Unified Stream Platform for Multi-Protocol Data Sampling
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Solution Overview
Problem
The proliferation of different stream-processing software platforms and protocols for delivering data streams complicates data processing, as developers must understand various SDKs and data types, making it challenging to develop applications that consume real-time data effectively.
Innovation Solution
A unified stream platform is provided that abstracts common functions across different stream platforms and protocols, allowing users to sample and visually represent data streams without needing expertise in specific platforms or protocols, using a client interface to connect to stream producers and a backend service to handle sampling and parsing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different stream platforms and protocols are used to deliver data streams, then data source diversity and adaptability are improved, but system complexity and difficulty of operation increase significantly
Solution Approach 1:
The patent implements a universal stream processing platform that can handle multiple different stream platforms and protocols (Apache Kafka, Azure Event Hubs, MQTT, AMQP, WebSocket) through a single unified interface. The system provides common abstracted functions that work across all these different protocols, eliminating the need for developers to learn and implement separate handling logic for each protocol. This multi-functional approach allows one system to serve multiple protocol types while maintaining consistent operation patterns.
Solution Approach 2:
The patent introduces an intermediary layer (the unified stream processing platform) that sits between the various stream protocols and the application logic. This intermediary handles protocol-specific details, data type conversions, and platform-specific SDK requirements, translating them into a common internal representation. Developers interact with this intermediary through standardized functions rather than directly with multiple different protocols, thus reducing complexity.
2Reliability
If developers must understand various SDKs and data types for different stream platforms, then protocol-specific functionality is optimized, but ease of operation and development time deteriorate
Solution Approach 1:
The system provides universal functions that work across all stream platforms while maintaining protocol-specific optimizations underneath. Common operations like connecting to a stream, sampling data, parsing events, and visualizing results are implemented once in a unified manner that adapts to each protocol's specifics automatically. This allows developers to use simple, consistent function calls regardless of which protocol is being used, while the system handles protocol-specific details in the background.
3Adaptability or versatility
If comprehensive data stream processing capabilities are provided across multiple platforms, then functionality and versatility are improved, but learning curve and operational difficulty increase
Solution Approach 1:
The patent extracts and isolates the complex protocol-specific knowledge into the intermediary layer, separating it from the application logic. Developers only need to learn the simplified unified interface functions, while the complex protocol handling, data type mappings, and platform-specific SDK requirements are extracted and encapsulated within the stream processing platform itself. This extraction reduces the learning burden on developers while preserving comprehensive functionality.
Data Source
AI summary
A stream tool is disclosed that allows a user to seamlessly connect with the different data streams, regardless of the streams' transmission platforms or communication protocols, in order to visually see a representation of the type of data that the data streams are transmitting. A user may specify a particular data stream and provide corresponding connection details. A collection of abstracted software functions enable interaction with the different stream platforms and protocols. Using these abstracted functions, a stream-processing service accesses a requested data stream and samples its data events for either sample timeframe or up to a threshold number of data events. The sampled data events are parsed and visually presented to the user in an easy-to-understand format. The user may then inspect the data stream's data for use in developing robust applications that may integrate and use such data.