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gcp-bigquery

Generate BigQuery configurations for datasets, schemas, queries, and security.

1|Updated Mar 2, 2026
Search Tags:#gcp#machine learning#data pipeline#sql#etl#bigquery#data warehousing
One-click install
npx skills add https://github.com/sitharaj88/claude-skills --skill gcp-bigquery
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gcp-bigquery
Source: https://github.com/sitharaj88/claude-skills/tree/main/skills/gcp-bigquery
Command: npx skills add https://github.com/sitharaj88/claude-skills --skill gcp-bigquery

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill empowers users to design, configure, and optimize Google Cloud BigQuery for robust data warehousing, advanced analytics, and machine learning pipelines.

Core Features & Use Cases

  • Dataset and Schema Design: Define BigQuery datasets and create partitioned, clustered, or standard table schemas using Terraform or SQL.
  • SQL Query Optimization: Generate efficient SQL queries for analytics, funnel analysis, and sessionization, including DML statements like MERGE and DELETE.
  • ML and Pipelines: Implement BigQuery ML models (classification, forecasting, clustering) and set up scheduled queries or materialized views for ETL/ELT.
  • Data Loading & Security: Configure external tables, streaming inserts, and implement column/row-level security and authorized views.
  • Use Case: A data engineer needs to set up a new analytics dataset in BigQuery, including defining schemas for event data, creating materialized views for daily aggregates, and securing access for the data science team.

Quick Start

Use the gcp-bigquery skill to generate a partitioned and clustered table schema for event data.

Frequently Asked Questions about gcp-bigquery

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a partitioned and clustered table schema in BigQuery?▼

To design a partitioned and clustered BigQuery table schema, you define partitioning fields and clustering columns using Terraform or SQL, optimizing query performance and cost for large analytics datasets.

Can I train machine learning models directly in BigQuery using SQL?▼

Yes, you can train machine learning models directly in BigQuery using SQL via BigQuery ML, supporting classification, forecasting, and clustering without exporting data to external training environments.

What is the best way to optimize SQL queries for funnel analysis in BigQuery?▼

The best way to optimize BigQuery SQL queries for funnel analysis is by using efficient query patterns, materialized views for daily aggregates, and appropriate partitioning to reduce data scanned.

How do I implement row-level and column-level security in BigQuery?▼

You implement BigQuery security by configuring column-level and row-level access policies, creating authorized views, and applying secure configurations using Terraform to restrict data visibility.

Does BigQuery support scheduled queries for ETL pipelines?▼

Yes, BigQuery supports scheduled queries for ETL and ELT pipelines, allowing you to automate data transformations, load data via streaming inserts, and refresh materialized views regularly.

Can I use Terraform to automate BigQuery dataset creation and configuration?▼

Yes, you can use Terraform to automate BigQuery dataset creation, schema design, external table configurations, and security policies, generating production-ready infrastructure as code.

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