>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Hey, welcome back to the Fira in Barcelona. My name is Dave Vellante. I'm here with David Nicholson, day four of MWC '23. Show's winding down a little bit, but it's still pretty packed here. Lot of innovation, planes, trains, automobiles, and we're talking 5G all week, private networks, connected breweries. It's super exciting. Really happy to have Warren Jackson here as the Edge Gateway Product Technologist at Dell Technologies, and Scott Waller, the CTO of the 5G Open Innovation Lab. Folks, welcome to theCUBE. >> Good to be here. >> Really interesting stories that we're going to talk about. Let's start, Scott, with you, what is the Open Innovation Lab? >> So it was hatched three years ago. Ideated about a bunch of guys from Microsoft who ran startup ventures program, started the developers program over at Microsoft, if you're familiar with MSDN. And they came three years ago and said, how does CSPs working with someone like T-Mobile who's in our backyard, I'm from Seattle. How do they monetize the edge? You need a developer ecosystem of applications and use cases. That's always been the thing. The carriers are building the networks, but where's the ecosystem of startups? So we built a startup ecosystem that is sponsored by partners, Dell being one sponsor, Intel, Microsoft, VMware, Aspirant, you name it. The enterprise folks who are also in the connectivity business. And with that, we're not like a Y Combinator or a Techstars where it's investment first and it's all about funding. It's all about getting introductions from a startup who might have a VR or AI type of application or observability for 5G slicing, and bring that in front of the Microsoft's of the world, or the Intel's and the Dell's of the world that they might not have the capabilities to do it because they're still a small little startup with an MVP. So we really incubate. We're the connectors and build a network. We've had 101 startups over the last three years. They've raised over a billion dollars. And it's really valuable to our partners like T-Mobile and Dell, et cetera, where we're bringing in folks like Expedo and GenXComm and Firecell. Start up private companies that are around here they were cohorts from our program in the past. >> That's awesome because I've often, I mean, I've seen Dell get into this business and I'm like, wow, they've done a really good job of finding these guys. I wonder what the pipeline is. >> We're trying to create the pipeline for the entire industry, whether it's 5G on the edge for the CSPs, or it's for private enterprise networks. >> Warren, what's this cool little thing you got here? >> Yeah, so this is very unique in the Dell portfolio. So when people think of Dell, they think of servers laptops, et cetera. But what this does is it's designed to be deployed at the edge in harsh environments and it allows customers to do analytics, data collection at the edge. And what's unique about it is it's got an extended temperature range. There's no fan in this and there's lots of ports on it for data ingestion. So this is a smaller box Edge Gateway 3200. This is the product that we're using in the brewery. And then we have a bigger brother of this, the Edge Gateway 5200. So the value of it, you can scale depending on what your edge compute requirements are at the edge. >> So tell us about the brewery story. And you covered it, I know you were in the Dell booth, but it's basically an analog brewery. They're taking measurements and temperatures and then writing it down and then entering it in and somebody from your company saw it and said, "We can help you with this problem." Explain the story. >> Yeah, so Scott and I did a walkthrough of the brewery back in November timeframe. >> It's in Framingham, Mass. >> Framingham, Mass, correct. And basically, we talked to him, and we said, what keeps you guys up at night? What's a problem that we can solve? Very simple, a kind of a lower budget, didn't have a lot money to spend on it, but what problem can we solve that will realize great benefit for you? So we looked at their fermentation process, which was completely analog. Somebody was walking around with a clipboard looking at analog gauges. And what we did is we digitized that process. So what this did for them rather than being completely reactive, and by the time they realized there was something going wrong with the fermentation process, it's too late. A batch of scrap. This allowed them to be proactive. So anytime, anywhere on the tablet or a phone, they can see if that fermentation process is going out of range and do something about it before the batch gets scrapped. >> Okay. Amazing. And Scott, you got a picture of this workflow here? >> Yeah, actually this is the final product. >> Explain that. >> As Warren mentioned, the data is actually residing in the industrial side of the network So we wanted to keep the IT/OT separation, which is critical on the factory floor. And so all the data is brought in from the sensors via digital connection once it's converted and into the edge gateway. Then there's a snapshot of it using Telit deviceWISE, their dashboarding application, that is decoding all the digital readings, putting them in a nice dashboard. And then when we gave them, we realized another problem was they're using cheap little Chromebooks that they spill beer on once a week and throw them out. That's why they bought the cheap ones 'cause they go through them so fast. So we got a Dell Latitude Rugged notebook. This is a brand new tablet, but they have the dashboarding software. So no matter if they're out there on the floor, but because the data resides there on the factory they have access to be able to change the parameters. This one's in the maturation cycle. This one's in the crashing cycle where they're bringing the temperature back down, stopping the fermentation process, getting it ready to go to the canning side of the house. >> And they're doing all that from this dashboard. >> They're doing all from the dashboard. They also have a giant screen that we put up there that in the floor instead of walking a hundred yards back behind a whole bunch of machinery equipment from a safety perspective, now they just look up on the screen and go, "Oh, that's red. That's out of range." They're actually doing a bunch of cleaning and a bunch of other things right now, too. So this is real time from Boston. >> Dave: Oh okay. >> Scott: This is actually real time from Boston. >> I'm no hop master, but I'm looking at these things flashing at me and I'm thinking something's wrong with my beer. >> We literally just lit this up last week. So we're still tweaking a few things, but they're also learning around. This is a new capability they never had. Oh, we have the ability to alert and monitor at different processes with different batches, different brews, different yeast types. Then now they're also training and learning. And we're going to turn that into eventually a product that other breweries might be able to use. >> So back to the kind of nuts and bolts of the system. The device that you have here has essentially wifi antennas on the back. >> Warren: Correct. >> Pull that up again if you would, please. >> Now I've seen this, just so people are clear, there are also paddle 5G antennas that go on the other side. >> Correct. >> That's sort of the connection from the 5G network that then gets transmogrified, technical term guys, into wifi so the devices that are physically connected to the brew vats, don't know what they're called. >> Fermentation tanks. >> Fermentation tanks, thank you. Those are wifi. That's a wifi signal that's going into this. Is that correct? >> Scott: No. >> No, it's not. >> It's a hard wire. >> Okay, okay. >> But, you're right. This particular gateway. >> It could be wifi if it's hard wire. >> It could be, yes. Could be any technology really. >> This particular gateway is not outfitted with 5G, but something that was very important in this application was to isolate the IT network, which is on wifi and physically connected from the OT network, which is the 5G connection. So we're sending the data directly from the gateway up to the cloud. The two partners that we worked with on this project were ifm, big sensor manufacturer that actually did the wired sensors into an industrial network called IO-Link. So they're physically wired into the gateway and then in the gateway we have a solution from our partner Telit that has deviceWISE software that actually takes the data in, runs the analytics on it, the logic, and then visualizes that data locally on those panels and also up to their cloud, which is what we're looking at. So they can look at it locally, they're in the plant and then up in the cloud on a phone or a tablet, whatever, when they're at home. >> We're talking about a small business here. I don't know how many employees they have, but it's not thousands. And I love that you're talking about an IT network and an OT network. And so they wanted, it is very common when we talk about industrial internet of things use cases, but we're talking about a tiny business here. >> Warren: Correct. >> They wanted to separate those networks because of cost, because of contention. Explain why. >> Yeah, just because, I mean, they're running their ERP system, their payroll, all of their kind of the way they run their business on their IT network and you don't want to have the same traffic out on the factory floor on that network, so it was pretty important. And the other thing is we really, one of the things that we didn't want to do in this project is interrupt their production process at all. So we installed this entire system in two days. They didn't have to shut down, they didn't have to stop. We didn't have to interrupt their process at all. It was like we were invisible there and we spun the thing up and within two days, very simple, easy, but tremendous value for their business. >> Talk about new markets here. I mean, it's like any company that's analog that needs to go digital. It's like 99% of the companies on the planet. What are you guys seeing out there in terms of the types of examples beyond breweries? >> Yeah, I could talk to that. So I spent a lot of time over the last couple years running my own little IoT company and a lot of it being in agriculture. So like in Washington state, 70% of the world's hops is actually grown in Washington state. It's my hometown. But in the Ag producing regions, there's lack of connectivity. So there's interest in private networks because the carriers aren't necessarily deploying it. But because we have the vast amount of hops there's a lot of IPAs, a lot of hoppy IPAs that come out of Seattle. And with that, there's a ton of craft breweries that are about the same size, some are a little larger. Anheuser-Busch and InBev and Heineken they've got great IoT platforms. They've done it. They're mass scale, they have to digitize. But the smaller shops, they don't, when we talk about IT/OT separation, they're not aware of that. They think it's just, I get local broadband and I get wifi and one hotspot inside my facility and it works. So a little bit of it was the education. I have got years in IT/OT security in my background so that education and we come forward with a solution that actually does that for them. And now they're aware of it. So now when they're asking questions of other vendors that are trying to sell them some type of solution, they're inherently aware of what should be done so they're not vulnerable to ransomware attacks, et cetera. So it's known as the Purdue Model. >> Well, what should they do? >> We came in and keep it completely separated and educated them because in the end too we'll build a design guide and a starter kit out of this that other brewers can use. Because I've toured dozens of breweries in Washington, the exact same scenario, analog gauges, analog process, very manual. And in the end, when you ask the brewer, what do they want out of this? It keeps them up at night because if the temperature goes out of range, because the chiller fails, >> They ruined. >> That's $30,000 lost in beer. That's a lot to a small business. However, it's also once they start digitizing the data and to Warren's point, it's read-only. We're not changing any of the process. We augmented on top of their existing systems. We didn't change their process. But now they have the ability to look at the data and see batch to batch consistency. Quality doesn't always mean best, it means consistency from batch to batch. Every beer from exhibit A from yesterday to two months from now of the same style of beer should be the same taste, flavor, boldness, et cetera. This is giving them the insights on it. >> It's like St. Louis Buds, when we were kids. We would buy the St. Louis Buds 'cause they tasted better than the Merrimack Buds. And then Budweiser made them all the same. >> Must be an East coast thing. >> It's an old guy thing, Dave. You weren't born yet. >> I was in high school. Yeah, I was in high school. >> We like the hops. >> We weren't 21. Do me a favor, clarify OT versus IT. It's something we talk about all the time, but not everyone's familiar with that separation. Define OT for me. >> It's really the factory floor. You got IT systems that are ERP systems, billing, you're getting your emails, stuff like that. Where the ransomware usually gets infected in. The OT side is the industrial control network. >> David: What's the 'O' stand for? >> Operation. >> David: Operation? >> Yeah, the operations side. >> 'Cause some people will think objects 'cause we think internet of things. >> The industrial operations, think of it that way. >> But in a sense those are things that are connected. >> And you think of that as they are the safety systems as well. So a machine, if someone doesn't push the stop button, you'd think if there's a lot of traffic on that network, it isn't guaranteed that that stop button actually stops that blade from coming down, someone's going to lose their arm. So it's very tied to safety, reliability, low latency. It is crafted in design that it never touches the internet inherently without having to go through a security gateway which is what we did. >> You mentioned the large companies like InBev, et cetera. You're saying they're already there. Are they not part of your target market? Or are there ways that you can help them? Is this really more of a small to mid-size company? >> For this particular solution, I think so, yeah. Because the cost to entry is low. I mean, you talk about InBev, they have millions of dollars of budgets to spend on OT. So they're completely automated from top to bottom. But these little craft brewers, which they're everywhere in the US. Vermont, Washington state, they're completely manual. A lot of these guys just started in their garage. And they just scaled up and they got a cult kind of following around their beers. One thing that we found here this week, when you talk around edge and 5G and beer, those things get people excited. In our booth we're serving beer, and all these kind of topics, it brings people together. >> And it lets the little guy compete more effectively with the big giants. >> Correct. >> And how do you do more with less as the little guy is kind of the big thing and to Warren's point, we have folks come up and say, "Great, this is for beer, but what about wine? What about the fermentation process of wine?" Same materials in the end. A vessel of some sort, maybe it's stainless steel. The clamps are the same, the sensors are the same. The parameters like temperature are key in any type of fermentation. We had someone talking about olive oil and using that. It's the same sanitary beverage style equipment. We grabbed sensors that were off the shelf and then we integrated them in and used the set of platforms that we could. How do we rapidly enable these guys at the lowest possible cost with stuff that's at the shelf. And there's four different companies in the solution. >> We were having a conversation with T-Mobile a little earlier and she mentioned the idea of this sounding scary. And this is a great example of showing that in fact, at a relatively small scale, this technology makes a lot of sense. So from that perspective, of course you can implement private 5G networks at an industrial scale with tens of millions of dollars of investment. But what about all of the other things below? And that seems to be a perfect example. >> Yeah, correct. And it's one of the things with the gateway and having flexibility the way Dell did a great job of putting really good modems in it. It had a wide spectrum range of what bands they support. So being able to say, at a larger facility, I mean, if Heineken wants to deploy something like this, oh, heck yeah, they probably could do it. And they might have a private 5G network, but let's say T-Mobile offers a private offering on their public via a slice. It's easy to connect that radio to it. You just change the sims. >> Is that how the CSPs fit here? How are they monetized? >> Yeah, correct. So one of our partners is T-Mobile and so we're working with them. We've got other telco partners that are coming on board in our lab. And so we'll do the same thing. We're going to take this back and put it in the lab and offer it up as others because the baseline building blocks or Lego blocks per se can be used in a bunch of different industries. It's really that starter point of giving folks the idea of what's possible. >> So small manufacturing, agriculture you mentioned, any other sort of use cases we should tune into? >> I think it's environmental monitoring, all of that stuff, I see it in IoT deployments all over the world. Just the simple starter kits 'cause a farmer doesn't want to get sold a solution, a platform, where he's got to hire a bunch of coders and partner with the big carriers. He just wants something that works. >> Another use case that we see a lot, a high cost in a lot of these places is the cost of energy. And a lot of companies don't know what they're spending on electricity. So a very simple energy monitoring system like that, it's a really good ROI. I'm going to spend five or $10,000 on a system like this, but I'm going to save $20,000 over a year 'cause I'm able to see, have visibility into that data. That's a lot of what this story's about, just giving visibility into the process. >> It's very cool, and like you said, it gets people excited. Is it a big market? How do you size it? Is it a big TAM? >> Yeah, so one thing that Dell brings to the table in this space is people are buying their laptops, their servers and whatnot from Dell and companies are comfortable in doing business with Dell because of our model direct to customer and whatnot. So our ability to bring a device like this to the OT space and have them have that same user experience they have with laptops and our client products in a ruggedized solution like this and bring a lot of partners to the table makes it easy for our customers to implement this across all kinds of industries. >> So we're talking to billions, tens of billions. Do we know how big this market is? What's the TAM? I mean, come on, you work for Dell. You have to do a TAM analysis. >> Yes, no, yeah. I mean, it really is in the billions. The market is huge for this one. I think we just tapped into it. We're kind of focused in on the brewery piece of it and the liquor piece of it, but the possibilities are endless. >> Yeah, that's tip of the spear. Guys, great story. >> It's scalable. I think the biggest thing, just my final feedback is working and partnering with Dell is we got something as small as this edge gateway that I can run a Packet Core on and run a 5G standalone node and then have one of the small little 5G radios out there. And I've got these deployed in a farm. Give the farmer an idea of what's possible, give him a unit on his tractor, and now he can do something that, we're providing connectivity he had never had before. But as we scale up, we've got the big brother to this. When we scale up from that, we got the telco size units that we can put. So it's very scalable. It's just a great suite of offerings. >> Yeah, outstanding. Guys, thanks for sharing the story. Great to have you on theCUBE. >> Good to be with you today. >> Stop by for beer later. >> You know it. All right, Dave Vellante for Dave Nicholson and the entire CUBE team, we're here live at the Fira in Barcelona MWC '23 day four. Keep it right there. (upbeat music)

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to Barcelona, Spain, everyone. It's theCUBE live at MWC '23, day three of four days of CUBE coverage. It's like a cannon of CUBE content coming right at you. I'm Lisa Martin with Dave Nicholson. We've got Dell and VMware here. Going to be talking about the ecosystem partnerships and what they're doing to further organizations in the telco industry. Please welcome Jared Woodrey, Director of Partner Engineering Open Telecom Ecosystem Lab, OTEL. Odded Solomon is here as well, Director of Product Management, VMware Service Provider and Edge Business Unit at VMware. Guys, great to have you on the program. >> Thank you for having me. >> Welcome to theCUBE. So Jared, first question for you. Talk about OTEL. I know there's a big announcement this week, but give the audience context and understanding of what OTEL is and how it works. >> Sure. So the Open Telecom Ecosystem Lab is physically located at Round Rock, Texas, it's the heart and soul of it. But this week we also just announced opening up the Cork, Ireland extension of OTEL. The reason for our existence is to to try and make it as easy as possible for both partners and customers to come together and to re-aggregate this disaggregated ecosystem. So that comes with a number of automation tools and basically just giving a known good testing environment so that tests that happen in our lab are as close to real world as they possibly can be and make it as transparent and open as possible for both partners like VMware as well as customers. >> Odded, talk about what you're doing with Dell and OTEL and give us a customer example of maybe one that you're working with or even even mentioning it by a high level descriptor if you have to. >> Yeah. So we provide a telco cloud platform, which is essentially a vertical in VMware. The telco cloud platform is serving network function vendors, such as Ericsson, Nokia, Mavenir, and so on. What we do with Dell as part of this partnership is essentially complementing the platform with some additional functionality that is not coming out of the box. We used to have a data protection in the past, but this is no longer our main business focus. So we do provide APIs that we can expose and work together with Dell PPDM solution so customer can benefit from this and leverage the partnership and have overall solution that is not coming out of the box from VMware. >> I'm curious, from a VMware perspective. VMware is associated often with the V in VMware, virtualization, and we've seen a transition over time between sort of flavors of virtualization and what is the mix currently today in the telecom space between environments that are leveraging what we would think of as more traditional virtualization with full blown Linux, Windows operating systems in a VM versus the world of containerized microservices? What does that mix look like today? Where do you see it going? >> Yeah, so the VMware telco cloud platform exists for about eight years. And the V started around that time. You might heard about open stack in addition to VMware. So this has definitely helped the network equipment providers with virtualizing their network functions. Those are typically VNF, virtualized network functions, inside the VMs. Essentially we have 4G applications, so core applications, EPC, we have IMS. Those are typically, I would say maybe 80 or 90% of the ecosystem right now. 5G is associated with cloud native network functions. So 5G is getting started now, getting deployed. There is an exponential growth on the core side. Now, when we expand towards the edge of the network we see more potential growth. This is 5G ran, we see the vRAN, we see the open RAN, we see early POCs, we see field trials that are starting. We obviously has production customer now. You just spoke to one. So this is really starting, cloud native is really starting I would say about 10 to 20% of the network functions these days are cloud native. >> Jared, question for you. You mentioned data protection, a huge topic there obviously from a security perspective. Data protection used to be the responsibility of the CSPs. You guys are changing that. Can you talk a little bit about how you're doing that and what Dell's play there is? >> Yeah, so PowerProtect Data Management is a product, but it's produced by Dell. So what this does is it enables data protection over virtual cloud as well as the physical infrastructure of specifically in this case of a telecoms ecosystem. So what this does is enables an ability to rapidly redeploy and back up existing configurations all the way up to the TCP and TCA that pulls the basis of our work here with VMware. >> So you've offloaded that responsibility from the CSPs. You freed them from that. >> So the work that we did, honestly was to make sure that we have a very clear and concise and accurate procedures for how to conduct this as well. And to put this through a realistic and real world as if it was in a telecoms own production network, what did that would actually look like, and what it would take to bring it back up as well. So our responsibility is to make sure that when we when we provide these products to the customers that not only do they work exactly as their intended to, but there is also documentation to help support them and to enable them to have their exact specifications met by as well. >> Got it. So talk about a little bit about OTEL expansion into Cork. What you guys are doing together to enable CSPs here in EMEA? >> Yeah, so the reason why we opened up a facility in Cork Island was to give, for an EMEA audience, for an EMEA CSPs and ability to look and feel and touch some of the products that we're working on. It also just facilitates and ease especially for European-based partners to have a chance to very easily come to a lab environment. The difference though, honestly, is the between Round Rock, Texas and Cork Island is that it's virtually an extension of the same thing. Like the physical locations can make it easier to provide access and obviously to showcase the products that we've developed with partners. But the reality is that it's more than just the physical location. It's more about the ability and ease by which customers and partners can access the labs. >> So we should be expecting a lot of Tito's vodka to be consumed in Cork at some point. Might change the national beverage. >> We do need to have some international exchange. >> Yeah, no, that's good to know. Odded, on the VMware side of things. There's a large group of folks who have VMware skillsets. >> Odded: Correct. >> The telecom industry is moving into this world of the kind of agility that those folks are familiar with. How do people come out of the traditional VMware virtualization world and move into that world of cloud native applications and serve the telecom space? What would your recommendation be? If you were speaking at a VMUG, a VMware Users Group meeting with all of your telecom background, what would you share with them that's critical to understand about how telecom is different, or how telecom's spot in its evolution might be different than the traditional IT space? >> So we're talking about the people with the knowledge and the background of. >> Yeah, I'm a V expert, let's say. And I'm looking into the future and I hear that there are 80,000 people in Barcelona at this event, and I hear that Dell is building optimized infrastructure specifically for telecom, and that VMware is involved. And I'm an expert in VMware and I want to be involved. What do I need to do? I know it's a little bit outside of the box question, but especially against the backdrop of economic headwinds globally, there are a lot of people facing transitions. What are your thoughts there? >> So, first of all, we understand the telco requirements, we understand the telco needs, and we make sure that what we learn from the customers, what we learn from the partners is being built into the VMware products. And simplicity is number one thing that is important for us. We want the customer experience, we want the user experience to be the same as they know even though we are transitioning into cloud native networks that require more frequent upgrades and they have more complexity to be honest. And what we do in our vertical inside VMware we are focusing on automation, telco cloud automation, telco cloud service assurance. Think of it as a wrapper around the SDDC stack that we have from VMware that really simplifies the operations for the telcos because it's really a challenge about skillset. You need to be a DevOps, SRE in order to operate these networks. And things are becoming really complex. We simplify it for them with the same VMware experience. We have a very good ability to do that. We sell products in VMware. Unlike our competition that is mostly selling professional services and support, we try to focus more on the products and delivering the value. Of course, we have services offering because telcos requires some customizations, but we do focus on automation simplicity throughout our staff. >> So just follow up. So in other words the investment in education in this VMware ecosystem absolutely can be extended and applied into the telecom world. I think it's an important thing. >> I was going to add to that. Our engagement in OTEL was also something that we created a solutions brief whether we released from Mobile World Congress this week. But in conjunction with that, we also have a white paper coming out that has a much more expansive explanation and documentation of what it was that we accomplished in the work that we've done together. And that's not something that is going to be a one-off thing. This is something that will stay evergreen that we'll continue to expand both the testing scope as well as the documentation for what this solution looks like and how it can be used as well as documentation on for the V experts for how they can then leverage and realize the the potential for what we're creating together. >> Jared, does Dell look at OTEL as having the potential to facilitate the continued evolution of the actual telco industry? And if so, how? >> Well, I mean, it would be a horrible answer if I were to say no to that. >> Right. >> I think, I honestly believe that one of the most difficult things about this idea of having desired ecosystem is not just trying to put it back together, but then also how to give yourself choice. So each time that you build one of those solution sets like that exists as an island out of all the other possibilities that comes with it. And OTEL seeks to not just be able to facilitate building that first solution set. Like that's what solutions engineering can do. And that's generally done relatively protected and internally. The Open Telecom Ecosystem seeks to build that then to also provide the ability to very easily change specific components of that whether that's a hardware component, a NIC, whether a security pass just came out or a change in either TCP or TCA or we talked a little bit about for this specific engagement that it was done on TCP 2.5. >> Odded: Correct. >> Obviously there's already a 2.7 and 3.0 is coming out. It's not like we're going to sit around and write our coattails of what 2.7 has happened. So this isn't intended to be a one and done thing. So when we talk about trying to make that easier and simpler and de-risk all of the risk that comes from trying to put all these things together, it's not just the the one single solution that you built in the lab. It's what's the next one? And how do I optimize this? And I have specific requirements as a CSP, how can I take something you built that doesn't quite match it, but how do I make that adjustment? So that's what we see to do and make it as easy and as painless as possible. >> What's the engagement model with CSPs? Is it led by Dell only, VMware partner? How does that work? >> Yeah, I can take that. So that depends on the customer, but typically customers they want to choose the cloud vendor. So they come to VMware, we want VMware. Typically, they come from the IT side. They said, "Oh, we want to manage the network side of the house the same way as we manage the IT. We don't want to have special skill sets, special teams." So they move from the IT to the network side and they want VMware there. And then obviously they have an RSP process and they have hardware choices. They can go with Dell, they can go with others. We leverage vSphere, other compatibility. So we can be flexible with the customer choice. And then depending on which customer, how large they are, they select the network equipment provider that the runs on top. We position our platform as multi-vendor. So many of them choose multiple network functions providers. So we work with Dell. So assuming that the customer is choosing Dell. We work very closely with them, offering the best solution for the customer. We work with them sometimes to even design the boxes to make sure that it fits their use cases and to make sure that it works properly. So we have a partnership validation certification end-to-end from the applications all the way down to the hardware. >> It's a fascinating place in history to be right now with 5G. Something that a lot of consumers sort of assume. It's like, "Oh, hey, yeah, we're already there. What's the 6G thing going to look like?" Well, wait a minute, we're just at the beginning stages. And so you talk about disaggregation, re-aggregation, or reintegration, the importance of that. Folks like Dell have experience in that space. Folks at VMware have a lot of experience in the virtualization space, but I heard that VMware is being acquired by Broadcom, if it all goes through, of course. You don't need to comment on it. But you mentioned something, SDDC, software-defined data center. That stack is sometimes misunderstood by the public at large and maybe the folks in the EU, I will editorialize for a moment here. It is eliminating capture in a way by larger hyperscale cloud providers. It absolutely introduces more competition into the market space. So it's interesting to hear Broadcom acknowledging that this is part of the future of VMware, no matter what else happens. These capabilities that spill into the telecom space are something that they say they're going to embrace and extend. I think that's important for anyone who's evaluating this if they're concern. Well, wait a minute. Yeah, when I reintegrate, do I want VMware as part of this mix? Is that an unknown? It's pretty clear that that's something that is part of the future of VMware moving forward. That's my personal opinion based on analysis. But you brought up SDDC, so I wanted to mention that. Again, I'm not going to ask you to get into trouble on that at all. What should we be, from a broad perspective, are there any services, outcomes that are going to come out of all of this work? The agility that's being built by you folks and folks in the open world. Are there any specific things that you personally are excited about? Or when we think about consumer devices, getting data, what are the other kinds of things that this facilitates? Anything cool, either one of you. >> So specific use cases? >> Yeah, anything. It's got to be cool though. If it's not cool we're going to ask you to leave. >> All right. I'll take that challenge. (laughs) I think one of the things that is interesting for something like OTEL as an exist, as being an Open Telecom Ecosystem, there are going to be some CSPs that it's very difficult for them to have this optionality existing for themselves. Especially when you start talking about tailoring it for specific CSPs and their needs. One of the things that becomes much more available to some of the smaller CSPs is the ability to leverage OTEL and basically act as one of their pre-production labs. So this would be something that would be very specific to a customer and we would obviously make sure that it's completely isolated but the intention there would be that it would open up the ability for what would normally take a much longer time period for them to receive some of the benefits of some of the changes that are happening within the industry. But they would have immediate benefit by leveraging specifically looking OTEL to provide them some of their solutions. And I know that you were also looking for specific use cases out of it, but like that's a huge deal for a lot of CSPs around the world that don't have the ability to lay out all the different permutations that they are most interested in and start to put each one of those through a test cycle. A specific use cases for what this looks like is honestly the most exciting that I've seen for right now is on the private 5G networks. Specifically within mining industry, we have a, sorry for the audience, but we have a demo at our booth that starts to lay out exactly how it was deployed and kind of the AB of what this looked like before the world of private 5G for this mining company and what it looks like afterwards. And the ability for both safety, as well as operational costs, as well as their ability to obviously do their job better is night and day. It completely opened up a very analog system and opened up to a very digitalized system. And I would be remiss, I didn't also mention OpenBrew, which is also an example in our booth. >> We saw it last night in action. >> We saw it. >> I hope you did. So OpenBrew is small brewery in Northeast America and we basically took a very manual process of checking temperature and pressure on multiple different tanks along the entire brewing process and digitized everything for them. All of that was enabled by a private 5G deployment that's built on Dell hardware. >> You asked for cool. I think we got it. >> Yeah, it's cool. >> Jared: I think beer. >> Cool brew, yes. >> Root beer, I think is trump card there. >> At least for folks from North America, we like our brew cool. >> Exactly. Guys, thank you so much for joining Dave and me talking about what Dell, OTEL, and VMware are doing together, what you're enabling CSPs to do and achieve. We appreciate your time and your insights. >> Absolutely. >> Thank you. >> All right, our pleasure. For our guests and for Dave Nicholson, I'm Lisa Martin. You watching theCUBE live from MWC '23. Day three of our coverage continues right after a short break. (upbeat music)

