>> Announcer: theCUBE's live coverage is made possible by funding from Dell Technologies creating technologies that drive human progress. (upbeat music) >> Hey everyone, welcome back to theCube's first day of coverage of MWC 23 from Barcelona, Spain. Lisa Martin here with Dave Vellante and Dave Nicholson. I'm literally in between two Daves. We've had a great first day of coverage of the event. There's been lots of conversations, Dave, on disaggregation, on the change of mobility. I want to be able to get your perspectives from both of you on what you saw on the show floor, what you saw and heard from our guests today. So we'll start with you, Dave V. What were some of the things that were our takeaways from day one for you? >> Well, the big takeaway is the event itself. On day one, you get a feel for what this show is like. Now that we're back, face-to-face kind of pretty much full face-to-face. A lot of excitement here. 2000 plus exhibitors, I mean, planes, trains, automobiles, VR, AI, servers, software, I mean everything. I mean, everybody is here. So it's a really comprehensive show. It's not just about mobile. That's why they changed the name from Mobile World Congress. I think the other thing is from the keynotes this morning, I mean, you heard, there's a lot of, you know, action around the telcos and the transformation, but in a lot of ways they're sort of protecting their existing past from the future. And so they have to be careful about how fast they move. But at the same time if they don't move fast, they're going to get disrupted. We heard some complaints, essentially, you know, veiled complaints that the over the top guys aren't paying their fair share and Telco should be able to charge them more. We heard the chairman of Ericsson talk about how we can't let the OTTs do that again. We're going to charge directly for access through APIs to our network, to our data. We heard from Chris Lewis. Yeah. They've only got, or maybe it was San Ji Choha, how they've only got eight APIs. So, you know the developers are the ones who are going to actually build out the innovation at the edge. The telcos are going to provide the connectivity and the infrastructure companies like Dell as well. But it's really to me all about the developers. And that's where the action's going to be. And it's going to be interesting to see how the developers respond to, you know, the gun to the head. If you want access, you're going to have to pay for it. Now maybe there's so much money to be made that they'll go for it, but I feel like there's maybe a different model. And I think some of the emerging telcos are going to say, you know what, here developers, here's a platform, have at it. We're not going to charge you for all the data until you succeed. Then we're going to figure out a monetization model. >> Right. A lot of opportunity for the developer. That skillset is certainly one that's in demand here. And certainly the transformation of the telecom industry is, there's a lot of conundrums that I was hearing going on today, kind of chicken and egg scenarios. But Dave, you had a chance to walk around the show floor. We were here interviewing all day. What were some of the things that you saw that really stuck out to you? >> I think I was struck by how much attention was being paid to private 5G networks. You sort of read between the lines and it appears as though people kind of accept that the big incumbent telecom players are going to be slower to move. And this idea of things like open RAN where you're leveraging open protocols in a stack to deliver more agility and more value. So it sort of goes back to the generalized IT discussion of moving to cloud for agility. It appears as though a lot of players realize that the wild wild west, the real opportunity, is in the private sphere. So it's really interesting to see how that works, how 5G implemented into an environment with wifi how that actually works. It's really interesting. >> So it's, obviously when you talk to companies like Dell, I haven't hit HPE yet. I'm going to go over there and check out their booth. They got an analyst thing going on but it's really early days for them. I mean, they started in this business by taking an X86 box, putting a name on it, you know, that sounded like it was edged, throwing it over, you know, the wall. That's sort of how they all started in this business. And now they're, you know, but they knew they had to form partnerships. They had to build purpose-built systems. Now with 16 G out, you're seeing that. And so it's still really early days, talking about O RAN, open RAN, the open RAN alliance. You know, it's just, I mean, not even, the game hasn't even barely started yet but we heard from Dish today. They're trying to roll out a massive 5G network. Rakuten is really focused on sort of open RAN that's more reliable, you know, or as reliable as the existing networks but not as nearly as huge a scale as Dish. So it's going to take a decade for this to evolve. >> Which is surprising to the average consumer to hear that. Because as far as we know 5G has been around for a long time. We've been talking about 5G, implementing 5G, you sort of assume it's ubiquitous but the reality is it is just the beginning. >> Yeah. And you know, it's got a fake 5G too, right? I mean you see it on your phone and you're like, what's the difference here? And it's, you know, just, >> Dave N.: What does it really mean? >> Right. And so I think your point about private is interesting, the conversation Dave that we had earlier, I had throughout, hey I don't think it's a replacement for wifi. And you said, "well, why not?" I guess it comes down to economics. I mean if you can get the private network priced close enough then you're right. Why wouldn't it replace wifi? Now you got wifi six coming in. So that's a, you know, and WiFi's flexible, it's cheap, it's good for homes, good for offices, but these private networks are going to be like kickass, right? They're going to be designed to run whatever, warehouses and robots, and energy drilling facilities. And so, you know the economics I don't think are there today but maybe they can be at volume. >> Maybe at some point you sort of think of today's science experiment becoming the enterprise-grade solution in the future. I had a chance to have some conversations with folks around the show. And I think, and what I was surprised by was I was reminded, frankly, I wasn't surprised. I was reminded that when we start talking about 5G, we're talking about spectrum that is managed by government entities. Of course all broadcast, all spectrum, is managed in one way or another. But in particular, you can't simply put a SIM in every device now because there are a lot of regulatory hurdles that have to take place. So typically what these things look like today is 5G backhaul to the network, communication from that box to wifi. That's a huge improvement already. So yeah, my question about whether, you know, why not put a SIM in everything? Maybe eventually, but I think, but there are other things that I was not aware of that are standing in the way. >> Your point about spectrum's an interesting one though because private networks, you're going to be able to leverage that spectrum in different ways, and tune it essentially, use different parts of the spectrum, make it programmable so that you can apply it to that specific use case, right? So it's going to be a lot more flexible, you know, because I presume the needs spectrum needs of a hospital are going to be different than, you know, an agribusiness are going to be different than a drilling, you know, unit, offshore drilling unit. And so the ability to have the flexibility to use the spectrum in different ways and apply it to that use case, I think is going to be powerful. But I suspect it's going to be expensive initially. I think the other thing we talked about is public policy and regulation, and it's San Ji Choha brought up the point, is telcos have been highly regulated. They don't just do something and ask for permission, you know, they have to work within the confines of that regulated environment. And there's a lot of these greenfield companies and private networks that don't necessarily have to follow those rules. So that's a potential disruptive force. So at the same time, the telcos are spending what'd we hear, a billion, a trillion and a half over the next seven years? Building out 5G networks. So they got to figure out, you know how to get a payback on that. They'll get it I think on connectivity, 'cause they have a monopoly but they want more. They're greedy. They see the over, they see the Netflixes of the world and the Googles and the Amazons mopping up services and they want a piece of that action but they've never really been good at it. >> Well, I've got a question for both of you. I mean, what do you think the odds are that by the time the Shangri La of fully deployed 5G happens that we have so much data going through it that effectively it feels exactly the same as 3G? What are the odds? >> That's a good point. Well, the thing that gets me about 5G is there's so much of it on, if I go to the consumer side when we're all consumers in our daily lives so much of it's marketing hype. And, you know all the messaging about that, when it's really early innings yet they're talking about 6G. What does actual fully deployed 5G look like? What is that going to enable a hospital to achieve or an oil refinery out in the middle of the ocean? That's something that interests me is what's next for that? Are we going to hear that at this event? >> I mean, walking around, you see a fair amount of discussion of, you know, the internet of things. Edge devices, the increase in connectivity. And again, what I was surprised by was that there's very little talk about a sim card in every one of those devices at this point. It's like, no, no, no, we got wifi to handle all that but aggregating it back into a central network that's leveraging 5G. That's really interesting. That's really interesting. >> I think you, the odds of your, to go back to your question, I think the odds are even money, that by the time it's all built out there's going to be so much data and so much new capability it's going to work similarly at similar speeds as we see in the networks today. You're just going to be able to do so many more things. You know, and your video's going to look better, the graphics are going to look better. But I think over the course of history, this is what's happening. I mean, even when you go back to dial up, if you were in an AOL chat room in 1996, it was, you know, yeah it took a while. You're like, (screeches) (Lisa laughs) the modem and everything else, but once you were in there- >> Once you're there, 2400 baud. >> It was basically real time. And so you could talk to your friends and, you know, little chat room but that's all you could do. You know, if you wanted to watch a video, forget it, right? And then, you know, early days of streaming video, stop, start, stop, start, you know, look at Amazon Prime when it first started, Prime Video was not that great. It's sort of catching up to Netflix. But, so I think your point, that question is really prescient because more data, more capability, more apps means same speed. >> Well, you know, you've used the phrase over the top. And so just just so we're clear so we're talking about the same thing. Typically we're talking about, you've got, you have network providers. Outside of that, you know, Netflix, internet connection, I don't need Comcast, right? Perfect example. Well, what about the over the top that's coming from direct satellite communications with devices. There are times when I don't have a signal on my, happens to be an Apple iPhone, when I get a little SOS satellite logo because I can communicate under very limited circumstances now directly to the satellite for very limited text messaging purposes. Here at the show, I think it might be a Motorola device. It's a dongle that allows any mobile device to leverage direct satellite communication. Again, for texting back to the 2,400 baud modem, you know, days, 1200 even, 300 even, go back far enough. What's that going to look like? Is that too far in the future to think that eventually it's all going to be over the top? It's all going to be handset to satellite and we don't need these RANs anymore. It's all going to be satellite networks. >> Dave V.: I think you're going to see- >> Little too science fiction-y? (laughs) >> No, I, no, I think it's a good question and I think you're going to see fragments. I think you're going to see fragmentation of private networks. I think you're going to see fragmentation of satellites. I think you're going to see legacy incumbents kind of hanging on, you know, the cable companies. I think that's coming. I think by 2030 it'll, the picture will be much more clear. The question is, and I think it's come down to the innovation on top, which platform is going to be the most developer friendly? Right, and you know, I've not heard anything from the big carriers that they're going to be developer friendly. I've heard "we have proprietary data that we're going to charge access for and developers are going to have to pay for that." But I haven't heard them saying "Developers, developers, developers!" You know, Steve Bomber running around, like bend over backwards for developers, they're asking the developers to bend over. And so if a network can, let's say the satellite network is more developer friendly, you know, you're going to see more innovation there potentially. You know, or if a dish network says, "You know what? We're going after developers, we're going after innovation. We're not going to gouge them for all this network data. Rather we're going to make the platform open or maybe we're going to do an app store-like model where we take a piece of the action after they succeed." You know, take it out of the backend, like a Silicon Valley VC as opposed to an East Coast VC. They're not going to get you in the front end. (Lisa laughs) >> Well, you can see the sort of disruptive forces at play between open RAN and the legacy, call it proprietary stack, right? But what is the, you know, if that's sort of a horizontal disruptive model, what's the vertically disruptive model? Is it private networks coming in? Is it a private 5G network that comes in that says, "We're starting from the ground up, everything is containerized. We're going to go find people at KubeCon who are, who understand how to orchestrate with Kubernetes and use containers in microservices, and we're going to have this little 5G network that's going to deliver capabilities that you can't get from the big boys." Is there a way to monetize that? Is there a way for them to be disrupted, be disruptive, or are these private 5G networks that everybody's talking about just relegated to industrial use cases where you're just squeezing better economics out of wireless communication amongst all your devices in your factory? >> That's an interesting question. I mean, there are a lot of those smart factory industrial use cases. I mean, it's basically industry 4.0 use cases. But yeah, I don't count the cloud guys out. You know, everybody says, "oh, the narrative is, well, the latency of the cloud." Well, not if the cloud is at the edge. If you take a local zone and put storage, compute, and data right next to each other and the cloud model with the cloud APIs, and then you got an asynchronous, you know, connection back. I think that's a reasonable model. I think the cloud guys figured out developers, right? Pretty well. Certainly Microsoft and, and Amazon and Google, they know developers. I don't see any reason why they can't bring their model to the edge. So, and that's really disruptive to the legacy telco guys, you know? So they have to be careful. >> One step closer to my dream of eliminating the word "cloud" from IT lexicon. (Lisa laughs) I contend that it has always been IT, and it will always be IT. And this whole idea of cloud, what is cloud? If AWS, for example, is delivering hardware to the edge where it needs to be, is that cloud? Do we go back to the idea that cloud is an operational model and not a question of physical location? I hope we get to that point. >> Well, what's Apex and GreenLake? Apex is, you know, Dell's as a service. GreenLake is- >> HPE. >> HPE's as a service. That's outposts. >> Dave N.: Right. >> Yeah. >> That's their outpost. >> Yeah. >> Well AWS's position used to be, you know, to use them as a proxy for hyperscale cloud. We'll just, we'll grow in a very straight trajectory forever on the back of net new stuff. Forget about the old stuff. As James T. Kirk said of the Klingons, "let them die." (Lisa laughs) As far as the cloud providers were concerned just, yeah, let, let that old stuff go away. Well then they found out, there came a point in time where they realized there's a lot of friction and stickiness associated with that. So they had to deal with the reality of hybridity, if that's the word, the hybrid nature of things. So what are they doing? They're pushing stuff out to the edge, so... >> With the same operating model. >> With the same operating model. >> Similar. I mean, it's limited, right? >> So you see- >> You can't run a lot of database on outpost, you can run RES- >> You see this clash of Titans where some may have written off traditional IT infrastructure vendors, might have been written off as part of the past. Whereas hyperscale cloud providers represent the future. It seems here at this show they're coming head to head and competing evenly. >> And this is where I think a company like Dell or HPE or Cisco has some advantages in that they're not going to compete with the telcos, but the hyperscalers will. >> Lisa: Right. >> Right. You know, and they're already, Google's, how much undersea cable does Google own? A lot. Probably more than anybody. >> Well, we heard from Google and Microsoft this morning in the keynote. It'd be interesting to see if we hear from AWS and then over the next couple of days. But guys, clearly there is, this is a great wrap of day one. And the crazy thing is this is only day one. We've got three more days of coverage, more news, more information to break down and unpack on theCUBE. Look forward to doing that with you guys over the next three days. Thank you for sharing what you saw on the show floor, what you heard from our guests today as we had about 10 interviews. Appreciate your insights and your perspectives and can't wait for tomorrow. >> Right on. >> All right. For Dave Vellante and Dave Nicholson, I'm Lisa Martin. You're watching theCUBE's day one wrap from MWC 23. We'll see you tomorrow. (relaxing music)

(upbeat instrumental music) >> TheCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (uplifting instrumental music) >> Hey everyone. Welcome to Barcelona, Spain. It's theCUBE Live at MWC '23. I'm Lisa Martin, Dave Vellante, our co-founder, our co-CEO of theCUBE, you know him, you love him. He's here as my co-host. Dave, we have a great couple of guests here to break down day one keynote. Lots of meat. I can't wait to be part of this conversation. Chris Lewis joins us, the founder and MD of Lewis Insight. And Sarbjeet Johal, one of you know him as well. He's a Cube contributor, cloud architect. Guys, welcome to the program. Thank you so much for joining Dave and me today. >> Lovely to be here. >> Thank you. >> Chris, I want to start with you. You have covered all aspects of global telecoms industries over 30 years working as an analyst. Talk about the evolution of the telecom industry that you've witnessed, and what were some of the things you heard in the keynote that excite you about the direction it's going? >> Well, as ever, MWC, there's no lack of glitz and glamour, but it's the underlying issues of the industry that are really at stake here. There's not a lot of new revenue coming into the telecom providers, but there's a lot of adjustment, readjustment of the underlying operational environment. And also, really importantly, what came out of the keynotes is the willingness and the necessity to really engage with the API community, with the developer community, people who traditionally, telecoms would never have even touched. So they're sorting out their own house, they're cleaning their own stables, getting the cost base down, but they're also now realizing they've got to engage with all the other parties. There's a lot of cloud providers here, there's a lot of other people from outside so they're realizing they cannot do it all themselves. It's quite a tough lesson for a very conservative, inward looking industry, right? So should we be spending all this money and all this glitz and glamour of MWC and all be here, or should would be out there really building for the future and making sure the services are right for yours and my needs in a business and personal lives? So a lot of new changes, a lot of realization of what's going on outside, but underlying it, we've just got to get this right this time. >> And it feels like that monetization is front and center. You mentioned developers, we've got to work with developers, but I'm hearing the latest keynote from the Ericsson CEOs, we're going to monetize through those APIs, we're going to charge the developers. I mean, first of all, Chris, am I getting that right? And Sarbjeet, as somebody who's close to the developer community, is that the right way to build bridges? But Chris, are we getting that right? >> Well, let's take the first steps first. So, Ericsson, of course, acquired Vonage, which is a massive API business so they want to make money. They expect to make money by bringing that into the mainstream telecom community. Now, whether it's the developers who pay for it, or let's face it, we are moving into a situation as the telco moves into a techco model where the techco means they're going to be selling bits of the technology to developer guys and to other application developers. So when he says he needs to charge other people for it, it's the way in which people reach in and will take going through those open APIs like the open gateway announced today, but also the way they'll reach in and take things like network slicing. So we're opening up the telecom community, the treasure chest, if you like, where developers' applications and other third parties can come in and take those chunks of technology and build them into their services. This is a complete change from the old telecom industry where everybody used to come and you say, "all right, this is my product, you've got to buy it and you're going to pay me a lot of money for it." So we are looking at a more flexible environment where the other parties can take those chunks. And we know we want collectivity built into our financial applications, into our government applications, everything, into the future of the metaverse, whatever it may be. But it requires that change in attitude of the telcos. And they do need more money 'cause they've said, the baseline of revenue is pretty static, there's not a lot of growth in there so they're looking for new revenues. It's in a B2B2X time model. And it's probably the middle man's going to pay for it rather than the customer. >> But the techco model, Sarbjeet, it looks like the telcos are getting their money on their way in. The techco company model's to get them on their way out like the app store. Go build something of value, build some kind of app or data product, and then when it takes off, we'll take a piece of the action. What are your thoughts from a developer perspective about how the telcos are approaching it? >> Yeah, I think before we came here, like I said, I did some tweets on this, that we talk about all kind of developers, like there's game developers and front end, back end, and they're all talking about like what they're building on top of cloud, but nowhere you will hear the term "telco developer," there's no API from telcos given to the developers to build IoT solutions on top of it because telco as an IoT, I think is a good sort of hand in hand there. And edge computing as well. The glimmer of hope, if you will, for telcos is the edge computing, I believe. And even in edge, I predicted, I said that many times that cloud players will dominate that market with the private 5G. You know that story, right? >> We're going to talk about that. (laughs) >> The key is this, that if you see in general where the population lives, in metros, right? That's where the world population is like flocking to and we have cloud providers covering the local zones with local like heavy duty presence from the big cloud providers and then these telcos are getting sidetracked by that. Even the V2X in cars moving the autonomous cars and all that, even in that space, telcos are getting sidetracked in many ways. What telcos have to do is to join the forces, build some standards, if not standards, some consortium sort of. They're trying to do that with the open gateway here, they have only eight APIs. And it's 2023, eight APIs is nothing, right? (laughs) So they should have started this 10 years back, I think. So, yeah, I think to entice the developers, developers need the employability, we need to train them, we need to show them some light that hey, you can build a lot on top of it. If you tell developers they can develop two things or five things, nobody will come. >> So, Chris, the cloud will dominate the edge. So A, do you buy it? B, the telcos obviously are acting like that might happen. >> Do you know I love people when they've got their heads in the clouds. (all laugh) And you're right in so many ways, but if you flip it around and think about how the customers think about this, business customers and consumers, they don't care about all this background shenanigans going on, do they? >> Lisa: No. >> So I think one of the problems we have is that this is a new territory and whether you call it the edge or whatever you call it, what we need there is we need connectivity, we need security, we need storage, we need compute, we need analytics, and we need applications. And are any of those more important than the others? It's the collective that actually drives the real value there. So we need all those things together. And of course, the people who represented at this show, whether it's the cloud guys, the telcos, the Nokia, the Ericssons of this world, they all own little bits of that. So that's why they're all talking partnerships because they need the combination, they cannot do it on their own. The cloud guys can't do it on their own. >> Well, the cloud guys own all of those things that you just talked about though. (all laugh) >> Well, they don't own the last bit of connectivity, do they? They don't own the access. >> Right, exactly. That's the one thing they don't own. So, okay, we're back to pipes, right? We're back to charging for connectivity- >> Pipes are very valuable things, right? >> Yeah, for sure. >> Never underestimate pipes. I don't know about where you live, plumbers make a lot of money where I live- >> I don't underestimate them but I'm saying can the telcos charge for more than that or are the cloud guys going to mop up the storage, the analytics, the compute, and the apps? >> They may mop it up, but I think what the telcos are doing and we've seen a lot of it here already, is they are working with all those major cloud guys already. So is it an unequal relationship? The cloud guys are global, massive global scale, the telcos are fundamentally national operators. >> Yep. >> Some have a little bit of regional, nobody has global scale. So who stitches it all together? >> Dave: Keep your friends close and your enemies closer. >> Absolutely. >> I know that saying never gets old. It's true. Well, Sarbjeet, one of the things that you tweeted about, I didn't get to see the keynote but I was looking at your tweets. 46% of telcos think they won't make it to the next decade. That's a big number. Did that surprise you? >> No, actually it didn't surprise me because the competition is like closing in on them and the telcos are competing with telcos as well and the telcos are competing with cloud providers on the other side, right? So the smaller ones are getting squeezed. It's the bigger players, they can hook up the newer platforms, I think they will survive. It's like that part is like any other industry, if you will. But the key is here, I think why the pain points were sort of described on the main stage is that they're crying out loud to tell the big tech cloud providers that "hey, you pay your fair share," like we talked, right? You are not paying, you're generating so much content which reverses our networks and you are not paying for it. So they are not able to recoup the cost of laying down their networks. By the way, one thing actually I want to mention is that they said the cloud needs earth. The cloud and earth, it's like there's no physical need to cloud, you know that, right? So like, I think it's the other way around. I think the earth needs the cloud because I'm a cloud guy. (Sarbjeet and Lisa laugh) >> I think you need each other, right? >> I think so too. >> They need each other. When they said cloud needs earth, right? I think they're still in denial that the cloud is a big force. They have to partner. When you can't compete with somebody, what do you do? Partner with them. >> Chris, this is your world. Are they in denial? >> No, I think they're waking up to the pragmatism of the situation. >> Yeah. >> They're building... As we said, most of the telcos, you find have relationships with the cloud guys, I think you're right about the industry. I mean, do you think what's happened since US was '96, the big telecom act when we started breaking up all the big telcos and we had lots of competition came in, we're seeing the signs that we might start to aggregate them back up together again. So it's been an interesting experiment for like 30 years, hasn't it too? >> It made the US less competitive, I would argue, but carry on. >> Yes, I think it's true. And Europe is maybe too competitive and therefore, it's not driven the investment needed. And by the way, it's not just mobile, it's fixed as well. You saw the Orange CEO was talking about the her investment and the massive fiber investments way ahead of many other countries, way ahead of the UK or Germany. We need that fiber in the ground to carry all your cloud traffic to do this. So there is a scale issue, there is a competition issue, but the telcos are very much aware of it. They need the cloud, by the way, to improve their operational environments as well, to change that whole old IT environment to deliver you and I better service. So no, it absolutely is changing. And they're getting scale, but they're fundamentally offering the basic product, you call it pipes, I'll just say they're offering broadband to you and I and the business community. But they're stepping on dangerous ground, I think, when saying they want to charge the over the top guys for all the traffic they use. Those over the top guys now build a lot of the global networks, the backbone submarine network. They're putting a lot of money into it, and by giving us endless data for our individual usage, that cat is out the bag, I think to a large extent. >> Yeah. And Orange CEO basically said that, that they're not paying their fair share. I'm for net neutrality but the governments are going to have to fund this unless you let us charge the OTT. >> Well, I mean, we could of course renationalize. Where would that take us? (Dave laughs) That would make MWC very interesting next year, wouldn't it? To renationalize it. So, no, I think you've got to be careful what we wish for here. Creating the absolute clear product that is required to underpin all of these activities, whether it's IoT or whether it's cloud delivery or whether it's just our own communication stuff, delivering that absolutely ubiquitously high quality for business and for consumer is what we have to do. And telcos have been too conservative in the past. >> I think they need to get together and create standards around... I think they have a big opportunity. We know that the clouds are being built in silos, right? So there's Azure stack, there's AWS and there's Google. And those are three main ones and a few others, right? So that we are fighting... On the cloud side, what we are fighting is the multicloud. How do we consume that multicloud without having standards? So if these people get together and create some standards around IoT and edge computing sort of area, people will flock to them to say, "we will use you guys, your API, we don't care behind the scenes if you use AWS or Google Cloud or Azure, we will come to you." So market, actually is looking for that solution. I think it's an opportunity for these guys, for telcos. But the problem with telcos is they're nationalized, as you said Chris versus the cloud guys are still kind of national in a way, but they're global corporations. And some of the telcos are global corporations as well, BT covers so many countries and TD covers so many... DT is in US as well, so they're all over the place. >> But you know what's interesting is that the TM forum, which is one of the industry associations, they've had an open digital architecture framework for quite some years now. Google had joined that some years ago, Azure in there, AWS just joined it a couple of weeks ago. So when people said this morning, why isn't AWS on the keynote? They don't like sharing the limelight, do they? But they're getting very much in bed with the telco. So I think you'll see the marriage. And in fact, there's a really interesting statement, if you look at the IoT you mentioned, Bosch and Nokia have been working together 'cause they said, the problem we've got, you've got a connectivity network on one hand, you've got the sensor network on the other hand, you're trying to merge them together, it's a nightmare. So we are finally seeing those sort of groups talking to each other. So I think the standards are coming, the cooperation is coming, partnerships are coming, but it means that the telco can't dominate the sector like it used to. It's got to play ball with everybody else. >> I think they have to work with the regulators as well to loosen the regulation. Or you said before we started this segment, you used Chris, the analogy of sports, right? In sports, when you're playing fiercely, you commit the fouls and then ask for ref to blow the whistle. You're now looking at the ref all the time. The telcos are looking at the ref all the time. >> Dave: Yeah, can I do this? Can I do that? Is this a fair move? >> They should be looking for the space in front of the opposition. >> Yeah, they should be just on attack mode and commit these fouls, if you will, and then ask for forgiveness then- >> What do you make of that AWS not you there- >> Well, Chris just made a great point that they don't like to share the limelight 'cause I thought it was very obvious that we had Google Cloud, we had Microsoft there on day one of this 80,000 person event. A lot of people back from COVID and they weren't there. But Chris, you brought up a great point that kind of made me think, maybe you're right. Maybe they're in the afternoon keynote, they want their own time- >> You think GSMA invited them? >> I imagine so. You'd have to ask GSMA. >> I would think so. >> Get Max on here and ask that. >> I'm going to ask them, I will. >> But no, and they don't like it because I think the misconception, by the way, is that everyone says, "oh, it's AWS, it's Google Cloud and it's Azure." They're not all the same business