VAST Forward 2026 | John Mao, VAST & Danny McGinniss, Cisco

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More from VAST Forward 2026

John Mao

Global Head of Business Development

VAST Data

Danny McGinniss

Vice President, Product Management, Compute BU

Cisco

Inside Cisco and Vast Data’s play to simplify — and secure — production-ready AI infrastructure

As enterprises race to deploy artificial intelligence, the focus is shifting from isolated tools to production-ready AI infrastructure that can deliver results at scale. In the process, companies are rethinking not just their technology architecture, but also the operating models needed to support AI in the real world.For Cisco Systems Inc. and Vast Data Inc., which builds scalable storage for AI and analytics, this new landscape brings an opportunity to collaborate on enterprise deployments

play_circle_outline Cisco and Vast Build AI Factory: Validated End-to-End AI Infrastructure Filling Storage Gap and Reducing Integration Burden

play_circle_outline One CPO, One Network: Cisco’s Unified Strategy to Solve AI Deployment Bottlenecks

play_circle_outline When NVIDIA Reference Architectures Meet Reality: Heavy Networking, GPU Integration, and the 100 kW Rack Challenge

play_circle_outline Societal impacts: productivity shifts, labor changes, centaur phase concerns about human roles

play_circle_outline Centers of excellence and GPU-as-a-service to improve utilization and operational efficiency

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John Mao, VAST & Danny McGinniss, Cisco

John Mao

Global Head of Business Development VAST Data

Danny McGinniss

Vice President, Product Management, Compute BU Cisco

In this exclusive interview, Danny McGinniss, vice president of product management for Cisco’s Compute Business Unit, and John Mao, vice president of business development and alliances at VAST Data, join theCUBE’s Dave Vellante and Rebecca Knight to unpack why “AI Factory” has become more than a catchy label – and why integration is now the product. McGinniss frames the partnership as a practical response to a hard reality: Enterprises want end-to-end, validated infrastructure that gets them to production faster, with security posture and observability design... Read more

explore Keep Exploring

What is the AI Factory concept? add

What changed at Cisco to cause the 180-degree turnaround in its AI and networking strategy? add

How do you view the NVIDIA–Cisco partnership for AI infrastructure, and what role does networking play in simplifying enterprise AI adoption? add

How will moving AI from development into production via "AI factories" change business models, operations, and pricing, what effects will it have on different types of labor, and what challenges does it create for human learning, judgment, and using the intelligence those AI systems produce? add

How can organizations improve efficiency and justify investment in GPU infrastructure for data science and machine learning (for example, by consolidating resources, providing GPU-as-a-service, and creating centers of excellence)? add

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John Mao, VAST & Danny McGinniss, Cisco

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Rebecca Knight

>> Hello, everyone, and welcome back to theCUBE's live coverage of VAST Forward 2026 here in Salt Lake City. I'm your host, Rebecca Knight, alongside Dave Vellante, my co-host and analyst. I would like to welcome two great guests to the show. Danny McGinniss, VP of Product Management Compute Business Unit at Cisco. Welcome, Danny.

Danny McGinniss

>> Hello, thank you. Thank you. Thank you for having me.

Rebecca Knight

>> And I'd like to welcome back John Mao. He is, of course, of Vast. Thank you so much for coming back on the show, John.

John Mao

>> Couldn't get rid of me.

Rebecca Knight

>> So let's start by framing this collaboration. Danny, at a high level, what brought Vast and Cisco together around this AI Factory concept? Was there a gap in the market that you were seeing, or conversations with customers that you were having that made you think, "We could do something here"?

Danny McGinniss

>> Yes, absolutely. So we're obviously Cisco, very strong in a couple layers of what the infrastructure stack would look like, but we don't have storage in that stack. And so, with what Vast is doing and brings to the table, it allows us to kind of create that entire end-to-end infrastructure for customers. So when we say that, you'll hear us talk a lot about AI pods, or secure AI Factory. And so that's our way of... Obviously we build infrastructure, but our way of going to market or bringing to a customer an entire solution is really around trying to help them move faster, and to elevate and have faster time to value, and have more reliability, and not have to go figure out how to make all of these pieces, disparate pieces work together. And so, Vast brings a really important piece to that stack.

Rebecca Knight

>> And let's back up even. Describe the AI Factory, as a concept.

