Distributed tracing is a critical methodology for debugging and monitoring in microservice architectures, where requests traverse multiple services and servers. By breaking down execution flows into smaller events, tracing helps identify the reasons for request failures or delays. Zipkin, a Java-based application originally developed by Twitter and based on Google Dapper, is widely used for distributed tracing to detect latency issues. It assigns unique identifiers to each request, enabling data collection and analysis through its UI. Integrating Zipkin with the ELK Stack enhances trace analysis by using Elasticsearch for long-term data retention and Kibana for deeper insights. The integration process involves setting up Zipkin, using Docker for installation, and configuring it with Elasticsearch as the storage type. Demonstrations show how to simulate requests between Java servlet services using Zipkin's Brave library. This setup allows users to visualize and analyze trace data in Kibana, helping to build comprehensive dashboards that provide actionable insights into service performance. As observability continues to be a challenge in microservices, tools like Zipkin and ELK Stack play an essential role in overcoming these hurdles.