Apache Airflow is a programming-based framework for automating authoring, scheduling, and monitoring Beam data pipelines. These Beam data pipelines are dynamic as they are built via programming, and you can use Airflow to create workflows as visualized graphics or directed acyclic graphs (DAGs) of tasks. Airflow also offers a user interface that makes it easy to visualize the pipelines in production, debug any issues that arise, and even track the pipelines’ progress. Another benefit of Airflow is that it is extensible, meaning you can build your own operators and extend the library to the desired level of abstraction for your environment. Airflow is also highly scalable, with the company’s official website boasting that it can scale indefinitely! No versioning of data pipelines,  unintuitive for new users, configuration overload right from the start, hard to use locally, and its scheduler are some of the bottlenecks faced by the users of Apache Airflow.

To contribute to this open-source project, head onto the link: https://github.com/apache/airflow

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