Workday HCM connector limitations

Beta

This feature is in Beta.

This page contains information about known limitations of the managed Workday HCM connector in Lakeflow Connect.

General limitations

When you run a scheduled pipeline, alerts don't trigger immediately. Instead, they trigger when the next update runs.
When a source table is deleted, the destination table is not automatically deleted. You must delete the destination table manually. This behavior is not consistent with Spark Declarative Pipelines on Lakeflow behavior.
During source maintenance periods, Databricks might not be able to access your data.
If a source table name conflicts with an existing destination table name, the pipeline update fails.
Multi-destination pipeline support is API-only.
You can optionally rename a table that you ingest. If you rename a table in your pipeline, it becomes an API-only pipeline, and you can no longer edit the pipeline in the UI.
If you select a column after a pipeline has already started, the connector does not automatically backfill data for the new column. To ingest historical data, manually run a full refresh on the table.
Databricks can't ingest two or more tables with the same name in the same pipeline, even if they come from different source schemas.
The source system assumes that the cursor columns are monotonically increasing.
The connector ingests raw data without transformations. Use downstream Spark Declarative Pipelines on Lakeflow pipelines for transformations.

The Workday HCM connector doesn't support column selection. The connector streams all data from a shared raw data stream. Because multiple materialized views are derived from the same stream, column selection at the individual materialized view level isn't supported.