(mellow music) >> Hello everyone, welcome to theCUBE's coverage of AWS Startup Showcase. This is the closing panel session on AI machine learning, the top startups generating generative AI on AWS. It's a great panel. This is going to be the experts talking about riding the wave in generative AI. We got Ankur Mehrotra, who's the director and general manager of AI and machine learning at AWS, and Clem Delangue, co-founder and CEO of Hugging Face, and Ori Goshen, who's the co-founder and CEO of AI21 Labs. Ori from Tel Aviv dialing in, and rest coming in here on theCUBE. Appreciate you coming on for this closing session for the Startup Showcase. >> Thanks for having us. >> Thank you for having us. >> Thank you. >> I'm super excited to have you all on. Hugging Face was recently in the news with the AWS relationship, so congratulations. Open source, open science, really driving the machine learning. And we got the AI21 Labs access to the LLMs, generating huge scale live applications, commercial applications, coming to the market, all powered by AWS. So everyone, congratulations on all your success, and thank you for headlining this panel. Let's get right into it. AWS is powering this wave here. We're seeing a lot of push here from applications. Ankur, set the table for us on the AI machine learning. It's not new, it's been goin' on for a while. Past three years have been significant advancements, but there's been a lot of work done in AI machine learning. Now it's released to the public. Everybody's super excited and now says, "Oh, the future's here!" It's kind of been going on for a while and baking. Now it's kind of coming out. What's your view here? Let's get it started. >> Yes, thank you. So, yeah, as you may be aware, Amazon has been in investing in machine learning research and development since quite some time now. And we've used machine learning to innovate and improve user experiences across different Amazon products, whether it's Alexa or Amazon.com. But we've also brought in our expertise to extend what we are doing in the space and add more generative AI technology to our AWS products and services, starting with CodeWhisperer, which is an AWS service that we announced a few months ago, which is, you can think of it as a coding companion as a service, which uses generative AI models underneath. And so this is a service that customers who have no machine learning expertise can just use. And we also are talking to customers, and we see a lot of excitement about generative AI, and customers who want to build these models themselves, who have the talent and the expertise and resources. For them, AWS has a number of different options and capabilities they can leverage, such as our custom silicon, such as Trainium and Inferentia, as well as distributed machine learning capabilities that we offer as part of SageMaker, which is an end-to-end machine learning development service. At the same time, many of our customers tell us that they're interested in not training and building these generative AI models from scratch, given they can be expensive and can require specialized talent and skills to build. And so for those customers, we are also making it super easy to bring in existing generative AI models into their machine learning development environment within SageMaker for them to use. So we recently announced our partnership with Hugging Face, where we are making it super easy for customers to bring in those models into their SageMaker development environment for fine tuning and deployment. And then we are also partnering with other proprietary model providers such as AI21 and others, where we making these generative AI models available within SageMaker for our customers to use. So our approach here is to really provide customers options and choices and help them accelerate their generative AI journey. >> Ankur, thank you for setting the table there. Clem and Ori, I want to get your take, because the riding the waves, the theme of this session, and to me being in California, I imagine the big surf, the big waves, the big talent out there. This is like alpha geeks, alpha coders, developers are really leaning into this. You're seeing massive uptake from the smartest people. Whether they're young or around, they're coming in with their kind of surfboards, (chuckles) if you will. These early adopters, they've been on this for a while; Now the waves are hitting. This is a big wave, everyone sees it. What are some of those early adopter devs doing? What are some of the use cases you're seeing right out of the gate? And what does this mean for the folks that are going to come in and get on this wave? Can you guys share your perspective on this? Because you're seeing the best talent now leaning into this. >> Yeah, absolutely. I mean, from Hugging Face vantage points, it's not even a a wave, it's a tidal wave, or maybe even the tide itself. Because actually what we are seeing is that AI and machine learning is not something that you add to your products. It's very much a new paradigm to do all technology. It's this idea that we had in the past 15, 20 years, one way to build software and to build technology, which was writing a million lines of code, very rule-based, and then you get your product. Now what we are seeing is that every single product, every single feature, every single company is starting to adopt AI to build the next generation of technology. And that works both to make the existing use cases better, if you think of search, if you think of social network, if you think of SaaS, but also it's creating completely new capabilities that weren't possible with the previous paradigm. Now AI can generate text, it can generate image, it can describe your image, it can do so many new things that weren't possible before. >> It's going to really make the developers really productive, right? I mean, you're seeing the developer uptake strong, right? >> Yes, we have over 15,000 companies using Hugging Face now, and it keeps accelerating. I really think that maybe in like three, five years, there's not going to be any company not using AI. It's going to be really kind of the default to build all technology. >> Ori, weigh in on this. APIs, the cloud. Now I'm a developer, I want to have live applications, I want the commercial applications on this. What's your take? Weigh in here. >> Yeah, first, I absolutely agree. I mean, we're in the midst of a technology shift here. I think not a lot of people realize how big this is going to be. Just the number of possibilities is endless, and I think hard to imagine. And I don't think it's just the use cases. I think we can think of it as two separate categories. We'll see companies and products enhancing their offerings with these new AI capabilities, but we'll also see new companies that are AI first, that kind of reimagine certain experiences. They build something that wasn't possible before. And that's why I think it's actually extremely exciting times. And maybe more philosophically, I think now these large language models and large transformer based models are helping us people to express our thoughts and kind of making the bridge from our thinking to a creative digital asset in a speed we've never imagined before. I can write something down and get a piece of text, or an image, or a code. So I'll start by saying it's hard to imagine all the possibilities right now, but it's certainly big. And if I had to bet, I would say it's probably at least as big as the mobile revolution we've seen in the last 20 years. >> Yeah, this is the biggest. I mean, it's been compared to the Enlightenment Age. I saw the Wall Street Journal had a recent story on this. We've been saying that this is probably going to be bigger than all inflection points combined in the tech industry, given what transformation is coming. I guess I want to ask you guys, on the early adopters, we've been hearing on these interviews and throughout the industry that there's already a set of big companies, a set of companies out there that have a lot of data and they're already there, they're kind of tinkering. Kind of reminds me of the old hyper scaler days where they were building their own scale, and they're eatin' glass, spittin' nails out, you know, they're hardcore. Then you got everybody else kind of saying board level, "Hey team, how do I leverage this?" How do you see those two things coming together? You got the fast followers coming in behind the early adopters. What's it like for the second wave coming in? What are those conversations for those developers like? >> I mean, I think for me, the important switch for companies is to change their mindset from being kind of like a traditional software company to being an AI or machine learning company. And that means investing, hiring machine learning engineers, machine learning scientists, infrastructure in members who are working on how to put these models in production, team members who are able to optimize models, specialized models, customized models for the company's specific use cases. So it's really changing this mindset of how you build technology and optimize your company building around that. Things are moving so fast that I think now it's kind of like too late for low hanging fruits or small, small adjustments. I think it's important to realize that if you want to be good at that, and if you really want to surf this wave, you need massive investments. If there are like some surfers listening with this analogy of the wave, right, when there are waves, it's not enough just to stand and make a little bit of adjustments. You need to position yourself aggressively, paddle like crazy, and that's how you get into the waves. So that's what companies, in my opinion, need to do right now. >> Ori, what's your take on the generative models out there? We hear a lot about foundation models. What's your experience running end-to-end applications for large foundation models? Any insights you can share with the app developers out there who are looking to get in? >> Yeah, I think first of all, it's start create an economy, where it probably doesn't make sense for every company to create their own foundation models. You can basically start by using an existing foundation model, either open source or a proprietary one, and start deploying it for your needs. And then comes the second round when you are starting the optimization process. You bootstrap, whether it's a demo, or a small feature, or introducing new capability within your product, and then start collecting data. That data, and particularly the human feedback data, helps you to constantly improve the model, so you create this data flywheel. And I think we're now entering an era where customers have a lot of different choice of how they want to start their generative AI endeavor. And it's a good thing that there's a variety of choices. And the really amazing thing here is that every industry, any company you speak with, it could be something very traditional like industrial or financial, medical, really any company. I think peoples now start to imagine what are the possibilities, and seriously think what's their strategy for adopting this generative AI technology. And I think in that sense, the foundation model actually enabled this to become scalable. So the barrier to entry became lower; Now the adoption could actually accelerate. >> There's a lot of integration aspects here in this new wave that's a little bit different. Before it was like very monolithic, hardcore, very brittle. A lot more integration, you see a lot more data coming together. I have to ask you guys, as developers come in and grow, I mean, when I went to college and you were a software engineer, I mean, I got a degree in computer science, and software engineering, that's all you did was code, (chuckles) you coded. Now, isn't it like everyone's a machine learning engineer at this point? Because that will be ultimately the science. So, (chuckles) you got open source, you got open software, you got the communities. Swami called you guys the GitHub of machine learning, Hugging Face is the GitHub of machine learning, mainly because that's where people are going to code. So this is essentially, machine learning is computer science. What's your reaction to that? >> Yes, my co-founder Julien at Hugging Face have been having this thing for quite a while now, for over three years, which was saying that actually software engineering as we know it today is a subset of machine learning, instead of the other way around. People would call us crazy a few years ago when we're seeing that. But now we are realizing that you can actually code with machine learning. So machine learning is generating code. And we are starting to see that every software engineer can leverage machine learning through open models, through APIs, through different technology stack. So yeah, it's not crazy anymore to think that maybe in a few years, there's going to be more people doing AI and machine learning. However you call it, right? Maybe you'll still call them software engineers, maybe you'll call them machine learning engineers. But there might be more of these people in a couple of years than there is software engineers today. >> I bring this up as more tongue in cheek as well, because Ankur, infrastructure's co is what made Cloud great, right? That's kind of the DevOps movement. But here the shift is so massive, there will be a game-changing philosophy around coding. Machine learning as code, you're starting to see CodeWhisperer, you guys have had coding companions for a while on AWS. So this is a paradigm shift. How is the cloud playing into this for you guys? Because to me, I've been riffing on some interviews where it's like, okay, you got the cloud going next level. This is an example of that, where there is a DevOps-like moment happening with machine learning, whether you call it coding or whatever. It's writing code on its own. Can you guys comment on what this means on top of the cloud? What comes out of the scale? What comes out of the benefit here? >> Absolutely, so- >> Well first- >> Oh, go ahead. >> Yeah, so I think as far as scale is concerned, I think customers are really relying on cloud to make sure that the applications that they build can scale along with the needs of their business. But there's another aspect to it, which is that until a few years ago, John, what we saw was that machine learning was a data scientist heavy activity. They were data scientists who were taking the data and training models. And then as machine learning found its way more and more into production and actual usage, we saw the MLOps become a thing, and MLOps engineers become more involved into the process. And then we now are seeing, as machine learning is being used to solve more business critical problems, we're seeing even legal and compliance teams get involved. We are seeing business stakeholders more engaged. So, more and more machine learning is becoming an activity that's not just performed by data scientists, but is performed by a team and a group of people with different skills. And for them, we as AWS are focused on providing the best tools and services for these different personas to be able to do their job and really complete that end-to-end machine learning story. So that's where, whether it's tools related to MLOps or even for folks who cannot code or don't know any machine learning. For example, we launched SageMaker Canvas as a tool last year, which is a UI-based tool which data analysts and business analysts can use to build machine learning models. So overall, the spectrum in terms of persona and who can get involved in the machine learning process is expanding, and the cloud is playing a big role in that process. >> Ori, Clem, can you guys weigh in too? 'Cause this is just another abstraction layer of scale. What's it mean for you guys as you look forward to your customers and the use cases that you're enabling? >> Yes, I think what's important is that the AI companies and providers and the cloud kind of work together. That's how you make a seamless experience and you actually reduce the barrier to entry for this technology. So that's what we've been super happy to do with AWS for the past few years. We actually announced not too long ago that we are doubling down on our partnership with AWS. We're excited to have many, many customers on our shared product, the Hugging Face deep learning container on SageMaker. And we are working really closely with the Inferentia team and the Trainium team to release some more exciting stuff in the coming weeks and coming months. So I think when you have an ecosystem and a system where the AWS and the AI providers, AI startups can work hand in hand, it's to the benefit of the customers and the companies, because it makes it orders of magnitude easier for them to adopt this new paradigm to build technology AI. >> Ori, this is a scale on reasoning too. The data's out there and making sense out of it, making it reason, getting comprehension, having it make decisions is next, isn't it? And you need scale for that. >> Yes. Just a comment about the infrastructure side. So I think really the purpose is to streamline and make these technologies much more accessible. And I think we'll see, I predict that we'll see in the next few years more and more tooling that make this technology much more simple to consume. And I think it plays a very important role. There's so many aspects, like the monitoring the models and their kind of outputs they produce, and kind of containing and running them in a production environment. There's so much there to build on, the infrastructure side will play a very significant role. >> All right, that's awesome stuff. I'd love to change gears a little bit and get a little philosophy here around AI and how it's going to transform, if you guys don't mind. There's been a lot of conversations around, on theCUBE here as well as in some industry areas, where it's like, okay, all the heavy lifting is automated away with machine learning and AI, the complexity, there's some efficiencies, it's horizontal and scalable across all industries. Ankur, good point there. Everyone's going to use it for something. And a lot of stuff gets brought to the table with large language models and other things. But the key ingredient will be proprietary data or human input, or some sort of AI whisperer kind of role, or prompt engineering, people are saying. So with that being said, some are saying it's automating intelligence. And that creativity will be unleashed from this. If the heavy lifting goes away and AI can fill the void, that shifts the value to the intellect or the input. And so that means data's got to come together, interact, fuse, and understand each other. This is kind of new. I mean, old school AI was, okay, got a big model, I provisioned it long time, very expensive. Now it's all free flowing. Can you guys comment on where you see this going with this freeform, data flowing everywhere, heavy lifting, and then specialization? >> Yeah, I think- >> Go ahead. >> Yeah, I think, so what we are seeing with these large language models or generative models is that they're really good at creating stuff. But I think it's also important to recognize their limitations. They're not as good at reasoning and logic. And I think now we're seeing great enthusiasm, I think, which is justified. And the next phase would be how to make these systems more reliable. How to inject more reasoning capabilities into these models, or augment with other mechanisms that actually perform more reasoning so we can achieve more reliable results. And we can count on these models to perform for critical tasks, whether it's medical tasks, legal tasks. We really want to kind of offload a lot of the intelligence to these systems. And then we'll have to get back, we'll have to make sure these are reliable, we'll have to make sure we get some sort of explainability that we can understand the process behind the generated results that we received. So I think this is kind of the next phase of systems that are based on these generated models. >> Clem, what's your view on this? Obviously you're at open community, open source has been around, it's been a great track record, proven model. I'm assuming creativity's going to come out of the woodwork, and if we can automate open source contribution, and relationships, and onboarding more developers, there's going to be unleashing of creativity. >> Yes, it's been so exciting on the open source front. We all know Bert, Bloom, GPT-J, T5, Stable Diffusion, that work up. The previous or the current generation of open source models that are on Hugging Face. It has been accelerating in the past few months. So I'm super excited about ControlNet right now that is really having a lot of impact, which is kind of like a way to control the generation of images. Super excited about Flan UL2, which is like a new model that has been recently released and is open source. So yeah, it's really fun to see the ecosystem coming together. Open source has been the basis for traditional software, with like open source programming languages, of course, but also all the great open source that we've gotten over the years. So we're happy to see that the same thing is happening for machine learning and AI, and hopefully can help a lot of companies reduce a little bit the barrier to entry. So yeah, it's going to be exciting to see how it evolves in the next few years in that respect. >> I think the developer productivity angle that's been talked about a lot in the industry will be accelerated significantly. I think security will be enhanced by this. I think in general, applications are going to transform at a radical rate, accelerated, incredible rate. So I think it's not a big wave, it's the water, right? I mean, (chuckles) it's the new thing. My final question for you guys, if you don't mind, I'd love to get each of you to answer the question I'm going to ask you, which is, a lot of conversations around data. Data infrastructure's obviously involved in this. And the common thread that I'm hearing is that every company that looks at this is asking themselves, if we don't rebuild our company, start thinking about rebuilding our business model around AI, we might be dinosaurs, we might be extinct. And it reminds me that scene in Moneyball when, at the end, it's like, if we're not building the model around your model, every company will be out of business. What's your advice to companies out there that are having those kind of moments where it's like, okay, this is real, this is next gen, this is happening. I better start thinking and putting into motion plans to refactor my business, 'cause it's happening, business transformation is happening on the cloud. This kind of puts an exclamation point on, with the AI, as a next step function. Big increase in value. So it's an opportunity for leaders. Ankur, we'll start with you. What's your advice for folks out there thinking about this? Do they put their toe in the water? Do they jump right into the deep end? What's your advice? >> Yeah, John, so we talk to a lot of customers, and customers are excited about what's happening in the space, but they often ask us like, "Hey, where do we start?" So we always advise our customers to do a lot of proof of concepts, understand where they can drive the biggest ROI. And then also leverage existing tools and services to move fast and scale, and try and not reinvent the wheel where it doesn't need to be. That's basically our advice to customers. >> Get it. Ori, what's your advice to folks who are scratching their head going, "I better jump in here. "How do I get started?" What's your advice? >> So I actually think that need to think about it really economically. Both on the opportunity side and the challenges. So there's a lot of opportunities for many companies to actually gain revenue upside by building these new generative features and capabilities. On the other hand, of course, this would probably affect the cogs, and incorporating these capabilities could probably affect the cogs. So I think we really need to think carefully about both of these sides, and also understand clearly if this is a project or an F word towards cost reduction, then the ROI is pretty clear, or revenue amplifier, where there's, again, a lot of different opportunities. So I think once you think about this in a structured way, I think, and map the different initiatives, then it's probably a good way to start and a good way to start