data-modeling

Design dimensional models and generate DDL with surrogate keys.

3|Updated May 28, 2026

Search Tags:#data-modeling #data-warehouse #star-schema #SCD #ddl #dimensional-model #data-vault

Ready to run

npx skills add https://github.com/mahg-es/araya --skill data-modeling-mahg-es

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-modeling
Source: https://github.com/mahg-es/araya/tree/main/skills/data-modeling
Command: npx skills add https://github.com/mahg-es/araya --skill data-modeling-mahg-es

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Design data models — conceptual, logical, and physical — using star schemas, dimensional modeling, slowly changing dimensions, and Data Vault patterns for analytics, reporting, and AI/ML readiness.

Core Features & Use Cases

Create dimensional models with star schemas to support analytics and reporting.
Apply SCD strategies Type 1/2/3 depending on history needs.
Generate DDL with surrogate keys and coordinate with db-schema for implementation.

Quick Start

Define the business processes and grain, then create dimensions and facts to implement a star-schema data model.

Frequently Asked Questions about data-modeling

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

FAQPage Schema

How do I design a star schema for a data warehouse?▼

Designing a star schema for a data warehouse involves defining the business process, establishing the grain, and creating fact and dimension tables to support analytics and reporting.

When should I use SCD Type 2 in dimensional modeling?▼

Use SCD Type 2 in dimensional modeling when you need to preserve historical attribute changes over time, whereas Type 1 overwrites data and Type 3 tracks limited historical changes.

What is the best way to generate DDL with surrogate keys for a data model?▼

The best way to generate DDL with surrogate keys is to apply dimensional modeling patterns that define explicit grain and star-schema integrity, producing implementation-ready DDL.

Can I apply Data Vault patterns for analytics on a lakehouse?▼

Yes, you can apply Data Vault patterns on a lakehouse to design scalable physical data models that support analytics, reporting, and AI/ML readiness alongside SCD strategies.

Does dimensional modeling work for BI-ready datasets and transactional processing?▼

Yes, dimensional modeling works for BI-ready datasets and transactional processing by creating logical and physical models with explicit grain, surrogate keys, and fact tables.

Why does defining an explicit grain matter in dimensional modeling?▼

Defining an explicit grain in dimensional modeling matters because it establishes the precise level of detail for each fact table record, ensuring star-schema integrity and accurate analytics.