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In Big Data, an enormous volume of data is used. Regarding data, we have two main challenges.The first challenge is how to collect large volume of data and the second challenge is to analyze the collected data. To overcome those challenges, you must need a messaging system.

Kafka is designed for distributed high throughput systems. Kafka tends to work very well as a replacement for a more traditional message broker.

In comparison to other messaging systems, Kafka has better throughput, built-in partitioning, replication and inherent fault-tolerance, which makes it a good fit for large-scale message processing applications.

Apache Kafka is a distributed publish-subscribe messaging system and a robust queue that can handle a high volume of data and enables you to pass messages from one end-point to another.

Kafka is suitable for both offline and online message consumption.

List of topics covered in this tutorial :

Apache Kafka - Introduction

Apache Kafka - Fundamentals

Apache Kafka - Cluster Architecture

Apache Kafka - WorkFlow

Apache Kafka - Installation Steps

Apache Kafka - Basic Operations

Apache Kafka - Simple Producer Example

Apache Kafka - Consumer Group Example

Apache Kafka - Integration With Storm

Apache Kafka - Integration With Spark

Real Time Application(Twitter)

Apache Kafka - Tools

Apache Kafka - Applications