>>from around the globe. It's the Cube >>with digital coverage of VM World 2020 brought to you by VM Ware and its ecosystem partners. Hello and welcome back to the VM World 2020 Virtual coverage with the Cube Virtual I'm John for day. Volonte your hosts our 11th year covering VM. We'll get a great guest Greg Lavender, SBP and the CTO of VM. Where, uh, welcome to the Cube. Virtual for VM World 2020 Virtual Great. Thanks for coming on. >>Privileged to be here. Thank you. >>Um, really. You know, one of the things Dave and I were commenting with Pat on just in general start 11th year covering VM world. Uh, a little difference not face to face. But it's always been a technical conference. Always a lot of technical innovation. Project Monterey's out there. It's pretty nerdy, but it's a it's called the catnip of the future. Right? People get excited by it, right? So there's really ah lot of awareness to it because it kinda it smells like a systems overhaul. It smells like an operating system. Feels like a, you know, a lot of moving parts that are, quite frankly, what distributed computing geeks and software geeks love to hear about and to end distributed software intelligence with new kinds of hardware innovations from and video and whatnot. Where's that innovation coming from? Can you share your thoughts on this direction? >>Yeah, I think first I should say this isn't like, you know, something that just, you know, we decided to do, you know, six months ago, actually, in the office of C T 04 years ago, we actually had a project. Um, you know, future looking project to get our core hyper visor technology running on arm processors and that incubated in the office of the CTO for three years. And then last December, move the engineering team that had done that research and advanced development work in the office of the CTO over to our cloud platforms business unit, you know, and smart Knicks, you know, kind of converged with that. And so we were already, you know, well along the innovation path there, and it's really now about building the partnerships we have with smart nick vendors and driving this technology out to the benefit of our customers who don't want to leverage it. >>You get >>Greg, I want if you could clarify something for me on that. So Pat talked about Monterey, a complete re architect ing of the i o Stack. And he talked about it affecting in video. Uh, intel, melon, ox and Sandoz part of that when he talks about the Iot stack, you know, specifically what are we talking about there? >>So you know any any computing server in the data center, you know, in a cola facility or even even in the cloud, you know? Ah, large portion of the, you know CPU resource is, and even some memory resource is can get consumed by just processing. You know, the high volumes of Iot that's going out, you know, storage devices, you know, communicating between the different parts of multi tiered applications. And so there's there's a there's an overhead that that gets consumed in the course server CPU, even if its multi core multi socket. And so by offloading that a lot of that I owe work onto the arm core and taking advantage of the of the hardware offloads there in the smart Knicks, you can You can offload that processing and free up even as much as 30% of the CPU of a server, multi socket, multicourse server, and give that back to the application so that the application gets the benefit of that extra compute and memory resource is >>So what about a single sort of low cost flash tear to avoid the complexities of tearing? Is that part of the equation? >>Well, you know, you can you can, um you know, much storage now is network attached. And so you could if it's all flash storage, you know, using something like envy me fabric over over Ethernet, you can essentially build large scale storage networks more efficiently, you know more cheaply and take advantage of that offload processing, uh, to begin to reduce the Iot Leighton. See, that's required taxes. That network attached storage and not just storage. But, you know, other devices, you know, that you can use you could better network attached. So disaggregated architectures is term. >>Uh, is that a yes? Or is that a stay tuned? >>Yeah, Yeah, yeah, yeah, yes. I mean the storage. You know, more efficient use of different classes of storage and storage. Tearing is definitely a prime use case there. >>Yeah, great. Thank you. Thanks for that. John, >>How could people think about the edge now? Because one of the things that's in this end to end is the edge. Pat brought it up multi cloud and edge or two areas that are extending off cloud and hybrid. What should people think about the innovation equation around those things? Is that these offload techniques? What specifically in the systems architecture? Er, do you guys see as the key keys there? >>So so, you know, edges very diversified, heterogeneous place, Uh, in the architectures of multi cloud services. So one thing we do know is, you know, workload. I would like to say workload follows data, and a lot of the data will be analyzed, the process at the edge. So the more that you can accelerate that data processing at the edge and apply some machine learning referencing at the edge were almost certainly gonna have kubernetes everywhere, including the edge. So I think you're seeing a convergence of the hardware architectures er the kubernetes control plane and services and machine learning workloads. You know, traveling to the edge where the where the data is going to be processed and actions could be taken autonomously at the edge. So I think we're in this convergence point in the industry where all that comes together. >>How important do you >>do you see that? Okay, John, >>how important is the intelligence piece? Because again, the potatoes at the edge. How do you guys see the data architecture being built out there? >>Um, well, again, it's depending on the other. The thick edge of the thin edge. You know, you're gonna have different, different types of data, and and again, a lot of the the inference thing that could happen at the edges. Going to, I think, for mawr, you know, again to take action at the edges, opposed to calling home to a cloud, you know, to decide what to do. So, depending on, you know, the computational power and the problem with its video processing or monitoring, you know, sensors, Aaron, oil. Well, the kind of interesting that will happen at the edge will will be dependent on that data type and what kind of decisions you want to make. So I think data will be moving, you know, from the edge to the cloud for historical analytics and maybe transitional training mechanisms. But, you know, the five G is gonna play heavily into this is well right for the network connectivity. So we read This unique point is often occurs in the industry every few years of all these technology innovations converging to open up an entirely new platform in a new way of computing that happens at the edge, not just in your data center at the cloud. >>So, Greg, you did a fairly major stint at a large bank. What would something you mentioned? You know, like an oil rig. But what would something like these changes mean for a new industry like banking or financial? Uh, will it have an impact there and put on your customer hat for a minute and take us through that >>e? You know, eight machines, you know, branches, chaos. You know, there's all make banks always been a very distributed computing platform. And so, you know, people want to deliver mawr user experience, services, more video services. You know all these things at the edge to interact positively with the customer without using the people in the loop. And so the banking industry has already gone through the SD when, and I want transformation to deliver the bandwidth more capably to the edge. And I just think that they'll just now be able to deliver Mawr Edge services that happened can happen more autonomously at the edge is opposed that having the hairpin home run everything back to the data center. >>Awesome. Well, Pat talks about the modern platform, the modern companies. Greg, I wanna ask you because we're seeing with Kovar, there's to use cases, you know, the people who don't have a tailwind, Um, companies that are, you know, not doing well because there's no business that you have there modernizing their business while they have some downtime. Other ones have a tailwind. They have a modern app that that takes advantage, this covert situation. So that brings up this idea of what is a modern app look like? Because now, if you're talking about a distributed architecture, some of things you're mentioning around inference, data edge. People are starting to think about these modern naps, and they are changing the game for the business. Now you have vertical industries. You mentioned oil and gas, you got financial services. It used to be you had industry solution. It worked like that and was siloed. Now you have a little bit of a different architectures. If we believe that we're looking up, not down. Does it matter by industry? How should people think about a modern application, how they move faster? Can you share your insights into into some of this conceptual? What is a modern approach and does it doesn't matter by vertical or industry. >>Yes, I mean, certainly over the course of my career, I mean, there's there's a massive diversity of applications. And of course, you know, the explosion of mobile and edge computing is just another sort of sort of use cases that will put demands on the infrastructure in the architecture and the networking. So a modern, a modern app I mean, we historically built sort of these monolithic app. So we sort of built these sort of three tier apse with, you know, sort of the client side, the