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BigQuery Basics

Free

Manage datasets and run queries in BigQuery easily.

by davila730.2k stars on davila7/claude-code-templates
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Updated Aug 10, 2026
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Free · Opens the source repo

What BigQuery Basics does

BigQuery Basics is a skill designed for developers and data analysts who need to work with Google BigQuery, a serverless data platform that allows for fast analysis of large datasets. This skill simplifies the management of datasets, tables, and jobs within BigQuery, making it easier to perform SQL queries and manage resources. It also integrates with BigQuery ML and Gemini, providing users the ability to leverage advanced data analytics and AI-driven insights. Whether you're ingesting data, running complex queries, or managing your BigQuery resources, this skill provides the necessary commands and workflows to streamline your tasks.

The skill includes essential setup instructions for enabling the BigQuery API, creating datasets, and managing tables. Users can quickly create tables by defining their schema in JSON format and executing commands through the bq command-line tool. Additionally, the skill offers guidance on running SQL queries, including examples that demonstrate how to access public datasets and filter results effectively. This makes it a practical resource for anyone looking to harness the power of BigQuery for data analysis.

Included in the skill are several reference documents that cover core concepts, command-line usage, client library integration, and best practices for IAM and security. This comprehensive approach ensures that users have access to all the information they need to effectively utilize BigQuery in their projects. Whether you're a beginner looking to learn the basics or an experienced developer seeking to optimize your data workflows, BigQuery Basics provides a solid foundation for working with this powerful data platform.

When to use it

Use this skill when you need to manage BigQuery resources or run SQL queries for data analysis.

When not to use it

This skill may not be suitable if you require advanced features not covered in the basic setup or if you're working with very specific BigQuery ML functionalities.

What you can build with it

Creating a New Dataset

Use the skill to quickly create a new dataset in BigQuery with a simple command, streamlining your data management process.

Running SQL Queries

Leverage the skill to run SQL queries against large datasets, allowing for efficient data analysis and insights.

Integrating with AI Applications

Utilize the skill to manage data for AI-driven applications, making it easier to ingest and analyze data in BigQuery.

How to install BigQuery Basics

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1. Install with the skills CLI

npx skills add davila7/claude-code-templates/bigquery-basics --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by davila7

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

  1. Enable the BigQuery API:

    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.

  • 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 search_documents tool.

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Frequently asked questions about BigQuery Basics

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