>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (bright music) >> Hey everyone! Welcome back. Good evening from Barcelona, Spain. It's theCUBE, the leader in live tech coverage. As you well know, Lisa Martin and Dave Nicholson. Day two of our coverage of MWC 23. Dave, we've been talking about sexy stuff all day. It's about to get, we're bringing sexy back. >> It's about to get hot. >> It's about to get hot. We've had two guests with us, two senior consultants from the product planning, networking and emerging server solutions group at Dell, Heather Raheel and James Bryan. Welcome guys. >> Thanks for having us. >> Thanks for having us. >> Really appreciate it. >> Lisa: Dude, you're bringing sexy back. >> I know. We are. We are. We wanted to bring it, yes. >> This is like XR8000 >> We've been talking about this all day. It's here... >> Yes. Yes. Talk to us about why this is so innovative. >> So, actually we wanted to bring this, getting a lot of attention here on site. Matter of fact, we even have a lot of our competition taking pictures of it. And why is it so innovative? So one of the things that we've done here is we've taken a lot of insights and feedback from our customers that are looking at 5G deployments and looking at how do they, basically, bring commercial off the shelf to a very proprietary industry. So what we've done is we've built a very flexible and scalable form factor in the XR8000. And so this is actually a product that we've purposely built for the telecommunications space. Specifically can be deployed for serving a virtual DU or DUC at a cell site for distributed ram. Or it can be put in a local data center, but outside a main data center to support centralized ram. We'll get into it, which is where the really excitement gets is it's sled-based in its design. And so because of that, it enables us to provide both functionality for telecommunications. Could be network, could be enterprise edge as well as being designed to be configured to whatever that workload is, and be cost-optimized for whatever that work. >> Ah, you're killing us! Let's see. Show, show it to us. >> Actually this is where I have to hand it off to my colleague Heather. But what I really want to show you here is the flexibility that we have and the scalability. So, right here what I'm going to show you first is a one U sled. So I'll set that out here, and I'll let Heather tell us all about it. >> Yeah. So XR8000. Let's talk about flexibility first. So the chassis is a two U chassis with a hot swap shared power supply on the right. Within it there are two form factors for the sleds. What James brought out here, this is the one U form factor. Each sled features one node or one CPU first sled. So we're calling the one U the highest, highest density sled right? Cause you can have up to four one node one U sleds in the chassis. The other form factor is a two U sled, on the right here. And that's just really building on top of the one U sled that adds two PCIe sleds on top. So this is really our general purpose sled. You could have up to two of these sleds within the chassis. So what's really cool about the flexibility is you can plug and play with these. So you could have two one Us, two two Us, or mix and match of each of those. >> Talk about the catalyst to build this for telco and some of the emerging trends that, that you guys have seen and said this needs to be purpose-built for the telco. There's so much challenge and complexity there, they need this. >> Want me to take this? So actually that, that's a great question by the way. It turns out that the market's growing. It's nascent right now. Different telecommunication providers have different needs. Their workloads are different. So they're looking for a form factor like this that, when we say flexible, they need to be able to configure it for theirs. They don't all configure the same way. And so they're looking for something that they can configure to their needs, but they also don't want to pay for things that they don't need. And so that's what led to the creation of, of this device the way we've created it. >> How is it specific for edge use cases, though? We think of the edge: it's emerging, it's burgeoning. What makes this so (pause) specific to edge use cases? >> Yeah, let's talk about some of the the ruggedized features of the product. So first of all, it is short depth. So only 430 millimeters. And this is designed for extreme temperatures, really for any environment. So the normal temperatures of operating are negative five to 55, but we've also developed an enhanced heat sink to get us even beyond that. >> Dave: That's Celsius? >> Celsius. Thank you. >> Lisa: Right. So this will get us all the way down to negative 20 boot in operating all the way up to 65 C. So this is one of the most extreme temperature edge offerings we've seen on the market so far. >> And so this is all outside the data center, so not your typical data center server. So not only are we getting those capabilities, but half the size when you look at a typical data center server. >> So these can go into a place where there's a rack, maybe, but definitely not, not doesn't have to be raised for... >> Could be a cell side cabinet. >> Yeah. Okay. >> Heather: Yeah. And we also have AC and DC power options that can be changed over time as well. >> So what can you pack into that one one U sled in terms of CPU cores and memory, just as an example? >> Yeah, great. So, each of the sleds will support the fourth generation of Intel Sapphire Rapids up to 32 corp. They'll also be supporting their new vRAN boost SKUs. And the benefit of those is it has an integrated FEC accelerator within the CPU. Traditionally, to get FEC acceleration, you would need a PCIe card that would take up one of the slots here. Now with it integrated, you're freeing up a PCIe slot, and there's also a power savings involved with that as well. >> So talk about the involvement of, of the telco customer here and then design, I know Dell is very tight with its customers. I imagine there was a lot of communications and collaboration with customers to, to deliver this. >> Interesting question. So it turns out that early on, we had had some initial insight, but it was actually through deep engagement with our customers that we actually redesigned the form factor to what you see here today. So we actually spent significant amount of time with various telecommunication customers from around the world, and they had a very strong influence in this form factor. Even to the point, like Lisa mentioned, we ended up redesigning it. >> Do, do you have a sense for how many of these, or in what kinds of configurations would you deploy in like the typical BBU? So if we're thinking about radio access network literally tran- tower transmitter receiver... somewhere down there (pause) in a cabinet, you have one of these, you have multiple units. I know, I know the answer is "it depends". >> You are right. >> But if, but if someone tells you, well you know, we have 20, 20 cellular sites, and we need (pause) we're we're moving to an open model, and we need the horsepower to do what we want to do. I'm trying to, I'm trying to gauge like what, one of these, what does that, what does that mean? Or is it more like four of these? >> So that, so we'll go >> It depends? >> Yeah it depends, you're absolutely right. However, we can go right there. So if you look in the two U >> Yeah. >> we have three PCIe slots, you know, as Heather mentioned. And so let's say you have a typical cell site, right? We could be able to support a cell site that could have it could have three radios in the configuration here, it could have a, multiply by three, right? It could have up to 18 radios, and we could actually support that. We could support multiple form factors or multiple deployments at a particular cell site. It really then to your point, it does depend, and that's one of the reasons that we've designed it the way we have. For example, if a customer says their initial deployment, they only need one compute node because maybe they're only going to have, you know, two or three carriers. So then, there, you've got maybe six or eight or nine radios. Well then, you put in a single node, but then they may want to scale over time. Well then, you actually have a chassis. They just come in, and they put in a new chassis. The other beauty of that is, is that maybe they wait, but then they want to do new technology. They don't even have to buy a whole new server. They can update to >> Heather: Yeah. the newest technology, same chassis put that in, connect to the radios, and keep going. >> But in this chassis, is it fair to say that most people will be shocked by how much traffic can go through something like this? In the sense that, if a tower is servicing 'n' number of conversations and data streams, going through something like this? I mean somehow blow, it blows my mind to think of thousands of people accessing something and having them all wrapped through something like this. >> It, it'll depend on what they're doing with that data. So you've probably talked a lot about a type of radios, right? Are we going to be massive MIMO or what type of radio? Is it going to be a mix of 4G or 5G? So it'll really depend on that type of radio, and then where this is located. Is it in a dense urban environment, or is it in a rural type of environment at that cell site shelter, but out in a suburban area. So will depend, but then, that's the beauty of this is then, (pause) I get the right CPU, I get the right number of adding cards to connect to the right radios. I purchase whatever, what I need. I may scale to that. I may be (pause) in a growing part of the city, like where we're from or where I'm from or in San Diego where Heather's from where she's in a new suburban, and they put out a new tower and the community grows rapidly. Well then, we may, they may put out one and then you may add another one and I can connect to more radios, more carriers. So it really just comes down to the type and what you're trying to put through that. It could edit a stadium where I may have a lot of people. I may have like, video streaming, and other things. Not only could I be a network connectivity, but I could do other functions like me, multi-axis axon point that you've heard about, talked about here. So I could have a GPU processing information on one side. I could do network on the other side. >> I do, I do. >> Go for it >> Yeah, no, no, I'm sorry. I'm sorry. I don't want to, don't want to hog all of the time. What about expansion beyond the chassis? Is there a scenario where you might load this chassis up with four of those nodes, but then because you need some type of external connectivity, you go to another chassis that has maybe some of these sleds? Or are these self-contained and independent of one another? >> They are all independent. >> Okay. >> So, and then we've done that for a reason. So one of the things that was clear from the customers, again and again and again, was cost, right? Total cost of ownership. So not only, how much does this cost when I buy it from you to what is it going to take to power and run it. And so basically we've designed that with that in mind. So we've separated the compute and isolated the compute from the chassis, from the power. So (pause) I can only deal with this. And the other thing is is it's, it's a sophisticated piece of equipment that people that would go out and service it are not used to. So they can just come out, pull it out without even bringing the system down. If they've got multiple nodes, pull it. They don't have to pull out a whole chassis or whole server. Put one in, connect it back up while the system is still running. If a power supply goes out, they can come and pull it out. We've got one, it's designed with a power infrastructure that if I lose one power supply, I'm not losing the whole system. So it's really that serviceability, total cost of ownership at the edge, which led us to do this as a configurable chassis. >> I was just going to ask you about TCO reduction but another thing that I'm curious about is: there seems to be like a sustainability angle here. Is that something that you guys talk with customers about in terms of reducing footprint and being able to pack more in with less reducing TCO, reducing storage, power consumption, that sort of thing? >> Go ahead. >> You want me to take that one as well? So yes, so it comes at me, varies by the customer, but it does come up and matter of fact one- in that vein, similar to this from a chassis perspective is, I don't, especially now with the technology changing so fast and and customers still trying to figure out well is this how we're really going to deploy it? You basically can configure, and so maybe that doesn't work. They reconfigure it, or, as I mentioned earlier, I purchased a single sled today, and I purchased a chassis. Well then the next generation comes. I don't have to purchase a new chassis. I don't have to purchase a new power supply. So we're trying to address those sustainability issues as we go, you know, again, back to the whole TCO. So they, they're kind of related to some extent. >> Right. Right, right. Definitely. We hear a lot from customers in every industry about ESG, and it's, and it's an important initiative. So Dell being able to, to help facilitate that for customers, I'm sure is part of what gives you that competitive advantage, but you talked about, James, that and, and we talked about it in an earlier segment that competitors are coming by, sniffing around your booth. What's going on? Talk about, from both of your lenses, the (pause) competitive advantage that you think this gives Dell in telco. Heather, we'll start with you. >> Heather: Yeah, I think the first one which we've really been hitting home with is the flexibility for scalability, right? This is really designed for any workload, from AI and inferencing on like a factory floor all the way to the cell site. I don't know another server that could say that. All in one box, right? And the second thing is, really, all of the TCO savings that will happen, you know, immediately at the point of sale and also throughout the life cycle of this product that is designed to have an extremely long lifetime compared to a traditional server. >> Yeah, I'll get a little geeky with you on that one. Heather mentioned that we'll be able to take this, eventually, to 65 C operating conditions. So we've even designed some of the thermal solutions enabling us to go there. We'll also help us become more power efficient. So, again, back to the flexibility even on how we cool it so it enables us to do that. >> So do, do you expect, you just mentioned maybe if I, if I heard you correctly, the idea that this might have a longer (pause) user-usable life than the average kind of refresh cycle we see in general IT. What? I mean, how often are they replacing equipment now in, kind of, legacy network environments? >> I believe the traditional life cycle of a of a server is, what? Three? Three to five years? Three to five years traditionally. And with the sled based design, like James said, we'll be designing new sleds, you know, every year two years that can just be plugged in, and swapped out. So the chassis is really designed to live much longer than, than just three to five years. >> James: We're having customers ask anywhere from seven to when it dies. So (pause) substantial increase in the life cycle as we move out because as you can, as you probably know, well, right? The further I get out on the edge, it, the more costly it is. >> Lisa: Yep. >> And, I don't want to change it if I don't have to. And so something has to justify me changing it. And so we're trying to build to support that both that longevity, but then with that longevity, things change. I mean, seven years is a long time in technology. >> Lisa: Yes it is. >> So we need to be there for those customers that are ready for that change, or something changed, and they want to still be able to, to adopt that without having to change a lot of their infrastructure. >> So customers are going to want to get their hands on this, obviously. We know, we, we can tell by your excitement. Is this GA now? Where is it GA, and where can folks go to learn more? >> Yeah, so we are here at Mobile World Congress in our booth. We've got a few featured here, and other booths throughout the venue. But if you're not here at Mobile World Congress, this will be launched live on the market at the end of May for Dell. >> Awesome. And what geographies? >> Worldwide. >> Worldwide. Get your hands on the XR8000. Worldwide in just a couple months. Guys, thank you >> James: Thank you very much. >> for the show and tell, talking to us about really why you're designing this for the telco edge, the importance there, what it's going to enable operators to achieve. We appreciate your time and your insights and your show and tell. >> Thanks! >> Thank you. >> For our guests and for Dave Nicholson, I'm Lisa Martin. You're watching theCUBE live, Spain in Mobile MWC 23. Be back with our sho- day two wrap with Dave Valente and some guests in just a minute. (bright music)

(lively music) >> Welcome back to our coverage of Cloud Native Security Con. I'm Dave Vellante, here in our Boston studio. We're connecting today, throughout the day, with Palo Alto on the ground in Seattle. And right now I'm here with Michael Foster with Red Hat. He's on the ground in Seattle. We're going to discuss the trends and containers and security and everything that's going on at the show in Seattle. Michael, good to see you, thanks for coming on. >> Good to see you, thanks for having me on. >> Lot of market momentum for Red Hat. The IBM earnings call the other day, announced OpenShift is a billion-dollar ARR. So it's quite a milestone, and it's not often, you know. It's hard enough to become a billion-dollar software company and then to have actually a billion-dollar product alongside. So congratulations on that. And let's start with the event. What's the buzz at the event? People talking about shift left, obviously supply chain security is a big topic. We've heard a little bit about or quite a bit about AI. What are you hearing on the ground? >> Yeah, so the last event I was at that I got to see you at was three months ago, with CubeCon and the talk was supply chain security. Nothing has really changed on that front, although I do think that the conversation, let's say with the tech companies versus what customers are actually looking at, is slightly different just based on the market. And, like you said, thank you for the shout-out to a billion-dollar OpenShift, and ACS is certainly excited to be part of that. We are seeing more of a consolidation, I think, especially in security. The money's still flowing into security, but people want to know what they're running. We've allowed, had some tremendous growth in the last couple years and now it's okay. Let's get a hold of the containers, the clusters that we're running, let's make sure everything's configured. They want to start implementing policies effectively and really get a feel for what's going on across all their workloads, especially with the bigger companies. I think bigger companies allow some flexibility in the security applications that they can deploy. They can have different groups that manage different ones, but in the mid to low market, you're seeing a lot of consolidation, a lot of companies that want basically one security tool to manage them all, so to speak. And I think that the features need to somewhat accommodate that. We talk supply chain, I think most people continue to care about network security, vulnerability management, shifting left and enabling developers. That's the general trend I see. Still really need to get some hands on demos and see some people that I haven't seen in a while. >> So a couple things on, 'cause, I mean, we talk about the macroeconomic climate all the time. We do a lot of survey data with our partners at ETR, and their recent data shows that in terms of cost savings, for those who are actually cutting their budgets, they're looking to consolidate redundant vendors. So, that's one form of consolidation. The other theme, of course, is there's so many tools out in the security market that consolidating tools is something that can help simplify, but then at the same time, you see opportunities open up, like IOT security. And so, you have companies that are starting up to just do that. So, there's like these countervailing trends. I often wonder, Michael, will this ever end? It's like the universe growing and tooling, what are your thoughts? >> I mean, I completely agree. It's hard to balance trying to grow the company in a time like this, at the same time while trying to secure it all, right? So you're seeing the consolidation but some of these applications and platforms need to make some promises to say, "Hey, we're going to move into this space." Right, so when you have like Red Hat who wants to come out with edge devices and help manage the IOT devices, well then, you have a security platform that can help you do that, that's built in. Then the messaging's easy. When you're trying to do that across different cloud providers and move into IOT, it becomes a little bit more challenging. And so I think that, and don't take my word for this, some of those IOT startups, you might see some purchasing in the next couple years in order to facilitate those cloud platforms to be able to expand into that area. To me it makes sense, but I don't want to hypothesize too much from the start. >> But I do, we just did our predictions post and as a security we put up the chart of candidates, and there's like dozens, and dozens, and dozens. Some that are very well funded, but I mean, you've seen some down, I mean, down rounds everywhere, but these many companies have raised over a billion dollars and it's like uh-oh, okay, so they're probably okay, maybe. But a lot of smaller firms, I mean there's just, there's too many tools in the marketplace, but it seems like there is misalignment there, you know, kind of a mismatch between, you know, what customers would like to have happen and what actually happens in the marketplace. And that just underscores, I think, the complexities in security. So I guess my question is, you know, how do you look at Cloud Native Security, and what's different from traditional security approaches? >> Okay, I mean, that's a great question, and it's something that we've been talking to customers for the last five years about. And, really, it's just a change in mindset. Containers are supposed to unleash developer speed, and if you don't have a security tool to help do that, then you're basically going to inhibit developers in some form or another. I think managing that, while also giving your security teams the ability to tell the message of we are being more secure. You know, we're limiting vulnerabilities in our cluster. We are seeing progress because containers, you know, have a shorter life cycle and there is security and speed. Having that conversation with the C-suites is a little different, especially when how they might be used to virtual machines and managing it through that. I mean, if it works, it works from a developer's standpoint. You're not taking advantage of those containers and the developer's speed, so that's the difference. Now doing that and then first challenge is making that pitch. The second challenge is making that pitch to then scale it, so you can get onboard your developers and get your containers up and running, but then as you bring in new groups, as you move over to Kubernetes or you get into more container workloads, how do you onboard your teams? How do you scale? And I tend to see a general trend of a big investment needed for about two years to make that container shift. And then the security tools come in and really blossom because once that core separation of responsibilities happens in the organization, then the security tools are able to accelerate the developer workflow and not inhibit it. >> You know, I'm glad you mentioned, you know, separation of responsibilities. We go to a lot of shows, as you know, with theCUBE, and many of them are cloud shows. And in the one hand, Cloud has, you know, obviously made the world, you know, more interesting and better in so many different ways and even security, but it's like new layers are forming. You got the cloud, you got the shared responsibility model, so the cloud is like the first line of defense. And then you got the CISO who is relying heavily on devs to, you know, the whole shift left thing. So we're asking developers to do a lot and then you're kind of behind them. I guess you have audit is like the last line of defense, but my question to you is how can software developers really ensure that cloud native tools that they're using are secure? What steps can they take to improve security and specifically what's Red Hat doing in that area? >> Yeah, well I think there's, I would actually move away from that being the developer responsibility. I think the job is the operators' and the security people. The tools to give them the ability to see. The vulnerabilities they're introducing. Let's say signing their images, actually verifying that the images that's thrown in the cloud, are the ones that they built, that can all be done and it can be done open source. So we have a DevSecOps validated pattern that Red Hat's pushed out, and it's all open source tools in the cloud native space. And you can sign your builds and verify them at runtime and make sure that you're doing that all for free as one option. But in general, I would say that the hope is that you give the developer the information to make responsible choices and that there's a dialogue between your security and operations and developer teams but security, we should not be pushing that on developer. And so I think with ACS and our tool, the goal is to get in and say, "Let's set some reasonable policies, have a conversation, let's get a security liaison." Let's say in the developer team so that we can make some changes over time. And the more we can automate that and the more we can build and have that conversation, the better that you'll, I don't say the more security clusters but I think that the more you're on your path of securing your environment. >> How much talk is there at the event about kind of recent high profile incidents? We heard, you know, Log4j, of course, was mentioned in the Keynote. Somebody, you know, I think yelled out from the audience, "We're still dealing with that." But when you think about these, you know, incidents when looking back, what lessons do you think we've learned from these events? >> Oh, I mean, I think that I would say, if you have an approach where you're managing your containers, managing the age and using containers to accelerate, so let's say no images that are older than 90 days, for example, you're going to avoid a lot of these issues. And so I think people that are still dealing with that aspect haven't set up the proper, let's say, disclosure between teams and update strategy and so on. So I don't want to, I think the Log4j, if it's still around, you know, something's missing there but in general you want to be able to respond quickly and to do that and need the tools and policies to be able to tell people how to fix that issue. I mean, the Log4j fix was seven days after, so your developers should have been well aware of that. Your security team should have been sending the messages out. And I remember even fielding all the calls, all the fires that we had to put out when that happened. But yeah. >> I thought Brian Behlendorf's, you know, talk this morning was interesting 'cause he was making an attempt to say, "Hey, here's some things that you might not be thinking about that are likely to occur." And I wonder if you could, you know, comment on them and give us your thoughts as to how the industry generally, maybe Red Hat specifically, are thinking about dealing with them. He mentioned ChatGPT or other GPT to automate Spear phishing. He said the identity problem is still not fixed. Then he talked about free riders sniffing repos essentially for known vulnerabilities that are slow to fix. He talked about regulations that might restrict shipping code. So these are things that, you know, essentially, we can, they're on the radar, but you know, we're kind of putting out, you know, yesterday's fire. What are your thoughts on those sort of potential issues that we're facing and how are you guys thinking about it? >> Yeah, that's a great question, and I think it's twofold. One, it's brought up in front of a lot of security leaders in the space for them to be aware of it because security, it's a constant battle, constant war that's being fought. ChatGPT lowers the barrier of entry for a lot of them, say, would-be hackers or people like that to understand systems and create, let's say, simple manifests to leverage Kubernetes or leverage a misconfiguration. So as the barrier drops, we as a security team in security, let's say group organization, need to be able to respond and have our own tools to be able to combat that, and we do. So a lot of it is just making sure that we shore up our barriers and that people are aware of these threats. The harder part I think is educating the public and that's why you tend to see maybe the supply chain trend be a little bit ahead of the implementation. I think they're still, for example, like S-bombs and signing an attestation. I think that's still, you know, a year, two years, away from becoming, let's say commonplace, especially in something like a production environment. Again, so, you know, stay bleeding edge, and then make sure that you're aware of these issues and we'll be constantly coming to these calls and filling you in on what we're doing and make sure that we're up to speed. >> Yeah, so I'm hearing from folks like yourself that the, you know, you think of the future of Cloud Native Security. We're going to see continued emphasis on, you know, better integration of security into the DevSecOps. You're pointing out it's really, you know, the ops piece, that runtime that we really need to shore up. You can't just put it on the shoulders of the devs. And, you know, using security focused tools and best practices. Of course you hear a lot about that and the continued drive toward automation. My question is, you know, automation, machine learning, how, where are we in that maturity cycle? How much of that is being adopted? Sometimes folks are, you know, they embrace automation but it brings, you know, unknown, unintended consequences. Are folks embracing that heavily? Are there risks associated around that, or are we kind of through that knothole in your view? >> Yeah, that's a great question. I would compare it to something like a smart home. You know, we sort of hit a wall. You can automate so much, but it has to actually be useful to your teams. So when we're going and deploying ACS and using a cloud service, like one, you know, you want something that's a service that you can easily set up. And then the other thing is you want to start in inform mode. So you can't just automate everything, even if you're doing runtime enforcement, you need to make sure that's very, very targeted to exactly what you want and then you have to be checking it because people start new workloads and people get onboarded every week or month. So it's finding that balance between policies where you can inform the developer and the operations teams and that they give them the information to act. And that worst case you can step in as a security team to stop it, you know, during the onboarding of our ACS cloud service. We have an early access program and I get on-calls, and it's not even security team, it's the operations team. It starts with the security product, you know, and sometimes it's just, "Hey, how do I, you know, set this policy so my developers will find this vulnerability like a Log4Shell and I just want to send 'em an email, right?" And these are, you know, they have the tools and they can do that. And so it's nice to see the operations take on some security. They can automate it because maybe you have a NetSec security team that doesn't know Kubernetes or containers as well. So that shared responsibility is really useful. And then just again, making that automation targeted, even though runtime enforcement is a constant thing that we talk about, the amount that we see it in the wild where people are properly setting up admission controllers and it's acting. It's, again, very targeted. Databases, cubits x, things that are basically we all know is a no-go in production. >> Thank you for that. My last question, I want to go to the, you know, the hardest part and 'cause you're talking to customers all the time and you guys are working on the hardest problems in the world. What is the hardest aspect of securing, I'm going to come back to the software supply chain, hardest aspect of securing the software supply chain from the perspective of a security pro, software engineer, developer, DevSecOps Pro, and then this part b of that is, is how are you attacking that specifically as Red Hat? >> Sure, so as a developer, it's managing vulnerabilities with updates. As an operations team, it's keeping all the cluster, because you have a bunch of different teams working in the same environment, let's say, from a security team. It's getting people to listen to you because there are a lot of things that need to be secured. And just communicating that and getting it actionable data to the people to make the decisions as hard from a C-suite. It's getting the buy-in because it's really hard to justify the dollars and cents of security when security is constantly having to have these conversations with developers. So for ACS, you know, we want to be able to give the developer those tools. We also want to build the dashboards and reporting so that people can see their vulnerabilities drop down over time. And also that they're able to respond to it quickly because really that's where the dollars and cents are made in the product. It's that a Log4Shell comes out. You get immediately notified when the feeds are updated and you have a policy in action that you can respond to it. So I can go to my CISOs and say, "Hey look, we're limiting vulnerabilities." And when this came out, the developers stopped it in production and we were able to update it with the next release. Right, like that's your bread and butter. That's the story that you want to tell. Again, it's a harder story to tell, but it's easy when you have the information to be able to justify the money that you're spending on your security tools. Hopefully that answered your question. >> It does. That was awesome. I mean, you got data, you got communication, you got the people, obviously there's skillsets, you have of course, tooling and technology is a big part of that. Michael, really appreciate you coming on the program, sharing what's happening on the ground in Seattle and can't wait to have you back. >> Yeah. Awesome. Thanks again for having me. >> Yeah, our pleasure. All right. Thanks for watching our coverage of the Cloud Native Security Con. I'm Dave Vellante. I'm in our Boston studio. We're connecting to Palo Alto. We're connecting on the ground in Seattle. Keep it right there for more coverage. Be right back. (lively music)

