Which solution will meet these requirements with the LEAST development effort?
Upload the data into the SageMaker Data Wrangler console directly. Perform data transformations and generate insights within Data Wrangler.
Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the S3 bucket into SageMaker Data Wrangler. Perform data transformations and generate insights within Data Wrangler.
Upload the data into the SageMaker Data Wrangler console directly. Allow SageMaker and Amazon QuickSight to access the data that is in an Amazon S3 bucket. Perform data transformations in Data Wrangler and save the transformed data into a second S3 bucket. Use QuickSight to generate data insights.
Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the bucket into SageMaker Data Wrangler. Perform data transformations in Data Wrangler. Save the data into a second S3 bucket. Use a SageMaker Studio notebook to generate data insights.
Explanations:
Uploading data directly into the SageMaker Data Wrangler console limits scalability and access control. It does not utilize Amazon S3 for data storage, which is designed for handling large datasets efficiently. Additionally, the solution does not mention generating insights, which may require additional tools.
Uploading the data to an Amazon S3 bucket allows for efficient storage and retrieval, and SageMaker can easily access the data from S3. Importing the data into SageMaker Data Wrangler enables quick data transformations and insights generation with minimal development effort, adhering to best practices for data handling.
While this option allows for data transformations in Data Wrangler and access to QuickSight for insights, uploading data directly to the Data Wrangler console is not necessary. It complicates the workflow by saving transformed data into a second S3 bucket without a clear necessity, making it less efficient compared to Option B.
This option also starts by uploading data to an S3 bucket, which is a good practice. However, it introduces unnecessary complexity by suggesting the use of a SageMaker Studio notebook for insights generation after data transformations in Data Wrangler. The additional step and tool may increase development effort compared to the more streamlined process in Option B.
I would say the answer is:
Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the S3 bucket into SageMaker Data Wrangler. Perform data transformations and generate insights within Data Wrangler.