Dell is stepping up its attack on Broadcom’s VMware customer base with a deal to run Nutanix software on its servers and add PowerFlex external storage support as well.
Since Broadcom took over VMware it has alienated a league of VMware customers with price and product package changes, as well as irritating VMware channel partners by cancelling contracts, forcing some of them to reapply and accept altered Ts and Cs.
Rivals are understandably trying to poach any dissatisfied VMware customers. With this in mind, Travis Vigil, Dell SVP for Product Management and Nutanix Product Management SVP Thomas Cornely highlighted in a joint blog that: “Dell Technologies and Nutanix are building on their 14+ year relationship with two new innovative solutions … sold and delivered by Dell.”
Thomas Cornely (left) and Travis Vigil (right)
The two don’t mention either Broadcom or VMware in their “Streamline Hybrid Cloud for Tomorrow’s Innovation “ missive, concentrating on what they are bringing to the market.
Nutanix and Dell signed a partnership deal in June to try and encourage upset Broadcom VMware customers to move to a joint Dell-Nutanix platform. At the time the two said Nutanix’ AHV hypervisor will run on Dell servers and a Dell AHV server will connect to storage-only HCI nodes
Now we have a Dell XC Plus appliance and PowerFlex support.
Dell PowerEdge XC server
The Dell XC Plus is a turnkey, HCI-based appliance integrating the Nutanix Cloud Platform software stack, including the AHV hypervisor and a centralized Prism control plane, on Dell PowerEdge servers. There are six models with varying CPU, GPU, memory, networking and storage capacities.
Dell PowerFlex is a scale-out virtual SAN and hyperconverged system with multi-hypervisor support, scaling out to thousands of nodes. PowerFlex will be the first external storage supported and integrated with the Nutanix Cloud Platform. The two SVPs say that joint customers can manage compute and storage independently, run their choice of hypervisor, and achieve extreme performance at scale while maintaining the full suite of Nutanix software features.
They add: “Over time, we intend to explore expanding Nutanix Cloud Platform integration with other Dell IP-based storage solutions.” Maybe we’ll see Dell file and object storage products supported with the XC Plus.
The PowerFlex Nutanix integration is currently in development and will be available to early access customers later this year.
A California jury has awarded MR Technologie of Germany $262 million in patent infringement damages in a jury trial against Western Digital.
MR Technologie (MRT) sued Western Digital in August 2022 saying it used IP patented by Dieter Suess, U.S. Patent Nos. 9,928,864 and 11,138,997, titled “Multilayer exchange spring recording media.” Patent 9,928,864 was filed in 2006 and awarded in March 2018, and 11,138,997 in October 2021. Suess has a number of such patents referring to multi-layer exchange spring recording media.
Dieter Suess
Suess is a professor and head of the Physics of Functional Materials department at the University of Vienna and MRT is his company. The two patents in question refer to ways to increase the signal to noise ratio in the recording medium layer of hard disk drives and using anisotropy – directionally dependent – magnetic effects in a multi-layer recording material to help switch bits from one magnetic direction to another.
His patent also refers to a nucleation host, a point in the recording medium’s detailed structure where an anisotropy phase change occurs. A “spring recording” effect embodying this helps increase the HDD’s area density and its overall capacity.
According to Law360, MRT’s lawyers accused WD of mis-using Suess’s IP “on a massive scale” by applying it in almost every disk drive it has produced since 2018. This meant it could increase areal density overall from 300 Gbit/sq in to 1,000 Gbit/sq in.
WD, in turn, claimed the firm had not infringed Suess’ patents as it used different technologies to increase the areal density in the recording medium, not needing a nucleation host component. The storage giant’s lawyers said WD had 400 researchers working in this area and claimed WD invented its areal density increase technology independently and without reference to Suess’ work.
According to Reuters, WD’s lawyer argued that: “MRT’s lawyers have given false credit, to a fairly magnificent extent, to Dr. Suess for the work of thousands of [Western Digital] engineers over decades and across the planet.”
The jury disagreed with WD’s argument. WD plans to appeal.
Bootnote
The case is MR Technologie GmbH v. Western Digital Technologies Inc, in the U.S. District Court for the Central District of California, No. 8:22-cv-01599.
Analysis. VAST Data is getting ready to launch its DataEngine software, providing automated pipeline operations to make AI faster and more responsive to events. We referenced a VAST Data Platform white paper writing this article. It’s long – 100 pages – but well worth a read.
Update: VAST DataEngine compute uses the Data Platform’s controller/compute nodes (C-nodes). 13 Aug 2024.VAST Co-founder says Cosmos event is not (just) about DataEngine. 15 Aug 2024.
The DataEngine is VAST’s latest layer of software on its Data Platform. This platform decouples compute logic from system state and has a DASE (Disaggregated and Shared-Everything Architecture) involving a VAST array consisting of x86-based controller nodes (C-nodes) which link to data storing all-flash D-nodes across an InfiniBand or RoCE with 200/400Gbps networking fabric. The C-node and D-node software can run in shared industry-standard servers.
VAST Data Platform diagram.
The controller software has been ported to Nvidia’s Arm-powered BlueField-3 DPU (Data Processing Units). These are located in the D-node storage enclosures and also the storage controllers. In general, VAST’s containerized VastOS software runs in stateless multi-protocol servers and these include the C-nodes and BlueField-3 DPUs. The VAST array BlueField3 DPUs can be linked to similar DPUs in Nvidia GPU servers.
VAST has built an AI-focused data infrastructure Data Platform stack atop its DASE hardware/software DataStore base:
DataCatalog – metadata about every file and object in the system
DataBase – transactional data warehouse
DataSpace – global namespace
DataEngine – AI pipeline infrastructure operations
VAST tells us that the VAST DataBase (the table management and storage bits) runs on the VAST cluster’s C-nodes. The VAST Catalog is a table in the VAST Database that holds the namespace metadata extracted from a snapshot periodically. So that runs across the C-nodes too.
