airflow

Apache Airflow is a platform designed to author, schedule, and monitor workflows. By defining workflows as code (Directed Acyclic Graphs or DAGs), they become more maintainable, versionable, and testable. The platform includes a scheduler that executes tasks on workers based on specified dependencies, a rich command-line interface for DAG management, and a user interface for visualizing pipelines and troubleshooting. Key capabilities include: - Dynamic Pipeline Generation: Workflows are defined in Python, allowing for dynamic generation and parameterization. - Extensibility: Provides a wide range of built-in operators and supports custom extensions. - Flexibility: Integrates the Jinja templating engine for rich customizations. - Broad Use Cases: While primarily used for data pipelines, it is also used to orchestrate machine learning workflows (training, evaluation, deployment) and AI/LLM-based workloads. - Monitoring UI: Features a comprehensive interface including DAG overviews, asset dependency graphs, grid and graph representations of runs, and backfilling tools. Airflow is designed for static or slowly changing workflows and emphasizes idempotent tasks. It is not a streaming solution but can process real-time data in batches. It supports various databases including PostgreSQL, MySQL, and SQLite (for development), and can be deployed via PyPI, Docker images, or Helm Charts.