Stateless Ingress Node for Real-Time Data Stream Routing
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Solution Overview
Problem
Current tools and technologies for real-time analytics in cloud environments face challenges in efficiently processing data streams from distributed devices, leading to unacceptable latency due to the need for offline data storage and processing.
Innovation Solution
The implementation of stateless ingress nodes that pre-process incoming data in real-time by transforming and routing it to appropriate real-time analytics services for stateful processing, avoiding intermediate storage and enabling genuine real-time analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If data is stored on storage device before analytics processing, then data can be processed systematically, but latency increases and real-time aspect is lost
Solution Approach 1:
The patent applies preliminary action by pre-processing data in real-time streams before storage, performing transformations, filtering, and enrichment operations on incoming data while it is being received. This allows the system to prepare data for analytics without waiting for complete data collection or batch storage, thereby reducing latency while maintaining systematic processing capabilities.
Solution Approach 2:
The patent extracts the real-time processing functionality from the traditional store-then-process workflow by implementing stream processing engines that operate independently on incoming data streams. This separation allows analytics operations to be performed on data in motion without requiring it to be persisted first, eliminating the storage bottleneck that causes latency.
2Productivity
If offline analytic processing is used, then comprehensive analysis can be performed, but decision-making speed decreases
Solution Approach 1:
The patent implements dynamics by creating a multi-speed analytics architecture where different processing speeds and depths coexist. Fast, simplified analytics operate on real-time streams for immediate decisions, while more comprehensive analytics are performed on subsets of data or with lower granularity. The system dynamically adjusts processing intensity based on requirements, enabling both rapid decision-making and thorough analysis where needed.
Solution Approach 2:
The patent applies partial action by performing selective analytics on data streams - not all data requires the same level of processing. The system identifies and applies appropriate analytics operations based on data type, source, and business requirements, performing comprehensive analysis only where necessary while using lighter processing for other streams. This maintains analytics comprehensiveness for critical decisions while preserving overall decision-making speed.
3Adaptability or versatility
If data streams from distributed devices are collected, then real-time insight is enabled, but system complexity increases
Solution Approach 1:
The patent applies universality by implementing a standardized stream processing engine that handles multiple data stream types from various distributed devices through a common architecture. The system uses uniform data formats, processing pipelines, and analytics operations that work across different data sources, eliminating the need for device-specific processing logic and reducing overall system complexity despite handling diverse distributed inputs.
Solution Approach 2:
The patent introduces intermediary components including stream processing engines and message queues that mediate between distributed data sources and analytics consumers. These intermediaries standardize data formats, buffer streams, and coordinate processing across multiple sources, simplifying the architecture by providing a uniform interface layer that abstracts the complexity of handling distributed data streams.
Data Source
AI summary
A method is described of pre-processing real-time data to be processed by one or more real-time analytics services. The method includes: receiving, at a stateless ingress node, data from one or more client devices; transforming the data in real-time at the stateless ingress node; determining in real-time, at the stateless ingress node, a real-time analytics service for the transformed data; and forwarding in real-time the transformed data to the determined real-time analytics service for stateful processing.