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Get Started Free →Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use for SQL queries, resource management, data ingestion, or AI applications on BigQuery.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 233% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 67% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 145% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 322% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 230% | 0% |
BigQuery is a serverless, AI-ready data platform that enables high-speed analysis of large datasets using SQL and Python. Its disaggregated architecture separates compute and storage, allowing them to scale independently while providing built-in machine learning, geospatial analysis, and business intelligence capabilities.
bash gcloud services enable bigquery.googleapis.com --quiet
bash bq mk --dataset --location=US my_dataset
Create a file named schema.json with your table schema:
json [ { "name": "name", "type": "STRING", "mode": "REQUIRED" }, { "name": "post_abbr", "type": "STRING", "mode": "NULLABLE" } ]
Then create the table with the bq tool:
bash bq mk --table my_dataset.mytable schema.json
bash bq query --use_legacy_sql=false \ 'SELECT name FROM bigquery-public-data.usa_names.usa_1910_2013 \ WHERE state = "TX" LIMIT 10'
workflows, and BigQuery Studio features.
bq command-line tooloperations for managing data and jobs.
client libraries for Python, Java, Node.js, and Go.
Gemini CLI extension.
datasets, tables, and reservations.
governance best practices.
If you need product information not found in these references, use the Developer Knowledge MCP server search_documents tool.
SKILL.md file for BigQuery AI and ML capabilities.
Reference files published for the BigQuery AI and ML skill.
Other measured skills in the registry, with their headline benchmark lift.