README.md

Marketing Intelligence Application

A powerful React-based marketing intelligence platform leveraging Google Cloud services for advanced analytics, machine learning, and real-time data processing.

🚀 Features

• Real-time Analytics Dashboard

  • BigQuery integration for large-scale data analysis
  • Interactive data visualization components
  • Custom report generation

• Machine Learning Capabilities

  • Vertex AI integration for predictive analytics
  • Gemini AI for personalized content generation
  • Automated lead scoring system

• Real-time Data Management

  • Firestore integration for live updates
  • Automated ETL pipelines
  • Data synchronization across clients

• Security

  • Firebase Authentication
  • Role-based access control
  • Secure data encryption

🛠 Tech Stack

• Frontend: React, TypeScript
• Cloud Services: Google Cloud Platform
• Database: Firestore
• Analytics: BigQuery
• AI/ML: Vertex AI, Gemini AI
• Authentication: Firebase Auth
• CI/CD: GitHub Actions

⚙️ Prerequisites

• Node.js (v18 or higher)
• Google Cloud CLI
• Firebase CLI
• Valid Google Cloud Project with enabled APIs
• Firebase Project

🏗 Installation

1. Clone the repository

git clone [repository-url]
cd mb-marketing

2. Install dependencies

npm install

3. Configure Google Cloud

gcloud init
gcloud config set project [YOUR_PROJECT_ID]
gcloud services enable bigquery.googleapis.com aiplatform.googleapis.com firestore.googleapis.com

4. Set up Firebase

npm install -g firebase-tools
firebase login
firebase init

5. Environment Configuration Create a .env file:

REACT_APP_GCP_PROJECT_ID=your-project-id
REACT_APP_GEMINI_API_KEY=your-gemini-api-key

🏃‍♂️ Running the Application

Development

npm run dev

Production Build

npm run build
npm start

📁 Project Structure

src/
├── components/
│   ├── analytics/     # Analytics components
│   ├── ml/            # Machine learning components
│   └── common/        # Shared components
├── services/          # API and service integrations
├── hooks/             # Custom React hooks
└── utils/             # Utility functions

🔒 Security Setup

1. Firebase Authentication

import { getAuth, signInWithPopup, GoogleAuthProvider } from "firebase/auth";

export const auth = getAuth();

2. Authorization Hook

const { user } = useAuth();

🚀 Deployment

Automated deployment via GitHub Actions:

name: Deploy to Production
on:
  push:
    branches: [main]

🧪 Testing

# Run tests
npm test

# Run tests with coverage
npm test -- --coverage

📈 Monitoring

1. Set up Google Cloud Monitoring
2. Configure error tracking
3. Implement usage analytics
4. Review maintenance documentation

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit changes
4. Push to the branch
5. Create a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE.md file for details.

⚡ Performance Optimization

• Implement code splitting
• Optimize bundle size
• Use lazy loading for components
• Configure caching strategies

🔄 CI/CD Pipeline

• Automated testing
• Build verification
• Deployment staging
• Production deployment

📚 Documentation

Detailed documentation for each component and service is available in the /docs directory:

• API Documentation
• Component Documentation
• Service Integration Guides
• Security Guidelines

ML Analytics Dashboard Integration Guide

1. Project Structure Setup

Initialize Core Directories

# Create main directories
mkdir -p src/{components,services,hooks,utils,contexts}
mkdir -p src/components/{analytics,ml,common}
mkdir -p src/services/{bigquery,firestore,vertex-ai,gemini-ai}

2. Dependencies Installation

# Core dependencies
npm install @google-cloud/bigquery @google-cloud/vertexai firebase 
npm install @google/generative-ai recharts flowbite-react
npm install tailwindcss @headlessui/react @heroicons/react

# Development dependencies
npm install -D typescript @types/react @types/node

3. Service Integration Implementation

A. Initialize Google Cloud Services

Create src/services/config.ts:

import { BigQuery } from '@google-cloud/bigquery';
import { VertexAI } from '@google-cloud/vertexai';

export const bigQueryClient = new BigQuery({
  projectId: process.env.NEXT_PUBLIC_GCP_PROJECT_ID
});

export const vertexAIClient = new VertexAI({
  project: process.env.NEXT_PUBLIC_GCP_PROJECT_ID,
  location: 'us-central1'
});

