Apache Kafka Tutorial – Kafka Architecture, Use Cases And More

In this tutorial, we will cover what is Apache Kafka with the architecture workflow and real-time Kafka use cases along with design patterns:

Apache Kafka helps us to resolve the challenges of the high load of data utilization from multiple sources with the help of the messaging system which has the capability of data analysis and overcomes such real-time challenges.

Kafka enables plenty of features, such as in-built data partitioning, most reliable replication techniques, and in-built fault-tolerance mechanisms, which provides the high scale message processing utilization.

It has the capabilities to define a distributed platform and subscribe to streams of records and publishes these records in a message queue.

In-Depth Tutorial on Apache Kafka

Apache Kafka Tutorial

What is Apache Kafka

Visit Apache Kafka Website

Apache Kafta maintains distributed systems that enable the best capacity performance in the production system. It can be the best approach to provide the most reliable performance to large-scale types of applications.

There are plenty of real-time use cases that we can find in our daily work activities, which can be applied to provide the best output result using Apache Kaffa.

Publish-Subscribe Kafka Architecture

In this Publish-Subscribe Kafka Architecture, we have a messaging process which performs as producers and publishes the message and consumers pull the data. This model works as loosely coupled microservices.

Publish-Subscribe Kafka Architecture

Kafka Architecture

We can divide the Kafka Architecture into four types of APIs, which have their specific functions and perform independently.

  1. Producer API
  2. Consumer API
  3. Streams API
  4. Connector API

Kafka Architecture

#1) Producer API: It has the mechanism of publishing a stream of records within one or more Kafka topics as an application.

#2) Consumer API: Using this API application can subscribe to more than one topic and it can also process the stream of records and produce it.

#3) Streams API: This API operates primarily as a stream processor, which utilizes the input stream from more than one topic and it allows the output stream within more than one output topic. This API can be consumed within the application.

#4) Connector API: This API runs as reusable producers or consumers. Using the connector API, we can use Kafka topics to the existing applications and data operations in a relational database.

Apache Kafka Use Case

We can find real-time use cases in our common areas, mainly Kafka provides the solution of optimization to load the data from multi-sources with the below types of use cases:

#1) Messaging: In messaging we have the concept of publishing and subscribing messages from the users to applications, the messaging technique logics we can maintain in the application, and with this technique, we need not maintain specific formatting.

  • It has the capability of a more responsive and fast solution, unlike the traditional message broker technique.
  • It provides strong stability guarantees using built-in partitioning and fault-tolerance features.
  • Using Kafka messaging, users can set customized preferences according to which they can send and receive messages.

#2) Metrics Technique: It has the feature of giving the best insights of data visualization which users can explore, monitor, and analyze the data in the form of various types of data sources in one single source of UI.

This includes different types of statistics and aggregated data from different location hosted applications and provides a centralized view in the form of a dashboard.

#3) Website Activity Tracking: This use case is beneficial for tracking the performance activity in a very high volume of real-time processing and real-time data loading in the data lake.

It produces a tracking pipeline with real-time publish-subscribe feeds that include multiple user access to the website at the same time, multiple page views, and user inputs, which allows the website data load and performance.

#4) Real-Time Log Analytics using Log Aggregation: To maintain real-time application activity, Kafka provides the log aggregation process that stores the log files in the Kafka server and stores them in a centralized place for processing, these logs not only can be from application logs but also system logs web server logs.

These collective logs can be beneficial for creating quick decision-making and analytics on certain problem areas.

#5) Stream-set Processing: Apache Kafka provides stream-set processing for different types of capabilities.

For example, ETL (Extract Transform Load), data integration, and data processing, in this competitive corporate world many organizations have already adopted the Kafka stream-set in different types of common use-cases such as collecting real-time data using IOT sensor ingestion, online security, online fraud detection, faster real-time transactional processing, and customer 360 views.

Java in Apache Kafka

Java is a programming language that supports Apache Kafka, and Java enables high processing speeds standards. It has the capability of community support for Kafka consumer clients.

What is Kafka Stream-set

Kafka stream runs dynamic real-time stream processing jobs using advanced pipelines that fetch data as per certain custom logic and shows the result in the visual user interface. This process helps the business to quickly get the data as per their requested data.

Apache Kafka is a classified streaming process that has the capability of providing reliable and effective results, and maintains the incoming data from multiple sources, and provides the most accurate result in the user interface.

Kafka Cluster

Developers can use it in the application and run the Kafka cluster from their local system environments, and it can be used as POC-Proof of Concept in application development activities.

It has the capability to balance the loads from multiple virtual server environments to handle the load, we can configure the Kafka cluster using the below workflow.

Kafka cluster

Benefits of Kafka Cluster

  1. Kafka is the central repository of the advanced streaming process, which acts as a database and has the benefits of Pub-Sub techniques.
  2. It is a flexible tool.
  3. Reliable in Classified, Partitioned, Replicated, and Fault-Tolerant ways.
  4. It is capable of being scalable and performs smooth runtime and quicker results.
  5. Durability – Kafka provides the “Distributed commit log” technique which sends the messages to continue on disk as efficiently as possible, with a durability mechanism.
  6. Performance – Using a fast stream set approach, it provides fast run-time performance from multi-loads of data.

