REAL-TIME DATA PROCESSING WITH KAFKA VS. PUB/SUB

Abstract

In the modern age of data-driven decision-making, organizations increasingly use scalable and efficient data processing systems to manage and analyze very large amounts of data in real time. It follows that real-time data streaming is becoming a fundamental technology in industries ranging from finance and e-commerce to healthcare and telecommunications. A message broker is a pivotal piece of middleware for streaming real-time data, as it allows the parts of a distributed system to communicate. Apache Kafka and Google Cloud Pub/Sub are two of the most well-known message brokers; both are solid choices for event-driven data processing. Kafka is an open-source distributed event streaming platform known for its throughput, scalability, and durability and is used in mission-critical systems. However, Pub/Sub is a fully managed messaging service from Google Cloud that can be utilized effortlessly and integrates perfectly with cloud-native apps, which is perfectly suited for companies operating in the cloud. In this paper, I compare Kafka and Pub/Sub in terms of performance, scalability, features, and real-world examples. It compares actual implementations of the tools—Kafka as real-time event streaming for an e-commerce application and Pub/Sub for IoT analytics. The article will present technical differences, performance comparisons, and best-fit tool selection considerations for scalable data processing. Finally, we will discuss how the analysis can help organizations select the appropriate messaging solution for their needs.