Apache Airflow is a workflow automation platform that schedules and monitors workflows in the data pipelines programmatically. Airflow makes it simpler to set up and operate an end-to-end data pipeline in the cloud. You can use Airflow to manage and create workflows without worrying about the infrastructure for scalability, availability, and security.
Apache Airflow is an open-source platform that began as an Apache incubator project in 2016. However, Airflow became a top-level Apache Software Foundation project in 2019. It is helpful for creating and managing workflows.
Airflow allows you to build workflows as Directed Acyclic Graphs (DAGs) of tasks. Airflow pipelines are configured as code, which allows for dynamic pipeline generation. This ensures that you can write code that can instantiate the pipeline dynamically. In addition, Airflow makes it easy to define the operators, executors, and extend the library to make it suitable for the environment. Another benefit of Airflow is that Airflow pipelines are lean and explicit, and the modular architecture of Airflow is perfectly fitted for scalability. You can have command-line utilities for performing complex tasks on DAGs. Additionally, the user interface provides the possibility to visualize the pipelines running in production, which can be monitored to track the progress and troubleshoot issues when required.
In this free-to-download warmup guide, you’ll learn everything you need to know to get started with Apache Airflow, including key terminology, installation, and use cases.