Global organizations increasingly rely on event-streaming tools like Redpanda and stream-processing platforms such as Databricks to handle large volumes of data for real-time decision-making. For instance, these tools can be used to recommend content to users based on their interactions with mobile or web applications, by streaming clickstreams through Redpanda to Databricks, where a recommendation engine processes the data. Redpanda serves as a fast, scalable alternative to Apache Kafka, featuring compatibility with Kafka's API, and it operates on various platforms, including virtual machines and Kubernetes. The tutorial demonstrates how to set up a data pipeline using Redpanda and Databricks, from producing data to a Redpanda topic to storing it in Databricks as CSV files, and subsequently analyzing the data in real-time. The process includes setting up Redpanda using Docker, configuring Databricks to process streaming data, and running Apache Spark queries to analyze and visualize the data. This setup enables organizations to efficiently analyze data in real-time for various projects, leveraging the speed and scalability of Redpanda, which integrates well with existing Kafka tooling and operates without dependencies on JVM or ZooKeeper.