Delve into Kafka Streams for real-time data processing. Gain insights into Kafka concepts, CLI tools, complex topologies, and Spring Boot integration, enabling you to build scalable, resilient applications.

kafka-streams-course.tar.gz

hello-world

stateless-topology

stateless-topology-test

stateful-topology

springboot

Kafka Streams is a Java library designed to develop applications and microservices tailored for real-time data stream processing. Harnessing Kafka’s capabilities, applications constructed using Kafka Streams exhibit exceptional scalability and fault tolerance. This versatile tool finds applications in various scenarios and domains.

The course starts with an introduction to Kafka Streams. Next, you will learn important Kafka concepts and gain experience using Kafka CLI tools. Then, you will learn how to build complex Kafka Streams topologies using stateless operators, error handling, and stateful operations. Finally, you will learn how to integrate Kafka Streams in Spring Boot applications, including using Spring’s monitoring and metrics capabilities.

After completing this course, you can build data-intensive real-time stream processing applications and microservices using Kafka Streams, enabling you to build powerful, resilient, and scalable products.

Kafka Streams for Software Developers

No flag is needed, this is the default behavior

Which flag would you use if you wanted to read the entire contents of a topic?

Which flag would you use if you wanted to read the entire contents of a topic?

**(Select all that apply.)** Suppose you started a console producer with the following flags: `--property key.separator=, --property parse.key=true`. Which option is valid?

(Select all that apply.) Suppose you started a console producer with the following flags: <code>--property key.separator=, --property parse.key=true</code>. Which option is valid?

It depends on the version of Kafka being used.

Which number does an offset begin from?

Producing a large amount of messages where each message has a different key

Producing a large amount of messages without keys

Producing a large amount of messages where most of them share a common key

Producing a large amount of messages where most of them share a common value

Given a topic with multiple partitions, which will most likely result in unevenly sized partitions?

Given a topic with multiple partitions, which will most likely result in unevenly sized partitions?

When it is necessary that messages with the same keys will be processed in the exact order in which they were produced.

When we want to maintain a uniform distribution of messages between the partitions of a topic.

When we want all the messages to be processed in the exact same order in which they were produced.

There is no good reason to use messages with keys.

What would be a good reason to use messages with keys?

What would be a good reason to use messages with keys?

One of the consumers will be assigned more than one partition.

Processing would be faster than it would be with only five consumers.

One of the partitions will not be populated with messages.

Given a topic with five partitions and a consumer group with six members, which statement is _true_?

Given a topic with five partitions and a consumer group with six members, which statement is true?

Test your knowledge of building a Kafka tool belt.

Kafka Streams Overview

Building Our Kafka Tool Belt

Stateless Processing

Error Handling

Stateful Processing

Kafka Streams with Spring Boot

Build an Order Processing Kafka Streams Application

Wrap Up

Quiz: Kafka Concepts and Commands