middleware side. The database back in is the system of record. I mean, this is even being more disaggregated in terms of, you know, the the consumer edges both not just web here, but mobile tear. And, you know, we'll see what emerges out of that. The one thing for sure that is that, um they're becoming less monolithic and mawr a conglomeration of sass and other services that are being brought together, whether it's from the cloud services or whether it's s, you know, SBS delivering, you know, bring your own software. Um, and they're becoming more distributed because people need operated higher degrees of scale. There's a limit to Virgil vertical scaling, so you have to go to horizontal scaling, which is what the cloud is really good at. So I think all these things were driving a whole new set of technologies like next generation AP gateways. Message Busses, service mesh. We're announcing Tanzi's service message being world. Um, you know, this is just allowing allowing that application to be disaggregated and then integrated with other APS assassin services that allow you to get faster time to market. So speed of delivery is everything. So modern C I. C d. Modern software, technology and ability to deploy and run that workload anywhere at the edge of the core in the data center in the cloud. >>So when you do in your re architecture like this, Greg, I mean you've seen over the course of history in our industry you've seen so many companies have hit a wall and in VM, whereas it's just amazing engineering culture. How are you able toe, you know, change the engine mid flight here and avoid like, serious technical debt. And I mean, it took, you know, you said started four years ago, but can you give us a peek inside? You know, that sort of transformation and how you're pulling that off? >>Well, I mean, we're providing were delivered the platform and, you know, spring Buddhas a key, you know, technology that's used widely across the industry already, which is what we've got is part of our pivotal acquisition. And so what we're just trying to do is just keep keep delivering the technology and the platform that allows people to go faster with quality security and safety and resiliency. That's what we do really well at VM ware. So I think you're seeing more people building these APS Cloud native is opposed to, you know, taking an existing legacy app In trying to re factor it, they might do what it called e think somebody's called two speed architectures. Take the user front, end the consumer front in, and put that cloud native in the cloud. But the back end system of record still runs in the private cloud in a highly resilient you know, backed up disaster recovered way. So you're having, I think, brand new cloud native APS we're seeing. And then you're seeing people very carefully because there's a cost to it of looking at How do I basically modernized the front end but maintain the reliability of the scalability of security and the reliability of that sort of system of record back in? So either way, it's it's winning for the companies because they could do faster delivery to their businesses and their clients and their partners. But you have to have the resiliency and reliability that were known for for running those mission critical workloads, >>right? So the scenario is that back end stays on premise on the last earnings call, I think, Pat said, or somebody said that, that I think I just they said on Prem or maybe the man hybrid 30 to 40% cheaper, then doing it in the cloud. I presume they were talking about those kind of back end systems that you know you don't wanna migrate. Can you add some color that again from your customer perspective That the economics? >>Yeah. You know, um, somebody asked me one time what's really a cloud. Greg and I said, automation, automation, automation you can take you can take You can take your current environments and highly automate the release. Lifecycle management develop more agile software delivery methods. And so therefore, you could you could get sort of cloud benefits, you know, from your existing applications by just highly optimizing them and, you know, on the cost of goods and services. And then again, the hybrid cloud model just gives customers more choice, which is okay. I want to reduce the number of data centers I have, but I need to maintain reliability, scalability, etcetera. Take advantage of, you know, the hybrid cloud that we offer. But you'll still run things. Cloud natives. I think you're seeing this true multi cloud technology and paradigm, you know, grow out as people have these choices. And then the question is okay. If you have those choices, how do you maintain security? How do you maintain reliability? How do you maintain up time yet be able to move quickly. And so I think there's different speeds in which those platforms will evolve. And our goal is to give you the ability to basically make those choices and and optimize for economics as well as technical. You know, capability. >>Great. I want to ask you a question with Cove it we're seeing and we've been reporting the Cube virtual evolve because we used to be it at events, but we're not there anymore. But the as everyone has realized with cove it it's exposed some projects that you might not want to double down on or highlighted some gaps in architecture. Er, I mean, certainly who would have forecast of the disruption of 100% work from home VP and provisioning to access and access management security, and it really is exposed. What kind of who's where in the journey, Right in digital transformation. So I gotta ask you, what's the most important story or thing to pay attention, Thio as the smart money and smart customers go, Hey, you know what? I'm gonna double down on that. I'm gonna kill that project or sunset. That or I'm not gonna re factor that I'm gonna contain Arise it and there's probably there's a lot of that going on. In our conversations with customers, they're like it's pretty obvious. It's critical path. It's like we stay in business. We build a modern app, but I'm doubling down. I'm transitioning. It's a whole nother ballgame. What >>is >>the most important thing that you see that people should pay attention to around maintaining an innovation and coming out on the other side? >>Yeah, well, I think I think it just generally goes to the whole thesis of software defined. I mean, you know the idea of taking an appliance physical, You know, you have to order the hardware, get it on your loading dock, install in your data center. You know, go configure it, mapping into the rest of your environment. You know, whereas or you could just spend up new, softer instances of load balancers, firewalls, etcetera. So I think you know what's What's really helped in the covert era is the maturity of software to find everything. Compute storage, networking. Lan really allowed customers and many of our customers toe, you know, rapidly make that pivot. And so you know what? It's the you know, the workspace, the remote workspace. You gotta secure it. That's a key part of it, and you've got to give it. You know, you gotta have the scalability back in your data centers or, if you don't have it, be able to run those virtual desktops you know, in the cloud. And I think so. This ability again to take your current environment and, more importantly, your operating model, which, you know the technology could be agile and fast. But if you're operating models not agile, you know you can't executed Well, One of the best comments I heard from a customer CEO was, you know, for six months we debated, you know, the virtual networking architecture and how to deploy the virtual network. And, you know, when covet hit. We made the decision that did it all in one week. So the question the CEO asked now is like Well, why do we Why do we have to operate in that six month model going forward? Let's operate in the one week model going forward. E. I think that that z yeah, that's e think that's the big That's a big inflection point is the operating model has to be agile. We got all kinds of agile technology and choices I mentioned it's like, How do you make your organization agile to take advantage of those technological offerings? That's really what I've been doing the last six months, helping our customers achieve. >>I think that's a key point worth calling out and doubling down on day because, you know, whether you talk about our q Q virtual, our operating model has changed and we're doing new things. But it's not bad. It's actually beneficial. We could talk to more people. This idea of virtual ization. I mean pun intended virtual izing workforces face to face interactions air now remote. This is a software defined operating business. This is the rial innovation. I think this is the exposure. As companies wake up and going. Why didn't we do that before? Reminds me of the old mainframe days. Days? You know, why do we have that mainframe? Because they're still clutching and grabbing onto it. They got a transition. So this is the new the new reality. >>We were joking earlier that you know it ain't broke, don't fix it. And all of a sudden Covic broke everything. And so you know, virtualization becomes a fundamental component of of of how you respond. But and I wonder if Greg you could talk about the security. Peace? How how that fits in. You know everybody you know, the bromide, of course, is security can't be a bolt on. It's gotta be designed in from the start, Pat Gelsinger said years ago in the Cube. Security is a do over. You guys have purchased many different security components you've built in. Security comes. So how should we think about? And how are you thinking about designing insecurity across that entire stack without really bolting in, You