Bigquery Basics
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.
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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:
bashgcloud services enable bigquery.googleapis.com --quietCreate a Dataset:
bashbq mk --dataset --location=US my_datasetCreate a Table:
Create a file named
schema.jsonwith your table schema:json[ { "name": "name", "type": "STRING", "mode": "REQUIRED" }, { "name": "post_abbr", "type": "STRING", "mode": "NULLABLE" } ]Then create the table with the
bqtool:bashbq mk --table my_dataset.mytable schema.jsonRun a Query:
bashbq query --use_legacy_sql=false \ 'SELECT name FROM `bigquery-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.