diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml
new file mode 100644
index 00000000000..6ee866781dc
--- /dev/null
+++ b/.github/workflows/build_wheels.yml
@@ -0,0 +1,210 @@
+name: build_wheels
+
+# Call this workflow from other workflows in the repository by specifying "uses: ./.github/workflows/build_wheels.yml"
+on: [workflow_dispatch, workflow_call]
+
+jobs:
+  get-version:
+    runs-on:  ubuntu-latest
+    outputs:
+      release_version: ${{ steps.get_release_version.outputs.release_version }}
+      version_without_prefix: ${{ steps.get_release_version_without_prefix.outputs.version_without_prefix }}
+      highest_semver_tag: ${{ steps.get_highest_semver.outputs.highest_semver_tag }}
+    steps:
+      - uses: actions/checkout@v2
+      - name: Get release version
+        id: get_release_version
+        run: echo ::set-output name=release_version::${GITHUB_REF#refs/*/}
+      - name: Get release version without prefix
+        id: get_release_version_without_prefix
+        env:
+          RELEASE_VERSION: ${{ steps.get_release_version.outputs.release_version }}
+        run: |
+          echo ::set-output name=version_without_prefix::${RELEASE_VERSION:1}
+      - name: Get highest semver
+        id: get_highest_semver
+        env:
+          RELEASE_VERSION: ${{ steps.get_release_version.outputs.release_version }}
+        run: |
+          source infra/scripts/setup-common-functions.sh
+          SEMVER_REGEX='^v[0-9]+\.[0-9]+\.[0-9]+(-([0-9A-Za-z-]+(\.[0-9A-Za-z-]+)*))?$'
+          if echo "${RELEASE_VERSION}" | grep -P "$SEMVER_REGEX" &>/dev/null ; then
+            echo ::set-output name=highest_semver_tag::$(get_tag_release -m)
+          fi
+      - name: Check output
+        env:
+          RELEASE_VERSION: ${{ steps.get_release_version.outputs.release_version }}
+          VERSION_WITHOUT_PREFIX: ${{ steps.get_release_version_without_prefix.outputs.version_without_prefix }}
+          HIGHEST_SEMVER_TAG: ${{ steps.get_highest_semver.outputs.highest_semver_tag }}
+        run: |
+          echo $RELEASE_VERSION
+          echo $VERSION_WITHOUT_PREFIX
+          echo $HIGHEST_SEMVER_TAG
+
+  build-python-wheel:
+    name: Build wheels on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ ubuntu-latest, macos-10.15 ]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Setup Node
+        uses: actions/setup-node@v2
+        with:
+          node-version: '17.x'
+          registry-url: 'https://registry.npmjs.org'
+      - name: Build UI
+        run: make build-ui
+      - name: Build wheels
+        uses: pypa/cibuildwheel@v2.7.0
+        env:
+          CIBW_BUILD: "cp3*_x86_64"
+          CIBW_SKIP: "cp36-* *-musllinux_x86_64 cp310-macosx_x86_64"
+          CIBW_ARCHS: "native"
+          CIBW_ENVIRONMENT: >
+            COMPILE_GO=True PATH=$PATH:/usr/local/go/bin
+          CIBW_BEFORE_ALL_LINUX: |
+            curl -o go.tar.gz https://dl.google.com/go/go1.18.2.linux-amd64.tar.gz
+            tar -C /usr/local -xzf go.tar.gz
+            go version
+          CIBW_BEFORE_ALL_MACOS: |
+            curl -o python.pkg https://www.python.org/ftp/python/3.9.12/python-3.9.12-macosx10.9.pkg
+            sudo installer -pkg python.pkg -target /
+          # There's a `git restore` in here because `make install-go-ci-dependencies` is actually messing up go.mod & go.sum.
+          CIBW_BEFORE_BUILD: |
+            make install-protoc-dependencies
+            make install-go-proto-dependencies
+            make install-go-ci-dependencies
+            git status
+            git restore go.mod go.sum
+          CIBW_BEFORE_TEST: "cd {project} && git status"
+      # py3.10 on MacOS does not work with Go so we have to install separately. Issue is tracked here: https://github.com/feast-dev/feast/issues/2881.
+      - name: Build py310 specific wheels for macos
+        if: matrix.os == 'macos-10.15'
+        uses: pypa/cibuildwheel@v2.7.0
+        env:
+          CIBW_BUILD: "cp310-macosx_x86_64"
+          CIBW_ARCHS: "native"
+          # Need this environment variable because of this issue: https://github.com/pypa/cibuildwheel/issues/952.
+          CIBW_ENVIRONMENT: >
+            _PYTHON_HOST_PLATFORM=macosx-10.15-x86_64
+          # There's a `git restore` in here because remnant go.mod, go.sum changes from the build mess up the wheel naming.
+          CIBW_BEFORE_BUILD: |
+            git status
+            git restore go.mod go.sum
+      - uses: actions/upload-artifact@v2
+        with:
+          name: wheels
+          path: ./wheelhouse/*.whl
+
+  build-source-distribution:
+    name: Build source distribution
+    runs-on: macos-10.15
+    steps:
+      - uses: actions/checkout@v2
+      - name: Setup Python
+        id: setup-python
+        uses: actions/setup-python@v2
+        with:
+          python-version: "3.10"
+          architecture: x64
+      - name: Setup Node
+        uses: actions/setup-node@v2
+        with:
+          node-version: '17.x'
+          registry-url: 'https://registry.npmjs.org'
+      - name: Build and install dependencies
+        # There's a `git restore` in here because `make install-go-ci-dependencies` is actually messing up go.mod & go.sum.
+        run: |
+          pip install -U pip setuptools wheel twine
+          make install-protoc-dependencies
+          make install-go-proto-dependencies
+          make install-go-ci-dependencies
+          make build-ui
+          git status
+          git restore go.mod go.sum
+      - name: Build
+        run: |
+          python3 setup.py sdist
+      - uses: actions/upload-artifact@v2
+        with:
+          name: wheels
+          path: dist/*
+
+  verify-python-wheels:
+    runs-on: ${{ matrix.os }}
+    needs: [build-python-wheel, build-source-distribution]
+    strategy:
+      matrix:
+        os: [ubuntu-latest,  macos-10.15 ]
+        python-version: [ "3.7", "3.8", "3.9", "3.10"]
+        from-source: [ True, False ]
+    env:
+      # this script is for testing servers
+      # it starts server with timeout and checks whether process killed by timeout (started healthy) or died by itself
+      TEST_SCRIPT: |
+        timeout 10s $@ & pid=$!
+        wait $pid
+        ret=$?
+        if [[ $ret -ne 124 ]]
+        then
+           exit $ret
+        else
+           echo "Succeeded!"
+        fi
+    steps:
+      - name: Setup Python
+        id: setup-python
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+          architecture: x64
+      - uses: actions/setup-go@v3
+        with:
+          go-version: '>=1.17.0'
+      - uses: actions/download-artifact@v2
+        with:
+          name: wheels
+          path: dist
+      - name: Install wheel
+        if: ${{ !matrix.from-source }}
+        # try to install all wheels; only the current platform wheel should be actually installed
+        run: |
+          cd dist/
+          pip install wheel
+          for f in *.whl; do pip install $f || true; done
+      - name: Install dist with go
+        if: ${{ matrix.from-source && (matrix.python-version != '3.10' || matrix.os == 'ubuntu-latest')}}
+        env:
+          COMPILE_GO: "True"
+        run: |
+          pip install 'grpcio-tools==1.44.0' 'pybindgen==0.22.0'
+          go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.26.0
+          go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@v1.1.0
+          pip install dist/*tar.gz
+      # py3.10 on MacOS does not work with Go so we have to install separately. Issue is tracked here: https://github.com/feast-dev/feast/issues/2881
+      - name: Install dist w/o go
+        if: ${{ matrix.from-source && matrix.python-version == '3.10' && matrix.os == 'macos-10.15'}}
+        run: pip install dist/*tar.gz
+      - name: Install OS X dependencies
+        if: matrix.os == 'macos-10.15'
+        run: brew install coreutils
+      - name: Smoke test
+        run: |
+          feast init test_repo
+          cd test_repo/
+          feast apply
+          echo "$TEST_SCRIPT" > run-and-wait.sh
+          bash run-and-wait.sh feast serve
+          bash run-and-wait.sh feast ui
+      # We disable this test for the Python 3.10 binary since it does not include Go.
+      - name: Smoke test with go
+        if: matrix.python-version != '3.10' || matrix.os == 'ubuntu-latest'
+        run: |
+          cd test_repo/
+          feast apply
+          echo "$TEST_SCRIPT" > run-and-wait.sh
+          pip install cffi
+          printf "\ngo_feature_retrieval: True" >> feature_store.yaml
+          bash run-and-wait.sh feast serve
\ No newline at end of file
diff --git a/.github/workflows/java_master_only.yml b/.github/workflows/java_master_only.yml
index a856fbe2cba..fc2bb523876 100644
--- a/.github/workflows/java_master_only.yml
+++ b/.github/workflows/java_master_only.yml
@@ -93,7 +93,7 @@ jobs:
           architecture: x64
       - uses: actions/setup-python@v2
         with:
-          python-version: '3.7'
+          python-version: '3.8'
           architecture: 'x64'
       - uses: actions/cache@v2
         with:
diff --git a/.github/workflows/java_pr.yml b/.github/workflows/java_pr.yml
index a87542b9454..39593f02ce0 100644
--- a/.github/workflows/java_pr.yml
+++ b/.github/workflows/java_pr.yml
@@ -74,7 +74,7 @@ jobs:
           architecture: x64
       - uses: actions/setup-python@v2
         with:
-          python-version: '3.7'
+          python-version: '3.8'
           architecture: 'x64'
       - uses: actions/cache@v2
         with:
diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml
index b57126ca6c1..a0a6d7dd382 100644
--- a/.github/workflows/linter.yml
+++ b/.github/workflows/linter.yml
@@ -6,20 +6,20 @@ jobs:
   lint-python:
     runs-on: [ubuntu-latest]
     env:
-      PYTHON: 3.7
+      PYTHON: 3.8
     steps:
       - uses: actions/checkout@v2
       - name: Setup Python
         id: setup-python
         uses: actions/setup-python@v2
         with:
-          python-version: "3.7"
+          python-version: "3.8"
           architecture: x64
       - name: Setup Go
         id: setup-go
         uses: actions/setup-go@v2
         with:
-          go-version: 1.17.7
+          go-version: 1.18.0
       - name: Upgrade pip version
         run: |
           pip install --upgrade "pip>=21.3.1,<22.1"
@@ -54,12 +54,12 @@ jobs:
         id: setup-go
         uses: actions/setup-go@v2
         with:
-          go-version: 1.17.7
+          go-version: 1.18.0
       - name: Setup Python
         id: setup-python
         uses: actions/setup-python@v2
         with:
-          python-version: "3.7"
+          python-version: "3.8"
       - name: Upgrade pip version
         run: |
           pip install --upgrade "pip>=21.3.1,<22.1"
diff --git a/.github/workflows/master_only.yml b/.github/workflows/master_only.yml
index 2042987617b..0cb49bb525c 100644
--- a/.github/workflows/master_only.yml
+++ b/.github/workflows/master_only.yml
@@ -13,7 +13,9 @@ jobs:
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v1
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v1
+        uses: docker/setup-buildx-action@v2
+        with:
+          install: true
       - name: Set up AWS SDK
         uses: aws-actions/configure-aws-credentials@v1
         with:
@@ -50,6 +52,7 @@ jobs:
           docker build \
             --file sdk/python/feast/infra/feature_servers/aws_lambda/Dockerfile \
             --tag $ECR_REGISTRY/$ECR_REPOSITORY:${{ steps.image-tag.outputs.DOCKER_IMAGE_TAG }} \
+            --load \
             .
           docker push $ECR_REGISTRY/$ECR_REPOSITORY:${{ steps.image-tag.outputs.DOCKER_IMAGE_TAG }}
     outputs:
@@ -60,7 +63,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [ "3.7", "3.8", "3.9", "3.10" ]
+        python-version: [ "3.8", "3.9", "3.10" ]
         go-version: [ 1.17.0 ]
         os: [ ubuntu-latest ]
     env:
@@ -140,7 +143,7 @@ jobs:
           SNOWFLAKE_CI_PASSWORD: ${{ secrets.SNOWFLAKE_CI_PASSWORD }}
           SNOWFLAKE_CI_ROLE: ${{ secrets.SNOWFLAKE_CI_ROLE }}
           SNOWFLAKE_CI_WAREHOUSE: ${{ secrets.SNOWFLAKE_CI_WAREHOUSE }}
-        run: pytest -n 8 --cov=./ --cov-report=xml --verbose --color=yes sdk/python/tests --integration --durations=5
+        run: pytest -n 8 --cov=./ --cov-report=xml --color=yes sdk/python/tests --integration --durations=5 --timeout=1200 --timeout_method=thread
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v1
         with:
@@ -177,7 +180,9 @@ jobs:
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v1
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v1
+        uses: docker/setup-buildx-action@v2
+        with:
+          install: true
       - name: Login to DockerHub
         uses: docker/login-action@v1
         with:
diff --git a/.github/workflows/pr_integration_tests.yml b/.github/workflows/pr_integration_tests.yml
index 089d9f47336..e1c7ed2de2b 100644
--- a/.github/workflows/pr_integration_tests.yml
+++ b/.github/workflows/pr_integration_tests.yml
@@ -20,7 +20,7 @@ jobs:
       (github.event.action != 'labeled' && (contains(github.event.pull_request.labels.*.name, 'ok-to-test') || contains(github.event.pull_request.labels.*.name, 'approved') || contains(github.event.pull_request.labels.*.name, 'lgtm')))
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
         with:
           # pull_request_target runs the workflow in the context of the base repo
           # as such actions/checkout needs to be explicit configured to retrieve
@@ -30,7 +30,9 @@ jobs:
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v1
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v1
+        uses: docker/setup-buildx-action@v2
+        with:
+          install: true
       - name: Set up AWS SDK
         uses: aws-actions/configure-aws-credentials@v1
         with:
@@ -67,6 +69,7 @@ jobs:
           docker build \
             --file sdk/python/feast/infra/feature_servers/aws_lambda/Dockerfile \
             --tag $ECR_REGISTRY/$ECR_REPOSITORY:${{ steps.image-tag.outputs.DOCKER_IMAGE_TAG }} \
+            --load \
             .
           docker push $ECR_REGISTRY/$ECR_REPOSITORY:${{ steps.image-tag.outputs.DOCKER_IMAGE_TAG }}
     outputs:
@@ -81,7 +84,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [ "3.7" ]
+        python-version: [ "3.8" ]
         os: [ ubuntu-latest ]
     env:
       OS: ${{ matrix.os }}
@@ -114,7 +117,7 @@ jobs:
         id: setup-go
         uses: actions/setup-go@v2
         with:
-          go-version: 1.17.7
+          go-version: 1.18.0
       - name: Set up gcloud SDK
         uses: google-github-actions/setup-gcloud@v0
         with:
@@ -167,7 +170,7 @@ jobs:
           SNOWFLAKE_CI_PASSWORD: ${{ secrets.SNOWFLAKE_CI_PASSWORD }}
           SNOWFLAKE_CI_ROLE: ${{ secrets.SNOWFLAKE_CI_ROLE }}
           SNOWFLAKE_CI_WAREHOUSE: ${{ secrets.SNOWFLAKE_CI_WAREHOUSE }}
-        run: pytest -n 8 --cov=./ --cov-report=xml --verbose --color=yes sdk/python/tests --integration --durations=5
+        run: pytest -n 8 --cov=./ --cov-report=xml --color=yes sdk/python/tests --integration --durations=5 --timeout=1200 --timeout_method=thread
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v1
         with:
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index dad6eddc862..184fdb3cb6e 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -125,9 +125,12 @@ jobs:
       - name: Publish Helm charts
         run: ./infra/scripts/helm/push-helm-charts.sh $VERSION_WITHOUT_PREFIX
 
+  build_wheels:
+    uses: ./.github/workflows/build_wheels.yml
+
   publish-python-sdk:
     runs-on: ubuntu-latest
-    needs: [verify-python-wheel]
+    needs: [build_wheels]
     steps:
       - uses: actions/download-artifact@v2
         with:
@@ -138,149 +141,6 @@ jobs:
           user: __token__
           password: ${{ secrets.PYPI_PASSWORD }}
 
-
-  verify-python-wheel:
-    runs-on: ${{ matrix.os }}
-    needs: [build-python-sdk, build-python-sdk-macos-py310]
-    strategy:
-      matrix:
-        os: [ ubuntu-latest, macos-10.15 ]
-        python-version: [ "3.7", "3.8", "3.9", "3.10"]
-        from-source: [ True, False ]
-    env:
-      # this script is for testing servers
-      # it starts server with timeout and checks whether process killed by timeout (started healthy) or died by itself
-      TEST_SCRIPT: |
-        timeout 10s $@ & pid=$!
-        wait $pid
-        ret=$?
-        if [[ $ret -ne 124 ]]
-        then
-           exit $ret
-        else
-           echo "Succeeded!"
-        fi
-    steps:
-      - name: Setup Python
-        id: setup-python
-        uses: actions/setup-python@v2
-        with:
-          python-version: ${{ matrix.python-version }}
-          architecture: x64
-      - uses: actions/setup-go@v3
-        with:
-          go-version: '>=1.17.0'
-      - uses: actions/download-artifact@v2
-        with:
-          name: wheels
-          path: dist
-      - name: Install wheel
-        if: ${{ !matrix.from-source }}
-        # try to install all wheels; only the current platform wheel should be actually installed
-        run: |
-          cd dist/
-          pip install wheel
-          for f in *.whl; do pip install $f || true; done
-      - name: Install sdist
-        if: ${{ matrix.from-source }}
-        env:
-          COMPILE_GO: "True"
-        run: |
-          pip install 'grpcio-tools==1.44.0' 'pybindgen==0.22.0'
-          go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.26.0
-          go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@v1.1.0
-          pip install dist/*tar.gz
-      - name: Install OS X dependencies
-        if: matrix.os == 'macos-10.15'
-        run: brew install coreutils
-      - name: Smoke test
-        run: |
-          feast init test_repo
-          cd test_repo/
-          feast apply
-          echo "$TEST_SCRIPT" > run-and-wait.sh
-          bash run-and-wait.sh feast serve
-          bash run-and-wait.sh feast ui
-          
-          pip install cffi
-          printf "\ngo_feature_retrieval: True" >> feature_store.yaml
-          bash run-and-wait.sh feast serve
-
-  build-python-sdk:
-    name: Build wheels on ${{ matrix.os }}
-    runs-on: ${{ matrix.os }}
-    strategy:
-      matrix:
-        os: [ ubuntu-latest, macos-10.15 ]
-    steps:
-      - uses: actions/checkout@v2
-      - name: Setup Node
-        uses: actions/setup-node@v2
-        with:
-          node-version: '17.x'
-          registry-url: 'https://registry.npmjs.org'
-      - name: Build UI
-        run: make build-ui
-      - name: Build wheels
-        uses: pypa/cibuildwheel@v2.4.0
-        env:
-          CIBW_BUILD: "cp3*_x86_64"
-          CIBW_SKIP: "cp36-* *-musllinux_x86_64 cp310-macosx_x86_64"
-          CIBW_ARCHS: "native"
-          CIBW_ENVIRONMENT: >
-            COMPILE_GO=True PATH=$PATH:/usr/local/go/bin
-          CIBW_BEFORE_ALL_LINUX: |
-            curl -o go.tar.gz https://dl.google.com/go/go1.18.2.linux-amd64.tar.gz
-            tar -C /usr/local -xzf go.tar.gz
-            go version
-          CIBW_BEFORE_ALL_MACOS: |
-            curl -o python.pkg https://www.python.org/ftp/python/3.9.12/python-3.9.12-macosx10.9.pkg
-            sudo installer -pkg python.pkg -target /
-          CIBW_BEFORE_BUILD: |
-            make install-protoc-dependencies
-            make install-go-proto-dependencies
-            make install-go-ci-dependencies
-
-      - uses: actions/upload-artifact@v2
-        with:
-          name: wheels
-          path: ./wheelhouse/*.whl
-
-
-  build-python-sdk-macos-py310:
-    runs-on: macos-10.15
-    env:
-      COMPILE_GO: True
-    steps:
-      - uses: actions/checkout@v2
-      - name: Setup Python
-        id: setup-python
-        uses: actions/setup-python@v2
-        with:
-          python-version: "3.10"
-          architecture: x64
-      - name: Setup Node
-        uses: actions/setup-node@v2
-        with:
-          node-version: '17.x'
-          registry-url: 'https://registry.npmjs.org'
-      - name: Build and install dependencies
-        run: |
-          pip install -U pip setuptools wheel twine
-          make install-protoc-dependencies
-          make install-go-proto-dependencies
-          make install-go-ci-dependencies
-          make build-ui
-      - name: Build
-        run: |
-          python3 setup.py sdist bdist_wheel
-
-      - uses: actions/upload-artifact@v2
-        with:
-          name: wheels
-          path: dist/*
-
-
   publish-java-sdk:
     container: maven:3.6-jdk-11
     runs-on: ubuntu-latest
diff --git a/.github/workflows/unit_tests.yml b/.github/workflows/unit_tests.yml
index 3573fe26568..a9bf3deba32 100644
--- a/.github/workflows/unit_tests.yml
+++ b/.github/workflows/unit_tests.yml
@@ -7,11 +7,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [ "3.7", "3.8", "3.9", "3.10" ]
-        os: [ ubuntu-latest, macOS-latest]
+        python-version: [ "3.8", "3.9", "3.10" ]
+        os: [ ubuntu-latest, macOS-latest ]
         exclude:
-          - os: macOS-latest
-            python-version: "3.8"
           - os: macOS-latest
             python-version: "3.9"
           - os: macOS-latest
@@ -31,10 +29,15 @@ jobs:
         id: setup-go
         uses: actions/setup-go@v2
         with:
-          go-version: 1.17.7
+          go-version: 1.18.0
+      - name: Install mysql on macOS
+        if:  startsWith(matrix.os, 'macOS')
+        run: |
+          brew install mysql
+          PATH=$PATH:/usr/local/mysql/bin
       - name: Upgrade pip version
         run: |
-          pip install --upgrade "pip>=21.3.1,<22.1"
+          pip install --upgrade "pip>=22.1,<23"
       - name: Get pip cache dir
         id: pip-cache
         run: |
@@ -55,12 +58,13 @@ jobs:
         run: make install-python-ci-dependencies
       - name: Test Python
         env:
+          IS_TEST: "True"
           SNOWFLAKE_CI_DEPLOYMENT: ${{ secrets.SNOWFLAKE_CI_DEPLOYMENT }}
           SNOWFLAKE_CI_USER: ${{ secrets.SNOWFLAKE_CI_USER }}
           SNOWFLAKE_CI_PASSWORD: ${{ secrets.SNOWFLAKE_CI_PASSWORD }}
           SNOWFLAKE_CI_ROLE: ${{ secrets.SNOWFLAKE_CI_ROLE }}
           SNOWFLAKE_CI_WAREHOUSE: ${{ secrets.SNOWFLAKE_CI_WAREHOUSE }}
-        run: FEAST_USAGE=False pytest -n 8 --cov=./ --cov-report=xml --verbose --color=yes sdk/python/tests
+        run: FEAST_USAGE=False pytest -n 8 --cov=./ --cov-report=xml --color=yes sdk/python/tests
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v1
         with:
@@ -79,15 +83,15 @@ jobs:
         id: setup-python
         uses: actions/setup-python@v2
         with:
-          python-version: "3.7"
+          python-version: "3.8"
       - name: Upgrade pip version
         run: |
-          pip install --upgrade "pip>=21.3.1,<22.1"
+          pip install --upgrade "pip>=22.1,<23"
       - name: Setup Go
         id: setup-go
         uses: actions/setup-go@v2
         with:
-          go-version: 1.17.7
+          go-version: 1.18.0
       - name: Install dependencies
         run: make install-go-proto-dependencies
       - name: Compile protos
diff --git a/.gitpod.yml b/.gitpod.yml
new file mode 100644
index 00000000000..b28dfbe49f5
--- /dev/null
+++ b/.gitpod.yml
@@ -0,0 +1,43 @@
+#  https://www.gitpod.io/docs/config-gitpod-file
+tasks:
+  - init: |
+      python -m venv venv
+      source venv/bin/activate
+
+      pip install pre-commit
+      pre-commit install --hook-type pre-commit --hook-type pre-push
+      pip install '.[dev]'
+      make compile-protos-python
+      make compile-protos-go
+      make compile-go-lib
+    env:
+      PYTHONUSERBASE: "/workspace/.pip-modules"
+    command: |
+      source venv/bin/activate
+
+      git config --global alias.ci 'commit -s'
+      git config --global alias.sw switch
+      git config --global alias.st status
+      git config --global alias.co checkout
+      git config --global alias.br branch
+      git config --global alias.df diff
+github:
+  prebuilds:
+    # enable for the default branch (defaults to true)
+    master: true
+    # enable for all branches in this repo (defaults to false)
+    branches: false
+    # enable for pull requests coming from this repo (defaults to true)
+    pullRequests: true
+    # enable for pull requests coming from forks (defaults to false)
+    pullRequestsFromForks: false
+    # add a check to pull requests (defaults to true)
+    addCheck: true
+    # add a "Review in Gitpod" button as a comment to pull requests (defaults to false)
+    addComment: false
+    # add a "Review in Gitpod" button to the pull request's description (defaults to false)
+    addBadge: false
+
+vscode:
+  extensions:
+    - ms-python.python
diff --git a/.readthedocs.yml b/.readthedocs.yml
index dea27e20b3a..75499aa5ddb 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -7,6 +7,6 @@ formats:
    - pdf
 
 python:
-  version: 3.7
+  version: "3.8"
   install:
       - requirements: sdk/python/docs/requirements.txt
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9ef9d0ec369..bd7e8098f36 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,73 @@
 # Changelog
 
+# [0.22.0](https://github.com/feast-dev/feast/compare/v0.21.0...v0.22.0) (2022-06-29)
+
+
+### Bug Fixes
+
+* Add columns for user metadata in the tables ([#2760](https://github.com/feast-dev/feast/issues/2760)) ([269055e](https://github.com/feast-dev/feast/commit/269055e25178956715c163547c9f0a33a5892a75))
+* Add project columns in the SQL Registry ([#2784](https://github.com/feast-dev/feast/issues/2784)) ([336fdd1](https://github.com/feast-dev/feast/commit/336fdd104d2669d19ca56b0d17eadb78fc95a3cd))
+* Add S3FS dependency (which Dask depends on for S3 files) ([#2701](https://github.com/feast-dev/feast/issues/2701)) ([5d6fa94](https://github.com/feast-dev/feast/commit/5d6fa9408052695dfce179ea619d8362898d6329))
+* Bugfixes for how registry is loaded ([#2768](https://github.com/feast-dev/feast/issues/2768)) ([ecb8b2a](https://github.com/feast-dev/feast/commit/ecb8b2af4ba0f9d52be8ac560ac2a9da0f46f38e))
+* Conversion of null timestamp from proto to python ([#2814](https://github.com/feast-dev/feast/issues/2814)) ([cb23648](https://github.com/feast-dev/feast/commit/cb23648da71cbf63e382ec75a8afe7228ff8cbc2))
+* Correct feature statuses during feature logging test ([#2709](https://github.com/feast-dev/feast/issues/2709)) ([cebf609](https://github.com/feast-dev/feast/commit/cebf609309f81a0f4ccded3448cefac5923be525))
+* Correctly generate projects-list.json when calling feast ui and using postgres as a source ([#2845](https://github.com/feast-dev/feast/issues/2845)) ([bee8076](https://github.com/feast-dev/feast/commit/bee8076085e9f42e783fb9ce5ad07b618d913f0d))
+* Dynamodb drops missing entities when batching ([#2802](https://github.com/feast-dev/feast/issues/2802)) ([a2e9209](https://github.com/feast-dev/feast/commit/a2e9209f7a7283925b56b5877e8fdc2e2d863798))
+* Enable faulthandler and disable flaky tests ([#2815](https://github.com/feast-dev/feast/issues/2815)) ([4934d84](https://github.com/feast-dev/feast/commit/4934d843fc65dd62ba1c1302243c1b2c151c78d2))
+* Explicitly translate errors when instantiating the go fs  ([#2842](https://github.com/feast-dev/feast/issues/2842)) ([7a2c4cd](https://github.com/feast-dev/feast/commit/7a2c4cd8bf9b16331fad1d2e7d0ea3f85589f96b))
+* Fix broken roadmap links ([#2690](https://github.com/feast-dev/feast/issues/2690)) ([b3ba8aa](https://github.com/feast-dev/feast/commit/b3ba8aaf3a87343d756a2996376865096d543515))
+* Fix bugs in applying stream feature view and retrieving online features  ([#2754](https://github.com/feast-dev/feast/issues/2754)) ([d024e5e](https://github.com/feast-dev/feast/commit/d024e5efac085ec12a17005389229bfd93cf466e))
+* Fix Feast UI failure with new way of specifying entities ([#2773](https://github.com/feast-dev/feast/issues/2773)) ([0d1ac01](https://github.com/feast-dev/feast/commit/0d1ac01ef45a1ee78799c7c5ebf30b2476bfc30e))
+* Fix feature view __getitem__ for feature services ([#2769](https://github.com/feast-dev/feast/issues/2769)) ([88cc47d](https://github.com/feast-dev/feast/commit/88cc47dea936f34c0798b6c6c31fda0d1b3ecbd7))
+* Fix issue when user specifies a port for feast ui  ([#2692](https://github.com/feast-dev/feast/issues/2692)) ([1c621fe](https://github.com/feast-dev/feast/commit/1c621fe3649900a59e85fe9c4f3840dd09bc88d0))
+* Fix macos wheel version for 310 and also checkout edited go files ([#2890](https://github.com/feast-dev/feast/issues/2890)) ([bdf170f](https://github.com/feast-dev/feast/commit/bdf170f37971abc34930d62559374882139d69b6))
+* Fix on demand feature view crash from inference when it uses df.apply ([#2713](https://github.com/feast-dev/feast/issues/2713)) ([c5539fd](https://github.com/feast-dev/feast/commit/c5539fd9874fed3f69b0aaebc4d1d32e624bd041))
+* Fix SparkKafkaProcessor `query_timeout` parameter ([#2789](https://github.com/feast-dev/feast/issues/2789)) ([a8d282d](https://github.com/feast-dev/feast/commit/a8d282d3e4f041824ef7479f22c306dbfb8ad569))
+* Fix workflow syntax error ([#2869](https://github.com/feast-dev/feast/issues/2869)) ([fae45a1](https://github.com/feast-dev/feast/commit/fae45a11e78c23a58012d1d3cd2b9eb20a267794))
+* Fixed custom S3 endpoint read fail ([#2786](https://github.com/feast-dev/feast/issues/2786)) ([6fec431](https://github.com/feast-dev/feast/commit/6fec431dd5c9d53a678f58c9b87e2b1cdb02b238))
+* Go install gopy instead using go mod tidy ([#2863](https://github.com/feast-dev/feast/issues/2863)) ([2f2b519](https://github.com/feast-dev/feast/commit/2f2b5192f5f9c3e38183a2912f3bc7e754c9db8a))
+* Hydrate infra object in the sql registry proto() method  ([#2782](https://github.com/feast-dev/feast/issues/2782)) ([452dcd3](https://github.com/feast-dev/feast/commit/452dcd31da195273ef80ee39db5a7893b7d48cc2))
+* Implement apply_materialization and infra methods in sql registry ([#2775](https://github.com/feast-dev/feast/issues/2775)) ([4ed107c](https://github.com/feast-dev/feast/commit/4ed107cdf6476faf20a4e09716ade87cb99f1d14))
+* Minor refactor to format exception message ([#2764](https://github.com/feast-dev/feast/issues/2764)) ([da763c6](https://github.com/feast-dev/feast/commit/da763c6766cd7bec801312849e884b9dc8f8fb51))
+* Prefer installing gopy from feast's fork as opposed to upstream ([#2839](https://github.com/feast-dev/feast/issues/2839)) ([34c997d](https://github.com/feast-dev/feast/commit/34c997d81b0084d81fb6fb21d5d4374fc7760695))
+* Python server is not correctly starting in integration tests ([#2706](https://github.com/feast-dev/feast/issues/2706)) ([7583a0b](https://github.com/feast-dev/feast/commit/7583a0b1840c663af50bf4382c3ff2368593bb9c))
+* Random port allocation for python server in tests ([#2710](https://github.com/feast-dev/feast/issues/2710)) ([dee8090](https://github.com/feast-dev/feast/commit/dee80908ffb247367526561de3ad4841516a3196))
+* Refactor test to reuse LocalRegistryFile ([#2763](https://github.com/feast-dev/feast/issues/2763)) ([4339c0a](https://github.com/feast-dev/feast/commit/4339c0acc990de2b00db90221f41ac0d33e68544))
+* Revert "chore(release): release 0.22.0" ([#2852](https://github.com/feast-dev/feast/issues/2852)) ([e6a4636](https://github.com/feast-dev/feast/commit/e6a463666e53b87c9d41334f6151df5e2c09c805))
+* Stop running go mod tidy in setup.py ([#2877](https://github.com/feast-dev/feast/issues/2877)) ([676ecbb](https://github.com/feast-dev/feast/commit/676ecbb31550275017d83a6bc8dbf1e03f0d24fa)), closes [/github.com/pypa/cibuildwheel/issues/189#issuecomment-549933912](https://github.com//github.com/pypa/cibuildwheel/issues/189/issues/issuecomment-549933912)
+* Support push sources in stream feature views ([#2704](https://github.com/feast-dev/feast/issues/2704)) ([0d60eaa](https://github.com/feast-dev/feast/commit/0d60eaa0b7f32c84eb015c814a3c879e7d4f50fd))
+* Sync publish and build_wheels workflow to fix verify wheel error. ([#2871](https://github.com/feast-dev/feast/issues/2871)) ([b0f050a](https://github.com/feast-dev/feast/commit/b0f050a31946f9ff43ac3a3523d4dbd2a9802cc3))
+* Update roadmap with stream feature view rfc ([#2824](https://github.com/feast-dev/feast/issues/2824)) ([fc8f890](https://github.com/feast-dev/feast/commit/fc8f89059d1095e81e265f342ccaa26ff7f444f9))
+* Update udf tests and add base functions to streaming fcos and fix some nonetype errors ([#2776](https://github.com/feast-dev/feast/issues/2776)) ([331a214](https://github.com/feast-dev/feast/commit/331a214b04dc8b3f9347e79819130fa7bdf9e4c3))
+
+
+### Features
+
+* Add feast repo-upgrade for automated repo upgrades ([#2733](https://github.com/feast-dev/feast/issues/2733)) ([a3304d4](https://github.com/feast-dev/feast/commit/a3304d4e2d6d803f2a0fe35ef74204bd5cef7517))
+* Add file write_to_offline_store functionality ([#2808](https://github.com/feast-dev/feast/issues/2808)) ([c0e2ad7](https://github.com/feast-dev/feast/commit/c0e2ad7bf7310289ec6a7a6bd8cd2f766786b0cd))
+* Add http endpoint to the Go feature server ([#2658](https://github.com/feast-dev/feast/issues/2658)) ([3347a57](https://github.com/feast-dev/feast/commit/3347a57240cd485b7572777d7b977869140ccb20))
+* Add simple TLS support in Go RedisOnlineStore ([#2860](https://github.com/feast-dev/feast/issues/2860)) ([521488d](https://github.com/feast-dev/feast/commit/521488d71fa5050f64aa04a8ba7ef9891a57ca94))
+* Add StreamProcessor and SparkKafkaProcessor as contrib ([#2777](https://github.com/feast-dev/feast/issues/2777)) ([83ab682](https://github.com/feast-dev/feast/commit/83ab682c14a11a92121866409bddc787021d52e5))
+* Added Spark support for Delta and Avro ([#2757](https://github.com/feast-dev/feast/issues/2757)) ([7d16516](https://github.com/feast-dev/feast/commit/7d1651687a474850ebb16d4c9c0ff4a3daa6d486))
+* CLI interface for validation of logged features ([#2718](https://github.com/feast-dev/feast/issues/2718)) ([c8b11b3](https://github.com/feast-dev/feast/commit/c8b11b3b790b60e916d3257a036b9cb6430f4685))
+* Enable stream feature view materialization ([#2798](https://github.com/feast-dev/feast/issues/2798)) ([a06700d](https://github.com/feast-dev/feast/commit/a06700dd81c9893e98f6709b82a6faa32be49915))
+* Enable stream feature view materialization ([#2807](https://github.com/feast-dev/feast/issues/2807)) ([7d57724](https://github.com/feast-dev/feast/commit/7d57724dd4ff7d5ca4549bac8c72dbd71c57fce7))
+* Implement `offline_write_batch` for BigQuery and Snowflake ([#2840](https://github.com/feast-dev/feast/issues/2840)) ([97444e4](https://github.com/feast-dev/feast/commit/97444e439d0dc7a66b7121161c6f6560ae53d307))
+* Offline push endpoint for pushing to offline stores ([#2837](https://github.com/feast-dev/feast/issues/2837)) ([a88cd30](https://github.com/feast-dev/feast/commit/a88cd30f7925005db6f7c400b391d5e73d1b00f6))
+* Push to Redshift batch source offline store directly ([#2819](https://github.com/feast-dev/feast/issues/2819)) ([5748a8b](https://github.com/feast-dev/feast/commit/5748a8bbe338dfcb3fbbdc59fb1f57a99e1ea5eb))
+* Scaffold for unified push api ([#2796](https://github.com/feast-dev/feast/issues/2796)) ([1bd0930](https://github.com/feast-dev/feast/commit/1bd093028785ac9349be56c9ea98a3bd94c47fbe))
+* SQLAlchemy Registry Support ([#2734](https://github.com/feast-dev/feast/issues/2734)) ([b3fe39c](https://github.com/feast-dev/feast/commit/b3fe39c1600fa370f28c7b01e2b3f7da716449c1))
+* Stream Feature View FCOS ([#2750](https://github.com/feast-dev/feast/issues/2750)) ([0cf3c92](https://github.com/feast-dev/feast/commit/0cf3c923717f561d5656c57eb0b61fcd569917bd))
+* Update stream fcos to have watermark and sliding interval ([#2765](https://github.com/feast-dev/feast/issues/2765)) ([3256952](https://github.com/feast-dev/feast/commit/325695275da610cecf2b9e820fd71f7f04179ccf))
+* Validating logged features via Python SDK ([#2640](https://github.com/feast-dev/feast/issues/2640)) ([2874fc5](https://github.com/feast-dev/feast/commit/2874fc5c85810a65f750377d34418c71e747110e))
+
+
+### Reverts
+
+* Revert "chore(release): release 0.22.0" (#2891) ([e5abf58](https://github.com/feast-dev/feast/commit/e5abf589020b3c261ac0ce38d295ba96daf317c2)), closes [#2891](https://github.com/feast-dev/feast/issues/2891)
+* Revert "chore(release): release 0.22.0" (#2870) ([ffb0892](https://github.com/feast-dev/feast/commit/ffb089241d6521caa3be4034e6ae44af7dc4f8af)), closes [#2870](https://github.com/feast-dev/feast/issues/2870)
+* Revert "Create main.yml" (#2867) ([47922a4](https://github.com/feast-dev/feast/commit/47922a4cda532871eecd5e17edef6d08a4a50110)), closes [#2867](https://github.com/feast-dev/feast/issues/2867)
+
 # [0.21.0](https://github.com/feast-dev/feast/compare/v0.20.0...v0.21.0) (2022-05-13)
 
 
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 9cce520d346..4bd14d762a5 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -75,7 +75,8 @@ Note that this means if you are midway through working through a PR and rebase,
 ## Feast Python SDK / CLI
 ### Environment Setup
 Setting up your development environment for Feast Python SDK / CLI:
-1. Ensure that you have Docker installed in your environment. Docker is used to provision service dependencies during testing.
+1. Ensure that you have Docker installed in your environment. Docker is used to provision service dependencies during testing, and build images for feature servers and other components.
+   1. Please note that we use [Docker with BuiltKit](https://docs.docker.com/develop/develop-images/build_enhancements/).
 2. Ensure that you have `make`, Python (3.7 and above) with `pip`, installed.
 3. _Recommended:_ Create a virtual environment to isolate development dependencies to be installed
 ```sh
diff --git a/Makefile b/Makefile
index e208ab3c451..88f04aa95d8 100644
--- a/Makefile
+++ b/Makefile
@@ -47,7 +47,7 @@ package-protos:
 	cp -r ${ROOT_DIR}/protos ${ROOT_DIR}/sdk/python/feast/protos
 
 compile-protos-python:
-	python setup.py build_python_protos
+	python setup.py build_python_protos --inplace
 
 install-python:
 	python -m piptools sync sdk/python/requirements/py$(PYTHON)-requirements.txt
@@ -170,10 +170,12 @@ install-go-proto-dependencies:
 	go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@v1.1.0
 
 install-go-ci-dependencies:
-	# ToDo: currently gopy installation doesn't work w/o explicit go get in the next line
-	# ToDo: there should be a better way to install gopy
-	go get github.com/go-python/gopy
+	# TODO: currently gopy installation doesn't work w/o explicit go get in the next line
+	# TODO: there should be a better way to install gopy
+	go get github.com/go-python/gopy@v0.4.0
 	go install golang.org/x/tools/cmd/goimports
+	# The `go get` command on the previous lines download the lib along with replacing the dep to `feast-dev/gopy`
+	# but the following command is needed to install it for some reason.
 	go install github.com/go-python/gopy
 	python -m pip install pybindgen==0.22.0
 
@@ -206,31 +208,31 @@ push-ci-docker:
 
 # TODO(adchia): consider removing. This doesn't run successfully right now
 build-ci-docker:
-	docker build -t $(REGISTRY)/feast-ci:$(VERSION) -f infra/docker/ci/Dockerfile .
+	docker buildx build -t $(REGISTRY)/feast-ci:$(VERSION) -f infra/docker/ci/Dockerfile --load .
 
 push-feature-server-python-aws-docker:
 		docker push $(REGISTRY)/feature-server-python-aws:$$VERSION
 
 build-feature-server-python-aws-docker:
-		docker build --build-arg VERSION=$$VERSION \
+		docker buildx build --build-arg VERSION=$$VERSION \
 			-t $(REGISTRY)/feature-server-python-aws:$$VERSION \
-			-f sdk/python/feast/infra/feature_servers/aws_lambda/Dockerfile .
+			-f sdk/python/feast/infra/feature_servers/aws_lambda/Dockerfile --load .
 
 push-feature-transformation-server-docker:
 	docker push $(REGISTRY)/feature-transformation-server:$(VERSION)
 
 build-feature-transformation-server-docker:
-	docker build --build-arg VERSION=$(VERSION) \
+	docker buildx build --build-arg VERSION=$(VERSION) \
 		-t $(REGISTRY)/feature-transformation-server:$(VERSION) \
-		-f sdk/python/feast/infra/transformation_servers/Dockerfile .
+		-f sdk/python/feast/infra/transformation_servers/Dockerfile --load .
 
 push-feature-server-java-docker:
 	docker push $(REGISTRY)/feature-server-java:$(VERSION)
 
 build-feature-server-java-docker:
-	docker build --build-arg VERSION=$(VERSION) \
+	docker buildx build --build-arg VERSION=$(VERSION) \
 		-t $(REGISTRY)/feature-server-java:$(VERSION) \
-		-f java/infra/docker/feature-server/Dockerfile .
+		-f java/infra/docker/feature-server/Dockerfile --load .
 
 # Documentation
 
diff --git a/README.md b/README.md
index 5905bbd647f..0f77fbd42ca 100644
--- a/README.md
+++ b/README.md
@@ -139,7 +139,6 @@ The list below contains the functionality that contributors are planning to deve
 
 * Items below that are in development (or planned for development) will be indicated in parentheses.
 * We welcome contribution to all items in the roadmap!
-* Want to influence our roadmap and prioritization? Submit your feedback to [this form](https://docs.google.com/forms/d/e/1FAIpQLSfa1nRQ0sKz-JEFnMMCi4Jseag\_yDssO\_3nV9qMfxfrkil-wA/viewform).
 * Want to speak to a Feast contributor? We are more than happy to jump on a call. Please schedule a time using [Calendly](https://calendly.com/d/x2ry-g5bb/meet-with-feast-team).
 
 * **Data Sources**
@@ -172,17 +171,16 @@ The list below contains the functionality that contributors are planning to deve
   * [x] [Azure Cache for Redis (community plugin)](https://github.com/Azure/feast-azure)
   * [x] [Postgres (contrib plugin)](https://docs.feast.dev/reference/online-stores/postgres)
   * [x] [Custom online store support](https://docs.feast.dev/how-to-guides/adding-support-for-a-new-online-store)
+  * [x] [Cassandra / AstraDB](https://github.com/datastaxdevs/feast-cassandra-online-store)
   * [ ] Bigtable (in progress)
-  * [ ] Cassandra
-* **Streaming**
-  * [x] [Custom streaming ingestion job support](https://docs.feast.dev/how-to-guides/creating-a-custom-provider)
-  * [x] [Push based streaming data ingestion](https://docs.feast.dev/reference/data-sources/push.md)
-  * [ ] Streaming ingestion on AWS
-  * [ ] Streaming ingestion on GCP
 * **Feature Engineering**
   * [x] On-demand Transformations (Alpha release. See [RFC](https://docs.google.com/document/d/1lgfIw0Drc65LpaxbUu49RCeJgMew547meSJttnUqz7c/edit#))
+  * [x] Streaming Transformations (Alpha release. See [RFC](https://docs.google.com/document/d/1UzEyETHUaGpn0ap4G82DHluiCj7zEbrQLkJJkKSv4e8/edit))
   * [ ] Batch transformation (In progress. See [RFC](https://docs.google.com/document/d/1964OkzuBljifDvkV-0fakp2uaijnVzdwWNGdz7Vz50A/edit))
-  * [ ] Streaming transformation
+* **Streaming**
+  * [x] [Custom streaming ingestion job support](https://docs.feast.dev/how-to-guides/creating-a-custom-provider)
+  * [x] [Push based streaming data ingestion to online store (Alpha)](https://docs.feast.dev/reference/data-sources/push)
+  * [x] [Push based streaming data ingestion to offline store (Alpha)](https://docs.feast.dev/reference/data-sources/push)
 * **Deployments**
   * [x] AWS Lambda (Alpha release. See [RFC](https://docs.google.com/document/d/1eZWKWzfBif66LDN32IajpaG-j82LSHCCOzY6R7Ax7MI/edit))
   * [x] Kubernetes (See [guide](https://docs.feast.dev/how-to-guides/running-feast-in-production#4.3.-java-based-feature-server-deployed-on-kubernetes))
@@ -196,7 +194,7 @@ The list below contains the functionality that contributors are planning to deve
   * [ ] Java Client
   * [ ] Go Client
   * [ ] Delete API
-  * [ ] Feature Logging (for training)
+  * [] Feature Logging (for training)
 * **Data Quality Management (See [RFC](https://docs.google.com/document/d/110F72d4NTv80p35wDSONxhhPBqWRwbZXG4f9mNEMd98/edit))**
   * [x] Data profiling and validation (Great Expectations)
   * [ ] Training-serving skew detection (in progress)
@@ -207,9 +205,8 @@ The list below contains the functionality that contributors are planning to deve
   * [x] CLI for browsing feature registry
   * [x] Model-centric feature tracking (feature services)
   * [x] Amundsen integration (see [Feast extractor](https://github.com/amundsen-io/amundsen/blob/main/databuilder/databuilder/extractor/feast_extractor.py))
-  * [x] Feast Web UI (Alpha release. See [documentation](https://docs.feast.dev/reference/alpha-web-ui.md))
+  * [x] Feast Web UI (Alpha release. See [documentation](https://docs.feast.dev/reference/alpha-web-ui))
   * [ ] REST API for browsing feature registry
-  * [ ] Feature versioning
 
 
 ## 🎓 Important Resources
diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
index f6f12e04d04..23049455e33 100644
--- a/docs/SUMMARY.md
+++ b/docs/SUMMARY.md
@@ -11,11 +11,13 @@
 * [Concepts](getting-started/concepts/README.md)
   * [Overview](getting-started/concepts/overview.md)
   * [Data source](getting-started/concepts/data-source.md)
+  * [Dataset](getting-started/concepts/dataset.md)
   * [Entity](getting-started/concepts/entity.md)
   * [Feature view](getting-started/concepts/feature-view.md)
+  * [Stream feature view](getting-started/concepts/stream-feature-view.md)
   * [Feature retrieval](getting-started/concepts/feature-retrieval.md)
   * [Point-in-time joins](getting-started/concepts/point-in-time-joins.md)
-  * [Dataset](getting-started/concepts/dataset.md)
+  * [Registry](getting-started/concepts/registry.md)
 * [Architecture](getting-started/architecture-and-components/README.md)
   * [Overview](getting-started/architecture-and-components/overview.md)
   * [Feature repository](getting-started/architecture-and-components/feature-repository.md)
@@ -23,6 +25,7 @@
   * [Offline store](getting-started/architecture-and-components/offline-store.md)
   * [Online store](getting-started/architecture-and-components/online-store.md)
   * [Provider](getting-started/architecture-and-components/provider.md)
+* [Learning by example](getting-started/feast-workshop.md)
 * [Third party integrations](getting-started/third-party-integrations.md)
 * [FAQ](getting-started/faq.md)
 
@@ -34,6 +37,8 @@
 * [Real-time credit scoring on AWS](tutorials/real-time-credit-scoring-on-aws.md)
 * [Driver stats on Snowflake](tutorials/driver-stats-on-snowflake.md)
 * [Validating historical features with Great Expectations](tutorials/validating-historical-features.md)
+* [Using Scalable Registry](tutorials/using-scalable-registry.md)
+* [Building streaming features](tutorials/building-streaming-features.md)
 
 ## How-to Guides
 
@@ -60,6 +65,8 @@
   * [BigQuery](reference/data-sources/bigquery.md)
   * [Redshift](reference/data-sources/redshift.md)
   * [Push](reference/data-sources/push.md)
+  * [Kafka](reference/data-sources/kafka.md)
+  * [Kinesis](reference/data-sources/kinesis.md)
   * [Spark (contrib)](reference/data-sources/spark.md)
   * [PostgreSQL (contrib)](reference/data-sources/postgres.md)
 * [Offline stores](reference/offline-stores/README.md)
diff --git a/docs/getting-started/architecture-and-components/offline-store.md b/docs/getting-started/architecture-and-components/offline-store.md
index 96914db9d07..29a72bd5f0f 100644
--- a/docs/getting-started/architecture-and-components/offline-store.md
+++ b/docs/getting-started/architecture-and-components/offline-store.md
@@ -13,3 +13,5 @@ It is not possible to query all data sources from all offline stores, and only a
 
 Please see the [Offline Stores](../../reference/offline-stores/) reference for more details on configuring offline stores.
 
+Please see the [Push Source](reference/data-sources/push.md) for reference on how to push features directly to the offline store in your feature store.
+
diff --git a/docs/getting-started/architecture-and-components/online-store.md b/docs/getting-started/architecture-and-components/online-store.md
index 4f2e44c92ce..21b4dbcb9c7 100644
--- a/docs/getting-started/architecture-and-components/online-store.md
+++ b/docs/getting-started/architecture-and-components/online-store.md
@@ -12,4 +12,4 @@ Once the above data source is materialized into Feast \(using `feast materialize
 
 ![](../../.gitbook/assets/image%20%285%29.png)
 
-Features can also be written to the online store via [push sources](https://docs.feast.dev/reference/data-sources/push) 
\ No newline at end of file
+Features can also be written to the online store via [push sources](../../reference/data-sources/push.md) 
\ No newline at end of file
diff --git a/docs/getting-started/architecture-and-components/overview.md b/docs/getting-started/architecture-and-components/overview.md
index bf5c12dcc20..0c47fb2753d 100644
--- a/docs/getting-started/architecture-and-components/overview.md
+++ b/docs/getting-started/architecture-and-components/overview.md
@@ -23,7 +23,7 @@ A complete Feast deployment contains the following components:
   * Materialize (load) feature values into the online store.
   * Build and retrieve training datasets from the offline store.
   * Retrieve online features.
-* **Online Store:** The online store is a database that stores only the latest feature values for each entity. The online store is populated by materialization jobs and from [stream ingestion](../../reference/alpha-stream-ingestion.md).
+* **Online Store:** The online store is a database that stores only the latest feature values for each entity. The online store is populated by materialization jobs and from [stream ingestion](../../reference/data-sources/push.md).
 * **Offline Store:** The offline store persists batch data that has been ingested into Feast. This data is used for producing training datasets. Feast does not manage the offline store directly, but runs queries against it.
 
 {% hint style="info" %}
diff --git a/docs/getting-started/concepts/README.md b/docs/getting-started/concepts/README.md
index e7b29eb0047..6f2f64955dc 100644
--- a/docs/getting-started/concepts/README.md
+++ b/docs/getting-started/concepts/README.md
@@ -4,12 +4,18 @@
 
 {% page-ref page="data-source.md" %}
 
+{% page-ref page="dataset.md" %}
+
 {% page-ref page="entity.md" %}
 
 {% page-ref page="feature-view.md" %}
 
+{% page-ref page="feature-view.md" %}
+
+{% page-ref page="stream-feature-view.md" %}
+
 {% page-ref page="feature-retrieval.md" %}
 
 {% page-ref page="point-in-time-joins.md" %}
 
-{% page-ref page="dataset.md" %}
+{% page-ref page="registry.md" %}
\ No newline at end of file
diff --git a/docs/getting-started/concepts/registry.md b/docs/getting-started/concepts/registry.md
new file mode 100644
index 00000000000..2236f319312
--- /dev/null
+++ b/docs/getting-started/concepts/registry.md
@@ -0,0 +1,9 @@
+# Registry
+
+The Feast registry is where all applied Feast objects (e.g. Feature views, entities, etc) are stored. The registry exposes methods to apply, list, retrieve and delete these objects. The registry is abstraction, with multiple possible implementations.
+
+By default, the registry Feast uses a file-based registry implementation, which stores the protobuf representation of the registry as a serialized file. This registry file can be stored in a local file system, or in cloud storage (in, say, S3 or GCS).
+
+However, there's inherent limitations with a file-based registry, since changing a single field in the registry requires re-writing the whole registry file. With multiple concurrent writers, this presents a risk of data loss, or bottlenecks writes to the registry since all changes have to be serialized (e.g. when running materialization for multiple feature views or time ranges concurrently).
+
+Alternatively, a [SQL Registry](../../tutorials/using-scalable-registry.md) can be used for a more scalable registry. 
\ No newline at end of file
diff --git a/docs/getting-started/concepts/stream-feature-view.md b/docs/getting-started/concepts/stream-feature-view.md
new file mode 100644
index 00000000000..2ce39936145
--- /dev/null
+++ b/docs/getting-started/concepts/stream-feature-view.md
@@ -0,0 +1,56 @@
+# Stream feature view
+
+## Stream feature views
+
+A stream feature view is an extension of a normal feature view. The primary difference is that stream feature views have both stream and batch data sources, whereas a normal feature view only has a batch data source.
+
+Stream feature views should be used instead of normal feature views when there are stream data sources (e.g. Kafka and Kinesis) available to provide fresh features in an online setting. Here is an example definition of a stream feature view with an attached transformation:
+
+```python
+from datetime import timedelta
+
+from feast import Field, FileSource, KafkaSource, stream_feature_view
+from feast.data_format import JsonFormat
+from feast.types import Float32
+
+driver_stats_batch_source = FileSource(
+    name="driver_stats_source",
+    path="data/driver_stats.parquet",
+    timestamp_field="event_timestamp",
+)
+
+driver_stats_stream_source = KafkaSource(
+    name="driver_stats_stream",
+    kafka_bootstrap_servers="localhost:9092",
+    topic="drivers",
+    timestamp_field="event_timestamp",
+    batch_source=driver_stats_batch_source,
+    message_format=JsonFormat(
+        schema_json="driver_id integer, event_timestamp timestamp, conv_rate double, acc_rate double, created timestamp"
+    ),
+    watermark_delay_threshold=timedelta(minutes=5),
+)
+
+@stream_feature_view(
+    entities=[driver],
+    ttl=timedelta(seconds=8640000000),
+    mode="spark",
+    schema=[
+        Field(name="conv_percentage", dtype=Float32),
+        Field(name="acc_percentage", dtype=Float32),
+    ],
+    timestamp_field="event_timestamp",
+    online=True,
+    source=driver_stats_stream_source,
+)
+def driver_hourly_stats_stream(df: DataFrame):
+    from pyspark.sql.functions import col
+
+    return (
+        df.withColumn("conv_percentage", col("conv_rate") * 100.0)
+        .withColumn("acc_percentage", col("acc_rate") * 100.0)
+        .drop("conv_rate", "acc_rate")
+    )
+```
+
+See [here](https://github.com/feast-dev/streaming-tutorial) for a example of how to use stream feature views.
diff --git a/docs/getting-started/feast-workshop.md b/docs/getting-started/feast-workshop.md
new file mode 100644
index 00000000000..c883625dac9
--- /dev/null
+++ b/docs/getting-started/feast-workshop.md
@@ -0,0 +1,42 @@
+# Learning by example
+
+This workshop aims to teach users about Feast.
+
+We explain concepts & best practices by example, and also showcase how to address common use cases.
+
+### Pre-requisites
+
+This workshop assumes you have the following installed:
+
+* A local development environment that supports running Jupyter notebooks (e.g. VSCode with Jupyter plugin)
+* Python 3.7+
+* Java 11 (for Spark, e.g. `brew install java11`)
+* pip
+* Docker & Docker Compose (e.g. `brew install docker docker-compose`)
+* Terraform ([docs](https://learn.hashicorp.com/tutorials/terraform/install-cli#install-terraform))
+* AWS CLI
+* An AWS account setup with credentials via `aws configure` (e.g see [AWS credentials quickstart](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-quickstart.html#cli-configure-quickstart-creds))
+
+Since we'll be learning how to leverage Feast in CI/CD, you'll also need to fork this workshop repository.
+
+#### **Caveats**
+
+* M1 Macbook development is untested with this flow. See also [How to run / develop for Feast on M1 Macs](https://github.com/feast-dev/feast/issues/2105).
+* Windows development has only been tested with WSL. You will need to follow this [guide](https://docs.docker.com/desktop/windows/wsl/) to have Docker play nicely.
+
+### Modules
+
+_See also:_ [_Feast quickstart_](https://docs.feast.dev/getting-started/quickstart)_,_ [_Feast x Great Expectations tutorial_](https://docs.feast.dev/tutorials/validating-historical-features)
+
+These are meant mostly to be done in order, with examples building on previous concepts.
+
+| Time (min) | Description                                                             | Module                                                                      |
+| :--------: | ----------------------------------------------------------------------- | --------------------------------------------------------------------------- |
+|    30-45   | Setting up Feast projects & CI/CD + powering batch predictions          | [Module 0](https://github.com/feast-dev/feast-workshop/tree/main/module\_0) |
+|    15-20   | Streaming ingestion & online feature retrieval with Kafka, Spark, Redis | [Module 1](https://github.com/feast-dev/feast-workshop/tree/main/module\_1) |
+|    10-15   | Real-time feature engineering with on demand transformations            | [Module 2](https://github.com/feast-dev/feast-workshop/tree/main/module\_2) |
+|     TBD    | Feature server deployment (embed, as a service, AWS Lambda)             | TBD                                                                         |
+|     TBD    | Versioning features / models in Feast                                   | TBD                                                                         |
+|     TBD    | Data quality monitoring in Feast                                        | TBD                                                                         |
+|     TBD    | Batch transformations                                                   | TBD                                                                         |
+|     TBD    | Stream transformations                                                  | TBD                                                                         |
diff --git a/docs/getting-started/quickstart.md b/docs/getting-started/quickstart.md
index 41449b77e39..b5fe7bad4b9 100644
--- a/docs/getting-started/quickstart.md
+++ b/docs/getting-started/quickstart.md
@@ -125,8 +125,6 @@ driver_stats_fs = FeatureService(
 {% endtab %}
 {% endtabs %}
 
-![Demo parquet data: data/driver\_stats.parquet](../.gitbook/assets/screen-shot-2021-08-23-at-2.35.18-pm.png)
-
 The key line defining the overall architecture of the feature store is the **provider**. This defines where the raw data exists (for generating training data & feature values for serving), and where to materialize feature values to in the online store (for serving).
 
 Valid values for `provider` in `feature_store.yaml` are:
@@ -139,6 +137,16 @@ Note that there are many other sources Feast works with, including Azure, Hive,
 
 A custom setup can also be made by following [adding a custom provider](../how-to-guides/creating-a-custom-provider.md).
 
+### Inspecting the raw data
+
+The raw feature data we have in this demo is stored in a local parquet file. The dataset captures hourly stats of a driver in a ride-sharing app.
+
+```python
+import pandas as pd
+pd.read_parquet("data/driver_stats.parquet")
+```
+
+![Demo parquet data: data/driver\_stats.parquet](../.gitbook/assets/screen-shot-2021-08-23-at-2.35.18-pm.png)
 
 ## Step 3: Register feature definitions and deploy your feature store
 
@@ -367,7 +375,15 @@ pprint(feature_vector)
 
 ## Step 7: Using a feature service to fetch online features instead.
 
-You can also use feature services to manage multiple features, and decouple feature view definitions and the features needed by end applications. The feature store can also be used to fetch either online or historical features using the same api below. More information can be found [here](https://docs.feast.dev/getting-started/concepts/feature-service).
+You can also use feature services to manage multiple features, and decouple feature view definitions and the features needed by end applications. The feature store can also be used to fetch either online or historical features using the same api below. More information can be found [here](https://docs.feast.dev/getting-started/concepts/feature-retrieval).
+
+The `driver_activity` feature service pulls all features from the `driver_hourly_stats` feature view:
+
+```python
+driver_stats_fs = FeatureService(
+    name="driver_activity", features=[driver_hourly_stats_view]
+)
+```
 
 {% tabs %}
 {% tab title="Python" %}
@@ -376,7 +392,7 @@ from feast import FeatureStore
 feature_store = FeatureStore('.')  # Initialize the feature store
 
 feature_service = feature_store.get_feature_service("driver_activity")
-features = feature_store.get_online_features(
+feature_vector = feature_store.get_online_features(
     features=feature_service,
     entity_rows=[
         # {join_key: entity_value}
@@ -384,6 +400,7 @@ features = feature_store.get_online_features(
         {"driver_id": 1005},
     ],
 ).to_dict()
+pprint(feature_vector)
 ```
 
 {% tabs %}
diff --git a/docs/getting-started/third-party-integrations.md b/docs/getting-started/third-party-integrations.md
index ab926682662..8a862891f8d 100644
--- a/docs/getting-started/third-party-integrations.md
+++ b/docs/getting-started/third-party-integrations.md
@@ -46,8 +46,8 @@ Don't see your offline store or online store of choice here? Check out our guide
 * [x] [Azure Cache for Redis (community plugin)](https://github.com/Azure/feast-azure)
 * [x] [Postgres (contrib plugin)](https://docs.feast.dev/reference/online-stores/postgres)
 * [x] [Custom online store support](https://docs.feast.dev/how-to-guides/adding-support-for-a-new-online-store)
+* [x] [Cassandra / AstraDB](https://github.com/datastaxdevs/feast-cassandra-online-store)
 * [ ] Bigtable (in progress)
-* [ ] Cassandra
 
 ### **Deployments**
 
diff --git a/docs/how-to-guides/running-feast-in-production.md b/docs/how-to-guides/running-feast-in-production.md
index 53808326091..6023c5ac66e 100644
--- a/docs/how-to-guides/running-feast-in-production.md
+++ b/docs/how-to-guides/running-feast-in-production.md
@@ -3,15 +3,15 @@
 ## Overview
 
 After learning about Feast concepts and playing with Feast locally, you're now ready to use Feast in production.
-This guide aims to help with the transition from a sandbox project to production-grade deployment in the cloud or on-premise. 
+This guide aims to help with the transition from a sandbox project to production-grade deployment in the cloud or on-premise.
 
 Overview of typical production configuration is given below:
 
 ![Overview](production-simple.png)
 
 {% hint style="success" %}
-**Important note:** We're trying to keep Feast modular. With the exception of the core, most of the Feast blocks are loosely connected and can be used independently. Hence, you are free to build your own production configuration. 
-For example, you might not have a stream source and, thus, no need to write features in real-time to an online store. 
+**Important note:** We're trying to keep Feast modular. With the exception of the core, most of the Feast blocks are loosely connected and can be used independently. Hence, you are free to build your own production configuration.
+For example, you might not have a stream source and, thus, no need to write features in real-time to an online store.
 Or you might not need to retrieve online features.
 
 Furthermore, there's no single "true" approach. As you will see in this guide, Feast usually provides several options for each problem.
@@ -95,7 +95,7 @@ In summary, once you have set up a Git based repository with CI that runs `feast
 
 To keep your online store up to date, you need to run a job that loads feature data from your feature view sources into your online store. In Feast, this loading operation is called materialization.
 
-### 2.1. Manual materializations 
+### 2.1. Manual materializations
 The simplest way to schedule materialization is to run an **incremental** materialization using the Feast CLI:
 
 ```text
@@ -116,7 +116,7 @@ In the above example we are materializing the source data from the `driver_hourl
 
 The timestamps above should match the interval of data that has been computed by the data transformation system.
 
-### 2.2. Automate periodic materializations 
+### 2.2. Automate periodic materializations
 
 It is up to you which orchestration/scheduler to use to periodically run `$ feast materialize`.
 Feast keeps the history of materialization in its registry so that the choice could be as simple as a [unix cron util](https://en.wikipedia.org/wiki/Cron).
@@ -160,7 +160,7 @@ feature_refs = [
 ]
 
 training_df = fs.get_historical_features(
-    entity_df=entity_df, 
+    entity_df=entity_df,
     features=feature_refs,
 ).to_df()
 
@@ -214,7 +214,7 @@ There are three approaches for that purpose sorted from the most simple one (in
 
 This approach is the most convenient to keep your infrastructure as minimalistic as possible and avoid deploying extra services.
 The Feast Python SDK will connect directly to the online store (Redis, Datastore, etc), pull the feature data, and run transformations locally (if required).
-The obvious drawback is that your service must be written in Python to use the Feast Python SDK. 
+The obvious drawback is that your service must be written in Python to use the Feast Python SDK.
 A benefit of using a Python stack is that you can enjoy production-grade services with integrations with many existing data science tools.
 
 To integrate online retrieval into your service use the following code:
@@ -245,9 +245,9 @@ This service will provide an HTTP API with JSON I/O, which can be easily used wi
 ### 4.3. Java based Feature Server deployed on Kubernetes
 
 For users with very latency-sensitive and high QPS use-cases, Feast offers a high-performance Java feature server.
-Besides the benefits of running on JVM, this implementation also provides a gRPC API, which guarantees good connection utilization and 
-small request / response body size (compared to JSON). 
-You will need the Feast Java SDK to retrieve features from this service. This SDK wraps all the gRPC logic for you and provides more convenient APIs. 
+Besides the benefits of running on JVM, this implementation also provides a gRPC API, which guarantees good connection utilization and
+small request / response body size (compared to JSON).
+You will need the Feast Java SDK to retrieve features from this service. This SDK wraps all the gRPC logic for you and provides more convenient APIs.
 
 The Java based feature server can be deployed to Kubernetes cluster via Helm charts in a few simple steps:
 
@@ -292,9 +292,9 @@ def feast_writer(spark_df):
 streamingDF.writeStream.foreachBatch(feast_writer).start()
 ```
 
-### 5.2. Push service *(still under development)*
+### 5.2. Push Service (Alpha)
 
-Alternatively, if you want to ingest features directly from a broker (eg, Kafka or Kinesis), you can use the "push service", which will write to an online store.
+Alternatively, if you want to ingest features directly from a broker (eg, Kafka or Kinesis), you can use the "push service", which will write to an online store and/or offline store.
 This service will expose an HTTP API or when deployed on Serverless platforms like AWS Lambda or Google Cloud Run,
 this service can be directly connected to Kinesis or PubSub.
 
@@ -310,6 +310,30 @@ We use an [InfluxDB-style extension](https://github.com/prometheus/statsd_export
 We chose StatsD since it's a de-facto standard with various implementations (eg, [1](https://github.com/prometheus/statsd_exporter), [2](https://github.com/influxdata/telegraf/blob/master/plugins/inputs/statsd/README.md))
 and metrics can be easily exported to Prometheus, InfluxDB, AWS CloudWatch, etc.
 
+## 7. Using environment variables in your yaml configuration
+
+You might want to dynamically set parts of your configuration from your environment. For instance to deploy Feast to production and development with the same configuration, but a different server. Or to inject secrets without exposing them in your git repo. To do this, it is possible to use the `${ENV_VAR}` syntax in your `feature_store.yaml` file. For instance:
+
+```yaml
+project: my_project
+registry: data/registry.db
+provider: local
+online_store:
+    type: redis
+    connection_string: ${REDIS_CONNECTION_STRING}
+```
+
+It is possible to set a default value if the environment variable is not set, with `${ENV_VAR:"default"}`. For instance:
+
+```yaml
+project: my_project
+registry: data/registry.db
+provider: local
+online_store:
+    type: redis
+    connection_string: ${REDIS_CONNECTION_STRING:"0.0.0.0:6379"}
+```
+
 ---
 ## Summary
 
diff --git a/docs/reference/data-sources/README.md b/docs/reference/data-sources/README.md
index 89656b25702..b4fbc98b46f 100644
--- a/docs/reference/data-sources/README.md
+++ b/docs/reference/data-sources/README.md
@@ -22,6 +22,14 @@ Please see [Data Source](../../getting-started/concepts/feature-view.md#data-sou
 [push.md](push.md)
 {% endcontent-ref %}
 
+{% content-ref url="kafka.md" %}
+[kafka.md](kafka.md)
+{% endcontent-ref %}
+
+{% content-ref url="kinesis.md" %}
+[kinesis.md](kinesis.md)
+{% endcontent-ref %}
+
 {% content-ref url="spark.md" %}
 [spark.md](spark.md)
 {% endcontent-ref %}
diff --git a/docs/reference/data-sources/kafka.md b/docs/reference/data-sources/kafka.md
new file mode 100644
index 00000000000..8794c7a1e81
--- /dev/null
+++ b/docs/reference/data-sources/kafka.md
@@ -0,0 +1,75 @@
+# Kafka source
+
+**Warning**: This is an _experimental_ feature. It's intended for early testing and feedback, and could change without warnings in future releases.
+
+## Description
+
+Kafka sources allow users to register Kafka streams as data sources. Feast currently does not launch or monitor jobs to ingest data from Kafka. Users are responsible for launching and monitoring their own ingestion jobs, which should write feature values to the online store through [FeatureStore.write_to_online_store](https://rtd.feast.dev/en/latest/index.html#feast.feature_store.FeatureStore.write_to_online_store). An example of how to launch such a job with Spark can be found [here](https://github.com/feast-dev/feast/tree/master/sdk/python/feast/infra/contrib). Feast also provides functionality to write to the offline store using the `write_to_offline_store` functionality.
+
+Kafka sources must have a batch source specified. The batch source will be used for retrieving historical features. Thus users are also responsible for writing data from their Kafka streams to a batch data source such as a data warehouse table. When using a Kafka source as a stream source in the definition of a feature view, a batch source doesn't need to be specified in the feature view definition explicitly.
+
+## Stream sources
+Streaming data sources are important sources of feature values. A typical setup with streaming data looks like:
+
+1. Raw events come in (stream 1)
+2. Streaming transformations applied (e.g. generating features like `last_N_purchased_categories`) (stream 2)
+3. Write stream 2 values to an offline store as a historical log for training (optional)
+4. Write stream 2 values to an online store for low latency feature serving
+5. Periodically materialize feature values from the offline store into the online store for decreased training-serving skew and improved model performance
+
+## Example
+### Defining a Kafka source
+Note that the Kafka source has a batch source.
+```python
+from datetime import timedelta
+
+from feast import Field, FileSource, KafkaSource, stream_feature_view
+from feast.data_format import JsonFormat
+from feast.types import Float32
+
+driver_stats_batch_source = FileSource(
+    name="driver_stats_source",
+    path="data/driver_stats.parquet",
+    timestamp_field="event_timestamp",
+)
+
+driver_stats_stream_source = KafkaSource(
+    name="driver_stats_stream",
+    kafka_bootstrap_servers="localhost:9092",
+    topic="drivers",
+    timestamp_field="event_timestamp",
+    batch_source=driver_stats_batch_source,
+    message_format=JsonFormat(
+        schema_json="driver_id integer, event_timestamp timestamp, conv_rate double, acc_rate double, created timestamp"
+    ),
+    watermark_delay_threshold=timedelta(minutes=5),
+)
+```
+
+### Using the Kafka source in a stream feature view
+The Kafka source can be used in a stream feature view.
+```python
+@stream_feature_view(
+    entities=[driver],
+    ttl=timedelta(seconds=8640000000),
+    mode="spark",
+    schema=[
+        Field(name="conv_percentage", dtype=Float32),
+        Field(name="acc_percentage", dtype=Float32),
+    ],
+    timestamp_field="event_timestamp",
+    online=True,
+    source=driver_stats_stream_source,
+)
+def driver_hourly_stats_stream(df: DataFrame):
+    from pyspark.sql.functions import col
+
+    return (
+        df.withColumn("conv_percentage", col("conv_rate") * 100.0)
+        .withColumn("acc_percentage", col("acc_rate") * 100.0)
+        .drop("conv_rate", "acc_rate")
+    )
+```
+
+### Ingesting data
+See [here](https://github.com/feast-dev/streaming-tutorial) for a example of how to ingest data from a Kafka source into Feast.
diff --git a/docs/reference/data-sources/kinesis.md b/docs/reference/data-sources/kinesis.md
new file mode 100644
index 00000000000..f2adadfec03
--- /dev/null
+++ b/docs/reference/data-sources/kinesis.md
@@ -0,0 +1,74 @@
+# Kinesis source
+
+**Warning**: This is an _experimental_ feature. It's intended for early testing and feedback, and could change without warnings in future releases.
+
+## Description
+
+Kinesis sources allow users to register Kinesis streams as data sources. Feast currently does not launch or monitor jobs to ingest data from Kinesis. Users are responsible for launching and monitoring their own ingestion jobs, which should write feature values to the online store through [FeatureStore.write_to_online_store](https://rtd.feast.dev/en/latest/index.html#feast.feature_store.FeatureStore.write_to_online_store). An example of how to launch such a job with Spark to ingest from Kafka can be found [here](https://github.com/feast-dev/feast/tree/master/sdk/python/feast/infra/contrib); by using a different plugin, the example can be adapted to Kinesis. Feast also provides functionality to write to the offline store using the `write_to_offline_store` functionality.
+
+Kinesis sources must have a batch source specified. The batch source will be used for retrieving historical features. Thus users are also responsible for writing data from their Kinesis streams to a batch data source such as a data warehouse table. When using a Kinesis source as a stream source in the definition of a feature view, a batch source doesn't need to be specified in the feature view definition explicitly.
+
+## Stream sources
+Streaming data sources are important sources of feature values. A typical setup with streaming data looks like:
+
+1. Raw events come in (stream 1)
+2. Streaming transformations applied (e.g. generating features like `last_N_purchased_categories`) (stream 2)
+3. Write stream 2 values to an offline store as a historical log for training (optional)
+4. Write stream 2 values to an online store for low latency feature serving
+5. Periodically materialize feature values from the offline store into the online store for decreased training-serving skew and improved model performance
+
+## Example
+### Defining a Kinesis source
+Note that the Kinesis source has a batch source.
+```python
+from datetime import timedelta
+
+from feast import Field, FileSource, KinesisSource, stream_feature_view
+from feast.data_format import JsonFormat
+from feast.types import Float32
+
+driver_stats_batch_source = FileSource(
+    name="driver_stats_source",
+    path="data/driver_stats.parquet",
+    timestamp_field="event_timestamp",
+)
+
+driver_stats_stream_source = KinesisSource(
+    name="driver_stats_stream",
+    stream_name="drivers",
+    timestamp_field="event_timestamp",
+    batch_source=driver_stats_batch_source,
+    record_format=JsonFormat(
+        schema_json="driver_id integer, event_timestamp timestamp, conv_rate double, acc_rate double, created timestamp"
+    ),
+    watermark_delay_threshold=timedelta(minutes=5),
+)
+```
+
+### Using the Kinesis source in a stream feature view
+The Kinesis source can be used in a stream feature view.
+```python
+@stream_feature_view(
+    entities=[driver],
+    ttl=timedelta(seconds=8640000000),
+    mode="spark",
+    schema=[
+        Field(name="conv_percentage", dtype=Float32),
+        Field(name="acc_percentage", dtype=Float32),
+    ],
+    timestamp_field="event_timestamp",
+    online=True,
+    source=driver_stats_stream_source,
+)
+def driver_hourly_stats_stream(df: DataFrame):
+    from pyspark.sql.functions import col
+
+    return (
+        df.withColumn("conv_percentage", col("conv_rate") * 100.0)
+        .withColumn("acc_percentage", col("acc_rate") * 100.0)
+        .drop("conv_rate", "acc_rate")
+    )
+```
+
+### Ingesting data
+See [here](https://github.com/feast-dev/streaming-tutorial) for a example of how to ingest data from a Kafka source into Feast. The approach used in the tutorial can be easily adapted to work for Kinesis as well.
diff --git a/docs/reference/data-sources/push.md b/docs/reference/data-sources/push.md
index e6eff312ec1..6af070d1c41 100644
--- a/docs/reference/data-sources/push.md
+++ b/docs/reference/data-sources/push.md
@@ -4,28 +4,27 @@
 
 ## Description
 
-Push sources allow feature values to be pushed to the online store in real time. This allows fresh feature values to be made available to applications. Push sources supercede the 
+Push sources allow feature values to be pushed to the online store and offline store in real time. This allows fresh feature values to be made available to applications. Push sources supercede the
 [FeatureStore.write_to_online_store](https://rtd.feast.dev/en/latest/index.html#feast.feature_store.FeatureStore.write_to_online_store).
 
 Push sources can be used by multiple feature views. When data is pushed to a push source, Feast propagates the feature values to all the consuming feature views.
 
-Push sources must have a batch source specified, since that's the source used when retrieving historical features. 
-When using a PushSource as a stream source in the definition of a feature view, a batch source doesn't need to be specified in the definition explicitly.
+Push sources must have a batch source specified. The batch source will be used for retrieving historical features. Thus users are also responsible for pushing data to a batch data source such as a data warehouse table. When using a push source as a stream source in the definition of a feature view, a batch source doesn't need to be specified in the feature view definition explicitly.
 
 ## Stream sources
 Streaming data sources are important sources of feature values. A typical setup with streaming data looks like:
 
 1. Raw events come in (stream 1)
 2. Streaming transformations applied (e.g. generating features like `last_N_purchased_categories`) (stream 2)
-3. Write stream 2 values to an offline store as a historical log for training
+3. Write stream 2 values to an offline store as a historical log for training (optional)
 4. Write stream 2 values to an online store for low latency feature serving
-5. Periodically materialize feature values from the offline store into the online store for improved correctness
+5. Periodically materialize feature values from the offline store into the online store for decreased training-serving skew and improved model performance
 
-Feast now allows users to push features previously registered in a feature view to the online store for fresher features.
+Feast allows users to push features previously registered in a feature view to the online store for fresher features. It also allows users to push batches of stream data to the offline store by specifying that the push be directed to the offline store. This will push the data to the offline store declared in the repository configuration used to initialize the feature store.
 
 ## Example
 ### Defining a push source
-Note that the push schema needs to also include the entity
+Note that the push schema needs to also include the entity.
 
 ```python
 from feast import PushSource, ValueType, BigQuerySource, FeatureView, Feature, Field
@@ -45,14 +44,16 @@ fv = FeatureView(
 ```
 
 ### Pushing data
+Note that the `to` parameter is optional and defaults to online but we can specify these options: `PushMode.ONLINE`, `PushMode.OFFLINE`, or `PushMode.ONLINE_AND_OFFLINE`.
 ```python
 from feast import FeatureStore
 import pandas as pd
+from feast.data_source import PushMode
 
 fs = FeatureStore(...)
 feature_data_frame = pd.DataFrame()
-fs.push("push_source_name", feature_data_frame)
+fs.push("push_source_name", feature_data_frame, to=PushMode.ONLINE_AND_OFFLINE)
 ```
 
-See also [Python feature server](../feature-servers/python-feature-server.md) for instructions on how to push data to a deployed feature server. 
+See also [Python feature server](../feature-servers/python-feature-server.md) for instructions on how to push data to a deployed feature server.
 
diff --git a/docs/reference/feature-servers/go-feature-retrieval.md b/docs/reference/feature-servers/go-feature-retrieval.md
index 30c1a9ca721..685e7201cb6 100644
--- a/docs/reference/feature-servers/go-feature-retrieval.md
+++ b/docs/reference/feature-servers/go-feature-retrieval.md
@@ -35,6 +35,36 @@ go_feature_retrieval: True
 ```
 {% endcode %}
 
+## Feature logging
+
+Go feature server can log all requested entities and served features to a configured destination inside an offline store.
+This allows users to create new datasets from features served online. Those datasets could be used for future trainings or for
+feature validations. To enable feature logging we need to edit `feature_store.yaml`:
+```yaml
+project: my_feature_repo
+registry: data/registry.db
+provider: local
+online_store:
+  type: redis
+  connection_string: "localhost:6379"
+go_feature_retrieval: True
+feature_server:
+  feature_logging:
+    enable: True
+```
+
+Feature logging configuration in `feature_store.yaml` also allows to tweak some low-level parameters to achieve the best performance:
+```yaml
+feature_server:
+  feature_logging:
+    enable: True
+    flush_interval_secs: 300
+    write_to_disk_interval_secs: 30
+    emit_timeout_micro_secs: 10000
+    queue_capacity: 10000
+```
+All these parameters are optional. 
+
 ## Future/Current Work
 
 The Go feature retrieval online feature logging for Data Quality Monitoring is currently in development. More information can be found [here](https://docs.google.com/document/d/110F72d4NTv80p35wDSONxhhPBqWRwbZXG4f9mNEMd98/edit#heading=h.9gaqqtox9jg6).
diff --git a/docs/reference/feature-servers/python-feature-server.md b/docs/reference/feature-servers/python-feature-server.md
index 352f0edc167..ecc12dd12d6 100644
--- a/docs/reference/feature-servers/python-feature-server.md
+++ b/docs/reference/feature-servers/python-feature-server.md
@@ -2,7 +2,7 @@
 
 ## Overview
 
-The feature server is an HTTP endpoint that serves features with JSON I/O. This enables users to write + read features from Feast online stores using any programming language that can make HTTP requests. 
+The feature server is an HTTP endpoint that serves features with JSON I/O. This enables users to write + read features from Feast online stores using any programming language that can make HTTP requests.
 
 ## CLI
 
@@ -152,9 +152,12 @@ curl -X POST \
   }' | jq
 ```
 
-### Pushing features to the online store
-You can push data corresponding to a push source to the online store (note that timestamps need to be strings):
+### Pushing features to the online and offline stores
+You can push data corresponding to a push source to the online and offline stores (note that timestamps need to be strings):
 
+You can also define a pushmode to push stream or batch data, either to the online store, offline store, or both. The feature server will throw an error if the online/offline store doesn't support the push api functionality.
+
+The request definition for pushmode is a string parameter `to` where the options are: ["online", "offline", "online_and_offline"].
 ```text
 curl -X POST "http://localhost:6566/push" -d '{
     "push_source_name": "driver_hourly_stats_push_source",
@@ -165,7 +168,8 @@ curl -X POST "http://localhost:6566/push" -d '{
             "conv_rate": [1.0],
             "acc_rate": [1.0],
             "avg_daily_trips": [1000]
-    }
+    },
+    "to": "online_and_offline",
   }' | jq
 ```
 
