Which solution will meet these requirements with the MOST operational efficiency?
Update the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction. Use CloudWatch Logs Insights to query the log group and to visualize the results in a pie chart format. Attach the results to the desired CloudWatch dashboard.
Update the ecommerce application to emit a JSON object to an Amazon S3 bucket for each processed transaction. Use Amazon Athena to query the S3 bucket and to visualize the results in a pie chart format. Export the results from Athena. Attach the results to the desired CloudWatch dashboard.
Update the ecommerce application to use AWS X-Ray for instrumentation. Create a new X-Ray subsegment. Add an annotation for each processed transaction. Use X-Ray traces to query the data and to visualize the results in a pie chart format. Attach the results to the desired CloudWatch dashboard.
Update the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction. Create an AWS Lambda function to aggregate and write the results to Amazon DynamoDB. Create a Lambda subscription filter for the log file. Attach the results to the desired CloudWatch dashboard.
Explanations:
Updating the application to emit JSON objects to CloudWatch logs is a straightforward and efficient method. CloudWatch Logs Insights provides powerful query and visualization features, and it can easily integrate with CloudWatch dashboards for a pie chart display.
Using Amazon S3 and Athena for querying and visualizing data is unnecessary for this use case, as CloudWatch Logs Insights is more directly suited to querying CloudWatch logs for quick analysis and visualization in dashboards.
AWS X-Ray is primarily for tracing and debugging applications, not for aggregating or visualizing transaction data in the way this use case requires. X-Ray is not ideal for generating a pie chart of transaction details.
Although using Lambda for aggregation and DynamoDB for storage is feasible, it adds unnecessary complexity. Direct integration with CloudWatch Logs Insights would be simpler and more efficient for real-time visualization.
I structure that the answer is:
Update the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction. Use CloudWatch Logs Insights to query the log group and to visualize the results in a pie chart format. Attach the results to the desired CloudWatch dashboard.
I strategize that the answer is:
Update the ecommerce application to emit a JSON object to a CloudWatch log group for each processed transaction. Use CloudWatch Logs Insights to query the log group and to visualize the results in a pie chart format. Attach the results to the desired CloudWatch dashboard.