You have CSV files in Amazon S3 that include a timestamp column formatted like: March 1st, 2020, 08:14pm. You hypothesize that weekday, month, and hour affect the target variable, so you need to add three new features (day of week, month, hour) to the dataset. Which approach adds these features while requiring the least operational overhead to produce a new file in S3?

SageMaker Data Wrangler is designed for data preparation and feature engineering with minimal coding, making it the most operationally efficient choice here. Its "Featurize date/time" transform directly extracts components like weekday, month, and hour from a timestamp column with a few clicks, then exports the result to S3. Provisioning an EMR cluster or writing custom Python/PySpark code for SageMaker Processing or AWS Glue jobs would require more development effort, resource management, and operational overhead compared to Data Wrangler's managed, low-code approach for this specific task.