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Building scalable, reproducible workflows for scientific computing often requires bridging the gap between research flexibility and enterprise reliability.
In this episode, Anja MacKenzie, Expert for Cheminformatics at Covestro, explains how her team uses Airflow and Kubernetes to create a shared, self-service platform for computational chemistry.
Key Takeaways:
(00:00) Introduction. (06:19) Custom scripts made sharing and reuse difficult. (09:29) Workflows are manually triggered with user traceability. (10:38) Customization supports varied compute requirements. (12:48) Persistent volumes allow tasks to share large amounts of data. (14:25) Custom operators separate logic from infrastructure. (16:43) Modified triggers connect dependent workflows. (18:36) UI plugins enable file uploads and secure access.
Resources Mentioned:
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.
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Interested in being a guest on The Data Flowcast? Fill out the form and we will be in touch.
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