(chill electronic music) >> Hello everyone, welcome to theCUBE's presentation of the AWS Startups Showcase, AI and machine learning, the top startups building generative AI on AWS. This is the season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talk about AI and machine learning. Can't believe it's three years and season one. I'm your host, John Furrier. Got a great guest today, we're joined by Joseph Nelson, the co-founder and CEO of Roboflow, doing some cutting edge stuff around computer vision and really at the front end of this massive wave coming around, large language models, computer vision. The next gen AI is here, and it's just getting started. We haven't even scratched a service. Thanks for joining us today. >> Thanks for having me. >> So you got to love the large language model, foundation models, really educating the mainstream world. ChatGPT has got everyone in the frenzy. This is educating the world around this next gen AI capabilities, enterprise, image and video data, all a big part of it. I mean the edge of the network, Mobile World Conference is happening right now, this month, and it's just ending up, it's just continue to explode. Video is huge. So take us through the company, do a quick explanation of what you guys are doing, when you were founded. Talk about what the company's mission is, and what's your North Star, why do you exist? >> Yeah, Roboflow exists to really kind of make the world programmable. I like to say make the world be read and write access. And our North Star is enabling developers, predominantly, to build that future. If you look around, anything that you see will have software related to it, and can kind of be turned into software. The limiting reactant though, is how to enable computers and machines to understand things as well as people can. And in a lot of ways, computer vision is that missing element that enables anything that you see to become software. So in the virtue of, if software is eating the world, computer vision kind of makes the aperture infinitely wide. It's something that I kind of like, the way I like to frame it. And the capabilities are there, the open source models are there, the amount of data is there, the computer capabilities are only improving annually, but there's a pretty big dearth of tooling, and an early but promising sign of the explosion of use cases, models, and data sets that companies, developers, hobbyists alike will need to bring these capabilities to bear. So Roboflow is in the game of building the community around that capability, building the use cases that allow developers and enterprises to use computer vision, and providing the tooling for companies and developers to be able to add computer vision, create better data sets, and deploy to production, quickly, easily, safely, invaluably. >> You know, Joseph, the word in production is actually real now. You're seeing a lot more people doing in production activities. That's a real hot one and usually it's slower, but it's gone faster, and I think that's going to be more the same. And I think the parallel between what we're seeing on the large language models coming into computer vision, and as you mentioned, video's data, right? I mean we're doing video right now, we're transcribing it into a transcript, linking up to your linguistics, times and the timestamp, I mean everything's data and that really kind of feeds. So this connection between what we're seeing, the large language and computer vision are coming together kind of cousins, brothers. I mean, how would you compare, how would you explain to someone, because everyone's like on this wave of watching people bang out their homework assignments, and you know, write some hacks on code with some of the open AI technologies, there is a corollary directly related to to the vision side. Can you explain? >> Yeah, the rise of large language models are showing what's possible, especially with text, and I think increasingly will get multimodal as the images and video become ingested. Though there's kind of this still core missing element of basically like understanding. So the rise of large language models kind of create this new area of generative AI, and generative AI in the context of computer vision is a lot of, you know, creating video and image assets and content. There's also this whole surface area to understanding what's already created. Basically digitizing physical, real world things. I mean the Metaverse can't be built if we don't know how to mirror or create or identify the objects that we want to interact with in our everyday lives. And where computer vision comes to play in, especially what we've seen at Roboflow is, you know, a little over a hundred thousand developers now have built with our tools. That's to the tune of a hundred million labeled open source images, over 10,000 pre-trained models. And they've kind of showcased to us all of the ways that computer vision is impacting and bringing the world to life. And these are things that, you know, even before large language models and generative AI, you had pretty impressive capabilities, and when you add the two together, it actually unlocks these kind of new capabilities. So for example, you know, one of our users actually powers the broadcast feeds at Wimbledon. So here we're talking about video, we're streaming, we're doing things live, we've got folks that are cropping and making sure we look good, and audio/visual all plugged in correctly. When you broadcast Wimbledon, you'll notice that the camera controllers need to do things like track the ball, which is moving at extremely high speeds and zoom crop, pan tilt, as well as determine if the ball bounced in or out. The very controversial but critical key to a lot of tennis matches. And a lot of that has been historically done with the trained, but fallible human eye and computer vision is, you know, well suited for this task to say, how do we track, pan, tilt, zoom, and see, track the tennis ball in real time, run at 30 plus frames per second, and do it all on the edge. And those are capabilities that, you know, were kind of like science fiction, maybe even a decade ago, and certainly five years ago. Now the interesting thing, is that with the advent of of generative AI, you can start to do things like create your own training data sets, or kind of create logic around once you have this visual input. And teams at Tesla have actually been speaking about, of course the autopilot team's focused on doing vision tasks, but they've combined large language models to add reasoning and logic. So given that you see, let's say the tennis ball, what do you want to do? And being able to combine the capabilities of what LLM's represent, which is really a lot of basically, core human reasoning and logic, with computer vision for the inputs of what's possible, creates these new capabilities, let alone multimodality, which I'm sure we'll talk more about. >> Yeah, and it's really, I mean it's almost intoxicating. It's amazing that this is so capable because the cloud scales here, you got the edge developing, you can decouple compute power, and let Moore's law and all the new silicone and the processors and the GPUs do their thing, and you got open source booming. You're kind of getting at this next segment I wanted to get into, which is the, how people should be thinking about these advances of the computer vision. So this is now a next wave, it's here. I mean I'd love to have that for baseball because I'm always like, "Oh, it should have been a strike." I'm sure that's going to be coming soon, but what is the computer vision capable of doing today? I guess that's my first question. You hit some of it, unpack that a little bit. What does general AI mean in computer vision? What's the new thing? Because there are old technology's been around, proprietary, bolted onto hardware, but hardware advances at a different pace, but now you got new capabilities, generative AI for vision, what does that mean? >> Yeah, so computer vision, you know, at its core is basically enabling machines, computers, to understand, process, and act on visual data as effective or more effective than people can. Traditionally this has been, you know, task types like classification, which you know, identifying if a given image belongs in a certain category of goods on maybe a retail site, is the shoes or is it clothing? Or object detection, which is, you know, creating bounding boxes, which allows you to do things like count how many things are present, or maybe measure the speed of something, or trigger an alert when something becomes visible in frame that wasn't previously visible in frame, or instant segmentation where you're creating pixel wise segmentations for both instance and semantic segmentation, where you often see these kind of beautiful visuals of the polygon surrounding objects that you see. Then you have key point detection, which is where you see, you know, athletes, and each of their joints are kind of outlined is another more traditional type problem in signal processing and computer vision. With generative AI, you kind of get a whole new class of problem types that are opened up. So in a lot of ways I think about generative AI in computer vision as some of the, you know, problems that you aimed to tackle, might still be better suited for one of the previous task types we were discussing. Some of those problem types may be better suited for using a generative technique, and some are problem types that just previously wouldn't have been possible absent generative AI. And so if you make that kind of Venn diagram in your head, you can think about, okay, you know, visual question answering is a task type where if I give you an image and I say, you know, "How many people are in this image?" We could either build an object detection model that might count all those people, or maybe a visual question answering system would sufficiently answer this type of problem. Let alone generative AI being able to create new training data for old systems. And that's something that we've seen be an increasingly prominent use case for our users, as much as things that we advise our customers and the community writ large to take advantage of. So ultimately those are kind of the traditional task types. I can give you some insight, maybe, into how I think about what's possible today, or five years or ten years as you sort go back. >> Yes, definitely. Let's get into that vision. >> So I kind of think about the types of use cases in terms of what's possible. If you just imagine a very simple bell curve, your normal distribution, for the longest time, the types of things that are in the center of that bell curve are identifying objects that are very common or common objects in context. Microsoft published the COCO Dataset in 2014 of common objects and contexts, of hundreds of thousands of images of chairs, forks, food, person, these sorts of things. And you know, the challenge of the day had always been, how do you identify just those 80 objects? So if we think about the bell curve, that'd be maybe the like dead center of the curve, where there's a lot of those objects present, and it's a very common thing that needs to be identified. But it's a very, very, very small sliver of the distribution. Now if you go out to the way long tail, let's go like deep into the tail of this imagined visual normal distribution, you're going to have a problem like one of our customers, Rivian, in tandem with AWS, is tackling, to do visual quality assurance and manufacturing in production processes. Now only Rivian knows what a Rivian is supposed to look like. Only they know the imagery of what their goods that are going to be produced are. And then between those long tails of proprietary data of highly specific things that need to be understood, in the center of the curve, you have a whole kind of messy middle, type of problems I like to say. The way I think about computer vision advancing, is it's basically you have larger and larger and more capable models that eat from the center out, right? So if you have a model that, you know, understands the 80 classes in COCO, well, pretty soon you have advances like Clip, which was trained on 400 million image text pairs, and has a greater understanding of a wider array of objects than just 80 classes in context. And over time you'll get more and more of these larger models that kind of eat outwards from that center of the distribution. And so the question becomes for companies, when can you rely on maybe a model that just already exists? How do you use your data to get what may be capable off the shelf, so to speak, into something that is usable for you? Or, if you're in those long tails and you have proprietary data, how do you take advantage of the greatest asset you have, which is observed visual information that you want to put to work for your customers, and you're kind of living in the long tails, and you need to adapt state of the art for your capabilities. So my mental model for like how computer vision advances is you have that bell curve, and you have increasingly powerful models that eat outward. And multimodality has a role to play in that, larger models have a role to play in that, more compute, more data generally has a role to play in that. But it will be a messy and I think long condition. >> Well, the thing I want to get, first of all, it's great, great mental model, I appreciate that, 'cause I think that makes a lot of sense. The question is, it seems now more than ever, with the scale and compute that's available, that not only can you eat out to the middle in your example, but there's other models you can integrate with. In the past there was siloed, static, almost bespoke. Now you're looking at larger models eating into the bell curve, as you said, but also integrating in with other stuff. So this seems to be part of that interaction. How does, first of all, is that really happening? Is that true? And then two, what does that mean for companies who want to take advantage of this? Because the old model was operational, you know? I have my cameras, they're watching stuff, whatever, and like now you're in this more of a, distributed computing, computer science mindset, not, you know, put the camera on the wall kind of- I'm oversimplifying, but you know what I'm saying. What's your take on that? >> Well, to the first point of, how are these advances happening? What I was kind of describing was, you know, almost uni-dimensional in that you have like, you're only thinking about vision, but the rise of generative techniques and multi-modality, like Clip is a multi-modal model, it has 400 million image text pairs. That will advance the generalizability at a faster rate than just treating everything as only vision. And that's kind of where LLMs and vision will intersect in a really nice and powerful way. Now in terms of like companies, how should they be thinking about taking advantage of these trends? The biggest thing that, and I think it's different, obviously, on the size of business, if you're an enterprise versus a startup. The biggest thing that I think if you're an enterprise, and you have an established scaled business model that is working for your customers, the question becomes, how do you take advantage of that established data moat, potentially, resource moats, and certainly, of course, establish a way of providing value to an end user. So for example, one of our customers, Walmart, has the advantage of one of the largest inventory and stock of any company in the world. And they also of course have substantial visual data, both from like their online catalogs, or understanding what's in stock or out of stock, or understanding, you know, the quality of things that they're going from the start of their supply chain to making it inside stores, for delivery of fulfillments. All these are are visual challenges. Now they already have a substantial trove of useful imagery to understand and teach and train large models to understand each of the individual SKUs and products that are in their stores. And so if I'm a Walmart, what I'm thinking is, how do I make sure that my petabytes of visual information is utilized in a way where I capture the proprietary benefit of the models that I can train to do tasks like, what item was this? Or maybe I'm going to create AmazonGo-like technology, or maybe I'm going to build like delivery robots, or I want to automatically know what's in and out of stock from visual input fees that I have across my in-store traffic. And that becomes the question and flavor of the day for enterprises. I've got this large amount of data, I've got an established way that I can provide more value to my own customers. How do I ensure I take advantage of the data advantage I'm already sitting on? If you're a startup, I think it's a pretty different question, and I'm happy to talk about. >> Yeah, what's startup angle on this? Because you know, they're going to want to take advantage. It's like cloud startups, cloud native startups, they were born in the cloud, they never had an IT department. So if you're a startup, is there a similar role here? And if I'm a computer vision startup, what's that mean? So can you share your your take on that, because there'll be a lot of people starting up from this. >> So the startup on the opposite advantage and disadvantage, right? Like a startup doesn't have an proven way of delivering repeatable value in the same way that a scaled enterprise does. But it does have the nimbleness to identify and take advantage of techniques that you can start from a blank slate. And I think the thing that startups need to be wary of in the generative AI enlarged language model, in multimodal world, is building what I like to call, kind of like sandcastles. A sandcastle is maybe a business model or a capability that's built on top of an assumption that is going to be pretty quickly wiped away by improving underlying model technology. So almost like if you imagine like the ocean, the waves are coming in, and they're going to wipe away your progress. You don't want to be in the position of building sandcastle business where, you don't want to bet on the fact that models aren't going to get good enough to solve the task type that you might be solving. In other words, don't take a screenshot of what's capable today. Assume that what's capable today is only going to continue to become possible. And so for a startup, what you can do, that like enterprises are quite comparatively less good at, is embedding these capabilities deeply within your products and delivering maybe a vertical based experience, where AI kind of exists in the background. >> Yeah. >> And we might not think of companies as, you know, even AI companies, it's just so embedded in the experience they provide, but that's like the vertical application example of taking AI and making it be immediately usable. Or, of course there's tons of picks and shovels businesses to be built like Roboflow, where you're enabling these enterprises to take advantage of something that they have, whether that's their data sets, their computes, or their intellect. >> Okay, so if I hear that right, by the way, I love, that's horizontally scalable, that's the large language models, go up and build them the apps, hence your developer focus. I'm sure that's probably the reason that the tsunami of developer's action. So you're saying picks and shovels tools, don't try to replicate the platform of what could be the platform. Oh, go to a VC, I'm going to build a platform. No, no, no, no, those are going to get wiped away by the large language models. Is there one large language model that will rule the world, or do you see many coming? >> Yeah, so to be clear, I think there will be useful platforms. I just think a lot of people think that they're building, let's say, you know, if we put this in the cloud context, you're building a specific type of EC2 instance. Well, it turns out that Amazon can offer that type of EC2 instance, and immediately distribute it to all of their customers. So you don't want to be in the position of just providing something that actually ends up looking like a feature, which in the context of AI, might be like a small incremental improvement on the model. If that's all you're doing, you're a sandcastle business. Now there's a lot of platform businesses that need to be built that enable businesses to get to value and do things like, how do I monitor my models? How do I create better models with my given data sets? How do I ensure that my models are doing what I want them to do? How do I find the right models to use? There's all these sorts of platform wide problems that certainly exist for businesses. I just think a lot of startups that I'm seeing right now are making the mistake of assuming the advances we're seeing are not going to accelerate or even get better. >> So if I'm a customer, if I'm a company, say I'm a startup or an enterprise, either one, same question. And I want to stand up, and I have developers working on stuff, I want to start standing up an environment to start doing stuff. Is that a service provider? Is that a managed service? Is that you guys? So how do you guys fit into your customers leaning in? Is it just for developers? Are you targeting with a specific like managed service? What's the product consumption? How do you talk to customers when they come to you? >> The thing that we do is enable, we give developers superpowers to build automated inventory tracking, self-checkout systems, identify if this image is malignant cancer or benign cancer, ensure that these products that I've produced are correct. Make sure that that the defect that might exist on this electric vehicle makes its way back for review. All these sorts of problems are immediately able to be solved and tackled. In terms of the managed services element, we have solutions as integrators that will often build on top of our tools, or we'll have companies that look to us for guidance, but ultimately the company is in control of developing and building and creating these capabilities in house. I really think the distinction is maybe less around managed service and tool, and more around ownership in the era of AI. So for example, if I'm using a managed service, in that managed service, part of their benefit is that they are learning across their customer sets, then it's a very different relationship than using a managed service where I'm developing some amount of proprietary advantages for my data sets. And I think that's a really important thing that companies are becoming attuned to, just the value of the data that they have. And so that's what we do. We tell companies that you have this proprietary, immense treasure trove of data, use that to your advantage, and think about us more like a set of tools that enable you to get value from that capability. You know, the HashiCorp's and GitLab's of the world have proven like what these businesses look like at scale. >> And you're targeting developers. When you go into a company, do you target developers with freemium, is there a paid service? Talk about the business model real quick. >> Sure, yeah. The tools are free to use and get started. When someone signs up for Roboflow, they may elect to make their work open source, in which case we're able to provide even more generous usage limits to basically move the computer vision community forward. If you elect to make your data private, you can use our hosted data set managing, data set training, model deployment, annotation tooling up to some limits. And then usually when someone validates that what they're doing gets them value, they purchase a subscription license to be able to scale up those capabilities. So like most developer centric products, it's free to get started, free to prove, free to poke around, develop what you think is possible. And then once you're getting to value, then we're able to capture the commercial upside in the value that's being provided. >> Love the business model. It's right in line with where the market is. There's kind of no standards bodies these days. The developers are the ones who are deciding kind of what the standards are by their adoption. I think making that easy for developers to get value as the model open sources continuing to grow, you can see more of that. Great perspective Joseph, thanks for sharing that. Put a plug in for the company. What are you guys doing right now? Where are you in your growth? What are you looking for? How should people engage? Give the quick commercial for the company. >> So as I mentioned, Roboflow is I think one of the largest, if not the largest collections of computer vision models and data sets that are open source, available on the web today, and have a private set of tools that over half the Fortune 100 now rely on those tools. So we're at the stage now where we know people want what we're working on, and we're continuing to drive that type of adoption. So companies that are looking to make better models, improve their data sets, train and deploy, often will get a lot of value from our tools, and certainly reach out to talk. I'm sure there's a lot of talented engineers that are tuning in too, we're aggressively hiring. So if you are interested in being a part of making the world programmable, and being at the ground floor of the company that's creating these capabilities to be writ large, we'd love to hear from you. >> Amazing, Joseph, thanks so much for coming on and being part of the AWS Startup Showcase. Man, if I was in my twenties, I'd be knocking on your door, because it's the hottest trend right now, it's super exciting. Generative AI is just the beginning of massive sea change. Congratulations on all your success, and we'll be following you guys. Thanks for spending the time, really appreciate it. >> Thanks for having me. >> Okay, this is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talking about the hottest things in tech. I'm John Furrier, your host. Thanks for watching. (chill electronic music)

(cheerful music) >> Welcome to theCUBE's continuing coverage of AMD's 4th Generation EPYC launch. I'm Dave Nicholson, and I'm here in our Palo Alto studios talking to Greg Gibby, senior product manager, data center products from AMD, and Mohan Rokkam, technical marketing engineer at Dell. Welcome, gentlemen. >> Mohan: Hello, hello. >> Greg: Thank you. Glad to be here. >> Good to see each of you. Just really quickly, I want to start out. Let us know a little bit about yourselves. Mohan, let's start with you. What do you do at Dell exactly? >> So I'm a technical marketing engineer at Dell. I've been with Dell for around 15 years now and my goal is to really look at the Dell powered servers and see how do customers take advantage of some of the features we have, especially with the AMD EPYC processors that have just come out. >> Greg, and what do you do at AMD? >> Yeah, so I manage our software-defined infrastructure solutions team, and really it's a cradle to grave where we work with the ISVs in the market, so VMware, Nutanix, Microsoft, et cetera, to integrate the features that we're putting into our processors and make sure they're ready to go and enabled. And then we work with our valued partners like Dell on putting those into actual solutions that customers can buy and then we work with them to sell those solutions into the market. >> Before we get into the details on the 4th Generation EPYC launch and what that means and why people should care. Mohan, maybe you can tell us a little about the relationship between Dell and AMD, how that works, and then Greg, if you've got commentary on that afterwards, that'd be great. Yeah, Mohan. >> Absolutely. Dell and AMD have a long standing partnership, right? Especially now with EPYC series. We have had products since EPYC first generation. We have been doing solutions across the whole range of Dell ecosystem. We have integrated AMD quite thoroughly and effectively and we really love how performant these systems are. So, yeah. >> Dave: Greg, what are your thoughts? >> Yeah, I would say the other thing too is, is that we need to point out is that we both have really strong relationships across the entire ecosystem. So memory vendors, the software providers, et cetera, we have technical relationships. We're working with them to optimize solutions so that ultimately when the customer buys that, they get a great user experience right out of the box. >> So, Mohan, I know that you and your team do a lot of performance validation testing as time goes by. I suspect that you had early releases of the 4th Gen EPYC processor technology. What have you been seeing so far? What can you tell us? >> AMD has definitely knocked it out of the park. Time and again, in the past four generations, in the past five years alone, we have done some database work where in five years, we have seen five exit performance. And across the board, AMD is the leader in benchmarks. We have done virtualization where we would consolidate from five into one system. We have world records in AI, we have world records in databases, we have world records in virtualization. The AMD EPYC solutions has been absolutely performant. I'll leave you with one number here. When we went from top of Stack Milan to top of Stack Genoa, we saw a performance bump of 120%. And that number just blew my mind. >> So that prompts a question for Greg. Often we, in industry insiders, think in terms of performance gains over the last generation or the current generation. A lot of customers in the real world, however, are N - 2. They're a ways back, so I guess two points on that. First of all, the kinds of increases the average person is going to see when they move to this architecture, correct me if I'm wrong, but it's even more significant than a lot of the headline numbers because they're moving two generations, number one. Correct me if I'm wrong on that, but then the other thing is the question to you, Greg. I like very long complicated questions, as you can tell. The question is, is it okay for people to skip generations or make the case for upgrades, I guess is the problem? >> Well, yeah, so a couple thoughts on that first too. Mohan talked about that five X over the generation improvements that we've seen. The other key point with that too is that we've made significant process improvements along the way moving to seven nanocomputer to now five nanocomputer and that's really reducing the total amount of power or the performance per watt the customers can realize as well. And when we look at why would a customer want to upgrade, right? And I want to rephrase that as to why aren't you? And there is a real cost of not upgrading. And so when you look at infrastructure, the average age of a server in the data center is over five years old. And if you look at the most popular processors that were sold in that timeframe, it's 8, 10, 12 cores. So now you've got a bunch of servers that you need in order to deliver the applications and meet your SLAs to your end users, and all those servers pull power. They require maintenance. They have the opportunity to go down, et cetera. You got to pay licensing and service and support costs and all those. And when you look at all the costs that roll up, even though the hardware is paid for just to keep the lights on, and not even talking about the soft costs of unplanned downtime, and, "I'm not meeting your SLAs," et cetera, it's very expensive to keep those servers running. Now, if you refresh, and now you have processors that have 32, 64, 96 cores, now you can consolidate that infrastructure and reduce your total power bill. You can reduce your CapEx, you reduce your ongoing OpEx, you improve your performance, and you improve your security profile. So it really is more cost effective to refresh than not to refresh. >> So, Mohan, what has your experience been double clicking on this topic of consolidation? I know that we're going to talk about virtualization in some of the results that you've seen. What have you seen in that regard? Does this favor better consolidation and virtualized environments? And are you both assuring us that the ROI and TCO pencil out on these new big, bad machines? >> Greg definitely hit the nail on the head, right? We are seeing tremendous savings really, if you're consolidating from two generations old. We went from, as I said, five is to one. You're going from five full servers, probably paid off down to one single server. That itself is, if you look at licensing costs, which again, with things like VMware does get pretty expensive. If you move to a single system, yes, we are at 32, 64, 96 cores, but if you compare to the licensing costs of 10 cores, two sockets, that's still pretty significant, right? That's one huge thing. Another thing which actually really drives the thing is we are looking at security, and in today's environment, security becomes a major driving factor for upgrades. Dell has its own setups, cyber-resilient architecture, as we call it, and that really is integrated from processor all the way up into the OS. And those are some of the features which customers really can take advantage of and help protect their ecosystems. >> So what kinds of virtualized environments did you test? >> We have done virtualization across primary codes with VMware, but the Azure Stack, we have looked at Nutanix. PowerFlex is another one within Dell. We have vSAN Ready Nodes. All of these, OpenShift, we have a broad variety of solutions from Dell and AMD really fits into almost every one of them very well. >> So where does hyper-converged infrastructure fit into this puzzle? We can think of a server as something that contains not only AMD's latest architecture but also latest PCIe bus technology and all of the faster memory, faster storage cards, faster nicks, all of that comes together. But how does that play out in Dell's hyper-converged infrastructure or HCI strategy? >> Dell is a leader in hyper-converged infrastructure. We have the very popular VxRail line, we have the PowerFlex, which is now going into the AWS ecosystem as well, Nutanix, and of course, Azure Stack. With all these, when you look at AMD, we have up to 96 cores coming in. We have PCIe Gen 5 which means you can now connect dual port, 100 and 200 gig nicks and get line rate on those so you can connect to your ecosystem. And I don't know if you've seen the