>>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.

>>Hello, everybody. Welcome to Von 2022, the live version. Yes, we're finally back live. Last time we did Von was 2019 live. Of course we did two subsequent years, uh, virtual. My name is Dave Valante and we've got two days of wall to wall coverage of VEON. As usual Veeam has brought together a number of customers, but it's really doing something different this year. Like many, uh, companies that you see, they have a big hybrid event. It's close to 40,000 people online and that's sort of driving the actual program where the content is actually different for the, the, the virtual viewers versus the onsite onsite. There's the, the V I P event going on, they got the keynotes. VM is a company who's a ancy occurred during the, the VMware rise. They brought in a new way of doing data protection. They didn't use agents. They, they protected at the hypervisor level. >>That changed the way that people did things. They're now doing it again in cloud, in SAS, in containers and ransomware. And so we're gonna dig into that. My cohost is Dave Nicholson this week, and we've got a special guest Zs Carava who is the principal at ZK research. He's an extraordinary analyst Zs. Great to see you, David. Thanks for coming out. Absolutely good to see you Beon. Great to be here. Yeah, we've done. Von act, live things have changed so dramatically. Uh, I mean the focus ransomware, it's now a whole new Tam, uh, the adjacency to security data protection. It's just a Zs. It's a whole new ballgame, isn't it? >>Well, it is. And, and in fact, um, during the keynote, they, they mentioned that they've, they're now tied at number one in, for, you know, back of a recovery, which is, I think it's safe to say Veeam. Does that really well? >>I think from a that's tied with Dell. Yes. Right. They didn't, I don't think they met Dell as >>Keto. And, uh, but I, you know, they've been rising Dell, EMC's been falling. And so I think >>It's somebody said 10 points that Dell lost and sharing the I data. >>It's not a big surprise. I mean, they haven't really invested a whole lot, >>I think anyway, >>Anyways, but I think from a Veeam perspective, the question is now that they've kind of hit that number one spot or close to it, what do they do next? This company, they mentioned, I was talking the CTO yesterday. You mentioned they're holding X bite of customer data. That is a lot of data. Right. And so they, they do back recovery really well. They do it arguably better than anybody. And so how do they take that data and then move into other adjacent markets to go create, not just a back recovery company, but a true data management platform company that has relevancy in cyber and analytics and artificial intelligence and data warehousing. Right? All those other areas I think are, are really open territory for this company right now. >>You know, Dave, you were a CTO at, at EMC when you, when you saw a lot of the acquisitions that the company made, uh, you, you know, they really never had a singular focus on data protection. They had a big data protection business, but that's the differentiator with Veeam. That's all it does. And you see that shine through from a, from a CTO's perspective. How do you see this market changing, evolving? And what's your sense as to how Vema is doing here? >>I think a lot of it's being driven by kind of, uh, unfortunately evil genius, uh, out in the market space. Yeah. I know we're gonna be hearing a lot about ransomware, uh, a lot about some concepts that we didn't really talk about outside of maybe the defense industry, air gaping, logical air gaping, um, Zs, you mentioned, you know, this, this, this question of what do you do when you have so many petabytes of data under management exabytes now exabytes, I'm sorry. Yeah, I see there I'm I'm already falling behind. One thing you could do is you could encrypt it all and then ask for Bitcoin in exchange for access to that data. >>Yes. That is what happens a >>Lot of them. So we're, we're getting, we're getting so much of the evil genius stuff headed our way. You start, you start thinking in those ways, but yet to, to your point, uh, dedicated backup products, don't address the scale and scope and variety of threats, not just from operational, uh, uh, you know, mishaps, uh, but now from so many bad actors coming in from the outside, it it's a whole new world. >>See us as analysts. We get inundated with ransomware solutions. Everybody's talking about it across the spectrum. The thing that interested me about what's happening here at VEON is they're, they're sort of trotting out this study that they do Veeam does some serious research, you know, thousands of customers that got hit by ransomware that they dug into. And then a, a larger study of all companies, many of whom didn't realize or said they hadn't been hit by ransomware, but they're really trying to inject thought leadership into the equation. You saw some