Operations | Monitoring | ITSM | DevOps | Cloud

Of all the polls, round-ups, and end-of-year wrap-ups, none gives us as much trepidation as the 2021 projected line-up for MVP hacks. As provider lists grow longer, and monitored data grows broader, we decided to distill our list of culprits down to the most likely suspects you’ll need to monitor. Ready? Okay!

You’ve probably heard about Prometheus, the leading open source project focused on metrics and alerting, and how it has changed the way the world does monitoring and observability. But if you’re brand-new to the technology, how can you dip your toes in and get started? I was in this position not long ago myself. I am a very hands-on type of learner, and usually when I want to explore new technologies, I start with “hello world” apps and small toy projects.

Hardware virtualization, also known as hardware assisted virtualization, is the creation of virtual versions of operating systems and computers. The technology was made by AMD and Intel for their server platforms. Its purpose was to improve the processor’s performance and meet virtualization challenges such as translating memory addresses and instructions. Many IT businesses have deployed servers that run only at a fraction of their total capacity.

If you’ve been using SquaredUp for Azure, you’re familiar with its abilities to treat Azure native virtual machines . You can create a number of amazing and useful visualizations with them, such as displaying their health state, performance charts, costs, and so on. This is all excellent and super useful, but one question we frequently get asked is: how do I do these things with my on-prem servers that I’ve connected to Azure Monitor?

Software developers are often only concerned about the functionality of their applications. When these applications are deployed in production, scalability and performance issues surface and application developers then have to worry about performance. Many a times, such situations warrant a complete restructuring of the application code, causing significant impact to new rollouts and current users.

This year, no sector has been spared from the business changes caused by the Covid-19 pandemic, which have almost always been painful ones: millions of dollars in losses for theaters, which still do not know where or when they will premiere their films, a dying tourism sector, the world of hospitality ruined, parents who fear taking their children to school, bankrupted real estate companies… and a lot of glances towards heaven waiting for an answer, from the Most High above or from the extraterre

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.