True reliability takes into account all of the services that exist in your software environment — which is why it can get so complicated. An ecommerce site, for example, might have services that update current inventory in near real time, process payments in the shopping cart, trigger email receipts to send, kick off fulfillment orders, etc. And if one of these services isn’t operating at its best, that can mean money — and in some cases, customers — lost for the company.
Time and resource consumption have become the driving forces of developing modern applications. While building cloud-native applications, it’s important to ensure that you have the most optimized code in place, and oftentimes that means leveraging concurrency. While writing concurrent code may sound overwhelming at first, Golang makes it extremely easy to get a handle on.
Introducing SL1 Eiffel. Designed to expand your ability to see, contextualize, and act in order to accelerate your journey to AIOps.
Containerization and Kubernetes have taken the DevOps world by storm in the past decade. More and more companies have turned to this technology to enhance their deployment workflows and cut costs. Examples like Pokemon Go and OpenAI would not have been feasible without Kubernetes. While Kubernetes is a growing technology in the current times, it comes with a relatively steep learning curve.
Ubuntu is fast becoming the platform of choice for data scientists worldwide – which is why HP is empowering its customers to launch Ubuntu environments directly on their Windows machines. In addition to native Ubuntu offerings, select Z by HP workstations are also available with Windows Subsystem for Linux 2 (WSL 2) pre-installed and pre-enabled, giving users the ability to accelerate data science workflows on Ubuntu straight out of the box, without leaving their native Windows OS.
Last year, International Data Corporation released its Data GlobalSphere Forecast, 2021-25, in which it outlined the projected 23% compound annual growth in data, leaping to 175 zettabytes of data globally. So the natural question becomes, what will the world do with that much data? And, more importantly, what can your business do with your data?
