Apache Kafka Tutorial

Do you want to understand Apache Kafka in-depth? You've reached the right place. Here, in this Apache Kafka tutorial, you'll get a brief explanation of all aspects that surround Apache Kafka. We'll begin with the basics and progress through all of Apache Kafka's major topics.

Apache Kafka is an open-source event streaming platform that collects, processes, stores, and integrates data at scale. Over 80% of Fortune 100 companies including, LinkedIn, Netflix, and Microsoft, also use Apache Kafka.

If you want to gain knowledge about Apache Kafka, then you are at the right place. In this Apache Kafka tutorial, we give the complete details from basics to advanced.

 Apache Kafka Tutorial - Table Of Content

What is Kafka?

Apache Kafka was launched in 2011 for message transfers and is written in Scala and Java programming languages. It can manage trillions of data per day. 

Kafka is a distributed channel that includes different servers and customers that communicate through a TCP network protocol. The programs enable us to read, write, save and process the events. An event is an independent piece of data that must be relayed from producer to consumer. 

Kafka allows you to create an app that continuously and accurately uses and processes multiple streams at high speed. It works for managing the data from various data sources. 

With Kafka, you can;

  • Publish and subscribe to the information or event.
  • Process the records as they occur.
  • Save reports consistently and adequately. 
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Why Learn Apache Kafka?

Here are some of the great reasons that describe the why to learn Apache Kafka:

1. Data Integration

Kafka can easily connect with any other information source in the traditional business data system, modern databases, or the cloud. It creates an efficient integration with built-in connectors without hiding logic or navigating inside brittle and centralized infrastructure. 

2. Publish-Subscribe Messaging

As a distributed pub/ sub messaging platform, Kafka performs as the best-modernized version of a traditional message broker. Any time a process that develops events must be disconnected from the processor receiving the events, Kafka is a measurable and flexible way to get the task done. 

3. Log Aggregation

A modern system is a distributed system, and logging data must be centralized from multiple system components to one place. Kafka serves as a single source of truth by concentrating information across all sources, rather than quantity or volume. 

4. Use Cases and Benefits

One lakh businesses worldwide use Kafka, and it’s supported by a thriving community of experts who constantly advance the state-of-the-art in optimizing processing together. Due to Kafka’s high throughput, resilience, scalability, and fault tolerance, many use cases exist in almost all industries, from fraud findings, in the banking sector to transportation and IoT.  

5. Stream Processing

To perform real-time calculations on event streams is a core competency of Kafka. From accurate data processing to dataflow programming, Kafka ingests, saves, and processes the data developed at any scale. 

6. Metrics & Monitoring

Kafka is often used for analyzing operational data. It includes aggregating statistics from distributed applications to develop centralized posts with actual metrics. 

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What is the Messaging System in Apache Kafka?

The primary task to streamline the system is to transfer the information from one application to another so that the app can mainly work on data without stressing sharing it.

Distributed messaging depends on the reliable message queuing process. The messages are queued as non – synchronously between the messaging system and customer applications. 

Here are two kinds of messaging portals are available: 

1. Publish-Subscribe Messaging System

In this messaging portal, messages remain as topics, but in a point-to-point messaging system, clients can take more than one Topic and use every message in that Topic. Those who generate messages are known as Publishers, and Kafka consumers are known as subscribers. 

2. Point to point messaging system

In this messaging system, messages continue to remain in the queue. More than one client can consume the message in the row, but one client uses only one message at one time. As the consumer reads the message, it will disappear from that row.

