Cloud Data Workflow Platform Without ETL or Local Downloads
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Solution Overview
Problem
Current data-heavy applications require integration, transformation, and downloading of data from cloud repositories for business intelligence analysis, leading to inefficiencies and complexities in managing enterprise data.
Innovation Solution
A cloud-native SaaS platform with no-code data-driven workflows that allows users to create, customize, and manage applications directly on cloud data without integration, transformation, or downloading, utilizing a graphical user interface for data mapping and automated processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data is copied or downloaded from cloud for business intelligence analysis, then local computation and workflow execution is enabled, but data integration and transformation complexity increases
Solution Approach 1:
The patent introduces a cloud data warehouse as an intermediary layer between cloud data sources and local workflows. This mediator enables local computation by providing a simplified data access interface while avoiding direct complex integration with multiple cloud sources. The data warehouse absorbs the complexity of data integration and transformation, presenting a clean API to local applications.
Solution Approach 2:
The system segments the data processing architecture into distinct components: cloud data sources, a centralized cloud data warehouse, and local workflow execution environments. This segmentation allows each component to be optimized independently - the cloud warehouse handles integration complexity while local systems focus on computation and workflow execution.
2Stability of the object's composition
If ETL or ELT processes are used to move data, then unified data repository is achieved, but processing time and operational complexity increase
Solution Approach 1:
The cloud data warehouse performs data integration, transformation, and consolidation as preliminary actions before workflows need the data. By pre-processing and preparing unified data in advance, the system eliminates the need for time-consuming ETL processes at workflow execution time, significantly reducing operational delays.
3Speed
If data is downloaded and stored locally, then workflow execution speed is improved, but data synchronization and updates become complex
Solution Approach 1:
The system implements feedback mechanisms where the cloud data warehouse continuously monitors cloud data sources for changes and automatically updates the warehouse when modifications occur. This feedback loop ensures local workflows always access the most current data without requiring manual synchronization or complex update management.
4Loss of information
If conventional data integration methods are used, then data accessibility is achieved, but business process efficiency decreases
Solution Approach 1:
The cloud data warehouse serves multiple functions simultaneously: it acts as a centralized data repository, performs data integration from various cloud sources, executes transformations, provides unified data access APIs, and enables real-time workflows. This multi-functionality consolidates what would otherwise require multiple separate systems, significantly improving business process efficiency.
Data Source
AI summary
The present disclosure provides platforms, systems and methods that may provide a data-driven workflow platform. A method of the present disclosure may comprise: mapping selected data objects to a data storage model of the data-driven workflow platform, where the selected data objects are stored in a data cloud configuration that is operatively coupled to the data-driven workflow platform; and displaying, on a graphical user interface (GUI), a flow for building a cloud application utilizing or managing the selected data objects. The interactive flow comprises at least one graphical element corresponding to a rule for automating an action triggered by a triggering event of the selected data objects.