VAST should not be viewed as a typical storage array supplier – focussed on stored data block, file and object I/O, with its arrays at the beck and call of system and application servers to which they ship read data and from which they receive write data.
VAST’s data infrastructure stack contains elements which traditionally run on servers. It has blurred the boundary between storage array and application/system servers and is now a hybrid storage hardware/software and AI data processing infrastructure software business. Looked at another way, VAST provides an AI infrastructure software stack with an integrated multi-protocol storage array base – its very own idea of a converged infrastructure – with compute (Dell, HPE, Intel, Lenovo Supermicro + AWS, Azure and GCP compute instances) and networking (Arista, Cisco, HPE, Nvidia-Mellanox) partners.
Regard the DataEngine as server-level and not storage array-level software. It is an engine – with a self-starter as the thing can trigger events – that executes and orchestrates AI pipeline events and functions without an AI developer having to explicitly code and locate routines within the AI stack. Its scope is an organization’s entire VAST installation, across both the distributed on-premises and public cloud environments.
Its operation is based around event triggers and functions – both are stored as elements in the VAST DataStore. Any data I/O event in the VAST Data Store can be an event trigger – meaning any create, read, update or delete, known by the unlovely acronym CRUD. For example, a new .jpg file stored in a Photos folder could trigger a metadata gathering operation. Other events could be or a new row in a VAST table, and this includes new Kafka topics since they are stored as table rows, internal counters reaching set points or triggered functions completing.
When an event trigger takes place, the DataEngine will execute a function according to set event trigger rules. These rules could specify the execution environment – such as the fastest processing hardware or the lowest-cost hardware. A function is made up of one or more elements which “defines the execution requirements of a given function, say a GPU-powered inference engine performing facial recognition.”
There is a global workflow optimization engine which “will choose which function to perform a task based on cost. The VAST DataEngine also keeps information on the cost, CPU resources, and execution time of each function each time it’s run, and uses these factors to run functions in the optimal location each time.”
The execution requirements “would include hardware and location dependencies for functions orchestrated by the VAST DataEngine, or simply the execution method for functions performed by the cloud or other services outside the VAST DataEngine’s compute environment.”
The DataEngine operates on a so-called VAST Computing Environment and this has the global workflow optimization engine “that responds to event triggers, figures out the best place to run each instance of each function, and calls the services that perform those functions, from scraping metadata to matching millions of genome segments.”
It also has an “execution and orchestration engine that manages the containers that deliver the function’s services.”
An example shows how this could work. “A workflow may start by scraping the embedded metadata out of the incoming files into The VAST Catalog. An event trigger may then detect that a subset of those files are – photographs taken in Las Vegas over the New Year’s weekend – and cause an inexpensive inference function to run on those files and identify a smaller subset of images that include the image of a red car. Those files are subjected to more extensive processing to ultimately return the license plate number for a hit-and-run.”
The VAST Execution Environment “provides the computing resources for the VAST Data Platform to perform its work, and for the VAST DataEngine to process user functions. The VAST Data Platform can orchestrate all of its computing functions across a single pool of servers optimizing their utilization.”
Before the VAST DataEngine VAST clusters ran all the code for a C-Node or D-Node in a single container, plus one container for the cluster’s VMS management service. There are multiple service modules within each container of course. Now the VAST Execution Environment uses the DataEngine as the control plane orchestrator of Kubernetes pod(s) that run VAST containers along side customer provided containers that train deep learning models, and, for example, infer the photos that have faces, and recognize whose face it is for a social media tag.
The VAST Execution Environment orchestrates user provided containers along with the VAST containers, that provide all the services of the VAST Data Platform from rebuilding erasure code data after an SSD failure to processing SQL queries and running the DataEngine’s optimization engine itself: VAST containers will also run functions users create with VAST’s Python toolkit in an AWS Lambda-like environment.
We understand that part of the VAST Data Engine that will be shipped, the query engine, today runs an embedded Spark cluster across a subset of the CNodes, with a customer allocating a pool for the Engine. That’s how the Kafka compatible event broker and event triggers will run. Customers will be able run functions on CNodes. In the future VAST may be able to orchestrate heavy-weight functions running on some other compute resource.
There is much more to learn about the DataEngine, such as metadata scraping, PII detection and ransomware anomaly detection. Read the whole white paper linked above if you’re interested.
VAST is preparing a DataEngine marketing blast, with a Cosmos Online event in October where, we’re told, viewers can witness AI in action and see how world-class organizations have VAST-powered AI data pipelines. VAST co-founder Jenn Denworth commented: “You’re a bit askew from what we’re looking to announce.”
Commvault has bagged the top-rating in GigaOm’s Sonar report for cloud-native data protection covering emerging technologies and market segments.
The Cloud-Native Data Protection Sonar looks at suppliers providing both SaaS and pure-play software to protect SaaS, hyperscaler-native, and private cloud workloads. These typically operate with a pay-as-you-go model, often based on consumed capacity. Customers may pay the supplier for the storage or bring their own, paying for it through their own cloud provider.
GigaOm analyst Chester Conforte said: “Commvault Cloud Platform delivers the broadest and deepest set of capabilities to address data threats, attack tactics, continuous validation, forensic analysis, and recovery needs across legacy on-prem, shared storage, endpoint, VM, public, private, and SaaS clouds all in a unified and extensible platform.”
The surveyed suppliers are Acronis, AWS, Backblaze, Clumio, Commvault (Metallic), Druva, Elastio, HYCU, KeepIt, Microsoft, N-able, Rubrik and Veritas. Cohesity was not included. The suppliers were evaluated across a number of dimensions, including cloud-native data plane architecture, management plane durability, retention and recoverability, end-to-end encryption, granularity, data movement optimization, data management and analytics, storage capacity efficiency, cyber resilience and diversity of data sources. They were rated on each of these (exceptional, capable, limited, no-applicable) and an average score computed:
Commvault – 2.9
Veritas -2.8
Druva – 2.6
HYCU – 2.6
Acronis – 2.4
Clumio – 1.9
Rubrik – 1.9
AWS – 1.5
Elastio – 1.5
KeepIt – 1.5
N-able – 1.4
Microsoft – 1.3
Backblaze -1.2
The Sonar chart is a semi-circle which ”assesses each vendor on its architecture approach (Innovation), while determining where each solution sits in terms of enabling rapid time to value (Feature Play) versus delivering a complex and robust solution (Platform Play).”