news, 200, 400 gig routers and switchers are selling out. That's not slowing down. The network infrastructure is booming. If you want to look at the AI/ML side of things, the VDI side of things, accelerator cards are becoming more and more powerful, more and more popular. And of course they need that higher end data path that PCIe Gen 5 brings to the table. GDDR5 is another huge improvement in terms of performance and latencies. So when we take all this together, you talk about hyper-converged, all of them add into making sure that A, with hyper-converged, you get ease of management, but B, just 'cause you have ease of management doesn't mean you need to compromise on anything. And the AMD servers effectively are a no compromise offering that we at Dell are able to offer to our customers. >> So Greg, I've got a question a little bit from left field for you. We covered Supercompute Conference 2022. We were in Dallas a couple of weeks ago, and there was a lot of discussion of the current processor manufacturer battles, and a lot of buzz around 4th Gen EPYC being launched and what's coming over the next year. Do you have any thoughts on what this architecture can deliver for us in terms of things like AI? We talk about virtualization, but if you look out over the next year, do you see this kind of architecture driving significant change in the world? >> Yeah, yeah, yeah, yeah. It has the real potential to do that from just the building blocks. So we have our chiplet architecture we call it. So you have an IO die and then you have your core complexes that go around that. And we integrate it all with our infinity fabric. That architecture allows you, if we wanted to, replace some of those CCDs with specific accelerators. And so when we look two, three, four years down the road, that architecture and that capability already built into what we're delivering and can easily be moved in. We just need to make sure that when you look at doing that, that the power that's required to do that and the software, et cetera, and those accelerators actually deliver better performance as a dedicated engine versus just using standard CPUs. The other things that I would say too is if you look at emerging workloads. So data center modernization is one of the buzzwords in cloud native, right? And these container environments, well, AMD'S architecture really just screams support for those type of environments, right? Where when you get into these larger core accounts and the consolidation that Mohan talked about. Now when I'm in a container environment, that blast radius so a lot of customers have concerns around, "Hey, having a single point of failure and having more than X number of cores concerns me." If I'm in containers, that becomes less of a concern. And so when you look at cloud native, containerized applications, data center modernization, AMD's extremely well positioned to take advantage of those use cases as well. >> Yeah, Mohan, and when we talk about virtualization, I think sometimes we have to remind everyone that yeah, we're talking about not only virtualization that has a full-blown operating system in the bucket, but also virtualization where the containers have microservices and things like that. I think you had something to add, Mohan. >> I did, and I think going back to the accelerator side of business, right? When we are looking at the current technology and looking at accelerators, AMD has done a fantastic job of adding in features like AVX-512, we have the bfloat16 and eight features. And some of what these do is they're effectively built-in accelerators for certain workloads especially in the AI and media spaces. And in some of these use cases we look at, for example, are inference. Traditionally we have used external accelerator cards, but for some of the entry level and mid-level use cases, CPU is going to work just fine especially with the newer CPUs that we are seeing this fantastic performance from. The accelerators just help get us to the point where if I'm at the edge, if I'm in certain use cases, I don't need to have an accelerator in there. I can run most of my inference workloads right on the CPU. >> Yeah, yeah. You know the game. It's an endless chase to find the bottleneck. And once we've solved the puzzle, we've created a bottleneck somewhere else. Back to the supercompute conversations we had, specifically about some of the AMD EPYC processor technology and the way that Dell is packaging it up and leveraging things like connectivity. That was one of the things that was also highlighted. This idea that increasingly connectivity is critically important, not just for supercomputing, but for high-performance computing that's finding its way out of the realms of Los Alamos and down to the enterprise level. Gentlemen, any more thoughts about the partnership or maybe a hint at what's coming in the future? I know that the original AMD announcement was announcing and previewing some things that are rolling out over the next several months. So let me just toss it to Greg. What are we going to see in 2023 in terms of rollouts that you can share with us? >> That I can share with you? Yeah, so I think look forward to see more advancements in the technology at the core level. I think we've already announced our product code name Bergamo, where we'll have up to 128 cores per socket. And then as we look in, how do we continually address this demand for data, this demand for, I need actionable insights immediately, look for us to continue to drive performance leadership in our products that are coming out and address specific workloads and accelerators where appropriate and where we see a growing market. >> Mohan, final thoughts. >> On the Dell side, of course, we have four very rich and configurable options with AMD EPYC servers. But beyond that, you'll see a lot more solutions. Some of what Greg has been talking about around the next generation of processors or the next updated processors, you'll start seeing some of those. and you'll definitely see more use cases from us and how customers can implement them and take advantage of the features that. It's just exciting stuff. >> Exciting stuff indeed. Gentlemen, we have a great year ahead of us. As we approach possibly the holiday seasons, I wish both of you well. Thank you for joining us. From here in the Palo Alto studios, again, Dave Nicholson here. Stay tuned for our continuing coverage of AMD's 4th Generation EPYC launch. Thanks for joining us. (cheerful music)

hi everybody welcome back to re invent 2022. this is thecube's exclusive coverage we're here at the satellite set it's up on the fifth floor of the Venetian Conference Center and this is part of the global startup program the AWS startup showcase series that we've been running all through last year and and into this year with AWS and featuring some of its its Global Partners Ed wallson series the CEO of chaos search many times Cube Alum and Kevin Miller there's also a cube Alum vice president GM of S3 at AWS guys good to see you again yeah great to see you Dave hi Kevin this is we call this our Super Bowl so this must be like your I don't know uh World Cup it's a pretty big event yeah it's the World Cup for sure yeah so a lot of S3 talk you know I mean that's what got us all started in 2006 so absolutely what's new in S3 yeah it's been a great show we've had a number of really interesting launches over the last few weeks and a few at the show as well so you know we've been really focused on helping customers that are running Mass scale data Lakes including you know whether it's structured or unstructured data we actually announced just a few just an hour ago I think it was a new capability to give customers cross-account access points for sharing data securely with other parts of the organization and that's something that we'd heard from customers is as they are growing and have more data sets and they're looking to to get more out of their data they are increasingly looking to enable multiple teams across their businesses to access those data sets securely and that's what we provide with cross-count access points we also launched yesterday our multi-region access point failover capabilities and so again this is where customers have data sets and they're using multiple regions for certain critical workloads they're now able to to use that to fail to control the failover between different regions in AWS and then one other launch I would just highlight is some improvements we made to storage lens which is our really a very novel and you need capability to help customers really understand what storage they have where who's accessing it when it's being accessed and we added a bunch of new metrics storage lens has been pretty exciting for a lot of customers in fact we looked at the data and saw that customers who have adopted storage lens typically within six months they saved more than six times what they had invested in turning storage lens on and certainly in this environment right now we have a lot of customers who are it's pretty top of mind they're looking for ways to optimize their their costs in the cloud and take some of those savings and be able to reinvest them in new innovation so pretty exciting with the storage lens launch I think what's interesting about S3 is that you know pre-cloud Object Store was this kind of a niche right and then of course you guys announced you know S3 in 2006 as I said and okay great you know cheap and deep storage simple get put now the conversations about how to enable value from from data absolutely analytics and it's just a whole new world and Ed you've talked many times I love the term yeah we built chaos search on the on the shoulders of giants right and so the under underlying that is S3 but the value that you can build on top of that has been key and I don't think we've talked about his shoulders and Giants but we've talked about how we literally you know we have a big Vision right so hard to kind of solve the challenge to analytics at scale we really focus on the you know the you know Big Data coming environment get analytics so we talk about the on the shoulders Giants obviously Isaac Newton's you know metaphor of I learned from everything before and we layer on top so really when you talk about all the things come from S3 like I just smile because like we picked it up naturally we went all in an S3 and this is where I think you're going Dave but everyone is so let's just cut the chase like so any of the data platforms you're using S3 is what you're building but we did it a little bit differently so at first people using a cold storage like you said and then they ETL it up into a different platforms for analytics of different sorts now people are using it closer they're doing caching layers and cashing out and they're that's where but that's where the attributes of a scale or reliability are what we did is we actually make S3 a database so literally we have no persistence outside that three and that kind of comes in so it's working really well with clients because most of the thing is we pick up all these attributes of scale reliability and it shows up in the clients environments and so when you launch all these new scalable things we just see it like our clients constantly comment like one of our biggest customers fintech in uh Europe they go to Black Friday again black Friday's not one days and they lose scale from what is it 58 terabytes a day and they're going up to 187 terabytes a day and we don't Flinch they say how do you do that well we built our platform on S3 as long as you can stream it to S3 so they're saying I can't overrun S3 and it's a natural play so it's it's really nice that but we take out those attributes but same thing that's why we're able to you know help clients get you know really you know Equifax is a good example maybe they're able to consolidate 12 their divisions on one platform we couldn't have done that without the scale and the performance of what you can get S3 but also they saved 90 I'm able to do that but that's really because the only persistence is S3 and what you guys are delivering but and then we really for focus on shoulders Giants we're doing on top of that innovating on top of your platforms and bringing that out so things like you know we have a unique data representation that makes it easy to ingest this data because it's kind of coming at you four v's of big data we allow you to do that make it performant on s3h so now you're doing hot analytics on S3 as if it's just a native database in memory but there's no memory SSC caching and then multi-model once you get it there don't move it leverage it in place so you know elasticsearch access you know Cabana grafana access or SQL access with your tools so we're seeing that constantly but we always talk about on the shoulders of giants but even this week I get comments from our customers like how did you do that and most of it is because we built on top of what you guys provided so it's really working out pretty well and you know we talk a lot about digital transformation of course we had the pleasure sitting down with Adam solipski prior John Furrier flew to Seattle sits down his annual one-on-one with the AWS CEO which is kind of cool yeah it was it's good it's like study for the test you know and uh and so but but one of the interesting things he said was you know we're one of our challenges going forward is is how do we go Beyond digital transformation into business transformation like okay well that's that's interesting I was talking to a customer today AWS customer and obviously others because they're 100 year old company and they're basically their business was they call them like the Uber for for servicing appliances when your Appliance breaks you got to get a person to serve it a service if it's out of warranty you know these guys do that so they got to basically have a you know a network of technicians yeah and they gotta deal with the customers no phone right so they had a completely you know that was a business transformation right they're becoming you know everybody says they're coming a software company but they're building it of course yeah right on the cloud so wonder if you guys could each talk about what's what you're seeing in terms of changing not only in the sort of I.T and the digital transformation but also the business transformation yeah I know I I 100 agree that I think business transformation is probably that one of the top themes I'm hearing from customers of all sizes right now even in this environment I think customers are looking for what can I do to drive top line or you know improve bottom line or just improve my customer experience and really you know sort of have that effect where I'm helping customers get more done and you know it is it is very tricky because to do that successfully the customers that are doing that successfully I think are really getting into the lines of businesses and figuring out you know it's probably a different skill set possibly a different culture different norms and practices and process and so it's it's a lot more than just a like you said a lot more than just the technology involved but when it you know we sort of liquidate it down into the data that's where absolutely we see that as a critical function for lines of businesses to become more comfortable first off knowing what data sets they have what data they they could access but possibly aren't today and then starting to tap into those data sources and then as as that progresses figuring out how to share and collaborate with data sets across a company to you know to correlate across those data sets and and drive more insights and then as all that's being done of course it's important to measure the results and be able to really see is this what what effect is this having and proving that effect and certainly I've seen plenty of customers be able to show you know this is a percentage increase in top or bottom line and uh so that pattern is playing out a lot and actually a lot of how we think about where we're going with S3 is related to how do we make it easier for customers to to do everything that I just described to have to understand what data they have to make it accessible and you know it's great to have such a great ecosystem of partners that are then building on top of that and innovating to help customers connect really directly with the businesses that they're running and driving those insights well and customers are hours today one of the things I loved that Adam said he said where Amazon is strategically very very patient but tactically we're really impatient and the customers out there like how are you going to help me increase Revenue how are you going to help me cut costs you know we were talking about how off off camera how you know software can actually help do that yeah it's deflationary I love the quote right so software's deflationary as costs come up how do you go drive it also free up the team and you nail it it's like okay everyone wants to save money but they're not putting off these projects in fact the digital transformation or the business it's actually moving forward but they're getting a little bit bigger but everyone's looking for creative ways to look at their architecture and it becomes larger larger we talked about a couple of those examples but like even like uh things like observability they want to give this tool set this data to all the developers all their sres same data to all the security team and then to do that they need to find a way an architect should do that scale and save money simultaneously so we see constantly people who are pairing us up with some of these larger firms like uh or like keep your data dog keep your Splunk use us to reduce the cost that one and one is actually cheaper than what you have but then they use it either to save money we're saving 50 to 80 hard dollars but more importantly to free up your team from the toil and then they they turn around and make that budget neutral and then allowed to get the same tools to more people across the org because they're sometimes constrained of getting the access to everyone explain that a little bit more let's say I got a Splunk or data dog I'm sifting through you know logs how exactly do you help so it's pretty simple I'll use dad dog example so let's say using data dog preservability so it's just your developers your sres managing environments all these platforms are really good at being a monitoring alerting type of tool what they're not necessarily great at is keeping the data for longer periods like the log data the bigger data that's where we're strong what you see is like a data dog let's say you're using it for a minister for to keep 30 days of logs which is not enough like let's say you're running environment you're finding that performance issue you kind of want to look to last quarter in last month in or maybe last Black Friday so 30 days is not enough but will charge you two eighty two dollars and eighty cents a gigabyte don't focus on just 280 and then if you just turn the knob and keep seven days but keep two years of data on us which is on S3 it goes down to 22 cents plus our list price of 80 cents goes to a dollar two compared to 280. so here's the thing what they're able to do is just turn a knob get more data we do an integration so you can go right from data dog or grafana directly into our platform so the user doesn't see it but they save money A lot of times they don't just save the money now they use that to go fund and get data dog to a lot more people make sense so it's a creativity they're looking at it and they're looking at tools we see the same thing with a grafana if you look at the whole grafana play which is hey you can't put it in one place but put Prometheus for metrics or traces we fit well with logs but they're using that to bring down their costs because a lot of this data just really bogs down these applications the alerting monitoring are good at small data they're not good at the big data which is what we're really good at and then the one and one is actually less than you paid for the one so it and it works pretty well so things are really unpredictable right now in the economy you know during the pandemic we've sort of lockdown and then the stock market went crazy we're like okay it's going to end it's going to end and then it looked like it was going to end and then it you know but last year it reinvented just just in that sweet spot before Omicron so we we tucked it in which which was awesome right it was a great great event we really really missed one physical reinvent you know which was very rare so that's cool but I've called it the slingshot economy it feels like you know you're driving down the highway and you got to hit the brakes and then all of a sudden you're going okay we're through it Oh no you're gonna hit the brakes again yeah so it's very very hard to predict and I was listening to jassy this morning he was talking about yeah consumers they're still spending but what they're doing is they're they're shopping for more features they might be you know buying a TV that's less expensive you know more value for the money so okay so hopefully the consumer spending will get us out of this but you don't really know you know and I don't yeah you know we don't seem to have the algorithms we've never been through something like this before so what are you guys seeing in terms of customer Behavior given that uncertainty well one thing I would highlight that I think particularly going back to what we were just talking about as far as business and digital transformation I think some customers are still appreciating the fact that where you know yesterday you may have had to to buy some Capital put out some capital and commit to something for a large upfront expenditure is that you know today the value of being able to experiment and scale up and then most importantly scale down and dynamically based on is the experiment working out am I seeing real value from it and doing that on a time scale of a day or a week or a few months that is so important right now because again it gets to I am looking for a ways to innovate and to drive Top Line growth but I I can't commit to a multi-year sort of uh set of costs to to do that so and I think plenty of customers are finding that even a few months of experimentation gives them some really valuable insight as far as is this going to be successful or not and so I think that again just of course with S3 and storage from day one we've been elastic pay for what you use if you're not using the storage you don't get charged for it and I think that particularly right now having the applications and the rest of the ecosystem around the storage and the data be able to scale up and scale down is is just ever more important and when people see that like typically they're looking to do more with it so if they find you usually find these little Department projects but they see a way to actually move faster and save money I think it is a mix of those two they're looking to expand it which can be a nightmare for sales Cycles because they take longer but people are looking well why don't you leverage this and go across division so we do see people trying to leverage it because they're still I don't think digital transformation is slowing down but a lot more to be honest a lot more approvals at this point for everything it is you know Adam and another great quote in his in his keynote he said if you want to save money the Cloud's a place to do it absolutely and I read an article recently and I was looking through and I said this is the first time you know AWS has ever seen a downturn because the cloud was too early back then I'm like you weren't paying attention in 2008 because that was the first major inflection point for cloud adoption where CFO said okay stop the capex we're going to Opex and you saw the cloud take off and then 2010 started this you know amazing cycle that we really haven't seen anything like it where they were doubling down in Investments and they were real hardcore investment it wasn't like 1998 99 was all just going out the door for no clear reason yeah so that Foundation is now in place and I think it makes a lot of sense and it could be here for for a while where people are saying Hey I want to optimize and I'm going to do that on the cloud yeah no I mean I've obviously I certainly agree with Adam's quote I think really that's been in aws's DNA from from day one right is that ability to scale costs with with the actual consumption and paying for what you use and I think that you know certainly moments like now are ones that can really motivate change in an organization in a way that might not have been as palatable when it just it didn't feel like it was as necessary yeah all right we got to go give you a last word uh I think it's been a great event I love all your announcements I think this is wonderful uh it's been a great show I love uh in fact how many people are here at reinvent north of 50 000. yeah I mean I feel like it was it's as big if not bigger than 2019. people have said ah 2019 was a record when you count out all the professors I don't know it feels it feels as big if not bigger so there's great energy yeah it's quite amazing and uh and we're thrilled to be part of it guys thanks for coming on thecube again really appreciate it face to face all right thank you for watching this is Dave vellante for the cube your leader in Enterprise and emerging Tech coverage we'll be right back foreign

(upbeat music) >> Good morning everybody. Welcome back to Las Vegas. This is day four of theCUBE's wall-to-wall coverage of our Super Bowl, aka AWS re:Invent 2022. I'm here with my co-host, Paul Gillin. My name is Dave Vellante. Sanjay Poonen is in the house, CEO and president of Cohesity. He's sitting in as our guest market watcher, market analyst, you know, deep expertise, new to the job at Cohesity. He was kind enough to sit in, and help us break down what's happening at re:Invent. But Paul, first thing, this morning we heard from Werner Vogels. He was basically given a masterclass on system design. It reminded me of mainframes years ago. When we used to, you know, bury through those IBM blue books and red books. You remember those Sanjay? That's how we- learned back then. >> Oh God, I remember those, Yeah. >> But it made me think, wow, now you know IBM's more of a systems design, nobody talks about IBM anymore. Everybody talks about Amazon. So you wonder, 20 years from now, you know what it's going to be. But >> Well- >> Werner's amazing. >> He pulled out a 24 year old document. >> Yup. >> That he had written early in Amazon's evolution about synchronous design or about essentially distributed architectures that turned out to be prophetic. >> His big thing was nature is asynchronous. So systems are asynchronous. Synchronous is an illusion. It's an abstraction. It's kind of interesting. But, you know- >> Yeah, I mean I've had synonyms for things. Timeless architecture. Werner's an absolute legend. I mean, when you think about folks who've had, you know, impact on technology, you think of people like Jony Ive in design. >> Dave: Yeah. >> You got to think about people like Werner in architecture and just the fact that Andy and the team have been able to keep him engaged that long... I pay attention to his keynote. Peter DeSantis has obviously been very, very influential. And then of course, you know, Adam did a good job, you know, watching from, you know, having watched since I was at the first AWS re:Invent conference, at time was President SAP and there was only a thousand people at this event, okay? Andy had me on stage. I think I was one of the first guest of any tech company in 2011. And to see now this become like, it's a mecca. It's a mother of all IT events, and watch sort of even the transition from Andy to Adam is very special. I got to catch some of Ruba's keynote. So while there's some new people in the mix here, this has become a force of nature. And the last time I was here was 2019, before Covid, watched the last two ones online. But it feels like, I don't know 'about what you guys think, it feels like it's back to 2019 levels. >> I was here in 2019. I feel like this was bigger than 2019 but some people have said that it's about the same. >> I think it was 60,000 versus 50,000. >> Yes. So close. >> It was a little bigger in 2019. But it feels like it's more active. >> And then last year, Sanjay, you weren't here but it was 25,000, which was amazing 'cause it was right in that little space between Omicron, before Omicron hit. But you know, let me ask you a question and this is really more of a question about Amazon's maturity and I know you've been following them since early days. But the way I get the question, number one question I get from people is how is Amazon AWS going to be different under Adam than it was under Andy? What do you think? >> I mean, Adam's not new because he was here before. In some senses he knows the Amazon culture from prior, when he was running sales and marketing prior. But then he took the time off and came back. I mean, this will always be, I think, somewhat Andy's baby, right? Because he was the... I, you know, sent him a text, "You should be really proud of what you accomplished", but you know, I think he also, I asked him when I saw him a few weeks ago "Are you going to come to re:Invent?" And he says, "No, I want to leave this to be Adam's show." And Adam's going to have a slightly different view. His keynotes are probably half the time. It's a little bit more vision. There was a lot more customer stories at the beginning of it. Taking you back to the inspirational pieces of it. I think you're going to see them probably pulling up the stack and not just focused in infrastructure. Many of their platform services are evolved. Many of their, even application services. I'm surprised when I talk to customers. Like Amazon Connect, their sort of call center type technologies, an app layer. It's getting a lot. I mean, I've talked to a couple of Fortune 500 companies that are moving off Ayer to Connect. I mean, it's happening and I did not know that. So it's, you know, I think as they move up the stack, the platform's gotten more... The data centric stack has gotten, and you know, in the area we're working with Cohesity, security, data protection, they're an investor in our company. So this is an important, you know, both... I think tech player and a partner for many companies like us. >> I wonder the, you know, the marketplace... there's been a big push on the marketplace by all the cloud companies last couple of years. Do you see that disrupting the way softwares, enterprise software is sold? >> Oh, for sure. I mean, you have to be a ostrich with your head in the sand to not see this wave happening. I mean, what's it? $150 billion worth of revenue. Even though the growth rates dipped a little bit the last quarter or so, it's still aggregatively between Amazon and Azure and Google, you know, 30% growth. And I think we're still in the second or third inning off a grand 1 trillion or 2 trillion of IT, shifting not all of it to the cloud, but significantly faster. So if you add up all of the big things of the on-premise world, they're, you know, they got to a certain size, their growth is stable, but stalling. These guys are growing significantly faster. And then if you add on top of them, platform companies the data companies, Snowflake, MongoDB, Databricks, you know, Datadog, and then apps companies on top of that. I think the move to the Cloud is inevitable. In SaaS companies, I don't know why you would ever implement a CRM solution on-prem. It's all gone to the Cloud. >> Oh, it is. >> That happened 15 years ago. I mean, begin within three, five years of the advent of Salesforce. And the same thing in HR. Why would you deploy a HR solution now? You've got Workday, you've got, you know, others that are so some of those apps markets are are just never coming back to an on-prem capability. >> Sanjay, I want to ask you, you built a reputation for being able to, you know, forecast accurately, hit your plan, you know, you hit your numbers, you're awesome operator. Even though you have a, you know, technology degree, which you know, that's a two-tool star, multi-tool star. But I call it the slingshot economy. This is like, I mean I've seen probably more downturns than anybody in here, you know, given... Well maybe, maybe- >> Maybe me. >> You and I both. I've never seen anything like this, where where visibility is so unpredictable. The economy is sling-shotting. It's like, oh, hurry up, go Covid, go, go go build, build, build supply, then pull back. And now going forward, now pulling back. Slootman said, you know, on the call, "Hey the guide, is the guide." He said, "we put it out there, We do our best to hit it." But you had CrowdStrike had issues you know, mid-market, ServiceNow. I saw McDermott on the other day on the, on the TV. I just want to pay, you know, buy from the guy. He's so (indistinct) >> But mixed, mixed results, Salesforce, you know, Octa now pre-announcing, hey, they're going to be, or announcing, you know, better visibility, forward guide. Elastic kind of got hit really hard. HPE and Dell actually doing really well in the enterprise. >> Yep. >> 'Course Dell getting killed in the client. But so what are you seeing out there? How, as an executive, do you deal with such poor visibility? >> I think, listen, what the last two or three years have taught us is, you know, with the supply chain crisis, with the surge that people thought you may need of, you know, spending potentially in the pandemic, you have to start off with your tech platform being 10 x better than everybody else. And differentiate, differentiate. 