Data modeling examples and best practices

Data models are important for generative AI features, like copilots, summarizers, and AI-supported search. Also, data models let you specify types of metadata, making it easier to search for assets with specific properties. Another major benefit is that data models let you tailor data consumption with GraphQL or an SDK generated from the data model.

This section has examples and best practices to apply when building and deploying containers, views, data models and instances, as well as for composing queries.

📄️ Modeling an asset hierarchy

This article describes the asset hierarchy template in the Cognite Data Fusion (CDF) templates repository. An asset hierarchy describes connections between larger assets and the smaller components inside them. The tools and information in the in the tools are provided as-is, without any support or warranties,

📄️ Building containers

This document outlines best practices when creating containers for the Cognite Data Fusion (CDF) Data Modeling service.

📄️ Optimizing data models for AI search

This article outlines best practices for documenting data models and optimize the accuracy of AI search in Cognite Data Fusion (CDF).