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.
{% code title="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{% endcode %}
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.
| 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.