of that in the analyst session this morning, it's now public. Uh, so we could talk about it. What were your thoughts on that data? >>Yeah, that was, uh, really fascinating data cuz it shows the ransomware industry, the response to it is largely reactive, right? We wait to get breach. We wait to, to uh, to get held at ransom I suppose. And then we, a lot of companies paid out. In fact, I thought there's one hospital in Florida, they're buying lots and lots of Bitcoin simply to pay out ransomware attacks. They didn't even really argue with them. They just pay it out. And I think Veeam's trying to change that mentality a little bit. You know, if you have the right strategy in place to be more preventative, you can do that. You can protect your data and then restore it right when you want to. So you don't have to be in that big bucket of companies that frankly pay and actually don't get their data back. Right. >>And like a third, I think roughly >>It's shocking amount of companies that get hit by that. And for a lot of companies, that's the end of their business. >>You know, a lot of the recovery process is manual is again a technologist. You understand that that's not the ideal way to go. In fact, it's probably a, a way to fail. >>Well, recovery's always the problem when I was in corporate, it used to joke that we were the best at backup, terrible at recovery. Well, you know, that's not atypical. >>My Fred Fred Moore, who was the vice president of strategy at a company called storage tech storage technology, corpor of storage tech. He had a great, uh, saying, he said, backup is one thing. Recovery is everything. And he started, he said that 30 years ago, but, but orchestration and automating that orchestration is, is really vital. We saw in the study, a lot of organizations are using scripts and scripts are fragile here they break. Right? >>Yeah, no, absolutely. Absolutely. Um, unfortunately the idea of the red run book on the shelf is still with us. Uh, uh, you know, scripting does not equal automation necessarily in every case, there's still gonna be a lot of manual steps in the process. Um, but you know, what I hope we get to talk about during the next couple of days is, you know, some of the factors that go into this, we've got day zero exploits that have already been uncovered that are stockpiled, uh, and tucked away. And it's inevitable that they're gonna hit. Yeah. So whether it's a manual recovery process or some level of automation, um, if you don't have something that is air gapped and cut off from the rest of the world in a physical or logical way, you can't guarantee >>That the, the problem with manual processes and scripting is even if you can set it up today, the environment changes so fast, right? With shadow it and business units buying their own services and users storing things and you know, wherever, um, you, you can't keep up with scripts in manual. Automation must be the way and I've been, and I don't care what part of it. You work in, whether it's this area in networking, communications, whatever automation must be the way I think prior to the pandemic, I saw a lot of resistance from it pros in the area of mission. Since the pandemic, I've seen a lot of warming up to it because I think it pros, I just realized they can't do their job without it. So, so you >>Don't, you don't think that edge devices, uh, lend themselves to manual >>Recovery, no process. In fact, I think that's one of the things they didn't talk about. What's that is, is edge. Edge is gonna be huge. More, every retailer, I talk to oil and gas, company's been using it for a long time. I've, you know, manufacturing organizations are looking at edge as a way to put more data in more places to improve experiences. Cuz you're moving the data closer, but we're creating a world where the fragmentation of data, you think it's bad now just wait a couple of years until the edge is a little more, you know, uh, to life here. And I think you ain't see nothing yet. This is this world of data. Everywhere is truly becoming that. And the thing with edge is there's no one definition, edge, you got IOT edge cellular edge, campus edge, right? Um, you know, you look at hotels, they have their own edge. I talked to major league baseball, right? They have every, stadium's got its own edge server in it. So we're moving into a world. We're putting more data in more places it's more fragmented than ever. And we need better ways of managing Of securing that data. But then also being able to recover for when >>Things happen. I was having that Danny Allen, he used the term that we coined called super cloud. He used that in the analyst meeting today. And, and that's a metaphor for this new layer of cloud. That's developing to your point, whether it's on-prem in a hybrid across clouds, not just running on the cloud, but actually abstracting away the complexity of the underlying primitives and APIs. And then eventually to your point, going