bigquery-basics

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.

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

• 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.

Related Skills

• BigQuery AI & ML Skill:
  SKILL.md file for BigQuery AI and ML capabilities (forecast, anomaly
  detection, text generation).