Innovation in orchestration is redefining how engineers approach both traditional ETL pipelines and emerging AI workloads. Understanding how to harness Airflow’s flexibility and observability is essential for teams navigating today’s evolving data landscape.

In this episode, Anu Pabla, Principal Engineer at The ODP Corporation, joins us to discuss her journey from legacy orchestration patterns to AI-native pipelines and why she sees Airflow as the future of AI workload orchestration.

Key Takeaways:

(03:43) Engaging with external technology communities fosters innovation.

(05:05) Mentoring early-career engineers builds confidence in a complex tech landscape.

(07:51) Orchestration patterns continue to evolve with modern data needs.

(08:41) Managing AI workflows requires structured and flexible orchestration.

(10:35) High-quality, meaningful data remains foundational across use cases.

(15:08) Community-driven open source tools offer lasting value.

(16:59) Self-healing systems support both legacy and AI pipelines.

(20:20) Orchestration platforms can drive future AI-native workloads.

Resources Mentioned:

Anu Pabla

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

The ODP Corporation

https://www.linkedin.com/company/the-odp-corporation/

The ODP Corporation | Website

https://www.theodpcorp.com/homepage

Apache Airflow

https://airflow.apache.org/

LlamaIndex

https://www.llamaindex.ai/

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/

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 #MachineLearning