Data Migration Validation via Multi-Protocol Comparison
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Solution Overview
Problem
Data migration from legacy systems to new systems often results in data loss or corruption, with existing validation methods only detecting issues at a service level, leaving user data migration success unknown.
Innovation Solution
Migrating data using multiple protocols to create multiple data sets, comparing them for discrepancies, and validating user data at a granular level before allowing access, with options for users to resolve discrepancies and refine future migrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is migrated using a single protocol, then the migration process is simple and fast, but data integrity and validation at user level cannot be ensured
Solution Approach 1:
The patent divides the data migration validation process into multiple independent migration operations using different protocols. Each protocol creates a separate data set that can be independently compared, allowing validation at the user level without requiring a monolithic complex validation system.
Solution Approach 2:
The patent creates multiple copies of the migrated data using different protocols (first protocol, second protocol) to generate comparable data sets. These copies serve as validation references against each other, enabling detection of data integrity issues without altering the original migration process.
2Measurement precision
If multiple protocols are used to migrate data, then data integrity can be validated, but the migration process becomes more complex and time-consuming
Solution Approach 1:
The patent performs multiple migrations using different protocols concurrently or in parallel during the initial migration phase, rather than sequentially validating after completion. This preliminary action approach allows validation to occur during the migration process itself, reducing overall time loss.
Solution Approach 2:
The patent changes the protocol parameter (using different protocols for different migrations) to create diverse data sets that can be compared for validation. This parameter variation enables precise validation without requiring additional time beyond what would be needed for standard multi-protocol migration operations.
3Loss of information
If corrupt item count validation is used, then service level data loss is detected, but user-level data migration success remains unknown
Solution Approach 1:
The patent segments the validation granularity from service level to user level by comparing data sets protocol by protocol and user by user. This segmentation allows identification of which specific users have data integrity issues, rather than only detecting aggregate service-level problems.
Solution Approach 2:
The patent implements a feedback mechanism where comparison results from multiple protocol migrations are used to identify and report user-level data integrity issues. This feedback loop provides precise information about which users have migration problems, enabling targeted remediation.
Data Source
AI summary
Migrating data from a source data store to a destination data store and validating the migrated data. The method includes migrating data from a first data store to a second data store using a first protocol to create a first set of comparison data. The method further includes migrating the data from the first data store to the second data store using a second protocol to create a second set of comparison data. The method further includes comparing the first set of comparison data to the second set of comparison data. The method further includes validating migration of the data from the first data store to the second data store based on comparing the first set of comparison data and the second set of comparison data.