Metadata Transformer for Automated Database Versioning
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Solution Overview
Problem
Current database versioning processes require manual intervention and complex scripts to export and import data, as well as modify metadata, which is inefficient and prone to errors, especially when transitioning between different database versions.
Innovation Solution
A method that automatically reads and processes metadata objects using predetermined rules, applying actions to change the metadata and subsequently updating the database structure, allowing for seamless transition between database versions without manual intervention, using a state machine approach that minimizes the need for manual transformations and business logic changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual scripts are used to export and import data during database versioning, then data can be transferred between versions, but the process becomes complex and error-prone
Solution Approach 1:
The system automatically applies transformation rules to metadata objects without requiring manual script intervention. The metadata transformer reads rules, identifies matching metadata objects, and applies transformations automatically, making the system self-sufficient and reducing human error in the versioning process
Solution Approach 2:
Manual mechanical operations (copy-pasting data, manually editing metadata) are replaced by an automated computational system that processes metadata through defined transformation rules, eliminating the need for manual scripting and reducing complexity
2Adaptability or versatility
If manual metadata changes are performed to resolve database version incompatibilities, then compatibility issues can be addressed, but productivity decreases due to time-consuming manual intervention
Solution Approach 1:
Transformation rules are pre-defined and stored in the system before versioning operations occur. When a versioning operation is needed, the system retrieves applicable rules and executes them automatically, eliminating the need for manual preparation and adjustment of metadata transformations
Solution Approach 2:
The metadata transformer autonomously identifies which metadata objects need transformation, selects appropriate rules, and applies changes without human intervention, significantly accelerating the compatibility resolution process while maintaining adaptability across different database versions
3Manufacturing precision
If comprehensive metadata transformation rules are applied to ensure accurate data migration, then data integrity is maintained, but the transformation process becomes more complex
Solution Approach 1:
The transformation system is divided into discrete, manageable rules that can be independently defined, stored, and executed. Each rule handles a specific transformation task, making the overall complex transformation process modular and easier to maintain while ensuring accurate data migration through comprehensive coverage
Solution Approach 2:
The metadata transformer acts as an intermediary layer between the source and target database versions. It reads transformation rules, identifies matching metadata objects, and applies appropriate transformations, simplifying the complexity management by centralizing the transformation logic in a dedicated system component
Data Source
AI summary
A computer automatically reads each object in a metadata that is descriptive of a database. Then the computer automatically applies a number of rules, to check if the object triggers a rule. If so, then the computer automatically changes the metadata as per an action identified by the triggered rule. After all rules are applied to the object, and after all objects in the metadata are processed, the resulting metadata is used to change the database's structure. The objects may be read from the metadata in an order (e.g. hierarchy) specified in meta-metadata that is descriptive of the metadata.