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Western Digital opens separate websites for HDD and flash businesses

Western Digital has advanced the previously announced split between its HDD and flash businesses by launching separate customer websites. The separation aims to improve operational focus and market agility in pursuit of improved sales and margins.

“We are now operating as two specialized websites: WesternDigital.com for HDDs and platforms, and SanDisk.com for flash technology, including SSDs, memory cards, USB flash drives, and more,” a canned statement from the storage titan read.

As announced in October last year, Western Digital plans to separate its HDD and flash businesses, creating two independent, public companies with market-specific, strategic focus. The separation will “better position each franchise to execute innovative technology and product development, capitalize on unique growth opportunities, extend respective market leadership positions, and operate more efficiently with distinct capital structures,” Western Digital explained. “The creation of two specialized websites is a necessary step in the company separation process.”

Although the websites are separate, there’s still some crossover between brands. On WesternDigital.com, you can shop for all HDD and platform products from the following brands: Western Digital, WD, WD_BLACK, and SanDisk Professional. On SanDisk.com, you can shop for all flash products, such as SSDs, memory cards, and USB flash drives from the Western Digital, WD, WD_BLACK, SanDisk, and SanDisk Professional brands.

For support, customers go through the relevant website, with G-Technology customers going through WesternDigital.com.

All warranty claims for HDDs and platform products from Western Digital, WD, WD_BLACK, SanDisk Professional, and G-Technology should be submitted through the Western Digital Support account. After signing in, select your registered product and “Request Warranty Replacement.” If you have not registered your product yet, select “Register a New Product.”

All warranty claims for flash products such as SSDs, memory cards, and USB flash drives from Western Digital, WD, WD_BLACK, SanDisk, and SanDisk Professional should be submitted through the SanDisk Support account.

The formal business split is expected to be completed in the “second half of 2024” and since last year the firm has established legal entities across around 20 countries. Once complete, both divisions will operate as publicly traded companies.

David Goeckeler, Western Digital’s CEO, will lead the SanDisk business, and Irving Tan, currently executive vice president of global operations, will become the CEO of WD.

Discover what it takes to build your AI-ready infrastructure strategy

SPONSORED POST: Organisations across the UK are rushing to find new ways of using artificial intelligence (AI) to streamline their operations and build new products and services for their customers and stakeholders.

A report published by the UK Office of National Statistics (ONS) last year suggests that the majority of UK organisations are yet to implement any form of AI within their business, though there has been a considerable expansion in awareness of the technology and willingness to explore its capabilities.

Certainly, the potential to extract intelligence and insight from the vast amount of data now at their disposal is huge. But identifying and implementing machine learning, deep learning, generative AI (GenAI) and other forms of the technology to fulfil that ambition still poses a significant challenge for many.

It’s not just about the tech stack – there are hurdles with data quality, supply chains, legacy systems, costs and operational complexity to navigate too. Building a hybrid IT infrastructure capable of handling AI is an important step. The larger the workload and the more data it consumes, the more likely that a data centre will be need to host and process all of that information – an environment that offers the scalability and agility to quickly expand further in support of additional and ever larger AI workloads and datasets as the business requires.

Few organisations will have that knowledge or infrastructure capability in-house, so choosing a partner with the expertise to guide them through the implementation journey will be critical.

Digital Realty has put together a white paper specifically designed to provide guidance on the importance of having a robust enterprise infrastructure to support an organisation’s AI needs. Titled “AI for IT Leaders: Deploying a Future-Proof IT Infrastructure”, it offers advice on the strategic goals of enterprise AI adoption together with the common AI workloads, challenges and solutions needed to deploy AI-ready infrastructure. Digital Realty also provides a list of evaluation criteria which will help you choose the right partners to build an AI infrastructure stack within your organisation that will yield the best performance.

You can register download a copy of the Digital Realty whitepaper by clicking this link.

Sponsored by Digital Realty.

Analyst warns of memory oversupply from AI server demand

Analyst Jim Handy thinks the memory market is being artificially buoyed by AI server demand, with oversupply and a price correction coming.

Jim Handy

Handy is a senior analyst at Objective Analysis. In a post titled How Long Will Memory Growth Continue? he writes: “The memory business is currently faring pretty well [but] the business is definitely off-trend.”

He says that the memory market is characterized by capacity-driven cycles. As market demand rises, suppliers build more fabs, but the market can’t often absorb their output, prices fall due to over-supply, and the market slumps.

The current memory market is demand-driven with a lot “coming from massive AI purchases in hyperscale datacenters.” Forecasting the duration of this cycle is tricky as “demand-driven cycles tend to be caused by factors that are very hard to predict.”

At the FMS event in Santa Clara in August, Handy “presented a chart showing the history of hyperscaler capital expenditures … It’s unusually high at the moment, but it’s not clear how long these companies will continue their high expenditures. They haven’t had a matching revenue surge, so they can’t fund accelerated spending forever.” 

Objective Analysis chart

He superimposes the current memory market revenue history on a second chart showing two previous demand cycles, “normalized to the market’s underlying trend, so they are all expressed in a percentage relating to how far off-trend they are, rather than in absolute revenues.”

Objective Analysis chart

Also, all three curves start from the same point, month 1, and then rise and fall over time. A 2017 cycle is shown by the red line and a 2021 cycle by a black line. Their duration, rise, and fall are identical within a two-month window.

The green line shows the current cycle, with the dashed extension being “a projection of where it might head if it performs as did the prior two cycles. Today we appear to be in Month 18.” If the projection is correct, this suggests the current cycle will peak in month 21, by the end of 2024, with the demand declining as 2025 progresses through to the fall.

Handy says this isn’t as scientific a forecast as those Objective Analysis usually produces for its clients, but adds: ”Today’s heavy AI spending can’t last forever, and when it does end, there will undoubtedly be an oversupply with a subsequent price correction, if not a collapse like those seen in 2018 and 2022.”

This line of analysis suggests that the high-bandwidth memory (HBM) boom being enjoyed by SK hynix and Micron, and pursued by Samsung, could be relatively short-lived. 

Pliops XDP LightningAI is a memory tier for GPU compute 

The Pliops LightningAI product functions as a memory tier for GPU servers and can provide a more than 2x speed up for large language model (LLM) responses.

Ido Bukspan, Pliops
Ido Bukspan

Pliops is an Israeli server CPU offload startup that has developed XDP (Extreme Data Processor) key-value store technology with its AccelKV software running in an FPGA to accelerate low-level storage stack processing, such as RocksDB. It has now developed a LightningAI product, using ASIC hardware inside a 1 or 2RU server, applicable to both training and inference LLM workloads.

CEO Ido Bukspan said: “We saw how we can leverage our technology to something even more that changed the needle significantly in the world. And the potential is huge.”

He said that Pliops developed the core product then “took it all the way and developed further than our product in order to show the end-to-end, amazing value of XDP to performance … It’s not just a specific area. It can be expanded. We did it all the way, developed all the stack and pieces of software needed in order to prove the value that our new AI tool can help AI developers get out much more from their existing GPUs.”

XDP LightningAI’s best fit is with inference workloads, where it enables an LLM, running a multi-tier inference process, to “remember” cached data but then replaces intermediate responses and data – the attention state – needed for a subsequent response, speeding up the end-to-end LLM processing time.

Pliops slide

The LLM, running in a GPU server with, for example, high-bandwidth memory, and accessing NoSQL and vector databases, runs out of memory capacity during a multi-tier response. This requires old data, previously evicted from the HBM prefill cache, to be reloaded. LightningAI serves as a persistent memory tier for such data, enabling the GPU to avoid the HBM reload time penalty. 

