Low Code Transformation Layer for Platform-Agnostic Data Pipelines
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Solution Overview
Problem
Existing data pipeline creation processes are complex and require specialized knowledge, making it difficult for non-technical users like business professionals to create platform-specific data pipelines, which are not scalable and lead to inefficiencies in time, resources, and network traffic.
Innovation Solution
A low code transformation layer system that enables users to create platform-agnostic data pipelines using a graphical user interface (GUI), allowing users to select nodes and link them to generate a data pipeline architecture, which is then converted into executable instructions for any platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data pipeline creation methods are used, then data pipelines can be created with specialized knowledge, but the process becomes complex and time-consuming
Solution Approach 1:
The patent introduces a low-code transformation layer as an intermediary between the user interface and the execution platform. This layer includes a visual pipeline builder that allows users to create data pipelines through graphical drag-and-drop operations rather than writing complex code. The transformation layer converts these visual definitions into executable code, resolving the contradiction by maintaining functionality while reducing creation complexity.
Solution Approach 2:
The patent uses template-based pipeline definitions where common data pipeline patterns are pre-defined and can be copied and reused. Instead of creating pipelines from scratch each time, users can replicate existing pipeline templates and modify them as needed, significantly reducing the complexity and time required to create new data pipelines while maintaining reliable functionality.
2Manufacturing precision
If platform-specific data pipelines are created, then optimization for a specific platform is achieved, but scalability to other platforms is lost
Solution Approach 1:
The patent creates a universal pipeline definition format that can execute on multiple different platforms. The low-code transformation layer generates platform-agnostic pipeline definitions that can be deployed to various execution environments (cloud platforms, on-premise systems, different programming languages) without requiring separate pipeline creations. This resolves the contradiction by achieving both platform optimization through targeted code generation and scalability through universal definitions.
Solution Approach 2:
The patent segments the data pipeline creation process into two independent parts: a platform-independent visual definition layer and a platform-specific code generation layer. The visual pipeline builder creates abstract pipeline definitions that are then transformed into platform-specific code through the low-code layer. This segmentation allows the same pipeline definition to be optimized for different platforms while maintaining scalability across multiple environments.
3Adaptability or versatility
If manual data pipeline creation is performed, then customization is possible, but the time required increases significantly
Solution Approach 1:
The patent implements pre-built pipeline templates and components that contain commonly used data processing logic already configured. Users can select from pre-defined templates for common scenarios (ETL processes, data validation, transformation patterns) and customize them by simply configuring parameters rather than building from scratch. This preliminary preparation of common patterns dramatically reduces creation time while maintaining the ability to customize pipelines for specific needs.
Solution Approach 2:
The patent merges commonly used data processing operations into integrated pipeline components that can be deployed as single units. Instead of manually configuring each individual transformation, filter, or aggregation step separately, related operations are combined into reusable components that can be added to pipelines with a single action, reducing the time required to create customized pipelines while maintaining full configurability.
4Ease of operation
If non-technical users attempt to create data pipelines, then accessibility is improved, but the lack of specialized knowledge creates barriers
Solution Approach 1:
The patent replaces the mechanical system of manual code writing and configuration with a visual drag-and-drop interface. Non-technical users can create data pipelines by dragging visual components onto a canvas and connecting them with arrows, eliminating the need to write or understand programming code. The system automatically handles the complex technical aspects of pipeline configuration, making data pipeline creation accessible to business users without specialized technical knowledge.
Solution Approach 2:
The patent implements intelligent defaults and auto-completion features that allow non-technical users to create pipelines without needing to understand the underlying technical parameters. The system automatically configures optimal settings based on the selected components and data types, and provides contextual guidance and validation to help users make correct decisions. This self-service approach enables accessibility while managing the complexity automatically.
Data Source
AI summary
Systems and methods for generating a platform-agnostic data pipeline via a low code transformation layer are disclosed. The system receives one or more user selections of (i) nodes and (ii) links linking the nodes, indicating a data pipeline architecture of transfer/management/flow of data via a GUI. In response to receiving a user selection to implement the data pipeline, the system automatically identifies/generates a set of code portions, based on one or more software objects (e.g., JSON objects) associated with the user selections indicating the data pipeline architecture. The system then identifies a platform identifier associated with a remote server and generates a set of executable instructions (e.g., a script, executable program, or other file) associated with the data pipeline architecture by using a transformation component. The system then provides the executable instructions to the remote server to host the data pipeline.