Executable Validation Code for Dynamic Data Pipeline Testing
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Solution Overview
Problem
Existing data transformation pipelines face challenges in scalable, dynamic, and adaptable data validation due to their modular and complex nature, leading to inefficiencies in manual validation methods and limitations of rule-based systems, which fail to capture dynamic conditions and user intervention, especially when dealing with sensitive data.
Innovation Solution
A process validation platform that generates simulated data based on metadata to validate data transformation pipelines, allowing user control and intervention, and dynamically generates metadata structures and test cases to ensure consistency and accuracy without exposing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual validation methods are used for data transformation pipelines, then validation can be performed with simple rules, but scalability and adaptability deteriorate due to the modular and complex nature of pipelines
Solution Approach 1:
The system enables self-service validation by automatically generating executable code samples that validate data transformation pipelines. The code generation model creates validation code that adapts to different pipeline configurations, eliminating the need for manual validation rule creation while maintaining scalability and adaptability across complex modular pipelines.
Solution Approach 2:
The patent replaces manual mechanical validation processes with an automated code generation system. Instead of manually creating and maintaining validation rules for each pipeline configuration, the system uses a code generation model to automatically produce adaptive validation code, substituting the mechanical process of manual rule-based validation with an intelligent automated system.
2Reliability
If rule-based validation systems are used, then validation logic can be explicitly defined, but dynamic conditions and user intervention capabilities deteriorate
Solution Approach 1:
The system introduces dynamics into validation by generating code that can adapt to changing pipeline configurations and data characteristics. The code generation model creates validation logic that is not static but can dynamically respond to different conditions, allowing the system to maintain reliability while adapting to dynamic conditions and user intervention requirements.
Solution Approach 2:
The patent utilizes parameter changes by generating validation code that adjusts its behavior based on pipeline-specific parameters and data characteristics. The code generation model modifies validation logic parameters dynamically, allowing the same validation framework to reliably handle diverse pipeline configurations and dynamic conditions through parameter adaptation rather than fixed rules.
3Object-affected harmful factors
If simulated data generation is implemented, then sensitive information privacy is protected, but data validation complexity increases due to metadata structure generation
Solution Approach 1:
The system applies copying by generating simulated data that replicates the structural and statistical properties of sensitive real data without containing actual sensitive information. The code generation model creates copies of data patterns and relationships, allowing validation to proceed on realistic simulated data while protecting privacy, thereby managing complexity through faithful replication rather than simplification.
Data Source
AI summary
The process validation platform disclosed herein enables dynamic, automated generation of code samples for data pipeline validation. For example, the process validation platform can retrieve a metadata structure and provide associated descriptors and record identifiers to a natural language generation model to generate a test dataset. The process validation platform can generate the test dataset for display on a user interface to enable detection of modifications to the test dataset. Based on such indications of such modifications, the process validation platform can generate an updated test dataset and provide the updated test dataset to the code generation model to generate a code sample for validating an associated data transformation platform.