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.

Kubernetes turns 9 this year and with its maturity each year, it brings new challenges that drive seismic influence across the rapidly changing cloud native ecosystem. Each year we see new tools created and existing solutions optimized from new lightweight distributions, new features across Kubernetes management platforms, and container security solutions, all adding value to users but simultaneously contributing to the complexity are facing to run Kubernetes successfully.

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.

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.

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.

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).

Kubernetes makes it easier for businesses to automate software deployment and manage applications in the cloud at scale. However, if you’ve ever deployed a cloud native app, you know how difficult it can be to keep it healthy and predictable. DevOps teams and SREs often use distributed tracing to get the insights they need to learn about application health and performance.

With the current macroeconomic situation, it's more important than ever to keep a close eye on our cloud costs. But here at Qovery, we don't just focus on cutting costs - we also want to make sure we're getting the most bang for our buck. In other words, we want to consume our budget in a way that gives us the most resources.

If you're reading this blog, you might ask yourself what container orchestration engines are, what problems they solve, and how the different engines distinguish themselves. Read on for a high-level overview of Kubernetes, Docker Swarm, and Apache Mesos, as well as a few of their notable similarities and differences.

2022 has been a year full of new releases, new events, and new projects for Open Source Calico. Let’s take a look at Project Calico’s 2022 highlights and see if you’ve missed any exciting news.