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role-database:data-modeling

Design database schemas across relational, document, graph, time-series, dimensional, and Data Vault paradigms.

14|3|Updated Feb 22, 2026
Search Tags:#database design#data modeling#data warehouse#graph#document#relational#data vault
One-click install
npx skills add https://github.com/rnavarych/alpha-engineer --skill role-database-data-modeling
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: role-database:data-modeling
Source: https://github.com/rnavarych/alpha-engineer/tree/main/plugins/roles/role-database/skills/data-modeling
Command: npx skills add https://github.com/rnavarych/alpha-engineer --skill role-database-data-modeling

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides comprehensive guidance on designing effective data models, addressing the complexities of various database paradigms and their specific modeling techniques.

Core Features & Use Cases

  • Multi-Paradigm Support: Covers relational (ER, normalization), document (embedding, patterns), graph (LPG, RDF), time-series, dimensional (star/snowflake, SCD), and Data Vault modeling.
  • Decision Frameworks: Offers decision tables and guides for choosing between modeling approaches (e.g., embedding vs. referencing) and normalization levels.
  • Use Case: When designing a new e-commerce platform, use this Skill to compare and contrast relational vs. document modeling for product catalogs, and to select the appropriate data warehouse schema for sales analytics.

Quick Start

Use the data-modeling skill to understand the differences between star schema and data vault modeling.

Frequently Asked Questions about role-database:data-modeling

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

FAQPage Schema
What is the best way to design a data model for a data warehouse?▼

Data warehouse data modeling uses dimensional schemas like star or snowflake designs, incorporating slowly changing dimension (SCD) types to manage historical data tracking effectively.

How do I choose between embedding and referencing in document database design?▼

Document database design uses decision frameworks to choose between embedding and referencing, evaluating read patterns and data relationship complexity to optimize query performance and structure.

How does Data Vault modeling differ from traditional relational data modeling?▼

Data Vault modeling separates raw data into hubs, links, and satellites for auditable historical records, whereas traditional relational data modeling relies on entity-relationship structures and normalization.

When do I need polyglot persistence for database schema design?▼

Polyglot persistence is needed for multi-database architectures when diverse data paradigms require combining relational, document, graph, and time-series models to meet specific application workloads.

Can I use this data modeling guidance for graph database paradigms?▼

Yes, the data modeling guidance covers graph database paradigms, providing syntax and patterns for both labeled property graph (LPG) and resource description framework (RDF) structures.

What are the limitations of normalization in relational data modeling?▼

Normalization in relational data modeling reduces redundancy but can limit query performance for complex joins, making denormalization or dimensional approaches preferable for heavy analytical workloads.

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