Commvault leads the pack, placed in the inner Leaders’ semi-circle with Veritas, Druva and HYCU, and Acronis accompanying it there too. All these suppliers are in the platform play side of the chart.
The outer Challenger’s half-ring has MSP-supplier N-able out on its own in a platform play start, with the rest clustered as feature-centric players on the opposite side of the chart.
GigaOm’s Conforte reckons Druva, HYCU and Rubrik are developing their offerings at a faster rate than all the other suppliers.
He points out that vendors in general “are leveraging AI and ML to enhance threat detection, automate data classification, and optimize backup processes. This allows for proactive identification of potential threats and efficient data management.”
Conforte says: ”Comparing the current Sonar graphic with the previous year’s chart reveals several notable changes. Some vendors have progressed as anticipated, moving closer to the Leaders band due to their continuous innovation and expanded capabilities. For example, vendors like Clumio and Druva have shown significant improvement, aligning with the expectations they set in the previous year.”
There is a SWOT type analysis of each supplier’s offerings in the body of the report.
Commvault has provided access to the full GigaOm Sonar report here.
VCs and angel investors are ploughing $100 million into two Nutanix veterans’ vision of a cloud-based platform for customer support and software development, valuing it at $1.15 billion.
Nutanix founding CEO Dheeraj Pandey and former SVP engineering, Manoj Agarwal, launched DevRev in 2020 and raised $50 million in seed funding the following year. DevRev refers to Developer (to) Revenue, and it is selling business infrastructure services that a startup can use to grow their operations without having to set up their own in-house operation for these back-office and front-office functions.
Dheeraj Pandey
Pandey said in a statement: “Design will play a key role in building trust with end users, who have inevitably begun to witness the AI hype cycle of broken prototypes and bespoke one-off GPT wrappers that are inherently unmaintainable and not secure.
“Trust is built by delivering fast, accurate, and personalized AI solutions. In under four years we’ve built an amazing multi-region platform to deliver enterprise-level security, combined with an experience that is consumer-grade.”
DevRev says its AgentOS combines “customer support, product management, and software development into a single integrated platform, creating a central knowledge graph for an enterprise. That knowledge graph powers a team of AI agents that automate workflows across every department.”
The pitch is that it will operationalize GenAI in the enterprise with one-click data migration from legacy systems and lightweight AI agents. The claim is that “DevRev’s simplified stack can co-exist or replace solutions such as Zendesk, Salesforce Service Cloud, Intercom, and Atlassian Jira.”
DevRev claims it has more than 1,000 customers, some with multi-million dollar deployments, including SaaS unicorns such as Uniphore, the largest AI chip designers and one of the world’s top 5 consumer banks.
Although this latest funding round is classed as a Series A round event, it actually includes investments accumulated over the past three years, culminating in its $1.15. billion valuation. Khosla Ventures, Mayfield Fund, and Dheeraj Pandey’s family office, Param Hansa Values, plus several accelerators and family offices of tech CEOs and VCs, are amongst the investors.
DevRev customer ticket screenshot
DevRev says that operationalizing GenAI in companies will require a “robust knowledge graph, one in which data from legacy systems is continuously being fed in real-time to an interlinked network of customer, product, employee, work, user, and session records that capture complex business relationships.”
It says that, without product and customer labeling, it is almost impossible to implement AI that is meaningful and reductive. This is why there is a need for an enterprise knowledge graph. DevRev’s model-based agents are powered by custom vector embeddings that cluster, classify, deflect, deduplicate, route, attribute and generate omnichannel conversations, tickets, incidents, issues, articles, recordings, code commits, release notes, and more.
There’s also “the AgentOS marketplace [which] has become a mecca for low-code snap-ins such as knowledge graph connectors, search crawlers, workflow nodes, and data visualization widgets that make AI achievable and delightfully simple for the younger generation and equally for the pre-GPT workforce.”
Vinod Khosla, founder of Khosla Ventures, said: “AI will transform enterprise customer support and product teams, changing the way companies do business globally. Having backed Dheeraj at Nutanix, and now at DevRev, he is one of the few people in the industry who can leverage foundation models and weave them together with enterprise architectures, customer collaboration, and monetization.”
Toshiba says it has increased the S300 Pro’s surveillance disk drive’s transfer rate by doubling its cache capacity.
The 3.5-inch form factor S300 Pro uses conventional magnetic recording with 4, 6, 8 and 10 TB capacities. It sits alongside the S300 shingled magnetic recording media drive in the Toshiba surveillance drive line-up. Both are air-filled drives with the S300 having 6, 8 and 10 TB capacity points. The 2020-era S300 Pro had a 256 MB cache and a maximum sustained transfer rate of 248 MBps. The new edition has a 512 MiB (537 MB) cache and its max transfer rate is 13.3 percent faster at 281 MBps.
The workload rate has increased as well, from the 2020 drive’s 180 TB/year to the new drive’s 300 TB/year. It supports 600,000 load/unload cycles, has a 1.2 million hours MTBF rating and a 3-year warranty. As before it has a SATA 6Gbitps interface and spins at 7,200rpm.
The new drive’s weight varies with its capacity: 10TB – 755g, 8TB – 730g, 6TB – 710g, 4TB – 690g. We understand this is because each 2TB increment in capacity adds a platter and read/write head assembly to the drive. That implies the 4TB drives has 2 x 2 TB platters and the 10TB version 5 x 2 TB platters.