thinking about these endeavors. >> Awesome. Clem, what's your take on this? What's your advice, folks out there? >> Yes, all of these are very good advice already. Something that you said before, John, that I disagreed a little bit, a lot of people are talking about the data mode and proprietary data. Actually, when you look at some of the organizations that have been building the best models, they don't have specialized or unique access to data. So I'm not sure that's so important today. I think what's important for companies, and it's been the same for the previous generation of technology, is their ability to build better technology faster than others. And in this new paradigm, that means being able to build machine learning faster than others, and better. So that's how, in my opinion, you should approach this. And kind of like how can you evolve your company, your teams, your products, so that you are able in the long run to build machine learning better and faster than your competitors. And if you manage to put yourself in that situation, then that's when you'll be able to differentiate yourself to really kind of be impactful and get results. That's really hard to do. It's something really different, because machine learning and AI is a different paradigm than traditional software. So this is going to be challenging, but I think if you manage to nail that, then the future is going to be very interesting for your company. >> That's a great point. Thanks for calling that out. I think this all reminds me of the cloud days early on. If you went to the cloud early, you took advantage of it when the pandemic hit. If you weren't native in the cloud, you got hamstrung by that, you were flatfooted. So just get in there. (laughs) Get in the cloud, get into AI, you're going to be good. Thanks for for calling that. Final parting comments, what's your most exciting thing going on right now for you guys? Ori, Clem, what's the most exciting thing on your plate right now that you'd like to share with folks? >> I mean, for me it's just the diversity of use cases and really creative ways of companies leveraging this technology. Every day I speak with about two, three customers, and I'm continuously being surprised by the creative ideas. And the future is really exciting of what can be achieved here. And also I'm amazed by the pace that things move in this industry. It's just, there's not at dull moment. So, definitely exciting times. >> Clem, what are you most excited about right now? >> For me, it's all the new open source models that have been released in the past few weeks, and that they'll keep being released in the next few weeks. I'm also super excited about more and more companies getting into this capability of chaining different models and different APIs. I think that's a very, very interesting development, because it creates new capabilities, new possibilities, new functionalities that weren't possible before. You can plug an API with an open source embedding model, with like a no-geo transcription model. So that's also very exciting. This capability of having more interoperable machine learning will also, I think, open a lot of interesting things in the future. >> Clem, congratulations on your success at Hugging Face. Please pass that on to your team. Ori, congratulations on your success, and continue to, just day one. I mean, it's just the beginning. It's not even scratching the service. Ankur, I'll give you the last word. What are you excited for at AWS? More cloud goodness coming here with AI. Give you the final word. >> Yeah, so as both Clem and Ori said, I think the research in the space is moving really, really fast, so we are excited about that. But we are also excited to see the speed at which enterprises and other AWS customers are applying machine learning to solve real business problems, and the kind of results they're seeing. So when they come back to us and tell us the kind of improvement in their business metrics and overall customer experience that they're driving and they're seeing real business results, that's what keeps us going and inspires us to continue inventing on their behalf. >> Gentlemen, thank you so much for this awesome high impact panel. Ankur, Clem, Ori, congratulations on all your success. We'll see you around. Thanks for coming on. Generative AI, riding the wave, it's a tidal wave, it's the water, it's all happening. All great stuff. This is season three, episode one of AWS Startup Showcase closing panel. This is the AI ML episode, the top startups building generative AI on AWS. I'm John Furrier, your host. Thanks for watching. (mellow music)

(soft music) >> Hello, everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase. AI and Machine Learning: Top Startups Building Foundational Model Infrastructure. This is season 3, episode 1 of the ongoing series covering the exciting stuff from the AWS ecosystem, talking about machine learning and AI. I'm your host, John Furrier and today we are excited to be joined by Luis Ceze who's the CEO of OctoML and Anna Connolly, VP of customer success and experience OctoML. Great to have you on again, Luis. Anna, thanks for coming on. Appreciate it. >> Thank you, John. It's great to be here. >> Thanks for having us. >> I love the company. We had a CUBE conversation about this. You guys are really addressing how to run foundational models faster for less. And this is like the key theme. But before we get into it, this is a hot trend, but let's explain what you guys do. Can you set the narrative of what the company's about, why it was founded, what's your North Star and your mission? >> Yeah, so John, our mission is to make AI sustainable and accessible for everyone. And what we offer customers is, you know, a way of taking their models into production in the most efficient way possible by automating the process of getting a model and optimizing it for a variety of hardware and making cost-effective. So better, faster, cheaper model deployment. >> You know, the big trend here is AI. Everyone's seeing the ChatGPT, kind of the shot heard around the world. The BingAI and this fiasco and the ongoing experimentation. People are into it, and I think the business impact is clear. I haven't seen this in all of my career in the technology industry of this kind of inflection point. And every senior leader I talk to is rethinking about how to rebuild their business with AI because now the large language models have come in, these foundational models are here, they can see value in their data. This is a 10 year journey in the big data world. Now it's impacting that, and everyone's rebuilding their company around this idea of being AI first 'cause they see ways to eliminate things and make things more efficient. And so now they telling 'em to go do it. And they're like, what do we do? So what do you guys think? Can you explain what is this wave of AI and why is it happening, why now, and what should people pay attention to? What does it mean to them? >> Yeah, I mean, it's pretty clear by now that AI can do amazing things that captures people's imaginations. And also now can show things that are really impactful in businesses, right? So what people have the opportunity to do today is to either train their own model that adds value to their business or find open models out there that can do very valuable things to them. So the next step really is how do you take that model and put it into production in a cost-effective way so that the business can actually get value out of it, right? >> Anna, what's your take? Because customers are there, you're there to make 'em successful, you got the new secret weapon for their business. >> Yeah, I think we just see a lot of companies struggle to get from a trained model into a model that is deployed in a cost-effective way that actually makes sense for the application they're building. I think that's a huge challenge we see today, kind of across the board across all of our customers. >> Well, I see this, everyone asking the same question. I have data, I want to get value out of it. I got to get these big models, I got to train it. What's it going to cost? So I think there's a reality of, okay, I got to do it. Then no one has any visibility on what it costs. When they get into it, this is going to break the bank. So I have to ask you guys, the cost of training these models is on everyone's mind. OctoML, your company's focus on the cost side of it as well as the efficiency side of running these models in production. Why are the production costs such a concern and where specifically are people looking at it and why did it get here? >> Yeah, so training costs get a lot of attention because normally a large number, but we shouldn't forget that it's a large, typically one time upfront cost that customers pay. But, you know, when the model is put into production, the cost grows directly with model usage and you actually want your model to be used because it's adding value, right? So, you know, the question that a customer faces is, you know, they have a model, they have a trained model and now what? So how much would it cost to run in production, right? And now without the big wave in generative AI, which rightfully is getting a lot of attention because of the amazing things that it can do. It's important for us to keep in mind that generative AI models like ChatGPT are huge, expensive energy hogs. They cost a lot to run, right? And given that model usage growth directly, model cost grows directly with usage, what you want to do is make sure that once you put a model into production, you have the best cost structure possible so that you're not surprised when it's gets popular, right? So let me give you an example. So if you have a model that costs, say 1 to $2 million to train, but then it costs about one to two cents per session to use it, right? So if you have a million active users, even if they use just once a day, it's 10 to $20,000 a day to operate that model in production. And that very, very quickly, you know, get beyond what you paid to train it. >> Anna, these aren't small numbers, and it's cost to train and cost to operate, it kind of reminds me of when the cloud came around and the data center versus cloud options. Like, wait a minute, one, it costs a ton of cash to deploy, and then running it. This is kind of a similar dynamic. What are you seeing? >> Yeah, absolutely. I think we are going to see increasingly the cost and production outpacing the costs and training by a lot. I mean, people talk about training costs now because that's what they're confronting now because people are so focused on getting models performant enough to even use in an application. And now that we have them and they're that capable, we're really going to start to see production costs go up a lot. >> Yeah, Luis, if you don't mind, I know this might be a little bit of a tangent, but, you know, training's super important. I get that. That's what people are doing now, but then there's the deployment side of production. Where do people get caught up and miss the boat or misconfigure? What's the gotcha? Where's the trip wire or so to speak? Where do people mess up on the cost side? What do they do? Is it they don't think about it, they tie it to proprietary hardware? What's the issue? >> Yeah, several things, right? So without getting really technical, which, you know, I might get into, you know, you have to understand relationship between performance, you know, both in terms of latency and throughput and cost, right? So reducing latency is important because you improve responsiveness of the model. But it's really important to keep in mind that it often leads diminishing returns. Below a certain latency, making it faster won't make a measurable difference in experience, but it's going to cost a lot more. So understanding that is important. Now, if you care more about throughputs, which is the time it takes for you to, you know, units per period of time, you care about time to solution, we should think about this throughput per dollar. And understand what you want is the highest throughput per dollar, which may come at the cost of higher latency, which you're not going to care about, right? So, and the reality here, John, is that, you know, humans and especially folks in this space want to have the latest and greatest hardware. And often they commit a lot of money to get access to them and have to commit upfront before they understand the needs that their models have, right? So common mistake here, one is not spending time to understand what you really need, and then two, over-committing and using more hardware than you actually need. And not giving yourself enough freedom to get your workload to move around to the more cost-effective choice, right? So this is just a metaphoric choice. And then another thing that's important here too is making a model run faster on the hardware directly translates to lower cost, right? So, but it takes a lot of engineers, you need to think of ways of producing very efficient versions of your model for the target hardware that you're going to use. >> Anna, what's the customer angle here? Because price performance has been around for a long time, people get that, but now latency and throughput, that's key because we're starting to see this in apps. I mean, there's an end user piece. I even seeing it on the infrastructure side where they're taking a heavy lifting away from operational costs. So you got, you know, application specific to the user and/or top of the stack, and then you got actually being used in operations where they want both. >> Yeah, absolutely. Maybe I can illustrate this with a quick story with the customer that we had recently been working with. So this customer is planning to run kind of a transformer based model for tech generation at super high scale on Nvidia T4 GPU, so kind of a commodity GPU. And the scale was so high that they would've been paying hundreds of thousands of dollars in cloud costs per year just to serve this model alone. You know, one of many models in their application stack. So we worked with this team to optimize our model and then benchmark across several possible targets. So that matching the hardware that Luis was just talking about, including the newer kind of Nvidia A10 GPUs. And what they found during this process was pretty interesting. First, the team was able to shave a quarter of their spend just by using better optimization techniques on the T4, the older hardware. But actually moving to a newer GPU would allow them to serve this model in a sub two milliseconds latency, so super fast, which was able to unlock an entirely new kind of user experience. So they were able to kind of change the value they're delivering in their application just because they were able to move to this new hardware easily. So they ultimately decided to plan their deployment on the more expensive A10 because of this, but because of the hardware specific optimizations that we helped them with, they managed to even, you know, bring costs down from what they had originally planned. And so if you extend this kind of example to everything that's happening with generative AI, I think the story we just talked about was super relevant, but the scale can be even higher, you know, it can be tenfold that. We were recently conducting kind of this internal study using GPT-J as a proxy to illustrate the experience of just a company trying to use one of these large language models with an example scenario of creating a chatbot to help job seekers prepare for interviews. So if you imagine kind of a conservative usage scenario where the model generates just 3000 words per user per day, which is, you know, pretty conservative for how people are interacting with these models. It costs 5 cents a session and if you're a company and your app goes viral, so from, you know, beginning of the year there's nobody, at the end of the year there's a million daily active active users in that year alone, going from zero to a million. You'll be spending about $6 million a year, which is pretty unmanageable. That's crazy, right? >> Yeah. >> For a company or a product that's just launching. So I think, you know, for us we see the real way to make these kind of advancements accessible and sustainable, as we said is to bring down cost to serve using these techniques. >> That's a great story and I think that illustrates this idea that deployment cost can vary from situation to situation, from model to model and that the efficiency is so strong with this new wave, it eliminates heavy lifting, creates more efficiency, automates intellect. I mean, this is the trend, this is radical, this is going to increase. So the cost could go from nominal to millions, literally, potentially. So, this is what customers are doing. Yeah, that's a great story. What makes sense on a financial, is there a cost of ownership? Is there a pattern for best practice for training? What do you guys advise cuz this is a lot of time and money involved in all potential, you know, good scenarios of upside. But you can get over your skis as they say, and be successful and be out of business if you don't manage it. I mean, that's what people are talking about, right? >> Yeah, absolutely. I think, you know, we see kind of three main vectors to reduce cost. I think one is make your deployment process easier overall, so that your engineering effort to even get your app running goes down. Two, would be get more from the compute you're already paying for, you're already paying, you know, for your instances in the cloud, but can you do more with that? And then three would be shop around for lower cost hardware to match your use case. So on the first one, I think making the deployment easier overall, there's a lot of manual work that goes into benchmarking, optimizing and packaging models for deployment. And because the performance of machine learning models can be really hardware dependent, you have to go through this process for each target you want to consider running your model on. And this is hard, you know, we see that every day. But for teams who want to incorporate some of these large language models into their applications, it might be desirable because licensing a model from a large vendor like OpenAI can leave you, you know, over provision, kind of paying for capabilities you don't need in your application or can lock you into them and you lose flexibility. So we have a customer whose team actually prepares models for deployment in a SaaS application that many of us use every day. And they told us recently that without kind of an automated benchmarking and experimentation platform, they were spending several days each to benchmark a single model on a single hardware type. So this is really, you know, manually intensive and then getting more from the compute you're already paying for. We do see customers who leave money on the table by running models that haven't been optimized specifically for the hardware target they're using, like Luis was mentioning. And for some teams they just don't have the time to go through an optimization process and for others they might lack kind of specialized expertise and this is something we can bring. And then on shopping around for different hardware types, we really see a huge variation in model performance across hardware, not just CPU vs. GPU, which is, you know, what people normally think of. But across CPU vendors themselves, high memory instances and across cloud providers even. So the best strategy here is for teams to really be able to, we say, look before you leap by running real world benchmarking and not just simulations or predictions to find the best software, hardware combination for their workload. >> Yeah. You guys sound like you have a very impressive customer base deploying large language models. Where would you categorize your current customer base? And as you look out, as you guys are growing, you have new customers coming in, take me through the progression. Take me through the profile of some of your customers you have now, size, are they hyperscalers, are they big app folks, are they kicking the tires? And then as people are out there scratching heads, I got to get in this game, what's their psychology like? Are they coming in with specific problems or do they have specific orientation point of view about what they want to do? Can you share some data around what you're seeing? >> Yeah, I think, you know, we have customers that kind of range across the spectrum of sophistication from teams that basically don't have MLOps expertise in their company at all. And so they're really looking for us to kind of give a full service, how should I do everything from, you know, optimization, find the hardware, prepare for deployment. And then we have teams that, you know, maybe already have their serving and hosting infrastructure up and ready and they already have models in production and they're really just looking to, you know, take the extra juice out of the hardware and just do really specific on that optimization piece. I think one place where we're doing a lot more work now is kind of in the developer tooling, you know, model selection space. And that's kind of an area that we're creating more tools for, particularly within the PyTorch ecosystem to bring kind of this power earlier in the development cycle so that as people are grabbing a model off the shelf, they can, you know, see how it might perform and use that to inform their development process. >> Luis, what's the big, I like this idea of picking the models because isn't that like going to the market and picking the best model for your data? It's like, you know, it's like, isn't there a certain approaches? What's your view on this? 