Kafka Streams with Spring Cloud Stream

Description

In this course, you will learn to create Kafka Streams microservices using the Spring cloud framework. This is an example-driven course, and you will learn to use Confluent Kafka distribution for all the examples. By the end of this course, you will learn to create Kafka Streams microservices using different types of serializations, Confluent schema registry, and creating stateless and stateful event processing applications.

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

Designing, developing, and testing stream processing applications
Kafka Streams binder implementation for Spring cloud streams
Working with JSON, AVRO, and other custom serializations
Spring cloud streams and Kafka Streams architecture
Kafka Streams DSL and programming with Kafka Streams API
Unit-testing Kafka Streams application

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.

Throughout the course, the author uses a unique progressive common-sense approach to teaching a complex subject. It is an interactive learning experience with working examples and exercises to convert your knowledge into a skill.

Explore Kafka Streams with JSON, AVRO, and other custom serialization * Learn Kafka architecture and programming with Kafka Streams API * Understand how to build applications using Spring Boot with streaming data

https://github.com/PacktPublishing/Kafka-Streams-with-Spring-Cloud-Stream

Prashant Kumar Pandey is passionate about helping people learn and grow in their careers by bridging the gap between their existing and required skills. In his journey to fulfill this mission, he is authoring books, publishing technical articles, and creating training videos to help IT professionals and students succeed in the industry. He is also the founder, lead author, and chief editor of the Learning Journal portal that has been providing various skill development courses, training sessions, and technical articles since 2018.

1. Before You Begin

2. Environment Setup on Window 10 Machine

3. Environment Setup on Mac Machine

4. Understanding the Technology Stack

5. Producing Data to Kafka

6. Processing Kafka Streams

7. Working with KStream

8. KTable and Aggregations

9. Timestamp and Windowing Aggregates

10. Joins in Kafka Streams

11. Kafka Streams in Functional Style and Unit Testing

12. Keep Learning