'Cause in a crowded market, but even in a market that's getting tougher, if you're not differentiating constantly through technology innovation, you're going to get left behind. So you named a few places, they're all technology innovators, but even if some of them are having challenges, and then I think you're constantly asking yourselves, how do you move from being a point product to a platform with more and more services where you're getting, you know, many of them moving really fast. In the case of Roe, I like him a lot. He's probably one of the most savvy operators, also that I respect. He calls these speedboats, and you know, his core platform started off with the firewall network security. But he's built now a very credible cloud security, cloud AI security business. And I think that's how you need to be thinking as a tech executive. I mean, if you got core, your core beachhead 10 x better than everybody else. And as you move to adjacencies in these new platforms, have you got now speedboats that are getting to a point where they are competitive advantage? Then as you think of the go-to-market perspective, it really depends on where you are as a company. For a company like our size, we need partners a lot more. Because if we're going to, you know, stand on the shoulders of giants like Isaac Newton said, "I see clearly because I stand on the shoulders giants." I need to really go and cultivate Amazon so they become our lead partner in cloud. And then appropriately Microsoft and Google where I need to. And security. Part of what we announced last week was, last month, yeah, last couple of weeks ago, was the data security alliance with the biggest security players. What was I trying to do with that? First time ever done in my industry was get Palo Alto, CrowdStrike, Wallace, Tenable, CyberArk, Splunk, all to build an alliance with me so I could stand on their shoulders with them helping me. If you're a bigger company, you're constantly asking yourself "how do you make sure you're getting your, like Amazon, their top hundred customers spending more with that?" So I think the the playbook evolves, and I'm watching some of these best companies through this time navigate through this. And I think leadership is going to be tested in enormously interesting ways. >> I'll say. I mean, Snowflake is really interesting because they... 67% growth, which is, I mean, that's best in class for a company that's $2 billion. And, but their guide was still, you know, pretty aggressive. You know, so it's like, do you, you know, when it when it's good times you go, "hey, we can we can guide conservatively and know we can beat it." But when you're not certain, you can't dial down too far 'cause your investors start to bail on you. It's a really tricky- >> But Dave, I think listen, at the end of the day, I mean every CEO should not be worried about the short term up and down in the stock price. You're building a long-term multi-billion dollar company. In the case of Frank, he has, I think I shot to a $10 billion, you know, analytics data warehousing data management company on the back of that platform, because he's eyeing the market that, not just Teradata occupies today, but now Oracle occupies or other databases, right? So his tam as it grows bigger, you're going to have some of these things, but that market's big. I think same with Palo Alto. I mean Datadog's another company, 75% growth. >> Yeah. >> At 20% margins, like almost rule of 95. >> Amazing. >> When they're going after, not just the observability market, they're eating up the sim market, security analytics, the APM market. So I think, you know, that's, you look at these case studies of companies who are going from point product to platforms and are steadily able to grow into new tams. You know, to me that's very inspiring. >> I get it. >> Sanjay: That's what I seek to do at our com. >> I get that it's a marathon, but you know, when you're at VMware, weren't you looking at the stock price every day just out of curiosity? I mean listen, you weren't micromanaging it. >> You do, but at the end of the day, and you certainly look at the days of earnings and so on so forth. >> Yeah. >> Because you want to create shareholder value. >> Yeah. >> I'm not saying that you should not but I think in obsession with that, you know, in a short term, >> Going to kill ya. >> Makes you, you know, sort of myopically focused on what may not be the right thing in the long term. Now in the long arc of time, if you're not creating shareholder value... Look at what happened to Steve Bomber. You needed Satya to come in to change things and he's created a lot of value. >> Dave: Yeah, big time. >> But I think in the short term, my comments were really on the quarter to quarter, but over a four a 12 quarter, if companies are growing and creating profitable growth, they're going to get the valuation they deserve. >> Dave: Yeah. >> Do you the... I want to ask you about something Arvind Krishna said in the previous IBM earnings call, that IT is deflationary and therefore it is resistant to the macroeconomic headwinds. So IT spending should actually thrive in a deflation, in a adverse economic climate. Do you think that's true? >> Not all forms of IT. I pay very close attention to surveys from, whether it's the industry analysts or the Morgan Stanleys, or Goldman Sachs. The financial analysts. And I think there's a gluc in certain sectors that will get pulled back. Traditional view is when the economies are growing people spend on the top line, front office stuff, sales, marketing. If you go and look at just the cloud 100 companies, which are the hottest private companies, and maybe with the public market companies, there's way too many companies focused on sales and marketing. Way too many. I think during a downsizing and recession, that's going to probably shrink some, because they were all built for the 2009 to 2021 era, where it was all about the top line. Okay, maybe there's now a proposition for companies who are focused on cost optimization, supply chain visibility. Security's been intangible, that I think is going to continue to an investment. So I tell, listen, if you are a tech investor or if you're an operator, pay attention to CIO priorities. And right now, in our business at Cohesity, part of the reason we've embraced things like ransomware protection, there is a big focus on security. And you know, by intelligently being a management and a security company around data, I do believe we'll continue to be extremely relevant to CIO budgets. There's a ransomware, 20 ransomware attempts every second. So things of that kind make you relevant in a bank. You have to stay relevant to a buying pattern or else you lose momentum. >> But I think what's happening now is actually IT spending's pretty good. I mean, I track this stuff pretty closely. It's just that expectations were so high and now you're seeing earnings estimates come down and so, okay, and then you, yeah, you've got the, you know the inflationary factors and your discounted cash flows but the market's actually pretty good. >> Yeah. >> You know, relative to other downturns that if this is not a... We're not actually not in a downturn. >> Yeah. >> Not yet anyway. It may be. >> There's a valuation there. >> You have to prepare. >> Not sales. >> Yeah, that's right. >> When I was on CNBC, I said "listen, it's a little bit like that story of Joseph. Seven years of feast, seven years of famine." You have to prepare for potentially your worst. And if it's not the worst, you're in good shape. So will it be a recession 2023? Maybe. You know, high interest rates, inflation, war in Russia, Ukraine, maybe things do get bad. But if you belt tightening, if you're focused in operational excellence, if it's not a recession, you're pleasantly surprised. If it is one, you're prepared for it. >> All right. I'm going to put you in the spot and ask you for predictions. Expert analysis on the World Cup. What do you think? Give us the breakdown. (group laughs) >> As my... I wish India was in the World Cup, but you can't get enough Indians at all to play soccer well enough, but we're not, >> You play cricket, though. >> I'm a US man first. I would love to see one of Brazil, or Argentina. And as a Messi person, I don't know if you'll get that, but it would be really special for Messi to lead, to end his career like Maradonna winning a World Cup. I don't know if that'll happen. I'm probably going to go one of the Latin American countries, if the US doesn't make it far enough. But first loyalty to the US team, and then after one of the Latin American countries. >> And you think one of the Latin American countries is best bet to win or? >> I don't know. It's hard to tell. They're all... What happens now at this stage >> So close, right? >> is anybody could win. >> Yeah. You just have lots of shots of gold. I'm a big soccer fan. It could, I mean, I don't know if the US is favored to win, but if they get far enough, you get to the finals, anybody could win. >> I think they get Netherlands next, right? >> That's tough. >> Really tough. >> But... The European teams are good too, but I would like to see US go far enough, and then I'd like to see Latin America with team one of Argentina, or Brazil. That's my prediction. >> I know you're a big Cricket fan. Are you able to follow Cricket the way you like? >> At god unearthly times the night because they're in Australia, right? >> Oh yeah. >> Yeah. >> I watched the T-20 World Cup, select games of it. Yeah, you know, I'm not rapidly following every single game but the World Cup games, I catch you. >> Yeah, it's good. >> It's good. I mean, I love every sport. American football, soccer. >> That's great. >> You get into basketball now, I mean, I hope the Warriors come back strong. Hey, how about the Warriors Celtics? What do we think? We do it again? >> Well- >> This year. >> I'll tell you what- >> As a Boston Celtics- >> I would love that. I actually still, I have to pay off some folks from Palo Alto office with some bets still. We are seeing unprecedented NBA performance this year. >> Yeah. >> It's amazing. You look at the stats, it's like nothing. I know it's early. Like nothing we've ever seen before. So it's exciting. >> Well, always a pleasure talking to you guys. >> Great to have you on. >> Thanks for having me. >> Thank you. Love the expert analysis. >> Sanjay Poonen. Dave Vellante. Keep it right there. re:Invent 2022, day four. We're winding up in Las Vegas. We'll be right back. You're watching theCUBE, the leader in enterprise and emerging tech coverage. (lighthearted soft music)

(bright music) >> Hello cloud computing friends and welcome back to theCUBE, where we are live from Las Vegas, Nevada, here at AWS re:Invent all week. My name is Savannah Peterson, very excited to be joined by Paul Gillan today. How are you doing? >> I'm doing great, Savannah. It's my first re:Invent. >> I was just going to ask you >> So it's quite an experience. >> If you've ever been to re:Invent. >> It's dazzling much like the sequins on your top. It's dazzling. >> Yes. >> It's a jam packed affair. I came to the COMDEX Conference for many years in Las Vegas, which was huge event and this really rivals it in terms of these crowd sizes. But I think there's more intensity here. There's more excitement. People are just jazzed about being here to the extent that I never saw at other computer conferences. >> I thought I would agree with you. It's my first re:Invent as well. I'm glad we could share this experience together. And the vibe, the pulse, I think being back in person is really contagious as well. Ooh, maybe the wrong word to use, but in a great way. The energy is definitely radiating between people here. I'll watch my words a little bit better. >> And in person we have with us Samuel Nicholls, the director of public cloud at Global Product Marketing at Veeam Software. Sam, is it Sam or Samuel? >> Depends if I'm in trouble, Paul. >> Savannah: But it depends on who's saying it out loud. >> Yeah, yeah. It's typically, Samuel is usually reserved for my mother, so- >> Yeah. >> (laughs) Well, Sam, thanks for joining us. >> We'll stick with Sam on the show. >> Yeah. >> So Veeam been a red hot company for several years. Really made its, uh, its reputation in the VMware world. Now you've got this whole-sail shift to the cloud, not that VMware is not important still, but how is that affecting, you're shifting with it, how is that affecting your role as a product manager and the business overall? >> Yeah, it's a fantastic question. Obviously Veeam was pioneered in terms of being the purpose-built backup and recovery company for VMware. And as these workloads are being transitioned from the data center into the cloud or just net new workloads being created in the cloud, there is that equal need for backup and recovery there. So it's incredibly important that we were able to provide a purpose-built backup and recovery solution for workloads that live in AWS as well. >> Paul: And how different is it backing up an AWS workload compared to a VMware workload? >> I think it depends on what kind of service a user is, is, is utilizing, right? There's infrastructure as a service, platform as a service, software as a service. And given the differences in what is exposed to that customer that can make backup and recovery quite challenging. So I would say that the primary thing that we want to look at is utilizing native snapshots is our first line of defense when it comes to backup and recovery, irrespective of what workload that right might be whether it's a virtual machine, Amazon EC2, some sort of database on Amazon RDS, a file share, so on. >> Savannah: I bet you're seeing a lot across verticals and across the industry given the support that you're giving customers. What are you seeing in the market and in customer environments? What are some of those trends? >> So I think the major trends that we highlight in our data protection trend support, which is a new update is coming very shortly in the new year, is- >> Savannah: We have to check that out. >> Yeah, absolutely. The physical server is on a decline within the data center. Virtualized workloads, namely VMware is relatively static, kind of flat. The real hockey stick is with the cloud workloads. And as I mentioned before, that is partially because workloads are being transitioned from physical to virtual machines to being cloud hosted but also we're creating more applications and the cloud has become lead de facto standard for new workloads. So you hear about cloud first initiatives, digital transformation, the cloud is central to that. >> You mentioned snapshotting, which is a relatively new phenomenon, although it's taken a hold rapidly, how does snapshotting work in the cloud versus in on your on-prem environment? >> Samuel: It's not wildly different at all. I think the snapshots is again, a great first line of defense for helping users achieve very low recovery point objectives. So the frequency that they can protect their data as well as very low recovery time objectives, how quickly that I can recover the data. Because that's why we're backing up, right? We need the ability to recover. However, snapshots certainly have their limitations as well. They are not independent of the workload that is being protected. So if there were to be some sort of cybersecurity event like ransomware that is prolific throughout pretty much every business, every vertical. When that snapshot is not independent, if the production system becomes compromised that snapshot's likely to be compromised as well. And then going back to the recovery piece, not going to have something to recover from. >> And it's not a one and done with ransomware. >> No. >> It's, yeah. So how, so what is the role that backup plays? I mean a lot of people, I feel like security is such a hot topic here in the show and just in general, attacks are coming in unique form factors for everyone. I mean, I feel like backup is, no pun intended, the backbone of a system here. How does that affect what you're creating, I mean? >> Yeah, absolutely. I think, like you say the backup is core to any comprehensive security strategy, right? I think when we talk about security, everyone tends to focus on the preventative, the proactive piece, stopping the bad guys from getting in. However, there is that remediative aspect as well because like you say, ransomware is relentless, right? You, you as a good guy have to pretty much fend off each and every single attack that comes your way. And that can be an infinite number of attacks. We're all human beings, we're fallible, right? And sometimes we can't defend against everything. So having a secure backup strategy is part of that remediative recovery component for a cybersecurity strategy is critical. And that includes things like encryption, immutability, logical separation of data and so forth. >> Paul: We know that ransomware is a scourge on-premises, typically begins with the end users, end user workstation. How does ransomware work in the cloud? And do the cloud providers have adequate protections against ransomware? Or can they? >> Samuel: Yeah, it's a, it's a fantastic question as well. I think when we look at the cloud, one of the common misconceptions is as we transition workloads to the cloud, we are transitioning responsibility to that cloud provider. And again, it's a misconception, right? It is a shared responsibility between the cloud provider in this case, AWS and the user. So as we transition these workloads across varying different services, infrastructure, platform, software as a service, we're always, always transitioning varying degrees of responsibility. But we always own our data and it is our responsibility to protect and secure that data, for the actual infrastructure components, the hardware that is on the onus of the cloud provider, so I'd say that's the major difference. >> Is ransomware as big a threat in the cloud as it is on-prem? >> Absolutely. There's no difference between a ransomware attack on-premises or in the cloud. Irrespective of where you are choosing to run your workloads, you need to have that comprehensive cybersecurity strategy in order to defend against that and ultimately recover as well if there's a successful attempt. >> Yeah, it's, ooh, okay. Let's get us out at the dark shadows real quick (laughs) and bring us back to a little bit of the business use case here. A lot of people using AWS. What do you think are some of the considerations, they should have when they're thinking about this, thinking about growing their (indistinct)? >> Well, if we're going to stick down the dark shadows, the cybersecurity piece. >> We can be the darkness. >> You and me kind of dark shadows business. >> Yeah, yeah. >> We can go rainbows and unicorns, nice and happy if you like. I think there's a number of considerations they need to keep up. Security is, is, is number one. The next piece is around the recovery as well. I think folks, when they, when we talk about backup and recovery, the focus is always on the backup piece of it. But again, we need to focus on why we're doing the backup. It's the recovery, it's the recovery component. So making sure that we have a clean verifiable backup that we're able to restore data from. Can we do that in a, in efficient and timely manner? And I think the other major consideration is looking at the entirety of our environments as well. Very few companies are a hundred percent sole sourced on a single cloud provider. It is typically hybrid cloud. It's around 80% of organizations are hybrid, right? So they have their on-premises data and they also have workloads running in one or multiple clouds. And when it comes to backup and recovery of all of these different infrastructures and environments, the way that we approach it is very different. And that often leads to multiple different point products from multiple different vendors. The average company utilizes three different backup products, sometimes as many as seven and that can introduce a management nightmare that's very complex, very resource intensive, expensive. So looking at the entirety of the environment and looking to utilize a backup provider that can cover the entirety of that environment while centralizing everything under a single management console helps folks be a lot more efficient, a lot more cost effective and ultimately better when it comes to data protection. >> Amazon and all cloud providers really are increasingly making regions transparent. Just at this conference, Amazon introduced failover controls from multiple multi-region access points. So you can, you can failover from one access from one region to another. What kind of challenges does that present to you as a backup provider? >> I don't think it represents any challenges. When we look at the native durability of the cloud, we look at availability zones, we look at multi-region failover. That is, that durability is ultimately founded on, on replication. And I wouldn't say that replication and backup, you would use one or the other. I would say that they are complimentary. So for replication, that is going to help with the failover scenario, that durability component. But then backup again is that independent copy. Because if we look at replication, if let's say the source data were to be compromised by ransomware or there was accidental deletion or corruption, that's simply going to be copied over to the target destination as well. Having that backup as an independent copy, again compliments that strategy as well. >> Paul: You need it in either, in any scenario. >> Samuel: In any scenario. >> I think the average person would probably say that backup is not the most exciting technology aspect of this industry. But, but you guys certainly made, build a great business on it. What excites you about what's coming in backup? What are the new technologies, new advancements that perhaps we haven't seen and productized yet that you think are going to change the game? >> I think actually what we offer right now is the most exciting piece which is just choice flexibility. So Veeam again is synonymous with VMware backup but we cover a multitude of environments including AWS, containerized workloads, Kubernetes physical systems and the mobility pieces is critical because as organizations look to act on their digital transformation, cloud first initiatives, they need to be able to mobilize their workloads across different infrastructures, maybe from on-premises into the cloud, one cloud to another, maybe it's cloud back to on-premises, 'cause we do also see that. That flexibility of choice is what excites me about Veeam because it's ultimately giving the users best in class data protection tool sets without any prescriptive approach from us in terms of where you should be running your workloads. That is the choice that you use. >> Yeah, Veeam is definitely more than VMware. We actually had a chance to chat with you all like KubeCon and CloudNativeCon in Detroit. So we, we've seen the multitude of things that you touch. I want to bring it back to something and something kind of fun because you talked a lot about the community and being able to serve them. It's very clear, actually I shouldn't say this, I shouldn't say it's very clear, but to me it appears clear that community is a big priority for Veeam. I just want to call this out 'cause this was one of the cooler pieces of swag. You all gave out a hundred massage guns. Okay, very hot topic. Hot Christmas gift for 2022. I feel like Vanna White right now. And, but I thought that I was actually really compelled by this because we do a swag segment on theCUBE but it's not just about the objects or getting stuff. It's really about who's looking out for their community and how are they saying thanks. I mean, swag is a brand activation but it's also a thank you and I loved that you were giving out massage guns to the AWS Heroes and Community Builders. >> Yep. >> What role does community play in the culture and the product development at Veeam? >> So community has always been at the heart of Veeam. If you have a look at pretty much every single development across all of our versions, across all of our products it's always did by the community, right? We have a wonderful Veeam forum where we got 400,000 plus users actively providing feedback on the product what they would like to see. And that is ultimately what steers the direction of the product. Of course market trends and technology chain. >> A couple other factors, I'm sure. >> A couple of other factors, but community is huge for us. And the same goes for AWS. So, you know, talking with the AWS Heroes, the Community Builders helps Veeam reach further into that, into that community and the AWS user base and empower those folks with data protection tools and massage guns, when your feet are tired from, you know, being standing on them all day in Vegas. >> (laughs) Yeah, well, I mean, everybody, everybody's working hard and it's nice to say, it's nice to say, thank you. So I love, I love to hear that and it's, it's clear from the breadth of products that you're creating, the ways that you're supporting your customers that you already, they care a lot about community. We have a new challenge on theCUBE this year at AWS re:Invent. Think of it as an Instagram reel of your thought leadership, your hot take on the show, key themes as we look into 2023. What do you think is the most important story or trend or thing going on here at the show? >> I think it's just the continuation of cybersecurity and the importance of backup as a comprehensive cybersecurity strategy. You know, some folks might say that secure backup is your last line of defense. Again, ransomware is relentless. These folks are going to keep coming and even if they're successful, it's not a one and done thing. It's going to happen again and again and again. So, you know, we have a look around the show floor, the presentations there is a huge cybersecurity focus and really just what folks should be doing as their best practice to secure their AWS environments. >> That's awesome. Well, Paul, any final, any final thoughts or questions? >> I just quickly, you've mentioned data security, you mentioned data protection and backup sort of interchangeably but they're not really the same thing, are they? I mean, what businesses do you see Veeam as being here? >> I would say that we are a data protection company because of, yes, there is backup, but there's also the replication component. There's the continuous data protection component where we've got, you know, near-zero RTOs and then we again look at the cybersecurity components of that. What can we do to really protect that data? So I would say that the two are different. Backup is a subset of data protection. >> Sam, thank you so much for being here with us on theCUBE. It's been a super insightful conversation. Hopefully we'll get you back soon and more of the teams, there seem to be celebrities here with us on theCUBE. Paul Gillan, thank you so much for being here with me. >> Pleasure Savannah. >> And I'm glad we get to celebrate our first re:Invent and most importantly, thank you to the audience for tuning in. Without you, we don't get to hang out here in fabulous Las Vegas, Nevada, where we're live from the show floor at AWS re:Invent. My name is Savannah Peterson with Paul Gillan. We're theCUBE and we are the leading source for high-tech coverage. (bright music)

(lively music) >> Good morning. Welcome back to theCUBE's coverage at Supercomputing Conference 2022, live here in Dallas. I'm Dave Nicholson with my co-host Paul Gillin. So far so good, Paul? It's been a fascinating morning Three days in, and a fascinating guest, Ian from AWS. Welcome. >> Thanks, Dave. >> What are we going to talk about? Batch computing, HPC. >> We've got a lot, let's get started. Let's dive right in. >> Yeah, we've got a lot to talk about. I mean, first thing is we recently announced our batch support for EKS. EKS is our Kubernetes, managed Kubernetes offering at AWS. And so batch computing is still a large portion of HPC workloads. While the interactive component is growing, the vast majority of systems are just kind of fire and forget, and we want to run thousands and thousands of nodes in parallel. We want to scale out those workloads. And what's unique about our AWS batch offering, is that we can dynamically scale, based upon the queue depth. And so customers can go from seemingly nothing up to thousands of nodes, and while they're executing their work they're only paying for the instances while they're working. And then as the queue depth starts to drop and the number of jobs waiting in the queue starts to drop, then we start to dynamically scale down those resources. And so it's extremely powerful. We see lots of distributed machine learning, autonomous vehicle simulation, and traditional HPC workloads taking advantage of AWS Batch. >> So when you have a Kubernetes cluster does it have to be located in the same region as the HPC cluster that's going to be doing the batch processing, or does the nature of batch processing mean, in theory, you can move something from here to somewhere relatively far away to do the batch processing? How does that work? 'Cause look, we're walking around here and people are talking about lengths of cables in order to improve performance. So what does that look like when you peel back the cover and you look at it physically, not just logically, AWS is everywhere, but physically, what does that look like? >> Oh, physically, for us, it depends on what the customer's looking for. We have workflows that are all entirely within a single region. And so where they could have a portion of say the traditional HPC workflow, is within that region as well as the batch, and they're saving off the results, say to a shared storage file system like our Amazon FSx for Lustre, or maybe aging that back to an S3 object storage for a little lower cost storage solution. Or you can have customers that have a kind of a multi-region orchestration layer to where they say, "You know what? "I've got a portion of my workflow that occurs "over on the other side of the country "and I replicate my data between the East Coast "and the West Coast just based upon business needs. "And I want to have that available to customers over there. "And so I'll do a portion of it in the East Coast "a portion of it in the West Coast." Or you can think of that even globally. It really depends upon the customer's architecture. >> So is the intersection of Kubernetes with HPC, is this relatively new? I know you're saying you're, you're announcing it. >> It really is. I think we've seen a growing perspective. I mean, Kubernetes has been a long time kind of eating everything, right, in the enterprise space? And now a lot of CIOs in the industrial space are saying, "Why am I using one orchestration layer "to manage my HPC infrastructure and another one "to manage my enterprise infrastructure?" And so there's a growing appreciation that, you know what, why don't we just consolidate on one? And so that's where we've seen a growth of Kubernetes infrastructure and our own managed Kubernetes EKS on AWS. >> Last month you announced a general availability of Trainium, of a chip that's optimized for AI training. Talk about what's special about that chip or what is is customized to the training workloads. >> Yeah, what's unique about the Trainium, is you'll you'll see 40% price performance over any other GPU available in the AWS cloud. And so we've really geared it to be that most price performance of options for our customers. And that's what we like about the silicon team, that we're part of that Annaperna acquisition, is because it really has enabled us to have this differentiation and to not just be innovating at the software level but the entire stack. That Annaperna Labs team develops our network cards, they develop our ARM cards, they developed this Trainium chip. And so that silicon innovation has become a core part of our differentiator from other vendors. And what Trainium allows you to do is perform similar workloads, just at a lower price performance. >> And you also have a chip several years older, called Inferentia- >> Um-hmm. >> Which is for inferencing. What is the difference between, I mean, when would a customer use one versus the other? How would you move the workload? >> What we've seen is customers traditionally have looked for a certain class of machine, more of a compute type that is not as accelerated or as heavy as you would need for Trainium for their inference portion of their workload. So when they do that training they want the really beefy machines that can grind through a lot of data. But when you're doing the inference, it's a little lighter weight. And so it's a different class of machine. And so that's why we've got those two different product lines with the Inferentia being there to support those inference portions of their workflow and the Trainium to be that kind of heavy duty training work. >> And then you advise them on how to migrate their workloads from one to the other? And once the model is trained would they switch to an Inferentia-based instance? >> Definitely, definitely. We help them work through what does that design of that workflow look like? And some customers are very comfortable doing self-service and just kind of building it on their own. Other customers look for a more professional services engagement to say like, "Hey, can you come in and help me work "through how I might modify my workflow to "take full advantage of these resources?" >> The HPC world has been somewhat slower than commercial computing to migrate to the cloud because- >> You're very polite. (panelists all laughing) >> Latency issues, they want to control the workload, they want to, I mean there are even issues with moving large amounts of data back and forth. What do you say to them? I mean what's the argument for ditching the on-prem supercomputer and going all-in on AWS? >> Well, I mean, to be fair, I started at AWS five years ago. And I can tell you when I showed up at Supercomputing, even though I'd been part of this community for many years, they said, "What is AWS doing at Supercomputing?" I know you care, wait, it's Amazon Web Services. You care about the web, can you actually handle supercomputing workloads? Now the thing that very few people appreciated is that yes, we could. Even at that time in 2017, we had customers that were performing HPC workloads. Now that being said, there were some real limitations on what we could perform. And over those past five years, as we've grown as a company, we've started to really eliminate those frictions for customers to migrate their HPC workloads to the AWS cloud. When I started in 2017, we didn't have our elastic fabric adapter, our low-latency interconnect. So customers were stuck with standard TCP/IP. So for their highly demanding open MPI workloads, we just didn't have the latencies to support