Pankaj Singh

Debugging Airflow failures in production can be harder than building the pipelines themselves. Engineers often encounter issues such as disappearing DAGs, hanging tasks, missing logs, zombie tasks, or sudden performance degradation, often with little visibility into the root cause.

Efficiently handling long-running workflows is crucial for scaling modern data pipelines. Apache Airflow’s deferrable operators help offload tasks during idle periods — freeing worker slots while tracking progress.

As a popular open-source library for analytics engineering, dbt is often combined with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models.