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

>>Welcome back everyone Cube's live coverage here. Day two, two sets, three days of cube coverage here at VMware Explorer. This is our 12th year covering VMware's annual conference, formally called world I'm Jean Dave ante. We'd love seeing the progress and we've got great security comes Tom Gill, senior rights, president general manager, networking and advanced security business group at VMware. Great to see you. Thanks for coming on. Thanks >>For having me. Yeah, really happy we could have you on, you know, I think, I think this is my sixth edition on the cube. Like, do I get freaking flyer points or anything? >>Yeah, you get first get the VIP badge. We'll make that happen. You can start getting credits. >>Okay. There we go. >>We won't interrupt you. No, seriously, you got a great story in security here. The security story is kind of embedded everywhere, so it's not like called out and, and blown up and talked specifically about on stage. It's kind of in all the narratives in, in the VM world for this year. Yeah. But you guys have an amazing security story. So let's just step back into set context. Tell us the security story for what's going on here at VMware and what that means to this super cloud multi-cloud and ongoing innovation with VMware. Yeah, >>Sure thing. So, so probably the first thing I'll point out is that, that security's not just built in at VMware it's built differently, right? So we're not just taking existing security controls and cut and pasting them into, into our software. But we can do things because of our platform because of the virtualization layer that you really can't do with other security tools and where we're very, very focused is what we call lateral security or east west movement of an attacker. Cuz frankly, that's the name of the game these days. Right? Attackers, you gotta assume that they're already in your network. Okay. Already assume that they're there, then how do we make it hard for them to get to what the, the stuff that you really want, which is the data that they're, they're going after. Right. And that's where we, >>We really should. All right. So we've been talking a lot coming into world VMware Explorer and here the event about two things security as a state. Yeah. I'm secure right now. Yeah. Or I, I think I'm secure right now, even though someone might be in my network or in my environment to the notion of being defensible. Yeah. Meaning I have to defend and be ready at a moment's notice to attack, fight, push back red team, blue team, whatever you're gonna call it, but something's happening. I gotta be a to defend. Yeah. >>So you, what you're talking about is the principle of zero trust. So the, the, when we, when I first started doing security, the model was we have a perimeter and everything on one side of the perimeter is dirty, ugly, old internet and everything on this side known good, trusted what could possibly go wrong. And I think we've seen that no matter how good you make that perimeter, bad guys find a way in. So zero trust says, you know what? Let's just assume they're already in. Let's assume they're there. How do we make it hard for them to move around within the infrastructure and get to the really valuable assets? Cuz for example, if they bust into your laptop, you click on a link and they get code running on your machine. They might find some interesting things on your machine, but they're not gonna find 250 million credit cards. Right. Or the, the script of a new movie or the super secret aircraft plans, right. That lives in a database somewhere. And so it's that movement from your laptop to that database. That's where the damage is done. Yeah. And that's where VMware shines. If they don't >>Have the right to get to that database, they're >>Not >>In and it's not even just the right, like, so they're so clever. And so sneaky that they'll steal a credential off your machine, go to another machine, steal a credential off of that. So it's like they have the key to unlock each one of these doors and we've gotten good enough where we can look at that lateral movement, even though it has a credential and a key where like, wait a minute, that's not a real CIS admin making a change. That's ransomware. Yeah. Right. And that's, that's where we, you have to earn your way in. That's right. That's >>Right. Yeah. And we're all, there's all kinds of configuration errors. But also some, some I'll just user problems. I've heard one story where there's so many passwords and username and passwords and systems that the bad guy's scour, the dark web for passwords that have been exposed. Correct. And go test them against different accounts. Oh one hit over here. Correct. And people don't change their passwords all the time. Correct? Correct. That's a known, known vector. We, >>We just, the idea that users are gonna be perfect and never make mistake. Like how long have we been doing this? Like humans with the weakest link. Right. So, so, so people are gonna make mistakes. Attackers are gonna be in here's another way of thinking about it. Remember log for J. Remember that whole ago, remember that was a Christmas time. That was nine months ago. And whoever came up with that, that vulnerability, they basically had a skeleton key that could access every network on the planet. I don't know if a single customer that was said, oh yeah, I wasn't impacted by log for J. So seers, some organized entity had access to every network on the planet. What was the big breach? What was that movie script that got stolen? So there wasn't one. Right? We haven't heard anything. So the point is the goal of attackers is to get in and stay in. Imagine someone breaks into your house, steals your laptop and runs. That's a breach. Imagine someone breaks into your house and stays for nine months. Like it's untenable, the real world. Right, right. >>We don't even go in there. They're still in there >>Watching your closet. Exactly. Moving around, nibbling on your ni line, your cookies. You know what I mean? Drinking your beer. >>Yeah. So, so let's talk about how this translates into the new reality of cloud native, because now know you hear about, you know, automated pen testing is a, a new hot thing right now you got antivirus on data. Yeah. Is hot is hot within APIs, for instance. Yeah. API security. So all kinds of new hot areas, cloud native is very iterative. You know, you, you can't do a pen test every week. Right. You gotta do it every second. Right. So this is where it's going. It's not so much simulation. It's actually real testing. Right. Right. How do you view that? How does that fit into this? Cuz that seems like a good direction to me. >>Yeah. It, it, it fits right in. And you were talking to my buddy AJ earlier about what VMware can do to help our customers build cloud native applications with, with Zu, my team is focused on how do we secure those applications? So where VMware wants to be the best in the world is securing these applications from within looking at the individual piece parts and how they talk to each other and figuring out, wait a minute. That, that, that, that, that should never happen by like almost having an x-ray machine on the ins of the application. So we do it for both for VMs and for container based applications. So traditional apps are VM based. Modern apps are container based and we, and we have a slightly different insertion mechanism. It's the same idea. So for VMs, we do it with the hypervisor, with NSX, we see all the inner workings in a container world. >>We have this thing called a service me that lets us look at each little snippet of code and how they talk to each other. And once you can see that stuff, then you can actually apply. It's almost like common sense logic of like, wait a minute. You know, this API is giving back credit card numbers and it gives five an hour. All of a sudden, it's now asking for 20,000 or a million credit card that doesn't make any sense. Right? The anomalies stick out like a sore thumb. If you can see them. And VMware, our unique focus in the infrastructure is that we can see each one of these little transactions and understand the conversation. That's what makes us so good at that east west or lateral >>Security. Yeah. You don't belong in this room, get out or that that's right. Some weird call from an in-memory database, something over >>Here. Exactly. Where other, other security solutions won't even see that. Right. It's not like there algorithms aren't as good as ours or, or better or worse. It's that, it's the access to the data. We see the, the, the, the inner plumbing of the app. And therefore we can protect >>The app from, and there's another dimension that I wanna get in the table here, cuz to my knowledge only AWS, Google, I, I believe Microsoft and Alibaba and VMware have this, it nitro the equivalent of a nitro. Yes. Project Monterey. Yeah. That's unique. It's the future of computing architectures. Everybody needs a nitro. I've I've written about this. Yeah. Right. So explain your version. Yeah. Project. It's now real. It's now in the market right. Or soon will be. Yeah. Here. Here's our mission salient aspects. Yeah. >>Here's our mission of VMware is that we wanna make every one of our enterprise customers. We want their private cloud to be as nimble, as agile, as efficient as the public cloud >>And secure >>And secure. In fact, I'll argue, we can make it actually more secure because we're thinking about putting security everywhere in this infrastructure. Right. Not just on the edges of it. So, so, so, okay. How do we go on that journey? As you pointed out, the public cloud providers realized, you know, five years ago that the right way to build computers was not just a CPU and a GPU graphics process, unit GPU, but there's this third thing that the industry's calling a DPU data processing unit. So there's kind of three pieces of a computer. And the DPU is sometimes called a smart Nick it's the network interface card. It does all that network handling and analytics and it takes it off the CPU. So they've been building and deploying those systems themselves. That's what nitro is. And so we have been working with the major Silicon vendors to bring that architecture to everybody. So, so with vSphere eight, we have the ability to take the network processing that east west inspection. I talked about, take it off of the CPU and put it into this dedicated processing element called the DPU and free up the CPU to run the applications that AJ and team are building. >>So no performance degradation at all, correct. >>To CPU >>Offload. So even the opposite, right? I mean you're running it basically bare metal speeds. >>Yes, yes. And yes. >>And, and, and you're also isolating the, the storage right from the, from the, the, the security, the management. And >>There's an isolation angle to this, which is that firewall that we're putting everywhere. Not just that the perimeter, we put it in each little piece of the server is running when it runs on one of these DPU, it's a different memory space. So even if, if an attacker gets to root in the OS, they it's very, very, never say never, but it's very difficult. >>So who has access to that? That, that resource >>Pretty much just the infrastructure layer, the cloud provider. So it's Google Microsoft, you know, and the enterprise, the >>Application can't get in, >>Can't get in there. Cause it, you would've to literally bridge from one memory space to another, never say never, but it would be very, very, >>It hasn't earned the trust >>To get it's more than Bob wire. It's, it's, it's multiple walls and, and >>It's like an air gap. It puts an air gap in the server itself so that if the server's compromised, it's not gonna get into the network really powerful. >>What's the big thing that you're seeing with this super cloud transition we're seeing, we're seeing, you know, multicloud and this new, not just SAS hosted on the cloud. Yeah. You're seeing a much different dynamic of combination of large scale CapEx, cloud native. And then now cloud native develops on premises and edge kind of changing what a cloud looks like if the cloud's on a cloud. So rubber customer, I'm building on a cloud and I have on-prem stuff. So I'm getting scale CapEx relief from the, from the cap, from the hyperscalers. >>I, I think there's an important nuance on what you're talking about, which is, is in the early days of the cloud customers. Remember those first skepticism? Oh, it'll never work. Oh, that's consumer grade. Oh, that's not really gonna work. And some people realize >>It's not secure. Yeah. >>It, it's not secure that one's like, no, no, no, it's secure. It works. And it, and it's good. So then there was this sort of over rush. Like let's put everything on the cloud. And I had a lot of customers that took VM based applications said, I'm gonna move those onto the cloud. You gotta take 'em all apart, put 'em on the cloud and put 'em all back together again. And little tiny details, like changing an IP address. It's actually much harder than it looks. So my argument is for existing workloads for VM based workloads, we are VMware. We're so good at running VM based workloads. And now we run them on anybody's cloud. So whether it's your east coast data center, your west coast data center, Amazon, Google, Microsoft, Alibaba, IBM keep going. Right. We pretty much every, and >>The benefit of the customer is what you >>Can literally vMotion and just pick it up and move it from private to public public, to private, private, to public, public, back and forth. >>Remember when we called VMO BS years ago. Yeah, yeah, yeah. >>We were really, skeptic is >>Powerful. We were very skeptical. We're like, that'll never happen. I mean, we were, I mean, it's supposed to be pat ourselves on the back. We, well, >>Because it's alchemy, it seems like what you can't possibly do that. Right. And so, so, so, and now we do it across clouds, right? So we can, you know, it's not quite VMO, but it's the same idea. You can just move these things over. I have one customer that had a production data center in the Ukraine, things got super tense, super fast, and they had to go from their private cloud data center in the Ukraine to a public cloud data center outta harm's way. They did it over a weekend, 48 hours. If you've ever migrated data, that's usually six months, right? And a lot of heartburn and a lot of angst, boom. They just drag and drop, moved it on over. That's the power of what we call the cloud operating model. And you can only do this when all your infrastructure's defined in software. >>If you're relying on hardware, load, balancers, hardware, firewalls, you can't move those. They're like a boat anchor. You're stuck with them. And by the way, really, really expensive. And by the way, they eat a lot of power, right? So that was an architecture from the nineties in the cloud operating model, your data center. And this goes back to what you were talking about is just racks and racks of X 86 with these magic DPU or smart necks to make any individual node go blisteringly fast and do all the functions that you used to do in network appliances. >>We just said, AJ taking us to school and everyone else to school on applications, middleware abstraction layer. Yeah. And kit Culver was also talking about this across cloud. We're talking super cloud, super pass. If this continues to happen, which we would think it will happen. What does the security posture look like? It has. It feels to me. And again, this is, this is your wheelhouse. If super cloud happens with this kind of past layer where there's B motioning going on, all kinds of yeah. Spanning applications and data. Yeah. Across environments. Yeah. Assume there's an operating system working on behind the scenes. Right. What's the security posture in all this. Yeah. >>So remember my narrative about like VA guys are getting in and they're moving around and they're so sneaky that they're using legitimate pathways. The only way to stop that stuff is you've gotta understand it at what, you know, we call layer seven at the application layer the in, you know, trying to do security, the infrastructure layer. It was interesting 20 years ago, kind of less interesting 10 years ago. And now it's becoming irrelevant because the infrastructure is oftentimes not even visible, right. It's buried in some cloud provider. So layer seven, understanding, application awareness, understanding the APIs and reading the content. That's the name of the game in security. That's what we've been focused on. Right. Nothing to do with >>The infras. And where's the progress bar on that, that paradigm early one at the 10, 10 being everyone's doing it >>Right now. Well, okay. So we, as a vendor can do this today. All the stuff I talked about about reading APIs, understanding the, the individual services looking at, Hey, wait a minute. This credit card anomalies, that's all shipping production code. Where is it in customer adoption life cycle, early days, 10%. So, so there's a whole lot of headroom. We, for people to understand, Hey, I can put these controls in place. There's software based. They don't require appliances. It's layer seven. So it has contextual awareness and it's works on every single cloud. >>You know, we talk about the pandemic. Being an accelerator really was a catalyst to really rethink. Remember we used to talk about pat his security a do over. He's like, yes, if it's the last thing I'm due, I'm gonna fix security. Well, he decided to go try to fix Intel instead, but, >>But, but he's getting some help from the government, >>But it seems like, you know, CISOs have totally rethought, you know, their security strategy. And, and at least in part is a function of the pandemic. >>When I started at VMware four years ago, pat sat me down in his office and he said to me what he said to you, which is like Tom, he said, I feel like we have fundamentally changed servers. We fundamentally changed storage. We fundamentally changed networking. The last piece of the puzzle of security. I want you to go fundamentally change it. And I'll argue that the work that we're doing with this, this horizontal security understanding the lateral movement east west inspection, it fundamentally changes how security works. It's got nothing to do with firewalls. It's got nothing to do with endpoint. It's a unique capability that VMware is uniquely suited to deliver on. And so pat, thanks for the mission. We delivered it and available >>Those, those wet like web applications firewall for instance are, are around. I mean, but to your point, the perimeter's gone. Exactly. And so you gotta get, there's no perimeter. So it's a surface area problem. Correct. And access and entry, correct. They're entering here easy from some manual error or misconfiguration or bad password that shouldn't be there. They're >>In. Think about it this way. You put the front door of your house, you put a big strong door and a big lock. That's a firewall bad guys, come in the window. Right. And >>Then the window's open and the window with a ladder room. Oh my >>God. Cause it's hot, bad user behavior. Trump's good security >>Every time. And then they move around room to room. We're the room to room people. Yeah. We see each little piece of the thing. Wait, that shouldn't happen. Right. >>I wanna get you a question that we've been seeing and maybe we're early on this, or it might be just a, a false data point. A lot of CSOs and we're talking to are, and people in industry in the customer environment are looking at CSOs and CSOs, two roles, chief information security officer, and then chief security officer Amazon, actually, Steven Schmidt is now CSO at reinforced. They actually called that out. Yeah. And the, and the interesting point that he made, we've had some other situations that verified. This is that physical security is now tied to online to your point about the service area. If I get a password, I still at the keys to the physical goods too. Right. Right. So physical security, whether it's warehouse for them is, or store or retail digital is coming in there. Yeah. So is there a CSO anymore? Is it just CSO? What's the role or are there two roles you see that evolving or is that just, >>Well, >>I circumstance, >>I, I think it's just one. And I think that, that, you know, the stakes are incredibly high in security. Just look at the impact that these security attacks are having on it. It, you know, companies get taken down, Equifax market cap was cut, you know, 80% with a security breach. So security's gone from being sort of a nuisance to being something that can impact your whole kind of business operation. And then there's a whole nother domain where politics get involved. Right. It determines the fate of nations. I know that sounds grand, but it's true. Yeah. And so, so, so companies care so much about it. They're looking for one liter, one throat to choke, you know, one person that's gonna lead security in the virtual domain, in the physical domain, in the cyber domain, in, in, you know, in the actual, well, it is, >>I mean, you mentioned that, but I mean, mean you look at Ukraine. I mean the, the, that, that, that cyber is a component of that war. I mean, that's very clear. I mean, that's, that's new, we've never seen >>This. And in my opinion, the stuff that we see happening in the Ukraine is small potatoes compared to what could happen. Yeah, yeah. Right. So the us, we have a policy of, of strategic deterrents where we develop some of the most sophisticated cyber weapons in the world. We don't use them and we hope never to use them because the, the, our adversaries who could do stuff like, oh, I don't know, wipe out every bank account in north America, or turn off the lights in New York city. They know that if they were to do something like that, we could do something back. >>I, this discuss, >>This is the red line conversation I wanna go there. So >>I had this discussion with Robert Gates in 2016 and he said, we have a lot more to lose, which is really >>Your point. So this brand, so I agree that there's the, to have freedom and Liberty, you gotta strike back with divorce and that's been our way to, to balance things out. Yeah. But with cyber, the red line, people are already in banks. So they're addresses are operating below the red line, red line, meaning before we know you're in there. So do we move the red line down because Hey, Sony got hacked the movie because they don't have their own militia. Yeah. If they were physical troops on the shores of LA breaking into the file cabinets. Yeah. The government would've intervened. >>I, I, I agree with you that it creates, it creates tension for us in the us because our, our adversaries don't have the clear delineation between public and private sector here. You're very, very clear if you're working for the government or you work for an private entity, there's no ambiguity on that. And so, so we have different missions in each department. Other countries will use the same cyber capabilities to steal intellectual, you know, a car design as they would to, you know, penetrate a military network. And that creates a huge hazard for us on the us. Cause we don't know how to respond. Yeah. Is that a civil issue? Is that a, a, a military issue? And so, so it creates policy ambiguity. I still love the clarity of separation of, you know, sort of the various branches of government separation of government from, >>But that, but, but bureau on multinational corporation, you then have to, your cyber is a defensible. You have to build the defenses >>A hundred percent. And I will also say that even though there's a clear D mark between government and private sector, there's an awful lot of cooperation. So, so our CSO, Alex toshe is actively involved in the whole intelligence community. He's on boards and standards and we're sharing because we have a common objective, right? We're all working together to fight these bad guys. And that's one of the things I love about cyber is that that even direct competitors, two big banks that are rivals on the street are working together to share security information and, and private, is >>There enough? Is collaboration Tom in the vendor community? I mean, we've seen efforts to try to, that's a good question, monetize private data, you know? Yeah. And private reports and, >>And, you know, like, so at VMware, we, we, I'm very proud of the security capabilities we've built, but we also partner with people that I think of as direct competitors, we've got firewall vendors and endpoint vendors that we work with and integrate. And so cooperation is something that exists. It's hard, you know, because when you have these kind of competing, you know, so could we do more? Of course we probably could, but I do think we've done a fair amount of cooperation, data sharing, product integration, et cetera, you know, and, you know, as the threats get worse, you'll probably see us continue to do more. >>And the governments is gonna trying to force that too. >>And, and the government also drives standards. So let's talk about crypto. Okay. So there's a new form of encryption coming out called quantum processing, calling out. Yeah. Yeah. Quantum, quantum computers have the potential to crack any crypto cipher we have today. That's bad. Okay. Right. That's not good at all because our whole system is built around these private communications. So, so the industry is having conversations about crypto agility. How can we put in place the ability to rapidly iterate the ciphers in encryption? So when the day quantum becomes available, we can change them and stay ahead of these quantum people. Well, >>Didn't this just put out a quantum proof algo that's being tested right now by the, the community. >>There's a lot of work around that. Correct. And, and, and this is taking the lead on this, but you know, Google's working on it, VMware's working on it. We're very, very active in how do we keep ahead of the attackers and the bad guys? Because this quantum thing is like a, it's a, it's a x-ray machine. You know, it's like, it's like a, a, a di lithium crystal that can power a whole ship. Right. It's a really, really, really powerful >>Tool. It's bad. Things will happen. >>Bad things could happen. >>Well, Tom, great to have you on the cube. Thanks for coming. Take the last minute to just give a plug for what's going on for you here at world this year, VMware explore this year. Yeah. >>We announced a bunch of exciting things. We announced enhancements to our, our NSX family, with our advanced load balancer, with our edge firewall. And they're all in service of one thing, which is helping our customers make their private cloud like the public cloud. So I like to say 0, 0, 0. If you are in the cloud operating model, you have zero proprietary appliances. You have zero tickets to launch a workload. You have zero network taps and zero trust built into everything you do. And that's, that's what we're working on and pushing that further and further. >>Tom Gill, senior vices president head of the networking at VMware. Thanks for coming up for you. Appreciate >>It. Yes. Thanks for having guys >>Always getting the security data. That's killer data and security of the two ops that get the most conversations around dev ops and cloud native. This is the queue bringing you all the action here in San Francisco for VMware. Explore 2022. I'm John furrier with Dave, Alan. Thanks for watching.

