Stop Debugging in Production: Shift-Left Airflow for Streaming Pipelines with AI

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