>> Announcer: TheCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (pleasant music) >> We're back. This is Dave Vellante for our live coverage of MWC '23 SiliconANGLE's wall to wall, four-day coverage. We're here with Greg Manganello, who's from Fuijitsu. He's the global head of network services business unit at the company. And Ryan McMeniman is the director of product management for the open telecom ecosystem. We've been talking about that all week, how this ecosystem has opened up. Ryan's with Dell Technologies. Gents, welcome to theCUBE. >> Thank you, Dave. >> Thank you. >> Good to be here. >> Greg, thanks for coming on. Let's hear Fuijitsu's story. We haven't heard much at this event from Fuijitsu. I'm sure you got a big presence, but welcome to theCUBE. Tell us your angle. >> Thanks very much. So Fuijitsu, we're big O-RAN advocates, open radio access network advocates. We're one of the leading founders of that open standard. We're also members of the Open RAN Policy Coalition. I'm a board member there. We're kind of all in on OpenRAN. The reason is it gives operators choices and much more vendor diversity and therefore a lot of innovation when they build out their 5G networks. >> And so as an entry point for Dell as well, I mean obviously you guys make a lot of hay with servers and storage and other sort of hardware, but O-RAN is just this disruptive change to this industry, but it's also compute intensive. So from Dell's perspective, what are the challenges of getting customers to the carriers to adopt O-RAN? How do you de-risk it for them? >> Right, I mean O-RAN really needs to be seen as a choice, right? And that choice comes with building out an ecosystem of partners, right? Working with people like Fuijitsu and others helps us build systems that the carriers can rely upon. Otherwise, it looks like another science experiment, a sandbox, and it's really anything but that. >> So what specifically are you guys doing together? Are you doing integrations, reference architectures engineered systems, all of the above? >> Yeah, so I think it's a little bit of all of the above. So we've announced our cooperation, so the engineering teams are linked, and that we're combining our both sweet spots together from Fuijitsu's virtual CU/DU, and our OpenRAN radios, and Dell's platforms and integration capabilities. And together we're offering a pre-integrated bundle to operators to reduce that risk and kind of help overcome some of the startup obstacles by shrinking the integration cost. >> So you've got Greenfield customers, that's pretty straightforward, white sheet of paper, go, go disrupt. And then there's traditional carriers, got 4G and 5G networks, and sort of hybrid if you will, and this integration there. Where do you see the action now? I presume it's Greenfield today, but isn't it inevitable that the traditional carriers have to go open? >> It is, a couple of different ways that they need to go and they want to go might be power consumption, it might be the cloudification of their network. They're going to have different reasons for doing it. And I think we have to make sure that when we work on collaborations like we do with Fuijitsu, we have to look at all of those vectors. What is it that somebody maybe here in Europe is dealing with high gas prices, high energy prices, in the U.S. or wherever it's expansion. They're going to be different justifications for it. >> Yeah, so power must be an increasing component of the operating expense, with energy costs up, and it's a power hungry environment. So how does OpenRAN solve that problem? >> So that's a great question. So by working together we can really optimize the configurations. So on the Fuijitsu side, our radios are multi-band and highly compact and super energy efficient so that the TCO for the carrier is much, much lower. And then we've also announced on the rApp side power savings, energy savings applications, which are really sophisticated AI enabled apps that can switch off the radio based upon traffic prediction models and we can save the operator 30% on their energy bill. That's a big number. >> And that intelligence that lives in the, does it live in the RIC, is it in the brain? >> In the app right above the RIC, absolutely. >> Okay, so it's a purpose-built app to deal with that. >> It's multi-vendor app, it can sit on anybody's O-RAN system. And one of the beauties of O-RAN is there is that open architecture, so that even if Dell and Fuijitsu only sell part of the, or none of the system, an app can be selected from any vendor including Fuijitsu. So that's one of the benefits of whoever's got the best idea, the best cost performance, the best energy performance, customers can really be enabled to make the choice and continue to make choices, not just way back at RFP time, but throughout their life cycle they can keep making choices. And so that's really meaning that, hey, if we miss the buying cycle then we're closed out for 5 or 10 years. No, it's constantly being reevaluated, and that's really exciting, the whole ecosystem. But what we really want to do is make sure we partner together with key partners, Dell and Fuijitsu, such that the customer, when they do select us they see a bundle, not just every person for themselves. It de-risks it. And we get a lot of that integration headache out of the way before we launch it. >> I think that's what's different. We've been talking about how we've kind of seen this move before, in the nineties we saw the move from the mainframe vertical stack to the horizontal stack. We talked about that, but there are real differences because back then you had, I don't know, five components of the stack and there was no integration, and even converged infrastructure was kind of bolts that brought that together. And then over time it's become engineered systems. When you talk to customers, Ryan, is the conversation today mostly TCO? Is it how to get the reliability and quality of service of traditional stacks? Where's the conversation today? >> Yeah, it's the flip side of choice, which is how do you make sure you have that reliability and that security to ensure that the full stack isn't just integrated, but it lives through that whole life cycle management. What are, if you're bringing in another piece, an rApp or an xApp, how do you actually make sure that it works together as a group? Because if you don't have that kind of assurance how can you actually guarantee that O-RAN in and of itself is going to perform better than a traditional RAN system? So overcoming that barrier requires partnerships and integration activity. That is an investment on the parts of our companies, but also the operators need to look back at us and say, yeah, that work has been done, and I trust as trusted advisors for the operators that that's been done. And then we can go validate it. >> Help our audience understand it. At what point in time do you feel that from a TCO perspective there'll be parity, or in my opinion it doesn't even have to be equal. It has to be close enough. And I don't know what that close enough is because the other benefits of openness, the innovation, so there's that piece of it as the cost piece and then there is the reliability. And I would say the same thing. It's got to be, well, maybe good enough is not good enough in this world, but maybe it is for some use cases. So really my question is around adoption and what are those factors that are going to affect adoption and when can we expect them to be? >> It's a good question, Dave, and what I would say is that the closed RAN vendors are making incremental improvements. And if you think in a snapshot there might be one answer, but if you think in kind of a flow model, a river over time, our O-RAN like-minded people are on a monster innovation curve. I mean the slope of the curve is huge. So in the OpenRAN policy coalition, 60 like-minded companies working together going north, and we're saying that let's bring all the innovation together, so you can say TCO, reliability, but we're bringing the innovation curve of software and integration curve from silicon and integration from system vendors all together to really out-innovate everybody else by working together. So that's the-- >> I like that curve analogy, Greg 'cause okay, you got the ogive or S curve, and you're saying that O-RAN is entering or maybe even before the steep part of the S curve, so you're going to go hyperbolic, whereas the traditional vendors are maybe trying to squeeze a little bit more out of the lemon. >> 1, 2%, and we're making 30% or more quantum leaps at a time every innovation. So what we tell customers is you can measure right now, but if you just do the time-based competition model, as an organization, as a group of us, we're going to be ahead. >> Is it a Moore's law innovation curve or is it actually faster because you've got the combinatorial factors of silicon, certain telco technologies, other integration software. Is it actually steeper than maybe historical Moore's law? >> I think it's steeper. I don't know Ryan's opinion, but I think it's steeper because Moore's law, well-known in silicon, and it's reaching five nanometers and more and more innovations. But now we're talking about AI software and machine learning as well as the system and device vendors. So when all that's combined, what is that? So that's why I think we're at an O-RAN conference today. I'm not sure we're at MWC. >> Well, it's true. It's funny they changed the name from Mobile World Congress and that was never really meant to be a consumer show, but these things change that, right? And so I think it's appropriate MWC because we're seeing really deep enterprise technology now enter, so that's your sweet spot, isn't it? >> It really is. But I think in some ways it's the path to that price performance parity, which we saw in IT a long time ago, making its way into telecom is there, but it doesn't work unless everybody is on board. And that involves players like this and even smaller companies and innovative startups, which we really haven't seen in this space for some time. And we've been having them at the Dell booth all week long. And there's really interesting stuff like Greg said, AI, ML, optimization and efficiency, which is exciting. And that's where O-RAN can also benefit the Industry. >> And as I say, there are other differences to your advantage. You've got engineered systems or you've been through that in enterprise IT, kind of learned how to do that. But you've also got the cloud, public cloud for experimentation, so you can fail cheaply, and you got AI, right, which is, really didn't have AI in the nineties. You had it, but nobody used it. And now you're like, everybody's using ChatGPT. >> Right, but now what's exciting, and the other thing that Ryan and we are working on together is linking our labs together because it's not about the first time system integration and connecting the hoses together, and okay, there it worked, but it's about the ongoing life cycle management of all the updates and upgrades. And by using Dell's OTEL Lab and Fuijitsu's MITC lab and linking them together, now we really have a way of giving operators confidence that as we bring out the new innovations it's battle tested by two organizations. And so two logos coming together and saying, we've looked at it from our different angles and then this is battle tested. There's a lot of value there. >> I think the labs are key. >> But it's interesting, the point there is by tying labs together, there's an acknowledged skills gap as we move into this O-RAN world that operators are looking to us and probably Fuijitsu saying, help our team understand how to thrive in this new environment because we're going from closed systems to open systems where they actually again, have more choice and more ability to be flexible. >> Yeah, if you could take away that plumbing, even though they're good plumbers. All right guys, we got to go. Thanks so much for coming on theCUBE. >> Thank you much. >> It's great to have you. >> Appreciate it, Dave. >> Okay, keep it right there. Dave Vellante, Lisa Martin, and Dave Nicholson will be back from the Fira in Barcelona on theCUBE. Keep it right there. (pleasant music)

>> Announcer: "theCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) >> Good evening, everyone. Live from Barcelona, Spain, it's "theCUBE". We are at Mobile World, MWC, excuse me, '23. New name this year. I'm Lisa Martin with Dave Vellante. Dave, we have had some great conversations. This is only day one of four days of coverage from "theCUBE" but one of the things that we've been talking about is disaggregation. You've wrote about it in your breaking analysis. We've been talking about it. Today is a big thing that's happening. We're going to be talking about that next. >> Yeah, open ecosystems require integration. Integration requires certification. And so, you got to have labs. We're going to talk about that and what value that brings to the community. >> Right. Please welcome Tibor Fabry-Asztalos, senior vice president of telecom systems and product engineering at Dell. >> Hi. >> And back to "theCUBE" after a couple of hours, Gautam Bhagra, vice president of partnerships at Dell. Guys, great to have you here. >> I love to be here. Thank you. >> Great to be here. >> So, day one, I'm sure lots of conversations, lots of meetings, lots of jet lag that we're all trying to get over. Talk about, Gautam, we'll start with you. Talk about the disaggregation era. What it is intended to support? What is it intended to enable? >> Yeah, so I mean, I think to be honest with you, Lisa, we spoke about this earlier also, like the whole vision with the disaggregation is to make sure our telco providers can take the benefits of having the innovation that comes along with it, right? So currently, we all know they're tied into like lock systems, which kind of constricts them in going after this whole innovative space. So, our hope is by working with our operators and our partners, we can help make that disaggregation journey a lot easier and work on some of these challenges, and make it easier for the telcos to innovate and consolidate going forward. So, we're working very closely and we talked about the community this morning. We're working very closely with Tibor and his team from an engineering perspective to help build those solutions with our partners and we're excited about the announcements we made this morning. >> When you hear challenges from this ecosystem, can you stack rank 'em? What are you hearing? Kind of what's top of mind? And so, the top three, if you would. >> Some of the challenges are just to define moving from a closed system and open system, just to making sure that the acceptance of that to see what's the value proposition is for an open system and then for the carriers to see the path going from a closed system to an open system. Of course, at the end, people realize the value at the end and speed of innovation that you're going to get all the new technologies and new features, functionality you get in an open system. But then the challenge comes with it, how you actually integrate those and then validate them, and you are to deploy them. So in a sense, that's the opportunity and also some of the challenge along the way. And that's where, as Gautam said, that's where we are also looking at playing the key role with the OTEL lab, the Open Telecom Ecosystem Lab, where we take these pieces of the open ecosystem and have combined them, validate them, and provide the pipeline to the customer. Pre-integration and then full integration into the production network. >> Those challenges, I presume, vary whether you're talking to a greenfield network operator versus somebody who's got a 40, 50 year history, a hundred-year history in the business, right? I mean migration is a big issue for them, right? Whereas the greenfield, we heard from DISH earlier, they want to drive innovation so they might be willing to sacrifice some other areas. So, is that a fair summarization and what are you hearing? >> [Tibor and Gautam] Yeah. >> Absolutely it is. I mean, that's where you see that DISH being kind of a leader in the space, as they were deploying in greenfield, they defined what the open ecosystem should look like, defined all the components of it, how you integrate them, validate them, and they were able to, well, go through it and deploy it. To your point, for an open, closed systems, as how you actually start transforming the existing network into the open one, that's going to go to a different process, right? You need to figure out how these new open systems can interrupt and work together with existing networks. So, that's one likely some of those carriers will start in an isolated area and grow from there. Deploy an open system in a rural area, for example, and then build from there. >> So, what a bank would do is they say, "Okay, we're going to write in our own abstraction layer." >> Gautam: Yeah. >> Right? "Using microservices, we're going to connect to the cloud. And we're going to, you know, put maybe some lower risk applications in the cloud first and then we're going to create our own cloud." Is there a similar dynamic here? >> Yeah, I mean, so I think you're spot on, right? Like, I think one of the things that we are seeing with the telco operators that we've spoken to is they're very risk averse. >> Yep. >> Right, they have very strong SLA requirements. They cannot go down even for a second. So, what that basically means is the innovation aspect is constrained by the risks that they perceive on any changes that you want to make on the architecture. So, the question that comes up is how do we make it easier for them to not worry about the bare minimum requirements of making sure the network's running and working while thinking about the new innovative technologies and solutions you want to build on the start. So, back to your bank example, nine years ago, no one in a bank even was thinking about like applications that will run on the cloud. Like for them, it was like a side project. They'll try and test something, see if it works, and then they'll think about cloud in the future, right? But now, core applications on banks are actually being built on public cloud. I think we see the same happening with the telco operators as well. Right now, they're understanding the move from a closed ecosystem to an open ecosystem. They understand the value proposition. On the core side, it's already happening a lot. And I think they are slowly moving there and that's where I think Tibor and team have been doing a great job working with our customers to make the transition happen. >> But there are so many permutations. >> Right. >> And integration points. How is Dell addressing that across the ecosystem? >> So, to give you an example, we talked about OTEL, which is our brand new, kind of 13,000 square feet lab that we kind of inaugurated last year based in Round Rock, Texas. >> Dave: Open Telecom. >> Dave and Tibor: Ecosystem Lab. >> Correct, great. And so, as part of that, that's a physical lab but more importantly, that's kind of a community where partners, customers come together to actually, and collaborate and work on these solutions. And as part of this, we also develop what we call the SIP, or Solution Integration Platform, to enable exactly what you just said. Making sure that we have a platform that actually can take all these various components, validate them individually, combine them, and then provide a DevOps and GitOps model, how you actually combine them, provide the BOM or SBOM, and then push that to pre-production and deployments for our customers. So, that's part of the challenge as we talked earlier. And that's how Dell and we are looking at actually enabling this basically, the validation of this disaggregated wall. >> Oh. >> Sorry, I just wanted to- >> Go ahead. >> just going to add one more point, right? So, when we look at the partners that we are working with as well in the OTEL and there are three ways we are working with them. At the bare minimum, we want to make sure that solution will run on the Dell infrastructure and the hardware, right? So, we have the self-certification process. We had a lot of good uptake on it and we are seeing a lot more come in. In fact, I had a check-in with "theCUBE" this morning in our side and it's more than a hundred plus partners already interested in going through that. Awesome. Then we have other places where we work on with partners to build reference architectures together, right? So, we want some sort of validated solution that will work together that we can take to the market. And then we also have engineered solutions that we are building with partners like the infrastructure block offering that we have taken where it's all pre-packaged, pre-built by Dell, working very closely with our partners. So, the telcos don't have to worry about deployment, integration, and everything else that comes along. >> And I presume the security supply chain is part of that- >> Yes. >> bill of materials- >> Absolutely. >> you just described. >> Yeah. >> Exactly. >> And that would include all those levels, the engineered systems, the reference architectures as well? And how do you decide like candidates, we can't do it all, right? So, it's the big markets get the engineered system, is that right? How do you adjudicate there? >> Yeah, so I mean, I think there are a couple of angles to look at it, right? I think the first and foremost is where we see the biggest demand is coming from the customers in terms of the stack they already have and where they have the pain points. >> Dave: Okay. >> Right, so this is why we are working with Red Hat and Wind River, as an example, because they are in most of the deployments that we are aware of with the customers and where we see an opportunity for Dell to partner with these partners. I think we are seeing a lot of new players also coming up the stack. And as they come up the stack and we find opportunities to co-build and co-innovate, absolutely we'll be building joint solutions with them as well. >> Where are you on, from a partnership perspective, on the strategic vision? You mentioned a number of things that have already been accomplished, quite a few. But from your journey perspective on that strategy, where are you? >> Yeah, so it's a really good question. I think we really want to be the partner of choice for all technology and services company within the telecom space. We're looking to drive the transformation in the network area, right? So, that's the vision that we have in the telecom system business from a partnership side. We have created some really good strategic partnerships with key providers, with independent software vendors, the network equipment providers. We're having some really good, strategic conversations with them. You've heard some of the announcement come out today, the work we are doing with Nokia, with Samsung, the Red Hat announcement, the Wind River, and so on and so forth. And there's a lot more in the pipeline. But more importantly, we want to grow the impact of the ecosystem. So, that's why we are launching the partner community today as well to make that happen. >> How does the lab work? Who has access to it? Can I self-certify? If I can self-certify, how do you make sure that I'm following the rules, all of the stuff- >> Sure. >> that you would- >> Absolutely. >> expect. >> So yes, you can self-certify, that's Gautam just mentioned. We already had quite a few ISVs go through that self-certification. And then there's also, there's reference architecture that's being done and other engineered solutions that we talked about earlier. And the lab is set up in a way that when needed, test lines can be isolated. So, only certain set of partners have access to it. So, it's made up in a way that enables collaborations. At the same times, it kind of enables a certain set of customers and partners working together without having challenges of having a completely open system. >> Okay, but so, if I want to do something with you guys and let's say, I am a candidate for an engineered system, so how does it work? Somebody's got to buy the equipment, right? He's got to ship it, right? There's a lot of Dell equipment involved. >> Tibor: That's correct. >> There's other third-party CapEx software, et cetera. So, you fund that, the partners fund that, it's a hybrid funding model, how does that all get done? >> So today, for obviously, we work closely with those partners. The engineered solutions we've developed so far, we've been funding it largely and as you said, is Dell infrastructure plus the cast layers and the cloud players we work with. So, we actually put those in place. We funded them, of course, with participation from them. And that's being done through those labs. >> Okay, great. So, you guys are providing that benefit to the ecosystem. Writing checks, bringing engineering talent to the table. >> Gautam: Yeah. >> Okay. >> And at the same time, I mean, it's a partnership at the end of the day, right? So, depending on the kind of partnership we are. So, if you're an ISV, it's fairly simple. Come into our labs. You don't have to worry about the infrastructure. >> Sure. >> Run it all in our labs and you're good. If you're a hardware vendor or a NEP, network equipment provider, that's where it gets interesting where they need to send us stuff, we need to send them stuff. And usually, like Tibor mentioned, it's a joint collaboration. We all put in our chips on the table and we work together. >> So, when you're having conversations with prospective partners, obviously different types of partners, Gautam, that you just talked about, what's in it for them? What's the value proposition? What does this community- >> Gautam: Yeah. >> give them from a competitive advantage standpoint? >> Yeah, so I mean there are, so the way I think about it, right? There are three things that Dell is bringing to the table. The first one is our experience and expertise on doing this transformation within the enterprise space and the learnings we have from there that we're bringing to telco now, right? So, Dell's been working with enterprises for many, many years. We are one of the big providers there. We all know what transformation enterprise went through. >> Tibor: Telco transformation, IT transformation. >> Exactly. And that's the experience we have, which we're bringing to telco. The second one is our investment, both from a go-to market side as well as the way we are working with our sales and marketing, and so on and so forth, with the engineering side. And finally, I think, and this for me is the best one, is Dell is a very partner-centric organization. >> Lisa: Yes. >> Our strategy is built around partnerships. So, that's the other piece that we bring to the table. >> Where are the labs? Oh, go ahead. >> And what's one more note on that, and also, we are talking about the engineered solutions. There's also the supply chain then because that's a basically appliance and then that goes to Dell's supply chain, which is best in class. >> Dave: And where are the labs? How many are there? >> So Round Rock, Texas is the biggest one, the 13,000 square feet. We also have extension to it. We just announced opening one in Cork for the EME market to making sure that we can cover any regulatory challenges. But also, basically any test lines that we need to cover that have latency challenges. That's why we want to make sure that we have labs in other areas as well. >> And the go-to market, is it an overlay organization, a dedicated organization? >> Yeah, so it's a bit of both as you know. But yeah, in the telecom business unit, we have a dedicated sales organization as well as an alliance organization working very closely with product and engineering to take it to market. >> Given the strength and the breadth of the partner program in the community, based on this is only day one of MWC but is there anything that you've heard today that excites you where telecom is going and where Dell and its ecosystem is going and really burgeoning? >> Oh, I've had I don't know how many meetings since 6:00 AM this morning. So, it's been an amazing event and we're just having so many great conversations with partners, our customers. And I think a lot of today is all about figuring out what our strategy and our vision is, where is each side going and what the overlap is. I think the end result's going to be follow up conversations with a lot of these partners that we are working with or will be working with soon. And then thinking about, do we build engineered solutions together? Do we go validated route? Like we going to figure that out. But I mean, for me, this is like the perfect place to come and share your vision and strategy and understand what we are trying to solve for. >> To me, what's been interesting that all the interactions and discussions are about how to get to or render open ecosystem. That's great to see that the focus is on how to make it work versus still questioning it and I think that's pretty good. >> Well, you guys launched this business I think during the pandemic, right? >> Yes. >> Yeah, that's right. >> So I mean, you could do a lot over Zoom, but as we were talking about earlier, having the face-to-face interaction, there's no replacement for it. The 6:00 AM meetings versus the 30 minute zoom calls and your body language, I mean, you learn so much that you can take away from these events. >> Absolutely. Seeing someone in 3D is so different and it's good to build that relationship and rapport as well with the folks. >> I agree. >> It is. There's so much value in the hallway conversations that you can't have over Zoom. So, I guess last question for you as we head into to day two, what are some of the things that we can be on the lookout for from Dell and its ecosystem? >> Hmm. >> Interesting. (Tibor chuckling) >> I mean, all our announcements are out. I think what you can look at for us to really be leading in this segment, taking a leadership role, and continuously looking at how we can really enable the open ecosystem and how we can provide more value there, and how we can see how we can lead in this space. >> How you can lead in this space. >> Yeah, I mean for me, I mean, day two is like, I have a lot more meetings in day two than day one so I don't know if it's like people flying in today or what, but it's amazing to just meet the partners and customers. >> So, that theme of velocity for you is going to keep going. >> Oh, it's not stopping. (Lisa laughing) That's for sure. We are excited about it. >> Well, thank you for carving out some time to talk to with us on "theCUBE" about the partner program, the open ecosystem and the commitment to growing that and enabling partners to really differentiate their services with Dell. We appreciate it. >> We appreciate it as well. >> Thank you very much. >> Thank you for having us. >> Thanks. >> Our pleasure. For our guests and for Dave Vellante, I'm Lisa Martin. You're watching "theCUBE" live in Barcelona, Spain at MWC '23. Day one of our coverage. Be right back with our final guest of the day so stick around. (upbeat music continues)

