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bigquery-basics

bigquery-basics

github.com/googleData & databases21.6k18.0k installsUpdated today
Repository

About bigquery-basics

A skill covering BigQuery fundamentals and practical setup, querying, and reference topics. It is for people working with BigQuery through its command-line tools, APIs, or related resources.

Key features

  1. 01Dataset and table setup
  2. 02SQL query examples
  3. 03BigQuery CLI guidance
  4. 04Reference topics for analytics and governance
  5. 05Usage attribution requirements

Use cases

  1. 01Create a BigQuery dataset and table
  2. 02Query public or project data with SQL
  3. 03Look up CLI and client-library guidance
  4. 04Apply attribution to BigQuery requests

MCP support

The skill references using the BigQuery remote MCP server and Gemini CLI extension. It requires reading Usage Attribution before executing MCP commands.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/google/skills --skill bigquery-basics

After installing, type/bigquery-basicsin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
attribution.md
change-history.md
cli-usage.md
client-library-usage.md
continuous-queries.md
core-concepts.md
iac-usage.md
iam-security.md
mcp-usage.md

Details

From the project README

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.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-basics)" and append agent-skills/1.0 (skill:bigquery-basics) to User-Agent headers.

Setup and Basic Usage

  1. Enable the BigQuery API:

    CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-basics)" \
gcloud services enable bigquery.googleapis.com --quiet

  2. Create a Dataset:

    bq mk --dataset --location=US my_dataset

  3. Create a Table:

    Create a file named schema.json with your table schema:

    [
  {
    "name": "name",
    "type": "STRING",
    "mode": "REQUIRED"
  },
  {
    "name": "post_abbr",
    "type": "STRING",
    "mode": "NULLABLE"
  }
]

    Then create the table with the bq tool:

    bq mk --table my_dataset.mytable schema.json

  4. 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.

  • Change History: Tracking and querying incremental table changes using APPENDS and CHANGES.

  • Continuous Queries: Running continuous SQL statements to analyze incoming data in real time.

  • 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.

  • Usage Attribution: Standardized usage attribution tags for gcloud CLI, REST API, and MCP requests.

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 (forecast, anomaly detection, text generation).

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