Observability in modern software systems revolves around the collection and analysis of telemetry signals to understand a system's internal state, with the foundational pillars being metrics, logs, and traces. These signals have evolved alongside technological advancements, with metrics providing raw numeric data from various sources, logs offering structured and unstructured data from infrastructure and applications, and traces recording user interactions in distributed architectures. Profiling has emerged as a proposed fourth pillar, offering deeper insights into code performance issues. Observability has transitioned from basic monitoring to encompass complex distributed systems, necessitating unified data platforms for holistic insights. Tools like OpenTelemetry are recommended for standardizing data formats and minimizing vendor incompatibility. As technology advances, observability frameworks are increasingly integrating AI/ML for predictive insights and real-time threat detection, emphasizing the need for robust data foundations to manage the constant evolution of telemetry signals.