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Cloud-native microservices app with container orchestration

Why Choose This Project

Modern enterprises need scalable, resilient, and modular applications. A cloud-native microservices architecture ensures independent deployment, fault tolerance, and better scalability. By integrating container orchestration tools like Kubernetes (K8s), Amazon EKS, Google GKE, or Azure AKS, this project demonstrates real-world deployment of microservices at scale.

What You Get

A production-ready cloud-native application.
Hands-on with Kubernetes orchestration (pods, services, deployments).
Microservices connected via REST APIs / gRPC with service discovery.
CI/CD pipeline for automated builds & deployments.
Cloud-native observability (logs, metrics, traces).

Key Features

Feature	Description
Microservices Design	Break down application into independent services (e.g., User Service, Payment Service, Order Service).
Containerization	Each microservice packaged in Docker containers for portability.
Kubernetes Orchestration	Deploy services on K8s cluster with scaling, rolling updates, and self-healing.
API Gateway	Central entry point (e.g., NGINX / Kong) for routing and authentication.
Service Discovery	K8s manages discovery and load balancing internally.
Cloud-Native Databases	Use Amazon RDS, Google Cloud SQL, or Azure Cosmos DB.
Monitoring & Logging	Integrated with Prometheus, Grafana, and ELK for observability.
CI/CD Pipeline	Automated builds with GitHub Actions, Jenkins, or GitLab CI/CD.
Scalability	Auto-scaling of services based on traffic (HPA in Kubernetes).

Technology Stack

Frontend Layer

React.js / Angular / Vue.js (SPA client)
API calls to backend microservices

Backend Layer

Microservices (Spring Boot, Node.js, or Python Flask/FastAPI)
REST APIs / gRPC for communication

Containerization & Orchestration

Docker for packaging microservices
Kubernetes (EKS/GKE/AKS) for orchestration

Database Layer

Relational: PostgreSQL / MySQL (managed cloud DB)
NoSQL (if needed): MongoDB / DynamoDB / Firestore

Cloud Integration

AWS EKS / GCP GKE / Azure AKS for K8s cluster
Load balancing with Ingress Controller / Cloud LB

CI/CD

GitHub Actions / Jenkins pipelines
Automated deployments to K8s

Monitoring & Logging

Prometheus + Grafana for metrics
ELK Stack (Elasticsearch, Logstash, Kibana) for logs
CloudWatch / Stackdriver / Azure Monitor integration

Working Flow

Developer builds microservices → Docker images pushed to container registry (ECR/GCR/ACR).
CI/CD pipeline triggers → Build, test, and deploy microservices to Kubernetes cluster.
Kubernetes schedules pods and exposes them via Services (ClusterIP, NodePort, LoadBalancer).
API Gateway routes traffic from frontend to appropriate microservice.
Databases and storage services handle persistent data.
Monitoring & logging systems collect metrics, traces, and logs for debugging and scaling.
Auto-scaling adjusts number of pods dynamically based on CPU/memory usage.