Beyond Monitoring: Lakeflow Observability for Operational Health

It is 3 AM. A dashboard is stale, an SLA slipped, and everyone is guessing which job, pipeline, or dependency broke first. That is when surface-level monitoring stops being useful. You need observability that shows the health of your workflows and helps you find the cause fast.In this session, we will show how Lakeflow Observability helps data engineering teams move from reactive monitoring to real operational health. You will see how to monitor runs, tasks, dependencies, alerts, and health metrics across jobs and pipelines, and how Databricks system tables provide the historical depth needed for root-cause analysis, performance analysis, cost tracking, and reliability reporting at scale.We will also cover practical patterns for tracking streaming backlog, freshness, SLA risk, and failure trends, and show how Genie Code can help teams troubleshoot faster using natural language. Your 3 AM self will thank you.