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Anysphere Audit Logs connector limitations

Beta

This feature is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Databricks previews.

General SaaS connector limitations​

The limitations in this section apply to all SaaS connectors in Lakeflow Connect.

  • 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.

Connector-specific limitations​

The limitations in this section apply to the Anysphere Audit Logs connector.

  • The connector doesn't support SCD type 2 for audit log event data.
  • The connector supports incremental ingestion for the audit_logs table using the timestamp cursor field. Only audit events from the last 30 days are available — Anysphere (Cursor) retains audit logs for approximately 30 days and caps the query window at 30 days. On the first pipeline run, expect the table to contain only recent events (typically from the last 30 days), not a full historical archive.
  • For the list of supported tables, see Supported source tables.
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