them. So the jobs didn't run as efficiently as they could. We didn't have Amazon FSx for Lustre, our managed lustre offering for high performant, POSIX-compliant file system, which is kind of the key to a large portion of HPC workloads is you have to have a high-performance file system. We didn't even, I mean, we had about 25 gigs of networking when I started. Now you look at, with our accelerated instances, we've got 400 gigs of networking. So we've really continued to grow across that spectrum and to eliminate a lot of those really, frictions to adoption. I mean, one of the key ones, we had a open source toolkit that was jointly developed by Intel and AWS called CFN Cluster that customers were using to even instantiate their clusters. So, and now we've migrated that all the way to a fully functional supported service at AWS called AWS Parallel Cluster. And so you've seen over those past five years we have had to develop, we've had to grow, we've had to earn the trust of these customers and say come run your workloads on us and we will demonstrate that we can meet your demanding requirements. And at the same time, there's been, I'd say, more of a cultural acceptance. People have gone away from the, again, five years ago, to what are you doing walking around the show, to say, "Okay, I'm not sure I get it. "I need to look at it. "I, okay, I, now, oh, it needs to be a part "of my architecture but the standard questions, "is it secure? "Is it price performant? "How does it compare to my on-prem?" And really culturally, a lot of it is, just getting IT administrators used to, we're not eliminating a whole field, right? We're just upskilling the people that used to rack and stack actual hardware, to now you're learning AWS services and how to operate within that environment. And it's still key to have those people that are really supporting these infrastructures. And so I'd say it's a little bit of a combination of cultural shift over the past five years, to see that cloud is a super important part of HPC workloads, and part of it's been us meeting the the market segment of where we needed to with innovating both at the hardware level and at the software level, which we're going to continue to do. >> You do have an on-prem story though. I mean, you have outposts. We don't hear a lot of talk about outposts lately, but these innovations, like Inferentia, like Trainium, like the networking innovation you're talking about, are these going to make their way into outposts as well? Will that essentially become this supercomputing solution for customers who want to stay on-prem? >> Well, we'll see what the future lies, but we believe that we've got the, as you noted, we've got the hardware, we've got the network, we've got the storage. All those put together gives you a a high-performance computer, right? And whether you want it to be redundant in your local data center or you want it to be accessible via APIs from the AWS cloud, we want to provide that service to you. >> So to be clear, that's not that's not available now, but that is something that could be made available? >> Outposts are available right now, that have this the services that you need. >> All these capabilities? >> Often a move to cloud, an impetus behind it comes from the highest levels in an organization. They're looking at the difference between OpEx versus CapEx. CapEx for a large HPC environment, can be very, very, very high. Are these HPC clusters consumed as an operational expense? Are you essentially renting time, and then a fundamental question, are these multi-tenant environments? Or when you're referring to batches being run in HPC, are these dedicated HPC environments for customers who are running batches against them? When you think about batches, you think of, there are times when batches are being run and there are times when they're not being run. So that would sort of conjure, in the imagination, multi-tenancy, what does that look like? >> Definitely, and that's been, let me start with your second part first is- >> Yeah. That's been a a core area within AWS is we do not see as, okay we're going to, we're going to carve out this super computer and then we're going to allocate that to you. We are going to dynamically allocate multi-tenant resources to you to perform the workloads you need. And especially with the batch environment, we're going to spin up containers on those, and then as the workloads complete we're going to turn those resources over to where they can be utilized by other customers. And so that's where the batch computing component really is powerful, because as you say, you're releasing resources from workloads that you're done with. I can use those for another portion of the workflow for other work. >> Okay, so it makes a huge difference, yeah. >> You mentioned, that five years ago, people couldn't quite believe that AWS was at this conference. Now you've got a booth right out in the center of the action. What kind of questions are you getting? What are people telling you? >> Well, I love being on the show floor. This is like my favorite part is talking to customers and hearing one, what do they love, what do they want more of? Two, what do they wish we were doing that we're not currently doing? And three, what are the friction points that are still exist that, like, how can I make their lives easier? And what we're hearing is, "Can you help me migrate my workloads to the cloud? "Can you give me the information that I need, "both from a price for performance, "for an operational support model, "and really help me be an internal advocate "within my environment to explain "how my resources can be operated proficiently "within the AWS cloud." And a lot of times it's, let's just take your application a subset of your applications and let's benchmark 'em. And really that, AWS, one of the key things is we are a data-driven environment. And so when you take that data and you can help a customer say like, "Let's just not look at hypothetical, "at synthetic benchmarks, let's take "actually the LS-DYNA code that you're running, perhaps. "Let's take the OpenFOAM code that you're running, "that you're running currently "in your on-premises workloads, "and let's run it on AWS cloud "and let's see how it performs." And then we can take that back to your to the decision makers and say, okay, here's the price for performance on AWS, here's what we're currently doing on-premises, how do we think about that? And then that also ties into your earlier question about CapEx versus OpEx. We have models where actual, you can capitalize a longer-term purchase at AWS. So it doesn't have to be, I mean, depending upon the accounting models you want to use, we do have a majority of customers that will stay with that OpEx model, and they like that flexibility of saying, "Okay, spend as you go." We need to have true ups, and make sure that they have insight into what they're doing. I think one of the boogeyman is that, oh, I'm going to spend all my money and I'm not going to know what's available. And so we want to provide the, the cost visibility, the cost controls, to where you feel like, as an HPC administrator you have insight into what your customers are doing and that you have control over that. And so once you kind of take away some of those fears and and give them the information that they need, what you start to see too is, you know what, we really didn't have a lot of those cost visibility and controls with our on-premises hardware. And we've had some customers tell us we had one portion of the workload where this work center was spending thousands of dollars a day. And we went back to them and said, "Hey, we started to show this, "what you were spending on-premises." They went, "Oh, I didn't realize that." And so I think that's part of a cultural thing that, at an HPC, the question was, well on-premises is free. How do you compete with free? And so we need to really change that culturally, to where people see there is no free lunch. You're paying for the resources whether it's on-premises or in the cloud. >> Data scientists don't worry about budgets. >> Wait, on-premises is free? Paul mentioned something that reminded me, you said you were here in 2017, people said AWS, web, what are you even doing here? Now in 2022, you're talking in terms of migrating to cloud. Paul mentioned outposts, let's say that a customer says, "Hey, I'd like you to put "in a thousand-node cluster in this data center "that I happen to own, but from my perspective, "I want to interact with it just like it's "in your data center." In other words, the location doesn't matter. My experience is identical to interacting with AWS in an AWS data center, in a CoLo that works with AWS, but instead it's my physical data center. When we're tracking the percentage of IT that's that is on-prem versus off-prem. What is that? Is that, what I just described, is that cloud? And in five years are you no longer going to be talking about migrating to cloud because people go, "What do you mean migrating to cloud? "What do you even talking about? "What difference does it make?" It's either something that AWS is offering or it's something that someone else is offering. Do you think we'll be at that point in five years, where in this world of virtualization and abstraction, you talked about Kubernetes, we should be there already, thinking in terms of it doesn't matter as long as it meets latency and sovereignty requirements. So that, your prediction, we're all about insights and supercomputing- >> My prediction- >> In five years, will you still be talking about migrating to cloud or will that be something from the past? >> In five years, I still think there will be a component. I think the majority of the assumption will be that things are cloud-native and you start in the cloud and that there are perhaps, an aspect of that, that will be interacting with some sort of an edge device or some sort of an on-premises device. And we hear more and more customers that are saying, "Okay, I can see the future, "I can see that I'm shrinking my footprint." And, you can see them still saying, "I'm not sure how small that beachhead will be, "but right now I want to at least say "that I'm going to operate in that hybrid environment." And so I'd say, again, the pace of this community, I'd say five years we're still going to be talking about migrations, but I'd say the vast majority will be a cloud-native, cloud-first environment. And how do you classify that? That outpost sitting in someone's data center? I'd say we'd still, at least I'll leave that up to the analysts, but I think it would probably come down as cloud spend. >> Great place to end. Ian, you and I now officially have a bet. In five years we're going to come back. My contention is, no we're not going to be talking about it anymore. >> Okay. >> And kids in college are going to be like, "What do you mean cloud, it's all IT, it's all IT." And they won't remember this whole phase of moving to cloud and back and forth. With that, join us in five years to see the result of this mega-bet between Ian and Dave. I'm Dave Nicholson with theCUBE, here at Supercomputing Conference 2022, day three of our coverage with my co-host Paul Gillin. Thanks again for joining us. Stay tuned, after this short break, we'll be back with more action. (lively music)

(techno music intro) >> Welcome back to theCube's coverage of SuperComputing Conference 2022. We are here at day three covering the amazing events that are occurring here. I'm Dave Nicholson, with my co-host Paul Gillin. How's it goin', Paul? >> Fine, Dave. Winding down here, but still plenty of action. >> Interesting stuff. We got a full day of coverage, and we're having really, really interesting conversations. We sort of wrapped things up at Supercomputing 22 here in Dallas. I've got two very special guests with me, Scott from Intel and David from Dell, to talk about yeah supercomputing, but guess what? We've got some really cool stuff coming up after this whole thing wraps. So not all of the holiday gifts have been unwrapped yet, kids. Welcome gentlemen. >> Thanks so much for having us. >> Thanks for having us. >> So, let's start with you, David. First of all, explain the relationship in general between Dell and Intel. >> Sure, so obviously Intel's been an outstanding partner. We built some great solutions over the years. I think the market reflects that. Our customers tell us that. The feedback's strong. The products you see out here this week at Supercompute, you know, put that on display for everybody to see. And then as we think about AI in machine learning, there's so many different directions we need to go to help our customers deliver AI outcomes. Right, so we recognize that AI has kind of spread outside of just the confines of everything we've seen here this week. And now we've got really accessible AI use cases that we can explain to friends and family. We can talk about going into retail environments and how AI is being used to track inventory, to monitor traffic, et cetera. But really what that means to us as a bunch of hardware folks is we have to deliver the right platforms and the right designs for a variety of environments, both inside and outside the data center. And so if you look at our portfolio, we have some great products here this week, but we also have other platforms, like the XR4000, our shortest rack server ever that's designed to go into Edge environments, but is also built for those Edge AI use cases that supports GPUs. It supports AI on the CPU as well. And so there's a lot of really compelling platforms that we're starting to talk about, have already been talking about, and it's going to really enable our customers to deliver AI in a variety of ways. >> You mentioned AI on the CPU. Maybe this is a question for Scott. What does that mean, AI on the CPU? >> Well, as David was talking about, we're just seeing this explosion of different use cases. And some of those on the Edge, some of them in the Cloud, some of them on Prem. But within those individual deployments, there's often different ways that you can do AI, whether that's training or inference. And what we're seeing is a lot of times the memory locality matters quite a bit. You don't want to have to pay necessarily a cost going across the PCI express bus, especially with some of our newer products like the CPU Max series, where you can have a huge about of high bandwidth memory just sitting right on the CPU. Things that traditionally would have been accelerator only, can now live on a CPU, and that includes both on the inference side. We're seeing some really great things with images, where you might have a giant medical image that you need to be able to do extremely high resolution inference on or even text, where you might have a huge corpus of extremely sparse text that you need to be able to randomly sample very efficiently. >> So how are these needs influencing the evolution of Intel CPU architectures? >> So, we're talking to our customers. We're talking to our partners. This presents both an opportunity, but also a challenge with all of these different places that you can put these great products, as well as applications. And so we're very thoughtfully trying to go to the market, see where their needs are, and then meet those needs. This industry obviously has a lot of great players in it, and it's no longer the case that if you build it, they will come. So what we're doing is we're finding where are those choke points, how can we have that biggest difference? Sometimes there's generational leaps, and I know David can speak to this, can be huge from one system to the next just because everything's accelerated on the software side, the hardware side, and the platforms themselves. >> That's right, and we're really excited about that leap. If you take what Scott just described, we've been writing white papers, our team with Scott's team, we've been talking about those types of use cases using doing large image analysis and leveraging system memory, leveraging the CPU to do that, we've been talking about that for several generations now. Right, going back to Cascade Lake, going back to what we would call 14th generation power Edge. And so now as we prepare and continue to unveil, kind of we're in launch season, right, you and I were talking about how we're in launch season. As we continue to unveil and launch more products, the performance improvements are just going to be outstanding and we'll continue that evolution that Scott described. >> Yeah, I'd like to applaud Dell just for a moment for its restraint. Because I know you could've come in and taken all of the space in the convention center to show everything that you do. >> Would have loved to. >> In the HPC space. Now, worst kept secrets on earth at this point. Vying for number one place is the fact that there is a new Mission Impossible movie coming. And there's also new stuff coming from Intel. I know, I think allegedly we're getting close. What can you share with us on that front? And I appreciate it if you can't share a ton of specifics, but where are we going? David just alluded to it. >> Yeah, as David talked about, we've been working on some of these things for many years. And it's just, this momentum is continuing to build, both in respect to some of our hardware investments. We've unveiled some things both here, both on the CPU side and the accelerator side, but also on the software side. OneAPI is gathering more and more traction and the ecosystem is continuing to blossom. Some of our AI and HPC workloads, and the combination thereof, are becoming more and more viable, as well as displacing traditional approaches to some of these problems. And it's this type of thing where it's not linear. It all builds on itself. And we've seen some of these investments that we've made for a better half of a decade starting to bear fruit, but that's, it's not just a one time thing. It's just going to continue to roll out, and we're going to be seeing more and more of this. >> So I want to follow up on something that you mentioned. I don't know if you've ever heard that the Charlie Brown saying that sometimes the most discouraging thing can be to have immense potential. Because between Dell and Intel, you offer so many different versions of things from a fit for function perspective. As a practical matter, how do you work with customers, and maybe this is a question for you, David. How do you work with customers to figure out what the right fit is? >> I'll give you a great example. Just this week, customer conversations, and we can put it in terms of kilowatts to rack, right. How many kilowatts are you delivering at a rack level inside your data center? I've had an answer anywhere from five all the way up to 90. There's some that have been a bit higher that probably don't want to talk about those cases, kind of customers we're meeting with very privately. But the range is really, really large, right, and there's a variety of environments. Customers might be ready for liquid today. They may not be ready for it. They may want to maximize air cooling. Those are the conversations, and then of course it all maps back to the workloads they wish to enable. AI is an extremely overloaded term. We don't have enough time to talk about all the different things that tuck under that umbrella, but the workloads and the outcomes they wish to enable, we have the right solutions. And then we take it a step further by considering where they are today, where they need to go. And I just love that five to 90 example of not every customer has an identical cookie cutter environment, so we've got to have the right platforms, the right solutions, for the right workloads, for the right environments. >> So, I like to dive in on this power issue, to give people who are watching an idea. Because we say five kilowatts, 90 kilowatts, people are like, oh wow, hmm, what does that mean? 90 kilowatts is more than 100 horse power if you want to translate it over. It's a massive amount of power, so if you think of EV terms. You know, five kilowatts is about a hairdryer's around a kilowatt, 1,000 watts, right. But the point is, 90 kilowatts in a rack, that's insane. That's absolutely insane. The heat that that generates has got to be insane, and so it's important. >> Several houses in the size of a closet. >> Exactly, exactly. Yeah, in a rack I explain to people, you know, it's like a refrigerator. But, so in the arena of thermals, I mean is that something during the development of next gen architectures, is that something that's been taken into consideration? Or is it just a race to die size? >> Well, you definitely have to take thermals into account, as well as just the power of consumption themselves. I mean, people are looking at their total cost of ownership. They're looking at sustainability. And at the end of the day, they need to solve a problem. There's many paths up that mountain, and it's about choosing that right path. We've talked about this before, having extremely thoughtful partners, we're just not going to common-torily try every single solution. We're going to try to find the ones that fit that right mold for that customer. And we're seeing more and more people, excuse me, care about this, more and more people wanting to say, how do I do this in the most sustainable way? How do I do this in the most reliable way, given maybe different fluctuations in their power consumption or their power pricing? We're developing more software tools and obviously partnering with great partners to make sure we do this in the most thoughtful way possible. >> Intel put a lot of, made a big investment by buying Habana Labs for its acceleration technology. They're based in Israel. You're based on the west coast. How are you coordinating with them? How will the Habana technology work its way into more mainstream Intel products? And how would Dell integrate those into your servers? >> Good question. I guess I can kick this off. So Habana is part of the Intel family now. They've been integrated in. It's been a great journey with them, as some of their products have launched on AWS, and they've had some very good wins on MLPerf and things like that. I think it's about finding the right tool for the job, right. Not every problem is a nail, so you need more than just a hammer. And so we have the Xeon series, which is incredibly flexible, can do so many different things. It's what we've come to know and love. On the other end of the spectrum, we obviously have some of these more deep learning focused accelerators. And if that's your problem, then you can solve that problem in incredibly efficient ways. The accelerators themselves are somewhere in the middle, so you get that kind of Goldilocks zone of flexibility and power. And depending on your use case, depending on what you know your workloads are going to be day in and day out, one of these solutions might work better for you. A combination might work better for you. Hybrid compute starts to become really interesting. Maybe you have something that you need 24/7, but then you only need a burst to certain things. There's a lot of different options out there. >> The portfolio approach. >> Exactly. >> And then what I love about the work that Scott's team is doing, customers have told us this week in our meetings, they do not want to spend developer's time porting code from one stack to the next. They want that flexibility of choice. Everyone does. We want it in our lives, in our every day lives. They need that flexibility of choice, but they also, there's an opportunity cost when their developers have to choose to port some code over from one stack to another or spend time improving algorithms and doing things that actually generate, you know, meaningful outcomes for their business or their research. And so if they are, you know, desperately searching I would say for that solution and for help in that area, and that's what we're working to enable soon. >> And this is what I love about oneAPI, our software stack, it's open first, heterogeneous first. You can take SYCL code, it can run on competitor's hardware. It can run on Intel hardware. It's one of these things that you have to believe long term, the future is open. Wall gardens, the walls eventually crumble. And we're just trying to continue to invest in that ecosystem to make sure that the in-developer at the end of the day really gets what they need to do, which is solving their business problem, not tinkering with our drivers. >> Yeah, I actually saw an interesting announcement that I hadn't been tracking. I hadn't been tracking this area. Chiplets, and the idea of an open standard where competitors of Intel from a silicone perspective can have their chips integrated via a universal standard. And basically you had the top three silicone vendors saying, yeah, absolutely, let's work together. Cats and dogs. >> Exactly, but at the end of the day, it's whatever menagerie solves the problem. >> Right, right, exactly. And of course Dell can solve it from any angle. >> Yeah, we need strong partners to build the platforms to actually do it. At the end of the day, silicone without software is just sand. Sand with silicone is poorly written prose. But without an actual platform to put it on, it's nothing, it's a box that sits in the corner. >> David, you mentioned that 90% of power age servers now support GPUs. So how is this high-performing, the growth of high performance computing, the demand, influencing the evolution of your server architecture? >> Great question, a couple of ways. You know, I would say 90% of our platforms support GPUs. 100% of our platforms support AI use cases. And it goes back to the CPU compute stack. As we look at how we deliver different form factors for customers, we go back to that range, I said that power range this week of how do we enable the right air coolant solutions? How do we deliver the right liquid cooling solutions, so that wherever the customer is in their environment, and whatever footprint they have, we're ready to meet it? That's something you'll see as we go into kind of the second half of launch season and continue rolling out products. You're going to see some very compelling solutions, not just in air cooling, but liquid cooling as well. >> You want to be more specific? >> We can't unveil everything at Supercompute. We have a lot of great stuff coming up here in the next few months, so. >> It's kind of like being at a great restaurant when they offer you dessert, and you're like yeah, dessert would be great, but I just can't take anymore. >> It's a multi course meal. >> At this point. Well, as we wrap, I've got one more question for each of you. Same question for each of you. When you think about high performance computing, super computing, all of the things that you're doing in your partnership, driving artificial intelligence, at that tip of the spear, what kind of insights are you looking forward to us being able to gain from this technology? In other words, what cool thing, what do you think is cool out there from an AI perspective? What problem do you think we can solve in the near future? What problems would you like to solve? What gets you out of bed in the morning? Cause it's not the little, it's not the bits and the bobs and the speeds and the feats, it's what we're going to do with them, so what do you think, David? >> I'll give you an example. And I think, I saw some of my colleagues talk about this earlier in the week, but for me what we could do in the past two years to unable our customers in a quarantine pandemic environment, we were delivering platforms and solutions to help them do their jobs, help them carry on in their lives. And that's just one example, and if I were to map that forward, it's about enabling that human progress. And it's, you know, you ask a 20 year version of me 20 years ago, you know, if you could imagine some of these things, I don't know what kind of answer you would get. And so mapping forward next decade, next two decades, I can go back to that example of hey, we did great things in the past couple of years to enable our customers. Just imagine what we're going to be able to do going forward to enable that human progress. You know, there's great use cases, there's great image analysis. We talked about some. The images that Scott was referring to had to do with taking CAT scan images and being able to scan them for tumors and other things in the healthcare industry. That is stuff that feels good when you get out of bed in the morning, to know that you're enabling that type of progress. >> Scott, quick thoughts? >> Yeah, and I'll echo that. It's not one specific use case, but it's really this wave front of all of these use cases, from the very micro of developing the next drug to finding the next battery technology, all the way up to the macro of trying to have an impact on climate change or even the origins of the universe itself. All of these fields are seeing these massive gains, both from the software, the hardware, the platforms that we're bringing to bear to these problems. And at the end of the day, humanity is going to be fundamentally transformed by the computation that we're launching and working on today. >> Fantastic, fantastic. Thank you, gentlemen. You heard it hear first, Intel and Dell just committed to solving the secrets of the universe by New Years Eve 2023. >> Well, next Supercompute, let's give us a little time. >> The next Supercompute Convention. >> Yeah, next year. >> Yeah, SC 2023, we'll come back and see what problems have been solved. You heard it hear first on theCube, folks. By SC 23, Dell and Intel are going to reveal the secrets of the universe. From here, at SC 22, I'd like to thank you for joining our conversation. I'm Dave Nicholson, with my co-host Paul Gillin. Stay tuned to theCube's coverage of Supercomputing Conference 22. We'll be back after a short break. (techno music)

