(light music) >> Hi, I'm Stu Miniman here at theCUBE studio in Palo Alto in the center of Silicon Valley. Happy to welcome back first of all a many time guest at theCUBE, Kevin Deierling with Mellanox, and also someone I've known for many years, but the first time we've actually gotten under the lights in front of the cameras, Marty Lans with Hewlett-Packard Enterprise. Here to talk a lot about networking today and not just networking but storage networking. So, you know, kind of one of the dark corners of the IT world that... There's those of us that have known each other for decades it seems. And, but you know, pretty critical to a lot of what goes on in the environment. Kevin, you know, let's start with you. You know, we've caught up with Mellanox a bunch. Obviously we do a lot of video with HPE. We'll be at the Discover show in Europe coming soon. But why'd you bring Marty along to talk about some of this stuff? >> Yeah, so HPE has been a long-time partner of Mellanox. We're really not necessarily known as a storage networking company, but in fact we're in a ton of storage platforms with our InfiniBand. So, we have super-high quality reliability. We're built into the major storage platforms in the world and Enterprise Appliances, and now with this new work that we're doing with Marty's team and HPE, we're really building what we consider to be the first Ethernet storage fabric that will scale out what we've done in other worlds with dedicated storage platforms. >> Okay, Marty, before we get into some of the things you're doing with Mellanox, tell us a little bit about your role, how you fit inside Hewlett-Packard Enterprise as it's made up today. >> I'm responsible for storage networking, or the connectivity for storage as well as our interoperability. So if you think about it, it's a very broad category from a role perspective. We have a lot of challenges with all the new types of storage technologies today. And that's where Mellanox gets to come in. >> So just elaborate a little bit. What products do you have? NICs and host bus adapters, switches, what falls under your purview? >> Pretty much everything, everything you just mentioned. We carry traditionally, all the traditional storage connectivity products, Fibre Channels, switches, adapters, optics cables, pretty much the whole ecosystem. >> So what we're talking about is the Ethernet storage fabric. So can one of you set it up for us, as to what that term means? And we talked about Fibre Channel. Fibre Channel is a bespoke network designed for storage, a lot of times run by storage people or storage networking people underneath that umbrella. What's happening with the Ethernet side? >> Yeah, I think when you look at the traditional SAN network it was Fibre Channel and the metrics that people evaluate that on are performance, and reliability, and intelligence, storage intelligence. Today when you look at that on all those metrics Ethernet actually wins. So we can get three times the performance for 1/3 the price. Everything is built in in terms of all of the new protocols like NVMe over Fabrics, which is a new one that's coming. Obviously iSCSI. And taking some of the things that we do in terms of intelligence, like RDMA, which is RoCE over Ethernet, that's what really enables NVMe over Fabrics. We have that end-to-end supply of switches, adapters, and cables. And working with HPE, we can bring all of the benefits of the platform that they have and all of the software to that world. Suddenly you've got something that's unmatched with Ethernet. And that's the internet storage fabric. >> So Marty, one of the things I've said a bunch over the last couple of years is nothing ever dies. But Fibre Channel, it's dead, right? Isn't that what this means? Why don't you help us a little bit with the nuance of what you're seeing, what customers are asking, and of course there are certain administrators that are like, I know it, I love it, I'm going to keep buying it for years. >> I guess Fibre Channel's still alive. It's doing very well. I think from a primary storage perspective, I mean that's where Fibre Channel is used, right? Today's storage has a lot of different technologies. And I like to look at this in a couple of ways. One, you look at the evolution of media. You're going from disk, we went from tape to disk, and now we're going from disk to Flash. And Flash to NVMe. And now we have things like performance and latency requirements that weren't there before. And the bottleneck is moved from the storage array to the network. So having a network that creates great latency is really the issue at stake. We have latency road maps. We don't have performance road maps from a storage perspective. So that's the big one. >> Kevin, I'm sure you want to comment on some of the latency piece. That's Mellanox's legacy. >> So with some of the things we're doing now, NVMe over Fabrics, we're adding 10 microseconds of latency. So you've got an NVMe Flash drive. When it was spinning rust, and it took 10 milliseconds, who cared what the network added? Today you really care. We're down to the tens of microseconds to access an NVMe Flash drive. When you move it out of the box, now you need to network it. And that's what we really do, is allow you to access NVMe over Fabrics and iSCSI and iSER and things like that in a remote box and you're adding less than 10 microseconds of latency. It's incredible. >> Yeah, Marty, I think back. Even 10 years ago, there was a lot of times, okay, do I want InfiniBand, do I want Ethernet, do I want Fibre Channel? And there were more political implications than there were technical, architectural implications. I said five years ago, the storage protocol wars are dead. That being said, it doesn't mean that we're still sorting those out. What do you hear from customers? Any more nuance you want to give on that piece? Architecturally, right, Ethernet can do it all today, right? >> Sure, yeah, yeah, it is. So I think those challenges are still there. You still have that... you mentioned political, and I think that's something that's still going to be there for quite some time. The nice thing we did with Mellanox, and what we did in our own technology for storage connectivity, we innovated in an area that I think really hasn't been innovated that was ripe for innovation. So creating an environment that gives the storage network administrator the same capabilities of what you get in Fibre Channel we can do on an Ethernet network today. >> And Marty, one of the things. When we get a partnership announcement like this, bring us inside. Talk to us about what engineering is being done. How is this more than just sticking a lovely new logo on it? What development, what's HPE been bringing to this offering? >> So we did, first when we started, before we get to the Ethernet side, we built something called Smart SAN. It's automation orchestration for Fibre Channel networks. And that was a big success. What we did after that was we looked at it from the Ethernet perspective. We said why can't we do it there? It's in-band, it's real-time access, and it gives you the ability to do all the nuances of what makes Ethernet hard. Automate and orchestrate all the Ethernet capabilities to behave much like a Fibre Channel network. So this is a four- to five-year development cycle that we're in, in terms of developing these products. And sitting down with Mellanox, this is not just a marketing relationship. There is a lot of engineering development work that we've done with Mellanox to storage optimize their products. To make them specifically designed to handle storage traffic. >> Kevin, it's interesting. I think back to, let's say the big other Ethernet company. When they got into Fibre Channel, they learned a lot from the storage side that they drove into some of their Ethernet products. So you kind of see learning going back and forth. It's a small industry we have here. What did HPE bring to the table, and more importantly, what's the latest as to what makes the Ethernet storage fabrics... What's going to move the needle on some of that storage adoption? >> I think the key thing is, as Marty said, if you look at it you've got to be able to be familiar with all of the same things. You need to provide the same level of protection. So whether you're using data center bridging to have a lossless network. We have zero packet loss switches, which means that our switches don't drop packets under the cases where you've actually over-subscribed a network. We can actually push back, we can use PFC, we can use ECN. All of that, and on top of that, what's happened is the look and feel to be able to manage things just like it's Fibre Channel. So all that intelligence that HPE has invested in so much over the years is now being brought to bear on Ethernet. One of the big things we see is in the cloud, people have already moved to a converged network where you're seeing compute and networking and storage all on the same fabric. And really that's Ethernet. And so what we're doing now is bringing all of those capabilities to the enterprise. So we think that 15 or 20 years ago there was really no choice. Fibre Channel was absolutely the right choice. Now we're really trying to make it as easy as possible to make that enterprise transformation to be cloud-like. >> It's funny. Marty, you and I worked for EMC back when that storage network was being designed. Architecturally, those of us who have been in networking since before Fibre Channel, we would have loved to do it with Ethernet, but there were limitations with CPU, the network itself. It would have been nice. But fast forward, it was like, Flash had been around for a long time before, oh wait, now it's ready for enterprise. Now it feels like Ethernet has gone through a lot of that journey. You're welcome to comment on that. But the question I want to have from the storage side, we're going through so many changes. HPE has a very large portfolio, a number of acquisitions as well as many things HPE's doing. We talked about NVMe, NVMe over Fabric, we talked about hyper-converge, we talked about scale-out NAS. Networking is not trivial when it comes to building out distributed architectures. And of course storage has very particular requirements when it comes to network. So what are you hearing from your customers from the storage side of the business? How does HPE pull those pieces together and how does this Ethernet storage fabric fit into it? >> I mentioned it earlier. We talked about the primary array being Fibre Channel. If you take a look at where storage has gone, you talk about the cloud, you talk about all these big data, now you've got secondary storage, you've got hyper-converged storage, you've got NAS scale-out, you've got object. I mean, you go on and on. And all these different storage technologies are representing almost 80% of all the data that's out there. Most of that data, or all that data, now that I think about it, is connected by Ethernet. Now what's interesting is, from our perspective, is that we have a purview of all that capability. I see that challenge that customers are having. And the problem that these customers are finding is they go through the first layer of the challenges which is the storage capabilities they need in these storage technologies. And then they get to the next layer that says oh, by the way, the network isn't that great. And so this is where we saw an opportunity to create something that created the same category of capabilities as you got in your primary to the rest of the storage technologies. They're already using Ethernet. It's a great opportunity to provide another dedicated network that does connectivity for all those other types of storage devices, including primary. >> Is there anything along the management of these type of environments? How similar, how much retraining do you need to do? If your customers are probably going to manage both for a while. >> From a usability perspective, it's quite easy. I think what customers are going to find. We use Fibre Channel as the lowest common denominator in terms of everything has to meet, the Ethernet network has to meet those kind of requirements. So what we did was we replicated that capability throughout the rest. With our automation orchestration capabilities it gives us the feature. From a customer perspective it's really a hands-off kind of solution. It's really nice. >> The other piece is... Kevin, how's the application portfolio changing? You mentioned a little bit, some of those really specific latencies that we have. What are you seeing from customers from the application portfolio? David Floyer from Wikibon has been talking for a long time. HPC is going to become mainstream in the enterprise which seems to pull all of these pieces together. >> That's Mellanox's heritage. We came from the InfiniBand world with HBC. We're really good at building giant supercomputers. And the cloud looks very much like that. And when you talk about things like big data, and Hadoop, and Spark, all of these activities for analytics, all these workloads. So it's not just the traditional enterprise database workloads that need the performance, but all of these new data intensive. And Marty really talked about the two different elements. One was the faster media, and the second was just the breadth of the offering. So it's not just primary block storage anymore. You're talking about object storage, and file storage, and hyper-converged systems. We're seeing all of that come into play here with the M-series switches that we're introducing with HPE. What's happening now is you've got a virtualized, containerized world that's using massive amounts of data on superfast storage media. And it needs the network to support that. All of the accelerations that we've built into our adapters all of the smarts that we're building into the switches and taking all of this management framework and automation that HPE's delivering, we've got a really nice solution together. >> Excellent. One thing I love when we talk networking here, is the containerized world, we're talking about serverless, some of this stuff is trying to explain it in a way that people can understand. Marty, an M-series is probably boxes. There's actually physical... You can buy the software, and everything critically important. Walk us through the product line and what sets it apart from what you've done before and what makes up the product line there. >> A lot of compliments to Mellanox and the way they've designed their products. We have, first and foremost I'd like to call out they have a smaller product that we're working with from an ASIC perspective. It's the 2100 series. It's nice because it's a half-width box. It allows you to get full redundancy on a single 1U tray if you want to think about it that way. From a real estate perspective it's really nice. And it's extremely powerful. So with that solution, you have the power and the cost savings being able to do what many different networks can do at three times the cost in a very small form factor. That's very nice. And with the software that we do, we talked about what kind of automation we have. It's all the basic stuff that you'd imagine like the discovery, the diagnostics, all the things that are manual in an Ethernet world we provide automated in a storage environment. >> What about some of the speeds and feeds? We've got so many different flavors of Ethernet now. I remember it took a decade for 10-gig to go from standards to most customer doing now. It wasn't just 40 and 100, but we've got 25 and 50 in there. So all of them, are there interoperability concerns? Any things that you want to say, yes this, or not ready for that? >> I'll say that the market has diverged on many different speeds and feeds. So we do support all of them in the technology. Even from a storage perspective, some of our platforms support 25 gig, some will support 40 gig. So with a solution, we can do one, we can do 10, 25, 40, 50, 100. What's nice is it gives you, regardless of what technology you're using you have the capability to use the technology. >> Kevin, I want to give you the opportunity. What are you hearing from the customers these days? What are the pain points? It used to be some of those speeds and feeds. Wait around, when can I do the upgrade? It's something that's a massive thing that we have to undertake from the backbone all the way through. So are we moving faster? I know we all talk, it's agility and speed, but how about the network? Is it keeping up? >> Yeah, I think we are keeping up. The thing we hear from customers is about efficiency of using their platform. So whether it's the server or the storage. And the network they don't want to be in the way. So you don't want to have stranded assets with an NVMe drive stuck inside of a server that's run at 10% and you've got another unit that's at 100% and needs more. And really that's what this disk aggregation and software-defined storage is all about is taking advantage and getting the most out of the infrastructure that you've invested in. One NVMe drive can saturate a 25-gig link. So we have people that are saying give me more bandwidth, give me more bandwidth. So we can saturate with 24 drives, 600-gig links. The bandwidth is incredible, and we're able to deliver that with zero packet loss technologies. So really that's what people are asking for. There's more data being generated and processed and analyzed to do efficient business models, new business models. And they don't want to worry about the network. They want it to configure itself automatically, and just work and not be the bottleneck. And we can do that. >> Marty, can you up-level for us a little bit here? When I think about HPE, it comes pre-configured, I know. That's what I've known HPE for. Of course HP for most of my career. Even back in some of the earliest jobs, it's like well, rack comes fully configured. Everything's in it. When I look at this announcement, HPE, server, storage, network, some of your pieces. What's important about this? How does this fit in to the overall picture? >> Customers are used to having that service level from us. Delivering those kind of solutions. And this is no different. We saw a lot of challenges with all these different types of networks. The network being the challenge with these new types of storage technologies. So having these solutions brought to you in the way that we've done with the primary storage array I think is going to make customers pretty happy about it. >> Kevin, want to give me the final word? What should we look for in this announcement? Any last things that we haven't covered? And what should we look for for the rest of 2017? >> I think as Marty said, this is a beginning. We have a strong relationship with HPE on the adapter side, on the cables, on the switches. Also on the synergy platform that we've done the switch for that as well. So 25, 50, 100-gig is here today. With shipping we're really saying 25 is the new 10. Because this faster storage needs faster networks and we're here to deliver. I think, pay attention, we're going to do some new things. There's lots of innovation coming. >> Kevin Deierling, Marty Lans, thanks so much for bringing us the update. And thank you for watching theCUBE. I'm Stu Miniman. (light music)

(upbeat music) >> In 2010 Wikibon predicted that the all flash data center was coming. The forecast at the time was that flash memory consumer volumes, would drive prices of enterprise flash down faster than those of high spin speed, hard disks. And by mid decade, buyers would opt for flash over 15K HDD for virtually all active data. That call was pretty much dead on and the percentage of flash in the data center continues to accelerate faster than that, of spinning disk. Now, the analyst that made this forecast was David FLoyer and he's with me today, along with Renen Hallak who is the founder and CEO of Vast Data. And they're going to discuss these trends and what it means for the future of data and the data center. Gentlemen, welcome to the program. Thanks for coming on. >> Great to be here. >> Thank you for having me. >> You're very welcome. Now David, let's start with you. You've been looking at this for over a decade and you know, frankly, your predictions have caused some friction, in the marketplace but where do you see things today? >> Well, what I was forecasting was based on the fact that the key driver in any technology is volume, volume reduces the cost over time and the volume comes from the consumers. So flash has been driven over the years by initially by the iPod in 2006 the Nano where Steve Jobs did a great job with Samsung and introducing large volumes of flash. And then the iPhone in 2008. And since then, all of mobile has been flash and mobile has been taking in a greater and greater percentage share. To begin with the PC dropped. But now the PCs are over 90% are using flash when there delivered. So flash has taken over the consumer market, very aggressively and that has driven down the cost of flash much much faster than the declining market of HDD. >> Okay and now, so Renen I wonder if we could come to you, we've got I want you to talk about the innovations that you're doing, but before we get there, talk about why you started Vast. >> Sure, so it was five years ago and it was basically the kill of the hard drive. I think what David is saying resonates very, very well. In fact, if you look at our original presentation for Vast Data. It showed flash and tape. There was no hard drive in the middle. And we said 10 years from now, and this was five years ago. So even the dates match up pretty well. We're not going to have hard drives anymore. Any piece of information that needs to be accessible at all will be on flash and anything that is dormant and never gets read will be on tape. >> So, okay. So we're entering this kind of new phase now, with which is being driven by QLC. David maybe you could give us a quick what is QLC? Just give us a bumper sticker there. >> There's 3D NAND, which is the thing that's growing, very very fast and it's growing on several dimensions. One dimension is the number of layers. Another dimension is the size of each of those pieces. And the third dimension is the number of bits which a QLC is five bits per cell. So those three dimensions have all been improving. And the result of that is that on a wafer of, that you create, more and more data can be stored on the whole wafer on the chip that comes from that wafer. And so QLC is the latest, set of 3D NAND flash NAND flash. That's coming off the lines at the moment. >> Okay, so my understanding is that there's new architectures that are entering the data center space, that could take advantage of QLC enter Vast. Someone said they've rented this, a nice set up for you and maybe before we get into the architecture, can you talk a little bit more about the company? I mean, maybe not everybody's familiar with with Vast, you share why you started it but what can you tell us about the business performance and any metrics you can share would be great? >> Sure, so the company as I said is five years old, about 170, 180 people today. We started selling product just around two years ago and have just hit $150 million in run rate. That's with eight sales people. And so, as you can imagine, there's a lot of demand for flash all the way down the stack in the way that David predicted. >> Wow, okay. So you got pretty comfortable. I think you've got product market fit, right? And now you're going to scale. I would imagine you're going to go after escape velocity and you're going to build your moat. Now part of that, I mean a lot of that is product, right? Product is sales. Those are the cool two golden pillars, but, and David when you think back to your early forecast last decade it was really about block storage. That was really what was under attack. You know, kind of fusion IO got it started with Facebook. They were trying to solve their SQL database performance problems. And then we saw pure storage. They hit escape velocity. They drove a truck through EMC sym metrics HDD based install base which precipitated the acquisition of XtremeIO by EMC. Something Renan knows a little bit about having led development, of the product but flash was late to the NAS party guys, Renan let me start with you. Why is that? And what is the relevance of QLC in that regard? >> The way storage has been always, it looks like a pyramid and you have your block devices up at the top and then your NAS underneath. And today you have object down at the bottom of that pyramid. And the pyramid basically represents capacity and the Y axis is price performance. And so if you could only serve a small subset of the capacity, you would go for block. And that is the subset that needed high performance. But as you go to QLC and PLC will soon follow the price of all flash systems goes down to a point where it can compete on the lower ends of that pyramid. And the capacity grows to a point where there's enough flash to support those workloads. And so now with QLC and a lot of innovation that goes with it it makes sense to build an all flash, NAS and object store. >> Yeah, okay. And David, you and I have talked about the volumes and Renan sort of just alluded to that, the higher volumes of NAS, not to mention the fact that NAS is hard, you know files difficult, but that's another piece of the equation here, isn't it? >> Absolutely, NAS is difficult. It's a large, very large scale. We're talking about petabytes of data. You're talking about very important data. And you're talking about data, which is at the moment very difficult to manage. It takes a lot of people to manage it, takes a lot of resources and it takes up a lot, a lot of space as well. So all of those issues with NAS and complexity is probably the biggest single problem. >> So maybe we could geek out a little bit here. You guys go at it, but Renan talk about the Vast architecture. I presume it was built from the ground up for flash since you were trying to kill HTD. What else do we need to know? >> It was built for flash. It was also built for Crosspoint which is a new technology that came out from Intel and micron about three years ago. Cross point is basically another level of persistent media above flash and below Ram. But what we really set out to do is, as I said to kill the hard drive, and for that what you need is to get the price parity. And of course, flash and hard drives are not at price parity today. As David said, they probably will be in a few years from now. And so we wanted to, jumpstart that, to accelerate that. And so we spent a lot of time in building a new type of architecture with a lot of new metadata structures and algorithms on top to bring that effective price down to a point where it's competitive today. And in fact, two years ago the way we did it was by going out to talk to these vendors Intel with 3D Crosspoint and QLC flash Mellanox with NVMe over fabrics, and very fast ethernet networks. And we took those building blocks and we thought how can we use this to build a completely different type of architecture, that doesn't just take flash one level down the stack but actually allows us to break that pyramid, to collapse it down and to build a single system that is as fast as your fastest all flash block device or faster but as affordable as your hard drive based archives. And once that happens you don't need to think about storage anymore. You have a single system that's big enough and cheap enough to throw everything at it. And it's fast enough such that everything is accessible as sub-millisecond latencies. The way the architecture is built is pretty much the opposite of the way scale-out storage has been done. It's not based on shared nothing. The way XtremIO was the way Isilon is the way Hadoop and the Google file system are. We're basing it on a concept called Dis-aggregated Shared Everything. And what that means is that we have the media on one set of devices, the logic running in containers, just software and you can scale each of those independently. So you can scale capacity independently from performance and you have this shared metadata space, that all of the containers can see. So the containers don't actually have to talk to each other in the synchronous path. That means that it's much more scalable. You can go up to hundreds of thousands of nodes rather than just a few dozen. It's much more resilient. You can have all of them fail and you still didn't lose any data. And it's much more easy to use to David's point about complexity. >> Thank you for that. And then you, you mentioned up front that you not only built for flash, but built for Crosspoint. So you're using Crosspoint today. It's interesting. There was always been this sort of debate about Crosspoint It's less expensive than Ram, or maybe I got that wrong but it's persistent, >> It is. >> Okay, but it's more expensive than flash. And it was sort of thought it was a fence sitter cause it didn't have the volume but you're using it today successfully. That's interesting. >> We're using it both to offset the deficiencies of the low cost flash. And the nice thing about QLC and PLC is that you get the same levels of read performance as you would from high-end flash. The only difference between high cost and low cost flash today is in right cycles and in right performance. And so Crosspoint helps us offset both of those. We use it as a large right buffer and we use it as a large metadata store. And that allows us not just to arrange the information in a very large persistent right buffer before we need to place it on the low cost flash. But it also allows us to develop new types of metadata structures and algorithms that allow us to make better use of the low cost flash and reduce the effective price down even lower than the rock capacity. >> Very cool. David, what are your thoughts on the architecture? give us kind of the independent perspective >> I think it's brilliant architecture. I'd like to just go one step down on the network side of things. The whole use of NBME over fabric allows the users all of the servers to get any data across this whole network directly to it. So you've got great performance right away across the stack. And then the other thing is that by using RDMA for NASS, you're able, if you need to, to get down in microseconds to the data. So overall that's a thousand times faster than any HDD system could manage. So this architecture really allows an any to any simple, single level of storage which is so much easier to think about, architect use or manage is just so much simpler. >> If you had I mean, I said I don't know if there's an answer to this question but if you had to pick one thing Renan that you really were dogmatic about and you bet on from an architectural standpoint, what would that be? >> I think what we bet on in the early days is the fact that the pyramid doesn't work anymore and that tiering doesn't work anymore. In fact, we stole Johnson and Johnson's tagline No More Tears. Only, It's not spelled the same way. The reason for that is not because of storage. It's because of the applications as we move to applications more and more that are machine-based and machines are now not just generating the data. They're also reading the data and analyzing it and providing insights for humans to consume. Then the workloads changed dramatically. And the one thing that we saw is that you can't choose which pieces of information need to be accessible anymore. These new algorithms, especially around AI and machine learning and deep learning they need fast access to the entirety of the dataset and they want to read it over and over and over again in order to generate those insights. And so that was the driving force behind us building this new type of architecture. And we're seeing every single day when we talk to customers how the old architecture is simply break down in the face of these new applications. >> Very cool speaking of customers. I wonder if you could talk about use cases, customers you know, and this NASS arena maybe you could add some color there. >> Sure, our customers are large in data. We started half a petabyte and we grow into the exabyte range. The system likes to be big as, as it grows it grows super linearly. If you have a 100 nodes or a 1000 nodes you get more than 10X in performance, in capacity efficiency and resilience, et cetera. And so that's where we thrive. And those workloads are today. Mainly analytics workloads, although not entirely. If you look at it geographically we have a lot of life science in Boston research institutes medical imaging, genomics universities pharmaceutical companies here in New York. We have a lot of financials, hedge funds, Analyzing everything from satellite imagery to trade data to Twitter feeds out in California. A lot of AI, autonomous driving vehicles as well as media and entertainment both generation of films like animation, as well as content distribution are being done on top of best. >> Great thank you and David, when you look at the forecast that you've made over the years and when I imagine that they match nicely with your assumptions. And so, okay, I get that, but that doesn't, not everybody agrees, David. I mean, certainly the HDD guys don't agree but they, they're obviously fighting to hang on to their awesome run for 50 years, but as well there's others to do in hybrids and the like, and they kind of challenge your assumptions and you don't have a dog in this fight. We just want the truth and try to do our best to report it. But let me start with this. One of the things I've seen is that you're comparing deduped and compressed flash with raw HDD. Is that true or false? >> It's in terms of the fundamentals of the forecast, et cetera, it's false. What I'm taking is the new egg price. And I did it this morning and I looked up a two terabyte disc drive, NAS disc drive. I think it was $54. And if you look at the cost of a a NAND for two terabytes, it's about $200. So it's a four to one ratio. >> So, >> So and that's coming down from what people saw last year, which was five or six and every year has been, that ratio has been coming down. >> The ratio between the cost Delta, between HDD is still cheaper. So Renan I wonder one of the other things that Floyer has said is that because of the advantages of flash, not only performance but also data sharing, et cetera, which really drives other factors like TCO. That it doesn't have to be at parody in order for customers to consume that. I certainly saw that on my laptop, I could have got more storage and it could have been cheaper for per bit for my laptop. I took the flash. I mean, no problem. That that was an intelligence test but what are you seeing from customers? And by the way Floyer I think is forecasting by what, 2026 there will be actually a raw to raw crossover. So then it's game over. But what are you seeing in terms of what customers are telling you or any evidence you have that it doesn't have to be, even that customers actually get more value even if it's more expensive from flash, what are you seeing? >> Yeah in the enterprise space customers aren't buying raw flash they're buying storage systems. And so even if the raw numbers flash versus hard drive are still not there there is a lot of things that can be done at the system level to equalize those two. In fact, a lot of our IP is based on that we are taking flash today is, as David said more expensive than hard drives, but at the system level it doesn't remain more expensive. And the reason for that is storage systems waste space. They waste it on metadata, they waste it on redundancy. We built our new metadata structures, such that they everything lives in Crosspoint and is so much smaller because of the way Crosspoint is accessible at byte level granularity, we built our erasure codes in a way where you can sustain 10, 20, 30 drive failures but you only pay two or 1% in overhead. We built our data reduction mechanisms such that they can reduce down data even if the application has already compressed it and already de-duplicated it. And so there's a lot of innovation that can happen at the software level as part of this new direct dis-aggregated shared everything architecture that allows us to bridge that cost gap today without having customers do fancy TCO calculations. And of course, as prices of flash over the next few years continue declining, all of those advantages remain and it will just widen the gap between hard drives and flash. And there really is no advantage to hard drives once the price thing is solved. >> So thank you. So David, the other thing I've seen around these forecasts is that the comments that you can't really data reduce effectively hard disk. And I understand why the overhead and of course you can in flash you can use all kinds of data reduction techniques and not affect performance, or it's not even noticeable like put the cloud guys, do it upstream. Others do it upstream. What's your comment on that? >> Yes, if you take sequential data and you do a lot of work upfront you can write out in very lot big blocks and that's a perfect sequentially, good way of doing it. The challenge for the HDD people is if they go for that for that sort of sequential type of application that the cheapest way of doing that is to use tape which comes back to the discussion that the two things that are going to remain are tape and flash. So that part of the HDD market in my assertion will go towards tape and tape libraries. And those are serving very well at the moment. >> Yeah I mean, It's just the economics of tape are really attractive. I just feel like I've said this many times that the marketing of tape is lacking. Like I'd like to see, better thinking around how it could play. Cause I think customers have this perception tape, but there's actually a lot of value there. I want to carry on, >> Small point there. Yeah, I mean, there's an opportunity in the same way that Vast have created an architecture for flash. There's an opportunity out there for the tech people with flash to make an architecture that allows you to take that workload and really lower the price, enormously. >> You've called it Flape >> Flape yes. >> There's some interesting metadata opportunities there but we won't go into that. And then David, I want