Big data has the potential to transform how organizations manage and apply knowledge in their projects, helping teams make better decisions, improve project outcomes, and foster continuous learning. But how exactly do these two concepts—big data and knowledge management—come together in a meaningful way? And what role does project learning play in connecting the dots?

The cloud has become an essential tool for businesses looking to scale, innovate, and remain competitive. But migrating to the cloud is not as simple as flipping a switch. The process requires careful planning, robust strategies, and a deep understanding of the potential risks and rewards. That’s why having a well-defined cloud migration strategy is crucial.

Ralph Marsten once said, “Don't lower your expectations to meet your performance. Raise your level of performance to meet your expectations.” Many organizations today seem to follow the opposite. If everything looks green on a dashboard, they assume all is well. But is it?

While it’s easy to think that everything is now in the cloud, there are still some use cases and some businesses that need private data centres. In this blog, we look at the benefits of colocation and explore the key considerations for businesses when choosing colocation services. Colocation, often shortened to ‘colo’, is a service provided by data centre owners and operators where the floor space and facilities, including electricity and connectivity, are rented to private enterprises.

I run a small Kubernetes cluster at home, which I originally set up as somewhere to experiment. Because it started as a playground, I never bothered to set up monitoring. However, as time passed, I’ve ended up dropping more production-esque workloads onto it, so I decided I should probably put some observability in place. Not having visibility into the cluster was actually a little odd, considering that even my fish tank can page me.

Grafana is a widely used open-source platform for monitoring and visualization. Grafana has a lot of built-in functionality and also provides a large amount of community templates that can improve your overall experience. However, Grafana requires quite a lot of configuration and the documentation can be a bit overwhelming for beginners. In this article, we explore seven alternatives that can be simpler to use and can provide seamless integration of traces, logs, and metrics.

At this point, it’s almost passé to write a blog post comparing events to the three pillars. Nobody really wants to give up their position. Regardless, I’m going to talk about how great events are and use some analogies to try to get that across. Maybe these will help folks learn to really appreciate them and to depreciate a certain understanding of the three pillars. Or maybe not.

Canonical, the publisher of Ubuntu, today announced the general availability of Data Science Stack (DSS), an out-of-the-box solution for data science that enables ML environments on your AI workstation. It is fully open source, free to use and native to Ubuntu. It is also accessible on other Linux distributions, on Windows using Windows Subsystem Linux (WSL), and on macOS with Multipass.

Today, we’re excited to unveil the Anomaly Detection feature. It will enable users to create smarter alerts based on dynamic metrics, moving beyond traditional fixed-threshold alerts. It will soon be available to all our users and is currently undergoing beta testing with select users. By detecting deviations from expected patterns, Anomaly Detection will help you stay informed about critical issues without getting overwhelmed by irrelevant alerts. Let’s dig in deeper.