Too often, tech teams rush to discard mainframes and legacy infrastructure, immediately aiming to build something shiny and new. However, just as the Saqqara was revolutionary when it was built, and acted as the origin point of Egypt’s 96 other pyramids, mainframes that we now classify as ‘legacy’ were the epitome of innovation when first set up.
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Scaling existing systems is hard. Scaling existing systems while also introducing new applications that need to be imminently scaled nationwide can feel impossible. This article examines some of the challenges that stand between Dev and Ops and closer cooperation and how to combat them.
Kubernetes isn’t just limited to containers. It is becoming an operating system for the cloud, both private and public. The technology began as an orchestrator for containerized apps, speeding deployment times and increasing app portability. See where it will go next!
What exactly is chaos engineering? Chaos Engineering is a practice and solution that creates resiliency and ultimately helps avoid expensive outages. When it comes to any system that is increasing in complexity through digital transformation, tech leaders need to consider all of the potential system weaknesses.
The foremost problem that organisations encounter when trying to automate is knowing their own environment. That simply won’t do. Organisations need to know their environments inside and out – they need to know where their nodes are located, they need to know what kind of web servers and operating systems they use and they need to know how certificates are used within their environment.
Growth in the global mobile app market is growing at an annual rate of over 18% and is driven by e-commerce, gaming, and IoT. However, according to a report, the percentage of organizations that struggle to keep pace with mobile app testing requirements is increasing.
In 2021, as the economy recovers, custom software will play the biggest role it’s played to date and any company looking to get ahead would be remiss not to explore this as an option. The trends that will dictate what those solutions look like may surprise you.
Why does DIY AIOps fail and what is the root cause? In many cases, all the time and effort put into a do-it-yourself project simply winds up being wasted. This article looks at how to safely encourage AIOps exploration and measure ROI from AIOps without the risk of failure.
The future of IoT is trending toward large-scale growth over the next several years. However, 2021 has the potential to be a pivotal year for the IoT Market as it aims to tackle major global social and economic issues associated with the pandemic. In this article, we’ll take a closer look at the economic and social factors shaping the Internet of Things market.
It is no secret that our world is changing. This article will explore how using a combination of Knative, the Kubernetes-native serverless platform, and Quarkus, Red Hat’s container-native approach to Java, can be used together to help simplify the modernization of application delivery.
There are a handful of similarities between the CLR and JVM – both are high performance software run times, both include methods for garbage collection, code-level security and rich frameworks and open source libraries. But there are also some very stark differences.
This article by Sergey Maximenko, Data Science engineer at MobiDev examines how to develop visual inspection software for manufacturing via building deep learning-based algorithms and training. Learn what questions you should ask before choosing a deep learning approach for visual inspection systems.
Observability can be used to improve how we think about our roles within companies. At the same time, linking observability to a business goal can be an effective way to ensure that other teams and departments care about the data involved. See what developers should know about observability.
Your organization is likely wasting money and time maintaining and integrating legacy and/or disconnected tools that aren’t worth the effort. As well, tools overload contributes needlessly to data noise rather than focusing on the data sets which are most critical to maintaining healthy service levels for the business.