Automated Data Pipeline Degradation Detection with Modular Log Monitoring
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Solution Overview
Problem
Existing technologies fail to efficiently detect data degradation in data sources and pipelines, leading to silent failures that can cause broader impacts in decentralized systems.
Innovation Solution
A system and method for automated data pipeline degradation detection that 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
1Reliability
If automated monitoring is implemented to detect data degradation, then detection reliability is improved, but system complexity increases
Solution Approach 1:
The system segments the monitoring task by dividing data stores into multiple data pipelines and further into individual log types. Each pipeline is monitored independently with its own baseline and threshold, allowing comprehensive coverage while managing complexity through modular processing units.
Solution Approach 2:
The patent introduces an intermediary monitoring system that acts as a mediator between data sources and analysts. This intermediary automatically collects log data, compares it against baselines, and generates alerts, eliminating the need for manual monitoring while maintaining system reliability.
2Measurement precision
If comprehensive log analysis is performed across all data stores, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by establishing baseline log counts for each log type before actual monitoring begins. These pre-computed baselines and thresholds enable rapid real-time comparison without requiring complex analysis during the monitoring phase, thus maintaining precision while reducing processing time.
Solution Approach 2:
The patent applies partial action by monitoring only the most critical log types and data pipelines that have established baselines. Not all logs require the same level of analysis, allowing the system to achieve sufficient detection precision with reduced processing overhead by focusing on key indicators.
Data Source
AI summary
A method analyzes dependency information for a first data store. Upon determining that a data pipeline associates a first log type generated by the first data store with a second log type generated by a second data store, a first number of logs of the first log type that are generated at a first time, a first baseline number, and a first threshold value are determined. Upon determining that the first number of logs differs from the first baseline number by less than the first threshold value, a second number of logs of the second log type that are generated at a second time, a second baseline number, and a second threshold value are determined. Upon determining that the second number of logs differs from the second baseline number by more than the second threshold value, the data pipeline is identified as degraded.