ATDD Framework for ETL Data Validation Automation
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Solution Overview
Problem
Automating and testing data accuracy and integrity within end-to-end ETL flows is challenging, especially in Big Data ecosystems, where there is a lack of mature vendor tools for ATDD automation, leading to cumbersome manual testing processes that fail to meet regulatory and financial reporting requirements for zero tolerance reconciliation with quick turnaround and traceability.
Innovation Solution
The PRANA framework provides a system and method for Acceptance Test Driven Development (ATDD) automation for ETL testing, utilizing core automation libraries developed in Java and Spark/Scala, which includes a user interface for selecting data testing options, generating feature files, executing data validation tests, and storing results in a cloud environment, enabling automated data validation and reconciliation across the ETL process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual testing approaches are used for ETL data validation, then flexibility and adaptability are maintained, but testing productivity is low and time consumption is high
Solution Approach 1:
The system enables self-service testing by allowing users to define validation rules and select data sources through a graphical interface without requiring programming knowledge. The automated testing framework executes tests independently based on user-defined parameters, eliminating the need for manual execution of complex validation scripts and significantly reducing time consumption.
Solution Approach 2:
The patent replaces manual mechanical testing processes with an automated software-based testing system. The framework uses programming languages like Python or Java to automatically execute validation tests, compare actual data against expected results, and generate reports, substituting the manual mechanical process of checking data accuracy with an automated computational system.
2Productivity
If automated testing frameworks are implemented, then testing productivity increases, but device complexity and implementation difficulty increase
Solution Approach 1:
The testing framework is segmented into modular components including data source connectors, validation rule engines, test execution managers, and reporting modules. Each component can be independently configured and selected, allowing users to build customized test scenarios without understanding the entire complex framework. This modular segmentation reduces the perceived complexity while maintaining high productivity.
Solution Approach 2:
The system introduces an intermediary layer in the form of a graphical user interface and configuration manager that mediates between the user and the complex underlying testing framework. This intermediary translates simple user selections into complex automated test executions, shielding users from framework complexity while delivering high productivity benefits.
3Reliability
If comprehensive data validation tests are executed, then data integrity and reliability are improved, but testing time and processing resources increase
Solution Approach 1:
The framework allows users to select specific validation rules and data sources based on their business needs rather than executing all possible tests. This partial action approach enables comprehensive validation of critical data elements while skipping less important checks, maintaining data integrity for essential fields without the time cost of validating every possible data attribute.
Solution Approach 2:
The system performs preliminary actions by pre-defining validation rules and expected results in the framework configuration. Users can load pre-configured test scenarios that have been prepared in advance, eliminating the need to create validation tests from scratch and reducing the time required to execute comprehensive data integrity checks.
Data Source
AI summary
Systems and methods according to exemplary embodiments provide a process and automation framework enabling Acceptance Test Driven Development (ATDD) automation for Extract, Transform, and Load (ETL) and Big Data testing. Exemplary embodiments include a user interface for executing end to end tests as part of an ATDD process during ETL. The user interface may act as a shopping cart where the user only has to pick and choose the flavor of tests he/she desires to run (e.g., Pre-Ingestion, Post Ingestion, Data Reconciliation, etc.), and the feature files associated with the tests are dynamically generated.