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.

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.

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: COMPUTE_WH
  database: FEAST
  schema: PUBLIC

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

	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

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	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

Snowflake

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

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.

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