The drive supports up to 64 video cameras and can be used in an up to 24-bay cabinet. Toshiba said in a statement that users “get the capacity to record and playback video’d events in real-time and high resolution, and with object identification and face recognition.”
Toshiba competitor Seagate launched a 10-platter, 20TB SkyHawk video surveillance drive in 2022. Its current video surveillance drive webpage lists 1, 2, 3, 4, 6 and 8TB Skyhawk drives only though.
Western Digital’s Purple brand drives are its video surveillance drives and these have a 1TB to 14 TB capacity range.The 14TB version has a 64 MB cache and an up to 110 MBps transfer rate, far slower than Toshiba’s claim for the S300 Pro.
WD also has a Purple Pro Smart Video Surveillance product, with an 8TB to 24TB capacity range, a 256 MB cache and an up to 267 MBps transfer rate; again slower than Toshiba’s S300 Pro.
Seagate and WD’s larger capacity video surveillance drives will have helium-filled enclosures and be more costly to manufacture than air-filled drives.
VAST Data has named its first ever chief financial officer in what could be a sign that it is edging closer to an IPO.
Putting aside the fact it seems strange that VAST has never had a CFO before, despite already raising hundreds of millions of dollars in funding, it has now selected and hired a very experienced bean counter.
Appointee Amy Shapero was previously the CFO at e-commerce giant Shopify. “As the company prepares for its next stage of growth, this strategic appointment will enable VAST to scale faster, better serve customers and further expand VAST’s global business,” says a VAST statement.
Amy Shapiro.
Shapero is said to have played a major role in raising Shopify’s annual revenue from $700 million to nearly $6 billion in less than five years, before leaving the company in 2022. Her previous work spans private and public technology companies in commerce, financial services, marketing, and information services.
According to her LinkedIn profile she has been an “advisor/mentor” to pre-IPO companies and founders, from November 2022 until this month and joining VAST.
Jeff Hoffmeister is the current CFO at Shopify after joining in October 2022. Shapero left Shopify after the company’s third quarter 2022 earnings announcement on October 27, 2022. Her exit at the time was part of a broader C-suite reshuffle at Shopify. On her departure, Shopify CEO Tobi Lütke had said: “I want to thank Amy for her significant contributions to our company. Over the past five years, Amy has been an important partner in helping to advance our strategy.”
Considering her pre-IPO advisor activity before joining VAST, the company seemingly wants to tap into that expertise following its past funding raises. VAST, founded in 2016, raised $40 million in an 2018 A-round, $40 million in a 2019 B-round, $100 million in a 2020 C-round, $83 million in a 2021 D-round, and $118 million in an E-round in 2023. That’s an impressive total of $381 million raised.
As a private company, VAST does not publicly disclose its sales figures, but at Blocks & Files we believe the firm’s sales growth is very solid, and that it may well be profitable or near-profitable. Such a state is, of course, an ideal pre-IPO state.
In December last year, VAST said it had tripled revenue year-on-year, and passed the $200 million annual recurring revenue point at the end of September, as sales responded to a surge in generative AI workloads. It was then valued at $9.1 billion, and had had a positive cash flow for several years, with a gross margin of nearly 90 percent. VAST Co-founder Jeff Denworth said the company would consider an IPO filing at the right time. It had not hired banks for a public listing, but a 2024 IPO could be “on the cards”, depending on the market conditions, according to Denworth.
Blocks & Files has asked whether VAST is now nearer to declaring an IPO plan with the appointment of Shapero. We will update this story when we get a response.
Renen Hallak.
“Amy’s extensive finance, strategy, and operating experience with disruptive, mission-driven, high-growth companies like ours will prove invaluable as VAST continues to scale and expand at a rapid pace,” said Renen Hallak, CEO and co-founder of VAST Data, in a company statement.
Shapero will oversee all financial operations, including budgeting, forecasting, financial reporting, and investor relations. In addition, she will play a “key role” in shaping the company’s strategic direction, added VAST, ensuring alignment between financial goals and business objectives by fostering a “dynamic finance team” to support customers, partners and product development.
“From my first conversations with Renen and the VAST leadership team, it was immediately clear to me that this is an exceptional company, with brilliant leadership and an incredible opportunity in front of us as AI’s impact grows,” said Shapero in another company statement. “I’ve always been data-driven, and throughout my career I’ve helped companies to harness data for insights to improve their customer experience, innovate to build new products, and use economies of scale to create new value.”
VAST’s unified AI data platform features an all-flash-based storage architecture, a “next-gen” database to organize all structured and unstructured data across a global namespace, and containerized Data Engine services running on connected DPU, CPU or GPU servers to power AI. The Data Engine provides the functional underpinnings for AI applications. VAST has moved on from its all-flash storage base and now has a multi-layered, AI-focussed software stack that extends beyond its storage arrays to run on servers.
Micron Technology has produced PCIe Gen6 datacenter SSD technology, as part of a portfolio of memory and storage products to support demand for AI.
The PCIe (Peripheral Control Interconnect Express) bus connects a host computer’s CPU to peripheral device controllers for devices such as SSDs, and graphics cards. The technology was showcased at this week’s FMS: The Future of Memory and Storage conference, in Santa Clara, California. At the conference, the company delivered a keynote focussing on how Micron’s products are impacting AI system architectures, while enabling “faster” and “more power-efficient” solutions to manage large datasets.
“AI and other data-intensive workloads are driving the need for higher performance storage in the datacenter,” said Alvaro Toledo, vice president and general manager of Micron’s datacenter storage group. “Our development of the industry’s first PCIe Gen6 SSD for ecosystem enablement is designed to meet these growing future demands, providing unprecedented speed for our customers’ highest-throughput workloads.”
Full duplex per lane speeds.
PCIe 6 is twice as fast as PCIe 5, with a 16 GBps per-lane speed. The specification can be accessed here. Current PCIe 5 SSDs, like Micron’s 9550, are delivering up to 14 GBps sequential read bandwidth across 4 lanes. With its PCIe Gen6 datacenter SSD technology, the firm says it is delivering sequential read bandwidths of over 26 GBps to partners. Host computer motherboards and SSDs supporting PCIe 6 could appear from next year onwards.
