The Art of Automation and Observability

The degree to which you can understand a complicated system's internal state or condition just by examining its exterior outputs is referred to as observability. The quicker and more accurately you can trace a performance issue back to its source without further testing or code, the more observable a system is.

Observability is an art of automation in cloud computing that describes methodologies and tools for gathering, correlating, and evaluating continuous data to reveal the performance of a software and its associated hardware. This enables you to successfully monitor, troubleshoot, and debug an application to meet service level agreements (SLAs) and customer experience objectives.

The term "observability" is sometimes used incorrectly in the IT space to refer to system monitoring or application performance monitoring (APM). In actuality, observability is a logical development of APM data gathering techniques that better meets the increasingly quick, dynamic and distributed deployments of cloud applications. Better monitoring and APM are enabled through observability rather than monitoring.

How is observability implemented?

Observability systems continually identify and collect performance metrics by integrating with existing instrumentation integrated into application and infrastructure components and by offering tools for adding instrumentation to these components.

These four key telemetry types are the focus of observability:


Logs are detailed, comprehensive, time-stamped records of application events that cannot be changed. A highly reliable, millisecond-by-millisecond record of every event, replete with context, can be made using logs so that engineers can "play back" the record for troubleshooting and debugging.


Metrics, also known as time series metrics, are basic indicators of the health of an application or system over a specified period of time. Examples of metrics include the amount of memory or CPU power used by an application over a five-minute period or the amount of latency it encounters during a period of high usage.


Traces track the whole "journey" of each user request from the user interface (UI) or mobile application through the full distributed architecture, and then back to the user.


Dependencies show how each application component depends on other application components, other applications, and IT resources. Dependencies are also known as dependency maps.

The platform collects this telemetry and correlates it in real-time to give SRE teams contextual knowledge about every event that might point to, cause, or be utilized to address an application performance issue.

Numerous observability platforms continuously search for new telemetry sources that may be present in the system (such as a new API call to another software application). Many platforms also feature AIOps (artificial intelligence for operations) capabilities that separate the signals (indications of the true problems) from the noise (data unrelated to issues) because they deal with so much more data than a traditional APM solution.

The Benefits of the Art of Automation and Observability

Find and fix problems that you're not even aware of

Observability identifies situations that you would not be aware of or think to check for, then analyzes how they relate to particular performance problems and gives the context for root cause analysis, expediting the resolution exercises in the process.

Fixes potential software problem in development stage

Catch and fix problems early in the process of app development thanks to observability, which incorporates monitoring from the start. Before they impact the customer satisfaction, DevOps teams can find and correct bugs in new code.

Automated observability scaling

For instance, you can configure a Kubernetes cluster to include aggregation of data and instrumentation, which will begin telemetry collection as soon as the cluster starts up and continue until it shuts down.

Activate self-healing application infrastructure and automated remediation

Combining observability, the art of automation and AIOps ML allows the application infrastructure to foresee difficulties using system outputs and implement remedies without human intervention.

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