[GitHub] apache/airflow

Apache Airflow is an open-source workflow automation and scheduling platform that enables users to define, execute, and monitor complex workflows by writing Python code. Its core functionality is "programmatically orchestrating workflows," which involves declaring task dependencies, execution logic, and scheduling rules in the form of directed acyclic graphs (DAGs) using code. Key technical highlights of the platform include: Python-based workflow definitions, providing high flexibility and scalability; a built-in powerful task scheduler supporting time-, event-, or external trigger-based scheduling; a web interface for managing task dependencies and monitoring execution status; and a rich set of connectors capable of integrating with various external systems such as cloud services, databases, and big data tools. Apache Airflow has become one of the de facto standard tools in the fields of data engineering and machine learning operations. It is widely applied in scenarios like ETL (extract, transform, load), data pipeline construction, and model training pipeline management, helping enterprises achieve automation and reliability for complex task workflows. Its over 45,000 GitHub stars reflect its large developer community and active ecosystem.