Operations | Monitoring | ITSM | DevOps | Cloud

Well, hello 2021: are you going to be good to me? In conversations with family, friends and coworkers, most are cautiously optimistic that with vaccines being rolled out things will start to return to some semblance of normalcy in a few months. Meanwhile, there is much work to be done. Enterprise IT managers and leaders always have big mandates and in 2020, those expectations exploded. Technology, after all, has been everyone’s lifeline during the pandemic.

In our last blog, "What's New in Splunk Cloud: Part 1," we reviewed a host of new Splunk Cloud features that we have delivered through our accelerated releases since the beginning of 2020. A large part of this effort focused on empowering Splunk Cloud admins and making their experience as self-service as possible. In this blog, we will examine our latest effort to continue this empowerment: Splunk Cloud’s Admin Configuration Service (ACS).

Python optimization is the solution to speed performance issues. But, when do you optimize, and what parts of the code should be optimized? This article will help you answer these questions. Developers always want to efficiently write neat code. However, things are quite different when working with a Python-based data science project. There will be situations where you need Python optimization. However, there are cases where optimization yields irrelevant results.

“We all get our credit cards replaced a lot. Our online ecosystem is getting more complex with the number of accounts we have. We wanted to simplify that, and make it less complicated to get those cards back on file,” says Katherine Chavez, Director of Marketing for Strivve (formerly Switch, Inc.). Strivve is a startup that aims to take the pain out of updating credit and debit cards by automating the updating process.

If you’re a veteran in this space, you probably understand the many incident response metrics and concepts, along with the many (at times exasperating) acronyms. For those new to the space, or even those with years of experience, the terminology is often overwhelming. If you’re one of those people who’s struggling to navigate through the world of DevOps metrics, we’ve created this article for you.

IT software technology is advancing at a rapid pace, with the internet of things, cloud, automation, and machine learning making networks and network management activities more complex and interdependent. As networks grow and become more complicated, it’s increasingly important for administrators to have access to the tools necessary to conduct essential network monitoring and management operations. One such tool is a network mapper, also known as a network topology mapper.

Kubernetes work queues are a great way to manage the prioritization and execution of long-running or expensive menial tasks, such as processing large volumes of employee migration to a new system, ranking and sorting all the planets in the universe by Twitter tags, or even post-processing every frame of the latest Avengers movie.

In its latest report, research firm Gartner tackles the trending subject of Observability. According to Gartner, "Observability is the evolution of monitoring into a process that offers insight into digital business applications, speeds innovation and enhances customer experience. I&O leaders should use observability to extend current monitoring capabilities, processes, and culture to deliver these benefits." This blog post gives you a sneak-peek of this new analyst report about observability.

Today’s blog post dives into the internals of Icinga 2 and will give you an overview how the config synchronization works internally. We will take a small cluster as an example and follow the configuration files through the synchronization mechanism. We assume some familiarity with distributed Icinga 2 setups as this post will not go into details on how to set up an Icinga 2 cluster.