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?
And you thought you were having a bad day…did you see what happened to the developers over at Nissan? The source code (Git repos) for Nissan’s mobile apps and internal tools was leaked to the Internet because the link was publicly accessible and the password easy to guess. 😬 Yikes.
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.
For developers and programmers, Amazon Web Services (AWS) offers many benefits. It gives you access to the computing and DevOps tools you need at the press of a button — which helps you get products out the door fast. However, it can be challenging to control your costs and identify waste. In this comprehensive guide, we will examine some practical steps you can take for AWS cost optimization.
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
2020 was an unpredictable year that presented several challenges, such as the outbreak of the coronavirus (COVID-19) pandemic. As part of the “new normal,” the world has adopted infection prevention procedures. The 2020 calendar year was defined by face coverings, constant sanitization and physical distancing. At its core, the year was an exhausting, surreal 12-month period for many.
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.
