Building reliable data pipelines at scale requires more than writing code. It depends on thoughtful design, infrastructure trade-offs and an understanding of how orchestration platforms evolve over time.

In this episode, Airflow best practices shaped by real-world implementation are examined. Bhavani Ravi, Independent Software Consultant and Apache Airflow Champion, shares lessons on pipeline design, architectural decisions and the evolution of the Airflow ecosystem in modern data environments.

Key Takeaways:

00:00 Introduction.

01:30 Independent consulting supports effective Airflow adoption.

02:38 Early challenges shaped modern Airflow practices.

03:21 Airflow setup has become significantly simpler.

04:30 New features expanded workflow capabilities.

06:03 Frequent releases support long-term sustainability.

07:34 Community and providers strengthen the ecosystem.

10:03 Pipeline design should come before coding.

10:55 Decoupling logic requires careful trade-offs.

13:30 Plugins extend Airflow into new use cases.

Resources Mentioned:

Bhavani Ravi

https://www.linkedin.com/in/bhavanicodes/

Apache Airflow

https://airflow.apache.org/

Kubernetes

https://kubernetes.io/

Azure Fabric

https://learn.microsoft.com/en-us/fabric/

Thanks for listening to “The Data Flowcast: Mastering Apache Airflow® for Data Engineering and AI.” If you enjoyed this episode, please leave a 5-star review to help get the word out about the show. And be sure to subscribe so you never miss any of the insightful conversations.

#AI #Automation #Airflow