(upbeat music) >> We are back, approaching the finish line here at Supercomputing 22, our last interview of the day, our last interview of the show. And I have to say Dave Nicholson, my co-host, My name is Paul Gillin. I've been attending trade shows for 40 years Dave, I've never been to one like this. The type of people who are here, the type of problems they're solving, what they talk about, the trade shows are typically, they're so speeds and feeds. They're so financial, they're so ROI, they all sound the same after a while. This is truly a different event. Do you get that sense? >> A hundred percent. Now, I've been attending trade shows for 10 years since I was 19, in other words, so I don't have necessarily your depth. No, but seriously, Paul, totally, completely, completely different than any other conference. First of all, there's the absolute allure of looking at the latest and greatest, coolest stuff. I mean, when you have NASA lecturing on things when you have Lawrence Livermore Labs that we're going to be talking to here in a second it's a completely different story. You have all of the academics you have students who are in competition and also interviewing with organizations. It's phenomenal. I've had chills a lot this week. >> And I guess our last two guests sort of represent that cross section. Armando Acosta, director of HPC Solutions, High Performance Solutions at Dell. And Matt Leininger, who is the HPC Strategist at Lawrence Livermore National Laboratory. Now, there is perhaps, I don't know you can correct me on this, but perhaps no institution in the world that uses more computing cycles than Lawrence Livermore National Laboratory and is always on the leading edge of what's going on in Supercomputing. And so we want to talk to both of you about that. Thank you. Thank you for joining us today. >> Sure, glad to be here. >> For having us. >> Let's start with you, Armando. Well, let's talk about the juxtaposition of the two of you. I would not have thought of LLNL as being a Dell reference account in the past. Tell us about the background of your relationship and what you're providing to the laboratory. >> Yeah, so we're really excited to be working with Lawrence Livermore, working with Matt. But actually this process started about two years ago. So we started looking at essentially what was coming down the pipeline. You know, what were the customer requirements. What did we need in order to make Matt successful. And so the beauty of this project is that we've been talking about this for two years, and now it's finally coming to fruition. And now we're actually delivering systems and delivering racks of systems. But what I really appreciate is Matt coming to us, us working together for two years and really trying to understand what are the requirements, what's the schedule, what do we need to hit in order to make them successful >> At Lawrence Livermore, what drives your computing requirements I guess? You're working on some very, very big problems but a lot of very complex problems. How do you decide what you need to procure to address them? >> Well, that's a difficult challenge. I mean, our mission is a national security mission dealing with making sure that we do our part to provide the high performance computing capabilities to the US Department of Energy's National Nuclear Security Administration. We do that through the Advanced Simulation computing program. Its goal is to provide that computing power to make sure that the US nuclear rep of the stockpile is safe, secure, and effective. So how we go about doing that? There's a lot of work involved. We have multiple platform lines that we accomplish that goal with. One of them is the advanced technology systems. Those are the ones you've heard about a lot, they're pushing towards exit scale, the GPU technologies incorporated into those. We also have a second line, a platform line, called the Commodity Technology Systems. That's where right now we're partnering with Dell on the latest generation of those. Those systems are a little more conservative, they're right now CPU only driven but they're also intended to be the everyday work horses. So those are the first systems our users get on. It's very easy for them to get their applications up and running. They're the first things they use usually on a day to day basis. They run a lot of small to medium size jobs that you need to do to figure out how to most effectively use what workloads you need to move to the even larger systems to accomplish our mission goals. >> The workhorses. >> Yeah. >> What have you seen here these last few days of the show, what excites you? What are the most interesting things you've seen? >> There's all kinds of things that are interesting. Probably most interesting ones I can't talk about in public, unfortunately, 'cause of NDA agreements, of course. But it's always exciting to be here at Supercomputing. It's always exciting to see the products that we've been working with industry and co-designing with them on for, you know, several years before the public actually sees them. That's always an exciting part of the conference as well specifically with CTS-2, it's exciting. As was mentioned before, I've been working with Dell for nearly two years on this, but the systems first started being delivered this past August. And so we're just taking the initial deliveries of those. We've deployed, you know, roughly about 1600 nodes now but that'll ramp up to over 6,000 nodes over the next three or four months. >> So how does this work intersect with Sandia and Los Alamos? Explain to us the relationship there. >> Right, so those three laboratories are the laboratories under the National Nuclear Security Administration. We partner together on CTS. So the architectures, as you were asking, how do we define these things, it's the labs coming together. Those three laboratories we define what we need for that architecture. We have a joint procurement that is run out of Livermore but then the systems are deployed at all three laboratories. And then they serve the programs that I mentioned for each laboratory as well. >> I've worked in this space for a very long time you know I've worked with agencies where the closest I got to anything they were actually doing was the sort of guest suite outside the secure area. And sometimes there are challenges when you're communicating, it's like you have a partner like Dell who has all of these things to offer, all of these ideas. You have requirements, but maybe you can't share 100% of what you need to do. How do you navigate that? Who makes the decision about what can be revealed in these conversations? You talk about NDA in terms of what's been shared with you, you may be limited in terms of what you can share with vendors. Does that cause inefficiency? >> To some degree. I mean, we do a good job within the NSA of understanding what our applications need and then mapping that to technical requirements that we can talk about with vendors. We also have kind of in between that we've done this for many years. A recent example is of course with the exit scale computing program and some things it's doing creating proxy apps or mini apps that are smaller versions of some of the things that we are important to us. Some application areas are important to us, hydrodynamics, material science, things like that. And so we can collaborate with vendors on those proxy apps to co-design systems and tweak the architectures. In fact, we've done a little bit that with CTS-2, not as much in CTS as maybe in the ATS platforms but that kind of general idea of how we collaborate through these proxy applications is something we've used across platforms. >> Now is Dell one of your co-design partners? >> In CTS-2 absolutely, yep. >> And how, what aspects of CTS-2 are you working on with Dell? >> Well, the architecture itself was the first, you know thing we worked with them on, we had a procurement come out, you know they bid an architecture on that. We had worked with them, you know but previously on our requirements, understanding what our requirements are. But that architecture today is based on the fourth generation Intel Xeon that you've heard a lot about at the conference. We are one of the first customers to get those systems in. All the systems are interconnected together with the Cornell Network's Omni-Path Network that we've used before and are very excited about as well. And we build up from there. The systems get integrated in by the operations teams at the laboratory. They get integrated into our production computing environment. Dell is really responsible, you know for designing these systems and delivering to the laboratories. The laboratories then work with Dell. We have a software stack that we provide on top of that called TOSS, for Tri-Lab Operating System. It's based on Redhead Enterprise Linux. But the goal there is that it allows us, a common user environment, a common simulation environment across not only CTS-2, but maybe older systems we have and even the larger systems that we'll be deploying as well. So from a user perspective they see a common user interface, a common environment across all the different platforms that they use at Livermore and the other laboratories. >> And Armando, what does Dell get out of the co-design arrangement with the lab? >> Well, we get to make sure that they're successful. But the other big thing that we want to do, is typically when you think about Dell and HPC, a lot of people don't make that connection together. And so what we're trying to do is make sure that, you know they know that, hey, whether you're a work group customer at the smallest end or a super computer customer at the highest end, Dell wants to make sure that we have the right setup portfolio to match any needs across this. But what we were really excited about this, this is kind of our, you know big CTS-2 first thing we've done together. And so, you know, hopefully this has been successful. We've made Matt happy and we look forward to the future what we can do with bigger and bigger things. >> So will the labs be okay with Dell coming up with a marketing campaign that said something like, "We can't confirm that alien technology is being reverse engineered." >> Yeah, that would fly. >> I mean that would be right, right? And I have to ask you the question directly and the way you can answer it is by smiling like you're thinking, what a stupid question. Are you reverse engineering alien technology at the labs? >> Yeah, you'd have to suck the PR office. >> Okay, okay. (all laughing) >> Good answer. >> No, but it is fascinating because to a degree it's like you could say, yeah, we're working together but if you really want to dig into it, it's like, "Well I kind of can't tell you exactly how some of this stuff is." Do you consider anything that you do from a technology perspective, not what you're doing with it, but the actual stack, do you try to design proprietary things into the stack or do you say, "No, no, no, we're going to go with standards and then what we do with it is proprietary and secret."? >> Yeah, it's more the latter. >> Is the latter? Yeah, yeah, yeah. So you're not going to try to reverse engineer the industry? >> No, no. We want the solutions that we develop to enhance the industry to be able to apply to a broader market so that we can, you know, gain from the volume of that market, the lower cost that they would enable, right? If we go off and develop more and more customized solutions that can be extraordinarily expensive. And so we we're really looking to leverage the wider market, but do what we can to influence that, to develop key technologies that we and others need that can enable us in the high forms computing space. >> We were talking with Satish Iyer from Dell earlier about validated designs, Dell's reference designs for for pharma and for manufacturing, in HPC are you seeing that HPC, Armando, and is coming together traditionally and more of an academic research discipline beginning to come together with commercial applications? And are these two markets beginning to blend? >> Yeah, I mean so here's what's happening, is you have this convergence of HPC, AI and data analytics. And so when you have that combination of those three workloads they're applicable across many vertical markets, right? Whether it's financial services, whether it's life science, government and research. But what's interesting, and Matt won't brag about, but a lot of stuff that happens in the DoE labs trickles down to the enterprise space, trickles down to the commercial space because these guys know how to do it at scale, they know how to do it efficiently and they know how to hit the mark. And so a lot of customers say, "Hey we want what CTS-2 does," right? And so it's very interesting. The way I love it is their process the way they do the RFP process. Matt talked about the benchmarks and helping us understand, hey here's kind of the mark you have to hit. And then at the same time, you know if we make them successful then obviously it's better for all of us, right? You know, I want to secure nuclear stock pile so I hope everybody else does as well. >> The software stack you mentioned, I think Tia? >> TOSS. >> TOSS. >> Yeah. >> How did that come about? Why did you feel the need to develop your own software stack? >> It originated back, you know, even 20 years ago when we first started building Linux clusters when that was a crazy idea. Livermore and other laboratories were really the first to start doing that and then push them to larger and larger scales. And it was key to have Linux running on that at the time. And so we had the. >> So 20 years ago you knew you wanted to run on Linux? >> Was 20 years ago, yeah, yeah. And we started doing that but we needed a way to have a version of Linux that we could partner with someone on that would do, you know, the support, you know, just like you get from an EoS vendor, right? Security support and other things. But then layer on top of that, all the HPC stuff you need either to run the system, to set up the system, to support our user base. And that evolved into to TOSS which is the Tri-Lab Operating System. Now it's based on the latest version of Redhead Enterprise Linux, as I mentioned before, with all the other HPC magic, so to speak and all that HPC magic is open source things. It's not stuff, it may be things that we develop but it's nothing closed source. So all that's there we run it across all these different environments as I mentioned before. And it really originated back in the early days of, you know, Beowulf clusters, Linux clusters, as just needing something that we can use to run on multiple systems and start creating that common environment at Livermore and then eventually the other laboratories. >> How is a company like Dell, able to benefit from the open source work that's coming out of the labs? >> Well, when you look at the open source, I mean open source is good for everybody, right? Because if you make a open source tool available then people start essentially using that tool. And so if we can make that open source tool more robust and get more people using it, it gets more enterprise ready. And so with that, you know, we're all about open source we're all about standards and really about raising all boats 'cause that's what open source is all about. >> And with that, we are out of time. This is our 28th interview of SC22 and you're taking us out on a high note. Armando Acosta, director of HPC Solutions at Dell. Matt Leininger, HPC Strategist, Lawrence Livermore National Laboratories. Great discussion. Hopefully it was a good show for you. Fascinating show for us and thanks for being with us today. >> Thank you very much. >> Thank you for having us >> Dave it's been a pleasure. >> Absolutely. >> Hope we'll be back next year. >> Can't believe, went by fast. Absolutely at SC23. >> We hope you'll be back next year. This is Paul Gillin. That's a wrap, with Dave Nicholson for theCUBE. See here in next time. (soft upbear music)

>>Hello >>Everyone and welcome back to Dallas where we're live from, Super computing. My name is Savannah Peterson, joined with my co-host David, and we have a rocket of a show for you this afternoon. The doctors are in the house and we are joined by nasa, ladies and gentlemen. So excited. Please welcome Dr. Dan Duffy and Dr. Bill Putman. Thank you so much for being here, guys. I know this is kind of last minute. How's it to be on the show floor? What's it like being NASA here? >>What's exciting? We haven't, we haven't been here for three years, so this is actually really exciting to come back and see everybody, to see the showroom floor, see the innovations that have happened over the last three years. It's pretty exciting. >>Yeah, it's great. And, and so, because your jobs are so cool, and I don't wanna even remotely give even too little of the picture or, or not do it justice, could you give the audience a little bit of background on what you do as I think you have one of the coolest jobs ever. YouTube bill. >>I, I appreciate that. I, I, I run high Performance Computing Center at NASA Goddard for science. It's high performance information technology. So we do everything from networking to security, to high performance computing, to data sciences, artificial intelligence and machine learning is huge for us now. Yeah, large amounts of data, big data sets, but we also do scientific visualizations and then cloud and commercial cloud computing, as well as on premises cloud computing. And quite frankly, we support a lot of what Bill and his team does. >>Bill, why don't you tell us what your team >>Does? Yeah, so I'm a, I'm an earth scientist. I work as the associate chief at the global modeling assimilation office. And our job is to really, you know, maximize the use of all the observations that NASA takes from space and build that into a coherent, consistent physical system of the earth. Right? And we're focused on utilizing the HC that, that Dan and the folks at the nccs provide to us, to the best of our abilities to integrate those observations, you know, on time scales from hours, days to, to seasonal to to monthly time scales. That's, that's the essence of our focus at the GMA o >>Casual modeling, all of NASA's earth data. That, that in itself as a sentence is pretty wild. I imagine you're dealing with a ton of data. >>Oh, massive amounts of data. Yes, >>Probably, I mean, as much as one probably could, now that I'm thinking about it. I mean, and especially with how far things have to travel. Bill, sticking with you, just to open us up, what technology here excites you the most about the future and that will make your job easier? Let's put it that way. >>To me, it's the accelerator technologies, right? So there's the limited, the limiting factor for, for us as scientists is how fast we can get an answer. And if we can get our answer faster through accelerated technologies, you know, with the support of the, of the nccs and the computing centers, but also the software engineers enabling that for us, then we can do more, right. And push the questions even further, you know, so once we've gotten fast enough to do what we want to do, there's always something next that we wanna look for. So, >>I mean, at nasa you have to exercise such patience, whether that be data, coming back, images from a rover, doesn't matter what it is. Sometimes there's a lot of time, days, hours, years, depending on the situation. Right? I really, I really admire that. What about you, Dan? What's got you really excited about the future here? So >>Bill talked about the, the accelerated technology, which is absolutely true and, and, and is needed to get us not to only to the point where we have the compute resources to do the simulations that Bill wants to do, and also do it in a energy efficient way. But it's really the software frameworks that go around that and the software frameworks, the technology that dealing with how to use those in an energy efficient and and most efficient way is extremely important. And that's some of the, you know, that's what I'm really here to try to understand better about is how can I support these scientists with not just the hardware, but the software frameworks by which they can be successful. >>Yeah. We've, we've had a lot of kind of philosophical discussion about this, the difference between the quantitative increases in power in computing that we're seeing versus the question of whether or not we need truly qualitative changes moving forward. Where do you see the limits of, of, of, you know, if you, if you're looking at the ability to gather more data and process more data more quickly, what you can do with that data changes when you're getting updates every second versus every month seems pretty obvious. Is there a, is there, but is there, is there a near term target that you have specifically where once you reach that target, if you weren't thinking ahead of that target, you'd kind of be going, Okay, well we solved that problem, we're getting the data in so fast that you can, you can ask me, what is the temperature in this area? And you can go, Oh, well, huh, an hour ago the data said this. Beyond that, do you need a qualitative change in our ability to process information and tease insight into out of chaos? Or do you just need more quantity to be able to get to the point where you can do things like predict weather six months in advance? What are, what are your thoughts on that? Yeah, >>It's an interesting question, right? And, and you ended it with predicting whether six months in advance, and actually I was thinking the other way, right? I was thinking going to finer and finer scales and shorter time scales when you talk about having data more frequently, right? So one of the things that I'm excited about as a modeler is going to hire resolution and representing smaller scale processes at nasa, we're, we're interested in observations that are global. So our models are global and we'd like to push those to as fine a resolution as possible to do things like severe storm predictions and so forth. So the faster we can get the data, the more data we can have, and that area would improve our ability to do that as well. So, >>And your background is in meteorology, right? >>Yes, I'm a meteorologist. >>Excellent. Okay. Yeah, yeah, >>Yeah. So, so I have to ask a question, and I'm sure all the audience cares about this. And I went through this when I was talking about the ghost satellites as well. What, what is it about weather that makes it so hard to predict? >>Oh, it's the classic chaos problem. The, the butterfly effects problem, and it's just true. You know, you always hear the story of a butterfly in Africa flaps, its rings and wings, and the weather changes in, in New York City, and it's just, computers are an excellent example of that, right? So we have a model of the earth, we can run it two times in a row and get the exact same answer, but if we flip a bit somewhere, then the answer changes 10 days later significantly. So it's a, it's a really interesting problem. So, >>Yeah. So do you have any issue with the fact that your colleague believes that butterflies are responsible for weather? No, I does that, does that, is it responsible for climate? Does that bother you at all? >>No, it doesn't. As a matter of fact, they actually run those butterfly like experi experiments within the systems where they do actually flip some bits and see what the uncertainties are that happen out 7, 8, 9 days out in advance to understand exactly what he's saying, to understand the uncertainties, but also the sensitivity with respect to the observations that they're taking. So >>Yeah, it's fascinating. It is. >>That is fascinating. Sticking with you for a second, Dan. So you're at the Center for Climate Simulation. Is that the center that's gonna help us navigate what happens over the next decade? >>Okay, so I, no one center is gonna help us navigate what's gonna happen over the next decade or the next 50 or a hundred years, right. It's gonna be everybody together. And I think NASA's role in that is really to pioneer the, the, the models that that bill and others are doing to understand what's gonna happen in not just the seasonal sub, but we also work with G, which is the God Institute for Space Studies. Yeah. Which does the decatal and, and the century long studies. Our, our job is to really help that research, understand what's happening with the client, but then feed that back into what observations we need to make next in order to better understand and better quantify the risks that we have to better quantify the mitigations that we can make to understand how and, and, and affect how the climate is gonna go for the future. So that's really what we trying to do. We're trying to do that research to understand the climate, understand what mitigations we can have, but also feedback into what observations we can make for the future. >>Yeah. And and what's the partnership ecosystem around that? You mentioned that it's gonna take all of us, I assume you work with a lot of >>Partners, Probably both of you. I mean, obviously the, the, the federal agencies work huge amounts together. Nasa, Noah is our huge partnerships. Sgs, a huge partnerships doe we've talked to doe several times this, so this, this this week already. So there's huge partnerships that go across the federal agency. We, we work also with Europeans as much as we can given the, the, the, you know, sort of the barriers of the countries and the financials. But we do collaborate as much as we can with, And the nice thing about NASA, I would say is the, all the observations that we take are public, they're paid for by the public. They're public, everybody can down them, anybody can down around the world. So that's also, and they're global measurements as Bill said, they're not just regional. >>Do you have, do you have specific, when you think about improving your ability to gain insights from data that that's being gathered? Yeah. Do you set out specific milestones that you're looking for? Like, you know, I hope by June of next year we will have achieved a place where we are able to accomplish X. Yeah. Do you, do you, Yeah. Bill, do you put, what, >>What milestones do we have here? So, yeah, I mean, do you have >>Yeah. Are, are you, are you sort of kept track of that way? Do you think of things like that? Like very specific things? Or is it just so fluid that as long as you're making progress towards the future, you feel okay? >>No, I would say we absolutely have milestones that we like to keep in track, especially from the modeling side of things, right? So whether it's observations that exist now that we want to use in our system, milestones to getting those observations integrated in, but also thinking even further ahead to the observations that we don't have yet. So we can use the models that we have today to simulate those kind of observations that we might want in the future that can help us do things that we can do right now. So those missions are, are aided by the work that we do at the GBO and, and the nccs, but, >>Okay, so if we, if we extrapolate really to the, to the what if future is really trying to understand the entire earth system as best as we can. So all the observations coming in, like you said, in in near real time, feeding that into an earth system model and to be able to predict short term, midterm or even long term predictions with, with some degree of certainty. And that may be things like climate change or it may be even more important, shorter term effects of, of severe weather. Yeah. Which is very important. And so we are trying to work towards that high resolution, immediate impact model that we can, that we can, you know, really share with the world and share those results as best, as best we can. >>Yeah. I, I have a quick, I have a quick follow up on that. I I bet we both did. >>So, so if you think about AI and ml, artificial intelligence and machine learning, something that, you know, people, people talk about a lot. Yeah. There's the concept of teaching a machine to go look for things, call it machine learning. A lot of it's machine teaching we're saying, you know, hit, you know, hit the rack on this side with a stick or the other side with the stick to get it to, to kind of go back and forth. Do you think that humans will be able to guide these systems moving forward enough to tease out the insights that we want? Or do you think we're gonna have to rely on what people think of as artificial intelligence to be able to go in with this massive amount of information with an almost infinite amount of variables and have the AI figure out that, you know what, it was the butterfly, It really was the butterfly. We all did models with it, but, but you understand the nuance that I'm saying. It's like we, we, we think we know what all the variables are and that it's chaotic because there's so many variables and there's so much data, but maybe there's something we're not taking into >>A account. Yeah, I I, I'm, I'm, I'm sure that's absolutely the case. And I'll, I'll start and let Bill, Bill jump in here. Yeah, there's a lot of nuances with a aiml. And so the, the, the, the real approach to get to where we want to be with this earth system model approach is a combination of both AI ML train models as best as we can and as unbiased way as we can. And there's a, there's a big conversation we have around that, but also with a physics or physical based model as well, Those two combined with the humans or the experts in the loop, we're not just gonna ask the artificial intelligence to predict anything and everything. The experts need to be in the loop to guide the training in as best as we, as, as we can in an unbiased, equitable way, but also interpret the results and not just give over to the ai. But that's the combination of that earth system model that we really wanna see. The future's a combination of AI l with physics based, >>But there's, there's a, there's an obvious place for a AI and ML in the modeling world that is in the parameterizations of the estimations that we have to do in our systems, right? So when we think about the earth system and modeling the earth system, there are many things like the equations of motions and thermodynamics that have fixed equations that we know how to solve on a computer. But there's a lot of things that happen physically in the atmosphere that we don't have equations for, and we have to estimate them. And machine learning through the use of high resolution models or observations in training the models to understand and, and represent that, yeah, that that's the place where it's really useful >>For us. There's so many factors, but >>We have to, but we have to make sure that we have the physics in that machine learning in those, in those training. So physics informed training isn't very important. So we're not just gonna go and let a model go off and do whatever it wants. It has to be constrained within physical constraints that the, that the experts know. >>Yeah. And with the wild amount of variables that affect our, our earth, quite frankly. Yeah, yeah. Which is geez. Which is insane. My god. So what's, what, what technology or what advancement needs to happen for your jobs to get easier, faster for our ability to predict to be even more successful than it is currently? >>You know, I think for me, the vision that I have for the future is that at some point, you know, all data is centrally located, essentially shared. We have our applications are then services that sit around all that data. I don't have to sit as a user and worry about, oh, is this all this data in place before I run my application? It's already there, it's already ready for me. My service is prepared and I just launch it out on that service. But that coupled with the performance that I need to get the result that I want in time. And I don't know when that's gonna happen, but at some point it might, you know, I don't know rooting for you, but that's, >>So there are, there are a lot of technologies we can talk about. What I'd like to mention is, is open science. So NASA is really trying to make a push and transformation towards open science. 2023 is gonna be the year of open science for nasa. And what does that mean? It means a lot of what Bill just said is that we have equity and fairness and accessibility and you can find the data, it's findability, it's fair data, you know, a fair findability accessibility reproducibility, and I forget what the eye stands for, but these are, these are tools and, and, and things that we need to, as, as a computing centers and including all the HC centers here, as well as the scientists need to support, to be as transparent as possible with the data sets and the, and the research that we're doing. And that's where I think is gonna be the best thing is if we can get this data out there that anybody can use in an equitable way and as transparent as possible, that's gonna eliminate, in my opinion, the bias over time because mistakes will be found and mistakes will be corrected over time. >>I love that. Yeah. The open source science end of this. No, it's great. And the more people that have access people I find in the academic world, especially people don't know what's going on in the private sector and vice versa. And so I love that you just brought that up. Closing question for you, because I suspect there might be some members of our audience who maybe have fantasized about working at nasa. You've both been working there for over a decade. Is it as cool as we all think of it? It is on the outside. >>I mean, it's, it's definitely pretty cool. >>You don't have to be modest about it, you know, >>I mean, just being at Goddard and being at the center where they build the James web web telescope and you can go to that clean room and see it, it's just fascinating. So it, it's really an amazing opportunity. >>Yeah. So NASA Goddard as a, as a center has, you know, information technologist, It has engineers, it has scientists, it has support staff, support team members. We have built more things, more instruments that have flown in this space than any other place in the world. The James Lab, we were part of that, part of a huge group of people that worked on James. We and James, we came through and was assembled in our, our, our clean room. It's one of the biggest clean rooms in, in, in the world. And we all took opportunities to go over and take selfies with this as they put those loveness mirrors on them. Yeah, it was awesome. It was amazing. And to see what the James we has done in such a short amount of time, the successes that they've gone through is just incredible. Now, I'm not a, I'm not a part of the James web team, but to be a, to be at the same center, to to listen to scientists like Bill talk about their work, to listen to scientists that, that talk about James, we, that's what's inspiring. And, and we get that all the time. >>And to have the opportunity to work with the astronauts that service the, the Hubble Telescope, you know, these things are, >>That's literally giving me goosebumps right now. I'm sitting over >>Here just, just an amazing opportunity. And woo. >>Well, Dan, Bill, thank you both so much for being on the show. I know it was a bit last minute, but I can guarantee we all got a lot out of it. David and I both, I know I speak for us in the whole cube audience, so thank you. We'll have you, anytime you wanna come talk science on the cube. Thank you all for tuning into our supercomputing footage here, live in Dallas. My name is Savannah Peterson. I feel cooler having sat next to these two gentlemen for the last 15 minutes and I hope you did too. We'll see you again soon.

