(inspiring music) >> Announcer: theCUBE presents HPE Discover 2022. Brought to you by HPE. >> Welcome back to theCUBE's coverage of HPE Discover 2022, from the Venetian in Las Vegas, the first Discover since 2019. I really think this is my 14th Discover, when you include HP, when you include Europe. And I got to say this Discover, I think has more energy than any one that I've ever seen, about 8,000 people here. Really excited to have one of HPE's longstanding partners, Veeam CTO, Danny Allen is here, joined by David Harvey, Vice President of Strategic Alliances at Veeam. Guys, good to see you again. It was just earlier, let's see, last month, we were together out here. >> Yeah, just a few weeks ago. It's fantastic to be back and what it's telling us, technology industry is coming back. >> And the events business, of course, is coming back, which we love. I think the expectations were cautious. You saw it at VeeamON, a little more than you expected, a lot of great energy. A lot of people, 'cause it was last month, it was their first time out, >> Yes. >> in two years. Here, I think people have started to go out more, but still, an energy that's palpable. >> You can definitely feel it. Last night, I think I went to four consecutive events and everyone's out having those discussions and having conversations, it's good to be back. >> You guys hosted the Storage party last night, which is epic. I left at midnight, I took a picture, it was still packed. I said, okay, time to go, nothing good happens after midnight kids. David, talk about the alliance with HPE, how it's evolved, and where you see it going? >> I appreciate it, and certainly this, as you said, has been a big alliance for us. Over 10 years or so, fantastic integrations across the board. And you touched on 2019 Discover. We launched with GreenLake at that event, we were one of the launch partners, and we've seen fantastic growth. Overall, what we're excited about, is that continuation of the movement of the customer's buying patterns in line with HPE's portfolio and in line with Veeam. We continue to be with all their primary, secondary storage, we continue to be a spearhead position with GreenLake, which we're really excited about. And we're also really excited to hear from HPE, unfortunately under NDA, some of their future stuff they're investing in, which is a really nice invigoration for what they're doing for their portfolio. And we see that being a big deal for us over the next 24 months. >> Your relationship with HPE predates the HP, HPE split. >> Mmm. >> Yes. >> But it was weird, because they had Data Protector, and that was a quasi-competitor, or really not, but it was a competitor, a legacy competitor, of what you guys have, kind of modern data protection I think is the tagline, if I got it right. Post the split, that was an S-curve moment, wasn't it, in terms of the partnership? >> It really was. If you go back 10 years, we did our first integration sending data to StoreOnce and we had some blueprints around that. But now, if you look what we have, we have integrations on the primary side, so, 3PAR, Primera, Nimble, all their top-tier storage, we can manage the snapshots. We have integration on the target side. We integrate with Catalyst in the movement of data and the management of data. And, as David alluded to, we integrate with GreenLake. So, customers who want to take this as a consumption model, we integrate with that. And so it's been, like you said, the strongest relationship that we have on the technology alliance side. >> So, V12, you announced at VeeamON. What does that mean for HPE customers, the relationship? Maybe you guys could both talk about that. >> Technology side, to touch on a few things that we're doing with them, ransomware has been a huge issue. Security's been a big theme, obviously, at the conference, >> Dave: Yeah, you bet. and one of the things we're doing in V12 is adding immutability for both StoreOnce and StoreEver. So, we take the features that our partners have, immutability being big in the security space, and we integrate that fully into the product. So a customer checks a box and says, hey, I want to make sure that the data is secure. >> Yeah, and also, it's another signification about the relationship. Every single release we've done has had HPE at the heart of it, and the same thing is being said with V12. And it shows to our customers, the continual commitment. Relationships come and go. They're hard, and the great news is, 10 years has proven that we get through good times and tricky situations, and we both continue to invest, et cetera. And I think there's a lot of peace of mind and the revenue figures prove that, which is what we're really excited about. >> Yeah I want to come back to that, but just to follow up, Danny, on that immutability, that's a feature that you check? It's service within GreenLake, or within Veeam? How does that all work? >> We have immutability now depending on the target. We introduced the ability to send data, for example, into S3 two years ago, and make it immutable when you send it to an S3 or S3 compatible environment. We added, in Version 11, the ability to take a Linux repository and make it, and harden it, essentially make it immutable. But what we're doing now is taking our partner systems like StoreOnce, like StoreEver, and when we send data there, we take advantage of an API flag or whatever it happens to be, that it makes the data, when it's written to that system, can't be deleted, can't be encrypted. Now, what does that mean for a customer? Well, we do all the hard work in the back end, it's just a check box. They say, I want to make it immutable, and we manage how long it's immutable. Because if you made everything immutable forever, that's hugely expensive, right? So, it's all about, how long is that immutable before you age it out and make sure the new data coming in is immutable. >> Dave: It's like an insurance policy, you have that overlap. >> Yes. >> Right, okay. And then David, you mentioned the revenue, Lou bears that out. I got the IDC guys comin' on later on today. I'll ask 'em about that, if that's their swim lane. But you guys are basically a statistical tie, with Dell for number one? Am I getting that right? And you're growing at a faster rate, I believe, it's hard to tell 'cause I don't think Dell reports on the pace of its growth within data protection. You guys obviously do, but is that right? It's a statistical tie, is it? >> Yeah, hundred percent. >> Yeah, statistical tie for first place, which we're super excited about. When I joined Veeam, I think we were in fifth place, but we've been in the leader's quadrant of the Gartner Magic- >> Cause and effect there or? (panelists laughing) >> No, I don't think so. >> Dave: Ha, I think maybe. >> We've been on a great trajectory. But statistical tie for first place, greatest growth sequentially, and year-over-year, of all of the data protection vendors. And that's a testament not just to the technology that we're doing, but partnerships with HPE, because you never do this, the value of a technology is not that technology alone, it's the value of that technology within the ecosystem. And so that's why we're here at HPE Discover. It's our joint technology solutions that we're delivering. >> What are your thoughts or what are you seeing in the field on As-a-service? Because of course, the messaging is all about As-a-service, you'd think, oh, a hundred percent of everything is going to be As-a-service. A lot of customers, they don't mind CapEx, they got good, balance sheet, and they're like, hey, we'll take care of this, and, we're going to build our own little internal cloud. But, what are you seeing in the market in terms of As-a-service, versus, just traditional licensing models? >> Certainly, there's a mix between the two. What I'd say, is that sources that are already As-a-service, think Microsoft 365, think AWS, Azure, GCP, the cloud providers. There's a natural tendency for the customer to want the data protection As-a-service, as well for those. But if you talk about what's on premises, customers who have big data centers deployed, they're not yet, the pendulum has not shifted for that to be data protection As-a-service. But we were early to this game ourselves. We have 10,000, what we call, Veeam Cloud Service Providers, that are offering data protection As-a-service, whether it be on premises, so they're remotely managing it, or cloud hosted, doing data protection for that. >> So, you don't care. You're providing the technology, and then your customers are actually choosing the delivery model. Is that correct? >> A hundred percent, and if you think about what GreenLake is doing for example, that started off as being a financial model, but now they're getting into that services delivery. And what we want to do is enable them to deliver it, As-a-service, not just the financial model, but the outcome for the customer. And so our technology, it's not just do backup, it's do backup for a multi-tenant, multi-customer environment that does all of the multi-tenancy and billing and charge back as part of that service. >> Okay, so you guys don't report on this, but I'm going to ask the question anyway. You're number one now, let's call you, let's declare number one, 'cause we're well past that last reporting and you're growin' faster. So go another quarter, you're now number one, so you're the largest. Do you spend more on R&D in data protection than any other company? >> Yes, I'm quite certain that we do. Now, we have an unfair advantage because we have 450,000 customers. I don't think there's any other data protection company out there, the size and scope and scale, that we have. But we've been expanding, our largest R&D operation center's in Prague, it's in Czech Republic, but we've been expanding that. Last year it grew 40% year on year in R&D, so big investment in that space. You can see this just through our product space. Five years ago, we did data protection of VMware only, and now we do all the virtual environments, all the physical environments, all the major cloud environments, Kubernetes, Microsoft 365, we're launching Salesforce. We announced that at VeeamON last month and it will be coming out in Q3. All of that is coming from our R&D investments. >> A lot of people expect that when a company like Insight, a PE company, purchases a company like Veeam, that one of the things they'll dial down is R&D. That did not happen in this case. >> No, they very much treat us as a growth company. We had 22% year-over-year growth in 2020, and 25% year-over-year last year. The growth has been tremendous, they continue to give us the freedom. Now, I expect they'll want returns like that continuously, but we have been delivering, they have been investing. >> One of my favorite conversations of the year was our supercloud conversation, which was awesome, thank you for doing that with me. But that's clearly an area of focus, what we call supercloud, and you don't use that term, I know, you do sometimes, but it's not your marketing, I get that. But that is an R&D intensive effort, is it not? To create that common experience. And you see HPE, attempting to do that as well, across all these different estates. >> A hundred percent. We focus on three things, I always say, our differentiators, simplicity, flexibility, and reliability. Making it simple for the customers is not an easy thing to do. Making that checkbox for immutability? We have to do a lot behind the scenes to make it simple. Same thing on flexibility. We don't care if they're using 3PAR, Primera, Nimble, whatever you want to choose as the primary storage, we will take that out of your hands and make it really easy. You mentioned supercloud. We don't care what the cloud infrastructure, it can be on GreenLake, it can be on AWS, can be on Azure, it can be on GCP, it can be on IBM cloud. It is a lot of effort on our part to abstract the cloud infrastructure, but we do that on behalf of our customers to take away that complexity, it's part of our platform. >> Quick follow-up, and then I want to ask a question of David. I like talking to you guys because you don't care where it is, right? You're truly agnostic to it all. I'm trying to figure out this repatriation thing, cause I hear a lot of hey, Dave, you should look into repatriation that's happened all over the place, and I see pockets of it. What are you seeing in terms of repatriation? Have customers over-rotated to the cloud and now they're pullin' back a little bit? Or is it, as I'm claiming, in pockets? What's your visibility on that? >> Three things I see happening. There's the customers who lifted up their data center, moved it into the cloud and they get the first bill. >> (chuckling) Okay. >> And they will repatriate, there's no question. If I talk to those customers who simply lifted up and moved it over because the CIO told them to, they're moving it back on premises. But a second thing that we see is people moving it over, with tweaks. So they'll take their SQL server database and they'll move it into RDS, they'll change some things. And then you have people who are building cloud-native, they're never coming back on premises, they are building it for the cloud environment. So, we see all three of those. We only really see repatriation on that first scenario, when they get that first bill. >> And when you look at the numbers, I think it gets lost, 'cause you see the cloud is growing so fast. So David, what are the conversations like? You had several events last night, The Veeam party, slash Storage party, from HPE. What are you hearing from your alliance partners and the customers at the event. >> I think Danny touched on that point, it's about philosophy of evolution. And I think at the end of the day, whether we're seeing it with our GSI alliances we've got out there, or with the big enterprise conversations we're having with HPE, it's about understanding which workloads they want to move. In our mind, the customers are getting much smarter in making that decision, rather than experimenting. They're really taking a really solid look. And the work we're doing with the GSIs on workplace modernization, data center transformation, they're really having that investment work up front on the workloads, to be able to say, this works for me, for my personality and my company. And so, to the point about movement, it's more about decisive decision at the start, and not feeling like the remit is, I have to do one thing or another, it's about looking at that workflow position. And that's what we've seen with the revenue part as well. We've seen our movement to GreenLake tremendously grow in the last 18 months to two years. And from our GSI work as well, we're seeing the types of conversations really focus on that workload, compared to, hey, I just need a backup solution, and that's really exciting. >> Are you having specific conversations about security, or is it a data protection conversation still, (David chuckles) that's an adjacency to security? >> That's a great question. And I think it's a complex one, because if you come to a company like Veeam, we are there, and you touched on it before, we provide a solution when something has happened with security. We're not doing intrusion detection, we're not doing that barrier position at the end of it, but it's part of an end-to-end assumption. And I don't think that at this particular point, I started in security with RSA and Check Point, it was about layers of protection. Now it's layers of protection, and the inevitability that at some point something will happen, so about the recovery. So the exciting conversations we're having, especially with the big enterprises, is not about the fear factor, it's about, at some point something's going to occur. Speed of recovery is the conversation. And so for us, and your question is, are they talking to us about security, or more, the continuity position? And that's where the synergy's getting a lot simpler, rather than a hard demark between security and backup. >> Yeah, when you look at the stock market, everything's been hit, but security, with the exception of Okta, 'cause it got that weird benign hack, but security, generally, is an area that CIOs have said, hey, we can't really dial that back. We can maybe, some other discretionary stuff, we'll steal and prioritize. But security seems to be, and I think data protection is now part of that discussion. You're not a security company. We've seen some of your competitors actually pivot to become security companies. You're not doing that, but it's very clearly an adjacency, don't you think? >> It's an adjacency, and it's a new conversation that we're having with the Chief Information Security Officer. I had a meeting an hour ago with a customer who was hit by ransomware, and they got the call at 2:00 AM in the morning, after the ransomware they recovered their entire portfolio within 36 hours, from backups. Didn't even contact Veeam, I found out during this meeting. But that is clearly something that the Chief Information Security Officer wants to know about. It's part of his purview, is the recovery of that data. >> And they didn't pay the ransom? >> And they did not pay the ransom, not a penny. >> Ahh, we love those stories. Guys, thanks so much for coming on theCUBE. Congratulations on all the success. Love when you guys come on, and it was such a fun event at VeeamON. Great event here, and your presence is, was seen. The Veeam green is everywhere, so appreciate your time. >> Thank you. >> Thanks, Dave. >> Okay, and thank you for watching. This is Dave Vellante for John Furrier and Lisa Martin. We'll be back right after this short break. You're watching theCUBE's coverage of HPE Discover 2022, from Las Vegas. (inspiring music)

(upbeat music) >> We're back at VeeamON 2022. We're here at the Aria in Las Vegas Dave Vellante with Dave Nicholson. Bill Andrews is here. He's the president and CEO of ExaGrid, mass boy. Bill, thanks for coming on theCUBE. >> Thanks for having me. >> So I hear a lot about obviously data protection, cyber resiliency, what's the big picture trends that you're seeing when you talk to customers? >> Well, I think clearly we were talking just a few minutes ago, data's growing like crazy, right This morning, I think they said it was 28% growth a year, right? So data's doubling almost just a little less than every three years. And then you get the attacks on the data which was the keynote speech this morning as well, right. All about the ransomware attacks. So we've got more and more data, and that data is more and more under attack. So I think those are the two big themes. >> So ExaGrid as a company been around for a long time. You've kind of been the steady kind of Eddy, if you will. Tell us about ExaGrid, maybe share with us some of the differentiators that you share with customers. >> Sure, so specifically, let's say in the Veeam world you're backing up your data, and you really only have two choices. You can back that up to disc. So some primary storage disc from a Dell, or a Hewlett Packard, or an NetApp or somebody, or you're going to back it up to what's called an inline deduplication appliance maybe a Dell Data Domain or an HPE StoreOnce, right? So what ExaGrid does is we've taken the best of both those but not the challenges of both those and put 'em together. So with disc, you're going to get fast backups and fast restores, but because in backup you keep weekly's, monthly's, yearly retention, the cost of this becomes exorbitant. If you go to a deduplication appliance, and let's say the Dell or the HPs, the data comes in, has to be deduplicated, compare one backup to the next to reduce that storage, which lowers the cost. So fixes that problem, but the fact that they do it inline slows the backups down dramatically. All the data is deduplicated so the restores are slow, and then the backup window keeps growing as the data grows 'cause they're all scale up technologies. >> And the restores are slow 'cause you got to rehydrate. >> You got to rehydrate every time. So what we did is we said, you got to have both. So our appliances have a front end disc cache landing zone. So you're right directed to the disc., Nothing else happens to it, whatever speed the backup app could write at that's the speed we take it in at. And then we keep the most recent backups in that landing zone ready to go. So you want to boot a VM, it's not an hour like a deduplication appliance it's a minute or two. Secondly, we then deduplicate the data into a second tier which is a repository tier, but we have all the deduplicated data for the long term retention, which gets the cost down. And on top of that, we're scale out. Every appliance has networking processor memory end disc. So if you double, triple, quadruple the data you double, triple, quadruple everything. And if the backup window is six hours at 100 terabyte it's six hours at 200 terabyte, 500 terabyte, a petabyte it doesn't matter. >> 'Cause you scale out. >> Right, and then lastly, our repository tier is non-network facing. We're the only ones in the industry with this. So that under a ransomware attack, if you get hold of a rogue server or you hack the media server, get to the backup storage whether it's disc or deduplication appliance, you can wipe out all the backup data. So you have nothing to recover from. In our case, you wipe it out, our landing zone will be wiped out. We're no different than anything else that's network facing. However, the only thing that talks to our repository tier is our object code. And we've set up security policies as to how long before you want us to delete data, let's say 10 days. So if you have an attack on Monday that data doesn't get deleted till like a week from Thursday, let's say. So you can freeze the system at any time and do restores. And then we have immutable data objects and all the other stuff. But the culmination of a non-network facing tier and the fact that we do the delayed deletes makes us the only one in the industry that can actually truly recover. And that's accelerating our growth, of course. >> Wow, great description. So that disc cache layer is a memory, it's a flash? >> It's disc, it's spinning disc. >> Spinning disc, okay. >> Yeah, no different than any other disc. >> And then the tiered is what, less expensive spinning disc? >> No, it's still the same. It's all SaaS disc 'cause you want the quality, right? So it's all SaaS, and so we use Western Digital or Seagate drives just like everybody else. The difference is that we're not doing any deduplication coming in or out of that landing zone to have fast backups and fast restores. So think of it like this, you've got disc and you say, boy it's too expensive. What I really want to do then is put maybe a deduplication appliance behind it to lower the cost or reverse it. I've got a deduplication appliance, ugh, it's too slow for backups and restores. I really want to throw this in front of it to have fast backups first. Basically, that's what we did. >> So where does the cost savings, Bill come in though, on the tier? >> The cost savings comes in the fact that we got deduplication in that repository. So only the most recent backup >> Ah okay, so I get it. >> are the duplicated data. But let's say you had 40 copies of retention. You know, 10 weekly's, 36 monthly's, a few yearly. All of that's deduplicated >> Okay, so you're deduping the stuff that's not as current. >> Right. >> Okay. >> And only a handful of us deduplicate at the layer we do. In other words, deduplication could be anywhere from two to one, up to 50 to one. I mean it's all over the place depending on the algorithm. Now it's what everybody's algorithms do. Some backup apps do two to one, some do five to one, we do 20 to one as well as much as 50 to one depending on the data types. >> Yeah, so the workload is going to largely determine the combination >> The content type, right. with the algos, right? >> Yeah, the content type. >> So the part of the environment that's behind the illogical air gap, if you will, is deduped data. >> Yes. >> So in this case, is it fair to say that you're trading a positive economic value for a little bit longer restore from that environment? >> No, because if you think about backup 95% of the customers restores are from the most recent data. >> From the disc cache. >> 95% of the time 'cause you think about why do you need fast restores? Somebody deleted a file, somebody overwrote a file. They can't go work, they can't open a file. It's encrypted, it's corrupted. That's what IT people are trying to keep users productive. When do you go for longer-term retention data? It's an SEC audit. It's a HIPAA audit. It's a legal discovery, you don't need that data right away. You have days and weeks to get that ready for that legal discovery or that audit. So we found that boundary where you keep users productive by keeping the most recent data in the disc cache landing zone, but anything that's long term. And by the way, everyone else is long term, at that point. >> Yeah, so the economics are comparable to the dedupe upfront. Are they better, obviously get the performance advance? >> So we would be a lot looped. The thing we replaced the most believe it or not is disc, we're a lot less expensive than the disc. I was meeting with some Veeam folks this morning and we were up against Cisco 3260 disc at a children's hospital. And on our quote was $500,000. The disc was 1.4 million. Just to give you an example of the savings. On a Data Domain we're typically about half the price of a Data Domain. >> Really now? >> The reason why is their front end control are so expensive. They need the fastest trip on the planet 'cause they're trying to do inline deduplication. >> Yeah, so they're chasing >> They need the fastest memory >> on the planet. >> this chips all the time. They need SSD on data to move in and out of the hash table. In order to keep up with inline, they've got to throw so much compute at it that it drives their cost up. >> But now in the case of ransomware attack, are you saying that the landing zone is still available for recovery in some circumstances? Or are you expecting that that disc landing zone would be encrypted by the attacker? >> Those are two different things. One is deletion, one is encryption. So let's do the first scenario. >> I'm talking about malicious encryption. >> Yeah, absolutely. So the first scenario is the threat actor encrypts all your primary data. What's does he go for next? The backup data. 