Monitoring a data lake's health and usage

Monitoring a Google Cloud data lake means using Cloud Monitoring, Cloud Logging, and service-specific metrics/audit logs to track storage health, access patterns, cost, and data freshness across Cloud Storage, BigQuery, and Dataproc/Dataplex. A well-monitored lake also enforces data quality and governance visibility via Dataplex and Data Catalog, ensuring issues like unauthorized access, stale partitions, or storage class misconfigurations are caught early.

1 · Learn the must-know

• 1Cloud Storage exposes bucket-level metrics (object count, total bytes, request counts/latencies by API method) in Cloud Monitoring, and Storage Insights can generate inventory reports for auditing objects and detecting misclassified storage classes.
• 2Cloud Audit Logs (Admin Activity and Data Access logs) record who accessed or modified objects/datasets; Data Access logs for Cloud Storage and BigQuery must be explicitly enabled since they are high-volume and cost extra.
• 3Dataplex provides data quality, data profiling, and lifecycle management scans across Cloud Storage and BigQuery assets, surfacing metrics like completeness, freshness, and validity rule pass rates directly in the console.
• 4BigQuery monitoring should track slot utilization, bytes scanned/billed, job errors, and long-running or failed queries via INFORMATION_SCHEMA views and Cloud Monitoring metrics, not just relying on the UI.
• 5Object Lifecycle Management events and storage class transitions (e.g., Standard to Nearline/Coldline/Archive) are not directly alertable by default, so tracking storage cost/age trends typically requires exporting billing or inventory data rather than assuming built-in alerts exist.
• 6Setting up alerting policies on Cloud Monitoring metrics (e.g., unusual spike in unauthorized access errors, sudden drop in ingestion rate, or storage growth anomalies) is the standard way to proactively catch data lake issues rather than relying solely on manual log review.

2 · Check your understanding