Which solution will meet these requirements with the LEAST operational overhead?
Provide the extracted insights to Amazon Athena for analysis. Store the extracted insights and analysis in an Amazon S3 bucket.
Store the extracted insights in an Amazon DynamoDB table. Use Amazon SageMaker to build a sentiment model.
Provide the extracted insights to Amazon Comprehend for analysis. Save the analysis to an Amazon S3 bucket.
Store the extracted insights in an Amazon S3 bucket. Use Amazon QuickSight to visualize and analyze the data.
Explanations:
While Amazon Athena can analyze data stored in S3, this option does not specify using Amazon Comprehend for sentiment analysis, which is essential for extracting insights related to sentiment.
Using DynamoDB requires managing a database, which adds operational overhead. Additionally, while SageMaker can build sentiment models, it does not directly integrate with Textract outputs without additional data processing steps.
This option effectively combines Amazon Textract for data extraction and Amazon Comprehend for sentiment analysis, streamlining the process with minimal operational overhead. The results can be saved in S3 for further analysis and reporting.
Although QuickSight is suitable for visualization, it does not perform sentiment analysis. This option lacks the required step to analyze sentiment, making it less aligned with the campaign’s needs.
I categorize that the answer is:
Provide the extracted insights to Amazon Comprehend for analysis. Save the analysis to an Amazon S3 bucket.
If I’m correct, the answer is:
Provide the extracted insights to Amazon Comprehend for analysis. Save the analysis to an Amazon S3 bucket.