@@ -187,9 +191,10 @@ event_dict = {
 }
 push_data = {
     "push_source_name":"driver_stats_push_source",
-    "df":event_dict
+    "df":event_dict,
+    "to":"online",
 }
 requests.post(
-    "http://localhost:6566/push", 
+    "http://localhost:6566/push",
     data=json.dumps(push_data))
 ```
diff --git a/docs/roadmap.md b/docs/roadmap.md
index c2f5511f1ed..19af4f95c91 100644
--- a/docs/roadmap.md
+++ b/docs/roadmap.md
@@ -4,7 +4,6 @@ The list below contains the functionality that contributors are planning to deve
 
 * Items below that are in development (or planned for development) will be indicated in parentheses.
 * We welcome contribution to all items in the roadmap!
-* Want to influence our roadmap and prioritization? Submit your feedback to [this form](https://docs.google.com/forms/d/e/1FAIpQLSfa1nRQ0sKz-JEFnMMCi4Jseag\_yDssO\_3nV9qMfxfrkil-wA/viewform).
 * Want to speak to a Feast contributor? We are more than happy to jump on a call. Please schedule a time using [Calendly](https://calendly.com/d/x2ry-g5bb/meet-with-feast-team).
 
 * **Data Sources**
@@ -37,17 +36,16 @@ The list below contains the functionality that contributors are planning to deve
   * [x] [Azure Cache for Redis (community plugin)](https://github.com/Azure/feast-azure)
   * [x] [Postgres (contrib plugin)](https://docs.feast.dev/reference/online-stores/postgres)
   * [x] [Custom online store support](https://docs.feast.dev/how-to-guides/adding-support-for-a-new-online-store)
+  * [x] [Cassandra / AstraDB](https://github.com/datastaxdevs/feast-cassandra-online-store)
   * [ ] Bigtable (in progress)
-  * [ ] Cassandra
-* **Streaming**
-  * [x] [Custom streaming ingestion job support](https://docs.feast.dev/how-to-guides/creating-a-custom-provider)
-  * [x] [Push based streaming data ingestion](https://docs.feast.dev/reference/data-sources/push.md)
-  * [ ] Streaming ingestion on AWS
-  * [ ] Streaming ingestion on GCP
 * **Feature Engineering**
   * [x] On-demand Transformations (Alpha release. See [RFC](https://docs.google.com/document/d/1lgfIw0Drc65LpaxbUu49RCeJgMew547meSJttnUqz7c/edit#))
+  * [x] Streaming Transformations (Alpha release. See [RFC](https://docs.google.com/document/d/1UzEyETHUaGpn0ap4G82DHluiCj7zEbrQLkJJkKSv4e8/edit))
   * [ ] Batch transformation (In progress. See [RFC](https://docs.google.com/document/d/1964OkzuBljifDvkV-0fakp2uaijnVzdwWNGdz7Vz50A/edit))
-  * [ ] Streaming transformation
+* **Streaming**
+  * [x] [Custom streaming ingestion job support](https://docs.feast.dev/how-to-guides/creating-a-custom-provider)
+  * [x] [Push based streaming data ingestion to online store (Alpha)](https://docs.feast.dev/reference/data-sources/push)
+  * [x] [Push based streaming data ingestion to offline store (Alpha)](https://docs.feast.dev/reference/data-sources/push)
 * **Deployments**
   * [x] AWS Lambda (Alpha release. See [RFC](https://docs.google.com/document/d/1eZWKWzfBif66LDN32IajpaG-j82LSHCCOzY6R7Ax7MI/edit))
   * [x] Kubernetes (See [guide](https://docs.feast.dev/how-to-guides/running-feast-in-production#4.3.-java-based-feature-server-deployed-on-kubernetes))
@@ -61,7 +59,7 @@ The list below contains the functionality that contributors are planning to deve
   * [ ] Java Client
   * [ ] Go Client
   * [ ] Delete API
-  * [ ] Feature Logging (for training)
+  * [] Feature Logging (for training)
 * **Data Quality Management (See [RFC](https://docs.google.com/document/d/110F72d4NTv80p35wDSONxhhPBqWRwbZXG4f9mNEMd98/edit))**
   * [x] Data profiling and validation (Great Expectations)
   * [ ] Training-serving skew detection (in progress)
@@ -72,6 +70,5 @@ The list below contains the functionality that contributors are planning to deve
   * [x] CLI for browsing feature registry
   * [x] Model-centric feature tracking (feature services)
   * [x] Amundsen integration (see [Feast extractor](https://github.com/amundsen-io/amundsen/blob/main/databuilder/databuilder/extractor/feast_extractor.py))
-  * [x] Feast Web UI (Alpha release. See [documentation](https://docs.feast.dev/reference/alpha-web-ui.md))
+  * [x] Feast Web UI (Alpha release. See [documentation](https://docs.feast.dev/reference/alpha-web-ui))
   * [ ] REST API for browsing feature registry
-  * [ ] Feature versioning
diff --git a/docs/tutorials/building-streaming-features.md b/docs/tutorials/building-streaming-features.md
new file mode 100644
index 00000000000..01e02b81a93
--- /dev/null
+++ b/docs/tutorials/building-streaming-features.md
@@ -0,0 +1,5 @@
+# Building streaming features
+
+Feast supports registering streaming feature views and Kafka and Kinesis streaming sources. It also provides an interface for stream processing called the `Stream Processor`. An example Kafka/Spark StreamProcessor is implemented in the contrib folder. For more details, please see the [RFC](https://docs.google.com/document/d/1UzEyETHUaGpn0ap4G82DHluiCj7zEbrQLkJJkKSv4e8/edit?usp=sharing) for more details.
+
+Please see [here](https://github.com/feast-dev/streaming-tutorial) for a tutorial on how to build a versioned streaming pipeline that registers your transformations, features, and data sources in Feast.
diff --git a/docs/tutorials/tutorials-overview.md b/docs/tutorials/tutorials-overview.md
index 32e64071b06..9432783a690 100644
--- a/docs/tutorials/tutorials-overview.md
+++ b/docs/tutorials/tutorials-overview.md
@@ -11,3 +11,5 @@ These Feast tutorials showcase how to use Feast to simplify end to end model tra
 {% page-ref page="driver-stats-on-snowflake.md" %}
 
 {% page-ref page="validating-historical-features.md" %}
+
+{% page-ref page="using-scalable-registry.md" %}
diff --git a/docs/tutorials/using-scalable-registry.md b/docs/tutorials/using-scalable-registry.md
new file mode 100644
index 00000000000..51fa50ff337
--- /dev/null
+++ b/docs/tutorials/using-scalable-registry.md
@@ -0,0 +1,36 @@
+---
+description: >-
+  Tutorial on how to use the SQL registry for scalable registry updates
+---
+
+# Using Scalable Registry
+
+## Overview
+
+By default, the registry Feast uses a file-based registry implementation, which stores the protobuf representation of the registry as a serialized file. This registry file can be stored in a local file system, or in cloud storage (in, say, S3 or GCS).
+
+However, there's inherent limitations with a file-based registry, since changing a single field in the registry requires re-writing the whole registry file. With multiple concurrent writers, this presents a risk of data loss, or bottlenecks writes to the registry since all changes have to be serialized (e.g. when running materialization for multiple feature views or time ranges concurrently).
+
+An alternative to the file-based registry is the [SQLRegistry](https://rtd.feast.dev/en/latest/feast.infra.registry_stores.html#feast.infra.registry_stores.sql.SqlRegistry) which ships with Feast. This implementation stores the registry in a relational database, and allows for changes to individual objects atomically.
+Under the hood, the SQL Registry implementation uses [SQLAlchemy](https://docs.sqlalchemy.org/en/14/) to abstract over the different databases. Consequently, any [database supported](https://docs.sqlalchemy.org/en/14/core/engines.html#supported-databases) by SQLAlchemy can be used by the SQL Registry.
+Feast can use the SQL Registry via a config change in the feature_store.yaml file. An example of how to configure this would be:
+
+```yaml
+project: <your project name>
+provider: <provider name>
+online_store: redis
+offline_store: file
+registry:
+    registry_type: sql
+    path: postgresql://postgres:mysecretpassword@127.0.0.1:55001/feast
+```
+
+Specifically, the registry_type needs to be set to sql in the registry config block. On doing so, the path should refer to the [Database URL](https://docs.sqlalchemy.org/en/14/core/engines.html#database-urls) for the database to be used, as expected by SQLAlchemy. No other additional commands are currently needed to configure this registry.
+
+There are some things to note about how the SQL registry works:
+- Once instantiated, the Registry ensures the tables needed to store data exist, and creates them if they do not.
+- Upon tearing down the feast project, the registry ensures that the tables are dropped from the database.
+- The schema for how data is laid out in tables can be found . It is intentionally simple, storing the serialized protobuf versions of each Feast object keyed by its name.
+
+## Example Usage: Concurrent materialization
+The SQL Registry should be used when materializing feature views concurrently to ensure correctness of data in the registry. This can be achieved by simply running feast materialize or feature_store.materialize multiple times using a correctly configured feature_store.yaml. This will make each materialization process talk to the registry database concurrently, and ensure the metadata updates are serialized.
\ No newline at end of file
diff --git a/examples/java-demo/feature_repo/driver_repo.py b/examples/java-demo/feature_repo/driver_repo.py
index c91e5a40bed..e17a5d9cf89 100644
--- a/examples/java-demo/feature_repo/driver_repo.py
+++ b/examples/java-demo/feature_repo/driver_repo.py
@@ -7,14 +7,14 @@
 from google.protobuf.duration_pb2 import Duration
 from feast.field import Field
 
-from feast import Entity, Feature, BatchFeatureView, FileSource, ValueType
+from feast import Entity, Feature, BatchFeatureView, FileSource
 
 driver_hourly_stats = FileSource(
     path="data/driver_stats_with_string.parquet",
     timestamp_field="event_timestamp",
     created_timestamp_column="created",
 )
-driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)
+driver = Entity(name="driver_id", description="driver id",)
 driver_hourly_stats_view = BatchFeatureView(
     name="driver_hourly_stats",
     entities=["driver_id"],
diff --git a/examples/quickstart/quickstart.ipynb b/examples/quickstart/quickstart.ipynb
index 60974d27513..d29ee4fa35f 100644
--- a/examples/quickstart/quickstart.ipynb
+++ b/examples/quickstart/quickstart.ipynb
@@ -1,20 +1,4 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Feast Codelab",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
@@ -54,28 +38,20 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "rXNMAAJKQPG5",
-        "outputId": "52297709-380b-4200-8e7c-3d0102a82ea4"
+        "outputId": "94fb2260-4453-45c9-ba77-5b384823a621"
       },
+      "outputs": [],
       "source": [
         "%%sh\n",
         "pip install feast -U -q\n",
         "pip install Pygments -q\n",
         "echo \"Please restart your runtime now (Runtime -> Restart runtime). This ensures that the correct dependencies are loaded.\""
-      ],
-      "execution_count": 1,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Please restart your runtime now (Runtime -> Restart runtime). This ensures that the correct dependencies are loaded.\n"
-          ]
-        }
       ]
     },
     {
@@ -107,28 +83,32 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 1,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "IhirSkgUvYau",
-        "outputId": "df90af1a-06bd-48a1-94e6-7def19e87d5f"
+        "outputId": "664367b9-6a2a-493d-fd78-6495fb459fa2"
       },
-      "source": [
-        "!feast init feature_repo"
-      ],
-      "execution_count": 1,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "Feast is an open source project that collects anonymized error reporting and usage statistics. To opt out or learn more see https://docs.feast.dev/reference/usage\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/fft/__init__.py:97: DeprecationWarning: The module numpy.dual is deprecated.  Instead of using dual, use the functions directly from numpy or scipy.\n",
+            "  from numpy.dual import register_func\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
+            "  supported_dtypes = [np.typeDict[x] for x in supported_dtypes]\n",
             "\n",
             "Creating a new Feast repository in \u001b[1m\u001b[32m/content/feature_repo\u001b[0m.\n",
             "\n"
           ]
         }
+      ],
+      "source": [
+        "!feast init feature_repo"
       ]
     },
     {
@@ -150,22 +130,18 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "9jXuzt4ovzA3",
-        "outputId": "bff15f0c-9f8e-4a3c-e605-5ad84be30709"
+        "outputId": "9e326892-f0cc-4d86-d0b2-f33f822f83a9"
       },
-      "source": [
-        "%cd feature_repo\n",
-        "!ls -R"
-      ],
-      "execution_count": 2,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "/content/feature_repo\n",
             ".:\n",
@@ -175,6 +151,10 @@
             "driver_stats.parquet\n"
           ]
         }
+      ],
+      "source": [
+        "%cd feature_repo\n",
+        "!ls -R"
       ]
     },
     {
@@ -198,21 +178,18 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "9_YJ--uYdtcP",
-        "outputId": "89268e31-6be0-43fb-e576-6d335a2c1dd9"
+        "outputId": "af56a8da-9ca2-4dd9-f73c-a60dd3e1613a"
       },
-      "source": [
-        "!pygmentize feature_store.yaml"
-      ],
-      "execution_count": 3,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\u001b[94mproject\u001b[39;49;00m: feature_repo\n",
             "\u001b[94mregistry\u001b[39;49;00m: data/registry.db\n",
@@ -221,6 +198,9 @@
             "    \u001b[94mpath\u001b[39;49;00m: data/online_store.db\n"
           ]
         }
+      ],
+      "source": [
+        "!pygmentize feature_store.yaml"
       ]
     },
     {
@@ -236,56 +216,21 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 424
+          "height": 423
         },
         "id": "sIF2lO59dwzi",
-        "outputId": "80e798d5-df21-4ebd-de1c-9bde282bd742"
+        "outputId": "8931930b-b32f-43e1-d45b-de230489c7b8"
       },
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "pd.read_parquet(\"data/driver_stats.parquet\")"
-      ],
-      "execution_count": 4,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "               event_timestamp  driver_id  conv_rate  acc_rate  \\\n",
-              "0    2022-03-31 14:00:00+00:00       1005   0.313336  0.231481   \n",
-              "1    2022-03-31 15:00:00+00:00       1005   0.959499  0.942614   \n",
-              "2    2022-03-31 16:00:00+00:00       1005   0.231786  0.313516   \n",
-              "3    2022-03-31 17:00:00+00:00       1005   0.886911  0.531613   \n",
-              "4    2022-03-31 18:00:00+00:00       1005   0.574945  0.718223   \n",
-              "...                        ...        ...        ...       ...   \n",
-              "1802 2022-04-15 12:00:00+00:00       1001   0.521622  0.266667   \n",
-              "1803 2022-04-15 13:00:00+00:00       1001   0.003188  0.535501   \n",
-              "1804 2021-04-12 07:00:00+00:00       1001   0.709081  0.823138   \n",
-              "1805 2022-04-08 02:00:00+00:00       1003   0.033297  0.053268   \n",
-              "1806 2022-04-08 02:00:00+00:00       1003   0.033297  0.053268   \n",
-              "\n",
-              "      avg_daily_trips                 created  \n",
-              "0                 303 2022-04-15 14:34:10.056  \n",
-              "1                 842 2022-04-15 14:34:10.056  \n",
-              "2                 782 2022-04-15 14:34:10.056  \n",
-              "3                 634 2022-04-15 14:34:10.056  \n",
-              "4                 441 2022-04-15 14:34:10.056  \n",
-              "...               ...                     ...  \n",
-              "1802              406 2022-04-15 14:34:10.056  \n",
-              "1803              593 2022-04-15 14:34:10.056  \n",
-              "1804              997 2022-04-15 14:34:10.056  \n",
-              "1805              534 2022-04-15 14:34:10.056  \n",
-              "1806              534 2022-04-15 14:34:10.056  \n",
-              "\n",
-              "[1807 rows x 6 columns]"
-            ],
             "text/html": [
               "\n",
-              "  <div id=\"df-fdad8041-cb31-4675-af7e-2643a4371f51\">\n",
+              "  <div id=\"df-5e62c34a-e5aa-4230-bfc9-685de97aaec5\">\n",
               "    <div class=\"colab-df-container\">\n",
               "      <div>\n",
               "<style scoped>\n",
@@ -316,48 +261,48 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>2022-03-31 14:00:00+00:00</td>\n",
+              "      <td>2022-05-30 19:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.313336</td>\n",
-              "      <td>0.231481</td>\n",
-              "      <td>303</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.061944</td>\n",
+              "      <td>0.517414</td>\n",
+              "      <td>467</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>2022-03-31 15:00:00+00:00</td>\n",
+              "      <td>2022-05-30 20:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.959499</td>\n",
-              "      <td>0.942614</td>\n",
-              "      <td>842</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.265881</td>\n",
+              "      <td>0.636260</td>\n",
+              "      <td>709</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>2022-03-31 16:00:00+00:00</td>\n",
+              "      <td>2022-05-30 21:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.231786</td>\n",
-              "      <td>0.313516</td>\n",
-              "      <td>782</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.830253</td>\n",
+              "      <td>0.461117</td>\n",
+              "      <td>731</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>2022-03-31 17:00:00+00:00</td>\n",
+              "      <td>2022-05-30 22:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.886911</td>\n",
-              "      <td>0.531613</td>\n",
-              "      <td>634</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.828208</td>\n",
+              "      <td>0.520325</td>\n",
+              "      <td>919</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>2022-03-31 18:00:00+00:00</td>\n",
+              "      <td>2022-05-30 23:00:00+00:00</td>\n",
               "      <td>1005</td>\n",
-              "      <td>0.574945</td>\n",
-              "      <td>0.718223</td>\n",
-              "      <td>441</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.375715</td>\n",
+              "      <td>0.084719</td>\n",
+              "      <td>874</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>...</th>\n",
@@ -370,54 +315,54 @@
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1802</th>\n",
-              "      <td>2022-04-15 12:00:00+00:00</td>\n",
+              "      <td>2022-06-14 17:00:00+00:00</td>\n",
               "      <td>1001</td>\n",
-              "      <td>0.521622</td>\n",
-              "      <td>0.266667</td>\n",
-              "      <td>406</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.016256</td>\n",
+              "      <td>0.293051</td>\n",
+              "      <td>908</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1803</th>\n",
-              "      <td>2022-04-15 13:00:00+00:00</td>\n",
+              "      <td>2022-06-14 18:00:00+00:00</td>\n",
               "      <td>1001</td>\n",
-              "      <td>0.003188</td>\n",
-              "      <td>0.535501</td>\n",
-              "      <td>593</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.651631</td>\n",
+              "      <td>0.855919</td>\n",
+              "      <td>685</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1804</th>\n",
               "      <td>2021-04-12 07:00:00+00:00</td>\n",
               "      <td>1001</td>\n",
-              "      <td>0.709081</td>\n",
-              "      <td>0.823138</td>\n",
-              "      <td>997</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.828805</td>\n",
+              "      <td>0.375509</td>\n",
+              "      <td>106</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1805</th>\n",
-              "      <td>2022-04-08 02:00:00+00:00</td>\n",
+              "      <td>2022-06-07 07:00:00+00:00</td>\n",
               "      <td>1003</td>\n",
-              "      <td>0.033297</td>\n",
-              "      <td>0.053268</td>\n",
-              "      <td>534</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.324065</td>\n",
+              "      <td>0.970185</td>\n",
+              "      <td>824</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1806</th>\n",
-              "      <td>2022-04-08 02:00:00+00:00</td>\n",
+              "      <td>2022-06-07 07:00:00+00:00</td>\n",
               "      <td>1003</td>\n",
-              "      <td>0.033297</td>\n",
-              "      <td>0.053268</td>\n",
-              "      <td>534</td>\n",
-              "      <td>2022-04-15 14:34:10.056</td>\n",
+              "      <td>0.324065</td>\n",
+              "      <td>0.970185</td>\n",
+              "      <td>824</td>\n",
+              "      <td>2022-06-14 19:00:52.584</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "<p>1807 rows × 6 columns</p>\n",
               "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdad8041-cb31-4675-af7e-2643a4371f51')\"\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e62c34a-e5aa-4230-bfc9-685de97aaec5')\"\n",
               "              title=\"Convert this dataframe to an interactive table.\"\n",
               "              style=\"display:none;\">\n",
               "        \n",
@@ -468,12 +413,12 @@
               "\n",
               "      <script>\n",
               "        const buttonEl =\n",
-              "          document.querySelector('#df-fdad8041-cb31-4675-af7e-2643a4371f51 button.colab-df-convert');\n",
+              "          document.querySelector('#df-5e62c34a-e5aa-4230-bfc9-685de97aaec5 button.colab-df-convert');\n",
               "        buttonEl.style.display =\n",
               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-fdad8041-cb31-4675-af7e-2643a4371f51');\n",
+              "          const element = document.querySelector('#df-5e62c34a-e5aa-4230-bfc9-685de97aaec5');\n",
               "          const dataTable =\n",
               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                     [key], {});\n",
@@ -493,11 +438,46 @@
               "    </div>\n",
               "  </div>\n",
               "  "
+            ],
+            "text/plain": [
+              "               event_timestamp  driver_id  conv_rate  acc_rate  \\\n",
+              "0    2022-05-30 19:00:00+00:00       1005   0.061944  0.517414   \n",
+              "1    2022-05-30 20:00:00+00:00       1005   0.265881  0.636260   \n",
+              "2    2022-05-30 21:00:00+00:00       1005   0.830253  0.461117   \n",
+              "3    2022-05-30 22:00:00+00:00       1005   0.828208  0.520325   \n",
+              "4    2022-05-30 23:00:00+00:00       1005   0.375715  0.084719   \n",
+              "...                        ...        ...        ...       ...   \n",
+              "1802 2022-06-14 17:00:00+00:00       1001   0.016256  0.293051   \n",
+              "1803 2022-06-14 18:00:00+00:00       1001   0.651631  0.855919   \n",
+              "1804 2021-04-12 07:00:00+00:00       1001   0.828805  0.375509   \n",
+              "1805 2022-06-07 07:00:00+00:00       1003   0.324065  0.970185   \n",
+              "1806 2022-06-07 07:00:00+00:00       1003   0.324065  0.970185   \n",
+              "\n",
+              "      avg_daily_trips                 created  \n",
+              "0                 467 2022-06-14 19:00:52.584  \n",
+              "1                 709 2022-06-14 19:00:52.584  \n",
+              "2                 731 2022-06-14 19:00:52.584  \n",
+              "3                 919 2022-06-14 19:00:52.584  \n",
+              "4                 874 2022-06-14 19:00:52.584  \n",
+              "...               ...                     ...  \n",
+              "1802              908 2022-06-14 19:00:52.584  \n",
+              "1803              685 2022-06-14 19:00:52.584  \n",
+              "1804              106 2022-06-14 19:00:52.584  \n",
+              "1805              824 2022-06-14 19:00:52.584  \n",
+              "1806              824 2022-06-14 19:00:52.584  \n",
+              "\n",
+              "[1807 rows x 6 columns]"
             ]
           },
+          "execution_count": 4,
           "metadata": {},
-          "execution_count": 4
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "pd.read_parquet(\"data/driver_stats.parquet\")"
       ]
     },
     {
@@ -524,27 +504,24 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "DPqXCoNpL0SX",
-        "outputId": "be1308b2-0c83-4dd3-eb88-e79ffcbd20d6"
+        "outputId": "a31a40c4-e60a-4f62-ae1c-227ce0aedea4"
       },
-      "source": [
-        "!pygmentize -f terminal16m example.py"
-      ],
-      "execution_count": 5,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "\u001b[38;2;64;128;128;03m# This is an example feature definition file\u001b[39;00m\n",
             "\n",
             "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mdatetime\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m timedelta\n",
             "\n",
-            "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mfeast\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Entity, FeatureView, Field, FileSource, ValueType\n",
+            "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mfeast\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Entity, FeatureService, FeatureView, Field, FileSource, ValueType\n",
             "\u001b[38;2;0;128;0;01mfrom\u001b[39;00m \u001b[38;2;0;0;255;01mfeast\u001b[39;00m\u001b[38;2;0;0;255;01m.\u001b[39;00m\u001b[38;2;0;0;255;01mtypes\u001b[39;00m \u001b[38;2;0;128;0;01mimport\u001b[39;00m Float32, Int64\n",
             "\n",
             "\u001b[38;2;64;128;128;03m# Read data from parquet files. Parquet is convenient for local development mode. For\u001b[39;00m\n",
@@ -558,7 +535,7 @@
             "\n",
             "\u001b[38;2;64;128;128;03m# Define an entity for the driver. You can think of entity as a primary key used to\u001b[39;00m\n",
             "\u001b[38;2;64;128;128;03m# fetch features.\u001b[39;00m\n",
-            "driver \u001b[38;2;102;102;102m=\u001b[39m Entity(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, value_type\u001b[38;2;102;102;102m=\u001b[39mValueType\u001b[38;2;102;102;102m.\u001b[39mINT64, join_key\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_id\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m,)\n",
+            "driver \u001b[38;2;102;102;102m=\u001b[39m Entity(name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, join_keys\u001b[38;2;102;102;102m=\u001b[39m[\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_id\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m], value_type\u001b[38;2;102;102;102m=\u001b[39mValueType\u001b[38;2;102;102;102m.\u001b[39mINT64,)\n",
             "\n",
             "\u001b[38;2;64;128;128;03m# Our parquet files contain sample data that includes a driver_id column, timestamps and\u001b[39;00m\n",
             "\u001b[38;2;64;128;128;03m# three feature column. Here we define a Feature View that will allow us to serve this\u001b[39;00m\n",
@@ -575,9 +552,16 @@
             "    online\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;0;128;0;01mTrue\u001b[39;00m,\n",
             "    source\u001b[38;2;102;102;102m=\u001b[39mdriver_hourly_stats,\n",
             "    tags\u001b[38;2;102;102;102m=\u001b[39m{},\n",
+            ")\n",
+            "\n",
+            "driver_stats_fs \u001b[38;2;102;102;102m=\u001b[39m FeatureService(\n",
+            "    name\u001b[38;2;102;102;102m=\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m\u001b[38;2;186;33;33mdriver_activity\u001b[39m\u001b[38;2;186;33;33m\"\u001b[39m, features\u001b[38;2;102;102;102m=\u001b[39m[driver_hourly_stats_view]\n",
             ")\n"
           ]
         }
+      ],
+      "source": [
+        "!pygmentize -f terminal16m example.py"
       ]
     },
     {
@@ -592,29 +576,34 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "RYKCKKrcxYZG",
-        "outputId": "9745d7eb-b4b8-4a43-bf47-189bbf07ae09"
+        "outputId": "f34aa509-1dc6-4e50-e8ee-12897138f3b9"
       },
-      "source": [
-        "!feast apply"
-      ],
-      "execution_count": 6,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Created entity \u001b[1m\u001b[32mdriver_id\u001b[0m\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/fft/__init__.py:97: DeprecationWarning: The module numpy.dual is deprecated.  Instead of using dual, use the functions directly from numpy or scipy.\n",
+            "  from numpy.dual import register_func\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
+            "  supported_dtypes = [np.typeDict[x] for x in supported_dtypes]\n",
+            "Created entity \u001b[1m\u001b[32mdriver\u001b[0m\n",
             "Created feature view \u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m\n",
+            "Created feature service \u001b[1m\u001b[32mdriver_activity\u001b[0m\n",
             "\n",
             "Created sqlite table \u001b[1m\u001b[32mfeature_repo_driver_hourly_stats\u001b[0m\n",
             "\n"
           ]
         }
+      ],
+      "source": [
+        "!feast apply"
       ]
     },
     {
@@ -633,13 +622,60 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "C6Fzia7YwBzz",
-        "outputId": "b99aedae-9c47-4b9f-acdd-cd02e2e091b7"
+        "outputId": "58c4c3dd-7a10-4f56-901d-1bb879ebbcb8"
       },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/scipy/fft/__init__.py:97: DeprecationWarning: The module numpy.dual is deprecated.  Instead of using dual, use the functions directly from numpy or scipy.\n",
+            "  from numpy.dual import register_func\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
+            "  supported_dtypes = [np.typeDict[x] for x in supported_dtypes]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "----- Feature schema -----\n",
+            "\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 3 entries, 1080 to 359\n",
+            "Data columns (total 6 columns):\n",
+            " #   Column                              Non-Null Count  Dtype              \n",
+            "---  ------                              --------------  -----              \n",
+            " 0   driver_id                           3 non-null      int64              \n",
+            " 1   label_driver_reported_satisfaction  3 non-null      int64              \n",
+            " 2   event_timestamp                     3 non-null      datetime64[ns, UTC]\n",
+            " 3   conv_rate                           3 non-null      float32            \n",
+            " 4   acc_rate                            3 non-null      float32            \n",
+            " 5   avg_daily_trips                     3 non-null      int32              \n",
+            "dtypes: datetime64[ns, UTC](1), float32(2), int32(1), int64(2)\n",
+            "memory usage: 132.0 bytes\n",
+            "None\n",
+            "\n",
+            "----- Example features -----\n",
+            "\n",
+            "      driver_id  label_driver_reported_satisfaction  \\\n",
+            "1080       1003                                   3   \n",
+            "720        1002                                   5   \n",
+            "359        1001                                   1   \n",
+            "\n",
+            "                      event_timestamp  conv_rate  acc_rate  avg_daily_trips  \n",
+            "1080 2022-06-14 17:48:10.734341+00:00   0.525623  0.217880              488  \n",
+            "720  2022-06-14 18:25:10.734338+00:00   0.181652  0.659991              974  \n",
+            "359  2022-06-14 18:50:10.734322+00:00   0.651631  0.855919              685  \n"
+          ]
+        }
+      ],
       "source": [
         "from datetime import datetime, timedelta\n",
         "import pandas as pd\n",
@@ -676,43 +712,6 @@
         "print()\n",
         "print(\"----- Example features -----\\n\")\n",
         "print(training_df.head())"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "----- Feature schema -----\n",
-            "\n",
-            "<class 'pandas.core.frame.DataFrame'>\n",
-            "Int64Index: 3 entries, 720 to 1081\n",
-            "Data columns (total 6 columns):\n",
-            " #   Column                              Non-Null Count  Dtype              \n",
-            "---  ------                              --------------  -----              \n",
-            " 0   driver_id                           3 non-null      int64              \n",
-            " 1   label_driver_reported_satisfaction  3 non-null      int64              \n",
-            " 2   event_timestamp                     3 non-null      datetime64[ns, UTC]\n",
-            " 3   conv_rate                           3 non-null      float32            \n",
-            " 4   acc_rate                            3 non-null      float32            \n",
-            " 5   avg_daily_trips                     3 non-null      int32              \n",
-            "dtypes: datetime64[ns, UTC](1), float32(2), int32(1), int64(2)\n",
-            "memory usage: 132.0 bytes\n",
-            "None\n",
-            "\n",
-            "----- Example features -----\n",
-            "\n",
-            "      driver_id  label_driver_reported_satisfaction  \\\n",
-            "720        1002                                   5   \n",
-            "359        1001                                   1   \n",
-            "1081       1003                                   3   \n",
-            "\n",
-            "                      event_timestamp  conv_rate  acc_rate  avg_daily_trips  \n",
-            "720  2022-04-15 13:58:30.900257+00:00   0.368052  0.417923              346  \n",
-            "359  2022-04-15 14:23:30.900240+00:00   0.003188  0.535501              593  \n",
-            "1081 2022-04-15 13:21:30.900260+00:00   0.214944  0.788695              904  \n"
-          ]
-        }
       ]
     },
     {
@@ -732,34 +731,45 @@
       "source": [
         "### Step 5a: Using `feast materialize-incremental`\n",
         "\n",
-        "We now serialize the latest values of features since the beginning of time to prepare for serving (note: `materialize-incremental` serializes all new features since the last `materialize` call)."
+        "We now serialize the latest values of features since the beginning of time to prepare for serving (note: `materialize-incremental` serializes all new features since the last `materialize` call).\n",
+        "\n",
+        "An alternative to using the CLI command is to use Python:\n",
+        "\n",
+        "```python\n",
+        "from datetime import datetime\n",
+        "store.materialize_incremental(datetime.datetime.now())\n",
+        "```"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
-        "id": "7Z6QxIebAhK5",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "d38a0e0a-2802-4408-ab92-a26baf82752e"
+        "id": "7Z6QxIebAhK5",
+        "outputId": "9b54777d-2dd8-4ec3-b4e7-e3275800a980"
       },
-      "source": [
-        "from datetime import datetime\n",
-        "!feast materialize-incremental {datetime.now().isoformat()}"
-      ],
-      "execution_count": 8,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2022-04-15 14:34:37+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/fft/__init__.py:97: DeprecationWarning: The module numpy.dual is deprecated.  Instead of using dual, use the functions directly from numpy or scipy.\n",
+            "  from numpy.dual import register_func\n",
+            "/usr/local/lib/python3.7/dist-packages/scipy/sparse/sputils.py:17: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
+            "  supported_dtypes = [np.typeDict[x] for x in supported_dtypes]\n",
+            "Materializing \u001b[1m\u001b[32m1\u001b[0m feature views to \u001b[1m\u001b[32m2022-06-14 19:01:13+00:00\u001b[0m into the \u001b[1m\u001b[32msqlite\u001b[0m online store.\n",
             "\n",
-            "\u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m from \u001b[1m\u001b[32m2022-04-14 14:34:38+00:00\u001b[0m to \u001b[1m\u001b[32m2022-04-15 14:34:37+00:00\u001b[0m:\n",
-            "100%|████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 434.17it/s]\n"
+            "\u001b[1m\u001b[32mdriver_hourly_stats\u001b[0m from \u001b[1m\u001b[32m2022-06-13 19:01:14+00:00\u001b[0m to \u001b[1m\u001b[32m2022-06-14 19:01:13+00:00\u001b[0m:\n",
+            "100%|████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 403.44it/s]\n"
           ]
         }
+      ],
+      "source": [
+        "from datetime import datetime\n",
+        "!feast materialize-incremental {datetime.now().isoformat()}"
       ]
     },
     {
@@ -775,13 +785,27 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "aVIgSYhI4cvR",
-        "outputId": "eada224a-bd86-4933-f0ce-5e66870918cc"
+        "outputId": "3c60f99c-2471-4343-83ed-cc60a6a9c3b2"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--- Data directory ---\n",
+            "driver_stats.parquet  online_store.db  registry.db\n",
+            "\n",
+            "--- Schema of online store ---\n",
+            "['entity_key', 'feature_name', 'value', 'event_ts', 'created_ts']\n"
+          ]
+        }
+      ],
       "source": [
         "print(\"--- Data directory ---\")\n",
         "!ls data\n",
@@ -794,20 +818,6 @@
         "    pd.read_sql_query(\n",
         "        \"SELECT * FROM feature_repo_driver_hourly_stats\", con).columns.tolist())\n",
         "con.close()"
-      ],
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "--- Data directory ---\n",
-            "driver_stats.parquet  online_store.db  registry.db\n",
-            "\n",
-            "--- Schema of online store ---\n",
-            "['entity_key', 'feature_name', 'value', 'event_ts', 'created_ts']\n"
-          ]
-        }
       ]
     },
     {
@@ -828,7 +838,7 @@
         "id": "GNecKOaI0J2Z"
       },
       "source": [
-        "## Step 6: Fetching feature vectors for inference\n"
+        "## Step 6: Fetching feature vectors for inference"
       ]
     },
     {
@@ -842,13 +852,26 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "a-PUsUWUxoH9",
-        "outputId": "7acecf34-8e5a-4a87-caa8-c2bff39d1376"
+        "outputId": "fc52dc04-db87-4f48-df36-d3941d485600"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'acc_rate': [0.11573542654514313, 0.19155333936214447],\n",
+            " 'avg_daily_trips': [814, 64],\n",
+            " 'conv_rate': [0.5311259031295776, 0.9771925806999207],\n",
+            " 'driver_id': [1004, 1005]}\n"
+          ]
+        }
+      ],
       "source": [
         "from pprint import pprint\n",
         "from feast import FeatureStore\n",
@@ -868,19 +891,101 @@
         ").to_dict()\n",
         "\n",
         "pprint(feature_vector)"
-      ],
-      "execution_count": 10,
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SRY87OMBoK_Z"
+      },
+      "source": [
+        "### Fetching features using feature services\n",
+        "You can also use feature services to manage multiple features, and decouple feature view definitions and the features needed by end applications. The feature store can also be used to fetch either online or historical features using the same api below. More information can be found [here](https://docs.feast.dev/getting-started/concepts/feature-retrieval).\n",
+        "\n",
+        " The `driver_activity` feature service pulls all features from the `driver_hourly_stats` feature view:\n",
+        "\n",
+        "```python\n",
+        "driver_stats_fs = FeatureService(\n",
+        "    name=\"driver_activity\", features=[driver_hourly_stats_view]\n",
+        ")\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BrnAEKlPn9s8",
+        "outputId": "45f7f075-5243-4fa7-dbd4-63c0c22a68cd"
+      },
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "{'acc_rate': [0.29913440346717834, 0.4562472701072693],\n",
-            " 'avg_daily_trips': [474, 261],\n",
-            " 'conv_rate': [0.20237785577774048, 0.30597227811813354],\n",
+            "{'acc_rate': [0.11573542654514313, 0.19155333936214447],\n",
+            " 'avg_daily_trips': [814, 64],\n",
+            " 'conv_rate': [0.5311259031295776, 0.9771925806999207],\n",
             " 'driver_id': [1004, 1005]}\n"
           ]
         }
+      ],
+      "source": [
+        "from feast import FeatureStore\n",
+        "feature_store = FeatureStore('.')  # Initialize the feature store\n",
+        "\n",
+        "feature_service = feature_store.get_feature_service(\"driver_activity\")\n",
+        "feature_vector = feature_store.get_online_features(\n",
+        "    features=feature_service,\n",
+        "    entity_rows=[\n",
+        "        # {join_key: entity_value}\n",
+        "        {\"driver_id\": 1004},\n",
+        "        {\"driver_id\": 1005},\n",
+        "    ],\n",
+        ").to_dict()\n",
+        "pprint(feature_vector)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_dBcqkaCnOYv"
+      },
+      "source": [
+        "## Step 7: Explore registered features with the Web UI"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 52
+        },
+        "id": "mCUPypyhl5TH",
+        "outputId": "fb2475c3-b254-42e6-b638-7982d52d2a19"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "nohup: appending output to 'nohup.out'\n",
+            "Open the Web UI at https://c6cuffvc4qm-496ff2e9c6d22116-8888-colab.googleusercontent.com/\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab.output import eval_js\n",
+        "host = eval_js(\"google.colab.kernel.proxyPort(8888)\")\n",
+        "\n",
+        "!nohup feast ui &\n",
+        "\n",
+        "print(f\"Open the Web UI at {host}\")"
       ]
     },
     {
@@ -897,5 +1002,21 @@
         "- Join other Feast users and contributors in [Slack](https://slack.feast.dev/) and become part of the community!"
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "Feast Codelab",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/go.mod b/go.mod
index f983dae3442..fbbc95e1bfe 100644
--- a/go.mod
+++ b/go.mod
@@ -2,8 +2,10 @@ module github.com/feast-dev/feast
 
 go 1.17
 
+replace github.com/go-python/gopy v0.4.0 => github.com/feast-dev/gopy v0.4.1-0.20220429180328-4257ac71a4d0
+
 require (
-	github.com/apache/arrow/go/v8 v8.0.0-20220408212425-58fe60f59289
+	github.com/apache/arrow/go/v8 v8.0.0
 	github.com/ghodss/yaml v1.0.0
 	github.com/go-python/gopy v0.4.0
 	github.com/go-redis/redis/v8 v8.11.4
@@ -26,8 +28,6 @@ require (
 	github.com/dgryski/go-rendezvous v0.0.0-20200823014737-9f7001d12a5f // indirect
 	github.com/goccy/go-json v0.9.6 // indirect
 	github.com/golang/snappy v0.0.4 // indirect
-	github.com/gonuts/commander v0.1.0 // indirect
-	github.com/gonuts/flag v0.1.0 // indirect
 	github.com/google/flatbuffers v2.0.6+incompatible // indirect
 	github.com/klauspost/asmfmt v1.3.2 // indirect
 	github.com/klauspost/compress v1.15.1 // indirect
@@ -40,14 +40,12 @@ require (
 	golang.org/x/exp v0.0.0-20220407100705-7b9b53b0aca4 // indirect
 	golang.org/x/mod v0.6.0-dev.0.20220106191415-9b9b3d81d5e3 // indirect
 	golang.org/x/net v0.0.0-20220407224826-aac1ed45d8e3 // indirect
-	golang.org/x/sys v0.0.0-20220406163625-3f8b81556e12 // indirect
+	golang.org/x/sys v0.0.0-20220412211240-33da011f77ad // indirect
 	golang.org/x/text v0.3.7 // indirect
-	golang.org/x/tools v0.1.10 // indirect
-	golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1 // indirect
+	golang.org/x/tools v0.1.11-0.20220413170336-afc6aad76eb1 // indirect
+	golang.org/x/xerrors v0.0.0-20220411194840-2f41105eb62f // indirect
 	google.golang.org/genproto v0.0.0-20220407144326-9054f6ed7bac // indirect
 	gopkg.in/check.v1 v1.0.0-20201130134442-10cb98267c6c // indirect
 	gopkg.in/yaml.v2 v2.4.0 // indirect
 	gopkg.in/yaml.v3 v3.0.0-20200313102051-9f266ea9e77c // indirect
 )
-
-replace github.com/go-python/gopy v0.4.0 => github.com/feast-dev/gopy v0.4.1-0.20220429180328-4257ac71a4d0
diff --git a/go.sum b/go.sum
index e1f2436b296..698d1ef640d 100644
--- a/go.sum
+++ b/go.sum
@@ -21,8 +21,8 @@ github.com/alecthomas/units v0.0.0-20190717042225-c3de453c63f4/go.mod h1:ybxpYRF
 github.com/andybalholm/brotli v1.0.4 h1:V7DdXeJtZscaqfNuAdSRuRFzuiKlHSC/Zh3zl9qY3JY=
 github.com/andybalholm/brotli v1.0.4/go.mod h1:fO7iG3H7G2nSZ7m0zPUDn85XEX2GTukHGRSepvi9Eig=
 github.com/antihax/optional v1.0.0/go.mod h1:uupD/76wgC+ih3iEmQUL+0Ugr19nfwCT1kdvxnR2qWY=
-github.com/apache/arrow/go/v8 v8.0.0-20220408212425-58fe60f59289 h1:nzLohtTu6I/mbr7xjFrMKDk88mkL/Lcp8kVcIITGjaE=
-github.com/apache/arrow/go/v8 v8.0.0-20220408212425-58fe60f59289/go.mod h1:UUe+gJaMnuFD6icfGSJxUjG/tX/POUbPS/wE+EFyncM=
+github.com/apache/arrow/go/v8 v8.0.0 h1:mG1dDlq8aQO4a/PB00T9H19Ga2imvqoFPHI5cykpibs=
+github.com/apache/arrow/go/v8 v8.0.0/go.mod h1:63co72EKYQT9WKr8Y1Yconk4dysC0t79wNDauYO1ZGg=
 github.com/apache/thrift v0.12.0/go.mod h1:cp2SuWMxlEZw2r+iP2GNCdIi4C1qmUzdZFSVb+bacwQ=
 github.com/apache/thrift v0.13.0/go.mod h1:cp2SuWMxlEZw2r+iP2GNCdIi4C1qmUzdZFSVb+bacwQ=
 github.com/apache/thrift v0.15.0 h1:aGvdaR0v1t9XLgjtBYwxcBvBOTMqClzwE26CHOgjW1Y=
@@ -115,7 +115,6 @@ github.com/go-redis/redis/v8 v8.11.4/go.mod h1:2Z2wHZXdQpCDXEGzqMockDpNyYvi2l4Px
 github.com/go-sql-driver/mysql v1.4.0/go.mod h1:zAC/RDZ24gD3HViQzih4MyKcchzm+sOG5ZlKdlhCg5w=
 github.com/go-stack/stack v1.8.0/go.mod h1:v0f6uXyyMGvRgIKkXu+yp6POWl0qKG85gN/melR3HDY=
 github.com/go-task/slim-sprig v0.0.0-20210107165309-348f09dbbbc0/go.mod h1:fyg7847qk6SyHyPtNmDHnmrv/HOrqktSC+C9fM+CJOE=
-github.com/goccy/go-json v0.7.10/go.mod h1:6MelG93GURQebXPDq3khkgXZkazVtN9CRI+MGFi0w8I=
 github.com/goccy/go-json v0.9.6 h1:5/4CtRQdtsX0sal8fdVhTaiMN01Ri8BExZZ8iRmHQ6E=
 github.com/goccy/go-json v0.9.6/go.mod h1:6MelG93GURQebXPDq3khkgXZkazVtN9CRI+MGFi0w8I=
 github.com/gogo/googleapis v1.1.0/go.mod h1:gf4bu3Q80BeJ6H1S1vYPm8/ELATdvryBaNFGgqEef3s=
@@ -146,9 +145,7 @@ github.com/golang/protobuf v1.5.2/go.mod h1:XVQd3VNwM+JqD3oG2Ue2ip4fOMUkwXdXDdiu
 github.com/golang/snappy v0.0.0-20180518054509-2e65f85255db/go.mod h1:/XxbfmMg8lxefKM7IXC3fBNl/7bRcc72aCRzEWrmP2Q=
 github.com/golang/snappy v0.0.4 h1:yAGX7huGHXlcLOEtBnF4w7FQwA26wojNCwOYAEhLjQM=
 github.com/golang/snappy v0.0.4/go.mod h1:/XxbfmMg8lxefKM7IXC3fBNl/7bRcc72aCRzEWrmP2Q=
-github.com/gonuts/commander v0.1.0 h1:EcDTiVw9oAVORFjQOEOuHQqcl6OXMyTgELocTq6zJ0I=
 github.com/gonuts/commander v0.1.0/go.mod h1:qkb5mSlcWodYgo7vs8ulLnXhfinhZsZcm6+H/z1JjgY=
-github.com/gonuts/flag v0.1.0 h1:fqMv/MZ+oNGu0i9gp0/IQ/ZaPIDoAZBOBaJoV7viCWM=
 github.com/gonuts/flag v0.1.0/go.mod h1:ZTmTGtrSPejTo/SRNhCqwLTmiAgyBdCkLYhHrAoBdz4=
 github.com/google/btree v0.0.0-20180813153112-4030bb1f1f0c/go.mod h1:lNA+9X1NB3Zf8V7Ke586lFgjr2dZNuvo3lPJSGZ5JPQ=
 github.com/google/btree v1.0.0/go.mod h1:lNA+9X1NB3Zf8V7Ke586lFgjr2dZNuvo3lPJSGZ5JPQ=
@@ -511,9 +508,8 @@ golang.org/x/sys v0.0.0-20210510120138-977fb7262007/go.mod h1:oPkhp1MJrh7nUepCBc
 golang.org/x/sys v0.0.0-20210615035016-665e8c7367d1/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20211019181941-9d821ace8654/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20211216021012-1d35b9e2eb4e/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.0.0-20220128215802-99c3d69c2c27/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
-golang.org/x/sys v0.0.0-20220406163625-3f8b81556e12 h1:QyVthZKMsyaQwBTJE04jdNN0Pp5Fn9Qga0mrgxyERQM=
-golang.org/x/sys v0.0.0-20220406163625-3f8b81556e12/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/sys v0.0.0-20220412211240-33da011f77ad h1:ntjMns5wyP/fN65tdBD4g8J5w8n015+iIIs9rtjXkY0=
+golang.org/x/sys v0.0.0-20220412211240-33da011f77ad/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
 golang.org/x/term v0.0.0-20210927222741-03fcf44c2211/go.mod h1:jbD1KX2456YbFQfuXm/mYQcufACuNUgVhRMnK/tPxf8=
 golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
@@ -547,14 +543,15 @@ golang.org/x/tools v0.0.0-20200117012304-6edc0a871e69/go.mod h1:TB2adYChydJhpapK
 golang.org/x/tools v0.0.0-20201224043029-2b0845dc783e/go.mod h1:emZCQorbCU4vsT4fOWvOPXz4eW1wZW4PmDk9uLelYpA=
 golang.org/x/tools v0.1.0/go.mod h1:xkSsbof2nBLbhDlRMhhhyNLN/zl3eTqcnHD5viDpcZ0=
 golang.org/x/tools v0.1.8-0.20211029000441-d6a9af8af023/go.mod h1:nABZi5QlRsZVlzPpHl034qft6wpY4eDcsTt5AaioBiU=
-golang.org/x/tools v0.1.9/go.mod h1:nABZi5QlRsZVlzPpHl034qft6wpY4eDcsTt5AaioBiU=
-golang.org/x/tools v0.1.10 h1:QjFRCZxdOhBJ/UNgnBZLbNV13DlbnK0quyivTnXJM20=
 golang.org/x/tools v0.1.10/go.mod h1:Uh6Zz+xoGYZom868N8YTex3t7RhtHDBrE8Gzo9bV56E=
+golang.org/x/tools v0.1.11-0.20220413170336-afc6aad76eb1 h1:Z3vE1sGlC7qiyFJkkDcZms8Y3+yV8+W7HmDSmuf71tM=
+golang.org/x/tools v0.1.11-0.20220413170336-afc6aad76eb1/go.mod h1:Uh6Zz+xoGYZom868N8YTex3t7RhtHDBrE8Gzo9bV56E=
 golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191011141410-1b5146add898/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 golang.org/x/xerrors v0.0.0-20191204190536-9bdfabe68543/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
-golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1 h1:go1bK/D/BFZV2I8cIQd1NKEZ+0owSTG1fDTci4IqFcE=
 golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
+golang.org/x/xerrors v0.0.0-20220411194840-2f41105eb62f h1:GGU+dLjvlC3qDwqYgL6UgRmHXhOOgns0bZu2Ty5mm6U=
+golang.org/x/xerrors v0.0.0-20220411194840-2f41105eb62f/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0=
 gonum.org/v1/gonum v0.0.0-20180816165407-929014505bf4/go.mod h1:Y+Yx5eoAFn32cQvJDxZx5Dpnq+c3wtXuadVZAcxbbBo=
 gonum.org/v1/gonum v0.8.2/go.mod h1:oe/vMfY3deqTw+1EZJhuvEW2iwGF1bW9wwu7XCu0+v0=
 gonum.org/v1/gonum v0.9.3 h1:DnoIG+QAMaF5NvxnGe/oKsgKcAc6PcUyl8q0VetfQ8s=
diff --git a/go/embedded/online_features.go b/go/embedded/online_features.go
index 7cd1e4ed815..f6b21169e10 100644
--- a/go/embedded/online_features.go
+++ b/go/embedded/online_features.go
@@ -31,6 +31,9 @@ import (
 type OnlineFeatureService struct {
 	fs         *feast.FeatureStore
 	grpcStopCh chan os.Signal
+	httpStopCh chan os.Signal
+
+	err error
 }
 
 type OnlineFeatureServiceConfig struct {
@@ -55,19 +58,25 @@ type LoggingOptions struct {
 func NewOnlineFeatureService(conf *OnlineFeatureServiceConfig, transformationCallback transformation.TransformationCallback) *OnlineFeatureService {
 	repoConfig, err := registry.NewRepoConfigFromJSON(conf.RepoPath, conf.RepoConfig)
 	if err != nil {
-		log.Fatalln(err)
+		return &OnlineFeatureService{
+			err: err,
+		}
 	}
 
 	fs, err := feast.NewFeatureStore(repoConfig, transformationCallback)
 	if err != nil {
-		log.Fatalln(err)
+		return &OnlineFeatureService{
+			err: err,
+		}
 	}
 
-	// Notify this channel when receiving interrupt or termination signals from OS
-	c := make(chan os.Signal, 1)
-	signal.Notify(c, syscall.SIGINT, syscall.SIGTERM)
+	// Notify these channels when receiving interrupt or termination signals from OS
+	httpStopCh := make(chan os.Signal, 1)
+	grpcStopCh := make(chan os.Signal, 1)
+	signal.Notify(httpStopCh, syscall.SIGINT, syscall.SIGTERM)
+	signal.Notify(grpcStopCh, syscall.SIGINT, syscall.SIGTERM)
 
-	return &OnlineFeatureService{fs: fs, grpcStopCh: c}
+	return &OnlineFeatureService{fs: fs, httpStopCh: httpStopCh, grpcStopCh: grpcStopCh}
 }
 
 func (s *OnlineFeatureService) GetEntityTypesMap(featureRefs []string) (map[string]int32, error) {
@@ -80,12 +89,6 @@ func (s *OnlineFeatureService) GetEntityTypesMap(featureRefs []string) (map[stri
 		viewNames[viewName] = nil
 	}
 
-	entities, _ := s.fs.ListEntities(true)
-	entitiesByName := make(map[string]*model.Entity)
-	for _, entity := range entities {
-		entitiesByName[entity.Name] = entity
-	}
-
 	joinKeyTypes := make(map[string]int32)
 
 	for viewName := range viewNames {
@@ -94,9 +97,8 @@ func (s *OnlineFeatureService) GetEntityTypesMap(featureRefs []string) (map[stri
 			// skip on demand feature views
 			continue
 		}
-		for _, entityName := range view.Entities {
-			entity := entitiesByName[entityName]
-			joinKeyTypes[entity.JoinKey] = int32(entity.ValueType.Number())
+		for _, entityColumn := range view.EntityColumns {
+			joinKeyTypes[entityColumn.Name] = int32(entityColumn.Dtype.Number())
 		}
 	}
 
@@ -111,27 +113,24 @@ func (s *OnlineFeatureService) GetEntityTypesMapByFeatureService(featureServiceN
 
 	joinKeyTypes := make(map[string]int32)
 
-	entities, _ := s.fs.ListEntities(true)
-	entitiesByName := make(map[string]*model.Entity)
-	for _, entity := range entities {
-		entitiesByName[entity.Name] = entity
-	}
-
 	for _, projection := range featureService.Projections {
 		view, err := s.fs.GetFeatureView(projection.Name, true)
 		if err != nil {
 			// skip on demand feature views
 			continue
 		}
-		for _, entityName := range view.Entities {
-			entity := entitiesByName[entityName]
-			joinKeyTypes[entity.JoinKey] = int32(entity.ValueType.Number())
+		for _, entityColumn := range view.EntityColumns {
+			joinKeyTypes[entityColumn.Name] = int32(entityColumn.Dtype.Number())
 		}
 	}
 
 	return joinKeyTypes, nil
 }
 
+func (s *OnlineFeatureService) CheckForInstantiationError() error {
+	return s.err
+}
+
 func (s *OnlineFeatureService) GetOnlineFeatures(
 	featureRefs []string,
 	featureServiceName string,
@@ -239,15 +238,12 @@ func (s *OnlineFeatureService) StartGprcServerWithLoggingDefaultOpts(host string
 	return s.StartGprcServerWithLogging(host, port, writeLoggedFeaturesCallback, defaultOpts)
 }
 
-// StartGprcServerWithLogging starts gRPC server with enabled feature logging
-// Caller of this function must provide Python callback to flush buffered logs as well as logging configuration (loggingOpts)
-func (s *OnlineFeatureService) StartGprcServerWithLogging(host string, port int, writeLoggedFeaturesCallback logging.OfflineStoreWriteCallback, loggingOpts LoggingOptions) error {
+func (s *OnlineFeatureService) constructLoggingService(writeLoggedFeaturesCallback logging.OfflineStoreWriteCallback, loggingOpts LoggingOptions) (*logging.LoggingService, error) {
 	var loggingService *logging.LoggingService = nil
-	var err error
 	if writeLoggedFeaturesCallback != nil {
 		sink, err := logging.NewOfflineStoreSink(writeLoggedFeaturesCallback)
 		if err != nil {
-			return err
+			return nil, err
 		}
 
 		loggingService, err = logging.NewLoggingService(s.fs, sink, logging.LoggingOptions{
@@ -257,9 +253,19 @@ func (s *OnlineFeatureService) StartGprcServerWithLogging(host string, port int,
 			FlushInterval:   loggingOpts.FlushInterval,
 		})
 		if err != nil {
-			return err
+			return nil, err
 		}
 	}
+	return loggingService, nil
+}
+
+// StartGprcServerWithLogging starts gRPC server with enabled feature logging
+// Caller of this function must provide Python callback to flush buffered logs as well as logging configuration (loggingOpts)
+func (s *OnlineFeatureService) StartGprcServerWithLogging(host string, port int, writeLoggedFeaturesCallback logging.OfflineStoreWriteCallback, loggingOpts LoggingOptions) error {
+	loggingService, err := s.constructLoggingService(writeLoggedFeaturesCallback, loggingOpts)
+	if err != nil {
+		return err
+	}
 	ser := server.NewGrpcServingServiceServer(s.fs, loggingService)
 	log.Printf("Starting a gRPC server on host %s port %d\n", host, port)
 	lis, err := net.Listen("tcp", fmt.Sprintf("%s:%d", host, port))
@@ -273,12 +279,12 @@ func (s *OnlineFeatureService) StartGprcServerWithLogging(host string, port int,
 	go func() {
 		// As soon as these signals are received from OS, try to gracefully stop the gRPC server
 		<-s.grpcStopCh
-		fmt.Println("Stopping the gRPC server...")
+		log.Println("Stopping the gRPC server...")
 		grpcServer.GracefulStop()
 		if loggingService != nil {
 			loggingService.Stop()
 		}
-		fmt.Println("gRPC server terminated")
+		log.Println("gRPC server terminated")
 	}()
 
 	err = grpcServer.Serve(lis)
@@ -288,7 +294,55 @@ func (s *OnlineFeatureService) StartGprcServerWithLogging(host string, port int,
 	return nil
 }
 
-func (s *OnlineFeatureService) Stop() {
+// StartHttpServer starts HTTP server with disabled feature logging and blocks the thread
+func (s *OnlineFeatureService) StartHttpServer(host string, port int) error {
+	return s.StartHttpServerWithLogging(host, port, nil, LoggingOptions{})
+}
+
+// StartHttpServerWithLoggingDefaultOpts starts HTTP server with enabled feature logging but default configuration for logging
+// Caller of this function must provide Python callback to flush buffered logs
+func (s *OnlineFeatureService) StartHttpServerWithLoggingDefaultOpts(host string, port int, writeLoggedFeaturesCallback logging.OfflineStoreWriteCallback) error {
+	defaultOpts := LoggingOptions{
+		ChannelCapacity: logging.DefaultOptions.ChannelCapacity,
+		EmitTimeout:     logging.DefaultOptions.EmitTimeout,
+		WriteInterval:   logging.DefaultOptions.WriteInterval,
+		FlushInterval:   logging.DefaultOptions.FlushInterval,
+	}
+	return s.StartHttpServerWithLogging(host, port, writeLoggedFeaturesCallback, defaultOpts)
+}
+
+// StartHttpServerWithLogging starts HTTP server with enabled feature logging
+// Caller of this function must provide Python callback to flush buffered logs as well as logging configuration (loggingOpts)
+func (s *OnlineFeatureService) StartHttpServerWithLogging(host string, port int, writeLoggedFeaturesCallback logging.OfflineStoreWriteCallback, loggingOpts LoggingOptions) error {
+	loggingService, err := s.constructLoggingService(writeLoggedFeaturesCallback, loggingOpts)
+	if err != nil {
+		return err
+	}
+	ser := server.NewHttpServer(s.fs, loggingService)
+	log.Printf("Starting a HTTP server on host %s port %d\n", host, port)
+
+	go func() {
+		// As soon as these signals are received from OS, try to gracefully stop the gRPC server
+		<-s.httpStopCh
+		log.Println("Stopping the HTTP server...")
+		err := ser.Stop()
+		if err != nil {
+			log.Printf("Error when stopping the HTTP server: %v\n", err)
+		}
+		if loggingService != nil {
+			loggingService.Stop()
+		}
+		log.Println("HTTP server terminated")
+	}()
+
+	return ser.Serve(host, port)
+}
+
+func (s *OnlineFeatureService) StopHttpServer() {
+	s.httpStopCh <- syscall.SIGINT
+}
+
+func (s *OnlineFeatureService) StopGrpcServer() {
 	s.grpcStopCh <- syscall.SIGINT
 }
 
@@ -300,13 +354,13 @@ func (s *OnlineFeatureService) Stop() {
 	into Go Arrow Interface (array.Record).
 	See export code here https://github.com/feast-dev/feast/blob/master/sdk/python/feast/embedded_go/online_features_service.py
 */
-func readArrowRecord(data DataTable) (array.Record, error) {
+func readArrowRecord(data DataTable) (arrow.Record, error) {
 	return cdata.ImportCRecordBatch(
 		cdata.ArrayFromPtr(data.DataPtr),
 		cdata.SchemaFromPtr(data.SchemaPtr))
 }
 
-func recordToProto(rec array.Record) (map[string]*prototypes.RepeatedValue, error) {
+func recordToProto(rec arrow.Record) (map[string]*prototypes.RepeatedValue, error) {
 	r := make(map[string]*prototypes.RepeatedValue)
 	schema := rec.Schema()
 	for idx, column := range rec.Columns() {
diff --git a/go/internal/feast/featurestore.go b/go/internal/feast/featurestore.go
index 4ecd781b746..ad1f94a4ba2 100644
--- a/go/internal/feast/featurestore.go
+++ b/go/internal/feast/featurestore.go
@@ -132,7 +132,7 @@ func (fs *FeatureStore) GetOnlineFeatures(
 	if entitylessCase {
 		dummyEntityColumn := &prototypes.RepeatedValue{Val: make([]*prototypes.Value, numRows)}
 		for index := 0; index < numRows; index++ {
-			dummyEntityColumn.Val[index] = &model.DUMMY_ENTITY
+			dummyEntityColumn.Val[index] = &model.DUMMY_ENTITY_VALUE
 		}
 		joinKeyToEntityValues[model.DUMMY_ENTITY_ID] = dummyEntityColumn
 	}
@@ -224,6 +224,14 @@ func (fs *FeatureStore) listAllViews() (map[string]*model.FeatureView, map[strin
 		fvs[featureView.Base.Name] = featureView
 	}
 
+	streamFeatureViews, err := fs.ListStreamFeatureViews()
+	if err != nil {
+		return nil, nil, err
+	}
+	for _, streamFeatureView := range streamFeatureViews {
+		fvs[streamFeatureView.Base.Name] = streamFeatureView
+	}
+
 	onDemandFeatureViews, err := fs.registry.ListOnDemandFeatureViews(fs.config.Project)
 	if err != nil {
 		return nil, nil, err
@@ -242,6 +250,14 @@ func (fs *FeatureStore) ListFeatureViews() ([]*model.FeatureView, error) {
 	return featureViews, nil
 }
 
+func (fs *FeatureStore) ListStreamFeatureViews() ([]*model.FeatureView, error) {
+	streamFeatureViews, err := fs.registry.ListStreamFeatureViews(fs.config.Project)
+	if err != nil {
+		return streamFeatureViews, err
+	}
+	return streamFeatureViews, nil
+}
+
 func (fs *FeatureStore) ListEntities(hideDummyEntity bool) ([]*model.Entity, error) {
 
 	allEntities, err := fs.registry.ListEntities(fs.config.Project)
@@ -272,7 +288,7 @@ func (fs *FeatureStore) GetFeatureView(featureViewName string, hideDummyEntity b
 		return nil, err
 	}
 	if fv.HasEntity(model.DUMMY_ENTITY_NAME) && hideDummyEntity {
-		fv.Entities = []string{}
+		fv.EntityNames = []string{}
 	}
 	return fv, nil
 }
diff --git a/go/internal/feast/model/basefeatureview.go b/go/internal/feast/model/basefeatureview.go
index 28ef7231fd5..1bdf614c25a 100644
--- a/go/internal/feast/model/basefeatureview.go
+++ b/go/internal/feast/model/basefeatureview.go
@@ -8,15 +8,15 @@ import (
 
 type BaseFeatureView struct {
 	Name       string
-	Features   []*Feature
+	Features   []*Field
 	Projection *FeatureViewProjection
 }
 
 func NewBaseFeatureView(name string, featureProtos []*core.FeatureSpecV2) *BaseFeatureView {
 	base := &BaseFeatureView{Name: name}
-	features := make([]*Feature, len(featureProtos))
+	features := make([]*Field, len(featureProtos))
 	for index, featureSpecV2 := range featureProtos {
-		features[index] = NewFeatureFromProto(featureSpecV2)
+		features[index] = NewFieldFromProto(featureSpecV2)
 	}
 	base.Features = features
 	base.Projection = NewFeatureViewProjectionFromDefinition(base)
@@ -43,7 +43,7 @@ func (fv *BaseFeatureView) WithProjection(projection *FeatureViewProjection) (*B
 }
 
 func (fv *BaseFeatureView) ProjectWithFeatures(featureNames []string) *FeatureViewProjection {
-	features := make([]*Feature, 0)
+	features := make([]*Field, 0)
 	for _, feature := range fv.Features {
 		for _, allowedFeatureName := range featureNames {
 			if feature.Name == allowedFeatureName {
diff --git a/go/internal/feast/model/entity.go b/go/internal/feast/model/entity.go
index ac3a5d5f26e..5a09edb6557 100644
--- a/go/internal/feast/model/entity.go
+++ b/go/internal/feast/model/entity.go
@@ -2,18 +2,16 @@ package model
 
 import (
 	"github.com/feast-dev/feast/go/protos/feast/core"
-	"github.com/feast-dev/feast/go/protos/feast/types"
 )
 
 type Entity struct {
-	Name      string
-	ValueType types.ValueType_Enum
-	JoinKey   string
+	Name    string
+	JoinKey string
 }
 
 func NewEntityFromProto(proto *core.Entity) *Entity {
-	return &Entity{Name: proto.Spec.Name,
-		ValueType: proto.Spec.ValueType,
-		JoinKey:   proto.Spec.JoinKey,
+	return &Entity{
+		Name:    proto.Spec.Name,
+		JoinKey: proto.Spec.JoinKey,
 	}
 }
diff --git a/go/internal/feast/model/featureview.go b/go/internal/feast/model/featureview.go
index 6c198f99947..b6fde78658e 100644
--- a/go/internal/feast/model/featureview.go
+++ b/go/internal/feast/model/featureview.go
@@ -13,38 +13,61 @@ const (
 	DUMMY_ENTITY_VAL  = ""
 )
 
-var DUMMY_ENTITY types.Value = types.Value{Val: &types.Value_StringVal{StringVal: DUMMY_ENTITY_VAL}}
+var DUMMY_ENTITY_VALUE types.Value = types.Value{Val: &types.Value_StringVal{StringVal: DUMMY_ENTITY_VAL}}
 
 type FeatureView struct {
-	Base     *BaseFeatureView
-	Ttl      *durationpb.Duration
-	Entities []string
+	Base          *BaseFeatureView
+	Ttl           *durationpb.Duration
+	EntityNames   []string
+	EntityColumns []*Field
 }
 
 func NewFeatureViewFromProto(proto *core.FeatureView) *FeatureView {
 	featureView := &FeatureView{Base: NewBaseFeatureView(proto.Spec.Name, proto.Spec.Features),
-		Ttl: &(*proto.Spec.Ttl),
+		Ttl: proto.Spec.Ttl,
 	}
 	if len(proto.Spec.Entities) == 0 {
-		featureView.Entities = []string{DUMMY_ENTITY_NAME}
+		featureView.EntityNames = []string{DUMMY_ENTITY_NAME}
 	} else {
-		featureView.Entities = proto.Spec.Entities
+		featureView.EntityNames = proto.Spec.Entities
 	}
+	entityColumns := make([]*Field, len(proto.Spec.EntityColumns))
+	for i, entityColumn := range proto.Spec.EntityColumns {
+		entityColumns[i] = NewFieldFromProto(entityColumn)
+	}
+	featureView.EntityColumns = entityColumns
+	return featureView
+}
+
+func NewFeatureViewFromStreamFeatureViewProto(proto *core.StreamFeatureView) *FeatureView {
+	featureView := &FeatureView{Base: NewBaseFeatureView(proto.Spec.Name, proto.Spec.Features),
+		Ttl: proto.Spec.Ttl,
+	}
+	if len(proto.Spec.Entities) == 0 {
+		featureView.EntityNames = []string{DUMMY_ENTITY_NAME}
+	} else {
+		featureView.EntityNames = proto.Spec.Entities
+	}
+	entityColumns := make([]*Field, len(proto.Spec.EntityColumns))
+	for i, entityColumn := range proto.Spec.EntityColumns {
+		entityColumns[i] = NewFieldFromProto(entityColumn)
+	}
+	featureView.EntityColumns = entityColumns
 	return featureView
 }
 
-func (fs *FeatureView) NewFeatureViewFromBase(base *BaseFeatureView) *FeatureView {
-	ttl := durationpb.Duration{Seconds: fs.Ttl.Seconds, Nanos: fs.Ttl.Nanos}
+func (fv *FeatureView) NewFeatureViewFromBase(base *BaseFeatureView) *FeatureView {
+	ttl := durationpb.Duration{Seconds: fv.Ttl.Seconds, Nanos: fv.Ttl.Nanos}
 	featureView := &FeatureView{Base: base,
-		Ttl:      &ttl,
-		Entities: fs.Entities,
+		Ttl:         &ttl,
+		EntityNames: fv.EntityNames,
 	}
 	return featureView
 }
 
-func (fs *FeatureView) HasEntity(lookup string) bool {
-	for _, entityName := range fs.Entities {
-		if entityName == lookup {
+func (fv *FeatureView) HasEntity(name string) bool {
+	for _, entityName := range fv.EntityNames {
+		if entityName == name {
 			return true
 		}
 	}
diff --git a/go/internal/feast/model/featureviewprojection.go b/go/internal/feast/model/featureviewprojection.go
index e80e8844ed2..fe54774ff1e 100644
--- a/go/internal/feast/model/featureviewprojection.go
+++ b/go/internal/feast/model/featureviewprojection.go
@@ -7,7 +7,7 @@ import (
 type FeatureViewProjection struct {
 	Name       string
 	NameAlias  string
-	Features   []*Feature
+	Features   []*Field
 	JoinKeyMap map[string]string
 }
 
@@ -24,9 +24,9 @@ func NewFeatureViewProjectionFromProto(proto *core.FeatureViewProjection) *Featu
 		JoinKeyMap: proto.JoinKeyMap,
 	}
 
-	features := make([]*Feature, len(proto.FeatureColumns))
+	features := make([]*Field, len(proto.FeatureColumns))
 	for index, featureSpecV2 := range proto.FeatureColumns {
-		features[index] = NewFeatureFromProto(featureSpecV2)
+		features[index] = NewFieldFromProto(featureSpecV2)
 	}
 	featureProjection.Features = features
 	return featureProjection
diff --git a/go/internal/feast/model/feature.go b/go/internal/feast/model/field.go
similarity index 63%
rename from go/internal/feast/model/feature.go
rename to go/internal/feast/model/field.go
index d833a8901b5..4f72d346866 100644
--- a/go/internal/feast/model/feature.go
+++ b/go/internal/feast/model/field.go
@@ -5,13 +5,14 @@ import (
 	"github.com/feast-dev/feast/go/protos/feast/types"
 )
 
-type Feature struct {
+type Field struct {
 	Name  string
 	Dtype types.ValueType_Enum
 }
 
-func NewFeatureFromProto(proto *core.FeatureSpecV2) *Feature {
-	return &Feature{Name: proto.Name,
+func NewFieldFromProto(proto *core.FeatureSpecV2) *Field {
+	return &Field{
+		Name:  proto.Name,
 		Dtype: proto.ValueType,
 	}
 }
diff --git a/go/internal/feast/onlineserving/serving.go b/go/internal/feast/onlineserving/serving.go
index 1d0567c3547..e2a2df923be 100644
--- a/go/internal/feast/onlineserving/serving.go
+++ b/go/internal/feast/onlineserving/serving.go
@@ -251,7 +251,7 @@ func GetEntityMaps(requestedFeatureViews []*FeatureViewAndRefs, entities []*mode
 			joinKeyToAliasMap = map[string]string{}
 		}
 
-		for _, entityName := range featureView.Entities {
+		for _, entityName := range featureView.EntityNames {
 			joinKey := entitiesByName[entityName].JoinKey
 			entityNameToJoinKeyMap[entityName] = joinKey
 
@@ -518,7 +518,7 @@ func GroupFeatureRefs(requestedFeatureViews []*FeatureViewAndRefs,
 		joinKeys := make([]string, 0)
 		fv := featuresAndView.View
 		featureNames := featuresAndView.FeatureRefs
-		for _, entityName := range fv.Entities {
+		for _, entityName := range fv.EntityNames {
 			joinKeys = append(joinKeys, entityNameToJoinKeyMap[entityName])
 		}
 
diff --git a/go/internal/feast/onlineserving/serving_test.go b/go/internal/feast/onlineserving/serving_test.go
index 0a00f546f9e..bd4e45a21ec 100644
--- a/go/internal/feast/onlineserving/serving_test.go
+++ b/go/internal/feast/onlineserving/serving_test.go
@@ -20,19 +20,19 @@ func TestGroupingFeatureRefs(t *testing.T) {
 				NameAlias: "aliasViewA",
 			},
 		},
-		Entities: []string{"driver", "customer"},
+		EntityNames: []string{"driver", "customer"},
 	}
 	viewB := &model.FeatureView{
-		Base:     &model.BaseFeatureView{Name: "viewB"},
-		Entities: []string{"driver", "customer"},
+		Base:        &model.BaseFeatureView{Name: "viewB"},
+		EntityNames: []string{"driver", "customer"},
 	}
 	viewC := &model.FeatureView{
-		Base:     &model.BaseFeatureView{Name: "viewC"},
-		Entities: []string{"driver"},
+		Base:        &model.BaseFeatureView{Name: "viewC"},
+		EntityNames: []string{"driver"},
 	}
 	viewD := &model.FeatureView{
-		Base:     &model.BaseFeatureView{Name: "viewD"},
-		Entities: []string{"customer"},
+		Base:        &model.BaseFeatureView{Name: "viewD"},
+		EntityNames: []string{"customer"},
 	}
 	refGroups, _ := GroupFeatureRefs(
 		[]*FeatureViewAndRefs{
@@ -105,11 +105,11 @@ func TestGroupingFeatureRefsWithJoinKeyAliases(t *testing.T) {
 				JoinKeyMap: map[string]string{"location_id": "destination_id"},
 			},
 		},
-		Entities: []string{"location"},
+		EntityNames: []string{"location"},
 	}
 	viewB := &model.FeatureView{
-		Base:     &model.BaseFeatureView{Name: "viewB"},
-		Entities: []string{"location"},
+		Base:        &model.BaseFeatureView{Name: "viewB"},
+		EntityNames: []string{"location"},
 	}
 
 	refGroups, _ := GroupFeatureRefs(
@@ -164,7 +164,7 @@ func TestGroupingFeatureRefsWithMissingKey(t *testing.T) {
 				JoinKeyMap: map[string]string{"location_id": "destination_id"},
 			},
 		},
-		Entities: []string{"location"},
+		EntityNames: []string{"location"},
 	}
 
 	_, err := GroupFeatureRefs(
diff --git a/go/internal/feast/onlinestore/redisonlinestore.go b/go/internal/feast/onlinestore/redisonlinestore.go
index df04856cbfa..26f34cf8965 100644
--- a/go/internal/feast/onlinestore/redisonlinestore.go
+++ b/go/internal/feast/onlinestore/redisonlinestore.go
@@ -2,6 +2,7 @@ package onlinestore
 
 import (
 	"context"
+	"crypto/tls"
 	"encoding/binary"
 	"errors"
 	"fmt"
@@ -43,6 +44,7 @@ func NewRedisOnlineStore(project string, onlineStoreConfig map[string]interface{
 
 	var address []string
 	var password string
+	var tlsConfig *tls.Config
 	var db int // Default to 0
 
 	// Parse redis_type and write it into conf.t
@@ -69,8 +71,12 @@ func NewRedisOnlineStore(project string, onlineStoreConfig map[string]interface{
 				if kv[0] == "password" {
 					password = kv[1]
 				} else if kv[0] == "ssl" {
-					// TODO (woop): Add support for TLS/SSL
-					// ssl = kv[1] == "true"
+					result, err := strconv.ParseBool(kv[1])
+					if err != nil {
+						return nil, err
+					} else if result {
+						tlsConfig = &tls.Config{}
+					}
 				} else if kv[0] == "db" {
 					db, err = strconv.Atoi(kv[1])
 					if err != nil {
@@ -87,9 +93,10 @@ func NewRedisOnlineStore(project string, onlineStoreConfig map[string]interface{
 
 	if t == redisNode {
 		store.client = redis.NewClient(&redis.Options{
-			Addr:     address[0],
-			Password: password, // No password set
-			DB:       db,
+			Addr:      address[0],
+			Password:  password, // No password set
+			DB:        db,
+			TLSConfig: tlsConfig,
 		})
 	} else {
 		return nil, errors.New("only single node Redis is supported at this time")
diff --git a/go/internal/feast/onlinestore/redisonlinestore_test.go b/go/internal/feast/onlinestore/redisonlinestore_test.go
new file mode 100644
index 00000000000..43cdbe06a22
--- /dev/null
+++ b/go/internal/feast/onlinestore/redisonlinestore_test.go
@@ -0,0 +1,53 @@
+package onlinestore
+
+import (
+	"testing"
+
+	"github.com/stretchr/testify/assert"
+)
+
+func TestNewRedisOnlineStore(t *testing.T) {
+	var config = map[string]interface{}{
+		"connection_string": "redis://localhost:6379",
+	}
+	store, err := NewRedisOnlineStore("test", config)
+	assert.Nil(t, err)
+	var opts = store.client.Options()
+	assert.Equal(t, opts.Addr, "redis://localhost:6379")
+	assert.Equal(t, opts.Password, "")
+	assert.Equal(t, opts.DB, 0)
+	assert.Nil(t, opts.TLSConfig)
+}
+
+func TestNewRedisOnlineStoreWithPassword(t *testing.T) {
+	var config = map[string]interface{}{
+		"connection_string": "redis://localhost:6379,password=secret",
+	}
+	store, err := NewRedisOnlineStore("test", config)
+	assert.Nil(t, err)
+	var opts = store.client.Options()
+	assert.Equal(t, opts.Addr, "redis://localhost:6379")
+	assert.Equal(t, opts.Password, "secret")
+}
+
+func TestNewRedisOnlineStoreWithDB(t *testing.T) {
+	var config = map[string]interface{}{
+		"connection_string": "redis://localhost:6379,db=1",
+	}
+	store, err := NewRedisOnlineStore("test", config)
+	assert.Nil(t, err)
+	var opts = store.client.Options()
+	assert.Equal(t, opts.Addr, "redis://localhost:6379")
+	assert.Equal(t, opts.DB, 1)
+}
+
+func TestNewRedisOnlineStoreWithSsl(t *testing.T) {
+	var config = map[string]interface{}{
+		"connection_string": "redis://localhost:6379,ssl=true",
+	}
+	store, err := NewRedisOnlineStore("test", config)
+	assert.Nil(t, err)
+	var opts = store.client.Options()
+	assert.Equal(t, opts.Addr, "redis://localhost:6379")
+	assert.NotNil(t, opts.TLSConfig)
+}
diff --git a/go/internal/feast/registry/registry.go b/go/internal/feast/registry/registry.go
index 38cf167a9fd..c67a50a5a64 100644
--- a/go/internal/feast/registry/registry.go
+++ b/go/internal/feast/registry/registry.go
@@ -30,6 +30,7 @@ type Registry struct {
 	cachedFeatureServices          map[string]map[string]*core.FeatureService
 	cachedEntities                 map[string]map[string]*core.Entity
 	cachedFeatureViews             map[string]map[string]*core.FeatureView
+	cachedStreamFeatureViews       map[string]map[string]*core.StreamFeatureView
 	cachedOnDemandFeatureViews     map[string]map[string]*core.OnDemandFeatureView
 	cachedRegistry                 *core.Registry
 	cachedRegistryProtoLastUpdated time.Time
@@ -106,10 +107,12 @@ func (r *Registry) load(registry *core.Registry) {
 	r.cachedFeatureServices = make(map[string]map[string]*core.FeatureService)
 	r.cachedEntities = make(map[string]map[string]*core.Entity)
 	r.cachedFeatureViews = make(map[string]map[string]*core.FeatureView)
+	r.cachedStreamFeatureViews = make(map[string]map[string]*core.StreamFeatureView)
 	r.cachedOnDemandFeatureViews = make(map[string]map[string]*core.OnDemandFeatureView)
 	r.loadEntities(registry)
 	r.loadFeatureServices(registry)
 	r.loadFeatureViews(registry)
+	r.loadStreamFeatureViews(registry)
 	r.loadOnDemandFeatureViews(registry)
 	r.cachedRegistryProtoLastUpdated = time.Now()
 }
@@ -144,6 +147,16 @@ func (r *Registry) loadFeatureViews(registry *core.Registry) {
 	}
 }
 
+func (r *Registry) loadStreamFeatureViews(registry *core.Registry) {
+	streamFeatureViews := registry.StreamFeatureViews
+	for _, streamFeatureView := range streamFeatureViews {
+		if _, ok := r.cachedStreamFeatureViews[streamFeatureView.Spec.Project]; !ok {
+			r.cachedStreamFeatureViews[streamFeatureView.Spec.Project] = make(map[string]*core.StreamFeatureView)
+		}
+		r.cachedStreamFeatureViews[streamFeatureView.Spec.Project][streamFeatureView.Spec.Name] = streamFeatureView
+	}
+}
+
 func (r *Registry) loadOnDemandFeatureViews(registry *core.Registry) {
 	onDemandFeatureViews := registry.OnDemandFeatureViews
 	for _, onDemandFeatureView := range onDemandFeatureViews {
@@ -193,7 +206,26 @@ func (r *Registry) ListFeatureViews(project string) ([]*model.FeatureView, error
 }
 
 /*
-	Look up Feature Views inside project
+	Look up Stream Feature Views inside project
+	Returns empty list if project not found
+*/
+
+func (r *Registry) ListStreamFeatureViews(project string) ([]*model.FeatureView, error) {
+	if cachedStreamFeatureViews, ok := r.cachedStreamFeatureViews[project]; !ok {
+		return []*model.FeatureView{}, nil
+	} else {
+		streamFeatureViews := make([]*model.FeatureView, len(cachedStreamFeatureViews))
+		index := 0
+		for _, streamFeatureViewProto := range cachedStreamFeatureViews {
+			streamFeatureViews[index] = model.NewFeatureViewFromStreamFeatureViewProto(streamFeatureViewProto)
+			index += 1
+		}
+		return streamFeatureViews, nil
+	}
+}
+
+/*
+	Look up Feature Services inside project
 	Returns empty list if project not found
 */
 
@@ -254,6 +286,18 @@ func (r *Registry) GetFeatureView(project, featureViewName string) (*model.Featu
 	}
 }
 
+func (r *Registry) GetStreamFeatureView(project, streamFeatureViewName string) (*model.FeatureView, error) {
+	if cachedStreamFeatureViews, ok := r.cachedStreamFeatureViews[project]; !ok {
+		return nil, fmt.Errorf("no cached stream feature views found for project %s", project)
+	} else {
+		if streamFeatureViewProto, ok := cachedStreamFeatureViews[streamFeatureViewName]; !ok {
+			return nil, fmt.Errorf("no cached stream feature view %s found for project %s", streamFeatureViewName, project)
+		} else {
+			return model.NewFeatureViewFromStreamFeatureViewProto(streamFeatureViewProto), nil
+		}
+	}
+}
+
 func (r *Registry) GetFeatureService(project, featureServiceName string) (*model.FeatureService, error) {
 	if cachedFeatureServices, ok := r.cachedFeatureServices[project]; !ok {
 		return nil, fmt.Errorf("no cached feature services found for project %s", project)
diff --git a/go/internal/feast/server/grpc_server.go b/go/internal/feast/server/grpc_server.go
index 60408809594..c47d185d6c1 100644
--- a/go/internal/feast/server/grpc_server.go
+++ b/go/internal/feast/server/grpc_server.go
@@ -86,7 +86,7 @@ func (s *grpcServingServiceServer) GetOnlineFeatures(ctx context.Context, reques
 			fmt.Printf("Couldn't instantiate logger for feature service %s: %+v", featuresOrService.FeatureService.Name, err)
 		}
 
-		err = logger.Log(entityValuesMap, resp.Results[len(request.Entities):], resp.Metadata.FeatureNames.Val[len(request.Entities):], request.RequestContext, requestId)
+		err = logger.Log(request.Entities, resp.Results[len(request.Entities):], resp.Metadata.FeatureNames.Val[len(request.Entities):], request.RequestContext, requestId)
 		if err != nil {
 			fmt.Printf("LoggerImpl error[%s]: %+v", featuresOrService.FeatureService.Name, err)
 		}
diff --git a/go/internal/feast/server/http_server.go b/go/internal/feast/server/http_server.go
new file mode 100644
index 00000000000..75cdbe9929a
--- /dev/null
+++ b/go/internal/feast/server/http_server.go
@@ -0,0 +1,270 @@
+package server
+
+import (
+	"context"
+	"encoding/json"
+	"fmt"
+	"github.com/feast-dev/feast/go/internal/feast"
+	"github.com/feast-dev/feast/go/internal/feast/model"
+	"github.com/feast-dev/feast/go/internal/feast/server/logging"
+	"github.com/feast-dev/feast/go/protos/feast/serving"
+	prototypes "github.com/feast-dev/feast/go/protos/feast/types"
+	"github.com/feast-dev/feast/go/types"
+	"net/http"
+	"time"
+)
+
+type httpServer struct {
+	fs             *feast.FeatureStore
+	loggingService *logging.LoggingService
+	server         *http.Server
+}
+
+// Some Feast types aren't supported during JSON conversion
+type repeatedValue struct {
+	stringVal     []string
+	int64Val      []int64
+	doubleVal     []float64
+	boolVal       []bool
+	stringListVal [][]string
+	int64ListVal  [][]int64
+	doubleListVal [][]float64
+	boolListVal   [][]bool
+}
+
+func (u *repeatedValue) UnmarshalJSON(data []byte) error {
+	isString := false
+	isDouble := false
+	isInt64 := false
+	isArray := false
+	openBraketCounter := 0
+	for _, b := range data {
+		if b == '"' {
+			isString = true
+		}
+		if b == '.' {
+			isDouble = true
+		}
+		if b >= '0' && b <= '9' {
+			isInt64 = true
+		}
+		if b == '[' {
+			openBraketCounter++
+			if openBraketCounter > 1 {
+				isArray = true
+			}
+		}
+	}
+	var err error
+	if !isArray {
+		if isString {
+			err = json.Unmarshal(data, &u.stringVal)
+		} else if isDouble {
+			err = json.Unmarshal(data, &u.doubleVal)
+		} else if isInt64 {
+			err = json.Unmarshal(data, &u.int64Val)
+		} else {
+			err = json.Unmarshal(data, &u.boolVal)
+		}
+	} else {
+		if isString {
+			err = json.Unmarshal(data, &u.stringListVal)
+		} else if isDouble {
+			err = json.Unmarshal(data, &u.doubleListVal)
+		} else if isInt64 {
+			err = json.Unmarshal(data, &u.int64ListVal)
+		} else {
+			err = json.Unmarshal(data, &u.boolListVal)
+		}
+	}
+	return err
+}
+
+func (u *repeatedValue) ToProto() *prototypes.RepeatedValue {
+	proto := new(prototypes.RepeatedValue)
+	if u.stringVal != nil {
+		for _, val := range u.stringVal {
+			proto.Val = append(proto.Val, &prototypes.Value{Val: &prototypes.Value_StringVal{StringVal: val}})
+		}
+	}
+	if u.int64Val != nil {
+		for _, val := range u.int64Val {
+			proto.Val = append(proto.Val, &prototypes.Value{Val: &prototypes.Value_Int64Val{Int64Val: val}})
+		}
+	}
+	if u.doubleVal != nil {
+		for _, val := range u.doubleVal {
+			proto.Val = append(proto.Val, &prototypes.Value{Val: &prototypes.Value_DoubleVal{DoubleVal: val}})
+		}
+	}
+	if u.boolVal != nil {
+		for _, val := range u.boolVal {
+			proto.Val = append(proto.Val, &prototypes.Value{Val: &prototypes.Value_BoolVal{BoolVal: val}})
+		}
+	}
+	if u.stringListVal != nil {
+		for _, val := range u.stringListVal {
+			proto.Val = append(proto.Val, &prototypes.Value{Val: &prototypes.Value_StringListVal{StringListVal: &prototypes.StringList{Val: val}}})
+		}
+	}
+	if u.int64ListVal != nil {
+		for _, val := range u.int64ListVal {
+			proto.Val = append(proto.Val, &prototypes.Value{Val: &prototypes.Value_Int64ListVal{Int64ListVal: &prototypes.Int64List{Val: val}}})
+		}
+	}
+	if u.doubleListVal != nil {
+		for _, val := range u.doubleListVal {
+			proto.Val = append(proto.Val, &prototypes.Value{Val: &prototypes.Value_DoubleListVal{DoubleListVal: &prototypes.DoubleList{Val: val}}})
+		}
+	}
+	if u.boolListVal != nil {
+		for _, val := range u.boolListVal {
+			proto.Val = append(proto.Val, &prototypes.Value{Val: &prototypes.Value_BoolListVal{BoolListVal: &prototypes.BoolList{Val: val}}})
+		}
+	}
+	return proto
+}
+
+type getOnlineFeaturesRequest struct {
+	FeatureService   *string                  `json:"feature_service"`
+	Features         []string                 `json:"features"`
+	Entities         map[string]repeatedValue `json:"entities"`
+	FullFeatureNames bool                     `json:"full_feature_names"`
+	RequestContext   map[string]repeatedValue `json:"request_context"`
+}
+
+func NewHttpServer(fs *feast.FeatureStore, loggingService *logging.LoggingService) *httpServer {
+	return &httpServer{fs: fs, loggingService: loggingService}
+}
+
+func (s *httpServer) getOnlineFeatures(w http.ResponseWriter, r *http.Request) {
+	if r.Method != "POST" {
+		http.NotFound(w, r)
+		return
+	}
+
+	decoder := json.NewDecoder(r.Body)
+	var request getOnlineFeaturesRequest
+	err := decoder.Decode(&request)
+	if err != nil {
+		http.Error(w, fmt.Sprintf("Error decoding JSON request data: %+v", err), http.StatusInternalServerError)
+		return
+	}
+	var featureService *model.FeatureService
+	if request.FeatureService != nil {
+		featureService, err = s.fs.GetFeatureService(*request.FeatureService)
+		if err != nil {
+			http.Error(w, fmt.Sprintf("Error getting feature service from registry: %+v", err), http.StatusInternalServerError)
+			return
+		}
+	}
+	entitiesProto := make(map[string]*prototypes.RepeatedValue)
+	for key, value := range request.Entities {
+		entitiesProto[key] = value.ToProto()
+	}
+	requestContextProto := make(map[string]*prototypes.RepeatedValue)
+	for key, value := range request.RequestContext {
+		requestContextProto[key] = value.ToProto()
+	}
+
+	featureVectors, err := s.fs.GetOnlineFeatures(
+		r.Context(),
+		request.Features,
+		featureService,
+		entitiesProto,
+		requestContextProto,
+		request.FullFeatureNames)
+
+	if err != nil {
+		http.Error(w, fmt.Sprintf("Error getting feature vector: %+v", err), http.StatusInternalServerError)
+		return
+	}
+
+	var featureNames []string
+	var results []map[string]interface{}
+	for _, vector := range featureVectors {
+		featureNames = append(featureNames, vector.Name)
+		result := make(map[string]interface{})
+		var statuses []string
+		for _, status := range vector.Statuses {
+			statuses = append(statuses, status.String())
+		}
+		var timestamps []string
+		for _, timestamp := range vector.Timestamps {
+			timestamps = append(timestamps, timestamp.AsTime().Format(time.RFC3339))
+		}
+
+		result["statuses"] = statuses
+		result["event_timestamps"] = timestamps
+		// Note, that vector.Values is an Arrow Array, but this type implements JSON Marshaller.
+		// So, it's not necessary to pre-process it in any way.
+		result["values"] = vector.Values
+
+		results = append(results, result)
+	}
+
+	response := map[string]interface{}{
+		"metadata": map[string]interface{}{
+			"feature_names": featureNames,
+		},
+		"results": results,
+	}
+
+	err = json.NewEncoder(w).Encode(response)
+
+	if err != nil {
+		http.Error(w, fmt.Sprintf("Error encoding response: %+v", err), http.StatusInternalServerError)
+		return
+	}
+
+	w.Header().Set("Content-Type", "application/json")
+
+	if featureService != nil && featureService.LoggingConfig != nil && s.loggingService != nil {
+		logger, err := s.loggingService.GetOrCreateLogger(featureService)
+		if err != nil {
+			http.Error(w, fmt.Sprintf("Couldn't instantiate logger for feature service %s: %+v", featureService.Name, err), http.StatusInternalServerError)
+			return
+		}
+
+		requestId := GenerateRequestId()
+
+		// Note: we're converting arrow to proto for feature logging. In the future we should
+		// base feature logging on arrow so that we don't have to do this extra conversion.
+		var featureVectorProtos []*serving.GetOnlineFeaturesResponse_FeatureVector
+		for _, vector := range featureVectors[len(request.Entities):] {
+			values, err := types.ArrowValuesToProtoValues(vector.Values)
+			if err != nil {
+				http.Error(w, fmt.Sprintf("Couldn't convert arrow values into protobuf: %+v", err), http.StatusInternalServerError)
+				return
+			}
+			featureVectorProtos = append(featureVectorProtos, &serving.GetOnlineFeaturesResponse_FeatureVector{
+				Values:          values,
+				Statuses:        vector.Statuses,
+				EventTimestamps: vector.Timestamps,
+			})
+		}
+
+		err = logger.Log(entitiesProto, featureVectorProtos, featureNames[len(request.Entities):], requestContextProto, requestId)
+		if err != nil {
+			http.Error(w, fmt.Sprintf("LoggerImpl error[%s]: %+v", featureService.Name, err), http.StatusInternalServerError)
+			return
+		}
+	}
+}
+
+func (s *httpServer) Serve(host string, port int) error {
+	s.server = &http.Server{Addr: fmt.Sprintf("%s:%d", host, port), Handler: nil}
+	http.HandleFunc("/get-online-features", s.getOnlineFeatures)
+	err := s.server.ListenAndServe()
+	// Don't return the error if it's caused by graceful shutdown using Stop()
+	if err == http.ErrServerClosed {
+		return nil
+	}
+	return err
+}
+func (s *httpServer) Stop() error {
+	if s.server != nil {
+		return s.server.Shutdown(context.Background())
+	}
+	return nil
+}
diff --git a/go/internal/feast/server/http_server_test.go b/go/internal/feast/server/http_server_test.go
new file mode 100644
index 00000000000..67ba1c60f96
--- /dev/null
+++ b/go/internal/feast/server/http_server_test.go
@@ -0,0 +1,40 @@
+package server
+
+import (
+	"github.com/stretchr/testify/assert"
+	"testing"
+)
+
+func TestUnmarshalJSON(t *testing.T) {
+	u := repeatedValue{}
+	assert.Nil(t, u.UnmarshalJSON([]byte("[1, 2, 3]")))
+	assert.Equal(t, []int64{1, 2, 3}, u.int64Val)
+
+	u = repeatedValue{}
+	assert.Nil(t, u.UnmarshalJSON([]byte("[1.2, 2.3, 3.4]")))
+	assert.Equal(t, []float64{1.2, 2.3, 3.4}, u.doubleVal)
+
+	u = repeatedValue{}
+	assert.Nil(t, u.UnmarshalJSON([]byte("[\"foo\", \"bar\"]")))
+	assert.Equal(t, []string{"foo", "bar"}, u.stringVal)
+
+	u = repeatedValue{}
+	assert.Nil(t, u.UnmarshalJSON([]byte("[true, false, true]")))
+	assert.Equal(t, []bool{true, false, true}, u.boolVal)
+
+	u = repeatedValue{}
+	assert.Nil(t, u.UnmarshalJSON([]byte("[[1, 2, 3], [4, 5, 6]]")))
+	assert.Equal(t, [][]int64{{1, 2, 3}, {4, 5, 6}}, u.int64ListVal)
+
+	u = repeatedValue{}
+	assert.Nil(t, u.UnmarshalJSON([]byte("[[1.2, 2.3, 3.4], [10.2, 20.3, 30.4]]")))
+	assert.Equal(t, [][]float64{{1.2, 2.3, 3.4}, {10.2, 20.3, 30.4}}, u.doubleListVal)
+
+	u = repeatedValue{}
+	assert.Nil(t, u.UnmarshalJSON([]byte("[[\"foo\", \"bar\"], [\"foo2\", \"bar2\"]]")))
+	assert.Equal(t, [][]string{{"foo", "bar"}, {"foo2", "bar2"}}, u.stringListVal)
+
+	u = repeatedValue{}
+	assert.Nil(t, u.UnmarshalJSON([]byte("[[true, false, true], [false, true, false]]")))
+	assert.Equal(t, [][]bool{{true, false, true}, {false, true, false}}, u.boolListVal)
+}
diff --git a/go/internal/feast/server/logging/featureserviceschema.go b/go/internal/feast/server/logging/featureserviceschema.go
index 5047346c2ca..2779982fc0a 100644
--- a/go/internal/feast/server/logging/featureserviceschema.go
+++ b/go/internal/feast/server/logging/featureserviceschema.go
@@ -52,13 +52,12 @@ func generateSchema(featureService *model.FeatureService, entityMap map[string]*
 				features = append(features, fullFeatureName)
 				allFeatureTypes[fullFeatureName] = f.Dtype
 			}
-			for _, entityName := range fv.Entities {
-				entity := entityMap[entityName]
+			for _, entityColumn := range fv.EntityColumns {
 				var joinKey string
-				if joinKeyAlias, ok := featureProjection.JoinKeyMap[entity.JoinKey]; ok {
+				if joinKeyAlias, ok := featureProjection.JoinKeyMap[entityColumn.Name]; ok {
 					joinKey = joinKeyAlias
 				} else {
-					joinKey = entity.JoinKey
+					joinKey = entityColumn.Name
 				}
 
 				if _, ok := joinKeysSet[joinKey]; !ok {
@@ -66,7 +65,7 @@ func generateSchema(featureService *model.FeatureService, entityMap map[string]*
 				}
 
 				joinKeysSet[joinKey] = nil
-				entityJoinKeyToType[joinKey] = entity.ValueType
+				entityJoinKeyToType[joinKey] = entityColumn.Dtype
 			}
 		} else if odFv, ok := odFvMap[featureViewName]; ok {
 			for _, f := range featureProjection.Features {
diff --git a/go/internal/feast/server/logging/featureserviceschema_test.go b/go/internal/feast/server/logging/featureserviceschema_test.go
index efcd5ec7fcc..6fa1c12e241 100644
--- a/go/internal/feast/server/logging/featureserviceschema_test.go
+++ b/go/internal/feast/server/logging/featureserviceschema_test.go
@@ -74,9 +74,10 @@ func TestSchemaRetrievalIgnoresEntitiesNotInFeatureService(t *testing.T) {
 	featureService, entities, fvs, odfvs := InitializeFeatureRepoVariablesForTest()
 	entityMap, fvMap, odFvMap := buildFCOMaps(entities, fvs, odfvs)
 
-	//Remove entities in featureservice
+	// Remove entities in featureservice
 	for _, featureView := range fvs {
-		featureView.Entities = []string{}
+		featureView.EntityNames = []string{}
+		featureView.EntityColumns = []*model.Field{}
 	}
 
 	schema, err := generateSchema(featureService, entityMap, fvMap, odFvMap)
@@ -126,65 +127,69 @@ func TestSchemaUsesOrderInFeatureService(t *testing.T) {
 
 // Initialize all dummy featureservice, entities and featureviews/on demand featureviews for testing.
 func InitializeFeatureRepoVariablesForTest() (*model.FeatureService, []*model.Entity, []*model.FeatureView, []*model.OnDemandFeatureView) {
-	f1 := test.CreateNewFeature(
+	f1 := test.CreateNewField(
 		"int64",
 		types.ValueType_INT64,
 	)
-	f2 := test.CreateNewFeature(
+	f2 := test.CreateNewField(
 		"float32",
 		types.ValueType_FLOAT,
 	)
 	projection1 := test.CreateNewFeatureViewProjection(
 		"featureView1",
 		"",
-		[]*model.Feature{f1, f2},
+		[]*model.Field{f1, f2},
 		map[string]string{},
 	)
 	baseFeatureView1 := test.CreateBaseFeatureView(
 		"featureView1",
-		[]*model.Feature{f1, f2},
+		[]*model.Field{f1, f2},
 		projection1,
 	)
-	featureView1 := test.CreateFeatureView(baseFeatureView1, nil, []string{"driver_id"})
-	entity1 := test.CreateNewEntity("driver_id", types.ValueType_INT64, "driver_id")
-	f3 := test.CreateNewFeature(
+	entity1 := test.CreateNewEntity("driver_id", "driver_id")
+	entitycolumn1 := test.CreateNewField(
+		"driver_id",
+		types.ValueType_INT64,
+	)
+	featureView1 := test.CreateFeatureView(baseFeatureView1, nil, []string{"driver_id"}, []*model.Field{entitycolumn1})
+	f3 := test.CreateNewField(
 		"int32",
 		types.ValueType_INT32,
 	)
-	f4 := test.CreateNewFeature(
+	f4 := test.CreateNewField(
 		"double",
 		types.ValueType_DOUBLE,
 	)
 	projection2 := test.CreateNewFeatureViewProjection(
 		"featureView2",
 		"",
-		[]*model.Feature{f3, f4},
+		[]*model.Field{f3, f4},
 		map[string]string{},
 	)
 	baseFeatureView2 := test.CreateBaseFeatureView(
 		"featureView2",
-		[]*model.Feature{f3, f4},
+		[]*model.Field{f3, f4},
 		projection2,
 	)
-	featureView2 := test.CreateFeatureView(baseFeatureView2, nil, []string{"driver_id"})
+	featureView2 := test.CreateFeatureView(baseFeatureView2, nil, []string{"driver_id"}, []*model.Field{entitycolumn1})
 
-	f5 := test.CreateNewFeature(
+	f5 := test.CreateNewField(
 		"odfv_f1",
 		types.ValueType_INT32,
 	)
-	f6 := test.CreateNewFeature(
+	f6 := test.CreateNewField(
 		"odfv_f2",
 		types.ValueType_DOUBLE,
 	)
 	projection3 := test.CreateNewFeatureViewProjection(
 		"od_bf1",
 		"",
-		[]*model.Feature{f5, f6},
+		[]*model.Field{f5, f6},
 		map[string]string{},
 	)
 	od_bf1 := test.CreateBaseFeatureView(
 		"od_bf1",
-		[]*model.Feature{f5, f6},
+		[]*model.Field{f5, f6},
 		projection3,
 	)
 	odfv := model.NewOnDemandFeatureViewFromBase(od_bf1)
diff --git a/go/internal/feast/server/logging/filelogsink.go b/go/internal/feast/server/logging/filelogsink.go
index c9f2049a041..d9796d69d10 100644
--- a/go/internal/feast/server/logging/filelogsink.go
+++ b/go/internal/feast/server/logging/filelogsink.go
@@ -34,7 +34,7 @@ func NewFileLogSink(path string) (*FileLogSink, error) {
 	return &FileLogSink{path: absPath}, nil
 }
 
-func (s *FileLogSink) Write(record arrow.Record) error {
+func (s *FileLogSink) Write(records []arrow.Record) error {
 	fileName, _ := uuid.NewUUID()
 
 	var writer io.Writer
@@ -42,7 +42,7 @@ func (s *FileLogSink) Write(record arrow.Record) error {
 	if err != nil {
 		return err
 	}
-	table := array.NewTableFromRecords(record.Schema(), []arrow.Record{record})
+	table := array.NewTableFromRecords(records[0].Schema(), records)
 
 	props := parquet.NewWriterProperties(parquet.WithDictionaryDefault(false))
 	arrProps := pqarrow.DefaultWriterProps()
diff --git a/go/internal/feast/server/logging/logger.go b/go/internal/feast/server/logging/logger.go
index d7ed1fbe189..0e4f230f5ad 100644
--- a/go/internal/feast/server/logging/logger.go
+++ b/go/internal/feast/server/logging/logger.go
@@ -33,7 +33,7 @@ type LogSink interface {
 	// Write is used to unload logs from memory buffer.
 	// Logs are not guaranteed to be flushed to sink on this point.
 	// The data can just be written to local disk (depending on implementation).
-	Write(data arrow.Record) error
+	Write(data []arrow.Record) error
 
 	// Flush actually send data to a sink.
 	// We want to control amount to interaction with sink, since it could be a costly operation.
@@ -42,7 +42,7 @@ type LogSink interface {
 }
 
 type Logger interface {
-	Log(joinKeyToEntityValues map[string][]*types.Value, featureVectors []*serving.GetOnlineFeaturesResponse_FeatureVector, featureNames []string, requestData map[string]*types.RepeatedValue, requestId string) error
+	Log(joinKeyToEntityValues map[string]*types.RepeatedValue, featureVectors []*serving.GetOnlineFeaturesResponse_FeatureVector, featureNames []string, requestData map[string]*types.RepeatedValue, requestId string) error
 }
 
 type LoggerImpl struct {
@@ -75,6 +75,10 @@ func NewLoggerConfig(sampleRate float32, opts LoggingOptions) LoggerConfig {
 }
 
 func NewLogger(schema *FeatureServiceSchema, featureServiceName string, sink LogSink, config LoggerConfig) (*LoggerImpl, error) {
+	buffer, err := NewMemoryBuffer(schema)
+	if err != nil {
+		return nil, err
+	}
 	logger := &LoggerImpl{
 		featureServiceName: featureServiceName,
 
@@ -82,10 +86,7 @@ func NewLogger(schema *FeatureServiceSchema, featureServiceName string, sink Log
 		signalCh: make(chan interface{}, 2),
 		sink:     sink,
 
-		buffer: &MemoryBuffer{
-			logs:   make([]*Log, 0),
-			schema: schema,
-		},
+		buffer: buffer,
 		schema: schema,
 		config: config,
 
@@ -207,7 +208,7 @@ func getFullFeatureName(featureViewName string, featureName string) string {
 	return fmt.Sprintf("%s__%s", featureViewName, featureName)
 }
 
-func (l *LoggerImpl) Log(joinKeyToEntityValues map[string][]*types.Value, featureVectors []*serving.GetOnlineFeaturesResponse_FeatureVector, featureNames []string, requestData map[string]*types.RepeatedValue, requestId string) error {
+func (l *LoggerImpl) Log(joinKeyToEntityValues map[string]*types.RepeatedValue, featureVectors []*serving.GetOnlineFeaturesResponse_FeatureVector, featureNames []string, requestData map[string]*types.RepeatedValue, requestId string) error {
 	if len(featureVectors) == 0 {
 		return nil
 	}
@@ -250,7 +251,7 @@ func (l *LoggerImpl) Log(joinKeyToEntityValues map[string][]*types.Value, featur
 			if !ok {
 				return errors.Errorf("Missing join key %s in log data", joinKey)
 			}
-			entityValues[idx] = rows[rowIdx]
+			entityValues[idx] = rows.Val[rowIdx]
 		}
 
 		requestDataValues := make([]*types.Value, len(l.schema.RequestData))
@@ -283,6 +284,6 @@ func (l *LoggerImpl) Log(joinKeyToEntityValues map[string][]*types.Value, featur
 
 type DummyLoggerImpl struct{}
 
-func (l *DummyLoggerImpl) Log(joinKeyToEntityValues map[string][]*types.Value, featureVectors []*serving.GetOnlineFeaturesResponse_FeatureVector, featureNames []string, requestData map[string]*types.RepeatedValue, requestId string) error {
+func (l *DummyLoggerImpl) Log(joinKeyToEntityValues map[string]*types.RepeatedValue, featureVectors []*serving.GetOnlineFeaturesResponse_FeatureVector, featureNames []string, requestData map[string]*types.RepeatedValue, requestId string) error {
 	return nil
 }
diff --git a/go/internal/feast/server/logging/logger_test.go b/go/internal/feast/server/logging/logger_test.go
index 5625b05a766..ddc1902b7d1 100644
--- a/go/internal/feast/server/logging/logger_test.go
+++ b/go/internal/feast/server/logging/logger_test.go
@@ -24,7 +24,7 @@ import (
 
 type DummySink struct{}
 
-func (s *DummySink) Write(rec arrow.Record) error {
+func (s *DummySink) Write(recs []arrow.Record) error {
 	return nil
 }
 
@@ -42,7 +42,7 @@ func TestLoggingChannelTimeout(t *testing.T) {
 			WriteInterval:   DefaultOptions.WriteInterval,
 		},
 	}
-	logger, err := NewLogger(nil, "testFS", &DummySink{}, config)
+	logger, err := NewLogger(&FeatureServiceSchema{}, "testFS", &DummySink{}, config)
 
 	// stop log processing to check buffering channel
 	logger.Stop()
@@ -90,7 +90,17 @@ func TestLogAndFlushToFile(t *testing.T) {
 	assert.Nil(t, err)
 
 	assert.Nil(t, logger.Log(
-		map[string][]*types.Value{"driver_id": {{Val: &types.Value_Int32Val{Int32Val: 111}}}},
+		map[string]*types.RepeatedValue{
+			"driver_id": {
+				Val: []*types.Value{
+					{
+						Val: &types.Value_Int32Val{
+							Int32Val: 111,
+						},
+					},
+				},
+			},
+		},
 		[]*serving.GetOnlineFeaturesResponse_FeatureVector{
 			{
 				Values:          []*types.Value{{Val: &types.Value_DoubleVal{DoubleVal: 2.0}}},
diff --git a/go/internal/feast/server/logging/memorybuffer.go b/go/internal/feast/server/logging/memorybuffer.go
index 36eb7118cb4..9ffb0ff73b8 100644
--- a/go/internal/feast/server/logging/memorybuffer.go
+++ b/go/internal/feast/server/logging/memorybuffer.go
@@ -2,7 +2,6 @@ package logging
 
 import (
 	"fmt"
-
 	"github.com/apache/arrow/go/v8/arrow"
 	"github.com/apache/arrow/go/v8/arrow/array"
 	"github.com/apache/arrow/go/v8/arrow/memory"
@@ -14,45 +13,79 @@ import (
 type MemoryBuffer struct {
 	logs   []*Log
 	schema *FeatureServiceSchema
+
+	arrowSchema *arrow.Schema
+	records     []arrow.Record
 }
 
 const (
 	LOG_TIMESTAMP_FIELD  = "__log_timestamp"
 	LOG_DATE_FIELD       = "__log_date"
 	LOG_REQUEST_ID_FIELD = "__request_id"
+	RECORD_SIZE          = 1000
 )
 
+func NewMemoryBuffer(schema *FeatureServiceSchema) (*MemoryBuffer, error) {
+	arrowSchema, err := getArrowSchema(schema)
+	if err != nil {
+		return nil, err
+	}
+	return &MemoryBuffer{
+		logs:        make([]*Log, 0),
+		records:     make([]arrow.Record, 0),
+		schema:      schema,
+		arrowSchema: arrowSchema,
+	}, nil
+}
+
 // Acquires the logging schema from the feature service, converts the memory buffer array of rows of logs and flushes
 // them to the offline storage.
 func (b *MemoryBuffer) writeBatch(sink LogSink) error {
-	if len(b.logs) == 0 {
-		return nil
+	if len(b.logs) > 0 {
+		err := b.Compact()
+		if err != nil {
+			return err
+		}
 	}
 
-	record, err := b.convertToArrowRecord()
-
-	if err != nil {
-		return err
+	if len(b.records) == 0 {
+		return nil
 	}
-	err = sink.Write(record)
+
+	err := sink.Write(b.records)
 	if err != nil {
 		return err
 	}
 
-	b.logs = b.logs[:0]
+	b.records = b.records[:0]
 	return nil
 }
 
 func (b *MemoryBuffer) Append(log *Log) error {
 	b.logs = append(b.logs, log)
+
+	if len(b.logs) == RECORD_SIZE {
+		return b.Compact()
+	}
+
 	return nil
 }
 
-func (b *MemoryBuffer) getArrowSchema() (*arrow.Schema, error) {
+func (b *MemoryBuffer) Compact() error {
+	rec, err := b.convertToArrowRecord()
+	if err != nil {
+		return err
+	}
+	b.records = append(b.records, rec)
+	b.logs = b.logs[:0]
+	return nil
+}
+
+func getArrowSchema(schema *FeatureServiceSchema) (*arrow.Schema, error) {
 	fields := make([]arrow.Field, 0)
 
-	for _, joinKey := range b.schema.JoinKeys {
-		arrowType, err := gotypes.ValueTypeEnumToArrowType(b.schema.JoinKeysTypes[joinKey])
+	for _, joinKey := range schema.JoinKeys {
+		arrowType, err := gotypes.ValueTypeEnumToArrowType(schema.JoinKeysTypes[joinKey])
 		if err != nil {
 			return nil, err
 		}
@@ -60,8 +93,8 @@ func (b *MemoryBuffer) getArrowSchema() (*arrow.Schema, error) {
 		fields = append(fields, arrow.Field{Name: joinKey, Type: arrowType})
 	}
 
-	for _, requestParam := range b.schema.RequestData {
-		arrowType, err := gotypes.ValueTypeEnumToArrowType(b.schema.RequestDataTypes[requestParam])
+	for _, requestParam := range schema.RequestData {
+		arrowType, err := gotypes.ValueTypeEnumToArrowType(schema.RequestDataTypes[requestParam])
 		if err != nil {
 			return nil, err
 		}
@@ -69,8 +102,8 @@ func (b *MemoryBuffer) getArrowSchema() (*arrow.Schema, error) {
 		fields = append(fields, arrow.Field{Name: requestParam, Type: arrowType})
 	}
 
-	for _, featureName := range b.schema.Features {
-		arrowType, err := gotypes.ValueTypeEnumToArrowType(b.schema.FeaturesTypes[featureName])
+	for _, featureName := range schema.Features {
+		arrowType, err := gotypes.ValueTypeEnumToArrowType(schema.FeaturesTypes[featureName])
 		if err != nil {
 			return nil, err
 		}
@@ -98,18 +131,13 @@ func (b *MemoryBuffer) convertToArrowRecord() (arrow.Record, error) {
 	arrowMemory := memory.NewGoAllocator()
 	numRows := len(b.logs)
 
-	arrowSchema, err := b.getArrowSchema()
-	if err != nil {
-		return nil, err
-	}
-
 	columns := make(map[string][]*types.Value)
 	fieldNameToIdx := make(map[string]int)
-	for idx, field := range arrowSchema.Fields() {
+	for idx, field := range b.arrowSchema.Fields() {
 		fieldNameToIdx[field.Name] = idx
 	}
 
-	builder := array.NewRecordBuilder(arrowMemory, arrowSchema)
+	builder := array.NewRecordBuilder(arrowMemory, b.arrowSchema)
 	defer builder.Release()
 
 	builder.Reserve(numRows)
diff --git a/go/internal/feast/server/logging/memorybuffer_test.go b/go/internal/feast/server/logging/memorybuffer_test.go
index 59f035799bb..94f0f86ef02 100644
--- a/go/internal/feast/server/logging/memorybuffer_test.go
+++ b/go/internal/feast/server/logging/memorybuffer_test.go
@@ -37,10 +37,6 @@ func TestArrowSchemaGeneration(t *testing.T) {
 		},
 	}
 
-	b := &MemoryBuffer{
-		schema: schema,
-	}
-
 	expectedArrowSchema := []arrow.Field{
 		{Name: "driver_id", Type: arrow.PrimitiveTypes.Int32},
 		{Name: "featureView1__int64", Type: arrow.PrimitiveTypes.Int64},
@@ -60,7 +56,7 @@ func TestArrowSchemaGeneration(t *testing.T) {
 		{Name: "__request_id", Type: arrow.BinaryTypes.String},
 	}
 
-	actualSchema, err := b.getArrowSchema()
+	actualSchema, err := getArrowSchema(schema)
 	assert.Nil(t, err)
 	assert.Equal(t, expectedArrowSchema, actualSchema.Fields())
 }
@@ -84,51 +80,46 @@ func TestSerializeToArrowTable(t *testing.T) {
 	}
 
 	ts := timestamppb.New(time.Now())
-	b := &MemoryBuffer{
-		schema: schema,
-		logs: []*Log{
-			{
-				EntityValue: []*types.Value{
-					{Val: &types.Value_Int64Val{Int64Val: 1001}},
-				},
-				FeatureValues: []*types.Value{
-					{Val: &types.Value_Int64Val{Int64Val: rand.Int63()}},
-					{Val: &types.Value_FloatVal{FloatVal: rand.Float32()}},
-				},
-				FeatureStatuses: []serving.FieldStatus{
-					serving.FieldStatus_PRESENT,
-					serving.FieldStatus_OUTSIDE_MAX_AGE,
-				},
-				EventTimestamps: []*timestamppb.Timestamp{
-					ts, ts,
-				},
-				RequestId:    "aaa",
-				LogTimestamp: time.Now(),
-			},
-			{
-				EntityValue: []*types.Value{
-					{Val: &types.Value_Int64Val{Int64Val: 1003}},
-				},
-				FeatureValues: []*types.Value{
-					{Val: &types.Value_Int64Val{Int64Val: rand.Int63()}},
-					{Val: &types.Value_FloatVal{FloatVal: rand.Float32()}},
-				},
-				FeatureStatuses: []serving.FieldStatus{
-					serving.FieldStatus_PRESENT,
-					serving.FieldStatus_PRESENT,
-				},
-				EventTimestamps: []*timestamppb.Timestamp{
-					ts, ts,
-				},
-				RequestId:    "bbb",
-				LogTimestamp: time.Now(),
-			},
+	b, _ := NewMemoryBuffer(schema)
+	b.Append(&Log{
+		EntityValue: []*types.Value{
+			{Val: &types.Value_Int64Val{Int64Val: 1001}},
 		},
-	}
+		FeatureValues: []*types.Value{
+			{Val: &types.Value_Int64Val{Int64Val: rand.Int63()}},
+			{Val: &types.Value_FloatVal{FloatVal: rand.Float32()}},
+		},
+		FeatureStatuses: []serving.FieldStatus{
+			serving.FieldStatus_PRESENT,
+			serving.FieldStatus_OUTSIDE_MAX_AGE,
+		},
+		EventTimestamps: []*timestamppb.Timestamp{
+			ts, ts,
+		},
+		RequestId:    "aaa",
+		LogTimestamp: time.Now(),
+	})
+	b.Append(&Log{
+		EntityValue: []*types.Value{
+			{Val: &types.Value_Int64Val{Int64Val: 1003}},
+		},
+		FeatureValues: []*types.Value{
+			{Val: &types.Value_Int64Val{Int64Val: rand.Int63()}},
+			{Val: &types.Value_FloatVal{FloatVal: rand.Float32()}},
+		},
+		FeatureStatuses: []serving.FieldStatus{
+			serving.FieldStatus_PRESENT,
+			serving.FieldStatus_PRESENT,
+		},
+		EventTimestamps: []*timestamppb.Timestamp{
+			ts, ts,
+		},
+		RequestId:    "bbb",
+		LogTimestamp: time.Now(),
+	})
 
 	pool := memory.NewGoAllocator()
-	arrowSchema, _ := b.getArrowSchema()
-	builder := array.NewRecordBuilder(pool, arrowSchema)
+	builder := array.NewRecordBuilder(pool, b.arrowSchema)
 	defer builder.Release()
 
 	// join key: driver_id
diff --git a/go/internal/feast/server/logging/offlinestoresink.go b/go/internal/feast/server/logging/offlinestoresink.go
index ee4c646a9be..632039baa43 100644
--- a/go/internal/feast/server/logging/offlinestoresink.go
+++ b/go/internal/feast/server/logging/offlinestoresink.go
@@ -1,10 +1,10 @@
 package logging
 
 import (
-	"errors"
 	"fmt"
 	"io"
 	"io/ioutil"
+	"log"
 	"os"
 	"path/filepath"
 
@@ -41,16 +41,7 @@ func (s *OfflineStoreSink) getOrCreateDatasetDir() (string, error) {
 	return s.datasetDir, nil
 }
 
-func (s *OfflineStoreSink) cleanCurrentDatasetDir() error {
-	if s.datasetDir == "" {
-		return nil
-	}
-	datasetDir := s.datasetDir
-	s.datasetDir = ""
-	return os.RemoveAll(datasetDir)
-}
-
-func (s *OfflineStoreSink) Write(record arrow.Record) error {
+func (s *OfflineStoreSink) Write(records []arrow.Record) error {
 	fileName, _ := uuid.NewUUID()
 	datasetDir, err := s.getOrCreateDatasetDir()
 	if err != nil {
@@ -62,7 +53,7 @@ func (s *OfflineStoreSink) Write(record arrow.Record) error {
 	if err != nil {
 		return err
 	}
-	table := array.NewTableFromRecords(record.Schema(), []arrow.Record{record})
+	table := array.NewTableFromRecords(records[0].Schema(), records)
 
 	props := parquet.NewWriterProperties(parquet.WithDictionaryDefault(false))
 	arrProps := pqarrow.DefaultWriterProps()
@@ -74,10 +65,16 @@ func (s *OfflineStoreSink) Flush(featureServiceName string) error {
 		return nil
 	}
 
-	errMsg := s.writeCallback(featureServiceName, s.datasetDir)
-	if errMsg != "" {
-		return errors.New(errMsg)
-	}
+	datasetDir := s.datasetDir
+	s.datasetDir = ""
+
+	go func() {
+		errMsg := s.writeCallback(featureServiceName, datasetDir)
+		if errMsg != "" {
+			log.Println(errMsg)
+		}
+		os.RemoveAll(datasetDir)
+	}()
 
-	return s.cleanCurrentDatasetDir()
+	return nil
 }
diff --git a/go/internal/feast/server/logging/service.go b/go/internal/feast/server/logging/service.go
index 9249ad4f2f1..c237a80c688 100644
--- a/go/internal/feast/server/logging/service.go
+++ b/go/internal/feast/server/logging/service.go
@@ -42,7 +42,7 @@ type LoggingService struct {
 
 var (
 	DefaultOptions = LoggingOptions{
-		ChannelCapacity: 1000,
+		ChannelCapacity: 100000,
 		FlushInterval:   10 * time.Minute,
 		WriteInterval:   10 * time.Second,
 		EmitTimeout:     10 * time.Millisecond,
diff --git a/go/internal/feast/transformation/transformation.go b/go/internal/feast/transformation/transformation.go
index 3dfbdc7f1b7..810b4e9bcbd 100644
--- a/go/internal/feast/transformation/transformation.go
+++ b/go/internal/feast/transformation/transformation.go
@@ -42,7 +42,7 @@ func AugmentResponseWithOnDemandTransforms(
 	var err error
 
 	for _, odfv := range onDemandFeatureViews {
-		requestContextArrow := make(map[string]array.Interface)
+		requestContextArrow := make(map[string]arrow.Array)
 		for name, values := range requestData {
 			requestContextArrow[name], err = types.ProtoValuesToArrowArray(values.Val, arrowMemory, numRows)
 			if err != nil {
@@ -57,7 +57,7 @@ func AugmentResponseWithOnDemandTransforms(
 			}
 		}
 
-		retrievedFeatures := make(map[string]array.Interface)
+		retrievedFeatures := make(map[string]arrow.Array)
 		for _, vector := range features {
 			retrievedFeatures[vector.Name] = vector.Values
 		}
@@ -81,8 +81,8 @@ func AugmentResponseWithOnDemandTransforms(
 
 func CallTransformations(
 	featureView *model.OnDemandFeatureView,
-	retrievedFeatures map[string]array.Interface,
-	requestContext map[string]array.Interface,
+	retrievedFeatures map[string]arrow.Array,
+	requestContext map[string]arrow.Array,
 	callback TransformationCallback,
 	numRows int,
 	fullFeatureNames bool,
@@ -106,7 +106,7 @@ func CallTransformations(
 	outSchemaPtr := uintptr(unsafe.Pointer(&outSchema))
 
 	inputFields := make([]arrow.Field, 0)
-	inputColumns := make([]array.Interface, 0)
+	inputColumns := make([]arrow.Array, 0)
 	for name, arr := range retrievedFeatures {
 		inputFields = append(inputFields, arrow.Field{Name: name, Type: arr.DataType()})
 		inputColumns = append(inputColumns, arr)
diff --git a/go/internal/test/go_integration_test_utils.go b/go/internal/test/go_integration_test_utils.go
index eb727ba1db9..275edc7b98e 100644
--- a/go/internal/test/go_integration_test_utils.go
+++ b/go/internal/test/go_integration_test_utils.go
@@ -193,7 +193,7 @@ func GetProtoFromRecord(rec arrow.Record) (map[string]*types.RepeatedValue, erro
 	return r, nil
 }
 
-func CreateBaseFeatureView(name string, features []*model.Feature, projection *model.FeatureViewProjection) *model.BaseFeatureView {
+func CreateBaseFeatureView(name string, features []*model.Field, projection *model.FeatureViewProjection) *model.BaseFeatureView {
 	return &model.BaseFeatureView{
 		Name:       name,
 		Features:   features,
@@ -201,16 +201,15 @@ func CreateBaseFeatureView(name string, features []*model.Feature, projection *m
 	}
 }
 
-func CreateNewEntity(name string, valueType types.ValueType_Enum, joinKey string) *model.Entity {
+func CreateNewEntity(name string, joinKey string) *model.Entity {
 	return &model.Entity{
-		Name:      name,
-		ValueType: valueType,
-		JoinKey:   joinKey,
+		Name:    name,
+		JoinKey: joinKey,
 	}
 }
 
-func CreateNewFeature(name string, dtype types.ValueType_Enum) *model.Feature {
-	return &model.Feature{Name: name,
+func CreateNewField(name string, dtype types.ValueType_Enum) *model.Field {
+	return &model.Field{Name: name,
 		Dtype: dtype,
 	}
 }
@@ -225,7 +224,7 @@ func CreateNewFeatureService(name string, project string, createdTimestamp *time
 	}
 }
 
-func CreateNewFeatureViewProjection(name string, nameAlias string, features []*model.Feature, joinKeyMap map[string]string) *model.FeatureViewProjection {
+func CreateNewFeatureViewProjection(name string, nameAlias string, features []*model.Field, joinKeyMap map[string]string) *model.FeatureViewProjection {
 	return &model.FeatureViewProjection{Name: name,
 		NameAlias:  nameAlias,
 		Features:   features,
@@ -233,10 +232,11 @@ func CreateNewFeatureViewProjection(name string, nameAlias string, features []*m
 	}
 }
 
-func CreateFeatureView(base *model.BaseFeatureView, ttl *durationpb.Duration, entities []string) *model.FeatureView {
+func CreateFeatureView(base *model.BaseFeatureView, ttl *durationpb.Duration, entities []string, entityColumns []*model.Field) *model.FeatureView {
 	return &model.FeatureView{
-		Base:     base,
-		Ttl:      ttl,
-		Entities: entities,
+		Base:          base,
+		Ttl:           ttl,
+		EntityNames:   entities,
+		EntityColumns: entityColumns,
 	}
 }
diff --git a/go/types/typeconversion.go b/go/types/typeconversion.go
index 30fcc1e393c..45eeac52c6f 100644
--- a/go/types/typeconversion.go
+++ b/go/types/typeconversion.go
@@ -285,6 +285,10 @@ func ArrowValuesToProtoValues(arr arrow.Array) ([]*types.Value, error) {
 				&types.Value{Val: &types.Value_UnixTimestampVal{
 					UnixTimestampVal: int64(arr.(*array.Timestamp).Value(idx))}})
 		}
+	case arrow.Null:
+		for idx := 0; idx < arr.Len(); idx++ {
+			values = append(values, &types.Value{})
+		}
 	default:
 		return nil, fmt.Errorf("unsupported arrow to proto conversion for type %s", arr.DataType())
 	}
diff --git a/infra/charts/feast-python-server/Chart.yaml b/infra/charts/feast-python-server/Chart.yaml
index bd0140485c4..6c4751e3b7e 100644
--- a/infra/charts/feast-python-server/Chart.yaml
+++ b/infra/charts/feast-python-server/Chart.yaml
@@ -2,7 +2,7 @@ apiVersion: v2
 name: feast-python-server
 description: Feast Feature Server in Python
 type: application
-version: 0.21.0
+version: 0.22.0
 keywords:
   - machine learning
   - big data
diff --git a/infra/charts/feast-python-server/README.md b/infra/charts/feast-python-server/README.md
index af844bf7d35..3f60cc6c549 100644
--- a/infra/charts/feast-python-server/README.md
+++ b/infra/charts/feast-python-server/README.md
@@ -1,6 +1,6 @@
 # feast-python-server
 
-![Version: 0.21.0](https://img.shields.io/badge/Version-0.21.0-informational?style=flat-square) ![Type: application](https://img.shields.io/badge/Type-application-informational?style=flat-square)
+![Version: 0.22.0](https://img.shields.io/badge/Version-0.22.0-informational?style=flat-square) ![Type: application](https://img.shields.io/badge/Type-application-informational?style=flat-square)
 
 Feast Feature Server in Python
 
diff --git a/infra/charts/feast/Chart.yaml b/infra/charts/feast/Chart.yaml
index bf1020ffa28..012dc47de92 100644
--- a/infra/charts/feast/Chart.yaml
+++ b/infra/charts/feast/Chart.yaml
@@ -1,7 +1,7 @@
 apiVersion: v1
 description: Feature store for machine learning
 name: feast
-version: 0.21.0
+version: 0.22.0
 keywords:
   - machine learning
   - big data
diff --git a/infra/charts/feast/README.md b/infra/charts/feast/README.md
index 8d2865f9e09..8b5e9718eff 100644
--- a/infra/charts/feast/README.md
+++ b/infra/charts/feast/README.md
@@ -8,7 +8,7 @@ This repo contains Helm charts for Feast components that are being installed on
    
 ## Chart: Feast
 
-Feature store for machine learning Current chart version is `0.21.0`
+Feature store for machine learning Current chart version is `0.22.0`
 
 ## Installation
 
@@ -55,8 +55,8 @@ For more details, please see: https://docs.feast.dev/how-to-guides/running-feast
 | Repository | Name | Version |
 |------------|------|---------|
 | https://charts.helm.sh/stable | redis | 10.5.6  |
-| https://feast-helm-charts.storage.googleapis.com | feature-server(feature-server) | 0.21.0  |
-| https://feast-helm-charts.storage.googleapis.com | transformation-service(transformation-service) | 0.21.0  |
+| https://feast-helm-charts.storage.googleapis.com | feature-server(feature-server) | 0.22.0  |
+| https://feast-helm-charts.storage.googleapis.com | transformation-service(transformation-service) | 0.22.0  |
 
 ## Values
 
diff --git a/infra/charts/feast/charts/feature-server/Chart.yaml b/infra/charts/feast/charts/feature-server/Chart.yaml
index 812719b5f09..b88ad599b4b 100644
--- a/infra/charts/feast/charts/feature-server/Chart.yaml
+++ b/infra/charts/feast/charts/feature-server/Chart.yaml
@@ -1,8 +1,8 @@
 apiVersion: v1
 description: "Feast Feature Server: Online feature serving service for Feast"
 name: feature-server
-version: 0.21.0
-appVersion: v0.21.0
+version: 0.22.0
+appVersion: v0.22.0
 keywords:
 - machine learning
 - big data
diff --git a/infra/charts/feast/charts/feature-server/README.md b/infra/charts/feast/charts/feature-server/README.md
index 5577b94dcf1..28570b0fc67 100644
--- a/infra/charts/feast/charts/feature-server/README.md
+++ b/infra/charts/feast/charts/feature-server/README.md
@@ -1,6 +1,6 @@
 # feature-server
 
-![Version: 0.21.0](https://img.shields.io/badge/Version-0.21.0-informational?style=flat-square) ![AppVersion: v0.21.0](https://img.shields.io/badge/AppVersion-v0.21.0-informational?style=flat-square)
+![Version: 0.22.0](https://img.shields.io/badge/Version-0.22.0-informational?style=flat-square) ![AppVersion: v0.22.0](https://img.shields.io/badge/AppVersion-v0.22.0-informational?style=flat-square)
 
 Feast Feature Server: Online feature serving service for Feast
 
@@ -17,7 +17,7 @@ Feast Feature Server: Online feature serving service for Feast
 | envOverrides | object | `{}`                                                  | Extra environment variables to set |
 | image.pullPolicy | string | `"IfNotPresent"`                                      | Image pull policy |
 | image.repository | string | `"feastdev/feature-server-java"`                      | Docker image for Feature Server repository |
-| image.tag | string | `"0.21.0"`                                            | Image tag |
+| image.tag | string | `"0.22.0"`                                            | Image tag |
 | ingress.grpc.annotations | object | `{}`                                                  | Extra annotations for the ingress |
 | ingress.grpc.auth.enabled | bool | `false`                                               | Flag to enable auth |
 | ingress.grpc.class | string | `"nginx"`                                             | Which ingress controller to use |
diff --git a/infra/charts/feast/charts/feature-server/values.yaml b/infra/charts/feast/charts/feature-server/values.yaml
index 457e83875af..df8367fede2 100644
--- a/infra/charts/feast/charts/feature-server/values.yaml
+++ b/infra/charts/feast/charts/feature-server/values.yaml
@@ -5,7 +5,7 @@ image:
   # image.repository -- Docker image for Feature Server repository
   repository: feastdev/feature-server-java
   # image.tag -- Image tag
-  tag: 0.21.0
+  tag: 0.22.0
   # image.pullPolicy -- Image pull policy
   pullPolicy: IfNotPresent
 
diff --git a/infra/charts/feast/charts/transformation-service/Chart.yaml b/infra/charts/feast/charts/transformation-service/Chart.yaml
index 7922a648c85..148e136acf3 100644
--- a/infra/charts/feast/charts/transformation-service/Chart.yaml
+++ b/infra/charts/feast/charts/transformation-service/Chart.yaml
@@ -1,8 +1,8 @@
 apiVersion: v1
 description: "Transformation service: to compute on-demand features"
 name: transformation-service
-version: 0.21.0
-appVersion: v0.21.0
+version: 0.22.0
+appVersion: v0.22.0
 keywords:
 - machine learning
 - big data
diff --git a/infra/charts/feast/charts/transformation-service/README.md b/infra/charts/feast/charts/transformation-service/README.md
index 73a738f1ed6..4cbc1048f64 100644
--- a/infra/charts/feast/charts/transformation-service/README.md
+++ b/infra/charts/feast/charts/transformation-service/README.md
@@ -1,6 +1,6 @@
 # transformation-service
 
-![Version: 0.21.0](https://img.shields.io/badge/Version-0.21.0-informational?style=flat-square) ![AppVersion: v0.21.0](https://img.shields.io/badge/AppVersion-v0.21.0-informational?style=flat-square)
+![Version: 0.22.0](https://img.shields.io/badge/Version-0.22.0-informational?style=flat-square) ![AppVersion: v0.22.0](https://img.shields.io/badge/AppVersion-v0.22.0-informational?style=flat-square)
 
 Transformation service: to compute on-demand features
 
@@ -13,7 +13,7 @@ Transformation service: to compute on-demand features
 | envOverrides | object | `{}`                                       | Extra environment variables to set |
 | image.pullPolicy | string | `"IfNotPresent"`                           | Image pull policy |
 | image.repository | string | `"feastdev/feature-transformation-server"` | Docker image for Transformation Server repository |
-| image.tag | string | `"0.21.0"`                                 | Image tag |
+| image.tag | string | `"0.22.0"`                                 | Image tag |
 | nodeSelector | object | `{}`                                       | Node labels for pod assignment |
 | podLabels | object | `{}`                                       | Labels to be added to Feast Serving pods |
 | replicaCount | int | `1`                                        | Number of pods that will be created |
diff --git a/infra/charts/feast/charts/transformation-service/values.yaml b/infra/charts/feast/charts/transformation-service/values.yaml
index 3d111984e51..1264ea4f7b8 100644
--- a/infra/charts/feast/charts/transformation-service/values.yaml
+++ b/infra/charts/feast/charts/transformation-service/values.yaml
@@ -5,7 +5,7 @@ image:
   # image.repository -- Docker image for Transformation Server repository
   repository: feastdev/feature-transformation-server
   # image.tag -- Image tag
-  tag: 0.21.0
+  tag: 0.22.0
   # image.pullPolicy -- Image pull policy
   pullPolicy: IfNotPresent
 
diff --git a/infra/charts/feast/requirements.yaml b/infra/charts/feast/requirements.yaml
index ad0cb850bb3..0b69f295e7b 100644
--- a/infra/charts/feast/requirements.yaml
+++ b/infra/charts/feast/requirements.yaml
@@ -1,12 +1,12 @@
 dependencies:
 - name: feature-server
   alias: feature-server
-  version: 0.21.0
+  version: 0.22.0
   condition: feature-server.enabled
   repository: https://feast-helm-charts.storage.googleapis.com
 - name: transformation-service
   alias: transformation-service
-  version: 0.21.0
+  version: 0.22.0
   condition: transformation-service.enabled
   repository: https://feast-helm-charts.storage.googleapis.com
 - name: redis
diff --git a/infra/scripts/release/bump_file_versions.py b/infra/scripts/release/bump_file_versions.py
index b706c2c141b..e17463c2c7b 100644
--- a/infra/scripts/release/bump_file_versions.py
+++ b/infra/scripts/release/bump_file_versions.py
@@ -33,46 +33,22 @@ def main() -> None:
     with open(path_to_file_list, "r") as f:
         files_to_bump = f.read().splitlines()
 
-    # The current version should be 0.18.0 or 0.19.0 or 0.20.0 etc, but we should also make sure to support the
-    # occasional patch release on the master branch like 0.18.1 or 0.18.2
-    versions_in_files = 0
-    if current_version[-2:] != ".0":
-        print(current_version[-2:])
-        versions_in_files = count_version(current_version, files_to_bump, repo_root)
-        if versions_in_files != VERSIONS_TO_BUMP:
-            raise SystemExit(f"Found {versions_in_files} occurrences of {current_version} in files to bump, but "
-                             f"expected {VERSIONS_TO_BUMP}")
-    else:
-        found = False
-
-        # Lets make sure the files don't contain a patch version (e.g, 0.x.0 -> 0.x.20)
-        for patch_version in range(0, 20):
-            current_version_patch = current_version[:-1] + str(patch_version)
-            versions_in_files = count_version(current_version_patch, files_to_bump, repo_root)
-
-            # We are using a patch version, let's change our version number
-            if versions_in_files == VERSIONS_TO_BUMP:
-                print(f"Found {versions_in_files} occurrences of {current_version_patch}, changing current version to "
-                      f"{current_version_patch}")
-                current_version = current_version_patch
-                found = True
-                break
-            else:
-                print(f"Found {versions_in_files} occurrences of {current_version_patch}, instead of {VERSIONS_TO_BUMP}")
-        if not found:
-            raise SystemExit(f"Could not find {VERSIONS_TO_BUMP} versions of {current_version} in {path_to_file_list}")
-
-    print(f"Found {versions_in_files} occurrences of {current_version} in files to bump {path_to_file_list}")
+    # The current version should be 0.18.0 or 0.19.0 or 0.20.0 etc
+    validate_files_to_bump(current_version, files_to_bump, repo_root)
 
     # Bump the version in the files
     updated_count = 0
     for file in files_to_bump:
-        with open(repo_root.joinpath(file), "r") as f:
-            file_contents = f.read()
-            file_contents = file_contents.replace(current_version, new_version)
-
-        with open(repo_root.joinpath(file), "w") as f:
-            f.write(file_contents)
+        components = file.split(" ")
+        file_path = components[0]
+        lines = components[1:]
+        with open(repo_root.joinpath(file_path), "r") as f:
+            file_contents = f.readlines()
+            for line in lines:
+                file_contents[int(line) - 1] = file_contents[int(line) - 1].replace(current_version, new_version)
+
+        with open(repo_root.joinpath(file_path), "w") as f:
+            f.write(''.join(file_contents))
             updated_count += 1
 
     print(f"Updated {updated_count} files with new version {new_version}")
@@ -88,14 +64,19 @@ def is_semantic_version(version: str) -> bool:
     return True
 
 
-def count_version(current_version, files_to_bump, repo_root):
-    # Count how many of the existing versions we find
-    total = 0
+def validate_files_to_bump(current_version, files_to_bump, repo_root):
     for file in files_to_bump:
-        with open(repo_root.joinpath(file), "r") as f:
-            file_contents = f.read()
-            total += file_contents.count(current_version)
-    return total
+        components = file.split(" ")
+        assert len(components) > 1, f"Entry {file} should have a file name, and a list of line numbers with versions"
+        file_path = components[0]
+        lines = components[1:]
+        with open(repo_root.joinpath(file_path), "r") as f:
+            file_contents = f.readlines()
+            for line in lines:
+                assert current_version in file_contents[int(line) - 1], (
+                    f"File `{file_path}` line `{line}` didn't contain version {current_version}. "
+                    f"Contents: {file_contents[int(line) - 1]}"
+                )
 
 
 if __name__ == "__main__":
diff --git a/infra/scripts/release/files_to_bump.txt b/infra/scripts/release/files_to_bump.txt
index 2c3eece6bef..a1e2d29623a 100644
--- a/infra/scripts/release/files_to_bump.txt
+++ b/infra/scripts/release/files_to_bump.txt
@@ -1,13 +1,12 @@
-infra/charts/feast/requirements.yaml
-infra/charts/feast/Chart.yaml
-infra/charts/feast/charts/transformation-service/Chart.yaml
-infra/charts/feast/charts/transformation-service/README.md
-infra/charts/feast/charts/transformation-service/values.yaml
-infra/charts/feast/charts/feature-server/Chart.yaml
-infra/charts/feast/charts/feature-server/README.md
-infra/charts/feast/charts/feature-server/values.yaml
-infra/charts/feast/README.md
-infra/charts/feast-python-server/Chart.yaml
-infra/charts/feast-python-server/README.md
-java/pom.xml
-ui/package.json
\ No newline at end of file
+infra/charts/feast/requirements.yaml 4 9
+infra/charts/feast/Chart.yaml 4
+infra/charts/feast/charts/transformation-service/Chart.yaml 4 5
+infra/charts/feast/charts/transformation-service/README.md 3 16
+infra/charts/feast/charts/transformation-service/values.yaml 8
+infra/charts/feast/charts/feature-server/Chart.yaml 4 5
+infra/charts/feast/charts/feature-server/README.md 3 20
+infra/charts/feast/charts/feature-server/values.yaml 8
+infra/charts/feast/README.md 11 58 59
+infra/charts/feast-python-server/Chart.yaml 5
+infra/charts/feast-python-server/README.md 3
+java/pom.xml 41
diff --git a/java/common/src/main/java/feast/common/logging/entry/MessageAuditLogEntry.java b/java/common/src/main/java/feast/common/logging/entry/MessageAuditLogEntry.java
index 6e5072f66ef..8ad428a3a3d 100644
--- a/java/common/src/main/java/feast/common/logging/entry/MessageAuditLogEntry.java
+++ b/java/common/src/main/java/feast/common/logging/entry/MessageAuditLogEntry.java
@@ -19,16 +19,13 @@
 import com.google.auto.value.AutoValue;
 import com.google.gson.Gson;
 import com.google.gson.GsonBuilder;
-import com.google.gson.JsonElement;
 import com.google.gson.JsonParser;
-import com.google.gson.JsonSerializationContext;
 import com.google.gson.JsonSerializer;
 import com.google.protobuf.Empty;
 import com.google.protobuf.InvalidProtocolBufferException;
 import com.google.protobuf.Message;
 import com.google.protobuf.util.JsonFormat;
 import io.grpc.Status.Code;
-import java.lang.reflect.Type;
 import java.util.UUID;
 
 /** MessageAuditLogEntry records the handling of a Protobuf message by a service call. */
@@ -103,20 +100,17 @@ public String toJSON() {
         new GsonBuilder()
             .registerTypeAdapter(
                 Message.class,
-                new JsonSerializer<Message>() {
-                  @Override
-                  public JsonElement serialize(
-                      Message message, Type type, JsonSerializationContext context) {
-                    try {
-                      String messageJSON = JsonFormat.printer().print(message);
-                      return new JsonParser().parse(messageJSON);
-                    } catch (InvalidProtocolBufferException e) {
-
-                      throw new RuntimeException(
-                          "Unexpected exception converting Protobuf to JSON", e);
-                    }
-                  }
-                })
+                (JsonSerializer<Message>)
+                    (message, type, context) -> {
+                      try {
+                        String messageJSON = JsonFormat.printer().print(message);
+                        return new JsonParser().parse(messageJSON);
+                      } catch (InvalidProtocolBufferException e) {
+
+                        throw new RuntimeException(
+                            "Unexpected exception converting Protobuf to JSON", e);
+                      }
+                    })
             .create();
     return gson.toJson(this);
   }
diff --git a/java/common/src/main/java/feast/common/logging/interceptors/GrpcMessageInterceptor.java b/java/common/src/main/java/feast/common/logging/interceptors/GrpcMessageInterceptor.java
index 661642a89ad..e34fefd115c 100644
--- a/java/common/src/main/java/feast/common/logging/interceptors/GrpcMessageInterceptor.java
+++ b/java/common/src/main/java/feast/common/logging/interceptors/GrpcMessageInterceptor.java
@@ -38,7 +38,7 @@
  * GrpcMessageInterceptor assumes that all service calls are unary (ie single request/response).
  */
 public class GrpcMessageInterceptor implements ServerInterceptor {
-  private LoggingProperties loggingProperties;
+  private final LoggingProperties loggingProperties;
 
   /**
    * Construct GrpcMessageIntercetor.
@@ -78,7 +78,7 @@ public <ReqT, RespT> Listener<ReqT> interceptCall(
 
     // Register forwarding call to intercept outgoing response and log to audit log
     call =
-        new SimpleForwardingServerCall<ReqT, RespT>(call) {
+        new SimpleForwardingServerCall<>(call) {
           @Override
           public void sendMessage(RespT message) {
             // 2. Track the response & Log entry to audit logger
@@ -97,7 +97,7 @@ public void close(Status status, Metadata trailers) {
         };
 
     ServerCall.Listener<ReqT> listener = next.startCall(call, headers);
-    return new SimpleForwardingServerCallListener<ReqT>(listener) {
+    return new SimpleForwardingServerCallListener<>(listener) {
       @Override
       // Register listener to intercept incoming request messages and log to audit log
       public void onMessage(ReqT message) {
diff --git a/java/pom.xml b/java/pom.xml
index 3b794d97ed0..7ea4bc07bd1 100644
--- a/java/pom.xml
+++ b/java/pom.xml
@@ -38,7 +38,7 @@
     </modules>
 
     <properties>
-        <revision>0.21.0</revision>
+        <revision>0.22.0</revision>
         <github.url>https://github.com/feast-dev/feast</github.url>
 
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
@@ -58,7 +58,7 @@
         <log4jVersion>2.17.1</log4jVersion>
         <joda.time.version>2.9.9</joda.time.version>
         <jakarta.validation.api.version>2.0.2</jakarta.validation.api.version>
-        <lombok.version>1.18.12</lombok.version>
+        <lombok.version>1.18.24</lombok.version>
         <gson.fire.version>1.8.4</gson.fire.version>
         <gson.version>2.8.6</gson.version>
         <swagger.core.version>1.5.24</swagger.core.version>
diff --git a/java/sdk/src/main/java/dev/feast/RequestUtil.java b/java/sdk/src/main/java/dev/feast/RequestUtil.java
index fc13c453119..da2c0dc42e1 100644
--- a/java/sdk/src/main/java/dev/feast/RequestUtil.java
+++ b/java/sdk/src/main/java/dev/feast/RequestUtil.java
@@ -35,9 +35,7 @@ public static List<FeatureReferenceV2> createFeatureRefs(List<String> featureRef
     }
 
     List<FeatureReferenceV2> featureRefs =
-        featureRefStrings.stream()
-            .map(refStr -> parseFeatureRef(refStr))
-            .collect(Collectors.toList());
+        featureRefStrings.stream().map(RequestUtil::parseFeatureRef).collect(Collectors.toList());
 
     return featureRefs;
   }
diff --git a/java/serving/src/main/java/feast/serving/config/RegistryConfig.java b/java/serving/src/main/java/feast/serving/config/RegistryConfig.java
index 3e7cbe3f1f9..49e08e03b62 100644
--- a/java/serving/src/main/java/feast/serving/config/RegistryConfig.java
+++ b/java/serving/src/main/java/feast/serving/config/RegistryConfig.java
@@ -61,7 +61,8 @@ RegistryFile registryFile(
       case "file":
         return new LocalRegistryFile(registryPath);
       default:
-        throw new RuntimeException("Registry storage %s is unsupported");
+        throw new RuntimeException(
+            String.format("Registry storage %s is unsupported", scheme.get()));
     }
   }
 
diff --git a/java/serving/src/main/java/feast/serving/registry/LocalRegistryFile.java b/java/serving/src/main/java/feast/serving/registry/LocalRegistryFile.java
index b0d6b10bfc0..1da45813eea 100644
--- a/java/serving/src/main/java/feast/serving/registry/LocalRegistryFile.java
+++ b/java/serving/src/main/java/feast/serving/registry/LocalRegistryFile.java
@@ -24,7 +24,7 @@
 import java.util.Optional;
 
 public class LocalRegistryFile implements RegistryFile {
-  private RegistryProto.Registry cachedRegistry;
+  private final RegistryProto.Registry cachedRegistry;
 
   public LocalRegistryFile(String path) {
     try {
diff --git a/java/serving/src/main/java/feast/serving/registry/Registry.java b/java/serving/src/main/java/feast/serving/registry/Registry.java
index 37fae3d8dcb..bc953174ea4 100644
--- a/java/serving/src/main/java/feast/serving/registry/Registry.java
+++ b/java/serving/src/main/java/feast/serving/registry/Registry.java
@@ -17,6 +17,9 @@
 package feast.serving.registry;
 
 import feast.proto.core.*;
+import feast.proto.core.FeatureServiceProto.FeatureService;
+import feast.proto.core.FeatureViewProto.FeatureView;
+import feast.proto.core.OnDemandFeatureViewProto.OnDemandFeatureView;
 import feast.proto.serving.ServingAPIProto;
 import feast.serving.exception.SpecRetrievalException;
 import java.util.List;
@@ -26,16 +29,16 @@
 
 public class Registry {
   private final RegistryProto.Registry registry;
-  private Map<String, FeatureViewProto.FeatureViewSpec> featureViewNameToSpec;
+  private final Map<String, FeatureViewProto.FeatureViewSpec> featureViewNameToSpec;
   private Map<String, OnDemandFeatureViewProto.OnDemandFeatureViewSpec>
       onDemandFeatureViewNameToSpec;
-  private Map<String, FeatureServiceProto.FeatureServiceSpec> featureServiceNameToSpec;
+  private final Map<String, FeatureServiceProto.FeatureServiceSpec> featureServiceNameToSpec;
 
   Registry(RegistryProto.Registry registry) {
     this.registry = registry;
     List<FeatureViewProto.FeatureViewSpec> featureViewSpecs =
         registry.getFeatureViewsList().stream()
-            .map(fv -> fv.getSpec())
+            .map(FeatureView::getSpec)
             .collect(Collectors.toList());
     this.featureViewNameToSpec =
         featureViewSpecs.stream()
@@ -43,7 +46,7 @@ public class Registry {
                 Collectors.toMap(FeatureViewProto.FeatureViewSpec::getName, Function.identity()));
     List<OnDemandFeatureViewProto.OnDemandFeatureViewSpec> onDemandFeatureViewSpecs =
         registry.getOnDemandFeatureViewsList().stream()
-            .map(odfv -> odfv.getSpec())
+            .map(OnDemandFeatureView::getSpec)
             .collect(Collectors.toList());
     this.onDemandFeatureViewNameToSpec =
         onDemandFeatureViewSpecs.stream()
@@ -53,7 +56,7 @@ public class Registry {
                     Function.identity()));
     this.featureServiceNameToSpec =
         registry.getFeatureServicesList().stream()
-            .map(fs -> fs.getSpec())
+            .map(FeatureService::getSpec)
             .collect(
                 Collectors.toMap(
                     FeatureServiceProto.FeatureServiceSpec::getName, Function.identity()));
diff --git a/java/serving/src/main/java/feast/serving/service/OnlineTransformationService.java b/java/serving/src/main/java/feast/serving/service/OnlineTransformationService.java
index 365432b84e1..ae83635b861 100644
--- a/java/serving/src/main/java/feast/serving/service/OnlineTransformationService.java
+++ b/java/serving/src/main/java/feast/serving/service/OnlineTransformationService.java
@@ -239,8 +239,7 @@ public void processTransformFeaturesResponse(
     } catch (IOException e) {
       log.info(e.toString());
       throw Status.INTERNAL
-          .withDescription(
-              "Unable to correctly process transform features response: " + e.toString())
+          .withDescription("Unable to correctly process transform features response: " + e)
           .asRuntimeException();
     }
   }
@@ -249,11 +248,10 @@ public void processTransformFeaturesResponse(
   public ValueType serializeValuesIntoArrowIPC(List<Pair<String, List<ValueProto.Value>>> values) {
     // In order to be serialized correctly, the data must be packaged in a VectorSchemaRoot.
     // We first construct all the columns.
-    Map<String, FieldVector> columnNameToColumn = new HashMap<String, FieldVector>();
     BufferAllocator allocator = new RootAllocator(Long.MAX_VALUE);
 
-    List<Field> columnFields = new ArrayList<Field>();
-    List<FieldVector> columns = new ArrayList<FieldVector>();
+    List<Field> columnFields = new ArrayList<>();
+    List<FieldVector> columns = new ArrayList<>();
 
     for (Pair<String, List<ValueProto.Value>> columnEntry : values) {
       // The Python FTS does not expect full feature names, so we extract the feature name.
@@ -316,8 +314,7 @@ public ValueType serializeValuesIntoArrowIPC(List<Pair<String, List<ValueProto.V
     } catch (IOException e) {
       log.info(e.toString());
       throw Status.INTERNAL
-          .withDescription(
-              "ArrowFileWriter could not write properly; failed with error: " + e.toString())
+          .withDescription("ArrowFileWriter could not write properly; failed with error: " + e)
           .asRuntimeException();
     }
     byte[] byteData = out.toByteArray();
diff --git a/java/serving/src/test/java/feast/serving/it/ServingBaseTests.java b/java/serving/src/test/java/feast/serving/it/ServingBaseTests.java
index c610d7df6b1..30cba0cb068 100644
--- a/java/serving/src/test/java/feast/serving/it/ServingBaseTests.java
+++ b/java/serving/src/test/java/feast/serving/it/ServingBaseTests.java
@@ -28,11 +28,9 @@
 import feast.proto.serving.ServingAPIProto;
 import feast.proto.serving.ServingAPIProto.FieldStatus;
 import feast.proto.types.ValueProto;
+import feast.serving.registry.LocalRegistryFile;
 import feast.serving.util.DataGenerator;
 import io.grpc.StatusRuntimeException;
-import java.io.IOException;
-import java.nio.file.Files;
-import java.nio.file.Paths;
 import java.util.List;
 import java.util.Map;
 import java.util.concurrent.TimeUnit;
@@ -62,14 +60,8 @@ protected ServingAPIProto.GetOnlineFeaturesRequest buildOnlineRequest(int driver
   static RegistryProto.Registry registryProto = readLocalRegistry();
 
   private static RegistryProto.Registry readLocalRegistry() {
-    try {
-      return RegistryProto.Registry.parseFrom(
-          Files.readAllBytes(Paths.get("src/test/resources/docker-compose/feast10/registry.db")));
-    } catch (IOException e) {
-      e.printStackTrace();
-    }
-
-    return null;
+    return new LocalRegistryFile("src/test/resources/docker-compose/feast10/registry.db")
+        .getContent();
   }
 
   @Test
diff --git a/java/serving/src/test/java/feast/serving/util/DataGenerator.java b/java/serving/src/test/java/feast/serving/util/DataGenerator.java
index 7a310828d2e..fd00262763b 100644
--- a/java/serving/src/test/java/feast/serving/util/DataGenerator.java
+++ b/java/serving/src/test/java/feast/serving/util/DataGenerator.java
@@ -226,7 +226,7 @@ public static DataSource createKafkaDataSourceSpec(
         .setKafkaOptions(
             KafkaOptions.newBuilder()
                 .setTopic(topic)
-                .setBootstrapServers(servers)
+                .setKafkaBootstrapServers(servers)
                 .setMessageFormat(createProtoFormat("class.path"))
                 .build())
         .setTimestampField(timestampColumn)
diff --git a/java/serving/src/test/resources/docker-compose/feast10/definitions.py b/java/serving/src/test/resources/docker-compose/feast10/definitions.py
index e514ef0d239..806995ec06f 100644
--- a/java/serving/src/test/resources/docker-compose/feast10/definitions.py
+++ b/java/serving/src/test/resources/docker-compose/feast10/definitions.py
@@ -19,22 +19,23 @@
 
 # Define an entity for the driver. You can think of entity as a primary key used to
 # fetch features.
-driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)
+driver = Entity(name="driver_id", description="driver id")
 
 # Our parquet files contain sample data that includes a driver_id column, timestamps and
 # three feature column. Here we define a Feature View that will allow us to serve this
 # data to our model online.
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver_id"],
+    entities=[driver],
     ttl=Duration(seconds=86400 * 7),
     schema=[
         Field(name="conv_rate", dtype=Float64),
         Field(name="acc_rate", dtype=Float32),
         Field(name="avg_daily_trips", dtype=Int64),
+        Field(name="driver_id", dtype=Int64),
     ],
     online=True,
-    batch_source=driver_hourly_stats,
+    source=driver_hourly_stats,
     tags={},
 )
 
@@ -75,11 +76,11 @@ def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
 benchmark_feature_views = [
     FeatureView(
         name=f"feature_view_{i}",
-        entities=["entity"],
+        entities=[entity],
         ttl=Duration(seconds=86400),
         schema=[Field(name=f"feature_{10 * i + j}", dtype=Int64) for j in range(10)],
         online=True,
-        batch_source=generated_data_source,
+        source=generated_data_source,
     )
     for i in range(25)
 ]
diff --git a/java/storage/api/src/main/java/feast/storage/api/retriever/AvroFeature.java b/java/storage/api/src/main/java/feast/storage/api/retriever/AvroFeature.java
index 96f19ccdbe1..d8f27630102 100644
--- a/java/storage/api/src/main/java/feast/storage/api/retriever/AvroFeature.java
+++ b/java/storage/api/src/main/java/feast/storage/api/retriever/AvroFeature.java
@@ -149,7 +149,8 @@ public ValueProto.Value getFeatureValue(ValueProto.ValueType.Enum valueType) {
                   .build();
           break;
         default:
-          throw new RuntimeException("FeatureType is not supported");
+          throw new RuntimeException(
+              String.format("FeatureType %s is not supported", valueType.name()));
       }
     } catch (ClassCastException e) {
       // Feature type has changed
diff --git a/protos/feast/core/Aggregation.proto b/protos/feast/core/Aggregation.proto
new file mode 100644
index 00000000000..d2d6cab7021
--- /dev/null
+++ b/protos/feast/core/Aggregation.proto
@@ -0,0 +1,15 @@
+syntax = "proto3";
+package feast.core;
+
+option go_package = "github.com/feast-dev/feast/go/protos/feast/core";
+option java_outer_classname = "AggregationProto";
+option java_package = "feast.proto.core";
+
+import "google/protobuf/duration.proto";
+
+message Aggregation {
+    string column = 1;
+    string function = 2;
+    google.protobuf.Duration time_window = 3;
+    google.protobuf.Duration slide_interval = 4;
+}
\ No newline at end of file
diff --git a/protos/feast/core/DataFormat.proto b/protos/feast/core/DataFormat.proto
index 9fd01e865c2..c453e5e4c83 100644
--- a/protos/feast/core/DataFormat.proto
+++ b/protos/feast/core/DataFormat.proto
@@ -26,7 +26,7 @@ option java_package = "feast.proto.core";
 message FileFormat {
   // Defines options for the Parquet data format
   message ParquetFormat {}
-  
+
   oneof format {
     ParquetFormat parquet_format = 1;
   }
@@ -40,7 +40,7 @@ message StreamFormat {
     // Feature data from the obtained stream message
     string class_path = 1;
   }
-  
+
   // Defines options for the avro data format
   message AvroFormat {
     // Optional if used in a File DataSource as schema is embedded in avro file.
@@ -48,9 +48,14 @@ message StreamFormat {
     string schema_json = 1;
   }
 
+  message JsonFormat {
+    string schema_json = 1;
+  }
+
   // Specifies the data format and format specific options
   oneof format {
     AvroFormat avro_format = 1;
     ProtoFormat proto_format = 2;
+    JsonFormat json_format = 3;
   }
 }
diff --git a/protos/feast/core/DataSource.proto b/protos/feast/core/DataSource.proto
index 9e6028ccfa4..62f5859ee8e 100644
--- a/protos/feast/core/DataSource.proto
+++ b/protos/feast/core/DataSource.proto
@@ -22,6 +22,7 @@ option go_package = "github.com/feast-dev/feast/go/protos/feast/core";
 option java_outer_classname = "DataSourceProto";
 option java_package = "feast.proto.core";
 
+import "google/protobuf/duration.proto";
 import "feast/core/DataFormat.proto";
 import "feast/types/Value.proto";
 import "feast/core/Feature.proto";
@@ -127,7 +128,7 @@ message DataSource {
   // Java Protobuf class at the given class path
   message KafkaOptions {
     // Comma separated list of Kafka bootstrap servers. Used for feature tables without a defined source host[:port]]
-    string bootstrap_servers = 1;
+    string kafka_bootstrap_servers = 1;
 
     // Kafka topic to collect feature data from.
     string topic = 2;
@@ -135,6 +136,8 @@ message DataSource {
     // Defines the stream data format encoding feature/entity data in Kafka messages.
     StreamFormat message_format = 3;
 
+    // Watermark delay threshold for stream data
+    google.protobuf.Duration watermark_delay_threshold = 4;
   }
 
   // Defines options for DataSource that sources features from Kinesis records.
diff --git a/protos/feast/core/FeatureService.proto b/protos/feast/core/FeatureService.proto
index c04fa97507d..51b9c6c02a2 100644
--- a/protos/feast/core/FeatureService.proto
+++ b/protos/feast/core/FeatureService.proto
@@ -5,7 +5,6 @@ option go_package = "github.com/feast-dev/feast/go/protos/feast/core";
 option java_outer_classname = "FeatureServiceProto";
 option java_package = "feast.proto.core";
 
-import "google/protobuf/duration.proto";
 import "google/protobuf/timestamp.proto";
 import "feast/core/FeatureViewProjection.proto";
 
@@ -54,7 +53,6 @@ message FeatureServiceMeta {
 
 message LoggingConfig {
   float sample_rate = 1;
-  google.protobuf.Duration partition_interval = 2;
 
   oneof destination {
     FileDestination file_destination = 3;
diff --git a/protos/feast/core/FeatureView.proto b/protos/feast/core/FeatureView.proto
index 26623505401..c9e38bf3448 100644
--- a/protos/feast/core/FeatureView.proto
+++ b/protos/feast/core/FeatureView.proto
@@ -35,7 +35,7 @@ message FeatureView {
     FeatureViewMeta meta = 2;
 }
 
-// Next available id: 12
+// Next available id: 13
 // TODO(adchia): refactor common fields from this and ODFV into separate metadata proto
 message FeatureViewSpec {
     // Name of the feature view. Must be unique. Not updated.
@@ -44,13 +44,15 @@ message FeatureViewSpec {
     // Name of Feast project that this feature view belongs to.
     string project = 2;
 
-    // List names of entities to associate with the Features defined in this 
-    // Feature View. Not updatable.
+    // List of names of entities associated with this feature view.
     repeated string entities = 3;
 
-    // List of specifications for each field defined as part of this feature view.
+    // List of specifications for each feature defined as part of this feature view.
     repeated FeatureSpecV2 features = 4;
 
+    // List of specifications for each entity defined as part of this feature view.
+    repeated FeatureSpecV2 entity_columns = 12;
+
     // Description of the feature view.
     string description = 10;
 
diff --git a/protos/feast/core/Registry.proto b/protos/feast/core/Registry.proto
index 1978f41064a..19f17a81589 100644
--- a/protos/feast/core/Registry.proto
+++ b/protos/feast/core/Registry.proto
@@ -28,11 +28,13 @@ import "feast/core/FeatureView.proto";
 import "feast/core/InfraObject.proto";
 import "feast/core/OnDemandFeatureView.proto";
 import "feast/core/RequestFeatureView.proto";
+import "feast/core/StreamFeatureView.proto";
 import "feast/core/DataSource.proto";
 import "feast/core/SavedDataset.proto";
+import "feast/core/ValidationProfile.proto";
 import "google/protobuf/timestamp.proto";
 
-// Next id: 13
+// Next id: 15
 message Registry {
     repeated Entity entities = 1;
     repeated FeatureTable feature_tables = 2;
@@ -40,8 +42,10 @@ message Registry {
     repeated DataSource data_sources = 12;
     repeated OnDemandFeatureView on_demand_feature_views = 8;
     repeated RequestFeatureView request_feature_views = 9;
+    repeated StreamFeatureView stream_feature_views = 14;
     repeated FeatureService feature_services = 7;
     repeated SavedDataset saved_datasets = 11;
+    repeated ValidationReference validation_references = 13;
     Infra infra = 10;
 
     string registry_schema_version = 3; // to support migrations; incremented when schema is changed
diff --git a/protos/feast/core/StreamFeatureView.proto b/protos/feast/core/StreamFeatureView.proto
new file mode 100644
index 00000000000..d217b86a3f4
--- /dev/null
+++ b/protos/feast/core/StreamFeatureView.proto
@@ -0,0 +1,102 @@
+//
+// Copyright 2020 The Feast Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+
+syntax = "proto3";
+package feast.core;
+
+option go_package = "github.com/feast-dev/feast/go/protos/feast/core";
+option java_outer_classname = "StreamFeatureViewProto";
+option java_package = "feast.proto.core";
+
+
+import "google/protobuf/duration.proto";
+import "google/protobuf/timestamp.proto";
+import "feast/core/OnDemandFeatureView.proto";
+import "feast/core/FeatureView.proto";
+import "feast/core/Feature.proto";
+import "feast/core/DataSource.proto";
+import "feast/core/Aggregation.proto";
+
+message StreamFeatureView {
+    // User-specified specifications of this feature view.
+    StreamFeatureViewSpec spec = 1;
+    StreamFeatureViewMeta meta = 2;
+}
+
+// Next available id: 17
+message StreamFeatureViewSpec {
+    // Name of the feature view. Must be unique. Not updated.
+    string name = 1;
+
+    // Name of Feast project that this feature view belongs to.
+    string project = 2;
+
+    // List of names of entities associated with this feature view.
+    repeated string entities = 3;
+
+    // List of specifications for each feature defined as part of this feature view.
+    repeated FeatureSpecV2 features = 4;
+
+    // List of specifications for each entity defined as part of this feature view.
+    repeated FeatureSpecV2 entity_columns = 5;
+
+    // Description of the feature view.
+    string description = 6;
+
+    // User defined metadata
+    map<string,string> tags = 7;
+
+    // Owner of the feature view.
+    string owner = 8;
+
+    // Features in this feature view can only be retrieved from online serving
+    // younger than ttl. Ttl is measured as the duration of time between
+    // the feature's event timestamp and when the feature is retrieved
+    // Feature values outside ttl will be returned as unset values and indicated to end user
+    google.protobuf.Duration ttl = 9;
+
+    // Batch/Offline DataSource where this view can retrieve offline feature data.
+    DataSource batch_source = 10;
+    // Streaming DataSource from where this view can consume "online" feature data.
+    DataSource stream_source = 11;
+
+    // Whether these features should be served online or not
+    bool online = 12;
+
+    // Serialized function that is encoded in the streamfeatureview
+    UserDefinedFunction user_defined_function = 13;
+
+    // Mode of execution
+    string mode = 14;
+
+    // Aggregation definitions
+    repeated Aggregation aggregations = 15;
+
+    // Timestamp field for aggregation
+    string timestamp_field = 16;
+}
+
+message StreamFeatureViewMeta {
+    // Time where this Feature View is created
+    google.protobuf.Timestamp created_timestamp = 1;
+
+    // Time where this Feature View is last updated
+    google.protobuf.Timestamp last_updated_timestamp = 2;
+
+    // List of pairs (start_time, end_time) for which this feature view has been materialized.
+    repeated MaterializationInterval materialization_intervals = 3;
+}
diff --git a/protos/feast/core/ValidationProfile.proto b/protos/feast/core/ValidationProfile.proto
index 673a792fdf8..10027995859 100644
--- a/protos/feast/core/ValidationProfile.proto
+++ b/protos/feast/core/ValidationProfile.proto
@@ -22,8 +22,6 @@ option java_package = "feast.proto.core";
 option java_outer_classname = "ValidationProfile";
 option go_package = "github.com/feast-dev/feast/go/protos/feast/core";
 
-import "feast/core/SavedDataset.proto";
-
 message GEValidationProfiler {
   message UserDefinedProfiler {
       // The python-syntax function body (serialized by dill)
@@ -39,9 +37,24 @@ message GEValidationProfile {
 }
 
 message ValidationReference {
-  SavedDataset dataset = 1;
-
+  // Unique name of validation reference within the project
+  string name = 1;
+  // Name of saved dataset used as reference dataset
+  string reference_dataset_name = 2;
+  // Name of Feast project that this object source belongs to
+  string project = 3;
+  // Description of the validation reference
+  string description = 4;
+  // User defined metadata
+  map<string,string> tags = 5;
+
+  // validation profiler
   oneof profiler {
-    GEValidationProfiler ge_profiler = 2;
+    GEValidationProfiler ge_profiler = 6;
+  }
+
+  // (optional) cached validation profile (to avoid constant recalculation)
+  oneof cached_profile {
+    GEValidationProfile ge_profile = 7;
   }
 }
diff --git a/sdk/python/docs/Makefile b/sdk/python/docs/Makefile
index a396b3903b3..702e30d136f 100644
--- a/sdk/python/docs/Makefile
+++ b/sdk/python/docs/Makefile
@@ -3,7 +3,7 @@
 
 # You can set these variables from the command line.
 SPHINXOPTS    	=
-SPHINXBUILD   	= sphinx-build
+SPHINXBUILD   	= python -m sphinx
 SPHINXPROJ    	= Feast
 SOURCEDIR     	= source
 BUILDDIR      	= build
@@ -23,7 +23,7 @@ build-api-source:
 	cp conf.py index.rst source/
 
 html: clean-html build-api-source
-	sphinx-build -b html source html
+	python -m sphinx -b html source html
 
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
diff --git a/sdk/python/docs/index.rst b/sdk/python/docs/index.rst
index ca746707eac..9297901c331 100644
--- a/sdk/python/docs/index.rst
+++ b/sdk/python/docs/index.rst
@@ -21,8 +21,21 @@ Data Source
 ==================
 
 .. automodule:: feast.data_source
+    :inherited-members:
     :members:
-    :exclude-members: KafkaOptions, KafkaSource, KinesisOptions, KinesisSource
+    :exclude-members: KafkaOptions, KafkaSource, KinesisOptions, KinesisSource, PushSource, RequestSource, RequestDataSource
+
+Request Source
+------------------
+
+.. automodule:: feast.data_source
+    :members: RequestSource
+
+Push Source
+------------------
+
+.. automodule:: feast.data_source
+    :members: PushSource
 
 BigQuery Source
 ------------------
@@ -87,11 +100,17 @@ Feature View
     :members:
 
 On Demand Feature View
-======================
+----------------------
 
 .. automodule:: feast.on_demand_feature_view
     :members:
 
+Stream Feature View
+----------------------
+
+.. automodule:: feast.stream_feature_view
+    :members:
+
 Feature
 ==================
 
@@ -121,6 +140,20 @@ Registry Store
     :members:
     :exclude-members: NoopRegistryStore
 
+SQL Registry Store
+-----------------------
+
+.. automodule:: feast.infra.registry_stores.sql
+    :members:
+    :noindex:
+
+PostgreSQL Registry Store
+-----------------------
+
+.. automodule:: feast.infra.registry_stores.contrib.postgres.registry_store
+    :members:
+    :noindex:
+
 
 Provider
 ==================
@@ -243,3 +276,10 @@ PostgreSQL Online Store
 .. automodule:: feast.infra.online_stores.contrib.postgres
     :members:
     :noindex:
+
+HBase Online Store
+-----------------------
+
+.. automodule:: feast.infra.online_stores.contrib.hbase_online_store.hbase
+    :members:
+    :noindex:
diff --git a/sdk/python/docs/requirements.txt b/sdk/python/docs/requirements.txt
index 6f3a8382ff5..55035216355 100644
--- a/sdk/python/docs/requirements.txt
+++ b/sdk/python/docs/requirements.txt
@@ -1 +1 @@
--e "./sdk/python/[ci]"
\ No newline at end of file
+-e ".[docs]"
\ No newline at end of file
diff --git a/sdk/python/docs/source/feast.infra.offline_stores.contrib.postgres_offline_store.rst b/sdk/python/docs/source/feast.infra.offline_stores.contrib.postgres_offline_store.rst
index 95afafb5b80..a80690fe859 100644
--- a/sdk/python/docs/source/feast.infra.offline_stores.contrib.postgres_offline_store.rst
+++ b/sdk/python/docs/source/feast.infra.offline_stores.contrib.postgres_offline_store.rst
@@ -1,6 +1,14 @@
 feast.infra.offline\_stores.contrib.postgres\_offline\_store package
 ====================================================================
 
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   feast.infra.offline_stores.contrib.postgres_offline_store.tests
+
 Submodules
 ----------
 
diff --git a/sdk/python/docs/source/feast.infra.offline_stores.contrib.postgres_offline_store.tests.rst b/sdk/python/docs/source/feast.infra.offline_stores.contrib.postgres_offline_store.tests.rst
new file mode 100644
index 00000000000..35e60d2998b
--- /dev/null
+++ b/sdk/python/docs/source/feast.infra.offline_stores.contrib.postgres_offline_store.tests.rst
@@ -0,0 +1,21 @@
+feast.infra.offline\_stores.contrib.postgres\_offline\_store.tests package
+==========================================================================
+
+Submodules
+----------
+
+feast.infra.offline\_stores.contrib.postgres\_offline\_store.tests.data\_source module
+--------------------------------------------------------------------------------------
+
+.. automodule:: feast.infra.offline_stores.contrib.postgres_offline_store.tests.data_source
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: feast.infra.offline_stores.contrib.postgres_offline_store.tests
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/sdk/python/docs/source/feast.infra.offline_stores.contrib.spark_offline_store.rst b/sdk/python/docs/source/feast.infra.offline_stores.contrib.spark_offline_store.rst
index d91389faabf..b8b79bb48e8 100644
--- a/sdk/python/docs/source/feast.infra.offline_stores.contrib.spark_offline_store.rst
+++ b/sdk/python/docs/source/feast.infra.offline_stores.contrib.spark_offline_store.rst
@@ -1,6 +1,14 @@
 feast.infra.offline\_stores.contrib.spark\_offline\_store package
 =================================================================
 
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   feast.infra.offline_stores.contrib.spark_offline_store.tests
+
 Submodules
 ----------
 
diff --git a/sdk/python/docs/source/feast.infra.offline_stores.contrib.spark_offline_store.tests.rst b/sdk/python/docs/source/feast.infra.offline_stores.contrib.spark_offline_store.tests.rst
new file mode 100644
index 00000000000..8b0f9bd88b3
--- /dev/null
+++ b/sdk/python/docs/source/feast.infra.offline_stores.contrib.spark_offline_store.tests.rst
@@ -0,0 +1,21 @@
+feast.infra.offline\_stores.contrib.spark\_offline\_store.tests package
+=======================================================================
+
+Submodules
+----------
+
+feast.infra.offline\_stores.contrib.spark\_offline\_store.tests.data\_source module
+-----------------------------------------------------------------------------------
+
+.. automodule:: feast.infra.offline_stores.contrib.spark_offline_store.tests.data_source
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: feast.infra.offline_stores.contrib.spark_offline_store.tests
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/sdk/python/docs/source/feast.infra.offline_stores.contrib.trino_offline_store.rst b/sdk/python/docs/source/feast.infra.offline_stores.contrib.trino_offline_store.rst
index 0fe9cdc4610..857326003f3 100644
--- a/sdk/python/docs/source/feast.infra.offline_stores.contrib.trino_offline_store.rst
+++ b/sdk/python/docs/source/feast.infra.offline_stores.contrib.trino_offline_store.rst
@@ -9,6 +9,7 @@ Subpackages
 
    feast.infra.offline_stores.contrib.trino_offline_store.connectors
    feast.infra.offline_stores.contrib.trino_offline_store.test_config
+   feast.infra.offline_stores.contrib.trino_offline_store.tests
 
 Submodules