Role of Pub/Sub in Apache Kafka

Apache Kafka provides a design pattern which we can consider as a Publish/subscribe messaging system which consists of the sender as a publisher and subscriber which performs as the receiver.

We can elaborate in better ways with the following techniques:

  1. Consumer -> Subscribe topic -> Consume messages on a specific topic
  2. Reciever -> Receives message -> Broadcast to the subscribers

Role of Pub/Sub in Apache Kafka

Kafka Design Patterns

We can divide the Kafka-Design pattern in two ways:

#1) Stream-Processing Design Patterns: This pattern is best for generating real-time data from different types of sources in our daily usage routine.

For example, Mobile Devices, websites, and various online communication mediums. Apache Kafka stream sets provide the capability to quickly process accurate data and scalability on stream-based applications. Stream-Design patterns are beneficial for generating real-time prediction models

#2) Single-Event Processing Pattern: In this design pattern, we use in our common real-time use case of aggregating the data, data processing, and decision-making types of streams. We can consider this patent as a map filter, which maps and cleans the unrecognized events.

This pattern performs it from the stream and transforms each event and then enables them to a different stream. You can consider it as a perfect example of an app that reads log messages from a stream and writes the results of a particular exception in an event using the High priority stream.

It has a load-balancing process that enables a quicker response time and performs the app without any failures.

Kafka Real-Time Streams for Microservices:

  1. Kafka Microservice Architecture introduces an application to configure an API that can communicate with a specific API microservice using Kafka as an agent.
  2. Kafka microservice enables the publish-subscribe model for handling the writing and reading of records.
  3. Kafka microservice architectures are scalable, secure, and more stable than traditional uniform application architectures.

Kafka microservice refers to the below mechanisms which we can use in our applications:

  • Kafka Ecosystem: Apache Kafka is dedicated to the easy and manageable and provides better connectivity to open-source system applications. Using Kafka Connect we can easily plugin Kafka into other data systems, which allows the data to stream and flow with low latency for consumption or further processing.
  • Integration with Existing Operations: Using Kafka microservices we can integrate the application with other different types of existing applications, we can easily transport with certain types of datasets.
  • Fault Tolerance and Scaling within Clustering: Kafka’s clustered design delivers it with the scalable and fault-tolerant methods, for a certain situation when the capacity of the message increases or in case the Kafka consumers change, in that case, Kafka can handle the load and scale the service.
  • Advanced Access Control: Kafka provides the technique of a centralized mechanism, which enables the read and write configuration which producers and consumers can easily access using the specified queues. It provides the best security model for access control mechanisms and using this method we can restrict the people as per their role and accessibility. For example: As we have two types of roles Data Scientists and Web Analytics, as per their respective roles they would be able to access only the assigned access page data scientists can access only error reporting and web analytics can analyze their outcomes on customer satisfaction.
  • Store and Process any Content: Using the store and process any content as per the business requirement change we can add and combine the producers and consumers. With the help of this, businesses can improve and grow without any investment in the infrastructure for service data processing. Using Kafka we can skip the option of knowing what the message contains, i.e., when your business needs change, you have the freedom to add any type of producer and consumer into the mix with this store and process any content business that can go faster without any infrastructure investments of taking service in data processing and data loading.

How to Download Apache Kafka

Apache Kafka can be easily downloaded from here. Kafka has the capability to get a live stream of real-time-based data using the publish-subscribe messaging technique, which sends messages within the process and applications.

Frequently Asked Questions

Q #1) What is a Kafka consumer?

Answer: Kafka is the system that collects messages, and the consumer is the component of your system that reads these messages from Kafka, it acts as a consumer, with no command-line tool. You can write Java code with the help of Kafka Consumer API for your production system.

Q #2) What is a Kafka producer?

Answer: After consumers read data from a Kafka cluster, producers write to a Kafka cluster. The Kafka producer is a custom Java code for your particular use case. The producer requires fine-tuning of the performance with the service-level agreement (SLA) guarantees and provides the solution with no errors.

Q #3) Where can I get Kafka training?

Answer: Many online training organizations provide training on the Solution Architect and a deep dive on Kafka architecture and best practices.

Q #4) How does Apache Kafka perform?

Answer: Apache Kafka performs with the help of two types of important features such as producers and brokers. Producers send a particular message to defined Kafka Brokers, and these messages are stored in a certain topic, and consumers can subscribe to the topic and use them in the application.

Q #5) Can Apache Kafka be defined as an ETL tool?

Answer: Yes, to receive fast real-time streaming data with the help of the Extract, Transform, Load process (ETL), we can consider Apache Kafka as an ETL tool.

Recommended Reading => BEST ETL Tools To Look For

Q #6) Can we consider Apache Kafka as a Database?

Answer: Apache Kafka is not a Database, it is open-source, and it provides the features of scaling that maintain the multi-loads of data from various sources.

Q #7) Does Netflix use Kafka?

Answer: Yes, Netflix uses Apache Kafka, which provides the high-throughput output of stream data with the best stability process.


We hope this Apache Kafka tutorial helped you understand the concept of Apache Kafka. Here we explained the Kafka architecture, use-cases, and real-time use case of microservices with an understanding of Kafka stream-sets and design patterns.

This tutorial will be helpful to professional developers from Java and .net backgrounds, solution architectures, ETL developers, and data science experts.