Danny McGinniss

>> Yeah, sure. So the AI Factory is absolutely an evolution. We've always had this concept of Cisco of Cisco Validated design, so that's where we take a lot of our infrastructure with third party partners like Vast, and build out an end-to-end validated stack. But what happened is that kind of morphed, especially with AI coming into the scene as like this very new infrastructure model, that frankly, some of the most sophisticated IT teams in the world are struggling to get that house of cards working. And so the factory was, "How do we provide not just an infrastructure stack to get the workload up and running, but how do we provide a security posture and all the observability tools around it, to really get it ready for production?" And so that's the concept, when we say secure AI Factory, that's really what we mean.

Dave Vellante

>> I'm going to go off script here for a minute, because let's see. Two, two and a half years ago maybe, I did a valuation of Cisco's AI strategy. And I was like, "This is a mess. It's disjointed-"

Danny McGinniss

>> We cleaned it up.

Dave Vellante

>> "... it doesn't make any sense. It's a bunch of stuff."

Rebecca Knight

>> Tell us what you really think, Dave.

Danny McGinniss

>> Yeah, you said all the-

John Mao

>> It was Cisco people....

Danny McGinniss

>> be careful with it.

Dave Vellante

>> "This is Cisco Game of Thrones." Everybody had their own AI strategy. I remember saying to management like, "Guys, you got to get your act together." Since then, networking clearly has become a bottleneck. Cisco was a fundamental, the mainspring of the AI network. The stuff you've done with Nvidia, with your optics division, which obviously has traction. Agentic as a tailwind, because of the inference demands on network. The AI summit that you guys just held, the fact that you're partnering with Vast, which is clearly an AI company. It's been a total 180.

Danny McGinniss

>> It has.

Dave Vellante

>> What changed?

Danny McGinniss

>> Oh man, there's been a lot to change. I mean, the industry changed, the customers changed, the dynamics of what people need and how they're evolving change. But I think the biggest thing, honestly, so for the first time in Cisco's history, we have a single chief product officer, that happened about two years ago. So G2, Patel came in. And I'd say inside Cisco, not that we didn't always do it, but it really forced this, I'll call it loosely coupled, tightly integrated type of mindset across all the business units, to start thinking more unified about that entire approach. And I think it just aligned well with the market needed, and as well as how we look at our ecosystem partners and solutions partnership.

Dave Vellante

>> And what Danny just described, John, is kind of interesting. Because sometimes, that type of structure, that has worked well in the industry. But now the sort of worlds coming together, you guys are about unifying data. Isn't it ironic? I wonder if you could talk about the history of the partnership.

John Mao

>> I mean, the history, there's a fun fact, which is that Cisco was actually the very first partner I brought Renen to go see when I joined the company in January '25.

Danny McGinniss

>> I don't even know that.

John Mao

>> Yeah, I know.

Dave Vellante

>> Because of your prior relationship with Cisco, yeah.

John Mao

>> I had previous relationships, so yeah. But anyways, there was no AI back then, per se. But no, I'm super... Look, I think this is an amazing partnership for the reasons you mentioned. I spent a lot of time with Nvidia on these AI Factory reference architectures, my team built a lot of those in partnership with Nvidia. At the core of most of those reference architectures, it's like 80% networking. I mean, that's like the lifeblood of a lot of these infrastructure projects. Cisco's obviously a major player and leader in that space, so very, very well positioned. I agree with Danny. I think it's about making it more integrated at a product, at an infrastructure level, to simplify the adoption. Most of these enterprises, gosh, you think about all they have to deal with when they think about AI infrastructure and AI solutions. From new data center considerations, liquid cooling, going to like 100 kilowatt racks, to new types of infrastructure to compute, new speeds and networking. All of this is all complex, and many enterprises don't have the skillsets, and just not the experience there, in the industry.

Danny McGinniss

>> And it's changing so fast.

John Mao

>> So fast.

Danny McGinniss

>> If you change one driver, or one version of your containers layer, everything has to kind of get reset and reevaluated. So a lot of customers frankly just don't have the time or the resources to do that, because at the same time, they're trying to focus on how to transform a business. They're thinking about, "How do we actually use AI to give us a competitive advantage?" And there's a whole bunch of thought and resources being spent there. And so, having to split your time and think about, "How do I get the infrastructure working?" It's kind of robbing you from being able to spend time on, "What do we do with this new technology we have available to us?"

