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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.
Text
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.
Text
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
To compare this set of functionality against other offline stores, please see the full functionality matrix.
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Description
Getting started
Disclaimer
Example
Functionality Matrix