Run several Python and SQL tasks daily with explicit dependencies and retryable tasks.
Let operators inspect run history and failed tasks.
Which automation approach is most appropriate?
Run an Apache Airflow DAG on Amazon MWAA and manage task dependencies, schedules, retries, and logs centrally.
Detailed explanation
TTL asynchronously deletes items and does not guarantee workflow execution.
TTL asynchronously deletes items and does not guarantee workflow execution.
Inventory lists objects and does not execute task dependencies.
Inventory lists objects and does not execute task dependencies.
A DAG models dependencies, schedules, retries, and task logs in one workflow.
A DAG models dependencies, schedules, retries, and task logs in one workflow.
Dependencies, history, permissions, and logs become fragmented and harder to operate.
Dependencies, history, permissions, and logs become fragmented and harder to operate.
Check official documentation
Compare the following points with the official documentation.
Amazon MWAA、Apache Airflow DAG、task dependencies、retryの公式説明を確認する。
What to confirm
DAGによる実行管理とcronの寄せ集めの差を説明できる。
Key points
- MWAA
- DAG
- Task dependency
- Retry
Notes
- Environment: AWS公式DEA-C01試験ガイドと各サービスの公式ドキュメントの確認
- Confirm current AWS service behavior in the official DEA-C01 documentation.
Foundation review
Retry boundary
Make tasks idempotent so only the failed task needs to run again.
Environment change
Review DAG code, dependencies, and connections as a deployable unit.