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At its re:Invent 2024 conference this week, AWS rolled out five new storage tools and services with the goal of making its offerings easier to work with, if not cheaper or better. Looking at this holistically, AWS is delivering updates across the data management/storage stack, from ingest to making data useful and usable to management.”
Supermicro announced the launch of a new storage system optimized for AI workloads using multiple Nvidia BlueField-3 data processing units (DPU) combined with an all-flash array. These units support 400Gb Ethernet or InfiniBand networking and provide hardware acceleration for demanding storage and networking workloads.
Also, as part of the partnership, Veeam will integrate Microsoft AI services and machine learning (ML) capabilities into its data resilience platform, Veeam Data Cloud. The platform, hosted on Microsoft Azure, uses zero trust and isolated Azure Blob Storage to secure backups.
Solidigm, a provider of NAND flash solid-state drives, and memory giant Micron have each introduced very high-capacity SSD drives meant for enterprise data centers, particularly in AI use cases. Solidigm intros 122TB PCIe SSD Solidigm unveiled its highest capacity PCIe drive yet, the 122TB Solidigm D5-P5336 data center SSD.
Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.
As data centers evolve from traditional compute and storage facilities into AI powerhouses, the demand for qualified professionals continues to grow exponentially and salaries are high. The rise of AI, in particular, is dramatically reshaping the technology industry, and data centers are at the epicenter of the changes.
Nvidia is touting a near 50% improvement in storage read bandwidth thanks to intelligence in its Spectrum-X Ethernet networking equipment, according to the vendors technical blog post. Storage is an overlooked element of AI that has been overshadowed by all the emphasis on processors, namely GPUs. Tbps of total bandwidth.
Enterprise datastorage skills are in demand, and that means storage certifications can be more valuable to organizations looking for people with those qualifications. No longer are storage skills a niche specialty, Smith says. Both vendor-specific and general storage certifications are valuable, Smith says.
The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data. The challenges of integrating data with AI workflows When I speak with our customers, the challenges they talk about involve integrating their data and their enterprise AI workflows.
In 2019, Gartner analyst Dave Cappuccio issued the headline-grabbing prediction that by 2025, 80% of enterprises will have shut down their traditional data centers and moved everything to the cloud. The enterprise data center is here to stay. Six years ago, nearly 60% of data center capacity was on-premises; thats down to 37% in 2024.
Hewlett Packard Enterprise has announced a pair of changes to its GreenLake Alletra MP block storage services, with a new program for on-premises users as well as support for Amazon Web Services. Now, HPE has added the Timeless Program for GreenLake Alletra MP block storage users with non-disruptive controller upgrades.
According to a report released this week by Bloom Energy, US data centers will need 55 gigawatts of new power capacity within the next five years. The report , based on a survey of 100 data center leaders, also shows that 30% of all sites will be using onsite power by 2030.
The flaws Overall, the study said, 74% of organizations had publicly exposed storage, some of which included sensitive data. And, it said, “as organizations ramp up their use of cloud-native applications so, too, does the amount of sensitive data they store there increase — including customer and employee information and business IP.
The chipmaker has released a series of what it calls Enterprise Reference Architectures (Enterprise RA), which are blueprints to simplify the building of AI-oriented data centers. Building an AI-oriented data center is no easy task, even by data center construction standards. After all, who has built AI factories before?
PEAK:AIO, maker of a dedicated AI storage infrastructure server software, has released a new version of its AI Data Server, with emphasis on performance, density, and sustainability. PEAK:AIO is a software defined storage platform designed running on Dell Technologies PowerEdge R7525 2U server with up to 24x NVMe drives.
As data volumes increase exponentially, the threat of data loss from cyberattacks, human error, or system failure has never been greater — which is why, in 2025, fortifying a data protection strategy has never made more sense. Backups continue to serve as the backbone of any data protection solution.
A growing number of buyers have reported purchasing supposedly new Seagate data center-grade hard drives, only to discover that they had been previously used for thousands of hours. Unlike traditional cryptocurrency mining that relies on GPUs, Chia mining is storage-intensive, leading to a surge in HDD demand during its peak.
IBM and data security and backup provider Cohesity have formed a new partnership, calling for Cohesity’s data protection functionality to be incorporated into an upcoming IBM storage product suite, dubbed Storage Defender, for better protection of end-user organizations’ critical information.
(Read our related feature: Enterprises reevaluate virtualization strategies amid Broadcom uncertainty ) However, if youre looking strictly for a replacement on-prem virtualization platform, the analyst firm Data Center Intelligence Group ( DCIG ) has identified five top alternatives to VMwares vSphere platform.
In the quest to reach the full potential of artificial intelligence (AI) and machine learning (ML), there’s no substitute for readily accessible, high-quality data. If the data volume is insufficient, it’s impossible to build robust ML algorithms. If the data quality is poor, the generated outcomes will be useless.
According to IDC data released this month, cloud and shared environments account for most AI server spending 72% in the first half of 2024. Companies can keep their data local, for example, or reduce lag by putting their computing capacity close to where it is needed. The data is their lifeblood. Its a brand-new skill set.
