Incident Data Pipeline for Real-Time Root Cause Analysis
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Solution Overview
Problem
Software deployments and changes in IT systems often lead to system outages and inefficiencies, with significant costs and risks due to unclear vulnerability assessment and complex, decentralized IT architectures.
Innovation Solution
A data pipeline system that aggregates and processes incident data through a collection point, front gate processor, data storage, and processing platform, utilizing machine learning for real-time analysis and correlation, to identify and resolve incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If decentralized personnel and systems are used in large IT organizations, then system complexity and adaptability are reduced, but incident resolution efficiency and coordination effectiveness deteriorate
Solution Approach 1:
The patent merges decentralized incident data from multiple sources into a centralized data pipeline that consolidates information flow. The collection point aggregates incidents from various decentralized systems, and the processing platform unifiedy processes all incidents, enabling efficient coordination despite organizational decentralization.
Solution Approach 2:
The data pipeline acts as an intermediary between decentralized incident sources and the processing platform. It receives data from distributed sources, standardizes formats, and delivers processed information to appropriate teams, mediating communication across decentralized organizations.
2Ease of operation
If individual ticket and time-separated resolutions are used, then operational simplicity is maintained, but overall incident resolution time and resource expenditure increase
Solution Approach 1:
The processing platform enables continuous incident processing by receiving data streams in real-time and immediately processing them through filtering, enrichment, and routing operations. This eliminates time-separated resolutions and maintains continuous useful action on incident data.
Solution Approach 2:
The system performs preliminary processing actions at the collection point and front gate processor, including data extraction, transformation, and initial filtering. This prepares data for downstream processing and reduces the time needed for final resolution by having data ready in advance.
3Speed
If real-time data processing is implemented, then incident detection speed and response time are improved, but processing complexity and computational resource requirements increase
Solution Approach 1:
The processing pipeline is segmented into distinct stages: collection point for data aggregation, front gate processor for initial processing and categorization, processing platform for real-time analysis, and data sink layers for storage and AI processing. Each segment handles specific processing tasks, managing overall complexity through modular architecture.
Solution Approach 2:
The front gate processor serves as an intermediary between raw data collection and complex processing. It performs preliminary processing, categorization, and filtering, reducing the complexity burden on downstream real-time processing components while maintaining fast incident detection.
4Measurement precision
If comprehensive data processing and analysis is performed, then incident accuracy and root cause analysis quality are improved, but processing time and computational cost increase
Solution Approach 1:
The system applies partial processing by filtering and prioritizing incidents based on severity and type. Not all incidents require the same level of analysis, so the system processes only the necessary depth of data for each incident, reducing overall processing time while maintaining high accuracy for critical issues.
Solution Approach 2:
The front gate processor performs preliminary data transformation, categorization, and enrichment actions that prepare data for accurate analysis. By performing these actions in advance, the real-time processing platform can focus on analysis rather than data preparation, reducing total processing time while maintaining accuracy.
Data Source
AI summary
A computer implemented method for processing data through a data pipeline is disclosed. The method includes: receiving, by a collection point, data from one or more data sources, the collection point being configured to at least one of extract, transform, or load the data; transferring the data from the collection point to a front gate processor, the front gate processor being configured to process the data; transferring the processed data from the front gate processor to a data storage system, the data storage system being configured to store the processed data; transferring the processed data from the front gate processor to a processing platform; and transferring the processed data from the processing platform to one or more data sink layers, each of the one or more data sink layers being configured to provide short term storage of the processed data.