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[Under Development] PulseFlow - Real-Time Social Media Analytics Platform

Overview

PulseFlow is a backend platform designed for real-time data processing and visualization, leveraging enterprise-grade technologies. This project showcases advanced capabilities in data simulation, monitoring, and analytics, all built on modern DevOps practices

Features

  • Real-time social media data processing
  • Sentiment analysis tracking
  • Platform usage analytics
  • Engagement metrics monitoring
  • Performance and health monitoring
  • Robust error handling and metrics tracking
  • Secure integration with cloud services

Tech Stack

Core Technologies

  • Backend: Node.js + Express with TypeScript
  • Database: MongoDB with Mongoose ODM
  • Containerization: Docker & Docker Compose
  • Message Queue: RabbitMQ with CloudAMQP
  • Testing: Jest with comprehensive test coverage
  • Code Quality: ESLint, Prettier, and automated pre-commit hooks

Architecture Components

  • Microservices-based Design: Modular architecture for scalability and maintainability.
  • MongoDB Data Storage: Efficient and scalable data management.
  • Real-time Metrics Collection: Continuous monitoring of system performance.
  • Custom Dashboards: Visual insights through Grafana and MongoDB Charts.

Message Queue Integration:

  • RabbitMQ with CloudAMQP: Utilizes a managed RabbitMQ service for distributed message processing.
  • Configuration: Secure, encrypted connections with elastic scaling.
  • Key Features: Reliable message queuing, automatic message routing, and error handling with dead-letter queues.
  • Message Flow: Synthetic tweet data is generated, published to RabbitMQ, processed asynchronously, and stored in MongoDB.

CI/CD & DevOps

  • Continuous Integration: GitHub Actions
  • Container Registry: GitHub Container Registry
  • Container Orchestration: Docker Compose
  • Pre-commit Hooks: Automated code quality and testing checks

Testing Strategy

The project implements a comprehensive testing approach:

  • Unit Tests: For individual components and functions
  • Integration Tests: Validates interactions between components
  • Automated Testing: Integrated with CI/CD and pre-commit hooks
  • Mocking: Sophisticated mocking for external dependencies

Security Improvements

See SECURITY.md file for details on:

  • Recent security updates addressing CVE-2023-45853
  • Container security hardening
  • Recommended security practices
  • Code quality enhancements

Monitoring Setup

  • Metrics: Prometheus with custom metrics collection
  • Dashboards: Grafana with real-time data visualization
  • Health Checks: Custom endpoints with prometheus-client
  • MongoDB Charts: For data-specific visualizations

The application exports metrics in Prometheus format and pushes them to Grafana Cloud. A local monitoring stack is also available through Docker Compose.

Dashboards

Project Demo Video

Video Thumbnail

Watch the Demo Video

Grafana Performance Dashboard

Grafana Dashboard Grafana Dashboard

>> SOON View LIVE Grafana Dashboard HERE <<

MongoDB Atlas Dashboard

MongoDB Atlas Dashboard

MongoDB Charts Dashboard

MongoDB Charts Dashboard

>> SOON View LIVE MongoDB Charts Dashboard HERE <<

RabbitMQ Cloud Management

RabbitMQ Dashboard

Data Generation Methodology

Simulated Data Approach

  • Data Source: Synthetic data generation
  • Randomization Techniques:
    • Probabilistic content creation
    • Realistic social media interaction patterns
    • Machine learning-based sentiment simulation

Data Generation Characteristics

  • Randomized user profiles
  • Contextually relevant tweet content
  • Simulated engagement metrics
  • Realistic temporal distribution
  • Sentiment spectrum simulation

Generation Algorithms

  • Markov chain text generation
  • Weighted sentiment scoring
  • Gaussian distribution for interaction rates
  • Time-based event probability modeling

Disclaimer

🚨 Note: All data is artificially generated and does not represent real social media interactions.

Deployment Infrastructure Workflow:

  1. Data Generation: Synthetic tweets created with Chance.js.
  2. Message Queuing: Data published to RabbitMQ, ensuring reliable processing.
  3. Data Storage: Tweets stored in MongoDB for efficient access.
  4. CI/CD: Automated tests and deployment via GitHub Actions.
  5. Hosting: Application deployed on Render for scalability.
  6. Monitoring: Metrics collected and visualized in Grafana.

Project Status & Quality Checks

Code Quality Badges

CodeFactor Snyk Security

Code Analysis Details

  • CodeFactor Grade: A (Excellent)
  • Snyk Security: No known vulnerabilities
  • Continuous Monitoring: Automated checks on every commit

Scan Highlights

  • Static code analysis
  • Dependency vulnerability scanning
  • Best practices enforcement
  • Security risk assessment

Compliance

  • OWASP Top 10 considerations
  • Secure coding standards
  • Regular automated inspections

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests.

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