Kafka Streams with Spring Cloud Stream: Mastering Real-Time Stream Processing Microservices Using Kafka Streams and Spring Cloud Streams

Kafka Streams with Spring Cloud Streams will help you understand stream processing in general and apply it to Kafka Streams Programming using Spring Boot. This course uses the Kafka Streams library compatible with Spring Cloud 2020. All the source code and examples used in this course have been tested by the author on Confluent Platform 6.0.0, which is compatible with Apache Kafka 2.6 open-source distribution. This is a fully example-driven course, and you will be working with multiple examples during the entire session. We will be making extensive use of IntelliJ IDEA as the preferred development IDE and Apache Maven and Gradle as the preferred build tool. However, based on your prior experience, you should be able to work with any other IDE designed for Spring application development and any other build tool designed for Java applications. This course also makes use of Log4J2 to teach you industry-standard log implementation in your application. We will be using JUnit5, which is the latest version of JUnit, to implement unit test cases. Working examples and exercises are the most critical tool to sharpen your skills. This course consists of some programming assignments as and when appropriate. These exercises will help you validate and check your concepts and apply your learning to solve programming problems. The code bundles for this course is available in https://github.com/PacktPublishing/Kafka-Streams-with-Spring-Cloud-Stream

Kafka Streams with Spring Cloud Streams is designed for software engineers willing to develop a stream processing application using the Kafka Streams library and Spring Boot. This course has also been created for data architects and data engineers responsible for designing and building the organization's data-centric infrastructure. Another group of people is the managers and architects who do not directly work with Kafka implementation, but they work with the people who implement Kafka Streams at the ground level.