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AI has the potential to profoundly change our lives with Dell Technologies. AI is easy to adopt, easy to manage and easy to scale. And there's nothing artificial about that. Yeah, yeah, from >>the Cube Studios in Palo Alto and Boston >>connecting with >>thought leaders all around the world. This is a cube conversation. Hi, I'm Stew Minimum. And welcome to this special launch with our friends at Dell Technologies. We're gonna be talking about AI and the reality of making artificial intelligence real happy to welcome to the program. Two of our Cube alumni Rob, depending 90. He's the senior vice president of server product management and very Pellegrino vice president, data centric workloads and solutions in high performance computing, both with Dell Technologies. Thank you both for joining thanks to you. So you know, is the industry we watch? You know, the AI has been this huge buzz word, but one of things I've actually liked about one of the differences about what I see when I listen to the vendor community talking about AI versus what I saw too much in the big data world is you know, it used to be, you know Oh, there was the opportunity. And data is so important. Yes, that's really But it was. It was a very wonky conversation. And the promise and the translation of what has been to the real world didn't necessarily always connect and We saw many of the big data solutions, you know, failed over time with AI on. And I've seen this in meetings from Dell talking about, you know, the business outcomes in general overall in i t. But you know how ai is helping make things real. So maybe we can start there for another product announcements and things we're gonna get into. But Robbie Interior talk to us a little bit about you know, the customers that you've been seeing in the impact that AI is having on their business. >>Sure, Teoh, I'll take us a job in it. A couple of things. For example, if you start looking at, uh, you know, the autonomous vehicles industry of the manufacturing industry where people are building better tools for anything they need to do on their manufacturing both. For example, uh, this is a good example of where that honors makers and stuff you've got Xeon ut It's actually a world war balcony. Now it is using our whole product suite right from the hardware and software to do multiple iterations off, ensuring that the software and the hardware come together pretty seamlessly and more importantly, ingesting, you know, probably tens of petabytes of data to ensure that we've got the right. They're training and gardens in place. So that's a great example of how we are helping some of our customers today in ensuring that we can really meet is really in terms of moving away from just a morning scenario in something that customers are able to use like today. >>Well, if I can have one more, Ah Yanai, one of our core and more partners than just customers in Italy in the energy sector have been been really, really driving innovation with us. We just deployed a pretty large 8000 accelerator cluster with them, which is the largest commercial cluster in the world. And where they're focusing on is the digital transformation and the development of energy sources. And it's really important not be an age. You know, the plan. It's not getting younger, and we have to be really careful about the type of energies that we utilize to do what we do every day on they put a lot of innovation. We've helped set up the right solution for them, and we'll talk some more about what they've done with that cluster. Later, during our chat, but it is one of the example that is tangible with the appointment that is being used to help there. >>Great. Well, we love starting with some of the customer stories. Really glad we're gonna be able to share some of those, you know, actual here from some of the customers a little bit later in this launch. But, Robbie, you know, maybe give us a little bit as to what you're hearing from customers. You know, the overall climate in AI. You know, obviously you know, so many challenges facing, you know, people today. But you know, specifically around ai, what are some of the hurdles that they might need to overcome Be able to make ai. Really? >>I think the two important pieces I can choose to number one as much as we talk about AI machine learning. One of the biggest challenges that customers have today is ensuring that they have the right amount and the right quality of data to go out and do the analytics percent. Because if you don't do it, it's giggle garbage in garbage out. So the one of the biggest challenges our customers have today is ensuring that they have the most pristine data to go back on, and that takes quite a bit of an effort. Number two. A lot of times, I think one of the challenges they also have is having the right skill set to go out and have the execution phase of the AI pod. You know, work done. And I think those are the two big challenges we hear off. And that doesn't seem to be changing in the very near term, given the very fact that nothing Forbes recently had an article that said that less than 15% off, our customers probably are using AI machine learning today so that talks to the challenges and the opportunities ahead for me. All right, >>So, Ravi, give us the news. Tell us the updates from Dell Technologies how you're helping customers with AI today, >>going back to one of the challenges, as I mentioned, which is not having the right skin set. One of the things we are doing at Dell Technologies is making sure that we provide them not just the product but also the ready solutions that we're working with. For example, Tier and his team. We're also working on validated and things are called reference architectures. The whole idea behind this is we want to take the guesswork out for our customers and actually go ahead and destroying things that we have already tested to ensure that the integration is right. There's rightsizing attributes, so they know exactly the kind of a product that would pick up our not worry about me in time and the resources needed you get to that particular location. So those are probably the two of the biggest things we're doing to help our customers make the right decision and execute seamlessly and on time. >>Excellent. So teary, maybe give us a little bit of a broader look as to, you know, Dell's part participation in the overall ecosystem when it comes to what's happening in AI on and you know why is this a unique time for what's happening in the in the industry? >>Yeah, I mean, I think we all live it. I mean, I'm right here in my home, and I'm trying to ensure that the business continues to operate, and it's important to make sure that we're also there for our customers, right? The fight against covered 19 is eyes changing what's happening around the quarantines, etcetera. So Dell, as a participant not only in the AI the world that we live in on enabling AI is also a participant in all of the community's s. So we've recently joined the covered 19 High Performance Computing Consortium on. We also made a lot of resources available to researchers and scientists leveraging AI in order to make progress towards you're and potentially the vaccine against Corbyn. 19 examples are we have our own supercomputers in the lab here in Austin, Texas, and we've given access to some of our partners. T. Gen. Is one example. The beginning of our chat I mentioned and I So not only did they have barely deport the cluster with us earlier this year that could 19 started hitting, so they've done what's the right thing to do for community and humanity is they made the resource available to scientists in Europe on tack just down the road here, which had the largest I can't make supercomputer that we deployed with them to. Ai's doing exactly the same thing. So this is one of the real examples that are very timely, and it's it's it's happening right now we hadn't planned for it. A booth there with our customers, the other pieces. This is probably going to be a trend, but healthcare is going through and version of data you mentioned in the beginning. You're talking about 2.3000 exabytes, about 3000 times the content of the Library of Congress. It's incredible, and that data is useless. I mean, it's great we can We can put that on our great ice on storage, but you can also see it as an opportunity to get business value out of it. That's going to be we're a lot more resource is with AI so a lot happening here. That's that's really if I can get into more of the science of it because it's healthcare, because it's the industry we see now that our family members at the M. Ware, part of the Dell Technologies Portfolio, are getting even more relevance in the discussion. The industry is based on virtualization, and the M ware is the number one virtualization solution for the industry. So now we're trying to weave in the reality in the I T environment with the new nodes of AI and data science and HPC. So you will see the VM Ware just added kubernetes control plane. This fear Andi were leveraging that to have a very flexible environment On one side, we can do some data science on the other side. We can go back to running some enterprise class hardware class software on top of it. So this is is great. And we're capitalizing on it with validates solutions, validated design on. And I think that's going to be adding a lot of ah power in the hands of our customers and always based on their feedback. And they asked back, >>Yeah, I may ask you just to build on that interesting comment that you made on we're actually looking at very shortly will be talking about how we're gonna have the ability to, for example, read or V Sphere and Allah servers begin. That essentially means that we're going to cut down the time our customers need to go ahead and deploy on their sites. >>Yeah, excellent. Definitely been, you know, very strong feedback from the community. We did videos around some of the B sphere seven launch, you know, theory. You know, we actually had done an interview with you. Ah, while back at your big lab, Jeff Frick. Otto, See the supercomputers behind what you were doing. Maybe bring us in a little bit inside as who? You know, some of the new pieces that help enable AI. You know, it often gets lost on the industry. You know, it's like, Oh, yeah, well, we've got the best hardware to accelerate or enable these kind of workloads. So, you know, bring us in its But what, You know, the engineering solution sets that are helping toe make this a reality >>of today. Yeah, and truly still you've been there. You've seen the engineers in the lab, and that's more than AI being real. That that is double real because we spend a lot of time analyzing workloads customer needs. We have a lot of PhD engineers in there, and what we're working on right now is kind of the next wave of HPC enablement Azaz. We all know the consumption model or the way that we want to have access to resources is evolving from something that is directly in front of us. 1 to 1 ratio to when virtualization became more prevalent. We had a one to many ratio on genes historically have been allocated on a per user. Or sometimes it is study modified view to have more than one user GP. But with the addition of big confusion to the VM our portfolio and be treated not being part of these fear. We're building up a GPU as a service solutions through a VM ware validated design that we are launching, and that's gonna give them flexibility. And the key here is flexibility. We have the ability, as you know, with the VM Ware environment, to bring in also some security, some flexibility through moving the workloads. And let's be honest with some ties into cloud models on, we have our own set of partners. We all know that the big players in the industry to But that's all about flexibility and giving our customers what they need and what they expect in the world. But really, >>Yeah, Ravi, I guess that brings us to ah, you know, one of the key pieces we need to look at here is how do we manage across all of these environments? Uh, and you know, how does AI fit into this whole discussion between what Dell and VM ware doing things like v Sphere, you know, put pulling in new workloads >>stew, actually a couple of things. So there's really nothing artificial about the real intelligence that comes through with all that foolish intelligence we're working out. And so one of the crucial things I think we need to, you know, ensure that we talk about is it's not just about the fact that it's a problem. So here are our stories there, but I think the crucial thing is we're looking at it from an end to end perspective from everything from ensuring that we have direct workstations, right servers, the storage, making sure that is well protected and all the way to working with an ecosystem of software renders. So first and foremost, that's the whole integration piece, making sure they realized people system. But more importantly, it's also ensuring that we help our customers by taking the guess work out again. I can't emphasize the fact that there are customers who are looking at different aliens off entry, for example, somebody will be looking at an F G. A. Everybody looking at GP use. API is probably, as you know, are great because they're price points and normal. Or should I say that our needs our lot lesser than the GP use? But on the flip side, there's a need for them to have a set of folks who can actually program right. It is why it's called the no programming programmable gate arrays of Saas fee programmable. My point being in all this, it's important that we actually provide dried end to end perspective, making sure that we're able to show the integration, show the value and also provide the options, because it's really not a cookie cutter approach of where you can take a particular solution and think that it will put the needs of every single customer. He doesn't even happen in the same industry, for that matter. So the flexibility that we provide all the way to the services is truly our attempt. At Dell Technologies, you get the entire gamut of solutions available for the customer to go out and pick and choose what says their needs the best. >>Alright, well, Ravi interior Thank you so much for the update. So we're gonna turn it over to actually hear from some of your customers. Talk about the power of ai. You're from their viewpoint, how real these solutions are becoming. Love the plan words there about, you know, enabling really artificial intelligence. Thanks so much for joining after the customers looking forward to the VM Ware discussion, we want to >>put robots into the world's dullest, deadliest and dirtiest jobs. We think that if we can have machines doing the work that put people at risk than we can allow people to do better work. Dell Technologies is the foundation for a lot of the >>work that we've done here. Every single piece of software that we developed is simulated dozens >>or hundreds of thousands of times. And having reliable compute infrastructure is critical for this. Yeah, yeah, A lot of technology has >>matured to actually do something really useful that can be used by non >>experts. We try to predict one system fails. We try to predict the >>business impatience things into images. On the end of the day, it's that >>now we have machines that learn how to speak a language from from zero. Yeah, everything >>we do really, at Epsilon centered around data and our ability >>to get the right message to >>the right person at the right >>time. We apply machine learning and artificial intelligence. So in real time you can adjust those campaigns to ensure that you're getting the most optimized message theme. >>It is a joint venture between Well, cars on the Amir are your progress is automated driving on Advanced Driver Assistance Systems Centre is really based on safety on how we can actually make lives better for you. Typically gets warned on distracted in cars. If you can take those kind of situations away, it will bring the accidents down about 70 to 80%. So what I appreciate it with Dell Technologies is the overall solution that they have to live in being able to deliver the full package. That has been a major differentiator compared to your competitors. >>Yeah. Yeah, alright, welcome back to help us dig into this discussion and happy to welcome to the program Chris Facade. He is the senior vice president and general manager of the B sphere business and just Simon, chief technologist for the High performance computing group, both of them with VM ware. Gentlemen, thanks so much for joining. Thank >>you for having us. >>All right, Krish. When vm Ware made the bit fusion acquisition. Everybody was looking the You know what this will do for space Force? GPU is we're talking about things like AI and ML. So bring us up to speed. As to you know, the news today is the what being worth doing with