Cloud Data Restoration via Pre-Restoration Transformation Routines
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Solution Overview
Problem
Existing backup and restore systems face compatibility issues when the data model changes after data extraction, making it impossible to restore extracted data to a cloud-based application.
Innovation Solution
A system and method that utilize a user-defined restoration flow, including pre-restoration, restoration, and post-restoration routines, to transform extracted data into a compatible format for restoration to a cloud-based application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is extracted from a cloud-based application for backup, then data backup capability is improved, but data compatibility for restoration deteriorates when data model changes occur
Solution Approach 1:
The system performs preliminary actions by extracting and storing not only the data itself but also the data model definition and metadata at the time of backup. This preliminary capture of the complete data context enables later restoration even when the live application's data model has changed, resolving the contradiction between reliable backup and restoration compatibility.
Solution Approach 2:
The patent introduces an intermediary data model layer that acts as a bridge between the extracted data and the current application schema. This intermediary model, stored separately from the application's live data model, mediates the restoration process by providing a compatible target schema that reflects the state at backup time, thus enabling restoration despite subsequent model changes.
2Adaptability or versatility
If a data model changes in the cloud-based application after data extraction, then the application's adaptability is improved, but the extractable data becomes incompatible with restoration
Solution Approach 1:
The system captures the data model definition at the time of backup as a static reference, preserving the historical schema state. This preliminary capture allows the application to evolve its data model freely while maintaining the ability to restore to any previous state by referencing the stored historical model definitions.
Solution Approach 2:
The patent creates a copy of the data model definition and stores it alongside the extracted data. This copied model serves as an immutable template that enables restoration compatibility, allowing the live application to modify its data model without affecting the ability to restore to previous states using the copied model as a reference.
3Device complexity
If traditional backup systems are used without transformation routines, then system complexity is reduced, but the ability to handle data model changes is lost
Solution Approach 1:
The backup system is segmented into distinct functional components: data extraction module, data model capture module, transformation routine module, and restoration module. This segmentation allows each component to specialize in its function, with the transformation routine specifically handling data model changes, thereby managing complexity through modular organization while enhancing adaptability.
Solution Approach 2:
The system introduces dynamic transformation routines that can adapt to different data model changes. Rather than a static backup process, the system dynamically generates and executes transformation code based on the detected differences between the extracted data model and the current application model, enabling flexible handling of various change scenarios.
Data Source
AI summary
The present disclosure relates to a system, method, and computer program for restoring extracted data to a cloud-based application. The system extracts a copy of data associated with a cloud-based application. The system provides a user interface that enables a user to enter a restoration flow for restoring the extracted data to the cloud-based application, where the restoration flow includes one or more routines for execution. The system receives a restoration flow comprising a pre-restoration routine and a restoration routine, where the pre-restoration routine specifies one or more data transformations to render the extracted data compatible with a restoration to the cloud-based application. The system executes the pre-restoration routine to transform the extracted data to be compatible with a restoration to the cloud-based application. The system executes the restoration routine to restore the transformed data to the cloud-based application. In certain embodiments, the restoration flow also includes a post-restoration routine.