Automated Data Mesh Code Generation for Scalable Governance
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Solution Overview
Problem
Conventional centrally managed, monolithic data lakes face complexities in implementation, data governance, scalability, ease of use, and adaptation to changing data landscapes, making them inefficient for managing large amounts of data across enterprise networks.
Innovation Solution
A federated, multi-product data mesh is implemented through automated source code generation based on governed and defined data models, using a method that includes receiving data models, generating software components, integrating customizations, initiating a CI/CD pipeline, and deploying services in a namespace, with data governance defined by a centrally governed data contract.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a centrally managed, monolithic data lake architecture is used, then data can be stored and managed in a centralized location, but the implementation becomes complex and time-consuming with difficulties in data governance and scalability
Solution Approach 1:
The patent divides the monolithic data lake into distributed data mesh components organized by domains. Each domain has its own data products and governance, breaking the complex centralized system into manageable independent units that can be implemented and governed separately, reducing overall system complexity and implementation burden
Solution Approach 2:
The patent introduces a data mesh platform as an intermediary layer that provides automated code generation, CI/CD pipelines, and governance enforcement. This intermediary handles the complexity of coordination and governance between domains, allowing individual domains to implement data products without directly managing the entire system's complexity
2Adaptability or versatility
If a centrally managed, monolithic data lake is implemented, then data storage is centralized, but adaptation to changing data landscapes and addition of new data domains becomes difficult
Solution Approach 1:
The patent creates a dynamic data mesh architecture where domains can be added, removed, or modified independently. The automated code generation and CI/CD pipelines enable the system to adapt to changing data requirements without manual reconfiguration of the entire system, allowing flexible response to evolving data landscapes while maintaining manageable complexity through automation
Solution Approach 2:
By segmenting the data lake into independent domains with their own governance and data products, the system allows new data domains to be added without affecting existing ones. Each domain can evolve independently, improving adaptability while the modular structure prevents complexity from propagating across the entire system
3Productivity
If automated code generation is used to build data mesh components, then the building process is accelerated and simplified, but the system requires sophisticated automation infrastructure
Solution Approach 1:
The patent pre-defines data mesh component templates, governance policies, and CI/CD pipeline configurations that can be automatically generated from domain models. By preparing these artifacts in advance and making them reusable, the system accelerates data mesh building while the templates encapsulate the automation infrastructure complexity, preventing it from becoming a bottleneck
Data Source
AI summary
A method for providing a federated, multi-product data mesh via automated code generation is disclosed. The method includes receiving, via an application programming interface, a data model, the data model including model artifacts that define data governance for a data product; automatically generating source code for software components based on the data model, the software components corresponding to data mesh components for the data product; integrating data product customizations into the software components, the data product customizations including business logics and testing configurations; initiating an automated continuous integration and continuous delivery pipeline to generate a service that corresponds to the data product based on the integrated software components; and deploying the generated service in a namespace that corresponds to the data product.