Kafka was created at LinkedIn (around 2010) by Jay Kreps, Neha Narkhede, and Jun Rao. They needed a high-throughput, distributed messaging system to handle LinkedIn's massive data streams.

Apache Spark helps us process huge amounts of data—fast. It's great when you already have all the data and want answers quickly. But what happens when the data never stops?

That’s the challenge many modern systems face. Imagine a food delivery app where thousands of drivers are constantly sharing their location. Or a security system that receives logins from all over the world, every second. In these cases, data keeps arriving all the time. We call this real-time data. To handle it, we need something that can collect and pass on the data as soon as it arrives.

This is where Apache Kafka comes in.

Fun fact: Kafka can handle millions of messages per second, with very low latency. That’s like streaming every click from millions of users in real time.

Apache Kafka

Apache Kafka is a platform that moves data from one place to another in real time. Think of it as a high-speed train built for messages. Instead of waiting for data to accumulate before processing, Kafka lets you stream the data as it happens.

Let’s say a restaurant app tracks every customer order, every rider’s location, and every payment event. These events happen in parallel and continuously. Kafka allows your system to capture all these live updates and deliver them where they’re needed—whether that’s a dashboard, a reporting tool, or an alert system. With Kafka, your data is always on the move, never stuck waiting in a file or a slow database.

Kafka is often used as the backbone of event-driven architectures, where each new data point is treated as an event that triggers action.

Producer-consumer model

To understand Kafka, you need to understand the producer-consumer model. It’s simple once you break it down.

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