know, pieces, whether it's carbon, carbon, black or other acquisitions that you've made? >>Yeah, I mean, I think that's that's the key. Inflection point we're in is an industry. I mean, getting back to my banking experience, I was responsible for cybersecurity, engineering the platforms that we engineered and deployed across the bank globally. And the challenge, the challenge. You know, that's I had, you know, 150 plus security products, and you go to bed at night wondering what? Which one did I forget to deploy or what did I get that gap? Do you think you think you're safe by the sheer number, but when you really boil down to it is like, you know, because you have to sort of like both all this stuff together to create a secure environment, you know, on a global level. And so really, our philosophy of VM where is Okay? Well, let's kind of break that model. That's what we call it intrinsic security, which is just, you know, we have the hyper visor. If you're running, the hyper visor is running on most of the service in your data center. If we have your if you have our network virtualization, we see all the traffic going between all those hyper visors and out to the cloud as well hybrid cloud or public cloud with our NSX technology. And then, you know, then you sort of bring into that the load balancers and the software to find firewalls. And pretty soon you have realized Okay, look, we have we have most of the estate. Therefore we could see everything and bring some intelligent machine learning to that and get proactive as opposed to reactive. Because our whole model now is we. All this technology and some alert pops and we get reactive. How about proactively telling me that something nasty is going on. >>I need to ask you a >>question. May be remediated. Sorry, John. It may be remediated at some point anyway. Bring in some machine intelligence tow. So instead of like you said, getting an alert actually tells me what what happened and how it was fixed, you know? Or at least recommending what I should dio, right? >>Yeah. I mean, part of the problem in the historic architectures is it was all these little silos. You know, every business unit had its own sort of technology. And Aziz, you make things virtualized. You you sort of do the virtual networking. The virtual stories of virtual compute all the software. You know, all of a sudden you have you have a different platform, you have lots of standardization. Therefore you don't have your operating model simplifies right and amount of and then it's about just collecting all the data and then making sense of the data. So you're not overwhelming the human's capacity to respond to it. And so I think that's really the fundamental thing we're all trying to get to. But the surface area is enlarged outside the data centers we've discussed out to the edge, whatever the edges, you know, into the cloud hybrid or public. So now you've got this big surface area where you've gotta have all that telemetry and all that visibility again, Back to getting proactive. So you got to do it in Band is opposed out of band. >>Great. I want to ask you a question on cyber security. We have an event on October 4th, the virtual event that Cuba is hosting with Cal Poly around this space and cybersecurity, symposiums, intersection of space and cyber. I noticed VM Ware recently announced last month that the United States Space Force has committed to the Tan Xue platform for for Continuous Dev ops operation for agility. I interviewed Lieutenant General John Thompson, Space Force, and we talked about that. He said quote, it's hard to do break fix in space. Uh, illustrating, really? Just can't send someone to swap out something in space. Not yet, at least. So they're looking at software defined as a key operating reality. Okay, so again, talk about the edge of space Isas edges. You're gonna get it. Need to be completely mad and talk about payloads and data. This >>is kind >>of interesting data point because you have security issues because space is gonna be contested and congested as an edge device. So it's actually the government's interested in that. But fundamentally, the death hops problem that you're you guys are involved in This >>is a >>reality. It's kind of connects this reality idea of operating models based in reality have to be software. What's >>your name? Yeah. I mean, I think the term we use now is def sec ops because you can't just do Dev ops. You have to have the security component in there, So, uh, yeah, the interesting. You know, like, there's a lot of interesting things happen just in fundamental networking, right? I mean, you know, the StarLink, you know, satellites at Testa. His launched Elon musk has launched and, you know, bringing sort of, you know, higher band with laurel agency to those. Yeah, we'll call it near space the and then again, just opens up all new opportunities for what we can dio. And so, Yeah, I think that's the software that the whole the whole saw for development ecosystem again, back to this idea. I think of three things. You gotta have speed. You gotta have scale and you gotta have security. And so that's really the emerging platform, whether it's a terrestrial or in near space, Uh, that's giving us the opportunity, Thio Do new architectures create service measures of services, some terrestrial, some some you know, far remote. And as you bring these new application architectures and system platform architectures together with all the underlying hardware and networking innovations that are occurring, you mentioned flash. But even getting into pmm persistent memory, right? So this this is so much happening that is converging. What's exciting to me about being a TV? Where is the CTO and we partner with all the hardware vendors? We partner with all the system providers, like in video and others. You know, the smart nick vendors. And then we get to come up with software architectures that sort of bring that together holistically and give people a platform. We can run your workloads to get work done wherever you need to land those workloads. And that's really the excitement about >>the candy store. And yet you've got problems hard problems to work on to solve. I mean, this really brings the whole project moderate, full circle because we think about space and networks and all these things you're talking about, You need to have smart everything. I mean, isn't that software? It's a complete tie into the Monterey. >>Yeah, yeah, yeah, Exactly. You're right. It's not just it's not just connecting everything and pushing data around its than having the intelligence to do it efficiently, economically, insecurely. And that's you know. So I see that you don't want to over hype machine learning. I did not to use the term AI, but use the machine learning technologies, you know, properly trained with the proper data sets, you know, and then the proper algorithms. You know that you can then a employee, you know, at the edge small edge, thick edge, you know, in the data center at the cloud is really Then you give the visibility so that we get to that proactive world I was talking about. >>Yeah, great stuff, Greg. Great insight, great conversation. Looking forward to talking mawr Tech with you. Obviously you are in the right spot was in the center of all the action across the board final point. If you could just close it out for us. What is the most important story at VM World 2020 this year. >>Um, well, I think you know, I like to say that I have the best job. I think you know that I've had in my career. I've had some great ones is you know, we get to be disruptive innovators, and we have a culture of perpetual innovation and really being world for us, Aly employees and all the people that work together to put it together is we get to showcase. You know, some of that obviously have more up our sleeves for the future. But, you know, being world is are, you know, coming coming out out show of the latest set of innovations and technologies. So there's going to be so much I have, ah, vision and innovation. Keynote kickoff, right. Do some lightning demos. And actually, I talk about work we're doing in sustainability, and we're putting a micro grid on our campus in Palo Alto and partnership with City of Palo Alto so that when the wildfires come through or there is power outages, you know we're in oasis of power generating capacity with our solar in our batteries. And so the city of Palo Alto could take their emergency command vehicles and plug into our batteries when the power is out in Palo Alto and operate city services and city emergency services. So we're not just innovating, you know, in cortex we're innovating to become a more, you know, sustainable company and provide sustainable, you know, carbon neutral technology for our customers to adopt. And I think that's an area we wanna talk about me. We talk about it next time, but I think you know our innovations. We're gonna basically help change the world with regard to climate as well. >>Let's definitely do that. Let's follow up for another in depth conversation on the societal impact. Of course, VM Ware VM Ware's VM World's 2020 is virtual is a ton of sessions. There's a Cloud City portion. Check out the 60 solution demos. Of course, they ask the expert, Greg, you're in there with Joe Beta Raghu, all the experts, um, engage and check it out. Thank you so much for the insight here on the Cube. Virtual. Thanks for coming on. >>Appreciate the opportunity. Great conversation and good questions. >>Great stuff. Thank you very much. Innovation that vm where it's the heart of their missions always has been, but they're doing well on the business side, Dave. Okay. The cube coverage. They're not there in person. Virtual. I'm John for day. Volonte. Thanks for watching.

