>> Announcer: From Austin, Texas it's theCube, covering Pure Storage Accelerate 2019. Brought to you by Pure Storage. >> Howdy from Austin, Lisa Martin with Dave Vellante we are on day one of our coverage of Pure Accelerate 2019. Welcoming a couple of guests to theCube. One is an alumni, Nathan Hall, VP of America's Systems Engineering from Pure, Nathan welcome back to theCube. >> Thanks, thanks very much. >> Lisa: And you brought a buddy from Cisco. We have Tom Sutliff, director of systems engineering and the America's data center, welcome to the Cube Tom. >> Thanks for having me. >> Dave: It's howdy you all. >> Howdy you all, okay. Thank you, it took the wicked smart guy from Boston to figure that out. >> A local. >> All right, so you all, let's talk about Cisco and Pure, you guys have been partners now since, Nathan we were chatting, since about the IPO, about four years ago. Let's start with you Nathan, our Pure guy. The Cisco, Pure partnership evolution, better together? What have you done over those last five years that sets you up for another first that you're going to share with us today? >> Sure, so it's a deep relationship that's only getting deeper and it's really at all levels. It starts with the executive alignment and think about Charlie Giancarlo from Cisco we've got a lot of just common, cross pollination there. But now it extends, certainly the field level, Tom and I are doing a lot of planning together in terms of having our teams go after common use cases. But now it extends to engineering as well, we had a UCS director plugin that we've had for some time now but Pure is now first in terms of having integration into Cisco intersight, so we are first and only to have storage integration of the Cisco intersight so that Cisco and Pure customers can really manage their environment from one console, so a lot of simplicity, just single SaaS interface for managing everything. >> Tom why Pure, why first with them? >> Well you know Nathan he articulated it well, we can look at the executive level, we talked about Charlie, but even, you know all of our Cisco executives but also to the engineering. We started really strong with the field sales teams but even if you look at the little things that our customers notice but a lot of people may not like the internal development of validated design guides, use cases. We churn them out with Pure as our top ecosystem partner, more than anybody and there's a lot of work being done, our customers see that and it's really helped drive our goal to market together it's really a very strong strategy. >> So there's a CVD around this is that right? >> Yeah there's many there's 22 right now and we're churning them out about one or two a quarter. With some vendors we might put out some initially we might do one or two things well, we do a lot of things well I guess you could say we do 22 things well with the CVD's but more than that. >> So this really started in the field if I understand correctly is that right? [Nathan] - Yes. >> So I always look for these deals and say is it a Barney deal, you know Barney deal I love you, you love me. And if there's real engineering going on then you say okay it's beyond a Barney deal. So it starts in the field with what, hey we should you know a customer wants us to work together and then how does the partnership evolve into where you're putting engineering resources and what does that look like? >> I think a lot of it evolves from just showing progress and showing success. If you look at, we just have a lot of common goals and from a portfolio perspective we fill in a lot of each others gaps so that's really where it started was having the success in the field and that drove, we should actually make greater investments in terms of engineering development, those 22 CVD's, the intersight integration, et cetera. >> So we were talking earlier about CI, HCI for audience members who it's kind of nuanced, how do you guys look at the intersection of those two? >> I say it's another better together story, for example we have a recent joint customer win where essentially across their entire SAP landscape we have Cisco hyper flex the HX managing the database portion, we have FlashStack with Pure Storage managing the Hanna portion, and really it all comes down to single console which is intersight. So we're really able to provide the best type of infrastructure for the right workload at the right time but all make it look like one single experience to the customer. >> So from a customer conversation perspective let's go back to you know we've talked about now this exciting new first engineering alignment. Going back to the field where customers have a multitude of workloads, SAP, Oracle, Microsoft, FEEdi, and there's FlashStack like 31 flavors of FlashStack right. What's that conversation like in terms of CI versus HCI when you guys come into play? Obviously FlashStack being I mentioned a number of flavors of that have been around for awhile, how do you help the customers determine what infrastructure is optimal for their workloads and their business objectives? >> You know there's a clear delineation between a hyper convergence, our HX platform, a hyper flex platform, and the converged infrastructure that we have with FlashStacks. If you look at a FlashStack it's an all in one solution, compute, fabric, storage. It's more for tier one apps, something that's you know scalable, something that's a highly dense tier one application. Latency obviously plays into this you know, I'd say it's a little less with the hyper flex platform and hyper convergence, much easier to stand up, much quicker to stand up within a half an hour. It's a storage play it does many of the similar same things but you know we're kind of closing the gap on both of them because even what you would call that smaller platform that started off at more tier one, excuse me tier two and tier three is now moving into the tier one space so. But it's really about scalability, ease of use, some of them are stronger in some markets like maybe a higher enterprise. But we can sell them across anywhere whether it be public sector, commercial, mid market, smaller customers. But they each have use cases that they fit in very well. >> This morning in the key notes we heard a lot about API's, I want to get into Multi Cloud in a second but before I do we talk a lot about infrastructures code, DevOps, we heard a lot about Kubernetes, a little bit about Kubernetes this morning. And the Cisco DevNet I've often said on theCUBE that they're the only large established company that's figured out how to do something for developers. Now does your partnership extend into sort of infrastructures code, how does that all sort of go through? Is DevNet a play here or even on the roadmap? >> Nathan: So from DevNet can you take that one? >> Well I can say yes it is a play, if you take a look at all of our solutions, primarily the compute and the fabric solutions, programmability is really a key function that we have and the customers can go in and they can actually working with our API's, API's that we work with separate with other vendors too that are dedicated to other vendors. It is a key thing and DevNet became to the forefront probably about five years ago and it was really built off of that development effort so that's critical for us going forward here there's a lot that we're doing I know we're going to talk about intersight and some other things where that was a key element of it. >> Yeah so this is important. You were at Cisco Live. >> And Cisco DevNet. >> And we were in the DevNet zone and you remember, you had many many booths, very specialized, then you have CCIE's learning python, learning how to program infrastructure for new use cases, edge comes in. Anything you'd add Nathan to sort of programmability? >> So I think just from day one from Pure Storage just having our restful API interface, having code.purestorage.com we've tried to make it as much automatable as possible, as easy for to really create a community of developers that can create these integrations very quickly, and honestly evidence of that is in intersight itself. How quickly we got that integration happening is because of that restful API interface. We were able to take the kind of AI Ops of Pure One and bring it into intersight, be able to get intersight to talk to Pure Storage very easily because of that strength of API first. >> What do we need to know about intersight? Add some color there, what is it, how's it work, what's the kind of history and how do you guys turn what you're doing in integration into customer value? >> So if I look at, going back to your comments around why converge versus hyper converge, it's often really a story of simplicity right? Customers want something simple for the data center, they know they can get it out in the Cloud but they can't always run their workloads out in the external Cloud. So simplicity is for intersight, no matter what it is, if it's converged or hyper converged, if it's Pure Storage, being able to have single interface to monitor your infrastructure, lifecycle it, to get really specific imagine a VMware administrator is able to in that single console, provision storage from Pure to a UCS server, format it for VMware ESX and VMFS, and in that single console so doesn't have to go to a bunch of different consoles, gets that Cloud like experience and that's what intersight delivers. So you get that simplicity whether its converged or hyper converged with intersight. >> Whether it's in the Cloud, it's the Edge, it's the Branch, Hybrid Cloud, instead of having to manage it I think that Nathan just hit on these single clusters of storage, compute, what have you. These can all be managed from one single console world wide no matter where they sit. >> So I want to talk about Multi Cloud if we can. So if I look at the players in Multi Cloud, the big whales, VMware, Red Hat, Google, Microsoft, and Cisco, you partner with all of those pretty much I think. AWS is not on the list but you figure they're kind of the facto part of the Multi Cloud scene but they're not going after Multi Cloud, Cisco was a relatively new entrant there. You got companies that have a Cloud like Microsoft and Google that want to participate, you've got companies that don't have a Cloud like Cisco that want to participate, where does Pure fit in to that Multi Cloud opportunity and how does it relate to the partnership? >> Well I think where we found a solid partnership with Cisco and Multi Cloud is the same approach to Multi Cloud and that is I'd call it open Multi Cloud. As opposed to having, forcing a single type of hyper visor on one side or a single Cloud, external Cloud on the other side, how do we make certain that our customers can run any app, anywhere? How do we appear and provide the data fabric having the most efficient amenity of fabric out there to kind of get around the data gravity problems of moving workloads, and we do that now with Pure Flash right on premises, Cloud block store out in the Cloud, our ability to Cloud snap to Azure, to AWS, and that's part of the story. The other part of the story is the fabric and the compute. So with ACI anywhere really that compeletes the any workload anywhere story, and keeping it open so it's not just one hyper visor or one Cloud provider on the other side. >> So you be the data plane in that equation, with the management of that data plane, and Cisco is the overall management framework the control plane I guess we could call that. Is that the right way to think about it? >> I'd say part of the control plane and the network fabric as well, and we're part of essentially the consistent data services no matter where you go. So really upleveling for example EBS to an enterprise grade of storage that it wasn't before, now we have something that whether you're on hardware on premises or in the cloud, you can run that monolithic application in places you couldn't do it before. >> So let's look at this in the real world in a customer environment, talk to me about whatever kind of whether it's a bank or an airline or what have you, what are the business benefits that, we'll use delta Airlines as an example, what would they get out of this if they think of all of the things that they need to achieve internally and be able to deliver to their customers? What's that you know TCO, ROI, what are all those sexy things that you guys are delivering? >> So I'd say they get essentially a lot of the barriers to getting the TCO you want for a given workload are based on compatibility. Maybe you want to run it out in Amazon but you can't get it there because it's this massive monolithic gap, the sync would take days, the SLA out there isn't quite what you want. Now being able to provide a consistent experience no matter where that data plane is, you get that choice. You can go and evaluate AWS or Azure and say that's ultimately the right TCO for my application and I know it could run out there because I've essentially standardized my data fabric anywhere, and it's the same story essentially now with ACI anywhere as well. So the ability to keep essentially the fundamental elements of the application, the infrastructure around it consistent no matter where it is, freeze that IT decision maker to put it in the right place. You don't have to be constrained by compatibility anymore. >> So internal operations can be dialed way up which means those folks are free to resources to work on other higher value projects, and the customer on the other end who doesn't know any of this stuff is under the hood is getting what they need when they want it. >> Exactly, yeah you can manage if you look at ACI you can manage the automation of the applications across the network fabric again wherever it may be, and there's robustness there, there's telemetry, there's measurements. So instead of just looking at the application you look at the robustness of that on the network and the network here us absolutely critical, none of this is going to run I think as Nathan hit on that it could be in the Cloud, it could be in the Branch, you still want the same level of performance the SLA, the five nines and that's where the network comes in that's what's critical. >> Well and the security piece as well. >> Absolutely. >> You guys are largely coming at the Multi Cloud from of course the network strength that you have but you've also got a security angle there because you can go deep packet inspection and that's a sweet spot for you guys. >> Tom: Absolutely. >> Talk about security and it's importance and so on. >> Well I think the security I mean one of the big plays that we have with ACI and with Tetration is being able to look in literally billions of packets a second and being able to track and make realtime decisions on any type of threat, threat defense that's built right in. So normally obviously you have firewall and you try to keep everything out but a lot of what will happen a lot of the penetration security hack happens inside. So this is able to look at all of the flows, at every single packet the flow of the application and the information to see if there's a threat in real time. It takes a lot of processing power a lot of storage and a lot of capacity but you know that's a Tetration product and it's a huge play, our security team is actually out selling that in addition to the data center teams. >> So is Wallingford Yankee's country or Red Sox country? >> Oh it's right on the border so I've got my in laws Yankee's, my parents Redsox, so it's very difficult at home. >> You're a Pat's fan of course, did you feel dirty watching the game on Sunday or? >> Tom: No not at all. >> Oh you felt good? >> Maybe 19 and O this year we'll see. >> And you're Switzerland in this whole debate? >> I try to be it's hard. >> Well you know this company is Warrior's so we can talk NBA too. >> You bet! >> There's a really interesting NBA season coming up now. Not so much for our team but. (laughter) >> Lisa: You never know! >> You never know. >> I had to try to be Switzerland too cause I was the West Coaster with the East Coaster boss, you know how it goes. So Tom last question for you, whole bunch of announcements that came out of Pure today as we look at all of the partnerships that Pure has we talked about that, that Cisco has as well, what are some of the things that as a partner as a valued strategic partner, that Cisco hears when they hear Pure talking about delivering everything as a service and what they're doing with AI and dialing up things there, what is Ciscos reaction to that news? >> Well the thing with Pure and it preceded this conference but you know I really heard it with the new announcements and Nate and I we have a lot of things we're going to work with our systems engineers on in the Americas, it's just the innovation which is pretty incredible. You know you kind of have the big four products here but primarily with the Flash arrays the CI platforms, the Flash blades, what's going on with Pure one, that's going to be critical going forward and we have very similar messages with Multi Cloud. We talked about the validated designs, this is really going to lead us to almost like it's kind of funny when you have an innovative partner you can do reboots every year and people don't think you're just throwing work at them or what have you. It's like now we really innovated again, 12, 15 months later we're going to hit this again and come at it. And so Pure is probably one of the only partners we have that type of relationship with. >> Alright well guys thank you so much for joining Dave and me on theCUBE today we appreciate it. We look forward to following the evolution of this Cisco Pure partnership, thanks for your time. >> Thank you. >> Thank you guys. >> For Dave Vellante, I'm Lisa Martin, you're watching theCUBE ya'll from Pure Accelerate in Austin, Texas. (upbeat music)

>> Announcer: Live from Miami Beach, Florida, it's theCUBE. Covering VeeamON 2019. Brought to you by Veeam. >> Welcome back to the Fontainebleau, Miami, everybody. My name is Dave Vellante, I'm here with my co-host for this segment, Justin Warren. Justin it's great to see you. This is theCUBE, the leader in live tech coverage, day two of our coverage of VeeamON 2019 here in Miami. Jason Buffington, @Jbuff is here, he's the vice president of solution strategy congratulations on the promotion and great to see you again, my friend. >> Thank you very much. >> Dave: And Nathan Hughes who is the IT director at Flex-N-Gate. Great to see you, thanks for coming on. We love to get the customer's perspective, so welcome. >> Great to be here. >> Okay, so, Jason let me start with you. Former analyst, you've been at Veeam now for long enough to A, get promoted, but also, get the Kool-Aid injection, you're wearing the green, and, what are the big trends that you're seeing in the market that are really driving this next era, what do guys call it? Act two of data protection? >> Sure. So, I preached on this even before I joined Veeam that every 10 years or so, when the industry shifts the platform of choice, the data protection vendors almost always reset, right? The people that lead in NetWare don't lead in Windows. The people that lead in Windows didn't lead in Vert. The next wave is we're moving from servers to services. Right, we're going from on prem into cloud and so, and every time the problem is the secret sauce doesn't line up, right? So you got to reinvent yourself each time. And what we saw in the past generations, what we learned from, is, you can't be so busy taking care of your install base that you forget to keep innovating on what that next platform is and so for us, act two is all about cloud. We're going to take everything we know about reliability but we're moving into cloud. The difference is, that in virtualization there was one hero scenario. VMs, right? This time around it's IaaS, it's SaaS, it's PaaS, it's using cloud storage, it's BaaS and DRaaS, there's not a single hero scenario which means we have a lot more innovation to do. That's round two. >> And you made that point today, you used the Archimedes quote, give me a lever and a fulcrum and I'll change the world. You used the analogy of backup as now becoming much more than just backup, it's data protection, it's data management, we're going to get into that. And test some of that with Nathan. So, Nathan, tell us about Flex-N-Gate what does the company do and what does your role as IT director entail? >> Okay, so Flex-N-Gate is a tier one automotive supplier. Which means that we provide parts, most of the things that go into a car besides electronics and glass, to the final automotive makers. So most of the companies that you're familiar with when you go to buy one. >> Okay, so you guys are global, I think you've got what, 24,000 associates worldwide, 64 locations. So what're some of the things that are, fundamental drivers of your business, that are rippling through to your IT strategy? >> Well, our business is varied in the sense that we do a lot of different things in house so, we do, obviously, manufacturing, that's a big part of what we do. And then, even that is broken down into different kinds and then beyond manufacturing we have advanced product development and engineering so we do a lot of that in house. >> Dave: You support it all? >> Yes. >> So you've got diverse lines of business, you've got different roles and personas, you know, engineers versus business people versus finance people. And you got to make 'em all happy. >> We've got to make 'em all happy. >> So, one of the things I love about manufacturing examples, is if you think about it it's the two extremes of high tech and low tech, right? On the low tech side of things you've got this manufacturing floor and it's just producing real stuff, not the zeros and ones that we live with, but real things come off this line. And then you have the engineering and R and D side. Where they're absolutely focused on stuff that comes out of some engineer's head into a computer, which is truly unique data, so, one of the things I love about the story is, talk about the downtime challenges you have around the manufacturing floor. Because I learned some things when we first met, that I think is phenomenal when it comes to manufacturing things that I didn't realize. >> Sure. So, we have a lot of different kinds of manufacturing environments. Some of them are more passive and some of them are more active. The most active environments are, a form of manufacturing known as sequencing. And it's sort of where you bring final assembly of parts together right before they go to the customer. The way that customers order up parts these days, it's not like they used to back in the 70s and 80s. Where they would warehouse huge volumes of everything on their site and then just draw it down if they needed it. And you just kept the queue full. Now they want everything just in time delivery. So they basically want parts to come to the line right when they're needed and actually in the order they're needed. So, a final car maker, they're not necessarily making, 300 of the same thing in a row, they're going to make one of this in blue and one of that in red and they're all going to be sequenced behind each other, one right after the other on the assembly line. And they want the parts from the suppliers to come in the exact right order for that environment. So, the challenge with that from our perspective is that we have trucking windows that are between 30, maybe 60 minutes on the high end, and if anything goes badly, you can put the customer down. And now you're talking about stopping production at Ford, Chrysler, GM, whatever. And that's a lot of money and a lot of other suppliers impacted. >> Dave: So this is a data problem isn't it? >> Yeah, it definitely is. And it's an interesting point, 'cause, you talk about sequencing. Veeam has their own sequence about how customers use the product and they start with backup, everything starts with backup, and then they move further to the right so that you get, ideally, to fully automated data protection. So, what are you actually using Veeam for today? And where do you see yourself going with Veeam? >> So, right now, we're using Veeam primarily as backup and recovery. It's how we started with it. We came from another product that was, great conceptually, but in the real world it had terrible reliability and its performance was very poor as time went on and so, when Veeam came on the scene it was a breath of fresh air because we got to the place where we knew that what we had was dependable, it was reliable. We got to understand how the product worked and to improve the way that we'd implemented it. And so, one of the key features in Veeam that really actually excited us, especially in those sequencing environments are these instant recovery options, right? So, we were used to the idea of having to write down a VM out of snapshot storage. And then being put in a position where it might take an hour, two hours, three hours before you could get that thing back online now, or again, to be able to launch that right out of snapshot storage was a blessing in the industry we're in. >> Yeah, did you see the tech demo yesterday where they were showing off how you could do an instant recovery directly from cloud storage? >> Yes, yeah. >> Did that get you excited? >> Yes. That is exciting. >> Are you using cloud at the moment or is this something that you're looking to move towards? >> Cloud is something we're sort of investigating but it's not something that we're actively utilizing right now. >> So this instance recovery, you guys obviously make a big deal out of that, I was talking to Danny Allan yesterday offline about it. He claims it's unique in the industry. And I asked him a question, I said specifically, if you lose the catalog, can I actually get the data back? And he said yes. And I'm like, that sounds like magic. So, so I guess my question to maybe both of you is, instant, how instant? And how does it actually work? (he laughs) >> It just works, isn't that? >> It just works! >> It's just magic, new tagline? >> I guess we don't have to get into the weeds but when you say, when I hear instant recovery, we're talking like, (fingers clicking) instantaneous recovery with, very short RTOs? >> To us what that means is that in practice, we can expect to have a VM from snapshot data back into production in about a five minute window. >> Dave: Five minutes? Okay. >> And that is sufficient for our needs in any environment. >> Okay, so now we're talking RTO, right? And then, what about, so we said 64 sites across the world, 24,000 associates, is Veeam your enterprise wide data protection strategy or are you rolling it out now? Where are you at? >> Yes, no. Veeam, we started with it in a handful of key sites. And we were using it to specifically back up SharePoint and a few other platforms. But once we understood what the product was capable of, and we were sort of reaching the end of our rope with this former product, yeah, we began an active roll out and we've now had Veeam in our facilities for five, six years. >> So you swept the floor of that previous product. And how complicated was it for you to move from the legacy product to Veeam? >> It was a challenge just rethinking the way that we do things, the previous product, one thing that it really had going for it, if this could be considered a positive, I guess, is that it was very very simple to set up. So, you could take an entry level IT administrator and they just next, next, next, next, next. And it would do all the things that they needed it to do. But the problem was that in the real world, that was sort of the Achilles' Heel, because, it meant that it wasn't very well customized and it meant also that, the way that they've developed that product, it became performance, it had poor performance. >> So the reason I ask that question is because, so many times customers are stuck. And it's like they don't want to move, because it's a pain. But the longer they go, the more costly it is, down the road. So I'm always looking to IT practitioners like, advice that you would give in terms of others, things that you might do differently if you had a mulligan, I don't know, maybe you would've started sooner, or maybe there were some things that you'd do differently. What would you advise? >> Yeah, I mean, if we'd understood, the whole context of what was happening with that other product, we would've moved sooner. And the one thing that I will say about Veeam is, it's not click and point. It does involve a little more setup. But the Veeam team is excellent when it comes to support. So there's nothing to fear in that category because they stand behind their product and it's very easy to get qualified technicians to help you out. >> Is that by design? >> I don't know if it's. Well, the being great to work with, yes, that's by design. >> Yeah, but I mean. >> I was talking to Danny yesterday and asked about the interface thing. Because there is always that tension between making it really really simple to use but then it doesn't have any knobs to change when you need to. >> That's what I'm asking. >> But it can't be too complex either. >> Our gap actually comes a little bit later in the process, right? So, you asked earlier about, in what ways do you use Veeam? And we think about Veeam as a progression, right? So, everybody if they're using Veeam at all, they're using it for Veeam backup and replication and because foundationally, until you can protect your stuff, right? Until you can reliably do that, all the other stuff that you'd like to do around data management is aspirational and unattainable at best, right? So, we think the journey comes in at yeah, it is pretty easy, to go next, next, next, finish. Just a few tweaks, right? To get backup going. But then when you go beyond that, now there's a whole range of other things you can do, right? So Danny, I'm sure, talked about DataLabs yesterday. The orchestration engine, those are not, next, next, next, finish. But anything that's worthwhile takes a little bit of effort, right? So as we pivot from, now that you've solved backup, then you can do those other things and that's where we really start going back into something which is really more expertise driven. >> Well, and it's early days too and as you get more data and more experience you can begin to automate things. >> Yeah, absolutely. So Justin was asking, Nathan, where the direction is. Today it's really backup. You've seen the stages where, talking about full automation. Is that something that, is on the horizon, it is sort of near term, midterm, longterm? >> I mean, coming to the conference, our experience with backup, or Veeam, is primarily backup and recovery operations but, I've seen a lot of things in the last few days that have piqued my interest. Particularly when it comes to the cloud integration. That's being actively baked into the product now. And, some of the automated, API stuff, that's being built into the product. Any place where I can get to where we simplify our procedures for recovery, that's a plus. So I'm really excited about the idea of the virtual labs, being able to actively test backup on a regular basis without human intervention and have reporting out of that. Those are things that I don't see in any other product that's out there. >> You know, there's another piece of the innovation that we should think through, and, so we've talked about the sequencing side which is where we focus on RTO, how fast can you get back and running again? And when you and I talked earlier, the example that we worked on was think of a zipper, right? You've got the bumpers coming in to a line of cars and if either side slows down, everything breaks, and at the end, by the way, is the truck, right? And everything has to come at the same time at the same rate, if there's downtime on either side of the source, you're done. But that's an RTO problem. The engineering side, for high tech, is an RPO problem, right? You have unique stuff coming out of somebody's brain into a PC and it'll never come out that way again. And so, when we look at backup and replication, that should be the next pieces to go on. And then as you mentioned, DataLabs becomes really interesting and orchestration, so. >> Well speaking of human brains, and you kind of touched on it, Nathan, that you came here to learn some things and you've learned things from different sessions. So, what is it about coming to VeeamON that is worth the time for IT practitioners like yourself? >> I think it's all those, I mean we were talking about Veeam, doing backup and recovery operations, fairly straightforwardly, in terms of getting in, but once you see some of this stuff here at a conference like this, you get a better sense of all the more, elaborate aspects of the product. And, you wouldn't get that >> See the possibilities. >> I think, if you were just sitting in front of it using it conventionally, this is a good place to really learn the depth and the level that you can go with it. >> And you're like most of your peers here, is that right, highly virtualized, is that right? Lot of Microsoft apps. And, they say, mid-sized global organization, actually kind of bumping up into big. >> Nathan: Sure. >> Yeah, cool. I asked about the data problem before, it sounds like the zipper's coming together, that's some funky math that you got to figure out to make sure everything's there. So, talk about the data angle. How important data is to your organization, we know much data's growing, data's the new oil, all those promides but, what about your organization specifically as it relates to a digital strategy? It's a buzzword that we hear a lot but, does it have meaning for you, and what does it mean? >> Data is vital in any organization. I mean, we were referencing earlier, how you've got low tech in manufacturing, or at least people think of it as lower tech. And then high tech in R and D, and how those things merge together in a single company. But the reality is all of that is data driven, right? Even when you go to the shop floor, all your scheduling, all your automation equipment, all this stuff is talking and it's all laying down data. You're putting rivets in the parts, you're probably taking pictures of that now with imagers when you're in manufacturing. And you do that so that if you get 300 bad ones you can see exactly when that started and what happened at the machine level, right? So, >> That's a good one. >> We're just constantly collecting massive volumes of new data, and being able to store that reliably is everything. >> Well, and the reason I'm asking is you guys have been around for a while and your a highly distributed organization so, in the old days, even still today, you'd build, you'd get a server for an application, you'd harden that application, you'd secure that box and the application running on it, you'd lock the data inside and, my question is, can, the backup approach, the data protection approach, the data management, or whatever we want to call it, can it help solve that data silo problem? Is that part of the strategy or is it just too early for that? >> I'm, sorry, I'm going to get you to repeat that question in a slightly different way. >> Yeah so, am I correct that you've got data in silos from all the years and years and years of building up applications and-- >> I mean, we have-- >> And can you use something like Veeam to help unify that data model? >> Draw that all together? Yeah. I think a lot of that has, it's more on the hosting side, right? So it depends on how those systems were rolled out originally and all that kind of thing. But yeah, as we've moved towards Veeam, we've necessarily rebuilt some of those systems in such a way that they are more aggregated and that Veeam can pick them up in an integrated kind of way. >> You see that as a common theme? Veeam as one of the levers of the fulcrum to new data architecture? >> We're getting there, so here's the trick. So, first you got to solve for basic protection, right? But the next thing along the way to really get towards data management is you got to know what you got, right? You got to know what's actually in those zeros and ones. And so, some of the things that you've already seen from us are around what we do around GDPR compliance, some of the things we do around sanitization of data for DevOps scenarios and reuse scenarios. All of that opens up a box of, okay, now that the data is curated. Now that it's ingested into our system, what else can you do with it? You know, when I talk to C-level execs, what I tell them is, data protection, no matter who it comes from, including Veeam, is really expensive if the only thing you do is put that data in a box and wait for bad things to happen, right? Now the good news is, bad things are going to happen, so you're going to get ROI. But better is don't just leave your data in a box, right? Do other stuff with that data, unlock the value of it and some of that value comes in, now that I'm more aware of it, let's reduce some of the copies, let's reduce some of the compliance mandates. Let's only put data that has sovereignty requirements where it goes, but to do all of that, you got to know what you got. >> Go ahead, please. >> There was some impressive demo yesterday about exactly that, so, we have the data. You can use the API to script it and you can do all kinds of, basically, you're limited by your imagination. So it's going to be fascinating to see what customers do with it once they've put it in place, they've got their data protected. And then they start playing with things, come to a conference like this and learn, ooh, I might just give that a try on my data when I get back home. >> That's right. >> We'll give the customer the last word, Nathan. Impressions of VeeamON 2019? >> It's been great. And like I say, if you're a company that's been using Veeam even for a while, and you have your entry level setup for backup and recovery and I think there's a lot of, probably, companies out there that use Veeam in that kind of way, this is a great place to have a better understanding of all that's available to you in that product. And there's a lot more than just meets the eye. >> And it's fun, good food, fun people. Thanks you guys for coming on, really appreciate it. >> Yeah, thank you. >> Alright, keep it right there, buddy, we'll be back with our next guest, you're watching theCUBE, Dave Vellante, Justin Warren, and Peter Burris is also here. VeeamON 2019, we'll be right back. (electronic music)

