A data science team is building a machine learning project that requires ingesting large volumes of raw data for analysis and feature engineering. They plan to use Amazon SageMaker and need an efficient workflow to ingest data, perform transformations, and store engineered features for future model training. Which approach is the MOST efficient for ingesting data, transforming it, and storing the engineered features?