From ETL to Airflow: Transforming Data Engineering at Deloitte Digital with Raviteja Tholupunoori

Data orchestration at scale presents unique challenges, especially when aiming for flexibility and efficiency across cloud environments. Choosing the right tools and frameworks can make all the difference.

In this episode, Raviteja Tholupunoori, Senior Engineer at Deloitte Digital, joins us to explore how Airflow enhances orchestration, scalability and cost efficiency in enterprise data workflows.

Key Takeaways:

(01:45) Early challenges in data orchestration before implementing Airflow.

(02:42) Comparing Airflow with ETL tools like Talend and why flexibility matters.

(04:24) The role of Airflow in enabling cloud-agnostic data processing.

(05:45) Key lessons from managing dynamic DAGs at scale.

(13:15) How hybrid executors improve performance and efficiency.

(14:13) Best practices for testing and monitoring workflows with Airflow.

(15:13) The importance of mocking mechanisms when testing DAGs.

(17:57) How Prometheus, Grafana and Loki support Airflow monitoring.

(22:03) Cost considerations when running Airflow on self-managed infrastructure.

(23:14) Airflow’s latest features, including hybrid executors and dark mode.

Resources Mentioned:

• Apache Airflow
• Grafana
• Astronomer Presents: Exploring Apache Airflow® 3 Roadshows
• 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 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