Ryan Hatter

This talk is the story of getting a PR merged into a Apache Airflow without writing a single line of code, using Apache Airflow itself as an agentic orchestration harness to replicate the functionality of Claude Code for any pluggable LLM.

Ready to contribute to Apache Airflow? In this hands-on workshop, we’ll help you jump straight into the project with real, beginner-friendly issues matched to your skills and interests.

Airflow 3 brings several exciting new features that better support MLOps: Native, intuitive backfills Removal of the unique execution date for dag runs Native support for event-driven scheduling These features, combined with the Airflow AI SDK, enable dag authors to easily build scalable, maintainable, and performant LLMOps pipelines.

Ready to contribute to Apache Airflow? In this hands-on workshop, you'll be expected to come prepared with your development environment already configured (Breeze installed is strongly recommended, but Codespaces works if you can't install Docker).

Because Airflow is 100% code, knowing the basics of Python is all it takes to get started writing DAGs. However, writing DAGs that are efficient, secure, and scalable requires some Airflow-specific finesse.

Much of the world sees Airflow as a hammer and ETL tasks as nails, but in reality, Airflow is much more of a sophisticated multitool, capable of orchestrating a wide variety of complex workflows.

Learn to contribute to the Apache Airflow ecosystem both with and without code. Post an article to the Airflow blog, improve documentation, or dive head-first into into Airflow's free and open source software community.