Micron reckons it is kickstarting a Gen6 PCIe ecosystem.
In June, Micron said its revenues rose 81.5 percent year-on-year in the third quarter ended May 30, 2024, as demand for AI server memory rocketed. Sales were worth $6.8 billion, compared to $3.8 billion a year earlier, when the memory market seemed to bottoming out.
Gartner’s latest Enterprise Backup and Recovery Software Solutions magic quadrant kicks Acronis out of the supplier list and has Veeam strengthening its position in the leaders quadrant with the highest Ability to Execute ranking.
A comparison with last year’s edition of this MQ shows that, in the Leaders’ quadrant, Dell falls back in completeness of vision while Veeam improves on that axis to join a close-packed, arrow head-like leading group alongside Commvault, Rubrik, Cohesity and Veritas. (Magic quadrant details and features are explained in a bootnote below.)
There are no Challenger suppliers listed. Arcserve was the sole challenger in 2023 but it has been demoted in Ability to Execute terms and is now a niche player. In the Niche Players quadrant Acronis exits the MQ completely, because, the report says: “its focus on prioritizing MSPs and edge/endpoint device workloads resulted in its inability to meet the inclusion criteria.” This leaves Arcserve, Unitrends, Microsoft and OpenText in the Niche Players’ box. As before, Druva, HYCU and IBM are the Visionaries, with IBM suffering a worse Completeness of Vision rating compared to 2023.
The Gartner report writers make some strategic planning assumptions:
By 2028, 75 percent of enterprises will use a common solution for backup and recovery of data residing on-premises and in cloud infrastructure, compared with 20 percent in 2024.
By 2028, 75 percent of enterprises will prioritize backup of SaaS applications as a critical requirement, compared with 15 percent in 2024.
By 2028, 90 percent of enterprise backup and recovery products will include embedded technology to detect and identify cyberthreats, compared with fewer than 45 percent in 2024.
By 2028, 75 percent of large enterprises will adopt backup as a service (BaaS), alongside on-premises tools, to back up cloud and on-premises workloads, compared with 15 percent in 2024.
By 2028, 75 percent of enterprise backup and recovery products will integrate generative AI (GenAI) to improve management and support operations, compared with fewer than 5 percent in 2024.
This is more or less an instruction to suppliers to put these features, if missing, on their development roadmaps.
A big question for us is: which of the three visionaries will make the jump into the leader’s box?
The report has detailed strengths and cautions note on each supplier and you can download a copy of this MQ from HYCU’s website (registration required).
Bootnote
The “Magic Quadrant” is a 2D space defined by axes labelled “Ability To Execute” and “Completeness of Vision”, and split into four squares tagged “Visionaries” and “Niche Players” at the bottom, and “Challengers” and “Leaders” at the top. The best placed vendors are in the top right Leaders box and with a balance between execution ability and vision completion. The nearer they are to the top right corner of that box the better.
Cloud storage provider Backblaze pulled in $31.3 million revenues during calendar Q2, up 27 percent YoY, and it reported a $10.3 million loss versus the $14.3 million net loss a year earlier. It expects $32.6 million, +/- $2 million revenues next quarter.
Backblaze’s revenue growth rate is accelerating.
B2 Cloud Storage revenue was $15.4 million, an increase of 43 percentYoY, while Computer Backup revenue was $15.9 million, an increase of 15 percent YoY.
William Blair analyst Jason Ader writes to subscribers, telling them: “The revenue mix continues to shift toward B2 Cloud, with B2 Cloud revenue growing 43% year-over-year in the quarter, representing 49% of total revenue. With strong growth expected to continue throughout 2024, we expect B2 Cloud will surpass 50% of revenue in the second half. Backblaze continues to position B2 as a low-cost cloud storage provider and noted that it has seen no impact from broader macro challenges. Meanwhile, Computer Backup continues to surprise to the upside, with last year’s price increase resulting in lower churn than expected in the first half of this year.”
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Marc Suidan.
Backblaze has hired Marc Suidan as its CFO, replacing the retiring Frank Patchel. Co-founder, chairman and CEO Gleb Budman said: “I would like to thank Frank for all of his contributions to Backblaze. He’s been an integral part of our company’s success, especially leading us through our successful IPO and for years after. We greatly appreciate his years of service and wish him well in retirement.” Suidan’s background includes leading a publicly held company as CFO, and advising and leading companies of all sizes in the technology and media industries, including numerous storage and software as a service (SaaS) cloud companies. CRO Jason Wakeam was hired in the quarter as well.
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Backup and data security supplier Commvault has expanded its cyber and data security ecosystem through strategic integrations with an array of security partners: Acante (data access governance), (Data Security Posture Management), Google Cloud (threat Intelligence), Splunk (threat detection and response), and Wiz (cloud security). These new integrations are available immediately through Commvault and its partners. For detailed product specifications, configuration guides, and additional resources, visit Commvault’s Partner page.
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Datalake service supplier Cribl announced an agreement with managed security services provider Vijilan Security. Zac Kilpatrick, VP of Global Go-to-Market Partners at Cribl, said: “By combining Cribl’s vendor agnostic data management solutions with Vijilan’s managed extended detection and response, joint customers are equipped to take complete control over their enterprise data to ensure the most secure digital environments.”
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FalconStor has reported calendar Q2, 2024, revenues of just $2.4 million, flat YoY, with a $30,963 net loss, better than the year-ago net loss of $456,785. CEO Todd Brooks said in a statement: “In Q2, we continued to effectively manage operating expenses and bolster our cash position, while we once again grew hybrid cloud ARR run-rate by over 100% compared to Q2 2023. Our growth is fueled by the expansion of FalconStor’s data protection and migration technology across the IBM global ecosystem, spanning on-premises, cloud, and MSP segments of the IBM Power customer base.” FalconStor obtained certification of its StorSafe and StorGuard integration with IBM Storage Ceph, IBM’s on-premises AI data lake solution.
