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As Apache Apache Airflow environments scale, teams face duplicated DAG patterns, slow debugging cycles, and technical debt that often surfaces only in production. This workshop explores how AI can shift pipeline quality left by enabling earlier detection of issues and improving code reliability during development.
Using IBM Bob, we demonstrate real-time code review and refactoring guidance across IDE and terminal workflows, helping engineers identify complexity and performance risks before deployment. We also show how AI accelerates DAG debugging and improves consistency across pipelines in environments that span Airflow and streaming systems such as Confluent.
Attendees will learn practical patterns to improve Airflow reliability, reduce technical debt, and shift debugging earlier in the lifecycle.