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Schema Evaluation

Design and evaluate warehouse schemas including star, snowflake, Vault, OBT patterns.

1|1|Updated Feb 21, 2026
Search Tags:#schema design#data modeling#data warehouse#SCD#star schema#data contracts#data vault
One-click install
npx skills add https://github.com/dtsong/claude-code-windows-setup --skill schema-evaluation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Schema Evaluation
Source: https://github.com/dtsong/claude-code-windows-setup/tree/main/skills/council/alchemist/schema-evaluation
Command: npx skills add https://github.com/dtsong/claude-code-windows-setup --skill schema-evaluation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you design and evaluate data warehouse schemas, ensuring they are efficient, scalable, and meet analytical needs.

Core Features & Use Cases

  • Schema Design: Supports Star, Snowflake, Data Vault, and OBT patterns.
  • Grain Definition: Clearly defines what each row in a fact table represents.
  • SCD Strategy: Implements Slowly Changing Dimension strategies (Type 1, 2, 3, 6).
  • Data Contracts: Establishes clear agreements between data producers and consumers.
  • Use Case: Design a new star schema for an e-commerce platform to track customer orders and product sales, ensuring efficient querying for monthly revenue reports.

Quick Start

Evaluate and design a data warehouse schema for an e-commerce domain, focusing on order and customer data.

Frequently Asked Questions about Schema Evaluation

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

FAQPage Schema
How do I design a star schema for an e-commerce data warehouse?▼

Designing a star schema involves defining the fact table grain, implementing slowly changing dimension strategies, and validating the schema against analytical query patterns to document optimal relationships and DDL.

What is the best way to implement Slowly Changing Dimension (SCD) strategies in data modeling?▼

Implementing Slowly Changing Dimension strategies involves selecting Type 1, 2, 3, or 6 methods to track historical changes in data warehouse dimensions, ensuring accurate historical tracking and efficient querying.

When should I use a data vault pattern instead of a snowflake schema for data warehouse design?▼

Use a data vault pattern for highly scalable, auditable raw data storage with historical tracking, whereas snowflake schemas normalize dimensions for analytical query efficiency rather than enterprise data integration.

How do I establish data contracts for analytical workloads?▼

Establishing data contracts involves defining clear agreements on schema structure, grain, and data quality between data producers and consumers to validate schemas against expected query patterns.

Does this schema design approach support One Big Table (OBT) patterns?▼

Yes, schema evaluation supports One Big Table (OBT) patterns alongside star, snowflake, and data vault designs, validating the chosen pattern against specific analytical query workloads and documenting the structure.

How do I define the grain of a fact table during data warehouse schema design?▼

Defining the grain of a fact table requires clearly specifying exactly what each row represents, such as an order line item, to ensure accurate aggregation and validate the schema against analytical needs.

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