>>from Menlo Park, California In the heart of Silicon Valley, it's the Cube covering cloud now. Awards 2020 Brought to you by Silicon Angle Media. Now here's Sonia category. >>Hi, and welcome to the Cube. I'm your host Sonia category, and we're on the ground at Facebook headquarters in Menlo Park, California covering Cloud now's top women entrepreneurs in Cloud Innovation Awards. >>Joining us today is Melon Yen, founder and partner of operator Collective Madeleine, Welcome to the Cube. Thank you so much. So tell us a little bit about your background. >>So Operator Collective is actually my fourth organization that been apart of starting, and all of them have had an aspect of it that had a strong community to it. And so that was one of the reasons why, um, as you hear about in a second, I could put together this kind of crazy idea for a fund that looks like no other. >>Um, So what inspired you to start this company? And how did you navigate getting funding? >>Sure. So? So, because that operator collective is my fourth company. The 1st 1 was actually a nonprofit. The 2nd 1 was a venture backed company that we took from 0 to 100 million in public in less than three years, and the 3rd 1 was something called Faster, which is the world's largest B two b B two b community for SAS Softwares of service, the company that was a venture backed startup that we took from 0 to 100 million in public in less than three years. Even though I helped launch it, I didn't actually officially joined as an employee until about 18 months in, and by that time it's employees 65 I noticed a number of things, which is there were largely homogenous group of people who were there before me, all really great people. But you tend to know people like you and the hyper growth stages of startups. You tend to turn around and say, Who can I get? And so you and you turn to the people that you know, And so you end up with companies that look like yourself and so spent a lot of time looking at what was going on in the venture world, which is that in the area that I focus on, which is enterprise and software enterprise software. It is over 90% male in terms of veces as well as founders and the world revolves around in the venture world revolves around veces and founders. And so I looked around and said, Well, where the operators, the people who build and grow and scale up these companies, they're largely not. They're not efficiently and effectively part of this ecosystem and then second, where the women and people of color And so but as I started to dig in more and talk to people, what I realized was that the VCs and founders actually wanted to bring in the operators. They wanted to bring in the people with different backgrounds, but the network's didn't naturally overlap. And so I thought, there's got to be a way to bring them in, because I know the operators and the operators also want to participate. But the system isn't optimized to make it efficient or friendly are comfortable for them to be able to participate. So that's why I decided to put operator collective together. >>Wow, So you are key noting today for cloud. Now, um, what has this experience been like? And what is the main message you want to give to the award winners and to the cloud now community. >>So it's incredibly inspiring to be with all of the women who are being honored tonight as well as, frankly, the organizers. The organization itself Cloud now is incredibly impactful. And so one of the reasons I was so excited to be asked is a number of the women who were being honored. I either know or have heard of. And the recognition is something that is very important because we need to tell the stories and recognize these people who are not. Maybe the usual suspects, the ones who maybe not our everyday names. And so I was super excited to be here. >>So you were talking about how it's about 90% male in the VC and founder community, Um, in one of your articles, which are amazing, by the way you said, Don't let the excuse of cultural fit be a vehicle for perpetuating sameness, and I thought that was so profound. So, um, are you still seeing this notion of cultural fit being a huge issue and if so, what can be done? Teoh mitigate it? Yeah, I think there's >>more awareness now of the fact that if you hire for cultural fit, you'll end up with 65 people who are exactly like you. And that's not optimizing for a successful company because right there studies that show that diverse teams outperform out innovate, homogeneous teams. But what's also interesting is the same study says that, but homogeneous teams are more certain that they've gotten to the right answer, even if they've got into the answer less less often than the diverse teams. And so when you have people who are just like you, then everyone agrees with each other than you don't realize that. Maybe there's another way of looking at something and so cultural fit is is a warning sign. I think to say that. Okay, well, there just like me, I'm very comfortable sometimes. Being uncomfortable is good. >>That's a great message. I think it's really hard to to say like, Oh, I'm okay with being comfortable. Um, so in, in in in one of your other articles, you bring up this idea of, um, don't check all the boxes, but rather fill in the gaps. So can you explain more about that? >>Yeah. So the idea behind that is, if you look for only the typical candidates. The ones who maybe think of a startup founder went to Stanford. Where's the hoodie? Right? Did computer science then that's fine. There are plenty of those people who have been successful, but you're ignoring all the people who didn't. And so, in fact, I'm the beneficiary of people who were willing to not just check all the boxes because I >>didn't >>check any of the boxes. If you look at, if you look at my background, I should not have been able to raise. Is the first time fund and a first time fund manager to be able to raise a $50 million fund because I'm a um Ah, let's see, I'm a solo GP, right? So, General partner who hasn't been a VC before with the first time fund, I don't have the traditional venture background. My previous background was I was an intellectual property attorney. Um, then help start a company as a result of that and then and then also when you check the boxes, 40% of the seas went to Stanford or Harvard, and when you look at the numbers, I didn't check all the boxes, but precisely because I didn't check all the boxes, I was able to actually look at this differently and say, Hey, that's not the model that that I want to build. And frankly, if I tried to build the same model that everyone else did, my background so doesn't look like anything. I wouldn't have been successful. And by taking it and saying, Look, I'm gonna build a model that's totally different from the ground up that allowed me to build a platform in a community that looked like no one else is as a result of that was able to raise money from institutional investors, for instance, which very rarely back first time funds. And so, by not checking all the boxes, um, I was able to build a model, but by other people also saying, Look, she doesn't check any of our typical boxes. But we >>would like this >>idea because it's so different than everyone else is. We will. We are now, you know, part of the fund >>and sometimes different is good, and it's what's what's needed? Absolutely. Um, so speaking of that, um, in terms of operator collective, what workplace environment are you trying to strive for. >>So what we say is we seek to back founders from all backgrounds who believe you share are believed that culture, diversity and operational excellence are a key part of building truly great companies. So we strive to be inclusive way. We strive to have a variety of backgrounds. We use a lot of the tools that of the companies, because we focus only on enterprise and B two B software and technology and infrastructure. And so we also try to use a lot of those tools. So we are mostly women team and we are distributed team. We largely work out of our homes and we work a lot on Zoom and we all a lot of us have kids too, and so what we do is we adjust the schedule so we can do drop off in the morning. We work like crazy, right? We work long hours, but we also do it so that people can can take their kids to doctor's appointments or pick up their kids at the end of the day. But we what was important to me was that we created environment that worked with our busy lives, and it wasn't that we were trying to take, take take these incredibly talented women and make it fit into just the corporate norm. Because you can have an incredibly successful work relationship. I mean, you can have an incredibly successful, um career if you don't have to sacrifice everything else in your life for it, >>right? Right. And that balance is so important. Um, so what advice would you give to aspiring female entrepreneurs who maybe have, ah, not so technical background or who are struggling to navigate in this male dominated industry. >>So one of the things >>I talked about in my keynote today was was that you never get this right. You're never going to raise a fund. If if you do this, you're never gonna raise a fund. And so when you're starting a company, you will go when you talk to a lot of people as you should, because you will get lots of great information. Ah, lot of people are going to say, Well, you're never gonna have a You're never going to start a company if you don't have a technical co founder never going to start a company. If you're gonna try to do X and So while you some might say, Well, you should just ignore those people actually say, Don't ignore those people because they are saying that other people are going to think that too. But think of a way to counter that. And that actually help make the operator collective business model stronger. Because we said Okay, we know that's gonna be the mindset. Let's turn it around and actually make this a strength. And so, for female founders or any founders, what I would say is listen to a lot of people talk to a lot of people here what they have to say. Ultimately, trust your instinct. Trust your gut. And because you know what's best for the company that you're trying to build. >>Great words of advice. Melon. Thank you so much for being on the Cube. Thank you >>so much for having me. Absolutely. >>I'm Sonita Gari. Thanks for watching the Cube. Stay tuned for more. >>Yeah, yeah, yeah.

