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 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. 1.
    Building training datasets from time-series features.
  2. 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 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.
Export as PDF
Copy link
Edit on GitHub