Introduction
Apache Airflow is a platform for programmatically defining, scheduling, and monitoring workflows as code (DAGs). It excels at composing complex data pipelines and task orchestration into maintainable, testable, and visual workflows, commonly used for ETL, data engineering, and scheduled automation.
Key Features
- Define dynamic, parameterized DAGs in Python.
- Extensive built-in and community providers/operators for integrating databases, cloud services, and messaging systems.
- Web UI for visualizing DAGs, dependencies, and task execution states to aid monitoring and troubleshooting.
Use Cases
- Scheduled ETL/ELT pipelines and data warehouse refresh jobs.
- Batch orchestration, model training pipelines, and periodic automation workflows.
- Scalable execution with Celery or Kubernetes executors for distributed workloads.
Technical Highlights
- Implemented in Python, supports multiple executors (Local, Celery, Kubernetes).
- Uses Jinja templating for parameterization and XCom for lightweight metadata passing.
- Active community, reproducible installation via constraint files, official Docker images and Helm charts for production deployments.