to ask you about NAND shortages. We saw this in 2016 and 2017. A lot of people saying there's an NAND shortage again. So that's a flaw in your forecast prices of you're assuming prices of flash continue to come down faster than those of HDD but the shortages of NAND could be problematic. What do you say to that? >> Well, I've looked at that in some detail and one of the big, important things is what's happening in the flash market and the Chinese, YMTC Chinese company has introduced a lot more volume into the market. They're making 100,000 wafers a month for this year. That's around six to 8% of market of NAND at this year, as a result, Samsung, micron, Intel, Hynix they're all increasing their volumes of NAND so that they're all investing. So I don't see that NAND itself is going to be a problem. There is certainly a shortage of processor chips which drive the intelligence in the NAND itself. But that's a problem for everybody. That's a problem for cars. It's a problem for disk drives. >> You could argue that's going to create an oversupply, potentially. Let's not go there, but you know what at the end of the day it comes back to the customer and all this stuff. It's interesting. I love talking about the architecture but it's really all about customer value. And so, so Renan, I want you to sort of close there. What should customers be paying attention to? And what should observers of Vast Data really watch as indicators for progress for you guys milestones and things in the market that we should be paying attention to but start with the customers. What's your advice to them? >> Sure, for any customer that I talked to I always ask the same thing. Imagine where you'll be five years from now because you're making an investment now that is at least five years long. In our case, we guaranteed the lifespan of the devices for a decade, such that you know that it's going to be there for you and imagine what is going to happen over those next five years. What we're seeing in most customers is that they have a lot of doormen data and with the advances in analytics and AI they want to make use of that data. They want to turn it from a cost center to a profit center and to gain insight from that data and to improve their business based on that information that they have the same way the hyperscalers are doing in order to do that, you need one thing you need fast access to all of that information. Once you have that, you have the foundation to step into this next generation type world where you can actually make money off of your information. And the best way to get very, very fast access to all of your information is to put it on Vast media like flash and Crosspoint. If I can give one example, Hedge Funds. Hedge funds do a lot of back-testing on Vast. And what makes sense for them is to test as much information back as they possibly can but because of storage limitations, they can't do that. And the other thing that's important to them is to have a real-time experience to be able to run those simulations in a few minutes and not as a batch process overnight, but because of storage limitations, they can't do that either. The third thing is if you have many different applications and many different users on the same system they usually step on each other's toes. And so the Vast architecture is solves those three problems. It allows you a lot of information very fast access and fast processing an amazing quality of service where different users of the system don't even notice that somebody else is accessing the same piece of information. And so Hedge Funds is one example. Any one of these verticals that make use of a lot of information will benefit from this architecture in this system. And if it doesn't cost any more, there's really no real reason delay this transition into all flash. >> Excellent very clear thinking. Thanks for laying that out. And what about, you know, things that we should how should we judge you? What are the things that we should watch? >> I think the most important way to judge us is to look at customer adoption and what we're seeing and what we're showing investors is a very high net dollar retention number. What that means is basically a customer buys a piece of kit today, how much more will they buy over the next year, over the next two years? And we're seeing them buy more than three times more, within a year of the initial purchase. And we see more than 90% of them buying more within that first year. And that to me indicates that we're solving a real problem and that they're making strategic decisions to stop buying any other type of storage system. And to just put everything on Vast over the next few years we're going to expand beyond just storage services and provide a full stack for these AI applications. We'll expand into other areas of infrastructure and develop the best possible vertically integrated system to allow those new applications to thrive. >> Nice, yeah. Think investors love that lifetime value story. If you can get above 3X of the customer acquisition cost is to IPO in the way. Guys hey, thanks so much for coming to the Cube. We had a great conversation and really appreciate your time. >> Thank you. >> Thank you. >> All right, Thanks for watching everybody. This is Dave Volante for the Cube. We'll see you next time. (gentle music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of VMworld 2020 brought to you by VMware and its ecosystem partners. >> Hello, welcome back to theCUBE's coverage of VMworld 2020. This is theCUBE virtual with VMworld 2020 virtual. I'm John Furrier, your host of theCUBE with Dave Vellante. It's our 11th year covering VMware. We're not in-person, we're virtual but all the content is flowing. Of course, we're here with Pat Gelsinger, the CEO of VMware who's been on theCUBE, all 11 years. This year virtual of theCUBE as we've been covering VMware from his early days in 2010 when theCUBE started, 11 years later, Pat, it's still changing and still exciting. Great to see you, thanks for taking the time. >> Hey, you guys are great. I love the interactions that we have, the energy, the fun, the intellectual sparring and of course the audiences have loved it now for 11 years, and I look forward to the next 11 that we'll be doing together. >> It's always exciting 'cause we have great conversations, Dave, and I like to drill in and really kind of probe and unpack the content that you're delivering at the keynotes, but also throughout the entire program. It is virtual this year which highlights a lot of the cloud native changes. Just want to get your thoughts on the virtual aspect, VMworld's not in-person, which is one of the best events of the year, everyone loves it, the great community. It's virtual this year but there's a slew of content, what should people take away from this virtual VMworld? >> Well, one aspect of it is that I'm actually excited about is that we're going to be well over 100,000 people which allows us to be bigger, right? You don't have the physical constraints, you also are able to reach places like I've gone to customers and maybe they had 20 people attend in prior years. This year they're having 100. They're able to have much larger teams also like some of the more regulated industries where they can't necessarily send people to events like this, The International Audience. So just being able to spread the audience much more. A digital foundation for an unpredictable world, and man, what an unpredictable world it has been this past year. And then key messages, lots of key products announcements, technology announcements, partnership announcements, and of course in all of the VMworld is that hands-on labs, the interactions that will be delivering a virtual. You come to VMware because the content is so robust and it's being delivered by the world's smartest people. >> Yeah, we've had great conversations over the years and we've talked about hybrid cloud, I think, 2012. A lot of the stuff I look back at a lot of the videos was early on we're picking out all these waves, but there was that moment four years ago or so, maybe even four three, I can't even remember it seems like yesterday. You gave the seminal keynote and you said, this is the way the world's going to happen. And since that keynote, I'll never forget, was in Moscone and since then, you guys have been performing extremely well both on the business front as well as making technology bets and it's paying off. So what's next, you got the cloud, cloud scale, is it Space, is it Cyber? All these things are going on what is next wave that you're watching and what's coming out and what can people extract out of VMworld this year about this next wave? >> Yeah, one of the things I really am excited about and I went to my buddy Jensen, I said, boy, we're doing this work in smart mix we really like to work with you and maybe some things to better generalize the GPU. And Jensen challenged me. Now usually, I'm the one challenging other people with bigger visions. This time Jensen said, "hey Pat, I think you're thinking too small. Let's do the entire AI landscape together, and let's make AI a enterprise class works load from the data center to the cloud and to the Edge. And so I'm going to bring all of my AI resources and make VMware and Tanzu the preferred infrastructure to deliver AI at scale. I need you guys to make the GPUs work like first-class citizens in the vSphere environment because I need them to be truly democratized for the enterprise, so that it's not some specialized AI Development Team, it's everybody being able to do that. And then we're going to connect the whole network together in a new and profound way with our Monterey program as well being able to use the Smart NIC, the DPU, as Jensen likes to call it. So now with CPU, GPU and DPU, all being managed through a distributed architecture of VMware. This is exciting, so this is one in particular that I think we are now re-architecting the data center, the cloud and the Edge. And this partnership is really a central point of that. >> Yeah, the NVIDIA thing's huge and I know Dave probably has some questions on that but I asked you a question because a lot of people ask me, is that just a hardware deal? Talking about SmartNICs, you talk about data processing units. It sounds like a motherboard in the cloud, if you will, but it's not just hardware. Can you talk about the aspect of the software piece? Because again, NVIDIA is known for GPUs, we all know that but we're talking about AI here so it's not just hardware. Can you just expand and share what the software aspect of all this is? >> Yeah well, NVIDIA has been investing in their AI stack and it's one of those where I say, this is Edison at work, right? The harder I work, the luckier I get. And NVIDIA was lucky that their architecture worked much better for the AI workload. But it was built on two decades of hard work in building a parallel data center architecture. And they have built a complete software stack for all the major AI workloads running on their platform. All of that is now coming to vSphere and Tanzu, that is a rich software layer across many vertical industries. And we'll talk about a variety of use cases, one of those that we highlight at VMworld is the University, California, San Francisco partnership, UCSF, one of the world's leading research hospitals. Some of the current vaccine use cases as well, the financial use cases for threat detection and trading benefits. It really is about how we bring that rich software stack. This is a decade and a half of work to the VMware platform, so that now every developer and every enterprise can take advantage of this at scale. That's a lot of software. So in many respects, yeah, there's a piece of hardware in here but the software stack is even more important. >> It's so well we're on the sort of NVIDIA, the arm piece. There's really interesting these alternative processing models, and I wonder if you could comment on the implications for AI inferencing at the Edge. It's not just as well processor implications, it's storage, it's networking, it's really a whole new fundamental paradigm, but how are you thinking about that, Pat? >> Yeah, and we've thought about there's three aspects, what we said, three problems that we're solving. One is the developer problem where we said now you develop once, right? And the developer can now say, "hey I want to have this new AI-centric app and I can develop and it can run in the data center on the cloud or at the Edge." Secondly, my Operations Team can be able to operate this just like I do all of my infrastructure, and now it's VMs containers and AI applications. And third, and this is where your question really comes to bear most significantly, is data gravity. Right, these data sets are big. Some of them need to be very low latency as well, they also have regulatory issues. And if I have to move these large regulated data sets to the cloud, boy, maybe I can't do that generally for my Apps or if I have low latency heavy apps at the Edge, huh, I can't pull it back to the cloud or to my data center. And that's where the uniform architecture and aspects of the Monterey Program where I'm able to take advantage of the network and the SmartNICs that are being built, but also being able to fully represent the data gravity issues of AI applications at scale. 'Cause in many cases, I'll need to do the processing, both the learning and the inference at the Edge as well. So that's a key part of our strategy here with NVIDIA and I do think is going to unlock a new class of apps because when you think about AI and containers, what am I using it for? Well, it's the next generation of applications. A lot of those are going to be Edge, 5G-based, so very critical. >> We've got to talk about security now too. I'm going to pivot a little bit here, John, if it's okay. Years ago, you said security is a do-over, you said that on theCUBE, it stuck with us. But there's been a lot of complacency. It's kind of if it ain't broke, don't fix it, but but COVID kind of broke it. And so you see three mega trends, you've got cloud security, you'll see in Z-scaler rocket, you've got Identity Access Management and Octo which I hope there's I think a customer of yours and then you got Endpoint, you're seeing Crowdstrike explode you guys paid 2.7 billion, I think, for Carbon Black, yet Crowdstrike has this huge valuation. That's a mega opportunity for you guys. What are you seeing there? How are you bringing that all together? You've got NSX components, EUC components, you've got sort of security throughout your entire stack. How should we be thinking about that? >> Well, one of the announcements that I am most excited about at VMworld is the release of Carbon Black workload. 'Cause we said we're going to take those carbon black assets and we're going to combine it with workspace one, we're going to build it in NSX, we're going to make it part of Tanzu, and we're going to make it part of vSphere. And Carbon Black workload is literally the vSphere embodiment of Carbon Black in an agent-less way. So now you don't need to insert new agents or anything, it becomes part of the hypervisor itself. Meaning that there's no attack surface available for the bad guys to pursue. But not only is this an exciting new product capability, but we're going to make it free, right? And what I'm announcing at VMworld and everybody who uses vSphere gets Carbon Black workload for free for an unlimited number of VMs for the next six months. And as I said in the keynote, today is a bad day for cyber criminals. This is what intrinsic security is about, making it part of the platform. Don't add anything on, just click the button and start using what's built into vSphere. And we're doing that same thing with what we're doing at the networking layer, this is the last line acquisition. We're going to bring that same workload kind of characteristic into the container, that's why we did the Octarine acquisition, and we're releasing the integration of workspace one with Carbon Black client and that's going to be the differentiator, and by the way, Crowdstrike is doing well, but guess what? So are we, and right both of us are eliminating the rotting dead carcasses of the traditional AV approach. So there's a huge market for both of us to go pursue here. So a lot of great things in security, and as you said, we're just starting to see that shift of the industry occur that I promised last year in theCUBE. >> So it'd be safe to say that you're a cloud native and a security company these days? >> Yeah well, absolutely. And the bigger picture of us is that we're this critical infrastructure layer for the Edge, for the cloud, for the Telco environment and for the data center from every endpoint, every application, every cloud. >> So, Pat, I want to ask you a virtual question we got from the community. I'm going to throw it out to you because a lot of people look at Amazon and the cloud and they say, okay we didn't see it coming, we saw it coming, we saw it scale all the benefits that are coming out of cloud well documented. The question for you is, what's next after cloud? As people start to rethink especially with COVID highlighting and all the scabs out there as people look at their exposed infrastructure and their software, they want to be modern, they want the modern apps. What's next after cloud, what's your vision? >> Well, with respect to cloud, we are taking customers on the multicloud vision, right, where you truly get to say, oh, this workload I want to be able to run it with Azure, with amazon, I need to bring this one on-premise, I want to run that one hosted. I'm not sure where I'm going to run that application, so develop it and then run it at the best place. And that's what we mean by our hybrid multicloud strategy, is being able for customers to really have cloud flexibility and choice. And even as our preferred relationship with Amazon is going super well, we're seeing a real uptick, we're also happy that the Microsoft Azure VMware service is now GA. So there in Marketplace, are Google, Oracle, IBM and Alibaba partnerships, and the much broader set of VMware Cloud partner programs. So the future is multicloud. Furthermore, it's then how do we do that in the Telco network for the 5G build out? The Telco cloud, and how do we do that for the Edge? And I think that might be sort of the granddaddy of all of these because increasingly in a 5G world, we'll be enabling Edge use cases, we'll be pushing AI to the Edge like we talked about earlier in this conversation, we'll be enabling these high bandwidth low latency use cases at the Edge, and we'll see more and more of the smart embodiment smart city, smart street, smart factory, the autonomous driving, all of those need these type of capabilities. >> Okay. >> So there's hybrid and there's multi, you just talked about multi. So hybrid are data, are data partner ETR they do quarterly surveys. We're seeing big uptick in VMware Cloud on AWS, you guys mentioned that in your call. We're also seeing the VMware Cloud, VMware Cloud Foundation and the other elements, clearly a big uptick. So how should we think about hybrid? It looks like that's an extension of on-prem maybe not incremental, maybe a share shift, whereas multi looks like it's incremental but today multi is really running on multiple clouds, but a vision toward incremental value. How are you thinking about that? >> Yeah, so clearly, the idea of multi is truly multiple clouds. Am I taking advantage of multiple clouds being my private clouds, my hosted clouds and of course my public cloud partners? We believe everybody will be running a great private cloud, picking a primary public cloud and then a secondary public cloud. Hybrid then is saying, which of those infrastructures are identical, so that I can run them without modifying any aspect of my infrastructure operations or applications? And in today's world where people are wanting to accelerate their move to the cloud, a hybrid cloud is spot-on with their needs. Because if I have to refactor my applications, it's a couple million dollars per app and I'll see you in a couple of years. If I can simply migrate my existing application to the hybrid cloud, what we're consistently seeing is the time is 1/4 and the cost is 1/8 or less. Those are powerful numbers. And if I need to exit a data center, I want to be able to move to a cloud environment to be able to access more of those native cloud services, wow, that's powerful. And that's why for seven years now, we've been preaching that hybrid is the future, it is not a way station to the future. And I believe that more fervently today than when I declared it seven years ago. So we are firmly on that path that we're enabling a multi and hybrid cloud future for all of our customers. >> Yeah, you addressed that like Cube 2013, I remember that interview vividly was not a weigh station I got hammered answered. Thank you, Pat, for clarifying that going back seven years. I love the vision, you always got the right wave, it's always great to talk to you but I got to ask you about these initiatives that you're seeing clearly. Last year, a year and a half ago, Project Pacific came out, almost like a guiding directional vision. It then put some meat on the bone Tanzu and now you guys have that whole cloud native initiative, it's starting to flower up, thousands of flowers are blooming. This year, Project Monterey has announced. Same kind of situation, you're showing out the vision. What are the plans to take that to the next level? And take a minute to explain how Project Monterey, what it means and how you see that filling out. I'm assuming it's going to take the same trajectory as Pacific. >> Yeah, Monterey is a big deal. This is re-architecting the core of vSphere and it really is ripping apart the IO stack from the intrinsic operation of vSphere and the SX itself because in many ways, the IO, we've been always leveraging the NIC and essentially virtual NICs, but we never leverage the resources of the network adapters themselves in any fundamental way. And as you think about SmartNICs, these are powerful resources now where they may have four, eight, 16 even 32 cores running in the SmartNIC itself. So how do I utilize that resource, but it also sits in the right place? In the sense that it is the network traffic cop, it is the place to do security acceleration, it is the place that enables IO bandwidth optimization across increasingly rich applications where the workloads, the data, the latency get more important both in the data center and across data centers, to the cloud and to the Edge. So this re-architecting is a big deal, we announced the three partners, Intel, NVIDIA Mellanox and Pensando that we're working with, and we'll begin the deliveries of this as part of the core vSphere offerings beginning next year. So it's a big re-architecting, these are our key partners, we're excited about the work that we're doing with them and then of course our system partners like Dell and Lenovo who've already come forward and says, "Yeah we're going to to be bringing these to market together with VMware." >> Pat, personal question for you. I want to get your personal take, your career going back to Intel, you've seen it all but the shift is consumer to enterprise and you look at just recently Snowflake IPO, the biggest ever in the history of Wall Street. It's an enterprise data company, and the enterprise is now relevant. The consumer enterprise feels consumery, we talked about consumerization of IT years and years ago. But now more than ever the hottest financial IPO enterprise, you guys are enterprise. You did enterprise at Intel (laughing), you know the enterprise, you're doing it here at VMware. The enterprise is the consumer now with cloud and all this new landscape. What is your view on this because you've seen the waves, have you seen the historical perspective? It was consumer, was the big thing now it's enterprise, what's your take on all this? How do you make sense of it because it's now mainstream, what's your view on this? >> Well, first I do want to say congratulations to my friend, Frank and the extraordinary Snowflake IPO. And by the way they use VMware, so I not only do I feel a sense of ownership 'cause Frank used to work for me for a period of time, but they're also a customer of ours so go Frank, go Snowflake. We're excited about that. But there is this episodic to the industry where for a period of time, it is consumer-driven and CES used to be the hottest ticket in the industry for technology trends. But as you say, it has now shifted to be more business-centric, and I've said this very firmly, for instance, in the case of 5G where I do not see consumer. A faster video or a better Facebook isn't going to be why I buy 5G. It's going to be driven by more business use cases where the latency, the security and the bandwidth will have radically differentiated views of the new applications that will be the case. So we do think that we're in a period of time and I expect that it's probably at least the next five years where business will be the technology drivers in the industry. And then probably, hey there'll be a wave of consumer innovation, and I'll have to get my black turtlenecks out again and start trying to be cool but I've always been more of an enterprise guy so I like the next five to 10 years better. I'm not cool enough to be a consumer guy and maybe my age is now starting to conspire against me as well. >> Hey, Pat I know you got to go but a quick question. So you guys, you gave guidance, pretty good guidance actually. I wonder, have you and Zane come up with a new algorithm to deal with all this uncertainty or is it kind of back to old school gut feel? >> (laughing) Well, I think as we thought about the year, as we came into the year, and obviously, COVID smacked everybody, we laid out a model, we looked at various industry analysts, what we call the Swoosh Model, right? Q2, Q3 and Q4 recovery, Q1 more so, Q2 more so. And basically, we built our own theories behind that, we tested against many analyst perspectives and we had Vs and we had Ws and we had Ls and so on. We picked what we thought was really sort of grounded in the best data that we could, put our own analysis which we have substantial data of our own customers' usage, et cetera and picked the model. And like any model, you put a touch of conservatism against it, and we've been pretty accurate. And I think there's a lot of things we've been able to sort of with good data, good thoughtfulness, take a view and then just consistently manage against it and everything that we said when we did that back in March has sort of proven out incrementally to be more accurate. And some are saying, "Hey things are coming back more quickly" and then, "Oh, we're starting to see the fall numbers climb up a little bit." Hey, we don't think this goes away quickly, there's still a lot of secondary things to get flushed through, the various economies as stimulus starts tailoring off, small businesses are more impacted, and we still don't have a widely deployed vaccine and I don't expect we will have one until second half of next year. Now there's the silver lining to that, as we said, which means that these changes, these faster to the future shifts in how we learn, how we work, how we educate, how we care for, how we worship, how we live, they will get more and more sedimented into the new normal, relying more and more on the digital foundation. And we think ultimately, that has extremely good upsides for us long-term, even as it's very difficult to navigate in the near term. And that's why we are just raving optimists for the long-term benefits of a more and more digital foundation for the future of every industry, every human, every workforce, every hospital, every educator, they are going to become more digital and that's why I think, going back to the last question this is a business-driven cycle, we're well positioned and we're thrilled for all of those who are participating with Vmworld 2020. This is a seminal moment for us and our industry. >> Pat, thank you so much for taking the time. It's an enabling model, it's what platforms are all about, you get that. My final parting question for you is whether you're a VC investing in startups or a large enterprise who's trying to get through COVID with a growth plan for that future. What does a modern app look like, and what does a modern company look like in your view? >> Well, a modern company would be that instead of having a lot of people looking down at infrastructure, the bulk of my IT resources are looking up at building apps, those apps are using modern CICD data pipeline approaches built for a multicloud embodiment, right, and of course VMware is the best partner that you possibly could have. So if you want to be modern cool on the front end, come and talk to us. >> All right, Pat Gelsinger, the CEO of VMware here on theCUBE for VMworld 2020 virtual, here with theCUBE virtual great to see you virtually, Pat, thanks for coming on, thanks for your time. >> Hey, thank you so much, love to see you in person soon enough but this is pretty good. >> Yeah. >> Thank you Dave. Thank you so much. >> Okay, you're watching theCUBE virtual here for VMworld 2020, I'm John Furrier, Dave Vellante with Pat Gelsinger, thanks for watching. (gentle music)

>> Narrator: From around the globe. It's theCUBE with digital coverage of VMworld 2020, brought to you by VMware and its ecosystem partners. >> Hello, welcome back to theCUBE's coverage of VMworld 2020. This is theCUBE virtual with VMworld 2020 virtual. I'm John Furrier your host of theCUBE with Dave Vellante. It's our 11th year covering VMware. We're not in person, we're virtual, but all the content is flowing. Of course, we're here with Pat Galsinger, the CEO of VMware. Who's been on theCUBE all 11 years. This year virtual of theCUBE as we've been covering VMware from his early days in 2010, when theCUBE started 11 years later, Pat is still changing and still exciting. Great to see you. Thanks for taking the time. >> Hey, you guys are great. I love the interactions that we have, the energy, the fun, the intellectual sparring. And of course that audiences have loved it now for 11 years. And I look forward to the next 11 that we'll be doing together. >> It's always exciting cause we'd love great conversations. Dave and I like to drill in and really kind of probe and unpack the content that you're delivering at the keynotes, but also throughout the entire program. It is virtual this year, which highlights a lot of the cloud native changes. Just want to get your thoughts on the virtual aspect of VMworld, not in person, which is one of the best events of the year. Everyone loves it. The great community. It's virtual this year, but there's a slew of content. What should people take away from this virtual VMworld? >> Well, one aspect of it is that I'm actually excited about is that we're going to be well over a hundred thousand people, which allows us to be bigger, right? You don't have the physical constraints. You also are able to reach places like I've gone to customers and maybe they had 20 people attend in prior years. This year they're having a hundred, they're able to have much larger teams. Also like some of the more regulated industries where they can't necessarily send people to events like this, the international audience. So just being able to spread the audience much more broadly well, also our key messages a digital foundation for unpredictable world. And man, what an unpredictable world it has been this past year? And then key messages, lots of key products announcements technology, announcements partnership, announcements and of course in all of the VMworld, is that hands on (murmurs) interactions that we'll be delivering our virtual, you come to the VMware because the content is so robust and it's being delivered by the world's smartest people. >> Yeah. We've had great conversations over the years. And we've talked about hybrid clothing 2012, a lot of this stuff I looked back in lot of the videos was early on, we're picking out all these waves, but it was that moment four years ago or so, maybe even four, three, I can't even remember, seems like yesterday. You gave the Seminole keynote and you said, "This is the way the world's going to happen." And since that keynote I'll never forget was in Moscone. And since then you guys have been performing extremely well both on the business as well as making technology bets and is paying off. So what's next? I mean, you've got the cloud scale. Is it space? Is it cyber? I mean, all these things are going on. What is next wave that you're watching and what's coming out and what can people extract out of VMworld this year about this next wave? >> Yeah, one of the things I really am excited about I went to my buddy Jensen. I said, "Boy, we're doing this work and smart. Next We really liked to work with you and maybe some things to better generalize the GPU." And Jensen challenged me. Now, usually, I'm the one challenging other people with bigger visions, this time Jensen said, "Hey Pat, I think you're thinking too small. Let's do the entire AI landscape together. And let's make AI a enterprise classwork stowed from the data center to the cloud and to the Edge. And so I'm going to bring all of my AI resources and make VMware, And Tansu the preferred infrastructure to deliver AI at scale. I need you guys to make the GPS work like first class citizens in the vSphere environment, because I need them to be truly democratized for the enterprise. so that it's not some specialized AI development team, it's everybody being able to do that. And then we're going to connect the whole network together in a new and profound way with our Monterey Program as well being able to use the SmartNIC, the DPU as Jensen likes to call it. So now it's CPU, GPU and DPU, all being managed through a distributed architecture of VMware." This is exciting. So this is one in particular that I think we are now rearchitecting the data center, the cloud in the Edge. And this partnership is really a central point of that. >> Yeah, the Nvid thing's huge. And I know Dave, Perharbs has some questions on that. But I ask you a question because a lot of people ask me, is it just a hardware deal? I mean, talking about SmartNIC, you talking about data processing units. It sounds like a motherboard in the cloud, if you will, but it's not just hardware. Can you talk about the aspect of the software piece? Because again, Nvidia is known for GP use, we all know that, but we're talking about AI here. So it's not just hardware. Can you just expand and share what the software aspect of all this is? >> Yeah. Well, Nvidia has been investing in their AI stack and it's one of those where I say, this is Edison at work, right? The harder I work, the luckier I get. And Nvidia was lucky that their architecture worked much better for the AI workload, but it was built on two decades of hard work in building a parallel data center architecture. And they have built a complete software stack for all of the major AI workloads running on their platform. All of that is now coming to vSphere and Tansu, that is a rich software layer across many vertical industries. And we'll talk about a variety of use cases. One of those that we highlight at Vmworld is the university of California, San Francisco partnership UCSF one of the world's leading research hospitals, some of the current vaccine use cases as well, the financial use cases for threat detection and trading benefits. It really is about how we bring that rich software stack. this is a decade and a half of work to the VMware platform so that now every developer and every enterprise could take advantage of this at scale, that's a lot of software. So in many respects, yeah, there's a piece of hardware in here, but the software stack is even more important. >> So well on the sort of Nvidia the arm piece, there's really interesting, these alternative processing models. And I wonder if you could comment on the implications for AI inferencing at the Edge. It's not just as well processor implications, it's storage, it's networking. It's really a whole new fundamental paradigm. How are you thinking about that Pat? >> Yeah, we've thought about, there's three aspects, but what we said three problems that we're solving. One is the developer problem, what we said, now you develop once, right? And the developer can now say, "Hey, I want to have this new AI centric app and I can develop, and it can run in the data center on the cloud or at the Edge." You'll secondly, my operations team can be able to operate this just like I do all my infrastructure. And now it's VMs containers and AI applications and third, and this is where your question really comes to bear. Most significantly is data gravity, right? These data sets are big. Some of them need to be very low latency as well. They also have regulatory issues. And if I have to move these large regulated data sets to the cloud, boy, maybe I can't do that generally for my apps or if I have low latency heavy apps at the Edge, ah, I can't pull it back to the cloud or to my data center. And that's where the uniform architecture and aspects of the Monterey program, where I'm able to take advantage of the network and the SmartNIC that are being built, but also being able to fully represent the data gravity issues of AI applications at scale 'cause in many cases I'll need to do the processing, both the learning and the inference at the Edge as well. So that's a key part of our strategy here with Nvidia. And I do think is going to be a lock, a new class of apps because when you think about AI and containers, what am I using it for? Well, it's the next generation of applications. A lot of those are going to be Edge 5G based. So very critical. >> We got to talk about security now, too. I mean, I'm going to pivot a little bit here John if it's okay. Years ago you said security is a do over. You said that on theCUBE, It stuck with us. There's there's been a lot of complacency it's kind of, if it didn't broke, don't fix it, but COVID kind of broke it. That's why you see three mega trends. You've got cloud security, you see in Z scaler rocket, you got identity access management and I'll check, I think a customer of yours. And then you've