Unified Data Platform for Analytics Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Organizations face challenges in effectively integrating analytics into their business operations due to the complexity and time required to develop and deploy new analytics, with data often siloed across different databases, making it difficult for data teams to quickly leverage relevant data for business insights.
Innovation Solution
A modular and adaptive data storage and processing system that allows for rapid prototyping and operationalization of analytics, featuring a key-value store with adaptive schema, efficient data indexing, and secure access controls, enabling quick identification and utilization of relevant data for analytics, and supporting multiple use cases across datasets within a single system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored across multiple siloed databases, then data security and organization are maintained, but data accessibility and analysis speed deteriorate
Solution Approach 1:
The patent combines multiple siloed databases into a unified data platform that maintains organized data structures while enabling cross-database queries. The system merges data from different sources into a common framework where analytics can operate across previously isolated data silos, improving accessibility without compromising security through unified access controls.
Solution Approach 2:
The system creates a universal data platform that handles multiple types of data storage and query operations simultaneously. The analytics engine can operate on data from various databases through a common interface, making the system multi-functional in handling both secure data isolation and integrated analysis needs.
2Measurement precision
If complex analytics are developed from scratch, then analytical accuracy is improved, but development time and complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing data into standardized formats before analytics are applied. Data is cleaned, transformed, and structured in advance, creating a ready-to-analyze foundation that reduces the time required for new analytics development while maintaining accuracy through consistent data preparation procedures.
Solution Approach 2:
The analytics development process is segmented into reusable modular components. The system breaks down complex analytics into standardized building blocks that can be independently developed, tested, and combined, reducing overall development time while maintaining analytical accuracy through systematic component assembly.
3Stability of the object's composition
If traditional data processing systems are used, then system stability is maintained, but adaptability to new analytics needs deteriorates
Solution Approach 1:
The system implements dynamic adaptability through configurable data models and flexible query interfaces that can accommodate new analytics requirements without restructuring the core system. The platform allows dynamic schema adjustments and supports evolving analytics needs while maintaining stable underlying data storage and processing mechanisms.
Data Source
AI summary
A system and method for data organization, optimization and analytics includes a web server, thrift server, distributed processing framework, key value store, distributed file system, and relational database. The web server provides a method whereby users issue control actions and query for records via interaction with the thrift server. The thrift server is the center of coordination and communication for the system and interacts with other system elements. The key value store organizes all of the operational data for the system. The key value store runs on a highly scalable distributed system, including a distributed file system for storage of data on disk. The distributed processing framework enables data to be processed in bulk and is used to execute analytical processing on the data. The relational database hold all of the administrative data in the system. Search queries are submitted by end user and results of the search query are sent from the web server to the end user. The web server sends control actions to queue background map reduce jobs. These jobs run in the distributed processing framework and are used to write data and indexes and execute bulk analytics against the key value store.