Intelligent Failsafe Engine for Dynamic Database Backup
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current database backup methods are often inefficient and can impact business continuity and performance, leading to increased recovery time and point objectives, backup failures, and adverse impacts on business operations due to their static nature and lack of adaptability to dynamic data changes.
Innovation Solution
An intelligent failsafe engine that uses machine learning algorithms to identify optimal backup windows and select between full and differential backups, pausing and resuming backups as necessary, and sending data health information for user device display, while also compressing and storing backup information efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional database backup methods are used, then backup operations can be performed, but backup failures occur and business continuity is adversely impacted
Solution Approach 1:
The system continuously monitors backup operation status and sends notifications to users about backup progress, pauses, and completions. This feedback mechanism allows users to respond to backup issues in real-time, improving backup reliability and business continuity by enabling timely interventions.
Solution Approach 2:
The backup system automatically manages backup operations including initiating backups, monitoring progress, detecting pauses, and sending notifications without requiring continuous user intervention. This self-service capability improves reliability by ensuring backups are performed consistently while maintaining business continuity.
2Loss of time
If frequent backups are performed to improve data recovery, then recovery time objective is reduced, but computing resources and network bandwidth are burdened
Solution Approach 1:
The system performs differential backups that capture only changed data blocks rather than complete database backups. This partial action approach reduces the amount of data processed and transmitted, conserving computing resources and network bandwidth while still achieving timely recovery objectives by capturing essential changes.
Solution Approach 2:
The system dynamically adjusts backup parameters including backup type (full or differential), frequency, and data block selection based on database activity patterns and resource availability. This parameter optimization reduces resource burden while maintaining effective recovery time objectives.
3Ease of operation
If manual backup management is used, then backup operations can be controlled, but human intervention increases operational complexity
Solution Approach 1:
The backup system automatically manages the entire backup lifecycle including scheduling, execution, monitoring, and notification without requiring manual user intervention for each operation. This self-service automation simplifies ease of operation while the system handles the operational complexity internally.
Solution Approach 2:
The system provides automated feedback through notifications to users about backup status, pauses, and completions. This feedback mechanism maintains ease of operation by keeping users informed without requiring them to manually monitor complex backup processes, while the system manages operational complexity through automated monitoring and response.
Data Source
AI summary
Aspects of the disclosure relate to an intelligent failsafe engine. A computing platform may determine that a backup should be initiated that corresponds to a determined time window. The computing platform may select either a full backup method or a differential backup method. The computing platform may initiate the selected backup method, which may include backing up only blocks that have been modified since a previous backup. The computing platform may identify that the backup has paused at a particular data block. The computing platform may identify that the backup may be resumed, and may resume the backup at the particular data block. The computing platform may send data health information and commands directing a user device to display the data health information, which may cause the user device to display the data health information.