bigquery-basics

Topics in the source

• BigQuery Basics
• Setup and Basic Usage
• Reference Directory
• Related Skills

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. 1. Enable the BigQuery API: 2. Create a Dataset: 3. Create a Table: Create a file named schema.json with your table schema: Then create the table with the bq tool: 4. Run a Query: Core Concepts: Storage types, analytics workflows, and BigQuery Studio features. CLI Usage: Essential bq command-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 searchdocuments tool. BigQuery AI & ML Skill: SKILL.md file for BigQuery AI and ML capabilities. BigQuery AI & ML

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