DEA-C01 Question 327
Single answerYou are designing a data processing pipeline in AWS for a company that requires processing high volumes of data in real-time. The workload is highly variable, with traffic spikes during business hours and minimal activity at night. The company also has a strict budget and prefers to minimize operational overhead. Which approach would best align with these requirements?
- A
Use Amazon Kinesis Data Streams with provisioned capacity and Amazon EC2 instances for processing.
- B
Use a serverless architecture with Amazon Kinesis Data Streams and AWS Lambda for processing.
- C
Deploy an Apache Kafka cluster on Amazon EC2 for data streaming and processing.
- D
Use Amazon Redshift with provisioned clusters for both data ingestion and processing.
Show answer and explanation
Correct answer: B
Explanation
The serverless architecture with Amazon Kinesis Data Streams and AWS Lambda is the best choice for this scenario. It allows automatic scaling to handle variable workloads, minimizes costs during low traffic periods, and reduces operational overhead by eliminating the need to manage infrastructure. This approach directly aligns with the company's requirements for real-time processing, variable workload handling, and cost efficiency.
- A. Incorrect.
Amazon Kinesis Data Streams with provisioned capacity and Amazon EC2 requires you to manage capacity and EC2 instances, which increases operational overhead and may not efficiently handle traffic spikes.
- B. Correct.
A serverless architecture with Amazon Kinesis Data Streams and AWS Lambda automatically scales with demand, minimizing costs during low traffic periods and eliminating the need for capacity management. It also aligns with the company's preference to reduce operational overhead.
- C. Incorrect.
Deploying Apache Kafka on Amazon EC2 involves significant operational overhead for managing the cluster. Additionally, scaling EC2 instances during traffic spikes requires manual intervention or custom automation, which is not ideal for a cost-conscious, variable workload.
- D. Incorrect.
Amazon Redshift is primarily designed for data warehousing and analytics, not real-time streaming and processing. It does not align with the need for a real-time data ingestion and processing pipeline.