Kafka Tutorial: Everything You Need to Learn

Apache Kafka is an open-source platform that handles real-time data storage. It mainly functions as a broker and handles copious data shared between sender and receiver. Keep reading to glance at the fundamental and advanced concepts of the Apache Kafka messaging system, its architecture and applications.

What is Apache Kafka? The History Behind Kafka

Apache Kafka is an open-source distributed streaming platform working as a subscribed messaging system to enable data exchange between servers, applications and processors. Developed under LinkedIn, Apache Kafka was transferred to the Apache Software Foundation and is currently regulated by Confluent. 

Before moving to the Kafka tutorial, let’s discuss Apache Kafka’s influence on the Big Data spectrum. 

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Understanding Kafka’s Popularity in Recent Times

Kafka is highly resilient with node features and automatic recovery systems. Moreover, its features have simplified integration and communication between the components of large-scale data systems. Since Kafka offers higher reliability, replication, and throughput, it has replaced conventional messaging brokers such as AMQP, JMS, etc.

Companies are always eager to hire Kafka professionals with practical fluency and experience. 

Messaging System in Kafka

The messaging system’s main task is to simplify the data sharing process between applications. The distributed messaging system is essentially based on a reliable message queue process. Kafka has two central messaging systems: a point-to-point messaging system and a published subscribe messaging system.

1. The point-to-point system

The point-to-point messaging system creates a queue for easy message consumption. However, there is a limitation: messages are sent one by one to the consumer. Therefore, as soon as they become the recipient and read the message, it will automatically be removed from the system.

2. The Published Subscribe Messaging system

This messaging system tends to be much more asynchronous. All forms of communication are conducted in service to service fashion for serverless and architecture of microservices. The whole model is published to subscribers, with the messages being received by all the users near instantaneously.

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Brief Overview of the Streaming Process

Apache Kafka leverages a top-notch messaging system to process data in connected systems, speeding up record publishing without worrying about previous record results. In addition, this streaming process simplifies streaming process execution and implementation. 

The streaming process in Kafka comes with the following features or capabilities:

• Processing starts as soon as the record streaming occurs. 
• Functions like an enterprise messaging system to subscribe and publish the stream of records. 
• It stores all the records safely.

Kafka APIs

To understand the concept of Apache Kafka in detail, you must be aware of the four core APIs, and they are: 

• Product API

This API allows application access to public records on one or more topics. 

• Consumer API

It allows an application to subscribe to one or more topics at a time and process the records produced to them. 

• Stream API

It enables a streaming application to transmit input streams to output streams. Here, the application works as a stream processor to consume an input stream from more than one topic and simultaneously deliver an output stream on more than one topic.

• Connector API 

This API executes reusable product APIs using the existing application and data systems. 

Why choose Kafka? 

Apache Kafka is a software platform with several convenient features. Let’s look at some of them:

• Apache Kafka handles extensive data and messages per second with relative ease.
• Apache Kafka serves as a mediator between the target and source systems. 
• Apache Kafka shows high performance through a lower latency value than 10ms and processes it with a well-versed software system. 
• Apache Kafka has a built-in resilient architecture, eliminating unusual data sharing complications. 
• Reputed global brands like Uber, Walmart and Netflix use Apache Kafka. 
• Apache Kafka is fault-tolerant. Being fault-tolerant implies Kafka prevents consumers from losing messages due to system errors. 
• Apache Kafka prevents issues with data reprocessing.

Key Kafka Components

By leveraging the following components, Kafka completes its messaging process:

• Kafka topic

Messages from particular categories are known as topics. Data is stored in topics, enabling users to categorise and replicate topics. Replication refers to partitions and copies of data. This feature gives Kafka scalability and fault tolerance.

• Kafka Zookeeper

Kafka ZooKeeper is employed in dispersed systems to enable synchronisation between services and the naming registry. In addition, it allows developers to keep track of the Kafka cluster and stay on top of topics and messages.

• Kafka broker 

Kafka broker maintains published data, leading every topic to have zero or more partitions.

Uses of Kafka 

There are several uses of Kafka:

• Messaging 

Kafka works as an alternative to traditional messaging systems. It offers better replication ability, higher throughput, top-notch built-in partitioning, and excellent fault tolerance, making Kafka a better solution for processing large amounts of data.

• Metrics 

Kafka allows developers to track metrics using motoring operational data. In addition, it provides access to complete statistics generating centralised feeds for quick review.

• Event sourcing 

Most streaming applications use Kafka for event sourcing since it supports large log data. 

Apache Kafka vs Apache Flume

Many platforms claim to provide Kafka’s unique experience and functionality, such as RabbitMQ, Active MQ, Storm, Apache Flume and Spark, but here’s why you should prefer Kafka: 

• Apache Kafka works for multiple consumers and producers, and therefore it can be used as a general-purpose tool. On the other hand, Apache Flume is a special-purpose tool with limited applications.
• Apache Kafka ensures maximum event replication using ingest pipelines. On the other hand, Apache Flume does not replicate the events. 

Conclusion 

This tutorial captures concepts of Kafka, its uses, components, and messaging system. Kafka’s unique benefits and features have helped it gain extensive popularity in big data. Developers can begin understanding Kafka fundamentals using this tutorial. A professional and complete Kafka certification course is recommended to gain practical experience through real-time projects.

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