'Cause this is where everyone, I think it's going to be a land rush for this and I want to get your thoughts. >> For sure, yeah. So, you know, I guess I'll start with saying the one main takeaway that we got from the GPT-J study is that, you know, having a different understanding of what your model's compute and memory requirements are, very quickly, early on helps with the much smarter AI model deployments, right? So, and in fact, you know, Anna just touched on this, but I want to, you know, make sure that it's clear that OctoML is putting that power into user's hands right now. So in partnership with AWS, we are launching this new PyTorch native profiler that allows you with a single, you know, one line, you know, code decorator allows you to see how your code runs on a variety of different hardware after accelerations. So it gives you very clear, you know, data on how you should think about your model deployments. And this ties back to choices of models. So like, if you have a set of choices that are equally good of models in terms of functionality and you want to understand after acceleration how are you going to deploy, how much they're going to cost or what are the options using a automated process of making a decision is really, really useful. And in fact, so I think these events can get early access to this by signing up for the Octopods, you know, this is exclusive group for insiders here, so you can go to OctoML.ai/pods to sign up. >> So that Octopod, is that a program? What is that, is that access to code? Is that a beta, what is that? Explain, take a minute and explain Octopod. >> I think the Octopod would be a group of people who is interested in experiencing this functionality. So it is the friends and users of OctoML that would be the Octopod. And then yes, after you sign up, we would provide you essentially the tool in code form for you to try out in your own. I mean, part of the benefit of this is that it happens in your own local environment and you're in control of everything kind of within the workflow that developers are already using to create and begin putting these models into their applications. So it would all be within your control. >> Got it. I think the big question I have for you is when do you, when does that one of your customers know they need to call you? What's their environment look like? What are they struggling with? What are the conversations they might be having on their side of the fence? If anyone's watching this, they're like, "Hey, you know what, I've got my team, we have a lot of data. Do we have our own language model or do I use someone else's?" There's a lot of this, I will say discovery going on around what to do, what path to take, what does that customer look like, if someone's listening, when do they know to call you guys, OctoML? >> Well, I mean the most obvious one is that you have a significant spend on AI/ML, come and talk to us, you know, putting AIML into production. So that's the clear one. In fact, just this morning I was talking to someone who is in life sciences space and is having, you know, 15 to $20 million a year cloud related to AI/ML deployment is a clear, it's a pretty clear match right there, right? So that's on the cost side. But I also want to emphasize something that Anna said earlier that, you know, the hardware and software complexity involved in putting model into production is really high. So we've been able to abstract that away, offering a clean automation flow enables one, to experiment early on, you know, how models would run and get them to production. And then two, once they are into production, gives you an automated flow to continuously updating your model and taking advantage of all this acceleration and ability to run the model on the right hardware. So anyways, let's say one then is cost, you know, you have significant cost and then two, you have an automation needs. And Anna please compliment that. >> Yeah, Anna you can please- >> Yeah, I think that's exactly right. Maybe the other time is when you are expecting a big scale up in serving your application, right? You're launching a new feature, you expect to get a lot of usage or, and you want to kind of anticipate maybe your CTO, your CIO, whoever pays your cloud bills is going to come after you, right? And so they want to know, you know, what's the return on putting this model essentially into my application stack? Am I going to, is the usage going to match what I'm paying for it? And then you can understand that. >> So you guys have a lot of the early adopters, they got big data teams, they're pushed in the production, they want to get a little QA, test the waters, understand, use your technology to figure it out. Is there any cases where people have gone into production, they have to pull it out? It's like the old lemon laws with your car, you buy a car and oh my god, it's not the way I wanted it. I mean, I can imagine the early people through the wall, so to speak, in the wave here are going to be bloody in the sense that they've gone in and tried stuff and get stuck with huge bills. Are you seeing that? Are people pulling stuff out of production and redeploying? Or I can imagine that if I had a bad deployment, I'd want to refactor that or actually replatform that. Do you see that too? >> Definitely after a sticker shock, yes, your customers will come and make sure that, you know, the sticker shock won't happen again. >> Yeah. >> But then there's another more thorough aspect here that I think we likely touched on, be worth elaborating a bit more is just how are you going to scale in a way that's feasible depending on the allocation that you get, right? So as we mentioned several times here, you know, model deployment is so hardware dependent and so complex that you tend to get a model for a hardware choice and then you want to scale that specific type of instance. But what if, when you want to scale because suddenly luckily got popular and, you know, you want to scale it up and then you don't have that instance anymore. So how do you live with whatever you have at that moment is something that we see customers needing as well. You know, so in fact, ideally what we want is customers to not think about what kind of specific instances they want. What they want is to know what their models need. Say, they know the SLA and then find a set of hybrid targets and instances that hit the SLA whenever they're also scaling, they're going to scale with more freedom, right? Instead of having to wait for AWS to give them more specific allocation for a specific instance. What if you could live with other types of hardware and scale up in a more free way, right? So that's another thing that we see customers, you know, like they need more freedom to be able to scale with whatever is available. >> Anna, you touched on this with the business model impact to that 6 million cost, if that goes out of control, there's a business model aspect and there's a technical operation aspect to the cost side too. You want to be mindful of riding the wave in a good way, but not getting over your skis. So that brings up the point around, you know, confidence, right? And teamwork. Because if you're in production, there's probably a team behind it. Talk about the team aspect of your customers. I mean, they're dedicated, they go put stuff into production, they're developers, there're data. What's in it for them? Are they getting better, are they in the beach, you know, reading the book. Are they, you know, are there easy street for them? What's the customer benefit to the teams? >> Yeah, absolutely. With just a few clicks of a button, you're in production, right? That's the dream. So yeah, I mean I think that, you know, we illustrated it before a little bit. I think the automated kind of benchmarking and optimization process, like when you think about the effort it takes to get that data by hand, which is what people are doing today, they just don't do it. So they're making decisions without the best information because it's, you know, there just isn't the bandwidth to get the information that they need to make the best decision and then know exactly how to deploy it. So I think it's actually bringing kind of a new insight and capability to these teams that they didn't have before. And then maybe another aspect on the team side is that it's making the hand-off of the models from the data science teams to the model deployment teams more seamless. So we have, you know, we have seen in the past that this kind of transition point is the place where there are a lot of hiccups, right? The data science team will give a model to the production team and it'll be too slow for the application or it'll be too expensive to run and it has to go back and be changed and kind of this loop. And so, you know, with the PyTorch profiler that Luis was talking about, and then also, you know, the other ways we do optimization that kind of prevents that hand-off problem from happening. >> Luis and Anna, you guys have a great company. Final couple minutes left. Talk about the company, the people there, what's the culture like, you know, if Intel has Moore's law, which is, you know, doubling the performance in few years, what's the culture like there? Is it, you know, more throughput, better pricing? Explain what's going on with the company and put a plug in. Luis, we'll start with you. >> Yeah, absolutely. I'm extremely proud of the team that we built here. You know, we have a people first culture, you know, very, very collaborative and folks, we all have a shared mission here of making AI more accessible and sustainable. We have a very diverse team in terms of backgrounds and life stories, you know, to do what we do here, we need a team that has expertise in software engineering, in machine learning, in computer architecture. Even though we don't build chips, we need to understand how they work, right? So, and then, you know, the fact that we have this, this very really, really varied set of backgrounds makes the environment, you know, it's say very exciting to learn more about, you know, assistance end-to-end. But also makes it for a very interesting, you know, work environment, right? So people have different backgrounds, different stories. Some of them went to grad school, others, you know, were in intelligence agencies and now are working here, you know. So we have a really interesting set of people and, you know, life is too short not to work with interesting humans. You know, that's something that I like to think about, you know. >> I'm sure your off-site meetings are a lot of fun, people talking about computer architectures, silicon advances, the next GPU, the big data models coming in. Anna, what's your take? What's the culture like? What's the company vibe and what are you guys looking to do? What's the customer success pattern? What's up? >> Yeah, absolutely. I mean, I, you know, second all of the great things that Luis just said about the team. I think one that I, an additional one that I'd really like to underscore is kind of this customer obsession, to use a term you all know well. And focus on the end users and really making the experiences that we're bringing to our user who are developers really, you know, useful and valuable for them. And so I think, you know, all of these tools that we're trying to put in the hands of users, the industry and the market is changing so rapidly that our products across the board, you know, all of the companies that, you know, are part of the showcase today, we're all evolving them so quickly and we can only do that kind of really hand in glove with our users. So that would be another thing I'd emphasize. >> I think the change dynamic, the power dynamics of this industry is just the beginning. I'm very bullish that this is going to be probably one of the biggest inflection points in history of the computer industry because of all the dynamics of the confluence of all the forces, which you mentioned some of them, I mean PC, you know, interoperability within internetworking and you got, you know, the web and then mobile. Now we have this, I mean, I wouldn't even put social media even in the close to this. Like, this is like, changes user experience, changes infrastructure. There's going to be massive accelerations in performance on the hardware side from AWS's of the world and cloud and you got the edge and more data. This is really what big data was going to look like. This is the beginning. Final question, what do you guys see going forward in the future? >> Well, it's undeniable that machine learning and AI models are becoming an integral part of an interesting application today, right? So, and the clear trends here are, you know, more and more competitional needs for these models because they're only getting more and more powerful. And then two, you know, seeing the complexity of the infrastructure where they run, you know, just considering the cloud, there's like a wide variety of choices there, right? So being able to live with that and making the most out of it in a way that does not require, you know, an impossible to find team is something that's pretty clear. So the need for automation, abstracting with the complexity is definitely here. And we are seeing this, you know, trends are that you also see models starting to move to the edge as well. So it's clear that we're seeing, we are going to live in a world where there's no large models living in the cloud. And then, you know, edge models that talk to these models in the cloud to form, you know, an end-to-end truly intelligent application. >> Anna? >> Yeah, I think, you know, our, Luis said it at the beginning. Our vision is to make AI sustainable and accessible. And I think as this technology just expands in every company and every team, that's going to happen kind of on its own. And we're here to help support that. And I think you can't do that without tools like those like OctoML. >> I think it's going to be an error of massive invention, creativity, a lot of the format heavy lifting is going to allow the talented people to automate their intellect. I mean, this is really kind of what we see going on. And Luis, thank you so much. Anna, thanks for coming on this segment. Thanks for coming on theCUBE and being part of the AWS Startup Showcase. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

>>Hello everyone. Welcome to the Super Cloud Event preview. I'm John Forry, host of the Cube, and with Dave Valante, host of the popular Super cloud events. This is Super Cloud two preview. I'm joined by industry leader and Cube alumni, Victoria Vigo, vice president of klos Cross Cloud Services at VMware. Vittorio. Great to see you. We're here for the preview of Super Cloud two on January 17th, virtual event, live stage performance, but streamed out to the audience virtually. We're gonna do a preview. Thanks for coming in. >>My pleasure. Always glad to be here. >>It's holiday time. We had the first super cloud on in August prior to VMware, explore North America prior to VMware, explore Europe prior to reinvent. We've been through that, but right now, super Cloud has got momentum. Super Cloud two has got some success. Before we dig into it, let's take a step back and set the table. What is Super Cloud and why is important? Why are people buzzing about it? Why is it a thing? >>Look, we have been in the cloud now for like 10, 15 years and the cloud is going strong and I, I would say that going cloud first was deliberate and strategic in most cases. In some cases the, the developer was going for the path of risk resistance, but in any sizable company, this caused the companies to end up in a multi-cloud world where 85% of the companies out there use two or multiple clouds. And with that comes what we call cloud chaos, because each cloud brings their own management tools, development tools, security. And so that increase the complexity and cost. And so we believe that it's time to usher a new era in cloud computing, which we, you call the super cloud. We call it cross cloud services, which allows our customers to have a single way to build, manage, secure, and access any application across any cloud. Lowering the cost and simplifying the environment. Since >>Dave Ante and I introduced and rift on the concept of Supercloud, as we talked about at reinvent last year, a lot has happened. Supercloud one, it was in August, but prior to that, great momentum in the industry. Great conversation. People are loving it, they're hating it, which means it's got some traction. Berkeley has come on board as with a position paper. They're kind of endorsing it. They call it something different. You call it cross cloud services, whatever it is. It's kind of the same theme we're seeing. And so the industry has recognized something is happening that's different than what Cloud one was or the first generation of cloud. Now we have something different. This Super Cloud two in January. This event has traction with practitioners, customers, big name brands, Sachs, fifth Avenue, Warner, media Financial, mercury Financial, other big names are here. They're leaning in. They're excited. Why the traction in the customer's industry converts over to, to the customer traction. Why is it happening? You, you get a lot of data. >>Well, in, in Super Cloud one, it was a vendor fest, right? But these vendors are smart people that get their vision from where, from the customers. This, this stuff doesn't happen in a vacuum. We all talk to customers and we tend to lean on the early adopters and the early adopters of the cloud are the ones that are telling us, we now are in a place where the complexity is too much. The cost is ballooning. We're going towards slow down potentially in the economy. We need to get better economics out of, of our cloud. And so every single customers I talked to today, or any sizable company as this problem, the developers have gone off, built all these applications, and now the business is coming to the operators and asking, where are my applications? Are they performing? What is the security posture? And how do we do compliance? And so now they're realizing we need to do something about this or it is gonna be unmanageable. >>I wanna go to a clip I pulled out from the, our video data lake and the cube. If we can go to that clip, it's Chuck Whitten Dell at a keynote. He was talking about what he calls multi-cloud by default, not by design. This is a state of the, of the industry. If we're gonna roll that clip, and I wanna get your reaction to that. >>Well, look, customers have woken up with multiple clouds, you know, multiple public clouds. On-premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was it can be, and it should be, it is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they wanna maintain an on-premise cloud. On-premise clouds are not going away. I mentioned edge Cloud, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud, by default we mean that's the state of the world. Today, our goal is to bring multi-cloud by design, as you heard. Yeah, I >>Mean, I, okay, Vittorio, that's, that's the head of Dell Technologies president. He obvious he runs it. Michael Dell's still around, but you know, he's the leader. This is a interesting observation. You know, he's not a customer. We have some customer equips we'll go to as well, but by default it kind of happened not by design. So we're now kind of in a zoom out issue where, okay, I got this environment just landed on me. What, what is the, what's your reaction to that clip of how multi-cloud has become present in, in everyone's on everyone's plate right now to deal with? Yeah, >>I it is, it is multi-cloud by default, I would call it by accident. We, we really got there by accident. I think now it's time to make it a strategic asset because look, we're using multiple cloud for a reason, because all these hyperscaler bring tremendous innovation that we want to leverage. But I strongly believe that in it, especially history repeat itself, right? And so if you look at the history of it, as was always when a new level of obstruction that simplify things, that we got the next level of innovation at the lower cost, you know, from going from c plus plus to Visual basic, going from integrating application at the bits of by layer to SOA and then web services. It's, it's only when we simplify the environment that we can go faster and lower cost. And the multi-cloud is ready for that level of obstruction today. >>You know, you've made some good points. You know, developers went crazy building great apps. Now they got, they gotta roll it out and operationalize it globally. A lot of compliance issues going on. The costs are going up. We got an economic challenge, but also agility with the cloud. So using cloud and or hybrid, you can get better agility. And also moving to the cloud, it's kind of still slow. Okay, so I get that at reinvent this year and at VMware explorer we were observing and we reported that you're seeing a transition to a new kind of ecosystem partner. Ones that aren't just ISVs anymore. You have ISVs, independent software vendors, but you got the emergence of bigger players that just, they got platforms, they have their own ecosystems. So you're seeing ecosystems on top of ecosystems where, you know, MongoDB CEO and the Databricks CEO both told me, we're not an isv, we're a platform built on a cloud. So this new kind of super cloudlike thing is going on. Why should someone pay attention to the super cloud movement? We're on two, we're gonna continue to do these out in the open. Anyone can participate. Why should people pay attention to this? Why should they come to the event? Why is this important? Is this truly an inflection point? And if they do pay attention, what should they pay attention to? >>I would pay attention to two things. If you are customers that are now starting to realize that you have a multi-cloud problem and the costs are getting outta control, look at what the leading vendors are saying, connect the dots with the early adopters and some of the customers that we are gonna have at Super Cloud two, and use those learning to not fall into the same trap. So I, I'll give you an example. I was talking to a Fortune 50 in Europe in my latest trip, and this is an a CIO that is telling me >>We build all these applications and now for compliance reason, the business is coming to me, I don't even know where they are, right? And so what I was telling him, so look, there are other customers that are already there. What did they do? They built a platform engineering team. What is the platform? Engineering team is a, is an operation team that understands how developers build modern applications and lays down the foundation across multiple clouds. So the developers can be developers and do their thing, which is writing code. But now you as a cio, as a, as a, as a governing body, as a security team can have the guardrail. So do you know that these applications are performing at a lower cost and are secure and compliant? >>Patura, you know, it's really encouraging and, and love to get your thoughts on this is one is the general consensus of industry leaders. I talked to like yourself in the round is the old way was soft complexity with more complexity. The cloud demand simplicity, you mentioned abstraction layer. This is our next inflection point. It's gotta be simpler and it's gotta be easy and it's gotta be performant. That's the table stakes of the cloud. What's your thoughts on this next wave of simplicity versus complexity? Because again, abstraction layers take away complexity, they should make it simpler. What's your thoughts? >>Yeah, so I'll give you few examples. One, on the development side and runtime. You, you one would think that Kubernetes will solve all the problems you have Kubernetes everywhere, just look at, but every cloud has a different distribution of Kubernetes, right? So for example, at VMware with tansu, we create a single place that allows you to deploy that any Kubernetes environment. But now you have one place to set your policies. We take care of the differences between this, this system. The second area is management, right? So once you have all everything deployed, how do you get a single object model that tells you where your stuff is and how it's performing, and then apply policies to it as well. So these are two areas and security and so on and so forth. So the idea is that figure out what you can abstract and make common across cloud. Make that simple and put it in one place while always allowing the developers to go underneath and use the differentiated features for innovation. >>Yeah, one of the areas I'm excited, I want to get your thoughts of too is, we haven't talked about this in the past, but it, I'll throw it out there. I think the, the new AI coming out chat, G P T and other things like lens, you see it and see new kinds of AI coming that's gonna be right in the heavy lifting opportunity to make things easier with AI and automation. I think AI will be a big factor in super cloud and, and cross cloud. What's your thoughts? >>Well, the one way to look at AI is, is one of the main, main services that you would want out of a multi-cloud, right? You want eventually, right now Google seems to have an edge, but you know, the competition creates, you know, innovation. So later on you wanna use something from Azure or from or from Oracle or something that, so you want at some point that is gonna be prone every single service in in the cloud is gonna be prone to obstruction and simplification. And I, I'm just excited about to see >>What book, I can't wait for the chat services to write code automatically for us. Well, >>They >>Do, they do. They're doing it now. They do. >>Oh, the other day, somebody, you know that I do this song par this for. So for fun sometimes. And somebody the other day said, ask the AI to write a parody song for multi-cloud. And so I have the lyrics stay >>Tuned. I should do that from my blog post. Hey, write a blog post on this January 17th, Victoria, thanks for coming in, sharing the preview bottom line. Why should people come? Why is it important? What's your final kind of takeaway? Billboard message >>History is repeat itself. It goes to three major inflection points, right? We had the inflection point with the cloud and the people that got left behind, they were not as competitive as the people that got on top o of this wave. The new wave is the super cloud, what we call cross cloud services. So if you are a customer that is experiencing this problem today, tune in to to hear from other customers in, in your same space. If you are behind, tune in to avoid the, the, the, the mistakes and the, the shortfalls of this new wave. And so that you can use multi-cloud to accelerate your business and kick butt in the future. >>All right. Kicking kick your names and kicking butt. Okay, we're here on J January 17th. Super Cloud two. Momentum continues. We'll be super cloud three. There'll be super cloud floor. More and more open conversations. Join the community, join the conversation. It's open. We want more voices. We want more, more industry. We want more customers. It's happening. A lot of momentum. Victoria, thank you for your time. Thank you. Okay. I'm John Farer, host of the Cube. Thanks for watching.