fusion. Yeah. >>Today we have a big announcement. I'm excited to announce that, you know, we're taking the next big step in the AI ML and more than application strategy. With the launch off bit fusion, we're just now being fully integrated with VCF. They're in black home, and we'll be releasing this very shortly to the market. As you said when we acquire institution A year ago, we had a showcase that's capable days as part of the animal event. And at that time we laid out a strategy that part of our institution as the cornerstone off our capabilities in the black home in the Iot space. Since then, we have had many customers take a look at the technology and we have had feedback from them as well as from partners and analysts. And the feedback has been tremendous. >>Excellent. Well, Chris, what does this then mean for customers? You know What's the value proposition that diffusion brings the VC? Yeah, >>if you look at our customers, they are in the midst of a big ah journey in digital transformation. And basically, what that means is customers are building a ton of applications and most of those applications some kind of data analytics or machine learning embedded in it. And what this is doing is that in the harbor and infrastructure industry, this is driving a lot of innovation. So you see the advent off a lot off specialized? Absolutely. There's custom a six FPs. And of course, the views being used to accelerate the special algorithms that these AI ml type applications need. And unfortunately, customer environment. Most of these specialized accelerators uh um bare metal kind of set up, but they're not taking advantage off optimization and everything that it brings to that. Also, with fusion launched today, we are essentially doing the accelerator space. What we need to compute several years ago and that is essentially bringing organization to the accelerators. But we take it one step further, which is, you know, we use the customers the ability to pull these accelerators and essentially going to be couple it from the server so you can have a pool of these accelerators sitting in the network. And customers are able to then target their workloads and share the accelerators get better utilization by a lot of past improvements and, in essence, have a smaller pool that they can use for a whole bunch of different applications across the enterprise. That is a huge angle for our customers. And that's the tremendous positive feedback that we get getting both from customers as well. >>Excellent. Well, I'm glad we've got Josh here to dig into some of the thesis before we get to you. They got Chris. Uh, part of this announcement is the partnership of VM Ware in Dell. So tell us about what the partnership is in the solutions for for this long. Yeah. >>We have been working with the Dell in the in the AI and ML space for a long time. We have ah, good partnership there. This just takes the partnership to the next level and we will have ah, execution solution. Support in some of the key. I am el targeted words like the sea for 1 40 the r 7 40 Those are the centers that would be partnering with them on and providing solutions. >>Excellent. Eso John. You know, we've watched for a long time. You know, various technologies. Oh, it's not a fit for virtualized environment. And then, you know, VM Ware does does what it does. Make sure you know, performance is there. And make sure all the options there bring us inside a little bit. You know what this solution means for leveraging GPS? Yeah. So actually, before I before us, answer that question. Let me say that the the fusion acquisition and the diffusion technology fits into a larger strategy at VM Ware around AI and ML. That I think matches pretty nicely the overall Dell strategy as well, in the sense that we are really focused on delivering AI ml capabilities or the ability for our customers to run their am ai and ml workloads from edge before the cloud. And that means running it on CPU or running it on hardware accelerators like like G fuse. Whatever is really required by the customer in this specific case, we're quite excited about using technology as it really allows us. As Chris was describing to extend our capabilities especially in the deep learning space where GPU accelerators are critically important. And so what this technology really brings to the table is the ability to, as Chris was outlining, to pull those resources those hardware resource together and then allow organizations to drive up the utilization of those GP Resource is through that pooling and also increase the degree of sharing that we support that supported for the customer. Okay, Jeff, take us in a little bit further as how you know the mechanisms of diffusion work. Sure, Yeah, that's a great question. So think of it this way. There there is a client component that we're using a server component. The server component is running on a machine that actually has the physical GPU is installed in it. The client machine, which is running the bit fusion client software, is where the user of the data scientist is actually running their machine machine learning application. But there's no GPU actually in that host. And what is happening with fusion technology is that it is essentially intercepting the cuda calls that are being made by that machine learning app, patience and promoting those protocols over to the bit fusion server and then injecting them into the local GPU on the server. So it's actually, you know, we call it into a position in the ability that remote these protocols, but it's actually much more sophisticated than that. There are a lot of underlying capabilities that are being deployed in terms of optimization who takes maximum advantage of the the networking link that sits between the client machine and the server machine. But given all of that, once we've done it with diffusion, it's now possible for the data scientist. Either consume multiple GP use for single GPU use or even fractional defuse across that Internet using the using technology. Okay, maybe it would help illustrate some of these technologies. If you got a couple of customers, Sure, so one example would be a retail customer. I'm thinking of who is. Actually it's ah, grocery chain. That is the flowing, ah, large number of video cameras into their to their stores in order to do things like, um, watch for pilfering, uh, identify when storage store shelves could be restocked and even looking for cases where, for example, maybe a customer has fallen down in denial on someone needs to go and help those multiple video streams and then multiple app patients that are being run that part are consuming the data from those video streams and doing analytics and ml on them would be perfectly suited for this type of environment where you would like to be ableto have these multiple independent applications running but having them be able to efficiently share the hardware resources of the GP use. Another example would be retailers who are deploying ml Howard Check out registers who helped reduce fraud customers who are buying, buying things with, uh, fake barcodes, for example. So in that case, you would not necessarily want to employ a single dedicated GPU for every single check out line. Instead, what you would prefer to do is have a full set of resource. Is that each inference operation that's occurring within each one of those check out lines could then consume collectively. That would be two examples of the use of this kind of pull in technology. Okay, great. So, Josh, a lot last question for you is this technology is this only for use and anything else. You can give us a little bit of a look forward to as to what we should be expecting from the big fusion technology. Yeah. So currently, the target is specifically NVIDIA GPU use with Cuda. The team, actually even prior to acquisition, had done some work on enablement of PJs and also had done some work on open CL, which is more open standard for a device that so what you will see over time is an expansion of the diffusion capabilities to embrace devices like PJs. The domain specific a six that first was referring to earlier will roll out over time. But we are starting with the NVIDIA GPU, which totally makes sense, since that is the primary hardware acceleration and for deep learning currently excellent. Well, John and Chris, thank you so much for the updates to the audience. If you're watching this live, please throwing the crowd chat and ask your questions. This faith, If you're watching this on demand, you can also go to crowdchat dot net slash make ai really to be able to see the conversation that we had. Thanks so much for joining. >>Thank you very much. >>Thank you. Managing your data center requires around the clock. Attention Dell, EMC open manage mobile enables I t administrators to monitor data center issues and respond rapidly toe unexpected events anytime, anywhere. Open Manage Mobile provides a wealth of features within a comprehensive user interface, including >>server configuration, push notifications, remote desktop augmented reality and more. The latest release features an updated Our interface Power and Thermal Policy Review. Emergency Power Reduction, an internal storage monitoring download Open Manage Mobile today.

>> Live from Orlando, Florida It's the que covering accelerate nineteen. Brought to you by important >> Welcome back to the Cube. Live from Orlando, Florida at Fortinet Accelerate twenty ninety nine. Lisa Martin Joining and welcoming to the queue for the first time, the CEO and president of Novy Flow. Dominique Jordan. Dominic. Great to have you joining on the Cube at accelerate. So here we are in Orlando, talking about all things cyber security. I just came from the keynote session where Fortinet was talking about how much they're innovating. What? How they're leading from a competitive perspective. What customers air saying why their security fabric is so differentiated? No, the flow is one of their security fabric ready partners. But before we talk about that, why don't you take a minute or two to describe to our audience who know the flow is and what you guys are doing in cybersecurity? >> Yeah, way We came in a little bit by accident. The cyber security. We we've been founded seven years ago, and the idea was to create the very programmable networks. It's very much in line with what we heard today on the keynote, and we became a technology leader in that field as the and software defined networking. And three, four years ago, customers started to use our product, obviously for cybersecurity application. We didn't even know about that. They don't necessarily tell us, and we spend a bit more focus into it. And over time we started to work with fortunate, for example. And now we have a developing. Is Greg relationship great solutions? Also for the for the customers. >> So one of the things that we understand from Fortinet and from all of the conversations that the Cube has globally is is that digital transformation is fundamental to every business to compete right. But as is secure transformation and security transformation, very challenging to do as businesses. And you think of any industry, financial services, retail, consumer packaged goods. As they expand digitally, so does the attack surface. So one of the things that fourteen it talks about is it's not enough anymore to have these point solutions pointed at different, you know, on Prem Cloud edge that the entire infrastructure as it's changing and they attacked services expanding has got to be protected more from an integrated perspective. This notion of the of the security fabric. Talk to us about a fabric ready partnership. What that means to know that though I know that's only in the last six months or so. So walk us through what you did to become a fabric ready partner and what it is that you in forging that are seeing in the market as challenges that you're helping to results. >> Yeah, what we see. Actually, I like to decide the defined that as a battlefield, the attacks are being waged, really, and and the band we feel is the networks of those carriers. There was a government agencies, large enterprise, etcetera, and those those companies are not really taking advantage of their position because, in fact, with the right network fabric the right tools to be able to react, they could actually be very much more powerful. So this is where we are working with forty nine to equip those customers with solutions that are much more agile, more programmable because the network is also evolving. It's not only that the attacks are broader, they also changing the nature of it is changing, and the fact that we came from a background of working at the edge of the networks mostly. Well, I wouldn't mention that before we deployed. Typically at the large tier one carriers all around the world are mentioned. A few tell Strike group, wait deployed at the Hutchison Group Young law, etcetera. And also a two of these five eyes. So government agencies that are engage in fighting these attacks. So So we come with a background of working in a decentralized approach anyway, So it was a very natural evolution. Work was done with Fortinet so far. So what we built so far together we built some integrated solutions s So far, we have two solutions that we are demonstrating two customers. The first one is to allow the large. It tends to be the larger customer fortunate that are making the transition from a in existing appliance to virtual eyes solutions. That's an area where we are very effective at helping them to scale. And those would be for customers that would have say, hundred gig of traffic or more. So we're fortunate we built a and undermanned solution. It's an integrated solution that enables those carriers to are. Those customers could be other kind of customers to gradually grow the number of the EMS that are used in real time for doing whatever Sabbath security job they have to do. And if they the demand comes down, these v EMS were released in the customer data centers. To do some other jobs like this is one of the products that we built together, and we are demonstrating. The second one is a. A feature of that is that we can process about the way this is Ah is able to scale all the way up to six point five terra bits per second. I'Ll repeat that six point five terror bits per second. This is a unheard of and this is, I think one of the interests of Fortinet is working with no visible. We already have developed not really the metal ring system, but all the O. N m features that you demand as a customer to be deployed in the real world. So so that's that. That's the base on. The second option is that we developed is a carry Great Nat again. Same idea. We can scale the Terra great net analysis up to one point six terabit per second. Former, very powerful. They're powerful solutions to meet this this raising the man which you talked about? They say this literally a wave of attacks coming more and more. >> So you mentioned some customers by name. Telstra, for example. CEO to CEO conversations tells, has been around for a long time as the organization expands digitally. And we talked about a minute ago as this the attack surface. What are some of the conversations that you're having with the scene? The C suite about security? It's not just talking to, you know, network security admits. Right? What are those conversations that you're having with the CEO in the C suite that are where they're saying these are my business problems? Dominic, help us solve these problems. >> Well, it comes to two words, basically its scale and are slow flexibility. It comes to that simple. Is this so they are struggling to see how they can cope with the especially the ones that are virtual izing because you end up. Imagine the model is that you go from a very powerful appliance and once you virtual eyes this appliance, you might end up with thirty different servers, you know, running in parallel, you have to have low balancers in front of it. That makes for a very complex and very expensive solution. So that's that's are they searching for? How can we reduce the complexity, for example, one of the advantages of our product working side by side with fortunate. Since we worked at six point five terabytes per second, we do some of the pre processing of the traffic before it hits the virtualized solution forty gate, for example. We have built some blacklist white list we can do also the load balancing. No need to install some additional law balancing can have. That is a kind of a black box I get that does all the required feature to increase the scaling off those those combined solution and the second, the second party flexibility. You got to be able to evolves your solution in time as these attacks are revolving now or product is built from bottom up, and it's built on and infrastructure typically white boxes that are running chips that are programmable