They say, “What happens in Vegas, stays in Vegas,” but I wanted to highlight the role our customers played at last month’s.conf22, our annual users’ event at the MGM Grand. It was awesome meeting customers in person again, and connecting virtually with thousands more. We had a terrific turnout with 8,200+ customers and partners representing 113 countries and more than 6,500 organizations.

When considering application source code, the way you maintain consistency throughout environments is mostly straightforward. You write application code, commit it to source control, and then build, test and deploy via a CI/CD pipeline. Since the application is defined by the source code living in source control, the build will be identical in all environments to which it’s deployed. But what about the infrastructure on which an application runs?

You have more data coming at you than ever before. Over the next five years, the total amount of digital data is going to be more than twice the amount of data created since the advent of digital storage. With the success of your company often determined by how you anticipate and respond to threats – and leverage meaningful insights – you need the ability to quickly search and find insights in your data, despite this increasing deluge of information.

People make mistakes, technology breaks down, and processes aren’t infallible. But, when incidents happen, what can we do about it? What can we learn? As with all things, learning isn’t a binary action, it’s a process. And, when an incident occurs, organizations typically conduct a post-mortem analysis and generate a post-incident review to uncover what went wrong and why.

Splunk Observability is incredibly good at details! Many of us use it as a metaphorical microscope through which we observe our software. But how do you observe the long-term trends and usage of that microscope? There are numerous organization-level metrics provided in Splunk Observability that can be used to chart organization-level concerns. These can be leveraged in various ways to understand things like uptake, billing and just how much value Observability is providing.

Managing source code with a defined method is one vital aspect of implementing effective application development. Today, two strategies for doing this stand above the rest: trunk-based development and GitFlow. Choosing the proper method for source code control is often dependent upon several factors, such as: In this article, let’s define and compare trunk-based development and GitFlow, look at the factors that drive an organization’s decision between the two.

In software development, the name of the game is to develop reliable systems in a fast-paced manner. As development shops have evolved to increase the speed of delivery, many organizations have embraced the Agile development practices of continuous integration and continuous deployment (CI/CD). But the very nature of fast-paced development introduces challenges — particularly around the quality and the reliability of the software being developed.

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.