A data scientist is preparing a dataset for a regression model that predicts customer lifetime value. One feature, 'annual_purchase_amount', is heavily right-skewed with a long tail of high-value customers, and the model's linear assumptions are being violated. The team uses AWS Glue DataBrew for their preparation workflow and wants to reduce skewness so the feature approximates a more normal distribution before training. Which transformation should they apply?