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.
The MsSQL offline store does not achieve full test coverage. Please do not assume complete stability.
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.
Below is a matrix indicating which functionality is supported by MsSqlServerRetrievalJob.
To compare this set of functionality against other offline stores, please see the full functionality matrix.
| 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
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