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

**(Select all that apply.)** What are some of the Serdes shipped with Kafka Streams? 

(Select all that apply.) What are some of the Serdes shipped with Kafka Streams?

What is a possible output of the `flatMap` operator?

What is a possible output of the <code>flatMap</code> operator?

The `filter` operator takes a predicate and only lets through records for which the predicate is `true`.

The `filter` operator takes a predicate and only lets through records for which the predicate is `false`.

The `filterNot` operator takes a predicate and only lets through records for which the predicate is `true`.

The `filterNot` operator takes a predicate and only lets through records for which the predicate is `false`.

**(Select all that apply.)** Which statement about the `filter` and `filterNot` operators is correct? 

(Select all that apply.) Which statement about the <code>filter</code> and <code>filterNot</code> operators is correct?

When we want to apply a 1:1 transformation to a record's value.

When we want to apply a one-to-many transformation to a record’s value.

When we want to apply a 1:1 transformation to a record’s value and key.

When we want to apply a one-to-many transformation to a record’s value and key.

When should we choose the `map` operator over the `mapValues` operator?

When should we choose the <code>map</code> operator over the <code>mapValues</code> operator?

Consider the following topology where the value of each record is an `int`:
```java
streams.split(Named.as("branch-"))
 .branch((k, v) -> v > 10, Branched.as("a"))
 .branch((k, v) -> v < 10, Branched.as("b"))
 .branch((k, v) -> v == 5, Branched.as("c"))
 .noDefaultBranch();
```

Given an incoming record with a value of `5`, what would be the name of the branch containing this record?

Consider the following topology where the value of each record is an <code>int</code>:
<pre><code class="language-java">streams.split(Named.as(&quot;branch-&quot;))
 .branch((k, v) -&gt; v &gt; 10, Branched.as(&quot;a&quot;))
 .branch((k, v) -&gt; v &lt; 10, Branched.as(&quot;b&quot;))
 .branch((k, v) -&gt; v == 5, Branched.as(&quot;c&quot;))
 .noDefaultBranch();
</code></pre>
Given an incoming record with a value of <code>5</code>, what would be the name of the branch containing this record?

Test your knowledge of stateless processing.

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: Stateless Processing