>>EDR NDR, what are the differences, which one's better? Are they better together? Today's security stack contains a lot of different tools and types of data and fortunate, as you know, this creates data silos, which leads to vis visibility gaps. EDR is endpoint detection and response. It's designed to monitor and mitigate endpoint attacks, which are typically focused on computers and servers, NDR network detection, and response. On the other hand, monitors network traffic to gain visibility into potential or active cyber threats, delivering real time visibility across the broader network. One of the biggest advantages that NDR has over EDR is that bad actors can hide or manipulate endpoint data, pretty easily network data. On the other hand, much harder to manipulate because attackers and malware can avoid detection at the endpoint. NDR, as you're gonna hear is the only real source for reliable, accurate, and comprehensive data. >>All endpoints use the network to communicate, which makes your network data, the ultimate source of truth. My name is Lisa Martin, and today on the special cube presentation, Tom Binkowski senior director of product marketing at net scout, and I are gonna explore the trends and the vital reasons why relying upon EDR is not quite enough. We're also gonna share with you the growing importance of advanced NDR. Welcome to the series, the growing importance of advanced NDR in the first segment, Tom's gonna talk with me about the trends that are driving enterprise security teams to implement multiple cyber security solutions that enable greater visibility, greater protection. We're also gonna explore Gartner's concept of the security operations center, SOC visibility triad, and the three main data sources for visibility, SIM EDR and NDR in segment two, Tom. And I will talk about the role of NDR and how it overcomes the challenges of EDR as Tom's gonna discuss, as you'll hear EDR is absolutely needed, but as he will explain it, can't be solely relied upon for comprehensive cybersecurity. And then finally, we'll come back for a third and final segment to discuss why not all NDR is created equal. Tom's gonna unpack the features and the capabilities that are most important when choosing an NDR solution. Let's do this. Here comes our first segment. >>Hey, everyone kicking things off. This is segment one. I'm Lisa Martin with Tom Binowski, senior director of product marketing at nets scout. Welcome to the growing importance of advanced NDR. Tom, great to have you on the program, >>Glad to be here. >>So we're gonna be talking about the trends that are driving enterprise security teams to implement multiple cyber security solutions that really enable greater visibility and protection. And there are a number of factors that continue to expand the ECAC service for enterprise networks. I always like to think of them as kind of the spreading amorphously you shared had shared some stats with me previously, Tom, some cloud adoption stats for 2022 94% of all enterprises today use a cloud service and more than 60% of all corporate data is store in the cloud. So, Tom, what are some of the key trends that nets scout is seeing in the market with respect to this? >>Yeah, so just to continue that, you know, those stats that, that migration of workloads to the cloud is a major trend that we're seeing in that was exasperated by the pandemic, right along with working from home. Those two things are probably the most dramatic changes that we we see out there today. But along with that is also this growing sophistication of the network, you know, today, you know, your network environment, isn't a simple hub and spoke or something like that. It is a very sophisticated combination of, you know, high speed backbones, potentially up to a hundred gigabits combination with partner networks. You have, like we said, workloads up in, in private clouds, pub public clouds. So you have this hybrid cloud environment. So, and then you have applications that are multi-tiered, there are pieces and parts. And in all of that, some on your premise, some up in a private cloud, some on a public cloud, some actually pulling data off when you a customer network or potentially even a, a partner network. So really, really sophisticated environment today. And that's requiring this need for very comprehensive network visibility, not only for, for cybersecurity purposes, but also just to make sure that those applications and networks are performing as you have designed them. >>So when it comes to gaining visibility into cyber threats, I, you talked about the, the sophistication and it sounds like even the complexity of these networks, Gartner introduced the concept of the security operations, visibility triad, or the SOC visibility triad break that down for us. It consists of three main data sources, but to break those three main data sources down for us. >>Sure. So Gartner came out a few years ago where they were trying to, you know, summarize where do security operations team get visibility into threats and they put together a triad and the three sides of the trier consists of one, the SIM security information event manager, two, the endpoint or, or data that you get from EDR systems, endpoint detection, response systems. And the third side is the network or the data you get from network detection, response systems. And, you know, they didn't necessarily say one is better than the other. They're basically said that you need all three in order to have comprehensive visibility for cybersecurity purposes. >>So talk, so all, all three perspectives are needed. Talk about what each provides, what are the different perspectives on threat detection and remediation? >>Yeah. So let's start with the SIM, you know, that is a device that is gathering alerts or logs from all kinds of different devices all over your network. Be it routers servers, you know, firewalls IDs, or even from endpoint detection and network detection devices too. So it is, it is the aggregator or consumer of all those alerts. The SIM is trying to correlate those alerts across all those different data sources and, and trying to the best it can to bubble up potentially the highest priority alerts or drawing correlations and, and, and, and giving you some guidance on, Hey, here's something that we think is, is really of importance or high priority. Here's some information that we have across these disparate data sources. Now go investigate the disadvantage of the SIM is that's all it gives you is just these logs or, or, or information. It doesn't give you any further context. >>Like what happened, what is really happening at the end point? Can I get visibility into the, into the files that were potentially manipulated or the, the registry setting or what, what happened on the network? And I get visibility into the packet date or things like that. It that's, so that's where it ends. And, and that's where the, so there other two sides of the equation come in, the endpoint will give you that deeper visibility, endpoint detection response. It will look for known and or unknown threats, you know, at that endpoint, it'll give you all kinds of additional information that is occurring in endpoint, whether it be a registry setting in memory on the file, et cetera. But you know, one of, some of its disadvantages, it's really difficult because really difficult to deploy pervasive because it requires an agent and, you know, not all devices can accept an agent, but what it miss, what is lacking is the context on the network. >>So if I was an analyst and I started pursuing from my SIM, I went down to the end point and, and said, I wanna investigate this further. And I hit a, I hit a dead end from some sort, or I realize that the device that's potentially I should be alerted to, or should be concerned about is an IOT device that doesn't even have an agent on it. My next source of visibility is on the network and that's where NDR comes in. It, it sees what's traversing. The entire network provides you visibility into that from both a metadata and even a ultimately a packer perspective. And maybe, you know, could be deployed a little bit more strategically, but you know, it doesn't have the perspective of the endpoint. So you can see how each of these sort of compliments each other. And that's why, you know, Gartner said that, that you need 'em all, then they all play a role. They all have their pros and cons or advantage and disadvantages, but, you know, bringing them and using 'em together is, is the key. >>I wanna kinda dig into some of the, the EDR gaps and challenges, as you talked about as, as the things evolve and change the network, environment's becoming far more sophisticated and as well as threat actors are, and malware is. So can you crack that open more on some of the challenges that EDR is presenting? What are some of those gaps and how can organizations use other, other, other data sources to solve them? >>Yeah, sure. So, you know, again, just be clear that EDR is absolutely required, right? We, we need that, but as sort of these network environments get more complex, are you getting all kinds of new devices being put on the network that devices being brought into the network that may be, you didn't know of B Y O D devices you have, I T devices, you know, popping up potentially by the thousands in, in, in some cases when new applications or world that maybe can't accept an and endpoint detection or an EDR agent, you may have environments like ICS and skate environments that just, you can't put an endpoint agent there. However, those devices can be compromised, right? You have different environments up in the cloud or SaaS environments again, where you may not be able to deploy an endpoint agent and all that together leaves visibility gaps or gaps in, in, in the security operation triad. Right. And that is basically open door for exploitation >>Open door. Go ahead. Sorry. >>Yeah. And then, then you just have the malware and the, and the attackers getting more sophisticated. They, they have malware that can detect an EDR agent running or some anti malware agent running on device. And they'll simply avoid that and move on to the next one, or they know how to hide their tracks, you know, whether it be deleting files, registry, settings, things like that. You know, so it's, that's another challenge that, that, that just an agent faces. Another one is there are certain applications like my SQL that are, you know, have ministry administrative rights into certain parts of the windows operate system that EDR doesn't have visibility into another area that maybe EDR may not have visibility is, is, is in, you know, malware that tries to compromise, you know, hardware, especially like bios or something like that. So there's a number of challenges as sort of the whole network environment and sophistication of bad actors and malware increases. >>Ultimately, I think one of the things that, that we've learned, and, and we've heard from you in this segment, is that doing business in, in today's digital economy, demands, agility, table stakes, right? Absolutely essential corporate digital infrastructures have changed a lot in response to the dynamic environment, but its businesses are racing to the clouds. Dave Alane likes to call it the forced March to the cloud, expanding activities across this globally distributed digital ecosystem. They also sounds like need to reinvent cybersecurity to defend this continuously expanding threat surface. And for that comprehensive network, visibility is, as I think you were saying is really, really fundamental and more advanced network detection is, and responses required. Is that right? >>That's correct. You know, you know, we, we at ESCO, this is, this is where we come from. Our perspective is the network. It has been over for over 30 years. And, and we, as well as others believe that that network visibility, comprehensive network visibility is fundamental for cyber security as well as network performance and application analysis. So it, it, it's sort of a core competency or need for, for modern businesses today. >>Excellent. And hold that thought, Tom, cause in a moment, you and I are gonna be back to talk about the role of NDR and how it overcomes the challenges of EDR. You're watching the cube, the leader in enterprise tech coverage. Hey everyone, welcome back. This is segment two kicking things off I'm Lisa Martin with Tom Binkowski, senior director of product marketing at nets scout, Tom, great to have you back on the program. >>Good to be here. >>We're gonna be talking about the growing importance of advanced NDR in this series. In this segment specifically, Tom's gonna be talking about the role of NDR and how it overcomes the challenges of EDR. So Tom, one of the things that we talked about previously is one of the biggest advantages that NDR has over EDR is that bad actors can hide or manipulate endpoint data pretty easily, whereas network data, much harder to manipulate. So my question, Tom, for you is, is NDR the only real source for reliable, accurate, comprehensive data. >>I'm sure that's arguable, right? Depending on who you are as a vendor, but you know, it's, it's our, our answer is yes, NDR solutions also bring an analyst down to the packet level. And there's a saying, you know, the, the packet is the ultimate source or source of truth. A bad actor cannot manipulate a packet. Once it's on the wire, they could certainly manipulate it from their end point and then blast it out. But once it hits the wire, that's it they've lost control of it. And once it's captured by a network detection or, or network monitoring device, they can't manipulate it. They can't go into that packet store and, and manipulate those packets. So the ultimate source of truth is, is lies within that packet somewhere. >>Got you. Okay. So as you said in segment one EDR absolutely necessary, right. But you did point out it can't organizations can't solely rely on it for comprehensive cybersecurity. So Tom, talk about the benefits of, of this complimenting, this combination of EDR and NDR and, and how can that deliver more comprehensive cybersecurity for organizations? >>Yeah, so, so one of the things we talked about in the prior segment was where EDR, maybe can't be deployed and it's either on different types of devices like IOT devices, or even different environments. They have a tough time maybe in some of these public cloud environments, but that's where NDR can, can step in, especially in these public cloud environments. So I think there's a misconception out there that's difficult to get packet level or network visibility and public clouds like AWS or Azure or Google and so on. And that's absolutely not true. They have all kinds of virtual tapping capabilities that an NDR solution or network based monitoring solution could take advantage of. And one of the things that we know we spoke about before some of that growing trends of migrating workloads to the cloud, that's, what's driving that those virtual networks or virtual taps is providing visibility into the performance and security of those workloads. >>As they're migrated to public clouds, NDR can also be deployed more strategically, you know, prior segment talking about how the, in order to gain pervasive visibility with EDR, you have to deploy an agent everywhere agents can't be deployed everywhere. So what you can do with NDR is there's a lot fewer places in a network where you can strategically deploy a network based monitoring device to give you visibility into not only that north south traffic. So what's coming in and out of your network, but also the, the, the, the east west traffic too west traversing, you know, within your network environment between different points of your op your, your multi-tiered application, things like that. So that's where, you know, NDR has a, a, a little bit more advantage. So fewer points of points in the network, if you will, than everywhere on every single endpoint. And then, you know, NDR is out there continuously gathering network data. It's both either before, during, and even after a threat or an attack is, is detected. And it provides you with this network context of, of, you know, what's happening on the wire. And it does that through providing you access to, you know, layer two through layer seven metadata, or even ultimately packets, you know, the bottom line is simply that, you know, NDR is providing, as we said before, that that network context that is potentially missing or is missing in EDR. >>Can you talk a little bit about XDR that kind of sounds like a superhero name to me, but this is extended detection and response, and this is an evolution of EDR talk to us about XDR and maybe EDR NDR XDR is really delivering that comprehensive cybersecurity strategy for organizations. >>Yeah. So, you know, it's, it's interesting. I think there's a lot of confusion out there in the industry. What is, what is XDR, what is XDR versus an advanced SIM, et cetera. So in some cases, there are some folks that don't think it's just an evolution of EDR. You know, to me, XDR is taking, look at these, all these disparate data sources. So going back to our, when our first segment, we talked about the, the, the security operations center triad, and it has data from different perspectives, as we were saying, right? And XCR, to me is the, is, is trying to bring them all together. All these disparate data source sets or sources bring them together, conduct some level of analysis on that data for the analyst and potentially, you know, float to the top. The most, you know, important events are events that we, that you know, that the system deems high priority or most risky and so on. But as I, as I'm describing this, I know there are many advanced Sims out there trying to do this today too. Or they do do this today. So this there's this little area of confusion around, you know, what exactly is XDR, but really it is just trying to pull together these different sources of information and trying to help that analyst figure out, you know, what, where's the high priority event that's they should be looking at, >>Right? Getting those high priority events elevated to the top as soon as possible. One of the things that I wanted to ask you about was something that occurred in March of this year, just a couple of months ago, when the white house released a statement from president Biden regarding the nation's cyber security, it included recommendations for private companies. I think a lot of you are familiar with this, but the first set of recommendations were best practices that all organizations should already be following, right? Multifactor authentication, patching against known vulnerabilities, educating employees on the phishing attempts on how to be effective against them. And the next statement in the president's release, focus on data safety practices, also stuff that probably a lot of corporations doing encryption maintaining offline backups, but where the statement focused on proactive measures companies should take to modernize and improve their cybersecurity posture. It was vague. It was deploy modern security tools on your computers and devices to continuously look for and mitigate threats. So my question to you is how do, how do you advise organizations do that? Deploy modern security tools look for and mitigate threats, and where do the data sources, the SOC tri that we talked about NDR XDR EDR, where did they help fit into helping organizations take something that's a bit nebulous and really figure out how to become much more secure? >>Yeah, it was, it was definitely a little vague there with that, with that sentence. And also if you, if you, I think if, if you look at the sentence, deploy modern security tools on your computers and devices, right. It's missing the network as we've been talking about there, there's, there's a key, key point of, of reference that's missing from that, from that sentence. Right. But I think what they mean by deploying monitor security tools is, is really taking advantage of all these, these ways to gain visibility into, you know, the threats like we've been talking about, you're deploying advanced Sims that are pulling logs from all kinds of different security devices or, and, or servers cetera. You're, you're deploying advanced endpoint detection systems, advanced NDR systems. And so on, you're trying to use, you're trying to utilize XDR new technology to pull data from all those different sources and analyze it further. And then, you know, the other one we, we haven't even mentioned yet. It was the, so the security operation and automation, right. Response it's now, now what do we do? We've detected something, but now help me automate the response to that. And so I think that's what they mean by leveraging modern, you know, security tools and so on >>When you're in customer conversations, I imagine they're coming to, to Netscale looking for advice like what we just talked through the vagueness in that statement and the different tools that organizations can use. So when you're talking to customers and they're talking about, we need to gain visibility across our entire network, across all of our devices, from your perspective from net Scout's perspective, what does that visibility actually look like and deliver across an organization that does it well? >>Yeah, we, I mean, I think the simple way to put it is you need visibility. That is both broad and deep. And what I mean by broad is that you need visibility across your network, no matter where that network may reside, no matter what protocols it's running, what, you know, technologies is it, is it virtualized or, or legacy running in a hundred gigabits? Is it in a private cloud, a public cloud, a combination of both. So that broadness, meaning wherever that network is or whatever it's running, that's, that's what you need visibility into. It has to be able to support that environment. Absolutely. And the, the, absolutely when I, we talk about being deep it's, it has to get down to a packet level. It can't be, you know, as high as say, just looking at net flow records or something like that, that they are valuable, they have their role. However, you know, when we talk about getting deep, it has to ultimately get down to the packet level and that's, and we've said this in this time that it's ultimately that source of truth. So that, that's what that's, I think that's what we need. >>Got it. That that depth is incredibly important. Thanks so much, Tom, for talking about this in a moment, you and I are gonna be back, we're gonna be talking about why not all NDR is created equally, and Tom's gonna actually share with you some of the features and capabilities that you should be looking for when you're choosing an NDR solution. You're watching the cube, the leader in enterprise tech coverage, >>And we're clear. >>All right. >>10 45. Perfect. You guys are >>Okay. Good >>Cruising. Well, >>Welcome back everyone. This is segment three. I'm Lisa Martin with Tom gin. Kowski senior director of product marketing at nets scout. Welcome back to the growing importance of advanced NDR in this segment, Tom and I are gonna be talking about the fact that not all NDR is created equally. He's gonna impact the features, the capabilities that are most important when organizations are choosing an NDR solution. Tom, it's great to have you back on the program. >>Great, great to be here. >>So we've, we've covered a lot of content in the first two segments, but as we, as we see enterprises expanding their it infrastructure, enabling the remote workforce, which is here to stay leveraging the crowd cloud, driving innovation, the need for cybersecurity approaches and strategies that are far more robust and deep is really essential. But in response to those challenges, more and more enterprises are relying on NDR solutions that fill some of the gaps that we talked about with some of the existing tool sets in the last segment, we talked about some of the gaps in EDR solutions, how NDR resolves those. But we also know that not all NDR tools are created equally. So what, in your perspective, Tom are some of the absolutely fundamental components of NDR tools that organizations need to have for those tools to really be robust. >>Yeah. So we, we, we touched upon this a little bit in the previous segment when we talked about first and foremost, your NDR solution is providing you comprehensive network visibility that must support whatever your network environment is. And it should be in a single tool. It shouldn't have a one vendor per providing you, you know, network visibility in the cloud and another vendor providing network visibility in a local network. It should be a single NDR solution that provides you visibility across your entire network. So we also talked about it, not only does it need to be broadened like that, but also has to be deep too, eventually down to a packet level. So those are, those are sort of fundamental table stakes, but the NDR solution also must give you the ability to access a robust source of layer two or layer three metadata, and then ultimately give you access to, to packets. And then last but not least that solution must integrate into your existing cybersecurity stack. So in the prior segments, we talked a lot about, you know, the, the SIM, so that, that, that NDR solution must have the ability to integrate into that SIM or into your XDR system or even into your source system. >>Let's kind of double click on. Now, the evolution of NDR can explain some of the differences between the previous generations and advanced NDR. >>Yeah. So let's, let's start with what we consider the most fundamental difference. And that is solution must be packet based. There are other ways to get network visibility. One is using net flow and there are some NDR solutions that rely upon net flow for their source of, of, of visibility. But that's too shallow. You ultimately, you need to get deeper. You need to get down to a pack level and that's again where some, so, you know, you, you want to make sure that your NDR or advanced NDR solution is packet based. Number two, you wanna make sure that when you're pulling packets off the wire, you can do it at scale, that full line rate and in any environment, as we, as we spoke about previously, whether it be your local environment or a public cloud environment, number three, you wanna be able to do this when your traffic is encrypted. As we know a lot of, lot of not of network traffic is encrypted today. So you have the ability to have to have the ability to decrypt that traffic and then analyze it with your NDR system. >>Another, another, another one number four is, okay, I'm not just pulling packets off the wire, throwing full packets into a data storage someplace. That's gonna, you know, fill up a disc in a matter of seconds, right? You want the ability to extract a meaningful set of metadata from layer two to layer seven, the OSI model look at key metrics and conducting initial set of analysis, have the ability to index and compress that data, that metadata as well as packets on these local storage devices on, you know, so having the ability to do this packet capture at scale is really important, storing that packets and metadata locally versus up in a cloud to, you know, help with some compliance and, and confidentiality issues. And then, you know, last final least when we talk about integration into that security stack, it's multiple levels of integration. Sure. We wanna send alerts up into that SIM, but we also want the ability to, you know, work with that XDR system to, or that, that source system to drill back down into that metadata packets for further analysis. And then last but not least that piece of integration should be that there's a robust set of information that these NDR systems are pulling off the wire many times in more advanced mature organizations, you know, security teams, data scientists, et cetera. They just want access to that raw data, let them do their own analysis outside, say the user interface with the boundaries of a, of a vendor's user interface. Right? So have the ability to export that data too is really important and advance in the systems. >>Got it. So, so essentially that the, the, the breadth, the visibility across the entire infrastructure, the depth you mentioned going down to a packet level, the scale, the metadata encryption, is that what net scout means when you talk about visibility without borders? >>Yeah, exactly. You know, we, we have been doing this for over 30 years, pulling packets off of wire, converting them using patent technology to a robust set of metadata, you know, at, at full line rates up to a hundred in any network environment, any protocols, et cetera. So that, that's what we mean by that breadth. And in depth of visibility, >>Can you talk a little bit about smart detection if we say, okay, advanced NDR needs to deliver this threat intelligence, but it also needs to enable smart detection. What does net scout mean by that? >>So what you wanna make sure you have multiple methods of detection, not just a methods. So, you know, not just doing behavioral analysis or not just detecting threats based on known indicators or compromise, what you wanna wanna have multiple ways of detecting threats. It could be using statistical behavioral analysis. It could be using curated threat intelligence. It could be using, you know, open source signature engine, like from Sara COTA or other threat analytics, but to, but you also wanna make sure that you're doing this both in real time and have the ability to do it historically. So after a, a threat has been detected, for example, with another, with another product, say an EDR device, you now want the ability to drill into the data from the network that had occurred in, in, you know, prior to this. So historically you want the ability to comb through a historical set of metadata or packets with new threat intelligence that you've you've gathered today. I wanna be able to go back in time and look through with a whole new perspective, looking for something that I didn't know about, but you know, 30 days ago. So that's, that's what we, what we mean by smart detection. >>So really what organizations need is these tools that deliver a far more comprehensive approach. I wanna get into a little bit more on in integration. You talked about that in previous segments, but can you, can you give us an example of, of what you guys mean by smart integration? Is that, what does that deliver for organizations specifically? >>Yeah, we really it's three things. One will say the integration to the SIM to the security operations center and so on. So when, when an ed, when an NDR device detects something, have it send an alert to the SIM using, you know, open standards or, or, or like syslog standards, et cetera, the other direction is from the SIM or from the so, so one, you know, that SIM that, so is receiving information from many different devices that are, or detecting threats. The analyst now wants the ability to one determine if that's a true threat or not a false positive, if it is a true threat, you know, what help me with the remediation effort. So, you know, an example could be an alert comes into a SIM slash. So, and part of the playbook is to go out and grab the metadata packets associated with this alert sometime before and sometime after when that alert came in. >>So that could be part of the automation coming from the SIM slash. So, and then last one, not least is we alluded to this before is having the ability to export that robust set of layer two through layer seven metadata and or packets to a third party data lake, if you will, and where analysts more sophisticated analysts, data scientists, and so on, can do their own correlation, enrich it with their own data, combined it with other data sets and so on, do their own analysis. So it's that three layers of, of integration, if you will, that really what should be an advanced NDR system? >>All right, Tom, take this home for me. How does nets scout deliver advanced NDRs for organizations? >>We do that via solution. We call Omni the security. This is Netscout's portfolio of, of multiple different cyber security products. It all starts with the packets. You know, our core competency for the last 30 years has been to pull packets off the wire at scale, using patented technologies, for example, adapt service intelligence technologies to convert those broad packets into robust set of layer seven layer two through seven metadata. We refer to that data as smart data with that data in hand, you now have the ability to conduct multiple types of threat detection using statistical behavioral, you know, curative threat intelligence, or even open source. So rules engine, you have the ability to detect threats both in real time, as well as historically, but then a solution goes beyond just detecting threats or investigating threats has the ability to influence the blocking of threats too. So we have integrations with different firewall vendors like Palo Alto, for example, where they could take the results of our investigation and then, you know, create policies, blocking policies into firewall. >>In addition to that, we have our own Omni a E D product or our Arbor edge defense. That's, that's a product that sits in front of the firewall and protects the firewall from different types of attacks. We have integration that where you can, you can also influence policies being blocked in the a E and in last but not least, our, our solution integrates this sort of three methods of integration. As we mentioned before, with an existing security system, sending alerts to it, allowing for automation and investigation from it, and having the ability to export our data for, you know, custom analysis, you know, all of this makes that security stack that we've been talking about better, all those different tools that we have. That's that operations triads that we talked about or visibility triad, we talked about, you know, our data makes that entire triad just better and makes the overall security staff better and makes overall security just, just better too. So that, that that's our solution on the security. >>Got it. On the security. And what you've talked about did a great job. The last three segments talking about the differences between the different technologies, data sources, why the complimentary and collaborative nature of them working together is so important for that comprehensive cybersecurity. So Tom, thank you so much for sharing such great and thoughtful information and insight for the audience. >>Oh, you're welcome. Thank you. >>My pleasure. We wanna thank you for watching the program today. Remember that all these videos are available@thecube.net, and you can check out today's news on Silicon angle.com and of course, net scout.com. We also wanna thank net scout for making this program possible and sponsoring the cube. I'm Lisa Martin for Tomski. Thanks for watching and bye for now.