>>The Cube presents Ignite 22, brought to you by Palo Alto Networks. >>Welcome back to Vegas. Guys. We're happy that you're here. Lisa Martin here covering with Dave Valante, Palo Alto Networks Ignite 22. We're at MGM Grand. This is our first day, Dave of two days of cube coverage. We've been having great conversations with the ecosystem with Palo Alto executives, with partners. One of the things that they have is unit 42. We're gonna be talking with them next about cyber intelligence. And the threat data that they get is >>Incredible. Yeah. They have all the data, they know what's going on, and of course things are changing. The state of play changes. Hold on a second. I got a text here. Oh, my Netflix account was frozen. Should I click on this link? Yeah. What do you think? Have you had a, it's, have you had a little bit more of that this holiday season? Yeah, definitely. >>Unbelievable, right? A lot of smishing going on. >>Yeah, they're very clever. >>Yeah, we're very pleased to welcome back one of our alumni to the queue. Wendy Whitmore is here, the SVP of Unit 42. Welcome back, Wendy. Great to have >>You. Thanks Lisa. So >>Unit 42 created back in 2014. One of the things that I saw that you said in your keynote this morning or today was everything old is still around and it's co, it's way more prolific than ever. What are some of the things that Unit 42 is seeing these days with, with respect to cyber threats as the landscape has changed so much the last two years alone? >>You know, it, it has. So it's really interesting. I've been responding to these breaches for over two decades now, and I can tell you that there are a lot of new and novel techniques. I love that you already highlighted Smishing, right? In the opening gate. Right. Because that is something that a year ago, no one knew what that word was. I mean, we, it's probably gonna be invented this year, right? But that said, so many of the tactics that we have previously seen, when it comes to just general espionage techniques, right? Data act filtration, intellectual property theft, those are going on now more than ever. And you're not hearing about them as much in the news because there are so many other things, right? We're under the landscape of a major war going on between Russia and Ukraine of ransomware attacks, you know, occurring on a weekly basis. And so we keep hearing about those, but ultimately these nations aid actors are using that top cover, if you will, as a great distraction. It's almost like a perfect storm for them to continue conducting so much cyber espionage work that like we may not be feeling that today, but years down the road, they're, the work that they're doing today is gonna have really significant impact. >>Ransomware has become a household word in the last couple of years. I think even my mom knows what it is, to some degree. Yeah. But the threat actors are far more sophisticated than they've ever written. They're very motivated. They're very well funded. I think I've read a stat recently in the last year that there's a ransomware attack once every 11 seconds. And of course we only hear about the big ones. But that is a concern that goes all the way up to the board. >>Yeah. You know, we have a stat in our ransomware threat report that talks about how often victims are posted on leak sites. And I think it's once every seven minutes at this point that a new victim is posted. Meaning a victim has had their data, a victim organization had their data stolen and posted on some leak site in the attempt to be extorted. So that has become so common. One of the shifts that we've seen this year in particular and in recent months, you know, a year ago when I was at Ignite, which was virtual, we talked about quadruple extortion, meaning four different ways that these ransomware actors would go out and try to make money from these attacks in what they're doing now is often going to just one, which is, I don't even wanna bother with encrypting your data now, because that means that in order to get paid, I probably have to decrypt it. Right? That's a lot of work. It's time consuming. It's kind of painstaking. And so what they've really looked to do now is do the extortion where they simply steal the data and then threaten to post it on these leak sites, you know, release it other parts of the web and, and go from there. And so that's really a blending of these techniques of traditional cyber espionage with intellectual property theft. Wow. >>How trustworthy are those guys in terms of, I mean, these are hackers, right? In terms of it's really the, the hacker honor system, isn't it? I mean, if you get compromised like that, you really beholden to criminals. And so, you >>Know, so that's one of the key reasons why having the threat intelligence is so important, right? Understanding which group that you're dealing with and what their likelihood of paying is, what's their modus operandi. It's become even more important now because these groups switch teams more frequently than NFL trades, you know, free agents during the regular season, right? Or players become free agents. And that's because their infrastructure. So the, you know, infrastructure, the servers, the systems that they're using to conduct these attacks from is actually largely being disrupted more from law enforcement, international intelligence agencies working together with public private partnerships. So what they're doing is saying, okay, great. All that infrastructure that I just had now is, is burned, right? It's no longer effective. So then they'll disband a team and then they'll recruit a new team and it's constant like mixing and matching in players. >>All that said, even though that's highly dynamic, one of the other areas that they pride themselves on is customer service. So, and I think it's interesting because, you know, when I said they're not wanting to like do all the decryption? Yeah. Cuz that's like painful techni technical slow work. But on the customer service side, they will create these customer service portals immediately stand one up, say, you know, hey it's, it's like an Amazon, you know, if you've ever had to return a package on Amazon for example, and you need to click through and like explain, you know, Hey, I didn't receive this package. A portal window pops up, you start talking to either a bot or a live agent on the backend. In this case they're hu what appeared to be very much humans who are explaining to you exactly what happened, what they're asking for, super pleasant, getting back within minutes of a response. And they know that in order for them to get paid, they need to have good customer service because otherwise they're not going to, you know, have a business. How, >>So what's the state of play look like from between nation states, criminals and how, how difficult or not so difficult is it for you to identify? Do you have clear signatures? My understanding in with Solar Winds it was a little harder, but maybe help us understand and help our audience understand what the state of play is right now. >>One of the interesting things that I think is occurring, and I highlighted this this morning, is this idea of convergence. And so I'll break it down for one example relates to the type of malware or tools that these attackers use. So traditionally, if we looked at a nation state actor like China or Russia, they were very, very specific and very strategic about the types of victims that they were going to go after when they had zero day. So, you know, new, new malware out there, new vulnerabilities that could be exploited only by them because the rest of the world didn't know about it. They might have one organization that they would target that at, at most, a handful and all very strategic for their objective. They wanted to keep that a secret as long as possible. Now what we're seeing actually is those same attackers going towards one, a much larger supply chain. >>So, so lorenzen is a great example of that. The Hafnia attacks towards Microsoft Exchange server last year. All great examples of that. But what they're also doing is instead of using zero days as much, or you know, because those are expensive to build, they take a lot of time, a lot of funding, a lot of patience and research. What they're doing is using commercially available tools. And so there's a tool that our team identified earlier this year called Brute Rael, C4 or BRC four for short. And that's a tool that we now know that nation state actors are using. But just two weeks ago we invested a ransomware attack where the ransomware actor was using that same piece of tooling. So to your point, yak can get difficult for defenders when you're looking through and saying, well wait, they're all using some of the same tools right now and some of the same approaches when it comes to nation states, that's great for them because they can blend into the noise and it makes it harder to identify as >>Quickly. And, and is that an example of living off the land or is that B BRC four sort of a homegrown hacker tool? Is it, is it a, is it a commercial >>Off the shelf? So it's a tool that was actually, so you can purchase it, I believe it's about 2,500 US dollars for a license. It was actually created by a former Red teamer from a couple well-known companies in the industry who then decided, well hey, I built this tool for work, I'm gonna sell this. Well great for Red teamers that are, you know, legitimately doing good work, but not great now because they're, they built a, a strong tool that has the ability to hide amongst a, a lot of protocols. It can actually hide within Slack and teams to where you can't even see the data is being exfiltrated. And so there's a lot of concern. And then now the reality that it gets into the wrong hands of nation state actors in ransomware actors, one of the really interesting things about that piece of malware is it has a setting where you can change wallpaper. And I don't know if you know offhand, you know what that means, but you know, if that comes to mind, what you would do with it. Well certainly a nation state actor is never gonna do something like that, right? But who likes to do that are ransomware actors who can go in and change the background wallpaper on a desktop that says you've been hacked by XYZ organization and let you know what's going on. So pretty interesting, obviously the developer doing some work there for different parts of the, you know, nefarious community. >>Tremendous amount of sophistication that's gone on the last couple of years alone. I was just reading that Unit 42 is now a founding member of the Cyber Threat Alliance includes now more than 35 organizations. So you guys are getting a very broad picture of today's threat landscape. How can customers actually achieve cyber resilience? Is it achievable and how do you help? >>So I, I think it is achievable. So let me kind of parse out the question, right. So the Cyber Threat Alliance, the J C D C, the Cyber Safety Review Board, which I'm a member of, right? I think one of the really cool things about Palo Alto Networks is just our partnerships. So those are just a handful. We've got partnerships with over 200 organizations. We work closely with the Ukrainian cert, for example, sharing information, incredible information about like what's going on in the war, sharing technical details. We do that with Interpol on a daily basis where, you know, we're sharing information. Just last week the Africa cyber surge operation was announced where millions of nodes were taken down that were part of these larger, you know, system of C2 channels that attackers are using to conduct exploits and attacks throughout the world. So super exciting in that regard and it's something that we're really passionate about at Palo Alto Networks in terms of resilience, a few things, you know, one is visibility, so really having a, an understanding of in a real, as much of real time as possible, right? What's happening. And then it goes into how you, how can we decrease operational impact. So that's everything from network segmentation to wanna add the terms and phrases I like to use a lot is the win is really increasing the time it takes for the attackers to get their work done and decreasing the amount of time it takes for the defenders to get their work done, right? >>Yeah. I I call it increasing the denominator, right? And the ROI equation benefit over or value, right? Equals equals or benefit equals value over cost if you can increase the cost to go go elsewhere, right? Absolutely. And that's the, that's the game. Yeah. You mentioned Ukraine before, what have we learned from Ukraine? I, I remember I was talking to Robert Gates years ago, 2016 I think, and I was asking him, yeah, but don't we have the best cyber technology? Can't we attack? He said, we got the most to lose too. Yeah. And so what have we learned from, from Ukraine? >>Well, I, I think that's part of the key point there, right? Is you know, a great offense essentially can also be for us, you know, deterrent. So in that aspect we have as an, as a company and or excuse me, as a country, as a company as well, but then as partners throughout all parts of the world have really focused on increasing the intelligence sharing and specifically, you know, I mentioned Ukrainian cert. There are so many different agencies and other sorts throughout the world that are doing everything they can to share information to help protect human life there. And so what we've really been concerned with, with is, you know, what cyber warfare elements are going to be used there, not only how does that impact Ukraine, but how does it potentially spread out to other parts of the world critical infrastructure. So you've seen that, you know, I mentioned CS rrb, but cisa, right? >>CISA has done a tremendous job of continuously getting out information and doing everything they can to make sure that we are collaborating at a commercial level. You know, we are sharing information and intelligence more than ever before. So partners like Mania and CrowdStrike, our Intel teams are working together on a daily basis to make sure that we're able to protect not only our clients, but certainly if we've got any information relevant that we can share that as well. And I think if there's any silver lining to an otherwise very awful situation, I think the fact that is has accelerated intelligence sharing is really positive. >>I was gonna ask you about this cause I think, you know, 10 or so years ago, there was a lot of talk about that, but the industry, you know, kind of kept things to themselves, you know, a a actually tried to monetize some of that private data. So that's changing is what I'm hearing from you >>More so than ever more, you know, I've, I mentioned I've been in the field for 20 years. You know, it, it's tough when you have a commercial business that relies on, you know, information to, in order to pay people's salaries, right? I think that has changed quite a lot. We see the benefit of just that continuous sharing. There are, you know, so many more walls broken down between these commercial competitors, but also the work on the public private partnership side has really increased some of those relationships. Made it easier. And you know, I have to give a whole lot of credit and mention sisa, like the fact that during log four J, like they had GitHub repositories, they were using Slack, they were using Twitter. So the government has really started pushing forward with a lot of the newer leadership that's in place to say, Hey, we're gonna use tools and technology that works to share and disseminate information as quickly as we can. Right? That's fantastic. That's helping everybody. >>We knew that every industry, no, nobody's spared of this. But did you notice in the last couple of years, any industries in particular that are more vulnerable? Like I think of healthcare with personal health information or financial services, any industries kind of jump out as being more susceptible than others? >>So I think those two are always gonna be at the forefront, right? Financial services and healthcare. But what's been really top of mind is critical infrastructure, just making sure right? That our water, our power, our fuel, so many other parts of right, the ecosystem that go into making sure that, you know, we're keeping, you know, houses heated during the winter, for example, that people have fresh water. Those are extremely critical. And so that is really a massive area of focus for the industry right now. >>Can I come back to public-private partnerships? My question is relates to regulations because the public policy tends to be behind tech, the technology industry as an understatement. So when you take something like GDPR is the obvious example, but there are many, many others, data sovereignty, you can't move the data. Are are, are, is there tension between your desire as our desire as an industry to share data and government's desire to keep data private and restrict that data sharing? How is that playing out? How do you resolve that? >>Well I think there have been great strides right in each of those areas. So in terms of regulation when it comes to breaches there, you know, has been a tendency in the past to do victim shaming, right? And for organizations to not want to come forward because they're concerned about the monetary funds, right? I think there's been tremendous acceleration. You're seeing that everywhere from the fbi, from cisa, to really working very closely with organizations to, to have a true impact. So one example would be a ransomware attack that occurred. This was for a client of ours within the United States and we had a very close relationship with the FBI at that local field office and made a phone call. This was 7:00 AM Eastern time. And this was an organization that had this breach gone public, would've made worldwide news. There would've been a very big impact because it would've taken a lot of their systems offline. >>Within the 30 minutes that local FBI office was on site said, we just saw this piece of malware last week, we have a decryptor for it from another organization who shared it with us. Here you go. And within 60 minutes, every system was back up and running. Our teams were able to respond and get that disseminated quickly. So efforts like that, I think the government has made a tremendous amount of headway into improving relationships. Is there always gonna be some tension between, you know, competing, you know, organizations? Sure. But I think that we're doing a whole lot to progress it, >>But governments will make exceptions in that case. Especially for something as critical as the example that you just gave and be able to, you know, do a reach around, if you will, on, on onerous regulations that, that ne aren't helpful in that situation, but certainly do a lot of good in terms of protecting privacy. >>Well, and I think there used to be exceptions made typically only for national security elements, right? And now you're seeing that expanding much more so, which I think is also positive. Right. >>Last question for you as we are wrapping up time here. What can organizations really do to stay ahead of the curve when it comes to, to threat actors? We've got internal external threats. What can they really do to just be ahead of that curve? Is that possible? >>Well, it is now, it's not an easy task so I'm not gonna, you know, trivialize it. But I think that one, having relationships with right organizations in advance always a good thing. That's a, everything from certainly a commercial relationships, but also your peers, right? There's all kinds of fantastic industry spec specific information sharing organizations. I think the biggest thing that impacts is having education across your executive team and testing regularly, right? Having a plan in place, testing it. And it's not just the security pieces of it, right? As security responders, we live these attacks every day, but it's making sure that your general counsel and your head of operations and your CEO knows what to do. Your board of directors, do they know what to do when they receive a phone call from Bloomberg, for example? Are they supposed supposed to answer? Do your employees know that those kind of communications in advance and training can be really critical and make or break a difference in an attack. >>That's a great point about the testing but also the communication that it really needs to be company wide. Everyone at every level needs to know how to react. Wendy, it's been so great having, >>Wait one last question. Sure. Do you have a favorite superhero growing up? >>Ooh, it's gotta be Wonder Woman. Yeah, >>Yeah, okay. Yeah, so cuz I'm always curious, there's not a lot of women in, in security in cyber. How'd you get into it? And many cyber pros like wanna save the world? >>Yeah, no, that's a great question. So I joined the Air Force, you know, I, I was a special agent doing computer crime investigations and that was a great job. And I learned about that from, we had an alumni day and all these alumni came in from the university and they were in flight suits and combat gear. And there was one woman who had long blonde flowing hair and a black suit and high heels and she was carrying a gun. What did she do? Because that's what I wanted do. >>Awesome. Love it. We >>Blonde >>Wonder Woman. >>Exactly. Wonder Woman. Wendy, it's been so great having you on the program. We, we will definitely be following unit 42 and all the great stuff that you guys are doing. Keep up the good >>Work. Thanks so much Lisa. Thank >>You. Day our pleasure. For our guest and Dave Valante, I'm Lisa Martin, live in Las Vegas at MGM Grand for Palo Alto Ignite, 22. You're watching the Cube, the leader in live enterprise and emerging tech coverage.

