DEA-C01 Question 168
Single answerA data engineering team is tasked with storing a high-volume data stream from IoT devices. The team requires low-latency writes and the ability to perform near real-time analytics on this data. Which AWS storage configuration is the most suitable for these requirements?
- A
Amazon S3 with S3 Standard storage class and Amazon Athena for querying
- B
Amazon RDS with a read replica for analytics workloads
- C
Amazon DynamoDB with DynamoDB Streams enabled
- D
Amazon Kinesis Data Streams with long-term storage in S3 Glacier
Show answer and explanation
Correct answer: C
Explanation
Amazon DynamoDB with DynamoDB Streams is the best choice for this scenario as it provides low-latency writes and enables real-time data processing through DynamoDB Streams. This setup is tailored for workloads such as IoT data ingestion and analytics, perfectly addressing the team's requirements. Other options either lack the low-latency or near real-time processing capabilities necessary for this use case.
- A. Incorrect.
Amazon S3 with S3 Standard is a durable and scalable storage option, but it does not provide low-latency writes or real-time processing capabilities. It is more suited for batch analytics rather than near real-time analytics.
- B. Incorrect.
Amazon RDS is a relational database service, which is not ideal for high-volume, low-latency writes from IoT devices. Additionally, it is not inherently optimized for real-time analytics on streaming data.
- C. Correct.
Amazon DynamoDB with DynamoDB Streams enabled is highly suitable for capturing high-volume, low-latency writes from IoT devices. DynamoDB Streams can be used to process the data in near real-time, meeting the stated requirements.
- D. Incorrect.
Amazon Kinesis Data Streams is a strong option for real-time data ingestion but is not designed for low-latency writes to the storage layer itself. Using S3 Glacier for long-term storage does not align with the requirement of performing near real-time analytics.