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docs/SUMMARY.md

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## Reference
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* [Repository Config](reference/repository-config.md)
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* [feature\_store.yaml](reference/feature-store-yaml.md)
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* [Python API reference](http://rtd.feast.dev/)
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## Feast on Kubernetes

docs/concepts/feature-repository.md

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* **Project**: A unique identifier for your project. The project name is used to isolate multiple feature stores when deploying to the same infrastructure.
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* **Registry**: The registry is used to persist feature definitions and related metadata. The registry is updated when the `apply` command is run to update infrastructure. The registry is read when users try to build training datasets or try to read from an online store. A registry can either be a local file or a file on an object store \(if it needs to be shared\).
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* **Provider**: The provider defines the target environment that will be used to configure your infrastructure. By selecting `local`, Feast will configure local infrastructure for storing and serving features. By selecting `gcp`, Feast will configure cloud infrastructure for storing and serving features.
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* **Online Store**: This option allows teams to configure the destination online store that should be used to store and serve online features. The type of online store that can be selected and configured depends on the `provider`. One provider may allow more than one online store to be configured.
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* **Online Store**: This option allows teams to configure the destination online store that should be used to store and serve online features. The type of online store that can be selected and configured depends on the `provider`. A provider may allow more than one online store to be configured.
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## Feature definitions
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docs/quickstart.md

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4. Materializing feature data to the online feature store
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5. Fetching feature vectors for inference
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The quickstart uses some example data about a ride hailing app to walk through Feast. Let's get into it!
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This quickstart uses some example data about a ride hailing app to walk through Feast. Let's get into it!
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### 1. Setting up Feast
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path: data/online_store.db
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```
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This file defines how the feature store is configured to run. The most important option here is `provider`, which specifies the environment that Feast will run in. We've initialized `provider=local`, indicating that Feast will run the feature store on our local machine. See [Repository Config](reference/repository-config.md) for more details.
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This file defines how the feature store is configured to run. The most important option here is `provider`, which specifies the environment that Feast will run in. We've initialized `provider=local`, indicating that Feast will run the feature store on our local machine. See [Repository Config](reference/feature-store-yaml.md) for more details.
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Next, take a look at `example.py`. This file defines some example features:
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# feature\_store.yaml
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### Overview
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`feature_store.yaml` is a file that is placed at the root of the [Feature Repository](../concepts/feature-repository.md). This file contains configuration about how the feature store runs. An example `feature_store.yaml` is shown below:
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{% code title="feature\_store.yaml" %}
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```yaml
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project: loyal_spider
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registry: data/registry.db
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provider: local
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online_store:
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type: sqlite
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path: data/online_store.db
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```
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{% endcode %}
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### Fields in feature\_store.yaml
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* **provider** \("local" or "gcp"\) — Defines the environment in which Feast will execute data flows.
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* **registry** \(a local or GCS filepath\) — Defines the location of the feature registry.
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* **online\_store** — Configures the online store. This field has two subfields:
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* **type** \("sqlite" or "datastore"\) — Defines the type of online store.
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* **path** \(a local filepath\) — Defines the path to the SQLite database file.
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* **project** — Defines a namespace for the entire feature store. Can be used to isolate multiple deployments in a single installation of Feast.
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### Providers
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The `provider` field defines the environment in which Feast will execute data flows. As a result, it also determines the default values for other fields.
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#### Local
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When using the local provider:
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* Feast can read from **Parquet data sources**,.
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* Feast performs historical feature retrieval \(point-in-time joins\) using **pandas.**
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* Feast performs online feature serving from a **SQLite database.**
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#### **GCP**
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When using the GCP provider:
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* Feast can read data from **BigQuery data sources.**
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* Feast performs historical feature retrieval \(point-in-time joins\) using **BigQuery.**
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* Feast performs online feature serving from **Google Cloud Firestore.**
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