Cloud Data Infrastructure Stack Generation and Deployment
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Solution Overview
Problem
Conventional database systems are inadequate for handling large volumes of data and require extensive development time, are difficult to deploy, and do not scale efficiently, especially with the rise of 'big data' and diverse data sources.
Innovation Solution
A cloud-based system generates and deploys data infrastructure stacks through predefined slices, capable of collecting, organizing, and connecting to various analysis tools, allowing for rapid deployment and scalability by transforming data from disparate sources into standardized formats and executing third-party services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional database systems are used to store data, then transaction data can be handled, but large amounts of data (greater than 10 TB) cannot be handled
Solution Approach 1:
The system segments the monolithic database functionality into multiple specialized data stores including columnar data stores for analytical queries, document data stores for flexible schemas, graph data stores for relationship analysis, and lake data stores for raw data. Each data store type is optimized for specific data volumes and query patterns, enabling the system to handle large-scale data (greater than 10 TB) while maintaining reliability through appropriate data routing and management strategies.
2Reliability
If recent solutions are implemented to address big data shortcomings, then data handling capability improves, but development time increases (months or years)
Solution Approach 1:
The system performs preliminary actions by automatically generating the data infrastructure stack, selecting appropriate data store types and configurations, establishing data pipelines, and configuring analysis tools before data ingestion begins. This pre-configuration approach eliminates months or years of manual development time while maintaining the ability to handle big data, as the infrastructure is prepared in advance through automated provisioning and orchestration.
Solution Approach 2:
The system enables self-service by providing automated infrastructure generation, automatic data store selection based on data characteristics, self-configured data pipelines, and automated tool provisioning. This self-service capability allows the system to rapidly deploy big data handling infrastructure without requiring extensive manual development, reducing deployment time from months to minutes while maintaining full functionality for handling large data volumes.
3Reliability
If recent solutions are deployed to handle big data, then data processing capability improves, but deployment difficulty increases
Solution Approach 1:
The system automates the entire deployment process through self-service mechanisms including automatic infrastructure generation, automated data store provisioning, self-configured data pipelines, and automatic tool deployment. This eliminates the complexity of manual deployment while maintaining robust data processing capabilities, as the system self-provisions and self-configures all necessary components without requiring expert intervention or complex deployment procedures.
Solution Approach 2:
The system implements a universal deployment framework that handles multiple data store types (columnar, document, graph, lake), various data formats, and diverse analysis tools through a single standardized interface. This multi-functional approach simplifies deployment by providing a unified method for deploying big data infrastructure regardless of the specific data store or tool requirements, making deployment easier while maintaining comprehensive data processing capabilities.
4Ease of operation
If traditional database systems are used, then simple deployment is possible, but scaling efficiency deteriorates
Solution Approach 1:
The system segments the data infrastructure into independent, scalable components including separate data stores, data pipelines, and analysis tools. Each component can be independently scaled based on specific data processing requirements without affecting other parts of the system. This segmented architecture maintains deployment simplicity through standardized interfaces while enabling efficient scaling of individual components to handle increasing data volumes and processing demands.
Solution Approach 2:
The system implements dynamic scaling capabilities where data infrastructure components can be automatically adjusted based on workload demands. Data stores, pipelines, and computational resources can be dynamically provisioned or de-provisioned according to actual data processing requirements, enabling efficient scaling while maintaining simple deployment through automated resource management and orchestration.
Data Source
AI summary
Generating, by a cloud-based system, a plurality of data infrastructure slices, each of the plurality of data infrastructure slices including a respective service; storing, by the cloud-based system, the plurality of data infrastructure slices; selecting, by the cloud-based server, at least two data infrastructure slices of the plurality of stored data infrastructure slices; generating, by the cloud-based system in response to the selection of the at least two data infrastructure slices of the plurality of data infrastructure slices, a data infrastructure stack comprising the selected stored data infrastructure slices, the data infrastructure stack capable of being executed in different third-party entity accounts of an on-demand cloud-computing platform; and deploying, by the cloud-based system, the data infrastructure stack in a particular third-party entity account of the on-demand cloud-computing platform.