Bigquery Basics
Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use for SQL queries, resource management, data ingestion, or AI applications on BigQuery.
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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
- CLI Usage: Essential
bqcommand-line tool
- Client Libraries: Using Google Cloud
- MCP Usage: Using the BigQuery remote MCP server and
- Infrastructure as Code: Terraform examples for
- IAM & Security: Roles, permissions, and data
*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. Reference files published for the BigQuery AI and ML skill.- bigquery_ai_detect_anomalies.md
- bigquery_ai_generate_bool.md
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