SummaryIn this episode of the Data Engineering Podcast Pete DeJoy, co-founder and product lead at Astronomer, talks about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3. Pete shares his journey into data engineering, discusses Astronomer's contributions to the Airflow project, and highlights the critical role of Airflow in powering operational data products. He covers the evolution of Airflow, its position in the data ecosystem, and the challenges faced by data engineers, including infrastructure management and observability. The conversation also touches on the upcoming Airflow 3 release, which introduces data awareness, architectural improvements, and multi-language support, and Astronomer's observability suite, Astro Observe, which provides insights and proactive recommendations for Airflow users.Announcements<ul><li>Hello and welcome to the Data Engineering Podcast, the show about modern data management</li><li>Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit <a href="https://www.dataengineeringpodcast.com/datafold" target="_blank">dataengineeringpodcast.com/datafold</a> today for the details.</li><li>Your host is Tobias Macey and today I'm interviewing Pete DeJoy about building and managing Airflow pipelines on Astronomer and the upcoming improvements in Airflow 3</li></ul>Interview<ul><li>Introduction</li><li>Can you describe what Astronomer is and the story behind it?</li><li>How would you characterize the relationship between Airflow and Astronomer?</li><li>Astronomer just released your State of Airflow 2025 Report yesterday and it is the largest data engineering survey ever with over 5,000 respondents. Can you talk a bit about top level findings in the report?</li><li>What about the overall growth of the Airflow project over time?</li><li>How have the focus and features of Astronomer changed since <a href="https://www.dataengineeringpodcast.com/astronomer-with-ry-walker-episode-6" target="_blank">it was last featured on the show</a> in 2017?</li><li>Astro Observe GA’d in early February, what does the addition of pipeline observability mean for your customers?&nbsp;</li><li>What are other capabilities similar in scope to observability that Astronomer is looking at adding to the platform?</li><li>Why is Airflow so critical in providing an elevated Observability–or cataloging, or something simlar - experience in a DataOps platform?&nbsp;<ul><li>What are the notable evolutions in the Airflow project and ecosystem in that time?</li></ul></li><li>What are the core improvements that are planned for Airflow 3.0?</li><li>What are the most interesting, innovative, or unexpected ways that you have seen Astro used?</li><li>What are the most interesting, unexpected, or challenging lessons that you have learned while working on Airflow and Astro?</li><li>What do you have planned for the future of Astro/Astronomer/Airflow?</li></ul>Contact Info<ul><li><a href="https://www.linkedin.com/in/pdejoy58/" target="_blank">LinkedIn</a></li></ul>Parting Question<ul><li>From your perspective, what is the biggest gap in the tooling or technology for data management today?</li></ul>Closing Announcements<ul><li>Thank you for listening! Don't forget to check out our other shows. <a href="https://www.pythonpodcast.com" target="_blank">Podcast.__init__</a> covers the Python language, its community, and the innovative ways it is being used. The <a href="https://www.aiengineeringpodcast.com" target="_blank">AI Engineering Podcast</a> is your guide to the fast-moving world of building AI systems.</li><li>Visit the <a href="https://www.dataengineeringpodcast.com" target="_blank">site</a> to subscribe to the show, sign up for the mailing list, and read the show notes.</li><li>If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story.</li></ul>Links<ul><li><a href="https://www.astronomer.io/" target="_blank">Astronomer</a></li><li><a href="https://airflow.apache.org/" target="_blank">Airflow</a></li><li><a href="https://www.linkedin.com/in/maximebeauchemin/" target="_blank">Maxime Beauchemin</a></li><li><a href="https://www.mongodb.com/" target="_blank">MongoDB</a></li><li><a href="https://www.databricks.com/" target="_blank">Databricks</a></li><li><a href="https://www.confluent.io/" target="_blank">Confluent</a></li><li><a href="https://spark.apache.org/" target="_blank">Spark</a></li><li><a href="https://kafka.apache.org/" target="_blank">Kafka</a></li><li><a href="https://dagster.io/" target="_blank">Dagster</a><ul><li><a href="https://www.dataengineeringpodcast.com/dagster-software-defined-assets-data-orchestration-episode-309" target="_blank">Podcast Episode</a></li></ul></li><li><a href="https://www.prefect.io/" target="_blank">Prefect</a></li><li><a href="https://www.astronomer.io/airflow/3-0/" target="_blank">Airflow 3</a></li><li><a href="https://www.freecodecamp.org/news/the-rise-of-the-data-engineer-91be18f1e603/" target="_blank">The Rise of the Data Engineer</a> blog post</li><li><a href="https://www.getdbt.com/" target="_blank">dbt</a></li><li><a href="https://jupyter.org/" target="_blank">Jupyter Notebook</a></li><li><a href="https://zapier.com/" target="_blank">Zapier</a></li><li><a href="https://www.astronomer.io/cosmos/" target="_blank">cosmos</a> library for dbt in Airflow</li><li><a href="https://docs.astral.sh/ruff/" target="_blank">Ruff</a></li><li><a href="https://airflow.apache.org/docs/apache-airflow/stable/howto/custom-operator.html" target="_blank">Airflow Custom Operator</a></li><li><a href="https://www.snowflake.com/en/" target="_blank">Snowflake</a></li></ul>The intro and outro music is from <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug" target="_blank">The Hug</a> by <a href="http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/" target="_blank">The Freak Fandango Orchestra</a> / <a href="http://creativecommons.org/licenses/by-sa/3.0/" target="_blank">CC BY-SA</a>

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