Automated Streaming Data Platform Generation
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Solution Overview
Problem
The generation of streaming platforms requires significant human resources and time to set up, making it inefficient for processing high volumes of data from various sources such as IoT devices and network traffic, which hinders timely data delivery to end users.
Innovation Solution
An automated method for generating and initiating a scalable, resilient streaming data platform in the cloud, allowing users to input parameters for creating a streaming data cluster, including coordination, streaming, and replication layers, which automatically configures and starts the necessary nodes and services without requiring extensive human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual setup process is used for streaming platform, then system reliability can be ensured through human verification, but time consumption and human resource requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-configuring streaming platform templates with all necessary coordination layers, streaming layers, and replication layers before actual deployment. When a user requests platform creation, the pre-configured template is automatically instantiated, eliminating the need for manual step-by-step setup while ensuring all necessary components are properly configured from the start.
Solution Approach 2:
The system enables self-service by allowing the streaming platform to automatically configure its own layers, nodes, and services without human intervention. The automated setup process includes self-verification of configuration parameters, automatic resource allocation, and self-starting of coordination, streaming, and replication services, thereby reducing both setup time and human resource requirements while maintaining system reliability.
2Productivity
If automated generation is implemented, then setup time and human resources are reduced, but system complexity increases due to automatic configuration of multiple layers and nodes
Solution Approach 1:
The system applies segmentation by dividing the streaming platform into distinct, independently manageable layers: coordination layer, streaming layer, and replication layer. Each layer is further segmented into specific nodes with dedicated functions. This segmentation allows the automated system to configure each layer separately using standardized procedures, reducing the overall complexity of automation while maintaining high deployment speed.
Solution Approach 2:
The system implements universality by creating a multi-functional automated configuration engine that handles all aspects of platform generation - from resource allocation to layer configuration to service startup. This universal engine uses standardized interfaces and procedures that can be applied across different platform configurations, reducing the complexity of automation by reusing the same configuration logic across multiple scenarios.
3Adaptability or versatility
If multiple layers and nodes are automatically configured, then platform scalability is improved, but the risk of configuration errors increases
Solution Approach 1:
The system implements feedback mechanisms at multiple stages of the automated configuration process. Before configuration, the system validates user input parameters against predefined constraints. During configuration, each layer and node is verified for proper setup. After deployment, the system monitors platform performance and configuration status, providing continuous feedback to detect and correct any configuration errors, thereby maintaining high scalability while ensuring configuration accuracy.
Data Source
AI summary
Aspects described herein generally improve the quality, efficiency, and speed of data processing by automatically generating a plurality of nodes for a streaming data platform based on user parameters. This combination of different platform nodes may allow for the generation and initiation of a streaming platform that may run in a cloud and may provide multi-availability-zone and multi-region capabilities to support replication and disaster recovery capabilities for data management. The streaming platform may include a coordination layer, a streaming layer, and a replication layer, and each layer may include a plurality of nodes.