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
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.
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
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BigQuery AI & ML Skill: SKILL.md file for BigQuery AI and ML capabilities.
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BigQuery AI & ML References: Reference files published for the BigQuery AI and ML skill.
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bigquery_ai_classify.md
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bigquery_ai_detect_anomalies.md
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bigquery_ai_forecast.md
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bigquery_ai_generate.md
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bigquery_ai_generate_bool.md
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bigquery_ai_generate_double.md
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bigquery_ai_generate_int.md
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bigquery_ai_if.md
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bigquery_ai_score.md
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bigquery_ai_search.md
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bigquery_ai_similarity.md