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

A Comprehensive Guide to Building a Modern Data Pipeline

Table of Contents

1. Introduction
2. System Architecture
3. Key Learnings
4. Technology Stack

Introduction

This project provides a hands-on experience in building a complete, end-to-end data engineering pipeline. It shows how to ingest, process, and store data using a combination of industry-standard tools and technologies. The entire project is containerized using Docker, making it easy to deploy and replicate the environment.

System Architecture

The project is designed with the following components:

• Data Source: We use 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.

Key Learnings

• 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

Technology Stack

• Apache Airflow: Data pipeline orchestration.
• Python: For data processing and Airflow DAGs.
• Apache Kafka: Real-time stream processing.
• Apache Zookeeper: Distributed coordination for Kafka.
• Apache Spark: Distributed data processing.
• Cassandra: Highly scalable database.
• PostgreSQL: Relational database for initial data storage.
• Docker: Containerization platform.