>> From the CUBE Studios in Palo Alto in Boston, bringing you data driven insights from the Cube and ETR. This is Breaking Analysis with Dave Vellante. >> A better than expected earnings report in late August got people excited about Snowflake again, but the negative sentiment in the market is weighed heavily on virtually all growth tech stocks and Snowflake is no exception. As we've stressed many times the company's management is on a long term mission to dramatically simplify the way organizations use data. Snowflake is tapping into a multi hundred billion dollar total available market and continues to grow at a rapid pace. In our view, Snowflake is embarking on its third major wave of innovation data apps, while its first and second waves are still bearing significant fruit. Now for short term traders focused on the next 90 or 180 days, that probably doesn't matter. But those taking a longer view are asking, "Should we still be optimistic about the future of this high flyer or is it just another over hyped tech play?" Hello and welcome to this week's Wiki Bond Cube Insights powered by ETR. Snowflake's Quarter just ended. And in this breaking analysis we take a look at the most recent survey data from ETR to see what clues and nuggets we can extract to predict the near term future in the long term outlook for Snowflake which is going to announce its earnings at the end of this month. Okay, so you know the story. If you've been investor in Snowflake this year, it's been painful. We said at IPO, "If you really want to own this stock on day one, just hold your nose and buy it." But like most IPOs we said there will be likely a better entry point in the future, and not surprisingly that's been the case. Snowflake IPOed a price of 120, which you couldn't touch on day one unless you got into a friends and family Delio. And if you did, you're still up 5% or so. So congratulations. But at one point last year you were up well over 200%. That's been the nature of this volatile stock, and I certainly can't help you with the timing of the market. But longer term Snowflake is targeting 10 billion in revenue for fiscal year 2028. A big number. Is it achievable? Is it big enough? Tell you what, let's come back to that. Now shorter term, our expert trader and breaking analysis contributor Chip Simonton said he got out of the stock a while ago after having taken a shot at what turned out to be a bear market rally. He pointed out that the stock had been bouncing around the 150 level for the last few months and broke that to the downside last Friday. So he'd expect 150 is where the stock is going to find resistance on the way back up, but there's no sign of support right now. He said maybe at 120, which was the July low and of course the IPO price that we just talked about. Now, perhaps earnings will be a catalyst, when Snowflake announces on November 30th, but until the mentality toward growth tech changes, nothing's likely to change dramatically according to Simonton. So now that we have that out of the way, let's take a look at the spending data for Snowflake in the ETR survey. Here's a chart that shows the time series breakdown of snowflake's net score going back to the October, 2021 survey. Now at that time, Snowflake's net score stood at a robust 77%. And remember, net score is a measure of spending velocity. It's a proprietary network, and ETR derives it from a quarterly survey of IT buyers and asks the respondents, "Are you adopting the platform new? Are you spending 6% or more? Is you're spending flat? Is you're spending down 6% or worse? Or are you leaving the platform decommissioning?" You subtract the percent of customers that are spending less or churning from those that are spending more and adopting or adopting and you get a net score. And that's expressed as a percentage of customers responding. In this chart we show Snowflake's in out of the total survey which ranges... The total survey ranges between 1,200 and 1,400 each quarter. And the very last column... Oh sorry, very last row, we show the number of Snowflake respondents that are coming in the survey from the Fortune 500 and the Global 2000. Those are two very important Snowflake constituencies. Now what this data tells us is that Snowflake exited 2021 with very strong momentum in a net score of 82%, which is off the charts and it was actually accelerating from the previous survey. Now by April that sentiment had flipped and Snowflake came down to earth with a 68% net score. Still highly elevated relative to its peers, but meaningfully down. Why was that? Because we saw a drop in new ads and an increase in flat spend. Then into the July and most recent October surveys, you saw a significant drop in the percentage of customers that were spending more. Now, notably, the percentage of customers who are contemplating adding the platform is actually staying pretty strong, but it is off a bit this past survey. And combined with a slight uptick in planned churn, net score is now down to 60%. That uptick from 0% and 1% and then 3%, it's still small, but that net score at 60% is still 20 percentage points higher than our highly elevated benchmark of 40% as you recall from listening to earlier breaking analysis. That 40% range is we consider a milestone. Anything above that is actually quite strong. But again, Snowflake is down and coming back to churn, while 3% churn is very low, in previous quarters we've seen Snowflake 0% or 1% decommissions. Now the last thing to note in this chart is the meaningful uptick in survey respondents that are citing, they're using the Snowflake platform. That's up to 212 in the survey. So look, it's hard to imagine that Snowflake doesn't feel the softening in the market like everyone else. Snowflake is guiding for around 60% growth in product revenue against the tough compare from a year ago with a 2% operating margin. So like every company, the reaction of the street is going to come down to how accurate or conservative the guide is from their CFO. Now, earlier this year, Snowflake acquired a company called Streamlit for around $800 million. Streamlit is an open source Python library and it makes it easier to build data apps with machine learning, obviously a huge trend. And like Snowflake, generally its focus is on simplifying the complex, in this case making data science easier to integrate into data apps that business people can use. So we were excited this summer in the July ETR survey to see that they added some nice data and pick on Streamlit, which we're showing here in comparison to Snowflake's core business on the left hand side. That's the data warehousing, the Streamlit pieces on the right hand side. And we show again net score over time from the previous survey for Snowflake's core database and data warehouse offering again on the left as compared to a Streamlit on the right. Snowflake's core product had 194 responses in the October, 22 survey, Streamlit had an end of 73, which is up from 52 in the July survey. So significant uptick of people responding that they're doing business in adopting Streamlit. That was pretty impressive to us. And it's hard to see, but the net scores stayed pretty constant for Streamlit at 51%. It was 52% I think in the previous quarter, well over that magic 40% mark. But when you blend it with Snowflake, it does sort of bring things down a little bit. Now there are two key points here. One is that the acquisition seems to have gained exposure right out of the gate as evidenced by the large number of responses. And two, the spending momentum. Again while it's lower than Snowflake overall, and when you blend it with Snowflake it does pull it down, it's very healthy and steady. Now let's do a little pure comparison with some of our favorite names in this space. This chart shows net score or spending velocity in the Y-axis, an overlap or presence, pervasiveness if you will, in the data set on the X-axis. That red dotted line again is that 40% highly elevated net score that we like to talk about. And that table inserted informs us as to how the companies are plotted, where the dots set up, the net score, the ins. And we're comparing a number of database players, although just a caution, Oracle includes all of Oracle including its apps. But we just put it in there for reference because it is the leader in database. Right off the bat, Snowflake jumps out with a net score of 64%. The 60% from the earlier chart, again included Streamlit. So you can see its core database, data warehouse business actually is higher than the total company average that we showed you before 'cause the Streamlit is blended in. So when you separate it out, Streamlit is right on top of data bricks. Isn't that ironic? Only Snowflake and Databricks in this selection of names are above the 40% level. You see Mongo and Couchbase, they know they're solid and Teradata cloud actually showing pretty well compared to some of the earlier survey results. Now let's isolate on the database data platform sector and see how that shapes up. And for this analysis, same XY dimensions, we've added the big giants, AWS and Microsoft and Google. And notice that those three plus Snowflake are just at or above the 40% line. Snowflake continues to lead by a significant margin in spending momentum and it keeps creeping to the right. That's that end that we talked about earlier. Now here's an interesting tidbit. Snowflake is often asked, and I've asked them myself many times, "How are you faring relative to AWS, Microsoft and Google, these big whales with Redshift and Synapse and Big Query?" And Snowflake has been telling folks that 80% of its business comes from AWS. And when Microsoft heard that, they said, "Whoa, wait a minute, Snowflake, let's partner up." 'Cause Microsoft is smart, and they understand that the market is enormous. And if they could do better with Snowflake, one, they may steal some business from AWS. And two, even if Snowflake is winning against some of the Microsoft database products, if it wins on Azure, Microsoft is going to sell more compute and more storage, more AI tools, more other stuff to these customers. Now AWS is really aggressive from a partnering standpoint with Snowflake. They're openly negotiating, not openly, but they're negotiating better prices. They're realizing that when it comes to data, the cheaper that you make the offering, the more people are going to consume. At scale economies and operating leverage are really powerful things at volume that kick in. Now Microsoft, they're coming along, they obviously get it, but Google is seemingly resistant to that type of go to market partnership. Rather than lean into Snowflake as a great partner Google's field force is kind of fighting fashion. Google itself at Cloud next heavily messaged what they call the open data cloud, which is a direct rip off of Snowflake. So what can we say about Google? They continue to be kind of behind the curve when it comes to go to market. Now just a brief aside on the competitive posture. I've seen Slootman, Frank Slootman, CEO of Snowflake in action with his prior companies and how he depositioned the competition. At Data Domain, he eviscerated a company called Avamar with their, what he called their expensive and slow post process architecture. I think he actually called it garbage, if I recall at one conference I heard him speak at. And that sort of destroyed BMC when he was at ServiceNow, kind of positioning them as the equivalent of the department of motor vehicles. And so it's interesting to hear how Snowflake openly talks about the data platforms of AWS, Microsoft, Google, and data bricks. I'll give you this sort of short bumper sticker. Redshift is just an on-prem database that AWS morphed to the cloud, which by the way is kind of true. They actually did a brilliant job of it, but it's basically a fact. Microsoft Excel, a collection of legacy databases, which also kind of morphed to run in the cloud. And even Big Query, which is considered cloud native by many if not most, is being positioned by Snowflake as originally an on-prem database to support Google's ad business, maybe. And data bricks is for those people smart enough to get it to Berkeley that love complexity. And now Snowflake doesn't, they don't mention Berkeley as far as I know. That's my addition. But you get the point. And the interesting thing about Databricks and Snowflake is a while ago in the cube I said that there was a new workload type emerging around data where you have AWS cloud, Snowflake obviously for the cloud database and Databricks data for the data science and EML, you bring those things together and there's this new workload emerging that's going to be very powerful in the future. And it's interesting to see now the aspirations of all three of these platforms are colliding. That's quite a dynamic, especially when you see both Snowflake and Databricks putting venture money and getting their hooks into the loyalties of the same companies like DBT labs and Calibra. Anyway, Snowflake's posture is that we are the pioneer in cloud native data warehouse, data sharing and now data apps. And our platform is designed for business people that want simplicity. The other guys, yes, they're formidable, but we Snowflake have an architectural lead and of course we run in multiple clouds. So it's pretty strong positioning or depositioning, you have to admit. Now I'm not sure I agree with the big query knockoffs completely. I think that's a bit of a stretch, but snowflake, as we see in the ETR survey data is winning. So in thinking about the longer term future, let's talk about what's different with Snowflake, where it's headed and what the opportunities are for the company. Snowflake put itself on the map by focusing on simplifying data analytics. What's interesting about that is the company's founders are as you probably know from Oracle. And rather than focusing on transactional data, which is Oracle's sweet spot, the stuff they worked on when they were at Oracle, the founder said, "We're going to go somewhere else. We're going to attack the data warehousing problem and the data analytics problem." And they completely re-imagined the database and how it could be applied to solve those challenges and reimagine what was possible if you had virtually unlimited compute and storage capacity. And of course Snowflake became famous for separating the compute from storage and being able to completely shut down compute so you didn't have to pay for it when you're not using it. And the ability to have multiple clusters hit the same data without making endless copies and a consumption/cloud pricing model. And then of course everyone on the planet realized, "Wow, that's a pretty good idea." Every venture capitalist in Silicon Valley has been funding companies to copy that move. And that today has pretty much become mainstream in table stakes. But I would argue that Snowflake not only had the lead, but when you look at how others are approaching this problem, it's not necessarily as clean and as elegant. Some of the startups, the early startups I think get it and maybe had an advantage of starting later, which can be a disadvantage too. But AWS is a good example of what I'm saying here. Is its version of separating compute from storage was an afterthought and it's good, it's... Given what they had it was actually quite clever and customers like it, but it's more of a, "Okay, we're going to tier to storage to lower cost, we're going to sort of dial down the compute not completely, we're not going to shut it off, we're going to minimize the compute required." It's really not true as separation is like for instance Snowflake has. But having said that, we're talking about competitors with lots of resources and cohort offerings. And so I don't want to make this necessarily all about the product, but all things being equal architecture matters, okay? So that's the cloud S-curve, the first one we're showing. Snowflake's still on that S-curve, and in and of itself it's got legs, but it's not what's going to power the company to 10 billion. The next S-curve we denote is the multi-cloud in the middle. And now while 80% of Snowflake's revenue is AWS, Microsoft is ramping up and Google, well, we'll see. But the interesting part of that curve is data sharing, and this idea of data clean rooms. I mean it really should be called the data sharing curve, but I have my reasons for calling it multi-cloud. And this is all about network effects and data gravity, and you're seeing this play out today, especially in industries like financial services and healthcare and government that are highly regulated verticals where folks are super paranoid about compliance. There not going to share data if they're going to get sued for it, if they're going to be in the front page of the Wall Street Journal for some kind of privacy breach. And what Snowflake has done is said, "Put all the data in our cloud." Now, of course now that triggers a lot of people because it's a walled garden, okay? It is. That's the trade off. It's not the Wild West, it's not Windows, it's Mac, it's more controlled. But the idea is that as different parts of the organization or even partners begin to share data that they need, it's got to be governed, it's got to be secure, it's got to be compliant, it's got to be trusted. So Snowflake introduced the idea of, they call these things stable edges. I think that's the term that they use. And they track a metric around stable edges. And so a stable edge, or think of it as a persistent edge is an ongoing relationship between two parties that last for some period of time, more than a month. It's not just a one shot deal, one a done type of, "Oh guys shared it for a day, done." It sent you an FTP, it's done. No, it's got to have trajectory over time. Four weeks or six weeks or some period of time that's meaningful. And that metric is growing. Now I think sort of a different metric that they track. I think around 20% of Snowflake customers are actively sharing data today and then they track the number of those edge relationships that exist. So that's something that's unique. Because again, most data sharing is all about making copies of data. That's great for storage companies, it's bad for auditors, and it's bad for compliance officers. And that trend is just starting out, that middle S-curve, it's going to kind of hit the base of that steep part of the S-curve and it's going to have legs through this decade we think. And then finally the third wave that we show here is what we call super cloud. That's why I called it multi-cloud before, so it could invoke super cloud. The idea that you've built a PAS layer that is purpose built for a specific objective, and in this case it's building data apps that are cloud native, shareable and governed. And is a long-term trend that's going to take some time to develop. I mean, application development platforms can take five to 10 years to mature and gain significant adoption, but this one's unique. This is a critical play for Snowflake. If it's going to compete with the big cloud players, it has to have an app development framework like Snowpark. It has to accommodate new data types like transactional data. That's why it announced this thing called UniStore last June, Snowflake a summit. And the pattern that's forming here is Snowflake is building layer upon layer with its architecture at the core. It's not currently anyway, it's not going out and saying, "All right, we're going to buy a company that's got to another billion dollars in revenue and that's how we're going to get to 10 billion." So it's not buying its way into new markets through revenue. It's actually buying smaller companies that can complement Snowflake and that it can turn into revenue for growth that fit in to the data cloud. Now as to the 10 billion by fiscal year 28, is that achievable? That's the question. Yeah, I think so. Would the momentum resources go to market product and management prowess that Snowflake has? Yes, it's definitely achievable. And one could argue to $10 billion is too conservative. Indeed, Snowflake CFO, Mike Scarpelli will fully admit his forecaster built on existing offerings. He's not including revenue as I understand it from all the new stuff that's in the pipeline because he doesn't know what it's going to look like. He doesn't know what the adoption is going to look like. He doesn't have data on that adoption, not just yet anyway. And now of course things can change quite dramatically. It's possible that is forecast for existing businesses don't materialize or competition picks them off or a company like Databricks actually is able in the longer term replicate the functionality of Snowflake with open source technologies, which would be a very competitive source of innovation. But in our view, there's plenty of room for growth, the market is enormous and the real key is, can and will Snowflake deliver on the promises of simplifying data? Of course we've heard this before from data warehouse, the data mars and data legs and master data management and ETLs and data movers and data copiers and Hadoop and a raft of technologies that have not lived up to expectations. And we've also, by the way, seen some tremendous successes in the software business with the likes of ServiceNow and Salesforce. So will Snowflake be the next great software name and hit that 10 billion magic mark? I think so. Let's reconnect in 2028 and see. Okay, we'll leave it there today. I want to thank Chip Simonton for his input to today's episode. Thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hove is our Editor in Chief over at Silicon Angle. He does some great editing for us. Check it out for all the news. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. Or you can email me to get in touch David.vallante@siliconangle.com. DM me @dvellante or comment on our LinkedIn post. And please do check out etr.ai, they've got the best survey data in the enterprise tech business. This is Dave Vellante for the CUBE Insights, powered by ETR. Thanks for watching, thanks for listening and we'll see you next time on breaking analysis. (upbeat music)

>>Good afternoon, friends. My name is Savannah Peterson here in the Cube Studios live from Detroit, Michigan, where we're at Cuban and Cloud Native Foundation, Cloud Native Con all week. Our last interview of the day served me a real treat and one that I wasn't expecting. It turns out that I am in the presence of two caddies. It's a literal episode of Caddy Shack up here on Cube. John Furrier. I don't think the audience knows that you were a caddy. Tell us about your caddy days. >>I used to caddy when I was a kid at the local country club every weekend. This is amazing. Double loops every weekend. Make some bang, two bags on each shoulder. Caddying for the members where you're going. Now I'm >>On show. Just, just really impressive >>Now. Now I'm caddying for the cube where I caddy all this great content out to the audience. >>He's carrying the story of emerging brands and established companies on their cloud journey. I love it. John, well played. I don't wanna waste any more of this really wonderful individual's time, but since we now have a new trend of talking about everyone's Twitter handle here on the cube, this may be my favorite one of the day, if not Q4 so far. Drew, not reply. AKA Drew ne Drew Nielsen, excuse me, there is here with us from Teleport. Drew, thanks so much for being here. >>Oh, thanks for having me. It's great to be here. >>And so you were a caddy on a whole different level. Can you tell us >>About that? Yeah, so I was in university and I got tired after two years and didn't have a car in LA and met a pro golfer at a golf course and took two years off and traveled around caddying for him and tried to get 'em through Q School. >>This is, this is fantastic. So if you're in school and your parents are telling you to continue going to school, know that you can drop out and be a caddy and still be a very successful television personality. Like both of the gentlemen at some point. >>Well, I never said my parents like >>That decision, but we'll keep our day jobs. Yeah, exactly. And one of them is Cloud Native Security. The hottest topic here at the show. Yep. I want to get into it. You guys are doing some really cool things. Are we? We hear Zero Trust, you know, ransomware and we even, I even talked with the CEO of Dockets morning about container security issues. Sure. There's a lot going on. So you guys are in the middle of teleport. You guys have a unique solution. Tell us what you guys got going on. What do you guys do? What's the solution and what's the problem you solve? >>So Teleport is the first and only identity native infrastructure access solution in the market. So breaking that down, what that really means is identity native being the combination of secret list, getting rid of passwords, Pam Vaults, Key Vaults, Yeah. Passwords written down. Basically the number one source of breach. And 50 to 80% of breaches, depending on whose numbers you want to believe are how organizations get hacked. >>But it's not password 1 23 isn't protecting >>Cisco >>Right >>Now. Well, if you think about when you're securing infrastructure and the second component being zero trust, which assumes the network is completely insecure, right? But everything is validated. Resource to resource security is validated, You know, it assumes work from anywhere. It assumes the security comes back to that resource. And we take the combination of those two into identity, native access where we cryptographically ev, validate identity, but more importantly, we make an absolutely frictionless experience. So engineers can access infrastructure from anywhere at any time. >>I'm just flashing on my roommates, checking their little code, changing Bob login, you know, dongle essentially, and how frustrating that always was. I mean, talk about interrupting workflow was something that's obviously necessary, but >>Well, I mean, talk about frustration if I'm an engineer. Yeah, absolutely. You know, back in the day when you had these three tier monolithic applications, it was kind of simple. But now as you've got modern application development environments Yeah, multi-cloud, hybrid cloud, whatever marketing term around how you talk about this, expanding sort of disparate infrastructure. Engineers are sitting there going from system to system to machine to database to application. I mean, not even a conversation on Kubernetes yet. Yeah. And it's just, you know, every time you pull an engineer or a developer to go to a vault to pull something out, you're pulling them out for 10 minutes. Now, applications today have hundreds of systems, hundreds of microservices. I mean 30 of these a day and nine minutes, 270 minutes times 60. And they also >>Do the math. Well, there's not only that, there's also the breach from manual error. I forgot to change the password. What is that password? I left it open, I left it on >>Cognitive load. >>I mean, it's the manual piece. But even think about it, TR security has to be transparent and engineers are really smart people. And I've talked to a number of organizations who are like, yeah, we've tried to implement security solutions and they fail. Why? They're too disruptive. They're not transparent. And engineers will work their way around them. They'll write it down, they'll do a workaround, they'll backdoor it something. >>All right. So talk about how it works. But I, I mean, I'm getting the big picture here. I love this. Breaking down the silos, making engineers lives easier, more productive. Clearly the theme, everyone they want, they be gonna need. Whoever does that will win it all. How's it work? I mean, you deploying something, is it code, is it in line? It's, >>It's two binaries that you download and really it starts with the core being the identity native access proxy. Okay. So that proxy, I mean, if you look at like the zero trust principles, it all starts with a proxy. Everything connects into that proxy where all the access is gated, it's validated. And you know, from there we have an authorization engine. So we will be the single source of truth for all access across your entire infrastructure. So we bring machines, engineers, databases, applications, Kubernetes, Linux, Windows, we don't care. And we basically take that into a single architecture and single access platform that essentially secures your entire infrastructure. But more importantly, you can do audit. So for all of the organizations that are dealing with FedRAMP, pci, hipaa, we have a complete audit trail down to a YouTube style playback. >>Oh, interesting. We're we're California and ccpa. >>Oh, gdpr. >>Yeah, exactly. It, it, it's, it's a whole shebang. So I, I love, and John, maybe you've heard this term a lot more than I have, but identity native is relatively new to me as as a term. And I suspect you have a very distinct way of defining identity. How do you guys define identity internally? >>So identity is something that is cryptographically validated. It is something you have. So it's not enough. If you look at, you know, credentials today, everyone's like, Oh, I log into my computer, but that's my identity. No, it's not. Right. Those are attributes. Those are something that is secret for a period of time until you write it down. But I can't change my fingerprint. Right. And now I >>Was just >>Thinking of, well no, perfect case in point with touch ID on your meth there. Yeah. It's like when we deliver that cryptographically validated identity, we use these secure modules in like modern laptops or servers. Yeah. To store that identity so that even if you're sitting in front of your computer, you can't get to it. But more importantly, if somebody were to take that and try to be you and try to log in with your fingerprint, it's >>Not, I'm not gonna lie, I love the apple finger thing, you know, it's like, you know, space recognition, like it's really awesome. >>It save me a lot of time. I mean, even when you go through customs and they do the face scan now it actually knows who you are, which is pretty wild in the last time you wanna provide ones. But it just shifted over like maybe three months ago. Well, >>As long as no one chops your finger off like they do in the James Bond movies. >>I mean, we try and keep it a light and fluffy here on the queue, but you know, do a finger teams, we can talk about that >>Too. >>Gabby, I was thinking more minority report, >>But you >>Knows that's exactly what I, what I think of >>Hit that one outta bounds. So I gotta ask, because you said you're targeting engineers, not IT departments. What's, is that, because I in your mind it is now the engineers or what's the, is always the solution more >>Targeted? Well, if you really look at who's dealing with infrastructure on a day-to-day basis, those are DevOps individuals. Those are infrastructure teams, Those are site reliability engineering. And when it, they're the ones who are not only managing the infrastructure, but they're also dealing with the code on it and everything else. And for us, that is who is our primary customer and that's who's doing >>It. What's the biggest problem that you're solving in this use case? Because you guys are nailing it. What's the problem that your identity native solution solves? >>You know, right out of the backs we remove the number one source of breach. And that is taking passwords, secrets and, and keys off the board. That deals with most of the problem right there. But there are really two problems that organizations face. One is scaling. So as you scale, you get more secrets, you get more keys, you get all these things that is all increasing your attack vector in real time. Oh >>Yeah. Across teams locations. I can't even >>Take your pick. Yeah, it's across clouds, right? Any of it >>On-prem doesn't. >>Yeah. Any of it. We, and we allow you to scale, but do it securely and the security is transparent and your engineers will absolutely love it. What's the most important thing about this product Engineers. Absolutely. >>What are they saying? What are some of those examples? Anecdotally, pull boats out from engineering. >>You're too, we should have invent, we should have invented this ourselves. Or you know, we have run into a lot of customers who have tried to home brew this and they're like, you know, we spend an in nor not of hours on it >>And IT or they got legacy from like Microsoft or other solutions. >>Sure, yeah. Any, but a lot of 'em is just like, I wish I had done it myself. Or you know, this is what security should be. >>It makes so much sense and it gives that the team such a peace of mind. I mean, you never know when a breach is gonna come, especially >>It's peace of mind. But I think for engineers, a lot of times it deals with the security problem. Yeah. Takes it off the table so they can do their jobs. Yeah. With zero friction. Yeah. And you know, it's all about speed. It's all about velocity. You know, go fast, go fast, go fast. And that's what we enable >>Some of the benefits to them is they get to save time, focus more on, on task that they need to work on. >>Exactly. >>And get the >>Job done. And on top of it, they answer the audit and compliance mail every time it comes. >>Yeah. Why are people huge? Honestly, why are people doing this? Because, I mean, identity is just such an hard nut to crack. Everyone's got their silos, Vendors having clouds have 'em. Identity is the most fragmented thing on >>The planet. And it has been fragmented ever since my first RSA conference. >>I know. So will we ever get this do over? Is there a driver? Is there a market force? Is this the time? >>I think the move to modern applications and to multi-cloud is driving this because as those application stacks get more verticalized, you just, you cannot deal with the productivity >>Here. And of course the next big thing is super cloud and that's coming fast. Savannah, you know, You know that's Rocket. >>John is gonna be the thought leader and keyword leader of the word super cloud. >>Super Cloud is enabling super services as the cloud cast. Brian Gracely pointed out on his Sunday podcast of which if that happens, Super Cloud will enable super apps in a new architectural >>List. Please don't, and it'll be super, just don't. >>Okay. Right. So what are you guys up to next? What's the big hot spot for the company? What are you guys doing? What are you guys, What's the idea guys hiring? You put the plug in. >>You know, right now we are focused on delivering the best identity, native access platform that we can. And we will continue to support our customers that want to use Kubernetes, that want to use any different type of infrastructure. Whether that's Linux, Windows applications or databases. Wherever they are. >>Are, are your customers all of a similar DNA or are you >>No, they're all over the map. They range everything from tech companies to financial services to, you know, fractional property. >>You seem like someone everyone would need. >>Absolutely. >>And I'm not just saying that to be a really clean endorsement from the Cube, but >>If you were doing DevOps Yeah. And any type of forward-leaning shift, left engineering, you need us because we are basically making security as code a reality across your entire infrastructure. >>Love this. What about the team dna? Are you in a scale growth stage right now? What's going on? Absolutely. Sounds I was gonna say, but I feel like you would have >>To be. Yeah, we're doing, we're, we have a very positive outlook and you know, even though the economic time is what it is, we're doing very well meeting. >>How's the location? Where's the location of the headquarters now? With remote work is pretty much virtual. >>Probably. We're based in downtown Oakland, California. >>Woohoo. Bay area representing on this stage right now. >>Nice. Yeah, we have a beautiful office right in downtown Oakland and yeah, it's been great. Awesome. >>Love that. And are you hiring right now? I bet people might be. I feel like some of our cube watchers are here waiting to figure out their next big play. So love to hear that. Absolutely love to hear that. Besides Drew, not reply, if people want to join your team or say hello to you and tell you how brilliant you looked up here, or ask about your caddy days and maybe venture a guest to who that golfer may have been that you were CAD Inc. For, what are the best ways for them to get in touch with you? >>You can find me on LinkedIn. >>Great. Fantastic. John, anything else >>From you? Yeah, I mean, I just think security is paramount. This is just another example of where the innovation has to kind of break through without good identity, everything could cripple. Then you start getting into the silos and you can start getting into, you know, tracking it. You got error user errors, you got, you know, one of the biggest security risks. People just leave systems open, they don't even know it's there. So like, I mean this is just, just identity is the critical linchpin to, to solve for in security to me. And that's totally >>Agree. We even have a lot of customers who use us just to access basic cloud consoles. Yeah. >>So I was actually just gonna drive there a little bit because I think that, I'm curious, it feels like a solution for obviously complex systems and stacks, but given the utility and what sounds like an extreme ease of use, I would imagine people use this for day-to-day stuff within their, >>We have customers who use it to access their AWS consoles. We have customers who use it to access Grafana dashboards. You know, for, since we're sitting here at coupon accessing a Lens Rancher, all of the amazing DevOps tools that are out there. >>Well, I mean true. I mean, you think about all the reasons why people don't adopt this new federated approach or is because the IT guys did it and the world we're moving into, the developers are in charge. And so we're seeing the trend where developers are taking the DevOps and the data and the security teams are now starting to reset the guardrails. What's your >>Reaction to that? Well, you know, I would say that >>Over the top, >>Well I would say that you know, your DevOps teams and your infrastructure teams and your engineers, they are the new king makers. Yeah. Straight up. Full stop. >>You heard it first folks. >>And that's >>A headline right >>There. That is a headline. I mean, they are the new king makers and, but they are being forced to do it as securely as possible. And our job is really to make that as easy and as frictionless as possible. >>Awesome. >>And it sounds like you're absolutely nailing it. Drew, thank you so much for being on the show. Thanks for having today. This has been an absolute pleasure, John, as usual a joy. And thank all of you for tuning in to the Cube Live here at CU Con from Detroit, Michigan. We look forward to catching you for day two tomorrow.