'Cause he knows that's your belt and suspend is to not pay the ransom. If it's disc he's going to go in and put delete commands at the disc, wipe out the disc. If it's a data domain or HPE StoreOnce, it's all going to be gone 'cause it's one tier. He's going to go after our landing zone, it's going to be gone too. It's going to wipe out our landing zone. Except behind that we have the most recent backup deduplicate in the repository as well as all the other backups. So what'll happen is they'll freeze the system 'cause we weren't going to delete anything in the repository for X days 'cause you set up a policy, and then you restore the most recent backup into the landing zone or we can restore it directly to your primary storage area, right? >> Because that tier is not network facing. >> That's right. >> It's fenced off essentially. >> People call us every day of the week saying, you saved me, you saved me again. People are coming up to me here, you saved me, you saved me. >> Tell us a story about that, I mean don't give me the names but how so. >> I'll actually do a funnier story, 'cause these are the ones that our vendors like to tell. 'Cause I'm self-serving as the CEO that's good of course, a little humor. >> It's your 15 minutes of job. >> That is my 15 minutes of fame. So we had one international company who had one ExaGrid at one location, 19 Data Domains at the other locations. Ransomware attack guess what? 19 Data Domains wiped out. The one ExaGrid, the only place they could restore. So now all 20 locations of course are ExaGrids, China, Russia, Mexico, Germany, US, et cetera. They rolled us out worldwide. So it's very common for that to occur. And think about why that is, everyone who's network facing you can get to the storage. You can say all the media servers are buttoned up, but I can find a rogue server and snake my way over the storage, I can. Now, we also of course support the Veeam Data Mover. So let's talk about that since we're at a Veeam conference. We were the first company to ever integrate the Veeam Data Mover. So we were the first actually ever integration with Veeam. And so that Veeam Data Mover is a protocol that goes from Veeam to the ExaGrid, and we run it on both ends. So that's a more secure protocol 'cause it's not an open format protocol like SaaS. So with running the Veeam Data Mover we get about 30% more performance, but you do have a more secure protocol layer. So if you don't get through Veeam but you get through the protocol, boom, we've got a stronger protocol. If you make it through that somehow, or you get to it from a rogue server somewhere else we still have the repository. So we have all these layers so that you can't get at it. >> So you guys have been at this for a while, I mean decade and a half plus. And you've raised a fair amount of money but in today's terms, not really. So you've just had really strong growth, sequential growth. I understand it, and double digit growth year on year. >> Yeah, about 25% a year right now >> 25%, what's your global strategy? >> So we have sales offices in about 30 countries already. So we have three sales teams in Brazil, and three in Germany, and three in the UK, and two in France, and a lot of individual countries, Chile, Argentina, Columbia, Mexico, South Africa, Saudi, Czech Republic, Poland, Dubai, Hong Kong, Australia, Singapore, et cetera. We've just added two sales territories in Japan. We're adding two in India. And we're installed in over 50 countries. So we've been international all along the way. The goal of the company is we're growing nicely. We have not raised money in almost 10 years. >> So you're self-funding. You're cash positive. >> We are cash positive and self-funded and people say, how have you done that for 10 years? >> You know what's interesting is I remember, Dave Scott, Dave Scott was the CEO of 3PAR, and he told me when he came into that job, he told the VCs, they wanted to give him 30 million. He said, I need 80 million. I think he might have raised closer to a hundred which is right around what you guys have raised. But like you said, you haven't raised it in a long time. And in today's terms, that's nothing, right? >> 100 is 500 in today's terms. >> Yeah, right, exactly. And so the thing that really hurt 3PAR, they were public companies so you could see all this stuff is they couldn't expand internationally. It was just too damn expensive to set up the channels, and somehow you guys have figured that out. >> 40% of our business comes out of international. We're growing faster internationally than we are domestically. >> What was the formula there, Bill, was that just slow and steady or? >> It's a great question. >> No, so what we did, we said let's build ExaGrid like a McDonald's franchise, nobody's ever done that before in high tech. So what does that mean? That means you have to have the same product worldwide. You have to have the same spares model worldwide. You have to have the same support model worldwide. So we early on built the installation. So we do 100% of our installs remotely. 100% of our support remotely, yet we're in large enterprises. Customers racks and stacks the appliances we get on with them. We do the entire install on 30 minutes to about three hours. And we've been developing that into the product since day one. So we can remotely install anywhere in the world. We keep spares depots all over the world. We can bring 'em up really quick. Our support model is we have in theater support people. So they're in Europe, they're in APAC, they're in the US, et cetera. And we assign customers to the support people. So they deal with the same support person all the time. So everything is scalable. So right now we're going to open up India. It's the same way we've opened up every other country. Once you've got the McDonald's formula we just stamp it all over the world. >> That's amazing. >> Same pricing, same product same model, same everything. >> So what was the inspiration for that? I mean, you've done this since day one, which is what like 15, 16 years ago. Or just you do engineering or? >> No, so our whole thought was, first of all you can't survive anymore in this world without being an international company. 'Cause if you're going to go after large companies they have offices all over the world. We have companies now that have 17, 18, 20, 30 locations. And there were in every country in the world, you can't go into this business without being able to ship anywhere in the world and support it for a single customer. You're not going into Singapore because of that. You're going to Singapore because some company in Germany has offices in the U.S, Mexico Singapore and Australia. You have to be international. It's a must now. So that was the initial thing is that, our goal is to become a billion dollar company. And we're on path to do that, right. >> You can see a billion. >> Well, I can absolutely see a billion. And we're bigger than everybody thinks. Everybody guesses our revenue always guesses low. So we're bigger than you think. The reason why we don't talk about it is we don't need to. >> That's the headline for our writers, ExaGrid is a billion dollar company and nobody's know about it. >> Million dollar company. >> On its way to a billion. >> That's right. >> You're not disclosing. (Bill laughing) But that's awesome. I mean, that's a great story. I mean, you kind of are a well kept secret, aren't you? >> Well, I dunno if it's a well kept secret. You know, smaller companies never have their awareness of big companies, right? The Dells of the world are a hundred billion. IBM is 70 billion, Cisco is 60 billion. Easy to have awareness, right? If you're under a billion, I got to give a funny story then I think we got to close out here. >> Oh go ahead please. >> So there's one funny story. So I was talking to the CIO of a super large Fortune 500 company. And I said to him, "Just so who do you use?" "I use IBM Db2, and I use, Cisco routers, and I use EMC primary storage, et cetera. And I use all these big." And I said, "Would you ever switch from Db2?" "Oh no, the switching costs would kill me. I could never go to Oracle." So I said to him, "Look would you ever use like a Pure Storage, right. A couple billion dollar company." He says, "Who?" >> Huh, interesting. >> I said to him, all right so skip that. I said, "VMware, would you ever think about going with Nutanix?" "Who?" Those are billion dollar plus companies. And he was saying who? >> Public companies. >> And he was saying who? That's not uncommon when I talk to CIOs. They see the big 30 and that's it. >> Oh, that's interesting. What about your partnership with Veeam? Tell us more about that. >> Yeah, so I would actually, and I'm going to be bold when I say this 'cause I think you can ask anybody here at the conference. We're probably closer first of all, to the Veeam sales force than any company there is. You talk to any Veeam sales rep, they work closer with ExaGrid than any other. Yeah, we are very tight in the field and have been for a long time. We're integrated with the Veeam Data Boomer. We're integrated with SOBR. We're integrated with all the integrations or with the product as well. We have a lot of joint customers. We actually do a lot of selling together, where we go in as Veeam ExaGrid 'cause it's a great end to end story. Especially when we're replacing, let's say a Dell Avamar to Dell Data Domain or a Dell Network with a Dell Data Domain, very commonly Veeam ExaGrid go in together on those types of sales. So we do a lot of co-selling together. We constantly train their systems engineers around the world, every given week we're training either inside sales teams, and we've trained their customer support teams in Columbus and Prague. So we're very tight with 'em we've been tight for over a decade. >> Is your head count public? Can you share that with us? >> So we're just over 300 employees. >> Really, wow. >> We have 70 open positions, so. >> Yeah, what are you looking for? Yeah, everything, right? >> We are looking for engineers. We are looking for customer support people. We're looking for marketing people. We're looking for inside sales people, field people. And we've been hiring, as of late, major account reps that just focus on the Fortune 500. So we've separated that out now. >> When you hire engineers, I mean I think I saw you were long time ago, DG, right? Is that true? >> Yeah, way back in the '80s. >> But systems guy. >> That's how old I am. >> Right, systems guy. I mean, I remember them well Eddie Castro and company. >> Tom West. >> EMV series. >> Tom West was the hero of course. >> The EMV 4000, the EMV 20,000, right? >> When were kids, "The Soul of a New Machine" was the inspirational book but anyway, >> Yeah Tracy Kidder, it was great. >> Are you looking for systems people, what kind of talent are you looking for in engineering? >> So it's a lot of Linux programming type stuff in the product 'cause we run on a Linux space. So it's a lot of Linux programs so its people in those storage. >> Yeah, cool, Bill, hey, thanks for coming on to theCUBE. Well learned a lot, great story. >> It's a pleasure. >> That was fun. >> Congratulations. >> Thanks. >> And good luck. >> All right, thank you. >> All right, and thank you for watching theCUBE's coverage of VeeamON 2022, Dave Vellante for Dave Nicholson. We'll be right back right after this short break, stay with us. (soft beat music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel AWS and our community partners >>Telephone. Welcome back to the cubes. Virtual coverage of AWS reinvent 2020. We got the cube virtual because we're not in person. Got a great remote interview. Slummy Mannheim. Who's the CEO? Co founder, uh, exciting company. Drake J Frog. We went public this year. Congratulations, Cube alumni. Really a successor of White. The cloud exists in all the greatness and goodness of technology. It's not great to see you. Thanks for coming off of the special reinvent segment. >>Thank you. Thank you for having me, John. Great to see you again. >>So you guys have your mission continues. You're growing. We're here at reinvent. What's the story? Give us the quick news. Visa vee. Reinvent N A W s. >>Well, we had Ah, wonderful, uh, wonderful. Two months. Uh, since we went public on September 16, um, the company actually going past and they have UPS. Industry is going along us along. Excite us. So we're very excited about it. Um, great. Here. Great journey. You guys met us two years ago. So So you know the swamp. Well, then we're very excited being reinvent again, although virtually defined. >>You know, when you get a tailwind and you have a trend that your friend you guys had certainly had that with the developer first. That's the mantra. Everyone's talking about that now. You guys saw it early. The future of binary lifecycle management Dev Ops was the lifeblood of Dev ops. Now more is happening. You got automation. You got everything as a service which makes the developer equation even more powerful. Abstracting away complexities is even more needed. What's your vision on this? How do you guys continue the momentum in this now Highly accelerated cove it and soon to be post covert environment. >>Yeah. You know, John co vid actually accelerated what we already so years ago. And, uh, what we've seen is that the war demands a better way to update software. Look at us. Even this interview is being powered by software, right? I'm staring at the camera. I e used to sit in your studio and everything we do we all the food by by software. Our kids are at home learning with software. So obviously the demand for most software and most software updates is there, and Dev Ops is just the vehicle now. Once you understand that, you have to ask yourself, what is the primary asset that we really need to automate in order to become faster and secure and to provide a seamless software really slow? And what we identify 12 years ago is that it's the software packages, the binaries azi. We were named by the community, the binary people. >>Yeah, and and this is cool because not only it's just not a tool, it's a platform. You guys don't have a platform view. We talked about this in 2017. I remember The conversation like this is pretty compelling. This is Ah, go big or go home. You guys went big, for sure and successful. How do you take that platform approach to Dev Ops, where you have to enable success, you gotta have the enterprise features you got now hybrid multiple environment with the edge and other clouds air happening. How are you looking at this? >>Yes, So today it's it's quite clear in the in the enterprise falls zero. Everybody understand. Developers are the rainmakers. The communities is what powers innovation and what makes changes Look a talker. Look at problematic. Look at cloud native. It didn't started the enterprise. It starts with the developer. The developer mind this is, I think, the biggest democracy. And when we realized that 10 years ago, our philosophy was very, very clear, we would like the developers to have the freedom of choice. We want them to have ah, universal solution that supports all technologies, all software packages. Then we want them to have a hybrid solution. They prefer to one in the cloud also fostered. We will be, um, completely for it. And then not just in the cloud, but also multi cloud. So the full the full freedom of choice coined by the community, the Switzerland of develops. And, uh, starting as you mentioned, we started without a factory housing factories. The database of them are posting all of your software packages, all type of software packages. Then J. Fogg, X ray, our security vulnerability and license compliance tool that natively integrate without the factory. Then J Fogg distribution that push your software packages to the edge. We acquired two companies cloud much for the dashboard, did oversee all the pipeline and ship a bell, which is today, Jeff Pipelines, Our C I c d. And then we did you know, it was a long journey, but very food food for us, and we are very proud to build it together with the community. >>Well, not only did you guys succeed execution wise, the vision was phenomenal. The execution with the acquisitions, you really knocked down some great accomplishments. Eso Congratulations. You just laid that out, you know? Good call out there. I do want to ask you about this liquid software narrative. Can you take a minute toe? Unpack that a little bit? Because this is new. It seems to be something that is about the collective vision. How does this come together? Because you gotta do act to now. Act one is over. You went public. You did all the work. You built the company. You got a durable business. Got great customers. Happy community. What's this liquid software thing? >>Well, think about it. Liquid software might be our vision J. Fogg vision, but it's the world's mission. Now we want to have Netflix podcasting to our home without any software update disturbing us. We want to have our iPhone being updated automatically and seamlessly without a reboot. We want our Tesla, uh, to be updated without shutting down the model and schedule and update. And this is our mission. This is the big picture. How can we make sure that software is running smoothly from the developers Single tips all the way to the edge, no matter what the edges. Now, in order to achieve that, you have to be fast. You have to be automated, you have to be secure. And you have to be focused on the assets that moved from the developer, the hands off from the developers to the op that goes all the way to the devices, the machines or whatever edge. And these are the binaries. So the vision of flick with software is a software updates slowing, uh, into your pipe seamlessly all the way from from the creator to the consumer. >>You know, that's the Holy Grail. That's the Nirvana. That's the dream of edge. You know, if you think about the old days, I'm old enough to remember back in the eighties, when we used to build purpose, built everything full stack developer hardware, ground up everything supply chain hardware, software done. Now you got an edge that still needs to be purpose built at the same time, you have a half of a software operating model. This to me, seems to be a great liquid software moment where I need to have special is, um, at the device. But I need a root of trust. I need quality. I need to have software operations, but I can't go down, whether it's in space or in the data center. What's your reaction to that? >>I think that, you know, liquid software is already happening. Um, if I would ask you what's version off Facebook are using, I bet you don't know what both version of Zuma we currently using, uh, for this interview. We don't know because it's happening behind the scene. Liquid software is happening and and you're right. It was It was the one big back that we had to take care of everything. And now it's a different way. But still developers are taking care of all the gates, all the stages. Think about all the, um, all the gates that kind of shifted left like security. Now it's in the hands of the developers, test automation developers automation in order to be fast and to scale fast developers and the option the and the depth kind of come together. This is already a cliche, so I don't need to again talk about Deva. But if you do it right from the moment you build and secure your software, then you will be faster than your competitors and organization realized that if you are not fastened secure, you will fall behind and you will lose your competitive advantage. So what we see now is the liquid doctor already happened and there is much more responsibility and much more expectations from the development organization. >>Yeah, it's awesome. You want to security Big 10. By the way, I'm running 10 15.7 uh, Catalina And when you run your >>you have to go liquid. >>When you when you go liquid, can you just make sure that always lands on a odd number? We know the even numbers are unlucky, so don't give me the, you know, make it work for me. Keep it liquid. Um, you >>know, one. I'm sorry. One of the biggest campaign we ever had was a big sign that says, imagine there's no version. Imagine There's no version. Imagine that you don't care what the version is because actually the consumer. My mother, she doesn't want to know what zoom version she used when she picked with me. >>Hey, we got server list. I could go version list, too. I mean, who doesn't want a version of this system? Look, this is critical. I love the hands on Hands off mindset. This is about non disruptive operations. You're starting to get into that kind of liquidity. What's next? What do you guys hearing at reinvent this year? Obviously, is virtual. So there's a lot of different touch points of over this three weeks. We got a lot of cube coverage. We're hearing speed, agility, agility has been around for a while. We're hearing speed is critical right now. It's the number one thing we're hearing across environments. That's the number one feature that we're hearing. What are you hearing? >>Yeah, well, John first, you know, I'm grateful as the CEO to have ah team off almost 700 employees worldwide doing this with the community, by the community and for the community. And we are very, very honored to have, um, over 6000 customers the majority. The vast majority of the Fortune 100 already powered by J Foe, the biggest bank, the biggest retail, the biggest tech company and what we hear from them. And I think that you know, a mental that stay humbled and listen to the community learns a lot. And the wisdom of the community is telling us the following number one double down on security because we still in the process in the transition of moving the responsibility to the developers. Even the system off the organization is still freaking out from from releases seven times a day. The second thing that we hear is that if software packages are the primary asset, then we want to have the freedom of choice. We want to integrate with whatever ecosystems I want to use Docker and dotnet and Java and pipe I and N P m. At the same time in the same resource. So consolidate consolidate this all for me And the last thing we hear is we We are also best of breed, But some some packages must come together and this is where the end to end solution coming from J. Prague is vital for the organization. You get the repository, the security, the distribution and the C I c d from the same vandal. Now take this and push the pedal even more, Uh, toe to the end. And you will see that the deployment environment that also got a bit more complex requires hybrid solution and multi cloud solution. There is no Fortune 100 company. It will just go with one cloud or with one solution. And when you come with unauthentic hybrid solution, multi cloud, that's a real This is a fanatic freedom of choice and the fanatic democracy that we give to developers. >>That's a great mission. Freedom of choice. No lock in lock ins. The new the new lock in his choice. New lock in his performance and scale. Slow me. Thank you for coming on The Cube behind CEO and co founder of Jay Frog. Mad props and congratulations to you and your team and swamp for great success having the right product at the right time. Developer first. Great stuff. Congratulations. Thanks for coming. >>Thank you very much and made the frog be with us and made this pandemic Thanks. Thank you very >>much. I want to get back to real life. I miss life. Thank you for coming. I miss it. This is the Cube. Virtual. We are cute. Virtual. Thanks for watching reinvent coverage. 2020. I'm John for your host. Yeah.

