SAA-C03 Question 252
Single answerA data analytics company processes large-scale log data daily to generate reports for its clients. The workload involves running highly parallelized compute-intensive tasks on the dataset, which vary in size each day depending on client requests. The company wants to minimize operational overhead and only pay for the resources it uses. Which AWS compute service is the most suitable for this workload?
- A
Amazon EC2 with Auto Scaling
- B
AWS Batch
- C
AWS Fargate
- D
Amazon EMR
Show answer and explanation
Correct answer: B
Explanation
AWS Batch is the most suitable service for this scenario because it is specifically designed to handle batch processing workloads. It automatically provisions and scales the necessary compute resources, minimizing operational overhead and ensuring cost-effectiveness by only using resources when jobs are running. This aligns perfectly with the company's requirements for processing dynamic workloads with minimal operational effort.
- A. Incorrect.
Amazon EC2 with Auto Scaling can scale instances based on demand, but it requires more operational overhead to set up and maintain the scaling policies and infrastructure. It is not the most optimal choice for highly parallelized batch workloads.
- B. Correct.
AWS Batch is designed specifically to process batch computing workloads. It dynamically provisions instances to run jobs, manages the underlying infrastructure, and scales based on the workload size, making it ideal for this scenario.
- C. Incorrect.
AWS Fargate is used for running containerized applications without managing servers. While it eliminates the need to manage infrastructure, it is not designed specifically for large-scale batch processing workloads like the one described.
- D. Incorrect.
Amazon EMR is optimized for big data processing using frameworks like Hadoop and Spark. While it supports parallel processing, it is more suited for analytical and data transformation tasks rather than generic, highly parallelized compute-intensive workloads.