>>The cube presents Ignite 22, brought to you by Palo Alto Networks. >>Good morning. Live from the MGM Grand. It's the cube at Palo Alto Networks Ignite 2022. Lisa Martin here with Dave Valante, day two, Dave of our coverage, or last live day of the year, which I can't believe, lots of good news coming out from Palo Alto Networks. We're gonna sit down with its Chief product officer next and dissect all of that. >>Yeah. You know, oftentimes in, in events like this, day two is product day. And look, it's all about products and sales. Yeah, I mean those, that's the, the, the golden rule. Get the product right, get the sales right, and everything else will take care of itself. So let's talk product. >>Yeah, let's talk product. Lee Claridge joins us, the Chief Product Officer at Palo Alto Networks. Welcome Lee. Great to have >>You. Thank you so much. >>So we didn't get to see your keynote yesterday, but we heard one of the things, you know, we've been talking about the threat landscape, the challenges. We had Unit 42, Wendy on yesterday. We had Nash on and near talking about the massive challenges in the threat landscape. But we understand, despite that you are optimistic. I am. Talk about your optimism given the massive challenges that every organization is facing today. >>Look, cybersecurity's hard and often in cybersecurity in the industry, a lot of people get sort of really focused on what the threat actors are doing, why they're successful. We investigate breaches and we think of it, it just starts to feel somewhat overwhelming for a lot of folks. And I just happen to think a little bit differently. I, I look at it and I think it's actually a solvable problem. >>Talk about cyber resilience. How does Palo Alto Networks define that and how does it help customers achieve that? Cuz that's the, that's the holy grail these days. >>Yes. Look, the, the way I think about cyber resilience is basically in two pieces. One, it's all about how do we prevent the threat actors from actually being successful in the first place. Second, we also have to be prepared for what happens if they happen to find a way to get through, and how do we make sure that that happens? The blast radius is, is as narrowly contained as possible. And so the, the way that we approach this is, you know, I, I kind of think in terms of like threes three core principles. Number one, we have to have amazing technology and we have to constantly be, keep keeping up with and ideally ahead of what attackers are doing. It's a big part of my job as the chief product officer, right? Second is we, you know, one of the, the big transformations that's happened is the advent of, of AI and the opportunity, as long as we can do it, a great job of collecting great data, we can drive AI and machine learning models that can start to be used for our advantage as defenders, and then further use that to drive automation. >>So we take the human out of the response as much as possible. What that allows us to do is actually to start using AI and automation to disrupt attackers as it's happening. The third piece then becomes natively integrating these capabilities into a platform. And when we do that, what allows us to do is to make sure that we are consistently delivering cybersecurity everywhere that it needs to happen. That we don't have gaps. Yeah. So great tech AI and automation deliver natively integrated through platforms. This is how we achieve cyber resilience. >>So I like the positivity. In fact, Steven Schmidt, who's now the CSO of, of Amazon, you know, Steven, and it was the CSO at AWS at the time, the first reinforced, he stood up on stage and said, listen, this narrative that's all gloom and doom is not the right approach. We actually are doing a good job and we have the capability. So I was like, yeah, you know, okay. I'm, I'm down with that. Now when I, my question is around the, the portfolio. I, I was looking at, you know, some of your alternatives and options and the website. I mean, you got network security, cloud security, you got sassy, you got capp, you got endpoint, pretty much everything. You got cider security, which you just recently acquired for, you know, this whole shift left stuff, you know, nothing in there on identity yet. That's good. You partner for that, but, so could you describe sort of how you think about the portfolio from a product standpoint? How you continue to evolve it and what's the direction? Yes. >>So the, the, the cybersecurity industry has long had this, I'm gonna call it a major flaw. And the major flaw of the cybersecurity industry has been that every time there is a problem to be solved, there's another 10 or 20 startups that get funded to solve that problem. And so pretty soon what you have is you're, if you're a customer of this is you have 50, a hundred, the, the record is over 400 different cybersecurity products that as a customer you're trying to operationalize. >>It's not a good record to have. >>No, it's not a good record. No. This is, this is the opposite of Yes. Not a good personal best. So the, so the reason I start there in answering your question is the, the way that, so that's one end of the extreme, the other end of the extreme view to say, is there such a thing as a single platform that does everything? No, there's not. That would be nice. That was, that sounds nice. But the reality is that cybersecurity has to be much broader than any one single thing can do. And so the, the way that we approach this is, is three fundamental areas that, that we, Palo Alto Networks are going to be the best at. One is network security within network security. This includes hardware, NextGen, firewalls, software NextGen, firewalls, sassy, all the different security services that tie into that. All of that makes up our network security platforms. >>So everything to do with network security is integrated in that one place. Second is around cloud security. The shift to the cloud is happening is very real. That's where Prisma Cloud takes center stage. C a P is the industry acronym. If if five letters thrown together can be called an acronym. The, so cloud native application protection platform, right? So this is where we bring all of the different cloud security capabilities integrated together, delivered through one platform. And then security, security operations is the third for us. This is Cortex. And this is where we bring together endpoint security, edr, ndr, attack, surface management automation, all of this. And what we had, what we announced earlier this year is x Im, which is a Cortex product for actually integrating all of that together into one SOC transformation platform. So those are the three platforms, and that's how we deliver much, much, much greater levels of native integration of capabilities, but in a logical way where we're not trying to overdo it. >>And cider will fit into two or three >>Into Prisma cloud into the second cloud to two. Yeah. As part of the shift left strategy of how we secure makes sense applications in the cloud >>When you're in customer conversations. You mentioned the record of 400 different product. That's crazy. Nash was saying yesterday between 30 and 50 and we talked with him and near about what's realistic in terms of getting organizations to, to be able to consolidate. I'd love to understand what does cybersecurity transformation look like for the average organization that's running 30 to 50 point >>Solutions? Yeah, look, 30 to 50 is probably, maybe normal. A hundred is not unusual. Obviously 400 is the extreme example. But all of those are, those numbers are too big right now. I think, I think realistic is high. Single digits, low double digits is probably somewhat realistic for most organizations, the most complex organizations that might go a bit above that if we're really doing a good job. That's, that's what I think. Now second, I do really want to point out on, on the product guy. So, so maybe this is just my way of thinking, consolidation is an outcome of having more tightly and natively integrated capabilities. Got you. And the reason I flip that around is if I just went to you and say, Hey, would you like to consolidate? That just means maybe fewer vendors that that helps the procurement person. Yes. You know, have to negotiate with fewer companies. Yeah. Integration is actually a technology statement. It's delivering better outcomes because we've designed multiple capabilities to work together natively ourselves as the developers so that the customer doesn't have to figure out how to do it. It just happens that by, by doing that, the customer gets all this wonderful technical benefit. And then there's this outcome sitting there called, you've just consolidated your complexity. How >>Specialized is the customer? I think a data pipelines, and I think I have a data engineer, have a data scientists, a data analyst, but hyper specialized roles. If, if, let's say I have, you know, 30 or 40, and one of 'em is an SD wan, you know, security product. Yeah. I'm best of breed an SD wan. Okay, great. Palo Alto comes in as you, you pointed out, I'm gonna help you with your procurement side. Are there hyper specialized individuals that are aligned to that? And how that's kind of part A and B, how, assuming that's the case, how does that integration, you know, carry through to the business case? So >>Obviously there are specializations, this is the, and, and cybersecurity is really important. And so there, this is why there had, there's this tendency in the past to head toward, well I have this problem, so who's the best at solving this one problem? And if you only had one problem to solve, you would go find the specialist. The, the, the, the challenge becomes, well, what do you have a hundred problems to solve? I is the right answer, a hundred specialized solutions for your a hundred problems. And what what I think is missing in this approach is, is understanding that almost every problem that needs to be solved is interconnected with other problems to be solved. It's that interconnectedness of the problems where all of a sudden, so, so you mentioned SD wan. Okay, great. I have Estee wan, I need it. Well what are you connecting SD WAN to? >>Well, ideally our view is you would connect SD WAN and branch to the cloud. Well, would you run in the cloud? Well, in our case, we can take our SD wan, connect it to Prisma access, which is our cloud security solution, and we can natively integrate those two things together such that when you use 'em together, way easier. Right? All of a sudden we took what seemed like two separate problems. We said, no, actually these problems are related and we can deliver a solution where those, those things are actually brought together. And that's just one simple example, but you could, you could extend that across a lot of these other areas. And so that's the difference. And that's how the, the, the mindset shift that is happening. And, and I I was gonna say needs to happen, but it's starting to happen. I'm talking to customers where they're telling me this as opposed to me telling them. >>So when you walk around the floor here, there's a visual, it's called a day in the life of a fuel member. And basically what it has, it's got like, I dunno, six or seven different roles or personas, you know, one is management, one is a network engineer, one's a coder, and it gives you an X and an O. And it says, okay, put the X on things that you spend your time doing, put the o on things that you wanna spend your time doing a across all different sort of activities that a SecOps pro would do. There's Xs and O's in every one of 'em. You know, to your point, there's so much overlap going on. This was really difficult to discern, you know, any kind of consistent pattern because it, it, it, unlike the hyper specialization and data pipelines that I just described, it, it's, it's not, it, it, there's way more overlap between those, those specialization roles. >>And there's a, there's a second challenge that, that I've observed and that we are, we've, we've been trying to solve this and now I'd say we've become, started to become a lot more purposeful in, in, in trying to solve this, which is, I believe cybersecurity, in order for cyber security vendors to become partners, we actually have to start to become more opinionated. We actually have to start, guys >>Are pretty opinionated. >>Well, yes, but, but the industry large. So yes, we're opinionated. We build these products, but that have, that have our, I'll call our opinions built into it, and then we, we sell the, the product and then, and then what happens? Customer says, great, thank you for the product. I'm going to deploy it however I want to, which is fine. Obviously it's their choice at the end of the day, but we actually should start to exert an opinion to say, well, here's what we would recommend, here's why we would recommend that. Here's how we envisioned it providing the most value to you. And actually starting to build that into the products themselves so that they start to guide the customer toward these outcomes as opposed to just saying, here's a product, good luck. >>What's, what's the customer lifecycle, not lifecycle, but really kind of that, that collaboration, like it's one thing to, to have products that you're saying that have opinions to be able to inform customers how to deploy, how to use, but where is their feedback in this cycle of product development? >>Oh, look, my, this, this is, this is my life. I'm, this is, this is why I'm here. This is like, you know, all day long I'm meeting with customers and, and I share what we're doing. But, but it's, it's a, it's a 50 50, I'm half the time I'm listening as well to understand what they're trying to do, what they're trying to accomplish, and how, what they need us to do better in order to help them solve the problem. So the, the, and, and so my entire organization is oriented around not just telling customers, here's what we did, but listening and understanding and bringing that feedback in and constantly making the products better. That's, that's the, the main way in which we do this. Now there's a second way, which is we also allow our products to be customized. You know, I can say, here's our best practices, we see it, but then allowing our customer to, to customize that and tailor it to their environment, because there are going to be uniquenesses for different customers in parti, we need more complex environments. Explain >>Why fire firewalls won't go away >>From your perspective. Oh, Nikesh actually did a great job of explaining this yesterday, and although he gave me credit for it, so this is like a, a circular kind of reference here. But if you think about the firewalls slightly more abstract, and you basically say a NextGen firewalls job is to inspect every connection in order to make sure the connection should be allowed. And then if it is allowed to make sure that it's secure, >>Which that is the definition of an NextGen firewall, by the way, exactly what I just said. Now what you noticed is, I didn't describe it as a hardware device, right? It can be delivered in hardware because there are environments where you need super high throughput, low latency, guess what? Hardware is the best way of delivering that functionality. There's other use cases cloud where you can't, you, you can't ship hardware to a cloud provider and say, can you install this hardware in front of my cloud? No, no, no. You deployed in a software. So you take that same functionality, you instantly in a software, then you have other use cases, branch offices, remote workforce, et cetera, where you say, actually, I just want it delivered from the cloud. This is what sassy is. So when I, when I look at and say, the firewall's not going away, what, what, what I see is the functionality needed is not only not going away, it's actually expanding. But how we deliver it is going to be across these three form factors. And then the customer's going to decide how they need to intermix these form factors for their environment. >>We put forth this notion of super cloud a while about a year ago. And the idea being you're gonna leverage the hyperscale infrastructure and you're gonna build a, a, you're gonna solve a common problem across clouds and even on-prem, super cloud above the cloud. Not Superman, but super as in Latin. But it turned into this sort of, you know, superlative, which is fun. But the, my, my question to you is, is, is, is Palo Alto essentially building a common cross-cloud on-prem, presumably out to the edge consistent experience that we would call a super cloud? >>Yeah, I don't know that we've ever used the term surfer cloud to describe it. Oh, you don't have to, but yeah. But yes, based on how you describe it, absolutely. And it has three main benefits that I describe to customers all the time. The first is the end user experience. So imagine your employee, and you might work from the office, you might work from home, you might work while from, from traveling and hotels and conferences. And, and by the way, in one day you might actually work from all of those places. So, so the first part is the end user experience becomes way better when it doesn't matter where they're working from. They always get the same experience, huge benefit from productivity perspective, no second benefit security operations. You think about the, the people who are actually administering these policies and analyzing the security events. >>Imagine how much better it is for them when it's all common and consistent across everywhere that has to happen. Cloud, on-prem branch, remote workforce, et cetera. So there's a operational benefit that is super valuable. Third, security benefit. Imagine if in this, this platform-based approach, if we come out with some new amazing innovation that is able to detect and block, you know, new types of attacks, guess what, we can deliver that across hardware, software, and sassi uniformly and keep it all up to date. So from a security perspective, way better than trying to figure out, okay, there's some new technology, you know, does my hardware provider have that technology or not? Does my soft provider? So it's bringing that in to one place. >>From a developer perspective, is there a, a, a PAs layer, forgive me super PAs, that a allows the developers to have a common experience across irrespective of physical location with the explicit purpose of serving the objective of your platform. >>So normally when I think of the context of developers, I'm thinking of the context of, of the people who are building the applications that are being deployed. And those applications may be deployed in a data center, increasing the data centers, depending private clouds might be deployed into, into public cloud. It might even be hybrid in nature. And so if you think about what the developer wants, the developer actually wants to not have to think about security, quite frankly. Yeah. They want to think about how do I develop the functionality I need as quickly as possible with the highest quality >>Possible, but they are being forced to think about it more and more. Well, but anyway, I didn't mean to >>Interrupt you. No, it's a, it is a good, it's a, it's, it's a great point. The >>Well we're trying to do is we're trying to enable our security capabilities to work in a way that actually enables what the developer wants that actually allows them to develop faster that actually allows them to focus on the things they want to focus. And, and the way we do that is by actually surfacing the security information that they need to know in the tools that they use as opposed to trying to bring them to our tools. So you think about this, so our customer is a security customer. Yet in the application development lifecycle, the developer is often the user. So we, we we're selling, we're so providing a solution to security and then we're enabling them to surface it in the developer tools. And by, by doing this, we actually make life easier for the developers such that they're not actually thinking about security so much as they're just saying, oh, I pulled down the wrong open source package, it's outdated, it has vulnerabilities. I was notified the second I did it, and I was told which one I should pull down. So I pulled down the right one. Now, if you're a developer, do you think that's security getting your way? Not at all. No. If you're a developer, you're thinking, thank god, thank you, thank, thank you. Yeah. You told me at a point where it was easy as opposed to waiting a week or two and then telling me where it's gonna be really hard to fix it. Yeah. Nothing >>More than, so maybe be talking to Terraform or some other hash corp, you know, environment. I got it. Okay. >>Absolutely. >>We're 30 seconds. We're almost out of time. Sure. But I'd love to get your snapshot. Here we are at the end of calendar 2022. What are you, we know you're optimistic in this threat landscape, which we're gonna see obviously more dynamics next year. What kind of nuggets can you drop about what we might hear and see in 23? >>You're gonna see across everything. We do a lot more focus on the use of AI and machine learning to drive automated outcomes for our customers. And you're gonna see us across everything we do. And that's going to be the big transformation. It'll be a multi-year transformation, but you're gonna see significant progress in the next 12 months. All >>Right, well >>What will be the sign of that progress? If I had to make a prediction, which >>I'm better security with less effort. >>Okay, great. I feel like that's, we can measure that. I >>Feel, I feel like that's a mic drop moment. Lee, it's been great having you on the program. Thank you for walking us through such great detail. What's going on in the organization, what you're doing for customers, where you're meeting, how you're meeting the developers, where they are. We'll have to have you back. There's just, just too much to unpack. Thank you both so much. Actually, our pleasure for Lee Cler and Dave Valante. I'm Lisa Martin. You're watching The Cube Live from Palo Alto Networks Ignite 22, the Cube, the leader in live, emerging and enterprise tech coverage.

(upbeat music) (logo swooshing) >> Good morning and welcome back to Dallas, ladies and gentlemen, we are here with theCUBE Live from Supercomputing 2022. David, my cohost, how are you doing? Exciting, day two, feeling good? >> Very exciting. Ready to start off the day. >> Very excited. We have two fascinating guests joining us to kick us off. Please welcome Pete and Armando. Gentlemen, thank you for being here with us. >> Thank you for having us. >> Thank you for having us. >> I'm excited that you're starting off the day because we've been hearing a lot of rumors about Ethernet as the fabric for HPC, but we really haven't done a deep dive yet during the show. You all seem all in on Ethernet. Tell us about that. Armando, why don't you start? >> Yeah, I mean, when you look at Ethernet, customers are asking for flexibility and choice. So when you look at HPC, InfiniBand's always been around, right? But when you look at where Ethernet's coming in, it's really our commercial in their enterprise customers. And not everybody wants to be in the top 500, what they want to do is improve their job time and improve their latency over the network. And when you look at Ethernet, you kind of look at the sweet spot between 8, 12, 16, 32 nodes, that's a perfect fit for Ethernet in that space and those types of jobs. >> I love that. Pete, you want to elaborate? >> Yeah, sure. I mean, I think one of the biggest things you find with Ethernet for HPC is that, if you look at where the different technologies have gone over time, you've had old technologies like, ATM, Sonic, Fifty, and pretty much everything is now kind of converged toward Ethernet. I mean, there's still some technologies such as InfiniBand, Omni-Path, that are out there. But basically, they're single source at this point. So what you see is that there is a huge ecosystem behind Ethernet. And you see that also the fact that Ethernet is used in the rest of the enterprise, is used in the cloud data centers, that is very easy to integrate HPC based systems into those systems. So as you move HPC out of academia into enterprise, into cloud service providers, it's much easier to integrate it with the same technology you're already using in those data centers, in those networks. >> So what's the state of the art for Ethernet right now? What's the leading edge? what's shipping now and what's in the near future? You're with Broadcom, you guys designed this stuff. >> Pete: Yeah. >> Savannah: Right. >> Yeah, so leading edge right now, got a couple things-- >> Savannah: We love good stage prop here on the theCUBE. >> Yeah, so this is Tomahawk 4. So this is what is in production, it's shipping in large data centers worldwide. We started sampling this in 2019, started going into data centers in 2020. And this is 25.6 terabytes per second. >> David: Okay. >> Which matches any other technology out there. Like if you look at say, InfinBand, highest they have right now that's just starting to get into production is 25.6 T. So state of the art right now is what we introduced, We announced this in August, This is Tomahawk 5, so this is 51.2 terabytes per second. So double the bandwidth, out of any other technology that's out there. And the important thing about networking technology is when you double the bandwidth, you don't just double the efficiency, actually, winds up being a factor of six efficiency. >> Savannah: Wow. >> 'Cause if you want, I can go into that, but... >> Why not? >> Well, what I want to know, please tell me that in your labs, you have a poster on the wall that says T five, with some like Terminator kind of character. (all laughs) 'Cause that would be cool. If it's not true, just don't say anything. I'll just... >> Pete: This can actually shift into a terminator. >> Well, so this is from a switching perspective. >> Yeah. >> When we talk about the end nodes, when we talk about creating a fabric, what's the latest in terms of, well, the nicks that are going in there, what speed are we talking about today? >> So as far as 30 speeds, it tends to be 50 gigabits per second. >> David: Okay. >> Moving to a hundred gig PAM-4. >> David: Okay. >> And we do see a lot of nicks in the 200 gig Ethernet port speed. So that would be four lanes, 50 gig. But we do see that advancing to 400 gig fairly soon, 800 gig in the future. But say state of the art right now, we're seeing for the end node tends to be 200 gig E based on 50 gig PAM-4. >> Wow. >> Yeah, that's crazy. >> Yeah, that is great. My mind is act actively blown. I want to circle back to something that you brought up a second ago, which I think is really astute. When you talked about HPC moving from academia into enterprise, you're both seeing this happen, where do you think we are on the adoption curve and sort of in that cycle? Armando, do you want to go? >> Yeah, well, if you look at the market research, they're actually telling you it's 50/50 now. So Ethernet is at the level of 50%, InfinBand's at 50%, right? >> Savannah: Interesting. >> Yeah, and so what's interesting to us, customers are coming to us and say, hey, we want to see flexibility and choice and, hey, let's look at Ethernet and let's look at InfiniBand. But what is interesting about this is that we're working with Broadcom, we have their chips in our lab, we their have switches in our lab. And really what we're trying to do is make it easy to simple and configure the network for essentially MPI. And so the goal here with our validated designs is really to simplify this. So if you have a customer that, hey, I've been InfiniBand but now I want to go Ethernet, there's going to be some learning curves there. And so what we want to do is really simplify that so that we can make it easy to install, get the cluster up and running and they can actually get some value out the cluster. >> Yeah, Pete, talk about that partnership. what does that look like? I mean, are you working with Dell before the T six comes out? Or you just say what would be cool is we'll put this in the T six? >> No, we've had a very long partnership both on the hardware and the software side. Dell's been an early adopter of our silicon. We've worked very closely on SI and Sonic on the operating system, and they provide very valuable feedback for us on our roadmap. So before we put out a new chip, and we have actually three different product lines within the switching group, within Broadcom, we've then gotten very valuable feedback on the hardware and on the APIs, on the operating system that goes on top of those chips. So that way when it comes to market, Dell can take it and deliver the exact features that they have in the current generation to their customers to have that continuity. And also they give us feedback on the next gen features they'd like to see again, in both the hardware and the software. >> So I'm fascinated by... I always like to know like what, yeah, exactly. Look, you start talking about the largest supercomputers, most powerful supercomputers that exist today, and you start looking at the specs and there might be two million CPUs, 2 million CPU cores. Exoflap of performance. What are the outward limits of T five in switches, building out a fabric, what does that look like? What are the increments in terms of how many... And I know it's a depends answer, but how many nodes can you support in a scale out cluster before you need another switch? Or what does that increment of scale look like today? >> Yeah, so this is 51.2 terabytes per second. Where we see the most common implementation based on this would be with 400 gig Ethernet ports. >> David: Okay. >> So that would be 128, 400 gig E ports connected to one chip. Now, if you went to 200 gig, which is kind of the state of the art for the nicks, you can have double that. So in a single hop, you can have 256 end nodes connected through one switch. >> Okay, so this T five, that thing right there, (all laughing) inside a sheet metal box, obviously you've got a bunch of ports coming out of that. So what's the form factor look like for where that T five sits? Is there just one in a chassis or you have.. What does that look like? >> It tends to be pizza boxes these days. What you've seen overall is that the industry's moved away from chassis for these high end systems more towardS pizza boxes. And you can have composable systems where, in the past you would have line cards, either the fabric cards that the line cards are plug into or interfaced to. These days what tends to happen is you'd have a pizza box and if you wanted to build up like a virtual chassis, what you would do is use one of those pizza boxes as the fabric card, one of them as the line card. >> David: Okay. >> So what we see, the most common form factor for this is they tend to be two, I'd say for North America, most common would be a 2RU, with 64 OSFP ports. And often each of those OSFP, which is an 800 gig E or 800 gig port, we've broken out into two 400 gig ports. >> So yeah, in 2RU, and this is all air cooled, in 2RU, you've got 51.2 T. We do see some cases where customers would like to have different optics and they'll actually deploy 4RU, just so that way they have the phase-space density. So they can plug in 128, say QSFP 112. But yeah, it really depends on which optics, if you want to have DAK connectivity combined with optics. But those are the two most common form factors. >> And Armando, Ethernet isn't necessarily Ethernet in the sense that many protocols can be run over it. >> Right. >> I think I have a projector at home that's actually using Ethernet physical connections. But, so what are we talking about here in terms of the actual protocol that's running over this? Is this exactly the same as what you think of as data center Ethernet, or is this RDMA over converged Ethernet? What Are we talking about? >> Yeah, so RDMA, right? So when you look at running, essentially HPC workloads, you have the NPI protocol, so message passing interface, right? And so what you need to do is you may need to make sure that that NPI message passing interface runs efficiently on Ethernet. And so this is why we want to test and validate all these different things to make sure that that protocol runs really, really fast on Ethernet. If you look at NPIs officially, built to, hey, it was designed to run on InfiniBand but now what you see with Broadcom, with the great work they're doing, now we can make that work on Ethernet and get same performance, so that's huge for customers. >> Both of you get to see a lot of different types of customers. I kind of feel like you're a little bit of a looking into the crystal ball type because you essentially get to see the future knowing what people are trying to achieve moving forward. Talk to us about the future of Ethernet in HPC in terms of AI and ML, where do you think we're going to be next year or 10 years from now? >> You want to go first or you want me to go first? >> I can start, yeah. >> Savannah: Pete feels ready. >> So I mean, what I see, I mean, Ethernet, what we've seen is that as far as on, starting off of the switch side, is that we've consistently doubled the bandwidth every 18 to 24 months. >> That's impressive. >> Pete: Yeah. >> Nicely done, casual, humble brag there. That was great, I love that. I'm here for you. >> I mean, I think that's one of the benefits of Ethernet, is the ecosystem, is the trajectory the roadmap we've had, I mean, you don't see that in any of the networking technology. >> David: More who? (all laughing) >> So I see that, that trajectory is going to continue as far as the switches doubling in bandwidth, I think that they're evolving protocols, especially again, as you're moving away from academia into the enterprise, into cloud data centers, you need to have a combination of protocols. So you'll probably focus still on RDMA, for the supercomputing, the AI/ML workloads. But we do see that as you have a mix of the applications running on these end nodes, maybe they're interfacing to the CPUs for some processing, you might use a different mix of protocols. So I'd say it's going to be doubling a bandwidth over time, evolution of the protocols. I mean, I expect that Rocky is probably going to evolve over time depending on the AI/ML and the HPC workloads. I think also there's a big change coming as far as the physical connectivity within the data center. Like one thing we've been focusing on is co-packed optics. So right now, this chip is, all the balls in the back here, there's electrical connections. >> How many are there, by the way? 9,000 plus on the back of that-- >> 9,352. >> I love how specific it is. It's brilliant. >> Yeah, so right now, all the SERDES, all the signals are coming out electrically based, but we've actually shown, we actually we have a version of Tomahawk 4 at 25.6 T that has co-packed optics. So instead of having electrical output, you actually have optics directly out of the package. And if you look at, we'll have a version of Tomahawk 5. >> Nice. >> Where it's actually even a smaller form factor than this, where instead of having the electrical output from the bottom, you actually have fibers that plug directly into the sides. >> Wow. Cool. >> So I see there's the bandwidth, there's radix's increasing, protocols, different physical connectivity. So I think there's a lot of things throughout, and the protocol stack's also evolving. So a lot of excitement, a lot of new technology coming to bear. >> Okay, You just threw a carrot down the rabbit hole. I'm only going to chase this one, okay? >> Peter: All right. >> So I think of individual discreet physical connections to the back of those balls. >> Yeah. >> So if there's 9,000, fill in the blank, that's how many connections there are. How do you do that many optical connections? What's the mapping there? What does that look like? >> So what we've announced for Tomahawk 5 is it would have FR4 optics coming out. So you'd actually have 512 fiber pairs coming out. So basically on all four sides, you'd have these fiber ribbons that come in and connect. There's actually fibers coming out of the sides there. We wind up having, actually, I think in this case, we would actually have 512 channels and it would wind up being on 128 actual fiber pairs because-- >> It's miraculous, essentially. >> Savannah: I know. >> Yeah. So a lot of people are going to be looking at this and thinking in terms of InfiniBand versus Ethernet, I think you've highlighted some of the benefits of specifically running Ethernet moving forward as HPC which sort of just trails slightly behind super computing as we define it, becomes more pervasive AI/ML. What are some of the other things that maybe people might not immediately think about when they think about the advantages of running Ethernet in that environment? Is it about connecting the HPC part of their business into the rest of it? What are the advantages? >> Yeah, I mean, that's a big thing. I think, and one of the biggest things that Ethernet has again, is that the data centers, the networks within enterprises, within clouds right now are run on Ethernet. So now, if you want to add services for your customers, the easiest thing for you to do is the drop in clusters that are connected with the same networking technology. So I think one of the biggest things there is that if you look at what's happening with some of the other proprietary technologies, I mean, in some cases they'll have two different types of networking technologies before they interface to Ethernet. So now you've got to train your technicians, you train your assist admins on two different network technologies. You need to have all the debug technology, all the interconnect for that. So here, the easiest thing is you can use Ethernet, it's going to give you the same performance and actually, in some cases, we've seen better performance than we've seen with Omni-Path, better than in InfiniBand. >> That's awesome. Armando, we didn't get to you, so I want to make sure we get your future hot take. Where do you see the future of Ethernet here in HPC? >> Well, Pete hit on a big thing is bandwidth, right? So when you look at, train a model, okay? So when you go and train a model in AI, you need to have a lot of data in order to train that model, right? So what you do is essentially, you build a model, you choose whatever neural network you want to utilize. But if you don't have a good data set that's trained over that model, you can't essentially train the model. So if you have bandwidth, you want big pipes because you have to move that data set from the storage to the CPU. And essentially, if you're going to do it maybe on CPU only, but if you do it on accelerators, well, guess what? You need a big pipe in order to get all that data through. And here's the deal, the bigger the pipe you have, the more data, the faster you can train that model. So the faster you can train that model, guess what? The faster you get to some new insight, maybe it's a new competitive advantage, maybe it's some new way you design a product, but that's a benefit of speed, you want faster, faster, faster. >> It's all about making it faster and easier-- for the users. >> Armando: It is. >> I love that. Last question for you, Pete, just because you've said Tomahawk seven times, and I'm thinking we're in Texas, stakes, there's a lot going on with that. >> Making me hungry. >> I know, exactly. I'm sitting out here thinking, man, I did not have big enough breakfast. How did you come up with the name Tomahawk? >> So Tomahawk, I think it just came from a list. So we have a tried end product line. >> Savannah: Ah, yes. >> Which is a missile product line. And Tomahawk is being kind of like the bigger and batter missile, so. >> Savannah: Love this. Yeah, I mean-- >> So do you like your engineers? You get to name it. >> Had to ask. >> It's collaborative. >> Okay. >> We want to make sure everyone's in sync with it. >> So just it's not the Aquaman tried. >> Right. >> It's the steak Tomahawk. I think we're good now. >> Now that we've cleared that-- >> Now we've cleared that up. >> Armando, Pete, it was really nice to have both you. Thank you for teaching us about the future of Ethernet and HCP. David Nicholson, always a pleasure to share the stage with you. And thank you all for tuning in to theCUBE live from Dallas. We're here talking all things HPC and supercomputing all day long. We hope you'll continue to tune in. My name's Savannah Peterson, thanks for joining us. (soft music)