>> connecting with thought leaders all around the world, this is a CUBE Conversation. Hi, everybody this is Dave Vellante of theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SEER model, the most popular SEER model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O our open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these great Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>> Starting the record, Dave in five, four, three. Hi, everybody this is Dave Vellante, theCUBE, and welcome back to my CXO series. I've been running this through really since the start of the COVID-19 crisis to really understand how leaders are dealing with this pandemic. Sri Ambati is here, he's the CEO and founder of H20. Sri, it's great to see you again, thanks for coming on. >> Thank you for having us. >> Yeah, so this pandemic has obviously given people fits, no question, but it's also given opportunities for companies to kind of reassess where they are. Automation is a huge watchword, flexibility, business resiliency and people who maybe really hadn't fully leaned into things like the cloud and AI and automation are now realizing, wow, we have no choice, it's about survival. Your thought as to what you're seeing in the marketplace. >> Thanks for having us. I think first of all, kudos to the frontline health workers who have been ruthlessly saving lives across the country and the world, and what you're really doing is a fraction of what we could have done or should be doing to stay away the next big pandemic. But that apart I think, I usually tend to say BC is before COVID. So if the world was thinking about going digital after COVID-19, they have been forced to go digital and as a result, you're seeing tremendous transformation across our customers, and a lot of application to kind of go in and reinvent their business models that allow them to scale as effortlessly as they could using the digital means. >> So, think about, doctors and diagnosis machines, in some cases, are helping doctors make diagnoses, they're sometimes making even better diagnosis, (mumbles) is informing. There's been a lot of talk about the models, you know how... Yeah, I know you've been working with a lot of healthcare organizations, you may probably familiar with that, you know, the Medium post, The Hammer and the Dance, and if people criticize the models, of course, they're just models, right? And you iterate models and machine intelligence can help us improve. So, in this, you know, you talk about BC and post C, how have you seen the data and in machine intelligence informing the models and proving that what we know about this pandemic, I mean, it changed literally daily, what are you seeing? >> Yeah, and I think it started with Wuhan and we saw the best application of AI in trying to trace, literally from Alipay, to WeChat, track down the first folks who were spreading it across China and then eventually the rest of the world. I think contact tracing, for example, has become a really interesting problem. supply chain has been disrupted like never before. We're beginning to see customers trying to reinvent their distribution mechanisms in the second order effects of the COVID, and the the prime center is hospital staffing, how many ventilator, is the first few weeks so that after COVID crisis as it evolved in the US. We are busy predicting working with some of the local healthcare communities to predict how staffing in hospitals will work, how many PPE and ventilators will be needed and so henceforth, but that quickly and when the peak surge will be those with the beginning problems, and many of our customers have begin to do these models and iterate and improve and kind of educate the community to practice social distancing, and that led to a lot of flattening the curve and you're talking flattening the curve, you're really talking about data science and analytics in public speak. That led to kind of the next level, now that we have somewhat brought a semblance of order to the reaction to COVID, I think what we are beginning to figure out is, is there going to be a second surge, what elective procedures that were postponed, will be top of the mind for customers, and so this is the kind of things that hospitals are beginning to plan out for the second half of the year, and as businesses try to open up, certain things were highly correlated to surgeon cases, such as cleaning supplies, for example, the obvious one or pantry buying. So retailers are beginning to see what online stores are doing well, e-commerce, online purchases, electronic goods, and so everyone essentially started working from home, and so homes needed to have the same kind of bandwidth that offices and commercial enterprises needed to have, and so a lot of interesting, as one side you saw airlines go away, this side you saw the likes of Zoom and video take off. So you're kind of seeing a real divide in the digital divide and that's happening and AI is here to play a very good role to figure out how to enhance your profitability as you're looking about planning out the next two years. >> Yeah, you know, and obviously, these things they get, they get partisan, it gets political, I mean, our job as an industry is to report, your job is to help people understand, I mean, let the data inform and then let public policy you know, fight it out. So who are some of the people that you're working with that you know, as a result of COVID-19. What's some of the work that H2O has done, I want to better understand what role are you playing? >> So one of the things we're kind of privileged as a company to come into the crisis, with a strong balance and an ability to actually have the right kind of momentum behind the company in terms of great talent, and so we have 10% of the world's top data scientists in the in the form of Kaggle Grand Masters in the company. And so we put most of them to work, and they started collecting data sets, curating data sets and making them more qualitative, picking up public data sources, for example, there's a tremendous amount of job loss out there, figuring out which are the more difficult kind of sectors in the economy and then we started looking at exodus from the cities, we're looking at mobility data that's publicly available, mobility data through the data exchanges, you're able to find which cities which rural areas, did the New Yorkers as they left the city, which places did they go to, and what's to say, Californians when they left Los Angeles, which are the new places they have settled in? These are the places which are now busy places for the same kind of items that you need to sell if you're a retailer, but if you go one step further, we started engaging with FEMA, we start engaging with the universities, like Imperial College London or Berkeley, and started figuring out how best to improve the models and automate them. The SaaS model, the most popular SaaS model, we added that into our Driverless AI product as a recipe and made that accessible to our customers in testing, to customers in healthcare who are trying to predict where the surge is likely to come. But it's mostly about information right? So the AI at the end of it is all about intelligence and being prepared. Predictive is all about being prepared and that's kind of what we did with general, lots of blogs, typical blog articles and working with the largest health organizations and starting to kind of inform them on the most stable models. What we found to our not so much surprise, is that the simplest, very interpretable models are actually the most widely usable, because historical data is actually no longer as effective. You need to build a model that you can quickly understand and retry again to the feedback loop of back testing that model against what really happened. >> Yeah, so I want to double down on that. So really, two things I want to understand, if you have visibility on it, sounds like you do. Just in terms of the surge and the comeback, you know, kind of what those models say, based upon, you know, we have some advanced information coming from the global market, for sure, but it seems like every situation is different. What's the data telling you? Just in terms of, okay, we're coming into the spring and the summer months, maybe it'll come down a little bit. Everybody says it... We fully expect it to come back in the fall, go back to college, don't go back to college. What is the data telling you at this point in time with an understanding that, you know, we're still iterating every day? >> Well, I think I mean, we're not epidemiologists, but at the same time, the science of it is a highly local response, very hyper local response to COVID-19 is what we've seen. Santa Clara, which is just a county, I mean, is different from San Francisco, right, sort of. So you beginning to see, like we saw in Brooklyn, it's very different, and Bronx, very different from Manhattan. So you're seeing a very, very local response to this disease, and I'm talking about US. You see the likes of Brazil, which we're worried about, has picked up quite a bit of cases now. I think the silver lining I would say is that China is up and running to a large degree, a large number of our user base there are back active, you can see the traffic patterns there. So two months after their last research cases, the business and economic activity is back and thriving. And so, you can kind of estimate from that, that this can be done where you can actually contain the rise of active cases and it will take masking of the entire community, masking and the healthy dose of increase in testing. One of our offices is in Prague, and Czech Republic has done an incredible job in trying to contain this and they've done essentially, masked everybody and as a result they're back thinking about opening offices, schools later this month. So I think that's a very, very local response, hyper local response, no one country and no one community is symmetrical with other ones and I think we have a unique situation where in United States you have a very, very highly connected world, highly connected economy and I think we have quite a problem on our hands on how to safeguard our economy while also safeguarding life. >> Yeah, so you can't just, you can't just take Norway and apply it or South Korea and apply it, every situation is different. And then I want to ask you about, you know, the economy in terms of, you know, how much can AI actually, you know, how can it work in this situation where you have, you know, for example, okay, so the Fed, yes, it started doing asset buys back in 2008 but still, very hard to predict, I mean, at this time of this interview you know, Stock Market up 900 points, very difficult to predict that but some event happens in the morning, somebody, you know, Powell says something positive and it goes crazy but just sort of even modeling out the V recovery, the W recovery, deep recession, the comeback. You have to have enough data, do you not? In order for AI to be reasonably accurate? How does it work? And how does at what pace can you iterate and improve on the models? >> So I think that's exactly where I would say, continuous modeling, instead of continuously learning continuous, that's where the vision of the world is headed towards, where data is coming, you build a model, and then you iterate, try it out and come back. That kind of rapid, continuous learning would probably be needed for all our models as opposed to the typical, I'm pushing a model to production once a year, or once every quarter. I think what we're beginning to see is the kind of where companies are beginning to kind of plan out. A lot of people lost their jobs in the last couple of months, right, sort of. And so up scaling and trying to kind of bring back these jobs back both into kind of, both from the manufacturing side, but also lost a lot of jobs in the transportation and the kind of the airlines slash hotel industries, right, sort of. So it's trying to now bring back the sense of confidence and will take a lot more kind of testing, a lot more masking, a lot more social empathy, I think well, some of the things that we are missing while we are socially distant, we know that we are so connected as a species, we need to kind of start having that empathy for we need to wear a mask, not for ourselves, but for our neighbors and people we may run into. And I think that kind of, the same kind of thinking has to kind of parade, before we can open up the economy in a big way. The data, I mean, we can do a lot of transfer learning, right, sort of there are new methods, like try to model it, similar to the 1918, where we had a second bump, or a lot of little bumps, and that's kind of where your W shaped pieces, but governments are trying very well in seeing stimulus dollars being pumped through banks. So some of the US case we're looking for banks is, which small medium business in especially, in unsecured lending, which business to lend to, (mumbles) there's so many applications that have come to banks across the world, it's not just in the US, and banks are caught up with the problem of which and what's growing the concern for this business to kind of, are they really accurate about the number of employees they are saying they have? Do then the next level problem or on forbearance and mortgage, that side of the things are coming up at some of these banks as well. So they're looking at which, what's one of the problems that one of our customers Wells Fargo, they have a question which branch to open, right, sort of that itself, it needs a different kind of modeling. So everything has become a very highly good segmented models, and so AI is absolutely not just a good to have, it has become a must have for most of our customers in how to go about their business. (mumbles) >> I want to talk a little bit about your business, you have been on a mission to democratize AI since the beginning, open source. Explain your business model, how you guys make money and then I want to help people understand basic theoretical comparisons and current affairs. >> Yeah, that's great. I think the last time we spoke, probably about at the Spark Summit. I think Dave and we were talking about Sparkling Water and H2O or open source platforms, which are premium platforms for democratizing machine learning and math at scale, and that's been a tremendous brand for us. Over the last couple of years, we have essentially built a platform called Driverless AI, which is a license software and that automates machine learning models, we took the best practices of all these data scientists, and combined them to essentially build recipes that allow people to build the best forecasting models, best fraud prevention models or the best recommendation engines, and so we started augmenting traditional data scientists with this automatic machine learning called AutoML, that essentially allows them to build models without necessarily having the same level of talent as these Greek Kaggle Grand Masters. And so that has democratized, allowed ordinary companies to start producing models of high caliber and high quality that would otherwise have been the pedigree of Google, Microsoft or Amazon or some of these top tier AI houses like Netflix and others. So what we've done is democratize not just the algorithms at the open source level. Now, we've made it easy for kind of rapid adoption of AI across every branch inside a company, a large organization, also across smaller organizations which don't have the access to the same kind of talent. Now, third level, you know, what we've brought to market, is ability to augment data sets, especially public and private data sets that you can, the alternative data sets that can increase the signal. And that's where we've started working on a new platform called Q, again, more license software, and I mean, to give you an idea there from business models endpoint, now majority of our software sales is coming from closed source software. And sort of so, we've made that transition, we still make our open source widely accessible, we continue to improve it, a large chunk of the teams are improving and participating in building the communities but I think from a business model standpoint as of last year, 51% of our revenues are now coming from closed source software and that change is continuing to grow. >> And this is the point I wanted to get to, so you know, the open source model was you know, Red Hat the one company that, you know, succeeded wildly and it was, put it out there open source, come up with a service, maintain the software, you got to buy the subscription okay, fine. And everybody thought that you know, you were going to do that, they thought that Databricks was going to do and that changed. But I want to take two examples, Hortonworks which kind of took the Red Hat model and Cloudera which does IP. And neither really lived up to the expectation, but now there seems to be sort of a new breed I mentioned, you guys, Databricks, there are others, that seem to be working. You with your license software model, Databricks with a managed service and so there's, it's becoming clear that there's got to be some level of IP that can be licensed in order to really thrive in the open source community to be able to fund the committers that you have to put forth to open source. I wonder if you could give me your thoughts on that narrative. >> So on Driverless AI, which is the closest platform I mentioned, we opened up the layers in open source as recipes. So for example, different companies build their zip codes differently, right, the domain specific recipes, we put about 150 of them in open source again, on top of our Driverless AI platform, and the idea there is that, open source is about freedom, right? It is not necessarily about, it's not a philosophy, it's not a business model, it allows freedom for rapid adoption of a platform and complete democratization and commodification of a space. And that allows a small company like ours to compete at the level of an SaaS or a Google or a Microsoft because you have the same level of voice as a very large company and you're focused on using code as a community building exercise as opposed to a business model, right? So that's kind of the heart of open source, is allowing that freedom for our end users and the customers to kind of innovate at the same level of that a Silicon Valley company or one of these large tech giants are building software. So it's really about making, it's a maker culture, as opposed to a consumer culture around software. Now, if you look at how the the Red Hat model, and the others who have tried to replicate that, the difficult part there was, if the product is very good, customers are self sufficient and if it becomes a standard, then customers know how to use it. If the product is crippled or difficult to use, then you put a lot of services and that's where you saw the classic Hadoop companies, get pulled into a lot of services, which is a reasonably difficult business to scale. So I think what we chose was, instead, a great product that builds a fantastic brand, that makes AI, even when other first or second.ai domain, and for us to see thousands of companies which are not AI and AI first, and even more companies adopting AI and talking about AI as a major way that was possible because of open source. If you had chosen close source and many of your peers did, they all vanished. So that's kind of how the open source is really about building the ecosystem and having the patience to build a company that takes 10, 20 years to build. And what we are expecting unfortunately, is a first and fast rise up to become unicorns. In that race, you're essentially sacrifice, building a long ecosystem play, and that's kind of what we chose to do, and that took a little longer. Now, if you think about the, how do you truly monetize open source, it takes a little longer and is much more difficult sales machine to scale, right, sort of. Our open source business actually is reasonably positive EBITDA business because it makes more money than we spend on it. But trying to teach sales teams, how to sell open source, that's a much, that's a rate limiting step. And that's why we chose and also explaining to the investors, how open source is being invested in as you go closer to the IPO markets, that's where we chose, let's go into license software model and scale that as a regular business. >> So I've said a few times, it's kind of like ironic that, this pandemic is as we're entering a new decade, you know, we've kind of we're exiting the era, I mean, the many, many decades of Moore's law being the source of innovation and now it's a combination of data, applying machine intelligence and being able to scale and with cloud. Well, my question is, what did we expect out of AI this decade if those are sort of the three, the cocktail of innovation, if you will, what should we expect? Is it really just about, I suggest, is it really about automating, you know, businesses, giving them more agility, flexibility, you know, etc. Or should we should we expect more from AI this decade? >> Well, I mean, if you think about the decade of 2010 2011, that was defined by software is eating the world, right? And now you can say software is the world, right? I mean, pretty much almost all conditions are digital. And AI is eating software, right? (mumbling) A lot of cloud transitions are happening and are now happening much faster rate but cloud and AI are kind of the leading, AI is essentially one of the biggest driver for cloud adoption for many of our customers. So in the enterprise world, you're seeing rebuilding of a lot of data, fast data driven applications that use AI, instead of rule based software, you're beginning to see patterned, mission AI based software, and you're seeing that in spades. And, of course, that is just the tip of the iceberg, AI has been with us for 100 years, and it's going to be ahead of us another hundred years, right, sort of. So as you see the discovery rate at which, it is really a fundamentally a math, math movement and in that math movement at the beginning of every century, it leads to 100 years of phenomenal discovery. So AI is essentially making discoveries faster, AI is producing, entertainment, AI is producing music, AI is producing choreographing, you're seeing AI in every walk of life, AI summarization of Zoom meetings, right, you beginning to see a lot of the AI enabled ETF peaking of stocks, right, sort of. You're beginning to see, we repriced 20,000 bonds every 15 seconds using H2O AI, corporate bonds. And so you and one of our customers is on the fastest growing stock, mostly AI is powering a lot of these insights in a fast changing world which is globally connected. No one of us is able to combine all the multiple dimensions that are changing and AI has that incredible opportunity to be a partner for every... (mumbling) For a hospital looking at how the second half will look like for physicians looking at what is the sentiment of... What is the surge to expect? To kind of what is the market demand looking at the sentiment of the customers. AI is the ultimate money ball in business and then I think it's just showing its depth at this point. >> Yeah, I mean, I think you're right on, I mean, basically AI is going to convert every software, every application, or those tools aren't going to have much use, Sri we got to go but thanks so much for coming to theCUBE and the great work you guys are doing. Really appreciate your insights. stay safe, and best of luck to you guys. >> Likewise, thank you so much. >> Welcome, and thank you for watching everybody, this is Dave Vellante for the CXO series on theCUBE. We'll see you next time. All right, we're clear. All right.

