Delta Lake API
Audience:Data Owners and Data Users
Content Summary: This page describes the Spark SQL options to substitute for the Delta Lake API a user may need.
When using Delta Lake, the API does not go through the normal Spark execution path. This means that Immuta's Spark extensions do not provide protection for the API. To solve this issue and ensure that Immuta has control over what a user can access, the Delta Lake API is blocked.
Spark SQL can be used instead to give the same functionality with all of Immuta's data protections.
Below is a table of the Delta Lake API with the Spark SQL that may be used instead.
|Delta Lake API||Spark SQL|
|DeltaTable.convertToDelta||CONVERT TO DELTA parquet.
|DeltaTable.delete||DELETE FROM [table_identifier delta.
|DeltaTable.generate||GENERATE symlink_format_manifest FOR TABLE [table_identifier delta.
|DeltaTable.history||DESCRIBE HISTORY [table_identifier delta.
|DeltaTable.update||UPDATE [table_identifier delta.
|DeltaTable.vacuum||VACUUM [table_identifier delta.
See here for a complete list of the Delta SQL Commands.
Merging Tables in Native Workspaces
When a table is created in a native workspace, you can merge a different Immuta data source from that workspace into that table you created.
- Create a table in the native workspace.
- Create a temporary view of the Immuta data source you want to merge into that table.
- Use that temporary view as the data source you add to the project workspace.
Run the following command:
MERGE INTO delta_native.target_native as target USING immuta_temp_view_data_source as source ON target.dr_number = source.dr_number WHEN MATCHED THEN UPDATE SET target.date_reported = source.date_reported