This article was originally published in The New Stack and is reposted here with permission. A consequence of living in a rapidly changing society is that the state of all systems changes just as rapidly, and with that comes inconsistencies in operations. But what if you could foresee these inconsistencies? What if you could take a peek into the future? This is where time-series data can help.
As the person on the front lines, you know that providing the best service possible can be what makes your ITSM organization succeed. Every day, you work to build the relationships that help your organization create value for end-users. However, when you have inefficient processes, you end up having to be the person responding to an upset user.
If you were asked to evaluate how good crews were at fighting forest fires, what metric would you use? Would you consider it a regression on your firefighters’ part if you had more fires this year than the last? Would the size and impact of a forest fire be a measure of their success? Would you look for the cause—such as a person lighting it, an environmental factor, etc—and act on it? Chances are that yes, that’s what you’d do.
Veteran programmer? Experienced application performance monitoring (APM) connoisseur? Whatever your specific tech chops, you know the importance of ensuring your applications are running optimally. Every minute a business app is down or slow to respond translates into lost revenue and frustrated customers. That’s why smart businesses rely on APM solutions to monitor and analyze their applications’ performance in real-time.
Every IT environment – whether it’s on-prem or in the cloud, and regardless of how it’s designed or what runs in it – is made up of endpoints. That’s why Endpoint Detection and Response, or EDR, has typically been one of the pillars of cybersecurity. EDR helps businesses monitor each of their endpoints for security risks so that they can detect problems before they escalate to other endpoints.
The whole point of our beloved networks is to deliver applications and services to real people sitting at computers. So, as network engineers, monitoring the performance and efficiency of our networks is a crucial part of our job. Flow data, in particular, is a powerful tool that provides valuable insights into what’s happening in our networks for ongoing monitoring and troubleshooting poor-performing applications.
An application programming interface (API) is a set of rules and protocols that enables different software applications to communicate and share data and functionality. The concept of an API has been around for a long time. However, APIs as you know them emerged in the late 1990s and early 2000s with the rise of the internet and web-based services. As more businesses began to offer online services, the need for a standardized way for these services to interact and share data became apparent.
Persistent storage is essential for any account-driven website. However, in Kubernetes, most resources are ephemeral and unsuitable for keeping data long-term. Regular storage is tied to the container and has a finite life span. Persistent storage has to be separately provisioned and managed. Making permanent storage work with temporary resources brings challenges that you need to solve if you want to get the most out of your Kubernetes deployments.
