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CAT Node's Architectural Quantum (Domain-Driven Design principle):

CAT Node uses the Architectural Quantum Domain-Driven Design principle described in Data Mesh of Data Products

This design principle enables effective cross-domain collaboration on Data Products across business and knowledge domains between cross-functional & multi-disciplinary teams and organizations.

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CAT’s architectural design and implementation are the result of applied Engineering, Computer Science, Network Science, and Social Science. CATs is software executing on a network client ontological to an MicroKernel Operating System. CATs’ is designed to enable Data Products implemented as compute node peers on a Data Mesh network that encapsulate code, data, metadata, and infrastructure to function as a service providing access to the business domain's analytical data as a product. Data Products use the Architectural Quantum domain-driven design principle for peer nodes that represent the “smallest unit of architecture that can be independently deployed with high functional cohesion, and includes all the structural elements required for its function” (“Data Mesh Principles and Logical Architecture” - Zhamak Dehghani, et al.).

Collaborative value of CATs Architectural Quantum:

The operation and maintenance of CATs’ Data Products on a Data Mesh can occur between independent teams that will operate, contribute, and maintain different portions of the entire cloud-service model in adherence to CATs' Architectural Quantum in a way suitable for their roles using the CATs’ API to serve individual Data Model entities on a Data Mesh for a variety of use-cases. CAT’s Data Product teams can be multidisciplinary due to the fact they can operate and maintain the different portions of the entire Web2 cloud service model based on role.

For example:

  • An Analyst or Data Scientist will use CATs Process interface deployed as FaaS for Analytical Data transformation
  • A Backend or ML-Engineer implementing a CAT application as SaaS for a Data Scientist to execute machine learning models / pipelines on a cloud managed Kubernetes PaaS as opposed to a machine learning handoff deployment.
  • An Infrastructure Engineer would use the Terraform to deploy multi-cloud IaaS for a CAT to be deployed by the Backend or ML-Engineer

Orginizational Value