BigQuery Basics
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.
Setup and Basic Usage
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Enable the BigQuery API:
gcloud services enable bigquery.googleapis.com --quiet -
Create a Dataset:
bq mk --dataset --location=US my_dataset -
Create a Table:
Create a file named
schema.jsonwith your table schema:[ { "name": "name", "type": "STRING", "mode": "REQUIRED" }, { "name": "post_abbr", "type": "STRING", "mode": "NULLABLE" } ]Then create the table with the
bqtool:bq mk --table my_dataset.mytable schema.json -
Run a Query:
bq query --use_legacy_sql=false \ 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` \ WHERE state = "TX" LIMIT 10'
Reference Directory
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Core Concepts: Storage types, analytics workflows, and BigQuery Studio features.
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CLI Usage: Essential
bqcommand-line tool operations for managing data and jobs. -
Client Libraries: Using Google Cloud client libraries for Python, Java, Node.js, and Go.
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MCP Usage: Using the BigQuery remote MCP server and Gemini CLI extension.
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Infrastructure as Code: Terraform examples for datasets, tables, and reservations.
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IAM & Security: Roles, permissions, and data governance best practices.
If you need product information not found in these references, use the
Developer Knowledge MCP server search_documents tool.
Related Skills
- BigQuery AI & ML Skill: SKILL.md file for BigQuery AI and ML capabilities (forecast, anomaly detection, text generation).