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 when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.
- License 许可
- Apache-2.0
- 最近更新
- 2026-11-08
- Stars
- ★ 1
- Forks
- 0
01 前言 Front Matter
- name
- bigquery-basics
- description
- Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.
02 资源文件 6
- 参考文档references/cli-usage.md2.0 KB
- 参考文档references/client-library-usage.md2.2 KB
- 参考文档references/core-concepts.md3.4 KB
- 参考文档references/iac-usage.md1.7 KB
- 参考文档references/iam-security.md1.9 KB
- 参考文档references/mcp-usage.md2.3 KB
03 SKILL.md 正文
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 --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
-
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.
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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.
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