by any stretch of the imagination. AWS has been doing loads of great work, they've been launching private network stuff over the last couple of weeks. Really interesting. Google's been playing catch up. We know that they came in readily late to the market. And Azure, they've all got slightly different angles on it. So perhaps it just wasn't right for AWS and the way they wanted to pitch things so they don't have to be there, do they? >> That's a good point. >> But the industry needs them there, that's the number one cloud. >> Dave, they're there working with the industry. >> Yeah, of course. >> They don't have to be on the keynote stage. And in fact, you think about this show and you mentioned the 80,000 people, the activity going on around in all these massive areas they're in, it's fantastic. That's where the business is done. The business isn't done up on the keynote stage. >> That's why there's the glitz and the glamour, Chris. (all laugh) >> Yeah. It's not glitz, it's espresso. It's not glamour anymore, it's just espresso. >> We need the espresso. >> Yeah. >> I think another thing is that it's interesting how an average European sees the tech market and an average North American, especially you from US, you have to see the market. Here, people are more like process oriented and they want the rules of the road already established before they can take a step- >> Chris: That's because it's your pension in the North American- >> Exactly. So unions are there and the more employee rights and everything, you can't fire people easily here or in Germany or most of the Europe is like that with the exception of UK. >> Well, but it's like I said, that Silicone Valley gets their money on the way out, you know? And that's how they do it, that's how they think it. And they don't... They ask for forgiveness. I think the east coast is more close to Europe, but in the EU, highly regulated, really focused on lifetime employment, things like that. >> But Dave, the issue is the telecom industry is brilliant, right? We keep paying every month whatever we do with it. >> It's a great business, to your point- >> It's a brilliant business model. >> Dave: It's fantastic. >> So it's about then getting the structure right behind it. And you know, we've seen a lot of stratification where people are selling off towers, Orange haven't sold their towers off, they made a big point about that. Others are selling their towers off. Some people are selling off their underlying network, Telecom Italia talking about KKR buying the whole underlying network. It's like what do you want to be in control of? It's a great business. >> But that's why they complain so much is that they're having to sell their assets because of the onerous CapEx requirements, right? >> Yeah, they've had it good, right? And dare I say, perhaps they've not planned well enough for the future. >> They're trying to protect their past from the future. I mean, that's... >> Actually, look at the... Every "n" number of years, there's a new faster network. They have to dig the ground, they have to put the fiber, they have to put this. Now, there are so many booths showing 6G now, we are not even done with 5G yet, now the next 6G you know, like then- >> 10G's coming- >> 10G, that's a different market. (Dave laughs) >> Actually, they're bogged down by the innovation, I think. >> And the generational thing is really important because we're planning for 6G in all sorts of good ways but actually what we use in our daily lives, we've gone through the barrier, we've got enough to do that. So 4G gives us enough, the fiber in the ground or even old copper gives us enough. So the question is, what are we willing to pay for more than that basic connectivity? And the answer to your point, Dave, is not a lot, right? So therefore, that's why the emphasis is on the business market on that B2B and B2B2X. >> But we'll pay for Netflix all day long. >> All day long. (all laugh) >> The one thing Chris, I don't know, I want to know your viewpoints and we have talked in the past as well, there's absence of think tanks in tech, right? So we have think tanks on the foreign policy and economic policy in every country, and we have global think tanks, but tech is becoming a huge part of the economy, global economy as well as national economies, right? But we don't have think tanks on like policy around tech. For example, this 4G is good for a lot of use cases. Then 5G is good for smaller number of use cases. And then 6G will be like, fewer people need 6G for example. Why can't we have sort of those kind of entities dictating those kind of like, okay, is this a wiser way to go about it? >> Lina Khan wants to. She wants to break up big tech- >> You're too young to remember but the IT used to have a show every four years in Geneva, there were standards around there. So I think there are bodies. I think the balance of power obviously has gone from the telecom to the west coast to the IT markets. And it's changing the balance about, it moves more quickly, right? Telecoms has never moved quickly enough. I think there is hope by the way, that telecoms now that we are moving to more softwarized environment, and God forbid, we're moving into CICD in the telecom world, right? Which is a massive change, but I think there's hopes for it to change. The mentality is changing, the culture is changing, but to change those old structured organizations from the British telecom or the France telecom into the modern world, it's a hell of a long journey. It's not an overnight journey at all. >> Well, of course the theme of the event is velocity. >> Yeah, I know that. >> And it's been interesting sitting here with the three of you talking about from a historic perspective, how slow and molasseslike telecom has been. They don't have a choice anymore. As consumers, we have this expectation we're going to get anything we want on our mobile device, 24 by seven. We don't care about how the sausage is made, we just want the end result. So do you really think, and we're only on day one guys... And Chris we'll start with you. Is the theme really velocity? Is it disruption? Are they able to move faster? >> Actually, I think invisibility is the real answer. (Lisa laughs) We want communication to be invisible, right? >> Absolutely. >> We want it to work. When we switch our phones on, we want it to work and we want to... Well, they're not even phones anymore, are they really? I mean that's the... So no, velocity, we've got... There is momentum in the industry, there's no doubt about that. The cloud guys coming in, making telecoms think about the way they run their own business, where they meet, that collision point on the edges you talked about Sarbjeet. We do have velocity, we've got momentum. There's so many interested parties. The way I think of this is that the telecom industry used to be inward looking, just design its own technology and then expect everyone else to dance to our tune. We're now flipping that 180 degrees and we are now having to work with all the different outside forces shaping us. Whether it's devices, whether it's smart cities, governments, the hosting guys, the Equinoxis, all these things. So everyone wants a piece of this telecom world so we've got to make ourselves more open. That's why you get in a more open environment. >> But you did... I just want to bring back a point you made during COVID, which was when everybody switched to work from home, started using their landlines again, telcos had to respond and nothing broke. I mean, it was pretty amazing. >> Chris: It did a good job. >> It was kind of invisible. So, props to the telcos for making that happen. >> They did a great job. >> So it really did. Now, okay, what have you done for me lately? So now they've got to deal with the future and they're talking monetization. But to me, monetization is all about data and not necessarily just the network data. Yeah, they can sell that 'cause they own that but what kind of incremental value are they going to create for the consumers that... >> Yeah, actually that's a problem. I think the problem is that they have been strangled by the regulation for a long time and they cannot look at their data. It's a lot more similar to the FinTech world, right? I used to work at Visa. And then Visa, we did trillion dollars in transactions in '96. Like we moved so much money around, but we couldn't look at these things, right? So yeah, I think regulation is a problem that holds you back, it's the antithesis of velocity, it slows you down. >> But data means everything, doesn't it? I mean, it means everything and nothing. So I think the challenge here is what data do the telcos have that is useful, valuable to me, right? So in the home environment, the fact that my broadband provider says, oh, by the way, you've got 20 gadgets on that network and 20 on that one... That's great, tell me what's on there. I probably don't know what's taking all my valuable bandwidth up. So I think there's security wrapped around that, telling me the way I'm using it if I'm getting the best out of my service. >> You pay for that? >> No, I'm saying they don't do it yet. I think- >> But would you pay for that? >> I think I would, yeah. >> Would you pay a lot for that? I would expect it to be there as part of my dashboard for my monthly fee. They're already charging me enough. >> Well, that's fine, but you pay a lot more in North America than I do in Europe, right? >> Yeah, no, that's true. >> You're really overpaying over there, right? >> Way overpaying. >> So, actually everybody's looking at these devices, right? So this is a radio operated device basically, right? And then why couldn't they benefit from this? This is like we need to like double click on this like 10 times to find out why telcos failed to leverage this device, right? But I think the problem is their reliance on regulations and their being close to the national sort of governments and local bodies and authorities, right? And in some countries, these telcos are totally controlled in very authoritarian ways, right? It's not like open, like in the west, most of the west. Like the world is bigger than five, six countries and we know that, right? But we end up talking about the major economies most of the time. >> Dave: Always. >> Chris: We have a topic we want to hit on. >> We do have a topic. Our last topic, Chris, it's for you. You guys have done an amazing job for the last 25 minutes talking about the industry, where it's going, the evolution. But Chris, you're registered blind throughout your career. You're a leading user of assertive technologies. Talk about diversity, equity, inclusion, accessibility, some of the things you're doing there. >> Well, we should have had 25 minutes on that and five minutes on- (all laugh) >> Lisa: You'll have to come back. >> Really interesting. So I've been looking at it. You're quite right, I've been using accessible technology on my iPhone and on my laptop for 10, 20 years now. It's amazing. And what I'm trying to get across to the industry is to think about inclusive design from day one. When you're designing an app or you're designing a service, make sure you... And telecom's a great example. In fact, there's quite a lot of sign language around here this week. If you look at all the events written, good to see that coming in. Obviously, no use to me whatsoever, but good for the hearing impaired, which by the way is the biggest category of disability in the world. Biggest chunk is hearing impaired, then vision impaired, and then cognitive and then physical. And therefore, whenever you're designing any service, my call to arms to people is think about how that's going to be used and how a blind person might use it or how a deaf person or someone with physical issues or any cognitive issues might use it. And a great example, the GSMA and I have been talking about the app they use for getting into the venue here. I downloaded it. I got the app downloaded and I'm calling my guys going, where's my badge? And he said, "it's top left." And because I work with a screen reader, they hadn't tagged it properly so I couldn't actually open my badge on my own. Now, they changed it overnight so it worked this morning, which is fantastic work by Trevor and the team. But it's those things that if you don't build it in from scratch, you really frustrate a whole group of users. And if you think about it, people with disabilities are excluded from so many services if they can't see the screen or they can't hear it. But it's also the elderly community who don't find it easy to get access to things. Smart speakers have been a real blessing in that respect 'cause you can now talk to that thing and it starts talking back to you. And then there's the people who can't afford it so we need to come down market. This event is about launching these thousand dollars plus devices. Come on, we need below a hundred dollars devices to get to the real mass market and get the next billion people in and then to educate people how to use it. And I think to go back to your previous point, I think governments are starting to realize how important this is about building the community within the countries. You've got some massive projects like NEOM in Saudi Arabia. If you have a look at that, if you get a chance, a fantastic development in the desert where they're building a new city from scratch and they're building it so anyone and everyone can get access to it. So in the past, it was all done very much by individual disability. So I used to use some very expensive, clunky blind tech stuff. I'm now using mostly mainstream. But my call to answer to say is, make sure when you develop an app, it's accessible, anyone can use it, you can talk to it, you can get whatever access you need and it will make all of our lives better. So as we age and hearing starts to go and sight starts to go and dexterity starts to go, then those things become very useful for everybody. >> That's a great point and what a great champion they have in you. Chris, Sarbjeet, Dave, thank you so much for kicking things off, analyzing day one keynote, the ecosystem day, talking about what velocity actually means, where we really are. We're going to have to have you guys back 'cause as you know, we can keep going, but we are out of time. But thank you. >> Pleasure. >> We had a very spirited, lively conversation. >> Thanks, Dave. >> Thank you very much. >> For our guests and for Dave Vellante, I'm Lisa Martin, you're watching theCUBE live in Barcelona, Spain at MWC '23. We'll be back after a short break. See you soon. (uplifting instrumental music)

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)

(upbeat music) >> Welcome back to Supercloud 2. This is Dave Vellante. We're here exploring the intersection of data and analytics in the future of cloud and data. In this segment, we're going to look deeper into the life sciences business with Jesse Cugliotta, who leads the Healthcare and Life Sciences industry practice at Snowflake. And Nicholas Nick Taylor, who's the executive director of Informatics at Ionis Pharmaceuticals. Gentlemen, thanks for coming in theCUBE and participating in the program. Really appreciate it. >> Thank you for having us- >> Thanks for having me. >> You're very welcome, okay, we're go really try to look at data sharing as a use case and try to understand what's happening in the healthcare industry generally and specifically, how Nick thinks about sharing data in a governed fashion whether tapping the capabilities of multiple clouds is advantageous long term or presents more challenges than the effort is worth. And to start, Jesse, you lead this industry practice for Snowflake and it's a challenging and vibrant area. It's one that's hyper-focused on data privacy. So the first question is, you know there was a time when healthcare and other regulated industries wouldn't go near the cloud. What are you seeing today in the industry around cloud adoption and specifically multi-cloud adoption? >> Yeah, for years I've heard that healthcare and life sciences has been cloud diverse, but in spite of all of that if you look at a lot of aspects of this industry today, they've been running in the cloud for over 10 years now. Particularly when you look at CRM technologies or HR or HCM, even clinical technologies like EDC or ETMF. And it's interesting that you mentioned multi-cloud as well because this has always been an underlying reality especially within life sciences. This industry grows through acquisition where companies are looking to boost their future development pipeline either by buying up smaller biotechs, they may have like a late or a mid-stage promising candidate. And what typically happens is the larger pharma could then use their commercial muscle and their regulatory experience to move it to approvals and into the market. And I think the last few decades of cheap capital certainly accelerated that trend over the last couple of years. But this typically means that these new combined institutions may have technologies that are running on multiple clouds or multiple cloud strategies in various different regions to your point. And what we've often found is that they're not planning to standardize everything onto a single cloud provider. They're often looking for technologies that embrace this multi-cloud approach and work seamlessly across them. And I think this is a big reason why we, here at Snowflake, we've seen such strong momentum and growth across this industry because healthcare and life science has actually been one of our fastest growing sectors over the last couple of years. And a big part of that is in fact that we run on not only all three major cloud providers, but individual accounts within each and any one of them, they had the ability to communicate and interoperate with one another, like a globally interconnected database. >> Great, thank you for that setup. And so Nick, tell us more about your role and Ionis Pharma please. >> Sure. So I've been at Ionis for around five years now. You know, when when I joined it was, the IT department was pretty small. There wasn't a lot of warehousing, there wasn't a lot of kind of big data there. We saw an opportunity with Snowflake pretty early on as a provider that would be a lot of benefit for us, you know, 'cause we're small, wanted something that was fairly hands off. You know, I remember the days where you had to get a lot of DBAs in to fine tune your databases, make sure everything was running really, really well. The notion that there's, you know, no indexes to tune, right? There's very few knobs and dials, you can turn on Snowflake. That was appealing that, you know, it just kind of worked. So we found a use case to bring the platform in. We basically used it as a logging replacement as a Splunk kind of replacement with a platform called Elysium Analytics as a way to just get it in the door and give us the opportunity to solve a real world use case, but also to help us start to experiment using Snowflake as a platform. It took us a while to A, get the funding to bring it in, but B, build the momentum behind it. But, you know, as we experimented we added more data in there, we ran a few more experiments, we piloted in few more applications, we really saw the power of the platform and now, we are becoming a commercial organization. And with that comes a lot of major datasets. And so, you know, we really see Snowflake as being a very important part of our ecology going forward to help us build out our infrastructure. >> Okay, and you are running, your group runs on Azure, it's kind of mono cloud, single cloud, but others within Ionis are using other clouds, but you're not currently, you know, collaborating in terms of data sharing. And I wonder if you could talk about how your data needs have evolved over the past decade. I know you came from another highly regulated industry in financial services. So what's changed? You sort of touched on this before, you had these, you know, very specialized individuals who were, you know, DBAs, and, you know, could tune databases and the like, so that's evolved, but how has generally your needs evolved? Just kind of make an observation over the last, you know, five or seven years. What have you seen? >> Well, we, I wasn't in a group that did a lot of warehousing. It was more like online trade capture, but, you know, it was very much on-prem. You know, being in the cloud is very much a dirty word back then. I know that's changed since I've left. But in, you know, we had major, major teams of everyone who could do everything, right. As I mentioned in the pharma organization, there's a lot fewer of us. So the data needs there are very different, right? It's, we have a lot of SaaS applications. One of the difficulties with bringing a lot of SaaS applications on board is obviously data integration. So making sure the data is the same between them. But one of the big problems is joining the data across those SaaS applications. So one of the benefits, one of the things that we use Snowflake for is to basically take data out of these SaaS applications and load them into a warehouse so we can do those joins. So we use technologies like Boomi, we use technologies like Fivetran, like DBT to bring this data all into one place and start to kind of join that basically, allow us to do, run experiments, do analysis, basically take better, find better use for our data that was siloed in the past. You mentioned- >> Yeah. And just to add on to Nick's point there. >> Go ahead. >> That's actually something very common that we're seeing across the industry is because a lot of these SaaS applications that you mentioned, Nick, they're with from vendors that are trying to build their own ecosystem in walled garden. And by definition, many of them do not want to integrate with one another. So from a, you know, from a data platform vendor's perspective, we see this as a huge opportunity to help organizations like Ionis and others kind of deal with the challenges that Nick is speaking about because if the individual platform vendors are never going to make that part of their strategy, we see it as a great way to add additional value to these customers. >> Well, this data sharing thing is interesting. There's a lot of walled gardens out there. Oracle is a walled garden, AWS in many ways is a walled garden. You know, Microsoft has its walled garden. You could argue Snowflake is a walled garden. But the, what we're seeing and the whole reason behind the notion of super-cloud is we're creating an abstraction layer where you actually, in this case for this use case, can share data in a governed manner. Let's forget about the cross-cloud for a moment. I'll come back to that, but I wonder, Nick, if you could talk about how you are sharing data, again, Snowflake sort of, it's, I look at Snowflake like the app store, Apple, we're going to control everything, we're going to guarantee with data clean rooms and governance and the standards that we've created within that platform, we're going to make sure that it's safe for you to share data in this highly regulated industry. Are you doing that today? And take us through, you know, the considerations that you have in that regard. >> So it's kind of early days for us in Snowflake in general, but certainly in data sharing, we have a couple of examples. So data marketplace, you know, that's a great invention. It's, I've been a small IT shop again, right? The fact that we are able to just bring down terabyte size datasets straight into our Snowflake and run analytics directly on that is huge, right? The fact that we don't have to FTP these massive files around run jobs that may break, being able to just have that on tap is huge for us. We've recently been talking to one of our CRO feeds- CRO organizations about getting their data feeds in. Historically, this clinical trial data that comes in on an FTP file, we have to process it, take it through the platforms, put it into the warehouse. But one of the CROs that we talked to recently when we were reinvestigate in what data opportunities they have, they were a Snowflake customer and we are, I think, the first production customer they have, have taken that feed. So they're basically exposing their tables of data that historically came in these FTP files directly into our Snowflake instance now. We haven't taken advantage of that. It only actually flipped the switch about three or four weeks ago. But that's pretty big for us again, right? We don't have to worry about maintaining those jobs that take those files in. We don't have to worry about the jobs that take those and shove them on the warehouse. We now have a feed that's directly there that we can use a tool like DBT to push through directly into our model. And then the third avenue that's came up, actually fairly recently as well was genetics data. So genetics data that's highly, highly regulated. We had to be very careful with that. And we had a conversation with Snowflake about the data white rooms practice, and we see that as a pretty interesting opportunity. We are having one organization run genetic analysis being able to send us those genetic datasets, but then there's another organization that's actually has the in quotes "metadata" around that, so age, ethnicity, location, et cetera. And being able to join those two datasets through some kind of mechanism would be really beneficial to the organization. Being able to build a data white room so we can put that genetic data in a secure place, anonymize it, and then share the amalgamated data back out in a way that's able to be joined to the anonymized metadata, that could be pretty huge for us as well. >> Okay, so this is interesting. So you talk about FTP, which was the common way to share data. And so you basically, it's so, I got it now you take it and do whatever you want with it. Now we're talking, Jesse, about sharing the same copy of live data. How common is that use case in your industry? >> It's become very common over the last couple of years. And I think a big part of it is having the right technology to do it effectively. You know, as Nick mentioned, historically, this was done by people sending files around. And the challenge with that approach, of course, while there are multiple challenges, one, every time you send a file around your, by definition creating a copy of the data because you have to pull it out of your system of record, put it into a file, put it on some server where somebody else picks it up. And by definition at that point you've lost governance. So this creates challenges in general hesitation to doing so. It's not that it hasn't happened, but the other challenge with it is that the data's no longer real time. You know, you're working with a copy of data that was as fresh as at the time at that when that was actually extracted. And that creates limitations in terms of how effective this can be. What we're starting to see now with some of our customers is live sharing of information. And there's two aspects of that that are important. One is that you're not actually physically creating the copy and sending it to someone else, you're actually exposing it from where it exists and allowing another consumer to interact with it from their own account that could be in another region, some are running in another cloud. So this concept of super-cloud or cross-cloud could becoming realized here. But the other important aspect of it is that when that other- when that other entity is querying your data, they're seeing it in a real time state. And this is particularly important when you think about use cases like supply chain planning, where you're leveraging data across various different enterprises. If I'm a manufacturer or if I'm a contract manufacturer and I can see the actual inventory positions of my clients, of my distributors, of the levels of consumption at the pharmacy or the hospital that gives me a lot of indication as to how my demand profile is changing over time versus working with a static picture that may have been from three weeks ago. And this has become incredibly important as supply chains are becoming more constrained and the ability to plan accurately has never been more important. >> Yeah. So the race is on to solve these problems. So it start, we started with, hey, okay, cloud, Dave, we're going to simplify database, we're going to put it in the cloud, give virtually infinite resources, separate compute from storage. Okay, check, we got that. Now we've moved into sort of data clean rooms and governance and you've got an ecosystem that's forming around this to make it safer to share data. And then, you know, nirvana, at least near term nirvana is we're going to build data applications and we're going to be able to share live data and then you start to get into monetization. Do you see, Nick, in the near future where I know you've got relationships with, for instance, big pharma like AstraZeneca, do you see a situation where you start sharing data with them? Is that in the near term? Is that more long term? What are the considerations in that regard? >> I mean, it's something we've been thinking about. We haven't actually addressed that yet. Yeah, I could see situations where, you know, some of these big relationships where we do need to share a lot of data, it would be very nice to be able to just flick a switch and share our data assets across to those organizations. But, you know, that's a ways off for us now. We're mainly looking at bringing data in at the moment. >> One of the things that we've seen in financial services in particular, and Jesse, I'd love to get your thoughts on this, is companies like Goldman or Capital One or Nasdaq taking their stack, their software, their tooling actually putting it on the cloud and facing it to their customers and selling that as a new monetization vector as part of their digital or business transformation. Are you seeing that Jesse at all in healthcare or is it happening today or do you see a day when that happens or is healthier or just too scary to do that? >> No, we're seeing the early stages of this as well. And I think it's for some of the reasons we talked about earlier. You know, it's a much more secure way to work with a colleague if you don't have to copy your data and potentially expose it. And some of the reasons that people have historically copied that data is that they needed to leverage some sort of algorithm or application that a third party was providing. So maybe someone was predicting the ideal location and run a clinical trial for this particular rare disease category where there are only so many patients around the world that may actually be candidates for this disease. So you have to pick the ideal location. Well, sending the dataset to do so, you know, would involve a fairly complicated process similar to what Nick was mentioning earlier. If the company who was providing the logic or the algorithm to determine that location could bring that algorithm to you and you run it against your own data, that's a much more ideal and a much safer and more secure way for this industry to actually start to work with some of these partners and vendors. And that's one of the things that we're looking to enable going into this year is that, you know, the whole concept should be bring the logic to your data versus your data to the logic and the underlying sharing mechanisms that we've spoken about are actually what are powering that today. >> And so thank you for that, Jesse. >> Yes, Dave. >> And so Nick- Go ahead please. >> Yeah, if I could add, yeah, if I could add to that, that's something certainly we've been thinking about. In fact, we'd started talking to Snowflake about that a couple of years ago. We saw the power there again of the platform to be able to say, well, could we, we were thinking in more of a data share, but could we share our data out to say an AI/ML vendor, have them do the analytics and then share the data, the results back to us. Now, you know, there's more powerful mechanisms to do that within the Snowflake ecosystem now, but you know, we probably wouldn't need to have onsite AI/ML people, right? Some of that stuff's very sophisticated, expensive resources, hard to find, you know, it's much better for us to find a company that would be able to build those analytics, maintain those analytics for us. And you know, we saw an opportunity to do that a couple years ago and we're kind of excited about the opportunity there that we can just basically do it with a no op, right? We share the data route, we have the analytics done, we get the result back and it's just fairly seamless. >> I mean, I could have a whole another Cube session on this, guys, but I mean, I just did a a session with Andy Thurai, a Constellation research about how difficult it's been for organization to get ROI because they don't have the expertise in house so they want to either outsource it or rely on vendor R&D companies to inject that AI and machine intelligence directly into applications. My follow-up question to you Nick is, when you think about, 'cause Jesse was talking about, you know, let the data basically stay where it is and you know bring the compute to that data. If that data lives on different clouds, and maybe it's not your group, but maybe it's other parts of Ionis or maybe it's your partners like AstraZeneca, or you know, the AI/ML partners and they're potentially on other clouds or that data is on other clouds. Do you see that, again, coming back to super-cloud, do you see it as an advantage to be able to have a consistent experience across those clouds? Or is that just kind of get in the way and make things more complex? What's your take on that, Nick? >> Well, from the vendors, so from the client side, it's kind of seamless with Snowflake for us. So we know for a fact that one of the datasets we have at the moment, Compile, which is a, the large multi terabyte dataset I was talking about. They're on AWS on the East Coast and we are on Azure on the West Coast. And they had to do a few tweaks in the background to make sure the data was pushed over from, but from my point of view, the data just exists, right? So for me, I think it's hugely beneficial that Snowflake supports this kind of infrastructure, right? We don't have to jump through hoops to like, okay, well, we'll download it here and then re-upload it here. They already have the mechanism in the background to do these multi-cloud shares. So it's not important for us internally at the moment. I could see potentially at some point where we start linking across different groups in the organization that do have maybe Amazon or Google Cloud, but certainly within our providers. We know for a fact that they're on different services at the moment and it just works. >> Yeah, and we learned from Benoit Dageville, who came into the studio on August 9th with first Supercloud in 2022 that Snowflake uses a single global instance across regions and across clouds, yeah, whether or not you can query across you know, big regions, it just depends, right? It depends on latency. You might have to make a copy or maybe do some tweaks in the background. But guys, we got to jump, I really appreciate your time. Really thoughtful discussion on the future of data and cloud, specifically within healthcare and pharma. Thank you for your time. >> Thanks- >> Thanks for having us. >> All right, this is Dave Vellante for theCUBE team and my co-host, John Furrier. Keep it right there for more action at Supercloud 2. (upbeat music)

