Operations | Monitoring | ITSM | DevOps | Cloud

AI and Machine Learning are becoming critical differentiators in the technology landscape. By their nature, AI and ML are computation hungry workloads. They require best-in-class distributed computing environments to thrive. AI and ML present a perfect use case for Kubernetes, the distributed computing platform engineered at Google to run their massive workloads.

Norwegian leader in fleet management, equipment and vehicle tracking, ABAX is one of Europe’s fastest-growing technology businesses. The company provides sophisticated fleet tracking, electronic mileage logs and equipment and vehicle control systems to more than 26,500 customers. ABAX manages over 250,000 active subscriptions that connect a variety of vehicles and industrial equipment subscriptions.

Data pipelines provide the ability to operate on streams of real-time data and process large data volumes. Monitoring data pipelines can present a challenge because many of the important metrics are unique. For example, with data pipelines, you need to understand the throughput of the pipeline, how long it takes data to flow through it and whether your data pipeline is resource-constrained.

For many years WordPress has invariably remained the CMS number 1, supporting about half of the world’s websites. All hosting service providers declare compatibility with WordPress – and in most cases this is true. WordPress has no excessive technical requirements. However, it can’t be denied that WP-based websites work better than elsewhere on the servers of certain providers. WordPress will work properly on any Apache or Nginx-based web hosting with a MySQL or MariaDB database.

Tags provide critical context for troubleshooting issues across any dimension of your environment. By applying best practices for tagging your systems, you can efficiently organize and analyze all your monitoring data, and set up automated multi alerts to streamline alerting workflows. Similar to any tags you would add to your services and infrastructure, monitor tags—tags that you apply to your monitors—are an essential feature for organizing and simplifying your workflows.

Container technologies have taken the infrastructure world by storm. Ideal for microservice architectures and environments that scale rapidly or have frequent releases, containers have seen a rapid increase in usage in recent years. But adopting Docker, containerd, or other container runtimes introduces significant complexity in terms of orchestration. That’s where Kubernetes comes into play.

As explained in Part 1 of this series, monitoring a Kubernetes environment requires a different approach than monitoring VM-based workloads or even unorchestrated containers. The good news is that Kubernetes is built around objects such as Deployments and DaemonSets, which provide long-lived abstractions on top of dynamic container workloads.

In the previous post in this series, we dug into the data you should track so you can properly monitor your Kubernetes cluster. Next, you will learn how you can start inspecting your Kubernetes metrics and logs using free, open source tools. In this post we’ll cover several ways of retrieving and viewing observability data from your Kubernetes cluster.