Pliops slide

It runs in a networked x86 server networked by NVMe-oF to a GPU, and enables the GPU to sidestep a memory wall, more than doubling its speed, and also be around 50 percent more power-efficient. Pliops sees it as a great benefit to inference workloads using retrieval-augmented generation (RAG) and vectors, where the GPU servers will have limited memory capacity and operate in power-constrained environments.

Pliops slide

A GPU will run the Pliops LLM KV-Cache Inference Plug-in software. It will use a Pliops API to issue standard GPU-initiated IO requesting Pliops CUDA key-value activity. The GPU servers’ BlueField DPUs send the request across a 400 GbE RDMA Ethernet fabric to ConnectX-7 NICs in the nearby (in-rack) XDP LightningAI server. There, it’s sent to the XDP-PRO ASIC, which wrangles the data operations using direct-attached SSDs. 

Pliops slide

The Pliops stack includes application (vLLM) modifications, a GPU CUDA library for NVMe key-value commands, and a NVMe-oF initial target for GPU and Lightning servers. The system can be deployed on standard 1 or 2RU ARM or x86-based servers, and is fully compliant with the vLLM framework. A single unit can serve multiple GPUs.

Pliops is working with potential customers, OEMs and ODMs. They can inspect demonstration and proof-of-concept XDP LightningAI units now, and the company will be at SC24 in Atlanta, November 17-22. We can expect additional GenAI applications to be supported beyond LLMs in the future, as well as even more LLM acceleration, between 2.5x and 3.0x.

NetApp responds to VAST with ONTAP Data Platform for AI

Analysis NetApp is one of the first of the major incumbent storage vendors to respond to VAST Data’s parallel NFS-based data access for AI work, with its internal ONTAP Data Platform for AI development, after HPE rolled out its AI-focused Nvidia partnership in March.

VAST Data has penetrated the enterprise data storage market with its DASE (Disaggregated Shared Everything) architecture, which provides a single tier of storage with stateless controllers driving low-latency, high-bandwidth, all-flash storage across an internal RDMA-type fabric with metadata stored in storage-class memory type drives. The company pitches its AI-focused software stack built on this base as providing costs that are close to that of disk, parallel access, and a single namespace used by a data catalog and unstructured data store plus a structured database, IO-event triggered data engine, and now an InsightEngine using Nvidia GPUs as compute node units and embedded NIM microservices.

VAST’s platform and products are being presented at its Cosmos marketing event. Until now, none of the enterprise storage incumbents – with the exception of HPE, which announced the AI-focused Nvidia partnership earlier this year – has responded to the tech other than to adopt lower-cost QLC (4 bits/cell) flash technology. HPE developed its Alletra MP hardware architecture and runs VAST’s file software on that with its own block storage offering separately available. Quantum’s Myriad OS development shares many of these concepts as well.

Now NetApp has just announced its own disaggregated compute/storage architecture development at its Insight event and a white paper, ONTAP – pioneering data management in the era of Deep Learning, fleshes out some details of this ONTAP Data Platform for AI project.

Currently, NetApp has three ONTAP storage hardware/software architectures:

  • FAS – clustered, dual-controller base Fabric-Attached Storage for unified files and blocks on hybrid disk and SSD drives
  • AFF – all-flash FAS with SSD drives only
  • ASA – all-flash SAN Array, AFF with block storage optimizations

Now a fourth ONTAP Data Platform for AI architecture is being developed, with NetApp saying it’s “a new design center in NetApp ONTAP built on the tenets of disaggregation and composable architecture.”

It’s a ground-up concept, starting with separate compute controllers, running ONTAP instances, “embellished with additional metadata and data services,” and storage nodes filled with NVMe SSDs, forming a single storage pool, accessed across a high-speed, low-latency, Ethernet-based RDMA fabric. Both compute units and storage nodes can be scaled out with dynamic workload balancing.

Blocks & Files diagram comparing VAST and NetApp storage stack approaches
Blocks & Files diagram comparing VAST and NetApp storage stack approaches

The system supports file, block, and object storage with underlying Write Anywhere File Layout (WAFL) storage and a single namespace. “Physical block space is now distributed across multiple [drive] enclosures, thus creating a single extensible namespace” and “each compute unit or node running the ONTAP OS has full view of and can directly communicate with the storage units providing the capacity.”

The ONTAP instances provide data protection (snaps, clones, replication, anti-ransomware), storage management (speeds, feeds, protocols, resiliency, scale), and intelligent data functions (exploration, insights, getting data AI-ready).

File locking can disrupt parallel access. NetApp is developing “the concept of independently consistent micro file system instances. Each micro file system instance operates as a fully functional file system and provides consistency across data and metadata operations … Since each micro file system instance has exclusive ownership of its resources at a given point in time, they can operate safely on file system internal data structures in parallel to other instances.”

NetApp white paper diagram
NetApp white paper diagram

NetApp says “these micro file system instances are decoupled from the front end or application facing constructs. As an example, a file system client mounting file shares and performing data and metadata operations has no visibility to which micro file system instance is processing the request. The client will communicate with the file server as per semantics prescribed during mount.”

The design achieves parallelism at three levels:

  • Client and server-side protocol stack 
  • File system namespace and object management subsystem 
  • File system block layer managing on-disk layout 

The white paper says the “WAFL on-disk layout will ensure that each individual file or a collection of files within a file share will have their data blocks distributed across multiple disk enclosures to drive massive parallelism and concurrency of access. Each instance of the ONTAP OS will have high bandwidth connectivity across the backend disk enclosures and can leverage RDMA constructs to maximize performance as well as ensure quality of service end to end.”

Metadata engine

A structured metadata engine “extracts the data attributes (or metadata) inline. Once the attributes are extracted, the metadata engine indexes and stores this metadata to enable fast lookups. A query interface allows applications to query for this metadata. The query  interface is extensible to enable semantic searches on the data if exact key words are not known.” 

It provides “a fast index and search capability through the metadata set. AI software ecosystems deployed for data labeling, classification, feature extraction or even a RAG framework deployed for generative AI inferencing use cases can significantly speed up time-to-value of their data by leveraging the structured view of unstructured data presented by the metadata engine.” 

The data in the system is made ready for AI as ”NetApp’s powerful SnapDiff API will track incremental changes to data in the most efficient manner. The metadata engine in ONTAP will record these changes and leverage its trigger functionality to initiate downstream operations for data classification, chunking and embedding creation. Specialized algorithms within ONTAP will generate highly compressible vector embeddings that significantly reduces both the on-disk and in-memory footprint of the vector database (significantly shrinking infrastructure cost). A novel in-memory re-ranking algorithm during retrieval ensures high precision semantic searches.”

The generated embeddings are stored in an integrated vector database backed by ONTAP volumes.

Looking ahead

NetApp’s ONTAP Data Platform for AI project validates VAST’s architectural approach and raises a question for the other enterprise storage incumbent suppliers. If NetApp sees a need to spend deeply on a new ONTAP data architecture, what does that mean for Dell, Hitachi Vantara, IBM, and Pure Storage? Do they have their product design engineers poring over VAST and working out how they could develop competing technology on PowerStore or Power Scale, VSP One, FlashSystem, and FlashArray/FlashBlade base architecture systems?

Secondly, with VAST, HPE, and NetApp providing, or soon to provide, parallel NFS-based data access for AI work, where does that leave previously HPC-focused parallel file system suppliers looking to sell their storage into enterprises for AI workloads? We’re thinking DDN (Lustre), IBM (StorageScale), Quobyte, and VDURA (PanFS). Is there some kind of middle ground where a parallel file system meets disaggregated architecture?