(lively music) >> Good morning. Welcome back to theCUBE's coverage at Supercomputing Conference 2022, live here in Dallas. I'm Dave Nicholson with my co-host Paul Gillin. So far so good, Paul? It's been a fascinating morning Three days in, and a fascinating guest, Ian from AWS. Welcome. >> Thanks, Dave. >> What are we going to talk about? Batch computing, HPC. >> We've got a lot, let's get started. Let's dive right in. >> Yeah, we've got a lot to talk about. I mean, first thing is we recently announced our batch support for EKS. EKS is our Kubernetes, managed Kubernetes offering at AWS. And so batch computing is still a large portion of HPC workloads. While the interactive component is growing, the vast majority of systems are just kind of fire and forget, and we want to run thousands and thousands of nodes in parallel. We want to scale out those workloads. And what's unique about our AWS batch offering, is that we can dynamically scale, based upon the queue depth. And so customers can go from seemingly nothing up to thousands of nodes, and while they're executing their work they're only paying for the instances while they're working. And then as the queue depth starts to drop and the number of jobs waiting in the queue starts to drop, then we start to dynamically scale down those resources. And so it's extremely powerful. We see lots of distributed machine learning, autonomous vehicle simulation, and traditional HPC workloads taking advantage of AWS Batch. >> So when you have a Kubernetes cluster does it have to be located in the same region as the HPC cluster that's going to be doing the batch processing, or does the nature of batch processing mean, in theory, you can move something from here to somewhere relatively far away to do the batch processing? How does that work? 'Cause look, we're walking around here and people are talking about lengths of cables in order to improve performance. So what does that look like when you peel back the cover and you look at it physically, not just logically, AWS is everywhere, but physically, what does that look like? >> Oh, physically, for us, it depends on what the customer's looking for. We have workflows that are all entirely within a single region. And so where they could have a portion of say the traditional HPC workflow, is within that region as well as the batch, and they're saving off the results, say to a shared storage file system like our Amazon FSx for Lustre, or maybe aging that back to an S3 object storage for a little lower cost storage solution. Or you can have customers that have a kind of a multi-region orchestration layer to where they say, "You know what? "I've got a portion of my workflow that occurs "over on the other side of the country "and I replicate my data between the East Coast "and the West Coast just based upon business needs. "And I want to have that available to customers over there. "And so I'll do a portion of it in the East Coast "a portion of it in the West Coast." Or you can think of that even globally. It really depends upon the customer's architecture. >> So is the intersection of Kubernetes with HPC, is this relatively new? I know you're saying you're, you're announcing it. >> It really is. I think we've seen a growing perspective. I mean, Kubernetes has been a long time kind of eating everything, right, in the enterprise space? And now a lot of CIOs in the industrial space are saying, "Why am I using one orchestration layer "to manage my HPC infrastructure and another one "to manage my enterprise infrastructure?" And so there's a growing appreciation that, you know what, why don't we just consolidate on one? And so that's where we've seen a growth of Kubernetes infrastructure and our own managed Kubernetes EKS on AWS. >> Last month you announced a general availability of Trainium, of a chip that's optimized for AI training. Talk about what's special about that chip or what is is customized to the training workloads. >> Yeah, what's unique about the Trainium, is you'll you'll see 40% price performance over any other GPU available in the AWS cloud. And so we've really geared it to be that most price performance of options for our customers. And that's what we like about the silicon team, that we're part of that Annaperna acquisition, is because it really has enabled us to have this differentiation and to not just be innovating at the software level but the entire stack. That Annaperna Labs team develops our network cards, they develop our ARM cards, they developed this Trainium chip. And so that silicon innovation has become a core part of our differentiator from other vendors. And what Trainium allows you to do is perform similar workloads, just at a lower price performance. >> And you also have a chip several years older, called Inferentia- >> Um-hmm. >> Which is for inferencing. What is the difference between, I mean, when would a customer use one versus the other? How would you move the workload? >> What we've seen is customers traditionally have looked for a certain class of machine, more of a compute type that is not as accelerated or as heavy as you would need for Trainium for their inference portion of their workload. So when they do that training they want the really beefy machines that can grind through a lot of data. But when you're doing the inference, it's a little lighter weight. And so it's a different class of machine. And so that's why we've got those two different product lines with the Inferentia being there to support those inference portions of their workflow and the Trainium to be that kind of heavy duty training work. >> And then you advise them on how to migrate their workloads from one to the other? And once the model is trained would they switch to an Inferentia-based instance? >> Definitely, definitely. We help them work through what does that design of that workflow look like? And some customers are very comfortable doing self-service and just kind of building it on their own. Other customers look for a more professional services engagement to say like, "Hey, can you come in and help me work "through how I might modify my workflow to "take full advantage of these resources?" >> The HPC world has been somewhat slower than commercial computing to migrate to the cloud because- >> You're very polite. (panelists all laughing) >> Latency issues, they want to control the workload, they want to, I mean there are even issues with moving large amounts of data back and forth. What do you say to them? I mean what's the argument for ditching the on-prem supercomputer and going all-in on AWS? >> Well, I mean, to be fair, I started at AWS five years ago. And I can tell you when I showed up at Supercomputing, even though I'd been part of this community for many years, they said, "What is AWS doing at Supercomputing?" I know you care, wait, it's Amazon Web Services. You care about the web, can you actually handle supercomputing workloads? Now the thing that very few people appreciated is that yes, we could. Even at that time in 2017, we had customers that were performing HPC workloads. Now that being said, there were some real limitations on what we could perform. And over those past five years, as we've grown as a company, we've started to really eliminate those frictions for customers to migrate their HPC workloads to the AWS cloud. When I started in 2017, we didn't have our elastic fabric adapter, our low-latency interconnect. So customers were stuck with standard TCP/IP. So for their highly demanding open MPI workloads, we just didn't have the latencies to support them. So the jobs didn't run as efficiently as they could. We didn't have Amazon FSx for Lustre, our managed lustre offering for high performant, POSIX-compliant file system, which is kind of the key to a large portion of HPC workloads is you have to have a high-performance file system. We didn't even, I mean, we had about 25 gigs of networking when I started. Now you look at, with our accelerated instances, we've got 400 gigs of networking. So we've really continued to grow across that spectrum and to eliminate a lot of those really, frictions to adoption. I mean, one of the key ones, we had a open source toolkit that was jointly developed by Intel and AWS called CFN Cluster that customers were using to even instantiate their clusters. So, and now we've migrated that all the way to a fully functional supported service at AWS called AWS Parallel Cluster. And so you've seen over those past five years we have had to develop, we've had to grow, we've had to earn the trust of these customers and say come run your workloads on us and we will demonstrate that we can meet your demanding requirements. And at the same time, there's been, I'd say, more of a cultural acceptance. People have gone away from the, again, five years ago, to what are you doing walking around the show, to say, "Okay, I'm not sure I get it. "I need to look at it. "I, okay, I, now, oh, it needs to be a part "of my architecture but the standard questions, "is it secure? "Is it price performant? "How does it compare to my on-prem?" And really culturally, a lot of it is, just getting IT administrators used to, we're not eliminating a whole field, right? We're just upskilling the people that used to rack and stack actual hardware, to now you're learning AWS services and how to operate within that environment. And it's still key to have those people that are really supporting these infrastructures. And so I'd say it's a little bit of a combination of cultural shift over the past five years, to see that cloud is a super important part of HPC workloads, and part of it's been us meeting the the market segment of where we needed to with innovating both at the hardware level and at the software level, which we're going to continue to do. >> You do have an on-prem story though. I mean, you have outposts. We don't hear a lot of talk about outposts lately, but these innovations, like Inferentia, like Trainium, like the networking innovation you're talking about, are these going to make their way into outposts as well? Will that essentially become this supercomputing solution for customers who want to stay on-prem? >> Well, we'll see what the future lies, but we believe that we've got the, as you noted, we've got the hardware, we've got the network, we've got the storage. All those put together gives you a a high-performance computer, right? And whether you want it to be redundant in your local data center or you want it to be accessible via APIs from the AWS cloud, we want to provide that service to you. >> So to be clear, that's not that's not available now, but that is something that could be made available? >> Outposts are available right now, that have this the services that you need. >> All these capabilities? >> Often a move to cloud, an impetus behind it comes from the highest levels in an organization. They're looking at the difference between OpEx versus CapEx. CapEx for a large HPC environment, can be very, very, very high. Are these HPC clusters consumed as an operational expense? Are you essentially renting time, and then a fundamental question, are these multi-tenant environments? Or when you're referring to batches being run in HPC, are these dedicated HPC environments for customers who are running batches against them? When you think about batches, you think of, there are times when batches are being run and there are times when they're not being run. So that would sort of conjure, in the imagination, multi-tenancy, what does that look like? >> Definitely, and that's been, let me start with your second part first is- >> Yeah. That's been a a core area within AWS is we do not see as, okay we're going to, we're going to carve out this super computer and then we're going to allocate that to you. We are going to dynamically allocate multi-tenant resources to you to perform the workloads you need. And especially with the batch environment, we're going to spin up containers on those, and then as the workloads complete we're going to turn those resources over to where they can be utilized by other customers. And so that's where the batch computing component really is powerful, because as you say, you're releasing resources from workloads that you're done with. I can use those for another portion of the workflow for other work. >> Okay, so it makes a huge difference, yeah. >> You mentioned, that five years ago, people couldn't quite believe that AWS was at this conference. Now you've got a booth right out in the center of the action. What kind of questions are you getting? What are people telling you? >> Well, I love being on the show floor. This is like my favorite part is talking to customers and hearing one, what do they love, what do they want more of? Two, what do they wish we were doing that we're not currently doing? And three, what are the friction points that are still exist that, like, how can I make their lives easier? And what we're hearing is, "Can you help me migrate my workloads to the cloud? "Can you give me the information that I need, "both from a price for performance, "for an operational support model, "and really help me be an internal advocate "within my environment to explain "how my resources can be operated proficiently "within the AWS cloud." And a lot of times it's, let's just take your application a subset of your applications and let's benchmark 'em. And really that, AWS, one of the key things is we are a data-driven environment. And so when you take that data and you can help a customer say like, "Let's just not look at hypothetical, "at synthetic benchmarks, let's take "actually the LS-DYNA code that you're running, perhaps. "Let's take the OpenFOAM code that you're running, "that you're running currently "in your on-premises workloads, "and let's run it on AWS cloud "and let's see how it performs." And then we can take that back to your to the decision makers and say, okay, here's the price for performance on AWS, here's what we're currently doing on-premises, how do we think about that? And then that also ties into your earlier question about CapEx versus OpEx. We have models where actual, you can capitalize a longer-term purchase at AWS. So it doesn't have to be, I mean, depending upon the accounting models you want to use, we do have a majority of customers that will stay with that OpEx model, and they like that flexibility of saying, "Okay, spend as you go." We need to have true ups, and make sure that they have insight into what they're doing. I think one of the boogeyman is that, oh, I'm going to spend all my money and I'm not going to know what's available. And so we want to provide the, the cost visibility, the cost controls, to where you feel like, as an HPC administrator you have insight into what your customers are doing and that you have control over that. And so once you kind of take away some of those fears and and give them the information that they need, what you start to see too is, you know what, we really didn't have a lot of those cost visibility and controls with our on-premises hardware. And we've had some customers tell us we had one portion of the workload where this work center was spending thousands of dollars a day. And we went back to them and said, "Hey, we started to show this, "what you were spending on-premises." They went, "Oh, I didn't realize that." And so I think that's part of a cultural thing that, at an HPC, the question was, well on-premises is free. How do you compete with free? And so we need to really change that culturally, to where people see there is no free lunch. You're paying for the resources whether it's on-premises or in the cloud. >> Data scientists don't worry about budgets. >> Wait, on-premises is free? Paul mentioned something that reminded me, you said you were here in 2017, people said AWS, web, what are you even doing here? Now in 2022, you're talking in terms of migrating to cloud. Paul mentioned outposts, let's say that a customer says, "Hey, I'd like you to put "in a thousand-node cluster in this data center "that I happen to own, but from my perspective, "I want to interact with it just like it's "in your data center." In other words, the location doesn't matter. My experience is identical to interacting with AWS in an AWS data center, in a CoLo that works with AWS, but instead it's my physical data center. When we're tracking the percentage of IT that's that is on-prem versus off-prem. What is that? Is that, what I just described, is that cloud? And in five years are you no longer going to be talking about migrating to cloud because people go, "What do you mean migrating to cloud? "What do you even talking about? "What difference does it make?" It's either something that AWS is offering or it's something that someone else is offering. Do you think we'll be at that point in five years, where in this world of virtualization and abstraction, you talked about Kubernetes, we should be there already, thinking in terms of it doesn't matter as long as it meets latency and sovereignty requirements. So that, your prediction, we're all about insights and supercomputing- >> My prediction- >> In five years, will you still be talking about migrating to cloud or will that be something from the past? >> In five years, I still think there will be a component. I think the majority of the assumption will be that things are cloud-native and you start in the cloud and that there are perhaps, an aspect of that, that will be interacting with some sort of an edge device or some sort of an on-premises device. And we hear more and more customers that are saying, "Okay, I can see the future, "I can see that I'm shrinking my footprint." And, you can see them still saying, "I'm not sure how small that beachhead will be, "but right now I want to at least say "that I'm going to operate in that hybrid environment." And so I'd say, again, the pace of this community, I'd say five years we're still going to be talking about migrations, but I'd say the vast majority will be a cloud-native, cloud-first environment. And how do you classify that? That outpost sitting in someone's data center? I'd say we'd still, at least I'll leave that up to the analysts, but I think it would probably come down as cloud spend. >> Great place to end. Ian, you and I now officially have a bet. In five years we're going to come back. My contention is, no we're not going to be talking about it anymore. >> Okay. >> And kids in college are going to be like, "What do you mean cloud, it's all IT, it's all IT." And they won't remember this whole phase of moving to cloud and back and forth. With that, join us in five years to see the result of this mega-bet between Ian and Dave. I'm Dave Nicholson with theCUBE, here at Supercomputing Conference 2022, day three of our coverage with my co-host Paul Gillin. Thanks again for joining us. Stay tuned, after this short break, we'll be back with more action. (lively music)

>>The Cube Presents UI Path Forward five. Brought to you by UI Path, >>Everybody. We're back in Las Vegas. The cube's coverage we're day one at UI Path forward. Five. Pascal Borne is here. He's an expert and bestselling author in the topic of AI and automation and the book Intelligent Automation. Welcome to the world of Hyper Automation, the first book on the topic. And of course, Ray Wong is back on the cube. He's the founder, chairman and principal analyst, Constellation Reese, also bestselling author of Everybody Wants To Rule the World. Guys, thanks so much for coming on The Cubes. Always a pleasure. Ray Pascal, First time on the Cube, I believe. >>Yes, thank you. Thanks for the invitation. Thank you. >>So what is artificial about artificial intelligence, >>For sure, not people. >>So, okay, so you guys are both speaking at the conference, Ray today. I think you're interviewing the co CEOs. What do you make of that? What's, what are you gonna, what are you gonna probe with these guys? Like, how they're gonna divide their divide and conquer, and why do you think the, the company Danielle in particular, decided to bring in Rob Sland? >>Well, you know what I mean, Like, you know, these companies are now at a different stage of growth, right? There's that early battle between RPA vendors. Now we're actually talking something different, right? We're talking about where does automation go? How do we get the decisioning? What's the next best action? That's gonna be the next step. And to take where UI path is today to somewhere else, You really want someone with that enterprise cred and experience the sales motions, the packages, the partnership capabilities, and who else better than Roblin? He, that's, he's done, he can do that in his sleep, but now he's gotta do that in a new space, taking whole category to another level. Now, Daniel on the other hand, right, I mean, he's the visionary founder. He put this thing from nothing to where he is today, right? I mean, at that point you want your founder thinking about the next set of ideas, right? So you get this interesting dynamic that we've seen for a while with co CEOs, those that are doing the operations, getting the stuff out the door, and then letting the founders get a chance to go back and rethink, take a look at the perspective, and hopefully get a chance to build the next idea or take the next idea back into the organization. >>Right? Very well said. Pascal, why did you write your book on intelligent automation and, and hyper automation, and what's changed since you've written that book? >>So, I, I wrote this book, An Intelligent Automation, two years ago. At that time, it was really a new topic. It was really about the key, the, the key, the key content of the, of the book is really about combining different technologies to automate the most complex end to end business processes in companies. And when I say capabilities, it's, we, we hear a lot about up here, especially here, robotic process automation. But up here alone, if you just trying to transform a company with only up here, you just fall short. Okay? A lot of those processes need more than execution. They need language, they need the capacity to view, to see, they need the capacity to understand and to, and to create insights. So by combining process automation with ai, natural language processing, computer vision, you give this capability to create impact by automating end to end processes in companies. >>I, I like the test, what I hear in the keynote with independent experts like yourself. So we're hearing that that intelligent automation or automation is a fundamental component of digital transformation. Is it? Or is it more sort of a back office sort of hidden in inside plumbing Ray? What do you think? >>Well, you start by understanding what's going on in the process phase. And that's where you see discover become very important in that keynote, right? And that's where process mining's playing a role. Then you gotta automate stuff. But when you get to operations, that's really where the change is going to happen, right? We actually think that, you know, when you're doing the digital transformation pieces, right? Analytics, automation and AI are coming together to create a concept we call decision velocity. You and I make a quick decision, boom, how long does it take to get out? Management committee could free forever, right? A week, two months, never. But if you're thinking about competing with the automation, right? These decisions are actually being done a hundred times per second by machine, even a thousand times per second. That asymmetry is really what people are facing at the moment. >>And the companies that are gonna be able to do that and start automating decisions are gonna be operating at another level. Back to what Pascal's book talking about, right? And there are four questions everyone has to ask you, like, when do you fully intelligently automate? And that happens right in the background when you augment the machine with a human. So we can find why did you make an exception? Why did you break a roll? Why didn't you follow this protocol so we can get it down to a higher level confidence? When do you augment the human with the machine so we can give you the information so you can act quickly. And the last one is, when do you wanna insert a human in the process? That's gonna be the biggest question. Order to cash, incident or resolution, Hire to retire, procure to pay. It doesn't matter. When do you want to put a human in the process? When do you want a man in the middle, person in the middle? And more importantly, when do you want insert friction? >>So Pascal, you wrote your book in the middle of the, the pandemic. Yes. And, and so, you know, pre pandemic digital transformation was kind of a buzzword. A lot of people gave it lip service, eh, not on my watch, I don't have to worry about that. But then it became sort of, you're not a digital business, you're out of business. So, so what have you seen as the catalyst for adoption of automation? Was it the, the pandemic? Was it sort of good runway before that? What's changed? You know, pre isolation, post isolation economy. >>You, you make me think about a joke. Who, who did your best digital transformation over the last years? The ceo, C H R O, the Covid. >>It's a big record ball, right? Yeah. >>Right. And that's exactly true. You know, before pandemic digital transformation was a competitive advantage. >>Companies that went into it had an opportunity to get a bit better than their, their competitors during the pandemic. Things have changed completely. Companies that were not digitalized and automated could not survive. And we've seen so many companies just burning out and, and, and those companies that have been able to capitalize on intelligent automation, digital transformations during the pandemic have been able not only to survive, but to, to thrive, to really create their place on the market. So that's, that has been a catalyst, definitely a catalyst for that. That explains the success of the book, basically. Yeah. >>Okay. Okay. >>So you're familiar with the concept of Stew the food, right? So Stew by definition is something that's delicious to eat. Stew isn't simply taking one of every ingredient from the pantry and throwing it in the pot and stirring it around. When we start talking about intelligent automation, artificial intelligence, augmented intelligence, it starts getting a bit overwhelming. My spy sense goes off and I start thinking, this sounds like mush. It doesn't sound like Stew. So I wanna hear from each of you, what is the methodical process that, that people need to go through when they're going through digital trans transmission, digital transformation, so that you get delicious stew instead of a mush that's just confused everything in your business. So you, Ray, you want, you want to, you wanna answer that first? >>Yeah. You know, I mean, we've been talking about digital transformation since 2010, right? And part of it was really getting the business model, right? What are you trying to achieve? Is that a new type of offering? Are you changing the way you monetize something? Are you taking existing process and applying it to a new set of technologies? And what do you wanna accomplish, right? Once you start there, then it becomes a whole lot of operational stuff. And it's more than st right? I mean, it, it could be like, well, I can't use those words there. But the point being is it could be a complete like, operational exercise. It could be a complete revenue exercise, it could be a regulatory exercise, it could be something about where you want to take growth into the next level. And each one of those processes, some of it is automation, right? There's a big component of it today. But most of it is really rethinking about what you want things to do, right? How do you actually make things to be successful, right? Do I reorganize a process? Do I insert a place to do monetization? Where do I put engagement in place? How do I collect data along the way so I can build better feedback loop? What can I do to build the business graph so that I have that knowledge for the future so I can go forward doing that so I can be successful. >>The Pascal should, should, should the directive be first ia, then ai? Or are these, are these things going to happen in parallel naturally? What's your position on that? Is it first, >>So it, so, >>So AI is part of IA because that's, it's, it's part of the big umbrella. And very often I got the question. So how do you differentiate AI in, I a, I like to say that AI is only the brain. So think of ai cuz I'm consider, I consider AI as machine learning, Okay? Think of AI in a, like a brain near jar that only can think, create, insight, learn, but doesn't do anything, doesn't have any arms, doesn't have any eyes, doesn't not have any mouth and ears can't talk, can't understand with ia, you, you give those capabilities to ai. You, you basically, you create a cap, the capability, technological capability that is able to do more than just thinking, learning and, and create insight, but also acting, speaking, understanding the environment, viewing it, interacting with it. So basically performing these, those end to end processes that are performed currently by people in companies. >>Yeah, we're gonna get to a point where we get to what we call a dynamic scenario generation. You're talking to me, you get excited, well, I changed the story because something else shows up, or you're talking to me and you're really upset. We're gonna have to actually ch, you know, address that issue right away. Well, we want the ability to have that sense and respond capability so that the next best action is served. So your data, your process, the journey, all the analytics on the top end, that's all gonna be served up and changed along the way. As we go from 2D journeys to 3D scenarios in the metaverse, if we think about what happens from a decentralized world to decentralized, and we think about what's happening from web two to web three, we're gonna make those types of shifts so that things are moving along. Everything's a choose your end venture journey. >>So I hope I remember this correctly from your book. You talked about disruption scenarios within industries and within companies. And I go back to the early days of, of our industry and East coast Prime, Wang, dg, they're all gone. And then, but, but you look at companies like Microsoft, you know, they were, they were able to, you know, get through that novel. Yeah. Ibm, you know, I call it survived. Intel is now going through their, you know, their challenge. So, so maybe it's inevitable, but how do you see the future in terms of disruption with an industry, Forget our industry for a second, all industry across, whether it's healthcare, financial services, manufacturing, automobiles, et cetera. How do you see the disruption scenario? I'm pretty sure you talked about this in your book, it's been a while since I read it, but I wonder if you could talk about that disruption scenario and, and the role that automation is going to play, either as the disruptor or as the protector of the incumbents. >>Let's take healthcare and auto as an example. Healthcare is a great example. If we think about what's going on, not enough nurses, massive shortage, right? What are we doing at the moment? We're setting five foot nine robots to do non-patient care. We're trying to capture enough information off, you know, patient analytics like this watch is gonna capture vitals from a going forward. We're doing a lot what we can do in the ambient level so that information and data is automatically captured and decisions are being rendered against that. Maybe you're gonna change your diet along the way, maybe you're gonna walk an extra 10 minutes. All those things are gonna be provided in that level of automation. Take the car business. It's not about selling cars. Tesla's a great example. We talk about this all the time. What Tesla's doing, they're basically gonna be an insurance company with all the data they have. They have better data than the insurance companies. They can do better underwriting, they've got better mapping information and insights they can actually suggest next best action do collision avoidance, right? Those are all the things that are actually happening today. And automation plays a big role, not just in the collection of that, that information insight, but also in the ability to make recommendations, to do predictions and to help you prevent things from going wrong. >>So, you know, it's interesting. It's like you talk about Tesla as the, the disrupting the insurance companies. It's almost like the over the top vendors have all the data relative to the telcos and mopped them up for lunch. Pascal, I wanna ask you, you know, the topic of future of work kind of was a bromide before, but, but now I feel like, you know, post pandemic, it, it actually has substance. How do you see the future of work? Can you even summarize what it's gonna look like? It's, it's, Or are we here? >>It's, yeah, it's, and definitely it's, it's more and more important topic currently. And you, you all heard about the great resignation and how employee experience is more and more important for companies according to have a business review. The companies that take care of their employee experience are four times more profitable that those that don't. So it's a, it's a, it's an issue for CEOs and, and shareholders. Now, how do we get there? How, how do we, how do we improve the, the quality of the employee experience, understanding the people, getting information from them, educating them. I'm talking about educating them on those new technologies and how they can benefit from those empowering them. And, and I think we've talked a lot about this, about the democratization local type of, of technologies that democratize the access to those technologies. Everyone can be empowered today to change their work, improve their work, and finally, incentivization. I think it's a very important point where companies that, yeah, I >>Give that. What's gonna be the key message of your talk tomorrow. Give us the bumper sticker, >>If you will. Oh, I'm gonna talk, It's a little bit different. I'm gonna talk for the IT community in this, in the context of the IT summit. And I'm gonna talk about the future of intelligent automation. So basically how new technologies will impact beyond what we see today, The future of work. >>Well, I always love having you on the cube, so articulate and, and and crisp. What's, what's exciting you these days, you know, in your world, I know you're traveling around a lot, but what's, what's hot? >>Yeah, I think one of the coolest thing that's going on right now is the fact that we're trying to figure out do we go to work or do we not go to work? Back to your other point, I mean, I don't know, work, work is, I mean, for me, work has been everywhere, right? And we're starting to figure out what that means. I think the second thing though is this notion around mission and purpose. And everyone's trying to figure out what does that mean for themselves? And that's really, I don't know if it's a great, great resignation. We call it great refactoring, right? Where you work, when you work, how we work, why you work, that's changing. But more importantly, the business models are changing. The monetization models are changing macro dynamics that are happening. Us versus China, G seven versus bricks, right? War on the dollar. All these things are happening around us at this moment and, and I think it's gonna really reshape us the way that we came out of the seventies into the eighties. >>Guys, always a pleasure having folks like yourself on, Thank you, Pascal. Been great to see you again. All right, Dave Nicholson, Dave Ante, keep it right there. Forward five from Las Vegas. You're watching the cue.

