Object-Based Data Integration Tool Version Control
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Solution Overview
Problem
Developers face challenges in managing and recovering from flawed data integration business logic during the design process, leading to increased time and effort in team development settings, as they often inadvertently modify working logic with inefficient or non-functioning logic, and current manual recovery methods are inefficient.
Innovation Solution
Integrating object-based data integration tools with version control systems using a relational database repository for persistence, allowing for tracking and management of metadata artifacts across centralized and distributed environments, enabling version control and collaboration among geographically distributed teams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developers manually re-create earlier data integration business logic to recover from flawed logic, then recovery from errors is possible, but developer productivity decreases and time to market increases
Solution Approach 1:
The system performs preliminary version control by automatically saving each modification to data integration business logic as a new version. This preliminary action ensures that previous working logic is preserved before any flawed changes are made, enabling automatic recovery without manual recreation efforts.
Solution Approach 2:
The system creates copies of the data integration business logic in the form of versioned metadata artifacts stored in a repository. When flawed logic is detected, the system can copy the previous working version back into place, replacing the need for manual recreation and significantly improving productivity.
2Reliability
If developers manually re-create earlier data integration business logic to recover from flawed logic, then recovery from errors is possible, but time to market increases
Solution Approach 1:
The system performs preliminary version control by automatically saving each modification to data integration business logic as a new version. This preliminary action ensures that previous working logic is preserved before any flawed changes are made, enabling automatic recovery without manual recreation efforts.
Solution Approach 2:
The system creates copies of the data integration business logic in the form of versioned metadata artifacts stored in a repository. When flawed logic is detected, the system can copy the previous working version back into place, replacing the need for manual recreation and significantly improving productivity.
3Reliability
If version control integration is implemented, then tracking and recovery of changes is improved, but system complexity increases
Solution Approach 1:
The system introduces a repository as an intermediary component that stores versioned metadata artifacts. This intermediary handles the complexity of version control operations, isolating the data integration tool from direct version management complexity while enabling robust tracking and recovery capabilities.
Solution Approach 2:
The repository serves multiple functions: storing metadata artifacts, managing versions, enabling recovery, and supporting collaboration. By making this component multi-functional, the system reduces overall complexity compared to having separate specialized systems for each function.
Data Source
AI summary
The present disclosure relates generally to a data integration system that integrate an object based data integration tool, such a GUI-based data integration tools, with version control systems using a relational database repository for persistence. Examples of distributed version control systems include Git, Mercurial, and Bazaar, and examples of centralized version control systems include Subversion, CVS etc. in centralized or distributed environments.