Kubernetes automation provider Kubermatic, a company from Germany simplifying cloud-native operations at scale, has released Kubermatic Kubernetes Platform (KKP) 2.16. The open-source platform would give IT teams the ability to fully automate the management of Kubernetes clusters across multi-cloud, on-premise, edge, and IoT environments.
Kubermatic’s 2.16 release introduces an out-of-the-box integration of the Open Policy Agent (OPA) for policy-based control across microservices, Kubernetes, CI/CD pipelines, API gateways, and more. OPA is an official CNCF project that graduated recently, thanks to its wide adoption in production by organizations such as Goldman Sachs, Netflix, T-Mobile, and many others.
“As enterprise cloud native adoption accelerates, it becomes increasingly essential for organizations across the globe to have access to policy enforcement tools that are particularly designed and built for cloud native environments,” said Sebastian Scheele, Chief Executive Officer (CEO) of Kubermatic. “With the KKP 2.16 release, we are proud to deliver on our promise to always provide our customers with best-in-class open-source technologies. Thanks to OPA’s streamlined policy language, our customers can now benefit from considerably facilitated policy enforcement across the entire stack.”
In addition to that, Kubermatic’s KKP 2.16 release would feature a number of improvements designed to bolster enterprise security, streamline operations and favor GitOps approaches, including:
- Dynamic Data Centers and Other Enhanced Admin Configurations – Dynamic data centers and other improved Preset Management functionalities would allow administrators to deliver a better and more secure user experience for everyone. All configurations can be adjusted in the KKP UI or via Infrastructure-as-Code, “helping organizations to deliver on their GitOps approach.”
- Machine Learning the Cloud Native Way – Machine learning workloads will increasingly move towards the cloud for its ability to scale on demand. As the de facto standard for orchestrating containerized workloads in the cloud, Kubernetes would be the perfect match for machine learning and data science. KKP 2.16 adds a Kubeflow integration to give operators the possibility to easily roll out the Kubeflow platform on top of KKP.
- Optimized Infrastructure with ARM Support – ARM-powered data centers and edge scenarios are enjoying increasing popularity for their system optimization and cost reduction potential. KKP 2.16 enables ARM support so organizations can “effortlessly” deploy and manage ARM-based clusters from the central KKP interface.