We live in a complicated world of Enterprise IT and software-driven consumer product design. The internet offers IT infrastructure services from remote data centers. Companies use these services as microservices and containers spread across infrastructure and platform services. Consumers anticipate frequent feature updates over the internet.

To fulfill these end-user demands, IT service providers and business organizations must increase the reliability and predictability of backend IT infrastructure operations. To enhance system dependability, we regularly monitor infrastructure performance indicators and statistics.

Though observability might seem like a buzzword, it is a traditional principle that drives monitoring procedures. System observability and monitoring are important components of system dependability, but they’re not the same. Monitoring vs Observability is a question that many have. Let’s examine the relationship between observability and monitoring in cloud-based business IT operations.

What is Observability in software?

Observability in software is the ability to deduce a system’s internal states from exterior outputs. Control theory is the ability to manipulate the internal states of a system by altering external inputs. It’s difficult to assess controllability quantitatively; therefore, system observability is used to evaluate outputs and draw meaningful inferences about system states.

In business IT, dispersed infrastructure components are virtualized and run on various abstraction levels. This setting makes analyzing and computing system controllability difficult.

Instead, most people use infrastructure performance logs and metrics to analyze specific hardware components’ and systems’ performance. Analyzing log data with AI (AIOps) helps detect future system failures. Then your IT staff may take proactive steps to minimize end-user impact.

Observability has three fundamental pillars:

  • Logs: An event log is a permanent record of discrete occurrences that may uncover unexpected behavior in a system and reveal what changed when things went wrong. It’s best to ingest logs in structured JSON format so log visualization tools can auto-index and query them.
  • Metrics: Metrics are the cornerstones of monitoring. They are measures or counts accumulated over time. Metrics inform you how much memory a function uses or how many requests a service handles per second.
  • Traces: A single trace shows a particular transaction or request moving from one node to another in a distributed system. Traces let you dive into specific requests to determine which components cause system problems, track module flow, and identify performance bottlenecks.

What is Monitoring?

Being observable means knowing a system’s internal status. Monitoring is described as actions involved in observability: observing system performance quality over time. Monitoring describes the performance, health, and other critical features of a system’s internal states. Monitoring in corporate IT refers to the practice of turning infrastructure log information into actionable insights.

The observability of a system involves how effectively infrastructure log metrics can infer individual component performance. Monitoring tools use infrastructure log metrics to provide actionable data and insights.

Monitoring vs. Observability

Let’s look at a vast, complicated data center’s infrastructure system monitored by log analysis and ITSM technologies. Too much data analysis generates needless alarms, data, and false flags. Without assessing the right measurements and thoroughly filtering out what’s unnecessary from all the information the system generates,  the infrastructure cannot be used for observability.

Single server machines can be readily monitored for hardware energy consumption, temperature, data transmission rates, and processor performance. These variables are highly linked with system health. So the system is observable. Performance, life expectancy, and risk of possible performance issues may be examined proactively using simple monitoring tools like energy and temperature measurement equipment.

The observability of a system depends on its simplicity, the metric representation, and the monitoring tools’ ability to recognize them. Despite a system’s intrinsic complexity, this combination provides essential insights.

Your teams should have the following to monitor and observe effectively:

  • System health reporting (Do my systems work? Do my systems have enough resources?).
  • Reporting on customer-experienced system condition (Do my customers know if my system is down?).
  • Key business and system metrics monitoring
  • Tools to understand and debug production systems.
  • Tooling to find information about things you did not previously know (that is, you can identify unknown unknowns).
  • Tools and data to trace, analyze and diagnose production infrastructure issues, including service interactions.

Observability and monitoring implementation

Monitoring and observability solutions are intended to:

  • Provide early warning signs of service breakdown.
  • Detect outages, bugs, and unauthorized activity.
  • Assist in the investigation of service disruptions.
  • Identify long-term patterns for business and capacity planning.
  • Expose unforeseen impacts of modifications or new features.

Installing a tool is not enough to fulfill DevOps goals, although tools can help or impede the endeavor. Monitoring methods should not be limited to a single person or team. Empowering all developers to use monitoring reduces outages.

Combining the forces of Monitoring and Observability

Though Observability and Monitoring are distinct tasks, they are linked. Both monitoring and observability technologies can help you identify issues. Monitoring and Observability go hand in hand since not all concerns deserve further investigation. Maybe your monitoring tools report a server offline, but it was part of a planned shutdown. You don’t need to collect and evaluate various data types. Just log the alert and go.

Observability data is essential when dealing with serious situations. Manually gathering the same data that observability technologies provide would be time-consuming. Observability tools always have data to understand a challenging scenario. Several solutions also provide ideas or automated assessments to help teams navigate complex observability data and identify fundamental causes.

With Apica, you can gather, process, and analyze behavioral data and use patterns from business systems to help you make better business choices and provide better user experiences. AI can evaluate operational data across apps and infrastructure to provide actionable insights that allow you to scale effectively. Sign up for a free trial today to take your business to the next level.