PagerDuty connector limitations

Important

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

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

General software as a service (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 PagerDuty connector.

• The incidents table captures inserts only. PagerDuty filters this table by created_at, so the connector does not reflect incident updates or deletions between syncs. Schedule a periodic full refresh to capture the latest incident status.
• The connector ingests services, users, teams, priorities, escalation_policies, schedules, and oncalls with full refresh only. The connector reloads these tables in full on each sync.
• The oncalls table reflects on-call assignments as of each sync. It is not an incremental change log.