>>My name is Dave Volonte, and with me are two world class technologists, visionaries and entrepreneurs. And Wa Dodgeville is the he co founded Snowflake, and he's now the president of the product division. And Florian Duetto is the co founder and CEO of Data Aiko. Gentlemen, welcome to the Cube to first timers. Love it. >>Great to be here >>now, Florian you and Ben Wa You have a number of customers in common. And I have said many times on the Cube that you know, the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud is really the scale platform. So is that premise your relevant to you? Do you buy that? And and why do you think snowflake and data ICU make a good match for customers? >>I think that because it's our values that are aligned when it's all about actually today allowing complexity for customers. So you close the gap or the democratizing access to data access to technology. It's not only about data data is important, but it's also about the impact of data. Who can you make the best out of data as fast as possible as easily as possible within an organization. And another value is about just the openness of the platform building the future together? Uh, I think a platform that is not just about the platform but also full ecosystem of partners around it, bringing the level off accessibility and flexibility you need for the 10 years away. >>Yeah, so that's key. But it's not just data. It's turning data into insights. Have been why you came out of the world of very powerful but highly complex databases. And we know we all know that you and the snowflake team you get very high marks for really radically simplifying customers lives. But can you talk specifically about the types of challenges that your customers air using snowflake to solve? >>Yeah, so So the really the challenge, you know, be four. Snowflake. I would say waas really? To put all the data, you know, in one place and run all the computers, all the workloads that you wanted to run, You know, against that data and off course, you know, existing legacy platforms. We're not able to support. You know that level of concurrency, Many workload. You know, we we talk about machine learning that a science that are engendering, you know, that our house big data were closed or running in one place didn't make sense at all. And therefore, you know what customers did is to create silos, silos of data everywhere, you know, with different system having a subset of the data. And of course, now you cannot analyze this data in one place. So, snowflake, we really solve that problem by creating a single, you know, architectural where you can put all the data in the cloud. So it's a really cloud native we really thought about You know how to solve that problem, how to create, you know, leverage, Cloud and the lessee cc off cloud to really put all the die in one place, but at the same time not run all workload at the same place. So each workload that runs in Snowflake that is dedicated, You know, computer resource is to run, and that makes it very Ajai, right? You know, Floyd and talk about, you know, data scientists having to run analysis, so they need you know a lot of compute resources, but only for, you know, a few hours on. Do you know, with snowflake they can run these new work lord at this workload to the system, get the compute resources that they need to run this workload. And when it's over, they can shut down. You know that their system, it will be automatically shut down. Therefore, they would not pay for the resources that they don't use. So it's a very Ajai system where you can do this, analyzes when you need, and you have all the power to run all this workload at the same time. >>Well, it's profound what you guys built to me. I mean, of course, everybody's trying to copy it now. It was like, remember that bringing the notion of bringing compute to the data and the Hadoop days, and I think that that Asai say everybody is sort of following your suit now are trying to Florian I gotta say the first data scientist I ever interviewed on the Cube was amazing. Hilary Mason, right after she started a bit Lee. And, you know, she made data science that sounds so compelling. But data science is hard. So same same question for you. What do you see is the biggest challenges for customers that they're facing with data science. >>The biggest challenge, from my perspective, is that owns you solve the issue of the data. Seidel with snowflake, you don't want to bring another Seidel, which would be a side off skills. Essentially, there is to the talent gap between the talented label of the market, or are it is to actually find recruits trained data scientist on what needs to be done. And so you need actually to simplify the access to technologies such as every organization can make it, whatever the talent, by bridging that gap and to get there, there is a need of actually breaking up the silos. And in a collaborative approach where technologists and business work together and actually put some their hands into those data projects together, >>it makes sense for flooring. Let's stay with you for a minute. If I can your observation spaces, you know it's pretty, pretty global, and and so you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends may be differences between regions or maybe within different industries. What are you seeing? >>Yes. Yeah, definitely. I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to start data in the last few years and no age, a level of maturity where they can invest more and get to the next steps. And it's really rely on the ability of certain medial certain organization actually to have built this long term strategy a few years ago and no start raping up the benefits. >>You know, a decade ago, Florian Hal Varian, we, you know, famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of change that to data scientists and then everybody. All the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What what skills >>do >>you see as critical for the next generation of data science? >>Yeah, it's a good question because I think the first generation of the patient is became the licenses because they could done some pipe and quickly on be flexible. And I think that the skills or the next generation of data sentences will definitely be different. It will be first about being able to speak the language of the business, meaning, oh, you translate data inside predictive modeling all of this into actionable insight or business impact. And it would be about you collaborate with the rest of the business. It's not just a farce. You can build something off fast. You can do a notebook in python or your credit models off themselves. It's about, oh, you actually build this bridge with the business. And obviously those things are important. But we also has become the center of the fact that technology will evolve in the future. There will be new tools and technologies, and they will still need to keep this level of flexibility and get to understand quickly, quickly. What are the next tools they need to use the new languages or whatever to get there. >>As you look back on 2020 what are you thinking? What are you telling people as we head into next year? >>Yeah, I I think it's Zaveri interesting, right? We did this crisis, as has told us that the world really can change from one day to the next. And this has, you know, dramatic, you know, and perform the, you know, aspect. For example, companies all the sudden, you know, So their revenue line, you know, dropping. And they had to do less meat data. Some of the companies was the reverse, right? All the sudden, you know, they were online, like in stock out, for example, and their business, you know, completely, you know, change, you know, from one day to the other. So this GT off, You know, I, you know, adjusting the resource is that you have tow the task a need that can change, you know, using solution like snowflakes, you know, really has that. And we saw, you know, both in in our customers some customers from one day to the to do the next where, you know, growing like big time because they benefited, you know, from from from from co vid and their business benefited, but also, as you know, had to drop. And what is nice with with with cloud, it allows to, you know, I just compute resources toe, you know, to your business needs, you know, and really adjusted, you know, in our, uh, the the other aspect is is understanding what is happening, right? You need to analyze the we saw all these all our customers basically wanted to understand. What is that going to be the impact on my business? How can I adapt? How can I adjust? And and for that, they needed to analyze data. And, of course, a lot of data which are not necessarily data about, you know, their business, but also data from the outside. You know, for example, coffee data, You know, where is the States? You know, what is the impact? You know, geographic impact from covitz, You know, all the time and access to this data is critical. So this is, you know, the promise off the data crowd, right? You know, having one single place where you can put all the data off the world. So our customers, all the Children you know, started to consume the cov data from our that our marketplace and and we had the literally thousands of customers looking at this data analyzing this data, uh, to make good decisions So this agility and and and this, you know, adapt adapting, you know, from from one hour to the next is really critical. And that goes, you know, with data with crowding adjusting, resource is on and that's, you know, doesn't exist on premise. So So So indeed, I think the lesson learned is is we are living in a world which machines changing all the time and we have for understanding We have to adjust and and And that's why cloud, you know, somewhere it's great. >>Excellent. Thank you. You know the kid we like to talk about disruption, of course. Who doesn't on And also, I mean, you look at a I and and the impact that is beginning to have and kind of pre co vid. You look at some of the industries that were getting disrupted by, you know, we talked about digital transformation and you had on the one end of the spectrum industries like publishing which are highly disrupted or taxis. And you could say Okay, well, that's, you know, bits versus Adam, the old Negroponte thing. But then the flip side of that look at financial services that hadn't been dramatically disrupted. Certainly healthcare, which is ripe for disruption Defense. So the number number of industries that really hadn't leaned into digital transformation If it ain't broke, don't fix it. Not on my watch. There was this complacency and then, >>of >>course, co vid broke everything. So, florian, I wonder if you could comment? You know what industry or industries do you think you're gonna be most impacted by data science and what I call machine intelligence or a I in the coming years and decades? >>Honestly, I think it's all of them artist, most of them because for some industries, the impact is very visible because we're talking about brand new products, drones like cars or whatever that are very visible for us. But for others, we are talking about sport from changes in the way you operate as an organization, even if financial industry itself doesn't seems to be so impacted when you look it from the consumer side or the outside. In fact, internally, it's probably impacted just because the way you use data on developer for flexibility, you need the kind off cost gay you can get by leveraging the latest technologies is just enormous, and so it will actually transform the industry that also and overall, I think that 2020 is only a where, from the perspective of a I and analytics, we understood this idea of maturity and resilience, maturity, meaning that when you've got a crisis, you actually need data and ai more than before. You need to actually call the people from data in the room to take better decisions and look for a while and not background. And I think that's a very important learning from 2020 that will tell things about 2021 and the resilience it's like, Yeah, Data Analytics today is a function consuming every industries and is so important that it's something that needs to work. So the infrastructure is to work in frustration in super resilient. So probably not on prime on a fully and prime at some point and the kind of residence where you need to be able to plan for literally anything like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we're just getting to the next step for the analytics. >>I wonder, Benoit, if you have anything to add to that. I mean, I often wonder, you know, winter machine's gonna be able to make better diagnoses than doctors. Some people say already, you know? Well, the financial services traditional banks lose control of payment systems. Uh, you know what's gonna happen to big retail stores? I mean, maybe bring us home with maybe some of your final thoughts. >>Yeah, I would say, you know, I I don't see that as a negative, right? The human being will always be involved very closely, but the machine and the data can really have, you know, see, Coalition, you know, in the data that that would be impossible for for for human being alone, you know, you know, to to discover so So I think it's going to be a compliment, not a replacement on. Do you know everything that has made us you know faster, you know, doesn't mean that that we have less work to do. It means that we can doom or and and we have so much, you know, to do, uh, that that I would not be worried about, You know, the effect off being more efficient and and and better at at our you know, work. And indeed, you know, I fundamentally think that that data, you know, processing off images and doing, you know, I ai on on on these images and discovering, you know, patterns and and potentially flagging, you know, disease, where all year that then it was possible is going toe have a huge impact in in health care, Onda and And as as as Ryan was saying, every you know, every industry is going to be impacted by by that technology. So So, yeah, I'm very optimistic. >>Great guys. I wish we had more time. I gotta leave it there. But so thanks so much for coming on. The Cube was really a pleasure having you.

>> My name is Dave Vellante. And with me are two world-class technologists, visionaries and entrepreneurs. Benoit Dageville, he co-founded Snowflake and he's now the President of the Product Division, and Florian Douetteau is the Co-founder and CEO of Dataiku. Gentlemen, welcome to the cube to first timers, love it. >> Yup, great to be here. >> Now Florian you and Benoit, you have a number of customers in common, and I've said many times on theCUBE, that the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation, is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake, and Dataiku make a good match for customers? >> I think that because it's our values that aligned, when it gets all about actually today, and knowing complexity of our customers, so you close the gap. Where we need to commoditize the access to data, the access to technology, it's not only about data. Data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible, within an organization. And another value is about just the openness of the platform, building a future together. Having a platform that is not just about the platform, but also for the ecosystem of partners around it, bringing the level of accessibility, and flexibility you need for the 10 years of that. >> Yeah, so that's key, that it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we know we all know that you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so the challenge before snowflake, I would say, was really to put all the data in one place, and run all the computes, all the workloads that you wanted to run against that data. And of course existing legacy platforms were not able to support that level of concurrency, many workload, we talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place didn't make sense at all. And therefore be what customers did this to create silos, silos of data everywhere, with different system, having a subset of the data. And of course now, you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data into cloud. So it's a really cloud native. We really thought about how solve that problem, how to create, leverage cloud, and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake, at its dedicated compute resources to run. And that makes it agile, right? Florian talked about data scientist having to run analysis, so they need a lot of compute resources, but only for a few hours. And with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system, it will automatically shut down. Therefore they would not pay for the resources that they don't use. So it's a very agile system, where you can do this analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. I mean to me, I mean of course everybody's trying to copy it now, it was like, I remember that bringing the notion of bringing compute to the data, in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say the first data scientist I ever interviewed on theCUBE, it was the amazing Hillary Mason, right after she started at Bitly, and she made data sciences sounds so compelling, but data science is a hard. So same question for you, what do you see as the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective, is that once you solve the issue of the data silo, with Snowflake, you don't want to bring another silo, which will be a silo of skills. And essentially, thanks to the talent gap, between the talent available to the markets, or are released to actually find recruits, train data scientists, and what needs to be done. And so you need actually to simplify the access to technologies such as, every organization can make it, whatever the talent, by bridging that gap. And to get there, there's a need of actually backing up the silos. Having a collaborative approach, where technologies and business work together, and actually all puts up their ends into those data projects together. >> It makes sense, Florain let's stay with you for a minute, if I can. Your observation space, it's pretty, pretty global. And so you have a unique perspective on how can companies around the world might be using data, and data science. Are you seeing any trends, maybe differences between regions, or maybe within different industries? What are you seeing? >> Yeah, definitely I do see trends that are not geographic, that much, but much more in terms of maturity of certain industries and certain sectors. Which are, that certain industries invested a lot, in terms of data, data access, ability to store data. As well as experience, and know region level of maturity, where they can invest more, and get to the next steps. And it's really relying on the ability of certain leaders, certain organizations, actually, to have built these long-term data strategy, a few years ago when no stats reaping of the benefits. >> A decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientist. And then everybody, all the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What skills do you see as critical for the next generation of data science? >> Yeah, it's a great question because I think the first generation of data scientists, became data scientists because they could have done some Python quickly, and be flexible. And I think that the skills of the next generation of data scientists will definitely be different. It will be, first of all, being able to speak the language of the business, meaning how you translates data insight, predictive modeling, all of this into actionable insights of business impact. And it would be about how you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python, or do predictive models of some sorts. It's about how you actually build this bridge with the business, and obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools, new technologies, and they will still need to keep this level of flexibility to understand quickly what are the next tools they need to use a new languages, or whatever to get there. >> As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right? This crises has told us that the world really can change from one day to the next. And this has dramatic and perform the aspects. For example companies all of a sudden, show their revenue line dropping, and they had to do less with data. And some other companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely changed from one day to the other. So this agility of adjusting the resources that you have to do the task, and need that can change, using solution like Snowflake really helps that. Then we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID, and their business benefited. But others had to drop. And what is nice with cloud, it allows you to adjust compute resources to your business needs, and really address it in house. The other aspect is understanding what happening, right? You need to analyze. We saw all our customers basically, wanted to understand what is the going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data which are not necessarily data about their business, but also they are from the outside. For example, COVID data, where is the States, what is the impact, geographic impact on COVID, the time. And access to this data is critical. So this is the premise of the data cloud, right? Having one single place, where you can put all the data of the world. So our customer obviously then, started to consume the COVID data from that our data marketplace. And we had delete already thousand customers looking at this data, analyzing these data, and to make good decisions. So this agility and this, adapting from one hour to the next is really critical. And that goes with data, with cloud, with interesting resources, and that doesn't exist on premise. So indeed I think the lesson learned is we are living in a world, which is changing all the time, and we have to understand it. We have to adjust, and that's why cloud some ways is great. >> Excellent thank you. In theCUBE we like to talk about disruption, of course, who doesn't? And also, I mean, you look at AI, and the impact that it's beginning to have, and kind of pre-COVID. You look at some of the industries that were getting disrupted by, everyone talks about digital transformation. And you had on the one end of the spectrum, industries like publishing, which are highly disrupted, or taxis. And you can say, okay, well that's Bits versus Adam, the old Negroponte thing. But then the flip side of, you say look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is ripe for disruption, defense. So there a number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science, and what I call machine intelligence, or AI, in the coming years and decade? >> Honestly, I think it's all of them, or at least most of them, because for some industries, the impact is very visible, because we have talking about brand new products, drones, flying cars, or whatever that are very visible for us. But for others, we are talking about a part from changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted, when you look at it from the consumer side, or the outside insights in Germany, it's probably impacted just because the way you use data (mumbles) for flexibility you need. Is there kind of the cost gain you can get by leveraging the latest technologies, is just the numbers. And so it's will actually comes from the industry that also. And overall, I think that 2020, is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience, maturity meaning that when you've got to crisis you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions, and look for one and a backlog. And I think that's a very important learning from 2020, that will tell things about 2021. And the resilience, it's like, data analytics today is a function transforming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient, so probably not on prem or not fully on prem, at some point. And the kind of resilience where you need to be able to blend for literally anything, like no hypothesis in terms of BLOs, can be taken for granted. And that's something that is new, and which is just signaling that we are just getting to a next step for data analytics. >> I wonder Benoir if you have anything to add to that. I mean, I often wonder, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? What's going to happen to big retail stores? I mean, maybe bring us home with maybe some of your finals thoughts. >> Yeah, I would say I don't see that as a negative, right? The human being will always be involved very closely, but then the machine, and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So I think it's going to be a compliment not a replacement. And everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do, that I will not be worried about the effect of being more efficient, and bare at our work. And indeed, I fundamentally think that data, processing of images, and doing AI on these images, and discovering patterns, and potentially flagging disease way earlier than it was possible. It is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, guys, I wish we had more time. I've got to leave it there, but so thanks so much for coming on theCUBE. It was really a pleasure having you.

>> Paige: Hello, everybody, and thank you for joining us today for the Virtual Vertica BDC 2020. Today's breakout session is entitled "Tapping Vertica's Integration with TensorFlow for Advanced Machine Learning." I'm Paige Roberts, Opensource Relations Manager at Vertica, and I'll be your host for this session. Joining me is Vertica Software Engineer, George Larionov. >> George: Hi. >> Paige: (chuckles) That's George. So, before we begin, I encourage you guys to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click submit. So, as soon as a question occurs to you, go ahead and type it in, and there will be a Q and A session at the end of the presentation. We'll answer as many questions as we're able to get to during that time. Any questions we don't get to, we'll do our best to answer offline. Now, alternatively, you can visit Vertica Forum to post your questions there, after the session. Our engineering team is planning to join the forums to keep the conversation going, so you can ask an engineer afterwards, just as if it were a regular conference in person. Also, reminder, you can maximize your screen by clicking the double-arrow button in the lower right corner of the slides. And, before you ask, yes, this virtual session is being recorded, and it will be available to view by the end this week. We'll send you a notification as soon as it's ready. Now, let's get started, over to you, George. >> George: Thank you, Paige. So, I've been introduced. I'm a Software Engineer at Vertica, and today I'm going to be talking about a new feature, Vertica's Integration with TensorFlow. So, first, I'm going to go over what is TensorFlow and what are neural networks. Then, I'm going to talk about why integrating with TensorFlow is a useful feature, and, finally, I am going to talk about the integration itself and give an example. So, as we get started here, what is TensorFlow? TensorFlow is an opensource machine learning library, developed by Google, and it's actually one of many such libraries. And, the whole point of libraries like TensorFlow is to simplify the whole process of working with neural networks, such as creating, training, and using them, so that it's available to everyone, as opposed to just a small subset of researchers. So, neural networks are computing systems that allow us to solve various tasks. Traditionally, computing algorithms were designed completely from the ground up by engineers like me, and we had to manually sift through the data and decide which parts are important for the task and which are not. Neural networks aim to solve this problem, a little bit, by sifting through the data themselves, automatically and finding traits and features which correlate to the right results. So, you can think of it as neural networks learning to solve a specific task by looking through the data without having human beings have to sit and sift through the data themselves. So, there's a couple necessary parts to getting a trained neural model, which is the final goal. By the way, a neural model is the same as a neural network. Those are synonymous. So, first, you need this light blue circle, an untrained neural model, which is pretty easy to get in TensorFlow, and, in edition to that, you need your training data. Now, this involves both training inputs and training labels, and I'll talk about exactly what those two things are on the next slide. But, basically, you need to train your model with the training data, and, once it is trained, you can use your trained model to predict on just the purple circle, so new training inputs. And, it will predict the training labels for you. You don't have to label it anymore. So, a neural network can be thought of as... Training a neural network can be thought of as teaching a person how to do something. For example, if I want to learn to speak a new language, let's say French, I would probably hire some sort of tutor to help me with that task, and I would need a lot of practice constructing and saying sentences in French. And a lot of feedback from my tutor on whether my pronunciation or grammar, et cetera, is correct. And, so, that would take me some time, but, finally, hopefully, I would be able to learn the language and speak it without any sort of feedback, getting it right. So, in a very similar manner, a neural network needs to practice on, example, training data, first, and, along with that data, it needs labeled data. In this case, the labeled data is kind of analogous to the tutor. It is the correct answers, so that the network can learn what those look like. But, ultimately, the goal is to predict on unlabeled data which is analogous to me knowing how to speak French. So, I went over most of the bullets. A neural network needs a lot of practice. To do that, it needs a lot of good labeled data, and, finally, since a neural network needs to iterate over the training data many, many times, it needs a powerful machine which can do that in a reasonable amount of time. So, here's a quick checklist on what you need if you have a specific task that you want to solve with a neural network. So, the first thing you need is a powerful machine for training. We discussed why this is important. Then, you need TensorFlow installed on the machine, of course, and you need a dataset and labels for your dataset. Now, this dataset can be hundreds of examples, thousands, sometimes even millions. I won't go into that because the dataset size really depends on the task at hand, but if you have these four things, you can train a good neural network that will predict whatever result you want it to predict at the end. So, we've talked about neural networks and TensorFlow, but the question is if we already have a lot of built-in machine-learning algorithms in Vertica, then why do we need to use TensorFlow? And, to answer that question, let's look at this dataset. So, this is a pretty simple toy dataset with 20,000 points, but it shows, it simulates a more complex dataset with some sort of two different classes which are not related in a simple way. So, the existing machine-learning algorithms that Vertica already has, mostly fail on this pretty simple dataset. Linear models can't really draw a good line separating the two types of points. Naïve Bayes, also, performs pretty badly, and even the Random Forest algorithm, which is a pretty powerful algorithm, with 300 trees gets only 80% accuracy. However, a neural network with only two hidden layers gets 99% accuracy in about ten minutes of training. So, I hope that's a pretty compelling reason to use neural networks, at least sometimes. So, as an aside, there are plenty of tasks that do fit the existing machine-learning algorithms in Vertica. That's why they're there, and if one of your tasks that you want to solve fits one of the existing algorithms, well, then I would recommend using that algorithm, not TensorFlow, because, while neural networks have their place and are very powerful, it's often easier to use an existing algorithm, if possible. Okay, so, now that we've talked about why neural networks are needed, let's talk about integrating them with Vertica. So, neural networks are best trained using GPUs, which are Graphics Processing Units, and it's, basically, just a different processing unit than a CPU. GPUs are good for training neural networks because they excel at doing many, many simple operations at the same time, which is needed for a neural network to be able to iterate through the training data many times. However, Vertica runs on CPUs and cannot run on GPUs at all because that's not how it was designed. So, to train our neural networks, we have to go outside of Vertica, and exporting a small batch of training data is pretty simple. So, that's not really a problem, but, given this information, why do we even need Vertica? If we train outside, then why not do everything outside of Vertica? So, to answer that question, here is a slide that Philips was nice enough to let us use. This is an example of production system at Philips. So, it consists of two branches. On the left, we have a branch with historical device log data, and this can kind of be thought of as a bunch of training data. And, all that data goes through some data integration, data analysis. Basically, this is where you train your models, whether or not they are neural networks, but, for the purpose of this talk, this is where you would train your neural network. And, on the right, we have a branch which has live device log data coming in from various MRI machines, CAT scan machines, et cetera, and this is a ton of data. So, these machines are constantly running. They're constantly on, and there's a bunch of them. So, data just keeps streaming in, and, so, we don't want this data to have to take any unnecessary detours because that would greatly slow down the whole system. So, this data in the right branch goes through an already trained predictive model, which need to be pretty fast, and, finally, it allows Philips to do some maintenance on these machines before they actually break, which helps Philips, obviously, and definitely the medical industry as well. So, I hope this slide helped explain the complexity of a live production system and why it might not be reasonable to train your neural networks directly in the system with the live device log data. So, a quick summary on just the neural networks section. So, neural networks are powerful, but they need a lot of processing power to train which can't really be done well in a production pipeline. However, they are cheap and fast to predict with. Prediction with a neural network does not require GPU anymore. And, they can be very useful in production, so we do want them there. We just don't want to train them there. So, the question is, now, how do we get neural networks into production? So, we have, basically, two options. The first option is to take the data and export it to our machine with TensorFlow, our powerful GPU machine, or we can take our TensorFlow model and put it where the data is. In this case, let's say that that is Vertica. So, I'm going to go through some pros and cons of these two approaches. The first one is bringing the data to the analytics. The pros of this approach are that TensorFlow is already installed, running on this GPU machine, and we don't have to move the model at all. The cons, however, are that we have to transfer all the data to this machine and if that data is big, if it's, I don't know, gigabytes, terabytes, et cetera, then that becomes a huge bottleneck because you can only transfer in small quantities. Because GPU machines tend to not be that big. Furthermore, TensorFlow prediction doesn't actually need a GPU. So, you would end up paying for an expensive GPU for no reason. It's not parallelized because you just have one GPU machine. You can't put your production system on this GPU, as we discussed. And, so, you're left with good results, but not fast and not where you need them. So, now, let's look at the second option. So, the second option is bringing the analytics to the data. So, the pros of this approach are that we can integrate with our production system. It's low impact because prediction is not processor intensive. It's cheap, or, at least, it's pretty much as cheap as your system was before. It's parallelized because Vertica was always parallelized, which we'll talk about in the next slide. There's no extra data movement. You get the benefit from model management in Vertica, meaning, if you import multiple TensorFlow models, you can keep track of their various attributes, when they were imported, et cetera. And, the results are right where you need them, inside your production pipeline. So, two cons are that TensorFlow is limited to just prediction inside Vertica, and, if you want to retrain your model, you need to do that outside of Vertica and, then, reimport. So, just as a recap of parallelization. Everything in Vertica is parallelized and distributed, and TensorFlow is no exception. So, when you import your TensorFlow model to your Vertica cluster, it gets copied to all the nodes, automatically, and TensorFlow will run in fenced mode which means that it the TensorFlow process fails for whatever reason, even though it shouldn't, but if it does, Vertica itself will not crash, which is obviously important. And, finally, prediction happens on each node. There are multiple threads of TensorFlow processes running, processing different little bits of data, which is faster, much faster, than processing the data line by line because it happens all in a parallelized fashion. And, so, the result is fast prediction. So, here's an example which I hope is a little closer to what everyone is used to than the usual machine learning TensorFlow example. This is the Boston housing dataset, or, rather, a small subset of it. Now, on the left, we have the input data to go back to, I think, the first slide, and, on the right, is the training label. So, the input data consists of, each line is a plot of land in Boston, along with various attributes, such as the level of crime in that area, how much industry is in that area, whether it's on the Charles River, et cetera, and, on the right, we have as the labels the median house value in that plot of land. And, so, the goal is to put all this data into the neural network and, finally, get a model which can train... I don't know, which can predict on new incoming data and predict a good housing value for that data. Now, I'm going to go through, step by step, how to actually use TensorFlow models in Vertica. So, the first step I won't go into much detail on because there are countless tutorials and resources online on how to use TensorFlow to train a neural network, so that's the first step. Second step is to save the model in TensorFlow's 'frozen graph' format. Again, this information is available online. The third step is to create a small, simple JSON file describing the inputs and outputs of the model, and what data type they are, et cetera. And, this is needed for Vertica to be able to translate from TensorFlow land into Vertica equal land, so that it can use a sequel table instead of the input set TensorFlow usually takes. So, once you have your model file and your JSON file, you want to put both of those files in a directory on a node, any node, in a Vertica cluster, and name that directory whatever you want your model to ultimately be called inside of Vertica. So, once you do that you can go ahead and import that directory into Vertica. So, this import model's function already exists in Vertica. All we added was a new category to be able to import. So, what you need to do is specify the pass to your neural network directory and specify that the category that the model is is a TensorFlow model. Once you successfully import, in order to predict, you run this brand new predict TensorFlow function, so, in this case, we're predicting on everything from the input table, which is what the star means. The model name is Boston housing net which is the name of your directory, and, then, there's a little bit of boilerplate. And, the two ID and value after the as are just the names of the columns of your outputs, and, finally, the Boston housing data is whatever sequel table you want to predict on that fits the import type of your network. And, this will output a bunch of predictions. In this case, values of houses that the network thinks are appropriate for all the input data. So, just a quick summary. So, we talked about what is TensorFlow and what are neural networks, and, then, we discussed that TensorFlow works best on GPUs because it needs very specific characteristics. That is TensorFlow works best for training on GPUs while Vertica is designed to use CPUs, and it's really good at storing and accessing a lot of data quickly. But, it's not very well designed for having neural networks trained inside of it. Then, we talked about how neural models are powerful, and we want to use them in our production flow. And, since prediction is fast, we can go ahead and do that, but we just don't want to train there, and, finally, I presented Vertica TensorFlow integration which allows importing a trained neural model, a trained neural TensorFlow model, into Vertica and predicting on all the data that is inside Vertica with few simple lines of sequel. So, thank you for listening. I'm going to take some questions, now.

