When we talk about metrics in software delivery, a lot of developers think of execution metrics — things like throughput, delivery and number of deploys. But in reality, those metrics don’t motivate anyone — at least not without connecting them to a bigger picture. I’ve worked in software for 23 years. I’m a three-time founder and four-time CTO, responsible for leading a 200+ member distributed engineering organization.

Wondering what error budgets (EBs) are and how they are useful? We explain what they are, how they are defined, and how they can help your team. An error budget is the amount of acceptable unreliability a service can have before customer happiness is impacted. If a service is well within its budget, the developers can take more risks in their releases. If not, developers need to make safer choices.

In the first article in this series, we explained how to set up your developer environment to begin creating Mattermost plugins. In the second, we examined the structure of server-side and web app plugins and how to deploy them. Now, it’s time to dive deeper into the server side of the application, which is written in Golang.

You’re going to run into network issues during normal operations—in part because so many kinds of errors can cause noticeable problems in your network. Identifying the root cause of each issue is critical and to do so successfully, you want to make sure you have the right network troubleshooting solutions in your arsenal before wading in. This helps ensure you have a clear understanding of the scope of the problem before you attempt any network troubleshooting steps.

Traditional systems monitoring solutions poll various counters (typically simple network management protocol [SNMP]), pull in data and react to it. If an issue requiring attention is found, an event is triggered—perhaps an email to an administrator or the firing of an alert. The admin subsequently responds as needed. This centralized pull approach is resource-intensive. Due to the pull nature of the requests, it results in data gaps and data that may not be granular enough.

“We want to work remotely.” That’s the major takeaway from Ivanti’s just-released survey on the Everywhere Workplace. Nearly 2,000 consumers across the U.S. and U.K. responded. While most of them were abruptly shifted into remote work due to circumstances outside their control – and those circumstances were scary and confusing – there has been a silver lining. They’re happier at home.

At Catchpoint, our mission is to provide customers with actionable data that will help them reduce MTTR and maintain a positive digital experience. We measure "from where the users are" to ensure the data reflects real end-user experience. As someone that's part of the Catchpoint on-call chain, this is extremely important to me. I do not want to be woken up at 2 AM because a server is misbehaving, only to find out that the application failed over gracefully and no users were impacted.

After weeks of rewriting the core aspect of Icinga for Windows – executing checks – we are happy to announce version 1.5.0 today. Why we changed the check handling? We are glad you asked and happy to share the new features!

In a relatively short amount of time, Kubernetes has evolved from an internal container orchestration tool at Google to the most important cloud-native technology across the world. Its rise in popularity has made Kubernetes the preferred way to build new software experiences and modernize existing applications at scale and across clouds. With Kubernetes, companies can host workloads running on a single cloud, as well as workloads across multiple clouds.

In our last episode, I wrote about some speed improvements to date_histogram and I was beside myself with excitement to see if I could apply the same principles to other aggregations. I've spent most of the past few months playing a small part developing runtime fields but eventually I found time to take a look at the terms aggregation.