Which solution meets this requirement with the LEAST amount of effort?
Export the Aurora MySQL database to Amazon S3 by using AWS Database Migration Service (AWS DMS). Use Amazon Comprehend to run sentiment analysis on the exported files.
Export the Aurora MySQL database to Amazon S3 by using AWS Database Migration Service (AWS DMS). Use Amazon SageMaker to run sentiment analysis on the exported files.
Set up Aurora native integration with Amazon Comprehend. Use SQL functions to extract sentiment analysis.
Set up Aurora native integration with Amazon SageMaker. Use SQL functions to extract sentiment analysis.
Explanations:
Exporting data to S3 and using Amazon Comprehend via AWS DMS adds unnecessary complexity and is not the most efficient solution for real-time analysis directly on the Aurora database.
Exporting data to S3 and using SageMaker for sentiment analysis is more complex and requires additional setup for model training and deployment, which increases effort compared to simpler options.
Aurora MySQL’s native integration with Amazon Comprehend allows for real-time sentiment analysis using SQL functions, making it the most efficient and least effort-intensive solution for automating feedback analysis.
Setting up integration with Amazon SageMaker for sentiment analysis in Aurora would require custom model training and deployment, which is more complex and less ideal than using Amazon Comprehend.
I chart that the answer is:
Set up Aurora native integration with Amazon Comprehend. Use SQL functions to extract sentiment analysis.