Data Model Output Manager for Multi-Instance Database Schema Views
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing multiple separate database schema instances for different geographic operations within an enterprise is time-consuming and error-prone, as it requires developing and maintaining multiple copies of data models, which can lead to inconsistencies and increased development efforts.
Innovation Solution
A data model output manager generates separate data views for each instance of a database schema using the same data model, allowing for efficient querying and output generation across multiple database schema instances, with features like model replication and refactoring to handle naming conflicts and autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple separate copies of data models are created for each database schema, then each data system can be maintained independently, but data management becomes time-consuming and error-prone
Solution Approach 1:
A single data model is designed to serve multiple database schemas across different geographic operations. The data model is configured with parameters that allow it to be universally applied to various schemas (e.g., US facilities schema, Europe facilities schema) without requiring separate customizations for each region.
Solution Approach 2:
The data model incorporates configurable parameters that can be adjusted to match different database schemas. By changing parameters such as schema names, table prefixes, or connection strings, the same data model can be deployed across multiple independent data systems, eliminating the need to create separate copies.
2Adaptability or versatility
If multiple separate copies of data models are created for each database schema, then each data system can be maintained independently, but consistency becomes difficult to maintain when new versions are released
Solution Approach 1:
A single data model serves multiple database schemas, ensuring that all systems use the same version and structure. When the data model is updated, the change is automatically applied to all connected schemas, maintaining consistency across all geographic operations while preserving their operational independence.
Solution Approach 2:
The system implements a centralized data model repository that tracks versions and automatically notifies all connected data systems when updates are available. This feedback mechanism ensures that all systems can be synchronized to the latest version, maintaining reliability and consistency across the enterprise.
3Adaptability or versatility
If multiple separate copies of data models are created for each database schema, then customization for specific regions is possible, but development effort increases significantly
Solution Approach 1:
The data model is designed with configurable parameters that allow regional customizations without requiring separate copies. A single data model can be adapted to different regions by modifying parameters such as locale settings, currency codes, or schema connections, significantly reducing development effort while maintaining regional adaptability.
Solution Approach 2:
The data model is pre-configured with parameter placeholders and template structures that anticipate regional variations. Common regional customizations are built into the model as configurable options, allowing rapid deployment to new regions without extensive development work.
Data Source
AI summary
A data model specific to a database schema is received in a database system. The received data model may be applied to a first instance of the database schema to generate a data view of the first database schema. The received data model may be applied to a second instance of the database schema to generate a second data view of the second database schema.