BigQuery Cost Observability

Challenge

Cloud analytics workloads can become expensive without visibility into query patterns. Teams need to see which queries, users, and workloads drive spend before cost becomes a surprise.

Approach

I created monitoring for expensive queries, heavy users, query patterns, and overall BigQuery spending so unusual usage shows up before the monthly invoice.

Architecture

• BigQuery usage and cost signals collected as first-class operational data
• Alerts and dashboards for expensive workloads and abnormal usage
• Visibility into pipeline reliability alongside spend
• Grafana, Datadog, and cloud monitoring used where they fit the workflow

Implementation

• Identified high-cost query patterns and the users or jobs behind them
• Built alerting around expensive BigQuery workloads and unusual spend
• Connected cost visibility to pipeline reliability so teams could act on both

Result

• Improved visibility into cloud costs
• Earlier intervention when abnormal usage occurred
• A clearer link between analytics usage and spend