The article explores the use of Redis in setting up a data pipeline with the ELK Stack, highlighting its role as a message broker and its popularity compared to Apache Kafka. It describes the deployment of components such as Elasticsearch, Logstash, Kibana, and Filebeat on a single Ubuntu machine in an Amazon EC2 environment. Redis serves as a buffering layer between Beats and Logstash, enhancing data processing and indexing capabilities. The author provides a detailed step-by-step guide for installing and configuring each component, ensuring proper data flow from log collection to analysis. The tutorial underscores the importance of message brokers like Redis and Kafka in managing data bursts and alleviating pressure on downstream components within logging pipelines.