>> from our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Hi! And welcome to the Cube studios from the Cube conversation, where we go in depth with thought leaders driving innovation across the tech industry on hosted a Peter Burress. What are we talking about today? One of the key indicators of success and additional business is how fast you can translate your data into new value streams. That means sharing it better, accelerating the rate at which you're running those models, making it dramatically easier to administrate large volumes of data at scale with a lot of different uses. That's a significant challenge. Is going to require a rethinking of how we manage many of those data assets and how we utilize him. Notto have that conversation. We're here with Le'Ron v. Bell, who was the CEO of work a Iot leering. Welcome back to the Cube. >> Thank you very much for having >> me. So before we get to the kind of a big problem, give us an update. What's going on at work a Iot these days? >> So very recently we announced around CIA financing for the company. Another 31.7 a $1,000,000 we've actually had a very unorthodox way of raising thiss round. Instead of going to the traditional VC lead round, we actually went to our business partners and joined forces with them into building a stronger where Collier for customers we started with and video that has seen a lot of success going with us to their customers. Because when Abel and Video to deploy more G pews so they're customers can either solve bigger problems or solve their problems faster. The second pillar off the data center is networking. So we've had melon ox investing in the company because there are the leader ofthe fast NETWORKINGS. So between and Vidia, melon, ox and work are yo u have very strong pillars. Iran compute network and storage performance is crucial, but it's not the only thing customers care about, so customers need extremely fast access to their data. But they're also accumulating and keeping and storing tremendous amount of it. So we've actually had the whole hard drive industry investing in us, with Sigi and Western Digital both investing in the company and finally one off a very successful go to market partner, Hewlett Pocket enterprise invested in us throw their Pathfinder program. So we're showing tremendous back from the industry, supporting our vision off, enabling next generation performance, two applications and the ability to scale to any workload >> graduations. And it's good money. But it's also smart money that has a lot of operational elements and just repeat it. It's a melon ox, our video video, H P E C Gate and Western Digital eso. It's It's an interesting group, but it's a group that will absolutely sustain and further your drive to try to solve some of these key data Orient problems. But let's talk about what some of those key day or data oriented problems where I set up front that one of the challenges that any business that has that generates a lot of it's value out of digital assets is how fast and how easily and with what kind of fidelity can I reuse and process and move those data assets? How are how is the industry attending? How's that working in the industry today, and where do you think we're going? >> So that's part on So businesses today, through different kind of workloads, need toe access, tremendous amount of data extremely quickly, and the question of how they're going to compare to their cohort is actually based on how quickly and how well they can go through the data and process it. And that's what we're solving for our customers. And we're now looking into several applications where speed and performance. On the one hand, I have to go hand in hand with extreme scale. So we see great success in machine learning, where in videos in we're going after Life Sciences, where the genomic models, the cryo here microscopy the computational chemistry all are now accelerated. And for the pharmacy, because for the research interested to actually get to conclusion, they serve to sift through a lot of data. We are working extremely well at financial analytics, either for the banks, for the hedge funds for the quantitative trading Cos. Because we allow them to go through data much, much quicker. Actually, only last week I had the grades to rate the customer where we were able to change the amount of time they go through one analytic cycle from almost two hours, four minutes. >> This is in a financial analytics >> Exactly. And I think last time I was here was telling you about one of their turn was driving companies using us taking, uh, time to I poke another their single up from two weeks to four hours. So we see consistent 122 orders of monk to speed time in wall clock. So we're not just showing we're faster for a benchmark. We're showing our customer that by leveraging our technology, they get results significantly faster. We're also successful in engineering around chip designed soft rebuild fluid dynamics. We've announced Melon ox as an idiot customer. The chip designed customers, so they're not only a partner, they have brought our technology in house, and they're leveraging us for the next chips. And recently we've also discovered that we are great help for running Noah scale databases in the clouds running ah sparkles plank or Cassandra over work. A Iot is more than twice faster than running over the Standard MPs elected elastic clock services. >> All right, so let's talk about this because your solving problems that really only recently have been within range of some of the technology, but we still see some struggling. The way I described it is that storage for a long time was focused on persisting data transactions executed. Make sure you persisted Now is moved to these life life sciences, machine learning, genomics, those types of outpatients of five workloads we're talking about. How can I share data? How can I deploy and use data faster? But the historian of the storage industry still predicated on this designs were mainly focused on persistent. You think about block storage and filers and whatnot. How is Wecker Io advancing that knowledge that technology space of, you know, reorganizing are rethinking storage for the types, performance and scale that some of these use cases require. >> This is actually a great question. We actually started the company. We We had a long legacy at IBM. We now have no Andy from, uh, metta, uh, kind of prints from the emcee. We see what happens. Page be current storage portfolio for the large Players are very big and very convoluted, and we've decided when we're starting to come see that we're solving it. So our aim is to solve all the little issues storage has had for the last four decades. So if you look at what customers used today, if they need the out most performance they go to direct attached. This's what fusion I awards a violin memory today, these air Envy me devices. The downside is that data is cannot be sure, but it cannot even be backed up. If a server goes away, you're done. Then if customers had to have some way of managing the data they bought Block san, and then they deployed the volume to a server and run still a local file system over that it wasn't as performance as the Daz. But at least you could back it up. You can manage it some. What has happened over the last 15 years, customers realized more. Moore's law has ended, so upscaling stopped working and people have to go out scaling. And now it means that they have to share data to stop to solve their problems. >> More perils more >> probably them out ofthe Mohr servers. More computers have to share data to actually being able to solve the problem, and for a while customers were able to use the traditional filers like Aneta. For this, kill a pilot like an eyes alone or the traditional parlor file system like the GP affair spectrum scale or luster, but these were significantly slower than sand and block or direct attached. Also, they could never scale matter data. You were limited about how many files that can put in a single, uh, directory, and you were limited by hot spots into that meta data. And to solve that, some customers moved to an object storage. It was a lot harder to work with. Performance was unimpressive. You had to rewrite our application, but at least he could scale what were doing at work a Iot. We're reconfiguring the storage market. We're creating a storage solution that's actually not part of any of these for categories that the industry has, uh, become used to. So we are fasted and direct attached, they say is some people hear it that their mind blows off were faster, the direct attached, whereas resilient and durable as San, we provide the semantics off shirt file, so it's perfect your ability and where as Kayla Bill for capacity and matter data as an object storage >> so performance and scale, plus administrative control and simplicity exactly alright. So because that's kind of what you just went through is those four things now now is we think about this. So the solution needs to be borrow from the best of these, but in a way that allows to be applied to work clothes that feature very, very large amounts of data but typically organized as smaller files requiring an enormous amount of parallelism on a lot of change. Because that's a big part of their hot spot with metadata is that you're constantly re shuffling things. So going forward, how does this how does the work I owe solution generally hit that hot spot And specifically, how are you going to apply these partnerships that you just put together on the investment toe actually come to market even faster and more successfully? >> All right, so these are actually two questions. True, the technology that we have eyes the only one that paralyzed Io in a perfect way and also meditate on the perfect way >> to strangers >> and sustains it parla Liz, um, buy load balancing. So for a CZ, we talked about the hot sport some customers have, or we also run natively in the cloud. You may get a noisy neighbor, so if you aren't employing constant load balancing alongside the extreme parallelism, you're going to be bound to a bottleneck, and we're the only solution that actually couples the ability to break each operation to a lot of small ones and make sure it distributed work to the re sources that are available. Doing that allows us to provide the tremendous performance at tremendous scale, so that answers the technology question >> without breaking or without without introducing unbelievable complexity in the administration. >> It's actually makes everything simpler because looking, for example, in the ER our town was driving example. Um, the reason they were able to break down from two weeks to four hours is that before us they had to copy data from their objects, George to a filer. But the father wasn't fast enough, so they also had to copy the data from the filer to a local file system. And these copies are what has added so much complexity into the workflow and made it so slow because when you copy, you don't compute >> and loss of fidelity along the way right? OK, so how is this money and these partnerships going to translate into accelerated ionization? >> So we are leveraging some off the funds for Mohr Engineering coming up with more features supporting Mohr enterprise applications were gonna leverage some of the funds for doing marketing. And we're actually spending on marketing programs with thes five good partners within video with melon ox with sick it with Western Digital and with Hewlett Packard Enterprise. But we're also deploying joint sales motion. So we're now plugged into in video and plugged, anted to melon ox and plugging booked the Western Digital and to Hillary Pocket Enterprise so we can leverage their internal resource now that they have realized through their business units and the investment arm that we make sense that we can actually go and serve their customers more effectively and better. >> Well, well, Kaio is introduced A road through the unique on new technology into makes perfect sense. But it is unique and it's relatively new, and sometimes enterprises might go well. That's a little bit too immature for me, but if the problem than it solves is that valuable will bite the bullet. But even more importantly, a partnership line up like this has got to be ameliorating some of the concerns that your fearing from the marketplace >> definitely so when and video tells the customers Hey, we have tested it in our laps. Where in Hewlett Packard Enterprise? Till the customer, not only we have tested it in our lab, but the support is going to come out of point. Next. Thes customers now have the ability to keep buying from their trusted partners. But get the intellectual property off a nor company with better, uh, intellectual property abilities another great benefit that comes to us. We are 100% channel lead company. We are not doing direct sales and working with these partners, we actually have their channel plans open to us so we can go together and we can implement Go to Market Strategy is together with they're partners that already know howto work with them. And we're just enabling and answering the technical of technical questions, talking about the roadmap, talking about how to deploy. But the whole ecosystem keeps running in the fishing way it already runs, so we don't have to go and reinvent the whales on how how we interact with these partners. Obviously, we also interact with them directly. >> You could focus on solving the problem exactly great. Alright, so once again, thanks for joining us for another cube conversation. Le'Ron zero ofwork I Oh, it's been great talking to you again in the Cube. >> Thank you very much. I always enjoy coming over here >> on Peter Burress until next time.