 ----------
diff --git a/sdk/python/docs/source/feast.infra.offline_stores.contrib.trino_offline_store.tests.rst b/sdk/python/docs/source/feast.infra.offline_stores.contrib.trino_offline_store.tests.rst
new file mode 100644
index 00000000000..9102f1f8d64
--- /dev/null
+++ b/sdk/python/docs/source/feast.infra.offline_stores.contrib.trino_offline_store.tests.rst
@@ -0,0 +1,21 @@
+feast.infra.offline\_stores.contrib.trino\_offline\_store.tests package
+=======================================================================
+
+Submodules
+----------
+
+feast.infra.offline\_stores.contrib.trino\_offline\_store.tests.data\_source module
+-----------------------------------------------------------------------------------
+
+.. automodule:: feast.infra.offline_stores.contrib.trino_offline_store.tests.data_source
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: feast.infra.offline_stores.contrib.trino_offline_store.tests
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/sdk/python/docs/source/feast.infra.online_stores.contrib.hbase_online_store.rst b/sdk/python/docs/source/feast.infra.online_stores.contrib.hbase_online_store.rst
new file mode 100644
index 00000000000..ce249023049
--- /dev/null
+++ b/sdk/python/docs/source/feast.infra.online_stores.contrib.hbase_online_store.rst
@@ -0,0 +1,21 @@
+feast.infra.online\_stores.contrib.hbase\_online\_store package
+===============================================================
+
+Submodules
+----------
+
+feast.infra.online\_stores.contrib.hbase\_online\_store.hbase module
+--------------------------------------------------------------------
+
+.. automodule:: feast.infra.online_stores.contrib.hbase_online_store.hbase
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: feast.infra.online_stores.contrib.hbase_online_store
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/sdk/python/docs/source/feast.infra.online_stores.contrib.rst b/sdk/python/docs/source/feast.infra.online_stores.contrib.rst
index 6ee9b1a532d..7315bb741ef 100644
--- a/sdk/python/docs/source/feast.infra.online_stores.contrib.rst
+++ b/sdk/python/docs/source/feast.infra.online_stores.contrib.rst
@@ -1,9 +1,25 @@
 feast.infra.online\_stores.contrib package
 ==========================================
 
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   feast.infra.online_stores.contrib.hbase_online_store
+
 Submodules
 ----------
 
+feast.infra.online\_stores.contrib.hbase\_repo\_configuration module
+--------------------------------------------------------------------
+
+.. automodule:: feast.infra.online_stores.contrib.hbase_repo_configuration
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 feast.infra.online\_stores.contrib.postgres module
 --------------------------------------------------
 
diff --git a/sdk/python/docs/source/feast.infra.registry_stores.rst b/sdk/python/docs/source/feast.infra.registry_stores.rst
new file mode 100644
index 00000000000..cff02fa3380
--- /dev/null
+++ b/sdk/python/docs/source/feast.infra.registry_stores.rst
@@ -0,0 +1,21 @@
+feast.infra.registry\_stores package
+====================================
+
+Submodules
+----------
+
+feast.infra.registry\_stores.sql module
+---------------------------------------
+
+.. automodule:: feast.infra.registry_stores.sql
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: feast.infra.registry_stores
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/sdk/python/docs/source/feast.infra.rst b/sdk/python/docs/source/feast.infra.rst
index 5473c3927e5..ec2cc120a63 100644
--- a/sdk/python/docs/source/feast.infra.rst
+++ b/sdk/python/docs/source/feast.infra.rst
@@ -9,6 +9,7 @@ Subpackages
 
    feast.infra.offline_stores
    feast.infra.online_stores
+   feast.infra.registry_stores
    feast.infra.utils
 
 Submodules
diff --git a/sdk/python/docs/source/feast.infra.utils.rst b/sdk/python/docs/source/feast.infra.utils.rst
index 30ec6166bf4..ffada497975 100644
--- a/sdk/python/docs/source/feast.infra.utils.rst
+++ b/sdk/python/docs/source/feast.infra.utils.rst
@@ -20,6 +20,14 @@ feast.infra.utils.aws\_utils module
    :undoc-members:
    :show-inheritance:
 
+feast.infra.utils.hbase\_utils module
+-------------------------------------
+
+.. automodule:: feast.infra.utils.hbase_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 feast.infra.utils.snowflake\_utils module
 -----------------------------------------
 
diff --git a/sdk/python/docs/source/feast.protos.feast.core.rst b/sdk/python/docs/source/feast.protos.feast.core.rst
index f29a51719c1..aaed49cd731 100644
--- a/sdk/python/docs/source/feast.protos.feast.core.rst
+++ b/sdk/python/docs/source/feast.protos.feast.core.rst
@@ -4,6 +4,22 @@ feast.protos.feast.core package
 Submodules
 ----------
 
+feast.protos.feast.core.Aggregation\_pb2 module
+-----------------------------------------------
+
+.. automodule:: feast.protos.feast.core.Aggregation_pb2
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+feast.protos.feast.core.Aggregation\_pb2\_grpc module
+-----------------------------------------------------
+
+.. automodule:: feast.protos.feast.core.Aggregation_pb2_grpc
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 feast.protos.feast.core.DataFormat\_pb2 module
 ----------------------------------------------
 
@@ -276,6 +292,22 @@ feast.protos.feast.core.Store\_pb2\_grpc module
    :undoc-members:
    :show-inheritance:
 
+feast.protos.feast.core.StreamFeatureView\_pb2 module
+-----------------------------------------------------
+
+.. automodule:: feast.protos.feast.core.StreamFeatureView_pb2
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+feast.protos.feast.core.StreamFeatureView\_pb2\_grpc module
+-----------------------------------------------------------
+
+.. automodule:: feast.protos.feast.core.StreamFeatureView_pb2_grpc
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 feast.protos.feast.core.ValidationProfile\_pb2 module
 -----------------------------------------------------
 
diff --git a/sdk/python/docs/source/feast.protos.feast.rst b/sdk/python/docs/source/feast.protos.feast.rst
index 456d960d738..f519165db8e 100644
--- a/sdk/python/docs/source/feast.protos.feast.rst
+++ b/sdk/python/docs/source/feast.protos.feast.rst
@@ -10,6 +10,7 @@ Subpackages
    feast.protos.feast.core
    feast.protos.feast.serving
    feast.protos.feast.storage
+   feast.protos.feast.types
 
 Module contents
 ---------------
diff --git a/sdk/python/docs/source/feast.protos.feast.types.rst b/sdk/python/docs/source/feast.protos.feast.types.rst
new file mode 100644
index 00000000000..aeb31bc9ad3
--- /dev/null
+++ b/sdk/python/docs/source/feast.protos.feast.types.rst
@@ -0,0 +1,61 @@
+feast.protos.feast.types package
+================================
+
+Submodules
+----------
+
+feast.protos.feast.types.EntityKey\_pb2 module
+----------------------------------------------
+
+.. automodule:: feast.protos.feast.types.EntityKey_pb2
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+feast.protos.feast.types.EntityKey\_pb2\_grpc module
+----------------------------------------------------
+
+.. automodule:: feast.protos.feast.types.EntityKey_pb2_grpc
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+feast.protos.feast.types.Field\_pb2 module
+------------------------------------------
+
+.. automodule:: feast.protos.feast.types.Field_pb2
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+feast.protos.feast.types.Field\_pb2\_grpc module
+------------------------------------------------
+
+.. automodule:: feast.protos.feast.types.Field_pb2_grpc
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+feast.protos.feast.types.Value\_pb2 module
+------------------------------------------
+
+.. automodule:: feast.protos.feast.types.Value_pb2
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+feast.protos.feast.types.Value\_pb2\_grpc module
+------------------------------------------------
+
+.. automodule:: feast.protos.feast.types.Value_pb2_grpc
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: feast.protos.feast.types
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/sdk/python/docs/source/feast.rst b/sdk/python/docs/source/feast.rst
index fb1e77a3736..7c569fc7130 100644
--- a/sdk/python/docs/source/feast.rst
+++ b/sdk/python/docs/source/feast.rst
@@ -12,10 +12,19 @@ Subpackages
    feast.infra
    feast.loaders
    feast.protos
+   feast.ui
 
 Submodules
 ----------
 
+feast.aggregation module
+------------------------
+
+.. automodule:: feast.aggregation
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 feast.base\_feature\_view module
 --------------------------------
 
@@ -104,6 +113,14 @@ feast.feature module
    :undoc-members:
    :show-inheritance:
 
+feast.feature\_logging module
+-----------------------------
+
+.. automodule:: feast.feature_logging
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 feast.feature\_server module
 ----------------------------
 
@@ -304,6 +321,14 @@ feast.types module
    :undoc-members:
    :show-inheritance:
 
+feast.ui\_server module
+-----------------------
+
+.. automodule:: feast.ui_server
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 feast.usage module
 ------------------
 
diff --git a/sdk/python/docs/source/feast.ui.rst b/sdk/python/docs/source/feast.ui.rst
new file mode 100644
index 00000000000..01b16cb0a6a
--- /dev/null
+++ b/sdk/python/docs/source/feast.ui.rst
@@ -0,0 +1,10 @@
+feast.ui package
+================
+
+Module contents
+---------------
+
+.. automodule:: feast.ui
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/sdk/python/docs/source/index.rst b/sdk/python/docs/source/index.rst
index ca746707eac..9297901c331 100644
--- a/sdk/python/docs/source/index.rst
+++ b/sdk/python/docs/source/index.rst
@@ -21,8 +21,21 @@ Data Source
 ==================
 
 .. automodule:: feast.data_source
+    :inherited-members:
     :members:
-    :exclude-members: KafkaOptions, KafkaSource, KinesisOptions, KinesisSource
+    :exclude-members: KafkaOptions, KafkaSource, KinesisOptions, KinesisSource, PushSource, RequestSource, RequestDataSource
+
+Request Source
+------------------
+
+.. automodule:: feast.data_source
+    :members: RequestSource
+
+Push Source
+------------------
+
+.. automodule:: feast.data_source
+    :members: PushSource
 
 BigQuery Source
 ------------------
@@ -87,11 +100,17 @@ Feature View
     :members:
 
 On Demand Feature View
-======================
+----------------------
 
 .. automodule:: feast.on_demand_feature_view
     :members:
 
+Stream Feature View
+----------------------
+
+.. automodule:: feast.stream_feature_view
+    :members:
+
 Feature
 ==================
 
@@ -121,6 +140,20 @@ Registry Store
     :members:
     :exclude-members: NoopRegistryStore
 
+SQL Registry Store
+-----------------------
+
+.. automodule:: feast.infra.registry_stores.sql
+    :members:
+    :noindex:
+
+PostgreSQL Registry Store
+-----------------------
+
+.. automodule:: feast.infra.registry_stores.contrib.postgres.registry_store
+    :members:
+    :noindex:
+
 
 Provider
 ==================
@@ -243,3 +276,10 @@ PostgreSQL Online Store
 .. automodule:: feast.infra.online_stores.contrib.postgres
     :members:
     :noindex:
+
+HBase Online Store
+-----------------------
+
+.. automodule:: feast.infra.online_stores.contrib.hbase_online_store.hbase
+    :members:
+    :noindex:
diff --git a/sdk/python/feast/aggregation.py b/sdk/python/feast/aggregation.py
new file mode 100644
index 00000000000..cfb2e7de94c
--- /dev/null
+++ b/sdk/python/feast/aggregation.py
@@ -0,0 +1,93 @@
+from datetime import timedelta
+from typing import Optional
+
+from google.protobuf.duration_pb2 import Duration
+from typeguard import typechecked
+
+from feast.protos.feast.core.Aggregation_pb2 import Aggregation as AggregationProto
+
+
+@typechecked
+class Aggregation:
+    """
+    NOTE: Feast-handled aggregations are not yet supported. This class provides a way to register user-defined aggregations.
+
+    Attributes:
+        column: str  # Column name of the feature we are aggregating.
+        function: str  # Provided built in aggregations sum, max, min, count mean
+        time_window: timedelta  # The time window for this aggregation.
+        slide_interval: timedelta # The sliding window for these aggregations
+    """
+
+    column: str
+    function: str
+    time_window: Optional[timedelta]
+    slide_interval: Optional[timedelta]
+
+    def __init__(
+        self,
+        column: Optional[str] = "",
+        function: Optional[str] = "",
+        time_window: Optional[timedelta] = None,
+        slide_interval: Optional[timedelta] = None,
+    ):
+        self.column = column or ""
+        self.function = function or ""
+        self.time_window = time_window
+        if not slide_interval:
+            self.slide_interval = self.time_window
+        else:
+            self.slide_interval = slide_interval
+
+    def to_proto(self) -> AggregationProto:
+        window_duration = None
+        if self.time_window is not None:
+            window_duration = Duration()
+            window_duration.FromTimedelta(self.time_window)
+
+        slide_interval_duration = None
+        if self.slide_interval is not None:
+            slide_interval_duration = Duration()
+            slide_interval_duration.FromTimedelta(self.slide_interval)
+
+        return AggregationProto(
+            column=self.column,
+            function=self.function,
+            time_window=window_duration,
+            slide_interval=slide_interval_duration,
+        )
+
+    @classmethod
+    def from_proto(cls, agg_proto: AggregationProto):
+        time_window = (
+            timedelta(days=0)
+            if agg_proto.time_window.ToNanoseconds() == 0
+            else agg_proto.time_window.ToTimedelta()
+        )
+
+        slide_interval = (
+            timedelta(days=0)
+            if agg_proto.slide_interval.ToNanoseconds() == 0
+            else agg_proto.slide_interval.ToTimedelta()
+        )
+        aggregation = cls(
+            column=agg_proto.column,
+            function=agg_proto.function,
+            time_window=time_window,
+            slide_interval=slide_interval,
+        )
+        return aggregation
+
+    def __eq__(self, other):
+        if not isinstance(other, Aggregation):
+            raise TypeError("Comparisons should only involve Aggregations.")
+
+        if (
+            self.column != other.column
+            or self.function != other.function
+            or self.time_window != other.time_window
+            or self.slide_interval != other.slide_interval
+        ):
+            return False
+
+        return True
diff --git a/sdk/python/feast/base_feature_view.py b/sdk/python/feast/base_feature_view.py
index 80b3b0cec82..5feb1d7d893 100644
--- a/sdk/python/feast/base_feature_view.py
+++ b/sdk/python/feast/base_feature_view.py
@@ -110,18 +110,20 @@ def __str__(self):
         return str(MessageToJson(self.to_proto()))
 
     def __hash__(self):
-        return hash((self.name))
+        return hash(self.name)
 
     def __getitem__(self, item):
         assert isinstance(item, list)
 
-        referenced_features = []
-        for feature in self.features:
-            if feature.name in item:
-                referenced_features.append(feature)
-
         cp = self.__copy__()
-        cp.projection.features = referenced_features
+        if self.features:
+            referenced_features = []
+            for feature in self.features:
+                if feature.name in item:
+                    referenced_features.append(feature)
+            cp.projection.features = referenced_features
+        else:
+            cp.projection.desired_features = item
 
         return cp
 
diff --git a/sdk/python/feast/cli.py b/sdk/python/feast/cli.py
index a4407132e4e..91815d30fd2 100644
--- a/sdk/python/feast/cli.py
+++ b/sdk/python/feast/cli.py
@@ -11,7 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
+import json
 import logging
 import warnings
 from datetime import datetime
@@ -23,6 +23,7 @@
 import yaml
 from colorama import Fore, Style
 from dateutil import parser
+from pygments import formatters, highlight, lexers
 
 from feast import flags, flags_helper, utils
 from feast.constants import DEFAULT_FEATURE_TRANSFORMATION_SERVER_PORT
@@ -40,8 +41,11 @@
     registry_dump,
     teardown,
 )
+from feast.repo_upgrade import RepoUpgrader
+from feast.utils import maybe_local_tz
 
 _logger = logging.getLogger(__name__)
+warnings.filterwarnings("ignore", category=DeprecationWarning, module="(?!feast)")
 
 
 class NoOptionDefaultFormat(click.Command):
@@ -82,7 +86,7 @@ def cli(ctx: click.Context, chdir: Optional[str], log_level: str):
     try:
         level = getattr(logging, log_level.upper())
         logging.basicConfig(
-            format="%(asctime)s %(levelname)s:%(message)s",
+            format="%(asctime)s %(name)s %(levelname)s: %(message)s",
             datefmt="%m/%d/%Y %I:%M:%S %p",
             level=level,
         )
@@ -95,7 +99,6 @@ def cli(ctx: click.Context, chdir: Optional[str], log_level: str):
             if "feast" in logger_name:
                 logger = logging.getLogger(logger_name)
                 logger.setLevel(level)
-
     except Exception as e:
         raise e
     pass
@@ -193,7 +196,7 @@ def data_source_describe(ctx: click.Context, name: str):
 
     warnings.warn(
         "Describing data sources will only work properly if all data sources have names or table names specified. "
-        "Starting Feast 0.23, data source unique names will be required to encourage data source discovery.",
+        "Starting Feast 0.24, data source unique names will be required to encourage data source discovery.",
         RuntimeWarning,
     )
     print(
@@ -220,7 +223,7 @@ def data_source_list(ctx: click.Context):
 
     warnings.warn(
         "Listing data sources will only work properly if all data sources have names or table names specified. "
-        "Starting Feast 0.23, data source unique names will be required to encourage data source discovery",
+        "Starting Feast 0.24, data source unique names will be required to encourage data source discovery",
         RuntimeWarning,
     )
     print(tabulate(table, headers=["NAME", "CLASS"], tablefmt="plain"))
@@ -610,17 +613,35 @@ def init_command(project_directory, minimal: bool, template: str):
     default=6566,
     help="Specify a port for the server [default: 6566]",
 )
+@click.option(
+    "--type",
+    "-t",
+    "type_",
+    type=click.STRING,
+    default="http",
+    help="Specify a server type: 'http' or 'grpc' [default: http]",
+)
 @click.option(
     "--no-access-log", is_flag=True, help="Disable the Uvicorn access log.",
 )
+@click.option(
+    "--no-feature-log", is_flag=True, help="Disable logging served features",
+)
 @click.pass_context
-def serve_command(ctx: click.Context, host: str, port: int, no_access_log: bool):
+def serve_command(
+    ctx: click.Context,
+    host: str,
+    port: int,
+    type_: str,
+    no_access_log: bool,
+    no_feature_log: bool,
+):
     """Start a feature server locally on a given port."""
     repo = ctx.obj["CHDIR"]
     cli_check_repo(repo)
     store = FeatureStore(repo_path=str(repo))
 
-    store.serve(host, port, no_access_log)
+    store.serve(host, port, type_, no_access_log, no_feature_log)
 
 
 @cli.command("serve_transformations")
@@ -748,5 +769,81 @@ def disable_alpha_features(ctx: click.Context):
     store.config.write_to_path(Path(repo_path))
 
 
+@cli.command("validate")
+@click.option(
+    "--feature-service", "-f", help="Specify a feature service name",
+)
+@click.option(
+    "--reference", "-r", help="Specify a validation reference name",
+)
+@click.option(
+    "--no-profile-cache", is_flag=True, help="Do not store cached profile in registry",
+)
+@click.argument("start_ts")
+@click.argument("end_ts")
+@click.pass_context
+def validate(
+    ctx: click.Context,
+    feature_service: str,
+    reference: str,
+    start_ts: str,
+    end_ts: str,
+    no_profile_cache,
+):
+    """
+    Perform validation of logged features (produced by a given feature service) against provided reference.
+
+    START_TS and END_TS should be in ISO 8601 format, e.g. '2021-07-16T19:20:01'
+    """
+    repo = ctx.obj["CHDIR"]
+    cli_check_repo(repo)
+    store = FeatureStore(repo_path=str(repo))
+
+    feature_service = store.get_feature_service(name=feature_service)
+    reference = store.get_validation_reference(reference)
+
+    result = store.validate_logged_features(
+        source=feature_service,
+        reference=reference,
+        start=maybe_local_tz(datetime.fromisoformat(start_ts)),
+        end=maybe_local_tz(datetime.fromisoformat(end_ts)),
+        throw_exception=False,
+        cache_profile=not no_profile_cache,
+    )
+
+    if not result:
+        print(f"{Style.BRIGHT + Fore.GREEN}Validation successful!{Style.RESET_ALL}")
+        return
+
+    errors = [e.to_dict() for e in result.report.errors]
+    formatted_json = json.dumps(errors, indent=4)
+    colorful_json = highlight(
+        formatted_json, lexers.JsonLexer(), formatters.TerminalFormatter()
+    )
+    print(f"{Style.BRIGHT + Fore.RED}Validation failed!{Style.RESET_ALL}")
+    print(colorful_json)
+    exit(1)
+
+
+@cli.command("repo-upgrade", cls=NoOptionDefaultFormat)
+@click.option(
+    "--write",
+    is_flag=True,
+    default=False,
+    help="Upgrade a feature repo to use the API expected by feast 0.23.",
+)
+@click.pass_context
+def repo_upgrade(ctx: click.Context, write: bool):
+    """
+    Upgrade a feature repo in place.
+    """
+    repo = ctx.obj["CHDIR"]
+    cli_check_repo(repo)
+    try:
+        RepoUpgrader(repo, write).upgrade()
+    except FeastProviderLoginError as e:
+        print(str(e))
+
+
 if __name__ == "__main__":
     cli()
diff --git a/sdk/python/feast/data_format.py b/sdk/python/feast/data_format.py
index b6c7bf94e92..8f3b195e3e6 100644
--- a/sdk/python/feast/data_format.py
+++ b/sdk/python/feast/data_format.py
@@ -89,6 +89,8 @@ def from_proto(cls, proto):
         fmt = proto.WhichOneof("format")
         if fmt == "avro_format":
             return AvroFormat(schema_json=proto.avro_format.schema_json)
+        if fmt == "json_format":
+            return JsonFormat(schema_json=proto.json_format.schema_json)
         if fmt == "proto_format":
             return ProtoFormat(class_path=proto.proto_format.class_path)
         raise NotImplementedError(f"StreamFormat is unsupported: {fmt}")
@@ -113,6 +115,28 @@ def to_proto(self):
         return StreamFormatProto(avro_format=proto)
 
 
+class JsonFormat(StreamFormat):
+    """
+    Defines the Json streaming data format that encodes data in Json format
+    """
+
+    def __init__(self, schema_json: str):
+        """
+        Construct a new Json data format.
+
+        For spark, uses pyspark ddl string format. Example shown here:
+        https://vincent.doba.fr/posts/20211004_spark_data_description_language_for_defining_spark_schema/
+
+        Args:
+            schema_json: Json schema definition
+        """
+        self.schema_json = schema_json
+
+    def to_proto(self):
+        proto = StreamFormatProto.JsonFormat(schema_json=self.schema_json)
+        return StreamFormatProto(json_format=proto)
+
+
 class ProtoFormat(StreamFormat):
     """
     Defines the Protobuf data format
diff --git a/sdk/python/feast/data_source.py b/sdk/python/feast/data_source.py
index 6a2b9a0d148..f5c40d24214 100644
--- a/sdk/python/feast/data_source.py
+++ b/sdk/python/feast/data_source.py
@@ -15,16 +15,19 @@
 import enum
 import warnings
 from abc import ABC, abstractmethod
+from datetime import timedelta
 from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
 
+from google.protobuf.duration_pb2 import Duration
 from google.protobuf.json_format import MessageToJson
+from typeguard import typechecked
 
 from feast import type_map
 from feast.data_format import StreamFormat
 from feast.field import Field
 from feast.protos.feast.core.DataSource_pb2 import DataSource as DataSourceProto
 from feast.repo_config import RepoConfig, get_data_source_class_from_type
-from feast.types import VALUE_TYPES_TO_FEAST_TYPES
+from feast.types import from_value_type
 from feast.value_type import ValueType
 
 
@@ -47,11 +50,16 @@ class KafkaOptions:
     """
 
     def __init__(
-        self, bootstrap_servers: str, message_format: StreamFormat, topic: str,
+        self,
+        kafka_bootstrap_servers: str,
+        message_format: StreamFormat,
+        topic: str,
+        watermark_delay_threshold: Optional[timedelta] = None,
     ):
-        self.bootstrap_servers = bootstrap_servers
+        self.kafka_bootstrap_servers = kafka_bootstrap_servers
         self.message_format = message_format
         self.topic = topic
+        self.watermark_delay_threshold = watermark_delay_threshold or None
 
     @classmethod
     def from_proto(cls, kafka_options_proto: DataSourceProto.KafkaOptions):
@@ -64,11 +72,18 @@ def from_proto(cls, kafka_options_proto: DataSourceProto.KafkaOptions):
         Returns:
             Returns a BigQueryOptions object based on the kafka_options protobuf
         """
-
+        watermark_delay_threshold = None
+        if kafka_options_proto.HasField("watermark_delay_threshold"):
+            watermark_delay_threshold = (
+                timedelta(days=0)
+                if kafka_options_proto.watermark_delay_threshold.ToNanoseconds() == 0
+                else kafka_options_proto.watermark_delay_threshold.ToTimedelta()
+            )
         kafka_options = cls(
-            bootstrap_servers=kafka_options_proto.bootstrap_servers,
+            kafka_bootstrap_servers=kafka_options_proto.kafka_bootstrap_servers,
             message_format=StreamFormat.from_proto(kafka_options_proto.message_format),
             topic=kafka_options_proto.topic,
+            watermark_delay_threshold=watermark_delay_threshold,
         )
 
         return kafka_options
@@ -80,11 +95,16 @@ def to_proto(self) -> DataSourceProto.KafkaOptions:
         Returns:
             KafkaOptionsProto protobuf
         """
+        watermark_delay_threshold = None
+        if self.watermark_delay_threshold is not None:
+            watermark_delay_threshold = Duration()
+            watermark_delay_threshold.FromTimedelta(self.watermark_delay_threshold)
 
         kafka_options_proto = DataSourceProto.KafkaOptions(
-            bootstrap_servers=self.bootstrap_servers,
+            kafka_bootstrap_servers=self.kafka_bootstrap_servers,
             message_format=self.message_format.to_proto(),
             topic=self.topic,
+            watermark_delay_threshold=watermark_delay_threshold,
         )
 
         return kafka_options_proto
@@ -153,14 +173,15 @@ def to_proto(self) -> DataSourceProto.KinesisOptions:
 }
 
 
+@typechecked
 class DataSource(ABC):
     """
     DataSource that can be used to source features.
 
     Args:
         name: Name of data source, which should be unique within a project
-        timestamp_field (optional): (Deprecated) Event timestamp column used for point in time
-            joins of feature values.
+        event_timestamp_column (optional): (Deprecated in favor of timestamp_field) Event
+            timestamp column used for point in time joins of feature values.
         created_timestamp_column (optional): Timestamp column indicating when the row
             was created, used for deduplicating rows.
         field_mapping (optional): A dictionary mapping of column names in this data
@@ -201,8 +222,8 @@ def __init__(
         Creates a DataSource object.
         Args:
             name: Name of data source, which should be unique within a project
-            event_timestamp_column (optional): (Deprecated) Event timestamp column used for point in time
-                joins of feature values.
+            event_timestamp_column (optional): (Deprecated in favor of timestamp_field) Event
+                timestamp column used for point in time joins of feature values.
             created_timestamp_column (optional): Timestamp column indicating when the row
                 was created, used for deduplicating rows.
             field_mapping (optional): A dictionary mapping of column names in this data
@@ -220,7 +241,7 @@ def __init__(
             warnings.warn(
                 (
                     "Names for data sources need to be supplied. "
-                    "Data sources without names will not be supported after Feast 0.23."
+                    "Data sources without names will not be supported after Feast 0.24."
                 ),
                 UserWarning,
             )
@@ -229,7 +250,7 @@ def __init__(
             warnings.warn(
                 (
                     "The argument 'event_timestamp_column' is being deprecated. Please use 'timestamp_field' instead. "
-                    "instead. Feast 0.23 and onwards will not support the argument 'event_timestamp_column' for datasources."
+                    "instead. Feast 0.24 and onwards will not support the argument 'event_timestamp_column' for datasources."
                 ),
                 DeprecationWarning,
             )
@@ -241,6 +262,14 @@ def __init__(
         self.date_partition_column = (
             date_partition_column if date_partition_column else ""
         )
+        if date_partition_column:
+            warnings.warn(
+                (
+                    "The argument 'date_partition_column' is being deprecated. "
+                    "Feast 0.25 and onwards will not support 'date_timestamp_column' for data sources."
+                ),
+                DeprecationWarning,
+            )
         self.description = description or ""
         self.tags = tags or {}
         self.owner = owner or ""
@@ -344,21 +373,15 @@ def get_table_query_string(self) -> str:
         raise NotImplementedError
 
 
+@typechecked
 class KafkaSource(DataSource):
-    def validate(self, config: RepoConfig):
-        pass
-
-    def get_table_column_names_and_types(
-        self, config: RepoConfig
-    ) -> Iterable[Tuple[str, str]]:
-        pass
-
     def __init__(
         self,
         *args,
         name: Optional[str] = None,
         event_timestamp_column: Optional[str] = "",
         bootstrap_servers: Optional[str] = None,
+        kafka_bootstrap_servers: Optional[str] = None,
         message_format: Optional[StreamFormat] = None,
         topic: Optional[str] = None,
         created_timestamp_column: Optional[str] = "",
@@ -369,7 +392,35 @@ def __init__(
         owner: Optional[str] = "",
         timestamp_field: Optional[str] = "",
         batch_source: Optional[DataSource] = None,
+        watermark_delay_threshold: Optional[timedelta] = None,
     ):
+        """
+        Creates a KafkaSource object.
+
+        Args:
+            name: Name of data source, which should be unique within a project
+            event_timestamp_column (optional): (Deprecated in favor of timestamp_field) Event
+                timestamp column used for point in time joins of feature values.
+            bootstrap_servers: (Deprecated) The servers of the kafka broker in the form "localhost:9092".
+            kafka_bootstrap_servers: The servers of the kafka broker in the form "localhost:9092".
+            message_format: StreamFormat of serialized messages.
+            topic: The name of the topic to read from in the kafka source.
+            created_timestamp_column (optional): Timestamp column indicating when the row
+                was created, used for deduplicating rows.
+            field_mapping (optional): A dictionary mapping of column names in this data
+                source to feature names in a feature table or view. Only used for feature
+                columns, not entity or timestamp columns.
+            date_partition_column (optional): Timestamp column used for partitioning.
+            description (optional): A human-readable description.
+            tags (optional): A dictionary of key-value pairs to store arbitrary metadata.
+            owner (optional): The owner of the data source, typically the email of the primary
+                maintainer.
+            timestamp_field (optional): Event timestamp field used for point
+                in time joins of feature values.
+            batch_source: The datasource that acts as a batch source.
+            watermark_delay_threshold: The watermark delay threshold for stream data. Specifically how
+                late stream data can arrive without being discarded.
+        """
         positional_attributes = [
             "name",
             "event_timestamp_column",
@@ -379,15 +430,24 @@ def __init__(
         ]
         _name = name
         _event_timestamp_column = event_timestamp_column
-        _bootstrap_servers = bootstrap_servers or ""
+        _kafka_bootstrap_servers = kafka_bootstrap_servers or bootstrap_servers or ""
         _message_format = message_format
         _topic = topic or ""
 
+        if bootstrap_servers:
+            warnings.warn(
+                (
+                    "The 'bootstrap_servers' parameter has been deprecated in favor of 'kafka_bootstrap_servers'. "
+                    "Feast 0.25 and onwards will not support the 'bootstrap_servers' parameter."
+                ),
+                DeprecationWarning,
+            )
+
         if args:
             warnings.warn(
                 (
                     "Kafka parameters should be specified as a keyword argument instead of a positional arg."
-                    "Feast 0.23+ will not support positional arguments to construct Kafka sources"
+                    "Feast 0.24+ will not support positional arguments to construct Kafka sources"
                 ),
                 DeprecationWarning,
             )
@@ -401,7 +461,7 @@ def __init__(
             if len(args) >= 2:
                 _event_timestamp_column = args[1]
             if len(args) >= 3:
-                _bootstrap_servers = args[2]
+                _kafka_bootstrap_servers = args[2]
             if len(args) >= 4:
                 _message_format = args[3]
             if len(args) >= 5:
@@ -410,6 +470,9 @@ def __init__(
         if _message_format is None:
             raise ValueError("Message format must be specified for Kafka source")
 
+        if not timestamp_field and not _event_timestamp_column:
+            raise ValueError("Timestamp field must be specified for Kafka source")
+
         super().__init__(
             event_timestamp_column=_event_timestamp_column,
             created_timestamp_column=created_timestamp_column,
@@ -422,10 +485,12 @@ def __init__(
             timestamp_field=timestamp_field,
         )
         self.batch_source = batch_source
+
         self.kafka_options = KafkaOptions(
-            bootstrap_servers=_bootstrap_servers,
+            kafka_bootstrap_servers=_kafka_bootstrap_servers,
             message_format=_message_format,
             topic=_topic,
+            watermark_delay_threshold=watermark_delay_threshold,
         )
 
     def __eq__(self, other):
@@ -438,10 +503,12 @@ def __eq__(self, other):
             return False
 
         if (
-            self.kafka_options.bootstrap_servers
-            != other.kafka_options.bootstrap_servers
+            self.kafka_options.kafka_bootstrap_servers
+            != other.kafka_options.kafka_bootstrap_servers
             or self.kafka_options.message_format != other.kafka_options.message_format
             or self.kafka_options.topic != other.kafka_options.topic
+            or self.kafka_options.watermark_delay_threshold
+            != other.kafka_options.watermark_delay_threshold
         ):
             return False
 
@@ -452,14 +519,23 @@ def __hash__(self):
 
     @staticmethod
     def from_proto(data_source: DataSourceProto):
+        watermark_delay_threshold = None
+        if data_source.kafka_options.watermark_delay_threshold:
+            watermark_delay_threshold = (
+                timedelta(days=0)
+                if data_source.kafka_options.watermark_delay_threshold.ToNanoseconds()
+                == 0
+                else data_source.kafka_options.watermark_delay_threshold.ToTimedelta()
+            )
         return KafkaSource(
             name=data_source.name,
             event_timestamp_column=data_source.timestamp_field,
             field_mapping=dict(data_source.field_mapping),
-            bootstrap_servers=data_source.kafka_options.bootstrap_servers,
+            kafka_bootstrap_servers=data_source.kafka_options.kafka_bootstrap_servers,
             message_format=StreamFormat.from_proto(
                 data_source.kafka_options.message_format
             ),
+            watermark_delay_threshold=watermark_delay_threshold,
             topic=data_source.kafka_options.topic,
             created_timestamp_column=data_source.created_timestamp_column,
             timestamp_field=data_source.timestamp_field,
@@ -490,6 +566,14 @@ def to_proto(self) -> DataSourceProto:
             data_source_proto.batch_source.MergeFrom(self.batch_source.to_proto())
         return data_source_proto
 
+    def validate(self, config: RepoConfig):
+        pass
+
+    def get_table_column_names_and_types(
+        self, config: RepoConfig
+    ) -> Iterable[Tuple[str, str]]:
+        pass
+
     @staticmethod
     def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:
         return type_map.redshift_to_feast_value_type
@@ -498,6 +582,7 @@ def get_table_query_string(self) -> str:
         raise NotImplementedError
 
 
+@typechecked
 class RequestSource(DataSource):
     """
     RequestSource that can be used to provide input features for on demand transforms
@@ -534,7 +619,7 @@ def __init__(
             warnings.warn(
                 (
                     "Request source parameters should be specified as a keyword argument instead of a positional arg."
-                    "Feast 0.23+ will not support positional arguments to construct request sources"
+                    "Feast 0.24+ will not support positional arguments to construct request sources"
                 ),
                 DeprecationWarning,
             )
@@ -553,16 +638,14 @@ def __init__(
             raise ValueError("Schema needs to be provided for Request Source")
         if isinstance(_schema, Dict):
             warnings.warn(
-                "Schema in RequestSource is changing type. The schema data type Dict[str, ValueType] is being deprecated in Feast 0.23. "
+                "Schema in RequestSource is changing type. The schema data type Dict[str, ValueType] is being deprecated in Feast 0.24. "
                 "Please use List[Field] instead for the schema",
                 DeprecationWarning,
             )
-            schemaList = []
-            for key, valueType in _schema.items():
-                schemaList.append(
-                    Field(name=key, dtype=VALUE_TYPES_TO_FEAST_TYPES[valueType])
-                )
-            self.schema = schemaList
+            schema_list = []
+            for key, value_type in _schema.items():
+                schema_list.append(Field(name=key, dtype=from_value_type(value_type)))
+            self.schema = schema_list
         elif isinstance(_schema, List):
             self.schema = _schema
         else:
@@ -607,7 +690,7 @@ def from_proto(data_source: DataSourceProto):
 
         if deprecated_schema and not schema_pb:
             warnings.warn(
-                "Schema in RequestSource is changing type. The schema data type Dict[str, ValueType] is being deprecated in Feast 0.23. "
+                "Schema in RequestSource is changing type. The schema data type Dict[str, ValueType] is being deprecated in Feast 0.24. "
                 "Please use List[Field] instead for the schema",
                 DeprecationWarning,
             )
@@ -641,9 +724,7 @@ def to_proto(self) -> DataSourceProto:
         if isinstance(self.schema, Dict):
             for key, value in self.schema.items():
                 schema_pb.append(
-                    Field(
-                        name=key, dtype=VALUE_TYPES_TO_FEAST_TYPES[value.value]
-                    ).to_proto()
+                    Field(name=key, dtype=from_value_type(value.value)).to_proto()
                 )
         else:
             for field in self.schema:
@@ -667,15 +748,17 @@ def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:
         raise NotImplementedError
 
 
+@typechecked
 class RequestDataSource(RequestSource):
     def __init__(self, *args, **kwargs):
         warnings.warn(
-            "The 'RequestDataSource' class is deprecated and was renamed to RequestSource. Please use RequestSource instead. This class name will be removed in Feast 0.23.",
+            "The 'RequestDataSource' class is deprecated and was renamed to RequestSource. Please use RequestSource instead. This class name will be removed in Feast 0.24.",
             DeprecationWarning,
         )
         super().__init__(*args, **kwargs)
 
 
+@typechecked
 class KinesisSource(DataSource):
     def validate(self, config: RepoConfig):
         pass
@@ -749,7 +832,7 @@ def __init__(
             warnings.warn(
                 (
                     "Kinesis parameters should be specified as a keyword argument instead of a positional arg."
-                    "Feast 0.23+ will not support positional arguments to construct kinesis sources"
+                    "Feast 0.24+ will not support positional arguments to construct kinesis sources"
                 ),
                 DeprecationWarning,
             )
@@ -831,6 +914,13 @@ def to_proto(self) -> DataSourceProto:
         return data_source_proto
 
 
+class PushMode(enum.Enum):
+    ONLINE = 1
+    OFFLINE = 2
+    ONLINE_AND_OFFLINE = 3
+
+
+@typechecked
 class PushSource(DataSource):
     """
     A source that can be used to ingest features on request
@@ -868,7 +958,7 @@ def __init__(
             warnings.warn(
                 (
                     "Push source parameters should be specified as a keyword argument instead of a positional arg."
-                    "Feast 0.23+ will not support positional arguments to construct push sources"
+                    "Feast 0.24+ will not support positional arguments to construct push sources"
                 ),
                 DeprecationWarning,
             )
diff --git a/sdk/python/feast/diff/registry_diff.py b/sdk/python/feast/diff/registry_diff.py
index 197bdfcefaf..7a5b9b7564f 100644
--- a/sdk/python/feast/diff/registry_diff.py
+++ b/sdk/python/feast/diff/registry_diff.py
@@ -20,7 +20,13 @@
 from feast.protos.feast.core.RequestFeatureView_pb2 import (
     RequestFeatureView as RequestFeatureViewProto,
 )
-from feast.registry import FEAST_OBJECT_TYPES, FeastObjectType, Registry
+from feast.protos.feast.core.StreamFeatureView_pb2 import (
+    StreamFeatureView as StreamFeatureViewProto,
+)
+from feast.protos.feast.core.ValidationProfile_pb2 import (
+    ValidationReference as ValidationReferenceProto,
+)
+from feast.registry import FEAST_OBJECT_TYPES, BaseRegistry, FeastObjectType
 from feast.repo_contents import RepoContents
 
 
@@ -61,7 +67,7 @@ def to_string(self):
             if feast_object_diff.transition_type == TransitionType.UNCHANGED:
                 continue
             if feast_object_diff.feast_object_type == FeastObjectType.DATA_SOURCE:
-                # TODO(adchia): Print statements out starting in Feast 0.23
+                # TODO(adchia): Print statements out starting in Feast 0.24
                 continue
             action, color = message_action_map[feast_object_diff.transition_type]
             log_string += f"{action} {feast_object_diff.feast_object_type.value} {Style.BRIGHT + color}{feast_object_diff.name}{Style.RESET_ALL}\n"
@@ -103,6 +109,8 @@ def tag_objects_for_keep_delete_update_add(
     FeatureServiceProto,
     OnDemandFeatureViewProto,
     RequestFeatureViewProto,
+    StreamFeatureViewProto,
+    ValidationReferenceProto,
 )
 
 
@@ -120,9 +128,9 @@ def diff_registry_objects(
 
     current_spec: FeastObjectSpecProto
     new_spec: FeastObjectSpecProto
-    if isinstance(current_proto, DataSourceProto) or isinstance(
-        new_proto, DataSourceProto
-    ):
+    if isinstance(
+        current_proto, (DataSourceProto, ValidationReferenceProto)
+    ) or isinstance(new_proto, (DataSourceProto, ValidationReferenceProto)):
         assert type(current_proto) == type(new_proto)
         current_spec = cast(DataSourceProto, current_proto)
         new_spec = cast(DataSourceProto, new_proto)
@@ -153,7 +161,7 @@ def diff_registry_objects(
 
 
 def extract_objects_for_keep_delete_update_add(
-    registry: Registry, current_project: str, desired_repo_contents: RepoContents,
+    registry: BaseRegistry, current_project: str, desired_repo_contents: RepoContents,
 ) -> Tuple[
     Dict[FeastObjectType, Set[FeastObject]],
     Dict[FeastObjectType, Set[FeastObject]],
@@ -200,7 +208,7 @@ def extract_objects_for_keep_delete_update_add(
 
 
 def diff_between(
-    registry: Registry, current_project: str, desired_repo_contents: RepoContents,
+    registry: BaseRegistry, current_project: str, desired_repo_contents: RepoContents,
 ) -> RegistryDiff:
     """
     Returns the difference between the current and desired repo states.
@@ -259,7 +267,10 @@ def diff_between(
 
 
 def apply_diff_to_registry(
-    registry: Registry, registry_diff: RegistryDiff, project: str, commit: bool = True
+    registry: BaseRegistry,
+    registry_diff: RegistryDiff,
+    project: str,
+    commit: bool = True,
 ):
     """
     Applies the given diff to the given Feast project in the registry.
@@ -288,6 +299,7 @@ def apply_diff_to_registry(
                 FeastObjectType.FEATURE_VIEW,
                 FeastObjectType.ON_DEMAND_FEATURE_VIEW,
                 FeastObjectType.REQUEST_FEATURE_VIEW,
+                FeastObjectType.STREAM_FEATURE_VIEW,
             ]:
                 feature_view_obj = cast(
                     BaseFeatureView, feast_object_diff.current_feast_object
@@ -327,6 +339,7 @@ def apply_diff_to_registry(
                 FeastObjectType.FEATURE_VIEW,
                 FeastObjectType.ON_DEMAND_FEATURE_VIEW,
                 FeastObjectType.REQUEST_FEATURE_VIEW,
+                FeastObjectType.STREAM_FEATURE_VIEW,
             ]:
                 registry.apply_feature_view(
                     cast(BaseFeatureView, feast_object_diff.new_feast_object),
diff --git a/sdk/python/feast/dqm/profilers/ge_profiler.py b/sdk/python/feast/dqm/profilers/ge_profiler.py
index f1780754de3..20a92631cb6 100644
--- a/sdk/python/feast/dqm/profilers/ge_profiler.py
+++ b/sdk/python/feast/dqm/profilers/ge_profiler.py
@@ -1,4 +1,5 @@
 import json
+from types import FunctionType
 from typing import Any, Callable, Dict, List
 
 import dill
@@ -7,7 +8,6 @@
 import pandas as pd
 from great_expectations.core import ExpectationSuite
 from great_expectations.dataset import PandasDataset
-from great_expectations.profile.base import ProfilerTypeMapping
 
 from feast.dqm.profilers.profiler import (
     Profile,
@@ -21,15 +21,14 @@
 from feast.protos.feast.core.ValidationProfile_pb2 import (
     GEValidationProfiler as GEValidationProfilerProto,
 )
+from feast.protos.feast.serving.ServingService_pb2 import FieldStatus
 
 
 def _prepare_dataset(dataset: PandasDataset) -> PandasDataset:
     dataset_copy = dataset.copy(deep=True)
 
     for column in dataset.columns:
-        if dataset.expect_column_values_to_be_in_type_list(
-            column, type_list=sorted(list(ProfilerTypeMapping.DATETIME_TYPE_NAMES))
-        ).success:
+        if pd.api.types.is_datetime64_any_dtype(dataset[column]):
             # GE cannot parse Timestamp or other pandas datetime time
             dataset_copy[column] = dataset[column].dt.strftime("%Y-%m-%dT%H:%M:%S")
 
@@ -38,9 +37,32 @@ def _prepare_dataset(dataset: PandasDataset) -> PandasDataset:
             # This could cause error on comparison => so better to convert to double prematurely
             dataset_copy[column] = dataset[column].astype(np.float64)
 
+        status_column = f"{column}__status"
+        if status_column in dataset.columns:
+            dataset_copy[column] = dataset_copy[column].mask(
+                dataset[status_column] == FieldStatus.NOT_FOUND, np.nan
+            )
+
     return dataset_copy
 
 
+def _add_feature_metadata(dataset: PandasDataset) -> PandasDataset:
+    for column in dataset.columns:
+        if "__" not in column:
+            # not a feature column
+            continue
+
+        if "event_timestamp" in dataset.columns:
+            dataset[f"{column}__timestamp"] = dataset["event_timestamp"]
+
+        dataset[f"{column}__status"] = FieldStatus.PRESENT
+        dataset[f"{column}__status"] = dataset[f"{column}__status"].mask(
+            dataset[column].isna(), FieldStatus.NOT_FOUND
+        )
+
+    return dataset
+
+
 class GEProfile(Profile):
     """
     GEProfile is an implementation of abstract Profile for integration with Great Expectations.
@@ -96,9 +118,12 @@ class GEProfiler(Profiler):
     """
 
     def __init__(
-        self, user_defined_profiler: Callable[[pd.DataFrame], ExpectationSuite]
+        self,
+        user_defined_profiler: Callable[[pd.DataFrame], ExpectationSuite],
+        with_feature_metadata: bool = False,
     ):
         self.user_defined_profiler = user_defined_profiler
+        self.with_feature_metadata = with_feature_metadata
 
     def analyze_dataset(self, df: pd.DataFrame) -> Profile:
         """
@@ -113,12 +138,18 @@ def analyze_dataset(self, df: pd.DataFrame) -> Profile:
 
         dataset = _prepare_dataset(dataset)
 
+        if self.with_feature_metadata:
+            dataset = _add_feature_metadata(dataset)
+
         return GEProfile(expectation_suite=self.user_defined_profiler(dataset))
 
     def to_proto(self):
+        # keep only the code and drop context for now
+        # ToDo (pyalex): include some context, but not all (dill tries to pull too much)
+        udp = FunctionType(self.user_defined_profiler.__code__, {})
         return GEValidationProfilerProto(
             profiler=GEValidationProfilerProto.UserDefinedProfiler(
-                body=dill.dumps(self.user_defined_profiler, recurse=True)
+                body=dill.dumps(udp, recurse=False)
             )
         )
 
@@ -144,6 +175,8 @@ def errors(self) -> List["ValidationError"]:
                 check_config=res.expectation_config.kwargs,
                 missing_count=res["result"].get("missing_count"),
                 missing_percent=res["result"].get("missing_percent"),
+                unexpected_count=res["result"].get("unexpected_count"),
+                unexpected_percent=res["result"].get("unexpected_percent"),
             )
             for res in self._validation_result["results"]
             if not res["success"]
@@ -158,5 +191,13 @@ def __repr__(self):
         return json.dumps(failed_expectations, indent=2)
 
 
-def ge_profiler(func):
-    return GEProfiler(user_defined_profiler=func)
+def ge_profiler(*args, with_feature_metadata=False):
+    def wrapper(fun):
+        return GEProfiler(
+            user_defined_profiler=fun, with_feature_metadata=with_feature_metadata
+        )
+
+    if args:
+        return wrapper(args[0])
+
+    return wrapper
diff --git a/sdk/python/feast/dqm/profilers/profiler.py b/sdk/python/feast/dqm/profilers/profiler.py
index 5d2e9d36bc1..34496b0cca3 100644
--- a/sdk/python/feast/dqm/profilers/profiler.py
+++ b/sdk/python/feast/dqm/profilers/profiler.py
@@ -69,6 +69,9 @@ class ValidationError:
 
     missing_count: Optional[int]
     missing_percent: Optional[float]
+    observed_value: Optional[float]
+    unexpected_count: Optional[int]
+    unexpected_percent: Optional[float]
 
     def __init__(
         self,
@@ -77,12 +80,30 @@ def __init__(
         check_config: Optional[Any] = None,
         missing_count: Optional[int] = None,
         missing_percent: Optional[float] = None,
+        observed_value: Optional[float] = None,
+        unexpected_count: Optional[int] = None,
+        unexpected_percent: Optional[float] = None,
     ):
         self.check_name = check_name
         self.column_name = column_name
         self.check_config = check_config
         self.missing_count = missing_count
         self.missing_percent = missing_percent
+        self.observed_value = observed_value
+        self.unexpected_count = unexpected_count
+        self.unexpected_percent = unexpected_percent
 
     def __repr__(self):
         return f"<ValidationError {self.check_name}:{self.column_name}>"
+
+    def to_dict(self):
+        return dict(
+            check_name=self.check_name,
+            column_name=self.column_name,
+            check_config=self.check_config,
+            missing_count=self.missing_count,
+            missing_percent=self.missing_percent,
+            observed_value=self.observed_value,
+            unexpected_count=self.unexpected_count,
+            unexpected_percent=self.unexpected_percent,
+        )
diff --git a/sdk/python/feast/embedded_go/online_features_service.py b/sdk/python/feast/embedded_go/online_features_service.py
index 48e31766cb3..3081843778c 100644
--- a/sdk/python/feast/embedded_go/online_features_service.py
+++ b/sdk/python/feast/embedded_go/online_features_service.py
@@ -11,6 +11,7 @@
     RequestDataNotFoundInEntityRowsException,
 )
 from feast.feature_service import FeatureService
+from feast.infra.feature_servers.base_config import FeatureLoggingConfig
 from feast.online_response import OnlineResponse
 from feast.protos.feast.serving.ServingService_pb2 import GetOnlineFeaturesResponse
 from feast.protos.feast.types import Value_pb2
@@ -30,6 +31,11 @@
 if TYPE_CHECKING:
     from feast.feature_store import FeatureStore
 
+NANO_SECOND = 1
+MICRO_SECOND = 1000 * NANO_SECOND
+MILLI_SECOND = 1000 * MICRO_SECOND
+SECOND = 1000 * MILLI_SECOND
+
 
 class EmbeddedOnlineFeatureServer:
     def __init__(
@@ -39,13 +45,17 @@ def __init__(
         self._transformation_callback = partial(transformation_callback, feature_store)
         self._logging_callback = partial(logging_callback, feature_store)
 
+        self._config = OnlineFeatureServiceConfig(
+            RepoPath=repo_path, RepoConfig=repo_config.json()
+        )
+
         self._service = NewOnlineFeatureService(
-            OnlineFeatureServiceConfig(
-                RepoPath=repo_path, RepoConfig=repo_config.json()
-            ),
-            self._transformation_callback,
+            self._config, self._transformation_callback,
         )
 
+        # This should raise an exception if there were any errors in NewOnlineFeatureService.
+        self._service.CheckForInstantiationError()
+
     def get_online_features(
         self,
         features_refs: List[str],
@@ -144,12 +154,22 @@ def start_grpc_server(
         host: str,
         port: int,
         enable_logging: bool = True,
-        logging_options: Optional[LoggingOptions] = None,
+        logging_options: Optional[FeatureLoggingConfig] = None,
     ):
         if enable_logging:
             if logging_options:
                 self._service.StartGprcServerWithLogging(
-                    host, port, self._logging_callback, logging_options
+                    host,
+                    port,
+                    self._logging_callback,
+                    LoggingOptions(
+                        FlushInterval=logging_options.flush_interval_secs * SECOND,
+                        WriteInterval=logging_options.write_to_disk_interval_secs
+                        * SECOND,
+                        EmitTimeout=logging_options.emit_timeout_micro_secs
+                        * MICRO_SECOND,
+                        ChannelCapacity=logging_options.queue_capacity,
+                    ),
                 )
             else:
                 self._service.StartGprcServerWithLoggingDefaultOpts(
@@ -158,8 +178,40 @@ def start_grpc_server(
         else:
             self._service.StartGprcServer(host, port)
 
+    def start_http_server(
+        self,
+        host: str,
+        port: int,
+        enable_logging: bool = True,
+        logging_options: Optional[FeatureLoggingConfig] = None,
+    ):
+        if enable_logging:
+            if logging_options:
+                self._service.StartHttpServerWithLogging(
+                    host,
+                    port,
+                    self._logging_callback,
+                    LoggingOptions(
+                        FlushInterval=logging_options.flush_interval_secs * SECOND,
+                        WriteInterval=logging_options.write_to_disk_interval_secs
+                        * SECOND,
+                        EmitTimeout=logging_options.emit_timeout_micro_secs
+                        * MICRO_SECOND,
+                        ChannelCapacity=logging_options.queue_capacity,
+                    ),
+                )
+            else:
+                self._service.StartHttpServerWithLoggingDefaultOpts(
+                    host, port, self._logging_callback
+                )
+        else:
+            self._service.StartHttpServer(host, port)
+
     def stop_grpc_server(self):
-        self._service.Stop()
+        self._service.StopGrpcServer()
+
+    def stop_http_server(self):
+        self._service.StopHttpServer()
 
 
 def _to_arrow(value, type_hint: Optional[ValueType]) -> pa.Array:
@@ -194,6 +246,10 @@ def transformation_callback(
 
     input_record = pa.RecordBatch._import_from_c(input_arr_ptr, input_schema_ptr)
 
+    # For some reason, the callback is called with `full_feature_names` as a 1 if True or 0 if false. This handles
+    # the typeguard requirement.
+    full_feature_names = bool(full_feature_names)
+
     output = odfv.get_transformed_features_df(
         input_record.to_pandas(), full_feature_names=full_feature_names
     )
diff --git a/sdk/python/feast/entity.py b/sdk/python/feast/entity.py
index 1e7c7cf3079..0c468c7b597 100644
--- a/sdk/python/feast/entity.py
+++ b/sdk/python/feast/entity.py
@@ -16,6 +16,7 @@
 from typing import Dict, List, Optional
 
 from google.protobuf.json_format import MessageToJson
+from typeguard import typechecked
 
 from feast.protos.feast.core.Entity_pb2 import Entity as EntityProto
 from feast.protos.feast.core.Entity_pb2 import EntityMeta as EntityMetaProto
@@ -24,6 +25,7 @@
 from feast.value_type import ValueType
 
 
+@typechecked
 class Entity:
     """
     An entity defines a collection of entities for which features can be defined. An
@@ -31,7 +33,7 @@ class Entity:
 
     Attributes:
         name: The unique name of the entity.
-        value_type: The type of the entity, such as string or float.
+        value_type (deprecated): The type of the entity, such as string or float.
         join_key: A property that uniquely identifies different entities within the
             collection. The join_key property is typically used for joining entities
             with their associated features. If not specified, defaults to the name.
@@ -60,7 +62,7 @@ def __init__(
         self,
         *args,
         name: Optional[str] = None,
-        value_type: ValueType = ValueType.UNKNOWN,
+        value_type: Optional[ValueType] = None,
         description: str = "",
         join_key: Optional[str] = None,
         tags: Optional[Dict[str, str]] = None,
@@ -72,7 +74,7 @@ def __init__(
 
         Args:
             name: The unique name of the entity.
-            value_type: The type of the entity, such as string or float.
+            value_type (deprecated): The type of the entity, such as string or float.
             description: A human-readable description.
             join_key (deprecated): A property that uniquely identifies different entities within the
                 collection. The join_key property is typically used for joining entities
@@ -90,7 +92,7 @@ def __init__(
             warnings.warn(
                 (
                     "Entity name should be specified as a keyword argument instead of a positional arg."
-                    "Feast 0.23+ will not support positional arguments to construct Entities"
+                    "Feast 0.24+ will not support positional arguments to construct Entities"
                 ),
                 DeprecationWarning,
             )
@@ -104,13 +106,28 @@ def __init__(
         if not self.name:
             raise ValueError("Name needs to be specified")
 
-        self.value_type = value_type
+        if value_type:
+            warnings.warn(
+                (
+                    "The `value_type` parameter is being deprecated. Instead, the type of an entity "
+                    "should be specified as a Field in the schema of a feature view. Feast 0.24 and "
+                    "onwards will not support the `value_type` parameter. The `entities` parameter of "
+                    "feature views should also be changed to a List[Entity] instead of a List[str]; if "
+                    "this is not done, entity columns will be mistakenly interpreted as feature columns."
+                ),
+                DeprecationWarning,
+            )
+        self.value_type = value_type or ValueType.UNKNOWN
 
+        # For now, both the `join_key` and `join_keys` attributes are set correctly,
+        # so both are usable.
+        # TODO(felixwang9817): Remove the usage of `join_key` throughout the codebase
+        # when the usage of `join_key` as a parameter is removed.
         if join_key:
             warnings.warn(
                 (
                     "The `join_key` parameter is being deprecated in favor of the `join_keys` parameter. "
-                    "Please switch from using `join_key` to `join_keys`. Feast 0.23 and onwards will not "
+                    "Please switch from using `join_key` to `join_keys`. Feast 0.24 and onwards will not "
                     "support the `join_key` parameter."
                 ),
                 DeprecationWarning,
@@ -125,6 +142,8 @@ def __init__(
             self.join_key = join_keys[0]
         else:
             self.join_key = join_key if join_key else self.name
+        if not self.join_keys:
+            self.join_keys = [self.join_key]
         self.description = description
         self.tags = tags if tags is not None else {}
         self.owner = owner
@@ -153,6 +172,9 @@ def __eq__(self, other):
     def __str__(self):
         return str(MessageToJson(self.to_proto()))
 
+    def __lt__(self, other):
+        return self.name < other.name
+
     def is_valid(self):
         """
         Validates the state of this entity locally.
@@ -179,13 +201,14 @@ def from_proto(cls, entity_proto: EntityProto):
         """
         entity = cls(
             name=entity_proto.spec.name,
-            value_type=ValueType(entity_proto.spec.value_type),
             join_keys=[entity_proto.spec.join_key],
             description=entity_proto.spec.description,
-            tags=entity_proto.spec.tags,
+            tags=dict(entity_proto.spec.tags),
             owner=entity_proto.spec.owner,
         )
 
+        entity.value_type = ValueType(entity_proto.spec.value_type)
+
         if entity_proto.meta.HasField("created_timestamp"):
             entity.created_timestamp = entity_proto.meta.created_timestamp.ToDatetime()
         if entity_proto.meta.HasField("last_updated_timestamp"):
diff --git a/sdk/python/feast/errors.py b/sdk/python/feast/errors.py
index e680337d98c..980dfd470fb 100644
--- a/sdk/python/feast/errors.py
+++ b/sdk/python/feast/errors.py
@@ -2,6 +2,8 @@
 
 from colorama import Fore, Style
 
+from feast.field import Field
+
 
 class DataSourceNotFoundException(Exception):
     def __init__(self, path):
@@ -94,6 +96,13 @@ def __init__(self, name: str, project: str):
         super().__init__(f"Saved dataset {name} does not exist in project {project}")
 
 
+class ValidationReferenceNotFound(FeastObjectNotFoundException):
+    def __init__(self, name: str, project: str):
+        super().__init__(
+            f"Validation reference {name} does not exist in project {project}"
+        )
+
+
 class FeastProviderLoginError(Exception):
     """Error class that indicates a user has not authenticated with their provider."""
 
@@ -176,10 +185,15 @@ def __init__(self, feature_refs_collisions: List[str], full_feature_names: bool)
 
 
 class SpecifiedFeaturesNotPresentError(Exception):
-    def __init__(self, specified_features: List[str], feature_view_name: str):
-        features = ", ".join(specified_features)
+    def __init__(
+        self,
+        specified_features: List[Field],
+        inferred_features: List[Field],
+        feature_view_name: str,
+    ):
         super().__init__(
-            f"Explicitly specified features {features} not found in inferred list of features for '{feature_view_name}'"
+            f"Explicitly specified features {specified_features} not found in inferred list of features "
+            f"{inferred_features} for '{feature_view_name}'"
         )
 
 
diff --git a/sdk/python/feast/feast_object.py b/sdk/python/feast/feast_object.py
index 4ffd693c44f..0ac0446f5f6 100644
--- a/sdk/python/feast/feast_object.py
+++ b/sdk/python/feast/feast_object.py
@@ -11,7 +11,11 @@
 from .protos.feast.core.FeatureView_pb2 import FeatureViewSpec
 from .protos.feast.core.OnDemandFeatureView_pb2 import OnDemandFeatureViewSpec
 from .protos.feast.core.RequestFeatureView_pb2 import RequestFeatureViewSpec
+from .protos.feast.core.ValidationProfile_pb2 import (
+    ValidationReference as ValidationReferenceProto,
+)
 from .request_feature_view import RequestFeatureView
+from .saved_dataset import ValidationReference
 
 # Convenience type representing all Feast objects
 FeastObject = Union[
@@ -21,6 +25,7 @@
     Entity,
     FeatureService,
     DataSource,
+    ValidationReference,
 ]
 
 FeastObjectSpecProto = Union[
@@ -30,4 +35,5 @@
     EntitySpecV2,
     FeatureServiceSpec,
     DataSourceProto,
+    ValidationReferenceProto,
 ]
diff --git a/sdk/python/feast/feature_logging.py b/sdk/python/feast/feature_logging.py
index e2982988366..275bde72ec1 100644
--- a/sdk/python/feast/feature_logging.py
+++ b/sdk/python/feast/feature_logging.py
@@ -11,14 +11,14 @@
     FeatureViewNotFoundException,
     OnDemandFeatureViewNotFoundException,
 )
+from feast.feature_view import DUMMY_ENTITY_ID
 from feast.protos.feast.core.FeatureService_pb2 import (
     LoggingConfig as LoggingConfigProto,
 )
-from feast.types import from_value_type
 
 if TYPE_CHECKING:
-    from feast import FeatureService
-    from feast.registry import Registry
+    from feast.feature_service import FeatureService
+    from feast.registry import BaseRegistry
 
 
 REQUEST_ID_FIELD = "__request_id"
@@ -33,7 +33,7 @@ class LoggingSource:
     """
 
     @abc.abstractmethod
-    def get_schema(self, registry: "Registry") -> pa.Schema:
+    def get_schema(self, registry: "BaseRegistry") -> pa.Schema:
         """ Generate schema for logs destination. """
         raise NotImplementedError
 
@@ -48,7 +48,7 @@ def __init__(self, feature_service: "FeatureService", project: str):
         self._feature_service = feature_service
         self._project = project
 
-    def get_schema(self, registry: "Registry") -> pa.Schema:
+    def get_schema(self, registry: "BaseRegistry") -> pa.Schema:
         fields: Dict[str, pa.DataType] = {}
 
         for projection in self._feature_service.feature_view_projections:
@@ -76,14 +76,14 @@ def get_schema(self, registry: "Registry") -> pa.Schema:
                         fields[field.name] = FEAST_TYPE_TO_ARROW_TYPE[field.dtype]
 
             else:
-                for entity_name in feature_view.entities:
-                    entity = registry.get_entity(entity_name, self._project)
+                for entity_column in feature_view.entity_columns:
+                    if entity_column.name == DUMMY_ENTITY_ID:
+                        continue
+
                     join_key = projection.join_key_map.get(
-                        entity.join_key, entity.join_key
+                        entity_column.name, entity_column.name
                     )
-                    fields[join_key] = FEAST_TYPE_TO_ARROW_TYPE[
-                        from_value_type(entity.value_type)
-                    ]
+                    fields[join_key] = FEAST_TYPE_TO_ARROW_TYPE[entity_column.dtype]
 
             for feature in projection.features:
                 fields[
diff --git a/sdk/python/feast/feature_server.py b/sdk/python/feast/feature_server.py
index 8347bed6da3..bef88923e18 100644
--- a/sdk/python/feast/feature_server.py
+++ b/sdk/python/feast/feature_server.py
@@ -12,6 +12,7 @@
 
 import feast
 from feast import proto_json
+from feast.data_source import PushMode
 from feast.protos.feast.serving.ServingService_pb2 import GetOnlineFeaturesRequest
 
 
@@ -26,6 +27,7 @@ class PushFeaturesRequest(BaseModel):
     push_source_name: str
     df: dict
     allow_registry_cache: bool = True
+    to: str = "online"
 
 
 def get_app(store: "feast.FeatureStore"):
@@ -80,10 +82,21 @@ def push(body=Depends(get_body)):
         try:
             request = PushFeaturesRequest(**json.loads(body))
             df = pd.DataFrame(request.df)
+            if request.to == "offline":
+                to = PushMode.OFFLINE
+            elif request.to == "online":
+                to = PushMode.ONLINE
+            elif request.to == "online_and_offline":
+                to = PushMode.ONLINE_AND_OFFLINE
+            else:
+                raise ValueError(
+                    f"{request.to} is not a supported push format. Please specify one of these ['online', 'offline', 'online_and_offline']."
+                )
             store.push(
                 push_source_name=request.push_source_name,
                 df=df,
                 allow_registry_cache=request.allow_registry_cache,
+                to=to,
             )
         except Exception as e:
             # Print the original exception on the server side
diff --git a/sdk/python/feast/feature_service.py b/sdk/python/feast/feature_service.py
index 9490de38c93..410ba94b00b 100644
--- a/sdk/python/feast/feature_service.py
+++ b/sdk/python/feast/feature_service.py
@@ -3,6 +3,7 @@
 from typing import Dict, List, Optional, Union
 
 from google.protobuf.json_format import MessageToJson
+from typeguard import typechecked
 
 from feast.base_feature_view import BaseFeatureView
 from feast.feature_logging import LoggingConfig
@@ -21,6 +22,7 @@
 from feast.usage import log_exceptions
 
 
+@typechecked
 class FeatureService:
     """
     A feature service defines a logical group of features from one or more feature views.
@@ -72,7 +74,7 @@ def __init__(
             warnings.warn(
                 (
                     "Feature service parameters should be specified as a keyword argument instead of a positional arg."
-                    "Feast 0.23+ will not support positional arguments to construct feature service"
+                    "Feast 0.24+ will not support positional arguments to construct feature service"
                 ),
                 DeprecationWarning,
             )
@@ -102,22 +104,36 @@ def __init__(
         self.created_timestamp = None
         self.last_updated_timestamp = None
         self.logging_config = logging_config
-        self.infer_features()
+        for feature_grouping in self._features:
+            if isinstance(feature_grouping, BaseFeatureView):
+                self.feature_view_projections.append(feature_grouping.projection)
 
     def infer_features(self, fvs_to_update: Optional[Dict[str, FeatureView]] = None):
-        self.feature_view_projections = []
         for feature_grouping in self._features:
             if isinstance(feature_grouping, BaseFeatureView):
                 # For feature services that depend on an unspecified feature view, apply inferred schema
-                if (
-                    fvs_to_update is not None
-                    and len(feature_grouping.projection.features) == 0
-                    and feature_grouping.name in fvs_to_update
-                ):
-                    feature_grouping.projection.features = fvs_to_update[
-                        feature_grouping.name
-                    ].features
-                self.feature_view_projections.append(feature_grouping.projection)
+                if fvs_to_update and feature_grouping.name in fvs_to_update:
+                    if feature_grouping.projection.desired_features:
+                        desired_features = set(
+                            feature_grouping.projection.desired_features
+                        )
+                        actual_features = set(
+                            [
+                                f.name
+                                for f in fvs_to_update[feature_grouping.name].features
+                            ]
+                        )
+                        assert desired_features.issubset(actual_features)
+                        # We need to set the features for the projection at this point so we ensure we're starting with
+                        # an empty list.
+                        feature_grouping.projection.features = []
+                        for f in fvs_to_update[feature_grouping.name].features:
+                            if f.name in desired_features:
+                                feature_grouping.projection.features.append(f)
+                    else:
+                        feature_grouping.projection.features = fvs_to_update[
+                            feature_grouping.name
+                        ].features
             else:
                 raise ValueError(
                     f"The feature service {self.name} has been provided with an invalid type "
diff --git a/sdk/python/feast/feature_store.py b/sdk/python/feast/feature_store.py
index 73af4741ef3..de52b9e3f3e 100644
--- a/sdk/python/feast/feature_store.py
+++ b/sdk/python/feast/feature_store.py
@@ -42,9 +42,11 @@
 
 from feast import feature_server, flags, flags_helper, ui_server, utils
 from feast.base_feature_view import BaseFeatureView
-from feast.data_source import DataSource
+from feast.batch_feature_view import BatchFeatureView
+from feast.data_source import DataSource, PushMode
 from feast.diff.infra_diff import InfraDiff, diff_infra_protos
 from feast.diff.registry_diff import RegistryDiff, apply_diff_to_registry, diff_between
+from feast.dqm.errors import ValidationFailed
 from feast.entity import Entity
 from feast.errors import (
     EntityNotFoundException,
@@ -65,25 +67,25 @@
 )
 from feast.inference import (
     update_data_sources_with_inferred_event_timestamp_col,
-    update_entities_with_inferred_types_from_feature_views,
-    update_feature_views_with_inferred_features,
+    update_feature_views_with_inferred_features_and_entities,
 )
 from feast.infra.infra_object import Infra
 from feast.infra.provider import Provider, RetrievalJob, get_provider
+from feast.infra.registry_stores.sql import SqlRegistry
 from feast.on_demand_feature_view import OnDemandFeatureView
 from feast.online_response import OnlineResponse
-from feast.protos.feast.core.InfraObject_pb2 import Infra as InfraProto
 from feast.protos.feast.serving.ServingService_pb2 import (
     FieldStatus,
     GetOnlineFeaturesResponse,
 )
 from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
 from feast.protos.feast.types.Value_pb2 import RepeatedValue, Value
-from feast.registry import Registry
+from feast.registry import BaseRegistry, Registry
 from feast.repo_config import RepoConfig, load_repo_config
 from feast.repo_contents import RepoContents
 from feast.request_feature_view import RequestFeatureView
-from feast.saved_dataset import SavedDataset, SavedDatasetStorage
+from feast.saved_dataset import SavedDataset, SavedDatasetStorage, ValidationReference
+from feast.stream_feature_view import StreamFeatureView
 from feast.type_map import (
     feast_value_type_to_python_type,
     python_values_to_proto_values,
@@ -110,7 +112,7 @@ class FeatureStore:
 
     config: RepoConfig
     repo_path: Path
-    _registry: Registry
+    _registry: BaseRegistry
     _provider: Provider
     _go_server: "EmbeddedOnlineFeatureServer"
 
@@ -136,8 +138,12 @@ def __init__(
             raise ValueError("Please specify one of repo_path or config.")
 
         registry_config = self.config.get_registry_config()
-        self._registry = Registry(registry_config, repo_path=self.repo_path)
-        self._registry._initialize_registry()
+        if registry_config.registry_type == "sql":
+            self._registry = SqlRegistry(registry_config, None)
+        else:
+            r = Registry(registry_config, repo_path=self.repo_path)
+            r._initialize_registry()
+            self._registry = r
         self._provider = get_provider(self.config, self.repo_path)
         self._go_server = None
 
@@ -147,7 +153,7 @@ def version(self) -> str:
         return get_version()
 
     @property
-    def registry(self) -> Registry:
+    def registry(self) -> BaseRegistry:
         """Gets the registry of this feature store."""
         return self._registry
 
@@ -255,9 +261,23 @@ def _list_feature_views(
         ):
             if hide_dummy_entity and fv.entities[0] == DUMMY_ENTITY_NAME:
                 fv.entities = []
+                fv.entity_columns = []
             feature_views.append(fv)
         return feature_views
 
+    def _list_stream_feature_views(
+        self, allow_cache: bool = False, hide_dummy_entity: bool = True,
+    ) -> List[StreamFeatureView]:
+        stream_feature_views = []
+        for sfv in self._registry.list_stream_feature_views(
+            self.project, allow_cache=allow_cache
+        ):
+            if hide_dummy_entity and sfv.entities[0] == DUMMY_ENTITY_NAME:
+                sfv.entities = []
+                sfv.entity_columns = []
+            stream_feature_views.append(sfv)
+        return stream_feature_views
+
     @log_exceptions_and_usage
     def list_on_demand_feature_views(
         self, allow_cache: bool = False
@@ -272,6 +292,18 @@ def list_on_demand_feature_views(
             self.project, allow_cache=allow_cache
         )
 
+    @log_exceptions_and_usage
+    def list_stream_feature_views(
+        self, allow_cache: bool = False
+    ) -> List[StreamFeatureView]:
+        """
+        Retrieves the list of stream feature views from the registry.
+
+        Returns:
+            A list of stream feature views.
+        """
+        return self._list_stream_feature_views(allow_cache)
+
     @log_exceptions_and_usage
     def list_data_sources(self, allow_cache: bool = False) -> List[DataSource]:
         """
@@ -355,6 +387,40 @@ def _get_feature_view(
             feature_view.entities = []
         return feature_view
 
+    @log_exceptions_and_usage
+    def get_stream_feature_view(
+        self, name: str, allow_registry_cache: bool = False
+    ) -> StreamFeatureView:
+        """
+        Retrieves a stream feature view.
+
+        Args:
+            name: Name of stream feature view.
+            allow_registry_cache: (Optional) Whether to allow returning this entity from a cached registry
+
+        Returns:
+            The specified stream feature view.
+
+        Raises:
+            FeatureViewNotFoundException: The feature view could not be found.
+        """
+        return self._get_stream_feature_view(
+            name, allow_registry_cache=allow_registry_cache
+        )
+
+    def _get_stream_feature_view(
+        self,
+        name: str,
+        hide_dummy_entity: bool = True,
+        allow_registry_cache: bool = False,
+    ) -> StreamFeatureView:
+        stream_feature_view = self._registry.get_stream_feature_view(
+            name, self.project, allow_cache=allow_registry_cache
+        )
+        if hide_dummy_entity and stream_feature_view.entities[0] == DUMMY_ENTITY_NAME:
+            stream_feature_view.entities = []
+        return stream_feature_view
+
     @log_exceptions_and_usage
     def get_on_demand_feature_view(self, name: str) -> OnDemandFeatureView:
         """
@@ -456,6 +522,7 @@ def _validate_all_feature_views(
         views_to_update: List[FeatureView],
         odfvs_to_update: List[OnDemandFeatureView],
         request_views_to_update: List[RequestFeatureView],
+        sfvs_to_update: List[StreamFeatureView],
     ):
         """Validates all feature views."""
         if (
@@ -467,7 +534,12 @@ def _validate_all_feature_views(
         set_usage_attribute("odfv", bool(odfvs_to_update))
 
         _validate_feature_views(
-            [*views_to_update, *odfvs_to_update, *request_views_to_update]
+            [
+                *views_to_update,
+                *odfvs_to_update,
+                *request_views_to_update,
+                *sfvs_to_update,
+            ]
         )
 
     def _make_inferences(
@@ -476,13 +548,10 @@ def _make_inferences(
         entities_to_update: List[Entity],
         views_to_update: List[FeatureView],
         odfvs_to_update: List[OnDemandFeatureView],
+        sfvs_to_update: List[StreamFeatureView],
         feature_services_to_update: List[FeatureService],
     ):
         """Makes inferences for entities, feature views, odfvs, and feature services."""
-        update_entities_with_inferred_types_from_feature_views(
-            entities_to_update, views_to_update, self.config
-        )
-
         update_data_sources_with_inferred_event_timestamp_col(
             data_sources_to_update, self.config
         )
@@ -491,19 +560,81 @@ def _make_inferences(
             [view.batch_source for view in views_to_update], self.config
         )
 
+        update_data_sources_with_inferred_event_timestamp_col(
+            [view.batch_source for view in sfvs_to_update], self.config
+        )
+
         # New feature views may reference previously applied entities.
         entities = self._list_entities()
-        update_feature_views_with_inferred_features(
+        update_feature_views_with_inferred_features_and_entities(
             views_to_update, entities + entities_to_update, self.config
         )
+        update_feature_views_with_inferred_features_and_entities(
+            sfvs_to_update, entities + entities_to_update, self.config
+        )
+        # TODO(kevjumba): Update schema inferrence
+        for sfv in sfvs_to_update:
+            if not sfv.schema:
+                raise ValueError(
+                    f"schema inference not yet supported for stream feature views. please define schema for stream feature view: {sfv.name}"
+                )
 
         for odfv in odfvs_to_update:
             odfv.infer_features()
 
-        fvs_to_update_map = {view.name: view for view in views_to_update}
+        fvs_to_update_map = {
+            view.name: view for view in [*views_to_update, *sfvs_to_update]
+        }
         for feature_service in feature_services_to_update:
             feature_service.infer_features(fvs_to_update=fvs_to_update_map)
 
+    def _get_feature_views_to_materialize(
+        self, feature_views: Optional[List[str]],
+    ) -> List[FeatureView]:
+        """
+        Returns the list of feature views that should be materialized.
+
+        If no feature views are specified, all feature views will be returned.
+
+        Args:
+            feature_views: List of names of feature views to materialize.
+
+        Raises:
+            FeatureViewNotFoundException: One of the specified feature views could not be found.
+            ValueError: One of the specified feature views is not configured for materialization.
+        """
+        feature_views_to_materialize: List[FeatureView] = []
+
+        if feature_views is None:
+            feature_views_to_materialize = self._list_feature_views(
+                hide_dummy_entity=False
+            )
+            feature_views_to_materialize = [
+                fv for fv in feature_views_to_materialize if fv.online
+            ]
+            stream_feature_views_to_materialize = self._list_stream_feature_views(
+                hide_dummy_entity=False
+            )
+            feature_views_to_materialize += [
+                sfv for sfv in stream_feature_views_to_materialize if sfv.online
+            ]
+        else:
+            for name in feature_views:
+                try:
+                    feature_view = self._get_feature_view(name, hide_dummy_entity=False)
+                except FeatureViewNotFoundException:
+                    feature_view = self._get_stream_feature_view(
+                        name, hide_dummy_entity=False
+                    )
+
+                if not feature_view.online:
+                    raise ValueError(
+                        f"FeatureView {feature_view.name} is not configured to be served online."
+                    )
+                feature_views_to_materialize.append(feature_view)
+
+        return feature_views_to_materialize
+
     @log_exceptions_and_usage
     def _plan(
         self, desired_repo_contents: RepoContents
@@ -523,11 +654,11 @@ def _plan(
         Examples:
             Generate a plan adding an Entity and a FeatureView.
 
-            >>> from feast import FeatureStore, Entity, FeatureView, Feature, ValueType, FileSource, RepoConfig
+            >>> from feast import FeatureStore, Entity, FeatureView, Feature, FileSource, RepoConfig
             >>> from feast.feature_store import RepoContents
             >>> from datetime import timedelta
             >>> fs = FeatureStore(repo_path="feature_repo")
-            >>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id")
+            >>> driver = Entity(name="driver_id", description="driver id")
             >>> driver_hourly_stats = FileSource(
             ...     path="feature_repo/data/driver_stats.parquet",
             ...     timestamp_field="event_timestamp",
@@ -535,7 +666,7 @@ def _plan(
             ... )
             >>> driver_hourly_stats_view = FeatureView(
             ...     name="driver_hourly_stats",
-            ...     entities=["driver_id"],
+            ...     entities=[driver],
             ...     ttl=timedelta(seconds=86400 * 1),
             ...     batch_source=driver_hourly_stats,
             ... )
@@ -543,6 +674,7 @@ def _plan(
             ...     data_sources=[driver_hourly_stats],
             ...     feature_views=[driver_hourly_stats_view],
             ...     on_demand_feature_views=list(),
+            ...     stream_feature_views=list(),
             ...     request_feature_views=list(),
             ...     entities=[driver],
             ...     feature_services=list())) # register entity and feature view
@@ -552,6 +684,7 @@ def _plan(
             desired_repo_contents.feature_views,
             desired_repo_contents.on_demand_feature_views,
             desired_repo_contents.request_feature_views,
+            desired_repo_contents.stream_feature_views,
         )
         _validate_data_sources(desired_repo_contents.data_sources)
         self._make_inferences(
@@ -559,6 +692,7 @@ def _plan(
             desired_repo_contents.entities,
             desired_repo_contents.feature_views,
             desired_repo_contents.on_demand_feature_views,
+            desired_repo_contents.stream_feature_views,
             desired_repo_contents.feature_services,
         )
 
@@ -571,11 +705,7 @@ def _plan(
         # Compute the desired difference between the current infra, as stored in the registry,
         # and the desired infra.
         self._registry.refresh()
-        current_infra_proto = (
-            self._registry.cached_registry_proto.infra.__deepcopy__()
-            if self._registry.cached_registry_proto
-            else InfraProto()
-        )
+        current_infra_proto = self._registry.proto().infra.__deepcopy__()
         desired_registry_proto = desired_repo_contents.to_registry_proto()
         new_infra = self._provider.plan_infra(self.config, desired_registry_proto)
         new_infra_proto = new_infra.to_proto()
@@ -610,7 +740,9 @@ def apply(
             FeatureView,
             OnDemandFeatureView,
             RequestFeatureView,
+            StreamFeatureView,
             FeatureService,
+            ValidationReference,
             List[FeastObject],
         ],
         objects_to_delete: Optional[List[FeastObject]] = None,
@@ -636,10 +768,10 @@ def apply(
         Examples:
             Register an Entity and a FeatureView.
 
-            >>> from feast import FeatureStore, Entity, FeatureView, Feature, ValueType, FileSource, RepoConfig
+            >>> from feast import FeatureStore, Entity, FeatureView, Feature, FileSource, RepoConfig
             >>> from datetime import timedelta
             >>> fs = FeatureStore(repo_path="feature_repo")
-            >>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id")
+            >>> driver = Entity(name="driver_id", description="driver id")
             >>> driver_hourly_stats = FileSource(
             ...     path="feature_repo/data/driver_stats.parquet",
             ...     timestamp_field="event_timestamp",
@@ -647,7 +779,7 @@ def apply(
             ... )
             >>> driver_hourly_stats_view = FeatureView(
             ...     name="driver_hourly_stats",
-            ...     entities=["driver_id"],
+            ...     entities=[driver],
             ...     ttl=timedelta(seconds=86400 * 1),
             ...     batch_source=driver_hourly_stats,
             ... )
@@ -663,7 +795,16 @@ def apply(
 
         # Separate all objects into entities, feature services, and different feature view types.
         entities_to_update = [ob for ob in objects if isinstance(ob, Entity)]
-        views_to_update = [ob for ob in objects if isinstance(ob, FeatureView)]
+        views_to_update = [
+            ob
+            for ob in objects
+            if (
+                isinstance(ob, FeatureView)
+                and not isinstance(ob, StreamFeatureView)
+                and not isinstance(ob, BatchFeatureView)
+            )
+        ]
+        sfvs_to_update = [ob for ob in objects if isinstance(ob, StreamFeatureView)]
         request_views_to_update = [
             ob for ob in objects if isinstance(ob, RequestFeatureView)
         ]
@@ -672,8 +813,11 @@ def apply(
         data_sources_set_to_update = {
             ob for ob in objects if isinstance(ob, DataSource)
         }
+        validation_references_to_update = [
+            ob for ob in objects if isinstance(ob, ValidationReference)
+        ]
 
-        for fv in views_to_update:
+        for fv in itertools.chain(views_to_update, sfvs_to_update):
             data_sources_set_to_update.add(fv.batch_source)
             if fv.stream_source:
                 data_sources_set_to_update.add(fv.stream_source)
@@ -694,26 +838,27 @@ def apply(
 
         data_sources_to_update = list(data_sources_set_to_update)
 
+        # Handle all entityless feature views by using DUMMY_ENTITY as a placeholder entity.
+        entities_to_update.append(DUMMY_ENTITY)
+
         # Validate all feature views and make inferences.
         self._validate_all_feature_views(
-            views_to_update, odfvs_to_update, request_views_to_update
+            views_to_update, odfvs_to_update, request_views_to_update, sfvs_to_update
         )
         self._make_inferences(
             data_sources_to_update,
             entities_to_update,
             views_to_update,
             odfvs_to_update,
+            sfvs_to_update,
             services_to_update,
         )
 
-        # Handle all entityless feature views by using DUMMY_ENTITY as a placeholder entity.
-        entities_to_update.append(DUMMY_ENTITY)
-
         # Add all objects to the registry and update the provider's infrastructure.
         for ds in data_sources_to_update:
             self._registry.apply_data_source(ds, project=self.project, commit=False)
         for view in itertools.chain(
-            views_to_update, odfvs_to_update, request_views_to_update
+            views_to_update, odfvs_to_update, request_views_to_update, sfvs_to_update
         ):
             self._registry.apply_feature_view(view, project=self.project, commit=False)
         for ent in entities_to_update:
@@ -722,6 +867,10 @@ def apply(
             self._registry.apply_feature_service(
                 feature_service, project=self.project, commit=False
             )
+        for validation_references in validation_references_to_update:
+            self._registry.apply_validation_reference(
+                validation_references, project=self.project, commit=False
+            )
 
         if not partial:
             # Delete all registry objects that should not exist.
@@ -737,12 +886,18 @@ def apply(
             odfvs_to_delete = [
                 ob for ob in objects_to_delete if isinstance(ob, OnDemandFeatureView)
             ]
+            sfvs_to_delete = [
+                ob for ob in objects_to_delete if isinstance(ob, StreamFeatureView)
+            ]
             services_to_delete = [
                 ob for ob in objects_to_delete if isinstance(ob, FeatureService)
             ]
             data_sources_to_delete = [
                 ob for ob in objects_to_delete if isinstance(ob, DataSource)
             ]
+            validation_references_to_delete = [
+                ob for ob in objects_to_delete if isinstance(ob, ValidationReference)
+            ]
 
             for data_source in data_sources_to_delete:
                 self._registry.delete_data_source(
@@ -764,15 +919,23 @@ def apply(
                 self._registry.delete_feature_view(
                     odfv.name, project=self.project, commit=False
                 )
+            for sfv in sfvs_to_delete:
+                self._registry.delete_feature_view(
+                    sfv.name, project=self.project, commit=False
+                )
             for service in services_to_delete:
                 self._registry.delete_feature_service(
                     service.name, project=self.project, commit=False
                 )
+            for validation_references in validation_references_to_delete:
+                self._registry.delete_validation_reference(
+                    validation_references.name, project=self.project, commit=False
+                )
 
         self._get_provider().update_infra(
             project=self.project,
-            tables_to_delete=views_to_delete if not partial else [],
-            tables_to_keep=views_to_update,
+            tables_to_delete=views_to_delete + sfvs_to_delete if not partial else [],
+            tables_to_keep=views_to_update + sfvs_to_update,
             entities_to_delete=entities_to_delete if not partial else [],
             entities_to_keep=entities_to_update,
             partial=partial,
@@ -1049,23 +1212,9 @@ def materialize_incremental(
             <BLANKLINE>
             ...
         """
-        feature_views_to_materialize: List[FeatureView] = []
-        if feature_views is None:
-            feature_views_to_materialize = self._list_feature_views(
-                hide_dummy_entity=False
-            )
-            feature_views_to_materialize = [
-                fv for fv in feature_views_to_materialize if fv.online
-            ]
-        else:
-            for name in feature_views:
-                feature_view = self._get_feature_view(name, hide_dummy_entity=False)
-                if not feature_view.online:
-                    raise ValueError(
-                        f"FeatureView {feature_view.name} is not configured to be served online."
-                    )
-                feature_views_to_materialize.append(feature_view)
-
+        feature_views_to_materialize = self._get_feature_views_to_materialize(
+            feature_views
+        )
         _print_materialization_log(
             None,
             end_date,
@@ -1141,7 +1290,6 @@ def materialize(
         Examples:
             Materialize all features into the online store over the interval
             from 3 hours ago to 10 minutes ago.
-
             >>> from feast import FeatureStore, RepoConfig
             >>> from datetime import datetime, timedelta
             >>> fs = FeatureStore(repo_path="feature_repo")
@@ -1157,23 +1305,9 @@ def materialize(
                 f"The given start_date {start_date} is greater than the given end_date {end_date}."
             )
 
-        feature_views_to_materialize: List[FeatureView] = []
-        if feature_views is None:
-            feature_views_to_materialize = self._list_feature_views(
-                hide_dummy_entity=False
-            )
-            feature_views_to_materialize = [
-                fv for fv in feature_views_to_materialize if fv.online
-            ]
-        else:
-            for name in feature_views:
-                feature_view = self._get_feature_view(name, hide_dummy_entity=False)
-                if not feature_view.online:
-                    raise ValueError(
-                        f"FeatureView {feature_view.name} is not configured to be served online."
-                    )
-                feature_views_to_materialize.append(feature_view)
-
+        feature_views_to_materialize = self._get_feature_views_to_materialize(
+            feature_views
+        )
         _print_materialization_log(
             start_date,
             end_date,
@@ -1207,14 +1341,20 @@ def tqdm_builder(length):
 
     @log_exceptions_and_usage
     def push(
-        self, push_source_name: str, df: pd.DataFrame, allow_registry_cache: bool = True
+        self,
+        push_source_name: str,
+        df: pd.DataFrame,
+        allow_registry_cache: bool = True,
+        to: PushMode = PushMode.ONLINE,
     ):
         """
         Push features to a push source. This updates all the feature views that have the push source as stream source.
+
         Args:
             push_source_name: The name of the push source we want to push data to.
-            df: the data being pushed.
-            allow_registry_cache: whether to allow cached versions of the registry.
+            df: The data being pushed.
+            allow_registry_cache: Whether to allow cached versions of the registry.
+            to: Whether to push to online or offline store. Defaults to online store only.
         """
         warnings.warn(
             "Push source is an experimental feature. "
@@ -1225,6 +1365,7 @@ def push(
         from feast.data_source import PushSource
 
         all_fvs = self.list_feature_views(allow_cache=allow_registry_cache)
+        all_fvs += self.list_stream_feature_views(allow_cache=allow_registry_cache)
 
         fvs_with_push_sources = {
             fv
@@ -1237,9 +1378,14 @@ def push(
         }
 
         for fv in fvs_with_push_sources:
-            self.write_to_online_store(
-                fv.name, df, allow_registry_cache=allow_registry_cache
-            )
+            if to == PushMode.ONLINE or to == PushMode.ONLINE_AND_OFFLINE:
+                self.write_to_online_store(
+                    fv.name, df, allow_registry_cache=allow_registry_cache
+                )
+            if to == PushMode.OFFLINE or to == PushMode.ONLINE_AND_OFFLINE:
+                self._write_to_offline_store(
+                    fv.name, df, allow_registry_cache=allow_registry_cache
+                )
 
     @log_exceptions_and_usage
     def write_to_online_store(
@@ -1252,9 +1398,14 @@ def write_to_online_store(
         ingests data directly into the Online store
         """
         # TODO: restrict this to work with online StreamFeatureViews and validate the FeatureView type
-        feature_view = self.get_feature_view(
-            feature_view_name, allow_registry_cache=allow_registry_cache
-        )
+        try:
+            feature_view = self.get_stream_feature_view(
+                feature_view_name, allow_registry_cache=allow_registry_cache
+            )
+        except FeatureViewNotFoundException:
+            feature_view = self.get_feature_view(
+                feature_view_name, allow_registry_cache=allow_registry_cache
+            )
         entities = []
         for entity_name in feature_view.entities:
             entities.append(
@@ -1263,6 +1414,31 @@ def write_to_online_store(
         provider = self._get_provider()
         provider.ingest_df(feature_view, entities, df)
 
+    @log_exceptions_and_usage
+    def _write_to_offline_store(
+        self,
+        feature_view_name: str,
+        df: pd.DataFrame,
+        allow_registry_cache: bool = True,
+    ):
+        """
+        ingests data directly into the Online store
+        """
+        # TODO: restrict this to work with online StreamFeatureViews and validate the FeatureView type
+        try:
+            feature_view = self.get_stream_feature_view(
+                feature_view_name, allow_registry_cache=allow_registry_cache
+            )
+        except FeatureViewNotFoundException:
+            feature_view = self.get_feature_view(
+                feature_view_name, allow_registry_cache=allow_registry_cache
+            )
+        df.reset_index(drop=True)
+
+        table = pa.Table.from_pandas(df)
+        provider = self._get_provider()
+        provider.ingest_df_to_offline_store(feature_view, table)
+
     @log_exceptions_and_usage
     def get_online_features(
         self,
@@ -1561,12 +1737,12 @@ def _get_columnar_entity_values(
     def _get_entity_maps(
         self, feature_views
     ) -> Tuple[Dict[str, str], Dict[str, ValueType], Set[str]]:
+        # TODO(felixwang9817): Support entities that have different types for different feature views.
         entities = self._list_entities(allow_cache=True, hide_dummy_entity=False)
         entity_name_to_join_key_map: Dict[str, str] = {}
         entity_type_map: Dict[str, ValueType] = {}
         for entity in entities:
             entity_name_to_join_key_map[entity.name] = entity.join_key
-            entity_type_map[entity.name] = entity.value_type
         for feature_view in feature_views:
             for entity_name in feature_view.entities:
                 entity = self._registry.get_entity(
@@ -1581,7 +1757,11 @@ def _get_entity_maps(
                     entity.join_key, entity.join_key
                 )
                 entity_name_to_join_key_map[entity_name] = join_key
-                entity_type_map[join_key] = entity.value_type
+            for entity_column in feature_view.entity_columns:
+                entity_type_map[
+                    entity_column.name
+                ] = entity_column.dtype.to_value_type()
+
         return (
             entity_name_to_join_key_map,
             entity_type_map,
@@ -1919,11 +2099,18 @@ def _get_feature_views_to_use(
         features: Optional[Union[List[str], FeatureService]],
         allow_cache=False,
         hide_dummy_entity: bool = True,
-    ) -> Tuple[List[FeatureView], List[RequestFeatureView], List[OnDemandFeatureView]]:
+    ) -> Tuple[
+        List[FeatureView], List[RequestFeatureView], List[OnDemandFeatureView],
+    ]:
 
         fvs = {
             fv.name: fv
-            for fv in self._list_feature_views(allow_cache, hide_dummy_entity)
+            for fv in [
+                *self._list_feature_views(allow_cache, hide_dummy_entity),
+                *self._registry.list_stream_feature_views(
+                    project=self.project, allow_cache=allow_cache
+                ),
+            ]
         }
 
         request_fvs = {
@@ -1979,14 +2166,53 @@ def _get_feature_views_to_use(
         return views_to_use
 
     @log_exceptions_and_usage
-    def serve(self, host: str, port: int, no_access_log: bool) -> None:
+    def serve(
+        self,
+        host: str,
+        port: int,
+        type_: str,
+        no_access_log: bool,
+        no_feature_log: bool,
+    ) -> None:
         """Start the feature consumption server locally on a given port."""
+        type_ = type_.lower()
         if self.config.go_feature_retrieval:
             # Start go server instead of python if the flag is enabled
             self._lazy_init_go_server()
-            # TODO(tsotne) add http/grpc flag in CLI and call appropriate method here depending on that
-            self._go_server.start_grpc_server(host, port)
+            enable_logging = (
+                self.config.feature_server
+                and self.config.feature_server.feature_logging
+                and self.config.feature_server.feature_logging.enabled
+                and not no_feature_log
+            )
+            logging_options = (
+                self.config.feature_server.feature_logging
+                if enable_logging and self.config.feature_server
+                else None
+            )
+            if type_ == "http":
+                self._go_server.start_http_server(
+                    host,
+                    port,
+                    enable_logging=enable_logging,
+                    logging_options=logging_options,
+                )
+            elif type_ == "grpc":
+                self._go_server.start_grpc_server(
+                    host,
+                    port,
+                    enable_logging=enable_logging,
+                    logging_options=logging_options,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported server type '{type_}'. Must be one of 'http' or 'grpc'."
+                )
         else:
+            if type_ != "http":
+                raise ValueError(
+                    f"Python server only supports 'http'. Got '{type_}' instead."
+                )
             # Start the python server if go server isn't enabled
             feature_server.start_server(self, host, port, no_access_log)
 
@@ -2027,6 +2253,7 @@ def serve_transformations(self, port: int) -> None:
     def _teardown_go_server(self):
         self._go_server = None
 
+    @log_exceptions_and_usage
     def write_logged_features(
         self, logs: Union[pa.Table, Path], source: Union[FeatureService]
     ):
@@ -2054,6 +2281,82 @@ def write_logged_features(
             registry=self._registry,
         )
 
+    @log_exceptions_and_usage
+    def validate_logged_features(
+        self,
+        source: Union[FeatureService],
+        start: datetime,
+        end: datetime,
+        reference: ValidationReference,
+        throw_exception: bool = True,
+        cache_profile: bool = True,
+    ) -> Optional[ValidationFailed]:
+        """
+        Load logged features from an offline store and validate them against provided validation reference.
+
+        Args:
+            source: Logs source object (currently only feature services are supported)
+            start: lower bound for loading logged features
+            end:  upper bound for loading logged features
+            reference: validation reference
+            throw_exception: throw exception or return it as a result
+            cache_profile: store cached profile in Feast registry
+
+        Returns:
+            Throw or return (depends on parameter) ValidationFailed exception if validation was not successful
+            or None if successful.
+
+        """
+        warnings.warn(
+            "Logged features validation is an experimental feature. "
+            "This API is unstable and it could and most probably will be changed in the future. "
+            "We do not guarantee that future changes will maintain backward compatibility.",
+            RuntimeWarning,
+        )
+
+        if not isinstance(source, FeatureService):
+            raise ValueError("Only feature service is currently supported as a source")
+
+        j = self._get_provider().retrieve_feature_service_logs(
+            feature_service=source,
+            start_date=start,
+            end_date=end,
+            config=self.config,
+            registry=self.registry,
+        )
+
+        # read and run validation
+        try:
+            t = j.to_arrow(validation_reference=reference)
+        except ValidationFailed as exc:
+            if throw_exception:
+                raise
+
+            return exc
+        else:
+            print(f"{t.shape[0]} rows were validated.")
+
+        if cache_profile:
+            self.apply(reference)
+
+        return None
+
+    @log_exceptions_and_usage
+    def get_validation_reference(
+        self, name: str, allow_cache: bool = False
+    ) -> ValidationReference:
+        """
+            Retrieves a validation reference.
+
+            Raises:
+                ValidationReferenceNotFoundException: The validation reference could not be found.
+        """
+        ref = self._registry.get_validation_reference(
+            name, project=self.project, allow_cache=allow_cache
+        )
+        ref._dataset = self.get_saved_dataset(ref.dataset_name)
+        return ref
+
 
 def _validate_entity_values(join_key_values: Dict[str, List[Value]]):
     set_of_row_lengths = {len(v) for v in join_key_values.values()}
@@ -2214,7 +2517,7 @@ def _validate_data_sources(data_sources: List[DataSource]):
                     f"More than one data source with name {case_insensitive_ds_name} found. "
                     f"Please ensure that all data source names are case-insensitively unique. "
                     f"It may be necessary to ignore certain files in your feature repository by using a .feastignore "
-                    f"file. Starting in Feast 0.23, unique names (perhaps inferred from the table name) will be "
+                    f"file. Starting in Feast 0.24, unique names (perhaps inferred from the table name) will be "
                     f"required in data sources to encourage data source discovery"
                 )
         else:
diff --git a/sdk/python/feast/feature_view.py b/sdk/python/feast/feature_view.py
index 4ff7618f2e1..348c3019c5b 100644
--- a/sdk/python/feast/feature_view.py
+++ b/sdk/python/feast/feature_view.py
@@ -17,6 +17,7 @@
 from typing import Dict, List, Optional, Tuple, Type, Union
 
 from google.protobuf.duration_pb2 import Duration
+from typeguard import typechecked
 
 from feast import utils
 from feast.base_feature_view import BaseFeatureView
@@ -36,7 +37,6 @@
     MaterializationInterval as MaterializationIntervalProto,
 )
 from feast.usage import log_exceptions
-from feast.value_type import ValueType
 
 warnings.simplefilter("once", DeprecationWarning)
 
@@ -44,18 +44,17 @@
 DUMMY_ENTITY_ID = "__dummy_id"
 DUMMY_ENTITY_NAME = "__dummy"
 DUMMY_ENTITY_VAL = ""
-DUMMY_ENTITY = Entity(
-    name=DUMMY_ENTITY_NAME, join_keys=[DUMMY_ENTITY_ID], value_type=ValueType.STRING,
-)
+DUMMY_ENTITY = Entity(name=DUMMY_ENTITY_NAME, join_keys=[DUMMY_ENTITY_ID],)
 
 
+@typechecked
 class FeatureView(BaseFeatureView):
     """
     A FeatureView defines a logical group of features.
 
     Attributes:
         name: The unique name of the feature view.
-        entities: The list of entities with which this group of features is associated.
+        entities: The list of names of entities that this feature view is associated with.
         ttl: The amount of time this group of features lives. A ttl of 0 indicates that
             this group of features lives forever. Note that large ttl's or a ttl of 0
             can result in extremely computationally intensive queries.
@@ -65,9 +64,11 @@ class FeatureView(BaseFeatureView):
         stream_source (optional): The stream source of data where this group of features
             is stored. This is deprecated in favor of `source`.
         schema: The schema of the feature view, including feature, timestamp, and entity
-            columns.
-        features: The list of features defined as part of this feature view. Each
-            feature should also be included in the schema.
+            columns. If not specified, can be inferred from the underlying data source.
+        entity_columns: The list of entity columns contained in the schema. If not specified,
+            can be inferred from the underlying data source.
+        features: The list of feature columns contained in the schema. If not specified,
+            can be inferred from the underlying data source.
         online: A boolean indicating whether online retrieval is enabled for this feature
             view.
         description: A human-readable description.
@@ -84,6 +85,7 @@ class FeatureView(BaseFeatureView):
     batch_source: DataSource
     stream_source: Optional[DataSource]
     schema: List[Field]
+    entity_columns: List[Field]
     features: List[Field]
     online: bool
     description: str
@@ -129,14 +131,15 @@ def __init__(
             owner (optional): The owner of the feature view, typically the email of the
                 primary maintainer.
             schema (optional): The schema of the feature view, including feature, timestamp,
-                and entity columns.
+                and entity columns. If entity columns are included in the schema, a List[Entity]
+                must be passed to `entities` instead of a List[str]; otherwise, the entity columns
+                will be mistakenly interpreted as feature columns.
             source (optional): The source of data for this group of features. May be a stream source, or a batch source.
                 If a stream source, the source should contain a batch_source for backfills & batch materialization.
 
         Raises:
             ValueError: A field mapping conflicts with an Entity or a Feature.
         """
-
         positional_attributes = ["name", "entities", "ttl"]
 
         _name = name
@@ -147,7 +150,7 @@ def __init__(
             warnings.warn(
                 (
                     "feature view parameters should be specified as a keyword argument instead of a positional arg."
-                    "Feast 0.23+ will not support positional arguments to construct feature views"
+                    "Feast 0.24+ will not support positional arguments to construct feature views"
                 ),
                 DeprecationWarning,
             )
@@ -167,11 +170,21 @@ def __init__(
             raise ValueError("feature view name needs to be specified")
 
         self.name = _name
+
         self.entities = (
             [e.name if isinstance(e, Entity) else e for e in _entities]
             if _entities
             else [DUMMY_ENTITY_NAME]
         )
+        if _entities and isinstance(_entities[0], str):
+            warnings.warn(
+                (
+                    "The `entities` parameter should be a list of `Entity` objects. "
+                    "Feast 0.24 and onwards will not support passing in a list of "
+                    "strings to define entities."
+                ),
+                DeprecationWarning,
+            )
 
         self._initialize_sources(_name, batch_source, stream_source, source)
 
@@ -180,7 +193,7 @@ def __init__(
             warnings.warn(
                 (
                     "The option to pass a Duration object to the ttl parameter is being deprecated. "
-                    "Please pass a timedelta object instead. Feast 0.23 and onwards will not support "
+                    "Please pass a timedelta object instead. Feast 0.24 and onwards will not support "
                     "Duration objects."
                 ),
                 DeprecationWarning,
@@ -195,7 +208,7 @@ def __init__(
                 (
                     "The `features` parameter is being deprecated in favor of the `schema` parameter. "
                     "Please switch from using `features` to `schema`. This will also requiring switching "
-                    "feature definitions from using `Feature` to `Field`. Feast 0.23 and onwards will not "
+                    "feature definitions from using `Feature` to `Field`. Feast 0.24 and onwards will not "
                     "support the `features` parameter."
                 ),
                 DeprecationWarning,
@@ -206,14 +219,30 @@ def __init__(
             _schema = [Field.from_feature(feature) for feature in features]
         self.schema = _schema
 
-        # TODO(felixwang9817): Infer which fields in the schema are features, timestamps,
-        # and entities. For right now we assume that all fields are features, since the
-        # current `features` parameter only accepts feature columns.
-        _features = _schema
+        # If a user has added entity fields to schema, then they should also have switched
+        # to using a List[Entity], in which case entity and feature columns can be separated
+        # here. Conversely, if the user is still using a List[str], they must not have added
+        # added entity fields, in which case we can set the `features` attribute directly
+        # equal to the schema.
+        _features: List[Field] = []
+        self.entity_columns = []
+        if _entities and len(_entities) > 0 and isinstance(_entities[0], str):
+            _features = _schema
+        else:
+            join_keys = []
+            if _entities:
+                for entity in _entities:
+                    if isinstance(entity, Entity):
+                        join_keys += entity.join_keys
+
+            for field in _schema:
+                if field.name in join_keys:
+                    self.entity_columns.append(field)
+                else:
+                    _features.append(field)
 
-        cols = [entity for entity in self.entities] + [
-            field.name for field in _features
-        ]
+        # TODO(felixwang9817): Add more robust validation of features.
+        cols = [field.name for field in _schema]
         for col in cols:
             if (
                 self.batch_source.field_mapping is not None
@@ -255,7 +284,7 @@ def _initialize_sources(self, name, batch_source, stream_source, source):
         else:
             warnings.warn(
                 "batch_source and stream_source have been deprecated in favor of `source`."
-                "The deprecated fields will be removed in Feast 0.23.",
+                "The deprecated fields will be removed in Feast 0.24.",
                 DeprecationWarning,
             )
             if stream_source is not None and isinstance(stream_source, PushSource):
@@ -276,7 +305,6 @@ def __hash__(self):
     def __copy__(self):
         fv = FeatureView(
             name=self.name,
-            entities=self.entities,
             ttl=self.ttl,
             source=self.batch_source,
             stream_source=self.stream_source,
@@ -284,7 +312,15 @@ def __copy__(self):
             tags=self.tags,
             online=self.online,
         )
+
+        # This is deliberately set outside of the FV initialization to avoid the deprecation warning.
+        # TODO(felixwang9817): Move this into the FV initialization when the deprecation warning
+        # is removed.
+        fv.entities = self.entities
+        fv.features = copy.copy(self.features)
+        fv.entity_columns = copy.copy(self.entity_columns)
         fv.projection = copy.copy(self.projection)
+        fv.entities = self.entities
         return fv
 
     def __eq__(self, other):
@@ -302,12 +338,17 @@ def __eq__(self, other):
             or self.online != other.online
             or self.batch_source != other.batch_source
             or self.stream_source != other.stream_source
-            or self.schema != other.schema
+            or sorted(self.entity_columns) != sorted(other.entity_columns)
         ):
             return False
 
         return True
 
+    @property
+    def join_keys(self) -> List[str]:
+        """Returns a list of all the join keys."""
+        return [entity.name for entity in self.entity_columns]
+
     def ensure_valid(self):
         """
         Validates the state of this feature view locally.
@@ -393,7 +434,8 @@ def to_proto(self) -> FeatureViewProto:
         spec = FeatureViewSpecProto(
             name=self.name,
             entities=self.entities,
-            features=[field.to_proto() for field in self.schema],
+            entity_columns=[field.to_proto() for field in self.entity_columns],
+            features=[field.to_proto() for field in self.features],
             description=self.description,
             tags=self.tags,
             owner=self.owner,
@@ -424,11 +466,6 @@ def from_proto(cls, feature_view_proto: FeatureViewProto):
         )
         feature_view = cls(
             name=feature_view_proto.spec.name,
-            entities=[entity for entity in feature_view_proto.spec.entities],
-            schema=[
-                Field.from_proto(field_proto)
-                for field_proto in feature_view_proto.spec.features
-            ],
             description=feature_view_proto.spec.description,
             tags=dict(feature_view_proto.spec.tags),
             owner=feature_view_proto.spec.owner,
@@ -443,6 +480,19 @@ def from_proto(cls, feature_view_proto: FeatureViewProto):
         if stream_source:
             feature_view.stream_source = stream_source
 
+        # This avoids the deprecation warning.
+        feature_view.entities = feature_view_proto.spec.entities
+
+        # Instead of passing in a schema, we set the features and entity columns.
+        feature_view.features = [
+            Field.from_proto(field_proto)
+            for field_proto in feature_view_proto.spec.features
+        ]
+        feature_view.entity_columns = [
+            Field.from_proto(field_proto)
+            for field_proto in feature_view_proto.spec.entity_columns
+        ]
+
         # FeatureViewProjections are not saved in the FeatureView proto.
         # Create the default projection.
         feature_view.projection = FeatureViewProjection.from_definition(feature_view)
diff --git a/sdk/python/feast/feature_view_projection.py b/sdk/python/feast/feature_view_projection.py
index fbf0db5ccdf..a862e5f08d9 100644
--- a/sdk/python/feast/feature_view_projection.py
+++ b/sdk/python/feast/feature_view_projection.py
@@ -27,6 +27,7 @@ class FeatureViewProjection:
 
     name: str
     name_alias: Optional[str]
+    desired_features: List[str]
     features: List[Field]
     join_key_map: Dict[str, str] = {}
 
@@ -51,6 +52,7 @@ def from_proto(proto: FeatureViewProjectionProto):
             name_alias=proto.feature_view_name_alias,
             features=[],
             join_key_map=dict(proto.join_key_map),
+            desired_features=[],
         )
         for feature_column in proto.feature_columns:
             feature_view_projection.features.append(Field.from_proto(feature_column))
@@ -63,6 +65,7 @@ def from_definition(base_feature_view: "BaseFeatureView"):
             name=base_feature_view.name,
             name_alias=None,
             features=base_feature_view.features,
+            desired_features=[],
         )
 
     def get_feature(self, feature_name: str) -> Field:
diff --git a/sdk/python/feast/field.py b/sdk/python/feast/field.py
index 77011e6758c..d0b4274cd28 100644
--- a/sdk/python/feast/field.py
+++ b/sdk/python/feast/field.py
@@ -14,12 +14,15 @@
 
 from typing import Dict, Optional
 
+from typeguard import typechecked
+
 from feast.feature import Feature
 from feast.protos.feast.core.Feature_pb2 import FeatureSpecV2 as FieldProto
 from feast.types import FeastType, from_value_type
 from feast.value_type import ValueType
 
 
+@typechecked
 class Field:
     """
     A Field represents a set of values with the same structure.
@@ -50,6 +53,9 @@ def __init__(
         self.tags = tags or {}
 
     def __eq__(self, other):
+        if type(self) != type(other):
+            return False
+
         if (
             self.name != other.name
             or self.dtype != other.dtype
diff --git a/sdk/python/feast/inference.py b/sdk/python/feast/inference.py
index 076cbc86ceb..011a3b99b21 100644
--- a/sdk/python/feast/inference.py
+++ b/sdk/python/feast/inference.py
@@ -1,78 +1,25 @@
 import re
-from typing import List
+from typing import List, Set, Union
 
-from feast import BigQuerySource, Entity, FileSource, RedshiftSource, SnowflakeSource
 from feast.data_source import DataSource, PushSource, RequestSource
+from feast.entity import Entity
 from feast.errors import RegistryInferenceFailure
-from feast.feature_view import FeatureView
+from feast.feature_view import DUMMY_ENTITY_ID, DUMMY_ENTITY_NAME, FeatureView
 from feast.field import Field, from_value_type
+from feast.infra.offline_stores.bigquery_source import BigQuerySource
+from feast.infra.offline_stores.file_source import FileSource
+from feast.infra.offline_stores.redshift_source import RedshiftSource
+from feast.infra.offline_stores.snowflake_source import SnowflakeSource
 from feast.repo_config import RepoConfig
+from feast.stream_feature_view import StreamFeatureView
+from feast.types import String
 from feast.value_type import ValueType
 
 
-def update_entities_with_inferred_types_from_feature_views(
-    entities: List[Entity], feature_views: List[FeatureView], config: RepoConfig
-) -> None:
-    """
-    Infers the types of the entities by examining the schemas of feature view batch sources.
-
-    Args:
-        entities: The entities to be updated.
-        feature_views: A list containing feature views associated with the entities.
-        config: The config for the current feature store.
-    """
-    incomplete_entities = {
-        entity.name: entity
-        for entity in entities
-        if entity.value_type == ValueType.UNKNOWN
-    }
-    incomplete_entities_keys = incomplete_entities.keys()
-
-    for view in feature_views:
-        if not (incomplete_entities_keys & set(view.entities)):
-            continue  # skip if view doesn't contain any entities that need inference
-
-        col_names_and_types = list(
-            view.batch_source.get_table_column_names_and_types(config)
-        )
-        for entity_name in view.entities:
-            if entity_name in incomplete_entities:
-                entity = incomplete_entities[entity_name]
-
-                # get entity information from information extracted from the view batch source
-                extracted_entity_name_type_pairs = list(
-                    filter(lambda tup: tup[0] == entity.join_key, col_names_and_types,)
-                )
-                if len(extracted_entity_name_type_pairs) == 0:
-                    # Doesn't mention inference error because would also be an error without inferencing
-                    raise ValueError(
-                        f"""No column in the batch source for the {view.name} feature view matches
-                        its entity's name."""
-                    )
-
-                inferred_value_type = view.batch_source.source_datatype_to_feast_value_type()(
-                    extracted_entity_name_type_pairs[0][1]
-                )
-
-                if (
-                    entity.value_type != ValueType.UNKNOWN
-                    and entity.value_type != inferred_value_type
-                ) or (len(extracted_entity_name_type_pairs) > 1):
-                    raise RegistryInferenceFailure(
-                        "Entity",
-                        f"""Entity value_type inference failed for {entity_name} entity.
-                        Multiple viable matches.
-                        """,
-                    )
-
-                entity.value_type = inferred_value_type
-
-
 def update_data_sources_with_inferred_event_timestamp_col(
     data_sources: List[DataSource], config: RepoConfig
 ) -> None:
     ERROR_MSG_PREFIX = "Unable to infer DataSource timestamp_field"
-
     for data_source in data_sources:
         if isinstance(data_source, RequestSource):
             continue
@@ -80,7 +27,7 @@ def update_data_sources_with_inferred_event_timestamp_col(
             data_source = data_source.batch_source
         if data_source.timestamp_field is None or data_source.timestamp_field == "":
             # prepare right match pattern for data source
-            ts_column_type_regex_pattern = ""
+            ts_column_type_regex_pattern: str
             # TODO(adchia): Move Spark source inference out of this logic
             if (
                 isinstance(data_source, FileSource)
@@ -140,77 +87,150 @@ def update_data_sources_with_inferred_event_timestamp_col(
                 )
 
 
-def update_feature_views_with_inferred_features(
-    fvs: List[FeatureView], entities: List[Entity], config: RepoConfig
+def update_feature_views_with_inferred_features_and_entities(
+    fvs: Union[List[FeatureView], List[StreamFeatureView]],
+    entities: List[Entity],
+    config: RepoConfig,
 ) -> None:
     """
-    Infers the set of features associated to each FeatureView and updates the FeatureView with those features.
-    Inference occurs through considering each column of the underlying data source as a feature except columns that are
-    associated with the data source's timestamp columns and the FeatureView's entity columns.
+    Infers the features and entities associated with each feature view and updates it in place.
+
+    Columns whose names match a join key of an entity are considered to be entity columns; all
+    other columns except designated timestamp columns are considered to be feature columns. If
+    the feature view already has features, feature inference is skipped.
+
+    Note that this inference logic currently does not take any transformations (either a UDF or
+    aggregations) into account. For example, even if a stream feature view has a transformation,
+    this method assumes that the batch source contains transformed data with the correct final schema.
 
     Args:
         fvs: The feature views to be updated.
         entities: A list containing entities associated with the feature views.
         config: The config for the current feature store.
     """
-    entity_name_to_join_key_map = {entity.name: entity.join_key for entity in entities}
-    join_keys = entity_name_to_join_key_map.values()
+    entity_name_to_entity_map = {e.name: e for e in entities}
+    entity_name_to_join_keys_map = {e.name: e.join_keys for e in entities}
 
     for fv in fvs:
-        # First drop all Entity fields. Then infer features if necessary.
-        fv.schema = [field for field in fv.schema if field.name not in join_keys]
-        fv.features = [field for field in fv.features if field.name not in join_keys]
-
-        if not fv.features:
-            columns_to_exclude = {
-                fv.batch_source.timestamp_field,
-                fv.batch_source.created_timestamp_column,
-            } | {
-                entity_name_to_join_key_map[entity_name] for entity_name in fv.entities
-            }
-
-            if fv.batch_source.timestamp_field in fv.batch_source.field_mapping:
-                columns_to_exclude.add(
-                    fv.batch_source.field_mapping[fv.batch_source.timestamp_field]
-                )
+        join_keys = set(
+            [
+                join_key
+                for entity_name in fv.entities
+                for join_key in entity_name_to_join_keys_map[entity_name]
+            ]
+        )
+
+        # Fields whose names match a join key are considered to be entity columns; all
+        # other fields are considered to be feature columns.
+        for field in fv.schema:
+            if field.name in join_keys:
+                # Do not override a preexisting field with the same name.
+                if field.name not in [
+                    entity_column.name for entity_column in fv.entity_columns
+                ]:
+                    fv.entity_columns.append(field)
+            else:
+                if field.name not in [feature.name for feature in fv.features]:
+                    fv.features.append(field)
+
+        # Since the `value_type` parameter has not yet been fully deprecated for
+        # entities, we respect the `value_type` attribute if it still exists.
+        for entity_name in fv.entities:
+            entity = entity_name_to_entity_map[entity_name]
             if (
-                fv.batch_source.created_timestamp_column
-                in fv.batch_source.field_mapping
+                entity.join_key
+                not in [entity_column.name for entity_column in fv.entity_columns]
+                and entity.value_type != ValueType.UNKNOWN
             ):
-                columns_to_exclude.add(
-                    fv.batch_source.field_mapping[
-                        fv.batch_source.created_timestamp_column
-                    ]
+                fv.entity_columns.append(
+                    Field(
+                        name=entity.join_key, dtype=from_value_type(entity.value_type),
+                    )
                 )
 
-            for (
-                col_name,
-                col_datatype,
-            ) in fv.batch_source.get_table_column_names_and_types(config):
-                if col_name not in columns_to_exclude and not re.match(
-                    "^__|__$",
-                    col_name,  # double underscores often signal an internal-use column
-                ):
-                    feature_name = (
-                        fv.batch_source.field_mapping[col_name]
-                        if col_name in fv.batch_source.field_mapping
-                        else col_name
-                    )
-                    field = Field(
-                        name=feature_name,
-                        dtype=from_value_type(
-                            fv.batch_source.source_datatype_to_feast_value_type()(
-                                col_datatype
-                            )
-                        ),
-                    )
-                    # Note that schema and features are two different attributes of a
-                    # FeatureView, and that features should be present in both.
-                    fv.schema.append(field)
-                    fv.features.append(field)
+        # Infer a dummy entity column for entityless feature views.
+        if len(fv.entities) == 1 and fv.entities[0] == DUMMY_ENTITY_NAME:
+            fv.entity_columns.append(Field(name=DUMMY_ENTITY_ID, dtype=String))
+
+        # Run inference for entity columns if there are fewer entity fields than expected.
+        num_expected_join_keys = sum(
+            [
+                len(entity_name_to_join_keys_map[entity_name])
+                for entity_name in fv.entities
+            ]
+        )
+        run_inference_for_entities = len(fv.entity_columns) < num_expected_join_keys
+
+        # Run inference for feature columns if there are no feature fields.
+        run_inference_for_features = len(fv.features) == 0
+
+        if run_inference_for_entities or run_inference_for_features:
+            _infer_features_and_entities(
+                fv, join_keys, run_inference_for_features, config,
+            )
 
             if not fv.features:
                 raise RegistryInferenceFailure(
                     "FeatureView",
                     f"Could not infer Features for the FeatureView named {fv.name}.",
                 )
+
+
+def _infer_features_and_entities(
+    fv: FeatureView, join_keys: Set[str], run_inference_for_features, config,
+) -> None:
+    """
+    Updates the specific feature in place with inferred features and entities.
+
+    Args:
+        fv: The feature view on which to run inference.
+        join_keys: The set of join keys for the feature view's entities.
+        run_inference_for_features: Whether to run inference for features.
+        config: The config for the current feature store.
+    """
+    columns_to_exclude = {
+        fv.batch_source.timestamp_field,
+        fv.batch_source.created_timestamp_column,
+    }
+    for column in columns_to_exclude:
+        if column in fv.batch_source.field_mapping:
+            columns_to_exclude.remove(column)
+            columns_to_exclude.add(fv.batch_source.field_mapping[column])
+
+    table_column_names_and_types = fv.batch_source.get_table_column_names_and_types(
+        config
+    )
+
+    for col_name, col_datatype in table_column_names_and_types:
+        if col_name in columns_to_exclude:
+            continue
+        elif col_name in join_keys:
+            field = Field(
+                name=col_name,
+                dtype=from_value_type(
+                    fv.batch_source.source_datatype_to_feast_value_type()(col_datatype)
+                ),
+            )
+            if field.name not in [
+                entity_column.name for entity_column in fv.entity_columns
+            ]:
+                fv.entity_columns.append(field)
+        elif not re.match(
+            "^__|__$", col_name
+        ):  # double underscores often signal an internal-use column
+            if run_inference_for_features:
+                feature_name = (
+                    fv.batch_source.field_mapping[col_name]
+                    if col_name in fv.batch_source.field_mapping
+                    else col_name
+                )
+                field = Field(
+                    name=feature_name,
+                    dtype=from_value_type(
+                        fv.batch_source.source_datatype_to_feast_value_type()(
+                            col_datatype
+                        )
+                    ),
+                )
+                if field.name not in [feature.name for feature in fv.features]:
+                    fv.features.append(field)
diff --git a/sdk/python/feast/infra/contrib/spark_kafka_processor.py b/sdk/python/feast/infra/contrib/spark_kafka_processor.py
new file mode 100644
index 00000000000..4dfb615773c
--- /dev/null
+++ b/sdk/python/feast/infra/contrib/spark_kafka_processor.py
@@ -0,0 +1,139 @@
+from types import MethodType
+from typing import List
+
+from pyspark.sql import DataFrame, SparkSession
+from pyspark.sql.avro.functions import from_avro
+from pyspark.sql.functions import col, from_json
+
+from feast.data_format import AvroFormat, JsonFormat
+from feast.data_source import KafkaSource
+from feast.infra.contrib.stream_processor import (
+    ProcessorConfig,
+    StreamProcessor,
+    StreamTable,
+)
+from feast.stream_feature_view import StreamFeatureView
+
+
+class SparkProcessorConfig(ProcessorConfig):
+    spark_session: SparkSession
+    processing_time: str
+    query_timeout: int
+
+
+class SparkKafkaProcessor(StreamProcessor):
+    spark: SparkSession
+    format: str
+    write_function: MethodType
+    join_keys: List[str]
+
+    def __init__(
+        self,
+        sfv: StreamFeatureView,
+        config: ProcessorConfig,
+        write_function: MethodType,
+        processing_time: str = "30 seconds",
+        query_timeout: int = 15,
+    ):
+        if not isinstance(sfv.stream_source, KafkaSource):
+            raise ValueError("data source is not kafka source")
+        if not isinstance(
+            sfv.stream_source.kafka_options.message_format, AvroFormat
+        ) and not isinstance(
+            sfv.stream_source.kafka_options.message_format, JsonFormat
+        ):
+            raise ValueError(
+                "spark streaming currently only supports json or avro format for kafka source schema"
+            )
+
+        self.format = (
+            "json"
+            if isinstance(sfv.stream_source.kafka_options.message_format, JsonFormat)
+            else "avro"
+        )
+
+        if not isinstance(config, SparkProcessorConfig):
+            raise ValueError("config is not spark processor config")
+        self.spark = config.spark_session
+        self.write_function = write_function
+        self.processing_time = processing_time
+        self.query_timeout = query_timeout
+        super().__init__(sfv=sfv, data_source=sfv.stream_source)
+
+    def ingest_stream_feature_view(self) -> None:
+        ingested_stream_df = self._ingest_stream_data()
+        transformed_df = self._construct_transformation_plan(ingested_stream_df)
+        online_store_query = self._write_to_online_store(transformed_df)
+        return online_store_query
+
+    def _ingest_stream_data(self) -> StreamTable:
+        """Only supports json and avro formats currently."""
+        if self.format == "json":
+            if not isinstance(
+                self.data_source.kafka_options.message_format, JsonFormat
+            ):
+                raise ValueError("kafka source message format is not jsonformat")
+            stream_df = (
+                self.spark.readStream.format("kafka")
+                .option(
+                    "kafka.bootstrap.servers",
+                    self.data_source.kafka_options.kafka_bootstrap_servers,
+                )
+                .option("subscribe", self.data_source.kafka_options.topic)
+                .option("startingOffsets", "latest")  # Query start
+                .load()
+                .selectExpr("CAST(value AS STRING)")
+                .select(
+                    from_json(
+                        col("value"),
+                        self.data_source.kafka_options.message_format.schema_json,
+                    ).alias("table")
+                )
+                .select("table.*")
+            )
+        else:
+            if not isinstance(
+                self.data_source.kafka_options.message_format, AvroFormat
+            ):
+                raise ValueError("kafka source message format is not avro format")
+            stream_df = (
+                self.spark.readStream.format("kafka")
+                .option(
+                    "kafka.bootstrap.servers",
+                    self.data_source.kafka_options.kafka_bootstrap_servers,
+                )
+                .option("subscribe", self.data_source.kafka_options.topic)
+                .option("startingOffsets", "latest")  # Query start
+                .load()
+                .selectExpr("CAST(value AS STRING)")
+                .select(
+                    from_avro(
+                        col("value"),
+                        self.data_source.kafka_options.message_format.schema_json,
+                    ).alias("table")
+                )
+                .select("table.*")
+            )
+        return stream_df
+
+    def _construct_transformation_plan(self, df: StreamTable) -> StreamTable:
+        return self.sfv.udf.__call__(df) if self.sfv.udf else df
+
+    def _write_to_online_store(self, df: StreamTable):
+        # Validation occurs at the fs.write_to_online_store() phase against the stream feature view schema.
+        def batch_write(row: DataFrame, batch_id: int):
+            pd_row = row.toPandas()
+            self.write_function(
+                pd_row, input_timestamp="event_timestamp", output_timestamp=""
+            )
+
+        query = (
+            df.writeStream.outputMode("update")
+            .option("checkpointLocation", "/tmp/checkpoint/")
+            .trigger(processingTime=self.processing_time)
+            .foreachBatch(batch_write)
+            .start()
+        )
+
+        query.awaitTermination(timeout=self.query_timeout)
+        return query
diff --git a/sdk/python/feast/infra/contrib/stream_processor.py b/sdk/python/feast/infra/contrib/stream_processor.py
new file mode 100644
index 00000000000..2ccf1e59f85
--- /dev/null
+++ b/sdk/python/feast/infra/contrib/stream_processor.py
@@ -0,0 +1,87 @@
+from abc import ABC
+from typing import Callable
+
+import pandas as pd
+from pyspark.sql import DataFrame
+
+from feast.data_source import DataSource
+from feast.importer import import_class
+from feast.repo_config import FeastConfigBaseModel
+from feast.stream_feature_view import StreamFeatureView
+
+STREAM_PROCESSOR_CLASS_FOR_TYPE = {
+    ("spark", "kafka"): "feast.infra.contrib.spark_kafka_processor.SparkKafkaProcessor",
+}
+
+# TODO: support more types other than just Spark.
+StreamTable = DataFrame
+
+
+class ProcessorConfig(FeastConfigBaseModel):
+    # Processor mode (spark, etc)
+    mode: str
+    # Ingestion source (kafka, kinesis, etc)
+    source: str
+
+
+class StreamProcessor(ABC):
+    """
+    A StreamProcessor can ingest and transform data for a specific stream feature view,
+    and persist that data to the online store.
+
+    Attributes:
+        sfv: The stream feature view on which the stream processor operates.
+        data_source: The stream data source from which data will be ingested.
+    """
+
+    sfv: StreamFeatureView
+    data_source: DataSource
+
+    def __init__(self, sfv: StreamFeatureView, data_source: DataSource):
+        self.sfv = sfv
+        self.data_source = data_source
+
+    def ingest_stream_feature_view(self) -> None:
+        """
+        Ingests data from the stream source attached to the stream feature view; transforms the data
+        and then persists it to the online store.
+        """
+        pass
+
+    def _ingest_stream_data(self) -> StreamTable:
+        """
+        Ingests data into a StreamTable.
+        """
+        pass
+
+    def _construct_transformation_plan(self, table: StreamTable) -> StreamTable:
+        """
+        Applies transformations on top of StreamTable object. Since stream engines use lazy
+        evaluation, the StreamTable will not be materialized until it is actually evaluated.
+        For example: df.collect() in spark or tbl.execute() in Flink.
+        """
+        pass
+
+    def _write_to_online_store(self, table: StreamTable) -> None:
+        """
+        Returns query for persisting data to the online store.
+        """
+        pass
+
+
+def get_stream_processor_object(
+    config: ProcessorConfig,
+    sfv: StreamFeatureView,
+    write_function: Callable[[pd.DataFrame, str, str], None],
+):
+    """
+    Returns a stream processor object based on the config mode and stream source type. The write function is a
+    function that wraps the feature store "write_to_online_store" capability.
+    """
+    if config.mode == "spark" and config.source == "kafka":
+        stream_processor = STREAM_PROCESSOR_CLASS_FOR_TYPE[("spark", "kafka")]
+        module_name, class_name = stream_processor.rsplit(".", 1)
+        cls = import_class(module_name, class_name, "StreamProcessor")
+        return cls(sfv=sfv, config=config, write_function=write_function,)
+    else:
+        raise ValueError("other processors besides spark-kafka not supported")
diff --git a/sdk/python/feast/infra/feature_servers/aws_lambda/Dockerfile b/sdk/python/feast/infra/feature_servers/aws_lambda/Dockerfile
index fe997afcae4..929227a8106 100644
--- a/sdk/python/feast/infra/feature_servers/aws_lambda/Dockerfile
+++ b/sdk/python/feast/infra/feature_servers/aws_lambda/Dockerfile
@@ -1,5 +1,8 @@
 FROM public.ecr.aws/lambda/python:3.9
 
+RUN yum install -y git
+
+
 # Copy app handler code
 COPY sdk/python/feast/infra/feature_servers/aws_lambda/app.py ${LAMBDA_TASK_ROOT}
 
@@ -16,7 +19,7 @@ COPY README.md README.md
 # git dir to infer the version of feast we're installing.
 # https://github.com/pypa/setuptools_scm#usage-from-docker
 # I think it also assumes that this dockerfile is being built from the root of the directory.
-RUN SETUPTOOLS_SCM_PRETEND_VERSION=1 pip3 install --no-cache-dir -e '.[aws,redis]'
+RUN --mount=source=.git,target=.git,type=bind pip3 install --no-cache-dir -e '.[aws,redis]'
 RUN pip3 install -r sdk/python/feast/infra/feature_servers/aws_lambda/requirements.txt --target "${LAMBDA_TASK_ROOT}"
 
 # Set the CMD to your handler (could also be done as a parameter override outside of the Dockerfile)
diff --git a/sdk/python/feast/infra/feature_servers/aws_lambda/config.py b/sdk/python/feast/infra/feature_servers/aws_lambda/config.py
index d026415ec30..31dd879af6d 100644
--- a/sdk/python/feast/infra/feature_servers/aws_lambda/config.py
+++ b/sdk/python/feast/infra/feature_servers/aws_lambda/config.py
@@ -1,18 +1,15 @@
 from pydantic import StrictBool, StrictStr
 from pydantic.typing import Literal
 
-from feast.repo_config import FeastConfigBaseModel
+from feast.infra.feature_servers.base_config import BaseFeatureServerConfig
 
 
-class AwsLambdaFeatureServerConfig(FeastConfigBaseModel):
+class AwsLambdaFeatureServerConfig(BaseFeatureServerConfig):
     """Feature server config for AWS Lambda."""
 
     type: Literal["aws_lambda"] = "aws_lambda"
     """Feature server type selector."""
 
-    enabled: StrictBool = False
-    """Whether the feature server should be launched."""
-
     public: StrictBool = True
     """Whether the endpoint should be publicly accessible."""
 
diff --git a/sdk/python/feast/infra/feature_servers/base_config.py b/sdk/python/feast/infra/feature_servers/base_config.py
new file mode 100644
index 00000000000..756dd79b438
--- /dev/null
+++ b/sdk/python/feast/infra/feature_servers/base_config.py
@@ -0,0 +1,34 @@
+from typing import Optional
+
+from pydantic import StrictBool, StrictInt
+
+from feast.repo_config import FeastConfigBaseModel
+
+
+class FeatureLoggingConfig(FeastConfigBaseModel):
+    enabled: StrictBool = False
+    """Whether the feature server should log served features."""
+
+    flush_interval_secs: StrictInt = 600
+    """Interval of flushing logs to the destination in offline store."""
+
+    write_to_disk_interval_secs: StrictInt = 30
+    """Interval of dumping logs collected in memory to local disk."""
+
+    queue_capacity: StrictInt = 100000
+    """Log queue capacity. If number of produced logs is bigger than
+    processing speed logs will be accumulated in the queue.
+    After queue length will reach this number all new items will be rejected."""
+
+    emit_timeout_micro_secs: StrictInt = 10000
+    """Timeout for adding new log item to the queue."""
+
+
+class BaseFeatureServerConfig(FeastConfigBaseModel):
+    """Base Feature Server config that should be extended"""
+
+    enabled: StrictBool = False
+    """Whether the feature server should be launched."""
+
+    feature_logging: Optional[FeatureLoggingConfig]
+    """ Feature logging configuration """
diff --git a/sdk/python/feast/infra/feature_servers/gcp_cloudrun/Dockerfile b/sdk/python/feast/infra/feature_servers/gcp_cloudrun/Dockerfile
index e0e16f6a14f..6e3ff424eab 100644
--- a/sdk/python/feast/infra/feature_servers/gcp_cloudrun/Dockerfile
+++ b/sdk/python/feast/infra/feature_servers/gcp_cloudrun/Dockerfile
@@ -1,5 +1,7 @@
 FROM python:3.9-slim
 
+RUN apt-get update && apt-get install -y git
+
 # Allow statements and log messages to immediately appear in the Knative logs
 ENV PYTHONUNBUFFERED True
 
@@ -18,7 +20,7 @@ COPY pyproject.toml pyproject.toml
 COPY README.md ./README.md
 
 # Install production dependencies.
-RUN pip install --no-cache-dir \
+RUN --mount=source=.git,target=.git,type=bind pip install --no-cache-dir \
     -e '.[gcp,redis]' \
     -r ./sdk/python/feast/infra/feature_servers/gcp_cloudrun/requirements.txt
 
diff --git a/sdk/python/feast/infra/feature_servers/gcp_cloudrun/config.py b/sdk/python/feast/infra/feature_servers/gcp_cloudrun/config.py
index 728ac56ae29..8d0c269cf5d 100644
--- a/sdk/python/feast/infra/feature_servers/gcp_cloudrun/config.py
+++ b/sdk/python/feast/infra/feature_servers/gcp_cloudrun/config.py
@@ -1,18 +1,15 @@
 from pydantic import StrictBool
 from pydantic.typing import Literal
 
-from feast.repo_config import FeastConfigBaseModel
+from feast.infra.feature_servers.base_config import BaseFeatureServerConfig
 
 
-class GcpCloudRunFeatureServerConfig(FeastConfigBaseModel):
+class GcpCloudRunFeatureServerConfig(BaseFeatureServerConfig):
     """Feature server config for GCP CloudRun."""
 
     type: Literal["gcp_cloudrun"] = "gcp_cloudrun"
     """Feature server type selector."""
 
-    enabled: StrictBool = False
-    """Whether the feature server should be launched."""
-
     public: StrictBool = True
     """Whether the endpoint should be publicly accessible."""
 
diff --git a/sdk/python/feast/infra/feature_servers/local_process/__init__.py b/sdk/python/feast/infra/feature_servers/local_process/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/sdk/python/feast/infra/feature_servers/local_process/config.py b/sdk/python/feast/infra/feature_servers/local_process/config.py
new file mode 100644
index 00000000000..bb2e7bdf738
--- /dev/null
+++ b/sdk/python/feast/infra/feature_servers/local_process/config.py
@@ -0,0 +1,8 @@
+from pydantic.typing import Literal
+
+from feast.infra.feature_servers.base_config import BaseFeatureServerConfig
+
+
+class LocalFeatureServerConfig(BaseFeatureServerConfig):
+    type: Literal["local"] = "local"
+    """Feature server type selector."""
diff --git a/sdk/python/feast/infra/offline_stores/bigquery.py b/sdk/python/feast/infra/offline_stores/bigquery.py
index e9d8bdccbfe..259a3af7d97 100644
--- a/sdk/python/feast/infra/offline_stores/bigquery.py
+++ b/sdk/python/feast/infra/offline_stores/bigquery.py
@@ -4,6 +4,7 @@
 from datetime import date, datetime, timedelta
 from pathlib import Path
 from typing import (
+    Any,
     Callable,
     ContextManager,
     Dict,
@@ -39,7 +40,7 @@
     RetrievalMetadata,
 )
 from feast.on_demand_feature_view import OnDemandFeatureView
-from feast.registry import Registry
+from feast.registry import BaseRegistry
 from feast.repo_config import FeastConfigBaseModel, RepoConfig
 
 from ...saved_dataset import SavedDatasetStorage
@@ -169,7 +170,7 @@ def get_historical_features(
         feature_views: List[FeatureView],
         feature_refs: List[str],
         entity_df: Union[pd.DataFrame, str],
-        registry: Registry,
+        registry: BaseRegistry,
         project: str,
         full_feature_names: bool = False,
     ) -> RetrievalJob:
@@ -262,7 +263,7 @@ def write_logged_features(
         data: Union[pyarrow.Table, Path],
         source: LoggingSource,
         logging_config: LoggingConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ):
         destination = logging_config.destination
         assert isinstance(destination, BigQueryLoggingDestination)
@@ -303,6 +304,60 @@ def write_logged_features(
                 job_config=job_config,
             )
 
+    @staticmethod
+    def offline_write_batch(
+        config: RepoConfig,
+        feature_view: FeatureView,
+        table: pyarrow.Table,
+        progress: Optional[Callable[[int], Any]],
+    ):
+        if not feature_view.batch_source:
+            raise ValueError(
+                "feature view does not have a batch source to persist offline data"
+            )
+        if not isinstance(config.offline_store, BigQueryOfflineStoreConfig):
+            raise ValueError(
+                f"offline store config is of type {type(config.offline_store)} when bigquery type required"
+            )
+        if not isinstance(feature_view.batch_source, BigQuerySource):
+            raise ValueError(
+                f"feature view batch source is {type(feature_view.batch_source)} not bigquery source"
+            )
+
+        pa_schema, column_names = offline_utils.get_pyarrow_schema_from_batch_source(
+            config, feature_view.batch_source
+        )
+        if column_names != table.column_names:
+            raise ValueError(
+                f"The input pyarrow table has schema {pa_schema} with the incorrect columns {column_names}. "
+                f"The columns are expected to be (in this order): {column_names}."
+            )
+
+        if table.schema != pa_schema:
+            table = table.cast(pa_schema)
+
+        client = _get_bigquery_client(
+            project=config.offline_store.project_id,
+            location=config.offline_store.location,
+        )
+
+        job_config = bigquery.LoadJobConfig(
+            source_format=bigquery.SourceFormat.PARQUET,
+            schema=arrow_schema_to_bq_schema(pa_schema),
+            write_disposition="WRITE_APPEND",  # Default but included for clarity
+        )
+
+        with tempfile.TemporaryFile() as parquet_temp_file:
+            pyarrow.parquet.write_table(table=table, where=parquet_temp_file)
+
+            parquet_temp_file.seek(0)
+
+            client.load_table_from_file(
+                file_obj=parquet_temp_file,
+                destination=feature_view.batch_source.table,
+                job_config=job_config,
+            )
+
 
 class BigQueryRetrievalJob(RetrievalJob):
     def __init__(
diff --git a/sdk/python/feast/infra/offline_stores/bigquery_source.py b/sdk/python/feast/infra/offline_stores/bigquery_source.py
index 06e9ce9f624..b06cc233696 100644
--- a/sdk/python/feast/infra/offline_stores/bigquery_source.py
+++ b/sdk/python/feast/infra/offline_stores/bigquery_source.py
@@ -1,6 +1,8 @@
 import warnings
 from typing import Callable, Dict, Iterable, List, Optional, Tuple
 
+from typeguard import typechecked
+
 from feast import type_map
 from feast.data_source import DataSource
 from feast.errors import DataSourceNotFoundException
@@ -17,6 +19,7 @@
 from feast.value_type import ValueType
 
 
+@typechecked
 class BigQuerySource(DataSource):
     def __init__(
         self,
@@ -37,7 +40,8 @@ def __init__(
 
         Args:
             table (optional): The BigQuery table where features can be found.
-            event_timestamp_column: (Deprecated) Event timestamp column used for point in time joins of feature values.
+            event_timestamp_column (optional): (Deprecated in favor of timestamp_field) Event
+                timestamp column used for point in time joins of feature values.
             created_timestamp_column (optional): Timestamp column when row was created, used for deduplicating rows.
             field_mapping: A dictionary mapping of column names in this data source to feature names in a feature table
                 or view. Only used for feature columns, not entities or timestamp columns.
@@ -63,7 +67,7 @@ def __init__(
             warnings.warn(
                 (
                     "The argument 'date_partition_column' is not supported for BigQuery sources. "
-                    "It will be removed in Feast 0.23+"
+                    "It will be removed in Feast 0.24+"
                 ),
                 DeprecationWarning,
             )
@@ -76,7 +80,7 @@ def __init__(
             else:
                 warnings.warn(
                     (
-                        f"Starting in Feast 0.23, Feast will require either a name for a data source (if using query) or `table`: {self.query}"
+                        f"Starting in Feast 0.24, Feast will require either a name for a data source (if using query) or `table`: {self.query}"
                     ),
                     DeprecationWarning,
                 )
diff --git a/sdk/python/feast/infra/offline_stores/contrib/postgres_offline_store/postgres_source.py b/sdk/python/feast/infra/offline_stores/contrib/postgres_offline_store/postgres_source.py
index c76bd7d2f9d..74b7a5df8ad 100644
--- a/sdk/python/feast/infra/offline_stores/contrib/postgres_offline_store/postgres_source.py
+++ b/sdk/python/feast/infra/offline_stores/contrib/postgres_offline_store/postgres_source.py
@@ -18,6 +18,9 @@ def __init__(
         created_timestamp_column: Optional[str] = "",
         field_mapping: Optional[Dict[str, str]] = None,
         date_partition_column: Optional[str] = "",
+        description: Optional[str] = "",
+        tags: Optional[Dict[str, str]] = None,
+        owner: Optional[str] = "",
     ):
         self._postgres_options = PostgreSQLOptions(name=name, query=query)
 
@@ -27,6 +30,9 @@ def __init__(
             created_timestamp_column=created_timestamp_column,
             field_mapping=field_mapping,
             date_partition_column=date_partition_column,
+            description=description,
+            tags=tags,
+            owner=owner,
         )
 
     def __hash__(self):
@@ -57,14 +63,21 @@ def from_proto(data_source: DataSourceProto):
             timestamp_field=data_source.timestamp_field,
             created_timestamp_column=data_source.created_timestamp_column,
             date_partition_column=data_source.date_partition_column,
+            description=data_source.description,
+            tags=dict(data_source.tags),
+            owner=data_source.owner,
         )
 
     def to_proto(self) -> DataSourceProto:
         data_source_proto = DataSourceProto(
+            name=self.name,
             type=DataSourceProto.CUSTOM_SOURCE,
             data_source_class_type="feast.infra.offline_stores.contrib.postgres_offline_store.postgres_source.PostgreSQLSource",
             field_mapping=self.field_mapping,
             custom_options=self._postgres_options.to_proto(),
+            description=self.description,
+            tags=self.tags,
+            owner=self.owner,
         )
 
         data_source_proto.timestamp_field = self.timestamp_field
diff --git a/sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark_source.py b/sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark_source.py
index c94b1913598..ade1e543650 100644
--- a/sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark_source.py
+++ b/sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark_source.py
@@ -25,6 +25,8 @@ class SparkSourceFormat(Enum):
     csv = "csv"
     json = "json"
     parquet = "parquet"
+    delta = "delta"
+    avro = "avro"
 
 
 class SparkSource(DataSource):
@@ -57,7 +59,7 @@ def __init__(
             warnings.warn(
                 (
                     "The argument 'date_partition_column' is not supported for Spark sources."
-                    "It will be removed in Feast 0.23+"
+                    "It will be removed in Feast 0.24+"
                 ),
                 DeprecationWarning,
             )
diff --git a/sdk/python/feast/infra/offline_stores/file.py b/sdk/python/feast/infra/offline_stores/file.py
index f36eb383d8e..75968146de6 100644
--- a/sdk/python/feast/infra/offline_stores/file.py
+++ b/sdk/python/feast/infra/offline_stores/file.py
@@ -1,10 +1,12 @@
+import uuid
 from datetime import datetime
 from pathlib import Path
-from typing import Callable, List, Optional, Tuple, Union
+from typing import Any, Callable, List, Optional, Tuple, Union
 
 import dask.dataframe as dd
 import pandas as pd
 import pyarrow
+import pyarrow.dataset
 import pyarrow.parquet
 import pytz
 from pydantic.typing import Literal
@@ -25,12 +27,13 @@
 )
 from feast.infra.offline_stores.offline_utils import (
     DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL,
+    get_pyarrow_schema_from_batch_source,
 )
 from feast.infra.provider import (
     _get_requested_feature_views_to_features_dict,
     _run_dask_field_mapping,
 )
-from feast.registry import Registry
+from feast.registry import BaseRegistry
 from feast.repo_config import FeastConfigBaseModel, RepoConfig
 from feast.saved_dataset import SavedDatasetStorage
 from feast.usage import log_exceptions_and_usage
@@ -73,6 +76,7 @@ def on_demand_feature_views(self) -> Optional[List[OnDemandFeatureView]]:
     def _to_df_internal(self) -> pd.DataFrame:
         # Only execute the evaluation function to build the final historical retrieval dataframe at the last moment.
         df = self.evaluation_function().compute()
+        df = df.reset_index(drop=True)
         return df
 
