Data Integration Hub With Reusable Migration Modules
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data migration systems require significant resources in time, personnel, and money due to their specialized configuration for specific data formats and sources, lacking flexibility and efficiency in handling varied data scenarios.
Innovation Solution
A data integration hub system with a library of executable modules that transform data agnostically across various sources and formats, utilizing a source bridge for access and customization layers to adapt to different environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specialized data migration systems are configured to access specific enterprise data and convert it to new formats, then data migration accuracy and reliability are improved, but device complexity and resource consumption (time, personnel, money) increase significantly
Solution Approach 1:
The patent implements a universal data migration system that can handle multiple data sources, formats, and target systems through a single configurable platform. The system uses abstracted data access layers and format converters that can be configured through metadata definitions rather than requiring specialized development for each migration scenario, thereby maintaining high reliability across diverse migrations while reducing overall system complexity.
Solution Approach 2:
The data migration system is divided into independent modular components including data access modules, format conversion modules, validation modules, and configuration modules. Each module can be independently configured, validated, and reused across different migration projects. This segmentation allows the system to maintain high reliability through focused validation of each component while reducing the complexity of the overall system through modular assembly.
2Reliability
If specialized data migration systems are developed for specific data scenarios, then data migration reliability is improved, but loss of time and productivity decrease due to significant development and validation resources required
Solution Approach 1:
The system performs preliminary configuration and validation by establishing data access templates, format conversion rules, and validation criteria before actual data migration begins. Migration profiles and configuration schemas are pre-defined and can be reused across multiple projects, eliminating the need to re-validate fundamental migration logic for each new scenario while maintaining reliability through consistent pre-established validation rules.
Solution Approach 2:
The system recovers and reuses migration configurations, templates, and validation rules from previous migration projects. By storing and reapplying proven migration patterns and configuration schemas across different data scenarios, the system maintains reliability through consistent validated approaches while dramatically reducing development and validation time for new migration projects.
3Reliability
If data migration systems are customized for specific enterprise data formats and sources, then data migration reliability is improved, but adaptability to new data scenarios decreases
Solution Approach 1:
The system implements dynamic configuration capabilities that allow migration parameters, data access methods, and format conversion rules to be adjusted based on the specific data scenario being migrated. The configuration system can adapt to new data sources and formats by loading appropriate migration profiles and validation rules, maintaining reliability through consistent validated processes while achieving adaptability through flexible configuration rather than fixed customization.
Data Source
AI summary
A data integration hub system and method is provided for migrating data from one or more source systems to a selection of target systems. Data migration is provided by a library of executable modules that are configured to transform input data to a form appropriate for a target data repository. The input data is made agnostic to the nature of the source of the data through a source bridge that is configured to access data from a variety of sources and convert the accessed data to a format acceptable to the executable modules. Embodiments also provide a mechanism for customizing the data integration hub by providing multiple development layers and restricting access to those layers.