google/skills

bigquery-basics

- Manages datasets, tables, and jobs in 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

  1. Enable the BigQuery API:
bash
    gcloud services enable bigquery.googleapis.com --quiet
  1. Create a Dataset:
bash
    bq mk --dataset --location=US my_dataset
  1. 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
  1. 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

workflows, and BigQuery Studio features.

incremental table changes using APPENDS and CHANGES.

SQL statements to analyze incoming data in real time.

operations for managing data and jobs.

client libraries for Python, Java, Node.js, and Go.

  • MCP Usage: Using the BigQuery remote MCP server and

Gemini CLI extension.

datasets, tables, and reservations.

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).

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