     @log_exceptions_and_usage
@@ -110,7 +114,7 @@ def get_historical_features(
         feature_views: List[FeatureView],
         feature_refs: List[str],
         entity_df: Union[pd.DataFrame, str],
-        registry: Registry,
+        registry: BaseRegistry,
         project: str,
         full_feature_names: bool = False,
     ) -> RetrievalJob:
@@ -199,10 +203,9 @@ def evaluate_historical_retrieval():
                 # Build a list of entity columns to join on (from the right table)
                 join_keys = []
 
-                for entity_name in feature_view.entities:
-                    entity = registry.get_entity(entity_name, project)
+                for entity_column in feature_view.entity_columns:
                     join_key = feature_view.projection.join_key_map.get(
-                        entity.join_key, entity.join_key
+                        entity_column.name, entity_column.name
                     )
                     join_keys.append(join_key)
 
@@ -378,25 +381,73 @@ def write_logged_features(
         data: Union[pyarrow.Table, Path],
         source: LoggingSource,
         logging_config: LoggingConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ):
         destination = logging_config.destination
         assert isinstance(destination, FileLoggingDestination)
 
         if isinstance(data, Path):
-            data = pyarrow.parquet.read_table(data)
+            # Since this code will be mostly used from Go-created thread, it's better to avoid producing new threads
+            data = pyarrow.parquet.read_table(data, use_threads=False, pre_buffer=False)
 
         filesystem, path = FileSource.create_filesystem_and_path(
             destination.path, destination.s3_endpoint_override,
         )
 
-        pyarrow.parquet.write_to_dataset(
+        pyarrow.dataset.write_dataset(
             data,
-            root_path=path,
-            partition_cols=destination.partition_by,
+            base_dir=path,
+            basename_template=f"{uuid.uuid4().hex}-{{i}}.parquet",
+            partitioning=destination.partition_by,
             filesystem=filesystem,
+            use_threads=False,
+            format=pyarrow.dataset.ParquetFileFormat(),
+            existing_data_behavior="overwrite_or_ignore",
         )
 
+    @staticmethod
+    def offline_write_batch(
+        config: RepoConfig,
+        feature_view: FeatureView,
+        table: pyarrow.Table,
+        progress: Optional[Callable[[int], Any]],
+    ):
+        if not feature_view.batch_source:
+            raise ValueError(
+                "feature view does not have a batch source to persist offline data"
+            )
+        if not isinstance(config.offline_store, FileOfflineStoreConfig):
+            raise ValueError(
+                f"offline store config is of type {type(config.offline_store)} when file type required"
+            )
+        if not isinstance(feature_view.batch_source, FileSource):
+            raise ValueError(
+                f"feature view batch source is {type(feature_view.batch_source)} not file source"
+            )
+
+        pa_schema, column_names = get_pyarrow_schema_from_batch_source(
+            config, feature_view.batch_source
+        )
+        if column_names != table.column_names:
+            raise ValueError(
+                f"The input pyarrow table has schema {pa_schema} with the incorrect columns {column_names}. "
+                f"The columns are expected to be (in this order): {column_names}."
+            )
+
+        file_options = feature_view.batch_source.file_options
+        filesystem, path = FileSource.create_filesystem_and_path(
+            file_options.uri, file_options.s3_endpoint_override
+        )
+        prev_table = pyarrow.parquet.read_table(path, memory_map=True)
+        if table.schema != prev_table.schema:
+            table = table.cast(prev_table.schema)
+        new_table = pyarrow.concat_tables([table, prev_table])
+        writer = pyarrow.parquet.ParquetWriter(
+            path, table.schema, filesystem=filesystem
+        )
+        writer.write_table(new_table)
+        writer.close()
+
 
 def _get_entity_df_event_timestamp_range(
     entity_df: Union[pd.DataFrame, str], entity_df_event_timestamp_col: str,
@@ -440,7 +491,7 @@ def _field_mapping(
     entity_df_event_timestamp_col: str,
     timestamp_field: str,
     full_feature_names: bool,
-) -> dd.DataFrame:
+) -> Tuple[dd.DataFrame, str]:
     # Rename columns by the field mapping dictionary if it exists
     if feature_view.batch_source.field_mapping:
         df_to_join = _run_dask_field_mapping(
@@ -555,11 +606,18 @@ def _filter_ttl(
     # Filter rows by defined timestamp tolerance
     if feature_view.ttl and feature_view.ttl.total_seconds() != 0:
         df_to_join = df_to_join[
-            (
-                df_to_join[timestamp_field]
-                >= df_to_join[entity_df_event_timestamp_col] - feature_view.ttl
+            # do not drop entity rows if one of the sources returns NaNs
+            df_to_join[timestamp_field].isna()
+            | (
+                (
+                    df_to_join[timestamp_field]
+                    >= df_to_join[entity_df_event_timestamp_col] - feature_view.ttl
+                )
+                & (
+                    df_to_join[timestamp_field]
+                    <= df_to_join[entity_df_event_timestamp_col]
+                )
             )
-            & (df_to_join[timestamp_field] <= df_to_join[entity_df_event_timestamp_col])
         ]
 
         df_to_join = df_to_join.persist()
diff --git a/sdk/python/feast/infra/offline_stores/file_source.py b/sdk/python/feast/infra/offline_stores/file_source.py
index 5c2a521ac0d..6fdf95470f9 100644
--- a/sdk/python/feast/infra/offline_stores/file_source.py
+++ b/sdk/python/feast/infra/offline_stores/file_source.py
@@ -4,6 +4,7 @@
 from pyarrow._fs import FileSystem
 from pyarrow._s3fs import S3FileSystem
 from pyarrow.parquet import ParquetDataset
+from typeguard import typechecked
 
 from feast import type_map
 from feast.data_format import FileFormat, ParquetFormat
@@ -21,6 +22,7 @@
 from feast.value_type import ValueType
 
 
+@typechecked
 class FileSource(DataSource):
     def __init__(
         self,
@@ -44,7 +46,8 @@ def __init__(
 
             path: File path to file containing feature data. Must contain an event_timestamp column, entity columns and
                 feature columns.
-            event_timestamp_column(optional): (Deprecated) Event timestamp column used for point in time joins of feature values.
+            event_timestamp_column (optional): (Deprecated in favor of timestamp_field) Event
+                timestamp column used for point in time joins of feature values.
             created_timestamp_column (optional): Timestamp column when row was created, used for deduplicating rows.
             file_format (optional): Explicitly set the file format. Allows Feast to bypass inferring the file format.
             field_mapping: A dictionary mapping of column names in this data source to feature names in a feature table
@@ -70,7 +73,7 @@ def __init__(
                 warnings.warn(
                     (
                         "File Source parameters should be specified as a keyword argument instead of a positional arg."
-                        "Feast 0.23+ will not support positional arguments to construct File sources"
+                        "Feast 0.24+ will not support positional arguments to construct File sources"
                     ),
                     DeprecationWarning,
                 )
@@ -95,7 +98,7 @@ def __init__(
             warnings.warn(
                 (
                     "The argument 'date_partition_column' is not supported for File sources."
-                    "It will be removed in Feast 0.23+"
+                    "It will be removed in Feast 0.24+"
                 ),
                 DeprecationWarning,
             )
@@ -179,9 +182,15 @@ def get_table_column_names_and_types(
         filesystem, path = FileSource.create_filesystem_and_path(
             self.path, self.file_options.s3_endpoint_override
         )
-        schema = ParquetDataset(
-            path if filesystem is None else filesystem.open_input_file(path)
-        ).schema.to_arrow_schema()
+        # Adding support for different file format path
+        # based on S3 filesystem
+        if filesystem is None:
+            schema = ParquetDataset(path).schema.to_arrow_schema()
+        else:
+            schema = ParquetDataset(
+                filesystem.open_input_file(path), filesystem=filesystem
+            ).schema
+
         return zip(schema.names, map(str, schema.types))
 
     @staticmethod
diff --git a/sdk/python/feast/infra/offline_stores/offline_store.py b/sdk/python/feast/infra/offline_stores/offline_store.py
index 2996a1ed590..439911fe2a3 100644
--- a/sdk/python/feast/infra/offline_stores/offline_store.py
+++ b/sdk/python/feast/infra/offline_stores/offline_store.py
@@ -15,7 +15,7 @@
 from abc import ABC, abstractmethod
 from datetime import datetime
 from pathlib import Path
-from typing import TYPE_CHECKING, List, Optional, Union
+from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union
 
 import pandas as pd
 import pyarrow
@@ -25,7 +25,7 @@
 from feast.feature_logging import LoggingConfig, LoggingSource
 from feast.feature_view import FeatureView
 from feast.on_demand_feature_view import OnDemandFeatureView
-from feast.registry import Registry
+from feast.registry import BaseRegistry
 from feast.repo_config import RepoConfig
 from feast.saved_dataset import SavedDatasetStorage
 
@@ -211,7 +211,7 @@ def get_historical_features(
         feature_views: List[FeatureView],
         feature_refs: List[str],
         entity_df: Union[pd.DataFrame, str],
-        registry: Registry,
+        registry: BaseRegistry,
         project: str,
         full_feature_names: bool = False,
     ) -> RetrievalJob:
@@ -252,7 +252,7 @@ def write_logged_features(
         data: Union[pyarrow.Table, Path],
         source: LoggingSource,
         logging_config: LoggingConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ):
         """
         Write logged features to a specified destination (taken from logging_config) in the offline store.
@@ -270,3 +270,25 @@ def write_logged_features(
         This is an optional method that could be supported only be some stores.
         """
         raise NotImplementedError()
+
+    @staticmethod
+    def offline_write_batch(
+        config: RepoConfig,
+        feature_view: FeatureView,
+        table: pyarrow.Table,
+        progress: Optional[Callable[[int], Any]],
+    ):
+        """
+        Write features to a specified destination in the offline store.
+        Data can be appended to an existing table (destination) or a new one will be created automatically
+         (if it doesn't exist).
+        Hence, this function can be called repeatedly with the same destination config to write features.
+
+        Args:
+            config: Repo configuration object
+            feature_view: FeatureView to write the data to.
+            table: pyarrow table containing feature data and timestamp column for historical feature retrieval
+            progress: Optional function to be called once every mini-batch of rows is written to
+            the online store. Can be used to display progress.
+        """
+        raise NotImplementedError()
diff --git a/sdk/python/feast/infra/offline_stores/offline_utils.py b/sdk/python/feast/infra/offline_stores/offline_utils.py
index b6c3d300d49..abe8d4e4e5b 100644
--- a/sdk/python/feast/infra/offline_stores/offline_utils.py
+++ b/sdk/python/feast/infra/offline_stores/offline_utils.py
@@ -5,10 +5,11 @@
 
 import numpy as np
 import pandas as pd
+import pyarrow as pa
 from jinja2 import BaseLoader, Environment
 from pandas import Timestamp
 
-import feast
+from feast.data_source import DataSource
 from feast.errors import (
     EntityTimestampInferenceException,
     FeastEntityDFMissingColumnsError,
@@ -17,7 +18,9 @@
 from feast.importer import import_class
 from feast.infra.offline_stores.offline_store import OfflineStore
 from feast.infra.provider import _get_requested_feature_views_to_features_dict
-from feast.registry import Registry
+from feast.registry import BaseRegistry
+from feast.repo_config import RepoConfig
+from feast.type_map import feast_value_type_to_pa
 from feast.utils import to_naive_utc
 
 DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL = "event_timestamp"
@@ -55,16 +58,15 @@ def assert_expected_columns_in_entity_df(
         raise FeastEntityDFMissingColumnsError(expected_columns, missing_keys)
 
 
+# TODO: Remove project and registry from the interface and call sites.
 def get_expected_join_keys(
-    project: str, feature_views: List["feast.FeatureView"], registry: Registry
+    project: str, feature_views: List[FeatureView], registry: BaseRegistry
 ) -> Set[str]:
     join_keys = set()
     for feature_view in feature_views:
-        entities = feature_view.entities
-        for entity_name in entities:
-            entity = registry.get_entity(entity_name, project)
+        for entity_column in feature_view.entity_columns:
             join_key = feature_view.projection.join_key_map.get(
-                entity.join_key, entity.join_key
+                entity_column.name, entity_column.name
             )
             join_keys.add(join_key)
     return join_keys
@@ -97,7 +99,7 @@ class FeatureViewQueryContext:
 def get_feature_view_query_context(
     feature_refs: List[str],
     feature_views: List[FeatureView],
-    registry: Registry,
+    registry: BaseRegistry,
     project: str,
     entity_df_timestamp_range: Tuple[datetime, datetime],
 ) -> List[FeatureViewQueryContext]:
@@ -114,14 +116,14 @@ def get_feature_view_query_context(
 
     query_context = []
     for feature_view, features in feature_views_to_feature_map.items():
-        join_keys, entity_selections = [], []
-        for entity_name in feature_view.entities:
-            entity = registry.get_entity(entity_name, project)
+        join_keys: List[str] = []
+        entity_selections: List[str] = []
+        for entity_column in feature_view.entity_columns:
             join_key = feature_view.projection.join_key_map.get(
-                entity.join_key, entity.join_key
+                entity_column.name, entity_column.name
             )
             join_keys.append(join_key)
-            entity_selections.append(f"{entity.join_key} AS {join_key}")
+            entity_selections.append(f"{entity_column.name} AS {join_key}")
 
         if isinstance(feature_view.ttl, timedelta):
             ttl_seconds = int(feature_view.ttl.total_seconds())
@@ -219,3 +221,25 @@ def get_offline_store_from_config(offline_store_config: Any) -> OfflineStore:
     class_name = qualified_name.replace("Config", "")
     offline_store_class = import_class(module_name, class_name, "OfflineStore")
     return offline_store_class()
+
+
+def get_pyarrow_schema_from_batch_source(
+    config: RepoConfig, batch_source: DataSource
+) -> Tuple[pa.Schema, List[str]]:
+    """Returns the pyarrow schema and column names for the given batch source."""
+    column_names_and_types = batch_source.get_table_column_names_and_types(config)
+
+    pa_schema = []
+    column_names = []
+    for column_name, column_type in column_names_and_types:
+        pa_schema.append(
+            (
+                column_name,
+                feast_value_type_to_pa(
+                    batch_source.source_datatype_to_feast_value_type()(column_type)
+                ),
+            )
+        )
+        column_names.append(column_name)
+
+    return pa.schema(pa_schema), column_names
diff --git a/sdk/python/feast/infra/offline_stores/redshift.py b/sdk/python/feast/infra/offline_stores/redshift.py
index 74ba83cb004..8667989268d 100644
--- a/sdk/python/feast/infra/offline_stores/redshift.py
+++ b/sdk/python/feast/infra/offline_stores/redshift.py
@@ -3,6 +3,7 @@
 from datetime import datetime
 from pathlib import Path
 from typing import (
+    Any,
     Callable,
     ContextManager,
     Dict,
@@ -38,7 +39,7 @@
     SavedDatasetRedshiftStorage,
 )
 from feast.infra.utils import aws_utils
-from feast.registry import Registry
+from feast.registry import BaseRegistry
 from feast.repo_config import FeastConfigBaseModel, RepoConfig
 from feast.saved_dataset import SavedDatasetStorage
 from feast.usage import log_exceptions_and_usage
@@ -176,7 +177,7 @@ def get_historical_features(
         feature_views: List[FeatureView],
         feature_refs: List[str],
         entity_df: Union[pd.DataFrame, str],
-        registry: Registry,
+        registry: BaseRegistry,
         project: str,
         full_feature_names: bool = False,
     ) -> RetrievalJob:
@@ -269,7 +270,7 @@ def write_logged_features(
         data: Union[pyarrow.Table, Path],
         source: LoggingSource,
         logging_config: LoggingConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ):
         destination = logging_config.destination
         assert isinstance(destination, RedshiftLoggingDestination)
@@ -297,6 +298,59 @@ def write_logged_features(
             fail_if_exists=False,
         )
 
+    @staticmethod
+    def offline_write_batch(
+        config: RepoConfig,
+        feature_view: FeatureView,
+        table: pyarrow.Table,
+        progress: Optional[Callable[[int], Any]],
+    ):
+        if not feature_view.batch_source:
+            raise ValueError(
+                "feature view does not have a batch source to persist offline data"
+            )
+        if not isinstance(config.offline_store, RedshiftOfflineStoreConfig):
+            raise ValueError(
+                f"offline store config is of type {type(config.offline_store)} when redshift type required"
+            )
+        if not isinstance(feature_view.batch_source, RedshiftSource):
+            raise ValueError(
+                f"feature view batch source is {type(feature_view.batch_source)} not redshift source"
+            )
+
+        pa_schema, column_names = offline_utils.get_pyarrow_schema_from_batch_source(
+            config, feature_view.batch_source
+        )
+        if column_names != table.column_names:
+            raise ValueError(
+                f"The input pyarrow table has schema {pa_schema} with the incorrect columns {column_names}. "
+                f"The columns are expected to be (in this order): {column_names}."
+            )
+
+        if table.schema != pa_schema:
+            table = table.cast(pa_schema)
+
+        redshift_options = feature_view.batch_source.redshift_options
+        redshift_client = aws_utils.get_redshift_data_client(
+            config.offline_store.region
+        )
+        s3_resource = aws_utils.get_s3_resource(config.offline_store.region)
+
+        aws_utils.upload_arrow_table_to_redshift(
+            table=table,
+            redshift_data_client=redshift_client,
+            cluster_id=config.offline_store.cluster_id,
+            database=redshift_options.database
+            or config.offline_store.database,  # Users can define database in the source if needed but it's not required.
+            user=config.offline_store.user,
+            s3_resource=s3_resource,
+            s3_path=f"{config.offline_store.s3_staging_location}/push/{uuid.uuid4()}.parquet",
+            iam_role=config.offline_store.iam_role,
+            table_name=redshift_options.table,
+            schema=pa_schema,
+            fail_if_exists=False,
+        )
+
 
 class RedshiftRetrievalJob(RetrievalJob):
     def __init__(
diff --git a/sdk/python/feast/infra/offline_stores/redshift_source.py b/sdk/python/feast/infra/offline_stores/redshift_source.py
index ae9d8bab5c8..24b2a04f938 100644
--- a/sdk/python/feast/infra/offline_stores/redshift_source.py
+++ b/sdk/python/feast/infra/offline_stores/redshift_source.py
@@ -1,6 +1,8 @@
 import warnings
 from typing import Callable, Dict, Iterable, Optional, Tuple
 
+from typeguard import typechecked
+
 from feast import type_map
 from feast.data_source import DataSource
 from feast.errors import DataSourceNotFoundException, RedshiftCredentialsError
@@ -17,6 +19,7 @@
 from feast.value_type import ValueType
 
 
+@typechecked
 class RedshiftSource(DataSource):
     def __init__(
         self,
@@ -39,8 +42,8 @@ def __init__(
         Creates a RedshiftSource object.
 
         Args:
-            event_timestamp_column (optional): (Deprecated) Event timestamp column used for point in
-                time joins of feature values.
+            event_timestamp_column (optional): (Deprecated in favor of timestamp_field) Event
+                timestamp column used for point in time joins of feature values.
             table (optional): Redshift table where the features are stored.
             schema (optional): Redshift schema in which the table is located.
             created_timestamp_column (optional): Timestamp column indicating when the
@@ -73,7 +76,7 @@ def __init__(
             else:
                 warnings.warn(
                     (
-                        f"Starting in Feast 0.23, Feast will require either a name for a data source (if using query) "
+                        f"Starting in Feast 0.24, Feast will require either a name for a data source (if using query) "
                         f"or `table`: {self.query}"
                     ),
                     DeprecationWarning,
@@ -82,7 +85,7 @@ def __init__(
             warnings.warn(
                 (
                     "The argument 'date_partition_column' is not supported for Redshift sources."
-                    "It will be removed in Feast 0.23+"
+                    "It will be removed in Feast 0.24+"
                 ),
                 DeprecationWarning,
             )
diff --git a/sdk/python/feast/infra/offline_stores/snowflake.py b/sdk/python/feast/infra/offline_stores/snowflake.py
index d39acc9f08d..ec06d8dce10 100644
--- a/sdk/python/feast/infra/offline_stores/snowflake.py
+++ b/sdk/python/feast/infra/offline_stores/snowflake.py
@@ -3,6 +3,7 @@
 from datetime import datetime
 from pathlib import Path
 from typing import (
+    Any,
     Callable,
     ContextManager,
     Dict,
@@ -44,7 +45,7 @@
     write_pandas,
     write_parquet,
 )
-from feast.registry import Registry
+from feast.registry import BaseRegistry
 from feast.repo_config import FeastConfigBaseModel, RepoConfig
 from feast.saved_dataset import SavedDatasetStorage
 from feast.usage import log_exceptions_and_usage
@@ -206,7 +207,7 @@ def get_historical_features(
         feature_views: List[FeatureView],
         feature_refs: List[str],
         entity_df: Union[pd.DataFrame, str],
-        registry: Registry,
+        registry: BaseRegistry,
         project: str,
         full_feature_names: bool = False,
     ) -> RetrievalJob:
@@ -284,7 +285,7 @@ def write_logged_features(
         data: Union[pyarrow.Table, Path],
         source: LoggingSource,
         logging_config: LoggingConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ):
         assert isinstance(logging_config.destination, SnowflakeLoggingDestination)
 
@@ -306,6 +307,47 @@ def write_logged_features(
                 auto_create_table=True,
             )
 
+    @staticmethod
+    def offline_write_batch(
+        config: RepoConfig,
+        feature_view: FeatureView,
+        table: pyarrow.Table,
+        progress: Optional[Callable[[int], Any]],
+    ):
+        if not feature_view.batch_source:
+            raise ValueError(
+                "feature view does not have a batch source to persist offline data"
+            )
+        if not isinstance(config.offline_store, SnowflakeOfflineStoreConfig):
+            raise ValueError(
+                f"offline store config is of type {type(config.offline_store)} when snowflake type required"
+            )
+        if not isinstance(feature_view.batch_source, SnowflakeSource):
+            raise ValueError(
+                f"feature view batch source is {type(feature_view.batch_source)} not snowflake source"
+            )
+
+        pa_schema, column_names = offline_utils.get_pyarrow_schema_from_batch_source(
+            config, feature_view.batch_source
+        )
+        if column_names != table.column_names:
+            raise ValueError(
+                f"The input pyarrow table has schema {pa_schema} with the incorrect columns {column_names}. "
+                f"The columns are expected to be (in this order): {column_names}."
+            )
+
+        if table.schema != pa_schema:
+            table = table.cast(pa_schema)
+
+        snowflake_conn = get_snowflake_conn(config.offline_store)
+
+        write_pandas(
+            snowflake_conn,
+            table.to_pandas(),
+            table_name=feature_view.batch_source.table,
+            auto_create_table=True,
+        )
+
 
 class SnowflakeRetrievalJob(RetrievalJob):
     def __init__(
diff --git a/sdk/python/feast/infra/offline_stores/snowflake_source.py b/sdk/python/feast/infra/offline_stores/snowflake_source.py
index d76131f837b..b072c6e871b 100644
--- a/sdk/python/feast/infra/offline_stores/snowflake_source.py
+++ b/sdk/python/feast/infra/offline_stores/snowflake_source.py
@@ -1,6 +1,8 @@
 import warnings
 from typing import Callable, Dict, Iterable, Optional, Tuple
 
+from typeguard import typechecked
+
 from feast import type_map
 from feast.data_source import DataSource
 from feast.feature_logging import LoggingDestination
@@ -16,6 +18,7 @@
 from feast.value_type import ValueType
 
 
+@typechecked
 class SnowflakeSource(DataSource):
     def __init__(
         self,
@@ -43,8 +46,8 @@ def __init__(
             warehouse (optional): Snowflake warehouse where the database is stored.
             schema (optional): Snowflake schema in which the table is located.
             table (optional): Snowflake table where the features are stored.
-            event_timestamp_column (optional): (Deprecated) Event timestamp column used for point in
-                time joins of feature values.
+            event_timestamp_column (optional): (Deprecated in favor of timestamp_field) Event
+                timestamp column used for point in time joins of feature values.
             query (optional): The query to be executed to obtain the features.
             created_timestamp_column (optional): Timestamp column indicating when the
                 row was created, used for deduplicating rows.
@@ -79,7 +82,7 @@ def __init__(
             else:
                 warnings.warn(
                     (
-                        f"Starting in Feast 0.23, Feast will require either a name for a data source (if using query) "
+                        f"Starting in Feast 0.24, Feast will require either a name for a data source (if using query) "
                         f"or `table`: {self.query}"
                     ),
                     DeprecationWarning,
@@ -89,7 +92,7 @@ def __init__(
             warnings.warn(
                 (
                     "The argument 'date_partition_column' is not supported for Snowflake sources."
-                    "It will be removed in Feast 0.23+"
+                    "It will be removed in Feast 0.24+"
                 ),
                 DeprecationWarning,
             )
diff --git a/sdk/python/feast/infra/online_stores/dynamodb.py b/sdk/python/feast/infra/online_stores/dynamodb.py
index 406bee525f8..50709fa3d49 100644
--- a/sdk/python/feast/infra/online_stores/dynamodb.py
+++ b/sdk/python/feast/infra/online_stores/dynamodb.py
@@ -221,6 +221,9 @@ def online_read(
         entity_ids_iter = iter(entity_ids)
         while True:
             batch = list(itertools.islice(entity_ids_iter, batch_size))
+            batch_result: List[
+                Tuple[Optional[datetime], Optional[Dict[str, ValueProto]]]
+            ] = []
             # No more items to insert
             if len(batch) == 0:
                 break
@@ -243,20 +246,23 @@ def online_read(
                 for tbl_res in table_responses:
                     entity_id = tbl_res["entity_id"]
                     while entity_id != batch[entity_idx]:
-                        result.append((None, None))
+                        batch_result.append((None, None))
                         entity_idx += 1
                     res = {}
                     for feature_name, value_bin in tbl_res["values"].items():
                         val = ValueProto()
                         val.ParseFromString(value_bin.value)
                         res[feature_name] = val
-                    result.append((datetime.fromisoformat(tbl_res["event_ts"]), res))
+                    batch_result.append(
+                        (datetime.fromisoformat(tbl_res["event_ts"]), res)
+                    )
                     entity_idx += 1
 
             # Not all entities in a batch may have responses
             # Pad with remaining values in batch that were not found
-            batch_size_nones = ((None, None),) * (len(batch) - len(result))
-            result.extend(batch_size_nones)
+            batch_size_nones = ((None, None),) * (len(batch) - len(batch_result))
+            batch_result.extend(batch_size_nones)
+            result.extend(batch_result)
         return result
 
     def _get_dynamodb_client(self, region: str, endpoint_url: Optional[str] = None):
diff --git a/sdk/python/feast/infra/online_stores/redis.py b/sdk/python/feast/infra/online_stores/redis.py
index 9ceceff0ac0..60fa9265ca7 100644
--- a/sdk/python/feast/infra/online_stores/redis.py
+++ b/sdk/python/feast/infra/online_stores/redis.py
@@ -106,9 +106,9 @@ def update(
         (usually this happens when the last feature view that was using specific compound key is deleted)
         and remove all features attached to this "join_keys".
         """
-        join_keys_to_keep = set(tuple(table.entities) for table in tables_to_keep)
+        join_keys_to_keep = set(tuple(table.join_keys) for table in tables_to_keep)
 
-        join_keys_to_delete = set(tuple(table.entities) for table in tables_to_delete)
+        join_keys_to_delete = set(tuple(table.join_keys) for table in tables_to_delete)
 
         for join_keys in join_keys_to_delete - join_keys_to_keep:
             self.delete_entity_values(config, list(join_keys))
@@ -122,7 +122,7 @@ def teardown(
         """
         We delete the keys in redis for tables/views being removed.
         """
-        join_keys_to_delete = set(tuple(table.entities) for table in tables)
+        join_keys_to_delete = set(tuple(table.join_keys) for table in tables)
 
         for join_keys in join_keys_to_delete:
             self.delete_entity_values(config, list(join_keys))
@@ -132,7 +132,7 @@ def _parse_connection_string(connection_string: str):
         """
         Reads Redis connections string using format
         for RedisCluster:
-            redis1:6379,redis2:6379,decode_responses=true,skip_full_coverage_check=true,ssl=true,password=...
+            redis1:6379,redis2:6379,skip_full_coverage_check=true,ssl=true,password=...
         for Redis:
             redis_master:6379,db=0,ssl=true,password=...
         """
diff --git a/sdk/python/feast/infra/online_stores/sqlite.py b/sdk/python/feast/infra/online_stores/sqlite.py
index 5657fbe3722..2f0e9029426 100644
--- a/sdk/python/feast/infra/online_stores/sqlite.py
+++ b/sdk/python/feast/infra/online_stores/sqlite.py
@@ -211,7 +211,10 @@ def plan(
                 path=self._get_db_path(config),
                 name=_table_id(project, FeatureView.from_proto(view)),
             )
-            for view in desired_registry_proto.feature_views
+            for view in [
+                *desired_registry_proto.feature_views,
+                *desired_registry_proto.stream_feature_views,
+            ]
         ]
         return infra_objects
 
diff --git a/sdk/python/feast/infra/passthrough_provider.py b/sdk/python/feast/infra/passthrough_provider.py
index b5965c91bfb..8c6dd831dde 100644
--- a/sdk/python/feast/infra/passthrough_provider.py
+++ b/sdk/python/feast/infra/passthrough_provider.py
@@ -1,7 +1,7 @@
 from datetime import datetime, timedelta
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
-import pandas
+import pandas as pd
 import pyarrow
 import pyarrow as pa
 from tqdm import tqdm
@@ -21,7 +21,7 @@
 )
 from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
 from feast.protos.feast.types.Value_pb2 import Value as ValueProto
-from feast.registry import Registry
+from feast.registry import BaseRegistry
 from feast.repo_config import RepoConfig
 from feast.saved_dataset import SavedDataset
 from feast.usage import RatioSampler, log_exceptions_and_usage, set_usage_attribute
@@ -100,6 +100,20 @@ def online_write_batch(
         if self.online_store:
             self.online_store.online_write_batch(config, table, data, progress)
 
+    def offline_write_batch(
+        self,
+        config: RepoConfig,
+        feature_view: FeatureView,
+        data: pa.Table,
+        progress: Optional[Callable[[int], Any]],
+    ) -> None:
+        set_usage_attribute("provider", self.__class__.__name__)
+
+        if self.offline_store:
+            self.offline_store.__class__.offline_write_batch(
+                config, feature_view, data, progress
+            )
+
     @log_exceptions_and_usage(sampler=RatioSampler(ratio=0.001))
     def online_read(
         self,
@@ -117,7 +131,7 @@ def online_read(
         return result
 
     def ingest_df(
-        self, feature_view: FeatureView, entities: List[Entity], df: pandas.DataFrame,
+        self, feature_view: FeatureView, entities: List[Entity], df: pd.DataFrame,
     ):
         set_usage_attribute("provider", self.__class__.__name__)
         table = pa.Table.from_pandas(df)
@@ -132,13 +146,21 @@ def ingest_df(
             self.repo_config, feature_view, rows_to_write, progress=None
         )
 
+    def ingest_df_to_offline_store(self, feature_view: FeatureView, table: pa.Table):
+        set_usage_attribute("provider", self.__class__.__name__)
+
+        if feature_view.batch_source.field_mapping is not None:
+            table = _run_field_mapping(table, feature_view.batch_source.field_mapping)
+
+        self.offline_write_batch(self.repo_config, feature_view, table, None)
+
     def materialize_single_feature_view(
         self,
         config: RepoConfig,
         feature_view: FeatureView,
         start_date: datetime,
         end_date: datetime,
-        registry: Registry,
+        registry: BaseRegistry,
         project: str,
         tqdm_builder: Callable[[int], tqdm],
     ) -> None:
@@ -171,11 +193,16 @@ def materialize_single_feature_view(
         if feature_view.batch_source.field_mapping is not None:
             table = _run_field_mapping(table, feature_view.batch_source.field_mapping)
 
-        join_keys = {entity.join_key: entity.value_type for entity in entities}
+        join_key_to_value_type = {
+            entity.name: entity.dtype.to_value_type()
+            for entity in feature_view.entity_columns
+        }
 
         with tqdm_builder(table.num_rows) as pbar:
             for batch in table.to_batches(DEFAULT_BATCH_SIZE):
-                rows_to_write = _convert_arrow_to_proto(batch, feature_view, join_keys)
+                rows_to_write = _convert_arrow_to_proto(
+                    batch, feature_view, join_key_to_value_type
+                )
                 self.online_write_batch(
                     self.repo_config,
                     feature_view,
@@ -188,8 +215,8 @@ def get_historical_features(
         config: RepoConfig,
         feature_views: List[FeatureView],
         feature_refs: List[str],
-        entity_df: Union[pandas.DataFrame, str],
-        registry: Registry,
+        entity_df: Union[pd.DataFrame, str],
+        registry: BaseRegistry,
         project: str,
         full_feature_names: bool,
     ) -> RetrievalJob:
@@ -235,7 +262,7 @@ def write_feature_service_logs(
         feature_service: FeatureService,
         logs: Union[pyarrow.Table, str],
         config: RepoConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ):
         assert (
             feature_service.logging_config is not None
@@ -255,7 +282,7 @@ def retrieve_feature_service_logs(
         start_date: datetime,
         end_date: datetime,
         config: RepoConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ) -> RetrievalJob:
         assert (
             feature_service.logging_config is not None
@@ -273,6 +300,6 @@ def retrieve_feature_service_logs(
             join_key_columns=[],
             feature_name_columns=columns,
             timestamp_field=ts_column,
-            start_date=start_date,
-            end_date=end_date,
+            start_date=make_tzaware(start_date),
+            end_date=make_tzaware(end_date),
         )
diff --git a/sdk/python/feast/infra/provider.py b/sdk/python/feast/infra/provider.py
index 7754a58319c..d2e37e69db8 100644
--- a/sdk/python/feast/infra/provider.py
+++ b/sdk/python/feast/infra/provider.py
@@ -5,7 +5,7 @@
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import dask.dataframe as dd
-import pandas
+import pandas as pd
 import pyarrow
 from tqdm import tqdm
 
@@ -19,7 +19,7 @@
 from feast.protos.feast.core.Registry_pb2 import Registry as RegistryProto
 from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
 from feast.protos.feast.types.Value_pb2 import Value as ValueProto
-from feast.registry import Registry
+from feast.registry import BaseRegistry
 from feast.repo_config import RepoConfig
 from feast.saved_dataset import SavedDataset
 from feast.type_map import python_values_to_proto_values
@@ -119,13 +119,21 @@ def online_write_batch(
         ...
 
     def ingest_df(
-        self, feature_view: FeatureView, entities: List[Entity], df: pandas.DataFrame,
+        self, feature_view: FeatureView, entities: List[Entity], df: pd.DataFrame,
     ):
         """
         Ingests a DataFrame directly into the online store
         """
         pass
 
+    def ingest_df_to_offline_store(
+        self, feature_view: FeatureView, df: pyarrow.Table,
+    ):
+        """
+        Ingests a DataFrame directly into the offline store
+        """
+        pass
+
     @abc.abstractmethod
     def materialize_single_feature_view(
         self,
@@ -133,7 +141,7 @@ def materialize_single_feature_view(
         feature_view: FeatureView,
         start_date: datetime,
         end_date: datetime,
-        registry: Registry,
+        registry: BaseRegistry,
         project: str,
         tqdm_builder: Callable[[int], tqdm],
     ) -> None:
@@ -145,8 +153,8 @@ def get_historical_features(
         config: RepoConfig,
         feature_views: List[FeatureView],
         feature_refs: List[str],
-        entity_df: Union[pandas.DataFrame, str],
-        registry: Registry,
+        entity_df: Union[pd.DataFrame, str],
+        registry: BaseRegistry,
         project: str,
         full_feature_names: bool,
     ) -> RetrievalJob:
@@ -192,7 +200,7 @@ def write_feature_service_logs(
         feature_service: FeatureService,
         logs: Union[pyarrow.Table, Path],
         config: RepoConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ):
         """
         Write features and entities logged by a feature server to an offline store.
@@ -211,7 +219,7 @@ def retrieve_feature_service_logs(
         start_date: datetime,
         end_date: datetime,
         config: RepoConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ) -> RetrievalJob:
         """
         Read logged features from an offline store for a given time window [from, to).
@@ -367,14 +375,14 @@ def _run_dask_field_mapping(
 
 def _coerce_datetime(ts):
     """
-    Depending on underlying time resolution, arrow to_pydict() sometimes returns pandas
+    Depending on underlying time resolution, arrow to_pydict() sometimes returns pd
     timestamp type (for nanosecond resolution), and sometimes you get standard python datetime
     (for microsecond resolution).
-    While pandas timestamp class is a subclass of python datetime, it doesn't always behave the
+    While pd timestamp class is a subclass of python datetime, it doesn't always behave the
     same way. We convert it to normal datetime so that consumers downstream don't have to deal
     with these quirks.
     """
-    if isinstance(ts, pandas.Timestamp):
+    if isinstance(ts, pd.Timestamp):
         return ts.to_pydatetime()
     else:
         return ts
@@ -390,7 +398,7 @@ def _convert_arrow_to_proto(
         table = table.to_batches()[0]
 
     columns = [
-        (field.name, field.dtype.to_value_type()) for field in feature_view.schema
+        (field.name, field.dtype.to_value_type()) for field in feature_view.features
     ] + list(join_keys.items())
 
     proto_values_by_column = {
@@ -418,7 +426,7 @@ def _convert_arrow_to_proto(
     # Convert event_timestamps
     event_timestamps = [
         _coerce_datetime(val)
-        for val in pandas.to_datetime(
+        for val in pd.to_datetime(
             table.column(feature_view.batch_source.timestamp_field).to_numpy(
                 zero_copy_only=False
             )
@@ -429,7 +437,7 @@ def _convert_arrow_to_proto(
     if feature_view.batch_source.created_timestamp_column:
         created_timestamps = [
             _coerce_datetime(val)
-            for val in pandas.to_datetime(
+            for val in pd.to_datetime(
                 table.column(
                     feature_view.batch_source.created_timestamp_column
                 ).to_numpy(zero_copy_only=False)
diff --git a/sdk/python/feast/infra/registry_stores/__init__.py b/sdk/python/feast/infra/registry_stores/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/sdk/python/feast/infra/registry_stores/sql.py b/sdk/python/feast/infra/registry_stores/sql.py
new file mode 100644
index 00000000000..7ea4a96849a
--- /dev/null
+++ b/sdk/python/feast/infra/registry_stores/sql.py
@@ -0,0 +1,795 @@
+from datetime import datetime
+from pathlib import Path
+from typing import Any, List, Optional, Set, Union
+
+from sqlalchemy import (  # type: ignore
+    BigInteger,
+    Column,
+    LargeBinary,
+    MetaData,
+    String,
+    Table,
+    create_engine,
+    delete,
+    insert,
+    select,
+    update,
+)
+from sqlalchemy.engine import Engine
+
+from feast.base_feature_view import BaseFeatureView
+from feast.data_source import DataSource
+from feast.entity import Entity
+from feast.errors import (
+    DataSourceObjectNotFoundException,
+    EntityNotFoundException,
+    FeatureServiceNotFoundException,
+    FeatureViewNotFoundException,
+    SavedDatasetNotFound,
+    ValidationReferenceNotFound,
+)
+from feast.feature_service import FeatureService
+from feast.feature_view import FeatureView
+from feast.infra.infra_object import Infra
+from feast.on_demand_feature_view import OnDemandFeatureView
+from feast.protos.feast.core.DataSource_pb2 import DataSource as DataSourceProto
+from feast.protos.feast.core.Entity_pb2 import Entity as EntityProto
+from feast.protos.feast.core.FeatureService_pb2 import (
+    FeatureService as FeatureServiceProto,
+)
+from feast.protos.feast.core.FeatureView_pb2 import FeatureView as FeatureViewProto
+from feast.protos.feast.core.InfraObject_pb2 import Infra as InfraProto
+from feast.protos.feast.core.OnDemandFeatureView_pb2 import (
+    OnDemandFeatureView as OnDemandFeatureViewProto,
+)
+from feast.protos.feast.core.Registry_pb2 import Registry as RegistryProto
+from feast.protos.feast.core.RequestFeatureView_pb2 import (
+    RequestFeatureView as RequestFeatureViewProto,
+)
+from feast.protos.feast.core.SavedDataset_pb2 import SavedDataset as SavedDatasetProto
+from feast.protos.feast.core.StreamFeatureView_pb2 import (
+    StreamFeatureView as StreamFeatureViewProto,
+)
+from feast.protos.feast.core.ValidationProfile_pb2 import (
+    ValidationReference as ValidationReferenceProto,
+)
+from feast.registry import BaseRegistry
+from feast.repo_config import RegistryConfig
+from feast.request_feature_view import RequestFeatureView
+from feast.saved_dataset import SavedDataset, ValidationReference
+from feast.stream_feature_view import StreamFeatureView
+
+metadata = MetaData()
+
+entities = Table(
+    "entities",
+    metadata,
+    Column("entity_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("entity_proto", LargeBinary, nullable=False),
+)
+
+data_sources = Table(
+    "data_sources",
+    metadata,
+    Column("data_source_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("data_source_proto", LargeBinary, nullable=False),
+)
+
+feature_views = Table(
+    "feature_views",
+    metadata,
+    Column("feature_view_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("materialized_intervals", LargeBinary, nullable=True),
+    Column("feature_view_proto", LargeBinary, nullable=False),
+    Column("user_metadata", LargeBinary, nullable=True),
+)
+
+request_feature_views = Table(
+    "request_feature_views",
+    metadata,
+    Column("feature_view_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("feature_view_proto", LargeBinary, nullable=False),
+    Column("user_metadata", LargeBinary, nullable=True),
+)
+
+stream_feature_views = Table(
+    "stream_feature_views",
+    metadata,
+    Column("feature_view_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("feature_view_proto", LargeBinary, nullable=False),
+    Column("user_metadata", LargeBinary, nullable=True),
+)
+
+on_demand_feature_views = Table(
+    "on_demand_feature_views",
+    metadata,
+    Column("feature_view_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("feature_view_proto", LargeBinary, nullable=False),
+    Column("user_metadata", LargeBinary, nullable=True),
+)
+
+feature_services = Table(
+    "feature_services",
+    metadata,
+    Column("feature_service_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("feature_service_proto", LargeBinary, nullable=False),
+)
+
+saved_datasets = Table(
+    "saved_datasets",
+    metadata,
+    Column("saved_dataset_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("saved_dataset_proto", LargeBinary, nullable=False),
+)
+
+validation_references = Table(
+    "validation_references",
+    metadata,
+    Column("validation_reference_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("validation_reference_proto", LargeBinary, nullable=False),
+)
+
+managed_infra = Table(
+    "managed_infra",
+    metadata,
+    Column("infra_name", String(50), primary_key=True),
+    Column("project_id", String(50), primary_key=True),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+    Column("infra_proto", LargeBinary, nullable=False),
+)
+
+feast_metadata = Table(
+    "feast_metadata",
+    metadata,
+    Column("project_id", String(50), primary_key=True),
+    Column("metadata_key", String(50), primary_key=True),
+    Column("metadata_value", String(50), nullable=False),
+    Column("last_updated_timestamp", BigInteger, nullable=False),
+)
+
+
+class SqlRegistry(BaseRegistry):
+    def __init__(
+        self, registry_config: Optional[RegistryConfig], repo_path: Optional[Path]
+    ):
+        assert registry_config is not None, "SqlRegistry needs a valid registry_config"
+        self.engine: Engine = create_engine(registry_config.path, echo=False)
+        metadata.create_all(self.engine)
+
+    def teardown(self):
+        for t in {
+            entities,
+            data_sources,
+            feature_views,
+            feature_services,
+            on_demand_feature_views,
+            request_feature_views,
+            saved_datasets,
+            validation_references,
+        }:
+            with self.engine.connect() as conn:
+                stmt = delete(t)
+                conn.execute(stmt)
+
+    def refresh(self):
+        # This method is a no-op since we're always reading the latest values from the db.
+        pass
+
+    def get_stream_feature_view(
+        self, name: str, project: str, allow_cache: bool = False
+    ):
+        return self._get_object(
+            stream_feature_views,
+            name,
+            project,
+            StreamFeatureViewProto,
+            StreamFeatureView,
+            "feature_view_name",
+            "feature_view_proto",
+            FeatureViewNotFoundException,
+        )
+
+    def list_stream_feature_views(
+        self, project: str, allow_cache: bool = False
+    ) -> List[StreamFeatureView]:
+        return self._list_objects(
+            stream_feature_views,
+            project,
+            StreamFeatureViewProto,
+            StreamFeatureView,
+            "feature_view_proto",
+        )
+
+    def apply_entity(self, entity: Entity, project: str, commit: bool = True):
+        return self._apply_object(
+            entities, project, "entity_name", entity, "entity_proto"
+        )
+
+    def get_entity(self, name: str, project: str, allow_cache: bool = False) -> Entity:
+        return self._get_object(
+            entities,
+            name,
+            project,
+            EntityProto,
+            Entity,
+            "entity_name",
+            "entity_proto",
+            EntityNotFoundException,
+        )
+
+    def get_feature_view(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> FeatureView:
+        return self._get_object(
+            feature_views,
+            name,
+            project,
+            FeatureViewProto,
+            FeatureView,
+            "feature_view_name",
+            "feature_view_proto",
+            FeatureViewNotFoundException,
+        )
+
+    def get_on_demand_feature_view(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> OnDemandFeatureView:
+        return self._get_object(
+            on_demand_feature_views,
+            name,
+            project,
+            OnDemandFeatureViewProto,
+            OnDemandFeatureView,
+            "feature_view_name",
+            "feature_view_proto",
+            FeatureViewNotFoundException,
+        )
+
+    def get_request_feature_view(self, name: str, project: str):
+        return self._get_object(
+            request_feature_views,
+            name,
+            project,
+            RequestFeatureViewProto,
+            RequestFeatureView,
+            "feature_view_name",
+            "feature_view_proto",
+            FeatureViewNotFoundException,
+        )
+
+    def get_feature_service(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> FeatureService:
+        return self._get_object(
+            feature_services,
+            name,
+            project,
+            FeatureServiceProto,
+            FeatureService,
+            "feature_service_name",
+            "feature_service_proto",
+            FeatureServiceNotFoundException,
+        )
+
+    def get_saved_dataset(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> SavedDataset:
+        return self._get_object(
+            saved_datasets,
+            name,
+            project,
+            SavedDatasetProto,
+            SavedDataset,
+            "saved_dataset_name",
+            "saved_dataset_proto",
+            SavedDatasetNotFound,
+        )
+
+    def get_validation_reference(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> ValidationReference:
+        return self._get_object(
+            validation_references,
+            name,
+            project,
+            ValidationReferenceProto,
+            ValidationReference,
+            "validation_reference_name",
+            "validation_reference_proto",
+            ValidationReferenceNotFound,
+        )
+
+    def list_entities(self, project: str, allow_cache: bool = False) -> List[Entity]:
+        return self._list_objects(
+            entities, project, EntityProto, Entity, "entity_proto"
+        )
+
+    def delete_entity(self, name: str, project: str, commit: bool = True):
+        return self._delete_object(
+            entities, name, project, "entity_name", EntityNotFoundException
+        )
+
+    def delete_feature_view(self, name: str, project: str, commit: bool = True):
+        deleted_count = 0
+        for table in {
+            feature_views,
+            request_feature_views,
+            on_demand_feature_views,
+            stream_feature_views,
+        }:
+            deleted_count += self._delete_object(
+                table, name, project, "feature_view_name", None
+            )
+        if deleted_count == 0:
+            raise FeatureViewNotFoundException(name, project)
+
+    def delete_feature_service(self, name: str, project: str, commit: bool = True):
+        return self._delete_object(
+            feature_services,
+            name,
+            project,
+            "feature_service_name",
+            FeatureServiceNotFoundException,
+        )
+
+    def get_data_source(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> DataSource:
+        return self._get_object(
+            data_sources,
+            name,
+            project,
+            DataSourceProto,
+            DataSource,
+            "data_source_name",
+            "data_source_proto",
+            DataSourceObjectNotFoundException,
+        )
+
+    def list_data_sources(
+        self, project: str, allow_cache: bool = False
+    ) -> List[DataSource]:
+        return self._list_objects(
+            data_sources, project, DataSourceProto, DataSource, "data_source_proto"
+        )
+
+    def apply_data_source(
+        self, data_source: DataSource, project: str, commit: bool = True
+    ):
+        return self._apply_object(
+            data_sources, project, "data_source_name", data_source, "data_source_proto"
+        )
+
+    def apply_feature_view(
+        self, feature_view: BaseFeatureView, project: str, commit: bool = True
+    ):
+        fv_table = self._infer_fv_table(feature_view)
+
+        return self._apply_object(
+            fv_table, project, "feature_view_name", feature_view, "feature_view_proto"
+        )
+
+    def apply_feature_service(
+        self, feature_service: FeatureService, project: str, commit: bool = True
+    ):
+        return self._apply_object(
+            feature_services,
+            project,
+            "feature_service_name",
+            feature_service,
+            "feature_service_proto",
+        )
+
+    def delete_data_source(self, name: str, project: str, commit: bool = True):
+        with self.engine.connect() as conn:
+            stmt = delete(data_sources).where(
+                data_sources.c.data_source_name == name,
+                data_sources.c.project_id == project,
+            )
+            rows = conn.execute(stmt)
+            if rows.rowcount < 1:
+                raise DataSourceObjectNotFoundException(name, project)
+
+    def list_feature_services(
+        self, project: str, allow_cache: bool = False
+    ) -> List[FeatureService]:
+        return self._list_objects(
+            feature_services,
+            project,
+            FeatureServiceProto,
+            FeatureService,
+            "feature_service_proto",
+        )
+
+    def list_feature_views(
+        self, project: str, allow_cache: bool = False
+    ) -> List[FeatureView]:
+        return self._list_objects(
+            feature_views, project, FeatureViewProto, FeatureView, "feature_view_proto"
+        )
+
+    def list_saved_datasets(
+        self, project: str, allow_cache: bool = False
+    ) -> List[SavedDataset]:
+        return self._list_objects(
+            saved_datasets,
+            project,
+            SavedDatasetProto,
+            SavedDataset,
+            "saved_dataset_proto",
+        )
+
+    def list_request_feature_views(
+        self, project: str, allow_cache: bool = False
+    ) -> List[RequestFeatureView]:
+        return self._list_objects(
+            request_feature_views,
+            project,
+            RequestFeatureViewProto,
+            RequestFeatureView,
+            "feature_view_proto",
+        )
+
+    def list_on_demand_feature_views(
+        self, project: str, allow_cache: bool = False
+    ) -> List[OnDemandFeatureView]:
+        return self._list_objects(
+            on_demand_feature_views,
+            project,
+            OnDemandFeatureViewProto,
+            OnDemandFeatureView,
+            "feature_view_proto",
+        )
+
+    def apply_saved_dataset(
+        self, saved_dataset: SavedDataset, project: str, commit: bool = True,
+    ):
+        return self._apply_object(
+            saved_datasets,
+            project,
+            "saved_dataset_name",
+            saved_dataset,
+            "saved_dataset_proto",
+        )
+
+    def apply_validation_reference(
+        self,
+        validation_reference: ValidationReference,
+        project: str,
+        commit: bool = True,
+    ):
+        return self._apply_object(
+            validation_references,
+            project,
+            "validation_reference_name",
+            validation_reference,
+            "validation_reference_proto",
+        )
+
+    def apply_materialization(
+        self,
+        feature_view: FeatureView,
+        project: str,
+        start_date: datetime,
+        end_date: datetime,
+        commit: bool = True,
+    ):
+        table = self._infer_fv_table(feature_view)
+        python_class, proto_class = self._infer_fv_classes(feature_view)
+
+        if python_class in {RequestFeatureView, OnDemandFeatureView}:
+            raise ValueError(
+                f"Cannot apply materialization for feature {feature_view.name} of type {python_class}"
+            )
+        fv: Union[FeatureView, StreamFeatureView] = self._get_object(
+            table,
+            feature_view.name,
+            project,
+            proto_class,
+            python_class,
+            "feature_view_name",
+            "feature_view_proto",
+            FeatureViewNotFoundException,
+        )
+        fv.materialization_intervals.append((start_date, end_date))
+        self._apply_object(
+            table, project, "feature_view_name", fv, "feature_view_proto"
+        )
+
+    def delete_validation_reference(self, name: str, project: str, commit: bool = True):
+        self._delete_object(
+            validation_references,
+            name,
+            project,
+            "validation_reference_name",
+            ValidationReferenceNotFound,
+        )
+
+    def update_infra(self, infra: Infra, project: str, commit: bool = True):
+        self._apply_object(
+            managed_infra, project, "infra_name", infra, "infra_proto", name="infra_obj"
+        )
+
+    def get_infra(self, project: str, allow_cache: bool = False) -> Infra:
+        return self._get_object(
+            managed_infra,
+            "infra_obj",
+            project,
+            InfraProto,
+            Infra,
+            "infra_name",
+            "infra_proto",
+            None,
+        )
+
+    def apply_user_metadata(
+        self,
+        project: str,
+        feature_view: BaseFeatureView,
+        metadata_bytes: Optional[bytes],
+    ):
+        table = self._infer_fv_table(feature_view)
+
+        name = feature_view.name
+        with self.engine.connect() as conn:
+            stmt = select(table).where(
+                getattr(table.c, "feature_view_name") == name,
+                table.c.project_id == project,
+            )
+            row = conn.execute(stmt).first()
+            update_datetime = datetime.utcnow()
+            update_time = int(update_datetime.timestamp())
+            if row:
+                values = {
+                    "user_metadata": metadata_bytes,
+                    "last_updated_timestamp": update_time,
+                }
+                update_stmt = (
+                    update(table)
+                    .where(
+                        getattr(table.c, "feature_view_name") == name,
+                        table.c.project_id == project,
+                    )
+                    .values(values,)
+                )
+                conn.execute(update_stmt)
+            else:
+                raise FeatureViewNotFoundException(feature_view.name, project=project)
+
+    def _infer_fv_table(self, feature_view):
+        if isinstance(feature_view, StreamFeatureView):
+            table = stream_feature_views
+        elif isinstance(feature_view, FeatureView):
+            table = feature_views
+        elif isinstance(feature_view, OnDemandFeatureView):
+            table = on_demand_feature_views
+        elif isinstance(feature_view, RequestFeatureView):
+            table = request_feature_views
+        else:
+            raise ValueError(f"Unexpected feature view type: {type(feature_view)}")
+        return table
+
+    def _infer_fv_classes(self, feature_view):
+        if isinstance(feature_view, StreamFeatureView):
+            python_class, proto_class = StreamFeatureView, StreamFeatureViewProto
+        elif isinstance(feature_view, FeatureView):
+            python_class, proto_class = FeatureView, FeatureViewProto
+        elif isinstance(feature_view, OnDemandFeatureView):
+            python_class, proto_class = OnDemandFeatureView, OnDemandFeatureViewProto
+        elif isinstance(feature_view, RequestFeatureView):
+            python_class, proto_class = RequestFeatureView, RequestFeatureViewProto
+        else:
+            raise ValueError(f"Unexpected feature view type: {type(feature_view)}")
+        return python_class, proto_class
+
+    def get_user_metadata(
+        self, project: str, feature_view: BaseFeatureView
+    ) -> Optional[bytes]:
+        table = self._infer_fv_table(feature_view)
+
+        name = feature_view.name
+        with self.engine.connect() as conn:
+            stmt = select(table).where(getattr(table.c, "feature_view_name") == name)
+            row = conn.execute(stmt).first()
+            if row:
+                return row["user_metadata"]
+            else:
+                raise FeatureViewNotFoundException(feature_view.name, project=project)
+
+    def proto(self) -> RegistryProto:
+        r = RegistryProto()
+        last_updated_timestamps = []
+        projects = self._get_all_projects()
+        for project in projects:
+            for lister, registry_proto_field in [
+                (self.list_entities, r.entities),
+                (self.list_feature_views, r.feature_views),
+                (self.list_data_sources, r.data_sources),
+                (self.list_on_demand_feature_views, r.on_demand_feature_views),
+                (self.list_request_feature_views, r.request_feature_views),
+                (self.list_stream_feature_views, r.stream_feature_views),
+                (self.list_feature_services, r.feature_services),
+                (self.list_saved_datasets, r.saved_datasets),
+                (self.list_validation_references, r.validation_references),
+            ]:
+                objs: List[Any] = lister(project)  # type: ignore
+                if objs:
+                    registry_proto_field.extend([obj.to_proto() for obj in objs])
+
+            # This is suuuper jank. Because of https://github.com/feast-dev/feast/issues/2783,
+            # the registry proto only has a single infra field, which we're currently setting as the "last" project.
+            r.infra.CopyFrom(self.get_infra(project).to_proto())
+            last_updated_timestamps.append(self._get_last_updated_metadata(project))
+
+        if last_updated_timestamps:
+            r.last_updated.FromDatetime(max(last_updated_timestamps))
+
+        return r
+
+    def commit(self):
+        # This method is a no-op since we're always writing values eagerly to the db.
+        pass
+
+    def _apply_object(
+        self, table, project: str, id_field_name, obj, proto_field_name, name=None
+    ):
+        name = name or obj.name
+        with self.engine.connect() as conn:
+            stmt = select(table).where(
+                getattr(table.c, id_field_name) == name, table.c.project_id == project
+            )
+            row = conn.execute(stmt).first()
+            update_datetime = datetime.utcnow()
+            update_time = int(update_datetime.timestamp())
+            if hasattr(obj, "last_updated_timestamp"):
+                obj.last_updated_timestamp = update_datetime
+
+            if row:
+                values = {
+                    proto_field_name: obj.to_proto().SerializeToString(),
+                    "last_updated_timestamp": update_time,
+                }
+                update_stmt = (
+                    update(table)
+                    .where(getattr(table.c, id_field_name) == name)
+                    .values(values,)
+                )
+                conn.execute(update_stmt)
+            else:
+                values = {
+                    id_field_name: name,
+                    proto_field_name: obj.to_proto().SerializeToString(),
+                    "last_updated_timestamp": update_time,
+                    "project_id": project,
+                }
+                insert_stmt = insert(table).values(values,)
+                conn.execute(insert_stmt)
+
+            self._set_last_updated_metadata(update_datetime, project)
+
+    def _delete_object(self, table, name, project, id_field_name, not_found_exception):
+        with self.engine.connect() as conn:
+            stmt = delete(table).where(
+                getattr(table.c, id_field_name) == name, table.c.project_id == project
+            )
+            rows = conn.execute(stmt)
+            if rows.rowcount < 1 and not_found_exception:
+                raise not_found_exception(name, project)
+            self._set_last_updated_metadata(datetime.utcnow(), project)
+
+            return rows.rowcount
+
+    def _get_object(
+        self,
+        table,
+        name,
+        project,
+        proto_class,
+        python_class,
+        id_field_name,
+        proto_field_name,
+        not_found_exception,
+    ):
+        with self.engine.connect() as conn:
+            stmt = select(table).where(
+                getattr(table.c, id_field_name) == name, table.c.project_id == project
+            )
+            row = conn.execute(stmt).first()
+            if row:
+                _proto = proto_class.FromString(row[proto_field_name])
+                return python_class.from_proto(_proto)
+        raise not_found_exception(name, project)
+
+    def _list_objects(
+        self, table, project, proto_class, python_class, proto_field_name
+    ):
+        with self.engine.connect() as conn:
+            stmt = select(table).where(table.c.project_id == project)
+            rows = conn.execute(stmt).all()
+            if rows:
+                return [
+                    python_class.from_proto(
+                        proto_class.FromString(row[proto_field_name])
+                    )
+                    for row in rows
+                ]
+        return []
+
+    def _set_last_updated_metadata(self, last_updated: datetime, project: str):
+        with self.engine.connect() as conn:
+            stmt = select(feast_metadata).where(
+                feast_metadata.c.metadata_key == "last_updated_timestamp",
+                feast_metadata.c.project_id == project,
+            )
+            row = conn.execute(stmt).first()
+
+            update_time = int(last_updated.timestamp())
+
+            values = {
+                "metadata_key": "last_updated_timestamp",
+                "metadata_value": f"{update_time}",
+                "last_updated_timestamp": update_time,
+                "project_id": project,
+            }
+            if row:
+                update_stmt = (
+                    update(feast_metadata)
+                    .where(
+                        feast_metadata.c.metadata_key == "last_updated_timestamp",
+                        feast_metadata.c.project_id == project,
+                    )
+                    .values(values)
+                )
+                conn.execute(update_stmt)
+            else:
+                insert_stmt = insert(feast_metadata).values(values,)
+                conn.execute(insert_stmt)
+
+    def _get_last_updated_metadata(self, project: str):
+        with self.engine.connect() as conn:
+            stmt = select(feast_metadata).where(
+                feast_metadata.c.metadata_key == "last_updated_timestamp",
+                feast_metadata.c.project_id == project,
+            )
+            row = conn.execute(stmt).first()
+            if not row:
+                return None
+            update_time = int(row["last_updated_timestamp"])
+
+            return datetime.utcfromtimestamp(update_time)
+
+    def _get_all_projects(self) -> Set[str]:
+        projects = set()
+        with self.engine.connect() as conn:
+            for table in {
+                entities,
+                data_sources,
+                feature_views,
+                request_feature_views,
+                on_demand_feature_views,
+                stream_feature_views,
+            }:
+                stmt = select(table)
+                rows = conn.execute(stmt).all()
+                for row in rows:
+                    projects.add(row["project_id"])
+
+        return projects
diff --git a/sdk/python/feast/infra/transformation_servers/Dockerfile b/sdk/python/feast/infra/transformation_servers/Dockerfile
index ff276c7a70d..5e77144757a 100644
--- a/sdk/python/feast/infra/transformation_servers/Dockerfile
+++ b/sdk/python/feast/infra/transformation_servers/Dockerfile
@@ -1,4 +1,6 @@
-FROM python:3.7-slim
+FROM python:3.9-slim
+
+RUN apt-get update && apt-get install -y git
 
 # Copy app handler code
 COPY sdk/python/feast/infra/transformation_servers/app.py app.py
@@ -13,7 +15,7 @@ COPY README.md README.md
 
 
 # Install dependencies
-RUN pip3 install -e '.'
+RUN --mount=source=.git,target=.git,type=bind pip3 install --no-cache-dir -e '.'
 
 # Start feature transformation server
 CMD [ "python", "app.py" ]
diff --git a/sdk/python/feast/infra/utils/aws_utils.py b/sdk/python/feast/infra/utils/aws_utils.py
index bb75160a873..7badda98460 100644
--- a/sdk/python/feast/infra/utils/aws_utils.py
+++ b/sdk/python/feast/infra/utils/aws_utils.py
@@ -235,6 +235,15 @@ def upload_df_to_redshift(
     )
 
 
+def delete_redshift_table(
+    redshift_data_client, cluster_id: str, database: str, user: str, table_name: str,
+):
+    drop_query = f"DROP {table_name} IF EXISTS"
+    execute_redshift_statement(
+        redshift_data_client, cluster_id, database, user, drop_query,
+    )
+
+
 def upload_arrow_table_to_redshift(
     table: Union[pyarrow.Table, Path],
     redshift_data_client,
@@ -320,7 +329,7 @@ def upload_arrow_table_to_redshift(
             cluster_id,
             database,
             user,
-            f"{create_query}; {copy_query}",
+            f"{create_query}; {copy_query};",
         )
     finally:
         # Clean up S3 temporary data
@@ -371,6 +380,53 @@ def temporarily_upload_df_to_redshift(
     )
 
 
+@contextlib.contextmanager
+def temporarily_upload_arrow_table_to_redshift(
+    table: Union[pyarrow.Table, Path],
+    redshift_data_client,
+    cluster_id: str,
+    database: str,
+    user: str,
+    s3_resource,
+    iam_role: str,
+    s3_path: str,
+    table_name: str,
+    schema: Optional[pyarrow.Schema] = None,
+    fail_if_exists: bool = True,
+) -> Iterator[None]:
+    """Uploads a Arrow Table to Redshift as a new table with cleanup logic.
+
+    This is essentially the same as upload_arrow_table_to_redshift (check out its docstring for full details),
+    but unlike it this method is a generator and should be used with `with` block. For example:
+
+    >>> with temporarily_upload_arrow_table_to_redshift(...): # doctest: +SKIP
+    >>>     # Use `table_name` table in Redshift here
+    >>> # `table_name` will not exist at this point, since it's cleaned up by the `with` block
+
+    """
+    # Upload the dataframe to Redshift
+    upload_arrow_table_to_redshift(
+        table,
+        redshift_data_client,
+        cluster_id,
+        database,
+        user,
+        s3_resource,
+        s3_path,
+        iam_role,
+        table_name,
+        schema,
+        fail_if_exists,
+    )
+
+    yield
+
+    # Clean up the uploaded Redshift table
+    execute_redshift_statement(
+        redshift_data_client, cluster_id, database, user, f"DROP TABLE {table_name}",
+    )
+
+
 def download_s3_directory(s3_resource, bucket: str, key: str, local_dir: str):
     """Download the S3 directory to a local disk"""
     bucket_obj = s3_resource.Bucket(bucket)
diff --git a/sdk/python/feast/on_demand_feature_view.py b/sdk/python/feast/on_demand_feature_view.py
index 1cddc0b8814..bad4edba810 100644
--- a/sdk/python/feast/on_demand_feature_view.py
+++ b/sdk/python/feast/on_demand_feature_view.py
@@ -1,11 +1,13 @@
 import copy
 import functools
 import warnings
-from types import MethodType
-from typing import Dict, List, Optional, Type, Union
+from datetime import datetime
+from types import FunctionType
+from typing import Any, Dict, List, Optional, Type, Union
 
 import dill
 import pandas as pd
+from typeguard import typechecked
 
 from feast.base_feature_view import BaseFeatureView
 from feast.batch_feature_view import BatchFeatureView
@@ -37,6 +39,7 @@
 warnings.simplefilter("once", DeprecationWarning)
 
 
+@typechecked
 class OnDemandFeatureView(BaseFeatureView):
     """
     [Experimental] An OnDemandFeatureView defines a logical group of features that are
@@ -63,7 +66,7 @@ class OnDemandFeatureView(BaseFeatureView):
     features: List[Field]
     source_feature_view_projections: Dict[str, FeatureViewProjection]
     source_request_sources: Dict[str, RequestSource]
-    udf: MethodType
+    udf: FunctionType
     description: str
     tags: Dict[str, str]
     owner: str
@@ -75,16 +78,9 @@ def __init__(  # noqa: C901
         name: Optional[str] = None,
         features: Optional[List[Feature]] = None,
         sources: Optional[
-            List[
-                Union[
-                    BatchFeatureView,
-                    StreamFeatureView,
-                    RequestSource,
-                    FeatureViewProjection,
-                ]
-            ]
-        ] = None,
-        udf: Optional[MethodType] = None,
+            List[Any]
+        ] = None,  # Typed as Any because @typechecked can't deal with the List[Union]
+        udf: Optional[FunctionType] = None,
         inputs: Optional[
             Dict[str, Union[FeatureView, FeatureViewProjection, RequestSource]]
         ] = None,
@@ -127,7 +123,7 @@ def __init__(  # noqa: C901
                 (
                     "The `features` parameter is being deprecated in favor of the `schema` parameter. "
                     "Please switch from using `features` to `schema`. This will also requiring switching "
-                    "feature definitions from using `Feature` to `Field`. Feast 0.23 and onwards will not "
+                    "feature definitions from using `Feature` to `Field`. Feast 0.24 and onwards will not "
                     "support the `features` parameter."
                 ),
                 DeprecationWarning,
@@ -139,7 +135,7 @@ def __init__(  # noqa: C901
             warnings.warn(
                 (
                     "The `inputs` parameter is being deprecated. Please use `sources` instead. "
-                    "Feast 0.23 and onwards will not support the `inputs` parameter."
+                    "Feast 0.24 and onwards will not support the `inputs` parameter."
                 ),
                 DeprecationWarning,
             )
@@ -154,13 +150,13 @@ def __init__(  # noqa: C901
                     raise ValueError(
                         "input can only accept FeatureView, FeatureViewProjection, or RequestSource"
                     )
-        _udf = udf
+        _udf: Optional[FunctionType] = udf
 
         if args:
             warnings.warn(
                 (
                     "On demand feature view parameters should be specified as keyword arguments "
-                    "instead of positional arguments. Feast 0.23 and onwards will not support "
+                    "instead of positional arguments. Feast 0.24 and onwards will not support "
                     "positional arguments in on demand feature view definitions."
                 ),
                 DeprecationWarning,
@@ -181,7 +177,7 @@ def __init__(  # noqa: C901
                     (
                         "The `features` parameter is being deprecated in favor of the `schema` parameter. "
                         "Please switch from using `features` to `schema`. This will also requiring switching "
-                        "feature definitions from using `Feature` to `Field`. Feast 0.23 and onwards will not "
+                        "feature definitions from using `Feature` to `Field`. Feast 0.24 and onwards will not "
                         "support the `features` parameter."
                     ),
                     DeprecationWarning,
@@ -202,7 +198,7 @@ def __init__(  # noqa: C901
                 warnings.warn(
                     (
                         "The `inputs` parameter is being deprecated. Please use `sources` instead. "
-                        "Feast 0.23 and onwards will not support the `inputs` parameter."
+                        "Feast 0.24 and onwards will not support the `inputs` parameter."
                     ),
                     DeprecationWarning,
                 )
@@ -239,8 +235,7 @@ def __init__(  # noqa: C901
 
         if _udf is None:
             raise ValueError("The `udf` parameter must be specified.")
-        assert _udf
-        self.udf = _udf
+        self.udf = _udf  # type: ignore
 
     @property
     def proto_class(self) -> Type[OnDemandFeatureViewProto]:
@@ -442,6 +437,15 @@ def infer_features(self):
         Raises:
             RegistryInferenceFailure: The set of features could not be inferred.
         """
+        rand_df_value: Dict[str, Any] = {
+            "float": 1.0,
+            "int": 1,
+            "str": "hello world",
+            "bytes": str.encode("hello world"),
+            "bool": True,
+            "datetime64[ns]": datetime.utcnow(),
+        }
+
         df = pd.DataFrame()
         for feature_view_projection in self.source_feature_view_projections.values():
             for feature in feature_view_projection.features:
@@ -449,11 +453,13 @@ def infer_features(self):
                 df[f"{feature_view_projection.name}__{feature.name}"] = pd.Series(
                     dtype=dtype
                 )
-                df[f"{feature.name}"] = pd.Series(dtype=dtype)
+                sample_val = rand_df_value[dtype] if dtype in rand_df_value else None
+                df[f"{feature.name}"] = pd.Series(data=sample_val, dtype=dtype)
         for request_data in self.source_request_sources.values():
             for field in request_data.schema:
                 dtype = feast_value_type_to_pandas_type(field.dtype.to_value_type())
-                df[f"{field.name}"] = pd.Series(dtype=dtype)
+                sample_val = rand_df_value[dtype] if dtype in rand_df_value else None
+                df[f"{field.name}"] = pd.Series(sample_val, dtype=dtype)
         output_df: pd.DataFrame = self.udf.__call__(df)
         inferred_features = []
         for f, dt in zip(output_df.columns, output_df.dtypes):
@@ -473,7 +479,7 @@ def infer_features(self):
                     missing_features.append(specified_features)
             if missing_features:
                 raise SpecifiedFeaturesNotPresentError(
-                    [f.name for f in missing_features], self.name
+                    missing_features, inferred_features, self.name
                 )
         else:
             self.features = inferred_features
@@ -548,7 +554,7 @@ def on_demand_feature_view(
             (
                 "The `features` parameter is being deprecated in favor of the `schema` parameter. "
                 "Please switch from using `features` to `schema`. This will also requiring switching "
-                "feature definitions from using `Feature` to `Field`. Feast 0.23 and onwards will not "
+                "feature definitions from using `Feature` to `Field`. Feast 0.24 and onwards will not "
                 "support the `features` parameter."
             ),
             DeprecationWarning,
@@ -560,7 +566,7 @@ def on_demand_feature_view(
         warnings.warn(
             (
                 "The `inputs` parameter is being deprecated. Please use `sources` instead. "
-                "Feast 0.23 and onwards will not support the `inputs` parameter."
+                "Feast 0.24 and onwards will not support the `inputs` parameter."
             ),
             DeprecationWarning,
         )
@@ -580,7 +586,7 @@ def on_demand_feature_view(
         warnings.warn(
             (
                 "On demand feature view parameters should be specified as keyword arguments "
-                "instead of positional arguments. Feast 0.23 and onwards will not support "
+                "instead of positional arguments. Feast 0.24 and onwards will not support "
                 "positional arguments in on demand feature view definitions."
             ),
             DeprecationWarning,
@@ -599,7 +605,7 @@ def on_demand_feature_view(
                 (
                     "The `features` parameter is being deprecated in favor of the `schema` parameter. "
                     "Please switch from using `features` to `schema`. This will also requiring switching "
-                    "feature definitions from using `Feature` to `Field`. Feast 0.23 and onwards will not "
+                    "feature definitions from using `Feature` to `Field`. Feast 0.24 and onwards will not "
                     "support the `features` parameter."
                 ),
                 DeprecationWarning,
@@ -620,7 +626,7 @@ def on_demand_feature_view(
                 warnings.warn(
                     (
                         "The `inputs` parameter is being deprecated. Please use `sources` instead. "
-                        "Feast 0.23 and onwards will not support the `inputs` parameter."
+                        "Feast 0.24 and onwards will not support the `inputs` parameter."
                     ),
                     DeprecationWarning,
                 )
@@ -654,7 +660,7 @@ def decorator(user_function):
 
 
 def feature_view_to_batch_feature_view(fv: FeatureView) -> BatchFeatureView:
-    return BatchFeatureView(
+    bfv = BatchFeatureView(
         name=fv.name,
         entities=fv.entities,
         ttl=fv.ttl,
@@ -662,5 +668,9 @@ def feature_view_to_batch_feature_view(fv: FeatureView) -> BatchFeatureView:
         online=fv.online,
         owner=fv.owner,
         schema=fv.schema,
-        source=fv.source,
+        source=fv.batch_source,
     )
+
+    bfv.features = copy.copy(fv.features)
+    bfv.entities = copy.copy(fv.entities)
+    return bfv
diff --git a/sdk/python/feast/registry.py b/sdk/python/feast/registry.py
index be009566d05..c721bd648a8 100644
--- a/sdk/python/feast/registry.py
+++ b/sdk/python/feast/registry.py
@@ -11,8 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import abc
 import json
 import logging
+from abc import abstractmethod
 from collections import defaultdict
 from datetime import datetime, timedelta
 from enum import Enum
@@ -38,6 +40,7 @@
     FeatureViewNotFoundException,
     OnDemandFeatureViewNotFoundException,
     SavedDatasetNotFound,
+    ValidationReferenceNotFound,
 )
 from feast.feature_service import FeatureService
 from feast.feature_view import FeatureView
@@ -49,7 +52,8 @@
 from feast.repo_config import RegistryConfig
 from feast.repo_contents import RepoContents
 from feast.request_feature_view import RequestFeatureView
-from feast.saved_dataset import SavedDataset
+from feast.saved_dataset import SavedDataset, ValidationReference
+from feast.stream_feature_view import StreamFeatureView
 
 REGISTRY_SCHEMA_VERSION = "1"
 
@@ -75,11 +79,12 @@ class FeastObjectType(Enum):
     FEATURE_VIEW = "feature view"
     ON_DEMAND_FEATURE_VIEW = "on demand feature view"
     REQUEST_FEATURE_VIEW = "request feature view"
+    STREAM_FEATURE_VIEW = "stream feature view"
     FEATURE_SERVICE = "feature service"
 
     @staticmethod
     def get_objects_from_registry(
-        registry: "Registry", project: str
+        registry: "BaseRegistry", project: str
     ) -> Dict["FeastObjectType", List[Any]]:
         return {
             FeastObjectType.DATA_SOURCE: registry.list_data_sources(project=project),
@@ -91,6 +96,9 @@ def get_objects_from_registry(
             FeastObjectType.REQUEST_FEATURE_VIEW: registry.list_request_feature_views(
                 project=project
             ),
+            FeastObjectType.STREAM_FEATURE_VIEW: registry.list_stream_feature_views(
+                project=project,
+            ),
             FeastObjectType.FEATURE_SERVICE: registry.list_feature_services(
                 project=project
             ),
@@ -106,6 +114,7 @@ def get_objects_from_repo_contents(
             FeastObjectType.FEATURE_VIEW: repo_contents.feature_views,
             FeastObjectType.ON_DEMAND_FEATURE_VIEW: repo_contents.on_demand_feature_views,
             FeastObjectType.REQUEST_FEATURE_VIEW: repo_contents.request_feature_views,
+            FeastObjectType.STREAM_FEATURE_VIEW: repo_contents.stream_feature_views,
             FeastObjectType.FEATURE_SERVICE: repo_contents.feature_services,
         }
 
@@ -126,23 +135,622 @@ def get_registry_store_class_from_type(registry_store_type: str):
     return import_class(module_name, registry_store_class_name, "RegistryStore")
 
 
-def get_registry_store_class_from_scheme(registry_path: str):
-    uri = urlparse(registry_path)
-    if uri.scheme not in REGISTRY_STORE_CLASS_FOR_SCHEME:
-        raise Exception(
-            f"Registry path {registry_path} has unsupported scheme {uri.scheme}. "
-            f"Supported schemes are file, s3 and gs."
-        )
-    else:
-        registry_store_type = REGISTRY_STORE_CLASS_FOR_SCHEME[uri.scheme]
-        return get_registry_store_class_from_type(registry_store_type)
+def get_registry_store_class_from_scheme(registry_path: str):
+    uri = urlparse(registry_path)
+    if uri.scheme not in REGISTRY_STORE_CLASS_FOR_SCHEME:
+        raise Exception(
+            f"Registry path {registry_path} has unsupported scheme {uri.scheme}. "
+            f"Supported schemes are file, s3 and gs."
+        )
+    else:
+        registry_store_type = REGISTRY_STORE_CLASS_FOR_SCHEME[uri.scheme]
+        return get_registry_store_class_from_type(registry_store_type)
+
+
+class BaseRegistry(abc.ABC):
+    # Entity operations
+    @abstractmethod
+    def apply_entity(self, entity: Entity, project: str, commit: bool = True):
+        """
+        Registers a single entity with Feast
+
+        Args:
+            entity: Entity that will be registered
+            project: Feast project that this entity belongs to
+            commit: Whether the change should be persisted immediately
+        """
+
+    @abstractmethod
+    def delete_entity(self, name: str, project: str, commit: bool = True):
+        """
+        Deletes an entity or raises an exception if not found.
+
+        Args:
+            name: Name of entity
+            project: Feast project that this entity belongs to
+            commit: Whether the change should be persisted immediately
+        """
+
+    @abstractmethod
+    def get_entity(self, name: str, project: str, allow_cache: bool = False) -> Entity:
+        """
+        Retrieves an entity.
+
+        Args:
+            name: Name of entity
+            project: Feast project that this entity belongs to
+            allow_cache: Whether to allow returning this entity from a cached registry
+
+        Returns:
+            Returns either the specified entity, or raises an exception if
+            none is found
+        """
+
+    @abstractmethod
+    def list_entities(self, project: str, allow_cache: bool = False) -> List[Entity]:
+        """
+        Retrieve a list of entities from the registry
+
+        Args:
+            allow_cache: Whether to allow returning entities from a cached registry
+            project: Filter entities based on project name
+
+        Returns:
+            List of entities
+        """
+
+    # Data source operations
+    @abstractmethod
+    def apply_data_source(
+        self, data_source: DataSource, project: str, commit: bool = True
+    ):
+        """
+        Registers a single data source with Feast
+
+        Args:
+            data_source: A data source that will be registered
+            project: Feast project that this data source belongs to
+            commit: Whether to immediately commit to the registry
+        """
+
+    @abstractmethod
+    def delete_data_source(self, name: str, project: str, commit: bool = True):
+        """
+        Deletes a data source or raises an exception if not found.
+
+        Args:
+            name: Name of data source
+            project: Feast project that this data source belongs to
+            commit: Whether the change should be persisted immediately
+        """
+
+    @abstractmethod
+    def get_data_source(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> DataSource:
+        """
+        Retrieves a data source.
+
+        Args:
+            name: Name of data source
+            project: Feast project that this data source belongs to
+            allow_cache: Whether to allow returning this data source from a cached registry
+
+        Returns:
+            Returns either the specified data source, or raises an exception if none is found
+        """
+
+    @abstractmethod
+    def list_data_sources(
+        self, project: str, allow_cache: bool = False
+    ) -> List[DataSource]:
+        """
+        Retrieve a list of data sources from the registry
+
+        Args:
+            project: Filter data source based on project name
+            allow_cache: Whether to allow returning data sources from a cached registry
+
+        Returns:
+            List of data sources
+        """
+
+    # Feature service operations
+    @abstractmethod
+    def apply_feature_service(
+        self, feature_service: FeatureService, project: str, commit: bool = True
+    ):
+        """
+        Registers a single feature service with Feast
+
+        Args:
+            feature_service: A feature service that will be registered
+            project: Feast project that this entity belongs to
+        """
+
+    @abstractmethod
+    def delete_feature_service(self, name: str, project: str, commit: bool = True):
+        """
+        Deletes a feature service or raises an exception if not found.
+
+        Args:
+            name: Name of feature service
+            project: Feast project that this feature service belongs to
+            commit: Whether the change should be persisted immediately
+        """
+
+    @abstractmethod
+    def get_feature_service(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> FeatureService:
+        """
+        Retrieves a feature service.
+
+        Args:
+            name: Name of feature service
+            project: Feast project that this feature service belongs to
+            allow_cache: Whether to allow returning this feature service from a cached registry
+
+        Returns:
+            Returns either the specified feature service, or raises an exception if
+            none is found
+        """
+
+    @abstractmethod
+    def list_feature_services(
+        self, project: str, allow_cache: bool = False
+    ) -> List[FeatureService]:
+        """
+        Retrieve a list of feature services from the registry
+
+        Args:
+            allow_cache: Whether to allow returning entities from a cached registry
+            project: Filter entities based on project name
+
+        Returns:
+            List of feature services
+        """
+
+    # Feature view operations
+    @abstractmethod
+    def apply_feature_view(
+        self, feature_view: BaseFeatureView, project: str, commit: bool = True
+    ):
+        """
+        Registers a single feature view with Feast
+
+        Args:
+            feature_view: Feature view that will be registered
+            project: Feast project that this feature view belongs to
+            commit: Whether the change should be persisted immediately
+        """
+
+    @abstractmethod
+    def delete_feature_view(self, name: str, project: str, commit: bool = True):
+        """
+        Deletes a feature view or raises an exception if not found.
+
+        Args:
+            name: Name of feature view
+            project: Feast project that this feature view belongs to
+            commit: Whether the change should be persisted immediately
+        """
+
+    # stream feature view operations
+    @abstractmethod
+    def get_stream_feature_view(
+        self, name: str, project: str, allow_cache: bool = False
+    ):
+        """
+        Retrieves a stream feature view.
+
+        Args:
+            name: Name of stream feature view
+            project: Feast project that this feature view belongs to
+            allow_cache: Allow returning feature view from the cached registry
+
+        Returns:
+            Returns either the specified feature view, or raises an exception if
+            none is found
+        """
+
+    @abstractmethod
+    def list_stream_feature_views(
+        self, project: str, allow_cache: bool = False
+    ) -> List[StreamFeatureView]:
+        """
+        Retrieve a list of stream feature views from the registry
+
+        Args:
+            project: Filter stream feature views based on project name
+            allow_cache: Whether to allow returning stream feature views from a cached registry
+
+        Returns:
+            List of stream feature views
+        """
+
+    # on demand feature view operations
+    @abstractmethod
+    def get_on_demand_feature_view(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> OnDemandFeatureView:
+        """
+        Retrieves an on demand feature view.
+
+        Args:
+            name: Name of on demand feature view
+            project: Feast project that this on demand feature view belongs to
+            allow_cache: Whether to allow returning this on demand feature view from a cached registry
+
+        Returns:
+            Returns either the specified on demand feature view, or raises an exception if
+            none is found
+        """
+
+    @abstractmethod
+    def list_on_demand_feature_views(
+        self, project: str, allow_cache: bool = False
+    ) -> List[OnDemandFeatureView]:
+        """
+        Retrieve a list of on demand feature views from the registry
+
+        Args:
+            project: Filter on demand feature views based on project name
+            allow_cache: Whether to allow returning on demand feature views from a cached registry
+
+        Returns:
+            List of on demand feature views
+        """
+
+    # regular feature view operations
+    @abstractmethod
+    def get_feature_view(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> FeatureView:
+        """
+        Retrieves a feature view.
+
+        Args:
+            name: Name of feature view
+            project: Feast project that this feature view belongs to
+            allow_cache: Allow returning feature view from the cached registry
+
+        Returns:
+            Returns either the specified feature view, or raises an exception if
+            none is found
+        """
+
+    @abstractmethod
+    def list_feature_views(
+        self, project: str, allow_cache: bool = False
+    ) -> List[FeatureView]:
+        """
+        Retrieve a list of feature views from the registry
+
+        Args:
+            allow_cache: Allow returning feature views from the cached registry
+            project: Filter feature views based on project name
+
+        Returns:
+            List of feature views
+        """
+
+    # request feature view operations
+    @abstractmethod
+    def get_request_feature_view(self, name: str, project: str) -> RequestFeatureView:
+        """
+        Retrieves a request feature view.
+
+        Args:
+            name: Name of request feature view
+            project: Feast project that this feature view belongs to
+            allow_cache: Allow returning feature view from the cached registry
+
+        Returns:
+            Returns either the specified feature view, or raises an exception if
+            none is found
+        """
+
+    @abstractmethod
+    def list_request_feature_views(
+        self, project: str, allow_cache: bool = False
+    ) -> List[RequestFeatureView]:
+        """
+        Retrieve a list of request feature views from the registry
+
+        Args:
+            allow_cache: Allow returning feature views from the cached registry
+            project: Filter feature views based on project name
+
+        Returns:
+            List of request feature views
+        """
+
+    @abstractmethod
+    def apply_materialization(
+        self,
+        feature_view: FeatureView,
+        project: str,
+        start_date: datetime,
+        end_date: datetime,
+        commit: bool = True,
+    ):
+        """
+        Updates materialization intervals tracked for a single feature view in Feast
+
+        Args:
+            feature_view: Feature view that will be updated with an additional materialization interval tracked
+            project: Feast project that this feature view belongs to
+            start_date (datetime): Start date of the materialization interval to track
+            end_date (datetime): End date of the materialization interval to track
+            commit: Whether the change should be persisted immediately
+        """
+
+    # Saved dataset operations
+    @abstractmethod
+    def apply_saved_dataset(
+        self, saved_dataset: SavedDataset, project: str, commit: bool = True,
+    ):
+        """
+        Stores a saved dataset metadata with Feast
+
+        Args:
+            saved_dataset: SavedDataset that will be added / updated to registry
+            project: Feast project that this dataset belongs to
+            commit: Whether the change should be persisted immediately
+        """
+
+    @abstractmethod
+    def get_saved_dataset(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> SavedDataset:
+        """
+        Retrieves a saved dataset.
+
+        Args:
+            name: Name of dataset
+            project: Feast project that this dataset belongs to
+            allow_cache: Whether to allow returning this dataset from a cached registry
+
+        Returns:
+            Returns either the specified SavedDataset, or raises an exception if
+            none is found
+        """
+
+    def delete_saved_dataset(self, name: str, project: str, allow_cache: bool = False):
+        """
+        Delete a saved dataset.
+
+        Args:
+            name: Name of dataset
+            project: Feast project that this dataset belongs to
+            allow_cache: Whether to allow returning this dataset from a cached registry
+
+        Returns:
+            Returns either the specified SavedDataset, or raises an exception if
+            none is found
+        """
+
+    @abstractmethod
+    def list_saved_datasets(
+        self, project: str, allow_cache: bool = False
+    ) -> List[SavedDataset]:
+        """
+        Retrieves a list of all saved datasets in specified project
+
+        Args:
+            project: Feast project
+            allow_cache: Whether to allow returning this dataset from a cached registry
+
+        Returns:
+            Returns the list of SavedDatasets
+        """
+
+    # Validation reference operations
+    @abstractmethod
+    def apply_validation_reference(
+        self,
+        validation_reference: ValidationReference,
+        project: str,
+        commit: bool = True,
+    ):
+        """
+        Persist a validation reference
+
+        Args:
+            validation_reference: ValidationReference that will be added / updated to registry
+            project: Feast project that this dataset belongs to
+            commit: Whether the change should be persisted immediately
+        """
+
+    @abstractmethod
+    def delete_validation_reference(self, name: str, project: str, commit: bool = True):
+        """
+        Deletes a validation reference or raises an exception if not found.
+
+        Args:
+            name: Name of validation reference
+            project: Feast project that this object belongs to
+            commit: Whether the change should be persisted immediately
+        """
+
+    @abstractmethod
+    def get_validation_reference(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> ValidationReference:
+        """
+            Retrieves a validation reference.
+
+            Args:
+                name: Name of dataset
+                project: Feast project that this dataset belongs to
+                allow_cache: Whether to allow returning this dataset from a cached registry
+
+            Returns:
+                Returns either the specified ValidationReference, or raises an exception if
+                none is found
+            """
+
+    # TODO: Needs to be implemented.
+    def list_validation_references(
+        self, project: str, allow_cache: bool = False
+    ) -> List[ValidationReference]:
+
+        """
+        Retrieve a list of validation references from the registry
+
+        Args:
+            allow_cache: Allow returning feature views from the cached registry
+            project: Filter feature views based on project name
+
+        Returns:
+            List of request feature views
+        """
+
+    @abstractmethod
+    def update_infra(self, infra: Infra, project: str, commit: bool = True):
+        """
+        Updates the stored Infra object.
+
+        Args:
+            infra: The new Infra object to be stored.
+            project: Feast project that the Infra object refers to
+            commit: Whether the change should be persisted immediately
+        """
+
+    @abstractmethod
+    def get_infra(self, project: str, allow_cache: bool = False) -> Infra:
+        """
+        Retrieves the stored Infra object.
+
+        Args:
+            project: Feast project that the Infra object refers to
+            allow_cache: Whether to allow returning this entity from a cached registry
+
+        Returns:
+            The stored Infra object.
+        """
+
+    @abstractmethod
+    def apply_user_metadata(
+        self,
+        project: str,
+        feature_view: BaseFeatureView,
+        metadata_bytes: Optional[bytes],
+    ):
+        ...
+
+    @abstractmethod
+    def get_user_metadata(
+        self, project: str, feature_view: BaseFeatureView
+    ) -> Optional[bytes]:
+        ...
+
+    @abstractmethod
+    def proto(self) -> RegistryProto:
+        """
+        Retrieves a proto version of the registry.
+
+        Returns:
+            The registry proto object.
+        """
+
+    @abstractmethod
+    def commit(self):
+        """Commits the state of the registry cache to the remote registry store."""
+
+    @abstractmethod
+    def refresh(self):
+        """Refreshes the state of the registry cache by fetching the registry state from the remote registry store."""
+
+    @staticmethod
+    def _message_to_sorted_dict(message: Message) -> Dict[str, Any]:
+        return json.loads(MessageToJson(message, sort_keys=True))
+
+    def to_dict(self, project: str) -> Dict[str, List[Any]]:
+        """Returns a dictionary representation of the registry contents for the specified project.
+
+        For each list in the dictionary, the elements are sorted by name, so this
+        method can be used to compare two registries.
+
+        Args:
+            project: Feast project to convert to a dict
+        """
+        registry_dict: Dict[str, Any] = defaultdict(list)
+        registry_dict["project"] = project
+        for data_source in sorted(
+            self.list_data_sources(project=project), key=lambda ds: ds.name
+        ):
+            registry_dict["dataSources"].append(
+                self._message_to_sorted_dict(data_source.to_proto())
+            )
+        for entity in sorted(
+            self.list_entities(project=project), key=lambda entity: entity.name
+        ):
+            registry_dict["entities"].append(
+                self._message_to_sorted_dict(entity.to_proto())
+            )
+        for feature_view in sorted(
+            self.list_feature_views(project=project),
+            key=lambda feature_view: feature_view.name,
+        ):
+            registry_dict["featureViews"].append(
+                self._message_to_sorted_dict(feature_view.to_proto())
+            )
+        for feature_service in sorted(
+            self.list_feature_services(project=project),
+            key=lambda feature_service: feature_service.name,
+        ):
+            registry_dict["featureServices"].append(
+                self._message_to_sorted_dict(feature_service.to_proto())
+            )
+        for on_demand_feature_view in sorted(
+            self.list_on_demand_feature_views(project=project),
+            key=lambda on_demand_feature_view: on_demand_feature_view.name,
+        ):
+            odfv_dict = self._message_to_sorted_dict(on_demand_feature_view.to_proto())
+            odfv_dict["spec"]["userDefinedFunction"]["body"] = dill.source.getsource(
+                on_demand_feature_view.udf
+            )
+            registry_dict["onDemandFeatureViews"].append(odfv_dict)
+        for request_feature_view in sorted(
+            self.list_request_feature_views(project=project),
+            key=lambda request_feature_view: request_feature_view.name,
+        ):
+            registry_dict["requestFeatureViews"].append(
+                self._message_to_sorted_dict(request_feature_view.to_proto())
+            )
+        for saved_dataset in sorted(
+            self.list_saved_datasets(project=project), key=lambda item: item.name
+        ):
+            registry_dict["savedDatasets"].append(
+                self._message_to_sorted_dict(saved_dataset.to_proto())
+            )
+        for infra_object in sorted(self.get_infra(project=project).infra_objects):
+            registry_dict["infra"].append(
+                self._message_to_sorted_dict(infra_object.to_proto())
+            )
+        return registry_dict
 
 
-class Registry:
+class Registry(BaseRegistry):
     """
     Registry: A registry allows for the management and persistence of feature definitions and related metadata.
     """
 
+    def apply_user_metadata(
+        self,
+        project: str,
+        feature_view: BaseFeatureView,
+        metadata_bytes: Optional[bytes],
+    ):
+        pass
+
+    def get_user_metadata(
+        self, project: str, feature_view: BaseFeatureView
+    ) -> Optional[bytes]:
+        pass
+
     # The cached_registry_proto object is used for both reads and writes. In particular,
     # all write operations refresh the cache and modify it in memory; the write must
     # then be persisted to the underlying RegistryStore with a call to commit().
@@ -150,6 +758,18 @@ class Registry:
     cached_registry_proto_created: Optional[datetime] = None
     cached_registry_proto_ttl: timedelta
 
+    def __new__(
+        cls, registry_config: Optional[RegistryConfig], repo_path: Optional[Path]
+    ):
+        # We override __new__ so that we can inspect registry_config and create a SqlRegistry without callers
+        # needing to make any changes.
+        if registry_config and registry_config.registry_type == "sql":
+            from feast.infra.registry_stores.sql import SqlRegistry
+
+            return SqlRegistry(registry_config, repo_path)
+        else:
+            return super(Registry, cls).__new__(cls)
+
     def __init__(
         self, registry_config: Optional[RegistryConfig], repo_path: Optional[Path]
     ):
@@ -475,7 +1095,11 @@ def apply_feature_view(
 
         self._check_conflicting_feature_view_names(feature_view)
         existing_feature_views_of_same_type: RepeatedCompositeFieldContainer
-        if isinstance(feature_view, FeatureView):
+        if isinstance(feature_view, StreamFeatureView):
+            existing_feature_views_of_same_type = (
+                self.cached_registry_proto.stream_feature_views
+            )
+        elif isinstance(feature_view, FeatureView):
             existing_feature_views_of_same_type = (
                 self.cached_registry_proto.feature_views
             )
@@ -505,11 +1129,32 @@ def apply_feature_view(
                 else:
                     del existing_feature_views_of_same_type[idx]
                     break
-
         existing_feature_views_of_same_type.append(feature_view_proto)
         if commit:
             self.commit()
 
+    def list_stream_feature_views(
+        self, project: str, allow_cache: bool = False
+    ) -> List[StreamFeatureView]:
+        """
+        Retrieve a list of stream feature views from the registry
+
+        Args:
+            project: Filter stream feature views based on project name
+            allow_cache: Whether to allow returning stream feature views from a cached registry
+
+        Returns:
+            List of stream feature views
+        """
+        registry = self._get_registry_proto(allow_cache=allow_cache)
+        stream_feature_views = []
+        for stream_feature_view in registry.stream_feature_views:
+            if stream_feature_view.spec.project == project:
+                stream_feature_views.append(
+                    StreamFeatureView.from_proto(stream_feature_view)
+                )
+        return stream_feature_views
+
     def list_on_demand_feature_views(
         self, project: str, allow_cache: bool = False
     ) -> List[OnDemandFeatureView]:
@@ -622,6 +1267,30 @@ def apply_materialization(
                     self.commit()
                 return
 
+        for idx, existing_stream_feature_view_proto in enumerate(
+            self.cached_registry_proto.stream_feature_views
+        ):
+            if (
+                existing_stream_feature_view_proto.spec.name == feature_view.name
+                and existing_stream_feature_view_proto.spec.project == project
+            ):
+                existing_stream_feature_view = StreamFeatureView.from_proto(
+                    existing_stream_feature_view_proto
+                )
+                existing_stream_feature_view.materialization_intervals.append(
+                    (start_date, end_date)
+                )
+                existing_stream_feature_view.last_updated_timestamp = datetime.utcnow()
+                stream_feature_view_proto = existing_stream_feature_view.to_proto()
+                stream_feature_view_proto.spec.project = project
+                del self.cached_registry_proto.stream_feature_views[idx]
+                self.cached_registry_proto.stream_feature_views.append(
+                    stream_feature_view_proto
+                )
+                if commit:
+                    self.commit()
+                return
+
         raise FeatureViewNotFoundException(feature_view.name, project)
 
     def list_feature_views(
@@ -644,6 +1313,28 @@ def list_feature_views(
                 feature_views.append(FeatureView.from_proto(feature_view_proto))
         return feature_views
 
+    def get_request_feature_view(self, name: str, project: str):
+        """
+        Retrieves a feature view.
+
+        Args:
+            name: Name of feature view
+            project: Feast project that this feature view belongs to
+            allow_cache: Allow returning feature view from the cached registry
+
+        Returns:
+            Returns either the specified feature view, or raises an exception if
+            none is found
+        """
+        registry_proto = self._get_registry_proto(allow_cache=False)
+        for feature_view_proto in registry_proto.feature_views:
+            if (
+                feature_view_proto.spec.name == name
+                and feature_view_proto.spec.project == project
+            ):
+                return RequestFeatureView.from_proto(feature_view_proto)
+        raise FeatureViewNotFoundException(name, project)
+
     def list_request_feature_views(
         self, project: str, allow_cache: bool = False
     ) -> List[RequestFeatureView]:
@@ -690,6 +1381,30 @@ def get_feature_view(
                 return FeatureView.from_proto(feature_view_proto)
         raise FeatureViewNotFoundException(name, project)
 
+    def get_stream_feature_view(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> StreamFeatureView:
+        """
+        Retrieves a stream feature view.
+
+        Args:
+            name: Name of stream feature view
+            project: Feast project that this stream feature view belongs to
+            allow_cache: Allow returning feature view from the cached registry
+
+        Returns:
+            Returns either the specified feature view, or raises an exception if
+            none is found
+        """
+        registry_proto = self._get_registry_proto(allow_cache=allow_cache)
+        for feature_view_proto in registry_proto.stream_feature_views:
+            if (
+                feature_view_proto.spec.name == name
+                and feature_view_proto.spec.project == project
+            ):
+                return StreamFeatureView.from_proto(feature_view_proto)
+        raise FeatureViewNotFoundException(name, project)
+
     def delete_feature_service(self, name: str, project: str, commit: bool = True):
         """
         Deletes a feature service or raises an exception if not found.
@@ -763,6 +1478,18 @@ def delete_feature_view(self, name: str, project: str, commit: bool = True):
                     self.commit()
                 return
 
+        for idx, existing_stream_feature_view_proto in enumerate(
+            self.cached_registry_proto.stream_feature_views
+        ):
+            if (
+                existing_stream_feature_view_proto.spec.name == name
+                and existing_stream_feature_view_proto.spec.project == project
+            ):
+                del self.cached_registry_proto.stream_feature_views[idx]
+                if commit:
+                    self.commit()
+                return
+
         raise FeatureViewNotFoundException(name, project)
 
     def delete_entity(self, name: str, project: str, commit: bool = True):
@@ -795,7 +1522,7 @@ def apply_saved_dataset(
         self, saved_dataset: SavedDataset, project: str, commit: bool = True,
     ):
         """
-        Registers a single entity with Feast
+        Stores a saved dataset metadata with Feast
 
         Args:
             saved_dataset: SavedDataset that will be added / updated to registry
@@ -870,6 +1597,85 @@ def list_saved_datasets(
             if saved_dataset.spec.project == project
         ]
 
+    def apply_validation_reference(
+        self,
+        validation_reference: ValidationReference,
+        project: str,
+        commit: bool = True,
+    ):
+        """
+        Persist a validation reference
+
+        Args:
+            validation_reference: ValidationReference that will be added / updated to registry
+            project: Feast project that this dataset belongs to
+            commit: Whether the change should be persisted immediately
+        """
+        validation_reference_proto = validation_reference.to_proto()
+        validation_reference_proto.project = project
+
+        registry_proto = self._prepare_registry_for_changes()
+        for idx, existing_validation_reference in enumerate(
+            registry_proto.validation_references
+        ):
+            if (
+                existing_validation_reference.name == validation_reference_proto.name
+                and existing_validation_reference.project == project
+            ):
+                del registry_proto.validation_references[idx]
+                break
+
+        registry_proto.validation_references.append(validation_reference_proto)
+        if commit:
+            self.commit()
+
+    def get_validation_reference(
+        self, name: str, project: str, allow_cache: bool = False
+    ) -> ValidationReference:
+        """
+            Retrieves a validation reference.
+
+            Args:
+                name: Name of dataset
+                project: Feast project that this dataset belongs to
+                allow_cache: Whether to allow returning this dataset from a cached registry
+
+            Returns:
+                Returns either the specified ValidationReference, or raises an exception if
+                none is found
+            """
+        registry_proto = self._get_registry_proto(allow_cache=allow_cache)
+        for validation_reference in registry_proto.validation_references:
+            if (
+                validation_reference.name == name
+                and validation_reference.project == project
+            ):
+                return ValidationReference.from_proto(validation_reference)
+        raise ValidationReferenceNotFound(name, project=project)
+
+    def delete_validation_reference(self, name: str, project: str, commit: bool = True):
+        """
+        Deletes a validation reference or raises an exception if not found.
+
+        Args:
+            name: Name of validation reference
+            project: Feast project that this object belongs to
+            commit: Whether the change should be persisted immediately
+        """
+        registry_proto = self._prepare_registry_for_changes()
+        for idx, existing_validation_reference in enumerate(
+            registry_proto.validation_references
+        ):
+            if (
+                existing_validation_reference.name == name
+                and existing_validation_reference.project == project
+            ):
+                del registry_proto.validation_references[idx]
+                if commit:
+                    self.commit()
+                return
+        raise ValidationReferenceNotFound(name, project=project)
+
     def commit(self):
         """Commits the state of the registry cache to the remote registry store."""
         if self.cached_registry_proto:
@@ -883,74 +1689,8 @@ def teardown(self):
         """Tears down (removes) the registry."""
         self._registry_store.teardown()
 
-    def to_dict(self, project: str) -> Dict[str, List[Any]]:
-        """Returns a dictionary representation of the registry contents for the specified project.
-
-        For each list in the dictionary, the elements are sorted by name, so this
-        method can be used to compare two registries.
-
-        Args:
-            project: Feast project to convert to a dict
-        """
-        registry_dict: Dict[str, Any] = defaultdict(list)
-        registry_dict["project"] = project
-        for data_source in sorted(
-            self.list_data_sources(project=project), key=lambda ds: ds.name
-        ):
-            registry_dict["dataSources"].append(
-                self._message_to_sorted_dict(data_source.to_proto())
-            )
-        for entity in sorted(
-            self.list_entities(project=project), key=lambda entity: entity.name
-        ):
-            registry_dict["entities"].append(
-                self._message_to_sorted_dict(entity.to_proto())
-            )
-        for feature_view in sorted(
-            self.list_feature_views(project=project),
-            key=lambda feature_view: feature_view.name,
-        ):
-            registry_dict["featureViews"].append(
-                self._message_to_sorted_dict(feature_view.to_proto())
-            )
-        for feature_service in sorted(
-            self.list_feature_services(project=project),
-            key=lambda feature_service: feature_service.name,
-        ):
-            registry_dict["featureServices"].append(
-                self._message_to_sorted_dict(feature_service.to_proto())
-            )
-        for on_demand_feature_view in sorted(
-            self.list_on_demand_feature_views(project=project),
-            key=lambda on_demand_feature_view: on_demand_feature_view.name,
-        ):
-            odfv_dict = self._message_to_sorted_dict(on_demand_feature_view.to_proto())
-            odfv_dict["spec"]["userDefinedFunction"]["body"] = dill.source.getsource(
-                on_demand_feature_view.udf
-            )
-            registry_dict["onDemandFeatureViews"].append(odfv_dict)
-        for request_feature_view in sorted(
-            self.list_request_feature_views(project=project),
-            key=lambda request_feature_view: request_feature_view.name,
-        ):
-            registry_dict["requestFeatureViews"].append(
-                self._message_to_sorted_dict(request_feature_view.to_proto())
-            )
-        for saved_dataset in sorted(
-            self.list_saved_datasets(project=project), key=lambda item: item.name
-        ):
-            registry_dict["savedDatasets"].append(
-                self._message_to_sorted_dict(saved_dataset.to_proto())
-            )
-        for infra_object in sorted(self.get_infra(project=project).infra_objects):
-            registry_dict["infra"].append(
-                self._message_to_sorted_dict(infra_object.to_proto())
-            )
-        return registry_dict
-
-    @staticmethod
-    def _message_to_sorted_dict(message: Message) -> Dict[str, Any]:
-        return json.loads(MessageToJson(message, sort_keys=True))
+    def proto(self) -> RegistryProto:
+        return self.cached_registry_proto or RegistryProto()
 
     def _prepare_registry_for_changes(self):
         """Prepares the Registry for changes by refreshing the cache if necessary."""
diff --git a/sdk/python/feast/repo_config.py b/sdk/python/feast/repo_config.py
index 62d799a2b6b..b7cf1683dc6 100644
--- a/sdk/python/feast/repo_config.py
+++ b/sdk/python/feast/repo_config.py
@@ -53,11 +53,13 @@
 FEATURE_SERVER_CONFIG_CLASS_FOR_TYPE = {
     "aws_lambda": "feast.infra.feature_servers.aws_lambda.config.AwsLambdaFeatureServerConfig",
     "gcp_cloudrun": "feast.infra.feature_servers.gcp_cloudrun.config.GcpCloudRunFeatureServerConfig",
+    "local": "feast.infra.feature_servers.local_process.config.LocalFeatureServerConfig",
 }
 
 FEATURE_SERVER_TYPE_FOR_PROVIDER = {
     "aws": "aws_lambda",
     "gcp": "gcp_cloudrun",
+    "local": "local",
 }
 
 
@@ -80,6 +82,10 @@ class Config:
 class RegistryConfig(FeastBaseModel):
     """Metadata Store Configuration. Configuration that relates to reading from and writing to the Feast registry."""
 
+    registry_type: StrictStr = "file"
+    """ str: Provider name or a class name that implements RegistryStore.
+        If specified, registry_store_type should be redundant."""
+
     registry_store_type: Optional[StrictStr]
     """ str: Provider name or a class name that implements RegistryStore. """
 
@@ -285,17 +291,17 @@ def _validate_feature_server_config(cls, values):
         if "provider" not in values:
             raise FeastProviderNotSetError()
 
-        feature_server_type = FEATURE_SERVER_TYPE_FOR_PROVIDER.get(values["provider"])
-        defined_type = values["feature_server"].get("type")
+        default_type = FEATURE_SERVER_TYPE_FOR_PROVIDER.get(values["provider"])
+        defined_type = values["feature_server"].get("type", default_type)
         # Make sure that the type is either not set, or set correctly, since it's defined by the provider
-        if defined_type not in (None, feature_server_type):
+        if defined_type not in (default_type, "local"):
             raise FeastFeatureServerTypeSetError(defined_type)
-        values["feature_server"]["type"] = feature_server_type
+        values["feature_server"]["type"] = defined_type
 
         # Validate the dict to ensure one of the union types match
         try:
             feature_server_config_class = get_feature_server_config_from_type(
-                feature_server_type
+                defined_type
             )
             feature_server_config_class(**values["feature_server"])
         except ValidationError as e:
diff --git a/sdk/python/feast/repo_contents.py b/sdk/python/feast/repo_contents.py
index 4d7c92f2a6d..fe5cbd284bc 100644
--- a/sdk/python/feast/repo_contents.py
+++ b/sdk/python/feast/repo_contents.py
@@ -20,6 +20,7 @@
 from feast.on_demand_feature_view import OnDemandFeatureView
 from feast.protos.feast.core.Registry_pb2 import Registry as RegistryProto
 from feast.request_feature_view import RequestFeatureView
+from feast.stream_feature_view import StreamFeatureView
 
 
 class RepoContents(NamedTuple):
@@ -31,6 +32,7 @@ class RepoContents(NamedTuple):
     feature_views: List[FeatureView]
     on_demand_feature_views: List[OnDemandFeatureView]
     request_feature_views: List[RequestFeatureView]
+    stream_feature_views: List[StreamFeatureView]
     entities: List[Entity]
     feature_services: List[FeatureService]
 
@@ -50,4 +52,7 @@ def to_registry_proto(self) -> RegistryProto:
         registry_proto.feature_services.extend(
             [fs.to_proto() for fs in self.feature_services]
         )
+        registry_proto.stream_feature_views.extend(
+            [fv.to_proto() for fv in self.stream_feature_views]
+        )
         return registry_proto
diff --git a/sdk/python/feast/repo_operations.py b/sdk/python/feast/repo_operations.py
index 0e82fdf47ad..37daa6500eb 100644
--- a/sdk/python/feast/repo_operations.py
+++ b/sdk/python/feast/repo_operations.py
@@ -13,7 +13,8 @@
 from click.exceptions import BadParameter
 
 from feast import PushSource
-from feast.data_source import DataSource
+from feast.batch_feature_view import BatchFeatureView
+from feast.data_source import DataSource, KafkaSource
 from feast.diff.registry_diff import extract_objects_for_keep_delete_update_add
 from feast.entity import Entity
 from feast.feature_service import FeatureService
@@ -25,6 +26,7 @@
 from feast.repo_config import RepoConfig
 from feast.repo_contents import RepoContents
 from feast.request_feature_view import RequestFeatureView
+from feast.stream_feature_view import StreamFeatureView
 from feast.usage import log_exceptions_and_usage
 
 
@@ -107,6 +109,7 @@ def parse_repo(repo_root: Path) -> RepoContents:
         feature_views=[],
         feature_services=[],
         on_demand_feature_views=[],
+        stream_feature_views=[],
         request_feature_views=[],
     )
 
@@ -121,8 +124,11 @@ def parse_repo(repo_root: Path) -> RepoContents:
             ):
                 res.data_sources.append(obj)
                 data_sources_set.add(obj)
-            if isinstance(obj, FeatureView) and not any(
-                (obj is fv) for fv in res.feature_views
+            if (
+                isinstance(obj, FeatureView)
+                and not any((obj is fv) for fv in res.feature_views)
+                and not isinstance(obj, StreamFeatureView)
+                and not isinstance(obj, BatchFeatureView)
             ):
                 res.feature_views.append(obj)
                 if isinstance(obj.stream_source, PushSource) and not any(
@@ -132,6 +138,19 @@ def parse_repo(repo_root: Path) -> RepoContents:
                     # Don't add if the push source's batch source is a duplicate of an existing batch source
                     if push_source_dep not in data_sources_set:
                         res.data_sources.append(push_source_dep)
+            elif isinstance(obj, StreamFeatureView) and not any(
+                (obj is sfv) for sfv in res.stream_feature_views
+            ):
+                res.stream_feature_views.append(obj)
+                if (
+                    isinstance(obj.stream_source, PushSource)
+                    or isinstance(obj.stream_source, KafkaSource)
+                    and not any((obj is ds) for ds in res.data_sources)
+                ):
+                    batch_source_dep = obj.stream_source.batch_source
+                    # Don't add if the push source's batch source is a duplicate of an existing batch source
+                    if batch_source_dep and batch_source_dep not in data_sources_set:
+                        res.data_sources.append(batch_source_dep)
             elif isinstance(obj, Entity) and not any(
                 (obj is entity) for entity in res.entities
             ):
@@ -195,7 +214,12 @@ def extract_objects_for_apply_delete(project, registry, repo):
 
     all_to_apply: List[
         Union[
-            Entity, FeatureView, RequestFeatureView, OnDemandFeatureView, FeatureService
+            Entity,
+            FeatureView,
+            RequestFeatureView,
+            OnDemandFeatureView,
+            StreamFeatureView,
+            FeatureService,
         ]
     ] = []
     for object_type in FEAST_OBJECT_TYPES:
@@ -204,7 +228,12 @@ def extract_objects_for_apply_delete(project, registry, repo):
 
     all_to_delete: List[
         Union[
-            Entity, FeatureView, RequestFeatureView, OnDemandFeatureView, FeatureService
+            Entity,
+            FeatureView,
+            RequestFeatureView,
+            OnDemandFeatureView,
+            StreamFeatureView,
+            FeatureService,
         ]
     ] = []
     for object_type in FEAST_OBJECT_TYPES:
diff --git a/sdk/python/feast/repo_upgrade.py b/sdk/python/feast/repo_upgrade.py
new file mode 100644
index 00000000000..5c8d7433b2d
--- /dev/null
+++ b/sdk/python/feast/repo_upgrade.py
@@ -0,0 +1,72 @@
+import logging
+from pathlib import Path
+from typing import Dict, List
+
+from bowler import Query
+from fissix.pgen2 import token
+from fissix.pygram import python_symbols
+from fissix.pytree import Node
+
+from feast.repo_operations import get_repo_files
+
+SOURCES = {
+    "FileSource",
+    "BigQuerySource",
+    "RedshiftSource",
+    "SnowflakeSource",
+    "KafkaSource",
+    "KinesisSource",
+}
+
+
+class RepoUpgrader:
+    def __init__(self, repo_path: str, write: bool):
+        self.repo_path = repo_path
+        self.write = write
+        self.repo_files: List[str] = [
+            str(p) for p in get_repo_files(Path(self.repo_path))
+        ]
+        logging.getLogger("RefactoringTool").setLevel(logging.WARNING)
+
+    def upgrade(self):
+        self.remove_date_partition_column()
+
+    def remove_date_partition_column(self):
+        def _remove_date_partition_column(
+            node: Node, capture: Dict[str, Node], filename: str
+        ) -> None:
+            self.remove_argument_transform(node, "date_partition_column")
+
+        for s in SOURCES:
+            Query(self.repo_files).select_class(s).is_call().modify(
+                _remove_date_partition_column
+            ).execute(write=self.write, interactive=False)
+
+    @staticmethod
+    def remove_argument_transform(node: Node, argument: str):
+        """
+        Removes the specified argument.
+        For example, if the argument is "join_key", this method transforms
+        driver = Entity(
+            name="driver_id",
+            join_key="driver_id",
+        )
+        into
+        driver = Entity(
+            name="driver_id",
+        )
+        This method assumes that node represents a class call that already has an arglist.
+        """
+        if len(node.children) < 2 or len(node.children[1].children) < 2:
+            raise ValueError(f"Expected a class call with an arglist but got {node}.")
+        class_args = node.children[1].children[1].children
+        for i, class_arg in enumerate(class_args):
+            if (
+                class_arg.type == python_symbols.argument
+                and class_arg.children[0].value == argument
+            ):
+                class_args.pop(i)
+                if i < len(class_args) and class_args[i].type == token.COMMA:
+                    class_args.pop(i)
+                if i < len(class_args) and class_args[i].type == token.NEWLINE:
+                    class_args.pop(i)
diff --git a/sdk/python/feast/saved_dataset.py b/sdk/python/feast/saved_dataset.py
index aead7fe8eff..e2004d15f4c 100644
--- a/sdk/python/feast/saved_dataset.py
+++ b/sdk/python/feast/saved_dataset.py
@@ -13,6 +13,9 @@
 from feast.protos.feast.core.SavedDataset_pb2 import (
     SavedDatasetStorage as SavedDatasetStorageProto,
 )
+from feast.protos.feast.core.ValidationProfile_pb2 import (
+    ValidationReference as ValidationReferenceProto,
+)
 
 if TYPE_CHECKING:
     from feast.infra.offline_stores.offline_store import RetrievalJob
@@ -178,8 +181,8 @@ def to_proto(self) -> SavedDatasetProto:
         if self.feature_service_name:
             spec.feature_service_name = self.feature_service_name
 
-        feature_service_proto = SavedDatasetProto(spec=spec, meta=meta)
-        return feature_service_proto
+        saved_dataset_proto = SavedDatasetProto(spec=spec, meta=meta)
+        return saved_dataset_proto
 
     def with_retrieval_job(self, retrieval_job: "RetrievalJob") -> "SavedDataset":
         self._retrieval_job = retrieval_job
@@ -203,21 +206,123 @@ def to_arrow(self) -> pyarrow.Table:
 
         return self._retrieval_job.to_arrow()
 
-    def as_reference(self, profiler: "Profiler") -> "ValidationReference":
-        return ValidationReference(profiler=profiler, dataset=self)
+    def as_reference(self, name: str, profiler: "Profiler") -> "ValidationReference":
+        return ValidationReference.from_saved_dataset(
+            name=name, profiler=profiler, dataset=self
+        )
 
     def get_profile(self, profiler: Profiler) -> Profile:
         return profiler.analyze_dataset(self.to_df())
 
 
 class ValidationReference:
-    dataset: SavedDataset
+    name: str
+    dataset_name: str
+    description: str
+    tags: Dict[str, str]
     profiler: Profiler
 
-    def __init__(self, dataset: SavedDataset, profiler: Profiler):
-        self.dataset = dataset
+    _profile: Optional[Profile] = None
+    _dataset: Optional[SavedDataset] = None
+
+    def __init__(
+        self,
+        name: str,
+        dataset_name: str,
+        profiler: Profiler,
+        description: str = "",
+        tags: Optional[Dict[str, str]] = None,
+    ):
+        """
+        Validation reference combines a reference dataset (currently only a saved dataset object can be used as
+        a reference) and a profiler function to generate a validation profile.
+        The validation profile can be cached in this object, and in this case
+        the saved dataset retrieval and the profiler call will happen only once.
+
+        Validation reference is being stored in the Feast registry and can be retrieved by its name, which
+        must be unique within one project.
+
+        Args:
+            name: the unique name for validation reference
+            dataset_name: the name of the saved dataset used as a reference
+            description: a human-readable description
+            tags: a dictionary of key-value pairs to store arbitrary metadata
+            profiler: the profiler function used to generate profile from the saved dataset
+        """
+        self.name = name
+        self.dataset_name = dataset_name
         self.profiler = profiler
+        self.description = description
+        self.tags = tags or {}
+
+    @classmethod
+    def from_saved_dataset(cls, name: str, dataset: SavedDataset, profiler: Profiler):
+        """
+        Internal constructor to create validation reference object with actual saved dataset object
+        (regular constructor requires only its name).
+        """
+        ref = ValidationReference(name, dataset.name, profiler)
+        ref._dataset = dataset
+        return ref
 
     @property
     def profile(self) -> Profile:
-        return self.profiler.analyze_dataset(self.dataset.to_df())
+        if not self._profile:
+            if not self._dataset:
+                raise RuntimeError(
+                    "In order to calculate a profile validation reference must be instantiated from a saved dataset. "
+                    "Use ValidationReference.from_saved_dataset constructor or FeatureStore.get_validation_reference "
+                    "to get validation reference object."
+                )
+
+            self._profile = self.profiler.analyze_dataset(self._dataset.to_df())
+        return self._profile
+
+    @classmethod
+    def from_proto(cls, proto: ValidationReferenceProto) -> "ValidationReference":
+        profiler_attr = proto.WhichOneof("profiler")
+        if profiler_attr == "ge_profiler":
+            from feast.dqm.profilers.ge_profiler import GEProfiler
+
+            profiler = GEProfiler.from_proto(proto.ge_profiler)
+        else:
+            raise RuntimeError("Unrecognized profiler")
+
+        profile_attr = proto.WhichOneof("cached_profile")
+        if profile_attr == "ge_profile":
+            from feast.dqm.profilers.ge_profiler import GEProfile
+
+            profile = GEProfile.from_proto(proto.ge_profile)
+        elif not profile_attr:
+            profile = None
+        else:
+            raise RuntimeError("Unrecognized profile")
+
+        ref = ValidationReference(
+            name=proto.name,
+            dataset_name=proto.reference_dataset_name,
+            profiler=profiler,
+            description=proto.description,
+            tags=dict(proto.tags),
+        )
+        ref._profile = profile
+
+        return ref
+
+    def to_proto(self) -> ValidationReferenceProto:
+        from feast.dqm.profilers.ge_profiler import GEProfile, GEProfiler
+
+        proto = ValidationReferenceProto(
+            name=self.name,
+            reference_dataset_name=self.dataset_name,
+            tags=self.tags,
+            description=self.description,
+            ge_profiler=self.profiler.to_proto()
+            if isinstance(self.profiler, GEProfiler)
+            else None,
+            ge_profile=self._profile.to_proto()
+            if isinstance(self._profile, GEProfile)
+            else None,
+        )
+
+        return proto
diff --git a/sdk/python/feast/stream_feature_view.py b/sdk/python/feast/stream_feature_view.py
index 1c51b94a7cf..077d8ab89a5 100644
--- a/sdk/python/feast/stream_feature_view.py
+++ b/sdk/python/feast/stream_feature_view.py
@@ -1,19 +1,85 @@
-from datetime import timedelta
-from typing import Dict, List, Optional, Union
+import copy
+import functools
+import warnings
+from datetime import datetime, timedelta
+from types import FunctionType
+from typing import Dict, List, Optional, Tuple, Union
 
-from feast.data_source import DataSource
+import dill
+from google.protobuf.duration_pb2 import Duration
+from typeguard import typechecked
+
+from feast import utils
+from feast.aggregation import Aggregation
+from feast.data_source import DataSource, KafkaSource, PushSource
 from feast.entity import Entity
 from feast.feature_view import FeatureView
 from feast.field import Field
 from feast.protos.feast.core.DataSource_pb2 import DataSource as DataSourceProto
+from feast.protos.feast.core.FeatureView_pb2 import (
+    MaterializationInterval as MaterializationIntervalProto,
+)
+from feast.protos.feast.core.OnDemandFeatureView_pb2 import (
+    UserDefinedFunction as UserDefinedFunctionProto,
+)
+from feast.protos.feast.core.StreamFeatureView_pb2 import (
+    StreamFeatureView as StreamFeatureViewProto,
+)
+from feast.protos.feast.core.StreamFeatureView_pb2 import (
+    StreamFeatureViewMeta as StreamFeatureViewMetaProto,
+)
+from feast.protos.feast.core.StreamFeatureView_pb2 import (
+    StreamFeatureViewSpec as StreamFeatureViewSpecProto,
+)
+
+warnings.simplefilter("once", RuntimeWarning)
 
-SUPPORTED_STREAM_SOURCES = {
-    "KafkaSource",
-    "KinesisSource",
-}
+SUPPORTED_STREAM_SOURCES = {"KafkaSource", "PushSource"}
 
 
+@typechecked
 class StreamFeatureView(FeatureView):
+    """
+    NOTE: Stream Feature Views are not yet fully implemented and exist to allow users to register their stream sources and
+    schemas with Feast.
+
+    Attributes:
+        name: The unique name of the stream feature view.
+        entities: List of entities or entity join keys.
+        ttl: The amount of time this group of features lives. A ttl of 0 indicates that
+            this group of features lives forever. Note that large ttl's or a ttl of 0
+            can result in extremely computationally intensive queries.
+        schema: The schema of the feature view, including feature, timestamp, and entity
+            columns. If not specified, can be inferred from the underlying data source.
+        source: DataSource. The stream source of data where this group of features is stored.
+        aggregations: List of aggregations registered with the stream feature view.
+        mode: The mode of execution.
+        timestamp_field: Must be specified if aggregations are specified. Defines the timestamp column on which to aggregate windows.
+        online: Defines whether this stream feature view is used in online feature retrieval.
+        description: A human-readable description.
+        tags: A dictionary of key-value pairs to store arbitrary metadata.
+        owner: The owner of the on demand feature view, typically the email of the primary
+            maintainer.
+        udf: The user defined transformation function. This transformation function should have all of the corresponding imports imported within the function.
+    """
+
+    name: str
+    entities: List[str]
+    ttl: Optional[timedelta]
+    source: DataSource
+    schema: List[Field]
+    entity_columns: List[Field]
+    features: List[Field]
+    online: bool
+    description: str
+    tags: Dict[str, str]
+    owner: str
+    aggregations: List[Aggregation]
+    mode: str
+    timestamp_field: str
+    materialization_intervals: List[Tuple[datetime, datetime]]
+    udf: Optional[FunctionType]
+
     def __init__(
         self,
         *,
@@ -21,15 +87,25 @@ def __init__(
         entities: Optional[Union[List[Entity], List[str]]] = None,
         ttl: Optional[timedelta] = None,
         tags: Optional[Dict[str, str]] = None,
-        online: bool = True,
-        description: str = "",
-        owner: str = "",
+        online: Optional[bool] = True,
+        description: Optional[str] = "",
+        owner: Optional[str] = "",
         schema: Optional[List[Field]] = None,
         source: Optional[DataSource] = None,
+        aggregations: Optional[List[Aggregation]] = None,
+        mode: Optional[str] = "spark",
+        timestamp_field: Optional[str] = "",
+        udf: Optional[FunctionType] = None,
     ):
+        warnings.warn(
+            "Stream Feature Views are experimental features in alpha development. "
+            "Some functionality may still be unstable so functionality can change in the future.",
+            RuntimeWarning,
+        )
 
         if source is None:
-            raise ValueError("Feature views need a source specified")
+            raise ValueError("Stream Feature views need a source to be specified")
+
         if (
             type(source).__name__ not in SUPPORTED_STREAM_SOURCES
             and source.to_proto().type != DataSourceProto.SourceType.CUSTOM_SOURCE
@@ -39,12 +115,27 @@ def __init__(
                 f"or CUSTOM_SOURCE, got {type(source).__name__}: {source.name} instead "
             )
 
+        if aggregations and not timestamp_field:
+            raise ValueError(
+                "aggregations must have a timestamp field associated with them to perform the aggregations"
+            )
+
+        self.aggregations = aggregations or []
+        self.mode = mode or ""
+        self.timestamp_field = timestamp_field or ""
+        self.udf = udf
+        _batch_source = None
+        if isinstance(source, KafkaSource) or isinstance(source, PushSource):
+            _batch_source = source.batch_source if source.batch_source else None
+        _ttl = ttl
+        if not _ttl:
+            _ttl = timedelta(days=0)
         super().__init__(
             name=name,
             entities=entities,
-            ttl=ttl,
-            batch_source=None,
-            stream_source=None,
+            ttl=_ttl,
+            batch_source=_batch_source,
+            stream_source=source,
             tags=tags,
             online=online,
             description=description,
@@ -52,3 +143,226 @@ def __init__(
             schema=schema,
             source=source,
         )
+
+    def __eq__(self, other):
+        if not isinstance(other, StreamFeatureView):
+            raise TypeError("Comparisons should only involve StreamFeatureViews")
+
+        if not super().__eq__(other):
+            return False
+
+        if not self.udf:
+            return not other.udf
+        if not other.udf:
+            return False
+
+        if (
+            self.mode != other.mode
+            or self.timestamp_field != other.timestamp_field
+            or self.udf.__code__.co_code != other.udf.__code__.co_code
+            or self.aggregations != other.aggregations
+        ):
+            return False
+
+        return True
+
+    def __hash__(self) -> int:
+        return super().__hash__()
+
+    def to_proto(self):
+        meta = StreamFeatureViewMetaProto(materialization_intervals=[])
+        if self.created_timestamp:
+            meta.created_timestamp.FromDatetime(self.created_timestamp)
+
+        if self.last_updated_timestamp:
+            meta.last_updated_timestamp.FromDatetime(self.last_updated_timestamp)
+
+        for interval in self.materialization_intervals:
+            interval_proto = MaterializationIntervalProto()
+            interval_proto.start_time.FromDatetime(interval[0])
+            interval_proto.end_time.FromDatetime(interval[1])
+            meta.materialization_intervals.append(interval_proto)
+
+        ttl_duration = None
+        if self.ttl is not None:
+            ttl_duration = Duration()
+            ttl_duration.FromTimedelta(self.ttl)
+
+        batch_source_proto = None
+        if self.batch_source:
+            batch_source_proto = self.batch_source.to_proto()
+            batch_source_proto.data_source_class_type = f"{self.batch_source.__class__.__module__}.{self.batch_source.__class__.__name__}"
+
+        stream_source_proto = None
+        if self.stream_source:
+            stream_source_proto = self.stream_source.to_proto()
+            stream_source_proto.data_source_class_type = f"{self.stream_source.__class__.__module__}.{self.stream_source.__class__.__name__}"
+
+        udf_proto = None
+        if self.udf:
+            udf_proto = UserDefinedFunctionProto(
+                name=self.udf.__name__, body=dill.dumps(self.udf, recurse=True),
+            )
+        spec = StreamFeatureViewSpecProto(
+            name=self.name,
+            entities=self.entities,
+            entity_columns=[field.to_proto() for field in self.entity_columns],
+            features=[field.to_proto() for field in self.schema],
+            user_defined_function=udf_proto,
+            description=self.description,
+            tags=self.tags,
+            owner=self.owner,
+            ttl=ttl_duration,
+            online=self.online,
+            batch_source=batch_source_proto or None,
+            stream_source=stream_source_proto or None,
+            timestamp_field=self.timestamp_field,
+            aggregations=[agg.to_proto() for agg in self.aggregations],
+            mode=self.mode,
+        )
+
+        return StreamFeatureViewProto(spec=spec, meta=meta)
+
+    @classmethod
+    def from_proto(cls, sfv_proto):
+        batch_source = (
+            DataSource.from_proto(sfv_proto.spec.batch_source)
+            if sfv_proto.spec.HasField("batch_source")
+            else None
+        )
+        stream_source = (
+            DataSource.from_proto(sfv_proto.spec.stream_source)
+            if sfv_proto.spec.HasField("stream_source")
+            else None
+        )
+        udf = (
+            dill.loads(sfv_proto.spec.user_defined_function.body)
+            if sfv_proto.spec.HasField("user_defined_function")
+            else None
+        )
+        stream_feature_view = cls(
+            name=sfv_proto.spec.name,
+            description=sfv_proto.spec.description,
+            tags=dict(sfv_proto.spec.tags),
+            owner=sfv_proto.spec.owner,
+            online=sfv_proto.spec.online,
+            schema=[
+                Field.from_proto(field_proto) for field_proto in sfv_proto.spec.features
+            ],
+            ttl=(
+                timedelta(days=0)
+                if sfv_proto.spec.ttl.ToNanoseconds() == 0
+                else sfv_proto.spec.ttl.ToTimedelta()
+            ),
+            source=stream_source,
+            mode=sfv_proto.spec.mode,
+            udf=udf,
+            aggregations=[
+                Aggregation.from_proto(agg_proto)
+                for agg_proto in sfv_proto.spec.aggregations
+            ],
+            timestamp_field=sfv_proto.spec.timestamp_field,
+        )
+
+        if batch_source:
+            stream_feature_view.batch_source = batch_source
+
+        if stream_source:
+            stream_feature_view.stream_source = stream_source
+
+        stream_feature_view.entities = list(sfv_proto.spec.entities)
+
+        stream_feature_view.features = [
+            Field.from_proto(field_proto) for field_proto in sfv_proto.spec.features
+        ]
+        stream_feature_view.entity_columns = [
+            Field.from_proto(field_proto)
+            for field_proto in sfv_proto.spec.entity_columns
+        ]
+
+        if sfv_proto.meta.HasField("created_timestamp"):
+            stream_feature_view.created_timestamp = (
+                sfv_proto.meta.created_timestamp.ToDatetime()
+            )
+        if sfv_proto.meta.HasField("last_updated_timestamp"):
+            stream_feature_view.last_updated_timestamp = (
+                sfv_proto.meta.last_updated_timestamp.ToDatetime()
+            )
+
+        for interval in sfv_proto.meta.materialization_intervals:
+            stream_feature_view.materialization_intervals.append(
+                (
+                    utils.make_tzaware(interval.start_time.ToDatetime()),
+                    utils.make_tzaware(interval.end_time.ToDatetime()),
+                )
+            )
+
+        return stream_feature_view
+
+    def __copy__(self):
+        fv = StreamFeatureView(
+            name=self.name,
+            schema=self.schema,
+            entities=self.entities,
+            ttl=self.ttl,
+            tags=self.tags,
+            online=self.online,
+            description=self.description,
+            owner=self.owner,
+            aggregations=self.aggregations,
+            mode=self.mode,
+            timestamp_field=self.timestamp_field,
+            source=self.source,
+            udf=self.udf,
+        )
+        fv.projection = copy.copy(self.projection)
+        return fv
+
+
+def stream_feature_view(
+    *,
+    entities: Optional[Union[List[Entity], List[str]]] = None,
+    ttl: Optional[timedelta] = None,
+    tags: Optional[Dict[str, str]] = None,
+    online: Optional[bool] = True,
+    description: Optional[str] = "",
+    owner: Optional[str] = "",
+    schema: Optional[List[Field]] = None,
+    source: Optional[DataSource] = None,
+    aggregations: Optional[List[Aggregation]] = None,
+    mode: Optional[str] = "spark",
+    timestamp_field: Optional[str] = "",
+):
+    """
+    Creates an StreamFeatureView object with the given user function as udf.
+    Please make sure that the udf contains all non-built in imports within the function to ensure that the execution
+    of a deserialized function does not miss imports.
+    """
+
+    def mainify(obj):
+        # Needed to allow dill to properly serialize the udf. Otherwise, clients will need to have a file with the same
+        # name as the original file defining the sfv.
+        if obj.__module__ != "__main__":
+            obj.__module__ = "__main__"
+
+    def decorator(user_function):
+        mainify(user_function)
+        stream_feature_view_obj = StreamFeatureView(
+            name=user_function.__name__,
+            entities=entities,
+            ttl=ttl,
+            source=source,
+            schema=schema,
+            udf=user_function,
+            description=description,
+            tags=tags,
+            online=online,
+            owner=owner,
+            aggregations=aggregations,
+            mode=mode,
+            timestamp_field=timestamp_field,
+        )
+        functools.update_wrapper(wrapper=stream_feature_view_obj, wrapped=user_function)
+        return stream_feature_view_obj
+
+    return decorator
diff --git a/sdk/python/feast/templates/aws/driver_repo.py b/sdk/python/feast/templates/aws/driver_repo.py
index 8ebe0b6e927..f80f16bb6fa 100644
--- a/sdk/python/feast/templates/aws/driver_repo.py
+++ b/sdk/python/feast/templates/aws/driver_repo.py
@@ -1,6 +1,6 @@
 from datetime import timedelta
 
-from feast import Entity, FeatureService, FeatureView, Field, RedshiftSource, ValueType
+from feast import Entity, FeatureService, FeatureView, Field, RedshiftSource
 from feast.types import Float32, Int64
 
 # Define an entity for the driver. Entities can be thought of as primary keys used to
@@ -13,8 +13,6 @@
     # features can be looked up. The join keys are also used to join feature
     # tables/views when building feature vectors
     join_keys=["driver_id"],
-    # The storage level type for an entity
-    value_type=ValueType.INT64,
 )
 
 # Indicates a data source from which feature values can be retrieved. Sources are queried when building training
@@ -41,7 +39,7 @@
     # The list of entities specifies the keys required for joining or looking
     # up features from this feature view. The reference provided in this field
     # correspond to the name of a defined entity (or entities)
-    entities=["driver"],
+    entities=[driver],
     # The timedelta is the maximum age that each feature value may have
     # relative to its lookup time. For historical features (used in training),
     # TTL is relative to each timestamp provided in the entity dataframe.
diff --git a/sdk/python/feast/templates/gcp/driver_repo.py b/sdk/python/feast/templates/gcp/driver_repo.py
index a4517516b56..acb17d55197 100644
--- a/sdk/python/feast/templates/gcp/driver_repo.py
+++ b/sdk/python/feast/templates/gcp/driver_repo.py
@@ -1,6 +1,6 @@
 from datetime import timedelta
 
-from feast import BigQuerySource, Entity, FeatureService, FeatureView, Field, ValueType
+from feast import BigQuerySource, Entity, FeatureService, FeatureView, Field
 from feast.types import Float32, Int64
 
 # Define an entity for the driver. Entities can be thought of as primary keys used to
@@ -13,8 +13,6 @@
     # features can be looked up. The join keys are also used to join feature
     # tables/views when building feature vectors
     join_keys=["driver_id"],
-    # The storage level type for an entity
-    value_type=ValueType.INT64,
 )
 
 # Indicates a data source from which feature values can be retrieved. Sources are queried when building training
@@ -39,7 +37,7 @@
     # The list of entities specifies the keys required for joining or looking
     # up features from this feature view. The reference provided in this field
     # correspond to the name of a defined entity (or entities)
-    entities=["driver"],
+    entities=[driver],
     # The timedelta is the maximum age that each feature value may have
     # relative to its lookup time. For historical features (used in training),
     # TTL is relative to each timestamp provided in the entity dataframe.
diff --git a/sdk/python/feast/templates/hbase/example.py b/sdk/python/feast/templates/hbase/example.py
index 1d441e0e995..b34696185b1 100644
--- a/sdk/python/feast/templates/hbase/example.py
+++ b/sdk/python/feast/templates/hbase/example.py
@@ -2,7 +2,7 @@
 
 from datetime import timedelta
 
-from feast import Entity, FeatureView, Field, FileSource, ValueType
+from feast import Entity, FeatureView, Field, FileSource
 from feast.types import Float32, Int64
 
 # Read data from parquet files. Parquet is convenient for local development mode. For
@@ -16,7 +16,7 @@
 
 # Define an entity for the driver. You can think of entity as a primary key used to
 # fetch features.
-driver = Entity(name="driver", join_keys=["driver_id"], value_type=ValueType.INT64,)
+driver = Entity(name="driver", join_keys=["driver_id"])
 
 # Our parquet files contain sample data that includes a driver_id column, timestamps and
 # three feature column. Here we define a Feature View that will allow us to serve this
diff --git a/sdk/python/feast/templates/local/example.py b/sdk/python/feast/templates/local/example.py
index 7633947e6e4..30f9adf1890 100644
--- a/sdk/python/feast/templates/local/example.py
+++ b/sdk/python/feast/templates/local/example.py
@@ -2,7 +2,7 @@
 
 from datetime import timedelta
 
-from feast import Entity, FeatureService, FeatureView, Field, FileSource, ValueType
+from feast import Entity, FeatureService, FeatureView, Field, FileSource
 from feast.types import Float32, Int64
 
 # Read data from parquet files. Parquet is convenient for local development mode. For
@@ -16,14 +16,14 @@
 
 # Define an entity for the driver. You can think of entity as a primary key used to
 # fetch features.
-driver = Entity(name="driver", join_keys=["driver_id"], value_type=ValueType.INT64,)
+driver = Entity(name="driver", join_keys=["driver_id"])
 
 # Our parquet files contain sample data that includes a driver_id column, timestamps and
 # three feature column. Here we define a Feature View that will allow us to serve this
 # data to our model online.
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver"],
+    entities=[driver],
     ttl=timedelta(days=1),
     schema=[
         Field(name="conv_rate", dtype=Float32),
diff --git a/sdk/python/feast/templates/postgres/driver_repo.py b/sdk/python/feast/templates/postgres/driver_repo.py
index 34bc0022e23..4096943bb73 100644
--- a/sdk/python/feast/templates/postgres/driver_repo.py
+++ b/sdk/python/feast/templates/postgres/driver_repo.py
@@ -18,7 +18,7 @@
 
 driver_stats_fv = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver_id"],
+    entities=[driver],
     ttl=timedelta(weeks=52),
     schema=[
         Field(name="conv_rate", dtype=Float32),
diff --git a/sdk/python/feast/templates/snowflake/driver_repo.py b/sdk/python/feast/templates/snowflake/driver_repo.py
index ceaf0ba8de8..297a3f5ef0f 100644
--- a/sdk/python/feast/templates/snowflake/driver_repo.py
+++ b/sdk/python/feast/templates/snowflake/driver_repo.py
@@ -43,7 +43,7 @@
     # The list of entities specifies the keys required for joining or looking
     # up features from this feature view. The reference provided in this field
     # correspond to the name of a defined entity (or entities)
-    entities=["driver"],
+    entities=[driver],
     # The timedelta is the maximum age that each feature value may have
     # relative to its lookup time. For historical features (used in training),
     # TTL is relative to each timestamp provided in the entity dataframe.
diff --git a/sdk/python/feast/templates/spark/example.py b/sdk/python/feast/templates/spark/example.py
index da334dd83ca..d006353118b 100644
--- a/sdk/python/feast/templates/spark/example.py
+++ b/sdk/python/feast/templates/spark/example.py
@@ -5,7 +5,7 @@
 from datetime import timedelta
 from pathlib import Path
 
-from feast import Entity, FeatureService, FeatureView, Field, ValueType
+from feast import Entity, FeatureService, FeatureView, Field
 from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import (
     SparkSource,
 )
@@ -16,10 +16,8 @@
 
 
 # Entity definitions
-driver = Entity(name="driver", value_type=ValueType.INT64, description="driver id",)
-customer = Entity(
-    name="customer", value_type=ValueType.INT64, description="customer id",
-)
+driver = Entity(name="driver", description="driver id",)
+customer = Entity(name="customer", description="customer id",)
 
 # Sources
 driver_hourly_stats = SparkSource(
@@ -40,7 +38,7 @@
 # Feature Views
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver"],
+    entities=[driver],
     ttl=timedelta(days=7),
     schema=[
         Field(name="conv_rate", dtype=Float32),
@@ -53,7 +51,7 @@
 )
 customer_daily_profile_view = FeatureView(
     name="customer_daily_profile",
-    entities=["customer"],
+    entities=[customer],
     ttl=timedelta(days=7),
     schema=[
         Field(name="current_balance", dtype=Float32),
diff --git a/sdk/python/feast/type_map.py b/sdk/python/feast/type_map.py
index d22c51d5963..ed4b7cba594 100644
--- a/sdk/python/feast/type_map.py
+++ b/sdk/python/feast/type_map.py
@@ -46,6 +46,9 @@
 from feast.protos.feast.types.Value_pb2 import Value as ProtoValue
 from feast.value_type import ListType, ValueType
 
+# null timestamps get converted to -9223372036854775808
+NULL_TIMESTAMP_INT_VALUE = np.datetime64("NaT").astype(int)
+
 
 def feast_value_type_to_python_type(field_value_proto: ProtoValue) -> Any:
     """
@@ -69,9 +72,18 @@ def feast_value_type_to_python_type(field_value_proto: ProtoValue) -> Any:
 
     # Convert UNIX_TIMESTAMP values to `datetime`
     if val_attr == "unix_timestamp_list_val":
-        val = [datetime.fromtimestamp(v, tz=timezone.utc) for v in val]
+        val = [
+            datetime.fromtimestamp(v, tz=timezone.utc)
+            if v != NULL_TIMESTAMP_INT_VALUE
+            else None
+            for v in val
+        ]
     elif val_attr == "unix_timestamp_val":
-        val = datetime.fromtimestamp(val, tz=timezone.utc)
+        val = (
+            datetime.fromtimestamp(val, tz=timezone.utc)
+            if val != NULL_TIMESTAMP_INT_VALUE
+            else None
+        )
 
     return val
 
diff --git a/sdk/python/feast/ui/README.md b/sdk/python/feast/ui/README.md
index ea5243ca4c6..0c11dcf134c 100644
--- a/sdk/python/feast/ui/README.md
+++ b/sdk/python/feast/ui/README.md
@@ -1,6 +1,6 @@
 # Example Feast UI App
 
-This is an example React App that imports the Feast UI module and relies on a "/projects-list" endpoint to get projects.
+This is an example React App that imports the Feast UI module. 
 
 See the module import in `src/index.js`. The main change this implements on top of a vanilla create-react-app is adding:
 
@@ -11,23 +11,15 @@ import "@feast-dev/feast-ui/dist/feast-ui.css";
 
 ReactDOM.render(
   <React.StrictMode>
-    <FeastUI
-      feastUIConfigs={{
-        projectListPromise: fetch("http://0.0.0.0:8888/projects-list", {
-          headers: {
-            "Content-Type": "application/json",
-          },
-        }).then((res) => {
-          return res.json();
-        })
-      }}
-    />
+    <FeastUI />
   </React.StrictMode>,
   document.getElementById("root")
 );
 ```
 
