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  1. Getting started
  2. Components

Offline store

PreviousRegistryNextOnline store

Last updated 26 days ago

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An offline store is an interface for working with historical time-series feature values that are stored in . The OfflineStore interface has several different implementations, such as the BigQueryOfflineStore, each of which is backed by a different storage and compute engine. For more details on which offline stores are supported, please see .

Offline stores are primarily used for two reasons:

  1. Building training datasets from time-series features.

  2. Materializing (loading) features into an online store to serve those features at low-latency in a production setting.

Offline stores are configured through the . When building training datasets or materializing features into an online store, Feast will use the configured offline store with your configured data sources to execute the necessary data operations.

Only a single offline store can be used at a time. Moreover, offline stores are not compatible with all data sources; for example, the BigQuery offline store cannot be used to query a file-based data source.

Please see for more details on how to push features directly to the offline store in your feature store.

data sources
Offline Stores
feature_store.yaml
Push Source