Introducing Apache Airflow® 3 with Vikram Koka and Jed Cunningham

The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI

The Airflow 3.0 release marks a significant leap forward in modern data orchestration, introducing architectural upgrades that improve scalability, flexibility and long-term maintainability.

In this episode, we welcome Vikram Koka, Chief Strategy Officer at Astronomer, and Jed Cunningham, Principal Software Engineer at Astronomer, to discuss the architectural foundations, new features and future implications of this milestone release. They unpack the rationale behind DAG versioning and task execution interface, explain how Airflow now integrates more seamlessly within broader data ecosystems and share how these changes lay the groundwork for multi-cloud deployments, language-agnostic workflows and stronger enterprise security.

Key Takeaways:

(02:28) Modern orchestration demands new infrastructure approaches.

(05:02) Removing legacy components strengthens system stability.

(06:26) Major releases provide the opportunity to reduce technical debt.

(08:31) Frontend and API modernization enable long-term adaptability.

(09:36) Event-based triggers expand integration possibilities.

(11:54) Version control improves visibility and execution reliability.

(14:57) Centralized access to workflow definitions increases flexibility.

(21:49) Decoupled architecture supports distributed and secure deployments.

(26:17) Community collaboration is essential for sustainable growth.

Resources Mentioned:

Astronomer Website

https://www.astronomer.io

Apache Airflow

https://airflow.apache.org/

Git Bundle

https://git-scm.com/book/en/v2/Git-Tools-Bundling

FastAPI

https://fastapi.tiangolo.com/

React

https://react.dev/

https://www.astronomer.io/events/roadshow/london/

https://www.astronomer.io/events/roadshow/new-york/

https://www.astronomer.io/events/roadshow/sydney/

https://www.astronomer.io/events/roadshow/san-francisco/

https://www.astronomer.io/events/roadshow/chicago/

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#AI #Automation #Airflow #MachineLearning