>>You can put this in a conference. >>Good morning and welcome back to Dallas. Ladies and gentlemen, we are here with the cube Live from, from Supercomputing 2022. David, my cohost, how you doing? Exciting. Day two. Feeling good. >>Very exciting. Ready to start off the >>Day. Very excited. We have two fascinating guests joining us to kick us off. Please welcome Pete and Armando. Gentlemen, thank you for being here with us. >>Having us, >>For having us. I'm excited that you're starting off the day because we've been hearing a lot of rumors about ethernet as the fabric for hpc, but we really haven't done a deep dive yet during the show. Y'all seem all in on ethernet. Tell us about that. Armando, why don't you start? >>Yeah. I mean, when you look at ethernet, customers are asking for flexibility and choice. So when you look at HPC and you know, infinite band's always been around, right? But when you look at where Ethernet's coming in, it's really our commercial and their enterprise customers. And not everybody wants to be in the top 500. What they want to do is improve their job time and improve their latency over the network. And when you look at ethernet, you kinda look at the sweet spot between 8, 12, 16, 32 nodes. That's a perfect fit for ethernet and that space and, and those types of jobs. >>I love that. Pete, you wanna elaborate? Yeah, yeah, >>Yeah, sure. I mean, I think, you know, one of the biggest things you find with internet for HPC is that, you know, if you look at where the different technologies have gone over time, you know, you've had old technologies like, you know, atm, Sonic, fitty, you know, and pretty much everything is now kind of converged toward ethernet. I mean, there's still some technologies such as, you know, InfiniBand, omnipath that are out there. Yeah. But basically there's single source at this point. So, you know, what you see is that there is a huge ecosystem behind ethernet. And you see that also, the fact that ethernet is used in the rest of the enterprise is using the cloud data centers that is very easy to integrate HPC based systems into those systems. So as you move HPC out of academia, you know, into, you know, into enterprise, into cloud service providers is much easier to integrate it with the same technology you're already using in those data centers, in those networks. >>So, so what's this, what is, what's the state of the art for ethernet right now? What, you know, what's, what's the leading edge, what's shipping now and what and what's in the near future? You, you were with Broadcom, you guys design this stuff. >>Yeah, yeah. Right. Yeah. So leading edge right now, I got a couple, you know, Wes stage >>Trough here on the cube. Yeah. >>So this is Tomahawk four. So this is what is in production is shipping in large data centers worldwide. We started sampling this in 2019, started going into data centers in 2020. And this is 25.6 tets per second. Okay. Which matches any other technology out there. Like if you look at say, infin band, highest they have right now that's just starting to get into production is 25 point sixt. So state of the art right now is what we introduced. We announced this in August. This is Tomahawk five. So this is 51.2 terabytes per second. So double the bandwidth have, you know, any other technology that's out there. And the important thing about networking technology is when you double the bandwidth, you don't just double the efficiency, it's actually winds up being a factor of six efficiency. Wow. Cause if you want, I can go into that, but why >>Not? Well, I, what I wanna know, please tell me that in your labs you have a poster on the wall that says T five with, with some like Terminator kind of character. Cause that would be cool if it's not true. Don't just don't say anything. I just want, I can actually shift visual >>It into a terminator. So. >>Well, but so what, what are the, what are the, so this is, this is from a switching perspective. Yeah. When we talk about the end nodes, when we talk about creating a fabric, what, what's, what's the latest in terms of, well, the kns that are, that are going in there, what's, what speed are we talking about today? >>So as far as 30 speeds, it tends to be 50 gigabits per second. Okay. Moving to a hundred gig pan four. Okay. And we do see a lot of Knicks in the 200 gig ethernet port speed. So that would be, you know, four lanes, 50 gig. But we do see that advancing to 400 gig fairly soon. 800 gig in the future. But say state of the art right now, we're seeing for the end nodes tends to be 200 gig E based on 50 gig pan four. Wow. >>Yeah. That's crazy. Yeah, >>That is, that is great. My mind is act actively blown. I wanna circle back to something that you brought up a second ago, which I think is really astute. When you talked about HPC moving from academia into enterprise, you're both seeing this happen. Where do you think we are on the adoption curve and sort of in that cycle? Armand, do you wanna go? >>Yeah, yeah. Well, if you look at the market research, they're actually telling it's 50 50 now. So ethernet is at the level of 50%. InfiniBand is at 50%. Right. Interesting. Yeah. And so what's interesting to us, customers are coming to us and say, Hey, we want to see, you know, flexibility and choice and hey, let's look at ethernet and let's look at InfiniBand. But what is interesting about this is that we're working with Broadcom, we have their chips in our lab, we have our switches in our lab. And really what we're trying to do is make it easy to simple and configure the network for essentially mpi. And so the goal here with our validated designs is really to simplify this. So if you have a customer that, Hey, I've been in fbe, but now I want to go ethernet, you know, there's gonna be some learning curves there. And so what we wanna do is really simplify that so that we can make it easy to install, get the cluster up and running, and they can actually get some value out of the cluster. >>Yeah. Peter, what, talk about that partnership. What, what, what does that look like? Is it, is it, I mean, are you, you working with Dell before the, you know, before the T six comes out? Or you just say, you know, what would be cool, what would be cool is we'll put this in the T six? >>No, we've had a very long partnership both on the hardware and the software side. You know, Dell has been an early adopter of our silicon. We've worked very closely on SI and Sonic on the operating system, you know, and they provide very valuable feedback for us on our roadmap. So before we put out a new chip, and we have actually three different product lines within the switching group within Broadcom, we've then gotten, you know, very valuable feedback on the hardware and on the APIs, on the operating system that goes on top of those chips. So that way when it comes to market, you know, Dell can take it and, you know, deliver the exact features that they have in the current generation to their customers to have that continuity. And also they give us feedback on the next gen features they'd like to see again in both the hardware and the software. >>So, so I, I'm, I'm just, I'm fascinated by, I I, I always like to know kind like what Yeah, exactly. Exactly right. Look, you, you start talking about the largest super supercomputers, most powerful supercomputers that exist today, and you start looking at the specs and there might be 2 million CPUs, 2 million CPU cores, yeah. Ex alop of, of, of, of performance. What are the, what are the outward limits of T five in switches, building out a fabric, what does that look like? What are the, what are the increments in terms of how many, and I know it, I know it's a depends answer, but, but, but how many nodes can you support in a, in a, in a scale out cluster before you need another switch? What does that increment of scale look like today? >>Yeah, so I think, so this is 51.2 terras per second. What we see the most common implementation based on this would be with 400 gig ethernet ports. Okay. So that would be 128, you know, 400 giggi ports connected to, to one chip. Okay. Now, if you went to 200 gig, which is kind of the state of the art for the Nicks, you can have double that. Okay. So, you know, in a single hop you can have 256 end nodes connected through one switch. >>So, okay, so this T five, that thing right there inside a sheet metal box, obviously you've got a bunch of ports coming out of that. So what is, what does that, what's the form factor look like for that, for where that T five sits? Is there just one in a chassis or you have, what does that look >>Like? It tends to be pizza boxes these days. Okay. What you've seen overall is that the industry's moved away from chassis for these high end systems more towards pizza, pizza boxes. And you can have composable systems where, you know, in the past you would have line cards, either the fabric cards that the line cards are plugged into or interface to these days, what tends to happen is you'd have a pizza box, and if you wanted to build up like a virtual chassis, what you would do is use one of those pizza boxes as the fabric card, one of them as the, the line card. >>Okay. >>So what we see, the most common form factor for this is they tend to be two, I'd say for North America, most common would be a two R U with 64 OSF P ports. And often each of those OSF p, which is an 800 gig e or 800 gig port, we've broken out into two 400 gig quarts. Okay. So yeah, in two r u you've got, and this is all air cooled, you know, in two re you've got 51.2 T. We do see some cases where customers would like to have different optics, and they'll actually deploy a four U just so that way they have the face place density, so they can plug in 128, say qsf P one 12. But yeah, it really depends on which optics, if you wanna have DAK connectivity combined with, with optics. But those are the two most common form factors. >>And, and Armando ethernet isn't, ethernet isn't necessarily ethernet in the sense that many protocols can be run over it. Right. I think I have a projector at home that's actually using ethernet physical connections. But what, so what are we talking about here in terms of the actual protocol that's running over this? Is this exactly the same as what you think of as data center ethernet, or, or is this, you know, RDMA over converged ethernet? What, what are >>We talking about? Yeah, so our rdma, right? So when you look at, you know, running, you know, essentially HPC workloads, you have the NPI protocol, so message passing interface, right? And so what you need to do is you may need to make sure that that NPI message passing interface runs efficiently on ethernet. And so this is why we want to test and validate all these different things to make sure that that protocol runs really, really fast on ethernet, if you look at NPI is officially, you know, built to, Hey, it was designed to run on InfiniBand, but now what you see with Broadcom and the great work they're doing now, we can make that work on ethernet and get, you know, it's same performance. So that's huge for customers. >>Both of you get to see a lot of different types of customers. I kind of feel like you're a little bit of a, a looking into the crystal ball type because you essentially get to see the future knowing what people are trying to achieve moving forward. Talk to us about the future of ethernet in hpc in terms of AI and ml. Where, where do you think we're gonna be next year or 10 years from now? >>You wanna go first or you want me to go first? I can start. >>Yeah. Pete feels ready. >>So I mean, what I see, I mean, ethernet, I mean, is what we've seen is that as far as on the starting off of the switch side, is that we've consistently doubled the bandwidth every 18 to 24 months. That's >>Impressive. >>Yeah. So nicely >>Done, casual, humble brag there. That was great. That was great. I love that. >>I'm here for you. I mean, I think that's one of the benefits of, of Ethan is like, is the ecosystem, is the trajectory, the roadmap we've had, I mean, you don't see that in any other networking technology >>More who, >>So, you know, I see that, you know, that trajectory is gonna continue as far as the switches, you know, doubling in bandwidth. I think that, you know, they're evolving protocols. You know, especially again, as you're moving away from academia into the enterprise, into cloud data centers, you need to have a combination of protocols. So you'll probably focus still on rdma, you know, for the supercomputing, the a AIML workloads. But we do see that, you know, as you have, you know, a mix of the applications running on these end nodes, maybe they're interfacing to the, the CPUs for some processing, you might use a different mix of protocols. So I'd say it's gonna be doubling a bandwidth over time evolution of the protocols. I mean, I expect that Rocky is probably gonna evolve over time depending on the a AIML and the HPC workloads. I think also there's a big change coming as far as the physical connectivity within the data center. Like one thing we've been focusing on is co-pack optics. So, you know, right now this chip is all, all the balls in the back here, there's electrical connections. How >>Many are there, by the way? 9,000 plus on the back of that >>352. >>I love how specific it is. It's brilliant. >>Yeah. So we get, so right now, you know, all the thirties, all the signals are coming out electrically based, but we've actually shown, we have this, actually, we have a version of Hawk four at 25 point sixt that has co-pack optics. So instead of having electrical output, you actually have optics directly out of the package. And if you look at, we'll have a version of Tomahawk five Nice. Where it's actually even a smaller form factor than this, where instead of having the electrical output from the bottom, you actually have fibers that plug directly into the sides. Wow. Cool. So I see, you know, there's, you know, the bandwidth, there's radis increasing protocols, different physical connectivity. So I think there's, you know, a lot of things throughout, and the protocol stack's also evolving. So, you know, a lot of excitement, a lot of new technology coming to bear. >>Okay. You just threw a carrot down the rabbit hole. I'm only gonna chase this one. Okay. >>All right. >>So I think of, I think of individual discreet physical connections to the back of those balls. Yeah. So if there's 9,000, fill in the blank, that's how many connections there are. How do you do that in many optical connections? What's, what's, what's the mapping there? What does that, what does that look like? >>So what we've announced for TAMA five is it would have fr four optics coming out. So you'd actually have, you know, 512 fiber pairs coming out. So you'd have, you know, basically on all four sides, you'd have these fiber ribbons that come in and connect. There's actually fibers coming out of the, the sides there. We wind up having, actually, I think in this case, we would actually have 512 channels and it would wind up being on 128 actual fiber pairs because >>It's, it's miraculous, essentially. It's, I know. Yeah, yeah, yeah, yeah. Yeah. So, so, you know, a lot of people are gonna be looking at this and thinking in terms of InfiniBand versus versus ethernet. I think you've highlighted some of the benefits of specifically running ethernet moving forward as, as hpc, you know, which is sort of just trails slightly behind supercomputing as we define it, becomes more pervasive AI ml. What, what are some of the other things that maybe people might not immediately think about when they think about the advantages of running ethernet in that environment? Is it, is it connecting, is it about connecting the HPC part of their business into the rest of it? What, or what, what are the advantages? >>Yeah, I mean, that's a big thing. I think, and one of the biggest things that ethernet has again, is that, you know, the data centers, you know, the networks within enterprises within, you know, clouds right now are run on ethernet. So now if you want to add services for your customers, the easiest thing for you to do is, you know, the drop in clusters that are connected with the same networking technology, you know, so I think what, you know, one of the biggest things there is that if you look at what's happening with some of the other proprietary technologies, I mean, in some cases they'll have two different types of networking technologies before they interface to ethernet. So now you've got to train your technicians, you train your, your assist admins on two different network technologies. You need to have all the, the debug technology, all the interconnect for that. So here, the easiest thing is you can use ethernet, it's gonna give you the same performance. And actually in some cases we seen better performance than we've seen with omnipath than, you know, better than in InfiniBand. >>That's awesome. Armando, we didn't get to you, so I wanna make sure we get your future hot take. Where do you see the future of ethernet here in hpc? >>Well, Pete hit on a big thing is bandwidth, right? So when you look at train a model, okay, so when you go and train a model in ai, you need to have a lot of data in order to train that model, right? So what you do is essentially you build a model, you choose whatever neural network you wanna utilize, but if you don't have a good data set that's trained over that model, you can't essentially train the model. So if you have bandwidth, you want big pipes because you have to move that data set from the storage to the cpu. And essentially, if you're gonna do it maybe on CPU only, but if you do it on accelerators, well guess what? You need a big pipe in order to get all that data through. And here's the deal. The bigger the pipe you have, the more data, the faster you can train that model. So the faster you can train that model, guess what? The faster you get to some new insight, maybe it's a new competitive advantage. Maybe it's some new way you design a product, but that's a benefit of speed you want faster, faster, faster. >>It's all about making it faster and easier. It is for, for the users. I love that. Last question for you, Pete, just because you've said Tomahawk seven times, and I'm thinking we're in Texas Stakes, there's a lot going on with with that making >>Me hungry. >>I know exactly. I'm sitting up here thinking, man, I did not have a big enough breakfast. How do you come up with the name Tomahawk? >>So Tomahawk, I think you just came, came from a list. So we had, we have a tri end product line. Ah, a missile product line. And Tomahawk is being kinda like, you know, the bigger and batter missile, so, oh, okay. >>Love this. Yeah, I, well, I >>Mean, so you let your engineers, you get to name it >>Had to ask. It's >>Collaborative. Oh good. I wanna make sure everyone's in sync with it. >>So just so we, it's not the Aquaman tried. Right, >>Right. >>The steak Tomahawk. I >>Think we're, we're good now. Now that we've cleared that up. Now we've cleared >>That up. >>Armando P, it was really nice to have both you. Thank you for teaching us about the future of ethernet N hpc. David Nicholson, always a pleasure to share the stage with you. And thank you all for tuning in to the Cube Live from Dallas. We're here talking all things HPC and Supercomputing all day long. We hope you'll continue to tune in. My name's Savannah Peterson, thanks for joining us.

>>Hello, and welcome to the cubes presentation of the AWS startup showcase. This is season two, episode four, the ongoing series covering exciting startups from the AWS ecosystem to talk about cyber security. I'm your host, John feer. And today we're excited to join by a Mediatel Walker, CEO of Quin security and sub IER, vice president of product management of sequence security gentlemen, thanks for joining us today on this showcase. >>Thank you, John PRAs. >>So the title of this session is continuous API protection life cycle to discover, detect, and defend security. APIs are part of it. They're hardened, everyone's using them, but they're they're target for malicious behavior. This is the focus of this segment. You guys are in the leading edge of this. What are the biggest challenges for organizations right now in assessing their security risks? Because you're seeing APIs all over the place in the news, just even this week, Twitter had a whistleblower come out from the security group, talking about their security plans, misleading the FTC on the bots and some of the malicious behavior inside the API interface of Twitter. This is really a mainstream Washington post is reporting on it. New York times, all the global outlets are talking about this story. This is the risk. I mean, yeah, this is what you guys do protect against this. >>Yeah, this is absolutely top of mind for a lot of security folks today. So obviously in the media and the type of attack that that is being discussed with this whistleblower coming out is called reputation bombing. This is not new. This has been going on since I would say at least eight to 10 years where the, the bad actors are using bots or automation and ultimately using APIs on these large social media platforms, whether it's Facebook, whether it's Twitter or some other social media platform and messing with the reputation system of those large platforms. And what I mean by that is they will do fake likes, fake commenting, fake retweeting in the case of Twitter. And what that means is that things that are, should not be very popular, all of a sudden become popular. That that way they're able to influence things like elections, shopping habits, personnel. >>We, we work with similar profile companies and we see this all the time. We, we mostly work on some of the secondary platforms like dating and other sort of social media platforms around music sharing and things like video sharing. And we see this all the time. These, these bots are bad. Actors are using bots, but ultimately it's an API problem. It's not just a bot problem. And that's what we've been trying to sort of preach to the world, which is your bot problem is subset of your API security challenges that you deal as an organization. >>You know, IMIA, we talked about this in the past on a previous conversation, but this really is front and center mainstream for the whole world to see around the challenges. All companies face, every CSO, every CIO, every board member organizations out there looking at this security posture that spans not just information technology, but physical and now social engineering. You have all kinds of new payloads of malicious behavior that are being compromised through, through things like APIs. This is not just about CSO, chief information security officer. This is chief security officer issues. What's your reaction >>Very much so I think the, this is a security problem, but it's also a reputation problem. In some cases, it's a data governance problem. We work with several companies which have very restrictive data governance and data regulations or data residency regulations there to conform to those regulations. And they have to look at that. It's not just a CSO problem anymore. In case of the, the news of the day to day, this is a platform problem. This goes all the way to the, that time CTO of Twitter. And now the CEO of Twitter, who was in charge of dealing with these problems. We see as just to give you an example, we, we work, we work with a similar sort of social media platform that allows Oop based login to their platform that is using tokens. You can sort of sign in with Facebook, sign in with Twitter, sign in with Google. These are API keys that are generated and trusted by these social media platforms. When we saw that Facebook leaked about 50 million of these login credentials or API keys, this was about three, four years ago. I wrote a blog about it. We saw a huge spike in those API keys being used to log to other social media platforms. So although one social platform might be taking care of its, you know, API or what problem, if something else gets reached somewhere else, it has a cascading impact on a variety of platforms. >>You know, that's a really interesting dynamic. And if you think about just the token piece that you mentioned, that's kind of under the coverage, that's a technology challenge, but also you get in the business logic. So let's go back and, and unpack that, okay, they discontinue the tokens. Now they're being reused here. In the case of Twitter, I was talking to an executive here in Silicon valley and they said, yeah, it's a cautionary tale, for sure. Although Twitter's a unique situation, but they abstract out the business value and say, Hey, they had an M and a deal on the table. And so if someone wants to unwind that deal, all I gotta say is, Hey, there's a bot problem. And now you have essentially new kinds of risk in the business have nothing to do with some sign the technology, okay. They got a security breach, but here with Twitter, you have an, an, an M and a deal, an acquisition that's being contested because of the, the APIs. So, so if you're in business, you gotta think to yourself, what am I risking with my API? So every organization should be assessing their security risks, tied to their APIs. This is a huge awakening for them. Where should they start? And that's the, that's the core question. Okay. You got my attention risks with the API. What do I do? >>So when I talked to you in my previous interview, the start is basically knowing what to, in most cases, you see these that are hitting the wire much. Every now there is a major in cases you'll find these APIs are targeted, that are not poorly protected. They're absolutely just not protected at all, which means the security team or any sort of team that is responsible for protecting these APIs are just completely unaware of these APIs being there in the first place. And this is where we talk about the shadow it or shadow API problem. Large enterprises have teams that are geo distributed, and this problem is escalated after the pandemic even more because now you have teams that are completely distributed. They do M and a. So they acquire new companies and have no visibility into their API or security practices. And so there are a lot of driving factors why these APIs are just not protected and, and just unknown even more to the security team. So the first step has to be discover your API attack surface, and then prioritize which APIs you wanna target in terms of runtime protection. >>Yeah. I wanna dig into that API kind of attack surface area management, runtime monitoring capability in a second, but so I wanna get you in here too, because we're talking about APIs, we're talking about attacks. What does an API attack look like? >>Yeah, that's a very good question, John, there are really two different forms of attacks of APIs, one type of attack, exploits, APIs that have known vulnerabilities or some form of vulnerabilities. For instance, APIs that may use a weak form of authentication or are really built with no authentication at all, or have some sort of vulnerability that makes them very good targets for an attacker to target. And the second form of attack is a more subtle one. It's called business logic abuse. It's, it's utilizing APIs in completely legitimate manner manners, but exploiting those APIs to exfiltrate information or key sensitive information that was probably not thought through by the developer or the designers or those APIs. And really when we do API protection, we really need to be able to handle both of those scenarios, protect against abuse of APIs, such as broken authentication, or broken object level authorization APIs with that problem, as well as protecting APIs from business logic abuse. And that's really how we, you know, differentiate against other vendors in this >>Market. So just what are the, those key differentiated ways to identify the, in the malicious intents with APIs? Can you, can you just summarize that real quick, the three ways? >>Sure. Yeah, absolutely. There are three key ways that we differentiate against our competition. One is in the, we have built out a, in the ability to actually detect such traffic. We have built out a very sophisticated threat intelligence network built over the entire lifetime of the company where we have very well curated information about malicious infrastructures, malicious operators around the world, including not just it address ranges, but also which infrastructures do they operate on and stuff like that, which actually helps a lot in, in many environments in especially B2C environments, that alone accounts for a lot of efficacy for us in detecting our weed out bad traffic. The second aspect is in analyzing the request that are coming in the API traffic that is coming in and from the request itself, being able to tell if there is credential abuse going on or credential stuffing going on or known patterns that the traffic is exhibiting, that looks like it is clearly trying to attack the attack, the APM. >>And the third one is, is really more sophisticated as they go farther and farther. It gets more sophisticated where sequence actually has a lot of machine learning models built in which actually profile the traffic that is coming in and separate. So the legitimate or learns the legitimate traffic from the anomalous or suspicious traffic. So as the traffic, as the API requests are coming in, it automatically can tell that this traffic does not look like legitimate traffic does not look like the traffic that this API typically gets and automatically uses that to figure out, okay, where is this traffic coming from? And automatically takes action to prevent that attack? >>You know, it's interesting APIs have been part of the goodness of cloud and cloud scale. And it reminds me of the old Andy Grove quote, founder of, in one of the founders of Intel, you know, let chaos, let, let the chaos happen, then reign it in it's APIs. You know, a lot of people have been creating them and you've got a lot of different stakeholders involved in creating them. And so now securing them and now manage them. So a lot of creation now you're starting to secure them and now you gotta manage 'em. This all is now big focus. As you pointed out, what are some of the dynamics that customers who have to deal with on the product side and, and organization, let, let chaos rain, and then rain in the chaos, as, as the saying goes, what, what do companies do? >>Yeah. Typically companies start off with like, like a mayor talked about earlier. Discovery is really the key thing to start with, like figuring out what your API attack surfaces and really getting your arms around that problem. And typically we are finding customers start that off from the security organization, the CSO organization to really go after that problem. And in some cases, in some customers, we even find like dedicated centers of excellence that are created for API security, which go after that problem to be able to get their arms around the whole API attack surface and the API protection problem statement. So that's where usually that problem starts to get addressed. >>I mean, organizations and your customers have to stop the attacks. A lot of different techniques, you know, run time. You mentioned that earlier, the surface area monitoring, what's the choice. What's the, where are, where are, where is everybody? Is everyone in the, in the boiling water, like the frog and boiling water or they do, they know it's happening? Like what did they do? What's their opportunity to get in >>Position? Yeah. So I, I think let's take a step back a little bit, right? What has happened is if you draw the cloud security market, if you will, right. Which is the journey to the cloud, the security of these applications or APIs at a container level, in terms of vulnerabilities and, and other things that market grew with the journey to the cloud, pretty much locked in lockstep. What has happened in the API side is the API space has kind of lacked behind the growth and explosion in the API space. So what that means is APIs are getting published way faster than the security teams are able to sort of control and secure them. APIs are getting published in environments that the security completely unaware of. We talked about in the past about the parameter, the parameter, as we know, it doesn't exist anymore. It used to be the case that you hit a CDN, you terminate your SSL, you stop your layer three and four DDoS. >>And then you go into the application and do the business logic. That parameter is just gone because it's now could be living in multi-cloud environment. It could be living in the on-prem environment, which is PubNet is friendly. And so security teams that are used to protecting apps, using a perimeter defense plus changes, it's gone. You need to figure out where your perimeter is. And therefore we sort of recommend an approach, which is have a uniform view across all your APIs, wherever they could be distributed and have a single point of control across those with a solution like sequence. And there are others also in this space, which is giving you that uniform view, which is first giving you that, you know, outside and looking view of what APIs to protect. And then let's, you sort of take the journey of securing the API life cycle. >>So I would say that every company now hear me out on this indulges me for a second. Every company in the world will be non perimeter based, except for maybe 5% because of maybe unique reason, proprietary lockdown, information, whatever. But for most, most companies, everyone will be in the cloud or some cloud native, non perimeter based security posture. So the question is, how does your platform fit into that trajectory? And specifically, why are you guys in the position in your mind to help customers solve this API problem? Because again, APIs have been the greatest thing about the cloud, right? Yeah. So the goodness is there because of APS. Now you gotta reign it in reign in the chaos. Yeah. What, what about your platform share? What is it, why is it win? Why should customers care about this? >>Absolutely. So if you think about it, you're right, the parameter doesn't exist. People have APIs deployed in multiple environments, multicloud hybrid, you name it sequence is uniquely positioned in a way that we can work with your environment. No matter what that environment is. We're the only player in this space that can protect your APIs purely as a SA solution or purely as an on-prem deployment. And that could be a SaaS platform. It doesn't need to be RackN, but we also support that and we could be a hybrid deployment. We have some deployments which are on your prem and the rest of this solution is in our SA. If you think about it, customers have secured their APIs with sequence with 15 minutes, you know, going live from zero to life and getting that protection instantaneously. We have customers that are processing a billion API calls per day, across variety of different cloud environments in sort of six different brands. And so that scale, that flexibility of where we can plug into your infrastructure or be completely off of your infrastructure is something unique to sequence that we offer that nobody else is offering >>Today. Okay. So I'll be, I'll be a naysayer. Yeah, look, it, we are perfectly coded APIs. We are the best in the business. We're locked down. Our APIs are as tight as a drum. Why do I need you? >>So that goes back to who's answer. Of course, >>Everyone's say that that's, that's great, but that's my argument. >>There are two types of API attacks. One is a tactic problem, which is exploiting a vulnerability in an API, right? So what you're saying is my APIs are secure. It does not have any vulnerability I've taken care of all vulnerabilities. The second type of attack that targets APIs is the business logic. Use this stuff in the news this week, which is the whistleblower problem, which is, if you think APIs that Twitter is publishing for users are perfectly secure. They are taking care of all the vulnerabilities and patching them when they find new ones. But it's the business logic of, you know, REWE liking or commenting that the bots are targeting, which they have no against. Right. And then none of the other social networks too. Yeah. So there are many examples. Uber wrote a program to impersonate users in different geo locations to find lifts, pricing, and driver information and passenger information, completely legitimate use of APIs for illegitimate, illegitimate purpose using bots. So you don't need bots by the way, don't, don't make this about bot versus not. Yeah. You can use APIs sort of for the, the purpose that they're not designed for sort of exploiting their business logic, either using a human interacting, a human farm, interacting with those APIs or a bot form targeting those APIs, I think. But that's the problem when you have, even when you've secured all your problem, all your APIs, you still have to worry about these of challenges. >>I think that's the big one. I think the business logic one, certainly the Twitter highlights that the Uber example is a good one. That is basically almost the, the backlash of having a simplistic API, which people design to. Right. Yeah. You know, as you point out, Twitter is very simple API, hardened, very strong security, but they're using it to maliciously manipulate what's inside. So in a way that perimeter's dead too. Right. So how do you stop that business logic? What's the, what's the solution what's the customer do about that? Because their goal is to create simple, scalable APIs. >>Yeah. I'll, I'll give you a little bit, and then I think Subaru should maybe go into a little bit of the depth of the problem, but what I think that the answer lies in what Subaru spoke earlier, which is our ML. AI is, is good at profiling plus split between the API users, are these legitimate users, humans versus bots. That's the first split we do. The split second split we do is even when these, these are classified users as bots, we will say there are some good bots that are necessary for the business and bad bots. So we are able to split this across three types of users, legitimate humans, good bots and bad bots. And just to give you an example of good bots is there are in the financial work, there are aggregators that are scraping your data and aggregating for end users to consume, right? Your, your, and other type of financial aggregators FinTech companies like MX. These are good bots and you wanna allow them to, you know, use your APIs, whereas you wanna stop the bad bots from using your APIs super, if you wanna add so, >>So good bots versus bad bots, that's the focus. Go ahead. Weigh in, weigh in on your thought on this >>Really breaks down into three key areas that we talk about here, sequence, right? One is you start by discovering all your APIs. How many APIs do I have in my environment that ly immediately highlight and say, Hey, you have, you know, 10,000 APIs. And that usually is an eye opener to many customers where they go, wow. I thought we had a 10th of that number. That usually is an eyeopener for them to, to at least know where they're at. The second thing is to tell them detection information. So discover, detect, and defend detect will tell them, Hey, your APIs are getting traffic from. So and so it addresses so and so infrastructure. So and so countries and so on that usually is another eye opener for them. They then get to see where their API traffic is coming from. Let's say, if you are a, if you're running a pizza delivery service out of California and your traffic is coming from Eastern Europe to go, wait a minute, nobody's trying, I'm not, I'm not, I don't deliver pizzas in Eastern Europe. Why am I getting traffic from that part of the world? So that sort of traffic immediately comes up and it will tell you that it is hitting your unauthenticated API. It is hitting your API. That has, that is vulnerable to a broken object level, that authorization, vulnerable be and so on. >>Yeah, I think, and >>Then comes the different aspect. Yeah. The different aspect is where you can take action and say, I wanna block certain types of traffic, or I wanna rate limit certain types of traffic. If, if you're seeing spikes there or you could maybe insert header so that it passes on to the end application and the application team can use that bit to essentially take a, a conscious response. And so, so the platform is very flexible in allowing them to take an action that suits their needs. >>Yeah. And I think this is the big trend. This is why I like what you guys are doing. One APIs we're built for the goodness of cloud. They're now the plumbing, you know, anytime you see plumbing involved, connection points, you know, that's pretty important. People are building it out and it has made the cloud what it is. Now, you got a security challenge. You gotta add more intelligence, more smarts to it. This is where I think platform versus tools matter. Can you guys just quickly share your thoughts on that? Cuz a lot of your customers and, and future customers have dealt with the sprawls of all these different tools. Right? I got a tool for this. I got a tool for that, but people are gravitating towards platforms, but how many platforms can a customer have? So again, this brings up the point point around how you guys are engaging with customers. Can you share your thoughts on tooling platforms? Your customers are constantly inundated with the same tsunami. Isn't new thing. Why, what, how should they look at this? >>Yeah, I mean, we don't wanna be, we don't wanna add to that alert fatigue problem that affects much of the cybersecurity industry by generating a whole bunch of alerts and so on. So what we do is we actually integrate very well with S IEM systems or so systems and allow customers to integrate the information that we are detecting or mitigating and feed them onto enterprise systems like a Splunk or a Datadog where they may have sophisticated processes built in to monitor, you know, spikes in anomalous traffic or actions that are taken by sequence. And that can be their dashboard where a whole bunch of alerting and reporting actually happens. So we play in the security ecosystem very well by integrating with other products and integrate very tightly with them, right outta the box. >>Okay. Mia, this is a wrap up now for the showcase. Really appreciate you guys sharing your awesome technology and very relevant product for your customers and where we are right now in this we call Supercloud or now multi-cloud or hybrid world of cloud. Share a, a little bit about the company, how people can get involved in your solution, how they can consume it and things they should know about, about sequence security. >>Yeah, we've been on this journey, an exciting journey it's been for, for about eight years. We have very large fortune 100 global 500 customers that use our platform on a daily basis. We have some amazing logos, both in Europe and, and, and in us customers are, this is basically not the shelf product customers not only use it, but depend on sequence. Several retailers. We are sitting in front of them handling, you know, black Friday, cyber, Monday, Christmas shopping, or any sort of holiday seasonality shopping. And we have handled that the journey starts by, by just simply looking at your API attack surface, just to a discover call with sequence, figure out where your APIs are posted work with you to prioritize how to protect them in a sort of a particular order and take the whole life cycle with sequence. This is, this is an exciting phase exciting sort of stage in the company's life. We just raised a very sort of large CDC round of funding in December from Menlo ventures. And we are excited to see, you know, what's next in, in, in the next, you know, 12 to 18 months. It certainly is the, you know, one of the top two or three items on the CSOs, you know, budget list for next year. So we are extremely busy, but we are looking for, for what the next 12 to 18 months are, are in store for us. >>Well, congratulations to all the success. So will you run the roadmap? You know, APIs are the plumbing. If you will, you know, they connection points, you know, you want to kind of keep 'em simple, as they say, keep the pipes dumb and make the intelligence around it. You seem to see more and more intelligence coming around, not just securing it, but does, where does this go in your mind? Where, where do we go beyond once we secure everything and manage it properly, APRs, aren't going away, they're only gonna get better and smarter. Where's the intelligence coming share a little bit. >>Absolutely. Yeah. I mean, there's not a dull moment in the space. As digital transformation happens to most enterprise systems, many applications are getting transformed. We are seeing an absolute explosion in the volume of APIs and the types of APIs as well. So the applications that were predominantly limited to data centers sort of deployments are now splintered across multiple different cloud environments are completely microservices based APIs, deep inside a Kubernetes cluster, for instance, and so on. So very exciting stuff in terms of proliferation of volume of APIs, as well as types of APIs, there's nature of APIs. And we are building very sophisticated machine learning models that can analyze traffic patterns of such APIs and automatically tell legitimate behavior from anomalous or suspicious behavior and so on. So very exciting sort of breadth of capabilities that we are looking at. >>Okay. I mean, yeah. I'll give you the final words since you're the CEO for the CSOs out there, the chief information security officers and the chief security officers, what do you want to tell them? If you could give them a quick shout out? What would you say to them? >>My shout out is just do an assessment with sequence. I think this is a repeating thing here, but really get to know your APIs first, before you decide what and where to protect them. That's the one simple thing I can mention for thes >>Am. Thank you so much for, for joining me today. Really appreciate it. >>Thank you. >>Thank you. Okay. That is the end of this segment of the eight of his startup showcase. Season two, episode four, I'm John for your host and we're here with sequin security. Thanks for watching.