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FMS 2024 Best of Show awards:
SSD category – Most Innovative Technology – KIOXIA’s RAID Offload data protection technology to offload RAID parity compute
Most Innovative Business Application – Graid Technology SupremeRAID, Solidigm SSDs, CheetahRAID Raptor edge servers, and Tuxera Fusion File Share
Most Innovative Memory Technology – Industry Standards Category – SNIA for the EDSFF Specification
Most Innovative Hyperscaler Implementation – Hammerspace for its support of Meta’s AI Research SuperCluster
Most Innovative Hyperscaler Implementation – All-Flash and Hybrid Storage Array Category Winner – Infinidat’s InfiniBox G4 Family
Most Innovative Artificial Intelligence (AI) Application – Unique Products – Neo Semiconductor 3D X-AI memory chip technology
Most Innovative Artificial Intelligence (AI) Application – Phison’s aiDAPTIV+ technology
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Data management supplier Komprise has published “The Komprise 2024 State of Unstructured Data Management” report which examines the challenges and opportunities with unstructured data in the enterprise. This report summarizes responses of 300 global enterprise IT leaders (director and above) at US firms with more than 1,000 employees. The survey was conducted by a third party in June 2024. Most (70 percent) organizations are still experimenting with new AI technologies as “preparing for AI” remains a top data storage and data management priority. Yet cost optimization is an even higher priority this year and they are trying to fit AI into existing IT budgets. Only 30 percent say they will increase their IT budgets to support AI projects. Get a copy of the report here (registration required.).
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LAM Research has a document discussing its cryogenic etching approach to 1,000 layer 3D NAND. Download it here.
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Mainframe app migrator to the public cloud startup Mechanical Orchard has raised $50m in a Series B round led by GV, formerly Google Ventures. It previously raised $24 million in an A-round in February this year. It must have demonstrated fast product development progress to get a B-round just six months later.
Mechanical Orchard team.
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MSP backup service provider N-able reported $119.4 million revenues in calendar Q2 of 2024, up 12.6 percent, with a $9.5 million profit, more than double the year-ago $4.5 million profit a year ago. CFO Tim O’Brien said: “Our second quarter performance marks our seventh consecutive quarter operating north of the Rule of 45 on a constant currency revenue growth and adjusted EBITDA basis.”
William Blair’s Ader said in a statement: “Outperformance in the quarter was driven by consistent demand for N-able’s backup and security suites (Cove and EDR/MDR were standouts) and enhanced focus on long-term contracts (drove some of the upside due to higher upfront revenue recognition). In addition, the company posted ACV bookings growth of 20% year-over-year (in our view, the best leading indicator), reflecting still strong secular trends around IT outsourcing for both SMBs and enterprises amid a maturing MSP market.”
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A comparison of Backblaze and N-able quarterly revenue growth rates show parallel curves. Both businesses are growing consistently and steadily, with N-able’s MSP channel bringing in a lot more revenue.
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NEO Semiconductor announced the development of its 3D X-AI chip technology, targeted to replace the current DRAM chips inside high bandwidth memory (HBM) to solve data bus bottlenecks by enabling AI processing in 3D DRAM. 3D X-AI can reduce the amount of data transferred between HBM and GPUs during AI workloads. NEO says this is set to revolutionize the performance, power consumption, and cost of AI Chips for AI applications like generative AI.
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Nimbus Data launched its ExaDrive EN line of Ethernet-native SSDs supporting NVMe-oF and NFS protocols. ExaDrive EN is based on an ARM SoC that provides processing power for functions including native NFS and NVMe-oF/TCP protocol support, AES-256 inline encryption, and full data checksums. ExaDrive EN adheres to the SNIA Native NVMe-oF Drive Specification v1.1 to ensure compatibility with EBOF (Ethernet Bunch of Flash) and Ethernet switch-based enclosures. ExaDrive EN will be initially available in 16 TB capacity using TLC flash with higher capacities expected in 2025.
Nimbus ExaDrive EN drives (top left), Nimbus FlashRack (top right) and Nimbus SSPs (bottom).
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Nimbus Data unveiled HALO Atmosphere storage software, encompassing block, file, and object storage. Its Flexspaces feature enables all data types and protocols to share one logical pool, including block (NVMe-oF, iSCSI, Fibre Channel, SRP), file (NFS, SMB), and object (S3- compliant) storage. HALO supports any workload type (mission-critical enterprise, extreme performance, maximum efficiency, data mobility) on one platform. HALO Atmosphere is available today with Nimbus Data’s all-flash systems. HALO will be available on the public cloud in Q4 2024.
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Nimbus Data also announced its new FlashRack all-flash storage systems powered by its HALO software. Nimbus says that with Federation, hundreds of FlashRacks can be centrally managed, simplifying administration at scale. FlashRack features Nimbus Data’s patented Parallel Memory Architecture (PMA), a stateless design that writes data to enterprise-grade flash memory in a single operation. We’re told that a single FlashRack cabinet offers up to 100 PB of effective capacity, 3 TBps of throughput, and 200 million IOPS, all while drawing 18 kW of power.
Using industry-standard 16 TB, 32 TB, and 64 TB NVMe SSDs, as well as a new option – SSPs, or Solid State Packs – combine multiple SSDs into one hot-pluggable and encrypted storage module of up to 512 TB. Using Flexspaces, SSPs can be mirrored, then split and unmounted. Customers can purchase FlashRack without any capacity and then add qualified SSDs from major vendors.
A 2RU FlashRack Turbo has up to 1.5PB raw capacity with 24 x 64 TB SSDs or 3 x 512 TB SSPs and needs 900w typical power. A 2RU FlashRack Ultra has up to 768 TB raw capacity with 24 x 32 TB SSDs or 3 x 256 TB SSPs, needing 700W typical power. Find out more here.