Answers to these questions will likely emerge in 2025, when we might also expect a VAST IPO.

Veeam survey uncovers apathy toward EU’s NIS2 security directive

A Veeam survey reveals that only 43 percent of EMEA IT decision-makers believe NIS2 will significantly enhance EU cybersecurity, yet 90 percent of respondents reported at least one security incident that the directive could have prevented in the past 12 months.

Some 44 percent of respondents experienced more than three cyber incidents, with 65 percent categorized as “highly critical.”

Andre Troskie, Veeam
Andre Troskie

Andre Troskie, EMEA Field CISO at Veeam, stated: “NIS2 brings responsibility for cybersecurity beyond IT teams into the boardroom. While many businesses recognize the importance of this directive, the struggle to comply found in the survey highlights significant systemic issues.”

The EU cybersecurity NIS2 (Network and Information Security 2) directive updates the 2016 NIS directive, which aimed to improve the cyber-resilience of critical infrastructure and services across the European Union. Operators of such services had to set up risk management practices and report significant incidents. EU member states had to set up national cybersecurity strategies and Computer Security Incident Response Teams (CSIRTs), and there was an EU-wide Cooperation Group to encourage cybersecurity information  sharing.

NIS2, which takes effect on October 18, broadens the scope of NIS, has stricter security requirements, faster incident reporting, a focus on supply chain security, harsher penalties for non-compliant organizations, harmonized rules across the EU, and better member state information sharing.

It represents an additional cost for businesses and other organizations. French digital services company Wallix states: ”Compliance with Directive NIS2 is non-negotiable and has significant financial implications for companies. According to the impact assessment associated with the directive, it is expected that companies will increase their spending on computer security by up to 22 percent in the first years following its implementation.”

Economics consultancy Frontier Economics assessed the NIS2 costs and its report states: “The direct costs of implementing the regulation on firms across the EU is €31.2 billion per year representing 0.31 percent of total turnover across all of the sectors that are affected by the NIS2 Directive … This represents a large increase in costs given that the EC estimated that average ICT security spending as a percentage of turnover was 0.52 percent in 2020.” 

It provided a chart of likely costs per affected economic sector:

Wallix suggests: “Although this increase in spending may seem substantial, it is expected to be offset by a significant reduction in costs associated with cybersecurity incidents.”

Veeam Software commissioned the survey from Censuswide, which gathered the views of more than 500 IT decision-makers from Belgium, France, Germany, the Netherlands, and the UK. The UK was included due to its significant business ties with EU countries. Nearly 80 percent of businesses are confident in their ability to eventually comply with NIS2 guidelines, but up to two-thirds state they will miss this deadline.

The main reasons cited were technical debt (24 percent), lack of leadership understanding (23 percent), and insufficient budget/investments (21 percent). The survey found that 42 percent of respondents who consider NIS2 insignificant for EU cybersecurity improvements attribute this to inadequate consequences of non-compliance. That’s led “to widespread apathy towards the directive.”

Troskie said: “The combined pressures of other business priorities and IT challenges can explain the delays, but this does not lessen the urgency. Given the rising frequency and severity of cyberthreats, the potential benefits of NIS2 in preventing critical incidents and bolstering data resilience can’t be overstated. Leadership teams must act swiftly to bridge these gaps and ensure compliance, not just for regulatory sake but to genuinely enhance organizational robustness and safeguard critical data.”

Veeam provides an NIS2 compliance checklist, assessment, and white paper.

VAST links up with Equinix and Cisco

VAST data is putting its AI storage and processing kit in Equinix colos and running its software on Cisco’s UCS servers as it broadens its routes to market. 

The company has an existing deal with Cisco concerning Cisco’s Nexus 9000 Ethernet switches used in HyperFabric AI clusters. VAST has certified Cisco Nexus Ethernet-based switches for validated designs with its storage. Cisco customers can monitor and correlate storage performance and latency using VAST’s APIs, feeding network and storage telemetry into the Nexus HyperFabric.

Now Cisco plans to offer the VAST Data Platform software natively on select UCS servers as an integrated system via its global sales team and channel partners. Cisco and VAST say “this full-stack enterprise AI solution simplifies the design, deployment, and management of AI infrastructure for Generative AI, RAG-based inferencing, and fine-tuning AI workloads.”

John Mao

John Mao, VP, Technology Alliances at VAST, stated: “This tight integration and joint selling motion between VAST and Cisco will help to accelerate enterprise AI adoption by providing end-to-end visibility of compute, networking, storage and data management – allowing organizations to seamlessly build and scale their AI operations.

Jeremy Foster, SVP and GM of Cisco Compute, claimed the VAST-Cisco partnership would “massively simplify the overall operation of AI-ready data centers, enabling customers to reduce time, resources and costs required by delivering an integrated stack of the next-generation of storage, compute and networking.”

Cisco UCS servers with VAST Data software and Cisco’s Nexus HyperFabric AI will be available in the first half of 2025.

Equinix and VAST

Equinix has an existing set of 236 International Business Exchange (IBX) globally-distributed co-location centres providing compute, storage and networking gear from suppliers such as Dell, NetApp, PureStorage and Seagate (Lyve Cloud). Some of these are made available to customers at 26 IBX locations through its Equinix Metal as-a-service business model.

It’s now going to provide VAST’s Data Platform for Nvidia DGX systems, including SuperPOD, and the Nvidia AI Enterprise platform in IBX colos as well. VAST and Equinix says this “leverages and supports Nvidia accelerated computing with the VAST Data Platform to deliver a parallel file and object storage system that is ideal for model training and distribution – speeding AI adoption and time to market.”

Renen Hallak

Renen Hallak, Founder and CEO of VAST Data, stated: “By combining supercomputing services with VAST’s AI-native offering for scalable and secure data services deployed in Equinix data centers, we’re setting a new standard in delivering high-performance, scalable, simple and sustainable accelerated computing infrastructure.”

Jon Lin, EVP and GM, Data Center Services at Equinix, said: “Equinix is helping customers access the benefits of AI by providing a fully managed global platform in close proximity to their data through private, high-bandwidth interconnections to cloud providers.”

VAST integrates Nvidia GPUs and NIM for AI insights

VAST Data has brought Nvidia GPU hardware and NIM microservices software into its AI storage and data processing to create an InsightEngine product providing real-time and automatically triggered AI model data access and analytical insights.

It has announced partnerships with Cisco and Equinix to widen its product’s route to market and set up a Cosmos AI user community centered on building an ecosystem of partners and users exchanging ideas around building AI deployments and use-cases using its products. We’ll cover the Cisco, Equinix and Cosmos news in separate stories and focus on the Nvidia GPU and NIM news here.

VAST slide deck diagram
VAST slide deck diagram

This VAST and Nvidia announcement builds on VAST’s storage+server data platform, which has a base of all-flash storage and a software stack comprising a Data Catalog, global namespace (DataSpace), unstructured DataStore, structured DataBase, and AI process-triggering Data Engine. Its all-flash storage has a DASE (Disaggregated and Shared-Everything Architecture) with scale-out x86-based controller nodes (C-nodes) linking to data-storing, all-flash, D-nodes across InfiniBand or RoCE links with a 200/400 Gbps networking fabric. The C-node and D-node software can run in shared industry-standard servers and the D-node software can also run in Nvidia BlueField3 DPUs.

The existing Nvidia partnership has VAST’s system certified for the DGX SuperPOD. This has been extended so that Nvidia GPUs can now be VAST controller nodes:

VAST slide deck diagram
VAST slide deck diagram

That means that the GPUs can work directly on data stored in the VAST array without it having to be first moved to the GPU server. Secondly, Nvidia’s NIM microservices software now runs natively inside the VAST software environment. NIM provides Gen AI Large language models (LLMs) as optimized containers. These simplify and accelerate the deployment of custom and pre-trained AI models across clouds, datacenters and workstations.

