MENU
A company's application has an API that retrieves workload metrics. The company needs to audit, analyze, and visualize these metrics from the application to detect issues at scale.
Which combination of steps will meet these requirements? (Select THREE).
The recommended architecture to audit, analyze, and visualize application workload metrics at scale involves:
Using Amazon EventBridge to schedule AWS Lambda invocations that call the application API and fetch metrics (Option A). The data is stored in Amazon S3, which is ideal for scalable, cost-effective storage of large datasets.
Cataloging the stored data with AWS Glue crawlers, enabling schema discovery and making data queryable via Amazon Athena (Option C).
Visualizing the data by creating Amazon QuickSight datasets from Athena views and building dashboards for analysis (Option E).
Option B and D introduce DynamoDB, which is less suitable for large-scale analytics and Athena querying. Option F suggests querying Athena via Lambda widgets in CloudWatch, which adds complexity without significant benefit over QuickSight.
Serverless Analytics Architecture on AWS:
'Use Lambda + S3 + Glue + Athena + QuickSight for scalable data ingestion, cataloging, querying, and visualization.'
(AWS Analytics Reference Architecture)
AWS Glue Crawlers and Athena:
'Glue crawlers catalog data stored in S3 and create Athena tables to enable SQL querying.'
(AWS Glue Documentation)
Amazon QuickSight for Visualization:
'QuickSight enables creating interactive dashboards from Athena query results.'
(Amazon QuickSight Overview)
Currently there are no comments in this discussion, be the first to comment!