(bright music) >> Welcome back everyone. theCube's live coverage here. Day two, of two sets, three days of theCube coverage here at VMware Explore. This is our 12th year covering VMware's annual conference, formerly called VM World. I'm John Furrier, with Dave Vellante. We'd love seeing the progress and we've got great security comes Tom Gill, senior vices, president general manager, networking and advanced security business group at VMware. Great to see you. Thanks for coming on. >> Thanks. for having me. >> Yeah, really happy we could have you on. >> I think this is my sixth edition on the theCube. Do I get frequent flyer points or anything? >> Yeah. >> You first get the VIP badge. We'll make that happen. You can start getting credits. >> Okay, there we go. >> We won't interrupt you. Seriously, you got a great story in security here. The security story is kind of embedded everywhere, so it's not called out and blown up and talked specifically about on stage. It's kind of in all the narratives in the VM World for this year. But you guys have an amazing security story. So let's just step back and to set context. Tell us the security story for what's going on here at VMware and what that means to this supercloud, multi-cloud and ongoing innovation with VMware. >> Yeah, sure thing. So probably the first thing I'll point out is that security's not just built in at VMware. It's built differently. So, we're not just taking existing security controls and cut and pasting them into our software. But we can do things because of our platform, because of the virtualization layer that you really can't do with other security tools. And where we're very, very focused is what we call lateral security or East-West movement of an attacker. 'Cause frankly, that's the name of the game these days. Attackers, you've got to assume that they're already in your network. Already assume that they're there. Then how do we make it hard for them to get to the stuff that you really want? Which is the data that they're going after. And that's where we really should. >> All right. So we've been talking a lot, coming into VMware Explore, and here, the event. About two things. Security, as a state. >> Yeah. >> I'm secure right now. >> Yeah. >> Or I think I'm secure right now, even though someone might be in my network or in my environment. To the notion of being defensible. >> Yeah. >> Meaning I have to defend and be ready at a moment's notice to attack, fight, push back, red team, blue team. Whatever you're going to call it. But something's happening. I got to be able to defend. >> Yeah. So what you're talking about is the principle of Zero Trust. When I first started doing security, the model was we have a perimeter. And everything on one side of the perimeter is dirty, ugly, old internet. And everything on this side, known good, trusted. What could possibly go wrong. And I think we've seen that no matter how good you make that perimeter, bad guys find a way in. So Zero Trust says, you know what? Let's just assume they're already in. Let's assume they're there. How do we make it hard for them to move around within the infrastructure and get to the really valuable assets? 'Cause for example, if they bust into your laptop, you click on a link and they get code running on your machine. They might find some interesting things on your machine. But they're not going to find 250 million credit cards. >> Right. >> Or the script of a new movie or the super secret aircraft plans. That lives in a database somewhere. And so it's that movement from your laptop to that database. That's where the damage is done and that's where VMware shines. >> So if they don't have the right to get to that database, they're not in. >> And it's not even just the right. So they're so clever and so sneaky that they'll steal a credential off your machine, go to another machine, steal a credential off of that. So, it's like they have the key to unlock each one of these doors. And we've gotten good enough where we can look at that lateral movement, even though it has a credential and a key, we're like wait a minute. That's not a real CIS Admin making a change. That's ransomware. And that's where you. >> You have to earn your way in. >> That's right. That's right. Yeah. >> And we're all kinds of configuration errors. But also some user problems. I've heard one story where there's so many passwords and username and passwords and systems that the bad guys scour, the dark web for passwords that have been exposed. >> Correct. >> And go test them against different accounts. Oh one hit over here. >> Correct. >> And people don't change their passwords all the time. >> Correct. >> That's a known vector. >> Just the idea that users are going to be perfect and never make a mistake. How long have we been doing this? Humans are the weakest link. So people are going to make mistakes. Attackers are going to be in. Here's another way of thinking about it. Remember log4j? Remember that whole fiasco? Remember that was at Christmas time. That was nine months ago. And whoever came up with that vulnerability, they basically had a skeleton key that could access every network on the planet. I don't know if a single customer that said, "Oh yeah, I wasn't impacted by log4j." So here's some organized entity had access to every network on the planet. What was the big breach? What was that movie script that got stolen? So there wasn't one, right? We haven't heard anything. So the point is, the goal of attackers is to get in and stay in. Imagine someone breaks into your house, steals your laptop and runs. That's a breach. Imagine someone breaks into your house and stays for nine months. It's untenable, in the real world, right? >> Right. >> We don't know in there, hiding in the closet. >> They're still in. >> They're watching everything. >> Hiding in your closet, exactly. >> Moving around, nibbling on your cookies. >> Drinking your beer. >> Yeah. >> So let's talk about how this translates into the new reality of cloud-native. Because now you hear about automated pentesting is a new hot thing right now. You got antivirus on data is hot within APIs, for instance. >> Yeah. >> API security. So all kinds of new hot areas. Cloud-native is very iterative. You know, you can't do a pentest every week. >> Right. >> You got to do it every second. >> So this is where it's going. It's not so much simulation. It's actually real testing. >> Right. Right. >> How do you view that? How does that fit into this? 'cause that seems like a good direction to me. >> Yeah. If it's right in, and you were talking to my buddy, Ahjay, earlier about what VMware can do to help our customers build cloud native applications with Tanzu. My team is focused on how do we secure those applications? So where VMware wants to be the best in the world is securing these applications from within. Looking at the individual piece parts and how they talk to each other and figuring out, wait a minute, that should never happen. By almost having an x-ray machine on the innards of the application. So we do it for both for VMs and for container based applications. So traditional apps are VM based. Modern apps are container based. And we have a slightly different insertion mechanism. It's the same idea. So for VMs, we do it with a hypervisor with NSX. We see all the inner workings. In a container world we have this thing called a service mesh that lets us look at each little snippet of code and how they talk to each other. And once you can see that stuff, then you can actually apply. It's almost like common sense logic of like, wait a minute. This API is giving back credit card numbers and it gives five an hour. All of a sudden, it's now asking for 20,000 or a million credit cards. That doesn't make any sense. The anomalies stick out like a sore thumb. If you can see them. At VMware, our unique focus in the infrastructure is that we can see each one of these little transactions and understand the conversation. That's what makes us so good at that East-West or lateral security. >> You don't belong in this room, get out or that that's some weird call from an in memory database, something over here. >> Exactly. Where other security solutions won't even see that. It's not like there algorithms aren't as good as ours or better or worse. It's the access to the data. We see the inner plumbing of the app and therefore we can protect the app from. >> And there's another dimension that I want to get in the table here. 'Cause to my knowledge only AWS, Google, I believe Microsoft and Alibaba and VMware have this. >> Correct >> It's Nitro. The equivalent of a Nitro. >> Yes. >> Project Monterey. >> Yeah. >> That's unique. It's the future of computing architectures. Everybody needs a Nitro. I've written about this. >> Yeah. >> Right. So explain your version. >> Yeah. >> It's now real. >> Yeah. >> It's now in the market, right? >> Yeah. >> Or soon will be. >> Here's our mission. >> Salient aspects. >> Yeah. Here's our mission of VMware. Is that we want to make every one of our enterprise customers. We want their private cloud to be as nimble, as agile, as efficient as the public cloud. >> And secure. >> And secure. In fact, I'll argue, we can make it actually more secure because we're thinking about putting security everywhere in this infrastructure. Not just on the edges of it. Okay. How do we go on that journey? As you pointed out, the public cloud providers realized five years ago that the right way to build computers was not just a CPU and a graphics process unit, GPU. But there's this third thing that the industry's calling a DPU, data processing unit. And so there's kind of three pieces of a computer. And the DPU is sometimes called a Smartnic. It's the network interface card. It does all that network handling and analytics and it takes it off the CPU. So they've been building and deploying those systems themselves. That's what Nitro is. And so we have been working with the major Silicon vendors to bring that architecture to everybody. So with vSphere 8, we have the ability to take the network processing, that East-West inspection I talked about, take it off of the CPU and put it into this dedicated processing element called the DPU and free up the CPU to run the applications that Ahjay and team are building. >> So no performance degradation at all? >> Correct. To CPU offload. >> So even the opposite, right? I mean you're running it basically Bare Metal speeds. >> Yes, yes and yes. >> And you're also isolating the storage from the security, the management, and. >> There's an isolation angle to this, which is that firewall, that we're putting everywhere. Not just that the perimeter, but we put it in each little piece of the server is running when it runs on one of these DPUs it's a different memory space. So even if an attacker gets to root in the OS, they it's very, very, never say never, but it's very difficult. >> So who has access to that resource? >> Pretty much just the infrastructure layer, the cloud provider. So it's Amazon, Google, Microsoft, and the enterprise. >> Application can't get in. >> Can't get in there. Cause you would've to literally bridge from one memory space to another. Never say never, but it would be very. >> But it hasn't earned the trust to get. >> It's more than barbwire. It's multiple walls. >> Yes. And it's like an air gap. It puts an air gap in the server itself so that if the server is compromised, it's not going to get into the network. Really powerful. >> What's the big thing that you're seeing with this supercloud transition. We're seeing multi-cloud and this new, not just SaaS hosted on the cloud. >> Yeah. >> You're seeing a much different dynamic of, combination of large scale CapEx, cloud-native, and then now cloud-native drills on premises and edge. Kind of changing what a cloud looks like if the cloud's on a cloud. >> Yeah. >> So we're the customer, I'm building on a cloud and I have on premise stuff. So, I'm getting scale CapEx relief from the hyperscalers. >> I think there's an important nuance on what you're talking about. Which is in the early days of the cloud customers. Remember those first skepticism? Oh, it'll never work. Oh, that's consumer grade. Oh, that's not really going to work. Oh some people realize. >> It's not secure. >> Yeah. It's not secure. >> That one's like, no, no, no it's secure. It works. And it's good. So then there was this sort of over rush. Let's put everything on the cloud. And I had a lot of customers that took VM based applications said, I'm going to move those onto the cloud. You got to take them all apart, put them on the cloud and put them all back together again. And little tiny details like changing an IP address. It's actually much harder than it looks. So my argument is, for existing workloads for VM based workloads, we are VMware. We're so good at running VM based workloads. And now we run them on anybody's cloud. So whether it's your east coast data center, your west coast data center, Amazon, Google, Microsoft, Alibaba, IBM keep going. We pretty much every. >> And the benefit of the customer is what. >> You can literally VMotion and just pick it up and move it from private to public, public to private, private to public, Back and forth. >> Remember when we called Vmotion BS, years ago? >> Yeah. Yeah. >> VMotion is powerful. >> We were very skeptical. We're like, that'll never happen. I mean we were. This supposed to be pat ourselves on the back. >> Well because alchemy. It seems like what you can't possibly do that. And now we do it across clouds. So it's not quite VMotion, but it's the same idea. You can just move these things over. I have one customer that had a production data center in the Ukraine. Things got super tense, super fast and they had to go from their private cloud data center in the Ukraine, to a public cloud data center out of harm's way. They did it over a weekend. 48 hours. If you've ever migrated a data center, that's usually six months. Right. And a lot of heartburn and a lot of angst. Boop. They just drag and dropped and moved it on over. That's the power of what we call the cloud operating model. And you can only do this when all your infrastructures defined in software. If you're relying on hardware, load balancers, hardware, firewalls, you can't move those. They're like a boat anchor. You're stuck with them. And by the way, they're really, really expensive. And by the way, they eat a lot of power. So that was an architecture from the 90's. In the cloud operating model your data center. And this comes back to what you were talking about is just racks and racks of X86 with these magic DPUs, or smart nics, to make any individual node go blisteringly fast and do all the functions that you used to do in network appliances. >> We just had Ahjay taking us to school, and everyone else to school on applications, middleware, abstraction layer. And Kit Culbert was also talking about this across cloud. We're talking supercloud, super pass. If this continues to happen, which we would think it will happen. What does the security posture look like? It feels to me, and again, this is your wheelhouse. If supercloud happens with this kind of past layer where there's vMotioning going on. All kinds of spanning applications and data across environments. >> Yeah. Assume there's an operating system working on behind the scenes. >> Right. >> What's the security posture in all this? >> Yeah. So remember my narrative about the bad guys are getting in and they're moving around and they're so sneaky that they're using legitimate pathways. The only way to stop that stuff, is you've got to understand it at what we call Layer 7. At the application layer. Trying to do security to the infrastructure layer. It was interesting 20 years ago, kind of less interesting 10 years ago. And now it's becoming irrelevant because the infrastructure is oftentimes not even visible. It's buried in some cloud provider. So Layer 7 understanding, application awareness, understanding the APIs and reading the content. That's the name of the game in security. That's what we've been focused on. Nothing to do with the infrastructure. >> And where's the progress bar on that paradigm. One to ten. Ten being everyone's doing it. >> Right now. Well, okay. So we as a vendor can do this today. All the stuff I talked about, reading APIs, understanding the individual services looking at, Hey, wait a minute this credit card anomalies, that's all shipping production code. Where is it in customer adoption life cycle? Early days 10%. So there's a whole lot of headroom for people to understand, Hey, I can put these controls in place. They're software based. They don't require appliances. It's Layer 7, so it has contextual awareness and it's works on every single cloud. >> We talked about the pandemic being an accelerator. It really was a catalyst to really rethink. Remember we used to talk about Pat as a security do over. He's like, yes, if it's the last thing I do, I'm going to fix security. Well, he decided to go try to fix Intel instead. >> He's getting some help from the government. >> But it seems like CISOs have totally rethought their security strategy. And at least in part, as a function of the pandemic. >> When I started at VMware four years ago, Pat sat me down in his office and he said to me what he said to you, which is like, "Tom," he said, "I feel like we have fundamentally changed servers. We fundamentally change storage. We fundamentally change networking. The last piece of the puzzle of security. I want you to go fundamentally change it." And I'll argue that the work that we're doing with this horizontal security, understanding the lateral movement. East- West inspection. It fundamentally changes how security works. It's got nothing to do with firewalls. It's got nothing to do with Endpoint. It's a unique capability that VMware is uniquely suited to deliver on. And so Pat, thanks for the mission. We delivered it and it's available now. >> Those WET web applications firewall for instance are around, I mean. But to your point, the perimeter's gone. >> Exactly. >> And so you got to get, there's no perimeter. so it's a surface area problem. >> Correct. And access. And entry. >> Correct. >> They're entering here easy from some manual error, or misconfiguration or bad password that shouldn't be there. They're in. >> Think about it this way. You put the front door of your house, you put a big strong door and a big lock. That's a firewall. Bad guys come in the window. >> And then the windows open. With a ladder. >> Oh my God. Cause it's hot, bad user behavior trumps good security every time. >> And then they move around room to room. We're the room to room people. We see each little piece of the thing. Wait, that shouldn't happen. Right. >> I want to get you a question that we've been seeing and maybe we're early on this or it might be just a false data point. A lot of CSOs and we're talking to are, and people in industry in the customer environment are looking at CISOs and CSOs, two roles. Chief information security officer, and then chief security officer. Amazon, actually Steven Schmidt is now CSO at Reinforce. They actually called that out. And the interesting point that he made, we had some other situations that verified this, is that physical security is now tied to online, to your point about the service area. If I get a password, I still got the keys to the physical goods too. >> Right. So physical security, whether it's warehouse for them or store or retail. Digital is coming in there. >> Yeah. So is there a CISO anymore? Is it just CSO? What's the role? Or are there two roles you see that evolving? Or is that just circumstance. >> I think it's just one. And I think that the stakes are incredibly high in security. Just look at the impact that these security attacks are having on. Companies get taken down. Equifax market cap was cut 80% with a security breach. So security's gone from being sort of a nuisance to being something that can impact your whole kind of business operation. And then there's a whole nother domain where politics get involved. It determines the fate of nations. I know that sounds grand, but it's true. And so companies care so much about it they're looking for one leader, one throat to choke. One person that's going to lead security in the virtual domain, in the physical domain, in the cyber domain, in the actual. >> I mean, you mention that, but I mean, you look at Ukraine. I mean that cyber is a component of that war. I mean, it's very clear. I mean, that's new. We've never seen. this. >> And in my opinion, the stuff that we see happening in the Ukraine is small potatoes compared to what could happen. >> Yeah. >> So the US, we have a policy of strategic deterrence. Where we develop some of the most sophisticated cyber weapons in the world. We don't use them. And we hope never to use them. Because our adversaries, who could do stuff like, I don't know, wipe out every bank account in North America. Or turn off the lights in New York City. They know that if they were to do something like that, we could do something back. >> This is the red line conversation I want to go there. So, I had this discussion with Robert Gates in 2016 and he said, "We have a lot more to lose." Which is really your point. >> So this brand. >> I agree that there's to have freedom and liberty, you got to strike back with divorce. And that's been our way to balance things out. But with cyber, the red line, people are already in banks. So they're are operating below the red line line. Red line meaning before we know you're in there. So do we move the red line down because, hey, Sony got hacked. The movie. Because they don't have their own militia. >> Yeah. >> If their were physical troops on the shores of LA breaking into the file cabinets. The government would've intervened. >> I agree with you that it creates tension for us in the US because our adversaries don't have the clear delineation between public and private sector. Here you're very, very clear if you're working for the government. Or you work for an private entity. There's no ambiguity on that. >> Collaboration, Tom, and the vendor community. I mean, we've seen efforts to try to. >> That's a good question. >> Monetize private data and private reports. >> So at VMware, I'm very proud of the security capabilities we've built. But we also partner with people that I think of as direct competitors. We've got firewall vendors and Endpoint vendors that we work with and integrate. And so coopetition is something that exists. It's hard. Because when you have these kind of competing. So, could we do more? Of course we probably could. But I do think we've done a fair amount of cooperation, data sharing, product integration, et cetera. And as the threats get worse, you'll probably see us continue to do more. >> And the government is going to trying to force that too. >> And the government also drives standards. So let's talk about crypto. Okay. So there's a new form of encryption coming out called processing quantum. >> Quantum. Quantum computers have the potential to crack any crypto cipher we have today. That's bad. Okay. That's not good at all because our whole system is built around these private communications. So the industry is having conversations about crypto agility. How can we put in place the ability to rapidly iterate the ciphers in encryption. So, when the day quantum becomes available, we can change them and stay ahead of these quantum people. >> Well, didn't NIST just put out a quantum proof algo that's being tested right now by the community? >> There's a lot of work around that. Correct. And NIST is taking the lead on this, but Google's working on it. VMware's working on it. We're very, very active in how do we keep ahead of the attackers and the bad guys? Because this quantum thing is a, it's an x-ray machine. It's like a dilithium crystal that can power a whole ship. It's a really, really, really powerful tool. >> Bad things will happen. >> Bad things could happen. >> Well, Tom, great to have you on the theCube. Thanks for coming on. Take the last minute to just give a plug for what's going on for you here at VMWorld this year, just VMware Explore this year. >> Yeah. We announced a bunch of exciting things. We announced enhancements to our NSX family, with our advanced load balancer. With our edge firewall. And they're all in service of one thing, which is helping our customers make their private cloud like the public cloud. So I like to say 0, 0, 0. If you are in the cloud operating model, you have zero proprietary appliances. You have zero tickets to launch a workload. You have zero network taps and Zero Trust built into everything you do. And that's what we're working on. Pushing that further and further. >> Tom Gill, senior vices president, head of the networking at VMware. Thanks for coming on. We do appreciate it. >> Thanks for having us. >> Always getting the security data. That's killer data and security of the two ops that get the most conversations around DevOps and Cloud Native. This is The theCube bringing you all the action here in San Francisco for VMware Explore 2022. I'm John Furrier with Dave Vellante. Thanks for watching. (bright music)

>>Welcome back everyone to the cubes, live coverage here at VMware Explorer, 22. We're here on the ground on the floor of Mosco. I'm John for David ante. We're at kit Goldberg, CTO of VMware, the star of the show, the headliner@supercloud.world. The event we had just a few weeks ago, kit. Great to see you super excited to, to chat with you. Thanks for coming on. Oh >>Yeah. Happy to be here, man. It's been a wild week. Tons of excitement. We are jazzed. We're jacked, like to look at things >>For both, of course, jacked up and jazzed. Ready to go. So you got UN stage loved your keynote, you know, very CTO oriented, hit the, all your marks cloud native, the vSphere eight intro. Yep. More performance, more power. Yeah, more efficiency. And now the cloud native over the top, you shipped a white paper a few weeks ago, which we discussed at our super cloud event. Yep. You know, really laying out the narrative of cloud native. This is the priority for you. Is that true? Is that your only priority? What are the things going on right now for you that are your top priorities, >>Top priorities. So absolutely at a high level, it's flushing out this vision that, that we're talking about in terms of what we call cross cloud services. Other people call multi-cloud, you guys have super cloud, but the point is, I think what we see is that there's these different sort of vertical silos, the different public clouds they're on-prem data center edge. And what we're looking at is trying to create a new type of cloud something that's more horizontal in architecture. And I think this is something that we realize we've been doing at VMware for a while, and we gave it a name, we call it cross cloud. But what's important is that while we do bring a lot of value there, we can't possibly do everything. This has to be an industrywide movement. And so I think what we're really excited about is figuring out, okay, how do we actually build an architecture and a framework such that there's clear sort of lines of responsibility. Here's what one company does. Here's what another one does make sure that there's clean sort of APIs between that basically an overall architecture and structure. So that's probably one of the, the high level things that we're doing as an organization right now. >>What's been the feedback here at VMware Explorer, obviously the new name, Explorer rag laid that out in the keynote. Yep. It's about moving forward. Not replacing the community. Yep. Extending the world core and exploring new frontiers multicloud. Obviously one of them key. Yeah. Very clever actually names dig into it. It's nuanced. What's been the reaction. Yep. You're right. Yep. You're crazy. I love it. I need it. It's it's too early. It's perfect timing. No, it's a bit of, what's the feedback always a little >>Bit of everything, you know, I think one of us firstno people didn't really understand it. I think people were confused about what it was, but now that we're here in person, I think generally speaking, I'm hearing a lot of positive things about it. We've been gone or been apart for three years now, right? Since the last in person one, and this is an interesting opportunity for recreation sort of rebirth, right? We've certainly lost some traditions during the COVID pandemic, but also gives us the opportunity to build new ones. And to your point, world was always associated with virtualization. And of course, we're still doing that. We're still doing cloud infrastructure, but we're doing so much more. And given this focus on multi-cloud that I just mentioned and how it is the go forward focus for VMware, we wanted to evolve the conference to have that focus. And so I've been actually really pleased to see how many folks for it's their first time here. Right? They haven't been Tom worlds before and you know, this broader sort of conference that we're creating to, to apply to the support, more disciplines, different focus areas, you know, application development, developers, platform teams, you got cloud management things with aria, public cloud management, networking security, and user computing, all in addition to the core infrastructure bits. >>So John all week's been paying homage to, to Andy Grove talking about, let chaos rain and then rain in the chaos. Right. And so when you talk to customers, that chaos message cloud chaos, how is it resonating? Are they aware of that chaos? Are they saying, yes, we have cloud chaos or some saying, eh, yeah. It's okay. Everything's good. And they just maybe have some blind spots. What do >>You think? Yeah. I'm actually surprised at how strongly it's resonating. I mean, I think we knew that we were onto something, but people even love the specific term. They're like cloud chaos. I never thought about it that way, but you're like, you're absolutely right. It was a movie. It's a great, yeah. I know. Sounds like a thriller, but, but what we sort of, the picture we paint there about these silos across clouds, the duplication of technologies, duplication of teams and training, all this stuff. People realize that's where they're at. And it's one of those things where there's this headlong rush to cloud for good reasons. People wanted to be in the agility, but now they're dealing with some of that complexity that, that gets built up there and it absolutely is chaos. And while speed is great, you need to somehow balance that speed with control things like security compliance. These are sort of enterprise requirements that are sort of getting left out. And I think that's the realization, that's the sort of chaos that we're hitting on. >>It's almost like when in bus, in business school, you had the economic lines when break even hits, you know, cloud had a lot of great goodness to it. Yep. A lot of great value. It still does on the CapEx side, but as distributed computing architectures become reality. Yep. Private cloud instantiation of hybrid cloud operations. Now you've got edge and opening up all these new, new net new applications. Yep. What are you seeing there? And it's a question we've been asked some of the folks in the partner network, what are some of those new next gen apps that are gonna be enabled by, by this next wave edge specifically? Yeah. More performance, more application development, more software. Yeah. More faster, cheaper going on here. Kind of a Moore's law vibe there. What's next. >>Yeah. So, you know, when we look at edge, so, okay. Take today. Today. Edge is oftentimes highly customized software and hardware. It's not general purpose or to cloud technologies. And while edge is certainly gonna be limited. You can't just infinitely scale. Like you can in the cloud and the network bandwidth might be a little bit limited. You still wanna imagine it or manage it as if it were another cloud location, right. That like, I wanna be able to address it. Just like I addressed a certain availabilities done within AWS. I wanna be able to say the specific edge location at, you know, wherever somewhere here in San Francisco, let's say right now there's a few different things though. The first of which is that you got to manage at scale. Cause you don't have with cloud, you got a small number of very large locations with edge. >>You got a large number of very small locations. And so it's the scale is inverted there. So what this means is that you probably can't exactly specify which edge you want to go to. What instead you wanna say is more relational. Like I've got an IOT device out there. I want my app to be in data to be near it. And the system needs to figure out, okay, where do I put that thing? And how do I get it near it? And there may be some different constraints. You have cost security, privacy, it may be your edge or maybe telco edge location, you know, one, one of these sorts of things. Right? And so I think where we're going there is to enable the movement of applications and data to the right place. And this again goes back to the whole cross cloud architecture, right? >>You don't wanna be limited in terms of where you put an app, you wanna have that flexibility. This is the whole, you know, we use the term cloud smart. Right. And that's what it means. It's like put the, the app where it needs to be sort of the right tool for the right job. And so I think the innovation though, it's gonna be huge. You're gonna see new application architectures that the app can be placed near a user near a device near like a, an iPhone or near an IOT device, like a video camera. And the way that you manage that is gonna be much kind of infrastructure is code base. Yeah. So I think there's huge possibilities there. And it's really amazing to see just real quick on the telco side, what's happening there as well. The move to 5g, the move to open ran telco is now starting to adopt these data center and cloud technologies kinda standard building blocks that we use now out at the edge. So I think, you know, the amount of innovation that we're gonna see, >>It's really the first time on telco, they actually have a viable, scalable opportunity to, to put real gear data center, liked capabilities yep. At a location for specific purpose. Yeah. The edge function. >>Yeah. And well, and what we, without >>Building a, a monster >>Facility. Exactly. Yeah. It's like the base of a cell tower or something telephone closet. But what we've been able to do is improve these general purpose technologies. Like you look at vSphere in our hypervisor today. We are great at real time workloads, right? Like as a matter of fact, you look at performance on vSphere versus bare metal. Oftentimes an app runs faster on vSphere now because of all the efficiency and scale and so forth we can bring. So it means that these telecom applications that are very latency sensitive can now run fun on there. But Hey, guess what? Once you have a general purpose server that can run some of the telecom apps, well, Hey, you got extra space to run other apps. Maybe you could sell that space to customers or partners. And you know, then you have this new architecture >>Is the dev skill, a, a barrier for the, for the telcos, where are we at >>With that? It, it, it is. I think the barriers are really, how do you provide, I dunno if it's a skill set. I mean, there's probably some skill set aspects. I think in my mind, it's more about giving them the APIs to get access to that. Like, as I said, you're not gonna have developers knowing, okay, here are the specific geographic locations of all the cell towers in San Francisco and set what you're gonna say again, I need to be near this thing. And so you used geolocation and figure out, just put it some, put it in the right place. I don't really care. Right. So again, I think it's an evolution of management evolution of the APIs that developers use to access. Like today, I'm gonna say, okay, I know my app needs to be on the east coast so I can use us east one. I know the specific AZs at a, at a cloud level. That makes sense at an edge level. It doesn't, you're not gonna know. Okay. Like the specific cross streets or whatever, you gotta let the system figure that >>Out kid. I know you gotta go on. Times's tight, real quick. You got a session here on web three. Yeah. The Cube's got the, you know, the cube versus coming soon. We might be heavy. The cube versus coming powered by arm token, we had all kinds of stuff going on. Yep. You saw the preview a couple years ago. We did with the Cuban. Anyway, you did a session on web three and DM. VMware's rolling real quick. What was that about? Yeah, what's the purpose? >>What's the direction. That was a fascinating conversation. So I was talking about web three. It was talking about why enterprises haven't really started even to scratch the surface of the potential of web three. So part of it was like, okay, what is web three? It's a buzz words. We talked through that. We talked through the use of blockchain, how that sits with the core of a lot of web three. We talked about the use of cryptocurrency and how that makes sense. We talked about the consumerization, continuing consumerization of it. We've seen it with end user devices. We may well see it with some of the web three changes around ownership, individual ownership of data, of assets, et cetera. That's gonna have a downstream impact on enterprises, how they go to market their commercial models. So it was a fascinating discussion that unfortunately it's hard to summarize, but gotten to a lot of the nuances of this and some of the, are >>You bullish on >>It? Very bullish, a hundred percent. Like I think blockchain is a hugely enabling technology and not from a cryptocurrency standpoint, put that aside. All the enterprise use cases, we have customers like broad bridge financial today leveraging VMware blockchain, doing a hundred billion in transactions a day with the sort of repo market >>You think defi is booming >>Defi. So I, I think we're just starting to get there. But what you find is oftentimes these trends start on the consumer side and then all of a sudden they surprise enterprises. >>They call it a tri tried tread five traditional fi finance >>Versus okay. >>Any >>Other way around? No, no, no. But I'm saying is that it's, these consumer trends will start to impact enterprises. But what I'm saying is that enterprises need to be ready now or start preparing now for those comings. >>And what's the preparation for that? Just education learning. Yeah. >>Education learning, looking at blockchain, use cases, looking at what will this enable consumers to do that they couldn't do before there is gonna be a democratization of access to data. You're still gonna wanna have gatekeepers. You're still gonna wanna have enterprises or services that add value on top of that, but it's gonna be a bit more of an open ecosystem now, and that's gonna change some of the market dynamics in subtle ways. >>Okay. So we got one minute left. I want to ask you, what's your impression of the super cloud event we had also, you were headlining and you guys were a big part of bringing the, a large C of great people together. Are you happy with the outcome? What do you think's next for? >>Absolutely. No. I was super excited to see how much reception and engagement it got from across the industry. Right? So many different entry participants, so many different customers, partners, et cetera, viewing it online have had a lot of conversations here at explore already. As you know, you know, VMware, we put out a white paper, our point of view on what is a multi-cloud service. What is the taxonomy of those services? Again, as I mentioned before, we need to get as an industry to a place where we have alignment about this overall architecture to enable interoperability. And I think that's really the key thing. If we're gonna make this industry architectural shift, which is what I see coming, this is what we got. >>And you're gonna be jumping all in with this and helping out if we need you >>Hundred percent. All right. >>All in. I really love your transparency on the, on your white paper. Check out the white paper online on vmware.com. It's the cross cloud cloud native. I, I call the, the mission statement. It's not a Jerry McGuire memo. It's more me than that. It's the, it's the direction of cloud native. Yep. And multi-cloud thanks for coming on and, and thanks for doing that too. >>No, of course. And thanks for having me. Thanks. Love the discussion. >>Okay. More live coverage here at world Explorer, VMware Explorer, after the short break.

