Designing Shared Storage for Hadoop, Elastic, Kafka, TensorFlow

Designing Shared Storage for Hadoop, Elastic, Kafka, TensorFlow. As analytics environments like Hadoop, Elastic, Kafka and TensorFlow continue to scale, organizations need to find a way to create a shared infrastructure that can deliver the bandwidth, flexibility, and efficiency that these environments need. In a recent Storage Intensity podcast, Tom Lyon, founder and chief scientist of DriveScale and George Crump, Lead Analyst of Storage Switzerland, sat down to discuss a wide range of subjects including Non-Volatile Memory express, (NVMe), Non-Volatile Memory express over Fabric (NVMe-oF), and Composable Infrastructures.

KubeCon 2019 wrap up

 KubeCon + CloudNative North America 2019 Conference (KubeCon) just ended. I wanted to jot down some impressions while they were fresh in my mind. The talks I found most [...]

C is for Container

In this second post of a four-part series, we restart our Kubernetes journey with containers. Microsoft tells us that: Containers are a technology for packaging and running Windows and [...]

K is for Kubernetes

Pat Gelsinger, CEO of VMware, recently said “Kubernetes is driving the biggest shift in enterprise architecture since Java, virtualization and cloud…”Well, that seems to clarify what you should be [...]

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