Workday HCM connector limitations

Important

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.

Workday HCM limitations

• 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.