Dave Vellante

>> So, I'd like to follow up on that. So you think about the history of computing. We built data centers managed by the companies, and they would bring in software on top of that, and applications. They would automate their whatever, their accounting, their back office workflows. Cloud comes in, get rid of my mess. Okay, great. SaaS changes the operating model, and the business model, great. Simplifies things. Now this thing, AI Factory. You get kind of energy, compute, obviously data, network is a part of that. And then intelligence comes out.

Danny McGinniss

>> Yep.

Dave Vellante

>> And it's like, "Okay, how do I tap that, through APIs or whatever?" It's a totally different operating model, business model, technical model. Pricing is going to change. So the AI Factory, it's kind of a marketing term, but it's different in that you're going to see these AI factories built with, I guess you guys don't call them neoclouds, you call them AI clouds.

John Mao

>> Yeah, neo clouds.

Dave Vellante

>> Clouds?

John Mao

>> Neo clouds, GPU clouds, whatever.

Dave Vellante

>> Mainstream enterprises aren't necessarily going to go put in liquid cooling and build all this stuff, but they're going to be able to tap that capability that is the compute, the storage, the network, and the data. How do you see that evolving when you talk to your customers, particularly the mainstream, not the JP Morgans, they could build their own data centers and put in the liquid cooling, and buy all the GPUs. But the tunas that are out there, the minnows, the startups. How do they get access to all this great AI?

Danny McGinniss

>> Well, I mean, you just said something that was really important, baked into this. If you think about the changes that we've seen in terms of IT, like in our lifetime here. Everything you just mentioned, whether it be virtualization, whether it be cloud, whether it be containers, it was all really to drive efficiency in IT. Everybody was either trying to save money, or have different consumption models. But if you build a... I mean, you think about even if you step aside from IT for a second, why does somebody build a factory? It's to produce something. The more efficient your factory is, the more bigger competitive advantage you have over... But there's also a decision that you make in what do you invest in your factory? How much automation do you want? How many robots do you want? How many tools do you want? And so I think that's-

Dave Vellante

>> How many tokens?

Danny McGinniss

>> How many tokens, exactly. And so that's... So what you're seeing now are, you have from the most senior leaders within any company saying, "How do I ensure that I do not have a competitive disadvantage against other people in my field or my industry?" And that means AI is most likely going to unlock that competitive advantage. So now you're re-architecting everything. You're re-architecting your data center, you're re-architecting how you stand up your infrastructure, you're rewriting your entire operational model around the technology. So before it was, "Let's use technology to be more efficient," now we're rewriting the way the company works around the technology available to us.

Rebecca Knight

>> But to Dave's point, that really requires an entirely new strategy.

Danny McGinniss

>> It does.

Rebecca Knight

>> And so the conversations that you're having with those senior leaders who say, "Okay, I need to really step up here, if I'm going to compete in this AI era." So what are you talking, what are the kinds of conversations you're having here at VAST Forward, and what are you hearing from customers about this?

John Mao

>> I mean, I think a lot of it is, most of the enterprises are on different stages of this journey. So it really depends on where they are in that journey. I think a lot of the customers that I've spoken to, some of them are the more advanced ones, they're thinking about how to move into production. Production means, "How do I make sure this thing is secure? How do I make sure this thing is actually scalable? How do I make sure I have optionality, and be able to leverage some of the neocloud capacity if they need to, over time?" There's a lot of considerations here for some of the mature ones. There's another end of the spectrum, which I think one of the advantages Vast has is, even if you're not ready for the AI journey at the extreme end, there's still a lot of work to be done in making sure you get your data house in order as well, right? So a lot of my conversations are also with companies just around data hygiene. How do you modernize your data to make that data AI ready? That also requires AI to get there, right? So that's an easy first step, if you're not ready to go all the way to the extreme. I think it's an inevitable conclusion, though. Most of the senior leadership that we speak with, like Danny said, it's not a question of if they will do this. It's a question of when and how.

Danny McGinniss

>> At what capacity?

John Mao

>> Yeah.

Danny McGinniss

>> And I think just to extend from John's point, you're asking, what are you conversation about? I think for us, a lot of it is consultative, in a way that you're trying to almost set a North Star for, "Look, you might be in a proof of concept mode today, or in a development mode today, trying to get this up and running. But there are things you'll have to think about like security end to end, like how are you going to operate this? What type of SLAs are you going to want to put on this, just like you do for any other application in your business? What type of safety requirements are we going to have?" We've got a bunch of little agents walking around representing people that have access permissions, that people have not figured all that out yet. And so there's a lot of planning that has to go from getting a dev environment moved into a production environment.

