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. Enable the BigQuery API: Create a Dataset: Create a Table: Create a file named schema.json with your table schema: Then create the table with the bq tool: 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.