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  1. Reference
  2. Offline stores

Azure Synapse + Azure SQL (contrib)

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Last updated 6 days ago

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Description

The MsSQL offline store provides support for reading . Specifically, it is developed to read from 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 .

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

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

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

The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the Spark offline store.

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

MsSQL Sources
Synapse SQL
tutorial
here
functionality matrix