got endpoint you're seeing CrowdStrike explode. You guys pay 2.7 billion I think for carbon black yet CrowdStrike has this huge valuation. That's a mega opportunity for you guys. What are you seeing there? How are you bringing that all together? You've got NSX components, EUC components. You've got sort of security throughout your entire stack. How should we be thinking about that? >> Well, one of the announcements that I am most excited about at Vmworld is the release of carbon black workload, this research we're going to take those carbon black assets and we're going to combine it with workspace one. We're going to build it in NSX. We're going to make it part of Tansu and we're going to make it part of vSphere. And carbon black workload is literally the vSphere embodiment of carbon black in an agentless way. Ans so now you don't need to insert new agents or anything. It becomes part of the hypervisor itself, meaning that there's no attack surface available for the bad guys to pursue, but not only is this an exciting new product capability, but we're going to make it free, right? And what I'm announcing at VMworld and everybody who uses vSphere gets carbon black workload for free for an unlimited number of VMs for the next six months. And as I said in the keynote today is a bad day for cybercriminals. This is what intrinsic security is about, making it part of the platform. Don't add anything on, just click the button and start using what's built into vSphere. And we're doing that same thing with what we're doing at the networking layer. This is the act, the last line acquisition. We're going to bring that same workload kind of characteristic into the container. That's why we did the Octarine acquisition. And we're releasing the integration of workspace one with a carbon black client, and that's going to be the differentiator. And by the way, CrowdStrike is doing well, but guess what? So are we, and like both of us are eliminating the rotting dead carcasses of the traditional AV approach. So there is a huge market for both of us to go pursue here. So a lot of great things in security. And as you said, we're just starting to see that shift of the industry occur that I promised last year in theCUBE. >> So it'd be safe to say that you're a cloud native in a security company these days? >> You all, absolutely. And the bigger picture of us, is that we're critical infrastructure layer for the Edge for the cloud, for the telco environment and for the data center from every end point, every application, every cloud. >> So Padagonia asked you a virtual question, we got from the community, I'm going to throw it out to you because a lot of people look at Amazon, The cloud and they say, "Okay, we didn't see it coming. We saw it coming. We saw it scale all the benefits that are coming out of cloud, Well-documented." The question for you is what's next after cloud, as people start to rethink, especially with COVID highlighting all the scabs out there. As people look at their exposed infrastructure and their software, they want to be modern. They want the modern apps. What's next after cloud. What's your vision? >> Well, with respect to cloud, we are taking customers on the multicloud vision, right? Where you truly get to say, "Oh, this workload, I want to be able to run it with Azure, with Amazon. I need to bring this one on premise. I want to run that one hosted. I'm not sure where I'm going to run that application." So develop it and then run it at the best place. And that's what we mean by our hybrid multicloud strategy is being able for customers to really have cloud flexibility and choice. And even as our preferred relationship with Amazon is going super well. We're seeing a real uptick. We're also happy that the Microsoft Azure VMware services now GA so they're in marketplace, our Google, Oracle, IBM and Alibaba partnerships in the much broader set of VMware cloud Partner Program. So the future is multicloud. Furthermore, it's then how do we do that in the Telco Network for the 5G build out, The Telco cloud? And how do we do that for the Edge? And I think that might be sort of the granddaddy of all of these because increasingly in a 5G world will be a nibbling Edge use cases. We'll be pushing AI to the Edge like we talked about earlier in this conversation, will be enabling these high bandwidth, with low latency use cases at the Edge, and we'll see more and more of the smart embodiment, smart cities, smart street, smart factory, or the autonomous driving. All of those need these type of capabilities. >> So there's hybrid and there's multi, you just talked about multi. So hybrid are data partner ETR, they do quarterly surveys. We're seeing big uptick in VMware cloud and AWS, you guys mentioned that in your call. we're also seeing the VMware cloud, VMware cloud Coundation and the other elements, clearly a big uptake. So how should we think about hybrid? It looks like that's an extension of on-prem maybe not incremental, maybe a share shift whereas multi looks like it's incremental, but today multi has really running on multiple clouds, but vision toward incremental value. How are you thinking about that? >> Yeah, so clearly the idea of multi is to link multiple. Am I taking advantage of multiple clouds being my private clouds, my hosted clouds. And of course my public cloud partners, we believe everybody will be running a great private cloud, picking a primary, a public cloud, and then a secondary public cloud. Hybrid then is saying, which of those infrastructures are identical so that I can run them without modifying any aspect of my infrastructure operations or applications. And in today's world where people are wanting to accelerate their move to the cloud, a hybrid cloud is spot on with their needs because if I have to refactor my applications it's a couple million dollars per app, And I'll see you in a couple of years. If I can simply migrate my existing application to the hybrid cloud, what we're consistently seeing is the time is one quarter and the cost is one eight, four less. Those are powerful numbers. And if I need to exit a data center, I want to be able to move to a cloud environment, to be able to access more of those native cloud services. Wow. That's powerful. And that's why for seven years now we've been preaching that hybrid is the future. It is not a waystation to the future. And I believe that more fervently today than when I declared it seven years ago. So we are firmly on that path that we're enabling a multi and a hybrid cloud future for all of our customers. >> Yeah. You addressed that like CUBE 2013. I remember that interview vividly was not a waystation. I got (murmurs) the answer. Thank you Pat, for clarifying than going back seven years. I love the vision. You're always got the right wave. It's always great to talk to you, but I got to ask you about these initiatives you seeing clearly last year or a year and a half ago, project Pacific name out almost like a guiding directional vision, and then put some meat on the bone Tansu and now you guys have that whole Cloud Native Initiative is starting to flower up thousand flowers are blooming. This year Project Monterrey has announced same kind of situation. You're showing out the vision. What are the plans to take that to the next level and take a minute to explain how project Monterey, what it means and how you see that filling out. I'm assuming it's going to take the same trajectory as Pacific. >> Yeah. Monetary is a big deal. This is rearchitecting The core of vSphere. It really is ripping apart the IO stack from the intrinsic operation of a vSphere and ESX itself, because in many ways, the IO we've been always leveraging the NIC and essentially virtual NICs, but we never leverage the resources of the network adapters themselves in any fundamental way. And as you think about SmartNICs, these are powerful resources now where they may have four, eight, 16, even 32 cores running in the smartNIC itself. So how do I utilize that resource? But it also sits in the right place in the sense that it is the network traffic cop. It is the place to do security acceleration. It is the place that enables IO bandwidth optimization across increasingly rich applications where the workloads, the data, the latency get more important both in the data center and across data centers to the cloud and to the Edge. So this rearchitecting is a big deal. We announced the three partners, Intel, Nvidia, Mellanox, and Penn Sandow that we're working with. And we'll begin the deliveries of this as part of the core vSphere offerings of beginning next year. So it's a big rearchitecting. These are our key partners. We're excited about the work that we're doing with them. And then of course our system partners like Dell and Lenovo, who've already come forward and says, "Yeah, we're going to be bringing these to market together with VMware." >> Pat, personal question for you. I want to get your personal take, your career, going back to Intel. You've seen it all, but the shift is consumer to enterprise. And you look at just recently snowflake IPO, the biggest ever in the history of wall street, an enterprise data's company. And the enterprise is now relevant. Enterprise feels consumer. We talked about consumerization of IT years and years ago, but now more than ever the hottest financial IPO enterprise, you guys are enterprise. You did enterprise at Intel. (laughs) You know the enterprise, you doing it here at VMware. The enterprise is the consumer now with cloud and all this new landscape. What is your view on this? Because you've seen the waves, and you've seen the historical perspective. It was consumer, was the big thing. Now it's enterprise, what's your take on all this? How do you make sense of it? Because it's now mainstream. what's your view on this? >> Well, first I do want to say congratulations to my friend Frank, and the extraordinary snowflake IPO, and by the way, they use VMware. So not only do I feel a sense of ownership 'cause Frank used to work for me for a period of time, but they're also a customer of ours. So go Frank, go snowflake. We're we're excited about that. But there is this episodic, this to the industry where for a period of time it is consumer-driven and CES used to be the hottest ticket in the industry for technology trends. But as you say, it is now shifted to be more business centric. And I've said this very firmly, for instance, in the case of 5G where I do not see consumer a faster video or a better Facebook, isn't going to be why I buy 5G. It's going to be driven by more business use cases where the latency, the security and the bandwidth will have radically differentiated views of the new applications that will be the case. So we do think that we're in a period of time and I expect that it's probably at least the next five years where business will be the technology drivers in the industry. And then probably, hey, there'll be a wave of consumer innovation and I'll have to get my black turtlenecks out again and start trying to be cool, but I've always been more of an enterprise guy. So I like the next five to 10 years better. I'm not cool enough to be a consumer guy. And maybe my age is now starting to conspire against me as well. >> Hey, Pat, I know you've got to go, but quick question. So you guys, you gave guidance, pretty good guidance, actually. I wondered have you and Zane come up with a new algorithm to deal with all this uncertainty or is it kind of back to old school gut feel? (laughs) >> Well, I think as we thought about the year as we came into the year and obviously, COVID smacked everybody, we laid out a model, we looked at various industry analysts, what we call the swoosh model, right? Q2, Q3 and Q4 recovery, Q1 more so, Q2 more so, and basically, we build our own theories behind that. We test it against many analysts, the perspectives, and we had vs and we had Ws and we had Ls and so on. We picked what we thought was really sort of grounded of the best data that we could put our own analysis, which we have substantial data of our own customer's usage, et cetera, and pick the model. And like any model, you put a touch of conservatism against it, and we've been pretty accurate. And I think there's a lot of things, we've been able to sort of, with good data good thoughtfulness, take a view and then just consistently manage against it and everything that we said when we did that back in March, sort of proven out incrementally to be more accurate. And some are saying, "Hey, things are coming back more quickly." And then, oh we're starting to see the fall numbers climb up a little bit. Hey, we don't think this goes away quickly. There's still a lot of secondary things to get flushed through the various economies, as stimulus starts tailoring off small businesses are more impacted and we still don't have a widely deployed vaccine. And I don't expect we will have one until second half of next year. Now there's the silver lining to that, as we said, which means that these changes, these faster to the future shifts in how we learn, how we work, how we educate, how we care for, how we worship, how we live, they will get more and more sedimented into the new normal relying more and more on the digital foundation. And we think ultimately that has extremely good upsides for us longterm, even as it's very difficult to navigate in the near term. And that's why we are just raving optimists for the longterm benefits of a more and more digital foundation for the future of every industry, every human, every workforce, every hospital, every educator, they are going to become more digital. And that's why I think going back to the last question, this is a business driven cycle, we're well positioned, and we're thrilled for all of those who are participating with VMworld 2020. This is a seminal moment for us and our industry. >> Pat, thank you so much for taking the time. It's an enabling model. It's what platforms are all about. You get that. My final parting question for you is whether you're a VC investing in startups or a large enterprise who's trying to get through COVID with a growth plan for that future. What is a modern app look like? And what does a modern company look like in your view? >> Well, a modern company would be that instead of having a lot of people looking down at infrastructure, the bulk of my IT resources are looking up at building apps. Those apps are using modern CICD data pipeline approaches built for a multicloud embodiment, right? And of course, VMware is the best partner that you possibly could have. So if you want to be modern, cool on the front end, come and talk to us. >> All right. Pat Galsinger the CEO of VMware here on theCUBE for VML 2020 virtual here with theCUBE virtual. Great to see you virtually Pat. Thanks for coming on. Thanks for your time. >> Hey, thank you so much. Love to see you in person soon enough, but this is pretty good. Thank you, Dave. Thank you so much. >> Okay. You're watching theCUBE virtual here for VMworld 2020. I'm John Furrier with Dave Vallente with Pat Gelsinger. Thanks for watching. (upbeat music)

>> Narrator: From around the globe, it's theCube. With digital coverage of VMworld 2020. Brought to you by VMware and its ecosystem partners. >> Hello, and welcome back to theCube virtual coverage of VMworld 2020. I'm John Furrier, host of theCube. VMworld's not in person this year, it's on the virtual internet. A lot of content, check it out, vmworld.com, a lot of great stuff, online demos, and a lot of great keynotes. Here we got a great conversation to unpack, the NVIDIA, the AI and all things Cloud Native. With Krish Prasad, who's the SVP and GM of Cloud Platform, Business Unit, and Manuvir Das head of enterprise computing at NVIDIA. Gentlemen, great to see you virtually. Thanks for joining me on the virtual Cube, for the virtual VMworld 2020. >> Thank you John. >> Pleasure to be here. >> Quite a world. And I think one of the things that obviously we've been talking about all year since COVID is the acceleration of this virtualized environment with media and everyone working at home remote. Really puts the pressure on digital transformation Has been well discussed and documented. You guys have some big news, obviously on the main stage NVIDIA CEO, Jensen there legend. And of course, you know, big momentum with with AI and GPUs and all things, you know, computing. Krish, what are your announcements today? You got some big news. Could you take a minute to explain the big announcements today? >> Yeah, John. So today we want to make two major announcements regarding our partnership with NVIDIA. So let's take the first one, and talk through it and then we can get to the second announcement later. In the first one, as you well know, NVIDIA is the leader in AI and VMware as the leader in virtualization and cloud. This announcement is about us teaming up, deliver a jointly engineered solution to the market to bring AI to every enterprise. So as you well know, VMware has more than 300,000 customers worldwide. And we believe that this solution would enable our customers to transform their data centers or AI applications running on top of their virtualized VMware infrastructure that they already have. And we think that this is going to vastly accelerate the adoption of AI and essentially democratize AI in the enterprise. >> Why AI? Why now Manuvir? Obviously we know the GPUs have set the table for many cool things, from mining Bitcoin to really providing a great user experience. But AI has been a big driver. Why now? Why VMware now? >> Yes. Yeah. And I think it's important to understand this is about AI more than even about GPUs, you know. This is a great moment in time where AI has finally come to life, because the hardware and software has come together to make it possible. And if you just look at industries and different parts of life, how is AI impacting? So for example, if you're a company on the internet doing business, everything you do revolves around making recommendations to your customers about what they should do next. This is based on AI. Think about the world we live in today, with the importance of healthcare, drug discovery, finding vaccines for something like COVID. That work is dramatically accelerated if you use AI. And what we've been doing in NVIDIA over the years is, we started with the hardware technology with the GPU, the Parallel Processor, if you will, that could really make these algorithms real. And then we worked very hard on building up the ecosystem. You know, we have 2 million developers today who work with NVIDIA AI. That's thousands of companies that are using AI today. But then if you think about what Krish said, you know about the number of customers that VMware has, which is in the hundreds of thousands, the opportunity before us really now is, how do we democratize this? How do we take this power of AI, that makes every customer and every person better and put it in the hands of every enterprise customer? And we need a great vehicle for that, and that vehicle is VMware. >> Guys, before we get to the next question, I would just want to get your personal take on this, because again, we've talked many times, both of you've been on theCube on this topic. But now I want to highlight, you mentioned the GPU that's hardware. This is software. VMware had hardware partners and then still software's driving it. Software's driving everything. Whether it's something in space, it's an IOT device or anything at the edge of the network. Software, is the value. This has become so obvious. Just share your personal take on this for folks who are now seeing this for the first time. >> Yeah. I mean, I'll give you my take first. I'm a software guy by background, I learned a few years ago for the first time that an array is a storage device and not a data structure in programming. And that was a shock to my system. Definitely the world is based on algorithms. Algorithms are implemented in software. Great hardware enables those algorithms. >> Krish, your thoughts. we live we're living in the future right now. >> Yeah, yeah. I would say that, I mean, the developers are becoming the center. They are actually driving the transformation in this industry, right? It's all about the application development, it's all about software, the infrastructure itself is becoming software defined. And the reason for that is you want the developers to be able to craft the infrastructure the way they need for the applications to run on top of. So it's all about software like I said. >> Software defined. Yeah, just want to get that quick self-congratulatory high five amongst ourselves virtually. (laughs) Congratulations. >> Exactly. >> Krish, last time we spoke at VMworld, we were obviously in person, but we talked about Tanzu and vSphere. Okay, you had Project Pacific. Does this expand? Does this announcement expand on that offering? >> Absolutely. As you know John, for the past several years, VMware has been on this journey to define the Hybrid Cloud Infrastructure, right? Essentially is the software stack that we have, which will enable our customers to provide a cloud operating model to their developers, irrespective of where they want to land their workloads. Whether they want to land their workloads On-Premise, or if they want it to be on top of AWS, Google, Azure, VMware stack is already running across all of them as you well know. And in addition to that, we have around, you know, 4,000, 5,000 service providers who are also running our Platform to deliver cloud services to their customers. So as part of that journey, last year, we took the Platform and we added one further element to it. Traditionally, our platform has been used by customers for running via VMs. Last year, we natively integrated Kubernetes into our platform. This was the big re architecture of vSphere, as we talked about. That was delivered to the market. And essentially now customers can use the same platform to run Kubernetes, Containers and VM workloads. The exact same platform, it is operationally the same. So the same skillsets, tools and processes can be used to run Kubernetes as well as VM applications. And the same platform runs, whether you want to run it On-Premise or in any of the clouds, as we talked about before. So that vastly simplifies the operational complexity that our customers have to deal with. And this is the next chapter in that journey, by doing the same thing for AI workload. >> You guys had great success with these Co-Engineering joined efforts. VMware and now with NVIDIA is interesting. It's very relevant and is very cool. So it's cool and relevant, so check, check. Manuvir, talk about this, because how do you bring that vision to the enterprises? >> Yeah, John, I think, you know, it's important to understand there is some real deep Computer Science here between the Engineers at VMware and NVIDIA. Just to lay that out, you can think of this as a three layer stack, right? The first thing that you need is, clearly you need the hardware that is capable of running these algorithms, that's what the GPU enable. Then you need a great software stack for AI, all the right Algorithmics that take advantage of that hardware. This is actually where NVIDIA spends most of its effort today. People may sometimes think of NVIDIA as a GPU company, but we have much more a software company now, where we have over the years created a body of work of all of the software that it actually takes to do good AI. But then how do you marry the software stack with the hardware? You need a platform in the middle that supports the applications and consumes the hardware and exposes it properly. And that's where vSphere, you know, as Krish described with either VMs or Containers comes into the picture. So the Computer Science here is, to wire all these things up together with the right algorithmics so that you get real acceleration. So as examples of early work that the two teams have done together, we have workloads in healthcare, for example. In cancer detection, where the acceleration we get with this new stack is 30X, right? The workload is running 30 times faster than it was running before this integration just on CPUs. >> Great performance increase again. You guys are hiring a lot of software developers. I can attest to knowing folks in Silicon Valley and around the world. So I know you guys are bringing the software jobs to the table on a great product by the way, so congratulations. Krish, Democratization of AI for the enterprise. This is a liberating opportunity, because one of the things we've heard from your customers and also from VMware, but mostly from the customer's successes, is that there's two types of extremes. There's the, I'm going to modernize my business, certainly COVID forcing companies, whether they're airlines or whatever, not a lot going on, they have an opportunity to modernize, to essentially modern apps that are getting a tailwind from these new digital transformation accelerated. How does AI democratize this? Cause you got people and you've got technology. (laughs) Right? So share your thoughts on how you see this democratizing. >> That's a very good question. I think if you look at how people are running AI applications today, like you go to an enterprise, you would see that there is a silo of bare metal sun works on the side, where the AI stack is run. And you have people with specialized skills and different tools and utilities that manage that environment. And that is what is standing in the way of AI taking off in the enterprise, right? It is not the use case. There are all these use cases which are mission critical that all companies want to do, right? Worldwide, that has been the case. It is about the complexity of life that is standing in the way. So what we are doing with this is we are saying, "hey, that whole solution stack that Manuvir talked about, is integrated into the VMware Virtualized Infrastructure." Whether it's On-Prem or in the cloud. And you can manage that environment with the exact same tools and processes and skills that you traditionally had for running any other application on VMware infrastructure. So, you don't need to have anything special to run this. And that's what is going to give us the acceleration that we talked about and essentially hive the Democratization of AI. >> That's a great point. I just want to highlight that and call that out, because AI's every use case. You could almost say theCube could have AI and we do actually have a little bit of AI and some of our transcriptions and work. But it's not so much just use cases, it's actually not just saying you got to do it. So taking down that blocker, the complexity, certainly is the key. And that's a great point. We're going to call that out after. Alright, let's move on to the second part of the announcement. Krish Project Monterey. This is a big deal. And it looks like a, you know, kind of this elusive, it's architectural thing, but it's directionally really strategic for VMware. Could you take a minute to explain this announcement? Frame this for us. >> Absolutely. I think John, you remember Pat got on stage last year at Vmworld and said, you know, "we are undertaking the biggest re architecture of the vSphere platform in the last 10 years." And he was talking about natively embedding Kubernetes, in vSphere, right? Remember Tanzu and Project Pacific. This year we are announcing Project Monterrey. It's a project that is significant with several partners in the industry, along with NVIDIA was one of the key partners. And what we are doing is we are reimagination of the data center for the next generation applications. And at the center of it, what we are going to do is rearchitect vSphere and ESX. So that the ESX can normally run on the CPU, but it'll also run on the Smart Mix. And what this gives us is the whole, let's say data center, infrastructure type services to be offloaded from running on the CPU onto the Smart Mix. So what does this provide the applications? The applications then will perform better. And secondly, it provides an extra layer of security for the next generation applications. Now we are not going to stop there. We are going to use this architecture and extended it so that we can finally eliminate one of the big silos that exist in the enterprise, which is the bare metal silo. Right? Today we have virtualized environments and bare metal, and what this architecture will do is bring those bare metal environments also under ESX management. So you ESX will manage environments which are virtualized and environments which are running bare metal OS. And so that's one big breakthrough and simplification for the elimination of silo or the elimination of, you know, specialized skills to keep it running. And lastly, but most importantly, where we are going with this. That just on the question you asked us earlier about software defined and developers being in control. Where we want to go with this is give developers, the application developers, the ability to really define and create their run time on the Fly, dynamically. So think about it. If dynamically they're able to describe how the application should run. And the infrastructure essentially kind of attaches computer resources on the Fly, whether they are sitting in the same server or somewhere in the network as pools of resources. Bring it all together and compose the runtime environment for them. That's going to be huge. And they won't be constrained anymore by the resources that are tied to the physical server that they are running on. And that's the vision of where we are taking it. It is going to be the next big change in the industry in terms of enterprise computing. >> Sounds like an Operating System to me. Yeah. Run time, assembly orchestration, all these things coming together, exciting stuff. Looking forward to digging in more after Vmworld. Manuvir, how does this connect to NVIDIA and AI? Tie that together for us. >> Yeah, It's an interesting question, because you would think, you know, okay, so NVIDIA this GPU company or this AI company. But you have to remember that INVIDIA is also a networking company. Because friends at Mellanox joined us not that long ago. And the interesting thing is that there's a Yin and Yang here, because, Krish described the software vision, which is brilliant. And what this does is it imposes a lot on the host CPU of the server to do. And so what we've be doing in parallel is developing hardware. A new kind of "Nick", if you will, we call it a DPU or a Data Processing Unit or a Smart Nick that is capable of hosting all this stuff. So, amusingly when Krish and I started talking, we exchanged slides and we basically had the same diagram for our vision of where things go with that software, the infrastructure software being offloaded, data center infrastructure on a chip, if you will. Right? And so it's a very natural confluence. We are very excited to be part of this, >> Yeah. >> Monterey program with Krish and his team. And we think our DPU, which is called the NVIDIA BlueField-2, is a pretty good device to empower the work that Krish's team is doing. >> Guys it's awesome stuff. And I got to say, you know, I've been covering Vmworld now 11 years with theCube, and I've known VMware since its founding, just the evolution. And just recently before VMworld, you know, you saw the biggest IPO in the history of Wall Street, Snowflake an Enterprise Data Cloud Company. The number one IPO ever. Enterprise tech is so exciting. This is really awesome. And NVIDIA obviously well known, great brand. You own some chip company as well, and get processors and data and software. Guys, customers are going to be very interested in this, so what should customers do to find out more? Obviously you've got Project Monterey, strategic direction, right? Framed perfectly. You got this announcement. If I'm a customer, how do I get involved? How do I learn more? And what's in it for me. >> Yeah, John, I would say, sorry, go ahead, Krish. >> No, I was just going to say sorry Manuvir. I was just going to say like a lot of these discussions are going to be happening, there are going to be panel discussions there are going to be presentations at Vmworld. So I would encourage customers to really look at these topics around Project Monterey and also about the AI work we are doing with NVIDIA and attend those sessions and be active and we will have a ways for them to connect with us in terms of our early access programs and whatnot. And then as Manuvir was about to say, I think Manuvir, I will give it to you about GTC. >> Yeah, I think right after that, we have the NVIDIA conference, which is GTC, where we'll also go over this. And I think some of this work is a lot closer to hand than people might imagine. So I would encourage watching all the sessions and learning more about how to get started. >> Yeah, great stuff. And just for the folks @vmworld.com watching, Cloud City's got 60 solution demos, go look for the sessions. You got the EX, the expert sessions, Raghu, Joe Beda amongst other people from VMware are going to be there. And of course, a lot of action on the content. Guys, thanks so much for coming on. Congratulations on the news, big news. NVIDIA on the Bay in Virtual stage here at VMworld. And of course you're in theCube. Thanks for coming. Appreciate it. >> Thank you for having us. Okay. >> Thank you very much. >> This is Cube's coverage of VMworld 2020 virtual. I'm John Furrier, host of theCube virtual, here in Palo Alto, California for VMworld 2020. Thanks for watching. (upbeat music)

