Offline store

An offline store is an interface for working with historical time-series feature values that are stored in data sources. The OfflineStore interface has several different implementations, such as 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.

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 feature_store.yaml. When building training datasets or materializing features into an online store, Feast will use the configured offline store along with your 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 Push Source for more details on how to push features directly to the offline store in your feature store.