>>Hello everyone, and welcome back to the Cube's Live coverage of Cuban here in Motor City, Michigan. My name is Savannah Peterson and I'm delighted to be joined for this segment by my co-host Lisa Martin. Lisa, how you doing? Good. >>We are, we've had such great energy for three days, especially on a Friday. Yeah, that's challenging to do for a tech conference. Go all week, push through the end of day Friday. But we're here, We're excited. We have a great conversation coming up. Absolutely. A little of our alumni is back with us. Love it. We have a great conversation about learning. >>There's been a lot of learning this week, and I cannot wait to hear what these folks have to say. Please welcome Tom and Victoria from Cast by Beam. You guys are swag up very well. You've got the Fanny pack. You've got the vest. You even were nice enough to give me a Carhartt Beanie. Carhartt being a Michigan company, we've had so much love for Detroit and, and locally sourced swag here. I've never seen that before. How has the week been for you? >>The week has been amazing, as you can say by my voice probably. >>So the mic helps. Don't worry. You're good. >>Yeah, so, So we've been talking to tons and tons of people, obviously some vendors, partners of ours. That was great seeing all those people face to face again, because in the past years we haven't really been able to meet up with those people. But then of course, also a lot of end users and most importantly, we've met a lot of people that wanted to learn Kubernetes, that came here to learn Kubernetes, and we've been able to help them. So feel very satisfied about that. >>When we were at VMware explorer, Tom, you were on the program with us, just, I guess that was a couple of months ago. I'm listening track. So many events are coming up. >>Time is a loop. It's >>Okay. It really is. You, you teased some new things coming from a learning perspective. What is going on there? >>All right. So I'm happy that you link back to VMware explorer there because Yeah, I was so excited to talk about it, but I couldn't, and it was frustrating. I knew it was coming up. That was was gonna be awesome. So just before Cuban, we launched Cube Campus, which is the rebrand of learning dot cast io. And Victoria is the great mind behind all of this, but what the gist of it, and then I'll let Victoria talk a little bit. The gist of Cube Campus is this all started as a small webpage in our own domain to bring some hands on lab online and let people use them. But we saw so many people who were interested in those labs that we thought, okay, we have to make this its own community, and this should not be a branded community or a company branded community. >>This needs to be its own thing because people, they like to be in just a community environment without the brand from the company being there. So we made it completely independent. It's a Cube campus, it's still a hundred percent free and it's still the That's right. Only platform where you actually learn Kubernetes with hands on labs. We have 14 labs today. We've been creating one per month and we have a lot of people on there. The most exciting part this week is that we had our first learning day, but before we go there, I suggest we let Victoria talk a little bit about that user experience of Cube Campus. >>Oh, absolutely. So Cube Campus is, and Tom mentioned it's a one year old platform, and we rebranded it specifically to welcome more and, you know, embrace this Kubernetes space total as one year anniversary. We have over 11,000 students and they've been taking labs Wow. Over 7,000. Yes. Labs taken. And per each user, if you actually count approximation, it's over three labs, three point 29. And I believe we're growing as per user if you look at the numbers. So it's a huge success and it's very easy to use overall. If you look at this, it's a number one free Kubernetes learning platform. So for you user journey for your Kubernetes journey, if you start from scratch, don't be afraid. That's we, we got, we got it all. We got you back. >>It's so important and, and I'm sure most of our audience knows this, but the, the number one challenge according to Gartner, according to everyone with Kubernetes, is the complexity. Especially when you're getting harder. I think it's incredibly awesome that you've decided to do this. 11,000 students. I just wanna settle on that. I mean, in your first year is really impressive. How did this become, and I'm sure this was a conversation you two probably had. How did this become a priority for CAST and by Beam? >>I have to go back for that. To the last virtual only Cuban where we were lucky enough to have set up a campaign. It was actually, we had an artist that was doing caricatures in a Zoom room, and it gave us an opportunity to actually talk to people because the challenge back in the days was that everything virtual, it's very hard to talk to people. Every single conversation we had with people asking them, Why are you at cu com virtual was to learn Kubernetes every single conversation. Yeah. And so that was, that is one data point. The other data point is we had one lab to, to use our software, and that was extremely popular. So as a team, we decided we should make more labs and not just about our product, but also about Kubernetes. So that initial page that I talked about that we built, we had three labs at launch. >>One was to learn install Kubernetes. One was to build a first application on Kubernetes, and then a third one was to learn how to back up and restore your application. So there was still a little bit of promoting our technology in there, but pretty soon we decided, okay, this has to become even more. So we added storage, we added security and, and a lot more labs. So today, 14 labs, and we're still adding one every month. The next step for the labs is going to be to involve other partners and have them bring their technologies in the lab. So that's our user base can actually learn more about Kubernetes related technologies and then hopefully with links to open source tools or free software tools. And it's, it's gonna continue to be a, a learning experience for Kubernetes. I >>Love how this seems to be, have been born out of the pandemic in terms of the inability to, to connect with customers, end users, to really understand what their challenges are, how do we help you best? But you saw the demand organically and built this, and then in, in the first year, not only 11,000 as Victoria mentioned, 11,000 users, but you've almost quadrupled the number of labs that you have on the platform in such a short time period. But you did hands on lab here, which I know was a major success. Talk to us about that and what, what surprised you about Yeah, the appetite to learn that's >>Here. Yeah. So actually I'm glad that you relay this back to the pandemic because yes, it was all online because it was still the, the tail end of the pandemic, but then for this event we're like, okay, it's time to do this in person. This is the next step, right? So we organized our first learning day as a co-located event. We were hoping to get 60 people together in a room. We did two labs, a rookie and a pro. So we said two times 30 people. That's our goal because it's really, it's competitive here with the collocated events. It's difficult >>Bringing people lots going on. >>And why don't I, why don't I let Victoria talk about the success of that learning day, because it was big part also her help for that. >>You know, our main goal is to meet expectations and actually see the challenges of our end user. So we actually, it also goes back to what we started doing research. We saw the pain points and yes, it's absolutely reflecting, reflecting on how we deal with this and what we see. And people very appreciative and they love platform because it's not only prerequisites, but also hands on lab practice. So, and it's free again, it's applied, which is great. Yes. So we thought about the user experience, user flow, also based, you know, the product when it's successful and you see the result. And that's where we, can you say the numbers? So our expectation was 60 >>People. You're kinda, you I feel like a suspense is starting killing. How many people came? >>We had over 350 people in our room. Whoa. >>Wow. Wow. >>And small disclaimer, we had a little bit of a technical issue in the beginning because of the success. There was a wireless problem in the hotel amongst others. Oh geez. So we were getting a little bit nervous because we were delayed 20 minutes. Nobody left that, that's, I was standing at the door while people were solving the issues and I was like, Okay, now people are gonna walk out. Right. Nobody left. Kind >>Of gives me >>Ose bump wearing that. We had a little reception afterwards and I talked to people, sorry about the, the disruption that we had under like, no, we, we are so happy that you're doing this. This was such a great experience. Castin also threw party later this week at the party. We had people come up to us like, I was at your learning day and this was so good. Thank you so much for doing this. I'm gonna take the rest of the classes online now. They love it. Really? >>Yeah. We had our instructors leading the program as well, so if they had any questions, it was also address immediately. So it was a, it was amazing event actually. I'm really grateful for people to come actually unappreciated. >>But now your boss knows how you can blow out metrics though. >>Yeah, yeah, yeah, yeah. Gonna >>Raise Victoria. >>Very good point. It's a very >>Good point. I can >>Tell. It's, it's actually, it's very tough to, for me personally, to analyze where the success came from. Because first of all, the team did an amazing job at setting the whole thing up. There was food and drinks for everybody, and it was really a very nice location in a hotel nearby. We made it a colocated event and we saw a lot of people register through the Cuban registration website. But we've done colocated events before and you typically see a very high no-show rate. And this was not the case right now. The a lot of, I mean the, the no-show was actually very low. Obviously we did our own campaign to our own database. Right. But it's hard to say like, we have a lot of people all over the world and how many people are actually gonna be in Detroit. Yeah. One element that also helped, I'm actually very proud of that, One of the people on our team, Thomas Keenan, he reached out to the local universities. Yes. And he invited students to come to learning day as well. I don't think it was very full with students. It was a good chunk of them. So there was a lot of people from here, but it was a good mix. And that way, I mean, we're giving back a little bit to the universities versus students. >>Absolutely. Much. >>I need to, >>There's a lot of love for Detroit this week. I'm all about it. >>It's amazing. But, but from a STEM perspective, that's huge. We're reaching down into that community and really giving them the opportunity to >>Learn. Well, and what a gateway for Castin. I mean, I can easily say, I mean, you are the number, we haven't really talked about casting at all, but before we do, what are those pins in front of you? >>So this is a physical pain. These are physical pins that we gave away for different programs. So people who took labs, for example, rookie level, they would get this p it's a rookie. >>Yes. I'm gonna hold this up just so they can do a little close shot on if you want. Yeah. >>And this is PR for, it's a, it's a next level program. So we have a program actually for IS to beginners inter intermediate and then pro. So three, three different levels. And this one is for Helman. It's actually from previous. >>No, Helmsman is someone who has taken the first three labs, right? >>Yes, it is. But we actually had it already before. So this one is, yeah, this one is, So we built two new labs for this event and it was very, very great, you know, to, to have a ready absolutely new before this event. So we launched the whole website, the whole platform with new labs, additional labs, and >>Before an event, honestly. Yeah. >>Yeah. We also had such >>Your expression just said it all. Exactly. >>You're a vacation and your future. I >>Hope so. >>We've had a couple of rough freaks. Yeah. This is part of it. Yeah. So, but about those labs. So in the classroom we had two, right? We had the, the, the rookie and the pro. And like I said, we wanted an audience for both. Most people stayed for both. And there were people at the venue one hour before we started because they did not want to miss it. Right. And what that chose to me is that even though Cuban has been around for a long time, and people have been coming back to this, there is a huge audience that considers themselves still very early on in their Kubernetes journey and wants to take and, and is not too proud to go to a rookie class for Kubernetes. So for us, that was like, okay, we're doing the right thing because yeah, with the website as well, more rookie users will keep, keep coming. And the big goal for us is just to accelerate their Kubernetes journey. Right. There's a lot of platforms out there. One platform I like as well is called the tech world with nana, she has a lot of instructional for >>You. Oh, she's a wonderful YouTuber. >>She, she's, yeah, her following is amazing. But what we add to this is the hands on part. Right? And, and there's a lot of auto resources as well where you have like papers and books and everything. We try to add those as well, but we feel that you can only learn it by doing it. And that is what we offer. >>Absolutely. Totally. Something like >>Kubernetes, and it sounds like you're demystifying it. You talked about one of the biggest things that everyone talks about with respect to Kubernetes adoption and some of the barriers is the complexity. But it sounds to me like at the, we talked about the demand being there for the hands on labs, the the cube campus.io, but also the fact that people were waiting an hour early, they're recognizing it's okay to raise, go. I don't really understand this. Yeah. In fact, another thing that I heard speaking of, of the rookies is that about 60% of the attendees at this year's cube con are Yeah, we heard that >>Out new. >>Yeah. So maybe that's smell a lot of those rookies showed up saying, >>Well, so even >>These guys are gonna help us really demystify and start learning this at a pace that works for me as an individual. >>There's some crazy macro data to support this. Just to echo this. So 85% of enterprise companies are about to start making this transition in leveraging Kubernetes. That means there's only 15% of a very healthy, substantial market that has adopted the technology at scale. You are teaching that group of people. Let's talk about casting a little bit. Number one, Kubernetes backup, 900% growth recently. How, how are we managing that? What's next for you, you guys? >>Yeah, so growth last year was amazing. Yeah. This year we're seeing very good numbers as well. I think part of the explanation is because people are going into production, you cannot sell back up to a company that is not in production with their right. With their applications. Right? So what we are starting to see is people are finally going into production with their Kubernetes applications and are realizing we have to back this up. The other trend that we're seeing is, I think still in LA last year we were having a lot of stateless first estate full conversations. Remember containers were created for stateless applications. That's no longer the case. Absolutely. But now the acceptance is there. We're not having those. Oh. But we're stateless conversations because everybody runs at least a database with some user data or application data, whatever. So all Kubernetes applications need to be backed up. Absolutely. And we're the number one product for that. >>And you guys just had recently had a new release. Yes. Talk to us a little bit about that before we wrap. It's new in the platform and, and also what gives you, what gives cast. And by being that competitive advantage in this new release, >>The competitive advantage is really simple. Our solution was built for Kubernetes. With Kubernetes. There are other products. >>Talk about dog fooding. Yeah. Yeah. >>That's great. Exactly. Yeah. And you know what, one of our successes at the show is also because we're using Kubernetes to build our application. People love to come to our booth to talk to our engineers, who we always bring to the show because they, they have so much experience to share. That also helps us with ems, by the way, to, to, to build those labs, Right? You need to have the, the experience. So the big competitive advantage is really that we're Kubernetes native. And then to talk about 5.5, I was going like, what was the other part of the question? So yeah, we had 5.5 launched also during the show. So it was really a busy week. The big focus for five five was simplicity. To make it even easier to use our product. We really want people to, to find it easy. We, we were using, we were using new helm charts and, and, and things like that. The second part of the launch was to do even more partner integrations. Because if you look at the space, this cloud native space, it's, you can also attest to that with, with Cube campus, when you build an application, you need so many different tools, right? And we are trying to integrate with all of those tools in the most easy and most efficient way so that it becomes easy for our customers to use our technology in their Kubernetes stack. >>I love it. Tom Victoria, one final question for you before we wrap up. You mentioned that you have a fantastic team. I can tell just from the energy you two have. That's probably the truth. You also mentioned that you bring the party everywhere you go. Where are we all going after this? Where's the party tonight? Yeah. >>Well, let's first go to a ballgame tonight. >>The party's on the court. I love it. Go Pistons. >>And, and then we'll end up somewhere downtown in a, in a good club, I guess. >>Yeah. Yeah. Well, we'll see how the show down with the hawks goes. I hope you guys make it to the game. Tom Victoria, thank you so much for being here. We're excited about what you're doing. Lisa, always a joy sharing the stage with you. My love. And to all of you who are watching, thank you so much for tuning into the cube. We are wrapping up here with one segment left in Detroit, Michigan. My name's Savannah Peterson. Thanks for being here.

(contemplative upbeat music) >> Hello and welcome to this CUBE Conversation. I'm John Furrier, your host of theCUBE, here in Palo Alto, California. Got a great topic on expanding capabilities for urgent computing. Dan Stanzione, he's Executive Director of TACC, the Texas Advanced Computing Center, and Rajesh Pohani, VP of PowerEdge, HPC Core Compute at Dell Technologies. Gentlemen, welcome to this CUBE Conversation. >> Thanks, John. >> Thanks, John, good to be here. >> Rajesh, you got a lot of computing in PowerEdge, HPC, Core Computing. I mean, I get a sense that you love compute, so we'll jump right into it. And of course, I got to love TACC, Texas Advanced Computing Center. I can imagine a lot of stuff going on there. Let's start with TACC. What is the Texas Advanced Computing Center? Tell us a little bit about that. >> Yeah, we're part of the University of Texas at Austin here, and we build large-scale supercomputers, data systems, AI systems, to support open science research. And we're mainly funded by the National Science Foundation, so we support research projects in all fields of science, all around the country and around the world. Actually, several thousand projects at the moment. >> But tied to the university, got a lot of gear, got a lot of compute, got a lot of cool stuff going on. What's the coolest thing you got going on right now? >> Well, for me, it's always the next machine, but I think science-wise, it's the machines we have. We just finished deploying Lonestar6, which is our latest supercomputer, in conjunction with Dell. A little over 600 nodes of those PowerEdge servers that Rajesh builds for us. Which makes more than 20,000 that we've had here over the years, of those boxes. But that one just went into production. We're designing new systems for a few years from now, where we'll be even larger. Our Frontera system was top five in the world two years ago, just fell out of the top 10. So we've got to fix that and build the new top-10 system sometime soon. We always have a ton going on in large-scale computing. >> Well, I want to get to the Lonestar6 in a minute, on the next talk track, but... What are some of the areas that you guys are working on that are making an impact? Take us through, and we talked before we came on camera about, obviously, the academic affiliation, but also there's a real societal impact of the work you're doing. What are some of the key areas that the TACC is making an impact? >> So there's really a huge range from new microprocessors, new materials design, photovoltaics, climate modeling, basic science and astrophysics, and quantum mechanics, and things like that. But I think the nearest-term impacts that people see are what we call urgent computing, which is one of the drivers around Lonestar and some other recent expansions that we've done. And that's things like, there's a hurricane coming, exactly where is it going to land? Can we refine the area where there's going to be either high winds or storm surge? Can we assess the damage from digital imagery afterwards? Can we direct first responders in the optimal routes? Similarly for earthquakes, and a lot recently, as you might imagine, around COVID. In 2020, we moved almost a third of our resources to doing COVID work, full-time. >> Rajesh, I want to get your thoughts on this, because Dave Vellante and I have been talking about this on theCUBE recently, a lot. Obviously, people see what cloud's, going on with the cloud technology, but compute and on-premises, private cloud's been growing. If you look at the hyperscale on-premises and the edge, if you include that in, you're seeing a lot more user consumption on-premises, and now, with 5G, you got edge, you mentioned first responders, Dan. This is now pointing to a new architectural shift. As the VP of PowerEdge and HPC and Core Compute, you got to look at this and go, "Hmm." If Compute's going to be everywhere, and in locations, you got to have that compute. How does that all work together? And how do you do advanced computing, when you have these urgent needs, as well as real-time in a new architecture? >> Yeah, John, I mean, it's a pretty interesting time when you think about some of the changing dynamics and how customers are utilizing Compute in the compute needs in the industry. Seeing a couple of big trends. One, the distribution of Compute outside of the data center, 5G is really accelerating that, and then you're generating so much data, whether what you do with it, the insights that come out of it, that we're seeing more and more push to AI, ML, inside the data center. Dan mentioned what he's doing at TACC with computational analysis and some of the work that they're doing. So what you're seeing is, now, this push that data in the data center and what you do with it, while data is being created out at the edge. And it's actually this interesting dichotomy that we're beginning to see. Dan mentioned some of the work that they're doing in medical and on COVID research. Even at Dell, we're making cycles available for COVID research using our Zenith cluster, that's located in our HPC and AI Innovation Lab. And we continue to partner with organizations like TACC and others on research activities to continue to learn about the virus, how it mutates, and then how you treat it. So if you think about all the things, and data that's getting created, you're seeing that distribution and it's really leading to some really cool innovations going forward. >> Yeah, I want to get to that COVID research, but first, you mentioned a few words I want to get out there. You mentioned Lonestar6. Okay, so first, what is Lonestar6, then we'll get into the system aspect of it. Take us through what that definition is, what is Lonestar6? >> Well, as Dan mentioned, Lonestar6 is a Dell technology system that we developed with TACC, it's located at the University of Texas at Austin. It consists of more than 800 Dell PowerEdge 6525 servers that are powered with 3rd Generation AMD EPYC processors. And just to give you an example of the scale of this cluster, it could perform roughly three quadrillion operations per second. That's three petaFLOPS, and to match what Lonestar6 can compute in one second, a person would have to do one calculation every second for a hundred million years. So it's quite a good-size system, and quite a powerful one as well. >> Dan, what's the role that the system plays, you've got petaFLOPS, what, three petaFLOPS, you mentioned? That's a lot of FLOPS! So obviously urgent computing, what's cranking through the system there? Take us through, what's it like? >> Sure, well, there there's a mix of workloads on it, and on all our systems. So there's the urgent computing work, right? Fast turnaround, near real-time, whether it's COVID research, or doing... Project now where we bring in MRI data and are doing sort of patient-specific dosing for radiation treatments and chemotherapy, tailored to your tumor, instead of just the sort of general for people your size. That all requires sort of real-time turnaround. There's a lot AI research going on now, we're incorporating AI in traditional science and engineering research. And that uses an awful lot of data, but also consumes a huge amount of cycles in training those models. And then there's all of our traditional, simulation-based workloads and materials and digital twins for aircraft and aircraft design, and more efficient combustion in more efficient photovoltaic materials, or photovoltaic materials without using as much lead, and things like that. And I'm sure I'm missing dozens of other topics, 'cause, like I said, that one really runs every field of science. We've really focused the Lonestar line of systems, and this is obviously the sixth one we built, around our sort of Texas-centric users. It's the UT Austin users, and then with contributions from Texas A&M , and Texas Tech and the University of Texas system, MD Anderson Healthcare Center, the University of North Texas. So users all around the state, and every research problem that you might imagine, those are into. We're just ramping up a project in disaster information systems, that's looking at the probabilities of flooding in coastal Texas and doing... Can we make building code changes to mitigate impact? Do we have to change the standard foundation heights for new construction, to mitigate the increasing storm surges from these sort of slow storms that sit there and rain, like hurricanes didn't used to, but seem to be doing more and more. All those problems will run on Lonestar, and on all the systems to come, yeah. >> It's interesting, you mentioned urgent computing, I love that term because it could be an event, it could be some slow kind of brewing event like that rain example you mentioned. It could also be, obviously, with the healthcare, and you mentioned COVID earlier. These are urgent, societal challenges, and having that available, the processing capability, the compute, the data. You mentioned digital twins. I can imagine all this new goodness coming from that. Compare that, where we were 10 years ago. I mean, just from a mind-blowing standpoint, you have, have come so far, take us through, try to give a context to the level of where we are now, to do this kind of work, and where we were years ago. Can you give us a feel for that? >> Sure, there's a lot of ways to look at that, and how the technology's changed, how we operate around those things, and then sort of what our capabilities are. I think one of the big, first, urgent computing things for us, where we sort of realized we had to adapt to this model of computing was about 15 years ago with the big BP Gulf Oil spill. And suddenly, we were dumping thousands of processors of load to figure out where that oil spill was going to go, and how to do mitigation, and what the potential impacts were, and where you need to put your containment, and things like that. And it was, well, at that point we thought of it as sort of a rare event. There was another one, that I think was the first real urgent computing one, where the space shuttle was in orbit, and they knew something had hit it during takeoff. And we were modeling, along with NASA and a bunch of supercomputers around the world, the heat shield and could they make reentry safely? You have until they come back to get that problem done, you don't have months or years to really investigate that. And so, what we've sort of learned through some of those, the Japanese tsunami was another one, there have been so many over the years, is that one, these sort of disasters are all the time, right? One thing or another, right? If we're not doing hurricanes, we're doing wildfires and drought threat, if it's not COVID. We got good and ready for COVID through SARS and through the swine flu and through HIV work, and things like that. So it's that we can do the computing very fast, but you need to know how to do the work, right? So we've spent a lot of time, not only being able to deliver the computing quickly, but having the data in place, and having the code in place, and having people who know the methods who know how to use big computers, right? That's been a lot of what the COVID Consortium, the White House COVID Consortium, has been about over the last few years. And we're actually trying to modify that nationally into a strategic computing reserve, where we're ready to go after these problems, where we've run drills, right? And if there's a, there's a train that derails, and there's a chemical spill, and it's near a major city, we have the tools and the data in place to do wind modeling, and we have the terrain ready to go. And all those sorts of things that you need to have to be ready. So we've really sort of changed our sort of preparedness and operational model around urgent computing in the last 10 years. Also, just the way we scheduled the system, the ability to sort of segregate between these long-running workflows for things that are really important, like we displaced a lot of cancer research to do COVID research. And cancer's still important, but it's less likely that we're going to make an impact in the next two months, right? So we have to shuffle how we operate things and then just, having all that additional capacity. And I think one of the things that's really changed in the models is our ability to use AI, to sort of adroitly steer our simulations, or prune the space when we're searching parameters for simulations. So we have the operational changes, the system changes, and then things like adding AI on the scientific side, since we have the capacity to do that kind of things now, all feed into our sort of preparedness for this kind of stuff. >> Dan, you got me sold, I want to come work with you. Come on, can I join the team over there? It sounds exciting. >> Come on down! We always need good folks around here, so. (laughs) >> Rajesh, when I- >> Almost 200 now, and we're always growing. >> Rajesh, when I hear the stories about kind of the evolution, kind of where the state of the art is, you almost see the innovation trajectory, right? The growth and the learning, adding machine learning only extends out more capabilities. But also, Dan's kind of pointing out this kind of response, rapid compute engine, that they could actually deploy with learnings, and then software, so is this a model where anyone can call up and get some cycles to, say, power an autonomous vehicle, or, hey, I want to point the machinery and the cycles at something? Is the service, do you guys see this going that direction, or... Because this sounds really, really good. >> Yeah, I mean, one thing that Dan talked about was, it's not just the compute, it's also having the right algorithms, the software, the code, right? The ability to learn. So I think when those are set up, yeah. I mean, the ability to digitally simulate in any number of industries and areas, advances the pace of innovation, reduces the time to market of whatever a customer is trying to do or research, or even vaccines or other healthcare things. If you can reduce that time through the leverage of compute on doing digital simulations, it just makes things better for society or for whatever it is that we're trying to do, in a particular industry. >> I think the idea of instrumenting stuff is here forever, and also simulations, whether it's digital twins, and doing these kinds of real-time models. Isn't really much of a guess, so I think this is a huge, historic moment. But you guys are pushing the envelope here, at University of Texas and at TACC. It's not just research, you guys got real examples. So where do you guys see this going next? I see space, big compute areas that might need some data to be cranked out. You got cybersecurity, you got healthcare, you mentioned oil spill, you got oil and gas, I mean, you got industry, you got climate change. I mean, there's so much to tackle. What's next? >> Absolutely, and I think, the appetite for computing cycles isn't going anywhere, right? And it's only going to, it's going to grow without bound, essentially. And AI, while in some ways it reduces the amount of computing we do, it's also brought this whole new domain of modeling to a bunch of fields that weren't traditionally computational, right? We used to just do engineering, physics, chemistry, were all super computational, but then we got into genome sequencers and imaging and a whole bunch of data, and that made biology computational. And with AI, now we're making things like the behavior of human society and things, computational problems, right? So there's this sort of growing amount of workload that is, in one way or another, computational, and getting bigger and bigger. So that's going to keep on growing. I think the trick is not only going to be growing the computation, but growing the software and the people along with it, because we have amazing capabilities that we can bring to bear. We don't have enough people to hit all of them at once. And so, that's probably going to be the next frontier in growing out both our AI and simulation capability, is the human element of it. >> It's interesting, when you think about society, right? If the things become too predictable, what does a democracy even look like? If you know the election's going to be over two years from now in the United States, or you look at these major, major waves >> Human companies don't know. >> of innovation, you say, "Hmm." So it's democracy, AI, maybe there's an algorithm for checking up on the AI 'cause biases... So, again, there's so many use cases that just come out of this. It's incredible. >> Yeah, and bias in AI is something that we worry about and we work on, and on task forces where we're working on that particular problem, because the AI is going to take... Is based on... Especially when you look at a deep learning model, it's 100% a product of the data you show it, right? So if you show it a biased data set, it's going to have biased results. And it's not anything intrinsic about the computer or the personality, the AI, it's just data mining, right? In essence, right, it's learning from data. And if you show it all images of one particular outcome, it's going to assume that's always the outcome, right? It just has no choice, but to see that. So how we deal with bias, how do we deal with confirmation, right? I mean, in addition, you have to recognize, if you haven't, if it gets data it's never seen before, how do you know it's not wrong, right? So there's about data quality and quality assurance and quality checking around AI. And that's where, especially in scientific research, we use what's starting to be called things like physics-informed or physics-constrained AI, where the neural net that you're using to design an aircraft still has to follow basic physical laws in its output, right? Or if you're doing some materials or astrophysics, you still have to obey conservation of mass, right? So I can't say, well, if you just apply negative mass on this other side and positive mass on this side, everything works out right for stable flight. 'Cause we can't do negative mass, right? So you have to constrain it in the real world. So this notion of how we bring in the laws of physics and constrain your AI to what's possible is also a big part of the sort of AI research going forward. >> You know, Dan, you just, to me just encapsulate the science that's still out there, that's needed. Computer science, social science, material science, kind of all converging right now. >> Yeah, engineering, yeah, >> Engineering, science, >> slipstreams, >> it's all there, >> physics, yeah, mmhmm. >> it's not just code. And, Rajesh, data. You mentioned data, the more data you have, the better the AI. We have a world what's going from silos to open control planes. We have to get to a world. This is a cultural shift we're seeing, what's your thoughts? >> Well, it is, in that, the ability to drive predictive analysis based on the data is going to drive different behaviors, right? Different social behaviors for cultural impacts. But I think the point that Dan made about bias, right, it's only as good as the code that's written and the way that the data is actually brought into the system. So making sure that that is done in a way that generates the right kind of outcome, that allows you to use that in a predictive manner, becomes critically important. If it is biased, you're going to lose credibility in a lot of that analysis that comes out of it. So I think that becomes critically important, but overall, I mean, if you think about the way compute is, it's becoming pervasive. It's not just in selected industries as damage, and it's now applying to everything that you do, right? Whether it is getting you more tailored recommendations for your purchasing, right? You have better options that way. You don't have to sift through a lot of different ideas that, as you scroll online. It's tailoring now to some of your habits and what you're looking for. So that becomes an incredible time-saver for people to be able to get what they want in a way that they want it. And then you look at the way it impacts other industries and development innovation, and it just continues to scale and scale and scale. >> Well, I think the work that you guys are doing together is scratching the surface of the future, which is digital business. It's about data, it's about out all these new things. It's about advanced computing meets the right algorithms for the right purpose. And it's a really amazing operation you guys got over there. Dan, great to hear the stories. It's very provocative, very enticing to just want to jump in and hang out. But I got to do theCUBE day job here, but congratulations on success. Rajesh, great to see you and thanks for coming on theCUBE. >> Thanks for having us, John. >> Okay. >> Thanks very much. >> Great conversation around urgent computing, as computing becomes so much more important, bigger problems and opportunities are around the corner. And this is theCUBE, we're documenting it all here. I'm John Furrier, your host. Thanks for watching. (contemplative music)

