Bigquery Basics

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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محتوى SKILL.md

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

  • 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.
  • 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.

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