Kafka Streams API for Developers Online Course

Course Content Overview

The Kafka Streams API for Developers Exam covers the following topics - 

Domain 1. Introduction and Course Setup

• Overview of the course and key learning objectives.
• Understanding the prerequisites required to get started.

Domain 2. Getting Started with Kafka Streams

• Introduction to Kafka Streams API and its capabilities.
• Explanation of key concepts, including topology and processor.
• Understanding the KStreams API and how it works.

Domain 3. Developing a Simple Kafka Streams App

• Introduction to the Greetings App, a basic Kafka Streams application.
• Setting up the project environment.
• Understanding the topology and components of the application.
• Implementing and launching a Kafka Streams app.
• Setting up Kafka locally and testing the application.

Domain 4. Exploring Kafka Streams Operators

• Using filter and filterNot to refine data streams.
• Understanding map and mapValues for data transformation.
• Using flatMapValues and flatMap for breaking down complex messages.
• Learning peek and merge for stream modifications.

Domain 5. Serialization and Deserialization in Kafka Streams

• Understanding how Kafka Streams serializes and deserializes key-value pairs.
• Configuring default serializers and deserializers in Kafka applications.
• Creating custom Serdes for message transformation.
• Using custom Serdes in the Greetings App for better message handling.

Domain 6. Building a Reusable Serializer/Deserializer

• Developing a generic serializer/deserializer for different message types.
• Integrating this generic serialization approach into Kafka applications.

Domain 7. Developing an Order Management System Using Kafka Streams

• Overview of the real-time order management system.
• Designing the data processing topology.
• Separating restaurant and retail orders using split and branch operators.
• Transforming order details into revenue-based data streams.

Domain 8. Understanding Internal Kafka Streams Mechanics

• Exploring how topology, stream, and tasks interact internally.
• Understanding the effects of modifying stream threads on processing behavior.

Domain 9. Handling Errors in Kafka Streams Applications

• Identifying failures in Kafka Streams processing.
• Understanding default deserialization error behavior.
• Implementing a custom deserialization error handler.
• Managing processing errors with custom solutions.
• Handling Kafka cluster failures effectively.

Domain 10. Working with KTable and GlobalKTable

• Introduction to KTable and GlobalKTable APIs.
• Implementing a KTable topology for data storage.
• Understanding how KTables function internally.

Domain 11. Performing Stateful Operations in Kafka Streams

• Understanding aggregation, joins, and windowing operations.
• Performing count-based aggregation in Kafka Streams.
• Using the reduce and aggregate operators for processing events.
• Storing aggregated data using materialized state stores.

Domain 12. Accessing Aggregated Data from State Stores

• Retrieving aggregation results from Kafka Streams.

Domain 13. Stateful Operations in Order Management System

• Counting total orders per store using count operator.
• Calculating total revenue per store using aggregate operator.

Domain 14. Rekeying Kafka Records for Stateful Operations

• Understanding the impact of null keys on stateful operations.
• Using the selectKey operator to rekey Kafka records.

Domain 15. Joining Data Streams in Kafka Streams

• Introduction to different types of joins in Kafka Streams.
• Implementing inner joins between KStreams, KTables, and GlobalKTables.
• Using left joins and outer joins for merging data streams.
• Understanding the co-partitioning requirements for joins.

Domain 16. Performing Joins in Order Management System

• Combining revenue data with store details using joins.

Domain 17. Working with Windowing in Kafka Streams

• Introduction to windowing techniques in Kafka.
• Implementing tumbling, hopping, and sliding windows.
• Controlling window result emission using suppress operator.

Domain 18. Windowing in Order Management System

• Extracting custom timestamps from events.
• Aggregating orders and revenue within time windows.
• Performing joins on windowed data.

Domain 19. Handling Out-of-Order Events in Windowing

• Managing records with earlier and future timestamps.

Domain 20. Developing Kafka Streams Applications with Spring Boot

• Setting up Kafka Streams with Spring Boot.
• Configuring Kafka Streams using application.yml.
• Developing Spring Boot Kafka Streams applications.

Domain 21. Spring Boot Auto-Configuration for Kafka Streams

• Understanding automatic configuration in Spring Boot Kafka Streams.

Domain 22. JSON Serialization/Deserialization in Spring Kafka Streams

• Implementing JsonSerde for JSON message handling.

Domain 23. Error Handling in Spring Kafka Streams

• Managing deserialization errors using different approaches.
• Handling uncaught exceptions in Kafka topology.

Domain 24. Building Order Management System with Spring Boot

• Developing Kafka Streams topology for order processing.

Domain 25. Building REST APIs for Interactive Queries

• Creating REST endpoints to query order count and revenue in real-time.

Domain 26. Testing Kafka Streams Applications

• Writing unit tests using TopologyTestDriver.
• Performing integration testing using Embedded Kafka.

Domain 27. Implementing Grace Period in Kafka Streams

• Understanding grace period behavior in windowing operations.

Domain 28. Packaging and Deploying Kafka Streams Applications

• Building Spring Boot Kafka Streams applications as executable JAR files.

Domain 29. Ensuring Exactly-Once Processing in Kafka Streams

• Setting up exactly-once semantics in Kafka Streams.
• Understanding transactions and idempotency in Kafka.

Domain 30. Running Kafka Streams Applications on Multiple Instances

• Configuring multiple Kafka Streams instances for scalability.