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chore: Fixing outdated documentation and roadmap. Also fixing FileOptions + PushSource (feast-dev#2543)
* chore: Fixing outdated documentation and roadmap. Making PushSource schema consistent with FV. Remove FileOptions warnings Signed-off-by: Danny Chiao <danny@tecton.ai> * fix Signed-off-by: Danny Chiao <danny@tecton.ai> * fix Signed-off-by: Danny Chiao <danny@tecton.ai> * fix Signed-off-by: Danny Chiao <danny@tecton.ai> * change request source example Signed-off-by: Danny Chiao <danny@tecton.ai>
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‎Makefile‎

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@@ -32,7 +32,7 @@ test: test-python test-java test-go
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protos: compile-protos-go compile-protos-python compile-protos-docs
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build: protos build-java build-docker build-html
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build: protos build-java build-docker
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# Python SDK
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‎README.md‎

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@@ -147,8 +147,8 @@ The list below contains the functionality that contributors are planning to deve
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* [x] [Synapse source (community plugin)](https://github.com/Azure/feast-azure)
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* [x] [Hive (community plugin)](https://github.com/baineng/feast-hive)
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* [x] [Postgres (community plugin)](https://github.com/nossrannug/feast-postgres)
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* [x] [Spark (community plugin)](https://github.com/Adyen/feast-spark-offline-store)
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* [x] Kafka source (with [push support into the online store](https://docs.feast.dev/reference/alpha-stream-ingestion))
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* [x] [Spark (community plugin)](https://docs.feast.dev/reference/data-sources/spark)
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* [x] Kafka / Kinesis sources (via [push support into the online store](https://docs.feast.dev/reference/data-sources/push)
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* [ ] HTTP source
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* **Offline Stores**
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* [x] [Snowflake](https://docs.feast.dev/reference/offline-stores/snowflake)
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* [x] [Hive (community plugin)](https://github.com/baineng/feast-hive)
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* [x] [Postgres (community plugin)](https://github.com/nossrannug/feast-postgres)
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* [x] [Trino (community plugin)](https://github.com/Shopify/feast-trino)
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* [x] [Spark (community plugin)](https://github.com/Adyen/feast-spark-offline-store)
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* [x] [Spark (community plugin)](https://docs.feast.dev/reference/offline-stores/spark)
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* [x] [In-memory / Pandas](https://docs.feast.dev/reference/offline-stores/file)
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* [x] [Custom offline store support](https://docs.feast.dev/how-to-guides/adding-a-new-offline-store)
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* **Online Stores**
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* [ ] Cassandra
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* **Streaming**
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* [x] [Custom streaming ingestion job support](https://docs.feast.dev/how-to-guides/creating-a-custom-provider)
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* [x] [Push based streaming data ingestion](reference/alpha-stream-ingestion.md)
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* [x] [Push based streaming data ingestion](https://docs.feast.dev/reference/data-sources/push.md)
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* [ ] Streaming ingestion on AWS
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* [ ] Streaming ingestion on GCP
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* **Feature Engineering**
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* [x] On-demand Transformations (Alpha release. See [RFC](https://docs.google.com/document/d/1lgfIw0Drc65LpaxbUu49RCeJgMew547meSJttnUqz7c/edit#))
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* [ ] Batch transformation (SQL. In progress. See [RFC](https://docs.google.com/document/d/1964OkzuBljifDvkV-0fakp2uaijnVzdwWNGdz7Vz50A/edit))
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* [ ] Batch transformation (In progress. See [RFC](https://docs.google.com/document/d/1964OkzuBljifDvkV-0fakp2uaijnVzdwWNGdz7Vz50A/edit))
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* [ ] Streaming transformation
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* **Deployments**
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* [x] AWS Lambda (Alpha release. See [RFC](https://docs.google.com/document/d/1eZWKWzfBif66LDN32IajpaG-j82LSHCCOzY6R7Ax7MI/edit))
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* [x] CLI for browsing feature registry
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* [x] Model-centric feature tracking (feature services)
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* [x] Amundsen integration (see [Feast extractor](https://github.com/amundsen-io/amundsen/blob/main/databuilder/databuilder/extractor/feast_extractor.py))
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* [ ] Feast Web UI (in progress)
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* [x] Feast Web UI (alpha)
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* [ ] REST API for browsing feature registry
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* [ ] Feature versioning
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‎docs/getting-started/architecture-and-components/online-store.md‎

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![](../../.gitbook/assets/image%20%285%29.png)
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Features can also be written to the online store via [push sources](https://docs.feast.dev/reference/data-sources/push)

