Which combination of steps should be taken to 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 AWS Glue as the persistent metadata store.
Use Amazon S3 as the persistent metadata store.
Explanations:
Amazon QuickSight is a business intelligence service that can easily connect to Amazon S3 and visualize data directly from .csv files. It is ideal for creating graphs and visualizations without needing significant setup.
Amazon Athena allows users to run SQL queries directly on data stored in Amazon S3, including .csv files. It is serverless, requires no infrastructure management, and integrates well with S3 for querying large datasets efficiently.
AWS Glue is a fully managed ETL service that can automatically extract, transform, and load metadata from .csv files in Amazon S3. It serves as an efficient metadata store and provides automated schema discovery.
AWS X-Ray is primarily used for tracing and monitoring applications, not for data visualization or querying. It cannot be used to filter or visualize data in the context of CSV files.
Amazon Redshift is a data warehouse solution, but it is generally overkill for this scenario. It would require additional setup and management, such as loading data into Redshift, making it less efficient than using Athena for direct querying of S3 data.
Amazon S3 is a storage service, not a dedicated metadata store. While it can store raw data, it does not have built-in features for managing and querying metadata in a structured way like AWS Glue.
I arrange that the answer is:
Filter the data through Amazon QuickSight to visualize the data.