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Kubernetes Capstone Project : 5 Proven Steps For Beginners

Posted on by Monika
Kubernetes Capstone Project

Introduction

Kubernetes Capstone Project In the world of modern software development, Kubernetes has emerged as the de facto standard for container orchestration. As organizations move towards cloud-native architectures, mastering Kubernetes is essential for developers, DevOps engineers, and system administrators alike. This Kubernetes Capstone Project  guides you through designing, deploying, and managing a real-world scalable application on Kubernetes, integrating key concepts and tools that demonstrate Kubernetes’ power and flexibility.

kubernetes capstone project

Project Overview

The goal of this Kubernetes Capstone Project is to build a fully functional, scalable, and resilient microservices-based web application deployed on a Kubernetes cluster. The project covers:

  • Designing containerized microservices

  • Writing Kubernetes manifests for Pods, Deployments, Services

  • Implementing resource management with Resource Quotas

  • Using Helm Charts for deployment automation

  • Configuring Horizontal Pod Autoscaling (HPA)

  • Monitoring with Prometheus and Grafana

  • Managing security using Pod Security Policies

  • Performing rolling updates and rollback

  • Upgrading Kubernetes clusters safely

Step 1: Designing the Application Architecture Kubernetes Capstone Project

The sample application will consist of multiple microservices:

  • Frontend Service: React-based UI serving user requests.

  • Backend API: Node.js Express REST API handling business logic.

  • Database: Stateful PostgreSQL deployment for data persistence.

  • Cache Layer: Redis to improve response time and reduce database load.

Each microservice will be containerized using Docker, creating isolated and reproducible environments.

Step 2: Containerizing Microservices

Create Dockerfiles for each microservice to build container images:

  • Frontend Dockerfile: Uses Nginx to serve static React build files.

  • Backend Dockerfile: Node.js environment with dependencies installed.

  • Database and Cache: Official PostgreSQL and Redis images used.

Push images to a container registry (Docker Hub, AWS ECR, etc.) for Kubernetes to pull.

Step 3: Writing Kubernetes Manifests

Kubernetes Capstone Project YAML files for each microservice, including:

  • Pods and Deployments: Define replicas, container specs, and resource limits.

  • Services: ClusterIP for internal communication, LoadBalancer for frontend.

  • ConfigMaps and Secrets: Store configuration and sensitive data like DB passwords securely.

  • StatefulSet: For PostgreSQL, ensuring persistent storage and stable network IDs.

Example: Backend deployment snippet:

apiVersion: apps/v1
kind: Deployment
metadata:
name: backend-deployment
spec:
replicas: 3
selector:
matchLabels:
app: backend
template:
metadata:
labels:
app: backend
spec:
containers:
- name: backend
image: yourrepo/backend:latest
ports:
- containerPort: 3000
resources:
requests:
memory: "256Mi"
cpu: "250m"
limits:
memory: "512Mi"
cpu: "500m"

Step 4: Resource Management with Resource Quotas and Limits

Set resource quotas per namespace to prevent any team or application from over-consuming cluster resources. For example:

apiVersion: v1
kind: ResourceQuota
metadata:
name: project-quota
spec:
hard:
requests.cpu: "4"
requests.memory: 8Gi
limits.cpu: "8"
limits.memory: 16Gi

This ensures efficient resource allocation and avoids noisy neighbor issues.

Step 5: Deploying with Helm Charts

Use Helm to package K8s manifests into reusable charts. Helm simplifies deploying and upgrading applications.

  • Create a Helm chart with templates for deployments, services, configmaps.

  • Use Helm values.yaml to configure deployments per environment (dev, staging, prod).

  • Commands like helm install and helm upgrade make release management easier.

Step 6: Implementing Horizontal Pod Autoscaling (HPA)

Configure HPA to automatically scale pods based on CPU usage:

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: backend-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: backend-deployment
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 60

This ensures application availability under load without manual intervention.

Step 7: Monitoring with Prometheus and Grafana

Deploy Prometheus to collect cluster and application metrics. Grafana connects to Prometheus and provides dashboards.

  • Set up Prometheus Operator for easy monitoring setup.

  • Create Grafana dashboards for CPU/memory usage, pod status, request latency.

  • Alerting configured for critical thresholds.

Monitoring helps in proactive troubleshooting and capacity planning.

Step 8: Security Best Practices: Pod Security Policies and Secrets Management

Implement Pod Security Policies to restrict privileges and enforce security standards:

  • Prevent containers running as root.

  • Restrict usage of host network and volumes.

Manage sensitive info with K8s Secrets and ensure encrypted etcd storage.

Step 9: Performing Rolling Updates and Rollbacks

Deploy new application versions without downtime by using K8s rolling updates.

  • Update Deployment manifest with new container image tag.

  • K8s gradually replaces pods with new ones, monitoring health.

  • If issues arise, rollback to previous stable version.

Example command:

kubectl rollout undo deployment/backend-deployment

Step 10: Upgrading Kubernetes Capstone Project Clusters

Regular upgrades maintain security and add features.

  • Upgrade control plane components first.

  • Drain and upgrade worker nodes sequentially.

  • Test workloads post-upgrade to ensure stability.

Plan OS upgrades carefully, ensuring compatibility with K8s version.

Challenges Kubernetes Capstone Project

  • Managing resource allocation is critical to prevent cluster instability.

  • Setting up monitoring early helps catch issues before they impact users.

  • Security must be integrated into the deployment pipeline, not an afterthought.

  • Automating deployments with Helm increases agility and consistency.

Conclusion

This  kubernetes capstone project demonstrated building a real-world microservices application on K8s, covering containerization, deployment, scaling, security, monitoring, and maintenance. Mastery of these skills prepares you for managing production-grade K8s environments and accelerates your journey in the cloud-native ecosystem.

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