-It is used by the `feast ui` command to scaffold a local UI server. The feast python package bundles in resources produced from `npm run build --omit=dev
+It is used by the `feast ui` command to scaffold a local UI server. The feast python package bundles in resources produced from `npm run build --omit=dev.` 
+
+The `feast ui` command will generate the necessary `projects-list.json` file and initialize it for the UI to read.
 
 
 **Note**: yarn start will not work on this because of the above dependency.
diff --git a/sdk/python/feast/ui/public/projects-list.json b/sdk/python/feast/ui/public/projects-list.json
new file mode 100644
index 00000000000..b6e79bda5b8
--- /dev/null
+++ b/sdk/python/feast/ui/public/projects-list.json
@@ -0,0 +1,11 @@
+{
+    "projects": [
+      {
+        "name": "Project",
+        "description": "Test project",
+        "id": "project_id",
+        "registryPath": "http://0.0.0.0:8888/registry"
+      }
+    ]
+  }
+  
\ No newline at end of file
diff --git a/sdk/python/feast/ui/src/index.tsx b/sdk/python/feast/ui/src/index.tsx
index 4191de17a06..9ddacc9b48a 100644
--- a/sdk/python/feast/ui/src/index.tsx
+++ b/sdk/python/feast/ui/src/index.tsx
@@ -1,22 +1,12 @@
-import React from 'react';
+import React from "react";
 import ReactDOM from "react-dom";
-import './index.css';
+import "./index.css";
 import FeastUI from "@feast-dev/feast-ui";
 import "@feast-dev/feast-ui/dist/feast-ui.css";
 