Dave Vellante

>> And they're validated designs, the integration work that you guys are doing all enables these AI factories to get built. But kind of back to what we were talking about before, and Danny, you brought this up. Executives, they're sleeping with one eye open because they're worried they're going to get disrupted. Scaling without labor is going to happen-

Danny McGinniss

>> Yes....

Dave Vellante

>> and it's happening. When we went from on-prem to SaaS, everything changed. The pricing model, the technology model, the operational model. But it really affected IT. To your points, now it's affecting the entire business. So it's interesting to hear and watch the SaaS attack in the market, and in HALO, heavy asset light obsolescence is the big thing now. But software, like economics, marginal economics, are going to recruit to all industries now, because of AI. And you guys are enabling that, but everything is going to change again.

Danny McGinniss

>> Yes.

Dave Vellante

>> And it's not just the IT department, it's the entire organization. It certainly has implications on white collar labor. Blue collar labor is exploding. Specialized engineers, and HVAC engineers.

Rebecca Knight

>> Plumbers, yeah.

John Mao

>> Plumbers, electricians, yeah.

Dave Vellante

>> Artisan plumbers, and so forth. And I'm an optimist, I think new jobs will be created. I think, you're not necessarily a pessimist-

Rebecca Knight

>> I believe that too-...

Dave Vellante

>> but there's a gap in between....

Rebecca Knight

>> but I believe that there is a lot of concerns.

Danny McGinniss

>> Some people do get left out, in the journey.

Rebecca Knight

>> Yeah. And I particularly worry about our children. Who I think, "Okay, if the AI is doing all the learning and the training and the synthesizing, how are you the human learning?" I think that's really important. We're all a little older, and we know how to learn, and we've done that on the job. But if the AI is doing that...

Dave Vellante

>> But to the point about AI Factories, what are you manufacturing?

Rebecca Knight

>> Yes, sorry. Yes.

Dave Vellante

>> You're manufacturing intelligence. And it's like, "Here you go." What do you do with that intelligence?

Rebecca Knight

>> Especially if you have not learned what you need to know.

Dave Vellante

>> Right, that's the challenge for people, but-

Rebecca Knight

>> To have the judgment, to make the... Anyway....

Dave Vellante

>> but having all that intelligence at your disposal, at continuously lower and lower costs. Because you and you are deflationary, that's what you guys do, that's what technology companies do. You bomb the economics.

John Mao

>> You read, you know?

Danny McGinniss

>> We like to think about

Dave Vellante

>> A wonderful part of our industry-

Danny McGinniss

>> We're democratizing intelligence. ...

Dave Vellante

>> our industry gets so much crap. What industry? Everybody's talking about affordability. Well, I mean, notwithstanding the NAND prices right now and memory prices, but generally speaking, IT has done a pretty good job of lowering cost and improving price performance.

Danny McGinniss

>> And it will continue to. Or, you can argue that it increases productivity as well, so one of the two. So does somebody really need to lower costs, or do they need to increase productivity? I think that's going to be the decision every single industry is going to go through in the next few years.

Rebecca Knight

>> And a lot of senior leaders would say, "Do both."

Dave Vellante

>> Well, And I think that the lower cost per token is going to enable us to absorb, to create more intelligence, to make it more ubiquitous, and it comes back to the data.

John Mao

>> I mean, I think a lot of this is about, at the end of the day, democratizing the intelligence that you can create out of AI Factory, because this is where the world is headed. And if you're not looking at this seriously, as an organization today? I don't know of anybody, I mean, I don't know what the statistics are these days, but I'm sure that survey has...

Danny McGinniss

>> A year ago, I think people were... But the thing, even if you look in just what changed in the last six months or 90 days, with how the models have... The mainstream models have just gotten smarter. I mean, if anyone has used Codex lately, if you tried to do AI coding a year ago versus what you can do today, I'm basically addicted to Codex right now. And it's pretty, pretty great.

Dave Vellante

>> So you code?

Danny McGinniss

>> Well. No, no, no. I used to code, 15 years ago. Now you don't have to code anymore, if you don't code.

Dave Vellante

>> You just talk to it, right.

