ETL ML Pipeline Validation with Auto-Generated Expected Outputs
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Solution Overview
Problem
ETL ML pipeline validation against multiple test datasets requires labor-intensive efforts, including manual conceptualization of ETL processes, dataset cleanup, and individual population of expected output datasets, which can lead to inefficiencies and potential invalid transformations.
Innovation Solution
A system and method that reduces labor-intensive efforts by generating an output schema, defining validator rules, and comparing actual and expected output datasets using a validation engine, allowing for iterative validation without repeated user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual conceptualization of ETL processes and individual population of expected output datasets is performed for each test dataset, then validation accuracy can be ensured, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating expected output datasets before validation occurs. The expected output engine creates these datasets in advance using the test input datasets and defined transformation logic, eliminating the need for manual population during validation and significantly reducing time consumption while maintaining accuracy
Solution Approach 2:
The validation system performs self-service through automated generation of expected output datasets. Instead of requiring manual intervention to create expected outputs, the system uses the test input datasets and transformation definitions to automatically generate what the outputs should be, enabling the system to validate itself without human labor
2Reliability
If manual conceptualization of ETL processes is required for each test dataset, then transformation accuracy can be verified, but ease of operation decreases due to repeated user interaction
Solution Approach 1:
The system enables self-service validation where the expected output engine automatically generates expected output datasets using test input datasets and transformation definitions. This eliminates repeated manual conceptualization and population operations, making the validation process easy to operate while maintaining verification of transformation accuracy through automated comparison
Solution Approach 2:
The system creates copies of the transformation logic and test input datasets to generate expected output datasets. By copying the transformation definitions and applying them automatically to test inputs, the system verifies transformation accuracy without requiring manual re-conceptualization for each validation case
3Measurement precision
If individual population of expected output datasets is performed for each test dataset, then validation precision can be maintained, but productivity decreases due to labor-intensive efforts
Solution Approach 1:
The expected output engine performs preliminary generation of expected output datasets before validation occurs. By pre-generating these datasets automatically from test input datasets and transformation definitions, the system maintains validation precision through accurate expected outputs while dramatically improving productivity by eliminating labor-intensive individual population operations
Solution Approach 2:
The system replaces the mechanical manual process of individually populating expected output datasets with an automated computational system. The expected output engine uses computational logic to generate expected outputs from test inputs and transformation definitions, maintaining precision through accurate data processing while improving efficiency by substituting manual labor with automation
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.