I'll suggest a comprehensive project that incorporates all these technologies. Let's build a Real-time Order Processing System for E-commerce.

🏢 Project: E-commerce Order Processing System

Architecture Overview:

User Browser → Load Balancer → API Gateway → Microservices → Kafka → Monitoring Dashboard
                ↓              ↓             ↓               ↓          ↓
              AWS ALB        ECS/EKS      Multiple       MSK/Kinesis  CloudWatch
                                                         Containers    Prometheus

---

📋 Step-by-Step Implementation

Phase 1: Project Setup & Local Development

1.1 Initialize the Project

# Create main project structure
mkdir ecommerce-microservices
cd ecommerce-microservices

# Initialize monorepo with multiple services
mkdir -p services/{order,product,user,notification}
mkdir -p load-balancer monitoring kafka-config cicd
mkdir -p infrastructure/aws

# Initialize each service
cd services/order
npx create-react-app frontend --template typescript
cd ../..

1.2 Microservices Setup

Order Service (Node.js + Express):

// services/order/server.js
const express = require('express');
const { Kafka } = require('kafkajs');
const promBundle = require('express-prom-bundle');

const app = express();
const metricsMiddleware = promBundle({ includeMethod: true });
app.use(metricsMiddleware);

const kafka = new Kafka({
  clientId: 'order-service',
  brokers: [process.env.KAFKA_BROKERS || 'localhost:9092']
});

const producer = kafka.producer();

app.post('/api/orders', async (req, res) => {
  const order = req.body;
  
  // Produce to Kafka
  await producer.send({
    topic: 'orders',
    messages: [{ value: JSON.stringify(order) }]
  });
  
  res.json({ status: 'processing', orderId: order.id });
});

// Health check endpoint for load balancer
app.get('/health', (req, res) => {
  res.json({ status: 'healthy', service: 'order' });
});

app.listen(3001, () => {
  console.log('Order Service running on port 3001');
  producer.connect();
});

Product Service (Similar structure) User Service (Similar structure)

1.3 Frontend React Application

// services/order/frontend/src/App.js
import React, { useState } from 'react';
import axios from 'axios';

function App() {
  const [order, setOrder] = useState({});
  
  const createOrder = async () => {
    const response = await axios.post(
      `${process.env.REACT_APP_API_URL}/api/orders`,
      { productId: '123', quantity: 2 }
    );
    setOrder(response.data);
  };

  return (
    <div>
      <button onClick={createOrder}>Create Order</button>
      <div>Order Status: {order.status}</div>
    </div>
  );
}

export default App;

Phase 2: Dockerize All Services

2.1 Create Dockerfiles

# services/order/Dockerfile
FROM node:16-alpine
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
EXPOSE 3001
CMD ["node", "server.js"]

2.2 Docker Compose for Local Development

# docker-compose.yml
version: '3.8'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:latest
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181

  kafka:
    image: confluentinc/cp-kafka:latest
    depends_on:
      - zookeeper
    ports:
      - "9092:9092"
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092

  order-service:
    build: ./services/order
    ports:
      - "3001:3001"
    environment:
      KAFKA_BROKERS: kafka:9092

  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
    volumes:
      - ./load-balancer/nginx.conf:/etc/nginx/nginx.conf

Phase 3: Load Balancer Configuration

3.1 Nginx Configuration

# load-balancer/nginx.conf
http {
    upstream order_services {
        server order-service-1:3001;
        server order-service-2:3001;
        server order-service-3:3001;
    }

    upstream product_services {
        server product-service-1:3002;
        server product-service-2:3002;
    }

    server {
        listen 80;
        
        location /api/orders {
            proxy_pass http://order_services;
            health_check;
        }
        
        location /api/products {
            proxy_pass http://product_services;
            health_check;
        }
        
        location /health {
            return 200 "healthy";
        }
    }
}

3.2 Load Balancer Health Check Endpoint

Each microservice must have:

app.get('/health', (req, res) => {
  const healthcheck = {
    uptime: process.uptime(),
    message: 'OK',
    timestamp: Date.now()
  };
  res.status(200).send(healthcheck);
});

Phase 4: Kafka Implementation

4.1 Kafka Producer Service

// services/kafka-producer/index.js
const { Kafka } = require('kafkajs');

class KafkaProducer {
  constructor() {
    this.kafka = new Kafka({
      clientId: 'ecommerce-producer',
      brokers: [process.env.KAFKA_BROKERS]
    });
    this.producer = this.kafka.producer();
  }

  async send(topic, message) {
    await this.producer.connect();
    await this.producer.send({
      topic,
      messages: [{ value: JSON.stringify(message) }]
    });
  }
}

module.exports = KafkaProducer;

4.2 Kafka Consumer Service

// services/kafka-consumer/index.js
const { Kafka } = require('kafkajs');

class KafkaConsumer {
  constructor(groupId) {
    this.kafka = new Kafka({
      clientId: 'ecommerce-consumer',
      brokers: [process.env.KAFKA_BROKERS]
    });
    this.consumer = this.kafka.consumer({ groupId });
  }

  async subscribe(topic, callback) {
    await this.consumer.connect();
    await this.consumer.subscribe({ topic, fromBeginning: true });
    
    await this.consumer.run({
      eachMessage: async ({ message }) => {
        callback(JSON.parse(message.value.toString()));
      }
    });
  }
}

