In recent years, the focus in data systems has shifted from passive storage to real-time data streaming, creating a central nervous system for technology companies like Uber, Netflix, and LinkedIn. This evolution has given rise to an ecosystem centered around processing real-time data streams with technologies like Kafka, Flink, and Spark, which have proven challenging for some organizations to integrate into their tech stacks. An event streaming platform, like Apache Kafka, acts as a central hub for data streams, enabling real-time applications and efficient data integration across geographically distributed systems. Kafka's design supports scalable, fault-tolerant processing and has been adopted widely beyond its origins at LinkedIn, where it transitioned the company to a stream-based architecture. This approach solves challenges in building real-time applications and data integration, offering a structured commit log model that supports low-latency processing and reliable data flow, effectively transforming businesses into dynamic data processing systems. The text highlights the advantages of event stream platforms over traditional messaging systems and emphasizes their role in modern digital companies, noting how Kafka and Confluent Platform are central to these advancements.