Real-Time Data Streaming, Processing with Kafka, Spark & Debezium : Event-Driven Architecture

In today's fast-paced world, real-time data processing is becoming more crucial across various industries. Whether it's financial transactions, social media monitoring, or tracking changes in large datasets, we often require tools that can capture changes in data automatically without any human intervention. This is where event-driven architectures come into play, offering scalable and flexible solutions to handle data streams efficiently,. That’s where Change Data Capture (CDC) mecanism comes in, and recently I had the chance to explore one of the most interesting CDC tools: Debezium.

👉 Debezium, as I've come to understand, listens to your data source for changes (insertions, updates, deletions...) and automatically publishes these events to a destination, often a message broker like Kafka. It connects seamlessly to a variety of databases [Transactional systems] and offers incredible flexibility in streaming real-time data to wherever it's needed. While some databases have built-in CDC capabilities, Debezium shines due to its simplicity, ease of integration, and reliability across different environments.

💡 To demonstrate the importance of this, I developed a real-time data streaming project using:
Debezium for CDC
Apache Kafka for data streaming
Spark Streaming for real-time data processing
Spring Boot and ReactJS for a real-time data visualization dashboard.

🚀 Let me walk you through the architecture of the project:
🔹 Debezium connects to a PostgreSQL database, capturing every change (insertions, updates, and deletions) as soon as they happen.
🔹The captured changes are then published to Kafka as events.
🔹Spark Streaming subscribes to these Kafka topics and processes the events in real time.
🔹After processing, the results are stored in a MySQL database, which serves as the real-time analytical layer. This allows business users to query the latest insights without delay.
🔹A Spring Boot backend fetches the processed data from MySQL and exposes it through APIs to a ReactJS frontend. Using ChartJS, the dashboard provides a visual representation of the data, updated in real time, giving users immediate insights into what's happening.

🚀In real-world scenarios, solutions like this are invaluable in industries like e-commerce, where inventory changes need to be tracked immediately, or in financial institutions for fraud detection, where real-time insights can prevent significant losses. This architecture ensures data accuracy, speed, and scalability, enabling businesses to make critical decisions based on the most up-to-date information available.


💻 Source Code:
Interested in trying it out yourself? The full project code and instructions to run it on your machine are available here: [https://github.com/aymane-maghouti/Re...]

0:00 - Solution Architecture
1:26 - Debezium Demo
3:20 - Spark Streaming Process
5:24 - Dashboard App [Spring boot - React]
6:23 - Notification service
7:33 - Dashboard Exploration
9:31 - Recap of the project

#RealTimeData #Kafka #Debezium #SparkStreaming #SpringBoot #ReactJS #CDC #Streaming #DataEngineering #BigData #RealTimeProcessing #PFE