Building Smarter Pipelines for AI, Maps, and ML with Airflow

If you’re working with spatial data, AI workflows, or massive batch jobs, you’ve probably hacked together more than a few pipelines. But what if there’s a better way?

In this episode, I sit down with Kenten Danas, Senior Manager of Developer Relations at ‪@Astronomer‬ to explore how Apache Airflow powers the modern data stack including real-world geospatial and climate risk modeling pipelines.

We cover:

What Airflow actually is (and why it’s everywhere)
How it’s used in geospatial pipelines, AI, and LLM workflows
New features in Airflow 3.0 like assets, remote execution, and backfills
Why orchestration is the key to scalable spatial data processing
Tools like the Airflow AI SDK that make LLM pipelines easier to manage

Links from the show:

Astronomer Academy (with courses + certifications): https://academy.astronomer.io/
Astronomer Webinars: https://www.astronomer.io/events/webi...
Astro CLI (for running Airflow locally): https://www.astronomer.io/docs/astro/...
Free trial of Astro: https://www.astronomer.io/lp/signup/
Airflow AI SDK (open source Python SDK for working with LLMs from Airflow): https://github.com/astronomer/airflow...
Vibrant Planet Geospatial + ML Airflow use case: https://www.astronomer.io/blog/airflo...

Whether you're building spatial features for machine learning or just want a more reliable way to manage your data workflows this is the episode for you.