Hybrid-Lambda Architecture for Sensor Data Processing
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Solution Overview
Problem
Current data processing architectures, such as Lambda processing, face challenges in integrating real-time transactional processing with long-term batch processing, leading to issues like data loss, high costs, and inconsistent results, particularly when handling large volumes of data in cloud services like AWS.
Innovation Solution
A hybrid-Lambda network architecture that combines transactional and batch processing, allowing for immediate storage and analysis of sensor data in immutable locations, enabling real-time and long-term analytics to influence each other, and supporting applications like ETA and congregation analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If Lambda processing architecture is used to combine transactional and batch processing, then real-time processing speed is improved, but data consistency and reliability deteriorate due to eventual consistency issues
Solution Approach 1:
The system segments data processing into distinct transactional processing paths and batch processing paths, with each path handling specific types of operations independently. Transactional processing handles real-time updates while batch processing handles analytical operations, preventing consistency issues from propagating between the two.
Solution Approach 2:
The patent introduces an intermediary mechanism that mediates between transactional and batch processing layers. This intermediary ensures that batch processing operations read consistent snapshots of data without interfering with ongoing transactional operations, thereby maintaining both speed and consistency.
2Loss of information
If continuous batch operations are performed to maintain data views, then long-term analytics capability is improved, but data loss occurs and costs increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and staging data in batch operations before it is needed for analytics. This allows the system to prepare data views in advance, reducing the need for continuous batch operations and minimizing data loss while lowering processing costs.
Solution Approach 2:
The patent implements copying mechanisms where batch processing creates copies of data for analytical purposes without affecting the original transactional data. This allows long-term analytics to be performed on copied data, preventing data loss in the source system and reducing the computational energy required for repeated batch operations.
3Duration of action of moving object
If batch processing is used for long-term analytics, then predictive modeling capability is improved, but real-time interaction responsiveness deteriorates
Solution Approach 1:
The system segments processing operations by time horizon, with transactional processing handling real-time interactions and batch processing handling long-term analytics. This segmentation allows each type of operation to be optimized independently, maintaining real-time responsiveness while enabling extensive long-term predictive modeling.
Solution Approach 2:
An intermediary layer is introduced that separates real-time transactional processing from batch analytics processing. This intermediary allows the system to maintain fast real-time responsiveness by handling immediate requests through the transactional path, while simultaneously performing long-term predictive modeling through the batch path without interfering with real-time operations.
4Adaptability or versatility
If Lambda architecture combines transactional and batch layers, then processing versatility is improved, but system complexity increases
Solution Approach 1:
The patent implements universal data structures and processing interfaces that serve both transactional and batch processing needs. This multi-functionality allows the system to handle diverse processing requirements through a unified architecture, reducing overall system complexity while maintaining high versatility.
Solution Approach 2:
The system employs homogeneous data formats and processing interfaces across both transactional and batch layers. This homogeneity simplifies the architecture by eliminating the need for complex data transformation layers, making the system easier to manage while maintaining the ability to handle diverse processing tasks through configuration rather than structural complexity.
Data Source
AI summary
A data processing system and method integrates speed or transactional sensor data processing with batch level processing of sensor data using a hybrid-Lambda network architecture. In such a hybrid-Lambda network architecture, speed or transactional processing is performed, batch level processing is performed, and batch level processing results can be combined and integrated with the transactional processing events, and visa-versa, such that real time results can be influenced by long term analytics, and long term analytics can be influenced by real time events. For such processing, both speed or transactional and batch level, can occur as result of any type of sensor data being received, processed, and substantially immediately stored in immutable storage locations, for later retrieval and analysis.