Crystallize logo

Data Modeling Design Patterns

Data modeling design patterns provide guidance on how to structure product information in a way that is consistent, scalable and easy to maintain across large or evolving catalogues. These patterns help teams make deliberate choices about how data is shaped, how meaning is shared, and how content is reused across products, markets and channels.

This collection focuses on practical patterns that can be applied when building product experiences in Crystallize. Each pattern includes an explanation of when it is useful, how it is structured, and considerations to be aware of when applying it. The goal is not to prescribe one correct model, but to provide clear mental models and reusable approaches that help improve clarity, flexibility and long-term maintainability.

The design patterns are intended for both technical and non-technical roles involved in shaping product information, including product managers, developers, content editors and architects.

Keep exploring

Semantic Classification Bridge

The Semantic Classification Bridge is a data modeling design pattern used to represent complex product attributes in a reusable and scalable way. Instead of relying on flat enums or repeated fields inside product shapes, this pattern separates classification data into dedicated documents that can be enriched, related and shared across many products. This provides better consistency, supports localization, and improves the storytelling capability of product data.