SAA-C03 Question 294
Select 2A company is designing a system to store and process customer data. The system has two distinct access patterns: one is read-intensive, where analytics applications frequently query the data, and the other is write-intensive, where new customer data is ingested at a high rate. The company wants to optimize for cost and performance while ensuring scalability. Which combination of AWS services should the company use?
- A
Amazon DynamoDB for write-intensive workloads and Amazon RDS Read Replicas for read-intensive workloads
- B
Amazon S3 for write-intensive workloads and Amazon Athena for read-intensive workloads
- C
Amazon Aurora with Auto Scaling for write-intensive workloads and Amazon ElastiCache for read-intensive workloads
- D
Amazon DynamoDB with DynamoDB Accelerator (DAX) for read-intensive workloads and write-intensive workloads
- E
Amazon Redshift for both write-intensive and read-intensive workloads
Show answer and explanation
Correct answers: A, B
Explanation
The correct answer combines Amazon DynamoDB for write-intensive workloads and Amazon RDS Read Replicas for read-intensive workloads, or Amazon S3 for write-intensive storage with Amazon Athena for analytics-focused reads. Both options align well with the described access patterns and optimize for performance, cost, and scalability. Other options either do not sufficiently address the access patterns or are less optimized for the specific workloads described.
- A. Correct.
Amazon DynamoDB is well-suited for write-intensive workloads because of its ability to handle high write throughput, and Amazon RDS Read Replicas can scale out read operations for read-intensive workloads. This solution is both scalable and optimized for the described access patterns.
- B. Correct.
Amazon S3 is ideal for write-intensive workloads because of its high durability and scalability, and Amazon Athena can efficiently query data stored in S3, addressing read-intensive requirements.
- C. Incorrect.
Amazon Aurora with Auto Scaling is a great choice for relational database workloads, but it is generally less optimal for handling extremely high write-throughput compared to DynamoDB or S3. Also, while ElastiCache is good for reducing latency in read-heavy applications, it may not be the best fit for analytics-focused read operations.
- D. Incorrect.
While DynamoDB is a good fit for write-intensive workloads, using DynamoDB Accelerator (DAX) is mainly beneficial for low-latency read operations rather than analytics-focused read-intensive patterns. This makes it less ideal in this scenario.
- E. Incorrect.
Amazon Redshift is highly optimized for analytics and read-heavy workloads, but it is not designed for handling high write-throughput workloads efficiently, making it unsuitable for this use case.