by us. So the software, the NASA's it's Gone, is complemented by some very easy to use porting layers if you like. So the Fortinet solution could be easily adapted to this platform and And that's how we can achieve this kind of throughput. And in fact, I will tell to your viewers that we already have built live demos of those solutions in the Sofia anti police lab in France. The labs of Fortinet, Where were you? We're doing demos for the for customers of those solutions. >> So I'MA tell Stir, though, and you said speed and flexibility scale rather the other sailor disability scale. Inflexibility. What are some? How does my business? What am I looking to achieve? A. My looking to scale to x number of users X number of regions. How does how is that measured from, say, a Telstra's perspective as a big business impact that Novy Flow and Fortinet are helping to them to achieve? >> Yeah, the It's really all dimensions way have some challenge just by handling the raw volume of traffics. Sometimes some customers are pumping terra bits of traffic between one country and the other, so that's one. And but it's also geography because your attack and come and any anywhere in your network that the periphery or inside your network so you have to be able to in a centralized away once you detect there's an attack you have to be able to respondent and in some time, and that's how we can do with our programmable infrastructure can actually reprogrammed those air routing tables. You can take some mitigation action, for example, some of some of the bad traffic on the blacklist. If you've looked at it, perhaps you could put it on a white list for serpent of time. Don't don't look at it over and over. Just wait, maybe a little bit those kind of off measures to alleviate the load. So, in fact, it's work more intelligently with the raw volume of traffic that comes to you. So this is one of the real advantage of is the end. So after defined networking applied to a cyber security problem, >> what are some of the other industries that you are seeing that have potential to dramatically benefit from suffer to find networking in cyber security? Knowing that he d threat landscape, it is exponentially growing. Yes, we've got tools like a I and Machine Learning, which we'LL talk about later on the program today with respective forty Gar labs, for example. But of course, so do the attackers have access to utilize artificial intelligence to create even smarter attacks. But from your perspective, what are some of the other industries that are really right to take advantage of SGN and cyber security practices? >> You know, I think all industries are moving to data. There's no exception. I was talking to some guy, an interpreter in Montreuil yesterday's doing farming, but it's high tech farming with several earlier. It's all based on a I. It's all based on data, even those industry that the forming industry thing that may be so every industry will rely on data, and that means it will rely on a network, and it all comes down to the network. You gotta be able to build a cyber security network ready fabric from the bottom up so that your network is one of the key features is actually stop the attacks, and that doesn't matter in which industry you are. I think they you can think about the industry where you have vast volumes of data. They will be most likely the first one to take benefit of these. You know, we talk about countries before, and this is one such an industry, but it certainly where you process the vast amount of traffic. So they taking advantage of our technology, for example. And but I think it will be probably most of the industry will be affected by that shorter later >> and hopefully sooner rather than later, considering how fast all of these opportunities, good and bad, are growing. One of the things sporting that talked a lot about this morning during this section and some of the press releases is this growth that they've experienced growing twenty percent year on year from last year one point eight billion in revenue over three hundred eighty five thousand customers. You're one of the fabric ready partners, of which there are fifty seven. So a lot of growth, a lot of potential. What excites you as the head of no be Flo and your recent and developing partnership with Fortinet for twenty nineteen and beyond were gonna latch onto that growth trajectory. >> Absolute well, you know, when you mentioned high volume of traffic that plays to our cards. So the market is actually coming where we are way have our product runs at six for five terabytes per second, and that's today because we have a *** plans to move to twelve Tara bits and so forth. So for us, it's exciting because we feel we have the right scaling platform and the right program ability. So our customers, fortunate customers together with us can start with the existing. They're powerful platform. But should that evolved, they'LL be able to move to a new level of software new capacity gradually over time. So that's very exciting for us. >> But what about some of the announcements that came out this morning? Over three hundred new features added, for example, that's a tremendous amount of innovation since last year's accelerate. >> Yeah, well, the's features needs also have the right, I would say filtered level of data to be able to do it more efficiently. And that's where we commend we're not inside the subway Security company. We are really complimenting the product of forty nine by playing upstream and doing a pre filtering controlled by the policy management of the Fortunate, the equipment but nevertheless taking up some of the load of it so that the equipment could be more efficient. But just as an example, I read in a magazine a couple days ago that Google is building a A two hundred fifty terabyte cable between North America and Europe. Think about that. It's it's mindboggling is three time Library of Congress per second. And those are the kind of volume of data did you see coming so suddenly? Six point five terabytes per second doesn't sound so big, does it? But in fact, that's the world win today, and we're lucky it may be flow. We invested early on in the software layer that runs on top of these extremely powerful white boxes and were taking advantage of it with Fortinet. >> Gotta deliver that scale, that flexibility and his son's more and more like Speed. Dominic, thank you so much for stopping by the Cuban joining me on the program today, talking about Novy float what you're doing with Fortinet and what excites you about the year ahead >> was a pleasure, Liza. Thank you for >> mine as well. I want to thank you for watching the Cube Lisa Martin live on the Cube from Fortinet Accelerate twenty nineteen in Orlando. Thanks for watching

(upbeat music) >> Hey, welcome back everybody, Jeff Frick here with theCube. We're at San Francisco International Gate 54B, at the Alaska Elevated Experience Event. It's pretty exciting, they're really used the opportunity on the Virgin integration, to kind of rebrand everything. Redo the planes, add a lot of new technology, and we're really happy to have our next guest , she's Annabel Chang, she's the vice president of the Bay area for Alaska. Annabelle great seeing you. >> Thank you for having me. >> So congratulations on the event,-- >> Yes. >> I'm sure there's a lot of work that went into this thing. >> Just to say the least, yes. >> So in your remarks during the pressor you spent a lot of time talking about the community involvement >> Yes >> I think you said that you guys invested over a million dollars in kind of local community >> Yes >> Types of activity. So highlight a couple of those organizations and why is it important for Alaska to play in the community that has nothing to do with me getting on an airplane and flying to Seatag. >> Ah, well it actually has everything to do with that. For example, last year we partnered up with the San Jose Mayor's office and the San Jose Public Library Foundation to offer the first ever free coding camp for girls. It was a week long coding camp. Parents didn't have to worry about providing breakfast or lunch. We had it all taken care of. Why does it matter to Alaska Airlines? We also need engineers to help create the apps, to help run the planes and it is super important that we have a diverse workforce that represents our community. Whether we fly and all of the focus that are onboard, as well. >> Right, so that's pretty interesting. Cause I don't think most people would think of you doing that, right? That's a little bit outside the seat mile, kind of calculation and really investing in the community. >> Yes >> A lot of conversation too about the investment in this terminal. You guys are at all 3 Bay Area airports thank you very much. I like to be able to hop on a plane if I'm delayed. >> Yeah >> But you guys are making a big investment here at SFO. >> Yes, so actually I will add a couple of things. We actually are at 6 Northern California airports. So in addition to our big Bay Area airports, we have flights out of Santa Rosa, right into wine country, Monterey and Sacramento. >> Flights out of Santa Rosa? >> Yes. You can bring that wine right onboard. Not, not a problem. Which is really exciting. But last week we just announced that we are going to be opening up a San Francisco lounge. 8500 sq ft. in 2020 on the third floor. You'll have stunning views of the runway. It'll be like nothing else. It'll be the highest domestic lounge at SFO. >> Right. I was wondering if you could just talk about, a little bit about, thinking about the entire customer experience. I had really interesting interview at GE Aviation. >> Yes >> Years ago, where even GE was thinking about kind from the time you leave your door at your house to the arrive at your destination, and all kind of that whole experience between. When you guys talk about lounges, and terminals, and gates, you really are trying to take a much more wholistic view then simply the travel of actual miles in the air. >> 100 percent. It is all about the guest experience. We are trying to be your favorite airline. And we have to earn that loyalty. So from the moment that you are thinking about booking the flight, we already want that to be as easy of a process as possible. From the moment that you deplane and get your bags. And hopefully, we are always looking for ways to be innovative. So, you know many years ago, Alaska Airlines was the first ever to have the kiosks and mobile check-in. And we continue to look for ways to be top in the field. And actually in flight, I'm proud to share that we have the most free movies in the sky, of any airlines. All I tend to watch a few of the same movies over and over again But literally you could scroll, scroll, scroll. It goes from A to Z. Most people kind of get stuck in like the Gs. >> They don't make it past the Gs. >> Yeah, but I promise there's some goodies in the back of the alphabet. >> Right, to just kind of close. You know you talk about WiFi, and you talked about movies, about kind of the role of technology and how Alaska continues to be innovative, leveraging technology with that, with the lounge, with the new C configurations. >> Yeah >> How important to you guys to be able to execute your vision. >> So we want to be your top west coast airline. And the west coast is obviously the tech hub of the entire world. So we know that our travelers care very much about technology. So we're looking ways creative, to make sure that everyone has power. As I always say ABC, always be charging. >> Right, right. >> So we want to make sure your tablets, your phone, your laptop is always available to charge. And we are looking for ways to be creative. So, for example, we know that everyone has personal mobile phones or laptops now. And we're looking for ways to make sure we can take advantage of that technology and offer it to you. >> Right. >> I know, number 1, fast WiFi is going to be key to our success. >> Well Annabel, thanks for taking a few minutes. We look forward to getting on the plane here >> Yeah. >> In a few minutes and >> We're going to have some ice cream aren't we. >> Oh we're going to have ice cream? Yes >> Salt and straw, you don't have to wait in line hopefully. >> Yeah, thank you. >> She's Annabel, I'm Jeff >> Thank you. >> You're watching theCube, we're here at SFO, Gate 54B. Soon we'll be at 35,000 feet. Thanks for watching. >> Awesome >> Catch you next time >> (upbeat music)

(bouncy music) >> Live, from Barcelona, Spain, it's theCUBE, covering Cisco Live Europe. Brought to you by Cisco and its ecosystem partners. >> Welcome back everyone. Live here in Barcelona it's theCUBE's exclusive coverage of Cisco Live 2019. I'm John Furrier. Dave Vellante, my co-host for the week, and Stu Miniman, who's also here doing interviews. Our next two guests is Mike Bundy, Senior Director of Global Cisco Alliance with Pure Storage, and Z, who's in charge of product strategy for Cisco. Welcome to theCUBE. Thanks for joining us. >> Thank you for having us here. >> You're welcome. >> Thank you. >> We're in the DevNet zone. It's packed with people learning real use cases, rolling up their sleeves. Talk about the Cisco Pure relationship. How do you guys fit into all this? What's the alliance? >> You want to start? >> Sure. So, we have a partnership with Cisco, primarily around a solution called Flashstack in the converged infrastructure space. And most recently, we've evolved a new use-case and application together for artificial intelligence that Z's business unit have just released a new platform that works with Cisco and NVIDEA to accomplish customer application needs mainly in machine learning but all aspects of artificial intelligence, so. >> So AI is obviously a hot trend in machine learning but today at Cisco, the big story was not about the data center as much anymore as it's the data at the center of the value proposition which spans the on-premises, IoT edge, and multiple clouds so data now is everywhere. You've got to store it. It's going to be stored in the cloud, it's on-premise. So data at the center means a lot of things. You can program with it. It's got to be addressable. It has to be smart and aware and take advantage of the networking. So with all of that as the background, backdrop, what is the AI approach? How should people think about AI in context to storing data, using data? Not just moving packets from point A to point B, but you're storing it, you're pulling it out, you're integrating it into applications. A lot of moving parts there. What's the-- >> Yeah, you got a really good point here. When people think about machine learning, traditionally they just think about training. But we look at it as more than just training. It's the whole data pack line that starts with collecting the data, store the data, analyze the data, train the data, and then deploy it. And then put the data back. So it's really a very, it's a cycle there. It's where you need to consider how you actually collect the data from edge, how you store them, in the speed that you can, and give the data to the training side. So I believe when we work with Pure, we try to create this as a whole data pack line and think about the entire data movement and the storage need that we look at here. >> So we're in the DevNet zone and I'm looking at the machine learning with Python, ML Library, (mumbles) Flow, Appache Spark, a lot of this data science type stuff. >> Yup. >> But increasingly, AI is a workload that's going mainstream. But what are the trends that you guys are seeing in terms of traditional IT's involvement? Is it still sort of AI off on an island? What are you seeing there? >> So I'll take a guess, a stab at it. So really, every major company industry that we work with have AI initiatives. It's the core of the future for their business. What we're trying to do is partner with IT to get ahead of the large infrastructure demands that will come from those smaller, innovative projects that are in pilot mode so that they are a partner to the business and the data scientists rather than a laggard in the business, the way that sometimes the reputation that IT gets. We want to be the infrastructure, solid, like a cloud-like experience for the data scientists so they can worry more about the applications, the data, what it means to the business, and less about the infrastructure. >> Okay. And so you guys are trying to simplify that infrastructure, whether it's converged infrastructure, and other unifying approaches. Are you seeing the shift of that heavy lifting, of people now shifting resources to new workloads like AI? Maybe you could discuss what the trends are there? >> Yeah, absolutely. So I think AI started with more like a data science experiment. You see a couple of data scientists experimenting. Now it's really getting into mainstream. More and more people are into that. And as, I apologize. >> Mike. >> Mike. >> Mike, can