The evolution of Airflow continues to shape data orchestration and monitoring strategies. Leveraging it beyond traditional ETL use cases opens powerful new possibilities for proactive support and internal operations.

In this episode, we are joined by Collin McNulty, Sr. Director of Global Support at Astronomer, who shares insights from his journey into data engineering and the lessons learned from leading Astronomer’s Customer Reliability Engineering (CRE) team.

Key Takeaways:

00:00 Introduction.

03:07 Lessons learned in adapting to major platform transitions.

05:18 How proactive monitoring improves reliability and customer experience.

08:10 Using automation to enhance internal support processes.

12:09 Why keeping systems current helps avoid unnecessary issues.

15:14 Approaches that strengthen system reliability and efficiency.

18:46 Best practices for simplifying complex orchestration dependencies.

23:24 Anticipated innovations that expand orchestration capabilities.

Resources Mentioned:

Collin McNulty

https://www.linkedin.com/in/collin-mcnulty/

Astronomer | LinkedIn

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

Astronomer | Website

https://www.astronomer.io

Apache Airflow

https://airflow.apache.org/

Prometheus

https://prometheus.io/

Splunk

https://www.splunk.com/

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