The digital landscape has transformed into a complex environment of microservices, serverless capabilities, and containerized workloads across multi-cloud ecosystems. The traditional observability framework, relying on metrics, logs, and traces, faces limitations in addressing the complexities of system operations. Observability 1.0 struggles with mean time to resolution and mean time to detect, which negatively affects user experiences and business outcomes. Observability 2.0 emerges as a comprehensive approach that enhances data collection by unifying and enriching it with context and intelligence, linking performance with business metrics to improve problem prioritization across teams.
For years, the solution has been 'observability,' often described by its 'three pillars': Metrics, Logs and Traces. These pillars have served us well, offering important information about system health.
Observability 2.0 isn't just a step-wise update; it's a fundamental shift in how we approach system understanding. It recognizes that in today's complex environments, merely collecting data isn't enough.
While individual metrics, logs and traces are indispensable, relying solely on them in isolation leads to several pain points: these limitations hinder mean time to resolution (MTTR) and mean time to detect (MTTD), directly impacting business outcomes and user experience.
Observability 2.0 isn't exclusive to SREs and DevOps engineers. It's about connecting technical health without delay to business effects.
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