Kridosz / Real-Time-Data-Streaming

An end-to-end data engineering pipeline that orchestrates data ingestion, processing, and storage using Apache Airflow, Python, Apache Kafka, Apache Zookeeper, Apache Spark, and Cassandra. All components are containerized with Docker for easy deployment and scalability.

Realtime Data Streaming | End-to-End Data Engineering Project

Table of Contents

• Introduction
• What You'll Learn
• [Technologies](#technologies

Introduction

This project serves as a comprehensive guide to building an end-to-end data engineering pipeline. It covers each stage from data ingestion to processing and finally to storage, utilizing a robust tech stack that includes Apache Airflow, Python, Apache Kafka, Apache Zookeeper, Apache Spark, and Cassandra. Everything is containerized using Docker for ease of deployment and scalability.

The project is designed with the following components:

• Data Source: I used randomuser.me API to generate random user data for our pipeline.
• Apache Airflow: Responsible for orchestrating the pipeline and storing fetched data in a PostgreSQL database.
• Apache Kafka and Zookeeper: Used for streaming data from PostgreSQL to the processing engine.
• Control Center and Schema Registry: Helps in monitoring and schema management of our Kafka streams.
• Apache Spark: For data processing with its master and worker nodes.
• Cassandra: Where the processed data will be stored.

What You'll Learn

• Setting up a data pipeline with Apache Airflow
• Real-time data streaming with Apache Kafka
• Distributed synchronization with Apache Zookeeper
• Data processing techniques with Apache Spark
• Data storage solutions with Cassandra and PostgreSQL
• Containerizing your entire data engineering setup with Docker

Technologies

• Apache Airflow
• Python
• Apache Kafka
• Apache Zookeeper
• Apache Spark
• Cassandra
• PostgreSQL
• Docker