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase Open Cloud Innovations. This is season two of the ongoing series. We're covering exciting start startups in the AWS ecosystem to talk about open source community stuff. I'm your host, Dave Nicholson. And I'm delighted today to have two guests from Weaveworks. Steve George, COO of Weaveworks, and Steve Waterworth, technical marketing engineer from Weaveworks. Welcome, gentlemen, how are you? >> Very well, thanks. >> Very well, thanks very much. >> So, Steve G., what's the relationship with AWS? This is the AWS Startup Showcase. How do Weaveworks and AWS interact? >> Yeah sure. So, AWS is a investor in Weaveworks. And we, actually, collaborate really closely around EKS and some specific EKS tooling. So, in the early days of Kubernetes when AWS was working on EKS, the Elastic Kubernetes Service, we started working on the command line interface for EKS itself. And due to that partnership, we've been working closely with the EKS team for a long period of time, helping them to build the CLI and make sure that users in the community find EKS really easy to use. And so that brought us together with the AWS team, working on GitOps and thinking about how to deploy applications and clusters using this GitOps approach. And we've built that into the EKS CLI, which is an open source tool, is a project on GitHub. So, everybody can get involved with that, use it, contribute to it. We love hearing user feedback about how to help teams take advantage of the elastic nature of Kubernetes as simply and easily as possible. >> Well, it's great to have you. Before we get into the specifics around what Weaveworks is doing in this area that we're about to discuss, let's talk about this concept of GitOps. Some of us may have gotten too deep into a Netflix series, and we didn't realize that we've moved on from the world of DevOps or DevSecOps and the like. Explain where GitOps fits into this evolution. >> Yeah, sure. So, really GitOps is an instantiation, a version of DevOps. And it fits within the idea that, particularly in the Kubernetes world, we have a model in Kubernetes, which tells us exactly what we want to deploy. And so what we're talking about is using Git as a way of recording what we want to be in the runtime environment, and then telling Kubernetes from the configuration that is stored in Git exactly what we want to deploy. So, in a sense, it's very much aligned with DevOps, because we know we want to bring teams together, help them to deploy their applications, their clusters, their environments. And really with GitOps, we have a specific set of tools that we can use. And obviously what's nice about Git is it's a very developer tool, or lots and lots of developers use it, the vast majority. And so what we're trying to do is bring those operational processes into the way that developers work. So, really bringing DevOps to that generation through that specific tooling. >> So Steve G., let's continue down this thread a little bit. Why is it necessary then this sort of added wrinkle? If right now in my organization we have developers, who consider themselves to be DevOps folks, and we give them Amazon gift cards each month. And we say, "Hey, it's a world of serverless, "no code, low code lights out data centers. "Go out and deploy your code. "Everything should be fine." What's the problem with that model, and how does GitOps come in and address that? >> Right. I think there's a couple of things. So, for individual developers, one of the big challenges is that, when you watch development teams, like deploying applications and running them, you watch them switching between all those different tabs, and services, and systems that they're using. So, GitOps has a real advantage to developers, because they're already sat in Git, they're already using their familiar tooling. And so by bringing operations within that developer tooling, you're giving them that familiarity. So, it's one advantage for developers. And then for operations staff, one of the things that it does is it centralizes where all of this configuration is kept. And then you can use things like templating and some other things that we're going to be talking about today to make sure that you automate and go quickly, but you also do that in a way which is reliable, and secure, and stable. So, it's really helping to bring that run fast, but don't break things kind of ethos to how we can deploy and run applications in the cloud. >> So, Steve W., let's start talking about where Weaveworks comes into the picture, and what's your perspective. >> So, yeah, Weaveworks has an engine, a set of software, that enables this to happen. So, think of it as a constant reconciliation engine. So, you've got your declared state, your desired state is declared in Git. So, this is where all your YAML for all your Kubernetes hangs out. And then you have an agent that's running inside Kubernetes, that's the Weaveworks GitOps agent. And it's constantly comparing the desired state in Git with the actual state, which is what's running in Kubernetes. So, then as a developer, you want to make a change, or an operator, you want to make a change. You push a change into Git. The reconciliation loop runs and says, "All right, what we've got in Git does not match "what we've got in Kubernetes. "Therefore, I will create story resource, whatever." But it also works the other way. So, if someone does directly access Kubernetes and make a change, then the next time that reconciliation loop runs, it's automatically reverted back to that single source of truth in Git. So, your Kubernetes cluster, you don't get any configuration drift. It's always configured as you desire it to be configured. And as Steve George has already said, from a developer or engineer point of view, it's easy to use. They're just using Git just as they always have done and continue to do. There's nothing new to learn. No change to working practices. I just push code into Git, magic happens. >> So, Steve W., little deeper dive on that. When we hear Ops, a lot of us start thinking about, specifically in terms of infrastructure, and especially since infrastructure when deployed and left out there, even though it's really idle, you're paying for it. So, anytime there's an Ops component to the discussion, cost and resource management come into play. You mentioned this idea of not letting things drift from a template. What are those templates based on? Are they based on... Is this primarily an infrastructure discussion, or are we talking about the code itself that is outside of the infrastructure discussion? >> It's predominantly around the infrastructure. So, what you're managing in Git, as far as Kubernetes is concerned, is always deployment files, and services, and horizontal pod autoscalers, all those Kubernetes entities. Typically, the source code for your application, be it in Java, Node.js, whatever it is you happen to be writing it in, that's, typically, in a separate repository. You, typically, don't combine the two. So, you've got one set of repository, basically, for building your containers, and your CLI will run off that, and ultimately push a container into a registry somewhere. Then you have a separate repo, which is your config. repo, which declares what version of the containers you're going to run, how many you're going to run, how the services are bound to those containers, et cetera. >> Yeah, that makes sense. Steve G., talk to us about this concept of trusted application delivery with GitOps, and frankly, it's what led to the sort of prior question. When you think about trusted application delivery, where is that intertwinement between what we think of as the application code versus the code that is creating the infrastructure? So, what is trusted application delivery? >> Sure, so, with GitOps, we have the ability to deploy the infrastructure components. And then we also define what the application containers are, that would go to be deployed into that environment. And so, this is a really interesting question, because some teams will associate all of the services that an application needs within an application team. And sometimes teams will deploy sort of horizontal infrastructure, which then all application teams services take advantage of. Either way, you can define that within your configuration, within your GitOps configuration. Now, when you start deploying speed, particularly when you have multiple different teams doing these sorts of deployments, one of the questions that starts to come up will be from the security team, or someone who's thinking about, well, what happens if we make a deployment, which is accidentally incorrect, or if there is a security issue in one of those dependencies, and we need to get a new version deployed as quickly as possible? And so, in the GitOps pipeline, one of the things that we can do is to put in various checkpoints to check that the policy is being followed correctly. So, are we deploying the right number of applications, the right configuration of an application? Does that application follow certain standards that the enterprise has set down? And that's what we talk about when we talk about trusted policy and trusted delivery. Because really what we're thinking about here is enabling the development teams to go as quickly as possible with their new deployments, but protecting them with automated guard rails. So, making sure that they can go fast, but they are not going to do anything which destroys the reliability of the application platform. >> Yeah, you've mentioned reliability and kind of alluded to scalability in the application environment. What about looking at this from the security perspective? There've been some recently, pretty well publicized breaches. Not a lot of senior executives in enterprises understand that a very high percentage of code that their businesses are running on is coming out of the open source community, where developers and maintainers are, to a certain degree, what they would consider to be volunteers. That can be a scary thing. So, talk about why an enterprise struggles today with security, policy, and governance. And I toss this out to Steve W. Or Steve George. Answer appropriately. >> I'll try that in a high level, and Steve W. can give more of the technical detail. I mean, I'll say that when I talk to enterprise customers, there's two areas of concern. One area of concern is that, we're in an environment with DevOps where we started this conversation of trying to help teams to go as quickly as possible. But there's many instances where teams accidentally do things, but, nonetheless, that is a security issue. They deploy something manually into an environment, they forget about it, and that's something which is wrong. So, helping with this kind of policy as code pipeline, ensuring that everything goes through a set of standards could really help teams. And that's why we call it developer guard rails, because this is about helping the development team by providing automation around the outside, that helps them to go faster and relieves them from that mental concern of have they made any mistakes or errors. So, that's one form. And then the other form is the form, where you are going, David, which is really around security dependencies within software, a whole supply chain of concern. And what we can do there, by, again, having a set of standard scanners and policy checking, which ensures that everything is checked before it goes into the environment. That really helps to make sure that there are no security issues in the runtime deployment. Steve W., anything that I missed there? >> Yeah, well, I'll just say, I'll just go a little deeper on the technology bit. So, essentially, we have a library of policies, which get you started. Of course, you can modify those policies, write your own. The library is there just to get you going. So, as a change is made, typically, via, say, a GitHub action, the policy engine then kicks in and checks all those deployment files, all those YAML for Kubernetes, and looks for things that then are outside of policy. And if that's the case, then the action will fail, and that'll show up on the pull request. So, things like, are your containers coming from trusted sources? You're not just pulling in some random container from a public registry. You're actually using a trusted registry. Things like, are containers running as route, or are they running in privileged mode, which, again, it could be a security? But it's not just about security, it can also be about coding standards. Are the containers correctly annotated? Is the deployment correctly annotated? Does it have the annotation fields that we require for our coding standards? And it can also be about reliability. Does the deployment script have the health checks defined? Does it have a suitable replica account? So, a rolling update. We'll actually do a rolling update. You can't do a rolling update with only one replica. So, you can have all these sorts of checks and guards in there. And then finally, there's an admission controller that runs inside Kubernetes. So, if someone does try and squeeze through, and do something a little naughty, and go directly to the cluster, it's not going to happen, 'cause that admission controller is going to say, "Hey, no, that's a policy violation. "I'm not letting that in." So, it really just stops. It stops developers making mistakes. I know, I know, I've done development, and I've deployed things into Kubernetes, and haven't got the conflict quite right, and then it falls flat on its face. And you're sitting there scratching your head. And with the policy checks, then that wouldn't happen. 'Cause you would try and put something in that has a slightly iffy configuration, and it would spit it straight back out at you. >> So, obviously you have some sort of policy engine that you're you're relying on. But what is the user experience like? I mean, is this a screen that is reminiscent of the matrix with non-readable characters streaming down that only another machine can understand? What does this look like to the operator? >> Yeah, sure, so, we have a console, a web console, where developers and operators can use a set of predefined policies. And so that's the starting point. And we have a set of recommendations there and policies that you can just attach to your deployments. So, set of recommendations about different AWS resources, deployment types, EKS deployment types, different sets of standards that your enterprise might be following along with. So, that's one way of doing it. And then you can take those policies and start customizing them to your needs. And by using GitOps, what we're aiming for here is to bring both the application configuration, the environment configuration. We talked about this earlier, all of this being within Git. We're adding these policies within Git as well. So, for advanced users, they'll have everything that they need together in a single unit of change, your application, your definitions of how you want to run this application service, and the policies that you want it to follow, all together in Git. And then when there is some sort of policy violation on the other end of the pipeline, people can see where this policy is being violated, how it was violated. And then for a set of those, we try and automate by showing a pull request for the user about how they can fix this policy violation. So, try and make it as simple as possible. Because in many of these sorts of violations, if you're a busy developer, there'll be minor configuration details going against the configuration, and you just want to fix those really quickly. >> So Steve W., is that what the Mega Leaks policy engine is? >> Yes, that's the Mega Leaks policy engine. So, yes, it's a SaaS-based service that holds the actual policy engine and your library of policies. So, when your GitHub action runs, it goes and essentially makes a call across with the configuration and does the check and spits out any violation errors, if there are any. >> So, folks in this community really like to try things before they deploy them. Is there an opportunity for people to get a demo of this, get their hands on it? what's the best way to do that? >> The best way to do it is have a play with it. As an engineer, I just love getting my hands dirty with these sorts of things. So, yeah, you can go to the Mega Leaks website and get a 30-day free trial. You can spin yourself up a little, test cluster, and have a play. >> So, what's coming next? We had DevOps, and then DevSecOps, and now GitOps. What's next? Are we going to go back to all infrastructure on premises all the time, back to waterfall? Back to waterfall, "Hot Tub Time Machine?" What's the prediction? >> Well, I think the thing that you set out right at the start, actually, is the prediction. The difference between infrastructure and applications is steadily going away, as we try and be more dynamic in the way that we deploy. And for us with GitOps, I think we're... When we talk about operations, there's a lots of depth to what we mean about operations. So, I think there's lots of areas to explore how to bring operations into developer tooling with GitOps. So, that's, I think, certainly where Weaveworks will be focusing. >> Well, as an old infrastructure guy myself, I see this as vindication. Because infrastructure still matters, kids. And we need sophisticated ways to make sure that the proper infrastructure is applied. People are shocked to learn that even serverless application environments involve servers. So, I tell my 14-year-old son this regularly, he doesn't believe it, but it is what it is. Steve W., any final thoughts on this whole move towards GitOps and, specifically, the Weaveworks secret sauce and superpower. >> Yeah. It's all about (indistinct)... It's all about going as quickly as possible, but without tripping up. Being able to run fast, but without tripping over your shoe laces, which you forgot to tie up. And that's what the automation brings. It allows you to go quickly, does lots of things for you, and yeah, we try and stop you shooting yourself in the foot as you're going. >> Well, it's been fantastic talking to both of you today. For the audience's sake, I'm in California, and we have a gentleman in France, and a gentlemen in the UK. It's just the wonders of modern technology never cease. Thanks, again, Steve Waterworth, Steve George from Weaveworks. Thanks for coming on theCUBE for the AWS Startup Showcase. And to the rest of us, keep it right here for more action on theCUBE, your leader in tech coverage. (upbeat music)

(bright music) >> Welcome to this Kube Conversation. I'm Dave Nicholson. And today we have a very special guest from Red Hat, Nick Barcet. Nick is the Senior Director of Technology, Technology Strategy at Red Hat. Nick, welcome back to theCUBE. >> Thank you. It's always a pleasure to be visiting you here virtually. >> It's fantastic to have you here. I see a new office surroundings at Red Hat. Have they taken a kind of a nautical theme at the office there? Where are you joining us from? >> I'm joining from my boat now, I've been living on my boat for the past few years, and that's where you'll find me most of the time. >> So would you consider your boat to be on the Edge? >> It's certainly one form of Edge. You know, there are multiple forms of Edge and a boat is one of those forms. >> Let's talk about Edge now. We're having this conversation in anticipation of KubeCon CloudNativeCon that's coming up North America 2021, coming up in Los Angeles. Let's talk about specifically the Edge, where the Edge, Edge computing and Kubernetes come together from a Red Hat perspective. Walk us through that, talk about some of the challenges that people are having at the Edge, why Kubernetes is something that would be considered at the edge. Walk us through that. >> Let's start from the premises that people have been doing stuff at the Edge for ages. I mean, nobody has been waiting for Kubernetes or any other technology to start implementing some form of computing that is happening in their stores, in their factories, wherever. What's really new today is when we talk about Edge computing, it's reusing the same technology we've been using to deploy inside of the data center and expand that all the way to the Edge. And that's what, from my perspective, constituents, Edge computing or the revolution it bring. So that means that the same GitOps, DevSecOps methodology that we were using into that center are now expandable all the way to those devices that leaves in where locations and that we can reuse the same methodology, the same tooling, and that includes Kubernetes. And all the efforts we've been doing over the past couple of years has been to make Kubernetes even more accessible for the various Edge typologies that we are encountering when discussing with our customer that have Edge projects. >> So typically when we think of a Kubernetes environment, you're talking about containers that are contained in pods, that live on physical clusters, despite all of the talk of a no-code and serverless, we still live in a world where applications and microservices run on physical servers. Are there practical limitations in terms of just how small you can scale Kubernetes? How far, how close to the Edge can you get with the Kubernetes deployment? >> So in theory, there is really no limit. As the smallest devices are always bigger than Kubernetes itself. But the reality is you never use just Kubernetes, you use Kubernetes with a series of other projects that makes it complete, or for example, stuff that is going to be reporting telemetry, components that are going to help you automatically scale, et cetera. And the further you go into the Edge, the less of these competence you can afford. So you have to make trade-offs when you reduce the size of the device. Today, what Red Hat offers, is really concentrated to where we can deliver a full OpenShift experience. So the smallest environments on which we would recommend to run OpenShift at the Edge is a single node is roughly 24 gigabytes of RAM, which is you could buy it, sorry, which is already a relatively big Edge device. And when you go a step lower then, that's where we would recommend using a standard rail for Edge configuration or something similar. Not Kubernetes anymore. >> So you said single node, are you let's double click on that for a second. Is that a single physical node that is abstracted in a way to create some level of logical redundancy? When you say single node, walk us through that. We've got containers that are in pods, so what are we talking about? >> You have, based on your requirements, you can have different way of addressing your compute need at the Edge. You can have those smallest of clusters. And this would be three nodes that are delivered, with is the control plane and the worker nodes integrated into one. When you want to go a step further, you could use worker nodes that are controlled remotely via a central control plane that is at a central site. And when you want to go, even one step further deploy Kubernetes on a very small machine, but that remains fully functional even if disconnected that's when you would use the thing that is not anymore a cluster, which is a single note, Kubernetes where you still have access to the full Kubernetes API, regardless of the connectivity of your site, whether it's active or not, whether you're at sea or in the air or not. And that's where we still offer some form of software high vulnerability, because Kubernetes, even on a single node, it'll still detect if a container dies and restarted and provide similar functionality like this, but it won't provide hardware availability since we are a single node. >> And that makes sense. Yeah, that makes, yeah, it makes perfect sense. And I would suggest that we refer to that as a single node cluster, just because we like to mix it up with terminology in our business and sometimes confuse people with it. >> Technically, that was the choice we made, actually. You like to call it a cluster because it's not a cluster >> Exactly. No, I appreciate that. Absolutely. So what's be explicit about what the trade-offs are there. Let's say that I'm thinking of deploying something at the Edge, and I'm going use Kubernetes to orchestrate my container environment and pretend for a moment that space and cost aren't huge limiting factors. I could put a three node cluster in, but the idea of putting in a single node is very, it's attractive. Where does, where's the line drawn in terms of what you would recommend from, you know, what are the trade offs? What am I losing, going to the single node cluster? See I just called that. >> Well, in a nutshell, you're losing hardware high availability. Meaning if one of your server fails since you only have one server, you lose everything. And there is no way around that. That's the biggest trade-off. Then you have also a trade-off on the memory used by the control plane, which you won't be able to use to do something else. So if I have a site with excellent connectivity and the biggest loss of connectivity might be counted in hours, maybe a remote worker use a better solution because this way, I have a single central-side that carries my control plane, and I can use all the RAM and all the CPU's on my local site to deploy my workloads, not to carries a control plane. To give you an example of these trade-off in the telco space, for example, if you're deploying an antenna in a city, you have plenty of antennas covering that city. And therefore, the loss of one antenna is not a big deal. So in that case, you will be tempted to use a remote worker because you will be maximizing your use of the RAM on the sites for the workload, which is let's have people establish communication using their phones. But now, we take another antenna that we are getting to locate in a very remote location. There, if this antenna fail, everybody fails. There's nobody that is able to make calls, even emergency vehicles cannot discuss together very often. So in that case, it's a lot better to have an autonomous deployment, something where the control plane and the workload itself are being run in one box. And this one box in fact can be duplicated. There could be a another box that is either seating in a truck in case of emergency or off, but on the antenna site, so that in case of a major failure, you have a possibility to re to restore it. So it really depends on what's your sets of constraints in terms of availability in SIM of efficiency of your RAM use is going to be that it's going to make you choose between one or the other of the deployment models. >> No, that's a great example. And so it sounds like it's not a one size fits all world, obviously. Now, from the perspective of the marketplace, looking in at Red Hat, participating in this business, some think of Red Hat as the company that deployed Linux 20 years ago. Help us make that connection between Red Hat today and what you've been doing for the last 20 years and this topic of Edge computing, 'cause some people don't automatically think of Red Hat an Edge computing. I do, I think they should, (chuckles) but help us understand that. >> Yeah, obviously a lot of people consider that Red Hat is Red Hat, Linux, and that's it. The Red Hat Enterprise Linux is what we've been known since our beginnings 25 years ago, and what has made our early success. But we consider ourselves more of an infrastructure company. We have been offering for the past 20 years, the various component that you need to deploy server, run and manage your workloads across data centers and make sure that you can store your data, and that you can automate your operations on top of this infrastructure. So we really consider ourselves much more of a company that offers everything that enables you to run your servers and run your workloads on top of your server. And that includes a tool to do virtualization, that includes tool to do continuous deployment of containers. And that's where Kubernetes entered in play about 10 years ago. Well, first it was OPAs that then became Kubernetes and the OpenShift offering that we have today. >> Yeah. Thanks for that. So I have, I've got a final question for you. It's a little bit off topic, but it's related, this is in the category of Nick predicts. So when does Nick predict that we will get to a point where we tip beyond the 50/50 point cloud versus on-premises IT spending, if you accept today that we're still in the neighborhood of 75 to 80% on-premises. When will we hit the 50/50 mark? I'm not asking you for the hundred percent cloud date, but give us a date, you give us a month and a year for 50/50. >> Given the progression of cloud, if there was no Edge, we could said two to three years from now, we would be at this 50/50 mark. But the funny thing is that at the same time, as the cloud progresses, people start realizing that they have needs that needs to be solved locally. And this is why we are deploying Edge-based solution, solution which reliably can provide answers, regardless of the connectivity to the cloud, regardless of the bandwidth. There are things that I would never want to do, like feeding a size on feeds from 4K cameras, into my cloud environment that won't scale, I won't have the bandwidth to do so. And therefore, maybe the answer to your question is, it's going to be asymptotic, and it's almost impossible to predict. >> So that is a much better answer than giving me an exact date and time, because (chuckles) because it reveals exactly the reality that we're living in. Again, there is, you know, it's fit for function. It's not cloud for cloud's sake, compute resources, data, resources have a place that they naturally belong oftentimes. And oftentimes that is on the Edge, whether it's on the edge of the edge of the world in a sailboat or out in a single server, not node, or I keep wanting to single node cluster, it's killing me. I dunno why, I think it's so funny, but a single node implementation of OpenShift where you can run Kubernetes on the Edge, it's a fascinating subject. Anything else that you want to share with us that we didn't get? >> I think one aspect that we never talk enough is how do you manage at the scale of Edge? Because even though each Edge site is very small, you can have thousands, even hundreds of thousands of these single node something that are running all over the place. And I think that what you're seeing in advent cluster management for Kubernetes, and particularly the 2.4 version that we are going to be announcing this week and actually releasing in November is I think a pretty good answer to that problem on how do I deploy with zero touch these devices? How do I update them, upgrade them? How do I deploy the workloads on top of that? How do I ensure to have the right tooling to deploy that at the scale? And we've done the testing now of ACM with up to 2,000 clusters, connected to a single ACMs. And in the future, we are planning on building federation of those, which really gives us the possibility to provide the tooling needed to manage at its scale. >> Excellent. Excellent. Yeah. That's whenever we start talking about anything in the realm of containerization and Kubernetes scale starts to become an issue. It's no longer a question of a human being managing 10 servers and 50 applications. We start talking about tens of thousands and hundreds of thousands of instances where it's beyond human scale. So that's obviously something that's very, very important. Well, Nick, I want to thank you for becoming a Kube veteran once again. Thanks for joining this Kube Conversation from Dave Nicholson, this has been a Kube Conversation in anticipation of KubeCon and CloudNativeCon North America 2021. Thanks for tuning in. (bright music)

>>Really excited about this year's data, citizens with so many of you together. Uh, I'm going to talk today about accessible data, because what good is the data. If you can get it into your hands and shop for it, but you can't understand it. Uh, and I'm here today with, uh, bald, really thrilled to be here with Paul. Paul is an award-winning author on all topics data. I think 20 books with 21st on the way over 300 articles, he's been a frequent speaker. He's an expert in future trends. Uh, he's a VP at cognitive systems, uh, over at IBM teachers' data also, um, at the business school and as a champion of diversity initiatives. Paul, thank you for being here, really the conformance, uh, to the session with you. >>Oh, thanks for having me. It's a privilege. >>So let's get started with, uh, our origins and data poll. Um, and I'll start with a little story of my own. So, uh, I trained as an engineer way back when, uh, and, um, in one of the courses we got as an engineer, it was about databases. So we got the stick thick book of CQL and me being in it for the programming. I was like, well, who needs this stuff? And, uh, I wanted to do my part in terms of making data accessible. So essentially I, I was the only book that I sold on. Uh, obviously I learned some hard lessons, uh, later on, as I did a master's in AI after that, and then joined the database research lab at the university that Libra spun off from. Uh, but Hey, we all learned along the way. And, uh, Paula, I'm really curious. Um, when did you awaken first to data? If you will? >>You know, it's really interesting Stan, because I come from the opposite side, an undergrad in economics, uh, with some, uh, information systems research at the higher level. And so I think I was always attuned to what data could do, but I didn't understand how to get at it and the kinds of nuances around it. So then I started this job, a database company, like 27 years ago, and it started there, but I would say the awakening has never stopped because the data game is always changing. Like I look at these epochs that I've been through data. I was a real relational databases thinking third normal form, and then no SQL databases. And then I watch no SQL be about no don't use SQL, then wait a minute. Not only sequel. And today it's really for the data citizens about wait, no, I need SQL. So, um, I think I'm always waking up in data, so I'll call it a continuum if you will. But that was it. It was trying to figure out the technology behind driving analytics in which I took in school. >>Excellent. And I fully agree with you there. Uh, every couple of years they seem to reinvent new stuff and they want to be able to know SQL models. Let me see. I saw those come and go. Uh, obviously, and I think that's, that's a challenge for most people because in a way, data is a very abstract concepts, um, until you get down in the weeds and then it starts to become really, really messy, uh, until you, you know, from that end button extract a certain insights. Um, and as the next thing I want to talk about with you is that challenging organizations, we're hearing a lot about data, being valuable data, being the new oil data, being the new soil, the new gold, uh, data as an asset is being used as a slogan all over. Uh, people are investing a lot in data over multiple decades. Now there's a lot of new data technologies, always, but still, it seems that organizations fundamentally struggle with getting people access to data. What do you think are some of the key challenges that are underlying the struggles that mud, that organizations seem to face when it comes to data? >>Yeah. Listen, Stan, I'll tell you a lot of people I think are stuck on what I call their data, acumen curves, and you know, data is like a gym membership. If you don't use it, you're not going to get any value on it. And that's what I mean by accurate. And so I like to think that you use the analogy of some mud. There's like three layers that are holding a lot of organizations back at first is just the amount of data. Now, I'm not going to give you some stat about how many times I can go to the moon and back with the data regenerate, but I will give you one. I found interesting stat. The average human being in their lifetime will generate a petabyte of data. How much data is that? If that was my apple music playlist, it would be about 2000 years of nonstop music. >>So that's some kind of playlist. And I think what's happening for the first layer of mud is when I first started writing about data warehousing and analytics, I would be like, go find a needle in the haystack. But now it's really finding a needle in a stack of needles. So much data. So little time that's level one of mine. I think the second thing is people are looking for some kind of magic solution, like Cinderella's glass slipper, and you put it on her. She turns into a princess that's for Disney movies, right? And there's nothing magical about it. It is about skill and acumen and up-skilling. And I think if you're familiar with the duper, you recall the Hadoop craze, that's exactly what happened, right? Like people brought all their data together and everyone was going to be able to access it and give insights. >>And it teams said it was pretty successful, but every line of business I ever talked to said it was a complete failure. And the third layer is governance. That's actually where you're going to find some magic. And the problem in governance is every client I talked to is all about least effort to comply. They don't want to violate GDPR or California consumer protection act or whatever governance overlooks, where they do business and governance. When you don't lead me separate to comply and try not to get fine, but as an accelerant to your analytics, and that gets you out of that third layer of mud. So you start to invoke what I call the wisdom of the crowd. Now imagine taking all these different people with intelligence about the business and giving them access and acumen to hypothesize on thousands of ideas that turn into hundreds, we test and maybe dozens that go to production. So those are three layers that I think every organization is facing. >>Well. Um, I definitely follow on all the days, especially the one where people see governance as a, oh, I have to comply to this, which always hurts me a little bit, honestly, because all good governance is about making things easier while also making sure that they're less riskier. Um, but I do want to touch on that Hadoop thing a little bit, uh, because for me in my a decade or more over at Libra, we saw it come as well as go, let's say around 2015 to 2020 issue. So, and it's still around. Obviously once you put your data in something, it's very hard to make it go away, but I've always felt that had do, you know, it seemed like, oh, now we have a bunch of clusters and a bunch of network engineers. So what, >>Yeah. You know, Stan, I fell for, I wrote the book to do for dummies and it had such great promise. I think the problem is there wasn't enough education on how to extract value out of it. And that's why I say it thinks it's great. They liked clusters and engineers that you just said, but it didn't drive lineup >>Business. Got it. So do you think that the whole paradigm with the clouds that we're now on is going to fundamentally change that or is just an architectural change? >>Yeah. You know, it's, it's a great comment. What you're seeing today now is the movement for the data lake. Maybe a way from repositories, like Hadoop into cloud object stores, right? And then you look at CQL or other interfaces over that not allows me to really scale compute and storage separately, but that's all the technical stuff at the end of the day, whether you're on premise hybrid cloud, into cloud software, as a service, if you don't have the acumen for your entire organization to know how to work with data, get value from data, this whole data citizen thing. Um, you're not going to get the kind of value that goes into your investment, right? And I think that's the key thing that business leaders need to understand is it's not about analytics for kind of science project sakes. It's about analytics to drive. >>Absolutely. We fully agree with that. And I want to touch on that point. You mentioned about the wisdom of the crowds, the concept that I love about, right, and your organization is a big grout full of what we call data citizens. Now, if I remember correctly from the book of the wisdom of the crowds, there's, there's two points that really, you have to take Canada. What is, uh, for the wisdom of the grounds to work, you have to have all the individuals enabled, uh, for them to have access to the right information and to be able to share that information safely kept from the bias from others. Otherwise you're just biasing the outcome. And second, you need to be able to somehow aggregate that wisdom up to a certain decision. Uh, so as Felix mentioned earlier, we all are United by data and it's a data citizen topic. >>I want to touch on with you a little bit, because at Collibra we look at it as anyone who uses data to do their job, right. And 2020 has sort of accelerated digitization. Uh, but apart from that, I've always believed that, uh, you don't have to have data in your title, like a data analyst or a data scientist to be a data citizen. If I take a look at the example inside of Libra, we have product managers and they're trying to figure out which features are most important and how are they used and what patterns of behavior is there. You have a gal managers, and they're always trying to know the most they can about their specific accounts, uh, to be able to serve as them best. So for me, the data citizen is really in its broadest sense. Uh, anyone who uses data to do their job, does that, does that resonate with you? >>Yeah, absolutely. It reminds me of myself. And to be honest in my eyes where I got started from, and I agree, you don't need the word data in your title. What you need to have is curiosity, and that is in your culture and in your being. And, and I think as we look at organizations to transform and take full advantage of their, their data investments, they're going to need great governance. I guarantee you that, but then you're going to have to invest in this data citizen concept. And the first thing I'll tell you is, you know, that kind of acumen, if you will, as a team sport, it's not a departmental sport. So you need to think about what are the upskilling programs of where we can reach across to the technical and the non-technical, you know, lots and lots of businesses rely on Microsoft Excel. >>You have data citizens right there, but then there's other folks who are just flat out curious about stuff. And so now you have to open this up and invest in those people. Like, why are you paying people to think about your business without giving the data? It would be like hiring Tom Brady as a quarterback and telling him not to throw a pass. Right. And I see it all the time. So we kind of limit what we define as data citizen. And that's why I love what you said. You don't need the word data in your title and more so if you don't build the acumen, you don't know how to bring the data together, maybe how to wrangle it, but where did it come from? And where can you fixings? One company I worked with had 17 definitions for a sales individual, 17 definitions, and the talent team and HR couldn't drive to a single definition because they didn't have the data accurate. So when you start thinking of the data citizen, concept it about enabling everybody to shop for data much. Like I would look for a USB cable on Amazon, but also to attach to a business glossary for definition. So we have a common version of what a word means, the lineage of the data who owns it, who did it come from? What did it do? So bring that all together. And, uh, I will tell you companies that invest in the data, citizen concept, outperform companies that don't >>For all of that, I definitely fully agree that there's enough research out there that shows that the ones who are data-driven are capturing the most markets, but also capturing the most growth. So they're capturing the market even faster. And I love what you said, Paul, about, um, uh, the brains, right? You've already paid for the brains you've already invested in. So you may as well leverage them. Um, you may as well recognize and, and enable the data citizens, uh, to get access to the assets that they need to really do their job properly. That's what I want to touch on just a little bit, if, if you're capable, because for me, okay. Getting access to data is one thing, right? And I think you already touched on a few items there, but I'm shopping for data. Now I have it. I have a cul results set in my hands. Let's say, but I'm unable to read and write data. Right? I don't know how to analyze it. I don't know maybe about bias. Uh, maybe I, I, I don't know how to best visualize it. And maybe if I do, maybe I don't know how to craft a compelling persuasion narrative around it to change my bosses decisions. So from your viewpoint, do you think that it's wise for companies to continuously invest in data literacy to continuously upgrade that data citizens? If you will. >>Yeah, absolutely. Forest. I'm going to tell you right now, data literacy years are like dog years stage. So fast, new data types, new sources of data, new ways to get data like API APIs and microservices. But let me take it away from the technical concept for a bit. I want to talk to you about the movie. A star is born. I'm sure most of you have seen it or heard it Bradley Cooper, lady Gaga. So everyone knows the movie. What most people probably don't know is when lady Gaga teamed up with Bradley Cooper to do this movie, she demanded that he sing everything like nothing could be auto-tuned everything line. This is one of the leading actors of Hollywood. They filmed this remake in 42 days and Bradley Cooper spent 18 months on singing lessons. 