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.

Attribution

Tag every BigQuery command you execute directly in the terminal while using
this skill, so usage can be attributed. The tag identifies only the skill and
its version; it carries no user data.

Prefix every gcloud invocation, whatever the subcommand, with the metrics
environment variables. Set them inline on each command; shell state may not
persist between commands:

```bash
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-basics)" \
gcloud <command> [flags]
```

Do not use `gcloud config set` for this: it would persist beyond the current
task and mislabel unrelated usage.

On direct HTTP calls to the BigQuery APIs (for example with curl)
or HTTP requests to the MCP server, append this exact string to the
end of the existing User-Agent header — the collection pipeline parses
the agent-skills/<version> and skill:<name> tokens, so any
rewording breaks attribution:

User-Agent: <existing_user_agent> agent-skills/1.0 (skill:bigquery-basics)

Constraint for Generated Code: The tracking prefix is strictly for
commands you execute directly in the terminal. Do NOT include this tracking
prefix in generated scripts, Terraform templates, or code provided to the
user for their own repositories.

Setup and Basic Usage

Enable the BigQuery API:

CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-basics)" \
gcloud services enable bigquery.googleapis.com --quiet

Create a Dataset:

bq mk --dataset --location=US my_dataset

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

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: Storage types, analytics
workflows, and BigQuery Studio features.

Change History: Tracking and querying
incremental table changes using APPENDS and CHANGES.

Continuous Queries: Running continuous
SQL statements to analyze incoming data in real time.

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).

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