foreign [Music] my name is Savannah Peterson and I am very excited to be coming to you today from the cube in Palo Alto we're going to be talking about kubecon giving a little preview of the hype and what you might be able to expect in Detroit with the one and only co-founder and CEO of the cube and siliconangle John ferriere John hello how are you today thanks for hosting and doing the preview with me my goodness a pleasure I we got acquainted this time last year how do you think the ecosystem has changed are you excited well first of all I missed kubecon Valencia because I had covid I was so excited to be there this big trip plan and then couldn't make it but so much has gone on I mean we've been at every kubecon the cube was there at the beginning when openstack was still going on kubernetes just started came out of Google we were there having beers with Lou Tucker and a bunch of The Luminaries when it all kind of came together and then watch it year by year progress through and how it's changed the industry and mainly how open source has been really the wave behind it combining with the Linux foundation and then cncf and then open source movement and good kubernetes has been amazing and under it all containers has been the real driver and all this so you know Docker containers Docker was a well-funded company they had to Pivot and were restructured now they're pure open source so containers have gone Supernova on top of that kubernetes and with that's a complete ecosystem of opportunity to create the next operating system in in software development so to me kubecon is at the center of software software 2030 what do you want to call it super cloud it's that it's really action it's not where the old school is it's where the new school is excellent so what has you most excited this year what's the biggest change from this time last year and now well two things I'm looking at this year uh carefully both from an editorial lens and also from a sponsorship lenses where is the funding going on the sponsorships because again a very diverse ecosystem of Builders but also vendors so I'm going to see how that Dynamics going on but also on the software side a lot of white space going on in the stack or in the map if you will you know the run times you've got observability you got a lot of competition maybe projects might be growing some Rising some falling maybe merge together I'm going to see how that but there's a lot of white spaces developing so I'm curious to see what's new on that area and then service meshes is a big deal this year so I'm looking for what's going on so it's been kind of a I won't say cold war but kind of like uh you know where is this going to go and because it's a super important part of of the of the orchestration and managing containers and so be very interested to see how service mesh does istio and other versions out there have been around for a while so that and also the other controversy is the number of stars on GitHub a project may have so sometimes that carries a lot of weight but we're going to look at which ones are rising which ones are falling again um which ones are getting the most votes by the developers vote with their code yeah absolutely well we did definitely miss you down in Los Angeles but it will be great to be in Detroit what has you most excited do you think that we're going to see the number of people in person that we have in the past I know you've seen it since the beginning so I think this year is going to be explosive from that psychology angle because I think it was really weird because La was on they were a bold to make that move we're all there is first conference back it was a lot a lot of like badges don't touch me only handshakes fist pumps but it was at the beginning of the covid second wave right so it was kind of still not yet released where everyone's was not worried about it so I think it's in the past year in the past eight months I mean I've been places with no masks people have no masks Vegas other places so I think it's going to be a year where it will be a lot more people in person because the growth and the opportunities are so big it's going to drive a lot of people in person just like Amazon reinvent those yeah absolutely and as the most important and prominent event in the kubernetes space I think everyone's very excited to to get back together when we think about this space do you think there that anyone's the clear winner yet or do you think it's still a bit of a open territory in terms of the companies and Partnerships I think Red Hat has done a great job and they're you know I think they're going to see how well they can turn this into gold for them because they've positioned themselves very well open shift years ago was kind of waffling I won't say it in a bad way but like but once they got view on containers and kubernetes red has done an exceptional job in how they position their company being bought by ibms can be very interesting to see how that influences change so if Red Hat can stay red hat I think IBM will win I think customers that's one company I like the startups we're seeing companies like platform nine Rafi systems young companies coming out in the kubernetes as a service space because I think whoever can make kubernetes easier because I think that's the hard part right now even though that the show is called kubecon is a lot more than kubernetes I think the container layer what docker's doing has been exceptional that's the real action the question is how does that impact the kubernetes layers so kubernetes is not a done deal yet I think it hasn't really crossed the chasm yet it's certainly popular but not every company is adopting it so we're starting to see that we need to see more adoption of kubernetes seeing that happen it's going to decide who the winners are totally agree with that if you look at the data a lot of companies are and people are excited about kubernetes but they haven't taken the plunge to shifting over their stack or fully embracing it because of that complexity so I'm very curious to see what we learn this week about who those players might be moving forward how does it feel to be in Detroit when was the last time you were here I was there in 2007 was the last time I was in that town so uh we'll see what's like wow yeah but things have changed yeah the lions are good this year they've got great hockey goalies there so you know all right you've heard that sports fans let John know what you're thinking your Sports predictions for this season I love that who do you hope to get to meet while we're at the show I want to meet more end user customers we're gonna have Envoy again on the cube I think Red Hat was going to be a big sponsor this year they've been great um we're looking for end user project most looking for some editorial super cloud like um commentary because the cncf is kind of the developer Tech Community that's powering in my opinion this next wave of software development Cloud native devops is now Cloud native developers devops is kind of going away that's killed I.T in my opinion data and security Ops is the new kind of Ops the new it so it's good to see how devops turns into more of a software engineering meet supercloud so I think you're going to start to see the infrastructure become more programmable it's infrastructure as code so I think if anything I'm more excited to hear more stories about how infrastructure as code is now the new standard so if when that truly happens the super cloud model be kicking into high gear I love that let's you touched on it a little bit right there but I want to dig in a bit since you've been around since the beginning what is it that you appreciate or enjoy so much about the kubernetes community and the people around this I think there are authentic people and I think they're they're building they're also Progressive they're very diverse um they're open and inclusive they try stuff and um they can be critical but they're not jerks about it so when people try something um they're open-minded of a failure so it's a classic startup mentality I think that is embodied throughout the Linux Foundation but CNC in particular has to bridge the entrepreneurial and corporate Vibe so they've done an exceptional job doing that and that's what I like about this money making involved but there's also a lot of development and Innovation that comes out of it so the next big name and startup could come out of this community and that's what I hope to see coming out here is that next brand that no one's heard of that just comes out of nowhere and just takes a big position in the marketplace so that's going to be interesting to see hopefully we have on our stage there yeah that's the goal we're going to interview them all a year from now when we're sitting here again what do you hope to be able to say about this space or this event that we might not be able to say today I think it's going to be more of clarity around um the new modern software development techniques software next gen using AI more faster silicon chips you see Amazon with what they're doing the custom silicon more processing but I think Hardware matters we've been talking a lot about that I think I think it's we're going to shift from what's been innovative and what's changed I think I think if you look at what's been going on in the industry outside of crypto the infrastructure hasn't really changed much except for AWS what they've done so I'm expecting to see more Innovations at the physics level way down in the chips and then that lower end of the stack is going to be dominated by either one of the three clouds probably AWS and then the middle layer is going to be this where the abstraction is around making infrastructure as code really happen I think that's going to be Clarity coming out of this year next year we should have some visibility into the vertical applications and of the AI and machine learning absolutely digging in on that actually even more because I like what you're saying a lot what verticals do you think that kubernetes is going to impact the most looking even further out than say a year I mean I think that hot ones Healthcare fintech are obvious to get the most money they're spending I think they're the ones who are already kind of creating these super cloud models where they're actually changed over their their spending from capex to Opex and they're driving top line revenue as part of that so you're seeing companies that wants customers of the I.T vendors are now becoming the providers that's a big super cloud Trend we see the other verticals are going to be served by a lot of men in Surprise oil and gas you know all the classic versus Healthcare I mentioned that one those are the classic verticals retail is going to I think be massively huge as you get more into the internet of things that's truly internet based you're going to start to see a lot more Edge use cases so Telecom I think it's going to be completely disrupted by new brands so I think once that you see see how that plays out but all verticals are going to be disrupted just a casual statement to say yeah yeah no doubt in my mind that's great I'm personally really excited about the edge applications that are possible here and can't wait to see can't wait to see what happens next I'm curious as to your thoughts how based given your history here and we don't have to say number of years that you've been participating in in Cape Cod but give them your history what's the evolution looked like from that Community perspective when you were all just starting out having that first drink did you anticipate that we would be here with thousands of people in Detroit you know I knew the moment was happening around um 2017-2018 Dan Coney no longer with us he passed away I ran into him randomly in China and it was like what are you doing here he was with a bunch of Docker guys so they were already investing in so I knew that the cncf was a great Steward for this community because they were already doing the work Dan led a great team at that time and then they were they were they were kicking ass and they were just really setting the foundation they dig in they set the architecture perfectly so I knew that that was a moment that was going to be pretty powerful at the early days when we were talking about kubernetes before it even started we were always always talking about if this this could be the tcpip of of cloud then we could have kind of a de facto interoperability and Lou Tucker was working for Cisco at the time and we were called it interclouding inter-networking what that did during the the revolution Cloud yeah the revolution of the client server and PC Revolution was about connectivity and so tcpip was the disruptive enable that created massive amounts of wealth created a lot of companies created a whole generation of companies so I think this next inflection point is kind of happening right now I think kubernetes is one step of this abstraction layer but you start to see companies like snowflake who's built on AWS and then moved to multiple clouds Goldman Sachs Capital One you're going to see insurance companies so we believe that the rise of the super cloud is here that's going to be Cloud 3.0 that's software 3.0 it's software three what do you want to call it it's not yesterday's Cloud lift and shift and run a SAS application it's a true Enterprise digital digital transformation so that's that's kind of the trend that we see riding in now and so you know if you're not on that side of the street you're going to get washed away from that wave so it's going to be interesting to see how how it all plays out so it's fun to watch who's on the wrong side it is very fun I hope you all are listening to this really powerful advice from John he's dropping some serious knowledge bombs on us well holding the back for kubecon because we've got we got all the great guests coming on and that's where all the content comes from I mean the best part of the community is that they're sharing yeah absolutely so just for old time's sake and it's because it's how I met your fabulous team last year Define kubernetes for the audience kubernetes is like what someone said it was a magical Christmas I heard that was a well good explanation with that when I heard that one um you mean the technical definition or like the business definition or maybe both you can give us an interpretive dance if you'd like I mean the simplest way to describe kubernetes is an orchestration layer that orchestrates containers that are containing applications and it's a way to keep things running and runtime assembly of like the of the data so if you've got you're running containers you can containerize applications kubernetes gives you that capability to run applications at scale which feeds into uh the development uh cycle of the pipelining of apps so if you're writing applications and you want to scale up it's a fast way to stand up massive amounts of scale using containers and kubernetes so a variety of other things that are in the in the in the system too so that was pretty good there's a lot more under the hood but that's the oversimplified version I think that's what we were going for I think it's actually I mean it's harder to oversimplify it sometimes in this case it connects it connects well it's the connective tissue between all the container applications yes last question for you John we are here at the cube we're very excited to be headed to Detroit very soon what can people expect from the cube at coupon this year so we'll be broadcasting Wednesday Thursday and Friday we'll be there early I'll be there Monday and Tuesday we'll do our normal kind of hanging around getting some scoop on the on the ground floor you'll see us there Monday and Tuesday probably in the in the lounge too um come up and say hi to us um again we're looking for more stories this year we believe this is the year that you're going to hear a lot more storytelling coming out of this community as people get more proof points so come up to us share your email your your handle give us yours give us your story we'll publish it we think we think this is going to be the year that cloud native developers start showing the signs of the of the rise of the supercloud that's going to come out of this this community so you know if you got something to say you know we're open to share stories so we're here all that speaking of John how can people say hi to you and the team on Twitter at Furrier at siliconangle at thecube thecube.net siliconangle.com LinkedIn Dave vellantis they were open on all channels all right signal Instagram WhatsApp perfect well pick your channel we really hope to hear from you John thank you so much for joining us for this preview session and thank you for tuning in my name is Savannah Peterson here in Palo Alto at thecube Studios looking forward to Detroit we can't wait to hear your thoughts do let us know in the comments and let us know if you're headed to Michigan cheers [Music] thank you

(upbeat music) >> Welcome back everyone to The Cubes coverage here in Seattle, Washington. For AWS's Marketplace Seller Conference. It's the big news within the Amazon partner network, combining with marketplace, forming the Amazon partner organization. Part of a big reorg as they grow to the next level, NextGen cloud, mid-game on the chessboard. Cube's got it covered. I'm John Furry, your host at Cube. Great guests here from Data bricks. Both cube alumni's. Jack Anderson, GM and VP of the Databricks partnership team for AWS. You handle that relationship and Joel Minick vice president of product and partner marketing. You guys have the keys to the kingdom with Databricks and AWS. Thanks for joining. Good to see you again. >> Thanks for having us back. >> Yeah, John, great to be here. >> So I feel like we're at Reinvent 2013. Small event, no stage, but there's a real shift happening with procurement. Obviously it's a no brainer on the micro, you know, people should be buying online. Self-service, Cloud Scale. But Amazon's got billions being sold through their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website, marketplace. Merge our partner organizations, have more synergy and frictionless experiences so everyone can make more money and customer's are going to be happier. >> Yeah, that's right. >> I mean, you're running relationship. You're in the middle of it. >> Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do, is to work with Amazon on these really, you know, unique use cases. >> You know, I interviewed Ali many times over the years. I remember many years ago, maybe six, seven years ago, we were talking. He's like, "we're all in on AWS." Obviously now the success of Databricks, you've got multiple clouds, see that. Customers have choice. But I remember the strategy early on. It was like, we're going to be deep. So this is, speaks volumes to the relationship you have. Years. Jack, take us through the relationship that Databricks has with AWS from a partner perspective. Joel, and from a product perspective. Because it's not like you guys are Johnny come lately, new to the scene. >> Right. >> You've been there, almost president creation of this wave. What's the relationship and how does it relate to what's going on today? >> So most people may not know that Databricks was born on AWS. We actually did our first $100 million of revenue on Amazon. And today we're obviously available on multiple clouds. But we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon, and marketplace broadens our reach. And so, we think of marketplace in three different aspects. We've got the marketplace private offer business, which we've been doing for a number of years. Matter of fact, we were driving well over a hundred percent year over year growth in private offers. And we have a nine figure business. So it's a very significant business. And when a customer uses a private offer, that private offer counts against their private pricing agreement with AWS. So they get pricing power against their private pricing. So it's really important it goes on their Amazon bill. In may we launched our pay as you go, on demand offering. And in five short months, we have well over a thousand subscribers. And what this does, is it really reduces the barriers to entry. It's low friction. So anybody in an enterprise or startup or public sector company can start to use Databricks on AWS, in a consumption based model, and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation, pilots, POCs. They're really learning the value of that first, use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offer, CPPO. Super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with Databricks on behalf of customers. >> So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data, coming in with the buyers you get. And obviously the integration piece all fitting in there. >> Exactly. >> Okay, so those are the offers, that's current, what's in marketplace today. Is that the products... What are people buying? >> Yeah. >> I mean, I guess what's the... Joel, what are people buying in the marketplace? And what does it mean for them? >> So fundamentally what they're buying is the ability to take silos out of their organization. And that is the problem that Databricks is out there to solve. Which is, when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data. And your teams are trying to use all of this data to solve really complicated problems. And as Databricks, as the Lakehouse Company, what we're helping customers do is, how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find, through the marketplace, those rapid adoption use cases where they can get rid of these data warehousing, data lake silos they've had in the past. Get their unstructured and structured data onto one data platform, an open data platform, that is no longer adherent to any proprietary formats and standards and something they can, very much, very easily, integrate into the rest of their data environment. Apply one common data governance layer on top of that. So that from the time they ingest that data, to the time they use that data, to the time they share that data, inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform, with that common governance solution, they'd being able to bring all of those use cases together. Across their real time streaming, their data engineering, their BI, their AI. All of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges they just couldn't solve before. A good example of this, you know, one of the world's now largest data streaming platforms runs on Databricks with AWS. And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses. That they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data, from what the customers are doing with their platform and the recommendations they want to push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex. But by building it on Databricks, they were able to release it in record time and have grown at a record pace to now be the number one platform. >> And this product, it's impacting product development. >> Absolutely. >> I mean, this is like the difference between lagging months of product development, to like days. >> Yes. >> Pretty much what you're getting at. >> Yes. >> So total agility. >> Mm-hmm. >> I got that. Okay, now, I'm a customer I want to buy in the marketplace, but you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from AWS's leadership, Chris, was up there speaking, and Mona was, "Hey, he's a CRO conference chief revenue officer" conversation. Which means someone's getting compensated. So, if I'm the sales rep at Databricks, what's my motion to the customer? Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Or, how do you handle it? >> Well, I'd add what Joel just talked about with, you know, with the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, it's the entire Data Bricks offering. And- >> The flagship, all the, the top stuff. >> Everything, the flagship, the complete offering. So it's not segmented. It's not a sub segment. >> Okay. >> It's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we view this two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend Databricks for the right situation. Same thing with Databricks, our sales force wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get Databricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars. You have $5 million of spend. You put that spend through the flywheel with AWS marketplace, and then you can use that in your negotiations with AWS to get better pricing overall. So that's how we view it. >> So customers are driving. This sounds like. >> Correct. For sure. >> So they're looking at this as saying, Hey, I'm going to just get purchasing power with all my relationships. Because it's a solution architectural market, right? >> Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider. If they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing power. >> Okay, Joel, we're going to date ourselves. At least I will. So back in the old days, (group laughter) It used to be, do a Barney deal with someone, Hey, let's go to market together. You got to get paper, you do a biz dev deal. And then you got to say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for Databricks, or any company is, to go find those partners and do deals, versus now Amazon is the center point for the customer. So you can still do those joint deals, but this seems to be flipping the script a little bit. >> Well, it is, but we still have vars and consulting partners that are doing implementation work. Very valuable work, advisory work, that can actually work with marketplace through the CPPO offering. So the marketplace allows multiple ways to procure your solution. >> So it doesn't change your business structure. It just makes it more efficient. >> That's correct. >> That's a great way to say it. >> Yeah, that's great. >> Okay. So, that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >> Yes. >> Absolutely. >> Economically. >> Economically, it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon. Especially when it comes back to co-selling, right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution. And our teams are working backwards from those use cases, you know, to collaborate and land them. >> Yeah. I want to get that out there. Go ahead, Joel. >> So one of the other things I might add to that too, you know, and why this is advantageous for companies like Databricks to work through the marketplace. Is it makes it so much easier for customers to deploy a solution. It's very, literally, one click through the marketplace to get Databricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. >> You know, it's interesting. I want to bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself, EDI back in the old days, you know, all that craziness. Now this is all the internet, basically through the console. I get the infrastructure side, you know, spin up and provision some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage. A little nuanced. Most enterprises aren't yet there of integrating tech, on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrators dilemma, not an innovator's dilemma. So like, I want to integrate. So now I have integration points with Databricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you got to build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or am I off because, no one's going to be buying software like they used to. They buy software to integrate it. >> Yeah, no- >> Because everything's integrated. >> I think AWS has done a great job at creating a partner ecosystem, right? To give customers the right tools for the right jobs. And those might be with third parties. Databricks is doing the same thing with our partner connect program, right? We've got customer partners like Five Tran and DBT that, you know, augment and enhance our platform. And so you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >> Yeah. It's almost like, you know, bundling and un bundling. I was talking about this with, with Dave Alante about Supercloud. Which is why wouldn't a customer want the best solution in their architecture? Period. In its class. If someone's got API security or an API gateway. Well, you know, I don't want to be forced to buy something because it's part of a suite. And that's where you see things get sub optimized. Where someone dominates a category and they have, oh, you got to buy my version of this. >> Joel and I were talking, we were actually saying, what's really important about Databricks, is that customers control the data, right? You want to comment on that? >> Yeah. I was going to say, you know, what you're pushing on there, we think is extraordinarily, you know, the way the market is going to go. Is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically, I think, really strong places, Databricks and AWS have lined up, is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the Lakehouse, one thing we've always been extremely committed to, as a company, is building the data platform on an open foundation. And we do that primarily through Delta Lake and making sure that, to Jack's point, with Databricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed Databricks to have the breadth of integrations that it has with all the other data tools out there. Because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >> When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what am I foreclosing if I go with something else that's not as open? What's the customer's downside as you think about what's around the corner in the industry? Because if you believe it's going to be open, open source, which I think open source software is the software industry, and integration is a big deal. Because software's going to be plentiful. >> Sure. >> Let's face it. It's a good time to be in software business. But Cloud's booming. So what's the downside, from your Databricks perspective? You see a buyer clicking on Databricks versus that alternative. What's potentially should they be a nervous about, down the road, if they go with a more proprietary or locked in approach? >> Yeah. >> Well, I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also, then, beholden to the pace at which that provider is able to innovate. >> Mm-hmm. >> And I think we've seen lots of times over history where, you know, a proprietary format may run ahead, for a while, on a lot of innovation. But as that market control begins to solidify, that desire to innovate begins to degrade. Whereas in the open formats- >> So extract rents versus innovation. (John laughs) >> Exactly. Yeah, exactly. >> I'll say it. >> But in the open world, you know, you have to continue to innovate. >> Yeah. >> And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you are always going to be at the forefront of what is the latest. >> You know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked with proprietary. >> Yeah. >> You know, SNA and IBM, deck net was digital. You know the rest. And then TCPIP was part of the open systems interconnect. >> Mm-hmm. >> Revolutionary (indistinct) a big part of that, as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. >> Yeah. >> But that helped inter operate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid-game, then you get the end game. You're not there at the end game yet at Cloud. But Cloud- >> There's, always some form of lock in, right? Andy Jazzy will address it, you know, when making a decision. But if you're going to make a decision you want to reduce- You don't want to be limited, right? So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now, is an AI driven business, right? And so it has to do with, can you get that data out of silos? Can you organize it and secure it? And then can you work with data scientists to feed those models? >> Yeah. >> In a very consistent manner. And so the tools of tomorrow will, to Joel's point, will be open and we want interoperability with those tools. >> And choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds, is that they have to compete directly too. Redshift competes directly with a lot of other stuff. But they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're going to be, they're onto it. This is the- >> To Amazon's credit by having these solutions that may compete with native services in marketplace, they are providing customers with choice, low price- >> And access to the core value. Which is the hardware- >> Exactly. >> Which is their platform. Okay. So I want to get you guys thought on something else I see emerging. This is, again, kind of Cube rumination moment. So on stage, Chris unpacked a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing, compensation, workflows, services behind the curtain. And one of those things he mentioned was, they talk about resellers or channel partners, depending upon what you talk about. We believe, Dave and I believe on the Cube, that the entire indirect sales channel of the industry is going to be disrupted radically. Because those players were selling hardware in the old days and software. That game is going to change. You mentioned you guys have a program, let me get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in. Which means that the old reseller channels are going to be rewritten. They're going to be refactored with this new kinds of access. Because you've got scale, you've got money and you've got product. And you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, a value added reseller or VAB or business. >> You've got to evolve. >> You got to, you got to be here. >> Yes. >> Yeah. >> How are you guys working with those partners? Because you say you have a product in your marketplace there. How do I make money if I'm a reseller with Databricks, with Amazon? Take me through that use case. >> Well I'll let Joel comment, but I think it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations. They need expertise from consulting and SI partners. If those consulting and SI partners happen to resell the solution as well. Well, that's another aspect of their business. But I really think it is the expertise that the partners bring to help customers get outcomes. >> Joel, channel big opportunity for Amazon to reimagine this. >> For sure. Yeah. And I think, you know, to your comment about how do resellers take advantage of that, I think what Jack was pushing on is spot on. Which is, it's becoming more and more about the expertise you bring to the table. And not just transacting the software. But now actually helping customers make the right choices. And we're seeing, you know, both SIs begin to be able to resell solutions and finding a lot of opportunity in that. >> Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's going to be the evolution that this goes. >> At the end of the day, it's about services, right? >> For sure. Yeah. >> I mean... >> You've got a great service. You're going to have high gross profits. >> Yeah >> Managed service provider business is alive and well, right? Because there are a number of customers that want that type of a service. >> I think that's going to be a really hot, hot button for you guys. I think being the way you guys are open, this channel, partner services model coming in, to the fold, really kind of makes for kind of that Supercloud like experience, where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on, within Databricks. >> For sure. >> On top of this ecosystem. How does that work? This is kind of like, hasn't been written up in business school and case studies yet. This is new. What is this? >> I think, you know, what it comes down to is, you're seeing ecosystems begin to evolve around the data platforms. And that's going to be one of the big, kind of, new horizons for us as we think about what drives ecosystems. It's going to be around, well, what's the data platform that I'm using? And then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around Databricks in its Lakehouse platform as well. As customers are looking at well, if I'm standing these Lakehouses up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >> I mean you think about ecosystem theory, we're living a whole nother dream. And I'm not kidding. It hasn't yet been written up and for business school case studies is that, we're now in a whole nother connective tissue, ecology thing happening. Where you have dependencies and value proposition. Economics, connectedness. So you have relationships in these ecosystems. >> And I think one of the great things about the relationships with these ecosystems, is that there's a high degree of overlap. >> Yeah. >> So you're seeing that, you know, the way that the cloud business is evolving, the ecosystem partners of Databricks, are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for you. >> Joel, Jack, I love it because you know what it means? The best ecosystem will win, if you keep it open. >> Sure, sure. >> You can see everything. If you're going to do it in the dark, you know, you don't know the outcome. I mean, this is really kind of what we're talking about. >> And John, can I just add that when I was at Amazon, we had a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? >> Yeah. >> And so there's a platform decision being made and that ecosystem is going to evolve around the platform. >> Yeah, and I totally agree. And the word innovation gets kicked around. That's why, you know, when we had our Supercloud panel, it was called the innovators dilemma, with a slash through it, called the integrater's dilemma. Innovation is the digital transformation. So- >> Absolutely. >> Like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating? If APIs are connective tissue, what's automation, what's the service messages look like? I mean, a whole nother set of, kind of thinking, goes on in these new ecosystems and these new products. >> And that thinking is, has been born in Delta Sharing, right? So the idea that you can have a multi-cloud implementation of Databricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud solution. >> Well, Databricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great growing ecosystem. And again, I think a shining example of what every enterprise is going to do. Build on top of something, operating model, get that operating model, driving revenue. >> Mm-hmm. >> Yeah. >> Whether, you're Goldman Sachs or capital one or XYZ corporation. >> S and P global, NASDAQ. >> Yeah. >> We've got, you know, the biggest verticals in the world are solving tough problems with Databricks. I think we'd be remiss because if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions. Whether it's the relationship we have with our engineering and service teams. Our marketing teams, you know, product development. And we're going to be at Reinvent. A big presence at Reinvent. We're looking forward to seeing you there, again. >> Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening. This NextGen Cloud is here. We're seeing this evolve, kind of new economics, new value propositions kind of scaling up. Producing more. So you guys are doing a great job. Thanks for coming on the Cube and taking the time. Joel, great to see you at the check. >> Thanks for having us, John. >> Okay. Cube coverage here. The world's changing as APN comes together with the marketplace for a new partner organization at Amazon web services. The Cube's got it covered. This should be a very big, growing ecosystem as this continues. Billions of being sold through the marketplace. And of course the buyers are happy as well. So we've got it all covered. I'm John Furry. your host of the cube. Thanks for watching. (upbeat music)

