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Kafka Streams API is an advanced API that’s part of the Kafka Ecosystem.
Using Kafka Streams API, we can :
Apply data transformations,
Data enrichment,
Branching the data into multiple data streams.
Aggregating the data or joining data from multiple Kafka topics.
Aggregate the Data into Window Buckets and more.
The Kafka Streams API For Developers using Java/SpringBoot course is structured to give you both the theoretical and coding experience of developing Kafka Streams Applications using Streams API and also covers the techniques to use Enterprise Standard Kafka Streams Application using SpringBoot and Streams API .
This is a pure hands-on oriented course where you will be learning the concepts through code. You will build a realtime Kafka Streams application by the end of this course.
By the end of this course, you will have a complete understanding of these concepts:
Building Kafka Streams Applications using Streams API
Building Kafka Streams Applications using SpringBoot & Streams API
Write Interactive Queries to retrieve the aggregated data from a state store and expose it via RESTFUL API.
Unit and Integration Testing Kafka Streams Applications using JUnit5
Getting Started to Kafka Streams
In this section, I will give you all an introduction to Kafka streams and the different terminologies that are involved in build a Kafka Streams Application.
Introduction to Kafka Streams
Kafka Streams Terminologies – Topology & Processor
Introduction to KStreams API
Greetings Kafka Streams App using KStreams API
In this section, we will build a simple Kafka Streams App and test it in our local.
Learn to build a Topology of the Greetings App
Build the Kafka Streams Launcher Application using which we can start and stop the application.
Operators in Kafka Streams using KStream API
In this section, we will explore some of the operators in the thats part of the Kafka Streams API.
Filter & FilterNot
Map/MapValues
FlatMapValues/FlatMap
peek
merge
Serialization and Deserialization in Kafka Streams
In this section, we will code and explore the serialization and deserialization in Kafka Streams.
How Key/Value serialization and deserialization works in Kafka Streams ?
Providing Default Serializer/Deserializer using Application Configuration
Build a Custom Serdes for Enhanced Greeting Messages
Reusable Generic Serializer/Deserializer (Recommended Approach)
In this section, I will show you the best approach to build a Generic Serializer and Deserializer that can be used for any type of Messages.
Build a Generic Serializer/Deserializer
Order Management Kafka Streams application – A real time use case
In this section, we will build a kafka streams application by implementing a Order Management system for a retail company
Topology, Stream and Tasks – Under the Hood
In this section, we will explore the internals of the Kafka Streams Application.
Internals of Topology, Stream and Tasks
Error/Exception Handling in Kafka Streams
In this section, we will explore different error handlers in Kafka Streams.
Failures in Kafka Streams
Default Deserialization Error Behavior
Custom Deserialization Error Handler
Default & Custom Processor Error Handler
Custom Production Error Handler
KTable & Global KTable
In this section, we will explore the KTable and GlobalKTable in KafkaStreams.
Introduction to KTable API
Build a topology for KTable
KTable – Under the Hood
GlobalKTable
StateFul Operations in Kafka Streams – Aggregate, Join and Windowing Events
In this section, I will give an introduction to stateful operators in Kafka Streams and explore the aggregation related operators in Kafka streams.
StateFul Operations in Kafka Streams
How aggregation works ? & Aggregation using “count” operator
Group Records by using groupBy operator
Aggregation using “reduce” operartor
Aggregation using “aggregate” operator
Using Materialized views for count & reduce operator
StateFul Operation Results – How to access them ?
In this section, I will explain about the options to retrieve the results of the aggregation.
Re-Keying Kafka Records for Stateful operations
In this section, we will code and explore the effect of null operator and the need to rekeying records during stateful operations.
StateFul Operations in Kafka Streams – Join
In this section, we will code and explore the different types of Joins in Kafka Streams Application.
Join in Order Management Application – A Real Time Use Case
In this section, we will implement join in the order management application that we have been working on so far.
Introduction to Joins & Types of Joins in Kafka Streams
Explore innerJoin using “join” operator – Joining KStream and KTable
Explore innerJoin using “join” operator – Joining KStream and GlobalKTable
Explore innerJoin using “join” operator – Joining KTable and KTable
Explore innerJoin using “join” operator – Joining KStream and KStream
Joining Kafka Streams using “leftJoin” operator
Joining Kafka Streams using “outerJoin” operator
Join – Under the hood
CoPartitioning Requirements in Joins & Joins Under the Hood
StateFul Operations in Kafka Streams – Windowing
In this section, we will explore the windowing concepts in Kafka Streams.
