Validation Definition Language for ETL ML Pipeline Correctness
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing ETL-based machine learning (ML) algorithms require labor-intensive efforts for validating their pipelines against multiple test datasets, as they depend on manual conceptualization and application of ETL processes, which can lead to inefficiencies and potential errors.
Innovation Solution
The proposed solution involves an ETL ML pipeline validation system that utilizes an output schema engine, a validator rule definition interface, an expected output engine, and a validation engine to automate the validation process. This system generates an output schema, defines validator rules, and compares actual and expected output datasets to determine the validity of the ETL ML pipeline without requiring extensive user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual conceptualization and application of ETL processes is used for validation, then validation can be performed with simple tools, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables self-service validation by automatically generating validation definitions from the ETL pipeline configuration and test datasets. The validation engine autonomously executes validation without requiring manual conceptualization of validation logic, allowing the system to validate itself through automated comparison of actual versus expected output datasets.
Solution Approach 2:
The patent replaces manual mechanical validation processes with an automated computational system. The validation engine substitutes human conceptualization and application of ETL processes with algorithmic automation, using processing devices to automatically generate validation definitions, execute transformations, and compare results without human intervention.
2Ease of operation
If manual validation is performed, then validation logic can be customized for specific needs, but the complexity of validation setup and operation increases
Solution Approach 1:
The system performs preliminary action by automatically generating validation definitions before execution. The validation definition generator creates comprehensive validation logic based on the ETL pipeline configuration and test datasets, preparing all necessary validation parameters and expected output datasets in advance so that validation execution becomes a simple automated process.
Solution Approach 2:
The validation engine provides universal functionality by handling multiple validation tasks through a single integrated system. It can generate validation definitions, execute transformations, compare datasets, and produce validation determinations automatically, eliminating the need for separate manual validation tools and procedures while reducing overall system complexity.
3Reliability
If secondary validation methods are used, then validation can be cross-checked, but reliability decreases due to reliance on non-independently validated ML models
Solution Approach 1:
The patent introduces an intermediary validation definition generator that creates independent validation criteria based on the ETL pipeline configuration and test datasets. This intermediary layer produces expected output datasets and validation definitions that serve as objective reference standards, enabling reliable validation determinations without relying on secondary ML models that may introduce additional uncertainty.
Data Source
AI summary
Systems and methods are provided for generating extract-transform-load (“ETL”) machine learning (“ML”) pipeline validation rules based on user-input, wherein the ETL ML pipeline validation rules may be applicable to validate an ETL ML pipeline against multiple test datasets. The ETL ML pipeline validation rules may comprise compute-type validation rules for computing expected values of data structures within a dataset output by the ETL ML pipeline. The ETL ML pipeline validation rules may comprise check-type validation rules for checking whether data structures within a dataset output by the ETL ML pipeline have intended characteristics.