>>Hello and welcome back to the Cube's coverage of AWS Reinvent 2022. I'm John Furrier, host of the Cube. We got a great conversation with Patrick Kauflin, vice president of Go to Market Strategy and specialization at Splunk. We're talking about the open cybersecurity scheme of framework, also known as the O C sf, a joint strategic collaboration between Splunk and aws. It's got a lot of traction momentum. Patrick, thanks for coming on the cube for reinvent coverage. >>John, great to be here. I'm excited for this. >>You know, I love this open source movement and open source and continues to add value, almost sets the standards. You know, we were talking at the CNCF Linux Foundation this past fall about how standards are coming outta open source. Not so much the the classic standards groups, but you start to see the developers voting with their code groups deciding what to adopt de facto standards and security is a real key part of that where data becomes key for resilience. And this has been the top conversation at reinvent and all around the industry, is how to make data a key part of building into cyber resilience. So I wanna get your thoughts about the problem that you see that's emerging that you guys are solving with this group kind of collaboration around the ocs f >>Yeah, well look, John, I I think, I think you, you've already, you've already hit the high notes there. Data is proliferating across the enterprise. The attack surface area is rapidly expanding. The threat landscape is ever changing. You know, we, we just had a, a lot of scares around open SSL before that we had vulnerabilities and, and Confluence and Atlassian, and you go back to log four J and SolarWinds before that and, and challenges with the supply chain. In this year in particular, we've had a, a huge acceleration in, in concerns and threat vectors around operational technology. In our customer base alone, we saw a huge uptake, you know, and double digit percentage of customers that we're concerned about the traditional vectors like, like ransomware, like business email compromise, phishing, but also from insider threat and others. So you've got this, this highly complex environment where data continues to proliferate and flow through new applications, new infrastructure, new services, driving different types of outcomes in the digitally transformed enterprise of today. >>And, and what happens there is, is our customers, particularly in security, are, are left with having to stitch all of this together. And they're trying to get visibility across multiple different services, infrastructure applications across a number of different point solutions that they've bought to help them protect, defend, detect, and respond better. And it's a massive challenge. And you know, when our, when our customers come to us, they are often looking for ways to drive more consolidation across a variety of different solutions. They're looking to drive better outcomes in terms of speed to detection. How do I detect faster? How do I bind the thing that when bang in the night faster? How do I then fix it quickly? And then how do I layer in some automation so hopefully I don't have to do it again? Now, the challenge there that really OCF Ocsf helps to, to solve is to do that effectively, to detect and to respond at the speed at which attackers are demanding. >>Today we have to have normalization of data across this entire landscape of tools, infrastructure, services. We have to have integration to have visibility, and these tools have to work together. But the biggest barrier to that is often data is stored in different structures and in different formats across different solution providers, across different tools that are, that are, that our customers are using. And that that lack of data, normalization, chokes the integration problem. And so, you know, several years ago, a number of very smart people, and this was, this was a initiative s started by Splunk and AWS came together and said, look, we as an industry have to solve this for our customers. We have to start to shoulder this burden for our customers. We can't, we can't make our customers have to be systems integrators. That's not their job. Our job is to help make this easier for them. And so OCS was born and over the last couple of years we've built out this, this collaboration to not just be AWS and Splunk, but over 50 different organizations, cloud service providers, solution providers in the cybersecurity space have come together and said, let's decide on a single unified schema for how we're gonna represent event data in this industry. And I'm very proud to be here today to say that we've launched it and, and I can't wait to see where we go next. >>Yeah, I mean, this is really compelling. I mean, it's so much packed in that, in that statement, I mean, data normalization, you mentioned chokes, this the, the solution and integration as you call it. But really also it's like data's not just stored in silos. It may not even be available, right? So if you don't have availability of data, that's an important point. Number two, you mentioned supply chain, there's physical supply chain that's coming up big time at reinvent this time as well as in open source, the software supply chain. So you now have the perimeter's been dead for multiple years. We've been talking with that for years, everybody knows that. But now combined with the supply chain problem, both physical and software, there's so much more to go on. And so, you know, the leaders in the industry, they're not sitting on their hands. They know this, but they're just overloaded. So, so how do leaders deal with this right now before we get into the ocs f I wanna just get your thoughts on what's the psychology of the, of the business leader who's facing this landscape? >>Yeah, well, I mean unfortunately too many leaders feel like they have to face these trade offs between, you know, how and where they are really focusing cyber resilience investments in the business. And, and often there is a siloed approach across security, IT developer operations or engineering rather than the ability to kind of drive visibility integration and, and connection of outcomes across those different functions. I mean, the truth is the telemetry that, that you get from an application for application performance monitoring or infrastructure monitoring is often incredibly valuable when there's a security incident and vice versa. Some of the security data that, that you may see in a security operation center can be incredibly valuable in trying to investigate a, a performance degradation in an application and understanding where that may come from. And so what we're seeing is this data layer is collapsing faster than the org charts are or the budget line items are in the enterprise. And so at Splunk here, you know, we believe security resilience is, is fundamentally a data problem. And one of the things that we do often is, is actually help connect the dots for our customers and bring our customers together across the silos they may have internally so that they can start to see a holistic picture of what resilience means for their enterprise and how they can drive faster detection outcomes and more automation coverage. >>You know, we recently had an event called Super Cloud, we're going into the next gen kind of a cloud, how data and security are all kind of part of this NextGen application. It's not just us. And we had a panel that was titled The Innovators Dilemma, kind of talk about you some of the challenges. And one of the panelists said, it's not the innovator's dilemma, it's the integrator's dilemma. And you mentioned that earlier, and I think this a key point right now into integration is so critical, not having the data and putting pieces together now open source is becoming a composability market. And I think having things snap together and work well, it's a platform system conversation, not a tool conversation. So I really wanna get into where the OCS f kind of intersects with this area people are working on. It's not just solution architects or cloud cloud native SREs, especially where DevSecOps is. So this that's right, this intersection is critical. How does Ocsf integrate into that integration of the data making that available to make machine learning and automation smarter and more relevant? >>Right, right. Well look, I mean, I I think that's a fantastic question because, you know, we talk about, we use Bud buzzwords like machine learning and, and AI all the time. And you know, I know they're all over the place here at Reinvent and, and the, there's so much promise and hope out there around these technologies and these innovations. However, machine learning AI is only as effective as the data is clean and normalized. And, and we will not realize the promise of these technologies for outcomes in resilience unless we have better ways to normalize data upstream and better ways to integrate that data to the downstream tools where detection and response is happening. And so Ocsf was really about the industry coming together and saying, this is no longer the job of our customers. We are going to create a unified schema that represents the, an event that we will all bite down on. >>Even some of us are competitors, you know, this is, this is that, that no longer matters because at the point, the point is how do we take this burden off of our customers and how do we make the industry safer together? And so 15 initial members came together along with AWS and Splunk to, to start to create that, that initial schema and standardize it. And if you've ever, you know, if you've ever worked with a bunch of technical grumpy security people, it's kind of hard to drive consensus about around just about anything. But, but I, I'm really happy to see how quickly this, this organization has come together, has open sourced the schema, and, and, and just as you said, like I think this, this unlocks the potential for real innovation that's gonna be required to keep up with the bad guys. But right now is getting stymied and held back by the lack of normalization and the lack of integration. >>I've always said Splunk was a, it eats data for breakfast, lunch, and dinner and turns it into insights. And I think you bring up the silo thing. What's interesting is the cross company sharing, I think this hits point on, so I see this as a valuable opportunity for the industry. What's the traction on that? Because, you know, to succeed it does take a village, it takes a community of security practitioners and, and, and architects and developers to kind of coalesce around this defacto movement has been, has been the uptake been good? How's traction? Can you share your thoughts on how this is translating across companies? >>Yeah, absolutely. I mean, look, I, I think cybersecurity has a, has a long track record of, of, of standards development. There's been some fantastic standards recently. Things like sticks and taxi for threat intelligence. There's been things like the, you know, the Mir attack framework coming outta mi mir and, and, and the adoption, the traction that we've seen with Attack in particular has been amazing to, to watch how that has kind of roared onto the scene in the last couple of years and has become table stakes for how you do security operations and incident response. And, you know, I think with ocs f we're gonna see something similar here, but, you know, we are in literally the first innings of, of this. So right now, you know, we're architecting this into our, into every part of our sort of backend systems here at Polan. I know our our collaborators at AWS and elsewhere are doing it too. >>And so I think it starts with bringing this standard now that the standard exists on a, you know, in schema format and there, there's, you know, confluence and Jira tickets around it, how do we then sort of build this into the code of, of the, the collaborators that have been leading the way on this? And you know, it's not gonna happen overnight, but I think in the coming quarters you'll start to see this schema be the standard across the leaders in this space. Companies like Splunk and AWS and others who are leading the way. And often that's what helps drive adoption of a standard is if you can get the, the big dogs, so to speak, to, to, to embrace it. And, and, you know, there's no bigger one than aws and I think there's no, no more important one than Splunk in the cybersecurity space. And so as we adopt this, we hope others will follow. And, and like I said, we've got over 50 organizations contributing to it today. And so I think we're off to a running >>Start. You know, it's interesting, choking innovation or having things kind of get, get slowed down has really been a problem. We've seen successes recently over the past few years. Like Kubernetes has really unlocked and accelerated the cloud native worlds of runtime with containers to, to kind of have the consensus of the community to say, Hey, if we just do this, it gets better. I think this is really compelling with the o the ocs F because if people can come together around this and get unified as well as all the other official standards, things can go highly accelerated. So I think, I think it looks really good and I think it's great initiative and I really appreciate your insight on that, on, on your relationship with Amazon. Okay. It's not just a partnership, it's a strategic collaboration. Could you share that relationship dynamic, how to start, how's it going, what's strategic about it? Share to the audience kind of the relationship between Splunk and a on this important OCS ocsf initiative. >>Look, I, I mean I think this, this year marks the, the 10th year anniversary that, that Splunk and AWS have been collaborating in a variety of different ways. I, I think our, our companies have a fantastic and, and long standing relationship and we've, we've partnered on a number of really important projects together that bring value obviously to our individual companies, but also to our shared customers. When I think about some of the most important customers at Splunk that I spend a significant amount of time with, I I I know how many of those are, are AWS customers as well, and I know how important AWS is to them. So I think it's, it's a, it's a collaboration that is rooted in, in a respect for each other's technologies and innovation, but also in a recognition that, that our shared customers want to see us work better together over time. And it's not, it's not two companies that have kind of decided in a back room that they should work together. It's actually our customers that are, that are pushing us. And I think we're, we're both very customer centric organizations and I think that has helped us actually be better collaborators and better partners together because we're, we're working back backwards from our customers >>As security becomes a physical and software approach. We've seen the trend where even Steven Schmidt at Amazon Web Services is, is the cso, he is not the CSO anymore. So, and I asked him why, he says, well, security's also physical stuff too. So, so he's that's right. Whole lens is now expanded. You mentioned supply chain, physical, digital, this is an important inflection point. Can you summarize in your mind why open cybersecurity schema for is important? I know the unification, but beyond that, what, why is this so important? Why should people pay attention to this? >>You know, I, if, if you'll let me be just a little abstract in meta for a second. I think what's, what's really meaningful at the highest level about the O C S F initiative, and that goes beyond, I think, the tactical value it will provide to, to organizations and to customers in terms of making them safer over the coming years and, and decades. I think what's more important than that is it's really the, one of the first times that you've seen the industry come together and say, we got a problem. We need to solve. That, you know, doesn't really have anything to do with, with our own economics. Our customers are, are hurt. And yeah, some of us may be competitors, you know, we got different cloud service providers that are participating in this along with aws. We got different cybersecurity solution providers participating in this along with Splunk. >>But, but folks who've come together and say, we can actually solve this problem if, if we're able to kind of put aside our competitive differences in the markets and approach this from the perspective of what's best for information security as a whole. And, and I think that's what I'm most proud of and, and what I hope we can do more of in other places in this industry, because I think that kind of collaboration from real market leaders can actually change markets. It can change the, the, the trend lines in terms of how we are keeping up with the bad guys. And, and I'd like to see a lot more of >>That. And we're seeing a lot more new kind of things emerging in the cloud next kind of this next generation architecture and outcomes are happening. I think it's interesting, you know, we always talk about sustainability, supply chain sustainability about making the earth a better place. But you're hitting on this, this meta point about businesses are under threat of going under. I mean, we want to keep businesses to businesses to be sustainable, not just, you know, the, the environment. So if a business goes outta business business, which they, their threats here are, can be catastrophic for companies. I mean, there is, there is a community responsibility to protect businesses so they can sustain and and stay Yeah. Stay producing. This is a real key point. >>Yeah. Yeah. I mean, look, I think, I think one of the things that, you know, we, we, we complain a lot of in, in cyber security about the lack of, of talent, the talent shortage in cyber security. And every year we kinda, we kind of whack ourselves over the head about how hard it is to bring people into this industry. And it's true. But one of the things that I think we forget, John, is, is how important mission is to so many people in what they do for a living and how they work. And I think one of the things that cybersecurity is strongest in information Security General and has been for decades is this sense of mission and people work in this industry be not because it's, it's, it's always the, the, the most lucrative, but because it, it really drives a sense of safety and security in the enterprises and the fabric of the economy that we use every day to go through our lives. And when I think about the spun customers and AWS customers, I think about the, the different products and tools that power my life and, and we need to secure them. And, and sometimes that means coming to work every day at that company and, and doing your job. And sometimes that means working with others better, faster, and stronger to help drive that level of, of, of maturity and security that this industry >>Needs. It's a human, is a human opportunity, human problem and, and challenge. That's a whole nother segment. The role of the talent and the human machines and with scale. Patrick, thanks so much for sharing the information and the insight on the Open cybersecurity schema frame and what it means and why it's important. Thanks for sharing on the Cube, really appreciate it. >>Thanks for having me, John. >>Okay, this is AWS Reinvent 2022 coverage here on the Cube. I'm John Furry, you're the host. Thanks for watching.

>>Hey, welcome back everyone. Day three of eight of us Reinvent 2022. I'm John Farmer with Dave Volante, co-host the q Dave. 10 years for us, the leader in high tech coverage is our slogan. Now 10 years of reinvent day. We've been to every single one except with the original, which we would've come to if Amazon actually marketed the event, but they didn't. It's more of a customer event. This is day three. Is the machine learning ai keynote sws up there. A lot of announcements. We're gonna break this down. We got, we got Andy Thra here, vice President, prince Constellation Research. Andy, great to see you've been on the cube before one of our analysts bringing the, bringing the, the analysis, commentary to the keynote. This is your wheelhouse. Ai. What do you think about Swami up there? I mean, he's awesome. We love him. Big fan Oh yeah. Of of the Cuban we're fans of him, but he got 13 announcements. >>A lot. A lot, >>A lot. >>So, well some of them are, first of all, thanks for having me here and I'm glad to have both of you on the same show attacking me. I'm just kidding. But some of the announcement really sort of like a game changer announcements and some of them are like, meh, you know, just to plug in the holes what they have and a lot of golf claps. Yeah. Meeting today. And you could have also noticed that by, when he was making the announcements, you know, the, the, the clapping volume difference, you could say, which is better, right? But some of the announcements are, are really, really good. You know, particularly we talked about, one of that was Microsoft took that out of, you know, having the open AI in there, doing the large language models. And then they were going after that, you know, having the transformer available to them. And Amazon was a little bit weak in the area, so they couldn't, they don't have a large language model. So, you know, they, they are taking a different route saying that, you know what, I'll help you train the large language model by yourself, customized models. So I can provide the necessary instance. I can provide the instant volume, memory, the whole thing. Yeah. So you can train the model by yourself without depending on them kind >>Of thing. So Dave and Andy, I wanna get your thoughts cuz first of all, we've been following Amazon's deep bench on the, on the infrastructure pass. They've been doing a lot of machine learning and ai, a lot of data. It just seems that the sentiment is that there's other competitors doing a good job too. Like Google, Dave. And I've heard folks in the hallway, even here, ex Amazonians saying, Hey, they're train their models on Google than they bring up the SageMaker cuz it's better interface. So you got, Google's making a play for being that data cloud. Microsoft's obviously putting in a, a great kind of package to kind of make it turnkey. How do they really stand versus the competition guys? >>Good question. So they, you know, each have their own uniqueness and the we variation that take it to the field, right? So for example, if you were to look at it, Microsoft is known for as industry or later things that they are been going after, you know, industry verticals and whatnot. So that's one of the things I looked here, you know, they, they had this omic announcement, particularly towards that healthcare genomics space. That's a huge space for hpz related AIML applications. And they have put a lot of things in together in here in the SageMaker and in the, in their models saying that, you know, how do you, how do you use this transmit to do things like that? Like for example, drug discovery, for genomics analysis, for cancer treatment, the whole, right? That's a few volumes of data do. So they're going in that healthcare area. Google has taken a different route. I mean they want to make everything simple. All I have to do is I gotta call an api, give what I need and then get it done. But Amazon wants to go at a much deeper level saying that, you know what? I wanna provide everything you need. You can customize the whole thing for what you need. >>So to me, the big picture here is, and and Swami references, Hey, we are a data company. We started, he talked about books and how that informed them as to, you know, what books to place front and center. Here's the, here's the big picture. In my view, companies need to put data at the core of their business and they haven't, they've generally put humans at the core of their business and data. And now machine learning are at the, at the outside and the periphery. Amazon, Google, Microsoft, Facebook have put data at their core. So the question is how do incumbent companies, and you mentioned some Toyota Capital One, Bristol Myers Squibb, I don't know, are those data companies, you know, we'll see, but the challenge is most companies don't have the resources as you well know, Andy, to actually implement what Google and Facebook and others have. >>So how are they gonna do that? Well, they're gonna buy it, right? So are they gonna build it with tools that's kind of like you said the Amazon approach or are they gonna buy it from Microsoft and Google, I pulled some ETR data to say, okay, who are the top companies that are showing up in terms of spending? Who's spending with whom? AWS number one, Microsoft number two, Google number three, data bricks. Number four, just in terms of, you know, presence. And then it falls down DataRobot, Anaconda data icu, Oracle popped up actually cuz they're embedding a lot of AI into their products and, and of course IBM and then a lot of smaller companies. But do companies generally customers have the resources to do what it takes to implement AI into applications and into workflows? >>So a couple of things on that. One is when it comes to, I mean it's, it's no surprise that the, the top three or the hyperscalers, because they all want to bring their business to them to run the specific workloads on the next biggest workload. As you was saying, his keynote are two things. One is the A AIML workloads and the other one is the, the heavy unstructured workloads that he was talking about. 