>> Announcer: theCube presents HPE Discover 2022 brought to you by HPE. >> Hello and welcome back to theCube's continuous coverage HPE Discover 2022 and from Las Vegas the formerly Sands Convention Center now Venetian, John Furrier and Dave Vellante here were excited to welcome in Jen Huffstetler. Who's the Chief product Sustainability Officer at Intel Jen, welcome to theCube thanks for coming on. >> Thank you very much for having me. >> You're really welcome. So you dial back I don't know, the last decade and nobody really cared about it but some people gave it lip service but corporations generally weren't as in tune, what's changed? Why has it become so top of mind? >> I think in the last year we've noticed as we all were working from home that we had a greater appreciation for the balance in our lives and the impact that climate change was having on the world. So I think across the globe there's regulations industry and even personally, everyone is really starting to think about this a little more and corporations specifically are trying to figure out how are they going to continue to do business in these new regulated environments. >> And IT leaders generally weren't in tune cause they weren't paying the power bill for years it was the facilities people, but then they started to come together. How should leaders in technology, business tech leaders, IT leaders, CIOs, how should they be thinking about their sustainability goals? >> Yeah, I think for IT leaders specifically they really want to be looking at the footprint of their overall infrastructure. So whether that is their on-prem data center, their cloud instances, what can they do to maximize the resources and lower the footprint that they contribute to their company's overall footprint. So IT really has a critical role to play I think because as you'll find in IT, the carbon footprint of the data center of those products in use is actually it's fairly significant. So having a focus there will be key. >> You know compute has always been one of those things where, you know Intel's been makes chips so that, you know heat is important in compute. What is Intel's current goals? Give us an update on where you guys are at. What's the ideal goal in the long term? Where are you now? You guys always had a focus on this for a long, long time. Where are we now? Cause I won't say the goalpost of changed, they're changing the definitions of what this means. What's the current state of Intel's carbon footprint and overall goals? >> Yeah, no thanks for asking. As you mentioned, we've been invested in lowering our environmental footprint for decades in fact, without action otherwise, you know we've already lowered our carbon footprint by 75%. So we're really in that last mile. And that is why when we recently announced a very ambitious goal Net-Zero 2040 for our scope one and two for manufacturing operations, this is really an industry leading goal. And partly because the technology doesn't even exist, right? For the chemistries and for making the silicon into the sand into, you know, computer chips yet. And so by taking this bold goal, we're going to be able to lead the industry, partner with academia, partner with consortia, and that drive is going to have ripple effects across the industry and all of the components in semiconductors. >> Is there a changing definition of Net-Zero? What that means, cause some people say they're Net-Zero and maybe in one area they might be but maybe holistically across the company as it becomes more of a broader mandate society, employees, partners, Wall Street are all putting pressure on companies. Is the Net-Zero conversation changed a little bit or what's your view on that? >> I think we definitely see it changing with changing regulations like those coming forth from the SEC here in the US and in Europe. Net-Zero can't just be lip service anymore right? It really has to be real reductions on your footprint. And we say then otherwise and even including in our supply chain goals what we've taken new goals to reduce, but our operations are growing. So I think everybody is going through this realization that you know, with the growth, how do we keep it lower than it would've been otherwise, keep focusing on those reductions and have not just renewable credits that could have been bought in one location and applied to a different geographical location but real credible offsets for where the the products manufactured or the computes deployed. >> Jen, when you talk about you've reduced already by 75% you're on that last mile. We listened to Pat Gelsinger very closely up until recently he was the number one most frequently had on theCube guest. He's been busy I guess. But as you apply that discipline to where you've been, your existing business and now Pat's laid out this plan to increase the Foundry business how does that affect your... Are you able to carry through that reduction to, you know, the new foundries? Do you have to rethink that? How does that play in? >> Certainly, well, the Foundry expansion of our business with IBM 2.0 is going to include the existing factories that already have the benefit of those decades of investment and focus. And then, you know we have clear goals for our new factories in Ohio, in Europe to achieve goals as well. That's part of the overall plan for Net-Zero 2040. It's inclusive of our expansion into Foundry which means that many, many many more customers are going to be able to benefit from the leadership that Intel has here. And then as we onboard acquisitions as any company does we need to look at the footprint of the acquisition and see what we can do to align it with our overall goals. >> Yeah so sustainable IT I don't know for some reason was always an area of interest to me. And when we first started, even before I met you, John we worked with PG&E to help companies get rebates for installing technologies that would reduce their carbon footprint. >> Jen: Very forward thinking. >> And it was a hard thing to get, you know, but compute was the big deal. And there were technologies and I remember virtualization at the time was one and we would go in and explain to the PG&E engineers how that all worked. Cause they had metrics and that they wanted to see, but anyway, so virtualization was clearly one factor. What are the technologies today that people should be paying, flash storage was another one. >> John: AI's going to have a big impact. >> Reduce the spinning disk, but what are the ones today that are going to have an impact? >> Yeah, no, that's a great question. We like to think of the built in acceleration that we have including some of the early acceleration for virtualization technologies as foundational. So built in accelerated compute is green compute and it allows you to maximize the utilization of the transistors that you already have deployed in your data center. This compute is sitting there and it is ready to be used. What matters most is what you were talking about, John that real world workload performance. And it's not just you know, a lot of specsmanship around synthetic benchmarks, but AI performance with the built in acceleration that we have in Xeon processors with the Intel DL Boost, we're able to achieve four X, the AI performance per Watts without you know, doing that otherwise. You think about the consolidation you were talking about that happened with virtualization. You're basically effectively doing the same thing with these built in accelerators that we have continued to add over time and have even more coming in our Sapphire Generation. >> And you call that green compute? Or what does that mean, green compute? >> Well, you are greening your compute. >> John: Okay got it. >> By increasing utilization of your resources. If you're able to deploy AI, utilize the telemetry within the CPU that already exists. We have customers KDDI in Japan has a great Proofpoint that they already announced on their 5G data center, lowered their data center power by 20%. That is real bottom line impact as well as carbon footprint impact by utilizing all of those built in capabilities. So, yeah. >> We've heard some stories earlier in the event here at Discover where there was some cooling innovations that was powering moving the heat to power towns and cities. So you start to see, and you guys have been following this data center and been part of the whole, okay and hot climates, you have cold climates, but there's new ways to recycle energy where's that cause that sounds very Sci-Fi to me that oh yeah, the whole town runs on the data center exhaust. So there's now systems thinking around compute. What's your reaction to that? What's the current view on re-engineering a system to take advantage of that energy or recycling? >> I think when we look at our vision of sustainable compute over this horizon it's going to be required, right? We know that compute helps to solve society's challenges and the demand for it is not going away. So how do we take new innovations looking at a systems level as compute gets further deployed at the edge, how do we make it efficient? How do we ensure that that compute can be deployed where there is air pollution, right? So some of these technologies that you have they not only enable reuse but they also enable some you know, closing in of the solution to make it more robust for edge deployments. It'll allow you to place your data center wherever you need it. It no longer needs to reside in one place. And then that's going to allow you to have those energy reuse benefits either into district heating if you're in, you know Northern Europe or there's examples with folks putting greenhouses right next to a data center to start growing food in what we're previously food deserts. So I don't think it's science fiction. It is how we need to rethink as a society. To utilize everything we have, the tools at our hand. >> There's a commercial on the radio, on the East Coast anyway, I don't know if you guys have heard of it, it's like, "What's your one thing?" And the gentleman comes on, he talks about things that you can do to help the environment. And he says, "What's your one thing?" So what's the one thing or maybe it's not just one that IT managers should be doing to affect carbon footprint? >> The one thing to affect their carbon footprint, there are so many things. >> Dave: Two, three, tell me. >> I think if I was going to pick the one most impactful thing that they could do in their infrastructure is it's back to John's comment. It's imagine if the world deployed AI, all the benefits not only in business outcomes, you know the revenue, lowering the TCO, but also lowering the footprint. So I think that's the one thing they could do. If I could throw in a baby second, it would be really consider how you get renewable energy into your computing ecosystem. And then you know, at Intel, when we're 80% renewable power, our processors are inherently low carbon because of all the work that we've done others have less than 10% renewable energy. So you want to look for products that have low carbon by design, any Intel based system and where you can get renewables from your grid to ask for it, run your workload there. And even the next step to get to sustainable computing it's going to take everyone, including every enterprise to think differently and really you know, consider what would it look like to bring renewables onto my site? If I don't have access through my local utility and many customers are really starting to evaluate that. >> Well Jen its great to have you on theCube. Great insight into the current state of the art of sustainability and carbon footprint. My final question for you is more about the talent out there. The younger generation coming in I'll say the pressure, people want to work for a company that's mission driven we know that, the Wall Street impact is going to be financial business model and then save the planet kind of pressure. So there's a lot of talent coming in. Is there awareness at the university level? Is there a course where can, do people get degrees in sustainability? There's a lot of people who want to come into this field what are some of the talent backgrounds of people learning or who might want to be in this field? What would you recommend? How would you describe how to onboard into the career if they want to contribute? What are some of those factors? Cause it's not new, new, but it's going to be globally aware. >> Yeah well there certainly are degrees with focuses on sustainability maybe to look at holistically at the enterprise, but where I think the globe is really going to benefit, we didn't really talk about the software inefficiency. And as we delivered more and more compute over the last few decades, basically the programming languages got more inefficient. So there's at least 35% inefficiency in the software. So being a software engineer, even if you're not an AI engineer. So AI would probably be the highest impact being a software engineer to focus on building new applications that are going to be efficient applications that they're well utilizing the transistor that they're not leaving zombie you know, services running that aren't being utilized. So I actually think-- >> So we got a program in assembly? (all laughing) >> (indistinct), would get really offended. >> Get machine language. I have to throw that in sorry. >> Maybe not that bad. (all laughing) >> That's funny, just a joke. But the question is what's my career path. What's a hot career in this area? Sustainability, AI totally see that. Anything else, any other career opportunities you see or hot jobs or hot areas to work on? >> Yeah, I mean, just really, I think it takes every architect, every engineer to think differently about their design, whether it's the design of a building or the design of a processor or a motherboard we have a whole low carbon architecture, you know, set of actions that are we're underway that will take to the ecosystem. So it could really span from any engineering discipline I think. But it's a mindset with which you approach that customer problem. >> John: That system thinking, yeah. >> Yeah sustainability designed in. Jen thanks so much for coming back in theCube, coming on theCube. It's great to have you. >> Thank you. >> All right. Dave Vellante for John Furrier, we're sustaining theCube. We're winding down day three, HPE Discover 2022. We'll be right back. (upbeat music)

>> (bright music) >> Narrator: The Cube, presents Kubecon and Cloudnativecon, year of 2022, brought to you by Red Hat, the cloud native computing foundation and its ecosystem partners. >> Now what we're opening. Welcome to Valencia, Spain in Kubecon Cloudnativecon, Europe, 2022. I'm Keith Townsend, along with my host, Paul Gillin, who is the senior editor for architecture at Silicon angle, Paul. >> Keith you've been asking me questions all these last two days. Let me ask you one. You're a traveling man. You go to a lot of conferences. What's different about this one. >> You know what, we're just talking about that pre-conference, open source conferences are usually pretty intimate. This is big. 7,500 people talking about complex topics, all in one big area. And then it's, I got to say it's overwhelming. It's way more. It's not focused on a single company's product or messaging. It is about a whole ecosystem, very different show. >> And certainly some of the best t-shirts I've ever seen. And our first guest, Jim has one of the better ones. >> I mean a bit cockroach come on, right. >> Jim Walker, principal product evangelist at CockroachDB and Christian Huning, tech director of cloud technologies at Finleap Connect, a financial services company that's based out of Germany, now offering services in four countries now. >> Basically all over Europe. >> Okay. >> But we are in three countries with offices. >> So you're CockroachDB customer and I got to ask the obvious question. Databases are hard and started the company in 2015 CockroachDB, been a customer since 2019, I understand. Why take the risk on a four year old database. I mean that just sounds like a world of risk and trouble. >> So it was in 2018 when we joined the company back then and we did this cloud native transformation, that was our task basically. We had very limited amount of time and we were faced with a legacy infrastructure and we needed something that would run in a cloud native way and just blend in with everything else we had. And the idea was to go all in with Kubernetes. Though early days, a lot of things were alpha beta, and we were running on mySQL back then. >> Yeah. >> On a VM, kind of small setup. And then we were looking for something that we could just deploy in Kubernetes, alongside with everything else. And we had to stack and we had to duplicate it many times. So also to maintain that we wanted to do it all the same like with GitOps and everything and Cockroach delivered that proposition. So that was why we evaluate the risk of relatively early adopting that solution with the proposition of having something that's truly cloud native and really blends in with everything else we do in the same way was something we considered, and then we jumped the leap of faith and >> The fin leap of faith >> The fin leap of faith. Exactly. And we were not dissatisfied. >> So talk to me a little bit about the challenges because when we think of MySQL, MySQL scales to amazing sizes, it is the de facto database for many cloud based architectures. What problems were you running into with MySQL? >> We were running into the problem that we essentially, as a finTech company, we are regulated and we have companies, customers that really value running things like on-prem, private cloud, on-prem is a bit of a bad word, maybe. So it's private cloud, hybrid cloud, private cloud in our own data centers in Frankfurt. And we needed to run it in there. So we wanted to somehow manage that and with, so all of the managed solution were off the table, so we couldn't use them. So we needed something that ran in Kubernetes because we only wanted to maintain Kubernetes. We're a small team, didn't want to use also like full blown VM solution, of sorts. So that was that. And the other thing was, we needed something that was HA distributable somehow. So we also looked into other solutions back at the time, like Vitis, which is also prominent for having a MySQL compliant interface and great solution. We also got into work, but we figured, this is from the scale, and from the sheer amount of maintenance it would need, we couldn't deliver that, we were too small for that. So that's where then Cockroach just fitted in nicely by being able to distribute BHA, be resilient against failure, but also be able to scale out because we had this problem with a single MySQL deployment to not really, as it grew, as the data amounts grew, we had trouble to operatively keep that under control. >> So Jim, every time someone comes to me and says, I have a new database, I think we don't need it, yet another database. >> Right. >> What problem, or how does CockroachDB go about solving the types of problems that Christian had? >> Yeah. I mean, Christian laid out why it exists. I mean, look guys, building a database isn't easy. If it was easy, we'd have a database for every application, but you know, Michael Stonebraker, kind of godfather of all database says it himself, it takes seven, eight years for a database to fully gestate to be something that's like enterprise ready and kind of, be relied upon. We've been billing for about seven, eight years. I mean, I'm thankful for people like Christian to join us early on to help us kind of like troubleshoot and go through some things. We're building a database, it's not easy. You're right. But building a distributor system is also not easy. And so for us, if you look at what's going on in just infrastructure in general, what's happening in Kubernetes, like this whole space is Kubernetes. It's all about automation. How do I automate scale? How do I automate resilience out of the entire equation of what we're actually doing? I don't want to have to think about active passive systems. I don't want to think about sharding a database. Sure you can scale MySQL. You know, how many people it takes to run three or four shards of MySQL database. That's not automation. And I tell you what, this world right now with the advances in data how hard it is to find people who actually understand infrastructure to hire them. This is why this automation is happening, because our systems are more complex. So we started from the very beginning to be something that was very different. This is a cloud native database. This is built with the same exact principles that are in Kubernetes. In fact, like Kubernetes it's kind of a spawn of borg, the back end of Google. We are inspired by Spanner. I mean, this started by three engineers that worked at Google, are frustrated, they didn't have the tools, they had at Google. So they built something that was, outside of Google. And how do we give that kind of Google like infrastructure for everybody. And that's, the advent of Cockroach and kind of why we're doing, what we're doing. >> As your database has matured, you're now beginning a transition or you're in a transition to a serverless version. How are you doing that without disrupting the experience for existing customers? And why go serverless at all? >> Yeah, it's interesting. So, you know, serverless was, it was kind of a an R&D project for us. And when we first started on a path, because I think you know, ultimately what we would love to do for the database is let's not even think about database, Keith. Like, I don't want to think about the database. What we're building too is, we want a SQL API in the cloud. That's it. I don't want to think about scale. I don't want to think about upgrades. I literally like. that stuff should just go away. That's what we need, right. As developers, I don't want to think about isolation levels or like, you know, give me DML and I want to be able to communicate. And for us the realization of that vision is like, if we're going to put a database on the planet for everybody to actually use it, we have to be really, really efficient. And serverless, which I believe really should be infrastructure less because I don't think we should be thinking of just about service. We got to think about, how do I take the context of regions out of this thing? How do I take the context of cloud providers out of what we're talking about? Let's just not think about that. Let's just code against something. Serverless was the answer. Now we've been building for about a year and a half. We launched a serverless version of Cockroach last October and we did it so that everybody in the public could have a free version of a database. And that's what serverless allows us to do. It's all consumption based up to certain limits and then you pay. But I think ultimately, and we spoke a little bit about this at the very beginning. I think as ISVs, people who are building software today the serverless vision gets really interesting because I think what's on the mind of the CTO is, how do I drive down my cost to the cloud provider? And if we can basically, drive down costs through either making things multi-tenant and super efficient, and then optimizing how much compute we use, spinning things down to zero and back up and auto scaling these sort of things in our software. We can start to make changes in the way that people are thinking about spend with the cloud provider. And ultimately we did that, so we could do things for free. >> So, Jim, I think I disagree Christian, I'm sorry, Jim. I think I disagree with you just a little bit. Christian, I think the biggest challenge facing CTOs are people. >> True. >> Getting the people to worry about cost and spend and implementation. So as you hear the concepts of CoachDB moving to a serverless model, and you're a large customer how does that make you think or react to your people side of your resources? >> Well, I can say that from the people side of resources luckily Cockroach is our least problem. So it just kind of, we always said, it's an operator stream because that was the part that just worked for us, so. >> And it's worked as you have scaled it? without you having ... >> Yeah. I mean, we use it in a bit of a, we do not really scale out like the Cockroach, like really large. It's like, more that we use it with the enterprise features of encryption in the stack and our customers then demand. If they do so, we have the Zas offering and we also do like dedicated stacks. So by having a fully cloud native solution on top of Kubernetes, as the foundational layer we can just use that and stamp it out and deploy it. >> How does that translate into services you can provide your customers? Are there services you can provide customers that you couldn't have, if you were running, say, MySQL? >> No, what we do is, we run this, so the SAS offering runs in our hybrid private cloud. And the other thing that we offer is that we run the entire stack at a cloud provider of their choosing. So if they are an AWS, they give us an AWS account, we put it in there. Theoretically, we could then also talk about using the serverless variant, if they like so, but it's not strictly required for us. >> So Christian, talk to me about that provisioning process because if I had a MySQL deployment before I can imagine how putting that into a cloud native type of repeatable CICD pipeline or Ansible script that could be difficult. Talk to me about that. How CockroachDB enables you to create new onboarding experiences for your customers? >> So what we do is, we use helm charts all over the place as probably everybody else. And then each application team has their parts of services, they've packaged them to helm charts, they've wrapped us in a super chart that gets wrapped into the super, super chart for the entire stack. And then at the right place, somewhere in between Cockroach is added, where it's a dependency. And as they just offer a helm chart that's as easy as it gets. And then what the teams do is they have an inner job, that once you deploy all that, it would spin up. And as soon as Cockroach is ready it's just the same reconcile loop as everything. It will then provision users, set up database schema, do all that. And initialize, initial data sets that might be required for a new setup. So with that setup, we can spin up a new cluster and then deploy that stack chart in there. And it takes some time. And then it's done. >> So talk to me about life cycle management. Because when I have one database, I have one schema. When I have a lot of databases I have a lot of different schemas. How do you keep your stack consistent across customers? >> That is basically part of the same story. We have get offs all over the place. So we have this repository, we see the super helm chart versions and we maintain like minus three versions and ensure that we update the customers and keep them up to date. It's part of the contract sometimes, down to the schedule of the customer at times. And Cockroach nicely supports also, these updates with these migrations in the background, the schema migrations in the background. So we use in our case, in that integration SQL alchemy, which is also nicely supported. So there was also part of the story from MySQL to Postgres, was supported by the ORM, these kind of things. So the skill approach together with the ease of helm charts and the background migrations of the schema is a very seamless upgrade operations. Before that we had to have downtime. >> That's right, you could have online schema changes. Upgrading the database uses the same concept of rolling upgrades that you have in Kubernetes. It's just cloud native. It just fits that same context, I think. >> Christian: It became a no-brainer. >> Yeah. >> Yeah. >> Jim, you mentioned the idea of a SQL API in the cloud, that's really interesting. Why does such a thing not exist? >> Because it's really difficult to build. You know, SQL API, what does that mean? Like, okay. What I'm going to, where does that endpoint live? Is there one in California one on the east coast, one in Europe, one in Asia? Okay. And I'm asking that endpoint for data. Where does that data live? Can you control where data lives on the planet? Because ultimately what we're fighting in software today in a lot of these situations is the speed of light. And so how do you intelligently place data on this planet? So that, you know, when you're asking for data, when you're maybe home, it's a different latency than when you're here in Valencia. Does that data follow and move you? These are really, really difficult problems to solve. And I think that we're at that layer of, we're at this moment in time in software engineering, we're solving some really interesting, interesting things cause we are budding against this speed of light problem. And ultimately that's one of the biggest challenges. But underneath, it has to have all this automation like the ease at which we can scale this database like the always on resilient, the way that we can upgrade the entire thing with just rolling upgrades. The cloud native concepts is really what's enabling us to do things at global scale it's automation. >> Let's alk about that speed of light in global scale. There's no better conference for speed of light, for scale, than Kubecon. Any predictions coming out of the show? >> It's less a prediction for me and more of an observation, you guys. Like look at two years ago, when we were here in Barcelona at QCon EU, it was a lot of hype. It's a lot of hype, a lot of people walking around, curious, fascinated, this is reality. The conversations that I'm having with people today, there's a reality. There's people really doing, they're becoming cloud native. And to me, I think what we're going to see over the next two to three years is people start to adopt this kind of distributed mindset. And it permeates not just within infrastructure but it goes up into the stack. We'll start to see much more developers using, Go and these kind of the threaded languages, because I think that distributed mindset, if it starts at the chip all the way to the fingertip of the person clicking and you're distributed everywhere in between. It is extremely powerful. And I think that's what Finleap, I mean, that's exactly what the team is doing. And I think there's a lot of value and a lot of power in that. >> Jim, Christian, thank you so much for coming on the Cube and sharing your story. You know what we're past the hype cycle of Kubernetes, I agree. I was a nonbeliever in Kubernetes two, three years ago. It was mostly hype. We're looking at customers from Microsoft, Finleap and competitors doing amazing things with this platform and cloud native in general. Stay tuned for more coverage of Kubecon from Valencia, Spain. I'm Keith Townsend, along with Paul Gillin and you're watching the Cube, the leader in high tech coverage. (bright music)

(soft intro music) >> Welcome back to theCUBE's coverage of VeeamON 2022. Dave Vellante with David Nicholson. Brian Schwarz is here. We're going to stay on cloud. He's the director of product management at Google Cloud. The world's biggest cloud, I contend. Brian, thanks for coming on theCUBE. >> Thanks for having me. Super excited to be here. >> Long time infrastructure as a service background, worked at Pure, worked at Cisco, Silicon Valley guy, techie. So we're going to get into it here. >> I love it. >> I was saying before, off camera. We used to go to Google Cloud Next every year. It was an awesome show. Guys built a big set for us. You joined, right as the pandemic hit. So we've been out of touch a little bit. It's hard to... You know, you got one eye on the virtual event, but give us the update on Google Cloud. What's happening generally and specifically within storage? >> Yeah. So obviously the Cloud got a big boost during the pandemic because a lot of work went online. You know, more things kind of being digitally transformed as people keep trying to innovate. So obviously the growth of Google Cloud, has got a big tailwind to it. So business has been really good, lots of R&D investment. We obviously have an incredible set of technology already but still huge investments in new technologies that we've been bringing out over the past couple of years. It's great to get back out to events to talk to people about 'em. Been a little hard the last couple of years to give people some of the insights. When I think about storage, huge investments, one of the things that some people know but I think it's probably underappreciated is we use the same infrastructure for Google Cloud that is used for Google consumer products. So Search and Photos and all the public kind of things that most people are familiar with, Maps, et cetera. Same infrastructure at the same time is also used for Google Cloud. So we just have this tremendous capability of infrastructure. Google's got nine products that have a billion users most of which many people know. So we're pretty good at storage pretty good at compute, pretty good at networking. Obviously a lot of that kind of shines through on Google Cloud for enterprises to bring their applications, lift and shift and/or modernize, build new stuff in the Cloud with containers and things like that. >> Yeah, hence my contention that Google has the biggest cloud in the world, like I said before. Doesn't have the most IS revenue 'cause that's a different business. You can't comment, but I've got Google Cloud running at $12 billion a year run rate. So a lot of times people go, "Oh yeah, Google they're third place going for the bronze." But that is a huge business. There aren't a lot of 10, $12 billion infrastructure companies. >> In a rapidly growing market. >> And if you do some back of napkin math, whatever, give me 10, 15, let's call it 15% of that, to storage. You've got a big storage business. I know you can't tell us how big, but it's big. And if you add in all the stuff that's not in GCP, you do a lot of storage. So you know storage, you understand the technology. So what is the state of technology? You have a background in Cisco, nearly a networking company, they used to do some storage stuff sort of on the side. We used to say they're going to buy NetApp, of course that never happened. That would've made no sense. Pure Storage, obviously knows storage, but they were a disk array company essentially. Cloud storage, what's different about it? What's different in the technology? How does Google think about it? >> You know, I always like to tell people there's some things that are the same and familiar to you, and there's some things that are different. If I start with some of the differences, object storage in the Cloud, like just fundamentally different. Object storage on-prem, it's been around for a while, often used as kind of like a third tier of storage, maybe a backup target, compliance, something like that. In the cloud, object storage is Tier one storage. Public reference for us, Spotify, okay, use object storage for all the songs out there. And increasingly we see a lot of growth in-- >> Well, how are you defining Tier one storage in that regard? Again, are you thinking streaming service? Okay. Fine. Transactional? >> Spotify goes down and I'm pissed. >> Yeah. This is true. (Dave laughing) >> Not just you, maybe a few million other people too. One is importance, business importance. Tier one applications like critical to the business, like business down type stuff. But even if you look at it for performance, for capabilities, object storage in the cloud, it's a different thing than it was. >> Because of the architecture that you're deploying? >> Yeah. And the applications that we see running on it. Obviously, a huge growth in our business in AI and analytics. Obviously, Google's pretty well known in both spaces, BigQuery, obviously on the analytics side, big massive data warehouses and obviously-- >> Gets very high marks from customers. >> Yeah, very well regarded, super successful, super popular with our customers in Google Cloud. And then obviously AI as well. A lot of AI is about getting structure from unstructured data. Autonomous vehicles getting pictures and videos around the world. Speech recognition, audio is a fundamentally analog signal. You're trying to train computers to basically deal with analog things and it's all stored in object storage, machine learning on top of it, creating all the insights, and frankly things that computers can deal with. Getting structure out of the unstructured data. So you just see performance capabilities, importance as it's really a Tier one storage, much like file and block is where have kind of always been. >> Depending on, right, the importance. Because I mean, it's a fair question, right? Because we're used to thinking, "Oh, you're running your Oracle transaction database on block storage." That's Tier one. But Spotify's pretty important business. And again, on BigQuery, it is a cloud-native born in the cloud database, a lot of the cloud databases aren't, right? And that's one of the reasons why BigQuery is-- >> Google's really had a lot of success taking technologies that were built for some of the consumer services that we build and turning them into cloud-native Google Cloud. Like HDFS, who we were talking about, open source technologies came originally from the Google file system. Now we have a new version of it that we run internally called Colossus, incredible technologies that are cloud scale technologies that you can use to build things like Google Cloud storage. >> I remember one of the early Hadoop worlds, I was talking to a Google engineer and saying, "Well, wow, that's so cool that Hadoop came. You guys were the main spring of that." He goes, "Oh, we're way past Hadoop now." So this is early days of Hadoop (laughs) >> It's funny whenever Google says consumer services, usually consumer indicates just for me. But no, a consumer service for Google is at a scale that almost no business needs at a point in time. So you're not taking something and scaling it up-- >> Yeah. They're Tier one services-- for sure. >> Exactly. You're more often pairing it down so that a fortune 10 company can (laughs) leverage it. >> So let's dig into data protection in the Cloud, disaster recovery in the Cloud, Ransomware protection and then let's get into why Google. Maybe you could give us the trends that you're seeing, how you guys approach it, and why Google. >> Yeah. One of the things I always tell people, there's certain best practices and principles from on-prem that are just still applicable in the Cloud. And one of 'em is just fundamentals around recovery point objective and recovery time objective. You should know, for your apps, what you need, you should tier your apps, get best practice around them and think about those in the Cloud as well. The concept of RPO and RTO don't just magically go away just 'cause you're running in the Cloud. You should think about these things. And it's one of the reasons we're here at the VeeamON event. It's important, obviously, they have a tremendous skill in technology, but helping customers implement the right RPO and RTO for their different applications. And they also help do that in Google Cloud. So we have a great partnership with them, two main offerings that they offer in Google. One is integration for their on-prem things to use, basically Google as a backup target or DR target and then cloud-native backups they have some technologies, Veeam backup for Google. And obviously they also bought Kasten a while ago. 