Join us in this panel with key members of the community behind the development of Apache Airflow where we will discuss the tentative scope for the next generation, i.e. Airflow 3.

As a data engineer, I’ve used Airflow extensively over the last 5 years: across 3 jobs, several different roles; for side projects, for critical infrastructure; for manually triggered jobs, for automated workflows; for IT (Ookla/Speedtest.net), for science (Allen Institute for Cell Science), for the commons (Openverse), for liberation (Orca Collective). Authoring a DAG has changed dramatically since 2018, thanks to improvements in both Airflow and the Python language. In this session, we’ll take a trip back in time to see how DAGs looked several years ago, and what the same DAGs might look like now. We’ll appreciate the many improvements that have been made towards simplifying workflow construction. I’ll also discuss the significant advancements that have been made around deploying Airflow. Lastly, I’ll give a brief overview of different use cases and ways I’ve seen Airflow leveraged.

We will describe how we were able to build a system in Airflow for MySQL to Redshift ETL pipelines defined in pure Python using dataclasses. These dataclasses are then used to dynamically generate DAGs depending on pipeline type. This setup allows us to implement robust testing, validation, alerts, and documentation for our pipelines. We will also describe the performance improvements we achieved by upgrading to Airflow 2.0.