Complex Data Transformation and Normalization Pipeline

Design a flexible Python data transformation framework capable of handling complex, multi-source datasets. Implement advanced normalization techniques including Min-Max, Z-score, and custom scaling methods. Create a modular system that can automatically detect data distributions, apply appropriate transformations, and generate detailed metadata about the preprocessing steps. Include robust error handling and support for both numerical and categorical data.