Text-Based Interactive Interpreter for Data Pipeline Management
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Solution Overview
Problem
Users face difficulties in accessing and interacting with data in database systems due to complex APIs and the need for extensive technical knowledge, making it time-consuming and cumbersome to retrieve, manipulate, and share data.
Innovation Solution
A text-based interactive interpreter and user interface that allows users to modify, transform, or filter data sets from a data pipeline system, enabling users to enter text-based instructions that are executed to retrieve, manipulate, and render data in various formats, with features like persistence, versioning, and access control, allowing for easy sharing and reuse of user-generated instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users directly access data through complex APIs, then data can be retrieved from the data pipeline system, but the process requires extensive technical knowledge and is time-consuming
Solution Approach 1:
The patent introduces a visual query builder and pre-configured data pipelines as intermediaries between users and the complex API system. Users interact with intuitive visual interfaces and drag-and-drop components rather than directly calling complex APIs, thereby eliminating the need for extensive technical knowledge while maintaining efficient data retrieval capabilities.
Solution Approach 2:
The system pre-configures data pipelines, transformations, and integrations beforehand, storing them as reusable templates. Users can directly utilize these pre-built pipelines for common data access scenarios, eliminating the time-consuming process of manually configuring complex API requests and data transformations from scratch.
2Adaptability or versatility
If users manually configure data pipelines and API requests, then data can be transformed and filtered, but the process is cumbersome and requires technical expertise
Solution Approach 1:
The patent segments complex data pipelines into modular, pre-configured components that users can selectively assemble. Instead of requiring users to configure entire complex pipelines, the system provides discrete, functional building blocks (data sources, transformations, filters, outputs) that can be combined through simple visual interfaces, reducing configuration complexity while maintaining transformation versatility.
Solution Approach 2:
The system creates universal, pre-configured pipeline templates that can serve multiple purposes and data types. A single pipeline template can be reused across different contexts and data sources, eliminating the need for users to create custom configurations for each scenario and reducing both complexity and the need for technical expertise.
3Productivity
If users explore and analyze data interactively, then data can be manipulated in real-time, but the process is time-consuming without automation
Solution Approach 1:
The patent implements real-time, automated data processing pipelines that continuously transform and prepare data as it flows through the system. Instead of requiring users to manually trigger processing steps during exploration, the system maintains continuous data flow and transformation, allowing users to interactively query and analyze data without waiting for manual processing cycles, thereby improving productivity while reducing exploration time.
Data Source
AI summary
A text-based interactive interpreter and user interface that sequentially allows a user to modify, transform, or filter data sets from a database system. Execution of user generated instructions results in output that can be rendered as a table, map, JSON, or other view. A user can easily retrieve a data set from a resource identifier for the data set. Instructions and results are presented in a sequential manner down the user interface page. Instructions can refer to variables and output data from previous blocks in the user interface page. The interpreted user-generated textual instruction areas can retrieve a data set specified in the textual blocks, parse and execute the textual instructions to transform, filter, or manipulate the data set. The resulting data set is rendered according to the data type, default settings, or preconfigured preferences into a table, map, JSON, or other view. Each or a subset of the textual instruction blocks can be persisted, versioned, and permissioned according to access control lists. The particular session of textual instruction blocks and transformations can be published as a service, which, if called again, can dynamically perform the user generated instructions and output the result data.