A 13% reduction in failure rates — this is how two data scientists at Astronomer revolutionized their data pipelines using Apache Airflow.

In this episode, we enter the world of data orchestration and AI with Maggie Stark and Marion Azoulai, both Senior Data Scientists at Astronomer. Maggie and Marion discuss how their team re-architected their use of Airflow to improve scalability, reliability and efficiency in data processing. They share insights on overcoming challenges with sensors and how moving to datasets transformed their workflows.

Key Takeaways:

(02:23 ) The data team’s role as a centralized hub within Astronomer.
(05:11 ) Airflow is the backbone of all data processes, running 60,000 tasks daily.
(07:13 ) Custom task groups enable efficient code reuse and adherence to best practices.
(11:33 ) Sensor-heavy architectures can lead to cascading failures and resource issues.
(12:09 ) Switching to datasets has improved reliability and scalability.
(14:19 ) Building a control DAG provides end-to-end visibility of pipelines.
(16:42 ) Breaking down DAGs into smaller units minimizes failures and improves management.
(19:02 ) Failure rates improved from 16% to 3% with the new architecture.

Resources Mentioned:

Maggie Stark -
https://www.linkedin.com/in/margaretstark/
Marion Azoulai -
https://www.linkedin.com/in/marionazoulai/
Astronomer | LinkedIn -
https://www.linkedin.com/company/astronomer/
Apache Airflow -
https://airflow.apache.org/
Astronomer | Website -
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