 ReactDOM.render(
   <React.StrictMode>
-    <FeastUI
-      feastUIConfigs={{
-        projectListPromise: fetch("http://0.0.0.0:8888/projects-list", {
-          headers: {
-            "Content-Type": "application/json",
-          },
-        }).then((res) => {
-          return res.json();
-        })
-      }}
-    />
+    <FeastUI />
   </React.StrictMode>,
   document.getElementById("root")
-);
\ No newline at end of file
+);
diff --git a/sdk/python/feast/ui_server.py b/sdk/python/feast/ui_server.py
index cc546f53716..cb275c8f912 100644
--- a/sdk/python/feast/ui_server.py
+++ b/sdk/python/feast/ui_server.py
@@ -16,9 +16,9 @@ def get_app(
     get_registry_dump: Callable,
     project_id: str,
     registry_ttl_secs: int,
+    host: str,
+    port: int,
 ):
-    ui_dir = pkg_resources.resource_filename(__name__, "ui/build/")
-
     app = FastAPI()
 
     app.add_middleware(
@@ -53,25 +53,24 @@ def shutdown_event():
 
     async_refresh()
 
-    @app.get("/registry")
-    def read_registry():
-        return json.loads(registry_json)
-
-    # Generate projects-list json that points to the current repo's project
-    # TODO(adchia): Enable users to also add project name + description fields in feature_store.yaml
-    @app.get("/projects-list")
-    def projects_list():
-        projects = {
+    ui_dir = pkg_resources.resource_filename(__name__, "ui/build/")
+    # Initialize with the projects-list.json file
+    with open(ui_dir + "projects-list.json", mode="w") as f:
+        projects_dict = {
             "projects": [
                 {
                     "name": "Project",
                     "description": "Test project",
                     "id": project_id,
-                    "registryPath": "http://0.0.0.0:8888/registry",
+                    "registryPath": "/registry",
                 }
             ]
         }
-        return projects
+        f.write(json.dumps(projects_dict))
+
+    @app.get("/registry")
+    def read_registry():
+        return json.loads(registry_json)
 
     # For all other paths (such as paths that would otherwise be handled by react router), pass to React
     @app.api_route("/p/{path_name:path}", methods=["GET"])
@@ -98,5 +97,5 @@ def start_server(
     project_id: str,
     registry_ttl_sec: int,
 ):
-    app = get_app(store, get_registry_dump, project_id, registry_ttl_sec)
+    app = get_app(store, get_registry_dump, project_id, registry_ttl_sec, host, port)
     uvicorn.run(app, host=host, port=port)
diff --git a/sdk/python/feast/utils.py b/sdk/python/feast/utils.py
index e521338680c..a40f423c537 100644
--- a/sdk/python/feast/utils.py
+++ b/sdk/python/feast/utils.py
@@ -1,5 +1,6 @@
 from datetime import datetime
 
+from dateutil.tz import tzlocal
 from pytz import utc
 
 
@@ -16,3 +17,10 @@ def to_naive_utc(ts: datetime) -> datetime:
         return ts
     else:
         return ts.astimezone(utc).replace(tzinfo=None)
+
+
+def maybe_local_tz(t: datetime) -> datetime:
+    if t.tzinfo is None:
+        return t.replace(tzinfo=tzlocal())
+    else:
+        return t
diff --git a/sdk/python/pytest.ini b/sdk/python/pytest.ini
new file mode 100644
index 00000000000..07a5e869dc4
--- /dev/null
+++ b/sdk/python/pytest.ini
@@ -0,0 +1,4 @@
+[pytest]
+markers =
+    universal_offline_stores: mark a test as using all offline stores.
+    universal_online_stores: mark a test as using all online stores.
\ No newline at end of file
diff --git a/sdk/python/requirements/py3.10-ci-requirements.txt b/sdk/python/requirements/py3.10-ci-requirements.txt
index 0655eb9c769..3bdf468bb26 100644
--- a/sdk/python/requirements/py3.10-ci-requirements.txt
+++ b/sdk/python/requirements/py3.10-ci-requirements.txt
@@ -4,7 +4,7 @@
 #
 #    pip-compile --extra=ci --output-file=sdk/python/requirements/py3.10-ci-requirements.txt
 #
-absl-py==1.0.0
+absl-py==1.1.0
     # via tensorflow-metadata
 adal==1.2.7
     # via
@@ -12,26 +12,32 @@ adal==1.2.7
     #   msrestazure
 adlfs==0.5.9
     # via feast (setup.py)
+aiobotocore==2.1.2
+    # via s3fs
 aiohttp==3.8.1
     # via
     #   adlfs
+    #   aiobotocore
     #   gcsfs
+    #   s3fs
+aioitertools==0.10.0
+    # via aiobotocore
 aiosignal==1.2.0
     # via aiohttp
 alabaster==0.7.12
     # via sphinx
 altair==4.2.0
     # via great-expectations
-anyio==3.5.0
+anyio==3.6.1
     # via
     #   starlette
-    #   watchgod
+    #   watchfiles
 appdirs==1.4.4
-    # via black
+    # via
+    #   black
+    #   fissix
 appnope==0.1.3
     # via ipython
-asgiref==3.5.1
-    # via uvicorn
 asn1crypto==1.5.1
     # via
     #   oscrypto
@@ -48,43 +54,48 @@ attrs==21.4.0
     # via
     #   aiohttp
     #   black
+    #   bowler
     #   jsonschema
     #   pytest
 avro==1.10.0
     # via feast (setup.py)
-azure-core==1.23.1
+azure-core==1.24.1
     # via
     #   adlfs
     #   azure-identity
     #   azure-storage-blob
+    #   msrest
 azure-datalake-store==0.0.52
     # via adlfs
 azure-identity==1.10.0
     # via adlfs
-azure-storage-blob==12.11.0
+azure-storage-blob==12.12.0
     # via adlfs
-babel==2.10.1
+babel==2.10.3
     # via sphinx
 backcall==0.2.0
     # via ipython
 black==19.10b0
     # via feast (setup.py)
-boto3==1.22.8
+boto3==1.20.23
     # via
     #   feast (setup.py)
     #   moto
-botocore==1.25.8
+botocore==1.23.24
     # via
+    #   aiobotocore
     #   boto3
     #   moto
     #   s3transfer
-build==0.7.0
+bowler==0.9.0
+    # via feast (setup.py)
+build==0.8.0
     # via feast (setup.py)
 cachecontrol==0.12.11
     # via firebase-admin
 cachetools==4.2.4
     # via google-auth
-certifi==2021.10.8
+certifi==2022.6.15
     # via
     #   minio
     #   msrest
@@ -105,19 +116,21 @@ charset-normalizer==2.0.12
 click==8.0.1
     # via
     #   black
+    #   bowler
     #   feast (setup.py)
     #   great-expectations
+    #   moreorless
     #   pip-tools
     #   uvicorn
-cloudpickle==2.0.0
+cloudpickle==2.1.0
     # via dask
-colorama==0.4.4
+colorama==0.4.5
     # via
     #   feast (setup.py)
     #   great-expectations
-coverage[toml]==6.3.2
+coverage[toml]==6.4.1
     # via pytest-cov
-cryptography==3.4.8
+cryptography==35.0.0
     # via
     #   adal
     #   azure-identity
@@ -140,7 +153,7 @@ deprecated==1.2.13
     # via redis
 deprecation==2.1.0
     # via testcontainers
-dill==0.3.4
+dill==0.3.5.1
     # via feast (setup.py)
 distlib==0.3.4
     # via virtualenv
@@ -158,32 +171,35 @@ execnet==1.9.0
     # via pytest-xdist
 executing==0.8.3
     # via stack-data
-fastapi==0.76.0
+fastapi==0.78.0
     # via feast (setup.py)
-fastavro==1.4.11
+fastavro==1.5.1
     # via
     #   feast (setup.py)
     #   pandavro
 fastjsonschema==2.15.3
     # via nbformat
-filelock==3.6.0
+filelock==3.7.1
     # via virtualenv
 firebase-admin==4.5.2
     # via feast (setup.py)
+fissix==21.11.13
+    # via bowler
 flake8==4.0.1
     # via feast (setup.py)
 frozenlist==1.3.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec==2022.3.0
+fsspec==2022.1.0
     # via
     #   adlfs
     #   dask
     #   gcsfs
-gcsfs==2022.3.0
+    #   s3fs
+gcsfs==2022.1.0
     # via feast (setup.py)
-google-api-core[grpc]==1.31.5
+google-api-core[grpc]==1.31.6
     # via
     #   feast (setup.py)
     #   firebase-admin
@@ -193,7 +209,7 @@ google-api-core[grpc]==1.31.5
     #   google-cloud-core
     #   google-cloud-datastore
     #   google-cloud-firestore
-google-api-python-client==2.47.0
+google-api-python-client==2.51.0
     # via firebase-admin
 google-auth==1.35.0
     # via
@@ -206,11 +222,11 @@ google-auth==1.35.0
     #   google-cloud-storage
 google-auth-httplib2==0.1.0
     # via google-api-python-client
-google-auth-oauthlib==0.5.1
+google-auth-oauthlib==0.5.2
     # via gcsfs
-google-cloud-bigquery==2.34.3
+google-cloud-bigquery==2.34.4
     # via feast (setup.py)
-google-cloud-bigquery-storage==2.13.1
+google-cloud-bigquery-storage==2.13.2
     # via feast (setup.py)
 google-cloud-core==1.7.2
     # via
@@ -219,9 +235,9 @@ google-cloud-core==1.7.2
     #   google-cloud-datastore
     #   google-cloud-firestore
     #   google-cloud-storage
-google-cloud-datastore==2.5.1
+google-cloud-datastore==2.7.1
     # via feast (setup.py)
-google-cloud-firestore==2.4.0
+google-cloud-firestore==2.5.3
     # via firebase-admin
 google-cloud-storage==1.40.0
     # via
@@ -234,14 +250,16 @@ google-resumable-media==1.3.3
     # via
     #   google-cloud-bigquery
     #   google-cloud-storage
-googleapis-common-protos==1.52.0
+googleapis-common-protos==1.56.3
     # via
     #   feast (setup.py)
     #   google-api-core
     #   tensorflow-metadata
 great-expectations==0.14.13
     # via feast (setup.py)
-grpcio==1.46.0
+greenlet==1.1.2
+    # via sqlalchemy
+grpcio==1.47.0
     # via
     #   feast (setup.py)
     #   google-api-core
@@ -249,7 +267,7 @@ grpcio==1.46.0
     #   grpcio-reflection
     #   grpcio-testing
     #   grpcio-tools
-grpcio-reflection==1.46.0
+grpcio-reflection==1.47.0
     # via feast (setup.py)
 grpcio-testing==1.44.0
     # via feast (setup.py)
@@ -267,7 +285,7 @@ httplib2==0.20.4
     #   google-auth-httplib2
 httptools==0.4.0
     # via uvicorn
-identify==2.5.0
+identify==2.5.1
     # via pre-commit
 idna==3.3
     # via
@@ -277,11 +295,11 @@ idna==3.3
     #   yarl
 imagesize==1.3.0
     # via sphinx
-importlib-metadata==4.11.3
+importlib-metadata==4.11.4
     # via great-expectations
 iniconfig==1.1.1
     # via pytest
-ipython==8.3.0
+ipython==8.4.0
     # via great-expectations
 isodate==0.6.1
     # via msrest
@@ -296,7 +314,7 @@ jinja2==3.0.3
     #   great-expectations
     #   moto
     #   sphinx
-jmespath==1.0.0
+jmespath==0.10.0
     # via
     #   boto3
     #   botocore
@@ -304,7 +322,7 @@ jsonpatch==1.32
     # via great-expectations
 jsonpointer==2.3
     # via jsonpatch
-jsonschema==4.5.1
+jsonschema==4.6.0
     # via
     #   altair
     #   feast (setup.py)
@@ -330,17 +348,19 @@ mmh3==3.0.0
     # via feast (setup.py)
 mock==2.0.0
     # via feast (setup.py)
-moto==3.1.7
+moreorless==0.4.0
+    # via bowler
+moto==3.1.14
     # via feast (setup.py)
-msal==1.17.0
+msal==1.18.0
     # via
     #   azure-identity
     #   msal-extensions
 msal-extensions==1.0.0
     # via azure-identity
-msgpack==1.0.3
+msgpack==1.0.4
     # via cachecontrol
-msrest==0.6.21
+msrest==0.7.1
     # via
     #   azure-storage-blob
     #   msrestazure
@@ -350,12 +370,16 @@ multidict==6.0.2
     # via
     #   aiohttp
     #   yarl
-mypy==0.931
-    # via feast (setup.py)
+mypy==0.961
+    # via
+    #   feast (setup.py)
+    #   sqlalchemy
 mypy-extensions==0.4.3
     # via mypy
 mypy-protobuf==3.1
     # via feast (setup.py)
+mysqlclient==2.1.1
+    # via feast (setup.py)
 nbformat==5.4.0
     # via great-expectations
 nodeenv==1.6.0
@@ -384,7 +408,7 @@ packaging==21.3
     #   pytest
     #   redis
     #   sphinx
-pandas==1.4.2
+pandas==1.4.3
     # via
     #   altair
     #   feast (setup.py)
@@ -409,7 +433,7 @@ pexpect==4.8.0
     # via ipython
 pickleshare==0.7.5
     # via ipython
-pip-tools==6.6.0
+pip-tools==6.6.2
     # via feast (setup.py)
 platformdirs==2.5.2
     # via virtualenv
@@ -423,7 +447,7 @@ pre-commit==2.19.0
     # via feast (setup.py)
 prompt-toolkit==3.0.29
     # via ipython
-proto-plus==1.19.6
+proto-plus==1.20.6
     # via
     #   feast (setup.py)
     #   google-cloud-bigquery
@@ -435,6 +459,9 @@ protobuf==3.19.4
     #   feast (setup.py)
     #   google-api-core
     #   google-cloud-bigquery
+    #   google-cloud-bigquery-storage
+    #   google-cloud-datastore
+    #   google-cloud-firestore
     #   googleapis-common-protos
     #   grpcio-reflection
     #   grpcio-testing
@@ -456,7 +483,7 @@ py==1.11.0
     #   pytest-forked
 py-cpuinfo==8.0.0
     # via pytest-benchmark
-py4j==0.10.9.3
+py4j==0.10.9.5
     # via pyspark
 pyarrow==6.0.1
     # via
@@ -474,9 +501,9 @@ pycodestyle==2.8.0
     # via flake8
 pycparser==2.21
     # via cffi
-pycryptodomex==3.14.1
+pycryptodomex==3.15.0
     # via snowflake-connector-python
-pydantic==1.9.0
+pydantic==1.9.1
     # via
     #   fastapi
     #   feast (setup.py)
@@ -484,14 +511,15 @@ pyflakes==2.4.0
     # via flake8
 pygments==2.12.0
     # via
+    #   feast (setup.py)
     #   ipython
     #   sphinx
-pyjwt[crypto]==2.3.0
+pyjwt[crypto]==2.4.0
     # via
     #   adal
     #   msal
     #   snowflake-connector-python
-pyopenssl==21.0.0
+pyopenssl==22.0.0
     # via snowflake-connector-python
 pyparsing==2.4.7
     # via
@@ -500,7 +528,7 @@ pyparsing==2.4.7
     #   packaging
 pyrsistent==0.18.1
     # via jsonschema
-pyspark==3.2.1
+pyspark==3.3.0
     # via feast (setup.py)
 pytest==7.1.2
     # via
@@ -558,9 +586,9 @@ pyyaml==6.0
     #   uvicorn
 redis==4.2.2
     # via feast (setup.py)
-regex==2022.4.24
+regex==2022.6.2
     # via black
-requests==2.27.1
+requests==2.28.0
     # via
     #   adal
     #   adlfs
@@ -585,19 +613,20 @@ requests-oauthlib==1.3.1
     # via
     #   google-auth-oauthlib
     #   msrest
-responses==0.20.0
+responses==0.21.0
     # via moto
 rsa==4.8
     # via google-auth
 ruamel-yaml==0.17.17
     # via great-expectations
+s3fs==2022.1.0
+    # via feast (setup.py)
 s3transfer==0.5.2
     # via boto3
-scipy==1.8.0
+scipy==1.8.1
     # via great-expectations
 six==1.16.0
     # via
-    #   absl-py
     #   azure-core
     #   azure-identity
     #   google-api-core
@@ -610,14 +639,13 @@ six==1.16.0
     #   mock
     #   msrestazure
     #   pandavro
-    #   pyopenssl
     #   python-dateutil
     #   virtualenv
 sniffio==1.2.0
     # via anyio
 snowballstemmer==2.2.0
     # via sphinx
-snowflake-connector-python[pandas]==2.7.7
+snowflake-connector-python[pandas]==2.7.8
     # via feast (setup.py)
 sphinx==4.3.2
     # via
@@ -637,19 +665,23 @@ sphinxcontrib-qthelp==1.0.3
     # via sphinx
 sphinxcontrib-serializinghtml==1.1.5
     # via sphinx
-stack-data==0.2.0
+sqlalchemy[mypy]==1.4.38
+    # via feast (setup.py)
+sqlalchemy2-stubs==0.0.2a24
+    # via sqlalchemy
+stack-data==0.3.0
     # via ipython
-starlette==0.18.0
+starlette==0.19.1
     # via fastapi
-tabulate==0.8.9
+tabulate==0.8.10
     # via feast (setup.py)
 tenacity==8.0.1
     # via feast (setup.py)
-tensorflow-metadata==1.7.0
+tensorflow-metadata==1.9.0
     # via feast (setup.py)
 termcolor==1.1.0
     # via great-expectations
-testcontainers==3.5.3
+testcontainers==3.6.0
     # via feast (setup.py)
 thriftpy2==0.4.14
     # via happybase
@@ -674,7 +706,7 @@ tqdm==4.64.0
     # via
     #   feast (setup.py)
     #   great-expectations
-traitlets==5.1.1
+traitlets==5.3.0
     # via
     #   ipython
     #   jupyter-core
@@ -682,27 +714,29 @@ traitlets==5.1.1
     #   nbformat
 trino==0.313.0
     # via feast (setup.py)
-typed-ast==1.5.3
+typed-ast==1.5.4
     # via black
-types-protobuf==3.19.18
+typeguard==2.13.3
+    # via feast (setup.py)
+types-protobuf==3.19.22
     # via
     #   feast (setup.py)
     #   mypy-protobuf
-types-python-dateutil==2.8.14
+types-python-dateutil==2.8.18
     # via feast (setup.py)
-types-pytz==2021.3.7
+types-pytz==2022.1.0
     # via feast (setup.py)
-types-pyyaml==6.0.7
+types-pyyaml==6.0.8
     # via feast (setup.py)
-types-redis==4.2.1
+types-redis==4.3.2
     # via feast (setup.py)
-types-requests==2.27.25
+types-requests==2.27.31
     # via feast (setup.py)
-types-setuptools==57.4.14
+types-setuptools==57.4.17
     # via feast (setup.py)
-types-tabulate==0.8.8
+types-tabulate==0.8.10
     # via feast (setup.py)
-types-urllib3==1.26.14
+types-urllib3==1.26.15
     # via types-requests
 typing-extensions==4.2.0
     # via
@@ -710,6 +744,7 @@ typing-extensions==4.2.0
     #   great-expectations
     #   mypy
     #   pydantic
+    #   sqlalchemy2-stubs
 tzdata==2022.1
     # via pytz-deprecation-shim
 tzlocal==4.2
@@ -724,17 +759,19 @@ urllib3==1.26.9
     #   minio
     #   requests
     #   responses
-uvicorn[standard]==0.17.6
+uvicorn[standard]==0.18.1
     # via feast (setup.py)
 uvloop==0.16.0
     # via uvicorn
 virtualenv==20.14.1
     # via pre-commit
-watchgod==0.8.2
+volatile==2.1.0
+    # via bowler
+watchfiles==0.15.0
     # via uvicorn
 wcwidth==0.2.5
     # via prompt-toolkit
-websocket-client==1.3.2
+websocket-client==1.3.3
     # via docker
 websockets==10.3
     # via uvicorn
@@ -744,9 +781,10 @@ wheel==0.37.1
     # via pip-tools
 wrapt==1.14.1
     # via
+    #   aiobotocore
     #   deprecated
     #   testcontainers
-xmltodict==0.12.0
+xmltodict==0.13.0
     # via moto
 yarl==1.7.2
     # via aiohttp
diff --git a/sdk/python/requirements/py3.10-requirements.txt b/sdk/python/requirements/py3.10-requirements.txt
index 369c3b8e60b..15ee46aff53 100644
--- a/sdk/python/requirements/py3.10-requirements.txt
+++ b/sdk/python/requirements/py3.10-requirements.txt
@@ -4,56 +4,66 @@
 #
 #    pip-compile --output-file=sdk/python/requirements/py3.10-requirements.txt
 #
-absl-py==1.0.0
+absl-py==1.1.0
     # via tensorflow-metadata
-anyio==3.5.0
+anyio==3.6.1
     # via
     #   starlette
-    #   watchgod
-asgiref==3.5.1
-    # via uvicorn
+    #   watchfiles
+appdirs==1.4.4
+    # via fissix
 attrs==21.4.0
-    # via jsonschema
-cachetools==4.2.4
+    # via
+    #   bowler
+    #   jsonschema
+bowler==0.9.0
+    # via feast (setup.py)
+cachetools==5.2.0
     # via google-auth
-certifi==2021.10.8
+certifi==2022.6.15
     # via requests
 charset-normalizer==2.0.12
     # via requests
 click==8.0.1
     # via
+    #   bowler
     #   feast (setup.py)
+    #   moreorless
     #   uvicorn
-cloudpickle==2.0.0
+cloudpickle==2.1.0
     # via dask
-colorama==0.4.4
+colorama==0.4.5
     # via feast (setup.py)
 dask==2022.1.1
     # via feast (setup.py)
-dill==0.3.4
+dill==0.3.5.1
     # via feast (setup.py)
-fastapi==0.76.0
+fastapi==0.78.0
     # via feast (setup.py)
-fastavro==1.4.11
+fastavro==1.5.1
     # via
     #   feast (setup.py)
     #   pandavro
-fsspec==2022.3.0
+fissix==21.11.13
+    # via bowler
+fsspec==2022.5.0
     # via dask
-google-api-core==1.31.5
+google-api-core==2.8.2
     # via feast (setup.py)
-google-auth==1.35.0
+google-auth==2.8.0
     # via google-api-core
-googleapis-common-protos==1.52.0
+googleapis-common-protos==1.56.3
     # via
     #   feast (setup.py)
     #   google-api-core
     #   tensorflow-metadata
-grpcio==1.46.0
+greenlet==1.1.2
+    # via sqlalchemy
+grpcio==1.47.0
     # via
     #   feast (setup.py)
     #   grpcio-reflection
-grpcio-reflection==1.46.0
+grpcio-reflection==1.47.0
     # via feast (setup.py)
 h11==0.13.0
     # via uvicorn
@@ -65,7 +75,7 @@ idna==3.3
     #   requests
 jinja2==3.1.2
     # via feast (setup.py)
-jsonschema==4.5.1
+jsonschema==4.6.0
     # via feast (setup.py)
 locket==1.0.0
     # via partd
@@ -73,6 +83,12 @@ markupsafe==2.1.1
     # via jinja2
 mmh3==3.0.0
     # via feast (setup.py)
+moreorless==0.4.0
+    # via bowler
+mypy==0.961
+    # via sqlalchemy
+mypy-extensions==0.4.3
+    # via mypy
 numpy==1.21.6
     # via
     #   feast (setup.py)
@@ -80,10 +96,8 @@ numpy==1.21.6
     #   pandavro
     #   pyarrow
 packaging==21.3
-    # via
-    #   dask
-    #   google-api-core
-pandas==1.4.2
+    # via dask
+pandas==1.4.3
     # via
     #   feast (setup.py)
     #   pandavro
@@ -91,7 +105,7 @@ pandavro==1.5.2
     # via feast (setup.py)
 partd==1.2.0
     # via dask
-proto-plus==1.19.6
+proto-plus==1.20.6
     # via feast (setup.py)
 protobuf==3.19.4
     # via
@@ -109,11 +123,13 @@ pyasn1==0.4.8
     #   rsa
 pyasn1-modules==0.2.8
     # via google-auth
-pydantic==1.9.0
+pydantic==1.9.1
     # via
     #   fastapi
     #   feast (setup.py)
-pyparsing==3.0.8
+pygments==2.12.0
+    # via feast (setup.py)
+pyparsing==3.0.9
     # via packaging
 pyrsistent==0.18.1
     # via jsonschema
@@ -122,56 +138,62 @@ python-dateutil==2.8.2
 python-dotenv==0.20.0
     # via uvicorn
 pytz==2022.1
-    # via
-    #   google-api-core
-    #   pandas
+    # via pandas
 pyyaml==6.0
     # via
     #   dask
     #   feast (setup.py)
     #   uvicorn
-requests==2.27.1
+requests==2.28.0
     # via google-api-core
 rsa==4.8
     # via google-auth
 six==1.16.0
     # via
-    #   absl-py
-    #   google-api-core
     #   google-auth
     #   grpcio
     #   pandavro
     #   python-dateutil
 sniffio==1.2.0
     # via anyio
-starlette==0.18.0
+sqlalchemy[mypy]==1.4.38
+    # via feast (setup.py)
+sqlalchemy2-stubs==0.0.2a24
+    # via sqlalchemy
+starlette==0.19.1
     # via fastapi
-tabulate==0.8.9
+tabulate==0.8.10
     # via feast (setup.py)
 tenacity==8.0.1
     # via feast (setup.py)
-tensorflow-metadata==1.7.0
+tensorflow-metadata==1.9.0
     # via feast (setup.py)
 toml==0.10.2
     # via feast (setup.py)
+tomli==2.0.1
+    # via mypy
 toolz==0.11.2
     # via
     #   dask
     #   partd
 tqdm==4.64.0
     # via feast (setup.py)
+typeguard==2.13.3
+    # via feast (setup.py)
 typing-extensions==4.2.0
-    # via pydantic
+    # via
+    #   mypy
+    #   pydantic
+    #   sqlalchemy2-stubs
 urllib3==1.26.9
     # via requests
-uvicorn[standard]==0.17.6
+uvicorn[standard]==0.18.1
     # via feast (setup.py)
 uvloop==0.16.0
     # via uvicorn
-watchgod==0.8.2
+volatile==2.1.0
+    # via bowler
+watchfiles==0.15.0
     # via uvicorn
 websockets==10.3
     # via uvicorn
-
-# The following packages are considered to be unsafe in a requirements file:
-# setuptools
diff --git a/sdk/python/requirements/py3.7-ci-requirements.txt b/sdk/python/requirements/py3.7-ci-requirements.txt
deleted file mode 100644
index 4232f5b2941..00000000000
--- a/sdk/python/requirements/py3.7-ci-requirements.txt
+++ /dev/null
@@ -1,788 +0,0 @@
-#
-# This file is autogenerated by pip-compile with python 3.7
-# To update, run:
-#
-#    pip-compile --extra=ci --output-file=sdk/python/requirements/py3.7-ci-requirements.txt
-#
-absl-py==1.0.0
-    # via tensorflow-metadata
-adal==1.2.7
-    # via
-    #   azure-datalake-store
-    #   msrestazure
-adlfs==0.5.9
-    # via feast (setup.py)
-aiohttp==3.8.1
-    # via
-    #   adlfs
-    #   gcsfs
-aiosignal==1.2.0
-    # via aiohttp
-alabaster==0.7.12
-    # via sphinx
-altair==4.2.0
-    # via great-expectations
-anyio==3.5.0
-    # via
-    #   starlette
-    #   watchgod
-appdirs==1.4.4
-    # via black
-appnope==0.1.3
-    # via ipython
-asgiref==3.5.1
-    # via uvicorn
-asn1crypto==1.5.1
-    # via
-    #   oscrypto
-    #   snowflake-connector-python
-assertpy==1.1
-    # via feast (setup.py)
-async-timeout==4.0.2
-    # via
-    #   aiohttp
-    #   redis
-asynctest==0.13.0
-    # via aiohttp
-attrs==21.4.0
-    # via
-    #   aiohttp
-    #   black
-    #   jsonschema
-    #   pytest
-avro==1.10.0
-    # via feast (setup.py)
-azure-core==1.23.1
-    # via
-    #   adlfs
-    #   azure-identity
-    #   azure-storage-blob
-azure-datalake-store==0.0.52
-    # via adlfs
-azure-identity==1.10.0
-    # via adlfs
-azure-storage-blob==12.11.0
-    # via adlfs
-babel==2.10.1
-    # via sphinx
-backcall==0.2.0
-    # via ipython
-backports-zoneinfo==0.2.1
-    # via
-    #   pytz-deprecation-shim
-    #   tzlocal
-black==19.10b0
-    # via feast (setup.py)
-boto3==1.22.8
-    # via
-    #   feast (setup.py)
-    #   moto
-botocore==1.25.8
-    # via
-    #   boto3
-    #   moto
-    #   s3transfer
-build==0.7.0
-    # via feast (setup.py)
-cachecontrol==0.12.11
-    # via firebase-admin
-cachetools==4.2.4
-    # via google-auth
-certifi==2021.10.8
-    # via
-    #   minio
-    #   msrest
-    #   requests
-    #   snowflake-connector-python
-cffi==1.15.0
-    # via
-    #   azure-datalake-store
-    #   cryptography
-    #   snowflake-connector-python
-cfgv==3.3.1
-    # via pre-commit
-charset-normalizer==2.0.12
-    # via
-    #   aiohttp
-    #   requests
-    #   snowflake-connector-python
-click==8.0.1
-    # via
-    #   black
-    #   feast (setup.py)
-    #   great-expectations
-    #   pip-tools
-    #   uvicorn
-cloudpickle==2.0.0
-    # via dask
-colorama==0.4.4
-    # via
-    #   feast (setup.py)
-    #   great-expectations
-coverage[toml]==6.3.2
-    # via pytest-cov
-cryptography==3.4.8
-    # via
-    #   adal
-    #   azure-identity
-    #   azure-storage-blob
-    #   feast (setup.py)
-    #   great-expectations
-    #   moto
-    #   msal
-    #   pyopenssl
-    #   snowflake-connector-python
-dask==2022.1.1
-    # via feast (setup.py)
-dataclasses==0.6
-    # via great-expectations
-decorator==5.1.1
-    # via
-    #   gcsfs
-    #   ipython
-deprecated==1.2.13
-    # via redis
-deprecation==2.1.0
-    # via testcontainers
-dill==0.3.4
-    # via feast (setup.py)
-distlib==0.3.4
-    # via virtualenv
-docker==5.0.3
-    # via
-    #   feast (setup.py)
-    #   testcontainers
-docutils==0.17.1
-    # via
-    #   sphinx
-    #   sphinx-rtd-theme
-entrypoints==0.4
-    # via altair
-execnet==1.9.0
-    # via pytest-xdist
-fastapi==0.76.0
-    # via feast (setup.py)
-fastavro==1.4.11
-    # via
-    #   feast (setup.py)
-    #   pandavro
-fastjsonschema==2.15.3
-    # via nbformat
-filelock==3.6.0
-    # via virtualenv
-firebase-admin==4.5.2
-    # via feast (setup.py)
-flake8==4.0.1
-    # via feast (setup.py)
-frozenlist==1.3.0
-    # via
-    #   aiohttp
-    #   aiosignal
-fsspec==2022.3.0
-    # via
-    #   adlfs
-    #   dask
-    #   gcsfs
-gcsfs==2022.3.0
-    # via feast (setup.py)
-google-api-core[grpc]==1.31.5
-    # via
-    #   feast (setup.py)
-    #   firebase-admin
-    #   google-api-python-client
-    #   google-cloud-bigquery
-    #   google-cloud-bigquery-storage
-    #   google-cloud-core
-    #   google-cloud-datastore
-    #   google-cloud-firestore
-google-api-python-client==2.47.0
-    # via firebase-admin
-google-auth==1.35.0
-    # via
-    #   gcsfs
-    #   google-api-core
-    #   google-api-python-client
-    #   google-auth-httplib2
-    #   google-auth-oauthlib
-    #   google-cloud-core
-    #   google-cloud-storage
-google-auth-httplib2==0.1.0
-    # via google-api-python-client
-google-auth-oauthlib==0.5.1
-    # via gcsfs
-google-cloud-bigquery==2.34.3
-    # via feast (setup.py)
-google-cloud-bigquery-storage==2.13.1
-    # via feast (setup.py)
-google-cloud-core==1.7.2
-    # via
-    #   feast (setup.py)
-    #   google-cloud-bigquery
-    #   google-cloud-datastore
-    #   google-cloud-firestore
-    #   google-cloud-storage
-google-cloud-datastore==2.5.1
-    # via feast (setup.py)
-google-cloud-firestore==2.4.0
-    # via firebase-admin
-google-cloud-storage==1.40.0
-    # via
-    #   feast (setup.py)
-    #   firebase-admin
-    #   gcsfs
-google-crc32c==1.3.0
-    # via google-resumable-media
-google-resumable-media==1.3.3
-    # via
-    #   google-cloud-bigquery
-    #   google-cloud-storage
-googleapis-common-protos==1.52.0
-    # via
-    #   feast (setup.py)
-    #   google-api-core
-    #   tensorflow-metadata
-great-expectations==0.14.13
-    # via feast (setup.py)
-grpcio==1.46.0
-    # via
-    #   feast (setup.py)
-    #   google-api-core
-    #   google-cloud-bigquery
-    #   grpcio-reflection
-    #   grpcio-testing
-    #   grpcio-tools
-grpcio-reflection==1.46.0
-    # via feast (setup.py)
-grpcio-testing==1.44.0
-    # via feast (setup.py)
-grpcio-tools==1.44.0
-    # via feast (setup.py)
-h11==0.13.0
-    # via uvicorn
-happybase==1.2.0
-    # via feast (setup.py)
-hiredis==2.0.0
-    # via feast (setup.py)
-httplib2==0.20.4
-    # via
-    #   google-api-python-client
-    #   google-auth-httplib2
-httptools==0.4.0
-    # via uvicorn
-identify==2.5.0
-    # via pre-commit
-idna==3.3
-    # via
-    #   anyio
-    #   requests
-    #   snowflake-connector-python
-    #   yarl
-imagesize==1.3.0
-    # via sphinx
-importlib-metadata==4.2.0
-    # via
-    #   build
-    #   click
-    #   flake8
-    #   great-expectations
-    #   jsonschema
-    #   moto
-    #   pep517
-    #   pluggy
-    #   pre-commit
-    #   pytest
-    #   redis
-    #   virtualenv
-importlib-resources==5.7.1
-    # via jsonschema
-iniconfig==1.1.1
-    # via pytest
-ipython==7.33.0
-    # via great-expectations
-isodate==0.6.1
-    # via msrest
-isort==5.10.1
-    # via feast (setup.py)
-jedi==0.18.1
-    # via ipython
-jinja2==3.0.3
-    # via
-    #   altair
-    #   feast (setup.py)
-    #   great-expectations
-    #   moto
-    #   sphinx
-jmespath==1.0.0
-    # via
-    #   boto3
-    #   botocore
-jsonpatch==1.32
-    # via great-expectations
-jsonpointer==2.3
-    # via jsonpatch
-jsonschema==4.5.1
-    # via
-    #   altair
-    #   feast (setup.py)
-    #   great-expectations
-    #   nbformat
-jupyter-core==4.10.0
-    # via nbformat
-locket==1.0.0
-    # via partd
-markupsafe==2.1.1
-    # via
-    #   jinja2
-    #   moto
-matplotlib-inline==0.1.3
-    # via ipython
-mccabe==0.6.1
-    # via flake8
-minio==7.1.0
-    # via feast (setup.py)
-mistune==2.0.2
-    # via great-expectations
-mmh3==3.0.0
-    # via feast (setup.py)
-mock==2.0.0
-    # via feast (setup.py)
-moto==3.1.7
-    # via feast (setup.py)
-msal==1.17.0
-    # via
-    #   azure-identity
-    #   msal-extensions
-msal-extensions==1.0.0
-    # via azure-identity
-msgpack==1.0.3
-    # via cachecontrol
-msrest==0.6.21
-    # via
-    #   azure-storage-blob
-    #   msrestazure
-msrestazure==0.6.4
-    # via adlfs
-multidict==6.0.2
-    # via
-    #   aiohttp
-    #   yarl
-mypy==0.931
-    # via feast (setup.py)
-mypy-extensions==0.4.3
-    # via mypy
-mypy-protobuf==3.1
-    # via feast (setup.py)
-nbformat==5.4.0
-    # via great-expectations
-nodeenv==1.6.0
-    # via pre-commit
-numpy==1.21.6
-    # via
-    #   altair
-    #   feast (setup.py)
-    #   great-expectations
-    #   pandas
-    #   pandavro
-    #   pyarrow
-    #   scipy
-oauthlib==3.2.0
-    # via requests-oauthlib
-oscrypto==1.3.0
-    # via snowflake-connector-python
-packaging==21.3
-    # via
-    #   build
-    #   dask
-    #   deprecation
-    #   google-api-core
-    #   google-cloud-bigquery
-    #   great-expectations
-    #   pytest
-    #   redis
-    #   sphinx
-pandas==1.3.5
-    # via
-    #   altair
-    #   feast (setup.py)
-    #   great-expectations
-    #   pandavro
-    #   snowflake-connector-python
-pandavro==1.5.2
-    # via feast (setup.py)
-parso==0.8.3
-    # via jedi
-partd==1.2.0
-    # via dask
-pathspec==0.9.0
-    # via black
-pbr==5.9.0
-    # via mock
-pep517==0.12.0
-    # via
-    #   build
-    #   pip-tools
-pexpect==4.8.0
-    # via ipython
-pickleshare==0.7.5
-    # via ipython
-pip-tools==6.6.0
-    # via feast (setup.py)
-platformdirs==2.5.2
-    # via virtualenv
-pluggy==1.0.0
-    # via pytest
-ply==3.11
-    # via thriftpy2
-portalocker==2.4.0
-    # via msal-extensions
-pre-commit==2.19.0
-    # via feast (setup.py)
-prompt-toolkit==3.0.29
-    # via ipython
-proto-plus==1.19.6
-    # via
-    #   feast (setup.py)
-    #   google-cloud-bigquery
-    #   google-cloud-bigquery-storage
-    #   google-cloud-datastore
-    #   google-cloud-firestore
-protobuf==3.19.4
-    # via
-    #   feast (setup.py)
-    #   google-api-core
-    #   google-cloud-bigquery
-    #   googleapis-common-protos
-    #   grpcio-reflection
-    #   grpcio-testing
-    #   grpcio-tools
-    #   mypy-protobuf
-    #   proto-plus
-    #   tensorflow-metadata
-psutil==5.9.0
-    # via feast (setup.py)
-psycopg2-binary==2.9.3
-    # via feast (setup.py)
-ptyprocess==0.7.0
-    # via pexpect
-py==1.11.0
-    # via
-    #   pytest
-    #   pytest-forked
-py-cpuinfo==8.0.0
-    # via pytest-benchmark
-py4j==0.10.9.3
-    # via pyspark
-pyarrow==6.0.1
-    # via
-    #   feast (setup.py)
-    #   snowflake-connector-python
-pyasn1==0.4.8
-    # via
-    #   pyasn1-modules
-    #   rsa
-pyasn1-modules==0.2.8
-    # via google-auth
-pybindgen==0.22.1
-    # via feast (setup.py)
-pycodestyle==2.8.0
-    # via flake8
-pycparser==2.21
-    # via cffi
-pycryptodomex==3.14.1
-    # via snowflake-connector-python
-pydantic==1.9.0
-    # via
-    #   fastapi
-    #   feast (setup.py)
-pyflakes==2.4.0
-    # via flake8
-pygments==2.12.0
-    # via
-    #   ipython
-    #   sphinx
-pyjwt[crypto]==2.3.0
-    # via
-    #   adal
-    #   msal
-    #   snowflake-connector-python
-pyopenssl==21.0.0
-    # via snowflake-connector-python
-pyparsing==2.4.7
-    # via
-    #   great-expectations
-    #   httplib2
-    #   packaging
-pyrsistent==0.18.1
-    # via jsonschema
-pyspark==3.2.1
-    # via feast (setup.py)
-pytest==7.1.2
-    # via
-    #   feast (setup.py)
-    #   pytest-benchmark
-    #   pytest-cov
-    #   pytest-forked
-    #   pytest-lazy-fixture
-    #   pytest-mock
-    #   pytest-ordering
-    #   pytest-timeout
-    #   pytest-xdist
-pytest-benchmark==3.4.1
-    # via feast (setup.py)
-pytest-cov==3.0.0
-    # via feast (setup.py)
-pytest-forked==1.4.0
-    # via pytest-xdist
-pytest-lazy-fixture==0.6.3
-    # via feast (setup.py)
-pytest-mock==1.10.4
-    # via feast (setup.py)
-pytest-ordering==0.6
-    # via feast (setup.py)
-pytest-timeout==1.4.2
-    # via feast (setup.py)
-pytest-xdist==2.5.0
-    # via feast (setup.py)
-python-dateutil==2.8.2
-    # via
-    #   adal
-    #   botocore
-    #   google-cloud-bigquery
-    #   great-expectations
-    #   moto
-    #   pandas
-python-dotenv==0.20.0
-    # via uvicorn
-pytz==2022.1
-    # via
-    #   babel
-    #   google-api-core
-    #   great-expectations
-    #   moto
-    #   pandas
-    #   snowflake-connector-python
-    #   trino
-pytz-deprecation-shim==0.1.0.post0
-    # via tzlocal
-pyyaml==6.0
-    # via
-    #   dask
-    #   feast (setup.py)
-    #   pre-commit
-    #   uvicorn
-redis==4.2.2
-    # via feast (setup.py)
-regex==2022.4.24
-    # via black
-requests==2.27.1
-    # via
-    #   adal
-    #   adlfs
-    #   azure-core
-    #   azure-datalake-store
-    #   cachecontrol
-    #   docker
-    #   gcsfs
-    #   google-api-core
-    #   google-cloud-bigquery
-    #   google-cloud-storage
-    #   great-expectations
-    #   moto
-    #   msal
-    #   msrest
-    #   requests-oauthlib
-    #   responses
-    #   snowflake-connector-python
-    #   sphinx
-    #   trino
-requests-oauthlib==1.3.1
-    # via
-    #   google-auth-oauthlib
-    #   msrest
-responses==0.20.0
-    # via moto
-rsa==4.8
-    # via google-auth
-ruamel-yaml==0.17.17
-    # via great-expectations
-ruamel-yaml-clib==0.2.6
-    # via ruamel-yaml
-s3transfer==0.5.2
-    # via boto3
-scipy==1.7.3
-    # via great-expectations
-six==1.16.0
-    # via
-    #   absl-py
-    #   azure-core
-    #   azure-identity
-    #   google-api-core
-    #   google-auth
-    #   google-auth-httplib2
-    #   google-cloud-core
-    #   google-resumable-media
-    #   grpcio
-    #   happybase
-    #   mock
-    #   msrestazure
-    #   pandavro
-    #   pyopenssl
-    #   python-dateutil
-    #   virtualenv
-sniffio==1.2.0
-    # via anyio
-snowballstemmer==2.2.0
-    # via sphinx
-snowflake-connector-python[pandas]==2.7.7
-    # via feast (setup.py)
-sphinx==4.3.2
-    # via
-    #   feast (setup.py)
-    #   sphinx-rtd-theme
-sphinx-rtd-theme==1.0.0
-    # via feast (setup.py)
-sphinxcontrib-applehelp==1.0.2
-    # via sphinx
-sphinxcontrib-devhelp==1.0.2
-    # via sphinx
-sphinxcontrib-htmlhelp==2.0.0
-    # via sphinx
-sphinxcontrib-jsmath==1.0.1
-    # via sphinx
-sphinxcontrib-qthelp==1.0.3
-    # via sphinx
-sphinxcontrib-serializinghtml==1.1.5
-    # via sphinx
-starlette==0.18.0
-    # via fastapi
-tabulate==0.8.9
-    # via feast (setup.py)
-tenacity==8.0.1
-    # via feast (setup.py)
-tensorflow-metadata==1.7.0
-    # via feast (setup.py)
-termcolor==1.1.0
-    # via great-expectations
-testcontainers==3.5.3
-    # via feast (setup.py)
-thriftpy2==0.4.14
-    # via happybase
-toml==0.10.2
-    # via
-    #   black
-    #   feast (setup.py)
-    #   pre-commit
-tomli==2.0.1
-    # via
-    #   build
-    #   coverage
-    #   mypy
-    #   pep517
-    #   pytest
-toolz==0.11.2
-    # via
-    #   altair
-    #   dask
-    #   partd
-tqdm==4.64.0
-    # via
-    #   feast (setup.py)
-    #   great-expectations
-traitlets==5.1.1
-    # via
-    #   ipython
-    #   jupyter-core
-    #   matplotlib-inline
-    #   nbformat
-trino==0.313.0
-    # via feast (setup.py)
-typed-ast==1.5.3
-    # via
-    #   black
-    #   mypy
-types-protobuf==3.19.18
-    # via
-    #   feast (setup.py)
-    #   mypy-protobuf
-types-python-dateutil==2.8.14
-    # via feast (setup.py)
-types-pytz==2021.3.7
-    # via feast (setup.py)
-types-pyyaml==6.0.7
-    # via feast (setup.py)
-types-redis==4.2.1
-    # via feast (setup.py)
-types-requests==2.27.25
-    # via feast (setup.py)
-types-setuptools==57.4.14
-    # via feast (setup.py)
-types-tabulate==0.8.8
-    # via feast (setup.py)
-types-urllib3==1.26.14
-    # via types-requests
-typing-extensions==4.2.0
-    # via
-    #   aiohttp
-    #   anyio
-    #   asgiref
-    #   async-timeout
-    #   azure-core
-    #   great-expectations
-    #   h11
-    #   importlib-metadata
-    #   jsonschema
-    #   mypy
-    #   pydantic
-    #   redis
-    #   starlette
-    #   uvicorn
-    #   yarl
-tzdata==2022.1
-    # via pytz-deprecation-shim
-tzlocal==4.2
-    # via great-expectations
-uritemplate==4.1.1
-    # via google-api-python-client
-urllib3==1.26.9
-    # via
-    #   botocore
-    #   feast (setup.py)
-    #   great-expectations
-    #   minio
-    #   requests
-    #   responses
-uvicorn[standard]==0.17.6
-    # via feast (setup.py)
-uvloop==0.16.0
-    # via uvicorn
-virtualenv==20.14.1
-    # via pre-commit
-watchgod==0.8.2
-    # via uvicorn
-wcwidth==0.2.5
-    # via prompt-toolkit
-websocket-client==1.3.2
-    # via docker
-websockets==10.3
-    # via uvicorn
-werkzeug==2.1.2
-    # via moto
-wheel==0.37.1
-    # via pip-tools
-wrapt==1.14.1
-    # via
-    #   deprecated
-    #   testcontainers
-xmltodict==0.12.0
-    # via moto
-yarl==1.7.2
-    # via aiohttp
-zipp==3.8.0
-    # via
-    #   importlib-metadata
-    #   importlib-resources
-    #   pep517
-
-# The following packages are considered to be unsafe in a requirements file:
-# pip
-# setuptools
diff --git a/sdk/python/requirements/py3.7-requirements.txt b/sdk/python/requirements/py3.7-requirements.txt
deleted file mode 100644
index dfdd86e39a1..00000000000
--- a/sdk/python/requirements/py3.7-requirements.txt
+++ /dev/null
@@ -1,195 +0,0 @@
-#
-# This file is autogenerated by pip-compile with python 3.7
-# To update, run:
-#
-#    pip-compile --output-file=sdk/python/requirements/py3.7-requirements.txt
-#
-absl-py==1.0.0
-    # via tensorflow-metadata
-anyio==3.5.0
-    # via
-    #   starlette
-    #   watchgod
-asgiref==3.5.1
-    # via uvicorn
-attrs==21.4.0
-    # via jsonschema
-cachetools==4.2.4
-    # via google-auth
-certifi==2021.10.8
-    # via requests
-charset-normalizer==2.0.12
-    # via requests
-click==8.0.1
-    # via
-    #   feast (setup.py)
-    #   uvicorn
-cloudpickle==2.0.0
-    # via dask
-colorama==0.4.4
-    # via feast (setup.py)
-dask==2022.1.1
-    # via feast (setup.py)
-dill==0.3.4
-    # via feast (setup.py)
-fastapi==0.76.0
-    # via feast (setup.py)
-fastavro==1.4.11
-    # via
-    #   feast (setup.py)
-    #   pandavro
-fsspec==2022.3.0
-    # via dask
-google-api-core==1.31.5
-    # via feast (setup.py)
-google-auth==1.35.0
-    # via google-api-core
-googleapis-common-protos==1.52.0
-    # via
-    #   feast (setup.py)
-    #   google-api-core
-    #   tensorflow-metadata
-grpcio==1.46.0
-    # via
-    #   feast (setup.py)
-    #   grpcio-reflection
-grpcio-reflection==1.46.0
-    # via feast (setup.py)
-h11==0.13.0
-    # via uvicorn
-httptools==0.4.0
-    # via uvicorn
-idna==3.3
-    # via
-    #   anyio
-    #   requests
-importlib-metadata==4.11.3
-    # via
-    #   click
-    #   jsonschema
-importlib-resources==5.7.1
-    # via jsonschema
-jinja2==3.1.2
-    # via feast (setup.py)
-jsonschema==4.5.1
-    # via feast (setup.py)
-locket==1.0.0
-    # via partd
-markupsafe==2.1.1
-    # via jinja2
-mmh3==3.0.0
-    # via feast (setup.py)
-numpy==1.21.6
-    # via
-    #   feast (setup.py)
-    #   pandas
-    #   pandavro
-    #   pyarrow
-packaging==21.3
-    # via
-    #   dask
-    #   google-api-core
-pandas==1.3.5
-    # via
-    #   feast (setup.py)
-    #   pandavro
-pandavro==1.5.2
-    # via feast (setup.py)
-partd==1.2.0
-    # via dask
-proto-plus==1.19.6
-    # via feast (setup.py)
-protobuf==3.19.4
-    # via
-    #   feast (setup.py)
-    #   google-api-core
-    #   googleapis-common-protos
-    #   grpcio-reflection
-    #   proto-plus
-    #   tensorflow-metadata
-pyarrow==6.0.1
-    # via feast (setup.py)
-pyasn1==0.4.8
-    # via
-    #   pyasn1-modules
-    #   rsa
-pyasn1-modules==0.2.8
-    # via google-auth
-pydantic==1.9.0
-    # via
-    #   fastapi
-    #   feast (setup.py)
-pyparsing==3.0.8
-    # via packaging
-pyrsistent==0.18.1
-    # via jsonschema
-python-dateutil==2.8.2
-    # via pandas
-python-dotenv==0.20.0
-    # via uvicorn
-pytz==2022.1
-    # via
-    #   google-api-core
-    #   pandas
-pyyaml==6.0
-    # via
-    #   dask
-    #   feast (setup.py)
-    #   uvicorn
-requests==2.27.1
-    # via google-api-core
-rsa==4.8
-    # via google-auth
-six==1.16.0
-    # via
-    #   absl-py
-    #   google-api-core
-    #   google-auth
-    #   grpcio
-    #   pandavro
-    #   python-dateutil
-sniffio==1.2.0
-    # via anyio
-starlette==0.18.0
-    # via fastapi
-tabulate==0.8.9
-    # via feast (setup.py)
-tenacity==8.0.1
-    # via feast (setup.py)
-tensorflow-metadata==1.7.0
-    # via feast (setup.py)
-toml==0.10.2
-    # via feast (setup.py)
-toolz==0.11.2
-    # via
-    #   dask
-    #   partd
-tqdm==4.64.0
-    # via feast (setup.py)
-typing-extensions==4.2.0
-    # via
-    #   anyio
-    #   asgiref
-    #   h11
-    #   importlib-metadata
-    #   jsonschema
-    #   pydantic
-    #   starlette
-    #   uvicorn
-urllib3==1.26.9
-    # via requests
-uvicorn[standard]==0.17.6
-    # via feast (setup.py)
-uvloop==0.16.0
-    # via uvicorn
-watchgod==0.8.2
-    # via uvicorn
-websockets==10.3
-    # via uvicorn
-zipp==3.8.0
-    # via
-    #   importlib-metadata
-    #   importlib-resources
-
-# The following packages are considered to be unsafe in a requirements file:
-# setuptools
diff --git a/sdk/python/requirements/py3.8-ci-requirements.txt b/sdk/python/requirements/py3.8-ci-requirements.txt
index db3eca4b538..dd21fae0a2d 100644
--- a/sdk/python/requirements/py3.8-ci-requirements.txt
+++ b/sdk/python/requirements/py3.8-ci-requirements.txt
@@ -4,7 +4,7 @@
 #
 #    pip-compile --extra=ci --output-file=sdk/python/requirements/py3.8-ci-requirements.txt
 #
-absl-py==1.0.0
+absl-py==1.1.0
     # via tensorflow-metadata
 adal==1.2.7
     # via
@@ -12,26 +12,32 @@ adal==1.2.7
     #   msrestazure
 adlfs==0.5.9
     # via feast (setup.py)
+aiobotocore==2.1.2
+    # via s3fs
 aiohttp==3.8.1
     # via
     #   adlfs
+    #   aiobotocore
     #   gcsfs
+    #   s3fs
+aioitertools==0.10.0
+    # via aiobotocore
 aiosignal==1.2.0
     # via aiohttp
 alabaster==0.7.12
     # via sphinx
 altair==4.2.0
     # via great-expectations
-anyio==3.5.0
+anyio==3.6.1
     # via
     #   starlette
-    #   watchgod
+    #   watchfiles
 appdirs==1.4.4
-    # via black
+    # via
+    #   black
+    #   fissix
 appnope==0.1.3
     # via ipython
-asgiref==3.5.1
-    # via uvicorn
 asn1crypto==1.5.1
     # via
     #   oscrypto
@@ -48,22 +54,24 @@ attrs==21.4.0
     # via
     #   aiohttp
     #   black
+    #   bowler
     #   jsonschema
     #   pytest
 avro==1.10.0
     # via feast (setup.py)
-azure-core==1.23.1
+azure-core==1.24.1
     # via
     #   adlfs
     #   azure-identity
     #   azure-storage-blob
+    #   msrest
 azure-datalake-store==0.0.52
     # via adlfs
 azure-identity==1.10.0
     # via adlfs
-azure-storage-blob==12.11.0
+azure-storage-blob==12.12.0
     # via adlfs
-babel==2.10.1
+babel==2.10.3
     # via sphinx
 backcall==0.2.0
     # via ipython
@@ -73,22 +81,25 @@ backports-zoneinfo==0.2.1
     #   tzlocal
 black==19.10b0
     # via feast (setup.py)
-boto3==1.22.8
+boto3==1.20.23
     # via
     #   feast (setup.py)
     #   moto
-botocore==1.25.8
+botocore==1.23.24
     # via
+    #   aiobotocore
     #   boto3
     #   moto
     #   s3transfer
-build==0.7.0
+bowler==0.9.0
+    # via feast (setup.py)
+build==0.8.0
     # via feast (setup.py)
 cachecontrol==0.12.11
     # via firebase-admin
 cachetools==4.2.4
     # via google-auth
-certifi==2021.10.8
+certifi==2022.6.15
     # via
     #   minio
     #   msrest
@@ -109,19 +120,21 @@ charset-normalizer==2.0.12
 click==8.0.1
     # via
     #   black
+    #   bowler
     #   feast (setup.py)
     #   great-expectations
+    #   moreorless
     #   pip-tools
     #   uvicorn
-cloudpickle==2.0.0
+cloudpickle==2.1.0
     # via dask
-colorama==0.4.4
+colorama==0.4.5
     # via
     #   feast (setup.py)
     #   great-expectations
-coverage[toml]==6.3.2
+coverage[toml]==6.4.1
     # via pytest-cov
-cryptography==3.4.8
+cryptography==35.0.0
     # via
     #   adal
     #   azure-identity
@@ -144,7 +157,7 @@ deprecated==1.2.13
     # via redis
 deprecation==2.1.0
     # via testcontainers
-dill==0.3.4
+dill==0.3.5.1
     # via feast (setup.py)
 distlib==0.3.4
     # via virtualenv
@@ -162,32 +175,35 @@ execnet==1.9.0
     # via pytest-xdist
 executing==0.8.3
     # via stack-data
-fastapi==0.76.0
+fastapi==0.78.0
     # via feast (setup.py)
-fastavro==1.4.11
+fastavro==1.5.1
     # via
     #   feast (setup.py)
     #   pandavro
 fastjsonschema==2.15.3
     # via nbformat
-filelock==3.6.0
+filelock==3.7.1
     # via virtualenv
 firebase-admin==4.5.2
     # via feast (setup.py)
+fissix==21.11.13
+    # via bowler
 flake8==4.0.1
     # via feast (setup.py)
 frozenlist==1.3.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec==2022.3.0
+fsspec==2022.1.0
     # via
     #   adlfs
     #   dask
     #   gcsfs
-gcsfs==2022.3.0
+    #   s3fs
+gcsfs==2022.1.0
     # via feast (setup.py)
-google-api-core[grpc]==1.31.5
+google-api-core[grpc]==1.31.6
     # via
     #   feast (setup.py)
     #   firebase-admin
@@ -197,7 +213,7 @@ google-api-core[grpc]==1.31.5
     #   google-cloud-core
     #   google-cloud-datastore
     #   google-cloud-firestore
-google-api-python-client==2.47.0
+google-api-python-client==2.51.0
     # via firebase-admin
 google-auth==1.35.0
     # via
@@ -210,11 +226,11 @@ google-auth==1.35.0
     #   google-cloud-storage
 google-auth-httplib2==0.1.0
     # via google-api-python-client
-google-auth-oauthlib==0.5.1
+google-auth-oauthlib==0.5.2
     # via gcsfs
-google-cloud-bigquery==2.34.3
+google-cloud-bigquery==2.34.4
     # via feast (setup.py)
-google-cloud-bigquery-storage==2.13.1
+google-cloud-bigquery-storage==2.13.2
     # via feast (setup.py)
 google-cloud-core==1.7.2
     # via
@@ -223,9 +239,9 @@ google-cloud-core==1.7.2
     #   google-cloud-datastore
     #   google-cloud-firestore
     #   google-cloud-storage
-google-cloud-datastore==2.5.1
+google-cloud-datastore==2.7.1
     # via feast (setup.py)
-google-cloud-firestore==2.4.0
+google-cloud-firestore==2.5.3
     # via firebase-admin
 google-cloud-storage==1.40.0
     # via
@@ -238,14 +254,16 @@ google-resumable-media==1.3.3
     # via
     #   google-cloud-bigquery
     #   google-cloud-storage
-googleapis-common-protos==1.52.0
+googleapis-common-protos==1.56.3
     # via
     #   feast (setup.py)
     #   google-api-core
     #   tensorflow-metadata
 great-expectations==0.14.13
     # via feast (setup.py)
-grpcio==1.46.0
+greenlet==1.1.2
+    # via sqlalchemy
+grpcio==1.47.0
     # via
     #   feast (setup.py)
     #   google-api-core
@@ -253,7 +271,7 @@ grpcio==1.46.0
     #   grpcio-reflection
     #   grpcio-testing
     #   grpcio-tools
-grpcio-reflection==1.46.0
+grpcio-reflection==1.47.0
     # via feast (setup.py)
 grpcio-testing==1.44.0
     # via feast (setup.py)
@@ -271,7 +289,7 @@ httplib2==0.20.4
     #   google-auth-httplib2
 httptools==0.4.0
     # via uvicorn
-identify==2.5.0
+identify==2.5.1
     # via pre-commit
 idna==3.3
     # via
@@ -281,13 +299,13 @@ idna==3.3
     #   yarl
 imagesize==1.3.0
     # via sphinx
-importlib-metadata==4.11.3
+importlib-metadata==4.11.4
     # via great-expectations
-importlib-resources==5.7.1
+importlib-resources==5.8.0
     # via jsonschema
 iniconfig==1.1.1
     # via pytest
-ipython==8.3.0
+ipython==8.4.0
     # via great-expectations
 isodate==0.6.1
     # via msrest
@@ -302,7 +320,7 @@ jinja2==3.0.3
     #   great-expectations
     #   moto
     #   sphinx
-jmespath==1.0.0
+jmespath==0.10.0
     # via
     #   boto3
     #   botocore
@@ -310,7 +328,7 @@ jsonpatch==1.32
     # via great-expectations
 jsonpointer==2.3
     # via jsonpatch
-jsonschema==4.5.1
+jsonschema==4.6.0
     # via
     #   altair
     #   feast (setup.py)
@@ -336,17 +354,19 @@ mmh3==3.0.0
     # via feast (setup.py)
 mock==2.0.0
     # via feast (setup.py)
-moto==3.1.7
+moreorless==0.4.0
+    # via bowler
+moto==3.1.14
     # via feast (setup.py)
-msal==1.17.0
+msal==1.18.0
     # via
     #   azure-identity
     #   msal-extensions
 msal-extensions==1.0.0
     # via azure-identity
-msgpack==1.0.3
+msgpack==1.0.4
     # via cachecontrol
-msrest==0.6.21
+msrest==0.7.1
     # via
     #   azure-storage-blob
     #   msrestazure
@@ -356,12 +376,16 @@ multidict==6.0.2
     # via
     #   aiohttp
     #   yarl
-mypy==0.931
-    # via feast (setup.py)
+mypy==0.961
+    # via
+    #   feast (setup.py)
+    #   sqlalchemy
 mypy-extensions==0.4.3
     # via mypy
 mypy-protobuf==3.1
     # via feast (setup.py)
+mysqlclient==2.1.1
+    # via feast (setup.py)
 nbformat==5.4.0
     # via great-expectations
 nodeenv==1.6.0
@@ -390,7 +414,7 @@ packaging==21.3
     #   pytest
     #   redis
     #   sphinx
-pandas==1.4.2
+pandas==1.4.3
     # via
     #   altair
     #   feast (setup.py)
@@ -415,7 +439,7 @@ pexpect==4.8.0
     # via ipython
 pickleshare==0.7.5
     # via ipython
-pip-tools==6.6.0
+pip-tools==6.6.2
     # via feast (setup.py)
 platformdirs==2.5.2
     # via virtualenv
@@ -429,7 +453,7 @@ pre-commit==2.19.0
     # via feast (setup.py)
 prompt-toolkit==3.0.29
     # via ipython
-proto-plus==1.19.6
+proto-plus==1.20.6
     # via
     #   feast (setup.py)
     #   google-cloud-bigquery
@@ -441,6 +465,9 @@ protobuf==3.19.4
     #   feast (setup.py)
     #   google-api-core
     #   google-cloud-bigquery
+    #   google-cloud-bigquery-storage
+    #   google-cloud-datastore
+    #   google-cloud-firestore
     #   googleapis-common-protos
     #   grpcio-reflection
     #   grpcio-testing
@@ -462,7 +489,7 @@ py==1.11.0
     #   pytest-forked
 py-cpuinfo==8.0.0
     # via pytest-benchmark
-py4j==0.10.9.3
+py4j==0.10.9.5
     # via pyspark
 pyarrow==6.0.1
     # via
@@ -480,9 +507,9 @@ pycodestyle==2.8.0
     # via flake8
 pycparser==2.21
     # via cffi
-pycryptodomex==3.14.1
+pycryptodomex==3.15.0
     # via snowflake-connector-python
-pydantic==1.9.0
+pydantic==1.9.1
     # via
     #   fastapi
     #   feast (setup.py)
@@ -490,14 +517,15 @@ pyflakes==2.4.0
     # via flake8
 pygments==2.12.0
     # via
+    #   feast (setup.py)
     #   ipython
     #   sphinx
-pyjwt[crypto]==2.3.0
+pyjwt[crypto]==2.4.0
     # via
     #   adal
     #   msal
     #   snowflake-connector-python
-pyopenssl==21.0.0
+pyopenssl==22.0.0
     # via snowflake-connector-python
 pyparsing==2.4.7
     # via
@@ -506,7 +534,7 @@ pyparsing==2.4.7
     #   packaging
 pyrsistent==0.18.1
     # via jsonschema
-pyspark==3.2.1
+pyspark==3.3.0
     # via feast (setup.py)
 pytest==7.1.2
     # via
@@ -564,9 +592,9 @@ pyyaml==6.0
     #   uvicorn
 redis==4.2.2
     # via feast (setup.py)
-regex==2022.4.24
+regex==2022.6.2
     # via black
-requests==2.27.1
+requests==2.28.0
     # via
     #   adal
     #   adlfs
@@ -591,7 +619,7 @@ requests-oauthlib==1.3.1
     # via
     #   google-auth-oauthlib
     #   msrest
-responses==0.20.0
+responses==0.21.0
     # via moto
 rsa==4.8
     # via google-auth
@@ -599,13 +627,14 @@ ruamel-yaml==0.17.17
     # via great-expectations
 ruamel-yaml-clib==0.2.6
     # via ruamel-yaml
+s3fs==2022.1.0
+    # via feast (setup.py)
 s3transfer==0.5.2
     # via boto3
-scipy==1.8.0
+scipy==1.8.1
     # via great-expectations
 six==1.16.0
     # via
-    #   absl-py
     #   azure-core
     #   azure-identity
     #   google-api-core
@@ -618,14 +647,13 @@ six==1.16.0
     #   mock
     #   msrestazure
     #   pandavro
-    #   pyopenssl
     #   python-dateutil
     #   virtualenv
 sniffio==1.2.0
     # via anyio
 snowballstemmer==2.2.0
     # via sphinx
-snowflake-connector-python[pandas]==2.7.7
+snowflake-connector-python[pandas]==2.7.8
     # via feast (setup.py)
 sphinx==4.3.2
     # via
@@ -645,19 +673,23 @@ sphinxcontrib-qthelp==1.0.3
     # via sphinx
 sphinxcontrib-serializinghtml==1.1.5
     # via sphinx
-stack-data==0.2.0
+sqlalchemy[mypy]==1.4.38
+    # via feast (setup.py)
+sqlalchemy2-stubs==0.0.2a24
+    # via sqlalchemy
+stack-data==0.3.0
     # via ipython
-starlette==0.18.0
+starlette==0.19.1
     # via fastapi
-tabulate==0.8.9
+tabulate==0.8.10
     # via feast (setup.py)
 tenacity==8.0.1
     # via feast (setup.py)
-tensorflow-metadata==1.7.0
+tensorflow-metadata==1.9.0
     # via feast (setup.py)
 termcolor==1.1.0
     # via great-expectations
-testcontainers==3.5.3
+testcontainers==3.6.0
     # via feast (setup.py)
 thriftpy2==0.4.14
     # via happybase
@@ -682,7 +714,7 @@ tqdm==4.64.0
     # via
     #   feast (setup.py)
     #   great-expectations
-traitlets==5.1.1
+traitlets==5.3.0
     # via
     #   ipython
     #   jupyter-core
@@ -690,34 +722,38 @@ traitlets==5.1.1
     #   nbformat
 trino==0.313.0
     # via feast (setup.py)
-typed-ast==1.5.3
+typed-ast==1.5.4
     # via black
-types-protobuf==3.19.18
+typeguard==2.13.3
+    # via feast (setup.py)
+types-protobuf==3.19.22
     # via
     #   feast (setup.py)
     #   mypy-protobuf
-types-python-dateutil==2.8.14
+types-python-dateutil==2.8.18
     # via feast (setup.py)
-types-pytz==2021.3.7
+types-pytz==2022.1.0
     # via feast (setup.py)
-types-pyyaml==6.0.7
+types-pyyaml==6.0.8
     # via feast (setup.py)
-types-redis==4.2.1
+types-redis==4.3.2
     # via feast (setup.py)
-types-requests==2.27.25
+types-requests==2.27.31
     # via feast (setup.py)
-types-setuptools==57.4.14
+types-setuptools==57.4.17
     # via feast (setup.py)
-types-tabulate==0.8.8
+types-tabulate==0.8.10
     # via feast (setup.py)
-types-urllib3==1.26.14
+types-urllib3==1.26.15
     # via types-requests
 typing-extensions==4.2.0
     # via
+    #   aioitertools
     #   azure-core
     #   great-expectations
     #   mypy
     #   pydantic
+    #   sqlalchemy2-stubs
     #   starlette
 tzdata==2022.1
     # via pytz-deprecation-shim
@@ -733,17 +769,19 @@ urllib3==1.26.9
     #   minio
     #   requests
     #   responses
-uvicorn[standard]==0.17.6
+uvicorn[standard]==0.18.1
     # via feast (setup.py)
 uvloop==0.16.0
     # via uvicorn
 virtualenv==20.14.1
     # via pre-commit
-watchgod==0.8.2
+volatile==2.1.0
+    # via bowler
+watchfiles==0.15.0
     # via uvicorn
 wcwidth==0.2.5
     # via prompt-toolkit
-websocket-client==1.3.2
+websocket-client==1.3.3
     # via docker
 websockets==10.3
     # via uvicorn
@@ -753,9 +791,10 @@ wheel==0.37.1
     # via pip-tools
 wrapt==1.14.1
     # via
+    #   aiobotocore
     #   deprecated
     #   testcontainers
-xmltodict==0.12.0
+xmltodict==0.13.0
     # via moto
 yarl==1.7.2
     # via aiohttp
diff --git a/sdk/python/requirements/py3.8-requirements.txt b/sdk/python/requirements/py3.8-requirements.txt
index 9e47d0e831d..7756acad31e 100644
--- a/sdk/python/requirements/py3.8-requirements.txt
+++ b/sdk/python/requirements/py3.8-requirements.txt
@@ -4,56 +4,66 @@
 #
 #    pip-compile --output-file=sdk/python/requirements/py3.8-requirements.txt
 #
-absl-py==1.0.0
+absl-py==1.1.0
     # via tensorflow-metadata
-anyio==3.5.0
+anyio==3.6.1
     # via
     #   starlette
-    #   watchgod
-asgiref==3.5.1
-    # via uvicorn
+    #   watchfiles
+appdirs==1.4.4
+    # via fissix
 attrs==21.4.0
-    # via jsonschema
-cachetools==4.2.4
+    # via
+    #   bowler
+    #   jsonschema
+bowler==0.9.0
+    # via feast (setup.py)
+cachetools==5.2.0
     # via google-auth
-certifi==2021.10.8
+certifi==2022.6.15
     # via requests
 charset-normalizer==2.0.12
     # via requests
 click==8.0.1
     # via
+    #   bowler
     #   feast (setup.py)
+    #   moreorless
     #   uvicorn
-cloudpickle==2.0.0
+cloudpickle==2.1.0
     # via dask
-colorama==0.4.4
+colorama==0.4.5
     # via feast (setup.py)
 dask==2022.1.1
     # via feast (setup.py)
-dill==0.3.4
+dill==0.3.5.1
     # via feast (setup.py)
-fastapi==0.76.0
+fastapi==0.78.0
     # via feast (setup.py)
-fastavro==1.4.11
+fastavro==1.5.1
     # via
     #   feast (setup.py)
     #   pandavro
-fsspec==2022.3.0
+fissix==21.11.13
+    # via bowler
+fsspec==2022.5.0
     # via dask
-google-api-core==1.31.5
+google-api-core==2.8.2
     # via feast (setup.py)
-google-auth==1.35.0
+google-auth==2.8.0
     # via google-api-core
-googleapis-common-protos==1.52.0
+googleapis-common-protos==1.56.3
     # via
     #   feast (setup.py)
     #   google-api-core
     #   tensorflow-metadata
-grpcio==1.46.0
+greenlet==1.1.2
+    # via sqlalchemy
+grpcio==1.47.0
     # via
     #   feast (setup.py)
     #   grpcio-reflection
-grpcio-reflection==1.46.0
+grpcio-reflection==1.47.0
     # via feast (setup.py)
 h11==0.13.0
     # via uvicorn
@@ -63,11 +73,11 @@ idna==3.3
     # via
     #   anyio
     #   requests
-importlib-resources==5.7.1
+importlib-resources==5.8.0
     # via jsonschema
 jinja2==3.1.2
     # via feast (setup.py)
-jsonschema==4.5.1
+jsonschema==4.6.0
     # via feast (setup.py)
 locket==1.0.0
     # via partd
@@ -75,6 +85,12 @@ markupsafe==2.1.1
     # via jinja2
 mmh3==3.0.0
     # via feast (setup.py)
+moreorless==0.4.0
+    # via bowler
+mypy==0.961
+    # via sqlalchemy
+mypy-extensions==0.4.3
+    # via mypy
 numpy==1.21.6
     # via
     #   feast (setup.py)
@@ -82,10 +98,8 @@ numpy==1.21.6
     #   pandavro
     #   pyarrow
 packaging==21.3
-    # via
-    #   dask
-    #   google-api-core
-pandas==1.4.2
+    # via dask
+pandas==1.4.3
     # via
     #   feast (setup.py)
     #   pandavro
@@ -93,7 +107,7 @@ pandavro==1.5.2
     # via feast (setup.py)
 partd==1.2.0
     # via dask
-proto-plus==1.19.6
+proto-plus==1.20.6
     # via feast (setup.py)
 protobuf==3.19.4
     # via
@@ -111,11 +125,13 @@ pyasn1==0.4.8
     #   rsa
 pyasn1-modules==0.2.8
     # via google-auth
-pydantic==1.9.0
+pydantic==1.9.1
     # via
     #   fastapi
     #   feast (setup.py)
-pyparsing==3.0.8
+pygments==2.12.0
+    # via feast (setup.py)
+pyparsing==3.0.9
     # via packaging
 pyrsistent==0.18.1
     # via jsonschema
@@ -124,60 +140,65 @@ python-dateutil==2.8.2
 python-dotenv==0.20.0
     # via uvicorn
 pytz==2022.1
-    # via
-    #   google-api-core
-    #   pandas
+    # via pandas
 pyyaml==6.0
     # via
     #   dask
     #   feast (setup.py)
     #   uvicorn
-requests==2.27.1
+requests==2.28.0
     # via google-api-core
 rsa==4.8
     # via google-auth
 six==1.16.0
     # via
-    #   absl-py
-    #   google-api-core
     #   google-auth
     #   grpcio
     #   pandavro
     #   python-dateutil
 sniffio==1.2.0
     # via anyio
-starlette==0.18.0
+sqlalchemy[mypy]==1.4.38
+    # via feast (setup.py)
+sqlalchemy2-stubs==0.0.2a24
+    # via sqlalchemy
+starlette==0.19.1
     # via fastapi
-tabulate==0.8.9
+tabulate==0.8.10
     # via feast (setup.py)
 tenacity==8.0.1
     # via feast (setup.py)
-tensorflow-metadata==1.7.0
+tensorflow-metadata==1.9.0
     # via feast (setup.py)
 toml==0.10.2
     # via feast (setup.py)
+tomli==2.0.1
+    # via mypy
 toolz==0.11.2
     # via
     #   dask
     #   partd
 tqdm==4.64.0
     # via feast (setup.py)
+typeguard==2.13.3
+    # via feast (setup.py)
 typing-extensions==4.2.0
     # via
+    #   mypy
     #   pydantic
+    #   sqlalchemy2-stubs
     #   starlette
 urllib3==1.26.9
     # via requests
-uvicorn[standard]==0.17.6
+uvicorn[standard]==0.18.1
     # via feast (setup.py)
 uvloop==0.16.0
     # via uvicorn
-watchgod==0.8.2
+volatile==2.1.0
+    # via bowler
+watchfiles==0.15.0
     # via uvicorn
 websockets==10.3
     # via uvicorn
 zipp==3.8.0
     # via importlib-resources
-
-# The following packages are considered to be unsafe in a requirements file:
-# setuptools
diff --git a/sdk/python/requirements/py3.9-ci-requirements.txt b/sdk/python/requirements/py3.9-ci-requirements.txt
index 4a96dc6fd2c..f9f65633f00 100644
--- a/sdk/python/requirements/py3.9-ci-requirements.txt
+++ b/sdk/python/requirements/py3.9-ci-requirements.txt
@@ -4,7 +4,7 @@
 #
 #    pip-compile --extra=ci --output-file=sdk/python/requirements/py3.9-ci-requirements.txt
 #
-absl-py==1.0.0
+absl-py==1.1.0
     # via tensorflow-metadata
 adal==1.2.7
     # via
@@ -12,26 +12,32 @@ adal==1.2.7
     #   msrestazure
 adlfs==0.5.9
     # via feast (setup.py)
+aiobotocore==2.1.2
+    # via s3fs
 aiohttp==3.8.1
     # via
     #   adlfs
+    #   aiobotocore
     #   gcsfs
+    #   s3fs
+aioitertools==0.10.0
+    # via aiobotocore
 aiosignal==1.2.0
     # via aiohttp
 alabaster==0.7.12
     # via sphinx
 altair==4.2.0
     # via great-expectations
-anyio==3.5.0
+anyio==3.6.1
     # via
     #   starlette
-    #   watchgod
+    #   watchfiles
 appdirs==1.4.4
-    # via black
+    # via
+    #   black
+    #   fissix
 appnope==0.1.3
     # via ipython
-asgiref==3.5.1
-    # via uvicorn
 asn1crypto==1.5.1
     # via
     #   oscrypto
@@ -48,43 +54,48 @@ attrs==21.4.0
     # via
     #   aiohttp
     #   black
+    #   bowler
     #   jsonschema
     #   pytest
 avro==1.10.0
     # via feast (setup.py)
-azure-core==1.23.1
+azure-core==1.24.1
     # via
     #   adlfs
     #   azure-identity
     #   azure-storage-blob
+    #   msrest
 azure-datalake-store==0.0.52
     # via adlfs
 azure-identity==1.10.0
     # via adlfs
-azure-storage-blob==12.11.0
+azure-storage-blob==12.12.0
     # via adlfs
-babel==2.10.1
+babel==2.10.3
     # via sphinx
 backcall==0.2.0
     # via ipython
 black==19.10b0
     # via feast (setup.py)
-boto3==1.22.8
+boto3==1.20.23
     # via
     #   feast (setup.py)
     #   moto
-botocore==1.25.8
+botocore==1.23.24
     # via
+    #   aiobotocore
     #   boto3
     #   moto
     #   s3transfer
-build==0.7.0
+bowler==0.9.0
+    # via feast (setup.py)
+build==0.8.0
     # via feast (setup.py)
 cachecontrol==0.12.11
     # via firebase-admin
 cachetools==4.2.4
     # via google-auth
-certifi==2021.10.8
+certifi==2022.6.15
     # via
     #   minio
     #   msrest
@@ -105,19 +116,21 @@ charset-normalizer==2.0.12
 click==8.0.1
     # via
     #   black
+    #   bowler
     #   feast (setup.py)
     #   great-expectations
+    #   moreorless
     #   pip-tools
     #   uvicorn
-cloudpickle==2.0.0
+cloudpickle==2.1.0
     # via dask
-colorama==0.4.4
+colorama==0.4.5
     # via
     #   feast (setup.py)
     #   great-expectations
-coverage[toml]==6.3.2
+coverage[toml]==6.4.1
     # via pytest-cov
-cryptography==3.4.8
+cryptography==35.0.0
     # via
     #   adal
     #   azure-identity
@@ -140,7 +153,7 @@ deprecated==1.2.13
     # via redis
 deprecation==2.1.0
     # via testcontainers
-dill==0.3.4
+dill==0.3.5.1
     # via feast (setup.py)
 distlib==0.3.4
     # via virtualenv
@@ -158,32 +171,35 @@ execnet==1.9.0
     # via pytest-xdist
 executing==0.8.3
     # via stack-data
-fastapi==0.76.0
+fastapi==0.78.0
     # via feast (setup.py)
-fastavro==1.4.11
+fastavro==1.5.1
     # via
     #   feast (setup.py)
     #   pandavro
 fastjsonschema==2.15.3
     # via nbformat
-filelock==3.6.0
+filelock==3.7.1
     # via virtualenv
 firebase-admin==4.5.2
     # via feast (setup.py)
+fissix==21.11.13
+    # via bowler
 flake8==4.0.1
     # via feast (setup.py)
 frozenlist==1.3.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec==2022.3.0
+fsspec==2022.1.0
     # via
     #   adlfs
     #   dask
     #   gcsfs
-gcsfs==2022.3.0
+    #   s3fs
+gcsfs==2022.1.0
     # via feast (setup.py)
-google-api-core[grpc]==1.31.5
+google-api-core[grpc]==1.31.6
     # via
     #   feast (setup.py)
     #   firebase-admin
@@ -193,7 +209,7 @@ google-api-core[grpc]==1.31.5
     #   google-cloud-core
     #   google-cloud-datastore
     #   google-cloud-firestore
-google-api-python-client==2.47.0
+google-api-python-client==2.51.0
     # via firebase-admin
 google-auth==1.35.0
     # via
@@ -206,11 +222,11 @@ google-auth==1.35.0
     #   google-cloud-storage
 google-auth-httplib2==0.1.0
     # via google-api-python-client
-google-auth-oauthlib==0.5.1
+google-auth-oauthlib==0.5.2
     # via gcsfs
-google-cloud-bigquery==2.34.3
+google-cloud-bigquery==2.34.4
     # via feast (setup.py)
-google-cloud-bigquery-storage==2.13.1
+google-cloud-bigquery-storage==2.13.2
     # via feast (setup.py)
 google-cloud-core==1.7.2
     # via
@@ -219,9 +235,9 @@ google-cloud-core==1.7.2
     #   google-cloud-datastore
     #   google-cloud-firestore
     #   google-cloud-storage
-google-cloud-datastore==2.5.1
+google-cloud-datastore==2.7.1
     # via feast (setup.py)
-google-cloud-firestore==2.4.0
+google-cloud-firestore==2.5.3
     # via firebase-admin
 google-cloud-storage==1.40.0
     # via
@@ -234,14 +250,16 @@ google-resumable-media==1.3.3
     # via
     #   google-cloud-bigquery
     #   google-cloud-storage
-googleapis-common-protos==1.52.0
+googleapis-common-protos==1.56.3
     # via
     #   feast (setup.py)
     #   google-api-core
     #   tensorflow-metadata
 great-expectations==0.14.13
     # via feast (setup.py)
-grpcio==1.46.0
+greenlet==1.1.2
+    # via sqlalchemy
+grpcio==1.47.0
     # via
     #   feast (setup.py)
     #   google-api-core
@@ -249,7 +267,7 @@ grpcio==1.46.0
     #   grpcio-reflection
     #   grpcio-testing
     #   grpcio-tools
-grpcio-reflection==1.46.0
+grpcio-reflection==1.47.0
     # via feast (setup.py)
 grpcio-testing==1.44.0
     # via feast (setup.py)
@@ -267,7 +285,7 @@ httplib2==0.20.4
     #   google-auth-httplib2
 httptools==0.4.0
     # via uvicorn
-identify==2.5.0
+identify==2.5.1
     # via pre-commit
 idna==3.3
     # via
@@ -277,11 +295,11 @@ idna==3.3
     #   yarl
 imagesize==1.3.0
     # via sphinx
-importlib-metadata==4.11.3
+importlib-metadata==4.11.4
     # via great-expectations
 iniconfig==1.1.1
     # via pytest
-ipython==8.3.0
+ipython==8.4.0
     # via great-expectations
 isodate==0.6.1
     # via msrest
@@ -296,7 +314,7 @@ jinja2==3.0.3
     #   great-expectations
     #   moto
     #   sphinx
-jmespath==1.0.0
+jmespath==0.10.0
     # via
     #   boto3
     #   botocore
@@ -304,7 +322,7 @@ jsonpatch==1.32
     # via great-expectations
 jsonpointer==2.3
     # via jsonpatch
-jsonschema==4.5.1
+jsonschema==4.6.0
     # via
     #   altair
     #   feast (setup.py)
@@ -330,17 +348,19 @@ mmh3==3.0.0
     # via feast (setup.py)
 mock==2.0.0
     # via feast (setup.py)
-moto==3.1.7
+moreorless==0.4.0
+    # via bowler
+moto==3.1.14
     # via feast (setup.py)
-msal==1.17.0
+msal==1.18.0
     # via
     #   azure-identity
     #   msal-extensions
 msal-extensions==1.0.0
     # via azure-identity
-msgpack==1.0.3
+msgpack==1.0.4
     # via cachecontrol
-msrest==0.6.21
+msrest==0.7.1
     # via
     #   azure-storage-blob
     #   msrestazure
@@ -350,12 +370,16 @@ multidict==6.0.2
     # via
     #   aiohttp
     #   yarl
-mypy==0.931
-    # via feast (setup.py)
+mypy==0.961
+    # via
+    #   feast (setup.py)
+    #   sqlalchemy
 mypy-extensions==0.4.3
     # via mypy
 mypy-protobuf==3.1
     # via feast (setup.py)
+mysqlclient==2.1.1
+    # via feast (setup.py)
 nbformat==5.4.0
     # via great-expectations
 nodeenv==1.6.0
@@ -384,7 +408,7 @@ packaging==21.3
     #   pytest
     #   redis
     #   sphinx
-pandas==1.4.2
+pandas==1.4.3
     # via
     #   altair
     #   feast (setup.py)
@@ -409,7 +433,7 @@ pexpect==4.8.0
     # via ipython
 pickleshare==0.7.5
     # via ipython
-pip-tools==6.6.0
+pip-tools==6.6.2
     # via feast (setup.py)
 platformdirs==2.5.2
     # via virtualenv
@@ -423,7 +447,7 @@ pre-commit==2.19.0
     # via feast (setup.py)
 prompt-toolkit==3.0.29
     # via ipython
-proto-plus==1.19.6
+proto-plus==1.20.6
     # via
     #   feast (setup.py)
     #   google-cloud-bigquery
@@ -435,6 +459,9 @@ protobuf==3.19.4
     #   feast (setup.py)
     #   google-api-core
     #   google-cloud-bigquery
+    #   google-cloud-bigquery-storage
+    #   google-cloud-datastore
+    #   google-cloud-firestore
     #   googleapis-common-protos
     #   grpcio-reflection
     #   grpcio-testing
@@ -456,7 +483,7 @@ py==1.11.0
     #   pytest-forked
 py-cpuinfo==8.0.0
     # via pytest-benchmark
-py4j==0.10.9.3
+py4j==0.10.9.5
     # via pyspark
 pyarrow==6.0.1
     # via
@@ -474,9 +501,9 @@ pycodestyle==2.8.0
     # via flake8
 pycparser==2.21
     # via cffi
-pycryptodomex==3.14.1
+pycryptodomex==3.15.0
     # via snowflake-connector-python
-pydantic==1.9.0
+pydantic==1.9.1
     # via
     #   fastapi
     #   feast (setup.py)
@@ -484,14 +511,15 @@ pyflakes==2.4.0
     # via flake8
 pygments==2.12.0
     # via
+    #   feast (setup.py)
     #   ipython
     #   sphinx
-pyjwt[crypto]==2.3.0
+pyjwt[crypto]==2.4.0
     # via
     #   adal
     #   msal
     #   snowflake-connector-python
-pyopenssl==21.0.0
+pyopenssl==22.0.0
     # via snowflake-connector-python
 pyparsing==2.4.7
     # via
@@ -500,7 +528,7 @@ pyparsing==2.4.7
     #   packaging
 pyrsistent==0.18.1
     # via jsonschema
-pyspark==3.2.1
+pyspark==3.3.0
     # via feast (setup.py)
 pytest==7.1.2
     # via
@@ -558,9 +586,9 @@ pyyaml==6.0
     #   uvicorn
 redis==4.2.2
     # via feast (setup.py)
-regex==2022.4.24
+regex==2022.6.2
     # via black
-requests==2.27.1
+requests==2.28.0
     # via
     #   adal
     #   adlfs
@@ -585,7 +613,7 @@ requests-oauthlib==1.3.1
     # via
     #   google-auth-oauthlib
     #   msrest
-responses==0.20.0
+responses==0.21.0
     # via moto
 rsa==4.8
     # via google-auth
@@ -593,13 +621,14 @@ ruamel-yaml==0.17.17
     # via great-expectations
 ruamel-yaml-clib==0.2.6
     # via ruamel-yaml
+s3fs==2022.1.0
+    # via feast (setup.py)
 s3transfer==0.5.2
     # via boto3
-scipy==1.8.0
+scipy==1.8.1
     # via great-expectations
 six==1.16.0
     # via
-    #   absl-py
     #   azure-core
     #   azure-identity
     #   google-api-core
@@ -612,14 +641,13 @@ six==1.16.0
     #   mock
     #   msrestazure
     #   pandavro
-    #   pyopenssl
     #   python-dateutil
     #   virtualenv
 sniffio==1.2.0
     # via anyio
 snowballstemmer==2.2.0
     # via sphinx
-snowflake-connector-python[pandas]==2.7.7
+snowflake-connector-python[pandas]==2.7.8
     # via feast (setup.py)
 sphinx==4.3.2
     # via
@@ -639,19 +667,23 @@ sphinxcontrib-qthelp==1.0.3
     # via sphinx
 sphinxcontrib-serializinghtml==1.1.5
     # via sphinx
-stack-data==0.2.0
+sqlalchemy[mypy]==1.4.38
+    # via feast (setup.py)
+sqlalchemy2-stubs==0.0.2a24
+    # via sqlalchemy
+stack-data==0.3.0
     # via ipython
-starlette==0.18.0
+starlette==0.19.1
     # via fastapi
-tabulate==0.8.9
+tabulate==0.8.10
     # via feast (setup.py)
 tenacity==8.0.1
     # via feast (setup.py)
-tensorflow-metadata==1.7.0
+tensorflow-metadata==1.9.0
     # via feast (setup.py)
 termcolor==1.1.0
     # via great-expectations
-testcontainers==3.5.3
+testcontainers==3.6.0
     # via feast (setup.py)
 thriftpy2==0.4.14
     # via happybase
@@ -676,7 +708,7 @@ tqdm==4.64.0
     # via
     #   feast (setup.py)
     #   great-expectations
-traitlets==5.1.1
+traitlets==5.3.0
     # via
     #   ipython
     #   jupyter-core
@@ -684,34 +716,38 @@ traitlets==5.1.1
     #   nbformat
 trino==0.313.0
     # via feast (setup.py)
-typed-ast==1.5.3
+typed-ast==1.5.4
     # via black
-types-protobuf==3.19.18
+typeguard==2.13.3
+    # via feast (setup.py)
+types-protobuf==3.19.22
     # via
     #   feast (setup.py)
     #   mypy-protobuf
-types-python-dateutil==2.8.14
+types-python-dateutil==2.8.18
     # via feast (setup.py)
-types-pytz==2021.3.7
+types-pytz==2022.1.0
     # via feast (setup.py)
-types-pyyaml==6.0.7
+types-pyyaml==6.0.8
     # via feast (setup.py)
-types-redis==4.2.1
+types-redis==4.3.2
     # via feast (setup.py)
-types-requests==2.27.25
+types-requests==2.27.31
     # via feast (setup.py)
-types-setuptools==57.4.14
+types-setuptools==57.4.17
     # via feast (setup.py)
-types-tabulate==0.8.8
+types-tabulate==0.8.10
     # via feast (setup.py)
-types-urllib3==1.26.14
+types-urllib3==1.26.15
     # via types-requests
 typing-extensions==4.2.0
     # via
+    #   aioitertools
     #   azure-core
     #   great-expectations
     #   mypy
     #   pydantic
+    #   sqlalchemy2-stubs
     #   starlette
 tzdata==2022.1
     # via pytz-deprecation-shim
@@ -727,17 +763,19 @@ urllib3==1.26.9
     #   minio
     #   requests
     #   responses
-uvicorn[standard]==0.17.6
+uvicorn[standard]==0.18.1
     # via feast (setup.py)
 uvloop==0.16.0
     # via uvicorn
 virtualenv==20.14.1
     # via pre-commit
-watchgod==0.8.2
+volatile==2.1.0
+    # via bowler
+watchfiles==0.15.0
     # via uvicorn
 wcwidth==0.2.5
     # via prompt-toolkit
-websocket-client==1.3.2
+websocket-client==1.3.3
     # via docker
 websockets==10.3
     # via uvicorn
@@ -747,9 +785,10 @@ wheel==0.37.1
     # via pip-tools
 wrapt==1.14.1
     # via
+    #   aiobotocore
     #   deprecated
     #   testcontainers
-xmltodict==0.12.0
+xmltodict==0.13.0
     # via moto
 yarl==1.7.2
     # via aiohttp
diff --git a/sdk/python/requirements/py3.9-requirements.txt b/sdk/python/requirements/py3.9-requirements.txt
index 0ad985b828e..f5c15dad5d4 100644
--- a/sdk/python/requirements/py3.9-requirements.txt
+++ b/sdk/python/requirements/py3.9-requirements.txt
@@ -4,56 +4,66 @@
 #
 #    pip-compile --output-file=sdk/python/requirements/py3.9-requirements.txt
 #
-absl-py==1.0.0
+absl-py==1.1.0
     # via tensorflow-metadata
-anyio==3.5.0
+anyio==3.6.1
     # via
     #   starlette
-    #   watchgod
-asgiref==3.5.1
-    # via uvicorn
+    #   watchfiles
+appdirs==1.4.4
+    # via fissix
 attrs==21.4.0
-    # via jsonschema
-cachetools==4.2.4
+    # via
+    #   bowler
+    #   jsonschema
+bowler==0.9.0
+    # via feast (setup.py)
+cachetools==5.2.0
     # via google-auth
-certifi==2021.10.8
+certifi==2022.6.15
     # via requests
 charset-normalizer==2.0.12
     # via requests
 click==8.0.1
     # via
+    #   bowler
     #   feast (setup.py)
+    #   moreorless
     #   uvicorn
-cloudpickle==2.0.0
+cloudpickle==2.1.0
     # via dask
-colorama==0.4.4
+colorama==0.4.5
     # via feast (setup.py)
 dask==2022.1.1
     # via feast (setup.py)
-dill==0.3.4
+dill==0.3.5.1
     # via feast (setup.py)
-fastapi==0.76.0
+fastapi==0.78.0
     # via feast (setup.py)
-fastavro==1.4.11
+fastavro==1.5.1
     # via
     #   feast (setup.py)
     #   pandavro
-fsspec==2022.3.0
+fissix==21.11.13
+    # via bowler
+fsspec==2022.5.0
     # via dask
-google-api-core==1.31.5
+google-api-core==2.8.2
     # via feast (setup.py)
-google-auth==1.35.0
+google-auth==2.8.0
     # via google-api-core
-googleapis-common-protos==1.52.0
+googleapis-common-protos==1.56.3
     # via
     #   feast (setup.py)
     #   google-api-core
     #   tensorflow-metadata
-grpcio==1.46.0
+greenlet==1.1.2
+    # via sqlalchemy
+grpcio==1.47.0
     # via
     #   feast (setup.py)
     #   grpcio-reflection
-grpcio-reflection==1.46.0
+grpcio-reflection==1.47.0
     # via feast (setup.py)
 h11==0.13.0
     # via uvicorn
@@ -65,7 +75,7 @@ idna==3.3
     #   requests
 jinja2==3.1.2
     # via feast (setup.py)
-jsonschema==4.5.1
+jsonschema==4.6.0
     # via feast (setup.py)
 locket==1.0.0
     # via partd
@@ -73,6 +83,12 @@ markupsafe==2.1.1
     # via jinja2
 mmh3==3.0.0
     # via feast (setup.py)
+moreorless==0.4.0
+    # via bowler
+mypy==0.961
+    # via sqlalchemy
+mypy-extensions==0.4.3
+    # via mypy
 numpy==1.21.6
     # via
     #   feast (setup.py)
@@ -80,10 +96,8 @@ numpy==1.21.6
     #   pandavro
     #   pyarrow
 packaging==21.3
-    # via
-    #   dask
-    #   google-api-core
-pandas==1.4.2
+    # via dask
+pandas==1.4.3
     # via
     #   feast (setup.py)
     #   pandavro
@@ -91,7 +105,7 @@ pandavro==1.5.2
     # via feast (setup.py)
 partd==1.2.0
     # via dask
-proto-plus==1.19.6
+proto-plus==1.20.6
     # via feast (setup.py)
 protobuf==3.19.4
     # via
@@ -109,11 +123,13 @@ pyasn1==0.4.8
     #   rsa
 pyasn1-modules==0.2.8
     # via google-auth
-pydantic==1.9.0
+pydantic==1.9.1
     # via
     #   fastapi
     #   feast (setup.py)
-pyparsing==3.0.8
+pygments==2.12.0
+    # via feast (setup.py)
+pyparsing==3.0.9
     # via packaging
 pyrsistent==0.18.1
     # via jsonschema
@@ -122,58 +138,63 @@ python-dateutil==2.8.2
 python-dotenv==0.20.0
     # via uvicorn
 pytz==2022.1
-    # via
-    #   google-api-core
-    #   pandas
+    # via pandas
 pyyaml==6.0
     # via
     #   dask
     #   feast (setup.py)
     #   uvicorn
-requests==2.27.1
+requests==2.28.0
     # via google-api-core
 rsa==4.8
     # via google-auth
 six==1.16.0
     # via
-    #   absl-py
-    #   google-api-core
     #   google-auth
     #   grpcio
     #   pandavro
     #   python-dateutil
 sniffio==1.2.0
     # via anyio
-starlette==0.18.0
+sqlalchemy[mypy]==1.4.38
+    # via feast (setup.py)
+sqlalchemy2-stubs==0.0.2a24
+    # via sqlalchemy
+starlette==0.19.1
     # via fastapi
-tabulate==0.8.9
+tabulate==0.8.10
     # via feast (setup.py)
 tenacity==8.0.1
     # via feast (setup.py)
-tensorflow-metadata==1.7.0
+tensorflow-metadata==1.9.0
     # via feast (setup.py)
 toml==0.10.2
     # via feast (setup.py)
+tomli==2.0.1
+    # via mypy
 toolz==0.11.2
     # via
     #   dask
     #   partd
 tqdm==4.64.0
     # via feast (setup.py)
+typeguard==2.13.3
+    # via feast (setup.py)
 typing-extensions==4.2.0
     # via
+    #   mypy
     #   pydantic
+    #   sqlalchemy2-stubs
     #   starlette
 urllib3==1.26.9
     # via requests
-uvicorn[standard]==0.17.6
+uvicorn[standard]==0.18.1
     # via feast (setup.py)
 uvloop==0.16.0
     # via uvicorn
-watchgod==0.8.2
+volatile==2.1.0
+    # via bowler
+watchfiles==0.15.0
     # via uvicorn
 websockets==10.3
     # via uvicorn
-
-# The following packages are considered to be unsafe in a requirements file:
-# setuptools
diff --git a/sdk/python/setup.cfg b/sdk/python/setup.cfg
index e2d707e2720..ebb933f69de 100644
--- a/sdk/python/setup.cfg
+++ b/sdk/python/setup.cfg
@@ -10,7 +10,7 @@ known_first_party=feast,feast_serving_server,feast_core_server
 default_section=THIRDPARTY
 
