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