The design goal of Apache Kafka

Below are some of the design considerations of Apache Kafka:

  • Low Latency - Kafka provides low latency at higher throughputs.
  • Scalability – The structure should manage scalability in all four dimensions ( event producer, event consumers, event connectors, and event processors)
  • Fault tolerance – The Kafka cluster should manage failures with the professionals and databases.
  • High Volume – Should have the potential to work with vast information of data streams.
  • Data Transformations – Kafka should support new information by using the data streams from producers.
  • Group – Kafka is the most active project in the Apache Software Foundation. The group organizes events like the Kafka Summit by Confluent. 
  • Connectivity – Kafka connect structure enables you to communicate with various event sources such as JMS and AWS S3.
Related Article: Apache Kafka Interview Questions

Kafka use cases

Kafka is used in multiple ways, but here are a few examples of various use cases that are shared on the Official Kafka Website:

  • It provides a base for data platforms, event-driven architectures, and microservices.
  • It monitors and tracks the transportation of vehicles in actual time.
  • It gathers and provides feedback to the customer communication.
  • Helps as a commit log for a distributed system.
  • Check the hospital patients.
  • Manage and streamline large–scale messaging.
  • Captures and optimizes the Sensor data.
  • Process financial transactions in actual time.

What is the Streaming Process?

A streaming process is the transformation of data in collateral-connected systems. This process enables several applications to limit the collateral data execution, where one record performs without waiting for the previous record result. Therefore, distributed streaming portals allow the user to clarify the work of optimizing process and collateral execution. However, a streaming portal in Kafka has the following vital potentials:

  • As soon as streams of data occur, it processes it.
  • It saves the streams of records in a fault-tolerant durable way.
  • It works similar to an agency messaging system wherein it publishes and subscribes streams of records.

Apache Kafka Core API’s

To learn and understand Apache Kafka, the applicant should know the following four core APIs:

  • Streams API: This API enables the aspirants to change the input flows effectively to the output flows. It allows an application to act as a stream processor that takes an input stream from one topic to another and builds an output stream to one or more output topics. 
  • Producer API: This API enables an applicant to publish streams of information to one or more topics. 
  • Connector API: This API performs the reusable producer and consumes APIs with the presenting information systems or applications. 
  • Consumer API: This API permits software to subscribe to different subjects and techniques the data stream produced to them.
Also Read: Kafka vs RabbitMQ

Kafka Components

By using these components, Kafka reaches messaging:

  • Kafka producers: The producer will publish the messages on more than one Kafka Topics. 
  • Kafka Broker: These are fundamentally systems that manage the published information. An individual broker can have zero or more partitions per topic. 
  • Kafka Topics: A collection of messages that relate to a single category is known as a Topic. Information is stored in topics, and also we can copy and partition issues. Here replicate means duplicate, and partition refers to division. Also, visualize them as logs wherein Kafka saves messages. However, Kafka’s error tolerance and scalability allow this to copy and portion topics in those factors. 
  • Kafka Consumer: The customer can select more than one Topic and use messages that are already published by bringing data from the brokers.  
  • Kafka Zookeeper: With the help of the zookeeper, Kafka gives the brokers Metadata regarding the processes to run the system and allow health checkups and broker leadership election.

Benefits of using Apache Kafka 

  • Kafka allows more than one purchaser to study any single flow of messages without interfering with every other. Each message can read N number of instances because statements are durable. 
  • Durable messages additionally approach that customers can work on historical statistics. However, Kafka supports actual-time processing as well. 
  • Kafka is entirely scalable, and bookers (nodes) may be brought or eliminated in runtime. The cluster wants not to be stopped.  
  • Kafka gives first-rate overall performance and manages millions of records according to the second within supporting hardware or infrastructure.
  • Kafka can offer high throughput while coping with more than one producer emitting statistics sets to a single subject matter or more than one Topic. This makes Kafka process bulk events/messages from front-end structures recording page-views, mouse monitoring, or consumer behaviour. 
  • Kafka gives first-rate overall performance and manages millions of records according to the second within supporting hardware or infrastructure.


Apache Kafka is an effective and powerful allotted system. Kafka’s scaling abilities allow it to deal with massive workloads. It’s frequently the preferred desire over different message queues for actual data pipelines. Overall, it’s a flexible platform that supports many use cases.

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Last updated: 03 Apr 2023
About Author


Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .

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