Kafka Tutorial: Everything You Need to Know

by | Aug 26, 2019 | Big Data

10 Min Read. |

We live in the age of big data. Even the smallest actions you take are somehow influenced by, or influence data science. Something as simple as driving to your office has become dependent on data science and big data. While these terms have been around for a while, there has been a recent addition to this buzz – Apache Kafka. This post will be a Kafka tutorial

Kafka is a messaging system that powers giants like LinkedIn, Twitter, Airbnb, etc. LinkedIn invented Kafka in 2011. LinkedIn’s Kafka deployment is the largest right now with over 1.4 trillion messages per day. 

By the end of this Apache Kafka tutorial, you will know Kafka meaning, how it works, the Kafka architecture, its advantages, and how learning Kafka can help your career.

What Is Apache Kafka?

Apache Kafka

Apache Kafka Source – Wikimedia

Kafka started as a messaging queue. It has now become an event streaming platform that is fast, reliable, robust, and scalable. It allows publishers and consumers to communicate using message-based topics. 

Kafka works with applications such as Flink, Spark, Flafka, and Hbase for analysing and processing data in real-time. It provides higher throughput and directs the data towards Hadoop’s big data lakes. 

Why did LinkedIn develop Kafka? They were looking for a way to utilise the vast quantities of data generated from the website. LinkedIn wanted to process this data in real-time.

While there are many applications where you collect and store data for processing at a later stage, there were none that would do it in real-time and at the pace that LinkedIn needed. 

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In today’s fast-paced world, speed is everything. Being able to handle real-time data enables organisations to make quick decisions based on the current scenario and requirement.

The Kafka meaning has become synonymous with speed and resilience.

You can also refer this video for a detailed introduction to Kafka tutorial.

How Does Kafka Work?

As you will learn in your Apache Kafka tutorial, it is a distributed messaging system. A distributed system uses a non-synchronous queueing process between the client and the messaging system.

There are primarily two types of distributed messaging systems, point-to-point and publish-subscribe. 

The Kafka tutorial will tell you that it is a publish-subscribe messaging system. What does it mean? And how does it improve Kafka’s performance?

The job of a messaging system is to transfer data between applications. It frees up the applications from worrying about the sharing of data and lets them concentrate on processing it.

Kafka Working

Kafka Working Source – Wikimedia

In a point-to-point messaging system, the queue contains the messages, and multiple consumers can access it. However, one consumer can only consume one particular message at a time.

A publish-subscribe system has topics, which are logical categories containing messages from the publishers for the consumers. A consumer is allowed to take multiple topics and consume the messages in them.

The message can be anything. It may contain information about an event or text message to trigger an event. Kafka system stores the messages for a previously specified retention period.

This allows applications to access the data during that time. They can even process or reprocess the messages as needed.

The messages in the Kafka tutorial are written into a log that is maintained by the topic. A data log is an append-only sequence of data that is ordered by time.

It is essentially the basic data structure of a database. The messages from the publisher are written into this data log in the topic. A topic can contain multiple data logs. The subscribers can read this data from the log. 

Apache Kafka Architecture

A Kafka tutorial cannot be complete without a detailed discussion on the Kafka architecture. Apache Kafka is deployed as a cluster.

The producers and consumers connect to this cluster. The Kafka meaning cluster consists of several servers. These servers are referred to as Kafka brokers.   

Apache Kafka Architecture

Apache Kafka Architecture Source – Wikimedia

The cluster stores the topics. The topics contain streams of messages or data. There are four core APIs in Kafka architecture

⇒ Producer API: It allows the application to distribute a stream of messages to the topics. 

⇒ Consumer API: It lets the application subscribe to topics. It also lets the application process the stream of records.

⇒ Streams API: It converts the input stream to the output stream.

⇒ Connector API: It is responsible for producing and executing reusable producers and consumers. These producers and consumers connect the applications to the Kafka topics and vice versa. 

The Kafka tutorial will also cover the various components that form the Kafka cluster. You need to know what role each component plays and its contribution to the Kafka architecture.  

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Kafka Broker

You can think of Kafka brokers as stateless servers. They are also known as Kafka nodes. Each broker can handle vast quantities of data.

True to its name, the broker mediates the conversation between the producer and the consumer.

It guarantees that the message from the producer gets delivered to the correct consumer. It is the Kafka brokers that host the topics. They are capable of handling thousands of reading and writes per second.   

Kafka Zookeeper

As mentioned in the previous subsection of the Apache Kafka tutorial, the brokers are stateless. It is the Zookeeper’s responsibility to maintain the state of the cluster. You can think of the Zookeeper as the manager of the cluster.

Whenever there is a new broker in the system or an existing broker suffers a failure, the Zookeeper informs the publishers and consumers. They take the necessary action based on this information. 

Kafka Producer

The producers push or send data to the broker. When the producer finds out from the Zookeeper that there is a new broker, it automatically starts sending the data to the new broker as well.

