Data Validation via Hash Comparison for Migration Efficiency
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Solution Overview
Problem
Current data validation processes for data record migrations in cloud platforms are inefficient, requiring significant signaling overhead, computational resources, and time due to full row-by-row comparisons between source and target databases.
Innovation Solution
Implementing a data validation process that compares key bits of information such as unique identifiers and timestamps using a hash function to determine if data records have been successfully migrated, reducing the need for full row-by-row comparisons and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a row-by-row comparison of data is performed between source and target databases, then data validation accuracy is improved, but computational resources and time are significantly increased
Solution Approach 1:
The patent extracts only the essential validation information (hash values and row counts) from the data records, comparing only these extracted elements rather than performing full row-by-row data comparison. This extraction approach maintains validation accuracy while dramatically reducing computational resource consumption.
Solution Approach 2:
The patent transforms the validation approach by changing the comparison parameters from full data records to hash values derived from those records. This parameter transformation enables accurate validation with significantly reduced computational overhead, as hash comparison is much more efficient than full data comparison.
2Measurement precision
If a row-by-row comparison of data is performed between source and target databases, then data validation accuracy is improved, but time consumption is significantly increased
Solution Approach 1:
The patent extracts only the essential validation information (hash values and row counts) from the data records, comparing only these extracted elements rather than performing full row-by-row data comparison. This extraction approach maintains validation accuracy while dramatically reducing time consumption.
Solution Approach 2:
The patent performs preliminary hashing of data records before migration and stores these hash values. During validation, only the hash values need to be compared rather than the full data records, significantly reducing validation time while maintaining accuracy.
3Reliability
If full row-by-row comparison is performed, then data validation completeness is improved, but signaling overhead is significantly increased
Solution Approach 1:
The patent extracts only the essential validation information (hash values and row counts) from the data records, comparing only these extracted elements rather than performing full row-by-row data comparison. This extraction approach maintains validation completeness while dramatically reducing signaling overhead.
Data Source
AI summary
Methods, systems, and devices for data validation are described. A user may store a set of data records on a source database and backup the set of data records at a target database through a data migration. A migration and validation server may initiate the data migration. After the data migration is complete, the migration and validation server may perform a validation process that includes comparing a calculated hash value from the source database and the target database that is based on unique identifiers and timestamps for each data record in the set of data records migrated from the source database to the target database. The migration and validation server may determine if the data migration was successful (e.g., the data was transferred correctly) if the hash value calculated for the data records at the target database equals the hash value calculated for the data records at the source database.