we restart that question? (all laughing) My deep apology. I need a GPU or something in my brain. I need to store that data better. >> You're on Fortnite. Go ahead. >> Yes, so as Mike has said earlier on, it's not just the data scientists. It's actually an IT challenge as well and I think with Cisco, what we're trying to do with Pure here is, you know that Cisco thing, we're saying, "We're a bridge." We want to bridge the gap between the data scientists and the IT and make it not just AI as an experiment but AI at scale, at production level, and be ready to actually create real impact with the technology infrastructure that we can enable. >> Mike, talk about Pure's position. You guys have announced Pure in the cloud? >> Yes. >> You're seeing that software focus. Software is the key here. >> Absolutely. >> You're getting into a software model. AI and machine learning, all this we're talking about is software. Data is now available to be addressed and managed in that software life cycle. How is the role of the software for you guys with converged infrastructure at the center of all the Cisco announcements. You were out on stage today with converged infrastructure to the edge. >> Yes, so, if you look at the platform that we built, it's referenced back, being called the Data Hub. The Data Hub has a very tight synergy with all the applications you're referring to: Spark, Tensor Flow, et cetera, et cetera, Cafe. So, we look at it as the next generation analytics and the platform has a super layer on top of all those applications because that's going to really make the integration possible for the data scientists so they can go quicker and faster. What we're trying to do underneath that is use the Data Hub that no matter what the size, whether it's small data, large data, transaction based or more bulk data warehouse type applications, the Data Hub and the FlashBlade solution underneath handle all of that very, very different and probably more optimized and easier than traditional legacy infrastructures. Even traditional, even Flash, from some of our competitors, because we built this purpose-built application for that. Not trying to go backwards in terms of technology. >> So I want to put both you guys on the spot for a question. We hear infrastructure as code going on many, many years since theCUBE started nine years ago. Infrastructure as code, now it's here. The network is programmable, the infrastructure is programmable, storage is programmable. When a customer or someone asks you, how is infrastructure, networks, and storage programmable and what do I do? I used to provision storage, I've got servers. I'm going to the cloud. What do I do? How do I become AI enabled so that I could program the infrastructure? How do you guys answer that question? >> So a lot of that comes to the infrastructure management layer. How do you actually, using policy and using the right infrastructure management to make the right configuration you want. And I think one thing from programmability is also flexibility. Instead of having just a fixed configuration, what we're doing with Pure here is really having that flexibility where you can put Pure storage, different kind of storage with different kind of compute that we have. No matter we're talking about two hour use, four hour use, that kind of compute power is different and can max with different storage, depending on what the customer use case is. So that flexibility driven to the programmability that is managed by the infrastructure management layer. And we're extending that. So Pure and Cisco's infrastructure management actually tying together. It's really single pane of glass within the side that we can actually manage both Pure and Cisco. That's the programmability that we're talking about. >> Your customers get Pure storage, end-to-end manageability? >> With the Cisco compute, it's a single pane of glass. >> Okay. >> So where do I buy? I want to get started. What do you got for me? (laughing) >> It's pretty simple. It's three basic components. Cisco Compute and a platform for machine learning that's powered by NVIDEA GPUs; Cisco FlashBlade, which is the Data Hub and storage component; and then network connectivity from the number one network provider in the world, from Cisco. It's very simple. >> And it's a SKU, it's a solution? >> Yup, it's very simple. It's data-driven. It's not tied to a specific SKU. It's more flexible than that so you have better optimization of the network. You don't buy a 1000 series X and then only use 50% of it. It's very customizable. >> Okay, do I can customize it for my, whatever, data science team or my IT workloads? >> Yes, and provision it for multi-purpose, same way a service provider would if you're a large IT organization. >> Trend around breaking silos has been discussed heavily. Can you talk about multiple clouds, on-premise in cloud and edge all coming together? How should companies think about their data architecture because silos are good for certain things, but to make multi-cloud work and all this end-to-end and intent-based networking and all the power of AI's around the corner, you got to have the data out there and it's got to be horizontally scalable, if you will. How do you break down those silos? What's your advice, is there a use case for an architecture? >> I think it's a classic example of how IT has evolved to not think just silos and be multi-cloud. So what we advocate is to have a data platform that transpires the entire community, whether it's development, test, engineering, production applications, and that runs holistically across the entire organization. That would include on-prem, it would include integration with the cloud because most companies now require that. So you can have different levels of high availability or lower cost if your data needs to be archived. So it's really building and thinking about the data as a platform across the company and not just silos for various applications. >> So replication never goes away. >> Never goes away. (laughing) >> It's going to be around for a long, long time. >> Dev Test never goes away either. >> Your thoughts on this? >> Yeah, so adding on top of that, we believe where your infrastructure should go is where the data goes. You want to follow where the data is and that's exactly why we want to partner with Pure here because we see a lot of the data are sitting today in the very important infrastructure which is built by Pure Storage and we want to make sure that we're not just building a silo box sitting there where you have to pour the data in there all the time, but actually connect to our server with Pure Storage in the most manageable way. And for IT, it's the same kind of manual layer. You're not thinking about, oh, I have to manage all this silo box, or the shadow IT that some data scientists would have under their desk. That's the least thing you want. >> And the other thing that came up in the key note today, which we've been saying on theCUBE, and all the experts reaffirm, is that moving data costs money. You've got latency costs and also just cost to move traffic around. So moving compute to the edge or moving compute to the data has been a big, hot trend. How has the compute equation changed? Because I've got storage. I'm not just moving packets around. I'm storing it, I'm moving it around. How does that change the compute? Does that put more emphasis on the compute? >> It's definitely putting a lot more emphasis on compute. I think it's where you want compute to happen. You can pull all the data and want it to happen in the center place. That's fine if that's the way you want to manage it. If you have already simplified the data, you want to put it in that's the way. If you want to do it at the edge, near where the data source is, you can also do the cleaning there. So we want to make sure that, no matter how you want to manage it, we have the portfolio that can actually help you to manage that. >> And it's alternative processors. You mentioned NVIDEA. >> Exactly. >> You guys are the first to do a deal with them. >> And other ways, too. You've got to take advantage of technology like Kubernetes, as an example. So you can move the containers where they need to be and have policy managers for the compute requirements and also storage, so that you don't have contention or data integrity issues. So embracing those technologies in a multi-cloud world is very, very essential. >> Mike, I want to ask you a question around customer trends. What are you seeing as a pattern from a customer standpoint, as they prepare for AI, and start re-factoring some of their IT and/or resources, is there a certain use-case that they set up with Pure in terms of how they set up their storage? Is it different by customer? Is there a common trend that you see? >> Yeah, there are some commonalities. Take financial services, quant-trading as an example. We have a number of customers that leverage our platform for that because it's very time-sensitive, high-availability data. So really, I think that the trend overall of that would be: step back, take a look at your data, and focus on, how can I correlate and organize that? And really get it ready so that whatever platform you use from a storage standpoint, you're thinking about all aspects of data and get it in a format, in a form, where you can manage and catalog, because that's kind of essential to the entire thing. >> It really highlights the key things that we've been saying in storage for a long time. High-availability, integrity of the data, and now you've got application developers programming with data. With APIs, you're slinging APIs around like it's-- >> The way it should be. >> That's the way it should be. This is like Nirvana finally got here. How far along are we in the progress? How far? Are we early? Are we moving the needle? Where are the customers? >> You mean in terms of a partnership? >> Partnership, customer AI, in general. You guys, you've got storage, you've got networking and compute all working together. It has to be flexible, elastic, like the cloud. >> My feeling, Mike can correct me, or you can disagree with me. (laughing) I think right now, if we look at what all the analysts are saying, and what we're saying, I think most of the companies, more than 50% of companies either have deployed AI MO or are considering a plan of deploying that. But having said that, we do see that we're still at a relatively early stage because the challenges of making AI deployment at scale, where data scientists and IT are really working together. You need that level of security and that level of skill of infrastructure and software and evolving DevNet. So my feeling is we're still at a relatively early stage. >> Yeah, I think we are in the early adopter phase. We've had customers for the last two years that have really been driving this. We work with about seven of the automated car-driving companies. But if you look at the data from Morgan Stanley and other analysts, there's about a $13 billion infrastructure that's required for AI over the next three years, from 2019-2021, so that is probably 6X, 7X what it is today, so we haven't quite hit that bell curve yet. >> So people are doing their homework right now, setting up their architecture? >> It's the leaders. It's leaders in the industry, not the mainstream. >> Got it. >> And everybody else is going to close that gap, and that's where you guys come in, is helping them do that. >> That's scale. (talking over one another) >> That's what we built this platform with Cisco on, is really, the Flashstack for AI is around scale, for tens and twenties of petabytes of data that will be required for these applications. >> And it's a targeted solution for AI with all the integration pieces with Cisco built in? >> Yes. >> Great, awesome. We'll keep track of it. It's exciting. >> Awesome. >> It's cliche to say future-proof but in this case, it literally is preparing for the future. The bridge to the future, as the new saying at Cisco goes. >> Yes, absolutely. >> This is theCube coverage live in Barcelona. We'll be back with more live coverage after this short break. Thanks for watching. I'm John Furrier with Dave Vallente. Stay with us. (upbeat electronic music)

>> Hello everyone. Welcome to this Cube conversation here in Palo Alto. On John Furrier, co host of the Cube. I'm here. Aaron Kalb is the co founder and VP of design and Alation. Great to see them on some fresh funding news. Aaron, Thanks for coming. And spend the time. Good to see you again. >> Good to see you, John. Thanks for having me >> So big news. You guys got a very big round of financing because you go to the next level. A startup. Certainly coming out that start up phase and growth phase super exciting news. You guys doing some very innovative things around, date around community around people and really kind of cracking the code on this humanization democratization of data, but actually helping businesses. I want to talk about it with you. First. Give us the update on the financing, the amount what it means to the company. A lot of cash. >> Yeah. So we're very excited to have raised a fifty million dollar round. Sapphire led the round, and we also had, you know, re ups from all of our existing investors. And, you know, as as a co founder, he always had big dreams for growth. And it's just validating tohave. Ah, a community of investors who can see the future, too, as well as our great community of over one hundred customers now who want to build this data democratized future with us. >> We've been following you guys since the founding obviously watching you guys great use of capital. Fifty million's a lot of capital, so obviously validation check. Good, good job. But now you go to a whole other level growth. What's the capital gonna be deployed for? What's going on with company where you guys I and in terms of innovation, what's the key focus? >> It's a great question. So you know, obviously we have revenue from our customers. But getting this extra infusion from VC lets us just supercharge our development. It's growth. It's going to more customers, both domestically and abroad, goingto a broader user base. And we're Enterprise-wide Adoption within those customers, as well as innovation in the core product, new technology, great design and futures. that are really going to change the organization's access and use data to make better decisions? >> What was the key Learnings As you guys went into this round of funding outside the validation to get through due diligence, all that good stuff. But you guys have made some successful milestones. What was the key? Notable accomplishments that Alation hit to kind of hit this trigger point here for the fifty million? >> Yeah, I'm glad you asked about that. I think that the key thing that's changed it's enabled this. This next phase is that the data catalog market has really come into its own right. In the beginning, in the early days, we were knocking on doors, trying to say, You know, we don't even know it was going to be called data catalog in our first few months. And even though we had the technology, we said, Hey, we got this thing and we know it's useful. Please buy it. Please want it. And the question was, you know, what's the data catalog by what I ever even look at that? And it's just turned a corner. Now, you know, Thanks. In part of things like Gartner telling companies you know, in the next year by twenty twenty, if you have a data catalog, you're goingto see twice the ROI from your existing data investments than if you don't your stories like that are making companies say? Of course, you want to data catalog. It just turned out a dime. Now they're asking, Which data catalog should we get? Why is yours the best in this change of the market maturing? I think it's the biggest change we've seen >> with one thing that we've observed. I want to get your reaction to This is that I'll stay with cloud computing economics, a phenomenally C scale data data science working the cloud. We see great success there. Now there's multiple clouds, multi clouds, a big trend, but also the validation that it's not just all cloud anymore. The on premises activity steel is relevant, although it might have a cloud. Operations really kind of changes the role of data. You mentioned the data catalogue kind of being kind of having a common mainstream visibility from the analysts like Gardner and others on Wiki Bond as well. It makes data the center of the innovation. Now you have data challenges around. Okay, where's the data deployed? Where my using the data? Because data scientists want ease of data, they want quality data. They want to make sure their their algorithm, whether it's machine learning component or software actually running a good data. So data effectiveness is now part of the operations of most businesses. What's your reaction to that? Which your thoughts. Is that how you see