Justin Boitano, Nvidia
Justin Boitano

Justin Boitano, VP Enterprise AI at Nvidia, stated: “Integrating Nvidia NIM into VAST InsightEngine with Nvidia helps enterprises more securely and efficiently access data at any scale to quickly convert it into actionable insights.”

VAST says its software with NIM embeds “the semantic meaning of incoming data using advanced models powered by Nvidia GPUs. The vector and graph embeddings are then stored in the VAST DataBase within milliseconds after the data is captured to ensure that any new file, object, table or streaming data* is instantly ready for advanced AI retrieval and inference operations.”

InsightEngine uses VAST’s DataEngine to trigger the Nvidia NIM embedding agent as soon as new data is written to the system, allowing for real-time creation of vector embeddings or graph relationships from unstructured data. Such vectors and graphs are used in RAG (Retrieval-Augmented Generation) whereby a customer’s proprietary data is used to inform LLM query responses to make them more accurate and less prone to fabricate data relationships (hallucinations).

The VAST DataBase can store “exabytes of both structured and unstructured enterprise datasets” and “trillions of embeddings,” and run “real-time similarity search across massive vector spaces and knowledge graphs.” 

VAST slide deck diagram
VAST slide deck diagram

Data indexing occurs at the point of data ingestion and “this architecture eliminates the need for separate data lakes and external SaaS platforms.” The data is held and processed securely. VAST says “any file system or object storage data update is atomically synced with the vector database and its indices, offering comprehensive, secure data access management and global data provenance to ensure data consistency across multi-tenant environments.”

VAST diagram
Jeff Denworth, VAST Data
Jeff Denworth

Jeff Denworth, co-founder at VAST Data, stated: “With the VAST Data Platform’s unique architecture, embedded with Nvidia NIM, we’re making it simple for organizations to extract insights from their data in real-time. By unifying all elements of the AI retrieval pipeline into an enterprise data foundation, VAST Data InsightEngine with Nvidia is the industry’s first solution to provide a universal view into all of an enterprise’s structured and unstructured data to achieve advanced AI-enabled decision-making.”

VAST’s InsightEngine with Nvidia will be generally available in early 2025. Learn more here.

Bootnote

*We understand block data support is coming to VAST Data.

VAST sets up Cosmos partner-user group to spread platform

VAST Data is setting up a Cosmos community of customers, partners and practitioners to exchange VAST Data use case information.

This announcement comes as VAST also announces Nvidia GPU and NIM adoption in its Data Platform, plus bundling its SW with Cisco’s UCS servers and making it available in Equinix IBX co-location sites.

VAST says Cosmos aims to streamline AI adoption for its members by offering a comprehensive, interconnected ecosystem that facilitates conversation, shares use cases, and provides learning opportunities through labs, vendor showcases, and general AI research news. As AI usage is still in its early days, VAST says, Cosmos will help members stay informed and be supported.

Renen Hallak

Renen Hallak, Founder and CEO of VAST Data, stated: “With the VAST Data Platform at the center of this comprehensive, interconnected AI ecosystem of technology leaders and AI practitioners, Cosmos will help accelerate discovery, empowering innovation, and enabling the transformation of entire industries.”

There are three main claimed membership benefits, with the first being faster AI development and deployment helped by AI Labs with pre-sales-type demos of working AI reference architecture systems. AI stack component providers can partner in infrastructure building exercises and, thirdly, Cosmos will facilitate knowledge sharing by hosting interactive events with industry insiders and AI experts.

These, VAST says, will “allow participants to ask in-depth questions, receive tailored advice, and gain clarity on complex topics.” It sounds very much like a classic legacy mainframe and enterprise app supplier user group.

Early Cosmos participants include VAST Data, of course, Nvidia, Elon Musk’s xAI human-centered AI business, server vendor Supermicro, consultancy Deloitte, technology services provider WWT, Cisco, GPU-as-a-service supplier CoreWeave, WWT’s data center platform Core42, healthcare and tech venture capital business NEA, tech systems house Impetus, AI infrastructure supplier Run:AI, and datalake supplier Dremio.

Cosmos is a selling opportunity for product and service-supplying members, as Jeetu Patel, EVP and Chief Product Officer at Cisco, indicated: “We’re in a new era. With the promise and the complexity of AI, data centers, both public and private, must be reimagined to meet the needs of these new AI workloads. The scale of this change will only be possible if we collaborate across the technology stack. Cisco is working with VAST, NVIDIA and others to build modular infrastructure that allows organizations to quickly deploy these AI workloads, including the networks to support them.”

Ditto Stephen Brown, AI Factory Leader at Deloitte, commented: “A strong data foundation is critical for successfully scaling AI and we look forward to collaborating with members of the Cosmos community to help clients extract tangible value from their GenAI initiatives.”

Mitch Ashley, Chief Technology Advisor for The Futurum Group possibly went a tad over the top in his statement: “Cosmos is a once-in-a-generation opportunity for industry and technology leaders to garner the once unimaginable benefits from AI, which would be unachievable if we go it alone. It’s incumbent upon us to take bold steps like Cosmos that can reshape our future solutions possible with AI.”

Interested potential Cosmos participants can join the community here, register for a World Tour with sessions in US, European and Asia-Pacific region cities, and read a VAST blog. There is also a Cosmos web event on October 2.

Eon secures $127 million to turn cloud backup headaches into searchable snapshots

Stealth-emerging Israeli startup Eon says it transforms traditional, hard-to-use cloud backups into easy-to-manage assets with granular restores and searchable database snapshots.

Eon, previously known as PolarKeep, was formally founded in January this year by CEO Ofir Ehrlich, CTO Ron Kimchi, and CRO Gonen Stein. It says it monitors cloud resource sprawl and brings cloud backup posture management (CBPM) to enterprises. Eon replaces legacy backup tools and generic snapshots, transforming backups into useful, easy-to-manage assets.

The three founders have an AWS background. Ehrlich co-founded Israeli DR startup CloudEndure in 2014 as VP for R&D. When it was bought by AWS for around $200 million in January 2019, he became AWS’s head of engineering, app migration services, and elastic DR – effectively the CloudEndure business unit inside AWS until March 2023. He is an angel investor with a substantial startup portfolio.

From left, Gonen Stein, Ofir Ehrlich and Ron Kimchi

Stein was a CTERA tech sales director then a co-founder of Cloud Endure. After the acquisition, he became AWS’s product owner for migration and DR services. Kimchi was AWS’s general manager for DR and cloud migration, joining AWS from Algotech in September 2019.

Since January 2024, Eon says it has secured a $20 million seed round led by Sequoia Capital, with participation from Vine Ventures, Meron Capital, and Eight Roads. Then a $30 million A-round was led by Lightspeed Venture Partners with participation from Sheva, and a $77 million B-round led by Greenoaks with participation from Quiet Ventures followed.

That makes a total of $127 million raised, from seed funding to B-round, in just nine months – surely some kind of startup funding record, and indicative of Eon hitting its product development milestones rapidly.

Ehrlich said: “We are fortunate to have supportive funding partners who deeply understand the value of unlocking cloud backups to be truly automated, globally searchable, portable, and useful.”

Greenoaks partner Patrick Backhouse said: “Storage and backup are among the largest parts of the IT budget. Yet customers are stuck with frustrating, outdated options, leaving them with poorly optimized costs, incomplete data inventories, and shallow classification. Eon has the team, the expertise, and the ambition to develop an entirely new product that we believe will become the cognitive referent for cloud-native backup.”

