Operations | Monitoring | ITSM | DevOps | Cloud

Most of the time the build and deployment pipelines we create will run on compute provided by the Azure DevOps cloud and the only decision we need to make is whether to select a Windows or Linux Agent. Sometimes though, the specification for the VM that Azure DevOps spins up may not be right for our needs. We may need more memory or a particular OS version. This is when custom agents and Agent Pools come into play.

Even if you don’t work with Prometheus day-to-day, you most likely have heard of it. After Kubernetes was donated to the Cloud Native Computing Foundation (CNCF), Prometheus became the second project to be incubated soon after. That was back in 2016 and it is still one of the most active CNCF projects. Why is it so popular? It’s the de facto monitoring tool for containerized workloads running on-prem and in the cloud – that is, it’s the monitoring tool for Kubernetes.

Being the most popular cloud solution provider, AWS needs no introduction. With its powerful and numerous services and solutions, many companies of all sizes and shapes run their applications and/or infrastructure on AWS. With AWS being integrated with other internal services as well as external solutions hosting the business apps, it is crucial to be aware of what's happening across the landscape and beyond, to ensure business continuity. The AWS plugin for SquaredUp helps you achieve exactly that.

The Ingress NGINX Controller is a widely used Kubernetes component for managing HTTP and HTTPS traffic routing. While it provides powerful observability through Prometheus metrics, it’s also notorious for generating an excessively high number of time series. The root cause lies in how the controller labels its metrics—tracking requests across multiple dimensions such as ingress name, host, path, status code, and upstream response times.

Modern systems throw off a lot of data—metrics, traces, logs—sometimes more than we know what to do with. When you're trying to understand how values spread out over time (like response times, memory usage, or queue lengths), averages alone don’t tell the full story. OpenTelemetry histograms help fill in those gaps. This guide walks through what they are, why they matter, and how DevOps engineers can use them to improve observability in real systems.