Grafana is an open-source platform for metric analytics, monitoring, and visualization. In this article, we will explore the basics of Grafana and learn how to deploy it to Kubernetes. You will find specific coding examples and screenshots you can follow to deploy Grafana.

Many organizations struggle with managing thousands of services and applications. A typical environment consists of a combination of modern cloud applications, on-premises workloads, and workloads that are in the process of being moved to the cloud. IT and operations teams can easily be overwhelmed by the large volume of data and activity that is generated across these systems.

Cloud computing and AI/machine learning (ML) are two powerful technologies that are even more impactful when used together. Cloud computing provides the infrastructure and resources needed to support AI/ML applications; while AI/ML enhances cloud computing by providing intelligent automation and decision-making capabilities.

If you’ve ever found yourself juggling multiple code versions and branches, desperately needing a toolkit to keep things organized, you’re in good company. We understand the challenges of version control, and that’s where these Git clients for Mac come into play.

In our last blog, we talked about the importance of setting memory requests when deploying applications to Kubernetes. We explained how memory requests lets you specify how much memory (RAM for short) Kubernetes should reserve for a pod before deploying it. However, this only helps your pod get deployed. What happens when your pod is running and gradually consumes more RAM over time?

Last Christmas, I bought my wife “Explain the cloud like I am 10” after she told me many times that it was hard for her to relate to what I am doing in my daily work at Qovery. While so far, I have been the sole reader to enjoy the book, I was wondering during my lecture if there were any resources to explain how to build all that. Most topics are software oriented.. So, in this article, I am going to explain how to build your own cloud network 🎊

What support do developers need at large enterprises to be productive? We often fall into the trap of evaluating coders on output, maybe even innate talent. We think that the best way to build secure and efficient software is to hire 10X developers, and get out of their way. But even if the individuals have massive intellectual firepower, operational work grows like entropy in the system.

I recently recorded a Lightboard presentation on securing open source software dependencies in the public cloud. This blog summarises, and expands upon, some of the key elements from that presentation: I think about this topic through two lenses: software supply chains and updating software dependencies while maintaining stability.

In Episode 1, we delve into the critical topic of overcoming “Analysis Paralysis” during cloud migration. We will explore how Tidal Accelerator can help organizations break free from the shackles of over-analysis and guide them towards successful cloud migration.

The telecommunications industry today is focused on delivering advanced, reliable connectivity and the highest possible performance to consumers, all while getting ahead of the cutthroat competition. And accomplishment in these key areas comes with its fair share of challenges for communications service providers (CSPs) to meet customer expectations.