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
- Enable the BigQuery API:
gcloud services enable bigquery.googleapis.com - 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 thebqtool:bq mk --table my_dataset.mytable schema.json - Run a Query: `
bq query --use_legacy_sql=false \ 'SELECT name FROMbigquery-public-data.usa_names.usa_1910_2013\ WHERE state = "TX" LIMIT 10'`
Reference Directory
- Core Concepts: Storage types, analytics workflows, and BigQuery Studio features.
- 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.
- MCP Usage: Using the BigQuery remote MCP server and Gemini CLI extension.
- Infrastructure as Code: Terraform examples for datasets, tables, and reservations.
- 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.
- BigQuery AI & ML References: Reference files published for the BigQuery AI and ML skill.
- bigquery_ai_classify.md - bigquery_ai_detect_anomalies.md - bigquery_ai_forecast.md - bigquery_ai_generate.md - bigquery_ai_generate_bool.md - bigquery_ai_generate_double.md - bigquery_ai_generate_int.md - bigquery_ai_if.md - bigquery_ai_score.md - bigquery_ai_search.md - bigquery_ai_similarity.md