Retries, notifications, and job monitoring

Retries help when a failure is transient, such as a short-lived service interruption. They do not fix deterministic code errors, invalid permissions, or bad input; retrying those can waste compute and repeat side effects. Configure attempt limits and delay appropriate to the dependency. Make tasks idempotent or use safe checkpoints so a retry does not duplicate external actions. Monitor run status, duration, task logs, output metrics, and notifications. Alert on meaningful conditions such as repeated failures, missed freshness objectives, or unusual runtime growth. Keep enough context to identify the failing task and input boundary without exposing secrets. Practice task: decide whether a rate-limit error, schema violation, and programming exception should be retried automatically. Applying the pattern: Classify the error, retry only bounded transient failures, notify the owning team, and verify data completeness after recovery. Checks and failure modes: A green job status does not guarantee correct output; include data-level checks and freshness monitoring. Before production, test both a representative input and a boundary case. Confirm the expected row grain, permissions, recovery behavior, and observable output for the task. Record the configuration needed to reproduce the run and compare its result with the agreed data contract. Practice task: Which failure is generally a poor candidate for blind retries?