Data Stream Processing Service for Real-Time Routing
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Solution Overview
Problem
Traditional data pipelines, both batch-oriented ETL and streaming, face inefficiencies due to duplication of services, lack of standardized integration, and inability to handle real-time data processing, leading to increased costs and latency issues as data volumes and complexity grow.
Innovation Solution
A programmable data stream processing service with a schema manager, processing services gateway, and transformation processor that enables dynamic routing and transformation of data across multiple sources and receivers, supporting real-time data processing and scalable, fault-tolerant data pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If batch-oriented ETL data pipelines are used, then data extraction and transformation can be performed with dedicated service windows, but real-time data processing capability is lost and latency increases to hours or days
Solution Approach 1:
The patent applies dynamics by enabling the data pipeline system to adapt its processing mode based on real-time requirements. The system dynamically routes data flows between batch processing paths and streaming processing paths, allowing organizations to switch between reliability-focused batch operations and speed-focused streaming operations as needs change, thereby resolving the contradiction between processing completeness and latency.
Solution Approach 2:
The patent segments the monolithic ETL pipeline into separate batch processing components and streaming processing components. This segmentation allows independent optimization of each pathway - batch processing ensures complete data extraction with proper error handling and retry logic, while streaming processing provides low-latency real-time data movement, eliminating the need to choose between reliability and speed.
2Loss of time
If multiple separate streaming data pipeline services are deployed, then real-time data processing capability is achieved, but service duplication increases development and operational costs
Solution Approach 1:
The patent implements universality through a unified data pipeline service that can perform multiple functions - batch processing, streaming processing, data routing, and transformation - all within a single system. This multi-functional platform eliminates the need for separate specialized services for each function, reducing operational complexity while maintaining real-time processing capabilities.
Solution Approach 2:
The patent merges previously separate batch ETL services and streaming data pipeline services into a single integrated platform. This consolidation combines the reliability benefits of batch processing with the speed benefits of streaming processing, while sharing common infrastructure, deployment pipelines, and operational tooling, thereby reducing development and operational costs.
3Loss of time
If separate streaming data pipeline services are used, then real-time processing is enabled, but lack of standardized integration increases operational complexity and retooling requirements
Solution Approach 1:
The patent applies parameter changes by introducing configurable parameters that control data routing behavior, transformation rules, and processing modes within the unified platform. These parameters can be adjusted without code changes, allowing standardized integration patterns to be flexibly adapted to different use cases while maintaining operational simplicity and reducing retooling requirements.
Data Source
AI summary
A computer implemented processing service for efficient streaming of data input from one or more sources to one or more receivers is disclosed. The processing service includes a schema manager that receives commands from a tenant of the data stream processing service. The processing service includes a processing services gateway that validates the data and sends the data to a transformation processor. The transformation processor receives the data and parses the data into the one or more data formats in accordance with the instructions and sends the data in the one or more data formats to an egress service. The egress service outputs the data in the one or more data formats to the one or more receivers, each receiver receiving the data in a data pipeline in one of the one or more formats.