EGUIDE:
The term hyper-convergence means different things to different people. But at its core, hyper-convergence brings together servers and storage network resources that are managed as a single unit using software. In this e-guide, learn more about the benefits of hyper-convergence and how you can derive the greatest value from your investment.
EGUIDE:
In this e-guide, Senior Analyst Mike Matchett walks you through how to use smarter storage infrastructure with embedded analytical intelligence to harvest the desired value from your data. Read on to learn the top 5 benefits of storage analytics, how to utilize storage-hosted lambda functionality, and more.
EGUIDE:
In this expert guide, SearchCloudStorage joins Avinash Lakshman, CEO and founder of Hedvig Inc, to demonstrate the flexibility achieved by employing a self-service public cloud provider that uses replication- and erasure coding-based storage. Read on to learn if replication-based SDS is more affordable than traditional storage.
EBOOK:
Learn about how DaaS providers build service-level agreements. The agreements don't usually favor the customer, and IT shops aren't as protected against downtime as they might think.
EGUIDE:
See how London Capital Group cut deployment times and datacenter footprint by ditching server-plus-SAN architecture for hyper-converged server/storage boxes.
WHITE PAPER:
In this white paper, you'll learn how one potential flash storage solution utilizes private cloud infrastructures to allow work to get done faster, make your enterprise more efficient, lower TCO and raise ROI.
EGUIDE:
As the number of VMs exploded, so did the complexity of the infrastructure required to support VMs. To tame this complexity, users have two options: converged vs. hyper-converged storage. In this e-guide, you will examine the differences between each—such as IT integration approaches and limitations, and which environments each best suits.
WHITE PAPER:
Highlighted in this custom white paper is a storage solution that offers the simplicity of NAS coupled with the scalability, performance, and simplified management that machine learning, AI, and analytics workloads require.
