Which solution will meet these requirements with the LEAST effort?
Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Create an Amazon EMR duster to read the S3 bucket and perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.
Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
Use AWS Database Migration Service (AWS DMS) to transfer the data from the PostgreSQL database to an Amazon S3 bucket. Use AWS Glue to read the data in the S3 bucket and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.
Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Studio for the prediction modeling.
Explanations:
This option involves multiple steps, including using AWS DMS to transfer data to S3 and setting up an Amazon EMR cluster, which requires some coding and operational knowledge. This complexity is not suitable for a no-code solution.
This option uses AWS Glue DataBrew, a no-code data preparation tool, to directly read from the PostgreSQL database and allows business analysts to prepare data easily. It also incorporates Amazon SageMaker Canvas, which enables no-code machine learning model building and predictions. This meets the requirement for ease of use without coding knowledge.
This option also starts with AWS DMS to transfer data to S3, which is unnecessary for a no-code solution and adds complexity. It then uses AWS Glue for data preparation, which, while potentially useful, is more complex than using DataBrew. The need for data transfer and extra services makes it less suitable for non-coders.
Although this option uses AWS Glue DataBrew for data preparation, it opts for Amazon SageMaker Studio for prediction modeling. SageMaker Studio requires some coding knowledge and familiarity with data science workflows, which does not align with the requirement for a no-code solution.
I gauge that the answer is:
Use AWS Glue DataBrew to read the data that is in the PostgreSQL database and to perform the data preparation. Use Amazon SageMaker Canvas for the prediction modeling.