(gentle upbeat music) >> Hello, and welcome to theCUBE presentation of the AWS Startup Showcase Open Cloud Innovations. This is season two episode one of an ongoing series covering setting status from the AWS ecosystem. Talking about innovation, here it's open source for this theme. We do this every episode, we pick a theme and have a lot of fun talking to the leaders in the industry and the hottest startups. I'm your host John Furrier here with Lisa Martin in our Palo Alto studios. Lisa great series, great to see you again. >> Good to see you too. Great series, always such spirited conversations with very empowered and enlightened individuals. >> I love the episodic nature of these events, we get more stories out there than ever before. They're the hottest startups in the AWS ecosystem, which is dominating the cloud sector. And there's a lot of them really changing the game on cloud native and the enablement, the stories that are coming out here are pretty compelling, not just from startups they're actually penetrating the enterprise and the buyers are changing their architectures, and it's just really fun to catch the wave here. >> They are, and one of the things too about the open source community is these companies embracing that and how that's opening up their entry to your point into the enterprise. I was talking with several customers, companies who were talking about the 70% of their pipeline comes from the open source community. That's using the premium version of the technology. So, it's really been a very smart, strategic way into the enterprise. >> Yeah, and I love the format too. We get the keynote we're doing now, opening keynote, some great guests. We have Sir John on from AWS started program, he is the global startups lead. We got Swami coming on and then closing keynote with Deepak Singh. Who's really grown in the Amazon organization from containers now, compute services, which now span how modern applications are being built. And I think the big trend that we're seeing that these startups are riding on that big wave is cloud natives driving the modern architecture for software development, not just startups, but existing, large ISV and software companies are rearchitecting and the customers who buy their products and services in the cloud are rearchitecting too. So, it's a whole new growth wave coming in, the modern era of cloud some say, and it's exciting a small startup could be the next big name tomorrow. >> One of the things that kind of was a theme throughout the conversations that I had with these different guests was from a modern application security perspective is, security is key, but it's not just about shifting lab. It's about doing so empowering the developers. They don't have to be security experts. They need to have a developer brain and a security heart, and how those two organizations within companies can work better together, more collaboratively, but ultimately empowering those developers, which goes a long way. >> Well, for the folks who are watching this, the format is very simple. We have a keynote, editorial keynote speakers come in, and then we're going to have a bunch of companies who are going to present their story and their showcase. We've interviewed them, myself, you Dave Vallante and Dave Nicholson from theCUBE team. They're going to tell their stories and between the companies and the AWS heroes, 14 companies are represented and some of them new business models and Deepak Singh who leads the AWS team, he's going to have the closing keynote. He talks about the new changing business model in open source, not just the tech, which has a lot of tech, but how companies are being started around the new business models around open source. It's really, really amazing. >> I bet, and does he see any specific verticals that are taking off? >> Well, he's seeing the contribution from big companies like AWS and the Facebook's of the world and large companies, Netflix, Intuit, all contributing content to the open source and then startups forming around them. So Netflix does some great work. They donated to open source and next thing you know a small group of people get together entrepreneurs, they form a company and they create a platform around it with unification and scale. So, the cloud is enabling this new super application environment, superclouds as we call them, that's emerging and this new supercloud and super applications are scaling data-driven machine learning and AI that's the new formula for success. >> The new formula for success also has to have that velocity that developers expect, but also that the consumerization of tech has kind of driven all of us to expect things very quickly. >> Well, we're going to bring in Serge Shevchenko, AWS Global Startup program into the program. Serge is our partner. He is the leader at AWS who has been working on this program Serge, great to see you. Thanks for coming on. >> Yeah, likewise, John, thank you for having me very excited to be here. >> We've been working together on collaborating on this for over a year. Again, season two of this new innovative program, which is a combination of CUBE Media partnership, and AWS getting the stories out. And this has been a real success because there's a real hunger to discover content. And then in the marketplace, as these new solutions coming from startups are the next big thing coming. So, you're starting to see this going on. So I have to ask you, first and foremost, what's the AWS startup showcase about. Can you explain in your terms, your team's vision behind it, and why those startup focus? >> Yeah, absolutely. You know John, we curated the AWS Startup Showcase really to bring meaningful and oftentimes educational content to our customers and partners highlighting innovative solutions within these themes and ultimately to help customers find the best solutions for their use cases, which is a combination of AWS and our partners. And really from pre-seed to IPO, John, the world's most innovative startups build on AWS. From leadership downward, very intentional about cultivating vigorous AWS community and since 2019 at re:Invent at the launch of the AWS Global Startup program, we've helped hundreds of startups accelerate their growth through product development support, go to market and co-sell programs. >> So Serge question for you on the theme of today, John mentioned our showcases having themes. Today's theme is going to cover open source software. Talk to us about how Amazon thinks about opensource. >> Sure, absolutely. And I'll just touch on it briefly, but I'm very excited for the keynote at the end of today, that will be delivered by Deepak the VP of compute services at AWS. We here at Amazon believe in open source. In fact, Amazon contributes to open source in multiple ways, whether that's through directly contributing to third-party project, repos or significant code contributions to Kubernetes, Rust and other projects. And all the way down to leadership participation in organizations such as the CNCF. And supporting of dozens of ISV myself over the years, I've seen explosive growth when it comes to open source adoption. I mean, look at projects like Checkov, within 12 months of launching their open source project, they had about a million users. And another great example is Falco within, under a decade actually they've had about 37 million downloads and that's about 300% increase since it's become an incubating project in the CNCF. So, very exciting things that we're seeing here at AWS. >> So explosive growth, lot of content. What do you hope that our viewers and our guests are going to be able to get out of today? >> Yeah, great question, Lisa. I really hope that today's event will help customers understand why AWS is the best place for them to run open source, commercial and which partner solutions will help them along their journey. I think that today the lineup through the partner solutions and Deepak at the end with the ending keynote is going to present a very valuable narrative for customers and startups in selecting where and which projects to run on AWS. >> That's great stuff Serge would love to have you on and again, I want to just say really congratulate your team and we enjoy working with them. We think this showcase does a great service for the community. It's kind of open source in its own way if I can co contributing working on out there, but you're really getting the voices out at scale. We've got companies like Armory, Kubecost, Sysdig, Tidelift, Codefresh. I mean, these are some of the companies that are changing the game. We even had Patreon a customer and one of the partners sneak with security, all the big names in the startup scene. Plus AWS Deepak saying Swami is going to be on the AWS Heroes. I mean really at scale and this is really a great. So, thank you so much for participating and enabling all of this. >> No, thank you to theCUBE. You've been a great partner in this whole process, very excited for today. >> Thanks Serge really appreciate it. Lisa, what a great segment that was kicking off the event. We've got a great lineup coming up. We've got the keynote, final keynote fireside chat with Deepak Singh a big name at AWS, but Serge in the startup showcase really innovative. >> Very innovative and in a short time period, he talked about the launch of this at re:Invent 2019. They've helped hundreds of startups. We've had over 50 I think on the showcase in the last year or so John. So we really gotten to cover a lot of great customers, a lot of great stories, a lot of great content coming out of theCUBE. >> I love the openness of it. I love the scale, the storytelling. I love the collaboration, a great model, Lisa, great to work with you. We also Dave Vallante and Dave Nicholson interview. They're not here, but let's kick off the show. Let's get started with our next guest Swami. The leader at AWS Swami just got promoted to VP of the database, but also he ran machine learning and AI at AWS. He is a leader. He's the author of the original DynamoDB paper, which is celebrating its 10th year anniversary really impacted distributed computing and open source. Swami's introduced many opensource aspects of products within AWS and has been a leader in the engineering side for many, many years at AWS, from an intern to now an executive. Swami, great to see you. Thanks for coming on our AWS startup showcase. Thanks for spending the time with us. >> My pleasure, thanks again, John. Thanks for having me. >> I wanted to just, if you don't mind asking about the database market over the past 10 to 20 years cloud and application development as you see, has changed a lot. You've been involved in so many product launches over the years. Cloud and machine learning are the biggest waves happening to your point to what you're doing now. Software is under the covers it's powering it all infrastructure is code. Open source has been a big part of it and it continues to grow and change. Deepak Singh from AWS talks about the business model transformation of how like Netflix donates to the open source. Then a company starts around it and creates more growth. Machine learnings and all the open source conversations around automation as developers and builders, like software as cloud and machine learning become the key pistons in the engine. This is a big wave, what's your view on this? How how has cloud scale and data impacting the software market? >> I mean, that's a broad question. So I'm going to break it down to kind of give some of the back data. So now how we are thinking about it first, I'd say when it comes to the open source, I'll start off by saying first the longevity and by ability of open sources are very important to our customers and that is why we have been a significant contributor and supporter of these communities. I mean, there are several efforts in open source, even internally by actually open sourcing some of our key Amazon technologies like Firecracker or BottleRocket or our CDK to help advance the industry. For example, CDK itself provides some really powerful way to build and configure cloud services as well. And we also contribute to a lot of different open source projects that are existing ones, open telemetries and Linux, Java, Redis and Kubernetes, Grafana and Kafka and Robotics Operating System and Hadoop, Leucine and so forth. So, I think, I can go on and on, but even now I'd say the database and observability space say machine learning we have always started with embracing open source in a big material way. If you see, even in deep learning framework, we championed MX Linux and some of the core components and we open sourced our auto ML technology auto Glue on, and also be open sourced and collaborated with partners like Facebook Meta on Fighter showing some major components and there, and then we are open search Edge Compiler. So, I would say the number one thing is, I mean, we are actually are very, very excited to partner with broader community on problems that really mattered to the customers and actually ensure that they are able to get amazing benefit of this. >> And I see machine learning is a huge thing. If you look at how cloud group and when you had DynamoDB paper, when you wrote it, that that was the beginning of, I call the cloud surge. It was the beginning of not just being a resource versus building a data center, certainly a great alternative. Every startup did it. That's history phase one inning and a half, first half inning. Then it became a large scale. Machine learning feels like the same way now. You feel like you're seeing a lot of people using it. A lot of people are playing around with it. It's evolving. It's been around as a science, but combined with cloud scale, this is a big thing. What should people who are in the enterprise think about how should they think about machine learning? How has some of your top customers thought about machine learning as they refactor their applications? What are some of the things that you can share from your experience and journey here? >> I mean, one of the key things I'd say just to set some context on scale and numbers. More than one and a half million customers use our database analytics or ML services end-to-end. Part of which machine learning services and capabilities are easily used by more than a hundred thousand customers at a really good scale. However, I still think in Amazon, we tend to use the phrase, "It's day one in the age of internet," even though it's an, or the phrase, "Now, but it's a golden one," but I would say in the world of machine learning, yes it's day one but I also think we just woke up and we haven't even had a cup of coffee yet. That's really that early, so. And, but when you it's interesting, you've compared it to where cloud was like 10, 12 years ago. That's early days when I used to talk to engineering leaders who are running their own data center and then we talked about cloud and various disruptive technologies. I still used to get a sense about like why cloud and basic and whatnot at that time, Whereas now with machine learning though almost every CIO, CEO, all of them never asked me why machine learning. Instead, the number one question, I get is, how do I get started with it? What are the best use cases? which is great, and this is where I always tell them one of the learnings that we actually learned in Amazon. So again, a few years ago, probably seven or eight years ago, and Amazon itself realized as a company, the impact of what machine learning could do in terms of changing how we actually run our business and what it means to provide better customer experience optimize our supply chain and so far we realized that the we need to help our builders learn machine learning and the help even our business leaders understand the power of machine learning. So we did two things. One, we actually, from a bottom-up level, we built what I call as machine learning university, which is run in my team. It's literally stocked with professors and teachers who offer curriculum to builders so that they get educated on machine learning. And now from a top-down level we also, in our yearly planning process, we call it the operational planning process where we write Amazon style narratives six pages and then answer FAQ's. We asked everyone to answer one question around, like how do you plan to leverage machine learning in your business? And typically when someone says, I really don't play into our, it does not apply. It's usually it doesn't go well. So we kind of politely encourage them to do better and come back with a better answer. This kind of dynamic on top-down and bottom-up, changed the conversation and we started seeing more and more measurable growth. And these are some of the things you're starting to see more and more among our customers too. They see the business benefit, but this is where to address the talent gap. We also made machine learning university curriculum actually now open source and freely available. And we launched SageMaker Studio Lab, which is a no cost, no set up SageMaker notebook service for educating learner profiles and all the students as well. And we are excited to also announce AIMLE scholarship for underrepresented students as well. So, so much more we can do well. >> Well, congratulations on the DynamoDB paper. That's the 10 year anniversary, which is a revolutionary product, changed the game that did change the world and that a huge impact. And now as machine learning goes to the next level, the next intern out there is at school with machine learning. They're going to be writing that next paper, your advice to them real quick. >> My biggest advice is, always, I encourage all the builders to always dream big, and don't be hesitant to speak your mind as long as you have the right conviction saying you're addressing a real customer problem. So when you feel like you have an amazing solution to address a customer problem, take the time to articulate your thoughts better, and then feel free to speak up and communicate to the folks you're working with. And I'm sure any company that nurtures good talent and knows how to hire and develop the best they will be willing to listen and then you will be able to have an amazing impact in the industry. >> Swami, great to know you're CUBE alumni love our conversations from intern on the paper of DynamoDB to the technical leader at AWS and database analyst machine learning, congratulations on all your success and continue innovating on behalf of the customers and the industry. Thanks for spending the time here on theCUBE and our program, appreciate it. >> Thanks again, John. Really appreciate it. >> Okay, now let's kick off our program. That ends the keynote track here on the AWS startup showcase. Season two, episode one, enjoy the program and don't miss the closing keynote with Deepak Singh. He goes into great detail on the changing business models, all the exciting open source innovation. (gentle bright music)

(calm electronic music) >> Hey, welcome back everyone to theCUBE coverage of AWS re:Invent 2021. I'm John Furrier, your host. We're here with two sets with live content, pumping out 120 years over the course of a couple of days, 28 hours of programming from the people making things happen, sharing the news, and the insight. We've got Sandy Carter, worldwide public sector vice president of partners and programs for Amazon web services. Sandy, CUBE alumni, welcome back to theCUBE. Great to see you. >> Great to see you too, John. It's so awesome to be here in person, right? >> The news is coming more and more. We got health care news. We got this news, we got all kinds of certification. We just recently talked on a segment about all the great stuff on certifications, but healthcare is booming, okay? We got talking about delivering the kind of performance that people need in healthcare with data, and you've got delivery, destination is healthcare. Let's talk health care, what's going on? >> Yeah, so we made a couple of really awesome announcements around healthcare today. So if you think about it, one of the big trends in healthcare is digitizing health records, so electronic healthcare records to really help and assist with patient care. So, because that is so big, we launched an initiative for electronic healthcare records, migration assistance. And what that means is that, we have now added technical subject matter experts and industry subject matter experts in the healthcare space who understand EHR, electronic health records, to help us migrate at least 500 ISV applications over to AWS. This is really big news, because so far, most of those applications are running on-premises. So getting them over to the cloud gives them the scalability, gives them the agility, that they need to provide all of us better healthcare. >> Well, one of the big themes is the Epic performance, the database on the cloud. Cloud has given so much agility and has changed the game. I mean, I'm old enough to remember, I mean, we can look back on the shifts in technology. You had that era of healthcare where data and the records were super important. Privacy, lock it down. Don't talk to each other. Are we going to respect the privacy of the individuals? That's all now changed with horizontal scalable data, as Swami pointed out, who's the SVP leader of AI and the data for AWS, whole new paradigm of data architecture. This is disrupting healthcare. >> Yes And you've got the Epic situation. Take us through, why is this important? Why are we talking about Epic? >> Well, so EHR is one of the announcements. And then the second big announcement, is our Epic on AWS announcement. So, you may have covered this back in the August, September timeframe, we announced a new EC2 instance, called the M6I. And Epic, which is one of the leading global healthcare providers in the world, has been migrated to the cloud. And so, they started testing themselves, Epic started testing on the M6I. And so what we saw is a 40% performance improvement. Now that is, that's huge, as well as a 30% reduction in total cost of ownership. So if you're a partner out there, you're going to see, as your application runs on top of Epic, you're going to get that performance gain. And Epic has an amazing ecosystem, John. They have what they call the code travelers. They kind of exist on Epic, cause everybody uses Epic. Those ISBs are now going to get that benefit, and 90% of the current Epic customers. And then, our consulting partners are also going to see the benefit, because of that total cost of ownership reduction of 30%. So imagine you're a consulting partner, you're now going to go into a hospital that's using Epic and tell them that you can reduce their total cost of ownership by 30%. That's amazing! >> Well, first of all, the cost thing is amazing. But also, when you mentioned the instances, what's happening with the graviton and the processors and the performance you're getting in the cloud now, the applications are running faster and lower cost. So, you know, databases, they really want the boast horsepower. So you've got the cloud performance, you've got the lower cost. Why wouldn't anyone want to run it anywhere else? This is what I'm saying on my story I wrote Sunday night. All the modern applications will go to the best performance, even legacy apps. >> That's right, and I think this is so important because you know, you need performance, you need speed. You need to get the rest of this application migrated over. That's why we got the EHR migration initiative. And then if you couple that with our third announcement around authority to operate that now gives you that security and compliance, right? Because if you're a hospital, you can't risk having that patient information exposed. And so we introduced as an authority to operate a program that enables our partners to get HIPAA and high trust authorization faster, cheaper, so that they can move with this new digital trend that's happening all across healthcare. I mean, it is our fastest growing area today, growing at 105%. >> Yeah, it points to examine, it's another one of those areas that is urgent under COVID. It's exploding because of the demand, just on performance. And Swami said it today, also in the keynote, the AI data keynote, governance should be an enabler, not an inhibitor. >> Sandy: That's right. So when you start getting into governance where you can start managing the data in a way that's cool for people to use the data, but protect the privacy, you then can have the modern apps. >> And if I could just add on one thing there, today we talked about, you know, when you go on your digital transformation journey, it requires digital security, especially in healthcare. And so as you have those requirements, you have to be able to, not just get stuff to the cloud, it's got to be secure. And that's why HIPAA and high trust exist today. >> And these fine grain controls now available are amazing. So again, I love the way you guys are going in this direction with AWS. I got to say every year, it's like, wow, again. But I want to get back to this ISV angle because I think this is super important. Again, I teased this out on my post Sunday night, when I, after my sit down with Adam Selipski was that, if I'm a software vendor, an ISV, an independent software vendor, or a software owner, I want my app to run faster, period, okay? I want my app to make money, which means valued by customers. I don't want my app to be slower and not be seen in front of my customers. So again, ISV is now an opportunity, Epic is a shining example of that, where now as an ISV, I can innovate and not have to do the heavy lifting. This is a huge point. Can you just share some color on this, because this is like, I think kind of the elephant in the room. The ISVs are going to go where the action is. >> That's right, and you know, the Epic ecosystem is such a force. Epic being a global healthcare leader, getting that performance level, all of those codes, they call them code travelers, that exist around Epic. All of those applications can now take advantage of that performance improvement, which for me is a game changer because all that data, I mean, I know that, you know, I was just in an emergency room with my daughter. She had a trouble, we thought she broke her elbow. And, you know, we were sitting there waiting as the person's entering and waiting and entering and waiting. So that performance really makes a difference, right? In your customer satisfaction, in your patient care, all the things that really matter, the business outcome areas, not just the technology side. It's a game changer for healthcare. >> It's the delivery of one, your health, your life. And two, hassle time, avoiding the steps, waiting in the wrong room, going here, waiting for this, getting a test you don't need. >> Sandy: That's right. >> It's a hassle for the customer, but also puts pressure on the supply chain, the operational bandwidth, and with telemedicine around the corner, you know, everything is happening with telemedicine. Why I might not need to go to the hospital if I don't have to, so again, another big wave coming is telemedicine. >> Yeah, that's right, and in fact, we launched that healthcare startup accelerator, where we invited healthcare companies from around the world to come in and get extra support as a brand new partner, as a next gen partner, and that was actually one of the top areas of focus. About 40% of the companies came in around telemedicine. And one of the really interesting partners that came in through that accelerator was a partner named Get lab. They do, you know, surgeon training, which is quite fascinating, and they were doing that and Time named them one of the top, most innovative companies of the year in 2021. And they accredited a lot of their success to the healthcare accelerator that we just launched as well. >> So much action going on. I got to get your thoughts on just in general healthcare, do you find the vibe to be more from the doctors and the service providers? Because they're the ones on the front lines. They're in the foxhole, so to speak. It always seems to me that they always wish things went faster, similar to government workers, right? It's like, I wish there wasn't red tape. I wish it was easier. Why aren't we doing this? That seems to have been like, the culture. And now it's shifting back to, all right, now we're having fun. We're delivering care. We're riding the right wave. >> I agree, you know, these business outcomes make a huge difference, I think. And I think that that transformation that you're talking about, is occurring much faster than anybody anticipated. I predict in 2022, you're going to see this increased focus, not just on telemedicine, patient care overall. Like how do you combine the two together? How are you able to move quicker to provide more diagnostics? So for example, one of our partners, GE Healthcare, was using AI and ML with one of our partner programs and was able to automate the radiology workflow. I mean, just think about radiology, reading X-rays, how fast that can be with AI and ML. It increased the diagnostic accuracy by 30%. I think you're going to see lots more use of technology to speed up diagnosis, to increase that customer, patient care. I think that's really going to be the trend in 2022. And it's great for all of us. >> And computer vision, by the way, with explainable AI, can come in, talk about analyzing x-rays and or film, more and more tech coming in and machine learning is driving a lot of it. >> I completely agree. Machine learning, I would say machine learning and analytics, you know? Now that we've got the data and you know, the data, IDC says that data coming in from IOT sensors increased by four x since COVID, so imagine, you know, there are now robots working in the hospital, gathering your readings of your, you know, how strong you breathe, your temperature, all your vital signs are now coming in from IOT sensors. So you're just seeing this explosion of data and healthcare, which only makes diagnosis and hopefully cures, new vaccines, more possible because now you've got more data to work with, right? That data accuracy is going up. Data sources are going up. It's just a really powerful combination. >> Yeah, healthcare is great. Sandy, it's been an amazing run on the healthcare side. It's continuing to change, in a good way, how care is managed and delivered and dispensed and cost savings. I do want to ask you if you could point out to the audience, just from within the partner base, what's the big trend there? Because obviously they're all engaged, seeing all kinds of new things. Where's the innovation vibe? What are some, what's the pattern in the partners, more software development, more cloud, more AI? What's the, what would you, how would you rank the activities of innovation? >> Yeah, I would say there are five prime drivers today on the technology side, you know. First and foremost right now is IOT, believe it or not. And IOT, because it's driving so much data and you have to have data for the second big trend, which is artificial intelligence and machine learning. So that data is essential for feeding all the modeling that's going on. We're also seeing the edge come to pass really fast, right? A lot of work on outpost. In fact, at the conference, we announced that we just opened an outpost innovation center with WWT and Intel MDC. We already have an innovation center for outpost in Seattle. So we opened one in DC for our partner community as well. So we're seeing a lot of focus on that edge. Containers, as we talked about earlier, 60% of customers want containers. So our partners to be, need to be all over it. And then another huge trend in public sector is blockchain. So if you think about, you know, Panama, El Salvador, Ukraine, they're all moving to Bitcoin. And I just went over to the Wynn hotel cause we're here in Vegas, did you see how many vendors are taking Bitcoin out? It's amazing! And so all of that is built on blockchain. So we also introduced a set of workshops and POCs with our partners around blockchain because we see it happening in states, in countries, and then the countries drive everything else to have to use or leverage that chain for Bitcoin. >> Great trends, the tailwind, the wave is here. It's a big wave, healthcare, public sector, a lot of change. Sandy Carter. Thank you for the great commentary. Great insight, great to see you. Thanks for coming back on theCUBE. >> Nice to see you too, yep. >> It's theCUBE coverage. I'm John Furrier, your host of theCUBE. We got two sets wall-to-wall coverage, here in person, live event, as well as hybrid, we have the software as well. You're watching theCUBE, the leader in global tech coverage. I'm John Furrier, thanks for watching. (calming electronic music)