Sequoia partner Shaun Maguire said: “In an industry where file restoration can take weeks, Eon’s novel backup solution pinpoints data instantly, saving time, money, and compliance headaches for customers.”

Eon notes that the global cloud infrastructure market is growing at an aggressive pace, expected to reach $838 billion by 2034, with enterprises estimating that 10 to 30 percent of their total cloud bill will be spent on backup storage and management. Current backup management methods, Eon claims, require time-consuming, manual data classification and tagging processes, agents and appliances, with mounting prohibitive costs, and ultimately produce backups that are not accessible.

Ehrlich states: “Eon has reimagined what backups can be for enterprises by introducing a new era of cloud backup storage and management.”

Eon claims its software, which is fully managed and portable, autonomously scans, maps, and classifies cloud resources continuously, providing backup recommendations based on business and compliance needs, and ensuring the appropriate backup policy is in use. Existing backup offerings, it claims, rely on snapshots, which are non-searchable black boxes that require full restores and are vendor-locked. 

In contrast, Eon’s backup storage provides global search capabilities, enabling customers to find and restore individual files. They can even run SQL queries on Eon’s backed-up database snapshots, which are searchable, without any resource provisioning. This suggests that Eon produces and stores backup file and database record metadata to provide the search repository.

We asked Gonen Stein questions about Eon’s technology.

Blocks & Files: Which cloud resources does Eon automatically scan, map and classify? 

Gonen Stein: Eon supports scanning and backing up cloud resources including; block, file, and object storage, as well as managed and unmanaged databases. Eon will continue to roll out support for additional cloud infrastructure resources.

Blocks & Files: How does Eon provide continuous backup recommendations based on business and compliance needs, and what do you mean by ‘the appropriate backup policy’?

    Gonen Stein: Eon continuously scans cloud resources and then maps and classifies them based on environment type (prod/dev/staging / QA…), and data classes (PII, PHI, financial info…), all with no tagging required.

    After mapping and classifying resources, Eon applies the appropriate backup policies on the mapped resources, based on the customer backup requirement (i.e.: production workloads containing PII data need to be backed up for 90 days, across cloud regions). This helps customers set backup retention to the right period, reducing storage costs associated with over-backing-up data, while preventing unnecessary business exposure.

    This automated approach is in contrast to today’s completely manual process, where customers need to constantly tag resources, and then manually associate backup policies based on resource tags.

    Blocks & Files: You say Eon’s next generation of backup storage is fully managed and portable- where is it portable to? 

    Gonen Stein: Eon creates a new tier of backup storage (we call them Eon snapshots), which does not rely on traditional vendor-locked cloud snapshots. Eon snapshots can be backed up from one cloud provider to another and also support restoration to a different cloud provider.

      Blocks & Files: How does it provide global search capabilities? 

        Gonen Stein: Eon’s new tier of storage (Eon snapshots), is automatically indexed, and unlike traditional black-boxed snapshots, is globally searchable. This means that a user can search for any backed-up files, or DB tables, without having to know what snapshot it was stored in.

        Blocks & Files: You have said that snapshots are non-searchable, so how do your customers find and restore individual files and run SQL queries on backed-up database snapshots? 

          Gonen Stein: To clarify, traditional snapshots (such as EBS snapshots and RDS snapshots) are not searchable. Eon snapshots are searchable. In addition to Eon’s global search capabilities, Eon also provides a database explorer, which allows customers to run a SQL query right on top of database backups, without requiring the customer to restore and provision full databases, before attempting to retrieve DB records.

          Blocks & Files: How do you restore individual files without any resource provisioning? 

            Gonen Stein: Eon Snapshots allows restoring files directly from the Eon console by searching for files in the backups, selecting specific files (from any backed-up version), and restoring them. This is in contrast to files stored in traditional snapshots, which require the customer to first figure out where the files are stored, then restore the whole snapshot (all or nothing), and only then locate the specific file in the restored volume.

            ****

            There is no connection, as we understand it, between Eon and Infortrend’s EonCloud products, nor between Eon and the similarly named German multinational electric utility company.

            AI-assisted malware resistance, response and recovery

            Malware detection
            Malware detection

            SPONSORED FEATURE: It’s the job of a storage array to be a data service, to store and serve data to its users. If that data is corrupted or stolen then the array is not doing its job.

            Storage array suppliers have increasingly emphasized cyber-resilience measures to safeguard the data they hold. An example of this Pure Storage adding a generative AI Copilot to help storage admin teams with security issues as well as performance investigations and fleet management.

            Now the epidemic of malware assailing virtually every organization getting worse with AI-assisted malware being more and more effective at opening and passing through doorways into IT systems and setting them up for data extraction and encryption. Emotet and TrickBot are examples of AI-assisted malware.

            This form of malware will be more effective at gaining entry to target systems. Organizations can themselves take advantage of AI to strengthen their attack response posture. Andy Stone, CTO for the Americas at Pure Storage, has written a 3-part blog looking at the before-, during -, and after-attack phases and how organizations can organize themselves to resist, respond and recover.

            Malware businesses are businesses; not short-sighted groups of disaffected hackers; teenagers in a basement. They can be strong, efficient, well-organized and determined. Pure VP R&D Customer Engineering, Shawn Rosemarin, commented on “the level of corporate maturity in some of these companies, like they are run by CEOs and CFOs they have fundamental formal support plans. You can even purchase extensive support contracts allowing you to call and get support with their tools if you’re having trouble getting them to do what you need them to do. In fact, some of them even offer “as-a-service” campaigns where they’ll take a piece of what it is that you are able to get access to, or even charge you on the amount of positive responses, or essentially the amount of breach that you’re able to cause.”

            All of this suggests that target organizations, meaning any organization, need to have the same kind of approach to incoming malware. That means a well thought through and informed stance.

            This starts with being prepared for an attack by maintaining a high standard of data hygiene, looking for active threats and having a rehearsed attack response plan. In a pre-attack or reconnaissance phase, attackers reconnoiter systems, initiate an attack plan, and try to gain entry through social engineering phishing techniques. It is during this pre-attack phase that AI-assisted malware can be most effective, crafting more sophisticated phishing approaches, extensive port log scanning, and also polymorphic signatures.

            With port scanning, Rosemarin said: “If I can get into a port and I can start to sniff that port and look at what’s happening, the ability for AI to actually filter and analyze what is potentially millions or hundreds of millions of logs makes it significantly easier. Finding vulnerabilities within those logs becomes easier with AI, because AI is very good at looking at massive amounts of information, finding something that’s interesting.”

            AI can also help improve social engineering phishing: If the attacker can find out even a little bit about an employee or officer of an organization and the way they behave, their access patterns they can more effectively pretend to be that officer or employee, and that person.

            Once an employee has been duped and malware code installed then, in Rosemarin’s view: “AI is changing the way these threats behave. You can call this concept ‘shape-shifting’, which you know is really probably a comic book or animation cartoon concept until now. What AI is allowing these threats to do is shape-shift and change their activity, on-the-fly. So if I see it in one place, it looks like this, but by the time I go to get rid of it, it’s changed its signature. it’s changed its behavior, which makes it significantly harder to identify and deal with.” Such polymorphic viruses are harder to detect and remediate.

            It is quite possible that, right now, there is malware lurking in your IT system, being used to scan and map out your system’s overall architecture so that vulnerabilities and targets can be identified.

