Microservices architecture, gaining popularity over traditional monolithic systems, offers improved scalability, maintainability, and independent deployment of individual services. However, it introduces complexity in monitoring system health, necessitating advanced observability strategies involving logs, metrics, and traces. Centralizing log storage, maintaining structured log formats, and labeling logs are crucial for effective microservice logging. Metrics, categorized into system, network, and business intelligence, provide insights into infrastructure performance, user experience, and business outcomes. Traces help track data flows and pinpoint errors, while service meshes facilitate distributed tracing without altering microservice code. These observability practices ensure timely error detection and resolution, with platforms like Coralogix employing machine learning to anticipate potential failures.