Analyze BigQuery Usage

Analyze BigQuery usage to identify expensive queries and heavy users.

112|305|Updated Oct 10, 2025

Search Tags:#gcp #data-analysis #cost-optimization #bigquery #cloud-costs #query-performance

Ready to run

npx skills add https://github.com/openshift-eng/ai-helpers --skill analyze-bigquery-usage

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Analyze BigQuery Usage
Source: https://github.com/openshift-eng/ai-helpers/tree/main/plugins/bigquery/skills/analyze-usage
Command: npx skills add https://github.com/openshift-eng/ai-helpers --skill analyze-bigquery-usage

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill identifies expensive BigQuery queries, analyzes usage patterns, and provides actionable recommendations to significantly reduce cloud costs while improving query performance.

Core Features & Use Cases

Cost Analysis: Pinpoint the most expensive queries and users driving your BigQuery spending.
Usage Pattern Detection: Identify inefficient query patterns, anti-patterns, and optimization opportunities.
Use Case: When your team's BigQuery costs are unexpectedly high, use this Skill to analyze the last 7 days of usage, discover that automated dashboard queries are scanning full tables unnecessarily, and get specific recommendations to save $50+ per day.

Quick Start

Analyze BigQuery usage for project 'my-gcp-project' over the last 24 hours to identify cost-saving opportunities.

Frequently Asked Questions about Analyze BigQuery Usage

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce BigQuery costs by identifying expensive queries?▼

Expensive queries can be identified by analyzing BigQuery job logs to find queries consuming the most data and compute resources. This Skill scans your project's query history over a specified timeframe, ranks queries by cost, and flags inefficient patterns like full table scans, so you can optimize or eliminate high-cost operations.

What's the best way to analyze BigQuery usage patterns across my team?▼

Analyze BigQuery usage patterns by examining per-user activity and query execution logs to detect inefficient practices and cost drivers. This Skill aggregates usage data by user and query type, revealing who and what consume the most resources, enabling targeted optimization and cost governance.

Can I use BigQuery cost optimization without manual query inspection?▼

Yes. This Skill automates BigQuery cost analysis by querying job metadata directly using the bq CLI, eliminating manual inspection. It requires only Cloud SDK authentication and bigquery.jobs.list permission to generate a structured optimization report with anti-patterns and actionable fixes.

How do I identify BigQuery anti-patterns that waste cloud spending?▼

BigQuery anti-patterns like unnecessary full-table scans and redundant queries waste cloud spending. This Skill detects these patterns by analyzing query execution details and data processed, then outputs specific recommendations to restructure queries, partition tables, or cache results for cost reduction.

What data do I need to start optimizing BigQuery performance and costs?▼

To optimize BigQuery performance and costs, provide your GCP Project ID, desired analysis timeframe in hours, and ensure the bq CLI and Cloud SDK are installed with appropriate authentication. This Skill then accesses your query job history to generate insights without requiring manual data export.