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‎docs/README.md‎

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## How can I get started?
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{% hint style="info" %}
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The best way to learn Feast is to use it. Head over to our [Quickstart](quickstart.md) and try it out!
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The best way to learn Feast is to use it. Head over to our [Quickstart](getting-started/quickstart.md) and try it out!
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{% endhint %}
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Explore the following resources to get started with Feast:
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* [Quickstart](quickstart.md) is the fastest way to get started with Feast
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* [Getting started ](getting-started/)provides a step-by-step guide to using Feast.
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* [Concepts](concepts/data-model-and-concepts.md#concepts) describes all important Feast API concepts.
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* [Quickstart](getting-started/quickstart.md) is the fastest way to get started with Feast
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* [Getting started ](how-to-guides/feast-gcp-aws/)provides a step-by-step guide to using Feast.
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* [Concepts](getting-started/concepts/data-model-and-concepts/#concepts) describes all important Feast API concepts.
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* [Reference](reference/feast-cli-commands.md) contains detailed API and design documents.
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* [Contributing](project/contributing.md) contains resources for anyone who wants to contribute to Feast.
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‎docs/SUMMARY.md‎

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# Table of contents
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* [Introduction](README.md)
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* [Quickstart](quickstart.md)
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* [Getting started](getting-started/README.md)
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* [Install Feast](getting-started/install-feast.md)
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* [Create a feature repository](getting-started/create-a-feature-repository.md)
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* [Deploy a feature store](getting-started/deploy-a-feature-store.md)
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* [Build a training dataset](getting-started/build-a-training-dataset.md)
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* [Load data into the online store](getting-started/load-data-into-the-online-store.md)
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* [Read features from the online store](getting-started/read-features-from-the-online-store.md)
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* [Community](community.md)
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* [Roadmap](roadmap.md)
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* [Changelog](https://github.com/feast-dev/feast/blob/master/CHANGELOG.md)
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## Getting started
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* [Quickstart](getting-started/quickstart.md)
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* [Concepts](getting-started/concepts/README.md)
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* [Overview](getting-started/concepts/overview.md)
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* [Data model](getting-started/concepts/data-model-and-concepts/README.md)
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* [Data source](getting-started/concepts/data-model-and-concepts/data-source.md)
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* [Entity](getting-started/concepts/data-model-and-concepts/entity.md)
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* [Feature view](getting-started/concepts/data-model-and-concepts/feature-view.md)
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* [Feature service](getting-started/concepts/data-model-and-concepts/feature-service.md)
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* [Feature retrieval](getting-started/concepts/data-model-and-concepts/feature-retrieval.md)
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* [Architecture](getting-started/concepts/architecture-and-components/README.md)
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* [Overview](getting-started/concepts/architecture-and-components/overview.md)
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* [Feature repository](getting-started/concepts/architecture-and-components/feature-repository.md)
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* [Offline store](getting-started/concepts/architecture-and-components/offline-store.md)
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* [Online store](getting-started/concepts/architecture-and-components/online-store.md)
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* [Provider](getting-started/concepts/architecture-and-components/provider.md)
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## Tutorials
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* [Overview](tutorials/tutorials-overview.md)
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* [Driver Ranking](tutorials/driver-ranking-with-feast.md)
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* [Fraud Detection](tutorials/fraud-detection.md)
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## Concepts
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* [Overview](concepts/overview.md)
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* [Feature view](concepts/feature-view.md)
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* [Data model](concepts/data-model-and-concepts.md)
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* [Online store](concepts/online-store.md)
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* [Offline store](concepts/offline-store.md)
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* [Provider](concepts/provider.md)
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* [Architecture](concepts/architecture-and-components.md)
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* [Feature service](concepts/feature-service.md)
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## How-to Guides
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* [Running Feast with GCP/AWS](how-to-guides/feast-gcp-aws/README.md)
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* [Install Feast](how-to-guides/feast-gcp-aws/install-feast.md)
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* [Create a feature repository](how-to-guides/feast-gcp-aws/create-a-feature-repository.md)
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* [Deploy a feature store](how-to-guides/feast-gcp-aws/deploy-a-feature-store.md)
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* [Build a training dataset](how-to-guides/feast-gcp-aws/build-a-training-dataset.md)
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* [Load data into the online store](how-to-guides/feast-gcp-aws/load-data-into-the-online-store.md)
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* [Read features from the online store](how-to-guides/feast-gcp-aws/read-features-from-the-online-store.md)
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* [Running Feast in production](how-to-guides/running-feast-in-production.md)
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* [Upgrading from Feast 0.9](https://docs.google.com/document/u/1/d/1AOsr_baczuARjCpmZgVd8mCqTF4AZ49OEyU4Cn-uTT0/edit)
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* [Creating a custom provider](how-to-guides/creating-a-custom-provider.md)

