Unified Storage System Inline Analytics
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Solution Overview
Problem
Current data storage solutions face challenges in integrating primary storage, data protection, and data analytics, leading to complex backup strategies, resource-intensive data movement, and time-consuming restore operations, with limitations in tracking data access and modifications, and requiring separate systems and additional software.
Innovation Solution
A unified system that merges primary storage, data protection, and analytics, using inline data analytics to track changes and store metadata for rapid search and restore, eliminating the need for separate backup streams and additional servers, with real-time data protection and minimal recovery time objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate backup and analytics systems are used, then data protection and analytics functions are provided, but system complexity and resource consumption increase
Solution Approach 1:
The patent combines backup and analytics functions into a single integrated system that shares storage resources and processing capabilities. The analytics system operates directly on backup data without requiring separate storage infrastructure, thereby reducing system complexity while maintaining data protection and analytics capabilities.
Solution Approach 2:
The backup system is designed to serve multiple purposes: data protection, disaster recovery, and analytics processing. By making the backup system universal, it eliminates the need for dedicated separate systems, reducing overall system complexity while providing comprehensive functionality.
2Reliability
If data is moved to backup and analytics silos, then data protection and analytics are achieved, but resource intensity and processing time increase
Solution Approach 1:
The patent extracts only the necessary data elements for analytics from the backup system, rather than moving entire datasets. By selectively extracting only the data needed for analytics processing, resource consumption during data movement is significantly reduced while maintaining analytics capability.
Solution Approach 2:
The system performs preliminary indexing and metadata generation during the backup process itself, before analytics are needed. This preliminary organization of data allows analytics to be performed efficiently without requiring intensive data movement or processing at analytics time, reducing overall resource consumption.
3Reliability
If restore operations are performed from backup, then data recovery is achieved, but access time and operational downtime increase
Solution Approach 1:
The system performs preliminary organization of backup data into easily accessible structures with comprehensive indexing and metadata during the backup process. When restore operations are needed, this pre-organized data can be quickly located and recovered without time-consuming search or processing, significantly reducing recovery time.
Solution Approach 2:
The patent replaces traditional mechanical restore processes with intelligent data retrieval systems that use metadata-based searching and selective data transfer. Instead of restoring entire backup sets sequentially, the system intelligently identifies and retrieves only the specific data needed, dramatically reducing restore time.
4Adaptability or versatility
If multiple third-party software layers are added, then data extraction and analytics capabilities are enhanced, but system complexity and cost increase
Solution Approach 1:
The backup system incorporates built-in analytics and data extraction capabilities as integrated functions rather than requiring separate third-party software layers. This multi-functional design provides comprehensive analytics capability while maintaining a simple, unified software architecture.
Solution Approach 2:
The system performs its own analytics and data extraction operations using its internal processing capabilities and metadata structures, eliminating the need for external third-party software. The backup system serves its own analytics needs through self-contained functionality.
Data Source
AI summary
A single system merges primary data storage, data protection, and intelligence. Intelligence is provided through in-line data analytics, and data intelligence and analytics are gathered on protected data and prior analytics, and stored in discovery points, all without impacting performance of primary storage. As data is written it is automatically mirrored for data protection as part of a High Availability (HA) process. Real-time analysis is done in-line with the HA processing, enabling a variety of data analytics. Data content can be mined from within files or blocks. The gathered intelligence is used to tag objects with extended metadata, enabling both valuable search options and rapid restore options. Data recovery begins with metadata restoration, followed by near-instantaneous access to “hot” regions of data being restored, allowing site operation to continue or resume while a restore is ongoing.