>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hi, I'm Stu Miniman, and welcome to a CUBE conversation. I'm coming to you from our Boston Area studio. And we're going to be digging into some interesting news regarding networking. Some important use cases these days, in 2020, of course, AI is a big piece of it. So happy to welcome to the program. First of all, I have one of our CUBE alumni, Kevin Deierling. He's the Senior Vice President of Marketing with Nvidia, part of the networking team there. And joining him is Scott Tease, someone we've known for a while, but first time on the program, who's the General Manager of HPC and AI, for the Lenovo Data Center Group. Scott and Kevin, thanks so much for joining us. >> It's great to be here Stu. >> Yeah, thank you. >> Alright, so Kevin, as I said, you you've been on the program a number of times, first when it was just Mellanox, now of course the networking team, there's some other acquisitions that have come in. If you could just set us up with the relationship between Nvidia and Lenovo. And there's some news today that we're here to talk about too. So let's start getting into that. And then Scott, you'll jump in after Kevin. >> Yeah, so we've been a long time partner with Lenovo, on our high performance computing. And so that's the InfiniBand piece of our business. And more and more, we're seeing that AI workloads are very, very similar to HPC workloads. And so that's been a great partnership that we've had for many, many years. And now we're expanding that, and we're launching a OEM relationship with Lenovo, for our Ethernet switches. And again, with our Ethernet switches, we really take that heritage of low latency, high performance networking that we built over many years in HPC, and we bring that to Ethernet. And of course that can be with HPC, because frequently in an HPC supercomputing environment, or in an AI supercomputing environment, you'll also have an Ethernet network, either for management, or sometimes for storage. And now we can offer that together with Lenovo. So it's a great partnership. We talked about it briefly last month, and now we're coming to market, and we'll be able to offer this to the market. >> Yeah, yeah, Kevin, we're super excited about it here in Lenovo as well. We've had a great relationship over the years with Mellanox, with Nvidia Mellanox. And this is just the next step. We've shown in HPC that the days of just taking an Ethernet card, or an InfiniBand card, plugging it in the system, and having it work properly are gone. You really need a system that's engineered for whatever task the customer is going to use. And we've known that in HPC for a long time, as we move into workloads, like artificial intelligence, where networking is a critical aspect of getting these systems to communicate with one another, and work properly together. We love from HPC perspective, to use InfiniBand, but most enterprise clients are using Ethernet. So where do we go? We go to a partner that we've trusted for a very long time. And we selected the Nvidia Mellanox Ethernet switch family. And we're really excited to be able to bring that end-to-end solution to our enterprise clients, just like we've been doing for HPC for a while. >> Yeah, well Scott, maybe if you could. I'd love to hear a little bit more about kind of that customer demand that those usages there. So you think traditionally, of course, is supercomputing, as you both talked about that move from InfiniBand, to leveraging Ethernet, is something that's been talked about for quite a while now in the industry. But maybe that AI specifically, could you talk about what are the networking requirements, how similar is it? Is it 95% of the same architecture, as what you see in HPC environments? And also, I guess the big question there is, how fast are customers adopting, and rolling out those AI solutions? And what kind of scale are they getting them to today? >> So yeah, there's a lot there of good things we can talk about. So I'd say in HPC, the thing that we've learned, is that you've got to have a fabric that's up to the task. When you're testing an HPC solution, you're not looking at a single node, you're looking at a combination of servers, and storage, management, all these things have to come together, and they come together over InfiniBand fabric. So we've got this nearly a purpose built fabric that's been fantastic for the HPC community for a long time. As we start to do some of that same type of workload, but in an enterprise environment, many of those customers are not used to InfiniBand, they're used to an Ethernet fabric, something that they've got all throughout their data center. And we want to try to find a way to do was, bring a lot of that rock solid interoperability, and pre-tested capability, and bring it to our enterprise clients for these AI workloads. Anything high performance GPUs, lots of inner internode communications, worries about traffic and congestion, abnormalities in the network that you need to spot. Those things happen quite often, when you're doing these enterprise AI solutions. You need a fabric that's able to keep up with that. And the Nvidia networking is definitely going to be able to do that for us. >> Yeah well, Kevin I heard Scott mention GPUs here. So this kind of highlights one of the reasons why we've seen Nvidia expand its networking capabilities. Could you talk a little bit about that kind of expansion, the portfolio, and how these use cases really are going to highlight what Nvidia helped bring to the market? >> Yeah, we like to really focus on accelerated computing applications. And whether those are HPC applications, or now they're becoming much more broadly adopted in the enterprise. And one of the things we've done is, tight integration at a product level, between GPUs, and the networking components in our business. Whether that's the adapters, or the DPU, the data processing unit, which we've talked about before. And now even with the switches here, with our friends at Lenovo, and really bringing that all together. But most importantly, is at a platform level. And by that I mean the software. And the enterprise here has all kinds of different verticals that are going after. And we invest heavily in the software ecosystem that's built on top of the GPU, and the networking. And by integrating all of that together on a platform, we can really accelerate the time to market for enterprises that wants to leverage these modern workloads, sort of cloud native workloads. >> Yeah, please Scott, if you have some follow up there. >> Yeah, if you don't mind Stu, I just like to say, five years ago, the roadmap that we followed was the processor roadmap. We all could tell you to the week when the next Xeon processor was going to come out. And that's what drove all of our roadmaps. Since that time what we found is that the items that are making the radical, the revolutionary improvements in performance, they're attached to the processor, but they're not the processor itself. It's things like, the GPU. It's things like that, especially networking adapters. So trying to design a platform that's solely based on a CPU, and then jam these other items on top of it. It no longer works, you have to design these systems in a holistic manner, where you're designing for the GPU, you're designing for the network. And that's the beauty of having a deep partnership, like we share with Nvidia, on both the GPU side, and on the networking side, is we can do all that upfront engineering to make sure that the platform, the systems, the solution, as a whole works exactly how the customer is going to expect it to. >> Kevin, you mentioned that a big piece of this is software now. I'm curious, there's an interesting piece that your networking team has picked up, relatively recently, that the Cumulus Linux, so help us understand how that fits into the Ethernet portfolio? And would it show up in these kind of applications that we're talking about? >> Yeah, that's a great question. So you're absolutely right, Cumulus is integral to what we're doing here with Lenovo. If you looked at the heritage that Mellanox had, and Cumulus, it's all about open networking. And what we mean by that, is we really decouple the hardware, and the software. So we support multiple network operating systems on top of our hardware. And so if it's, for example, Sonic, or if it's our Onyx or Dents, which is based on switch def. But Cumulus who we just recently acquired, has been also on that same access of open networking. And so they really support multiple platforms. Now we've added a new platform with our friends at Lenovo. And really they've adopted Cumulus. So it is very much centered on, Enterprise, and really a cloud like experience in the Enterprise, where it's Linux, but it's highly automated. Everything is operationalized and automated. And so as a result of that, you get sort of the experience of the cloud, but with the economics that you get in the Enterprise. So it's kind of the best of both worlds in terms of network analytic, and all of the ability to do things that the cloud guys are doing, but fully automated, and for an Enterprise environment. >> Yeah, so Kevin, I mean, I just want to say a few things about this. We're really excited about the Cumulus acquisition here. When we started our negotiations with Mellanox, we were still planning to use Onyx. We love Onyx, it's been our IB nodes of choice. Our users love, our are architects love it. But we were trying to lean towards a more open kind of futuristic, node as we got started with this. And Cumulus is really perfect. I mean it's a Linux open source based system. We love open source in HPC. The great thing about it is, we're going to be able to take all the great learnings that we've had with Onyx over the years, and now be able to consolidate those inside of Cumulus. We think it's the perfect way to start this relationship with Nvidia networking. >> Well Scott, help us understand a little more. What you know what does this expansion of the partnership mean? If you're talking about really the full solutions that Lenovo opens in the think agile brand, as well as the hybrid and cloud solutions. Is this something then that, is it just baked into the solution, is it a reseller, what should customers, and your your channel partners understand about this? >> Yeah, so any of the Lenovo solutions that require a switch to perform the functionality needed across the solution, are going to show up with the networking from Nvidia inside of it. Reasons for that, a couple of reasons. One is even something as simple as solution management for HPC, the switch is so integral to how we do all that, how we push all those functions down, how we deploy systems. So you've got to have a switch, in a connectivity methodology, that ensures that we know how to deploy these systems. And no matter what scale they are, from a few systems up, to literally thousands of systems, we've got something that we know how to do. Then when we're we're selling these solutions, like an SAP solution, for instance. The customer is not buying a server anymore, they're buying a solution, they're buying a functionality. And we want to be able to test that in our labs to ensure that that system, that rack, leaves our factory ready to do exactly what the customer is looking for. So any of the systems that are going to be coming from us, pre configured, pre tested, are all going to have Nvidia networking inside of them. >> Yeah, and I think that's, you mentioned the hybrid cloud. I think that's really important. That's really where we cut our teeth first in InfiniBand, but also with our Ethernet solutions. And so today, we're really driving a bunch of the big hyper scalars, as well as the big clouds. And as you see things like SAP or Azure, it's really important now that you're seeing Azure stack coming into a hybrid environment, that you have the known commodity here. So we're something that we're built in to many of those different platforms, with our Spectrum ASIC, as well as our adapters. And so now the ability with Nvidia, and Lenovo together, to bring that to enterprise customers, is really important. I think it's a proven set of components that together forms a solution. And that's the real key, as Scott said, is delivering a solution, not just piece parts, we have a platform, that software, hardware, all of it integrated. >> Well, it's great to see you. We've had an existing partnership for a while. I want to give you both the opportunity, anything specific, you've been hearing kind of the customer demand leading up this. Is it people that might be transitioning from InfiniBand to Ethernet? Or is it just general market adoption of new solutions that you have out there? (speakers talk over each other) >> You go ahead and start. >> Okay, so I think that there's different networks for different workloads, is what we've seen. And InfiniBand certainly is going to continue to be the best platform out there for HPC, and often for AI. But as Scott said, the enterprise frequently is not familiar with that, and for various reasons, would like to leverage Ethernet. So I think we'll see two different cases, one where there's Ethernet with an InfiniBand network. And the other is for new enterprise workloads that are coming, that are very AI centric, modern workloads, sort of cloud native workloads. You have all of the infrastructure in place with our Spectrum ASICs, and our Connectx adapters, and now integrated with GPUs, that we'll be able to deliver solutions rather than just compliments. And that's the key. >> Yeah, I think Stu, a great example, I think of where you need that networking, like we've been used to an HPC, is when you start looking at deep learning in training, scale out training. A lot of companies have been stuck on a single workstation, because they haven't been able to figure out how to spread that workload out, and chop it up, like we've been doing in HPC, because they've been running into networking issues. They can't run over an unoptimized network. With this new technology, we're hoping to be able to do a lot of the same things that HPC customers take for granted every day, about workload management, distribution of workload, chopping jobs up into smaller portions, and feeding them out to a cluster. We're hoping that we're going to be able to do those exact same things for our enterprise clients. And it's going to look magical to them, but it's the same kind of thing we've been doing forever. With Mellanox, in the past, now Nvidia networking, we're just going to take that to the enterprise. I'm really excited about it. >> Well, it's so much flexibility. We used to look at, it would take a decade to roll out some new generations. Kevin, if you could just give us latest speeds and feeds. If I look at Ethernet, did I see that this has from n gig, all the way up to 400 gig? I think I lose track a little bit of some of the pieces. I know the industry as a whole is driving it. But where are we with the general customer adoption of some of the some of the speeds today? >> Yeah indeed, we're coming up on the 40th anniversary of the first specification of Ethernet. And we're about 4000 times faster now, 40,000 times faster at 400 gigabits, versus 10 megabits. So yeah, we're shipping today at the adapter level, 100 gig, and even 200 gig. And then at the switch level, 400 gig. And people sort of ask, "Do we really need all that performance?" The answer is absolutely. So the amount of data that the GPU can crunch, and these AI workloads, these giant neural networks, it needs massive amounts of data. And then as you're scaling out, as Scott was talking about, much along the lines of InfiniBand Ethernet needs that same level of performance, throughput, latency and offloads, and we're able to deliver. >> Yeah, so Kevin, thank you so much. Scott, I want to give you a final word here. Anything else you want your customers to understand regarding this partnerships? >> Yeah, just a quick one Stu, quick one. So we've been really fortunate in working really closely with Mellanox over the years, and with Nvidia. And now the two together, we're just excited about what the future holds. We've done some really neat things in HPC, with being one of the first watercool an InfiniBand card. We're one of the first companies to deploy Dragonfly topology. We've done some unique things where we can share a single IP adapter, across multiple users. We're looking forward to doing a lot of that same exact kind of innovation, inside of our systems as we look to Ethernet. We often think that as speeds of Ethernet continue to go higher, we may see more and more people move from InfiniBand to Ethernet. I think that now having both of these offerings inside of our lineup, is going to make it really easy for customers to choose what's best for them over time. So I'm excited about the future. >> Alright, well Kevin and Scott, thank you so much. Deep integration and customer choice, important stuff. Thank you so much for joining us. >> Thank you Stu. >> Thanks Stu. >> Alright, I'm Stu Miniman, and thank you. Thanks for watching theCUBE. (upbeat music)

>> Narrator: From around the global its theCUBE, covering HPE Discover Virtual Experience, brought to you by HPE. >> Hi, I'm Stu Miniman and this is theCUBE's coverage of HPE, discover the virtual experience for 2020, getting to talk to Hp executives, their partners, the ecosystem, where they are around the globe, this session we're going to be digging in about artificial intelligence, obviously a super important topic these days. And to help me do that, I've got two guests from Nvidia, sitting in the window next to me, we have Paresh Kharya, he's director of product marketing and sitting next to him in the virtual environment is Kevin Deierling, who is this senior vice president of marketing as I mentioned both with Nvidia. Thank you both so much for joining us. >> Thank you, so great to be here. >> Great to be here. >> All right, so Paresh when you set the stage for us? AI, obviously, one of those mega trends to talk about but just, give us the stages, where Nvidia sits, where the market is, and your customers today, that they think about AI. >> Yeah, so we are basically witnessing a massive changes that are happening across every industry. And it's basically the confluence of three things. One is of course, AI, the second is 5G and IOT, and the third is the ability to process all of the data that we have, that's now possible. For AI we are now seeing really advanced models, from computer vision, to understanding natural language, to the ability to speak in conversational terms. In terms of IOT and 5G, there are billions of devices that are sensing and inferring information. And now we have the ability to act, make decisions in various industries, and finally all of the processing capabilities that we have today, at the data center, and in the cloud, as well as at the edge with the GPUs as well as advanced networking that's available, we can now make sense all of this data to help industrial transformation. >> Yeah, Kevin, you know it's interesting when you look at some of these waves of technology and we say, "Okay, there's a lot of new pieces here." You talk about 5G, it's the next generation but architecturally some of these things remind us of the past. So when I look at some of these architectures, I think about, what we've done for high performance computing for a long time, obviously, you know, Mellanox, where you came from through NVIDIA's acquisition, strong play in that environment. So, maybe give us a little bit compare, contrast, what's the same, and what's different about this highly distributed, edge compute AI, IOT environment and what's the same with what we were doing with HPC in the past. >> Yeah, so we've--Mellanox has now been a part of Nvidia for a little over a month and it's great to be part of that. We were both focused on accelerated computing and high performance computing. And to do that, what it means is the scale and the type of problems that we're trying to solve are just simply too large to fit into a single computer. So if that's the case, then you connect a lot of computers. And Jensen talked about this recently at the GTC keynote where he said that the new unit computing, it's really the data center. So it's no longer the box that sits on your desk or even in Iraq, it's the entire data center because that's the scale of the types of problems that we're solving. And so the notion of scale up and scale out, the network becomes really, really critical. And we're doing high-performance networking for a long time. When you move to the edge, instead of having, a single data center with 10,000 computers, you have 10,000 data centers, each of which as a small number of servers that is processing all of that information that's coming in. But in a sense, the problems are very, very similar, whether you're at the edge or you're doing massive HPC, scientific computing or cloud computing. And so we're excited to be part of bringing together the AI and the networking because they are really optimizing at the data center scale across the entire stack. >> All right, so it's interesting. You mentioned, Nvidia CEO, Jensen. I believe if I saw right in there, he actually could, wrote a term which I had not run across, it was the data processing unit or DPU in that, data center, as you talked about. Help us wrap our heads around this a little bit. I know my CPU, when I think about GPUs, I obviously think of Nvidia. TPUs, in the cloud and everything we're doing. So, what is DPUs? Is this just some new AI thing or, is this kind of a new architectural model? >> Yeah. I think what Jensen highlighted is that there's three key elements of this accelerated disaggregated infrastructure that the data center has becoming. And so that's the CPU, which is doing traditional single threaded workloads but for all of the accelerated workloads, you need the GPU. And that does massive parallelism deals with massive amounts of data, but to get that data into the GPU and also into the CPU, you need really an intelligent data processing because the scale and scope of GPUs and CPUs today, these are not single core entities. These are hundreds or even thousands of cores in a big system. And you need to steer the traffic exactly to the right place. You need to do it securely. You need to do it virtualized. You need to do it with containers and to do all of that, you need a programmable data processing unit. So we have something called our BlueField, which combines our latest, greatest, 100 gig and 200 gig network connectivity with Arm processors and a whole bunch of accelerators for security, for virtualization, for storage. And all of those things then feed these giant parallel engines which are the GPU. And of course the CPU, which is really the workload at the application layer for non-accelerated outs. >> Great, so Paresh, Kevin talked about, needing similar types of services, wherever the data is. I was wondering if you could really help expand for us a little bit, the implications of it AI at the edge. >> Sure, yeah, so AI is basically not just one workload. AI is many different types of models and AI also means training as well as inferences, which are very different workloads or AI printing, for example, we are seeing the models growing exponentially, think of any AI model, like a brain of a computer or like a brain, solving a particular use case a for simple models like computer vision, we have models that are smaller, bugs have computer vision but advanced models like natural language processing, they require larger brains or larger models, so on one hand we are seeing the size of the AI models increasing tremendously and in order to train these models, you need to look at computing at the scale of data center, many processors, many different servers working together to train a single model, on the other hand because of these AI models, they are so accurate today from understanding languages to speaking languages, to providing the right recommendations whether it's for products or for content that you may want to consume or advertisements and so on. These models are so effective and efficient that they are being powered by AI today. These applications are being powered by AI and each application requires a small amount of acceleration, so you need the ability to scale out or, and support many different applications. So with our newly launched MPR architecture, just couple of weeks to go that Jensen announced, in the virtual keynote for the first time, we are now able to provide both, scale up and scale out both training data analytics as well as imprints on the single architecture and that's very exciting. >> Yeah, so look at that. The other thing that's interesting is you're talking about at the edge and scale out versus scale up, the networking is critical for both of those. And there's a lot of different workloads. And as Paresh was describing, you've got different workloads that require different amounts of GPU or storage or networking. And so part of that vision of this data center as the computer is that, the DPU lets you scale independently, everything. So you can compose, you desegregate into DPUs and storage and CPUs, and then you compose exactly the computer that you need on the fly container, right, to solve the problem that you're solving right now. So these new way of programming is programming the entire data center at once and you'll go grab all of it and it'll run for a few hundred milliseconds even and then it'll come back down and recompose itself onsite. And to do that, you need this very highly efficient networking infrastructure. And the good news is we're here at HPE Discover. We've got a great partner with HPE. You know, they have our M series switches that uses the Mellanox hundred gig and now even 200 and 400 gig ethernet switches, we have all of our adapters and they have great platforms. The Apollo platform for example, is break for HPC and they have other great platforms that we're looking at with the new telco that we're doing or 5G and accelerating that. >> Yeah, and on the edge computing side, there's the edge line set of products which are very interesting, the other sort of aspect that I wanted to touch upon, is the whole software stack that's needed for the edge. So edge is different in the sense that it's not centrally managed, the edge computing devices are distributed remote locations. And so managing the workflow of running and updating software on it is important and needs to be done in a very secure manner. The second thing that's, that's very different again, for the edges, these devices are going to require connectivity. As Kevin was pointing out, the importance of networking so we also announced, a couple of weeks ago at our GTC, our EGX product that combines the Mellanox NIC and our GPUs into a single a processor, Mellanox NIC provides a fast connectivity, security, as well as the encryption and decryption capabilities, GPUs provide acceleration to run the advanced DI models, that are required for applications at the edge. >> Okay, and if I understood that, right. So, you've got these throughout the HPE the product line, HPE's got long history of making, flexible configurations, I remember when they first came out with a Blade server it was, different form factors, different connectivity options, they pushed heavily into composable infrastructure. So it sounds like this is just a kind of extending, you know, what HP has been doing for a couple of decades. >> Yeah, I think HP is a great partner there and these new platforms, the EGX, for example that was just announced, a great workload there is a 5G telco. So we'll be working with our friends at HPE to take that to market as well. And, you know, really, there's a lot of different workloads and they've got a great portfolio of products across the spectrum from regular servers. And 1U, 2U, and then all the way up to their big Apollo platform. >> Well I'm glad you brought up telco, I'm curious, are there any specific, applications or workloads that, where the low hanging fruit or the kind of the first targets that you use for AI acceleration? >> Yeah, so you know, the 5G workload is just awesome. We're introduced with the EGX, a new platform called Ariel which is a programming framework and there were lots of partners there that were part of that, including, folks like Ericsson. And the idea there is that you have a software defined hardware accelerated radio area network, so a cloud RAM and it really has all of the right attributes of the cloud and what's nice there is now you can change on the fly, the algorithms that you're using for the baseband codex without having to go climb a radio tower and change the actual physical infrastructure. So that's a critical part. Our role in that, on the networking side, we introduced the technology that's part of EGX then are connected, It's like the DX adapter, it's called 5T for 5G. And one of the things that happens is you need this time triggered transport or a telco technology. That's the 5T's for 5G. And the reason is because you're doing distributed baseband unit, distributed radio processing and the timing between each of those server nodes needs to be super precise, 20 nanosecond. It's something that simply can't be done in software. And so we did that in hardware. So instead of having an expensive FPGA, I try to synchronize all of these boxes together. We put it into our NIC and now we put that into industry standard servers HP has some fantastic servers. And then with the EGX platform, with that we can build, really scale out software to client cloud RAM. >> Awesome, Paresh, anything else on the application side you'd like to add in just about what Kevin spoke about. >> Oh yeah, so from application perspective, every industry has applications that touch on edge. If you take a look at the retail, for example, there is, you know, all the way from supply chain to inventory management, to keeping the right stock units in the shelves, making sure there is a there is no slippage or shrinkage. So to telecom, to healthcare, we are re-looking at constantly monitoring patients and taking actions for the best outcomes to manufacturing. We are looking to automate production detecting failures much early on in the production cycle and so on every industry has different applications but they all use AI. They can all leverage the computing capabilities and high-speed networking at the edge to transform their business processes. >> All right, well, it's interesting almost every time we've talked about AI, networking has come up. So, you know, Kevin, I think that probably ease up a little bit why, Nvidia, spent around $7 billion for the acquisition of Mellanox and not only was it the Mellanox acquisition, Cumulus Networks, very known in the network space for software defined really, operating system for networking but give us strategically, does this change the direction of Nvidia, how should we be thinking about Nvidia in the overall network? >> Yeah, I think the way to think about it is going back to that data center as the computer. And if you're thinking about the data center as computer then networking becomes the back plane, if you will of that data center computer and having a high performance network is really critical. And Mellanox has been a leader in that for 20 years now with our InfiniBand and our Ethernet product. But beyond that, you need a programmatic interface because one of the things that's really important in the cloud is that everything is software defined and it's containerized now and there is no better company in the world then Cumulus, really the pioneer and building Cumulus clinics, taking the Linux operating system and running that on multiple homes. So not just hardware from Mellanox but hardware from other people as well. And so that whole notion of an open networking platform more committed to, you need to support that and now you have a programmatic interface that you can drop containers on top of, Cumulus has been the leader in the Linux FRR, it's Free Range Routing, which is the core routing algorithm. And that really is at the heart of other open source network operating systems like Sonic and DENT so we see a lot of synergy here, all the analytics that Cumulus is bringing to bear with NetQ. So it's really great that they're going to be part here of the Nvidia team. >> Excellent, well thank you both much. Want to give you the final word, what should they do, HPE customers in their ecosystem know about the Nvidia and HPE partnership? >> Yeah, so I'll start you know, I think HPE has been a longtime partner and a customer of ours. If you have accelerated workloads, you need to connect those together. The HPE server portfolio is an ideal place. We can combine some of the work we're doing with our new amp years and existing GPUs and then also to connect those together with the M series, which is their internet switches that are based on our spectrum switch platforms and then all of the HPC related activities on InfiniBand, they're a great partner there. And so all of that, pulling it together, and now as at the edge, as edge becomes more and more important, security becomes more and more important and you have to go to this zero trust model, if you plug in a camera that's somebody has at the edge, even if it's on a car, you can't trust it. So everything has to become, validated authenticated, all the data needs to be encrypted. And so they're going to be a great partner because they've been a leader and building the most secure platforms in the world. >> Yeah and on the data center, server, portfolio side, we really work very closely with HP on various different lines of products and really fantastic servers from the Apollo line of a scale up servers to synergy and ProLiant line, as well as the Edgeline for the edge and on the super computing side with the pre side of things. So we really work to the fullest spectram of solutions with HP. We also work on the software side, wehere a lot of these servers, are also certified to run a full stack under a program that we call NGC-Ready so customers get phenomenal value right off the bat, they're guaranteed, to have accelerated workloads work well when they choose these servers. >> Awesome, well, thank you both for giving us the updates, lots happening, obviously in the AI space. Appreciate all the updates. >> Thanks Stu, great to talk to you, stay well. >> Thanks Stu, take care. >> All right, stay with us for lots more from HPE Discover Virtual Experience 2020. I'm Stu Miniman and thank you for watching theCUBE. (bright upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. (smooth music) >> Hi, I'm Stu Miniman, and welcome to this special exclusive presentation from theCUBE. We're digging into Pensando and their Future Proof Your Enterprise event. To help kick things off, welcoming in a friend of the program, Scott Raynovich. He is the principal analyst at Futuriom coming to us from Montana. I believe first time we've had a guest on the program in the state of Montana, so Scott, thanks so much for joining us. >> Thanks, Stu, happy to be here. >> All right, so we're going to dig a lot into Pensando. They've got their announcement with Hewlett Packard Enterprise. Might help if we give a little bit of background, and definitely I want Scott and I to talk a little bit about where things are in the industry, especially what's happening in networking, and how some of the startups are helping to impact what's happening on the market. So for those that aren't familiar with Pensando, if you followed networking I'm sure you are familiar with the team that started them, so they are known, for those of us that watch the industry, as MPLS, which are four people, not to be confused with the protocol MPLS, but they had very successfully done multiple spin-ins for Cisco, Andiamo, Nuova and Insieme, which created Fibre Channel switches, the Cisco UCS, and the ACI product line, so multiple generations to the Nexus, and Pensando is their company. They talk about Future Proof Your Enterprise is the proof point that they have today talking about the new edge. John Chambers, the former CEO of Cisco, is the chairman of Pensando. Hewlett Packard Enterprise is not only an investor, but also a customer in OEM piece of this solution, and so very interesting piece, and Scott, I want to pull you into the discussion. The waves of technology, I think, the last 10, 15 years in networking, a lot it has been can Cisco be disrupted? So software-defined networking was let's get away from hardware and drive towards more software. Lots of things happening. So I'd love your commentary. Just some of the macro trends you're seeing, Cisco's position in the marketplace, how the startups are impacting them. >> Sure, Stu. I think it's very exciting times right now in networking, because we're just at the point where we kind of have this long battle of software-defined networking, like you said, really pushed by the startups, and there's been a lot of skepticism along the way, but you're starting to see some success, and the way I describe it is we're really on the third generation of software-defined networking. You have the first generation, which was really one company, Nicira, which VMware bought and turned into their successful NSX product, which is a virtualized networking solution, if you will, and then you had another round of startups, people like Big Switch and Cumulus Networks, all of which were acquired in the last year. Big Switch went to Arista, and Cumulus just got purchased by... Who were they purchased by, Stu? >> Purchased by Nvidia, who interestingly enough, they just picked up Mellanox, so watching Nvidia build out their stack. >> Sorry, I was having a senior moment. It happens to us analysts. (chuckling) But yeah, so Nvidia's kind of rolling up these data center and networking plays, which is interesting because Nvidia is not a traditional networking hardware vendor. It's a chip company. So what you're seeing is kind of this vision of what they call in the industry disaggregation. Having the different components sold separately, and then of course Cisco announced the plan to roll out their own chip, and so that disaggregated from the network as well. When Cisco did that, they acknowledged that this is successful, basically. They acknowledged that disaggregation is happening. It was originally driven by the large public cloud providers like Microsoft Azure and Amazon, which started the whole disaggregation trend by acquiring different components and then melding it all together with software. So it's definitely the future, and so there's a lot of startups in this area to watch. I'm watching many of them. They include ArcOS, which is a exciting new routing vendor. DriveNets, which is another virtualized routing vendor. This company Alkira, which is going to do routing fully in the cloud, multi-cloud networking. Aviatrix, which is doing multi-cloud networking. All these are basically software companies. They're not pitching hardware as part of their value add, or their integrated package, if you will. So it's a different business model, and it's going to be super interesting to watch, because I think the third generation is the one that's really going to break this all apart. >> Yeah, you brought up a lot of really interesting points there, Scott. That disaggregation, and some of the changing landscape. Of course that more than $1 billion acquisition of Nicira by VMware caused a lot of tension between VMware and Cisco. Interesting. I think back when to Cisco created the UCS platform it created a ripple effect in the networking world also. HP was a huge partner of Cisco's before UCS launched, and not long after UCS launched HP stopped selling Cisco gear. They got heavier into the networking component, and then here many years later we see who does the MPLS team partner with when they're no longer part of Cisco, and Chambers is no longer the CEO? Well, it's HPE front and center there. You're going to see John Chambers at HPE Discover, so it was a long relationship and change. And from the chip companies, Intel, of course, has built a sizeable networking business. We talked a bit about Mellanox and the acquisitions they've done. One you didn't mention but caused a huge impact in the industry, and something that Pensando's responding to is Amazon, but Annapurna Labs, and Annapurna Labs, a small Israeli company, and really driving a lot of the innovation when it comes to compute and networking at Amazon. The Graviton, Compute, and Nitro is what powers their Outposts solutions, so if you look at Amazon, they buy lots of pieces. It's that mixture of hardware and software. In early days people thought that they just bought kind of off-the-shelf white boxes and did it cheap, but really we see Amazon really hyper optimizes what they're doing. So Scott, let's talk a little bit about Pensando if we can. Amazon with the Nitro solutions built to Outposts, which is their hybrid solution, so the same stack that they put in Amazon they can now put in customers' data center. What Pensando's positioning is well, other cloud providers and enterprise, rather than having to buy something from Amazon, we're going to enable that. So what do you think about what you've seen and heard from Pensando, and what's that need in the market for these type of solutions? >> Yes, okay. So I'm glad you brought up Outposts, because I should've mentioned this next trend. We have, if you will, the disaggregated open software-based networking which is going on. It started in the public cloud, but then you have another trend taking hold, which is the so-called edge of the network, which is going to be driven by the emergence of 5G, and the technology called CBRS, and different wireless technologies that are emerging at the so-called edge of the network, and the purpose of the edge, remember, is to get closer to the customer, get larger bandwidth, and compute, and storage closer to the customer, and there's a lot of people excited about this, including the public cloud providers, Amazon's building out their Outposts, Microsoft has an Edge stack, the Azure Edge Stack that they've built. They've acquired a couple companies for $1 billion. They acquired Metaswitch, they acquired Affirmed Networks, and so all these public cloud providers are pushing their cloud out to the edge with this infrastructure, a combination of software and hardware, and that's the opportunity that Pensando is going after with this Outposts theme, and it's very interesting, Stu, because the coopetition is very tenuous. A lot of players are trying to occupy this edge. If you think about what Amazon did with public cloud, they sucked up all of this IT compute power and services applications, and everything moved from these enterprise private clouds to the public cloud, and Amazon's market cap exploded, right, because they were basically sucking up all the money for IT spending. So now if this moves to the edge, we have this arms race of people that want to be on the