In part 4 of the Spring for Apache Kafka Deep Dive blog series, the focus is on building event streaming applications using Spring Cloud Data Flow, Apache Kafka, and Spring Cloud Stream. The article explains how to create and deploy event streaming pipelines that utilize Kafka topics as named destinations, allowing developers to produce and consume data to and from these topics with ease. It delves into the use of Stream DSL syntax for defining streams, illustrating how to handle user/click events and apply filtering, business logic, and storage in an RDBMS. The blog also covers advanced topics like fan-in/fan-out, partitioning for content-based routing, and function composition to dynamically attach business logic to streaming applications. Developers can employ the Spring Cloud Data Flow shell to register, deploy, update, and manage event streaming applications, with support for continuous deployment and version control. The series emphasizes the versatility and integration capabilities of Spring Cloud Data Flow in building and managing scalable event streaming solutions on Apache Kafka, while also highlighting new features in Apache Kafka 3.8.0.