Overview
Functionality
In Feast, each batch data source is associated with corresponding offline stores. For example, a SnowflakeSource can only be processed by the Snowflake offline store, while a FileSource can be processed by both File and DuckDB offline stores. Otherwise, the primary difference between batch data sources is the set of supported types. Feast has an internal type system, and aims to support eight primitive types (bytes, string, int32, int64, float32, float64, bool, and timestamp) along with the corresponding array types. However, not every batch data source supports all of these types.
For more details on the Feast type system, see here.
Functionality Matrix
There are currently four core batch data source implementations: FileSource, BigQuerySource, SnowflakeSource, and RedshiftSource. There are several additional implementations contributed by the Feast community (PostgreSQLSource, SparkSource, and TrinoSource), which are not guaranteed to be stable or to match the functionality of the core implementations. Details for each specific data source can be found here.
Below is a matrix indicating which data sources support which types.
| File | BigQuery | Snowflake | Redshift | Postgres | Spark | Trino | |
|---|---|---|---|---|---|---|---|
| yes | yes | yes | yes | yes | yes | yes |
| yes | yes | yes | yes | yes | yes | yes |
| yes | yes | yes | yes | yes | yes | yes |
| yes | yes | yes | yes | yes | yes | yes |
| yes | yes | yes | yes | yes | yes | yes |
| yes | yes | yes | yes | yes | yes | yes |
| yes | yes | yes | yes | yes | yes | yes |
| yes | yes | yes | yes | yes | yes | yes |
array types | yes | yes | yes | no | yes | yes | no |
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