A retail company is preparing a customer dataset in AWS Glue for a churn prediction model. The source data was merged from multiple CRM systems and contains many duplicate customer records that are not exact matches — names have typos, addresses use different abbreviations, and phone formats vary. These fuzzy duplicates would inflate certain customers' behavior signals and bias the training data. The data engineering team needs a scalable way to identify and remove these near-duplicate records within their existing Glue ETL workflow. Which approach best addresses this requirement?