it? Is there something different there? What's going on with the whole date at the center? >> Absolutely hit on two key themes for us. One of that idea of the center and the other is your point about data quality and data trust. So, so centrality, we think, is really essential. You know, we're seeing cataloging technology crop up more and more. A lot of people were coming out with catalogs or catalog kind of add ons to their products. But what our customers really tell us is they want the data catalog to be the hub, that one stop shop where they go to to access any data, wherever it lives, whether it's in the cloud or on Prem, whether it's in a relational database or a file system, so is one of Alations key. Differentiators early on was being that central index, much like Google is out of the front page to the Internet, even though it's linking to ad pages all over the place. And the other thing in terms of that data quality and data trustworthiness has been a differentiator, and this was something that was part of our technology when we launched that we didn't put the label out till later. Is this idea of Behavior IO, that's kind of looking at previous human behavior to influence future human behavior to be better. And there's another place we really took some inspiration from Google and Terry Winograd at Stanford before that, you know, he observed. You know, if you remember back before Google search sucked, frankly, right, the results on top are not the most development were not the most trustworthy. And the reason was those algorithms were based on saying, how often does your key word appear in that website? Built, in other words, and so you'd get results on top. That might just not be very good. Or even that were created by spammers who put in a lot of words to get SEO and and, you know, that isn't the best result for you on what Google did was turned that around with page rank and say, Let's use the signals that other people are getting behind about the pages they find valuable to get the best result on top. And Alation is the exact same thing our patented proprietary behavior technology lets us say Who's using this data? How were they using it? Is it reputable? And that enables us to get the right data and transfer the data in front of decision makers. >> And you call that Behavioral IO >> Behavior IO, that's right. >> I mean, certainly remember Google algorithmic search was pooh poohed. It first had to be a portal. Everyone kind of my age. You can't remember those those days and the results were key word stuff by spammer's. But algorithmic search accelerated the quality. So I got to ask you the behavioral Io to kind of impact a little bit. Go a little deeper. What does that mean for customers? Because now I'll see as people start thinking, OK, I need to catalogue my data because now I need to have replication, all kinds of least technical things that are going on around integrity of the data. But why Behavioral Aya? What's the angle on that? What's the impact of the customer? Why is this important? Absolutely so. >> Might have to work through an example, you know we joke about. You might be looking around in your SharePoint drive and find an Excel file called Q three Numbers final. Underscore final. Okay, that seems that'S inject the final numbers, and then you see next to it when it says underscore final underscore, final underscore finalist. Okay, well, is that one final? And it turns out what Data says about itself is less reliable than what other people say about the data. Same thing with Google that if everyone's linking with Wikipedia Page, that's a more reliable page than one that just has, you know, paid for a higher placement, Right? So what a means an organization is with Alation will tell you. You know, this is the data table that was refreshed yesterday and that the CFO and everybody in this department is using every day. That's a really strong signal. That's trustworthy data, as opposed to something that was only used once a year ago. >> So relevance is key there. >> Absolutely. It's relevant. And trustworthiness. We find both all right, indicated more strongly by who's using it and how than by the data itself. >> Are you seeing adoption with data scientist and people who were wrangling date or data analysts that if the date is not high quality, they abandoned. The usage is they're getting kind of stats around that are because that we're hearing a lot of Hey, you know, that I'm not going to really work on the data. But I'm not going to do all the heavy lifting on the front end the data qualities, not there. >> Absolutely. We see a really cool upward spiral. So in Alation, we have a mix of manual, human curated metadata, you know, data stewards and that a curator saying, this is endorsed data. It's a certified data. This is applicable for this context. But we also do this automatic behavior. Io. We parse the query logs. These logs were, you know, put there for audit on debugging purposes. But we were mining that for behavioral insight, and we'll show them side by side on what we see is overtime on day one. There's no manual curation. But as that curation gets added in, we see a strong correlation between the best highest quality data and the most used data. And we also see an upward spiral where, if on day one. People are using data that isn't trustworthy that stale or miscalculated as soon as Ah, an Alation steward slaps a deprecation or a warning on the data asset because of technology like trust check talking about last time I was here, that technology, that's the O part of behavior IO We then stop the future behavior from being on bad data, and we see an upward spiral where suddenly the bad sata is no longer being used and everyone's guided put the pound. >> One thing I'm really impressed with you guys on is you have a great management team and overall team with mixed disciplines. Okay, I think last night about your role, Stanford and the human side of the world. But you have to search analogy, which is interesting because you have search folks. You got hardcore data data geeks all working together. And if you think about Discovery and navigation, which is the Google parent, I need to find a Web page and go, Go, go to it. You guys were in that same business of helping people discover data and act on it or take action. Same kind of paradigm, so explain some customer impact anecdotes. People who bought Alation, what your service and offering and what happened after and what was it like before? We talk about some of that? And because I think you're onto something pretty big here with this discovery. Actionable data perspective. >> Yeah, well, one of our values, it Alation, is that we measure our success through customer impact, you know, not do financing or other other milestones that we are excited about them. So I I would love to talk about our customers. One example of a business impact is an example that our champion at Safeway Albertsons describes where, after safe, it was acquired by Albertson's. They've been sort of pioneers of sort of digital, ah, loyalty and engagement. And there was a move to kind of stop that in its tracks and switch should just mailing people big books of coupons that of customizing, you know, deals for you based on your buying behavior. And they talked about getting a thirty x  ROI on the dollars they've spent on Alation by basically proving the value of their program and kind of maximizing their relationship with their customers. But the stories they're even more exciting to me, then just business impacts in dollars and cents when we can leave a positive impact on people's lives with data. There's a few examples of that Munich reinsurance, the biggest being sure and also a primary ensure in Europe, had some coverage and Forbes about the way that they use Alation, other data tools to be able to help people get back on their feet more quickly after, ah, earthquakes and other natural disasters. And similarly, there's a piece in The Wall Street Journal about how Pfizer is able to create diagnostics and treatments for rare diseases where it wouldn't have been a good ROI even invest in those if they didn't get that increased efficient CNN analytics from Alation on the other data. >> So it's not just one little vertical. It's kind of mean data is horizontally. Scaleable is not like one. Industry is going to leverage Alation, >> Absolutely so you know, I mentioned just now. Insurance and health care and retail were also in tech were in basically every vertical you can imagine and even multiple sectors. You know, I've been focusing on industry, but there's another case that you can read about at the city of San Diego were there. They're doing an open data initiative, enabling people to figure out everything from where parking is easiest, the hardest to anything else. >> The behavioral Io. And it's all about context and behavior, role of data and all this. It's kind of fundamental to businesses. >> That's right. It's all about taking everything about how people using data today and driving people to be even more data driven, more accurate, better able to satisfy their curiosity and be more rational in >> the future. So if I'm a from a potential customer and I heard a rAlation, get the buzz out there, why would I need you? What air? Some signals that would indicate that I should call Alation. What's some of that Corvette? What's the pitch? >> Yeah, it's a great question. No, I sometimes joke with the team that you know every five minutes another enterprise reaches that point where they can't do it the old way anymore. And the needle ations. And the reason for that is that data is growing exponentially and people can only grow at most, you know, linearly. So I compare it a bit again to the days of of Yahoo When the Internet was small, you make a table of contents for it. But as there came to be trillions of red pages, you needed an automatic index with pay drink to make sense of it. So I would say, once you find that your analytics team has spread out and they're spending, you know eighty percent of their time calling up other people to find where development data is, you're asked to Your point is this data high quality show even spend my time on it? You know that's probably not money is well spent with these highly paid people spending other times scrounging If you switch from scrounging to finding understanding and trusting their data for quick and accurate analysis, give us >> a call. So basically the pitches, if you want to be like Yahoo, do it the old way. We know what happened. Yeah, you want to be like Google, two algorithmic and have data >> God rAlation, and you'll be around for a while very well. After that, maybe the one see that that's my words. >> And and that's part of turning that corner. I think in the beginning we were trying to tell people this could be a nice toe have. And now customers are coming to us realizing it's a must have to stay a relevant, you know, And if you've made all these investments in data infrastructure and data people, but you can't connect the dots is you said, between the human side and the tech side that money's all wasted and you're going to not be able to compete against your competitors and impact of customers what you want. >> Well, Eric, congratulations. Certainly is the co founder. It's great success. And how hard is that you start ups? You guys worked hard and again. Why following you guys? Been interesting to see that growth and this innovation involved in creative, A lot of energy. You guys do a good job. So final question, talk about the secret sauce of Alation. What's the key innovation formula? And now that you got the funding where you're going to double down on, where's the innovation going to come next? So the innovation formula and where the innovation, the future, >> absolutely innovation has been critical for us to get here on our customers didn't just buy the exciting features with behavioral and trust. Check that we had but also are buying into the idea that we're going to continue to be the leaders and to innovate. Andi, we're going to do that. So I think the secret sauce which we've had in the past, we're going to continue to innovate in this vein, is to be really conscious of water computers great at and what humans uniquely good at what you humans like doing and trying to have the human and computers work together to really help the human achieve their goals. Right? So, Doctor, the Google example. You know, there's a bunch of systems for collaboratively ranking things, but it takes work to, you know, write a review on the upper Amazon. Google had the insight that we could leverage people are already doing and make it about it. Out of that, we're going to continue to do that. >> The other kind of innovation you'll see is bringing Alation to a wider and wider audience, with less and less technical skill needed. So I came from Syria Apple, and the idea is you have to learn a programming language to Queria database. You could just speak in English. That helps you ask answer questions like What's the weather today? Imagine taking that same kind of experience of seamless integration to the more important questions enterprises are asking. >> We'll have to tap your expertise is we want to have an app called the Cube Syria, which is a cube. What's the innovation in Silicon Valley and have it just spit out a video on the kidding? Final question just to double down on that piece, because I think the human interactions a big part of what you're saying I've always loved that about with your vision is. But this points to a major problems. Seeing whether it's, you know, media, the news cycle These days, people are challenging the efficacy of finding the research and the real deep research on the media. So I was seeing scale on data scale is a huge challenge. You mentioned the growth of data. Computers can scale things, but the knowledge and the curation kind of dynamic of packaging it, finding it, acting on it. It's kind of where you guys are hitting. Talk about that tie name, my getting that right and set is that important? Because, you know, certainly scale is table stakes these days. >> That is super insightful John, because I think human cognition and human thought excuse me, is the bottleneck four being data driven right we have on the Internet trillions of Web pages, you know, more than the Library of Alexandria a hundred times over, and we have in databases millions of columns and trillions of rose. But for that to actually impact the business and impact the world in a positive way, it's got to go through a person who could understand it. And so, in the same way that Google became the mechanism by which the Internet becomes accessible, we think that Alation for organizations is becoming the way that data can become actionable. And the other thing I would say is, you know, in this age of alternative facts and mistrust of data, you know, we've sort of realizing the just having more information out there doesn't actually make people wiser and better able to reason. It can actually be a lot of noise that muddies the signal and confuses people. So we think Alation by also using human computer interaction to help separate the signal from the noise and the quality from the garbage can help stop the garbage in garbage out and make people more rational and more curious and have more trust than what there. Hearing understanding >> build that Paige rang kind of metaphor is interesting because the human gestures, whether it's work or engaging on the data, is a signal tube, not just algorithmic meta data extraction. >> Absolutely anything you do with data and any tool, even outside of Alation. Alation will capture that and use it to guide future behavior for you and your appears to be better and smarter. >> Fifty million dollars. Where's this all going to lead to wins the next innovation. What do you guys see? The future for rAlation? >> Well, you know, I, uh I was just thinking before the show I used to be an apple kind of in the golden Age when Apple was really innovative. And there was the joke where they released something new and say, Redman, start your photocopier. So in this interview, I'm going to be a little close to the chest about the specifics, but we're releasing. But I will tell you we have a room that we're really excited about to go to a broader and broader audience that impactor customers more fully >> well you feel free to say one more thing? >> Yeah. I think the secret to the future is Aaron. Thanks for coming on. >> Really preachy. Congratulations on the funding. He has got a very innovative formula. Good luck. And we'll be following you guys. Thanks, but come on, keep commerce. Thanks so much. Eric Kalb, co founder and VP of designing Alation. Interesting formula. Great. Successful. Former great innovation. Alation. Check him out. I'm Jennifer here in Palo Alto for cube conversation. Thanks for watching.