out to the edge, I don't know if anyone who has an aggressive edge strategy Veeam to its credit, you know, has gone well beyond just virtualization and gone to bare metal into cloud. They were the containers. There was first at SAS. They acquired Caston who was a partner of theirs and they tried to acquire them earlier, but there was some government things and you know, that whole thing that got cleaned up and now they've, they own Caston. And I think the edge is next. I mean, it's gotta be, there's gonna be so much data at the edge. I guess the question is where is it today? How much of that is actually persisted? How much goes back to the cloud? I don't think people really have a good answer for that yet. >>No. In fact, a lot of edge services will be very ephemeral in nature. So it's not like with cloud where we'll take data and we'll store it there forever with the edge, we're gonna take data, we'll store it there for the time, point in time we need it. But I think one of the interesting things about Veeam is because they're decoupled from the airline hardware, they can run virtual machines and containers, porting Veeam to whatever platform you have next actually isn't all that difficult. Right? And so then if you need to be able to go back to a certain point in time, they can do that instantly. It's, it's a fascinating way to do backup. Are >>You you' point about it? I mean, you remember the signs up and down, you know, near the EMC facility, right outside of Southborough no hardware agenda that that was Jeremy Burton when he was running Verto of course they've got a little hardware agenda. So, but Veeam doesn't Veeam is, you know, they they're friendly with all the hardware players of pure play software, couple other stats on them. So they're a billion dollar company. They've now started to talk about their ARR growth. They grew, uh, 27% last year in, in, in annual recurring revenue, uh, 25%, uh, in the most recent quarter. And so they're in, in the vast majority of their business is subscription. I think they said, uh, 73% is now subscription based. So they really trans transitioned that business. The other thing about vem is they they've come up with a licensing model that's very friendly. >>Um, and they sort of removed that friction early on in the process. I remember talking to TIR about this. He said, we are gonna incent our partners and make it transparent to them, whether it's, you know, that when we shift from, you know, the, the, the, the crack of, of perpetual license to a subscription model, we're gonna make that transparent to partners. We'll take care of that. Essentially. They funded that transition. So that's worked very well. So they do stand out, I think from some of the larger companies at these big portfolios, although the big portfolio companies, you know, they get board level contacts and they can elbow their ways in your thoughts on that sort of selling dynamic. >>So navigating that transition to a subscription model is always fraught with danger. Everybody wants you to be there, but they want you to be there now. Mm-hmm <affirmative>, they don't like the transition that happens over 1824 months to get there. Um, >>As a private company, they're somewhat shielded from what they would've been if they were appli. Sure, >>Exactly. But, but that, but that bodes well from a, from a, a Veeam perspective. Um, the other interesting thing is that they sit where customers sit today in the real world, a hybrid world, not everything is in the cloud or a single cloud, uh, still a lot of on-prem things to take care of. And, >>And there will be for >>A long time exactly. Back to this idea. Yeah. There's a very long tail on that. So it's, it's, it's well enough to have a niche product that addresses a certain segment of the market, but to be able to go in and say all data everywhere, it doesn't matter where it lives. We have you covered. Um, that's a powerful message. And we were talking earlier. I think they, they stand a really good shot at taking market share, you know, on an ongoing basis. >>Yeah. The interesting thing about this market, Dave is they're, you know, although, you know, they're tied to number one with Dell now, they're, it's 12%, right? This reminds me of the security industry five, six years ago, where it's so fragmented. There's so many vendors, no one really stood out right. Then what happened in security? It's a little company called Palo Alto networks came around, they created a platform story. They moved into adjacent markets like SDWAN, they did a lot of smart acquisitions and they took off. I think vem is at that similar point where they've now, you know, that 12% number they've got some capital. Now they could go do some acquisitions that they want do. There's lots of adjacent markets as they talk about this company could be the Palo Alto of the data management market, if you know, and based on good execution. But there's certainly the opportunities there with