Which option provides a scalable application architecture to handle peak seasons with the LEAST development effort?
Migrate the backend services to AWS Lambda. Increase the read and write capacity of DynamoDB
Migrate the backend services to AWS Lambda. Configure DynamoDB to use global tables
Use Auto Scaling groups for the backend services. Use DynamoDB auto scaling
Use Auto Scaling groups for the backend services. Use Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB
Explanations:
While migrating to AWS Lambda can improve scalability, simply increasing the read and write capacity of DynamoDB may not be sufficient during peak seasons, and this option does not address potential performance bottlenecks effectively.
Migrating to AWS Lambda can enhance scalability, but using global tables is more relevant for cross-region replication rather than directly addressing peak traffic in a single region. This option also adds complexity without guaranteeing significant performance improvement during high traffic.
Utilizing Auto Scaling groups for the backend services allows the application to dynamically adjust its capacity based on demand, ensuring efficient resource use during peak traffic. Coupling this with DynamoDB auto scaling allows the database to automatically adjust capacity based on the workload, providing a scalable solution with minimal development effort.
This option introduces additional complexity by incorporating Amazon SQS and AWS Lambda for writes to DynamoDB. While it can help with managing burst traffic, it adds layers of development and management overhead that may not be necessary compared to simply using Auto Scaling with DynamoDB auto scaling.
I have a feeling that the answer is:
Use Auto Scaling groups for the backend services. Use DynamoDB auto scaling
As far as I’m aware, the answer is:
Use Auto Scaling groups for the backend services. Use DynamoDB auto scaling