Which combination of steps will meet these requirements with the LEAST amount of effort?
(Choose three.)
Filter the data through AWS X-Ray to visualize the data.
Filter the data through Amazon QuickSight to visualize the data.
Query the data with Amazon Athena.
Query the data with Amazon Redshift.
Use the AWS Glue Data Catalog as the persistent metadata store.
Use Amazon DynamoDB as the persistent metadata store.
Explanations:
Amazon QuickSight is a business intelligence service that can visualize data stored in Amazon S3, including querying data and creating graphs. It integrates easily with S3 and Athena.
Amazon Athena is a serverless interactive query service that allows querying data directly from S3 in CSV format. It’s efficient and minimizes setup, making it ideal for querying large datasets.
AWS Glue Data Catalog provides a persistent metadata store for structured and unstructured data, making it an appropriate solution for managing metadata of CSV files stored in S3.
AWS X-Ray is used for tracing and analyzing microservices performance, not for visualizing data stored in S3 or querying CSV files.
Amazon Redshift is a data warehouse service. While it can query data, it requires more setup and effort compared to Athena, and the scenario asks for the least effort.
Amazon DynamoDB is a NoSQL database service suited for key-value or document data storage. It is not an appropriate solution for storing metadata of CSV files in S3.
I figure that the answer is:
Filter the data through Amazon QuickSight to visualize the data.
I weigh that the answer is:
Filter the data through Amazon QuickSight to visualize the data.