(upbeat music) >> Announcer: theCUBE presents UiPath Forward5. Brought to you by UiPath. >> Hi everybody, we're back in Las Vegas. We're live with Cube's coverage of Forward 5 2022. Dave Vellante with Dave Nicholson Ted Kumer this year is the Executive Vice President, product and engineering at UiPath. Brought on to do a lot of the integration and bring on new capabilities for the platform and we've seen that over the last several years. And he's joined by Dr. Edward Challis, who's the co-founder of the recent acquisition that UiPath made, company called Re:infer. We're going to learn about those guys. Gents, welcome to theCUBE. Ted, good to see you again. Ed, welcome. >> Good to be here. >> First time. >> Thank you. >> Yeah, great to be here with you. >> Yeah, so we have seen, as I said, this platform expanding. I think you used the term business automation platform. It's kind of a new term you guys introduced at the conference. Where'd that come from? What is that? What are the characteristics that are salient to the platform? >> Well, I see the, the evolution of our platform in three chapters. You understand the first chapter, we call that the RPA chapter. And that's where we saw the power of UI automation applied to the old problems of how do I integrate apps? How do I automate processes? That was chapter one. You know, chapter two gets us to Forward3 in 2019, and the definition of this end-to-end automation platform you know, with the capabilities from discover to measure, and building out that core platform. And as the platform's progressed, what we've seen happen with our customers is the use of it goes from being very heavy in automating the repetitive and routine to being more balanced, to now where they're implementing new brought business process, new capability for their organization. So that's where the name, Business Automation Platform, came from. Reflecting now that it's got this central role, as a strategic tool, sitting between their application landscape, their processes, their people, helping that move forward at the rate that it needs to. >> And process mining and task mining, that was sort of the enabler of chapter two, is that right? >> Well, I'd say chapter two was, you know, first the robots got bigger in terms of what they could cover and do. API integration, long running workflows, AI and ML skills integrated document processing, citizen development in addition to professional development, engaging end users with things like user interfaces built with UiPath apps. And then the discovery. >> So, more robustness of the? Yeah, okay. >> Yeah. Just an expansion of the whole surface area which opened up a lot of things for our customers to do. That went much broader than where core RPA started. And so, and the other thing about this progression to the business automation platform is, you know, we see customers now talking more about outcomes. Early on they talk a lot about hours saved and that's great, but then what about the business outcomes it's enabling? The transformations in their business. And the other thing we're doing in the platform is thinking about, well, where can we land with solutions capabilities that more directly land on business, measurable business outcomes? And so we had started, for example, offering an email automation solution, big business problem for a lot of our customers last year. And we'd started encountering this company Re:infer as we were working with customers. And then, and we encountered Re:infer being used with our platform together. And we saw we can accelerate this. And what that is giving us now is a solution now that aligns with a very defined business outcome. And this way, you know, we can help you process communications and do it efficiently and provide better service for your customers. And that's beginning of another important progression for us in our platform. >> So that's a nice segue, Ed. Tell about Re:infer. Why did you start the company? >> Right, yeah, so my whole career has been in machine learning and AI and I finished my PhD around 2013, it was a very exciting time in AI. And me and my co-founders come from UCL, this university in London, and Deep Mind, this company which Google acquired a few years later, came from our same university. So very exciting time amongst the people that really knew about machine learning and AI. And everyone was thinking, you know, how do we, these are just really big breakthroughs. And you could just see there was going to be a whole bunch of subsequent breakthroughs and we thought NLP would be the next breakthrough. So we were really focused on machine reading problems. And, but we also knew as people that had like built machine learning production systems. 'Cause I'd also worked in industry that built that journey from having a hypothesis that machine learning can solve a problem to getting machine learning into production. That journey is of painful, painful journey and that, you know, you can see that you've got these advances, but getting into broad is just way too hard. >> So where do you fit in the platform? >> Yeah, so I think when you look in the enterprise just so many processes start with a message start with a no, start with a case ticket or, you know, some other kind of request from a colleague or a customer. And so it's super exciting to be able to, you know, take automation one step higher in that process chain. So, you could automatically read that request, interpret it, get all the structured data you need to drive that process forward. So it's about bringing automation into these human channels. >> So I want to give the audience a sense here. So we do a lot of events at the Venetian Conference Center, and it's usually very booth heavy, you know, brands and big giant booths. And here the booths are all very small. They're like kiosks, and they're all pretty much the same size. So it's not like one vendor trying to compete with the other. And there are all these elements, you know I feel like there's clouds and there's, you know, of course orange is the color here. And one of the spots is, it has this really kind of cool sitting area around customer stories. And I was in there last night reading about Deutsche Bank. Deutsche Bank was also up on stage. Deutsche Bank, you guys were talking about a Re:infer. So share with our audience what Deutsche Bank are doing with UiPath and Re:infer. >> Yeah, so I mean, you know, before we automate something, we often like to do what we call communications mining. Which is really understanding what all of these messages are about that might be hitting a part of the business. And at Deutsche Bank and in many, you know, like many large financial services businesses, huge volumes of messages coming in from the clients. We analyze those, interpret the high volume query types and then it's about automating against those to free up capacity. Which ultimately means you can provide faster, higher quality service because you've got more time to do it. And you're not dealing with all of those mundane tasks. So it's that whole journey of mining to automation of the coms that come into the corporate bank. >> So how do I invoke the service? So is it mother module or what's the customer onboarding experience like? >> So, I think the first thing that we do is we generate some understanding of actually the communications data they want to observe, right? And we call it mining, but you know, what we're trying to understand is like what are these communications about? What's the intent? What are they trying to accomplish? Tone can be interesting, like what's the sentiment of this customer? And once you understand that, you essentially then understand categories of conversations you're having and then you apply automations to that. And so then essentially those individual automations can be pointed to sets of emails for them to automate the processing of. And so what we've seen is customers go from things they're handling a hundred percent manual to now 95% of them are handled basically with completely automated processing. The other thing I think is super interesting here and why communications mining and automation are so powerful together is communications about your business can be very, very dynamic. So like, new conversations can emerge, something happens right in your business, you have an outage, whatever, and the automation platform, being a very rapid development platform, can help you adapt quickly to that in an automated way. Which is another reason why this is such a powerful thing to put the two things together. >> So, you can build that event into the automation very quickly you're saying? >> Speaker 1: Yeah. >> Speaker 2: That's totally right. >> Cool. >> So Ed, on the subject of natural language processing and machine learning versus machine teaching. If I text my wife and ask her would you like to go to an Italian restaurant tonight? And she replies, fine. Okay, how smart is your machine? And, of course, context usually literally denotes things within the text, and a short response like that's very difficult to do this. But how do you go through this process? Let's say you're implementing this for a given customer. And we were just talking about, you know, the specific customer requirements that they might have. What does that process look like? Do you have an auditor that goes through? And I mean do you get like 20% accuracy, and then you do a pass, and now you're at 80% accuracy, and you do a pass? What does that look? >> Yeah, so I mean, you know when I was talking about the pain of getting a machine learning model into production one of the principle drivers of that is this process of training the machine learning model. And so what we use is a technique called active learning which is effectively where the AI and ML model queries the user to say, teach me about this data point, teach me about this sentence. And that's a dynamic iterative process. And by doing it in that way you make that training process much, much faster. But critically that means that the user has, when you train the model the user defines how you want to encode that interpretation. So when you were training it you would say fine from my wife is not good, right? >> Sure, so it might be fine, do you have a better suggestion? >> Yeah, but that's actually a very serious point because one of the things we do is track the quality of service. Our customers use us to attract the quality of service they deliver to their clients. And in many industries people don't use flowery language, like, thank you so much, or you know, I'm upset with you, you know. What they might say is fine, and you know, the person that manages that client, that is not good, right? Or they might say I'd like to remind you that we've been late the last three times, you know. >> This is urgent. >> Yeah, you know, so it's important that the client, our client, the user of Re:infer, can encode what their notions of good and bad are. >> Sorry, quick follow up on that. Differences between British English and American English. In the U.K., if you're thinking about becoming an elected politician, you stand for office, right? Here in the U.S., you run for office. That's just the beginning of the vagaries and differences. >> Yeah, well, I've now got a lot more American colleagues and I realize my English phrasing often goes amiss. So I'm really aware of the problem. We have customers that have contact centers, some of them are in the U.K., some of them are in America, and they see big differences in the way that the customers get treated based on where the customer is based. So we've actually done analysis in Re:infer to look at how agents and customers interact and how you should route customers to the contact centers to be culturally matched. Because sometimes there can be a little bit of friction just for that cultural mapping. >> Ted, what's the what's the general philosophy when you make an acquisition like this and you bring in new features? Do you just wake up one day and all of a sudden there's this new capability? Is it a separate sort of for pay module? Does it depend? >> I think it depends. You know, in this case we were really led here by customers. We saw a very high value opportunity and the beginnings of a strategy and really being able to mine all forms of communication and drive automated processing of all forms of communication. And in this case we found a fantastic team and a fantastic piece of software that we can move very quickly to get in the hands of our customer's via UiPath. We're in private preview now, we're going to be GA in the cloud right after the first of the year and it's going to continue forward from there. But it's definitely not one size fits all. Every single one of 'em is different and it's important to approach 'em that way. >> Right, right. So some announcements, StudioWeb was one that I think you could. So I think it came out today. Can't remember what was today. I think we talked about it yesterday on the keynotes anyway. Why is that important? What is it all about? >> Well we talked, you know, at a very top level. I think every development platform thinks about two things for developers. They think, how do I make it more expressive so you can do other things, richer scenarios. And how do I make it simpler? 'Cause fast is always better, and lower learning curves is always better, and those sorts of things. So, Re:infer's a great example of look the runtime is becoming more and more expressive and now you can buy in communications state as part of your automation, which is super cool. And then, you know StudioWeb is about kind of that second point and Studios and Studio X are already low code visual, but they're desktop. And part of our strategy here is to elevate all of that experience into the web. Now we didn't elevate all of studio there, it's a subset. It is API integration and web based application automation, Which is a great foundation for a lot of apps. It's a complete reimagining of the studio user interface and most importantly it's our first cross-platform developer strategy. And so that's been another piece of our strategy, is to say to the customers we want to be everywhere you need us to be. We did cross-platform deployment with the automation suite. We got cross-platform robots with linear robots, serverless robots, Mac support and now we got a cross-platform devs story. So we're starting out with a subset of capabilities maybe oriented toward what you would associate with citizen scenarios. But you're going to see more roadmap, bringing more and more of that. But it's pretty exciting for us. We've been working on this thing for a couple years now and like this is a huge milestone for the team to get to this, this point. >> I think my first conversation on theCUBE with a customer was six years ago maybe at one of the earlier Forwards, I think Forward2. And the pattern that I saw was basically people taking existing processes and making them better, you know taking the mundane away. I remember asking customers, yeah, aren't you kind of paving the cow path? Aren't there sort of new things that you can do, new process? And they're like, yeah, that's sort of the next wave. So what are you seeing in terms of automating existing processes versus new processes? I would see Re:infer is going to open up a whole new vector of new processes. How should we think about that? >> Yeah, I think, you know, I mean in some ways RPA has this reputation because there's so much value that's been provided in the automating of the repetitive and routine. But I'd say in my whole time, I've been at the company now for two and a half years, I've seen lots of new novel stuff stood up. I mean just in Covid we saw the platform being used in PPP loan processing. We saw it in new clinical workflows for COVID testing. We see it and we've just seen more and more progression and it's been exciting that the conference, to see customers now talking about things they built with UiPath apps. So app experiences they've been delivering, you know. I talked about one in healthcare yesterday and basically how they've improved their patient intake processing and that sort of thing. And I think this is just the front end. I truly believe that we are seeing the convergence happen and it's happening already of categories we've talked about separately, iPass, BPM, low-code, RPA. It's happening and it's good for customers 'cause they want one thing to cover more stuff and you know, I think it just creates more opportunity for developers to do more things. >> Your background at Microsoft probably well prepared you for a company that you know, was born on-prem and then went all in on the cloud and had, you know, multiple code bases to deal with. UiPath has gone through a similar transformation and we talked to Daniel last night about this and you're now cloud first. So how is that going just in terms of managing multiple code bases? >> Well it's actually not multiple Code bases. >> Oh, it's the same one, Right, deployment models I should say. >> Is the first thing, Yeah, the deployment models. Another thing we did along the way was basically replatform at an infrastructure level. So we now can deploy into a Kubernetes Docker world, what you'd call the cloud native platform. And that allows us to have much more of a shared infrastructure layer as we look to deliver to the automation cloud. The same workload to the automation cloud that we now deliver in the automation suite for deployment on-prem or deploying a public cloud for a customer to manage. Interesting and enough, that's how Re:infer was built, which is it was built also in the cloud native platform. So it's going to be pretty easy. Well, pretty easy, there's some work to do, but it's going to be pretty easy for us to then bring that into the platform 'cause they're already working on that same platform and provide those same services both on premises and in the cloud without having your developers have to think too much about both. >> Okay, I got to ask you, so I could wrap my stack in a container and put it into AWS or Azure or Google and it'll run great. As well, I could tap some of the underlying primitives of those respective clouds, which are different and I could run them just fine. Or/and I could create an abstraction layer that could hide those underlying primitives and then take the best of each and create an automation cloud, my own cloud. Does that resonate? Is that what you're doing architecturally? Is that a roadmap, or? >> Certainly going forward, you know, in the automation cloud. The automation cloud, we announced a great partnership or a continued partnership with Microsoft. And just Azure and our platform. We obviously take advantage of anything we can to make that great and native capabilities. And I think you're going to see in the Automation Suite us doing more and more to be in a deployment model on Azure, be more and more optimized to using those infrastructure services. So if you deploy automation suite on-prem we'll use our embedded distro then when we deploy it say on Azure, we'll use some of their higher level managed services instead of our embedded distro. And that will just give customers a better optimized experience. >> Interesting to see how that'll develop. Last question is, you know what should we expect going forward? Can you show us a little leg on on the future? >> Well, we've talked about a number of directions. This idea of semantic automation is a place where you know, you're going to, I think, continue to see things, shoots, green shoots, come up in our platform. And you know, it's somewhat of an abstract idea but the idea that the platform is just going to become semantically smarter. You know, I had to serve Re:infer as a way, we're semantically smarter now about communications data and forms of communications data. We're getting semantically smarter about documents, screens you know, so developers aren't dealing with, like, this low level stuff. They can focus on business problem and get out of having to deal with all this lower level mechanism. That is one of many areas I'm excited about, but I think that's an area you're going to see a lot from us in the next coming years. >> All right guys, hey, thanks so much for coming to theCUBE. Really appreciate you taking us through this. Awesome >> Yeah Always a pleasure. >> Platform extension. Ed. All right, keep it right there, everybody. Dave Nicholson, I will be back right after this short break from UiPath Forward5, Las Vegas. (upbeat music)

(intro upbeat music) >> TheCUBE presents UiPath Forward5, Brought to you by UiPath. >> Welcome to day two of Forward5 UiPath Customer Conference. You're watching theCUBE. My name is Dave Vellante. My co-host is David Nicholson. Yesterday, Dave, we heard about the extension into an enterprise platform. We heard about, from the two CEOs, a new go-to-market strategy. We heard from a lot of customers how they're implementing UiPath generally and automation, specifically, scaling, hyper-automation, and all the buzzwords you hear. Todd Foley is the CDO and CSO of Lydonia Technologies and Devika Saharya is the director of ERP and RPA at MongoDB. Folks, welcome to theCUBE. Thanks for taking time out of your busy day and coming on. >> Thank you Dave. >> Thank you so much. >> So let's start with the roles. So Devika, ERP and RPA. >> Yes. >> It's like peanut butter and jelly, or how do those things relate? What's your, what's your role? >> Absolutely. So I started at Mongo as an ERP manager, and you know, as we were growing, the one thing that came out of, you know, the every year goals for the company, one big goal that came out was how we have to scale. There are so many barriers to scale. How can we become a billion dollar company? What do we need to do? And when we started drilling down into, you know, different areas, we figured it out that people do a lot of stuff manually. It's like comparing sheets, you know, copying data from one place to the other, and so on and so forth. So one thing that we realized was we definitely need some kind of automation. At that time, we didn't know about automation, but we did our own market research and here we are. >> Let's automate. Yeah, right. (Devika laughs) Sounds easy. All right, thank you. Todd, CDO, Chief Data or Chief Dig, and CSO, I'm assuming Chief Data? >> Chief Data. >> And the Chief Information Security Officer. Tell us about Lydonia and also your role. >> Sure, Lydonia, we started just over three years ago. We looked at the RPA market. We saw great opportunity, but we also saw a challenge. We saw that a lot of people had deployed RPA but weren't getting the promised, you know, immediate ROI, rapid deployment that was out there. And when we looked at it, we saw that it really wasn't a technical challenge. Sometimes it was how technology was applied, but there were a lot of things that people were doing in their process and how they were treating RPA, often as if it were traditional technology that slowed them down. So we built our practice, our company, around the idea of being able to help people scale very quickly and drive that faster. And we're finding now with the RPA being pretty ubiquitous, that it's the one thing that's in the greatest demand among our clients. >> Okay, so you're the implementation partner for Mongo, is that right? >> We are. >> Okay, so relatively new. Very new actually, but a specialist. Why'd you choose Lydonia? >> So, that's an interesting question. When we came last year to UiPath Forward, we were looking for, you know, the right kind of people who can, you know, put us on track. We had the technology, we had everything in place, we did the POC, everybody liked it, but we didn't know how to, you know, basically go in that direction. We were missing that direction. And then we, you know, we were doing our homework here, we found, we accidentally stumbled with Lydonia, and I had follow up conversations with Todd, and they were just so tapered. I knew exactly what Todd was explaining me, and we knew we are, we are in safe hands. >> So, where did you start? >> So we, the first thing that we did was a POC for the finance side of business. And right after that POC, we realized that, you know, how much time people were actually investing manually, like things that were done in three to four days was turning into a 30 minute process. And that gave us, you know, the idea that we should start drilling down into different departments and try to find where there are, you know, areas where we can improve. And we did all of that. And then we met with Todd, and Todd explained that how his Reignite process works. So we took Reignite as our first step and, you know, took it from there. We chose one department, we worked with them. We had about 10 processes highlighted, thanks to Todd, he worked with them, and he literally drilled and nailed it down that what we need to do. And as of today, all those 10 are automated. >> Wow. Okay. >> Todd, does this interaction between Lydonia and MongoDB, as a customer, apply equally in the field when you're going out and talking to clients that might be running MongoDB, they might be customers of MongoDB, they may have financial applications that are backended with MongoDB, is there a synergy there that you've been able to gain? >> I think there is. I think there's one thing that's kind of unique about RPA, and that the traditional questions around integration and applicability aren't as important when you have a platform that can work with anything that people can use. I think also, you know, when we look at what we typically do with people, some of the things we see at Mongo are very common use cases you know, across all of our clients. So I, there's definitely the ability for us to take things we've done and have clients get leverage out of them. At the same time, the platform itself is, makes it different than a traditional model where, you know if somebody has worked in a particular area or built an automation for a particular application, there's some kind of utility to do it faster for another client. What we find is that that's not really the case. And that oftentimes we'll compete with people who use different tool sets than UiPath who have that kind of value story around having done it before, we come in and we do it twice as fast as they could. >> So you've, you're a veteran of complex integrations. >> Oh yeah. (Todd laughs) >> I know that from our paths have crossed in the past. So you're saying that in this world of RPA, that this tool set like UiPath as a platform, we've been talking a lot about the difference between being a tool set and being a platform. >> Right. >> That this platform can sort of hover above things without that same layer of complexity, or level of complexity, that you've experienced in the past. Because that speaks to the idea that UiPath, as a platform, is going to work moving forward in a big way. >> Exactly, right. I think we've seen for years and years that regardless of the type of development environment you're using, a developer's value sometimes is based on what reusable libraries they've created, what they have to cut and paste from their old code to be able to do things faster. The challenge with that is it has to be maintained, when things change, they've got to update those libraries. It's a value prop that's very high touch. With UiPath, they've created the ultimate in reusability. The platform, especially since they acquired cloud elements and built all of those API integrations into their platform. The platform maintains the reusability and the libraries in such a way where they're drag and drop from a development standpoint and you don't have to maintain them. It's the ultimate expression of reusability as a platform. >> Yeah, cloud elements, API automation, obviously a key pick by UiPath. Devika, what's the scale of your operation today? Like how many bots and where do you see it going? >> Yes. So we, we started with one bot. Last year we experimented a lot that, you know, we were just trying to make our footprint in the company, trying to understand that, you know, people understand what RPA is, what UiPath is. Initially we got a lot of pushback. We got a pushback from our security team as well, because they could not understand, you know, that what UiPath is and how secure it is. And we had to explain them that how we would host it over AWS, how we will work, how we will not save passwords, et cetera. When we did all of that and they got comfort, we started picking, you know, very small processes around to show, you know, people the capability of RPA and UiPath per se. When we did that, people started just coming with bigger processes, and one specific team that I can think of came that we do, you know, fuzzy logic in Excel, and we do it twice a week, but it takes a lot of time. We automated it, they run it daily, every single day, two times now. And the exponential growth that we saw just with that one automation was mind boggling. I couldn't believe that, you know. We were tracking our insights and we were like, oh my God, what happened? It just blew out of proportion. >> Okay. So then did you need more bots? Are you still running one bot, or? >> Nope. Now at the moment we have nine. >> Okay. >> And we are still looking to grow. >> Okay. So the initial friction, you said there was some, you know, concern, it was primarily security or were there others, people afraid they're going to lose their jobs? Was there any of that? >> There was no risk of losing the job. The major, you know, pushback was, one was from security, the other one was from different system owners because a lot of people were not sure why we want UI access, or why we want API access, and why are we accessing their systems? What type of information we are trying to gather out of their systems. Are we writing into their system? Because a lot of people have issues when we start saying that we will write or override data. So most of the processes that we are working around are either writing, comparing, and reading and comparing, and if it is writing, we take special permission that this is what we are going to do. >> So what did you have to do to get through the security mottle, a AWS SOC 2 report, did you have to show them the UiPath pen test? >> Absolutely. >> Did you have to change any of your processes? What was that sort of punch list like? >> Everything. >> Yeah. >> So we had to start from pen test. We had to start, we had to explain that UiPath is in the process of, you know, acquiring SOC. We also explained that how things are hosted on AWS. We had to, you know, bring our consultants in who explained that how on, on AWS, this will be a very secured way of doing things. And when we did our first process, which was actually for the auditors, which is, you know, interesting. >> Yeah. >> What we did was we did segregation of duties, which I think is very important in every field and every sphere we work in. So for example, the the writeup that we were building for auditors, we made sure that it is approved by a physical or a human, you know, and not everything is done by the bot. The biggest piece of the puzzle was writing, you know, because it was taking a lot of time. People were going into different systems, gathering information, putting it on Excel, and then you know, comparing and submitting it to PWC. >> When you say write, you mean any update to a system of record? >> Correct. >> Required some scrutiny? >> Some scrutiny, yes, yes. >> Okay, initially by a human until there was comfort level and then it's like these bots know what they're doing. >> Correct, correct. >> Okay. And now you're a NetSuite customer, correct? >> Yes. >> That's your ERP? >> That's right. >> Now we were talking about Oracle is going to acquire OCR capabilities. Will that, and we've been talking, Dave and I, a week about, okay well ServiceNow has, you know, RPA, and Salesforce, and SAP, et cetera. How will that affect your thinking about adopting UiPath? >> I don't think it should matter because I think all these systems kind of coexist in a bigger ecosystem, you know, and I also feel that all these systems have their own plus points and minus points. Not one system in, per se, can do everything within a company. So it could be that, for example, NetSuite might be very strong for financials in the space we are in, but not extremely good around sales and marketing. So for that company chose Salesforce. So you know, you have those smaller smaller multiple systems that build into a bigger ecosystem, right. And I think the other piece of the puzzle is that UiPath helps bridge that gap between these systems. You know, it could happen that certain things can get integrated, certain things cannot because of the nature of business, the nature of work that the teams are trying to do. And I think UiPath is leveraging that gap, you know, and putting, you know, those strings together. >> As you scale - >> Mm hmm. >> How will, and Todd I presume you're going to assist in this process, but how will you decide what processes to prioritize, and is that a process driven decision? Is it data led? Both? If so, what kind of data? Can you describe how you guys are going to approach that? >> Yep. Todd, would you like to take that first before I start? >> Sure, yeah. >> Maybe some best practices and then we can maybe get specific to Mongo. >> Absolutely. Our guidance is always that it should be a business decision, right? And it should be data driven, based on a business defined metric around the business case for that particular automation. Our guidance to customers is don't automate it unless you know why you're automating it, and what the value is. We see sometimes there are challenges with people being able to articulate the business case for an automation, and it can almost always be resolved by having that business case be the first step, and qualifying and identifying an automation candidate. >> And how does that apply to Mongo? Do you, where are you thinking about scaling, in your opinion? >> It's interesting because, you know, initially we thought that we will, you know, explore one area in MongoDB. And the other thing that we did was we did road shows. So because we had to create some awareness in the company that we have UiPath there's something called bots. There's something called, you know, automation that we can do, so we created a presentation with small demos inside it and, you know, circulated it within the company. Different departments tried to explain what we can achieve. And based off of that, you know, we came up with a laundry list of all the automations that different departments needed. And out of that, you know, we started doing the business case, the value, you know, trying to come up with complexity, effort. We did a full estimation matrix and based off of that we came, okay, these are the top 20 that we should build first. And as soon as we built those top 20, we saw a skyrocket, you know, growth and - >> And you're looking for hard dollars, right? >> Yes, yes. Absolutely. >> Okay, just to be clear. >> Devika, I think Mongo also is great at taking a data driven approach to looking at their program. Do you want to share how you do that? >> Yes, absolutely. So one thing that we were very sure was we have to talk in terms of numbers because that's the only solid way to see growth. And what we did was, you know, we got insights, we started doing full metrics in terms of dollar saved, hour saved, and we are trying to track how every process is impacting, you know, in the grand scheme of things. Like say for example, for finance, are we shortening the close cycle in any shape or form by doing these two or three automations that we are doing? And I'm happy to report that we have really shortened our close cycle from where we started. >> Your quarter end or month end close. >> Correct, yes. >> Daily? You at the daily close yet, (all laugh) or the "John Chambers"? >> Drive everyone nuts. First I have to say, I could feel the audience sort of smiling as they see, as they hear from MongoDB, disruptor of legacy databases being cautious in their internal approach to change. As everyone else is. >> Exactly, yeah. >> But Todd, just sort of, double clicking on this idea of kind of stove pipes of capabilities in the RPA space. I mean OCR, being added to NetSuite, I'm not sure if that's the greatest example, but the point is Lydonia will work with all of those technologies to synthesize something. Is that correct? Or are you a UiPath only? >> Both. So we exclusively use UiPath with our customers. We don't use other RPA platforms. >> Okay. >> And we don't because, not because we can't, but because we don't believe that anything else is going to be as quick or as effective. Also, it's the only platform that is as broad and comprehensive as it needs to be to deliver outcomes to our customers. We have partnerships with other companies that have gaps where UiPath isn't currently playing, but the number of companies and the number of gaps has shrunk down to almost nothing these days. And we're well placed as UiPath continues to grow their platform to take advantage of that and leverage that to deliver outcomes to customers. >> It was a great story of starting small, being careful. >> Yes. >> And prudent, from a security standpoint, especially as a public company. And then it sounds like there's virtually unlimited opportunity. >> Yes, absolutely, absolutely. >> For you guys. Great story, thank you very much for sharing it. Appreciate it. >> Thank you. >> All right, good luck. All right, thank you for watching. Keep it right there. Dave Nicholson and Dave Vellante will be back from UiPath Forward5 from the Venetian in Las Vegas. Be right back. (upbeat music playing)