>> From the Aria Resort in Las Vegas, it's theCUBE. Covering AWS marketplace. Brought to you by Amazon Web Services. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We are kicking off three crazy days at AWS re:Invent. It is the place to be the week after Thanksgiving. There's got to be 50,000 people, we haven't got the official word, but it's packed and it kicks off tonight with a reception. We're here at the AWS Marketplace and Service Catalog Experience over at the Aria, in the quad, come check us out. A lot of good stuff going on. A lot of fun stuff going on. And we're excited to have first time to theCUBE, he's Nathan Dyer, Senior Product Manager for Tenable. Great to see you. >> Jeff, great to be here. Thanks for having me. >> Yeah, have the energy the opened the doors the people are streaming in. >> I don't know if it's the food or the drinks or the vendors. >> All of the above. Probably more the food and the drinks. All right. So give us an overview of Tenable for people who aren't familiar with the company. >> Yeah, so Tenable, we are the cyber exposure company. We help organizations assess, manage, and measure their cyber risk across their entire organization, across their monitored tax surface. And so what we try to do is help answer four fundamental questions around security. How exposed are we? How do we prioritize based on risk, how are we doing over time from a measurement standpoint, and then how do we compare with our peers? And so, if you haven't heard of Tenable, chances are you've heard of Nessus, which is one of our flagship brands. Nessus just turned 20 years young earlier this year. If you're pen tester, if you're a consultant if you're a practitioner, you know Nessus. But over the years we've added some other brands as well. Security Center which is now renamed Tenable.sc which is our On-Prem vulnerability management solution. And then tenable.io which was released in 2017 which is our cloud based vulnerability management solution and built on AWS. >> Right. So I was doing some research, I love your guys' little mantra here, it's security for code, for clouds and containers. You got all the C's there. The containers, you know, what's going on with Docker over the last couple of years and now obviously the huge groundswell with Kubernetes, you know this container thing, depending on who you talk to has been around for a long time but it certainly didn't have the momentum. How's the kind of the growth of the container world impacted the securities base? >> Oh, it's massive. Containers are everywhere. In fact there's a strong affinity to cloud and containers. So a lot of our large AWS customers love containers. They've been dabbling with containers for quite some time. They're moving more and more workloads to be containerized and on Kubernetes, Dockers, et cetera. From a securities standpoint that introduces a lot of challenges, right. They're short lived life cycles of docker containers make it very hard for us in security to assess or discover them. They're part of the whole immutable infrastructure phenomenon, so you can't patch it in production, right. Infrastructure is code. You have to tear down the container, fix the image and then redeploy. So from our perspective, we think you have to secure containers by focusing on the container image. Specifically as developers are spinning up new code, compiling new builds, creating new container images, is it running quality assurance checks? Security has to be a critical part of that quality assurance process. As you're doing integration tests, unit testing, API testing, security has to be a critical test looking for vulnerabilities and malwares is part of that process. >> But the rate of change in those images is pretty high. I mean, the rate of deployments is super high, but like you said a lot of them have short life spans, they're up or they're down. So, have people baked that in to their process? I mean, obviously, I hope they are. Or how are you helping them to make sure that security is a really key piece to that image. Because once that image goes out it has access to all kinds of things. >> So, the new news with containers, and then by focusing on the image it forces security teams to talk to their development peers. In order to secure DevOps and secure containers, security has to be embedded into continuous integration, into continuous delivery cycles or systems. And if you're focusing on development, you have a much greater chance of making sure that vulnerable container images are not escaping into the wild. And you guys should get a hold of those vulnerable images and make sure they adhere to policies before they're released into production. So that's the new news. >> Well, it's funny because you reference the DevOps. 'Cause DevOps has now been around for a while and clearly is the way the code gets deployed in a very rapid iteration. So they're some significant lessons from the DevOps security angle that you're now using then on the container side. Yeah, well first thing with secure DevOps and Devops in general, is that you have to get the developers and security teams to talk. You have to have a shared understanding of what makes each other tick. What are the goals, what are the responsibilities, priorities, understand each other and it turns out there's actually a lot of shared understanding and mutual benefit between infosec and application developments. When security is focused on solving for vulnerabilities and looking for security issues, that's improving code quality. That's removing some of the software defects from the development code and developers love that. They love producing high quality code. On the flip side, security teams can learn a lot about agile development. DevOps principles. Bringing DevOps into the security discipline, and help security teams start to leverage automation and continuous testing, continuous delivery, and make them much more scalable and productive in their organizations. So there's a lot of mutual of understanding there. >> Right. So I'd imagine there's a lot of, kind of similarities between classic waterfall and the moat, versus now kind of the DevOps and the continuous and ongoing constant process. >> That's exactly right. >> Yeah. So we're here at the AWS Marketplace. So you guys are selling through the marketplace, how has that been for the company? How has the experience been working with the AWS marketplace team? >> Oh, it's been great. I mean, Amazon is a great partner to work with. Tenable.io which is our cloud based vulnerability management solution is built on Amazon. We have a great relationship with Amazon engineers. Now for the marketplace, we've been selling Nessus for quite some time through the marketplace. So if you're a Nessus subscriber, if you're a tenable.io or securities center or tenable.sc subscriber, you get access to unlimited Nessus scanners and you can provision them very easily through the marketplace. It's super easy. Just recently, we now unveiled tenable.io through the marketplace and so far it's been a great success. Now customers who prefer to buy through Amazon marketplace AWS marketplace, can do so with a couple of clicks and be provisioned and get up and running with tenable.io. It's super easy, you can learn about the product. Kick the tires with a free evaluation, and really provision the product very simply. >> Yeah, I would imagine the touch from your guys side goes down significantly when they're just coming right through the marketplace. >> Exactly. That's the idea. Make it super easy for customers to invest in tenable.io and get a great experience in doing it. >> What about your own sales guys though. Is there a little channel conflict? They're like hey come one, I want to sell hat thing, we don't want to go through Amazon. >> Not at all. Our mantra is we want our customer to purchase through the channel they're comfortable with. And if they want to purchase through the AWS marketplace we have a channel for them, if they want to go through our three chair model we have obviously a great experience there as well. >> And clearly Amazon brings a lot of customer eyeballs to the table. >> They're a great partner. >> So, just before we wrap, you guys came out with the vulnerability intelligence report. I wonder if you can share some of the highlights of the things. You guys are obviously keeping track of this, you talked about benchmarking against your peers. And I know there's also a lot of sharing of information within security companies, to kind of know what the bad guys are and some of the patterns and best practices. So, I'm wondering if you can share some of the current trends. What are you seeing? How's the landscape changing? >> Well first of all, we have phenomenal tenable research team. They're phenomenal in terms of the data science, in terms of the vulnerability intelligence. We have a wealth of data in our hands from various deployments and so there's a lot of great number crunching and analysis we can generate from that. What we discovered in the vulnerability and intelligence report, is that security teams are just bombarded with vulnerabilities, literally, bombarded. Last year in 2017 we saw over 15,000 CVE's and unique vulnerabilities hitting the marketplace or hitting the industry. And by the end of this year we're expected to be between 18,000 and 19,000 vulnerabilities. So the trend is just going up, up, up. I think what makes matters worse though, is that when you start looking at those 19,000 vulnerabilities, over 60% of those vulnerabilities are classified as either high risk or critical. >> 65%? >> Around 60%. >> Of the, what was the numerator? 18,000? >> Of those 18,000 to 19,000 vulnerabilities, are classified as high risk or critical risk. So, that's a lot of fire drills that security teams need to chase. And so, what we're trying to achieve is helping our customers, helping the market at large understand what are the true risks out there, not the theoretical risks. What are the actual cyber risks. Meaning what are the vulnerabilities that could be easily exploitable, that have exploit kits already developed. We have our data science team looking at the characteristics of vulnerabilities and which ones would be leveraged by the bad guys and which ones would not be. And we significantly boil that number down so that organizations can focus on only 5% of the number of vulnerabilities that they otherwise would be chasing without changing their overall security risk to the organization. So, prioritization is super, super critical for those organizations. >> Nathan I think we all that separating the signal from the noise. (laughs) >> Jeff, well thanks for having me. >> Nathan, thank you very much, it's great to see you and have a great show. >> Thanks. You too. >> All right, I'm Jeff he's Nathan, you're watching theCUBE. We are at the AWS marketplace and service catalog experience at the Aria, at the quad. Come on by. We're serving free food and drink. See you next time. (lively music)

>> From Tavern on the Green in Central Park, New York it's theCUBE. Covering Veritas Vision Solution Day, brought to you by Veritas. >> Welcome back to New York City everybody. We're here in the heart of Central Park at Tavern On the Green, a beautiful facility. I'm surrounded by Yankee fans so I'm like a fish out of water. But that's okay, it's a great time of the year. We love it, we're still in it up in Boston so we're happy. Dave Vellante here, you're watching theCUBE, the leader in live tech coverage. Nathan Hall is here, he's the field CTO at Pure Storage. Nathan, good to see you. >> Good to see you too. >> Thanks for coming on. >> Thanks. >> So you guys made some announcements today with Veritas, what's that all about? >> It's pretty exciting and Veritas, being the market leader in data protection software. Now our customers are able to take Veritas's net backup software and use it to drive the policy engine of Snapshots for our FlashArrays. They're also able to take Veritas and back up our data hub, which is our new strategy with FlashBlade to really unify all of data analytics onto a single platform. So Veritas really is the solution net back up that's able to back up all the workloads and Pure is the solution that's able to run all the workloads. >> So what if I could follow-up on that, maybe push you a little bit? A lot of these announcements that you see, we call them Barney deals, I love you, you love me, we go to market together and everything's wonderful. Are we talking about deeper integration than that or is just kind of press release? >> Absolutely deeper integration. So you'll see not just how-to guides, white papers, et cetera, but there's actual engineering-level integration that's happening here. We're available as an advanced disk target within that back up, we've integrated into CloudPoint as well. We certify all of our hardware platforms with Veritas. So this is deep, deep engineering-level integration. >> Yeah, we're excited about Pure, we followed you guys since the early days. You know we saw Scott Dietzen, what he built, very impressive modern architecture, you won't be a legacy for 20, 25 years so you've got a lot going for you. Presumably it's easier to integrate with such a modern architecture, but now at the same time you got to integrate with Veritas, it's been around for about 25 years. We heard a lot about how they're investing in API-based architectures, and microservices, and containers and the like, so what is that like in terms of integrating with a 25-year-old company? >> Well I think, from Pure's perspective we are API first, we're RESTfull APIs first. We've done a ton of integrations across multiple platforms whether it's Kubernetes, Docker, VMware, et cetera, so we have a lot of experience in terms of how to integrate with various flavors of other infrastructure. I think Veritas has done a lot of work as well in terms of maturing their API to really be this kind of cloud-first type of API, this RESTful API, that made our cross-integration much easier. >> You guys like being first, there were a number of firsts, you guys were kind of the first, or one of the first with flash for block. You were kind of the first for file. You guys have hit AI pretty hard, everybody's now doing that. You guys announced the first partnership with NVIDIA, everybody's now doing that. (laughs) You guys announced giving away NVME as part of the Stack for no upcharge, everybody's now doing that. So, you like to be first. Culturally, you've worked at some other companies, what's behind that? >> Well culturally, this is best company I've worked at in terms of culture, period, and really it all starts with the culture of the company. I think that's why we're first in so many places and it's not just first in terms of first to market. It's really about first in terms of customer feedback. If you look at the Gartner Magic Quadrant we're up, we've been at leaders quadrant for five years in a row. But this year, we're indisputably the leader. Furthest to the right on the X-axis, furthest north on the Y-axis and that's all driven by just a customer-obsessed culture. We've got a Net Promoter Score of 86.6 which is stratospheric. It's something that puts us in the top 1% of all business-to-business companies, not just tech companies. So, it's really that culture about customer obsession that drives us to be first. Both to market, in a lot of cases, but also just first in terms of customer perception of our technology. >> You guys were a first at really escape velocity, the billion dollar unicorn status, and now you're kind of having that fly-wheel effect where you're able to throw off different innovations in different areas. Can you talk more about the data hub and the relevance to what you're doing with Veritas and data protection? Let's unpack that a little bit. >> Sure, sure, the data hub, we had a great keynote this morning with Jyothi the VP of Marketing for Veritas and he had an interesting customer tidbit. He had some sort of unnamed government agency customer that actually gets penalized when they're unable to retrieve data fast enough. That's not something that many of our customers have, but they do get penalized in terms of opportunity costs. The reason why is 'cause customers just have their data siloed into all these different split-up locations and that prevents them from being able to get insight out of that data. If you look at AI luminaries like Andrew Ng or even people like Dominique Brezinski at Apple, they all agree that you have to, in order to be successful with your data strategy, you have to unify these data silos. And that's what the data hub does. For the first time we're able to unify everything from data warehousing, to data lakes, to streaming analytics, to AI and now even backup all onto a single platform with multidimensional performance. That's FlashBlade and that is our data hub, we think it's revolutionary and we're challenging the rest of the storage industry to follow suit. Let's make less silos, let's unify the data into a data hub so that our customers can get real actionable information out of their data. >> I was on a crowd chat the other day, you guys put out an open letter to the storage community, an open challenge, so that was kind of both a little controversial but also some fun. That's a very important point you're making about sort of putting data at the core. I make an observation, it's not so much true about Facebook anymore 'cause after the whole fake news thing their market value dropped. But if you look at the top five companies in terms of market value, include Facebook in there, they and Berkshire keep doing this, but let's assume for a second that Facebook's up there. Apple, Google, Facebook, Microsoft, and Amazon, top five in terms of US market value. Of course markets ebb and they flow, but it's no coincidence that those are data companies. They all have a lot of hard assets at those companies. They've got data at their core so it's interesting to hear you talk about data hub because one of the challenges that we see for traditional companies, call them incumbents, is they have data in stovepipes. For them to compete they've got to put it in the digital world, they've got to put data at their core. It's not just for start-ups and people doing Greenfield, it's for folks that are established and don't want to get disrupted. Long-winded question, how do they get, let's think of traditional company, an incumbent company, how do they get from point A to point B with the data hub? >> I think Andrew Ng has a great talked-point on this. He basically talks about your data strategy and you need to think about, as a company, how do you acquire data and then how do you unify into a single data hub? It's not just around putting it on a single platform, such as FlashBlade. A valuable byproduct of that is if you have all the stove-piped data, though you probably in terms of your data scientist trying to get access to it, now have to, they have 10 different stovepipes you've got 10 different VPs that you have to go talk to in order to get access to that data. So it really starts with stopping the bleeding and starting to have a data strategy around how do we acquire and how do we make certain or storing data in the same place and have a single unified data hub in order to maximize the value we are able to get out of that data. >> You know when I talked to, I'll throw my two cents in, I talk to a lot of chief data officers. To me, the ones that are most insightful talk about their five imperatives. First of all, is they got to understand how data contributes to monetization. Whether it's saving money or making money, it's not necessarily selling your data. I think a lot of people make that mistake, oh I'm going to monetize my data, I mean I'm going to sell my data, no, it's all about how it contributes to value. The second is, what about data sources? And then how do I get access to data sources? There's a lot implied there in terms of governance and security and who has access to that. And in the same time, how do I scale up my business so that I get the right people who can act on that data? Then how do I form relationships with a line of business so that I can maximize that monetization? Those are, I think, sensible steps that aren't trivial. They require a lot of thought and a lot of cultural change and I would imagine that's what a lot of your customers are going through right now. >> I think they are and I think as IT practitioners out there, I think that we have a duty to get closer to our business and be able to kind of educate them around these data strategies. To give them the same level of insight that you're talking about, you see in some chief data officers. But if I looked out at the, there's a recent study on the Fortune 50, the CXOs, and these aren't even CIOs, they're actually, we think as IT practitioners that the cloud is the most disruptive thing that we see, but the CEOs and the CFOs are actually five times more likely to talk about AI and data as being more disruptive to their business. But most of them have no data strategy, most of them don't know how AI works. It's up to us as IT practitioners to educate the business. To say here's what's possible, here's what we have to do in order to maximize the value out of data, so that you can get a business advantage out of this. It's incumbent on us as IT leaders. >> So Nathan, I think again, that's really insightful because let's face it, if you're moving at the speed of the CIO, which is what many companies want to do, because that's the so called, fat middle and that's where the money is. But you're behind, I mean we're moving into a new era, the cloud era, no pun intended, is here, it's solid but we're entering that data of machine intelligence and we built the foundation with the dupe even, there's a lot of data now what do we do with it? We see, and I wonder if you could comment on this, is the innovation engine of the future changing it? It use to be Moore's Law, we marched to the cadence of Moore's Law for years. Now it's data applying machine intelligence and then, of course, using the cloud for scale and attracting start-ups and innovation. That's fine because we want to program infrastructure, we don't want to deploy infrastructure. If you think about Pure, you got data for sure. You're going hard after machine intelligence. And cloud, if I understand your cloud play, you sell to cloud providers whether they're on-prem or in the public cloud but what do you think about those? That innovation sandwich that I just described and how do you guys play? >> Well, cloud is where we get over 30% of our revenue so we're actually selling to the cloud, cloud service providers, et cetera. For example, one of the biggest cloud service providers out there that I think today's announcement helps them out a lot from a policy perspective actually used FlashBlade to reduce their SLAs, to reduce their restore time from, I think, it was 30 hours down to 38 minutes. They were paying money before to their customers. What we see in our cloud strategy is one of empowering cloud providers, but also we think that cloud is increasingly, at the infrastructure layer, going to be commoditized and it's going to be about how do we enable multicloud? So how do we enable customers to get around data gravity problems? I've got this big, weighty database that I want to see if I can move it up to the cloud but that takes me forever. So how do we help customers be able to move to one cloud or even exit a cloud to another or back to on-prem? We think there's a lot of value in applying our, for example deduplication technology, et cetera, to helping customers with those data gravity problems, to making a more open world in terms of sharing data to and from the cloud. >> Great, well we looked at Pure and Veritas getting together, do some hard core engineering, going to market, solving some real problems. Thanks Nathan for hanging out, this iconic beautiful Tavern on the Green in the heart of New York City. Appreciate you coming on theCUBE. >> Thanks Dave. >> All right, keep it right there everybody, Dave Vallante. We'll be right back right after this short break. You're watching theCUBE from Veritas Solutions Day, #VeritasVision, be right back. (digital music)

