Data engineering is entering a new era, where orchestration and automation are redefining how large-scale projects operate. This episode features Vasantha Kosuri-Marshall, Data and ML Ops Engineer at Ford Motor Company. Vasantha shares her expertise in managing complex data pipelines. She takes us through Ford's transition to cloud platforms, the adoption of Airflow and the intricate challenges of orchestrating data in a diverse environment.

Key Takeaways:

(03:10) Vasantha’s transition to the Advanced Driving Assist Systems team at Ford.

(05:42) Early adoption of Airflow to orchestrate complex data pipelines.

(09:29) Ford's move from on-premise data solutions to Google Cloud Platform.

(12:03) The importance of Airflow's scheduling capabilities for efficient data management.

(16:12) Using Kubernetes to scale Airflow for large-scale data processing.

(19:59) Vasantha’s experience in overcoming challenges with legacy orchestration tools.

(22:22) Integration of data engineering and data science pipelines at Ford.

(28:03) How deferrable operators in Airflow improve performance and save costs.

(32:12) Vasantha’s insights into tuning Airflow properties for thousands of DAGs.

(36:09) The significance of monitoring and observability in managing Airflow instances.

Resources Mentioned:

Vasantha Kosuri-Marshall -

https://www.linkedin.com/in/vasantha-kosuri-marshall-0b0aab188/

Apache Airflow -

https://airflow.apache.org/

Google Cloud Platform (GCP) -

https://cloud.google.com/

Ford Motor Company | LinkedIn -

https://www.linkedin.com/company/ford-motor-company/

Ford Motor Company | Website -

https://www.ford.com/

Astronomer -

https://www.astronomer.io/

Thanks for listening to “The Data Flowcast: Mastering Airflow for Data Engineering & 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