Pure Storage is adding features to its storage-as-a-service (STaaS) platform, including Real-time Enterprise File, Universal Credits, and a virtual machine assessment to help admins optimize their VMs. We’re calling this a global storage pool, and this is the ability to not have to fix your allocations or reserve things in advance.”
The MonTitan RDK is designed for enterprise data center use and Silicon Motion says its sequential read speeds exceed 14 GB/s, ranking among the fastest external SSDs, with random read performance of over 3.3 data center specifications. million IOPS, a respectable throughput rate. The 128TB SSD RDK also supports NVMe 2.0
IBM has broadened its support of Nvidia technology and added new features that are aimed at helping enterprises increase their AI production and storage capabilities. Content-aware IBM Storage Scale On the storage front, IBM said it would add Nvidia awareness to its recently introduced content-aware storage (CAS) technology.
The issue allows attackers to perform name-squatting on AWS S3 (simple storage service) staging buckets, a temporary storage location within an organization’s cloud application deployment infrastructure to hold data for later processing.
Yet, as transformative as GenAI can be, unlocking its full potential requires more than enthusiasm—it demands a strong foundation in data management, infrastructure flexibility, and governance. Trusted, Governed Data The output of any GenAI tool is entirely reliant on the data it’s given.
Pure Storage is upping its AI game with a pair of announcements, including a new storage-as-a-service offering that’s designed for AI workloads and new AI-driven security features to detect and prevent ransomware. The company bills its new Evergreen//One product as the first purpose-built AI storage as-a-service offering.
also supports HPEs Data Fabric architecture which aims supply a unified and consistent data layer that allows data access across premises data centers, public clouds, and edge environments with the idea of bringing together a single, logical view of data, regardless of where it resides, according to HPE.
Migration to the cloud, data valorization, and development of e-commerce are areas where rubber sole manufacturer Vibram has transformed its business as it opens up to new markets. Data is the heart of our business, and its centralization has been fundamental for the group,” says Emmelibri CIO Luca Paleari.
As enterprises begin to deploy and use AI, many realize they’ll need access to massive computing power and fast networking capabilities, but storage needs may be overlooked. In that case, Duos needs super-fast storage that works alongside its AI computing units. “If If you have a broken wheel, you want to know right now,” he says. “We
In estimating the cost of a large-scale VMware migration , Gartner cautions: VMwares server virtualization platform has become the point of integration for its customers across server, storage and network infrastructure in the data center. HCI vendors include Nutanix , Scale, Microsoft Azure Stack and others.
(SPONSORED) As we adopt hybrid work environments for good, here's how storage leaders can improve data center success and longevity while lowering total cost of ownership.
Dell also is offering a new solution for general professional users and data scientists, called Dell Agentic RAG with Nvidia. Its RAG agents handle company-specific data access and regulatory challenges. The first move will be support for Nvidia H100 NVL GPUs in upcoming PowerEdge servers.
BlueField data processing units (DPUs) are designed to offload and accelerate networking traffic and specific tasks from the CPU like security and storage. It can process large amounts of data from network traffic, logs, and endpoint behavior to identify anomalies and potential threats.
This approach enhances the agility of cloud computing across private and public locations—and gives organizations greater control over their applications and data. Public and private cloud infrastructure is often fundamentally incompatible, isolating islands of data and applications, increasing workload friction, and decreasing IT agility.
AI’s ability to automate repetitive tasks leads to significant time savings on processes related to content creation, data analysis, and customer experience, freeing employees to work on more complex, creative issues. Building a strong, modern, foundation But what goes into a modern data architecture?
Life on Earth emerged as early as 4 billion years ago. It is the product of the codes stored in DNA. Now, we are repurposing DNA to store other forms of information.
They are intently aware that they no longer have an IT staff that is large enough to manage an increasingly complex compute, networking, and storage environment that includes on-premises, private, and public clouds. At 11:11, we offer real data on what it will take to migrate to our platform and achieve multi- and hybrid cloud success. “At
The Singapore government is advancing a green data center strategy in response to rising demand for computing resources, driven in large part by resource-hungry AI projects. Singapore wants to go beyond that and reduce energy use for air-cooling by raising the temperatures at which servers and storage racks can safely operate.
growth this year, with data center spending increasing by nearly 35% in 2024 in anticipation of generative AI infrastructure needs. Data center spending will increase again by 15.5% in 2025, but software spending — four times larger than the data center segment — will grow by 14% next year, to $1.24 trillion, Gartner projects.
The Data Encryption Policy’s purpose is to define for employees, computer users and IT department staff the encryption requirements to be used on all computer, device, desktop, laptop, server, network storage and storage area network disks, and drives that access or store organization information to prevent unauthorized access to organization communications, (..)
The patchwork nature of traditional data management solutions makes testing response and recovery plans cumbersome and complex. To address these challenges, organizations need to implement a unified data security and management system that delivers consistent backup and recovery performance.
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