>> Live from Barcelona, Spain. It's the cue covering Sisqo. Live Europe, Brought to you by Cisco and its ecosystem partners. >> Welcome back to Barcelona, Everybody watching the Cube, the leader in live tech coverage. My name is Dave Valentin here with my co host Student events. Do Myself and John for Be here all week. Eric Hurt, Saugus Here Long time Cuba Long friend. Great to see you again. He's the CMO of IBM IBM Storage division. He's joined by James Amy's, who's the head of networks at advance. The service provider Guys, Welcome to the Cube. Good to see again. >> Great. Thanks for having us loved being on the cute. >> So we love having you So, James, let's start with you. Tell us a little bit about advanced to want to dig into some of the networking trends. We're hearing a lot about it here. It's just go live. >> Yeah, I think so. Advanced are a manage service provider software software company based in the UK, one of the largest software companies in the UK, providing interim solutions for lots of different Marchal market verticals, including healthcare, local government, regional government, national infrastructure projects we've got involved with as well as charity sector legal sector. A lot of education work we do is real diverse portfolio of products we offer on with the manage services piece. We also offer complete outsourcing. So this is desktop support. Telephony support, printer support all the >> way back into integration with public cloud platforms and private cloud platforms, the majority of >> which is our in. >> So so Eric advanced are both a customer and a partner, right? Right, right. And so you love you. Love versus Stack. These guys are presumed versus stack customers. Well >> stacked customer in the Versace tack, as you know, Integrate. Cisco, UCS, Cisco Networking Infrastructure, IBM Storage of all types entry products up into the fastest all flash raise with our software spectrum virtualized spectrum, Accelerate Family and James's company is using versus tax is part of their infrastructure, which they then offer, as you know, to a service toe and uses. James just described. >> So let's talk about some of the big trends that you guys are seeing and how you're both responding to customers and you're responding to your customer. So we're seeing two hearing today. Lot about multi cloud. We've been hearing that for a while the network is flattening your network expert love to get your your thoughts on that. Security, obviously, is a huge topic. End end management, another big topic, something that IBM is focused on. So so James, what 1 of the big mega trends that you're seeing that a driving your business decisions and your customer's activity? One >> of the big changes we're seeing is a change from large scare enterprise scale deployments off a particular type of technology on customers are now choosing because they're informed the best fit for a particular application or particular service on that may be coming to a service provider like ourselves to offer our services products to them. Uh, or they're looking for us to run in infrastructure service for them or integrate with a public cloud offering. So the competition of the public cloud for service providers is key on DH. I think people were looking around a few years ago thinking, How do we compete to this well, with partnerships that we have in our Francisco? It gives us a very compelling competitive offering. But we can turn around and say, Well, we can give you a like for like, but we can give you a slightly better service because we could give you guaranteed availability. We give you guaranteed price point on, and this is all backed with key vendor certified designs. So we're not talking about going out on developing a solution that takes maybe eighteen months to take to market. This is understanding a requirement for a quick, you know, Q and A with a customer a line that, too a reference architecture that we can literally just pick up off the shelf, deploy into our data centers using the standard building rocks that we use across the business. So Nexus nine K seven k's or our standard bread and butter inside the data center environment. As Eric pointed out, Cisco UCS is our our key Intel computer platform that we used on DH. The store wise IBM product has been a real true success story for us. So we started off being a a mixed then the house where we would align storage requirement paste with what we could find in the market. That was, that was a good fit. But the store was products is basically just allowed us to standardize on the speed of deployment is one of the key things. So we started out with a very lengthy lead time tio service ready, which is when we start charging for revenue on if we want a ninety day build. Well, we've got a lot of special service time, A lot of engineering time getting that ready Teo, Teo and take to the customer and then we turn it on. We can start seeing revenue from that platform with versus Stack. This enabled us to accelerate how quickly we can turn that on. And we've seen that drop, too. They're literally days through standardisation elements of automation as well. Many of our environments are bespoke because we have such a wide arrange off different types of customers with different needs, but it allows us to take those standing building blocks, align them to their needs and deliver that service. >> James James, we found the peas are often in the middle of those discussions that customers are having on multi clouds. You talked a lot about the services you build. Are they also coming to you? If if you tie into the public Cloud services or yes, maybe you can help explain a little bit on how that worked Five years ago, it was the public loud there are going to kill them and service providers. And what we see is customers can't sort out half of what's going on. They've got to be able to turn two partners like you to be able to figure this out. >> Yeah, that's a fantastic question. I think three years ago we'd be talking to our customers and they were I am going to this public cloud or I am going to build this infrastructure. Where is now? They're They're making Mohr informed select decisions based on the drive to the hosted office and voice platforms offered by Microsoft. There's a big driving. Many of our customers are going in that direction, but it's how we integrate that with legacy applications. Some of the solutions that some of our customers use have have have had millions of pounds of investment into them, and that's not something I can just turn off the water away from overnight. So it is how we're integrating that. We're doing that at the network level, so it's how we're appearing with different service providers, bringing that in integrating that, I'm offering it to them as a solution. What we try tio, we try to try position ourselves is really it's the same experience, regardless of where we're placing it. Consumption. Workload doesn't know whether it's inside our data centers, whether we're talking one of the public cloud platforms or even on premise. So we have quite a few customers that still have significant presence on premises because that's right for their business, depending on on what they're doing, especially some of the research scientists. >> So you've got to deliver flexibility in your architecture, and you talk a lot about software to find you guys made a big move to software to find, you know, a couple years ago, actually, maybe discuss how that fits in to how you're servicing advanced another client? >> Sure. So you know, IBM Storage has embraced multi Cloud for several years. So our solutions. While, of course, they work with IBM, Cloud and IBM cloud private work with Amazon. They work with azure Google Cloud and in fact, some are products. For example, the versus stack not only is advanced using it, but we've got pry forty or fifty public, small, medium sized cloud providers that our public references for the vs Tag and Spectrum Protect you Know which is our backup product Number one in the Enterprise. Back up space Expect from detectives Got at least three hundred cloud providers. Medium, small and big. Who offered the engine underneath for their backup is a service is spectrum protect, So we make sure that weather PR transparent cloud tearing our cyber resiliency technology. What we doing? Backup archive object storage works with essentially all cloud providers. That way, someone like James A. CSP MSP can leverage our products. And we, like I said, we have tons of public records around versus Stack for that, but so can an enterprise. And in fact, I saw survey recently that it was done in Europe and in North America that when you look at a roughly two billion US size revenue and up the average company of that sizing up, we use five different public cloud riders at one time. Where that it be due to legal reasons whether that be procurement. You know, the Web is really the Internet. And, yeah, Cloud is really just It's been around for twenty some years. So in bigger accounts, guess what is now involved Procurement Well, we love that you did that deal with IBM club, but you are going to get a competitive quote now from Amazon and Microsoft, right? So that's driven it legal's driven it. Certain countries, right? The data needs to stay in that country, even if your cloud if eyeing it, it's so to speak. So if the clap water doesn't have a data center there, guess what? Another geographer used different. And then you, of course, still have some large entities that still allow regional buying pattern so they'll have three or four different cloud providers that air quote certified by corporate. And then you could use whichever one you want, so we make sure that we could take advantage of that. Wade and IBM. We ride the wave, We don't fight the way. >> So you've got in that situation. You these multi cloud you got different AP eyes, You get different frameworks potty, you abstract all that complexity you got, Francisco coming at it from a networking standpoint, I b m. Now with Red Hat is good. Be a big player in that that world. VM where What do you guys do? James, in terms of of simplifying all that multi cloud complexity >> for people. I think some of it is actually the mystifying on its engaging with our partners to understand what the proposition is on, how we can develop that on a line, that to mind your own business, but more importantly, to the needs of our customers. We've got some really, really talented technicians worked within within advance, and we've got a number of different forums that allow them to feed back their ideas. But we've got the alignments between those partners and and some of those communities, so that we can have an open discussion on drive. Some of that thinking forward about ultimately see engaging with customers. So the customers feedback is key on how we shape and deliver no need service to them, but also to the service to other customers. We have a number of customers that are very similar, but they may work in different spaces, some somewhere even competitive. So we have to tread that line very safe, very carefully and safely. But it is. It's a good one to one relationship between the client service managers, technical technicians. We have inside business having that to complete three sixty communication is key, but that's that's that's really the bottom takes. Its creation >> came like youto dig into security for us a little bit. You know, I think we surpassed a couple of years ago. I'm not going to go to the cloud to it because it's not secure to Oh, I understand it's time for me to least reevaluate meant security and, most likely, you know, manage service fighters. Public clouds are probably more secure than what I had in my data center, but if I've got multiple environment, there's a lot of complexity there. So how do you traverse that? Make sure that you've got a comprehensive security practice, not just all these point solutions for security all over the place. >> Ah, so that's that comes onto visibility. So its visibility understanding where all the control points are within a given infrastructure on how the landscape looks. So we were working quite closely with a number actually of key Cisco and IBM partners, as well as IBM and Cisco themselves directly tohave a comprehensive offering that allows us to position to our customers. You used to once upon a time you had one game, right? So we need it is from good security on your Internet. Facing viable For now, you might have a ten. Twenty, thirty of those. We need tohave consistent policies across those. We need to understand how they're performing, but also potentially, if there's any attempt attack vector on one of them. How that how someone is trying to looking to compromise that so centralized intelligence on That's where we start to look at my eye operations to gather all that information. The long gone are the days where you have twenty people sharing a room just reading streams. Those twenty people now need thio. See reams and reams of information instantly. Something needs to be called up to them. They could make a decision quickly on Active planet on DH. That's really where we we're positioning ourselves in the market to differentiate. I'm working with key part, Mr >> Never talk about your announcement cadence. Good idea as a big show. Think coming up in a couple weeks cubes gonna be there. Of course. What can we expect from from you guys? >> So we're actually gonna announce on the fifth before things way, want to drive end users and our business partners to storage campus, which is one of the largest campuses at IBM, think we'll have over fifteen pedestals of demo and actually multiple demos because we have such a broad portfolio, from the all flash arrays to our versus stack offering to a whole set of modern data protection management control for storage, which manages in control storage, that's not ours, right? Our competitors storage as well, and, of course, our software to find storage. So we're going to do a big announcement. The focus of that will be around our storage solutions. These air solutions blueprints reference architectures is Jane, you mentioned that use our software and our storage systems that allow reseller or end user to configure systems easily. Think of it as the ultimate wrestling recipe for that German chocolate cake. But it's the perfect recipe. It's tried. It's true, it's tested. It's been on the Food Channel twenty seven times and everybody loves it. That's what we do with our our solutions. Blueprints. We'll have some announcements around modern data protection, and obviously a big focus of IBM. Storage is been in the space. So both storage as an Aye aye platform for aye aye, applications are workloads but also the incorporation of technology into our own storage systems and software. So be having announcements around that on February fifth going into think, which will then be the week after in San Francisco. >> Great. So I'm here and trusted data protection plays into that. Aye, aye. Intelligence machine intelligence. And I'm also hearing header of Geneti multiple platforms. Whether it's your storage, you said our competitors now does that also include sort of the clouds? Fear we're not announcing anything. But you guys have you know, you've seen your pictures. That's azure itt's a w a s. I mean, that continues >> so absolutely so. Whether it be what we do from backup in archive, right, let's take the easy one. So we support not only the protocol of IBM clad object storage which we acquired and allows you to have object storage either on premise or in a cloud in stance e ation. But we also support the s three protocol. So, for example, our spectrum scale software giant scale out. In fact, the two fastest supercomputers world you spectrum scale over four hundred fifty petabytes running on spectrum scale, and they continue their to an object store that supports us three. Or it can tear toe IBM clad object stories through that IBM clad object storage customer. That's great for using the S three protocol. You, Khun, Tear to that as well. That's just one example. Same thing we do for cyber resiliency. So from a cyber resents me perspective, we could do things with any cloud vendor oven air cat air gap, right? And so you could do that, eh? With tape. But you could also do that with the clouds. So if your cloud is your backup archive replication repository, then you can always roll back to a known good copy. You don't have to pay the ransom writer. When you clean up the malware, you can roll back to a known good copy, and we provide that across all of the platforms in a number of ways. Our protect family, our new products, a safeguard copy for the main friend that we announced October. So all that allows us to be multi cloud resiliency as well as how do we connect a multi cloud backup archive automated tearing all kinds of clouds, whether the IBM cloud and, of course, I'm a shareholder. So I love that, but at the same time were realistic. Lots of people use Amazon Google Azar. And like I said, there's thousands of mid two small cloud providers all over the world, and we support them, too. We engage with everyone. >> What about SAS? You know, that's one of the questions we've been trying to squint through and understand is because when you talk about five cloud providers is obviously infrastructures of service. And then there's their service providers like like Advanced. And then there's like a gazillion SAS Companies >> write a lot of data >> in there and a lot of data in there. How should we think about, you know, protecting that data? Securing that data is that sort of up to the SAS vendor, and thou shalt not touch. Or should that be part of the scope of AH, storage company? Well, so what we do >> is we engage with the SAS vendor, so we have a number of different sass coming is, in fact, one of them was on the Cube two years ago with us. They were startup in the cyber security space and all of its delivered over SAS. So what they do is in that case, the use our flash system product line, they get the performance they need to deliver south. They want no bottlenecks because obviously you have to go over the network when you're doing SAS Andi. Also, what they do is data encryption at rest. So when the data is brought in because we have on our flash arrays capability and most of our product line especially the flash systems to have no performance hit on encrypt their decrypt because its hardware embedded, they're able to have the data at rest encrypted for all their customers. That gives them a level of security when it's at rest on their site. At the same time, we've given the right performance. They need tohave soft reserve, so we engage with all we pry have three hundred, four hundred different SAS companies who are the actual software vendor and their deployment model. This software's interest, by the way, we do that as well as I mentioned, over three hundred cloud providers today have a backup is a service and the engine ease their spectrum. Protect or spectrum protect. Plus, but they may call it something else. In fact, we just had a public reference out from Silver String, which is out in the UK, and all they do is cyber resiliency. Backup in archive. That's their service. They have their own product, but then spectrum Protect and Spectrum Check plus is the engine underneath their Prada. So that's an example. In this case, the backup is a service, which, I would argue is not infrastructure, but more of an application. But then true what you call real application providers like cyber security vendors, we have a vendor who in fact, does something for all of the universities and colleges. United States. They have about eight thousand of them, including the junior colleges, and they run all their bookstores. So when you place an order, all their air NPR, everything they do is from this SAS vendor that's based in there in the Northeast. And they've got, like I said, about a thousand colleges and universities in the U. S. And Canada, and they offer this if you will bookstore as a sass service and the students use it. University uses it. And, of course, the bookstores are designed to, you know, make a little money for the university, and they all use that so that's another example. And they use are flash systems as well. And then they back up that data internally with spectrum protectors. They obviously it's the financial data as well as the inventory of all of these book stores all over the United States at the collegiate >> level right now. James Way gotta wrap, but just sort of give you the final word. UK specialist, right? So Brexit really doesn't affect you. Is that a fair statement? >> Uh, we'll do? Yes. >> How so? >> I think it's too early to tell. No one really knows. I think that's all the debates are about. AJ's trying to understand that on DH for us. We're just watching and observing. >> Staying focused on your customers, obviously. So no predictions as to what's going to happen. I was not from a weeks ago. I got hurt both sides. You know, it's definitely gonna happen, All right, Not happen, but okay, again give you the last word. You know? What's your focus? Over the next twelve eighteen months? >> Eso all our focus is really about visibility, So they they they've touched on that. We're talking about security for customers. Understanding whether data is whether exposure point saw. That's our keep. Keep focusing on DH versus stack on dh thie IBM store wise product underpin all of those offerings that we have on. That will continue to be to be so forward. >> Guys. Great to see you. Thanks so much for coming on the Cube and our pleasure hosting you. Thanks. Appreciate, Really welcome. Alright, Keep right, everybody. We'll be back. Day Volante was stew Minutemen from Cisco live in Barcelona. >> No.