>>Fly from San Francisco. It's the cube covering RSA conference, 2020 San Francisco brought to you by Silicon angle media. >>Hello and welcome back to the cube coverage here in San Francisco, the Moscone center for RSA Congress 2020 for all the coverage period for three days. I'm John, host of the cube. You know, as cybersecurity goes to the next level, as cloud computing goes, continues to go more enterprise, large scale AI and machine learning have become critical managing the data. We've got a great guest here from Intel, Kaz Borzynski, senior director of the AI price with Intel. Thanks for joining us. Oh thanks. So data is a huge, huge data problem when it comes down to cybersecurity, uh, and and generally across the enterprise. Now it's well known, well documented, but you're here giving a talk about machine learning privacy because everyone wants to know who the bad guys are. So do the bad guys deserve privacy? Okay, we'll get to that later. But first tell about your talk and give a talk here at RSA. >>We'll get into other stuff later. I gave a talk, so thanks for having me. I gave a talk on a whole suite of exciting new techniques known as privacy preserving machine learning. So this is a set of machine learning techniques that help people realize the promise of AI and machine learning. But we know that machine learning systems rely on underlying data to train. So how can you also respect the privacy and the security of the underlying data while still being able to train and use AI systems and just take it, where are you within the Intel sphere? Because Intel osseous surgery obviously chips and power to all the enterprises in large. Skip. How are you on the software side AI group? Explain where you are. And so I'm in the AI group at Intel, but I have the most fun job at Intel. I think so cause I work in the CTO office of the AI group, which means I get to think about more futuristic, you know, where is AI going? >>What are some of the major inflection points? One of these that we've been looking at for the last couple of years is this kind of collision course between the need for data to train machine learning systems to unlock all the power of AI, but still the need to keep data private. Yeah, and I think that's generally consistent with our editorial in our research, which is the confluence of cloud native, large scale cloud computing, multi-cloud and AI or machine learning, all kinds of coming together. Those are multigenerational technologies that are coming. So that's, this wave is big. That's right. And I think one thing that's kind of maybe underappreciated about machine learning, especially in production is it's almost always a multi-party interaction. So you'll have, let's say one party that owns data and other party may own a model. They're running a system on somebody else's hardware. So because of the nature of digital data, if you want to share things, you have to worry about what other parties may be doing with those data. >>Because you bring up a great point I want to get your reaction and thoughts on is that, is that it's multidisciplinary. Now as people aren't breaking into the field. I mean people are really excited about AI. I mean you talk to someone who's 12 years old, they see a Tesla, they see software, they see all these things, they see all this cool stuff. So machine learning, which powers AI is very enticing to anyone that's got kind of technical or nerdy background and social attracting a lot of young people. So it's not just getting a computer science degree. There's so much more to AI because you talk about why, what someone needs to be successful too. And to engage in the AI wave. You don't need to just be a code or you could be outside the scope because it's an integrated model or is it's very much, so my group at Intel is better, very heterogeneous. >>So what have got a, you know, kind of mathematicians, but I also have coders. I have, uh, an attorney who's a public policy expert. I have cryptographers. Uh, I think there's a number of ways to get involved in, in meaning my, my background is actually a neuroscience. So, um, it makes sense. Good. Stitch it all together. Yeah. Well, societal changes has to be the, the algorithm needs training they need to learn. So having the most diverse input seems to me to be a, a posture the industry is taking and what's, is that right? Is that the right way to think about it? How should we be thinking about how to make AI highly effective versus super scary? Right. Well, one of the efforts that we're making, part of my message here is that to make these systems better, generally more data helps, right? If you can expand the availability of data, that's always going to help machine learning systems. >>And so we're trying to unlock data silos that may exist across countries, across the organizations. So for example, you know, in healthcare you could have multiple hospitals that have patient data. If somehow they could pool all their data together, you would get much more effective models, much better patient outcomes, but for very good privacy reasons, they're not allowed to do that. So there's these interesting ideas like federated learning where you could somehow decentralize the machine learning process so that you can still respect privacy but get the statistical power. That's a double down on that for a second cause I want to explore that. I think this is the most important story that's not being talked about. It's nuance a little bit. Yeah. You know, healthcare, you had HIPAA, which was built for all the right reasons back then, but now when you start to get into much more of a cross pollination of data, you need to manage the benefit of why it existed with privacy. >>So encryption, homomorphic encryption for instance, data and use. Yes. Okay. When it's being used, not just in flight or being arrested becomes, now you have the three triads of data. Yes. This is now causing a new formula for encryption privacy. What are some of the state of the art mindset thinkings around how to make data open a usable but yet either secure, encrypted or protected. That's right. So it's kind of this paradox of how do I use the data but not actually get the data. You mentioned homomorphic encryption. So this is one of the most kind of leading edge techniques in this area where somehow you're able to, there are ways of doing math on the data while it stays encrypted and the answer that comes out, it's still encrypted and it's only the actual owner of the data who can reveal the answer. So it seems like magic, but with this capability you enable all kinds of new use cases that wouldn't be possible before where third parties can act on, you know, your sensitive data without ever being exposed to it in any way. >>So discovery and leverage of the days that what you're getting at in terms of the benefits, I mean use cases. So stay on that. They used cases of the, of this new idea. Yeah. Is discovery and usage. How would that work? Well, so when we talked about federated learning and pooling across hospitals, that's one set of techniques. Homomorphic encryption would be, for example, suppose that some AI system has already been trained, but I'd like to use it on sensitive data. How do I do that in such a way that the third party service isn't, you know, this what makes, I think machine learning different from different types of data. You know, security problems is that machine learning, you have to operate on the data. You're not just storing it, you're not just moving it around. So how do you, yeah, and this is a key thing. >>So I've got to ask you the question because one of the things that's a real interesting trade off these days is AI and machine learning is really can create great benefits, but also people just go the knee jerk reaction of, you know, Oh my God, it's scary. My privacy. So it's a frontline with Amazon, just facial recognition. Oh my God, it's evil. Yeah. So there's a lot of scared people that might not be informed. Yeah. How should companies invest in machine learning and AI from your opinion? On how should they think about the next 10 year trajectory starting today, thinking about how to invest, what's the right way to think about it, build a team. Yeah. What's your thoughts on that? Because, and this is the number one challenge right now. Yeah. Well I think the, uh, some of this scary issues that you mentioned, you know, there are legitimately scary. >>They're going to have to be resolved, not by companies, but probably, you know, by society and kind of our delegates. So lawmakers, regulators, part of what we're trying to do at the technical level is give society and regulators a, a more flexible set of tools around which you can slice and dice data privacy and so on, so that it's not just all or none. Right. I think that's kind of my main goal as a, as an organization. I think again, the, this idea of having a heterogeneous set of talents, you know, you're going to need policy experts and applied mathematicians and linguists and you know, neuroscientists. So diversity is a huge opportunity, very much so. Not just diversity of people, but diverse data, diverse data, diverse kind of mindsets, approaches to problems that are hard but very promising. If so. Okay. Let's flip to the other side of the spectrum, which is what should people not do? >>What does, what's a, what's a fail failure formula one dimensional thinking? What's a, what's an identification of something that's not, may not go in the right way? Well, you know, one, uh, distinguishing feature of the machine learning field, and it's kind of a cultural thing, but it's given it a lot of traction is it's fundamentally, it had been a very open culture. So there's a lot of, uh, sharing of methods. It's a very, uh, collaborative academic field. So I think within a company you want to kind of be re you want to be part of that culture too. So every company is going to have its secret sauce. It's things that it needs to keep proprietary, but it's very important for companies to engage this broader community of researchers. So you're saying, which I would want, maybe I'm what I would agree with, but I'll just say it. >>You can agree or disagree to be successful, you got to be open. If you're data-driven, you've gotta be open. That's right. There's more JD equals better data. That's why more data, more approaches to data, kind of more eyes on the problem. But you know, still you can definitely keep your proprietary, you know, it kind of forces organizations to think about what are our core strengths that we really want to keep proprietary. But then other things let's, you know, open. All right. So what's the coolest thing you've working on right now? What are some of the fun projects you guys are digging into and you've got a great job. Sounds like you're excited about it. I mean, AI I think is the most exciting thing. I mean I wish I could be 20 again in computer science or whatever field. Cause I think AI is more than a multigenerational things. >>Super exciting as a technical person. But what are you working on that you're excited about? So I'm very excited about taking some of these things like homomorphic encryption and making them much more available to developers, to data scientists because it's asking too much for a data scientist to also be a kind of a post quantum crypto expert. So we've written an open source package called H E transformer, H G for homomorphic encryption. It allows the data scientists to kind of do their normal data science and Python or whatever they're used to, but then they kind of flick a switch and suddenly their model is able to run on encrypted data. Can you just take a minute to explain why homomorphic encryption trend right now is really important? I mean, give a peek into the why because this is something that is now becoming much more real. >>Yeah. The data in use kind of philosophy. Why now? Why is it so important right now? Well, I think, uh, the, because of cloud in the, the power of cloud and the fact that you know, data are collected in one place and possibly processed in another place, you're going to have to, you know, your data are moving around and they're being operated on. So if you can know that, you know, as long as my data are moving around and people are operating on it but it's staying encrypted the whole time, you know, not just in transit, that gives a much higher level of comfort around and the applications are going to probably be onboarded. I mean you can almost imagine new applications will emerge from this application discovery cataloging and API integration points. I mean you can almost imagine the trust will go up and you can also kind of end up with these different business models where you have entities that compete in some spheres but they may decide to collaborate in other ways. >>So for example, banks could compete on, you know, lending and so on under normal activities. But in terms of fraud detection, they may decide, Hey, maybe we can make some Alliance where we cross check with each other as models on certain transactions, but I'm not actually giving you any transaction data. So that's maybe okay. Right. So that's a very powerful, it's really interesting. I mean I think the uh, the compute power has allowed, the overhead seems to be much more robust because people are working on this for in the eighties and nineties I remember. Yes. But it was just so expensive overhead while that's right. Yeah. So you bring up a great point here. So, and this is one of the areas where Intel is really pushing, my team is pushing these techniques have been around for 20 years. Initially they were maybe like 10 million times slower than real time. >>So people thought, okay, this is interesting, you know, mathematically, but not practical. There've been massive improvements just in the last two years where now things are running, you know, a hundred times slower than, than kind of un-encrypted math. But still that, that means that something that you know would take 50 milliseconds now takes five seconds. That's still not an unreasonable, you're my new friend. Now, my best friend on AI. Um, and I got a business to run and I'm going to ask you, what should I do? I really want to leverage machine learning and AI in my business. Okay, I'm investing in more tech. I got cloud and building my own software. How should I be investing? How do I build out a great machine learning AI scene and then ultimately capabilities? How should I do that? Okay, well I would start with a team that has a kind of a research mindset, not because you want them to come in and like write research papers, but the path from research into production is so incredibly short in AI. >>You know, you have things that are papers one year and they're going into production at Google search and within a year. So you kind of need that research mindset. I think another thing is that you want to, uh, you're gonna, you're going to require a very close collaboration between this data science team and your CIO and kind of, you know, systems. And a lot of the challenges around AI are not just coming up with the model, but how do you actually scale it up and you know, go to production with it and interesting about the research. I totally agree with you. I think, you know, you can almost call that product management kind of new fangled Prague product management because if it's applied research, you kind of have your eye on a market generally, but you're not making hardcore product decisions. You're researching it, you're writing it so that you got to, got to do the homework, you know, dream it before you can build it. >>Well, I'm just saying that the field is moving so fast that you're going to need on your team, uh, people who can kind of consume the latest papers. Oh, you're saying consume the research as well. Yeah, I mean if they can contribute, that's great too. I mean, I think this is this kind of open culture where, you know, people consume, they find some improvement. They can then publish it at the next year's conference. It's just been this incredibly healthy eco software. Acceleration's a big part of the cloud. Awesome. Well I really appreciate your insight. This is great topic. I could go for an hour. One of my favorite things. I love the homophobic uh, encryption. I think that's going to be a game changer. I think we're going to start to see some interesting discoveries there. Uh, give a quick plug for Intel. What are you working on now? >>What are you looking to do? What's your plans, highs hiring, doing more research, what's going on? Well, so we think that this intersection of privacy and AI is kind of at the core of, of Intel's data centric mission. So we're trying to figure out, you know, whatever it takes to enable the community, whether it's, you know, uh, optimize software libraries. It could be custom Silicon, it could be even services where, you know, we really want to listen to customers, figure out what they need. Funding. Moore's law is always going to be around the next wave is going to have more compute. It's never going away. More storage, more data. It's just gets better and better. Yeah. Thanks for coming on Catherine. Thanks for having can we have Intel inside the cube breaking down the future of AI. Really exciting stuff on the technology front security day. That's all going to happen at large scale. Of course, it's the cube bringing you all the data here at RSA. I'm John furrier. Thanks for watching.

