The Splunk Operator for Kubernetes team is extremely pleased to announce the release of version 2.0! This represents the culmination of many months of work by our team and continues to deliver on our commitment to provide a high-quality experience for our customers wishing to deploy Splunk on the Kubernetes platform.

Today we’re proud to announce the general availability of best-in-class Synthetic Monitoring capabilities within Splunk Observability Cloud. Now, IT and engineering teams can proactively measure, monitor and troubleshoot their critical user flows, APIs and services, connected across Splunk Observability.

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.

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.

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?

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.