Introduction to Windowing and time concepts
Windowing in Kafka Streams – Tumbling Windows
Control emission of windowed results using “supress” operartor
Windowing in Kafka Streams – Hopping Windows
Windowing in Kafka Streams – Sliding Windows
Widowing in Order Management Application – A Real Time Use Case
In this section, we will code and explore the new requirement to implement the windowing in the Orders Stream Application.
Behavior of Records with Future & Older Timestamp in Windowing
In this section, we will explore the behavior of records with the older and future timestamp in a Kafka Streams Application.
Build Kafka Streams Application using SpringBoot
In this section, we will build a simple kafka streams app using SpringBoot.
Introduction to SpringBoot and Kafka Streams
Setup the Project – Greeting Streams app using Spring Kafka Streams
Configuring the Kafka Stream using application.yml
Build the Greeting Topology
Test Greeting App in Local
SpringBoot AutoConfiguration of Kafka Streams
In this section, I will show you how spring boot auto configures Kafka Streams Application.
JSON Serialization/Deserialization in Spring Kafka Streams
In this section, we will implement the JSON Serialization/Deserialization in Kafka Streams using SpringBoot.
Error Handling in Spring Kafka Streams
In this section, I will show you error handling in Kafka Streams using SpringBoot.
Handle DeSerialization Error – Approach 1
Handle DeSerialization Error using Custom Error Handler – Approach 2
Handle Deserialization Error – Approach 3 ( Spring Specific Approach)
Handle UncaughtExceptions in the Topology
Handle Production Errors
Build Orders Kafka Streams Application using SpringBoot
In this section, we will set up the Spring Boot Project for orders streams.
Interactive Queries – Querying State Stores using RESTFUL APIs
Build a GET Endpoint to retrieve the OrderCount by OrderType – Part 1
Build a GET Endpoint to retrieve the OrderCount by OrderType – Part 2
Retrieve OrderCount by OrderType & LocationId
Build a GET Endpoint to retrieve the OrderCount for All OrderTypes
Build a GET Endpoint to retrieve the Revenue by OrderType
Global Error Handling for useful Client Error Messages
Interactive Queries – Querying Window State Stores using RESTFUL APIs
Build a GET Endpoint to Retrieve OrderCount by OrderType
Build a GET Endpoint to Retrieve the windowed OrderCount for All OrderTypes
Build a GET endpoint to retrieve the windowed OrderCount within a Time Range
Build a GET Endpoint to retrieve the Revenue by OrderType
Testing Kafka Streams Using TopologyTestDriver & JUnit5
In this section, we will code and learn about how to write automated tests for Kafka Streams app.
Testing Kafka Streams using TopologyTestDriver
Unit Testing Greetings App – Writing Data to a Output Topic
Unit Testing Greetings App – Testing Multiple Messages
Unit Testing Greetings App – Error Scenario
Unit Testing OrdersCount – Writing Data to a State Store
Unit Testing OrdersRevenue – Writing Data to a State Store
Unit Testing OrdersRevenue By Windows – Writing Data to a State Store
Limitations of TopologyTestDriver
Testing Kafka Streams in SpringBoot Using TopologyTestDriver & JUnit5
In this section, we will code and learn how to write unit tests in our Kafka Streams application that’s build using SpringBoot.
Integration Testing Spring KafkaStreams App using @EmbeddedKafka
In this section, we will code and learn about writing integration tests for the Kafka Streams app using EmbeddedKafka.
Introduction & SetUp Integration Test
Integration Test for OrdersCount
Integration Test for OrdersRevenue
Integration Test for OrdersRevenue By Windows
Grace Period in Kafka Streams
In this section I will explain the concept of grace period and how it can be used in kafka streams application.
Build and Package the SpringBoot App as an Executable
In this section, we will package the kafka streams app as an executable and launch the app.
By the end of this course you will have a complete understanding of Kafka Streams API and the different kinds of applications that can be built using Kafka Streams API.
Who this course is for:
Advanced Java Developers
Kafka Developers who are curious to learn Kafka Streams API
Kafka Developers who are interested in building advanced streaming applications
Developers who wish to learn the techniques to test Kafka Streams Application using TopologyTestDriver
Requirements
Java Knowledge is required
Prior experience building Kafka Applications
Prior experience working with IntelliJ or any other IDEA
Java 17 is required
Gradle or Maven Knowledge is needed
Using Kafka Streams API, we can :
Apply data transformations,
Data enrichment,
Branching the data into multiple data streams.