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Hello and welcome to Cube conversations. I'm James Kabila's lead analyst at Wicked Bond. Today we've got an excellent guest. Who's a Cube alumnus? Par excellence. It's your own Haviv who is the founder and CEO of a guajillo. Hello. You're wrong. Welcome in. I think you're you're coming in from Tel Aviv. If I'm not mistaken, >> right? Really? Close the deal of any thanks from my seeing you again. >> Yeah. Nice to see you again. So I'm here in our Palo Alto studios. And so I'm always excited when I can hear your own and meet with your room because he always has something interesting in new to share. But what they're doing in the areas of cloud and serve earless and really time streaming analytics And now, data science. I wasn't aware of how deeply they're involved in the whole data Science pipelines, so ah, your own. This is great to have you. So my first question really is. Can you sketch out? What are the emerging marketplace requirements that USA gua Si are seeing in the convergence of all these spaces? Especially riel time streaming analytics edge computing server lis and data science and A I can you give us a sort of ah broad perspective and outlook on the convergence and really the new opportunities or possibilities that the convergence of those technologies enable for enterprises that are making deep investments. >> Yeah, so I think we were serving dissipated. What's happening now? We just call them different names will probably get into into this discussion in a minute. I think what you see is the traditional analytics and even data scientist Science was starting at sort of a research labs, people exploring cancer, expressing, you know, impact. Whether on, you know, people's moved its era. And now people are trying to make real or a Y from a guy in their assigned, so they have to plug it within business applications. Okay, so it's not just a veil. A scientist Inning the silo, you know, with a bunch of large that he got from his friends, the data engineer in the scan them and Derrickson Namesake runs to the boss and says, You know what? You know, we could have made some money in a year ago. We've done something so that doesn't make a lot of impact on the business, where the impact on the business is happening is when you actually integrate a I in jackpot in recommendation engines in doing predictive analytics on analyzing failures and saving saving failures on, you know, saving people's life. Those kind of use cases. Doctors are the ones that record a tighter integration between the application and the data and algorithms that come from the day I. And that's where we started to think about our platform. Way worked on a real time data, which is where you know, when you're going into more production environment of not fatal accident. Very good, very fast integration with data. And we have this sort of fast computation layer, which was a one micro services, and now everyone talks about micro services. We sort of started with this area, and that is allowing people to build those intelligent application that are integrated into the business applications. And the biggest challenges they see today for organizations is moving from this process of books on research, on data in a historical data and translating that into a visit supplication or into impact on business application. This is where people can spend the year. You know, I've seen the tweet saying with build a machine learning model in, like, a few weeks. And now we've waited eleven months for the product ization. So that artifact, >> Yes, that's what we're seeing it wicked bomb. Which is that A. I is the heart of modern applications in business and the new generation of application developers, in many ways, our data scientists, or have you know, lovers the skills and tools for data science. Now, looking at a glass zeros portfolio, you evolve so rapidly and to address a broader range of use cases I've seen. And you've explained it over the years that in position to go, as well as being a continuous data platform and intelligent edge platform, a surveillance platform. And now I see that you're a bit of a data science workbench or pipeline tooling. Clever. Could you connect these dots here on explain what is a guajillo fully >> role, Earl? Nice mark things for this in technology that we've built, OK, just over the years, you know, people, four years when we started, So we have to call it something else. Well, that I thought that analytic sort of the corporate state of science. And when we said continued analytics, we meant essentially feeding data and running, some of them speaking some results. This is the service opposed to the trend of truth which was dating the lady Throw data in and then you run the batch that analytic and they're like, Do you have some insight? So continue statistics was served a term that we've came up with a B, not the basket. You know, describe that you're essentially thinking, needing from different forces crunching it, Prue algorithms and generating triggers and actions are responsible user requests. Okay on that will serve a pretty unique and serve the fireman here in this industry even before they called it streaming or in a real time, data science or whatever. Now, if you look at our architecture are architecture, as I explained before, is comprised of three components. The first event is a real time, full time model database. You know, you know about it really exceptional in his performance and its other capabilities. The second thing is a pursue miss engine that allows us to essentially inject applications. Various guys, initially we started with application. I sense you do analytics, you know, grouping joining, you know, correlating. And then we start just adding more functions and other things like inference, saying humans recognitions and analysis. It's Arab is we have dysfunction engine. It allows us a lot of flexibility and find the really fast for the engine on a really fast data there endure it, remarkable results and then this return calling this turn this micro assume it's finger serve Ellis who certainly even where have the game of this or service gang. And the third element of our platform is a sense she having a fully manage, passed a platform where a ll those micro services our data and it threw a self service into face surfing over there is a mini cloud. You know, we've recently the last two years we've shifted to working with coronaries versus using our own A proprietary micro spurs does or frustration originally. So we went into all those three major technologies. Now, those pit into different application when they're interesting application. If you think about edge in the engine in serving many clouds, you need variety of data, sources and databases. With you, no problem arose streaming files. Terra. We'LL support all of them when our integrated the platform and then you need to go micro services that developed in the cloud and then just sort of shift into the enforcement point in the edge. And you need for an orchestration there because you want to do suffer upgrades, you need to protect security. So having all the integrated separated an opportunity for us to work with providers of agin, you may have noticed our joint announcement with Google around solution for hedge around retailers and an i O. T. We've made some announcement with Microsoft in the fast. We're going to do some very interesting announcement very soon. We've made some joint that nonsense with Samsung and in video, all around those errands, we continue. It's not that we're limited to EJ just what happens because we have extremely high density data platform, very power of fish and very well integrated. It has a great feat in the India, but it's also the same platform that we sell in. The cloud is a service or we sell two on from customers s so they can run. The same things is in the clouds, which happens to be the fastest, most real time platform on the Advantage service. An essential feature cannot just ignore. >> So you're wrong. Europe. Yeah, Iguazu is a complete cloud, native development and run time platform. Now serve earless in many ways. Seems to be the core of your capability in your platform. New Cleo, which is your technology you've open sourced. It's bill for Prem bays to private clouds. But also it has is extensible to be usable in broader hybrid cloud scenarios. Now, give us a sense for how nuclear and civilised functions become valuable or useful for data science off or for executing services or functions of data of the data science pipeline kick you connect the dots of nuclear and data science and a I from the development standpoint >> church. So So I think you know, the two pillars that we have technology that the most important ones are the data. You know, we have things like twelve batons on our data engine is very high performance and nuclear functions, and also they're very well integrated because usually services stateless. So you know, you you end up. If you want to practice that they have some challenges with service with No, no, you can't. You stay for use cases. You can mount files. You have real time connections to data, so that makes it a lot more interesting than just along the functions. The other thing, with no clothes that is extremely high performance has about two hundred times faster than land. So that means that you can actually go and build things like the stream processing and joins in real time all over practice, their base activities. You can just go and do collectors. We call them those like things. Go fetch information from whether services from routers for the X cybersecurity analysis for all sorts of sensors. So those functions are becoming like, you know, those nanobots technology of off the movies is that you just send them over to go and do things for you, whether it's the daily collection and crunching, whether it's the influencing engines, those things that, for example, get a picture of very put the model, decide what's in the picture, and that this is where we're really comes into play. They nothing important you see now an emergence off a service patterns in data science. So there are many companies that do like mother influencing as a service city what they do, they launch an end point of your eleven point and serve runs the model inside you send the Vector America values and get back in the Americans and their conversion. It's not really different and service it just wait more limited because I don't just want to send a vector off numbers because usually I understand really like a geo location of my cellphone, which are user I D. And I need dysfunction to cross correlated with other information about myself with the location. Then came commendation of which a product they need to buy. So and then those functions also have all sorts of dependency exam on different packages. Different software environment, horribles, build structures, all those. This is really where service technologies are much more suitable now. It's interesting that if you'LL go to Amazon, they have a product called Sage Maker. I'm sure yes, which is dinner, then a science block. Okay, now sage mint for although you would say that's a deal use case for after Onda functions actually don't use Amazon London functions in sage maker, and you ask yourself, Why aren't they using Lambda Stage Maker just telling you, you know you could use Lambda is a blue logic around sage maker. And that's because because London doesn't feed the use case. Okay, because lambda doesn't it is not capable of storing large content and she learning miles could be hundreds of megabytes or Landa is extremely slow. So you cannot do hi concurrency influencing with will land the function so essentially had to create another surveillance and college with a different name. Although if they just would have approved Landa, maybe it was one or a Swiss are So we're looking, We've took it, were taken the other approach We don't have the resources that I have so we created a monster virus Engine one servant attention does batch Frost is saying scream processing, consort, lots of data, even rocketeer services to all the different computation pattern with a single engine. And that's when you started taking all this trend because