edge. The way to visualize it is a mini cloud. Whether this mini cloud is at the edge of Costco, so that when Stu's shopping at Costco there's AI that follows you in the store, knows everything you're going to do, and predicts you're going to buy this cereal and "We're going to give you a deal today. "Here's a coupon." This kind of big brother-ish AI tracking thing, which is happening whether you like it or not. Or autonomous vehicles that need to connect to the edge, and have self-driving, and have very low latency services very close to them, whether that's on the edge of the highway or wherever you're going in the car. You might not have time to go back to the public cloud to get the data, so it's about pushing these compute and data services closer to the customers at the edge, and having very low latency, and having lots of resources there, compute, storage, and networking. And that's the opportunity that Pensando's going after, and of course HPE is going after that, too, and HPE, as we know, is competing with its other big mega competitors, primarily Dell, the Dell/VMware combo, and the Cisco... The Cisco machine. At the same time, the service providers are interested as well. By the way, they have infrastructure. They have central offices all over the world, so they are thinking that can be an edge. Then you have the data center people, the Equinixes of the world, who also own real estate and data centers that are closer to the customers in the metro areas, so you really have this very interesting dynamic of all these big players going after this opportunity, putting in money, resources, and trying to acquire the right technology. Pensando is right in the middle of this. They're going after this opportunity using the P4 networking language, and a specialized ASIC, and a NIC that they think is going to accelerate processing and networking of the edge. >> Yeah, you've laid out a lot of really good pieces there, Scott. As you said, the first incarnation of this, it's a NIC, and boy, I think back to years ago. It's like, well, we tried to make the NIC really simple, or do we build intelligence in it? How much? The hardware versus software discussion. What I found interesting is if you look at this team, they were really good, they made a chip. It's a switch, it's an ASIC, it became compute, and if you look at the technology available now, they're building a lot of your networking just in a really small form factor. You talked about P4. It's highly programmable, so the theme of Future Proof Your Enterprise. With anything you say, "Ah, what is it?" It's a piece of hardware. Well, it's highly programmable, so today they position it for security, telemetry, observability, but if there's other services that I need to get to edge, so you laid out really well a couple of those edge use cases and if something comes up and I need that in the future, well, just like we've been talking about for years with software-defined networking, and network function virtualization, I don't want a dedicated appliance. It's going to be in software, and a form factor like Pensando does, I can put that in lots of places. They're positioning they have a cloud business, which they sell direct, and expect to have a couple of the cloud providers using this solution here in 2020, and then the enterprise business, and obviously a huge opportunity with HPE's position in the marketplace to take that to a broad customer base. So interesting opportunity, so many different pieces. Flexibility of software, as you relayed, Scott. It's a complicated coopetition out there, so I guess what would you want to see from the market, and what is success from Pensando and HPE, if they make this generally available this month, it's available on ProLiant, it's available on GreenLake. What would you want to be hearing from customers or from the market for you to say further down the road that this has been highly successful? >> Well, I want to see that it works, and I want to see that people are buying it. So it's not that complicated. I mean I'm being a little superficial there. It's hard sometimes to look in these technologies. They're very sophisticated, and sometimes it comes down to whether they perform, they deliver on the expectation, but I think there are also questions about the edge, the pace of investment. We're obviously in a recession, and we're in a very strange environment with the pandemic, which has accelerated spending in some areas, but also throttled back spending in other areas, and 5G is one of the areas that it appears to have been throttled back a little bit, this big explosion of technology at the edge. Nobody's quite sure how it's going to play out, when it's going to play out. Also who's going to buy this stuff? Personally, I think it's going to be big enterprises. It's going to start with the big box retailers, the Walmarts, the Costcos of the world. By the way, Walmart's in a big competition with Amazon, and I think one of the news items you've seen in the pandemic is all these online digital ecommerce sales have skyrocketed, obviously, because people are staying at home more. They need that intelligence at the edge. They need that infrastructure. And one of the things that I've heard is the thing that's held it back so far is the price. They don't know how much it's going to cost. We actually ran a survey recently targeting enterprises buying 5G, and that was one of the number one concerns. How much does this infrastructure cost? So I don't actually know how much Pensando costs, but they're going to have to deliver the right ROI. If it's a very expensive proprietary NIC, who pays for that, and does it deliver the ROI that they need? So we're going to have to see that in the marketplace, and by the way, Cisco's going to have the same challenge, and Dell's going to have the same challenge. They're all racing to supply this edge stack, if you will, packaged with hardware, but it's going to come down to how is it priced, what's the ROI, and are these customers going to justify the investment is the trick. >> Absolutely, Scott. Really good points there, too. Of course the HPE announcement, big move for Pensando. Doesn't mean that they can't work with the other server vendors. They absolutely are talking to all of them, and we will see if there are alternatives to Pensando that come up, or if they end up singing with them. All right, so what we have here is I've actually got quite a few interviews with the Pensando team, starting with I talked about MPLS. We have Prem, Jane, and Sony Giandoni, who are the P and the S in MPLS as part of it. Both co-founders, Prem is the CEO. We have Silvano Guy who, anybody that followed this group, you know writes the book on it. If you watched all the way this far and want to learn even more about it, I actually have a few copies of Silvano's book, so if you reach out to me, easiest way is on Twitter. Just hit me up at @Stu. I've got a few copies of the book about Pensando, which you can go through all those details about how it works, the programmability, what changes and everything like that. We've also, of course, got Hewlett Packard Enterprise, and while we don't have any customers for this segment, Scott mentioned many of the retail ones. Goldman Sachs is kind of the marquee early customer, so did talk with them. I have Randy Pond, who's the CFO, talking about they've actually seen an increase beyond what they expected at this point of being out of stealth, only a little over six months, even more, which is important considering that it's tough times for many startups coming out in the middle of a pandemic. So watch those interviews. Please hit us up with any other questions. Scott Raynovich, thank you so much for joining us to help talk about the industry, and this Pensando partnership extending with HPE. >> Thanks, Stu. Always a pleasure to join theCUBE team. >> All right, check out thecube.net for all the upcoming, as well as if you just search "Pensando" on there, you can see everything we had on there. I'm Stu Miniman, and thank you for watching theCUBE. 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>> Jeff: Hello everybody, and thank you for joining us today for the virtual Vertica BDC 2020. Today's break-out session has been titled, "Migrating Your Vertica Cluster to the Cloud." I'm Jeff Healey, and I'm in Vertica marketing. I'll be your host for this break-out session. Joining me here are Sumeet Keswani and Chris Daly, Vertica product technology engineers and key members of our customer success team. Before we begin, I encourage you to submit questions and comments during the virtual session. You don't have to wait, just type your question or comment in the question box below the slides and click Submit. As always, there will be a Q&A session at the end of the presentation. We'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to answer them offline. And alternatively, you can visit Vertica forums at forum.vertica.com to post your questions there after the session. Our engineering team is planning to join the forums to keep the conversation going. Also as a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slides. And yes, this virtual session is being recorded and will be available to view on demand this week. We'll send you a notification as soon as it's ready. Now let's get started. Over to you, Sumeet. >> Sumeet: Thank you, Jeff. Hello everyone, my name is Sumeet Keswani, and I will be talking about planning to deploy or migrate your Vertica cluster to the Cloud. So you may be moving an on-prem cluster or setting up a new cluster in the Cloud. And there are several design and operational considerations that will come into play. You know, some of these are cost, which industry you are in, or which expertise you have, in which Cloud platform. And there may be a personal preference too. After that, you know, there will be some operational considerations like VM and cluster sizing, what Vertica mode you want to deploy, Eon or Enterprise. It depends on your use keys. What are the DevOps skills available, you know, what elasticity, separation you need, you know, what is your backup and DR strategy, what do you want in terms of high availability. And you will have to think about, you know, how much data you have and where it's going to live. And in order to understand the cost, or the cost and the benefit of deployment and you will have to understand the access patterns, and how you are moving data from and to the Cloud. So things to consider before you move a deployment, a Vertica deployment to the Cloud, right, is one thing to keep in mind is, virtual CPUs, or CPUs in the Cloud, are not the same as the usual CPUs that you've been familiar with in your data center. A vCPU is half of a CPU because of hyperthreading. There is definitely the noisy neighbor effect. There is, depending on what other things are hosted in the Cloud environment, you may see performance, you may occasionally see performance issues. There are I/O limitations on the instance that you provision, so that what that really means is you can't always scale up. You might have to scale up, basically, you have to add more instances rather than getting bigger or the right size instances. Finally, there is an important distinction here. Virtualization is not free. There can be significant overhead to virtualization. It could be as much as 30%, so when you size and scale your clusters, you must keep that in mind. Now the other important aspect is, you know, where you put Vertica cluster is important. The choice of the region, how far it is from your various office locations. Where will the data live with respect to the cluster. And remember, popular locations can fill up. So if you want to scale out, additional capacity may or may not be available. So these are things you have to keep in mind when picking or choosing your Cloud platform and your deployment. So at this point, I want to make a plug for Eon mode. Eon mode is the latest mode, is a Cloud mode from Vertica. It has been designed with Cloud economics in mind. It uses shared storage, which is durable, available, and very cheap, like S3 storage or Google Cloud storage. It has been designed for quick scaling, like scale out, and highly elastic deployments. It has also been designed for high workload isolation, where each application or user group can be isolated from the other ones, so that they'll be paid and monitored separately, without affecting each other. But there are some disadvantages, or perhaps, you know, there's a cost for using Eon mode. Storage in S3 is neither cheap nor efficient. So there is a high latency of I/O when accessing data from S3. There is API and data access cost. There is API and data access cost associated with accessing your data in S3. Vertica in Eon mode has a pay as you go model, which you know, works for some people and does not work for others. And so therefore it is important to keep that in mind. And performance can be a little bit variable here, because it depends on cache, it depends on the local depot, which is a cache, and it is not as predictable as EE mode, so that's another trade-off. So let's spend about a minute and see how a Vertica cluster in Eon mode looks like. A Vertica cluster in Eon mode has S3 as the durability layer where all the data sits. There are subclusters, which are essentially just aggregation groups, which is separated compute, which will service different workloads. So for in this example, you may have two subclusters, one servicing ETL workload and the other one servicing (mic interference obscures speaking). These clusters are isolated, and they do not affect each other's performance. This allows you to scale them independently and isolate workloads. So this is the new Vertica Eon mode which has been specifically designed by us for use in the Cloud. But beyond this, you can use EE mode or Eon mode in the Cloud, it really depends on what your use case is. But both of these are possible, and we highly recommend Eon mode wherever possible. Okay, let's talk a little bit about what we mean by Vertica support in the Cloud. Now as you know, a Cloud is a shared data center, right. Performance in the Cloud can vary. It can vary between regions, availability zones, time of the day, choice of instance type, what concurrency you use, and of course the noisy neighbor effect. You know, we in Vertica, we performance, load, and stress test our product before every release. We have a bunch of use cases, we go through all of them, make sure that we haven't, you know, regressed any performance, and make sure that it works up to standards and gives you the high performance that you've come to expect. However, your solution or your workload is unique to you, and it is still your responsibility to make sure that it is tuned appropriately. To do this, one of the easiest things you can do is you know, pick a tested operating system, allocate the virtual machine, you know, with enough resources. It's something that we recommend, because we have tested it thoroughly. It goes a long way in giving you predictability. So after this I would like to now go into the various platforms, Cloud platforms, that Vertica has worked on. And I'll start with AWS, and my colleague Chris will speak about Azure and GCP. And our thoughts forward. So without further ado, let's start with the Amazon Web Services platform. So this is Vertica running on the Amazon Web Services platform. So as you probably are all aware, Amazon Web Services is the market leader in this space, and indeed really our biggest provider by far, and have been here for a very long time. And Vertica has a deep integration in the Amazon Web Services space. We provide a marketplace offering which has both pay as you go or a bring your own license model. We have many, you know, knowledge base articles, best practices, scripts, and resources that help you configure and use a Vertica database in the Cloud. We have several customers in the Cloud for many, many years now, and we have managed and console-based point and click deployments, you know, for ease of use in the Cloud. So Vertica has a deep integration in the Amazon space, and has been there for quite a bit now. So we communicate a lot of experience here. So let's talk about sizing on AWS. And sizing on any platform comes down to you know, these four or five different things. It comes down to picking the right instance type, picking the right disk volume and type, tuning and optimizing your networking, and finally, you know, some operational concerns like security, maintainability, and backup. So let's go into each one of these on the AWS ecosystem. So the choice of instance type is one of the important choices that you will make. In Eon mode, you know, you don't really need persistent disk. You can, you should probably choose ephemeral disk because it gives you extra speed, and speed with the instance type. We highly recommend the i3.4x instance types, which are very economical, have a big, 4 terabyte depot or cache per node. The i3.metal is similar to the i3.4, but has got significantly better performance, for those subclusters that need this extra oomph. The i3.2 is good for scale out of small ad hoc clusters. You know, they have a smaller cache and lower performance but it's cheap enough to use very indiscriminately. If you were in EE mode, well we don't use S3 as the layer of durability. Your local volumes is where we persist the data. Hence you do need an EBS volume in EE mode. In order to make sure that, you know, that the instance or the deployment is manageable, you might have to use some sort of a software RAID array over the EBS volumes. The most common instance type you see in EE mode is the r4.4x, the c4, or the m4 instance types. And then of course for temp space and depot we always recommend instance volumes. They're just much faster. Okay. So let's go, let's talk about optimizing your network or tuning your network. So the best, the best thing you can do about tuning your network, especially in Eon mode but in other modes too, is to get a VPC S3 endpoint. This is essentially a route table that makes sure that all traffic between your cluster and S3 goes over an internal fabric. This makes it much faster, you don't pay for egress cost, especially if you're doing external tables or your communal storage, but you do need to create it. Many times people will forget doing it. So you really do have to create it. And best of all, it's free. It doesn't cost you anything extra. You just have to create it during cluster creation time, and there's a significant performance difference for using it. The next thing about tuning your network is, you know, sizing it correctly. Pick the closest geographical region to where you'll consume the data. Pick the right availability zone. We highly recommend using cluster placement groups. In fact, they are required for the stability of the cluster. A cluster placement group is essentially, it operates this notion of rack. Nodes in a cluster placement group, are, you know, physically closer to each other than they would otherwise be. And this allows, you know, a 10 Gbps, bidirectional, TCP/IP flow between the nodes. And this makes sure that, you know, you get a high amount of Gbps per second. As you probably are all aware, the Cloud does not support broadcast or UDP broadcast. Hence you must use point-to-point UDP for spread in the Cloud, or in AWS. Beyond that, you know, point-to-point UDP does not scale very well beyond 20 nodes. So you know, as your cluster sizes increase, you must switch over to large cluster mode. And finally, use instances with enhanced networking or SR-IOV support. Again, it's free, it comes with the choice of the instance type and the operating system. We highly recommend it, it makes a big difference in terms of how your workload will perform. So let's talk a little bit about security, configuration, and orchestration. As I said, we provide CloudFormation scripts to make the ease of deployment. You can use the MC point and click. With regard to security, you know, Vertica does support instance profiles out of the box in Amazon. We recommend you use it. This is highly desirable so that you're not passing access keys and secret keys around. If you use our marketplace image, we have picked the latest operating systems, we have patched them, Amazon actually validates everything on marketplace and scans them for security vulnerabilities. So you get that for free. We do some basic configuration, like we disable root ssh access, we disallow any password access, we turn on encryption. And we run a basic set of security checks to make sure that the image is secure. Of course, it could be made more secure. But we try to balance out security, performance, and convenience. And finally, let's talk about backups. Especially in Eon mode I get the question, "Do we really need to back up our system, "since the data is in S3?" And the answer is yes, you do. Because you know, S3's not going to protect you against an accidental drop table. You know, S3 has a finite amount of reliability, durability, and availability. And you may want to be able to restore data differently. Also, backups are important if you're doing DR, or if you have additional cluster in a different region. The other cluster can be considered a backup. And finally, you know, why not create a backup or a disaster recovery cluster, you know, storage is cheap in the Cloud. So you know, we highly recommend you use it. So with this, I would like to hand it over to my colleague Christopher Daly, who will talk about the other two platforms that we support, that is Google and Azure. Over to you, Chris, thank you. >> Chris: Thanks, Sumeet, and hi everyone. So while there's no argument that we here at Vertica have a long history of running within the Amazon Web Services space, there are other alternative Cloud service providers where we do have a presence, such as Google Cloud Platform, or GCP. For those of you who are unfamiliar with GCP, it's considered the third-largest Cloud service provider in the marketspace, and it's priced very competitively to its peers. Has a lot of similarities to AWS in the products and services that it offers, but it tends to be the go-to place for newer businesses or startups. We officially started supporting GCP a little over a year ago with our first entry into their GCP marketplace. So a solution that deployed a fully-functional and ready-to-use Enterprise mode cluster. We followed up on that with the release and the support of Google storage buckets, and now I'm extremely pleased to announce that with the launch of Vertica 10, we're officially supporting Eon mode architecture in GCP as well. But that's not all, as we're adding additional offerings into the GCP marketplace. With the launch of version 10 we'll be introducing a second listing in the marketplace that allows for the deployment of an Eon mode cluster. It's all being driven by our own management consult. This will allow customers to quickly spin up Eon-based clusters within the GCP space. And if that wasn't enough, I'm also pleased to tell you that very soon after the launch we're going to be offering Vertica by the hour in GCP as well. And while we've done a lot to automate the solutions coming out of the marketplace, we recognize the simple fact that for a lot of you, building your cluster manually is really the only option. So with that in mind, let's talk about the things you need to understand in GCP to get that done. So wag me if you think this slide looks familiar. Well nope, it's not an erroneous duplicate slide from Sumeet's AWS section, it's merely an acknowledgement of all the things you need to consider for running Vertica in the Cloud. In Vertica, the choice of the operational mode will dictate some of the choices you'll need to make in the infrastructure, particularly around storage. Just like on-prem solutions, you'll need to understand the disk and networking capacities to get the most out of your cluster. And one of the most attractive things in GCP is the pricing, as it tends to run a little less than the others. But it does translate into less choices and options within the environment. If nothing else, I want you to take one thing away from this slide, and Sumeet said this earlier. VMs running, about AWS, Sumeet said this about AWS earlier. VMs running in the GCP space run on top of hardware that has hyperthreading enabled. And that a vCPU doesn't equate to a core, but rather a processing thread. This becomes particularly important if you're moving from an on-prem environment into the Cloud. Because a physical Vertica node with 32 cores is not the same thing as a VM with 32 vCPUs. In fact, with 32 vCPUs, you're only getting about 16 cores worth of performance. GCP does offer a handful of VM types, which they categorize by letter, but for us, most of these don't make great choices for Vertica nodes. The M series, however, does offer a good core to memory ratio, especially when you're looking at the high-mem variants. Also keep in mind, performance in I/O, such as network and disk, are partially dependent on the VM size, so customers in GCP space should be focusing on 16 vCPU VMs and above for their Vertica nodes. Disk options in GCP can be broken down into two basic types, persistent disks and local disks, which are ephemeral. Persistent disks come in two forms, standard or SSD. For Vertica in Eon mode, we recommend that customers use persistent SSD disks for the catalog, and either local SSD disks or persistent SSD disks for the depot and the temp space. Couple of things to think about here, though. Persistent disks are provisioned as a single device with a settable size. Local disks are provisioned as multiple disk devices with a fixed size, requiring you to use some kind of software RAIDing to create a single storage device. So while local SSD disks provide much more throughput, you're using CPU resources to maintain that RAID set. So you're giving, it's a little bit of a trade-off. Persistent disks offer redundancy, either within the zone that they exist or within the region, and if you're selecting regional redundancy, the disks are replicated across multiple zones in the region. This does have an effect in the performance to VM, so we don't recommend this. What we do recommend is the zonal redundancy when you're using persistent disks, as it gives you that redundancy level without actually affecting the performance. Remember also, in the Cloud space, all I/O is network I/O, as disks are basically block storage devices. This means that disk actions can and will slow down network traffic. And finally, the storage bucket access in GCP is based on GCP interoperability mode, which means that it's basically compliant with the AWS S3 API. In interoperability mode, access to the bucket is granted by a key pair that GCP refers to as HMAC keys. HMAC keys can be generated for individual users or for service accounts. We will recommend that when you're creating HMAC keys, choose a service account to ensure that the keys are not tied to a single employee. When thinking about storage for Enterprise mode, things change a little bit. We still recommend persistent SSD disks over standard ones. However, the use of local SSD disks for anything other than temp space is highly discouraged. I said it before, local SSD disks are ephemeral, meaning that the data's lost if the machine is turned off or goes down. So not really a place you want to store your data. In GCP, multiple persistent disks placed into a software RAID set does not create more throughput like you can find in other Clouds. The I/O saturation usually hits the VM limit long before it hits the disk limit. In fact, performance of a persistent disk is determined not just by the size of the disk but also by the size of the VM. So a good rule of thumb in GCP is to maximize your I/O throughput for persistent disks, is that the size tends to max out at two terabytes for SSDs and 10 terabytes for standard disks. Network performance in GCP can be thought of in two distinct ways. There's node-to-node traffic, and then there's egress traffic. Node-to-node performance in GCP is really good within the zone, with typical traffic between nodes falling in the 10-15 gigabits per second range. This might vary a little from zone to zone and region to region, but usually it's only limited, they're only limited by the existing traffic where the VMs exist. So kind of a noisy neighbor effect. Egress traffic from a VM, however, is subject to throughput caps, and these are based on the size of the VM. So the speed is set for the number of vCPUs in the VM at two gigabits per second per vCPU, and tops out at 32 gigabits per second. So the larger the VM, the more vCPUs you get, the larger the cap. So some things to consider in the NAV ring space for your Vertica cluster, pick a region that's physically close to you, even if you're connecting to the GCP network from a corporate LAN as opposed to the internet. The further the packets have to travel, the longer it's going to take. Also, GCP, like most Clouds, doesn't support UDP broadcast traffic on their virtual NAV ring, so you do have to use the point-to-point flag for spread when you're creating your cluster. And since the network cap on VMs is set at 32 gigabits per second per VM, maximize your network egress throughput and don't use VMs that are smaller than 16 vCPUs for your Vertica nodes. And that gets us to the one question I get asked the most often. How do I get my data into and out of the Cloud? Well, GCP offers many different methods to support different speeds and different price points for data ingress and egress. There's the obvious one, right, across the internet either directly to the VMs or into the storage bucket. Or you can, you know, light up a VPN tunnel to encrypt all that traffic. But additionally, GCP offers direct network interconnect from your corporate network. These get provided either by Google or by a partner, and they vary in speed. They also offer things called direct or carrier peering, which is connecting the edges of the networks between your network and GCP, and you can use a CDN interconnect, which creates, I believe, an on-demand connection from the GCP network, your network to the GCP network provided by a large host of CDN service providers. So GCP offers a lot of ways to move your data around in and out of the GCP Cloud. It's really a matter of what price point works for you, and what technology your corporation is looking to use. So we've talked about AWS, we've talked about GCP, it really only leaves one more Cloud. So last, and by far not the least, there's the Microsoft Azure environment. Holding on strong to the number two place in the major Cloud providers, Azure offers a very robust Cloud offering that's attractive to customers that already consume services from Microsoft. But what you need to keep in mind is that the underlying foundation of their Cloud is based on the Microsoft Windows products. And this makes their Cloud offering a little bit different in the services and offerings that they have. The good news here, though, is that Microsoft has done a very good job of getting their virtualization drivers baked into the modern kernels of most Linux operating systems, making running Linux-based VMs in Azure fairly seamless. So here's the slide again, but now you're going to notice some slight differences. First off, in Azure we only support Enterprise mode. This is because the Azure storage product is very different from Google Cloud storage and S3 on AWS. So while we're working on getting this supported, and we're starting to focus on this, we're just not there yet. This means that since we're only supporting Enterprise mode in Azure, getting the local disk performance right is one of the keys to success of running Vertica here, with the other major key being making sure that you're getting the appropriate networking speeds. Overall, Azure's a really good platform for Vertica, and its performance and pricing are very much on par with AWS. But keep in mind that the newer versions of the Linux operating systems like RHEL and CentOS run much better here than the older versions. Okay, so first things first again, just like GCP, in Azure VMs are running on top of hardware that has hyperthreading enabled. And because of the way Hyper-V, Azure's virtualization engine works, you can actually see this, right? So if you look down into the CPU information of the VM, you'll actually see how it groups the vCPUs by core and by thread. Azure offers a lot of VM types, and is adding new ones all the time. But for us, we see three VM types that make the most sense for Vertica. For customers that are looking to run production workloads in Azure, the Es_v3 and the Ls_v2 series are the two main recommendations. While they differ slightly in the CPU to memory ratio and the I/O throughput, the Es_v3 series is probably the best recommendation for a generalized Vertica node, with the Ls_v2 series being recommended for workloads with higher I/O requirements. If you're just looking to deploy a sandbox environment, the Ds_v3 series is a very suitable choice that really can reduce your overall Cloud spend. VM storage in Azure is provided by a grouping of four different types of disks, all offering different levels of performance. Introduced at the end of last year, the Ultra Disk option is the highest-performing disk type for VMs in Azure. It was designed for database workloads where high throughput and low latency is very desirable. However, the Ultra Disk option is not available in all regions yet, although that's been changing slowly since their launch. The Premium SSD option, which has been around for a while and is widely available, can also offer really nice performance, especially higher capacities. And just like other Cloud providers, the I/O throughput you get on VMs is dictated not only by the size of the disk, but also by the size of the VM and its type. So a good rule of thumb here, VM types with an S will have a much better throughput rate than ones that don't, meaning, and the larger VMs will have, you know, higher I/O throughput than the smaller ones. You can expand the VM disk throughput by using multiple disks in Azure and using a software RAID. This overcomes limitations of single disk performance, but keep in mind, you're now using CPU cycles to maintain that raid, so it is a bit of a trade-off. The other nice thing in Azure is that all their managed disks are encrypted by default on the server side, so there's really nothing you need to do here to enable that. And of course I mentioned this earlier. There is no native access to Azure storage yet, but it is something we're working on. We have seen folks using third-party applications like MinIO to access Azure's storage as an S3 bucket. So it might be something you want to keep in mind and maybe even test out for yourself. Networking in Azure comes in two different flavors, standard and accelerated. In standard networking, the entire network stack is abstracted and virtualized. So this works really well, however, there are performance limitations. Standard networking tends to top out around four gigabits per second. Accelerated networking in Azure is based on single root I/O virtualization of the Mellanox adapter. This is basically the VM talking directly to the physical network card in the host hardware, and it can produce network speeds up to 20 gigabits per second, so much, much faster. Keep in mind, though, that not all VM types and operating systems actually support accelerated networking, and you know, just like disk throughput, network throughput is based on VM type and size. So what do you need to think about for networking in the Azure space? Again, stay close to home. Pick regions that are geographically close to your location. Yes, the backbones between the regions are very, very fast, but the more hops your packets have to make, the longer it takes. Azure offers two types of groupings of their VMs, availability sets and availability zones. Availability zones offer good redundancy across multiple zones, but this actually increases the node-to-node latency, so we recommend you avoid this. Availability sets, on the other hand, keep all your VMs grouped together within a single zone, but makes sure that no two VMs are running on the same host hardware, for redundancy. And just like the other Clouds, UDP broadcast is not supported. So you have to use the point-to-point flag when you're creating your database to ensure that the spread works properly. Spread time out, okay, this is a good one. So recently, Microsoft has started monthly rolling updates of their environment. What this looks like is VMs running on top of hardware that's receiving an update can be paused. And this becomes problematic when the pausing of the VM exceeds eight seconds, as the unpaused members of the cluster now think the paused VM is down. So consider adjusting the spread time out for your clusters in Azure to 30 seconds, and this will help avoid a little of that. If you're deploying a large cluster in Azure, more than 20 nodes, use large closer mode, as point-to-point for spread doesn't really scale well with a lot of Vertica nodes. And finally, you know, pick VM types and operating systems that support accelerated networking. The difference in the node-to-node speeds can be very dramatic. So how do we move data around in Azure, right? So Microsoft views data egress a little differently than other Clouds, as it classifies any data being transmitted by a VM as egress. However, it only bills for data egress that actually leaves the Azure environment. Egress speed limits in Azure are based entirely on the VM type and size, and then they're limited by your connection to them. While not offering as many pathways to access their Cloud as GCP, Azure does offer a direct network-to-network connection called ExpressRoute. Offered by a large group of third-party processors, partners, the ExpressRoute offers multiple tiers of performance that are based on a flat charge for inbound data and a metered charge for outbound data. And of course you can still access these via the internet, and securely through a VPN gateway. So on behalf of Jeff, Sumeet, and myself, I'd like to thank you for listening to our presentation today, and we're now ready for Q&A.

