Snowflake

Description

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.

Example

feature_store.yaml

project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
  type: snowflake.offline
  account: snowflake_deployment.us-east-1
  user: user_login
  password: user_password
  role: sysadmin
  warehouse: demo_wh
  database: FEAST

The full set of configuration options is available in SnowflakeOfflineStoreConfig.

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 Snowflake offline store.

	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

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

	Snowflake
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.)	yes
export to data warehouse	yes
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.