Azure Synapse + Azure SQL (contrib)

Description

The MsSQL offline store provides support for reading MsSQL Sources. Specifically, it is developed to read from Synapse SQL on Microsoft Azure

Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[azure]'. You can get started by then following this tutorial.

Disclaimer

The MsSQL offline store does not achieve full test coverage. Please do not assume complete stability.

Example

feature_store.yaml

registry:
  registry_store_type: AzureRegistryStore
  path: ${REGISTRY_PATH} # Environment Variable
project: production
provider: azure
online_store:
    type: redis
    connection_string: ${REDIS_CONN} # Environment Variable
offline_store:
    type: mssql
    connection_string: ${SQL_CONN}  # Environment Variable

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 Spark offline store.

MsSql

	MsSql
`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)	no
`write_logged_features` (persist logged features to offline store)	no

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)

write_logged_features (persist logged features to offline store)

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

	MsSql
export to dataframe	yes
export to arrow table	yes
export to arrow batches	no
export to SQL	no
export to data lake (S3, GCS, etc.)	no
export to data warehouse	no
local execution of Python-based on-demand transforms	no
remote execution of Python-based on-demand transforms	no
persist results in the offline store	yes

MsSql

export to dataframe

yes

export to arrow table

yes

export to arrow batches

export to SQL

export to data lake (S3, GCS, etc.)

export to data warehouse

local execution of Python-based on-demand transforms

remote execution of Python-based on-demand transforms

persist results in the offline store

yes

To compare this set of functionality against other offline stores, please see the full functionality matrix.

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