1. Getting Started with the Course FREE CHAPTER

• Course Introduction
• Prerequisites

2. Getting Started with Kafka Streams

• Introduction to Kafka Streams
• Kafka Streams Terminologies - Topology and Processor
• Introduction to KStreams API

3. Greetings Kafka Streams App Using KStreams API

• Overview of the Greetings App
• Set Up the Greetings App
• Topology of the Greetings App
• Build the Kafka Streams Launcher Application
• Setting Up Kafka Environment and Testing Our Greeting App

4. Operators in Kafka Streams Using KStreams API

• Filter and FilterNot
• Map/MapValues
• FlatMapValues/FlatMap
• peek
• merge

5. Serialization and Deserialization in Kafka Streams

• How Key/Value Serialization and Deserialization Works in Kafka Streams
• Providing Default Serializer/Deserializer Using Application Configuration
• Build Custom Serdes for Enhanced Greeting Messages
• Usage of Custom Serde in the Greeting App

6. Reusable Generic Serializer/Deserializer (Recommended Approach)

• Build a Generic Serializer/Deserializer
• Integrate Generic Serializer/Deserializer into the Greeting App

7. Order Management Kafka Streams Application - A Real-Time Use Case

• Overview of the Retail App
• Build the Topology for the Orders Management App
• Split the Restaurant/Retail Shopping Orders - Using Split and Branch Operator
• Transform the Order Domain to a Revenue Domain Type

8. Topology, Stream, and Tasks - Under the Hood

• Internals of Topology, Stream, and Tasks
• Explore the Behavior of Streams by Modifying the Stream Threads

9. Error/Exception Handling in Kafka Streams

• Failures in Kafka Streams
• Default Deserialization Error Behavior
• Custom Deserialization Error Handler
• Default and Custom Processor Error Handler
• Custom Production Error Handler
• Error Handling When Kafka Cluster Is Down

10. KTable and Global KTable

• Introduction to KTable API
• Build a Topology for KTable
• KTable - Under the Hood
• GlobalKTable

11. StateFul Operations in Kafka Streams - Aggregate, Join, and Windowing Events

• StateFul Operations in Kafka Streams
• What Is Aggregation and How It Works?
• Aggregation Using "count" Operator
• Aggregation Using "reduce" Operator
• Aggregation Using "aggregate" Operator
• Using Materialized Store for count and reduce Operators

12. StateFul Operation Results - How to Access Them?

• How to Access the Results of Aggregation

13. Aggregation in Order Management Application - A Real-Time Use Case

• Total Number of Orders by Each Store Using "count" Operator
• Total Revenue by Each Store Using "aggregate" Operator

14. Rekeying Kafka Records for Stateful Operations

• Effect of null Key in Stateful Operations and Repartition of Kafka Records
• Rekeying Using the "selectKey" Operator

15. StateFul Operations in Kafka Streams - Join

• Introduction to Joins and 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
• Co-Partitioning Requirements in Joins

16. Join in Order Management Application - A Real-Time Use Case

• Join Aggregate Revenue with StoreDetails KTable

17. StateFul Operations in Kafka Streams - Windowing

• Introduction to Windowing and Time Concepts
• Windowing in Kafka Streams - Tumbling Windows
• Control Emission of Windowed Results Using "suppress" Operator
• Windowing in Kafka Streams - Hopping Windows
• Windowing in Kafka Streams - Sliding Windows

18. Widowing in Order Management Application - A Real-Time Use Case

• New Requirements for the Order Management Application
• Implementing a CustomTimeStamp Extractor
• Aggregate "Number of Orders" by Windows
• Aggregate Revenue by Windows
• Joins on the Windowed Data

19. Behavior of Records with Future and Older Timestamp in Windowing

• Records with Timestamps before and after the CurrentTimestamp

20. Build Kafka Streams Application Using Spring Boot

• Introduction to Spring Boot and Kafka Streams
• Set Up 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

21. Spring Boot Autoconfiguration of Kafka Streams

• Internals of Autoconfiguring Kafka Streams in Spring Boot

22. JSON Serialization/Deserialization in Spring Kafka Streams

• JSON Serialization/Deserialization Using JsonSerde
• JsonSerde Using Custom ObjectMapper

23. Error Handling in Spring Kafka Streams

• Handle Deserialization Error - Approach 1
• Handle Deserialization Error Using Custom Error Handler - Approach 2
• Handle Deserialization Errors Using Spring Specific Approach- Approach 3
• Handle Uncaught Exceptions in the Topology
• Handle Production Errors

24. Build Orders Kafka Streams Application Using Spring Boot

• Set Up the Base Project for Orders Kafka Streams App
• Create the OrdersTopology

25. 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 and 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

26. 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

27. Testing Kafka Streams Using TopologyTestDriver and JUnit5

• 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

28. Testing Kafka Streams in Spring Boot Using TopologyTestDriver and JUnit5

• Unit Test Using TopologyTestDriver in Spring Boot

29. Integration Testing Spring KafkaStreams App Using @EmbeddedKafka

• Introduction and Set Up Integration Test
• Integration Test for OrdersCount
• Integration Test for OrdersRevenue
• Integration Test for OrdersRevenue By Windows

30. Grace Period in Kafka Streams

• Grace Period in Windowing

31. Build and Package the Spring Boot App as an Executable

• Package the Spring Boot App and Execute It as a Jar File

32. Exactly Once Processing/Semantics in Kafka Streams

• What Is Exactly Once Processing and Why Is It Needed?
• Set Up Exactly Once Processing in Kafka Streams
• Transactions and Idempotent Producer - Under the Hood
• Limitations and Performance Impacts of Kafka Transactions

33. Running Kafka Streams Applications as Multiple Instances (Spring Boot)

• Running Kafka Streams Applications as Multiple Instances
• Set Up to Run the Kafka Streams as Multiple Instances
• Kafka Streams Metadata
• Aggregate Data from Multiple Instances - Overview
• Aggregate Data from Multiple Instances - Fetching Metadata - Part 1
• Aggregate Data from Multiple Instances - Building RestClients - Part 2
• Aggregate Data from Multiple Instances - Testing End to End - Part 3
• Key-Based Queries with Multiple Instances - Overview
• Key-Based Queries Multiple Instances - Fetching Metadata- Part 1
• Key-Based Queries Multiple Instances - Building RestClient and Testing- Part 2
• Refactor the Code for OrderCount for All OrderTypes Endpoint - /v1/orders/count
• Fix the Test Cases
• What about the Other Endpoints?