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone, and welcome to this week's episode of theCUBE insights, powered by ETR. In this breaking analysis, I'm going to provide a little detail on the recent announcement that Insight Partners was acquiring Veeam for five billion dollars. There was a lot of information on the announcement in press releases and in news articles, so what I really want to focus on is what it means for the industry generally, and for the data protection community specifically. So, very briefly this was a five billion dollar exit for Veeam on top of a five hundred million dollar investment lead by the same Insight Partners last year. I think it had earlier investments, kind of a rent, with an option to buy. New management is being promoted from within, which I think is significant, to replace the two founders. Andrei Baronov and Ratmir Timashev are going to step down after the transition and give up their board seats. Veeam is a fascinating company. It started in the 2006, 2007 time frame, after the two founders, who met in college, formed and sold Aleta software to Quest. Then they started a company called AMUST Software, from which they created Veeam. You never hear about AMUST, but I believe it's the engineering and development arm of Veeam. Now the new CEO of Veeam, Bill Largent told theCUBE that AMUST is now a wholly owned subsidiary of Veeam and it won't effect any of the engineering assets that exist in Prague and in Russia. So this I the thing about Veeam, it's a very closely held company controlled by it's two founders, with a domicile in Switzerland. My understanding is Baronov is, well he's the technical guru, and he's a resident of that country in Switzerland, and the HQ there is very lean, the sizable engineering teams, as they say, is in Russia and Prague. Timashev resides in the US, and he's a marketing genius, who helped create this company, and it's always punched above it's weight class with, epic parties, and great products. Now interestingly, Veeam's rise, it coincided with the ascendancy of VMware. Veeam became the standard backup software for small to medium size companies within VMware shops. Their products are renowned for being simple, and working as advertised, and their customer support is outstanding by all accounts. But the US business lagged, despite the fact that most of VMware's business is in the Americas. You'd think you think if they super glued themself to VMware their Americas business would be higher. So a few years ago they decided to really go hard after the enterprise and they brought in Peter Mckay, from VMware, and he began to build up a US presence. But the enterprise business, it requires a lot of things that were kind of antithetical to Veeam. So think about long sales cycles, expensive sales people, belly to belly selling, with the expectations of, road maps, and clarity around enterprise feature sets. Now McKay was named CEO with Baronov, who continued to run engineering. So it was a bit of a culture clash. You got the sales oriented leader wanting the engineering team to turn on a dime and help close large deals, and satiate partners like HPE and Sysco, and you've got this genius co-leader, slash engineer, with an incredible track record of delivering features that the customer loves. So it really didn't work out and then Veeam scaled back on it's ambitions some what. At it's annual user conference in Miami last year, Ratmir came on theCUBE, and he talked about how Veeam's act one was all about dominance in virtualized environment. Let's listen to what he said about act two and then we'll come back and talk about it >> That was act one, we dominated it, we grew from zero to one billion within 10, 12 years. We added three hundred fifty thousand customers over that time frame, and now it's act two. What is act two? Act two is the, again, the new major industry transformation to a hybrid cloud. What are the similarities? Again, Veeam is in a great position because we're at the right time at the right place with a brilliant product. >> Now what we know is that act two is about a few things, one, as Ratmir said, hybrid cloud, multi cloud management, etcetera. But it's also about an awesome exit for it's two founders. Wow five billion dollars, five x revenue multiple, handing over the reigns is really the third thing this is about and creating more traditional governance structure for Veeam. Now they're moving from a governance structure that was closely held and opaque to one that is still going to be closely held, but ideally somewhat less opaque. Which brings me to inside partners. In the money world, you basically have a spectrum of investors. On the one side you've got banks, who are the most conservative. On the other side you've got VCs, now they're the most aggressive, of course. Now somewhere in the middle, you have private equity firms. Now they traditionally invest in companies, and they squeeze them for EBITDA, and they suck money out. But inside is more of a hybrid. They invest in a number of companies as VCs, they take a portion of the ownership. And to me they're more of a rule of forty PE, meaning it's not just about EBITDA, it's about growth plus EBITDA. So a rule of thirty or a rule of forty PE company, they can dial down EBITDA and go for growth, or dial up EBIT and moderate growth. So it's a great model. So I would expect Insight to bring structure and leadership to Veeam, with the goal of taking the company public at some point, because they like to sell to companies for all cash, I don't see a logical buyer at these kind of price points for this company in this market. It's growing market but it's still not a giant market. All right let's shift gears a little bit and get into some of the ETR data. Here's a narrative they put out recently that, to me, sums it up well. ETR said Veeam is one of the few vendors growing share among customers vs previous surveys in the storage sector. And that said, spending intentions are decelerating and continue to look poor in the largest sectors and Veeam trails Rubrik and Cohesity, although on a larger user base. So you can see by this statement that Veeam is of course doing well, but there are some cracks in the enterprise armor that I want to talk about and drill into a little bit. Now this now this Arline customer quote also, to me, sums up one of the reasons for Veeam's success. What this person said is if I want to do a Veeam back up to the cloud, it's basically point and click, very easy to use. Now I've talked to dozens, if not hundreds of Veeam customers, and they all say the same thing, it just works, that's kind of their motto. So this is the big reason why Veeam has steadily gained gained share over time. Now take a look at this chart, which shows the progression over time of Veeam's progress in terms of what ETR calls market share. Now remember, market share is a measure of pervasiveness in the ETR data set. And you can see, in the data, that Veeam has had a steady rise since ETR started tracking them at critical mass back in 2014. And you can see the steady decline in the survey for Veritas and Commvault and what appears to be, rapid momentum building for Rubrik and Cohesity, two companies that I said in my 2020 predictions breaking analysis that would continue to do well this year. Now notice I had to black out the January 2020 survey, which is ending shortly, so stay tuned for those results. But let's drill into Veeam's performance a little bit more. What this chart shows is a candlestick of net score and market share across all the respondents in the ETR survey for Veeam. Remember net score is a measure of spending momentum that subtracts customers that are spending less, the red, from those spending more, the greens. And it's represented over time by this blue line that you see. You can see that this blue line, it bounces around but it holds steady in the past couple of years pretty generally, and really in that thirty to forty percent range which you see on the left hand axis. Now that yellow line, is market share or pervasiveness, it also continues to climb steadily as I showed you in the previous chart. Now again this is amongst all respondents. Let's now take a look at this chart which isolates Veeam's performance in the largest companies, that enterprise push. Notice the pictures is somewhat choppier. Market share is okay, although unlike the previous chart, it's not steady. This is stunning. Peter McKay left in October 2018, and that's when Veeam really pulled back on it's big enterprise push, and you can see, there's a noticeable and steady drop there based on ETR data. So what's happening here is we are entering a new chapter for Veeam, act two so to speak. With new leadership and new governance. Danny Allen is taking over CTO, he previously ran strategy, Bill Largent is going to be CEO, the HQ is moving into the US. So in my opinon, Veeam's issues in the US have been more execution related than anything else. Veeam is a leader. So partnerships with Nutanix, Sysco, HPE, NetApp, should continue to improve and be somewhat productive, actually largely productive. Let's talk a little bit about Veeam's architecture, and a point of discussion that you often hear in the community. Veeam's a Window's based architecture. Now is that a blessing or is that a curse? Well the pros are that the Veeam team came out of a Windows world, and they know the platform very well. They are amazingly good at adding function, without screwing up performance somewhere else. You saw this a couple years back when they were making a big push on the enterprise and they increased the file sizes, and the number of objects that they could support. Another example is when Veeam added cloud back up, it was a really good product, version one. Unlink many products, when they first tried to port to the cloud, that wasn't the case. Recovery from the cloud is very tricky. Things are out of sync, you got a metadata challenge, and generally Veeam was able to achieve consistent levels of performance with it's cloud product. Now flip side of this, is that if you look at most, if not all, modern architectures today, are based on Linux. And once you start getting into mulit cloud, and cross cloud management, you're going to bump into and be interfacing with lots of Linux based systems. So Veeam is going to have migrate code, and maintaining consistent performance is going to be tougher. But as David Fourier, my colleague points out, there are many many ways to skin a cat, and Veeam's engineering team has really, based on it's track record, has proven that it can solve tough problems, and really deliver a great product consistently. I think the bigger issue and challenge for Veeam again, is execution in the US, and of course the enterprise. Customers in EBC's executive briefing centers, they want to see road maps, and enterprise features, and specials. And so we'll see, if that's something that Veeam has an appetite for. If they do, and I'm one of the incumbents, I'd be worried that Veeam could do a land and expand. Where Veeam isn't as strong in large enterprises, big companies they buy from Veeam. Maybe it's a smaller division, or remote location, but it's not like they don't do business in large accounts, they do. So in a way, they've already landed and they have an opportunity to expand, so that's something to pay attention to. If I'm an enterprise customer, I would be pressing Veeam on it's roadmap, and having them clarify their vision around hybrid and multi cloud management. Will Veeam be more transparent and willing to do specials for the enterprise, and their big partners, who expect them, when they say jump, they expect Veeam to say how high. How will Veeam's culture change, is the other thing I want to focus on. As the two founders step down, are they going to be able to main their engineering ethos, and customer loyalty, and can they figure out the enterprise. I'm a big fan of founder lead companies, when founders leave cultures often change. When founders stay, they're intensely committed, even beyond great CEOs who aren't founders. Look at Michael Dell. He went to the mat to keep his company against the great icon, now look at Dell technologies, after the EMC acquisition, it was completely transformed. Look at Oracle, look at the lengths that Larry Ellison goes to win. Compare that to a great CEO Joe Tucci, when he was at EMC, but you know when he was done, he was done, it was over. It wasn't his baby. So my point is how will this effect Veeam's culture and prospects in the long term. For me the bottom line is the big opportunity's in the US. And that's about execution. And I expect with the move to US HQ, new management, I expect they're going to see consistent market share gains, that's going to continue. The enterprise however, that's going to take longer, it's going to require more patience and more money. And with Veeam transitioning from essentially the two founder's lifestyle business into a company that's really built for an exit, they're going to have more money to invest, greater transparency, I hope, and a path to really build on their past successes. So this Dave Vellante signing out from the latest episode of theCUBE insights, powered by ETR. Thanks for watching everybody and we'll see you next time. (upbeat music)

(upbeat music) >> Announcer: From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a Cube conversation. >> Everyone, welcome to this special Cube conversation here in Palo Alto Cube studios. I'm John Furrier, host of the Cube. We have special breaking news here, with Sri Ambati who is the founder and CEO of H2O.ai with big funding news. Great to see you Cube alumni, hot startup, you got some hot funding news, share with us. >> We are very excited to announce our Series D. Goldman Sachs, one of our leading customers and Ping An from China are leading our round. It's a round of $72 million, and bringing our total fundraise to 147. This is an endorsement of their support of our mission to democratize AI and an endorsement of the amazing teamwork behind the company and its customer centricity. Customers have now come to lead two of our rounds. Last round was Series C led by Wells Fargo and NVIDIA and I think it just goes to say how critical a thing we are for their success in AI. >> Well congratulations, I've been watching you guys build this company from scratch, we've had many conversations going back to 2013, '14 on The Cube. You call it-- >> You covered us long before. >> You guys were always on the wave, and you really created a category, this is a new category that Cloud 2.0 is creating which is a DevOps mindset, entrepreneurial mindset, creating a category to enable people to have the kind of infrastructure and tooling and software to enable them to do all the heavy lifting of AI without doing the heavy lifting. As the quote for cloud is, that Amazon always quotes is you do all of the undifferentiated heavy lifting that's required to stand up stuff and then provide tooling for the heavy differentiated lifting to make it easy to use. This has been a key thing. Has that been the-- >> Customers have be core to our, company building. H2O is here to build an amazing piece of innovation and technology and innovation is not new for Silicon Valley, as you know. But I think innovation, with a purpose and with a focus of customer success is something we represent and that's been kind of the key north finder for us. In terms of making things simpler, when we started, it was a grassroots movement in open source and we wanted the mind share of millions of users worldwide and that mind share got us a lot of feedback. And that feedback is how we then built the second generation of the product lines, which is driverless AI. We are also announcing our mission to make every company an AI company, this funding will power that transformation of several businesses that can then go on to build the AI superpower. >> And certainly, cloud computing, more compute more elastic resources is always a great tailwind. What are you guys going to do with the funding in terms of focus? >> You mentioned cloud which is a great story. We're obviously going to make things easier for folks who are doing the cloud, but they are the largest players, as well, Google, Microsoft, Amazon. They're right there, trying to innovate. AI is at the center of every software moment because AI eating software, software is eating the world. And so, all the software players are right there, trying to build a large AI opportunity for the world and we think in ecosystems, not just empires. So our mission is to uplift the entire AI to the place where businesses can use it, verticalize it, build new products, globalize. We are building our sales and marketing efforts now with a much bigger, faster systems-- >> So a lot of, go to market expansion, more customer focus. More field sales and support kind of thing. >> Build our center for AI research in Prague, within the CND, now we are building it in Chennai and Ottawa, and so globalizing the operation, going to China, going to build focus in Asia as well. >> So nice step up on funding at 72 million, you said? >> 72.5 million. >> 72.5 million, that's almost double what you've raised to date, nice kickup. So global expansion, nice philosophy. That's important to you guys, isn't it? >> The world has become a small village. There's no changing that, and data is global. Things are a wide global trend, it's amazing to see that AI is not just transforming the US, it's also transforming China, it's also transforming India. It's transforming Africa. Pay through mobile is a very common theme worldwide and I think data is being collected globally. I think there is no way to unbox it and box it back to a small place, so our vision is very borderless and global and we want the AI companies of the valley to also compete in a global arena and I think that's kind of why we think it's important to be-- >> Love competition, that's certainly going to force everyone to be more open. I got to ask you about the role of the developer. I love the democratization, putting AI in the hands of everybody, it's a great mission. You guys do a lot of AI for Good efforts. So congratulations on that, but how does this change the nature of the developer, because you're seeing with cloud and DevOps, developers are becoming closer to the front lines, they're becoming kingmakers. They're becoming really, really important. So the role of the developer is important. How do you change that role, if any. How do you expand it, what happens? >> There are two important transformations happening right now in the tech world. One is the role of data scientists and the role of the software engineer. Right, so they're coming closer in many ways, in actually in some of the newer places, software engineers are deploying data science models, data scientists are deploying software engineering. So Python has been a good new language, the new languages that are coming up that help that happen more closely. Software engineering as we know it, which was looking at data creating the rules and the logic that runs a program is now being automated to a degree where that logic is being generated from data using data science. So that's where the brains behind how programs run how computers build is now being, is AI inside. And so that's where the world is transforming, software engineers now get to do a lot more with a lot less of tinkering on a daily basis for little modules. They can probably build a whole slew of an application what would take 18 months to build is now compressing into 18 weeks or 18 days. >> Sri, I love how you talk about software engineering and data scientists, very specific. I was having a debate with my young son around what is computer science was the question. Well, computer science is the study of computers the science of computers. It used to be if you were a CS or a comp sci major which is not cool to say anymore but, when you were a computer science major, you were really a software engineer, that was the discipline. Now, computer science as a field has spread so far and so broad, you've got software engineering you've got data science, you have newer roles are emerging. But that brings up the question I want to put to you which is, the whole idea of, I'm a full stack developer. Well, if what you're saying you're doing is true, you're essentially cutting the stack in half. So it's a half stack developer on one end and a data scientist that's got the other half. So the notion of the full stack developer kind of goes away with the idea of horizontally scalable infrastructure and vertically specialized data and AI. Your thoughts, what's your reaction to that? >> I think the most... I would say the most scarce resource in the world is empathy, right? When developers have empathy for their users, they now start building design that cares for the users. So the design becomes still the limiting factor where you can't really automate a lot of that design. So the full stack engineer is now going closer to the front and understanding their users and making applications that are perceptive of how the users are using them and building that empathy into the product. A lot of the full stack, we used to learn how to build up a kernel, deploy it on cloud, scale it on your own servers. All of that is coming together in reasonably easier ways. With cloud is helping there, AI is helping there, data is helping there, and lessons from the data. But I think what has not gone away is imagination, creativity, and how to power that creativity with AI and get it in the hands of someone quickly. Marketing has become easier in the new world. So it's not just enough to make products, you have to make markets for your products and then deliver and get that success for customers-- >> So what you're saying-- >> The developers become-- >> The consistency of the lower end of the stack of wiring together the plumbing and the kernel and everything else is done for you. So you can move up. >> Up the stack. >> So the stack's growing, so it's still kind of full. No one calls themselves a half stack developer. I haven't met anyone say "Yeah I'm a half stack developer." They're full stack developers, but the roles are changing. >> I think what-- >> There's more to do on the front end of creativity so the stack's extending. >> Creativity is changing, I think the one thing we have learned. We've gone past Moore's Law in the valley and people are innovating architectures to run AI faster. So AI is beginning to eat hardware. So you've seen the transformation in microprocessors as well I think once AI starts being part of the overall conversation, you'll see a much more richer coexistence with being how a human programmer and a computer programmer is going to be working closely. But I think this is just the beginning of a real richness when you talk about rich interactive applications, you're going to talk about rich interactive appliances, where you start seeing intelligence really spread around the form. >> Sri, if we really want to have some fun we can just talk about what a 10x engineer is. No I'm only kidding, we're not going to go there. It's always a good debate on Twitter what a 10x engineer is. Sri, congratulations on the funding. $72.5 million in finance for global expansion on the team side as well as in geographies, congratulations. >> Thank you. >> H2O.ai >> The full stack engineer of the future is, finishing up your full stack engineer conversation is going to get that courage and become a leader. Going from managers to leaders, developers to founders. I think it's become easier to democratize entrepreneurship now than ever before and part of our mission as a company is to democratize things, democratize AI, democratize H2O like in the AI for Good, democratize water. But also democratize the art of making more entrepreneurs and remove the common ways to fail and that's also a way to create more opportunity more ownership in the world and so-- >> And I think society will benefit from this globally because in the data is truth, in the data is the notion of being transparent, if it's all there and we're going to get to the data faster and that's where AI helps us. >> That's what it is. >> Sri, congratulations, $72 million of funding for H2O. We're here with the founder and CEO Sri Ambati. Great success story here in Silicon Valley and around the world. I'm John Furrier with the Cube, thanks for watching. >> Sri: Thank you. (upbeat music)

