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Bettey-k/medical-telegram-warehouse

Domain:

healthcaredigital infrastructure

Record type:

softwareproject
Creator:
Bet
Host:
End-to-end data pipeline for scraping, transforming, enriching, and exposing analytics from Ethiopian medical Telegram channels. πŸ₯ Medical Telegram Analytics Pipeline An end-to-end data engineering & analytics pipeline that ingests Telegram data, transforms it into a dimensional data warehouse, enriches it with computer vision, exposes analytical insights via an API, and orchestrates the entire workflow using Dagster. πŸ“Œ Project Overview This project analyzes medical-related Telegram channels to extract insights about: Product mentions and engagement Channel activity and trends Visual content usage (images) Promotional vs non-promotional posts The pipeline follows modern data engineering best practices: Data Lake β†’ Data Warehouse β†’ Analytics API β†’ Orchestration Telegram Scraper β”‚ β–Ό Data Lake (JSON files) β”‚ β–Ό PostgreSQL (raw schema) β”‚ β–Ό dbt Transformations (staging + star schema) β”‚ β–Ό YOLO Image Enrichment β”‚ β–Ό Analytics Data Mart β”‚ β–Ό FastAPI Analytical API β”‚ β–Ό Dagster Orchestration πŸ“‚ Project Structure medical-telegram-warehouse/ β”œβ”€β”€ api/ # FastAPI application β”‚ β”œβ”€β”€ main.py β”‚ β”œβ”€β”€ database.py β”‚ └── schemas.py β”œβ”€β”€ data/ β”‚ └── raw/ β”‚ └── telegram_messages/ β”œβ”€β”€ medical_warehouse/ # dbt project β”‚ β”œβ”€β”€ dbt_project.yml β”‚ β”œβ”€β”€ profiles.yml β”‚ β”œβ”€β”€ models/ β”‚ β”‚ β”œβ”€β”€ staging/ β”‚ β”‚ └── marts/ β”‚ └── tests/ β”œβ”€β”€ src/ β”‚ β”œβ”€β”€ scrape_telegram.py β”‚ β”œβ”€β”€ load_raw_to_postgres.py β”‚ └── yolo_detect.py β”œβ”€β”€ pipeline.py # Dagster pipeline β”œβ”€β”€ docker-compose.yml β”œβ”€β”€ Dockerfile β”œβ”€β”€ requirements.txt β”œβ”€β”€ .env └── README.md Task Breakdown βœ… Task 1 – Data Scraping Goal: Collect Telegram messages and media Output: JSON files stored in a data lake structure Scrapes messages and images from medical Telegram channels Stores data in date-partitioned folders Handles malformed or empty messages safely πŸ“ Example: data/raw/telegram_messages/2026-01-15/messages.json ask 2 – Data Modeling & Transformation (dbt) Goal: Build a trusted analytical data warehouse Raw Layer raw.telegram_messages Staging Layer stg_telegram_messages Cleaned fields Type …

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