that's about yeah, we need two version our code. We need to, you know, record all our back into dependencies. And although yes, service doesn't so if we just had to go and tied more into the existing frameworks and you've looked at our frantically product called Tokyo Jupiter, which is essentially a scientist, right, some code in his data's passport book and then in clicks. One command called nuclear Deploy, it automatically compiles, is their science artifact in notebooks, that server and converted into a real hand function that can listen in on your next city. People can listen on streams and keep the scheduled on various timing. It could do magic. So many other things. So, and the interesting point is that if you think about their scientists there, not the farmers, because they should be a scientist on this's means that they actually have a bigger barrier to write in code. So if you serve in this framework that also automates the law daughter scaling the security provisioning of data, the versions of everything in fact fantasies, they just need to focus on writing other them's. It's actually a bigger back for the book. Now, if you just take service into them, Epstein's and they will tell you, Yeah, you know, we know how to explain, Doctor. We know all those things, so they're very their eyes is smaller than the value in the eyes of their scientists. So that's why we're actually seeing this appeal that those those people that essentially focus in life trying math and algorithms and all sorts of those sophisticated things they don't want to deal with. Coding and maintenance are refreshed. And by also doing so by oppression analyzing their cool for service, you can come back to market. You can address calle ability to avoid rewriting of code. All those big challenges the organizations are facing. >> You're gonna have to ask you, that's great. You have the tools to build, uh, help customers build serve Ellis functions for and so forth inside of Jupiter notebooks. And you mentioned Sage Maker, which is in a WS solution, which is up in coming in terms of supporting a full data science tool chain for pipeline development. You know, among teams you have a high profile partnerships with Microsoft and Google and Silver. Do you incorporate or integrator support either of these cloud providers own data science workbench offerings or third party offerings from? There's dozens of others in this space. What are you doing in terms of partnerships in that area? >> Yeah, obviously we don't want to lock us out from any of those, and, you know, if someone already has his work bench that I don't know my customers say they were locking me into your world back in our work when things are really cool because like our Jupiter is connected for real time connections to the database. And yes, serve other cool features that sentir getting like a huge speed boost we have. But that's on A with an within vigna of round Heads and Integration, which reviews are creating a pool of abuse from each of one of the data scientist running on African essentially launch clubs on this full of civilians whose off owning the abuse, which are extremely expensive, is you? No. But what we've done is because of her. The technology beside the actual debate engine is open source. We can accept it essentially just going any sold packages. And we demonstrate that to Google in danger. The others we can essentially got just go and load a bunch of packages into their work match and make it very proposed to what we provide in our manage platform. You know, not with the same performance levels. Well, functionality wise, the same function. >> So how can you name some reference customers that air using a guajillo inside a high performance data science work flows is ah, are you Are there you just testing the waters in that market for your technology? Your technology's already fairly mature. >> That says, I told you before, although you know, sort of changed messaging along the lines. We always did the same thing. So when we were continuous analytics and we've spoken like a year or two ago both some news cases that we Iran like, you know, tell cooperators and running really time, you know, health, a predictive health, monitoring their networks and or killing birds and those kind of things they all use algorithms. You control those those positions. We worked with Brian nailing customers so we can feed a lot of there there in real time maps and do from detection. And another applications are on all those things that we've noticed that all of the use cases that we're working with involved in a science in some cases, by the way, because of sort of politics that with once we've said, we have analytics for continuous analytics, we were serving send into sent into the analytic schools with the organization, which more focused on survey data warehouse because I know the case is still serve. They were saying, and I do. And after the people that build up can serve those data science applications and serve real time. Aye, aye. OK, Ianto. Business applications or more, the development and business people. This is also why we sort of change are our name, because we wanted to make it very clear that we're aren't the carnage is about building a new applications. It's not about the warehousing or faster queries. On a day of Eros is about generating value to the business, if you ask it a specific amplification. And we just announced two weeks in the investment off Samsung in Iguazu, former that essentially has two pillars beyond getting a few million dollars, It says. One thing is that they're adopted. No cure. Is there a service for the internal clouds on the second one is, we're working with them on a bunch of us, Della sighs. Well, use case is one of them was even quoted in enough would make would be There are no I can not say, but says she knows our real business application is really a history of those that involves, you know, in in intercepting data from your sister's customers, doing real time on analytics and responding really quickly. One thing that we've announced it because of youse off nuclear sub picture. We're done with inferior we actually what were pulled their performance. >> You're onto you see if you see a fair number of customers embedding machine learning inside of Realtor time Streaming stream computing back ones. This is the week of Flink forward here in San San Francisco. I I was at the event earlier this week and I I saw the least. They're presenting a fair amount of uptake of ml in sight of stream computing. Do you see that as being a coming meet Mainstream best practice. >> Streaming is still the analytics bucket. OK, because what we're looking for is a weakness which are more interactive, you know, think about like, uh, like a chatterbox or like doing a predictive analytic. It's all about streaming. Streaming is still, you know, it's faster flow data, but it's still, sir has delay the social. It's not responses, you know. It's not the aspect of legacy. Is that pickle in streaming? Okay, the aspect of throughput is is higher on streaming, but not necessarily the response that I think about sparks streaming. You know, it's good at crossing a lot of data. It's definitely not good at three to one on would put spark as a way to respond to user request on the Internet S O. We're doing screaming, and we see that growth. But think where we see the real growth is panic to reel of inches. The ones with the customer logs in and sends a request or working with telcos on scenarios where conditions of LA car, if the on the tracks and they settled all sorts of information are a real time invent train. Then the customer closer says, I need a second box and they could say No, this guy needs to go away to that customer because how many times you've gotten technician coming to your house and said I don't have that more exactly. You know, they have to send a different guy. So they were. How do you impact the business on three pillars of business? Okay, the three pillars are one is essentially improving your china Reducing the risk is essentially reducing your calls. Ask him. The other one is essentially audio, rap or customer from a more successful. So this is around front and application and whether it's box or are doing, you know our thing or those kind of us kisses. And also under you grow your market, which is a together on a recommendation in at this time. So all those fit you if you want, have hey, I incorporated in your business applications. In few years you're probably gonna be dead. I don't see any bits of sustained competition without incorporating so ability to integrate really real data with some customer data and essentially go and react >> changes. Something slightly you mentioned in video as a partner recently, Of course, he announced that few weeks ago. At their event on, they have recently acquired Melon ox, and I believe you used to be with Melon Axe, so I'd like to get your commentary on that acquisition or merger. >> Right? Yes, yes, I was VP Data Center man Ox. Like my last job, I feel good friends off off the Guider, including the CEO and the rest of the team with medicines. And last week I was in Israel's with talk to the media. Kansas. Well, I think it's a great merger if you think about men in Ox Head as sort of the best that breaking and storage technology answer Silicon Side and the video has the best view technologies, man. It's also acquired some compute cheap technologies, and they also very, very nice. Photonics technologies and men are today's being by all the club providers. Remiss Troll was essentially only those technical engagement would like the seizures and you know the rest of the gas. So now VP running with the computation engine in and minerals coming, we serve the rest of the pieces were our storage and make them a very strong player. And I think it's our threatens intel because think about it until they haven't really managed to high speed networking recently. They haven't really managed to come with Jiffy use at your combat and big technology, and so I think that makes a video, sort of Ah, pretty. You know, vendor and suspect. >> And another question is not related to that. But you're in Tel Aviv, Israel. And of course, Israel is famous for the start ups in the areas of machine learning. And so, especially with a focus on cyber security of the Israel, is like near the top of the world in terms of just the amount of brainpower focused on cyber security there. What are the hot ML machine? Learning related developments or innovations you see, coming out of Israel recently related to cybersecurity and distributed cloud environments, anything in terms of just basic are indeed technology that we should all be aware of that will be finding its way into mainstream Cloud and Cooper Netease and civilised environments. Going forward, your thoughts. >> Yes, I think there are different areas, you know, The guys in Israel also look at what happens in sort of the U. S. And their place in all the different things. I think with what's unique about us is a small country is always trying to think outside of the box because we know we cannot compete in a very large market. It would not have innovation. So that's what triggers this ten of innovation part because of all this tippy expects in the country. And also there's a lot of cyber, you know, it's time. I think I've seen one cool startup. There's also backed by our VC selling. Serve, uh, think about like face un recognition, critical technology off sent you a picture and make it such that you machine learning will not be able to recognize Recognize that, you know, sort of out of the cyber attack for image recognition. So that's something pretty unique that I've heard. But there are other starts working on all the aspects on their ops and information in our animal and also cyber automated cyber security and hope. Curious aspect. >> Right, Right. Thank you very much. Your own. This has been an excellent conversation, and we've really enjoyed hearing your comments. And Iguazu. It was a great company. Quite quite an innovator is always a pleasure to have you on the Cube. With that, I'm going to sign off. This is James Kabila's with wicked bond with your own haviv on dh er we bid You all have a good day. >> Thank you.

