You have historical CSV data in S3 containing product inventory demand and need to prepare it for Amazon Forecast. How should you transform and upload the data so Forecast can train models effectively?

Amazon Forecast requires input data to be split into specific datasets: a target time series dataset (containing the demand data) and an item metadata dataset (containing static item attributes). AWS Glue ETL is an excellent choice for this transformation because it's a fully managed, scalable service designed for data preparation and movement, making it efficient for processing data stored in S3. After transformation, Forecast reads these datasets directly from S3. Incorrect options: Uploading to Amazon Aurora is incorrect because Forecast reads data directly from S3, not relational databases. Uploading directly from a local machine is not scalable or practical for large datasets and doesn't leverage AWS services for data ingestion. Also, Forecast doesn't inherently require a "related time series dataset" if the original data only contains target and item metadata. Converting to protobuf RecordIO is not a standard or required format for Forecast input; it primarily uses CSV files from S3.