A Guide to Data Transformation

This post details the challenges and best practices for data transformation, focusing on maintaining trustworthiness as teams grow. It outlines core transformation techniques in SQL and Python (cleaning, reshaping, joining, aggregating, CTEs), discusses the evolving ownership of transformation work by analytics engineers, and highlights common issues like metric definition drift, lack of documentation, and the 'bus factor of one'. It emphasizes making transformation work visible and collaborative through version control, code review, and documentation, and contrasts the limitations of traditional BI tools and semantic layers in addressing these issues.