Test cases, data, and validation procedures are crucial for data transformations, requiring an understanding of transformation requirements, scenarios, and specific techniques for accuracy and integrity.
Data transformations require complex testing due to their sophisticated logic, computations, and dependency on real-time data streams. This necessitates extensive test case design, representative data, automation tools, and robust validation procedures.
Part 1 of this blog series explained the basic concepts netherlands rcs data of data transformation testing. This section discussed how transformation-related errors usually rank among other errors in data pipelines and several important factors to consider when planning data transformation testing (e.g., scope of testing, primary goals, specific errors to test).
Below, part 2 highlights test ideas for basic and complex data transformations.
Testing Basic Data Transformations
Data transformations in data pipelines are crucial for data structure, enhancement, and analysis. Test scenarios for top 10 functions are essential for maintaining data quality and reliability.