From Theory to Production: Architecting a Cloud-Native Weather Platform on AWS
I wanted to build a production-grade, highly available full-stack application that mirrors enterprise architecture. The result? A decoupled Cloud-Native Weather Platform using a Next.js frontend and a Java Spring Boot backend, entirely hosted and automated on AWS.
Here is a breakdown of how I built it, the AWS services I leveraged, and the key lessons I learned along the way.
- The Frontend Edge: AWS Amplify & Route 53
For the user interface, I built a server-side rendered application using Next.js. My goal was fast global delivery and seamless deployment.
I deployed the frontend using AWS Amplify, which automatically handles the hosting, edge caching via Amazon CloudFront, and CI/CD for the Next.js framework. To give it a professional touch, I configured Amazon Route 53 for secure, custom DNS routing, ensuring users experience low-latency, encrypted HTTPS connections from the moment they hit the URL.
2.The Compute Layer: AWS Elastic Beanstalk
The brain of the application is a Java Spring Boot REST API that processes and serves the weather data. I needed an environment that could handle the heavy lifting of server provisioning, load balancing, and scaling without requiring me to manually manage individual Linux instances.
AWS Elastic Beanstalk was the perfect Platform-as-a-Service (PaaS) solution. Behind the scenes, Beanstalk orchestrated the foundational infrastructure:
- Application Load Balancer (ALB): I configured an ALB to serve as the single entry point for the API, handling SSL/TLS certificate termination and intelligently distributing incoming HTTP traffic.
- Auto Scaling Group (ASG): To ensure high availability and cost efficiency, the backend dynamically scales Amazon EC2 instances out and in based on real-time traffic volume and CPU utilization.
- Internal Reverse Proxy: Within the EC2 environment, Elastic Beanstalk utilizes Nginx as a local reverse proxy, smoothly routing public traffic from port 80 directly to my embedded Spring Boot Tomcat server on port 5000.
3.Automating the Flow: AWS CodePipeline & CodeBuild
A modern application is only as good as its deployment strategy. I wanted to achieve zero-downtime releases without manual intervention.
I engineered a fully automated continuous integration and continuous deployment (CI/CD) pipeline using native AWS developer tools:
- Source: GitHub webhooks trigger AWS CodePipeline the moment new code is merged into the main branch.
- Build: AWS CodeBuild spins up a managed container, compiles the Java source code, runs tests, and packages the executable Maven .jar file.
- Artifact Store: The compiled .jar is securely stored in an Amazon S3 bucket.
- Deploy: Finally, CodePipeline pulls the artifact from S3 and orchestrates a rolling deployment to the Elastic Beanstalk environments.
4.Observability: Amazon CloudWatch
You cannot manage what you cannot measure. I centralized all application telemetry—including Nginx access logs, JVM standard output, and EC2 health metrics—into Amazon CloudWatch. This gave me real-time visibility into the system's performance and the data required to drive my Auto Scaling policies.
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