Did you know that the name "Airflow" was inspired by the airflow patterns inside the data center at Airbnb?

Having practiced Airflow for almost 2 years now, I have tried all sorts of things to get better:

• Go through Airflow tutorials and repos

• Watch Youtube videos on Airflow

• Listen to podcasts about data engineering

And all of these things helped me a ton.

But if I had to start all over again (as a beginner), this is the simple, 5-step framework I wish I had for learning Airflow:

Step 1: Understand the Basics

This includes understanding the key concepts such as DAGs (Directed Acyclic Graphs), operators, tasks, and workflows. Familiarizing yourself with these concepts will provide a solid foundation to build upon.

Step 2: Build a Simple DAG

Start by creating a DAG with a single task, then gradually add more complexity as you become more comfortable with the syntax and structure of Airflow.

Step 3: Experiment with Operators

Airflow provides a wide range of operators that can be used to perform different tasks within a DAG, such as BashOperators, PythonOperators, and DummyOperators. You will soon observe and capture the true capabilities of Airflow.

Step 4: Integrate External Systems

Databases, APIs, and cloud services are at your fingertips with Airflow. With these integrated, you can build more complex and powerful workflows. Start by integrating something simple, such as a local database, and gradually work your way up to more complex systems.

Step 5: Practice, Practice, Practice

Build a repo with different types of workflows, operators and external systems. Then share it with the world! This will help you develop a deeper understanding of the tool and its capabilities.

By following these 5 steps, you can learn Airflow quickly and effectively. With practice, you can become proficient in managing data pipelines, helping organizations achieve better data management outcomes.