Let’s start with simple definitions. Time series data is largely what it sounds like – a stream of numerical data representing events that happen in sequence. One can analyze this data for any number of use cases, but here we will be focusing on two: forecasting and anomaly detection. First, you can use time series data to extrapolate the future.
As discussed in a previous blog, here at LogicMonitor, we are in the process of rolling out a new user interface (UI) which is designed to streamline workflows, reduce clicks, and include powerful new features. So what’s new in the alerts page?
For your team to effectively respond to incidents, you need a shared, unambiguous incident definition so you can recognize when an incident has occurred and assign the appropriate severity. Definitions of an incident differ across teams, but whatever definition you use, identifying and monitoring key service level indicators (SLIs) can help you understand when your service is operating normally—and when its performance has degraded to the point where you need to trigger an incident.
One of the most common complaints we hear from operations and site reliability engineers is about the quality of life impacts and the resulting stress imposed by their on-call responsibilities. Most of us are already aware that a proper on-call rotation is critical to our engineering organization’s health in terms of both immediate incident response and long-term sustainable growth.
