SAA-C03 Question 251
Single answerA company processes large volumes of data to generate analytics reports. The data is stored in Amazon S3, and the processing tasks require distributed computation over hundreds of nodes. The company wants to minimize operational overhead while optimizing for cost. Which AWS compute service is the most appropriate for this use case?
- A
AWS Batch
- B
Amazon EMR
- C
AWS Fargate
- D
Amazon EC2 Auto Scaling
Show answer and explanation
Correct answer: B
Explanation
Amazon EMR is the most appropriate service for this scenario because it is specifically designed for distributed data processing and analytics using frameworks like Apache Hadoop and Apache Spark. It minimizes operational overhead by managing the underlying infrastructure and integrates seamlessly with Amazon S3 for storing large datasets. Other options like AWS Batch, AWS Fargate, and Amazon EC2 Auto Scaling are not optimized for this type of workload.
- A. Incorrect.
AWS Batch is designed for batch processing jobs, but it does not provide the specific optimizations for distributed data processing frameworks like Hadoop or Spark, which are commonly used to process large datasets.
- B. Correct.
Amazon EMR is specifically designed for cost-effective, distributed data processing using frameworks such as Apache Hadoop and Apache Spark. It integrates well with Amazon S3 for data storage and is ideal for processing large volumes of data.
- C. Incorrect.
AWS Fargate is a serverless compute engine for containers, but it is not optimized for distributed data processing over large datasets using frameworks like Hadoop or Spark. It is more suitable for containerized microservices or simpler workloads.
- D. Incorrect.
Amazon EC2 Auto Scaling can scale EC2 instances to handle increased demand, but it requires significant operational overhead to set up and manage distributed computation frameworks manually. It is not the best choice for this specific use case.