Use a single-stage ETL process to load all data into a machine learning platform and perform all transformations and analysis there, without considering data preparation for machine learning models.

Design a multi-stage ETL pipeline with data preprocessing and feature engineering steps to prepare the data for machine learning models, leveraging Apache Spark's machine learning libraries and frameworks.

Use a traditional statistical analysis approach to prepare the data for machine learning models, as it is more accurate than using an ETL pipeline.

Focus only on the ETL process and ignore the data preparation aspect, as it is not relevant to machine learning models.