Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has firmly established itself as the leading choice for container orchestration thanks to its robust ecosystem and flexibility, allowing users to scale their workloads easily. However, the complexity of Kubernetes can make it challenging to set up and may pose a significant barrier for organizations looking to adopt cloud native technology and containers as part of their modernization efforts.

Hi there! If you're in the tech world, you've probably heard about the trend of companies moving from Heroku to AWS. At Qovery, we've seen an increasing demand from companies looking to make this move. And for good reasons! Here are the top 4 reasons why companies are choosing AWS over Heroku.

Application performance monitoring (APM) involves a mix of tools and practices to track specific performance metrics. Engineers use APM to monitor and maintain the health of their applications and ensure a better user experience. This is crucial to high quality architecture, development, and operations, but it can be difficult to achieve in Kubernetes since the container orchestration system doesn’t provide an easy way to monitor application data like it does for other cluster components.

In this article, we will dive into Kubernetes network monitoring and metrics, examining these concepts in detail and exploring how metrics in an application can be transformed into tangible, human-readable reports. The article will also include a step-by-step tutorial on how to enable Calico’s integration with Prometheus, a free and open-source CNCF project created for monitoring the cloud.

We’re pleased to announce the availability of Kubewarden 1.5.0! This release brings the usual amount of small bug fixes, dependency updates, and a major security enhancement. Let’s take a closer look!

If you need to deploy a lot of microservices at once and manage them at scale, Kubernetes is hard to beat. But Kubernetes also brings additional complexity that you just might not need. You would be smart to ask yourself these three questions before getting started with Kubernetes.

We have entered the next phase of the digital revolution in which the data center has stretched to the edge of the network and where myriad Internet of Things (IoT) devices gather and process data with the aid of artificial intelligence (AI).

There are instances in which organizations miss the opportunity to improve their infrastructure and gain competitive advantages by clinging to entrenched, well-worn technologies with which they are comfortable. This is the case with Terraform and Cluster API (CAPI). When explaining to various organizations the benefits they could gain through CAPI, I have heard hesitant responses such as.

How is your organization handling Kubernetes observability? What tools are you using to monitor Kubernetes? Is it a time-consuming, manual process to collect, store and visualize your logging, metrics and tracing data? And, what are you actually getting out of all that investment? At Logz.io we’re trying to make this process easier for customers who are serious about Kubernetes observability. We’ve made significant investments in this area for Kubernetes use cases.

Many organizations rely on distributed tracing in Datadog APM to gain end-to-end visibility into the performance of their Kubernetes applications. But as teams grow, it can become impractical for them to manually configure each new application with the libraries and environment variables needed for tracing.