Apache Airflow

Airflow is a platform designed to programmatically author, schedule, and monitor data pipelines. With Airflow, workflows are created as Directed Acyclic Graphs (DAGs) of tasks. The Airflow scheduler executes your tasks across a set of workers while adhering to specified dependencies. A rich set of command-line utilities makes performing complex operations on DAGs a breeze. The intuitive user interface allows for easy visualization of pipelines running in production, monitoring progress, and troubleshooting when necessary. **Principles** - **Dynamic**: Airflow pipelines are configured as code (Python), enabling dynamic pipeline generation. This allows for writing code that dynamically instantiates pipelines. - **Extensible**: Easily define your own operators, executors, and extend the library to fit the abstraction level that suits your environment. - **Elegant**: Airflow pipelines are lean and explicit. Parameterized scripts are built into Airflow’s core using the powerful Jinja templating engine. - **Scalable**: Airflow features a modular architecture and uses message queues to orchestrate any number of workers. It’s built to scale infinitely.

Apache Airflow is a platform designed to programmatically author, schedule, and monitor data pipelines. It enables users to define workflows as tasks within directed acyclic graphs (DAGs), and provides capabilities for visualizing, tracking progress, and troubleshooting in production environments.