Which solution will meet these requirements?
Run a daily Amazon EMR job to transform the data and load the data into Amazon Redshift. Use Amazon Redshift ML to create and train the ML models.
Run a daily Amazon EMR job to transform the data and load the data into Amazon Aurora Serverless. Use Amazon Aurora ML to create and train the ML models.
Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless. Use Amazon Redshift ML to create and train the ML models.
Run a daily AWS Glue job to transform the data and load the data into Amazon Athena tables. Use Amazon Athena ML to create and train the ML models.
Explanations:
Amazon EMR can transform data, but it is not serverless and requires management overhead. Amazon Redshift is an MPP data warehouse but does not provide serverless capabilities. While Amazon Redshift ML can create and train ML models, this option does not fully utilize serverless services.
Similar to option A, Amazon EMR is not serverless. While Amazon Aurora Serverless provides some serverless capabilities, it is not optimized for MPP workloads like Amazon Redshift. Additionally, Aurora ML does not offer the same capabilities for large-scale ML model training as Redshift ML.
AWS Glue is a serverless data integration service that can efficiently transform data and load it into Amazon Redshift Serverless, which provides MPP capabilities. Amazon Redshift ML allows for SQL-based machine learning, making this option a fit for the requirements.
AWS Glue can transform data, but loading it into Amazon Athena does not meet the MPP requirement since Athena is not a traditional data warehouse and is optimized for ad-hoc querying. Additionally, while Amazon Athena has machine learning capabilities, they are not as robust as those provided by Redshift ML for creating and training models.
I organize that the answer is:
Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless. Use Amazon Redshift ML to create and train the ML models.