bigquery-basics

# 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 **MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you MUST read [Usage Attribution](references/attribution.md). For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix `gcloud` with `CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-basics)"` and append `agent-skills/1.0 (skill:bigquery-basics)` to `User-Agent` headers. ## Setup and Basic Usage 1. **Enable the BigQuery API:** ```bash CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-basics)" \ gcloud services enable bigquery.googleapis.com --quiet ``` 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](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. - [Usage Attribution](references/attribution.md): Standardized usage attribution tags for `gcloud` CLI, REST API, and MCP requests. *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).