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data-modeling

Provides guidance on data modeling patterns for OLTP and OLAP systems.

1|Updated Jan 6, 2026
Search Tags:#schema design#data modeling#normalization#data warehouse#star schema#dimensional modeling
One-click install
npx skills add https://github.com/hyukudan/ai-skills --skill data-modeling-hyukudan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-modeling
Source: https://github.com/hyukudan/ai-skills/tree/main/examples/skills/data-modeling
Command: npx skills add https://github.com/hyukudan/ai-skills --skill data-modeling-hyukudan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides comprehensive guidance on designing robust and efficient data models for both transactional (OLTP) and analytical (OLAP) systems, addressing common challenges and offering modern solutions.

Core Features & Use Cases

  • Modeling Patterns: Explains Normalization (1NF, 2NF, 3NF), Star Schema, Snowflake Schema, Data Vault, and Wide Tables.
  • Slowly Changing Dimensions (SCD): Details Type 1, Type 2, and Type 3 implementations.
  • Use Case: You need to design a data warehouse schema for sales data. This Skill will guide you through creating fact and dimension tables, implementing SCD for customer data, and optimizing for analytical queries.

Quick Start

Use the data-modeling skill to design a star schema for sales data, assuming an OLAP system type.

Frequently Asked Questions about data-modeling

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

FAQPage Schema
What is the difference between star schema and snowflake schema for data warehouse design?▼

Star schema uses denormalized dimension tables directly connected to fact tables for faster analytical queries, while snowflake schema normalizes dimensions into multiple related tables to reduce data redundancy. This Skill provides SQL examples and best practices for implementing both patterns in OLAP environments.

How do I implement slowly changing dimensions (SCD) in SQL for a data warehouse?▼

Slowly changing dimensions (SCD) are implemented in SQL by tracking historical changes in dimension tables. This Skill details Type 1 (overwrite), Type 2 (historical row tracking with effective dates), and Type 3 (limited history with separate columns) implementations with practical SQL examples for OLAP systems.

When should I use Data Vault modeling instead of traditional dimensional modeling?▼

Data Vault modeling is ideal for large enterprise data warehouses requiring high scalability and historical tracking from multiple sources, whereas dimensional modeling optimizes analytical query performance. This Skill covers Data Vault alongside star schema and normalization patterns to guide your architectural choice.

How do I design a normalized data model for an OLTP transactional system?▼

Designing a normalized data model for OLTP systems involves applying 1NF, 2NF, and 3NF rules to reduce data redundancy and ensure data integrity. This Skill provides comprehensive guidance on normalization patterns and best practices tailored specifically for transactional environments.

What are modern data modeling approaches like wide tables and activity schemas?▼

Modern data modeling approaches like wide tables and activity schemas optimize analytical querying by consolidating data into broad, flat structures or event-based tables. This Skill explains these contemporary patterns alongside traditional dimensional modeling for building efficient OLAP systems.

Does this data modeling guidance cover both transactional and analytical systems?▼

Yes, this data modeling guidance covers both transactional (OLTP) and analytical (OLAP) systems. It includes specific patterns like normalization for OLTP and star schema or Data Vault for OLAP, ensuring robust schema design across different system architectures.

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