news-pipeline

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End-to-end news data pipeline using Apache Airflow, Kafka, MinIO, PostgreSQL, Docker, and Grafana. Automates web scraping, streaming ingestion, ETL, and Medallion (Bronze, Silver, Gold) storage for scalable analytics, monitoring, and reliable data engineering workflows. Built with Python and Docker Compose for scalable, reliable, automated flows!!!

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# 📰 News Data Pipeline

🚀 **An end-to-end Data Engineering pipeline for automated news collection, processing, storage, and analytics.**

Designed using a modern **Medallion Architecture (Bronze, Silver, Gold)** to automate web scraping, real-time data ingestion, ETL processing, data warehousing, and monitoring.

![Python](https://img.shields.io/badge/Python-3.x-blue?logo=python)
![Apache Airflow](https://img.shields.io/badge/Apache-Airflow-red?logo=apacheairflow)
![Apache Kafka](https://img.shields.io/badge/Apache-Kafka-black?logo=apachekafka)
![PostgreSQL](https://img.shields.io/badge/PostgreSQL-Database-blue?logo=postgresql)
![Docker](https://img.shields.io/badge/Docker-Container-blue?logo=docker)
![Grafana](https://img.shields.io/badge/Grafana-Monitoring-orange?logo=grafana)

</div>

---

# 📖 Overview

The **News Data Pipeline** is a modern Data Engineering project that automatically collects articles from multiple online news sources and processes them through a scalable ETL architecture.

The pipeline scrapes news websites, streams articles using **Apache Kafka**, stores raw data inside **MinIO** (Bronze Layer), transforms and enriches the data using **Apache Airflow** (Silver Layer), loads curated datasets into **PostgreSQL** (Gold Layer), and monitors the entire workflow through **Grafana dashboards**.

The project follows the **Medallion Architecture** to ensure high data quality, scalability, and maintainability.

---

# ✨ Features

- 📰 Automated web scraping from multiple news sources
- ⚡ Real-time streaming with Apache Kafka
- 🥉 Bronze Layer for raw data storage
- 🥈 Silver Layer for cleaning and enrichment
- 🥇 Gold Layer for analytics-ready datasets
- 🔍 Data validation and normalization
- 🌍 Language detection and metadata extraction
- 📦 Object storage using MinIO
- 🗄️ PostgreSQL Data Warehouse
- 🔄 Workflow orchestration with Apache Airflow
- 🐳 Dockerized deployment
- 📊 Real-time monitoring with Grafana
- 📈 Scalable ETL architecture

---

# 🏗️ Pipeline Architecture

```text
🌐 News Websites
        │
        ▼
📰 Python Web Scraper
        │
        ▼
⚡ Apache Kafka
        │
        ▼
🥉 Bronze Layer (MinIO)
        │
        ▼
🔄 Apache Airflow ETL
        │
        ▼
🥈 Silver Layer
(Data Cleaning & Enrichment)
        │
        ▼
🥇 Gold Layer
(PostgreSQL Data Warehouse)
        │
        ▼
📊 Grafana Dashboard
```

---

# 🛠️ Technologies

| Technology | Description |
|------------|-------------|
| 🐍 Python | Core application development |
| ⚡ Apache Kafka | Real-time data streaming |
| 🔄 Apache Airflow | Workflow orchestration |
| 🗄️ PostgreSQL | Data Warehouse |
| 📦 MinIO | Object Storage |
| 🐳 Docker & Docker Compose | Containerization |
| 📊 Grafana | Monitoring & Visualization |
| 🌐 BeautifulSoup | Web Scraping |
| 📡 Requests | HTTP Requests |

---

# 🚀 Project Objectives

- 📰 Automate news collection from multiple sources
- ⚡ Build a scalable real-time ingestion pipeline
- 🔄 Implement a complete ETL workflow
- 🏗️ Apply the Medallion Architecture
- 📦 Store structured and unstructured data efficiently
- 📊 Enable analytics-ready datasets
- 📈 Monitor pipeline health and performance
- 🐳 Ensure reproducibility through Docker

---

# 📂 Project Structure

```bash
News-Data-Pipeline/
│
├── airflow/
│   ├── dags/
│   └── plugins/
│
├── scraper/
├── kafka/
├── postgres/
├── minio/
├── grafana/
├── docker-compose.yml
├── requirements.txt
└── README.md
```

---

# 🎯 Learning Outcomes

This project provided hands-on experience with:

- 🏗️ Data Engineering pipelines
- ⚡ Apache Kafka streaming
- 🔄 Apache Airflow orchestration
- 📦 Object storage with MinIO
- 🗄️ PostgreSQL Data Warehousing
- 📊 Monitoring with Grafana
- 🐳 Docker & Docker Compose
- 🌐 Web scraping using BeautifulSoup
- 📈 ETL design and implementation
- 🥉🥈🥇 Medallion Architecture

---

# 👨‍💻 Author

**Hamza Zaidi**

🎓 Data Engineering & Artificial Intelligence Student

📍 Casablanca, Morocco

📧 zaidihamza1373@gmail.com

💼 LinkedIn: www.linkedin.com/in/hamza-zaidi-789b84262

⭐ If you found this project useful, consider giving it a **Star** on GitHub!

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