B. Firestore Integration

Create src/services/firestore/client.ts:

import { initializeApp } from 'firebase/app';
import { getFirestore } from 'firebase/firestore';

const firebaseConfig = {
  apiKey: process.env.NEXT_PUBLIC_FIREBASE_API_KEY,
  projectId: process.env.NEXT_PUBLIC_FIREBASE_PROJECT_ID,
  // Add other config values
};

const app = initializeApp(firebaseConfig);
export const db = getFirestore(app);

4. Analytics Components Implementation

A. Dashboard Component

Create src/components/analytics/Dashboard.tsx:

import React from 'react';
import { LineChart, XAxis, YAxis, CartesianGrid, Line } from 'recharts';

interface DashboardProps {
  data: any[];
}

export const Dashboard: React.FC<DashboardProps> = ({ data }) => {
  return (
    <div className="p-4">
      <h2 className="text-2xl font-bold mb-4">Analytics Dashboard</h2>
      <LineChart width={800} height={400} data={data}>
        <XAxis dataKey="name" />
        <YAxis />
        <CartesianGrid strokeDasharray="3 3" />
        <Line type="monotone" dataKey="value" stroke="#8884d8" />
      </LineChart>
    </div>
  );
};

5. ML Integration Components

A. Model Prediction Component

Create src/components/ml/PredictiveModel.tsx:

import { VertexAIService } from '../../services/vertex-ai/client';
import React, { useState } from 'react';

export const PredictiveModel = () => {
  const [prediction, setPrediction] = useState(null);
  const vertexAI = new VertexAIService();

  const handlePredict = async (data: any) => {
    const result = await vertexAI.predictLeadScore(data);
    setPrediction(result);
  };

  return (
    <div className="p-4">
      {/* Add prediction UI components */}
    </div>
  );
};

6. Authentication Setup

A. Firebase Auth Configuration

Create src/services/auth/firebase.ts:

import { getAuth, signInWithPopup, GoogleAuthProvider } from 'firebase/auth';

export const auth = getAuth();
const provider = new GoogleAuthProvider();

export const signInWithGoogle = async () => {
  try {
    const result = await signInWithPopup(auth, provider);
    return result.user;
  } catch (error) {
    console.error('Authentication error:', error);
    throw error;
  }
};

7. API Routes Setup

A. Create BigQuery API Route

Create pages/api/analytics/data.ts:

import type { NextApiRequest, NextApiResponse } from 'next';
import { bigQueryClient } from '../../../services/config';

export default async function handler(
  req: NextApiRequest,
  res: NextApiResponse
) {
  try {
    const query = req.body.query;
    const [rows] = await bigQueryClient.query(query);
    res.status(200).json(rows);
  } catch (error) {
    res.status(500).json({ error: 'Failed to fetch data' });
  }
}

8. Environment Configuration

Create .env.local:

NEXT_PUBLIC_GCP_PROJECT_ID=your-project-id
NEXT_PUBLIC_FIREBASE_API_KEY=your-api-key
NEXT_PUBLIC_FIREBASE_PROJECT_ID=your-firebase-project-id
NEXT_PUBLIC_GEMINI_API_KEY=your-gemini-api-key

9. Integration Steps

1. Initialize Services

   • Set up Google Cloud Project
   • Enable necessary APIs in Google Cloud Console
   • Configure Firebase project
   • Set up authentication

2. Component Implementation

   • Implement analytics dashboard
   • Create ML model integration
   • Set up real-time data sync with Firestore

3. Testing

   # Run tests
   npm test
   
   # Start development server
   npm run dev

10. CI/CD Setup

Create .github/workflows/deploy.yml:

name: Deploy
on:
  push:
    branches: [ main ]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - uses: actions/setup-node@v2
      - run: npm ci
      - run: npm test
      - run: npm run build

11. Monitoring Setup

1. Set up Google Cloud Monitoring
2. Implement error tracking
3. Configure usage analytics
4. Set up performance monitoring

12. Security Considerations

1. Implement proper authentication flows
2. Set up Firebase security rules
3. Configure CORS policies
4. Implement rate limiting
5. Set up API key rotation

Next Steps

1. Review and implement error handling
2. Add comprehensive logging
3. Implement user feedback mechanisms
4. Set up monitoring alerts
5. Document API endpoints
6. Create user guides

Additional Resources

• Google Cloud Documentation
• Firebase Documentation
• Vertex AI Documentation
• Next.js Documentation