            Rosemarin said: ”Ultimately the attacks will come in, and they’ll come in from an application or user device, and they’ll find a way to move through your environment, either north, south or east, west, depending on how they can and where they’ll find the most valuable or the biggest payload is, ‘honey pot’ as it’s called in the industry. And they’ll dwell undetected until the timing is right to attack that particular honey pot.”

            Once malware code is dwelling in a system it can watch what staff do in a typical day. It can watch for 45 days, 60 days, or even longer and detect that, say, every Tuesday at 8pm the employee does some sort of download. Rosemarin suggested: “That would be the time for me to move my malware to these particular systems, because that would not be considered out of the ordinary.”

            He said: “I can arm the malware to attack against the latest CVEs (Common Vulnerabilities and Exposures), what are often zero day attacks. Because, in a large environment, it currently can take a few weeks, days or weeks to get CVEs actioned due to change control and outage windows. CVEs provide excellent entry points allowing malware to assess your data estate, and assess the appropriate payload to release.”

            Where Pure Storage can help

            What can Pure Storage do to better detect these attacks? Rosemarin told us: “The opportunity for us that’s unique is that, unlike our competitors, we have visibility into the life of an IO all the way from the storage controller through to the flash media. Specifically, we are able to follow the particular command set that’s coming from the app through the storage controller down to the flash, allowing us to see exactly what’s happening to that IO at every element of the storage stack. We leverage this full telemetry, as well as the metadata management associated with it, to make it easier for us to spot potential anomalies.”

            “This is already in use today within Pure1’s ransomware detection mechanisms learning what’s normal in the life of an IO all the way down to the flash level.” He says no other storage array supplier can do this due to their reliance on third party SSDs and the disparate firmware therein.

            Pure also has ransomware detection built in now to Pure1. Customers can go in and turn on the feature to look through recorded logs and alert them within the Pure1 console.

            At a higher level, Pure works with security companies like Palo Alto Networks allowing them to use this information: ”There’s a standard called Open Telemetry, which is essentially taking my metrics and converting them to a format that is easily integrated into third party systems,” says Rosemarin. “Today, we use telemetry from infrastructure in the open telemetry standard, and then we put this data directly into SecOps workflows so that customers can gain visibility all the way down to their storage level.”

            That means it can be correlated with what’s happening at the user authentication schema level; for example who’s logged into which systems, and play a role at the SIEM, SOAR, and security operations center level.

            But there’s more: “As we find these particular signatures or patterns within the telemetry, we take automated action to protect the data that’s within it.”

            Pure’s malware security infrastructure also features automated action scripts. Rosemarin said these “integrate with SIEM, SOAR and XDR (Extended Detection and Response) so that if we natively find a threat, or if the SecOps workflow finds a threat, we engage certain capabilities like SafeMode snaps that would protect that particular volume in the case of an anomaly detection without human intervention.”

            This automated execution is a major advantage, as opposed to an alert says Rosemarin. When someone gets an email or a page or a text alert, they then have to go in and do something, which takes time. As Pure’s system has complete storage telemetry visibility and automated script actions, it has the capabilities to lock down that storage instantly. That can be a huge benefit in damage limitation to its customers.

            Safemode for resilience and space efficiency

            In addition, a feature of Pure’s volume snapshotting is its “SafeMode” immutability, notable not only for its resilience but also its space-efficiency. Rosemarin says: “The easiest way to restore a data set is a snap,” and Pure has: “the most space-efficient snapshot technology in the industry.” With other suppliers: “snapshot technology can consume a significant amount of space. Cyber criminals know that the average organization only does snaps for 60 days, say, because they don’t want to consume a ton of expensive space, and so they’ll let the malware dwell for 65 days, knowing that now it’s taken you to the point where your only restoration mechanism is from backups.”

            “And not only is that slower and more kludgy, it also represents all sorts of additional risks, I have to restore the applications, then I have to replay logs, because transactions have occurred since the last full backup. And if I’m in banking or in insurance or any kind of institution, I could potentially lose valuable transactions. People made deposits, they took withdrawals. Equipment moved. Customers placed orders.” It all adds up and can make restoring from backup painful and risky.

            The benefit, he explains, is that “with Pure I can get more space-efficient snapshots, which allows me to have more days of protection for the same amount of potential use of the storage capacity. And those snapshots will be immutable to the point where, even if the credentials are phished to Pure1, the attackers will not be able to go in and encrypt my snaps. SafeMode immutability is protected beyond access credentials with named users and secondary passcodes. Snapshot strategy is so important, because, in the event of an attack, I’m just going to use my snaps to instantly restore myself to a period pre-breach.”

            Once an attack is detected the affected systems have to be identified, locked down, disconnected from the network and quarantined. Rosemarin said: “The array is put into solitary confinement.”

            Attack detection should trigger a SecOps response plan: ”This is no different than any element of your DR plan. Ultimately it should not be that the phone rings at 2am or someone gets paged, and now everybody’s got to come in and figure out what they’re going to do.”

            “This is part of the SecOps playbook, part of the SecOps mandate. Most organizations now have a SecOps organization, and it’s not necessarily a full-time job function. It’s individuals from specific groups that have been pulled into SecOps to not only build but practice and execute this plan in the event of an attack.

            “But it’s also not just the company itself. It includes their cyber-insurers, potentially law enforcement, and even government organizations like the CIA and FBI who get engaged in these pursuits. There is an established process, an ’In case of emergency, break glass’ type of book that spells out the process and the people involved.”

            “This is a formal team, a little bit like what we had back with Y2K. This team has to practice. It has to know what processes they’re going to follow. If you just leave this to your network and system administrators it’s usually ineffective. It requires business leadership, critical partners. It might involve your legal teams, external law enforcement as well as your cyber insurance providers.”

            Time for data restoration and recovery

            Once the attack is halted, and in the initial post-attack stage, this is where data restoration and recovery take place. The affected data must be identified and restored to fresh and clean systems. This requires replacement clean hardware as the attacked system hardware is now corrupt and quarantined. It may be required for forensic analysis use which can take appreciable time; days and weeks.

            Rosemarin again: “You’ll have a recovery environment, whether off-site, on-site, rented, leased or as-a-service. And that’ll give you line of sight to new hardware. In many cases we deal with this at Pure through Evergreen/One. We actually offer a ransomware recovery SLA and we take care of it. We guarantee the shipment of equipment, we guarantee the migration of the data and the restoration of the systems, and we actually ensure that the systems are up and running.”

            The attack recovery process is complex. Rosemarin told us: “You want to update your credentials and passwords. You want to make sure that you know if any information was posted on your site by the attackers. You remove it. You contact the search engines to clear the cache, so that any kind of breach fallout is minimized. Then you have to mobilize your emergency response team, which gets us back to SecOps.

            Rosemarin thinks attacked organizations should share their attack data. They “should consider publicizing these attacks and the activity to help their peers deal with similar attacks. They can do that either anonymously through case studies or cybersecurity forums. Some of the biggest are the Information Sharing and Analysis Center (ISACs) in financial services and healthcare.

            “Some of these are protected in that you need to be a member of a given organization and you need to be vetted. But there’s also less formal clearinghouse called the Information Sharing and Analysis Organizations (ISOs). They’re similar, but they’re more flexible in terms of membership. What this does is give companies the ability to have collective defense community support, the concept of ‘you give to get.’”

            Rosemarin thinks that: “As humans, we are the largest weakness in the security chain. And the good news is, when I look at AI, the ability for AI to augment our systems to protect us against these threats is the way forward. That augmentation is going to come on the back of clear and present visibility, and I think that organizations who have the ability to most effectively gather that telemetry and connect it, will be in the best position to deal with attacks.”

            Sponsored by Pure Storage.

