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Difficulty: MediumServerless and Automated Scaling Architectures for Cost Efficiency

A logistics company is building a serverless tracking application. The application must process incoming telemetry events that are highly unpredictable, with long periods of zero activity followed by sudden bursts of thousands of requests per second. The telemetry events must be processed in the exact order they are received to ensure tracking accuracy. Additionally, the application requires a continuous, 24/7 background reconciliation process to run reports on the accumulated telemetry data. The company wants to minimize compute and database costs, ensuring that they do not pay for idle resources during periods of no traffic. Which two of the following architectural options should the Solutions Architect select to design a cost-effective and functionally correct solution? (Select TWO.)

  1. Store the telemetry data in Amazon DynamoDB configured with on-demand capacity mode.Answer
  2. Deploy the 24/7 background reconciliation process on Amazon ECS with AWS Fargate.Answer
  3. C
    Implement the 24/7 background reconciliation process using an AWS Lambda function configured to trigger itself recursively.
  4. D
    Store the telemetry data in Amazon DynamoDB configured with provisioned capacity mode and Auto Scaling.
  5. E
    Use a standard Amazon SQS queue to ingest the telemetry events and guarantee first-in, first-out (FIFO) ordering during spikes.

Answer

Store the telemetry data in Amazon DynamoDB configured with on-demand capacity mode, and deploy the 24/7 background reconciliation process on Amazon ECS with AWS Fargate.
The correct architecture uses Amazon DynamoDB on-demand capacity mode for the telemetry storage and Amazon ECS on AWS Fargate for the continuous 24/7 background reconciliation engine. Amazon DynamoDB on-demand mode is optimal for spiky, unpredictable workloads because it scales to zero throughput costs when idle, charging only for active reads and writes. For the continuous 24/7 background reporting engine, containerized compute on Amazon ECS with AWS Fargate is more cost-effective than serverless function runtimes, which are priced for short-lived event-driven execution.

Step-by-Step Solution

1
Analyze the database capacity mode requirement for unpredictable, spiky telemetry events with long idle periods.
Identify that Amazon DynamoDB on-demand capacity mode scales down to zero throughput cost when there is no traffic, making it more cost-effective than provisioned capacity mode.
Choosing provisioned capacity mode (even with Auto Scaling) incurs a continuous hourly charge for the minimum configured write/read capacity units during idle periods.
2
Analyze the compute requirement for the 24/7 continuous reconciliation process.
Identify that running containerized compute on Amazon ECS with AWS Fargate is more cost-effective for continuous 24/7 execution compared to AWS Lambda.
AWS Lambda is designed for short-lived, event-driven executions, and running it continuously leads to high invocation and duration costs.
3
Combine the database and compute architectures for cost efficiency.
Select the combination of Amazon DynamoDB on-demand capacity mode and Amazon ECS with AWS Fargate.
This combination ensures zero database costs during idle periods and minimizes continuous compute costs for the 24/7 background engine.

Key Concept

Designing serverless and automated scaling architectures that align cost structures with usage patterns, specifically using on-demand capacity for unpredictable workloads with idle times and containerized compute for continuous, long-running processes.
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Serverless and Automated Scaling Architectures for Cost Efficiency