For near-real-time data processing with minimal effort and operational overhead, Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose are the most cost-effective solutions. Amazon Kinesis Data Streams will continuously capture and stream the data in real-time, while Amazon Kinesis Data Firehose will handle the near-real-time data transformation and loading into Amazon Redshift. This combination allows for seamless integration of streaming data with minimal manual intervention and operational complexity.
The application experiences a predictable spike in CPU utilization on the first day of every month at midnight due to the month-end financial calculation batch. To handle this predictable workload and avoid downtime, configuring an EC2 Auto Scaling scheduled scaling policy based on the monthly schedule is the best solution. Scheduled scaling allows you to automatically increase the number of EC2 instances just before the spike occurs, ensuring that additional resources are available to handle the increased load. This proactive approach prevents the application from slowing down due to immediate 100% CPU utilization.
To make the web application more resilient to periodic increases in request rates, adding an Aurora Replica and an Amazon CloudFront distribution would be effective. Aurora Replicas can offload read traffic from the primary database, thereby handling increased read request rates and enhancing the database's scalability and availability. An Amazon CloudFront distribution can cache content at edge locations, reducing the load on the EC2 instances behind the Application Load Balancer by serving cached content quickly and efficiently, which reduces latency and improves user experience. This combination ensures both the database and the web content delivery can handle increased traffic more resiliently.
To separate read requests from write requests in an Amazon Aurora Multi-AZ deployment, the best approach is to create a read replica and modify the application to use the appropriate endpoint. Aurora uses a storage model that allows multiple read replicas to share the same underlying data as the primary instance without needing to create multiple sets of data. This setup enables the read replicas to handle read traffic effectively, reducing the load on the primary instance and minimizing latency for write operations. This method leverages Aurora's architecture designed for high availability and scalability in read-heavy workloads.
The suitable solution involves using AWS Snowball for the initial transfer and AWS Direct Connect for ongoing connectivity. AWS Snowball is optimal for one-time data transfers involving large volumes of data (50 TB per application in this case), mitigating the need for extended network use and avoiding potentially high online data transfer costs. Following the initial transfer, AWS Direct Connect ensures ongoing network connectivity with consistent throughput, meeting the requirement for secure connections as it provides private, dedicated connections to AWS. This combination is effective in satisfying both the migration timeframe and the need for stable, secure connectivity afterward.