Athena offline store (contrib)

Description

The Athena offline store provides support for reading AthenaSources.

Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe.

Disclaimer

The Athena offline store does not achieve full test coverage. Please do not assume complete stability.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[aws]'.

Example

{% code title="feature_store.yaml" %}

yaml

project: my_project
registry: data/registry.db
provider: local
offline_store:
  type: athena
  data_source: AwsDataCatalog
  region: us-east-1
  database: my_database
  workgroup: primary
online_store:
    path: data/online_store.db

{% endcode %}

The full set of configuration options is available in AthenaOfflineStoreConfig.

Functionality Matrix

The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the Athena offline store.

	Athena
`get_historical_features` (point-in-time correct join)	yes
`pull_latest_from_table_or_query` (retrieve latest feature values)	yes
`pull_all_from_table_or_query` (retrieve a saved dataset)	yes
`offline_write_batch` (persist dataframes to offline store)	no
`write_logged_features` (persist logged features to offline store)	yes

Below is a matrix indicating which functionality is supported by AthenaRetrievalJob.

	Athena
export to dataframe	yes
export to arrow table	yes
export to arrow batches	no
export to SQL	yes
export to data lake (S3, GCS, etc.)	no
export to data warehouse	no
export as Spark dataframe	no
local execution of Python-based on-demand transforms	yes
remote execution of Python-based on-demand transforms	no
persist results in the offline store	yes
preview the query plan before execution	yes
read partitioned data	yes

To compare this set of functionality against other offline stores, please see the full functionality matrix.