>>Live from San Diego, California at the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back. This is the cubes coverage of CubeCon cloud date of con 2019 in San Diego, 12,000 in attendance. I'm just two minute and my cohost is John trier. And welcome back to the program. A multi-time cube alumni. You're on Aviv, who is the CTO and cofounder of a Gwoza. We've had quite a lot of, you know, founders, CTOs, you know, their big brains at this show, your own. So you know, let, let, let's start, you know, there's, there's really a gathering, uh, there's a lot of effort building out, you know, a very complicated ecosystem. Give us first, kind of your overall impressions of the show in this ecosystem. Yeah, so we're very early on on Desecco system. We were one of the first in the first batch of CNCF members when there were a few dozens of those. Not like a thousand of those. Uh, so I've been, I've been to all those shows. >>Uh, we're part of the CNCF committees for different things. And any initiating, I think this has become much more mainstream. I told you before, it's sort of the new van world. You know, I lot a lot more, uh, all day infrastructure vendors along with middleware and application vendor are coming here. All right, so, so one of the things we like having you on the program you're on is you don't pull any punches. So we've seen certain waves of technology come with big promise and fall short, you know, big data was going to allow us to leverage everything and you know, large percentage of, uh, solutions, you know, had to stop or be pulled back. Um, give us, what's the cautionary tale that we should learn and make sure that we don't repeat, you know, so I've been a CTO for many years in different companies and, and what everyone used to say about it, I'm always right. >>I'm only one year off usually. I'm usually a little more optimistic. So, you know, we've been talking about Cloudera and Hadoop world sort of going down and Kubernetes and cloud services, essentially replacing them. We were talking about it four years ago and what do you see that's actually happening? You know, with the collapse of my par and whore, then we're going to Cloudera things are going down, customer now Denon guys, we need equivalent solution for Kubernetes. We're not going to maintain two clusters. So I think in general we've been, uh, picking on many of those friends. We've, we've invented serverless before it was even called serverless with, with nuclear and now we're expanding it further and now we see the new emerging trends really around machine learning and AI. That's sort of the big thing. I'm surprised, you know, that's our space where essentially you're doing a data science platform as a service fully automated around serverless constructs so people can, can develop things really, really quickly. >>And what I see that, you know, third of the people I talk to are, have some relations to machine learning and AI. Yeah. Maybe explain that for our audience a little bit. Because when, you know, Kubernetes first started very much an infrastructure discussion, but the last year or two, uh, very much application specific, we hear many people talking about those data use cases, AI and ML early days. But you know how, how does that fit into the overall? It's simple. You know there, if you're moving to the cloud are two workloads. There is lift and shift workloads and there are new workloads. Okay, lift and ship. Why? Why bother moving them to Kubernetes? Okay, so you end up with new workloads. Everyone is trying to be cloud native server, elastic services and all that. Everyone has to feed data and machine learning into those new applications. This is why you see those trends that talk about old data integration, various frameworks and all that in that space. >>So I don't think it's by coincidence. I think it's, that's because new applications incorporate the intelligence. That's why you hear a lot of the talk about those things. What I loved about the architecture, what you just said is like people don't want to run into another cluster. I don't want to run two versions of Kubernetes, you know, if I'm moving there you, because you, but you're still built on that, that kind of infrastructure framework and, and knowledge of, of how to do serverless and how to make more nodes and fewer nodes and persistent storage and all that sort of good stuff and uh, and, and run TensorFlow and run, you know, all these, all these big data apps. But you can, um, you can talk about that just as a, as a, the advantage to your customer cause you could, it seems like you could, you could run it on top of GKE. >>You could run it on prem. I could run my own Coobernetti's you could, you could just give me a, uh, so >> we, we say Kubernetes is not interesting. I didn't know. I don't want anyone to get offended. Okay. But Kubernetes is not the big deal. The big deal is organizations want to be competitive in this sort of digital world. They need to build new applications. Old ones are sort of in sort of a maintenance mode. And the big point is about delivering new application with elastic scaling because your, your customers may, may be a million people behind some sort of, uh, you know, uh, app. Okay. Um, so that's the key thing and Kubernetes is a way to deliver those microservices. But what we figured out, it's still very complicated for people. Okay. Especially in, in the data science work. Uh, he takes him a few weeks to deliver a model on a Jupiter notebook, whatever. >>And then productizing it is about the year. That's something we've seen between six months to a year to productize things that are relatively simple. Okay. And that's because people think about the container, the TensorFlow, the Kuda driver, whatever, how to scale it, how to make it perform, et cetera. So let's, we came up with is traditionally there's a notion of serverless, which is abstraction with very slow performance, very limited set of use cases. We sell services about elastic scaling paper, use, full automation around dev ops and all that. Okay. Why cannot apply to other use cases are really high concurrency, high-speed batch, no distributed training, distributed workload. Because we're coming, if you know my background, you know, been beeping in Mellanox and other high-performance companies. So where I have a, we have a high performance DNA so we don't know how to build things are extremely slow. >>It sort of irritates me. So the point is that how can we apply this notion of abstraction and scaling and all that to variety of workloads and this is essentially what it was. It is a combination of high speed data technology for like, you know, moving data around on between those function and extremely high speed set though functions that work on the different domains of data collection and ingestion, data analytics, you know, machine learning, training and CIN learning model serving. So a customer can come on on our platform and we have testimonials around that, that you know, things that they thought about building on Amazon or even on prem for months and months. They'd built in our platform in few weeks with fewer people because the focus is on building the application. The focus is not about joining your Kubernetes. Now we go to customers, some of them are large banks, et cetera. >>They say, Alrighty, likes Kubernetes, we have our own Kubernetes. So you know what, we don't butter. Initially we, we used to bring our own Kubernetes, but then you know, I don't mind, you know, we do struggle sometimes because our level of expertise in Coobernetti's is way more sophisticated than what they have to say. Okay, we've installed Kubernetes and we come with our software stack. No you didn't, you know, you didn't configure the security, they didn't configure ingress, et cetera. So sometimes it's easier for us to bring, but we don't want him to get into this sort of tension with it. Our focus is to accelerate development on the new application that are intelligent, you know, move applications from, if you think of the traditional data analytics and data science, it's about reporting and what people want to do. And some applications we've announced this week and application around real time cyber collection, it's being used in some different governments is that you can collect a lot of information, SMS, telephony, video, et cetera. >>And in real time you could detect terrorists. Okay. So those application requires high concurrency always on rolling upgrades, things that weren't there in the traditional BI, Oracle, you know, kind of reporting. So you have this wave of putting intelligence into more highly concurrent online application. It requires all the dev ops sort of aspects, but all the data analytics and machine learning aspects to to come to come along. Alright. So speaking of those workloads for, for machine learning, uh, cube flow is a project, uh, moving the, moving in that space along it. Give us the update there. Yeah. So, so there is sort of a rising star in the Kubernetes community around how to automate machine learning workflows. That's cube flow. Uh, I'm personally, I one of the committers and killed flow and what we've done, because it's very complicated cause Google developed the cube cube flow as one of the services on, on a GKE. >>Okay. And the tweaked everything. It works great in GK, even that it's relatively new technology and people want to move around it in a more generic. So one of the things in our platform is a managed cube flow that works natively with all the rest of the solutions. And other thing that we've done is we make it, we made it fully. So instead of queue flow approach is very con, you know, Kubernetes oriented containers, the ammos, all that. Uh, in our flavor of Coupa we can just create function and you just like chain functions and you click and it runs. Just, you've mentioned a couple of times, uh, how does serverless, as you defined it, fit in with, uh, Coobernetti's? Is that working together just functions on top or I'm just trying to make here, >> you'll, you'll hear different things. I think when most people say serverless, they mean sort of front end application things that are served low concurrency, a Terra, you know, uh, when we mean serverless, it's, we have eight different engines that each one is very good in, in different, uh, domain like distributed deep learning, you know, distributed machine learning, et cetera. >>And we know how to fit the thing into any workloads. So for me, uh, we deliver the elastic scaling, the paper use and the ease of use of sort of no dev ops across all the eight workloads that we're addressing. For most people it's like a single Dreek phony. And I think really that the future is, is moving to that. And if you think about serverless, there's another aspect here which is very important for machine learning and Israel's ability. I'm not going to develop any algorithm in the world. Okay. There are a bunch of companies or users or developers that can develop an algorithm and I can just consume it. So the future in data science but not just data science is essentially to have like marketplaces of algorithms premade or analytic tools or maybe even vendors licensing their technology through sort of prepackaged solution. >>So we're a great believer of forget about the infrastructure, focus on the business components and Daisy chain them in to a pipeline like UFO pipeline and run them. And that will allow you most reusability that, you know, lowest amount of cost, best performance, et cetera. That's great. I just want to double click on the serverless idea one more time, but, so you're, you're developing, it's an architectural pattern, uh, and you're developing these concepts yourself. You're not actually, sometimes the concept gets confused with the implementations of other people's serverless frameworks or things like that. Is that, is that correct? I think there are confusion. I'm getting asked a lot of times. How do you compare your technology compared to let's say a? You've heard the term gay native is just a technology or open FAS or, yeah. Hold on. Pfizer's a CGIs or Alito. An open community is very nice for hobbies, but if you're an enterprise and it's security, Eldep integration, authentication for anything, you need DUIs, you need CLI, you need all of those things. >>So Amazon provides that with Lambda. Can you compare Lambda to K native? No. Okay. Native is, I need to go from get and build and all that. Serverless is about taking a function and clicking and deploying. It's not about building. And the problem is that this conference is about people, it people in crowd for people who like to build. So they, they don't like to get something that work. They want to get the build the Lego building blocks so they can play. So in our view, serverless is not open FAS or K native. Okay. It's something that you click and it works and have all the enterprise set of features. We've extended it to different levels of magnitude of performance. I'll give you an anecdote. I did a comparison for our customer asking me the same question, not about Canadian, but this time Lambda. How do you guys compare with London? >>Know Nokia is extremely high performance. You know we are doing up to 400,000 events on a single process and the customer said, you know what, I have a use case. I need like 5,000 events per second. How do you guys compare a total across all my functions? How do you compare against Lambda? We went into, you know the price calculator, 5,000 events per second on Lambda. That's $50,000 okay. $50,000 we do about, let's say even in simple function, 60,000 per process, $500 VM on Amazon, $500 VM on Amazon with our technology stick, 2000 transactions per second, 5,000 events per second on Lambda. That's 50,000. Okay. 100 times more expensive. So it depends on the design point. We designed our solution to be extremely efficient, high concurrency. If you just need something to do a web hook, use Lambda, you know, if you are trying to build a high concurrency application efficient, you know, an enterprise application on it, on a serverless architecture construct come to us. >>Yeah. So, so just a, I'll pause at this for you because a, it reminds me what you were talking about about the builders here in the early days of VMware to get it to work the way I wanted to. People need to participate and build it and there's the Ikea effect. If I actually helped build it a little bit, I like it more to get to the vast majority, uh, to uh, adopt those things. It needs to become simplified and I can't have, you know, all the applications move over to this environment if I have to constantly tweak that. Everything. So that's the trend we've been really seeing this year is some of that simplification needs to get there. There's focus on, you know, the operators, the day two operations, the applications so that anybody can get there without having to build themselves. So we know there's still work to be done. >>Um, but if we've crossed the chasm and we want the majority to now adopt this, it can't be that I have to customize it. It needs to be more turnkey. Yeah. And I think it's a friendly and attitude between what you'll see in Amazon reinvent in couple of weeks. And then what you see here, because there is those, the focus of we're building application a what kind of tools and the Jess is gonna just launch today on the, on the floor. Okay. So we can just consume it and build our new application. They're not thinking, how did Andy just, he built his tools. Okay. And I think that's the opposite here is like how can you know Ali's is still working inside underneath dude who cares about his team. You know, you care about having connectivity between two points and and all that. How do you implement it that, you know, let someone else take care of it and then you can apply your few people that you have on solving your business problem, not on infrastructure. >>You know, I just met a guy, came to our booth, we've seen our demo. Pretty impressive how we rise people function and need scales and does everything automatically said we want to build something like you're doing, you know, not really like only 10% of what you just showed me. And we have about six people and for three months where it just like scratching our head. I said, okay, you can use our platform, pay us some software license and now you'll get, you know, 10 times more functionality and your six people can do something more useful. Says right, let's do a POC. So, so that's our intention and I think people are starting to get it because Kubernetes is not easy. Again, people tell me we installed Kubernete is now installed your stack and then they haven't installed like 20% of all the things that you need to stop so well your own have Eve always pleasure to catch up with you. Thanks for the all the updates and I know we'll catch up with you again soon. Sure. All right. For John Troyer, I'm Stu Miniman. We'll be back with more coverage here from CubeCon cloud date of con in San Diego. Thanks for watching the cube.