>> Live, from Washington, D.C. It's theCUBE, covering the AWS Public Sector Summit 2018. Brought to you by Amazon Web Services, and its ecosystem partners. (futuristic music) >> Hey, welcome back everyone. We're live in Washington, D.C. for Amazon Web Services Public Sector Summit. This is their big show for the public sector. It's like a mini reinvent for specifically the public sector. I'm John Furrier, your host, with Stu Miniman, my co-host this segment, and Stewart Mclaurin, president of the White House Historic Association, is our guest. I heard him speak last night at a private dinner with Teresa Carlson and their top customers. Great story here, Amazon success story, but I think something more we can all relate to. Stewart, thank you for joining us and taking the time, appreciate it. >> Thanks John, it's just great to be with you. >> Okay, so let's jump into it; what's your story? You work for the White House Historical Association, which means you preserve stuff? Or, you provide access? Tell the story. >> Well, we have a great and largely untold story, and a part of our partnership with Amazon Web Services is to blow that open so more people know who we are and what we do, and have access to the White House, because it's the people's house. It doesn't belong to any one particular president; it's your house. We were founded in 1961 by First Lady Jacqueline Kennedy, who realized that the White House needed a nonprofit, nonpartisan partner. We have no government funding whatsoever, completely private. So we fund the acquisition of art, furnishings, decorative arts for the White House, if a new rug is needed, or new draperies are needed on the State Floor, or a frame needs to be regilded. We also acquire the china, the presidential and first lady portraits that are done; we fund those. But more importantly, in my view, is our education mission that Mrs. Kennedy also started, to teach and tell the stories of White House history going back to 1792, when George Washington selected that plot of land and the architect to build that house that we know today. So we unpack those stories through publications, programs, lectures, symposia, and now this new multifaceted partnership with AWS. >> Let's talk about, first of all, a great mission. This is the people's house; I love that. But it's always the secret cloak and dagger, kind of what's going on in there? The tours are not always, they're probably packed when people go through there, but the average person on the street doesn't have access. >> Sure, well, your cable news channels handle the politics and the policy of the place. We handle the building and the history, and all that's taken place there, including innovation and technology. If you think of Thomas Edison and Alexander Graham Bell, and others that evolved their early technologies through the White House, about 500,000 people get a chance to go through the White House every year. And when you think about in that small space, the president and his family lives, the president and his staff work, it's the ceremonial stage upon which our most important visitors are received, and then about 500,000 people schlep through, so you imagine 500,000 people that are going through your house, and all of that takes place. But it's very important to us for people to be able to see up close and personal, and walk through these spaces where Lincoln walked, and Roosevelt worked. >> Is that what the book you have, and share the book 'cause it's really historic, and the app that you have with Amazon, I think this is a great-- >> Sure, this is a real prize from our office. Mrs. Kennedy wanted us to teach and tell the stories of White House history, and so the first thing she wanted was a guide book, because the White House never had one. So in 1962, she published this guide book with us, and this is her actual copy. Her hands held this book. This was her copy of the book. Now, we continue to update this. It's now in its 24th edition, and each new edition has the latest renovations and updates that the latest president has added. But it's now 2018. So books are great, but we want to be able to impart this information and experience to people not only around Washington, who are going through the White House, but across the country and around the world. So this app that we've developed, you get through WHExperience at the App Store, you have three different tours. If you're walking through the White House, tours are self-guided, so unless you know what you're looking at, you don't know what you're looking at. So you can hold up an image, you can see, it brings to life for you everything that you're looking at in every room. Two other types of tours; if you're outside the White House in President's Park, it will unpack and open the doors of these rooms for you virtually, so you can see the Oval Office, and the Cabinet Room, and the Blue Room, and the Green Room. If you're around the world, there's a third tour experience, but the best part of it is, empowered by Amazon recognition technology, and it allows people to take a selfie, and it analyzes that selfie against all presidential portraits and first lady portraits, and the spatial features of your face, and it will tell you you're 47% Ronald Reagan, or 27% Jackie Kennedy, and people have a lot of fun with that part of the app. >> (laughs) That's awesome. >> Stewart, fascinating stuff. You know, when I go to a museum a lot of times, it's like, oh, the book was something you get on the way home, because maybe you couldn't take photos, or the book has beautiful photos. Can you speak a little bit about how the technology's making the tours a little bit more interactive? >> Sure, well we love books, and we'll publish six hardbound books this year on the history of the White House, and those are all available at our website, whitehousehistory.org. But the three facets of technology that we're adapting with Amazon, it's the app that I've spoken about, and that has the fun gamification element of portrait analysis, but it also takes you in a deeper depth in each room, even more so than the book does. And we can update it for seasons, like we'll update it for the Fall Garden Tour, we'll update it for the Christmas decorations, we'll update it for the Easter Egg Roll. But another part of the partnership is our digital library. We have tens of thousands of images of the White House that have literally been in a domestic freezer, frozen for decades, and with AWS, we're unpacking those and digitizing them, and it's like bringing history to life for the first time. We're seeing photographs of Kennedy, Johnson, other presidents, that haven't been seen by anybody in decades, and those are becoming available through our digital library. And then third, we're launching here a chatbot, so that through a Lex and Polly technology, AWS technology, you'll be able to go to Alexa and ask questions about White House history and the spaces in the White House, or keyboard to our website and ask those questions as well. >> It's going to open up a lot of windows to the young folks in education too. >> It is. >> It's like you're one command away; Hey, Alexa! >> It takes a one-dimensional picture off of a page, or off of a website, and it gives the user an experience of touring the White House. >> Talk about your vision around modernization. We just had a conversation with the CEO of Tellus, when we're talking about government has a modernization approach, and I think Obama really put the stake in the ground on that; former President Obama. And that means something to a lot of people, for you guys it's extending it forward. But your digital strategy is about bringing the experience digitally online from historical documents, and then going forward. So is there plans in the future, for virtual reality and augmented reality, where I can pop in and-- >> That's right. We're looking to evolve the app, and to do other things that are AR and VR focused, and keep it cool and fun, but we're here in a space that's all about the future. I was talking at this wonderful talk last night, about hundreds of thousands of people living and working on Mars, and that's really great. But we all need to remember our history and our roots. History applies to no matter what field you're in, medicine, law, technology; knowing your history, knowing the history of this house, and what it means to our country. There are billions of people around the world that know what this symbol means, this White House. And those are billions of people who will never come to our country, and certainly never visit the White House. Most of them won't even meet an American, but through this app, they'll be able to go into the doors of the White House and understand it more fully. >> Build a community around it too; is there any online social component? You guys looking around that at all? >> All of this is just launched, and so we do want to build some interactive, because it's important for us to know who these people are. One simple thing we're doing with that now, is we're asking people to socially post and tag us on these comparative pictures they take with presidents and first ladies. So there's been some fun from that. >> So Stewart, one of the things I've found interesting is your association, about 50 people, and what you were telling me off-camera, there's not a single really IT person inside there, so walk us through a little bit about how this partnership began, who helps you through all of these technical decisions, and how you do some pretty fun tech on your space. >> Unfortunately, a lot of historical organizations are a little dusty, or at least perceived to be that way. And so we want to be a first mover in this space, and an influencer of our peer institutions. Later this summer, we're convening 200 presidential sites from around the country, libraries, birthplaces, childhood homes, and we're going to share with them the experience that we've had with AWS. We'll partner or collaborate with them like we're already doing with some, like the Lincoln Library in Illinois, where we have a digitization partnership with them. So with us, it's about collaboration and partnership. We are content rich, but we are reach-challenged, and a way to extend our reach and influence is through wonderful partnerships like AWS, and so that's what we're doing. Now another thing we get with AWS is we're not just hiring an IT vendor of some type. They know our mission, they appreciate our mission, and they support our mission. Teresa Carlson was at the White House with us last Friday, and she had the app, and she was going through and looking at things, and it came to life for her in a new real and fresh way, and she'd been to the White House many times on business. >> That's great; great story. And the thing is, it's very inspirational on getting these other historic sites online. It's interesting. It's a digital library, it's a digital version. So, super good. Content rich, reach-challenged; I love that line. What else is going on? Who funds you guys? How do you make it all work? Who pays the bills? Do you guys do donations, is it philanthropy, is it-- >> We do traditional philanthropy, and we'd love for anybody to engage us in that. During the Reagan Administration in 1981, someone had the brilliant idea, now if I'd been in the room when this happened, I probably would have said, "Okay, fine, do that." But thank goodness we did, because it has funded our organization all these years. And that's the creation of the annual, official White House Christmas ornament, and we feature a different president each year sequentially so we don't have to make a political decision. This year, it's Harry Truman, and that ornament comes with a booklet, and it has elements of that ornament that talk about those years in the White House. So with Truman, it depicts the south balcony, the Truman Balcony on the south portico. The Truman seal that eventually evolved into being the Presidential Seal. On the reverse is the Truman Blue Room of the White House. So these are teaching tools, and we sell a lot of those ornaments. People collect them; once you start, you can't stop. A very traditional thing, but it's an important thing, and that's been a lifeblood. Actually, Teresa Carlson chairs our National Council on White House History. John Wood, that you just had on before me, is on our National Council on White House History. These are some of our strong financial supporters who believe in our mission, and who are collaborating it with us on innovative ways, and it's great to have them involved with us because it brings life in new ways, rather than just paper books. >> Stewart, I had a non-technical question for you. According to your mission, you also obtained pieces. I'm curious; what's the mission these days? What sort of things are you pulling in? >> Well, there's a curator in the White House. It's a government employee that actually manages the White House collection. Before President and Mrs. Kennedy came into the White House, a new president could come in and get rid of anything they wanted to, and they did. That's how they funded the new, by selling the old. That's not the case anymore. With the Kennedys, there's a White House collection, like a museum, and so we'll work with the White House and take their requests. For example, a recent acquisition was an Alma Thomas painting. Alma Thomas is the first African American female artist to have a work in the White House collection; a very important addition. And to have a work in the White House collection, the artist should be deceased and the work over 25 years old, so we're getting more of the 21st century. The great artists of the American 20th century are becoming eligible to have their works in the collection. >> Stewart, thanks so much for coming on theCUBE and sharing your story. It's good to see you speak, and thanks for the ornament we got last night. >> Sure. Well, you've teased this ornament. Everybody's going to want and need one now, so go to whitehousehistory.org. >> John, come on, you have to tell the audience who you got face matched recognition with on the app. >> So who did you get face matched with? >> I think I'm 20% James Buchanan, but you got the Gipper. >> I'm Ronald Reagan. Supply-side economics, trickle-down, what do they call it? Voodoo economics, was his famous thing? >> That's right. >> He had good hair, John. >> Well, you know, our job is to be story tellers, and thank you for letting us share a little bit of our story here today. We love to make good friends through our social channels, and I hope everyone will download this app and enjoy visiting the White House. >> We will help with the reach side and promote your mission. Love the mission, love history, love the digital convergence while preserving and maintaining the great history of the United States. And a great, good tool. It's going to open up-- >> Amazon gave us these stickers for everybody who had downloaded the app, so I'm officially giving you your downloaded app sticker to wear. Stu, this is yours. >> Thank you so much. >> Thanks guys, really appreciate it. >> Thank so much, great mission. Check out the White House-- >> Historical Association. >> Historicalassociation.org, and get the White House app, which is WHExperience on the App Store. >> That's right. >> Okay, thanks so much. Be back with more, stay with us. Live coverage here at AWS, Amazon Web Services Public Sector Summit. We'll be right back. (futuristic music)

