The reasons to move to Kubernetes are many and compelling. This post doesn’t make the case that you should migrate, but assumes you have already decided that you want to. When you’re clear on what you want to do and why you want to do it, the questions of “When?” and “How?” become your focus. What follows centers on the question of how to approach making Kubernetes the platform on which your workloads thrive.
Today, you probably wouldn’t blink at the idea of involving security in your development process. In fact, “shifting security left” has become so commonplace in the security industry, that there are conferences and job titles dedicated to SecOps (or DevSecOps or SecDevOps *eye roll emoji*). Yet, it wasn’t that long ago that the massive transition to this mindset took place.
Over the past few months, we published a number of articles showing how to snap desktop applications written in different languages – Rust, Java, C/C++, and others. In each one of these zero-to-hero guides, we went through a representative snapcraft.yaml file and highlighted the specific bits and pieces developers need to successfully build a snap. Today, we want to diverge from this journey and focus on the server side of things.
Apache Flink is an open source framework, written in Java and Scala, for stateful processing of real-time and batch data streams. Flink offers robust libraries and layered APIs for building scalable, event-driven applications for data analytics, data processing, and more. You can run Flink as a standalone cluster or use infrastructure management technologies such as Mesos and Kubernetes.
This article will cover the most common challenges you might find when trying to use Prometheus at scale. Prometheus is one of the foundations of the cloud-native environment. It has become the de-facto standard for visibility in Kubernetes environments, creating a new category called Prometheus monitoring. The Prometheus journey is usually tied to the Kubernetes journey and the different development stages, from proof of concept to production.
Runtime security for Rancher environments requires putting controls in place to detect unexpected behavior that could be malicious or anomalous. Even with processes in place for vulnerability scanning and implementing pod security policies and network policies in Rancher, not every risk will be addressed. You still need mechanisms to confirm these security barriers are effective and provide a last line of defense when they fail.
Docker is an essential bridge in modern DevOps. Despite Kubernetes overtaking Docker on orchestrating containers, the Docker container itself remains the standard and likely will for the foreseeable future. We developed the Docker Metrics collector to operate as its own container that will run Metricbeat using the modules you are running in real time. Now, in addition to the Docker module, we are now releasing an AWS module for operations in the cloud.
In our previous announcement for the removal of the Codefresh Docker registry, we presented a timeline of the migration phases along with the actions expected from our customers. One of the milestones in the migration process is the introduction of our new Image API along with several enhancements on the pipeline build step. We believe that these features deserve a dedicated explanation of why they are useful on their own (regardless of the registry removal).
This is a repost from the JFrog dev.io blog site. When you’re new to an industry, you encounter a lot of new concepts. This can make it really difficult to get your feet underneath you on an unfamiliar landscape, especially for junior engineers. In this series, I’ll cover tools and terminology common to the DevOps space, plus the occasional newbie-friendly tutorial for emerging or established technologies. If you have a request or suggestion, let me know!
