Operations | Monitoring | ITSM | DevOps | Cloud

No matter how careful developers are or how comprehensive tests are applied before deployment, there will always be some level of issues to deal with in production. When it comes to managing issues and ensuring application quality, two main metrics should be on our radar: time to discover and time to resolve issues.

The reasons to move to Kubernetes are many and compelling. This post doesn’t make the case that you should migrate, but assumes you have already decided that you want to. When you’re clear on what you want to do and why you want to do it, the questions of “When?” and “How?” become your focus. What follows centers on the question of how to approach making Kubernetes the platform on which your workloads thrive.

It’s easy to see the value of sensor data that enables acting in time. Just picture driving as the scene of a massive traffic accident unfolds and ambulances race to the rescue…what a difference a few seconds can make. What if past traffic patterns could help city operators predict and manage traffic flow at critical times?

In my last post I walked through a brief introduction to Application Insights, and APM tools in general, and hopefully the simple outside-in availability test I concluded the chapter with has been a useful starting point for you on your APM journey. In this post, I’m going to dig deeper into Application Insights and walk you through a use case that should make the purpose of Application Insights really clear.

With all the buzz surrounding user experience these days, I thought it might be worthwhile to start “at 50,000 feet” and dissect user experience into its component parts and see what all the fuss is about. User experience (UX) is somewhat different than customer experience (CX). Customer experience has been defined as the quality of all a consumer’s encounters with a company’s products, services, and brand. Today, user experience typically refers to the digital user experience.