Danny McGinniss

>> You just have to know what you want.

Dave Vellante

>> I used to code too, a long, long time ago. I wasn't a good coder, so I got into this. But with that whole OpenClaw trend, I started getting into it again.

Danny McGinniss

>> It's fun.

Dave Vellante

>> And I'm like, "Wow, this is amazing."

Danny McGinniss

>> The level of sophistication is incredible.

Dave Vellante

>> And you have this infinitely patient AI to teach you how to do stuff.

Danny McGinniss

>> Things you would've troubleshoot for a day or two days, you can now have an answer in minutes. And so, the frustration level doesn't really grow enough.

Dave Vellante

>> So if you're a consternation, I would say you saw the gig economy, everybody now has a side gig. Except us. But I think there's going to be... With all that intelligence, your ability to launch a company, or launch a new idea, talk to a system and create and build, is going to be so much easier.

Rebecca Knight

>> Well, I think that it's-

Dave Vellante

>> And that's what's going to close the gap.

Rebecca Knight

>> The head of Anthropic recently described this period that we're in now as the centaur phase, talking about the Greek mythological creature that was half man, half horse, and the AI being the horse here. But he said that this is going to be a brief hybrid period, because the human is just not going to be needed as the machines get better and better. So that I think is the concern is... Okay. I'm an AI-

Danny McGinniss

>> We just deviated from infrastructure in this call really quick, but.

Dave Vellante

>> I like it. I like it.

Danny McGinniss

>> So do I.

Rebecca Knight

>> Okay, but let's-

Danny McGinniss

>> But very philosophically, we can get into it....

Rebecca Knight

>> let's vast-forward here, to 2036, and talk about what this partnership will look like then. And what your greatest hopes for, if they are realized, in terms of-

Dave Vellante

>> Well, Renen was talking 2036 this morning.

Rebecca Knight

>> I know, yeah. So you, yeah.

Dave Vellante

>> And we were, "Wow."

Rebecca Knight

>> Yeah.

Danny McGinniss

>> I don't even know if I'd want to put a 10-year timeline.

Rebecca Knight

>> Okay, so let's go with years.

Danny McGinniss

>> But this is what I would say. I think when we talk about strategy together, and vision, and what we're ultimately trying to accomplish. I think the sum of the parts is greater than that of the whole. And so yes, Vast can provide technology, Cisco can provide technology, Nvidia can provide technology. I think for us it's about how do you get people using that technology in a way that is reliable, in a way that's scalable, in a way that's secure, in the fastest time possible. And so, a lot of the effort that we do, the joint effort that we do, is really about trying to create that blueprint for a customer so they know how to do what they're trying to do, and have some confidence in entering it. Like you said, the most sophisticated team, they're going to be just fine, but there's a lot of other people that are trying to enter into this space, and they could really use some help and some guidance. And I think that's really where a lot of the investment goes, in the work we do together from a partnership.

John Mao

>> I thought you were just going to say world domination, at the end of the day, so. You're done.

Rebecca Knight

>> That's

John Mao

>> No, I think, I'm very bullish on the partnership. I think there's a lot of opportunity, obviously, in front of us right now. I do think for both companies, it does mean that we have to move up the stack a little bit. Because a lot of the value, at the end of the day, infrastructure is a major piece of it. But the applications, the software, understanding how those pipelines work to connect that into the value of the business, is where they get that ROI, right? I do also, just one last thing. I think, what is encouraging that I've seen, that I did not see a year ago, is you are now starting to see enterprises write very large checks,. Like, relatively large checks, nine figure checks, on infrastructure as a way to make an investment in moving in the right direction. If you go back like 12, 18 months ago, you didn't see that kind of spend. So I think something has-

Danny McGinniss

>> Unless it was just a proof of concept, or let's just go-

John Mao

>> But it's not a nine figure check, right?

Danny McGinniss

>> Correct.

John Mao

>> So yes, people were always buying smaller amounts, but it wasn't at that scale. So I think something is starting to tip a little bit in the market. Obviously not everyone's there yet, but I do see this acceleration that's going to start coming around the corner.