            Storage news ticker – October 1

            Assured Data Protection, the largest global Managed Service Provider (MSP) for Rubrik, has launched in the Middle East offering fully managed backup and cyber recovery services to businesses of all sizes. Assured has been rapidly growing its footprint in 2024 following successful launches in Canada and Latin America. This Middle East expansion is supported by a strategic partnership with Mindware, a leading Value-Added Distributor (VAD) in the region. The collaboration will establish local datacenters to help clients manage data sovereignty issues and minimize latency in data transfer, enhancing operational efficiency and security.

            Storage exec Mike Canavan
            Mike Canavan

            Object storage supplier Cloudian has appointed Mike Canavan as worldwide VP of sales to drive revenue growth, customer engagement, and lead all field operations across the worldwide sales team. He has a broad storage industry background, having most recently headed Americas sales at Model9, the mainframe VTL and data migration business bought by BMC. Prior to that he served as Global VP of Sales for the Emerging Solutions Business at Hitachi Vantara, and previously leading global sales for Pure Storage’s FlashBlade business. There was a stint at EMC in his CV as well. Cloudian recently took in $23 million in funding. Combine that with this appointment and it indicates Cloudian has business expansion in mind.

            Dell and Nvidia have integrated Dell’s AI Factory with Nvidia’s Llama Stack with agentic (GenAI software that makes decisions on your behalf) workflows in mind. The reference architecture uses Dell’s PowerEdge 9680 server fitted with Nvidia H100 GPUs. Llama 3.2 introduces a versatile suite of multilingual models, ranging from 1B to 90B parameters, capable of processing both text and images. These models include lightweight text-only options (1B and 3B) as well as vision LLMs (11B and 90B), supporting long context lengths and optimized for inference with advanced query attention mechanisms. Of the updates for Meta Llama 3.2, a particularly interesting update allows enterprises to use new multimodal models to securely utilize different models for various applications, such as detecting manufacturing defects, enhancing healthcare diagnostic accuracy, and improving retail inventory management. Read about this in a Dell blog

            B&F diagram on agentic AI
            B&F diagram

            Data mover Fivetran has surpassed $300 million in annual recurring revenue (ARR), up from $200 million in 2023. Fivetran has consistently driven elevated ARR growth and recently reaccelerated year-over-year gains over the past two consecutive quarters.

            HighPoint Technologies announced PCIe Gen5 and Gen4 x16 NVMe Switch series AICs and Adapters using Broadcom’s PCIe Switch ICs, supporting up to 32x 2.5-inch NVMe devices and speeds up to 60GB/sec and 7.5 million IOPS. They support features like the 2×2 drive mode for enhanced workload distribution, include Broadcom’s Hardware Secure Boot technology, and are natively supported by all modern Linux and Windows platforms. Rocket 1600 Series PCIe Gen5 x16 are equipped with Broadcom’s PEX89048 switch IC and Rocket 15xx series PCIe Gen4 x16 AICs and Adapters utilize Broadcom’s PEX88048 switch IC. Both series provide 48 lanes of internal bandwidth, and enable each AIC or Adapter to allocate 16 lanes of dedicated upstream bandwidth (to the host platform), and 4 lanes to each device port to ensure each hosted device performs optimally. The 2×2 mode splits the connection of a single NVMe SSD into two separate logical “paths,” each of which is assigned 2 PCIe lanes. The operating system will recognize each path as a distinct drive.

            Hitachi Vantara announced significant growth in its data storage business in the first quarter of its 2024 fiscal year (ended June 30) with a 27 percent increase of quarter-over-quarter (Q/Q) product revenue growth compared to Q1 FY23, exceeding the market CAGR of 11.31 percent. The growth was even more pronounced in the United States, which saw an increase of 54 percent Q/Q compared to the previous year.

            Storage exe Brian Babineau
            Brian Babineau

            SaaS-based dat protector HYCU announced its first chief customer officer, Brian Babineau. He joins from Barracuda where he led the success and support organizations for the MSP business before becoming chief customer officer. As HYCU continues to grow its customer base from 4,200+ customers in 78 countries worldwide, Brian’s experience will be instrumental in supporting customers throughout their experience with HYCU. He was an integral part of Barracuda’s shift from an appliance to SaaS business model across several security solutions, and also has extensive experience with working with teams at scale.

            Cloud provider Lyrid announced its open source database as a service offer based on Percona Everest. Percona announced Everest at the Open Source Summit event last week, and Lyrid is using the Everest cloud data platform to power its service. It uses Percona Everest to provide a flexible and open DBaaS where customers can choose their database and approach without the fear of vendor lock-in. This contrasts with the vast majority of DBaaS options that are tied to specific cloud providers or database options, limiting customer choice. Lyrid offers this service based on its datacenter partners, Biznet Gio and American Cloud, to provide customers with hassle-free database automation at lower costs. Customers can also choose to run this on their own datacenter environments, enjoying a fully  configured and privately managed DBaaS without lock-in. 

            We’re told that what makes Everest unique is that it is fully open source, so any organization can run their choice of database (PostgreSQL, MySQL or MongoDB) on their preferred cloud service, including OpenStack, and on any flavor of Kubernetes as well. Using Kubernetes, Lyrid can deliver the same kind of automated database service that other DBaaS products offer, but at both lower cost and without lock-in.

            Cloud file services suppluer Nasuni is further integrating with Microsoft 365 Copilot. Through the Microsoft Graph Connector, Nasuni managed data is fully accessible and operational with Microsoft Search and Microsoft 365 Copilot, expanding data access for Microsoft’s AI services. The Graph Connector enables organizations to leverage Nasuni’s managed data repositories, enabling Nasuni managed files to be indexed into Microsoft’s semantic index, and so provide contextually relevant answers and insights across Microsoft 365 applications. There is single-pane-of-glass access to customers’ Microsoft 365 data (including SharePoint and OneDrive) and Nasuni. This unified view allows for efficient searching and interaction with documents across the entire unstructured file stack, inclusive of Nasuni-managed data. 

            NetApp has expanded its AWS relationship with a new Strategic Collaboration Agreement (SCA) to accelerate generative AI efforts, delivering data-rich experiences through workload migration and new application deployments on AWS. The two will enable increased AWS Marketplace purchases, especially for NetApp CloudOps solutions, to streamline processes for customers. Instaclustr by NetApp manages open source vector databases – a crucial component in the delivery of fast and accurate results in RAG architectures. The close collaboration between AWS and NetApp on advanced workloads makes it simpler and faster for customers to unlock value from their data using RAG. NetApp is the only enterprise storage vendor with a first-party data storage service natively built on AWS with Amazon FSx for NetApp ONTAP.

            Database-as-a-service (DBaaS) supplier Tessell is partnering Microsoft Azure and NetApp to deliver a ubiquitous Copilot for Cloud Databases. It integrates an enterprise-grade Database PaaS with one-click functionality for any database on Azure, leveraging Azure NetApp Files (ANF) as enterprise cloud storage and Tessell as the unified Database Service. For the first time, customers of Azure and NetApp will have access to an enterprise-grade Managed Instance for Oracle on Azure, fully integrated with ANF and supporting any virtual machine (VM) family across all Azure regions. Azure Saving Plans and NetApp effective capacity pricing are available, ensuring Co-Sell incentives and Microsoft Azure Consumption Credits (MACC) enablement. The bundled offering includes 24x7x365 support with a 15-minute response time for issues related to Azure, ANF, and Tessell Oracle PaaS. 

            Tessell claims customers can experience up to a 45 percent reduction in total cost of ownership (TCO) across four vectors: infrastructure optimization, third-party software optimization, database license optimization, and operational cost optimization. More information here.