The producer does not require acknowledgments from the broker. It just sends the messages as fast as the broker can handle. 

Kafka Consumer

Kafka Consumer

Kafka Consumer Source – Wikimedia

The consumer pulls or reads the data from the broker. Since the broker is stateless, the consumer uses the partition offset to keep track of the messages that have been consumed.

When the consumer acknowledges an offset, it automatically implies that it has consumed all the previous messages. The offset value can also be used to skip to or rewind to any point in the partition.

Consumers can also form a consumer group. All the consumers in a group will subscribe to the same topic

Kafka Topic

We have been talking about topics from the start of the Kafka tutorial. A Kafka topic is a unique category or feeds within the cluster to which the publisher writes the data and from which the consumer reads the data. 

The topic can contain multiple partitions. The messages are stored in the partition in a sequence that cannot be altered.

Every message will have a unique offset. This offset is the identifier which the consumer uses to read the message. Multiple consumers can read from the same topic simultaneously thanks to the partitions. 

Usually, a message is randomly written in any of the partitions of the topic. However, if the message carries a key, then all the messages with that key will be written to the same partition. 

The topics are replicated across multiple brokers. This safeguards the messages in case of broker failure.                  

Advantages of Apache Kafka

Apache Kafka has gained huge popularity within a short amount of time. It has also lead to a rise in the demand for Kafka tutorials and the need to understand Kafka’s meaning. All of this makes you wonder, is it worth the hype? Is Kafka as useful as they say?

However, once you read about its advantages, you will realize that Kafka is a revolutionary product. 


Kafka can accommodate a large number of producers and consumers. Since it is a distributed system, it is easier to scale. Moreover, the scaling would not lead to a downtime for the service. 

Durability and Reliability


Reliability Source – PicsServer

Kafka replicates the topics and stores them in multiple brokers. This way, even if one broker suffers an issue, your messages will be safe.

Moreover, the cluster stores the data for the specified retention period. You can retrieve it anytime within this period without any hassle. All of this makes the system extremely reliable and durable. 

Enhanced Performance

The Kafka architecture lets you read and write thousands of messages per second even when it is storing terabytes of data. There is no other messaging system that can offer this level of performance.

Traditional real-time applications such as RabbitMQ can process around 20,000 messages per second, whereas Kafka can manage 100,000 messages per second.

Kafka achieves this level of performance by reducing its load by not maintaining indexes on the messages. It is the consumer’s job to specify the correct offset for the messages. 


Kafka can be easily integrated with other Apache applications such as HBase and Storm. You can increase its functionality and make it more versatile this way. 

Operational Metrics

The producers and consumers in the Kafka architecture periodically publish their message counts to the topics. In case of a data loss, you can retrieve this information and compare the counts. This feature makes Kafka more robust and fault-tolerant. 

Hadoop Infrastructure

Hadoop Infrastructure

Hadoop Infrastructure Source – Wikimedia

Kafka can direct data to the Hadoop big data lakes. You can utilize Kafka for real-time data processing solutions that demand super-fast messaging. 

Track Web Activities

It should come as no surprise that most of the big data applications are based on the information gathered from the internet. You can use Kafka to track and store real-time web activity. You will learn how to do this in your Kafka tutorial

Log Aggregation

Kafka can transform the data in different formats to a single standard format. An organization can use it to collect data from various services and convert them to a standard format before giving it to clients. It reduces ambiguity and increases uniformity.    

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Why You Should Take Kafka Tutorial

Kafka only came into existence in 2011. It has been around for less than a decade. But if you look at its application across various websites, you will be surprised by its proliferation. LinkedIn, Netflix, Uber, Goldman Sachs, Intuit, and many more are dependent on the Kafka architecture.

A look at these names should be enough to gauge the amount of data that they generate per second. If Kafka is the trusted messaging service for these companies, then it is one of the best in the industry.      

The number of jobs that list Kafka as a requirement has more than doubled since 2014, in five years!

Career Scope inn Apache Kafka

Career Scope in Apache Kafka Source – Data Flair

In addition to all this, there are not a lot of trained Kafka professionals. Since it is a relatively new field that has become popular over the last 8 years, the supply has not kept up with the demand.

The difference has pushed up the salary for Kafka developers to an average of USD 110,000. This figure has been growing steadily and shows no signs of decline. It is safe to assume that the rise would continue for a few more years at least. 

There is also a lot of scope for improvement and learning in Kafka. It is an open-source platform and with adequate training, you can contribute to its betterment. If you love challenges and want a job that requires constant innovation, then this is the field for you. 

Start the Kafka Tutorial Today

Has this Kafka tutorial inspired you? Would you like to learn more about Apache Kafka? Then enroll for the Data Science Course. The course covers all the popular big data tools such as Hive, Hadoop, Spark, and, of course, Kafka.

The course is taken by experts who have industry experience and can offer valuable guidance. There are plenty of assignments and projects that will help you get a good grasp of the Kafka concepts.

What are you waiting for? Start your Apache Kafka tutorial today!

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