18 months on a guitar lessons had a voice coach and it's so much and so forth. >>And so I think here's the point. If one of the best actors in the world has to invest three and a half years for 42 days to hit a movie out of the park. Why do we think we don't need a continuous investment in data literacy? Even once you've done your initial training, if you will, over the data, citizen, things are going to change. I don't, you don't. If I, you Stan, if you go to the gym and workout every day for three months, you'll never have to work out for the rest of your life. You would tell me I was ridiculous. So your data literacy is no different. And I will tell you, I have managed thousands of individuals, some of the most technical people around distinguished engineers, fellows, and data literacy comes from curiosity and a culture of never ending learning. That is the number one thing to success. >>And that curiosity, I hire people who are curious, I'll give you one more story. It's about Mozart. And this 21 year old comes to Mozart and he says, Mozart, can you teach me how to compose a symphony? And Mozart looks at this person that says, no, no, you're too young, too young. You compose your fourth symphony when you were 12 and Mozart looks at him and says, yeah, but I didn't go around asking people how to compose a symphony. Right? And so the notion of that story is curiosity. And those people who show up in always want to learn, they're your home run individuals. And they will bring data literacy across the organization. >>I love it. And I'm not going to try and be Mozart, but you know, three and a half years, I think you said two times, 18 months, uh, maybe there's hope for me yet in a singing, you'll be a good singer. Um, Duchy on the, on the, some of the sports references you've made, uh, Paul McGuire, we first connected, uh, I'm not gonna like disclose where you're from, but, uh, I saw he did come up and I know it all sorts of sports that drive to measure everything they can right on the field of the field. So let's imagine that you've done the best analysis, right? You're the most advanced data scientists schooled in the classics, as well as the modernist methods, the best tools you've made a beautiful analysis, beautiful dashboards. And now your coach just wants to put their favorite player on the game, despite what you're building to them. How do you deal with that kind of coaches? >>Yeah. Listen, this is a great question. I think for your data analytics strategy, but also for anyone listening and watching, who wants to just figure out how to drive a career forward? I would give the same advice. So the story you're talking about, indeed hockey, you can figure out where I'm from, but it's around the Ottawa senators, general manager. And he made a quote in an interview and he said, sometimes I want to punch my analytics, people in the head. Now I'm going to tell you, that's not a good culture for analytics. And he goes on to say, they tell me not to play this one player. This one player is very tough. You know, throws four or five hits a game. And he goes, I'd love my analytics people to get hit by bore a wacky and tell me how it feels. That's the player. >>Sure. I'm sure he hits hard, but here's the deal. When he's on the ice, the opposing team gets more shots on goal than the senators do on the opposing team. They score more goals, they lose. And so I think whenever you're trying to convince a movement forward, be it management, be it a project you're trying to fund. I always try to teach something that someone didn't previously know before and make them think, well, I never thought of it that way before. And I think the great opportunity right now, if you're trying to get moving in a data analytics strategy is around this post COVID era. You know, we've seen post COVID now really accelerate, or at least post COVID in certain parts of the world, but accelerate the appetite for digital transformation by about half a decade. Okay. And getting the data within your systems, as you digitize will give you all kinds of types of projects to make people think differently than the way they thought before. >>About data. I call this data exhaust. I'll give you a great example, Uber. I think we're all familiar with Uber. If we all remember back in the days when Uber would offer you search pricing. Okay? So basically you put Uber on your phone, they know everything about you, right? Who are your friends, where you going, uh, even how much batteries on your phone? Well, in a data science paper, I read a long time ago. They recognize that there was a 70% chance that you would accept a surge price. If you had less than 10% of your battery. So 10% of battery on your phone is an example of data exhaust all the lawns that you generate on your digital front end properties. Those are logs. You can take those together and maybe show executive management with data. We can understand why people abandoned their cart at the shipping phase, or what is the amount of shipping, which they abandoned it. When is the signal when our systems are about to go to go down. So, uh, I think that's a tremendous way. And if you look back to the sports, I mean the Atlanta Falcons NFL team, and they monitor their athletes, sleep performance, the Toronto Raptors basketball, they're running AI analytics on people's personalities and everything they tweet and every interview to see if the personality fits. So in sports, I think athletes are the most important commodity, if you will, or asset a yet all these teams are investing in analytics. So I think that's pretty telling, >>Okay, Paul, it looks like we're almost out of time. So in 30 seconds or less, what would you recommend to the data citizens out there? >>Okay. I'm going to give you a four tips in 30 seconds. Number one, remember learning never ends be curious forever. You'll drive your career. Number two, remember companies that invest in analytics and data, citizens outperform those that don't McKinsey says it's about 1.4 times across many KPIs. Number three, stop just collecting the dots and start connecting them with that. You need a strong governance strategy and that's going to help you for the future because the biggest thing in the future is not going to be about analytics, accuracy. It's going to be about analytics, explainability. So accuracy is no longer going to be enough. You're going to have to explain your decisions and finally stay positive and forever test negative. >>Love it. Thank you very much fall. Um, and for all the data seasons is out there. Um, when it comes down to access to data, it's more than just getting your hands on the data. It's also knowing what you can do with it, how you can do that and what you definitely shouldn't be doing with it. Uh, thank you everyone out there and enjoy your learning and interaction with the community. Stay healthy. Bye-bye.

>>from around >>the globe. It's >>the cube presenting future >>Cloud one >>event. A >>world of opportunities >>brought to you by Cisco. >>Okay. We're here with costume. Does, who is the Senior Vice President, General Manager of Cloud and compute at Cisco And VJ Venugopal, who is the Senior Director for Product management for Cloud, compute at Cisco. KTV J Good to see you guys welcome. >>Great to see you. Dave >>to be here. >>Katie. Let's talk about cloud. You. And I, last time we're face to face was in Barcelona where we love talking about cloud. And I always say to people look, Cisco is not a hyper scaler, but the big public cloud players, they're like giving you a gift. They spent almost actually over $100 billion last year on Capex. The big four. So you can build on that infrastructure. Cisco is all about hybrid cloud. So help us understand the strategy. There may be how you can leverage that build out and importantly what a customer is telling you they want out of hybrid cloud. >>Yeah, no, that's that's that's a perfect question to start with Dave. So yes, so the hybrid hyper scholars have invested heavily building out their assets. There's a great lot of innovation coming from that space. Um there's also a great innovation set of innovation coming from open source and and that's another source of uh a gift, in fact the I. T. Community. But when I look at my customers, they're saying, well how do I in the context of my business, implement a strategy that takes into consideration everything that I have to manage um in terms of my contemporary work clothes, in terms of my legacy, in terms of everything my developer community wants to do on devops and really harness that innovation that's built in the public cloud, that built an open source that built internally to me and that naturally leads them down the path of a hybrid cloud strategy. And Siskel's mission is to provide for that imperative, the simplest, more power, more powerful platform to deliver hybrid cloud. And that platform uh is inter site, we've been investing in Inner side, it's a it's a SAS um service um inner side delivers to them that bridge between their estates of today. There were clothes of today, the need for them to be guardians of enterprise grade resiliency with the agility uh that's needed for the future. The embracing of cloud, Native of new paradigms of deVOPS models, the embracing of innovation coming from public cloud and an open source and bridging those two is what inner side has been doing. That's kind of, that's kind of the crux of our strategy. Of course, we have the entire portfolio behind it to support any any version of that, Whether that is on prem in the cloud, hybrid, cloud, multi, cloud and so forth. >>But but if I understand it correctly from what I heard earlier today, the inter site is really a linchpin of that strategy, is it not? >>It really is and may take a second to uh to totally familiarize those who don't know inner side with what it is. We started building this platform quite a few years back and we we built a ground up to be an immensely scalable SAs super simple hybrid cloud platform and it's a platform that provides a slew of service is inherently. And then on top of that there are suites of services, the sweets of services that are tied to infrastructure, automation. Cisco, as well as Cisco partners, their suite of services that have nothing to do with Cisco um products from a hardware perspective and it's got to do with more cloud orchestration and cloud native and inner side and its suite of services um continue to kind of increase in pace and velocity of delivery video. It's just over the last two quarters we've announced a whole number of things will go a little bit deeper into some of those, but they span everything from infrastructure automation to kubernetes and delivering community than service to workload optimization and having visibility into your cloud estate. How much it's costing into your on premise state into your work clothes and how they're performing. It's got integrations with other tooling with both Cisco Abdi uh as well as non Cisco um, assets. And then and then it's got a whole slew of capabilities around orchestration because at the end of the day, the job of it is to deliver something that works and works at scale that you can monitor and make sure is resilient and that includes that. That includes a workflow and ability to say, you know, do this and do this and do this. Or it includes other ways of automation, like infrastructure as code and so forth. So it includes self service that so that expand that. But inside the world's simplest hybrid cloud platform, rapidly evolving rapidly delivering new services. And uh, we'll talk about some more of those days. >>Great. Thank you. Katie VJ Let's bring you into the discussion. You guys recently made an announcement with the ASCII corp. I was stoked because even though it seemed like a long time ago, pre covid, I mean in my predictions post, I said, ha she was a name to watch our data partners. Et are you look at the survey data and they really have become mainstream. You know, particularly we think very important in the whole multi cloud discussion. And as well, they're attractive to customers. They have open source offerings. You can very easily experiment, smaller organizations can take advantage, but if you want to upgrade to enterprise features like clustering or whatever, you can plug right in. Not a big complicated migration. So a very, very compelling story there. Why is this important? Why is this partnership important to Cisco's customers? >>Mhm. Absolutely. When the spot on every single thing that you said, let me just start by paraphrasing what ambition statement is in the cloud and compute group right ambition statement is to enable a cloud operating model for hybrid cloud. And what we mean by that is the ability to have extreme amounts of automation orchestration and observe ability across your hybrid cloud idea operations now. Uh So developers >>and applications >>team get a great amount of agility in public clouds and we're on a mission to bring that kind of agility and automation to the private cloud and to the data centers. And inter site is a quickie platform and lynchpin to enable that kind of operations. Uh, Cloud like operations in the in the private clouds and the key uh as you rightly said, harsher Carp is the, you know, they were the inventors of the concept of infrastructure at school and in terra form, they have the world's number one infrastructure as code platform. So it became a natural partnership for Cisco to enter into a technology partnership with Harsher Card to integrate inter site with hardship cops, terra form to bring the benefits of infrastructure as code to the to hybrid cloud operations. And we've entered into a very tight integration and uh partnership where we allow developers devops teams and infrastructure or administrators to allow the use of infrastructure as code in a SAS delivered manner for both public and private club. So it's a very unique partnership and a unique integration that allows the benefits of cloud managed. I see to be delivered to hybrid cloud operations and we've been very happy and proud to be partnering with Russia Carbonara. >>Yeah, telephone gets very high marks from customers. The a lot of value there, the inner side integration adds to that value. Let's stay on cloud Native for a minute. We all talk about cloud native cady was sort of mentioning before you got the the core apps uh you want to protect those, make sure their enterprise create but they gotta be cool as well for developers. You're connecting to other apps in the cloud or wherever. How are you guys thinking about this? Cloud native trend. What other movies are you making in this regard? >>I mean cloud Native is there is one of the paramount I. D. Trends of today and we're seeing massive amounts of adoption of cloud native architecture in all modern applications. Now. Cloud native has become synonymous with kubernetes these days and communities has emerged as a de facto cloud native platform for modern cloud native app development. Now, what Cisco has done is we have created a brand new SAs delivered kubernetes service that is integrated with inter site, we call it the inter site community service for a. Ks and this just gave a little over one month ago now, what interstate Kubernetes service does is it delivers a cloud managed and cloud delivered kubernetes service that can be deployed on any supportive target infrastructure. It could be a Cisco infrastructure, it could be a third party infrastructure or it could even be public club. But think of it as kubernetes anywhere delivered, as says, managed from inside. It's a very powerful capability that we've just released into inter site to enable the power of communities and cognitive to be used to be used anywhere. But today we made a very important aspect because we have today announced the brand new Cisco service mess manager. The Cisco service mesh manager, which is available as an extension to I K s are doing decide basically we see service measures as being the future of networking. Right in the past we had layer to networking and layer three networking and now with service measures, application networking and layer seven networking is the next frontier of of networking. But you need to think about networking for the application age very differently, how it is managed, how it is deployed, it needs to be ready, developer friendly and developer centric. And so what we have done is we've built out an application networking strategy and built out the service match manager as a very simple way to deliver application networking through the consumers, like like developers and application teams. This is built on an acquisition that Cisco made recently of Banzai Cloud. And we've taken the assets of Banzai Cloud and deliver the Cisco service mash Manager as an extension to KS. That brings the promise of future networking and modern networking to application and development gives >>God thank you BJ. And so Katie, let's let's let's wrap this up. I mean, there was a lot in this announcement today, a lot of themes around openness, heterogeneity and a lot of functionality and value. Give us your final thoughts. >>Absolutely. So couple of things to close on. First of all. Um, inner side is the simplest, most powerful hybrid cloud platform out there. It enables that that cloud operating model that VJ talked about but enables that across cloud. So it's sad, it's relatively easy to get into it and give it a spin so that I'd highly encouraged anybody who's not familiar with it to try it out and anybody who is familiar with it to look at it again, because they're probably services in there that you didn't notice or didn't know last time you looked at it because we're moving so fast. So that's the first thing, the second thing I close with is, um we've been talking about this bridge that's kind of bridging, bridging uh your your on prem your open source, your cloud estates. And it's so important to to make that mental leap because uh in past generation we used to talk about integrating technologies together and then with Public cloud, we started talking about move to public cloud, but it's really how do we integrate, how do we integrate all of that innovation that's coming from the hyper scale is everything they're doing to innovate superfast. All of that innovation is coming from open source, all of that innovation that's coming from from companies around the world including Cisco. How do we integrate that to deliver an outcome? Because at the end of the day, if you're a cloud of Steam, if you're an idea of Steam, your job is to deliver an outcome and our mission is to make it super simple for you to do that. That's the mission we're on and we're hoping that everybody that's excited as we are about how simple we made that. >>Great, thank you a lot in this announcement today, appreciate you guys coming back on and help us unpack you know, some of the details. Thanks so much. Great having you. >>Thank you. Dave. >>Thank you everyone for spending some time with us. This is Dave Volonte and you're watching the cube, the leader in tech event >>coverage. >>Mm mm.

>>mhm, mm. All right. Mhm. Mhm, mm mm. Mhm. Yeah, mm. Mhm. Yeah, yeah. Mhm, mm. Okay. Mm. Yeah, Yeah. >>Mhm. Mhm. Yeah. Welcome to future cloud made possible by Cisco. My name is Dave Volonte and I'm your host. You know, the cloud is evolving like the universe is expanding at an accelerated pace. No longer is the cloud. Just a remote set of services, you know, somewhere up there. No, the cloud, it's extending to on premises. Data centers are reaching into the cloud through adjacent locations. Clouds are being connected together to each other and eventually they're gonna stretch to the edge and the far edge workloads, location latency, local laws and economics will define the value customers can extract from this new cloud model which unifies the operating experience independent of location. Cloud is moving rapidly from a spare capacity slash infrastructure resource to a platform for application innovation. Now, the challenge is how to make this new cloud simple, secure, agile and programmable. Oh and it has to be cloud agnostic. Now, the real opportunity for customers is to tap into a layer across clouds and data centers that abstracts the underlying complexity of the respective clouds and locations. And it's got to accommodate both mission critical workloads as well as general purpose applications across the spectrum cost, effectively enabling simplicity with minimal labor costs requires infrastructure i. E. Hardware, software, tooling, machine intelligence, AI and partnerships within an ecosystem. It's kind of accommodate a variety of application deployment models like serverless and containers and support for traditional work on VMS. By the way, it also requires a roadmap that will take us well into the next decade because the next 10 years they will not be like the last So why are we here? Well, the cube is covering Cisco's announcements today that connect next generation compute shared memory, intelligent networking and storage resource pools, bringing automation, visibility, application assurance and security to this new decentralized cloud. Now, of course in today's world you wouldn't be considered modern without supporting containers ai and operational tooling that is demanded by forward thinking practitioners. So sit back and enjoy the cubes, special coverage of Cisco's future cloud >>From around the globe. It's the Cube presenting future cloud one event, a world of opportunities brought to you by Cisco. >>We're here with Dejoy Pandey, a VP of emerging tech and incubation at Cisco. V. Joy. Good to see you. Welcome. >>Good to see you as well. Thank you Dave and pleasure to be here. >>So in 2020 we kind of had to redefine the notion of agility when it came to digital business or you know organizations, they had to rethink their concept of agility and business resilience. What are you seeing in terms of how companies are thinking about their operations in this sort of new abnormal context? >>Yeah, I think that's a great question. I think what what we're seeing is that pretty much the application is the center of the universe. And if you think about it, the application is actually driving brand recognition and the brand experience and the brand value. So the example I like to give is think about a banking app uh recovered that did everything that you would expect it to do. But if you wanted to withdraw cash from your bank you would actually have to go to the ATM and punch in some numbers and then look at your screen and go through a process and then finally withdraw cash. Think about what that would have, what what that would do in a post pandemic era where people are trying to go contact less. And so in a situation like this, the digitization efforts that all of these companies are going through and and the modernization of the automation is what is driving brand recognition, brand trust and brand experience. >>Yeah. So I was gonna ask you when I heard you say that, I was gonna say well, but hasn't it always been about the application, but it's different now, isn't it? So I wonder if you talk more about how the application is experience is changing. Yes. As a result of this new digital mandate. But how should organizations think about optimizing those experiences in this new world? >>Absolutely. And I think, yes, it's always been about the application, but it's becoming the center of the universe right now because all interactions with customers and consumers and even businesses are happening through that application. So if the application is unreliable or if the application is not available is untrusted insecure, uh, there's a problem. There's a problem with the brand, with the company and the trust that consumers and customers have with our company. So if you think about an application developer, the weight he or she is carrying on their shoulders is tremendous because you're thinking about rolling features quickly to be competitive. That's the only way to be competitive in this world. You need to think about availability and resiliency. Like you pointed out and experience, you need to think about security and trust. Am I as a customer or consumer willing to put my data in that application? So velocity, availability, Security and trust and all of that depends on the developer. So the experience, the security, the trust, the feature, velocity is what is driving the brand experience now. >>So are those two tensions that say agility and trust, you know, Zero Trust used to be a buzzword now it's a mandate. But are those two vectors counter posed? Can they be merged into one and not affect each other? Does the question makes sense? Right? Security usually handcuffs my speed. But how do you address that? >>Yeah that's a great question. And I think if you think about it today that's the way things are. And if you think about this developer all they want to do is run fast because they want to build those features out and they're going to pick and choose a piece and services that matter to them and build up their app and they want the complexities of the infrastructure and security and trust to be handled by somebody else is not that they don't care about it but they want that abstraction so that is handled by somebody else. And typically within an organization we've seen in the past where this friction between Netapp Sec ops I. T. Tops and and the cloud platform Teams and the developer on one side and these these frictions and these meetings and toil actually take a toll on the developer and that's why companies and apps and developers are not as agile as they would like to be. So I think but it doesn't have to be that way. So I think if there was something that would allow a developer to pick and choose, discover the apis that they would like to use connect those api is in a very simple manner and then be able to scale them out and be able to secure them and in fact not just secure them during the run time when it's deployed. We're right off the back when the fire up that I'd and start developing the application. Wouldn't that be nice? And as you do that, there is a smooth transition between that discovery connectivity and ease of consumption and security with the idea cops. Netapp psych ops teams and see source to ensure that they are not doing something that the organization won't allow them to do in a very seamless manner. >>I want to go back and talk about security but I want to add another complexity before we do that. So for a lot of organizations in the public cloud became a staple of keeping the lights on during the pandemic but it brings new complexities and differences in terms of latency security, which I want to come back to deployment models etcetera. So what are some of the specific networking challenges that you've seen with the cloud native architecture is how are you addressing those? >>Yeah. In fact, if you think about cloud, to me that is a that is a different way of seeing a distributed system. And if you think about a distributed system, what is at the center of the distributed system is the network. So my my favorite comment here is that the network is the wrong time for all distribute systems and modern applications. And that is true because if you think about where things are today, like you said, there's there's cloud assets that a developer might use in the banking example that I gave earlier. I mean if you want to build a contact less app so that you get verified, a customer gets verified on the app. They walk over to the ATM and they were broadcast without touching that ATM. In that kind of an example, you're touching the mobile Rus, let's say U S A P is you're touching cloud API is where the back end might sit. You're touching on primary PS maybe it's an oracle database or a mainframe even where transactional data exists. You're touching branch pipes were the team actually exists and the need for consistency when you withdraw cash and you're carrying all of this and in fact there might be customer data sitting in salesforce somewhere. So it's cloud API is a song premise branch. It's ass is mobile and you need to bring all of these things together and over time you will see more and more of these API is coming from various as providers. So it's not just cloud providers but saas providers that the developer has to use. And so this complexity is very, very real. And this complexity is across the wide open internet. So the application is built across this wide open internet. So the problems of discovery ability, the problems of being able to simply connect these apis and manage the data flow across these apis. The problems of consistency of policy and consumption because all of these areas have their own nuances and what they mean, what the arguments mean and what the A. P. I. Actually means. How do you make it consistent and easy for the developer? That is the networking problem. And that is a problem of building out this network, making traffic engineering easy, making policy easy, making scale out, scale down easy, all of that our networking problems. And so we are solving those problems uh Francisco. >>Yeah the internet is the new private network but it's not so private. So I want to go back to security. I often say that the security model of building a moat, you dig the moat, you get the hardened castle that's just outdated now that the queen is left her castle, I always say it's dangerous out there. And the point is you touched on this, it's it's a huge decentralized system and with distributed apps and data, that notion of perimeter security, it's just no longer valid. So I wonder if you could talk more about how you're thinking about this problem and you definitely address some of that in your earlier comments. But what are you specifically doing to address this and how do you see it evolving? >>Yeah, I mean, that's that's a very important point. I mean, I think if you think about again the wide open internet being the wrong time for all modern applications, what is perimeter security in this uh in this new world? I mean, it's to me it boils down to securing an API because again, going with that running example of this contact lists cash withdrawal feature for a bank, the ap wherever it's it's entre branch SAs cloud, IOS android doesn't matter that FBI is your new security perimeter. And the data object that is trying to access is also the new security perimeter. So if you can secure ap to ap communication and P two data object communication, you should be good. So that is the new frontier. But guess what software is buggy? Everybody's software not saying Cisco software, everybody's Softwares buggy. Uh software is buggy, humans are not reliable and so things mature, things change, things evolve over time. So there needs to be defense in depth. So you need to secure at the API layer had the data object layer, but you also need to secure at every layer below it so that you have good defense and depth if any layer in between is not working out properly. So for us that means ensuring ap to ap communication, not just during long time when the app has been deployed and is running, but during deployment and also during the development life cycle. So as soon as the developer launches an ID, they should be able to figure out that this api is security uses reputable, it has compliant, it is compliant to my to my organization's needs because it is hosted, let's say from Germany and my organization wants appears to be used only if they are being hosted out of Germany so compliance needs and and security needs and reputation. Is it available all the time? Is it secure? And being able to provide that feedback all the time between the security teams and the developer teams in a very seamless real time manner. Yes, again, that's something that we're trying to solve through some of the services that we're trying to produce in san Francisco. >>Yeah, I mean those that layered approach that you're talking about is critical because every layer has, you know, some vulnerability. And so you you've got to protect that with some depth in terms of thinking about security, how should we think about where where Cisco's primary value add is, I mean as parts of the interview has a great security business is growing business, Is it your intention to to to to add value across the entire value chain? I mean obviously you can't do everything so you've got a partner but so has the we think about Cisco's role over the next I'm thinking longer term over the over the next decade. >>Yeah, I mean I think so, we do come in with good strength from the runtime side of the house. So if you think about the security aspects that we haven't played today, uh there's a significant set of assets that we have around user security around around uh with with do and password less. We have significant assets in runtime security. I mean, the entire portfolio that Cisco brings to the table is around one time security, the secure X aspects around posture and policy that will bring to the table. And as you see, Cisco evolve over time, you will see us shifting left. I mean, I know it's an overused term, but that is where security is moving towards. And so that is where api security and data security are moving towards. So learning what we have during runtime because again, runtime is where you learn what's available and that's where you can apply all of the M. L. And I models to figure out what works what doesn't taking those learnings, Taking those catalogs, taking that reputation database and moving it into the deployment and development life cycle and making sure that that's part of that entire they have to deploy to runtime chain is what you will see. Cisco do overtime. >>That's fantastic phenomenal perspective video. Thanks for coming on the cube. Great to have you and look forward to having you again. >>Absolutely. Thank you >>in a moment. We'll talk hybrid cloud applications operations and potential gaps that need to be addressed with costume, Das and VJ Venugopal. You're watching the cube the global leader in high tech coverage. Mhm >>You were cloud. It isn't just a cloud. It's everything flowing through it. It's alive. Yeah, connecting users, applications, data and devices and whether it's cloud, native hybrid or multi cloud, it's more distributed than ever. One company takes you inside, giving you the visibility and the insight you need to take action. >>One company >>has the vision to understand it, all the experience, to securely connect at all on any platform in any environment. So you can work wherever work takes you in a cloud first world between your cloud and being cloud smart, there's a bridge. Cisco the bridge to possible. >>Okay. We're here with costume does, who is the Senior Vice President, General Manager of Cloud and compute at Cisco. And VJ Venugopal, who is the Senior Director for Product Management for cloud compute at Cisco. KTV. J. Good to see you guys welcome. >>Great to see you. Dave to be here. >>Katie, let's talk about cloud you And I last time we're face to face was in Barcelona where we love talking about cloud and I always say to people look, Cisco is not a hyper Scaler, but the big public cloud players, they're like giving you a gift. They spent almost actually over $100 billion last year on Capex. The big four. So you can build on that infrastructure. Cisco is all about hybrid cloud. So help us understand the strategy. There may be how you can leverage that build out and importantly what a customer is telling you they want out of hybrid cloud. >>Yeah, no that's that's that's a perfect question to start with. Dave. So yes. So the hybrid hyper scholars have invested heavily building out their assets. There's a great lot of innovation coming from that space. Um There's