Status pages have become the end-users window into your team’s operations. Companies with status pages are doing the right thing for their users — building in some transparency while mitigating frustration and support contact. For the benefits of status pages to pay off, organizations need to treat them as something more than active wiki-pages run by support.

Observability is a mindset that lets you use data to answer questions about business processes. In short, collecting as much data as possible from the components of your business — including applications and key business metrics — then using an AI-powered tool to help consolidate and make sense of this huge volume of data gives you observability into your business. Having observability for your business and applications lets you make smarter decisions, faster.

What if there was a way to deploy a new feature into production — and not actually turn it on until you’re ready? There is! These tools are called feature flags (or feature toggles or flippers, depending on whom you ask). Feature flags are a powerful way to fine-tune your control over which features are enabled within a software deployment. Of course, feature flags aren’t the right solution in all cases.

Two categories a software organization should always strive to improve in are: Data analysis is one way that your organization can improve the efficiency of incident management and overall application quality. However, the questions remain – which metrics should be collected? How can analysis of these metrics facilitate these improvements? Read on to hear about five key metrics essential to incident response.

With the release of Splunk 9.0 came support for SmartStore in Azure. Previously to achieve this, you’d have to use some form of S3-compliant broker API, but now we can use native Azure APIs. The addition of this capability means that Splunk now offers complete SmartStore support for all three of the big public cloud vendors. This blog will describe a little bit about how it works, and help you set it up yourself.

If you’re new to the concept or just trying to keep up with the conversation, Gartner defines Observability as the evolution of monitoring into a process that offers insight into digital business applications, speeds innovation and enhances customer experience. Some folks think that Observability is a new buzzword, but in fact the term was coined in 1960 by Rudolf E. Kalman, a Hungarian-American engineer.

You’ve written code, you tested it and built it. Now, your release is ready to deploy into production. But: is your production environment ready for the release? That’s a question every IT professional and platform engineer should be asking before accepting a new release — whether the release is an update of an existing app or a totally new deployment. To that end, here’s a checklist to make sure that your production environment is ready to go.

Load balancers and content delivery networks (CDNs) are critical tools for delivering modern, cloud-native applications. They play essential roles in ensuring the smooth flow of data between applications and end-users. If you don’t have both a load balancer and a CDN in place, you’re probably in a poor position to guarantee the uptime of your application across a wide geographic area. That does not mean, however, that load balancers and CDNs do the same thing.

DevOps has never been more popular than it is today. Since first popularized nearly 15 years ago by Patrick Debois and Gene Kim, DevOps has become the standard approach for managing IT. In this blog post, we’ll look at key trends and data that paint a picture of today’s State of DevOps. You can learn more about the history and fundamentals of the topic in our article What is DevOps and why is it important?.

Extended Berkeley Packet Filter (eBPF) is an exciting technology that provides secure, high-performance kernel programmability directly from the operating system. It can expose a wide range of applications and kernel telemetry that is otherwise unavailable. But with operating systems frequently processing very large volumes of network data, even with an efficient framework and cheap eBPF program runs, costs can add up quickly.