BigQuery Basics

Setup and Basic Usage

1. Enable the BigQuery API:
   bash

   gcloud services enable bigquery.googleapis.com

2. Create a Dataset:
   bash

   bq mk --dataset --location=US my_dataset

3. 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

4. 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.
• Infrastructure as Code: Terraform examples for datasets, tables, and reservations.
• IAM & Security: Roles, permissions, and data governance best practices.

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Related Skills

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