Kafka Streams API For Developers using Java/SpringBoot 3.X

Master Kafka Streams: Real-time Data Processing with Java & Spring Boot

Get Hands-on with Kafka Streams API for Powerful Data Transformations

Unlock the potential of real-time data processing with our comprehensive course on the Kafka Streams API. This advanced API, a core component of the Kafka Ecosystem, empowers you to build sophisticated data pipelines. Learn to apply data transformations, perform data enrichment, and branch data streams for various processing needs.

Key Skills You'll Acquire:

• Data Aggregation & Joining: Aggregate data from multiple Kafka topics and join data streams for comprehensive insights.
• Windowing & State Management: Aggregate data into time-based window buckets and manage stateful operations for complex analytics.
• Real-time Application Development: Build robust real-time Kafka Streams applications using both the pure Streams API and Spring Boot.
• Interactive Data Queries: Write interactive queries to retrieve aggregated data from state stores and expose it via RESTful APIs.
• Comprehensive Testing: Master unit and integration testing techniques using JUnit5 to ensure application reliability.

Course Curriculum: From Fundamentals to Advanced Applications

1. Getting Started with Kafka Streams:

• Introduction to Kafka Streams and essential terminologies (Topology, Processor).
• Hands-on setup: Greetings App and Kafka Streams Launcher.
• Exploring Kafka Streams Operators using the KStream API (Filter, Map, FlatMap, etc.).
• Serialization & Deserialization: Implement and customize serialization/deserialization for enhanced data handling.

2. Building Real-World Kafka Streams Applications:

• Order Management Use Case: Develop a complete Order Management Kafka Streams application to understand practical applications.
• Topology, Streams, and Tasks: Dive deep into the internal workings of Kafka Streams applications.
• Error Handling: Implement robust error handling mechanisms, including default and custom error handlers.
• KTable & Global KTable: Utilize KTable and Global KTable for stateful operations.
• Stateful Operations: Master stateful operations like aggregation, joining, and windowing.
• Re-Keying: Learn to re-key Kafka records for efficient stateful processing.
• Windowing Techniques: Implement various windowing strategies (Tumbling, Hopping, Sliding) for time-based analysis.

3. Kafka Streams with Spring Boot:

• Building Kafka Streams applications with Spring Boot: Setup, configuration, and auto-configuration.
• JSON Serialization/Deserialization in Spring Kafka Streams.
• Error Handling in Spring Kafka Streams: Comprehensive approaches to manage application errors.
• Interactive Queries with RESTful APIs: Build GET endpoints to retrieve aggregated and windowed data.
• Testing Kafka Streams: Write effective unit tests (Greetings App, OrdersCount, OrdersRevenue) and integration tests (using @EmbeddedKafka).
• Grace Period: Understand and implement the grace period concept in Kafka Streams.
• Packaging & Deployment: Package your Spring Boot Kafka Streams application as an executable.

Who is this course for?

This course is designed for developers with Java and Spring Boot experience who want to leverage the power of Kafka Streams for building scalable and real-time data processing solutions.

By the end of this course, you will be able to:

• Design and implement complex Kafka Streams applications.
• Utilize Spring Boot for simplified development and deployment.
• Build interactive data pipelines with RESTful APIs.
• Ensure the reliability and correctness of your Kafka Streams applications through comprehensive testing.