in the beginning there were mainframes a highly centralized secure command and control environment open systems brought a spate of innovation innovations that were powered by machines servers storage arrays networks that had to be configured deployed and managed by specialists virtualization that made that simpler but it was still a machine centric world the cloud devops and importantly containers created an inflection point in the industry where no longer do developers have to do a handoff to an infrastructure guru to deploy and often reconfigure systems which could cause other problems containers essentially codified the infrastructure to the point where developers could now be responsible for the full stack with consistency that allows stretching if you will of applications between on-prem to the cloud across clouds and out to the edge kubernetes in particular has enabled organizations to host applications and containers with automation so you can now deploy as many instances of your application as required and communicate between different services used by those applications in a consistent manner manner what this does is enables rolling updates security patches in a run anywhere environment that is changing how organizations build and manage their applications hello and welcome to this cube conversation and preview to kubecon cloud nativecon north america 2021 i'm pleased to welcome my friend and guest stu miniman director of market insights for cloud platforms at red hat stu man great to see you so good to see you dave thanks for having me you're very welcome so you heard my little spiel up front a little narrative what are the big trends that you're seeing that you're watching that you think people should know about they're important yeah well well dave i'm so glad you started out talking about the application because dave i mean you know my background your background very much too is started in infrastructure and for so long we talked about well let's dig different increments that we talk about the infrastructure but there was that huge divide between the people that run the infrastructure and the people that build and own the applications and when agile and devops came out we talked about not throwing things over the wall but when we look at containers and kubernetes really what it is is an application to build our application to modernize our application to run our application as you said they they have to be more that that right once go anywhere has been something we've wanted for a while and from a developer viewpoint i haven't wanted to think about the infrastructure so we want to enable that we want developers to be able to do their thing what we've done at red hat is try to have that consistency in every environment because kubernetes is only a single a very thin layer there's lots that needs to be done on top of that but one of the biggest trends is from an application standpoint the same thing that we've seen in other environments dave when you say okay well what apps did you have well you know it's great to say i have the cool micro service new stuff but what about older applications what about modernizing things can i lift things over can i have a broader spectrum of applications and yes that's where we are with kubernetes we don't just have stateless applications that are you know written in this new modern way we have a broad spectrum and there's another word that i really keyed off of in your intro talking about automation dave if you talk about scale and you talk about automation that's what container was built for if you look at what you know the the predecessor kubernetes was borg at google and if you think about just building things at scale and building things for with automation at their core that's what we've done and that's where this ecosystem is building towards so not saying everybody needs to be google but when you start talking about ai applications when you start talking about different ways to really have automation built into your environment this is where containers and kubernetes really shines because you know that's where we've really gone beyond human scale dave and we've gone to that machine scale so we need to make sure not just to remove humans to remove errors but to be able to have that agility and flexibility and scale which is what offers in this space so all the cool kids of course they want to develop in the cloud but i feel like for every app that's developed in the cloud there's like 10 on prem that are screaming to be modernized and we have a we have a chart on this but so what kind of applications are you seeing going in to containers and kubernetes yeah so so two two charts here for the survey we actually did for kubecon europe leading up to it the one on the left talks about the data is it stateless applications is it stateful applications well what do you know dave it's a mix of both of those right you'll remember dave in the virtualization days it took us about a decade to solve those storage and networking things how do we make sure that things really run at the virtual machine layer how do we have things like moving all over the place and still not break the connection that we had there that was a lot of hard work that we as an industry did well you know here we are six seven years into kubernetes we've solved a lot of those same issues so storage and networking work much better today in kubernetes environments than it did in the early days it started out oh stateless applications but if you look at the data on the second side what kind of applications are there the answer dave is yes you want your cool new modern databases absolutely ai and ml absolutely uh you know through kind of your isv you know more traditional applications the the answer is yes so customers are doing a whole lot of it when i'm meeting with customers one of the first questions we always have dave we've worked on silo busting for for many decades in this industry but if you talk to the infrastructure team and you ask them well what apps are you putting on there if they don't have a good answer the first thing we do is hey you really need to get the developers in the room you really need to understand this because if you stand up a platform just because kubernetes is cool and it's great it helps you build your resume you're not going to have success down the road you want to make sure they're involved up front understand what the requirements so you know kubernetes uh that one of the joke is you know containers and kubernetes add some magic and you know yippee you win it's like well there's a little bit more to that uh to actually have it work you mentioned it took decade plus to actually you know kind of work it out in the virtualization days i mean you remember the api you know stuff and we have the scars from their revenues right exactly but it's interesting when i look at this chart that you know because like you said it started off it's kind of stateless database yes all kinds of applications but database is number one and so you've got a lot of stateful applications enterprise apps security sensitive i mean everything's security sensitive today but hyper security sensitive so do you feel like that time frame relative to you know two decades ago is going to be compressed yes it seems like it's compressing quite rapidly absolutely the cncf always puts out a survey around the event as to where adoption is it's a little bit of a self-selecting for the community but containers and kubernetes broad adoption we've really not only crossed the chasm we're into the you know solid majority of of adoption here and yeah the the databases i mean dave you've covered things like the postgres uh world uh companies like crunchy data uh and some of these modern databases are really built for this type of environment and as you said they shouldn't have to think as much about okay i'm in a cloud or i'm in a different cloud this containerized platform that for applications can live in a lot of different places and that goes to kind of what we're seeing changing in the in the infrastructure world uh over the last couple years i'm glad to mention that a database i was interviewing josh uh at the postgres event and he was explaining to me how far kubernetes has actually come and and how much you know more trustworthy it is today still still some gaps but much different than even two or three years ago yeah i guess one of the highlights interesting at the kubecon europe uh there was the general availability of both the pipelines project and the get ops project it was it's argo cd is the project for for get ops and when that went ga for red hat we actually have that built into openshift at ga and not only was it ready to go we actually had a few customers that were ready to say hey we're using this and we're using the production so we had xa insurance one of the largest payers in the globe and the largest bank in turkey uh were two of the ones that we had saying hey we're using this for the audience if you're not familiar with git ops it's everything we use github as the repository of records so that this is kind of if you think about the old days we had the gold cd or the gold server well we do that for our entire stack that whole infrastructure's code that we've been talking about so many years but it will manage that for us so i patch it at the github level and it will enforce what i have in my environment so if somebody oh wait let me make a change no it's constantly validating things at github so it keeps it rather regimented so we've had uh as i mentioned a couple of customers we've seen a lot of interest in the public sector space because of course dave they're very concerned around security and patching and access and we want to keep that least access necessary so if we can keep that at the github level that's one of the things that will help your environment it really ties into the whole kind of git ops ai ops modern environment so it really ties all of it together as to kind of the the culture of the application and the infrastructure so your files your config files your policies same api same console that is how you get the scale yeah absolutely it's we we don't want the people to have to manage that as much you can let them focus on where they're going to add value to the business so let's talk about cloud cloud the definition of cloud is changing the cloud is expanding it's going on-prem there's hybrid connections to to a cloud or multiple clouds across clouds now as seems to be becoming more real we could talk about that and then maybe eventually out to the edge they're all real in their own right but how much is actually being connected together is something that i'm interested in but what are you seeing there what role is kubernetes playing yeah so first you talked about where applications live the latest data i've seen from kind of the the industry watchers is what are we dave 20 25 of applications are in the cloud that means there's a lot still in the data center if i look at open shift customers yes do we have a lot of them in the data center but then they are also using the public cloud so we have deep partnerships with amazon and azure to do public services in the cloud and our value is we give consistency across all of those environments so are using data center yes most customers still have data center do you have one or more clouds absolutely you know i used to love the andy jassy line um you know multi-cloud doesn't mean that you spread evenly across all the clouds most customers i talk to they have a primary provider that they partner with but things change over time we've seen plenty of customers go two or three years in and say well i have a strategic initiative sometimes they make an acquisition and they'll do another cloud or you know there's lots of factors why i might be doing more than one cloud there's certain industries where basically you have to have relationships with multiple vendors or there's there's regulations that you need to be concerned about so the answer is yes what we've been talking about more than a decade at red hat is open hybrid cloud and what does that mean today you might have not have planned it out but you're hybrid today and what are you going to be in the next decade you're going to be even more hybrid so edge if we talk about it everyone is talking about one of the biggest trends here is how does kubernetes go out to the edge even more that consistency message that i talked about where does openshift live openshift lives anywhere that red hat enterprise linux lives so rel am i going to have linux out of these small environments without a lot of resources what else are you going to have other than linux that's going to be the foundation of what you have so if i can have management and consistency that push out to all of those environments and we've been building out a portfolio something that you'll see us talking about more at kubecon in la is single node openshift so this is a really small footprint openshift but still have the consistency to work across all these environments and we've had different footprints basically to be able to do edge and remote offices whether you're talking from a service provider out to a full customer premise data center but there's there's a lot going on in the edge space we actually have we already have a public use case with verizon who's doing some of the ai use cases i'm sure you can picture with verizon being such a large telco the touch points that they have not only at the service provider but to their customer environments and openshift is the platform for enabling that innovation i mean if i had a big application portfolio on-prem you know legacy company with you know 100-year history obviously i'm going to be doing some stuff in the cloud i would be building some kind of abstraction layer that would could obviously modernize my on-premise state i would want to i would probably start with amazon i'd want to take advantage of aws cloud native tooling but i would absolutely be doing the same thing in azure and google and i would want to build my own cloud right and and and service my customers or or my company have people log into that cloud hide the underlying complexity of the technology and just simplify everything up level it and build a stack around that and probably build it on on openshift why not and of course kubernetes but there are alternatives there's there's eks anywhere for example which presumably is a competitor what do you how is that impacting the marketplace yeah so so dave as you said everybody is kind of extending beyond where they live so microsoft azure has their arc offering google has anthos and amazon was the last one i mean dave you'll remember this when we talked about hybrid and multi-cloud for a bunch of years it was like amazon doesn't talk about hybrid or multi-cloud and you know back when i sat on the analyst side i was like well you can't talk about hybrid and multi-cloud without talking about amazon so they've now uh eks anywhere something they announced back at re invent it just went generally available recently and so they have a distribution of kubernetes that you can use on your own so you could have completely disconnected in your data center running only on vmware is the only way that they support it today and they have in beta there's something called an eks connector so if you want it to be managed from the cloud and have someone more of that consistency they have the way to do that they've had eks which is their kubernetes service in amazon for a bunch of years but as a friend of the program corey quinn says there's actually 17 different ways to run containers in amazon today that's supported by amazon and you laugh at it but you know dave it's it's no different you know remember the storage world okay how many different storage products did emc have do you know how many compute and storage products amazon have they have a lot growing so one of those offerings that they have natively in the console is red hat openshift service for aws so is eksd a competitor well if you're an amazon customer and you want everything amazon and you want to use their environment in a hybrid environment yes you can do that part of the strategy for amazon is outpost we've got on our roadmap to be able to support openshift on outposts so you know we look at our our positioning is we are much more than kubernetes if you talk about the stack of tooling that we build on top of it we've done a real lot to make sure that developers have the tooling that they need from an amazon environment it's just the kubernetes piece it's a in the cloud it's a managed control plane in your own data center it's here's a kubernetes distribution good luck with it if you want monitoring and observability if you want more security if you want all these other pieces you need to build them on top of that as opposed to openshift gives you a full application development platform you know forrester wave we were you know far and away the top and to the right on on that uh spectrum with the leading position for both developers and operators so you know great to see amazon you know i i i hate to say they're like validating something that we do but look everybody's going to do it's true this is true i know that's the marketing line but and and i hate to do the the marketing line but um it's you will you see everyone rolls out their pieces and you say what is the game that they are playing it's amazon wants you to consume as much of their services as you can from a red hat standpoint it's well everywhere that rel can go we can go so openshift can live a lot of places we are going to give you the best experience in your data center in amazon in azure in google in your hosted in the edge we're going to work in all of those environments and we've got years of experience with thousands of production employments like in the data center eks anywhere sitting on top of vsphere as far as i know we have at red hat the most production kubernetes deployments on vmware are openshift actually at vmworld i'll be talking about i'm i'm on a panel talking about openshift on vsphere with vmware so long deep partnership that we've had there no one can speak to the breadth and depth of uh what we've done there uh what's the little line amazon always says there's no compression algorithm for experience well i like it okay but that's why i like your edge strategy because i've said many times the edge is going to be won by developers it's not going to be won by taking a you know x86 box throwing it over the fence and saying okay we got edge and i think you know that's tongue-in-cheek i think that the traditional enterprise hardware vendors are understanding that but they're not in a great position with developers you know maybe cisco a little bit with devnet but generally speaking you know vmware obviously uh it always has been struggling the edge is you know the challenge with the edge is you always have to look through it as to what your perspective is so we have a long and deep relationship with a lot of the telecommunications providers uh people will disparage openstack some but that's actually the solutions that we've sold the most into are network function virtualization for the telco and a lot of them have followed what they worked with us on openstack and continued that into openshift and verizon being one of those proof points you've seen my etr data and i tell you openstack keeps popping up and when you dig into it it's oh that's telco there may not be maybe there's not a region there and it's telcos developing their own cloud essentially and you know they're monetizing it so let's talk about um a cncf the ecosystem uh it's we have another slide on this if you guys wouldn't mind bringing it up i mean it's a complicated matter right you got here's the picture i mean it's like you can't read it because there's just so many people that wants to stop this from becoming you know kind of openstack too yeah that's a great question so chris wright our cto i thought really boiled it down really well one of the big problems with openstack is we were building a complete stack so when they said oh there's all these projects it's like okay well we're going to create a big tent and under that big tent you have to have all of these pieces and they all need to work together and while they were modular projects i needed to have that full stack validated and managing and maintaining that was a nightmare what is the cncf landscape it is you know what doesn't hundred more projects that are independent of what they had so yes kubernetes is the one that gets the most attention but takes something like service mesh service mesh has been around for a few years it's hot we're still early on the adoption trend service mesh works with kubernetes but it isn't limited to kubernetes it's one of those venn diagram it works with it but you can also work with my virtual environment it works in other places and that's true of a lot of these projects often they are complementary to kubernetes but i can adopt them standalone so the challenge is it is that paradox of choice when you go out there there are some people that want to go to the grocery store and buy all of their various pieces and put it all together well other people will come to us and say hey i just want my developers to get working i don't want them to spend all their time fighting over what they had and at red hat we say great we're going to have an opinionated platform and if you come down later and say oh there's a piece of it i don't want to use or i have some other tool i can have its batters are included they're optional and they're swappable so that's what's nice in this developer environment so you know we also work with you know companies like hashicorp a lot of our customers use vault for their secrets uh you know git lab is is another pure var in this industry that have a lot of developer tools they're not a kubernetes provider they usually sit higher up in the stack than we do so there's a lot of players there's a lot of room for activity and innovation yes we've seen a cambrian explosion of projects there and there has been some consolidation that's part of the job of the cncf is in the observability world they took uh i can't remember there were two projects that were kind of similar and they got them in a room and got them to agree to put them into a single project and put those together so we do see some consolidation over time but there's still room for a lot of growth standards are good but so is optionality i think is your point there so the event is october 11th to the 15th it's actually an in-person event you're planning on being there so i i am it's it's hybrid i know a lot of people will be online the other thing i'd point out there are a lot of day zero events so these are really awesome there's a git ops day there's security day there's so many different pieces i'll actually be for the day zero i'll be emceeing the openshift commons where we get a bunch of end users to just tell their stories projects they're working on deployments that they have have some good partner ecosystem discussion there it's usually a lot of fun we hope a bunch of people come to those in purses and then you know the day itself uh the the three days of the show itself are always hopping and lots of learning to be done uh whether you're there in person or online fantastic so i'm glad you pointed out it is a hybrid event that's kind of the nature of these things these days and i think we'll be for for some time i think potentially indefinitely i think people are realizing hey you know what as much of a pain in the neck as virtual events are we can reach a lot more people and it's a good on-demand experience so have at it stu thanks so much for for coming into the cube studios we miss you glad to see you're thriving and uh good luck at the show and uh we'll see you around the block thanks dave i know i'll be seeing john on the cube there too absolutely okay thanks for watching everybody this is dave vellante we'll see you next time you

>>Mhm Hello and welcome to the cubes coverage of AWS amazon web services, Global public Sector partner awards program. I'm john for your host of the cube here we're gonna talk about the best covid solution to great guests. Benham or with healthcare and life sciences lead at palantir Ben welcome to the cube SAm Michaels, Director of automation and compound management and Cats. National Center for advancing translational sciences and Cats. Part of the NIH National sort of health Gentlemen, thank you for coming on and and congratulations on the best covid solution. >>Thank you so much john >>so I gotta, I gotta ask you the best solution is when can I get the vaccine? How fast how long it's gonna last but I really appreciate you guys coming on. I >>hope you're vaccinated. I would say john that's outside of our hands. I would say if you've not got vaccinated, go get vaccinated right now, have someone stab you in the arm, you know, do not wait and and go for it. That's not on us. But you got that >>opportunity that we have that done. I got to get on a plane and all kinds of hoops to jump through. We need a better solution anyway. You guys have a great technical so I wanna I wanna dig in all seriousness aside getting inside. Um you guys have put together a killer solution that really requires a lot of data can let's step back and and talk about first. What was the solution that won the award? You guys have a quick second set the table for what we're talking about. Then we'll start with you. >>So the national covered cohort collaborative is a secure data enclave putting together the HR records from more than 60 different academic medical centers across the country and they're making it available to researchers to, you know, ask many and varied questions to try and understand this disease better. >>See and take us through the challenges here. What was going on? What was the hard problem? I'll see everyone had a situation with Covid where people broke through and cloud as he drove it amazon is part of the awards, but you guys are solving something. What was the problem statement that you guys are going after? What happened? >>I I think the problem statement is essentially that, you know, the nation has the electronic health records, but it's very fragmented, right. You know, it's been is highlighted is there's there's multiple systems around the country, you know, thousands of folks that have E H. R. S. But there is no way from a research perspective to actually have access in any unified location. And so really what we were looking for is how can we essentially provide a centralized location to study electronic health records. But in a Federated sense because we recognize that the data exist in other locations and so we had to figure out for a vast quantity of data, how can we get data from those 60 sites, 60 plus that Ben is referencing from their respective locations and then into one central repository, but also in a common format. Because that's another huge aspect of the technical challenge was there's multiple formats for electronic health records, there's different standards, there's different versions. And how do you actually have all of this data harmonised into something which is usable again for research? >>Just so many things that are jumping in my head right now, I want to unpack one at the time Covid hit the scramble and the imperative for getting answers quickly was huge. So it's a data problem at a massive scale public health impact. Again, we were talking before we came on camera, public health records are dirty, they're not clean. A lot of things are weird. I mean, just just massive amount of weird problems. How did you guys pull together take me through how this gets done? What what happened? Take us through the the steps He just got together and said, let's do this. How does it all happen? >>Yeah, it's a great and so john, I would say so. Part of this started actually several years ago. I explain this when people talk about in three C is that and Cats has actually established what we like to call, We support a program which is called the Clinical translation Science Award program is the largest single grant program in all of NIH. And it constitutes the bulk of the Cats budget. So this is extra metal grants which goes all over the country. And we wanted this group to essentially have a common research environment. So we try to create what we call the secure scientific collaborative platforms. Another example of this is when we call the rare disease clinical research network, which again is a consortium of 20 different sites around the nation. And so really we started working this several years ago that if we want to Build an environment that's collaborative for researchers around the country around the world, the natural place to do that is really with a cloud first strategy and we recognize this as and cats were about 600 people now. But if you look at the size of our actual research community with our grantees were in the thousands. And so from the perspective that we took several years ago was we have to really take a step back. And if we want to have a comprehensive and cohesive package or solution to treat this is really a mid sized business, you know, and so that means we have to treat this as a cloud based enterprise. And so in cats several years ago had really gone on this strategy to bring in different commercial partners, of which one of them is Palin tear. It actually started with our intramural research program and obviously very heavy cloud use with AWS. We use your we use google workspace, essentially use different cloud tools to enable our collaborative researchers. The next step is we also had a project. If we want to have an environment, we have to have access. And this is something that we took early steps on years prior that there is no good building environment if people can't get in the front door. So we invested heavily and create an application which we call our Federated authentication system. We call it unified and cats off. So we call it, you know, for short and and this is the open source in house project that we built it and cats. And we wanted to actually use this for all sorts of implementation, acting as the front door to this collaborative environment being one of them. And then also by by really this this this interest in electronic health records that had existed prior to the Covid pandemic. And so we've done some prior work via mixture of internal investments in grants with collaborative partners to really look at what it would take to harmonize this data at scale. And so like you mentioned, Covid hit it. Hit really hard. Everyone was scrambling for answers. And I think we had a bit of these pieces um, in play. And then that's I think when we turned to ban and the team at volunteer and we said we have these components, we have these pieces what we really need. Something independent that we can stand up quickly to really address some of these problems. One of the biggest one being that data ingestion and the harmonization step. And so I can let Ben really speak to that one. >>Yeah. Ben Library because you're solving a lot of collaboration problems, not just the technical problem but ingestion and harmonization ingestion. Most people can understand is that the data warehousing or in the database know that what that means? Take us through harmonization because not to put a little bit of shade on this, but most people think about, you know, these kinds of research or non profits as a slow moving, you know, standing stuff up sandwich saying it takes time you break it down. By the time you you didn't think things are over. This was agile. So take us through what made it an agile because that's not normal. I mean that's not what you see normally. It's like, hey we'll see you next year. We stand that up. Yeah. At the data center. >>Yeah, I mean so as as Sam described this sort of the question of data on interoperability is a really essential problem for working with this kind of data. And I think, you know, we have data coming from more than 60 different sites and one of the reasons were able to move quickly was because rather than saying oh well you have to provide the data in a certain format, a certain standard. Um and three C. was able to say actually just give us the data how you have it in whatever format is easiest for you and we will take care of that process of actually transforming it into a single standard data model, converting all of the medical vocabularies, doing all of the data quality assessment that's needed to ensure that data is actually ready for research and that was very much a collaborative endeavor. It was run out of a team based at johns Hopkins University, but in collaboration with a broad range of researchers who are all adding their expertise and what we were able to do was to provide the sort of the technical infrastructure for taking the transformation pipelines that are being developed, that the actual logic and the code and developing these very robust kind of centralist templates for that. Um, that could be deployed just like software is deployed, have changed management, have upgrades and downgrades and version control and change logs so that we can roll that out across a large number of sites in a very robust way very quickly. So that's sort of that, that that's one aspect of it. And then there was a bunch of really interesting challenges along the way that again, a very broad collaborative team of researchers worked on and an example of that would be unit harmonization and inference. So really simple things like when a lab result arrives, we talked about data quality, um, you were expected to have a unit right? Like if you're reporting somebody's weight, you probably want to know if it's in kilograms or pounds, but we found that a very significant proportion of the time the unit was actually missing in the HR record. And so unless you can actually get that back, that becomes useless. And so an approach was developed because we had data across 60 or more different sites, you have a large number of lab tests that do have the correct units and you can look at the data distributions and decide how likely is it that this missing unit is actually kilograms or pounds and save a huge portion of these labs. So that's just an example of something that has enabled research to happen that would not otherwise have been able >>just not to dig in and rat hole on that one point. But what time saving do you think that saves? I mean, I can imagine it's on the data cleaning side. That's just a massive time savings just in for Okay. Based on the data sampling, this is kilograms or pounds. >>Exactly. So we're talking there's more than 3.5 billion lab records in this data base now. So if you were trying to do this manually, I mean, it would take, it would take to thousands of years, you know, it just wouldn't be a black, it would >>be a black hole in the dataset, essentially because there's no way it would get done. Ok. Ok. Sam take me through like from a research standpoint, this normalization, harmonization the process. What does that enable for the, for the research and who decides what's the standard format? So, because again, I'm just in my mind thinking how hard this is. And then what was the, what was decided? Was it just on the base records what standards were happening? What's the impact of researchers >>now? It's a great quite well, a couple things I'll say. And Ben has touched on this is the other real core piece of N three C is the community, right? You know, And so I think there's a couple of things you mentioned with this, johN is the way we execute this is, it was very nimble, it was very agile and there's something to be said on that piece from a procurement perspective, the government had many covid authorities that were granted to make very fast decisions to get things procured quickly. And we were able to turn this around with our acquisition shop, which we would otherwise, you know, be dead in the water like you said, wait a year ago through a normal acquisition process, which can take time, but that's only one half the other half. And really, you're touching on this and Ben is touching on this is when he mentions the research as we have this entire courts entire, you know, research community numbering in the thousands from a volunteer perspective. I think it's really fascinating. This is a really a great example to me of this public private partnership between the companies we use, but also the academic participants that are actually make up the community. Um again, who the amount of time they have dedicated on this is just incredible. So, so really, what's also been established with this is core governance. And so, you know, you think from assistance perspective is, you know, the Palin tear this environment, the N three C environment belongs to the government, but the N 33 the entire actually, you know, program, I would say, belongs to the community. We have co governance on this. So who decides really is just a mixture between the folks on End Cats, but not just end cast as folks at End Cats, folks that, you know, and I proper, but also folks and other government agencies, but also the, the academic communities and entire these mixed governance teams that actually set the stage for all of this. And again, you know, who's gonna decide the standard, We decide we're gonna do this in Oman 5.3 point one um is the standard we're going to utilize. And then once the data is there, this is what gets exciting is then they have the different domain teams where they can ask different research questions depending upon what has interest scientifically to them. Um and so really, you know, we viewed this from the government's perspective is how do we build again the secure platform where we can enable the research, but we don't really want to dictate the research. I mean, the one criteria we did put your research has to be covid focused because very clearly in response to covid, so you have to have a Covid focus and then we have data use agreements, data use request. You know, we have entire governance committees that decide is this research in scope, but we don't want to dictate the research types that the domain teams are bringing to the table. >>And I think the National Institutes of Health, you think about just that their mission is to serve the public health. And I think this is a great example of when you enable data to be surfaced and available that you can really allow people to be empowered and not to use the cliche citizen analysts. But in a way this is what the community is doing. You're doing research and allowing people from volunteers to academics to students to just be part of it. That is citizen analysis that you got citizen journalism. You've got citizen and uh, research, you've got a lot of democratization happening here. Is that part of it was a result of >>this? Uh, it's both. It's a great question. I think it's both. And it's it's really by design because again, we want to enable and there's a couple of things that I really, you know, we we clamor with at end cats. I think NIH is going with this direction to is we believe firmly in open science, we believe firmly in open standards and how we can actually enable these standards to promote this open science because it's actually nontrivial. We've had, you know, the citizen scientists actually on the tricky problem from a governance perspective or we have the case where we actually had to have students that wanted access to the environment. Well, we actually had to have someone because, you know, they have to have an institution that they come in with, but we've actually across some of those bridges to actually get students and researchers into this environment very much by design, but also the spirit which was held enabled by the community, which, again, so I think they go they go hand in hand. I planned for >>open science as a huge wave, I'm a big fan, I think that's got a lot of headroom because open source, what that's done to software, the software industry, it's amazing. And I think your Federated idea comes in here and Ben if you guys can just talk through the Federated, because I think that might enable and remove some of the structural blockers that might be out there in terms of, oh, you gotta be affiliate with this or that our friends got to invite you, but then you got privacy access and this Federated ID not an easy thing, it's easy to say. But how do you tie that together? Because you want to enable frictionless ability to come in and contribute same time you want to have some policies around who's in and who's not. >>Yes, totally, I mean so Sam sort of already described the the UNa system which is the authentication system that encounters has developed. And obviously you know from our perspective, you know we integrate with that is using all of the standard kind of authentication protocols and it's very easy to integrate that into the family platform um and make it so that we can authenticate people correctly. But then if you go beyond authentication you also then to actually you need to have the access controls in place to say yes I know who this person is, but now what should they actually be able to see? Um And I think one of the really great things in Free C has done is to be very rigorous about that. They have their governance rules that says you should be using the data for a certain purpose. You must go through a procedure so that the access committee approves that purpose. And then we need to make sure that you're actually doing the work that you said you were going to. And so before you can get your data back out of the system where your results out, you actually have to prove that those results are in line with the original stated purpose and the infrastructure around that and having the access controls and the governance processes, all working together in a seamless way so that it doesn't, as you say, increase the friction on the researcher and they can get access to the data for that appropriate purpose. That was a big component of what we've been building out with them three C. Absolutely. >>And really in line john with what NIH is doing with the research, all service, they call this raz. And I think things that we believe in their standards that were starting to follow and work with them closely. Multifactor authentication because of the point Ben is making and you raised as well, you know, one you need to authenticate, okay. This you are who you say you are. And and we're recognizing that and you're, you know, the author and peace within the authors. E what do you authorized to see? What do you have authorization to? And they go hand in hand and again, non trivial problems. And especially, you know, when we basis typically a lot of what we're using is is we'll do direct integrations with our package. We using commons for Federated access were also even using login dot gov. Um, you know, again because we need to make sure that people had a means, you know, and login dot gov is essentially a runoff right? If they don't have, you know an organization which we have in common or a Federated access to generate a login dot gov account but they still are whole, you know beholden to the multi factor authentication step and then they still have to get the same authorizations because we really do believe access to these environment seamlessly is absolutely critical, you know, who are users are but again not make it restrictive and not make it this this friction filled process. That's very that's very >>different. I mean you think about nontrivial, totally agree with you and if you think about like if you were in a classic enterprise, I thought about an I. T. Problem like bring your own device to work and that's basically what the whole world does these days. So like you're thinking about access, you don't know who's coming in, you don't know where they're coming in from, um when the churn is so high, you don't know, I mean all this is happening, right? So you have to be prepared two Provisions and provide resource to a very lightweight access edge. >>That's right. And that's why it gets back to what we mentioned is we were taking a step back and thinking about this problem, you know, an M three C became the use case was this is an enterprise I. T. Problem. Right. You know, we have users from around the world that want to access this environment and again we try to hit a really difficult mark, which is secure but collaborative, Right? That's that's not easy, you know? But but again, the only place this environment could take place isn't a cloud based environment, right? Let's be real. You know, 10 years ago. Forget it. You know, Again, maybe it would have been difficult, but now it's just incredible how much they advanced that these real virtual research organizations can start to exist and they become the real partnerships. >>Well, I want to Well, that's a great point. I want to highlight and call out because I've done a lot of these interviews with awards programs over the years and certainly in public sector and open source over many, many years. One of the things open source allows us the code re use and also when you start getting in these situations where, okay, you have a crisis covid other things happen, nonprofits go, that's the same thing. They, they lose their funding and all the code disappears. Saying with these covid when it becomes over, you don't want to lose the momentum. So this whole idea of re use this platform is aged deplatforming of and re factoring if you will, these are two concepts with a cloud enables SAM, I'd love to get your thoughts on this because it doesn't go away when Covid's >>over, research still >>continues. So this whole idea of re platform NG and then re factoring is very much a new concept versus the old days of okay, projects over, move on to the next one. >>No, you're absolutely right. And I think what first drove us is we're taking a step back and and cats, you know, how do we ensure that sustainability? Right, Because my background is actually engineering. So I think about, you know, you want to build things to last and what you just described, johN is that, you know, that, that funding, it peaks, it goes up and then it wanes away and it goes and what you're left with essentially is nothing, you know, it's okay you did this investment in a body of work and it goes away. And really, I think what we're really building are these sustainable platforms that we will actually grow and evolve based upon the research needs over time. And I think that was really a huge investment that both, you know, again and and Cats is made. But NIH is going in a very similar direction. There's a substantial investment, um, you know, made in these, these these these really impressive environments. How do we make sure the sustainable for the long term? You know, again, we just went through this with Covid, but what's gonna come next? You know, one of the research questions that we need to answer, but also open source is an incredibly important piece of this. I think Ben can speak this in a second, all the harmonization work, all that effort, you know, essentially this massive, complex GTL process Is in the N three Seagate hub. So we believe, you know, completely and the open source model a little bit of a flavor on it too though, because, you know, again, back to the sustainability, john, I believe, you know, there's a room for this, this marriage between commercial platforms and open source software and we need both. You know, as we're strong proponents of N cats are both, but especially with sustainability, especially I think Enterprise I. T. You know, you have to have professional grade products that was part of, I would say an experiment we ran out and cast our thought was we can fund academic groups and we can have them do open source projects and you'll get some decent results. But I think the nature of it and the nature of these environments become so complex. The experiment we're taking is we're going to provide commercial grade tools For the academic community and the researchers and let them use them and see how they can be enabled and actually focus on research questions. And I think, you know, N3C, which we've been very successful with that model while still really adhering to the open source spirit and >>principles as an amazing story, congratulated, you know what? That's so awesome because that's the future. And I think you're onto something huge. Great point, Ben, you want to chime in on this whole sustainability because the public private partnership idea is the now the new model innovation formula is about open and collaborative. What's your thoughts? >>Absolutely. And I mean, we uh, volunteer have been huge proponents of reproducibility and openness, um in analyses and in science. And so everything done within the family platform is done in open source languages like python and R. And sequel, um and is exposed via open A. P. I. S and through get repository. So that as SaM says, we've we've pushed all of that E. T. L. Code that was developed within the platform out to the cats get hub. Um and the analysis code itself being written in those various different languages can also sort of easily be pulled out um and made available for other researchers in the future. And I think what we've also seen is that within the data enclave there's been an enormous amount of re use across the different research projects. And so actually having that security in place and making it secure so that people can actually start to share with each other securely as well. And and and be very clear that although I'm sharing this, it's still within the range of the government's requirements has meant that the, the research has really been accelerated because people have been able to build and stand on the shoulders of what earlier projects have done. >>Okay. Ben. Great stuff. 1000 researchers. Open source code and get a job. Where do I sign up? I want to get involved. This is amazing. Like it sounds like a great party. >>We'll send you a link if you do a search on on N three C, you know, do do a search on that and you'll actually will come up with a website hosted by the academic side and I'll show you all the information of how you can actually connect and john you're welcome to come in. Billion by all means >>billions of rows of data being solved. Great tech he's working on again. This is a great example of large scale the modern era of solving problems is here. It's out in the open, Open Science. Sam. Congratulations on your great success. Ben Award winners. You guys doing a great job. Great story. Thanks for sharing here with us in the queue. Appreciate it. >>Thank you, john. >>Thanks for having us. >>Okay. It is. Global public sector partner rewards best Covid solution palantir and and cats. Great solution. Great story. I'm john Kerry with the cube. Thanks for watching. Mm mm. >>Mhm

