Unified Data Platform for Siloed Applications
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Solution Overview
Problem
Existing data management systems operate in separate silos, leading to inefficiencies, data inconsistencies, and challenges in facilitating data-driven decision-making across disparate applications within an enterprise.
Innovation Solution
A centralized data platform that integrates data lake, OLAP, and OLTP components through a unified data exchange, enabling seamless data management, processing, and integration across various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data management systems operate in separate silos (data lake, OLAP, OLTP systems), then each system can be optimized for its specific function, but data consistency and integration across applications deteriorate
Solution Approach 1:
The patent merges data lake, OLAP, and OLTP systems into a unified centralized data platform. The data lake component stores raw data from multiple sources, the OLAP component provides analytical processing, and the OLTP component handles transactional operations, all integrated under a single platform architecture that ensures data consistency across applications while maintaining functional optimization.
Solution Approach 2:
The centralized data platform provides multi-functional capabilities through a single system that can handle data storage, analytical processing, and transactional operations. The platform serves multiple applications simultaneously, enabling them to access consistent data through a unified interface while maintaining their specific functional requirements.
2Ease of operation
If separate systems with separate interfaces are used for data management, then each system can be independently optimized, but ease of operation and data access across applications deteriorates
Solution Approach 1:
The unified data platform provides a single interface that serves multiple applications and data management functions. Applications can access data through this common interface without needing separate connections to data lake, OLAP, or OLTP systems, significantly improving ease of operation while reducing the number of interfaces required.
3Reliability
If data is stored and processed in separate systems, then processing speed for specific functions can be optimized, but data redundancy and information loss increase
Solution Approach 1:
By combining data storage, analytical processing, and transactional operations into a single unified platform, the system eliminates data redundancy that would otherwise occur across multiple separate systems. The centralized architecture ensures a single source of truth for all data, improving data integrity while reducing unnecessary duplication.
Data Source
AI summary
A computer-implemented method that includes executing a data lake application that includes computing instructions operable to perform operations including receiving raw data, cleansing the raw data using validation rules to generate curated raw data, and storing the curated raw data. The method also can include executing an OLAP application that includes computing instructions operable to perform operations including building a data layer comprising transaction objects from the curated raw data, storing the data layer in a data store, and performing batch processing using the data layer. The method additionally can include executing an OLTP application that includes computing instructions operable to perform operations including performing real-time processing based on data received from one or more of the curated raw data or the data layer. Communications to and from the data lake application, the OLAP application, and the OLTP application with other applications are processed through a unified data exchange. Other embodiments are described.