Handling errors in pipelines, notebooks, and jobs

Robust Databricks pipelines combine language-level exception handling (try/except in Python/Scala/SQL logic) with platform-level controls in Jobs—retries, timeouts, task dependencies, and alerting—to gracefully manage and recover from failures. Delta Live Tables adds declarative data-quality error handling through expectations, while structured streaming relies on checkpointing and idempotent writes for fault tolerance.

1 · Learn the must-know

• 1Jobs let you configure automatic retries (with retry interval) and a timeout per task, so transient failures don't require manual intervention.
• 2Multi-task jobs support 'Run if' dependency conditions (e.g., All succeeded, At least one failed) to control downstream task execution based on upstream outcomes.
• 3You can attach email or webhook notifications at the job or task level for start, success, and failure events to enable proactive alerting.
• 4dbutils.notebook.run() raises a WorkflowException when the called notebook fails or times out, so wrapping it in try/except lets the caller handle child notebook failures programmatically.
• 5dbutils.notebook.exit() returns a value from a notebook and can be used with try/except/finally blocks to implement custom success/failure signaling in notebook orchestration.
• 6Delta Live Tables uses expectations (expect, expect_or_drop, expect_or_fail) to handle bad records declaratively—logging, dropping, or failing the pipeline based on data quality constraints, separate from code-level exception handling.

2 · Check your understanding