Monitoring cloud-native systems is increasingly complex due to their distributed nature and the diversity of third-party frameworks they encompass. Observability, which involves using telemetry data to ask and answer questions about system performance, emerges as a key solution to this challenge. This approach relies on three pillars: metrics for detecting issues, logs for diagnosing them, and traces for isolating them, all of which contribute to a comprehensive understanding of system behavior. The open-source landscape for observability is thriving, with tools like Prometheus, ELK Stack, and OpenTelemetry leading the way in providing metrics, logs, and traces, respectively. However, the proliferation of tools has led to challenges like tool sprawl and recent changes in licensing, prompting a need for greater integration and consolidation. As open-source observability tools evolve, emphasis is being placed on user experience, regulatory compliance, and the integration of AI and machine learning to automate issue detection and diagnosis. OpenTelemetry, in particular, is gaining traction as a unified framework for telemetry data collection, blending APIs, libraries, and protocols across metrics, logs, and traces, and is anticipated to become a standard in the industry.