Phase 5: Monitoring Setup

5.1 Prometheus Configuration

# monitoring/prometheus.yml
global:
  scrape_interval: 15s

scrape_configs:
  - job_name: 'order-service'
    static_configs:
      - targets: ['order-service:3001']
    metrics_path: '/metrics'

  - job_name: 'product-service'
    static_configs:
      - targets: ['product-service:3002']
    metrics_path: '/metrics'

  - job_name: 'node-exporter'
    static_configs:
      - targets: ['node-exporter:9100']

5.2 Grafana Dashboard

Create dashboard JSON files for:

• Service health monitoring
• Request rates and latency
• Error rates
• Kafka message throughput
• Container resource usage

Phase 6: AWS Cloud Deployment

6.1 Infrastructure as Code (Terraform)

# infrastructure/aws/main.tf
provider "aws" {
  region = "us-east-1"
}

# ECS Cluster
resource "aws_ecs_cluster" "ecommerce_cluster" {
  name = "ecommerce-cluster"
}

# Load Balancer
resource "aws_lb" "main" {
  name               = "ecommerce-lb"
  internal           = false
  load_balancer_type = "application"
  security_groups    = [aws_security_group.lb_sg.id]
  subnets            = aws_subnet.public.*.id
}

# Target Groups for each service
resource "aws_lb_target_group" "order_service" {
  name     = "order-service-tg"
  port     = 3001
  protocol = "HTTP"
  vpc_id   = aws_vpc.main.id
  
  health_check {
    path = "/health"
  }
}

6.2 ECS Task Definitions

{
  "family": "order-service",
  "networkMode": "awsvpc",
  "containerDefinitions": [{
    "name": "order-service",
    "image": "your-ecr-repo/order-service:latest",
    "portMappings": [{
      "containerPort": 3001,
      "hostPort": 3001
    }],
    "environment": [
      {"name": "KAFKA_BROKERS", "value": "your-msk-brokers"}
    ]
  }]
}

Phase 7: CI/CD Pipeline

7.1 GitHub Actions Workflow

# .github/workflows/deploy.yml
name: Deploy to ECS

on:
  push:
    branches: [ main ]

jobs:
  build-and-deploy:
    runs-on: ubuntu-latest
    
    steps:
    - uses: actions/checkout@v2
    
    - name: Configure AWS credentials
      uses: aws-actions/configure-aws-credentials@v1
      with:
        aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
        aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
        aws-region: us-east-1
    
    - name: Login to Amazon ECR
      id: login-ecr
      uses: aws-actions/amazon-ecr-login@v1
    
    - name: Build and push Docker image
      run: |
        docker build -t order-service ./services/order
        docker tag order-service:latest ${{ steps.login-ecr.outputs.registry }}/order-service:latest
        docker push ${{ steps.login-ecr.outputs.registry }}/order-service:latest
    
    - name: Deploy to ECS
      run: |
        aws ecs update-service \
          --cluster ecommerce-cluster \
          --service order-service \
          --force-new-deployment

Phase 8: Complete Integration

8.1 Environment Configuration

# .env.example
KAFKA_BROKERS=your-kafka-brokers:9092
AWS_REGION=us-east-1
ECS_CLUSTER=ecommerce-cluster
MONITORING_ENDPOINT=http://prometheus:9090

8.2 Deploy Script

#!/bin/bash
# deploy.sh

# 1. Build all services
docker-compose build

# 2. Run tests
npm test

# 3. Push to ECR
aws ecr get-login-password | docker login --username AWS --password-stdin your-account-id.dkr.ecr.region.amazonaws.com

# 4. Deploy to ECS
aws ecs update-service --cluster ecommerce-cluster --service order-service --force-new-deployment

🚀 Deployment Steps Summary:

1. Local Development:

   • Set up all microservices locally
   • Test with Docker Compose
   • Verify Kafka message flow

2. AWS Setup:

   • Create VPC, subnets, security groups
   • Set up ECR repositories
   • Configure MSK (Managed Streaming for Kafka)
   • Create ECS cluster

3. CI/CD Pipeline:

   • Configure GitHub Secrets
   • Test the pipeline with dummy deployment
   • Set up staging environment

4. Monitoring & Alerting:

   • Deploy Prometheus and Grafana
   • Set up CloudWatch alarms
   • Configure Slack/Email alerts

5. Load Testing:

   • Use Apache Bench or k6
   • Test auto-scaling
   • Monitor performance metrics

📊 Monitoring Dashboard Metrics:

1. Service Health (HTTP 200 rates)
2. Response Time P95, P99
3. Error Rates (4xx, 5xx)
4. Kafka Lag per consumer group
5. Container CPU/Memory usage
6. API Request Rate per service

🔧 Tools & Services Used:

• Frontend: React, TypeScript, Axios
• Backend: Node.js, Express
• Message Queue: Apache Kafka (AWS MSK)
• Containerization: Docker
• Orchestration: AWS ECS/EKS
• Load Balancer: AWS ALB + Nginx
• Monitoring: Prometheus, Grafana, AWS CloudWatch
• CI/CD: GitHub Actions, AWS CodePipeline
• Infrastructure: Terraform, AWS CDK

This architecture provides scalability, fault tolerance, and real-time processing capabilities. Start with basic implementations and gradually add complexity as you become comfortable with each component.