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Optimizing data models for AI

Learn how to model data to support natural language queries and enable Atlas AI agents across domains and workflows.

Designing scalable data models

Learn best practices for designing scalable data models in CDF, including layered architecture, design principles, and performance optimization.

Building an asset hierarchy

Build an asset hierarchy with the Cognite core data model and populate it with transformations.

Extending the core data model

Create custom data models by extending the core data model using GraphQL data modeling language.

Integrate with files

Connect files to your data model instances using the Cognite core data model.

Integrate with time series

Link time series data to your data model instances for operational insights.

Performance considerations

Understand key factors that impact performance when ingesting and querying data.

Debug query performance

Use debug notices to analyze and optimize your data model queries.

Handling latency variability

Learn why query latency can vary and how to design applications that tolerate occasional slow requests.

Clean up nodes without data

Delete nodes that don’t have associated container data using a Python script.
Deploying data models with CI/CD is a Cognite Toolkit workflow. See Deploying with the Cognite Toolkit in the Deploy tab.
Last modified on September 24, 2026