Apache Airflow exemplifies production DAG orchestration

Airflow remains the clearest mental model for “code-defined orchestration” because it turns pipeline logic into auditable graph structure rather than hidden scheduler magic.

• –Airflow DAGs encode dependencies explicitly, which improves reviewability and incident debugging in complex data/ML pipelines.
• –Parallel DAG runs and task-level concurrency controls make backfills and high-throughput workloads operationally practical.
• –Trigger rules and upstream failure states provide predictable failure propagation semantics instead of ad hoc retry chains.
• –The ecosystem depth (operators, cloud integrations, managed offerings) keeps Airflow relevant even as newer orchestrators emerge.