Apache Airflow
Apache Airflow is an open-source platform for authoring, scheduling, and monitoring programmatic workflows (workflow orchestration) as directed acyclic graphs (DAGs).
- Python-based workflow definition as directed acyclic graphs (workflow orchestration)
- Pluggable executors for running tasks on various compute backends (job execution)
- Scheduler and web-based user interface for managing DAGs and task runs (operations management)
- Extensible operators, hooks, and sensors for integrating with external systems and data platforms (integration framework)
- Role-Based Access Control (RBAC) and deployment options for multi-tenant and enterprise environments (platform governance)
More About Apache Airflow
Apache Airflow is an open-source workflow orchestration platform (workflow orchestration) designed to create, schedule, and monitor complex workflows defined as directed acyclic graphs (DAGs). It targets scenarios where organizations need to coordinate dependent tasks, automate data pipelines, and manage recurring jobs across heterogeneous systems. Workflows are defined in Python code, which allows users to treat workflows as software artifacts that can be version-controlled, tested, and deployed using existing software delivery practices.
The core model centers on DAGs, which capture task dependencies and execution order. Individual units of work are implemented as tasks that can invoke operators (task execution framework) to perform functions such as running shell commands, executing Structured Query Language (SQL), interacting with cloud services, or moving data between systems. Airflow includes a scheduler (job scheduling) that parses DAGs, resolves dependencies, and queues tasks for execution based on time or external triggers. A choice of executors (execution backends), such as local or distributed options, enables deployment on a single machine or across clusters and container platforms.
Airflow provides a web-based user interface (operations management) for visualizing DAGs, inspecting task states, managing configuration, and performing administrative actions such as manually triggering runs or clearing failed tasks. Logging and monitoring capabilities integrate with the UI to present run histories, logs, and status views for operators and support teams. RBAC (security and governance), when enabled, allows organizations to manage user permissions for viewing and modifying DAGs and operational metadata.
The project emphasizes extensibility through plugins, custom operators, hooks, and sensors (integration framework). Hooks abstract connections to external systems such as databases, message queues, and cloud APIs, while sensors allow tasks to wait for external conditions like file arrival or downstream job completion. This design supports integration with data warehouses, data lakes, and analytics platforms in enterprise environments. Airflow can operate as a central orchestration layer that coordinates Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) processes (data engineering), reporting workflows, Machine Learning (ML) pipelines, and other batch-oriented workloads.
Enterprises typically deploy Airflow as a multi-component service, including a metadata database (relational database), a scheduler, web server, and one or more worker processes or containers. It fits into categories such as workflow orchestration, job scheduling, and data pipeline management. Within a technical directory or catalog, Apache Airflow can be positioned under data engineering orchestration platforms, general-purpose workflow schedulers, and integration frameworks used to manage and automate complex, dependency-driven workloads across infrastructure and application domains.