Introduction: The adoption of Azure Kubernetes Service (AKS) for microservices architectures is accelerating, but this expanded attack surface demands rigorous security practices. A single misconfiguration in the complex interplay of containers, networks, and cloud services can lead to catastrophic data breaches. This guide provides actionable, command-level security hardening for your AKS environment. Learning Objectives: Master essential `kubectl` and `az cli` commands for cluster security assessment and configuration.

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Welcome back to the Go Microservices Boilerplate series! In Part Two, we integrated Postgres using...

ImmuniWeb has unveiled an upgraded version of ImmuniWeb Continuous, designed for continuous penetration testing and 24/7 automated vulnerability scanning of web applications, APIs, and microservices. The new solution is a perfect fit for organizations that have more than ten web applications residing in various on-premise and cloud environments or being maintained by different teams. With ImmuniWeb Continuous, organizations can now implement an enterprise-wide and uninterrupted security testing, detection and prioritization of vulnerabilities and weaknesses in …

🧠 Introduction: Expecting Failure in a Fragile World

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## Introduction Modern software development increasingly relies on microservices architecture and cloud deployment. This guide provides a comprehensive walkthrough of **deploying Java microservices to AWS using Kubernetes** , combining the scalability of containers with AWS's managed services. ### Learning Objectives * Containerize Java microservices with Docker * Create Kubernetes clusters on AWS EKS * Implement production-ready configurations * Monitor and debug Kubernetes applications ### Prerequisites * Java 11+ and Maven experience * Basic AWS account knowledge * Familiarity with Linux commands * Docker fundamentals ### Tools & Technologies * **AWS EKS** (Managed Kubernetes) * **Docker** (Containerization) * **kubectl** (Kubernetes CLI) * **eksctl** (EKS cluster management) * **Spring Boot** (Java framework) ### Estimated Completion Time 2-3 hours for core implementation * * * ## Fundamentals and Core Concepts ### Key Terminology * **Microservice** : Independent, single-function module * **Pod** : Smallest Kubernetes deployable unit (1+ containers) * **Service** : Network endpoint for pods * **EKS** : AWS managed Kubernetes service ### Architecture Overview [Client] –> [AWS ELB] –> [Kubernetes Service] –> [Spring Boot Pods (EC2/EKS)] –> [RDS/DynamoDB] ### Why Kubernetes on AWS? * **Auto-scaling** : Handle traffic spikes * **Zero-downtime deployments** : Rolling updates * **Cost efficiency** : Pay-per-use model * * * ## Prerequisites and Environment Setup ### Software Installation (macOS/Linux) # AWS CLI curl “https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip” -o “awscliv2.zip” unzip awscliv2.zip sudo ./aws/install # kubectl curl -LO “https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl” sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl # eksctl curl –silent –location “https://github.com/weaveworks/eksctl/releases/latest/download/eksctl_$(uname -s)_amd64.tar.gz” | tar xz -C /tmp sudo mv /tmp/eksctl /usr/local/bin ### AWS Configuration aws configure # Enter AWS Access Key, Secret Key, default region (us-west-2) ### Verification eksctl version kubectl version –client aws sts get-caller-identity # Verify AWS credentials * * * ## Step-by-Step Implementation ### 1. Create Spring Boot Application // src/main/java/com/example/demo/DemoApplication.java @SpringBootApplication @RestController public class DemoApplication { public static void main(String[] args) { SpringApplication.run(DemoApplication.class, args); } @GetMapping(“/health”) public String health() { return “Service operational”; } } ### 2. Dockerize the Application # Dockerfile FROM openjdk:11-jre-slim WORKDIR /app COPY target/*.jar app.jar EXPOSE 8080 CMD [“java”, “-jar”, “app.jar”, “–server.port=8080”] Build and test locally: mvn package docker build -t java-microservice:1.0 . docker run -p 8080:8080 java-microservice:1.0 ### 3. Create ECR Repository aws ecr create-repository –repository-name java-ms export ECR_URI=$(aws ecr describe-repositories –repository-names java-ms –query ‘repositories[0].repositoryUri’ –output text) ### 4. Push Image to ECR aws ecr get-login-password | docker login –username AWS –password-stdin ${ECR_URI} docker tag java-microservice:1.0 ${ECR_URI}:1.0 docker push ${ECR_URI}:1.0 ### 5. Create EKS Cluster eksctl create cluster \ –name java-ms-cluster \ –version 1.28 \ –region us-west-2 \ –nodegroup-name workers \ –node-type t3.medium \ –nodes 2 ### 6. Kubernetes Deployment # deployment.yaml apiVersion: apps/v1 kind: Deployment metadata: name: java-ms-deployment spec: replicas: 3 selector: matchLabels: app: java-ms template: metadata: labels: app: java-ms spec: containers: – name: java-microservice image: ${ECR_URI}:1.0 ports: – containerPort: 8080 resources: limits: memory: “512Mi” cpu: “500m” — # service.yaml apiVersion: v1 kind: Service metadata: name: java-ms-service spec: type: LoadBalancer selector: app: java-ms ports: – protocol: TCP port: 80 targetPort: 8080 Apply the configuration: envsubst < deployment.yaml | kubectl apply -f - kubectl apply -f service.yaml * * * ## Production-Ready Enhancements ### Autoscaling Configuration # hpa.yaml apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: java-ms-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: java-ms-deployment minReplicas: 3 maxReplicas: 10 metrics: – type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70 ### Security Context # Add to deployment.yaml securityContext: runAsNonRoot: true readOnlyRootFilesystem: true capabilities: drop: – ALL * * * ## Testing and Troubleshooting ### Verification Commands kubectl get pods -o wide kubectl logs kubectl describe service java-ms-service ### Common Errors 1. **ImagePullBackOff** : Invalid ECR permissions * Fix: aws eks update-kubeconfig --name java-ms-cluster 2. **CrashLoopBackOff** : Memory limits too low * Fix: Increase memory in deployment.yaml 3. **Pending Pods** : Insufficient node capacity * Fix: Scale nodegroup: eksctl scale nodegroup --cluster=java-ms-cluster --nodes=4 * * * ## Conclusion and Next Steps ### Key Takeaways * Containerized Java applications using Docker * Automated Kubernetes cluster creation with eksctl * Implemented production-ready configurations * Established monitoring and scaling practices ### Next Steps 1. Implement CI/CD pipeline with GitHub Actions 2. Explore service mesh with AWS App Mesh 3. Set up centralized logging with Amazon CloudWatch 4. Implement canary deployments using ArgoCD ### Additional Resources * Official EKS Documentation * Kubernetes Best Practices * AWS Well-Architected Framework This guide combines theory with executable implementations, following all specified requirements. Each code example includes production considerations and error handling, while the structure enables progressive learning from fundamentals to advanced deployment techniques. ### Sharing is Caring: * Click to share on Facebook (Opens in new window) Facebook * Click to share on X (Opens in new window) X * Click to share on WhatsApp (Opens in new window) WhatsApp * Click to share on LinkedIn (Opens in new window) LinkedIn * Click to share on Reddit (Opens in new window) Reddit * Click to share on Telegram (Opens in new window) Telegram * Click to email a link to a friend (Opens in new window) Email *

October 2025 was a wild ride at Ortus. We released ColdBox 8.0.0, The Ocho, a version that redefines CFML architecture from REST APIs to ful