Autoscaling? Kubernetes Pods vs. Nodes

Knowing Which Way to Scale a Cluster Helps Optimize Performance and Resources

Not only does it deploy and manage containers, Kubernetes autoscaling enables users to automatically scale the overall solution in numerous ways. This is a tremendous asset, especially in the modern cloud, where costs are based… Read more…

Running Spark with Jupyter Notebook & HDFS on Kubernetes

Saving cost, improving performance of data science workloads with k8s

Kublr and Kubernetes can help make your favorite data science tools easier to deploy and manage. Hadoop Distributed File System (HDFS) carries the burden of storing big data; Spark provides many powerful tools to process… Read more…

Kubernetes and the Data Layer

Orchestrating Storage with Kubernetes and Implications for Persisting State

Leverage persistence solutions like Ceph, Rook as a storage orchestrator for Kuberentes storage and remain cloud-agnostic and flexible.

Deploying Kubernetes in Highly Restrictive Environments

It’s possible and here’s what you need to know

Balancing cloud-native with enterprise governance and security requirements Cloud-native technologies are radically changing the IT landscape. Developed for the cloud, cloud-native is certainly not cloud-bound. Increasingly more companies seek to benefit from cloud-like features in… Read more…

Centralizing Container and Kubernetes Management

Modern enterprise requirements for an agile release lifecycle

While Docker and Kubernetes are at the core of today’s app development paradigm shift, running the new stack reliably and securely requires much more – from security and governance to visibility into applications and infrastructure.

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