Description
Currently, the sempy.fabric.lakehouse functions such as list_lakehouse_tables(count_rows=True, extended=True) do not work on Lakehouses with schemas enabled. Attempting to run these functions results in the following error:
Error: {"requestId":"01d5abd1-1a43-41a0-b98b-74c202a8b1c6","errorCode":"UnsupportedOperationForSchemasEnabledLakehouse","message":"The operation is not supported for Lakehouse with schemas enabled."}
Expected Behavior
list_lakehouse_tables should support schema-enabled Lakehouses, returning:
- Table names
- Table sizes
- Row counts
- Table last refreshed date and time
- Other metadata (similar to classic Lakehouses)
Describe alternatives you've considered
Current workaround is to use native Spark queries to enumerate tables and count rows by iterating through the schemas.
Additional context
High-value for automated scripts, dashboards, and workspace audits.