'Cause they also got excited about the container trend and obviously great technologies for those customers to use those in Google Cloud as well. >> So RPO and RTO is kind of IT terms, right? But we think of them as sort of the business requirement. Here's the business language. How much data are you willing to lose? And the business person says, "What? I don't want to lose any data." Oh, how big's your budget, right? Oh, okay. That's RPO. RTO is how fast you want to get it back? "How fast do you want to get it back if there's an outage?" "Instantly." "How much money do you want to spend on that?" "Oh." Okay. And then your application value will determine that. Okay. So that's what RPO and RTO is for those who you may not know that. Sometimes we get into the acronym too much. Okay. Why Google Cloud? >> Yeah. When I think about some of the infrastructure Google has and like why does it matter to a customer of Google Cloud? The first couple things I usually talk about is networking and storage. Compute's awesome, we can talk about containers and Kubernetes in a little bit, but if you just think about core infrastructure, networking, Google's got one of the biggest networks in the world, obviously to service all these consumer applications. Two things that I often tell people about the Google network, one, just tremendous backbone bandwidth across the regions. One of the things to think about with data protection, it's a large data set. When you're going to do recoveries, you're pushing lots of terabytes often and big pipes matter. Like it helps you hit the right recovery time objective 'cause you, "I want to do a restore across the country." You need good networks. And obviously Google has a tremendous network. I think we have like 20 subsea cables that we've built underneath the the world's oceans to connect the world on the internet. >> Awesome. >> The other thing that I think is really underappreciated about the Google network is how quickly you get into it. One of the reasons all the consumer apps have such good response time is there's a local access point to get into the Google network somewhere close to you almost anywhere in the world. I'm sure you can find some obscure place where we don't have an access point, but look Search and Photos and Maps and Workspace, they all work so well because you get in the Google network fast, local access points and then we can control the quality of service. And that underlying substrate is the same substrate we have in Google Cloud. So the network is number one. Second one in storage, we have some really incredible capabilities in cloud storage, particularly around our dual region and multi-region buckets. The multi-region bucket, the way I describe it to people, it's a continent sized bucket. Single bucket name, strongly consistent that basically spans a continent. It's in some senses a little bit of the Nirvana of storage. No more DR failover, right? In a lot of places, traditionally on-prem but even other clouds, two buckets, failover, right? Orchestration, set up. Whenever you do orchestration, the DR is a lot more complicated. You got to do more fire drills, make sure it works. We have this capability to have a single name space that spans regions and it has strong read after write consistency, everything you drop into it you can read back immediately. >> Say I'm on the west coast and I have a little bit of an on-premises data center still and I'm using Veeam to back something up and I'm using storage within GCP. Trace out exactly what you mean by that in terms of a continent sized bucket. Updates going to the recovery volume, for lack of a better term, in GCP. Where is that physically? If I'm on the west coast, what does that look like? >> Two main options. It depends again on what your business goals are. First option is you pick a regional bucket, multiple zones in a Google Cloud region are going to store your data. It's resilient 'cause there's three zones in the region but it's all in one region. And then your second option is this multi-region bucket, where we're basically taking a set of the Google Cloud regions from around North America and storing your data basically in the continent, multiple copies of your data. And that's great because if you want to protect yourself from a regional outage, right? Earthquake, natural disaster of some sort, this multi-region, it basically gives you this DR protection for free and it's... Well, it's not free 'cause you have to pay for it of course, but it's a free from a failover perspective. Single name space, your app doesn't need to know. You restart the app on the east coast, same bucket name. >> Right. That's good. >> Read and write instantly out of the bucket. >> Cool. What are you doing with Veeam? >> So we have this great partnership, obviously for data protection and DR. And I really often segment the conversation into two pieces. One is for traditional on-prem customers who essentially want to use the Cloud as either a backup or a DR target. Traditional Veeam backup and replication supports Google Cloud targets. You can write to cloud storage. Some of these advantages I mentioned. Our archive storage, really cheap. We just actually lowered the price for archive storage quite significantly, roughly a third of what you find in some of the other competitive clouds if you look at the capabilities. Our archive class storage, fast recovery time, right? Fast latency, no hours to kind of rehydrate. >> Good. Storage in the cloud is overpriced. >> Yeah. >> It is. It is historically overpriced despite all the rhetoric. Good. I didn't know that. I'm glad to hear. >> Yeah. So the archive class store, so you essentially read and write into this bucket and restore. So it's often one of the things I joke with people about. I live in Silicon Valley, I still see the tape truck driving around. I really think people can really modernize these environments and use the cloud as a backup target. You get a copy of your data off-prem. >> Don't you guys use tape? >> Well, we don't talk a lot about-- >> No comment. Just checking. >> And just to be clear, when he says cloud storage is overpriced, he thinks that a postage stamp is overpriced, right? >> No. >> If I give you 50 cents, are you going to deliver a letter cross country? No. Cloud storage, it's not overpriced. >> Okay. (David laughing) We're going to have that conversation. I think it's historically overpriced. I think it could be more attractive, relative to the cost of the underlying technology. So good for you guys pushing prices. >> Yeah. So this archive class storage, is one great area. The second area we really work with Veeam is protecting cloud-native workloads. So increasingly customers are running workloads in the Cloud, they run VMware in the Cloud, they run normal VMs, they run containers. Veeam has two offerings in Google that essentially help customers protect that data, hit their RPO, RTO objectives. Another thing that is not different in the Cloud is the need to meet your compliance regulations, right? So having a product like Veeam that is easy to show back to your auditor, to your regulator to make sure that you have copies of your data, that you can hit an appropriate recovery time objective if you're in finance or healthcare, energy. So there's some really good Veeam technologies that work in Google Cloud to protect applications that actually run in Google Cloud all in. >> To your point about the tape truck I was kind of tongue in cheek, but I know you guys use tape. But the point is you shouldn't have to call the tape truck, right, you should go to Google and say, "Okay. I need my data back." Now having said that sometimes the highest bandwidth in the world is putting all this stuff on the truck. Is there an option for that? >> Again, it gets back to this networking capability that I mentioned. Yes. People do like to joke about, okay, trucks and trains and things can have a lot of bandwidth, big networks can push a lot of data around, obviously. >> And you got a big network. >> We got a huge network. So if you want to push... I've seen statistics. You can do terabits a second to a single Google Cloud storage bucket, super computing type performance inside Google Cloud, which from a scale perspective, whether it be network compute, these are things scale. If there's one thing that Google's really, really good at, it's really high scale. >> If your's companies can't afford to. >> Yeah, if you're that sensitive, avoid moving the data altogether. If you're that sensitive, have your recovery capability be in GCP. >> Yeah. Well, and again-- >> So that when you're recovering you're not having to move data. >> It's approximate to, yeah. That's the point. >> Recovering GCV, fail over your VMware cluster. >> Exactly. >> And use the cloud as a DR target. >> We got very little time but can you just give us a rundown of your portfolio in storage? >> Yeah. So storage, cloud storage for object storage got a bunch of regional options and classes of storage, like I mentioned, archive storage. Our first party offerings in the file area, our file store, basic enterprise and high scale, which is really for highly concurrent paralyzed applications. Persistent disk is our block storage offering. We also have a very high performance cash block storage offering and local SSDs. So that's the main kind of food groups of storage, block file object, increasingly doing a lot of work in data protection and in transfer and distributed cloud environments where the edge of the cloud is pushing outside the cloud regions themselves. But those are our products. Also, we spend a lot of time with our partners 'cause Google's really good at building and open sourcing and partnering at the same time hence with Veeam, obviously with file. We partner with NetApp and Dell and a bunch of folks. So there's a lot of partnerships we have that are important to us as well. >> Yeah. You know, we didn't get into Kubernetes, a great example of open source, Istio, Anthos, we didn't talk about the on-prem stuff. So Brian we'll have to have you back and chat about those things. >> I look forward to it. >> To quote my friend Matt baker, it's not a zero sum game out there and it's great to see Google pushing the technology. Thanks so much for coming on. All right. And thank you for watching. Keep it right there. Our next guest will be up shortly. This is Dave Vellante for Dave Nicholson. We're live at VeeamON 2022 and we'll be right back. (soft beats music)

(digital pulsing music) >> We're back at VeeamON 2022. My name is Dave Vellante. I'm here with my co-host David Nicholson. I've got another mass boy coming on. Patrick Osborne is the vice president of the storage business unit at HPE. Good to see you again, my friend. It's been a long time. >> It's been way too long, thank you very much for having me. >> I can't even remember the last time we saw each other. It might have been in our studios in the East Coast. Well, it's good to be here with you. Lots have been going on, of course, we've been following from afar, but give us the update, what's new with HPE? We've done some stuff on GreenLake, we've covered that pretty extensively and looks like you got some momentum there. >> Quite a bit of momentum, both on the technology front and certainly the customer acquisition front. The message is certainly resonating with our customers. GreenLake is, that's the transformation that's fueling the future of Hewlett Packard Enterprise. So the momentum is great on the technology side. We're at well over 50 services that we're providing on the GreenLake platform. Everything from solutions and workloads to compute, networking and storage. So it's been really fantastic to see the platform and being able to really delight the customers and then the momentum on the sales and the customer acquisition side, the customers are voting with their dollars, so they're very happy with the platform, certainly from an operational perspective and a financial consumption perspective and so our target goal, which we've said a bunch of times is we want to be the hyperscaler on on-prem. We want to provide that customer experience to the folks that are investing in the platform. It's going really well. >> I'll ask you a question, as a former analyst, it could be obnoxious and so forth, so I'll be obnoxious for a minute. I wrote a piece in 2010 called At Your Storage Service, saying the future of storage and infrastructure as a service, blah, blah, blah. Now, of course, you don't want to over-rotate when there's no market, there was no market for GreenLake in 2010. Do you feel like your timing was right on, a little bit late, little bit early? Looking back now, how do you feel about that? >> Well, it's funny you say that. On the timing side, we've seen iterations of this stops and start forever. >> That's true. Financial gimmicks. >> I started my career at Sun Microsystems. We talked about the big freaking Web-tone switch and a lot of the network is the computer. You saw storage networks, you've seen a lot, a ton of iterations in this category, and so, I think the timing's right right now. Obviously, the folks in the hyperscaler class have proved out that this is something that's working. I think for us, the big thing that's really resonating with the customers is they want the operational model and they want the consumption model that they're getting from that as a service experience, but they still are going to run a number of their workloads on-prem and that's the best place to do it for them economically and we've proved that out. So I think the time is here to have that bifurcated experience from operational and financial perspective and in the past, the technology wasn't there and the ability to deliver that for the customers in a manner that was useful wasn't there. So I think the timing's perfect right now to provide them. >> As you know, theCUBE has had a presence at HPE Discover. Previous, even HP Discover and same with Veeam. But we got a long history with HP/HPE. When Hewlett Packard split into two companies, we made the observation, Wow, this opens up a whole new ecosystem opportunity for HPE generally, in storage business specifically, especially in data protection and backup, and the Veeam relationship, the ink wasn't dry and all of a sudden you guys were partnering, throwing joint activities, and so talk about how that relationship has evolved. >> From my perspective, we've always been a big partnering company, both on the route to market side, so our distributors and partners, and we work with them in big channel business. And then on the software partnership side, that's always evolving and growing. We're a very open ecosystem and we like to provide choice for our customers and I think, at the end of the day, we've got a lot of things that we work on jointly, so we have a great value prop. First phase of that relationship was partnering, we've got a full boat of product integrations that we do for customers. The second was a lot of special sauce that we do for our customers for co-integration and co-development. We had a huge session today with Rick Vanover and Frederico on our team here to talk about ransomware. We have big customers suffering from this plague right now and we've done a lot together on the engineering side to provide a very, very well-engineered, well thought out process to help avoid some of these things. And so that wave, too, of how do we do a ton of co-innovation together to really delight our customers and help them run their businesses, and I think the evolution of where we're going now, we have a lot of things that are very similar, strategically, in terms of, we all talk about data services and outcomes for our customers. So at the end of the day, when we think about GreenLake, like our virtual machine backup as a service or disaster recovery, it's all about what workloads are you running, what are the most important ones, where do you need help protecting that data? And essentially, how can we provide that outcome to you and you pay it as an outcome. And so we have a lot of things that we're working on together in that space. >> Let's take a little bit of a closer look at that. First of all, I'm from California, so I'm having a really hard time understanding what either of you were saying. Your accents are so thick. >> We could talk in Boston. >> Your accents are so thick. (Dave laughing) I could barely, but I know I heard you say something about Veaam at one point. Take a closer look at that. What does that look like from a ransomware perspective in terms of this concept of air gaping or immutable, immutable volumes and just as an aside, it seems like Veeam is a perfect partnership for you since customers obviously are going to be in hybrid mode for a long time and Veeam overlays that nicely. But what does it look like specifically? Immutable, air gap, some of the things we've been hearing a lot about. >> I'm exec sponsor for a number of big HPE customers and I'll give you an example. One of our customers, they have their own cloud service for time management and essentially they're exploited and they're not able to provide their service. It has huge ripple effect, if you think about, on inability to do their service and then how that affects their customers and their customers' employees and all that. It's a disaster, no pun intended. And the thing is, we learn from that and we can put together a really good architectures and best practices. So we're talking today about 3-2-1-1, so having three copies of your data, two different types of media, having an offline copy, an offsite copy and an offline copy. And now we're thinking about all the things you need to do to mitigate against all the different ways that people are going to exploit you. We've seen it all. You have keys that are erased, primary storage that is compromised and encrypted, people that come in and delete your backup catalog, they delete your backups, they delete your snapshots. So they get it down to essentially, "I'm either going to have one set of data, it's encrypted, I'm going to make you pay for it," and 40 percent of the time they pay and they get the data back, 60 percent of the time they pay and they get maybe some of the data back. But for the most part, you're not getting your data back. The best thing that we can do for our customers that come with a very prescriptive set of T-shirt configuration sizes, standardization, best practices on how they can take this entire ecosystem together and make it really easy for the customers to implement. But I wouldn't say, it's never bulletproof, but essentially, do as much as you can to avoid having to pay that ransomware. >> So 3-2-1-1, three copies, meaning local. >> Patrick: Yeah. >> So you can do fast recovery if you need to. Two different types of media, so tape fits in here? Not necessarily flashing and spinning disks. Could it be tape? >> A lot of times we have customers that have almost four different types. So they are running their production on flash. We have Alletras with HPE networking and servers running specific workloads, high performance. We have secondary storage on-prem for fast recovery and then we have some form of offsite and offline. Offsite could be object storage in the cloud and then offline would be an actual tape backup. The tape is out of the tape library in a vault so no one can actually access it through the network and so it's a physical copy that's offline. So you always have something to restore. >> Patrick, where's the momentum today, specifically, we're at VeeamON, but with regard to the Veeam partnership, is it security and ransomware, which is a new thing for this world. The last two years, it's really come to the top. Is it cloud migration? Is it data services and data management? Where's the momentum, all of the above, but maybe you could help us parse that. >> What we're seeing here at Hewlett Packard Enterprise, especially through GreenLake, is just an overall focus on data services. So what we're doing is we've got great platforms, we always had. HPE is known as an engineering company. We have fantastic products and solutions that customers love. What we're doing right now is taking, essentially, a lot of the beauty of those products and elevating them into an operational experience in the cloud, so you have a set of platforms that you want to run, you have machine critical platform, business critical, secondary storage, archival, data analytics and I want to be able to manage those from the cloud. So fleet management, HCI management, protocol management, block service, what have you, and then I want a set of abstracted data services that are on top of it and that's essentially things like disaster recovery, backup, data immutability, data vision, understanding what kind of data you have, and so we'll be able to provide those services that are essentially abstracted from the platforms themselves that run across multiple types of platforms. We can charge them on outcome based. They're based on consumption, so you think about something like DR, you have a small set of VMs that you want to protect with a very tight RPO, you can pay for those 100 VMs that are the most important that you have. So for us driving that operational experience and then the cloud data service experience into GreenLake gives customers a really, gives them a cloud experience. >> So have you heard the term super cloud? >> Patrick: Yeah. (chuckles) >> Have you? >> Patrick: Absolutely. >> It's term that we kind of coined, but I want to ask you about it specifically, in terms of how it fits into your strategy. So the idea is, and you kind of just described it, I think, whether your data is on-prem, it's in the cloud, multiple clouds, we'll talk about the edge later, but you're hiding the underlying complexities of the cloud's APIs and primitives, you're taking care of that for your customers, irrespective of physical location. It's the common experience across all those platforms. Is that a reasonable vision, maybe, even from a technical standpoint, is it part of HPE strategy and what does it take to actually do that, 'cause it sounds nice, but it's probably pretty intense? >> So the proof's in the pudding for us. We have a number of platforms that are providing, whether it's compute or networking or storage, running those workloads that they plum up into the cloud, they have an operational experience in the cloud and now they have data services that are running in the cloud for us in GreenLake. So it's a reality. We have a number of platforms that support that. We're going to have a set of big announcements coming up at HPE Discover. So we led with Alletra and we have a block service, we have VM backup as a service and DR On top of that. That's something that we're providing today. GreenLake has over, I think, it's actually over 60 services right now that we're providing in the GreenLake platform itself. Everything from security, single sign on, customer IDs, everything, so it's real. We have the proof point for it. >> So, GreenLake is essentially, I've said it, it's the HPE cloud. Is that a fair statement? >> A hundred percent. >> You're redefining cloud. And one of the hallmarks of cloud is ecosystem. Roughly, and I want to talk more about you got to grow that ecosystem to be successful in cloud, no question about it. And HPE's got the chops to do that. What percent of those services are HPE versus ecosystem partners and how do you see that evolving over time? >> We have a good number of services that are based on HPE, our tried and true intellectual property. >> You got good tech. >> Absolutely, so a number of that. And then we have partners in GreenLake today. We have a pretty big ecosystem and it's evolving, too. So we have customers and partners that are focused, our customers want our focus on data services. We have a number of opportunities and partnerships around data analytics. As you know, that's a really dynamic space. A lot of folks providing support on open source, analytics and that's a fast moving ecosystem, so we want to support that. We've seen a lot of interest in security. Being able to bring in security companies that are focused on data security. Data analytics to understand what's in your data from a customer perspective, how to secure that. So we have a pretty big ecosystem there. Just like our path at HPE, we've always had a really strong partnership with tons of software companies and we're going to continue to do that with GreenLake. >> You guys have been partner-friendly, I'll give you that. I'm going to ask Antonio this at Discover in a couple of weeks, but I want to ask you, when you think about, again, to go back to AWS as the prototypical cloud, you look at a Snowflake and a Redshift. The Redshift guys probably hate Snowflake, but the EC2 guys love them, sell a lot of compute. Now you as a business unit manager, do you ever see the day where you're side by side with one of your competitors? I'm guessing Antonio would say absolutely. Culturally, how does that play inside of HPE? I'm testing your partner-friendliness. How would you- >> Who will you- >> How do you think about that? >> At the end of the day, for us, the opportunity for us is to delight our customers. So we've always talked about customer choice and how to provide that best outcome. I think the big thing for us is that, from a cost perspective, we've seen a lot of customers coming back to HPE repatriation, from a repatriation perspective for a certain class of workloads. From my perspective, we're providing the best infrastructure and the best operational services at the best price at scale for these costumers. >> Really? It definitely, culturally, HPE has to, I think you would agree, it has to open up. You might not, you're going to go compete, based on the merit- >> Absolutely. >> of your product and technology. The repatriation thing is interesting. 'Cause I've always been a repatriation skeptic. Are you actually starting to see that in a meaningful way? Do you think you'll see it in the macro numbers? I mean, cloud doesn't seem to be slowing down, the public cloud growth, I mean, the 35, 40 percent a year. >> We're seeing it in our numbers. We're seeing it in the new logo and existing customer acquisition within GreenLake. So it's real for us. >> And they're telling you? Pure cost? >> Cost. >> So it's that's simple. >> Cost. >> So, they get the cloud bill, you do, too. I'd get the email from my CFO, "Why the cloud bill so high this month?" Part of that is it's consumption-based and it's not predictable. >> And also, too, one of the things that you said around unlocking a lot of the customer's ability from a resourcing perspective, so if we can take care of all the stuff underneath, the under cloud for the customer, the platform, so the stores, the serving, the networking, the automation, the provisioning, the health. As you guys know, we have hundreds of thousands of customers on the Aruba platform. We've got hundreds of thousands of customers calling home through InfoSight. So we can provide a very rich set of analytics, automated environment, automated health checking, and a very good experience that's going to help them move away from managing boxes to doing operational services with GreenLake. >> We talk about repatriation often. There was a time when I think a lot of us would've agreed that no one who was born in the cloud will ever do anything other than grow in the cloud. Are you seeing organizations that were born in the cloud realizing, "Hey, we know what our 80 percent steady state is and we've modeled this. Why rent it when we can own it? Or why rent it here when we can have it as operational cost there?" Are you seeing those? >> We're seeing some of that. We're certainly seeing folks that have a big part of their native or their digital business. It's a cost factor and so I think, one of the other areas, too, that we're seeing is there's a big transformation going on for our partners as well, too, on the sell-through side. So you're starting to see more niche SaaS offerings. You're starting to see more vertically focused offerings from our service provider partners or MSPs. So it's not just in either-or type of situation. You're starting to see now some really, really specific things going on in either verticals, customer segmentation, specific SaaS or data services and for us, it's a really good ecosystem, because we work with our SP partners, our MSP partners, they use our tech, they use our services, they provide services to our joint customers. For example, I know you guys have talked to iland here in the past. It's a great example for us for customers that are looking for DR as a service, backup as a service hosting, so it's a nice triangle for us to be able to please those customers. >> They're coming on to tomorrow. They're on 11/11. I think you're right on. The one, I think, obvious place where this repatriation could happen, it's the Sarah Wong and Martin Casano scenario where a SaaS companies cost a good sold become dominated by cloud costs. And they say, "Okay, well, maybe, I'm not going to build my own data centers. That's probably not going to happen, but I can go to Equinix and do a colo and I'm going to save a ton of dough, managing my own infrastructure with automation or outsourcing it." So Patrick, got to go. I could talk with you forever. Thank you so much for coming back in theCUBE. >> Always a pleasure. >> Go, Celts. How you feeling about the, we always talk sports here in VeeamON. How are you feeling about the Celts today? >> My original call today was Celtics in six, but we'll see what happens. >> Stephen, you like Celtics? Celtics six. >> Stephen: Celtics six. >> Even though tonight, they got a little- >> Stephen: Still believe, you got to believe. >> All right, I believe. >> It'd be better than the Miami's Mickey Mouse run there, in the bubble, a lot of astronauts attached to that. (Dave laughing) >> I love it. You got to believe here on theCUBE. All right, keep it right- >> I don't care. >> Keep it right there. You don't care, 'cause you're not from a sports town. Where are you in California? >> We have no sports. >> All right, keep it right there. This is theCUBE's coverage of VeeamON 2022. Dave Vellante for Dave Nicholson. We'll be right back. (digital music)

>>Time series data is any data that's stamped in time in some way that could be every second, every minute, every five minutes, every hour, every nanosecond, whatever it might be. And typically that data comes from sources in the physical world like devices or sensors, temperature, gauges, batteries, any device really, or things in the virtual world could be software, maybe it's software in the cloud or data and containers or microservices or virtual machines. So all of these items, whether in the physical or virtual world, they're generating a lot of time series data. Now time series data has been around for a long time, and there are many examples in our everyday lives. All you gotta do is punch up any stock, ticker and look at its price over time and graphical form. And that's a simple use case that anyone can relate to and you can build timestamps into a traditional relational database. >>You just add a column to capture time and as well, there are examples of log data being dumped into a data store that can be searched and captured and ingested and visualized. Now, the problem with the latter example that I just gave you is that you gotta hunt and Peck and search and extract what you're looking for. And the problem with the former is that traditional general purpose databases they're designed as sort of a Swiss army knife for any workload. And there are a lot of functions that get in the way and make them inefficient for time series analysis, especially at scale. Like when you think about O T and edge scale, where things are happening super fast, ingestion is coming from many different sources and analysis often needs to be done in real time or near real time. And that's where time series databases come in. >>They're purpose built and can much more efficiently support ingesting metrics at scale, and then comparing data points over time, time series databases can write and read at significantly higher speeds and deal with far more data than traditional database methods. And they're more cost effective instead of throwing processing power at the problem. For example, the underlying architecture and algorithms of time series databases can optimize queries and they can reclaim wasted storage space and reuse it. At scale time, series databases are simply a better fit for the job. Welcome to moving the world with influx DB made possible by influx data. My name is Dave Valante and I'll be your host today. Influx data is the company behind InfluxDB. The open source time series database InfluxDB is designed specifically to handle time series data. As I just explained, we have an exciting program for you today, and we're gonna showcase some really interesting use cases. >>First, we'll kick it off in our Palo Alto studios where my colleague, John furrier will interview Evan Kaplan. Who's the CEO of influx data after John and Evan set the table. John's gonna sit down with Brian Gilmore. He's the director of IOT and emerging tech at influx data. And they're gonna dig into where influx data is gaining traction and why adoption is occurring and, and why it's so robust. And they're gonna have tons of examples and double click into the technology. And then we bring it back here to our east coast studios, where I get to talk to two practitioners, doing amazing things in space with satellites and modern telescopes. These use cases will blow your mind. You don't want to miss it. So thanks for being here today. And with that, let's get started. Take it away. Palo Alto. >>Okay. Today we welcome Evan Kaplan, CEO of influx data, the company behind influx DB. Welcome Evan. Thanks for coming on. >>Hey John, thanks for having me >>Great segment here on the influx DB story. What is the story? Take us through the history. Why time series? What's the story >><laugh> so the history history is actually actually pretty interesting. Um, Paul dicks, my partner in this and our founder, um, super passionate about developers and developer experience. And, um, he had worked on wall street building a number of time series kind of platform trading platforms for trading stocks. And from his point of view, it was always what he would call a yak shave, which means you had to do a ton of work just to start doing work, which means you had to write a bunch of extrinsic routines. You had to write a bunch of application handling on existing relational databases in order to come up with something that was optimized for a trading platform or a time series platform. And he sort of, he just developed this real clear point of view is this is not how developers should work. And so in 2013, he went through why Combinator and he built something for, he made his first commit to open source in flu DB at the end of 2013. And, and he basically, you know, from my point of view, he invented modern time series, which is you start with a purpose-built time series platform to do these kind of workloads. And you get all the benefits of having something right outta the box. So a developer can be totally productive right away. >>And how many people in the company what's the history of employees and stuff? >>Yeah, I think we're, I, you know, I always forget the number, but it's something like 230 or 240 people now. Um, the company, I joined the company in 2016 and I love Paul's vision. And I just had a strong conviction about the relationship between time series and IOT. Cuz if you think about it, what sensors do is they speak time, series, pressure, temperature, volume, humidity, light, they're measuring they're instrumenting something over time. And so I thought that would be super relevant over long term and I've not regretted it. >>Oh no. And it's interesting at that time, go back in the history, you know, the role of databases, well, relational database is the one database to rule the world. And then as clouds started coming in, you starting to see more databases, proliferate types of databases and time series in particular is interesting. Cuz real time has become super valuable from an application standpoint, O T which speaks time series means something it's like time matters >>Time. >>Yeah. And sometimes data's not worth it after the time, sometimes it worth it. And then you get the data lake. So you have this whole new evolution. Is this the momentum? What's the momentum, I guess the question is what's the momentum behind >>You mean what's causing us to grow. So >>Yeah, the time series, why is time series >>And the >>Category momentum? What's the bottom line? >>Well, think about it. You think about it from a broad, broad sort of frame, which is where, what everybody's trying to do is build increasingly intelligent systems, whether it's a self-driving car or a robotic system that does what you want to do or a self-healing software system, everybody wants to build increasing intelligent systems. And so in order to build these increasing intelligent systems, you have to instrument the system well, and you have to instrument it over time, better and better. And so you need a tool, a fundamental tool to drive that instrumentation. And that's become clear to everybody that that instrumentation is all based on time. And so what happened, what happened, what happened what's gonna happen? And so you get to these applications like predictive maintenance or smarter systems. And increasingly you want to do that stuff, not just intelligently, but fast in real time. So millisecond response so that when you're driving a self-driving car and the system realizes that you're about to do something, essentially you wanna be able to act in something that looks like real time, all systems want to do that, want to be more intelligent and they want to be more real time. And so we just happen to, you know, we happen to show up at the right time in the evolution of a >>Market. It's interesting near real time. Isn't good enough when you need real time. >><laugh> yeah, it's not, it's not. And it's like, and it's like, everybody wants, even when you don't need it, ironically, you want it. It's like having the feature for, you know, you buy a new television, you want that one feature, even though you're not gonna use it, you decide that your buying criteria real time is a buying criteria >>For, so you, I mean, what you're saying then is near real time is getting closer to real time as possible, as fast as possible. Right. Okay. So talk about the aspect of data, cuz we're hearing a lot of conversations on the cube in particular around how people are implementing and actually getting better. So iterating on data, but you have to know when it happened to get, know how to fix it. So this is a big part of how we're seeing with people saying, Hey, you know, I wanna make my machine learning algorithms better after the fact I wanna learn from the data. Um, how does that, how do you see that evolving? Is that one of the use cases of sensors as people bring data in off the network, getting better with the data knowing when it happened? >>Well, for sure. So, so for sure, what you're saying is, is, is none of this is non-linear, it's all incremental. And so if you take something, you know, just as an easy example, if you take a self-driving car, what you're doing is you're instrumenting that car to understand where it can perform in the real world in real time. And if you do that, if you run the loop, which is I instrumented, I watch what happens, oh, that's wrong? Oh, I have to correct for that. I correct for that in the software. If you do that for a billion times, you get a self-driving car, but every system moves along that evolution. And so you get the dynamic of, you know, of constantly instrumenting watching the system behave and do it. And this and sets up driving car is one thing. But even in the human genome, if you look at some of our customers, you know, people like, you know, people doing solar arrays, people doing power walls, like all of these systems are getting smarter. >>Well, let's get into that. What are the top applications? What are you seeing for your, with in, with influx DB, the time series, what's the sweet spot for the application use case and some customers give some >>Examples. Yeah. So it's, it's pretty easy to understand on one side of the equation that's the physical side is sensors are sensors are getting cheap. Obviously we know that and they're getting the whole physical world is getting instrumented, your home, your car, the factory floor, your wrist, watch your healthcare, you name it. It's getting instrumented in the physical world. We're watching the physical world in real time. And so there are three or four sweet spots for us, but, but they're all on that side. They're all about IOT. So they're think about consumer IOT projects like Google's nest todo, um, particle sensors, um, even delivery engines like rapid who deliver the Instacart of south America, like anywhere there's a physical location do and that's on the consumer side. And then another exciting space is the industrial side factories are changing dramatically over time. Increasingly moving away from proprietary equipment to develop or driven systems that run operational because what, what has to get smarter when you're building, when you're building a factory is systems all have to get smarter. And then, um, lastly, a lot in the renewables sustainability. So a lot, you know, Tesla, lucid, motors, Cola, motors, um, you know, lots to do with electric cars, solar arrays, windmills, arrays, just anything that's gonna get instrumented that where that instrumentation becomes part of what the purpose >>Is. It's interesting. The convergence of physical and digital is happening with the data IOT. You mentioned, you know, you think of IOT, look at the use cases there, it was proprietary OT systems. Now becoming more IP enabled internet protocol and now edge compute, getting smaller, faster, cheaper AI going to the edge. Now you have all kinds of new capabilities that bring that real time and time series opportunity. Are you seeing IOT going to a new level? What was the, what's the IOT where's the IOT dots connecting to because you know, as these two cultures merge yeah. Operations, basically industrial factory car, they gotta get smarter, intelligent edge is a buzzword, but I mean, it has to be more intelligent. Where's the, where's the action in all this. So the >>Action, really, it really at the core, it's at the developer, right? Because you're looking at these things, it's very hard to get an off the shelf system to do the kinds of physical and software interaction. So the actions really happen at the developer. And so what you're seeing is a movement in the world that, that maybe you and I grew up