bigquery-basics

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

Enable the BigQuery API:

gcloud services enable bigquery.googleapis.com --quiet

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.

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