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Observability vs. Monitoring: A Guide for IT Operations

What is observability, how is it achieved, and what are the attributes of successful operations teams who have achieved total observability.

What is 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.

Monitoring vs. Observability

Monitoring

  • Monitoring conveys what is happening
  • Monitoring is designed around collection of metrics and logs
  • Built around ‘known’ entities — decide what to monitor
  • Good for overall health status
  • Monitoring remains a key task for IT operations, DevOps and SREs

Observability

  • Observability explains why something is happening and provides actionable insights
  • Observable systems inherently offer data about their state through instrumentation
  • Collects monitoring data and enriches; adds in other data sources and enables questions to be answered that could never be asked before
  • Designed for granular insight, context and debugging
  • Observability includes monitoring but extends it; it is both an outcome and a culture (similar in a way to DevOps)

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Challenges in Achieving Observability

Complexity of continuous change

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 they know little about.

Automation brings benefits and risks

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.

Too many tools, too few skills

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.

Requirements to Obtain Effective Observability

Enterprisewide instrumentation calls for centralized log management as its foundation

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.

Context to answer “Why?”

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.

Visualize the stack to understand and convey what is happening and why

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.

Harness AIOps as a key pillar of observability

Organizations are just starting to explore AIOps in DevOps workflows. It is no longer viable or effective to chase release and deployment errors across thousands 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.

Extend to unleash business innovation

Data about application or infrastructure states offers limited value if you can’t act on it. Every organization 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.