Bulk Data Automatic Correction Tool for SaaS Migration
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Solution Overview
Problem
Bulk data migration from different sources to enterprise applications is cumbersome and time-consuming due to format, encoding, and semantic differences, making it challenging to ensure compatibility with specific enterprise application requirements.
Innovation Solution
The Bulk Data Automatic Correction and Migration Tool (BDACMT) performs a preview process by uploading a subset of data, analyzing errors, and applying corrective rules to ensure data format and encoding compliance with enterprise application APIs, thereby facilitating successful data migration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bulk data is migrated directly from different sources to enterprise application, then data transfer speed is improved, but data compatibility and error rate deteriorate due to format, encoding, and semantic differences
Solution Approach 1:
The system performs preliminary actions by uploading a subset of bulk data before full migration to detect format, encoding, and semantic errors in advance. This allows corrective rules to be generated and applied to the remaining data, ensuring compatibility while maintaining efficient bulk transfer.
Solution Approach 2:
The system implements feedback by analyzing errors returned from the enterprise application after uploading a data subset, then using this feedback to generate corrective rules that are applied to the bulk data before complete migration, resolving compatibility issues while preserving transfer efficiency.
2Reliability
If a subset of data is uploaded to validate format and encoding, then data compatibility is improved, but migration time increases due to the preview and correction process
Solution Approach 1:
The migration process is segmented into phases: uploading a representative subset for validation, generating corrective rules from errors, applying rules to bulk data, and completing migration. This segmentation ensures compatibility through targeted validation while minimizing overall time loss by processing only essential subsets.
Solution Approach 2:
Instead of validating the entire bulk data set, the system performs partial action by uploading only a representative subset for error detection. This subset is sufficient to identify format, encoding, and semantic issues, allowing corrective rules to be applied to the full data set without the time cost of complete validation.
3Reliability
If corrective rules are applied to convert data formats, then data compatibility with enterprise application APIs is improved, but processing complexity increases
Solution Approach 1:
The system implements self-service by automatically generating corrective rules from errors detected during subset validation. The system autonomously identifies format, encoding, and semantic issues, creates appropriate correction rules, and applies them to bulk data without manual intervention, reducing processing complexity despite the need for format conversion.
Data Source
AI summary
The present invention may include a method for automatic correction and migration of a plurality of bulk data. The method may identify sources, where the sources include the plurality of bulk data. The method may identify a subset of the bulk data, where the subset represents one or more records selected from the plurality of bulk data. The method may upload the subset to an enterprise application. The method may determine an error list from the enterprise application based on the uploaded subset. The method may roll back the subset from the enterprise application and based on the determined error list, the method may match one or more rules to a one or more errors in the error list.