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google/bigquery-basics

google

bigquery-basics

Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.

global
0installs0uses—~1.2k
v1.0Saved May 2, 2026

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

  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: 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

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Overall Score

79/100

Grade

B

Good

Safety

85

Quality

78

Clarity

82

Completeness

72

Summary

A foundational skill for managing BigQuery datasets, tables, and jobs using the bq CLI, client libraries, Terraform, and MCP integration. It covers dataset/table creation, SQL queries, data loading, IAM security, and Infrastructure as Code patterns with references to advanced ML and AI capabilities.

Detected Capabilities

gcloud CLI execution (bigquery.googleapis.com API enablement)bq command-line tool operations (dataset/table management, querying, job control)Client library usage (Python, Java, Node.js, Go)Terraform infrastructure provisioning for BigQuery resourcesMCP server integration for remote data queriesIAM and security configuration (roles, CMEK, row/column-level security)Data loading from Cloud Storage (CSV, JSON)SQL query execution and dry-run estimation

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

bigquery datasetrun sql queryload data bigquerybigquery terraformiam security bigquerybigquery mlgemini bigquery analyticsbigquery mcp integration

Risk Signals

INFO

gcloud services enable command

SKILL.md, Setup and Basic Usage section
INFO

bq CLI commands for data manipulation (load, insert, delete)

references/cli-usage.md, Table Management section
INFO

Service account impersonation via gcloud config

references/iam-security.md, Service Accounts section
INFO

MCP execute_sql tool restricted to SELECT statements only

references/mcp-usage.md, MCP Tools section
INFO

IAM and encryption best practices documented

references/iam-security.md

Referenced Domains

External domains referenced in skill content, detected by static analysis.

ai.google.devcloud.google.comdataframes.bigquery.devdocs.cloud.google.comgithub.comgoogleapis.devmcp-toolbox.devregistry.terraform.iowww.apache.org

Use Cases

  • Set up and manage BigQuery datasets and tables
  • Run SQL queries and analyze large datasets
  • Load data from Cloud Storage into BigQuery
  • Configure IAM roles and data governance
  • Deploy BigQuery infrastructure using Terraform
  • Integrate BigQuery with AI/ML workflows via Gemini

Quality Notes

  • Well-structured skill with clear reference architecture: core concepts, CLI usage, client libraries, MCP integration, IaC, and security.
  • Practical examples provided for each interface (bq CLI, Python, Java, Node.js, Go, Terraform).
  • Security guidance is comprehensive: least privilege principle, CMEK, VPC Service Controls, row/column-level security, and audit logging all documented.
  • MCP integration clearly documents tool restrictions (execute_sql limited to SELECT statements), reducing misuse risk.
  • References are well-organized and externally linked, but the main SKILL.md does not include inline operational guidance for error handling or edge cases.
  • Missing: explicit guidance on query cost estimation, handling large result sets, retry logic for transient errors, and data validation patterns before insert/load operations.
  • Missing: explicit scope boundaries around which operations are scoped to a project vs. organization level.
  • Terraform examples are minimal but sufficient; no mention of state management best practices or drift detection.
  • BigFrames (pandas-like API) is mentioned but not elaborated on—could benefit from basic usage example.
Model: claude-haiku-4-5-20251001Analyzed: May 2, 2026

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