Managing data pipelines at scale is not just a technical challenge. It is also an organizational one. At Lyft, success means empowering dozens of teams to build with autonomy while enforcing governance and best practices across thousands of workflows.
In this episode, we speak with Yunhao Qing, Software Engineer at Lyft, about building a governed data-engineering platform powered by Airflow that balances flexibility, standardization and scale.
Key Takeaways:
(03:17) Supporting internal teams with a centralized orchestration platform.
(04:54) Migrating to a managed service to reduce infrastructure overhead.
(06:04) Embedding platform-level governance into custom components.
(08:02) Consolidating and regulating the creation of custom code.
(09:48) Identifying and correcting inefficient workflow patterns.
(11:17) Replacing manual workarounds with native platform features.
(14:32) Preparing teams for major version upgrades.
(16:03) Leveraging asset-based scheduling for smarter triggers.
(18:13) Envisioning GenAI and semantic search for future productivity.
Resources Mentioned:
https://www.linkedin.com/in/yunhao-qing
Lyft | LinkedIn
https://www.linkedin.com/company/lyft/
Lyft | Website
https://www.lyft.com/
https://airflow.apache.org/
https://www.astronomer.io/
https://kubernetes.io/
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