Aggregating the data or joining data from multiple Kafka topics.
Aggregate the Data into Window Buckets and more.
The Kafka Streams API For Developers using Java/SpringBoot course is structured to give you both the theoretical and coding experience of developing Kafka Streams Applications using Streams API and also covers the techniques to use Enterprise Standard Kafka Streams Application using SpringBoot and Streams API .
This is a pure hands-on oriented course where you will be learning the concepts through code. You will build a realtime Kafka Streams application by the end of this course.
By the end of this course, you will have a complete understanding of these concepts:
Building Kafka Streams Applications using Streams API
Building Kafka Streams Applications using SpringBoot & Streams API
Write Interactive Queries to retrieve the aggregated data from a state store and expose it via RESTFUL API.
Unit and Integration Testing Kafka Streams Applications using JUnit5
Getting Started to Kafka Streams
In this section, I will give you all an introduction to Kafka streams and the different terminologies that are involved in build a Kafka Streams Application.
Introduction to Kafka Streams
Kafka Streams Terminologies – Topology & Processor
Introduction to KStreams API
Greetings Kafka Streams App using KStreams API
In this section, we will build a simple Kafka Streams App and test it in our local.
Learn to build a Topology of the Greetings App
Build the Kafka Streams Launcher Application using which we can start and stop the application.
Operators in Kafka Streams using KStream API
In this section, we will explore some of the operators in the thats part of the Kafka Streams API.
Filter & FilterNot
Map/MapValues
FlatMapValues/FlatMap
peek
merge
Serialization and Deserialization in Kafka Streams
In this section, we will code and explore the serialization and deserialization in Kafka Streams.
How Key/Value serialization and deserialization works in Kafka Streams ?
Providing Default Serializer/Deserializer using Application Configuration
Build a Custom Serdes for Enhanced Greeting Messages
Reusable Generic Serializer/Deserializer (Recommended Approach)
In this section, I will show you the best approach to build a Generic Serializer and Deserializer that can be used for any type of Messages.
Build a Generic Serializer/Deserializer
Order Management Kafka Streams application – A real time use case
In this section, we will build a kafka streams application by implementing a Order Management system for a retail company
Topology, Stream and Tasks – Under the Hood
In this section, we will explore the internals of the Kafka Streams Application.
Internals of Topology, Stream and Tasks
Error/Exception Handling in Kafka Streams
In this section, we will explore different error handlers in Kafka Streams.
Failures in Kafka Streams
Default Deserialization Error Behavior
Custom Deserialization Error Handler
Default & Custom Processor Error Handler
Custom Production Error Handler
KTable & Global KTable
In this section, we will explore the KTable and GlobalKTable in KafkaStreams.
Introduction to KTable API
Build a topology for KTable
KTable – Under the Hood
GlobalKTable
StateFul Operations in Kafka Streams – Aggregate, Join and Windowing Events
In this section, I will give an introduction to stateful operators in Kafka Streams and explore the aggregation related operators in Kafka streams.
StateFul Operations in Kafka Streams
How aggregation works ? & Aggregation using “count” operator
Group Records by using groupBy operator
Aggregation using “reduce” operartor
Aggregation using “aggregate” operator
Using Materialized views for count & reduce operator
StateFul Operation Results – How to access them ?
In this section, I will explain about the options to retrieve the results of the aggregation.
Re-Keying Kafka Records for Stateful operations
In this section, we will code and explore the effect of null operator and the need to rekeying records during stateful operations.
StateFul Operations in Kafka Streams – Join
In this section, we will code and explore the different types of Joins in Kafka Streams Application.
Join in Order Management Application – A Real Time Use Case
In this section, we will implement join in the order management application that we have been working on so far.
Introduction to Joins & Types of Joins in Kafka Streams
Explore innerJoin using “join” operator – Joining KStream and KTable
Explore innerJoin using “join” operator – Joining KStream and GlobalKTable
Explore innerJoin using “join” operator – Joining KTable and KTable
Explore innerJoin using “join” operator – Joining KStream and KStream
Joining Kafka Streams using “leftJoin” operator
Joining Kafka Streams using “outerJoin” operator
Join – Under the hood
CoPartitioning Requirements in Joins & Joins Under the Hood
StateFul Operations in Kafka Streams – Windowing
In this section, we will explore the windowing concepts in Kafka Streams.
