Building real-time data processing platforms often leads developers to struggle with traditional tools that can't handle massive data streams efficiently. Apache Flink offers a powerful solution to these challenges, and this talk demonstrates how to leverage it effectively using Kotlin.
We'll explore the DataStream API for efficient stream processing and show how to write cleaner, more maintainable code using Kotlin's language features. Through practical examples, you'll learn:

Building expressive stream processing pipelines that handle unbounded data efficiently
Working with Flink's Table API and SQL features while maintaining type safety
Managing application state effectively for complex event processing
Implementing robust testing strategies for stream-processing applications

The session includes real-world examples of how Flink solved critical performance challenges in production systems. You'll learn how to handle common pitfalls, implement best practices, and structure your applications for scalability. Whether building real-time analytics dashboards or processing event streams, you'll walk away with practical knowledge to implement Flink in your next project.