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Nimbus Data unwrapped BatArray, a fusion of the company’s FlashRack all-flash systems with Tesla’s Cybertruck, creating the world’s first mobile flash storage data center. BatArray uses six FlashRack Turbo systems, each storing 1.5 PB, to house 9 PB of raw all-flash storage. After considering redundancy and 3:1 data reduction, 25 PB of effective capacity is possible. Cybertruck provides a 240V 40A power circuit in the truck bed. It’s possible to run the whole infrastructure from this single circuit. With its 123 kWh battery, Cybertruck can power the entire storage system for up 24 hours entirely from its EV battery.
NImbusDatastand at FMS showing BatArray Cybertruckon the right.
With its patented Parallel Memory Architecture, BatArray delivers up to 360 GBps of ingress performance, or approximately 3 terabits per second. Nimbus Data claims this data rate is three times faster than the massive AWS Snowmobile, Amazon’s original data transfer vehicle based on a 45-foot long semi-trailer truck. All data is automatically encrypted in hardware using AES-256 with KMIP support. Egress speed is even faster, reaching up to 600 GBps, or nearly 5 terabits per second.
At maximum performance, a user can fill BatArray to capacity in about 7 hours, still leaving more than 200 miles over range in the Cybertruck battery. With 400 Gigabit Ethernet FR4 fiber cabling and transceivers, this transmission rate is possible over 2 km, allowing for some distance between BatArray and the source or destination connection points. BatArray supports industry standard NFS, SMB, S3, and NVMe-oF protocols.
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NVM Express, Inc. today released three new specifications and eight updated specifications. The three new specifications are the NVMe Boot specification, the Subsystem Local Memory command set and the Computational Programs command set. The updated specifications are the NVMe 2.1 Base specification, Command Set specifications (NVM Command Set, ZNS Command Set, Key Value Command Set), Transport specifications (PCIe Transport, Fibre Channel Transport, RDMA Transport and TCP Transport) and the NVMe Management Interface specification. The NVM Express specifications and the new feature specifications are available for download on the NVM Express website.
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Semiconductor designer Rambus says it has advanced data center server performance with the industry-first Gen 4 DDR5 Register Clock Driver (RCD). This technology boosts the data rate to 7200 MT/s, setting a new benchmark for performance. It enables a 50 percent increase in memory bandwidth over today’s 4800 MT/s DDR5 module solutions. You can read more about it here.
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Data protector Rubrik has a tech integration and partnership deal with Mandiant, part of the Google Cloud, aiming to expedite customers’ threat detection and path to cyber recovery. Mandiant Threat Intelligence is now integrated directly in the Rubrik Security Cloud. Breaking intrusions, active campaigns, and evolving threats detected by Mandiant Threat Intelligence are now integrated into Rubrik’s Threat Monitoring capability providing threat intelligence to Rubrik Enterprise Edition customers. Rubrik’s Threat Hunting and Threat Monitoring capabilities are used to identify a safe recovery point by automatically applying Mandiant Threat Intelligence’s thousands of knowledge points against every Rubrik backup. Rubrik Clean Room Recovery allows customers to recover and store data in a clean Google Cloud environment or multi-cloud environments. Rubrik and Mandiant can bring together their respective Ransomware Response and Incident Response teams to provide victims with additional investigative and recovery support. Read more about all this in a Rubrik blog.
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Silicon Motion announced its SM2508 – the best power efficiency PCIe Gen5 NVMe 2.0 client SSD controller for AI PCs and gaming consoles. It’s the world’s first PCIe Gen5 client SSD controller using TSMC’s 6nm EUV process, offering a 50 percent reduction in power consumption compared to competitive offerings in the 12nm process. With less than 7W power consumption for the entire SSD, we’re told it delivers 1.7x better power efficiency than PCIe Gen4 SSDs and up to 70 percent better than current competitive PCIe Gen5 offerings on the market.
Silicon MOtion SM2508.
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South Korean memory, NAND and SSD manufacturer SK hynix will receive up to $450 million in funding and access to $50 million in loans as part of the US CHIPS and Science Act for its investment to build a production base for semiconductor packaging in Indiana. lt plans to seek from the U.S. Department of the Treasury a tax benefit equivalent of up to 25% of the qualified capital expenditures through the Investment Tax Credit program. This follows SK hynix’s announcement in April that it intends to invest $3.87 billion to build a production base for advanced packaging in Indiana, creating an expected 1,000 jobs.
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Western Digital launched two new automotive flash products – the Western Digital AT EN610 NVMe SSD and iNAND AT EU75 – for next-generation, high-performance, centralized computing (HPCC), advanced driver-assistance systems (ADAS), and other autonomous driving systems, at FMS 2024. It also launched the RapidFlex Interposer, which converts PCIe SSD signals to Ethernet so PCIe eSSDs can be deployed in either an Ethernet-switched or a PCIe-switched EBOF NVMe-oF architecture. It unveiled the world’s first 8TB SD card, the SanDisk SDUC UHS-1, and a 16TB external SSD at FMS 2024 as well as a new 64TB eSSD for storage-intensive applications. WD previewed two PCIe 5.0 x 4 lane M.2 2280 NVMe SSDs; one performance focussed and the other a DRAM-less mainstream drive. Both used BiCS8 218-layer NAND.
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Winbond Electronics unveiled the W25N01KW, a 1Gb 1.8V QspiNAND flash device. It’s designed to meet the increasing demands of wearable and battery-operated IoT devices with low standby power, small-form-factor package, and continuous read for fast boot and instant-on support, achieving up to 52 MBps in both Continuous Read and Sequential Read modes. It’s available in compact WSON8 (8mm x 6mm) and WSON8 (6mm x 5mm) packages.
ExaGrid has updated its Tiered Backup Storage system with extra support for Veeam workloads.
The company’s appliances ingest backup data to a disk cache landing zone, with post-ingest deduplication to a repository tier providing efficient capacity usage.
