Automated Data Degradation Detection for Stores and Pipelines
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Solution Overview
Problem
Existing technologies fail to efficiently detect data degradation in decentralized systems, leading to silent failures that can cause broader impacts on key data sources and critical detection failures.
Innovation Solution
A data monitoring system that automatically identifies degraded data stores and pipelines by analyzing log types, impact scores, and dependency information, generating reports with priority alerts for maintenance teams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring and analysis of data sources is performed, then detection accuracy can be maintained, but response time increases and productivity decreases
Solution Approach 1:
The system performs self-monitoring and self-diagnosis by automatically analyzing log files, detecting degradation patterns, and identifying root causes without requiring manual intervention. The degradation detection system monitors itself and other data sources continuously, generating alerts and impact assessments autonomously.
Solution Approach 2:
The patent replaces manual mechanical monitoring processes with automated electronic systems that continuously collect, analyze, and interpret data from multiple sources. The system uses computational algorithms to detect degradation patterns that would otherwise require human analysts to identify through manual review of logs and metrics.
2Reliability
If comprehensive monitoring of all data sources is implemented, then detection coverage improves, but device complexity increases
Solution Approach 1:
The system divides the monitoring task into modular components, each responsible for specific data sources or degradation types. The degradation detection system can independently monitor individual data sources, analyze specific log types, and generate targeted alerts, making the overall complex system manageable through functional segmentation.
Solution Approach 2:
The patent creates a universal monitoring framework that can handle multiple data sources, log types, and degradation scenarios through a single integrated system. The same core infrastructure monitors diverse data sources using standardized protocols, reducing complexity compared to separate specialized monitoring systems for each data source.
3Productivity
If automated log analysis is performed without baseline comparison, then processing speed increases, but measurement precision decreases
Solution Approach 1:
The system establishes baseline characteristics for log patterns, error rates, and performance metrics during normal operation before degradation occurs. These pre-computed baselines serve as reference points that enable rapid and accurate detection of deviations, allowing the system to maintain both speed and precision by comparing current data against established norms rather than analyzing every data point from scratch.
Data Source
AI summary
A method includes analyzing a first log of a first log type generated by a first data store and determining if the first log has a first format of the first log type. In response to determining that the first log does not have the first format of the first log type, the first data store and the first log type are identified as degraded. A first impact score is determined for the first log type. First dependency information is analyzed for the first data store. In response to determining that no data store receives data items from the first data store, a report is generated. The report includes an identification that the first data store is degraded, an identification that the first log type is degraded, and the first impact score associated with the first log type.