Realtime Data Architecture: Hiring Platform

Table of Contents

• Introduction
• System Architecture
• Technologies
• Project Files
• Getting Started

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 multiple storages, utilizing a robust tech stack that includes Apache Airflow, Python, Apache Kafka, Apache Zookeeper, Apache Spark, Cassandra, MySQL, and Grafana. Everything is containerized using Docker for ease of deployment and scalability.

System Architecture

The project is designed with the following components:

• Data Source: randomuser.me API provides mock user data.
• 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.
• Apache Spark: For data processing with its master and worker nodes.
• Cassandra: Where the raw user and tracking data will be stored.
• MySQL: Stored transformed and aggregated tracking data for analysis.
• Grafana: Create real-time dashboards, pulling data directly from MySQL.

Technologies

• Apache Airflow
• Python
• Apache Kafka
• Apache Zookeeper
• Apache Spark
• Cassandra
• MySQL
• Grafana
• Docker

Project Files

• docker-compose.yml: Configure containers for each technology
• kafka_stream.py: Streams data from the API to Kafka
• spark_stream.py: Processes data from Kafka and stores it in Cassandra
• faking_log.py: Generates sample interaction log data for testing
• spark_cdc.py: Captures changes in Cassandra, transforms and pushes them to MySQL

Getting Started

1. Clone the repository:

   git clone https://github.com/MarcusLe02/realtime-pipeline-hiring-platform.git

2. Navigate to the project directory:

   cd realtime-pipeline-hiring-platform

3. Run Docker Compose to spin up the services:

   docker-compose up -d