>> Voiceover: Live from San Francisco, celebrating 10 years of high tech coverage, it's theCUBE. Covering VMworld 2019. Brought to you by VMware and its ecosystem partners. >> Welcome back, everyone, CUBE's live coverage for VMworld 2019 in Moscone North, in San Francisco, California. I'm John Furrier with Dave Vellante. Dave, our 10 years, we have Eric Herzog, the CMO and vice president of Global Storage Channels at IBM. CUBE alum, this is his 11th appearance on theCUBE at VMworld. That's the number one position. >> Dave: It's just at VMworld. >> Congratulations, welcome back. >> Well, thank you very much. Always love to come to theCUBE. >> John: Sporting the nice shirt and the IBM badge, well done. >> Thank you, thank you. >> What's going on with IBM in VMworld? First, get the news out. What's happening for you guys here? >> So for us, we just had a big launch actually in July. That was all about big data, storage for big data and AI, and also storage for cyber-resiliency. So we just had a big launch in July, so we're just sort of continuing that momentum. We have some exciting things coming out on September 12th in the high end of our storage product line, and then some additional things very heavily around containers at the end of October. >> So the open shift is the first question I have that pops into my head. You know, I think of IBM, I think of IBM Storage, I think of Red Hat, the acquisition, OpenShift's been very successful. Pat Gelsinger was talking containers, Kubernetes-- >> Eric: Right. >> OpenShift has been a big part of Red Hat's offering, now part of IBM. Has that Red Shift, I mean OpenShift's come in, to your world, and how do you guys view that? I mean, it's containers, obviously, is there any impact there at all? >> So from a storage perspective, no. IBM storage has been working with Red Hat for over 15 years, way before the company ever thought about buying them. So we went to the old Red Hat Summits, it was two guys, a dog, and a note, and IBM was there. So we've been supporting Red Hat for years, and years, and years. So for the storage division, it's probably one of the least changes to the direction, compared to the rest of IBM 'cause we were already doing so much with Red Hat. >> You guys were present at the creation of the whole Red Hat movement. >> Yeah, I mean we were-- >> We've seen the summits, but I was kind of teeing up the question, but legitimately though, now that you have that relationship under your belt-- >> Eric: Right. >> And IBM's into creating OpenShift in all the services, you're starting to see Red Hat being an integral part across IBM-- >> Eric: Right. >> Does that impact you guys at all? >> So we've already talked about our support for Red Hat OpenShift. We do support it. We also support any sort of container environment. So we've made sure that if it's not OpenShift and someone's going to leverage something else, that our storage will work with it. We've had support for containers now for two and half years. We also support the CSI Standard. We publicly announced that earlier in the year, that we'd be having products at the end of the year and into the next year around the CSI specification. So, we're working on that as well. And then, IBM also came out with a thing that are called the Cloud Paks. These Cloud Paks are built around Red Hat. These are add-ons that across multiple divisions, and from that perspective, we're positioned as, you know, really that ideal rock solid foundation underneath any of those Cloud Paks with our support for Red Hat and the container world. >> How about protecting containers? I mean, you guys obviously have a lot of history in data protection of containers. They're more complicated. There's lots of them. You spin 'em up, spin 'em down. If they don't spin 'em down, they're an attack point. What are your thoughts on that? >> Well, first thing I'd say is stay tuned for the 22nd of October 'cause we will be doing a big announcement around what we're doing for modern data protection in the container space. We've already publicly stated we would be doing stuff. Right, already said we'd be having stuff either the end of this year in Q4 or in Q1. So, we'll be doing actually our formal launch on the 22nd of October from Prague. And we'll be talking much more detail about what we're doing for modern data protection in the container space. >> Now, why Prague? What's your thinking? >> Oh, IBM has a big event called TechU, it's a Technical University, and there'll be about 2,000 people there. So, we'll be doing our launch as part of the TechU process. So, Ed Walsh, who you both know well and myself will be doing a joint keynote at that event on the 22nd. >> So, talk a little bit more about multi-cloud. You hear all kinds of stuff on multi-cloud here, and we've been talkin' on theCUBE for a while. It's like you got IBM Red Hat, you got Google, CISCO's throwin' a hat in the ring. Obviously, VMware has designs on it. You guys are an arms dealer, but of course, you're, at the same time, IBM. IBM just bought Red Hat so what are your thoughts on multi-cloud? First, how real is it? Sizeable opportunity, and from a storage perspective, storage divisions perspective, what's your strategy there? >> Well, from our strategy, we've already been takin' hybrid multi-cloud for several years. In fact, we came to Wikibon, your sister entity, and actually, Ed and I did a presentation to you in July of 2017. I looked it up, the title says hybrid multi-cloud. (Dave laughs) Storage for hybrid multi-cloud. So, before IBM started talkin' about it, as a company, which now is, of course, our official line hybrid multi-cloud, the IBM storage division was supporting that. So, we've been supporting all sorts of cloud now for several years. What we have called transparent cloud tiering where we basically just see cloud as a tier. Just the way Flash would see hard drive or tape as a tier, we now see cloud as a tier, and our spectrum virtualized for cloud sits in a VM either in Amazon or in IBM Cloud, and then, several of our software products the Spectrum line, Spectrum Protect, Spectrum Scale, are available on the AWS Marketplace as well as the IBM Cloud Marketplace. So, for us, we see multi-cloud from a software perspective where the cloud providers offer it on their marketplaces, our solutions, and we have several, got some stuff with Google as well. So, we don't really care what cloud, and it's all about choice, and customers are going to make that choice. There's been surveys done. You know, you guys have talked about it that certainly in the enterprise space, you're not going to use one cloud. You use multiple clouds, three, four, five, seven, so we're not going to care what cloud you use, whether it be the big four, right? Google, IBM, Amazon, or Azure. Could it be NTT in Japan? We have over 400 small and medium cloud providers that use our Spectrum Protect as the engine for their backup as a service. We love all 400 of them. By the way, there's another 400 we'd like to start selling Spectrum Protect as a service. So, from our perspective, we will work with any cloud provider, big, medium, and small, and believe that that's where the end users are going is to use not just one cloud provider but several. So, we want to be the storage connected. >> That's a good bet, and again, you bring up a good point, which I'll just highlight for everyone watching, you guys have made really good bets early, kind of like we were just talking to Pat Gelsinger. He was making some great bets. You guys have made some, the right calls on a lot of things. Sometimes, you know, Dave's critical of things in there that I don't really have visibility in the storage analyst he is, but generally speaking, you, Red Hat, software, the systems group made it software. How would you describe the benefits of those bets paying off today for customers? You mentioned versatility, all these different partners. Why is IBM relevant now, and from those bets that you've made, what's the benefit to the customers? How would you talk about that? Because it's kind of a big message. You got a lot going on at IBM Storage, but you've made some good bets that turned out to be on the right side of tech history. What are those bets? And what are they materializing into? >> Sure, well, the key thing is you know I always wear a Hawaiian shirt on theCUBE. I think once maybe I haven't. >> You were forced to wear a white shirt. You were forced to wear the-- >> Yes, an IBM white shirt, and once, I actually had a shirt from when I used to work for Pat at the EMC, but in general, Hawaiian shirt, and why? Because you don't fight the wave, you ride the wave, and we've been riding the wave of technology. First, it was all about AI and automation inside of storage. Our easy tier product automatically tiers. You don't have, all you do is set it up once, and after that, it automatically moves data back and forth, not only to our arrays, but over 450 arrays that aren't ours, and the data that's hottest goes to the fastest tier. If you have 15,000 RPM drives, that's your fastest, it automatically knows that and moves data back and forth between hot, fast, and cold. So, one was putting AI and automation in storage. Second wave we've been following was clearly Flash. It's all about Flash. We create our own Flash, we buy raw Flash, create our own modules. They are in the industry standard form factor, but we do things, for example, like embed encryption with no performance hit into the Flash. Latency as low as 20 microseconds, things that we can do because we take the Flash and customize it, although it is in industry standard form factor. The other one is clearly storage software and software-defined storage. All of our arrays come with software. We don't sell hardware. We sell a storage solution. They either come with Spectrum Virtualize or Spectrum Scale, but those packages are also available stand-alone. If you want to go to your reseller or your distributor and buy off-the-shelf white-box componentry, storage-rich servers, you can create your own array with Spectrum Virtualize for block, Spectrum Scale for File, IBM Object Storage for Cloud. So, if someone wants to buy software only, just the way Pat was talking about software-defined networking, we'll sell 'em software for file blocker object, and they don't buy any infrastructure from us. They only buy the software, so-- >> So, is that why you have a large customer base? Is that why there's so much, diverse set of implementations? >> Well, we've got our customers that are system-oriented, right, some you have Flash system. Got other customers that say, "Look, I just want to buy Spectrum Scale. "I don't want to buy your infrastructure. "Just I'll build my own," and we're fine with that. And the other aspect we have, of course, is we've got the modern data protection with Spectrum Protect. So, you've got a lot of vendors out on the floor. They only sell backup. That's all they sell, and you got other people on the floor, they only sell an array. They have nice little arrays, but they can't do an array and software-defined storage and modern data protection one throat to choke, one tech support, entity to deal with one set of business partners to deal with, and we can do that, which is why it's so diverse. We have people who don't have any of IBM storage at all, but they back up everything with Spectrum Protect. We have other customers who have Flash systems, but they use backup from one of our competitors, and that's okay 'cause we'll always get a PO one way or another, right? >> So, you want the choice as factor. >> Right. >> Question on the ecosystem and your relationship with VMware. As John said, 10th year at VMworld, if you go back 10 years, storage, VMware storage was limited. They had very few resources. They were throwin' out APIs to the storage industry and sayin' here, you guys, fix this problem, and you had this cartel, you know, it was EMC, IBM was certainly in there, and NetApp, a couple others, HPE, HP at the time, Dell, I don't know, I'm not sure if Dell was there. They probably were, but you had the big Cos that actually got the SDK early, and then, you'd go off and try to sell all the storage problems. Of course, EMC at the time was sort of puttin' the brakes on VMware. Now, it's totally different. You've got, actually similar cartel. Although, you've got different ownership structure with Dell, EMC, and you got (mumbles) VMwware's doin' its own software finally. The cuffs are off. So, your thoughts on the changes that have gone on in the ecosystem. IBM's sort of position and your relationship with VMware, how that's evolved. >> So, the relationship for us is very tight. Whether it be the old days of VASA, VAAI, V-center op support, right, then-- >> Dave: V-Vault, yeah yeah. >> Now, V-Vault two so we've been there every single time, and again, we don't fight the wave, we ride the wave. Virtualization's a wave. It's swept the industry. It swept the end users. It's swept every aspect of compute. We just were riding that wave and making sure our storage always worked with it with VMware, as well as other hypervisors as well, but we always supported VMware first. VMware also has a strong relationship with the cloud division, as you know, they've now solved all kinds of different things with IBM Cloud so we're making sure that we stay there with them and are always up front and center. We are riding all the waves that they start. We're not fighting it. We ride it. >> You got the Hawaiian shirt. You're riding the waves. You're hanging 10, as you used to say. Toes on the nose, as the expression goes. As Pat Gelsinger says, ride the new wave, you're a driftwood. Eric, great to see you, CMO of IBM Storage, great to have you all these years and interviewing you, and gettin' the knowledge. You're a walking storage encyclopedia, Wikipedia, thanks for comin' on. >> Great, thank you. >> All right, it's more CUBE coverage here live in San Francisco. I'm John Furrier for Dave Vellante, stay with us. I got Sanjay Putin coming up, and we have all the big executives who run the different divisions. We're going to dig into them. We're going to get the data, share with you. We'll be right back. (upbeat music)

>> Live from Washington, D.C., it's theCUBE! Covering Inforum D.C. 2018. Brought to you by Infor. >> Well, welcome back to Washington, D.C., We are alive here at the Convention Center at Inforum 18, along with Dave Vellante, I'm John Walls. It's a pleasure now, welcome to theCUBE, Leigh Martin, who is the Senior Director of the Dynamic Science Labs at Infor, and good afternoon to you Leigh! >> Good afternoon, thank you for having me. >> Thanks for comin' on. >> Thank you for being here. Alright, well tell us about the Labs first off, obviously, data science is a big push at Infor. What do you do there, and then why is data science such a big deal? >> So Dynamic Science Labs is based in Cambridge, Massachusetts, we have about 20 scientists with backgrounds in math and science areas, so typically PhDs in Statistics and Operations Research, and those types of areas. And, we've really been working over the last several years to build solutions for Infor customers that are Math and Science based. So, we work directly with customers, typically through proof of concept, so we'll work directly with customers, we'll bring in their data, and we will build a solution around it. We like to see them implement it, and make sure we understand that they're getting the value back that we expect them to have. Once we prove out that piece of it, then we look for ways to deliver it to the larger group of Infor customers, typically through one of the Cloud Suites, perhaps functionality, that's built into a Cloud Suite, or something like that. >> Well, give me an example, I mean it's so, as you think-- you're saying that you're using data that's math and science based, but, for application development or solution development if you will. How? >> So, I'll give you an example, so we have a solution called Inventory Intelligence for Healthcare, it's moving towards a more generalized name of Inventory Intelligence, because we're going to move it out of the healthcare space and into other industries, but this is a product that we built over the last couple of years. We worked with a couple of customers, we brought in their loss and data, so their loss in customers, we bring the data into an area where we can work on it, we have a scientist in our team, actually, she's one of the Senior Directors in the team, Dawn Rose, who led the effort to design and build this, design and build the algorithm underlying the product; and what it essentially does is, it allows hospitals to find the right level of inventory. Most hospitals are overstocked, so this gives them an opportunity to bring down their inventory levels, to a manageable place without increasing stockouts, so obviously, it's very important in healthcare, that you're not having a lot of stockouts. And so, we spent a lot of time working with these customers, really understanding what the data was like that they were giving to us, and then Dawn and her team built the algorithm that essentially says, here's what you've done historically, right? So it's based on historic data, at the item level, at the location level. What've you done historically, and how can we project out the levels you should have going forward, so that they're at the right level where you're saving money, but again, you're not increasing stockouts, so. So, it's a lot of time and effort to bring those pieces together and build that algorithm, and then test it out with the customers, try it out a couple of times, you make some tweaks based on their business process and exactly how it works. And then, like I said, we've now built that out into originally a stand-alone application, and in about a month, we're going to go live in Cloud Suite Financials, so it's going to be a piece of functionality inside of Cloud Suite Financials. >> So, John, if I may, >> Please. >> I'm going to digress for a moment here because the first data scientist that I ever interviewed was the famous Hilary Mason, who's of course now at Cloudera, but, and she told me at the time that the data scientist is a part mathematician, part scientist, part statistician, part data hacker, part developer, and part artist. >> Right. (laughs) >> So, you know it's an amazing field that Hal Varian, who is the Google Economist said, "It's going to be the hottest field, in the next 10 years." And this is sort of proven true, but Leigh, my question is, so you guys are practitioners of data science, and then you bring that into your product, and what we hear from a lot of data scientists, other than that sort of, you know, panoply of skill sets, is, they spend more time wrangling data, and the tooling isn't there for collaboration. How are you guys dealing with that? How has that changed inside of Infor? >> It is true. And we actually really focus on first making sure we understand the data and the context of the data, so it's really important if you want to solve a particular business problem that a customer has, to make sure you understand exactly what is the definition of each and every piece of data that's in all of those fields that they sent over to you, before you try to put 'em inside an algorithm and make them do something for you. So it is very true that we spend a lot of time cleaning and understanding data before we ever dive into the problem solving aspect of it. And to your point, there is a whole list of other things that we do after we get through that phase, but it's still something we spend a lot of time on today, and that has been the case for, a long time now. We, wherever we can, we apply new tools and new techniques, but actually just the simple act of going in there and saying, "What am I looking at, how does it relate?" Let me ask the customer to clarify this to make sure I understand exactly what it means. That part doesn't go away, because we're really focused on solving the customer solution and then making sure that we can apply that to other customers, so really knowing what the data is that we're working with is key. So I don't think that part has actually changed too much, there are certainly tools that you can look at. People talk a lot about visualization, so you can start thinking, "Okay, how can I use some visualization to help me understand the data better?" But, just that, that whole act of understanding data is key and core to what we do, because, we want to build the solution that really answers the answers the business problem. >> The other thing that we hear a lot from data scientists is that, they help you figure out what questions you actually have to ask. So, it sort of starts with the data, they analyze the data, maybe you visualize the data, as you just pointed out, and all these questions pop out. So what is the process that you guys use? You have the data, you've got the data scientist, you're looking at the data, you're probably asking all these questions. You get, of course, get questions from your customers as well. You're building models maybe to address those questions, training the models to get better and better and better, and then you infuse that into your software. So, maybe, is that the process? Is it a little more complicated than that? Maybe you could fill in the gaps. >> Yeah, so, I, my personal opinion, and I think many of my colleagues would agree with me on this is, starting with the business problem, for us, is really the key. There are ways to go about looking at the data and then pulling out the questions from the data, but generally, that is a long and involved process. Because, it takes a lot of time to really get that deep into the data. So when we work, we really start with, what's the business problem that the customer's trying to solve? And then, what's the data that needs to be available for us to be able to solve that? And then, build the algorithm around that. So for us, it's really starting with the business problem. >> Okay, so what are some of the big problems? We heard this morning, that there's a problem in that, there's more job openings than there are candidates, and productivity, business productivity is not being impacted. So there are two big chewy problems that data scientists could maybe attack, and you guys seem to be passionate about those, so. How does data science help solve those problems? >> So, I think that, at Infor, I'll start off by saying at Infor there's actually, I talked about the folks that are in our office in Cambridge, but there's quite a bit of data science going on outside of our team, and we are the data science team, but there are lots of places inside of Infor where this is happening. Either in products that contains some sort of algorithmic approach, the HCM team for sure, the talent science team which works on HCM, that's a team that's led by Jill Strange, and we work with them on certain projects in certain areas. They are very focused on solving some of those people-related problems. For us, we work a little bit more on the, some of the other areas we work on is sort of the manufacturing and distribution areas, we work with the healthcare side of things, >> So supply chain, healthcare? >> Exactly. So some of the other areas, because they are, like I said, there are some strong teams out there that do data science, it's just, it's also incorporated with other things, like the talent science team. So, there's lots of examples of it out there. In terms of how we go about building it, so we, like I was saying, we work on answering the business, the business question upfront, understanding the data, and then, really sitting with the customer and building that out, and, so the problems that come to us are often through customers who have particular things that they want to answer. So, a lot of it is driven by customer questions, and particular problems that they're facing. Some of it is driven by us. We have some ideas about things that we think, would be really useful to customers. Either way, it ends up being a customer collaboration with us, with the product team, that eventually we'll want to roll it out too, to make sure that we're answering the problem in the way that the product team really feels it can be rolled out to customers, and better used, and more easily used by them. >> I presume it's a non-linear process, it's not like, that somebody comes to you with a problem, and it's okay, we're going to go look at that. Okay now, we got an answer, I mean it's-- Are you more embedded into the development process than that? Can you just explain that? >> So, we do have, we have a development team in Prague that does work with us, and it's depending on whether we think we're going to actually build a more-- a product with aspects to it like a UI, versus just a back end solution. Depends on how we've decided we want to proceed with it. so, for example, I was talking about Inventory Intelligence for Healthcare, we also have Pricing Science for Distribution, both of those were built initially with UIs on them, and customers could buy those separately. Now that we're in the Cloud Suites, that those are both being incorporated