Which solution meets these requirements MOST cost-effectively?
Store the images and geographic codes in a database table. Use Oracle running on an Amazon RDS Multi-AZ DB instance.
Store the images in Amazon S3 buckets. Use Amazon DynamoDB with the geographic code as the key and the image S3 URL as the value.
Store the images and geographic codes in an Amazon DynamoDB table. Configure DynamoDB Accelerator (DAX) during times of high load.
Store the images in Amazon S3 buckets. Store geographic codes and image S3 URLs in a database table. Use Oracle running on an Amazon RDS Multi-AZ DB instance.
Explanations:
Using Oracle on Amazon RDS Multi-AZ provides high availability but may not be cost-effective for storing millions of high-resolution GIS images due to high storage and licensing costs. Additionally, RDS is not optimized for handling frequent updates at scale, particularly during natural disasters.
Storing images in Amazon S3 is highly cost-effective and scalable, allowing for easy management of large datasets. Using Amazon DynamoDB with geographic codes as the key and S3 URLs as values enables fast access and updates during high-load situations. This setup maximizes performance and minimizes costs.
While using Amazon DynamoDB is scalable and provides low-latency access, storing images directly in DynamoDB can be expensive and inefficient for high-resolution images. Additionally, configuring DynamoDB Accelerator (DAX) adds complexity and cost, and may not be necessary if the images are stored in S3.
This option combines S3 for image storage (which is good), but the use of an Oracle RDS instance to manage geographic codes and S3 URLs is not as cost-effective as using DynamoDB. RDS would add unnecessary costs and complexity, especially considering the expected high load during natural disasters.
I think the answer is:
Store the images in Amazon S3 buckets. Use Amazon DynamoDB with the geographic code as the key and the image S3 URL as the value.