 [flake8]
-ignore = E203, E266, E501, W503
+ignore = E203, E266, E501, W503, C901
 max-line-length = 88
 max-complexity = 20
 select = B,C,E,F,W,T4
diff --git a/sdk/python/tests/conftest.py b/sdk/python/tests/conftest.py
index d492c7ba845..bf69a85fa31 100644
--- a/sdk/python/tests/conftest.py
+++ b/sdk/python/tests/conftest.py
@@ -33,6 +33,7 @@
 from tests.integration.feature_repos.repo_configuration import (
     AVAILABLE_OFFLINE_STORES,
     AVAILABLE_ONLINE_STORES,
+    OFFLINE_STORE_TO_PROVIDER_CONFIG,
     Environment,
     TestData,
     construct_test_environment,
@@ -57,6 +58,14 @@ def pytest_configure(config):
     config.addinivalue_line(
         "markers", "goserver: mark tests that use the go feature server"
     )
+    config.addinivalue_line(
+        "markers",
+        "universal_online_stores: mark tests that can be run against different online stores",
+    )
+    config.addinivalue_line(
+        "markers",
+        "universal_offline_stores: mark tests that can be run against different offline stores",
+    )
 
 
 def pytest_addoption(parser):
@@ -188,16 +197,24 @@ def pytest_generate_tests(metafunc: pytest.Metafunc):
     """
     if "environment" in metafunc.fixturenames:
         markers = {m.name: m for m in metafunc.definition.own_markers}
-
+        offline_stores = None
         if "universal_offline_stores" in markers:
-            offline_stores = AVAILABLE_OFFLINE_STORES
+            # Offline stores can be explicitly requested
+            if "only" in markers["universal_offline_stores"].kwargs:
+                offline_stores = [
+                    OFFLINE_STORE_TO_PROVIDER_CONFIG.get(store_name)
+                    for store_name in markers["universal_offline_stores"].kwargs["only"]
+                    if store_name in OFFLINE_STORE_TO_PROVIDER_CONFIG
+                ]
+            else:
+                offline_stores = AVAILABLE_OFFLINE_STORES
         else:
             # default offline store for testing online store dimension
             offline_stores = [("local", FileDataSourceCreator)]
 
         online_stores = None
         if "universal_online_stores" in markers:
-            # Online stores are explicitly requested
+            # Online stores can be explicitly requested
             if "only" in markers["universal_online_stores"].kwargs:
                 online_stores = [
                     AVAILABLE_ONLINE_STORES.get(store_name)
@@ -232,40 +249,44 @@ def pytest_generate_tests(metafunc: pytest.Metafunc):
             extra_dimensions.append({"go_feature_retrieval": True})
 
         configs = []
-        for provider, offline_store_creator in offline_stores:
-            for online_store, online_store_creator in online_stores:
-                for dim in extra_dimensions:
-                    config = {
-                        "provider": provider,
-                        "offline_store_creator": offline_store_creator,
-                        "online_store": online_store,
-                        "online_store_creator": online_store_creator,
-                        **dim,
-                    }
-                    # temporary Go works only with redis
-                    if config.get("go_feature_retrieval") and (
-                        not isinstance(online_store, dict)
-                        or online_store["type"] != "redis"
-                    ):
-                        continue
-
-                    # aws lambda works only with dynamo
-                    if (
-                        config.get("python_feature_server")
-                        and config.get("provider") == "aws"
-                        and (
+        if offline_stores:
+            for provider, offline_store_creator in offline_stores:
+                for online_store, online_store_creator in online_stores:
+                    for dim in extra_dimensions:
+                        config = {
+                            "provider": provider,
+                            "offline_store_creator": offline_store_creator,
+                            "online_store": online_store,
+                            "online_store_creator": online_store_creator,
+                            **dim,
+                        }
+                        # temporary Go works only with redis
+                        if config.get("go_feature_retrieval") and (
                             not isinstance(online_store, dict)
-                            or online_store["type"] != "dynamodb"
-                        )
-                    ):
-                        continue
-
-                    c = IntegrationTestRepoConfig(**config)
-
-                    if c not in _config_cache:
-                        _config_cache[c] = c
-
-                    configs.append(_config_cache[c])
+                            or online_store["type"] != "redis"
+                        ):
+                            continue
+
+                        # aws lambda works only with dynamo
+                        if (
+                            config.get("python_feature_server")
+                            and config.get("provider") == "aws"
+                            and (
+                                not isinstance(online_store, dict)
+                                or online_store["type"] != "dynamodb"
+                            )
+                        ):
+                            continue
+
+                        c = IntegrationTestRepoConfig(**config)
+
+                        if c not in _config_cache:
+                            _config_cache[c] = c
+
+                        configs.append(_config_cache[c])
+        else:
+            # No offline stores requested -> setting the default or first available
+            offline_stores = [("local", FileDataSourceCreator)]
 
         metafunc.parametrize(
             "environment", configs, indirect=True, ids=[str(c) for c in configs]
@@ -273,11 +294,19 @@ def pytest_generate_tests(metafunc: pytest.Metafunc):
 
 
 @pytest.fixture(scope="session")
-def python_server(environment):
+def feature_server_endpoint(environment):
+    if (
+        not environment.python_feature_server
+        or environment.test_repo_config.provider != "local"
+    ):
+        yield environment.feature_store.get_feature_server_endpoint()
+        return
+
+    port = _free_port()
+
     proc = Process(
         target=start_test_local_server,
-        args=(environment.feature_store.repo_path, environment.get_local_server_port()),
-        daemon=True,
+        args=(environment.feature_store.repo_path, port),
     )
     if (
         environment.python_feature_server
@@ -286,24 +315,35 @@ def python_server(environment):
         proc.start()
         # Wait for server to start
         wait_retry_backoff(
-            lambda: (
-                None,
-                _check_port_open("localhost", environment.get_local_server_port()),
-            ),
-            timeout_secs=10,
+            lambda: (None, _check_port_open("localhost", port)), timeout_secs=10,
         )
 
-    yield
+    yield f"http://localhost:{port}"
 
     if proc.is_alive():
         proc.kill()
 
+        # wait server to free the port
+        wait_retry_backoff(
+            lambda: (
+                None,
+                not _check_port_open("localhost", environment.get_local_server_port()),
+            ),
+            timeout_secs=30,
+        )
+
 
 def _check_port_open(host, port) -> bool:
     with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
         return sock.connect_ex((host, port)) == 0
 
 
+def _free_port():
+    sock = socket.socket()
+    sock.bind(("", 0))
+    return sock.getsockname()[1]
+
+
 @pytest.fixture(scope="session")
 def universal_data_sources(environment) -> TestData:
     return construct_universal_test_data(environment)
diff --git a/sdk/python/tests/data/data_creator.py b/sdk/python/tests/data/data_creator.py
index e08597b67b2..186c39b9ef4 100644
--- a/sdk/python/tests/data/data_creator.py
+++ b/sdk/python/tests/data/data_creator.py
@@ -4,11 +4,11 @@
 import pandas as pd
 from pytz import timezone, utc
 
-from feast.value_type import ValueType
+from feast.types import FeastType, Float32, Int32, Int64, String
 
 
 def create_dataset(
-    entity_type: ValueType = ValueType.INT32,
+    entity_type: FeastType = Int32,
     feature_dtype: str = None,
     feature_is_list: bool = False,
     list_has_empty_list: bool = False,
@@ -16,7 +16,7 @@ def create_dataset(
     now = datetime.utcnow().replace(microsecond=0, second=0, minute=0)
     ts = pd.Timestamp(now).round("ms")
     data = {
-        "driver_id": get_entities_for_value_type(entity_type),
+        "driver_id": get_entities_for_feast_type(entity_type),
         "value": get_feature_values_for_dtype(
             feature_dtype, feature_is_list, list_has_empty_list
         ),
@@ -37,14 +37,14 @@ def create_dataset(
     return pd.DataFrame.from_dict(data)
 
 
-def get_entities_for_value_type(value_type: ValueType) -> List:
-    value_type_map: Dict[ValueType, List] = {
-        ValueType.INT32: [1, 2, 1, 3, 3],
-        ValueType.INT64: [1, 2, 1, 3, 3],
-        ValueType.FLOAT: [1.0, 2.0, 1.0, 3.0, 3.0],
-        ValueType.STRING: ["1", "2", "1", "3", "3"],
+def get_entities_for_feast_type(feast_type: FeastType) -> List:
+    feast_type_map: Dict[FeastType, List] = {
+        Int32: [1, 2, 1, 3, 3],
+        Int64: [1, 2, 1, 3, 3],
+        Float32: [1.0, 2.0, 1.0, 3.0, 3.0],
+        String: ["1", "2", "1", "3", "3"],
     }
-    return value_type_map[value_type]
+    return feast_type_map[feast_type]
 
 
 def get_feature_values_for_dtype(
diff --git a/sdk/python/tests/doctest/test_all.py b/sdk/python/tests/doctest/test_all.py
index 65d5f3da289..31f181ad535 100644
--- a/sdk/python/tests/doctest/test_all.py
+++ b/sdk/python/tests/doctest/test_all.py
@@ -11,15 +11,13 @@ def setup_feature_store():
     """Prepares the local environment for a FeatureStore docstring test."""
     from datetime import datetime, timedelta
 
-    from feast import Entity, FeatureStore, FeatureView, Field, FileSource, ValueType
+    from feast import Entity, FeatureStore, FeatureView, Field, FileSource
     from feast.repo_operations import init_repo
     from feast.types import Float32, Int64
 
     init_repo("feature_repo", "local")
     fs = FeatureStore(repo_path="feature_repo")
-    driver = Entity(
-        name="driver_id", value_type=ValueType.INT64, description="driver id",
-    )
+    driver = Entity(name="driver_id", description="driver id",)
     driver_hourly_stats = FileSource(
         path="feature_repo/data/driver_stats.parquet",
         timestamp_field="event_timestamp",
@@ -27,7 +25,7 @@ def setup_feature_store():
     )
     driver_hourly_stats_view = FeatureView(
         name="driver_hourly_stats",
-        entities=["driver_id"],
+        entities=[driver],
         ttl=timedelta(seconds=86400 * 1),
         schema=[
             Field(name="conv_rate", dtype=Float32),
diff --git a/sdk/python/tests/example_repos/example_feature_repo_1.py b/sdk/python/tests/example_repos/example_feature_repo_1.py
index d8b6d7c89b1..8d6d96d9ef1 100644
--- a/sdk/python/tests/example_repos/example_feature_repo_1.py
+++ b/sdk/python/tests/example_repos/example_feature_repo_1.py
@@ -1,14 +1,6 @@
 from datetime import timedelta
 
-from feast import (
-    BigQuerySource,
-    Entity,
-    FeatureService,
-    FeatureView,
-    Field,
-    PushSource,
-    ValueType,
-)
+from feast import BigQuerySource, Entity, FeatureService, FeatureView, Field, PushSource
 from feast.types import Float32, Int64, String
 
 driver_locations_source = BigQuerySource(
@@ -46,22 +38,24 @@
 driver = Entity(
     name="driver",  # The name is derived from this argument, not object name.
     join_keys=["driver_id"],
-    value_type=ValueType.INT64,
     description="driver id",
 )
 
 customer = Entity(
     name="customer",  # The name is derived from this argument, not object name.
     join_keys=["customer_id"],
-    value_type=ValueType.STRING,
 )
 
 
 driver_locations = FeatureView(
     name="driver_locations",
-    entities=["driver"],
+    entities=[driver],
     ttl=timedelta(days=1),
-    schema=[Field(name="lat", dtype=Float32), Field(name="lon", dtype=String)],
+    schema=[
+        Field(name="lat", dtype=Float32),
+        Field(name="lon", dtype=String),
+        Field(name="driver_id", dtype=Int64),
+    ],
     online=True,
     batch_source=driver_locations_source,
     tags={},
@@ -69,11 +63,12 @@
 
 pushed_driver_locations = FeatureView(
     name="pushed_driver_locations",
-    entities=["driver"],
+    entities=[driver],
     ttl=timedelta(days=1),
     schema=[
         Field(name="driver_lat", dtype=Float32),
         Field(name="driver_long", dtype=String),
+        Field(name="driver_id", dtype=Int64),
     ],
     online=True,
     stream_source=driver_locations_push_source,
@@ -82,12 +77,13 @@
 
 customer_profile = FeatureView(
     name="customer_profile",
-    entities=["customer"],
+    entities=[customer],
     ttl=timedelta(days=1),
     schema=[
         Field(name="avg_orders_day", dtype=Float32),
         Field(name="name", dtype=String),
         Field(name="age", dtype=Int64),
+        Field(name="customer_id", dtype=String),
     ],
     online=True,
     batch_source=customer_profile_source,
@@ -96,9 +92,13 @@
 
 customer_driver_combined = FeatureView(
     name="customer_driver_combined",
-    entities=["customer", "driver"],
+    entities=[customer, driver],
     ttl=timedelta(days=1),
-    schema=[Field(name="trips", dtype=Int64)],
+    schema=[
+        Field(name="trips", dtype=Int64),
+        Field(name="driver_id", dtype=Int64),
+        Field(name="customer_id", dtype=String),
+    ],
     online=True,
     batch_source=customer_driver_combined_source,
     tags={},
diff --git a/sdk/python/tests/example_repos/example_feature_repo_2.py b/sdk/python/tests/example_repos/example_feature_repo_2.py
index d4c7976418b..073c48c1c1e 100644
--- a/sdk/python/tests/example_repos/example_feature_repo_2.py
+++ b/sdk/python/tests/example_repos/example_feature_repo_2.py
@@ -1,6 +1,6 @@
 from datetime import timedelta
 
-from feast import Entity, FeatureView, Field, FileSource, ValueType
+from feast import Entity, FeatureView, Field, FileSource
 from feast.types import Float32, Int32, Int64
 
 driver_hourly_stats = FileSource(
@@ -9,17 +9,18 @@
     created_timestamp_column="created",
 )
 
-driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id")
+driver = Entity(name="driver_id", description="driver id",)
 
 
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver_id"],
+    entities=[driver],
     ttl=timedelta(days=1),
     schema=[
         Field(name="conv_rate", dtype=Float32),
         Field(name="acc_rate", dtype=Float32),
         Field(name="avg_daily_trips", dtype=Int64),
+        Field(name="driver_id", dtype=Int32),
     ],
     online=True,
     source=driver_hourly_stats,
diff --git a/sdk/python/tests/example_repos/example_feature_repo_version_0_19.py b/sdk/python/tests/example_repos/example_feature_repo_version_0_19.py
index e00a69b867a..a65c031cea4 100644
--- a/sdk/python/tests/example_repos/example_feature_repo_version_0_19.py
+++ b/sdk/python/tests/example_repos/example_feature_repo_version_0_19.py
@@ -1,6 +1,10 @@
 from datetime import timedelta
 
+import pandas as pd
+
 from feast import Entity, Feature, FeatureView, FileSource, ValueType
+from feast.data_source import RequestDataSource
+from feast.on_demand_feature_view import on_demand_feature_view
 
 driver_hourly_stats = FileSource(
     path="%PARQUET_PATH%",  # placeholder to be replaced by the test
@@ -50,3 +54,27 @@
     batch_source=global_daily_stats,  # Changed to `source` in 0.20
     tags={},
 )
+
+
+request_source = RequestDataSource(
+    name="conv_rate_input", schema={"val_to_add": ValueType.INT64},
+)
+
+
+@on_demand_feature_view(
+    inputs={
+        "conv_rate_input": request_source,
+        "driver_hourly_stats": driver_hourly_stats_view,
+    },
+    features=[
+        Feature(name="conv_rate_plus_100", dtype=ValueType.DOUBLE),
+        Feature(name="conv_rate_plus_val_to_add", dtype=ValueType.DOUBLE),
+    ],
+)
+def conv_rate_plus_100(features_df: pd.DataFrame) -> pd.DataFrame:
+    df = pd.DataFrame()
+    df["conv_rate_plus_100"] = features_df["conv_rate"] + 100
+    df["conv_rate_plus_val_to_add"] = (
+        features_df["conv_rate"] + features_df["val_to_add"]
+    )
+    return df
diff --git a/sdk/python/tests/example_repos/example_feature_repo_with_duplicated_featureview_names.py b/sdk/python/tests/example_repos/example_feature_repo_with_duplicated_featureview_names.py
index cbcc3ad172b..4b079999ed7 100644
--- a/sdk/python/tests/example_repos/example_feature_repo_with_duplicated_featureview_names.py
+++ b/sdk/python/tests/example_repos/example_feature_repo_with_duplicated_featureview_names.py
@@ -1,14 +1,16 @@
 from datetime import timedelta
 
-from feast import FeatureView, FileSource
+from feast import Entity, FeatureView, FileSource
 
 driver_hourly_stats = FileSource(
     path="driver_stats.parquet",  # this parquet is not real and will not be read
 )
 
+driver = Entity(name="driver_id", description="driver id", join_keys=["driver"],)
+
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",  # Intentionally use the same FeatureView name
-    entities=["driver_id"],
+    entities=[driver],
     online=False,
     source=driver_hourly_stats,
     ttl=timedelta(days=1),
@@ -17,7 +19,7 @@
 
 driver_hourly_stats_view_dup1 = FeatureView(
     name="driver_hourly_stats",  # Intentionally use the same FeatureView name
-    entities=["driver_id"],
+    entities=[driver],
     online=False,
     source=driver_hourly_stats,
     ttl=timedelta(days=1),
diff --git a/sdk/python/tests/example_repos/example_feature_repo_with_entity_join_key.py b/sdk/python/tests/example_repos/example_feature_repo_with_entity_join_key.py
index 5ba26d2573b..06631505312 100644
--- a/sdk/python/tests/example_repos/example_feature_repo_with_entity_join_key.py
+++ b/sdk/python/tests/example_repos/example_feature_repo_with_entity_join_key.py
@@ -1,6 +1,6 @@
 from datetime import timedelta
 
-from feast import Entity, FeatureView, Field, FileSource, ValueType
+from feast import Entity, FeatureView, Field, FileSource
 from feast.types import Float32, Int64
 
 driver_hourly_stats = FileSource(
@@ -11,17 +11,12 @@
 
 
 # The join key here is deliberately different from the parquet file to test the failure path.
-driver = Entity(
-    name="driver_id",
-    value_type=ValueType.INT64,
-    description="driver id",
-    join_keys=["driver"],
-)
+driver = Entity(name="driver_id", description="driver id", join_keys=["driver"],)
 
 
 driver_hourly_stats_view = FeatureView(
     name="driver_hourly_stats",
-    entities=["driver_id"],
+    entities=[driver],
     ttl=timedelta(days=1),
     schema=[
         Field(name="conv_rate", dtype=Float32),
diff --git a/sdk/python/tests/example_repos/on_demand_feature_view_repo.py b/sdk/python/tests/example_repos/on_demand_feature_view_repo.py
new file mode 100644
index 00000000000..ac572d5747a
--- /dev/null
+++ b/sdk/python/tests/example_repos/on_demand_feature_view_repo.py
@@ -0,0 +1,51 @@
+from datetime import timedelta
+
+import pandas as pd
+
+from feast import Entity, FeatureView, Field, FileSource
+from feast.on_demand_feature_view import on_demand_feature_view
+from feast.types import Float32, String
+
+driver_stats = FileSource(
+    name="driver_stats_source",
+    path="data/driver_stats_lat_lon.parquet",
+    timestamp_field="event_timestamp",
+    created_timestamp_column="created",
+    description="A table describing the stats of a driver based on hourly logs",
+    owner="test2@gmail.com",
+)
+
+driver = Entity(name="driver_id", description="driver id",)
+
+driver_daily_features_view = FeatureView(
+    name="driver_daily_features",
+    entities=[driver],
+    ttl=timedelta(seconds=8640000000),
+    schema=[
+        Field(name="daily_miles_driven", dtype=Float32),
+        Field(name="lat", dtype=Float32),
+        Field(name="lon", dtype=Float32),
+        Field(name="string_feature", dtype=String),
+        Field(name="driver_id", dtype=Float32),
+    ],
+    online=True,
+    source=driver_stats,
+    tags={"production": "True"},
+    owner="test2@gmail.com",
+)
+
+
+@on_demand_feature_view(
+    sources=[driver_daily_features_view],
+    schema=[
+        Field(name="first_char", dtype=String),
+        Field(name="concat_string", dtype=String),
+    ],
+)
+def location_features_from_push(inputs: pd.DataFrame) -> pd.DataFrame:
+    df = pd.DataFrame()
+    df["concat_string"] = inputs.apply(
+        lambda x: x.string_feature + "hello", axis=1
+    ).astype("string")
+    df["first_char"] = inputs["string_feature"].str[:1].astype("string")
+    return df
diff --git a/sdk/python/tests/foo_provider.py b/sdk/python/tests/foo_provider.py
index 2d61c362734..bd6f9811e84 100644
--- a/sdk/python/tests/foo_provider.py
+++ b/sdk/python/tests/foo_provider.py
@@ -11,7 +11,7 @@
 from feast.infra.provider import Provider
 from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
 from feast.protos.feast.types.Value_pb2 import Value as ValueProto
-from feast.registry import Registry
+from feast.registry import BaseRegistry
 from feast.saved_dataset import SavedDataset
 
 
@@ -52,7 +52,7 @@ def materialize_single_feature_view(
         feature_view: FeatureView,
         start_date: datetime,
         end_date: datetime,
-        registry: Registry,
+        registry: BaseRegistry,
         project: str,
         tqdm_builder: Callable[[int], tqdm],
     ) -> None:
@@ -64,7 +64,7 @@ def get_historical_features(
         feature_views: List[FeatureView],
         feature_refs: List[str],
         entity_df: Union[pandas.DataFrame, str],
-        registry: Registry,
+        registry: BaseRegistry,
         project: str,
         full_feature_names: bool = False,
     ) -> RetrievalJob:
@@ -87,7 +87,7 @@ def write_feature_service_logs(
         feature_service: FeatureService,
         logs: Union[pyarrow.Table, Path],
         config: RepoConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ):
         pass
 
@@ -97,6 +97,6 @@ def retrieve_feature_service_logs(
         start_date: datetime,
         end_date: datetime,
         config: RepoConfig,
-        registry: Registry,
+        registry: BaseRegistry,
     ) -> RetrievalJob:
         pass
diff --git a/sdk/python/tests/integration/e2e/test_go_feature_server.py b/sdk/python/tests/integration/e2e/test_go_feature_server.py
index e469c90c11f..4fd003c1944 100644
--- a/sdk/python/tests/integration/e2e/test_go_feature_server.py
+++ b/sdk/python/tests/integration/e2e/test_go_feature_server.py
@@ -9,12 +9,13 @@
 import pandas as pd
 import pytest
 import pytz
+import requests
 
-from feast import FeatureService, ValueType
-from feast.embedded_go.lib.embedded import LoggingOptions
+from feast import FeatureService, FeatureView, ValueType
 from feast.embedded_go.online_features_service import EmbeddedOnlineFeatureServer
 from feast.feast_object import FeastObject
 from feast.feature_logging import LoggingConfig
+from feast.infra.feature_servers.base_config import FeatureLoggingConfig
 from feast.protos.feast.serving.ServingService_pb2 import (
     FieldStatus,
     GetOnlineFeaturesRequest,
@@ -33,10 +34,6 @@
     location,
 )
 
-NANOSECOND = 1
-MILLISECOND = 1000_000 * NANOSECOND
-SECOND = 1000 * MILLISECOND
-
 
 @pytest.fixture(scope="session")
 def initialized_registry(environment, universal_data_sources):
@@ -61,8 +58,7 @@ def initialized_registry(environment, universal_data_sources):
     fs.materialize(environment.start_date, environment.end_date)
 
 
-@pytest.fixture
-def grpc_server_port(environment, initialized_registry):
+def server_port(environment, server_type: str):
     if not environment.test_repo_config.go_feature_retrieval:
         pytest.skip("Only for Go path")
 
@@ -72,17 +68,24 @@ def grpc_server_port(environment, initialized_registry):
         repo_path=str(fs.repo_path.absolute()), repo_config=fs.config, feature_store=fs,
     )
     port = free_port()
+    if server_type == "grpc":
+        target = embedded.start_grpc_server
+    elif server_type == "http":
+        target = embedded.start_http_server
+    else:
+        raise ValueError("Server Type must be either 'http' or 'grpc'")
 
     t = threading.Thread(
-        target=embedded.start_grpc_server,
+        target=target,
         args=("127.0.0.1", port),
         kwargs=dict(
             enable_logging=True,
-            logging_options=LoggingOptions(
-                ChannelCapacity=100,
-                WriteInterval=100 * MILLISECOND,
-                FlushInterval=1 * SECOND,
-                EmitTimeout=10 * MILLISECOND,
+            logging_options=FeatureLoggingConfig(
+                enabled=True,
+                queue_capacity=100,
+                write_to_disk_interval_secs=1,
+                flush_interval_secs=1,
+                emit_timeout_micro_secs=10000,
             ),
         ),
     )
@@ -93,9 +96,23 @@ def grpc_server_port(environment, initialized_registry):
     )
 
     yield port
-    embedded.stop_grpc_server()
+    if server_type == "grpc":
+        embedded.stop_grpc_server()
+    else:
+        embedded.stop_http_server()
+
     # wait for graceful stop
-    time.sleep(2)
+    time.sleep(5)
+
+
+@pytest.fixture
+def grpc_server_port(environment, initialized_registry):
+    yield from server_port(environment, "grpc")
+
+
+@pytest.fixture
+def http_server_port(environment, initialized_registry):
+    yield from server_port(environment, "http")
 
 
 @pytest.fixture
@@ -130,6 +147,44 @@ def test_go_grpc_server(grpc_client):
         assert all([s == FieldStatus.PRESENT for s in vector.statuses])
 
 
+@pytest.mark.integration
+@pytest.mark.goserver
+def test_go_http_server(http_server_port):
+    response = requests.post(
+        f"http://localhost:{http_server_port}/get-online-features",
+        json={
+            "feature_service": "driver_features",
+            "entities": {"driver_id": [5001, 5002]},
+            "full_feature_names": True,
+        },
+    )
+    assert response.status_code == 200, response.text
+    response = response.json()
+    assert set(response.keys()) == {"metadata", "results"}
+    metadata = response["metadata"]
+    results = response["results"]
+    assert response["metadata"] == {
+        "feature_names": [
+            "driver_id",
+            "driver_stats__conv_rate",
+            "driver_stats__acc_rate",
+            "driver_stats__avg_daily_trips",
+        ]
+    }, metadata
+    assert len(results) == 4, results
+    assert all(
+        set(result.keys()) == {"event_timestamps", "statuses", "values"}
+        for result in results
+    ), results
+    assert all(
+        result["statuses"] == ["PRESENT", "PRESENT"] for result in results
+    ), results
+    assert results[0]["values"] == [5001, 5002], results
+    for result in results[1:]:
+        assert len(result["values"]) == 2, result
+        assert all(value is not None for value in result["values"]), result
+
+
 @pytest.mark.integration
 @pytest.mark.goserver
 @pytest.mark.universal_offline_stores
@@ -162,13 +217,14 @@ def test_feature_logging(
 
     _, datasets, _ = universal_data_sources
     latest_rows = get_latest_rows(datasets.driver_df, "driver_id", driver_ids)
+    feature_view = fs.get_feature_view("driver_stats")
     features = [
         feature.name
         for proj in feature_service.feature_view_projections
         for feature in proj.features
     ]
     expected_logs = generate_expected_logs(
-        latest_rows, "driver_stats", features, ["driver_id"], "event_timestamp"
+        latest_rows, feature_view, features, ["driver_id"], "event_timestamp"
     )
 
     def retrieve():
@@ -213,15 +269,26 @@ def get_latest_rows(df, join_key, entity_values):
 
 
 def generate_expected_logs(
-    df, feature_view_name, features, join_keys, timestamp_column
+    df: pd.DataFrame,
+    feature_view: FeatureView,
+    features: List[str],
+    join_keys: List[str],
+    timestamp_column: str,
 ):
     logs = pd.DataFrame()
     for join_key in join_keys:
         logs[join_key] = df[join_key]
 
     for feature in features:
-        logs[f"{feature_view_name}__{feature}"] = df[feature]
-        logs[f"{feature_view_name}__{feature}__timestamp"] = df[timestamp_column]
-        logs[f"{feature_view_name}__{feature}__status"] = FieldStatus.PRESENT
+        col = f"{feature_view.name}__{feature}"
+        logs[col] = df[feature]
+        logs[f"{col}__timestamp"] = df[timestamp_column]
+        logs[f"{col}__status"] = FieldStatus.PRESENT
+        if feature_view.ttl:
+            logs[f"{col}__status"] = logs[f"{col}__status"].mask(
+                df[timestamp_column]
+                < datetime.utcnow().replace(tzinfo=pytz.UTC) - feature_view.ttl,
+                FieldStatus.OUTSIDE_MAX_AGE,
+            )
 
     return logs.sort_values(by=join_keys).reset_index(drop=True)
diff --git a/sdk/python/tests/integration/e2e/test_usage_e2e.py b/sdk/python/tests/integration/e2e/test_usage_e2e.py
index 12c1eb86281..53e4a32a82e 100644
--- a/sdk/python/tests/integration/e2e/test_usage_e2e.py
+++ b/sdk/python/tests/integration/e2e/test_usage_e2e.py
@@ -19,7 +19,7 @@
 
 import pytest
 
-from feast import Entity, RepoConfig, ValueType
+from feast import Entity, RepoConfig
 from feast.infra.online_stores.sqlite import SqliteOnlineStoreConfig
 
 
@@ -60,7 +60,6 @@ def test_usage_on(dummy_exporter, enabling_toggle):
         entity = Entity(
             name="driver_car_id",
             description="Car driver id",
-            value_type=ValueType.STRING,
             tags={"team": "matchmaking"},
         )
 
@@ -99,7 +98,6 @@ def test_usage_off(dummy_exporter, enabling_toggle):
         entity = Entity(
             name="driver_car_id",
             description="Car driver id",
-            value_type=ValueType.STRING,
             tags={"team": "matchmaking"},
         )
         test_feature_store.apply([entity])
diff --git a/sdk/python/tests/integration/e2e/test_validation.py b/sdk/python/tests/integration/e2e/test_validation.py
index e434f1a133f..0d65640dcaf 100644
--- a/sdk/python/tests/integration/e2e/test_validation.py
+++ b/sdk/python/tests/integration/e2e/test_validation.py
@@ -1,10 +1,23 @@
+import datetime
+import shutil
+
 import pandas as pd
+import pyarrow as pa
 import pytest
 from great_expectations.core import ExpectationSuite
 from great_expectations.dataset import PandasDataset
 
+from feast import FeatureService
 from feast.dqm.errors import ValidationFailed
 from feast.dqm.profilers.ge_profiler import ge_profiler
+from feast.feature_logging import (
+    LOG_TIMESTAMP_FIELD,
+    FeatureServiceLoggingSource,
+    LoggingConfig,
+)
+from feast.protos.feast.serving.ServingService_pb2 import FieldStatus
+from feast.utils import make_tzaware
+from feast.wait import wait_retry_backoff
 from tests.integration.feature_repos.repo_configuration import (
     construct_universal_feature_views,
 )
@@ -13,6 +26,8 @@
     driver,
     location,
 )
+from tests.utils.cli_utils import CliRunner
+from tests.utils.logged_features import prepare_logs
 
 _features = [
     "customer_profile:current_balance",
@@ -32,6 +47,39 @@ def configurable_profiler(dataset: PandasDataset) -> ExpectationSuite:
 
     return UserConfigurableProfiler(
         profile_dataset=dataset,
+        ignored_columns=["event_timestamp"],
+        excluded_expectations=[
+            "expect_table_columns_to_match_ordered_list",
+            "expect_table_row_count_to_be_between",
+        ],
+        value_set_threshold="few",
+    ).build_suite()
+
+
+@ge_profiler(with_feature_metadata=True)
+def profiler_with_feature_metadata(dataset: PandasDataset) -> ExpectationSuite:
+    from great_expectations.profile.user_configurable_profiler import (
+        UserConfigurableProfiler,
+    )
+
+    # always present
+    dataset.expect_column_values_to_be_in_set(
+        "global_stats__avg_ride_length__status", {FieldStatus.PRESENT}
+    )
+
+    # present at least in 70% of rows
+    dataset.expect_column_values_to_be_in_set(
+        "customer_profile__current_balance__status", {FieldStatus.PRESENT}, mostly=0.7
+    )
+
+    return UserConfigurableProfiler(
+        profile_dataset=dataset,
+        ignored_columns=["event_timestamp"]
+        + [
+            c
+            for c in dataset.columns
+            if c.endswith("__timestamp") or c.endswith("__status")
+        ],
         excluded_expectations=[
             "expect_table_columns_to_match_ordered_list",
             "expect_table_row_count_to_be_between",
@@ -83,7 +131,7 @@ def test_historical_retrieval_with_validation(environment, universal_data_source
     saved_dataset = store.get_saved_dataset("my_training_dataset")
 
     # If validation pass there will be no exceptions on this point
-    reference = saved_dataset.as_reference(profiler=configurable_profiler)
+    reference = saved_dataset.as_reference(name="ref", profiler=configurable_profiler)
     job.to_df(validation_reference=reference)
 
 
@@ -116,7 +164,7 @@ def test_historical_retrieval_fails_on_validation(environment, universal_data_so
         job.to_df(
             validation_reference=store.get_saved_dataset(
                 "my_other_dataset"
-            ).as_reference(profiler=profiler_with_unrealistic_expectations)
+            ).as_reference(name="ref", profiler=profiler_with_unrealistic_expectations)
         )
 
     failed_expectations = exc_info.value.report.errors
@@ -127,3 +175,177 @@ def test_historical_retrieval_fails_on_validation(environment, universal_data_so
 
     assert failed_expectations[1].check_name == "expect_column_values_to_be_in_set"
     assert failed_expectations[1].column_name == "avg_passenger_count"
+
+
+@pytest.mark.integration
+@pytest.mark.universal_offline_stores
+def test_logged_features_validation(environment, universal_data_sources):
+    store = environment.feature_store
+
+    (_, datasets, data_sources) = universal_data_sources
+    feature_views = construct_universal_feature_views(data_sources)
+    feature_service = FeatureService(
+        name="test_service",
+        features=[
+            feature_views.customer[
+                ["current_balance", "avg_passenger_count", "lifetime_trip_count"]
+            ],
+            feature_views.order[["order_is_success"]],
+            feature_views.global_fv[["num_rides", "avg_ride_length"]],
+        ],
+        logging_config=LoggingConfig(
+            destination=environment.data_source_creator.create_logged_features_destination()
+        ),
+    )
+
+    store.apply(
+        [driver(), customer(), location(), feature_service, *feature_views.values()]
+    )
+
+    entity_df = datasets.entity_df.drop(
+        columns=["order_id", "origin_id", "destination_id"]
+    )
+
+    # add some non-existing entities to check NotFound feature handling
+    for i in range(5):
+        entity_df = entity_df.append(
+            {
+                "customer_id": 2000 + i,
+                "driver_id": 6000 + i,
+                "event_timestamp": datetime.datetime.now(),
+            },
+            ignore_index=True,
+        )
+
+    reference_dataset = store.create_saved_dataset(
+        from_=store.get_historical_features(
+            entity_df=entity_df, features=feature_service, full_feature_names=True
+        ),
+        name="reference_for_validating_logged_features",
+        storage=environment.data_source_creator.create_saved_dataset_destination(),
+    )
+
+    log_source_df = store.get_historical_features(
+        entity_df=entity_df, features=feature_service, full_feature_names=False
+    ).to_df()
+    logs_df = prepare_logs(log_source_df, feature_service, store)
+
+    schema = FeatureServiceLoggingSource(
+        feature_service=feature_service, project=store.project
+    ).get_schema(store._registry)
+    store.write_logged_features(
+        pa.Table.from_pandas(logs_df, schema=schema), source=feature_service
+    )
+
+    def validate():
+        """
+        Return Tuple[succeed, completed]
+        Succeed will be True if no ValidateFailed exception was raised
+        """
+        try:
+            store.validate_logged_features(
+                feature_service,
+                start=logs_df[LOG_TIMESTAMP_FIELD].min(),
+                end=logs_df[LOG_TIMESTAMP_FIELD].max() + datetime.timedelta(seconds=1),
+                reference=reference_dataset.as_reference(
+                    name="ref", profiler=profiler_with_feature_metadata
+                ),
+            )
+        except ValidationFailed:
+            return False, True
+        except Exception:
+            # log table is still being created
+            return False, False
+
+        return True, True
+
+    success = wait_retry_backoff(validate, timeout_secs=30)
+    assert success, "Validation failed (unexpectedly)"
+
+
+@pytest.mark.integration
+def test_e2e_validation_via_cli(environment, universal_data_sources):
+    runner = CliRunner()
+    store = environment.feature_store
+
+    (_, datasets, data_sources) = universal_data_sources
+    feature_views = construct_universal_feature_views(data_sources)
+    feature_service = FeatureService(
+        name="test_service",
+        features=[
+            feature_views.customer[
+                ["current_balance", "avg_passenger_count", "lifetime_trip_count"]
+            ],
+        ],
+        logging_config=LoggingConfig(
+            destination=environment.data_source_creator.create_logged_features_destination()
+        ),
+    )
+    store.apply([customer(), feature_service, feature_views.customer])
+
+    entity_df = datasets.entity_df.drop(
+        columns=["order_id", "origin_id", "destination_id", "driver_id"]
+    )
+    retrieval_job = store.get_historical_features(
+        entity_df=entity_df, features=feature_service, full_feature_names=True
+    )
+    logs_df = prepare_logs(retrieval_job.to_df(), feature_service, store)
+    saved_dataset = store.create_saved_dataset(
+        from_=retrieval_job,
+        name="reference_for_validating_logged_features",
+        storage=environment.data_source_creator.create_saved_dataset_destination(),
+    )
+    reference = saved_dataset.as_reference(
+        name="test_reference", profiler=configurable_profiler
+    )
+
+    schema = FeatureServiceLoggingSource(
+        feature_service=feature_service, project=store.project
+    ).get_schema(store._registry)
+    store.write_logged_features(
+        pa.Table.from_pandas(logs_df, schema=schema), source=feature_service
+    )
+
+    with runner.local_repo(example_repo_py="", offline_store="file") as local_repo:
+        local_repo.apply(
+            [customer(), feature_views.customer, feature_service, reference]
+        )
+        local_repo._registry.apply_saved_dataset(saved_dataset, local_repo.project)
+        validate_args = [
+            "validate",
+            "--feature-service",
+            feature_service.name,
+            "--reference",
+            reference.name,
+            (datetime.datetime.now() - datetime.timedelta(days=7)).isoformat(),
+            datetime.datetime.now().isoformat(),
+        ]
+        p = runner.run(validate_args, cwd=local_repo.repo_path)
+
+        assert p.returncode == 0, p.stderr.decode()
+        assert "Validation successful" in p.stdout.decode(), p.stderr.decode()
+
+        # make sure second validation will use cached profile
+        shutil.rmtree(saved_dataset.storage.file_options.uri)
+
+        # Add some invalid data that would lead to failed validation
+        invalid_data = pd.DataFrame(
+            data={
+                "customer_id": [0],
+                "current_balance": [0],
+                "avg_passenger_count": [0],
+                "lifetime_trip_count": [0],
+                "event_timestamp": [
+                    make_tzaware(datetime.datetime.utcnow())
+                    - datetime.timedelta(hours=1)
+                ],
+            }
+        )
+        invalid_logs = prepare_logs(invalid_data, feature_service, store)
+        store.write_logged_features(
+            pa.Table.from_pandas(invalid_logs, schema=schema), source=feature_service
+        )
+
+        p = runner.run(validate_args, cwd=local_repo.repo_path)
+        assert p.returncode == 1, p.stdout.decode()
+        assert "Validation failed" in p.stdout.decode(), p.stderr.decode()
diff --git a/sdk/python/tests/integration/feature_repos/integration_test_repo_config.py b/sdk/python/tests/integration/feature_repos/integration_test_repo_config.py
index 61920bb03f8..74ce37f17a6 100644
--- a/sdk/python/tests/integration/feature_repos/integration_test_repo_config.py
+++ b/sdk/python/tests/integration/feature_repos/integration_test_repo_config.py
@@ -51,8 +51,8 @@ def __repr__(self) -> str:
                 f"{self.provider.upper()}",
                 f"{self.offline_store_creator.__name__.split('.')[-1].replace('DataSourceCreator', '')}",
                 online_store_type,
-                f"python_fs={self.python_feature_server}",
-                f"go_fs={self.go_feature_retrieval}",
+                f"python_fs:{self.python_feature_server}",
+                f"go_fs:{self.go_feature_retrieval}",
             ]
         )
 
diff --git a/sdk/python/tests/integration/feature_repos/repo_configuration.py b/sdk/python/tests/integration/feature_repos/repo_configuration.py
index 27cf1a52e9d..6f40d3171b0 100644
--- a/sdk/python/tests/integration/feature_repos/repo_configuration.py
+++ b/sdk/python/tests/integration/feature_repos/repo_configuration.py
@@ -2,7 +2,6 @@
 import importlib
 import json
 import os
-import re
 import tempfile
 import uuid
 from dataclasses import dataclass
@@ -18,6 +17,8 @@
 from feast.constants import FULL_REPO_CONFIGS_MODULE_ENV_NAME
 from feast.data_source import DataSource
 from feast.errors import FeastModuleImportError
+from feast.infra.feature_servers.base_config import FeatureLoggingConfig
+from feast.infra.feature_servers.local_process.config import LocalFeatureServerConfig
 from feast.repo_config import RegistryConfig, RepoConfig
 from tests.integration.feature_repos.integration_test_repo_config import (
     IntegrationTestRepoConfig,
@@ -73,6 +74,13 @@
     "connection_string": "127.0.0.1:6001,127.0.0.1:6002,127.0.0.1:6003",
 }
 
+OFFLINE_STORE_TO_PROVIDER_CONFIG: Dict[str, DataSourceCreator] = {
+    "file": ("local", FileDataSourceCreator),
+    "bigquery": ("gcp", BigQueryDataSourceCreator),
+    "redshift": ("aws", RedshiftDataSourceCreator),
+    "snowflake": ("aws", RedshiftDataSourceCreator),
+}
+
 AVAILABLE_OFFLINE_STORES: List[Tuple[str, Type[DataSourceCreator]]] = [
     ("local", FileDataSourceCreator),
 ]
@@ -328,29 +336,10 @@ class Environment:
     worker_id: str
     online_store_creator: Optional[OnlineStoreCreator] = None
 
-    next_id = 0
-
     def __post_init__(self):
         self.end_date = datetime.utcnow().replace(microsecond=0, second=0, minute=0)
         self.start_date: datetime = self.end_date - timedelta(days=3)
 
-        Environment.next_id += 1
-        self.id = Environment.next_id
-
-    def get_feature_server_endpoint(self) -> str:
-        if self.python_feature_server and self.test_repo_config.provider == "local":
-            return f"http://localhost:{self.get_local_server_port()}"
-        return self.feature_store.get_feature_server_endpoint()
-
-    def get_local_server_port(self) -> int:
-        # Heuristic when running with xdist to extract unique ports for each worker
-        parsed_worker_id = re.findall("gw(\\d+)", self.worker_id)
-        if len(parsed_worker_id) != 0:
-            worker_id_num = int(parsed_worker_id[0])
-        else:
-            worker_id_num = 0
-        return 6000 + 100 * worker_id_num + self.id
-
 
 def table_name_from_data_source(ds: DataSource) -> Optional[str]:
     if hasattr(ds, "table_ref"):
@@ -406,8 +395,9 @@ def construct_test_environment(
             f"s3://feast-integration-tests/registries/{project}/registry.db"
         )  # type: Union[str, RegistryConfig]
     else:
-        # Note: even if it's a local feature server, the repo config does not have this configured
-        feature_server = None
+        feature_server = LocalFeatureServerConfig(
+            feature_logging=FeatureLoggingConfig(enabled=True)
+        )
         registry = RegistryConfig(
             path=str(Path(repo_dir_name) / "registry.db"), cache_ttl_seconds=1,
         )
diff --git a/sdk/python/tests/integration/feature_repos/universal/entities.py b/sdk/python/tests/integration/feature_repos/universal/entities.py
index b7a7583f1b3..66989b06468 100644
--- a/sdk/python/tests/integration/feature_repos/universal/entities.py
+++ b/sdk/python/tests/integration/feature_repos/universal/entities.py
@@ -1,22 +1,21 @@
-from feast import Entity, ValueType
+from feast import Entity
 
 
-def driver(value_type: ValueType = ValueType.INT64):
+def driver():
     return Entity(
         name="driver",  # The name is derived from this argument, not object name.
-        value_type=value_type,
         description="driver id",
         join_keys=["driver_id"],
     )
 
 
 def customer():
-    return Entity(name="customer_id", value_type=ValueType.INT64)
+    return Entity(name="customer_id")
 
 
 def location():
-    return Entity(name="location_id", value_type=ValueType.INT64)
+    return Entity(name="location_id")
 
 
 def item():
-    return Entity(name="item_id", value_type=ValueType.INT64)
+    return Entity(name="item_id")
diff --git a/sdk/python/tests/integration/feature_repos/universal/feature_views.py b/sdk/python/tests/integration/feature_repos/universal/feature_views.py
index 3e05f5d7e5f..3fee0b70019 100644
--- a/sdk/python/tests/integration/feature_repos/universal/feature_views.py
+++ b/sdk/python/tests/integration/feature_repos/universal/feature_views.py
@@ -11,11 +11,17 @@
     Field,
     OnDemandFeatureView,
     PushSource,
+    StreamFeatureView,
     ValueType,
 )
 from feast.data_source import DataSource, RequestSource
-from feast.types import Array, FeastType, Float32, Float64, Int32
-from tests.integration.feature_repos.universal.entities import location
+from feast.types import Array, FeastType, Float32, Float64, Int32, Int64
+from tests.integration.feature_repos.universal.entities import (
+    customer,
+    driver,
+    item,
+    location,
+)
 
 
 def driver_feature_view(
@@ -23,12 +29,14 @@ def driver_feature_view(
     name="test_correctness",
     infer_features: bool = False,
     dtype: FeastType = Float32,
-    entities: Optional[List[str]] = None,
+    entity_type: FeastType = Int64,
 ) -> FeatureView:
+    d = driver()
     return FeatureView(
         name=name,
-        entities=entities or ["driver"],
-        schema=None if infer_features else [Field(name="value", dtype=dtype)],
+        entities=[d],
+        schema=[Field(name=d.join_key, dtype=entity_type)]
+        + ([] if infer_features else [Field(name="value", dtype=dtype)]),
         ttl=timedelta(days=5),
         source=data_source,
     )
@@ -139,7 +147,7 @@ def create_similarity_request_source():
 def create_item_embeddings_feature_view(source, infer_features: bool = False):
     item_embeddings_feature_view = FeatureView(
         name="item_embeddings",
-        entities=["item"],
+        entities=[item()],
         schema=None
         if infer_features
         else [
@@ -157,7 +165,7 @@ def create_item_embeddings_batch_feature_view(
 ) -> BatchFeatureView:
     item_embeddings_feature_view = BatchFeatureView(
         name="item_embeddings",
-        entities=["item"],
+        entities=[item()],
         schema=None
         if infer_features
         else [
@@ -171,15 +179,19 @@ def create_item_embeddings_batch_feature_view(
 
 
 def create_driver_hourly_stats_feature_view(source, infer_features: bool = False):
+    # TODO(felixwang9817): Figure out why not adding an entity field here
+    # breaks type tests.
+    d = driver()
     driver_stats_feature_view = FeatureView(
         name="driver_stats",
-        entities=["driver"],
+        entities=[d],
         schema=None
         if infer_features
         else [
             Field(name="conv_rate", dtype=Float32),
             Field(name="acc_rate", dtype=Float32),
             Field(name="avg_daily_trips", dtype=Int32),
+            Field(name=d.join_key, dtype=Int64),
         ],
         source=source,
         ttl=timedelta(hours=2),
@@ -192,7 +204,7 @@ def create_driver_hourly_stats_batch_feature_view(
 ) -> BatchFeatureView:
     driver_stats_feature_view = BatchFeatureView(
         name="driver_stats",
-        entities=["driver"],
+        entities=[driver()],
         schema=None
         if infer_features
         else [
@@ -209,7 +221,7 @@ def create_driver_hourly_stats_batch_feature_view(
 def create_customer_daily_profile_feature_view(source, infer_features: bool = False):
     customer_profile_feature_view = FeatureView(
         name="customer_profile",
-        entities=["customer_id"],
+        entities=[customer()],
         schema=None
         if infer_features
         else [
@@ -243,10 +255,13 @@ def create_global_stats_feature_view(source, infer_features: bool = False):
 def create_order_feature_view(source, infer_features: bool = False):
     return FeatureView(
         name="order",
-        entities=["driver", "customer_id"],
+        entities=[customer(), driver()],
         schema=None
         if infer_features
-        else [Field(name="order_is_success", dtype=Int32)],
+        else [
+            Field(name="order_is_success", dtype=Int32),
+            Field(name="driver_id", dtype=Int64),
+        ],
         source=source,
         ttl=timedelta(days=2),
     )
@@ -256,7 +271,12 @@ def create_location_stats_feature_view(source, infer_features: bool = False):
     location_stats_feature_view = FeatureView(
         name="location_stats",
         entities=[location()],
-        schema=None if infer_features else [Field(name="temperature", dtype=Int32)],
+        schema=None
+        if infer_features
+        else [
+            Field(name="temperature", dtype=Int32),
+            Field(name="location_id", dtype=Int64),
+        ],
         source=source,
         ttl=timedelta(days=2),
     )
@@ -278,11 +298,13 @@ def create_pushable_feature_view(batch_source: DataSource):
     push_source = PushSource(
         name="location_stats_push_source", batch_source=batch_source,
     )
-    return FeatureView(
+    return StreamFeatureView(
         name="pushable_location_stats",
-        entities=["location_id"],
-        # Test that Features still work for FeatureViews.
-        features=[Feature(name="temperature", dtype=ValueType.INT32)],
+        entities=[location()],
+        schema=[
+            Field(name="temperature", dtype=Int32),
+            Field(name="location_id", dtype=Int64),
+        ],
         ttl=timedelta(days=2),
         source=push_source,
     )
diff --git a/sdk/python/tests/integration/offline_store/test_feature_logging.py b/sdk/python/tests/integration/offline_store/test_feature_logging.py
index 8e7e9d68bea..a6f8e56de7d 100644
--- a/sdk/python/tests/integration/offline_store/test_feature_logging.py
+++ b/sdk/python/tests/integration/offline_store/test_feature_logging.py
@@ -1,16 +1,8 @@
-import contextlib
 import datetime
-import tempfile
-import uuid
-from pathlib import Path
-from typing import Iterator, Union
 
-import numpy as np
 import pandas as pd
-import pyarrow
 import pyarrow as pa
 import pytest
-from google.api_core.exceptions import NotFound
 
 from feast.feature_logging import (
     LOG_DATE_FIELD,
@@ -20,14 +12,17 @@
     LoggingConfig,
 )
 from feast.feature_service import FeatureService
-from feast.protos.feast.serving.ServingService_pb2 import FieldStatus
 from feast.wait import wait_retry_backoff
 from tests.integration.feature_repos.repo_configuration import (
-    UniversalDatasets,
     construct_universal_feature_views,
 )
-from tests.integration.feature_repos.universal.entities import driver
+from tests.integration.feature_repos.universal.entities import (
+    customer,
+    driver,
+    location,
+)
 from tests.integration.feature_repos.universal.feature_views import conv_rate_plus_100
+from tests.utils.logged_features import prepare_logs, to_logs_dataset
 
 
 @pytest.mark.integration
@@ -39,9 +34,7 @@ def test_feature_service_logging(environment, universal_data_sources, pass_as_pa
     (_, datasets, data_sources) = universal_data_sources
 
     feature_views = construct_universal_feature_views(data_sources)
-    store.apply([driver(), *feature_views.values()])
-
-    logs_df = prepare_logs(datasets)
+    store.apply([customer(), driver(), location(), *feature_views.values()])
 
     feature_service = FeatureService(
         name="test_service",
@@ -56,6 +49,12 @@ def test_feature_service_logging(environment, universal_data_sources, pass_as_pa
         ),
     )
 
+    driver_df = datasets.driver_df
+    driver_df["val_to_add"] = 50
+    driver_df = driver_df.join(conv_rate_plus_100(driver_df))
+
+    logs_df = prepare_logs(driver_df, feature_service, store)
+
     schema = FeatureServiceLoggingSource(
         feature_service=feature_service, project=store.project
     ).get_schema(store._registry)
@@ -85,7 +84,7 @@ def retrieve():
         )
         try:
             df = retrieval_job.to_df()
-        except NotFound:
+        except Exception:
             # Table was not created yet
             return None, False
 
@@ -102,44 +101,3 @@ def retrieve():
         persisted_logs.sort_values(REQUEST_ID_FIELD).reset_index(drop=True),
         check_dtype=False,
     )
-
-
-def prepare_logs(datasets: UniversalDatasets) -> pd.DataFrame:
-    driver_df = datasets.driver_df
-    driver_df["val_to_add"] = 50
-    driver_df = driver_df.join(conv_rate_plus_100(driver_df))
-    num_rows = driver_df.shape[0]
-
-    logs_df = driver_df[["driver_id", "val_to_add"]]
-    logs_df[REQUEST_ID_FIELD] = [str(uuid.uuid4()) for _ in range(num_rows)]
-    logs_df[LOG_TIMESTAMP_FIELD] = pd.Series(
-        np.random.randint(0, 7 * 24 * 3600, num_rows)
-    ).map(lambda secs: pd.Timestamp.utcnow() - datetime.timedelta(seconds=secs))
-    logs_df[LOG_DATE_FIELD] = logs_df[LOG_TIMESTAMP_FIELD].dt.date
-
-    for view, features in (
-        ("driver_stats", ("conv_rate", "avg_daily_trips")),
-        (
-            "conv_rate_plus_100",
-            ("conv_rate_plus_val_to_add", "conv_rate_plus_100_rounded"),
-        ),
-    ):
-        for feature in features:
-            logs_df[f"{view}__{feature}"] = driver_df[feature]
-            logs_df[f"{view}__{feature}__timestamp"] = driver_df["event_timestamp"]
-            logs_df[f"{view}__{feature}__status"] = FieldStatus.PRESENT
-
-    return logs_df
-
-
-@contextlib.contextmanager
-def to_logs_dataset(
-    table: pyarrow.Table, pass_as_path: bool
-) -> Iterator[Union[pyarrow.Table, Path]]:
-    if not pass_as_path:
-        yield table
-        return
-
-    with tempfile.TemporaryDirectory() as temp_dir:
-        pyarrow.parquet.write_to_dataset(table, root_path=temp_dir)
-        yield Path(temp_dir)
diff --git a/sdk/python/tests/integration/offline_store/test_offline_write.py b/sdk/python/tests/integration/offline_store/test_offline_write.py
new file mode 100644
index 00000000000..30ead983894
--- /dev/null
+++ b/sdk/python/tests/integration/offline_store/test_offline_write.py
@@ -0,0 +1,231 @@
+import random
+from datetime import datetime, timedelta
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from feast import FeatureView, Field
+from feast.types import Float32, Int32
+from tests.integration.feature_repos.universal.entities import driver
+
+
+@pytest.mark.integration
+@pytest.mark.universal_offline_stores(only=["file", "redshift"])
+@pytest.mark.universal_online_stores(only=["sqlite"])
+def test_writing_columns_in_incorrect_order_fails(environment, universal_data_sources):
+    # TODO(kevjumba) handle incorrect order later, for now schema must be in the order that the filesource is in
+    store = environment.feature_store
+    _, _, data_sources = universal_data_sources
+    driver_stats = FeatureView(
+        name="driver_stats",
+        entities=["driver"],
+        schema=[
+            Field(name="avg_daily_trips", dtype=Int32),
+            Field(name="conv_rate", dtype=Float32),
+        ],
+        source=data_sources.driver,
+    )
+
+    now = datetime.utcnow()
+    ts = pd.Timestamp(now).round("ms")
+
+    entity_df = pd.DataFrame.from_dict(
+        {"driver_id": [1001, 1002], "event_timestamp": [ts - timedelta(hours=3), ts]}
+    )
+
+    store.apply([driver(), driver_stats])
+    df = store.get_historical_features(
+        entity_df=entity_df,
+        features=["driver_stats:conv_rate", "driver_stats:avg_daily_trips"],
+        full_feature_names=False,
+    ).to_df()
+
+    assert df["conv_rate"].isnull().all()
+    assert df["avg_daily_trips"].isnull().all()
+
+    expected_df = pd.DataFrame.from_dict(
+        {
+            "driver_id": [1001, 1002],
+            "event_timestamp": [ts - timedelta(hours=3), ts],
+            "conv_rate": [random.random(), random.random()],
+            "avg_daily_trips": [random.randint(0, 10), random.randint(0, 10)],
+            "created": [ts, ts],
+        },
+    )
+    with pytest.raises(ValueError):
+        store._write_to_offline_store(
+            driver_stats.name, expected_df, allow_registry_cache=False
+        )
+
+
+@pytest.mark.integration
+@pytest.mark.universal_offline_stores(only=["file", "redshift"])
+@pytest.mark.universal_online_stores(only=["sqlite"])
+def test_writing_incorrect_schema_fails(environment, universal_data_sources):
+    # TODO(kevjumba) handle incorrect order later, for now schema must be in the order that the filesource is in
+    store = environment.feature_store
+    _, _, data_sources = universal_data_sources
+    driver_stats = FeatureView(
+        name="driver_stats",
+        entities=["driver"],
+        schema=[
+            Field(name="avg_daily_trips", dtype=Int32),
+            Field(name="conv_rate", dtype=Float32),
+        ],
+        source=data_sources.driver,
+    )
+
+    now = datetime.utcnow()
+    ts = pd.Timestamp(now).round("ms")
+
+    entity_df = pd.DataFrame.from_dict(
+        {"driver_id": [1001, 1002], "event_timestamp": [ts - timedelta(hours=3), ts]}
+    )
+
+    store.apply([driver(), driver_stats])
+    df = store.get_historical_features(
+        entity_df=entity_df,
+        features=["driver_stats:conv_rate", "driver_stats:avg_daily_trips"],
+        full_feature_names=False,
+    ).to_df()
+
+    assert df["conv_rate"].isnull().all()
+    assert df["avg_daily_trips"].isnull().all()
+
+    expected_df = pd.DataFrame.from_dict(
+        {
+            "event_timestamp": [ts - timedelta(hours=3), ts],
+            "driver_id": [1001, 1002],
+            "conv_rate": [random.random(), random.random()],
+            "incorrect_schema": [random.randint(0, 10), random.randint(0, 10)],
+            "created": [ts, ts],
+        },
+    )
+    with pytest.raises(ValueError):
+        store._write_to_offline_store(
+            driver_stats.name, expected_df, allow_registry_cache=False
+        )
+
+
+@pytest.mark.integration
+@pytest.mark.universal_offline_stores
+@pytest.mark.universal_online_stores(only=["sqlite"])
+def test_writing_consecutively_to_offline_store(environment, universal_data_sources):
+    store = environment.feature_store
+    _, _, data_sources = universal_data_sources
+    driver_stats = FeatureView(
+        name="driver_stats",
+        entities=["driver"],
+        schema=[
+            Field(name="avg_daily_trips", dtype=Int32),
+            Field(name="conv_rate", dtype=Float32),
+            Field(name="acc_rate", dtype=Float32),
+        ],
+        source=data_sources.driver,
+        ttl=timedelta(
+            minutes=10
+        ),  # This is to make sure all offline store data is out of date since get_historical_features() only searches backwards for a ttl window.
+    )
+
+    now = datetime.utcnow()
+    ts = pd.Timestamp(now, unit="ns")
+
+    entity_df = pd.DataFrame.from_dict(
+        {
+            "driver_id": [1001, 1001],
+            "event_timestamp": [ts - timedelta(hours=4), ts - timedelta(hours=3)],
+        }
+    )
+
+    store.apply([driver(), driver_stats])
+    df = store.get_historical_features(
+        entity_df=entity_df,
+        features=["driver_stats:conv_rate", "driver_stats:avg_daily_trips"],
+        full_feature_names=False,
+    ).to_df()
+
+    assert df["conv_rate"].isnull().all()
+    assert df["avg_daily_trips"].isnull().all()
+
+    first_df = pd.DataFrame.from_dict(
+        {
+            "event_timestamp": [ts - timedelta(hours=4), ts - timedelta(hours=3)],
+            "driver_id": [1001, 1001],
+            "conv_rate": [random.random(), random.random()],
+            "acc_rate": [random.random(), random.random()],
+            "avg_daily_trips": [random.randint(0, 10), random.randint(0, 10)],
+            "created": [ts, ts],
+        },
+    )
+    store._write_to_offline_store(
+        driver_stats.name, first_df, allow_registry_cache=False
+    )
+
+    after_write_df = store.get_historical_features(
+        entity_df=entity_df,
+        features=["driver_stats:conv_rate", "driver_stats:avg_daily_trips"],
+        full_feature_names=False,
+    ).to_df()
+
+    assert len(after_write_df) == len(first_df)
+    assert np.where(
+        after_write_df["conv_rate"].reset_index(drop=True)
+        == first_df["conv_rate"].reset_index(drop=True)
+    )
+    assert np.where(
+        after_write_df["avg_daily_trips"].reset_index(drop=True)
+        == first_df["avg_daily_trips"].reset_index(drop=True)
+    )
+
+    second_df = pd.DataFrame.from_dict(
+        {
+            "event_timestamp": [ts - timedelta(hours=1), ts],
+            "driver_id": [1001, 1001],
+            "conv_rate": [random.random(), random.random()],
+            "acc_rate": [random.random(), random.random()],
+            "avg_daily_trips": [random.randint(0, 10), random.randint(0, 10)],
+            "created": [ts, ts],
+        },
+    )
+
+    store._write_to_offline_store(
+        driver_stats.name, second_df, allow_registry_cache=False
+    )
+
+    entity_df = pd.DataFrame.from_dict(
+        {
+            "driver_id": [1001, 1001, 1001, 1001],
+            "event_timestamp": [
+                ts - timedelta(hours=4),
+                ts - timedelta(hours=3),
+                ts - timedelta(hours=1),
+                ts,
+            ],
+        }
+    )
+
+    after_write_df = store.get_historical_features(
+        entity_df=entity_df,
+        features=[
+            "driver_stats:conv_rate",
+            "driver_stats:acc_rate",
+            "driver_stats:avg_daily_trips",
+        ],
+        full_feature_names=False,
+    ).to_df()
+
+    expected_df = pd.concat([first_df, second_df])
+    assert len(after_write_df) == len(expected_df)
+    assert np.where(
+        after_write_df["conv_rate"].reset_index(drop=True)
+        == expected_df["conv_rate"].reset_index(drop=True)
+    )
+    assert np.where(
+        after_write_df["acc_rate"].reset_index(drop=True)
+        == expected_df["acc_rate"].reset_index(drop=True)
+    )
+    assert np.where(
+        after_write_df["avg_daily_trips"].reset_index(drop=True)
+        == expected_df["avg_daily_trips"].reset_index(drop=True)
+    )
diff --git a/sdk/python/tests/integration/offline_store/test_push_offline_retrieval.py b/sdk/python/tests/integration/offline_store/test_push_offline_retrieval.py
new file mode 100644
index 00000000000..5cea8a36ef4
--- /dev/null
+++ b/sdk/python/tests/integration/offline_store/test_push_offline_retrieval.py
@@ -0,0 +1,67 @@
+import datetime
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from feast.data_source import PushMode
+from tests.integration.feature_repos.repo_configuration import (
+    construct_universal_feature_views,
+)
+from tests.integration.feature_repos.universal.entities import (
+    customer,
+    driver,
+    location,
+)
+
+
+@pytest.mark.integration
+@pytest.mark.universal_offline_stores
+@pytest.mark.universal_online_stores(only=["sqlite"])
+def test_push_features_and_read_from_offline_store(environment, universal_data_sources):
+    store = environment.feature_store
+
+    (_, _, data_sources) = universal_data_sources
+    feature_views = construct_universal_feature_views(data_sources)
+    now = pd.Timestamp(datetime.datetime.utcnow()).round("ms")
+
+    store.apply([driver(), customer(), location(), *feature_views.values()])
+    entity_df = pd.DataFrame.from_dict({"location_id": [1], "event_timestamp": [now]})
+
+    before_df = store.get_historical_features(
+        entity_df=entity_df,
+        features=["pushable_location_stats:temperature"],
+        full_feature_names=False,
+    ).to_df()
+
+    data = {
+        "event_timestamp": [now],
+        "location_id": [1],
+        "temperature": [4],
+        "created": [now],
+    }
+    df_ingest = pd.DataFrame(data)
+    assert np.where(
+        before_df["location_id"].reset_index(drop=True)
+        == df_ingest["location_id"].reset_index(drop=True)
+    )
+    assert np.where(
+        before_df["temperature"].reset_index(drop=True)
+        != df_ingest["temperature"].reset_index(drop=True)
+    )
+
+    store.push("location_stats_push_source", df_ingest, to=PushMode.OFFLINE)
+
+    df = store.get_historical_features(
+        entity_df=entity_df,
+        features=["pushable_location_stats:temperature"],
+        full_feature_names=False,
+    ).to_df()
+    assert np.where(
+        df["location_id"].reset_index(drop=True)
+        == df_ingest["location_id"].reset_index(drop=True)
+    )
+    assert np.where(
+        df["temperature"].reset_index(drop=True)
+        == df_ingest["temperature"].reset_index(drop=True)
+    )
diff --git a/sdk/python/tests/integration/offline_store/test_universal_historical_retrieval.py b/sdk/python/tests/integration/offline_store/test_universal_historical_retrieval.py
index d5f49a1f958..2076ab2aedf 100644
--- a/sdk/python/tests/integration/offline_store/test_universal_historical_retrieval.py
+++ b/sdk/python/tests/integration/offline_store/test_universal_historical_retrieval.py
@@ -22,7 +22,6 @@
     DEFAULT_ENTITY_DF_EVENT_TIMESTAMP_COL,
 )
 from feast.types import Float32, Int32
-from feast.value_type import ValueType
 from tests.integration.feature_repos.repo_configuration import (
     construct_universal_feature_views,
     table_name_from_data_source,
@@ -728,10 +727,10 @@ def test_historical_features_from_bigquery_sources_containing_backfills(environm
         created_timestamp_column="created",
     )
 
-    driver = Entity(name="driver", join_keys=["driver_id"], value_type=ValueType.INT64)
+    driver = Entity(name="driver", join_keys=["driver_id"])
     driver_fv = FeatureView(
         name="driver_stats",
-        entities=["driver"],
+        entities=[driver],
         schema=[Field(name="avg_daily_trips", dtype=Int32)],
         batch_source=driver_stats_data_source,
         ttl=None,
diff --git a/sdk/python/tests/integration/online_store/test_e2e_local.py b/sdk/python/tests/integration/online_store/test_e2e_local.py
index d3cb86716a2..c5b66e7ddce 100644
--- a/sdk/python/tests/integration/online_store/test_e2e_local.py
+++ b/sdk/python/tests/integration/online_store/test_e2e_local.py
@@ -41,7 +41,7 @@ def _assert_online_features(
         full_feature_names=True,
     )
 
-    # Float features should still be floats from the online store...
+    # Float features should still be floats.
     assert (
         response.proto.results[
             list(response.proto.metadata.feature_names.val).index(
@@ -67,6 +67,49 @@ def _assert_online_features(
     assert "global_daily_stats__num_rides" in result
     assert "global_daily_stats__avg_ride_length" in result
 
+    # Test the ODFV if it exists.
+    odfvs = store.list_on_demand_feature_views()
+    if odfvs and odfvs[0].name == "conv_rate_plus_100":
+        response = store.get_online_features(
+            features=[
+                "conv_rate_plus_100:conv_rate_plus_100",
+                "conv_rate_plus_100:conv_rate_plus_val_to_add",
+            ],
+            entity_rows=[{"driver_id": 1001, "val_to_add": 100}],
+            full_feature_names=True,
+        )
+
+        # Check that float64 feature is stored correctly in proto format.
+        assert (
+            response.proto.results[
+                list(response.proto.metadata.feature_names.val).index(
+                    "conv_rate_plus_100__conv_rate_plus_100"
+                )
+            ]
+            .values[0]
+            .double_val
+            > 0
+        )
+
+        result = response.to_dict()
+        assert len(result) == 3
+        assert "conv_rate_plus_100__conv_rate_plus_100" in result
+        assert "conv_rate_plus_100__conv_rate_plus_val_to_add" in result
+        assert (
+            abs(
+                result["conv_rate_plus_100__conv_rate_plus_100"][0]
+                - (_get_last_feature_row(driver_df, 1001, max_date)["conv_rate"] + 100)
+            )
+            < 0.01
+        )
+        assert (
+            abs(
+                result["conv_rate_plus_100__conv_rate_plus_val_to_add"][0]
+                - (_get_last_feature_row(driver_df, 1001, max_date)["conv_rate"] + 100)
+            )
+            < 0.01
+        )
+
 
 def _test_materialize_and_online_retrieval(
     runner: CliRunner,
@@ -87,7 +130,7 @@ def _test_materialize_and_online_retrieval(
         cwd=Path(store.repo_path),
     )
 
-    assert r.returncode == 0
+    assert r.returncode == 0, f"stdout: {r.stdout}\n stderr: {r.stderr}"
     _assert_online_features(store, driver_df, end_date - timedelta(days=7))
 
     # Test `feast materialize-incremental` and online retrieval.
@@ -95,7 +138,7 @@ def _test_materialize_and_online_retrieval(
         ["materialize-incremental", end_date.isoformat()], cwd=Path(store.repo_path),
     )
 
-    assert r.returncode == 0
+    assert r.returncode == 0, f"stdout: {r.stdout}\n stderr: {r.stderr}"
     _assert_online_features(store, driver_df, end_date)
 
 
diff --git a/sdk/python/tests/integration/online_store/test_universal_online.py b/sdk/python/tests/integration/online_store/test_universal_online.py
index 259a094426e..b01448e7ccc 100644
--- a/sdk/python/tests/integration/online_store/test_universal_online.py
+++ b/sdk/python/tests/integration/online_store/test_universal_online.py
@@ -13,7 +13,7 @@
 import requests
 from botocore.exceptions import BotoCoreError
 
-from feast import Entity, FeatureService, FeatureView, Field, ValueType
+from feast import Entity, FeatureService, FeatureView, Field
 from feast.errors import (
     FeatureNameCollisionError,
     RequestDataNotFoundInEntityRowsException,
@@ -117,13 +117,13 @@ def test_write_to_online_store_event_check(environment):
     }
     dataframe_source = pd.DataFrame(data)
     with prep_file_source(df=dataframe_source, timestamp_field="ts_1") as file_source:
-        e = Entity(name="id", value_type=ValueType.STRING)
+        e = Entity(name="id")
 
         # Create Feature View
         fv1 = FeatureView(
             name="feature_view_123",
             schema=[Field(name="string_col", dtype=String)],
-            entities=["id"],
+            entities=[e],
             batch_source=file_source,
             ttl=timedelta(minutes=5),
         )
@@ -288,6 +288,7 @@ def _get_online_features_dict_remotely(
 
 def get_online_features_dict(
     environment: Environment,
+    endpoint: str,
     features: Union[List[str], FeatureService],
     entity_rows: List[Dict[str, Any]],
     full_feature_names: bool = False,
@@ -305,7 +306,6 @@ def get_online_features_dict(
     assertpy.assert_that(online_features).is_not_none()
     dict1 = online_features.to_dict()
 
-    endpoint = environment.get_feature_server_endpoint()
     # If endpoint is None, it means that a local / remote feature server aren't configured
     if endpoint is not None:
         dict2 = _get_online_features_dict_remotely(
@@ -325,7 +325,6 @@ def get_online_features_dict(
 
 
 @pytest.mark.integration
-@pytest.mark.universal
 def test_online_retrieval_with_shared_batch_source(environment, universal_data_sources):
     # Addresses https://github.com/feast-dev/feast/issues/2576
 
@@ -442,12 +441,88 @@ def test_online_retrieval_with_event_timestamps(
     )
 
 
+@pytest.mark.integration
+@pytest.mark.universal_online_stores
+@pytest.mark.goserver
+@pytest.mark.parametrize("full_feature_names", [True, False], ids=lambda v: str(v))
+def test_stream_feature_view_online_retrieval(
+    environment, universal_data_sources, feature_server_endpoint, full_feature_names
+):
+    """
+    Tests materialization and online retrieval for stream feature views.
+
+    This test is separate from test_online_retrieval since combining feature views and
+    stream feature views into a single test resulted in test flakiness. This is tech
+    debt that should be resolved soon.
+    """
+    # Set up feature store.
+    fs = environment.feature_store
+    entities, datasets, data_sources = universal_data_sources
+    feature_views = construct_universal_feature_views(data_sources)
+    pushable_feature_view = feature_views.pushed_locations
+    fs.apply([location(), pushable_feature_view])
+
+    # Materialize.
+    fs.materialize(
+        environment.start_date - timedelta(days=1),
+        environment.end_date + timedelta(days=1),
+    )
+
+    # Get online features by randomly sampling 10 entities that exist in the batch source.
+    sample_locations = datasets.location_df.sample(10)["location_id"]
+    entity_rows = [
+        {"location_id": sample_location} for sample_location in sample_locations
+    ]
+
+    feature_refs = [
+        "pushable_location_stats:temperature",
+    ]
+    unprefixed_feature_refs = [f.rsplit(":", 1)[-1] for f in feature_refs if ":" in f]
+
+    online_features_dict = get_online_features_dict(
+        environment=environment,
+        endpoint=feature_server_endpoint,
+        features=feature_refs,
+        entity_rows=entity_rows,
+        full_feature_names=full_feature_names,
+    )
+
+    # Check that the response has the expected set of keys.
+    keys = set(online_features_dict.keys())
+    expected_keys = set(
+        f.replace(":", "__") if full_feature_names else f.split(":")[-1]
+        for f in feature_refs
+    ) | {"location_id"}
+    assert (
+        keys == expected_keys
+    ), f"Response keys are different from expected: {keys - expected_keys} (extra) and {expected_keys - keys} (missing)"
+
+    # Check that the feature values match.
+    tc = unittest.TestCase()
+    for i, entity_row in enumerate(entity_rows):
+        df_features = get_latest_feature_values_from_location_df(
+            entity_row, datasets.location_df
+        )
+
+        assert df_features["location_id"] == online_features_dict["location_id"][i]
+        for unprefixed_feature_ref in unprefixed_feature_refs:
+            tc.assertAlmostEqual(
+                df_features[unprefixed_feature_ref],
+                online_features_dict[
+                    response_feature_name(
+                        unprefixed_feature_ref, feature_refs, full_feature_names
+                    )
+                ][i],
+                delta=0.0001,
+            )
+
+
 @pytest.mark.integration
 @pytest.mark.universal_online_stores
 @pytest.mark.goserver
 @pytest.mark.parametrize("full_feature_names", [True, False], ids=lambda v: str(v))
 def test_online_retrieval(
-    environment, universal_data_sources, python_server, full_feature_names
+    environment, universal_data_sources, feature_server_endpoint, full_feature_names
 ):
     fs = environment.feature_store
     entities, datasets, data_sources = universal_data_sources
@@ -547,6 +622,7 @@ def test_online_retrieval(
 
     online_features_dict = get_online_features_dict(
         environment=environment,
+        endpoint=feature_server_endpoint,
         features=feature_refs,
         entity_rows=entity_rows,
         full_feature_names=full_feature_names,
@@ -556,6 +632,7 @@ def test_online_retrieval(
     # feature isn't requested.
     online_features_no_conv_rate = get_online_features_dict(
         environment=environment,
+        endpoint=feature_server_endpoint,
         features=[ref for ref in feature_refs if ref != "driver_stats:conv_rate"],
         entity_rows=entity_rows,
         full_feature_names=full_feature_names,
@@ -616,6 +693,7 @@ def test_online_retrieval(
     # Check what happens for missing values
     missing_responses_dict = get_online_features_dict(
         environment=environment,
+        endpoint=feature_server_endpoint,
         features=feature_refs,
         entity_rows=[{"driver_id": 0, "customer_id": 0, "val_to_add": 100}],
         full_feature_names=full_feature_names,
@@ -635,6 +713,7 @@ def test_online_retrieval(
     with pytest.raises(RequestDataNotFoundInEntityRowsException):
         get_online_features_dict(
             environment=environment,
+            endpoint=feature_server_endpoint,
             features=feature_refs,
             entity_rows=[{"driver_id": 0, "customer_id": 0}],
             full_feature_names=full_feature_names,
@@ -642,6 +721,7 @@ def test_online_retrieval(
 
     assert_feature_service_correctness(
         environment,
+        feature_server_endpoint,
         feature_service,
         entity_rows,
         full_feature_names,
@@ -659,6 +739,7 @@ def test_online_retrieval(
     ]
     assert_feature_service_entity_mapping_correctness(
         environment,
+        feature_server_endpoint,
         feature_service_entity_mapping,
         entity_rows,
         full_feature_names,
@@ -854,8 +935,13 @@ def get_latest_feature_values_for_location_df(entity_row, origin_df, destination
     }
 
 
+def get_latest_feature_values_from_location_df(entity_row, location_df):
+    return get_latest_row(entity_row, location_df, "location_id", "location_id")
+
+
 def assert_feature_service_correctness(
     environment,
+    endpoint,
     feature_service,
     entity_rows,
     full_feature_names,
@@ -866,6 +952,7 @@ def assert_feature_service_correctness(
 ):
     feature_service_online_features_dict = get_online_features_dict(
         environment=environment,
+        endpoint=endpoint,
         features=feature_service,
         entity_rows=entity_rows,
         full_feature_names=full_feature_names,
@@ -905,6 +992,7 @@ def assert_feature_service_correctness(
 
 def assert_feature_service_entity_mapping_correctness(
     environment,
+    endpoint,
     feature_service,
     entity_rows,
     full_feature_names,
@@ -914,6 +1002,7 @@ def assert_feature_service_entity_mapping_correctness(
     if full_feature_names:
         feature_service_online_features_dict = get_online_features_dict(
             environment=environment,
+            endpoint=endpoint,
             features=feature_service,
             entity_rows=entity_rows,
             full_feature_names=full_feature_names,
@@ -948,6 +1037,7 @@ def assert_feature_service_entity_mapping_correctness(
         with pytest.raises(FeatureNameCollisionError):
             get_online_features_dict(
                 environment=environment,
+                endpoint=endpoint,
                 features=feature_service,
                 entity_rows=entity_rows,
                 full_feature_names=full_feature_names,
diff --git a/sdk/python/tests/integration/registration/test_cli.py b/sdk/python/tests/integration/registration/test_cli.py
index ce23ed66a60..ecc17fc06c5 100644
--- a/sdk/python/tests/integration/registration/test_cli.py
+++ b/sdk/python/tests/integration/registration/test_cli.py
@@ -201,6 +201,37 @@ def test_nullable_online_store(test_nullable_online_store) -> None:
             runner.run(["teardown"], cwd=repo_path)
 
 
+@pytest.mark.integration
+@pytest.mark.universal_offline_stores
+def test_odfv_apply(environment) -> None:
+    project = f"test_odfv_apply{str(uuid.uuid4()).replace('-', '')[:8]}"
+    runner = CliRunner()
+
+    with tempfile.TemporaryDirectory() as repo_dir_name:
+        try:
+            repo_path = Path(repo_dir_name)
+            feature_store_yaml = make_feature_store_yaml(
+                project, environment.test_repo_config, repo_path
+            )
+
+            repo_config = repo_path / "feature_store.yaml"
+
+            repo_config.write_text(dedent(feature_store_yaml))
+
+            repo_example = repo_path / "example.py"
+            repo_example.write_text(get_example_repo("on_demand_feature_view_repo.py"))
+            result = runner.run(["apply"], cwd=repo_path)
+            assertpy.assert_that(result.returncode).is_equal_to(0)
+
+            # entity & feature view list commands should succeed
+            result = runner.run(["entities", "list"], cwd=repo_path)
+            assertpy.assert_that(result.returncode).is_equal_to(0)
+            result = runner.run(["on-demand-feature-views", "list"], cwd=repo_path)
+            assertpy.assert_that(result.returncode).is_equal_to(0)
+        finally:
+            runner.run(["teardown"], cwd=repo_path)
+
+
 @contextmanager
 def setup_third_party_provider_repo(provider_name: str):
     with tempfile.TemporaryDirectory() as repo_dir_name:
diff --git a/sdk/python/tests/integration/registration/test_feature_store.py b/sdk/python/tests/integration/registration/test_feature_store.py
index db4c6700cec..88a4b9f2491 100644
--- a/sdk/python/tests/integration/registration/test_feature_store.py
+++ b/sdk/python/tests/integration/registration/test_feature_store.py
@@ -27,10 +27,8 @@
 from feast.infra.offline_stores.file import FileOfflineStoreConfig
 from feast.infra.online_stores.dynamodb import DynamoDBOnlineStoreConfig
 from feast.infra.online_stores.sqlite import SqliteOnlineStoreConfig
-from feast.protos.feast.types import Value_pb2 as ValueProto
 from feast.repo_config import RepoConfig
 from feast.types import Array, Bytes, Float64, Int64, String
-from feast.value_type import ValueType
 from tests.utils.data_source_utils import (
     prep_file_source,
     simple_bq_source_using_query_arg,
@@ -93,10 +91,7 @@ def feature_store_with_s3_registry():
 )
 def test_apply_entity_success(test_feature_store):
     entity = Entity(
-        name="driver_car_id",
-        description="Car driver id",
-        value_type=ValueType.STRING,
-        tags={"team": "matchmaking"},
+        name="driver_car_id", description="Car driver id", tags={"team": "matchmaking"},
     )
 
     # Register Entity
@@ -108,7 +103,6 @@ def test_apply_entity_success(test_feature_store):
     assert (
         len(entities) == 1
         and entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -127,10 +121,7 @@ def test_apply_entity_success(test_feature_store):
 )
 def test_apply_entity_integration(test_feature_store):
     entity = Entity(
-        name="driver_car_id",
-        description="Car driver id",
-        value_type=ValueType.STRING,
-        tags={"team": "matchmaking"},
+        name="driver_car_id", description="Car driver id", tags={"team": "matchmaking"},
     )
 
     # Register Entity
@@ -142,7 +133,6 @@ def test_apply_entity_integration(test_feature_store):
     assert (
         len(entities) == 1
         and entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -151,7 +141,6 @@ def test_apply_entity_integration(test_feature_store):
     entity = test_feature_store.get_entity("driver_car_id")
     assert (
         entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -173,6 +162,8 @@ def test_apply_feature_view_success(test_feature_store):
         date_partition_column="date_partition_col",
     )
 
+    entity = Entity(name="fs1_my_entity_1", join_keys=["entity_id"])
+
     fv1 = FeatureView(
         name="my_feature_view_1",
         schema=[
@@ -180,15 +171,16 @@ def test_apply_feature_view_success(test_feature_store):
             Field(name="fs1_my_feature_2", dtype=String),
             Field(name="fs1_my_feature_3", dtype=Array(String)),
             Field(name="fs1_my_feature_4", dtype=Array(Bytes)),
+            Field(name="entity_id", dtype=Int64),
         ],
-        entities=["fs1_my_entity_1"],
+        entities=[entity],
         tags={"team": "matchmaking"},
         batch_source=batch_source,
         ttl=timedelta(minutes=5),
     )
 
     # Register Feature View
-    test_feature_store.apply([fv1])
+    test_feature_store.apply([entity, fv1])
 
     feature_views = test_feature_store.list_feature_views()
 
@@ -217,13 +209,11 @@ def test_apply_feature_view_success(test_feature_store):
 @pytest.mark.parametrize("dataframe_source", [lazy_fixture("simple_dataset_1")])
 def test_feature_view_inference_success(test_feature_store, dataframe_source):
     with prep_file_source(df=dataframe_source, timestamp_field="ts_1") as file_source:
-        entity = Entity(
-            name="id", join_keys=["id_join_key"], value_type=ValueType.INT64
-        )
+        entity = Entity(name="id", join_keys=["id_join_key"])
 
         fv1 = FeatureView(
             name="fv1",
-            entities=["id"],
+            entities=[entity],
             ttl=timedelta(minutes=5),
             online=True,
             batch_source=file_source,
@@ -232,7 +222,7 @@ def test_feature_view_inference_success(test_feature_store, dataframe_source):
 
         fv2 = FeatureView(
             name="fv2",
-            entities=["id"],
+            entities=[entity],
             ttl=timedelta(minutes=5),
             online=True,
             batch_source=simple_bq_source_using_table_arg(dataframe_source, "ts_1"),
@@ -241,7 +231,7 @@ def test_feature_view_inference_success(test_feature_store, dataframe_source):
 
         fv3 = FeatureView(
             name="fv3",
-            entities=["id"],
+            entities=[entity],
             ttl=timedelta(minutes=5),
             online=True,
             batch_source=simple_bq_source_using_query_arg(dataframe_source, "ts_1"),
@@ -296,6 +286,8 @@ def test_apply_feature_view_integration(test_feature_store):
         date_partition_column="date_partition_col",
     )
 
+    entity = Entity(name="fs1_my_entity_1", join_keys=["test"])
+
     fv1 = FeatureView(
         name="my_feature_view_1",
         schema=[
@@ -303,15 +295,16 @@ def test_apply_feature_view_integration(test_feature_store):
             Field(name="fs1_my_feature_2", dtype=String),
             Field(name="fs1_my_feature_3", dtype=Array(String)),
             Field(name="fs1_my_feature_4", dtype=Array(Bytes)),
+            Field(name="test", dtype=Int64),
         ],
-        entities=["fs1_my_entity_1"],
+        entities=[entity],
         tags={"team": "matchmaking"},
         batch_source=batch_source,
         ttl=timedelta(minutes=5),
     )
 
     # Register Feature View
-    test_feature_store.apply([fv1])
+    test_feature_store.apply([fv1, entity])
 
     feature_views = test_feature_store.list_feature_views()
 
@@ -364,13 +357,9 @@ def test_apply_object_and_read(test_feature_store):
         created_timestamp_column="timestamp",
     )
 
-    e1 = Entity(
-        name="fs1_my_entity_1", value_type=ValueType.STRING, description="something"
-    )
+    e1 = Entity(name="fs1_my_entity_1", description="something")
 
-    e2 = Entity(
-        name="fs1_my_entity_2", value_type=ValueType.STRING, description="something"
-    )
+    e2 = Entity(name="fs1_my_entity_2", description="something")
 
     fv1 = FeatureView(
         name="my_feature_view_1",
@@ -379,8 +368,9 @@ def test_apply_object_and_read(test_feature_store):
             Field(name="fs1_my_feature_2", dtype=String),
             Field(name="fs1_my_feature_3", dtype=Array(String)),
             Field(name="fs1_my_feature_4", dtype=Array(Bytes)),
+            Field(name="fs1_my_entity_1", dtype=Int64),
         ],
-        entities=["fs1_my_entity_1"],
+        entities=[e1],
         tags={"team": "matchmaking"},
         batch_source=batch_source,
         ttl=timedelta(minutes=5),
@@ -393,8 +383,9 @@ def test_apply_object_and_read(test_feature_store):
             Field(name="fs1_my_feature_2", dtype=String),
             Field(name="fs1_my_feature_3", dtype=Array(String)),
             Field(name="fs1_my_feature_4", dtype=Array(Bytes)),
+            Field(name="fs1_my_entity_2", dtype=Int64),
         ],
-        entities=["fs1_my_entity_1"],
+        entities=[e2],
         tags={"team": "matchmaking"},
         batch_source=batch_source,
         ttl=timedelta(minutes=5),
@@ -406,7 +397,6 @@ def test_apply_object_and_read(test_feature_store):
     fv1_actual = test_feature_store.get_feature_view("my_feature_view_1")
     e1_actual = test_feature_store.get_entity("fs1_my_entity_1")
 
-    assert fv1 == fv1_actual
     assert e1 == e1_actual
     assert fv2 != fv1_actual
     assert e2 != e1_actual
@@ -434,13 +424,13 @@ def test_apply_remote_repo():
 def test_reapply_feature_view_success(test_feature_store, dataframe_source):
     with prep_file_source(df=dataframe_source, timestamp_field="ts_1") as file_source:
 
-        e = Entity(name="id", join_keys=["id_join_key"], value_type=ValueType.STRING)
+        e = Entity(name="id", join_keys=["id_join_key"])
 
         # Create Feature View
         fv1 = FeatureView(
             name="my_feature_view_1",
             schema=[Field(name="string_col", dtype=String)],
-            entities=["id"],
+            entities=[e],
             batch_source=file_source,
             ttl=timedelta(minutes=5),
         )
@@ -470,7 +460,7 @@ def test_reapply_feature_view_success(test_feature_store, dataframe_source):
         fv1 = FeatureView(
             name="my_feature_view_1",
             schema=[Field(name="int64_col", dtype=Int64)],
-            entities=["id"],
+            entities=[e],
             batch_source=file_source,
             ttl=timedelta(minutes=5),
         )
@@ -485,10 +475,12 @@ def test_reapply_feature_view_success(test_feature_store, dataframe_source):
 
 def test_apply_conflicting_featureview_names(feature_store_with_local_registry):
     """Test applying feature views with non-case-insensitively unique names"""
+    driver = Entity(name="driver", join_keys=["driver_id"])
+    customer = Entity(name="customer", join_keys=["customer_id"])
 
     driver_stats = FeatureView(
         name="driver_hourly_stats",
-        entities=["driver_id"],
+        entities=[driver],
         ttl=timedelta(seconds=10),
         online=False,
         batch_source=FileSource(path="driver_stats.parquet"),
@@ -497,7 +489,7 @@ def test_apply_conflicting_featureview_names(feature_store_with_local_registry):
 
     customer_stats = FeatureView(
         name="DRIVER_HOURLY_STATS",
-        entities=["id"],
+        entities=[customer],
         ttl=timedelta(seconds=10),
         online=False,
         batch_source=FileSource(path="customer_stats.parquet"),
diff --git a/sdk/python/tests/integration/registration/test_inference.py b/sdk/python/tests/integration/registration/test_inference.py
index 6cf49c31db8..935aa2d1a66 100644
--- a/sdk/python/tests/integration/registration/test_inference.py
+++ b/sdk/python/tests/integration/registration/test_inference.py
@@ -24,14 +24,13 @@
 from feast.field import Field
 from feast.inference import (
     update_data_sources_with_inferred_event_timestamp_col,
-    update_entities_with_inferred_types_from_feature_views,
-    update_feature_views_with_inferred_features,
+    update_feature_views_with_inferred_features_and_entities,
 )
 from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import (
     SparkSource,
 )
 from feast.on_demand_feature_view import on_demand_feature_view
-from feast.types import Float32, String, UnixTimestamp
+from feast.types import Float32, Float64, Int64, String, UnixTimestamp
 from tests.utils.data_source_utils import (
     prep_file_source,
     simple_bq_source_using_query_arg,
@@ -39,47 +38,6 @@
 )
 
 
-def test_update_entities_with_inferred_types_from_feature_views(
-    simple_dataset_1, simple_dataset_2
-):
-    with prep_file_source(
-        df=simple_dataset_1, timestamp_field="ts_1"
-    ) as file_source, prep_file_source(
-        df=simple_dataset_2, timestamp_field="ts_1"
-    ) as file_source_2:
-
-        fv1 = FeatureView(
-            name="fv1", entities=["id"], batch_source=file_source, ttl=None,
-        )
-        fv2 = FeatureView(
-            name="fv2", entities=["id"], batch_source=file_source_2, ttl=None,
-        )
-
-        actual_1 = Entity(name="id", join_keys=["id_join_key"])
-        actual_2 = Entity(name="id", join_keys=["id_join_key"])
-
-        update_entities_with_inferred_types_from_feature_views(
-            [actual_1], [fv1], RepoConfig(provider="local", project="test")
-        )
-        update_entities_with_inferred_types_from_feature_views(
-            [actual_2], [fv2], RepoConfig(provider="local", project="test")
-        )
-        assert actual_1 == Entity(
-            name="id", join_keys=["id_join_key"], value_type=ValueType.INT64
-        )
-        assert actual_2 == Entity(
-            name="id", join_keys=["id_join_key"], value_type=ValueType.STRING
-        )
-
-        with pytest.raises(RegistryInferenceFailure):
-            # two viable data types
-            update_entities_with_inferred_types_from_feature_views(
-                [Entity(name="id", join_keys=["id_join_key"])],
-                [fv1, fv2],
-                RepoConfig(provider="local", project="test"),
-            )
-
-
 def test_infer_datasource_names_file():
     file_path = "path/to/test.csv"
     data_source = FileSource(path=file_path)
@@ -225,7 +183,7 @@ def test_view_with_missing_feature(features_df: pd.DataFrame) -> pd.DataFrame:
         test_view_with_missing_feature.infer_features()
 
 
-# TODO(kevjumba): remove this in feast 0.23 when deprecating
+# TODO(kevjumba): remove this in feast 0.24 when deprecating
 @pytest.mark.parametrize(
     "request_source_schema",
     [
@@ -287,7 +245,10 @@ def test_view_with_missing_feature(features_df: pd.DataFrame) -> pd.DataFrame:
         test_view_with_missing_feature.infer_features()
 
 
-def test_update_feature_views_with_inferred_features():
+def test_feature_view_inference_respects_basic_inference():
+    """
+    Tests that feature view inference respects the basic inference that occurs during creation.
+    """
     file_source = FileSource(name="test", path="test path")
     entity1 = Entity(name="test1", join_keys=["test_column_1"])
     entity2 = Entity(name="test2", join_keys=["test_column_2"])
@@ -312,26 +273,132 @@ def test_update_feature_views_with_inferred_features():
     )
 
     assert len(feature_view_1.schema) == 2
-    assert len(feature_view_1.features) == 2
+    assert len(feature_view_1.features) == 1
+    assert len(feature_view_1.entity_columns) == 1
 
-    # The entity field should be deleted from the schema and features of the feature view.
-    update_feature_views_with_inferred_features(
+    update_feature_views_with_inferred_features_and_entities(
         [feature_view_1], [entity1], RepoConfig(provider="local", project="test")
     )
-    assert len(feature_view_1.schema) == 1
+    assert len(feature_view_1.schema) == 2
     assert len(feature_view_1.features) == 1
+    assert len(feature_view_1.entity_columns) == 1
 
     assert len(feature_view_2.schema) == 3
-    assert len(feature_view_2.features) == 3
+    assert len(feature_view_2.features) == 1
+    assert len(feature_view_2.entity_columns) == 2
 
-    # The entity fields should be deleted from the schema and features of the feature view.
-    update_feature_views_with_inferred_features(
+    update_feature_views_with_inferred_features_and_entities(
         [feature_view_2],
         [entity1, entity2],
         RepoConfig(provider="local", project="test"),
     )
-    assert len(feature_view_2.schema) == 1
+    assert len(feature_view_2.schema) == 3
     assert len(feature_view_2.features) == 1
+    assert len(feature_view_2.entity_columns) == 2
+
+
+def test_feature_view_inference_on_entity_columns(simple_dataset_1):
+    """
+    Tests that feature view inference correctly infers entity columns.
+    """
+    with prep_file_source(df=simple_dataset_1, timestamp_field="ts_1") as file_source:
+        entity1 = Entity(name="test1", join_keys=["id_join_key"])
+        feature_view_1 = FeatureView(
+            name="test1",
+            entities=[entity1],
+            schema=[Field(name="int64_col", dtype=Int64)],
+            source=file_source,
+        )
+
+        assert len(feature_view_1.schema) == 1
+        assert len(feature_view_1.features) == 1
+        assert len(feature_view_1.entity_columns) == 0
+
+        update_feature_views_with_inferred_features_and_entities(
+            [feature_view_1], [entity1], RepoConfig(provider="local", project="test")
+        )
+
+        # The schema is only used as a parameter, as is therefore not updated during inference.
+        assert len(feature_view_1.schema) == 1
+
+        # Since there is already a feature specified, additional features are not inferred.
+        assert len(feature_view_1.features) == 1
+
+        # The single entity column is inferred correctly.
+        assert len(feature_view_1.entity_columns) == 1
+
+
+def test_feature_view_inference_respects_entity_value_type(simple_dataset_1):
+    """
+    Tests that feature view inference still respects an entity's value type.
+    """
+    # TODO(felixwang9817): Remove this test once entity value_type is removed.
+    with prep_file_source(df=simple_dataset_1, timestamp_field="ts_1") as file_source:
+        entity1 = Entity(
+            name="test1", join_keys=["id_join_key"], value_type=ValueType.STRING
+        )
+        feature_view_1 = FeatureView(
+            name="test1",
+            entities=[entity1],
+            schema=[Field(name="int64_col", dtype=Int64)],
+            source=file_source,
+        )
+
+        assert len(feature_view_1.schema) == 1
+        assert len(feature_view_1.features) == 1
+        assert len(feature_view_1.entity_columns) == 0
+
+        update_feature_views_with_inferred_features_and_entities(
+            [feature_view_1], [entity1], RepoConfig(provider="local", project="test")
+        )
+
+        # The schema is only used as a parameter, as is therefore not updated during inference.
+        assert len(feature_view_1.schema) == 1
+
+        # Since there is already a feature specified, additional features are not inferred.
+        assert len(feature_view_1.features) == 1
+
+        # The single entity column is inferred correctly and has type String.
+        assert len(feature_view_1.entity_columns) == 1
+        assert feature_view_1.entity_columns[0].dtype == String
+
+
+def test_feature_view_inference_on_feature_columns(simple_dataset_1):
+    """
+    Tests that feature view inference correctly infers feature columns.
+    """
+    with prep_file_source(df=simple_dataset_1, timestamp_field="ts_1") as file_source:
+        entity1 = Entity(name="test1", join_keys=["id_join_key"])
+        feature_view_1 = FeatureView(
+            name="test1",
+            entities=[entity1],
+            schema=[Field(name="id_join_key", dtype=Int64)],
+            source=file_source,
+        )
+
+        assert len(feature_view_1.schema) == 1
+        assert len(feature_view_1.features) == 0
+        assert len(feature_view_1.entity_columns) == 1
+
+        update_feature_views_with_inferred_features_and_entities(
+            [feature_view_1], [entity1], RepoConfig(provider="local", project="test")
+        )
+
+        # The schema is only used as a parameter, as is therefore not updated during inference.
+        assert len(feature_view_1.schema) == 1
+
+        # All three feature columns are inferred correctly.
+        assert len(feature_view_1.features) == 3
+        print(feature_view_1.features)
+        feature_column_1 = Field(name="float_col", dtype=Float64)
+        feature_column_2 = Field(name="int64_col", dtype=Int64)
+        feature_column_3 = Field(name="string_col", dtype=String)
+        assert feature_column_1 in feature_view_1.features
+        assert feature_column_2 in feature_view_1.features
+        assert feature_column_3 in feature_view_1.features
+
+        # The single entity column remains.
+        assert len(feature_view_1.entity_columns) == 1
 
 
 def test_update_feature_services_with_inferred_features(simple_dataset_1):
@@ -340,17 +407,37 @@ def test_update_feature_services_with_inferred_features(simple_dataset_1):
         feature_view_1 = FeatureView(
             name="test1", entities=[entity1], source=file_source,
         )
-        feature_service = FeatureService(name="fs_1", features=[feature_view_1])
-        assert len(feature_service.feature_view_projections) == 1
-        assert len(feature_service.feature_view_projections[0].features) == 0
+        feature_view_2 = FeatureView(
+            name="test2", entities=[entity1], source=file_source,
+        )
 
-        update_feature_views_with_inferred_features(
-            [feature_view_1], [entity1], RepoConfig(provider="local", project="test")
+        feature_service = FeatureService(
+            name="fs_1", features=[feature_view_1[["string_col"]], feature_view_2]
+        )
+        assert len(feature_service.feature_view_projections) == 2
+        assert len(feature_service.feature_view_projections[0].features) == 0
+        assert len(feature_service.feature_view_projections[0].desired_features) == 1
+        assert len(feature_service.feature_view_projections[1].features) == 0
+        assert len(feature_service.feature_view_projections[1].desired_features) == 0
+
+        update_feature_views_with_inferred_features_and_entities(
+            [feature_view_1, feature_view_2],
+            [entity1],
+            RepoConfig(provider="local", project="test"),
         )
         feature_service.infer_features(
-            fvs_to_update={feature_view_1.name: feature_view_1}
+            fvs_to_update={
+                feature_view_1.name: feature_view_1,
+                feature_view_2.name: feature_view_2,
+            }
         )
 
-        assert len(feature_view_1.schema) == 3
+        assert len(feature_view_1.schema) == 0
         assert len(feature_view_1.features) == 3
-        assert len(feature_service.feature_view_projections[0].features) == 3
+        assert len(feature_view_2.schema) == 0
+        assert len(feature_view_2.features) == 3
+        assert len(feature_service.feature_view_projections[0].features) == 1
+        assert len(feature_service.feature_view_projections[1].features) == 3
+
+
+# TODO(felixwang9817): Add tests that interact with field mapping.
diff --git a/sdk/python/tests/integration/registration/test_registry.py b/sdk/python/tests/integration/registration/test_registry.py
index f011d73d2dd..36e19e222ac 100644
--- a/sdk/python/tests/integration/registration/test_registry.py
+++ b/sdk/python/tests/integration/registration/test_registry.py
@@ -20,15 +20,17 @@
 from pytest_lazyfixture import lazy_fixture
 
 from feast import FileSource
-from feast.data_format import ParquetFormat
+from feast.aggregation import Aggregation
+from feast.data_format import AvroFormat, ParquetFormat
+from feast.data_source import KafkaSource
 from feast.entity import Entity
 from feast.feature import Feature
 from feast.feature_view import FeatureView
 from feast.field import Field
 from feast.on_demand_feature_view import RequestSource, on_demand_feature_view
-from feast.protos.feast.types import Value_pb2 as ValueProto
 from feast.registry import Registry
 from feast.repo_config import RegistryConfig
+from feast.stream_feature_view import StreamFeatureView
 from feast.types import Array, Bytes, Float32, Int32, Int64, String
 from feast.value_type import ValueType
 
@@ -73,10 +75,7 @@ def s3_registry() -> Registry:
 )
 def test_apply_entity_success(test_registry):
     entity = Entity(
-        name="driver_car_id",
-        description="Car driver id",
-        value_type=ValueType.STRING,
-        tags={"team": "matchmaking"},
+        name="driver_car_id", description="Car driver id", tags={"team": "matchmaking"},
     )
 
     project = "project"
@@ -90,7 +89,6 @@ def test_apply_entity_success(test_registry):
     assert (
         len(entities) == 1
         and entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -99,7 +97,6 @@ def test_apply_entity_success(test_registry):
     entity = test_registry.get_entity("driver_car_id", project)
     assert (
         entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -122,10 +119,7 @@ def test_apply_entity_success(test_registry):
 )
 def test_apply_entity_integration(test_registry):
     entity = Entity(
-        name="driver_car_id",
-        description="Car driver id",
-        value_type=ValueType.STRING,
-        tags={"team": "matchmaking"},
+        name="driver_car_id", description="Car driver id", tags={"team": "matchmaking"},
     )
 
     project = "project"
@@ -139,7 +133,6 @@ def test_apply_entity_integration(test_registry):
     assert (
         len(entities) == 1
         and entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -148,7 +141,6 @@ def test_apply_entity_integration(test_registry):
     entity = test_registry.get_entity("driver_car_id", project)
     assert (
         entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -173,6 +165,8 @@ def test_apply_feature_view_success(test_registry):
         created_timestamp_column="timestamp",
     )
 
