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.
Getting started
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 .
Please be aware that here is a restriction/limitation for using SQL query string in Feast with Snowflake. Try to avoid the usage of single quote in SQL query string. For example, the following query string will fail:
That 'value' will fail in Snowflake. Instead, please use pairs of dollar signs like $$value$$ as .
The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the Snowflake offline store.
Snowflake
Below is a matrix indicating which functionality is supported by SnowflakeRetrievalJob.
Snowflake
To compare this set of functionality against other offline stores, please see the full .
offline_write_batch (persist dataframes to offline store)
yes
write_logged_features (persist logged features to offline store)
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