A company needs a data warehouse that can run complex aggregation and analytical queries at high speed against large volumes of data collected from multiple systems. Which AWS service is MOST suited for this requirement?
A question asking which service provides a data warehouse for large-scale analytical workloads.
Amazon DynamoDB
DynamoDB is a low-latency key-value NoSQL database that excels at simple read and write operations.
It is not suited for data warehouse workloads that require complex aggregation and analysis across large datasets, so this is incorrect.
Amazon Athena
Amazon Athena is a serverless analytical service that runs SQL queries directly against data stored in S3.
Although it is convenient for ad hoc analysis, it is not a dedicated platform (data warehouse) for continuously running complex aggregation queries at high speed against large datasets — columnar storage makes Redshift more appropriate, so this is incorrect.
Amazon Redshift
This is correct. Amazon Redshift is a managed data warehouse (OLAP) that can execute complex aggregation and analytical queries at high speed against petabyte-scale datasets. Its columnar storage and parallel processing efficiently support analytics for BI and reporting workloads.
Amazon RDS
Amazon RDS is a relational database for transactional processing (OLTP), such as daily inserts and updates.
Because of its row-oriented design, it cannot match the performance of columnar Redshift for complex aggregation and analytical queries (OLAP) over large datasets, making it unsuitable for data warehouse use cases, so this is incorrect.
'Data warehouse' and 'large-scale aggregation and analytics (OLAP)' point to Amazon Redshift. Distinguish it from RDS for daily transactions (OLTP), DynamoDB for low-latency NoSQL, and Athena for ad hoc SQL queries against S3.