>> Narrator: Live from the Bill Graham Auditorium in San Francisco. It's the Cube. Covering Pure Storage Accelerate 2018. Brought to you by Pure Storage. >> Welcome back to Pure Storage Accelerate 2018. I'm Lisa Martin with the Cube. I'm with Dave Vellante. We are here in San Francisco at the Bill Graham Civic Auditorium which is why we're sporting some concert t-shirts. >> Who. >> The Who and the Clong. >> Roger. Roger Delchi. >> Roger. We are here with the CIO of the David Geffen School of Medicine at UCLA, Pure customer, Ben Nathan. Ben, welcome to the Cube. Thanks for having me. So, talk to us about the shool of medicine at UCLA. You are the CIO there, you've been there for about three years. Give us a little bit of the 10,000 foot view of what your organization looks like to support the school of medicine. >> Sure. We're about 170 people. We have changed a lot over the last three years. So, when I got to UCLA there was 25 separate IT organizations, all smaller groups, operating in each individual department. And, they had built their own sets of managed infrastructure, distributed throughout every closet, nook and cranny in the school. We've consolidated all that under one set of service lines, one organization, and that's including consolidating all the systems and applications as well. So, we've brought all those together and now we're additionally running IT for three more health sciences schools at UCLA, nursing, dentistry, and school of public health, Fielding School of Public Health. Like a lot of CIOs, you serve many masters. You got the administration, you got the students, right. You've got the broader constituency. The community, UCLA. Where do you start? What's the quote on quote customer experience that you're trying to achieve? That's a great way to put it. There's really sort of four pillars that we try to serve. The patient being first and foremost. So, for us, everything is built around a great patient experience. And, that means that when we're educating students it's so they can be great providers of patient care. When we're doing research, When we're doing that research in an effort to eradicate disease et cetera. And, when we're doing community outreach it's also around improving health and peoples lives, so, in IT, we try to stay very connected to those missions. I think it's a large part of what drives people to be a part of an organization that's healthcare or that's a provider. That mission is really, really important. So, yes. We're serving all four of those things at once. >> So, you had lots of silos, lots of data, that's all continuing to grow but, this is data that literally life and death decisions can be made on this. Talk to us about the volumes of data, all the different sources that are generating data. People, sensors, things and how did you make this decision to consolidate leveraging Pure Storage as that foundation? >> Yeah, there's and incredible amount of work going on at UCLA. Particularly in their research education and patient care spaces. We had every brand of server in storage that you've never heard of. Things bought at lowest, bitter methods but, the technical data that we had incurred as part of that was enormous. Right, it's unsustainable. It's unsupportable. It's insecure-able. When I got there and we started to think about how do we deal with all of this? We knew we had an opportunity to green field an infrastructure and consolidate everything onto it. That was the first, that was started us down the road that led us to Pure as one of our major storage vendors. I had worked with them before but, they won on their merits, right? We do these very rigorous RFP processes when we buy things. The thing that really, I think, got them the the victory is us is that the deduplication of data got us to something like an eight to one ratio of virtual to physical. So, we get a lot of virtual servers running on relatively small amount of storage. And, that it's encrypted you know, sort of the time, right? There's not like a switch you might flip or something a vendor says they'll do but it >> Always on. >> doesn't really do, it is always on. And, it's critical for us. We're really building a far more secure and manageable set of services and so all the vendors we work with meet that criteria. >> So, is as a CIO, I would imagine you don't want to wake up every day and think of storage. With all due respect to our friends at Pure. >> That's true. >> So, has bringing it in for infrastructure in, like Pure, that prides itself on simplicity, allowed you to do the things that you really want to do and need to do for your organization? >> Yeah. I'll give you a two part answer. I mean one is simply, I think, it's operationally a really great service. I think that it's well designed, and run, and managed. And, we get great use of out it. I think the thing that makes it so that I don't have to think about it is actually, the business model that they have. So, the fact that I know that it's not going to really obsolete on its own, as long as you're like in the support model, you're upgrading the system every few years, changes, you know the, model for me, 'cause I don't have to think about these new, massive capitalization efforts, it's more of a predictable operational costs and that helps me sleep well because I know what we look like over the next few years and I can explain that to my financial organization. >> Just a follow up on that, a large incumbent storage supplier or system vendor might say, "Well, we can make that transparent to you. We can use our financial services to hide that complexity or make a cloud-like rental experience or you know, play financial games to hide that. Why does that not suffice for you? >> Well, I think, first and foremost we sort of want to run our financials on our own and we're pretty anxious about having anyone else in the middle of all that. Number two is it seems to me different in terms of Pure having built that model from the ground up as part of their service offerings. So, I don't think we see that with too many other vendors and I think that obviously there's far less technical than what I had in the previous design but it can still add up if you're not careful about whatever, what server mechanism you have in place, et cetera. >> But, it eliminates the forklift upgrade, right. Even with those financial incentives or tricks, you still got to forklift it and it's a disruption to your operation. >> Yeah, and I'm sure that's true, yeah. >> So, when you guys were back a year and a half or so, maybe two years ago, looking at this consolidation, where were your thoughts in terms of beyond consolidation and looking at being able to harness the power of AI, for example, we heard a lot of AI today already and this need for legacy infrastructures are insufficient to support that. Was that also part of your plan, was not simply to consolidate and bring your (speaks very rapidly) environment unto Pure source but also to leverage a modern platform that can allow you to harness the power of AI? >> Yeah. That was sort of the later phase bonus period that we're starting to enter now. So, after we sort of consolidate and secure everything, now, we can actually do far more interesting things that would've been much more difficult before. And, in terms of Pure, when we had set out to do this we imagined doing a lot of our analytics and AI machine learning kind of cloud only and we tried that. We're doing a lot of really great things in the cloud but not all of it is makes sense in that environment. Either from a cost perspective or from a capabilities perspective. Particularly with what Pure has been announcing lately, I think there's a really good opportunity for us to build high performance computing clusters in our on premise environment that leverage Pure as a potential storage back end. And that's where our really interesting data goes. We can do the analytics or the AI machine learning on the data that's in our electronic medical record or in our genomics workflows or things like that can all flow through a service like that and there's some interesting discoveries that ought to come from it. >> There's a lot of talk at this event about artificial intelligence, machine intelligence, how do you see AI in health care, generally? And specifically, how you're going to apply it? Is it helping doctors with diagnosis? Is it maybe maintaining better compliance? Or, talk about that a little. >> I think there's two things that I can think of off the top of my head. The first is decision support. So this is helping physicians when they're working directly with patients there's only, there's so many systems, so many data sets, so many way to analyze, and yet getting it all in front of them in some kind of real time way so that they can use it effectively is tricky. So, AI, machine learning, have a chance to help us funnel that into something that's immediately useful in the moment. And then the other thing that we're seeing is that most of the research on genomics and the outcomes that have resulted in changes to clinical care are around individualized mutations in a single nucleotide so there's, those are I guess, quote, relatively easy for a researcher to pick out. There's a letter here that is normally a different letter. But, there are other scenarios where there's not a direct easy tie from a single mutation to an outcome. so, like in autism or diabetes, we're not sure what the genetic components are but we think that with AI machine learning, those things will start to identify patterns in genomic sequences that humans aren't finding with their typical approaches and so, we're really excited to see our genomic platforms built up to a point where they have sequences in them to do that sort of analysis and you need big compute, fast storage to do that kind of thing. >> How is it going to help the big compute, fast storage, this modern infrastructure, help whether its genomics or clinicians be able to sort through masses amounts of data to try to find those needles in the haystack 'cause I think the staff this morning that Charlie Jean and Carla mentioned was that half a percent of data in the world is analyzed. So, how would that under the hood infrastructure going to help facilitate your smart folks getting those needles in the haystack just to start really making big impacts? >> UCLA has an incredible faculty, like brilliant researchers, and sometimes what I've found since I've gotten there, the only ingredient that's missing is the platform where they can do some of this stuff. So, some of them are incredibly enterprising, they've built their own platforms for their own analysis. Others we work with they have a lot of data sets they don't have a place to put them where they can properly interrelate them and do, apply their algorithms at scale. So, we've run into people that are trying to do these massive analysis on a laptop or a little computer or whatever it just fails, right? Or it runs forever. So, giving them, providing a way to have the infrastructure that they can run these things is really the ingredient that we're trying to add and so, that's about storage and compute, et cetera. >> How do you see the role of the CIO evolving? We hear a lot of people on the Cube and these conferences talk about digital transformation and the digital CIO, how much of that is permeating your organization and what do you think it means to the CIO world going forward? >> I wish I knew the real answer to that question. I don't know, time will tell. But, I think that certainly we're trying to follow the trends that we see more broadly which is there's a job of keeping the lights on of operations. And you're not really, you shouldn't have a seat at any other table and so those things are quite excellent. >> Table stakes. >> Yeah. Right. Exactly, table stakes. Security, all that stuff. Once, you've got that, you know, my belief is you need to deeply understand the business and find your way into helping to solve problems for it and so, you know, our realm, a lot of that these days is how do we understand the student journey from prior to, from when they maybe want to apply all the way 'til when they go out and become a resident and then a physician. There's a ton of data that's gathered along that way. We got to ask a lot of questions we don't have easy answers to but, if we put the data together properly, we start to, right? On the research side, same sort of idea, right? Where the more we know about the particular clinical outcomes they're trying to achieve or even just basic science research that they're looking into, the better that we can better micro target a solution to them. Whether it's a on prem, private cloud, or public cloud, either one of those can be harnessed for really specific workloads and I think when we start to do that, we've enabled our faculty to do things that have been tougher for them to do before. Once, we understand the business in those ways I think we really start to have an impact at the strategic level, the organization. >> You've got this centralized services model that was a strategic initiative that you put in place. You've got the foundation there that's going to allow you to start opening up other opportunities. I'm curious, in the UCLA system, maybe the UC system, are there other organizations or schools that are looking at what you're doing as a model to maybe replicate across the system? >> I think there's I don't know about a model. I think there's certainly efforts among some to find, to centralize at least some services because of economies to scale or security or all the normal things. With the anticipated, and then anticipating that that could ultimately provide more value once the baseline stuff is out of the way. UC is vast and varied system so there's a lot of amazing things going on in different realms and we're I think, doing more than ever working together and trying to find common solutions to problems. So, we'll see whose model works out. >> Well, Ben. Thanks so much for stopping by the Cube and sharing the impact that your making at the UCLA School of Medicine, leveraging storage and all the different capabilities that that is generating. We thank you for your time. >> Thanks so much for having me. >> We want to thank you for watching the Cube. I'm Lisa Martin with Dave Vellante. We are live at Pure Accelerate 2018 in San Francisco. Stick around, we'll be right back with our next guest.

(upbeat music) >> Announcer: Live from Orlando Florida, it's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to theCUBE's live coverage of PentahoWorld, brought to you of course by Hitachi Vantara. My name is Rebecca Knight, and I'm here with Dave Vellante, my co-host. We are joined by Nathan Hart, he is the Development Manager at NextGear Capital. Thanks so much for coming on theCUBE, Nathan. >> Thanks for having me. >> So let's start by telling our viewers a little bit about what NextGear Capital is, and what you do there. >> Sure, NextGear Capital is a, we do auto financing for auto dealerships, so if a dealer goes to an auction and wants to buy some inventory, we're going to be the ones who actually finance that and purchase it for them, and then they pay us back. >> Great, and your role as a development manager. >> Yep, I am over our integrations team, so we are responsible for basically getting data in and out of the company, a lot of that is getting data to and from our sister companies, all under Cox Automotive. >> And the data we're talking about is? >> Uh, it's a whole lot of things, obviously it's a lot of financial data, as we are a finance company, but a lot of things like inventory, unit statuses, where a car is located, we have credit scores, and that sort of work as well, so all kinds of data are coming in and out and then into our systems. >> So, are the cars instrumented to the point where you can kind of track where they are in an automated way, or is it? >> Yes, we do have some GPS units, not on all that inventory, just because we have quite a few open floor plans, about 500,000 I believe. But yes, we do have some select units that are GPS'd that we can track that way, or we have inspectors that go to lots. >> Okay so as a developer you know this story well, back in the day if you had a big data problem, you'd buy a Unix box and you'd stuff all the data in there and then you'd buy a bunch of Oracle licenses, and if you had any money left over, you could maybe do something, maybe buy a little storage, or conduct business. Okay that changed, quite dramatically. I wonder, if you could tell us your version of that story and how it's affected your business. >> Sure, so, uh. (laughter) >> Dave: Is it a fair representation? >> Not, not... >> Dave: Is the old world, was it a big data warehouse world? >> Yeah, so. >> Where it's sort of expensive to get stuff in and get stuff out and has that changed? Or is that sort of? >> Yeah, it has changed greatly, we're not quite that bad, but we do currently have an older monolithic database system that we are trying to get away from. >> Dave: It's hard. >> Yeah, exactly. And so a lot of our processes right now, go in and come out of this so obviously, if anything in that breaks, it hurts everywhere. >> Dave: Right. >> So yes. >> Dave: Sort of a chain reaction. >> Exactly. >> Okay, but so how have you, talk about the journey of bringing in Pentaho and how that has affected you. >> Sure, Pentaho has been great for us, just in terms of being able to be really flexible with our data. Like I said, we're trying to get away from this monolithic service, so we have, in Pentaho, we can easily branch off and say, go to the monolithic database, but also talk to another service that is going to replace it. And then it's just one click of a button, and now this is off, this is on, or we can do both and have some replication going, just so we have that flexibility, and that kind of adaptability around those changes. >> So why Pentaho, I mean, a lot of tools out there, there's open source, you could roll your own, you could do everything in the cloud, why Pentaho? >> We liked Pentaho because of the, I guess the freedom and independence it kind of offers, in the sense that it allows us to have a large set of steps and tools that are already prebuilt, that we can just use right out of the box, and, it's just a massive library, far greater than most of the competition that we looked at. And then it also is just built on this great Java platform that we can, if we need to, write a custom Java class, pop it in, and then that can do what we need to, if we don't have something out of the box. >> Dave: So it's integrated, >> Yep >> but it's customizable. >> Nathan: Exactly. >> If you need it to be. >> Nathan: Yep. >> Okay, and one of the things that customers like you tell us about Pentaho is that they like the sort of end-to-end integration. >> Nathan: Yep. >> We were talking off camera, you had mentioned that you've got an initiative to move toward the cloud. Maybe you could talk about that a little bit. >> Yeah, so right now, just Cox, as a whole, is kind of investigating the cloud. I definitely don't want to speak out of turn, or say we're definitely going there, but that is the current initiatives are to start experimenting with how we can leverage this more. I know one of the, kind of the first steps that we're taking towards that is we have large archives, we keep all of the files we've ever received or sent out, and we don't access them much, we don't need them much, but we want to keep them, just so we have this history, and we can always look back if we need to. So using the cloud for something like that, where's it's just like a deep storage, where we can just upload it and forget it, and if we ever need it, it's there and easily accessible, and this way we don't have to pay for as much storage on print. >> Very workload specific, cheap storage. >> Nathan: Yep. >> Probably a lot of test and dev. >> Nathan: Exactly. >> So going back to the Pentaho, and why Pentaho, and you mentioned the freedom and the flexibility that it provides, can you talk about some of the best practices that you've discovered that could help some other Hitachi Vantara customers? >> Absolutely, the biggest change, learning curve that we went through, my first introduction was Pentaho when I started at NextGear, and it was a real huge learning curve for the whole team. We all started within about a month of each other, and there were only three of us to start. So, it was a real learning curve of, okay, here's how we do this, here's how we do this. So, once we kind of got the workflow going and understanding what we were trying to do, the next step was figuring out okay we can make this very modular, we can build a sub job that does a very specific task, and we can use it everywhere. And we just did that again and again and again, so now we have a library of about 118 different utilities that we can just plug and drop anywhere and they just do what they need to do, we don't need to re-test them, we don't need to think about them ever. And of course, if we update one of those, it updates every single job that it touches. As soon as we kind of unlocked that and figured we didn't have to make a custom solution for every single job, that we could use a lot of reuseability. It really sped up our development, and how we do things. >> Could you talk about data sources, have they or how have they evolved over the last decade? >> Sure, I can't speak for the whole decade, I haven't actually been in the industry that long, but a lot of what we came into and inherited when I came in, were flat files, just everything is CSV, TXT, either in or out, and we still do a lot of that, that's still kind of our bread and butter, just by the nature of our current role, but as it's changing we are interacting more and more with APIs. We're shifting away from this monolithic database into micro services so we're having to interact with those a lot more and figure out how we can get that real time communication and get the data where it needs to go so it's all in its happy place. >> One of the things that Brian Householder, the CEO, got up on the main stage and talked about how, for companies, the two most important assets are the people and the data. I want to talk to you about the people aspect. >> Nathan: Okay. >> We're hearing so much about the shortage, the tech shortage of data scientists, and other kinds of talent in this industry. How hard is it for you to recruit? Your company, as you said, is based in Carmel, Indiana is that right? >> Nathan: Yep. >> What are you finding out there? >> The greater Indianapolis area, like many other places, is very starved for tech talent. It's very, very easy as a developer to throw a stone and get an interview. It's definitely a challenge. We actually currently have two openings on my team. Just, do less with more and do what we can. So, it's definitely a challenge, but I think that there's a lot of really great young talent coming out of colleges right now that are coming in, they've grown up with this right? They're a lot further along than necessarily I was when I came out of school and some of our other developers. So they can step in and already understand a lot of these complex architectures that we're dealing with and can just hit the ground running. >> So at least 10 times a week, I get somebody hitting me on LinkedIn about hey do you need development resources? (Nathan laughing) As a developer, it must happen to you 100 times a week, but there's obviously challenges of off-shoring and managing that remotely. I'm sure you've thought about it. What are your thoughts on off-shoring? You want someone there in a bee hive effect? Maybe talk about that a little bit. So, at NextGear we've been fairly rigid about butts in the seats, in the office, real collaborative environment, where you're at the morning stand up, you're there in the meetings, and it's a very present environment. And we are being a little bit more adaptable with that, just as time changes and other companies, obviously do offer more remote from home or what have you, so that is shifting a little bit, as far as necessarily off-shoring, that's way above my pay grade to even make that call, I have worked in previous environments where that was a large part of it. In a previously life we had a US based team and then we had a Malaysia based team, and I thought it was a really great experience cause we basically all had our own counterparts over there, so at the end of your day, you just email your notes, here's what I did today, here's where I left off, and they pick it up and do the same, then we had about a weekly meeting. So I think it definitely can work, I'm all for the global tech community all coming up together, when appropriate and when it works. >> But you've got to have the right infrastructure and processes in place, >> Nathan: Absolutely. >> Or it's just, it sucks all your productivity out. >> Nathan: Absolutely, if you spend half your day trying to figure out what the other person did, then you've lost your day. >> Yeah, right. And you follow the sun, yes and no right, you've got to wait for the sun sometimes. Pentaho, back to Pentaho, what are the things that, as a customer, you want them to do. What's on their to-do list, you know, when you're talking to Donna Prlich and her team, what are you pushing them for? >> So, the biggest things kind of on our wish list and that we're seeing is interacting more natively with those microservices like I mentioned and I was really glad that that came up in the keynote as something that they're focusing on and it's something that is going to come up in 8.0, at least the kind of stepping stones to go in that direction. So, that's really exciting stuff for us, just it answers a lot of questions we're currently having of how are we going to interact with those, and the answer can still be Pentaho moving forward. >> I was struck in the keynote, when Brian was asking hands up please, how many people are doing business with Hitachi outside of Pentaho, and just a smattering, right, I presume your hand was down. >> Nathan: My hand was down. >> And then, had you heard of Hitachi Vantara? >> I read the press release when they first announced Vantara, but that's about the extent of it. Obviously I knew about Hitachi from when they purchased Pentaho. We actually were having a week long, kind of a tech support get together that week that it happened, so I think on the Tuesday or something, our rep was like I now work for Hitachi. It was a fun thing, but yeah I'm not terribly familiar with Hitachi's products or, obviously I know where they're going with the Vantara concept, but. >> As a developer in a very focused area, >> Yep. >> Cox Automotive, obviously has some IOT initiatives, I'm sure, >> Absolutely. >> And some process automation, but I presume you haven't really dug into that yet, but when you think about the messaging that you heard this morning. What does it mean to you? Do you say, okay, nice, but I've got other problems? Or do you see the potential to leverage some of the technologies down the road? I definitely see the potential to start, at least exploring that direction, and figuring out what can we get out of this, right. It makes a lot more sense to play in a singular ecosystem and have all those tools at our hand just in one bucket instead of trying to figure out how does this play nice with this, how does this play nice over here, if we just can have a singular ecosystem that does it all together, that definitely makes our jobs a lot easier. >> How about the event, is this your first PentahoWorld? >> Yep, this is my first PentahoWorld. >> So it's early, but why do you come to events like this, and what do you hope to take away? >> Sure, I came to this event, cause I was specifically invited to. That's really it. It was nothing more than that, but I definitely come to kind of, see what's next and learn about the new technologies, and get that chance to visit some of the booths and some of the breakout sessions for maybe things that I don't get to do in my day to day life. We're very heads down in PDI so I don't get to spend too much time learning about the analytics and playing with those tools. So it's a lot of fun to come here and kind of see what's out there and be like, oh could we leverage this, or how could I adapt, or what are some of the other professionals doing that maybe I can bring back and improve our processes. >> And it's early days, but what are your thoughts on 8.0? >> I liked what I saw, and then I stopped by the booth and got another demo and I can definitely already see a couple of use cases where we can improve existing jobs with some of the new streaming features that they have in play, so I'm excited for that to come out and for us to start working with that. >> So that, the integration of streaming, Kafka, and the like was appealing to you? >> Yep, absolutely, and that'll be something that we can probably use right out of the gate, so excited for that. >> Well great, Nathan thank you so much for coming on theCUBE. >> Nathan: Yeah, thank you. >> I'm Rebecca Knight for Dave Vellante, we will have more from PentahoWorld just after this. (upbeat music)

(techno music) >> Hey welcome back everybody, Jeff Frick here with The CUBE. We're having a cube conversation in the Palo Alto studio. It's a different kind of format of CUBE. Not in the context of a big show. Got a great guest here lined up who we just had on at a show recently. He's Nathan Trueblood, he's the vice president of product management for DataTorrent. Nathan great to see you. >> Thanks for having me. >> We just had you on The CUBE at Hadoop, or Data Works now, >> That's right. >> not Hadoop Summit anymore. So just a quick follow up on that, we were just talking before we turned the cameras on. You said that was a pretty good show for you guys. >> Yeah it was a really great show. In fact as a software company one of the things you really want to see at shows is a lot of customer flow and a lot of good customer discussions, and that's definitely what happened at Data Works. It was also really good validation for us that everyone was coming and talking to us about what can you do from a real time analytics perspective? So that was also a good strong signal that we're onto something in this marketplace. >> It's interesting, I heard your quote from somewhere, that really the streaming and the real time streaming in the big data space is really grabbing all the attention. Obviously we do Spark Summit. We did Flink Forward. So we're seeing more and more activity around streaming and it's so logical that now that we have the compute horsepower, the storage horsepower, the networking horsepower, to enable something that we couldn't do very effectively before but now it's opening up a whole different way to look at data. >> Yeah it really is and I think as someone who's been working the tech world for a while, I'm always looking for simplifying ways to explain what this means. 'Cause people say streaming and real time and all of that stuff. For us what it really comes down to is the faster I can make decisions or the closer to when something happens I can make a decision, that gives me competitive advantage. And so if you look at the whole big data evolution. It's always been towards how quickly can we analyze this data so that we can respond to what it's telling us? And in many ways that means being more responsive to my customer. So a lot of this came out of course originally from very large scale systems at some of the big internet companies like Yahoo where Hadoop was born. But really it all comes down to if I'm more responsive to my customer, I'm more competitive and I win. And I think what a lot of customers are saying across many different verticals is real time means more responsiveness and that means competitive advantage. >> Right and even we hear all the time moving into a predictive model, and then even to a prescriptive model where you're offloading a lot of the grunt work of the decision making, letting the machine do a lot more of that, and so really it's the higher value stuff that finally gets to the human at the end of the interaction who's got to make a judgment. >> That's exactly right, that's right. And so to me all the buzz about streaming is really representative of just this is now the next evolution of where big data architecture has been going which is towards moving away from a batch oriented world into something where we're making decisions as close to the time of data creation as possible. >> So you've been involved in not only tech for a long time but Hadoop specifically and Big Data specifically. And one of the knocks, I remember that first time I ever heard about Hadoop, is actually from Bill Schmarzo at EMC the dean of Big Data. And I was talking to a friend of it and he goes yeah but what Bill didn't tell you, there's not enough people. You know Hadoop's got all this great promise, there just aren't enough people for all the enterprises at the individual company level to implement this stuff. Huge part of the problem. And now you're at DataTorrent and as we talked before, interesting kind of shift in strategy and going to really an application focus strategy as opposed to more of a platform focus strategy so that you can help people at companies solve problems faster. >> That's right we've definitely focused, especially recently on more of an application strategy. But to kind of peel that back a little bit, you need a platform with all the capabilities that a platform has to be able to deliver large scale operable streaming analytics. But customers aren't looking for platforms, they're looking for please solve my business problem, give me that competitive advantage. I think it's a long standing problem in technology and particularly in Big Data where you build a tremendous platform but there's only a handful of people who know how to actually construct the applications to deliver that value. And I think increasingly in big data but also across all of tech, customers are looking for outcomes now and the way for us to deliver outcomes is to deliver applications that run on our platform. So we've built a tremendous platform and now we are working with customers and delivering applications for that platform so that it takes a lot of the complexity out of the equation for them. And we kind of think of it like if in the past it required sort of an architect level person in order to construct an application on our platform, now we're gearing towards a much larger segment of developers in the enterprise who are tremendously capable but don't have that deep Big Data experience that they need to build an application from scratch. >> And it's pretty interesting too 'cause another theme we see over and over and over and over, especially around the innovation theme is the democratization of the access to the data, the democratization of the tools to access the data so that anyone in the company or a much greater set of individuals inside the company have the opportunity to have a hypothesis, to explore the hypothesis, to come back with solutions. And so by kind of removing this ivory tower, either the data scientists or the super smart engineer who's the only one that has the capability to play with the data and the tools. That's really how you open up innovation is democratizing access and ability to test and try things. >> That's right, to me I look at it very simply, when you have large scale adoption of a technology, usually it comes down to simplifying abstractions of one kind or another. And the big simplifying abstraction really of Big Data is providing the ability to break up a huge amount of data and make some sense of it, using of course large scale distributed computing. The abstraction we're delivering at DataTorrent now is building on all that stuff, on all those layers, we've obscured all of that and now you can download with our software an application that produces an outcome. So for example one of the applications we're shipping shortly is a Omni-Channel credit card fraud prevention application. Now our customers in the past have already constructed applications like this on our platform. But now what we're doing like you said is democratizing access to those kinds of applications by providing an application that works out of the box. And that's a simplifying abstraction. Now truthfully there's still a lot of complexity in there but we are providing the pattern, the foundational application that then the customer can focus on customizing to their particular situation, their integrations, their fraud rules and so forth. And so that just means getting you closer to that outcome much more quickly. >> Watching your video from Data Works, one of the interesting topics you brought up is really speed and how faster, better, cheaper, which is innovative for a little while, becomes the new norm. And as soon as you reset the bar on speed, then they just want it, well can you go faster. So whether you went from a week to a day, a day to an hour, there's just this relentless pressure to be able to get the data, analyze the data, make a decision faster and faster and faster. And you've seen this just changing by leap years right over time. >> Right and I literally started my career in the days of ETL extracting data from tape that was data produced weeks or months ago, down to now we're analyzing data at volumes that were inconceivable and producing insight in less than a second, which is kind of mind boggling. And I think the interesting thing that's happening when we think about speed, and I've had a few discussions with other folks about this, they say well speed really only matters for some very esoteric applications. It's one of the things that people bring up. But no one has ever said well I wish my data was less fresh or my insight was not as current. And so when you start to look at the kinds of customers that want to bring real time data processing and analytics, it turns out that nearly every vertical that we look at has a whole host of applications where if you could bring real time analytics you could be more responsive to what your customer's doing. >> Right right. >> Right and that can be, certainly that's the case in retail, but we see it in industrial automation and IoT. All I think of is IoT is a way to sense what's going on in the world, bring that data in, get insight and take action from it. And so real time analytics is a huge part of that, which you know again, healthcare, insurance, banking, all these different places have used cases. And so what we're aiming to do at DataTorrent is make it easy for the businesses in those different verticals to really get the outcome they're looking for, not produce a platform and say imagine what you could do, but produce an application that actually delivers on a particular problem they have. >> It's funny too the speed equation, you saw it in Flash, remembering to shift gears a little bit into the hardware space right, is people said well it's only super low latency, super high volume transactions, financial services, is the only benefit we're going to get from Flash. >> Right yeah we've had the same knock for real time analytics. >> Same thing right, but as soon as you put it in, there's all these second order impacts, third order impacts that nobody ever thought of, that speed that delivers, that aren't directly tied to that transactional speed, but now enable you because of that transactional speed, to do so many other things that you couldn't even imagine to do and so that's why I think we see this pervasiveness of Flash, why wouldn't you want Flash? I mean why wouldn't you want to go faster? 'Cause there's so much upside. >> Yeah so again all of these innovations in IT come down to how can I be more flexible and more responsive to changing conditions? More responsive to my customer, more flexible when it comes to changing business conditions and so forth. And so now as we start to instrument the world and have technologies like machine learning and artificial intelligence, that all needs to be fed by data that is delivered as quickly as possible and then it can be analyzed to make decisions in real time. >> So I wanted to shift gears a little bit, kind of back to the application strategies. So you said you had the first app that's going to be, (Jeff drowned out by Nathan) >> Yeah so the first application yes it was fraud prevention. That's an important distinction there because the distinction between detection and prevention is the competitive advantage of real time. Because what we deliver in DataTorrent is the ability to process massive amounts of data in very very low time frame. Sub seconds time frames. And so that's the kind of fundamental capability you need in order to do something like respond to some kind of fraud event. And what we see in the market is that fraud is becoming a greater and greater problem. The market itself is expanding. But I think as we see fraud is also evolving in terms of the ways it can take place across e-commerce and point of sale and so forth. And so merchants and processors and everyone in the whole spectrum of that market is facing a massive problem and an evolving problem. And so that's where we're focused in one of our first I would say vertically oriented business applications is it's really easy to be able to take in new sources of data with our application but also to be able to process all that data and then run it through a decision engine to decide if something is fraudulent or not in a short period of time. So you need to be able to take in all that data to be able to make a good decision. And you need to be able to decide quickly if it's going to matter. And you also need to be able to have a really strong model for making decisions so that you avoid things like false positives which are as big a problem as preventing fraud itself if you deliver bad customer experience. And we've all had that experience as well which is your card gets shut down for what you think is a legitimate activity. >> It's just so ironic that false positives are the biggest problem with credit card fraud. >> Yeah it's one of yeah. >> You would think we would be thankful for a false positive but all you hear over and over and over is that false positive and the customer experience. It shows that we're so good at it is the thing that really irks people. >> Well if you think about that, having an application that allows you to make better decisions more quickly and prevent those false positives and take care of fraud is a huge competitive advantage for all the different players in that industry. And it's not just for the credit card companies of course, it's for the whole spectrum of people from the merchant all the way to the bank that are trying to deal with this problem. And so that's why it's one of the applications that we think of as a key example where we see a lot of opportunity. And certainly people that are looking at credit card fraud have been thinking about this problem for a while. But there's the complexity like we were discussing earlier of finding the talent, on being able to deliver these kinds of applications finding the technology that can actually scale to the processing volume. And so by delivering Omni-Channel fraud prevention as a Big Data application, that just puts our customers so much closer to the outcome that they want. And it makes it a lot easier to adopt. >> So as you sit, shift gears a little bit, as your VP of product hat, and there's a huge wide world of opportunity in front of you, we talked about IoT a little bit, obviously fraud, you've talked about Omni-Channel retail. How are you guys going to figure out where you want to go next? How are you prioritizing the world, and as you build up more of these applications is it going to be vertically focused, horizontally focused, what are you thoughts as you start down the application journey? >> So a few thoughts on that. Certainly one of the key indicators for me as a product manager when I look at where to go next and what applications we should build next, it comes down to what signal are the customers giving us? As we mentioned earlier, we built a platform for real time analytics and decision making, and one of the things that we see is broad adoption across a lot of different verticals. So I mentioned industrial IoT and financial services fraud prevention and advertising technology, and, and, and. We have a company that we're working with in GPS geofencing. So the possibilities are pretty interesting. But when it comes to prioritizing those different applications we have to also look at what are the economics involved for the customer and for us. So certainly one of the reasons we chose fraud prevention is that the economics are pretty obvious for our customers. Some of these other things are going to take a little bit longer for the economics to show up when it comes to the applications. So you'll certainly see us focusing on vertically oriented business applications because again the horizontals tend to be more like a platform and it's not close enough to delivering an outcome for a customer. But it's worth noting one of the things we see is that while we will deliver vertically oriented applications that oftentimes switching from one vertical app to another is really not a lot more than changing the kind of data we're analyzing, and changing the decision engine. But the fundamental idea of processing data in a pipeline at very high volume with fault tolerance and low latency, that remains the same in every case. So we see a lot of opportunity essentially as we solve an application in one vertical, to rescan it into another. >> So you can say you're tweaking the dials and tweaking the UDI. >> Tweaking the data and the rules that you apply to that data. So if you think about Omni-Channel fraud prevention, well it's not that big of a leap to look at healthcare fraud or into look at all the other kinds of fraud in different verticals that you might see. >> Do you ever see that you'll potentially break out the algorithm, I forget which one we're at, people are talking about algorithms as a service. Or is that too much of a bit, does there need to be a little bit more packaging? >> No I mean I think there will be cases where we will have an algorithm out of the box that provides some basics for the decisions support. But as we see a huge market springing up around AI and machine learning and machine scoring and all of that, there's a whole industry that's growing up around essentially, we provide you the best way to deliver that algorithm or that decision engine, that you train on your data and so forth. So that's certainly an area where we're looking from a partnership perspective. Where we already today partner with some of the AI vendors for what I would say is some custom applications that customers have deployed. But you'll see more of that in our applications coming up in the future. But as far as algorithms as a service, I think that's already here in the form of being able to query against some kind of AI with a question, you know essentially a model and then getting an answer back. >> Right well Nathan, exciting times, and your Big Data journey continues. >> It certainly does, thanks a lot Jeff. >> Thanks Nathan Trueblood from DataTorrent. I'm Jeff Frick, you're watching The CUBE, we'll see you next time, thanks for watching. (techno music)

>> Narrator: From the computer history museum in the heart of Silicon Valley, it's The Cube, covering Food IT: Fork to Farm. Brought to you by Western Digital. >> Hi, welcome to The Cube, I am Lisa Martin. We are at the fourth annual Food IT: Fork to Farm event in Silicon Valley at the computer history museum. An incredible event talking with ag-tech experts, technologists, and really understanding how people that the produce the food can get together with those that are innovating technology and really improve the supply chain or the food chain. My next two guests are George Kellerman, COO and general partner of Yamaha Motor Ventures and laboratory here in Silicon Valley. Welcome George! >> Welcome, thank you. >> Great to have you, and we have Nathan Dorn, COO of Food-Origins and you're also an advisor to the mixing bowl. >> Thank you Lisa. >> Absolutely. So this is a really interesting event for us. We cover a lot of tech innovation events and looking at now even the title kind of threw me when I saw Fork to Farm. We're so used to the trend of farm to table, farm to fork, and I kept reading, is that right? One of the things is that everyone's tech-enabled, right? We've got computers in our pockets. I'd love to understand Nathan from your perspective, how are you seeing the consumer, this tech-enabled food consumer really drive the food and agriculture industry which is not only contending with demanding consumers, but environmental sustainability, climate change. How is that consumer being that influential? >> They're getting vocal with their dollar, with their pocket book, and they're able to say, "I'm buying based upon values and the values just aren't cost." So they're paying up for the opportunity to know more data behind the product and contribute to the farmer. A lot of people talk about their experience at farmer's markets. It's because of their direct relationship and the feel that they have control in their engagement, that they're really becoming more empowered. The agricultural industry is taking notice and they're starting to buy into that. >> Tell us about Food-Origins, the genesis of that in context with what you just mentioned. >> So I'm a technologist in agriculture. I've been involved in agriculture since I was a child, and recently worked in a major winery and vineyard team and then later with a berry company, and realized that most of the innovations that we brought, they lacked context of economics because we just couldn't see deep enough, more granular, and measure things that mattered from people movement to where the product actually came from, the impact on whether it was quality or not, and whether the economic-- There was economic differences. We accepted that as natural variation because a farmer's job is to grow something and make it successful. If you buy a seed and you put it in the ground and you do well and make money at it, you're going to do it again. You do more of it. Their job is if I can do this well, I'll do more, rather then reinvent it. Somebody had to take on that job of reinvention and we thought Food-Origins was a big part of that. >> So from a technology perspective, if we look at the food chain from planting to evaluating soil health and fertilizer requirements, and then the post-harvest, where are you seeing the biggest opportunities for farmers to use big data analytics, connected devices, GPS devices' sensors, to glean this information, learn from these machines, to improve from we'll say farm to fork? >> The amazing thing is there's so many great companies out there that are bringing pieces of the data, whether it's soil moisture or weather, or they're imaging, flying over my fields and telling me how healthy my plants are. But the gap is in connecting that data, going from pretty pictures that are standalone or great inventions that are standalone to this is the cause and these three attributes are the effect. You know, these three attributes lead to this effect. If I can do that, if I can make that connection, we've closed the big gap. We can create that continuous learning cycle that happens automatically within a farm, We can take this art to farming, leave it as an art, but take pieces of it and make it science and allow people to connect what soil moisture does to this product that was sold weeks later. How it affected the roots, then the plant, then the fruit, and then we can make all those connections. It's in that linkage, that's where the biggest opportunities are. >> So facilitating machine leaning-- >> Yeah, absolutely. >> For the next generation farm. >> And then once you've got that machine learning, you've got the knowledge base to make those improvements, like buying the right robot for the right task, buying and having assets available at the moment they're needed, because a lot of these businesses-- Picking a berry is much different than picking a watermelon or picking an apple or a tree nut, or a piece of corn in a field. So by doing it, by having so much differences, knowing all the data ahead of time allows an innovator, a robotics company to do amazing work and make the most of their dollar asset. >> Speaking of robotics, George, Yamaha. My first thought was motorcycles. >> Absolutely. >> So tell us about Yamaha Motor Ventures. You're based here in Silicon Valley. What was the opportunity that Yamaha saw to get into the robotics space, specifically in the food and agriculture industry? >> Well when we launched Yamaha Motor Ventures two years ago, our mandate was autonomous vehicles, robotics, and industrial automation. We actually weren't looking at agriculture per se, but after meeting people like Nathan and others in the industry, it was obvious that there were opportunities for all of those, autonomous vehicles, automation, and robotics. It was just the application was a little different. Yamaha has actually a robotics division, so we have vehicles, we have robotics. Now we're looking at those platforms and technologies and looking at how we can marry them in the agricultural space. Maybe also how we can innovate new products and services. >> So in terms of adoption, what are you seeing from whether it's a generational small farm or a larger farm, where is the biggest opportunity that you see for adoption in the food chain? Is it planting, harvesting? Is it looking at drones or aerial vehicles to evaluate the health of crops? >> So I have a two part answer to that. One is people have to understand that agriculture is not just complicated. Complicated means with enough time, we could figure it out. It's complex. It's a complex system, meaning there's lots of different elements to it. We can't just assume that we can do a series of steps and it'll work, because there's going to be downstream consequences and you then have to think of those as well. It really is going to take a lot of people and a lot of different approaches, and there isn't going to be one solution or one area. You mentioned a lot of different things: drones, data collection, sensors, network connectivity, IoT. It's going to be all of those in a complex system. The system we're dealing with is complex, so the solution is also going to be complex and we have to figure out how to integrate that. It's not just enough to say here's a robot and we'll put it in the field. It's going to be well, what is the data that it's basing its decisions on and how is collecting, when? As Nathan said, knowing when to put it in the field. That's also a lot of data collection up till that point. I think actually what Nathan's focusing on is we have to start with data. We need to build that historical data where we can apply machine learning to it. We have to start somewhere, and that data is going to come from drones, from sensors, from a lot of different networks. It might just be putting sensors on the vehicles that are in the field now. >> Right. But they're connecting different kinds of data, not just GPS, but they might be collecting hyper spectral imagery to detect disease and insect infestation, the health, the vitality of the plants and the fruit. So there's a lot of opportunities, but this is not a five year solution. This is a generational, multi-generational solution that we have to come up with. >> And is it also a multi-educational step process with farms across the US to really understand how to maybe deconstruct this complexity so they can understand the value that can be gleaned? >> A lot of the farmers I talk with, they'll tell me point blank, they're not farmers. They're people who foster and help the biological system of plants growing and creating produce. They're there to facilitate that. They're not there to do that, but think about innovation as a whole. A farmer has a super multi-skilled, multi-disciplinary skill set. Whatever innovations we bring have to fit in an entire skill set of a farmer, whether it's human resources manager, chemist, biological expert, soil scientist, mechanic. It has to fit an economist. They have to be able to match all those things, so it's going to take people that want to be engaged and have a passion for changing that system and being involved in that system to help carry it to that next step I think. It's going to take people like Yamaha Ventures. >> Well I think fortunate for them that they have people like you who are leading them on the way. George and Nathan, we want to thank you so much for sharing your insights on The Cube with us today. We wish you the best of luck in ventures. >> Thank you. >> Yeah, appreciate it. >> We want to thank you for watching The Cube. Again, we are at the Food IT: Fork to Farm event at the computer history museum in Silicon Valley. I am Lisa Martin, stick around. We have great guests coming up next. (electronic music)

>> Narrator: Live, from San Jose, in the heart of Silicon Valley, it's The Cube. Covering, DataWorks Summit, 2017. Brought to you by Hortonworks. >> Welcome back to The Cube. We are live on day two of the DataWorks Summit. From the heart of Silicon Valley. I am Lisa Martin, my co-host is George Gilbert. We're very excited to be joined by our next guest from DataTorrent, we've got Nathan Trueblood, VP of Product, hey Nathan. >> Hi. >> Lisa: And, the man who gave me my start in high tech, 12 years ago, the SVP of Marketing, Jeff Bettencourt. Welcome, Jeff. >> Hi, Lisa, good to see ya. >> Lisa: Great to see you, too, so. Tell us about the SVP of Marketing, who is DataTorrent, what do you guys do, what are doing in the big data space? >> Jeff: So, DataTorrent is all about real time streaming. So, it's really taken a different paradigm to handling information as it comes from the different sources that are out there, so you think, big IOT, you think, all of these different new things that are creating pieces of information. It could be humans, it could be machines. Sensors, whatever it is. And taking that in realtime, rather than putting it traditionally just in a data lake and then later on coming back and investigating the data that you stored. So, we started about 2011, started by some of the early founders, people that started Yahoo. And, we're pioneers in Hadoop with Hadoop yarn. This is one of the guys here, too. And so we're all about building realtime analytics for our customers, making sure that they can get business decisions done in realtime. As the information is created. And, Nathan will talk a little bit about what we're doing on the application side of it, as well. Building these hard application pipelines for our customers to assist them to get started faster. >> Lisa: Excellent. >> So, alright, let's turn to those realtime applications. Umm, my familiarity with DataTorrent started probably about five years ago, I think, where it was, I think the position is, I don't think that there was so much talk about streaming but it was like, you know, realtime data feed, but, now we have, I mean, streaming is sort of center of gravity. Sort of, appear to big data. >> Nathan: Yeah. >> So, tell us how someone whose building apps, should think about the two solution categories how they compliment each other and what sort of applications we can build now that we couldn't build before? >> So, I think the way I look at it, is not so much two different things that compliment each other, but streaming analytics and realtime data processing and analytics is really just a natural progression of where big data has been going. So, you know, when we were at Yahoo and we're running Hadoop in scale, you know, first thing on the scene was just simply the ability to produce insight out of a massive amount of data. But then there was this constant pressure, well, okay, now we've produced that insight in a day, can you do it in an hour? You know, can you do it in half an hour? And particularly at Yahoo at the time that Ah-mol, our CTO and I were there, there was just constant pressure of can you produce insight from a huge volume of data more quickly? And, so we kind of saw at that time, two major trends. One, was that we were kind of reaching a limit of where you could go with the Hadoop and batch architecture at that time. And so a new approach was required. And that's what really was sort of, the foundation of the Apache Apex project and of DataTorrent the company, was simply realizing that a new approach was required because the more that Yahoo or other businesses can take information from the world around them and take action on that as quickly as possible, that's going to make you more competitive. So I'd look at streaming as really just a natural progression. Where, now it's possible to get inside and take action on data as close to the time of data creation as possible and if you can do that, then, you're going to be competitive. And so we see this coming across a whole bunch of different verticals. So that's how I kind of look at the sort of it's not too much complimentary, as a trend in where big data is going. Now, the kinds of things that weren't possible before this, are, you know, the kinds of applications where now you can take insight whether it's from IOD or from sensors or from retail, all the things that are going on. Whereas before, you would land this in a data lake, do a bunch of analysis, produce some insight, maybe change your behavior, but ultimately, you weren't being as responsive as you could be to customers. So now what we are seeing, why I think the center of mass is moved into realtime and streaming, is that now it's possible to, you know, give the customer an offer the second they walk into a store. Based on what you know about them and their history. This was always something that the internet properties were trying to move towards, but now we see, that same technology is being made available across a whole bunch of different verticals. A whole bunch of different industries and that's why you know, when you look at Apex and DataTorrent, we're involved not only in things like adtech, but in industrial automation and IOT, and we're involved in, you know, retail and customer 360 because in every one of these cases, insurance, finance, security and fraud prevention, it's a huge competitive advantage if you can get insight and make a decision, close to the time of the data creation. So, I think that's really where the shift is coming from. And then the other thing I would mention here, is that a big thrust of our company, and of Apache Apex and this is, so we saw streaming was going to be something that every one was going to need. The other thing we saw from our experience at Yahoo, was that, really getting something to work at a POC level, showing that something is possible, with streaming analytics is really only a small part of the problem. Being able to take and put something into production at scale and run a business on it, is a much bigger part of the problem. And so, we put into both the Apache Apex problem as well as into our product, the ability to not only get insight out of this data in motion, but to be able to put that into production at scale. And so, that's why we've had quite a few customers who have put our product, in production at scale and have been running that way, you know, in some cases for years. And so that's another sort of key area where we're forging a path, which is, it's not enough to do POC and show that something is possible. You have to be able to run a business on it. >> Lisa: So, talk to us about where DataTorrent sits within a modern data architecture. You guys are kind of playing in a couple of, integrated in a couple of different areas. What goes through what that looks like? >> So, in terms of a modern data architecture, I mean part of it is what I just covered in that, we're moving sort of from a batch to streaming world where the notion of batch is not going away, but now when you have something, you know a streaming application, that's something that's running all the time, 24/7, there's no concept of batch. Batch is really more the concept of how you are processing data through that streaming application so, what we're seeing in the modern data architecture, is that, you know, typically you have people taking data, extracting it and eventually loading it into some kind of a data lake, right? What we're doing is, shifting left of the data lake. You know, analyzing information when it's created. Produce insight from it, take action on it, and then, yes, land it in the data lake, but once you land it in the data lake, now, all of the purposes of what you're doing with that data have shifted. You know, we're producing insight, taking action to the left of the data lake and then we use that data lake to do things, like train your you know, your machine learning model that we're then going to use to the left of the data lake. Use the data lake to do slicing and dicing of your data to better understand what kinds of campaigns you want to run, things like that. But ultimately, you're using the realtime portion of this to be able to take those campaigns and then measure the impacts you're having on your customers in realtime. >> So, okay, cause that was going to be my followup question, which is, there does seem to be a role, for a historical repository for richer context. >> Nathan: Absolutely. >> And you're acknowledging that. Like, did the low legacy analytics happen first? Then, store up for a richer model, you know, later? >> Nathan: Correct. >> Umm. So, there are a couple things then that seem to be like requirements, next steps, which is, if you're doing the modeling, the research model, in the cloud, how do you orchestrate its distribution towards the sources of the realtime data, umm, and in other words, if you do training up in the cloud where you have, the biggest data or the richest data. Is DataTorrent or Apex a part of the process of orchestrating the distribution and coherence of the models that should be at the edge, or closer to where the data sources are? >> So, I guess there's a couple different ways we can think about that problem. So, you know we have customers today who are essentially providing into the streaming analytics application, you know, the models that have been trained on the data from the data lake. And, part of the approach we take in Apex and DataTorrent, is that you can reload and be changing those models all of the time. So, our architecture is such that it's full tolerant it stays up all the time so you can actually change the application and evolve it over time. So, we have customers that are reloading models on a regular basis, so that's whether it's machine learning or even just a rules engine, we're able to reload that on a regular basis. The other part of your question, if I understood you, was really about the distribution of data. And the distribution of models, and the distribution of data and where do you train that. And I think that you're going to have data in the cloud, you're going to have data on premises, you're going to have data at the edge, again, what we allow customers to do, is to be able to take and integrate that data and make decisions on it, regardless kind of where it lives, so we'll see streaming applications that get deployed into the cloud. But they may be synchronized in some portion of the data, to on premises or vis versa. So, certainly we can orchestrate all of that as part of an overall streaming application. >> Lisa: I want to ask Jeff, now. Give us a cross section of your customers. You've got customers ranging from small businesses, to fortune 10. >> Jeff: Yep. >> Give us some, kind of used cases that really took out of you, that really showcased the great potential that DataTorrent gives. >> Jeff: So if you think about the heritage of our company coming out of the early guys that were in Yahoo, adtech is obviously one that we hit hard and it's something we know how to do really really well. So, adtech is one of those things where they're constantly changing so you can take that same model and say, if I'm looking at adtech and saying, if I applied that to a distribution of products, in a manufacturing facility, it's kind of all the same type of activities, right? I'm managing a lot of inventory, I'm trying to get that inventory to the right place at the right time and I'm trying to fill that aspect of it. So that's kind of where we kind of started but we've got customers in the financial sector, right, that are really looking at instantaneous type of transactions that are happening. And then how do you apply knowledge and information to that while you're bringing that source data in so that you can make decisions. Some of those decisions have people involved with them and some of them are just machine based, right, so you take the people equation out. We kind of have this funny thing that Guy Churchward our CEO talks about, called the do loop and the do loop is where the people come in and how do we remove people out of that do loop and really make it easier for companies to act, prevent? So then if you take that aspect of it, we've got companies like in the publishing space. We've got companies in the IOT space, so they're doing interview management, stuff like that, so, we go from very you know, medium sized customers all the way up to very very large enterprises. >> Lisa: You're really turning up a variety of industries and to tech companies, because they have to be these days. >> Nathan: Right, well and one other thing I would mention, there, which is important, especially as we look at big data and a lot of customer concern about complexity. You know, I mentioned earlier about the challenge of not just coming up with an idea but being able to put that into production. So, one of the other big ares of focus for DataTorrent, as a company, is that not only have we developed platform for streaming analytics and applications but we're starting to deliver applications that you can download and run on our platform that deliver an outcome to a customer immediately. So, increasingly as we see in different verticals, different applications, then we turn those into applications we can make available to all of our customers that solve business problems immediately. One of the challenges for a long time in IT is simply how do you eliminate complexity and there's no getting away from the fact that this is big data in its complex systems. But to drive mass adoption, we're focused on how can we deliver outcomes for our customers as quickly as possible and the way to do that is by making applications available across all these different verticals. >> Well you guys, this has been so educational. We wish you guys continued success, here. It sounds like you're really being quite disruptive in an of yourselves, so if you haven't heard of them, DataTorrent.com, check them out. Nathan, Jeff, thanks so much for giving us your time this afternoon. >> Great, thanks for the opportunity. >> Lisa: We look forward to having you back. You've been watching The Cube, live from day two of the DataWorks Summit, from the heart of Silicon Valley, for my co-host George Gilbert, I'm Lisa Martin, stick around, we'll be right back. (upbeat music)

>> Announcer: Live from San Francisco, it's theCUBE, covering Spark Summit 2017. Brought to you by Databricks. >> Welcome back to theCUBE, and we are pleased to have our first guest here today. He is a customer of Databricks, and also doing some exciting things with Spark. So welcome Nathan Murith, our senior software development manager from Autodesk. Welcome, Nathan. >> Thank you. >> Are you happy to be here? >> Absolutely, very exciting. >> Is this your first Spark Summit? >> It is, absolutely, yep. First time here, first time at a Spark Summit. Lot of fun, lot of people, lot of energy. So I'm very happy to be here. >> Well, before we dive into some of the exciting things you're doing with Spark, maybe tell me what you were hoping to learn at this summit. >> I think, I'm really interested in learning what's coming next. You know, Autodesk is a technology company. We build products, we build software, and we're always looking at the future, figuring out what we can build and what we can leverage this amazing technology for, in our own tools that we then offer to our customers. >> And did you just attend the Keynote? >> Nathan: I did. >> And what did you think? What stood out to you? >> A lot of interesting things that I want to go home and try, basically, or take back to the office and try, 'cause a lot of these things are very applicable to what we're doing on a day to day basis. >> All right, we've also got George Gilbert on the show. And George, we're going to dig in a little bit. Maybe you have a question for Nathan about what he's doing with ... >> Yeah, Nathan, for those of us who are antediluvian, in other words, having been born before the big flood that floated Noah's Ark, what was, tell us about the types of that Autodesk builds, and how Spark helps people who use those tools. >> Sure, so Autodesk, as a company, we do a lot of different things. Autodesk primarily builds software for the design and make space in three or four different verticals and disciplines. One is media and entertainment. One is manufacturing. One is architecture, engineering, and construction. The group that I'm a part of, and the software that our team is responsible for building, is mainly around the cloud and mobile products for the architecture, engineering, and construction industry. Specifically, we have a suite of products that we brand BIM 360, that basically are tailored to the construction industry and various personas, and various steps, depending on where you are in the life cycle. The building a vertical structure, a bridge, a hospital, a stadium, and we provide software for those individuals. >> Can you tell us a little bit about that life cycle, and then the life cycle of a project like that, and where Spark can help a customer who's thinking 360, and not a particular product. >> Absolutely. So the life cycle is pretty complex, but it starts, usually, on the computer, like this, where you'll design your building, or your structure, or whatever it is. Heavy 3D graphics, that's where Autodesk, the company, started. From that point on, you do a number of coordination exercises with the various disciplines in a construction project, so your architecture, your structural, your plumbing, your heat/ventilation/air conditioning people, that are all specific disciplines. Then you actually go on site, and you start building this structure, whatever it may be. And when you build, the building process that typically could take multiple months to multiple years, depending on how large the structure is, is when people are going to start leveraging some of our tools even more. Typically some of the things that we see a lot of is, when you're managing a construction site, you will see on a daily basis hundreds, probably, if not thousands of construction issues. This sheet of glass here is broken. The drywall, you know, fell off. This beam is going through a wall, you name it. Construction site problems happen every day. >> You want to know if the beam's going through the wall. >> Exactly, absolutely. So typically that generates a lot of data, to the point where our customers can possibly feel overwhelmed by their own data, because there just is so many things that get generated in our system. >> So this actually sounds like where IT operations is the the discipline of I've got all this infrastructure, and I'm getting all these alerts when little things go wrong, and I don't know where the, necessarily, the root cause is, or what I should attack first. Is that sort of what you're... >> It's where we're going. Yeah, absolutely. Right now, we're tackling, we're starting, we're still early stages with kind of the machine learning, data science applications to the products that we do and build. But right now, what we're tackling, we're just trying to help our customers gain insights on their own data, so when you're swimming in this vast ocean of data, and you don't know where to start, or typically a construction site the size of a stadium, or a campus, or a huge office building, you don't know where to start, typically. >> So what does this vast pool of data look like, and how, specifically, are you using Spark to help make sense out of it, or prioritize what you should look at? >> So a lot of what we're doing now is, we're using image data, and text data, so what happens is your superintendents, when they walk around a construction site to figure out what's going on, what's broken, what's working, what should I focus on today? They will walk around with our mobile devices, or their mobile devices using our mobile software, and take pictures, and write descriptions of things that they see walking around the construction site. So they've generated hundreds of thousands of these construction issues, and where we're leveraging Spark, is to help build classification models on top of that data, be it image and textual data, to help bring to the surface, and bring to the top the things that are most critical to our customers. Typically, one example is on a construction site any problem that's related to water is usually considered a big problem. So if we can help among hundreds of thousands of issues that happen on construction site, kind of identify what. Hey, Mister Superintendent, you have these 10 problems that are related to water, whatever they may be, you should probably focus on those first. And that's where we're leveraging things like, you know, Spark technologies, machine learning, data science to build our products. >> And are you are you learning from all the customers who use the product, or in other words, do you need their data to get smarter, or is it rules that you're building? >> So right now we're working with a subset of our customers, through which we've gone into a number of agreements where they were okay with us working with them very closely to possibly use some of their data that was generated in our tools and systems to help them to help build our model. So we're absolutely not looking at the entire data set, per se. >> Did you see anything in the keynote, with the Structured Streaming that's now down to a millisecond, which is truly impressive for Spark, or in the deep learning that might simplify traditional machine learning. Did you see anything there that looks like it might have an impact on the type of app you could build? >> Very much so. So I think all the streaming applications are very relevant, because more and more on the construction sites, or more and more construction sites are being censored with, whether it's webcams, cameras, temperature detection, dust detection, air quality detection. >> George: IoT. >> Exactly, IoT all over the place, so when we can start collecting the data from those devices, streaming into our systems, we can more proactively notify, warn our customers, people on site, either security risk, any dangerous situation, or simply this is happening right now on your construction site, you might have to wake up because it's the middle of the night, and go check out what's going on. >> This is actually of great interest to us, because one of our themes now, where customers are telling us that they're trying to figure out what type of analytics, especially the machine learning training, would happen in the cloud, and what type of analytics would happen on site, or on customer premise. Are you doing the training up in the Autodesk cloud, and then, are you doing would the models be evaluating and executing sort of on site, close to where the data is being captured? >> So right now, again, early stages. So a lot of those questions we're still trying to figure out and understand what's best, what's best for our customers, what's, obviously the most secure, and things like that. A lot of the training that we're doing today is in the Autodesk cloud, so we use a lot of our cloud infrastructure where the data resides for our products, of course, to build and train our models, essentially. >> Well, we only have a couple of minutes left, but I wanted to dig in to, maybe some of the lessons learned. You said it was early days. So what are some things you could share with the community here on theCUBE that would help them? So maybe just getting started with Spark and some of the valuable lessons you've learned you'd want to share. >> I would say, I would say probably, get started now, is probably my piece of advice. I think we're all going in this direction, a lot of technology, and it's interesting 'cause even the construction space that I'm in is maybe not considered the highest tech, you know, discipline, which your industry, which makes sense and is obvious, but even in the construction space, we're going in the direction of using machine learning, data science, Spark-like technology. So I would say, get started now. That would be my piece of advice, 'cause there's a lot to learn, and things move really fast. >> Okay, so if you could complete this sentence: Spark has finally enabled Autodesk to blank. And start with Spark. I'm trying to get a sound bite out of you. >> Yeah, I think so. Spark has finally allowed Autodesk to build valuable customer-facing machine learning and data science products for our customers. >> And then the business outcomes for that, are being closely watched by executive sponsors, or how does that work? >> They are, but again, early days, right? Any large corporation like Autodesk, early days is a lot of moving parts so we're still feeling the waters of it now. >> Right, George, the last question goes to you. >> I guess, from what you've seen today, and anything you've heard about also coming down the roadmap, how might you expand the application that you are building in terms of thinking new possibilities, pushing the boundary? >> I think Internet of things is something that we're looking at, and I can very well foresee being part of this solution and ecosystem, as well as just allowing, I think, allowing our customers to push and pull their data into our systems to leverage our technologies, or to pull it back out, to plug it into their BI tools, or things like that. And I think that's something that, at least for our enterprise customers, will be very valuable. >> All right. Well, Nathan Murith from Autodesk, thank you so much for spending some time >> Absolutely, thank you. >> here on theCUBE. We're going to let you get back to the show. It looks like the show floor is open now, so get out and network with some of those 3,000 attendees. >> Perfect, thank you very much. >> All right, thank you so much. And thank you for watching. We'll be back soon with more guests, here on theCUBE. (techno music)

>>Welcome back to NYC the Cube's coverage of Mongo DB 2022, a few thousand people here at least bigger than many people, perhaps expected, and a lot of buzz going on and we're gonna talk devs. I'm really excited to welcome back. Robbie Bellson who's the developer relations lead at Verizon and Ian Massingham. Who's the vice president of developer relations at Mongo DB Jens. Good to see you. Great >>To be here. >>Thanks having you. So Robbie, we just met a few weeks ago at the, the red hat summit in Boston and was blown away by what Verizon is doing in, in developer land. And of course, Ian, you know, Mongo it's rayon Detra is, is developers start there? Why is Mongo so developer friendly from your perspective? >>Well, it's been the ethos of MongoDB since day one. You know, back when we launched the first version of MongoDB back in 2009, we've always been about making developers lives easier. And then in 2016, we announced and released MongoDB Atlas, which is our cloud managed service for MongoDB, you know, starting with a small number of regions built on top of AWS and about 2,500 adoption events per week for MongoDB Atlas. After the first year today, MongoDB Atlas provides a managed service for MongoDB developers around the world. We're present in almost a hundred cloud regions across S DCP and Azure. And that adoption number is now running at about 25,000 developers a week. So, you know, the proof are in proof is really in the metrics. MongoDB is an incredibly popular platform for developers that wanna build data-centric applications. You just can't argue with the metrics really, >>You know, Ravi, sometimes there's an analyst who come up with these theories and one of the theories I've been spouting for a long time is that developers are gonna win the edge. And now to, to see you at Verizon building out this developer community was really exciting to me. So explain how you got this started with this journey. >>Absolutely. As you think about Verizon 5g edge or mobile edge computing portfolio, we knew from the start that developers would play a central role and not only consuming the service, but shaping the roadmap for what it means to build a 5g future. And so we started this journey back in late 20, 19 and fast forward to about a year ago with Mongo, we realized, well, wait a minute, you look at the core service offerings available at the edge. We didn't know really what to do with data. We wanted to figure it out. We wanted the vote of confidence from developers. So there I was in an apartment in Colorado racing, your open source Mongo against that in the region edge versus region, what would you see? And we saw tremendous performance improvements. It was so much faster. It's more than 40% faster for thousands and thousands of rights. And we said, well, wait a minute. There's something here. So what often starts is an organic developer, led intuition or hypothesis can really expand to a much broader go to market motion that really brings in the enterprise. And that's been our strategy from day one. Well, >>It's interesting. You talk about the performance. I, I just got off of a session talking about benchmarks in the financial services industry, you know, amazing numbers. And that's one of the hallmarks of, of Mongo is it can play in a lot of different places. So you guys both have developer relations in your title. Is that how you met some formal developer relations? >>We were a >>Program. >>Yeah, I would say that Verizon is one of the few customers that we also collaborate with on a developer relations effort. You know, it's in our mutual best interest to try to drive MongoDB consumption amongst developers using Verizon's 5g edge network and their platform. So of course we work together to help, to increase awareness of MongoDB amongst mobile developers that want to use that kind of technology. >>But so what's your story on this? >>I mean, as I, as I mentioned, everything starts with an organic developer discovery. It all started. I just cold messaged a developer advocate on Twitter and here we are at MongoDB world. It's amazing how things turn out. But one of the things that's really resonated with me as I was speaking with one of, one of your leads within your organization, they were mentioning that as Mongo DVIA developed over the years, the mantra really became, we wanna make software development easy. Yep. And that really stuck with me because from a network perspective, we wanna make networking easy. Developers are not gonna care about the internals of 5g network. In fact, they want us to abstract away those complexities so that they can focus on building their apps. So what better co-innovation opportunity than taking MongoDB, making software easy, and we make the network easy. >>So how do you think about the edge? How does you know variety? I mean, to me, you know, there's a lot of edge use cases, you know, think about the home Depot or lows. Okay, great. I can put like a little mini data center in there. That's cool. That's that's edge. Like, but when I think of Verizon, I mean, you got cell towers, you've got the far edge. How do you think about edge Robbie? >>Well, the edge is a, I believe a very ambiguous term by design. The edge is the device, the mobile device, an IOT device, right? It could be the radio towers that you mentioned. It could be in the Metro edge. The CDN, no one edge is better than the other. They're all just serving different use cases. So when we talk about the edge, we're focused on the mobile edge, which we believe is most conducive to B2B applications, a fleet of IOT devices that you can control a manufacturing plant, a fleet of ground and aerial robotics. And in doing so you can create a powerful compute mesh where you could have a private network and private mobile edge computing by way of say an AWS outpost and then public mobile edge computing by way of AWS wavelength. And why keep them separate. You could have a single compute mesh even with MongoDB. And this is something that we've been exploring. You can extend Atlas, take a cluster, leave it in the region and then use realm the mobile portfolio and spread it all across the edge. So you're creating that unified compute and data mesh together. >>So you're describing what we've been expecting is a new architecture emerging, and that's gonna probably bring new economics of new use cases, right? Where are we today in that first of all, is that a reasonable premise that this is a sort of a new architecture that's being built out and where are we in that build out? How, how do you think about the, the future of >>That? Absolutely. It's definitely early days. I think we're still trying to figure it out, but the architecture is definitely changing the idea to rip out a mobile device that was initially built and envisioned for the device and only for the device and say, well, wait a minute. Why can't it live at the edge? And ultimately become multi-tenant if that's the data volume that may be produced to each of those edge zones with hypothesis that was validated by developers that we continue to build out, but we recognize that we can't, we can't get that static. We gotta keep evolving. So one of our newest ideas as we think about, well, wait a minute, how can Mongo play in the 5g future? We started to get really clever with our 5g network APIs. And I, I think we talked about this briefly last time, 5g, programmability and network APIs have been talked about for a while, but developers haven't had a chance to really use them and our edge discovery service answering the question in this case of which database is the closest database, doesn't have to be invoked by the device anymore. You can take a thin client model and invoke it from the cloud using Atlas functions. So we're constantly permuting across the entire portfolio edge or otherwise for what it means to build at the edge. We've seen such tremendous results. >>So how does Mongo think about the edge and, and, and playing, you know, we've been wondering, okay, which database is actually gonna be positioned best for the edge? >>Well, I think if you've got an ultra low latency access network using data technology, that adds latency is probably not a great idea. So MongoDB since the very formative years of the company and product has been built with performance and scalability in mind, including things like in memory storage for the storage engine that we run as well. So really trying to match the performance characteristics of the data infrastructure with the evolution in the mobile network, I think is really fundamentally important. And that first principles build of MongoDB with performance and scalability in mind is actually really important here. >>So was that a lighter weight instance of, of Mongo or not >>Necessarily? No, not necessarily. No, no, not necessarily. We do have edge cashing with realm, the mobile databases Robbie's already mentioned, but the core database is designed from day one with those performance and scalability characteristics in mind, >>I've been playing around with this. This is kind of a, I get a lot of heat for this term, but super cloud. So super cloud, you might have data on Preem. You might have data in various clouds. You're gonna have data out at the edge. And, and you've got an abstraction that allows a developer to, to, to tap services without necessarily if, if he or she wants to go deep into the S great, but then there's a higher level of services that they can actually build for their customers. So is that a technical reality from a developer standpoint, in your view, >>We support that with the Mongo DB multi-cloud deployment model. So you can place Mongo DB, Atlas nodes in any one of the three hyperscalers that we mentioned, AWS, GCP or Azure, and you can distribute your data across nodes within a cluster that is spread across different cloud providers. So that kinds of an kind of answers the question about how you do data placement inside the MongoDB clustered environment that you run across the different providers. And then for the abstraction layer. When you say that I hear, you know, drivers ODMs the other intermediary software components that we provide to make developers more productive in manipulating data in MongoDB. This is one of the most interesting things about the technology. We're not forcing developers to learn a different dialect or language in order to interact with MongoDB. We meet them where they are by providing idiomatic interfaces to MongoDB in JavaScript in C sharp, in Python, in rust, in that in fact in 12 different pro programming languages that we support as a first party plus additional community contributed programming languages that the community have created drivers for ODMs for. So there's really that model that you've described in hypothesis exist in reality, using >>Those different Compli. It's not just a series of siloed instances in, >>In different it's the, it's the fabric essentially. Yeah. >>What, what does the Verizon developer look like? Where does that individual come from? We talked about this a little bit a few weeks ago, but I wonder if you could describe it. >>Absolutely. My view is that the Verizon or just mobile edge ecosystem in general for developers are present at this very conference. They're everywhere. They're building apps. And as Ian mentioned, those idiomatic interfaces, we need to take our network APIs, take the infrastructure that's being exposed and make sure that it's leveraging languages, frameworks, automation, tools, the likes of Terraform and beyond. We wanna meet developers where they are and build tools that are easy for them to use. And so you had talked about the super cloud. I often call it the cloud continuum. So we, we took it P abstraction by abstraction. We started with, will it work in one edge? Will it work in multiple edges, public and private? Will it work in all of the edges for a given region, public or private, will it work in multiple regions? Could it work in multi clouds? We've taken it piece by piece by piece and in doing so abstracting way, the complexity of the network, meaning developers, where they are providing those idiomatic interfaces to interact with our API. So think the edge discovery, but not in a silo within Atlas functions. So the way that we're able to converge portfolios, using tools that dev developers already use know and love just makes it that much easier. Do, >>Do you feel like I like the cloud continuum cause that's really what it is. The super cloud does the security model, how does the security model evolve with that? >>At least in the context of the mobile edge, the attack surface is a lot smaller because it's only for mobile traffic not to say that there couldn't be various configuration and human error that could be entertained by a given application experience, but it is a much more secure and also reliable environment from a failure domain perspective, there's more edge zones. So it's less conducive to a regionwide failure because there's so many more availability zones. And that goes hand in hand with security. Mm. >>Thoughts on security from your perspective, I mean, you added, you've made some announcements this week, the, the, the encryption component that you guys announced. >>Yeah. We, we issued a press release this morning about a capability called queryable encryption, which actually as we record this Mark Porter, our CTO is talking about in his keynote, and this is really the next generation of security for data stored within databases. So the trade off within field level encryption within databases has always been very hard, very, very rigid. Either you have keys stored within your database, which means that your memory, so your data is decrypted while it's resident in memory on your database engine. This allow, of course, allows you to perform query operations on that data. Or you have keys that are managed and stored in the client, which means the data is permanently OBS from the engine. And therefore you can't offload query capabilities to your data platform. You've gotta do everything in the client. So if you want 10 records, but you've got a million encrypted records, you have to pull a million encrypted records to the client, decrypt them all and see performance hit in there. Big performance hit what we've got with queryable encryption, which we announced today is the ability to keep data encrypted in memory in the engine, in the database, in the data platform, issue queries from the client, but use a technology called structural encryption to allow the database engine, to make decisions, operate queries, and find data without ever being able to see it without it ever being decrypted in the memory of the engine. So it's groundbreaking technology based on research in the field of structured encryption with a first commercial database provided to bring this to market. >>So how does the mobile edge developer think about that? I mean, you hear a lot about shifting left and not bolting on security. I mean, is this, is this an example of that? >>It certainly could be, but I think the mobile edge developer still stuck with how does this stuff even work? And I think we need to, we need to be mindful of that as we build out learning journeys. So one of my favorite moments with Mongo was an immersion day. We had hosted earlier last year where we, our, from an enterprise perspective, we're focused on BW BS, but there's nothing stopping us. You're building a B2C app based on the theme of the winner Olympics. At the time, you could take a picture of Sean White or of Nathan Chen and see that it was in fact that athlete and then overlaid on that web app was the number of medals they accrued with the little trumpeteer congratulating you for selecting that athlete. So I think it's important to build trust and drive education with developers with a more simple experience and then rapidly evolve overlaying the features that Ian just mentioned over time. >>I think one of the keys with cryptography is back to the familiar messaging for the cloud offloading heavy lifting. You actually need to make it difficult to impossible for developers to get this wrong, and you wanna make it as easy as possible for developers to deal with cryptography. And that of course is what we're trying to do with our driver technology combined with structure encryption, with query encryption. >>But Robbie, your point is lots of opportunity for education. I mean, I have to say the developers that I work with, it's, I'm, I'm in awe of how they solve problems and I, and the way they solve problems, if they don't know the answer, they figure out how to go get it. So how, how are your two communities and other communities, you know, how are they coming together to, to solve such problems and share whether it's best practices or how do I do this? >>Well, I'm not gonna lie in person. Events are a bunch of fun. And one of the easiest domain knowledge exchange opportunities, when you're all in person, you can ideate, you can whiteboard, you can brainstorm. And often those conversations are what leads to that infrastructure module that an immersion day features. And it's just amazing what in person events can do, but community groups of interest, whether it's a Twitch stream, whether it's a particular code sample, we rely heavily on digital means today to upscale the developer community, but also build on by, by means of a simple port request, introduce new features that maybe you weren't even thinking of before. >>Yeah. You know, that's a really important point because when you meet people face to face, you build a connection. And so if you ask a question, you're more likely perhaps to get an answer, or if one doesn't exist in a, in a search, you know, you, oh, Hey, we met at the, at the conference and let's collaborate on this guys. Congratulations on, on this brave new world. You're in a really interesting spot. You know, developers, developers, developers, as Steve bomber says screamed. And I was glad to see Dave was not screaming and jumping up and down on the stage like that, but, but the message still resonates. So thank you, definitely appreciate. All right, keep it right there. This is Dave ante for the cubes coverage of Mago DB world 2022 from New York city. We'll be right back.

(bright music) >> You know, there's a lot of negative press around the technology industry these days. The tech lash is somewhat understandable, people are struggling and yet the tech industry is booming, creating incredible wealth for a relatively select group of people. I get it. But the reality is, that the technology industry has guided us through the pandemic, allowing us to work remotely, securing our employees, keeping goods and services flowing, and using data and analytics to track COVID and accelerate the development of vaccines. And many in the tech industry are passionate about giving back and applying their talents to solve real world problems. I'll give you an example. After accidents, cancer is the number one cause of death among young people. In the middle of the 20th century, the survival rate for kids with cancer was 0.0%. Today, it's above 85%. Cancer in kids is much different than in adults. The types of cancer, the diagnoses, the treatments, they vary. Different types of research are required to attack the problem. And that takes money. And one of the people here in Boston and beyond that's using his talents, his creativity, his network, and yeah, his wealth, to attack this problem, is my friend, Chris Lynch, entrepreneur, investor, and philanthropist. Chris, awesome to see ya. Welcome back to theCUBE my friend. >> Thanks, Dave. It's great to be here. >> So, listen, this personal story of yours, how'd you get into, where's the passion come from for kids with cancer? >> Dave, it's actually related to one of my startup endeavors. When you're starting, bootstrapping your company, you're typically staying at people's homes to save money. >> Sleeping on couches. Yeah. >> Yeah, yeah. Pretty much. And for the years of these startups, I've developed relationships with families all over the world, 'cause I've literally lived with them for periods of time until the companies got to points where we didn't have to do that. And there was a family in Seattle that I used to stay with, and they had a son that was a similar age to one of mine and he ultimately passed of cancer. And I stayed with the family, and I stayed with them a few times while they were going through this, and I was touched, I was inspired by their courage, how positive they were. I was thinking in my own circumstance, how could I, I would just hate the world. And in these families, I stay there, they call me Uncle Chris. And I was having dinner at the family home and I was looking at the boy, and I excused myself, went to the bathroom and I started sobbing, and he knocks on the door, comes in and says, "Uncle Chris, it's okay. My dad tells me you can do anything. Just do whatever you can so that other kids don't have what I have." You know and... >> Wow. Wow. And I can see the emotion that you're feeling right now, bringing us back to that moment. >> Well. Yeah. >> It's unbelievable. All right, so you got Tech Tackles Cancer. Is this your latest venture? I think the last one was 2018. It's coming back, took a break 'cause of COVID, and this is going to go down on the 21st at The Sinclair in Harvard Square. Bring a bunch of people in. We got a number of people who have signed up to, actually you're one of them, of course, but to sing karaoke, raise a bunch of dough, and then there's like a little contest, right? So... (he chuckles) Alex, bring up that slide. I got to show the audience who we got here. And this is, Chris, this is your competition. So, here you go. We got, Steve Duplessie, right? That's a great picture, Steve. Thanks for doing this, right. Nathan Hall, who's at Pure Storage. Steiny, Ken Steinhardt, from INFINIDAT. And you got George Hope at HPE. And Joe Lemay, who's an inventor, he's the CEO of Rocketbook. Any of these guys worry you? >> I'm going to sleep easy tonight. (Dave laughs) >> So, how did you get into rock and roll? You wrote a blog one time. You quoted Nietzsche saying that life without music would be a mistake. Rock and roll. Rock on. How'd you get into rock and what's your passion there? >> Well, I always loved rock and roll but I had someone that was staying with us who was a student at BU, and he went to his semester abroad, he went to the UK. And he came back with all this punk rock music, the Sex Pistols and all this stuff. And I heard it and it just triggered something in me. And then I didn't want to do anything but play music and try to be a musician, and my grades and everything else suffered as a result. But music's always inspired me, the creativity, the boldness. A lot of things that I think I apply to my startup life. >> How could people help? Let's say they want to get involved. I mean, obviously, they can attend the event, they donate. What should people do? They could sing? >> Yeah. So they can certainly sponsor the event. There are a number of sponsorship opportunities. They can participate. They can volunteer for the event. It is an all-volunteer organization. Every dollar that we raise goes to the charities that we've listed. And we handle everything else through a lot of arm twisting and whatnot. >> Great. So it's June 24th, sorry, June 21st, at The Sinclair, which is right in Harvard Square. So it's live band karaoke, right? >> Correct. >> I've seen some of the, we're going to share a little clip there. And so, it's a call to action to all you rock and roll technology gods out there. You know, we showed you the five folks plus Chris who were doing it, and so we're dying to see you up there again, you must be really excited about it. >> I am, I am. I'm going to be much better than last time. >> Okay. Well, so just on that note we'll close with a little taste of what's in store for June 21st. We'll see you there. ♪ Now my loneliness ♪ ♪ Is killing me now ♪ ♪ You know I still believe ♪ ♪ Midnight, midnight to six ♪ ♪ Midnight, midnight to six ♪ ♪ Midnight, midnight to six ♪ ♪ Believe in things that you don't understand ♪ ♪ then you're su... ♪ (bright music)

(upbeat music) >> On June 21st in Cambridge mass at the Sinclair in Harvard Square, Tech Tackles Cancer is back after a COVID hiatus with live band karaoke and some local tech celebrities raising money for a great cause. The Cube is a media sponsor of the event and Josh Epstein, local marketing exec and one of the events organizers is here to tell us more. Josh, good to see you, welcome. >> Good to be here, Dave. >> So tell us about this event. What's going on? What are the logistics? How's that all work? >> Yeah, we're super excited. So as you said, June 21st at the Sinclair in Harvard Square, Sinclair, if you haven't been there is just the great old school rock club. So we'll be there from 6:00 to 10:00. We will have live band karaoke. So the main event and kind of the primary fundraising approach here is that we have some celebrity technology rock gods these featured performers like Chris Lynch who was the founder of Tech Tackles Cancer, who are are raising money from basically now, up until June 21st. Then at the event, their fundraising will culminate with them singing a live song backed by a live band. And the awards will be given out to the most money raised, the best performance and the best stage presence. So it will be a lot of fun. >> So the fundraising format is I sign up to sing do the karaoke with a live band which is a little bit different. And then I raise as much dough as possible. So obviously that's competitive. >> It's competitive, I think that we ask for a minimum of $10,000 targeted for each of the fundraisers but knowing these guys, knowing guys like Chris Lynch, they don't like to lose. So the bet here is that people are going to go out, they're going to hit their network and they are going to look to kind of raise the most money. So we anticipate this to be a great event with a lot of money raised and a lot of fun. >> So we have a graphic from Alex. If you could bring that up of the people who have signed up for this already. We got Steve Duplessie, founder of of ESG, senior analyst. They sold their company to Tech Target, which is awesome. Congratulations to those guys and thank you for stepping up. George Hope, who heads partner sales for HPE, Joe Lemay of Rocketbook Nathan Hall from Pure Storage, system engineering guy and of course, Steiny, Ken Steinhardt from Infinidat. He was at EMC, he's the field CTO now. He's going to be up there singing. So of course, Chris. >> Absolutely, these are just the early entrance here. So we just started really working our networks. And obviously, I'm a Boston tech guy kind of working the storage networks, the networking networks and kind of the other folks that are around. So as we come out of stealth here in April and start really recruiting, we anticipate having probably 10 to 15 of these featured performers, really fundraising performers that we'll sing. And then we're also obviously soliciting broader donations from anyone who wants to come to the event or just give to the cause and the corporate sponsorships as well. >> All right, so you got corporate sponsorships. You can sing, you can donate you can be there just to support it. That's fantastic and the awards, how's that work? >> Yeah, so we're excited. So first off, most money raised wins an award. So we'll have a leaderboard on the website, we'll be able to kind of track who's raised what, at the event, we're going to have some celebrity judges that will be actually voting for their favorites and then have a crowdsource component as well. So we'll introduce what that mechanism is. But as people, either at the events or a watching in streamed live on LinkedIn live, we'll actually vote for their favorite performance as well as their their pick for best stage presence which we know in rock and roll is half the battle. >> Now this cause has raised a bunch of, I think last time, you guys did this, it was probably a quarter million or close to it and you support multiple causes. What causes are you supporting? >> Sure, yeah, actually I think since they founded the event several years ago they raised over $2 million. This year for this format where we're looking, we can really up our game here but this year we're supporting two really great causes that are both focused on pediatric cancer. The first is St. Batrick's that is really committed to raising funds for research to really help stamp out pediatric cancer really. The approach to researching cures and treatments to pediatric cancer is very different from regular adult cancer. So St. Batrick's does a great job of picking those research projects that really target in on those pediatric cancer causes. And then the second is one mission. And one mission really outlooks to help make pediatric cancer patients that are spending time in the hospital, making their time less stressful, less painful, less sad, less boring. And so they do a lot of fundraising and contributions targeting children's hospitals, really around the country for those pediatric cancer floors. >> Josh, amazing cause. Thanks so much for coming onto the Cube and explaining all that. >> Great, thanks David. >> All right, June 21st, go to ttcfund.org, Tech Tackles Cancer fund, ttcffund.org for more information and you can donate. We'll see you there. (soft music)

[Music] you know there's a lot of negative press around the technology industry these days the tech lash that's somewhat understandable people are struggling and yet the tech industry is booming creating incredible wealth for a relatively select group of people i get it but the reality is that the technology industry has guided us through the pandemic allowing us to work remotely securing our employees keeping goods and services flowing and using data and analytics to track covet and accelerate the development of vaccines and many in the tech industry are passionate about giving back and applying their talents to solve real world problems i'll give an example after accidents cancer is the number one cause of death among young people in the middle of the 20th century the survival rate for kids with cancer was 0.0 percent today it's above 85 percent cancer in kids is a much different than in adults the types of cancer the diagnoses the treatments they vary different types of research are required to attack the problem and that takes money and one of the people here in boston and beyond that's using his talents his creativity his network and yeah his wealth to attack this problem is my friend chris lynch entrepreneur investor and philanthropist chris awesome to see you welcome back to thecube my friend thanks dave it's great to be here so listen this personal story of yours how did you get into where's the passion come from for kids with cancer dave it's actually related to one of my startup endeavors when you're starting bootstrapping company you're typically staying at people's homes and you know to save money sleeping on couches yeah yeah pretty much and um you know through the years of these startups i've developed relationships with families all over the world you know because i've literally lived with them you know for periods of time until the companies get to points where we didn't have to do that and um there was a family in seattle that i used to stay with and they had a son that was a similar age to one of mine and he ultimately passed of cancer and i stayed with the family and i stayed with them a few times while they were going through this and i was touched i was inspired by their courage how positive they were i was thinking in my own circumstance how could i i would just hate the world and you know in the you know in these families i stay there you know they call me uncle chris and um i was having dinner at the you know at the family home and i was looking at the boy and uh i excused myself went to the to the bathroom and i started sobbing and um he knocks on the door comes comes in and says uncle chris it's okay my dad tells me you can do anything just do whatever you can so that other kids don't have what i have you know in it wow wow and i can see the emotion that you're feeling right now bringing bringing us back to that moment it's it's unbelievable and and the thing is when you started st baldrick's it wasn't it was obviously about the kids but it was also about the family as well right because they're going through right i mean you know we all know as parents how hard it is to be a parent can you imagine having a parent that's you know got a disease like that so it's not just about you know the the cancer and the research it's about the supporting the families as well right that's right and that's why one mission is you know one one of our um you know big beneficiaries you know of of the work we do um because it's obviously we want to find cures um but people you know families are affected every day and we need to provide them the kind of support um you know that that they any child should have and any family should have in this circumstance all right so you got tech tackles cancer this is your latest venture i think the last one was uh 2018. it's coming back took a break because of covid obviously uh but so it's live band karaoke it's the tech industry your network and beyond really kind of giving back how does that all work well basically you know we we once i learned that pediatric cancer was different and that there was it was underfunded we wanted to raise awareness for that we wanted to raise funds to take a different approach applying sort of venture principles how i invest in companies and find the best research in the world which is not in any four walls of any sort of research center so we get the best research from around the world and that we decided to put the money invest the money as well as the support services around those that you know are affected today yeah okay so we've got actually so what's going to happen and this is going to go down on the the 21st at the sinclair and harvard square bring a bunch of people in we've got a number of people who have signed up to actually you're one of them of course but to to sing karaoke raise a bunch of dough and then there's a little contest right no alex bring up that slide i gotta i gotta show the audience who we got here this is chris this is your competition uh so here you go you got we got steve duplessi right he has great picture steve thanks for doing this right nathan hall who's at pure storage steiny ken steinhardt from infinidat and you got george hope at hpe and joe lemay who's uh he's inventor he's a ceo a rocket book any of these guys where are you i'm going to sleep easy tonight [Laughter] how did you get into rock and roll you wrote a blog one time you you quoted nietzsche is saying that life without music would be a mistake you know rock and roll rock on how did you get into rock and roll well i always loved rock and roll but i had that was staying with us he was a student at bu and he he went to his semester abroad he went to the uk and he came back with all this punk rock music the sex pistols and all the stuff and um i heard it and it just triggered something in me and that i didn't want to do anything but play music and you know try to be a musician and um you know my grades and everything else suffered as a result but music's always inspired me the creativity the boldness a lot of things that i think i apply to my startup life how can people help let's say they want to get involved i mean obviously they can attend the event they donate what what should people do they could sing yeah so they could they could certainly sponsor the event there are a number of sponsorship opportunities um they can participate they can volunteer for the event it is an all volunteer organization every dollar that we raise goes to the charities that we've listed um and we handle everything else through a lot of arm twisting and you know and whatnot great so it's june 24th uh sorry june 21st at the sinclair which is right in harvard square so it's live band karaoke right i've seen some of the we're gonna share a little a little a little clip there and so it's a call to action to all you you rock and roll technology gods out there you know we showed you the the five folks plus chris who were doing it um and so we're dying to to see you up there again you must be really excited about it i am i am i'm going to be much better than last time okay well so just on that note we'll close with a little taste of what's in store for june 21st we'll see you there [Music] midnight midnight midnight midnight six midnight midnight six things that you don't understand in yourself [Music] you

>>With me now or Maneesh outer wall, senior director of product management for a HyperFlex. It's Cisco at flash for all. Number four. I love that on Twitter and Darren Williams, the director of business development and sales for Cisco, Mr. HyperFlex at Mr. HyperFlex on Twitter. Thanks guys. Hey, we're going to talk about some news and in HyperFlex and what role it plays in accelerating the hybrid cloud journey. Gentlemen, welcome to the cube. Good to see you. >>Thanks David. >>Hi, Darren. Let's start with you. So for hybrid cloud, you got to have on-prem connection, right? So you got to have basically a private cloud. What are your thoughts on that? >>Yeah, we agree. You can't, you can't have a hybrid cloud without that private adamant. And you've got to have a strong foundation in terms of how you set up the, the whole benefit of the cloud model you build in, in terms of what you want to try and get back from the cloud. You need a strong foundation. High conversions provides that we see more and more customers requiring a private cloud in their building with hyper conversions in particular HyperFlex, Mexican bank, all that work. They need a good strong cloud operations model to be able to connect both the private and the public. And that's where we look at insight. We've got solution around that to be able to connect that around a SAS offering Nathan looks around simplified operations, give some optimization and also automation to bring both private and public together in that hybrid world. >>Darren let's stay with you for a minute. When you talk to your customers, what are they thinking these days? W when it comes to implementing hyper-converged infrastructure in both the enterprise and at the edge, what are they trying to achieve? >>So, so there's many things they're trying to achieve. My probably the most brutal, honest is they're trying to save money. That's probably the quickest answer, but I think they're trying to look at, in terms of simplicity, how can they remove laser components they've had before in their infrastructure, we see obviously collapsing of storage into hyperconversions and storage networking. And we got customers that have saved 80% worth of savings by doing that class into a hyper conversion infrastructure away from their three tier infrastructure, also about scalability. They don't know the end game. So they're looking about how they can size for what they know now and how they can grow that with hyper-conversion. It's very easy. It's one of the major factors and benefits of hyperconversions. They also obviously need performance and consistent performance. They don't want to compromise performance around their virtual machines when they want to run multiple workloads, they need that consistency all the way through. >>And then probably one of the biggest ones is that around the simplicity model is the management layer, ease of management to make it easier for their operations. And we've got customers that have told us they've saved 50% of costs in that operations model, deploying flex also around the time-savings. They make massive time savings, which they can reinvest in their infrastructure and their operations teams in being able to innovate and go forward. And then I think probably one of the biggest pieces where you've seen as people move away from the three tier architecture is the deployment elements. And the ease of deployment gets easy with hyper-converged, especially with edge edge is a major, key use case for us. And what our customers want to do is get the benefit of the data center at the edge without a big investment. They don't want to compromise on performance, and they want that simplicity in both management and employment. >>And we've seen our analyst recommendations around what their readers are telling them in terms of how management deployments key for it, operations teams and how much they're actually saving by deploying edge and taking the burden away when they deploy hyper conversions. And as I said, the savings elements, the key there, and again, not always, but obviously there's all case studies around about public cloud being quite expensive at times over time for the wrong workloads. So by bringing them back, people could make savings. And we again have customers that have made 50% savings over three years compared to their public cloud usage. So I'd say that's the key things that customers are looking for. Yeah. >>Great. Thank you for that, Darren, uh, Monisha, we have some hard news. You've been working a lot on evolving the hyper flex line. What's the big news that you've just announced. >>Yeah. Thanks Dave. Um, so there are several things that we are seeing today. The first one is a new offer, um, called HyperFlex express. This is, uh, you know, Cisco intersite lend and Cisco intersect managed it HyperFlex configurations that we feel are the fastest spot to hybrid cloud. The second is we're expanding our service portfolio by adding support for each X on EMD rack, uh, UCS M D rack. And the code is a new capability that we're introducing that we calling, um, local and containerized witness and get, let me take a minute to explain what this is. This is a pretty nifty, uh, capability to optimize for, for an edge environments. So, you know, this leverage is the Cisco's ubiquitous presence, uh, of the networking, um, products that we have in the environments worldwide. So the smallest HyperFlex configuration that we have is, uh, configuration, which is primarily used in edge environments, think of a, you know, a backup woman or department store, or it might even be a smaller data center somewhere on the blue for these two, not two configurations. >>There is always a need for a third entity that, uh, you know, industry down for that is either a witness or an arbitrator. Uh, we had that for HyperFlex as well. And the problem that customers face is where do you host this witness? It cannot be on the cluster because it's the job of the witnesses to when the infrastructure is going. Now, it basically breaks, um, sort of, uh arbitrates which node gets to survive. So it needs to be outside of the cluster, but finding infrastructure, uh, to actually host this is a problem, especially in the edge environments where these are resource constrained environments. So what we've done is we've taken that test. We've converted it into a container or a form factor, and then qualified a very large slew of Cisco networking products that we have, right from ISR ESR, mixers, catalyst, industrial routers, uh, even, uh, even as we buy that can host host this witness, eliminating the need for you to find yet another piece of infrastructure are doing any, um, you know, Caden feeding or that infrastructure. You can host it on something that already exists in the environment. So those are the three things that we are announcing today. >>I want to ask you about HyperFlex express. You know, obviously the, the whole demand and supply chain is out of whack. Everybody's, you know, global supply chain issues are in the news, everybody's dealing with it. Can you expand on that a little bit more? Can, can HyperFlex express help customers respond to some of these issues? >>Yeah, indeed. The, um, you know, the primary motivation for HyperFlex express was indeed, uh, an idea that, uh, you know, one of the folks on my team had, we was to build a set of HyperFlex configurations that are, you know, would have a shorter lead time, but as we were brainstorming, we were actually able to tag on multiple other things and, uh, make sure that, uh, you know, that is in it for something in it for customers, for sales, as well as our partners. Uh, so for example, uh, you know, for customers, uh, we've been able to dramatically simplify the configuration and the install for HyperFlex express. These are still high-paced configurations, and you would at the end of it, get a HyperFlex cluster, but the part to that cluster is much, much, uh, simplifying. Uh, second is that we've added an flexibility where you can now deploy these, uh, these are data center configurations, but you can deploy these with, or without fabric interconnects, meaning you can deploy with your existing top of rack. >>Um, we've also added a, uh, attractive price point for these. And, uh, of course, uh, you know, these will have a better lead times because we made sure, uh, that, uh, you know, we are using components that are, um, that we have clear line of sight from a supply perspective for partner and sales. This is represents a high velocity sales motion, a foster doughnut around time, uh, and a frictionless sales motion for our distributors. Uh, this is actually a set of distinct friendly configurations, which they would find very easy to stock. And with a quick turnaround time, this would be very attractive for, uh, the disease as well. >>It's interesting Maneesh, I'm looking at some fresh survey data set more than 70% of the customers that were surveyed. This is ETR survey. Again, I mentioned them at the top more than the 70% said they had difficulty procuring a server hardware and networking was also a huge problem. So, so that's encouraging. Um, what about ministry, uh, AMD that's new for HyperFlex? What's that going to give customers that they couldn't get before? >>Yeah, Dave, so, uh, you know, in the short time that we've had UCS EMD direct support, we've had several record breaking benchmark results that we've published. So it's a, it's a, it's a powerful platform with a lot of performance in it. And HyperFlex, uh, you know, the differentiator that we've had from day one is that it is, it has the industry leading storage performance. So with this, we are going to get the masters compute together with the foster storage and this, we are logging that will, it'll basically unlock, you know, a, um, unprecedented level of performance and efficiency, but also unlock several new workloads, uh, that were previously locked out from the hyper-converged experience. >>Yeah. Cool. Um, so Darren, can you, can you give us an idea as to how HyperFlex is doing in the field? >>Sure, absolutely. So I've made, Maneesha been involved right from the Stein before it was called hype and we we've had a great journey and it's very exciting to see where we're taking, where we've been with the $10 year. So we have over 5,000 customers worldwide, and we're currently growing faster year over year than the market. Um, the majority of our customers are repeat buyers, which is always a good sign in terms of coming back when they've, uh, approved for technology and are comfortable with the technology. They repeat by expanded capacity, putting more workloads on they use in different use cases on that. And from an age perspective, more numbers of science. So really good endorsement, the technology, um, we get used across all verticals or segments, um, to house mission critical, uh, applications, as well as the, uh, traditional virtual server infrastructures, uh, and where the lifeblood of our customers around those mission critical customers. >>They want example, and I apologize for the worldwide audience, but this resonates with the American audiences, uh, the super bowl. So, uh, the like, uh, stadium that house, the soup, well actually has Cisco HyperFlex, right? In all the management services through, from the entire stadium for digital signage, 4k video distribution, and it's compete completely cashless. So if that were to break during the super bowl, that would have been a big, uh, news article, but it was run perfectly. We in the design of the solution were able to collapse down nearly 200 service into a few nodes, across a few racks and at a hundred, 120 virtual machines running the whole stadium without missing a heartbeat. And that is mission critical for you to run super bowl and not be on the front of the press afterwards for the wrong reasons. That's a win for us. So we really are really happy with the high place where it's going, what it's doing. And some of the use cases we're getting involved in very, very excited. >>He come on Darren Superbowl, NFL, that's, uh, that's international now. And you know, it's, it's dating London. Of course, I see the, the picture of the real football over your shoulder. But anyway, last question for minis. Give us a little roadmap. What's the future hold for HyperFlex. >>Yeah, so, you know, as Dan said, what data and I have been involved with type of flicks since the beginning, uh, but, uh, I think the best is we have to come. Uh, there are three main pillars for, uh, for HyperFlex. Um, one is intersite is central to our strategy. It provides a lot of customer benefit from a single pane of glass, um, management, but we are going to date this beyond the lifecycle management, which is a for HyperFlex, which is integrated. You're going to say today and element management, we're going to take it beyond that and start delivering customer value on the dimensions of AI ops, because intersect really provides us a ideal platform to gather slides from all the clusters across the globe, do AIML and do some predictive analysis with that and return it back as, uh, you know, customer value, um, actionable insights. >>So that is one, uh, the second is UCS expand the HyperFlex portfolio, go beyond UCS to third party server platforms and newer, uh, UCS, several platforms as well. But the highlight, there is one that I'm really, really excited about and think that there is a lot of potential in terms of the number of customers we can help is HX on X, CDs, uh, extra users. And other thing that'd be able to, uh, you know, uh, uh, get announcing a bunch of capabilities on in this particular launch. Uh, but each Axonics cities will have that by the end of this calendar year. And that should unlock with the flexibility of X of hosting, a multitude of workloads and the simplicity of HyperFlex. We were hoping that would bring a lot of benefits to new workloads, uh, that were locked out previously. And then the last thing is HyperFlex need a platform. >>This is the heart of the offering today, and you'll see the hyperlinks data platform itself. It's a distributed architecture, a unique architecture, primarily where we get our, you know, uh, they got bidding performance wrong. You'll see it get foster a more scalable, more resilient, and we'll optimize it for, uh, you know, containerized workloads, meaning it will get a granular container, a container, granular management capabilities and optimize for public cloud. So those are some things that we are, the team is busy working on, and we should see that come to fruition. I'm hoping that we'll be back at this forum in maybe before the end of the year and talking about some of these new capabilities. >>That's great. Thank you very much for that. Okay guys, we gotta leave it there. And, you know, Monisha was talking about the HX on X series. That's huge. Customers are gonna love that. And it's a great transition because in a moment I'll be back with VKS Ratana and Jim leech, and we're going to dig into X series. Some real serious engineering went into this platform and we're gonna explore what it all means. You're watching simplifying hybrid cloud on the cube. You're a leader in enterprise tech coverage.

(upbeat music) (upbeat music) >> Hello, and welcome back to theCUBE's coverage of Red Hat Summit 2021 virtual. I'm John furrier, host of theCUBE We've got a great segment with a customer Roberto Calandrini, Head of Architecture, Digital and AI services for Snam customer need to leak oil and gas and AI services for Snam customer need to leak oil and gas great industrial IOT and digital transformation. Roberto, thank you for coming on the cube and spending the time. >> Hi, John. Good to see you. Thank you for inviting me. >> That's awesome. Before we get started, I love the story and again I think security edge and in, in in disease industry for disruptions is huge story here. But before we get started, talk about Snam. Give me a quick overview of Snam, who you guys are. What's your focus customers you have and your role there. >> Of course. So it was not is one of the major global energy infrastructure company and is managing a international and a national asset specifically and a national asset specifically in the natural gas utility segment. There's what the story Kelly Snam did. And it recently positioned itself as a leader of the energy transition, investing a lot in startups of the energy transition, investing a lot in startups mostly focused on, for example, H2 so hydrogen, these the very recent topic, bio Nathan with numb for environment sustainable mobility, energy efficiency, and reforestation. So we kind of So we kind of expanded our core businesses in terms of positioning ourselves much more within the energy transition segments and still developing a lot, what we used to do in the natural gas, in the natural gas industry. And my role there is, as you said, Head of Architecture And my role there is, as you said, Head of Architecture Digital and AI Services. So I'm basically responsible for managing the entire technology stack of Snam and focusing a lot on developing artificial intelligence services for our business lines. >> That's awesome. Well, thanks for sharing that. Let's talk about the digital transmission you've been rearchitecting. You guys redesign your applications map impacting your architecture from the data center to the edge recently, even the center of that your responsibility for the business. What were the business drivers and objectives for you to reach that transformation goal and target? >> Yeah, thanks for, for the question. So they basically, we were mainly interested in exploiting three main three main objectives with our transformation. The first was very much related to our business strategy. So having a more agile So having a more agile and flexible digital architecture that will still on one one end provide us with the reliability that we need in order to sustain our business critical application. And on the other end, provide the agility And on the other end, provide the agility and flexibility the speed in some sense that our new business line will lead in order to succeed. So let's say speed and agility. The second one was a focus on platformization and servitization of our industry specific application. So what we used to develop, as So what we used to develop, as let's say, very focused full stack application now, thanks to the modern architectures can be developed on top of platforms or using microservices. on top of platforms or using microservices. And that will apart from providing us agility And that will apart from providing us agility and flexibility will give us more alignment will give us more alignment between what we invest. So the cost of our software development efforts So the cost of our software development efforts and the business value we derive and the business value we derive from the software we produce basically. >> John: Can I... >> So I focus on value. >> Can I ask you real quick on the business drivers? Can you talk about the impact of domain expertise? One of the trends we're seeing is you want to scale of cloud and having an architecture that's going to enable value creation and customer value for your customers but in these vertical disruptions these new opportunities in these industries like you're a very specialized industry get natural gas and you still need that domain expertise if you want to tap in and advantage of the AI. >> Absolutely. >> Can you share your vision on how you're doing that and how that relates to the business driver? >> Yeah. So let's say that this is very, very aligned with >> Yeah. So let's say that this is very, very aligned with with our strategy that focuses with our strategy that focuses on platformization servitization. So if you think So if you think about how we can explore the best, the value of our people so our industry specific expertise, there are two main ways. The first is to build from scratch as we used to do The first is to build from scratch as we used to do in the past full stack applications that are really focused on a specific, this specific need of a business line. And so focused on the business side of the industry or we can leverage modern architecture and develop services that serve that specific need. and develop services that serve that specific need. So this will let us basically being able to So this will let us basically being able to So this will let us basically being able to satisfy our internal customer. So our internal clients and the business need and at the same time, being able to use that software so that service for an external customer or potential potentially for, for our peers. So in order to provide value exploiting our business expertise, in order to, for example you cited AI using what we developed as an AI system, for example, for two in order to solve demand for customer problems and provide that same business value for, for for other companies that are are they share our same business need. >> Yeah. It's a data workload. I mean, it's at the end of the day you need the data >> Exactly. >> and that's going to come back. I want to unpack the data workload when we talk about the edge, but real quick, I want to talk about the role red hat played in your journey to execute your architecture and transformation. Can you share how Red Hat helped you in this? >> Sure. So let's say that, you know, >> Sure. So let's say that, you know, it all began in two, 2018. it all began in two, 2018. When we started to set up our cloud readiness map When we started to set up our cloud readiness map in order to assess what we will, we'll be able to transform. in order to assess what we will, we'll be able to transform. So scale lift and shift or refactor of of our application map into a modern architecture application. into a modern architecture application. So this cloud readiness journey started So this cloud readiness journey started with assessing the level of modularity with assessing the level of modularity with assessing the level of modularity in some way of some of our main applications. And what we started to do is to develop the first blueprints in order to start to develop new system in order to start to develop new system and new application on a cloud native framework and new application on a cloud native framework and Red Hat really Apple with this but providing a container orchestration platform OpenShift on which we started to build up our new, our new application, that up our new, our new application, that so the cloud native application by application map so the cloud native application by application map then in 2019, we started to accelerate this then in 2019, we started to accelerate this let's say moving to a CNA environment journey. let's say moving to a CNA environment journey. let's say moving to a CNA environment journey. And we started to move the first 10 to 20% And we started to move the first 10 to 20% of our workload on the platform as a service environment. of our workload on the platform as a service environment. So an OpenShift and this is something that we are still doing while at the same time, developing different project at the same time, developing different project that tries to turn what we used to have developed that tries to turn what we used to have developed as custom application toward platforms. as custom application toward platforms. So we are basically transforming our application map leveraging the power for what regards to the customer application of modern architectures. So microservices bays So microservices bays and the container orchestration platform provided by Red Hat OpenShift. And at the same time the other main technological driver is platform migration. the other main technological driver is platform migration. So with basically trying to leverage, especially for the processes that are already very standardized. for the processes that are already very standardized. So usually corporate processes. So staff SEF function processes what we're doing there is to build on top of very what we're doing there is to build on top of very let's say industry standard platform. I don't want to, to provide you with names but you can imagine most but you can imagine most of them are software as a service platforms. And this is really happiness because we are as a target. And this is really happiness because we are as a target. We are, we have as, as a target for 2022 to basically have the number for 2022 to basically have the number of application with respect to the number of application our application map of 2018. our application map of 2018. >> So big, big step increase in applications. >> Yeah, yeah, yeah >> That's great. That's cool. And then the ecosystem of energy efficiency and aiming for lower carbon emissions that's a goal you guys are helping with. How is Red Hat helping in the ecosystem in your ecosystem? Do you see them going above and beyond? >> You know, the, for what regards to new business lines? I think that the container orchestration platform I think that the container orchestration platform so OpenShift would provide us with the right level so OpenShift would provide us with the right level of flexibility and agility to move of flexibility and agility to move at the speed of those businesses. That is quite different with respect to our classical ones and frequently needs a much higher speed of development. and frequently needs a much higher speed of development. >> Yeah. Awesome. Well, that's great. Great to see that success with Red Hat let's let's shift gears to the topic of the edge. >> Yeah We've been reporting on Silicon angle industrial edge for many years now. And we were calling out the security potential there as risky, obviously it's, it's it's industrial there's you also got generic edge which is consumer edge and everything in between the edge is just part of the network. And you think about this, this is important for you are what are you doing for you are what are you doing with the edge and IOT from a use case standpoint? What have you already done? And what are you planning to deploy soon? Take us through your, your edge IOT use case how it is today and how you see it tomorrow. >> So let's say that Snam has long OT history that basically started that Snam has long OT history that basically started at the very beginning of our SCADA system. So what we have right now is quite complex Brown So what we have right fields situation for what regards edges and gateways fields situation for what regards edges and gateways fields situation for what regards edges and gateways and technical component that resides on, on the field. and technical component that resides on, on the field. So you can, you, you, you must consider that the Italian network is for the modern that the Italian network is for the modern modern 34,000 kilometers and modern 34,000 kilometers and as many different plants, small, medium, and as many different plants, small, medium, and and large plants spread across the country. and large plants spread across the country. And what we are trying to do leveraging also Red Hat technologies among with Red Hat technologies among with with others is trying to get the benefit with others is trying to get the benefit of containers and microservice development. So the benefit coming from cloud native application and getting those to the edge. from cloud native application and getting those to the edge. So the usual problem So the usual problem with OT as historically been a standardization with OT as historically been a standardization so a very heterogeneous number of components Virginia's protocols of components Virginia's protocols in order for them to communicate with the charters and relatively low level of security. with the charters and relatively low level of security. This is, this was mainly due to the segregation principle This is, this was mainly due to the segregation principle physical segregation principle that used to physical segregation principle that used to dominate the OT field with IOT. Of course, as you were saying we are terrifically expanding the attack surface we are terrifically expanding the attack surface from the cybersecurity standpoint, but at the same time that is mainly why we are approaching that is mainly why we are approaching in a very structural way. Our technology stack implementation including security by design in all our architectural blueprints and implementation. And we strongly believe that pushing the capability And we strongly believe that pushing the capability of container orchestration and containerization to the edge and being able to orchestrate that from the cloud or from our data centers will provide us with a very high level of high-quality and flexibility and the capability to exploited best the geographical distribution of the data. to exploited best the geographical distribution of the data. You know, you were saying a center point will be You know, you were saying a center point will be was soaked around data, and it is correct, but it in our specific case, our data basically came from points in our specific case, our data basically came from points in our specific case, our data basically came from points as I was saying, spread it all across the country. So having different data, gravity points enabled So having different data, gravity points enabled by container rise and centrally orchestrated by container rise and centrally orchestrated by container rise and centrally orchestrated environments will enable us to get the best also environments will enable us to get the best also in terms of, from the cybersecurity perspective because what will be acquired on the centralized environment is only exclusively on the centralized environment is only exclusively what is needed at the centralized environment. what is needed at the centralized environment. All the rest on our target architecture will be entirely elaborated on the field, very close to where the data physically on the field, very close to where the data physically and this will be excludable exclusively enabled by by a containerized approach. >> That's awesome. Great, great. A use case there, Roberto, what's next A use case there, Roberto, what's next for your future plans and your technology journey? Obviously AI is going to be very important and data and leveraging that you've got the core cloud data center edge perspective. >> Yeah, of course. Yeah. What, what, what's next? >> What's your future? Let's say, let's say that what we currently implemented is Let's say, let's say that what we currently implemented is and in average cloud environment so we basically have two data center and one cloud tenant, our infrastructure due to, again and one cloud tenant, our infrastructure due to, again and one cloud tenant, our infrastructure due to, again the use of OpenShifts will be easily extensible the use of OpenShifts will be easily extensible the use of OpenShifts will be easily extensible to other potentially to other cloud providers. So we will move, we're evaluating the move to a multicloud So we will move, we're evaluating the move to a multicloud a hybrid multicloud environment. At the same time our main focus right now is to close our IOT foundation. our main focus right now is to close our IOT foundation. And within the IOT foundation I think the main focus right now is on gateways and edges. I think the main focus right now is on gateways and edges. As you were saying, these are quite complex components As you were saying, these are quite complex components and must be greatly evaluated, especially from the cybersecurity standpoint and last from the cybersecurity standpoint and last but not least the data we need to. but not least the data we need to we started our data platform journey and we currently are acquiring data from legacy systems and we currently are acquiring data from legacy systems different kinds of legacy system and SCADA system. What we would like to reach is a complete IOT What we would like to reach is a complete IOT What we would like to reach is a complete IOT acquisition system that will be directly connected to our components, acquiring data on the field. Right now we are in, let's say Right now we are in, let's say in the middle of this digital transformation and we are hemming to close our and we are hemming to close our our journey in the next couple of years. >> That's great, Roberto, great story. Love the conversation. First of all, I love your title Head of Architecture, Digital AI Services. I mean, that speaks to this modern error of, of, of cloud distributed computing. You hit all the hit, all the key things, right? It's an architectural system distributed system. It's a digital business. Now, even though there's physical assets offline, online coming together in a modern way and AI really speaks to the underlying data which is combination of many, many things, you know you're you get all the action there. >> Roberto: Yeah! >> How do you feel? What's your advice to other people in the same boat you're in? >> No, I, I think that, that the interesting part of what we do that the interesting part of what we do at least in, in my specific area, and this is what digital at least in, in my specific area, and this is what digital or sustained for is digital service design. This is something new that is quite uncommon within the utility sector. And it is basically a group of people that apart And it is basically a group of people that apart from being technologists focus a lot on the interaction from being technologists focus a lot on the interaction design of what we are or what we are trying to build design of what we are or what we are trying to build in terms of the technology stack. So these are people that basically try to make the very So these are people that basically try to make the very complex technology stack we talk about in our interview much more simple the, to the final user and think about the level of interaction, complexity about the level of interaction, complexity that all our user will have with our technology stack. Especially when we talk about IOT now, and you start to interact, not just with digital systems, but also with digital or physical systems. with digital or physical systems. So yes, we, we, we have a lot on our plate >> It reminds me of the late eighties, early nineties when open standards really hit the scene and then incubated and then accelerated was seeing that same dynamic happening now with cloud. And you're a pioneer and really appreciate you taking the time to come on The Cube and speak with me about this and share your story. And more importantly than Red Hat success there. 'cause it's Red Hat summit, a story here, Roberto. Thank you very much for sharing your insights and experiences. >> Thank you for your time, John. This has been a pleasure. >> Really appreciate it. Okay. That's Red Hat CUBE coverage here with theCUBE. I'm John furrier. Thanks for watching. (upbeat music)

>>Hello. My name is Isaac twang and I am on the faculty at MIT in electrical engineering and computer science and in physics. And it is a pleasure for me to be presenting at today's NTT research symposium of 2020 to share a little bit with you about programmable quantum simulators theory and practice the simulation of physical systems as described by their Hamiltonian. It's a fundamental problem which Richard Fineman identified early on as one of the most promising applications of a hypothetical quantum computer. The real world around us, especially at the molecular level is described by Hamiltonians, which captured the interaction of electrons and nuclei. What we desire to understand from Hamiltonian simulation is properties of complex molecules, such as this iron molded to them. Cofactor an important catalyst. We desire there are ground States, reaction rates, reaction dynamics, and other chemical properties, among many things for a molecule of N Adams, a classical simulation must scale exponentially within, but for a quantum simulation, there is a potential for this simulation to scale polynomials instead. >>And this would be a significant advantage if realizable. So where are we today in realizing such a quantum advantage today? I would like to share with you a story about two things in this quest first, a theoretical optimal quantum simulation, awkward them, which achieves the best possible runtime for generic Hamiltonian. Second, let me share with you experimental results from a quantum simulation implemented using available quantum computing hardware today with a hardware efficient model that goes beyond what is utilized by today's algorithms. I will begin with the theoretically optimal quantum simulation uncle rhythm in principle. The goal of quantum simulation is to take a time independent Hamiltonian age and solve Schrodinger's equation has given here. This problem is as hard as the hardest quantum computation. It is known as being BQ P complete a simplification, which is physically reasonable and important in practice is to assume that the Hamiltonian is a sum over terms which are local. >>For example, due to allow to structure these local terms, typically do not commute, but their locality means that each term is reasonably small, therefore, as was first shown by Seth Lloyd in 1996, one way to compute the time evolution that is the exponentiation of H with time is to use the lead product formula, which involves a successive approximation by repetitive small time steps. The cost of this charterization procedure is a number of elementary steps, which scales quadratically with the time desired and inverse with the error desired for the simulation output here then is the number of local terms in the Hamiltonian. And T is the desired simulation time where Epsilon is the desired simulation error. Today. We know that for special systems and higher or expansions of this formula, a better result can be obtained such as scaling as N squared, but as synthetically linear in time, this however is for a special case, the latest Hamiltonians and it would be desirable to scale generally with time T for a order T time simulation. >>So how could such an optimal quantum simulation be constructed? An important ingredient is to transform the quantum simulation into a quantum walk. This was done over 12 years ago, Andrew trials showing that for sparse Hamiltonians with around de non-zero entries per row, such as shown in this graphic here, one can do a quantum walk very much like a classical walk, but in a superposition of right and left shown here in this quantum circuit, where the H stands for a hazard market in this particular circuit, the head Mar turns the zero into a superposition of zero and one, which then activate the left. And the right walk in superposition to graph of the walk is defined by the Hamiltonian age. And in doing so Childs and collaborators were able to show the walk, produces a unitary transform, which goes as E to the minus arc co-sign of H times time. >>So this comes close, but it still has this transcendental function of age, instead of just simply age. This can be fixed with some effort, which results in an algorithm, which scales approximately as towel log one over Epsilon with how is proportional to the sparsity of the Hamiltonian and the simulation time. But again, the scaling here is a multiplicative product rather than an additive one, an interesting insight into the dynamics of a cubit. The simplest component of a quantum computer provides a way to improve upon this single cubits evolve as rotations in a sphere. For example, here is shown a rotation operator, which rotates around the axis fi in the X, Y plane by angle theta. If one, the result of this rotation as a projection along the Z axis, the result is a co-sign squared function. That is well-known as a Ravi oscillation. On the other hand, if a cubit is rotated around multiple angles in the X Y plane, say around the fee equals zero fee equals 1.5 and fee equals zero access again, then the resulting response function looks like a flat top. >>And in fact, generalizing this to five or more pulses gives not just flattered hops, but in fact, arbitrary functions such as the Chevy chef polynomial shown here, which gets transplants like bullying or, and majority functions remarkably. If one does rotations by angle theta about D different angles in the X Y plane, the result is a response function, which is a polynomial of order T in co-sign furthermore, as captured by this theorem, given a nearly arbitrary degree polynomial there exists angles fi such that one can achieve the desired polynomial. This is the result that derives from the Remez exchange algorithm used in classical discreet time signal processing. So how does this relate to quantum simulation? Well recall that a quantum walk essentially embeds a Hamiltonian insight, the unitary transform of a quantum circuit, this embedding generalize might be called and it involves the use of a cubit acting as a projector to control the application of H if we generalize the quantum walk to include a rotation about access fee in the X Y plane, it turns out that one obtains a polynomial transform of H itself. >>And this it's the same as the polynomial in the quantum signal processing theorem. This is a remarkable result known as the quantum synchrony value transformed theorem from contrast Julian and Nathan weep published last year. This provides a quantum simulation auger them using quantum signal processing. For example, can start with the quantum walk result and then apply quantum signal processing to undo the arc co-sign transformation and therefore obtain the ideal expected Hamiltonian evolution E to the minus I H T the resulting algorithm costs a number of elementary steps, which scales as just the sum of the evolution time and the log of one over the error desired this saturates, the known lower bound, and thus is the optimal quantum simulation algorithm. This table from a recent review article summarizes a comparison of the query complexities of the known major quantum simulation algorithms showing that the cubitus station and quantum sequel processing algorithm is indeed optimal. >>Of course, this optimality is a theoretical result. What does one do in practice? Let me now share with you the story of a hardware efficient realization of a quantum simulation on actual hardware. The promise of quantum computation traditionally rests on a circuit model, such as the one we just used with quantum circuits, acting on cubits in contrast, consider a real physical problem from quantum chemistry, finding the structure of a molecule. The starting point is the point Oppenheimer separation of the electronic and vibrational States. For example, to connect it, nuclei, share a vibrational mode, the potential energy of this nonlinear spring, maybe model as a harmonic oscillator since the spring's energy is determined by the electronic structure. When the molecule becomes electronically excited, this vibrational mode changes one obtains, a different frequency and different equilibrium positions for the nuclei. This corresponds to a change in the spring, constant as well as a displacement of the nuclear positions. >>And we may write down a full Hamiltonian for this system. The interesting quantum chemistry question is known as the Frank Condon problem. What is the probability of transition between the original ground state and a given vibrational state in the excited state spectrum of the molecule, the Frank content factor, which gives this transition probability is foundational to quantum chemistry and a very hard and generic question to answer, which may be amiable to solution on a quantum computer in particular and natural quantum computer to use might be one which already has harmonic oscillators rather than one, which has just cubits. This has provided any Sonic quantum processors, such as the superconducting cubits system shown here. This processor has both cubits as embodied by the Joseph's injunctions shown here, and a harmonic oscillator as embodied by the resonant mode of the transmission cavity. Given here more over the output of this planar superconducting circuit can be connected to three dimensional cavities instead of using cubit Gates. >>One may perform direct transformations on the bull's Arctic state using for example, beam splitters, phase shifters, displacement, and squeezing operators, and the harmonic oscillator, and may be initialized and manipulated directly. The availability of the cubit allows photon number resolve counting for simulating a tri atomic two mode, Frank Condon factor problem. This superconducting cubits system with 3d cavities was to resonators cavity a and cavity B represent the breathing and wiggling modes of a Triumeq molecule. As depicted here. The coupling of these moles was mediated by a superconducting cubit and read out was accomplished by two additional superconducting cubits, coupled to each one of the cavities due to the superconducting resonators used each one of the cavities had a, a long coherence time while resonator States could be prepared and measured using these strong coupling of cubits to the cavity. And Posana quantum operations could be realized by modulating the coupling cubit in between the two cavities, the cavities are holes drilled into pure aluminum, kept superconducting by millikelvin scale. >>Temperatures microfiber, KT chips with superconducting cubits are inserted into ports to couple via a antenna to the microwave cavities. Each of the cavities has a quality factor so high that the coherence times can reach milliseconds. A coupling cubit chip is inserted into the port in between the cavities and the readout and preparation cubit chips are inserted into ports on the sides. For sake of brevity, I will skip the experimental details and present just the results shown here is the fibrotic spectrum obtained for a water molecule using the Pulsonix superconducting processor. This is a typical Frank content spectrum giving the intensity of lions versus frequency in wave number where the solid line depicts the theoretically expected result and the purple and red dots show two sets of experimental data. One taken quickly and another taken with exhaustive statistics. In both cases, the experimental results have good agreement with the theoretical expectations. >>The programmability of this system is demonstrated by showing how it can easily calculate the Frank Condon spectrum for a wide variety of molecules. Here's another one, the ozone and ion. Again, we see that the experimental data shown in points agrees well with the theoretical expectation shown as a solid line. Let me emphasize that this quantum simulation result was obtained not by using a quantum computer with cubits, but rather one with resonators, one resonator representing each one of the modes of vibration in this trial, atomic molecule. This approach represents a far more efficient utilization of hardware resources compared with the standard cubit model because of the natural match of the resonators with the physical system being simulated in comparison, if cubit Gates had been utilized to perform the same simulation on the order of a thousand cubit Gates would have been required compared with the order of 10 operations, which were performed for this post Sonic realization. >>As in topically, the Cupid motto would have required significantly more operations because of the need to retire each one of the harmonic oscillators into some max Hilbert space size compared with the optimal quantum simulation auger rhythms shown in the first half of this talk, we see that there is a significant gap between available quantum computing hardware can perform and what optimal quantum simulations demand in terms of the number of Gates required for a simulation. Nevertheless, many of the techniques that are used for optimal quantum simulation algorithms may become useful, especially if they are adapted to available hardware, moving for the future, holds some interesting challenges for this field. Real physical systems are not cubits, rather they are composed from bolt-ons and from yawns and from yawns need global anti-Semitism nation. This is a huge challenge for electronic structure calculation in molecules, real physical systems also have symmetries, but current quantum simulation algorithms are largely governed by a theorem, which says that the number of times steps required is proportional to the simulation time. Desired. Finally, real physical systems are not purely quantum or purely classical, but rather have many messy quantum classical boundaries. In fact, perhaps the most important systems to simulate are really open quantum systems. And these dynamics are described by a mixture of quantum and classical evolution and the desired results are often thermal and statistical properties. >>I hope this presentation of the theory and practice of quantum simulation has been interesting and worthwhile. Thank you.