all the data that they're holding. >>That's a really interesting point. I wanna stay that in a second. So there's obviously, there's, there's backup, there's recovery, there's data protection, there's ransomware protection, there's SAS data protection. And now all of a sudden you're seeing even a company like Rubrik is kind of repositioning as a security play. Yeah. Which I'm not sure that's the right move for a company that's really been focused on, on backup to really dive into that fragmented market. But it's clearly an adjacency and we heard Anan the new CEO today in the analyst segment, you know, we asked him, what's your kinda legacy gonna look like? And he said, I want to, I want to, defragment this market he's looking at. Yeah. He wants 25 to 45% of the market, which I think is really ambitious. I love that goal now to your point, agree, he, he sure. But that doubles yeah. >>From today or more, and he gets there to your point, possibly through acquisitions, they've made some really interesting tuck-ins with Castin. They certainly bought an AWS, uh, cloud play years ago. But my, my so, uh, Veeam was purchased by, uh, private equity inside capital inside capital in January of 2020, just before COVID for 5 billion. And at the time, then COVID hit right after you were like uhoh. And then of course the market took off so great acquisition by insight. But I think an IPO is in their future and that's, uh, Zs when they can start picking up some of these adjacent markets through every day. >>And I think one of the challenges for them is now that the Holden XAB bited data, they need to be able to tell customers things they, the customer doesn't know. Right. And that's where a lot of the work they're doing in artificial intelligence machine learning comes into play. Right. And, and nobody does that better than AWS, right? AWS is always looking at your data and telling you things you don't know, which makes you buy more. And so I think from a Veeam perspective, they need to now take all this, this huge asset they have and, and find a way to monetize it. And that's by revealing these key insights to customers that the customers don't even know they have. And >>They've got that monitor monitoring layer. Um, it's if you called it, Danny, didn't like to use the term, but he called it an AI. It's really machine learning that monitors. And then I think makes recommendations. I want to dig into that a little bit with it. >>Well, you can see the platform story starting to build here. Right. And >>Here's a really good point. Yeah. Because they really have been historically a point product company. This notion of super cloud is really a platform play. >>Right. And if you look in the software industry, look across any, any segment of the software industry, those companies that were niche that became big became platforms, Salesforce, SAP, Oracle. Right. And, and they find a way to allow others to build on their platform. You know, companies, they think like a Citrix, they never did that. Yeah. And they kind of taped, you know, petered out at a certain level of growth and had to, you know, change. They're still changing their business model, in fact. But I think that's Veeam's at that inflection point, right. They either build a platform story, enable others to do more on their platform or they stagnate >>HP software is another good example. They never were able to get that platform. And we're not able bunch of spoke with it, a non used to work there. Why is it so important Dave, to have a platform over a product? >>Well, cynical, Dave says, uh, you have a platform because it attracts investment and it makes you look cooler than maybe you really are. Um, but, uh, but really for longevity, you have, you, you, you have to be a platform. So what's >>The difference. How do you know when you have platform versus it? APIs? Is it, yeah. Brett, is it ecosystem? >>Some of it is. Some of it is semantics. Look at when, when I'm worried about my critical assets, my data, um, I think of a platform, a portfolio of point solutions for backing up edge data stuff. That's in the cloud stuff that exists in SAS. I see that holistically. And I think guys, you're doing enough. This is good. Don't, don't dilute your efforts. Just keep focusing on making sure that you can back up my data wherever it lives and we'll both win together. So whenever I hear a platform, I get a little bit, a little bit sketchy, >>Well platform, beats products, doesn't >>It? Yeah. To me, it's a last word. You said ecosystem. Yes. When you think of the big platform players, everybody B in the customer, uh, experience space builds to build for Salesforce. First, if you're a small security vendor, you build for Palo Alto first, right? Right. If you're in the database, you build for Oracle first and when you're that de facto platform, you create an ecosystem around you that you no longer have to fund and build yourself. It just becomes self-fulfilling. And that