DEA-C01 Question 169
Single answerA data engineering team is responsible for managing a high-performance analytics workload that requires low-latency and high-throughput for processing large volumes of data. The team needs to select a storage solution that can integrate seamlessly with an Amazon EMR cluster running Spark jobs. Which storage service and configuration would best meet these performance demands?
- A
Amazon S3 with the S3 Standard storage class
- B
Amazon EBS Provisioned IOPS SSD (io2) volumes attached to the EMR cluster
- C
Amazon S3 with the S3 Intelligent-Tiering storage class
- D
Amazon Elastic File System (EFS) with General Purpose performance mode
Show answer and explanation
Correct answer: B
Explanation
Amazon EBS Provisioned IOPS SSD (io2) volumes are the best choice for this scenario because they provide the low-latency and high-throughput performance necessary for processing large-scale analytics workloads on Amazon EMR. While S3 is suitable for many use cases, it is not designed for the high-performance demands of Spark jobs running on EMR. Similarly, EFS and S3 Intelligent-Tiering address other use cases but are not optimized for intensive computational workloads requiring low-latency storage.
- A. Incorrect.
Amazon S3 with the S3 Standard storage class is highly durable and scalable but does not provide the low-latency and high-throughput performance required for intensive analytics workloads on Amazon EMR.
- B. Correct.
Amazon EBS Provisioned IOPS SSD (io2) volumes are specifically designed for high-performance and low-latency use cases, making them ideal for attaching to EMR nodes to support Spark jobs with demanding performance requirements.
- C. Incorrect.
Amazon S3 with the S3 Intelligent-Tiering storage class is optimized for cost efficiency by automatically moving data between storage tiers based on access patterns. However, it does not offer the low-latency and high-throughput performance required for this scenario.
- D. Incorrect.
Amazon EFS with General Purpose performance mode is suited for scalable and shared file storage but is not optimized for the level of performance needed for high-throughput Spark workloads on Amazon EMR.