into the Cloud Suite. So, we have, going back to where I was talking about our team in Prague, we sometimes build product, sort of a fully encased product, working with them, and sometimes we work very closely with the development teams from the various Cloud Suites. And the product management team is always there to help us, to figure out sort of the long term plan and how the different pieces fit together. >> You know, kind of big picture, you've got AI right, and then machine learning, pumping all kinds of data your way. So, in a historical time frame, this is all pretty new, this confluence right? And in terms of development, but, where do you see it like 10 years from now, 20 years from now? What potential is there, we've talked about human potential, unlocking human potential, we'll unlock it with that kind of technology, what are we looking at, do you think? >> You know, I think that's such a fascinating area, and area of discussion, and sort of thinking, forward thinking. I do believe in sort of this idea of augmented intelligence, and I think Charles was talking a little bit about, about that this morning, although not in those particular terms; but this idea that computers and machines and technology will actually help us do better, and be better, and being more productive. So this idea of doing sort of the rote everyday tasks, that we no longer have to spend time doing, that'll free us up to think about the bigger problems, and hopefully, and my best self wants to say we'll work on famine, and poverty, and all those problems in the world that, really need our brains to focus on, and work. And the other interesting part of it is, if you think about, sort of the concept of singularity, and are computers ever going to actually be able to think for themselves? That's sort of another interesting piece when you talk about what's going to happen down the line. Maybe it won't happen in 10 years, maybe it will never happen, but there's definitely a lot of people out there, who are well known in sort of tech and science who talk about that, and talk about the fears related to that. That's a whole other piece, but it's fascinating to think about 10 years, 20 years from now, where we are going to be on that spectrum? >> How do you guys think about bias in AI and data science, because, humans express bias, tribalism, that's inherent in human nature. If machines are sort of mimicking humans, how do you deal with that and adjudicate? >> Yeah, and it's definitely a concern, it's another, there's a lot of writings out there and articles out there right now about bias in machine learning and in AI, and it's definitely a concern. I actually read, so, just being aware of it, I think is the first step, right? Because, as scientists and developers develop these algorithms, going into it consciously knowing that this is something they have to protect against, I think is the first step, for sure. And then, I was just reading an article just recently about another company (laughs) who is building sort of a, a bias tracker, so, a way to actually monitor your algorithm and identify places where there is perhaps bias coming in. So, I do think we'll see, we'll start to see more of those things, it gets very complicated, because when you start talking about deep learning and networks and AI, it's very difficult to actually understand what's going on under the covers, right? It's really hard to get in and say this is the reason why, your AI told you this, that's very hard to do. So, it's not going to be an easy process but, I think that we're going to start to see that kind of technology come. >> Well, we heard this morning about some sort of systems that could help, my interpretation, automate, speed up, and minimize the hassle of performance reviews. >> Yes. (laughs) >> And that's the classic example of, an assertive woman is called abrasive or aggressive, an assertive man is called a great leader, so it's just a classic example of bias. I mentioned Hilary Mason, rock star data scientist happens to be a woman, you happen to be a woman. Your thoughts as a woman in tech, and maybe, can AI help resolve some of those biases? >> Yeah. Well, first of all I want to say, I'm very pleased to work in an organization where we have some very strong leaders, who happen to be women, so I mentioned Dawn Rose, who designed our IIH solution, I mentioned Jill Strange, who runs the talent science organization. Half of my team is women, so, particularly inside of sort of the science area inside of Infor, I've been very pleased with the way we've built out some of that skill set. And, I'm also an active member of WIN, so the Women's Infor Network is something I'm very involved with, so, I meet a lot of people across our organization, a lot of women across our organization who have, are just really strong technology supporters, really intelligent, sort of go-getter type of people, and it's great to see that inside of Infor. I think there's a lot of work to be done, for sure. And you can always find stories, from other, whether it's coming out of Silicon Valley, or other places where you hear some, really sort of arcane sounding things that are still happening in the industry, and so, some of those things it's, it's disappointing, certainly to hear that. But I think, Van Jones said something this morning about how, and I liked the way he said it, and I'm not going to be able say it exactly, but he said something along the lines of, "The ground is there, the formation is starting, to get us moving in the right direction." and I think, I'm hopeful for the future, that we're heading in that way, and I think, you know, again, he sort of said something like, "Once the ground swell starts going in that direction, people will really jump in, and will see the benefits of being more diverse." Whether it's across, having more women, or having more people of color, however things expand, and that's just going to make us all better, and more efficient, and more productive, and I think that's a great thing. >> Well, and I think there's a spectrum, right? And on one side of the spectrum, there's intolerable and unacceptable behavior, which is just, should be zero tolerance in my opinion, and the passion of ours in theCUBE. The other side of that spectrum is inclusion, and it's a challenge that we have as a small company, and I remember having a conversation, earlier this year with an individual. And we talk about quotas, and I don't think that's the answer. Her comment was, "No, that's not the answer, you have to endeavor to reach deeper beyond your existing network." Which is hard sometimes for us, 'cause you're so busy, you're running around, it's like okay it's the convenient thing to do. But you got to peel the onion on that network, and actually take the extra time and make it a priority. I mean, your thoughts on that? >> No, I think that's a good point, I mean, if I think about who my circle is, right? And the people that I know and I interact with. If I only reach out to the smallest group of people, I'm not getting really out beyond my initial circle. So I think that's a very good point, and I think that that's-- we have to find ways to be more interactive, and pull from different areas. And I think it's interesting, so coming back to data science for a minute, if you sort of think about the evolution of where we got to, how we got to today where, now we're really pulling people from science areas, and math areas, and technology areas, and data scientists are coming from lots of places, right? And you don't always have to have a PhD, right? You don't necessary have to come up through that system to be a good data scientist, and I think, to see more of that, and really people going beyond, beyond just sort of the traditional circles and the traditional paths to really find people that you wouldn't normally identify, to bring into that, that path, is going to help us, just in general, be more diverse in our approach. >> Well it certainly it seems like it's embedded in the company culture. I think the great reason for you to be so optimistic going forward, not only about your job, but about the way companies going into that doing your job. >> What would you advise, young people generally, who want to crack into the data science field, but specifically, women, who have clearly, are underrepresented in technology? >> Yeah, so, I think the, I think we're starting to see more and more women enter the field, again it's one of those, people know it, and so there's less of a-- because people are aware of it, there's more tendency to be more inclusive. But I definitely think, just go for it, right? I mean if it's something you're interested in, and you want to try it out, go to a coding camp, and take a science class, and there's so many online resources now, I mean there's, the massive online courses that you can take. So, even if you're hesitant about it, there are ways you can kind of be at home, and try it out, and see if that's the right thing for you. >> Just dip your toe in the water. >> Yes, exactly, exactly! Try it out and see, and then just decide if that's the right thing for you, but I think there's a lot of different ways to sort of check it out. Again, you can take a course, you can actually get a degree, there's a wide range of things that you can do to kind of experiment with it, and then find out if that's right for you. >> And if you're not happy with the hiring opportunities out there, just start a company, that's my advice. >> That's right. (laughing together) >> Agreed, I definitely agree! >> We thank you-- we appreciate the time, and great advice, too. >> Thank you so much. >> Leigh Martin joining us here at Inforum 18, we are live in Washington, D.C., you're watching the exclusive coverage, right here, on theCUBE. (bubbly music)

from New York it's the cube covering blockchain week now here's John furry hello everyone welcome back to this special cube exclusive on the water coverage of the awesome cryptocurrency event going on this week blockchain week New York City D central Anthony do re oh seven a big special event launching some great killer products me up to cube alumni that we introduced at polycon 2018 Genevieve Dec Monroe and Jeromy Gartner great to see you guys thanks for having us so you guys look fabulous you look beautiful you're smart we're on a boat we're partying it feels like Prague it feels like prom feels like we are at the top of another bubble couldn't feel better five more boat parties and then the bubbles officially at the top but we're only had the first boat party well the real existential question is what do we view next you know we've we've graduated from nightclubs and strip clubs and now two super yachts like do we go on a spaceship neck's or a Boeing Jets yeah I mean the options are somewhat limited in how we scale up the crypto parties I actually heard today one of my clients is launching in space a crypto mining operation that's fueled by solar power so we might be going to space Elon Musk wants to get involved I agree like where are we going you guys are awesome I love the creative so this party to me is really a testament of the community talk about the community I see polycon was great in Puerto Rico they had restart week and that but I heard these guys saying here at the central that the community's fragmented is the community fragmented seems like it's not out there or just only one pocket of the community I think the community so we have 10,000 people at consensus okay so these are 10,000 people that have gone down the rabbit hole and they're all at the Hilton in midtown Manhattan kind of going like how'd you get involved why are you here 10,000 people is a lot but I think that yeah we're we're at the decentral party so some of the yeast communities are being fragmented but I think we're having like infrastructure built to kind of connect the broader world to the things whether it's custodial services whether it's like tonight the jocks 2.0 wallet and you know everything that's getting involved there I don't know Jeremy Jeremy it's like an international traveler so you Carly Jeremy it's 100 percent in an echo chamber more importantly rabbit holes are like dark and confusing places that there are they're winding and a lot of people are here for very different reasons and thus when you have all these new entrants to the industry to this technology here for all these different reasons of course you have some fragmentation you know in many regards the ideological and philosophical roots of Bitcoin and blotchy technology have been lost son on many of the new entrants and and so it takes time to get to the point where we're all winding I think different blockchains and different applications of this technology will have different kind of approaches to how people think about investors always gonna be pragma because this is a massively growing industry that touches upon every kind of business and governmental and non-governmental it's actually fragmentation is a relative chairman is Genevieve you I saw you and you guys are working with things from cannabis coin I think you had to cannabis cabin this week in New Yorker yeah we're doing that tomorrow night actually so crypto and cannabis are two the hottest millennial sectors right and so we kind of like to say Agri capital we like to dance on the edge of chaos I actually found out about a cannabis company in Vancouver so just outside Vancouver that is using a crypto mining operation and all the excess heat that is coming off that to power a grow-op so we're literally at the intersection of crypto and cannabis not just for our handling money but handling energy in a different way which is so fast that's real mission impact investing right there you know using energy to grow weed that's the Seidel impact isn't it good bad I mean even as you look at it you know better cannabis healthy cannabis is a mission people look care about we're helping people's wallets and we're helping people's minds right in like ways that the government banks and pharmaceutical companies are fighting against so you know if you can't beat them join them so I welcome Astra Zeneca and the Bank of Canada to come on board our mission this is specially turning into a cube after dark episode Jeremy I gotta get your thoughts on these industries because look at cannabis we joke about it but that's an example of another market this zilean markets that are coming online that are gonna be impacted so fragmentation is a relative terms but hey look at it I mean energy tech is infrastructure tech and solid that's what I'm concerned about who nails the infrastructure for network effects and what's the instrumentation for that that's the number one question that is essential question for the protocols whether it's Theory amore Bitcoin oreos Definity so forth the protocol that provides the strongest and and most adaptable and infrastructure and foundational technology is going to be one of the main ones are those will be the main winners and so the names I mentioned they're up there they're very competitive but it's anybody's game right now I think any blockchain can come along right now and be the winner a decade from now and for entrepreneurs represents a challenge because you have to figure out what blocks came to go build on this is why I am big on investing in interoperable Ledger's technologies that enable the kind of transfer smart contracts and crypto assets between blockchains it's a great great segue let's just get an update since we last talked what are you working on what are you investing in what's new in your world share the update on strangers so now my fund is officially launched where how much we launched with just over 15 million dollars and amazingly we launched at the perfect time we're already up 55% and we got making an investment for a venture fund we actually did the exact WA T investment which transferred over from my personal investment portfolio but doing great I have really run the gamut in terms of investments we're making on the equity side of things and in crypto assets but what we're seeing is really accomplished entrepreneurs coming to this space continue actually more optimism than I had felt at polygon poly car and I was like this market needs to correct in a real way today I think that Corrections been prolonged if we were gonna feel a lot of pain it was gonna be two months ago but instead I think it's gonna be one to three years before the market goes through the correction that we need to see for the real shakeout to happen because so many of these teams that I think are garbage have so much money yeah and they're just floating around they got has worked their way out it's just like a bad burrito at some point it's got a pass Genevieve what are you working on I'll see you've got grit capital what's the update on your end what's new yeah amazing actually literally tonight probably about 60 minutes ago my business partner and I signed one of the fastest-growing exchanges in Canada called Einstein exchanges of quiet so these guys have only ever raised like one and a half million u.s. and they're the biggest exchange in Canada by sign ups active accounts so they're probably doing like almost a hundred million in top-line transaction volumes and they're probably never going public somebody's probably gonna buy them but we're gonna be marketing them across the country getting customers I mean the tagline is it doesn't take I'm Stein to open an account it shouldn't take n Stein it by Bitcoin you can literally get this account set up in under 60 seconds so they're vampires ease-of-use surety reducing the steps it takes to do it and get it up and running fast absolutely like my dad could do it and like alright so we say now follow you on Instagram and Facebook which is phenomenal by the way I got a great lifestyle what's the coolest thing you've done since we last talked to Polycom Wow polycon was kind of a high really peaked and then everyone got sick like our team got said polymath untraceable cuz everybody just got the flu yeah we were like on adrenaline and we kept going ah what's the coolest thing that we've done since then I think it's signing up like cool companies like Einstein we also signed a big cannabis company in Colombia called Chiron they're about to go public I don't know Cole what do you think I don't know maybe what's the coolest thing you've done travel what's your good so last night Jeremy and I just met we're together on a blockchain Research Institute project that Sonova Financial is backing and meeting him so you guys working together on a special project right now how's that going what's that about JCO which is a new sort of financial services firm they're creating what it could effectively be understood as a compliant coin offering that is available to more than just accredited investors and that's they're making ico something that falls within the pre-existing regulatory framework and also accessible to your average Joe which I think it's really important if we're going to follow the initial vision for both blockchain technology and offerings all right final question I know you guys want to get back to your dancing and schmoozing networking doing big deals having fun what is blockchain New York we call about we could pop chain we here in New York what the hell's happening there's been a lot of events what's your guy's assessment of you observed and saw anything can you share for the people who didn't make it to New York or not online reading all the action what's happened so as someone that did not attend consensus spoke at three other events or speaking at three other events I can say with certainty that the New York box chain week has been about bringing together virtually everyone in the industry to connect and kind of catch up with one another which is really important we we don't have that many events Miami was too short the industry's gotten too big but having a full week of activities in New York City has enabled me to kind of foster relationships are oh I yeah man get a lot of work John well I've gotten so much work done I haven't had to actually be a date conferences to reconnect with just about everyone that I want to industry that's really special Genevieve what is your observation what have you observed share some in anecdote some insight on what happened this week I know fluid he started I saw Bilt's I was just chatting with him about it it was started in over the weekend it's gone up and we're now into Thursday tomorrow coming up well I don't think it's a coincidence that Goldman Sachs came out today and said that they were launching some sort of digital currency marketing yeah exactly using the power of the 10,000 people i consensus but yeah i know i agree with what jeremy says it's not really about being at consensus it's about what happens like behind closed doors it's all these decentralized parties that are happening yeah open doors but like it's you know like we hosted a core capital asset we had a hundred people in a suite at the dream hotel and it was just like you put the biggest CEOs of the mining companies in the world together and like put those with investors in a room it's like you know 100 people and that's where the deals happen it's not like in the big you know huge auditorium where like nobody looks at each other and everyone's on their phone well I gotta tell you how do we know we the Entrepreneurship side is booming so I totally love the entrepreneurial side check check check access to capital new kinds of business model stuff economics so we reported on all that to me the big story is Wall Street in New York City has been kind of stuck the products kind of like our old is antiquated like the financial products and like that's why Goldman's coming out they got nothing what they don't have anything what are they got so you see in a stagnant they got a traditional product approximately nothing really like new fresh so you got in comes crypto just do a crypto washer so I think I see the New York crowd going this is something that is exciting and we could product ties potentially so I don't think they know yet what that is but I think some of the things that are going on you guys I like I like so I my dad's always the kind of barometer to this whole thing and he's like when are they gonna come out with like a Salesforce stock column for the blockchain right like some sort of application that it doesn't matter if you're like illegal if you're like in investment banking like some sort of pervasive application that just goes wild you have that yet what is that happening Jeremy Jeremy did the date was it's the Netscape moment if you will the moment that blotching technology becomes tangible and now and in retrospect a few years out we may decide that's great for all the young browsers is a browser the original browse for the Internet that was that moment may have already happened we don't really know it maybe it been something like a theory a more augered you know something where there's a use case but people haven't wrapped their heads around it yet but if that hasn't happened yet it's coming it's where we're on the cusp of it because people know what bitcoin is they've heard of the blockchain it is part of the zeitgeist now and and that cultural relevance it's so important for having that Netscape moment Jeremy Jeremy thanks so much to spend the time here on the ground on the water for our special cube coverage of blockchain week new york city consensus you had all kinds of different events you had the crypto house where we were at tons of fluidity conference all this stuff going on good to see you guys you look great thanks for sharing the update here and the cube special coverage I'm John Faria thanks for watching Thanks

