An offline store is an interface for working with historical time-series feature values that are stored in data sources. The
OfflineStoreinterface 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.
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 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
BigQueryoffline 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.