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What is observability, how is it achieved, and what are the attributes of successful operations teams who have achieved total observability.
Observability is the practice of achieving actionable insights from data that is generated by instrumented IT and software systems. The goal is to understand both when an event or issue happened, and why. The concept isn’t new – it comes from control theory introduced by Rudolf Kalman in linear dynamic systems, and is defined as a measure of how well internal states of a system can be inferred from external outputs. It is no surprise that observability is gaining buzz in the DevOps and SRE communities. In today’s complex, hybrid, constantly evolving IT infrastructures, with microservices, serverless and automation as code, it is critical to be able to easily observe and know what is happening, where it is happening, and why.
Let’s settle two things. First, observability is not just a fancy word for monitoring. Second, observability isn’t just about collecting as many logs, event, metrics and traces as you can.
Monitoring and observability are distinct concepts, but both require modern log analytics.
Legacy apps and workloads are finding their home in rapidly changing hybrid environments alongside new cloud-native technologies. The deconstruction of monolithic environments into thousands of microservices at scale mean that operations teams are now responsible for maintaining multiple domains and environments that they know little about.
Operations pros have to embrace automation to make their teams more effective and efficient. But at the same time, these automated delivery pipelines open another surface layer and abstractions that must be monitored. Monitoring of the automation itself poses new risks.
According to a 451 Research report, cloud-native tech adoption and cloud migration increased both tool proliferation and time to resolution. Juggling multiple tools and swiveling among dashboards across these hybrid environments is resource intensive for your team and causes delays for your impatient stakeholders.
Gain visibility into the entire surface area of your business’s applications and infrastructure. Embrace source-agnostic platforms to collect telemetry, spanning logs, events, metrics, and traces from all infrastructures, hosts, containers, devices, endpoints, and all applications types - cloud-native, legacy or proprietary.
Raw logs, traces, alerts and events alone aren’t enough. Context is critical to developing a rich and accurate understanding of the situation. When things go wrong your ops team must be all hands on deck- gathering, correlating data and ultimately piecing together the full story. Observability platforms make these investigations go more quickly and help generate meaningful notifications.
Observability needs to work for both operations professionals and stakeholders. SREs and DevOps shouldn’t have to make hard choices between building yet another dashboard or debugging. Modern observability platforms achieve the right balance – conveying the overall health of services while easily revealing root-cause across complex dependencies, dynamic topologies and real-time data relationships.
Organizations are just starting to explore AIOps in DevOps workflows. It is no longer viable or effective to chase release and deployment errors across 1000s of applications and complex cloud infrastructures. AIOps capabilities, such as anomaly detection, clustering and usage forecasting, need to be part of modern observability platforms so DevOps teams can focus on building better apps and Ops can focus on automation.
Data about application or infrastructure states offers limited value if the business can’t act on it. Every business has unique KPIs and metrics, even those in the same industry. Observability platforms must be extensible so operations team can build applications that support innovation and enable data-driven decisions.
Watch this on-demand webinar to learn how simplifying your monitoring stack is key to achieving total observability.
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