Multi-way Data Store Conversion via Virtual Targets
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Solution Overview
Problem
Current data store migration techniques are limited to single conversion pairs, restricting the ability to migrate data between multiple sources and targets, and often require the creation of real target data stores, which can be costly and resource-intensive.
Innovation Solution
Implementing a data store conversion application that supports many-to-one, one-to-many, and many-to-many data store migration, allowing for the use of virtual targets to perform migrations without creating real target data stores, and enabling the generation of conversion scripts and reports for multiple sources and targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data store migration techniques are used, then data can be migrated between single conversion pairs, but the system is restricted and cannot handle multiple sources and targets efficiently
Solution Approach 1:
The conversion application is designed to handle multiple conversion scenarios (one-to-one, one-to-many, many-to-one, many-to-many) through a single unified system. The application accepts multiple source data stores and multiple target data stores as inputs, and can perform conversions across all combinations, making the system universally applicable to various migration needs without requiring separate specialized tools for each scenario.
Solution Approach 2:
The patent introduces a virtual target data store as an intermediary concept that allows conversion analysis and script generation to occur without requiring actual target data stores to exist. This virtual intermediary enables the system to plan and prepare conversions beforehand, reducing the complexity of actual migration execution while maintaining high adaptability to different target systems.
2Reliability
If real target data stores are created for migration, then data conversion can be performed, but the process becomes costly and resource-intensive
Solution Approach 1:
The system performs conversion analysis and generates conversion scripts in advance, before actual data migration is executed. By analyzing the source data store schema and generating appropriate conversion scripts beforehand, the system ensures conversion accuracy is planned and validated prior to execution, while avoiding the need to create and populate actual target data stores during the analysis phase, thus reducing resource consumption.
Solution Approach 2:
The patent uses virtual target data stores that are conceptual copies or representations of actual target systems, rather than creating full instances of target data stores. This allows the system to perform all necessary analysis, validation, and script generation against these virtual copies, ensuring conversion accuracy without consuming the resources required to create and maintain real target data store instances.
3Adaptability or versatility
If multiple data formats are introduced to reduce storage complexity, then data management becomes more flexible, but data processing complexity increases due to format incompatibility
Solution Approach 1:
The conversion application is designed to universally handle multiple data store types and formats (relational databases, NoSQL databases, data warehouses, data lakes) through a single platform. It accepts diverse source formats and can convert to various target formats, maintaining high adaptability while managing processing complexity through automated schema analysis and conversion script generation that handles format incompatibilities systematically.
Data Source
AI summary
Conversion that includes multiple targets or sources of a data store migration may be performed. A request that specifies a mapping between multiple sources and one or more targets, one or more sources and multiple targets, or multiple sources and multiple targets may be created for data store migration. Metadata may be obtained for the selected sources and targets for the mapping. Conversion scripts may then be generated for the mapping based on an analysis using the metadata. The conversion scripts may then be stored for later execution.