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Why NEAT?#

There was no easy way to make data models and knowledge graphs, so we build NEAT.

NEAT is great for:

  • Onboarding of domain experts to the semantic data modeling and knowledge graphs
  • Semantic data model development and deployment to Cognite Data Fusion
  • Conversion of (meta)data to knowledge graphs
  • Simplification of large and complex domain data models and knowledge graphs
  • Enrichment of knowledge graphs with additional information
  • Ingestion of knowledge graphs to CDF
  • Staging and playground area for data models and knowledge graphs

NEAT is not meant as:

  • Long-term solution for storage of data models and knowledge graphs
  • Reasoning over semantic data models and knowledge graphs

Domain Expert Centric#

NEAT is a domain expert-centric application, meaning that it is designed to be used by subject matter experts rather than just developers. Overall, NEAT's domain expert-centric approach helps to bridge the gap between business needs and technical implementation, resulting in more effective and efficient data modeling.

Batteries Included#

NEAT provides a batteries-included environment for creating data modeling and knowledge graph workflows. This means that the application includes pre-built components that enable users to quickly and easily create desired solutions.

Modular Design#

NEAT's well-defined interfaces and separation of concerns make it simple to add missing features. The application's modular architecture allows for easy integration of new components, and the well-defined interfaces ensure that new components can be added without disrupting the existing underlaying library. This approach enables developers to quickly add missing features and extend the functionality of NEAT, without needing to rewrite large portions of the codebase. Overall, NEAT's architecture provides a flexible and scalable platform for data modeling and knowledge graph workflows.

Data QC/QA#

NEAT provides extensive QA/QC (Quality Assurance/Quality Control) features that safeguard Cognite Data Fusion from 'rubbish' data models and graphs. These features ensure that the data models and knowledge graphs created in NEAT are accurate and reliable, prior landing in Cognite Data Fusion, and that they meet the required standards and specifications. The QA/QC features include data validation, error checking, and data lineage tracking, which help to identify and correct errors in the data models.

Meaningful Errors#

NEAT provides meaningful errors that help users rapidly fix errors in their data models and knowledge graphs. The application's error messages are designed to be clear and concise, making it easy for users to understand what went wrong and how to fix it. This approach helps to reduce the time and effort required to correct errors, enabling users to quickly iterate and improve their models.

Linear Learning Curve#

NEAT enables a linear learning curve due to its thorough design. The application's user-friendly interface and pre-built components make it easy for users to quickly learn and understand the data modeling process.

Rapid Knowledge Onboarding#

NEAT's features, including its batteries-included approach, modular design, extensive QA/QC features, meaningful errors, linear learning curve, and ability to run anywhere, enables rapid knowledge (i.e.,data models and graphs) onboarding.