>> Narrator: Live from Austin Texas. It's theCUBE, covering KubeKon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux Foundation, and theCUBE's ecosystem partners. >> Welcome back everyone, live here in Austin Texas, theCUBE's exclusive coverage of CloudNativeConference and KubeKon, for Kubernetes' Conference. I'm John Furrier, co-founder of SiliconANGLE and my co-host Stu Miniman, our analyst. And next is Matt Klein, a software engineer at Lyft, ride-hailing service, car sharing, social network, great company, everyone knows that everyone loves Lyft. Thanks for coming on. >> Thanks very much for having me. >> All right so you're a customer of all this technology. You guys built, and I think this is like the shiny use cases of our generation, entrepreneurs and techies build their own stuff because they can't get product from the general market. You guys had a large-scale demand for the service, you had to go out and build your own with open source and all those tools, you had a problem you had to solve, you build it, used some open source and then give it back to open source and be part of the community, and everybody wins, you donated it back. This is, this is the future, this is what it's going to be like, great community work. What problem were you solving? Obviously Lyft, everyone knows it's hard, they see their car, lot of real time going on, lot of stuff happening >> Matt: Yeah, sure. >> magic's happening behind the scenes, you had to build that. Talk about the problem you solved. >> Well, I think, you know, when people look at Lyft, like you were saying, they look at the app and the car, and I think many people think that it's a relative simple thing. Like how hard could it be to bring up your app and say, I want a ride, and you know, get that car from here to there, but it turns out that it's really complicated. There's a lot of real-time systems involved in actually finding what are all the cars that are near you, and what's the fastest route, all of that stuff. So, I think what people don't realize is that Lyft is a very large, real-time system that, at current scale, operates at millions of requests per second, and has a lot of different use cases around databases, and caching, you know, all those technologies. So, Lyft was built on open source, as you say, and, you know Lyft grew from what I think most companies do, which is a very simple, monolithic stack, you know, it starts with a PHP application, we're a big user of MongoDB, and some load balancer, and then, you know-- >> John: That breaks (laughs) >> Well, well no but but people do that because that's what's very quick to do. And I think what happened, like most companies, is, or that most companies that become very successful, is Lyft grew a lot, and like the few companies that can become very successful, they start to outgrow some of that basic software, or the basic pieces that they're actually using. So, as Lyft started to grow a lot, things just didn't actually start working, so then we had to start fixing and building different things. >> Yeah, Matt, scale is one of those things that gets talked about a lot. But, I mean Lyft, you know, really does operate at a significant scale. >> Matt: Yeah, sure. >> Maybe you can talk a little bit about, you know, what kind of things were breaking, >> Matt: Absolutely, yeah, and then what led to Envoy and why that happened. >> Yeah, sure. I mean, I think there's two different types of scale, and I think this is something that people don't talk about enough. There's scale in terms of things that people talk about, in terms of data throughput or requests per second, or stuff like that. But there's also people scale, right. So, as organizations grow, we go from 10 developers to 50 developers to 100, where Lyft is now many hundreds of developers and we're continuing to grow, and what I think people don't talk about enough is the human scale, so you know, so we have a lot of people that are trying to edit code, and at a certain size, that number of people, you can't all be editing on that same code base. So that's I think the biggest move where people start moving towards this microservice or service-oriented architecture, so you start splitting that apart to get people-scale. People-scale probably usually comes with requests per second scale and data scale and that kind of stuff. But these problems come hand in hand, where as you grow the number of people, you start going into microservices, and then suddenly you have actual scale problems. The database is not working, or the network is not actually reliable. So from Envoy perspective, so Envoy is an open source proxy we built at Lyft, it's now part of CNCF, it's having tremendous uptake across the industry, which is fantastic, and the reason that we built Envoy is what we're seeing now in the industry is people are moving towards polyglot architectures, so they're moving towards architectures with many different applications, or many different languages. And it used to be that you could use Java and you could have one particular library that would do all of your networking and service discovery and load balancing, and now you might have six different languages. So how as an organization do you actually deal with that? And what we decided to do was build an out-of-process proxy, which allows people to build a lot of functionality into one place, around load balancing, and service discovery, and rate limiting, and buffering, and all those kinds of things, and also most importantly, observability. So things like tracing and stats and logging. And that allowed us to actually understand what was going on in the network, so that when problems were happening, we could actually debug what was going on. And what we saw at Lyft, about three years ago, is we had started our microservices journey, but it was actually almost, it was almost stopped, because what people found is they had started to build services because supposedly it was faster than the monolith, but then we would start having problems with tail latency and other things, and they didn't know hot to debug it. So they didn't trust those services, and then at that point they say, not surprisingly, we're just going to go back and we're going to build it back into the monolith. So, we're almost in that situation where things are kind of in that split. >> So Matt I have to think that's the natural, where you led to service mesh, and Istio specifically and Lyft, Google, IBM all working on that. Talk a little bit about, more about what Istio, it was really the buzz coming in with service mesh, there's also there's some competing offerings out there, Conduit, new one announced this week, maybe give us the landscape, kind of where we are, and what you're seeing. >> So I think service mesh is, it's incredible to look around this conference, I think there's 15 or more talks on service mesh between all of the Buoyant talks on Linker D and Conduit and Istio and Envoy, it's super fantastic. I think the reason that service mesh is so compelling to people is that we have these problems where people want to build in five or six languages, they have some common problems around load balancing and other types of things, and this is a great solution for offloading some of those problems into a common place. So, the confusion that I see right now around the industry is service mesh is really split into two pieces. It's split into the data plane, so the proxy, and the control plane. So the proxy's the thing that actually moves the bytes, moves the requests, and the control plane is the thing that actually tells all the proxies what to do, tells it the topology, tells it all the configurations, all the settings. So the landscape right now is essentially that Envoy is a proxy, it's a data plane. Envoy has been built into a bunch of control planes, so Istio is a control plane, it's reference proxy is Envoy, though other companies have shown that they can integrate with Istio. Linker D has shown that, NGINX has shown that. Buoyant just came out with a new combined control plane data plane service mesh called Conduit, that was brand new a couple days ago, and I think we're going to see other companies get in there, because this is a very popular paradigm, so having the competition is good. I think it's going to push everyone to be better. >> How do companies make sense of this, I mean, if I'm just a boring enterprise with complexity, legacy, you know I have a lot of stuff, maybe not the kind of scale in terms of transactions per second, because they're not Lyft, but they still have a lot of stuff. They got servers, they got data center, they got stuff in the cloud, they're trying to put this cloud native package in because the developer movement is clearly pushing the legacy guy, old guard, into cloud. So how does your stuff translate into the mainstream, how would you categorize it? >> Well, what I counsel people is, and I think that's actually a problem that we have within the industry, is that I think sometimes we push people towards complexity that they don't necessarily need yet. And I'm not saying that all of these cloud native technologies aren't great, right, I mean people here are doing fantastic things. >> You know how to drive a car, so to speak, you don't know how to use the tech. >> Right, and I advise companies and organizations to use the technology and the complexity that they need. So I think that service mesh and microservices and tracing and a lot of the stuff that's being talked about at this conference are very important if you have the scale to have a service-oriented microservice architecture. And, you know, some enterprises they're segmented enough where they may not actually need a full microservice real-time architecture. So I think that the thing to actually decide is, number one, do you need a microservice architecture, and it's okay if you don't, that's just fine, take the complexity that you need. If you do need a microservice architecture, then I think you're going to have a set of common problems around things like networking, and databases, and those types of things, and then yes, you are probably going to need to build in more complicated technologies to actually deal with that. But the key takeaway is that as you bring on, as you bring on more complexity, the complexity is a snowballing effect. More complexity yields more complexity. >> So Matt, might be a little bit out of bounds for what we're talking about, but when I think about autonomous vehicles, that's just going to put even more strain on the kind of the distributed natured systems, you know, things that have to have the edge, you know. Are we laying the groundwork at a conference like this? How's Lyft looking at this? >> For sure, and I mean, we're obviously starting to look into autonomous a lot, obviously Uber's doing that a fair amount, and if you actually start looking at the sheer amount of data that is generated by these cars when they're actually moving around, it's terabytes and terabytes of data, you start thinking through the complexity of ingesting that data from the cars into a cloud and actually analyzing it and doing things with it either offline or in real-time, it's pretty incredible. So, yes, I think that these are just more massive scale real-time systems that require more data, more hard drives, more networks, and as you manage more things with more people, it becomes more complicated for sure. >> What are you doing inside Lyft, your job. I mean obviously, you're involved in open source. Like, what are you coding specifically these days, what's the current assignment? >> Yeah, so I'm a software engineer at Lyft, I lead our networking team. Our networking team owns obviously all the stuff that we do with Envoy, we own our edge system, so basically how internet traffic comes into Lyft, all of our service discovery systems, rate limiting, auth between services. We're increasingly owning our GRPC communications, so how people define their APIs, moving from a more polling-based API to a more push-based API. So our team essentially owns the end-to-end pipe from all of our back-end services to the client, so that's APIs, analytics, stats, logging, >> So to the app >> Yeah, right, right, to the app, so, on the phone. So that's my job. I also help a lot with general kind of infrastructure architecture, so we're increasingly moving towards Kubernetes, so that's a big thing that we're doing at Lyft. Like many companies of Lyft's kind of age range, we started on VMs and AWS and we used SaltStack and you know, it's the standard story from companies that were probably six or eight years old. >> Classic dev ops. >> Right, and >> Gen One devops. >> And now we're trying to move into the, as you say, Gen Two world, which is pretty fantastic. So this is becoming, probably, the most applicable conference for us, because we're obviously doing a lot with service mesh, and we're leading the way with Envoy. But as we integrate with technologies like Istio and increasingly use Kubernetes, and all of the different related technologies, we are trying to kind of get rid of all of our bespoke stuff that many companies like Lyft had, and we're trying to get on that general train. >> I mean you guys, I mean this is going to be written in the history books, you look at this time in a generation, I mean this is going to define open source for a long, long time, because, I say Gen one kind of sounds pejorative but it's not. It's really, you need to build your own, you couldn't just buy Oracle database, because, you probably have some maybe Oracle in there, but like, you build your own. Facebook did it, you guys are doing it. Why, because you're badass, you had to. Otherwise you don't build customers. >> Right and I absolutely agree about that. I think we are in a very unique time right now, and I actually think that if you look out 10 years, and you look at some of the services that are coming online, and like Amazon just did Fargate, that whole container scheduling system, and Azure has one, and I think Google has one, but the idea there is that in 10 years' time, people are really going to be writing business logic, they're going to insert that business logic >> They may do a powerpoint slides. >> That would be nice. >> I mean it's easy to me, like powerpoint, it's so easy, that's, I'm not going to say that's coding, but that's the way it should be. >> I absolutely agree, and we'll keep moving towards that, but the way that's going to happen is, more and more plumbing if you will, will get built into these clouds, so that people don't have to worry about all this stuff. But we're in this intermediate time, where people are building these massive scale systems, and the pieces that they need is not necessarily there. >> I've been saying in theCUBE now for multiple events, all through this last year, kind of crystallized and we were talking about with Kelsey about this, Hightower, yesterday, craft is coming back to programming. So you've got software engineering, and you've got craftsmanship. And so, there's real software engineering being done, it's engineering. Application development is going to go back to the old school of real craft. I mean, Agile, all it did was create a treadmill of de-risking rapid build scale, by listening to data and constantly iterating, but it kind of took the craft out of it. >> I agree. >> But that turned into engineering. Now you have developers working on say business logic or just solving, building a healthcare app. That's just awesome software. Do you agree with this craft? >> I absolutely agree, and actually what we say about Envoy, so kind of the catchword buzz phrase of Envoy is to make the network transparent to applications. And I think most of what's happening in infrastructure right now is to get back to a time where application developers can focus on business logic, and not have to worry about how some of this plumbing actually works. And what you see around the industry right now, is it is just too painful for people to operate some of these large systems. And I think we're heading in the right direction, all of the trends are there, but it's going to take a lot more time to actually make that happen. >> I remember when I was graduating college in the 80s, sound old but, not to date myself, but the jobs were for software engineering. I mean that is what they called it, and now we're back to this devops brought it, cloud, the systems kind of engineering, really at a large scale, because you got to think about these things. >> Yeah, and I think what's also kind of interesting is that companies have moved toward this devops culture, or expecting developers to operate their systems, to be on call for them and I think that's fantastic, but what we're not doing as an industry is we're not actually teaching and helping people how to do this. So like we have this expectation that people know how to be on-call and know how to make dashboards, and know how to do all this work, but they don't learn it in school, and actually we come into organizations where we may not help them learn these skills. >> Every company has different cultures, that complicates things. >> So I think we're also, as an industry, we are figuring out how to train people and how to help them actually do this in a way that makes sense. >> Well, fascinating conversation Matt. Congratulations on all your success. Obviously a big fan of Lyft, one of the board members gave a keynote, she's from Palo Alto, from Floodgate. Great investors, great fans of the company. Congratulations, great success story, and again open source, this is the new playbook, community scale contribution, innovation. TheCUBE's doing it's share here live in Austin, Texas, for KubeKon, for Kubernetes conference and CloudNativeCon. I'm John Furrrier, for Stu Miniman, we'll be back with more after this short break. (futuristic music)

