Getting Started with Spring Boot and Apache Kafka

Apache Kafka has become a popular choice for building scalable and real-time data streaming applications. When combined with the power of Spring Boot, developers can easily create robust and efficient applications that handle the complexities of data streaming. In this blog, we’ll explore the integration of Spring Boot with Apache Kafka, along with step-by-step examples to help you get started.

What is Apache Kafka?

Apache Kafka is a distributed event streaming platform that is designed for high-throughput, fault-tolerant, and real-time data streaming. It is commonly used for building applications that involve real-time data ingestion, processing, and distribution. Kafka uses a publish-subscribe model where producers send messages to topics, and consumers subscribe to these topics to receive messages.

Why Use Spring Boot with Apache Kafka?

Spring Boot is a popular framework for building Java applications, known for its simplicity and rapid development capabilities. Integrating Spring Boot with Apache Kafka simplifies the process of creating Kafka producers and consumers by providing abstractions and ready-to-use components. This integration also offers benefits like dependency management, auto-configuration, and ease of testing.

Here’s a list of real-time use cases where Apache Kafka can be employed effectively:

  1. Financial Transactions: Kafka can be used to process real-time financial transactions, such as stock trading or online payments, ensuring that every transaction is reliably recorded and analyzed.
  2. IoT Data Processing: Kafka’s ability to handle high-throughput data makes it ideal for processing and analyzing data from Internet of Things (IoT) devices, enabling real-time monitoring and decision-making.
  3. Social Media Activity: Real-time tracking of social media activities like tweets, likes, and shares can be achieved using Kafka, allowing businesses to respond quickly to trends or customer interactions.
  4. Fraud Detection: Kafka can be used to process and analyze patterns of user behavior across various systems, helping in real-time fraud detection and prevention.
  5. Log Aggregation: For applications deployed across multiple servers, Kafka can aggregate logs from different sources in real-time, enabling centralized monitoring, troubleshooting, and analysis.

These use cases highlight Kafka’s versatility in handling real-time data streams across various industries and applications.

Setting Up the Project

Let’s start by setting up a Spring Boot project with Apache Kafka integration. If you haven’t already, you can use Spring Initializr ( to generate a new Spring Boot project. Make sure to include the “Spring for Apache Kafka” dependency in your project.

Maven Dependency:

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Gradle Dependency:

implementation group: 'org.springframework.kafka', name: 'spring-kafka', version: '3.0.9'

Creating a Kafka Producer

A Kafka producer is responsible for sending messages to Kafka topics. Here’s how you can create a simple Kafka producer using Spring Boot:

import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.stereotype.Service;

public class KafkaProducerService {

    private KafkaTemplate<String, String> kafkaTemplate;

    public void sendMessage(String topic, String message) {
        kafkaTemplate.send(topic, message);

In this example, the KafkaTemplate simplifies the process of sending messages to a Kafka topic.

Creating a Kafka Consumer

A Kafka consumer reads messages from Kafka topics. Here’s how you can create a Kafka consumer using Spring Boot:

import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.stereotype.Service;

public class KafkaConsumerService {

    @KafkaListener(topics = "my-topic", groupId = "my-group")
    public void consumeMessage(String message) {
        System.out.println("Received message: " + message);

The @KafkaListener annotation marks a method as a Kafka consumer. It specifies the topic to listen to and the consumer group.

Putting It All Together

Now that we have our producer and consumer, let’s see how they work together:

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.ConfigurableApplicationContext;

public class KafkaApplication {

    public static void main(String[] args) {
        ConfigurableApplicationContext context =, args);

        KafkaProducerService producerService = context.getBean(KafkaProducerService.class);
        producerService.sendMessage("my-topic", "Hello, Kafka!");

        // Keep the application running for a while to see the consumer in action

When you run the application, the producer sends a message to the “my-topic” topic, and the consumer, which is listening to the same topic, receives and prints the message.


Spring Boot and Apache Kafka make a powerful combination for building real-time data streaming applications. With Spring Boot’s abstractions and easy-to-use components, you can quickly create Kafka producers and consumers without diving into the intricacies of Kafka’s low-level APIs. This blog covered the basics of integrating Spring Boot with Kafka and provided simple examples to help you get started on your Kafka journey. From here, you can explore more advanced features, error handling, and fine-tuning to build robust and efficient data streaming applications.

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