SAA-C03 Question 224
Single answerA company is building a data analytics platform to process and store large volumes of time-series data from IoT devices. The platform must ensure high throughput for frequent writes and provide scalable storage that can accommodate the growing dataset over time. Which storage solution should the company use?
- A
Amazon S3 with S3 Standard storage class
- B
Amazon RDS with a MySQL database
- C
Amazon DynamoDB with on-demand capacity mode
- D
Amazon EFS with General Purpose performance mode
Show answer and explanation
Correct answer: C
Explanation
The best solution for storing and processing large volumes of time-series data with high throughput and scalability requirements is Amazon DynamoDB. It is a fully managed, serverless NoSQL database designed to handle frequent writes and automatically scale to accommodate growing datasets. The on-demand capacity mode further simplifies scaling for unpredictable workloads, making it an ideal fit for this scenario.
- A. Incorrect.
Amazon S3 is a highly durable and scalable object storage service, but it is not optimized for frequent, low-latency writes such as those required for time-series data. It is better suited for storing large, unstructured files like logs or backups.
- B. Incorrect.
Amazon RDS with MySQL is a relational database service that is not ideal for handling high-frequency writes at scale for time-series data. It may face performance bottlenecks as the dataset grows.
- C. Correct.
Amazon DynamoDB is a high-performance NoSQL database that supports frequent writes at scale with low latency. The on-demand capacity mode ensures automatic scaling to handle variable workloads, making it a suitable choice for time-series data.
- D. Incorrect.
Amazon EFS is a file storage service that is optimized for shared file access, not for handling high-throughput, low-latency writes in a scalable manner. It is not the best fit for time-series data storage.