Introduction to Windowing and time concepts
Windowing in Kafka Streams – Tumbling Windows
Control emission of windowed results using “supress” operartor
Windowing in Kafka Streams – Hopping Windows
Windowing in Kafka Streams – Sliding Windows
Widowing in Order Management Application – A Real Time Use Case
In this section, we will code and explore the new requirement to implement the windowing in the Orders Stream Application.
Behavior of Records with Future & Older Timestamp in Windowing
In this section, we will explore the behavior of records with the older and future timestamp in a Kafka Streams Application.
Build Kafka Streams Application using SpringBoot
In this section, we will build a simple kafka streams app using SpringBoot.
Introduction to SpringBoot and Kafka Streams
Setup the Project – Greeting Streams app using Spring Kafka Streams
Configuring the Kafka Stream using application.yml
Build the Greeting Topology
Test Greeting App in Local
SpringBoot AutoConfiguration of Kafka Streams
In this section, I will show you how spring boot auto configures Kafka Streams Application.
JSON Serialization/Deserialization in Spring Kafka Streams
In this section, we will implement the JSON Serialization/Deserialization in Kafka Streams using SpringBoot.
Error Handling in Spring Kafka Streams
In this section, I will show you error handling in Kafka Streams using SpringBoot.
Handle DeSerialization Error – Approach 1
Handle DeSerialization Error using Custom Error Handler – Approach 2
Handle Deserialization Error – Approach 3 ( Spring Specific Approach)
Handle UncaughtExceptions in the Topology
Handle Production Errors
Build Orders Kafka Streams Application using SpringBoot
In this section, we will set up the Spring Boot Project for orders streams.
Interactive Queries – Querying State Stores using RESTFUL APIs
Build a GET Endpoint to retrieve the OrderCount by OrderType – Part 1
Build a GET Endpoint to retrieve the OrderCount by OrderType – Part 2
Retrieve OrderCount by OrderType & LocationId
Build a GET Endpoint to retrieve the OrderCount for All OrderTypes
Build a GET Endpoint to retrieve the Revenue by OrderType
Global Error Handling for useful Client Error Messages
Interactive Queries – Querying Window State Stores using RESTFUL APIs
Build a GET Endpoint to Retrieve OrderCount by OrderType
Build a GET Endpoint to Retrieve the windowed OrderCount for All OrderTypes
Build a GET endpoint to retrieve the windowed OrderCount within a Time Range
Build a GET Endpoint to retrieve the Revenue by OrderType
Testing Kafka Streams Using TopologyTestDriver & JUnit5
In this section, we will code and learn about how to write automated tests for Kafka Streams app.
Testing Kafka Streams using TopologyTestDriver
Unit Testing Greetings App – Writing Data to a Output Topic
Unit Testing Greetings App – Testing Multiple Messages
Unit Testing Greetings App – Error Scenario
Unit Testing OrdersCount – Writing Data to a State Store
Unit Testing OrdersRevenue – Writing Data to a State Store
Unit Testing OrdersRevenue By Windows – Writing Data to a State Store
Limitations of TopologyTestDriver
Testing Kafka Streams in SpringBoot Using TopologyTestDriver & JUnit5
In this section, we will code and learn how to write unit tests in our Kafka Streams application that’s build using SpringBoot.
Integration Testing Spring KafkaStreams App using @EmbeddedKafka
In this section, we will code and learn about writing integration tests for the Kafka Streams app using EmbeddedKafka.
Introduction & SetUp Integration Test
Integration Test for OrdersCount
Integration Test for OrdersRevenue
Integration Test for OrdersRevenue By Windows
Grace Period in Kafka Streams
In this section I will explain the concept of grace period and how it can be used in kafka streams application.
Build and Package the SpringBoot App as an Executable
In this section, we will package the kafka streams app as an executable and launch the app.
By the end of this course you will have a complete understanding of Kafka Streams API and the different kinds of applications that can be built using Kafka Streams API.
Who this course is for:
Advanced Java Developers
Kafka Developers who are curious to learn Kafka Streams API
Kafka Developers who are interested in building advanced streaming applications
Developers who wish to learn the techniques to test Kafka Streams Application using TopologyTestDriver
Requirements
Java Knowledge is required
Prior experience building Kafka Applications
Prior experience working with IntelliJ or any other IDEA
Java 17 is required
Gradle or Maven Knowledge is needed