[Narrator] Live from Barcelona, Spain, it's theCUBE covering Cisco live Europe. Brought to you by Cisco and it's ecosystem partners. >> Welcome back to Barcelona everybody, you're watching theCUBE, the leader in live teach coverage. My name is Dave Vellante. I'm here with my co-host Stu Miniman. Stu, myself, and John Fur will be here all week. Eric Herzog is here, long time Cube alumn friend, great to see you again. He's the CMO of IBM storage division. he's joined by James Amies who's the head of networks at Advanced, the service provider guys. Welcome to theCUBE. Good to see you again. >> Great thanks for having us. Love being on theCUBE. >> So we love having you. So James let's start with you. Tell us a little bit about Advanced, do you want to dig into some of the networking trends? We're hearing a lot about it here at Cisco Live. >> Yeah thanks, Advanced are a manage service provider, software company based in the UK, one of the largest software companies in the UK, providing entrance solutions for lots of different market verticals, including healthcare, local government, regional government, national infrastructure projects we get involved with, as well charity sector, legal sector, a lot of education work that we do. And it's just real diverse portfolio products that we offer. And with the manage services piece, we also offer complete IT outsourcing. So this is desktop support, telephony support, printer support, all the way back into integration with public cloud platforms and private cloud platforms. The majority of which is our own. >> So Eric, Advanced are both a customer and a partner. >> Right >> Right and so you love Versastack, These guys are I presume are Versastack customers as well? >> Yes Versastack customer in the Versastack as you know integrates Cisco UCS Cisco networking infrastructure, IBM storage of all types, entry products up into the fastest off flash rays with our software spectrum virtualizer, spectrum accelerate family, and James' company is using Versastacks as part of their infrastructure. Which they then offer as a service to end users as James just described. >> So let's talk about some of the big trends that guys are seeing and how you're both responding to customers, and you're responding to your customers. So we're seeing here today, a lot about multi-cloud. We've been hearing that for a while. The network is flattening, you're a network expert, love to get your thoughts on that. Security obviously is a huge topic. End to end management, another big topic, something that IBM is focused on. So James what are the big mega trends that you're seeing that are driving your business decisions and your customers' activities. >> So I think one of the big changes we're seeing is a change from large enterprise scale deployments of a particular type of technology and customers are now choosing because they're informed, the best fit for a particular application or a particular service, and that may be coming to a service provider like ourselves, or for our service product to them, or they're looking for us to run an infrastructure service for them, or integrate with a public cloud offering. So the competition of the public cloud for service providers is key. And I think people were looking around a few years ago, thinking how do we compete to this. Well with the partnerships that we have with IBM and Cisco, it gives us a very compelling, competitive offering where we can turn around and say, well we can give you a like for like, but we can give you a slightly better service, because we can give you guaranteed availability. We can give you guaranteed price points, and we this is all backed with key vendor certified designs, so we're not talking about going out and developing a solution that takes as maybe 18 months, to take to market, this is understanding a requirement for a quick Q and A with a customer, align that to a reference architecture, that we can literally just pick up off the shelf, deploy into our data centers using the standard building blocks that we use across the business. So Nexus, nine K seven K's, or our standard` bread and butter inside the data center environment, as Eric pointed out, Cisco UCS is our key intel compute platform that we use. And the storewise IBM product has been a real true success story for us. So we started off being a mixed vendor house, where we would align storage requirement based with what we could find in the market that was a good fit. But the storewise products just basically just allowed us to standardize, and the speed of deployment is one of the key things. So we started out with a very lengthy lead time to serve as ready. Which is when we start charging for revenue. And if we want a 90 day build, well we've got a lot of professional service time, a lot of engineering time getting that ready to go and take to the customer, and then we turn it on, and then we can start seeing revenue from that platform. With Versastack, it's enabled us to accelerate how quickly we can turn that on. And we've seen that drop to literally days through standardization, elements of automation as well. Many of our environments are bespoke because we have such a wide range of different types of customers with different needs. But it allows us to take those standard building blocks, algin them to their needs, and deliver that service. >> James we found the MSP's are often in the middle of those discussions that customers are having on multi-cloud, so you talked a lot about the services you build. Are they also coming to you? Do you tie into the public cloud services? >> Yes. >> Maybe you can help expand a little bit on how that works. Five years ago it was, the public clouds were all going to kill the manage service providers, and what we see is customers can't sort out half of what's going on. They've got to be able to turn to partners like you to be able to figure this out. >> Yeah that's a fantastic question. Because I think three years ago, we'd be talking to our customers, and they were "I am going to this public cloud" or " I am going to build this infrastructure." Whereas now they're making more informed select decisions based on (mumbles) The drive to the hosted office and voice platforms, often by microsoft, is a big drive in many of our ITO customers are going in that direction. But it's how we integrate that with their legacy applications. Some of the ERP solutions that some of our customers use have had millions of pounds of investment into them. And that's not something that I can just turn off and walk away from overnight. So it's how we're integrating that, and we're doing that at the network level, so it's how we're pairing with different service providers, bringing that and integrating that, and offering it to them as a solution. And what we try to position ourselves is really, the same experience regardless of where we're placing IT consumption workload. It doesn't matter if it's inside our data centers, whether we're talking on one of the public cloud platforms, or even on premise, we have quite a few customers that still have significant presence on premise. Because that's right for their business, depending on what they're doing. Especially with some of the research scientists. >> So you've got to deliver flexibility in your architecture. I know you talk a lot about software define, you guys made a big move to software define a couple years ago actually. Maybe discuss how that fits into how you're servicing Advanced and other clients. >> Sure so IBM storage has embraced multi-cloud for several years now. So our solutions, well of course they work with IBM cloud, and IBM cloud private work with Amazon. They work with Azure, Google Cloud. And in fact, some of our products for example, the Versastack not only is Advanced using it, but we've got probably 40 or 50 public small medium sized cloud providers, that are public references for the Versastack, and spectrum protect, which is our back-up product, number one in the enterprise back-up space, spectrum protect has got at least 300 cloud providers, medium, small, and big who offer the engine underneath, for their backup as a service, is spectrum protect. So we make sure that whether it be our transparent cloud tiering, our cyber resiliency technology, what we do in back up archive. Object storage works with essentially, all cloud providers, that way someone like James, a CSP, MSP, can leverage our products, and we like I said, we got tons of public references around Versastack for that. But so can an enterprise, and in fact I saw a survey recently, and it was done in Europe, and in North American, that when you look at a roughly, the two billion US size revenue and up, the average company of that sizing up, will use five different public cloud providers at one time, whether that be due to legal reasons, whether that procurement, the web is really the internet. And the cloud is really just, it's been around for 20 some years. So in bigger accounts, guess who is now involved? Procurement, well we love that you did that deal with IBM cloud, but you are going to get a competitive quote now from Amazon and Microsoft right. So that's driven it, legal's driven it, certain countries right the data needs to stay in that country, even if you're cloudafying it, so to speak. So If the cloud provider doesn't have a data center there, guess what, another GI use different, and then you of course still have some large entities that still allow regional buying patterns, so they'll have three or four different cloud providers, that are quote, certified by corporate, and then you can use whichever one you want. So we make sure that we can take advantage of that wave. At IBM we ride the wave. We don't fight the wave. >> So you've got in that situation, you've got these multi clouds, you've got different API's. You've got different frameworks. How do you abstract all that complexity, you got Cisco coming at it from a networking standpoint, IBM now with red hat is good. They'd be a big player in that, that world VM ware. What do you guys do James, in terms of simplifying all that multi cloud complexity for people? >> I think with some of it, is actually demystifying and it's engaging with our partners to understand what the proposition is, and how we can develop that and align that to, not only in your own business, but more importantly to the needs of our customers. We've got some really really talented technicians work within Advanced. We've got a number of different forums that allow them to feedback their ideas. And we've got the alignments between those partners, and some of those communities, so that we can have an open discussion, and drive some of that thinking forward. But ultimately it's engaging with the customers. So the customers' feedback is key on how we shape and deliver, not only the service to them, but also to the service to other customers. We have a number of customers that are very similar, but they may work in different spaces. Some are even competitive, so we have to tread that line very carefully and safely. But it's a good one to one relationship between the client service managers, the technicians we have inside the business, having that complete 360 communication is key. And that's really the bottom too, is communication. >> James I'd like you to dig into security a little bit. I think we surpassed a couple years ago. I'm not going to go to the cloud because it's not secured to, oh I understand, it's time for me to at least re-evaluate my security, and most likely manage service providers, public clouds are probably more secure than what I had in my data center. But if I've got multiple environments, there's a lot of complexity there, so how do you traverse that, make sure that you've got a comprehensive security practice, not sure all these point solutions, all over the place? >> Yeah so that comes down to visibility. So it's visibility, understanding where all the control points are, within a given infrastructure. And how the landscape looks, so we're working quite closely with a number actually of key Cisco and IBM partners, as well as IBM and Cisco themselves directly. To have a comprehensive offering that allows us to position to our customers, you used to once upon a time. You had one gate. So all we needed is good security on your internet fighting firewall. But now you may have a 10, 20, 30 of those, we need to have consistent policies across those. We need to understand how they're performing, but also potentially if there's any attack vector on one of them, how somebody's trying to look into compromise that. So it's centralized intelligence, and that's where we're starting to look at AI operations to gather all our information. Long gone are the days where you have 20 people sitting in a room just reading screens. Those 20 people now need to see reams and reams of information instantly. Something needs to be caught up to them, so they can make their decision quickly, and access upon it. And that's really where we're positioning ourselves in the market to differentiate. I'm working with few partners to be able to do that. >> Eric talk about your announcement cadence. IBM has big show, Think, coming up in a couple weeks, Cube's going to be there of course. What can we expect from you guys? >> So we're actually going to announce on the fifth before Think. We want to drive end users and our business partners to the storage campus, which probably one of the largest campuses at IBM Think. We'll have over 15 pedestals of demo. And actually multiple demos because we have such a broad portfolio from the all flash arrays to our Versastack offering, to a whole set of modern day protection, management and control for storage. Which manage is going to control storage that's not ours right, our competitor's storage as well. And of course our software Defined storage. So we're going to do a big announcement. The focus of that will be around our storage solutions. These are solutions, blueprints, references, architectures, Jame you mentioned that use our software, and our storage systems that allow reseller or end user to configure systems easily. Think of it as the ultimate recipe for the german chocolate cake, but it's the perfect recipe. It's tried it's true it's tested, it's been on the food channel 27 times and everybody loves it. That's what we do with our solutions blueprints. We'll all have some announcements around modern data protection and obviously a big focus of IBM storage is been in the AI space. So both storage as an AI platform for AI applications workloads, but also the incorporation of AI technology into our own storage systems and software. So we'll be having announcements around that on February fifth, going into Think, which will be the week after in San Francisco. >> Great so I'm hearing trusted, data protection plays into that. Ai intelligence, machine intelligence and I'm also hearing heterogeneity, multiple platforms whether it's your storage you said, or competitor's storage. Now does that also include the cloud sphere? Without announcing anything, but you guys have -- >> Yeah. >> I've seen your pictures ads Azure. It's AWS, I mean that continues yes? >> Absolutely so whether it be what we do from back up in archive right. Let's take the easy one, so we support not only the protocol of IBM cloud object storage, which we acquired, and allows you to have object storage either on premise or in a cloud instantiation. But we also support the S3 protocol, so for example our spectrum scale software, giant scale out in fact, the two fastest super computers in the world, use spectrum scale. Over 450 petabytes running on spectrum scale. And they can tier to an object store that supports S3. Or it can tier to IBM cloud and object storage. So we have IBM storage customer that's great. If you're using the S3 protocol, you can tier to that at well. So that's just one example. Same thing we do for cyber resiliency, so for a cyber resiliency perspective, we can do things with any cloud vendor of an air gap right. And so you can do that, A with tape, but you can also do that with the cloud. So if your cloud is your backup archive replication repository, then you can always roll back to a known good copy. You don't have to pay the ransom right. Or when you clean up the malware, you can roll back to a known good copy, and we provide that across all of the platforms in a number of different ways, our protect family, our new product safe guard copy for the main frame that we announced it on October. So all that allows us to be multi-cloud resiliency, as well as how do we connect to multi-cloud, back up archive automated tiering to all kinds of clouds, whether it be IBM cloud, and of course I'm a share holder, so I love that. But at the same time we're realistic. Lots of people us Amazon, Google, Azure, and like I said there's thousands of mid to small cloud providers all over the world. And we support them too. We engage with everyone. >> What about SAS, one of the questions we've been trying to squint through, and understand is, because when you talk about five cloud providers, there's obviously infrastructures of service, and then there's service providers like Advanced, and then there's like a Gazillion SAS companies. >> Right. >> Lot of data in there. >> And a lot of Data in there. How should we think about protecting that data, securing that data? Is that up to the SAS vendor, and thou shalt not touch or should that be part of the scope of a storage company? >> Well so what we do is we engage with the SAS vendor, so we have a number of different SAS companies in fact, one was on theCUBE two years ago with us. They were a start up in the cybersecurity space, and all of it's delivered over SAS. What they do is in that case, they use our flash system product line, they get the performance they need to deliver SAS. They want no bottle necks. Because obviously you have to go over the network when you're doing SAS. And then also what they do is data encryption at rest. So when the data is brought it because we have on our flash arrays, the capability in most of our product line, especially the flash systems, to have no performance suit on encrypt or decrypt because it's hardware embedded, they're able to have the data at rest encrypted for all their customers that gives them a level of security when it's at rest on their site. At the same time we give them the right performance they need to have softwares and service. So we probably have 300,400 different SAS companies who are the actual software vendor and their deployment model is softwares and service, by the way we do that as well. As I mentioned over 300 cloud providers today have a backup as a service and the engine needs a spectrum protect or spectrum protect plus, but they may call it something else. In fact we just had a public reference out from Silver String, which is out in the UK. And all they do is Cyber resiliency backup and archive, that's their service. They have their own product, but then spectrum protect, and spectrum protect plus is the engine underneath their product. So that's an example, in this case, of back up as a service, which I would argue is not infrastructure. But more of an application. But then true what you call real application providers like cybersecurity vendors. We have a vendor who in fact, does something for all of the universities and colleges in the United States. They have about 8,000 of them, including the junior colleges. And they run all of their bookstores, so when you place an order all their AR and PR, everything they do is from this SAS vendor. They're in the northeast and they've got like I said, about 8,000 colleges and universities in the US and Canada. And they offer this, if you will, bookstore as a SAS service. And the students use it, the university uses it. And of course the bookstores are designed to at least make a little money for the University. And they all use that. So that's another example, and they use our flash systems as well. And then they back up that data internally with spectrum protect because they obviously it's the financial data as well as the inventory of all of these bookstores all over the United States at the colligate level. >> Right. >> Now James we got to wrap, but just to give you the final word, UK specialist right, so Brexit really doesn't affect you. Is that a fair statement or? >> It will do yes. >> How so? >> I think it's too early to tell. And no one really knows. I think that's what all the debates are about, is trying to understand that. And for us, I think we're just watching and observing. >> And staying focused on your customers obviously >> Yeah. >> So no predictions as to what's going to happen. When I was in the UK-- >> Not from me. a few weeks ago I heard both sides. You know oh it's definitely going to happen, oh it might not happen. But okay, again give you the last word. What's your focus over the next 12, 18 months? >> Our focus is really about visibility so Dave touched on that when we were talking about the security. For customers understanding where their data is, where their exposure points are. That's our key focus. And Versastack and the IBM storewise products underpin all of those offerings that we have. And that will continue to be so moving forward. >> Guys great to see you. Thanks so much for coming to theCUBE. And our pleasure hosting you. >> Great thank you really appreciate it. >> You're really welcome, alright keep it right there everybody. We'll be back. Dave Velante with Stu Minamin from Cisco live in Barcelona. (electronic music)

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

>>Good afternoon everyone, and thank you so much for joining us. My name is Savannah Peterson, joined by my co-host Paul for the afternoon. Very excited. Oh, Savannah. Hello. I'm, I'm pumped for this. This is our first bit together. Exactly. >>It's gonna be fun. Yes. We have a great guest to kick off with. >>We absolutely do. We're at Supercomputing 2022 today, and very excited to talk to our next guest. We're gonna be talking about data at scale and data that really matters to us joining us. Kelly Gayer, thank you so much for being here and you are with tech. Tell everyone what TAC is. >>Tech is the Texas Advanced Computing Center at the University of Texas at Austin. And thank you so much for having me here. >>It is wonderful to have you. Your smile's contagious. And one of the themes that's come up a lot with all of our guests, and we just talked about it, is how good it is to be back in person, how good it is to be around our hardware, community tech. You did some very interesting research during the pandemic. Can you tell us about that? >>I can. I did. So when we realized sort of mid-March, we realized that, that this was really not normal times and the pandemic was statement. Yes. That pandemic was really gonna touch everyone. I think a lot of us at the center and me personally, we dropped everything to plug in and that's what we do. So UT's tagline is what starts here changes the world and tax tagline is powering discoveries that change the world. So we're all about impact, but I plugged in with the research group there at UT Austin, Dr. Lauren Myers, who's an epidemiologist, and just we figured out how to plug in and compute so that we could predict the spread of, of Covid 19. >>And you did that through the use of mobility data, cell phone signals. Tell us more about what exactly you were choreographing. >>Yeah, so that was really interesting. Safe graph during the pandemic made their mobility data. Typically it was used for marketing purposes to know who was going into Walmart. The offenses >>For advertising. >>Absolutely, yeah. They made all of their mobility data available for free to people who were doing research and plugging in trying to understand Covid. 19, I picked that data up and we used it as a proxy for human behavior. So we knew we had some idea, we got weekly mobility updates, but it was really mobility all day long, you know, anonymized. I didn't know who they were by cell phones across the US by census block group or zip code if we wanted to look at it that way. And we could see how people were moving around. We knew what their neighbor, their home neighborhoods were. We knew how they were traveling or not traveling. We knew where people were congregating, and we could get some idea of, of how people were behaving. Were they really, were they really locking down or were they moving in their neighborhoods or were they going outside of their neighborhoods? >>What a, what a fascinating window into our pandemic lives. So now that you were able to do this for this pandemic, as we look forward, what have you learned? How quickly could we forecast? What's the prognosis? >>Yeah, so we, we learned a tremendous amount. I think during the pandemic we were reacting, we were really trying. It was a, it was an interesting time as a scientist, we were reacting to things almost as if the earth was moving underneath us every single day. So it was something new every day. And I've told people since I've, I haven't, I haven't worked that hard since I was a graduate student. So it was really daylight to dark 24 7 for a long period of time because it was so important. And we knew, we, we knew we were, we were being a part of history and affecting something that was gonna make a difference for a really long time. And, and I think what we've learned is that indeed there is a lot of data being collected that we can use for good. We can really understand if we get organized and we get set up, we can use this data as a means of perhaps predicting our next pandemic or our next outbreak of whatever. It is almost like using it as a canary in the coal mine. There's a lot in human behavior we can use, given >>All the politicization of, of this last pandemic, knowing what we know now, making us better prepared in theory for the next one. How confident are you that at least in the US we will respond proactively and, and effectively when the next one comes around? >>Yeah, I mean, that's a, that's a great question and, and I certainly understand why you ask. I think in my experience as a scientist, certainly at tech, the more transparent you are with what you do and the more you explain things. Again, during the pandemic, things were shifting so rapidly we were reacting and doing the best that we could. And I think one thing we did right was we admitted where we felt uncertain. And that's important. You have to really be transparent to the general public. I, I don't know how well people are gonna react. I think if we have time to prepare, to communicate and always be really transparent about it. I think those are three factors that go into really increasing people's trust. >>I think you nailed it. And, and especially during times of chaos and disaster, you don't know who to trust or what to believe. And it sounds like, you know, providing a transparent source of truth is, is so critical. How do you protect the sensitive data that you're working with? I know it's a top priority for you and the team. >>It is, it is. And we, we've adopted the medical mantra, do no harm. So we have, we feel a great responsibility there. There's, you know, two things that you have to really keep in mind when you've got sensitive data. One is the physical protection of it. And so that's, that's governed by rule, federal rules, hipaa, ferpa, whatever, whatever kind of data that you have. So we certainly focus on the physical protection of it, but there's also sort of the ethical protection of it. What, what is the quote? There's lies, damn lies and statistics. >>Yes. Twain. >>Yeah. So you, you really have to be responsible with what you're doing with the data, how you're portraying the results. And again, I think it comes back to transparency is is basically if people are gonna reproduce what I did, I have to be really transparent with what I did. >>I, yeah, I think that's super important. And one of the themes with, with HPC that we've been talking about a lot too is, you know, do people trust ai? Do they trust all the data that's going into these systems? And I love that you just talked about the storytelling aspect of that, because there is a duty, it's not, you can cut data kind of however you want. I mean, I come from marketing background and we can massage it to, to do whatever we want. So in addition to being the deputy director at Tech, you are also the DEI officer. And diversity I know is important to you probably both as an individual, but also in the work that you're doing. Talk to us about that. >>Yeah, I mean, I, I very passionate about diversity, equity and inclusion in a sense of belongingness. I think that's one of the key aspects of it. Core >>Of community too. >>I got a computer science degree back in the eighties. I was akin to a unicorn in a, in an engineering computer science department. And, but I was really lucky in a couple of respects. I had a, I had a father that was into science that told me I could do anything I, I wanted to set my mind to do. So that was my whole life, was really having that support system. >>He was cheers to dad. >>Yeah. Oh yeah. And my mom as well, actually, you know, they were educators. I grew up, you know, in that respect, very, very privileged, but it was still really hard to make it. And I couldn't have told you back in that time why I made it and, and others didn't, why they dropped out. But I made it a mission probably back, gosh, maybe 10, 15 years ago, that I was really gonna do all that I could to change the needle. And it turns out that there are a number of things that you can do grassroots. There are certainly best practices. There are rules and there are things that you really, you know, best practices to follow to make people feel more included in an organization, to feel like they belong it, shared mission. But there are also clever things that you can do with programming to really engage students, to meet people and students where they are interested and where they are engaged. And I think that's what, that's what we've done over, you know, the course of our programming over the course of about maybe since 2016. We have built a lot of programming ATAC that really focuses on that as well, because I'm determined the needle is gonna change before it's all said and done. It just really has to. >>So what, what progress have you made and what goals have you set in this area? >>Yeah, that, that's a great question. So, you know, at first I was a little bit reluctant to set concrete goals because I really didn't know what we could accomplish. I really wasn't sure what grassroots efforts was gonna be able to, you're >>So honest, you can tell how transparent you are with the data as well. That's >>Great. Yeah, I mean, if I really, most of the successful work that I've done is both a scientist and in the education and outreach space is really trust relationships. If I break that trust, I'm done. I'm no longer effective. So yeah, I am really transparent about it. But, but what we did was, you know, the first thing we did was we counted, you know, to the extent that we could, what does the current picture look like? Let's be honest about it. Start where we are. Yep. It was not a pretty picture. I mean, we knew that anecdotally it was not gonna be a great picture, but we put it out there and we leaned into it. We said, this is what it is. We, you know, I hesitated to say we're gonna look 10% better next year because I'm, I'm gonna be honest, I don't always know we're gonna do our best. >>The things that I think we did really well was that we stopped to take time to talk and find out what people were interested in. It's almost like being present and listening. My grandmother had a saying, you have two errors in one mouth for a reason, just respect the ratio. Oh, I love that. Yeah. And I think it's just been building relationships, building trust, really focusing on making a difference, making it a priority. And I think now what we're doing is we've been successful in pockets of people in the center and we are, we are getting everybody on board. There's, there's something everyone can do, >>But the problem you're addressing doesn't begin in college. It begins much, much, that's right. And there's been a lot of talk about STEM education, particularly for girls, how they're pushed out of the system early on. Also for, for people of color. Do you see meaningful progress being made there now after years of, of lip service? >>I do. I do. But it is, again, grassroots. We do have a, a, a researcher who was a former teacher at the center, Carol Fletcher, who is doing research and for CS for all we know that the workforce, so if you work from the current workforce, her projected workforce backwards, we know that digital skills of some kind are gonna be needed. We also know we have a, a, a shortage. There's debate on how large that shortage is, but about roughly about 1 million unmet jobs was projected in 2020. It hasn't gotten a lot better. We can work that problem backwards. So what we do there is a little, like a scatter shot approach. We know that people come in all forms, all shapes, all sizes. They get interested for all different kinds of reasons. We expanded our set of pathways so that we can get them where they can get on to the path all the way back K through 12, that's Carol's work. Rosie Gomez at the center is doing sort of the undergraduate space. We've got Don Hunter that does it, middle school, high school space. So we are working all parts of the problem. I am pretty passionate about what we consider opportunity youth people who never had the opportunity to go to college. Is there a way that we can skill them and get, get them engaged in some aspect and perhaps get them into this workforce. >>I love that you're starting off so young. So give us an example of one of those programs. What are you talking to kindergartners about when it comes to CS education? >>You know, I mean, gaming. Yes. Right. It's what everybody can wrap their head around. So most kids have had some sort of gaming device. You talk in the context, in the context of something they understand. I'm not gonna talk to them about high performance computing. It, it would go right over their heads. And I think, yeah, you know, I, I'll go back to something that you said Paul, about, you know, girls were pushed out. I don't know that girls are being pushed out. I think girls aren't interested and things that are being presented and I think they, I >>Think you're generous. >>Yeah. I mean, I was a young girl and I don't know why I stayed. Well, I do know why I stayed with it because I had a father that saw something in me and I had people at critical points in my life that saw something in me that I didn't see. But I think if we ch, if we change the way we teach it, maybe in your words they don't get pushed out or they, or they won't lose interest. There's, there's some sort of computing in everything we do. Well, >>Absolutely. There's also the bro culture, which begins at a very early >>Age. Yeah, that's a different problem. Yeah. That's just having boys in the classroom. Absolutely. You got >>It. That's a whole nother case. >>That's a whole other thing. >>Last question for you, when we are sitting here, well actually I've got, it's two parter, let's put it that way. Is there a tool or something you wish you could flick a magic wand that would make your job easier? Where you, you know, is there, can you identify the, the linchpin in the DEI challenge? Or is it all still prototyping and iterating to figure out the best fit? >>Yeah, that is a, that's a wonderful question. I can tell you what I get frustrated with is that, that >>Counts >>Is that I, I feel like a lot of people don't fully understand the level of effort and engagement it takes to do something meaningful. The >>Commitment to a program, >>The commitment to a program. Totally agree. It's, there is no one and done. No. And in fact, if I do that, I will lose them forever. They'll be, they will, they will be lost in the space forever. Rather. The engagement is really sort of time intensive. It's relationship intensive, but there's a lot of follow up too. And the, the amount of funding that goes into this space really is not, it, it, it's not equal to the amount of time and effort that it really takes. And I think, you know, I think what you work in this space, you realize that what you gain is, is really more of, it's, it really feels good to make a difference in somebody's life, but it's really hard to do on a shoer budget. So if I could kind of wave a magic wand, yes, I would increase understanding. I would get people to understand that it's all of our responsibility. Yes, everybody is needed to make the difference and I would increase the funding that goes to the programs. >>I think that's awesome, Kelly, thank you for that. You all heard that. More funding for diversity, equity, and inclusion. Please Paul, thank you for a fantastic interview, Kelly. Hopefully everyone is now inspired to check out tac perhaps become a, a Longhorn, hook 'em and, and come deal with some of the most important data that we have going through our systems and predicting the future of our pandemics. Ladies and gentlemen, thank you for joining us online. We are here in Dallas, Texas at Supercomputing. My name is Savannah Peterson and I look forward to seeing you for our next segment.

