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Bigquery Basics

GoogleOfficialPublished by Google

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Low risk
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From the author

Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

BigQuery Basics provides a structured approach to interacting with Google BigQuery, a serverless data platform. This skill covers essential resource management, including creating datasets and tables, executing SQL queries, and tracking data changes. It emphasizes standardized usage attribution for all CLI and API interactions to ensure proper telemetry within agent environments.

What it does

  • Enables the BigQuery API for cloud projects.
  • Creates and manages BigQuery datasets and tables.
  • Executes SQL queries against public and private datasets.
  • Tracks incremental table changes using APPENDS and CHANGES functions.
  • Provides guidance on IAM, security, and infrastructure-as-code practices.
  • Implements mandatory usage attribution for CLI and API requests.

When to use it

  • When you need to perform data ingestion or analysis within BigQuery.
  • When managing BigQuery resources like datasets, tables, or views via CLI.
  • When you need to audit or process incremental data changes in a table.
  • When integrating BigQuery operations into automated workflows or infrastructure scripts.

Advantages

  • Standardized command attribution for better observability.
  • Comprehensive coverage of core BigQuery resource management.
  • Includes advanced features like change history tracking.
  • Provides clear examples for both CLI and programmatic access.

Usage examples

“Create a new BigQuery dataset named sales_data in the US location.”
The agent executes the bq mk command with the appropriate flags to create the dataset in the specified region.
“Run a SQL query to select all rows from my_dataset.orders where the status is 'complete'.”
The agent uses the bq query command with --use_legacy_sql=false to execute the SQL statement and return the results.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google/bigquery-basics/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip bigquery-basics.zip -d .agents/skills

Documentation by Google

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).

Files

references/core-concepts.md

BigQuery Core Concepts

BigQuery is a fully managed, AI-ready data platform that helps you manage and analyze your data with built-in features like machine learning, search, geospatial analysis, and business intelligence. BigQuery's serverless architecture lets you use languages like SQL and Python to answer your organization's biggest questions with zero infrastructure management.

BigQuery provides a uniform way to work with both structured and unstructured data and supports open table formats like Apache Iceberg. BigQuery streaming supports continuous data ingestion and analysis while BigQuery's scalable, distributed analysis engine lets you query terabytes in seconds and petabytes in minutes.

Architecture

BigQuery's architecture separates compute and storage, connected by a petabit-scale network.

  • BigQuery Storage: A columnar storage format optimized for analytical queries. It can be replicated across multiple locations for high availability.

  • BigQuery Analytics: A scalable, distributed analysis engine that can process data in BigQuery and in external sources.

Resource Hierarchy

BigQuery organizes resources in a structured hierarchy:

  1. Organization/Folder/Project: Standard Google Cloud resource containers.

  2. Dataset: The top-level container for tables and views.

  3. Table/View: The basic unit of data storage and logical representation.

Analytics Workflows

  • Ad Hoc Analysis: Using GoogleSQL for interactive queries.

  • Geospatial Analysis: Analyzing and visualizing spatial data using geography types.

  • Machine Learning (BigQuery ML): Creating and executing ML models directly in BigQuery using SQL.

  • Gemini in BigQuery: AI-powered assistance for data preparation, SQL generation, and visualization. Refer to the Gemini Models for more information.

  • Stream Processing (BigQuery continuous queries): Long running SQL statements that analyze and transform incoming data in near real time as it arrives in BigQuery. This feature enables unbounded streaming pipelines for real-time AI inference (using Vertex AI) and Reverse ETL to downstream systems. Results can be exported to Pub/Sub, Bigtable, Spanner, or other BigQuery tables. See Continuous Queries for more detail.

BigQuery Studio

A unified workspace for data engineering, analysis, and predictive modeling.

  • SQL Editor: With code completion and generation.

  • Python Notebooks: Built-in support for Colab Enterprise and BigQuery DataFrames (BigFrames).

  • Data Discovery: Integrated with Dataplex for search and profiling.

Pricing

BigQuery pricing consists of two main components: compute (analysis) costs and storage costs.

  • Storage: Storage costs are based on the amount of data stored in BigQuery tables. Storage is classified as either active storage (any table or partition modified in the last 90 days) and long-term storage (data that hasn't been modified for 90 consecutive days, resulting in a price drop of approximately 50%).

  • Analysis: Billed based on bytes processed (On-demand) or dedicated slots (Capacity/Reservations).

For the latest pricing details, visit: BigQuery Pricing.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs a command-line tool

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/bigquery-basics · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Google or Google.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download bigquery-basics.zip