>> From the Silicon Angle Media Office in Boston, Massachusetts, it's The Cube. Now, here's your host Dave Vellante. >> Hi everybody. Welcome to this Cube Insights powered by ETR. In this breaking analysis we're going to look at recent spending data from the ETR Spending Intentions Survey. We believe tech spending is slowing down. Now, it's not falling off a cliff but it is reverting to pre-2018 spending levels. There's some concern in the bellwethers of specifically financial services and insurance accounts and large telcos. We're also seeing less redundancy. What we mean by that is in 2017 and 2018 you had a lot of experimentation going on. You had a lot of digital initiatives that were going into, not really production, but sort of proof of concept. And as a result you were seeing spending on both legacy infrastructure and emerging technologies. What we're seeing now is more replacements. In other words people saying, "Okay, we're now going into production. We've tried that. We're not going to go with A, we're going to double down on B." And we're seeing less experimentation with the emerging technology. So in other words people are pulling out, actually some of the legacy technologies. And they're not just spraying and praying across the entire emerging technology sector. So, as a result, spending is more focused. As they say, it's not a disaster, but it's definitely some cause for concern. So, what I'd like to do, Alex if you bring up the first slide. I want to give you some takeaways from the ETR, the Enterprise Technology Research Q4 Pulse Check Survey. ETR has a data platform of 4,500 practitioners that it surveys regularly. And the most recent spending intention survey will actually be made public on October 16th at the ETR Webcast. ETR is in its quiet period right now, but they've given me a little glimpse and allowed me to share with you, our Cube audience, some of the findings. So as I say, you know, overall tech spending is clearly slowing, but it's still healthy. There's a uniform slowdown, really, across the board. In virtually all sectors with very few exceptions, and I'll highlight some of the companies that are actually quite strong. Telco, large financial services, insurance. That's rippling through to AMIA, which is, as I've said, is over-weighted in banking. The Global 2000 is looking softer. And also the global public and private companies. GPP is what ETR calls it. They say this is one of the best indicators of spending intentions and is a harbinger for future growth or deceleration. So it's the largest public companies and the largest private companies. Think Mars, Deloitte, Cargo, Coke Industries. Big giant, private companies. We're also seeing a number of changes in responses from we're going to increase to more flat-ish. So, again, it's not a disaster. It's not falling off the cliff. And there are some clear winners and losers. So adoptions are really reverting back to 2018 levels. As I said, replacements are arising. You know, digital transformation is moving from test everything to okay, let's go, let's focus now and double-down on those technologies that we really think are winners. So this is hitting both legacy companies and the disrupters. One of the other key takeaways out of the ETR Survey is that Microsoft is getting very, very aggressive. It's extending and expanding its TAM further into cloud, into collaboration, into application performance management, into security. We saw the Surface announcement this past week. Microsoft is embracing Android. Windows is not the future of Microsoft. It's all these other markets that they're going after. They're essentially building out an API platform and focusing in on the user experience. And that's paying off because CIOs are clearly more comfortable with Microsoft. Okay, so now I'm going to take you through some themes. I'm going to make some specific vendor comments, particularly in Cloud, software, and infrastructure. And then we'll wrap. So here's some major themes that really we see going on. Investors still want growth. They're punishing misses on earnings and they're rewarding growth companies. And so you can see on this slide that it's really about growth metrics. What you're seeing is companies are focused on total revenue, total revenue growth, annual recurring revenue growth, billings growth. Companies that maybe aren't growing so fast, like Dell, are focused on share gains. Lately we've seen pullbacks in the software companies and their stock prices really due to higher valuations. So, there's some caution there. There's actually a somewhat surprising focus given the caution and all the discussion about, you know, slowing economy. There's some surprising lack of focus on key performance indicators like cash flow. A few years ago, Splunk actually stopped giving, for example, cash flow targets. You don't see as much focus on market capitalization or shareholders returns. You do see that from Oracle. You see that last week from the Dell Financial Analyst Meeting. I talked about that. But it's selective. You know these are the type of metrics that Oracle, Dell, VMware, IBM, HPE, you know generally HP Inc. as well will focus on. Another thing we see is the Global M&A across all industries is back to 2016 levels. It basically was down 16% in Q3. However, well and that's by the way due to trade wars and other uncertainties and other economic slowdowns and Brexit. But tech M&A has actually been pretty robust this year. I mean, you know take a look at some examples. I'll just name a few. Google with Looker, big acquisitions. Sales Force, huge acquisition. A $15 billion acquisition of Tableau. It also spent over a billion dollars on Click software. Facebook with CTRL-labs. NVIDIA, $7 billion acquisition of Mellanox. VMware just plunked down billion dollars for Carbon Black and its own, you know, sort of pivotal within the family. Splunk with a billion dollar plus acquisition of SignalFx. HP over a billion dollars with Cray. Amazon's been active. Uber's been active. Even nontraditional enterprise tech companies like McDonald's trying to automate some of the drive-through technology. Mastercard with Nets. And of course the stalwart M&A companies Apple, Intel, Microsoft have been pretty active as well as many others. You know but generally I think what's happening is valuations are high and companies are looking for exits. They've got some cool tech so they're putting it out there. That you know, hey now's the time to buy. They want to get out. That maybe IPO is not the best option. Maybe they don't feel like they've got, you know, a long-term, you know, plan that is going to really maximize shareholder value so they're, you know, putting forth themselves for M&A today. And so that's been pretty robust. And I would expect that's going to continue for a little bit here as there are, again, some good technology companies out there. Okay, now let's get into, Alex if you pull up the next slide of the Company Outlook. I want to start with Cloud. Cloud, as they say here, continues it's steady march. I'm going to focus on the Big 3. Microsoft, AWS, and Google. In the ETR Spending Surveys they're all very clearly strong. Microsoft is very strong. As I said it's expanding it's total available market. It's into collaboration now so it's going after Slack, Box, Dropbox, Atlassian. It's announced application performance management capabilities, so it's kind of going after new relic there. New SIM and security products. So IBM, Splunk, Elastic are some targets there. Microsoft is one of the companies that's gaining share overall. Let me talk about AWS. Microsoft is growing faster in Cloud than AWS, but AWS is much, much larger. And AWS's growth continues. So it's not as strong as 2018 but it's stronger, in fact, much stronger than its peers overall in the marketplace. AWS appears to be very well positioned according to the ETR Surveys in database and AI it continues to gain momentum there. The only sort of weak spot is the ECS, the container orchestration area. And that looks a little soft likely due to Kubernetes. Drop down to Google. Now Google, you know, there's some strength in Google's business but it's way behind in terms of market share, as you all know, Microsoft and AWS. You know, its AI and machine learning gains have stalled relative to Microsoft and AWS which continue to grow. Google's strength and strong suit has always been analytics. The ETR data shows that its holdings serve there. But there's deceleration in data warehousing, and even surprisingly in containers given, you know, its strength in contributing to the Kubernetes project. But the ETR 3 Year Outlook, when they do longer term outlook surveys, shows GCP, Google's Cloud platform, gaining. But there's really not a lot of evidence in the existing data, in the near-term data to show that. But the big three, you know, Cloud players, you know, continue to solidify their position. Particularly AWS and Microsoft. Now let's turn our attention to enterprise software. Just going to name a few. ETR will have an extensive at their webcast. We'll have an extensive review of these vendors, and I'll pick up on that. But I just want to pick out a few here. Some of the enterprise software winners. Workday continues to be very, very strong. Especially in healthcare and pharmaceutical. Salesforce, we're seeing a slight deceleration but it's pretty steady. Very strong in Fortune 100. And Einstein, its AI offering appears to be gaining as well. Some of the acquisitions Mulesoft and Tableu are also quite strong. Demandware is another acquisition that's also strong. The other one that's not so strong, ExactTarget is somewhat weakening. So Salesforce is a little bit mixed, but, you know, continues to be pretty steady. Splunk looks strong. Despite some anecdotal comments that point to pricing issues, and I know Splunk's been working on, you know, tweaking its pricing model. And maybe even some competition. There's no indication in the ETR data yet that Splunk's, you know, momentum is attenuating. Security as category generally is very, very strong. And it's lifting all ships. Splunk's analytics business is showing strength is particularly in healthcare and pharmaceuticals, as well as financial services. I like the healthcare and pharmaceuticals exposure because, you know, in a recession healthcare will, you know, continue to do pretty well. Financial services in general is down, so there's maybe some exposure there. UiPath, I did a segment on RPA a couple weeks ago. UiPath continues its rapid share expansion. The latest ETR Survey data shows that that momentum is continuing. And UiPath is distancing itself in the spending surveys from its broader competition as well. Another company we've been following and I did a segment on the analytics and enterprise data warehousing sector a couple weeks ago is Snowflake. Snowflake continues to expand its share. Its slightly slower than its previous highs, which were off the chart. We shared with you its Net Score. Snowflake and UiPath have some of the highest Net Scores in the ETR Survey data of 80+%. Net Score remembers. You take the we're adding the platform, we're spending more and you subtract we're leaving the platform or spending less and that gives you the Net Score. Snowflake and UiPath are two of the highest. So slightly slower than previous ties, but still very very strong. Especially in larger companies. So that's just some highlights in the software sector. The last sector I want to focus on is enterprise infrastructure. So Alex if you'd bring that up. I did a segment at the end of Q2, post Q2 looking at earning statements and also some ETR data on the storage spending segment. So I'll start with Pure Storage. They continue to have elevative spending intentions. Especially in that giant public and private, that leading indicator. There are some storage market headwinds. The storage market generally is still absorbing that all flash injection. I've talked about this before. There's still some competition from Cloud. When Pure came out with its earnings last quarter, the stock dropped. But then when everybody else announced, you know, negative growth or, in Dell's case, Dell's the leader, they were flat. Pure Storage bounced back because on a relative basis they're doing very well. The other indication is Pure storage is very strong in net app accounts. Net apps mix, they don't call them out here but we'll do some further analysis down the road of net apps. So I would expect Pure to continue to gain share and relative to the others in that space. But there are some headwinds overall in the market. VMware, let's talk about VMware. VMware's spending profile, according to ETR, looks like 2018. It's still very strong in Fortune 1000, or 100 rather, but weaker in Fortune 500 and the GPP, the global public and private companies. That's a bit of a concern because GPP is one of the leading indicators. VMware on Cloud on AWS looks very strong, so that continues. That's a strategic area for them. Pivotal looks weak. Carbon Black is not pacing with CrowdStrike. So clearly VMware has some work to do with some of its recent acquisitions. It hasn't completed them yet. But just like the AirWatch acquisition, where AirWatch wasn't the leader in that space, really Citrix was the leader. VMware brought that in, cleaned it up, really got focused. So that's what they're going to have to do with Carbon Black and Security, which is going to be a tougher road to hoe I would say than end user computing and Pivotal. So we'll see how that goes. Let's talk about Dell, Dell EMC, Dell Technologies. The client side of the business is holding strong. As I've said many times server and storage are decelerating. We're seeing market headwinds. People are spending less on server and storage relative to some of the overall initiatives. And so, that's got to bounce back at some point. People are going to still need compute, they're still going to need storage, as I say. Both are suffering from, you know, the Cloud overhang. As well, storage there was such a huge injection of flash it gave so much headroom in the marketplace that it somewhat tempered storage demand overall. Customers said, "Hey, I'm good for a while. Cause now I have performance headroom." Whereas before people would buy spinning discs, they buy the overprovision just to get more capacity. So, you know, that was kind of a funky value proposition. The other thing is VxRail is not as robust as previous years and that's something that Dell EMC talks about as, you know, one of the market share leaders. But it's showing a little bit of softness. So we'll keep an eye on that. Let's talk about Cisco. Networking spend is below a year ago. The overall networking market has been, you know, somewhat decelerating. Security is a bright spot for Cisco. Their security business has grown in double digits for the last couple of quarters. They've got work to do in multi-Cloud. Some bright spots Meraki and Duo are both showing strength. HP, talk about HPE it's mixed. Server and storage markets are soft, as I've said. But HPE remains strong in Fortune 500 and that critical GPP leading indicator. You know Nimble is growing, but maybe not as fast as it used to be and Simplivity is really not as strong as last year. So we'd like to see a little bit of an improvement there. On the bright side, Aruba is showing momentum. Particularly in Fortune 500. I'll make some comments about IBM, even though it's really, you know, this IBM enterprise infrastructure. It's really services, software, and yes some infrastructure. The Red Hat acquisition puts it firmly in infrastructure. But IBM is also mixed. It's bouncing back. IBM Classic, the core IBM is bouncing back in Fortune 100 and Fortune 500 and in that critical GPP indicator. It's showing strength, IBM, in Cloud and it's also showing strength in services. Which is over half of its business. So that's real positive. Its analytics and EDW software business are a little bit soft right now. So that's a bit of a concern that we're watching. The other concern we have is Red Hat has been significantly since the announcement of the merger and acquisition. Now what we don't know, is IBM able to inject Red Hat into its large service and outsourcing business? That might be hidden in some of the spending intention surveys. So we're going to have to look at income statement. And the public statements post earnings season to really dig into that. But we'll keep an eye on that. The last comment is Cloudera. Cloudera once was the high-flying darling. They are hitting all-time lows. They made the acquisition of Hortonworks, which created some consolidation. Our hope was that would allow them to focus and pick up. CEO left. Cloudera, again, hitting all-time lows. In particular, AWS and Snowflake are hurting Cloudera's business. They're particularly strong in Cloudera's shops. Okay, so let me wrap. Let's give some final thoughts. So buyers are planning for a slowdown in tech spending. That is clear, but the sky is not falling. Look we're in the tenth year of a major tech investment cycle, so slowdown, in my opinion, is healthy. Digital initiatives are really moving into higher gear. And that's causing some replacement on legacy technologies and some focus on bets. So we're not just going to bet on every new, emerging technology, were going to focus on those that we believe are going to drive business value. So we're moving from a try-everything mode to a more focused management style. At least for a period of time. We're going to absorb the spend, in my view, of the last two years and then double-down on the winners. So not withstanding the external factors, the trade wars, Brexit, other geopolitical concerns, I would expect that we're going to have a period of absorption. Obviously it's October, so the Stock Market is always nervous in October. You know, we'll see if we get Santa Claus rally going into the end of the year. But we'll keep an eye on that. This is Dave Vellante for Cube Insights powered by ETR. Thank you for watching this breaking analysis. We'll see you next time. (upbeat tech music)

live from San Francisco it's the cube covering IBM thing 2019 brought to you by IBM good afternoon and welcome back to the cubes continuing coverage of IBM think 2019 I'm Lisa Martin and sake San Francisco with Dave Volante hey Dave hey Lisa we're staying dry though we are the most part exactly there are there looks like the Moscone notices maybe having a few little areas of improvement I think just running water through the pipes as we would say is a little trial that's true so we're welcoming back to the queue but guess that hasn't been with us for a while Paul is a couple of vice president of Big Data at cognitive systems at IBM Paul welcome back oh thank you and thanks for get my name right that was good so you are an accomplished author I talked to you on Twitter 19 books ever 350 articles I know you do a lot of speaking you've been IBM a long time this events massive great 30,000 people or so yesterday was standing room only in fact they shut the doors to Judy's keynote because there were so many people I'm curious some of the announcements that came out with cognitive yesterday what are some of what are some of the things that you saw yesterday that kind of piqued your interest well the Watson the Watson anywhere was I person have said that's a long time coming and they come on we got to have Watson on any cloud right not just the IBM cloud so that was I thought a big deal and then there were a bunch of announcements around enabling hybrid I think there were 20 plus services so you know it's not kind of vogue you know we're in this multi cloud world I need a way to get to hybrid so those are two standouts so your group's been busy basically that's right that's right I mean you hit it right Watson anywhere cloud everywhere so it's about AI in that drink I have to tell you that when I hear all the announcements there's tons of them right one of my favorite ones probably doesn't go as notice and it was Watson machine learning accelerator and that is really about looking at the journey for AI and clients over the next course of the years on that journey see most clients are just getting started there's some clients in the middle phase and there's some clients now that are hitting what I call the enterprise worthiness stage of AI right and so when we look at our announcements they're actually taking you from just getting started all the way to enterprise hardened explainable and algorithms and how to manage that because we're gonna go from this world where AI is sitting in the corner offices for the privileged few we have to democratize for the many but today it's like here's a little data science team they have their own server here's our programmer on their laptop you know and hanging out working there we want to bring this all together for enterprise so things like workload management which is what watching machine learning accelerated really does is how do I get everything together and working in a concurrent environment as organizations go from having 10 20 algorithms to trying to deploy thousands of them that's all they'll define themselves well you know when you get a bunch of data scientists in the room and you talk about citizens data scientists they kind of look at he like me there's no such thing but the fact is that if you can operationalize a you can open it up to a lot more people you know as a line of business person you'd much rather not have to go to a data scientist every time you want to do something with a because otherwise you're just kind of repeating the old decision-support world cells right what do you guys do when to operationalize yeah so it's a great question we're trying to taking the friction and so a lot of people will come and say oh gee p you acceleration so yeah it's about training stuff faster it's an open architecture and power and so you've seen the work with NVIDIA and that's unique to what Nvidia can do with with our cognitive systems is to accelerate the CPU GPU communications but there's a broader pipeline when you go to as the say I journey and we want to flatten that curve so one is how do I get up and running I don't know if you remember open source changes all the time so we're Enterprise hardening back testing getting you ready for here's the platform to deploy built on open source and where 80% of a data scientist time is spent right now is in what I call data preparation wrangling data labeling data gets stuff together now none of that is data science like none of that is data science at all and that's where the time and once I get the data ready I train the model ok so you've heard a lot about that and then the next thing I do is have to optimize the model so I think about where data scientist should be spending their time and that's on stage for we call that exploring the hyper parameter space another thing that Watson machine learning accelerator is all about how do we make the model perform now for data science geeks perform means how well is it classifying or how accurate is Hardware people often think performance means how fast you go right and then finally go to inference so we're looking at all five of those stages and one of them the biggest one is that 80% sink time we're trying to drop that to 20% and open it up for the rest of the enterprise so how do you democratize AI you mentioned that a lot of enterprises are really at the beginning of that journey yeah but when you're out talking with customers is there some sort of paralysis there where they're like Paul where do we start right right I think there's two areas where I see inertia or friction and so one is where do we start so let me say that start with the data you have you don't have to step up to the plate and hit a homerun you just get started and it's the things the little things you do every day not the big things you do once in a while and we always hear about disruption disruption you hear about uber and airbnb as the disruptors I actually believed they were the disruptors of yesterday I think right now we're in this list shift rift or cliff moment the disruptors of tomorrow will be those at the head of the analytics Renaissance that work with the data they have we know the outcomes we call that supervised learning and that's where you get started and the other piece is how do I get more people to participate talk about the lift shift rift or cliff intersection I saw that you've seen talked about that on social media can you break that down a little bit more and also talk to us about how you're helping customers actually kind of break through that or maybe it's avoid that altogether yeah well I mean you want to take two of those four and not take the other two right and I think that we do this lift if cliff moment in two ways one is as individuals so the people in the audience to people watching here all of us as practitioners we have got to get our skills moving forward I always say skill years are like dog years right like they age instantly and so you should be waking up every day like a newbie in this world and learning every single day and if you do that you'll have nothing to worry about as an individual and as organizations you had better put analytics at the forefront that means from the boardroom that means we encourage the culture of analytics everywhere and so those that's what I mean by lift chef rift or cliff moment so what comes back to sort of opening it up for average everyday line of business people you got a you got a demo yeah I'm gonna see what can I show to you all right so you know you were talking about the data scientist and citizen data scientist so I'm gonna propose to you this thing I call the wisdom of the crowd right today data scientists have to build things they're not domain experts imagine if I could invite the many to participate in this storyline and in this story line everyday line of business people could create an application based on an idea or a model and maybe we'd have thousands of them and out of those thousands we might vet I don't know 50 or hundred and out of that we would team up with data science deploy ten or twenty into production and then do the whole thing over again so let me show you how I could create this application here without building a single line of code and I actually use you Dave as an example because I wanted to see how much face time you get on the cube when John is up here with you doing this I get the short end of the stick the data tell the truth right so I had this intuition as a line of business user and I went to explore this so you can see here that we'll have two videos here and on the first video see where I put this here will say host screen time that's actually gonna measure the amount of time that you're on screen and I will be like that yeah and I actually built that in this modern way that democratizes for the many I'll just start it out here and on the bottom I built it the old-fashioned way so you can see we got John in there and they start out pretty good to start right there both recognizing both of them so let me go in pause these now the first thing you should notice is I've got a timer on the bottom I got a timer on the bottom cuz actually I had time to build that my dev ops team kind of put that in there for me so we'll continue this move it over here and let these things run now look at the accuracy of these models do you notice on the top you guys are both identified increasing this green counter and on the bottom I can't see you so in computer vision is very interesting if I wanted to teach a computer to tell me what the number eight was I could show it a picture of an eight but no more when I moved it sideways it would have no idea what it was I need to train it with lots and lots of data and so the bottom is the way the data scientists work so what did I have to do to do that I had to go collect some video had to reformat it had to put it down to a 480 and I had to write some code fire away and you see the code there now in order to get just to MVP so this model clearly doesn't score well Dave turns his head and it doesn't know who it is anymore all I said is your Dave Valente and if you're not then you're John so what do you do if you've got a third person in there all right and this is where we democratize it so this is our power I vision we've been talking a lot about this and I want to kind of invite everybody to take part in this kind of data science Renaissance all you do is you would go and upload some video here and you go capture some frames we could auto capture those frames every five seconds and let's say I wanted to add a new person like Arvin into this list here so I want to go develop and figure out how the algorithm can find out Arvin is now my last demo I showed you that was a linear classifier that wasn't easy here we'll go type in Arvind add Arvind and then I'm just gonna highlight it and box Arvind and now I've started to train the model there's no code at all you just train them all you just said this is Arvind when I see this so I'm leaving the model and then I'd have to go set it off to training and I'll look I'll do one other thing for you here I'll go and say well here's the think logo and maybe I want to track some logo detection that's it that's how I built the model now it's all about how much supervised label data you have so I asked I said who are the disruptors of the future and it's all about the compute power and the workload management power to train this stuff so economy systems is really all about both so we obviously know about the power in the workload management how do I go and actually generate the data so once I train this model I could click auto label it'll actually go through the rest of the video and go and find out from what it saw but here's where things get beautiful and everything I've showed you is someone writing lines of code now replaced with a clicker so I click on mint data we call these morphological operations I want you to notice something we have a hundred nineteen images labeled of Dave and John so as I click here I'm gonna apply these morphological operations Gaussian blurs sharpening blur that all means stuff to data scientists now I have four thousand two hundred and forty nine data points and I will generate that automatically that's all driven by line of business and finally we can come over here and go actually look at the model here's my model this model is actually scoring pretty really well but even if it wasn't scoring well and that's seventy percent this is now when I pass it to the data scientist team to do what their exceptional at the the hyper parameter tuning for the performance score the algorithm and so here I'll just finish this off by I think I had a picture of you I'll just drag it in here and now it's actually going out and scoring it we're scoring at 96% okay accuracy and I can expose this as a rest of API with the click of a button so I just have one thing the way I found out with the AI for you Dave at the end of it from what I can see John is getting about 50% more screen time than you and it's all good actually yeah oh you thought it was worse and if you notice your name here is Dave dapper Volante because we can't help but notice funny we can't even always tell well-dressed you a scientist you're well-dressed and it's pretty accurate but you're not getting the ROI on those outfits that you need for screen time that's what we found with it stuff with my business partner John but that's that's pretty good now you're saying you wrote the code right to identify either John or Dave and and at what point did you bring the data scientist in yeah so I didn't write any code on the top right on the bottom which the model did not perform well when he turns Ivy conceived that's the code we wrote now would take iterations iterations there was no code written there we built the model and then we brought a dev person in to try to build us a timer it was a couple lines of code took him about half an hour and in this case I didn't really bring the data scientist in yet because I'm scoring at 96% but I can easily pass it on into workflow and that's the story it's a pipeline workflow across so I'll pull the data scientists and I need to but 96% accuracy without a data scientist presence pretty good so a more complex use case you know you might not get 96 percent accuracy you might be at 50 percent forty percent more than 70 percent now you bring the data scientists in for the last mile absolutely let's say I was only scoring 50% and you don't think that's impressive I think it's pretty impressive that I did that in a half an hour and now this is engineer from the wisdom of the crowd I'm a line of business user and I'd like to know what kind of screen time you're getting maybe that's not a sporting event and I'd actually like a new business model where I charge Toyota by the second that they show up on the screen that's my idea data scientist never gonna think that I get it started and then they join the Renaissance that's how you democratize AI for the money yeah so maybe you could talk a little bit about how what was the compute power behind this the infrastructure behind this and then maybe we could talk about power and how you're applying that for AI infrastructure yeah that's a great great question so the bottom video actually trained on my laptop it ran for about a day and a half just so you know who's saying it is my laptop on the top of the video we actually leveraged our para AI architecture and ran that through with Watson machine learning accelerator and I gotta tell you the models train in about 30 minutes and in fact we had trained a model on your last show with your last guest in the amount of time it when you finish to when I came on stage 20 loads yeah so I mean that's the that's the accelerated compute and it's not and I hope what you're seeing here this isn't just a hardware component tree story this is a kind of coexistence in an almost synergy of software and hardware together and that's what's needed in the AI era well it's interesting I know when when you guys change the name of the you know power systems group to cognitive systems they had you know and I inferred of course we got a guy running it who used to run the software business so the different software component so it is clearly more than than software what are some of the sort of more interesting use cases that you guys are seeing with with clients specifically in terms of operationalizing this yeah for sure so in use cases of AI is I think it's we're in this world of precision so we're in precision agriculture precision risk or underwriting precision finance precision retail so the use cases are everywhere and it's really taking in all this kind of data in the operationalizing I think that we're helping people on all the levels you think about it I almost see three segments the first segment is we're not really sure what to do this AI and everyone says they're doing AI reminds me of the Hadoop days and the big data Lake and you know all that stuff turned out so how do we get you started so you can get down the path and build kind of MVPs and that's what I just showed you is the MVP the next group of people are the folks that have maybe one or two models deployed and now they're trying to say how do we scale out to hundreds and thousands of models what is the path now to make this bigger because we got it moving here and then the final phase with few people are at are those who are getting the challenge of I'm getting to a thousand algorithms deployed and now how do I get all this stuff running and so that entire path goes like this and our story line goes across that entire path how unique is this in the marketplace I'm interested in your commentary on IBM's competitive advantage is this so you guys have only guys who can do this and and how you know why are you winning in the market how how differentiable is this yeah so I think I'll answer that in two ways one is from the brand in which I participate in a larger company called IBM in terms of the acceleration there's nobody doing what we're doing and the reason is you took this kind of power processor and created the open power project and just like software evolved through open innovation that's what hardware is done so you look at Mellanox and Nvidia so I'll give you an example Dave the NV link exists on Intel and exists on power but they operate in two very different ways and nobody realizes that so envy link accelerates GPU to GPU communications does that an Intel does that on power but because of open power Envy link also allows the GPU to talk to the CPU so GPUs accelerate ai training because there's thousands of cores there right but they still got to talk to the CPU on top of that they don't have much memory so there's an example that's completely unique in the industry to make you train faster I think our workflow model is completely unique the tools that I showed you and around the workload management and then you look at the bigger part of IBM and how I can mix this with API calls to clouds clouds based Watson services or local but on top of that is now it's about how do you build the data that you can trust and how do you look at things like the explained ability of the model with their Watson open scale and that kind of stuff so it's a bigger story and nobody else has that end end story well and it's showing up in the in the in in the results we saw last quarter the your line of business was a bright star you know we're seeing some momentum obviously there's a lot of activity going on in Linux clearly you know cognitive is a big play there so congratulations that's it's exciting to see and of course maybe a lot of people don't realize it when you guys did the work to bring in little-endian compatibility and you know and tire you know software Suites now that it's you know it's not just this sort of niche proprietary platform anymore it's mainstream and so it's starting to show up in the business results so that's great to see yeah when I say democratize for the many I mean for the people for the enterprise and across the entire spectrum so well Paul thank you for confirming my suspicions here that John is my partner John Ferrier is sucking up all the camera time John I'm gonna have to elbow my way in a lot more so appreciate that having the data John's very data-driven so appreciate that yeah to have you on yeah as I see you again all right take deep right there everybody we'll be back with our next guest we're live from IBM think 2019 you're watching the cube

