I loved my first car, a 1970’s-era Mini Cooper, but from my perspective today as a software engineer, I know that from both an observability and monitoring perspective, it was a disaster.

A 1970s-era Mini Cooper.

We can’t drive modern IT systems without both observability and monitoring, not unless we like being surprised when our systems fail.

Fortunately for drivers, modern cars have better monitoring tools than they did in 1970. Even the dreaded “check engine” light, which my 1970-era Mini Cooper lacked, provides useful information to the end user. In my old Mini you only knew something was wrong with the engine when you found yourself coasting to the side of the road without power.

Obviously, if you’re working on an enterprise application or service, the equivalent loss of service – surprise downtime or degradation – can have serious consequences.

A car computer that has the check engine output light for monitoring works by observing the state of various systems the automobile. In other words, monitoring is what you do after a system is observable. Without some level of observability, monitoring is virtually impossible.

We need both monitoring and observability in the devOps world – even more than in antique Minis. Let’s look at these two concepts in more detail.


Monitoring can be reactive, which isn’t always a bad thing. Usually, monitoring systems on networks and SaaS products provide an alert via a software component which manages devices. These systems maintain the data for the products and report, as needed, to other managing systems. The detail provided helps engineering teams reduce repair time when an incident occurred.

In addition to answering the question, “what’s broken, and why?, ” monitoring can show what component utilization looks like. Monitoring can provide a great deal of insight into the health of networks, apps and systems. It’s also a great resource for looking at historical data.

However, monitoring by itself will not however prevent failure or downtime.

Traditional monitoring solutions do have a role to play for many environments, but they offer only limited, siloed visibility across distributed applications that impact the overall digital experience. For example, there can be limited visibility for application services, networks, infrastructure, clouds, databases, and logs. Typically, the restricted view from monitoring systems is inadequate for managing services in cloud native architectures.


The concept of observability originates from control theory. It refers to the degree to which the internal condition of a complex system can be understood if you know just its outputs. According to the theory, the higher the degree of observability, the easier it is to find and issue’s cause and then resolve the problem. Observability differs from domain monitoring by enabling users to track multiple processes across complex operating environments as an observability tools can identify the factors that contribute to problems occurring within a distributed system, making them easier to resolve.

The most comprehensive solutions provide full-stack observability to provide gain insight into potential problems across an entire array of applications and infrastructure.

Observability tools collect and analyze a broad spectrum of data, including application health and performance, business metrics like conversion rates, user experience mapping, and infrastructure and network telemetry — to resolve issues before they impact business KPIs.

The three pillars of observability

Observability is broen down into three main components:

  1. Metrics are numerical representations of data that can be used to determine a service or component’s overall behavior over time, for example how much of the total amount of memory is used by a method, or how many requests a service handles per second, system uptime, response time and how much processing power an application is using, for example. Engineering teams and ops engineers use metrics to trigger alerts whenever a system value goes above a specified threshold.
  2. Logs are structured and unstructured lines of text a system produces when certain processes run (or fail). Most application frameworks, libraries, and languages come with support for logging. Log files can provide comprehensive system details, such as a fault, and the specific time when the fault occurred. By analyzing the logs, you can troubleshoot code and identify where (and sometimes why) an error happened.
  3. A Trace represents the entire journey of a request or action as it moves through all the nodes of a system. Traces allow you to profile and observe systems, especially containerized applications, serverless architectures, or microservices architecture. Traces allow you to get into the details of requests to determine which components cause system errors, monitor flow through modules, and find performance bottlenecks. Traces are a key pillar of observability because they can provide context for the other components of observability.

Tools for observability

You cannot fix what you cannot see, and the more you see, the more you solve.

To manage distributed system infrastructures, set up a dedicated set of tools to visualize your operational states and alert engineer teams when a failure occurs. No matter how carefully you build a system, there will always be something that can go wrong. Cisco has several full-stack observability architectures to help transform your operations today, and there are integrations across tools, including  AppDynamics, Cisco ThousandEyes, Cisco Intersight, and Cisco Secure Application.

  • AppDynamics Allows developers to build better web and mobile applications with deep performance visibility in test, pre-production, and production environments.
  • ThousandEyes integrations enable application performance to be correlated to the network components that connects users and services.
  • Application performance integrations with Cisco Intersight provides full-stack visibility and multicloud resource management, from bare-metal servers, hypervisors, to Kubernetes clusters, serverless and applications components..

Observability solutions like these can help teams move beyond siloed domain monitoring to gain insights that can lead to insights and action. Full-stack observability solutions enable delivery of unmatched application experiences and streamlined operations. By centralizing and correlating application performance analytics across the full stack, teams can better collaborate to isolate issues and optimize application experiences.

Full-stack observability and business telemetry gives us the power to prioritize actions and deliver flawless experiences that drive revenue streams — while accelerating digital transformation.

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Stuart Clark

Senior Developer Advocate Of Community, AWS