(upbeat music) >> Welcome back to the Quantcast Industry Summit on the demise of third-party cookies, The Cookie Conundrum, A Recipe for Success. We're here with Peter Day, the CTO, Quantcast and Shruti Koparkar, Head of Product Marketing Quancast. Thanks for coming on. Talk about the changing advertising landscape. >> Thanks for having us. >> Thank you for having us. >> So we've been hearing the story out to the big players, want to keep the data, make that centralized, control all the leverage, and then you've got the other end. You've got the open internet that still wants to be free and valuable for everyone. What's what are you guys doing to solve this problem? Because if cookies go away, what's going to happen there? How do people track things? You guys are in this business? First question, what is Quancast strategy to adapt to third-party cookies going away? What's going to be the answer? >> Yeah, so very rightly said, John. The mission, the Quancast mission is to champion of free and open internet. And with that in mind, our approach to this a world without third party cookies is really grounded in three fundamental things. First is industry standards. We think it's really important to participate and to work with organizations who are defining the standards that will guide the future of advertising. So with that in mind we've been participating with IAB Tech Lab, We've been part of their project, we are same thing with Prebid, who's kind of trying to figure out the pipes of identity the ID pipes of the future. And then also is W3C which is the World Wide Web Consortium. And our engineers and our engineering team are participating in their weekly meetings, trying to figure out what's happening with the browsers and keeping up with the progress there on things such as Google's FLoC. The second sort of thing is interoperability. As you've mentioned that a lots of different ID solutions that are emerging. You have UID 2.0, you have LiveRamp, you have Google's FLoC, and there will be more, there are more, and they will continue to be more. We really think it is important to build a platform that can ingest all of these signals. And so that's what we've done. The reason really is to meet our customers where they are at. Today our customers use multiple Data Management Platforms, DMPs. And that's why we support multiple of those. This is not going to be much different than that. We have to meet our customers where we are, or where they are at. And then finally, of course, which is at the very heart of who Quancast is, is innovation. As you can imagine being able to take all of these multiple signals in, including the IDs and the cohorts, but also others like contextual first party consent is becoming more and more important. And then there are many other signals like time, language, geolocation. So all of these signals can help us understand user behavior, intent and interests. In absence of third party cookies. However there's something to note about these. They're very raw, they're complex, they're messy, all of these different signals. They are changing all the time, their real time. And those incomplete in information isolation, just one of these signals can not help you build up true and complete picture. So what you really need is a technology like AI and Machine Learning, to really bring all of these signals together, combine them statistically, and get an understanding of user behavior intent and interest, and then act on it. Be it in terms of providing audience insights, or responding to bid requests and so on and so forth. So those are sort of the three fundamentals that our approach is grounded in which is industry standards, interoperability, and innovation. And you know, you have Peter here >> Yeah. who is the expert so you can dive much deeper into it. >> So Peter is CTO. You've got to tell us, how is this going to actually work? What are you guys doing from a technology standpoint to help with data-driven advertising and a third-party cookieless world? >> Well, we've been this is not a shock. You know, I think anyone who's been close to this space has known that the third party cookie has been reducing in quality in terms of its pervasiveness and its longevity for many years now. And the kind of death knell is really Google Chrome, making the changes that, they're going to be making. So we've been embarrassing in this space for many years and we've had to make a number of hugely diverse investments. So one of them is in how to, as a marketer how do I tell it my marketing still working in a world without (indistinct). The majority of marketers, completely relying on third party cookies today. It's tell them if their marketing is working or not. And so we've had to invest heavily and statistical techniques, which are closer to kind of echo metric models that marketers are used to have things like out of home advertising. It's going to be establishing whether their advertising is working or not in a digital environment. And actually this as with often the case in these kind of times of massive disruption, there's always opportunity to make things better. And we really think that's true. And you know, digital measurement is often mistaken precision for accuracy and there's a real opportunity to kind of see the wood for the trees if you'd like. And start to come up with better methods of measuring the effectiveness of advertising without third party cookies. And we've had to make countless other investments in areas like contextual modeling, and targeting that third-party cookies and connecting directly to publishers rather than going through this kind of loom escape that's going to tied together third party cookies. So I could, if I was to enumerate all the investments we've made I think it would be here till midnight, but we've had to make a number of investments over a number years. And that level investments only increasing at the moment. >> Peter, on that contextual, can you just double click on that and tell us more? >> Yeah, I mean, contextual it is, unfortunately when I think this is really poorly defined. It can mean everything from a publisher saying, Hey trust us this page is about SUV's, it's a what's possible now. And it's only really been possible the last couple of years which is to build statistical models of the entire internet based on the content that people are actually consuming. And this type of technology requires massive data processing capabilities, it's able to take advantage of the latest innovations in areas like natural language processing. And really gives computers, that kind of much deeper and richer understanding of the internet, which ultimately makes it possible to kind of organize the internet, in terms of the types of content of pages. So this type of technology has only been possible for the last few years. And, but we've been using contextual signals since our inception. Had always been massively predictive in terms of audience behaviors, in terms of where advertising is likely to work. And so we've been very fortunate to keep that investment going and take advantage of many of these innovations that are happening in academia and in kind of an adjacent areas >> On the AI and Machine Learning aspect. That seems to be a great differentiator in this day and age for getting the most out of the data. How is machine learning and AI factoring into your platform? >> I think it's how we've always operated, right from our inception. When we started as a measurement company. The way that we were giving our customers at the time we were just publishers, just the publisher side of our business. Insights into who their audience was, which was using Machine Learning techniques. And that's never really changed. The foundation of our platform has always been Machine Learning from before it was cool. A lot of our, kind of a lot of our co-teams have backgrounds in Machine Learning, and the PhDs in statistics and Machine Learning. And that really drives our decision-making. I mean, data is only useful if you can make sense of it and if you can organize it, and if you can take action on it, and to do that at this kind of scale it's absolutely necessary to use Machine Learning technology. >> So you mentioned contextual also, you know, in advertising we have everyone knows and that world that you got the contextual and behavioral dynamics. The behavior that's kind of generally can everyone's believing is happening. The consensus is undeniable is that, people are wanting to expect an environment where there's trust, there's truth, but also they want to be locked in. They don't want to get walled into a walled garden. Nobody wants to be in a wall garden. They want to be free to pop around and visit sites. It's more horizontal scalability than ever before yet. The bigger players are becoming walled garden vertical platforms. So with future of AI, the experience is going to come from this data. So the behaviors out there. How do you get >> Yeah. that contextual relevance and provide the horizontal scale that users expect? >> Yeah, I think it's a really good point and we're definitely at this kind of tipping point, we think in the broader industry. I think, you know, every publisher, right? We're really blessed to work with the biggest publishers in the world. All the way through to my mom's blog, right? So we get to hear the perspectives of the publishers at every scale. And they consistently tell us the same thing. Which is they want some more directly connect to consumers. They don't want to be tied into these walled gardens, which dictate how they must present their content. And in some cases what content they're allowed to present. And so, you know, our job as a company is to really provide level the playing field a little bit. Provide them the same capabilities they're only used to in the walled gardens, but let, give them more choice. In terms of how they structure their content, how they organize their content, how they organize their audiences, but make sure that they can fund that effectively. By making their audiences and their environments discoverable by marketers, measurable by marketers, and connect them as directly as possible to make that kind of ad funded economic model, as effective in the open internet as it is in social. And so a lot of the investments we've made over recent years have been really to kind of realize that vision, which is, it should be as easy for a marketer to be able to understand people on the open internet, as it is in social media. It should be as effective for them to reach people in that environment, is really high quality content as it is on Facebook. And so we've invested a lot of our R&D dollars in making that true. And we're now live with the Quantcast Platform which does exactly that. And as third party cookies go away, it only kind of exaggerate all kind of further emphasizes the need for direct connections between brands and publishers. And so we just want to build a technology that helps make that true, and gives the kind of technology to these marketers and publishers to connect, and to deliver great experiences without relying on these kind of walled gardens. >> Yeah. The direct to consumer, direct to audience is a new trend. You're seeing it everywhere. How do you guys support this new kind of signaling from for that's happening in these new world? How do you ingest the content, ingest this consent signaling? >> We were really fortunate to have an amazing an amazing R&D team. And, you know, we've had to do all sorts to make this, you know, to realize our vision. This has meant things like we, you know we have crawlers which stand the entire internet at this point, extract the content of the pages, and kind of make sense of it, and organize it. And organize it for publishers so that they can understand how their audiences overlap with potentially their competitors or collaborators, but more importantly, organize it for marketers. So they can understand what kind of high-impact opportunities are there for them there. So, you know, we've had to build a lot of technology. We've had to build analytics engines which can get answers back in seconds, so that, you know marketers and publishers can kind of interact with it with their own data and make sense of it and present it in a way that is compelling and then help them drive their strategy as well as their execution. We've had to invest in areas like consent management. Because we believe that a free and open internet is absolutely reliant on trust. And therefore we spend a lot of our time thinking about how do we make it easy for end-users to understand who has access to that data and easy friendly and users to be able to opt out. And as a result of that, we've now got the world's most widely adopted consent management platform. So it's hard to tackle one of these problems without tackling all of them. And we're fortunate enough to have had a large enough R&D budget over the last four or five years, make a number of investments, everything from consent and identity, through to contextual signals, through to measurement technologies, which really bring advertisers and publishers closer together. >> Great insight there. Shruti last word for you. What's the customer view here as you bring these new capabilities of the platform. What's what are you guys seeing as the highlight from a platform perspective? >> So the initial response that we've seen from our customers has been very encouraging. Both on the publisher side, as well as the marketer side. I think, you know, one of the things we hear quite a lot is you guys are at least putting forth a solution and action solution for us to test. Peter mentioned measurement. That really is where we started because you cannot optimize what you cannot measure. So that is where his team has started. And we have some measurement, very very initial capabilities still in alpha, but they are available in the platform for marketers to test out today. So the initial response has been very encouraging. People want to engage with us. Of course, our, you know, our fundamental value proposition which is that the Quantcast platform was never built to be reliant on third party data, these stale segments. Like we operate we've always operated on real time live data. The second thing is our premium publisher relationships. We have had the privilege of working like Peter served with some of the biggest publishers but we also have a very wide footprint. We have first party tags across over a hundred million plus web and mobile destinations. And, you know, as you must've heard like that sort of first party footprint, is going to come in really handy in a world without third party cookies. We are encouraging all of our customers, publishers and marketers to grow their first party data. And so that's something that's a strong point that customers love about us and lean into it quite a bit. So, yeah, the initial response has been great. Of course it doesn't hurt that we've made all these R&D investments. We can talk about consent, and, you know, I often say that consent it sounds simple, but it isn't, there's a lot of technology involved. But there's lots of legal work involved as it as well. We have a very strong legal team who has expertise built in. So yeah, a very good response initially. >> Democratization, everyone's a publisher, everyone's a media company. They have to think about being a platform. You guys provide that. So congratulations Peter, thanks for dropping the gems there. Shruti thanks for sharing the product highlights. Thanks for your time. >> Thank you. >> Okay, this is the Quancast Industry Summit on the demise of third-party cookies and what's next The Cookie Conundrum, the Recipe for success with Quancast I'm John Berger with theCUBE. Thanks for watching. (upbeat music)

>>10 years ago, a group of industry storage veterans formed a company called Infinidat. The DNA of the company was steeped in the heritage of its founder, Moshe Yanai, who had a reputation for relentlessly innovating on three main areas, the highest performance, rock solid availability, and the lowest possible cost. Now these elements have historically represented the superpower triumvirate of a successful storage platform. Now, as Infinidat evolved, landed on a fourth vector, that has been a key differentiator and its value proposition, and that is petabyte scale. Hello everyone. And welcome to this Qube conversation. My name is Dave Vellante and I'm pleased to welcome in two longtime friends of theCube. Phil Bullinger is newly minted CEO of Infinidat and of course, Lee Caswell, VMware's VP of Marketing for the cloud platform business unit. Gents, welcome. >>Great to be here. Always good to see you guys. Phil, so you're joining at the 10 year anniversary mark. Congratulations on the appointment. What attracted you to the company? >>You know I spent a long time in my career at enterprise storage and, and enjoying many of the opportunities, you know, through a number of companies. Last fall when I became aware of the Infinidat opportunity and it immediately captured my attention because of frankly my respect for the product through several opportunities I've had with enterprise customers in selling cycles of different products, if they happened to be customers of Infinidat, , they were not bashful about talking about their satisfaction with the product, their level of delight with it. And so I think from, from the sidelines, I've always had a lot of respect for the Infinidat platform, the implementation of the product quality and reliability that it's kind of legendary for. And so when the opportunity came along, it really captured my interest in of course behind a great product is almost always a great team. >>And as I got to know the company and the board, and, you know, some of the leaders, and learned about the momentum and the business, it was just a very, very compelling opportunity for me. And I'll have to say just, you know, 60 days into the job. Everything I hoped for is here, not only a warm welcome to the company, but an exciting opportunity with respect to where Infinidat is at today with the growth of the business. The company has achieved a level of consistent growth through 2020, cashflow positive, EBITDA positive. And now it's a matter of scaling, scaling the business and it's something that I have had success with several times in my career and really, really enjoying the opportunity here at Infinidat to do that. >>That's great. Thanks for that. Now, of course, Lee, VMware was founded nearly a quarter century ago and carved out a major piece of the enterprise pie and predominantly that's been on prem, but the data center's evolving the cloud is evolving, and this universe is expanding. How do you see the future of that on-prem data center? >>No, I think Satya recently said, right, that, that we've reached max consolidation almost right. You pointed that out earlier. I thought that was really interesting, right. You know, we believe in the distributed hybrid cloud and you know, the reasons for that actually turn out to be storage led in there and in, in the real thinking about it, because we're going to have distributed environments and, you know, one of the things that we're doing with Infinidat here today, right, is we're showing how customers can invest intelligently and responsibly on prem and have bridges in across the hybrid cloud. We do that through something called the VMware Cloud Foundation. That's a full stack offering that, uh, an interesting here, right? It started off with a HCI element, but it's expanded into storage and storage at scale, you know, because storage is going to exist... We have very powerful storage value propositions, and you're seeing customers go and deploy both. We're really excited about seeing Infinidat lean into the VMware Cloud Foundation and vVols actually as a way to match the pace of change in today's application world. >>These trends, I mean, building bridges is what we called it. And so that takes a lot of hard work, especially when you're doing from on-prem into hybrid, across clouds, eventually the edge, you know, that's a, that's a non-trivial task. How do you see this playing out in market trends? >>Yeah. You know, we're, we're in the middle of this every day as, as you know, Dave, uh, and certainly Lee, uh, data center architectures ebb and flow from centralized to decentralized, but clearly data locality, I think, is driving a lot of the growth of the distributed data center architecture, the edge data centers, but core is still very significant for, for most enterprise. Uh, and it's, it's, it has, it has a lot to do with the fact that most enterprises want to own their own cloud. You know, when a Fortune 15 or a Fortune 50 or Fortune 100 customer, when they talk about their cloud, they don't want to talk about, you know, the AWS cloud or the GCP cloud or the Azure cloud. They want to talk about their cloud. And almost always, these are hybrid architectures with a large on-prem or colo footprint. >>Uh, the reason for that number of reasons, right? Data sovereignty is a big deal, uh, among the highest priorities for enterprise today. The control of the security, the, the ability to recover quickly from ransomware attacks, et cetera. These, these are the things that are just fundamentally important, uh, to the business continuity and enterprise risk management plan for these companies. But I think one thing that has changed the on prem data center is the fact that it's the core operating characteristics have to take on kind of that public cloud characteristic. It has to be a transparent, seamless scalability. I think the days of, of CIO's  you know, even tolerating people showing up in their data centers with, with disk trays under their arms to add capacity is, is over. Um, they want to seamlessly add capacity. They want nonstop operation, a hundred percent uptime is the bar. >>Now it has to be a consolidation. Massive consolidation is clearly the play for TCO and efficiency. They don't want to have any compromises between scale and availability and performance. You know, the, the very characteristics that you talked about upfront, Dave, that make Infinidat unique, I think are fundamentally the characteristics that enterprises are looking for when they build their cloud on prem. Uh, I, I think our architecture also really does provide a, a set it and forget it, uh, kind of experience. Um, when we install a new Infinidat frame in an enterprise data center, our intentions are we're, we're not going to come back. We don't intend to come back, uh, to, to help fiddle with the bits or, uh, you know, tweak the configuration as applications and, and multitenant users are added. And then of course, flexible economic models. I mean, everybody takes this for granted, but you really, really do have to be completely flexible between the two rails, the CapEx rail and the OpEx rail and every, uh, every step in between. And importantly, when a customer, when an enterprise customer needs to add capacity, they don't have a sales conversation. They just want to have it right. They're already running in their data center. And that's the experience that we provide. >>Yeah. You guys are aligned in that vision, that layer, that abstracts the complexity from the underlying wherever cloud on prem, et cetera. Right. Let's talk about the VMware and Infinidat relationship. I mean, every, every year at VMworld, up until last year, thank you COVID, Infinidat would host this awesome dinner. You'd have the top customers there. Very nice Vegas steak restaurant. I, of course, I always made a point to stop by not just for the food. I mean, I was able to meet some customers and I've talked to many dozens over the years, Phil, and I can echo that sentiment, but, you know, why is the VMware ecosystem so important to Infinidat? And I guess the question there is, is, is petabyte scale that really that prominent in the VMware customer base? >>It's a, it's a very, very important point. VMware is the longest standing Alliance partner of Infinidat. It goes back to really, almost the foundation of the company, certainly starting with the release one, the very first commercial release of Infinidat VMware and a very tight integration with the VMware was a core part of that. Uh, we, we have a capability. We call the Host PowerTools, which drives a consistent best practices implementation around our, our VMware, uh, integration and, and how it's actually used in the data center. And we built on that through the years through just a deep level of integration. And, um, our customers typically are, are at scale petabyte scale or average deployment as a petabyte and up, um, and over 90% of our customers use VMware. So you would say, I, I think I can safely say we're we serve the VMware environment for some of VMware's largest enterprise footprints, uh, in the market. >>I know it's like children, you got, you love all your partners, but is there anything about Infinidat that, that stands out to you a particular area where, where they shine that from your perspective? >>Yeah, I think so. You know, the, the best partnerships, one are ones that are customer driven. It turns out right. And the idea that we have joint customers at large scale and listen storage is a tough business to get, right, right. It takes time to go and mature to harden a code base. Right. And particularly when you're talking about petabyte scale, right now, you've basically got customers buying in for the largest systems. And what we're seeing overall is customers are trying to do more things with fewer component elements, makes sense, right? And so the scale here is important because it's not just scale in terms of like capacity, right. It's scale in terms of performance as well. And so, as you see customers trying to expand the number of different types of applications, this is one of the things we're seeing, right. Is new applications, which could be container-based Kubernetes orchestrated our Tanzu portfolio helps with that. >>Right. If you see what we're doing with Nvidia, for example, we announced some AI work, right. Uh, this week with vSphere. And so what you're starting to see is like the changing nature of applications and the fast pace of applications is really helping customers save us. And I want to go and find solutions that can meet the majority of my needs. And that's one of the things that we're seeing. And particularly with the vVols integration at scale, that we just haven't seen before, uh, and Infinidat has set the bar and is really setting a new, a new record for that. >>Yeah. Let me, let me comment on that a little bit, Dave, we've been a core part of the VMware Cloud Solutions Lab, which is a very, very exciting engaging, investment that VMware has made. A lot of people have contributed to in the industry, but in the, in the VMware Cloud Solutions Lab, we recently demonstrated on a single Infinidat frame over 200,000 vVols on a single system. And I think that not only edges up the bar, I think it completely redefines what, what scale means when you're talking about a vVols implementation. >>So not to geek out here, but vVols, they're kind of a game changer because instead of admins, having to manually allocate storage to performance tiers. An array, that is VASA certified, VASA is VMware, or actually vStorage API for, for storage awareness, VASA, anyway, with vVols, you can dynamically provision storage that matches the way I say it as a match as device attributes to the data and the application requirements of the VM. So Phil,  it seems like so much in VMware land hearkens back to the way mainframes used to solve problems in a modern way. Right. And vVols is a real breakthrough in that regard in terms of storage. So, so how do you guys see it? I, I presume you're, you're sort of vVols certified based on what you just said in the lab. >>Yeah. We recently announced our vVols release and we're not the first to market with the vVols, but from, from the start of the engineering project, we wanted to do it. We wanted to do it the way we think. We think at scale in everything we do, and our customers were very prescriptive about the kind of scale and performance and availability that they wanted to experience in vVols. And we're now seeing quite a bit of customer interest with traction in it. Uh, as I said, we, we redefined the bar for vVols scalability. We support on a single array now, um, a thousand storage containers. Uh, and I think most of our competition is like at one or maybe 10 or 13 or something like that. So, uh, our customers are, again at scale, they said, if you're going to do vVols, we want it... We want it at scale. We want it to embody the characteristics of your, of your platform. We really liked vVols because it, it helps, it helps separate kind of the roles and responsibilities between the VI administrator and the storage system administrator. If you're going to put a majority of your most critical bits on Infinidat in your data center, you're going to want to, you're going to want to have control over how that resource is used, but yet the vVols mplementation and the tools that we provide with that deep level of integration, give the VI, the VI administrator, all of the flexibility they need to manage applications. And vVols of course gives the VI administrator the native use of our snapshot technology. And so it makes it incredibly easy for them to administrate the platform without having to worry about the physical infrastructure, but yet the people worried about the physical infrastructure still have control over that resource. So it's, it's a game changer as far as we're concerned. >>Yeah. Storage has come a long way. Hasn't it, Lee? I'm wondering if you could add some color here, it seems in talking to ... Uh, so that's interesting. You've had, you had a hand in the growth of vSAN and it was very successful product, but he chose Infinidat for that higher end application. It seems like vVols are a key innovation in that regard. How's the vVols uptake going from your perspective. >>Yeah, I think we you know, we're in the second phase of vVols adoption, right? First phase was, Hey, technically interesting, intriguing. Um, but adoption was relatively low, I think because, you know, up until five years ago, um, applications, weren't actually changing that fast. I mean, think about it, right? The applications, ERP systems, CRM systems, you weren't changing those at the pace of what we're doing today. Now what's happening is every business is a software business. Every business, when you work, when you interact with your healthcare provider right now, it's about the apps. Like, can you go and get your schedules online? Can you email your doctors? Right? Can you go and get your labs? Right? The pace of new application development, we have some data showing that there will be more apps developed in the next five years, and then the past 40 years of computing combined. >>And so when you think about that, what's changed now is trying to manage that all from the kind of storage hardware side was just actually getting in the way you want to organize around the fastest beat rate in your infrastructure today. That's the application. So what vVols has helped you do is it allows the vSphere administrator, who's managing VMs and looking at the apps and the changing pace, and be able to basically select storage attributes, including QoS, capacity, IOPS, and do that from the vCenter console, and then be able to rectify things and manage them right from the console right next to the apps. And that provides a really integrated way. So when you have a close interaction, like what we're talking about today, or, you know, integration, um, that the Infinidat has provided now, you've got this ability to have a faster moving activity. And, you know, consolidation is one of the themes you've heard from time to time from VMware, we're consolidating the management so that the vSphere administrator can now go and manage more things. What traditional VMs yes. VMs across HCI. Sure. Plus now, plus storage and into the hybrid cloud and into like containers. It's that consolidated management, which is getting us speed and basically a consumer like experience for infrastructure deployments. >>Yeah. Now Phil mentioned the solutions lab. We've got a huge ecosystem. Several years ago, you launched this, this via the VMware. I think it's called the VMware Cloud Solutions Lab is the official name. What, explain what it does for collaboration and joint solutions development. And then Phil, I want you to go into more detail about what your participation is, but Lee, why don't you explain it? >>Yeah. You know, we don't take just any products that, because listen, there's a mixing. What we take is things that really expand that innovation frontier. And that's what we saw with Infinidat was expanding the frontier on like large capacity for many, many different mixed workloads and a commitment, right. To go and bring in, not just vVols support, of course, all the things we do for just a normal interaction with vSphere. But, uh, bringing vVols in was certainly important in showing how we operate at scale. And then importantly, as we expanded the VCF, VMware Cloud Foundation, to include storagee systems for a customer, for example, right, who has storage and HCI, right? And it looks for how to go and use them. And that's an individual choice at a customer level. We think this is strategically important. Now, as we expand a multicloud experience, that's different from the hyperscalers. Hyperscalers are coming in with two kind of issues, maybe, right? So one is it's single cloud. And the other one is there's a potential competitive aspect or from some right around the ongoing, underlying business and a hyperscaler business model. And so what VMware uniquely is doing is extending a common control plane across storage systems and HCI, and doing that in a way that basically gives customers choice. And we love that the cloud lab is really designed to go and make that a reality for customers strip out perceived and real risk. >>Yeah. To Lee's point of, it's not like there's not dozens and dozens and dozens of logos on the slide for the lab. I think there's like, you know, 10 or 12 from what I saw and Infinidat is one of them. Maybe you could talk a little bit more about your participation in the program and what it does for customers. >>Yeah, absolutely. And I would agree it's I, we liked the lab because it's not just supposed to be one of everything eye candy it's a purpose-built lab to do real things. And we like it because we can really explore, you know, some of the most contemporary, workloads in that environment, as well as solutions to what I considered some of the most contemporary industry problems. We're participating in a couple of ways. I believe we're the only petabyte scale storage solution in the Cloud Solutions Lab at VMware. One of the projects we're working on with VMware is their machine learning platform.  That's one of the first cloud solutions lab projects that we worked on at Infinidat. And we're also a core part of, of what VMware is driving from a data for good initiative. This was inspired by the idea that that tech can be used as a force for good in the world. And right now it's focused on the technology needs of nonprofits. And so we're closely working in, in the cloud solutions lab with, the VMware cloud foundation layers, as well as, their Tanzu and Kubernetes environments and learning a lot and proving a lot. And it's also a great way to demonstrate the capabilities of our platform. >>Yeah. So, yeah, it was just the other day I was on the VMware analyst meeting virtually of course in Zane and Sanjay and a number of other execs were giving the update. And, and just to sort of emphasize what we've been talking about here, this expansion of on-prem the cloud experience, the data from, especially from our survey data, we have a partner UTR that did great surveys on a regular quarterly basis, the VMware cloud on AWS, doing great for sure, but the VMware Cloud Foundation, the on-prem cloud, the hybrid cloud is really exploding and resonating with customers. And that's a good example of this sort of equilibrium that we're seeing between the public and private coming together >>Well on the VMware Cloud Foundation right now with, uh, you know, over a thousand customers, but importantly over 400 of the global 2000, it's the largest customers. And that's actually where the Venn diagram between the work that VMware Cloud Foundation is doing and Infinidat right, you know, this large scale, actually the, you know, interesting crossover, right. And, you know, listen for customers to go and take on a new store system. We always know that it's a high bar, right. So they have to see some really unique value, like how is this going to help? Right. And today that value is I want to spend less time looking down at the storage and more time looking up at the apps, that's how we're working together. Right. And how vVols fits into that, you know, with the VMware Cloud Foundation, it's the hype that hybrid cloud offering really gives customers that future-proofing right. And the degrees of freedom they're most likely to exercise. >>Right. Well, let's close with a, kind of a glimpse of the future. What do you see as the future of the data center specifically, and also your, your collaborations Lee? Why don't you start? >>I think what we hope to be true is turning out to be true. So, you know, if you've looked at the, you know, what's happening in the cloud, not everything is migrating in the cloud, but the public cloud, for example, and I'm talking about public cloud there. The public cloud offers some really interesting, unique value and VMware is doing really interesting things about like DR as a service and other things, right? So we're helping customers tap into that at the same time. Right. We're seeing that the on-prem investment is not stalling at all because of data sovereignty because of bandwidth limitations. Right. And because of really the economics of what it means to rent versus buy. And so, you know, partnering with  leaders on, in storage, right, is a core part of our strategy going forward. And we're looking forward to doing more right with Infinidat, as we see VCF evolve, as we see new applications, including container based applications running on our platform, lots of futures, right. As the pace of application change, you know, doesn't slow down. >>So what do you see for the next 10 years for Infinidat? >>Yeah, well, um, we, I appreciated your introduction because of this speak to sort of the core characteristics of Infinidat. And I think a company like us and at our, at our juncture of evolution, it's important to know exactly who you are. And we clearly are focused in that on-prem hybrid data center environment. We want to be the storage tier that companies use to build their clouds. And, uh, the partnership with VMware, uh, we talked about the Venn diagram. I think it just could not be more complimentary. And so we're certainly going to continue to focus on VMware as our largest and most consequential Alliance partner for our business going forward. Um, I'm excited about, about the data center landscape going forward. I think it's going to continue to ebb and flow. We'll see growth in distributed architectures. We'll see growth at the edge in the core data center. >>I think the, the old, the old days where customers would buy a storage system for a application environment, um, those days are over, it's all about consolidating multiple apps and thousands of users on a single platform. And to do that, you have to be really good at, uh, at a lot of things that we are very good at. Our, our strategy going forward is to evolve as media evolves, but never stray far from what has made Infinidat unique and special and highly differentiated in the marketplace. I think the work that VMware is doing and in Kubernetes >>Is very exciting. We're starting to see that really pick up in our business as well. So as we think about, um, uh, you know, not only staying relevant, but keeping very contemporary with application workloads, you know, we have some very small amount of customers that still do some bare metal, but predominantly as I said, 90% or above is VMware infrastructure. Uh, but we also see, uh, Kubernetes, our CSI driver works well with the VMware suite above it. Uh, so that, that complimentary relationship we see extending forward as, as the application environment evolves. Great, thank you. You know, many years ago when I attended my first, uh, VMworld, the practitioners that were there, you talked to them, half the conversations, they were complaining about storage and how it was so complicated and you needed guys in lab coats to solve problems. And, you know, VMware really has done a great job, publishing the APIs and encouraging the ecosystem. And so if you're a practitioner you're interested in how vVols and Infinidat and VMware were kind of raising the bar and on petabyte scale, there's some good blogs out there. Check out the Virtual Blocks blog for more information, guys. Thanks so much great to have you in the program. Really appreciate it. Thanks so much. Thank you for watching this Cube conversation, Dave Vellante. We'll see you next time.

