Which solution requires the LEAST operational overhead to create a new dataset with the added features?
Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.
Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
Explanations:
Creating an Amazon EMR cluster requires significant setup and management, including configuring the cluster and scaling resources. The development of PySpark code also adds complexity, which may not be necessary for a straightforward transformation task.
While Amazon SageMaker can handle the processing, setting up a processing job involves more steps, including managing the environment and infrastructure, which adds operational overhead compared to other options focused on simplicity.
Using Amazon SageMaker Data Wrangler allows for a low-code approach to transform the timestamp into separate variables with minimal operational overhead. The Featurize date/time transform can be applied directly without the need for extensive coding or resource management, making it the most efficient option for this task.
Although AWS Glue is designed for ETL tasks, it requires writing and managing code, similar to EMR, and involves additional setup and operational overhead. It is less straightforward compared to using SageMaker Data Wrangler for this specific transformation task.
I assess that the answer is:
Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.