Data Lineage Recovery Across Multi-Store Derived Tables
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Solution Overview
Problem
Data in one data table can become corrupted and propagate errors to multiple derivative tables across different data stores, leading to widespread data integrity issues.
Innovation Solution
A data recovery system utilizing data lineage and versioning tools to trace and restore corrupted data to its previous correct versions across multiple interconnected data stores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data transformation and propagation occur across multiple data stores to serve different purposes, then data versatility and utility are improved, but data integrity and reliability deteriorate when errors are propagated
Solution Approach 1:
The patent implements data validation and quality checks before data is propagated to derivative tables. This preliminary action prevents bad data from being transformed and distributed across multiple data stores, thus maintaining data integrity while still allowing versatile data usage. The system validates source data before it enters the transformation pipeline.
Solution Approach 2:
The patent establishes feedback mechanisms that track data lineage and propagate error information back to source systems. When data quality issues are detected in derivative tables, the system traces the lineage back to the source and notifies the originating system, enabling corrective action. This feedback loop maintains reliability across the versatile data ecosystem.
2Adaptability or versatility
If data is propagated to multiple derivative tables across different data stores, then data utility for different purposes is improved, but the spread of errors and data corruption worsens
Solution Approach 1:
The patent introduces data quality intermediaries or gateways that sit between source systems and derivative tables. These intermediaries validate, clean, and sanitize data before it is propagated to multiple data stores. By placing this intermediary layer, the system maintains high data utility across different purposes while preventing error propagation to derivative tables.
Solution Approach 2:
The patent converts the potentially harmful effect of data propagation into a benefit by using the same propagation mechanisms to distribute data quality metadata and lineage information. The system tracks where data goes and uses this information to prevent error spread, turning the propagation infrastructure itself into a protective mechanism.
3Reliability
If comprehensive data validation and error prevention mechanisms are implemented across multiple data stores, then data integrity is improved, but system complexity and computational overhead worsen
Solution Approach 1:
The patent segments data validation and quality assurance into modular, reusable components that can be applied at different stages of the data pipeline. Rather than implementing a single complex validation system across all data stores, the system uses segmented validation rules and quality checks that can be independently configured and maintained for different data sources and destinations.
Solution Approach 2:
The patent creates universal data validation frameworks and quality assurance mechanisms that can be applied across multiple data stores and transformation processes. These multi-functional tools handle various validation scenarios (data type checking, format validation, quality scoring) in a unified manner, reducing overall system complexity while maintaining comprehensive data integrity protection.
Data Source
AI summary
Embodiments disclosed herein provide systems, methods, and computer readable media for data lineage based multi-data store recovery. In a particular embodiment, a method provides identifying first data in a first table of a plurality of tables stored in a plurality of data stores and restoring the first data to a first correct version of the first data in a prior version of the first table. The method further provides identifying a second table of the plurality of tables that descends from the first table and includes second descendent data that stems from the first data. The method also provides restoring the second descendent data to a second correct version of the second descendent data in a prior version of the second table.