The most logical and efficient workflow begins with understanding the data's structure. AWS Glue crawlers are designed specifically to scan data stores like Amazon S3, automatically infer schemas from unstructured or semi-structured data (like the described .csv files), and populate the AWS Glue Data Catalog. Once the schema is defined, AWS Glue DataBrew provides a visual interface to clean, normalize, and perform feature engineering on the dataset. It is the ideal tool for handling sparse, unlabeled data without writing extensive code. Finally, the prepared, clean dataset must be stored. Amazon S3 is the standard and most integrated storage service for machine learning workflows on AWS, making the processed data readily available for model training with services like Amazon SageMaker.

1. AWS Glue Crawlers: The AWS Glue Developer Guide states, "You can use a crawler to populate the AWS Glue Data Catalog with tables. This is the primary method used by most AWS Glue users." A crawler automatically discovers the schema of your data.

Source: AWS Glue Developer Guide, "Defining Crawlers".

URL: https://docs.aws.amazon.com/glue/latest/dg/add-crawler.html

2. AWS Glue DataBrew: The AWS Glue DataBrew Developer Guide explains, "AWS Glue DataBrew is a visual data preparation tool that you can use to clean and normalize data... You can then use this prepared data for analytics and machine learning."

Source: AWS Glue DataBrew Developer Guide, "What Is AWS Glue DataBrew?".

URL: https://docs.aws.amazon.com/databrew/latest/dg/what-is-databrew.html

3. Storing Data in Amazon S3: The Amazon SageMaker Developer Guide specifies that for training a model, "You store training data in an Amazon S3 bucket." This establishes S3 as the standard location for storing datasets prepared for ML training.

Source: Amazon SageMaker Developer Guide, "Train a Model with Amazon SageMaker".

URL: https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html