‎docs/getting-started/concepts/README.md‎

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# Concepts
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{% page-ref page="overview.md" %}
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{% page-ref page="data-model-and-concepts/" %}
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{% page-ref page="architecture-and-components/" %}
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‎docs/getting-started/concepts/architecture-and-components/README.md‎

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# Architecture
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{% page-ref page="overview.md" %}
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{% page-ref page="feature-repository.md" %}
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{% page-ref page="offline-store.md" %}
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{% page-ref page="online-store.md" %}
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{% page-ref page="provider.md" %}
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‎docs/getting-started/concepts/architecture-and-components/feature-repository.md‎

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# Feature repository
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Feast users use Feast to manage two important sets of configuration:
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* Configuration about how to run Feast on your infrastructure
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* Feature definitions
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With Feast, the above configuration can be written declaratively and stored as code in a central location. This central location is called a feature repository. The feature repository is the declarative source of truth for what the desired state of a feature store should be.
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The Feast CLI uses the feature repository to configure, deploy, and manage your feature store.
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An example structure of a feature repository is shown below:
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```text
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$ tree -a
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.
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├── data
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│ └── driver_stats.parquet
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├── driver_features.py
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├── feature_store.yaml
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└── .feastignore
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1 directory, 4 files
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```
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For more details, see the [Feature repository](../../../reference/feature-repository/) reference.
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docs/concepts/offline-store.md renamed to docs/getting-started/concepts/architecture-and-components/offline-store.md

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1. Building training datasets from time-series features.
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2. Materializing \(loading\) features from the offline store into an online store in order to serve those features at low latency for prediction.
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Offline stores are configured through the [feature\_store.yaml](../reference/offline-stores/). When building training datasets or materializing features into an online store, Feast will use the configured offline store along with the data sources you have defined as part of feature views to execute the necessary data operations.
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Offline stores are configured through the [feature\_store.yaml](../../../reference/offline-stores/). When building training datasets or materializing features into an online store, Feast will use the configured offline store along with the data sources you have defined as part of feature views to execute the necessary data operations.
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It is not possible to query all data sources from all offline stores, and only a single offline store can be used at a time. For example, it is not possible to query a BigQuery table from a `File` offline store, nor is it possible for a `BigQuery` offline store to query files from your local file system.
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Please see the [Offline Stores](../reference/offline-stores/) reference for more details on configuring offline stores.
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Please see the [Offline Stores](../../../reference/offline-stores/) reference for more details on configuring offline stores.
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docs/concepts/online-store.md renamed to docs/getting-started/concepts/architecture-and-components/online-store.md

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Example batch data source
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![](../.gitbook/assets/image%20%286%29.png)
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![](../../../.gitbook/assets/image%20%286%29.png)
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Once the above data source is materialized into Feast \(using `feast materialize`\), the feature values will be stored as follows:
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![](../../../.gitbook/assets/image%20%285%29.png)
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docs/concepts/architecture-and-components.md renamed to docs/getting-started/concepts/architecture-and-components/overview.md

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# Architecture
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# Overview
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![Feast Architecture Diagram](../.gitbook/assets/image%20%284%29.png)
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![Feast Architecture Diagram](../../../.gitbook/assets/image%20%284%29.png)
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A complete Feast deployment contains the following components:
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* **Feast Online Serving:** Provides low-latency access to feature values stores in the online store. This component is optional. Teams can also read feature values directly from the online store if necessary.
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* **Feast Registry**: An object store \(GCS, S3\) based registry used to persist feature definitions that are registered with the feature store. Systems can discover feature data by interacting with the registry through the Feast SDK.
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* **Feast Python SDK/CLI:** The primary user facing SDK. Used to:
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* Manage version controlled feature definitions.

docs/concepts/provider.md renamed to docs/getting-started/concepts/architecture-and-components/provider.md

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Please see [feature\_store.yaml](../reference/feature-repository/feature-store-yaml.md#overview) for configuring providers.
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Please see [feature\_store.yaml](../../../reference/feature-repository/feature-store-yaml.md#overview) for configuring providers.
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