(upbeat electronic music) >> Welcome back to everyone, to "theCUBE's" coverage here in Seattle, Washington for Amazon Web Services Partner Marketplace Seller Conference, combining their partner network with Marketplace forming a new organization called AWS Partner Organization. This is "theCUBE" coverage. I'm John Furrier, your host. We've got great "Cube" alumni here from Zscaler, a very successful cloud company doing great work. Stelio D'Alo, senior director of cloud business development and Raveesh Chugh, VP of Public Cloud Partnerships at Zscaler. Welcome back to "theCUBE." Good to see you guys. Thanks for coming on. >> Thank you. >> Thanks having us, John. >> So we've been doing a lot of coverage of Zscaler, what a great success story. I mean, the numbers are great. The business performance, it's in the top two, three, one, two, three in all metrics on public companies, SaaS. So you guys, check. Good job. >> Yes, thank you. >> So you guys have done a good job. Now you're here, selling through the Marketplace. You guys are a world class performing company in cloud SaaS, so you're in the front lines doing well. Now, Marketplace is a procurement front end opportunity for people to buy. Hey, self-service, buy and put things together. Sounds novel, what a great concept. Great cloud life. >> Yes. >> You guys are participating and now sellers are coming together. The merger of the public, the partner network with Marketplace. It feels like this is a second act for AWS to go to the next level. They got their training wheels done with partners. Now they're going to the next level. What do you guys think about this? >> Well, I think you're right, John. I think it is very much something that is in keeping with the way AWS does business. Very Amazonian, they're working back from the customer. What we're seeing is, our customers and in general, the market is gravitating towards purchase mechanisms and route to market that just are lower friction. So in the same way that companies are going through their digital transformations now, really modernizing the way they host applications and they reach the internet. They're also modernizing on the purchasing side, which is super exciting, because we're all motivated to help customers with that agility. >> You know, it's fun to watch and again I'm being really candid and props to you guys as a company. Now, everyone else is kind of following that. Okay, lift and shift, check, doing some things. Now they go, whoa, I can really build on this. People are building their own apps for their companies. Going to build their own stuff. They're going to use piece parts. They're going to put it together in a really scalable way. That's the new normal. Okay, so now they go okay, I'm going to just buy through the market, I get purchasing power. So you guys have been a real leader with AWS. Can you share what you guys are doing in the Marketplace? I think you guys are a nice example of how to execute the Marketplace. Take us through. What are you guys offering there? What's the contract look like? Is it multi-pronged? What's the approach? What do customers get if they go to the marketplace for Zscaler? >> Yeah, so it's been a very exciting story and been a very pleasing one for us with AWS marketplace. We see a huge growth potentially. There are more than 350,000 customers that are actively buying through Marketplace today. We expect that number to grow to around a million customers by the next, I would say, five to ten years and we want to be part of this wave. We see AWS Marketplace to be a channel where not only our resalers or our channel partners can come and transact, but also our GSIs like Accenture want to transact through this channel. We are doing a lot, in terms of bringing new customers through Marketplace, who want to not only close their deals, but close it in the next few hours. That's the beauty of Marketplace, the agility, the flexibility in terms of pricing that it provides to ISVs like us. If a customer wants to delay their payments by a couple of quarters, Marketplace supports that. If a customer wants to do monthly payments, Marketplace supports that. We are seeing lot of customers, big customers, that have signed EDPs, enterprise discount plans with AWS. These are multi-year cloud commits coming to us and saying we can retire our EDPs with AWS if we transact your solution through AWS Marketplace. So what we have done, as of today, we have all of our production services enabled through AWS Marketplace. What that means for customers, they can now retire their EDPs by buying Zscaler products through AWS Marketplace and in return get the full benefit of maximizing their EDP commits with AWS. >> So you guys are fully committed, no toe on the water, as we heard. You guys are all in. >> Absolutely, that's exactly the way to put it. We're all in, all of our solutions are available in the marketplace. As you mentioned, we're a SaaS provider. So we're one of the vendors in the Marketplace that have SaaS solutions. So unlike a lot of customers and even the market in general, associate the Marketplace for historical reasons, the way it started with a lot of monthly subscriptions and just dipping your toe in it from a consumer perspective. Whereas we're doing multimillion dollar, multi-year SaaS contracts. So the most complicated kinds of transactions you'd normally associate with enterprise software, we're doing in very low friction ways. >> On the Zscaler side going in low friction. >> Yep, yeah, that's right. >> How about the customer experience? >> So it is primarily the the customer that experiences. >> Driving it? >> Yeah, they're driving it and it's because rather than traditional methods of going through paperwork, purchase orders- >> What are some of the things that customers are saying about this, bcause I see two benefits, I'll say that. The friction, it's a channel, okay, for Zscaler. Let's be clear, but now you have a customer who's got a lot of Amazon. They're a trusted partner too. So why wouldn't they want to have one point of contact to use their purchasing power and you guys are okay with that. >> We're absolutely okay with it. The reason being, we're still doing the transaction and we can do the transaction with our... We're a channel first company, so that's another important distinction of how people tend to think of the Marketplace. We go through channel. A lot of our transactions are with traditional channel partners and you'd be surprised the kinds of, even the Telcos, carrier providers, are starting to embrace Marketplace. So from a customer perspective, it's less paperwork, less legal work. >> Yeah, I'd love to get your reaction to something, because I think this highlights to me what we've been reporting on with "theCUBE" with super cloud and other trends that are different in a good way. Taking it to the next level and that is that if you look at Zscaler, SaaS, SaaS is self-service, the scale, there's efficiencies. Marketplace first started out as a self-service catalog, a website, you know, click and choose, but now it's a different. He calls it a supply chain, like the CICD pipeline of buying software. He mentions that, there's also services. He put the Channel partners can come in. The GSIs, global system integrators can come in. So it's more than just a catalog now. It's kind of self-service procurement more than it is just a catalog of buy stuff. >> Yes, so yeah, I feel CEOs, CSOs of today should understand what Marketplace brings to the bear in terms of different kinds of services or Zscaler solutions that they can acquire through Marketplace and other ISV solutions, for that matter. I feel like we are at a point, after the pandemic, where there'll be a lot of digital exploration and companies can do more in terms of not just Marketplace, but also including the channel partners as part of deals. So you talked about channel conflict. AWS addressed this by bringing a program called CPPO in the picture, Channel Partner Private Offers. What that does is, we are not only bringing all our channel partners into deals. For renewals as well, they're the partner of record and they get paid alongside with the customer. So AWS does all the heavy lifting, in terms of disbursements of payments to us, to the channel partner, so it's a win-win situation for all. >> I mean, private offers and co-sale has been very popular. >> It has been, and that is our bread and butter in the Marketplace. Again, we do primarily three year contracts and so private offers work super well. A nice thing for us as a vendor is it provides a great amount of flexibility. Private Offer gives you a lot of optionality, in terms of how the constructs of the deal and whether or not you're working with a partner, how the partner is utilizing as well to resell to the end user. So, we've always talked about AWS giving IT agility. This gives purchasing and finance business agility. >> Yeah, and I think this comes up a lot. I just noticed this happening a lot more, where you see dedicated sessions, not just on DevOps and all the goodies of the cloud, financial strategy. >> Yeah. >> Seeing a lot more conversation around how to operationalize the business transactions in the cloud. >> Absolutely. >> This is the new, I mean it's not new, it's been thrown around, but not at a tech conference. You don't see that. So I got to ask you guys, what's the message to the CISOs and executives watching the business people about Zscaler in the Marketplace? What should they be looking at? What is the pitch for Zscaler for the Marketplace buyer? >> So I would say that we are a cloud-delivered network security service. We have been in this game for more than a decade. We have years of early head start with lots of features and functionality versus our competitors. If customers were to move into AWS Cloud, they can get rid of their next-gen firewalls and just have all the traffic routed through our Zscaler internet access and use Zscaler private access for accessing their private applications. We feel we have done everything in our capacity, in terms of enabling customers through Marketplace and will continue to participate in more features and functionality that Marketplace has to offer. We would like these customers to take advantage of their EDPs as well as their retirement and spend for the multi-commit through AWS Marketplace. Learn about what we have to offer and how we can really expedite the motion for them, if they want to procure our solutions through Marketplace >> You know, we're seeing an ability for them to get more creative, more progressive in terms of the purchasing. We're also doing, we're really excited about the ability to serve multiple markets. So we've had an immense amount of success in commercial. We also are seeing increasing amount of public sector, US federal government agencies that want to procure this way as well for the same reasons. So there's a lot of innovation going on. >> So you have the FedRAMP going on, you got all those certifications. >> Exactly right. So we are the first cloud-native solution to provide IL5 ATO, as well as FedRAMP pie and we make that all available, GSA schedule pricing through the AWS Marketplace, again through FSIs and other resellers. >> Public private partnerships have been a big factor, having that span of capability. I got to ask you about, this is a cool conversation, because now you're like, okay, I'm selling through the Marketplace. Companies themselves are changing how they operate. They don't just buy software that we used to use. So general purpose, bundled stuff. Oh yeah, I'm buying this product, because this has got a great solution and I have to get forced to use this firewall, because I bought this over here. That's not how companies are architecting and developing their businesses. It's no longer buying IT. They're building their company digitally. They have to be the application. So they're not sitting around, saying hey, can I get a solution? They're building and architecting their solution. This is kind of like the new enterprise that no one's talking about. They kind of, got to do their own work. >> Yes. >> There's no general purpose solution that maps every company. So they got to pick the best piece parts and integrate them. >> Yes and I feel- >> Do you guys agree with that? >> Yeah, I agree with that and customers don't want to go for point solutions anymore. They want to go with a platform approach. They want go with a vendor that can not only cut down their vendors from multi-dozens to maybe a dozen or less and that's where, you know, we kind of have pivoted to the platform-centric approach, where we not only help customers with Cloud Network Security, but we also help customers with Cloud Native Application Protection Platform that we just recently launched. It's going by the name of the different elements, including Cloud Security Posture Management, Cloud Identity Event Management and so we are continuously doing more and more on the configuration and vulnerability side space. So if a customer has an AWS S3 bucket that is opened it can be detected and can be remediated. So all of those proactive steps we are taking, in terms of enhancing our portfolio, but we have come a long way as a company, as a platform that we have evolved in the Marketplace. >> What's the hottest product? >> The hottest product? >> In Marketplace right now. >> Well, the fastest growing products include our digital experience products and we have new Cloud Protection. So we've got Posture and Workload Protection as well and those are the fastest growing. For AWS customers a strong affinity also for ZPA, which provides you zero trust access to your workloads on AWS. So those are all the most popular in Marketplace. >> Yeah. >> So I would like to add that we recently launched and this has been a few years, a couple of years. We launched a product called Zscaler Digital X, the ZDX. >> Mm-hmm. >> What that product does is, let's say you're making a Zoom call and your WiFi network is laggy or it's a Zoom server that's laggy. It kind of detects where is the problem and it further tells the IT department you need to fix either the server on which Zoom is running, or fix your home network. So that is the beauty of the product. So I think we are seeing massive growth with some of our new editions in the portfolio, which is a long time coming. >> Yeah and certainly a lot of growth opportunities for you guys, as you come in. Where do you see Zscaler's big growth coming from product-wise? What's the big push? Actually, this is great upside for you here. >> Yeah. >> On the go to market side. Where's the big growth for Zscaler right now? So I think we are focused as a company on zero trust architecture. We want to securely connect users to apps, apps to apps, workloads to workloads and machines to machines. We want to give customers an experience where they have direct access to the apps that's hidden from the outside world and they can securely connect to the apps in a very succinct fashion. The user experience is second to none. A lot of customers use us on the Microsoft Office 365 side, where they see a lag in connecting to Microsoft Office 365 directly. They use the IE service to securely connect. >> Yeah, latency kills. >> Microsoft Office 365. >> Latency kills, as we always say, you know and security, you got to look at the pattern, you want to see that data. >> Yeah, and emerging use cases, there is an immense amount of white space and upside for us as well in emerging use cases, like OT, 5G, IOT. >> Yeah. >> Federal government, DOD. >> Oh god, tactical edge government. >> Security at the edge, absolutely, yeah. >> Where's the big edge? What's the edge challenge right now, if you have to put your finger on the edge, because right now that's the hot area, we're watching that. It's going to be highly contested. It's not yet clear, I mean certainly hybrid is the operating model, cloud, distributing, computing, but edge has got unique things that you can't really point to on premises that's the same. It's highly dynamic, you need high bandwidth, low latency, compute at the edge. The data has to be processed right there. What's the big thing at the edge right now? >> Well, so that's probably an emerging answer. I mean, we're working with our customers, they're inventing and they're kind of finding the use cases for those edge, but one of the good things about Zscaler is that we are able to, we've got low latency at the edge. We're able to work as a computer at the edge. We work on Outpost, Snowball, Snowcone, the Snow devices. So we can be wherever our customers need us. Mobile devices, there are a lot of applications where we've got to be either on embedded devices, on tractors, providing security for those IOT devices. So we're pretty comfortable with where we are being the- >> So that's why you guys are financially doing so well, performance wise. I got to ask you though, because I think that brings up the great point. If this is why I like the Marketplace, if I'm a customer, the edge is highly dynamic. It's changing all the time. I don't want to wait to buy something. If I got my solution architects on a product, I need to know I'm going to have zero trust built in and I need to push the button on Zscaler. I don't want to wait. So how does the procurement side impact? What have you guys seen? Share your thoughts on how Marketplace is working from the procurement standpoint, because it seems to me to be fast. Is that right, or is it still slow on their side? On the buyer side, because this to me would be a benefit to developers, if we say, hey, the procurement can just go really fast. I don't want to go through a bunch of PO approvals or slow meetings. >> It can be, that manifests itself in several ways, John. It can be, for instance, somebody wants to do a POC and traditionally you could take any amount of time to get budget approval, take it through. What if you had a pre-approved cloud budget and that was spent primarily through AWS Marketplace, because it's consolidated data on your AWS invoice. The ability to purchase a POC on the Marketplace could be done literally within minutes of the decision being made to go forward with it. So that's kind of a front end, you know, early stage use case. We've got examples we didn't talk about on our recent earnings call of how we have helped customers bring in their procurement with large million dollar, multimillion dollar deals. Even when a resaler's been involved, one of our resaler partners. Being able to accelerate deals, because there's so much less legal work and traditional bureaucratic effort. >> Agility. >> That agility purchasing process has allowed our customers to pull into the quarter, or the end of month, or end of quarter for them, deals that would've otherwise not been able to be done. >> So this is a great example of where you can set policy and kind of create some guard rails around innovation and integration deals, knowing if it's something that the edge is happening, say okay, here's some budget. We approved it, or Amazon gives credits and partnership going on. Then I'd say, hey, well green light this, not to exceed a million dollars, or whatever number in their range and then let people have the freedom to execute. >> You're absolutely right, so from the purchasing side, it does give them that agility. It eliminates a lot of the processes that would push out a purchase in actual execution past when the business decision is made and quite frankly, to be honest, AWS has been very accommodative. They're a great partner. They've invested a lot in Marketplace, Marketplace programs, to help customers do the right thing and do it more quickly as well as vendors like us to help our customers make the decisions they need to. >> Rising tide, a rising tide floats all boats and you guys are a great example of an independent company. Highly successful on your own. >> Yep. >> Certainly the numbers are clear. Wall Street loves Zscaler and economics are great. >> Our customer CSAT numbers are off the scale as well. >> Customers are great and now you've got the Marketplace. This is again, a new normal. A new kind of ecosystem is developing where it's not like the old monolithic ecosystems. The value creation and extraction is happening differently now. It's kind of interesting. >> Yes and I feel we have a long way to go, but what I can tell you is that Zscaler is in this for the long run. We are seeing some of the competitors erupt in the space as well, but they have a long way to go. What we have built requires years worth of R&D and features and thousands of customer's use cases which kind of lead to something what Zscaler has come up with today. What we have is very unique and is going to continuously be an innovation in the market in the years to come. In terms of being more cloud-savvy or more cloud-focused or more cloud-native than what the market has seen so far in the form of next-gen firewalls. >> I know you guys have got a lot of AI work. We've had many conversations with Howie over there. Great stuff and really appreciate you guys participating in our super cloud event we had and we'll see more of that where we're talking about the next generation clouds, really enabling that new disruptive, open-spanning capabilities across multiple environments to run cloud-native modern applications at scale and secure. Appreciate your time to come on "theCUBE". >> Thank you. >> Thank you very much. >> Thanks for having us. >> Thanks, I totally appreciate it. Zscaler, leading company here on "theCUBE" talking about their relationship with Marketplace as they continue to grow and succeed as technology goes to the next level in the cloud. Of course "theCUBE's" covering it here in Seattle. I'm John Furrier, your host. Thanks for watching. (peaceful electronic music)

>>Okay, welcome back everyone. It's the cubes coverage here in Seattle, Washington for Amazon web services, marketplace seller event. Really the big news here is the combination of the partner network with marketplace to one organization called the Amazon web services partner organization. Again, great news. Things are coming together, getting simplified and I'm John furry host of the cube. You've got a great guest here. Trish TRO head of worldwide Alliance at Wiz the fastest growing software company in history. Congratulations. Welcome to the cube. >>Thank you so much. And thanks for having us. >>So we were talking on camera. You had a little insight to a AWS. You jumped on this company. Oh my God. Amazing team. Take us through the story real quick. It's worth noting Wiz the company fastest growth. We're seeing take us through the quick soundbite. >>Sure. So the quick soundbite. So I was at AWS and my husband shared an article with me on cnbc.com about Wiz. They just done a big funding raise and he's like, you really have to read this. And I read it. And I said, oh my God, every single customer that I've met with the last year and a half has this problem. I have to find a way to be there. I don't care if I have to sweep the floors, lucky enough, they needed someone to run channels and alliances. So I did not have to sweep the floors, but for me, you know, when I think about our success, it's really this convergence of a series of things it's it's right time. Right? COVID forced everybody to the cloud, probably a little faster than they were ready to, you know, right market. And we have this convergence of the incredible product market fit, helping customers accelerate their cloud journey securely. And then I can't say enough about the team. You know, I thought it was fascinating, you know, as great as our product is when I got on board, everyone kept telling me, you know, they bought our product because of the team. And I was like, okay, cool. What about the product? And then I met the team and I understood. So jumped >>On one off one rocket ship. Yeah. To go onto another one. Yeah. You like the rocket, you like to ride those big, fast growth companies. You >>Know, I, I wish I was the kind of person where, you know, I just, I need excitement. Right? I'm I love to build. And I've had really good luck that I've always been able to find myself in a place, whether it's at a massive company or a startup to find myself as a builder, which has always been awesome. >>Well, tr it's great to have you on the cube. And a little fun fact is your sister was interviewed here on the cube in 2019 by myself. And so we have the first sisters, both cube alumni. Congratulations. >>I think that's, you know, honestly of all the accomplishments in my career, that's definitely one. I gotta make sure I get a plaque for that. You >>Will get a VIP sticker too. Yes, we, we all >>Sticker. Let's not get crazy now. >>All right. We'll designate in the front page. We'll have a very big story. L fund all good. We'd love the queue. We'd love to get the insight. So I wanna get your thoughts. Okay. You you've seen the Amazon side. You've been on that side. Now you're another side of the table with a partner growing. We're here to seller our conference. Big mission here is let's make things simpler and easier to procure software since you're already fast growing, what's in it for the customer to work through AWS, to get Wiz. Obviously you guys got a lot of demand. Yeah. A lot of money flowing through. You guys have a direct sales force. Are you going through the marketplace? What's the relationship between Wiz and Aish marketplace. >>So huge, honestly, and it's been a huge contributor to our success. We were lucky because we're, we were born during COVID, we're born in the cloud company. We got to build it from the ground up. This wasn't something that we had to go and figure out how to integrate into our existing ecosystem. Our ecosystem is actually built around the marketplace motion. You know, it's, it's interesting as you know, coming from AWS and now being on the other side, you know, something we really put a focus on is, you know, I see a lot of the companies that I was working with, you know, cloud was very much this thing. That's kind of in a silo and it's its own box and it competes internally. And really when you, you get deeper and deeper into the marketplace, it becomes about how do I use the cloud to really accelerate what I'm doing and to integrate it across my different channels. And for us, you know, AWS is our deepest relationship on the partner side. We invested heavily early and often, and it's been amazing. You >>Know, tr I was talking one of the data brick guys as well, and other companies that are big successes. This is a unique time here at the marketplace. We're on the ground floor. You can see here, we're at the, there's no stage. It's the smaller Q small venue, very intimate event. But it reminds me of 2013 when reinvent was starting to get traction second year, small, intimate, little bit bigger, obviously, but this is gonna feel like it's gonna explode. And you mentioned that you guys are building emotions around the ecosystem of the marketplace because you were born, born in the cloud. And COVID, so it's almost like if you're a startup today, why wouldn't you be in the marketplace first? Why even have that motion? So reminds me of the old days of you're a startup. Why not use the cloud? Why build a data center? >>No, and I think that's a really great analogy, you know, at least from what I've seen, it's, it's super interesting as a startup, because part of when you come out with a new technology in a perfect world, customers would already know what you were gonna make and have funding allocated for it. And we would all have this much easier sales cycle. That's not how it works. The customers, you know, as much as they might wanna get your solution, they have real things like budgets to deal with. And so it's really cool because when you work with the marketplace, it's a pool of funding that the customer has allocated on the customer side. It burns down their commit with the, with their different contracts. So that's usually powerful for them, right? Being able to consolidate your it, spend, reduce your overall total cost of ownership is, is usually powerful to the customer. And it on our side is a startup. So not only are they the financial benefits, it also helps you elevate the conversation. You know, a lot of times in the security industry, it's really all about like speeds and beads. That's how we sell cyber crime is 300% on the rise and stuff like that. Right. But being able to kind of get above that and help the customer, you know, have that financial conversation is, is really helpful too. >>So if I'm a startup, I'm a company, what would be the playbook for me and say, you know what, I'm gonna go all in, in the marketplace, I'm just gonna build the best kick ass product. Okay. I got product market fit. I'm gonna focus all my creative energy on building the best tech with the best, best team. All my friends and colleagues, and none of this non says go to market direct Salesforce, go all in on AWS. I know the product market fits there. What's the playbook. What do I do? Do just list it. >>So list, I think this is one of the mistakes that a lot of companies make when, when they first start out with the marketplace, right? They're like I will get to the marketplace and then AWS will sell my solution. I'm done the marketplace really? >>Where's the money back up the truck, come on. >>Exactly. Right? Like they have all these customers, they should just all come to me. Right. And I think that's one of the mistakes that organizations stumble on initially, cuz they go to the marketplace and then AWS is not selling their solution for them immediately. And they're like, the marketplace is a failure and it's really not. It's just the beginning of that. Being able to go into the marketplace, being able, honestly, to set expectations internally and understanding the journey that really comes into play here. You know, building, you know, one of the things that I talk to a lot about my team with is like building success within the sales reps and helping them be big advocates and champions for the marketplace. And the other thing is like, don't assume people know, I can't tell you. I feel like my, my real job at Wiz is I'm like the marketplace evangelist and cheap cuz that's all I do is talk about why they should use the marketplace and how it can solve all these different problems. Don't assume that people know how to do these things. Like you have to keep reiterating the message. You have to find sellers that are ready for it. And then you have to really, you have to teach them how to do it and then align your sales process accordingly. Like confidentiality come up a whole bunch at this conference today. It's important. You need it. >>It's huge. How big is your sales force right now? >>On >>The direct side. >>On the direct side, I think we're like a hundred or something like >>That. So you have, you have people out there on the streets knocking on doors selling. How's that comp decision go internally as you guys have that, what's the, what's the uptake in the marketplace for you guys right now? Is it high? Is it it's >>Been really high honestly. Yeah. It's and we've been really great. We have some incredible champions internally who are really great about sharing their experience, helping other sellers understand like we've, we've honestly had amazing co-sell stories at AWS where they've been so supportive and helpful. And it's amazing. Like we've had so many sellers that have done their first marketplace transaction ever. And now it's like for some of our sellers, they're at the point where they're like, I don't wanna, I don't wanna not do a marketplace transaction. It's just, it's so much easier. Take us >>For the procurement benefits. Take, walk me through what happens on the procurement side. What's the benefits for using the marketplace as you, as the procurement process goes through? >>Oh, from a, from a procurement side, right? It's like, it's simple, right? Like you, you essentially click a button and it's done like from the seller's side, like imagine not having to like chase down 15 different signatures and make sure nobody's on vacation. Right? So it just takes this really convoluted ti process that they would normally deal with. It makes it a lot simpler on the customer side. Right. Being able to have one consolidated is super powerful, burning down against commit, super powerful. And I think that's something that's really helped. Our sellers too, is being able, like we, we spend a tremendous amount of resources on educating our sellers. Not only about how it's gonna help them, but also how it's gonna help the customer too, >>Too. So good internally for you guys frictionless easier, better, better. Sounds like a better path >>On that. Oh, I won't say frictionless. I mean we're, we're about a year into this, but it wasn't so much frictionless, but it's not a hassle itself. Right. It's not a hassle. And it's all about >>On scale one to 10, 10 being frictionless. Would you get a, an eight or >>I'd say like an eight. Yeah. Okay. Okay. Cool. But it's important for organizations to understand that, right? Like that just because there's a little bit of friction at first. Like the most important thing I told my team is they were like, look like, well, why doesn't everybody wanna do this? This is so easy. And a, a good seller will take the hard time every way when they know what the defined outcome is. Yeah. The marketplace to them feels like a shortcut at first. Yeah. So a very much helps them become like, Hey look, this isn't a shortcut. This is gonna help you. Like, this is a good thing. And once you get that adoption like that, that's where the primary friction is. They almost go, is this, is this too good to be true? This can't be real. >>It, it, it almost sounds too good to be true when you think about, okay, so lemme take, I'm gonna put them a sales rep for a second. Like I'm selling WIS and I go and knock on a door and there's a company and I get an, a champion inside the company and says, oh, I love this product. I wanna buy it. I gotta get my PO approved and I gotta go get, I tell my boss about it. Does it go through that kind of normal kind of normal sales motion where you got buy in and now they gotta commit and close and get contract or they just go to the person who runs the account, click the button, like, like, is there, I mean, I'd like to see that shortcut happen. Like so on the customer side, what, what do you see as the process? Is it just go to the console and hit by and >>You know, depends on the customer honestly, and kind of where they are in their cloud journey. You know, really mature customers tend to have a little bit more of a mature process, you know, earlier customers, it tends to be a little less, let's say structured, but no, it's definitely not. The customer just clicks the button and it's done. That would be quite nice. We're just not there yet, but it's definitely a much simpler process cuz you know, you think about it on the customer side when they decide they wanna buy something, especially something new, they don't have allocated funding for us. They have to go build all this justification for funding. They still have to do that. Right. But then now there's a pot of money that they can go to and be able to retire against. There, there, it does help in that sense. A >>Lot. Chris, Chris grew has talked about on his keynote, the buyer journey survey. That seems to be on the, on the customer side. Yeah. Having those processes where they can forecast against it, they kind of know what they're getting. That's that's that's sounds like a great thing that's happening. I wanna get back to this comp issue again. Cause this came up. I heard that a lot. We talked with Chris about the competing thing. That's not an issue in my mind, but I think the factor to me, if I'm looking at this is that if you get the comp right, they can sell it at Amazon. You get comped, your sales people get comped goes through the marketplace. How do you look at that? How do company her look? How do they look at the comp what's what's the deciding factor or is it a non-issue what's the, what's the core. >>So I'm opportunity. I'm gonna be honest. I think I got a little lucky because I think the getting alignment at the executive level that this was something we should do to be totally honest here. Wasn't wasn't super hard. When we presented a clear plan, how we were gonna do it, what other companies were doing, what it did for their business to our executives. We do, we get some pushback. Sure. Healthy questions. Sure. But like it, it really >>Was it margin related or more like operational costs. >>It wasn't even margin related. It was again, more of like, is this, this feels too good to be true kind of thing. So it was more like proving it to them. Like no, like it really can be that easy. Yeah. And then on the, the comp side, right. For us, we look at it as like cost of sales. So yeah. You know, we, we treat it the same way. We treat all other channels and we wanted to make sure for our reps that, you know, when we think about the channel, whether, you know, from, especially with marketplace, like it can't be harder for them to do a marketplace transaction or less incentive for them to do that than a direct one that doesn't incentivize the right behaviors. >>So it's more of an indirect channel play. >>Yeah. So it's all for us. It was about aligning the right incentives to drive the right behaviors. It wasn't, it actually was a pretty short discussion on the confidentiality. Everyone was like, no, this, this makes sense. We should do that. >>Yeah. I mean, I think it's, I think it's an easy, easy, but you have to be organized for it. Like, like Chris said, don't put the toe in the water. Right. Put your flagship offering in there, make it valuable. And then the flag wheel gets going, the Amazon sales people can sell it. Right. They get calm. That's always a good thing. >>Yeah. And I think that's something that was really interesting. Like when we started on the marketplace journey, like I said, it's not just, you get in a marketplace and you're done, you know, Chris talked a lot about ISV accelerate and you know, how you elevate yourself within that program, doing things with ACE, like putting in different opportunities to, to start to essentially build that groundswell to drive co-sell it's, it's gets that first step into it. But there's so much more that, that we're still discovering and learning today is we're building it >>Out. And you said you had some good co-sell examples. >>Oh yeah. So we've had some great Cosell. >>What's your best one. Best one to >>Share. Oh, so my favorite one, I won't say the customer name, but we were in the final stages and a customer was really like, oh, like this is a lot of money. I'm really nervous. And the, they, I think what's crazy is that at AWS you have a different relationship with customers. Like you are truly a trusted advisor and rightfully so. Yeah. AWS really does a great job with making sure their account teams do what's best for the customer. And so an AWS seller or technical resource on an account says, Hey, no, this is the right thing for your business. That is huge for the customer. So we at Wiz actually spend a lot of time investing in enabling and educating the AWS account teams. So they feel comfortable when they get into that situation where the customers nervous of being saying like, no, this is you need to do this. This is >>Gonna be, you carry a lot of weight with the customers. >>Absolutely. >>And so you almost have to treat them like a lunch and learn, get 'em up, find, share. So it's kind of like an indirect relationship for you, but for them it's a part, you know, this is basically a channel. >>Yeah. And I think that's the thing that, that really is something we we've really heavily invested in is, is building. I like call the ground game within AWS. Right? Yeah. Making sure we spend time with enabling their reps. We enable their technical teams lunch and learns, right? Like there's so much energy at AWS to really invest in technical solutions that help their customers. Awesome. Which you don't always find that a lot of partners honestly. >>Well, Trish, great. Great to have you on sharing the AWS relationship story with WIS, gotta ask you, what's it like to be working for the fastest growing startup? What's it like? It's, it's, it's pretty fun. >>You know, it's, let's say I don't ever wake up on a day and say, man, I just wish I had more things to do. No, it's, it's been an incredible journey. The people, you know, my favorite part of a startup is, you know, getting to do this with a bunch of really incredible, awesome people. It's, it's the most fun thing in the world. We've, I've learned more in the last, you know, we like to joke that we're a five year old company and a one year old company at the exact same time. Yeah. And what's cool is we get to learn and, and I I've learned so much this year. >>When was the company officially >>Formed? It was officially formed before. Like, so it was officially formed in February, 2020. We started officially operating in the January following 21. So 21. Yep. >>Yeah. So one and a half years, >>One and a half years. Isn't that crazy? Great. >>And a hundred million ARR already. Yeah. Hitting that. >>Yep. It's been a, a wild journey. I I'll put it that way >>Is the, what's the success of the businesses? It, the onboarding the, is it the business model of freemium? What's the product market fit dynamic. Why is so fast? I mean, that's the needs there? Pandemic fresh, clean piece, piece of paper doing it, right. What's the, why is it? Why is that going so fast? >>Well, I think about this, I've been in the security industry for too many years. And when you think about normal security products, like there's so much time to value, you have to deploy all this infrastructure and then you gotta wait till something happens that you find that's scary, that will excite the customer. Right? It's, it's, it's a lot of time to show value. What blew my mind is the way that we approach our, the problem that we're solving is essentially immediate time to value. So the customer connects within minutes, they're immediately presented with here's your, your top risks. And then they can take action on them. Right? Like it's not just, here's these big threats and detecting, it's actually giving, empowering the customer to go and, and fix things. That's that's powerful for them. Yeah. Yeah. >>So, and the renewals are there coming in, people like the product, >>I mean, we've only been around for a year and a half, so there aren't that many renewals yet, but let's say we have extremely strong renewal rate from our customer base. >>Yeah. I mean you can have when you have a great product. Yeah. Well, thanks for coming on sharing. What's your assessment so far of the database marketplace kind of reorg with APN partner network to have one organization. What does that mean to the, to the market? What does that what's that tell you? >>So I was really excited. So we're actually built this way. So I run both our channels and alliances organization and it was, it was great because it allows these two things to work together and, and very well. And AWS, I think, is realizing the power of bringing those two groups together. So when I saw that, I was like, that's gonna be great. It's gonna make it simpler, easier. And at least for us, it's been really powerful. >>Awesome. Thanks for coming on the cube. Really appreciate it. We'll get you that plaque shortly. >>I thought I was getting a sticker too. >>Don't forget the sticker. Oh, the sticker definitely guaranteed. And we'll give you a VIP icon on our cube alumni network. All >>Right. I like that. >>Thanks for coming out. Alls great stuff. Thanks. Awesome. Thanks for having all best growing company history here on the cube, bringing all the action again, the new flywheel is gonna be procured through the marketplaces. This is obvious how it all kind of works and forms. It's kind of happening in real time. Cube's got you covered on the ground floor here in Seattle with more coverage after the short break.