(upbeat music) >> Hey guys, welcome back to the show floor of KubeCon + CloudNativeCon '22 North America from Detroit, Michigan. Lisa Martin here with John Furrier. This is day one, John at theCUBE's coverage. >> CUBE's coverage. >> theCUBE's coverage of KubeCon. Try saying that five times fast. Day one, we have three wall-to-wall days. We've been talking about Kubernetes, containers, adoption, cloud adoption, app modernization all morning. We can't talk about those things without addressing security. >> Yeah, this segment we're going to hear container and Kubernetes security for modern application 'cause the enterprise are moving there. And this segment with Red Hat's going to be important because they are the leader in the enterprise when it comes to open source in Linux. So this is going to be a very fun segment. >> Very fun segment. Two guests from Red Hat join us. Please welcome Doron Caspin, Senior Principal Product Manager at Red Hat. Michael Foster joins us as well, Principal Product Marketing Manager and StackRox Community Lead at Red Hat. Guys, great to have you on the program. >> Thanks for having us. >> Thank you for having us. >> It's awesome. So Michael StackRox acquisition's been about a year. You got some news? >> Yeah, 18 months. >> Unpack that for us. >> It's been 18 months, yeah. So StackRox in 2017, originally we shifted to be the Kubernetes-native security platform. That was our goal, that was our vision. Red Hat obviously saw a lot of powerful, let's say, mission statement in that, and they bought us in 2021. Pre-acquisition we were looking to create a cloud service. Originally we ran on Kubernetes platforms, we had an operator and things like that. Now we are looking to basically bring customers in into our service preview for ACS as a cloud service. That's very exciting. Security conversation is top notch right now. It's an all time high. You can't go with anywhere without talking about security. And specifically in the code, we were talking before we came on camera, the software supply chain is real. It's not just about verification. Where do you guys see the challenges right now? Containers having, even scanning them is not good enough. First of all, you got to scan them and that may not be good enough. Where's the security challenges and where's the opportunity? >> I think a little bit of it is a new way of thinking. The speed of security is actually does make you secure. We want to keep our images up and fresh and updated and we also want to make sure that we're keeping the open source and the different images that we're bringing in secure. Doron, I know you have some things to say about that too. He's been working tirelessly on the cloud service. >> Yeah, I think that one thing, you need to trust your sources. Even if in the open source world, you don't want to copy paste libraries from the web. And most of our customers using third party vendors and getting images from different location, we need to trust our sources and we have a really good, even if you have really good scanning solution, you not always can trust it. You need to have a good solution for that. >> And you guys are having news, you're announcing the Red Hat Advanced Cluster Security Cloud Service. >> Yes. >> What is that? >> So we took StackRox and we took the opportunity to make it as a cloud services so customer can consume the product as a cloud services as a start offering and customer can buy it through for Amazon Marketplace and in the future Azure Marketplace. So customer can use it for the AKS and EKS and AKS and also of course OpenShift. So we are not specifically for OpenShift. We're not just OpenShift. We also provide support for EKS and AKS. So we provided the capability to secure the whole cloud posture. We know customer are not only OpenShift or not only EKS. We have both. We have free cloud or full cloud. So we have open. >> So it's not just OpenShift, it's Kubernetes, environments, all together. >> Doron: All together, yeah. >> Lisa: Meeting customers where they are. >> Yeah, exactly. And we focus on, we are not trying to boil the ocean or solve the whole cloud security posture. We try to solve the Kubernetes security cluster. It's very unique and very need unique solution for that. It's not just added value in our cloud security solution. We think it's something special for Kubernetes and this is what Red that is aiming to. To solve this issue. >> And the ACS platform really doesn't change at all. It's just how they're consuming it. It's a lot quicker in the cloud. Time to value is right there. As soon as you start up a Kubernetes cluster, you can get started with ACS cloud service and get going really quickly. >> I'm going to ask you guys a very simple question, but I heard it in the bar in the lobby last night. Practitioners talking and they were excited about the Red Hat opportunity. They actually asked a question, where do I go and get some free Red Hat to test some Kubernetes out and run helm or whatever. They want to play around. And do you guys have a program for someone to get start for free? >> Yeah, so the cloud service specifically, we're going to service preview. So if people sign up, they'll be able to test it out and give us feedback. That's what we're looking for. >> John: Is that a Sandbox or is that going to be in the cloud? >> They can run it in their own environment. So they can sign up. >> John: Free. >> Doron: Yeah, free. >> For the service preview. All we're asking for is for customer feedback. And I know it's actually getting busy there. It's starting December. So the quicker people are, the better. >> So my friend at the lobby I was talking to, I told you it was free. I gave you the sandbox, but check out your cloud too. >> And we also have the open source version so you can download it and use it. >> Yeah, people want to know how to get involved. I'm getting a lot more folks coming to Red Hat from the open source side that want to get their feet wet. That's been a lot of people rarely interested. That's a real testament to the product leadership. Congratulations. >> Yeah, thank you. >> So what are the key challenges that you have on your roadmap right now? You got the products out there, what's the current stake? Can you scope the adoption? Can you share where we're at? What people are doing specifically and the real challenges? >> I think one of the biggest challenges is talking with customers with a slightly, I don't want to say outdated, but an older approach to security. You hear things like malware pop up and it's like, well, really what we should be doing is keeping things into low and medium vulnerabilities, looking at the configuration, managing risk accordingly. Having disparate security tools or different teams doing various things, it's really hard to get a security picture of what's going on in the cluster. That's some of the biggest challenges that we talk with customers about. >> And in terms of resolving those challenges, you mentioned malware, we talk about ransomware. It's a household word these days. It's no longer, are we going to get hit? It's when? It's what's the severity? It's how often? How are you guys helping customers to dial down some of the risk that's inherent and only growing these days? >> Yeah, risk, it's a tough word to generalize, but our whole goal is to give you as much security information in a way that's consumable so that you can evaluate your risk, set policies, and then enforce them early on in the cluster or early on in the development pipeline so that your developers get the security information they need, hopefully asynchronously. That's the best way to do it. It's nice and quick, but yeah. I don't know if Doron you want to add to that? >> Yeah, so I think, yeah, we know that ransomware, again, it's a big world for everyone and we understand the area of the boundaries where we want to, what we want to protect. And we think it's about policies and where we enforce it. So, and if you can enforce it on, we know that as we discussed before that you can scan the image, but we never know what is in it until you really run it. So one of the thing that we we provide is runtime scanning. So you can scan and you can have policy in runtime. So enforce things in runtime. But even if one image got in a way and get to your cluster and run on somewhere, we can stop it in runtime. >> Yeah. And even with the runtime enforcement, the biggest thing we have to educate customers on is that's the last-ditch effort. We want to get these security controls as early as possible. That's where the value's going to be. So we don't want to be blocking things from getting to staging six weeks after developers have been working on a project. >> I want to get you guys thoughts on developer productivity. Had Docker CEO on earlier and since then I had a couple people messaging me. Love the vision of Docker, but Docker Hub has some legacy and it might not, has does something kind of adoption that some people think it does. Are people moving 'cause there times they want to have these their own places? No one place or maybe there is, or how do you guys see the movement of say Docker Hub to just using containers? I don't need to be Docker Hub. What's the vis-a-vis competition? >> I mean working with open source with Red Hat, you have to meet the developers where they are. If your tool isn't cutting it for developers, they're going to find a new tool and really they're the engine, the growth engine of a lot of these technologies. So again, if Docker, I don't want to speak about Docker or what they're doing specifically, but I know that they pretty much kicked off the container revolution and got this whole thing started. >> A lot of people are using your environment too. We're hearing a lot of uptake on the Red Hat side too. So, this is open source help, it all sorts stuff out in the end, like you said, but you guys are getting a lot of traction there. Can you share what's happening there? >> I think one of the biggest things from a developer experience that I've seen is the universal base image that people are using. I can speak from a security standpoint, it's awesome that you have a base image where you can make one change or one issue and it can impact a lot of different applications. That's one of the big benefits that I see in adoption. >> What are some of the business, I'm curious what some of the business outcomes are. You talked about faster time to value obviously being able to get security shifted left and from a control perspective. but what are some of the, if I'm a business, if I'm a telco or a healthcare organization or a financial organization, what are some of the top line benefits that this can bubble up to impact? >> I mean for me, with those two providers, compliance is a massive one. And just having an overall look at what's going on in your clusters, in your environments so that when audit time comes, you're prepared. You can get through that extremely quickly. And then as well, when something inevitably does happen, you can get a good image of all of like, let's say a Log4Shell happens, you know exactly what clusters are affected. The triage time is a lot quicker. Developers can get back to developing and then yeah, you can get through it. >> One thing that we see that customers compliance is huge. >> Yes. And we don't want to, the old way was that, okay, I will provision a cluster and I will do scans and find things, but I need to do for PCI DSS for example. Today the customer want to provision in advance a PCI DSS cluster. So you need to do the compliance before you provision the cluster and make all the configuration already baked for PCI DSS or HIPAA compliance or FedRAMP. And this is where we try to use our compliance, we have tools for compliance today on OpenShift and other clusters and other distribution, but you can do this in advance before you even provision the cluster. And we also have tools to enforce it after that, after your provision, but you have to do it again before and after to make it more feasible. >> Advanced cluster management and the compliance operator really help with that. That's why OpenShift Platform Plus as a bundle is so popular. Just being able to know that when a cluster gets provision, it's going to be in compliance with whatever the healthcare provider is using. And then you can automatically have ACS as well pop up so you know exactly what applications are running, you know it's in compliance. I mean that's the speed. >> You mentioned the word operator, I get triggering word now for me because operator role is changing significantly on this next wave coming because of the automation. They're operating, but they're also devs too. They're developing and composing. It's almost like a dashboard, Lego blocks. The operator's not just manually racking and stacking like the old days, I'm oversimplifying it, but the new operators running stuff, they got observability, they got coding, their servicing policy. There's a lot going on. There's a lot of knobs. Is it going to get simpler? How do you guys see the org structures changing to fill the gap on what should be a very simple, turn some knobs, operate at scale? >> Well, when StackRox originally got acquired, one of the first things we did was put ACS into an operator and it actually made the application life cycle so much easier. It was very easy in the console to go and say, Hey yeah, I want ACS my cluster, click it. It would get provisioned. New clusters would get provisioned automatically. So underneath it might get more complicated. But in terms of the application lifecycle, operators make things so much easier. >> And of course I saw, I was lucky enough with Lisa to see Project Wisdom in AnsibleFest. You going to say, Hey, Red Hat, spin up the clusters and just magically will be voice activated. Starting to see AI come in. So again, operations operator is got to dev vibe and an SRE vibe, but it's not that direct. Something's happening there. We're trying to put our finger on. What do you guys think is happening? What's the real? What's the action? What's transforming? >> That's a good question. I think in general, things just move to the developers all the time. I mean, we talk about shift left security, everything's always going that way. Developers how they're handing everything. I'm not sure exactly. Doron, do you have any thoughts on that. >> Doron, what's your reaction? You can just, it's okay, say what you want. >> So I spoke with one of our customers yesterday and they say that in the last years, we developed tons of code just to operate their infrastructure. That if developers, so five or six years ago when a developer wanted VM, it will take him a week to get a VM because they need all their approval and someone need to actually provision this VM on VMware. And today they automate all the way end-to-end and it take two minutes to get a VM for developer. So operators are becoming developers as you said, and they develop code and they make the infrastructure as code and infrastructure as operator to make it more easy for the business to run. >> And then also if you add in DataOps, AIOps, DataOps, Security Ops, that's the new IT. It seems to be the new IT is the stuff that's scaling, a lot of data's coming in, you got security. So all that's got to be brought in. How do you guys view that into the equation? >> Oh, I mean you become big generalists. I think there's a reason why those cloud security or cloud professional certificates are becoming so popular. You have to know a lot about all the different applications, be able to code it, automate it, like you said, hopefully everything as code. And then it also makes it easy for security tools to come in and look and examine where the vulnerabilities are when those things are as code. So because you're going and developing all this automation, you do become, let's say a generalist. >> We've been hearing on theCUBE here and we've been hearing the industry, burnout, associated with security professionals and some DataOps because the tsunami of data, tsunami of breaches, a lot of engineers getting called in the middle of the night. So that's not automated. So this got to get solved quickly, scaled up quickly. >> Yes. There's two part question there. I think in terms of the burnout aspect, you better send some love to your security team because they only get called when things get broken and when they're doing a great job you never hear about them. So I think that's one of the things, it's a thankless profession. From the second part, if you have the right tools in place so that when something does hit the fan and does break, then you can make an automated or a specific decision upstream to change that, then things become easy. It's when the tools aren't in place and you have desperate environments so that when a Log4Shell or something like that comes in, you're scrambling trying to figure out what clusters are where and where you're impacted. >> Point of attack, remediate fast. That seems to be the new move. >> Yeah. And you do need to know exactly what's going on in your clusters and how to remediate it quickly, how to get the most impact with one change. >> And that makes sense. The service area is expanding. More things are being pushed. So things will, whether it's a zero day vulnerability or just attack. >> Just mix, yeah. Customer automate their all of things, but it's good and bad. Some customer told us they, I think Spotify lost the whole a full zone because of one mistake of a customer because they automate everything and you make one mistake. >> It scale the failure really. >> Exactly. Scaled the failure really fast. >> That was actually few contact I think four years ago. They talked about it. It was a great learning experience. >> It worked double edge sword there. >> Yeah. So definitely we need to, again, scale automation, test automation way too, you need to hold the drills around data. >> Yeah, you have to know the impact. There's a lot of talk in the security space about what you can and can't automate. And by default when you install ACS, everything is non-enforced. You have to have an admission control. >> How are you guys seeing your customers? Obviously Red Hat's got a great customer base. How are they adopting to the managed service wave that's coming? People are liking the managed services now because they maybe have skills gap issues. So managed service is becoming a big part of the portfolio. What's your guys' take on the managed services piece? >> It's just time to value. You're developing a new application, you need to get it out there quick. If somebody, your competitor gets out there a month before you do, that's a huge market advantage. >> So you care how you got there. >> Exactly. And so we've had so much Kubernetes expertise over the last 10 or so, 10 plus year or well, Kubernetes for seven plus years at Red Hat, that why wouldn't you leverage that knowledge internally so you can get your application. >> Why change your toolchain and your workflows go faster and take advantage of the managed service because it's just about getting from point A to point B. >> Exactly. >> Well, in time to value is, you mentioned that it's not a trivial term, it's not a marketing term. There's a lot of impact that can be made. Organizations that can move faster, that can iterate faster, develop what their customers are looking for so that they have that competitive advantage. It's definitely not something that's trivial. >> Yeah. And working in marketing, whenever you get that new feature out and I can go and chat about it online, it's always awesome. You always get customers interests. >> Pushing new code, being secure. What's next for you guys? What's on the agenda? What's around the corner? We'll see a lot of Red Hat at re:Invent. Obviously your relationship with AWS as strong as a company. Multi-cloud is here. Supercloud as we've been saying. Supercloud is a thing. What's next for you guys? >> So we launch the cloud services and the idea that we will get feedback from customers. We are not going GA. We're not going to sell it for now. We want to get customers, we want to get feedback to make the product as best what we can sell and best we can give for our customers and get feedback. And when we go GA and we start selling this product, we will get the best product in the market. So this is our goal. We want to get the customer in the loop and get as much as feedback as we can. And also we working very closely with our customers, our existing customers to announce the product to add more and more features what the customer needs. It's all about supply chain. I don't like it, but we have to say, it's all about making things more automated and make things more easy for our customer to use to have security in the Kubernetes environment. >> So where can your customers go? Clearly, you've made a big impact on our viewers with your conversation today. Where are they going to be able to go to get their hands on the release? >> So you can find it on online. We have a website to sign up for this program. It's on my blog. We have a blog out there for ACS cloud services. You can just go there, sign up, and we will contact the customer. >> Yeah. And there's another way, if you ever want to get your hands on it and you can do it for free, Open Source StackRox. The product is open source completely. And I would love feedback in Slack channel. It's one of the, we also get a ton of feedback from people who aren't actually paying customers and they contribute upstream. So that's an awesome way to get started. But like you said, you go to, if you search ACS cloud service and service preview. Don't have to be a Red Hat customer. Just if you're running a CNCF compliant Kubernetes version. we'd love to hear from you. >> All open source, all out in the open. >> Yep. >> Getting it available to the customers, the non-customers, they hopefully pending customers. Guys, thank you so much for joining John and me talking about the new release, the evolution of StackRox in the last season of 18 months. Lot of good stuff here. I think you've done a great job of getting the audience excited about what you're releasing. Thank you for your time. >> Thank you. >> Thank you. >> For our guest and for John Furrier, Lisa Martin here in Detroit, KubeCon + CloudNativeCon North America. Coming to you live, we'll be back with our next guest in just a minute. (gentle music)

(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of VMware Explore 2022. We are at Moscone West. Lisa Martin and Dave Nicholson here. Excited, really excited, whereas they were saying in the VMware keynote, pumped and jacked and jazzed to be back in-person with a lot of folks here. Keynote with standing room only. We've just come from that. We've got a couple of guests here from Expedient, going to unpack their relationship with VMware. Please welcome Brian Smith, the Senior Vice President and Chief Strategy Officer at Expedient. And Brent Meadows, the Vice President of Advanced Solution Architecture at Expedient. Guys it's great to have you on the program. >> Appreciate it bringing us on. >> Yep, welcome. >> Isn't it great to be back in person? >> It is phenomenal to be back. >> So let's talk about obviously three years since the last, what was called VMworld, so many dynamics in the market. Talk to us about what's going on at Expedient, we want to dig into Cloud Different, but kind of give us a lay of the land of what's going on and then we're going to uncrack the VMware partnership as well. >> Sure, so Expedient we're a full stack cloud service provider. So we have physical data centers that we run and then have VMware-based cloud and we've seen a huge shift from the client perspective during the pandemic in how they've really responded from everything pre-pandemic was very focused with Cloud First and trying to go that route only with hyper scaler. And there's been a big evolution with how people have to change how they think about their transformation to get the end result they're looking for. >> Talk about Cloud Different and what it's helping customers to achieve as everyone's in this accelerated transformation. >> Yeah. So, Cloud Different is something that Expedient branded. It's really about how the transformation works. And traditionally, companies thought about doing their transformation, at first they kept everything in house that they were doing and they started building their new applications out into a hyper scale cloud. And what that really is like is, a good analogy would be, it's like living in a house while you're renovating it. And I know what that's like from my relationship versus if you build a new house, or move to a new property that's completed already. And that's really the difference in that experience from a Cloud Different approach from transformation is you think of all the things that you have internally, and there's a lot of technical debt there, and that's a lot of weight that you're carrying when you're trying to do that transformation. So if you kind of flip that around and instead look to make that transformation and move all that technical debt into a cloud that's already built to run those same types of applications, a VMware-based cloud, now you can remove all of that noise, move into a curated stack of technology and everything just works. It has the security in place, your teams know how to run it, and then you can take that time you really reclaim and apply that towards new applications and new things that are strategic to the business. >> That's really critical, Brent, to get folks in the IT organization across the business, really focused on strategic initiatives rather than a lot of the mundane tasks that they just don't have time for. Brent, what are you hearing in the last couple of years with the dynamics we talked about, what are you hearing from the customer? >> Right. So, one of the big things and the challenges in the current dynamic is kind of that staffing part. So as people have built their infrastructure over the years, there's a lot of tribal knowledge that's been created during that process and every day more and more of that knowledge is walking out the door. So taking some of that technical debt that Brian mentioned and kind of removing that so you don't have to have all that tribal knowledge, really standardizing on the foundational infrastructure pieces, allows them to make that transition and not have to carry that technical debt along with them as they make their digital transformations. >> We heard a lot this morning in the keynote guys about customers going, most of them still being in cloud chaos, but VMware wanting them to get to cloud smart. What does that mean, Brian, from Expedient's perspective? What does cloud smart look like to Expedient and its customers? >> Yeah, we completely agree with that message. And it's something we've been preaching for a couple years in part of that Cloud Different story. And it's really about having a consistent wrapper across all of your environments. It doesn't matter if it's things that you're running on-premises that's legacy to things that are in a VMware-based cloud, like an Expedient cloud or things that are in a hyper scale, but having one consistent security, one consistent automation, one consistent cost management, really gives you the governance so that you can get the value out of cloud that you are hoping for and remove a lot of the noise and think less about the technology and more about what the business is getting out of the technology. >> So what does that look like as a practical matter? I imagine you have customers whose on-premises VMware environments look different than what you've created within Expedient data centers. I'm thinking of things like the level of adoption of NSX, how well a customer may embrace VSAN on-prem as an example. Is part of this transmogrification into your data center, kind of nudging people to adopt frameworks that are really necessary for success in the future? >> It's less of a nudge because a lot of times as a service provider, we don't talk about the technology, we talk more about the outcome. So the nice thing with VMware is we can move that same virtual machine or that container into the platform and the client doesn't always know exactly what's underneath because we have that standardized VMware stack and it just works. And that's part of the beauty of the process. I dunno if you want to talk about a specific client or... >> Yeah, so one of the ones we worked with is Bob Evans Foods. So they were in that transformation stage of refreshing, not only their office space and their data center, but also their VMware environment. So we helped them go through and first thing is looking at their existing environment, figuring out what they currently have, because you can't really make a good decision of what you need to change until you know where you're starting from. So we worked with them through that process, completely evacuated their data center. And from a business perspective, what that allowed them to do as well is have more flexibility in the choice of their next corporate office, because they didn't have to have a data center attached to it. So just from that data center perspective, we gave them some flexibility there. But then from an operations perspective, really standardize that process, offloaded some of those menial tasks that you mentioned earlier, and allow them to really look more towards business-driving projects, instead of just trying to keep those lights on, keeping the backups running, et cetera. >> Brian, question for you, here we are, the theme of the event is "The Center of the Multi-cloud Universe" which seems like a Marvel movie, I haven't seen any new superheroes yet, but I suspect there might be some here. But as customers end up and land in multi-cloud by default not by strategy, how does Expedient and VMware help them actually take the environment that they have and make it strategic so that the business can achieve the outcomes, improving revenue, finding new revenue streams, new products, new routes to market to delight those customers. How do you turn that kind of cloud chaos into a strategy? >> Yeah. I'd say there's a couple different components. One is really time. How can you give them time back for things that are creating noise and aren't really strategic to the business? And so if you can give that time back, that's the first way that you can really impact the business. And the second is through that standardization, but also a lot of times when people think of that new standard, they're only thinking if you're building from scratch. And what VMware has really helped is by taking those existing workloads and giving a standard that works for those applications and what you're building new and brings those together under a common platform and so had a really significant impact to the speed that somebody can get to that cloud operating model, that used to be a multi-year process and most of our clients can go from really everything or almost everything on-prem and a little bit in a cloud to a complete cloud operating model, on average, in four to six months. >> Wow! >> So if I have an on-premises environment and some of my workloads are running in a VMware context, VMware would make the pitch in an agnostic way that, "Well, you can go and deploy that "on top of a stack of infrastructure "and anybody and anywhere now." Why do customers come to you instead of saying, "Oh, we'll go to "pick your flavor of hyper scale cloud provider." What's kind of your superpower? You've mentioned a couple of things, but really hone it in on, why would someone want to go to Expedient? >> Yeah. In a single word, service. I mean, we have a 99% client retention rate and have for well over a decade. So it's really that expertise that wraps around all the different technology so that you're not worried about what's happening and you're not worried about trying to keep the lights on and doing the firefighting. You're really focused on the business. And the other way to, I guess another analogy is, if you think about a lot of the technology and the way people go to cloud, it's like if you got a set of Legos without the box or the instructions. So you can build stuff, it could be cool, but you're not going to get to that end state-- >> Hold on. That's how Legos used to work. Just maybe you're too young to remember a time-- >> You see their sales go up because now you buy a different set for this-- >> I build those sets with my son, but I do it grudgingly. >> Do you ever step on one? >> Of course I do. >> Yeah, there's some pain involved. Same thing happens in the transformation. So when they're buying services from an Expedient, you're buying that box set where you have a picture of what your outcome's going to be, the instructions are there. So you also have confidence that you're going to get to the end outcome much faster than you would if you're trying to assemble everything yourself. (David laughing) >> In my mind, I'm imagining the things that I built with Lego, before there were instructions. >> No death star? >> No. Nothing close with the death star. Definitely something that you would not want your information technology to depend upon. >> Got it. >> Brent, we've seen obviously, it seems like every customer these days, regardless of industry has a cloud first initiative. They have competitors in the rear view mirror who are, if they're able to be more agile and faster to market, are potential huge competitive threat. As we see the rise of multi-cloud in the last 12 months, there's also been a lot of increased analyst coverage for alternate specialty hybrid cloud. Talk to us about, Expedient was in the recent Gartner market guide for specialty cloud. How are these related? What's driving this constant change out in the customer marketplace? >> Sure. So a lot of that agility that clients are getting and trying to do that digital transformation or refactor their applications requires a lot of effort from the developers and the internal IT practitioners. So by moving to a model with an enterprise kind of like Expedient, that allows them to get a consistent foundational level for those technical debt, the 'traditional workloads' where they can start focusing their efforts more on that refactoring of their applications, to get that agility, to get the flexibility, to get the market advantage of time to market with their new refactored applications. That takes them much faster to market, allows them to get ahead of those competitors, if they're not already ahead of them, get further ahead of them or catch up the ones that may have already made that transition. >> And I would add that the analyst coverage you've seen in the last 9 to 12 months, really accelerate for our type of cloud because before everything was hyper scale, everything's going to be hyper scale and they realized that companies have been trying to go to the cloud really for