Dave Vellante

>> Well, and I'm in the middle of a research project right now, and I can name names. So there was basically a premise that you got to get your data house in order before you spend any money on AI infrastructure. I've talked to JP Morgan, Walmart, Capital One, Lilly, a couple others. And what they said is, "Yeah, we thought that too. Wrong strategy. Best strategy is get your data infrastructure in order, and then go hard after AI projects, and let the AI cleanse and harmonize that data. Get your first project done, it might take 12, 15 months to really perfect it. The second one's going to go faster, second one's going to go faster." What they found is they get to project 18, 19, 20, much, much faster. It's throwing off, it's self-funding, it's throwing off ROI and cash flow. And it sort of flies in the face of convention that you got to get your house in order before. So now, what you guys are delivering with your validated designs and your partnerships, is making it easier for mainstream organizations to tap that intelligence.

Danny McGinniss

>> Yeah. I mean, you're trying to get it to a point where you can fail fast on the AI application, or the AI use case. I mean, for every dozen use cases that you come up with, one or two of those might prove to have good ROI associated with it.

Dave Vellante

>> And it's so not risky to test it, right?

Danny McGinniss

>> But what I think was really important in what you just said there, it's beyond just the data hygiene. There's also centers of excellence around everything. "How do I network these things?"

Dave Vellante

>> Yes.

Danny McGinniss

>> "I need a containers level to work. I'm going to have to figure out what enterprise software I'm going to use. There's security, there's tooling, there's completely new components that I've never worked with before." So you have to build all those skills, and that just does not happen overnight.

Dave Vellante

>> And the learnings from the research, we're getting on that curve, that experience curve allowed you to go much, much faster. And then it accrues benefits. So this was like, we studied a couple of things. We modeled out a $10 billion division of a $40 billion company, but then all those advantages confer, because they had the muscle memory.

Danny McGinniss

>> Correct.

Dave Vellante

>> And so, the message is, get on that curve. Or to your point-

Danny McGinniss

>> And I'll give you one more example, because in that same exact, any of those companies that you just mentioned. I'll bet you when they first got started, you had multiple different groups, lines of business all sort of working on their own-

Dave Vellante

>> Experimenting-...

Danny McGinniss

>> projects....

Dave Vellante

>> right.

Danny McGinniss

>> But where the true, I think, efficiency comes is when you start to consolidate, the IT team is now providing GPU as a service back to the lines of business, or to the data scientists. They've created the centers of excellence and economies of scale associated with that. And now you're truly working on multi-tenanted environments, that GPU utilization is... I mean, you've got some pretty expensive infrastructure sitting there. So if you're not utilizing that infrastructure 100%, it's really hard to justify the renewal of that, or the refresh of that -

John Mao

>> Or the expansion of it.

Dave Vellante

>> And that's the other thing we learned, is that when you looked at the utilization of those on-prem instances, and you may have had visibility on this. They were way underutilized, and now it's popped up.

Danny McGinniss

>> Because there's so many things that have to work. Your network has to scale, your data has to be ready. The IO.

Dave Vellante

>> The IO.

Danny McGinniss

>> Capacity has to be there.

Dave Vellante

>> They didn't know what to do with that GB200 rack. It was new, and it was melting GPUs, and...

Danny McGinniss

>> Yeah. And I think the biggest recommend, I mean, we should probably try to make this actionable. But I mean, the biggest recommendation is, to whoever's getting in there. So you really have to think about all of that, and how the weakest link in the chain can be what makes or breaks everything we're talking about here.

Dave Vellante

>> It's a system mindset. And to your earlier point, you can't do it all yourselves, so you got to have partnerships. And you got to have... And not Barney partnerships, press releases and so forth. It's got to be-

Danny McGinniss

>> Yeah, you have to trust your vendors. I mean, there's a reason we have dozens and dozens of engineers sitting in labs working together to get this stuff tuned and optimized, so that all of our customers and our partners benefit from it.

John Mao

>> That's exactly right.

Rebecca Knight

>> Excellent.

Dave Vellante

>> Thank you, guys. Appreciate it.

Rebecca Knight

>> Well, great note to end on. Danny, John, thank you so much for coming on theCUBE.

John Mao

>> Thanks for having us.

Danny McGinniss

>> Thank you for having us.

John Mao

>> Great.

Rebecca Knight

>> I'm Rebecca Knight, for Dave Vellante, stay tuned for more of theCUBE's live coverage of VAST Forward, presented by Solidigm. You're watching theCUBE, the leader in enterprise tech news and analysis.

Dave Vellante

>> When we first spoke to Vestata in 2019, we flat-