Today, IT pros field an increasing number of assets they need to monitor to secure systems and understand what’s happening in their database and applications. This is further complicated as organizations adopt hybrid IT strategies and IT pros are tasked with managing tech stacks that span across on-premises and public clouds.
To cut through complexity and create end-to-end visibility into their IT systems, organizations must enact an observability approach.
Monitoring Vs. Observability
When it comes to observability, many think it means the same as monitoring. Whereas monitoring is a verb; observability is a measure of how well the internal states and conditions of a system can be inferred from its external outputs.
When monitoring systems are set up, dashboards are built around certain assumptions that an IT pro may expect will happen. When monitoring databases, teams are building multi-level component IP systems. The algorithms must process that data, and the user interface must interact with it.
However, to make that system highly observable, performance information and other kinds of metrics across all kinds of different endpoints must be incorporated. Especially since IT pros must deal with microservices, mesh, IoT or containers orchestrated through Kubernetes.
How to Monitor Effectively
In the past, IT pros would typically monitor a SQL Server or even other database platforms like PostgreSQL or MySQL. In this new era of observability, on the other hand, IT pros need to do something different with the front-end code with regard to the different kinds of algorithms processed against that data set. Typically, developer teams would write the code, but there wasn’t anything written in that code to send out debug signals.
Today that has changed. To make sure our systems are fully observable (because we can’t anticipate all possible problems!), we must log different steps to keep track of the many moving pieces. Organizations must collect as much of everything as possible. The more data, the more information organizations have about what’s happening in their application, from the database through the virtualization or containerization layer to the virtualization host or the orchestration to the bare metal. If organizations can get visibility into those elements, we can enable our systems to be fully observable and more easily diagnose any problem or anomaly that happens.
An Observability Approach
When using the observability approach, high-level dashboards are handy. They can help IT pros roll up everything that’s happening and get to a root cause analysis if there is a problem. However, sometimes there’s not a specific problem; there’s just a general performance issue. With observability platforms, teams can quickly discover, map and understand dependencies in ways that make it easier to achieve and maintain service level objectives (SLOs).
As IT teams increasingly distinguish between merely monitoring IT environments and achieving observability, they can proactively surface anomalies using machine learning algorithms and other forms of data science. And the depth and insight that can be applied to managing IT is increasing dramatically. Observability is quickly becoming table stakes for any modern IT organization.