also a great innovation set of innovation coming from open source and and that's another source of uh a gift. In fact the I. T. Community. But when I look at my customers they're saying well how do I in the context of my business implement a strategy that takes into consideration everything that I have to manage um in terms of my contemporary work clothes, in terms of my legacy, in terms of everything my developer community wants to do on DEVOPS and really harnessed that innovation that's built in the public cloud, that built an open source that built internally to me, and that naturally leads them down the path of a hybrid cloud strategy. And Siskel's mission is to provide for that imperative, the simplest more power, more powerful platform to deliver hybrid cloud and that platform. Uh It's inter site we've been investing in. Inner side, it's a it's a SAS um service um inner side delivers to them that bridge between their estates of today that were closer today, the need for them to be guardians of enterprise grade resiliency with the agility uh that's needed for the future. The embracing of cloud. Native of new paradigms of deVOPS models, the embracing of innovation coming from public cloud and an open source and bridging those two is what inner side has been doing. That's kind of that's kind of the crux of our strategy. Of course we have the entire portfolio behind it to support any, any version of that, whether that is on prem in the cloud, hybrid, cloud, multi cloud and so forth. >>But but if I understand it correctly from what I heard earlier today, the inter site is really a linchpin of that strategy, is it not? >>It really is and may take a second to totally familiarize those who don't know inner side with what it is. We started building this platform quite a few years back and we we built a ground up to be an immensely scalable SAs, super simple hybrid cloud platform and it's a platform that provides a slew of service is inherently and then on top of that there are suites of services, the sweets of services that are tied to infrastructure, automation. Cisco, as well as Cisco partners. The streets of services that have nothing to do with Cisco um products from a hardware perspective. And it's got to do with more cloud orchestration and cloud native and inner side and its suite of services um continue to kind of increase in pace and velocity of delivery video. Just over the last two quarters we've announced a whole number of things will go a little bit deeper into some of those but they span everything from infrastructure automation to kubernetes and delivering community than service to workload optimization and having visibility into your cloud estate. How much it's costing into your on premise state into your work clothes and how they're performing. It's got integrations with other tooling with both Cisco Abdi uh as well as non Cisco um, assets and then and then it's got a whole slew of capabilities around orchestration because at the end of the day, the job of it is to deliver something that works and works at scale that you can monitor and make sure is resilient and that includes that. That includes a workflow and ability to say, you know, do this and do this and do this. Or it includes other ways of automation, like infrastructure as code and so forth. So it includes self service that so that expand that. But inside the world's simplest hybrid cloud platform, rapidly evolving rapidly delivering new services. And uh we'll talk about some more of those day. >>Great, thank you, Katie VJ. Let's bring you into the discussion. You guys recently made an announcement with the ASCIi corp. I was stoked because even though it seemed like a long time ago, pre covid, I mean in my predictions post, I said, ha, she was a name to watch our data partners. Et are you look at the survey data and they really have become mainstream? You know, particularly we think very important in the whole multi cloud discussion. And as well, they're attractive to customers. They have open source offerings. You can very easily experiment. Smaller organizations can take advantage. But if you want to upgrade to enterprise features like clustering or whatever, you can plug right in. Not a big complicated migration. So a very, very compelling story there. Why is this important? Why is this partnership important to Cisco's customers? Mhm. >>Absolutely. When the spot on every single thing that you said, let me just start by paraphrasing what ambition statement is in the cloud and computer group. Right ambition statement is to enable a cloud operating model for hybrid cloud. And what we mean by that is the ability to have extreme amounts of automation orchestration and observe ability across your hybrid cloud idea operations now. Uh So developers and applications team get a great amount of agility in public clouds and we're on a mission to bring that kind of agility and automation to the private cloud and to the data centers and inter site is a quickie platform and lynchpin to enable that kind of operations. Uh, Cloud like operations in the in the private clouds and the key uh As you rightly said, harsher car is the, you know, they were the inventors of the concept of infrastructure at school and in terra form, they have the world's number one infrastructure as code platform. So it became a natural partnership for Cisco to enter into a technology partnership with harsher card to integrate inter site with hardship cops, terra form to bring the benefits of infrastructure as code to the to hybrid cloud operations. And we've entered into a very tight integration and uh partnership where we allow developers devops teams and infrastructure or administrators to allow the use of infrastructure as code in a SAS delivered manner for both public and private club. So it's a very unique partnership and a unique integration that allows the benefits of cloud managed i E C. To be delivered to hybrid cloud operations. And we've been very happy and proud to be partnering with Russian government shutdown. >>Yeah, Terra form gets very high marks from customers. The a lot of value there. The inner side integration adds to that value. Let's stay on cloud native for a minute. We all talk about cloud native cady was sort of mentioning before you got the the core apps, uh you want to protect those, make sure their enterprise create but they gotta be cool as well for developers. You're connecting to other apps in the cloud or wherever. How are you guys thinking about this? Cloud native trend? What other movies are you making in this regard? >>I mean cloud native is there is one of the paramount I. D. Trends of today and we're seeing massive amounts of adoption of cloud native architecture in all modern applications. Now, Cloud Native has become synonymous with kubernetes these days and communities has emerged as a de facto cloud native platform for modern cloud native app development. Now, what Cisco has done is we have created a brand new SAs delivered kubernetes service that is integrated with inter site, we call it the inter site community service for A. Ks. And this just geared a little over one month ago. Now, what interstate kubernetes service does is it delivers a cloud managed and cloud delivered kubernetes service that can be deployed on any supported target infrastructure. It could be a Cisco infrastructure, it could be a third party infrastructure or it could even be public club. But think of it as kubernetes anywhere delivered as says, managed from inside. It's a very powerful capability that we've just released into inter site to enable the power of communities and clog native to be used to be used anywhere. But today we made a very important aspect because we are today announced the brand new Cisco service mess manager, the Cisco service mesh manager, which is available as an extension to the KS are doing decide basically we see service measures as being the future of networking right in the past we had layer to networking and layer three networking and now with service measures, application networking and layer seven networking is the next frontier of, of networking. But you need to think about networking for the application age very differently how it is managed, how it is deployed. It needs to be ready, developer friendly and developer centric. And so what we've done is we've built out an application networking strategy and built out the service match manager as a very simple way to deliver application networking through the consumers, like like developers and application teams. This is built on an acquisition that Cisco made recently of Banzai Cloud and we've taken the assets of Banzai Cloud and deliver the Cisco service mesh manager as an extension to KS. That brings the promise of future networking and modern networking to application and development gives >>God thank you. BJ. And so Katie, let's let's let's wrap this up. I mean, there was a lot in this announcement today, a lot of themes around openness, heterogeneity and a lot of functionality and value. Give us your final thoughts. >>Absolutely. So, couple of things to close on, first of all, um Inner side is the simplest, most powerful hybrid cloud platform out there. It enables that that cloud operating model that VJ talked about, but enables that across cloud. So it's sad, it's relatively easy to get into it and give it a spin so that I'd highly encouraged anybody who's not familiar with it to try it out and anybody who is familiar with it to look at it again, because they're probably services in there that you didn't notice or didn't know last time you looked at it because we're moving so fast. So that's the first thing. The second thing I close with is um, we've been talking about this bridge that's kind of bridging, bridging uh your your on prem your open source, your cloud estates. And it's so important to to make that mental leap because uh in past generation, we used to talk about integrating technologies together and then with public cloud, we started talking about move to public cloud, but it's really how do we integrate, how do we integrate all of that innovation that's coming from the hyper scale, is everything they're doing to innovate superfast, All of that innovation is coming from open source, all of that innovation that's coming from from companies around the world, including Cisco, How do we integrate that to deliver an outcome? Because at the end of the day, if you're a cloud of Steam, if you're an idea of Steam, your job is to deliver an outcome and our mission is to make it super simple for you to do that. That's the mission we're on and we're hoping that everybody that's excited as we are about how simple we made that. >>Great, thank you a lot in this announcement today, appreciate you guys coming back on and help us unpack you know, some of the details. Thank thanks so much. Great having you. >>Thank you >>Dave in a moment. We're gonna come back and talk about disruptive technologies and futures in the age of hybrid cloud with Vegas Rattana and James leach. You're watching the cube, the global leader in high tech coverage. >>What if your server box >>wasn't a box at >>all? What if it could do anything run anything? >>Be any box you >>need with massive scale precision and intelligence managed and optimized from the cloud integrated with all your clouds, private, public or hybrid. So you can build whatever you need today and tomorrow. The potential of this box is unlimited. Unstoppable unseen ever before. Unbox the future with Cisco UCS X series powered by inter site >>Cisco. >>The bridge to possible. Yeah >>we're here with Vegas Rattana who's the director of product management for Pcs at Cisco. And James Leach is the director of business development for U. C. S. At the Cisco as well. We're gonna talk about computing in the age of hybrid cloud. Welcome gentlemen. Great to see you. >>Thank you. >>Thank you because let's start with you and talk about a little bit about computing architectures. We know that they're evolving. They're supporting new data intensive and other workloads especially as high performance workload requirements. What's this guy's point of view on all this? I mean specifically interested in your thoughts on fabrics. I mean it's kind of your wheelhouse, you've got accelerators. What are the workloads that are driving these evolving technologies and how how is it impacting customers? What are you seeing? >>Sure. First of all, very excited to be here today. You're absolutely right. The pace of innovation and foundational platform ingredients have just been phenomenal in recent years. The fabric that's writers that drives the processing power, the Golden city all have been evolving just an amazing place and the peace will only pick up further. But ultimately it is all about applications and the way applications leverage those innovations. And we do see applications evolving quite rapidly. The new classes of applications are evolving to absorb those innovations and deliver much better business values. Very, very exciting time step. We're talking about the impact on the customers. Well, these innovations have helped them very positively. We do see significant challenges in the data center with the point product based approach of delivering these platforms, innovations to the applications. What has happened is uh, these innovations today are being packaged as point point products to meet the needs of a specific application and as you know, the different applications have no different needs. Some applications need more to abuse, others need more memory, yet others need, you know, more course, something different kinds of fabrics. As a result, if you walk into a data center today, it is very common to see many different point products in the data center. This creates a manageability challenge. Imagine the aspect of managing, you know, several different form factors want you to you purpose built servers. The variety of, you know, a blade form factor, you know, this reminds me of the situation we had before smartphones arrived. You remember the days when you when we used to have a GPS device for navigation system, a cool music device for listening to the music. A phone device for making a call camera for taking the photos right? And we were all excited about it. It's when a smart phones the right that we realized all those cool innovations could be delivered in a much simpler, much convenient and easy to consume through one device. And you know, I could uh, that could completely transform our experience. So we see the customers were benefiting from these innovations to have a way to consume those things in a much more simplistic way than they are able to go to that. >>And I like to look, it's always been about the applications. But to your point, the applications are now moving in a much faster pace. The the customer experience is expectation is way escalated. And when you combine all these, I love your analogy there because because when you combine all these capabilities, it allows us to develop new Applications, new capabilities, new customer experiences. So that's that I always say the next 10 years, they ain't gonna be like the last James Public Cloud obviously is heavily influencing compute design and and and customer operating models. You know, it's funny when the public cloud first hit the market, everyone we were swooning about low cost standard off the shelf servers in storage devices, but it quickly became obvious that customers needed more. So I wonder if you could comment on this. How are the trends that we've seen from the hyper scale, Is how are they filtering into on prem infrastructure and maybe, you know, maybe there's some differences there as well that you could address. >>Absolutely. So I'd say, first of all, quite frankly, you know, public cloud has completely changed the expectations of how our customers want to consume, compute, right? So customers, especially in a public cloud environment, they've gotten used to or, you know, come to accept that they should consume from the application out, right? They want a very application focused view, a services focused view of the world. They don't want to think about infrastructure, right? They want to think about their application, they wanna move outward, Right? So this means that the infrastructure basically has to meet the application where it lives. So what that means for us is that, you know, we're taking a different approach. We're we've decided that we're not going to chase this single pane of glass view of the world, which, frankly, our customers don't want, they don't want a single pane of glass. What they want is a single operating model. They want an operating model that's similar to what they can get at the public with the public cloud, but they wanted across all of their cloud options they wanted across private cloud across hybrid cloud options as well. So what that means is they don't want to just consume infrastructure services. They want all of their cloud services from this operating model. So that means that they may want to consume infrastructure services for automation Orchestration, but they also need kubernetes services. They also need virtualization services, They may need terror form workload optimization. All of these services have to be available, um, from within the operating model, a consistent operating model. Right? So it doesn't matter whether you're talking about private cloud, hybrid cloud anywhere where the application lives. It doesn't matter what matters is that we have a consistent model that we think about it from the application out. And frankly, I'd say this has been the stumbling block for private cloud. Private cloud is hard, right. This is why it hasn't been really solved yet. This is why we had to take a brand new approach. And frankly, it's why we're super excited about X series and inter site as that operating model that fits the hybrid cloud better than anything else we've seen >>is acute. First, first time technology vendor has ever said it's not about a single pane of glass because I've been hearing for decades, we're gonna deliver a single pane of glass is going to be seamless and it never happens. It's like a single version of the truth. It's aspirational and, and it's just not reality. So can we stay in the X series for a minute James? Uh, maybe in this context, but in the launch that we saw today was like a fire hose of announcements. So how does the X series fit into the strategy with inter site and hybrid cloud and this operating model that you're talking about? >>Right. So I think it goes hand in hand, right. Um the two pieces go together very well. So we have uh, you know, this idea of a single operating model that is definitely something that our customers demand, right? It's what we have to have, but at the same time we need to solve the problems of the cost was talking about before we need a single infrastructure to go along with that single operating model. So no longer do we need to have silos within the infrastructure that give us different operating models are different sets of benefits when you want infrastructure that can kind of do all of those configurations, all those applications. And then, you know, the operating model is very important because that's where we abstract the complexity that could come with just throwing all that technology at the infrastructure so that, you know, this is, you know, the way that we think about is the data center is not centered right? It's no longer centered applications live everywhere. Infrastructure lives everywhere. And you know, we need to have that consistent operating model but we need to do things within the infrastructure as well to take full advantage. Right? So we want all the sas benefits um, of a Ci CD model of, you know, the inter site can bring, we want all that that proactive recommendation engine with the power of A I behind it. We want the connected support experience went all of that. They want to do it across the single infrastructure and we think that that's how they tie together, that's why one or the other doesn't really solve the problem. But both together, that's why we're here. That's why we're super excited. >>So Vegas, I make you laugh a little bit when I was an analyst at I D C, I was deep in infrastructure and then when I left I was doing, I was working with application development heads and like you said, uh infrastructure, it was just a, you know, roadblock but but so the target speakers with Cisco announced UCS a decade ago, I totally missed it. I didn't understand it. I thought it was Cisco getting into the traditional server business and it wasn't until I dug in then I realized that your vision was really to transform infrastructure, deployment and management and change them all. I was like, okay, I got that wrong uh but but so let's talk about the the ecosystem and the joint development efforts that are going on there, X series, how does it fit into this, this converged infrastructure business that you've, you've built and grown with partners, you got storage partners like Netapp and Pure, you've got i SV partners in the ecosystem. We see cohesive, he has been a while since we we hung out with all these companies at the Cisco live hopefully next year, but tell us what's happening in that regard. >>Absolutely, I'm looking forward to seeing you in the Cisco live next year. You know, they have absolutely you brought up a very good point. You see this is about the ecosystem that it brings together, it's about making our customers bring up the entire infrastructure from the core foundational hardware all the way to the application level so that they can, you know, go off and running pretty quick. The converse infrastructure has been one of the corners 2.5 hour of the strategy, as you pointed out in the last decade. And and and I'm I'm very glad to share that converse infrastructure continues to be a very popular architecture for several enterprise applications. Seven today, in fact, it is the preferred architecture for mission critical applications where performance resiliency latency are the critical requirements there almost a de facto standards for large scale deployments of virtualized and business critical data bases and so forth with X series with our partnerships with our Stories partners. Those architectures will absolutely continue and will get better. But in addition as a hybrid cloud world, so we are now bringing in the benefits of canvas in infrastructure uh to the world of hybrid cloud will be supporting the hybrid cloud applications now with the CIA infrastructure that we have built together with our strong partnership with the Stories partners to deliver the same benefits to the new ways applications as well. >>Yeah, that's what customers want. They want that cloud operating model. Right, go ahead please. >>I was going to say, you know, the CIA model will continue to thrive. It will transition uh it will expand the use cases now for the new use cases that were beginning to, you know, say they've absolutely >>great thank you for that. And James uh have said earlier today, we heard this huge announcement, um a lot of lot of parts to it and we heard Katie talk about this initiative is it's really computing built for the next decade. I mean I like that because it shows some vision and you've got a road map that you've thought through the coming changes in workloads and infrastructure management and and some of the technology that you can take advantage of beyond just uh, you know, one or two product cycles. So, but I want to understand what you've done here specifically that you feel differentiates you from other competitive architectures in the industry. >>Sure. You know that's a great question. Number one. Number two, um I'm frankly a little bit concerned at times for for customers in general for our customers customers in general because if you look at what's in the market, right, these rinse and repeat systems that were effectively just rehashes of the same old design, right? That we've seen since before 2000 and nine when we brought you C. S to market these are what we're seeing over and over and over again. That's that's not really going to work anymore frankly. And I think that people are getting lulled into a false sense of security by seeing those things continually put in the market. We rethought this from the ground up because frankly future proofing starts now, right? If you're not doing it right today, future proofing isn't even on your radar because you're not even you're not even today proved. So we re thought the entire chassis, the entire architecture from the ground up. Okay. If you look at other vendors, if you look at other solutions in the market, what you'll see is things like management inside the chassis. That's a great example, daisy chaining them together >>like who >>needs that? Who wants that? Like that kind of complexity is first of all, it's ridiculous. Um, second of all, um, if you want to manage across clouds, you have to do it from the cloud, right. It's just common sense. You have to move management where it can have the scale and the scope that it needs to impact your entire domain, your world, which is much larger now than it was before. We're talking about true hybrid cloud here. Right. So we had to solve certain problems that existed in the traditional architecture. You know, I can't tell you how many times I heard you talk about the mid plane is a great example. You know, the mid plane and a chastity is a limiting factor. It limits us on how much we can connect or how much bandwidth we have available to the chassis. It limits us on air flow and other things. So how do you solve that problem? Simple. Just get rid of it. Like we just we took it out, right. It's not no longer a problem. We designed an architecture that doesn't need it. It doesn't rely on it. No forklift upgrades. So, as we start moving down the path of needing liquid cooling or maybe we need to take advantage of some new, high performance, low latency fabrics. We can do that with almost. No problem at all. Right, So, we don't have any forklift upgrades. Park your forklift on the side. You won't need it anymore because you can upgrade gradually. You can move along as technologies come into existence that maybe don't even exist. They they may not even be on our radar today to take advantage of. But I like to think of these technologies, they're really important to our customers. These are, you know, we can call them disruptive technologies. The reality is that we don't want to disrupt our customers with these technologies. We want to give them these technologies so they can go out and be disruptive themselves. Right? And this is the way that we've designed this from the ground up to be easy to consume and to take advantage of what we know about today and what's coming in the future that we may not even know about. So we think this is a way to give our customers that ultimate capability flexibility and and future proofing. >>I like I like that phrase True hybrid cloud. It's one that we've used for years and but to me this is all about that horizontal infrastructure that can support that vision of what true hybrid cloud is. You can support the mission critical applications. You can you can develop on the system and you can support a variety of workload. You're not locked into one narrow stovepipe and that does have legs, Vegas and James. Thanks so much for coming on the program. Great to see you. >>Yeah. Thank you. Thank you. >>When we return shortly thomas Shiva who leads Cisco's data center group will be here and thomas has some thoughts about the transformation of networking I. T. Teams. You don't wanna miss what he has to say. You're watching the cube. The global leader in high tech company. Okay, >>mm. Mhm, mm. Okay. Mhm. Yeah. Mhm. Yeah. >>Mhm. Yes. Yeah. Okay. We're here with thomas Shiva who is the Vice president of Product Management, A K A VP of all things data center, networking STN cloud. You name it in that category. Welcome thomas. Good to see you again. >>Hey Sam. Yes. Thanks for having me on. >>Yeah, it's our pleasure. Okay, let's get right into observe ability. When you think about observe ability, visibility, infrastructure monitoring problem resolution across the network. How does cloud change things? In other words, what are the challenges that networking teams are currently facing as they're moving to the cloud and trying to implement hybrid cloud? >>Yeah. Yeah, visibility as always is very, very important. And it's quite frankly, it's not just it's not just the networking team is actually the application team to write. And as you pointed out, the underlying impetus to what's going on here is the data center is where the data is. And I think we set us a couple years back and really what happens the applications are going to be deployed uh in different locations, right. Whether it's in a public cloud, whether it's on prayer, uh, and they are built differently right there, built as microservices, they might actually be distributed as well at the same application. And so what that really means is you need as an operator as well as actually a user better visibility. Where are my pieces and you need to be able to correlate between where the app is and what the underlying network is that is in place in these different locations. So you have actually a good knowledge while the app is running so fantastic or sometimes not. So I think that's that's really the problem statement. What what we're trying to go afterwards, observe ability. >>Okay, and let's double click on that. So a lot of customers tell me that you gotta stare at log files until your eyes bleed and you gotta bring in guys with lab coats who have phds to figure all this stuff out. So, so you just described, it's getting more complex, but at the same time you have to simplify things. So how how are you doing that, >>correct? So what we basically have done is we have this fantastic product that that is called 1000 Ice. And so what this does is basically as the name, which I think is a fantastic fantastic name. You have these sensors everywhere. Um, and you can have a good correlation on uh links between if I run from a site to aside from a site to a cloud, from a cloud to cloud and you basically can measure what is the performance of these links. And so what we're, what we're doing here is we're actually extending the footprint of these thousands agent. Right? Instead of just having uh inversion machine clouds, we are now embedding them with the Cisco network devices. Right? We announced this with the catalyst 9000 and we're extending this now to our 8000 catalyst product line for the for the SD were in products as well as to the data center products the next line. Um and so what you see is is, you know, half a saying, you have 1000 eyes, you get a million insights and you get a billion dollar of improvements uh for how your applications run. And this is really uh, the power of tying together the footprint of where the network is with the visibility, what is going on. So you actually know the application behavior that is attached to this network. >>I see. So okay. So as the cloud evolves and expands it connects your actually enabling 1000 eyes to go further, not just confined within a single data center location, but out to the network across clouds, et cetera, >>correct. Wherever the network is, you're going to have 1000 I sensor and you can't bring this together and you can quite frankly pick if you want to say, hey, I have my application in public cloud provider, a uh, domain one and I have another one domain to, I can't do monitor that link. I can also monitor have a user that has a campus location or branch location. I kind of put an agent there and then I can monitor the connectivity from that branch location all the way to the let's say corporations that data centre, our headquarter or to the cloud. And I can have these probes and just we have visibility and saying, hey, if there's a performance, I know where the issue is and then I obviously can use all the other foods that we have to address those. >>All right, let's talk about the cloud operating model. Everybody tells us it's really the change in the model that drives big numbers in terms of R. O. I. And I want you to maybe address how you're bringing automation and devops to this world of of hybrid and specifically how is Cisco enabling I. T. Organizations to move to a cloud operating model? Is that cloud definition expands? >>Yeah, no that's that's another interesting topic beyond the observe ability. So really, really what we're seeing and this is going on for uh I want to say a couple of years now, it's really this transition from operating infrastructure as a networking team more like a service like what you would expect from a cloud provider. Right? It's really around the network team offering services like a cloud provided us. And that's really what the meaning is of cloud operating model. Right? But this is infrastructure running your own data center where that's linking that infrastructure was whatever runs on the public club is operating and like a cloud service. And so we are on this journey for why? So one of the examples uh then we have removing some of the control software assets, the customers that they can deploy on prayer uh to uh an instance that they can deploy in a cloud provider and just busy, insane. She ate things there and then just run it that way. Right. And so the latest example for this is what we have our identity service engine that is now limited availability available on AWS and will become available in mid this year, both in Italy as unusual as a service. You can just go to market place, you can load it there and now you create, you can start running your policy control in a cloud, managing your access infrastructure in your data center, in your campus wherever you want to do it. And so that's just one example of how we see our customers network operations team taking advantage of a cloud operating model and basically employing their, their tools where they need them and when they need them. >>So what's the scope of, I hope I'm saying it right. Ice, right. I see. I think it's called ice. What's the scope of that like for instance, turn in effect my or even, you know, address simplify my security approach. >>Absolutely. That's now coming to what is the beauty of the product itself? Yes. What you can do is really is that there's a lot of people talking about else. How do I get to zero trust approach to networking? How do I get to a much more dynamic, flexible segmentation in my infrastructure. Again, whether this is only campus X as well as a data center and Ice help today, you can use this as a point to define your policies and then any connect from there. Right. In this particular case we would instant Ice in the cloud as a software load. You now can connect and say, hey, I want to manage and program my network infrastructure and my data center on my campus, going to the respective control over this DNA Center for campus or whether it is the A. C. I. Policy controller. And so yes, what you get as an effect out of this is a very elegant way to automatically manage in one place. What is my policy and then drive the right segmentation in your network infrastructure? >>zero. Trust that, you know, it was pre pandemic. It was kind of a buzzword. Now it's become a mandate. I wonder if we could talk about right. I mean I wonder if you talk about cloud native apps, you got all these developers that are working inside organizations. They're maintaining legacy apps. They're connecting their data to systems in the cloud there, sharing that data. I need these developers, they're rapidly advancing their skill sets. How is Cisco enabling its infrastructure to support this world of cloud? Native making infrastructure more responsive and agile for application developers? >>Yeah. So, you know, we're going to the top of his visibility, we talked about the operating model, how how our network operators actually want to use tools going forward. Now, the next step to this is it's not just the operator. How do they actually, where do they want to put these tools, how they, how they interact with these tools as well as quite frankly as how, let's say, a devops team on application team or Oclock team also wants to take advantage of the program ability of the underlying network. And this is where we're moving into this whole cloud native discussion, right? Which is really two angles, that is the cloud native way, how applications are being built. And then there is the cloud native way, how you interact with infrastructure. Right? And so what we have done is we're a putting in place the on ramps between clouds and then on top of it we're exposing for all these tools, a P I S that can be used in leverage by standard uh cloud tools or uh cloud native tools. Right. And one example or two examples we always have and again, we're on this journey for a while is both answerable uh script capabilities that exist from red hat as well as uh Ashitaka from capabilities that you can orchestrate across infrastructure to drive infrastructure, automation and what what really stands behind it is what either the networking operations team wants to do or even the ap team. They want to be able to describe the application as a code and then drive automatically or programmatically in situation of infrastructure needed for that application. And so what you see us doing is providing all these capability as an interface for all our network tools. Right. Whether it's this ice that I just mentioned, whether this is our D. C. And controllers in the data center, uh whether these are the controllers in the in the campus for all of those, we have cloud native interfaces. So operator or uh devops team can actually interact directly with that infrastructure the way they would do today with everything that lives in the cloud, with everything how they brought the application. >>This is key. You can't even have the conversation of op cloud operating model that includes and comprises on prem without programmable infrastructure. So that's that's very important. Last question, thomas our customers actually using this, they made the announcement today. There are there are there any examples of customers out there doing this? >>We do have a lot of customers out there that are moving down the past and using the D. D. Cisco high performance infrastructure, but also on the compute side as well as on an exercise one of the customers. Uh and this is like an interesting case. It's Rakuten uh record and is a large tackle provider, a mobile five G. Operator uh in Japan and expanding and is in different countries. Uh and so people something oh, cloud, you must be talking about the public cloud provider, the big the big three or four. But if you look at it, there's a lot of the tackle service providers are actually cloud providers as well and expanding very rapidly. And so we're actually very proud to work together with with Rakuten and help them building a high performance uh, data and infrastructure based on hard gig and actually phone a gig uh to drive their deployment to. It's a five G mobile cloud infrastructure, which is which is uh where the whole the whole world where traffic is going. And so it's really exciting to see this development and see the power of automation visibility uh together with the high performance infrastructure becoming reality and delivering actually services, >>you have some great points you're making there. Yes, you have the big four clouds, your enormous, but then you have a lot of actually quite large clouds. Telcos that are either approximate to those clouds or they're in places where those hyper scholars may not have a presence and building out their own infrastructure. So so that's a great case study uh thomas, hey, great having you on. Thanks so much for spending some time with us. >>Yeah, same here. I appreciate it. Thanks a lot. >>I'd like to thank Cisco and our guests today V Joy, Katie VJ, viscous James and thomas for all your insights into this evolving world of hybrid cloud, as we said at the top of the next decade will be defined by an entirely new set of rules. And it's quite possible things will evolve more quickly because the cloud is maturing and has paved the way for a new operating model where everything is delivered as a service, automation has become a mandate because we just can't keep throwing it labor at the problem anymore. And with a I so much more as possible in terms of driving operational efficiencies, simplicity and support of the workloads that are driving the digital transformation that we talk about all the time. This is Dave Volonte and I hope you've enjoyed today's program. Stay Safe, be well and we'll see you next time.

