/bigquery-basics

name: bigquery-basics
metadata:
  category: BigDataAndAnalytics
description: >-
  Manages datasets, tables, and jobs in BigQuery. Use when you need to interact
  with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables,
  views), or perform basic data ingestion and analysis.

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:**

    gcloud services enable bigquery.googleapis.com --quiet

2. **Create a Dataset:**

    bq mk --dataset --location=US my_dataset

3. **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

4. **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](references/core-concepts.md): Storage types, analytics

workflows, and BigQuery Studio features.

• [Change History](references/change-history.md): Tracking and querying

incremental table changes using APPENDS and CHANGES.

• [Continuous Queries](references/continuous-queries.md): Running continuous

SQL statements to analyze incoming data in real time.

• [CLI Usage](references/cli-usage.md): Essential `bq` command-line tool

operations for managing data and jobs.

• [Client Libraries](references/client-library-usage.md): Using Google Cloud

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

• [MCP Usage](references/mcp-usage.md): Using the BigQuery remote MCP server and

Gemini CLI extension.

• [Infrastructure as Code](references/iac-usage.md): Terraform examples for

datasets, tables, and reservations.

• [IAM & Security](references/iam-security.md): 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

• [BigQuery AI & ML Skill](../bigquery-ai-ml):

SKILL.md file for BigQuery AI and ML capabilities (forecast, anomaly detection, text generation).