drives a level of stickiness that can't be replicated through product. >>Well, look at the ecosystem that, that these guys are forming. I mean, it's clear. Yeah. So are they becoming in your view >>Of platform? I think they are becoming a platform and I think that's one of the reasons they brought on and in, I think he's got some good experience doing that. You could argue that ring kind of became that. Right. The, when, you know, when he was ring central. >>Yeah. >>Yeah. And, uh, so I think some, some of his experiences and then moving into adjacencies, I think is really the reason they brought him in to lead this company to the next level. >>Excellent guys, thanks so much for setting up VEON 20, 22, 2 days of coverage on the cube. We're here at the area. It's a, it's a great venue. I >>Love the area. >>Yeah. It's nice. It's a nice intimate spot. A lot of customers here. Of course, there's gonna be a big Veeam party. They're famous for their parties, but, uh, we'll, we'll be here to cover it and, uh, keep it right there. We'll be back with the next segment. You're watching the cube VEON 20, 22 from Las Vegas.

>> Welcome back to Los Angeles. The cube is live. It feels so good to say that. I'm going to say that again. The cube is alive in Los Angeles. We are a coop con cloud native con 21. Lisa Martin with Dave Nicholson. We're talking to storm forge next. Cool name, right? We're going to get to the bottom of that. Please welcome Matt Provo, the founder and CEO of storm forge and Tom Ellery, the SVP of revenue storm forge, guys, welcome to the program. Thanks for having us. So storm forge, you have to say it like that. Like I feel like do you guys wear Storm trooper outfits on Halloween. >> Sometimes Storm trooper? The colors are black. You know, we hit anvils from time to time. >> I thought I, I thought they, that I saw >> Or may not be a heavy metal band that might be infringing on our name. It's all good. That's where we come from. >> I see. So you, so you started the company in 2015. Talk to me about the Genesis of the company. What were some of the gaps in the market that you saw that said we got to come in here and solve this? >> Yeah, so I was fortunate to always know. I think when you start a company, sometimes you, you know exactly the set of problems that you want to go after and potentially why you might be uniquely set up to solve it. What we knew at the beginning was we had a number of really talented data scientists. I was frustrated by the buzzwords around AI and machine learning when under the hood, this really a lot of vaporware. And so at the outset, really the, the point was build something real at the core, connect that to a set of problems that could drive value. And when we looked at really the beginnings of Kubernetes and containerization five, six years ago at its Genesis, we saw just a bunch of opportunity for machine learning, to play the right kind of role if we could build it correctly. And so at the outset it was what's going on. Why are people are people moving content workloads over to containers in the first place? And, you know, because of the flexibility and the portability around Kubernetes, we then ran into quickly its complexity. And within that complexity was really the foundation to set up the company and the solution for prob a set of problems uniquely and most beneficially solved by using machine learning. And so when we sort of brought that together and designed out some ideas, we, we did what any, any founder with a product background would do. We went and talked to a bunch of potential users and kind of tried to validate the problems themselves and, and got a really positive response. So. >> So Tom, from a business perspective, what, what attracted you to this? >> Well, initially I wasn't attracted just, I'll say that just from a startup standpoint. So I've been in the industry for 30 years, I've done six or seven pre IPO companies. I was exiting a private company. I did not want to go do another startup company, but being in the largest enterprise companies for the last 20 years, you see Kubernetes like wildfire in these places. And you knew there was huge amount of complexity and sophistication when they deployed it. So I started talking to Matt early on. He explained what they were doing and how unique the offer was around machine learning. I already knew the problems that customers had at scale with Kubernetes. So it was for me, I said, all right, I'm going to take one more run at this with Matt. I think we're, we're in a great position to differentiate ourselves. So that was really the launch pad for me, was really the technology and the market space. Those, those two things in combination are very exciting for us as a business. >> And, you know, a couple of bottles of amazing wine and a number of dinners that. >> Helps as well. >> That definitely helped twist his arm? >> Now tell us, just really kind of get into the technology. What does it do? How