80%, 90% of the data that's coming off is unstructured. So how do you analyze that? Such as the geospatial data. He was talking about the volumes of data you need to analyze the, the neural deep neural net drug you ought to use, only hyperscale can do it, right? So that's no wonder all of them on top for the data, one of the things they announced, which not many people paid attention, there was a zero eight L that that they talked about. >>What that does is a little bit of a game changing moment in a sense that you don't have to, for example, if you were to train the data, data, if the data is distributed everywhere, if you have to bring them all together to integrate it, to do that, it's a lot of work to doing the dl. So by taking Amazon, Aurora, and then Rich combine them as zero or no ETL and then have Apaches Apaches Spark applications run on top of analytical applications, ML workloads. That's huge. So you don't have to move around the data, use the data where it is, >>I, I think you said it, they're basically filling holes, right? Yeah. They created this, you know, suite of tools, let's call it. You might say it's a mess. It's not a mess because it's, they're really powerful but they're not well integrated and now they're starting to take the seams as I say. >>Well yeah, it's a great point. And I would double down and say, look it, I think that boring is good. You know, we had that phase in Kubernetes hype cycle where it got boring and that was kind of like, boring is good. Boring means we're getting better, we're invisible. That's infrastructure that's in the weeds, that's in between the toes details. It's the stuff that, you know, people we have to get done. So, you know, you look at their 40 new data sources with data Wrangler 50, new app flow connectors, Redshift Auto Cog, this is boring. Good important shit Dave. The governance, you gotta get it and the governance is gonna be key. So, so to me, this may not jump off the page. Adam's keynote also felt a little bit of, we gotta get these gaps done in a good way. So I think that's a very positive sign. >>Now going back to the bigger picture, I think the real question is can there be another independent cloud data cloud? And that's the, to me, what I try to get at my story and you're breaking analysis kind of hit a home run on this, is there's interesting opportunity for an independent data cloud. Meaning something that isn't aws, that isn't, Google isn't one of the big three that could sit in. And so let me give you an example. I had a conversation last night with a bunch of ex Amazonian engineering teams that left the conversation was interesting, Dave. They were like talking, well data bricks and Snowflake are basically batch, okay, not transactional. And you look at Aerospike, I can see their booth here. Transactional data bases are hot right now. Streaming data is different. Confluence different than data bricks. Is data bricks good at hosting? >>No, Amazon's better. So you start to see these kinds of questions come up where, you know, data bricks is great, but maybe not good for this, that and the other thing. So you start to see the formation of swim lanes or visibility into where people might sit in the ecosystem, but what came out was transactional. Yep. And batch the relationship there and streaming real time and versus you know, the transactional data. So you're starting to see these new things emerge. Andy, what do you, what's your take on this? You're following this closely. This seems to be the alpha nerd conversation and it all points to who's gonna have the best data cloud, say data, super clouds, I call it. What's your take? >>Yes, data cloud is important as well. But also the computational that goes on top of it too, right? Because when, when the data is like unstructured data, it's that much of a huge data, it's going to be hard to do that with a low model, you know, compute power. But going back to your data point, the training of the AIML models required the batch data, right? That's when you need all the, the historical data to train your models. And then after that, when you do inference of it, that's where you need the streaming real time data that's available to you too. You can make an inference. One of the things, what, what they also announced, which is somewhat interesting, is you saw that they have like 700 different instances geared towards every single workload. And there are some of them very specifically run on the Amazon's new chip. The, the inference in two and theran tr one chips that basically not only has a specific instances but also is run on a high powered chip. And then if you have that data to support that, both the training as well as towards the inference, the efficiency, again, those numbers have to be proven. They claim that it could be anywhere between 40 to 60% faster. >>Well, so a couple things. You're definitely right. I mean Snowflake started out as a data warehouse that was simpler and it's not architected, you know, in and it's first wave to do real time inference, which is not now how, how could they, the other second point is snowflake's two or three years ahead when it comes to governance, data sharing. I mean, Amazon's doing what always does. It's copying, you know, it's customer driven. Cuz they probably walk into an account and they say, Hey look, what's Snowflake's doing for us? This stuff's kicking ass. And they go, oh, that's a good idea, let's do that too. You saw that with separating compute from storage, which is their tiering. You saw it today with extending data, sharing Redshift, data sharing. So how does Snowflake and data bricks approach this? They deal with ecosystem. They bring in ecosystem partners, they bring in open source tooling and that's how they compete. I think there's unquestionably an opportunity for a data cloud. >>Yeah, I think, I think the super cloud conversation and then, you know, sky Cloud with Berkeley Paper and other folks talking about this kind of pre, multi-cloud era. I mean that's what I would call us right now. We are, we're kind of in the pre era of multi-cloud, which by the way is not even yet defined. I think people use that term, Dave, to say, you know, some sort of magical thing that's happening. Yeah. People have multiple clouds. They got, they, they end up by default, not by design as Dell likes to say. Right? And they gotta deal with it. So it's more of they're inheriting multiple cloud environments. It's not necessarily what they want in the situation. So to me that is a big, big issue. >>Yeah, I mean, again, going back to your snowflake and data breaks announcements, they're a data company. So they, that's how they made their mark in the market saying that, you know, I do all those things, therefore you have, I had to have your data because it's a seamless data. And, and Amazon is catching up with that with a lot of that announcements they made, how far it's gonna get traction, you know, to change when I to say, >>Yeah, I mean to me, to me there's no doubt about Dave. I think, I think what Swamee is doing, if Amazon can get corner the market on out of the box ML and AI capabilities so that people can make it easier, that's gonna be the end of the day tell sign can they fill in the gaps. Again, boring is good competition. I don't know mean, mean I'm not following the competition. Andy, this is a real question mark for me. I don't know where they stand. Are they more comprehensive? Are they more deeper? Are they have deeper services? I mean, obviously shows to all the, the different, you know, capabilities. Where, where, where does Amazon stand? What's the process? >>So what, particularly when it comes to the models. So they're going at, at a different angle that, you know, I will help you create the models we talked about the zero and the whole data. We'll get the data sources in, we'll create the model. We'll move the, the whole model. We are talking about the ML ops teams here, right? And they have the whole functionality that, that they built ind over the year. So essentially they want to become the platform that I, when you come in, I'm the only platform you would use from the model training to deployment to inference, to model versioning to management, the old s and that's angle they're trying to take. So it's, it's a one source platform. >>What about this idea of technical debt? Adrian Carro was on yesterday. John, I know you talked to him as well. He said, look, Amazon's Legos, you wanna buy a toy for Christmas, you can go out and buy a toy or do you wanna build a, to, if you buy a toy in a couple years, you could break and what are you gonna do? You're gonna throw it out. But if you, if you, if part of your Lego needs to be extended, you extend it. So, you know, George Gilbert was saying, well, there's a lot of technical debt. Adrian was countering that. Does Amazon have technical debt or is that Lego blocks analogy the right one? >>Well, I talked to him about the debt and one of the things we talked about was what do you optimize for E two APIs or Kubernetes APIs? It depends on what team you're on. If you're on the runtime gene, you're gonna optimize for Kubernetes, but E two is the resources you want to use. So I think the idea of the 15 years of technical debt, I, I don't believe that. I think the APIs are still hardened. The issue that he brings up that I think is relevant is it's an end situation, not an or. You can have the bag of Legos, which is the primitives and build a durable application platform, monitor it, customize it, work with it, build it. It's harder, but the outcome is durability and sustainability. Building a toy, having a toy with those Legos glued together for you, you can get the play with, but it'll break over time. Then you gotta replace it. So there's gonna be a toy business and there's gonna be a Legos business. Make your own. >>So who, who are the toys in ai? >>Well, out of >>The box and who's outta Legos? >>The, so you asking about what what toys Amazon building >>Or, yeah, I mean Amazon clearly is Lego blocks. >>If people gonna have out the box, >>What about Google? What about Microsoft? Are they basically more, more building toys, more solutions? >>So Google is more of, you know, building solutions angle like, you know, I give you an API kind of thing. But, but if it comes to vertical industry solutions, Microsoft is, is is ahead, right? Because they have, they have had years of indu industry experience. I mean there are other smaller cloud are trying to do that too. IBM being an example, but you know, the, now they are starting to go after the specific industry use cases. They think that through, for example, you know the medical one we talked about, right? So they want to build the, the health lake, security health lake that they're trying to build, which will HIPPA and it'll provide all the, the European regulations, the whole line yard, and it'll help you, you know, personalize things as you need as well. For example, you know, if you go for a certain treatment, it could analyze you based on your genome profile saying that, you know, the treatment for this particular person has to be individualized this way, but doing that requires a anomalous power, right? So if you do applications like that, you could bring in a lot of the, whether healthcare, finance or what have you, and then easy for them to use. >>What's the biggest mistake customers make when it comes to machine intelligence, ai, machine learning, >>So many things, right? I could start out with even the, the model. Basically when you build a model, you, you should be able to figure out how long that model is effective. Because as good as creating a model and, and going to the business and doing things the right way, there are people that they leave the model much longer than it's needed. It's hurting your business more than it is, you know, it could be things like that. Or you are, you are not building a responsibly or later things. You are, you are having a bias and you model and are so many issues. I, I don't know if I can pinpoint one, but there are many, many issues. Responsible ai, ethical ai. All >>Right, well, we'll leave it there. You're watching the cube, the leader in high tech coverage here at J three at reinvent. I'm Jeff, Dave Ante. Andy joining us here for the critical analysis and breaking down the commentary. We'll be right back with more coverage after this short break.

>>We are back in the final stretch at Supercomputing 22 here in Dallas. I'm your host Paul Gillum with my co-host Dave Nicholson, and we've been talking to so many smart people this week. It just, it makes, boggles my mind are next guest. J Poso is the HPC and AI technology strategist at Dell. Jay also has a PhD in astronomy from the University of Texas. And I'm guessing you were up watching the Artemis launch the other night? >>I, I wasn't. I really should have been, but, but I wasn't, I was in full super computing conference mode. So that means discussions at, you know, various venues with people into the wee hours. >>How did you make the transition from a PhD in astronomy to an HPC expert? >>It was actually really straightforward. I did theoretical astrophysics and I was modeling what white dwarfs look like when they create matter and then explode as type one A super Novi, which is a class of stars that blow up. And it's a very important class because they blow up almost exactly the same way. So if you know how bright they are physically, not just how bright they appear in the sky, but if you can determine from first principles how bright they're, then you have a standard ruler for the universe when they go off in a galaxy, you know how far the galaxy is about how faint it is. So to model these though, you had to understand equations of physics, including electron degeneracy pressure, as well as normal fluid dynamics kinds of of things. And so you were solving for an explosive burning front, ripping through something. And that required a supercomputer to have anywhere close to the fat fidelity to get a reasonable answer and, and hopefully some understanding. >>So I've always said electrons are degenerate. I've always said it and I, and I mentioned to Paul earlier, I said, finally we're gonna get a guest to consort through this whole dark energy dark matter thing for us. We'll do that after, after, after the segment. >>That's a whole different, >>So, well I guess super computing being a natural tool that you would use. What is, what do you do in your role as a strategist? >>So I'm in the product management team. I spend a lot of time talking to customers about what they want to do next. HPC customers are always trying to be maximally productive of what they've got, but always wanting to know what's coming next. Because if you think about it, we can't simulate the entire human body cell for cell on any supercomputer day. We can simulate parts of it, cell for cell or the whole body with macroscopic physics, but not at the, you know, atomic level, the entire organism. So we're always trying to build more powerful computers to solve larger problems with more fidelity and less approximations in it. And so I help people try to understand which technologies for their next system might give them the best advance in capabilities for their simulation work, their data analytics work, their AI work, et cetera. Another part of it is talking to our great technology partner ecosystem and learning about which technologies they have. Cause it feeds the first thing, right? So understanding what's coming, and Dell has a, we're very proud of our large partner ecosystem. We embrace many different partners in that with different capabilities. So understanding those helps understand what your future systems might be. That those are two of the major roles in it. Strategic customers and strategic technologies. >>So you've had four days to wander the, this massive floor here and lots of startups, lots of established companies doing interesting things. What have you seen this week that really excites you? >>So I'm gonna tell you a dirty little secret here. If you are working for someone who makes super computers, you don't get as much time to wander the floor as you would think because you get lots of meetings with people who really want to understand in an NDA way, not just in the public way that's on the floor, but what's, what are you not telling us on the floor? What's coming next? And so I've been in a large number of customer meetings as well as being on the floor. And while I can't obviously share the everything, that's a non-disclosure topic in those, some things that we're hearing a lot about, people are really concerned with power because they see the TDP on the roadmaps for all the silicon providers going way up. And so people with power comes heat as waste. And so that means cooling. >>So power and cooling has been a big topic here. Obviously accelerators are, are increasing in importance in hpc not just for AI calculations, but now also for simulation calculations. And we are very proud of the three new accelerator platforms we launched here at the show that are coming out in a quarter or so. Those are two of the big topics we've seen. You know, there's, as you walk the floor here, you see lots of interesting storage vendors. HPC community's been do doing storage the same way for roughly 20 years. But now we see a lot of interesting players in that space. We have some great things in storage now and some great things that, you know, are coming in a year or two as well. So it's, it's interesting to see that diversity of that space. And then there's always the fun, exciting topics like quantum computing. We unveiled our first hybrid classical quantum computing system here with I on Q and I can't say what the future holds in this, in this format, but I can say we believe strongly in the future of quantum computing and that this, that future will be integrated with the kind of classical computing infrastructure that we make and that will help make quantum computing more powerful downstream. >>Well, let's go down that rabbit hole because, oh boy, boy, quantum computing has been talked about for a long time. There was a lot of excitement about it four or five years ago, some of the major vendors were announcing quantum computers in the cloud. Excitement has kind of died down. We don't see a lot of activity around, no, not a lot of talk around commercial quantum computers, yet you're deep into this. How close are we to have having a true quantum computer or is it a, is it a hybrid? More >>Likely? So there are probably more than 20 and I think close to 40 companies trying different approaches to make quantum computers. So, you know, Microsoft's pursuing a topol topological approach, do a photonics based approach. I, on Q and i on trap approach. These are all different ways of trying to leverage the quantum properties of nature. We know the properties exist, we use 'em in other technologies. We know the physics, but trying the engineering is very difficult. It's very difficult. I mean, just like it was difficult at one point to split the atom. It's very difficult to build technologies that leverage quantum properties of nature in a consistent and reliable and durable way, right? So I, you know, I wouldn't wanna make a prediction, but I will tell you I'm an optimist. I believe that when a tremendous capability with, with tremendous monetary gain potential lines up with another incentive, national security engineering seems to evolve faster when those things line up, when there's plenty of investment and plenty of incentive things happen. >>So I think a lot of my, my friends in the office of the CTO at Dell Technologies, when they're really leading this effort for us, you know, they would say a few to several years probably I'm an optimist, so I believe that, you know, I, I believe that we will sell some of the solution we announced here in the next year for people that are trying to get their feet wet with quantum. And I believe we'll be selling multiple quantum hybrid classical Dell quantum computing systems multiple a year in a year or two. And then of course you hope it goes to tens and hundreds of, you know, by the end of the decade >>When people talk about, I'm talking about people writ large, super leaders in supercomputing, I would say Dell's name doesn't come up in conversations I have. What would you like them to know that they don't know? >>You know, I, I hope that's not true, but I, I, I guess I understand it. We are so good at making the products from which people make clusters that we're number one in servers, we're number one in enterprise storage. We're number one in so many areas of enterprise technology that I, I think in some ways being number one in those things detracts a little bit from a subset of the market that is a solution subset as opposed to a product subset. But, you know, depending on which analyst you talk to and how they count, we're number one or number two in the world in supercomputing revenue. We don't always do the biggest splashy systems. We do the, the frontier system at t, the HPC five system at ENI in Europe. That's the largest academic supercomputer in the world and the largest industrial super >>That's based the world on Dell. Dell >>On Dell hardware. Yep. But we, I think our vision is really that we want to help more people use HPC to solve more problems than any vendor in the world. And those problems are various scales. So we are really concerned about the more we're democratizing HPC to make it easier for more people to get in at any scale that their budget and workloads require, we're optimizing it to make sure that it's not just some parts they're getting, that they are validated to work together with maximum scalability and performance. And we have a great HPC and AI innovation lab that does this engineering work. Cuz you know, one of the myths is, oh, I can just go buy a bunch of servers from company X and a network from company Y and a storage system from company Z and then it'll all work as an equivalent cluster. Right? Not true. It'll probably work, but it won't be the highest performance, highest scalability, highest reliability. So we spend a lot of time optimizing and then we are doing things to try to advance the state of HPC as well. What our future systems look like in the second half of this decade might be very different than what they look like right. Now. >>You mentioned a great example of a limitation that we're running up against right now. You mentioned an entire human body as a, as a, as an organism >>Or any large system that you try to model at the atomic level, but it's a huge macro system, >>Right? So will we be able to reach milestones where we can get our arms entirely around something like an entire human organism with simply quantitative advances as opposed to qualitative advances? Right now, as an example, let's just, let's go down to the basics from a Dell perspective. You're in a season where microprocessor vendors are coming out with next gen stuff and those next NextGen microprocessors, GPUs and CPUs are gonna be plugged into NextGen motherboards, PCI e gen five, gen six coming faster memory, bigger memory, faster networking, whether it's NS or InfiniBand storage controllers, all bigger, better, faster, stronger. And I suspect that systems like Frontera, I don't know, but I suspect that a lot of the systems that are out there are not on necessarily what we would think of as current generation technology, but maybe they're n minus one as a practical matter. So, >>But yeah, I mean they have a lifetime, so Exactly. >>The >>Lifetime is longer than the evolution. >>That's the normal technologies. Yeah. So, so what some people miss is this is, this is the reality that when, when we move forward with the latest things that are being talked about here, it's often a two generation move for an individual, for an individual organization. Yep. >>So now some organizations will have multiple systems and they, the system's leapfrog and technology generations, even if one is their real large system, their next one might be newer technology, but smaller, the next one might be a larger one with newer technology and such. Yeah. So the, the biggest super computing sites are, are often running more than one HPC system that have been specifically designed with the latest technologies and, and designed and configured for maybe a different subset of their >>Workloads. Yeah. So, so the, the, to go back to kinda the, the core question, in your opinion, do we need that qualitative leap to something like quantum computing in order to get to the point, or is it simply a question of scale and power at the, at the, at the individual node level to get us to the point where we can in fact gain insight from a digital model of an entire human body, not just looking at a, not, not just looking at an at, at an organ. And to your point, it's not just about human body, any system that we would characterize as being chaotic today, so a weather system, whatever. Do you, are there any milestones that you're thinking of where you're like, wow, you know, I have, I, I understand everything that's going on, and I think we're, we're a year away. We're a, we're, we're a, we're a compute generation away from being able to gain insight out of systems that right now we can't simply because of scale. It's a very, very long question that I just asked you, but I think I, but hopefully, hopefully you're tracking it. What, what are your, what are your thoughts? What are these, what are these inflection points that we, that you've, in your mind? >>So I, I'll I'll start simple. Remember when we used to buy laptops and we worried about what gigahertz the clock speed was Exactly. Everybody knew the gigahertz of it, right? There's some tasks at which we're so good at making the hardware that now the primary issues are how great is the screen? How light is it, what's the battery life like, et cetera. Because for the set of applications on there, we we have enough compute power. We don't, you don't really need your laptop. Most people don't need their laptop to have twice as powerful a processor that actually rather up twice the battery life on it or whatnot, right? We make great laptops. We, we design for all of those, configure those parameters now. And what, you know, we, we see some customers want more of x, somewhat more of y but the, the general point is that the amazing progress in, in microprocessors, it's sufficient for most of the workloads at that level. Now let's go to HPC level or scientific and technical level. And when it needs hpc, if you're trying to model the orbit of the moon around the earth, you don't really need a super computer for that. You can get a highly accurate model on a, on a workstation, on a server, no problem. It won't even really make it break a sweat. >>I had to do it with a slide rule >>That, >>That >>Might make you break a sweat. Yeah. But to do it with a, you know, a single body orbiting with another body, I say orbiting around, but we both know it's really, they're, they're both ordering the center of mass. It's just that if one is much larger, it seems like one's going entirely around the other. So that's, that's not a super computing problem. What about the stars in a galaxy trying to understand how galaxies form spiral arms and how they spur star formation. Right now you're talking a hundred billion stars plus a massive amount of inter stellar medium in there. So can you solve that on that server? Absolutely not. Not even close. Can you solve it on the largest super computer in the world today? Yes and no. You can solve it with approximations on the largest super computer in the world today. But there's a lot of approximations that go into even that. >>The good news is the simulations produce things that we see through our great telescopes. So we know the approximations are sufficient to get good fidelity, but until you really are doing direct numerical simulation of every particle, right? Right. Which is impossible to do. You need a computer as big as the universe to do that. But the approximations and the science in the science as well as the known parts of the science are good enough to give fidelity. So, and answer your question, there's tremendous number of problem scales. There are problems in every field of science and study that exceed the der direct numerical simulation capabilities of systems today. And so we always want more computing power. It's not macho flops, it's real, we need it, we need exo flops and we will need zeta flops beyond that and yada flops beyond that. But an increasing number of problems will be solved as we keep working to solve problems that are farther out there. So in terms of qualitative steps, I do think technologies like quantum computing, to be clear as part of a hybrid classical quantum system, because they're really just accelerators for certain kinds of algorithms, not for general purpose algorithms. I do think advances like that are gonna be necessary to solve some of the very hardest problem. It's easy to actually formulate an optimization problem that is absolutely intractable by the larger systems in the world today, but quantum systems happen to be in theory when they're big and stable enough, great at that kind of problem. >>I, that should be understood. Quantum is not a cure all for absolutely. For the, for the shortage of computing power. It's very good for certain, certain >>Problems. And as you said at this super computing, we see some quantum, but it's a little bit quieter than I probably expected. I think we're in a period now of everybody saying, okay, there's been a lot of buzz. We know it's gonna be real, but let's calm down a little bit and figure out what the right solutions are. And I'm very proud that we offered one of those >>At the show. We, we have barely scratched the surface of what we could talk about as we get into intergalactic space, but unfortunately we only have so many minutes and, and we're out of them. Oh, >>I'm >>J Poso, HPC and AI technology strategist at Dell. Thanks for a fascinating conversation. >>Thanks for having me. Happy to do it anytime. >>We'll be back with our last interview of Supercomputing 22 in Dallas. This is Paul Gillen with Dave Nicholson. Stay with us.

