AWS Certified Solutions Architect – Associate (SAA-C02) — Question 499

A company has an application that scans millions of connected devices for security threats and pushes the scan logs to an Amazon S3 bucket. A total of 70 GB of data is generated each week, and the company needs to store 3 years of data for historical reporting. The company must process, aggregate, and enrich the data from Amazon S3 by performing complex analytical queries and joins in the least amount of time. The aggregated dataset is visualized on an Amazon QuickSight dashboard.
What should a solutions architect recommend to meet these requirements?

Answer options

• A. Create and run an ETL job in AWS Glue to process the data from Amazon S3 and load it into Amazon Redshift. Perform the aggregation queries on Amazon Redshift.
• B. Use AWS Lambda functions based on S3 PutObject event triggers to copy the incremental changes to Amazon DynamoDB. Perform the aggregation queries on DynamoDB.
• C. Use AWS Lambda functions based on S3 PutObject event triggers to copy the incremental changes to Amazon Aurora MySQL. Perform the aggregation queries on Aurora MySQL.
• D. Use AWS Glue to catalog the data in Amazon S3. Perform the aggregation queries on the cataloged tables by using Amazon Athena. Query the data directly from Amazon S3.

Correct answer: A

Explanation

Amazon Redshift is a petabyte-scale data warehouse optimized for running complex analytical queries and joins over massive datasets, which fits the requirement of querying nearly 11 TB of historical data in the least amount of time. AWS Glue is the ideal serverless integration service to perform the necessary ETL processes to transform and load the S3 data into Redshift. Other options like DynamoDB and Aurora are OLTP-focused and not optimized for complex analytical joins at this scale, while Athena would perform slower than Redshift for these highly complex queries.