Data Event Management System for Heterogeneous Data Ecosystems
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Solution Overview
Problem
Inefficient data management across heterogeneous data sources in large data ecosystems leads to bottlenecks, data replication, and quality issues, making it difficult to improve data-driven actions and optimize data flow.
Innovation Solution
A data event management system that includes a data portal for monitoring and processing data events, a pipeline service for additional processing and analysis, and a centralized repository for logging and analyzing data events, providing recommendations for improving query performance and data store restructuring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is accessed across multiple heterogeneous data stores, then data coverage and completeness are improved, but system complexity and difficulty of management increase
Solution Approach 1:
The patent introduces a data portal as an intermediary layer between user applications and heterogeneous data stores. The portal provides a unified interface that abstracts the complexity of multiple data sources, allowing applications to access diverse data without directly managing the heterogeneity underlying the system.
Solution Approach 2:
The data portal implements universal access mechanisms that handle multiple types of data stores through a single interface. It provides multi-functional capabilities including monitoring, analysis, and optimization across different data sources without requiring separate access methods for each source.
2Loss of information
If comprehensive monitoring of data events is implemented, then data transparency and insight are improved, but processing overhead and computational resources increase
Solution Approach 1:
The patent extracts and monitors only the most relevant data events rather than processing all possible events. The system identifies and focuses on key performance indicators and critical data access patterns, reducing the volume of events requiring comprehensive processing while maintaining essential transparency.
Solution Approach 2:
The system implements self-service mechanisms where the data portal automatically analyzes monitored events and generates optimization recommendations without requiring extensive external computational resources. The monitoring system serves itself by providing actionable insights directly from the collected data.
3Productivity
If real-time analysis of data events is performed, then responsiveness and optimization capability are improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and categorizing data events as they occur. By preparing and organizing event data in advance, the system reduces the computational burden during real-time analysis, enabling faster generation of optimization recommendations without sacrificing comprehensive processing.
Data Source
AI summary
Generally, the present disclosure relates to solving issues with inefficient management of data within an organization by building a central repository of events within the data ecosystem. The data event management system, disclosed herein, offers a platform agnostic ledger of the data ecosystem that enables data transparency and identifies patterns/bottlenecks to properly adjust to meeting the needs of the data consumer. With this information, data driven decisions can be made to prioritize workload and ensure funding is supporting the consumer prioritized assets.