(upbeat music) >> 10 years ago, a group of industry storage veterans formed a company called INFINIDAT. The DNA of the company was steeped in the heritage of its founder, Moshe Yanai who had a reputation for relentlessly innovating on three main areas, the highest performance, rock solid availability and the lowest possible cost. Now these elements have historically represented the superpower triumvirate of a successful storage platform. Now as INFINIDAT evolved it landed on a fourth vector that has been a key differentiator in its value proposition and that is petabyte scale. Hello everyone and welcome to this Cube Conversation. My name is Dave Vellante and I'm pleased to welcome in two long time friends of the cube, Phil Bullinger is newly minted CEO of INFINIDAT and of course, Lee Caswell, VMware's VP of marketing for the cloud platform business unit. Gents welcome. >> Thank you so much. Yeah. Great to be here Dave. >> Yeah. Great to be here Dave. Thanks. >> Always good to see you guys. Phil, so you're joining at the 10 year anniversary, Mark, congratulations on the appointment. What attracted you to the company? >> Yeah that's a great question Dave. I spent a long time in my career at enterprise storage and enjoyed many of the opportunities through a number of companies. Last fall when I became aware of the INFINIDAT opportunity and immediately captured my attention because of frankly my respect for the product. Through several opportunities I've had with enterprise customers in selling cycles of different products, if they happen to be customers of INFINIDAT they were not bashful about talking about their satisfaction with the product, their level of delight with it. And so I think from the sidelines I have always had a lot of respect for the INFINIDAT platform, the implementation of the product quality and reliability that it's kind of legendary for. And so when the opportunity came along it really captured my interest and of course behind a great product is almost always a great team and as I got to know the company and the board and some of the leaders and learned about the momentum and the business it was just a very, very compelling opportunity for me. And I'll have to say just 60 days into the job everything I hoped for is here not only a warm welcome to the company but an exciting opportunity with respect to where INFINIDAT is at today with growth of the business, the company has achieved a level of consistent growth through 2020 cashflow, positive, even thought positive and now it's a matter of scaling the business and it's something that I have had success with at several times in my career and I'm really, really enjoying the opportunity here at INFINIDAT to do that. >> That's great. Thanks for that. Now, of course Lee, VMware was founded nearly a quarter century ago and carved out a major piece of the enterprise pie and predominantly that's been on prem but the data centers evolving, the cloud is evolving and this universe is expanding. How do you see the future of that on-prem data center? >> I think Satya recently said, right? That we've reached max consolidation almost right. You pointed that out earlier. I thought that was really interesting, right? We believe in the distributed hybrid cloud and the reasons for that actually turn out to be storage led in there and in the real thinking about it because we're going to have distributed environments. And one of the things that we're doing with INFINIDAT here today, right? Is we're showing how customers can invest intelligently and responsibly on prem and have bridges in across the hybrid cloud. We do that through something called the VMware Cloud Foundation. That's a full stack offering that... And interesting here, right? It started off with a HCI element but it's expanded into storage and storage at scale. Because storage is going to exist we have very powerful storage value propositions and you're seeing customers go and deploy both. We're really excited about seeing INFINIDAT lean into the VMware Cloud Foundation and VVol has actually a way to match the pace of change in today's application world. >> Yes, so Phil you see these trends, I mean building bridges is what we called it. And so that takes a lot of hard work especially when you're doing from on-prem into hybrid, across clouds, eventually the edge, that's a non-trivial task. How do you see this playing out in market trends? >> We're in the middle of this every day and as you know Dave and certainly Lee, data center architecture is urban flow from centralized to decentralized but clearly data locality I think is driving a lot of the growth of the distributed data center architecture, the edge data centers but core is still very significant for most enterprise. And it has a lot to do with the fact that most enterprises want to own their own cloud when a Fortune 15 or a Fortune 50 or a Fortune 100 customer, when they talk about their cloud they don't want to talk about the AWS cloud or the GCP cloud or the Azure cloud. They want to talk about their cloud and almost always these are hybrid architectures with a large on-prem or colo footprint. The reason for that number of reasons, right? Data sovereignty is a big deal among the highest priorities for enterprise today. The control, the security, the ability to recover quickly from ransomware attacks, et cetera. These are the things that are just fundamentally important to the business continuity and enterprise risk management plan for these companies. But I think one thing that has changed the on-prem data center is the fact that it's the core operating characteristics have to take on kind of that public cloud characteristic, it has to be a transparent seamless scalability. I think the days of CIOs even tolerating people showing up in their data centers with disk trays under their arms to add capacity is over. They want to seamlessly add capacity, they want nonstop operation, a hundred percent uptime is the bar now it has to be a consolidation, massive consolidation, is clearly the play for TCO and efficiency. They don't want to have any compromises between scale and availability and performance. The very characteristics that you talked about upfront Dave, that make INFINIDAT unique I think are fundamentally the characteristics that enterprises are looking for when they build their cloud on prem. I think our architecture also really does provide a set it and forget it kind of experience when we install a new INFINIDAT frame in an enterprise data center, our intentions are we're not going to come back. We don't intend to come back to help fiddle with the bits or tweak the configuration and as applications and multi tenant users are added. And then of course, flexible economic models. I mean, everybody takes this for granted but you really really do have to be completely flexible between the two rails, the cap X rail and the objects rail and every step in between. And importantly when an enterprise customer needs to add capacity they don't have a sales conversation. They just want to have it right there already running in their data center. And that's the experience that we provide. >> Yeah. You guys are aligned in that vision, that layer that abstracts the complexity from the underlying wherever cloud on prem, et cetera. >> Right? >> Let's talk about VMware and INFINIDAT their relationship, I mean, every year at VMworld up until last year, thank you COVID, INFINIDAT would host this awesome dinner, you'd have his top customers there, very nice Vegas steak restaurant. I of course, I always made a point to stop by not just for the food. I mean, I was able to meet some customers and I've talked to many dozens over the years Phil, and I can echo that sentiment, why is the VMware ecosystem so important to INFINIDAT? And I guess the question there is, is petabyte scale really that prominent in the VMware customer base? >> It's a very, very important point. VMware is the longest standing alliance partner of INFINIDAT. It goes back to really almost the foundation of the company certainly starting with the release one, the very first commercial release of INFINIDAT, VMware and a very tight integration where VMware was a core part of that. We have a capability we call the host power tools which drives a consistent best practices implementation around our VMware integration and how it's actually used in the data center. And we built on that through the years through just a deep level of integration and our customers typically are at scale, petabyte scale or average deployment as a petabyte and up and over 90% of our customers use VMware. I think I can safely say we serve the VMware environment for some of VMware's largest enterprise footprints in the market. >> So Lee It's like children, you love all your partners but is there anything about INFINIDAT that stands out to you, a particular area where they shine from your perspective? >> Yeah, I think so. The best partnerships won are ones that are customer driven it turns out, right? And the idea that we have joint customers at large-scale, I must say storage is a tough business to go, right? Right, it takes time to go and mature to harden a code base, right? And particularly when you talk about petabyte scale right now, you've basically got customers buying in for the largest systems. And what we're seeing overall is customers are trying to do more things with fewer component elements. Makes sense, right? And so the scale here is important because it's not just scale in terms of like capacity, right? It's scale in terms of performance as well. And so, as you see customers trying to expand the number of different types of applications and this is one of the things we're seeing, right? Is new applications which could be container-based, Kubernetes orchestrated, our Tansu portfolio helps with that, right? If you see what we're doing with Nvidia, for example we announced some AI work, right? This week with vSphere. And so what you're starting to see is like the changing nature of applications and the fast pace of applications is really helping customers say, listen I want to go and find solutions that can meet the majority of my needs. And that's one of the things that we're seeing and particularly with the VVol'sintegration at scale that we just haven't seen before, INFINIDAT is setting the bar and really setting a new record for that. >> Yeah. Let me comment on that a little bit, Dave. We've been a core part of the VMware Cloud Solutions Lab, which is a very very exciting engaging investment that VMware has made. A lot of people have contributed to in the industry but in the VMware Cloud Solutions Lab we recently demonstrated on a single INFINIDAT frame over 200,000 VVols on a single system. And I think that not only edges up the bar I think it completely redefines what scale means when you're talking about a VVol implementation >> So lets talk about both those things. Not to geek out here but VVols they're kind of a game changer because instead of admins having to manually allocate storage to performance tiers, an array that is VASA certified, VASA is VMware or actually the storage API for storage awareness, VASA, anyway with VVols you can dynamically provision storage that matches, the way I say it as matches device attributes to the data and the application requirements of the VM. So Phil, it seems like so much in VMware land harkens back to the way mainframes used to solve problems in a modern way, right? And VVol is a real breakthrough in that regard in terms of simplifying storage. So how do you guys see it? I presume you're sort of VVol certified based on what you just said in the lab. >> Yeah. We recently announced our VVols release and we're not the first to market with VVols but from the start of the engineering project we wanted to do it. We wanted to do it the way we think. We think at scale in everything we do and our customers were very prescriptive and the kind of scale and performance and availability that they wanted to experience in VVols. And we're now seeing quite a bit of customer interest with traction in it. As I said, we redefined the bar for VVol scalability. We support on a single array now a thousand storage containers. And I think most of our competition is like at one or maybe 10 or 13 or something like that. So our customers are again at scale, they said if you're going to do VVols we want it at scale. We want it to embody the characteristics of your platform. We really liked VVols because it helps separate kind of the roles and responsibilities between the BI administrator and the storage system administrator. If you're going to put the majority of your most critical bits on INFINIDAT in your data center you're going to want to have control over how that resource is used, the at the VVols in rotation and the tools that we provide with that deep level of integration give the BI administrator all of the flexibility they need to manage applications and VVols of course gives the BI administrator the native use of our in minute snapshot technology. And so it makes it incredibly easy for them to administrate the platform without having to worry about the physical infrastructure but yet the people worried about the physical infrastructure still have control over that resource. So it's a game changer as far as we're concerned. >> Yeah. Storage has come a long way hasn't it Lee? If you could add some color here it seems in talking needs so VASA that's interesting you had a hand in the growth of VASA and very successful product but he chose INFINIDAT for that higher end application. It seemed like VVols are a key innovation in that regard. How's the VVol uptake going from your perspective. >> Yeah, I think we're in the second phase of VVol adoption, right? First phase was, hey, it technically interesting, intriguing but adoption was relatively low I think because you know up until five years ago applications weren't actually changing that fast. I mean, think about it, right? The applications, ERP systems, CRM systems, you weren't changing those at the pace of what we're doing today. Now what's happening is every business is a software business. Every business when you work, when you interact with your healthcare provider right now it's about the apps. Like, can you go and get your schedules online? Can you email your doctors, right? Can you go and get your labs, right? The pace of new application development, we have some data showing that there will be more apps developed in the next five years and then the past 40 years of computing combined. And so when you think about that what's changed now is trying to manage that all from the kind of storage hardware side was just actually getting in the way you want to organize around the fastest beat rate in your infrastructure, today that's the application. So what VVOls helps you do is it allows the vSphere administrator who's managing VMs and looking at the apps and the changing pace and be able to basically select storage attributes including QoS, capacity, IOPS and do that from the V center console and then be able to rectify things and manage them, right? From the console right next to the apps. And that provides a really integrated way. So when you have a close interaction like what we're talking about today or integration that the INFINIDAT has provided now you've got this ability to have a faster moving activity. And consolidation is one of the themes you've heard from time to time from VMware, we're consolidating the management so that the vSphere administrator can now go and manage more things. What traditional VMs, yes, VMs across HI sure put now plus storage and into the hybrid cloud and into like containers, it's that consolidated management which is getting us speed and basically a consumer like experience for infrastructure deployments. >> Yeah. Now Phil mentioned the solutions lab. We've got a huge ecosystem. Several years ago you launched this, the VMware, I think it's called the VMware Cloud Solutions Lab is the official name. Explain what it does for collaboration and joint solutions development. And then Phil, I want you to go in more detail about what your participation has been but Lee why don't you explain it? >> Yeah. We don't take just any products that because listen there's a mixing, what we take is things that really expand that innovation frontier. And that's what we saw with INFINIDAT was expanding the frontier on like large capacity for many many different mixed workloads and a commitment, right? To go and bring in not just VVol support, of course all the things we do for just normal interaction with vSphere but bringing VVOls in was certainly important in showing how we operate at scale. And then importantly as we expanded the vSphere or cloud foundation to include store systems, fair customer for example, right? Who has storage and HCI, right? And it looks for how to go and use them. And that's an individual choice at a customer level. We think this is strategically important now as we expand a multi-cloud experience that's different from the hyperscalers, right? Hyperscalers are coming in with two kind of issues, maybe, right? So one is it's single cloud. And the other one is there's a potential competitive aspect from some right around the ongoing underlying business and a hyperscaler business model. And so what VMware uniquely is doing is extending a common control plane across storage systems and HCI and doing that in a way that basically gives customers choice. And we love that the cloud lab is really designed to go and make that a reality for customers strip out perceived and real risk. >> Yeah. Phil to Lee's point, it's not dozens and dozens and dozens of logos on the slide for the lab. I think there's like 10 or 12 from what I saw and INFINIDAT is one of them. Maybe you could talk a little bit more about your participation in the program and what it does for customers. >> Yeah, absolutely. And I would agree it's, we like the lab because it's not just supposed to be one of everything I can do it, it's a purpose-built lab to do real things. And we like it because we can really explore some of the most contemporary workloads in that environment as well as solutions to what I centered as some of the most contemporary industry problems we're participating in a couple of ways. I believe we're the only petabyte scale storage solution in the cloud solutions lab at VMware. One of the projects we're working on with VMware is their machine learning platform. That's one of the first cloud solutions lab projects that we worked on with INFINIDAT. And we're also a core part of what VMware is driving from at but we call it data for good initiative. This was inspired by the idea that tech can be used as a force for good in the world. And right now it's focused on the technology needs of nonprofits. And so we're closely working in the cloud solutions lab with the VMware Cloud Foundation layers as well as the Tansu and Kubernetes environments and learning a lot and proving a lot. And it's also a great way to demonstrate the capabilities of our platform. >> Yeah. So Lee, I was just the other day I was under VMware analyst meeting virtually of course and Zane and Sanjay and a number of other execs were given the update. And just to sort of emphasize what we've been talking about here this expansion of on-prem, the cloud experience, the data especially from our survey data we have a partner at ETR they do great surveys on quarterly basis. The VMware cloud on AWS do great for sure but the VMware Cloud Foundation, the on-prem cloud, the hybrid cloud is really exploding and resonating with customers. And that's a good example of this sort of equilibrium that we're seeing between the public and private coming together. >> Well, VMware Cloud Foundation right now with over a thousand customers but importantly over 400 of the global 2000, right? It's the largest customers. And that's actually where the Venn diagram between the work that VMware Cloud Foundation is doing and INFINIDAT, right? This large scale actually the interesting crossover, right? And listen for customers to go and take on a new storage system we always know that it's a high bar, right? So they have to see some really unique value, like how is this going to help, right? And today that value is I want to spend less time looking down at the storage and more time looking up at the apps, that's how we're working together, right? And how VVols fits into that with the VMware Cloud Foundation, it's that hybrid cloud offering really gives customers that future-proofing, right? And the degrees of freedom they're most likely to exercise. >> Right. Well, let's close with a kind of a glimpse of the future. What do you two see as the future of the data center specifically and also your collaborations Lee? Why don't you start? >> So I think what we hope to be true is turning out to be true. So, if you've looked at what's happening in the cloud not everything is migrating in the cloud but the public cloud for example and I'm talking about public cloud there, the public cloud offers some really interesting unique value. And VMware is doing really interesting things about like Dr as a service and other things, right? So we're helping customers tap into that at the same time, right? We're seeing that the on-prem investment is not stalling at all because of data sovereignty because of bandwidth limitations, right? And because of really the economics of what it means to rent versus buy. And so partnering with leaders in storage, right? Is a core part of our strategy going forward. And we're looking forward to doing more, right? With INFINIDAT as we see VCF evolve, as we see new applications including container-based applications running on our platform, lots of futures, right? As the pace of application change doesn't slow down. >> So Phil, what do you see for the next 10 years for INFINIDAT? >> Yeah, well, I appreciated your introduction because it does speak to sort of the core characteristics of INFINIDAT. And I think a company like us and at our juncture of evolution it's important to know exactly who you are. And we clearly are focused in that on-prem hybrid data center environment. We want to be the storage tier that companies use to build their clouds. The partnership with VMware we talked about the Venn diagram, I think it just could not be more complimentary. And so we're certainly going to continue to focus on VMware as our largest and most consequential alliance partner for our business going forward. I'm excited about the data center landscape going forward. I think it's going to continue to ebb and flow. We'll see growth and distributed architectures, we'll see growth at the edge. In the core data center I think the old days where customers would buy a storage system for a application environment, those days are over it's all about consolidating multiple apps and thousands of users on a single platform. And to do that you have to be really good at a lot of things that we are very good at. Our strategy going forward is to evolve as media evolves but never stray far from what has made INFINIDAT unique and special and highly differentiated in the marketplace. I think the work that VMware is doing in Kubernetes is very exciting. We're starting to see that really pick up in our business as well. So as we think about not only staying relevant but keeping very contemporary with application workloads, we have some very small amount of customers that still do some bare metal but predominantly as I said 90% or above is a VMware infrastructure. But we also see Kubernetes, our CSI driver works well with the VMware suite above it. So that that complimentary relationship we see extending forward as the application environment evolves. >> It's great. Thank you. Many years ago when I attended my first VMworld the practitioners that were there you talked to them, half the conversations they were complaining about storage and how it was so complicated and you needed guys in lab coats to solve problems. And VMware really has done a great job publishing the APIs and encouraging the ecosystem. And so if you're a practitioner you're interested in in how VVols and INFINIDAT and VMware, we're kind of raising the bar and on petabyte scale there's some good blogs out there. Check out the virtual blocks blog for more information. Guys thanks so much. Great to have you in the program. Really appreciate it. >> Thanks so much, Dave. >> All right. Thank you for watching this cute conversation, Dave Vellante, we'll see you next time. (upbeat music)

>> Narrator: From around the globe. It's theCUBE with digital coverage of Exascale Day. Made possible by Hewlett Packard Enterprise. >> Welcome everyone to theCUBE's celebration of Exascale Day. Dr. Mark Fernandez is here. He's the HPC technology officer for the Americas at Hewlett Packard enterprise. And he's a developer of the spaceborne computer, which we're going to talk about today. Mark, welcome. It's great to see you. >> Great to be here. Thanks for having me. >> You're very welcome. So let's start with Exascale Day. It's on 10 18, of course, which is 10 to the power of 18. That's a one followed by 18 zeros. I joke all the time. It takes six commas to write out that number. (Mark laughing) But Mark, why don't we start? What's the significance of that number? >> So it's a very large number. And in general, we've been marking the progress of our computational capabilities in thousands. So exascale is a thousand times faster than where we are today. We're in an era today called the petaflop era which is 10 to the 15th. And prior to that, we were in the teraflop era, which is 10 to the 12th. I can kind of understand a 10 to the 12th and I kind of can discuss that with folks 'cause that's a trillion of something. And we know a lot of things that are in trillions, like our national debt, for example. (Dave laughing) But a billion, billion is an exascale and it will give us a thousand times more computational capability than we have in general today. >> Yeah, so when you think about going from terascale to petascale to exascale I mean, we're not talking about orders of magnitude, we're talking about a much more substantial improvement. And that's part of the reason why it's sort of takes so long to achieve these milestones. I mean, it kind of started back in the sixties and seventies and then... >> Yeah. >> We've been in the petascale now for more than a decade if I think I'm correct. >> Yeah, correct. We got there in 2007. And each of these increments is an extra comma, that's the way to remember it. So we want to add an extra comma and get to the exascale era. So yeah, like you say, we entered the current petaflop scale in 2007. Before that was the terascale, teraflop era and it was in 1997. So it took us 10 years to get that far, but it's taken us, going to take us 13 or 14 years to get to the next one. >> And we say flops, we're talking about floating point operations. And we're talking about the number of calculations that can be done in a second. I mean, talk about not being able to get your head around it, right? Is that's what talking about here? >> Correct scientists, engineers, weather forecasters, others use real numbers and real math. And that's how you want to rank those performance is based upon those real numbers times each other. And so that's why they're floating point numbers. >> When I think about supercomputers, I can't help but remember whom I consider the father of supercomputing Seymour Cray. Cray of course, is a company that Hewlett Packard Enterprise acquired. And he was kind of an eclectic fellow. I mean, maybe that's unfair but he was an interesting dude. But very committed to his goal of really building the world's fastest computers. When you look at back on the industry, how do you think about its developments over the years? >> So one of the events that stands out in my mind is I was working for the Naval Research Lab outside Stennis Space Center in Mississippi. And we were doing weather modeling. And we got a Cray supercomputer. And there was a party when we were able to run a two week prediction in under two weeks. So the scientists and engineers had the math to solve the problem, but the current computers would take longer than just sitting and waiting and looking out the window to see what the weather was like. So when we can make a two week prediction in under two weeks, there was a celebration. And that was in the eighties, early nineties. And so now you see that we get weather predictions in eight hours, four hours and your morning folks will get you down to an hour. >> I mean, if you think about the history of super computing it's really striking to consider the challenges in the efforts as we were just talking about, I mean, decade plus to get to the next level. And you see this coming to fruition now, and we're saying exascale likely 2021. So what are some of the innovations in science, in medicine or other areas you mentioned weather that'll be introduced as exascale computing is ushered in, what should people expect? >> So we kind of alluded to one and weather affects everybody, everywhere. So we can get better weather predictions, which help everybody every morning before you get ready to go to work or travel or et cetera. And again, storm predictions, hurricane predictions, flood predictions, the forest fire predictions, those type things affect everybody, everyday. Those will get improved with exascale. In terms of medicine, we're able to take, excuse me, we're able to take genetic information and attempt to map that to more drugs quicker than we have in the past. So we'll be able to have drug discovery happening much faster with an exascale system out there. And to some extent that's happening now with COVID and all the work that we're doing now. And we realize that we're struggling with these current computers to find these solutions as fast as everyone wants them. And exascale computers will help us get there much faster in the future in terms of medicine. >> Well, and of course, as you apply machine intelligence and AI and machine learning to the applications running on these supercomputers, that just takes it to another level. I mean, people used to joke about you can't predict the weather and clearly we've seen that get much, much better. Now it's going to be interesting to see with climate change. That's another wildcard variable but I'm assuming the scientists are taking that into consideration. I mean, actually been pretty accurate about the impacts of climate change, haven't they? >> Yeah, absolutely. And the climate change models will get better with exascale computers too. And hopefully we'll be able to build a confidence in the public and the politicians in those results with these better, more powerful computers. >> Yeah let's hope so. Now let's talk about the spaceborne computer and your involvement in that project. Your original spaceborne computer it went up on a SpaceX reusable rocket. Destination of course, was the international space station. Okay, so what was the genesis of that project and what was the outcome? So we were approached by a long time customer NASA Ames. And NASA Ames says its mission is to model rocket launches and space missions and return to earth. And they had the foresight to realize that their supercomputers here on earth, could not do that mission when we got to Mars. And so they wanted to plan ahead and they said, "Can you take a small part of our supercomputer today and just prove that it can work in space? And if it can't figure out what we need to do to make it work, et cetera." So that's what we did. We took identical hardware, that's present at NASA Ames. We put it on a SpaceX rocket no special preparations for it in terms of hardware or anything of that sort, no special hardening, because we want to take the latest technology just before we head to Mars with us. I tell people you wouldn't want to get in the rocket headed to Mars with a flip phone. You want to take the latest iPhone, right? And all of the computers on board, current spacecrafts are about the 2007 era that we were talking about, in that era. So we want to take something new with us. We got the spaceone computer on board. It was installed in the ceiling because in space, there's no gravity. And you can put computers in the ceiling. And we immediately made a computer run. And we produced a trillion calculations a second which got us into the teraflop range. The first teraflop in space was pretty exciting. >> Well, that's awesome. I mean, so this is the ultimate example of edge computing. >> Yes. You mentioned you wanted to see if it could work and it sounds like it did. I mean, there was obviously a long elapse time to get it up and running 'cause you have to get it up there. But it sounds like once you did, it was up and running very quickly so it did work. But what were some of the challenges that you encountered maybe some of the learnings in terms of getting it up and running? >> So it's really fascinating. Astronauts are really cool people but they're not computer scientists, right? So they see a cord, they see a place to plug it in, they plug it in and of course we're watching live on the video and you plugged it in the wrong spot. So (laughs) Mr. Astronaut, can we back up and follow the procedure more carefully and get this thing plugged in carefully. They're not computer technicians used to installing a supercomputer. So we were able to get the system packaged for the shake, rattle and roll and G-forces of launch in the SpaceX. We were able to give astronaut instructions on how to install it and get it going. And we were able to operate it here from earth and get some pretty exciting results. >> So our supercomputers are so easy to install even an astronaut can do it. I don't know. >> That's right. (both laughing) Here on earth we have what we call a customer replaceable units. And we had to replace a component. And we looked at our instructions that are tried and true here on earth for average Joe, a customer to do that and realized without gravity, we're going to have to update this procedure. And so we renamed it an astronaut replaceable unit and it worked just fine. >> Yeah, you can't really send an SE out to space to fix it, can you? >> No sir. (Dave laughing) You have to have very careful instructions for these guys but they're great. It worked out wonderfully. >> That's awesome. Let's talk about spaceborne two. Now that's on schedule to go back to the ISS next year. What are you trying to accomplish this time? >> So in retrospect, spaceborne one was a proof of concept. Can we package it up to fit on SpaceX? Can we get the astronauts to install it? And can we operate it from earth? And if so, how long will it last? And do we get the right answers? 100% mission success on that. Now spaceborne two is, we're going to release it to the community of scientists, engineers and space explorers and say, "Hey this thing is rock solid, it's proven. Come use it to improve your edge computing." We'd like to preserve the network downlink bandwidth for all that imagery, all that genetic data, all that other data and process it on the edge as the whole world is moving to now. Don't move the data, let's compute at the edge and that's what we're going to do with spaceborne two. And so what's your expectation for how long the project is going to last? What does success look like in your mind? So spaceborne one was given a one year mission just to see if we could do it but the idea then was planted it's going to take about three years to get to Mars and back. So if you're successful, let's see if this computer can last three years. And so we're going up February 1st, if we go on schedule and we'll be up two to three years and as long as it works, we'll keep computing and computing on the edge. >> That's amazing. I mean, I feel like, when I started the industry, it was almost like there was a renaissance in supercomputing. You certainly had Cray and you had all these other companies, you remember thinking machines and convex spun out tried to do a mini supercomputer. And you had, really a lot of venture capital and then things got quiet for a while. I feel like now with all this big data and AI, we're seeing in all the use cases that you talked about, we're seeing another renaissance in supercomputing. I wonder if you could give us your final thoughts. >> Yeah, absolutely. So we've got the generic like you said, floating point operations. We've now got specialized image processing processors and we have specialized graphics processing units, GPUs. So all of the scientists and engineers are looking at these specialized components and bringing them together to solve their missions at the edge faster than ever before. So there's heterogeneity of computing is coming together to make humanity a better place. And how are you going to celebrate Exascale Day? You got to special cocktail you going to shake up or what are you going to do? It's five o'clock somewhere on 10 18, and I'm a Parrothead fan. So I'll probably have a margarita. There you go all right. Well Mark, thanks so much for sharing your thoughts on Exascale Day. Congratulations on your next project, the spaceborne two. Really appreciate you coming to theCUBE. Thank you very much I've enjoyed it. All right, you're really welcome. And thank you for watching everybody. Keep it right there. This is Dave Vellante for thecUBE. We're celebrating Exascale Day. We'll be right back. (upbeat music)