SAA-C03 · Question #762
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…
The correct answer is A. Create and run an ETL job in AWS Glue to process the data from Amazon S3 and load it into. Amazon Redshift is optimized for complex analytical queries, joins, and aggregations on large datasets. Using AWS Glue to ETL the S3 data into Redshift allows for high-performance querying and integration with Amazon QuickSight for dashboards. This approach minimizes query time…
Question
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?
Options
- ACreate and run an ETL job in AWS Glue to process the data from Amazon S3 and load it into
- BUse AWS Lambda functions based on S3 PutObject event triggers to copy the incremental
- CUse AWS Lambda functions based on S3 PutObject event triggers to copy the incremental
- DUse AWS Glue to catalog the data in Amazon S3. Perform the aggregation queries on the
How the community answered
(52 responses)- A62% (32)
- B6% (3)
- C21% (11)
- D12% (6)
Explanation
Amazon Redshift is optimized for complex analytical queries, joins, and aggregations on large datasets. Using AWS Glue to ETL the S3 data into Redshift allows for high-performance querying and integration with Amazon QuickSight for dashboards. This approach minimizes query time compared with querying raw S3 data directly.
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