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

Enable the BigQuery API:

bash

gcloud services enable bigquery.googleapis.com --quiet

Create a Dataset:

bash

bq mk --dataset --location=US my_dataset

Create a Table:

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

json

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

Then create the table with the bq tool:

bash

bq mk --table my_dataset.mytable schema.json

Run a Query:

bash

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 [blocked]: Storage types, analytics workflows, and BigQuery Studio features.
CLI Usage [blocked]: Essential bq command-line tool operations for managing data and jobs.
Client Libraries [blocked]: Using Google Cloud client libraries for Python, Java, Node.js, and Go.
MCP Usage [blocked]: Using the BigQuery remote MCP server and Gemini CLI extension.
Infrastructure as Code [blocked]: Terraform examples for datasets, tables, and reservations.
IAM & Security [blocked]: 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).