Real-Time Real Estate Price Analysis System using event-driven architecture (EDA) with cloud-native technologies and serverless functions

A scalable, enterprise-grade real estate price analysis system designed to deliver real-time insights and predictions on property prices. This solution leverages a serverless architecture on AWS, Node.js, Next.js, and machine learning for advanced data analysis and visualization.

Table of Contents

• Overview
• Architecture
• Features
• Tech Stack
• Installation
• Usage
• Project Structure
• Contributing
• License

Overview

This system enables real-time analysis of property prices by ingesting data from multiple sources, processing it with a serverless data pipeline, and applying machine learning models to predict future price trends. The frontend, built with Next.js, provides an intuitive interface for users to visualize data, track trends, and receive notifications on market changes.

Architecture

This project follows an event-driven, microservices-based architecture. Key components include:

• Data Ingestion: Real-time data is ingested using AWS Lambda and processed for analytics.
• Machine Learning: AWS SageMaker or an equivalent ML framework for price prediction models.
• Event-Driven Architecture: Leveraging AWS services (e.g., Kinesis, SNS, SQS) for data streaming and notifications.
• Serverless Functions: Microservices-based approach with AWS Lambda for cost-effective scalability.

Features

• Real-Time Data Ingestion and Processing: Ingest and process data from APIs, handling large data volumes with AWS Lambda and Kinesis.
• Machine Learning Predictions: Predict property prices and trends using a trained ML model.
• Data Visualization: Interactive charts and visualizations using D3.js or Chart.js for exploring data insights.
• Scalable Cloud Architecture: Deployed on AWS using serverless and cloud-native technologies.
• Alerts & Notifications: Automated alerts based on pricing changes, driven by AWS SNS.

Tech Stack

• Cloud Provider: AWS (Lambda, S3, Kinesis, SageMaker, API Gateway)
• Backend: Node.js, Express, AWS Lambda
• Frontend: Next.js, React, Tailwind CSS, D3.js
• Machine Learning: Python (SageMaker, Scikit-learn, TensorFlow/PyTorch)
• Database: Amazon RDS (PostgreSQL) and DynamoDB
• CI/CD: GitHub Actions, Serverless Framework
• Monitoring: AWS CloudWatch, Sentry

Installation

Prerequisites

• Node.js v14 or higher
• AWS CLI
• Serverless Framework (optional for deployment)

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/real-estate-analysis.git
   cd real-estate-analysis

2. Install dependencies:

   cd backend && npm install
   cd ../frontend && npm install

3. Environment Variables: Create .env files in both backend and frontend directories for your configuration. Refer to .env.example for required variables.

4. Run Locally:

   • Backend:

     cd backend
     npm start

   • Frontend:

     cd frontend
     npm run dev

Usage

• Data Ingestion: Triggered by AWS Lambda functions to pull data from APIs.
• ML Predictions: Process data and make predictions using the ML model, accessed via API.
• Frontend: Navigate to http://localhost:3000 for the Next.js application to explore data, charts, and predictions.

Project Structure

real-estate-analysis/
│
├── backend/                    # Serverless functions, data processing, ML model scripts
├── frontend/                   # Next.js frontend
├── infra/                      # Infrastructure as code (Serverless Framework, AWS CDK)
├── data/                       # Sample data for local testing
├── docs/                       # Project documentation
├── tests/                      # Unit and integration tests
├── logs/                       # Log storage
└── README.md                   # Project documentation

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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