            Nutanix Unified Storage (distributed file system with commodity server-based scale-out) ranked first in the “image classification” workload category and second for the “image segmentation” workload offering top performance, faster throughput, and linear scaling in the latest round of MLPerf benchmarks, the AI storage benchmark by MLCommons. For the image classification workload, a single node was able to power 33 AI accelerators and deliver over 5,970 MB/sec throughput. The same results were achieved at scale, showing linear performance with 32-node deployment driving 1,056 AI accelerators and over 19,1043 MB/sec of throughput. Nutanix said AI is rapidly increasing the demand for sustained high-throughput and high-performance storage solutions due to the massive datasets and complex computations required for training and inference.

            Oak Ridge National Laboratory (ORNL) is disposing of 32,494 disk drives as it is closing down its Summit supercomputer. The 20 tons of drives are being fed into a mobile ShredPro Secure shredder – a waist-high, 4 foot wide unit. The drives are fed by a technician into an opening at the top of the machine, where counter-rotating metal teeth tear the drives apart and reduce them to small, irregular strips a few inches in size. The mobile shredder can shred one hard drive every 10 seconds, with a theoretical capacity to process up to 3,500 hard drives a day. A conveyor belt gathers the material and deposits the waste into a bin, which is then transferred to larger containers and taken to be recycled through ORNL’s metal recycling program.

            Collecting useless HDDs at ORNL for shredding
            Collecting useless HDDs at ORNL for shredding

            Pure Storage and Rubrik are partnering to add 3-layer protection to Flash Array data with a reference architecture. Layer 1 is Pure’s immutable snapshot technology, with auto-on safe mode governance providing instant recovery from a secure enclave accessible only to designated contacts authenticated through Pure Storage Support. Layer 2 is compliant immutable and on-site backup via the Rubrik Secure Vault, providing anomaly detection, threat monitoring and hunting, sensitive data monitoring, user intelligence and orchestrated recovery. Layer 3 is archival FlashBlade//S and //E storage, with massive scale-out and immutable, cost-effective storage designed for rapid recovery, even for data stored over extended periods. More info available here.

            Rubrik has partnered with Okta to provide Okta Identity Threat Protection with user context to accelerate threat detection and response. Rubrik shares with Okta important user context such as email and the types of sensitive files they have accessed. By combining Rubrik’s Security Cloud user access risk signals with threat context from other security products used by an organization (such as Endpoint Detection and Response or EDR), Okta can determine overall risk levels more effectively and automate threat response actions to mitigate identity-based threats. A Rubrik blog provides more information.

            Rubrik Okta partnership

            According to Tom’s Hardware, Samsung has announced and is mass-producing PM9E1 Gen 5 M.2 SSDs with speeds up to 14.5GB/sec read and 13GB/sec write bandwidth. The PM9E1 has a 2,400 TBW (Terabytes written) lifespan rating, double that of its PCIe 4 PM9A1 predecessor. It is also 50 percent more power-efficient. Device Authentication and Firmware Tampering Attestation security features are included through the  v1.2 Security Protocol and Data Model (SPDM).

            Samsung PM9E1 storage
            Samsung PM9E1


            SK hynix has begun mass production of the world’s first 12-layer HBM3E product with the largest (36GB) capacity of existing HBM to date, using DRAM chips made 40 percent thinner to increase capacity by 50 percent at the same thickness as the previous 8-layer product. It’s increased the speed of memory operations to 9.6Gbit/sec, the highest memory speed available today. If Llama 3 70B, a Large Language Model (LLM), is driven by a single GPU equipped with four HBM3E products, it can read 70 billion total parameters 35 times within a second. Samsung Electronics and SK hynix are expected to post record sales in the third 2024 quarter, driven by AI chip demand. SK hynix could even overtake Intel in sales, becoming the third largest semiconductor supplier.

            Enterprise app data manager Syniti is working with tyre supplier Bridgestone’s EMEA unit to remove siloes across the organization by consolidating business processes across its SAP ERP Central Component (SAP ECC) system into one SAP S/4HANA Cloud instance allowing the company to improve operational efficiency, data accuracy and set the stage for future growth. Read the full case study here.

            Research house TrendForce claims concerns over a potential HBM oversupply in 2025 have been growing in the market. Its latest findings indicate that Samsung, SK hynix, and Micron have all submitted their first HBM3e 12-Hi samples in the first half and third quarter of 2024, respectively, and are currently undergoing validation. SK hynix and Micron are making faster progress and are expected to complete validation by the end of this year. The market is concerned that aggressive capacity expansion by some DRAM suppliers could lead to oversupply and price declines in 2025. If an oversupply does occur, it is more likely to affect older-generation products such as HBM2e and HBM3. TrendForce maintains its outlook for the DRAM industry, forecasting that HBM will account for 10 percent of total DRAM bit output in 2025, doubling its share in 2024. HBM’s contribution to total DRAM market revenue is expected to exceed 30 percent given its high ASP.

            University of Southampton boffins, who are developing 5-dimensional silicon glass storage that basically lasts forever – it’s stable at room temperature for 300 quintillion years – stored the full human genome on a crystal of the glass which is in the Memory of Mankind archive located within a salt cave in Hallstatt, Austria. It’s the usual kind of eye-catching but useless demo of a technology that is years away from commercial use.

            The boffins, led by Professor Peter Kazansky, used ultra-fast lasers to inscribe data into 20nm size nano-structured voids orientated within the silica. The crystal is inscribed with a visual key showing the universal elements (hydrogen, oxygen, carbon and nitrogen); the four bases of the DNA molecule (adenine, cytosine, guanine and thymine) with their molecular structure; their placement in the double helix structure of DNA; and how genes position into a chromosome, which can then be inserted into a cell.

            The boffins’ release proclaims: “Although it is not currently possible to synthetically create humans using genetic information alone, the longevity of the 5D crystal means the information will be available if these advances are ever made in the future.” Gee whiz.

            Storage exec Itay Nebenzahl
            Itay Nebenzahl

            Open source-based hyperconverged cloud technology supplier Virtuozzo has appointed Itay Nebenzahl as its new CFO. He joins Virtuozzo from Logz.io, where he served as CFO and was instrumental in managing the company’s multi-currency cash exposure and global customer portfolio. Prior to Logz.io, Itay held the CFO position at Au10tix Limited where he led a multi-hundred-million dollar round with a leading VC, preparing the team for a multi-million special purpose acquisition company (SPAC) IPO.

            Storage exec Lauren Vaccarello
            Lauren Vaccarello

            WekaIO announced that its Data Platform has been certified as a high-performance data store for Nvidia Partner Network Cloud Partners. Nvidia Cloud Partners can leverage the WEKA Data Platform’s performance, scalability, operational efficiency, and ease of use through the jointly validated WEKA Reference Architecture for Nvidia Cloud Partners using Nvidia HGX H100 systems. Also Lauren Vaccarello has been apointed as WEKA’s first CMO. Vaccarello is a veteran marketing executive and celebrated author, board member, and angel investor with a proven track record of accelerating revenue growth for enterprise software companies. She has previously served as the CMO of Salesloft and Talend and held executive leadership positions at Box, Salesforce, and Adroll. Before joining WEKA, she was an entrepreneur-in-residence at Scale Venture Partners. She sits on the boards of Thryv and USA for UNFPA.

            Xinnor’s xiRAID technology, combined with Lustre, has enhanced HPC capabilities at German research university Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Highlights:

            • Write throughput increased from 11.3GB/sec to 67GB/sec
            • Read throughput improved from 23.4GB/sec to 90.6GB/sec
            • Nearly doubled usable storage capacity