Data Migration Framework Using Staging Tables
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Solution Overview
Problem
Migrating data from one business data system to another is complex and often results in inaccuracies or data loss due to format differences between the source and target systems, especially when the source is not a traditional data system but a database.
Innovation Solution
A data migration architecture that retrieves user data from a source data system, generates a mapping between the data system and staging tables, and uses this mapping to transfer and validate data, ensuring accurate conversion and storage in the target system, even when the source and target systems have different formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is transferred directly from source to target system without mapping, then migration speed is faster, but data accuracy deteriorates due to format differences
Solution Approach 1:
The patent introduces a staging table as an intermediary structure between the source and target data systems. Data is first loaded into the staging table with mapping applied, then validated and finally transferred to the target system. This intermediary approach allows complex mapping and validation operations to be performed without directly complicating the source-to-target transfer path, thereby maintaining data accuracy while managing process complexity.
Solution Approach 2:
The migration process is divided into distinct segments: data extraction from source, loading into staging table, validation against target schema, and final insertion into target system. This segmentation allows each step to be independently optimized and error-handled, improving overall data accuracy while making the complex migration process more manageable through modular processing.
2Measurement precision
If comprehensive validation is performed during migration, then data accuracy is improved, but migration time increases
Solution Approach 1:
The patent performs validation operations in advance during the staging table population phase, before the final data transfer to the target system. By validating data against the target schema and business rules during the staging phase, the system ensures data accuracy is established early, preventing rework during the actual migration and ultimately reducing total migration time despite the additional validation steps.
3Productivity
If data is migrated sequentially from source to target, then data accuracy is maintained, but productivity decreases due to inability to process multiple data types simultaneously
Solution Approach 1:
The staging table is designed as a universal intermediate structure that can accommodate multiple data types and formats from different source systems. By loading various data types into the same staging table structure with appropriate mapping, the system enables parallel processing of heterogeneous data sources while maintaining consistent validation and transfer procedures, thereby improving productivity without proportionally increasing complexity.
Data Source
AI summary
User data is retrieved from an instance of a data system. A mapping between the data system and one or more staging tables is generated and a package that includes the data and the mapping is generated. The user data is saved to the one or more staging tables using the mapping.