>>from around the globe. It's >>the cube >>with coverage of >>Kublai khan and >>Cloud Native Con, Europe 2021 virtual brought >>to you by red hat, >>the cloud Native >>Computing foundation and ecosystem partners. Hello, welcome back to the cubes coverage of Kublai khan. Cloud Native Con 2021 part of the CNC. S continuing cube partnership virtual here because we're not in person soon, we'll be out of the pandemic and hopefully in person for the next event. I'm john for your host of the key. We're here with ricardo. Roach computing engineers sir. In CUBA. I'm not great to see you ricardo. Thanks for remote ng in all the way across the world. Thanks for coming in. >>Hello, Pleasure. Happy to be here. >>I saw your talk with Priyanka on linkedin and all around the web. Great stuff as always, you guys do great work over there at cern. Talk about what's going on with you and the two speaking sessions you have it coop gone pretty exciting news and exciting sessions happening here. So take us through the sessions. >>Yeah. So actually the two sessions are kind of uh showing the two types of things we do with kubernetes. We we are doing we have a lot of uh services moving to kubernetes, but the first one is more on the services we have in the house. So certain is known for having a lot of data and requests, requiring a lot of computing capacity to analyze all this data. But actually we have also very large community and we have a lot of users and people interested in the stuff we do. So the first question will actually show how we've been uh migrating our group of infrastructure into the into communities and in this case actually open shift. And uh the challenge there is to to run a very large amount of uh global websites on coordinators. Uh we run more than 1000 websites and there will be a demonstration on how we do all the management of the website um life cycle, including upgrading and deploying new new websites and an operator that was developed for this purpose. And then more on the other side will give with a colleague also talk about machine learning. Machine learning has been a big topic for us. A lot of our workloads are migrating to accelerators and can benefit a lot from machine learning. So we're giving a talk about a new service that we've deployed on top of Cuban areas where we try to manage to uh lifecycle of machine learning workloads from data preparation all the way to serving the bottles, also exploring the communities features and integrating accelerators and a lot of accelerators. >>So one part of the one session, it's a large scale deployment kubernetes key to there and now the machine learning essentially service for other people to use that. Right? Like take me through the first large scale deployment. What's the key innovation there in your opinion? >>Yeah, I think compared to the infrastructure we had before, is this notion that we can develop an operator that will uh, manage resource, in this case a website. And this is uh, something that is not always obvious when people start with kubernetes, it's not just an orchestra, it's really the ap and the capability of managing a huge amount of resources, including custom resources. So the possibility to develop this operator and then uh, manage the lifecycle of uh, something that was defined in the house and that fits our needs. Uh, There are challenges there because we have a large amount of websites and uh, they can be pretty active. Uh, we also have to some scaling issues on the storage that serves these these websites and we'll give some details uh during the talk as well, >>so kubernetes storage, this is all kind of under the covers, making this easier. Um and the machine learning, it plays nicely in that what if you take us for the machine learning use case, what's going on there, wow, what was the discovery, How did you guys put that together? What's the key elements there? >>Right, so the main challenge there has been um that machine learning is is quite popular but it's quite spread as well, so we have multiple groups focusing on this, but there's no obvious way to centralize not only the resource usage and make it more efficient, but also centralize the knowledge of how these procedures can be done. So what we are trying to do is just offer a service to all our users where we help them with infrastructure so that they don't have to focus on that and they could focus just on their workloads and we do everything from exposing the data systems that we have in the house so that they can do access to the data and data preparation and then doing um some iteration using notebooks and then doing distributed training with potentially large amount of gps and that storage and serving up the models and all of this is uh is managed with the coordinates cluster underneath. Uh We had a lot of knowledge of how to handle kubernetes and uh all the features that everyone likes scalability. The reliability out of scaling is very important for this type of workload. This is, this is key. >>Yeah, it's interesting to see how kubernetes is maturing, um congratulations on the projects. Um they're going to probably continue to scale. Remember this reminds me of when I was uh you know coming into the business in the 98 late eighties early nineties with TCP I. P. And the S. I. Model, you saw the standards evolve and get settled in and then boom innovation everywhere. And that took about a year to digest state and scale up. It's happening much faster now with kubernetes I have to ask you um what's your experience with the question that people are looking to get answered? Which is as kubernetes goes, the next generation of the next step? Um People want to integrate. So how is kubernetes exposing a. P. I. S. To say integration points for tools and other things? Can you share your experience and where this is going, what's happening now and where it goes? Because we know there's no debate. People like the kubernetes aspect of it, but now it's integration is the conversation. Can you share your thoughts on that? >>I can try. Uh So it's uh I would say it's a moving target, but I would say the fact that there's such a rich ecosystem around kubernetes with all the cloud, David projects, uh it's it's uh like a real proof that the popularity of the A. P. I. And this is also something that we after we had the first step of uh deploying and understanding kubernetes, we started seeing the potential that it's not reaching only the infrastructure itself, it's reaching all the layers, all the stack that we support in house and premises. And also it's opening up uh doors to easily scale into external resources as as well. So what we've been trying to tell our users is to rely on these integrations as much as possible. So this means like the application lifecycle being managed with things like Helmand getups, but also like the monitoring being managed with Prometheus and once you're happy with your deployment in house we have ways to scale out to external resources including public clouds. And this is really like see I don't know a proof that all these A. P. I. S are not only popular but incredibly useful because there's such a rich ecosystem around it. >>So talk about the role of data in this obviously machine learning pieces something that everyone is interested in as you get infrastructure as code and devops um and def sec ops as everything's shifting left. I love that, love that narrative day to our priests. All this is all proving mature, mature ization. Um data is critical. Right? So now you get real time information, real time data. The expectations for the apps is to integrate the data. What's your view on how this is progressing from your standpoint because machine learning and you mentioned you know acceleration or being part of another system. Cashing has always done that would say databases. Right. So you've got now is databases get slower, caches are getting faster now they're all the ones so it's all changing. So what's your thoughts on this next level data equation into kubernetes? Because you know stateless is cool but now you've got state issues. >>Yeah so uh yeah we we've always had huge needs for for data we store and I I think we are over half an exhibit of data available on the premises but we we kind of have our own storage systems which are external and that's for for like the physics data, the raw data and one particular charity that we had with our workloads until recently is that we we call them embarrassing parallel in the sense that they don't really need uh very tight connectivity between the different workloads. So if it's people always say tens of thousands of jobs to do some analysis, they're actually quite independent, they will produce a lot more data but we can store them independently. Machine learning is is posing a challenge in the sense that this is a training tends to be a lot more interconnected. Um so it can be a benefit from from um systems that we are not so familiar with. So for us it's it's maybe not so much the cashing layers themselves is really understanding how our infrastructure needs to evolve on premises to support this kind of workloads. We had some smallish uh more high performance computing clusters with things like infinite and for low latency. But this is not the bulk of our workloads. This is not what we are experts on these days. This is the transition we are doing towards uh supporting this machine learning workers >>um just as a reference for the folks watching you mentioned embarrassing parallel and that's a quote that you I read on your certain tech blog. So if you go to tech blog dot web dot search dot ch or just search cern tech blog, you'll see the post there um and good stuff there and in there you go, you lay out a bunch of other things too where you start to see the deployment services and customer resource definitions being part of this, is it going to get to the point where automation is a bigger part of the cluster management setting stuff up quicker. Um As you look at some of the innovations you're doing with machines and Coubertin databases and thousands of other point things that you're working on there, I mean I know you've got a lot going on there, it's in the post but um you know, we don't want to have the problem of it's so hard to stand up and manage and this is what people want to make simpler. How do you how do you answer that when people say say we want to make it easier? >>Yeah. So uh for us it's it's really automate everything and up to now it has been automate the deployment in the kubernetes clusters right now we are looking at automating the kubernetes clusters themselves. So there's some really interesting projects, uh So people are used to using things like terra form to manage the deployment of clusters, but there are some projects like cross playing, for example, that allows us to have the clusters themselves being resources within kubernetes. Uh and this is something we are exploring quite a bit. Uh This allows us to also abstract the kubernetes clusters themselves uh as uh as carbonated resources. So this this idea of having a central cluster that will manage a much larger infrastructure. So this is something that we're exploring the getups part is really key for us to, it's something that eases the transition from from from people that are used already to manage large scale systems but are not necessarily experts on core NATO's. Uh they see that there's an easier past there if they if they can be introduced slowly through through the centralized configuration. >>You know, you mentioned cross plane, I had some on earlier, he's awesome dude, great guy and I was smiling because you know I still have you know flashbacks and trigger episodes from the Hadoop world, you know when it was such so promising that technology but it was just so hard to stand up and managed to be like really an expert to do that. And I think you mentioned cross plane, this comes up to the whole operator notion of operating the clusters, right? So you know, this comes back down to provisioning and managing the infrastructure, which is, you know, we all know is key, right? But when you start getting into multi cloud and multiple environments, that's where it becomes challenging. And I think I like what they're doing is that something that's on your mind to around hybrid and multi cloud? Can you share your thoughts on that whole trajectory? >>Absolutely. So I actually gave an internal seminar just last week describing what we've been playing with in this area and I showed some demo of using cross plane to manage clusters on premises but also manage clusters running on public clouds. A. W. S. Uh google cloud in nature and it's really like the goal there. There are many reasons we we want to explore external resources. We are kind of used to this because we have a lot of sites around the world that collaborate with us, but specifically for public clouds. Uh there are some some motivations there. The first one is this idea that we have periodic load spikes. So we knew we have international conferences, the number of analysis and job requests goes up quite a bit, so we need to be able to like scale on demand for short periods instead of over provisioning this uh in house. The second one is again coming back to machine learning this idea of accelerators. We have a lot of Cpus, we have a lot less gPS uh so it would be nice to go on fish uh for those in the public clouds. And then there's also other accelerators that are quite interesting, like CPUs and I p u s that will definitely play a role and we probably, or maybe we will never have among premises, will only be able to to use them externally. So in that, in that respect, actually coming back to your previous question, this idea of storage then becomes quite important. So what we've been playing with is not only managing this external cluster centrally, but also managing the wall infrastructure from a central place. So this means uh, making all the clusters, whatever they are look very, very much the same, including like the monitoring and the aggregation of the monitoring centrally. And then as we talked about storage, this idea of having local storage that that will be allow us to do really quick software distribution but also access to the data, >>what you guys are doing as we say, cool. And relevant projects. I mean you got the large scale deployments and the machine learning to really kind of accelerate which will drive a lot of adoption in terms of automation. And as that kicks in when you got to get the foundational work done, I see that clearly the right trajectory, you know, reminds me ricardo, um you know, again not do a little history lesson here, but you know, back when network protocols were moving from proprietary S N A for IBM deck net for digital back in the history the old days the os I Open Systems Interconnect Standard stack was evolving and you know when TCP I P came around that really opened up this interoperability, right? And SAM and I were talking about this kind of cross cloud connections or inter clouding as lou lou tucker. And I talked that open stack in 2013 about inter networking or interconnections and it's about integration and interoperability. This is like the next gen conversation that kubernetes is having. So as you get to scale up which is happening very fast as you get machine learning which can handle data and enable modern applications really it's connecting networks and connecting systems together. This is a huge architectural innovation direction. Could you share your reaction to that? >>Yeah. So actually we are starting the easy way, I would say we are starting with the workloads that are loosely coupled that we don't necessarily have to have this uh tighten inter connectivity between the different deployments, I would say that this is this is already giving us a lot because our like the bulk of our workloads are this kind of batch, embarrassing parallel, uh and we are also doing like co location when we have large workloads that made this kind of uh close inter connectivity then we kind of co locate them in the same deployment, same clouds in region. Um I think like what you describe of having cross clouds interconnectivity, this will be like a huge topic. It is already, I would say so we started investigating a lot of service measure options to try to learn what we can gain from it. There is clearly a benefit for managing services but there will be definitely also potential to allow us to kind of more easily scale out across regions. There's we've seen this by using the public cloud. Some things that we found is for example, this idea of infinite, infinite capacity which is kind of sometimes uh it feels kind of like that even at the scale we have for Cpus But when you start using accelerators, Yeah, you start negotiating like maybe use multiple regions because there's not enough capacity in a single region and you start having to talk to the cloud providers to negotiate this. And this makes the deployments more complicated of course. So this, this interconnectivity between regions and clouds will be a big thing. >>And, and again, low hanging fruit is just a kind of existing market but has thrown the vision out there mainly to kind of talk about what what we're seeing which is the world's are distributed computer. And if you have the standards, good things happen. Open systems, open innovating in the open really could make a big difference is going to be the difference between real value for the society of global society or are we going to get into the silo world? So I think the choice is the industry and I think, you know, Cern and C and C. F and Lennox Foundation and all the companies that are investing in open really is a key inflection point for us right now. So congratulations. Thanks for coming on the cube. Yeah, appreciate it. Thank you. Okay, Ricardo, rocha computing engineer cern here in the cube coverage of the CN Cf cube con cloud, native con europe. I'm john for your host of the cube. Thanks for watching.

>>Hello, and welcome to this cube conversation. I'm Sean for, with the cube here in Palo Alto, California, actually coming out of the pandemic this year. Hopefully we'll be back to real life soon. Uh it's uh, in March, shouldn't it be? April spring, 2021. Got a great guest Chris Lynch, who is executive chairman, CEO of scale, who took over at the helm of this company about two and a half years ago, or so, um, lots of going on Chris. Great to see you, uh, remotely, uh, in Boston, we're here in Palo Alto. Great to see you. >>Great to see you as well, but hope to see you in person, this sprint. >>Yeah. I got to say people really missing real life. And I started to see events coming back to vaccines out there, but a lot going on. I mean, Dave and I Volante, I was just talking about how, um, you know, when we first met you and big data world was kicking ass and taking names a lot's changed at Duke went the way it went. Um, you know, Vertica coming, you led, did extremely well sold. HP continue to be a crown jewel for HPE. Now the world has changed in the data and with COVID more than ever, you starting to see more and more people really doubling down. You can see who the winners and losers are. You starting to see kind of the mega trend, and now you've got the edge and other things. So I want to get your take at scale, took advantage of that pivot. You've been in charge. Give us the update. What's the current strategy of that scale? >>Sure. Well, when I took the company over about two and a half years ago, it was very focused on accelerating the dupe instances. And, uh, as you mentioned earlier, the dupe is sort of plateaued, but the ability to take that semantic layer and deliver it in the cloud is actually even more relevant with the advent of snowflake and Databricks and the emergence of, uh, Google big query, um, and Azure as the analytic platforms, in addition to Amazon, which obviously was, was the first mover in the space. So I would say that while people present big day in as sort of a passing concept, I think it's been refined and matured and companies are now digitizing their environment to take advantage of being able to deliver all of this big data in a way that, um, they could get actionable insights, which I don't think has been the case through the early stages of the development of big data concepts. >>Yeah, Chris, we've always followed your career. You've been a strong operator, but also see things a little bit early, get on the wave, uh, and help helps companies turn around also on public, a great career. You've had, I got to ask you in your opinion and you, and you can make sense for customers and make sure customers see the value proposition. So I got to ask you in this new world of the semantic layer, you mentioned snowflake, Amazon and cloud scales. Huge. Why is the semantic layer important? What is it and why is it important for customers? What are they really buying with this? >>Well, they're buying a few things, the buying freedom and choice because we're multicloud, um, they're, they're buying the ability to evolve their environments versus your evolution versus revolution. When they think about how they move forward in the next generation of their enterprise architecture. And the reason that you need the semantic layer, particularly in the cloud is that we separate the source from the actual presentation of the data. So we allow data to stay where it is, but we create one logical view that was important for legacy data workloads, but it's even more important in a world of hybrid compute models in multi-vendor cloud models. So having one source of truth, consistency, consistent access, secure access, and actual insights to wall, and we deliver this with no code and we allow you to turbocharge the stacks of Azure and Amazon Redshift and Google big query while being able to use the data that you've created your enterprise. So, so there's a demand for big data and big data means being able to access all your data into one logical form, not pockets of data that are in the cloud that are behind the firewall that are constrained by, um, vendor lock-in, but open access to all of the data to make the best decisions. >>So if I'm an enterprise and I'm used to on-premise data warehouses and data management, you know, from whether it's playing with a dupe clusters or whatever, I see snowflake, I see the cloud scale. How do I get my teams kind of modernized if you had to kind of go in and say, cause most companies actually have a hard time doing that. They're like they got to turn their existing it into cloud powerhouses. That's what they want to do. So how do you get them there? What's the secret in your opinion, to take a team and a company that's used to doing it on prem, on premises to the cloud? >>Sure. It's a great question. So as I mentioned before, the difference between evolution and revolution today, without outscale to do what you're suggesting is a revolution. And you know, it's very difficult to perform heart surgery on the patient while he's running the Boston marathon. And that's the analog I would give you for trying to digitize your environment without this semantic layer that allows you to first create a logical layer, right? This information in a logical mapping so that you can gradually move data to the appropriate place. Without us. You're asked to go from, you know, one spot to another and do that while you're running your business. And that's what discourages companies or creates tremendous risk with digitizing your environment or moving to cloud. They have to be able to do it in a way that's non-disruptive to their business and seamless with respect to their current workflows. >>No, Chris, I got to ask you without, I know you probably not expecting this question, but um, most people don't know that you are also an investor before you as CEO, um, angel investor as well. You did an angel investment deal with a chemical data robot. We've had a good outcome. And so you've seen the wave, you've seen a kind of how the progress, you mentioned snowflake earlier. Um, as you look at those kinds of deals, as they've evolved, you know, you're seeing this acceleration with data science, what's your take on this because you know, those companies that have become successful or been acquired that you've invested in now, you're operating at scale as a company, you got to direct the company into the right direction. Where is that? Where are you taking this thing? >>Sure. It's a great, great question. So with respect to AI and ML and the investment that I made almost 10 years ago and data robot, um, I believe then, and I believe now more than ever that AI is going to be the next step function in industrial productivity. And I think it's going to change, you know, the composition of our lives. And, um, I think I have enough to have been around when the web was commercialized in the internet, the impact that's having had on the world. I think that impact pales in comparison to what AI, the application of AI to all walks of life has gone going to do. Um, I think that, um, within the next 24 months companies that don't have an AI strategy will be shorted on wall street. I think every phone, every, every vertical function in the marketplace is going to be impacted by AI. >>And, um, we're just seeing the infancy of mass adoption application when it comes to at scale. I think we're going to be right in the middle of that. We're about the democratization of those AI and machine learning models. One of the interesting things we developed it, this ML ops product, where we're able to allow you with your current BI tool, we're able to take machine learning models and just all the legacy BI data into those models, providing better models, more accurate, and precise models, and then re publish that data back out to the BI tool of your choice, whether it be Tableau, Microsoft power, BI Excel, we don't care. >>So I got to ask you, okay, the enterprises are easy targets, large enterprises, you know, virtualization of the, of this world that we're living with. COVID virtualization being more, you know, virtual events, virtual meetings, virtual remote, not, not true virtualization, as we know it, it virtualization, but like life of virtualization of life companies, small companies like the, even our size, the cube, we're getting more data. So you start to see people becoming more data full, not used to dealing with data city mission. They see opportunities to pivot, leverage the data and take advantage of the cloud scale. McKinsey, just put out a report that we covered. There's a trillion dollars of new Tam in innovation, new use cases around data. So a small company, the size of the cube Silicon angle could be out there and innovate and build a use case. This is a new dynamic. This is something that was seen, this mid-market opportunity where people are starting to realize they can have a competitive advantage and disrupt the big guys and the incumbents. How do you see this mid market opportunity and how does at-scale fit into that? >>So you're as usual you're spot on John. And I think the living breathing example of snowflake, they brought analytics to the masses and to small and medium enterprises that didn't necessarily have the technical resources to implement. And we're taking a page out of their book. We're beginning to deliver the end of this quarter, integrated solutions, that map SME data with public markets, data and models, all integrated in their favorite SAS applications to make it simple and easy for them to get EnLink insight and drive it into their business decisions. And we think we're very excited about it. And, you know, if, if we can be a fraction, um, if we can, if we get a fraction of the adoption that snowflake has will be very soon, we'll be very successful and very happy with the results this year. >>Great to see you, Chris, I want to ask you one final question. Um, as you look at companies coming out of the pandemic, um, growth strategies is going to be in play some projects going to be canceled. There's pretty obvious, uh, you know, evidence that, that has been exposed by working at remote and everyone working at home, you can start to see what worked, what wasn't working. So that's going to be clear. You're gonna start to see pattern of people doubling down on certain projects. Um, at scales, a company has a new trajectory for folks that kind of new the old company, or might not have the update. What is at scale all about what are what's the bumper sticker? What's the value proposition what's working that you're doubling down on. >>We want to deliver advanced multi-dimensional analytics to customers in the cloud. And we want to do that by delivering, not compromising on the complexity of analytics, um, and to do that, you have to deliver it, um, in a seamless and easy to use way. And we figure out a way to do that by delivering it through the applications that they know and love today, whether it be their Salesforce or QuickBooks or you name, the SAS picked that application, we're going to turbocharge them with big data and machine learning in a way that's going to enhance their operations without, uh, increase the complexity. So it's about delivering analytics in a way that customers can absorb big customers and small customers alike. >>While I got you here, one final final question, because you're such an expert at turnarounds, as well as growing companies that have a growth opportunity. There's three classes of companies that we see emerging from this new cloud scale model where data's involved or whatever new things out there, but mainly data and cloud scale. One is use companies that are either rejuvenating their business model or pivoting. Okay. So they're looking at cost optimization, things of that nature, uh, class number two innovation strategy, where they're using technology and data to build new use cases or changed existing use cases for kind of new capabilities and finally pioneers, pioneering new net, new paradigms or categories. So each one has its own kind of profile. All, all are winning with data as a former investor and now angel investor and someone who's seen turnarounds and growing companies that are on the innovation wave. What's your takeaway from this because it's pretty miraculous. If you think about what could happen in each one of those cases, there's an opportunity for all three categories with cloud and data. What's your personal take on that? >>So I think if you look at, um, ways we've seen in the past, you know, particularly the, you know, the internet, it created a level of disruption that croup that delivered basically a renewed, um, playing field so that the winners and losers really could be reset and be based on their ability to absorb and leverage the new technology. I think the same as an AI and ML. So I think it creates an opportunity for businesses that were laggerts to catch, operate, or even supersede the competitors. Um, I think it has that kind of an impact. So from my, my view, you're going to see as big data and analytics and artificial intelligence, you know, mature and coalesce, um, vertical integration. So you're going to see companies that are full stack businesses that are delivered through AI and cloud, um, that are completely new and created or read juvenile based on leveraging these new fundamentals. >>So I think you're going to see a set of new businesses and business models that are created by this ubiquitous access to analytics and data. And you're going to see some laggerts catch up that you're going to see some of the people that say, Hey, if it isn't broke, don't fix it. And they're going to go by the wayside and it's going to happen very, very quickly. When we started this business, John, the cycle of innovation was five it's now, you know, under a year, maybe, maybe even five months. So it's like the difference between college for some professional sports, same football game, the speed of the game is completely different. And the speed of the game is accelerating. >>That's why the startup actions hot, and that's why startups are going from zero to 60, if you will, uh, very quickly, um, highly accelerated great stuff. Chris Lynch veteran the industry executive chairman CEO of scale here on the cube conversation with John furrier, the host. Thank you for watching Chris. Great to see you. Thanks for coming on. >>Great to see you, John, take care. Hope to see you soon. >>Okay. Let's keep conversation. Thanks for watching.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. As I've said many times on the Cube for years, decades, even we've marched to the cadence of Moore's law, relying on the doubling of performance every 18 months or so. But no longer is this the mainspring of innovation for technology. Rather, it's the combination of data applying machine intelligence and the cloud supported by the relentless reduction of the cost of compute and storage and the build out of a massively distributed computer network. Very importantly, in the last several years, alternative processors have emerged to support offloading work and performing specific Test GP use of the most widely known example of this trend, with the ascendancy of in video for certain applications like gaming and crypto mining and, more recently, machine learning. But in the middle of last decade, we saw the early development focused on the DPU, the data processing unit, which is projected to make a huge impact on data centers in the coming years. As we move into the next era of Cloud. And with me is deep. Sindhu, who's this co founder and CEO of Fungible, a company specializing in the design and development of GPU deep Welcome to the Cube. Great to see you. >>Thank you, Dave. And thank you for having me. >>You're very welcome. So okay, my first question is, don't CPUs and GP use process data already? Why do we need a DPU? >>Um you know that that is a natural question to ask on. CPUs have been around in one form or another for almost, you know, 55 maybe 60 years. And, uh, you know, this is when general purpose computing was invented, and essentially all CPI use went to x 80 60 x 86 architecture. Uh, by and large arm, of course, is used very heavily in mobile computing, but x 86 primarily used in data center, which is our focus. Um, now, you can understand that that architectural off general purpose CPUs has been refined heavily by some of the smartest people on the planet. And for the longest time, uh, improvements you refer the Moore's Law, which is really the improvements off the price performance off silicon over time. Um, that, combined with architectural improvements, was the thing that was pushing us forward. Well, what has happened is that the architectural refinements are more or less done. Uh, you're not going to get very much. You're not going to squeeze more blood out of that storm from the general purpose computer architectures. What has also happened over the last decade is that Moore's law, which is essentially the doubling off the number of transistors, um, on a chip has slowed down considerably on and to the point where you're only getting maybe 10 20% improvements every generation in speed off the grandest er. If that. And what's happening also is that the spacing between successive generations of technology is actually increasing from 2, 2.5 years to now three, maybe even four years. And this is because we are reaching some physical limits in Seamus. Thes limits are well recognized, and we have to understand that these limits apply not just to general purpose if use, but they also apply to GP use now. General purpose, if used, do one kind of confrontation. They really general on bacon do lots and lots of different things. It is actually a very, very powerful engine, Um, and then the problem is it's not powerful enough to handle all computations. So this is why you ended up having a different kind of processor called the GPU, which specializes in executing vector floating point arithmetic operations much, much better than CPL. Maybe 2030 40 times better. Well, GPS have now been around for probably 15, 20 years, mostly addressing graphics computations. But recently, in the last decade or so, they have been used heavily for AI and analytics computations. So now the question is, why do you need another specialized engine called the DPU? Well, I started down this journey about almost eight years ago, and I recognize I was still at Juniper Networks, which is another company that I found it. I recognize that in the data center, um, as the workload changes due to addressing Mawr and Mawr, larger and larger corpus is of data number one. And as people use scale out as the standard technique for building applications, what happens is that the amount of East West traffic increases greatly. And what happens is that you now have a new type off workload which is coming, and today probably 30% off the workload in a data center is what we call data centric. I want to give you some examples of what is the data centric E? >>Well, I wonder if I could interrupt you for a second, because Because I want you to. I want those examples, and I want you to tie it into the cloud because that's kind of the topic that we're talking about today and how you see that evolving. It's a key question that we're trying to answer in this program. Of course, Early Cloud was about infrastructure, a little compute storage, networking. And now we have to get to your point all this data in the cloud and we're seeing, by the way, the definition of cloud expand into this distributed or I think the term you use is disaggregated network of computers. So you're a technology visionary, And I wonder, you know how you see that evolving and then please work in your examples of that critical workload that data centric workload >>absolutely happy to do that. So, you know, if you look at the architectural off cloud data centers, um, the single most important invention was scale out scale out off identical or near identical servers, all connected to a standard i p Internet network. That's that's the architectural. Now, the building blocks of this architecture er is, uh, Internet switches, which make up the network i p Internet switches. And then the servers all built using general purpose X 86 CPUs with D ram with SSD with hard drives all connected, uh, inside the CPU. Now, the fact that you scale these, uh, server nodes as they're called out, um, was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose computer? But this architectures, Dave, is it compute centric architectures and the reason it's a compute centric architectures. If you open this a server node, what you see is a connection to the network, typically with a simple network interface card. And then you have CP use, which are in the middle of the action. Not only are the CPUs processing the application workload, but they're processing all of the aisle workload, what we call data centric workload. And so when you connect SSD and hard drives and GPU that everything to the CPU, um, as well as to the network, you can now imagine that the CPUs is doing to functions it z running the applications, but it's also playing traffic cop for the I O. So every Io has to go to the CPU and you're executing instructions typically in the operating system, and you're interrupting the CPU many, many millions of times a second now. General Purpose CPUs and the architecture of the CPS was never designed to play traffic cop, because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's. It's critical that in this new architecture, where there's a lot of data, a lot of East West traffic, the percentage of work clothes, which is data centric, has gone from maybe 1 to 2% to 30 to 40%. I'll give you some numbers, which are absolutely stunning if you go back to, say, 1987 and which is, which is the year in which I bought my first personal computer. Um, the network was some 30 times slower. Then the CPI. The CPI was running at 50 megahertz. The network was running at three megabits per second. Well, today the network runs at 100 gigabits per second and the CPU clock speed off. A single core is about 3 to 2.3 gigahertz. So you've seen that there is a 600 x change in the ratio off I'll to