Apache Airflow

Apache Airflow is a platform for programmatically authoring, scheduling and monitoring workflows. It allows you to define your workflows as Python code, making them maintainable, versionable, testable, and collaborative.

Key Features

• Workflow Authoring:

  • Define workflows as Python DAGs (Directed Acyclic Graphs)
  • Rich set of operators and hooks for various integrations
  • Extensible through custom operators and hooks
  • Jinja templating support for dynamic configuration

• Scheduling & Monitoring:

  • Flexible scheduling with cron-like syntax
  • Backfilling and catchup for historical runs
  • Rich UI for monitoring workflow status
  • REST API for programmatic control
  • Notifications and alerts

• Execution & Scaling:

  • Multiple executor types (Local, Celery, Kubernetes)
  • Horizontal scaling with worker nodes
  • Task retries and error handling
  • Resource management and queueing

• Enterprise Features:

  • Role-based access control (RBAC)
  • Audit logging
  • REST API authentication
  • External authentication support
  • Database backend support (PostgreSQL, MySQL)

Who Should Use Airflow

Airflow is ideal for:

• Data Engineers building ETL/ELT pipelines
• ML Engineers orchestrating training workflows
• DevOps Teams automating infrastructure tasks
• Analytics Teams scheduling report generation
• Organizations needing workflow orchestration at scale

Getting Started

Airflow can be installed via pip or deployed using Docker. For production environments, it's recommended to:

1. Use a supported database backend (PostgreSQL recommended)
2. Configure appropriate executor (Celery/Kubernetes for scaling)
3. Set up proper authentication and access control
4. Plan for monitoring and maintenance

The platform provides extensive documentation and an active community to help users get started with workflow automation.