in with it or OT moving increasingly that developer driven capability. And so all of these IOT systems they're bespoke, they don't come out of the box. And so the developer, the architect, the CTO, they define what's my business. What am I trying to do? Am I trying to sequence a human genome and figure out when these genes express theself or am I trying to figure out when the next heart rate monitor's gonna show up on my apple watch, right? What am I trying to do? What's the system I need to build. And so starting with the developers where all of the good stuff happens here, which is different than it used to be, right. Used to be you'd buy an application or a service or a SA thing for, but with this dynamic, with this integration of systems, it's all about bespoke. It's all about building >>Something. So let's get to the developer real quick, real highlight point here is the data. I mean, I could see a developer saying, okay, I need to have an application for the edge IOT edge or car. I mean, we're gonna have, I mean, Tesla's got applications of the car it's right there. I mean, yes, there's the modern application life cycle now. So take us through how this impacts the developer. Does it impact their C I C D pipeline? Is it cloud native? I mean, where does this all, where does this go to? >>Well, so first of all, you're talking about, there was an internal journey that we had to go through as a company, which, which I think is fascinating for anybody who's interested is we went from primarily a monolithic software that was open sourced to building a cloud native platform, which means we had to move from an agile development environment to a C I C D environment. So to a degree that you are moving your service, whether it's, you know, Tesla monitoring your car and updating your power walls, right. Or whether it's a solar company updating the arrays, right. To degree that that service is cloud. Then increasingly remove from an agile development to a C I C D environment, which you're shipping code to production every day. And so it's not just the developers, all the infrastructure to support the developers to run that service and that sort of stuff. I think that's also gonna happen in a big way >>When your customer base that you have now, and as you see, evolving with infl DB, is it that they're gonna be writing more of the application or relying more on others? I mean, obviously there's an open source component here. So when you bring in kind of old way, new way old way was I got a proprietary, a platform running all this O T stuff and I gotta write, here's an application. That's general purpose. Yeah. I have some flexibility, somewhat brittle, maybe not a lot of robustness to it, but it does its job >>A good way to think about this is versus a new way >>Is >>What so yeah, good way to think about this is what, what's the role of the developer slash architect CTO that chain within a large, within an enterprise or a company. And so, um, the way to think about it is I started my career in the aerospace industry <laugh> and so when you look at what Boeing does to assemble a plane, they build very, very few of the parts. Instead, what they do is they assemble, they buy the wings, they buy the engines, they assemble, actually, they don't buy the wings. It's the one thing they buy the, the material for the w they build the wings, cuz there's a lot of tech in the wings and they end up being assemblers smart assemblers of what ends up being a flying airplane, which is pretty big deal even now. And so what, what happens with software people is they have the ability to pull from, you know, the best of the open source world. So they would pull a time series capability from us. Then they would assemble that with, with potentially some ETL logic from somebody else, or they'd assemble it with, um, a Kafka interface to be able to stream the data in. And so they become very good integrators and assemblers, but they become masters of that bespoke application. And I think that's where it goes, cuz you're not writing native code for everything. >>So they're more flexible. They have faster time to market cuz they're assembling way faster and they get to still maintain their core competency. Okay. Their wings in this case, >>They become increasingly not just coders, but designers and developers. They become broadly builders is what we like to think of it. People who start and build stuff by the way, this is not different than the people just up the road Google have been doing for years or the tier one, Amazon building all their own. >>Well, I think one of the things that's interesting is is that this idea of a systems developing a system architecture, I mean systems, uh, uh, systems have consequences when you make changes. So when you have now cloud data center on premise and edge working together, how does that work across the system? You can't have a wing that doesn't work with the other wing kind of thing. >>That's exactly. But that's where the that's where the, you know, that that Boeing or that airplane building analogy comes in for us. We've really been thoughtful about that because IOT it's critical. So our open source edge has the same API as our cloud native stuff that has enterprise on pre edge. So our multiple products have the same API and they have a relationship with each other. They can talk with each other. So the builder builds it once. And so this is where, when you start thinking about the components that people have to use to build these services is that you wanna make sure, at least that base layer, that database layer, that those components talk to each other. >>So I'll have to ask you if I'm the customer. I put my customer hat on. Okay. Hey, I'm dealing with a lot. >>That mean you have a PO for <laugh> >>A big check. I blank check. If you can answer this question only if the tech, if, if you get the question right, I got all this important operation stuff. I got my factory, I got my self-driving cars. This isn't like trivial stuff. This is my business. How should I be thinking about time series? Because now I have to make these architectural decisions, as you mentioned, and it's gonna impact my application development. So huge decision point for your customers. What should I care about the most? So what's in it for me. Why is time series >>Important? Yeah, that's a great question. So chances are, if you've got a business that was, you know, 20 years old or 25 years old, you were already thinking about time series. You probably didn't call it that you built something on a Oracle or you built something on IBM's DB two, right. And you made it work within your system. Right? And so that's what you started building. So it's already out there. There are, you know, there are probably hundreds of millions of time series applications out there today. But as you start to think about this increasing need for real time, and you start to think about increasing intelligence, you think about optimizing those systems over time. I hate the word, but digital transformation. Then you start with time series. It's a foundational base layer for any system that you're gonna build. There's no system I can think of where time series, shouldn't be the foundational base layer. If you just wanna store your data and just leave it there and then maybe look it up every five years. That's fine. That's not time. Series time series is when you're building a smarter, more intelligent, more real time system. And the developers now know that. And so the more they play a role in building these systems, the more obvious it becomes. >>And since I have a PO for you and a big check, yeah. What is, what's the value to me as I, when I implement this, what's the end state, what's it look like when it's up and running? What's the value proposition for me. What's an >>So, so when it's up and running, you're able to handle the queries, the writing of the data, the down sampling of the data, they're transforming it in near real time. So that the other dependencies that a system that gets for adjusting a solar array or trading energy off of a power wall or some sort of human genome, those systems work better. So time series is foundational. It's not like it's, you know, it's not like it's doing every action that's above, but it's foundational to build a really compelling, intelligent system. I think that's what developers and archs are seeing now. >>Bottom line, final word. What's in it for the customer. What's what, what's your, um, what's your statement to the customer? What would you say to someone looking to do something in time series on edge? >>Yeah. So, so it's pretty clear to clear to us that if you're building, if you view yourself as being in the build business of building systems that you want 'em to be increasingly intelligent, self-healing autonomous. You want 'em to operate in real time that you start from time series. But I also wanna say what's in it for us influx what's in it for us is people are doing some amazing stuff. You know, I highlighted some of the energy stuff, some of the human genome, some of the healthcare it's hard not to be proud or feel like, wow. Yeah. Somehow I've been lucky. I've arrived at the right time, in the right place with the right people to be able to deliver on that. That's that's also exciting on our side of the equation. >>Yeah. It's critical infrastructure, critical, critical operations. >>Yeah. >>Yeah. Great stuff, Evan. Thanks for coming on. Appreciate this segment. All right. In a moment, Brian Gilmore director of IOT and emerging technology that influx day will join me. You're watching the cube leader in tech coverage. Thanks for watching >>Time series data from sensors systems and applications is a key source in driving automation and prediction in technologies around the world. But managing the massive amount of timestamp data generated these days is overwhelming, especially at scale. That's why influx data developed influx DB, a time series data platform that collects stores and analyzes data influx DB empowers developers to extract valuable insights and turn them into action by building transformative IOT analytics and cloud native applications, purpose built and optimized to handle the scale and velocity of timestamped data. InfluxDB puts the power in your hands with developer tools that make it easy to get started quickly with less code InfluxDB is more than a database. It's a robust developer platform with integrated tooling. That's written in the languages you love. So you can innovate faster, run in flex DB anywhere you want by choosing the provider and region that best fits your needs across AWS, Microsoft Azure and Google cloud flex DB is fast and automatically scalable. So you can spend time delivering value to customers, not managing clusters, take control of your time series data. So you can focus on the features and functionalities that give your applications a competitive edge. Get started for free with influx DB, visit influx data.com/cloud to learn more. >>Okay. Now we're joined by Brian Gilmore director of IOT and emerging technologies at influx data. Welcome to the show. >>Thank you, John. Great to be here. >>We just spent some time with Evan going through the company and the value proposition, um, with influx DV, what's the momentum, where do you see this coming from? What's the value coming out of this? >>Well, I think it, we're sort of hitting a point where the technology is, is like the adoption of it is becoming mainstream. We're seeing it in all sorts of organizations, everybody from like the most well funded sort of advanced big technology companies to the smaller academics, the startups and the managing of that sort of data that emits from that technology is time series and us being able to give them a, a platform, a tool that's super easy to use, easy to start. And then of course will grow with them is, is been key to us. Sort of, you know, riding along with them is they're successful. >>Evan was mentioning that time series has been on everyone's radar and that's in the OT business for years. Now, you go back since 20 13, 14, even like five years ago that convergence of physical and digital coming together, IP enabled edge. Yeah. Edge has always been kind of hyped up, but why now? Why, why is the edge so hot right now from an adoption standpoint? Is it because it's just evolution, the tech getting better? >>I think it's, it's, it's twofold. I think that, you know, there was, I would think for some people, everybody was so focused on cloud over the last probably 10 years. Mm-hmm <affirmative> that they forgot about the compute that was available at the edge. And I think, you know, those, especially in the OT and on the factory floor who weren't able to take Avan full advantage of cloud through their applications, you know, still needed to be able to leverage that compute at the edge. I think the big thing that we're seeing now, which is interesting is, is that there's like a hybrid nature to all of these applications where there's definitely some data that's generated on the edge. There's definitely done some data that's generated in the cloud. And it's the ability for a developer to sort of like tie those two systems together and work with that data in a very unified uniform way. Um, that's giving them the opportunity to build solutions that, you know, really deliver value to whatever it is they're trying to do, whether it's, you know, the, the out reaches of outer space or whether it's optimizing the factory floor. >>Yeah. I think, I think one of the things you also mentions genome too, dig big data is coming to the real world. And I think I, OT has been kind of like this thing for OT and, and in some use case, but now with the, with the cloud, all companies have an edge strategy now. So yeah, what's the secret sauce because now this is hot, hot product for the whole world and not just industrial, but all businesses. What's the secret sauce. >>Well, I mean, I think part of it is just that the technology is becoming more capable and that's especially on the hardware side, right? I mean, like technology compute is getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the micro controller layer with our, um, you know, our client libraries and then working hard to make our applications, especially the database as small as possible so that it can be located as close to sort of the point of origin of that data in the edge as possible is, is, is fantastic. Now you can take that. You can run that locally. You can do your local decision making. You can use influx DB as sort of an input to automation control the autonomy that people are trying to drive at the edge. But when you link it up with everything that's in the cloud, that's when you get all of the sort of cloud scale capabilities of parallelized, AI and machine learning and all of that. >>So what's interesting is the open source success has been something that we've talked about a lot in the cube about how people are leveraging that you guys have users in the enterprise users that IOT market mm-hmm <affirmative>, but you got developers now. Yeah. Kind of together brought that up. How do you see that emerging? How do developers engage? What are some of the things you're seeing that developers are really getting into with InfluxDB >>What's? Yeah. Well, I mean, I think there are the developers who are building companies, right? And these are the startups and the folks that we love to work with who are building new, you know, new services, new products, things like that. And, you know, especially on the consumer side of IOT, there's a lot of that, just those developers. But I think we, you gotta pay attention to those enterprise developers as well, right? There are tons of people with the, the title of engineer in, in your regular enterprise organizations. And they're there for systems integration. They're there for, you know, looking at what they would build versus what they would buy. And a lot of them come from, you know, a strong, open source background and they, they know the communities, they know the top platforms in those spaces and, and, you know, they're excited to be able to adopt and use, you know, to optimize inside the business as compared to just building a brand new one. >>You know, it's interesting too, when Evan and I were talking about open source versus closed OT systems, mm-hmm <affirmative> so how do you support the backwards compatibility of older systems while maintaining open dozens of data formats out there? Bunch of standards, protocols, new things are emerging. Everyone wants to have a control plane. Everyone wants to leverage the value of data. How do you guys keep track of it all? What do you guys support? >>Yeah, well, I mean, I think either through direct connection, like we have a product called Telegraph, it's unbelievable. It's open source, it's an edge agent. You can run it as close to the edge as you'd like, it speaks dozens of different protocols in its own, right? A couple of which MQTT B, C U a are very, very, um, applicable to these T use cases. But then we also, because we are sort of not only open source, but open in terms of our ability to collect data, we have a lot of partners who have built really great integrations from their own middleware, into influx DB. These are companies like ke wear and high bite who are really experts in those downstream industrial protocols. I mean, that's a business, not everybody wants to be in. It requires some very specialized, very hard work and a lot of support, um, you know, and so by making those connections and building those ecosystems, we get the best of both worlds. The customers can use the platforms they need up to the point where they would be putting into our database. >>What's some of customer testimonies that they, that share with you. Can you share some anecdotal kind of like, wow, that's the best thing I've ever used. This really changed my business, or this is a great tech that's helped me in these other areas. What are some of the, um, soundbites you hear from customers when they're successful? >>Yeah. I mean, I think it ranges. You've got customers who are, you know, just finally being able to do the monitoring of assets, you know, sort of at the edge in the field, we have a customer who's who's has these tunnel boring machines that go deep into the earth to like drill tunnels for, for, you know, cars and, and, you know, trains and things like that. You know, they are just excited to be able to stick a database onto those tunnel, boring machines, send them into the depths of the earth and know that when they come out, all of that telemetry at a very high frequency has been like safely stored. And then it can just very quickly and instantly connect up to their, you know, centralized database. So like just having that visibility is brand new to them. And that's super important. On the other hand, we have customers who are way far beyond the monitoring use case, where they're actually using the historical records in the time series database to, um, like I think Evan mentioned like forecast things. So for predictive maintenance, being able to pull in the telemetry from the machines, but then also all of that external enrichment data, the metadata, the temperatures, the pressure is who is operating the machine, those types of things, and being able to easily integrate with platforms like Jupyter notebooks or, you know, all of those scientific computing and machine learning libraries to be able to build the models, train the models, and then they can send that information back down to InfluxDB to apply it and detect those anomalies, which >>Are, I think that's gonna be an, an area. I personally think that's a hot area because I think if you look at AI right now, yeah. It's all about training the machine learning albums after the fact. So time series becomes hugely important. Yeah. Cause now you're thinking, okay, the data matters post time. Yeah. First time. And then it gets updated the new time. Yeah. So it's like constant data cleansing data iteration, data programming. We're starting to see this new use case emerge in the data field. >>Yep. Yeah. I mean, I think you agree. Yeah, of course. Yeah. The, the ability to sort of handle those pipelines of data smartly, um, intelligently, and then to be able to do all of the things you need to do with that data in stream, um, before it hits your sort of central repository. And, and we make that really easy for customers like Telegraph, not only does it have sort of the inputs to connect up to all of those protocols and the ability to capture and connect up to the, to the partner data. But also it has a whole bunch of capabilities around being able to process that data, enrich it, reform at it, route it, do whatever you need. So at that point you're basically able to, you're playing your data in exactly the way you would wanna do it. You're routing it to different, you know, destinations and, and it's, it's, it's not something that really has been in the realm of possibility until this point. Yeah. Yeah. >>And when Evan was on it's great. He was a CEO. So he sees the big picture with customers. He was, he kinda put the package together that said, Hey, we got a system. We got customers, people are wanting to leverage our product. What's your PO they're sell. He's selling too as well. So you have that whole CEO perspective, but he brought up this notion that there's multiple personas involved in kind of the influx DB system architect. You got developers and users. Can you talk about that? Reality as customers start to commercialize and operationalize this from a commercial standpoint, you got a relationship to the cloud. Yep. The edge is there. Yep. The edge is getting super important, but cloud brings a lot of scale to the table. So what is the relationship to the cloud? Can you share your thoughts on edge and its relationship to the cloud? >>Yeah. I mean, I think edge, you know, edges, you can think of it really as like the local information, right? So it's, it's generally like compartmentalized to a point of like, you know, a single asset or a single factory align, whatever. Um, but what people do who wanna pro they wanna be able to make the decisions there at the edge locally, um, quickly minus the latency of sort of taking that large volume of data, shipping it to the cloud and doing something with it there. So we allow them to do exactly that. Then what they can do is they can actually downsample that data or they can, you know, detect like the really important metrics or the anomalies. And then they can ship that to a central database in the cloud where they can do all sorts of really interesting things with it. Like you can get that centralized view of all of your global assets. You can start to compare asset to asset, and then you can do those things like we talked about, whereas you can do predictive types of analytics or, you know, larger scale anomaly detections. >>So in this model you have a lot of commercial operations, industrial equipment. Yep. The physical plant, physical business with virtual data cloud all coming together. What's the future for InfluxDB from a tech standpoint. Cause you got open. Yep. There's an ecosystem there. Yep. You have customers who want operational reliability for sure. I mean, so you got organic <laugh> >>Yeah. Yeah. I mean, I think, you know, again, we got iPhones when everybody's waiting for flying cars. Right. So I don't know. We can like absolutely perfectly predict what's coming, but I think there are some givens and I think those givens are gonna be that the world is only gonna become more hybrid. Right. And then, you know, so we are going to have much more widely distributed, you know, situations where you have data being generated in the cloud, you have data gen being generated at the edge and then there's gonna be data generated sort sort of at all points in between like physical locations as well as things that are, that are very virtual. And I think, you know, we are, we're building some technology right now. That's going to allow, um, the concept of a database to be much more fluid and flexible, sort of more aligned with what a file would be like. >>And so being able to move data to the compute for analysis or move the compute to the data for analysis, those are the types of, of solutions that we'll be bringing to the customers sort of over the next little bit. Um, but I also think we have to start thinking about like what happens when the edge is actually off the planet. Right. I mean, we've got customers, you're gonna talk to two of them, uh, in the panel who are actually working with data that comes from like outside the earth, like, you know, either in low earth orbit or you know, all the way sort of on the other side of the universe. Yeah. And, and to be able to process data like that and to do so in a way it's it's we gotta, we gotta build the fundamentals for that right now on the factory floor and in the mines and in the tunnels. Um, so that we'll be ready for that one. >>I think you bring up a good point there because one of the things that's common in the industry right now, people are talking about, this is kind of new thinking is hyper scale's always been built up full stack developers, even the old OT world, Evan was pointing out that they built everything right. And the world's going to more assembly with core competency and IP and also property being the core of their apple. So faster assembly and building, but also integration. You got all this new stuff happening. Yeah. And that's to separate out the data complexity from the app. Yes. So space genome. Yep. Driving cars throws off massive data. >>It >>Does. So is Tesla, uh, is the car the same as the data layer? >>I mean the, yeah, it's, it's certainly a point of origin. I think the thing that we wanna do is we wanna let the developers work on the world, changing problems, the things that they're trying to solve, whether it's, you know, energy or, you know, any of the other health or, you know, other challenges that these teams are, are building against. And we'll worry about that time series data and the underlying data platform so that they don't have to. Right. I mean, I think you talked about it, uh, you know, for them just to be able to adopt the platform quickly, integrate it with their data sources and the other pieces of their applications. It's going to allow them to bring much faster time to market on these products. It's gonna allow them to be more iterative. They're gonna be able to do more sort of testing and things like that. And ultimately it will, it'll accelerate the adoption and the creation of >>Technology. You mentioned earlier in, in our talk about unification of data. Yeah. How about APIs? Cuz developers love APIs in the cloud unifying APIs. How do you view view that? >>Yeah, I mean, we are APIs, that's the product itself. Like everything, people like to think of it as sort of having this nice front end, but the front end is B built on our public APIs. Um, you know, and it, it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then, you know, sort of data extraction to bring it to other platforms or other applications, microservices, whatever it might be. So, I mean, it is a world of APIs right now and you know, we, we bring a very sort of useful set of them for managing the time series data. These guys are all challenged with. It's >>Interesting. You and I were talking before we came on camera about how, um, data is, feels gonna have this kind of SRE role that DevOps had site reliability engineers, which manages a bunch of servers. There's so much data out there now. Yeah. >>Yeah. It's like reigning data for sure. And I think like that ability to be like one of the best jobs on the planet is gonna be to be able to like, sort of be that data Wrangler to be able to understand like what the data sources are, what the data formats are, how to be able to efficiently move that data from point a to point B and you know, to process it correctly so that the end users of that data aren't doing any of that sort of hard upfront preparation collection storage's >>Work. Yeah. That's data as code. I mean, data engineering is it is becoming a new discipline for sure. And, and the democratization is the benefit. Yeah. To everyone, data science get easier. I mean data science, but they wanna make it easy. Right. <laugh> yeah. They wanna do the analysis, >>Right? Yeah. I mean, I think, you know, it, it's a really good point. I think like we try to give our users as many ways as there could be possible to get data in and get data out. We sort of think about it as meeting them where they are. Right. So like we build, we have the sort of client libraries that allow them to just port to us, you know, directly from the applications and the languages that they're writing, but then they can also pull it out. And at that point nobody's gonna know the users, the end consumers of that data, better than those people who are building those applications. And so they're building these user interfaces, which are making all of that data accessible for, you know, their end users inside their organization. >>Well, Brian, great segment, great insight. Thanks for sharing all, all the complexities and, and IOT that you guys helped take away with the APIs and, and assembly and, and all the system architectures that are changing edge is real cloud is real. Yeah, absolutely. Mainstream enterprises. And you got developer attraction too, so congratulations. >>Yeah. It's >>Great. Well, thank any, any last word you wanna share >>Deal with? No, just, I mean, please, you know, if you're, if you're gonna, if you're gonna check out influx TV, download it, try out the open source contribute if you can. That's a, that's a huge thing. It's part of being the open source community. Um, you know, but definitely just, just use it. I think when once people use it, they try it out. They'll understand very, >>Very quickly. So open source with developers, enterprise and edge coming together all together. You're gonna hear more about that in the next segment, too. Right. Thanks for coming on. Okay. Thanks. When we return, Dave LAN will lead a panel on edge and data influx DB. You're watching the cube, the leader in high tech enterprise coverage. >>Why the startup, we move really fast. We find that in flex DB can move as fast as us. It's just a great group, very collaborative, very interested in manufacturing. And we see a bright future in working with influence. My name is Aaron Seley. I'm the CTO at HBI. Highlight's one of the first companies to focus on manufacturing data and apply the concepts of data ops, treat that as an asset to deliver to the it system, to enable applications like overall equipment effectiveness that can help the factory produce better, smarter, faster time series data. And manufacturing's really important. If you take a piece of equipment, you have the temperature pressure at the moment that you can look at to kind of see the state of what's going on. So without that context and understanding you can't do what manufacturers ultimately want to do, which is predict the future. >>Influx DB represents kind of a new way to storm time series data with some more advanced technology and more importantly, more open technologies. The other thing that influx does really well is once the data's influx, it's very easy to get out, right? They have a modern rest API and other ways to access the data. That would be much more difficult to do integrations with classic historians highlight can serve to model data, aggregate data on the shop floor from a multitude of sources, whether that be P C U a servers, manufacturing execution systems, E R P et cetera, and then push that seamlessly into influx to then be able to run calculations. Manufacturing is changing this industrial 4.0, and what we're seeing is influx being part of that equation. Being used to store data off the unified name space, we recommend InfluxDB all the time to customers that are exploring a new way to share data manufacturing called the unified name space who have open questions around how do I share this new data that's coming through my UNS or my QTT broker? How do I store this and be able to query it over time? And we often point to influx as a solution for that is a great brand. It's a great group of people and it's a great technology. >>Okay. We're now going to go into the customer panel and we'd like to welcome Angelo Fasi. Who's a software engineer at the Vera C Ruben observatory in Caleb McLaughlin whose senior spacecraft operations software engineer at loft orbital guys. Thanks for joining us. You don't wanna miss folks this interview, Caleb, let's start with you. You work for an extremely cool company. You're launching satellites into space. I mean, there, of course doing that is, is highly complex and not a cheap endeavor. Tell us about loft Orbi and what you guys do to attack that problem. >>Yeah, absolutely. And, uh, thanks for having me here by the way. Uh, so loft orbital is a, uh, company. That's a series B startup now, uh, who and our mission basically is to provide, uh, rapid access to space for all kinds of customers. Uh, historically if you want to fly something in space, do something in space, it's extremely expensive. You need to book a launch, build a bus, hire a team to operate it, you know, have a big software teams, uh, and then eventually worry about, you know, a bunch like just a lot of very specialized engineering. And what we're trying to do is change that from a super specialized problem that has an extremely high barrier of access to a infrastructure problem. So that it's almost as simple as, you know, deploying a VM in, uh, AWS or GCP is getting your, uh, programs, your mission deployed on orbit, uh, with access to, you know, different sensors, uh, cameras, radios, stuff like that. >>So that's, that's kind of our mission. And just to give a really brief example of the kind of customer that we can serve. Uh, there's a really cool company called, uh, totem labs who is working on building, uh, IOT cons, an IOT constellation for in of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor T, which means you have this little modem inside a container container that you, that you track from anywhere in the world as it's going across the ocean. Um, so they're, it's really little and they've been able to stay a small startup that's focused on their product, which is the, uh, that super crazy complicated, cool radio while we handle the whole space segment for them, which just, you know, before loft was really impossible. So that's, our mission is, uh, providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving, you know, a huge variety of customers with all kinds of different missions, um, and obviously generating a ton of data in space, uh, that we've gotta handle. Yeah. >>So amazing Caleb, what you guys do, I, now I know you were lured to the skies very early in your career, but how did you kinda land on this business? >>Yeah, so, you know, I've, I guess just a little bit about me for some people, you know, they don't necessarily know what they wanna do like early in their life. For me, I was five years old and I knew, you know, I want to be in the space industry. So, you know, I started in the air force, but have, uh, stayed in the space industry, my whole career and been a part of, uh, this is the fifth space startup that I've been a part of actually. So, you know, I've, I've, uh, kind of started out in satellites, did spent some time in working in, uh, the launch industry on rockets. Then, uh, now I'm here back in satellites and you know, honestly, this is the most exciting of the difference based startups. That I've been a part of >>Super interesting. Okay. Angelo, let's, let's talk about the Ruben observatory, ver C Ruben, famous woman scientist, you know, galaxy guru. Now you guys the observatory, you're up way up high. You're gonna get a good look at the Southern sky. Now I know COVID slowed you guys down a bit, but no doubt. You continued to code away on the software. I know you're getting close. You gotta be super excited. Give us the update on, on the observatory and your role. >>All right. So yeah, Rubin is a state of the art observatory that, uh, is in construction on a remote mountain in Chile. And, um, with Rubin, we conduct the, uh, large survey of space and time we are going to observe the sky with, uh, eight meter optical telescope and take, uh, a thousand pictures every night with a 3.2 gig up peaks of camera. And we are going to do that for 10 years, which is the duration of the survey. >>Yeah. Amazing project. Now you, you were a doctor of philosophy, so you probably spent some time thinking about what's out there and then you went out to earn a PhD in astronomy, in astrophysics. So this is something that you've been working on for the better part of your career, isn't it? >>Yeah, that's that's right. Uh, about 15 years, um, I studied physics in college, then I, um, got a PhD in astronomy and, uh, I worked for about five years in another project. Um, the dark energy survey before joining rubing in 2015. >>Yeah. Impressive. So it seems like you both, you know, your organizations are looking at space from two different angles. One thing you guys both have in common of course is, is, is software. And you both use InfluxDB as part of your, your data infrastructure. How did you discover influx DB get into it? How do you use the platform? Maybe Caleb, you could start. >>Uh, yeah, absolutely. So the first company that I extensively used, uh, influx DBN was a launch startup called, uh, Astra. And we were in the process of, uh, designing our, you know, our first generation rocket there and testing the engines, pumps, everything that goes into a rocket. Uh, and when I joined the company, our data story was not, uh, very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. Um, and at first there, you know, that's the way that a lot of engineers and scientists are used to working. Um, and at first that was, uh, like people weren't entirely sure that that was a, um, that that needed to change, but it's something the nice thing about InfluxDB is that, you know, it's so easy to deploy. So as the, our software engineering team was able to get it deployed and, you know, up and running very quickly and then quickly also backport all of the data that we collected thus far into influx and what, uh, was amazing to see. >>And as kind of the, the super cool moment with influx is, um, when we hooked that up to Grafana Grafana as the visualization platform we used with influx, cuz it works really well with it. Uh, there was like this aha moment of our engineers who are used to this post process kind of method for dealing with their data where they could just almost instantly easily discover data that they hadn't been able to see before and take the manual processes that they would run after a test and just throw those all in influx and have live data as tests were coming. And, you know, I saw them implementing like crazy rocket equation type stuff in influx, and it just was totally game changing for how we tested. >>So Angelo, I was explaining in my open, you know, you could, you could add a column in a traditional RDBMS and do time series, but with the volume of data that you're talking about, and the example of the Caleb just gave you, I mean, you have to have a purpose built time series database, where did you first learn about influx DB? >>Yeah, correct. So I work with the data management team, uh, and my first project was the record metrics that measured the performance of our software, uh, the software that we used to process the data. So I started implementing that in a relational database. Um, but then I realized that in fact, I was dealing with time series data and I should really use a solution built for that. And then I started looking at time series databases and I found influx B. And that was, uh, back in 2018. The another use for influx DB that I'm also interested is the visits database. Um, if you think about the observations we are moving the telescope all the time in pointing to specific directions, uh, in the Skype and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, uh, we call a visit. So we want to record the metadata about those visits and flex to, uh, that time here is going to be 10 years long, um, with about, uh, 1000 points every night. It's actually not too much data compared to other, other problems. It's, uh, really just a different, uh, time scale. >>The telescope at the Ruben observatory is like pun intended, I guess the star of the show. And I, I believe I read that it's gonna be the first of the next gen telescopes to come online. It's got this massive field of view, like three orders of magnitude times the Hub's widest camera view, which is amazing, right? That's like 40 moons in, in an image amazingly fast as well. What else can you tell us about the telescope? >>Um, this telescope, it has to move really fast and it also has to carry, uh, the primary mirror, which is an eight meter piece of glass. It's very heavy and it has to carry a camera, which has about the size of a small car. And this whole structure weighs about 300 tons for that to work. Uh, the