Big Data Integration via Domain-Specific Language and Code Generation
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Solution Overview
Problem
Current big data systems lack an efficient end-to-end process for defining and deploying solutions, particularly in integrating and configuring disparate technologies.
Innovation Solution
A system and method utilizing a domain-specific language and code generation tools to define and package components, generate relevant artifacts, replace environment-dependent variables, and deploy them into a target big data environment, simplifying the integration and configuration process across dispersed technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual integration and configuration methods are used for big data solutions, then flexibility and control are maintained, but the complexity and time required for deployment increase significantly
Solution Approach 1:
The patent segments the big data solution deployment into distinct modular components including infrastructure layer, data layer, processing layer, and application layer. Each layer can be independently configured, deployed, and managed through standardized interfaces, reducing overall system complexity while improving deployment efficiency.
Solution Approach 2:
The patent introduces an integration platform as an intermediary layer between disparate big data technologies and business applications. This platform provides standardized adapters, connectors, and abstraction layers that simplify integration complexity while enabling efficient deployment of big data solutions.
2Adaptability or versatility
If environment-specific configurations are hardcoded for each deployment, then deployment reliability improves, but adaptability to different environments decreases
Solution Approach 1:
The patent implements dynamic configuration mechanisms that allow the system to automatically adapt to different deployment environments through environment detection, configuration inheritance, and runtime parameter adjustment. This enables the same artifact to be reliably deployed across multiple environments without hardcoding environment-specific settings.
Solution Approach 2:
The patent utilizes parameter-based configuration management where environment-specific values are defined as replaceable parameters or variables. Configuration templates define structure and relationships, while actual values are substituted during deployment based on target environment parameters, ensuring both adaptability and reliability.
3Manufacturing precision
If comprehensive configuration management is implemented, then deployment accuracy improves, but the time and resources required for configuration increase
Solution Approach 1:
The patent implements configuration templates and artifact definitions that are prepared in advance during the development phase. These pre-configured templates include validation rules, dependency relationships, and best practices that ensure deployment accuracy while reducing on-site configuration time through automated generation and validation.
Data Source
AI summary
A system, method, and computer program product are provided for big data integration and configuration. In use, a plurality of components associated with a big data solution are defined in a domain specific language utilizing one or more code generation tools. Additionally, relevant artifacts for the plurality of components associated with the big data solution are generated. Further, the relevant artifacts are packaged into a manifest, the manifest including elements required to support at least one business process. In addition, environment dependent variables of the manifest are replaced with target values associated with a target big data environment to which the manifest is to be deployed. Moreover, the manifest is deployed into operation in the target big data environment.