Understanding why feature transformations must be fit on training data before joining across large datasets.
You are building a feature pipeline over a large dataset using distributed joins to enrich transactions with aggregated user-level statistics (e.g., mean spend). To avoid data leakage while computing these aggregate features, what is the most correct approach?