Kubernetes has become the de-facto solution for container orchestration. While it has, in some ways, simplified the management and deployment of your distributed applications and services, it has also introduced new levels of complexity. When maintaining a Kubernetes cluster, one must be mindful of all the different abstractions in its ecosystem and how the various pieces and layers interact with each other in order to avoid failed deployments, resource exhaustion, and application crashes.

Two malicious Python libraries, jeilyfish (with a capital i and a lowercase L in the original name) and python3-dateutil, were detected on PyPI (Python Package Index) on December 1st. They were typosquatting similar named legitimate libraries jellyfish (with a double lowercase L) and python-dateutil libraries, a malicious technique aiming to trick developers to use the similar named modified libraries.

Organizations are rapidly moving more and more mission-critical applications to Kubernetes (K8s) and the cloud to reduce costs, achieve faster deployment times, and improve operational efficiencies, but are struggling to achieve a strong security posture because of their inability to apply conventional security practices in the cloud environment. Commitment to cloud security grows, but security safeguards are not keeping up with the increased use of the various cloud platforms.

Single-node deployments of Kubernetes are more common than what one would expect. In some scenarios, single-node clusters make much more sense. For development purposes or testing, there’s no need to deploy a full-blown production-grade cluster. Single-node deployments are also handy for appliances and IoT applications since they have a much smaller footprint. Enter MicroK8s, a tool by Canonical that enables you to easily deploy a lightweight single-node cluster in your local environment.

If you have selected Gitlab as your git provider, you may automatically think about selecting GitlabCI as your CI/CD solution as well. Using the same vendor for source control and CI/CD might seem natural, but is not always the best combination. In fact, choosing a “good-enough” option just because it is part of the same solution is unwise in the long run if it doesn’t cover your needs.

Big data, AI, machine learning, and numerous others are all buzzwords we seem to throw around lightly in recent years. Even though they are hugely different from one another, they all have one thing in common. Data! Huge amounts of data that need to be managed. The downside of that is that the more data you have the more of a headache it is to store, query, and make sense of.

Kubernetes 1.17 is about to be released! This short-cycle release is focused on small improvements and house cleaning. There are implementation optimizations all over the place, new features like the promising topology aware routing, and improvements to the dual-stack support. Here is the list of what’s new in Kubernetes 1.17.

The last fifteen years have seen huge increases in developer productivity for several reasons, including the arrival of open source into the mainstream and the ability to better emulate target environments. In addition, the process of resetting a development environment back to the last known stable version has been vastly improved by Vagrant and then Docker.

Having completed a series of twelve Lighthouse Roadshow events across Europe and North America over the past six months, I’ve had time to reflect on what I’ve learnt about the rapid growth of the Kubernetes ecosystem, the importance of community and my personal development.

Yesterday, we announced the beta release of Logz.io Infrastructure Monitoring — our Grafana-based monitoring solution, and the planned release of a Jaeger-based tracing solution. These additions to our platform complement our ELK-based Log Management product, together constituting what is the world’s only open source-based observability platform for monitoring, troubleshooting and securing distributed cloud workloads.