2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)
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Abstract

Accurate and dependable data is critical when making crucial business decisions. However, verifying the accuracy of complex and extensive datasets can be both error-prone and time-consuming when done manually. We developed a PyDaQu, our automated framework that creates data quality checks code based on a standardized template. PyDaQu offers a variety of quality assurance measures, including validation, completeness, and consistency checks. These measures ensure exceptional data quality while simultaneously streamlining your data management processes. With PyDaQu, creating data quality checks requires significantly less time and effort. We have thoroughly evaluated PyDaQu using data from two different industry domains.
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