A beginner’s friendly introduction to Airflow in Python
Introduction
If you’ve ever worked with a data pipeline, you know that managing the process can be a challenge. There are many moving parts, and if any one of them breaks, the whole system can come to a screeching halt. This is where Apache Airflow can help.
Apache Airflow was created by Airbnb in 2015 as an internal solution to managing their data workflows. Airflow quickly gained popularity in the open source community as a way to define and orchestrate complex data pipelines. In 2018, the project was accepted into the Apache Software Foundation and became a top-level project. Today, Airflow is used by companies all over the world to manage their data workflows.
What is Airflow
Airflow is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
Airflow is not just a workflow engine; it is also a platform to author and monitor workflows. The core of Airflow is a web server, a scheduler, and a metadata database.
The web server exposes a simple REST API that you can use to trigger DAGs, view DAG runs, and log DAGs. The scheduler monitors all…