over a decade, really 15 years, that digital transformation, but most companies, when you look at the analysts say they're about 30% there, they've hit a plateau. So they need to look at a different way to approach that. And they're realizing that a VMware-based cloud or the specialty cloud providers give a different mode of cloud. Because you had of a pendulum that everything was on-premises, everything swung to cloud first and then it swung to multi-cloud, which meant multiple hyper scale providers and now it's really landing at that equilibrium where you have different modes of cloud. So it's similar like if you want to travel the world, you don't use one mode of transportation to get from one continent to the other. You have to use different modes. Same thing to get all the way to that cloud transformation, you need to use different modes of cloud, an enterprise cloud, a hyper scale cloud, working them together with that common management plan. >> And with that said Brian, where have customer conversations gone in the last couple of years? Obviously this has got to be an executive level, maybe even a board level conversation. Talk to us about how your customer conversations have changed. Have the stakeholders changed? Has things gone up to stack? >> Yeah. The business is much more involved than what it's been in the past and some of the drivers, even through the pandemic, as people reevaluate office space, a lot of times data centers were part of the same building. Or they were added into a review that nobody ever asked, "Well, why are you only using 20% of your data center?" So now that conversation is very active and they're reevaluating that and then the conversation shifts to "Where's the best place?" And that's a lot of, the conference also talks about the best place for your application for the workload in the right location. >> My role here is to dive down into the weeds constantly to stay away from business outcomes and things like that. But somewhere in the middle there's this question of how what you provide is consumed. So fair to assume that often people are moving from CapEx model to an OPEX model where they're consuming by the glass, by the drink. What does that mean organizationally for your customers? And do you help them work through that journey, reorganizing their internal organization to take advantage of cloud? Is that something that Expedient is a part of, or do you have partners that help them through that? How does that work? >> Yeah. There's some unique things that an enterprise doesn't understand when they think about what they've done on-prem versus a service provider is. There's whole models that they can purchase with us in consumption, not just the physical hardware, but licensing as well. Do you want to talk about how clients actually step in and start to do that evaluation? >> Sure. So it really kind of starts on the front end of evaluating what they have. So going through an assessment process, because traditionally, if you have a big data center full of hardware, you've already paid for it. So as you're deploying new workloads, it's "free to deploy." But when you go to that cloud operating model, you're paying for each drink that you're taking. So we want to make sure that as they're going into that cloud operating model, that they are right sized on the front end. They're not over-provisioned on anything that they're going to just waste money and resources on after they make that transition. So it's really about giving them great data on the front end, doing all that collection from a foundational level, from a infrastructure level, but also from a business and IT operations perspective and figuring out where they're spending, not just their money, but also their time and effort and helping them streamline and simplify those IT operations. >> Let's talk about one of the other elephants in the room and that is the remote hybrid workforce. Obviously it's been two and a half years, which is hard to believe. I think I'm one of the only people that hates working from home. Most people, do you too? Okay, good. Thank you, we're normal. >> Absolutely. (Lisa laughing) But VMware was talking about desktop as a service, there was so much change and quick temporary platform set up to accommodate offsite workers during the pandemic. What are some of the experiences that your clients are having and how is Expedient plus VMware helping businesses adapt and really create them the right hybrid model for them going forward? >> Sure. So as part of being that full sack cloud service provider, desktop in that remote user has to be part of that consideration. And one of the biggest things we saw with the pandemic was people stood up what we call pandemic VDI, very temporary solutions. And you saw the news articles that they said, "We did it in 10 days." And how many big transformational events do people plan and execute in 10 days that transform their workforce? So now they're having to come back and say, "Okay, what's the right way to deploy it?" And do you want to talk about some of the specifics of what we're seeing in the adjustments that they're doing? >> Sure. So it is, when you look at it from the end user perspective, it's how they're operating, how they're getting their tools through their day to day job, but it's also the IT administrators that are having to provide that service to the end users. So it's really kind of across the board, it's affecting everyone. So it's really kind of going through and helping them figure out how they're going to support their users going forward. So we've spun up things like VMware desktop as a service providing that multi-tenant ability to consume on a per desktop basis, but then we've also wrapped around with a lot of security features. So one of the big things is as people are going and distributing where they're working from, that data and access to data is also opened up to those locations. So putting those protections in place to be able to protect the environment and then be able, if something does get in, to be able to detect what's going on. And then of course, with a lot of the other components, being able to recover those environments. So building the desktops, the end user access into the disaster recovery plans. >> And talk more, a little bit Brent, about the security aspect. We've seen the threat landscape change dramatically in the last couple of years, ransomware is a household word. I'm pretty sure even my mom knows what that means, to some degree. Where is that in customer conversations? I can imagine in certain industries like financial services and healthcare with PII, it's absolutely critical to ensure that that data is, they know where it is. It's protected and it's recoverable, 'cause everyone's talking about cyber resilience these days. >> Right. And if it's not conversation 1, it's conversation 1A. So it's really kind of core to everything that we do when we're talking to clients. It's whether it's production DR or the desktops, is building that security in place to help them build their security practice up. So when you think about it, it's doing it at layers. So starting with things like more advanced antivirus to see what's actually going on the desktop and then kind of layering above there. So even up to micro-segmentation, where you can envelop each individual desktop in their own quasi network, so that they're only allowed kind of that zero trust model where, Hey, if you can get to a file share, that's the only place you should be going or do I need web apps to get my day to day job done, but really restricting that access and making sure that everything is more good traffic versus unknown traffic. >> Yeah. >> And also on the, you asked about the clouds smarter earlier. And you can really weave the desktop into that because when you're thinking of your production compute environment and your remote desktop environment, and now you can actually share storage together, you can share security together and you start to get economies of scale across those different environments as well. >> So as we are in August, I think still yeah, 2022, barely for a couple more days, lot of change going on at VMware. Expedient has been VMware America's partner of the year before. Talk to us about some of the things that you think from a strategic perspective are next for the partnership. >> That it's definitely the multi-cloud world is here. And it's how we can go deeper, how we're going to see that really mature. You know, one of the things that we've actually done together this year was we worked on a project and evaluated over 30 different companies of what they spend on IT. Everything from the physical data center to the entire stack, to people and actually build a cloud transformation calculator that allows you to compare strategies, so that if you look at Strategy A over a five year period, doing your current transformation, versus that Cloud Different approach, it can actually help quantify the number of hours difference that you can get, the total cost of ownership and the speed that you can get there. So it's things like that that help people make easier decisions and simplify information are going to be part of it. But without a doubt, it's going to be how you can have that wrapper across all of your different environments that really delivers that cloud-like environment that panacea people have been looking for. >> Yeah. That panacea, that seems like it's critical for every organization to achieve. Last question for you. When customers come to you, when they've hit that plateau. They come to Expedient saying, "Guys, with VMware, help us accelerate past this. "We don't have the time, we need to get this done quickly." How do you advise them to move forward? >> Sure. So it goes back to that, what's causing them to hit that plateau? Is it more on the development side of things? Is it the infrastructure teams, not being able to respond fast enough to the developers? And really putting a plan in place to really get rid of those plateaus. It could be getting rid of the technical debt. It could be changing the IT operations and kind of that, the way that they're looking at a cloud transformation model, to help them kind of get accelerated and get them back on the right path. >> Back on the right path. I think we all want to get back on the right path. Guys, thank you so much for joining David and me on theCUBE today, talking about Expedient Cloud Different, what you're seeing in the marketplace, and how Expedient and VMware are helping customers to succeed. We appreciate your time. >> Yep. >> Thanks for having us. >> For our guests and Dave Nicholson, I'm Lisa Martin. You're watching theCUBE live from VMware Explorer '22, stick around, Dave and I will be back shortly with our next guest. (gentle upbeat music)

(contemplative upbeat music) >> Hello and welcome to this CUBE Conversation. I'm John Furrier, your host of theCUBE, here in Palo Alto, California. Got a great topic on expanding capabilities for urgent computing. Dan Stanzione, he's Executive Director of TACC, the Texas Advanced Computing Center, and Rajesh Pohani, VP of PowerEdge, HPC Core Compute at Dell Technologies. Gentlemen, welcome to this CUBE Conversation. >> Thanks, John. >> Thanks, John, good to be here. >> Rajesh, you got a lot of computing in PowerEdge, HPC, Core Computing. I mean, I get a sense that you love compute, so we'll jump right into it. And of course, I got to love TACC, Texas Advanced Computing Center. I can imagine a lot of stuff going on there. Let's start with TACC. What is the Texas Advanced Computing Center? Tell us a little bit about that. >> Yeah, we're part of the University of Texas at Austin here, and we build large-scale supercomputers, data systems, AI systems, to support open science research. And we're mainly funded by the National Science Foundation, so we support research projects in all fields of science, all around the country and around the world. Actually, several thousand projects at the moment. >> But tied to the university, got a lot of gear, got a lot of compute, got a lot of cool stuff going on. What's the coolest thing you got going on right now? >> Well, for me, it's always the next machine, but I think science-wise, it's the machines we have. We just finished deploying Lonestar6, which is our latest supercomputer, in conjunction with Dell. A little over 600 nodes of those PowerEdge servers that Rajesh builds for us. Which makes more than 20,000 that we've had here over the years, of those boxes. But that one just went into production. We're designing new systems for a few years from now, where we'll be even larger. Our Frontera system was top five in the world two years ago, just fell out of the top 10. So we've got to fix that and build the new top-10 system sometime soon. We always have a ton going on in large-scale computing. >> Well, I want to get to the Lonestar6 in a minute, on the next talk track, but... What are some of the areas that you guys are working on that are making an impact? Take us through, and we talked before we came on camera about, obviously, the academic affiliation, but also there's a real societal impact of the work you're doing. What are some of the key areas that the TACC is making an impact? >> So there's really a huge range from new microprocessors, new materials design, photovoltaics, climate modeling, basic science and astrophysics, and quantum mechanics, and things like that. But I think the nearest-term impacts that people see are what we call urgent computing, which is one of the drivers around Lonestar and some other recent expansions that we've done. And that's things like, there's a hurricane coming, exactly where is it going to land? Can we refine the area where there's going to be either high winds or storm surge? Can we assess the damage from digital imagery afterwards? Can we direct first responders in the optimal routes? Similarly for earthquakes, and a lot recently, as you might imagine, around COVID. In 2020, we moved almost a third of our resources to doing COVID work, full-time. >> Rajesh, I want to get your thoughts on this, because Dave Vellante and I have been talking about this on theCUBE recently, a lot. Obviously, people see what cloud's, going on with the cloud technology, but compute and on-premises, private cloud's been growing. If you look at the hyperscale on-premises and the edge, if you include that in, you're seeing a lot more user consumption on-premises, and now, with 5G, you got edge, you mentioned first responders, Dan. This is now pointing to a new architectural shift. As the VP of PowerEdge and HPC and Core Compute, you got to look at this and go, "Hmm." If Compute's going to be everywhere, and in locations, you got to have that compute. How does that all work together? And how do you do advanced computing, when you have these urgent needs, as well as real-time in a new architecture? >> Yeah, John, I mean, it's a pretty interesting time when you think about some of the changing dynamics and how customers are utilizing Compute in the compute needs in the industry. Seeing a couple of big trends. One, the distribution of Compute outside of the data center, 5G is really accelerating that, and then you're generating so much data, whether what you do with it, the insights that come out of it, that we're seeing more and more push to AI, ML, inside the data center. Dan mentioned what he's doing at TACC with computational analysis and some of the work that they're doing. So what you're seeing is, now, this push that data in the data center and what you do with it, while data is being created out at the edge. And it's actually this interesting dichotomy that we're beginning to see. Dan mentioned some of the work that they're doing in medical and on COVID research. Even at Dell, we're making cycles available for COVID research using our Zenith cluster, that's located in our HPC and AI Innovation Lab. And we continue to partner with organizations like TACC and others on research activities to continue to learn about the virus, how it mutates, and then how you treat it. So if you think about all the things, and data that's getting created, you're seeing that distribution and it's really leading to some really cool innovations going forward. >> Yeah, I want to get to that COVID research, but first, you mentioned a few words I want to get out there. You mentioned Lonestar6. Okay, so first, what is Lonestar6, then we'll get into the system aspect of it. Take us through what that definition is, what is Lonestar6? >> Well, as Dan mentioned, Lonestar6 is a Dell technology system that we developed with TACC, it's located at the University of Texas at Austin. It consists of more than 800 Dell PowerEdge 6525 servers that are powered with 3rd Generation AMD EPYC processors. And just to give you an example of the scale of this cluster, it could perform roughly three quadrillion operations per second. That's three petaFLOPS, and to match what Lonestar6 can compute in one second, a person would have to do one calculation every second for a hundred million years. So it's quite a good-size system, and quite a powerful one as well. >> Dan, what's the role that the system plays, you've got petaFLOPS, what, three petaFLOPS, you mentioned? That's a lot of FLOPS! So obviously urgent computing, what's cranking through the system there? Take us through, what's it like? >> Sure, well, there there's a mix of workloads on it, and on all our systems. So there's the urgent computing work, right? Fast turnaround, near real-time, whether it's COVID research, or doing... Project now where we bring in MRI data and are doing sort of patient-specific dosing for radiation treatments and chemotherapy, tailored to your tumor, instead of just the sort of general for people your size. That all requires sort of real-time turnaround. There's a lot AI research going on now, we're incorporating AI in traditional science and engineering research. And that uses an awful lot of data, but also consumes a huge amount of cycles in training those models. And then there's all of our traditional, simulation-based workloads and materials and digital twins for aircraft and aircraft design, and more efficient combustion in more efficient photovoltaic materials, or photovoltaic materials without using as much lead, and things like that. And I'm sure I'm missing dozens of other topics, 'cause, like I said, that one really runs every field of science. We've really focused the Lonestar line of systems, and this is obviously the sixth one we built, around our sort of Texas-centric users. It's the UT Austin users, and then with contributions from Texas A&M , and Texas Tech and the University of Texas system, MD Anderson Healthcare Center, the University of North Texas. So users all around the state, and every research problem that you might imagine, those are into. We're just ramping up a project in disaster information systems, that's looking at the probabilities of flooding in coastal Texas and doing... Can we make building code changes to mitigate impact? Do we have to change the standard foundation heights for new construction, to mitigate the increasing storm surges from these sort of slow storms that sit there and rain, like hurricanes didn't used to, but seem to be doing more and more. All those problems will run on Lonestar, and on all the systems to come, yeah. >> It's interesting, you mentioned urgent computing, I love that term because it could be an event, it could be some slow kind of brewing event like that rain example you mentioned. It could also be, obviously, with the healthcare, and you mentioned COVID earlier. These are urgent, societal challenges, and having that available, the processing capability, the compute, the data. You mentioned digital twins. I can imagine all this new goodness coming from that. Compare that, where we were 10 years ago. I mean, just from a mind-blowing standpoint, you have, have come so far, take us through, try to give a context to the level of where we are now, to do this kind of work, and where we were years ago. Can you give us a feel for that? >> Sure, there's a lot of ways to look at that, and how the technology's changed, how we operate around those things, and then sort of what our capabilities are. I think one of the big, first, urgent computing things for us, where we sort of realized we had to adapt to this model of computing was about 15 years ago with the big BP Gulf Oil spill. And suddenly, we were dumping thousands of processors of load to figure out where that oil spill was going to go, and how to do mitigation, and what the potential impacts were, and where you need to put your containment, and things like that. And it was, well, at that point we thought of it as sort of a rare event. There was another one, that I think was the first real urgent computing one, where the space shuttle was in orbit, and they knew something had hit it during takeoff. And we were modeling, along with NASA and a bunch of supercomputers around the world, the heat shield and could they make reentry safely? You have until they come back to get that problem done, you don't have months or years to really investigate that. And so, what we've sort of learned through some of those, the Japanese tsunami was another one, there have been so many over the years, is that one, these sort of disasters are all the time, right? One thing or another, right? If we're not doing hurricanes, we're doing wildfires and drought threat, if it's not COVID. We got good and ready for COVID through SARS and through the swine flu and through HIV work, and things like that. So it's that we can do the computing very fast, but you need to know how to do the work, right? So we've spent a lot of time, not only being able to deliver the computing quickly, but having the data in place, and having the code in place, and having people who know the methods who know how to use big computers, right? That's been a lot of what the COVID Consortium, the White House COVID Consortium, has been about over the last few years. And we're actually trying to modify that nationally into a strategic computing reserve, where we're ready to go after these problems, where we've run drills, right? And if there's a, there's a train that derails, and there's a chemical spill, and it's near a major city, we have the tools and the data in place to do wind modeling, and we have the terrain ready to go. And all those sorts of things that you need to have to be ready. So we've really sort of changed our sort of preparedness and operational model around urgent computing in the last 10 years. Also, just the way we scheduled the system, the ability to sort of segregate between these long-running workflows for things that are really important, like we displaced a lot of cancer research to do COVID research. And cancer's still important, but it's less likely that we're going to make an impact in the next two months, right? So we have to shuffle how we operate things and then just, having all that additional capacity. And I think one of the things that's really changed in the models is our ability to use AI, to sort of adroitly steer our simulations, or prune the space when we're searching parameters for simulations. So we have the operational changes, the system changes, and then things like adding AI on the scientific side, since we have the capacity to do that kind of things now, all feed into our sort of preparedness for this kind of stuff. >> Dan, you got me sold, I want to come work with you. Come on, can I join the team over there? It sounds exciting. >> Come on down! We always need good folks around here, so. (laughs) >> Rajesh, when I- >> Almost 200 now, and we're always growing. >> Rajesh, when I hear the stories about kind of the evolution, kind of where the state of the art is, you almost see the innovation trajectory, right? The growth and the learning, adding machine learning only extends out more capabilities. But also, Dan's kind of pointing out this kind of response, rapid compute engine, that they could actually deploy with learnings, and then software, so is this a model where anyone can call up and get some cycles to, say, power an autonomous vehicle, or, hey, I want to point the machinery and the cycles at something? Is the service, do you guys see this going that direction, or... Because this sounds really, really good. >> Yeah, I mean, one thing that Dan talked about was, it's not just the compute, it's also having the right algorithms, the software, the code, right? The ability to learn. So I think when those are set up, yeah. I mean, the ability to digitally simulate in any number of industries and areas, advances the pace of innovation, reduces the time to market of whatever a customer is trying to do or research, or even vaccines or other healthcare things. If you can reduce that time through the leverage of compute on doing digital simulations, it just makes things better for society or for whatever it is that we're trying to do, in a particular industry. >> I think the idea of instrumenting stuff is here forever, and also simulations, whether it's digital twins, and doing these kinds of real-time models. Isn't really much of a guess, so I think this is a huge, historic moment. But you guys are pushing the envelope here, at University of Texas and at TACC. It's not just research, you guys got real examples. So where do you guys see this going next? I see space, big compute areas that might need some data to be cranked out. You got cybersecurity, you got healthcare, you mentioned oil spill, you got oil and gas, I mean, you got industry, you got climate change. I mean, there's so much to tackle. What's next? >> Absolutely, and I think, the appetite for computing cycles isn't going anywhere, right? And it's only going to, it's going to grow without bound, essentially. And AI, while in some ways it reduces the amount of computing we do, it's also brought this whole new domain of modeling to a bunch of fields that weren't traditionally computational, right? We used to just do engineering, physics, chemistry, were all super computational, but then we got into genome sequencers and imaging and a whole bunch of data, and that made biology computational. And with AI, now we're making things like the behavior of human society and things, computational problems, right? So there's this sort of growing amount of workload that is, in one way or another, computational, and getting bigger and bigger. So that's going to keep on growing. I think the trick is not only going to be growing the computation, but growing the software and the people along with it, because we have amazing capabilities that we can bring to bear. We don't have enough people to hit all of them at once. And so, that's probably going to be the next frontier in growing out both our AI and simulation capability, is the human element of it. >> It's interesting, when you think about society, right? If the things become too predictable, what does a democracy even look like? If you know the election's going to be over two years from now in the United States, or you look at these major, major waves >> Human companies don't know. >> of innovation, you say, "Hmm." So it's democracy, AI, maybe there's an algorithm for checking up on the AI 'cause biases... So, again, there's so many use cases that just come out of this. It's incredible. >> Yeah, and bias in AI is something that we worry about and we work on, and on task forces where we're working on that particular problem, because the AI is going to take... Is based on... Especially when you look at a deep learning model, it's 100% a product of the data you show it, right? So if you show it a biased data set, it's going to have biased results. And it's not anything intrinsic about the computer or the personality, the AI, it's just data mining, right? In essence, right, it's learning from data. And if you show it all images of one particular outcome, it's going to assume that's always the outcome, right? It just has no choice, but to see that. So how we deal with bias, how do we deal with confirmation, right? I mean, in addition, you have to recognize, if you haven't, if it gets data it's never seen before, how do you know it's not wrong, right? So there's about data quality and quality assurance and quality checking around AI. And that's where, especially in scientific research, we use what's starting to be called things like physics-informed or physics-constrained AI, where the neural net that you're using to design an aircraft still has to follow basic physical laws in its output, right? Or if you're doing some materials or astrophysics, you still have to obey conservation of mass, right? So I can't say, well, if you just apply negative mass on this other side and positive mass on this side, everything works out right for stable flight. 'Cause we can't do negative mass, right? So you have to constrain it in the real world. So this notion of how we bring in the laws of physics and constrain your AI to what's possible is also a big part of the sort of AI research going forward. >> You know, Dan, you just, to me just encapsulate the science that's still out there, that's needed. Computer science, social science, material science, kind of all converging right now. >> Yeah, engineering, yeah, >> Engineering, science, >> slipstreams, >> it's all there, >> physics, yeah, mmhmm. >> it's not just code. And, Rajesh, data. You mentioned data, the more data you have, the better the AI. We have a world what's going from silos to open control planes. We have to get to a world. This is a cultural shift we're seeing, what's your thoughts? >> Well, it is, in that, the ability to drive predictive analysis based on the data is going to drive different behaviors, right? Different social behaviors for cultural impacts. But I think the point that Dan made about bias, right, it's only as good as the code that's written and the way that the data is actually brought into the system. So making sure that that is done in a way that generates the right kind of outcome, that allows you to use that in a predictive manner, becomes critically important. If it is biased, you're going to lose credibility in a lot of that analysis that comes out of it. So I think that becomes critically important, but overall, I mean, if you think about the way compute is, it's becoming pervasive. It's not just in selected industries as damage, and it's now applying to everything that you do, right? Whether it is getting you more tailored recommendations for your purchasing, right? You have better options that way. You don't have to sift through a lot of different ideas that, as you scroll online. It's tailoring now to some of your habits and what you're looking for. So that becomes an incredible time-saver for people to be able to get what they want in a way that they want it. And then you look at the way it impacts other industries and development innovation, and it just continues to scale and scale and scale. >> Well, I think the work that you guys are doing together is scratching the surface of the future, which is digital business. It's about data, it's about out all these new things. It's about advanced computing meets the right algorithms for the right purpose. And it's a really amazing operation you guys got over there. Dan, great to hear the stories. It's very provocative, very enticing to just want to jump in and hang out. But I got to do theCUBE day job here, but congratulations on success. Rajesh, great to see you and thanks for coming on theCUBE. >> Thanks for having us, John. >> Okay. >> Thanks very much. >> Great conversation around urgent computing, as computing becomes so much more important, bigger problems and opportunities are around the corner. And this is theCUBE, we're documenting it all here. I'm John Furrier, your host. Thanks for watching. (contemplative music)