>> From Seattle, Washington, it's theCUBE! Covering KubeCon and CloudNativeCon North America 2018. Brought to you by Red Hat, the Cloud Native Computing Foundation, and it's ecosystem partners. >> Welcome back everyone, it's theCUBE's live coverage, day three of three days of coverage here at KubeCon 2018, and CloudNativeCon put on by the Linux Foundation and CNCF. I'm John Furrier with theCUBE with Stu Miniman, breaking down all the action. Our next guest is Hussein Khazaal, who's the Vice President of Marketing and Partners of Nuage Networks. Thanks for coming on, good to see you! >> Thanks, John, good to see you. >> Love that shirt, automation... >> Yeah. >> That's the theme. >> That is! (chuckles) >> Cloud native, cloud operations, thanks for coming on. So take a minute just to talk about what you guys are doing with the show, what's the key value proposition you guys are part of, what conversations you're having. >> Right so, for Nuage we basically deliver a software-based virtual networking solution. And a lot of our customers appreciate the value it brings because they have multi cloud environments, they have workloads in on-prem. Those are mixed, some VM, some bare metal, some containers, they have workloads in public cloud, and what we enable them with our software is to stitch all that together using an API-driven networking model that has policy applied to the workload, and you have that mixed workload environment with network policy and security built into that platform. And that's kind of where we help not really break what Kubernetes brings to developers, but maintain that, giving the IT and infrastructure folks the ability to have visibility control and maintain that. >> We were just talking with a partner from Google, we always talk to the same companies, so some of the senior people at AWS, and all the clouds. Obviously cloud operations is what everyone wants, that's the preferred environment, whether you're on-premises or in the cloud, Edge is now on the horizon. Storage, networking and compute is still the core, it's just a little bit different. But there's new jobs that are emerging around Kubernetes, you see the job board, but it's also revitalizing older roles, the network guy, the storage guy, the server guy, traditional IT enterprises are seeing those roles transform. So I got to ask you, as you guys are in the middle of all the networking side, how do see that person, that role, that piece of the puzzle in an IT enterprise change with Kubernetes? >> Absolutely, I mean, the one thing that we had some of our customers do is that these roles are no longer defined by a specific, you have to have these mixed skills, you have to understand what the developer needs as an infrastructure person, and the developer needs what kind of tools that they need to implement so you can do your job, and that's why Kubernetes, and when you're talking about networking and security, you have to understand Linux, you have to understand programming, to be able to give the developers the tools that they need to develop and understand the requirements and then by the same token, they need to make sure that from an intercom perspective, you need to understand, you still need the visibility, you still need control, right? And that balance can only be achieved if you kind of do the exchange roles, right? You get to work with the developers, and then the developers need to look at infrastructure and that's kind of where you stick at Kubernetes, and with what Red Hat is doing with OpenShift, and a lot of the vendors in terms of integrating with CNI, to be able to plug in and tap in and be able to deliver that security and that relief. >> I get what you're saying. I think you've got a great thread there that I want to pull on a little bit. So, I think back at networking over the last few decades, we used to call it multi-vendor, now we call it multi-cloud, we've been talking about automation forever, but it's different now. So, I think that thread you were going on is part of that answer, but explain why now, multi cloud and automation, what's that's real about that compared to what we were talking about the dominant, hardware-led environment that we lived in for decades? >> Absolutely, I mean just you look at how people develop, look at containers, the lifetime of a container is very short compared to like a monolithic application, things that are more dynamic. Some enterprises need to scale up operations, and then that's where they kind of... So early on it was more like a developer testing things in their lab and when you go into production and the rate and the scale at which you operate, dictates that, you know, look, I need to work in public cloud, I need to work with bare metal, and then that, the amount of the infrastructure guys meet that demand otherwise those enterprises are not going to be able to serve their end customers. And that's why they're kind of working with us, and even the community's coming together to address these, and we're looking with-- for performance with the vendors and then even for networking and that's what's driving that. >> Yeah, I want to get your reaction, I was talking to somebody here at the show and they said "Kubernetes is a reset for SDN." >> Yep, it is! I mean the thing is, Kubernetes as it is is perfect, there's no reason to reinvent the wheel, right? There's a lot of adaption from developers' infrastructure. What we're trying to do is build around it, you'll see orchestration on top, you'll see networking, this is such a good thing that everybody is, and you can see by the level of attendance, the level of interest, and engagement, now what we're trying to do is like grow the operation. What are the problems that are left for an enterprise to solve? And that's the multi-cloud piece, right? How do you do policy, network and security policy in that hybrid environment, right? For example, you look at a retailer, they have users using mobile apps, they have remote stores, they have data centers, they have public cloud, and then they're using containers (mumbles) how do you stitch all that together? And that's for us, the challenge that we're addressing. >> And Kubernetes gives you a lot of policy knobs, how are you guys seeing that opportunity? 'Cause that's where people see that kind of piece. >> The three letters, API, right? This API makes integration such an easy thing to do. And then we have obviously, using a CNI plug-in from a (mumbles) perspective, to be able to work in that eco-system and deliver what we do. We have, obviously you guys know that in OpenStack, they're running Kubernetes inside OpenStack and then you have people running Kubernetes on bare metal, right? But it's still Kubernetes and that's how we're able to serve our customers to kind of stitch between between those different stories. >> Alright, Hussein, let's talk about security. So, you know, when containers first came out it was all this argument of how do I architect it? Do I have to shove the thing in a VM, or now is it a micro VM? How do I make sure I ensure security? What's working well? What do we still have a lot of work to do in the security space? >> I think if you look at the three areas: visibility, protection and then the third one is dynamic further response, right? So you can't protect what you can't see and visibility is kind of the first thing that we as networking, because we move packets around, can deliver to the enterprise. The second one is isolation, is that everything you have in a pod is contained. Now between pods, if you're running in public cloud, as a bank, you may want to encrypt that traffic, right? You need to do some level of protection, whether that's in-flight protection or separation between them. The third one is, as you're moving things around and you see bad things happen, you need to not wait for a person, because you're looking at scale, like thousands of these instances that are moving around. The network is intelligent enough to act based on rules that you give it to, like if there's a threat, we'll just quarantine the source or remove traffic. This combination is what's missing and that's kind of what a lot of... >> I think that's an opportunity that's clear, but most people look at networking and say "oh, let's move it from A to B, point A to point B." It's now so much more than that, it's more headroom. What is the specific headroom on top of that? Because there's a lot of security opportunities, things are moving around, you can see the bad guys and all kinds of different threats, but not just moving packets, it's other things. What's the other key things that people should pay attention to when really designing these architectures? >> So the one thing, obviously, when you're doing things in a lab, you're not really going by scale. You're not looking at throughput, latency, things like that that's part of networking and that's kind of the work we're doing with some of the, like Mellanox, you know? On terms of providing high-throughput, providing low latency for specific applications. The other one is, how do you provide that intelligence? Like all this data has to go somewhere to be processed, to work with other security solutions. Those are the two things that maybe people don't give that much thought early on, but as you scale your operations, they become real bottlenecks for you. >> So I want to get a chance for you to get a plug in for the company, DevOps. This infrastructure, this code has kind of been kicking around since the beginning. It's actually happening, a programmable infrastructure. You know, at the app layer for coding, but now network's programmable. What are you guys doing in that area? How are you guys extending that value proposition to your customers? Why are they going with you guys? Why are you guys winning? What's the one thing that people should know about in order to come to you guys? >> Flexibility and openness, that's the key one. We are hardware agnostic, any switch, any network, any hypervisor, any CMS, content management system, that's our focus is our networking and security. Similar to Kubernertes, you can run Kubernetes anywhere. That's how we provide networking and we have an open eco-system that gives you scale, performance and security without really limiting your options. And the thing is, we have all, going forward, like people can do stuff on premises today, they may move to cloud, we don't lock you in to one architecture. The architecture's fluid and it could be whatever. You may see the future one way today, but in a couple of months as we all know, things change. >> Why would someone call you guys up? What's the paying point? What's the value? When will they know, oh okay I've got to get Nuage involved? >> Scale, multi-cloud, that's basically it. If you're looking for multi-cloud, multiple workloads and you're running things at scale, you need to talk to us because that's basically where we help you solve it. >> Hussein, talk a little bit about how Edge fits into it too. You know when you think back to even before cloud, think back to the XSPs. Networking securities have always been the choke point, physics still rules the day. We know it's only getting more complicated with Edge, more surface area for security, but I have to imagine that applies into what you're doing. >> Absolutely, I mean we've done, so as you decompose these things and you move them apart, your attack services increase, right? So the security is, as you move, those communication channels have to be protected somehow. We have an extension which is basically part of getting into the Edge, adding more intelligence at the Edge, because that traffic is coming from the Edge to the core, it goes to public cloud. And being able, as a networking solution, to steer that traffic securely using encryption or whatever have you in terms of visibility, provides those enterprises with a secure, sound platform to really do their business. >> What's your take on the show? 8,000 people up from 4,000. We were comparing it earlier to Adobe's Reinvent. A rising tide, is it a tsunami? >> Absolutely, I mean I couldn't believe the number when they said it because obviously we saw they'd sold out the tickets, but coming here to see all that many people and there have been earlier shows and the growth is tremendous. >> Well thanks for coming for coming on and sharing your insight and congratulations on the scale, we love it. Data, scale, programmable networks, it's all part of the new evolution of cloud native. It's on premises, it's in the cloud, multiple workloads, multiple clouds. This is the choice everyone has, they're rebuilding. Don't forget networking compute and storage, it's still a Holy Trinity there. Congratulations, thanks for coming on. >> Thank you very much. >> More live coverage here at theCUBE, here in Seattle for KubeCon and CloudNativeCon, day three of three days of coverage, this is theCUBE, we'll be right back after this short break. (upbeat music)

[Music] [Applause] [Music] five universe and welcome to another cube conversation on being joined today by Tom burns who's the senior vice president of networking solutions at WMC Tom welcome back to the cube thanks Peter it's great to be here again good to see you so Tom this is gonna be a very very exciting talk conversation we're gonna have and it's going to be about AI so when you go out and talk to customers specifically what are you hearing them is they described their needs their wants their aspirations as they pertain to AI yeah you know Pete we've always been looking at this is this whole digital transformation some studies say that about 70% of enterprises today are looking how to take advantage of the digital transformation that's occurring in fact you're probably familiar with the del 2030 survey where we went out and talked to about 400 different companies of very different sizes and they're looking at all these connected devices at edge computing and all the various changes that are happening from a technology standpoint and certainly AI is one of the hottest areas there's a report I think that was co-sponsored by ServiceNow over 62 percent of the CIOs and the fortune 500 are looking at AI as far as managing their business in the future and it's really about user outcomes it's about how do they improve their businesses their operations their processes their decision making using the capability of compute coming down from a cost perspective and the number of connected devices exploding bringing more and more data to their companies that they can use analyze and put to use cases that really make a difference in their business but they make a difference in their business but they're also often these use cases are a lot more complex they're not we have this little bromide that we use that the first 50 years of computing we're about known process unknown technology we're now entering into an era where we know a little bit more about the technology it's gonna be cloud like but we don't know what the processes are because we're engaging directly with customers or partners in much more complex domains that suggests a lot of things how does how our customers dealing with that new level of complexity and where are they looking to simplify you actually nailed it on the head you know what's happening in our customers environment is they're hiring these data scientists to really look at this data and instead of looking at analyzing the data that's being connected that's being analyzed connected they're spending more time worried about the infrastructure and building the components and looking about allocations of capacity in order to make these data scientist productive and really what we're trying to do is help them get through that particular hurdle so you have the data scientists that are frustrated because they're waiting for the IT department to help them set up and scale the capacity that they need an infrastructure that they need in order to do their job and then you got the IT departments that are very frustrated because they don't know how to manage all this infrastructure so the question around do I go to the cloud or remain on Prem all of this is things that our companies or our customers are continuing to be challenged with now the ideal would be that you can have a cloud experience but have the data reside where it most naturally resides given physics given the cost given bandwidth limitations given regulatory regimes etc so how are you at Dell EMC hoping to provide that sense of an experience based on what the workload is and where the data resides as opposed to some other set of infrastructure choices well that's exciting part is that we're getting ready to announce a new solution called the readied solution for AI and what we've been doing is working with our customers over the last several years looking at these challenges around infrastructure the data analytics the connected devices but giving them an experience that's real-time not letting them worry about how am I going to set this up or management and so forth so we're introducing the ready solution for AI which really focuses on three things one is simplify the AI process the second thing is to ensure that we give them deep in real-time analytics and lastly provide them the level of expertise that they need in a partner in order to make those tools useful and that information useful to their business now we want to not only provide AI to the business but we also want to start utilizing some of these advanced technologies directly into the infrastructure elements themselves to make it more simple is that a big feature of what the writing system for AI is absolutely as I said one of the key value proposition is around making eyes I simple you know we are experts at building infrastructure we have IP around compute storage networking infinity band the things that are capable of putting this infrastructure together so we've tested that based upon customer's input using traditional data analytics libraries and tool sets that the data scientists are going to use already pre tested and certified and then we're bringing this to them in a way which allows them through a service provisioning portal to basically set up and get to work much faster you know the previous tools that were available out there some from our competition there were fifteen twenty twenty-five different steps just to log on just to get enough automation or enough capability in order to get the information that they need the infrastructure allocated for this big data analytics through this service portal we've actually gotten it down to about five clicks with a very user-friendly GUI no CLI required and basically again interacting with the tools that they're used to immediately router the gate like in stage three and then getting them to work and Stage four and Stage five so that they're not worried about the infrastructure not worried about capacity or is it gonna work they basically are one two three four clicks away and they're up and working on the analytics that you know everyone wants them to work on and heaven knows these guys are not cheap so you're talking about the data scientist so presumably when you're saying they're not worried about all those things they're also not worried about when the IT department can get around to doing it so this is gives them the opportunity to self provisioning I got that right that's correct they don't need the IT to come in and set up the network to do the CLI for the provisioning to make sure that there's you know enough VMs or workloads that are properly scheduled in order to give them the capacity that they need they basically are set with the preset platform again let's think about what Dell EMC is really working towards and that's becoming the you know infrastructure provider we believe that the silos of server storage and networking are becoming eliminated that companies want a platform that they can enable this capabilities so you're absolutely right the part about the simplicity or the simplifying the AEA process is really giving the data scientists the tools they need to provision the infrastructure they need very quickly and so that means that the AI or the rather the IT group can actually start acting more like a DevOps organization as opposed to specialists in one or another technology correct but we've also given them the capability by giving the the usual automation and configuration tools that they're used to from some of our software partners such as cloud era so in other words you still want the IT department involved making sure that the infrastructure is meeting the requirements of the users they're giving them what they want but we're simplifying the tools and processes around the IT standpoint as well now we've done a lot of research into what's happening in the big data and now is likely to happen in the AI world and a lot of the problems that companies had with big data was they conflated or they confused the objectives the outcome of a big data project with just getting the infrastructure to work and they walked away off and because they fail to get the infrastructure to work so it sounds as though what you're doing is you're trying to take the infrastructure out of the equation while at the same time going back to the customer saying wherever you want this job to run or this workload to run you're gonna get the same experience irregardless correct but we're gonna get an approved experience as well because of the products that we've put together in this particular solution combined with our compute our scale out now solution from a storage perspective our partnership with Mellanox from an infinity ban or Ethernet switch capability we're going to give them deeper insights and faster insights the performance and scalability of this particular platform is tremendous we believe in certain benchmark studies based upon the Resnick 50 benchmark we've performed anywhere between two and a half to almost three times faster than competition in addition from a storage standpoint you know all of these workloads all the various characteristics have happened you need a ton of AI ops and there's no one in the industry that has the IAP performance that we have with our all-flash Isilon product the capabilities that we have there we believe are somewhere around nine times the competition again the scale out performance while simplifying the overall architecture very very incredible so as we think about where this solution goes and where Dell EMC as a partner goes in this burgeoning and increasingly crucial space of AI how do you regard or how do you think customers are going to be looking to you in a couple years for example with that portal for data scientists just be a portal that focus on provision or do you anticipate the ecosystem getting stronger I think the ecosystem will continue to get stronger and I think that leads to kind of our third value proposition and that is we're building a team of experts we have a services organization that helps customers in the implementation of these particular projects not just provisioning of the infrastructure we have a spectacular lab that we've built based upon the experiences that we have with our customers so we can jointly look at some of these particular areas with our capability and our resources in our labs along with the customer and then obviously we'll continue to do training we have partnerships with companies such with Nvidia and so forth that helps companies build up the AI expertise within their particular space to their businesses so it's not just about us becoming the infrastructure provider it's also being seen as an expert in the industry and helping them go through this digital transformation this journey of being able to use artificial intelligence and deep learning to truly help their business overall from the outcome I once had a CIO tell me that in my world the infrastructure must do no harm that's true that's true they don't fix what's not broken or something but but the truth in matter is is that the way technology is moving today you know CIOs are really challenged with really moving to kind of this future data center software-defined everything the capability of again eliminating the silos and the management and people that are related to those silos and building a platform so that you can enable new applications new workloads new use cases very very quickly that's really what the digital transformation is all about and that's what dello EMC is very focused on and nowhere is that more important than crucial new kinds of workloads like AI get to the outcome don't screw around with the piece parts in between correct let us do that let us do the testing let us do the certifications let us provide already proven libraries and tool sets that they're used to using let us jointly as a community improve on this provisioning portal so that it makes it even easier for the data scientists to focus on what they're really good at and that's building the use cases the algorithms the very most change the models that that help you know every vertical market we've seen different use cases in healthcare automobile transportation manufacturing you know things such as fraud and anomaly detection the capability to look at object recognition etc I think these are going to continue to evolve over time and we've got a host of customers that are already actively starting to work on these particular areas and they're already seeing tremendous business benefits Tom burns senior vice-president of networking and solutions at Dell EMC thanks for beyond - thank you very much [Music]

[Music] [Applause] [Music] welcome to the special cube conversations here in Palo Alto cube studios I'm John Ferrier the founder of Silicon angle in the cube we're here with divest cargoes the founder and CEO of arcus Inc our curse com ar-are see us calm and Steve Herod General Partner at at General Catalyst VCU's funded him congratulations on your launch these guys launched on Monday a hot new product software OS for networking powering white boxes in a whole new generation of potentially cloud computing welcome to this cube conversation congratulations on your >> launch thank you John >> so today I should talk about this this >> startup when do you guys were founded let's get to the specifics date you were founded some of the people on the team and the funding and we were formally incorporated in February of 2016 we really got going in earnest in August of 2016 and have you know chosen to stay in stealth the the founding team consists of myself a gentleman by the name of Kop tell he's our CTO we also have a gentleman by the name of Derek Young he's our chief architect and our backgrounds are a combination of the semiconductor industry I spent a lot of time in the semiconductor industry most recently I was president of easy chip and we sold that company to Mellanox and Kher and Derek our networking protocol experts spent 20 plus years at places like Cisco and arguably some of the best protocol guys in the world so the three of us got together and basically saw an opportunity to to bring some of the insights and and architectural innovation you know we had in mind to the Mobius a pedigree in there some some top talent absolutely some of the things that they've done in the past from some notable yeah I mean you know some if you if you'd like some just high-level numbers we have 600 plus years of experience of deep networking expertise within the company our collective team has shipped over 400 products to production we have over 200 IETF RFC papers that have been filed by the team as well as 150 plus patents so we really can do something on the pedigree for sure yeah we absolutely focused on getting the best talent in the world because we felt that it would be a significant differentiation to be able to start from a clean sheet of paper and so really having people who have that expertise allowed us to kind of take a step back and you know reimagine what could be possible with an operating system and gave us the benefit of being able to you know choose >> best-in-class approaches so what's the >> cap the point that this all came >> together what was the guiding vision was it network os's are going to be cloud-based was it going to be more I owe t what was the some of the founding principles that really got this going because clearly we see a trend where you know Intel's been dominating we see what NVIDIA is doing competitively certainly on the GPU side you're seeing the white box has become a trend Google makes their own stuff apples big making their own silicon seeking the that's kind of a whole big scale world out there that has got a lot of hardware experience what was the catalyst for you guys when you found this kinda was the guiding principle yeah I would say there were three John and you hit you hit on a couple of them in your reference to Intel and NVIDIA with some of the innovation but if I start at the top level the market the networking market is a large market and it's also very strategic and foundational in a hyper-connected world that market is also dominated by a few people and there's essentially three vertically integrated OEM so that dominate that market and when you have that type of dominance it leads to ultimately high prices and muted innovations so we felt number one the market was going through tremendous change but at the same time it had been tightly controlled by a few people the other part of it was that there was a tremendous amount of innovation that was happening at the silicon component level coming from the semiconductor industry I was early at Broadcom very you know involved in some of the networking things that happened in the early stages of the company we saw tremendous amounts of innovation feature velocity that was happening at the silicon component level that in turn led to a lot of system hardware people coming into the market and producing systems based on this wide variety of choices for you know for the silicon but the missing link was really an operating system that would unleash all that innovation so Silicon Valley is back Steve you you know you're a VC now but you were the CTO at VMware one of the companies that actually changed how data centers operate certainly as it certainly as a pretext and cloud computing was seeing with micro services and the growth of cloud silicon's hot IT operations is certainly being decimated as we old knew it in the past everything's being automated away you need more function now there's a demand this is this penny how you see I mean you always see things are a little early as of technologist now VC what got you excited about these guys what's the what's the bottom line yeah maybe two points on that which so one silicon is is definitely become interesting again if you will in the in the Silicon Valley area and I think that's partly because cloud scale and web scale allows these environments where you can afford to put in new hardware and really take advantage of it I was a semiconductor I first austerity too so it's exciting for me to see that but um you know is the fish that it's kind of a straightforward story you know especially in a world of whether it's cloud or IOT or everything networking is you know like literally the core to all of us working going forward and the opportunity to rethink it in a new design and in software first mentality felt kind of perfect right now I think I I think device even sell the team a little short even is with all the numbers that are there kr for instance this co-founder was sort of everyone you talk to will call him mister BGP which is one of the main routing protocols in the internet so just a ridiculously deep team trying to take this on and there been a few companies trying to do something kind of like this and I think what do they say that the second Mouse gets the cheese and I think I think we've seen some things that didn't work the first time around and we can really I think improve on them and have a >> chance to make a major impact on the networking market you know just to kind of go on a tangent here for a second >> because you know as you're talking kind of my brain is kind of firing away because you know one of things I've been talking about on the cube a lot is ageism and if you look at the movement of the cloud that's brought us systems mindset back you look at all the best successes out there right now it's almost a old guys and gals but it's really systems people people who understand networking and systems because the cloud is an operating system you have an operating system for networking so you're seeing that trend certainly happened that's awesome the question I have for you device is what is the difference what's the impact of this new network OS because I'm almost envisioning if I think through my mind's eye you got servers and server list certainly big train seeing and cloud it's one resource pools one operating system and that needs to have cohesiveness and connectedness through services so is this how you guys are thinking about how are you guys think about the network os what's different about what you guys are doing with ARC OS versus what's out there today now that's a great question John so in terms of in terms of what we've done the the third piece you know of the puzzle so to speak when we were talking about our team I talked a little bit about the market opportunity I talked a little bit about the innovation that was happening at the semiconductor and systems level and said the missing link was on the OS and so as I said at the onset we had the benefit of hiring some of the best people in the world and what that gave us the opportunity was to look at the twenty plus years of development that had happened on the operating system side for networking and basically identify those things that really made sense so we had the benefit of being able to adopt what worked and then augment that with those things that were needed for a modern day networking infrastructure environment and so we set about producing a product we call it our Co s and the the characteristics of it that are unique are that its first of all its best-in-class protocols we have minimal dependency on open source protocols and the reason for that is that no serious network operator is going to put an open source networking protocol in the core of their network they're just not going to risk their business and the efficacy and performance of their network for something like that so we start with best-in-class protocols and then we captured them in a very open modular Services microservices based architecture and that allows us the flexibility and the extensibility to be able to compose it in a manner that's consistent with what the end-use case is going to be so it's designed from the onset to be very scalable and very versatile in terms of where it can be deployed we can deploy it you know in a physical environment we can deploy it visa via a container or we could deploy it in the cloud so we're agnostic to all of those use case scenarios and then in addition to that we knew that we had to make it usable it makes no sense to have the best-in-class protocols if our end customers can't use them so what we've done is we've adopted open config yang based models and we have programmable api's so in any environment people can leverage their existing tools their existing applications and they can relatively easily and efficiently integrate our Co s into their networking environment and then similarly we did the same thing on the hardware side we have something that we call D pal it's a data plane adaptation layer it's an intelligent how and what that allows us to do is be Hardware agnostic so we're indifferent to what the underlying hardware is and what we want to do is be able to take advantage of the advancements in the silicon component level as well as at the system level and be able to deploy our go S anywhere it's let's take a step back so you guys so the protocols that's awesome what's the value proposition for our Co S and who's the target audience you mentioned data centers in the past is a data center operators is it developers is it service providers who was your target customer yeah so so the the piece of the puzzle that wraps everything together is we wanted to do it at massive scale and so we have the ability to support internet scale with deep routing capabilities within our Co s and as a byproduct of that and all the other things that we've done architectural II were the world's first operating system that's been ported to the high-end Broadcom strata DNX family that product is called jericho plus in the marketplace and as a byproduct of that we can ingest a full internet routing table and as a byproduct of that we can be used in the highest end applications for network operators so performance is a key value public performance as measured by internet scale as measured by convergence times as measured by the amount of control visibility and access that we provide and by virtue of being able to solve that high-end problem it's very easy for us to come down so in terms of your specific question about what are the use cases we have active discussions in data center centric applications for the leaf and spine we have active discussions for edge applications we have active discussions going on for cloud centric applications arcus can be used anywhere who's the buyer those network operator so since we can go look a variety of personas network operator large telco that's right inner person running a killer app that's you know high mission-critical high scale is that Mike right yeah you're getting you're absolutely getting it right basically anybody that has a network and has a networking infrastructure that is consuming networking equipment is a potential customer for ours now the product has the extensibility to be used anywhere in the data center at the edge or in the cloud we're very focused on some of the use cases that are in the CDN peering and IP you know route reflector IP peering use cases great Steve I want to get your thoughts because I say I know how you invest you guys a great great firm over there you're pretty finicky on investments certainly team check pedigrees they're on the team so that's a good inside market tamp big markets what's the market here for you but how do you see this market what's the bet for you guys on the market side yeah it's pretty pretty straightforward as you look at the size of the networking market with you know three major players around here and you know a longer tail owning a small piece of Haitian giant market is a great way to get started and if you believe in the and the secular trends that are going on with innovation and hardware and the ability to take advantage of them I think we have identified a few really interesting starting use cases and web-scale companies that have a lot of cost and needs in the networking side but what I would love about the software architecture it reminds me a lot of things do have kind of just even the early virtualization pieces if you if you can take advantage of movement in advantages and hardware as they improve and really bring them into a company more quickly than before then those companies are gonna be able to have you know better economics on their networking early on so get a great layer in solve a particular use case but then the trends of being able to take advantage of new hardware and to be able to provide the data and the API is to programmatic and to manage it who one would that it's creative limp limitless opportunity because with custom silicon that has you know purpose-built protocols it's easy to put a box together and in a large data center or even boxes yeah you can imagine the vendors of the advances and the chips really love that there's a good company that can take advantage of them more quickly than others can so cloud cloud service refined certainly as a target audience here large the large clouds would love it there's an app coming in Broadcom as a customer they a partner of you guys in two parts first comes a partner so we we've ported arc OS onto multiple members of the Broadcom switching family so we have five or six of their components their networking system on chip components that we've ported to including the two highest end which is the jericho plus and you got a letter in the Broadcom buying CA and that's gonna open up IT operations to you guys and volge instead of applications and me to talk about what you just said extensibility of taking what you just said about boxes and tying applique and application performance you know what's going to see that vertically integrated and i think i think eloping yeah from from a semiconductor perspective since i spent a lot of time in the industry you know one of the challenges i had founded a high court count multi processor company and one of the challenges we always had was the software and at easy chip we had the world's highest and network processor challenge with software and i think if you take all the innovation in the silicon industry and couple it with the right software the combination of those two things opens up a vast number of opportunities and we feel that with our Co s we provide you know that software piece that's going to help people take advantage of all the great innovation that's happening you mentioned earlier open source people don't want to bring open source at the core the network yet the open source communities are growing really at an exponential rate you starting to see open source be the lingua franca for all developers especially the modern software developers wine not open sourcing the core the amino acids gotta be bulletproof you need security obviously answers there but that seems difficult to the trend on open source what's the what's the answer there on why not open source in the core yeah so we we take advantage of open source where it makes sense so we take advantage of open and onl open network Linux and we have developed our protocols that run on that environment the reason we feel that the protocols being developed in-house as opposed to leveraging things from the open source community are the internet scale multi-threading of bgp integrating things like open config yang based models into that environment right well it's not only proven but our the the the capabilities that we're able to innovate on and bring unique differentiation weren't really going back to a clean sheet of paper and so we designed it ground-up to really be optimized for the needs of today Steve your old boss Palmer rich used to talk about the harden top mmm-hmm similar here right you know one really no one's really gonna care if it works great it's under the under the harden top where you use open source as a connection point for services and opportunities to grow that similar concept yes I mean at the end of the day open source is great for certain things and for community and extensibility and for visibility and then on the flip side they look to a company that's accountable and for making sure it performs and as high quality and so I think I think that modern way for especially for the mission critical infrastructure is to have a mix of both and to give back to community where it makes sense to be responsible for hardening things are building them when they don't expense so how'd you how'd you how'd you land these guys you get him early and don't sit don't talk to any other VCS how did it all come together between you guys we've actually been friends for a while which has been great in it at one point we actually decided to ask hey what do you actually do I found that I was a venture investor and he is a network engineer but now I actually have actually really liked the networking space as a whole as much as people talk about the cloud or open source or storage being tough networking is literally everywhere and will be everywhere and whatever our world looks like so I always been looking for the most interesting companies in that space and we always joke like the investment world kind of San Francisco's applications mid here's sort of operating systems and the lower you get the more technical it gets and so well there's a vaccine I mean we're a media company I think we're doing things different we're team before we came on camera but I think media is undervalued I wrote just wrote a tweet on that got some traction on that but it's shifting back to silicon you're seeing systems if you look at some of the hottest areas IT operations is being automated away AI ops you know Auto machine learning starting to see some of these high-end like home systems like that's exactly where I was gonna go it's like the vid I I especially just love very deep intellectual property that is hard to replicate and that you can you know ultimately you can charge a premium for something that is that hard to do and so that's that's really something I get drugs in the deal with in you guys you have any other syndicates in the video about soda sure you know so our initial seed investor was clear ventures gentleman by the name of Chris rust is on our board and then Steve came in and led our most recent round of funding and he also was on the board what we've done beyond that institutional money is we have a group of very strategic individual investors two people I would maybe highlight amongst the vast number of advisers we have our gentleman by the name of Pankaj Patel punka JH was the chief development officer at Cisco he was basically number two at Cisco for a number of years deep operating experience across all facets of what we would need and then there's another gentleman by the name of Amarjeet Gill I've been friends with armored teeth for 30 years he's probably one of the single most successful entrepreneurs in the he's incubated companies that have been purchased by Broadcom by Apple by Google by Facebook by Intel by EMC so we were fortunate enough to get him involved and keep him busy great pedigree great investors with that kind of electoral property and those smart mines they're a lot of pressure on you as the CEO not to screw it up right I mean come on now get all those smart man come on okay you got it look at really good you know I I welcome it actually I enjoy it you know we look when you have a great team and you have as many capable people surrounding you it really comes together and so I don't think it's about me I actually think number one it's about I was just kidding by the way I think it's about the team and I'm merely a spokesperson to represent all the great work that our team has done so I'm really proud of the guys we have and frankly it makes my job easier you've got a lot of people to tap for for advice certainly the shared experiences electively in the different areas make a lot of sense in the investors certainly yeah up to you absolutely absolutely and it's not it's not just at the at the board it's just not at the investor level it's at the adviser level and also at you know at our individual team members when we have a team that executes as well as we have you know everything falls into place well we think the software worlds change we think the economics are changing certainly when you look at cloud whether it's cloud computing or token economics with blockchain and new emerging tech around AI we think the world is certainly going to change so you guys got a great team to kind of figure it out I mean you got a-you know execute in real time you got a real technology play with IP question is what's the next step what is your priorities now that you're out there congratulations on your launch thank you in stealth mode you got some customers you've got Broadcom relationships and looking out in the landscape what's your what's your plan for the next year what's your goals really to take every facet of what you said and just scale the business you know we're actively hiring we have a lot of customer activity this week happens to be the most recent IETF conference that happened in Montreal given our company launch on Monday there's been a tremendous amount of interest in everything that we're doing so that coupled with the existing customer discussions we have is only going to expand and then we have a very robust roadmap to continue to augment and add capabilities to the baseline capabilities that we brought to the market so I I really view the next year as scaling the business in all aspects and increasingly my time is going to be focused on commercially centric activities right well congratulations got a great team we receive great investment cube conversation here I'm John furry here the hot startup here launching this week here in California in Silicon Valley where silicon is back and software is back it's the cube bringing you all the action I'm John Fourier thanks for watching [Music]