Demo Dive Series
Debugging data pipelines often means chasing failures across orchestration layers, SQL transformations, and upstream dependencies—a time-consuming and manual process.
In this session, we’ll walk through a real-world example of using Snowflake Cortex Code to triage and resolve multiple broken workflows. Instead of relying on manual investigation, an AI agent analyzes pipeline failures, traces dependencies, identifies root causes, and proposes actionable fixes directly within the data environment.
We’ll break down how the system reasons across schemas, queries, and job metadata, and where it meaningfully reduces engineering effort versus where human oversight is still required. Attendees will leave with a practical understanding of how AI can accelerate incident response, reduce debugging overhead, and fit into production-grade data workflows.
You’ll learn how to:
- Accelerate incident response: use Snowflake Cortex Code to analyze pipeline failures in seconds instead of hours of manual lineage-chasing.
- Trace root causes across the stack: let the AI agent reason across schemas, queries, and job metadata to surface the source of a break.
- Know when to trust the agent: see the specific failure modes where human review still beats automation, and how to build that check into production workflows.
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