📘 Introduction

Modern applications generate massive amounts of data — clicks, payments, sensor readings, and more — all happening in real time. To handle this flow efficiently, systems need a reliable way to move data between components instantly.

That’s where Apache Kafka comes in. Kafka is a distributed data streaming platform built for high-performance, real-time communication between systems. It’s the backbone for event-driven architectures at companies like Netflix, LinkedIn, and Uber.

Let’s break down how Kafka works — from topics and partitions to brokers, producers, and consumers — and see how it powers real-time data pipelines at scale.

🧩 Kafka Architecture Overview

At its core, Kafka is a publish-subscribe messaging system distributed across a cluster of servers.

  • Producers send data.
  • Consumers read data.
  • Topics organize messages.
  • Brokers manage storage and delivery.
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These components work together to deliver fast, reliable, and scalable data streams across systems.

⚙️ What is a Kafka Topic?

A topic is a category or feed name where records are published. Think of it as a data channel.

Examples:

  • orders for purchase events
  • user_activity for website clicks
  • payments for transaction data
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Producers write messages to topics, and consumers read from them — independently and asynchronously. This loose coupling enables real-time, scalable communication between systems.

🏗️ What Are Kafka Partitions?

Each topic is divided into partitions, which store ordered sequences of messages. Partitions allow Kafka to:

  • Scale horizontally by distributing data across servers.
  • Process in parallel for higher throughput.
  • Track progress using message offsets.

Example:

Partition 0: [0] login-A, [1] login-B  
Partition 1: [0] login-C, [1] login-D
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If a consumer restarts, it can resume from its last offset — ensuring no duplicate processing.

⚙️ What Are Kafka Brokers?

A broker is a Kafka server that stores and serves data. A Kafka cluster typically contains multiple brokers, each managing some partitions.

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