A company has used Amazon Elastic MapReduce (Amazon EMR) clusters to capture data about user actions and push it to Amazon Simple Storage Service (S3). The database grows up to 50GB per day. Then it uses Apache Hive for querying user-activity data. However, the DevOps lead is unsatisfied with its performance, cost, and management complexity. You have proposed to use AWS Athena to query the data instead. Which benefits does this new solution bring? (Select TWO.)

Answer options:

A.AWS Athena charges every hour at an extremely low cost. It is more cost-effective than the previous solution.
B.Amazon Athena is serverless. So you do not need to set up the instance or consider if the compute capability is enough or not.
C.Amazon Athena can query data in S3 easily using standard SQL only when the data format is CSV. Standard SQL cannot be used for other formats.
D.AWS Athena can automatically execute queries in parallel. So most results come back within seconds.
E.Amazon Athena can query data from any data sources, including S3, AWS RDS, EFS and Glacier.

Correct Answer – B, D
Amazon Athena is a query service that uses standard SQL to analyze data in Amazon S3.
Check https://aws.amazon.com/athena/ for its various features.
Option A is incorrect because, with Amazon Athena, you only pay for the queries. And you are charged based on the amount of data scanned by each query. It does not charge every hour.
Option B is CORRECT because Amazon Athena is totally serverless, meaning that users only need to consider how to query the data without managing the infrastructure.
Option C is incorrect because various standard data formats are supported, including CSV, JSON, ORC, Avro, and Parquet.
Option D is CORRECT because Amazon Athena can automatically allocate resources for queries. As a result, performance has been improved if compared with traditional solutions.
Option E is incorrect because the source data should be located in S3 only. AWS RDS, EFS and Glacier are not valid.
Check out the Amazon Athena workflow as below.