>>When Oracle acquired my SQL via the Sun acquisition, nobody really thought the company would put much effort into the platform preferring to focus all the wood behind its leading Oracle database, Arrow pun intended. But two years ago, Oracle surprised many folks by announcing my SQL Heatwave a new database as a service with a massively parallel hybrid Columbia in Mary Mary architecture that brings together transactional and analytic data in a single platform. Welcome to our latest database, power panel on the cube. My name is Dave Ante, and today we're gonna discuss Oracle's MySQL Heat Wave with a who's who of cloud database industry analysts. Holgar Mueller is with Constellation Research. Mark Stammer is the Dragon Slayer and Wikibon contributor. And Ron Westfall is with Fu Chim Research. Gentlemen, welcome back to the Cube. Always a pleasure to have you on. Thanks for having us. Great to be here. >>So we've had a number of of deep dive interviews on the Cube with Nip and Aggarwal. You guys know him? He's a senior vice president of MySQL, Heatwave Development at Oracle. I think you just saw him at Oracle Cloud World and he's come on to describe this is gonna, I'll call it a shock and awe feature additions to to heatwave. You know, the company's clearly putting r and d into the platform and I think at at cloud world we saw like the fifth major release since 2020 when they first announced MySQL heat wave. So just listing a few, they, they got, they taken, brought in analytics machine learning, they got autopilot for machine learning, which is automation onto the basic o l TP functionality of the database. And it's been interesting to watch Oracle's converge database strategy. We've contrasted that amongst ourselves. Love to get your thoughts on Amazon's get the right tool for the right job approach. >>Are they gonna have to change that? You know, Amazon's got the specialized databases, it's just, you know, the both companies are doing well. It just shows there are a lot of ways to, to skin a cat cuz you see some traction in the market in, in both approaches. So today we're gonna focus on the latest heat wave announcements and we're gonna talk about multi-cloud with a native MySQL heat wave implementation, which is available on aws MySQL heat wave for Azure via the Oracle Microsoft interconnect. This kind of cool hybrid action that they got going. Sometimes we call it super cloud. And then we're gonna dive into my SQL Heatwave Lake house, which allows users to process and query data across MyQ databases as heatwave databases, as well as object stores. So, and then we've got, heatwave has been announced on AWS and, and, and Azure, they're available now and Lake House I believe is in beta and I think it's coming out the second half of next year. So again, all of our guests are fresh off of Oracle Cloud world in Las Vegas. So they got the latest scoop. Guys, I'm done talking. Let's get into it. Mark, maybe you could start us off, what's your opinion of my SQL Heatwaves competitive position? When you think about what AWS is doing, you know, Google is, you know, we heard Google Cloud next recently, we heard about all their data innovations. You got, obviously Azure's got a big portfolio, snowflakes doing well in the market. What's your take? >>Well, first let's look at it from the point of view that AWS is the market leader in cloud and cloud services. They own somewhere between 30 to 50% depending on who you read of the market. And then you have Azure as number two and after that it falls off. There's gcp, Google Cloud platform, which is further way down the list and then Oracle and IBM and Alibaba. So when you look at AWS and you and Azure saying, hey, these are the market leaders in the cloud, then you start looking at it and saying, if I am going to provide a service that competes with the service they have, if I can make it available in their cloud, it means that I can be more competitive. And if I'm compelling and compelling means at least twice the performance or functionality or both at half the price, I should be able to gain market share. >>And that's what Oracle's done. They've taken a superior product in my SQL heat wave, which is faster, lower cost does more for a lot less at the end of the day and they make it available to the users of those clouds. You avoid this little thing called egress fees, you avoid the issue of having to migrate from one cloud to another and suddenly you have a very compelling offer. So I look at what Oracle's doing with MyQ and it feels like, I'm gonna use a word term, a flanking maneuver to their competition. They're offering a better service on their platforms. >>All right, so thank you for that. Holger, we've seen this sort of cadence, I sort of referenced it up front a little bit and they sat on MySQL for a decade, then all of a sudden we see this rush of announcements. Why did it take so long? And and more importantly is Oracle, are they developing the right features that cloud database customers are looking for in your view? >>Yeah, great question, but first of all, in your interview you said it's the edit analytics, right? Analytics is kind of like a marketing buzzword. Reports can be analytics, right? The interesting thing, which they did, the first thing they, they, they crossed the chasm between OTP and all up, right? In the same database, right? So major engineering feed very much what customers want and it's all about creating Bellevue for customers, which, which I think is the part why they go into the multi-cloud and why they add these capabilities. And they certainly with the AI capabilities, it's kind of like getting it into an autonomous field, self-driving field now with the lake cost capabilities and meeting customers where they are, like Mark has talked about the e risk costs in the cloud. So that that's a significant advantage, creating value for customers and that's what at the end of the day matters. >>And I believe strongly that long term it's gonna be ones who create better value for customers who will get more of their money From that perspective, why then take them so long? I think it's a great question. I think largely he mentioned the gentleman Nial, it's largely to who leads a product. I used to build products too, so maybe I'm a little fooling myself here, but that made the difference in my view, right? So since he's been charged, he's been building things faster than the rest of the competition, than my SQL space, which in hindsight we thought was a hot and smoking innovation phase. It kind of like was a little self complacent when it comes to the traditional borders of where, where people think, where things are separated between OTP and ola or as an example of adjacent support, right? Structured documents, whereas unstructured documents or databases and all of that has been collapsed and brought together for building a more powerful database for customers. >>So I mean it's certainly, you know, when, when Oracle talks about the competitors, you know, the competitors are in the, I always say they're, if the Oracle talks about you and knows you're doing well, so they talk a lot about aws, talk a little bit about Snowflake, you know, sort of Google, they have partnerships with Azure, but, but in, so I'm presuming that the response in MySQL heatwave was really in, in response to what they were seeing from those big competitors. But then you had Maria DB coming out, you know, the day that that Oracle acquired Sun and, and launching and going after the MySQL base. So it's, I'm, I'm interested and we'll talk about this later and what you guys think AWS and Google and Azure and Snowflake and how they're gonna respond. But, but before I do that, Ron, I want to ask you, you, you, you can get, you know, pretty technical and you've probably seen the benchmarks. >>I know you have Oracle makes a big deal out of it, publishes its benchmarks, makes some transparent on on GI GitHub. Larry Ellison talked about this in his keynote at Cloud World. What are the benchmarks show in general? I mean, when you, when you're new to the market, you gotta have a story like Mark was saying, you gotta be two x you know, the performance at half the cost or you better be or you're not gonna get any market share. So, and, and you know, oftentimes companies don't publish market benchmarks when they're leading. They do it when they, they need to gain share. So what do you make of the benchmarks? Have their, any results that were surprising to you? Have, you know, they been challenged by the competitors. Is it just a bunch of kind of desperate bench marketing to make some noise in the market or you know, are they real? What's your view? >>Well, from my perspective, I think they have the validity. And to your point, I believe that when it comes to competitor responses, that has not really happened. Nobody has like pulled down the information that's on GitHub and said, Oh, here are our price performance results. And they counter oracles. In fact, I think part of the reason why that hasn't happened is that there's the risk if Oracle's coming out and saying, Hey, we can deliver 17 times better query performance using our capabilities versus say, Snowflake when it comes to, you know, the Lakehouse platform and Snowflake turns around and says it's actually only 15 times better during performance, that's not exactly an effective maneuver. And so I think this is really to oracle's credit and I think it's refreshing because these differentiators are significant. We're not talking, you know, like 1.2% differences. We're talking 17 fold differences, we're talking six fold differences depending on, you know, where the spotlight is being shined and so forth. >>And so I think this is actually something that is actually too good to believe initially at first blush. If I'm a cloud database decision maker, I really have to prioritize this. I really would know, pay a lot more attention to this. And that's why I posed the question to Oracle and others like, okay, if these differentiators are so significant, why isn't the needle moving a bit more? And it's for, you know, some of the usual reasons. One is really deep discounting coming from, you know, the other players that's really kind of, you know, marketing 1 0 1, this is something you need to do when there's a real competitive threat to keep, you know, a customer in your own customer base. Plus there is the usual fear and uncertainty about moving from one platform to another. But I think, you know, the traction, the momentum is, is shifting an Oracle's favor. I think we saw that in the Q1 efforts, for example, where Oracle cloud grew 44% and that it generated, you know, 4.8 billion and revenue if I recall correctly. And so, so all these are demonstrating that's Oracle is making, I think many of the right moves, publishing these figures for anybody to look at from their own perspective is something that is, I think, good for the market and I think it's just gonna continue to pay dividends for Oracle down the horizon as you know, competition intens plots. So if I were in, >>Dave, can I, Dave, can I interject something and, and what Ron just said there? Yeah, please go ahead. A couple things here, one discounting, which is a common practice when you have a real threat, as Ron pointed out, isn't going to help much in this situation simply because you can't discount to the point where you improve your performance and the performance is a huge differentiator. You may be able to get your price down, but the problem that most of them have is they don't have an integrated product service. They don't have an integrated O L T P O L A P M L N data lake. Even if you cut out two of them, they don't have any of them integrated. They have multiple services that are required separate integration and that can't be overcome with discounting. And the, they, you have to pay for each one of these. And oh, by the way, as you grow, the discounts go away. So that's a, it's a minor important detail. >>So, so that's a TCO question mark, right? And I know you look at this a lot, if I had that kind of price performance advantage, I would be pounding tco, especially if I need two separate databases to do the job. That one can do, that's gonna be, the TCO numbers are gonna be off the chart or maybe down the chart, which you want. Have you looked at this and how does it compare with, you know, the big cloud guys, for example, >>I've looked at it in depth, in fact, I'm working on another TCO on this arena, but you can find it on Wiki bod in which I compared TCO for MySEQ Heat wave versus Aurora plus Redshift plus ML plus Blue. I've compared it against gcps services, Azure services, Snowflake with other services. And there's just no comparison. The, the TCO differences are huge. More importantly, thefor, the, the TCO per performance is huge. We're talking in some cases multiple orders of magnitude, but at least an order of magnitude difference. So discounting isn't gonna help you much at the end of the day, it's only going to lower your cost a little, but it doesn't improve the automation, it doesn't improve the performance, it doesn't improve the time to insight, it doesn't improve all those things that you want out of a database or multiple databases because you >>Can't discount yourself to a higher value proposition. >>So what about, I wonder ho if you could chime in on the developer angle. You, you followed that, that market. How do these innovations from heatwave, I think you used the term developer velocity. I've heard you used that before. Yeah, I mean, look, Oracle owns Java, okay, so it, it's, you know, most popular, you know, programming language in the world, blah, blah blah. But it does it have the, the minds and hearts of, of developers and does, where does heatwave fit into that equation? >>I think heatwave is gaining quickly mindshare on the developer side, right? It's not the traditional no sequel database which grew up, there's a traditional mistrust of oracles to developers to what was happening to open source when gets acquired. Like in the case of Oracle versus Java and where my sql, right? And, but we know it's not a good competitive strategy to, to bank on Oracle screwing up because it hasn't worked not on Java known my sequel, right? And for developers, it's, once you get to know a technology product and you can do more, it becomes kind of like a Swiss army knife and you can build more use case, you can build more powerful applications. That's super, super important because you don't have to get certified in multiple databases. You, you are fast at getting things done, you achieve fire, develop velocity, and the managers are happy because they don't have to license more things, send you to more trainings, have more risk of something not being delivered, right? >>So it's really the, we see the suite where this best of breed play happening here, which in general was happening before already with Oracle's flagship database. Whereas those Amazon as an example, right? And now the interesting thing is every step away Oracle was always a one database company that can be only one and they're now generally talking about heat web and that two database company with different market spaces, but same value proposition of integrating more things very, very quickly to have a universal database that I call, they call the converge database for all the needs of an enterprise to run certain application use cases. And that's what's attractive to developers. >>It's, it's ironic isn't it? I mean I, you know, the rumor was the TK Thomas Curian left Oracle cuz he wanted to put Oracle database on other clouds and other places. And maybe that was the rift. Maybe there was, I'm sure there was other things, but, but Oracle clearly is now trying to expand its Tam Ron with, with heatwave into aws, into Azure. How do you think Oracle's gonna do, you were at a cloud world, what was the sentiment from customers and the independent analyst? Is this just Oracle trying to screw with the competition, create a little diversion? Or is this, you know, serious business for Oracle? What do you think? >>No, I think it has lakes. I think it's definitely, again, attriting to Oracle's overall ability to differentiate not only my SQL heat wave, but its overall portfolio. And I think the fact that they do have the alliance with the Azure in place, that this is definitely demonstrating their commitment to meeting the multi-cloud needs of its customers as well as what we pointed to in terms of the fact that they're now offering, you know, MySQL capabilities within AWS natively and that it can now perform AWS's own offering. And I think this is all demonstrating that Oracle is, you know, not letting up, they're not resting on its laurels. That's clearly we are living in a multi-cloud world, so why not just make it more easy for customers to be able to use cloud databases according to their own specific, specific needs. And I think, you know, to holder's point, I think that definitely lines with being able to bring on more application developers to leverage these capabilities. >>I think one important announcement that's related to all this was the JSON relational duality capabilities where now it's a lot easier for application developers to use a language that they're very familiar with a JS O and not have to worry about going into relational databases to store their J S O N application coding. So this is, I think an example of the innovation that's enhancing the overall Oracle portfolio and certainly all the work with machine learning is definitely paying dividends as well. And as a result, I see Oracle continue to make these inroads that we pointed to. But I agree with Mark, you know, the short term discounting is just a stall tag. This is not denying the fact that Oracle is being able to not only deliver price performance differentiators that are dramatic, but also meeting a wide range of needs for customers out there that aren't just limited device performance consideration. >>Being able to support multi-cloud according to customer needs. Being able to reach out to the application developer community and address a very specific challenge that has plagued them for many years now. So bring it all together. Yeah, I see this as just enabling Oracles who ring true with customers. That the customers that were there were basically all of them, even though not all of them are going to be saying the same things, they're all basically saying positive feedback. And likewise, I think the analyst community is seeing this. It's always refreshing to be able to talk to customers directly and at Oracle cloud there was a litany of them and so this is just a difference maker as well as being able to talk to strategic partners. The nvidia, I think partnerships also testament to Oracle's ongoing ability to, you know, make the ecosystem more user friendly for the customers out there. >>Yeah, it's interesting when you get these all in one tools, you know, the Swiss Army knife, you expect that it's not able to be best of breed. That's the kind of surprising thing that I'm hearing about, about heatwave. I want to, I want to talk about Lake House because when I think of Lake House, I think data bricks, and to my knowledge data bricks hasn't been in the sites of Oracle yet. Maybe they're next, but, but Oracle claims that MySQL, heatwave, Lakehouse is a breakthrough in terms of capacity and performance. Mark, what are your thoughts on that? Can you double click on, on Lakehouse Oracle's claims for things like query performance and data loading? What does it mean for the market? Is Oracle really leading in, in the lake house competitive landscape? What are your thoughts? >>Well, but name in the game is what are the problems you're solving for the customer? More importantly, are those problems urgent or important? If they're urgent, customers wanna solve 'em. Now if they're important, they might get around to them. So you look at what they're doing with Lake House or previous to that machine learning or previous to that automation or previous to that O L A with O ltp and they're merging all this capability together. If you look at Snowflake or data bricks, they're tacking one problem. You look at MyQ heat wave, they're tacking multiple problems. So when you say, yeah, their queries are much better against the lake house in combination with other analytics in combination with O ltp and the fact that there are no ETLs. So you're getting all this done in real time. So it's, it's doing the query cross, cross everything in real time. >>You're solving multiple user and developer problems, you're increasing their ability to get insight faster, you're having shorter response times. So yeah, they really are solving urgent problems for customers. And by putting it where the customer lives, this is the brilliance of actually being multicloud. And I know I'm backing up here a second, but by making it work in AWS and Azure where people already live, where they already have applications, what they're saying is, we're bringing it to you. You don't have to come to us to get these, these benefits, this value overall, I think it's a brilliant strategy. I give Nip and Argo wallet a huge, huge kudos for what he's doing there. So yes, what they're doing with the lake house is going to put notice on data bricks and Snowflake and everyone else for that matter. Well >>Those are guys that whole ago you, you and I have talked about this. Those are, those are the guys that are doing sort of the best of breed. You know, they're really focused and they, you know, tend to do well at least out of the gate. Now you got Oracle's converged philosophy, obviously with Oracle database. We've seen that now it's kicking in gear with, with heatwave, you know, this whole thing of sweets versus best of breed. I mean the long term, you know, customers tend to migrate towards suite, but the new shiny toy tends to get the growth. How do you think this is gonna play out in cloud database? >>Well, it's the forever never ending story, right? And in software right suite, whereas best of breed and so far in the long run suites have always won, right? So, and sometimes they struggle again because the inherent problem of sweets is you build something larger, it has more complexity and that means your cycles to get everything working together to integrate the test that roll it out, certify whatever it is, takes you longer, right? And that's not the case. It's a fascinating part of what the effort around my SQL heat wave is that the team is out executing the previous best of breed data, bringing us something together. Now if they can maintain that pace, that's something to to, to be seen. But it, the strategy, like what Mark was saying, bring the software to the data is of course interesting and unique and totally an Oracle issue in the past, right? >>Yeah. But it had to be in your database on oci. And but at, that's an interesting part. The interesting thing on the Lake health side is, right, there's three key benefits of a lakehouse. The first one is better reporting analytics, bring more rich information together, like make the, the, the case for silicon angle, right? We want to see engagements for this video, we want to know what's happening. That's a mixed transactional video media use case, right? Typical Lakehouse use case. The next one is to build more rich applications, transactional applications which have video and these elements in there, which are the engaging one. And the third one, and that's where I'm a little critical and concerned, is it's really the base platform for