Airflow Review
Airflow
A platform created by the community to programmatically author, schedule and monitor workflows.
Apache Airflow is an open-source workflow orchestration platform for data teams that programmatically define DAG-based pipelines in Python. It schedules monitors and automates batch workflows across databases cloud services and integrations for ETL analytics and data engineering operations reliably at scale today.
What is Airflow
Apache Airflow is an open-source workflow orchestration platform for data engineers, analytics teams, and platform operators who manage recurring pipelines. Apache Airflow uses Python code to define directed acyclic graphs (DAGs), schedule jobs, manage task dependencies, retry failures, trigger event-driven runs, and monitor execution from a web UI. It connects with databases, cloud storage, warehouses, Kubernetes, Spark, and messaging systems through providers and integrations. Teams use Apache Airflow to centralize automation, enforce workflow logic, and run batch processes with version-controlled code.
Common use cases include building ETL and ELT pipelines, refreshing BI datasets, moving data between systems, orchestrating machine learning training jobs, and coordinating multi-step operational workflows across internal tools. Logs, alerts, backfills, variables, connections, and role-based access controls support day-to-day operations and governed production environments. Managed distributions such as Astronomer and cloud-hosted offerings extend deployment options, while the open-source core remains widely adopted. In the modern data stack, Apache Airflow owns the orchestration layer that coordinates warehouses, compute engines, transformation tools, and downstream reporting systems.