(click) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Node Summit 2017 in San Francisco at the Mission Bay Convention Center. We've been coming here for years. A really active community, a lot of good mojo, about 800 developers here. About to the limits that the Mission Bay center can hold. Now we're excited to have our next guest. He just came off a panel. It's Jacob Groundwater. He's an engineering manager for Electron at Github. Jacob, welcome. >> Thank you, it's great to be here. >> So really interesting panel, Electron. I hadn't heard about Electron before, I was kind digging in a little bit while the panel was going on, but for the folks that aren't familiar, what is Electron? >> Yeah. Electron, there's a good chance that people who haven't even heard of it might already be using it. >> (chuckles) That's always a good thing. >> Yeah. Electron is a project that's started by Github and it's open source and you can use it to build desktop applications but with web technologies. We're leveraging the Google Chrome project to do a lot of that. And Node. And Node. Node.js is a big part of it as well. >> So build desktop apps using web technologies. >> Yep. >> And why would somebody want to do that? >> You know, I think at the root of that question, it's always the same answer which is just economics right now. Developers are in demand, software developers are in demand. The web is taking over and the web is becoming the most common skillset that people have. So you get a few benefits by using Electron. You get to distribute to three platforms automatically, you get Linux, Mac, and Windows. Sometimes it's like super easy. Sometimes you do a little bit of building to get that to happen, but it's, you know, you could cut your team size down by maybe two thirds if you do it that way. >> Wow, that's a pretty significant cut. Now you said one 1.0 released year, and how's the, how's the adoption? >> I actually can't even keep up with the number of applications that are being published on top of Electron. I'm often surprised, I'll go to a company and I'll say, oh I work on Electron at Github. And they'll be like, oh we're developing an Electron app, or we're working on an Electron app. So it, it's kind of unreal. Like I've never really been in this situation before where something that I'm working on is being used so much. I think it's out, it's out there, it's in production, it's running in millions of laptops and desktops. >> Yeah. That's great though, 'cause that's the whole promise of software, right? That's why people want to get into software. >> Yeah. >> 'Cause you can actually write something that people use and you can change the world. It could be distributed all over the world with millions of users before you even know it. >> There's this wonderful thought of like writing something once and then it running in millions of places potentially. I just love it. I love it. I think it's super cool. Yeah. So as it's grown what have been some of the main kind of concerns, issues, what are some of the things you're managing within that growth that's not pure technical? >> Yeah. That's a great question. One of the biggest things that I found interesting is when I got on our website and check the analytics, it's almost uniform across the globe. People are interested in it from everywhere. So there's challenges like, right now I had to set up a core meeting to talk about some of the like, updates to Electron and that had to be at midnight pacific time because we had to include the Prague time zone, Tokyo time zone, and Chennai in India. And we're trying to see if we can squeeze in someone from Australia. And just the global distributive nature of Electron, like people around the world are working on this and using it. >> Right. The other part you mentioned in the session, was the management of the community. And you made an interesting, you know, we go to a lot of conferences, everyone's got their code of conduct published these days which is kind of sad. It's good, but it's kind of sad that people don't have basic manners it seems like anymore. We've covered a lot of opensource communities. One that jumps to mind is OpenStack and watch that evolve over time and there's kind of community management issues that come up as these things grow. And you brought up, kind of an interesting paradigm, if you've got a great technical contributor who's just not a good person for, I don't know you didn't really define kind of the negative side but got some issues that may impact the cohesiveness of the community going forward, especially because community is so important in these projects. But if you got a great technical mind, I never really heard that particular challenge. >> I think it comes up a lot more than people realize. And it's something that I think about a lot. And one thing I want to focus on is, what we're really zeroing in on is bad behavior. >> Bad behavior. That was the word. >> And not a bad person. >> Right, right. >> One of the best ways to, to maybe get around that happening is to set an expectation early about what is acceptable behavior and alert people early when they're doing things that are going to cause harm to the community or cause harm to others. And also frame it in a way where they know, we're trying to keep other people safe, but we're also trying to keep those offenders, give them the space to change. If you choose not to change, that's a whole different story. So I think that by keeping the community strong, we encourage people around the globe to work on this project and we've already seen great returns by doing this far, so that's why I'm really focused on keeping it, keeping it a place where you know you can come and show up and do your work and do your best work. >> Right. Right. Well hopefully that's not taking too many of your cycles, you don't got too many of those, of those characters. >> Every hour I put in, I get like 10s and 20, like hours and hours back in return from the people who give back. So it's well worth it. It's the best use of my time. >> Alright good. So great growth over the year. As you look forward to next calendar year, kind of what are some of your priorities? What are some of the community's priorities? Where is Electron going? And if we touch base a year from now, what are we going to be talking about? >> Excellent question. So strengthening, formalizing some aspects of the community that we have so far, it's a little ad hoc, would be great. We want to look to having people outside of Github that feel more ownership over the project. For example, we have contributors who probably should be reviewing and committing code on their own, without necessarily needing to loop in someone from my team. So really turning this into a community project. In addition, we are focusing up on what might go into a version 2 release. And we're really focusing on security as a key feature in version two. >> Yeah, security's key and it's got to be baked in all the way to the bottom. >> Yeah. >> Alright Jacob, well it sounds like you've got your work cut out for you >> Thank you. and it should be an exciting year. >> Yeah, thanks very much. >> Alright. He's Jacob Groundwater. He's from the Electron project at Github. I'm Jeff Frick. You're watching theCUBE. We'll see you next time. Thanks for watching. (sharp music)

>> Announcer: Live, from Boston, Massachusetts, it's The Cube, covering OpenStack Summit 2017. Brought to you by the OpenStack Foundation, Red Hat, and additional ecosystem support. (upbeat techno music) >> Welcome back, I'm Stu Miniman, joined by my co-host for the week, John Troyer. We've been talking this week about how OpenStack, there's real clouds, there's real deployments. I'm happy to welcome to the program two people that have done this with Adobe Advertising Cloud. We have Joseph Sandoval, who is the engineering manager at Adobe Advertising Cloud, and Nicolas Brousse, who is director of operations engineering. Gentlemen, thanks so much for joining us. >> Thank you for letting us join. >> Thank you. >> Nicolas, I'm sorry Joseph, we actually had you on the program at the Silicon Valley OpenStack Days a little while ago. Refresh our audience, though, a little bit, your background, how OpenStack fits in with your role, and what you do. >> Sure. Now, I've been in, a long time, in the OpenStack community, at that time when I was at the Silicon Valley event, I was with Lithium Technologies, so we also were an OpenStack user, but we were also kind of going through some transformation, I think, I would say we kind of really pushed the Kubernetes button for the community at that time. So I think I kind of got a little rep about being kind of like an agitator in this community to try to make the product really, you know, work for people who are actually consuming it. >> Right, so not only have you deployed OpenStack, you've done it at two different jobs now already. >> Joseph: Yes. >> People think we're still so early there, but we're already seeing that progression. Alright, Joseph, a little bit of background, yourself, what brought you to the current role in interaction with OpenStack? >> Yeah, so it's Nicolas. >> I'm sorry, yeah Nicolas, sorry, yeah. >> It's okay. >> I come from this startup company called TubeMogul that got acquired by Adobe last year, and one of our challenges as a startup was to be able to scale our cloud infrastructure and our infrastructure in general. We were a newer user of a public cloud at that time, but over the years we faced multiple challenges, not only as a cost challenge, where it gets like easily out of control with public clouds, but also technical challenge. We were in like an IP goals environment with very lean team operation. So we had to figure out a way where we can scale some of our technology and some of our platforms. And so my first technical prime was to have a reasonable cost control. And so we started to look at different cloud solutions. At the time it was like Eucalyptus, CloudStack, Open Nebula, and we tried many of these to get control, to get some time to figure out what was the solution. And we moved quickly to OpenStack and started to implement and get like some known, couple of here a journey to implement that and scale a little faster too. >> Stu, I want to point out something. In that story, at least what I took away from it, usually when you have a problem state of a lean team and you're trying to hyper growth >> Stu: In scaling. >> In scaling, the answer is public cloud. Oh, we'll just go to the public cloud, that'll solve all that problem. You chose a different way and chose a different architecture. >> Nicholas: Correct. >> Anything that brought you to that decision? >> Yeah, so there was a few factors. First one was like, well cost growth on public clouds was growing faster than the revenue in some ways, so that doesn't line up. You need to have a story that makes more sense. And the second one was really like technical. We had some very specific challenge where we're in the real-time bidding advertising, so we have a huge amount of traffic. We do want to try billions, HTTP request on the platform. All of those need to be answered in a few milliseconds, so the proximity of our partner, you can always see that as a smaller stock exchange for advertising. So we need to be close to our partner so all this auction process is happening very quickly. And we have to store huge amount of data. Any of the solution you will find on the public cloud will end up having like 50 minutes ago that's 50 milliseconds that doesn't necessarily fit our use case. >> Yeah, just maybe you can bring us inside the architecture a little bit. >> Joseph: Sure. >> Talk about, look, public cloud isn't simple, obviously costs people, you know, we understand that and there's the debate as to where those pieces fit. But you know, OpenStack, speak a little bit to how it is to put that together. Simplicity is not usually what we hear when we talk, but what worked, what didn't work, what did you have to kind of customize to kind of get things working? >> You know, I think the one thing is just coming through like, you know, two different implementations is that, yeah there is complexity. And what I really got out of this was that you know, you really just have to consume the things that you need, so we've been very lean about the APIs that we consume, what services that we think are meaningful to our business. Instead of taking really all as a service type parts of this framework, we really narrowed it down to what matched our business requirements. I think as well as kind of like how you're consuming, and I think if you noticed the keynote on Monday, all of a sudden we're seeing this new pivot of like, let us manage your cloud. And it still kind of speaks to some of the challenges that you know, the end users of OpenStack have. And I think the part that's really important for anyone that's really going on this journey is that, you know, it's how you decide to consume it, like can you start really running it within like a CICD model so that you're really getting into that dev-ops aspect of it. Even within Amazon, I think in my journey, that's one thing that I think a lot of people miss is that when they try to lift and shift, like they want to race to the public cloud, you're going to still be challenged because you haven't really fundamentally changed how you're consuming the cloud product. You're not making yourself cloud native. And I think in my journey, I've made those same mistakes. I've learned from it enough. I'm actually really realizing that it's almost bigger than OpenStack. It's almost like how as business you operate and how your teams fundamentally build their tools and how they kind of like make open source a true strategy. >> I'd love to hear about the applications that you're using in this environment. We hear it in some of the keynotes on some of the users, you know, rapid move from where they started to adding applications. You mentioned cloud native. What are the class of applications, what percentage of your business runs on that? >> Sure, yeah so the code name we've given our platform is CloudMogul. And really it comprises bare metal, primarily OpenStack, and yet we still also use Amazon, so we have all different frameworks in there, depending on the type of, you know, workload that's there. As far as like OpenStack specifically, we really just consume the court. It's compute, storage, and network. Storage is probably a little bit secondary for us, the way we have designed our platform. Network is the really key thing. And as Nicholas mentioned earlier, I mean, that's the thing that in Amazon, you'll see great choices for compute, great choices for memory, but if you try to find an affordable network, you know, intensive instance, and that's what you know, we have decided why we're doing the data center. So we really have stuck really with just the core OpenStack services. Currently our developers are looking at now rolling out Kubernetes, and they're kind of doing it in a more, you know, dev POC. And as well as we're trying to balance out like the broader Adobe strategies, like they want to move to multi-cloud, they want to use Azure. So there's quite a bit that we're trying to consume, but with the lean team, we have to really be judicious about what we decide to roll in. >> Nicholas, can you comment maybe on the applications you mentioned some of the costs. The keynote, cost compliance capabilities, does that resonate with you, and how do you choose between the public and >> I think it's more like to get back to this lean operation, it would drive like some of our info on it, like we're a technology company in some way. I mean, we are building software, we are building certain solutions. You know, our goal is to develop like an advertising solution and trend solutions at several customer. So we're on a tier to be like a storage solution for OpenStack or compute solution for OpenStack or public cloud. So we really had to focus on what is selling or best use case or solve one problem, as that's where we had like to really look at cutting the fat in some way on OpenStack and really just looking at what is going to be the best use case for us. So we liberate OpenStack for most of our bidding system and manage all those calculating for the VMs, but we also integrate that very easily with like a flat network designed with open maker, so we are about to really like get the best of both worlds, between like Permital, OpenStack, and virtualization, and know we are also like implementing like reverting on the, be able to offload some of the workload back to Amazon in Ozone, we are starting to look at Ozone like a cloud provider but for trying to revert like what's the best and consider like all the terms we have. >> Can you give us a little insight to that cloud bursting is a term that, you know, gets attention because data's tough to move, you know, where the application lives, is that you know, container, Kubernetes stuff that you're doing, expand that a little for us. >> So it's definitely challenging. It's not something that, and then we got a very quick iteration and we have been able to liberate it easily first because we are like a very simple design on the way we were managing our kernel environment on OpenStack prime mount. So it was to very easily integrate, have a direct connect to a VPC on Amazon and just offload some of the compute of these onto this VPC. So a challenge we had to learn is we are trying to understand we're in the workload and that was in iteration, when we did move back to in house, understanding like the network traffic you are getting and understanding like the back and forth between your backend and your frontend. That's something you don't really see or understand easily on public cloud. When you move back in house, then you start to see the bottlenecks and you start to learn about what is really your workload, and we are to do this again, like with cloud bursting, okay, what kind of back and forth are going between our compute services and all the backend service that it needs to access. And latency being very critical for us, we had to really measure that. >> Yeah, you never know til you try it, right? >> Exactly. >> You crawl, walk, run. Hey Joseph, you talked about CICD and rate of change. I'm kind of curious how you're seeing the rate of change of your infrastructure stack, so OpenStack, versus you said you're now kind of experimenting with Kubernetes containers in the talk. A lot of talk about containers here at the show. For me, it's becoming a little more clear where in the architectural pie, layer cake, that that, pie, layer cake, that that fits in. Can you talk about rate of change? Are you looking at, does your infrastructure need to change at the same rate as the application on top of it, or how are you all looking at it? >> You know, in just beginning this journey, the one thing that I've really took away and that was one guy on my team when I was at Lithium, where he would always talk about like really meeting your developers where they're at. And yes, there's so much change, and you have to really kind of balance it. And you know, some of these companies we've been with, we've had some software stacks that are almost a decade old. They're just not made with cloud nativeness in mind. And that's where, you know, I've always been a really like let's move forward, and that was one of the early individuals saying, you know, I was at OpenStack Prague and we were doing, you know, Kubernetes under the control plane. In hindsight I was like, well, it was a little kind of premature. It was almost a little reckless. But I think that the thing that I'm trying to do now is really just try to leverage like where our product's at. Can I help evolve the platform so that, are we 12-factored, can we get there? You know, we have big data kind of workloads. How do we like start taking frameworks that allow us so that you know, we can be in this multi-cloud world. So I think there is a challenge, you know, you're hearing all these new great things that are happening. You know, you're coming to these summits, and you're getting all this hype. But then you really got to walk away. And I just kind of do that sniff test, testing something out to see like, is it really ready? And especially with where we're at in enterprise, you know, we really have to map to security compliance. And I think those are some of the gates that we're challenged with, as well as like, is the workload that we're bringing in, have we adapted it enough so that we can really kind of push what we're doing. Cause I'd love to see us get to the point where we have the frameworks of containers and Kubernetes. But not everything for us can get there. You know, so like on the edge, we're doing billions of requests per second. Bare metal is the key thing for us. And we're running HA proxy on the edge. So the key thing for us is like, run it as code, let's count how much can we do to get this so that we can fully automate this and make it repeatable. And I think that's kind of the core ethos for the team. >> You talked about coming to different summits over the years, kind of the sniff test. What's the mood of the attendees here at OpenStack summit here in Boston this year in 2017 and is it different from previous years? >> You know, I think we seen kind of some interesting ebb and flows. I think when I was in Barcelona, it was definitely different. I was kind of like surprised, it just felt like it was a little bit less energy. Austin I thought was tremendous, it was a great event. And I kind of feel like, I think there's a little bit more pragmatism that set in, which I think is really healthy and a sign of maturity that you know, people are really kind of understanding instead of getting caught up in that, the cloud hype, you know, public versus private and all these things. I think now we're starting to see a more mature audience. I think OpenStack foundation and the community has also kind of adapted as well. I know they try to be everything for everybody under the cloud in a data center. And I think now we're actually seeing a more healthy approach, so for me I think there's still a lot of energy there. Maybe it's getting a little boring, which to me in my world, that's a good thing. >> Nicholas, I'm curious, do you either at this show or at other events, how are you working with your peers in the industry to understand that kind of hybrid multi-cloud model and sort that out, you know, resources you go to, conversations you have, you know, how do you create that learning? >> So, first it's I come from the culture that's from the startup to Mogul that got a prior where we're ready for costs on the customer and the end goal of what we are trying to build. And we are not necessarily driven by the technology itself, we really try to devise technology to solve a problem. We have a lot of geek on our team, and that's what drives some of our discussion. But we're really more trying to look at how we drive the product for our world. And that's really like most of the discussion, even with our product, like we started a year ago to use the Fastly file pen to sort some specific problem where we can't have like a global footprint as much as the city and provider. And they were able to address like some of a specific use case, where they can do like a synchronous looking for us. And that was something like a specific business case for us, and every time we go like to an event or technology, we are trying to see like what are we trying to solve? And that's what drives most of our discussions. >> Joseph, sounds like you've given feedback and been on some of the leading edge of some of the activities. Is there anything you look at where you're hoping for a little bit more maturity, either OpenStack in general or the vendor community out there, you know, what are you hoping to see, you know, as we mature this even further? >> Sure, I mean I would say one thing about, you know, the OpenStack community. And I know this was always kind of one of my early beefs about it. It felt so vendor-centric, and very vendor-influenced that it just didn't really for me feel like the actual consumers, the individuals who really are using these platforms are really being heard. So I think they need to still kind of really force it, really listen to that feedback from the community, what's working, what's not working. As far as what I'd love to see, is you know, I think there's been a little bit more of like a correction I guess in a sense of like all the kind of like services that were out there, these side projects. I think there was a lot of messaging about like let's all work together, which I think is kind of, I just kind of wince a little bit. But I'm like, it's good, I'm glad that they've kind of come to this recognition. I'd love to see more and more of that. But I also want to make sure that the OpenStack community, like stay distinct. I'm not sure if I 100% think like, leveraging off the Kubernetes community, like yes, work together, let's make these things, you know, coexist and stuff. But I do hear some things where like, hey, we should just make this service be the backend for Kubernetes. I'm like, hmm. I don't think you've really looked at the framework of some of these APIs and how they're going to integrate in that environment. And I actually would like to see them develop, you know, distinctly, but you know, find some really friendly integration points so that me as a consumer, I can like easily use these as we evolve and our platform evolves, I can easily kind of start roadmapping these into our platform. >> Alright, Nicholas and Justin, really appreciate you giving us the update, and we'd love to get that real practitioner viewpoint. John and I will be back with more coverage here from OpenStack 2017 in Boston. You're watching The Cube. (upbeat techno music)