does it help facilitate the Kubernetes environment? >> Yeah, absolutely. So when organizations start moving workloads over to Kubernetes and get their applications up and running, there's a number of amazing organizations, whether it's through cloud providers or otherwise that that sort of solved that day one problem, those challenges. And as I was mentioning, you know, they moved because of flexibility and so developers love it and it starts to create a great experience, but there's these set of expectations. >> Where, where typically are these moving from? What you, what, what are the, what are the top three environments these are, that these are moving out of? >> Yeah. I mean, of course, non containerized environments, more generally. They could be coming from, you know, bare metal environment and it could be coming from kind of a VM driven environment. >> Okay. >> So when you look back at kind of the, the growth and Genesis and of VMs, you see a lot of parallels to what we're seeing now with, with containerization. And so as you move, it's, it's exciting. And then you get smacked in the face with the complexity, for all of the knobs that are able to be turned within a Kubernetes environment. It gives developers a lot of flexibility. These knobs, as you turn them, you have no visibility into how into the impact on the application itself. And so often organizations are become, you know, becoming more agile shipping, you know, shipping code more quickly, but then all of a sudden the, the cloud bill comes and they've, over-provisioned by 80, 90%, the, they didn't need nearly as many resources. And so what we do is we help understand the unique goals and requirements for each of the applications that are running in Kubernetes. And we have machine learning capabilities that can predict very accurately what organizations will need from a resource standpoint, in order to meet their goals, not just from a cost standpoint, but also from a performance standpoint. And so we allow organizations to typically save usually between 40 and 60% off their cloud bill and usually increased performance between 30 and 50%. Historically developers had to choose between cost and performance and their worldview on the application environment was very limited to a small set of what we would call parameters or metrics that they could choose from. And machine learning allows that world to just be blown open and not many humans are, are sophisticated in the way we think about multidimensional math to be able to make those kinds of predictions. You're talking about billions and billions of combinations, not just in a static environment, but an ongoing basis. So our technology sits in the middle of all that chaos and, and allows it to allows organizations just to re reap a whole lot of benefits that they otherwise may not ever find. >> Those numbers that you mentioned were, were big from a cost savings perspective than a performance increased perspective, which is so critical these days is in the last 18 months, we've seen so much change. We've seen massive pivots from companies in every industry to survive first of all, and then to be able to thrive and be able to iterate quickly enough to develop new products and services and get them to market to be competitive. >> Yeah. >> Yeah. Sorry. I mean, the thing that's interesting, there was an article by Andreessen Horowitz. I don't know if you've taken to the cloud paradox. So we actually, if you start looking at that great example would be some of these cloud companies that are growing like astronomical rates, snowflakes, like phenomenal what they're doing, but go look at their cogs and what it's doing. Also, it's growing almost proportionately as the revenues growing. So you need to be able to solve that problem in a way that is sophisticated enough with machine learning algorithms, that people don't have to be in the loop to do it. And that the math can prove out the solution as you go out and scale your environments. And a lot of companies now are all transitioning over SAS based platforms, and they're going to start running into these problems that they go as they go to scale. And those are the areas that we're really focused and concentrating on as an organization. >> As the leader of sales, talk to me about the voice of the customer. What are some- you've been there six months or so we heard, we heard about the wine and the dinners is obvious. >> We haven't done a lot of that over the last 18 months. >> You'll have to make for lost time then >> As soon as he closes more business. >> Oh, oh there we go, we got that on camera! >> There's, there's been three, a market spaces that we've had some really good success in that. So we talked about a SAS marketplace. So there's a company that does Drupal and Matt knows very well up in Boston, Aquia. And they have every customer is a unique snowflake customer. So they need to optimize each of their customers in order to ensure