>> Announcer: Live from Las Vegas, it's theCUBE. Covering NAB 2017, brought to you by HGST. >> Welcome back to theCUBE. We are live at NAB 2017 on day three from Las Vegas, I am Lisa Martin. Excited to introduce you to our next guest, Jim Blakley the GM of the Visual Cloud Division at Intel. Hey Jim, welcome back to theCUBE. >> Thank you, thank you, it's good to be back. >> Great to have you here again, you are a CUBE alumni. You've been at NAB, you said this is your third or fourth year, >> Yeah. >> Talk to us about, from cloud perspective, technology perspective, what are some of the trends that you've seen really emerge as leading technologies? >> Well I would say this year particularly there's much more focus on the things that are near and dear to Intel's heart which are virtualization, IP networks, the drive to move all the workflows to standard, compute platforms, and the thing we've seen in many, many industries over the years and we've talked about it here before at NAB, but this is the first time that I'm really seeing it taking hold. Really exciting, yeah. >> So talk to us about visual cloud, what are the benefits of visual cloud for studios, for broadcast news for streaming companies, producers? >> Yeah, there's two real values. One is, it's just a simplification of the infrastructure in the longterm. It just makes it easier to procure equipment and easier to run a software based infrastructure as opposed to having to do it all with purpose built hardware which this industry currently does use a lot of. But the other thing that's really critical is it starts to open up the opportunity for new types of experiences. Things like augmented reality, virtual reality, What we refer to as media analytics which is the application of artificial intelligence to media. Those sorts of capabilities give you the ability to tell a story in a way that you weren't able to tell it before. >> Talk to us about how a movie studio, speaking of that storytelling which, sometimes technology, a lot of times it's phenomenal. But there are times where you see where it actually gets in the way of storytelling and you lose ... We were talking to some folks the other day here, I think on Monday, about really leveraging analytics to determine even the sequence of a movie trailer. How much time should the lead actor or lead actress be on camera, in a trailer. Give us an example of a studio where they're really leveraging analytics to improve the viewing experience. Right, nowadays, a lot of the younger audience isn't going out to movie theaters because they're used to having access on tablets, mobile devices, etc. >> Yeah, I guess I haven't seen a lot of it. I've seen a few of the studios that are doing work in that area. We do see research happening at some of the bigger universities, particularly those that are tied to the studios. >> Okay, maybe UFC with their-- >> UFC yeah. We just actually announced here a collaboration, what's called an Intel Science and Technology Center, at Carnegie Melon and Stanford, that is doing research in this area and they're partnered with some of the UC schools to be able to do those sorts of analytics to be able to understand how directors, certain directors change scenes. How many shots from this angle, how many shots from that angle? Coloring and so forth. Using the analytics to understand how another story was told in order to apply it in the creation that they're making for that. >> Interesting. So cloud adoption, you're seeing that on the rise maybe in media and entertainment? >> Yeah. >> Whereas some of the things like analytics maybe are more emerging? >> It's much earlier. It's much earlier both for the technology behind artificial intelligence, media analytics. Deep learning has come on enormously over the last couple years and it's being applied very heavily in this space. But it's still early in terms of real applications where you can see a real result from it. >> The amount of data, and content that studios, broadcast news, streaming companies are generating we're talking petabyte scale, media archives. How do studios, broadcasters, etc., how do they evolve their IT infrastructure to get to the visual cloud? What's that journey like? >> Yeah, so there's a few things to focus on in that. There's how do you manage your compute and applications in that environment? What sort of infrastructure do you have? And that's where a move to a standard virtualized infrastructure really makes sense. We've seen that in a lot of different industries that you first have to make the decision that you're going to move to that sort of an infrastructure. Then networking becomes very critical because especially in this industry, because the size of the data is so large, moving it from place to place becomes one of the big constraints. So you need to think through your networking infrastructure, that can be a shift from SDI over to IP based networks that give you much more flexibility both within your environment but also to move things out into cloud environments, service provider environments. Or other services that you can get access to. Of course storage is a huge portion of the transformation. In traditional storage systems from the traditional vendors, they're great for file-based storage. That typically is the way we see people do it. More and more a lot of those platforms are also built out of standard hardware, standard equipment but really building an expertise on how to operate your cloud infrastructure across those three domains is the critical first step. >> Where is the conversation typically? Is it with IT, is it with the business? Do you see those two sides aligning to facilitate and plan this journey together? >> Yeah, over time, yeah. The initial, frankly the initial seeds usually come out of the CTO office. Whoever is looking at the edge of technology and pushing the transformation. In companies that listen to their CTO, which not all companies actually do that, but the CTO typically goes through an exploration process, understanding what the technologies are and how to apply it in their particular space. Then as that learning takes place through a group of concepts, through testing, evaluation, vendor cooperation, learning from peers in the industry, that's how it begins to deploy. >> We were talking the other day to a guest who was driving large scale rendering through the cloud. How can visual cloud enable this large scale rendering, these workloads that studios are now-- >> It already is. Most of the large render farms are in fact large clouds. They're made up of servers that are tied together often with special purpose network that gives you really good performance to share between them. But effectively they are clouds. Specially set aside for rendering. Some of the opensource software, like Renderman, that's in that space has facilitated the ability of people who may not have been creators of rendering farms to be able to pick it up and do it fairly quickly. >> You mentioned storage, cloud, compute, tell us a bit about what Intel is doing on the alliances side to enable visual cloud. >> Intel has always been an ecosystem player. We don't typically sell direct to most people. 100% of my job or 90% of my job is making sure that our relationships are in place with the equipment providers, the systems providers, the solution providers. People like Erikson, Harmonic, Cisco, as well as many of the smaller players to really help them adopt the technologies, go through this journey themselves as they transition their products from more purpose built systems to open standards, cloud oriented systems. We act as both a technical advisor to them and of course if you've seen any of Jim Parson's recent Intel ads, Intel is 98% of the cloud infrastructure. >> One of my favorite shows, The Big Bang Theory. From a perspective of industries obviously here at NAB entertainative and media, as we look at a lot of companies like an Envidea for example, who's really, and a lot of companies like them and others across industries that are starting to leverage technology for social impact. Almost every company these days is a tech company. What other industries do you work with that are great candidates for visual cloud that are generating a tremendous amount of video content, besides, media? >> I think healthcare environments are very big, not so much from the video creation but in terms of image processing and being able to look at medical images and CAT scans. Create 3D models out of all the data sets that they have so they can manipulate and view them and make diagnosis off of them. That's a big industry. The other one that we think particularly for virtual reality and augmented reality will be education. Both in terms of the typical K-12 and college but also enterprise based training. So if you're trying to learn how to assemble a new machine, you could do that assembly through a virtual augmented reality system. It scales much better than having to have everybody get their own machine to work on. >> Absolutely. Jim thanks so much for stopping by theCUBE again. It's great to have you back on the program and we hope you have a great rest of your day three at NAB. >> Thank you very much. Thanks much for being here. >> We want to thank >> Absolutely, we want to thank you for watching theCUBE. Again, we're live in Las Vegas from NAB 2017. Stick around, I'm Lisa Martin, we'll be right back. (upbeat music)