driver_trips:average_daily_rides) for the features that you would like to retrieve from the offline store. These features can come from multiple feature tables. The only requirement is that the feature tables that make up the feature references have the same entity (or composite entity), and that they aren't located in the same offline store.
event_timestampand all entities (primary keys) necessary to join feature tables onto. All entities found in feature views that are being joined onto the entity dataframe must be found as column on the entity dataframe.
event_timestampcolumn and a
driver_identity column. Pandas based entity dataframes may need to be uploaded into an offline store, which may result in longer wait times compared to a SQL based entity dataframe.
get_historical_features(). This method launches a job that executes a point-in-time join of features from the offline store onto the entity dataframe. Once completed, a job reference will be returned. This job reference can then be converted to a Pandas dataframe by calling