ELV Data Testing Engine for Automated ETL Validation
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Solution Overview
Problem
Current methods for testing data integration and business intelligence systems are inefficient due to the complexity of multiple data sources, large data volumes, and the lack of automation, particularly in validating data integrity and performance across different load modes and report scenarios, leading to unreliable results and user distrust.
Innovation Solution
A modularized client-server based test system with a plug-and-play adaptor module, test case definition, validation module, and graphical user interface for automating the extraction, loading, and validation of data from multiple sources, enabling rigorous regression and performance testing through the Extract, Load, and Validate (ELV) architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing methods are used to compare data between source and target data sources, then testing can be performed with simple tools, but the testing process becomes tedious and time-consuming due to large data volumes and multiple test cases
Solution Approach 1:
The testing system performs self-service by automatically executing test cases, extracting data from multiple sources, and comparing results without requiring manual intervention. The system autonomously manages the entire testing workflow including data extraction, transformation, loading, and validation, thereby resolving the contradiction between ensuring data quality and reducing testing time.
Solution Approach 2:
The patent replaces manual mechanical testing operations with an automated computer-based system. The testing engine automatically executes test cases, extracts data from sources, transforms data, loads it to targets, and validates results, substituting the manual mechanical process with an automated computational system that significantly reduces testing time while maintaining reliability.
2Extent of automation
If custom scripts are developed to automate data comparison between data sources, then some automation is achieved, but the automation is limited to smaller data samples and requires coding expertise
Solution Approach 1:
The patent introduces a testing engine as an intermediary between the user and the complex automation processes. This engine provides a high-level interface that abstracts away the complexity of data extraction, transformation, loading, and validation operations. Users can define test cases at a high level without needing to write complex custom scripts, while the engine handles the intricate automation tasks, thus achieving extensive automation without increasing user-facing complexity.
Solution Approach 2:
The testing engine is designed as a universal platform that can handle multiple data sources, various test case types, and different validation scenarios through a single unified system. It provides multi-functional capabilities including data extraction from diverse sources, transformation operations, loading to multiple targets, and comprehensive validation, replacing the need for multiple custom scripts with one versatile automation engine.
3Measurement precision
If separate test cases are created for incremental and full load modes of ETL process, then accurate testing of each mode is possible, but the testing process becomes more complex and requires maintaining multiple test case sets
Solution Approach 1:
The testing system incorporates dynamics by allowing test cases to be configured with mode-specific parameters that can be dynamically adjusted. A single test case framework can adapt to different ETL modes (incremental or full load) by dynamically setting appropriate parameters and execution contexts, eliminating the need for separate rigid test case sets while maintaining precise testing accuracy for each mode.
Solution Approach 2:
The patent utilizes parameter changes to differentiate between incremental and full load testing within a unified test case structure. By changing execution parameters such as date ranges, data selection criteria, and validation thresholds based on the ETL mode being tested, the system achieves mode-specific testing accuracy without requiring separate test cases, thereby reducing test case management complexity.
4Ease of operation
If OLAP tool metadata model provides abstraction layer for reporting, then user-friendly reporting is enabled, but testing becomes challenging due to large permutation and combination of possible reporting queries
Solution Approach 1:
The testing system applies partial action by focusing test efforts on representative subsets of possible reporting queries rather than attempting to test every permutation and combination. It selects critical test cases that cover key functionality and edge cases, achieving sufficient testing coverage without requiring exhaustive testing of all possible queries, thus managing complexity while maintaining ease of operation benefits.
Solution Approach 2:
The patent segments the complex space of possible reporting queries into manageable categories or groups based on their functional characteristics, data sources, and complexity levels. By dividing the testing scope into segments such as basic reports, complex aggregations, and edge case scenarios, the system can systematically test each segment with appropriate test cases, reducing overall testing complexity while preserving the usability benefits of the metadata abstraction layer.
Data Source
AI summary
The present method and apparatus provides for automated testing of data integration and business intelligence projects using Extract, Load and Validate (ELV) architecture. The method and computer program product provides a testing framework that automates the querying, extraction and loading of test data into a test result database from plurality of data sources and application interfaces using source specific adaptors. The test data available for extraction using the adaptors include metadata such as the database query generated by the OLAP Tools that are critical to validate the changes in business intelligence systems. A validation module helps define validation rules for verifying the test data loaded into the test result database. The validation module further provides a framework for comparing the test data with previously archived test data as well as benchmark test data.