>> Voiceover: Live from San Francisco, it's The Cube. Covering Samsung Developer Conference 2017. Brought to you by Samsung. >> Okay, welcome back, everyone. Live here in San Francisco, this is The Cube's exclusive coverage of Samsung Developer Conference, SDC 2017. I'm John Furrier, co-founder of SiliconANGLE Media, and co-host of The Cube. My next guest is Dominic Venuto, who is the General Manager of the consumer division of The Weather Channel, and Watson Advertising, which is part of The Weather Company. Welcome to The Cube. >> Thank you for having me. >> Finally, I got the consumer guy on. I've interviewed The Weather Company folks from the IBM side, two different brands. One's the data, big data science operation going on, the whole Weather Company. But Weather Channel, the consumer stuff, Weather Underground, that's your product. >> Yes, you saved the best for last. We touch the consumer. >> So, weather content is good. So obviously, the hurricanes have been in the news over the years. Out here in California, the fires. People are interested in whether the impact, it used to be a unique thing on cable, go to the Weather Channel, check the forecast, read the paper. Now with online apps, weather is constantly a utility for users. So it's not a long-tail editorial product. It's pretty fundamental. >> Yeah, we want to be where our consumers are. Fundamentally we want to help people make better decisions and propel the world. And since weather touches everything, we need to be where the consumers are. So now, with all the digital touchpoints, whether that's your phone, or its a watch, your television, desktop if you still have one and you're still using it, as some of us do. We want to be there, for that very reason. And in fact, what we're aiming for, is to move from a utility, because if we are going to help people make better decisions, a utility only goes so far, would be a platform to anticipate behavior and drive decisions. >> So tell me about the Weather Underground and the weather.com consumer product. They're all one in the same now? Obviously one was very successful, with user generated content. This is not going away. Explain the product side of The Weather Channel consumer division. >> Yeah, so we have two brands in our portfolio, Weather Underground, which is more of a challenger brand. It's very data rich, and visualizes data in a number of different ways, that a certain user group really loves. So if you're a weather geek, as we call them, an avid aficionado of weather, and you really want to really get in there and understand what's happening, and look at the data, then Weather Underground is a platform. >> So for users to tie into, to put up weather stations, and other things that might be relevant. >> Exactly so, we started out in 2001, originally the first IOT implementation at the consumer level, connected devices. Where you could connect a personal weather station, put one in your back yard, and connect it to our platform, and feed hyper-local data into our network. And then we feed that into our forecast, to improve that, and actually validate whether the forecast is right or not, based on what people have at home. And we've hit a recent milestone. We've got over 250,000 personal weather stations connected to the network, which we are super thrilled about. And now, what we are doing is, we are extending that network to other connected devices, and air quality is a big topic right now, in other parts of the world, especially in Asia, where air quality is not always where it should be, that's a big thing we think we can... >> That's a big innovation opportunity for you, I mean, you point out the underground product was part of maker-culture, people do-it-yourself weather stations, evolve now into really strong products. That same dynamic could be used for air control, not just micro-climates. >> Exactly, yeah. >> In California, we had a problem this week. >> Exactly, California is a good example, really topical, where cities may have had great air quality, and all of the sudden the environment changes, and you want to know, what is it like? What is the breathing quality like outside right now? And you can come to our network and see that. And we're growing the air quality sensors every month, it's only been up a few months right now, so that's expanding quite well. >> So for the folks that don't know, The Weather Channel back end, has a huge data-driven product. I don't want to get into that piece, because we've talked about it. Go to youtube.com/siliconangle, search Weather Company. You'll see all our great videos from the IBM events, that are out, if you want the detail. But I do want to ask you, what's really happening with you guys, there's two things. One is, it's an app and content for devices, like Samsung is using. And two, essentially you're an IOT network. Sensors are sensors, whether they're user-generated, or user-populated, you guys are deploying a serious IOT capability. >> Absolutely, it's one of the reasons that IBM acquired The Weather Company, which houses the brands of Weather Underground and The Weather Channel, is that we have this fantastic infrastructure, this IOT infrastructure, ingesting large amounts of data, processing it, and then serving it back out to consumers at scale globally. >> What are you guys doing there with Samsung? Anything just particular in the IOT side, or? >> We've got a couple of initiatives going on with Samsung, a few I can't mention right now, but stay tuned. Some really cool things in the connect-at-home, that we're excited about, that builds on some of the work... >> Nest competitor? >> Not exactly a Nest competitor. Think more kitchen. >> Kitchen, okay. >> Think more kitchen. >> We had the goods, cooking in the kitchen, from our previous guest. So the question is, IOT personal, I get that. What else is going on with IOT, with you guys, that you can share? Lifestyle, in the home is great, but... >> So again, going back to how do we help people make better decisions, now that we are collecting data from not just personal weather stations, but air quality monitors, we are collecting it from cars, we are collecting it from the cell phone. We are really able to ingest data at scale, and when you're doing that, we've got hundreds of thousands of data sets that we are feeding into our models, when you do that, we've solved the computing challenge, now we are applying machine-learning and artificial intelligence to process this and extract insights. To validate data sets, in our forecast, and then deliver that back to the end user. >> One of the tech geek themes we talk about all of the time is policy-based something. Programming, setting the policy. So, connecting the dots from what you're saying is, I'm driving my car, and I want to know if it's hot, or the road temperature. I might want to know if I'm running too fast, and my sensor device on me wants to impact the weather, for comfortable breathing for me, for instance. The lifestyle impacts, the content of data, is not just watching a video on The Weather Channel. >> No, it's not. >> So this is a new user experience. It's immersive, it's lifestyle-oriented, it's relevant. What are some of the products you're doing with Samsung, that can enable this new user expectation? >> One of the products that we have right now, we we're one of the initial partners for the Made for Samsung program, is, we've got calendar integration in our app. So now we know, if you've got a meeting coming up, and you need to travel to get there, maybe there's a car trip involved, we know, obviously, the forecast. We know what traffic might be, and we can give you heads up, an alert, that says, hey you might want to leave 15 minutes early for that meeting coming up. That's in the Samsung product right now, which is really, again, helping people make better decisions. So we've got a lot of examples like that. But again, the calendar integration in the Made for Samsung app is really exciting. We recently announced, in fact I think it was this morning, we announced integration with Trip Advisor. So similarly, if we see time on your calendar, and the weather is fine for the weekend, we might suggest outdoor activities for you to go and explore, using Trip Advisor's almost one-billion library of events that they have. >> What's the coolest thing you guys are working on right now? >> Oh, that's a very long list. I say that I'm probably the luckiest guy in IBM right now, because I get to work with millions of consumers, we reach 250 million consumers a month, and I'm also bringing Watson to consumers, and artificial intelligence, which is a unique challenge to solve. Introducing consumers to a new paradigm of user interaction and abilities. So, I think the most exciting thing is taking artificial intelligence and machine-learning, and bringing that to consumers at scale, and solving some of the challenges there. >> Well contratulations. I'm a big fan of IBM, what they're doing with weather data, The Weather Company, The Weather Channel. Bringing that data and immersing it into these new networks that are being created, new capabilities, really helps the consumer, so. Hope to see you at the Think conference coming up next year. >> Yes, we are excited about that, and stay tuned, we may have some more exciting stuff to unveil. >> Make sure our writers get ahold of it, break the stories. It's The Cube, bringing you the data. The weather's fine in San Francisco today. I'm John Farrier with The Cube. More live from San Francisco, from the SDC Samsung Developer Conference, after this short break. (electronic music)

>> Announcer: Live from Las Vegas, it's The Cube covering Dell EMC World 2017 brought to you by Dell EMC. >> Welcome back here on The Cube, the Flagship broadcast of SiliconANGLE TV. We rap up our coverage today, day two of Dell EMC World 2017. We're live in Las Vegas. I'm John Walls along with Rebecca Knight and joining us now all the way from Tobacco Road Patrick Williams, who is the IT Infrastructure Architect at North Carolina State University in beautiful Raleigh, North Carolina. Patrick, thank you for joining us. >> Thank you. >> Tell us first of all, you know, academically, you're the first, somebody from that community that we have a chance to speak with over the past two days. What are you seeing here that you are going to find of interest that you might want to take back with you to Raleigh, that maybe you're going to put into practice? >> Right, so we're really taking a look at the technologies that we have in play, and there's been a lot of new announcements at the conference this year, so we have Unity Storage, we have Data Domain and there's been announcements pretty much across that product spectrum, so we've been looking, going to breakout sessions talking to the experts and trying to take a look at the technology and see how we can take advantage of the new features that are offered in our environment. >> So before the cameras were rolling, you were setting the scene a little bit and describing the kinds of data needs, security needs that you have for a busy, thriving, and large college campus. Can you lay that out for our viewers? >> Right so for a college campus, one of the biggest concerns is around security, so there's a mandate or desire, probably as part of the academic culture to be as open as possible because the goal is to exchange ideas and to share resources between the university and across our set of institutions. So contrast that with the reality that we have to maintain a high level of security now, so there's obviously a lot of incidents. We are a Google Mail university and as you know, there was recently an attack on Gmail, right? So one of the things that we've had to do is to say, "We're going to implement Two Factor Authentication. "We're going to develop a classification system "around how we assess and manage data," so depending on the category, there's different levels of security that are in put in place in our (mumbles) environment, while also trying to remain as open as possible. >> So you have a lot of competing interests, it seems, in trying to balance those interests, is how much of your job? >> 100%. (laughing) Yeah, so what I would say is that in order to be able to get proposals forward, I have to be able to make the case on all sides of the equation, so I have to make the correct academic case. I have to make the correct business case. I have to make the correct cultural case and if I can make those cases coincide, then we can succeed and move things forward and get proposals. >> 'Cause you're saying that at NC State, it's not central IT. You're in IT, but there are some more schools that have options, they can make their own decisions, and so I would think coordination, integration, are not barriers, but certainly challenges. >> That's right, so we are, we call ourselves a central IT group; however, there is no mandate for each of the colleges to use central IT services, so our goal is to create kind of a foundational set of services that the consumers then in come and build on top of rather than building their own resources and we like to see that grow kind of organically rather than to mandate it, use of central services, and we've actually had great success. So we've had a lot of resources to come back from the edge into the central folds and be able to grow that centrally, put a higher level of resiliency on top of those services and satisfy our customers. >> In terms of one of the challenges, though, cost is a huge one, and then making sure that things do come within budget and not a penny over. Can you talk a little bit about some of those obstacles and how you've overcome them? >> Right, so cost is everything for us. Our budgets have been flat for the past three years, but the demand for growth in capacity and existing environments and the demand for new services is ongoing. What we've been able to do is to work really hard on assessing our resources. We've implemented Cloud IQ a year ago when it was first announced to get a kind of a long-term view of our environment and kind of track our growth, and that has enabled us to put the right data in the appropriate tier and be able to maximize our investment and that's really helped us be able to continue to grow our environments as we move forward. >> When you're talking about the different clients or constituencies you're trying to please: you've got the students, the faculty, the administration, and the staff, what do students want, what do faculty want, and how do you give them what they want? >> That's right. So students, is really interesting because the student perspective has really changed over the past couple of years and it caught us off-guard. We have a pervasive data network on our campus. We have all the dorms wired. We have about 21,000 students total. About 8000 stay on campus. All those dorms were hard-wired, but we did not have wifi enabled in all the dorms and we survey students every year. Last year we surveyed them and we got very bad marks because that, even a jack was not enough for them. If you look at what you typically show up with now, how many devices have a hard-wired jack, none, right? So they show up with four devices. They couldn't use any of them on our data network and their response on the survey was, the one that I remember the most was, "Our lives depend on wifi," that was the quote. >> Of course. >> We, of course, immediately went and looked at how we roll out 4000 access points right away. We did that over a summer. That was able to succeed. We also have a very unique set of challenges in that because I mentioned that, we only have 4000 students, slightly more than that, that stay on campus. The majority of them move back and forth between classes so 10 AM when 5000 people walk by one access point. >> When they've just woken up. >> That's right. >> 10 AM. >> Or log in to check their email, et cetera. So those are unique challenges so what we had to do is what are the tools to track the application resources? What's normal application performance? What's a normal peak and what's a breakout that's outside of the normal, and how do we profile that and we want to be well ahead of the demand so that we can put those resources in place ahead of the need. >> So what do you do about the challenge of future deployments? Your budget's going to be somewhat constrained. You know your needs are increasing. You know your constituents have new and growing demand. So, I mean, tough nut to crack, isn't it? You're trying to make your cloud strategy. What are you going to do with that? The 4GG server coming on board now, how do you find, or how do you balance that from the academic perspective? >> You mentioned that and also I didn't mention that one of our data centers is aging and so on top of all that, we're also starting to see, put a strain on our data center resources. What we really hope to be able to do is to leverage some type of a hybrid cloud strategy. The challenge for us has been, what is our application profile? If you look at applications that are a great fit in cloud and applications that are not a great fit in cloud, the traditional backend applications, the core infrastructure applications are not necessarily a great fit, and so what we're trying to figure out is what is the best hybrid solution that will help you move our environment forward and still leverage existing resources. >> So looking ahead, what does the college campus of the future, the technology-enabled college campus of the future look like? Give us a picture. >> I think one of the best examples i can give is our Hunt Library, so we opened a new library on what we call our Centennial Campus a few years ago. It was designed from the ground up as kind of a new model of what does the next generational library look like because it's not, if you think of a library now, you don't think of a traditional, okay, here's a building and stacks and stacks and stacks of books. So they put the books off in a corner and there is a large robotic library that's designed to handle the books and the bulk of it is about collaborative spaces, so there are high-end collaborative work stations, consolidated areas. There are students that are in the design school. If you want to go and practice your DJ skills, you can do that there as well, so that's where things are really headed. >> So Patrick, before we let you go, my final question is, when are you going to beat Carolina and Duke at basketball? >> We're waiting, so we have that US Championship banners from the '80s and I'm tired of looking at that, so we're really looking forward to-- >> Those days are long-gone. >> Right. >> Right, Patrick Williams, NC State, thank you for being with us here on The Cube. Safe travels back home and continued success at Raleigh. >> Thank you. >> Appreciate the time. >> Alright. >> Good. That raps it up here on The Cube, day two is in the books. We'll see you back here tomorrow morning at 11:30 central time, that's 2:30 on the East Coast, for more interviews live from Las Vegas, until then. For Rebecca Knight, I'm John Walls. Have a good night.