Organizations around the globe recognize the importance of digital transformation to respond to the demands of the modern world. Organizations can advance their business by adapting technology, processes and tools. By doing so, they can increase flexibility, efficiency, security and improve customer experiences to boost success and gain competitive advantage. However, digital transformation is not always easy.
When you want to direct your observability data in a uniform fashion, you want to run an OpenTelemetry collector. If you have a Kubernetes cluster handy, that’s a useful place to run it. Helm is a quick way to get it running in Kubernetes; it encapsulates all the YAML object definitions that you need. OpenTelemetry publishes a Helm chart for the collector. When you install the OpenTelemetry collector with Helm, you’ll give it some configuration.
Over the last few months, a common and recurring theme in our conversations with users has been about managing observability costs, which is increasing at a rate faster than the footprint of the applications and infrastructure being monitored. As enterprises lean into cloud native architectures and the popularity of Prometheus continues to grow, it is not surprising that metrics cardinality (a cartesian combination of metrics and labels) also grows.
Do you have an application built with Django and PostgreSQL that you’d like to run on Kubernetes? If so, you’re in luck! In this tutorial, you’ll learn how to orchestrate your Django application with Kubernetes. Since we’re working with multiple microservices, it can be difficult to ensure all parts work together. This tutorial will demystify all that.
The real beauty of this modern, cloud-fueled, DevOps-driven world that we are living in is that it’s so highly composable. In so many ways, we’ve been freed from the limitations and structures of the previous annals of software and technology history to build things the way that we want to, and however we choose to do so.
Nowadays, the software development paradigm is based on containerizing applications to deploy on pods to let Kubernetes manage it. Containerized applications can then allow Kubernetes to manage its deployment, replication, high availability, metrics and other capabilities so that the application can focus on doing what it was designed to do. This technology is used for projects and by customers all over the globe.
Cloud Service Providers (CSPs) offer an ever-expanding array of instance types, ensuring that for any given workload there exists the perfect hosting option that matches the exact needs of that app or business service. But with this expansion comes an ever-increasing challenge to match the workloads to the offerings – there are many things to consider.
Kubernetes has broken down barriers as the cornerstone of cloud-native application infrastructure in recent years. In addition, cloud vendors offer flexibility, speedy operations, high availability, SLAs (service-level agreement) that guarantee your service availability, and a large catalog of embedded services. But as organizations mature in their Kubernetes journey, monitoring and optimizing costs is the next stage in their cloud-native transformation.