‎docs/getting-started/concepts/feature-view.md‎

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@@ -17,8 +17,8 @@ driver_stats_fv = FeatureView(
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Field(name="trips_today", dtype=Int64),
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Field(name="rating", dtype=Float32),
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],
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batch_source=BigQuerySource(
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table_ref="feast-oss.demo_data.driver_activity"
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source=BigQuerySource(
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table="feast-oss.demo_data.driver_activity"
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)
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)
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```
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Feature views are used during
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* The generation of training datasets by querying the data source of feature views in order to find historical feature values. A single training dataset may consist of features from multiple feature views.
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* Loading of feature values into an online store. Feature views determine the storage schema in the online store. Feature values can be loaded from batch sources or from [stream sources](../../reference/alpha-stream-ingestion.md).
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* Loading of feature values into an online store. Feature views determine the storage schema in the online store. Feature values can be loaded from batch sources or from [stream sources](../../reference/data-sources/push.md).
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* Retrieval of features from the online store. Feature views provide the schema definition to Feast in order to look up features from the online store.
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{% hint style="info" %}
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## Feature views without entities
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If a feature view contains features that are not related to a specific entity, the feature view can be defined without entities.
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If a feature view contains features that are not related to a specific entity, the feature view can be defined without entities (only event timestamps are needed for this feature view).
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{% tabs %}
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{% tab title="global_stats.py" %}
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schema=[
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Field(name="total_trips_today_by_all_drivers", dtype=Int64),
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],
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batch_source=BigQuerySource(
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table_ref="feast-oss.demo_data.global_stats"
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source=BigQuerySource(
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table="feast-oss.demo_data.global_stats"
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)
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)
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```
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schema=[
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Field(name="temperature", dtype=Int32)
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],
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batch_source=BigQuerySource(
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table_ref="feast-oss.demo_data.location_stats"
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source=BigQuerySource(
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table="feast-oss.demo_data.location_stats"
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),
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)
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```

‎docs/getting-started/concepts/point-in-time-joins.md‎

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@@ -9,6 +9,7 @@ The above table can be registered with Feast through the following feature view:
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```python
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from feast import FeatureView, Field, FileSource
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from feast.types import Float32, Int64
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from datetime import timedelta
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driver_stats_fv = FeatureView(
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name="driver_hourly_stats",
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Field(name="earnings_today", dtype=Float32),
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],
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ttl=timedelta(hours=2),
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batch_source=FileSource(
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source=FileSource(
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path="driver_hourly_stats.parquet"
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)
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)

‎docs/getting-started/quickstart.md‎

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driver_hourly_stats_view = FeatureView(
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name="driver_hourly_stats",
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entities=["driver"], # reference entity by name
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ttl=Duration(seconds=86400 * 1),
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ttl=timedelta(seconds=86400 * 1),
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schema=[
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Field(name="conv_rate", dtype=Float32),
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Field(name="acc_rate", dtype=Float32),
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Field(name="avg_daily_trips", dtype=Int64),
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],
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online=True,
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batch_source=driver_hourly_stats,
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source=driver_hourly_stats,
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tags={},
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```
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driver_hourly_stats_view = FeatureView(
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entities=["driver"], # reference entity by name
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ttl=Duration(seconds=86400 * 1),
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ttl=timedelta(seconds=86400 * 1),
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schema=[
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Field(name="conv_rate", dtype=Float32),
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Field(name="acc_rate", dtype=Float32),
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Field(name="avg_daily_trips", dtype=Int64),
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],
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online=True,
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batch_source=driver_hourly_stats,
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source=driver_hourly_stats,
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tags={},
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)
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```

‎docs/getting-started/third-party-integrations.md‎

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* [x] [Synapse source (community plugin)](https://github.com/Azure/feast-azure)
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* [x] [Hive (community plugin)](https://github.com/baineng/feast-hive)
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* [x] [Postgres (community plugin)](https://github.com/nossrannug/feast-postgres)
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* [x] [Spark (community plugin)](https://github.com/Adyen/feast-spark-offline-store)
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* [x] Kafka source (with [push support into the online store](https://docs.feast.dev/reference/alpha-stream-ingestion))
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* [x] [Spark (community plugin)](https://docs.feast.dev/reference/data-sources/spark)
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* [x] Kafka / Kinesis sources (via [push support into the online store](https://docs.feast.dev/reference/data-sources/push)
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* [ ] HTTP source
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### Offline Stores
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* [x] [Hive (community plugin)](https://github.com/baineng/feast-hive)
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* [x] [Postgres (community plugin)](https://github.com/nossrannug/feast-postgres)
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* [x] [Trino (community plugin)](https://github.com/Shopify/feast-trino)
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* [x] [Spark (community plugin)](https://github.com/Adyen/feast-spark-offline-store)
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* [x] [Spark (community plugin)](https://docs.feast.dev/reference/offline-stores/spark)
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* [x] [In-memory / Pandas](https://docs.feast.dev/reference/offline-stores/file)
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* [x] [Custom offline store support](https://docs.feast.dev/how-to-guides/adding-a-new-offline-store)
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‎docs/how-to-guides/adding-a-new-offline-store.md‎

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driver_hourly_stats_view = FeatureView(
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source=driver_hourly_stats,
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...
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```

‎docs/how-to-guides/running-feast-in-production.md‎

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def feast_writer(spark_df):
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store.push("driver_hourly_stats", pandas_df)
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streamingDF.writeStream.foreachBatch(feast_writer).start()
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‎docs/reference/alpha-on-demand-feature-view.md‎

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### **Registering transformations**
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We register `RequestDataSource` inputs and the transform in `on_demand_feature_view`:
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We register `RequestSource` inputs and the transform in `on_demand_feature_view`:
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```python
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from feast import Field, RequestSource
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from feast.types import Float64
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from feast.types import Float64, Int64
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import pandas as pd
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# Define a request data source which encodes features / information only
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# available at request time (e.g. part of the user initiated HTTP request)
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input_request = RequestDataSource(
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input_request = RequestSource(
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name="vals_to_add",
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schema={
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"val_to_add": ValueType.INT64,
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"val_to_add_2": ValueType.INT64
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}
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schema=[
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Field(name='val_to_add', dtype=Int64),
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Field(name='val_to_add_2', dtype=Int64)
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]
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)
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# Use the input data and feature view features to create new features

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