Key metrics for monitoring Databricks

Databricks is a lakehouse platform that combines open data formats, scalable object storage, ACID transactions, governance, analytics, AI, and data-management capabilities in shared workspaces divided between control and compute planes. Its workloads include scheduled jobs, Spark Declarative Pipelines, SQL warehouses, streaming applications, and Model Serving endpoints, running on either customer-managed classic compute or Databricks-managed serverless compute. Effective monitoring relies on control-plane system tables, Spark execution signals, query telemetry, infrastructure metrics where host access is available, and endpoint metrics to identify failures, latency, resource saturation, data-processing backlogs, and autoscaling limitations. Important indicators include job outcomes and durations, pipeline quality errors, task failures, shuffle spills, garbage-collection time, warehouse queue waits and query latency, streaming input-versus-processing rates, CPU and memory use, inference error rates and tail latency, and GPU memory pressure. Because Databricks billing is based on elastic per-second DBU consumption, tracking usage and estimated cost is also necessary to detect anomalies caused by misconfigured infrastructure, retries, or inefficient workloads.

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