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A data science team is working on a machine learning project that requires them to ingest large volumes of raw data for analysis and feature engineering. The team plans to use Amazon SageMaker for the project, and they need to efficiently ingest the data into a platform where they can perform transformations and store features for future model training. Which of the following approaches is the most efficient for ingesting data, performing data transformations, and then storing the engineered features?