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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.

Data Product Team Example:

  • Applied discipline for Functions (FaaS)
    • Data Science involves exploratory data analysis (EDA), data cleaning and visualization, and predictive modeling / machine learning to inform Control Plane decisions and strategies.
    • Machine Learning Engineering involves the development, training, performance optimizing, and deployment of machine learning models as scalable Integration sub-Processes (FaaS) orchestrated by InfraFunction (FaaS).
    • Data Analysis involves the composition of Data Product’s InfraFunctions as data processing language (integration).
    • The CAT Order is updated with the inclusion of resulting mutated Functions (FaaS) for execution processed by CATs Factory Client.
  • Applied discipline for Structure (PaaS as IaC)
    • Data Platform / Cloud / Infrastructure Engineering involves the design and IaC development, and automation of the provisioning and management of Structure (PaaS) executing Function. This is accomplished using IaC to provision InfraStructure (IaaS) as the execution paradigm of the Plant (SaaS) as well as contributing to InfraFunctions’ (FaaS) execution configurations of Plant (SaaS) operations.

Orginizational Value