By
Bob Reno
As Apache Airflow expands beyond batch into real-time, event-driven architectures, data teams face a new set of challenges: duplicated DAG patterns, fragile Kafka-triggered workflows, and debugging cycles that happen too late—often in production.
In this session, we introduce a shift-left approach to pipeline reliability for environments combining Airflow with streaming platforms like Confluent. We’ll explore how event-driven pipelines increase complexity—and why traditional debugging and validation approaches no longer scale. You’ll see how IBM Bob, an AI-powered assistant for data engineers, brings real-time code review, refactoring guidance, and debugging insights directly into developer workflows.
From catching DAG anti-patterns early to improving consistency across batch and streaming pipelines, we’ll demonstrate how teams can prevent issues before they reach production.
We’ll also share practical patterns to: -Improve Airflow code quality across distributed teams -Standardize DAG development for batch and streaming use cases -Reduce MTTD (Mean Time to Detections) and MTTR (Mean Time To Resolution) -Automate DAG tracking across your enterprise through lineage graphs -Minimize technical debt as pipeline complexity grows
We’ll close with a preview of our hands-on workshop, where attendees can apply these concepts in a live lab—using AI to debug, optimize, and standardize Airflow pipelines in real-world scenarios.