Data Migration via Source Database Classification and Mapping
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Solution Overview
Problem
Data migration between disparate database systems is complex due to varying hardware configurations and stringent functionality requirements, such as minimum downtime, making efficient migration a longstanding challenge in data management.
Innovation Solution
A system and method for data migration using source classification and mapping, where source databases are classified based on predetermined sizes, and resource requirements are determined to generate target databases with appropriate hardware configurations, enabling efficient migration planning, scheduling, and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data migration is performed between disparate database systems with varying hardware configurations, then migration coverage and adaptability are improved, but migration complexity and difficulty increase
Solution Approach 1:
The patent applies parameter changes by transforming diverse hardware configurations into standardized database shapes through classification. Source databases with varying hardware specs are assessed and mapped to predetermined target shapes (small, medium, large, xlarge), converting heterogeneous parameters into a standardized framework that simplifies migration planning while maintaining adaptability across different source systems.
Solution Approach 2:
The patent segments the migration process into distinct phases: source database assessment, classification into size categories, shape determination, and target system provisioning. This segmentation breaks down the complex migration task into manageable steps, reducing overall complexity while handling diverse database systems systematically.
2Ease of manufacture
If traditional data migration methods are used without classification, then implementation is simpler, but migration time and resource allocation inefficiency increase
Solution Approach 1:
The patent implements preliminary action by classifying source databases and determining target shapes before actual data migration begins. This advance classification and resource allocation planning enables more efficient execution during the migration phase, reducing overall migration time while maintaining implementation simplicity through automated classification processes.
Solution Approach 2:
The patent changes the parameter of resource allocation from reactive to proactive by determining target database shapes and resource requirements in advance. This allows for optimized resource provisioning and faster migration execution, improving productivity without significantly increasing implementation complexity.
3Adaptability or versatility
If database sizes are not standardized, then flexibility in handling diverse sources is maintained, but resource allocation efficiency and consistency decrease
Solution Approach 1:
The patent applies parameter changes by converting diverse database sizes into standardized shape categories (small, medium, large, xlarge). This transformation maintains flexibility in accepting various source databases while achieving consistency in target provisioning, as all source databases are mapped to standardized target shapes that ensure uniform resource allocation and migration consistency.
Solution Approach 2:
The patent creates a universal classification framework that can handle diverse source databases through a common shape determination process. The standardized shapes serve as a universal interface between various source systems and target systems, maintaining adaptability while ensuring consistent resource allocation and migration procedures across all databases.
4Measurement precision
If detailed source database information is collected and analyzed, then migration accuracy is improved, but information processing time and system complexity increase
Solution Approach 1:
The patent segments information collection into essential categories (hardware specs, database size, workload characteristics) and processes them through a structured classification framework. This segmentation enables accurate migration planning by focusing on key parameters while reducing unnecessary data processing time, achieving a balance between precision and efficiency.
Solution Approach 2:
The patent transforms detailed source database information into classified shape categories through parameter aggregation and threshold-based classification. This parameter transformation reduces processing time by converting granular details into standardized categories while maintaining sufficient accuracy for effective migration planning and resource allocation.
Data Source
AI summary
Embodiments include systems and methods for performing data migration using source database classification. Information about source databases can be received from a source system, including source hardware types and processor information for the source databases. Each of the source databases can be classified to one of a plurality of predetermined database sizes based on the received information. The source system can be interrogated to derive information about the source databases, such as a processor utilization per database. Characteristics of the classified database sizes can be adjusted based on the derived information and resource requirements at a target system for the classified source databases can be determined. A shape for the target databases can be generated based on the resource requirements, the shape including target databases of predetermined database sizes implemented by target hardware, where the target databases are configured to receive migration data from the source databases.