Time-Series-Streaming-RabbitMQ

1. 🏭 Project Overview

This project demonstrates a time-series streaming pipeline using RabbitMQ, MongoDB, Pandas, and Flask.
It simulates how electricity grid data (consumption, production, energy mix) can be ingested, persisted, analyzed, and visualized — a workflow similar to downstream IoT data analytics.

The focus is on systems engineering: message brokers, persistence, analytics, and containerization.

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2. 🔑 Key Features

• Message Broker Integration: RabbitMQ streams simulated time-series data.
• Database Persistence: MongoDB stores incoming messages for durability.
• Analytics Engine: Pandas computes export/import hours, energy mix, and hourly consumption patterns.
• Visualization: Matplotlib plots (daily averages, energy distribution pie, hourly consumption).
• Frontend: Flask app with login (hardcoded credentials) and report page.
• Testing: Pytest suite for routes and analytics functions.
• Containerization: Docker + Docker Compose for reproducible deployment.

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3. 🛠️ Technical Stack

• Language: Python 3.14
• Libraries: Flask, Pandas, Matplotlib, Pytest, Pika (RabbitMQ), PyMongo
• Message Broker: RabbitMQ
• Database: MongoDB
• Containerization: Docker, Docker Compose

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4. 🏗️ Architecture

[Producer] → RabbitMQ → [Consumer] → MongoDB → [Analytics (Pandas)] → Flask Dashboard

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5. 🚀 How to Run

Local

python app.py

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6. 📷 Output Screenshots

First Prototype:

Post WCAG Compliance Enhancements: