>>from around the globe. It's the Q with digital coverage of exa scale day made possible by Hewlett Packard Enterprise. Welcome, everyone to the Cube celebration of Exa Scale Day. Shaheen Khan is here. He's the founding partner, an analyst at Orion X And, among other things, he is the co host of Radio free HPC Shaheen. Welcome. Thanks for coming on. >>Thanks for being here, Dave. Great to be here. How are you >>doing? Well, thanks. Crazy with doing these things, Cove in remote interviews. I wish we were face to face at us at a supercomputer show, but, hey, this thing is working. We can still have great conversations. And And I love talking to analysts like you because you bring an independent perspective. You're very wide observation space. So So let me, Like many analysts, you probably have sort of a mental model or a market model that you look at. So maybe talk about your your work, how you look at the market, and we could get into some of the mega trends that you see >>very well. Very well. Let me just quickly set the scene. We fundamentally track the megatrends of the Information Age And, of course, because we're in the information age, digital transformation falls out of that. And the megatrends that drive that in our mind is Ayotte, because that's the fountain of data five G. Because that's how it's gonna get communicated ai and HBC because that's how we're gonna make sense of it Blockchain and Cryptocurrencies because that's how it's gonna get transacted on. That's how value is going to get transferred from the place took place and then finally, quantum computing, because that exemplifies how things are gonna get accelerated. >>So let me ask you So I spent a lot of time, but I D. C and I had the pleasure of of the High Performance computing group reported into me. I wasn't an HPC analyst, but over time you listen to those guys, you learning. And as I recall, it was HPC was everywhere, and it sounds like we're still seeing that trend where, whether it was, you know, the Internet itself were certainly big data, you know, coming into play. Uh, you know, defense, obviously. But is your background mawr HPC or so that these other technologies that you're talking about it sounds like it's your high performance computing expert market watcher. And then you see it permeating into all these trends. Is that a fair statement? >>That's a fair statement. I did grow up in HPC. My first job out of school was working for an IBM fellow doing payroll processing in the old days on and and And it went from there, I worked for Cray Research. I worked for floating point systems, so I grew up in HPC. But then, over time, uh, we had experiences outside of HPC. So for a number of years, I had to go do commercial enterprise computing and learn about transaction processing and business intelligence and, you know, data warehousing and things like that, and then e commerce and then Web technology. So over time it's sort of expanded. But HPC is a like a bug. You get it and you can't get rid of because it's just so inspiring. So supercomputing has always been my home, so to say >>well and so the reason I ask is I wanted to touch on a little history of the industry is there was kind of a renaissance in many, many years ago, and you had all these startups you had Kendall Square Research Danny Hillis thinking machines. You had convex trying to make many supercomputers. And it was just this This is, you know, tons of money flowing in and and then, you know, things kind of consolidate a little bit and, uh, things got very, very specialized. And then with the big data craze, you know, we've seen HPC really at the heart of all that. So what's your take on on the ebb and flow of the HPC business and how it's evolved? >>Well, HBC was always trying to make sense of the world, was trying to make sense of nature. And of course, as much as we do know about nature, there's a lot we don't know about nature and problems in nature are you can classify those problems into basically linear and nonlinear problems. The linear ones are easy. They've already been solved. The nonlinear wants. Some of them are easy. Many of them are hard, the nonlinear, hard, chaotic. All of those problems are the ones that you really need to solve. The closer you get. So HBC was basically marching along trying to solve these things. It had a whole process, you know, with the scientific method going way back to Galileo, the experimentation that was part of it. And then between theory, you got to look at the experiment and the data. You kind of theorize things. And then you experimented to prove the theories and then simulation and using the computers to validate some things eventually became a third pillar of off science. On you had theory, experiment and simulation. So all of that was going on until the rest of the world, thanks to digitization, started needing some of those same techniques. Why? Because you've got too much data. Simply, there's too much data to ship to the cloud. There's too much data to, uh, make sense of without math and science. So now enterprise computing problems are starting to look like scientific problems. Enterprise data centers are starting to look like national lab data centers, and there is that sort of a convergence that has been taking place gradually, really over the past 34 decades. And it's starting to look really, really now >>interesting, I want I want to ask you about. I was like to talk to analysts about, you know, competition. The competitive landscape is the competition in HPC. Is it between vendors or countries? >>Well, this is a very interesting thing you're saying, because our other thesis is that we are moving a little bit beyond geopolitics to techno politics. And there are now, uh, imperatives at the political level that are driving some of these decisions. Obviously, five G is very visible as as as a piece of technology that is now in the middle of political discussions. Covert 19 as you mentioned itself, is a challenge that is a global challenge that needs to be solved at that level. Ai, who has access to how much data and what sort of algorithms. And it turns out as we all know that for a I, you need a lot more data than you thought. You do so suddenly. Data superiority is more important perhaps than even. It can lead to information superiority. So, yeah, that's really all happening. But the actors, of course, continue to be the vendors that are the embodiment of the algorithms and the data and the systems and infrastructure that feed the applications. So to say >>so let's get into some of these mega trends, and maybe I'll ask you some Colombo questions and weaken geek out a little bit. Let's start with a you know, again, it was one of this when I started the industry. It's all it was a i expert systems. It was all the rage. And then we should have had this long ai winter, even though, you know, the technology never went away. But But there were at least two things that happened. You had all this data on then the cost of computing. You know, declines came down so so rapidly over the years. So now a eyes back, we're seeing all kinds of applications getting infused into virtually every part of our lives. People trying to advertise to us, etcetera. Eso So talk about the intersection of AI and HPC. What are you seeing there? >>Yeah, definitely. Like you said, I has a long history. I mean, you know, it came out of MIT Media Lab and the AI Lab that they had back then and it was really, as you mentioned, all focused on expert systems. It was about logical processing. It was a lot of if then else. And then it morphed into search. How do I search for the right answer, you know, needle in the haystack. But then, at some point, it became computational. Neural nets are not a new idea. I remember you know, we had we had a We had a researcher in our lab who was doing neural networks, you know, years ago. And he was just saying how he was running out of computational power and we couldn't. We were wondering, you know what? What's taking all this difficult, You know, time. And it turns out that it is computational. So when deep neural nets showed up about a decade ago, arm or it finally started working and it was a confluence of a few things. Thalib rhythms were there, the data sets were there, and the technology was there in the form of GPS and accelerators that finally made distractible. So you really could say, as in I do say that a I was kind of languishing for decades before HPC Technologies reignited it. And when you look at deep learning, which is really the only part of a I that has been prominent and has made all this stuff work, it's all HPC. It's all matrix algebra. It's all signal processing algorithms. are computational. The infrastructure is similar to H B. C. The skill set that you need is the skill set of HPC. I see a lot of interest in HBC talent right now in part motivated by a I >>mhm awesome. Thank you on. Then I wanna talk about Blockchain and I can't talk about Blockchain without talking about crypto you've written. You've written about that? I think, you know, obviously supercomputers play a role. I think you had written that 50 of the top crypto supercomputers actually reside in in China A lot of times the vendor community doesn't like to talk about crypto because you know that you know the fraud and everything else. But it's one of the more interesting use cases is actually the primary use case for Blockchain even though Blockchain has so much other potential. But what do you see in Blockchain? The potential of that technology And maybe we can work in a little crypto talk as well. >>Yeah, I think 11 simple way to think of Blockchain is in terms off so called permission and permission less the permission block chains or when everybody kind of knows everybody and you don't really get to participate without people knowing who you are and as a result, have some basis to trust your behavior and your transactions. So things are a lot calmer. It's a lot easier. You don't really need all the supercomputing activity. Whereas for AI the assertion was that intelligence is computer herbal. And with some of these exa scale technologies, we're trying to, you know, we're getting to that point for permission. Less Blockchain. The assertion is that trust is computer ble and, it turns out for trust to be computer ble. It's really computational intensive because you want to provide an incentive based such that good actors are rewarded and back actors. Bad actors are punished, and it is worth their while to actually put all their effort towards good behavior. And that's really what you see, embodied in like a Bitcoin system where the chain has been safe over the many years. It's been no attacks, no breeches. Now people have lost money because they forgot the password or some other. You know, custody of the accounts have not been trustable, but the chain itself has managed to produce that, So that's an example of computational intensity yielding trust. So that suddenly becomes really interesting intelligence trust. What else is computer ble that we could do if we if we had enough power? >>Well, that's really interesting the way you described it, essentially the the confluence of crypto graphics software engineering and, uh, game theory, Really? Where the bad actors air Incentive Thio mined Bitcoin versus rip people off because it's because because there are lives better eso eso so that so So Okay, so make it make the connection. I mean, you sort of did. But But I want to better understand the connection between, you know, supercomputing and HPC and Blockchain. We know we get a crypto for sure, like in mind a Bitcoin which gets harder and harder and harder. Um and you mentioned there's other things that we can potentially compute on trust. Like what? What else? What do you thinking there? >>Well, I think that, you know, the next big thing that we are really seeing is in communication. And it turns out, as I was saying earlier, that these highly computational intensive algorithms and models show up in all sorts of places like, you know, in five g communication, there's something called the memo multi and multi out and to optimally manage that traffic such that you know exactly what beam it's going to and worth Antenna is coming from that turns out to be a non trivial, you know, partial differential equation. So next thing you know, you've got HPC in there as and he didn't expect it because there's so much data to be sent, you really have to do some data reduction and data processing almost at the point of inception, if not at the point of aggregation. So that has led to edge computing and edge data centers. And that, too, is now. People want some level of computational capability at that place like you're building a microcontroller, which traditionally would just be a, you know, small, low power, low cost thing. And people want victor instructions. There. People want matrix algebra there because it makes sense to process the data before you have to ship it. So HPCs cropping up really everywhere. And then finally, when you're trying to accelerate things that obviously GP use have been a great example of that mixed signal technologies air coming to do analog and digital at the same time, quantum technologies coming so you could do the you know, the usual analysts to buy to where you have analog, digital, classical quantum and then see which, you know, with what lies where all of that is coming. And all of that is essentially resting on HBC. >>That's interesting. I didn't realize that HBC had that position in five G with multi and multi out. That's great example and then I o t. I want to ask you about that because there's a lot of discussion about real time influencing AI influencing at the edge on you're seeing sort of new computing architectures, potentially emerging, uh, video. The acquisition of arm Perhaps, you know, amore efficient way, maybe a lower cost way of doing specialized computing at the edge it, But it sounds like you're envisioning, actually, supercomputing at the edge. Of course, we've talked to Dr Mark Fernandez about space born computers. That's like the ultimate edge you got. You have supercomputers hanging on the ceiling of the International space station, but But how far away are we from this sort of edge? Maybe not. Space is an extreme example, but you think factories and windmills and all kinds of edge examples where supercomputing is is playing a local role. >>Well, I think initially you're going to see it on base stations, Antenna towers, where you're aggregating data from a large number of endpoints and sensors that are gathering the data, maybe do some level of local processing and then ship it to the local antenna because it's no more than 100 m away sort of a thing. But there is enough there that that thing can now do the processing and do some level of learning and decide what data to ship back to the cloud and what data to get rid of and what data to just hold. Or now those edge data centers sitting on top of an antenna. They could have a half a dozen GPS in them. They're pretty powerful things. They could have, you know, one they could have to, but but it could be depending on what you do. A good a good case study. There is like surveillance cameras. You don't really need to ship every image back to the cloud. And if you ever need it, the guy who needs it is gonna be on the scene, not back at the cloud. So there is really no sense in sending it, Not certainly not every frame. So maybe you can do some processing and send an image every five seconds or every 10 seconds, and that way you can have a record of it. But you've reduced your bandwidth by orders of magnitude. So things like that are happening. And toe make sense of all of that is to recognize when things changed. Did somebody come into the scene or is it just you know that you know, they became night, So that's sort of a decision. Cannot be automated and fundamentally what is making it happen? It may not be supercomputing exa scale class, but it's definitely HPCs, definitely numerically oriented technologies. >>Shane, what do you see happening in chip architectures? Because, you see, you know the classical intel they're trying to put as much function on the real estate as possible. We've seen the emergence of alternative processors, particularly, uh, GP use. But even if f b g A s, I mentioned the arm acquisition, so you're seeing these alternative processors really gain momentum and you're seeing data processing units emerge and kind of interesting trends going on there. What do you see? And what's the relationship to HPC? >>Well, I think a few things are going on there. Of course, one is, uh, essentially the end of Moore's law, where you cannot make the cycle time be any faster, so you have to do architectural adjustments. And then if you have a killer app that lends itself to large volume, you can build silicon. That is especially good for that now. Graphics and gaming was an example of that, and people said, Oh my God, I've got all these cores in there. Why can't I use it for computation? So everybody got busy making it 64 bit capable and some grass capability, And then people say, Oh, I know I can use that for a I And you know, now you move it to a I say, Well, I don't really need 64 but maybe I can do it in 32 or 16. So now you do it for that, and then tens, of course, come about. And so there's that sort of a progression of architecture, er trumping, basically cycle time. That's one thing. The second thing is scale out and decentralization and distributed computing. And that means that the inter communication and intra communication among all these notes now becomes an issue big enough issue that maybe it makes sense to go to a DPU. Maybe it makes sense to go do some level of, you know, edge data centers like we were talking about on then. The third thing, really is that in many of these cases you have data streaming. What is really coming from I o t, especially an edge, is that data is streaming and when data streaming suddenly new architectures like F B G. A s become really interesting and and and hold promise. So I do see, I do see FPG's becoming more prominent just for that reason, but then finally got a program all of these things on. That's really a difficulty, because what happens now is that you need to get three different ecosystems together mobile programming, embedded programming and cloud programming. And those are really three different developer types. You can't hire somebody who's good at all three. I mean, maybe you can, but not many. So all of that is challenges that are driving this this this this industry, >>you kind of referred to this distributed network and a lot of people you know, they refer to this. The next generation cloud is this hyper distributed system. When you include the edge and multiple clouds that etcetera space, maybe that's too extreme. But to your point, at least I inferred there's a There's an issue of Leighton. See, there's the speed of light s So what? What? What is the implication then for HBC? Does that mean I have tow Have all the data in one place? Can I move the compute to the data architecturally, What are you seeing there? >>Well, you fundamentally want to optimize when to move data and when to move, Compute. Right. So is it better to move data to compute? Or is it better to bring compute to data and under what conditions? And the dancer is gonna be different for different use cases. It's like, really, is it worth my while to make the trip, get my processing done and then come back? Or should I just developed processing capability right here? Moving data is really expensive and relatively speaking. It has become even more expensive, while the price of everything has dropped down its price has dropped less than than than like processing. So it is now starting to make sense to do a lot of local processing because processing is cheap and moving data is expensive Deep Use an example of that, Uh, you know, we call this in C two processing like, you know, let's not move data. If you don't have to accept that we live in the age of big data, so data is huge and wants to be moved. And that optimization, I think, is part of what you're what you're referring to. >>Yeah, So a couple examples might be autonomous vehicles. You gotta have to make decisions in real time. You can't send data back to the cloud flip side of that is we talk about space borne computers. You're collecting all this data You can at some point. You know, maybe it's a year or two after the lived out its purpose. You ship that data back and a bunch of disk drives or flash drives, and then load it up into some kind of HPC system and then have at it and then you doom or modeling and learn from that data corpus, right? I mean those air, >>right? Exactly. Exactly. Yeah. I mean, you know, driverless vehicles is a great example, because it is obviously coming fast and furious, no pun intended. And also, it dovetails nicely with the smart city, which dovetails nicely with I o. T. Because it is in an urban area. Mostly, you can afford to have a lot of antenna, so you can give it the five g density that you want. And it requires the Layton sees. There's a notion of how about if my fleet could communicate with each other. What if the car in front of me could let me know what it sees, That sort of a thing. So, you know, vehicle fleets is going to be in a non opportunity. All of that can bring all of what we talked about. 21 place. >>Well, that's interesting. Okay, so yeah, the fleets talking to each other. So kind of a Byzantine fault. Tolerance. That problem that you talk about that z kind of cool. I wanna I wanna sort of clothes on quantum. It's hard to get your head around. Sometimes You see the demonstrations of quantum. It's not a one or zero. It could be both. And you go, What? How did come that being so? And And of course, there it's not stable. Uh, looks like it's quite a ways off, but the potential is enormous. It's of course, it's scary because we think all of our, you know, passwords are already, you know, not secure. And every password we know it's gonna get broken. But give us the give us the quantum 101 And let's talk about what the implications. >>All right, very well. So first off, we don't need to worry about our passwords quite yet. That that that's that's still ways off. It is true that analgesic DM came up that showed how quantum computers can fact arise numbers relatively fast and prime factory ization is at the core of a lot of cryptology algorithms. So if you can fact arise, you know, if you get you know, number 21 you say, Well, that's three times seven, and those three, you know, three and seven or prime numbers. Uh, that's an example of a problem that has been solved with quantum computing, but if you have an actual number, would like, you know, 2000 digits in it. That's really harder to do. It's impossible to do for existing computers and even for quantum computers. Ways off, however. So as you mentioned, cubits can be somewhere between zero and one, and you're trying to create cubits Now there are many different ways of building cubits. You can do trapped ions, trapped ion trapped atoms, photons, uh, sometimes with super cool, sometimes not super cool. But fundamentally, you're trying to get these quantum level elements or particles into a superimposed entanglement state. And there are different ways of doing that, which is why quantum computers out there are pursuing a lot of different ways. The whole somebody said it's really nice that quantum computing is simultaneously overhyped and underestimated on. And that is that is true because there's a lot of effort that is like ways off. On the other hand, it is so exciting that you don't want to miss out if it's going to get somewhere. So it is rapidly progressing, and it has now morphed into three different segments. Quantum computing, quantum communication and quantum sensing. Quantum sensing is when you can measure really precise my new things because when you perturb them the quantum effects can allow you to measure them. Quantum communication is working its way, especially in financial services, initially with quantum key distribution, where the key to your cryptography is sent in a quantum way. And the data sent a traditional way that our efforts to do quantum Internet, where you actually have a quantum photon going down the fiber optic lines and Brookhaven National Labs just now demonstrated a couple of weeks ago going pretty much across the, you know, Long Island and, like 87 miles or something. So it's really coming, and and fundamentally, it's going to be brand new algorithms. >>So these examples that you're giving these air all in the lab right there lab projects are actually >>some of them are in the lab projects. Some of them are out there. Of course, even traditional WiFi has benefited from quantum computing or quantum analysis and, you know, algorithms. But some of them are really like quantum key distribution. If you're a bank in New York City, you very well could go to a company and by quantum key distribution services and ship it across the you know, the waters to New Jersey on that is happening right now. Some researchers in China and Austria showed a quantum connection from, like somewhere in China, to Vienna, even as far away as that. When you then put the satellite and the nano satellites and you know, the bent pipe networks that are being talked about out there, that brings another flavor to it. So, yes, some of it is like real. Some of it is still kind of in the last. >>How about I said I would end the quantum? I just e wanna ask you mentioned earlier that sort of the geopolitical battles that are going on, who's who are the ones to watch in the Who? The horses on the track, obviously United States, China, Japan. Still pretty prominent. How is that shaping up in your >>view? Well, without a doubt, it's the US is to lose because it's got the density and the breadth and depth of all the technologies across the board. On the other hand, information age is a new eyes. Their revolution information revolution is is not trivial. And when revolutions happen, unpredictable things happen, so you gotta get it right and and one of the things that these technologies enforce one of these. These revolutions enforce is not just kind of technological and social and governance, but also culture, right? The example I give is that if you're a farmer, it takes you maybe a couple of seasons before you realize that you better get up at the crack of dawn and you better do it in this particular season. You're gonna starve six months later. So you do that to three years in a row. A culture has now been enforced on you because that's how it needs. And then when you go to industrialization, you realize that Gosh, I need these factories. And then, you know I need workers. And then next thing you know, you got 9 to 5 jobs and you didn't have that before. You don't have a command and control system. You had it in military, but not in business. And and some of those cultural shifts take place on and change. So I think the winner is going to be whoever shows the most agility in terms off cultural norms and governance and and and pursuit of actual knowledge and not being distracted by what you think. But what actually happens and Gosh, I think these exa scale technologies can make the difference. >>Shaheen Khan. Great cast. Thank you so much for joining us to celebrate the extra scale day, which is, uh, on 10. 18 on dso. Really? Appreciate your insights. >>Likewise. Thank you so much. >>All right. Thank you for watching. Keep it right there. We'll be back with our next guest right here in the Cube. We're celebrating Exa scale day right back.

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven to you by activity. >> Hi, everyone. Welcome back to Boston. This is the Cube, the leader, and on the ground tech coverage. My name is David. Want a stupid woman. And John for you have been here. Uh, all day we've been plowing through some great interviews. This is active FiOS data driven 19 conference, the second conference. They've had this kind of about 500 people here in Boston. Shaheen peruses here. He's the chief technical officer and chief information security officer at data endure Cuba. LEM, good to see you. Thanks very much for coming back on. >> Thank you. Thanks for having me. >> You're very welcome. So, um, let's talk about backup. Gave a talk today. What is your backup done for you lately? Essentially. You know, so interesting question, right? You look at the data. A lot of customers air rethinking their backup. We sort of saw this with the ascendancy of virtual ization. We're seeing again with cloud and digital transformation. What's that? What was the theme of your talk? What was the catalyst behind the thoughts there? >> I really walk through the concept that storage has continued to evolve so aggressively and so fast with Moore's law and everything else and what has really proliferated. Part of that is that our data keeps growing and growing fast, and a very big contributor of that is copy data management. So we take a backup of something, but we don't ever use that backup. We restore it, and now we have a second copy of production so that development could do work in it. Then we restore it somewhere else so that analytics can happen against that. Then we restored another place, and pretty soon you have 45456 ten 10 20 copies of the exact same data and that proliferation keeps growing and growing. And it's time to think about backup differently. And almost all traditional backup players have not changed the way they operate have not changed the way they deal with backups. They continue to do it the same way, and their programs were written to go to tape versus to Cloud or to do copy data management >> properly. So it's That's a color today, if you would, sir, you said to do it the same way it was meant to go to take meeting. What? It's just a designed to be essentially a serial process. Exactly designed. Maybe maybe recovery is sort of a hope. We never have to recover kind of kind of thought, and that's it. Back up and no other additional value >> and file it somewhere. So just in case something, it's an insurance policy. >> So how should be done >> so before I get into how it should be done, One of the other attributes that makes backup a challenge with traditional players is they convert the data into their proprietary format, so you can't use the data unless you rehydrate it and put it back into its native format. Then you can start doing analytics or a I, or whatever you wanna do against that. So what activity was done differently, which is what I feel is that how you should do it is they keep the data in native format, and then when you need to access that copy of that data, they create a virtual copy of that data. So you're not taking a penny dis space, but its performance, because the underlying this subsystem that you assigned to active fio is have whatever performance, you want to assign it. So now you can spend up 10 copies of the same server without ever taking up 10 copies of storage and give the give all of your constituents that development team, the analytics team, whatever teams the ability to access it in real time. >> Why did the traditional vendors do it that way? Because they want to reduce it to save cost is they wanna optimize on on performance or they want to have control. From a catalog standpoint, Wise >> said, the popular if you go back to tape tape, was really slow. And it was a serial right, like you were saying earlier. And so you had to write software that would know how to take advantage of that slow speed and not make any mistakes and then be able to recover from it. So they were converting it to a format that was easier to write, easier to read. But that format doesn't play anymore in today's world, however, they haven't really adopted their king technologies to today's world and what I 50 0 did differently when they came out 10 years ago, they said. We need to reshape this whole backup landscape on DH. They created this copy data management space and all other backup players. Air tryingto ad copy data management to compete. But active Theo isn't a backup solution. It's a copy data management solution and backup is a nice artifact. >> Okay, so you deliver services on top of this and other technologies, right? Maybe talk a little bit more about your business and what you're going to market >> way help companies that our whole go to market is around this concept of digital resilient. So the ability to survive and thrive in the middle of an attack and whether that be Mother Nature or that be a cyber attack, or that your system's crashing on you and the in order to do that, let's just pick security. Let's parts that for a second. If you have a ransomware attack, for example, you can have the best controls. However, if a foothold gets into your environment and encrypts your data, your only choices recovery. And if you can't recover, you have to pay the ransom to get the encryption back way had a customer who had challenges on their their backups were on the virtual ization platform, which got encrypted and they weren't able to recover. So their only option was to pay ransom ware and, uh, fair to say they weren't the customer until after that happened. But the But the reality is that solutions like after Theo in by nature of the way they act, the way they store the data off promises in cloud or the way they store it s so that it's not easily it's immutable. It makes it a lot easier for a organization to leverage it and be able to recover quickly from it and have offsite copies or multiple data center copies. So that's the That's the challenge. I would say that a solution like activity of >> Psalms, where our customers I've got to take a little change for second and ask you CTO and a C. So I was taking a little security knowledge servant, test your security knowledge, and I actually did really well. I was like, 90% on. But what I got wrong was, you know, if you get hit with ransom where it said you should should pay it, and I said, Well, yeah, I guess so. They said, Nah, you're wrong, like, well, how else would I get my data back? If that's the way you know, I could avoid it if I were. I work with numbers like yours, but should people pay the ransom? >> So the odds of getting an encryption key that allows you to recover your data are minimal there. What usually happens is they don't want to get caught, so they don't want to send you the encryption key. They get the money and run because the more interactions they have with you, the more opportunity for somebody to trail them and figure out how >> to. So you shouldn't pay. You shouldn't, because your chances of infant testable that you're going to get your data back. >> The only way to pay in this customer they happen to have cyber insurance. And so their actual out of pocket expense was a fraction of the ransom. But not all cyber insurance covers all ransomware scenario. So it's They're not all kind of like, so it's a really it's a really complex question. Actually, I was >> wondering if you could do a smart contract. Yeah. Wait. What? >> You get the keys >> and you could be right. Yeah, on, then, then that's the challenge. right. It's leased like who's Who's way >> got to do it at the same time. But yeah, it's it's typically my recommendation is don't pay, but ideally, if you have, if you don't have a backup, then you really don't have an option. >> So part of your your job is obviously information security, which is the fast moving. I mean, that market is exploding. It feels like it's a big do over, You know that's going on. Um, you know, we all know the narrative. It's you know, there is no perimeter. All the money has been spent, you know, sort of hardening, you know, the perimeter building that moat. But now the queen leaves the castle so the whole paradigm changes. So how are you addressing that for your customers? >> So a couple ways Number one, the endpoint is the perimeter now, So the device that's sitting in front of here is an example is where you have to to treat the security, you need to monitor the activity of the behavior that's happening on that device. And if there's something that moves away from baseline, so if you're capturing a baseline of how you operate, what you do day today, and if all of a sudden you start encrypting your files and you never did before, the flag should go off. And those flags need to be able to get back to a central location, which is the business we operate. We offer a sock is a service. We deployed tools on the end points. We collect data from the perimeter, the firewalls around hers, the switches So we see the health of the network. But then we also monitor the end point to make sure if something's happening at that end point, we want to know we want to stop it before it spreads to anywhere else. >> It is a manage service. So another question around, you know, this is the buzz words of multi Cloud. It's a hot space, but it looks legit. I mean, multi cloud, I've always said, is the son of almost a symptom of multi vendor right versus a strategy. But increasingly, people are saying, Okay, we need a strategy. There's horses for courses, certain clouds or better for certain things, and that's where we're going. We're going, maybe rain in the shadow it in the line of business, or at least support them. So we need a strategy. Their So what? Your thoughts on multi cloud. How are you participating in that space? Is there any role for active fio? There >> absolutely is active fio supports all of before the major clouds out there. So they support a WS czar, G, C, P and IBM. And having that strategy allows a customer who's leveraging activity to protect their data to be able to spin up workloads in any of those clouds. And, for example, GP is known for better. Aye, Aye. And analytics. So spin up a copy of your data in G C. P. Do your analytics and then shut it down. Um uh, a czar is known integrate better with any Microsoft platform so spent up your Microsoft workloads and his whore and used them for whatever purposes, whether it's analytics or other and shut him down. Andi, each cloud does have its attributes and benefits that are better >> universes just good. Yeah, well, >> they have a lead, right? They've done a lot of application ecosystem, right? And then IBM, with Watson is kind of taking a lead in the Aye aye space. So, really, it's you as a company. As a architect cloud architect, you need to decide what cloud has the benefits you need and the ability to move between them with a technology like Octavio is pretty key >> thoughts on, uh, security. The cloud In the early days that was a real blocker. You know, people were concerned about security of Cloud, and today it's almost becoming an advantage. Do you buy that? >> So sort of. I've been I've been a c T O N C So for the last 15 years, and early clouds start ups and number one objection I always got was security in the cloud. You can't put your data there. The reality is, the cloud is no different than another data center. It's You can't abdicate your responsibility to secure your infrastructure just because it's in somebody else's data center, it's You still have to do what you would do. Apply your security policies, apply your security controls and manages if it's another one of your polos, for example, and that's where people forget. They think just because it's somewhere else I'm protected. The only benefit that the cloud gives us from a security perspective is the physical security. So nobody can get into that data center because they have great security controls. But that doesn't mean electronically people can't get it. That you're still you. You haven't really gained anything by going to cloud other than reliability and availability. >> Yeah, your point about endpoint security before a bad user behavior is going to trump great security every single time. Exactly. Okay, final thoughts on this event, your business, your partnership, the marketplace take us home. >> I think I think is a great event. Lots of great topics are covered some great partnerships. Way heard some great information about analytics from IBM. I think that active FiOS uniquely positioned where you can take that one, back up your data and then be able to use it in so many different facets of your business rather than, like I said, creating the copies and exploding your data growth. And so because of that, you're seeing the partnership in the ecosystem coming together. The other attributes that makes it powerful is that they've got the AP integration. Anything you could do in the user interface, you can do the FBI, so that allows third party companies to come in and do integrations. That extend the capability and leverage that data even better on DH. So I think this event is good to help show people some of those capabilities and how some of those integration >> support that's here. It's all about creating incremental value with data as opposed to just below one out copies. So great. Appreciate it. Should you? Thanks very much for coming on the Q. Thank you. Good to see you again. Good to see you. All right. Thanks for watching everybody. We'll be back with our next guest right after this short break. You watching the Cube from data driven 19.