Scaling a data orchestration platform to manage thousands of tasks daily demands innovative solutions and strategic problem-solving. In this episode, we explore the complexities of scaling Airflow and the challenges of orchestrating thousands of tasks in dynamic data environments. Jonathan Rainer, Former Platform Engineer at Monzo Bank, joins us to share his journey optimizing data pipelines, overcoming UI limitations and ensuring DAG consistency in high-stakes scenarios. 

Key Takeaways:

(03:11) Using Airflow to schedule computation in BigQuery.

(07:02) How DAGs with 8,000+ tasks were managed nightly.

(08:18) Ensuring accuracy in regulatory reporting for banking.

(11:35) Handling task inconsistency and DAG failures with automation.

(16:09) Building a service to resolve DAG consistency issues in Airflow.

(25:05) Challenges with scaling the Airflow UI for thousands of tasks.

(27:03) The role of upstream and downstream task management in Airflow.

(37:33) The importance of operational metrics for monitoring Airflow health.

(39:19) Balancing new tools with root cause analysis to address scaling issues.

(41:35) Why scaling solutions require both technical and leadership buy-in

Resources Mentioned:

Jonathan Rainer -

https://www.linkedin.com/in/jonathan-rainer/

Monzo Bank -

https://www.linkedin.com/company/monzo-bank/

Apache Airflow -

https://airflow.apache.org/

BigQuery -

https://airflow.apache.org/docs/apache-airflow-providers-google/stable/operators/cloud/bigquery.html

Kubernetes -

https://kubernetes.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