The use of Apache Airflow in financial services demands a balance between innovation and compliance. Agile Engine’s approach to orchestration showcases how secure, auditable workflows can scale even within the constraints of regulatory environments.
In this episode, Valentyn Druzhynin, Senior Data Engineer at AgileEngine, discusses how his team leverages Airflow for ETF calculations, data validation and workflow reliability within tightly controlled release cycles.
Key Takeaways:
00:00 Introduction.
03:24 The orchestrator ensures secure and auditable workflows.
05:13 Validations before and after computation prevent errors.
08:24 Release freezes shape prioritization and delivery plans.
11:14 Migration plans must respect managed service constraints.
13:04 Versioning, backfills and event triggers increase reliability.
15:08 UI and integration improvements simplify operations.
18:05 New contributors should start small and seek help.
Resources Mentioned:
https://www.linkedin.com/in/valentyn-druzhynin/
AgileEngine | LinkedIn
https://www.linkedin.com/company/agileengine/
AgileEngine | Website
https://agileengine.com/
https://airflow.apache.org/
https://www.astronomer.io/
https://aws.amazon.com/managed-workflows-for-apache-airflow/
Google Cloud Composer (Managed Airflow)
https://cloud.google.com/composer
https://airflowsummit.org/
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