We recently wrote about Hadoop’s HDFS and how it was once mighty and instrumental in big data, but today the file system’s flaws leave it buckling under modern analytics and AI workloads. Anyone working in those spaces needs to be aware of these disadvantages. But what about the rest of…

I was at a conference of cryo-EM scientists recently, and if you read our recent blog posts about cryo-EM and 3D protein modelling, you know that these scientists are some of the most data-hungry, bandwidth-constrained specialists in the world. They need infrastructure able to absorb and manage petabytes for both…

Over the last five years, massive scale-out workloads have arrived in the enterprise. Driven by new big data analytics technologies like Hadoop or Spark, or more recently machine learning (ML) and AI, enterprises today have to deal with unprecedented amounts of data. …

Scale-out workloads used to be the domain of high-performance computing (HPC). However, over the past couple of years scale-out workloads have arrived in the enterprise with applications like machine learning or big data/analytics. Enterprise environments, and increasingly HPC users, require 24/7 availability of the storage system. What is BeeGFS? BeeGFS is an open-source…

New holes emerge as enterprises embrace containers Increasingly, corporations are in the data business, they collect more of it, they rely on it to make key decisions, and it essentially drives the business. However, they are not good at protecting it. Why? Most data analytic applications are built on modern architectures, like containers and Kubernetes, and few…

Kubernetes, the popular container management system, offers enterprises many benefits: agility, scalability, faster development, and management. Consequently, the open source orchestration software has quickly been gaining traction. One ripple effect is that many enterprise storage systems are struggling to keep pace with its growth. …

In a recent post, we discussed the technological advances happening around protein folding, crystallography, atomic-resolution visualization, and modeling. Success in this space carries the promise of life sciences labs being able to study existing, unmapped proteins with unprecedented speed, which in turn assists new predictive modeling techniques. Collectively, these advances…

Some readers may recall the rise and spread of “mad cow disease” in the early 1990s, more accurately known as bovine spongiform encephalopathy (BSE). With symptoms that resembled dementia and wasting, the fatal disease proved transmissible to humans via ingestion of contaminated tissue and resulted in millions of cows being…

Summary: Detailed, data-driven analysis of plankton can yield remarkable insights into the state of oceanic health and the world’s food chain. Collecting and crunching that data relies on efforts by Oregon State University’s Hatfield Marine Science Center and the OSU Center for Quantitative Life Sciences. Executing this ongoing big data…