telescope needs to be, uh, very compact and stiff. Uh, and one thing that's amazing about it's design is that the telescope, um, is 300 tons structure. It sits on a tiny film of oil, which has the diameter of, uh, human hair. And that makes an almost zero friction interface. In fact, a few people can move these enormous structure with only their hands. Uh, as you said, uh, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So each image has, uh, in diameter the size of about seven full moons. And, uh, with that, we can map the entire sky in only, uh, three days. And of course doing operations everything's, uh, controlled by software and it is automatic. Um there's a very complex piece of software, uh, called the scheduler, which is responsible for moving the telescope, um, and the camera, which is, uh, recording 15 terabytes of data every night. >>Hmm. And, and, and Angela, all this data lands in influx DB. Correct. And what are you doing with, with all that data? >>Yeah, actually not. Um, so we are using flex DB to record engineering data and metadata about the observations like telemetry events and commands from the telescope. That's a much smaller data set compared to the images, but it is still challenging because, uh, you, you have some high frequency data, uh, that the system needs to keep up and we need to, to start this data and have it around for the lifetime of the price. Mm, >>Got it. Thank you. Okay, Caleb, let's bring you back in and can tell us more about the, you got these dishwasher size satellites. You're kind of using a multi-tenant model. I think it's genius, but, but tell us about the satellites themselves. >>Yeah, absolutely. So, uh, we have in space, some satellites already that as you said, are like dishwasher, mini fridge kind of size. Um, and we're working on a bunch more that are, you know, a variety of sizes from shoebox to, I guess, a few times larger than what we have today. Uh, and it is, we do shoot to have effectively something like a multi-tenant model where, uh, we will buy a bus off the shelf. The bus is, uh, what you can kind of think of as the core piece of the satellite, almost like a motherboard or something where it's providing the power. It has the solar panels, it has some radios attached to it. Uh, it handles the attitude control, basically steers the spacecraft in orbit. And then we build also in house, what we call our payload hub, which is, has all, any customer payloads attached and our own kind of edge processing sort of capabilities built into it. >>And, uh, so we integrate that. We launch it, uh, and those things, because they're in lower orbit, they're orbiting the earth every 90 minutes. That's, you know, seven kilometers per second, which is several times faster than a speeding bullet. So we've got, we have, uh, one of the unique challenges of operating spacecraft and lower orbit is that generally you can't talk to them all the time. So we're managing these things through very brief windows of time, uh, where we get to talk to them through our ground sites, either in Antarctica or, you know, in the north pole region. >>Talk more about how you use influx DB to make sense of this data through all this tech that you're launching into space. >>We basically previously we started off when I joined the company, storing all of that as Angelo did in a regular relational database. And we found that it was, uh, so slow in the size of our data would balloon over the course of a couple days to the point where we weren't able to even store all of the data that we were getting. Uh, so we migrated to influx DB to store our time series telemetry from the spacecraft. So, you know, that's things like, uh, power level voltage, um, currents counts, whatever, whatever metadata we need to monitor about the spacecraft. We now store that in, uh, in influx DB. Uh, and that has, you know, now we can actually easily store the entire volume of data for the mission life so far without having to worry about, you know, the size bloating to an unmanageable amount. >>And we can also seamlessly query, uh, large chunks of data. Like if I need to see, you know, for example, as an operator, I might wanna see how my, uh, battery state of charge is evolving over the course of the year. I can have a plot and an influx that loads that in a fraction of a second for a year's worth of data, because it does, you know, intelligent, um, I can intelligently group the data by, uh, sliding time interval. Uh, so, you know, it's been extremely powerful for us to access the data and, you know, as time has gone on, we've gradually migrated more and more of our operating data into influx. >>You know, let's, let's talk a little bit, uh, uh, but we throw this term around a lot of, you know, data driven, a lot of companies say, oh, yes, we're data driven, but you guys really are. I mean, you' got data at the core, Caleb, what does that, what does that mean to you? >>Yeah, so, you know, I think the, and the clearest example of when I saw this be like totally game changing is what I mentioned before at Astro where our engineer's feedback loop went from, you know, a lot of kind of slow researching, digging into the data to like an instant instantaneous, almost seeing the data, making decisions based on it immediately, rather than having to wait for some processing. And that's something that I've also seen echoed in my current role. Um, but to give another practical example, uh, as I said, we have a huge amount of data that comes down every orbit, and we need to be able to ingest all of that data almost instantaneously and provide it to the operator. And near real time, you know, about a second worth of latency is all that's acceptable for us to react to, to see what is coming down from the spacecraft and building that pipeline is challenging from a software engineering standpoint. >>Um, our primary language is Python, which isn't necessarily that fast. So what we've done is started, you know, in the, in the goal of being data driven is publish metrics on individual, uh, how individual pieces of our data processing pipeline are performing into influx as well. And we do that in production as well as in dev. Uh, so we have kind of a production monitoring, uh, flow. And what that has done is allow us to make intelligent decisions on our software development roadmap, where it makes the most sense for us to, uh, focus our development efforts in terms of improving our software efficiency. Uh, just because we have that visibility into where the real problems are. Um, it's sometimes we've found ourselves before we started doing this kind of chasing rabbits that weren't necessarily the real root cause of issues that we were seeing. Uh, but now, now that we're being a bit more data driven, there we are being much more effective in where we're spending our resources and our time, which is especially critical to us as we scale to, from supporting a couple satellites, to supporting many, many satellites at >>Once. Yeah. Coach. So you reduced those dead ends, maybe Angela, you could talk about what, what sort of data driven means to, to you and your teams? >>I would say that, um, having, uh, real time visibility, uh, to the telemetry data and, and metrics is, is, is crucial for us. We, we need, we need to make sure that the image that we collect with the telescope, uh, have good quality and, um, that they are within the specifications, uh, to meet our science goals. And so if they are not, uh, we want to know that as soon as possible and then, uh, start fixing problems. >>Caleb, what are your sort of event, you know, intervals like? >>So I would say that, you know, as of today on the spacecraft, the event, the, the level of timing that we deal with probably tops out at about, uh, 20 Hertz, 20 measurements per second on, uh, things like our, uh, gyroscopes, but the, you know, I think the, the core point here of the ability to have high precision data is extremely important for these kinds of scientific applications. And I'll give an example, uh, from when I worked at, on the rocket at Astra there, our baseline data rate that we would ingest data during a test is, uh, 500 Hertz. So 500 samples per second. And in some cases we would actually, uh, need to ingest much higher rate data, even up to like 1.5 kilohertz. So, uh, extremely, extremely high precision, uh, data there where timing really matters a lot. And, uh, you know, I can, one of the really powerful things about influx is the fact that it can handle this. >>That's one of the reasons we chose it, uh, because there's times when we're looking at the results of a firing where you're zooming in, you know, I talked earlier about how on my current job, we often zoom out to look, look at a year's worth of data. You're zooming in to where your screen is preoccupied by a tiny fraction of a second. And you need to see same thing as Angela just said, not just the actual telemetry, which is coming in at a high rate, but the events that are coming out of our controllers. So that can be something like, Hey, I opened this valve at exactly this time and that goes, we wanna have that at, you know, micro or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at, you know, at this exact moment, was that before or after this valve open, those kind of, uh, that kind of visibility is critical in these kind of scientific, uh, applications and absolutely game changing to be able to see that in, uh, near real time and, uh, with a really easy way for engineers to be able to visualize this data themselves without having to wait for, uh, software engineers to go build it for them. >>Can the scientists do self-serve or are you, do you have to design and build all the analytics and, and queries for your >>Scientists? Well, I think that's, that's absolutely from, from my perspective, that's absolutely one of the best things about influx and what I've seen be game changing is that, uh, generally I'd say anyone can learn to use influx. Um, and honestly, most of our users might not even know they're using influx, um, because what this, the interface that we expose to them is Grafana, which is, um, a generic graphing, uh, open source graphing library that is very similar to influx own chronograph. Sure. And what it does is, uh, let it provides this, uh, almost it's a very intuitive UI for building your queries. So you choose a measurement and it shows a dropdown of available measurements. And then you choose a particular, the particular field you wanna look at. And again, that's a dropdown, so it's really easy for our users to discover. And there's kind of point and click options for doing math aggregations. You can even do like perfect kind of predictions all within Grafana, the Grafana user interface, which is really just a wrapper around the APIs and functionality of the influx provides putting >>Data in the hands of those, you know, who have the context of domain experts is, is key. Angela, is it the same situation for you? Is it self serve? >>Yeah, correct. Uh, as I mentioned before, um, we have the astronomers making their own dashboards because they know what exactly what they, they need to, to visualize. Yeah. I mean, it's all about using the right tool for the job. I think, uh, for us, when I joined the company, we weren't using influx DB and we, we were dealing with serious issues of the database growing to an incredible size extremely quickly, and being unable to like even querying short periods of data was taking on the order of seconds, which is just not possible for operations >>Guys. This has been really formative it's, it's pretty exciting to see how the edge is mountaintops, lower orbits to be space is the ultimate edge. Isn't it. I wonder if you could answer two questions to, to wrap here, you know, what comes next for you guys? Uh, and is there something that you're really excited about that, that you're working on Caleb, maybe you could go first and an Angela, you can bring us home. >>Uh, basically what's next for loft. Orbital is more, more satellites, a greater push towards infrastructure and really making, you know, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, uh, making that happen, it's extremely exciting and extremely exciting time to be in this company and to be in this industry as a whole, because there are so many interesting applications out there. So many cool ways of leveraging space that, uh, people are taking advantage of. And with, uh, companies like SpaceX and the now rapidly lowering cost, cost of launch, it's just a really exciting place to be. And we're launching more satellites. We are scaling up for some constellations and our ground system has to be improved to match. So there's a lot of, uh, improvements that we're working on to really scale up our control software, to be best in class and, uh, make it capable of handling such a large workload. So >>You guys hiring >><laugh>, we are absolutely hiring. So, uh, I would in we're we need, we have PE positions all over the company. So, uh, we need software engineers. We need people who do more aerospace, specific stuff. So, uh, absolutely. I'd encourage anyone to check out the loft orbital website, if there's, if this is at all interesting. >>All right. Angela, bring us home. >>Yeah. So what's next for us is really, uh, getting this, um, telescope working and collecting data. And when that's happen is going to be just, um, the Lu of data coming out of this camera and handling all, uh, that data is going to be really challenging. Uh, yeah. I wanna wanna be here for that. <laugh> I'm looking forward, uh, like for next year we have like an important milestone, which is our, um, commissioning camera, which is a simplified version of the, of the full camera it's going to be on sky. And so yeah, most of the system has to be working by them. >>Nice. All right, guys, you know, with that, we're gonna end it. Thank you so much, really fascinating, and thanks to influx DB for making this possible, really groundbreaking stuff, enabling value creation at the edge, you know, in the cloud and of course, beyond at the space. So really transformational work that you guys are doing. So congratulations and really appreciate the broader community. I can't wait to see what comes next from having this entire ecosystem. Now, in a moment, I'll be back to wrap up. This is Dave ante, and you're watching the cube, the leader in high tech enterprise coverage. >>Welcome Telegraph is a popular open source data collection. Agent Telegraph collects data from hundreds of systems like IOT sensors, cloud deployments, and enterprise applications. It's used by everyone from individual developers and hobbyists to large corporate teams. The Telegraph project has a very welcoming and active open source community. Learn how to get involved by visiting the Telegraph GitHub page, whether you want to contribute code, improve documentation, participate in testing, or just show what you're doing with Telegraph. We'd love to hear what you're building. >>Thanks for watching. Moving the world with influx DB made possible by influx data. I hope you learn some things and are inspired to look deeper into where time series databases might fit into your environment. If you're dealing with large and or fast data volumes, and you wanna scale cost effectively with the highest performance and you're analyzing metrics and data over time times, series databases just might be a great fit for you. Try InfluxDB out. You can start with a free cloud account by clicking on the link and the resources below. Remember all these recordings are gonna be available on demand of the cube.net and influx data.com. So check those out and poke around influx data. They are the folks behind InfluxDB and one of the leaders in the space, we hope you enjoyed the program. This is Dave Valante for the cube. We'll see you soon.

(upbeat music) >> Okay, we've heard from the folks at Pluribus Networks and NVIDIA about their effort to transform cloud networking and unify bespoke infrastructure. Now, let's get the perspective from an independent analyst. And to do so, we welcome in ESG senior analyst, Bob Laliberte. Bob, good to see you. Thanks for coming into our East Coast studios. >> Oh, thanks for having me. It's great to be here. >> So this idea of unified cloud networking approach, how serious is it? What's driving it? >> There's certainly a lot of drivers behind it, but probably the first and foremost is the fact that application environments are becoming a lot more distributed, right? So the IT pendulum tends to swing back and forth, and we're definitely on one that's swinging from consolidated to distributed. And so applications are being deployed in multiple private data centers, multiple public cloud locations, edge locations. And as a result of that, what you're seeing is a lot of complexity. So organizations are having to deal with this highly disparate environment. They have to secure it. They have to ensure connectivity to it. And all that's driving up complexity. In fact, when we asked, in one of our last surveys last year about network complexity, more than half, 54% came out and said, "Hey, our network environment is now either more or significantly more complex than it used to be." And as a result of that, what you're seeing is it's really impacting agility. So everyone's moving to these modern application environments, distributing them across areas so they can improve agility, yet it's creating more complexity. So a little bit counter to the fact and really counter to their overarching digital transformation initiatives. From what we've seen, 9 out 10 organizations today are either beginning, in process, or have a mature digital transformation process or initiative, but their top goals, when you look at them, and it probably shouldn't be a surprise, the number one goal is driving operational efficiency. So it makes sense. I've distributed my environment to create agility but I've created a lot of complexity. So now, I need these tools that are going to help me drive operational efficiency, drive better experiences. >> Got it. I mean, I love how you bring in the data. ESG does a great job with that. The question is, is it about just unifying existing networks or is there sort of a need to rethink, kind of do over how networks are built? >> That's a really good point. Because certainly, unifying networks helps, right. Driving any kind of operational efficiency helps. But in this particular case, because we've made the transition to new application architectures and the impact that's having as well, it's really about changing and bringing in new frameworks and new network architectures to accommodate those new application architectures. And by that, what I'm talking about is the fact that these new modern application architectures, microservices, containers, are driving a lot more east-west traffic. So in the old days, it used to be easier. North-south coming out of the server, one application per server, things like that. Right now, you've got hundreds, if not thousands, of microservices communicating with each other, users communicating to 'em. So there's a lot more traffic, and a lot of it's taking place within the servers themselves. The other issue that you're starting to see as well, from that security perspective, when we were all consolidated, we had those perimeter-based, legacy, castle-and-moat security architectures, but that doesn't work anymore when the applications aren't in the castle, right. When everything's spread out, that no longer happens. So we're absolutely seeing organizations trying to make a shift. And I think much like, if you think about the shift that we're seeing with all the remote workers in the SASE framework to enable a secure framework there, it's almost the same thing. We're seeing this distributed services framework come up to support the applications better within the data centers, within the cloud data centers, so that you can drive that security closer to those applications and make sure they're fully protected. And that's really driving a lot of the zero trust stuff you hear, right? So never trust, always verify, making sure that everything is really secure. Microsegmentation's another big area. So ensuring that these applications, when they're connected to each other, they're fully segmented out. And again, because if someone does get a breach, if they are in your data center, you want to limit the blast radius, you want to limit the amount of damage that's done. So that by doing that, it really makes it a lot harder for them to see everything that's in there. >> You mentioned zero trust. It used to be a buzzword and now it's become a mandate. And I love the moat analogy. You build a moat to protect the queen in the castle. The queen's left the castle. It's just distributed. So how should we think about this Pluribus and NVIDIA solution? There's a spectrum. Help us understand that. You got appliances. You got pure software solutions. You got what Pluribus is doing with NVIDIA. Help us understand that. >> Yeah, absolutely. I think as organizations recognize the need to distribute their services closer to the applications, they're trying different models. So from a legacy approach, from a security perspective, they've got decentralized firewalls that they're deploying within their data centers. The hard part for that is, if you want all this traffic to be secured, you're actually sending it out of the server, up through the rack, usually to a different location in the data center and back. So with the need for agility, with the need for performance, right, that adds a lot of latency. Plus, when you start needing to scale, that means adding more and more network connections, more and more appliances. So it can get very costly, as well as impacting the performance. The other way that organizations are seeking to solve this problem is by taking the software itself and deploying it on the servers, okay? So it's a great approach, right? It brings it really close to the applications. But the things you start running into there, there's a couple of things. One is that you start seeing that the DevOps team start taking on that networking and security responsibility. >> Which they don't want to do. >> They don't want to do, right. And the operations teams loses a little bit of visibility into that. Plus, when you load the software onto the server, you're taking up precious CPU cycles. So if you're really wanting your applications to perform at an optimized state, having additional software on there isn't going to do it. So when we think about all those types of things, right, and certainly, the other side effects of that is the impact in the performance, but there's also a cost. So if you have to buy more servers, because your CPUs are being utilized, right, and you have hundreds or thousands of servers, right, those costs are going to add up. So what NVIDIA and Pluribus have done by working together is to be able to take some of those services and be able to deploy them onto a SmartNIC, right, be able to deploy the DPU-based SmartNIC into the servers themselves, and then Pluribus has come in and said, "We're going to create that unified fabric across the networking space into those networking services all the way down to the server." So the benefits of having that are pretty clear in that you're offloading that capability from the server. So your CPUs are optimized. You're saving a lot of money. You're not having to go outside of the server and go to a different rack somewhere else in the data center. So your performance is going to be optimized as well. You're not going to incur any latency hit for every round trip to the firewall and back. So I think all those things are really important, plus the fact that you're going to see, from an organizational aspect, we talked about the DevOps and NetOps teams, the network operations teams now can work with the security teams to establish the security policies and the networking policies so that the DevOps teams don't have to worry about that. So essentially, they just create the guardrails and let the DevOps team run, 'cause that's what they want. They want that agility and speed. >> Your point about CPU cycles is key. I mean, it's estimated that 25 to 30% of CPU cycles in the data center are wasted. The cores are wasted doing storage offload or networking or security offload. And I've said many times, everybody needs a Nitro, like the Amazon Nitro. You can only buy Amazon Nitro if you go into AWS, right. But everybody needs a Nitro. So is that how we should think about this? >> Yeah, that's a great analogy to think about this. And I think I would take it a step further because it's almost the opposite end of the spectrum, because Pluribus and NVIDIA are doing this in a very open way. And so Pluribus has always been a proponent of open networking. And so what they're trying to do is extend that now to these distributed services. So Leverage working with NVIDIA is also open as well, being able to bring that to bear so that organizations cannot only take advantage of these distributed services, but also that unified networking fabric, that unified cloud fabric, across that environment from the server across the switches. The other key piece of what Pluribus is doing, because they've been doing this for a while now and they've been doing it with the older application environments and the older server environments, they're able to provide that unified networking experience across a host of different types of servers and platforms. So you can have not only the modern application supported, but also the legacy environments, bare metal. You could go any type of virtualization, you can run containers, et cetera. So a wide gambit of different technologies hosting those applications, supported by a unified cloud fabric from Pluribus. >> So what does that mean for the customer? I don't have to rip and replace my whole infrastructure right? >> Yeah, well, think what it does, again, from that operational efficiency, when you're going from a legacy environment to that modern environment, it helps with the migration, it helps you accelerate that migration because you're not switching different management systems to accomplish that. You've got the same, unified networking fabric that you've been working with to enable you to run your legacy as well as transfer over to those modern applications as well. >> Got it, so your people are comfortable with the skillsets, et cetera. All right, I'll give you the last word. Give us the bottom line here. >> So I think, obviously, with all the modern applications that are coming out, the distributed application environments, it's really posing a lot of risk on these organizations to be able to get not only security, but also visibility into those environments. And so organizations have to find solutions. As I said at the beginning, they're looking to drive operational efficiency. So getting operational efficiency from a unified cloud networking solution, that it goes from the server across the servers to multiple different environments, right, in different cloud environments, is certainly going to help organizations drive that operational efficiency, it's going to help them save money for visibility, for security, and even open networking. So a great opportunity for organizations, especially large enterprises, cloud providers, who are trying to build that hyperscale-like environment. You mentioned the Nitro card. This is a great way to do it with an open solution. >> Love it. Bob, thanks so much for coming in and sharing your insights. I appreciate it. >> You're welcome, thanks. >> All right, in a moment, I'll be back to give you some closing thoughts on unified cloud networking and the key takeaways from today. You're watching "theCUBE", your leader in enterprise tech coverage. (upbeat music)

(bright upbeat music) >> According to the latest survey from Enterprise Technology Research, container orchestration is the number one category as measured by customer spending momentum. It's ahead of AIML, it's ahead of cloud computing, and it's ahead of robotic process automation. All of which also show highly elevated levels of customer spending velocity. Now, we drill deeper into the survey of more than 1200 CIOs and IT buyers, and we find that a whopping 70% of respondents are spending more on Kubernetes initiatives in 2022 as compared to last year. The rise of Kubernetes came about through a series of improbable events that change the way applications are developed, deployed and managed. Very early on Kubernetes committers chose to focus on simplicity in massive adoption rather than deep enterprise functionality. It's why initially virtually all activity around Kubernetes focused on stateless applications. That has changed. As Kubernetes adoption has gone mainstream, the need for stronger enterprise functionality has become much more pressing. You hear this constantly when you attend the various developer conference, and the talk is all around, let's say, shift left to improve security and better cluster management, more complete automation capabilities, support for data-driven workloads and very importantly, vastly better application performance in visibility and management. And that last topic is what we're here to talk about today. Hello, this is Dave Vellante, and welcome to this special CUBE conversation where we invite into our East Coast Studios Matt Provo, who's the founder and CEO of StormForge and Chandler Hoisington, the general manager of EKS Edge in Hybrid at AWS. Gentlemen, welcome, it's good to see you. >> Thanks. >> Thanks for having us. >> So Chandler, you have this convergence, you've got application performance, you've got developer speed and velocity and you've got cloud economics all coming together. What's driving that convergence and why is it important for customers? >> Yeah, yeah, great question. I think it's important to kind of understand how we got here in the first place. I think Kubernetes solves a lot of problems for users, but the complexity of Kubernetes of just standing up a cluster to begin with is not always simple. And that's where services like EKS comes in and where Amazon tried to solve that problem for users saying, "Hey the control plane, it's made up of 10, 15 different components, standing all these up, patching them, you know, handling the CBEs for it et cetera, et cetera, is a very complicated process, let me help you do that." And where EKS has been so successful and with EKS Anywhere which we launched last year, that's what we're helping customers do, a very similar thing in their own data centers. So we're kind of solving this problem of bringing the cluster online and helping customers launch their first application on it. But then what do you do once your application's there? That's the question. And so now you launched your application and does it have enough resources? Did you tune the right CPU? Did you tune the right amount of memory for it? All those questions need to be answered and that's where working with folks like StormForge come in. >> Well, it's interesting Matt because you're all about optimization and trying to maximize the efficiency which might mean people's lower their AWS bill, but that's okay with Amazon, right? You guys have shown the cheaper it is, the more they buy, well. >> Yeah. And it's all about loyalty and developer experience. And so when you can help create or add to the developer experience itself, over time that loyalty's there. And so when we can come alongside EKS and services from Amazon, well, number one StormForge is built on Amazon, on AWS, and so it's a nice fit, but when we don't have to require developers to choose between things like cost and performance, but they can focus on, you know, innovation and connecting the applications that they're managing on Kubernetes as they operationalize them to the actual business objectives that they have, it's a pretty powerful combination. >> So your entry into the market was in pre-production. >> Yeah. >> You can kind of simulate what performance is going to look like and now you've announced optimized live. >> Yep. >> So that should allow you to turn the crank a little bit more. >> Yeah. >> Get a little bit more accurate and respond more quickly. >> Yeah. So we're the only ones that give you both views. And so we want to, you know, we want to provide a view in what we call kind of our experimentation side of our platform, which is pre-production, as well as on ongoing and continuous view which we kind of call our observation, the observation part of our solution, which is in production. And so for us, it's about providing that view, it's also about taking an increased number of data inputs into the platform itself so that our machine learning can learn from that and ultimately be able to automate the right kinds of tasks alongside the developers to meet their objectives. >> So, Chandler, in my intro I was talking about the spending velocity and how Kubernetes was at the top. But when we had other survey questions that ETR did, and this is post pandemic, it was interesting. We asked what's the most important initiative? And the two top ones were security, no surprise, and it popped up really after the pandemic hit in the lockdown even more prominent and cloud migration, >> Right. >> was number two. And so how are you working with StormForge to effect cloud migrations? Talk about that relationship. >> Yeah. I think it's, you know, different enterprises to have different strategies on how they're going to get their workloads to the cloud. Some of 'em want to have modernize in place in their data centers and then take those modernized applications and move them to the cloud, and that's where something like I mentioned earlier, EKS Anywhere comes into play really nicely because we can bring a consistent experience, a Kubernetes experience to your data center, you can modernize your applications and then you can bring those to EKS in the cloud. And as you're moving them back and forth you have a more consistent experience with Kubernetes. And luckily StormForge works on prem as well even in air gapped environments for StormForge. So, you know, that's, you can get your applications tuned correctly for your data center workloads, and then you're going to tune them differently when you move them to the cloud and you can get them tuned correctly there but StormForge can run consistently in both environments. >> Now, can you add some color as to how you optimize EKS? >> Yeah, so I think from a EKS standpoint, when you, again, when the number of parameters that you have to look at for your application inside of EKS and then the associated services that will go alongside that the packages that are coming in from a Kubernetes standpoint itself, and then you start to transition and operationalize where more and more of these are in production, they're, you know, connected to the business, we provide the ability to go beyond what developers typically do which is sort of take the, either the out of the box defaults or recommendations that ship with the services that they put into their application or the any human's ability to kind of keep up with a couple parameters at a time. You know, with two parameters for the typical Kubernetes application, you might have about a 100 different possible combinations that you could choose from. And sometimes humans can keep up with that, at least statically. And so for us, we want to blow that wide open. We want developers to be able to take advantage of the entire footprint or environment itself. And, you know, by using machine learning to help augment what the developers themselves are doing, not replacing them, augmenting them and having them be a part of that process. Now this whole new world of optimization opens up to them, which is pretty fantastic. And so how the actual workloads are configured, you know, on an ongoing basis and predictively based on upcoming business events, or even unknowns many times is a pretty powerful position to be in. >> I mean, you said not to replace development. I mentioned robotic process automation in my intro, and of course in the early days, I was like, oh, it's going to replace my job. What's actually happened is it's replacing all the mundane tasks. >> Yeah. >> So you can actually do your job. >> Yeah. >> Right? We're all working 24/7, 365 these days, so that the extent that you can automate the things that I hate doing, >> Yeah. >> That's a huge win. So Chandler, how do people get started? You mentioned EKS Anywhere, are they starting on prem and then kind of moving into the cloud? If I'm a customer and I'm interested and I'm sort of at the beginning, where do I start? >> Yeah. Yeah. I mean, it really depends on your workload. Any workload that can run in the cloud should run in the cloud. I'm not just saying that because I work at Amazon but I truly think that that is the case. And I think customers think that as well. More and more customers are trying to move workloads to the cloud for that elasticity and all the benefits of using these huge platforms and, you know, hundreds of services that you have advantage of in the cloud but some workloads just can't move to the cloud yet. You have workloads that have latency requirements like some gaming workloads, for example, where we don't have regions close enough to the consumers yet. So, you know, you want to put workloads in Turkey to service Egypt customers or something like this. You also have workloads that are, you know, on cruise ships and they lose connectivity in the middle of the Atlantic, or maybe you have highly secure workloads in air gapped environments or something like this. So there's still a lot of use cases that keep workloads on prem and sometimes customers just have existing investments in hardware that they don't want to eat yet, right? And they want to slowly phase those out as they move to the cloud. And again, that's where EKS Anywhere really plays well for the workloads that you want to keep on prem, but then as you move to the cloud you can take advantage of obviously EKS. >> I'll put you in the spot. >> Sure. >> And don't hate me for doing this, but so Andy Jassy, Adam Selipsky, I've certainly heard Maylan Thompson Bukavek talk about this, and in fullness of time, all workloads will be in the cloud. >> Yeah. >> And I've said the cloud is expanding. We're going to bring the cloud to the edge. Edge is in your title. >> Yeah. >> Is that a correct interpretation and obvious it relates >> Absolutely. >> to Kubernetes. >> And you'll see that in Amazon strategy. I mean, without posts and wavelengths and local zones, like we're, at the end of the day, Amazon tries to satisfy customers. And if customers are saying, "Hey, I need workloads in San, I want to run a workload in San Francisco. And it's really important to me that it's close to those users, the end users that are in that area," we're going to help them do that at Amazon. And there's a variety of options now to do that. EKS Anywhere is actually only one piece of that kind of whole strategy. >> Yeah. I mean, here you have your best people working on the speed of light problem, but until that's solved, sure, sure. >> That's right. >> We'll give you the last word. >> How do you know about that? >> Yeah. Yeah. (all laughing) >> It's a top secret. Sorry. You heard it on the CUBE first. Matt, we'll give you the last word, bring us home. >> I, so I couldn't agree more. The, you know, the cloud is where workloads are going. Whether what I love is the ability to look at, you know, for the same enterprises, a lot of the ones we work with, want a, they want a public and a private view, public cloud, private cloud view. And they want that flexibility to, depending on the nature of the applications to be able to shift between from time to time where, you know, really decide. And I love EKS Anywhere. I think it's a fantastic addition to the, you know, to the ecosystem. And, you know, I think for us, we're about staying focused on the set of problems that we solve. No developer that I've ever met and probably neither of you have met, gets super excited about getting out of bed to manually tune their applications. And so what we find is that, you know, the time spent doing that, literally just is, there's like a one-to-one correlation. It means they're not innovating and they're not doing what they love to be doing. And so when we can come alongside that and automate away the manual task to your point, I think there are a lot of parallels to RPA in that case, it becomes actually a pretty empowering process for our users, so that they feel like they're, again, meeting the business objectives that they have, they get to innovate and yet, you know, they're exploring this whole new world around not having to choose between something like cost and performance for their applications. >> Well, and we're entering an entire new era of scale. >> Yeah. >> We've never seen before and human just are not going to be able to keep up with that. >> Yep. >> And that affect quality and speed and everything else. Guys, hey, thanks so much for coming in a great conversation. And thank you for watching this CUBE conversation. This is Dave Vellante, and we'll see you next time. (upbeat music)