>> From around the globe, it's theCUBE, with coverage of KubeCon and CloudNativeCon Europe 2021 Virtual. Brought to you by Red Hat, the Cloud Native Computing Foundation and ecosystem partners. >> Hello, welcome back to theCUBE's coverage of KubeCon 2021 CloudNativeCon Europe Virtual, I'm John Furrier your host, preview with Brian Gracely from Red Hat Senior Director Product Strategy Cloud Business Unit Brian Gracely great to see you. Former CUBE host CUBE alumni, big time strategist at Red Hat, great to see you, always great. And also the founder of Cloudcast which is an amazing podcast on cloud, part of the cloud (indistinct), great to see you Brian. Hope's all well. >> Great to see you too, you know for years, theCUBE was always sort of the ESPN of tech, I feel like, you know ESPN has become nothing but highlights. This is where all the good conversation is. It's theCUBE has become sort of the the clubhouse of tech, if you will. I know that's that's an area you're focused on, so yeah I'm excited to be back on and good to talk to you. >> It's funny you know, with all the events going away loved going out extracting the signal from the noise, you know, game day kind of vibe. CUBE Virtual has really expanded, so it's been so much more fun because we can get more people easy to dial in. So we're going to keep that feature post COVID. You're going to hear more about theCUBE Virtual hybrid events are going to be a big part of it, which is great because as you know and we've talked about communities and ecosystems are huge advantage right now it's been a big part of the Red Hat story. Now part of IBM bringing that mojo to the table the role of ecosystems with hybrid cloud is so critical. Can you share your thoughts on this? Because I know you study it, you have podcasts you've had one for many years, you understand that democratization and this new direct to audience kind of concept. Share your thoughts on this new ecosystem. >> Yeah, I think so, you know, we're sort of putting this in the context of what we all sort of familiarly call KubeCon but you know, if we think about it, it started as KubeCon it was sort of about this one technology but it's always been CloudNativeCon and we've sort of downplayed the cloud native part of it. But even if we think about it now, you know Kubernetes to a certain extent has kind of, you know there's this feeling around the community that, that piece of the puzzle is kind of boring. You know, it's 21 releases in, and there's lots of different offerings that you can get access to. There's still, you know, a lot of innovation but the rest of the ecosystem has just exploded. So it's, you know, there are ecosystem partners and companies that are working on edge and miniaturization. You know, we're seeing things like Kubernetes now getting into outer space and it's in the space station. We're seeing, you know, Linux get on Mars. But we're also seeing, you know, stuff on the other side of the spectrum. We're sort of seeing, you know awesome people doing database work and streaming and AI and ML on top of Kubernetes. So, you know, the ecosystem is doing what you'd expect it to do once one part of it gets stable. The innovation sort of builds on top of it. And, you know, even though we're virtual, we're still seeing just tons and tons of contributions, different companies different people stepping up and leading. So it's been really cool to watch the last few years. >> Yes, interesting point about the CloudNativeCon. That's an interesting insight, and I totally agree with you. And I think it's worth double clicking on. Let me just ask you, because when you look at like, say Kubernetes, okay, it's enabled a lot. Okay, it's been called the dial tone of Cloud native. I think Pat Gelsinger of VMware used that term. We call it the kind of the interoperability layer it enables more large scale deployments. So you're seeing a lot more Kubernetes enablement on clusters. Which is causing more hybrid cloud which means more Cloud native. So it actually is creating a network effect in and of itself with more Cloud native components and it's changing the development cycle. So the question I want to ask you is one how does a customer deal with that? Because people are saying, I like hybrid. I agree, Multicloud is coming around the corner. And of course, Multicloud is just a subsystem of resource underneath hybrid. How do I connect it all? Now I have multiple vendors, I have multiple clusters. I'm cross-cloud, I'm connecting multiple clouds multiple services, Kubernetes clusters, some get stood up some gets to down, it's very dynamic. >> Yeah, it's very dynamic. It's actually, you know, just coincidentally, you know, our lead architect, a guy named Clayton Coleman, who was one of the Kubernetes founders, is going to give a talk on sort of Kubernetes is this hybrid control plane. So we're already starting to see the tentacles come out of it. So you know how we do cross cloud networking how we do cross cloud provisioning of services. So like, how do I go discover what's in other clouds? You know and I think like you said, it took people a few years to figure out, like how do I use this new thing, this Kubernetes thing. How do I harness it. And, but the demand has since become "I have to do multi-cloud." And that means, you know, hey our company acquires companies, so you know, we don't necessarily know where that next company we acquire is going to run. Are they going to run on AWS? Are they going to, you know, run on Azure I've got to be able to run in multiple places. You know, we're seeing banking industries say, "hey, look cloud's now a viable target for you to put your applications, but you have to treat multiple clouds as if they're your backup domains." And so we're, you know, we're seeing both, you know the way business operates whether it's acquisitions or new things driving it. We're seeing regulations driving hybrid and multi-cloud and, even you know, even if the stalwart were to you know, set for a long time, well the world's only going to be public cloud and sort of you know, legacy data centers even those folks are now coming around to "I've got to bring hybrid to, to these places." So it's been more than just technology. It's been, you know, industries pushing it regulations pushing it, a lot of stuff. So, but like I said, we're going to be talking about kind of our future, our vision on that, our future on that. And, you know Red Hat everything we end up doing is a community activity. So we expect a lot of people will get on board with it >> You know, for all the old timers out there they can relate to this. But I remember in the 80's the OSI Open Systems Interconnect, and I was chatting with Paul Cormier about this because we were kind of grew up through that generation. That disrupted network protocols that were proprietary and that opened the door for massive, massive growth massive innovation around just getting that interoperability with TCP/IP, and then everything else happened. So Kubernetes does that, that's a phenomenal impact. So Cloud native to me is at that stage where it's totally next-gen and it's happening really fast. And a lot of people getting caught off guard, Brian. So you know, I got to to ask you as a product strategist, what's your, how would you give them the navigation of where that North star is? If I'm a customer, okay, I got to figure out where I got to navigate now. I know it's super volatile, changing super fast. What's your advice? >> I think it's a couple of pieces, you know we're seeing more and more that, you know, the technology decisions don't get driven out of sort of central IT as much anymore right? We sort of talk all the time that every business opportunity, every business project has a technology component to it. And I think what we're seeing is the companies that tend to be successful with it have built up the muscle, built up the skill set to say, okay, when this line of business says, I need to do something new and innovative I've got the capabilities to sort of stand behind that. They're not out trying to learn it new they're not chasing it. So that's a big piece of it, is letting the business drive your technology decisions as opposed to what happened for a long time which was we built out technology, we hope they would come. You know, the other piece of it is I think because we're seeing so much push from different directions. So we're seeing, you know people put technology out at the edge. We're able to do some, you know unique scalable things, you know in the cloud and so forth That, you know more and more companies are having to say, "hey, look, I'm not, I'm not in the pharmaceutical business. I'm not in the automotive business, I'm in software." And so, you know the companies that realize that faster, and then, you know once they sort of come to those realizations they realize, that's my new normal, those are the ones that are investing in software skills. And they're not afraid to say, look, you know even if my existing staff is, you know, 30 years of sort of history, I'm not afraid to bring in some folks that that'll break a few eggs and, you know, and use them as a lighthouse within their organization to retrain and sort of reset, you know, what's possible. So it's the business doesn't move. That's the the thing that drives all of them. And it's, if you embrace it, we see a lot of success. It's the ones that, that push back on it really hard. And, you know the market tends to sort of push back on them as well. >> Well we're previewing KubeCon CloudNativeCon. We'll amplify that it's CloudNativeCon as well. You guys bought StackRox, okay, so interesting company, not an open source company they have soon to be, I'm assuring, but Advanced Cluster Security, ACS, as it's known it's really been a key part of Red Hat. Can you give us the strategy behind that deal? What does that product, how does it fit in that's a lot of people are really talking about this acquisition. >> Yeah so here's the way we looked at it, is we've learned a couple of things over the last say five years that we've been really head down in Kubernetes, right? One is, we've always embedded a lot of security capabilities in the platform. So OpenShift being our core Kubernetes platform. And then what's happened over time is customers have said to us, "that's great, you've made the platform very secure" but the reality is, you know, our software supply chain. So the way that we build applications that, you know we need to secure that better. We need to deal with these more dynamic environments. And then once the applications are deployed they interact with various types of networks. I need to better secure those environments too. So we realized that we needed to expand our functionality beyond the core platform of OpenShift. And then the second thing that we've learned over the last number of years is to be successful in this space, it's really hard to take technology that wasn't designed for containers, or it wasn't designed for Kubernetes and kind of retrofit it back into that. And so when we were looking at potential acquisition targets, we really narrowed down to companies whose fundamental technologies were you know, Kubernetes-centric, you know having had to modify something to get to Kubernetes, and StackRox was really the leader in that space. They really, you know have been the leader in enterprise Kubernetes security. And the great thing about them was, you know not only did they have this Kubernetes expertise but on top of that, probably half of their customers were already OpenShift customers. And about 3/4 of their customers were using you know, native Kubernetes services and other clouds. So, you know, when we went and talked to them and said, "Hey we believe in Kubernetes, we believe in multi-cloud. We believe in open source," they said, "yeah, those are all the foundational things for us." And to your point about it, you know, maybe not being an open source company, they actually had a number of sort of ancillary projects that were open source. So they weren't unfamiliar to it. And then now that the acquisition's closed, we will do what we do with every piece of Red Hat technology. We'll make sure that within a reasonable period of time that it's made open source. And so you know, it's good for the community. It allows them to keep focusing on their innovation. >> Yeah you've got to get that code out there cool. Brian, I'm hearing about Platform Plus what is that about? Take us through that. >> Yeah, so you know, one of the things that our customers, you know, have come to us over time is it's you know, it's like, I've been saying kind of throughout this discussion, right? Kubernetes is foundational, but it's become pretty stable. The things that people are solving for now are like, you highlighted lots and lots of clusters, they're all over the place. That was something that our advanced cluster management capabilities were able to solve for people. Once you start getting into lots of places you've got to be able to secure things everywhere you go. And so OpenShift for us really allows us to bundle together, you know, sort of the complete set of the portfolio. So the platform, security management, and it also gives us the foundational pieces or it allows our customers to buy the foundational pieces that are going to help them do multi and hybrid cloud. And, you know, when we bundle that we can save them probably 25% in terms of sort of product acquisition. And then obviously the integration work we do you know, saves a ton on the operational side. So it's a new way for us to, to not only bundle the platform and the technologies but it gets customers in a mindset that says, "hey we've moved past sort of single environments to hybrid and multi-cloud environments. >> Awesome, well thanks for the update on that, appreciate it. One of the things going into KubeCon, and that we're watching closely is this Cloud native developer action. Certainly end users want to get that in a separate section with you but the end user contribution, which is like exploding. But on the developer side there's a real trend towards adding stronger consistency programmability support for more use cases okay. Where it's becoming more of a data platform as a requirement. >> Brian: Right. >> So how, so that's a trend so I'm kind of thinking, there's no disagreement on that. >> Brian: No, absolutely. >> What does that mean? Like I'm a customer, that sounds good. How do I make that happen? 'Cause that's the critical discussion right now in the DevOps, DevSecOps day, two operations. What you want to call it. This is the number one concern for developers and that solution architect, consistency, programmability more use cases with data as a platform. >> Yeah, I think, you know the way I kind of frame this up was you know, for any for any organization, the last thing you want to to do is sort of keep investing in lots of platforms, right? So platforms are great on their surface but once you're having to manage five and six and, you know 10 or however many you're managing, the economies of scale go away. And so what's been really interesting to watch with Kubernetes is, you know when we first got started everything was Cloud native application but that really was sort of, you know shorthand for stateless applications. We quickly saw a move to, you know, people that said, "Hey I can modernize something, you know, a Stateful application and we add that into Kubernetes, right? The community added the ability to do Stateful applications and that got people a certain amount of the way. And they sort of started saying, okay maybe Kubernetes can help me peel off some things of an existing platform. So I can peel off, you know Java workloads or I can peel off, what's been this explosion is the data community, if you will. So, you know, the TensorFlows the PItorches, you know, the Apache community with things like Couchbase and Kafka, TensorFlow, all these things that, you know maybe in the past didn't necessarily, had their own sort of underlying system are now defaulting to Kubernetes. And what we see because of that is, you know people now can say, okay, these data workloads these AI and ML workloads are so important to my business, right? Like I can directly point to cost savings. I can point to, you know, driving innovation and because Kubernetes is now their default sort of way of running, you know we're seeing just sort of what used to be, you know small islands of clusters become these enormous footprints whether they're in the cloud or in their data center. And that's almost become, you know, the most prevalent most widely used use case. And again, it makes total sense. It's exactly the trends that we've seen in our industry, even before Kubernetes. And now people are saying, okay, I can consolidate a lot of stuff on Kubernetes. I can get away from all those silos. So, you know, that's been a huge thing over the last probably year plus. And the cool thing is we've also seen, you know the hardware vendors. So whether it's Intel or Nvidia, especially around GPUs, really getting on board and trying to make that simpler. So it's not just the software ecosystem. It's also the hardware ecosystem, really getting on board. >> Awesome, Brian let me get your thoughts on the cloud versus the power dynamics between the cloud players and the open source software vendors. So what's the Red Hat relationship with the cloud players with the hybrid architecture, 'cause you want to set up the modern day developer environment, we get that right. And it's hybrid, what's the relationship with the cloud players? >> You know, I think so we we've always had two philosophies that haven't really changed. One is, we believe in open source and open licensing. So you haven't seen us look at the cloud as, a competitive threat, right? We didn't want to make our business, and the way we compete in business, you know change our philosophy in software. So we've always sort of maintained open licenses permissive licenses, but the second piece is you know, we've looked at the cloud providers as very much partners. And mostly because our customers look at them as partners. So, you know, if Delta Airlines or Deutsche Bank or somebody says, "hey that cloud provider is going to be our partner and we want you to be part of that journey, we need to be partners with that cloud as well." And you've seen that sort of manifest itself in terms of, you know, we haven't gone and set up new SaaS offerings that are Red Hat offerings. We've actually taken a different approach than a lot of the open source companies. And we've said we're going to embed our capabilities, especially, you know OpenShift into AWS, into Azure into IBM cloud working with Google cloud. So we'd look at them very much as a partner. I think it aligns to how Red Hat's done things in the past. And you know, we think, you know even though it maybe easy to sort of see a way of monetizing things you know, changing licensing, we've always found that, you've got to allow the ecosystem to compete. You've got to allow customers to go where they want to go. And we try and be there in the most consumable way possible. So that's worked out really well for us. >> So I got to bring up the end user participation component. That's a big theme here at KubeCon going into it and around the event is, and we've seen this trend happen. I mean, Envoy, Lyft the laying examples are out there. But they're more end-use enterprises coming in. So the enterprise class I call classic enterprise end user participation is at an all time high in opensource. You guys have the biggest portfolio of enterprises in the business. What's the trend that you're seeing because it used to be limited to the hyperscalers the Lyfts and the Facebooks and the big guys. Now you have, you know enterprises coming in the business model is working, can you just share your thoughts on CloudNativeCons participation for end users? >> Yeah, I think we're definitely seeing a blurring of lines between what used to be the Silicon Valley companies were the ones that would create innovation. So like you mentioned Lyft, or, you know LinkedIn doing Kafka or Twitter doing you know, whatever. But as we've seen more and more especially enterprises look at themselves as software companies right. So, you know if you talk about, you know, Ford or Volkswagen they think of themselves as a software company, almost more than they think about themselves as a car company, right. They're a sort of mobile transportation company you know, something like that. And so they look at themselves as I've got to I've got to have software as an expertise. I've got to compete for the best talent, no matter where that talent is, right? So it doesn't have to be in Detroit or in Germany or wherever I can go get that anywhere. And I think what they really, they look for us to do is you know, they've got great technology chops but they don't always understand kind of the the nuances and the dynamics of open-source right. They're used to having their own proprietary internal stuff. And so a lot of times they'll come to us, not you know, "Hey how do we work with the project?" But you know like here's new technology. But they'll come to us and they'll say "how do we be good, good stewards in this community? How do we make sure that we can set up our own internal open source office and have that group, work with communities?" And so the dynamics have really changed. I think a lot of them have, you know they've looked at Silicon Valley for years and now they're modeling it, but it's, you know, for us it's great because now we're talking the same language, you know we're able to share sort of experiences we're able to share best practices. So it is really, really interesting in terms of, you know, how far that whole sort of software is eating the world thing is materialized in sort of every industry. >> Yeah and it's the workloads of expanding Cloud native everywhere edge is blowing up big time. Brian, final question for you before we break. >> You bet. >> Thanks for coming on and always great to chat with you. It's always riffing and getting the data out too. What's your expectation for KubeCon CloudNativeCon this year? What are you expecting to see? What highlights do you expect will come out of CloudNativeCon KubeCon this year? >> Yeah, I think, you know like I said, I think it's going to be much more on the Cloud native side, you know we're seeing a ton of new communities come out. I think that's going to be the big headline is the number of new communities that are, you know have sort of built up a following. So whether it's Crossplane or whether it's, you know get-ops or whether it's, you know expanding around the work that's going on in operators we're going to see a whole bunch of projects around, you know, developer sort of frameworks and developer experience and so forth. So I think the big thing we're going to see is sort of this next stage of, you know a thousand flowers are blooming and we're going to see probably a half dozen or so new communities come out of this one really strong and you know the trends around those are going to accelerate. So I think that'll probably be the biggest takeaway. And then I think just the fact that the community is going to come out stronger after the pandemic than maybe it did before, because we're learning you know, new ways to work remotely, and that, that brings in a ton of new companies and contributors. So I think those two big things will be the headlines. And, you know, the state of the community is strong as they, as they like to say >> Yeah, love the ecosystem, I think the values are going to be network effect, ecosystems, integration standards evolving very quickly out in the open. Great to see Brian Gracely Senior Director Product Strategy at Red Hat for the cloud business unit, also podcasts are over a million episode downloads for the cloud cast podcast, thecloudcast.net. What's it Brian, what's the stats now. >> Yeah, I think we've, we've done over 500 shows. We're you know, about a million and a half listeners a year. So it's, you know again, it's great to have community followings and, you know, and meet people from around the world. So, you know, so many of these things intersect it's a real pleasure to work with everybody >> You're going to create a culture, well done. We're all been there, done that great job. >> Thank you >> Check out the cloud cast, of course, Red Hat's got the great OpenShift mojo going on into KubeCon. Brian, thanks for coming on. >> Thanks John. >> Okay so CUBE coverage of KubeCon, CloudNativeCon Europe 2021 Virtual, I'm John Furrier with theCUBE virtual. Thanks for watching. (upbeat music)

>>C G technology is focused on data where we have long believed that data is in our DNA. We help maximize humanity's potential by delivering world class, precision engineered data solutions developed through sustainable and profitable partnerships. Included in our offerings are hard disk drives. As I'm sure many of you know, ah, hard drive consists of a slider also known as a drive head or transducer attached to a head gimbal assembly. I had stack assembly made up of multiple head gimbal assemblies and a drive enclosure with one or more platters, or just that the head stacked assembles into. And while the concept hasn't changed, hard drive technology has progressed well beyond the initial five megabytes, 500 quarter inch drives that Seagate first produced. And, I think 1983. We have just announced in 18 terabytes 3.5 inch drive with nine flatters on a single head stack assembly with dual head stack assemblies this calendar year, the complexity of these drives further than need to incorporate Edge analytics at operation sites, so G Edward stemming established the concept of continual improvement and everything that we do, especially in product development and operations and at the end of World War Two, he embarked on a mission with support from the US government to help Japan recover from its four time losses. He established the concept of continual improvement and statistical process control to the leaders of prominent organizations within Japan. And because of this, he was honored by the Japanese emperor with the second order of the sacred treasure for his teachings, the only non Japanese to receive this honor in hundreds of years. Japan's quality control is now world famous, as many of you may know, and based on my own experience and product development, it is clear that they made a major impact on Japan's recovery after the war at Sea Gate. The work that we've been doing and adopting new technologies has been our mantra at continual improvement. As part of this effort, we embarked on the adoption of new technologies in our global operations, which includes establishing machine learning and artificial intelligence at the edge and in doing so, continue to adopt our technical capabilities within data science and data engineering. >>So I'm a principal engineer and member of the Operations and Technology Advanced Analytics Group. We are a service organization for those organizations who need to make sense of the data that they have and in doing so, perhaps introduce a different way to create an analyzed new data. Making sense of the data that organizations have is a key aspect of the work that data scientist and engineers do. So I'm a project manager for an initiative adopting artificial intelligence methodologies for C Gate manufacturing, which is the reason why I'm talking to you today. I thought I'd start by first talking about what we do at Sea Gate and follow that with a brief on artificial intelligence and its role in manufacturing. And I'd like them to discuss how AI and machine Learning is being used at Sea Gate in developing Edge analytics, where Dr Enterprise and Cooper Netease automates deployment, scaling and management of container raised applications. So finally, I like to discuss where we are headed with this initiative and where Mirant is has a major role in case some of you are not conversant in machine learning, artificial intelligence and difference outside some definitions. To cite one source, machine learning is the scientific study of algorithms and statistical bottles without computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference Instead, thus, being seen as a subset of narrow artificial intelligence were analytics and decision making take place. The intent of machine learning is to use basic algorithms to perform different functions, such as classify images to type classified emails into spam and not spam, and predict weather. The idea and this is where the concept of narrow artificial intelligence comes in, is to make decisions of a preset type basically let a machine learn from itself. These types of machine learning includes supervised learning, unsupervised learning and reinforcement learning and in supervised learning. The system learns from previous examples that are provided, such as images of dogs that are labeled by type in unsupervised learning. The algorithms are left to themselves to find answers. For example, a Siris of images of dogs can be used to group them into categories by association that's color, length of coat, length of snout and so on. So in the last slide, I mentioned narrow a I a few times, and to explain it is common to describe in terms of two categories general and narrow or weak. So Many of us were first exposed to General Ai in popular science fiction movies like 2000 and One, A Space Odyssey and Terminator General Ai is a I that can successfully perform any intellectual task that a human can. And if you ask you Lawn Musk or Stephen Hawking, this is how they view the future with General Ai. If we're not careful on how it is implemented, so most of us hope that is more like this is friendly and helpful. Um, like Wally. The reality is that machines today are not only capable of weak or narrow, a I AI that is focused on a narrow, specific task like understanding, speech or finding objects and images. Alexa and Google Home are becoming very popular, and they can be found in many homes. Their narrow task is to recognize human speech and answer limited questions or perform simple tasks like raising the temperature in your home or ordering a pizza as long as you have already defined the order. Narrow. AI is also very useful for recognizing objects in images and even counting people as they go in and out of stores. As you can see in this example, so artificial intelligence supplies, machine learning analytics inference and other techniques which can be used to solve actual problems. The two examples here particle detection, an image anomaly detection have the potential to adopt edge analytics during the manufacturing process. Ah, common problem in clean rooms is spikes in particle count from particle detectors. With this application, we can provide context to particle events by monitoring the area around the machine and detecting when foreign objects like gloves enter areas where they should not. Image Anomaly detection historically has been accomplished at sea gate by operators in clean rooms, viewing each image one at a time for anomalies, creating models of various anomalies through machine learning. Methodologies can be used to run comparative analyses in a production environment where outliers can be detected through influence in an automated real Time analytics scenario. So anomaly detection is also frequently used in machine learning to find patterns or unusual events in our data. How do you know what you don't know? It's really what you ask, and the first step in anomaly detection is to use an algorithm to find patterns or relationships in your data. In this case, we're looking at hundreds of variables and finding relationships between them. We can then look at a subset of variables and determine how they are behaving in relation to each other. We use this baseline to define normal behavior and generate a model of it. In this case, we're building a model with three variables. We can then run this model against new data. Observations that do not fit in the model are defined as anomalies, and anomalies can be good or bad. It takes a subject matter expert to determine how to classify the anomalies on classify classification could be scrapped or okay to use. For example, the subject matter expert is assisting the machine to learn the rules. We then update the model with the classifications anomalies and start running again, and we can see that there are few that generate these models. Now. Secret factories generate hundreds of thousands of images every day. Many of these require human toe, look at them and make a decision. This is dull and steak prone work that is ideal for artificial intelligence. The initiative that I am project managing is intended to offer a solution that matches the continual increased complexity of the products we manufacture and that minimizes the need for manual inspection. The Edge Rx Smart manufacturing reference architecture er, is the initiative both how meat and I are working on and sorry to say that Hamid isn't here today. But as I said, you may have guessed. Our goal is to introduce early defect detection in every stage of our manufacturing process through a machine learning and real time analytics through inference. And in doing so, we will improve overall product quality, enjoy higher yields with lesser defects and produce higher Ma Jin's. Because this was entirely new. We established partnerships with H B within video and with Docker and Amaranthus two years ago to develop the capability that we now have as we deploy edge Rx to our operation sites in four continents from a hardware. Since H P. E. And in video has been an able partner in helping us develop an architecture that we have standardized on and on the software stack side doctor has been instrumental in helping us manage a very complex project with a steep learning curve for all concerned. To further clarify efforts to enable more a i N M l in factories. Theobald active was to determine an economical edge Compute that would access the latest AI NML technology using a standardized platform across all factories. This objective included providing an upgrade path that scales while minimizing disruption to existing factory systems and burden on factory information systems. Resource is the two parts to the compute solution are shown in the diagram, and the gateway device connects to see gates, existing factory information systems, architecture ER and does inference calculations. The second part is a training device for creating and updating models. All factories will need the Gateway device and the Compute Cluster on site, and to this day it remains to be seen if the training devices needed in other locations. But we do know that one devices capable of supporting multiple factories simultaneously there are also options for training on cloud based Resource is the stream storing appliance consists of a kubernetes cluster with GPU and CPU worker notes, as well as master notes and docker trusted registries. The GPU nodes are hardware based using H B E l 4000 edge lines, the balance our virtual machines and for machine learning. We've standardized on both the H B E. Apollo 6500 and the NVIDIA G X one, each with eight in video V 100 GP use. And, incidentally, the same technology enables augmented and virtual reality. Hardware is only one part of the equation. Our software stack consists of Docker Enterprise and Cooper Netease. As I mentioned previously, we've deployed these clusters at all of our operations sites with specific use. Case is planned for each site. Moran Tous has had a major impact on our ability to develop this capability by offering a stable platform in universal control plane that provides us, with the necessary metrics to determine the health of the Kubernetes cluster and the use of Dr Trusted Registry to maintain a secure repository for containers. And they have been an exceptional partner in our efforts to deploy clusters at multiple sites. At this point in our deployment efforts, we are on prem, but we are exploring cloud service options that include Miranda's next generation Docker enterprise offering that includes stack light in conjunction with multi cluster management. And to me, the concept of federation of multi cluster management is a requirement in our case because of the global nature of our business where our operation sites are on four continents. So Stack Light provides the hook of each cluster that banks multi cluster management and effective solution. Open source has been a major part of Project Athena, and there has been a debate about using Dr CE versus Dr Enterprise. And that decision was actually easy, given the advantages that Dr Enterprise would offer, especially during a nearly phase of development. Cooper Netease was a natural addition to the software stack and has been widely accepted. But we have also been a work to adopt such open source as rabbit and to messaging tensorflow and tensor rt, to name three good lab for developments and a number of others. As you see here, is well, and most of our programming programming has been in python. The results of our efforts so far have been excellent. We are seeing a six month return on investment from just one of seven clusters where the hardware and software cost approached close to $1 million. The performance on this cluster is now over three million images processed per day for their adoption has been growing, but the biggest challenge we've seen has been handling a steep learning curve. Installing and maintaining complex Cooper needs clusters in data centers that are not used to managing the unique aspect of clusters like this. And because of this, we have been considering adopting a control plane in the cloud with Kubernetes as the service supported by Miranda's. Even without considering, Kubernetes is a service. The concept of federation or multi cluster management has to be on her road map, especially considering the global nature of our company. Thank you.