compute just the raw clock speed. Now you can tell me that. Hey, um, typical CPUs have lots of lots, of course, but even when you factor that in, there's bean close toe two orders of magnitude change in the amount of ill to compute. There is no way toe address that without changing the architectures on this is where the DPU comes in on the DPU actually solves two fundamental problems in cloud data centers on these air. Fundamental. There's no escaping it, no amount off. Clever marketing is going to get around these problems. Problem number one is that in a compute centric cloud architectures the interactions between server notes are very inefficient. Okay, that's number one problem number one. Problem number two is that these data center computations and I'll give you those four examples the network stack, the storage stack, the virtualization stack and the security stack. Those four examples are executed very inefficiently by CBS. Needless to say that that if you try to execute these on GPS, you'll run into the same problem, probably even worse because GPS are not good at executing these data centric computations. So when U. S o What we were looking to do it fungible is to solve these two basic problems and you don't solve them by by just using taking older architectures off the shelf and applying them to these problems because this is what people have been doing for the for the last 40 years. So what we did was we created this new microprocessor that we call the DPU from ground doctor is a clean sheet design and it solve those two problems. Fundamental. >>So I want to get into that. But I just want to stop you for a second and just ask you a basic question, which is so if I understand it correctly, if I just took the traditional scale out, If I scale out compute and storage, you're saying I'm gonna hit a diminishing returns, It z Not only is it not going to scale linear linearly, I'm gonna get inefficiencies. And that's really the problem that you're solving. Is that correct? >>That is correct. And you know this problem uh, the workloads that we have today are very data heavy. You take a I, for example, you take analytics, for example. It's well known that for a I training, the larger the corpus of data relevant data that you're training on, the better the result. So you can imagine where this is going to go, especially when people have figured out a formula that, hey, the more data I collect, I can use those insights to make money. >>Yeah, this is why this is why I wanted to talk to you, because the last 10 years we've been collecting all this data. Now I want to bring in some other data that you actually shared with me beforehand. Some market trends that you guys cited in your research and the first thing people said is they want to improve their infrastructure on. They want to do that by moving to the cloud, and they also there was a security angle there as well. That's a whole nother topic. We could discuss the other staff that jumped out at me. There's 80% of the customers that you surveyed said they'll be augmenting their X 86 CPUs with alternative processing technology. So that's sort of, you know, I know it's self serving, but z right on the conversation we're having. So I >>want to >>understand the architecture. Er, aan den, how you've approached this, You've you've said you've clearly laid out the X 86 is not going to solve this problem. And even GP use are not going to solve this problem. So help us understand the architecture and how you do solve this problem. >>I'll be I'll be very happy to remember I use this term traffic cough. Andi, I use this term very specifically because, uh, first let me define what I mean by a data centric computation because that's the essence off the problem resolved. Remember, I said two problems. One is we execute data centric work clothes, at least in order of magnitude, more efficiently than CPUs or GPS, probably 30 times more efficiently on. The second thing is that we allow notes to interact with each other over the network much, much more efficiently. Okay, so let's keep those two things in mind. So first, let's look at the data centric piece, the data centric piece, um, for for workload to qualify as being data centric. Four things have to be true. First of all, it needs to come over the network in the form of packets. Well, this is all workloads, so I'm not saying anything new. Secondly, uh, this workload is heavily multiplex in that there are many, many, many computations that are happening concurrently. Thousands of them. Yeah, that's number two. So a lot of multiplexing number three is that this workload is state fel. In other words, you have to you can't process back. It's out of order. You have to do them in order because you're terminating network sessions on the last one Is that when you look at the actual computation, the ratio off I Oto arithmetic is medium to high. When you put all four of them together, you actually have a data centric workout, right? And this workload is terrible for general purpose, C p s not only the general purpose, C p is not executed properly. The application that is running on the CPU also suffers because data center workloads are interfering workloads. So unless you designed specifically to them, you're going to be in trouble. So what did we do? Well, what we did was our architecture consists off very, very heavily multi threaded, general purpose CPUs combined with very heavily threaded specific accelerators. I'll give you examples of some some of those accelerators, um, de Emma accelerators, then radio coding accelerators, compression accelerators, crypto accelerators, um, compression accelerators, thes air, just something. And then look up accelerators. These air functions that if you do not specialized, you're not going to execute them efficiently. But you cannot just put accelerators in there. These accelerators have to be multi threaded to handle. You know, we have something like 1000 different threads inside our DPU toe address. These many, many, many computations that are happening concurrently but handle them efficiently. Now, the thing that that is very important to understand is that given the paucity off transistors, I know that we have hundreds of billions of transistors on a chip. But the problem is that those transistors are used very inefficiently today. If the architecture, the architecture of the CPU or GPU, what we have done is we've improved the efficiency of those transistors by 30 times. Yeah, so you can use >>the real estate. You can use their real estate more effectively, >>much more effectively because we were not trying to solve a general purpose computing problem. Because if you do that, you know, we're gonna end up in the same bucket where General Focus CPS are today. We were trying to solve the specific problem off data centric computations on off improving the note to note efficiency. So let me go to Point number two, because that's equally important, because in a scale out architecture, the whole idea is that I have many, many notes and they're connected over a high performance network. It might be shocking for your listeners to hear that these networks today run at a utilization of no more than 20 to 25%. Question is why? Well, the reason is that if I tried to run them faster than that, you start to get back. It drops because there are some fundamental problems caused by congestion on the network, which are unsolved as we speak today. There only one solution, which is to use DCP well. DCP is a well known is part of the D. C. P I. P. Suite. DCP was never designed to handle the agencies and speeds inside data center. It's a wonderful protocol, but it was invented 42 year 43 years ago, now >>very reliable and tested and proven. It's got a good track record, but you're a >>very good track record, unfortunately, eats a lot off CPU cycles. So if you take the idea behind TCP and you say, Okay, what's the essence of TCP? How would you apply to the data center? That's what we've done with what we call F C P, which is a fabric control protocol which we intend toe open way. Intend to publish standards on make it open. And when you do that and you you embed F c p in hardware on top of his standard I P Internet network, you end up with the ability to run at very large scale networks where the utilization of the network is 90 to 95% not 20 to 25% on you end up with solving problems of congestion at the same time. Now, why is this important today that zall geek speak so far? But the reason this stuff is important is that it such a network allows you to disaggregate pool and then virtualized, the most important and expensive resource is in the data center. What are those? It's computer on one side, storage on the other side. And increasingly even things like the Ram wants to be disaggregated in food. Well, if I put everything inside a general purpose server, the problem is that those resource is get stranded because they're they're stuck behind the CPI. Well, once you disaggregate those resources and we're saying hyper disaggregate, the meaning, the hyper and the hyper disaggregate simply means that you can disaggregate almost all the resources >>and then you're gonna re aggregate them, right? I mean, that's >>obviously exactly and the network is the key helping. So the reason the company is called fungible is because we are able to disaggregate virtualized and then pull those resources and you can get, you know, four uh, eso scale out cos you know the large aws Google, etcetera. They have been doing this aggregation and pulling for some time, but because they've been using a compute centric architecture, er that this aggregation is not nearly as efficient as we could make on their off by about a factor of three. When you look at enterprise companies, they're off by any other factor of four. Because the utilization of enterprises typically around 8% off overall infrastructure, the utilization the cloud for A W S and G, C, P and Microsoft is closer to 35 to 40%. So there is a factor off almost, uh, 4 to 8, which you can gain by disaggregated and pulling. >>Okay, so I wanna interrupt again. So thes hyper scaler zehr smart. A lot of engineers and we've seen them. Yeah, you're right. They're using ah, lot of general purpose. But we've seen them, uh, move Make moves toward GP use and and embrace things like arm eso I know, I know you can't name names but you would think that this is with all the data that's in the cloud again Our topic today you would think the hyper scaler zehr all over this >>all the hyper scale is recognized it that the problems that we have articulated are important ones on they're trying to solve them. Uh, with the resource is that they have on all the clever people that they have. So these air recognized problems. However, please note that each of these hyper scale er's has their own legacy now they've been around for 10, 15 years, and so they're not in a position to all of a sudden turn on a dime. This is what happens to all companies at some >>point. Have technical debt. You mean they >>have? I'm not going to say they have technical debt, but they have a certain way of doing things on. They are in love with the compute centric way of doing things. And eventually it will be understood that you need a third element called the DPU to address these problems. Now, of course, you heard the term smart neck, and all your listeners must have heard that term. Well, a smart thing is not a deep you what a smart Nick is. It's simply taking general purpose arm cores put in the network interface on a PC interface and integrating them all in the same chip and separating them from the CPI. So this does solve the problem. It solves the problem off the data centric workload, interfering with the application work, work. Good job. But it does not address the architectural problem. How to execute data centric workloads efficiently. >>Yeah, it reminds me. It reminds me of you I I understand what you're saying. I was gonna ask you about smart. Next. It does. It's almost like a bridge or a Band Aid. It's always reminds me of >>funny >>of throwing, you know, a flash storage on Ah, a disc system that was designed for spinning disk gave you something, but it doesn't solve the fundamental problem. I don't know if it's a valid analogy, but we've seen this in computing for a long time. >>Yeah, this analogy is close because, you know. Okay, so let's let's take hyper scaler X. Okay, one name names. Um, you find that, you know, half my CPUs are twiddling their thumbs because they're executing this data centric workload. Well, what are you going to do? All your code is written in, uh, C c plus plus, um, on x 86. Well, the easiest thing to do is to separate the cores that run this workload. Put it on a different Let's say we use arm simply because you know x 86 licenses are not available to people to build their own CPUs. So arm was available, so they put a bunch of encores. Let's stick a PC. I express and network interface on you. Port that quote from X 86 Tow arm. Not difficult to do, but it does yield you results on, By the way, if, for example, um, this hyper scaler X shall we call them if they're able to remove 20% of the workload from general purpose CPUs? That's worth billions of dollars. So of course you're going to do that. It requires relatively little innovation other than toe for quote from one place to another place. >>That's what that's what. But that's what I'm saying. I mean, I would think again. The hyper scale is why Why can't they just, you know, do some work and do some engineering and and then give you a call and say, Okay, we're gonna We're gonna attack these workloads together. You know, that's similar to how they brought in GP use. And you're right. It's it's worth billions of dollars. You could see when when the hyper scale is Microsoft and and Azure, uh, and and AWS both announced, I think they depreciated servers now instead of four years. It's five years, and it dropped, like a billion dollars to their bottom line. But why not just work directly with you guys. I mean, Z the logical play. >>Some of them are working with us. So it's not to say that they're not working with us. So you know, all of the hyper scale is they recognize that the technology that we're building is a fundamental that we have something really special, and moreover, it's fully programmable. So you know, the whole trick is you can actually build a lump of hardware that is fixed function. But the difficulty is that in the place where the DPU would sit, which is on the boundary off a server, and the network is literally on that boundary, that place the functionality needs to be programmable. And so the whole trick is how do you come up with an architectural where the functionality is programmable? But it is also very high speed for this particular set of applications. So the analogy with GPS is nearly perfect because GP use, and particularly in video that's implemented or they invented coulda, which is a programming language for GPS on it made them easy to use mirror fully programmable without compromising performance. Well, this is what we're doing with DP use. We've invented a new architectures. We've made them very easy to program. And they're these workloads or not, Workload. The computation that I talked about, which is security virtualization storage and then network. Those four are quintessential examples off data centric, foreclosed on. They're not going away. In fact, they're becoming more and more and more important over time. >>I'm very excited for you guys, I think, and really appreciate deep we're gonna have you back because I really want to get into some of the secret sauce you talked about these accelerators, Erasure coding, crypto accelerators. I want to understand that. I know there's envy me in here. There's a lot of hardware and software and intellectual property, but we're seeing this notion of programmable infrastructure extending now, uh, into this domain, this build out of this I like this term dis aggregated, massive disaggregated network s so hyper disaggregated. Even better. And I would say this on way. I gotta go. But what got us here the last decade is not the same is what's gonna take us through the next decade. Pretty Thanks. Thanks so much for coming on the cube. It's a great company. >>You have it It's really a pleasure to speak with you and get the message of fungible out there. >>E promise. Well, I promise we'll have you back and keep it right there. Everybody, we got more great content coming your way on the Cube on Cloud, This is David. Won't stay right there.

>> As I've said many times on theCUBE for years, decades even we've marched to the cadence of Moore's law relying on the doubling of performance every 18 months or so, but no longer is this the main spring of innovation for technology rather it's the combination of data applying machine intelligence and the cloud supported by the relentless reduction of the cost of compute and storage and the build-out of a massively distributed computer network. Very importantly, the last several years alternative processors have emerged to support offloading work and performing specific tests. GPUs are the most widely known example of this trend with the ascendancy of Nvidia for certain applications like gaming and crypto mining and more recently machine learning. But in the middle of last decade we saw the early development focused on the DPU, the data processing unit, which is projected to make a huge impact on data centers in the coming years as we move into the next era of cloud. And with me is Pradeep Sindhu who's the co-founder and CEO of Fungible, a company specializing in the design and development of DPUs. Pradeep, welcome to theCUBE. Great to see you. >> Thank-you, Dave and thank-you for having me. >> You're very welcome. So okay, my first question is don't CPUs and GPUs process data already. Why do we need a DPU? >> That is a natural question to ask. And CPUs have been around in one form or another for almost 55, maybe 60 years. And this is when general purpose computing was invented and essentially all CPUs went to x86 architecture by and large and of course is used very heavily in mobile computing, but x86 is primarily used in data center which is our focus. Now, you can understand that that architecture of a general purpose CPUs has been refined heavily by some of the smartest people on the planet. And for the longest time improvements you refer to Moore's law, which is really the improvements of the price, performance of silicon over time that combined with architectural improvements was the thing that was pushing us forward. Well, what has happened is that the architectural refinements are more or less done. You're not going to get very much, you're not going to squeeze more blood out of that storm from the general purpose computer architecture. what has also happened over the last decade is that Moore's law which is essentially the doubling of the number of transistors on a chip has slowed down considerably and to the point where you're only getting maybe 10, 20% improvements every generation in speed of the transistor if that. And what's happening also is that the spacing between successive generations of technology is actually increasing from two, two and a half years to now three, maybe even four years. And this is because we are reaching some physical limits in CMOS. These limits are well-recognized. And we have to understand that these limits apply not just to general purposive use but they also apply to GPUs. Now, general purpose CPUs do one kind of competition they're really general and they can do lots and lots of different things. It is actually a very, very powerful engine. And then the problem is it's not powerful enough to handle all computations. So this is why you ended up having a different kind of a processor called the GPU which specializes in executing vector floating-point arithmetic operations much, much better than CPU maybe 20, 30, 40 times better. Well, GPUs have now been around for probably 15, 20 years mostly addressing graphics computations, but recently in the last decade or so they have been used heavily for AI and analytics computations. So now the question is, well, why do you need another specialized engine called the DPU? Well, I started down this journey about almost eight years ago and I recognize I was still at Juniper Networks which is another company that I founded. I recognize that in the data center as the workload changes to addressing more and more, larger and larger corpuses of data, number one and as people use scale-out as these standard technique for building applications, what happens is that the amount of east-west traffic increases greatly. And what happens is that you now have a new type of workload which is coming. And today probably 30% of the workload in a data center is what we call data-centric. I want to give you some examples of what is a data-centric workload. >> Well, I wonder if I could interrupt you for a second. >> Of course. >> Because I want those examples and I want you to tie it into the cloud 'cause that's kind of the topic that we're talking about today and how you see that evolving. I mean, it's a key question that we're trying to answer in this program. Of course, early cloud was about infrastructure, little compute, little storage, little networking and now we have to get to your point all this data in the cloud. And we're seeing, by the way the definition of cloud expand into this distributed or I think a term you use is disaggregated network of computers. So you're a technology visionary and I wonder how you see that evolving and then please work in your examples of that critical workload, that data-centric workload. >> Absolutely happy to do that. So if you look at the architecture of our cloud data centers the single most important invention was scale-out of identical or near identical servers all connected to a standard IP ethernet network. That's the architecture. Now, the building blocks of this architecture is ethernet switches which make up the network, IP ethernet switches. And then the server is all built using general purpose x86 CPUs with DRAM, with SSD, with hard drives all connected to inside the CPU. Now, the fact that you scale these server nodes as they're called out was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose compute. But this architecture did is it compute centric architecture and the reason it's a compute centric architecture is if you open this server node what you see is a connection to the network typically with a simple network interface card. And then you have CPUs which are in the middle of the action. Not only are the CPUs processing the application workload but they're processing all of the IO workload, what we call data-centric workload. And so when you connect SSDs, and hard drives, and GPUs, and everything to the CPU, as well as to the network you can now imagine the CPUs is doing two functions. It's running the applications but it's also playing traffic cop for the IO. So every IO has to go through the CPU and you're executing instructions typically in the operating system and you're interrupting the CPU many, many millions of times a second. Now, general purpose CPUs and the architecture CPUs was never designed to play traffic cop because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's critical that in this new architecture where there's a lot of data, a lot of these stress traffic the percentage of workload, which is data-centric has gone from maybe one to 2% to 30 to 40%. I'll give you some numbers which are absolutely stunning. If you go back to say 1987 and which is the year in which I bought my first personal computer the network was some 30 times slower than the CPU. The CPU is running at 15 megahertz, the network was running at three megabits per second. Or today the network runs at a 100 gigabits per second and the CPU clock speed of a single core is about three to 2.3 gigahertz. So you've seen that there's a 600X change in the ratio of IO to compute just the raw clock speed. Now, you can tell me that, hey, typical CPUs have lots, lots of cores, but even when you factor that in there's been close to two orders of magnitude change in the amount of IO to compute. There is no way to address that without changing the architecture and this is where the DPU comes in. And the DPU actually solves two fundamental problems in cloud data centers. And these are fundamental there's no escaping it. No amount of clever marketing is going to get around these problems. Problem number one is that in a compute centric cloud architecture the interactions between server nodes are very inefficient. That's number one, problem number one. Problem number two is that these data-centric computations and I'll give you those four examples. The network stack, the storage stack, the virtualization stack, and the security stack. Those four examples are executed very inefficiently by CPUs. Needless to say that if you try to execute these on GPUs you will run into the same problem probably even worse because GPUs are not good at executing these data-centric computations. So what we were looking to do at Fungible is to solve these two basic problems. And you don't solve them by just taking older architectures off the shelf and applying them to these problems because this is what people have been doing for the last 40 years. So what we did was we created this new microprocessor that we call DPU from ground up. It's a clean sheet design and it solves those two problems fundamentally. >> So I want to get into that. And I just want to stop you for a second and just ask you a basic question which is if I understand it correctly, if I just took the traditional scale out, if I scale out compute and storage you're saying I'm going to hit a diminishing returns. It's not only is it not going to scale linearly I'm going to get inefficiencies. And that's really the problem that you're solving. Is that correct? >> That is correct. And the workloads that we have today are very data-heavy. You take AI for example, you take analytics for example it's well known that for AI training the larger the corpus of relevant data that you're training on the better the result. So you can imagine where this is going to go. >> Right. >> Especially when people have figured out a formula that, hey the more data I collect I can use those insights to make money- >> Yeah, this is why I wanted to talk to you because the last 10 years we've been collecting all this data. Now, I want to bring in some other data that you actually shared with me beforehand. Some market trends that you guys cited in your research. And the first thing people said is they want to improve their infrastructure and they want to do that by moving to the cloud. And they also, there was a security angle there as well. That's a whole another topic we could discuss. The other stat that jumped out at me, there's 80% of the customers that you surveyed said there'll be augmenting their x86 CPU with alternative processing technology. So that's sort of, I know it's self-serving, but it's right on the conversation we're having. So I want to understand the architecture. >> Sure. >> And how you've approached this. You've clearly laid out this x86 is not going to solve this problem. And even GPUs are not going to solve the problem. >> They re not going to solve the problem. >> So help us understand the architecture and how you do solve this problem. >> I'll be very happy to. Remember I use this term traffic cop. I use this term very specifically because, first let me define what I mean by a data-centric computation because that's the essence of the problem we're solving. Remember I said two problems. One is we execute data-centric workloads at least an order of magnitude more efficiently than CPUs or GPUs, probably 30 times more efficient. And the second thing is that we allow nodes to interact with each other over the network much, much more efficiently. Okay, so let's keep those two things in mind. So first let's look at the data-centric piece. The data-centric piece for workload to qualify as being data-centric four things have to be true. First of all, it needs to come over the network in the form of packets. Well, this is all workloads so I'm not saying anything. Secondly, this workload is heavily multiplex in that there are many, many, many computations that are happening concurrently, thousands of them, okay? That's the number two. So a lot of multiplexing. Number three is that this workload is stateful. In other words you can't process back it's out of order. You have to do them in order because you're terminating network sessions. And the last one is that when you look at the actual computation the ratio of IO to arithmetic is medium to high. When you put all four of them together you actually have a data-centric workload, right? And this workload is terrible for general purpose CPUs. Not only the general purpose CPU is not executed properly the application that is running on the CPU also suffers because data center workloads are interfering workloads. So unless you designed specifically to them you're going to be in trouble. So what did we do? Well, what we did was our architecture consists of very, very heavily multi-threaded general purpose CPUs combined with very heavily threaded specific accelerators. I'll give you examples of some of those accelerators, DMA accelerators, then ratio coding accelerators, compression accelerators, crypto accelerators, compression accelerators. These are just some, and then look up accelerators. These are functions that if you do not specialize you're not going to execute them efficiently. But you cannot just put accelerators in there, these accelerators have to be multi-threaded to handle. We have something like a 1,000 different treads inside our DPU to address these many, many, many computations that are happening concurrently but handle them efficiently. Now, the thing that is very important to understand is that given the velocity of transistors I know that we have hundreds of billions of transistors on a chip, but the problem is that those transistors are used very inefficiently today if the architecture of a CPU or a GPU. What we have done is we've improved the efficiency of those transistors by 30 times, okay? >> So you can use a real estate much more effectively? >> Much more effectively because we were not trying to solve a general purpose computing problem. Because if you do that we're going to end up in the same bucket where general purpose CPUs are today. We were trying to solve a specific problem of data-centric computations and of improving the note to note efficiency. So let me go to point number two because that's equally important. Because in a scalar or architecture the whole idea is that I have many, many notes and they're connected over a high performance network. It might be shocking for your listeners to hear that these networks today run at a utilization of no more than 20 to 25%. Question is why? Well, the reason is that if I tried to run them faster than that you start to get back at drops because there are some fundamental problems caused by congestion on the network which are unsolved as we speak today. There are only one solution which is to use TCP. Well, TCP is a well-known, is part of the TCP IP suite. TCP was never designed to handle the latencies and speeds inside data center. It's a wonderful protocol but it was invented 43 years ago now. >> Yeah, very reliable and tested and proven. It's got a good track record but you're right. >> Very good track record, unfortunately eats a lot of CPU cycles. So if you take the idea behind TCP and you say, okay, what's the essence of TCP? How would you apply it to the data center? That's what we've done with what we call FCP which is a fabric control protocol, which we intend to open. We intend to publish the standards and make it open. And when you do that and you embed FCP in hardware on top of this standard IP ethernet network you end up with the ability to run at very large-scale networks where the utilization of the network is 90 to 95%, not 20 to 25%. >> Wow, okay. >> And you end up with solving problems of congestion at the same time. Now, why is this important today? That's all geek speak so far. The reason this stuff is important is that it such a network allows you to disaggregate, pull and then virtualize the most important and expensive resources in the data center. What are those? It's computer on one side, storage on the other side. And increasingly even things like DRAM wants to be disaggregated. And well, if I put everything inside a general purpose server the problem is that those resources get stranded because they're stuck behind a CPU. Well, once you disaggregate those resources and we're saying hyper disaggregate meaning the hyper and the hyper disaggregate simply means that you can disaggregate almost all the resources. >> And then you going to reaggregate them, right? I mean, that's obviously- >> Exactly and the network is the key in helping. >> Okay. >> So the reason the company is called Fungible is because we are able to disaggregate, virtualize and then pull those resources. And you can get for so scale-out companies the large AWS, Google, et cetera they have been doing this aggregation tooling for some time but because they've been using a compute centric architecture their disaggregation is not nearly as efficient as we can make. And they're off by about a factor of three. When you look at enterprise companies they are off by another factor of four because the utilization of enterprise is typically around 8% of overall infrastructure. The utilization in the cloud for AWS, and GCP, and Microsoft is closer to 35 to 40%. So there is a factor of almost four to eight which you can gain by dis-aggregating and pulling. >> Okay, so I want to interrupt you again. So these hyperscalers are smart. They have a lot of engineers and we've seen them. Yeah, you're right they're using a lot of general purpose but we've seen them make moves toward GPUs and embrace things like Arm. So I know you can't name names, but you would think that this is with all the data that's in the cloud, again, our topic today. You would think the hyperscalers are all over this. >> Well, the hyperscalers recognized here that the problems that we have articulated are important ones and they're trying to solve them with the resources that they have and all the clever people that they have. So these are recognized problems. However, please note that each of these hyperscalers has their own legacy now. They've been around for 10, 15 years. And so they're not in a position to all of a sudden turn on a dime. This is what happens to all companies at some point. >> They have technical debt, you mean? (laughs) >> I'm not going to say they have technical debt, but they have a certain way of doing things and they are in love with the compute centric way of doing things. And eventually it will be understood that you need a third element called the DPU to address these problems. Now, of course, you've heard the term SmartNIC. >> Yeah, right. >> Or your listeners must've heard that term. Well, a SmartNIC is not a DPU. What a SmartNIC is, is simply taking general purpose ARM cores, putting the network interface and a PCI interface and integrating them all on the same chip and separating them from the CPU. So this does solve a problem. It solves the problem of the data center workload interfering with the application workload, good job, but it does not address the architectural problem of how to execute data center workloads efficiently. >> Yeah, so it reminds me of, I understand what you're saying I was going to ask you about SmartNICs. It's almost like a bridge or a band-aid. >> Band-aid? >> It almost reminds me of throwing a high flash storage on a disc system that was designed for spinning disc. Gave you something but it doesn't solve the fundamental problem. I don't know if it's a valid analogy but we've seen this in computing for a longtime. >> Yeah, this analogy is close because okay, so let's take a hyperscaler X, okay? We won't name names. You find that half my CPUs are crippling their thumbs because they're executing this data-centric workload. Well, what are you going to do? All your code is written in C++ on x86. Well, the easiest thing to do is to separate the cores that run this workload. Put it on a different let's say we use Arm simply because x86 licenses are not available to people to build their own CPUs so Arm was available. So they put a bunch of Arm cores, they stick a PCI express and a network interface and you bought that code from x86 to Arm. Not difficult to do but and it does you results. And by the way if for example this hyperscaler X, shall we called them, if they're able to remove 20% of the workload from general purpose CPUs that's worth billions of dollars. So of course, you're going to do that. It requires relatively little innovation other than to port code from one place to another place. >> Pradeep, that's what I'm saying. I mean, I would think again, the hyperscalers why can't they just do some work and do some engineering and then give you a call and say, okay, we're going to attack these workloads together. That's similar to how they brought in GPUs. And you're right it's worth billions of dollars. You could see when the hyperscalers Microsoft, and Azure, and AWS bolt announced, I think they depreciated servers now instead of four years it's five years. And it dropped like a billion dollars to their bottom line. But why not just work directly with you guys? I mean, let's see the logical play. >> Some of them are working with us. So that's not to say that they're not working with us. So all of the hyperscalers they recognize that the technology that we're building is a fundamental that we have something really special and moreover it's fully programmable. So the whole trick is you can actually build a lump of hardware that is fixed function. But the difficulty is that in the place where the DPU would sit which is on the boundary of a server and the network, is literally on that boundary, that place the functionality needs to be programmable. And so the whole trick is how do you come up with an architecture where the functionality is programmable but it is also very high speed for this particular set of applications. So the analogy with GPUs is nearly perfect because GPUs and particularly Nvidia implemented or they invented CUDA which is the programming language for GPUs. And it made them easy to use, made it fully programmable without compromising performance. Well, this is what we're doing with DPUs. We've invented a new architecture, we've made them very easy to program. And they're these workloads, not workloads, computation that I talked about which is security, virtualization, storage and then network. Those four are quintessential examples of data center workloads and they're not going away. In fact, they're becoming more, and more, and more important over time. >> I'm very excited for you guys, I think, and really appreciate Pradeep, we have your back because I really want to get into some of the secret sauce. You talked about these accelerators, eraser code and crypto accelerators. But I want to understand that. I know there's NBMe in here, there's a lot of hardware and software and intellectual property, but we're seeing this notion of programmable infrastructure extending now into this domain, this build-out of this, I like this term disaggregated, massive disaggregated network. >> Hyper disaggregated. >> It's so hyper disaggregated even better. And I would say this and then I got to go. But what got us here the last decade is not the same as what's going to take us through the next decade. >> That's correct. >> Pradeep, thanks so much for coming on theCUBE. It's a great conversation. >> Thank-you for having me it's really a pleasure to speak with you and get the message of Fungible out there. >> Yeah, I promise we'll have you back. And keep it right there everybody we've got more great content coming your way on theCUBE on cloud. This is Dave Vellante. Stay right there. >> Thank-you, Dave.