Automated Data Transformation Subscription Generation
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Solution Overview
Problem
Software upgrades often result in significant downtime due to manual and error-prone processes for identifying and transforming data to be compatible with upgraded systems, leading to lengthy downtimes and productivity losses.
Innovation Solution
Automatically identifying complex transformations and generating subscriptions for data replication, using specific commands like SQL commands, to transform data from a source system to a target system with different code levels, enabling real-time or near real-time data replication and fine-grain access control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to identify and transform data for software upgrades, then data transformation can be performed, but system downtime increases and productivity is lost
Solution Approach 1:
The system performs self-service by automatically comparing data models between source and target systems, identifying transformations needed, and generating the transformation logic without requiring manual administrator intervention. This automation eliminates the time-consuming manual review process while maintaining transformation accuracy through systematic comparison algorithms.
Solution Approach 2:
The system performs preliminary actions by pre-identifying all data model differences and generating transformation subscriptions before the actual data replication occurs. This allows the transformation logic to be prepared in advance, reducing the actual downtime during upgrade execution while ensuring accurate transformations through thorough preliminary analysis.
2Reliability
If manual review of software code is performed to identify data model changes, then data compatibility can be ensured, but the process becomes lengthy and error-prone
Solution Approach 1:
The system replaces the mechanical manual review process with an automated computational system that systematically compares data models, identifies transformations, and generates transformation logic. This substitution maintains data compatibility through thorough systematic analysis while dramatically increasing upgrade execution speed by eliminating manual intervention bottlenecks.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the source and target data models. This intermediary automatically identifies differences and generates transformation subscriptions, ensuring data compatibility through systematic comparison while accelerating the upgrade process by removing manual review steps.
3Loss of time
If automated data replication is implemented, then system downtime is reduced, but complex transformations require sophisticated subscription generation
Solution Approach 1:
The system segments the complex transformation generation process into distinct automated steps: comparing data models to identify differences, analyzing transformation requirements, generating transformation logic, and creating structured subscriptions. This segmentation manages the inherent complexity by breaking it down into systematic, automated tasks that can be executed sequentially without manual intervention, thereby reducing system downtime.
Data Source
AI summary
According to embodiments of the present invention, machines, systems, methods and computer program products as part of a data replication process are provided. One or more complex transformations are identified from source code files of installed software products on a target system. A subscription is created for each complex transformation, the subscription containing instructions for transforming data within the source system into a form compatible with the target system. The instructions are executed within the target system to transform source data of the source system into a form compatible with the target system.