Hybrid Search System Configuration Sharing via Shared Data Store
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Businesses face challenges in analyzing and identifying patterns in large volumes of heterogeneous data, particularly in understanding user behavior and system performance, due to the unstructured nature of the data and the difficulty in applying semantic meaning, which can lead to missed correlations and strategic insights.
Innovation Solution
A hybrid search system combining cloud-based and on-premises data intake and query systems, utilizing a shared data store for configuration information to promote scalability, data isolation, and efficient data leverage, while enabling secure sharing of configuration data among clusters to enhance cluster security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If configuration information is shared directly between clusters, then configuration sharing capability is improved, but cluster security deteriorates
Solution Approach 1:
A shared data store acts as an intermediary between clusters, allowing configuration information to be stored centrally while clusters access it through controlled retrieval operations. This mediator architecture enables configuration sharing without requiring direct cluster-to-cluster communication, thus maintaining security boundaries while achieving the goal of configuration reuse across multiple clusters
2Productivity
If sensitive data is moved to cloud-based systems, then data processing scalability is improved, but data security deteriorates
Solution Approach 1:
The system applies different data handling approaches to different types of data based on their security requirements. Sensitive data remains stored and processed on-premises where it can be protected with appropriate security measures, while non-sensitive data is moved to cloud-based systems to leverage their scalability and processing power. This localized quality approach ensures each data type is handled according to its specific security needs
3Difficulty of detecting and measuring
If all data is analyzed centrally, then pattern identification capability is improved, but system complexity deteriorates
Solution Approach 1:
The data analysis system is segmented into multiple independent clusters that can operate autonomously. Each cluster can perform local pattern identification on its own data, reducing the need for centralized analysis of all data. Configuration information is shared through the shared data store to enable coordinated analysis across clusters without requiring a single complex centralized system, thus maintaining pattern identification capability while reducing overall system complexity
Data Source
AI summary
Various embodiments describe multi-site cluster-based data intake and query systems, including cloud-based data intake and query systems. Using a hybrid search system that includes cloud-based data intake and query systems working in concert with so-called “on-premises” data intake and query systems can promote the scalability of search functionality. In addition, the hybrid search system can enable data isolation in a manner in which sensitive data is maintained “on premises” and information or data that is not sensitive can be moved to the cloud-based system. Further, the cloud-based system can enable efficient leveraging of data that may already exist in the cloud.