Real-Time Data Stream Analysis System Using Segmented Processing
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Solution Overview
Problem
Existing data analysis systems lack scalability and efficiency in handling complex, high-volume concurrent data streams, particularly in real-time or near real-time scenarios, and struggle to analyze diverse data types such as images, audio, and video effectively.
Innovation Solution
A system and method for analyzing multiple data streams in real-time, utilizing an integration engine, workload manager, and analytical engines to process and scale data streams, generate metadata, and apply business rules, allowing for flexible protocol compatibility and distributed processing across multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data streams are converted to platform-specific formats using resource conversion, then data can be analyzed by specific applications, but the conversion process consumes additional transport resources and reduces efficiency
Solution Approach 1:
The system segments data streams into multiple channels based on different data types and characteristics, processing each segment through specialized analytical engines rather than converting all data to a single platform format. This maintains efficiency while providing targeted analysis for each data type.
Solution Approach 2:
The system implements a universal data intake service that accepts multiple data types (metrics, logs, traces, events) in their native formats and routes them to appropriate analytical engines. This multi-functional approach eliminates the need for format conversion while maintaining compatibility with various analysis requirements.
2Adaptability or versatility
If multiple data types are analyzed simultaneously, then comprehensive analysis coverage is achieved, but system complexity increases
Solution Approach 1:
The system divides complex multi-type data analysis into separate processing channels, with dedicated analytical engines for metrics, logs, traces, and events. This segmentation reduces the complexity each component must handle while maintaining comprehensive coverage.
Solution Approach 2:
The system introduces intermediary components including a data intake service, query service, and result service that mediate between diverse data sources and analysis targets. These intermediaries standardize interfaces and protocols, reducing overall system complexity while enabling multi-type data analysis.
3Speed
If data analysis is performed in real-time, then responsiveness is improved, but processing resources are consumed at high rates
Solution Approach 1:
The system dynamically adjusts processing resources based on incoming data rates and query demands. Analytical engines can scale processing capacity up or down in real-time, maintaining responsive analysis while optimizing resource utilization according to actual workload conditions.
Solution Approach 2:
The system changes processing parameters such as sampling rates, aggregation intervals, and analysis depth based on data characteristics and resource availability. This allows real-time analysis responsiveness while adapting resource consumption to actual processing needs rather than maintaining constant high-rate processing.
Data Source
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AI summary
Data analysis plays a crucial role to get significant information out of the data. A real time system and method for analyzing data streams have been provided. The system can utilize many different types of data formats such as numeric, text, video, audio, image, or combination thereof. The analysis takes place as per the requirement using an analytical engine and an intermediate output is generated. The intermediate output is further processed using a distributed real time business rule processing engine to determine required conditions in the data. The business rules comprise one or more set of meta data. On match of the business rule, the system triggers an alert or propagates the required information to integrating solution for required actions. The system and method are technology and communication protocol agnostic, and designed with highly efficient load balanced technique, thereby facilitating highly concurrent data processing with minimal latency.