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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 APIBuilding Kafka Streams Applications using SpringBoot & Streams APIWrite 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 JUnit5Getting Started to Kafka StreamsIn 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 StreamsKafka Streams Terminologies - Topology & ProcessorIntroduction to KStreams APIGreetings Kafka Streams App using KStreams APIIn this section, we will build a simple Kafka Streams App and test it in our local.Learn to build a Topology of the Greetings AppBuild the Kafka Streams Launcher Application using which we can start and stop the application.Operators in Kafka Streams using KStream APIIn this section, we will explore some of the operators in the thats part of the Kafka Streams API.Filter & FilterNotMap/MapValuesFlatMapValues/FlatMappeekmergeSerialization and Deserialization in Kafka StreamsIn 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 ConfigurationBuild a Custom Serdes for Enhanced Greeting MessagesReusable 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/DeserializerOrder Management Kafka Streams application - A real time use caseIn this section, we will build a kafka streams application by implementing a Order Management system for a retail companyTopology, Stream and Tasks - Under the HoodIn this section, we will explore the internals of the Kafka Streams Application.Internals of Topology, Stream and TasksError/Exception Handling in Kafka StreamsIn this section, we will explore different error handlers in Kafka Streams.Failures in Kafka StreamsDefault Deserialization Error BehaviorCustom Deserialization Error HandlerDefault & Custom Processor Error HandlerCustom Production Error HandlerKTable & Global KTableIn this section, we will explore the KTable and GlobalKTable in KafkaStreams.Introduction to KTable APIBuild a topology for KTable KTable - Under the HoodGlobalKTableStateFul Operations in Kafka Streams - Aggregate, Join and Windowing EventsIn 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 StreamsHow aggregation works ? & Aggregation using "count" operatorGroup Records by using groupBy operatorAggregation using "reduce" operartorAggregation using "aggregate" operatorUsing Materialized views for count & reduce operatorStateFul 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 operationsIn 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 - JoinIn 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 CaseIn 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 StreamsExplore innerJoin using "join" operator - Joining KStream and KTableExplore innerJoin using "join" operator - Joining KStream and GlobalKTableExplore innerJoin using "join" operator - Joining KTable and KTableExplore innerJoin using "join" operator - Joining KStream and KStreamJoining Kafka Streams using "leftJoin" operatorJoining Kafka Streams using "outerJoin" operatorJoin - Under the hoodCoPartitioning Requirements in Joins & Joins Under the HoodStateFul Operations in Kafka Streams - WindowingIn this section, we will explore the windowing concepts in Kafka Streams.Introduction to Windowing and time conceptsWindowing in Kafka Streams - Tumbling WindowsControl emission of windowed results using "supress" operartorWindowing in Kafka Streams - Hopping WindowsWindowing in Kafka Streams - Sliding WindowsWidowing in Order Management Application - A Real Time Use CaseIn 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 WindowingIn 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 SpringBootIn this section, we will build a simple kafka streams app using SpringBoot.Introduction to SpringBoot and Kafka StreamsSetup the Project - Greeting Streams app using Spring Kafka StreamsConfiguring the Kafka Stream using application.ymlBuild the Greeting Topology Test Greeting App in LocalSpringBoot AutoConfiguration of Kafka StreamsIn this section, I will show you how spring boot auto configures Kafka Streams Application.JSON Serialization/Deserialization in Spring Kafka StreamsIn this section, we will implement the JSON Serialization/Deserialization in Kafka Streams using SpringBoot.Error Handling in Spring Kafka StreamsIn this section, I will show you error handling in Kafka Streams using SpringBoot.Handle DeSerialization Error - Approach 1Handle DeSerialization Error using Custom Error Handler - Approach 2Handle Deserialization Error - Approach 3 ( Spring Specific Approach)Handle UncaughtExceptions in the TopologyHandle Production ErrorsBuild Orders Kafka Streams Application using SpringBootIn this section, we will set up the Spring Boot Project for orders streams.Interactive Queries - Querying State Stores using RESTFUL APIsBuild a GET Endpoint to retrieve the OrderCount by OrderType - Part 1Build a GET Endpoint to retrieve the OrderCount by OrderType - Part 2Retrieve OrderCount by OrderType & LocationIdBuild a GET Endpoint to retrieve the OrderCount for All OrderTypesBuild a GET Endpoint to retrieve the Revenue by OrderTypeGlobal Error Handling for useful Client Error MessagesInteractive Queries - Querying Window State Stores using RESTFUL APIsBuild a GET Endpoint to Retrieve OrderCount by OrderTypeBuild a GET Endpoint to Retrieve the windowed OrderCount for All OrderTypesBuild a GET endpoint to retrieve the windowed OrderCount within a Time RangeBuild a GET Endpoint to retrieve the Revenue by OrderTypeTesting Kafka Streams Using TopologyTestDriver & JUnit5In this section, we will code and learn about how to write automated tests for Kafka Streams app.Testing Kafka Streams using TopologyTestDriverUnit Testing Greetings App - Writing Data to a Output TopicUnit Testing Greetings App - Testing Multiple MessagesUnit Testing Greetings App - Error ScenarioUnit Testing OrdersCount - Writing Data to a State StoreUnit Testing OrdersRevenue - Writing Data to a State StoreUnit Testing OrdersRevenue By Windows - Writing Data to a State StoreLimitations of TopologyTestDriverTesting Kafka Streams in SpringBoot Using TopologyTestDriver & JUnit5In 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 @EmbeddedKafkaIn this section, we will code and learn about writing integration tests for the Kafka Streams app using EmbeddedKafka.Introduction & SetUp Integration TestIntegration Test for OrdersCountIntegration Test for OrdersRevenueIntegration Test for OrdersRevenue By WindowsGrace Period in Kafka StreamsIn 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 ExecutableIn 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.

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