Remote Database Backup Merging With Parallel Containers
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Solution Overview
Problem
Merging data from multiple databases into a single database is a time-consuming and labor-intensive process, especially when databases are located in different geographical locations, leading to increased costs and potential errors due to substantial manual work by database administrators.
Innovation Solution
An automated algorithm that uses parallel processing to consolidate data from multiple databases into a single target database, handling exceptions such as primary and foreign key violations, and includes features for data transformation, mapping, and integration, while being scalable and adaptable to various scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data merging process is used by database administrators, then data integrity can be maintained through human oversight, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables automated self-service data merging through parallel processing algorithms that independently handle multiple database consolidations simultaneously. The algorithm automatically queries databases, creates containers, restores data, and merges information without requiring continuous human intervention, thereby maintaining data integrity while dramatically reducing the time required for the merging process.
2Productivity
If automated parallel processing is used to merge databases, then processing time is reduced and productivity increases, but the system complexity increases
Solution Approach 1:
The automated merging system divides the complex task of database consolidation into distinct sequential segments: querying databases for data, creating separate containers for each database, restoring data to individual containers, and finally merging from each container to the target database. This segmentation allows parallel processing of multiple databases simultaneously while managing system complexity through structured, modular operations.
Solution Approach 2:
The system introduces temporary container databases as intermediary structures between the source databases and the final target database. These containers serve as intermediate storage points that facilitate the parallel processing workflow, allowing data to be restored and prepared in isolation before being merged into the target database, thereby simplifying the overall complex merging operation.
3Adaptability or versatility
If multiple databases are merged into a single target database, then data consolidation is achieved, but data consistency and constraint violations may occur
Solution Approach 1:
The system performs preliminary actions by querying each source database beforehand to identify and extract the specific data to be merged. It then creates dedicated containers and restores data to these containers before the actual merging process. This preliminary preparation allows for proper handling of primary and foreign key constraints, ensuring data consistency is maintained throughout the consolidation process while achieving versatile data consolidation capability.
Data Source
AI summary
A computer-implemented method is disclosed in which a number of remote database backups are received from remote locations. Containers are created where each container hosts remote data from a respective one of the remote database backups. The remote data from each container is merged with a target database. The method can be implemented, for example, in a cloud service.