Data Model Design

Design data models that correctly represent business domains and support required queries efficiently.

Context

You are modeling a business domain in a database. The data model must reflect business semantics and perform well.

Domain Context

Based on relational theory and domain-driven design:

• Entities: Things with identity (User, Order)
• Attributes: Properties of entities (name, email)
• Relationships: How entities connect (User has many Orders)
• Normalization: Reduce redundancy; improves write performance
• Denormalization: Intentionally duplicate data; improves read performance

Instructions

1. Identify Entities: What are the main concepts? List with primary key.
2. Identify Attributes: For each entity, list properties.
3. Define Relationships: One-to-one, one-to-many, many-to-many.
4. Normalize: Remove update anomalies through normal forms.
5. Denormalize Strategically: Add calculated columns or tables for read performance.

Anti-Patterns

• Premature Normalization: Third-normal-form everywhere. Result: slow queries. Guard: Design for reads; denormalize if needed.
• Flat Tables: No normalization; entity data scattered. Result: update anomalies. Guard: At least second-normal-form.

Further Reading

• Relational Database Design — normalization fundamentals
• Domain-Driven Design — modeling business domains