Keeping data pipelines reliable at scale requires more than just the right tools — it demands constant innovation. In this episode, Nick Bilozerov, Senior Data Engineer at Stripe, and Sharadh Krishnamurthy, Engineering Manager at Stripe, discuss how Stripe customizes Airflow for its needs, the evolution of its data orchestration framework and the transition to Airflow 2. They also share insights on scaling data workflows while maintaining performance, reliability and developer experience. 

Key Takeaways:



(02:04) Stripe’s mission is to grow the GDP of the internet by supporting businesses with payments and data.

(05:08) 80% of Stripe engineers use data orchestration, making scalability critical.

(06:06) Airflow powers business reports, regulatory needs and ML workflows.

(08:02) Custom task frameworks improve dependencies and validation.

(08:50) "User scope mode" enables local testing without production impact.

(10:39) Migrating to Airflow 2 improves isolation, safety and scalability.

(16:40) Monolithic DAGs caused database issues, prompting a service-based shift.

(19:24) Frequent Airflow upgrades ensure stability and access to new features.

(21:38) DAG versioning and backfill improvements enhance developer experience.

(23:38) Greater UI customization would offer more flexibility.

Resources Mentioned:

Nick Bilozerov -

https://www.linkedin.com/in/nick-bilozerov/

Sharadh Krishnamurthy -

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

Apache Airflow -

https://airflow.apache.org/

Stripe | LinkedIn -

https://www.linkedin.com/company/stripe/

Stripe | Website -

https://stripe.com/

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#AI #Automation #Airflow #MachineLearning