ExaGrid’s systems include a non-network-facing tier to create a data security air gap, and data object immutability protection against ransomware and other malicious attacks.
The newly launched version 7.0.0 platform supports Veeam writing to ExaGrid Tiered Backup Storage as an object store target using the S3 protocol, as well as supporting Veeam Backup for Microsoft 365 directly to ExaGrid. Veeam added the ability to write backups directly to object storage appliances in February 2023.
ExaGrid has achieved “Veeam Ready-Object” status with immutability, and “Veeam SOSAPI (Smart Object Storage API)” certification, to verify the new features. Veeam states that, to interact with object storage, SOSAPI uses API requests. It sends API requests to an S3 compatible object storage repository, like Exagrid, and gets the necessary information in a set of XML files. These files contain details on the backup target system, object storage repository capacity and correct storage usage, object storage capabilities and a state of the backup processing.
Bill Andrews.
“We continue to update our Tiered Backup Storage solution, as we know that data is not truly protected by backups if the backup solution itself is vulnerable to threat actors,” said Bill Andrews, president and CEO of ExaGrid. “In addition to the S3 Object Locking for Veeam in Version 7.0.0, ExaGrid also provides Retention Time-Lock with a non-network-facing tier, delayed delete policy, and immutable data objects – it is double security.”
ExaGrid’s Landing Zone is designed to support fast backups and restores, and “instant” VM recoveries, and its Repository Tier offers the “lowest cost” for long-term retention, claims the supplier.
The Marlborough, Massachusetts-headquartered firm says around half of its sales are now generated outside the US. Last month, the privately-owned business said it added 137 new customers in its latest quarter, claiming it added 64 six- and seven-figure deals as part of that, to break its own sales records. It wasn’t obliged to reveal the actual sales figures.
Contextual AI is using WEKA’s Data Platform parallel filesystem software to speed AI training runs as it develops counter-hallucinatory retrieval augmented generation (RAG) 2.0 software.
Contextual AI’s CEO and co-founder Douwe Kiela was part of the team that pioneered RAG at Facebook AI Research (FAIR) in 2020, by augmenting a language model with a retriever to access data from external sources (e.g. Wikipedia, Google, internal company documents).
A typical RAG system uses a frozen off-the-shelf model for embeddings, a vector database for retrieval, and a blackbox language model for generation, stitched together through prompting or an orchestration framework. And it can be unreliable, producing misleading, innacurate and false responses (hallucinations).
Kiela and his team are developing RAG 2.0 to address what Contextual says are the inherent challenges with the original RAG system. RAG 2.0 end-to-end optimizes the language model and retriever as a single system, we’re told. It pretrains, fine-tunes, and aligns with human feedback (RLHF – Reinforcement learning from human feedback) all components as a single integrated system, back-propagating through both the language model and the retriever to maximize performance.
Contextual founders CEO Douwe Kiela (left) and CTO Amanpreet Singh (right)
Contextual claims: “Using RAG 2.0, we’ve created our first set of Contextual Language Models (CLMs), which achieve state-of-the-art performance on a wide variety of industry benchmarks. CLMs outperform strong RAG baselines based on GPT-4 and the best open-source models by a large margin, according to our research and our customers.”
It produced test results by comparing its Contextual Language Models (CLMs) with frozen RAG systems across a variety of axes:
Open domain question answering: Contextualizes the canonical Natural Questions (NQ) and TriviaQA datasets to test each model’s ability to correctly retrieve relevant knowledge and accurately generate an answer. It also evaluates models on the HotpotQA (HPQA) dataset in the single-step retrieval setting. All datasets use the exact match (EM) metric.
Faithfulness: HaluEvalQA and TruthfulQA are used to measure each model’s ability to remain grounded in retrieved evidence and hallucinations.
Freshness: It measures the ability of each RAG system to generalize to changing world knowledge using a web search index and showing accuracy on the recent FreshQA benchmark.
Each of these axes is important for building production-grade RAG systems, Contextual says. CLMs significantly improve performance over a variety of strong frozen RAG systems built using GPT-4 or state-of-the-art open source models like Mixtral.
Contextual builds its large language model (LLM) on the Google Cloud, with a training environment consisting of A3 VMs featuring NVIDIA H100 Tensor Core GPUs, and runs it there. It originally used Google’s Filestore but this wasn’t fast enough and nor did it scale to the extent required, it said.
Its AI training used Python which utilized a large amount of tiny files, causing it to be extremely slow to load files within Google FileStore. Also long model checkpointing times meant the training would stop for up to 5 minutes while the checkpoint was being written. Contextual needed a file store to move data from storage to GPU compute faster, with quicker metadata handling, training run checkpointing, and data preprocessing.
With its consultancy partner Accenture, Contextual looked at alternative filesystems from DDN (Lustre), IBM (Storage Scale) and WEKA (Matrix – now the Data Platform), also checking out local SSDs on the GPU servers, comparing them in a proof-of-concept environment in Google’s Cloud.
We’re told WEKA outperformed Google Filestore, with 212 percent higher aggregate read bandwidth, 497 percent higher aggregate write bandwidth, 212 percent higher aggregate read IOPs, 282 percent higher aggregate write IOPs, 70 percent lower average latency and reducing model checkpoint times by 4x times. None of the other contenders came close to this.
Contextual uses WEKA to manage its 100TB AI training data sets. The WEKA software runs on a 10-node cluster of GCE C2-std-16 VMs, providing a high-performance data layer built on NVMe devices attached to each VM, for a total of 50 TB of flash capacity. The single WEKA namespace extends to an additional 50 TB of Google Object Storage, providing a data lakelet to retain training data sets and the final production models.
Overall Contextual cloud storage costs have dropped by 38 percent per TB, it says, adding that its developers are more productive. Contextual, which was founded in 2023, raised $80 million earlier this month in a Series A round.
There’s more detail about RAG 2.0 and CLMs in a Contextual AI blog which we’ve referenced for this article.
