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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:
gcloud services enable bigquery.googleapis.com --quiet- Create a Dataset:
bq mk --dataset --location=US my_dataset- 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- 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
bqcommand-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
SKILL.md file for BigQuery AI and ML capabilities (forecast, anomaly detection, text generation).