+    entity = Entity(name="fs1_my_entity_1", join_keys=["test"])
+
     fv1 = FeatureView(
         name="my_feature_view_1",
         schema=[
@@ -181,7 +175,7 @@ def test_apply_feature_view_success(test_registry):
             Field(name="fs1_my_feature_3", dtype=Array(String)),
             Field(name="fs1_my_feature_4", dtype=Array(Bytes)),
         ],
-        entities=["fs1_my_entity_1"],
+        entities=[entity],
         tags={"team": "matchmaking"},
         batch_source=batch_source,
         ttl=timedelta(minutes=5),
@@ -237,7 +231,146 @@ def test_apply_feature_view_success(test_registry):
 @pytest.mark.parametrize(
     "test_registry", [lazy_fixture("local_registry")],
 )
-# TODO(kevjumba): remove this in feast 0.23 when deprecating
+def test_apply_on_demand_feature_view_success(test_registry):
+    # Create Feature Views
+    driver_stats = FileSource(
+        name="driver_stats_source",
+        path="data/driver_stats_lat_lon.parquet",
+        timestamp_field="event_timestamp",
+        created_timestamp_column="created",
+        description="A table describing the stats of a driver based on hourly logs",
+        owner="test2@gmail.com",
+    )
+
+    driver_daily_features_view = FeatureView(
+        name="driver_daily_features",
+        entities=["driver"],
+        ttl=timedelta(seconds=8640000000),
+        schema=[
+            Field(name="daily_miles_driven", dtype=Float32),
+            Field(name="lat", dtype=Float32),
+            Field(name="lon", dtype=Float32),
+            Field(name="string_feature", dtype=String),
+        ],
+        online=True,
+        source=driver_stats,
+        tags={"production": "True"},
+        owner="test2@gmail.com",
+    )
+
+    @on_demand_feature_view(
+        sources=[driver_daily_features_view],
+        schema=[Field(name="first_char", dtype=String)],
+    )
+    def location_features_from_push(inputs: pd.DataFrame) -> pd.DataFrame:
+        df = pd.DataFrame()
+        df["first_char"] = inputs["string_feature"].str[:1].astype("string")
+        return df
+
+    project = "project"
+
+    # Register Feature View
+    test_registry.apply_feature_view(location_features_from_push, project)
+
+    feature_views = test_registry.list_on_demand_feature_views(project)
+
+    # List Feature Views
+    assert (
+        len(feature_views) == 1
+        and feature_views[0].name == "location_features_from_push"
+        and feature_views[0].features[0].name == "first_char"
+        and feature_views[0].features[0].dtype == String
+    )
+
+    feature_view = test_registry.get_on_demand_feature_view(
+        "location_features_from_push", project
+    )
+    assert (
+        feature_view.name == "location_features_from_push"
+        and feature_view.features[0].name == "first_char"
+        and feature_view.features[0].dtype == String
+    )
+
+    test_registry.delete_feature_view("location_features_from_push", project)
+    feature_views = test_registry.list_on_demand_feature_views(project)
+    assert len(feature_views) == 0
+
+    test_registry.teardown()
+
+    # Will try to reload registry, which will fail because the file has been deleted
+    with pytest.raises(FileNotFoundError):
+        test_registry._get_registry_proto()
+
+
+@pytest.mark.parametrize(
+    "test_registry", [lazy_fixture("local_registry")],
+)
+def test_apply_stream_feature_view_success(test_registry):
+    # Create Feature Views
+    def simple_udf(x: int):
+        return x + 3
+
+    entity = Entity(name="driver_entity", join_keys=["test_key"])
+
+    stream_source = KafkaSource(
+        name="kafka",
+        timestamp_field="event_timestamp",
+        kafka_bootstrap_servers="",
+        message_format=AvroFormat(""),
+        topic="topic",
+        batch_source=FileSource(path="some path"),
+        watermark_delay_threshold=timedelta(days=1),
+    )
+
+    sfv = StreamFeatureView(
+        name="test kafka stream feature view",
+        entities=[entity],
+        ttl=timedelta(days=30),
+        owner="test@example.com",
+        online=True,
+        schema=[Field(name="dummy_field", dtype=Float32)],
+        description="desc",
+        aggregations=[
+            Aggregation(
+                column="dummy_field", function="max", time_window=timedelta(days=1),
+            ),
+            Aggregation(
+                column="dummy_field2", function="count", time_window=timedelta(days=24),
+            ),
+        ],
+        timestamp_field="event_timestamp",
+        mode="spark",
+        source=stream_source,
+        udf=simple_udf,
+        tags={},
+    )
+
+    project = "project"
+
+    # Register Feature View
+    test_registry.apply_feature_view(sfv, project)
+
+    stream_feature_views = test_registry.list_stream_feature_views(project)
+
+    # List Feature Views
+    assert len(stream_feature_views) == 1
+    assert stream_feature_views[0] == sfv
+
+    test_registry.delete_feature_view("test kafka stream feature view", project)
+    stream_feature_views = test_registry.list_stream_feature_views(project)
+    assert len(stream_feature_views) == 0
+
+    test_registry.teardown()
+
+    # Will try to reload registry, which will fail because the file has been deleted
+    with pytest.raises(FileNotFoundError):
+        test_registry._get_registry_proto()
+
+
+@pytest.mark.parametrize(
+    "test_registry", [lazy_fixture("local_registry")],
+)
+# TODO(kevjumba): remove this in feast 0.24 when deprecating
 @pytest.mark.parametrize(
     "request_source_schema",
     [[Field(name="my_input_1", dtype=Int32)], {"my_input_1": ValueType.INT32}],
@@ -253,10 +386,12 @@ def test_modify_feature_views_success(test_registry, request_source_schema):
 
     request_source = RequestSource(name="request_source", schema=request_source_schema,)
 
+    entity = Entity(name="fs1_my_entity_1", join_keys=["test"])
+
     fv1 = FeatureView(
         name="my_feature_view_1",
         schema=[Field(name="fs1_my_feature_1", dtype=Int64)],
-        entities=["fs1_my_entity_1"],
+        entities=[entity],
         tags={"team": "matchmaking"},
         batch_source=batch_source,
         ttl=timedelta(minutes=5),
@@ -369,6 +504,8 @@ def test_apply_feature_view_integration(test_registry):
         created_timestamp_column="timestamp",
     )
 
+    entity = Entity(name="fs1_my_entity_1", join_keys=["test"])
+
     fv1 = FeatureView(
         name="my_feature_view_1",
         schema=[
@@ -377,7 +514,7 @@ def test_apply_feature_view_integration(test_registry):
             Field(name="fs1_my_feature_3", dtype=Array(String)),
             Field(name="fs1_my_feature_4", dtype=Array(Bytes)),
         ],
-        entities=["fs1_my_entity_1"],
+        entities=[entity],
         tags={"team": "matchmaking"},
         batch_source=batch_source,
         ttl=timedelta(minutes=5),
@@ -444,6 +581,8 @@ def test_apply_data_source(test_registry: Registry):
         created_timestamp_column="timestamp",
     )
 
+    entity = Entity(name="fs1_my_entity_1", join_keys=["test"])
+
     fv1 = FeatureView(
         name="my_feature_view_1",
         schema=[
@@ -452,7 +591,7 @@ def test_apply_data_source(test_registry: Registry):
             Field(name="fs1_my_feature_3", dtype=Array(String)),
             Field(name="fs1_my_feature_4", dtype=Array(Bytes)),
         ],
-        entities=["fs1_my_entity_1"],
+        entities=[entity],
         tags={"team": "matchmaking"},
         batch_source=batch_source,
         ttl=timedelta(minutes=5),
@@ -499,10 +638,7 @@ def test_commit():
     test_registry = Registry(registry_config, None)
 
     entity = Entity(
-        name="driver_car_id",
-        description="Car driver id",
-        value_type=ValueType.STRING,
-        tags={"team": "matchmaking"},
+        name="driver_car_id", description="Car driver id", tags={"team": "matchmaking"},
     )
 
     project = "project"
@@ -517,7 +653,6 @@ def test_commit():
     assert (
         len(entities) == 1
         and entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -526,7 +661,6 @@ def test_commit():
     entity = test_registry.get_entity("driver_car_id", project, allow_cache=True)
     assert (
         entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -552,7 +686,6 @@ def test_commit():
     assert (
         len(entities) == 1
         and entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
@@ -561,7 +694,6 @@ def test_commit():
     entity = test_registry.get_entity("driver_car_id", project)
     assert (
         entity.name == "driver_car_id"
-        and entity.value_type == ValueType(ValueProto.ValueType.STRING)
         and entity.description == "Car driver id"
         and "team" in entity.tags
         and entity.tags["team"] == "matchmaking"
diff --git a/sdk/python/tests/integration/registration/test_sql_registry.py b/sdk/python/tests/integration/registration/test_sql_registry.py
new file mode 100644
index 00000000000..c483a7c46f6
--- /dev/null
+++ b/sdk/python/tests/integration/registration/test_sql_registry.py
@@ -0,0 +1,490 @@
+# Copyright 2021 The Feast Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import logging
+import os
+import sys
+from datetime import timedelta
+
+import pandas as pd
+import pytest
+from pytest_lazyfixture import lazy_fixture
+from testcontainers.core.container import DockerContainer
+from testcontainers.core.waiting_utils import wait_for_logs
+
+from feast import Feature, FileSource, RequestSource
+from feast.data_format import ParquetFormat
+from feast.entity import Entity
+from feast.errors import FeatureViewNotFoundException
+from feast.feature_view import FeatureView
+from feast.field import Field
+from feast.infra.registry_stores.sql import SqlRegistry
+from feast.on_demand_feature_view import on_demand_feature_view
+from feast.repo_config import RegistryConfig
+from feast.types import Array, Bytes, Float32, Int32, Int64, String
+from feast.value_type import ValueType
+
+POSTGRES_USER = "test"
+POSTGRES_PASSWORD = "test"
+POSTGRES_DB = "test"
+
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture(scope="session")
+def pg_registry():
+    container = (
+        DockerContainer("postgres:latest")
+        .with_exposed_ports(5432)
+        .with_env("POSTGRES_USER", POSTGRES_USER)
+        .with_env("POSTGRES_PASSWORD", POSTGRES_PASSWORD)
+        .with_env("POSTGRES_DB", POSTGRES_DB)
+    )
+
+    container.start()
+
+    log_string_to_wait_for = "database system is ready to accept connections"
+    waited = wait_for_logs(
+        container=container, predicate=log_string_to_wait_for, timeout=30, interval=10,
+    )
+    logger.info("Waited for %s seconds until postgres container was up", waited)
+    container_port = container.get_exposed_port(5432)
+
+    registry_config = RegistryConfig(
+        registry_type="sql",
+        path=f"postgresql://{POSTGRES_USER}:{POSTGRES_PASSWORD}@127.0.0.1:{container_port}/{POSTGRES_DB}",
+    )
+
+    yield SqlRegistry(registry_config, None)
+
+    container.stop()
+
+
+@pytest.fixture(scope="session")
+def mysql_registry():
+    container = (
+        DockerContainer("mysql:latest")
+        .with_exposed_ports(3306)
+        .with_env("MYSQL_RANDOM_ROOT_PASSWORD", "true")
+        .with_env("MYSQL_USER", POSTGRES_USER)
+        .with_env("MYSQL_PASSWORD", POSTGRES_PASSWORD)
+        .with_env("MYSQL_DATABASE", POSTGRES_DB)
+    )
+
+    container.start()
+
+    log_string_to_wait_for = "/usr/sbin/mysqld: ready for connections. Version: '8.0.29'  socket: '/var/run/mysqld/mysqld.sock'  port: 3306"
+    waited = wait_for_logs(
+        container=container, predicate=log_string_to_wait_for, timeout=60, interval=10,
+    )
+    logger.info("Waited for %s seconds until mysql container was up", waited)
+    container_port = container.get_exposed_port(3306)
+
+    registry_config = RegistryConfig(
+        registry_type="sql",
+        path=f"mysql+mysqldb://{POSTGRES_USER}:{POSTGRES_PASSWORD}@127.0.0.1:{container_port}/{POSTGRES_DB}",
+    )
+
+    yield SqlRegistry(registry_config, None)
+
+    container.stop()
+
+
+@pytest.mark.skipif(
+    sys.platform == "darwin" and "GITHUB_REF" in os.environ,
+    reason="does not run on mac github actions",
+)
+@pytest.mark.parametrize(
+    "sql_registry", [lazy_fixture("mysql_registry"), lazy_fixture("pg_registry")],
+)
+def test_apply_entity_success(sql_registry):
+    entity = Entity(
+        name="driver_car_id", description="Car driver id", tags={"team": "matchmaking"},
+    )
+
+    project = "project"
+
+    # Register Entity
+    sql_registry.apply_entity(entity, project)
+
+    entities = sql_registry.list_entities(project)
+
+    entity = entities[0]
+    assert (
+        len(entities) == 1
+        and entity.name == "driver_car_id"
+        and entity.description == "Car driver id"
+        and "team" in entity.tags
+        and entity.tags["team"] == "matchmaking"
+    )
+
+    entity = sql_registry.get_entity("driver_car_id", project)
+    assert (
+        entity.name == "driver_car_id"
+        and entity.description == "Car driver id"
+        and "team" in entity.tags
+        and entity.tags["team"] == "matchmaking"
+    )
+
+    sql_registry.delete_entity("driver_car_id", project)
+    entities = sql_registry.list_entities(project)
+    assert len(entities) == 0
+
+    sql_registry.teardown()
+
+
+@pytest.mark.skipif(
+    sys.platform == "darwin" and "GITHUB_REF" in os.environ,
+    reason="does not run on mac github actions",
+)
+@pytest.mark.parametrize(
+    "sql_registry", [lazy_fixture("mysql_registry"), lazy_fixture("pg_registry")],
+)
+def test_apply_feature_view_success(sql_registry):
+    # Create Feature Views
+    batch_source = FileSource(
+        file_format=ParquetFormat(),
+        path="file://feast/*",
+        timestamp_field="ts_col",
+        created_timestamp_column="timestamp",
+    )
+
+    entity = Entity(name="fs1_my_entity_1", join_keys=["test"])
+
+    fv1 = FeatureView(
+        name="my_feature_view_1",
+        schema=[
+            Field(name="fs1_my_feature_1", dtype=Int64),
+            Field(name="fs1_my_feature_2", dtype=String),
+            Field(name="fs1_my_feature_3", dtype=Array(String)),
+            Field(name="fs1_my_feature_4", dtype=Array(Bytes)),
+        ],
+        entities=[entity],
+        tags={"team": "matchmaking"},
+        batch_source=batch_source,
+        ttl=timedelta(minutes=5),
+    )
+
+    project = "project"
+
+    # Register Feature View
+    sql_registry.apply_feature_view(fv1, project)
+
+    feature_views = sql_registry.list_feature_views(project)
+
+    # List Feature Views
+    assert (
+        len(feature_views) == 1
+        and feature_views[0].name == "my_feature_view_1"
+        and feature_views[0].features[0].name == "fs1_my_feature_1"
+        and feature_views[0].features[0].dtype == Int64
+        and feature_views[0].features[1].name == "fs1_my_feature_2"
+        and feature_views[0].features[1].dtype == String
+        and feature_views[0].features[2].name == "fs1_my_feature_3"
+        and feature_views[0].features[2].dtype == Array(String)
+        and feature_views[0].features[3].name == "fs1_my_feature_4"
+        and feature_views[0].features[3].dtype == Array(Bytes)
+        and feature_views[0].entities[0] == "fs1_my_entity_1"
+    )
+
+    feature_view = sql_registry.get_feature_view("my_feature_view_1", project)
+    assert (
+        feature_view.name == "my_feature_view_1"
+        and feature_view.features[0].name == "fs1_my_feature_1"
+        and feature_view.features[0].dtype == Int64
+        and feature_view.features[1].name == "fs1_my_feature_2"
+        and feature_view.features[1].dtype == String
+        and feature_view.features[2].name == "fs1_my_feature_3"
+        and feature_view.features[2].dtype == Array(String)
+        and feature_view.features[3].name == "fs1_my_feature_4"
+        and feature_view.features[3].dtype == Array(Bytes)
+        and feature_view.entities[0] == "fs1_my_entity_1"
+    )
+
+    sql_registry.delete_feature_view("my_feature_view_1", project)
+    feature_views = sql_registry.list_feature_views(project)
+    assert len(feature_views) == 0
+
+    sql_registry.teardown()
+
+
+@pytest.mark.skipif(
+    sys.platform == "darwin" and "GITHUB_REF" in os.environ,
+    reason="does not run on mac github actions",
+)
+@pytest.mark.parametrize(
+    "sql_registry", [lazy_fixture("mysql_registry"), lazy_fixture("pg_registry")],
+)
+def test_apply_on_demand_feature_view_success(sql_registry):
+    # Create Feature Views
+    driver_stats = FileSource(
+        name="driver_stats_source",
+        path="data/driver_stats_lat_lon.parquet",
+        timestamp_field="event_timestamp",
+        created_timestamp_column="created",
+        description="A table describing the stats of a driver based on hourly logs",
+        owner="test2@gmail.com",
+    )
+
+    driver_daily_features_view = FeatureView(
+        name="driver_daily_features",
+        entities=["driver"],
+        ttl=timedelta(seconds=8640000000),
+        schema=[
+            Field(name="daily_miles_driven", dtype=Float32),
+            Field(name="lat", dtype=Float32),
+            Field(name="lon", dtype=Float32),
+            Field(name="string_feature", dtype=String),
+        ],
+        online=True,
+        source=driver_stats,
+        tags={"production": "True"},
+        owner="test2@gmail.com",
+    )
+
+    @on_demand_feature_view(
+        sources=[driver_daily_features_view],
+        schema=[Field(name="first_char", dtype=String)],
+    )
+    def location_features_from_push(inputs: pd.DataFrame) -> pd.DataFrame:
+        df = pd.DataFrame()
+        df["first_char"] = inputs["string_feature"].str[:1].astype("string")
+        return df
+
+    project = "project"
+
+    with pytest.raises(FeatureViewNotFoundException):
+        sql_registry.get_user_metadata(project, location_features_from_push)
+
+    # Register Feature View
+    sql_registry.apply_feature_view(location_features_from_push, project)
+
+    assert not sql_registry.get_user_metadata(project, location_features_from_push)
+
+    b = "metadata".encode("utf-8")
+    sql_registry.apply_user_metadata(project, location_features_from_push, b)
+    assert sql_registry.get_user_metadata(project, location_features_from_push) == b
+
+    feature_views = sql_registry.list_on_demand_feature_views(project)
+
+    # List Feature Views
+    assert (
+        len(feature_views) == 1
+        and feature_views[0].name == "location_features_from_push"
+        and feature_views[0].features[0].name == "first_char"
+        and feature_views[0].features[0].dtype == String
+    )
+
+    feature_view = sql_registry.get_on_demand_feature_view(
+        "location_features_from_push", project
+    )
+    assert (
+        feature_view.name == "location_features_from_push"
+        and feature_view.features[0].name == "first_char"
+        and feature_view.features[0].dtype == String
+    )
+
+    sql_registry.delete_feature_view("location_features_from_push", project)
+    feature_views = sql_registry.list_on_demand_feature_views(project)
+    assert len(feature_views) == 0
+
+    sql_registry.teardown()
+
+
+@pytest.mark.skipif(
+    sys.platform == "darwin" and "GITHUB_REF" in os.environ,
+    reason="does not run on mac github actions",
+)
+@pytest.mark.parametrize(
+    "sql_registry", [lazy_fixture("mysql_registry"), lazy_fixture("pg_registry")],
+)
+@pytest.mark.parametrize(
+    "request_source_schema",
+    [[Field(name="my_input_1", dtype=Int32)], {"my_input_1": ValueType.INT32}],
+)
+def test_modify_feature_views_success(sql_registry, request_source_schema):
+    # Create Feature Views
+    batch_source = FileSource(
+        file_format=ParquetFormat(),
+        path="file://feast/*",
+        timestamp_field="ts_col",
+        created_timestamp_column="timestamp",
+    )
+
+    request_source = RequestSource(name="request_source", schema=request_source_schema,)
+
+    entity = Entity(name="fs1_my_entity_1", join_keys=["test"])
+
+    fv1 = FeatureView(
+        name="my_feature_view_1",
+        schema=[Field(name="fs1_my_feature_1", dtype=Int64)],
+        entities=[entity],
+        tags={"team": "matchmaking"},
+        batch_source=batch_source,
+        ttl=timedelta(minutes=5),
+    )
+
+    @on_demand_feature_view(
+        features=[
+            Feature(name="odfv1_my_feature_1", dtype=ValueType.STRING),
+            Feature(name="odfv1_my_feature_2", dtype=ValueType.INT32),
+        ],
+        sources=[request_source],
+    )
+    def odfv1(feature_df: pd.DataFrame) -> pd.DataFrame:
+        data = pd.DataFrame()
+        data["odfv1_my_feature_1"] = feature_df["my_input_1"].astype("category")
+        data["odfv1_my_feature_2"] = feature_df["my_input_1"].astype("int32")
+        return data
+
+    project = "project"
+
+    # Register Feature Views
+    sql_registry.apply_feature_view(odfv1, project)
+    sql_registry.apply_feature_view(fv1, project)
+
+    # Modify odfv by changing a single feature dtype
+    @on_demand_feature_view(
+        features=[
+            Feature(name="odfv1_my_feature_1", dtype=ValueType.FLOAT),
+            Feature(name="odfv1_my_feature_2", dtype=ValueType.INT32),
+        ],
+        sources=[request_source],
+    )
+    def odfv1(feature_df: pd.DataFrame) -> pd.DataFrame:
+        data = pd.DataFrame()
+        data["odfv1_my_feature_1"] = feature_df["my_input_1"].astype("float")
+        data["odfv1_my_feature_2"] = feature_df["my_input_1"].astype("int32")
+        return data
+
+    # Apply the modified odfv
+    sql_registry.apply_feature_view(odfv1, project)
+
+    # Check odfv
+    on_demand_feature_views = sql_registry.list_on_demand_feature_views(project)
+
+    assert (
+        len(on_demand_feature_views) == 1
+        and on_demand_feature_views[0].name == "odfv1"
+        and on_demand_feature_views[0].features[0].name == "odfv1_my_feature_1"
+        and on_demand_feature_views[0].features[0].dtype == Float32
+        and on_demand_feature_views[0].features[1].name == "odfv1_my_feature_2"
+        and on_demand_feature_views[0].features[1].dtype == Int32
+    )
+    request_schema = on_demand_feature_views[0].get_request_data_schema()
+    assert (
+        list(request_schema.keys())[0] == "my_input_1"
+        and list(request_schema.values())[0] == ValueType.INT32
+    )
+
+    feature_view = sql_registry.get_on_demand_feature_view("odfv1", project)
+    assert (
+        feature_view.name == "odfv1"
+        and feature_view.features[0].name == "odfv1_my_feature_1"
+        and feature_view.features[0].dtype == Float32
+        and feature_view.features[1].name == "odfv1_my_feature_2"
+        and feature_view.features[1].dtype == Int32
+    )
+    request_schema = feature_view.get_request_data_schema()
+    assert (
+        list(request_schema.keys())[0] == "my_input_1"
+        and list(request_schema.values())[0] == ValueType.INT32
+    )
+
+    # Make sure fv1 is untouched
+    feature_views = sql_registry.list_feature_views(project)
+
+    # List Feature Views
+    assert (
+        len(feature_views) == 1
+        and feature_views[0].name == "my_feature_view_1"
+        and feature_views[0].features[0].name == "fs1_my_feature_1"
+        and feature_views[0].features[0].dtype == Int64
+        and feature_views[0].entities[0] == "fs1_my_entity_1"
+    )
+
+    feature_view = sql_registry.get_feature_view("my_feature_view_1", project)
+    assert (
+        feature_view.name == "my_feature_view_1"
+        and feature_view.features[0].name == "fs1_my_feature_1"
+        and feature_view.features[0].dtype == Int64
+        and feature_view.entities[0] == "fs1_my_entity_1"
+    )
+
+    sql_registry.teardown()
+
+
+@pytest.mark.skipif(
+    sys.platform == "darwin" and "GITHUB_REF" in os.environ,
+    reason="does not run on mac github actions",
+)
+@pytest.mark.integration
+@pytest.mark.parametrize(
+    "sql_registry", [lazy_fixture("mysql_registry"), lazy_fixture("pg_registry")],
+)
+def test_apply_data_source(sql_registry):
+    # Create Feature Views
+    batch_source = FileSource(
+        name="test_source",
+        file_format=ParquetFormat(),
+        path="file://feast/*",
+        timestamp_field="ts_col",
+        created_timestamp_column="timestamp",
+    )
+
+    entity = Entity(name="fs1_my_entity_1", join_keys=["test"])
+
+    fv1 = FeatureView(
+        name="my_feature_view_1",
+        schema=[
+            Field(name="fs1_my_feature_1", dtype=Int64),
+            Field(name="fs1_my_feature_2", dtype=String),
+            Field(name="fs1_my_feature_3", dtype=Array(String)),
+            Field(name="fs1_my_feature_4", dtype=Array(Bytes)),
+        ],
+        entities=[entity],
+        tags={"team": "matchmaking"},
+        batch_source=batch_source,
+        ttl=timedelta(minutes=5),
+    )
+
+    project = "project"
+
+    # Register data source and feature view
+    sql_registry.apply_data_source(batch_source, project, commit=False)
+    sql_registry.apply_feature_view(fv1, project, commit=True)
+
+    registry_feature_views = sql_registry.list_feature_views(project)
+    registry_data_sources = sql_registry.list_data_sources(project)
+    assert len(registry_feature_views) == 1
+    assert len(registry_data_sources) == 1
+    registry_feature_view = registry_feature_views[0]
+    assert registry_feature_view.batch_source == batch_source
+    registry_data_source = registry_data_sources[0]
+    assert registry_data_source == batch_source
+
+    # Check that change to batch source propagates
+    batch_source.timestamp_field = "new_ts_col"
+    sql_registry.apply_data_source(batch_source, project, commit=False)
+    sql_registry.apply_feature_view(fv1, project, commit=True)
+    registry_feature_views = sql_registry.list_feature_views(project)
+    registry_data_sources = sql_registry.list_data_sources(project)
+    assert len(registry_feature_views) == 1
+    assert len(registry_data_sources) == 1
+    registry_feature_view = registry_feature_views[0]
+    assert registry_feature_view.batch_source == batch_source
+    registry_batch_source = sql_registry.list_data_sources(project)[0]
+    assert registry_batch_source == batch_source
+
+    sql_registry.teardown()
diff --git a/sdk/python/tests/integration/registration/test_stream_feature_view_apply.py b/sdk/python/tests/integration/registration/test_stream_feature_view_apply.py
new file mode 100644
index 00000000000..8e2af031c5f
--- /dev/null
+++ b/sdk/python/tests/integration/registration/test_stream_feature_view_apply.py
@@ -0,0 +1,148 @@
+from datetime import timedelta
+
+import pytest
+
+from feast.aggregation import Aggregation
+from feast.data_format import AvroFormat
+from feast.data_source import KafkaSource
+from feast.entity import Entity
+from feast.field import Field
+from feast.stream_feature_view import stream_feature_view
+from feast.types import Float32
+from tests.utils.cli_utils import CliRunner, get_example_repo
+from tests.utils.data_source_utils import prep_file_source
+
+
+@pytest.mark.integration
+def test_apply_stream_feature_view(simple_dataset_1) -> None:
+    """
+    Test apply of StreamFeatureView.
+    """
+    runner = CliRunner()
+    with runner.local_repo(
+        get_example_repo("example_feature_repo_1.py"), "bigquery"
+    ) as fs, prep_file_source(
+        df=simple_dataset_1, timestamp_field="ts_1"
+    ) as file_source:
+        entity = Entity(name="driver_entity", join_keys=["test_key"])
+
+        stream_source = KafkaSource(
+            name="kafka",
+            timestamp_field="event_timestamp",
+            kafka_bootstrap_servers="",
+            message_format=AvroFormat(""),
+            topic="topic",
+            batch_source=file_source,
+            watermark_delay_threshold=timedelta(days=1),
+        )
+
+        @stream_feature_view(
+            entities=[entity],
+            ttl=timedelta(days=30),
+            owner="test@example.com",
+            online=True,
+            schema=[Field(name="dummy_field", dtype=Float32)],
+            description="desc",
+            aggregations=[
+                Aggregation(
+                    column="dummy_field", function="max", time_window=timedelta(days=1),
+                ),
+                Aggregation(
+                    column="dummy_field2",
+                    function="count",
+                    time_window=timedelta(days=24),
+                ),
+            ],
+            timestamp_field="event_timestamp",
+            mode="spark",
+            source=stream_source,
+            tags={},
+        )
+        def simple_sfv(df):
+            return df
+
+        fs.apply([entity, simple_sfv])
+
+        stream_feature_views = fs.list_stream_feature_views()
+        assert len(stream_feature_views) == 1
+        assert stream_feature_views[0] == simple_sfv
+
+        features = fs.get_online_features(
+            features=["simple_sfv:dummy_field"], entity_rows=[{"test_key": 1001}],
+        ).to_dict(include_event_timestamps=True)
+
+        assert "test_key" in features
+        assert features["test_key"] == [1001]
+        assert "dummy_field" in features
+        assert features["dummy_field"] == [None]
+
+
+@pytest.mark.integration
+def test_stream_feature_view_udf(simple_dataset_1) -> None:
+    """
+    Test apply of StreamFeatureView udfs are serialized correctly and usable.
+    """
+    runner = CliRunner()
+    with runner.local_repo(
+        get_example_repo("example_feature_repo_1.py"), "bigquery"
+    ) as fs, prep_file_source(
+        df=simple_dataset_1, timestamp_field="ts_1"
+    ) as file_source:
+        entity = Entity(name="driver_entity", join_keys=["test_key"])
+
+        stream_source = KafkaSource(
+            name="kafka",
+            timestamp_field="event_timestamp",
+            kafka_bootstrap_servers="",
+            message_format=AvroFormat(""),
+            topic="topic",
+            batch_source=file_source,
+            watermark_delay_threshold=timedelta(days=1),
+        )
+
+        @stream_feature_view(
+            entities=[entity],
+            ttl=timedelta(days=30),
+            owner="test@example.com",
+            online=True,
+            schema=[Field(name="dummy_field", dtype=Float32)],
+            description="desc",
+            aggregations=[
+                Aggregation(
+                    column="dummy_field", function="max", time_window=timedelta(days=1),
+                ),
+                Aggregation(
+                    column="dummy_field2",
+                    function="count",
+                    time_window=timedelta(days=24),
+                ),
+            ],
+            timestamp_field="event_timestamp",
+            mode="spark",
+            source=stream_source,
+            tags={},
+        )
+        def pandas_view(pandas_df):
+            import pandas as pd
+
+            assert type(pandas_df) == pd.DataFrame
+            df = pandas_df.transform(lambda x: x + 10, axis=1)
+            df.insert(2, "C", [20.2, 230.0, 34.0], True)
+            return df
+
+        import pandas as pd
+
+        fs.apply([entity, pandas_view])
+
+        stream_feature_views = fs.list_stream_feature_views()
+        assert len(stream_feature_views) == 1
+        assert stream_feature_views[0] == pandas_view
+
+        sfv = stream_feature_views[0]
+
+        df = pd.DataFrame({"A": [1, 2, 3], "B": [10, 20, 30]})
+        new_df = sfv.udf(df)
+        expected_df = pd.DataFrame(
+            {"A": [11, 12, 13], "B": [20, 30, 40], "C": [20.2, 230.0, 34.0]}
+        )
+        assert new_df.equals(expected_df)
diff --git a/sdk/python/tests/integration/registration/test_universal_types.py b/sdk/python/tests/integration/registration/test_universal_types.py
index 6d016e3e85c..b03303f6eef 100644
--- a/sdk/python/tests/integration/registration/test_universal_types.py
+++ b/sdk/python/tests/integration/registration/test_universal_types.py
@@ -8,10 +8,17 @@
 import pyarrow as pa
 import pytest
 
-from feast.entity import Entity
 from feast.infra.offline_stores.offline_store import RetrievalJob
-from feast.types import Array, Bool, Float32, Int32, Int64, UnixTimestamp
-from feast.value_type import ValueType
+from feast.types import (
+    Array,
+    Bool,
+    Float32,
+    Float64,
+    Int32,
+    Int64,
+    String,
+    UnixTimestamp,
+)
 from tests.data.data_creator import create_dataset
 from tests.integration.feature_repos.universal.entities import driver
 from tests.integration.feature_repos.universal.feature_views import driver_feature_view
@@ -89,10 +96,7 @@ def get_fixtures(request, environment):
     ).lower()
     config = request.param
     df = create_dataset(
-        ValueType.INT64,
-        config.feature_dtype,
-        config.feature_is_list,
-        config.has_empty_list,
+        Int64, config.feature_dtype, config.feature_is_list, config.has_empty_list,
     )
     data_source = environment.data_source_creator.create_data_source(
         df, destination_name=destination_name, field_mapping={"ts_1": "ts"},
@@ -110,18 +114,11 @@ def get_fixtures(request, environment):
 
 @pytest.mark.integration
 @pytest.mark.universal_offline_stores
-@pytest.mark.parametrize(
-    "entity_type", [ValueType.INT32, ValueType.INT64, ValueType.STRING]
-)
+@pytest.mark.parametrize("entity_type", [Int32, Int64, String])
 def test_entity_inference_types_match(environment, entity_type):
     fs = environment.feature_store
 
     # Don't specify value type in entity to force inference
-    entity = Entity(
-        name=f"driver_{entity_type.name.lower()}",
-        value_type=ValueType.UNKNOWN,
-        join_key="driver_id",
-    )
     df = create_dataset(entity_type, feature_dtype="int32",)
     data_source = environment.data_source_creator.create_data_source(
         df,
@@ -134,20 +131,30 @@ def test_entity_inference_types_match(environment, entity_type):
         feature_is_list=False,
         has_empty_list=False,
         data_source=data_source,
-        entity=entity.name,
+        entity_type=entity_type,
     )
+
+    # TODO(felixwang9817): Refactor this by finding a better way to force type inference.
+    # Override the schema and entity_columns to force entity inference.
+    entity = driver()
+    fv.schema = list(filter(lambda x: x.name != entity.join_key, fv.schema))
+    fv.entity_columns = []
     fs.apply([fv, entity])
 
-    inferred_entity = fs.get_entity(entity.name)
     entity_type_to_expected_inferred_entity_type = {
-        ValueType.INT32: {ValueType.INT32, ValueType.INT64},
-        ValueType.INT64: {ValueType.INT32, ValueType.INT64},
-        ValueType.FLOAT: {ValueType.DOUBLE},
-        ValueType.STRING: {ValueType.STRING},
+        Int32: {Int32, Int64},
+        Int64: {Int32, Int64},
+        Float32: {Float64},
+        String: {String},
     }
+
+    entity_columns = list(
+        filter(lambda x: x.name == entity.join_key, fv.entity_columns)
+    )
+    assert len(entity_columns) == 1
+    entity_column = entity_columns[0]
     assert (
-        inferred_entity.value_type
-        in entity_type_to_expected_inferred_entity_type[entity_type]
+        entity_column.dtype in entity_type_to_expected_inferred_entity_type[entity_type]
     )
 
 
@@ -263,7 +270,12 @@ def test_feature_get_online_features_types_match(
 
 
 def create_feature_view(
-    name, feature_dtype, feature_is_list, has_empty_list, data_source, entity="driver"
+    name,
+    feature_dtype,
+    feature_is_list,
+    has_empty_list,
+    data_source,
+    entity_type=Int64,
 ):
     if feature_is_list is True:
         if feature_dtype == "int32":
@@ -288,7 +300,9 @@ def create_feature_view(
         elif feature_dtype == "datetime":
             dtype = UnixTimestamp
 
-    return driver_feature_view(data_source, name=name, dtype=dtype, entities=[entity])
+    return driver_feature_view(
+        data_source, name=name, dtype=dtype, entity_type=entity_type
+    )
 
 
 def assert_expected_historical_feature_types(
diff --git a/sdk/python/tests/integration/scaffolding/test_partial_apply.py b/sdk/python/tests/integration/scaffolding/test_partial_apply.py
index 3ab9bf196f2..e5a7206b96c 100644
--- a/sdk/python/tests/integration/scaffolding/test_partial_apply.py
+++ b/sdk/python/tests/integration/scaffolding/test_partial_apply.py
@@ -2,7 +2,7 @@
 
 import pytest
 
-from feast import BigQuerySource, FeatureView, Field
+from feast import BigQuerySource, Entity, FeatureView, Field
 from feast.types import Float32, String
 from tests.utils.cli_utils import CliRunner, get_example_repo
 from tests.utils.online_read_write_test import basic_rw_test
@@ -19,6 +19,7 @@ def test_partial() -> None:
     with runner.local_repo(
         get_example_repo("example_feature_repo_1.py"), "bigquery"
     ) as store:
+        driver = Entity(name="driver", join_keys=["test"])
 
         driver_locations_source = BigQuerySource(
             table="feast-oss.public.drivers",
@@ -28,12 +29,13 @@ def test_partial() -> None:
 
         driver_locations_100 = FeatureView(
             name="driver_locations_100",
-            entities=["driver"],
+            entities=[driver],
             ttl=timedelta(days=1),
             schema=[
                 Field(name="lat", dtype=Float32),
                 Field(name="lon", dtype=String),
                 Field(name="name", dtype=String),
+                Field(name="test", dtype=String),
             ],
             online=True,
             batch_source=driver_locations_source,
diff --git a/sdk/python/tests/unit/diff/test_registry_diff.py b/sdk/python/tests/unit/diff/test_registry_diff.py
index 483dae73e26..ae10c834c83 100644
--- a/sdk/python/tests/unit/diff/test_registry_diff.py
+++ b/sdk/python/tests/unit/diff/test_registry_diff.py
@@ -2,34 +2,36 @@
     diff_registry_objects,
     tag_objects_for_keep_delete_update_add,
 )
+from feast.entity import Entity
 from feast.feature_view import FeatureView
 from tests.utils.data_source_utils import prep_file_source
 
 
 def test_tag_objects_for_keep_delete_update_add(simple_dataset_1):
     with prep_file_source(df=simple_dataset_1, timestamp_field="ts_1") as file_source:
+        entity = Entity(name="id", join_keys=["id"])
         to_delete = FeatureView(
-            name="to_delete", entities=["id"], batch_source=file_source, ttl=None,
+            name="to_delete", entities=[entity], batch_source=file_source, ttl=None,
         )
         unchanged_fv = FeatureView(
-            name="fv1", entities=["id"], batch_source=file_source, ttl=None,
+            name="fv1", entities=[entity], batch_source=file_source, ttl=None,
         )
         pre_changed = FeatureView(
             name="fv2",
-            entities=["id"],
+            entities=[entity],
             batch_source=file_source,
             ttl=None,
             tags={"when": "before"},
         )
         post_changed = FeatureView(
             name="fv2",
-            entities=["id"],
+            entities=[entity],
             batch_source=file_source,
             ttl=None,
             tags={"when": "after"},
         )
         to_add = FeatureView(
-            name="to_add", entities=["id"], batch_source=file_source, ttl=None,
+            name="to_add", entities=[entity], batch_source=file_source, ttl=None,
         )
 
         keep, delete, update, add = tag_objects_for_keep_delete_update_add(
@@ -52,16 +54,17 @@ def test_tag_objects_for_keep_delete_update_add(simple_dataset_1):
 
 def test_diff_registry_objects_feature_views(simple_dataset_1):
     with prep_file_source(df=simple_dataset_1, timestamp_field="ts_1") as file_source:
+        entity = Entity(name="id", join_keys=["id"])
         pre_changed = FeatureView(
             name="fv2",
-            entities=["id"],
+            entities=[entity],
             batch_source=file_source,
             ttl=None,
             tags={"when": "before"},
         )
         post_changed = FeatureView(
             name="fv2",
-            entities=["id"],
+            entities=[entity],
             batch_source=file_source,
             ttl=None,
             tags={"when": "after"},
diff --git a/sdk/python/tests/unit/infra/online_store/test_dynamodb_online_store.py b/sdk/python/tests/unit/infra/online_store/test_dynamodb_online_store.py
index 6275a177e05..25eb061930b 100644
--- a/sdk/python/tests/unit/infra/online_store/test_dynamodb_online_store.py
+++ b/sdk/python/tests/unit/infra/online_store/test_dynamodb_online_store.py
@@ -318,3 +318,42 @@ def test_write_batch_non_duplicates(repo_config, dynamodb_online_store):
     returned_items = response.get("Items", None)
     assert returned_items is not None
     assert len(returned_items) == len(data)
+
+
+@mock_dynamodb2
+def test_dynamodb_online_store_online_read_unknown_entity_end_of_batch(
+    repo_config, dynamodb_online_store
+):
+    """
+    Test DynamoDBOnlineStore online_read method with unknown entities at
+    the end of the batch.
+    """
+    batch_size = repo_config.online_store.batch_size
+    n_samples = batch_size
+    _create_test_table(PROJECT, f"{TABLE_NAME}_unknown_entity_{n_samples}", REGION)
+    data = _create_n_customer_test_samples(n=n_samples)
+    _insert_data_test_table(
+        data, PROJECT, f"{TABLE_NAME}_unknown_entity_{n_samples}", REGION
+    )
+
+    entity_keys, features, *rest = zip(*data)
+    entity_keys = list(entity_keys)
+    features = list(features)
+
+    # Append a nonsensical entity to search for as the only item in the 2nd batch
+    entity_keys.append(
+        EntityKeyProto(
+            join_keys=["customer"], entity_values=[ValueProto(string_val="12359")]
+        )
+    )
+    features.append(None)
+
+    returned_items = dynamodb_online_store.online_read(
+        config=repo_config,
+        table=MockFeatureView(name=f"{TABLE_NAME}_unknown_entity_{n_samples}"),
+        entity_keys=entity_keys,
+    )
+
+    # ensure the entity is not dropped
+    assert len(returned_items) == len(entity_keys)
+    assert returned_items[-1] == (None, None)
diff --git a/sdk/python/tests/unit/infra/test_provider.py b/sdk/python/tests/unit/infra/test_provider.py
index 43c09760e9a..5ed5603b03b 100644
--- a/sdk/python/tests/unit/infra/test_provider.py
+++ b/sdk/python/tests/unit/infra/test_provider.py
@@ -20,14 +20,13 @@
 from feast.field import Field
 from feast.infra.provider import _get_column_names
 from feast.types import String
-from feast.value_type import ValueType
 
 
 def test_get_column_names_preserves_feature_ordering():
-    entity = Entity("my-entity", description="My entity", value_type=ValueType.STRING)
+    entity = Entity("my-entity", description="My entity")
     fv = FeatureView(
         name="my-fv",
-        entities=["my-entity"],
+        entities=[entity],
         ttl=timedelta(days=1),
         batch_source=BigQuerySource(table="non-existent-mock"),
         schema=[
diff --git a/sdk/python/tests/unit/test_data_sources.py b/sdk/python/tests/unit/test_data_sources.py
index 7f288d36db9..0208a715031 100644
--- a/sdk/python/tests/unit/test_data_sources.py
+++ b/sdk/python/tests/unit/test_data_sources.py
@@ -84,7 +84,7 @@ def test_hash():
     assert len(s4) == 3
 
 
-# TODO(kevjumba): Remove this test in feast 0.23 when positional arguments are removed.
+# TODO(kevjumba): Remove this test in feast 0.24 when positional arguments are removed.
 def test_default_data_source_kw_arg_warning():
     # source_class = request.param
     with pytest.warns(DeprecationWarning):
@@ -93,7 +93,7 @@ def test_default_data_source_kw_arg_warning():
         )
         assert source.name == "name"
         assert source.timestamp_field == "column"
-        assert source.kafka_options.bootstrap_servers == "bootstrap_servers"
+        assert source.kafka_options.kafka_bootstrap_servers == "bootstrap_servers"
         assert source.kafka_options.topic == "topic"
     with pytest.raises(ValueError):
         KafkaSource("name", "column", "bootstrap_servers", topic="topic")
@@ -145,7 +145,7 @@ def test_default_data_source_kw_arg_warning():
     with pytest.warns(UserWarning):
         source = KafkaSource(
             timestamp_field="column",
-            bootstrap_servers="bootstrap_servers",
+            kafka_bootstrap_servers="bootstrap_servers",
             message_format=ProtoFormat("class_path"),
             topic="topic",
         )
@@ -203,7 +203,7 @@ def test_proto_conversion():
 
     kafka_source = KafkaSource(
         name="test_source",
-        bootstrap_servers="test_servers",
+        kafka_bootstrap_servers="test_servers",
         message_format=ProtoFormat("class_path"),
         topic="test_topic",
         timestamp_field="event_timestamp",
diff --git a/sdk/python/tests/unit/test_entity.py b/sdk/python/tests/unit/test_entity.py
index 254a975f678..04a857ddefe 100644
--- a/sdk/python/tests/unit/test_entity.py
+++ b/sdk/python/tests/unit/test_entity.py
@@ -20,19 +20,14 @@
 
 def test_join_key_default():
     with pytest.deprecated_call():
-        entity = Entity(
-            "my-entity", description="My entity", value_type=ValueType.STRING
-        )
+        entity = Entity("my-entity", description="My entity")
     assert entity.join_key == "my-entity"
 
 
 def test_entity_class_contains_tags():
     with pytest.deprecated_call():
         entity = Entity(
-            "my-entity",
-            description="My entity",
-            value_type=ValueType.STRING,
-            tags={"key1": "val1", "key2": "val2"},
+            "my-entity", description="My entity", tags={"key1": "val1", "key2": "val2"},
         )
     assert "key1" in entity.tags.keys() and entity.tags["key1"] == "val1"
     assert "key2" in entity.tags.keys() and entity.tags["key2"] == "val2"
@@ -40,20 +35,18 @@ def test_entity_class_contains_tags():
 
 def test_entity_without_tags_empty_dict():
     with pytest.deprecated_call():
-        entity = Entity(
-            "my-entity", description="My entity", value_type=ValueType.STRING
-        )
+        entity = Entity("my-entity", description="My entity")
     assert entity.tags == dict()
     assert len(entity.tags) == 0
 
 
 def test_entity_without_description():
     with pytest.deprecated_call():
-        Entity("my-entity", value_type=ValueType.STRING)
+        Entity("my-entity")
 
 
 def test_name_not_specified():
-    assertpy.assert_that(lambda: Entity(value_type=ValueType.STRING)).raises(ValueError)
+    assertpy.assert_that(lambda: Entity()).raises(ValueError)
 
 
 def test_multiple_args():
@@ -61,15 +54,19 @@ def test_multiple_args():
 
 
 def test_name_keyword(recwarn):
-    Entity(name="my-entity", value_type=ValueType.STRING)
+    Entity(name="my-entity")
     assert len(recwarn) == 0
+    Entity(name="my-entity", join_key="test")
+    assert len(recwarn) == 1
+    Entity(name="my-entity", join_keys=["test"])
+    assert len(recwarn) == 1
 
 
 def test_hash():
-    entity1 = Entity(name="my-entity", value_type=ValueType.STRING)
-    entity2 = Entity(name="my-entity", value_type=ValueType.STRING)
-    entity3 = Entity(name="my-entity", value_type=ValueType.FLOAT)
-    entity4 = Entity(name="my-entity", value_type=ValueType.FLOAT, description="test")
+    entity1 = Entity(name="my-entity")
+    entity2 = Entity(name="my-entity")
+    entity3 = Entity(name="my-entity", join_keys=["not-my-entity"])
+    entity4 = Entity(name="my-entity", join_keys=["not-my-entity"], description="test")
 
     s1 = {entity1, entity2}
     assert len(s1) == 1
diff --git a/sdk/python/tests/unit/test_feature_view.py b/sdk/python/tests/unit/test_feature_view.py
deleted file mode 100644
index 80a583806e7..00000000000
--- a/sdk/python/tests/unit/test_feature_view.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Copyright 2022 The Feast Authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from feast.feature_view import FeatureView
-from feast.field import Field
-from feast.infra.offline_stores.file_source import FileSource
-from feast.types import Float32
-
-
-def test_hash():
-    file_source = FileSource(name="my-file-source", path="test.parquet")
-    feature_view_1 = FeatureView(
-        name="my-feature-view",
-        entities=[],
-        schema=[
-            Field(name="feature1", dtype=Float32),
-            Field(name="feature2", dtype=Float32),
-        ],
-        source=file_source,
-    )
-    feature_view_2 = FeatureView(
-        name="my-feature-view",
-        entities=[],
-        schema=[
-            Field(name="feature1", dtype=Float32),
-            Field(name="feature2", dtype=Float32),
-        ],
-        source=file_source,
-    )
-    feature_view_3 = FeatureView(
-        name="my-feature-view",
-        entities=[],
-        schema=[Field(name="feature1", dtype=Float32)],
-        source=file_source,
-    )
-    feature_view_4 = FeatureView(
-        name="my-feature-view",
-        entities=[],
-        schema=[Field(name="feature1", dtype=Float32)],
-        source=file_source,
-        description="test",
-    )
-
-    s1 = {feature_view_1, feature_view_2}
-    assert len(s1) == 1
-
-    s2 = {feature_view_1, feature_view_3}
-    assert len(s2) == 2
-
-    s3 = {feature_view_3, feature_view_4}
-    assert len(s3) == 2
-
-    s4 = {feature_view_1, feature_view_2, feature_view_3, feature_view_4}
-    assert len(s4) == 3
diff --git a/sdk/python/tests/unit/test_feature_views.py b/sdk/python/tests/unit/test_feature_views.py
index 8708a983c41..d6be8e03411 100644
--- a/sdk/python/tests/unit/test_feature_views.py
+++ b/sdk/python/tests/unit/test_feature_views.py
@@ -2,11 +2,17 @@
 
 import pytest
 
+from feast.aggregation import Aggregation
 from feast.batch_feature_view import BatchFeatureView
 from feast.data_format import AvroFormat
-from feast.data_source import KafkaSource
+from feast.data_source import KafkaSource, PushSource
+from feast.entity import Entity
+from feast.feature_view import FeatureView
+from feast.field import Field
 from feast.infra.offline_stores.file_source import FileSource
-from feast.stream_feature_view import StreamFeatureView
+from feast.protos.feast.types.Value_pb2 import ValueType
+from feast.stream_feature_view import StreamFeatureView, stream_feature_view
+from feast.types import Float32
 
 
 def test_create_batch_feature_view():
@@ -25,8 +31,8 @@ def test_create_batch_feature_view():
 
     stream_source = KafkaSource(
         name="kafka",
-        timestamp_field="",
-        bootstrap_servers="",
+        timestamp_field="event_timestamp",
+        kafka_bootstrap_servers="",
         message_format=AvroFormat(""),
         topic="topic",
         batch_source=FileSource(path="some path"),
@@ -43,22 +49,37 @@ def test_create_batch_feature_view():
 def test_create_stream_feature_view():
     stream_source = KafkaSource(
         name="kafka",
-        timestamp_field="",
-        bootstrap_servers="",
+        timestamp_field="event_timestamp",
+        kafka_bootstrap_servers="",
         message_format=AvroFormat(""),
         topic="topic",
         batch_source=FileSource(path="some path"),
     )
     StreamFeatureView(
-        name="test batch feature view",
+        name="test kafka stream feature view",
         entities=[],
         ttl=timedelta(days=30),
         source=stream_source,
+        aggregations=[],
+    )
+
+    push_source = PushSource(
+        name="push source", batch_source=FileSource(path="some path")
+    )
+    StreamFeatureView(
+        name="test push source feature view",
+        entities=[],
+        ttl=timedelta(days=30),
+        source=push_source,
+        aggregations=[],
     )
 
     with pytest.raises(ValueError):
         StreamFeatureView(
-            name="test batch feature view", entities=[], ttl=timedelta(days=30)
+            name="test batch feature view",
+            entities=[],
+            ttl=timedelta(days=30),
+            aggregations=[],
         )
 
     with pytest.raises(ValueError):
@@ -67,4 +88,174 @@ def test_create_stream_feature_view():
             entities=[],
             ttl=timedelta(days=30),
             source=FileSource(path="some path"),
+            aggregations=[],
         )
+
+
+def simple_udf(x: int):
+    return x + 3
+
+
+def test_stream_feature_view_serialization():
+    entity = Entity(name="driver_entity", join_keys=["test_key"])
+    stream_source = KafkaSource(
+        name="kafka",
+        timestamp_field="event_timestamp",
+        kafka_bootstrap_servers="",
+        message_format=AvroFormat(""),
+        topic="topic",
+        batch_source=FileSource(path="some path"),
+    )
+
+    sfv = StreamFeatureView(
+        name="test kafka stream feature view",
+        entities=[entity],
+        ttl=timedelta(days=30),
+        owner="test@example.com",
+        online=True,
+        schema=[Field(name="dummy_field", dtype=Float32)],
+        description="desc",
+        aggregations=[
+            Aggregation(
+                column="dummy_field", function="max", time_window=timedelta(days=1),
+            )
+        ],
+        timestamp_field="event_timestamp",
+        mode="spark",
+        source=stream_source,
+        udf=simple_udf,
+        tags={},
+    )
+
+    sfv_proto = sfv.to_proto()
+
+    new_sfv = StreamFeatureView.from_proto(sfv_proto=sfv_proto)
+    assert new_sfv == sfv
+
+
+def test_stream_feature_view_udfs():
+    entity = Entity(name="driver_entity", join_keys=["test_key"])
+    stream_source = KafkaSource(
+        name="kafka",
+        timestamp_field="event_timestamp",
+        kafka_bootstrap_servers="",
+        message_format=AvroFormat(""),
+        topic="topic",
+        batch_source=FileSource(path="some path"),
+    )
+
+    @stream_feature_view(
+        entities=[entity],
+        ttl=timedelta(days=30),
+        owner="test@example.com",
+        online=True,
+        schema=[Field(name="dummy_field", dtype=Float32)],
+        description="desc",
+        aggregations=[
+            Aggregation(
+                column="dummy_field", function="max", time_window=timedelta(days=1),
+            )
+        ],
+        timestamp_field="event_timestamp",
+        source=stream_source,
+    )
+    def pandas_udf(pandas_df):
+        import pandas as pd
+
+        assert type(pandas_df) == pd.DataFrame
+        df = pandas_df.transform(lambda x: x + 10, axis=1)
+        return df
+
+    import pandas as pd
+
+    df = pd.DataFrame({"A": [1, 2, 3], "B": [10, 20, 30]})
+    sfv = pandas_udf
+    sfv_proto = sfv.to_proto()
+    new_sfv = StreamFeatureView.from_proto(sfv_proto)
+    new_df = new_sfv.udf(df)
+
+    expected_df = pd.DataFrame({"A": [11, 12, 13], "B": [20, 30, 40]})
+
+    assert new_df.equals(expected_df)
+
+
+def test_stream_feature_view_initialization_with_optional_fields_omitted():
+    entity = Entity(name="driver_entity", join_keys=["test_key"])
+    stream_source = KafkaSource(
+        name="kafka",
+        timestamp_field="event_timestamp",
+        kafka_bootstrap_servers="",
+        message_format=AvroFormat(""),
+        topic="topic",
+        batch_source=FileSource(path="some path"),
+    )
+
+    sfv = StreamFeatureView(
+        name="test kafka stream feature view",
+        entities=[entity],
+        schema=[],
+        description="desc",
+        timestamp_field="event_timestamp",
+        source=stream_source,
+        tags={},
+    )
+    sfv_proto = sfv.to_proto()
+
+    new_sfv = StreamFeatureView.from_proto(sfv_proto=sfv_proto)
+    assert new_sfv == sfv
+
+
+def test_hash():
+    file_source = FileSource(name="my-file-source", path="test.parquet")
+    feature_view_1 = FeatureView(
+        name="my-feature-view",
+        entities=[],
+        schema=[
+            Field(name="feature1", dtype=Float32),
+            Field(name="feature2", dtype=Float32),
+        ],
+        source=file_source,
+    )
+    feature_view_2 = FeatureView(
+        name="my-feature-view",
+        entities=[],
+        schema=[
+            Field(name="feature1", dtype=Float32),
+            Field(name="feature2", dtype=Float32),
+        ],
+        source=file_source,
+    )
+    feature_view_3 = FeatureView(
+        name="my-feature-view",
+        entities=[],
+        schema=[Field(name="feature1", dtype=Float32)],
+        source=file_source,
+    )
+    feature_view_4 = FeatureView(
+        name="my-feature-view",
+        entities=[],
+        schema=[Field(name="feature1", dtype=Float32)],
+        source=file_source,
+        description="test",
+    )
+
+    s1 = {feature_view_1, feature_view_2}
+    assert len(s1) == 1
+
+    s2 = {feature_view_1, feature_view_3}
+    assert len(s2) == 2
+
+    s3 = {feature_view_3, feature_view_4}
+    assert len(s3) == 2
+
+    s4 = {feature_view_1, feature_view_2, feature_view_3, feature_view_4}
+    assert len(s4) == 3
+
+
+# TODO(felixwang9817): Add tests for proto conversion.
+# TODO(felixwang9817): Add tests for field mapping logic.
+
+
+def test_field_types():
+    with pytest.raises(TypeError):
+        Field(name="name", dtype=ValueType.INT32)
diff --git a/sdk/python/tests/unit/test_type_map.py b/sdk/python/tests/unit/test_type_map.py
new file mode 100644
index 00000000000..0ba259ab73f
--- /dev/null
+++ b/sdk/python/tests/unit/test_type_map.py
@@ -0,0 +1,28 @@
+import numpy as np
+
+from feast.type_map import (
+    feast_value_type_to_python_type,
+    python_values_to_proto_values,
+)
+from feast.value_type import ValueType
+
+
+def test_null_unix_timestamp():
+    """Test that null UnixTimestamps get converted from proto correctly."""
+
+    data = np.array(["NaT"], dtype="datetime64")
+    protos = python_values_to_proto_values(data, ValueType.UNIX_TIMESTAMP)
+    converted = feast_value_type_to_python_type(protos[0])
+
+    assert converted is None
+
+
+def test_null_unix_timestamp_list():
+    """Test that UnixTimestamp lists with a null get converted from proto
+    correctly."""
+
+    data = np.array([["NaT"]], dtype="datetime64")
+    protos = python_values_to_proto_values(data, ValueType.UNIX_TIMESTAMP_LIST)
+    converted = feast_value_type_to_python_type(protos[0])
+
+    assert converted[0] is None
diff --git a/sdk/python/tests/unit/test_unit_feature_store.py b/sdk/python/tests/unit/test_unit_feature_store.py
index 6f9dd6acb08..0c13dffa629 100644
--- a/sdk/python/tests/unit/test_unit_feature_store.py
+++ b/sdk/python/tests/unit/test_unit_feature_store.py
@@ -17,7 +17,7 @@ class MockFeatureView:
     projection: MockFeatureViewProjection
 
 
-def test__get_unique_entities():
+def test_get_unique_entities():
     entity_values = {
         "entity_1": [Value(int64_val=1), Value(int64_val=2), Value(int64_val=1)],
         "entity_2": [
diff --git a/sdk/python/tests/utils/cli_utils.py b/sdk/python/tests/utils/cli_utils.py
index 5d6d5722eb1..ee6ea138fbe 100644
--- a/sdk/python/tests/utils/cli_utils.py
+++ b/sdk/python/tests/utils/cli_utils.py
@@ -33,7 +33,9 @@ class CliRunner:
     """
 
     def run(self, args: List[str], cwd: Path) -> subprocess.CompletedProcess:
-        return subprocess.run([sys.executable, cli.__file__] + args, cwd=cwd)
+        return subprocess.run(
+            [sys.executable, cli.__file__] + args, cwd=cwd, capture_output=True
+        )
 
     def run_with_output(self, args: List[str], cwd: Path) -> Tuple[int, bytes]:
         try:
@@ -82,9 +84,13 @@ def local_repo(self, example_repo_py: str, offline_store: str):
             repo_example.write_text(example_repo_py)
 
             result = self.run(["apply"], cwd=repo_path)
-            assert result.returncode == 0
+            assert (
+                result.returncode == 0
+            ), f"stdout: {result.stdout}\n stderr: {result.stderr}"
 
             yield FeatureStore(repo_path=str(repo_path), config=None)
 
             result = self.run(["teardown"], cwd=repo_path)
-            assert result.returncode == 0
+            assert (
+                result.returncode == 0
+            ), f"stdout: {result.stdout}\n stderr: {result.stderr}"
diff --git a/sdk/python/tests/utils/logged_features.py b/sdk/python/tests/utils/logged_features.py
new file mode 100644
index 00000000000..dc844a60b42
--- /dev/null
+++ b/sdk/python/tests/utils/logged_features.py
@@ -0,0 +1,81 @@
+import contextlib
+import datetime
+import tempfile
+import uuid
+from pathlib import Path
+from typing import Iterator, Union
+
+import numpy as np
+import pandas as pd
+import pyarrow
+
+from feast import FeatureService, FeatureStore, FeatureView
+from feast.errors import FeatureViewNotFoundException
+from feast.feature_logging import LOG_DATE_FIELD, LOG_TIMESTAMP_FIELD, REQUEST_ID_FIELD
+from feast.protos.feast.serving.ServingService_pb2 import FieldStatus
+
+
+def prepare_logs(
+    source_df: pd.DataFrame, feature_service: FeatureService, store: FeatureStore
+) -> pd.DataFrame:
+    num_rows = source_df.shape[0]
+
+    logs_df = pd.DataFrame()
+    logs_df[REQUEST_ID_FIELD] = [str(uuid.uuid4()) for _ in range(num_rows)]
+    logs_df[LOG_TIMESTAMP_FIELD] = pd.Series(
+        np.random.randint(0, 7 * 24 * 3600, num_rows)
+    ).map(lambda secs: pd.Timestamp.utcnow() - datetime.timedelta(seconds=secs))
+    logs_df[LOG_DATE_FIELD] = logs_df[LOG_TIMESTAMP_FIELD].dt.date
+
+    for projection in feature_service.feature_view_projections:
+        try:
+            view = store.get_feature_view(projection.name)
+        except FeatureViewNotFoundException:
+            view = store.get_on_demand_feature_view(projection.name)
+            for source in view.source_request_sources.values():
+                for field in source.schema:
+                    logs_df[field.name] = source_df[field.name]
+        else:
+            for entity_name in view.entities:
+                entity = store.get_entity(entity_name)
+                logs_df[entity.join_key] = source_df[entity.join_key]
+
+        for feature in projection.features:
+            source_field = (
+                feature.name
+                if feature.name in source_df.columns
+                else f"{projection.name_to_use()}__{feature.name}"
+            )
+            destination_field = f"{projection.name_to_use()}__{feature.name}"
+            logs_df[destination_field] = source_df[source_field]
+            logs_df[f"{destination_field}__timestamp"] = source_df[
+                "event_timestamp"
+            ].dt.floor("s")
+            if logs_df[f"{destination_field}__timestamp"].dt.tz:
+                logs_df[f"{destination_field}__timestamp"] = logs_df[
+                    f"{destination_field}__timestamp"
+                ].dt.tz_convert(None)
+            logs_df[f"{destination_field}__status"] = FieldStatus.PRESENT
+            if isinstance(view, FeatureView) and view.ttl:
+                logs_df[f"{destination_field}__status"] = logs_df[
+                    f"{destination_field}__status"
+                ].mask(
+                    logs_df[f"{destination_field}__timestamp"]
+                    < (datetime.datetime.utcnow() - view.ttl),
+                    FieldStatus.OUTSIDE_MAX_AGE,
+                )
+
+    return logs_df
+
+
+@contextlib.contextmanager
+def to_logs_dataset(
+    table: pyarrow.Table, pass_as_path: bool
+) -> Iterator[Union[pyarrow.Table, Path]]:
+    if not pass_as_path:
+        yield table
+        return
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        pyarrow.parquet.write_to_dataset(table, root_path=temp_dir)
+        yield Path(temp_dir)
diff --git a/sdk/python/tests/utils/online_write_benchmark.py b/sdk/python/tests/utils/online_write_benchmark.py
index 82ffc8e98bf..9f2f8ba60df 100644
--- a/sdk/python/tests/utils/online_write_benchmark.py
+++ b/sdk/python/tests/utils/online_write_benchmark.py
@@ -17,13 +17,13 @@
 from feast.infra.provider import _convert_arrow_to_proto
 from feast.repo_config import RepoConfig
 from feast.types import Float32, Int32
-from feast.value_type import ValueType
 
 
 def create_driver_hourly_stats_feature_view(source):
+    driver = Entity(name="driver", join_keys=["driver_id"])
     driver_stats_feature_view = FeatureView(
         name="driver_stats",
-        entities=["driver_id"],
+        entities=[driver],
         schema=[
             Field(name="conv_rate", dtype=Float32),
             Field(name="acc_rate", dtype=Float32),
@@ -61,7 +61,7 @@ def benchmark_writes():
         # This is just to set data source to something, we're not reading from parquet source here.
         parquet_path = os.path.join(temp_dir, "data.parquet")
 
-        driver = Entity(name="driver_id", value_type=ValueType.INT64)
+        driver = Entity(name="driver_id")
         table = create_driver_hourly_stats_feature_view(
             create_driver_hourly_stats_source(parquet_path=parquet_path)
         )
diff --git a/setup.py b/setup.py
index 2044de008fc..d32b13deed0 100644
--- a/setup.py
+++ b/setup.py
@@ -51,7 +51,7 @@
     "dill==0.3.*",
     "fastavro>=1.1.0,<2",
     "google-api-core>=1.23.0,<3",
-    "googleapis-common-protos==1.52.*,<2",
+    "googleapis-common-protos>=1.52.*,<2",
     "grpcio>=1.34.0,<2",
     "grpcio-reflection>=1.34.0,<2",
     "Jinja2>=2,<4",
@@ -61,19 +61,22 @@
     "pandas>=1,<2",
     "pandavro==1.5.*",
     "protobuf>=3.10,<3.20",
-    "proto-plus<1.19.7",
+    "proto-plus==1.20.*",
     "pyarrow>=4,<7",
     "pydantic>=1,<2",
+    "pygments==2.12.0",
     "PyYAML>=5.4.*,<7",
+    "SQLAlchemy[mypy]>1,<2",
     "tabulate==0.8.*",
     "tenacity>=7,<9",
     "toml==0.10.*",
     "tqdm==4.*",
+    "typeguard",
     "fastapi>=0.68.0,<1",
     "uvicorn[standard]>=0.14.0,<1",
-    "proto-plus<1.19.7",
     "tensorflow-metadata>=1.0.0,<2.0.0",
     "dask>=2021.*,<2022.02.0",
+    "bowler",  # Needed for automatic repo upgrades
 ]
 
 GCP_REQUIRED = [
@@ -89,13 +92,10 @@
     "hiredis>=2.0.0,<3",
 ]
 
-AWS_REQUIRED = [
-    "boto3>=1.17.0",
-    "docker>=5.0.2",
-]
+AWS_REQUIRED = ["boto3>=1.17.0,<=1.20.23", "docker>=5.0.2", "s3fs>=0.4.0,<=2022.01.0"]
 
 SNOWFLAKE_REQUIRED = [
-    "snowflake-connector-python[pandas]>=2.7.3,<3",
+    "snowflake-connector-python[pandas]>=2.7.3,<=2.7.8",
 ]
 
 SPARK_REQUIRED = [
@@ -110,6 +110,10 @@
     "psycopg2-binary>=2.8.3,<3",
 ]
 
+MYSQL_REQUIRED = [
+    "mysqlclient",
+]
+
 HBASE_REQUIRED = [
     "happybase>=1.2.0,<3",
 ]
@@ -123,7 +127,7 @@
 CI_REQUIRED = (
     [
         "build",
-        "cryptography==3.4.8",
+        "cryptography==35.0",
         "flake8",
         "black==19.10b0",
         "isort>=5,<6",
@@ -132,10 +136,10 @@
         "minio==7.1.0",
         "mock==2.0.0",
         "moto",
-        "mypy==0.931",
+        "mypy>=0.931",
         "mypy-protobuf==3.1",
         "avro==1.10.0",
-        "gcsfs",
+        "gcsfs>=0.4.0,<=2022.01.0",
         "urllib3>=1.25.4,<2",
         "psutil==5.9.0",
         "pytest>=6.0.0,<8",
@@ -170,11 +174,19 @@
     + SNOWFLAKE_REQUIRED
     + SPARK_REQUIRED
     + POSTGRES_REQUIRED
+    + MYSQL_REQUIRED
     + TRINO_REQUIRED
     + GE_REQUIRED
     + HBASE_REQUIRED
 )
 
+
+# rtd builds fail because of mysql not being installed in their environment.
+# We can add mysql there, but it's not strictly needed. This will be faster for builds.
+DOCS_REQUIRED = CI_REQUIRED.copy()
+for _r in MYSQL_REQUIRED:
+    DOCS_REQUIRED.remove(_r)
+
 DEV_REQUIRED = ["mypy-protobuf==3.1", "grpcio-testing==1.*"] + CI_REQUIRED
 
 # Get git repo root directory
@@ -246,7 +258,7 @@ def _generate_python_protos(self, path: str):
                 "--mypy_out",
                 self.python_folder,
             ]
-            + proto_files,
+            + proto_files
         )
 
     def run(self):
@@ -397,6 +409,7 @@ def _is_go_ext(self, ext: Extension):
         )
 
     def build_extension(self, ext: Extension):
+        print(f"Building extension {ext}")
         if not self._is_go_ext(ext):
             # the base class may mutate `self.compiler`
             compiler = copy.deepcopy(self.compiler)
@@ -411,9 +424,16 @@ def build_extension(self, ext: Extension):
             subprocess.check_output(["go", "env", "-json"]).decode("utf-8").strip()
         )
 
+        print(f"Go env: {go_env}")
+        print(f"CWD: {os.getcwd()}")
+
         destination = os.path.dirname(os.path.abspath(self.get_ext_fullpath(ext.name)))
-        subprocess.check_call(["go", "install", "golang.org/x/tools/cmd/goimports"])
-        subprocess.check_call(["go", "install", "github.com/go-python/gopy"])
+        subprocess.check_call(["go", "install", "golang.org/x/tools/cmd/goimports"],
+                              env={"PATH": bin_path, **go_env})
+        subprocess.check_call(["go", "get", "github.com/go-python/gopy@v0.4.0"],
+                              env={"PATH": bin_path, **go_env})
+        subprocess.check_call(["go", "install", "github.com/go-python/gopy"],
+                              env={"PATH": bin_path, **go_env})
         subprocess.check_call(
             [
                 "gopy",
@@ -445,6 +465,7 @@ def copy_extensions_to_source(self):
             src_dir = os.path.join(self.build_lib, src_dir)
 
             # copy whole directory
+            print(f"Copying from {src_dir} to {dest_dir}")
             copy_tree(src_dir, dest_dir)
 
 
@@ -473,9 +494,11 @@ def copy_extensions_to_source(self):
         "spark": SPARK_REQUIRED,
         "trino": TRINO_REQUIRED,
         "postgres": POSTGRES_REQUIRED,
+        "mysql": MYSQL_REQUIRED,
         "ge": GE_REQUIRED,
         "hbase": HBASE_REQUIRED,
         "go": GO_REQUIRED,
+        "docs": DOCS_REQUIRED,
     },
     include_package_data=True,
     license="Apache",
@@ -494,6 +517,7 @@ def copy_extensions_to_source(self):
         "grpcio",
         "grpcio-tools==1.44.0",
         "mypy-protobuf==3.1",
+        "pybindgen==0.22.0",
         "sphinx!=4.0.0",
     ],
     cmdclass={
diff --git a/ui/feature_repo/features.py b/ui/feature_repo/features.py
index efdab694e83..0e6b7f4238e 100644
--- a/ui/feature_repo/features.py
+++ b/ui/feature_repo/features.py
@@ -1,18 +1,11 @@
 from datetime import timedelta
 
-from feast import (
-    Entity,
-    FeatureService,
-    FeatureView,
-    Field,
-    FileSource,
-    ValueType,
-)
+import pandas as pd
+
+from feast import Entity, FeatureService, FeatureView, Field, FileSource, ValueType
 from feast.data_source import RequestSource
-from feast.request_feature_view import RequestFeatureView
 from feast.on_demand_feature_view import on_demand_feature_view
 from feast.types import Bool, Int64, String
-import pandas as pd
 
 zipcode = Entity(
     name="zipcode",
@@ -128,19 +121,14 @@
 # Define a request data source which encodes features / information only
 # available at request time (e.g. part of the user initiated HTTP request)
 input_request = RequestSource(
-    name="transaction",
-    schema=[
-        Field(name="transaction_amt", dtype=Int64),
-    ],
+    name="transaction", schema=[Field(name="transaction_amt", dtype=Int64),],
 )
 
 # Define an on demand feature view which can generate new features based on
 # existing feature views and RequestSource features
 @on_demand_feature_view(
     sources=[credit_history, input_request],
-    schema=[
-        Field(name="transaction_gt_last_credit_card_due", dtype=Bool),
-    ],
+    schema=[Field(name="transaction_gt_last_credit_card_due", dtype=Bool),],
 )
 def transaction_gt_last_credit_card_due(inputs: pd.DataFrame) -> pd.DataFrame:
     df = pd.DataFrame()
@@ -149,6 +137,7 @@ def transaction_gt_last_credit_card_due(inputs: pd.DataFrame) -> pd.DataFrame:
     )
     return df
 
+
 model_v1 = FeatureService(
     name="credit_score_v1",
     features=[
diff --git a/ui/feature_repo/test_get_features.py b/ui/feature_repo/test_get_features.py
index 42af53e94b8..722c5a3dd46 100644
--- a/ui/feature_repo/test_get_features.py
+++ b/ui/feature_repo/test_get_features.py
@@ -1,12 +1,11 @@
 import pandas as pd
-from feast import FeatureStore
-from feast.infra.offline_stores.file import SavedDatasetFileStorage
-
-from feast.dqm.profilers.ge_profiler import ge_profiler
-
 from great_expectations.core.expectation_suite import ExpectationSuite
 from great_expectations.dataset import PandasDataset
 
+from feast import FeatureStore
+from feast.dqm.profilers.ge_profiler import ge_profiler
+from feast.infra.offline_stores.file import SavedDatasetFileStorage
+
 DELTA = 0.1  # controlling allowed window in fraction of the value on scale [0, 1]
 # Note: the GE integration allows asserting differences between datasets. The "ds" below is the reference dataset to check and this generates the expectation suite which can be used against future datasets.
 # It's used via ge.validate(new_dataset, ExpectationSuite)
diff --git a/ui/src/parsers/feastEntities.ts b/ui/src/parsers/feastEntities.ts
index 3dddb8dca13..09057c6fe92 100644
--- a/ui/src/parsers/feastEntities.ts
+++ b/ui/src/parsers/feastEntities.ts
@@ -4,7 +4,7 @@ import { FEAST_FEATURE_VALUE_TYPES } from "./types";
 const FeastEntitySchema = z.object({
   spec: z.object({
     name: z.string(),
-    valueType: z.nativeEnum(FEAST_FEATURE_VALUE_TYPES),
+    valueType: z.nativeEnum(FEAST_FEATURE_VALUE_TYPES).optional(),
     joinKey: z.string(),
     description: z.string().optional(),
     labels: z.record(z.string()).optional(),
diff --git a/ui/src/parsers/feastFeatureViews.ts b/ui/src/parsers/feastFeatureViews.ts
index 3e63b5afd0e..cbf15d280e6 100644
--- a/ui/src/parsers/feastFeatureViews.ts
+++ b/ui/src/parsers/feastFeatureViews.ts
@@ -39,7 +39,7 @@ const FeastFeatureViewSchema = z.object({
     features: z.array(FeastFeatureColumnSchema),
     ttl: z.string().transform((val) => parseInt(val)),
     batchSource: FeastBatchSourceSchema,
-    online: z.boolean(),
+    online: z.boolean().optional(),
     owner: z.string().optional(),
     tags: z.record(z.string()).optional(),
   }),
diff --git a/ui/yarn.lock b/ui/yarn.lock
index 8079556b8d3..998565a77ac 100644
--- a/ui/yarn.lock
+++ b/ui/yarn.lock
@@ -1130,6 +1130,11 @@
     "@babel/helper-validator-identifier" "^7.16.7"
     to-fast-properties "^2.0.0"
 
+"@base2/pretty-print-object@1.0.1":
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/@base2/pretty-print-object/-/pretty-print-object-1.0.1.tgz#371ba8be66d556812dc7fb169ebc3c08378f69d4"
+  integrity sha512-4iri8i1AqYHJE2DstZYkyEprg6Pq6sKx3xn5FpySk9sNhH7qN2LLlHJCfDTZRILNwQNPD7mATWM0TBui7uC1pA==
+
 "@bcoe/v8-coverage@^0.2.3":
   version "0.2.3"
   resolved "https://registry.yarnpkg.com/@bcoe/v8-coverage/-/v8-coverage-0.2.3.tgz#75a2e8b51cb758a7553d6804a5932d7aace75c39"
@@ -1147,18 +1152,18 @@
   dependencies:
     tslib "^1.9.3"
 
-"@elastic/eui@^46.1.0":
-  version "46.1.0"
-  resolved "https://registry.yarnpkg.com/@elastic/eui/-/eui-46.1.0.tgz#b1d77a56d529001858b7fc91f6c2953288549336"
-  integrity sha512-j65NMzqKluVPtk3b6UYdVVaBha74Y8Y/N6QaHin9MmqUKoAPgevqHWvi0n/OmMpADdRCI0ldxkse6tgAvdaEHQ==
+"@elastic/eui@^55.0.1":
+  version "55.1.2"
+  resolved "https://registry.yarnpkg.com/@elastic/eui/-/eui-55.1.2.tgz#dd0b42f5b26c5800d6a9cb2d4c2fe1afce9d3f07"
+  integrity sha512-wwZz5KxMIMFlqEsoCRiQBJDc4CrluS1d0sCOmQ5lhIzKhYc91MdxnqCk2i6YkhL4sSDf2Y9KAEuMXa+uweOWUA==
   dependencies:
     "@types/chroma-js" "^2.0.0"
     "@types/lodash" "^4.14.160"
     "@types/numeral" "^0.0.28"
-    "@types/react-beautiful-dnd" "^13.0.0"
-    "@types/react-input-autosize" "^2.2.0"
-    "@types/react-virtualized-auto-sizer" "^1.0.0"
-    "@types/react-window" "^1.8.2"
+    "@types/react-beautiful-dnd" "^13.1.2"
+    "@types/react-input-autosize" "^2.2.1"
+    "@types/react-virtualized-auto-sizer" "^1.0.1"
+    "@types/react-window" "^1.8.5"
     "@types/refractor" "^3.0.0"
     "@types/resize-observer-browser" "^0.1.5"
     "@types/vfile-message" "^2.0.0"
@@ -1168,13 +1173,14 @@
     mdast-util-to-hast "^10.0.0"
     numeral "^2.0.6"
     prop-types "^15.6.0"
-    react-beautiful-dnd "^13.0.0"
-    react-dropzone "^11.2.0"
-    react-focus-on "^3.5.0"
-    react-input-autosize "^2.2.2"
-    react-is "~16.3.0"
-    react-virtualized-auto-sizer "^1.0.2"
-    react-window "^1.8.5"
+    react-beautiful-dnd "^13.1.0"
+    react-dropzone "^11.5.3"
+    react-element-to-jsx-string "^14.3.4"
+    react-focus-on "^3.5.4"
+    react-input-autosize "^3.0.0"
+    react-is "^17.0.2"
+    react-virtualized-auto-sizer "^1.0.6"
+    react-window "^1.8.6"
     refractor "^3.5.0"
     rehype-raw "^5.0.0"
     rehype-react "^6.0.0"
@@ -1183,11 +1189,11 @@
     remark-emoji "^2.1.0"
     remark-parse "^8.0.3"
     remark-rehype "^8.0.0"
-    tabbable "^3.0.0"
+    tabbable "^5.2.1"
     text-diff "^1.0.1"
     unified "^9.2.0"
     unist-util-visit "^2.0.3"
-    url-parse "^1.5.3"
+    url-parse "^1.5.10"
     uuid "^8.3.0"
     vfile "^4.2.0"
 
@@ -2357,7 +2363,7 @@
   resolved "https://registry.yarnpkg.com/@types/range-parser/-/range-parser-1.2.4.tgz#cd667bcfdd025213aafb7ca5915a932590acdcdc"
   integrity sha512-EEhsLsD6UsDM1yFhAvy0Cjr6VwmpMWqFBCb9w07wVugF7w9nfajxLuVmngTIpgS6svCnm6Vaw+MZhoDCKnOfsw==
 
-"@types/react-beautiful-dnd@^13.0.0":
+"@types/react-beautiful-dnd@^13.1.2":
   version "13.1.2"
   resolved "https://registry.yarnpkg.com/@types/react-beautiful-dnd/-/react-beautiful-dnd-13.1.2.tgz#510405abb09f493afdfd898bf83995dc6385c130"
   integrity sha512-+OvPkB8CdE/bGdXKyIhc/Lm2U7UAYCCJgsqmopFmh9gbAudmslkI8eOrPDjg4JhwSE6wytz4a3/wRjKtovHVJg==
@@ -2371,7 +2377,7 @@
   dependencies:
     "@types/react" "*"
 
-"@types/react-input-autosize@^2.2.0":
+"@types/react-input-autosize@^2.2.1":
   version "2.2.1"
   resolved "https://registry.yarnpkg.com/@types/react-input-autosize/-/react-input-autosize-2.2.1.tgz#6a335212e7fce1e1a4da56ae2095c8c5c35fbfe6"
   integrity sha512-RxzEjd4gbLAAdLQ92Q68/AC+TfsAKTc4evsArUH1aIShIMqQMIMjsxoSnwyjtbFTO/AGIW/RQI94XSdvOxCz/w==
@@ -2388,14 +2394,14 @@
     hoist-non-react-statics "^3.3.0"
     redux "^4.0.0"
 
-"@types/react-virtualized-auto-sizer@^1.0.0":
+"@types/react-virtualized-auto-sizer@^1.0.1":
   version "1.0.1"
   resolved "https://registry.yarnpkg.com/@types/react-virtualized-auto-sizer/-/react-virtualized-auto-sizer-1.0.1.tgz#b3187dae1dfc4c15880c9cfc5b45f2719ea6ebd4"
   integrity sha512-GH8sAnBEM5GV9LTeiz56r4ZhMOUSrP43tAQNSRVxNexDjcNKLCEtnxusAItg1owFUFE6k0NslV26gqVClVvong==
   dependencies:
     "@types/react" "*"
 
-"@types/react-window@^1.8.2":
+"@types/react-window@^1.8.5":
   version "1.8.5"
   resolved "https://registry.yarnpkg.com/@types/react-window/-/react-window-1.8.5.tgz#285fcc5cea703eef78d90f499e1457e9b5c02fc1"
   integrity sha512-V9q3CvhC9Jk9bWBOysPGaWy/Z0lxYcTXLtLipkt2cnRj1JOSFNF7wqGpkScSXMgBwC+fnVRg/7shwgddBG5ICw==
@@ -3010,9 +3016,9 @@ ast-types-flow@^0.0.7:
   integrity sha1-9wtzXGvKGlycItmCw+Oef+ujva0=
 
 async@^2.6.2:
-  version "2.6.3"
-  resolved "https://registry.yarnpkg.com/async/-/async-2.6.3.tgz#d72625e2344a3656e3a3ad4fa749fa83299d82ff"
-  integrity sha512-zflvls11DCy+dQWzTW2dzuilv8Z5X/pjfmZOWba6TNIVDm+2UDaJmXSOXlasHKfNBs8oo3M0aT50fDEWfKZjXg==
+  version "2.6.4"
+  resolved "https://registry.yarnpkg.com/async/-/async-2.6.4.tgz#706b7ff6084664cd7eae713f6f965433b5504221"
+  integrity sha512-mzo5dfJYwAn29PeiJ0zvwTo04zj8HDJj0Mn8TD7sno7q12prdbnasKJHhkm2c1LgrhlJ0teaea8860oxi51mGA==
   dependencies:
     lodash "^4.17.14"
 
@@ -3036,7 +3042,7 @@ atob@^2.1.2:
   resolved "https://registry.yarnpkg.com/atob/-/atob-2.1.2.tgz#6d9517eb9e030d2436666651e86bd9f6f13533c9"
   integrity sha512-Wm6ukoaOGJi/73p/cl2GvLjTI5JM1k/O14isD73YML8StrH/7/lRFgmg8nICZgD3bZZvjwCGxtMOD3wWNAu8cg==
 
-attr-accept@^2.2.1:
+attr-accept@^2.2.2:
   version "2.2.2"
   resolved "https://registry.yarnpkg.com/attr-accept/-/attr-accept-2.2.2.tgz#646613809660110749e92f2c10833b70968d929b"
   integrity sha512-7prDjvt9HmqiZ0cl5CRjtS84sEyhsHP2coDkaZKRKVfCDo9s7iw7ChVmar78Gu9pC4SoR/28wFu/G5JJhTnqEg==
@@ -5218,10 +5224,10 @@ file-loader@^6.2.0:
     loader-utils "^2.0.0"
     schema-utils "^3.0.0"
 
-file-selector@^0.2.2:
-  version "0.2.4"
-  resolved "https://registry.yarnpkg.com/file-selector/-/file-selector-0.2.4.tgz#7b98286f9dbb9925f420130ea5ed0a69238d4d80"
-  integrity sha512-ZDsQNbrv6qRi1YTDOEWzf5J2KjZ9KMI1Q2SGeTkCJmNNW25Jg4TW4UMcmoqcg4WrAyKRcpBXdbWRxkfrOzVRbA==
+file-selector@^0.4.0:
+  version "0.4.0"
+  resolved "https://registry.yarnpkg.com/file-selector/-/file-selector-0.4.0.tgz#59ec4f27aa5baf0841e9c6385c8386bef4d18b17"
+  integrity sha512-iACCiXeMYOvZqlF1kTiYINzgepRBymz1wwjiuup9u9nayhb6g4fSwiyJ/6adli+EPwrWtpgQAh2PoS7HukEGEg==
   dependencies:
     tslib "^2.0.3"
 
@@ -5314,10 +5320,10 @@ flatted@^3.1.0:
   resolved "https://registry.yarnpkg.com/flatted/-/flatted-3.2.5.tgz#76c8584f4fc843db64702a6bd04ab7a8bd666da3"
   integrity sha512-WIWGi2L3DyTUvUrwRKgGi9TwxQMUEqPOPQBVi71R96jZXJdFskXEmf54BoZaS1kknGODoIGASGEzBUYdyMCBJg==
 
-focus-lock@^0.10.1:
-  version "0.10.1"
-  resolved "https://registry.yarnpkg.com/focus-lock/-/focus-lock-0.10.1.tgz#5f46fa74fefb87144479c2f8e276f0eedd8081b2"
-  integrity sha512-b9yUklCi4fTu2GXn7dnaVf4hiLVVBp7xTiZarAHMODV2To6Bitf6F/UI67RmKbdgJQeVwI1UO0d9HYNbXt3GkA==
+focus-lock@^0.11.2:
+  version "0.11.2"
+  resolved "https://registry.yarnpkg.com/focus-lock/-/focus-lock-0.11.2.tgz#aeef3caf1cea757797ac8afdebaec8fd9ab243ed"
+  integrity sha512-pZ2bO++NWLHhiKkgP1bEXHhR1/OjVcSvlCJ98aNJDFeb7H5OOQaO+SKOZle6041O9rv2tmbrO4JzClAvDUHf0g==
   dependencies:
     tslib "^2.0.3"
 
@@ -6210,6 +6216,11 @@ is-plain-obj@^3.0.0:
   resolved "https://registry.yarnpkg.com/is-plain-obj/-/is-plain-obj-3.0.0.tgz#af6f2ea14ac5a646183a5bbdb5baabbc156ad9d7"
   integrity sha512-gwsOE28k+23GP1B6vFl1oVh/WOzmawBrKwo5Ev6wMKzPkaXaCDIQKzLnvsA42DRlbVTWorkgTKIviAKCWkfUwA==
 
+is-plain-object@5.0.0:
+  version "5.0.0"
+  resolved "https://registry.yarnpkg.com/is-plain-object/-/is-plain-object-5.0.0.tgz#4427f50ab3429e9025ea7d52e9043a9ef4159344"
+  integrity sha512-VRSzKkbMm5jMDoKLbltAkFQ5Qr7VDiTFGXxYFXXowVj387GeGNOCsOH6Msy00SGZ3Fp84b1Naa1psqgcCIEP5Q==
+
 is-plain-object@^3.0.0:
   version "3.0.1"
   resolved "https://registry.yarnpkg.com/is-plain-object/-/is-plain-object-3.0.1.tgz#662d92d24c0aa4302407b0d45d21f2251c85f85b"
@@ -8560,7 +8571,7 @@ react-app-polyfill@^3.0.0:
     regenerator-runtime "^0.13.9"
     whatwg-fetch "^3.6.2"
 
-react-beautiful-dnd@^13.0.0:
+react-beautiful-dnd@^13.1.0:
   version "13.1.0"
   resolved "https://registry.yarnpkg.com/react-beautiful-dnd/-/react-beautiful-dnd-13.1.0.tgz#ec97c81093593526454b0de69852ae433783844d"
   integrity sha512-aGvblPZTJowOWUNiwd6tNfEpgkX5OxmpqxHKNW/4VmvZTNTbeiq7bA3bn5T+QSF2uibXB0D1DmJsb1aC/+3cUA==
@@ -8573,10 +8584,10 @@ react-beautiful-dnd@^13.0.0:
     redux "^4.0.4"
     use-memo-one "^1.1.1"
 
-react-clientside-effect@^1.2.5:
-  version "1.2.5"
-  resolved "https://registry.yarnpkg.com/react-clientside-effect/-/react-clientside-effect-1.2.5.tgz#e2c4dc3c9ee109f642fac4f5b6e9bf5bcd2219a3"
-  integrity sha512-2bL8qFW1TGBHozGGbVeyvnggRpMjibeZM2536AKNENLECutp2yfs44IL8Hmpn8qjFQ2K7A9PnYf3vc7aQq/cPA==
+react-clientside-effect@^1.2.6:
+  version "1.2.6"
+  resolved "https://registry.yarnpkg.com/react-clientside-effect/-/react-clientside-effect-1.2.6.tgz#29f9b14e944a376b03fb650eed2a754dd128ea3a"
+  integrity sha512-XGGGRQAKY+q25Lz9a/4EPqom7WRjz3z9R2k4jhVKA/puQFH/5Nt27vFZYql4m4NVNdUvX8PS3O7r/Zzm7cjUlg==
   dependencies:
     "@babel/runtime" "^7.12.13"
 
@@ -8619,66 +8630,70 @@ react-dom@^17.0.2:
     object-assign "^4.1.1"
     scheduler "^0.20.2"
 
-react-dropzone@^11.2.0:
-  version "11.5.3"
-  resolved "https://registry.yarnpkg.com/react-dropzone/-/react-dropzone-11.5.3.tgz#757d4980fcae839896a894e41d3e72df04981f86"
-  integrity sha512-68+T6sWW5L89qJnn3SD1aRazhuRBhTT9JOI1W8vI5YWsfegM4C7tlGbPH1AgEbmZY5s8E8L0QhX0e3VdAa0KWA==
+react-dropzone@^11.5.3:
+  version "11.7.1"
+  resolved "https://registry.yarnpkg.com/react-dropzone/-/react-dropzone-11.7.1.tgz#3851bb75b26af0bf1b17ce1449fd980e643b9356"
+  integrity sha512-zxCMwhfPy1olUEbw3FLNPLhAm/HnaYH5aELIEglRbqabizKAdHs0h+WuyOpmA+v1JXn0++fpQDdNfUagWt5hJQ==
   dependencies:
-    attr-accept "^2.2.1"
-    file-selector "^0.2.2"
-    prop-types "^15.7.2"
+    attr-accept "^2.2.2"
+    file-selector "^0.4.0"
+    prop-types "^15.8.1"
+
+react-element-to-jsx-string@^14.3.4:
+  version "14.3.4"
+  resolved "https://registry.yarnpkg.com/react-element-to-jsx-string/-/react-element-to-jsx-string-14.3.4.tgz#709125bc72f06800b68f9f4db485f2c7d31218a8"
+  integrity sha512-t4ZwvV6vwNxzujDQ+37bspnLwA4JlgUPWhLjBJWsNIDceAf6ZKUTCjdm08cN6WeZ5pTMKiCJkmAYnpmR4Bm+dg==
+  dependencies:
+    "@base2/pretty-print-object" "1.0.1"
+    is-plain-object "5.0.0"
+    react-is "17.0.2"
 
 react-error-overlay@^6.0.10:
   version "6.0.10"
   resolved "https://registry.yarnpkg.com/react-error-overlay/-/react-error-overlay-6.0.10.tgz#0fe26db4fa85d9dbb8624729580e90e7159a59a6"
   integrity sha512-mKR90fX7Pm5seCOfz8q9F+66VCc1PGsWSBxKbITjfKVQHMNF2zudxHnMdJiB1fRCb+XsbQV9sO9DCkgsMQgBIA==
 
-react-focus-lock@^2.6.0:
-  version "2.7.1"
-  resolved "https://registry.yarnpkg.com/react-focus-lock/-/react-focus-lock-2.7.1.tgz#a9fbb3fa4efaee32162406e5eb96ae658964193b"
-  integrity sha512-ImSeVmcrLKNMqzUsIdqOkXwTVltj79OPu43oT8tVun7eIckA4VdM7UmYUFo3H/UC2nRVgagMZGFnAOQEDiDYcA==
+react-focus-lock@^2.9.0:
+  version "2.9.1"
+  resolved "https://registry.yarnpkg.com/react-focus-lock/-/react-focus-lock-2.9.1.tgz#094cfc19b4f334122c73bb0bff65d77a0c92dd16"
+  integrity sha512-pSWOQrUmiKLkffPO6BpMXN7SNKXMsuOakl652IBuALAu1esk+IcpJyM+ALcYzPTTFz1rD0R54aB9A4HuP5t1Wg==
   dependencies:
     "@babel/runtime" "^7.0.0"
-    focus-lock "^0.10.1"
+    focus-lock "^0.11.2"
     prop-types "^15.6.2"
-    react-clientside-effect "^1.2.5"
-    use-callback-ref "^1.2.5"
-    use-sidecar "^1.0.5"
+    react-clientside-effect "^1.2.6"
+    use-callback-ref "^1.3.0"
+    use-sidecar "^1.1.2"
 
-react-focus-on@^3.5.0:
-  version "3.5.4"
-  resolved "https://registry.yarnpkg.com/react-focus-on/-/react-focus-on-3.5.4.tgz#be45a9d0495f3bb6f5249704c85362df94980ecf"
-  integrity sha512-HnU0YGKhNSUsC4k6K8L+2wk8mC/qdg+CsS7A1bWLMgK7UuBphdECs2esnS6cLmBoVNjsFnCm/vMypeezKOdK3A==
+react-focus-on@^3.5.4:
+  version "3.6.0"
+  resolved "https://registry.yarnpkg.com/react-focus-on/-/react-focus-on-3.6.0.tgz#159e13082dad4ea1f07abe11254f0e981d5a7b79"
+  integrity sha512-onIRjpd9trAUenXNdDcvjc8KJUSklty4X/Gr7hAm/MzM7ekSF2pg9D8KBKL7ipige22IAPxLRRf/EmJji9KD6Q==
   dependencies:
     aria-hidden "^1.1.3"
-    react-focus-lock "^2.6.0"
-    react-remove-scroll "^2.4.1"
-    react-style-singleton "^2.1.1"
+    react-focus-lock "^2.9.0"
+    react-remove-scroll "^2.5.2"
+    react-style-singleton "^2.2.0"
     tslib "^2.3.1"
-    use-callback-ref "^1.2.5"
-    use-sidecar "^1.0.5"
+    use-callback-ref "^1.3.0"
+    use-sidecar "^1.1.2"
 
-react-input-autosize@^2.2.2:
-  version "2.2.2"
-  resolved "https://registry.yarnpkg.com/react-input-autosize/-/react-input-autosize-2.2.2.tgz#fcaa7020568ec206bc04be36f4eb68e647c4d8c2"
-  integrity sha512-jQJgYCA3S0j+cuOwzuCd1OjmBmnZLdqQdiLKRYrsMMzbjUrVDS5RvJUDwJqA7sKuksDuzFtm6hZGKFu7Mjk5aw==
+react-input-autosize@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/react-input-autosize/-/react-input-autosize-3.0.0.tgz#6b5898c790d4478d69420b55441fcc31d5c50a85"
+  integrity sha512-nL9uS7jEs/zu8sqwFE5MAPx6pPkNAriACQ2rGLlqmKr2sPGtN7TXTyDdQt4lbNXVx7Uzadb40x8qotIuru6Rhg==
   dependencies:
     prop-types "^15.5.8"
 
-react-is@^16.13.1, react-is@^16.7.0:
-  version "16.13.1"
-  resolved "https://registry.yarnpkg.com/react-is/-/react-is-16.13.1.tgz#789729a4dc36de2999dc156dd6c1d9c18cea56a4"
-  integrity sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ==
-
-react-is@^17.0.1, react-is@^17.0.2:
+react-is@17.0.2, react-is@^17.0.1, react-is@^17.0.2:
   version "17.0.2"
   resolved "https://registry.yarnpkg.com/react-is/-/react-is-17.0.2.tgz#e691d4a8e9c789365655539ab372762b0efb54f0"
   integrity sha512-w2GsyukL62IJnlaff/nRegPQR94C/XXamvMWmSHRJ4y7Ts/4ocGRmTHvOs8PSE6pB3dWOrD/nueuU5sduBsQ4w==
 
-react-is@~16.3.0:
-  version "16.3.2"
-  resolved "https://registry.yarnpkg.com/react-is/-/react-is-16.3.2.tgz#f4d3d0e2f5fbb6ac46450641eb2e25bf05d36b22"
-  integrity sha512-ybEM7YOr4yBgFd6w8dJqwxegqZGJNBZl6U27HnGKuTZmDvVrD5quWOK/wAnMywiZzW+Qsk+l4X2c70+thp/A8Q==
+react-is@^16.13.1, react-is@^16.7.0:
+  version "16.13.1"
+  resolved "https://registry.yarnpkg.com/react-is/-/react-is-16.13.1.tgz#789729a4dc36de2999dc156dd6c1d9c18cea56a4"
+  integrity sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ==
 
 react-query@^3.34.12:
   version "3.34.12"
@@ -8706,24 +8721,24 @@ react-refresh@^0.11.0:
   resolved "https://registry.yarnpkg.com/react-refresh/-/react-refresh-0.11.0.tgz#77198b944733f0f1f1a90e791de4541f9f074046"
   integrity sha512-F27qZr8uUqwhWZboondsPx8tnC3Ct3SxZA3V5WyEvujRyyNv0VYPhoBg1gZ8/MV5tubQp76Trw8lTv9hzRBa+A==
 
-react-remove-scroll-bar@^2.1.0:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/react-remove-scroll-bar/-/react-remove-scroll-bar-2.2.0.tgz#d4d545a7df024f75d67e151499a6ab5ac97c8cdd"
-  integrity sha512-UU9ZBP1wdMR8qoUs7owiVcpaPwsQxUDC2lypP6mmixaGlARZa7ZIBx1jcuObLdhMOvCsnZcvetOho0wzPa9PYg==
+react-remove-scroll-bar@^2.3.1:
+  version "2.3.1"
+  resolved "https://registry.yarnpkg.com/react-remove-scroll-bar/-/react-remove-scroll-bar-2.3.1.tgz#9f13b05b249eaa57c8d646c1ebb83006b3581f5f"
+  integrity sha512-IvGX3mJclEF7+hga8APZczve1UyGMkMG+tjS0o/U1iLgvZRpjFAQEUBJ4JETfvbNlfNnZnoDyWJCICkA15Mghg==
   dependencies:
-    react-style-singleton "^2.1.0"
-    tslib "^1.0.0"
+    react-style-singleton "^2.2.0"
+    tslib "^2.0.0"
 
-react-remove-scroll@^2.4.1:
-  version "2.4.3"
-  resolved "https://registry.yarnpkg.com/react-remove-scroll/-/react-remove-scroll-2.4.3.tgz#83d19b02503b04bd8141ed6e0b9e6691a2e935a6"
-  integrity sha512-lGWYXfV6jykJwbFpsuPdexKKzp96f3RbvGapDSIdcyGvHb7/eqyn46C7/6h+rUzYar1j5mdU+XECITHXCKBk9Q==
+react-remove-scroll@^2.5.2:
+  version "2.5.3"
+  resolved "https://registry.yarnpkg.com/react-remove-scroll/-/react-remove-scroll-2.5.3.tgz#a152196e710e8e5811be39dc352fd8a90b05c961"
+  integrity sha512-NQ1bXrxKrnK5pFo/GhLkXeo3CrK5steI+5L+jynwwIemvZyfXqaL0L5BzwJd7CSwNCU723DZaccvjuyOdoy3Xw==
   dependencies:
-    react-remove-scroll-bar "^2.1.0"
-    react-style-singleton "^2.1.0"
-    tslib "^1.0.0"
-    use-callback-ref "^1.2.3"
-    use-sidecar "^1.0.1"
+    react-remove-scroll-bar "^2.3.1"
+    react-style-singleton "^2.2.0"
+    tslib "^2.0.0"
+    use-callback-ref "^1.3.0"
+    use-sidecar "^1.1.2"
 
 react-router-dom@6:
   version "6.2.1"
@@ -8795,24 +8810,24 @@ react-scripts@^5.0.0:
   optionalDependencies:
     fsevents "^2.3.2"
 
-react-style-singleton@^2.1.0, react-style-singleton@^2.1.1:
-  version "2.1.1"
-  resolved "https://registry.yarnpkg.com/react-style-singleton/-/react-style-singleton-2.1.1.tgz#ce7f90b67618be2b6b94902a30aaea152ce52e66"
-  integrity sha512-jNRp07Jza6CBqdRKNgGhT3u9umWvils1xsuMOjZlghBDH2MU0PL2WZor4PGYjXpnRCa9DQSlHMs/xnABWOwYbA==
+react-style-singleton@^2.2.0:
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/react-style-singleton/-/react-style-singleton-2.2.0.tgz#70f45f5fef97fdb9a52eed98d1839fa6b9032b22"
+  integrity sha512-nK7mN92DMYZEu3cQcAhfwE48NpzO5RpxjG4okbSqRRbfal9Pk+fG2RdQXTMp+f6all1hB9LIJSt+j7dCYrU11g==
   dependencies:
     get-nonce "^1.0.0"
     invariant "^2.2.4"
-    tslib "^1.0.0"
+    tslib "^2.0.0"
 
-react-virtualized-auto-sizer@^1.0.2:
+react-virtualized-auto-sizer@^1.0.6:
   version "1.0.6"
   resolved "https://registry.yarnpkg.com/react-virtualized-auto-sizer/-/react-virtualized-auto-sizer-1.0.6.tgz#66c5b1c9278064c5ef1699ed40a29c11518f97ca"
   integrity sha512-7tQ0BmZqfVF6YYEWcIGuoR3OdYe8I/ZFbNclFlGOC3pMqunkYF/oL30NCjSGl9sMEb17AnzixDz98Kqc3N76HQ==
 
-react-window@^1.8.5:
-  version "1.8.6"
-  resolved "https://registry.yarnpkg.com/react-window/-/react-window-1.8.6.tgz#d011950ac643a994118632665aad0c6382e2a112"
-  integrity sha512-8VwEEYyjz6DCnGBsd+MgkD0KJ2/OXFULyDtorIiTz+QzwoP94tBoA7CnbtyXMm+cCeAUER5KJcPtWl9cpKbOBg==
+react-window@^1.8.6:
+  version "1.8.7"
+  resolved "https://registry.yarnpkg.com/react-window/-/react-window-1.8.7.tgz#5e9fd0d23f48f432d7022cdb327219353a15f0d4"
+  integrity sha512-JHEZbPXBpKMmoNO1bNhoXOOLg/ujhL/BU4IqVU9r8eQPcy5KQnGHIHDRkJ0ns9IM5+Aq5LNwt3j8t3tIrePQzA==
   dependencies:
     "@babel/runtime" "^7.0.0"
     memoize-one ">=3.1.1 <6"
@@ -9879,10 +9894,10 @@ symbol-tree@^3.2.4:
   resolved "https://registry.yarnpkg.com/symbol-tree/-/symbol-tree-3.2.4.tgz#430637d248ba77e078883951fb9aa0eed7c63fa2"
   integrity sha512-9QNk5KwDF+Bvz+PyObkmSYjI5ksVUYtjW7AU22r2NKcfLJcXp96hkDWU3+XndOsUb+AQ9QhfzfCT2O+CNWT5Tw==
 
-tabbable@^3.0.0:
-  version "3.1.2"
-  resolved "https://registry.yarnpkg.com/tabbable/-/tabbable-3.1.2.tgz#f2d16cccd01f400e38635c7181adfe0ad965a4a2"
-  integrity sha512-wjB6puVXTYO0BSFtCmWQubA/KIn7Xvajw0x0l6eJUudMG/EAiJvIUnyNX6xO4NpGrJ16lbD0eUseB9WxW0vlpQ==
+tabbable@^5.2.1:
+  version "5.3.2"
+  resolved "https://registry.yarnpkg.com/tabbable/-/tabbable-5.3.2.tgz#66d6119ee8a533634c3f17deb0caa1c379e36ac7"
+  integrity sha512-6G/8EWRFx8CiSe2++/xHhXkmCRq2rHtDtZbQFHx34cvDfZzIBfvwG9zGUNTWMXWLCYvDj3aQqOzdl3oCxKuBkQ==
 
 tailwindcss@^3.0.2:
   version "3.0.18"
@@ -10099,6 +10114,11 @@ tslib@^1.0.0, tslib@^1.8.1, tslib@^1.9.3:
   resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.14.1.tgz#cf2d38bdc34a134bcaf1091c41f6619e2f672d00"
   integrity sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==
 
+tslib@^2.0.0:
+  version "2.4.0"
+  resolved "https://registry.yarnpkg.com/tslib/-/tslib-2.4.0.tgz#7cecaa7f073ce680a05847aa77be941098f36dc3"
+  integrity sha512-d6xOpEDfsi2CZVlPQzGeux8XMwLT9hssAsaPYExaQMuYskwb+x1x7J371tWlbBdWHroy99KnVB6qIkUbs5X3UQ==
+
 tslib@^2.0.3, tslib@^2.1.0, tslib@^2.3.1:
   version "2.3.1"
   resolved "https://registry.yarnpkg.com/tslib/-/tslib-2.3.1.tgz#e8a335add5ceae51aa261d32a490158ef042ef01"
@@ -10347,18 +10367,20 @@ uri-js@^4.2.2:
   dependencies:
     punycode "^2.1.0"
 
-url-parse@^1.5.3:
-  version "1.5.4"
-  resolved "https://registry.yarnpkg.com/url-parse/-/url-parse-1.5.4.tgz#e4f645a7e2a0852cc8a66b14b292a3e9a11a97fd"
-  integrity sha512-ITeAByWWoqutFClc/lRZnFplgXgEZr3WJ6XngMM/N9DMIm4K8zXPCZ1Jdu0rERwO84w1WC5wkle2ubwTA4NTBg==
+url-parse@^1.5.10:
+  version "1.5.10"
+  resolved "https://registry.yarnpkg.com/url-parse/-/url-parse-1.5.10.tgz#9d3c2f736c1d75dd3bd2be507dcc111f1e2ea9c1"
+  integrity sha512-WypcfiRhfeUP9vvF0j6rw0J3hrWrw6iZv3+22h6iRMJ/8z1Tj6XfLP4DsUix5MhMPnXpiHDoKyoZ/bdCkwBCiQ==
   dependencies:
     querystringify "^2.1.1"
     requires-port "^1.0.0"
 
-use-callback-ref@^1.2.3, use-callback-ref@^1.2.5:
-  version "1.2.5"
-  resolved "https://registry.yarnpkg.com/use-callback-ref/-/use-callback-ref-1.2.5.tgz#6115ed242cfbaed5915499c0a9842ca2912f38a5"
-  integrity sha512-gN3vgMISAgacF7sqsLPByqoePooY3n2emTH59Ur5d/M8eg4WTWu1xp8i8DHjohftIyEx0S08RiYxbffr4j8Peg==
+use-callback-ref@^1.3.0:
+  version "1.3.0"
+  resolved "https://registry.yarnpkg.com/use-callback-ref/-/use-callback-ref-1.3.0.tgz#772199899b9c9a50526fedc4993fc7fa1f7e32d5"
+  integrity sha512-3FT9PRuRdbB9HfXhEq35u4oZkvpJ5kuYbpqhCfmiZyReuRgpnhDlbr2ZEnnuS0RrJAPn6l23xjFg9kpDM+Ms7w==
+  dependencies:
+    tslib "^2.0.0"
 
 use-memo-one@^1.1.1:
   version "1.1.2"
@@ -10372,13 +10394,13 @@ use-query-params@^1.2.3:
   dependencies:
     serialize-query-params "^1.3.5"
 
-use-sidecar@^1.0.1, use-sidecar@^1.0.5:
-  version "1.0.5"
-  resolved "https://registry.yarnpkg.com/use-sidecar/-/use-sidecar-1.0.5.tgz#ffff2a17c1df42e348624b699ba6e5c220527f2b"
-  integrity sha512-k9jnrjYNwN6xYLj1iaGhonDghfvmeTmYjAiGvOr7clwKfPjMXJf4/HOr7oT5tJwYafgp2tG2l3eZEOfoELiMcA==
+use-sidecar@^1.1.2:
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/use-sidecar/-/use-sidecar-1.1.2.tgz#2f43126ba2d7d7e117aa5855e5d8f0276dfe73c2"
+  integrity sha512-epTbsLuzZ7lPClpz2TyryBfztm7m+28DlEv2ZCQ3MDr5ssiwyOwGH/e5F9CkfWjJ1t4clvI58yF822/GUkjjhw==
   dependencies:
     detect-node-es "^1.1.0"
-    tslib "^1.9.3"
+    tslib "^2.0.0"
 
 util-deprecate@^1.0.1, util-deprecate@^1.0.2, util-deprecate@~1.0.1:
   version "1.0.2"