End-to-end data pipeline for Ethiopian medical business analytics using Telegram data, dbt, YOLOv8, FastAPI, and Dagster.
# 🏥 Medical Telegram Warehouse
An end-to-end data pipeline for analyzing Ethiopian medical businesses from public Telegram channels.
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## 📋 Table of Contents
- Overview
- Business Questions
- Architecture
- Tech Stack
- Project Structure
- Quick Start
- Environment Variables
- Tasks
- API Endpoints
- Results
- Testing
- CI/CD Pipeline
- Docker Support
- Monitoring
- Deployment
- Branch Strategy
- Troubleshooting
- Contributing
- License
- Acknowledgments
- Contact
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## 📖 Overview
This project builds a robust data platform that generates actionable insights about Ethiopian medical businesses using data scraped from public Telegram channels. The platform implements a modern ELT (Extract, Load, Transform) framework with a layered data architecture.
### Business Need
A well-designed data platform significantly enhances data analysis. This platform answers key business questions such as:
- What are the top 10 most frequently mentioned medical products or drugs across all channels?
- How does the price or availability of a specific product vary across different channels?
- Which channels have the most visual content (e.g., images of pills vs. creams)?
- What are the daily and weekly trends in posting volume for health-related topics?
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## 🎯 Business Questions
| Question | How It's Answered |
|---|---|
| Top 10 mentioned products | API: `/api/reports/top-products` |
| Product price/availability across channels | API: `/api/channels/{channel_name}/activity` |
| Channels with most visual content | API: `/api/reports/visual-content` |
| Daily/weekly posting trends | API: `/api/channels/{channel_name}/activity` |
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## 🏗️ Architecture
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