Apache Airflow

Apache Airflow is an open source platform for programmatically authoring, scheduling, and monitoring workflows. Workflows are defined as code (DAGs), making them maintainable, versionable, and easier to test and operate at scale.

Key Features

• Define workflows in Python with dynamic DAG generation and parametrization
• Scheduling and dependency management for complex task graphs
• Scalable execution using a scheduler and distributed workers, typically backed by a message queue
• Web UI to visualize DAGs, monitor runs, inspect logs, and troubleshoot failures
• Extensible architecture with a large ecosystem of operators, hooks, and provider integrations
• Templating support (Jinja) for runtime parameters and task configuration

Use Cases

• Orchestrating ETL/ELT data pipelines and batch data processing
• Running scheduled machine learning and analytics workflows
• Coordinating infrastructure or application automation that requires dependency-aware execution

Limitations and Considerations

• Best suited for mostly static, slowly changing workflow structures rather than highly dynamic per-run graphs
• Not a streaming engine; common patterns process near-real-time data in batches
• Tasks should be idempotent and should avoid passing large datasets between tasks (use external storage/services and pass metadata instead)

Apache Airflow is a strong fit when you need reliable, observable orchestration for batch workflows with clear dependencies and operational controls. Its extensibility and broad integration ecosystem make it adaptable across many data and automation environments.