the cost as well as performance for that customer on their site works appropriately. So that's one example of a SAS based company that where we can go in and help them optimize without humans doing the optimization and the math and the machine learning from storm forge doing that. So that's an area, the other area that we've seen some really good traction Cantonese with GSI. So part of our go to market model is with GSI. So if you think about what a GSI does, a lot of times customers are struggling either initially deploying Kubernetes or putting it in for 12, 18 months and realizing we're starting to scale, we got all kinds of performance issues. How do I solve it? A lot of these people go to the Accentures, the cognizance and other ones, and start flying their ninjas into kind of solve the problem. So we're getting a lot of traction with them because they're using our tool as a way to help solve the customer's problems. And they're in the largest enterprise customers as possible. >> So if I'm hearing what you're saying correctly, you're saying that when I deploy server less applications, I may in fact, get a bill for servers that are being used? Is it, is that what you're telling us? >> They're there in fact may be a bill for what was coined as server less. That is very difficult to understand, by the way, >> That's crazy talk, Matt. >> And connect back. >> Yeah. But absolutely we deal with that all the time. It's a, it's a painful process from time to time. >> Have you, have you, have you seen the statistics that's going on with how people, I mean, there was huge inertia from every CIO that you had have a cloud strategy in place. Everyone ran out and had a cloud strategy in place. And then they started deploying on Kubernetes. Now they're realizing, oh wow, we can run it, but it's costing us more than it ever costs us on prem and the operational complexity associated with that. So there's not enough people in the industry to help solve that problem, especially at the grass roots, that's where you need sophisticated solutions like storm forge and machine learning to help solve this at scale problem in a way that humans could never solve. >> And I would, I would just add to that, that the, the same humans managing the Kubernetes application environments today are likely the same humans that we're managing it in a, in a BM world. So there's a huge skills gap. I love what Castin announced at KU KU con this year around their learning environment where it's free. Come learn Kubernetes and this, and we need more of that. There's an enormous skills gap and, and the problems are complex enough in and of themselves. But when we have, when you add that to the skills gap, it it's, it presents a lot of challenges for organizations. >> What are some the ways in which you think that gap can start to be made smaller. >> Yeah. I mean, I think as more workloads get moved over, over, you know, over time, you see, you see more and more people becoming comfortable in an environment where scale is a part of what they have to manage and take care of. I love what the Linux foundation and the CNCF are doing around Kubernetes certifications, you know, more and more training. I think you're going to see training, you know, availability for more and more developers and practitioners be adopted more widely. You know, and I think that, you know, as the tool chain itself hardens within a CCD world in a containerized world, as that hardens, you're going to, you're going to start seeing more and more individuals who are comfortable across all these different tools. If you look at the CNCF landscape, I mean, today compared to four or five years ago, it's growing like crazy. And so, but, but there's also consolidation taking place within the tools. And people have an opportunity to, to learn and gain expertise within us. Which is very marketable by the way, >> Absolutely >> My employees often show me their LinkedIn profiles and remind me of how , how much they're getting recruited, but they've been loyal. So it's been a fantastic. >> Are there are so many parallels when you look at a VM in virtualization and what's happening with covers, obviously all the abstractions and stuff, but there was this whole concept of VM sprawl, you know, maybe 10 years in, if you think about the Kubernetes environment, that is exponentially bigger problem because of how many they're spitting up versus how, how many you spun up in VM. So those things ultimately need to be solved. It's not just going to be solved with people. It needs to be solved with sophisticated software. That's the only way you're going to solve a problem at scale like that. No matter how many people you have in the industry, it's just never going to solve the problem. >> So when you're in customer conversations, Tom, what are you say are like the top three differentiators that really set storm forage apart? >> Well, so the first one is we're very focused on Kubernetes only. So