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).