The Snowflake offline store provides support for reading SnowflakeSources.
All joins happen within Snowflake.
Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Snowflake as a temporary table in order to complete join operations.
In order to use this offline store, you'll need to run pip install 'feast[snowflake]'.
If you're using a file based registry, then you'll also need to install the relevant cloud extra (pip install 'feast[snowflake, CLOUD]' where CLOUD is one of aws, gcp, azure)
You can get started by then running feast init -t snowflake.
The full set of configuration options is available in SnowflakeOfflineStoreConfig.
The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the Snowflake offline store.
Below is a matrix indicating which functionality is supported by SnowflakeRetrievalJob.
To compare this set of functionality against other offline stores, please see the full functionality matrix.
| Snowflake | |
|---|---|
| Snowflake | |
|---|---|
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)
yes
write_logged_features (persist logged features to offline store)
yes
export to dataframe
yes
export to arrow table
yes
export to arrow batches
yes
export to SQL
yes
export to data lake (S3, GCS, etc.)
yes
export to data warehouse
yes
export as Spark dataframe
yes
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