that's all we do is just Kubernetes environment. So we understand not just the applications that run in Kubernetes, but we understand the underlying architectures and techniques, which we think is really important. From a solution standpoint, >> So you're specialists? >> We are absolutely specialists. The other areas obviously are machine learning and the sophistication of our machine learning. And Matt said this really well, early on, I mean, the buzzwords are all out there. You can read them all up, all over the place for the last five to seven year AI and ML. And a lot of them are very hollow, but our whole foundation was based on machine learning and PhDs from Harvard. That's where we came out of from a technology background. So we were solving more, we weren't just solving the Kubernetes problems. We were solving machine learning problems. And so that's another really big area of differential for us. And I think the ability to actually scale and not just deal with small problems, but very large problems, because our focus is the fortune 2000 companies. And most of them have been deploying like financial services and stuff, Kubernetes for three, four or five years. And so they have had scale challenges that they're trying to solve. >> Yeah. It's Lisa and I talk about this concept of machine learning and looking under the covers and trying to find out is the machine really learning? Is it really learning or is it people are telling the machine, you need to do this. If you see that Where's the machine actually making those correlations and doing something intelligently. So can you give us an example of something that is actually happening that's intelligent? >> Well, so the, the, if this, then that problem is actually a huge source of my original frustration for starting the company, because you, you, you tag AI as a buzzword onto a lot of stuff. And we see that growing like crazy. And so I literally at the beginning said, if we can't actually build something real, that solves problems, like we're going to hang it up. And, you know, as Tom said, we came out of Harvard and, you know, there was a challenge initially of, are we just going to build like a really amazing algorithm? That's so heavy, it can never be productized or commercialized and it really should have just stayed in academia. And, you know, I the I, I will say a couple of things. One is I do not believe that that black box AI is a thing. We believe in what we would call human, augmented AI. So we want to empower practitioners and developers into the process instead of automate them out. We just want to give them the information and we want to save time for them and make their lives easier. But there's a kill switch on the technology. They can intervene at any point in time. They can direct the technology as they see fit. And what's really, really interesting is because their worldview of this application environment gets opened up by all the predictions and all of the learning that actually is taking place and, you know, give it because that worldview is open, they then get into a kind of a tinkering or experimental mindset with the technology. And they start thinking about all these other scenarios that they never were able to explore previously with the application. And, and so the machine learning itself is on an ongoing basis. Understanding changes in traffic, understanding and changes, changes in workloads for the application or demand. If you thought about like surge pricing for Uber, you know, because of a, a big game that took place. And you know, that, that change in peaks and valleys in demand, our, our technology not only understands those reactively, but it starts to build models and predict proactively in advance of the events that are going to take place on, on what ne- what kind of resources need to be allocated. And why that's the other piece around it is often solutions are giving you a little bit of a what, but they certainly are not giving you any explanation of the why. So the holy grail really like in our world is kind of truly explainable AI, which we're not there yet. Nobody's there yet. But human augmented AI with, with actual intelligence that's taking place that also is relevant to business outcomes is, is pretty exciting. So that's why where try to operate. >> Very exciting guys. Thanks for joining us, talking to us about storm forage, to feel like we need some store in forge. T-shirts what do you think? >> (unintelligible) >> See, I'm not even asking for the bottle of wine. I liked that idea. I thank Matt and Tom, thank you so much for joining us exciting company. Congratulations on your success. And we look forward to seeing what great things are to come from storm forage. >> Thanks so much for the time. >> Our pleasure. For Dave Nicholson. I'm Lisa Martin. We are alive in Los Angeles, the cube covering Kube con and cloud native con 21 stick around. Dave and I will be right back with our next guest.