# 10Academy-Kifiya-Week-7
# Building a Scalable Data Pipeline for Ethiopian Medical Businesses:
## Introduction
In the rapidly evolving landscape of Ethiopian e-commerce, structured and accessible data is a key enabler for informed decision-making. At **Kara Solutions**, embarking on a mission to build a **comprehensive data warehouse** for Ethiopian medical businesses by leveraging data scraped from **Telegram channels**, the approach focuses on creating a **scalable, modular, and efficient pipeline**, ensuring seamless data collection, transformation, and analysis.
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## Task 1: Data Scraping from Telegram
### **Objective:**
The goal of this task was to **collect raw data from Telegram channels** where medical businesses actively post their products, services, and contact details. Given the decentralized nature of business listings in Ethiopia, Telegram serves as a crucial data source.
### **Approach:**
A **scalable Telegram scraper** was developed using **Telethon**, an asynchronous Python library for interacting with the Telegram API. The scraper extracts messages from targeted channels, including:
- **Business Listings** (clinic and pharmacy details, supplier information)
- **Product Advertisements** (medications, equipment, medical supplies)
- **Contact Information** (phone numbers, addresses, website links)
- **Media Attachments** (images, PDFs, and videos related to the listings)
### **Key Features Implemented:**
✅ **Asynchronous scraping** for high efficiency and reduced API rate limitations
✅ **Storage in MongoDB**, ensuring structured collection and retrieval
✅ **Metadata extraction**, including timestamps, message IDs, and sender details
✅ **Handling media files**, downloading and storing paths for images and videos
✅ **Filtering relevant messages** based on keywords and patterns
### **Challenges Faced & Solutions:**
**1. API Rate Limits:** implemented adaptive request throttling to avoid Telegram restrictions.
**2. Unstructured Data:** Message …