artificial intelligence, right? To run deep learning to run things automatically because they have all the data in one place can create in one way. >>And that's where Oracle, I know that Ron talked about Invidia for a moment, but that's where Oracle doesn't have the strongest best story. Nonetheless, the two other main use cases of the lake house are very strong, very well only concern is four 50 terabyte sounds long. It's an arbitrary limitation. Yeah, sounds as big. So for the start, and it's the first word, they can make that bigger. You don't want your lake house to be limited and the terabyte sizes or any even petabyte size because you want to have the certainty. I can put everything in there that I think it might be relevant without knowing what questions to ask and query those questions. >>Yeah. And you know, in the early days of no schema on right, it just became a mess. But now technology has evolved to allow us to actually get more value out of that data. Data lake. Data swamp is, you know, not much more, more, more, more logical. But, and I want to get in, in a moment, I want to come back to how you think the competitors are gonna respond. Are they gonna have to sort of do a more of a converged approach? AWS in particular? But before I do, Ron, I want to ask you a question about autopilot because I heard Larry Ellison's keynote and he was talking about how, you know, most security issues are human errors with autonomy and autonomous database and things like autopilot. We take care of that. It's like autonomous vehicles, they're gonna be safer. And I went, well maybe, maybe someday. So Oracle really tries to emphasize this, that every time you see an announcement from Oracle, they talk about new, you know, autonomous capabilities. It, how legit is it? Do people care? What about, you know, what's new for heatwave Lakehouse? How much of a differentiator, Ron, do you really think autopilot is in this cloud database space? >>Yeah, I think it will definitely enhance the overall proposition. I don't think people are gonna buy, you know, lake house exclusively cause of autopilot capabilities, but when they look at the overall picture, I think it will be an added capability bonus to Oracle's benefit. And yeah, I think it's kind of one of these age old questions, how much do you automate and what is the bounce to strike? And I think we all understand with the automatic car, autonomous car analogy that there are limitations to being able to use that. However, I think it's a tool that basically every organization out there needs to at least have or at least evaluate because it goes to the point of it helps with ease of use, it helps make automation more balanced in terms of, you know, being able to test, all right, let's automate this process and see if it works well, then we can go on and switch on on autopilot for other processes. >>And then, you know, that allows, for example, the specialists to spend more time on business use cases versus, you know, manual maintenance of, of the cloud database and so forth. So I think that actually is a, a legitimate value proposition. I think it's just gonna be a case by case basis. Some organizations are gonna be more aggressive with putting automation throughout their processes throughout their organization. Others are gonna be more cautious. But it's gonna be, again, something that will help the overall Oracle proposition. And something that I think will be used with caution by many organizations, but other organizations are gonna like, hey, great, this is something that is really answering a real problem. And that is just easing the use of these databases, but also being able to better handle the automation capabilities and benefits that come with it without having, you know, a major screwup happened and the process of transitioning to more automated capabilities. >>Now, I didn't attend cloud world, it's just too many red eyes, you know, recently, so I passed. But one of the things I like to do at those events is talk to customers, you know, in the spirit of the truth, you know, they, you know, you'd have the hallway, you know, track and to talk to customers and they say, Hey, you know, here's the good, the bad and the ugly. So did you guys, did you talk to any customers my SQL Heatwave customers at, at cloud world? And and what did you learn? I don't know, Mark, did you, did you have any luck and, and having some, some private conversations? >>Yeah, I had quite a few private conversations. The one thing before I get to that, I want disagree with one point Ron made, I do believe there are customers out there buying the heat wave service, the MySEQ heat wave server service because of autopilot. Because autopilot is really revolutionary in many ways in the sense for the MySEQ developer in that it, it auto provisions, it auto parallel loads, IT auto data places it auto shape predictions. It can tell you what machine learning models are going to tell you, gonna give you your best results. And, and candidly, I've yet to meet a DBA who didn't wanna give up pedantic tasks that are pain in the kahoo, which they'd rather not do and if it's long as it was done right for them. So yes, I do think people are buying it because of autopilot and that's based on some of the conversations I had with customers at Oracle Cloud World. >>In fact, it was like, yeah, that's great, yeah, we get fantastic performance, but this really makes my life easier and I've yet to meet a DBA who didn't want to make their life easier. And it does. So yeah, I've talked to a few of them. They were excited. I asked them if they ran into any bugs, were there any difficulties in moving to it? And the answer was no. In both cases, it's interesting to note, my sequel is the most popular database on the planet. Well, some will argue that it's neck and neck with SQL Server, but if you add in Mariah DB and ProCon db, which are forks of MySQL, then yeah, by far and away it's the most popular. And as a result of that, everybody for the most part has typically a my sequel database somewhere in their organization. So this is a brilliant situation for anybody going after MyQ, but especially for heat wave. And the customers I talk to love it. I didn't find anybody complaining about it. And >>What about the migration? We talked about TCO earlier. Did your t does your TCO analysis include the migration cost or do you kind of conveniently leave that out or what? >>Well, when you look at migration costs, there are different kinds of migration costs. By the way, the worst job in the data center is the data migration manager. Forget it, no other job is as bad as that one. You get no attaboys for doing it. Right? And then when you screw up, oh boy. So in real terms, anything that can limit data migration is a good thing. And when you look at Data Lake, that limits data migration. So if you're already a MySEQ user, this is a pure MySQL as far as you're concerned. It's just a, a simple transition from one to the other. You may wanna make sure nothing broke and every you, all your tables are correct and your schema's, okay, but it's all the same. So it's a simple migration. So it's pretty much a non-event, right? When you migrate data from an O LTP to an O L A P, that's an ETL and that's gonna take time. >>But you don't have to do that with my SQL heat wave. So that's gone when you start talking about machine learning, again, you may have an etl, you may not, depending on the circumstances, but again, with my SQL heat wave, you don't, and you don't have duplicate storage, you don't have to copy it from one storage container to another to be able to be used in a different database, which by the way, ultimately adds much more cost than just the other service. So yeah, I looked at the migration and again, the users I talked to said it was a non-event. It was literally moving from one physical machine to another. If they had a new version of MySEQ running on something else and just wanted to migrate it over or just hook it up or just connect it to the data, it worked just fine. >>Okay, so every day it sounds like you guys feel, and we've certainly heard this, my colleague David Foyer, the semi-retired David Foyer was always very high on heatwave. So I think you knows got some real legitimacy here coming from a standing start, but I wanna talk about the competition, how they're likely to respond. I mean, if your AWS and you got heatwave is now in your cloud, so there's some good aspects of that. The database guys might not like that, but the infrastructure guys probably love it. Hey, more ways to sell, you know, EC two and graviton, but you're gonna, the database guys in AWS are gonna respond. They're gonna say, Hey, we got Redshift, we got aqua. What's your thoughts on, on not only how that's gonna resonate with customers, but I'm interested in what you guys think will a, I never say never about aws, you know, and are they gonna try to build, in your view a converged Oola and o LTP database? You know, Snowflake is taking an ecosystem approach. They've added in transactional capabilities to the portfolio so they're not standing still. What do you guys see in the competitive landscape in that regard going forward? Maybe Holger, you could start us off and anybody else who wants to can chime in, >>Happy to, you mentioned Snowflake last, we'll start there. I think Snowflake is imitating that strategy, right? That building out original data warehouse and the clouds tasking project to really proposition to have other data available there because AI is relevant for everybody. Ultimately people keep data in the cloud for ultimately running ai. So you see the same suite kind of like level strategy, it's gonna be a little harder because of the original positioning. How much would people know that you're doing other stuff? And I just, as a former developer manager of developers, I just don't see the speed at the moment happening at Snowflake to become really competitive to Oracle. On the flip side, putting my Oracle hat on for a moment back to you, Mark and Iran, right? What could Oracle still add? Because the, the big big things, right? The traditional chasms in the database world, they have built everything, right? >>So I, I really scratched my hat and gave Nipon a hard time at Cloud world say like, what could you be building? Destiny was very conservative. Let's get the Lakehouse thing done, it's gonna spring next year, right? And the AWS is really hard because AWS value proposition is these small innovation teams, right? That they build two pizza teams, which can be fit by two pizzas, not large teams, right? And you need suites to large teams to build these suites with lots of functionalities to make sure they work together. They're consistent, they have the same UX on the administration side, they can consume the same way, they have the same API registry, can't even stop going where the synergy comes to play over suite. So, so it's gonna be really, really hard for them to change that. But AWS super pragmatic. They're always by themselves that they'll listen to customers if they learn from customers suite as a proposition. I would not be surprised if AWS trying to bring things closer together, being morely together. >>Yeah. Well how about, can we talk about multicloud if, if, again, Oracle is very on on Oracle as you said before, but let's look forward, you know, half a year or a year. What do you think about Oracle's moves in, in multicloud in terms of what kind of penetration they're gonna have in the marketplace? You saw a lot of presentations at at cloud world, you know, we've looked pretty closely at the, the Microsoft Azure deal. I think that's really interesting. I've, I've called it a little bit of early days of a super cloud. What impact do you think this is gonna have on, on the marketplace? But, but both. And think about it within Oracle's customer base, I have no doubt they'll do great there. But what about beyond its existing install base? What do you guys think? >>Ryan, do you wanna jump on that? Go ahead. Go ahead Ryan. No, no, no, >>That's an excellent point. I think it aligns with what we've been talking about in terms of Lakehouse. I think Lake House will enable Oracle to pull more customers, more bicycle customers onto the Oracle platforms. And I think we're seeing all the signs pointing toward Oracle being able to make more inroads into the overall market. And that includes garnishing customers from the leaders in, in other words, because they are, you know, coming in as a innovator, a an alternative to, you know, the AWS proposition, the Google cloud proposition that they have less to lose and there's a result they can really drive the multi-cloud messaging to resonate with not only their existing customers, but also to be able to, to that question, Dave's posing actually garnish customers onto their platform. And, and that includes naturally my sequel but also OCI and so forth. So that's how I'm seeing this playing out. I think, you know, again, Oracle's reporting is indicating that, and I think what we saw, Oracle Cloud world is definitely validating the idea that Oracle can make more waves in the overall market in this regard. >>You know, I, I've floated this idea of Super cloud, it's kind of tongue in cheek, but, but there, I think there is some merit to it in terms of building on top of hyperscale infrastructure and abstracting some of the, that complexity. And one of the things that I'm most interested in is industry clouds and an Oracle acquisition of Cerner. I was struck by Larry Ellison's keynote, it was like, I don't know, an hour and a half and an hour and 15 minutes was focused on healthcare transformation. Well, >>So vertical, >>Right? And so, yeah, so you got Oracle's, you know, got some industry chops and you, and then you think about what they're building with, with not only oci, but then you got, you know, MyQ, you can now run in dedicated regions. You got ADB on on Exadata cloud to customer, you can put that OnPrem in in your data center and you look at what the other hyperscalers are, are doing. I I say other hyperscalers, I've always said Oracle's not really a hyperscaler, but they got a cloud so they're in the game. But you can't get, you know, big query OnPrem, you look at outposts, it's very limited in terms of, you know, the database support and again, that that will will evolve. But now you got Oracle's got, they announced Alloy, we can white label their cloud. So I'm interested in what you guys think about these moves, especially the industry cloud. We see, you know, Walmart is doing sort of their own cloud. You got Goldman Sachs doing a cloud. Do you, you guys, what do you think about that and what role does Oracle play? Any thoughts? >>Yeah, let me lemme jump on that for a moment. Now, especially with the MyQ, by making that available in multiple clouds, what they're doing is this follows the philosophy they've had the past with doing cloud, a customer taking the application and the data and putting it where the customer lives. If it's on premise, it's on premise. If it's in the cloud, it's in the cloud. By making the mice equal heat wave, essentially a plug compatible with any other mice equal as far as your, your database is concern and then giving you that integration with O L A P and ML and Data Lake and everything else, then what you've got is a compelling offering. You're making it easier for the customer to use. So I look the difference between MyQ and the Oracle database, MyQ is going to capture market more market share for them. >>You're not gonna find a lot of new users for the Oracle debate database. Yeah, there are always gonna be new users, don't get me wrong, but it's not gonna be a huge growth. Whereas my SQL heatwave is probably gonna be a major growth engine for Oracle going forward. Not just in their own cloud, but in AWS and in Azure and on premise over time that eventually it'll get there. It's not there now, but it will, they're doing the right thing on that basis. They're taking the services and when you talk about multicloud and making them available where the customer wants them, not forcing them to go where you want them, if that makes sense. And as far as where they're going in the future, I think they're gonna take a page outta what they've done with the Oracle database. They'll add things like JSON and XML and time series and spatial over time they'll make it a, a complete converged database like they did with the Oracle database. The difference being Oracle database will scale bigger and will have more transactions and be somewhat faster. And my SQL will be, for anyone who's not on the Oracle database, they're, they're not stupid, that's for sure. >>They've done Jason already. Right. But I give you that they could add graph and time series, right. Since eat with, Right, Right. Yeah, that's something absolutely right. That's, that's >>A sort of a logical move, right? >>Right. But that's, that's some kid ourselves, right? I mean has worked in Oracle's favor, right? 10 x 20 x, the amount of r and d, which is in the MyQ space, has been poured at trying to snatch workloads away from Oracle by starting with IBM 30 years ago, 20 years ago, Microsoft and, and, and, and didn't work, right? Database applications are extremely sticky when they run, you don't want to touch SIM and grow them, right? So that doesn't mean that heat phase is not an attractive offering, but it will be net new things, right? And what works in my SQL heat wave heat phases favor a little bit is it's not the massive enterprise applications which have like we the nails like, like you might be only running 30% or Oracle, but the connections and the interfaces into that is, is like 70, 80% of your enterprise. >>You take it out and it's like the spaghetti ball where you say, ah, no I really don't, don't want to do all that. Right? You don't, don't have that massive part with the equals heat phase sequel kind of like database which are more smaller tactical in comparison, but still I, I don't see them taking so much share. They will be growing because of a attractive value proposition quickly on the, the multi-cloud, right? I think it's not really multi-cloud. If you give people the chance to run your offering on different clouds, right? You can run it there. The multi-cloud advantages when the Uber offering comes out, which allows you to do things across those installations, right? I can migrate data, I can create data across something like Google has done with B query Omni, I can run predictive models or even make iron models in different place and distribute them, right? And Oracle is paving the road for that, but being available on these clouds. But the multi-cloud capability of database which knows I'm running on different clouds that is still yet to be built there. >>Yeah. And >>That the problem with >>That, that's the super cloud concept that I flowed and I I've always said kinda snowflake with a single global instance is sort of, you know, headed in that direction and maybe has a league. What's the issue with that mark? >>Yeah, the problem with the, with that version, the multi-cloud is clouds to charge egress fees. As long as they charge egress fees to move data between clouds, it's gonna make it very difficult to do a real multi-cloud implementation. Even Snowflake, which runs multi-cloud, has to pass out on the egress fees of their customer when data moves between clouds. And that's really expensive. I mean there, there is one customer I talked to who is beta testing for them, the MySQL heatwave and aws. The only reason they didn't want to do that until it was running on AWS is the egress fees were so great to move it to OCI that they couldn't afford it. Yeah. Egress fees are the big issue but, >>But Mark the, the point might be you might wanna root query and only get the results set back, right was much more tinier, which been the answer before for low latency between the class A problem, which we sometimes still have but mostly don't have. Right? And I think in general this with fees coming down based on the Oracle general E with fee move and it's very hard to justify those, right? But, but it's, it's not about moving data as a multi-cloud high value use case. It's about doing intelligent things with that data, right? Putting into other places, replicating it, what I'm saying the same thing what you said before, running remote queries on that, analyzing it, running AI on it, running AI models on that. That's the interesting thing. Cross administered in the same way. Taking things out, making sure compliance happens. Making sure when Ron says I don't want to be American anymore, I want to be in the European cloud that is gets migrated, right? So tho those are the interesting value use case which are really, really hard for enterprise to program hand by hand by developers and they would love to have out of the box and that's yet the innovation to come to, we have to come to see. But the first step to get there is that your software runs in multiple clouds and that's what Oracle's doing so well with my SQL >>Guys. Amazing. >>Go ahead. Yeah. >>Yeah. >>For example, >>Amazing amount of data knowledge and, and brain power in this market. Guys, I really want to thank you for coming on to the cube. Ron Holger. Mark, always a pleasure to have you on. Really appreciate your time. >>Well all the last names we're very happy for Romanic last and moderator. Thanks Dave for moderating us. All right, >>We'll see. We'll see you guys around. Safe travels to all and thank you for watching this power panel, The Truth About My SQL Heat Wave on the cube. Your leader in enterprise and emerging tech coverage.

>>Good afternoon, brilliant humans, and welcome back to the Cube. We're live in Detroit, Michigan at Cub Con, and I'm joined by John Furrier. John three exciting days buzzing. How you doing? >>That's great. I mean, we're coming down to the third day. We're keeping the energy going, but this segment's gonna be awesome. The CD foundation's doing amazing work. Developers are gonna be running businesses and workflows are changing. Productivity's the top conversation, and you're gonna start to see a coalescing of the communities who are continuous delivery, and it's gonna be awesome. >>And, and our next guess is an outstanding person to talk about this. We are joined by Stephen Chin, the chair of the CD Foundation. Steven, thanks so much for being here. >>No, no, my pleasure. I mean, this has been an amazing week quote that CubeCon with all of the announcements, all of the people who came out here to Detroit and, you know, fantastic. Like just walking around, you bump into all the right people here. Plus we held a CD summit zero day events, and had a lot of really exciting announcements this week. >>Gotta love the shirt. I gotta say, it's one of my favorites. Love the logos. Love the love the branding. That project got traction. What's the news in the CD foundation? I tried to sneak in the back. I got a little laid into your co-located event. It was packed. Everyone's engaged. It was really looked, look really cool. Give us the update. >>What's the news? Yeah, I know. So we, we had a really, really powerful event. All the key practitioners, the open source leads and folks were there. And one of, one of the things which I think we've done a really good job in the past six months with the CD foundation is getting back to the roots and focusing on technical innovation, right? This is what drives foundations, having strong projects, having people who are building innovation, and also bringing in a new innovation. So one of the projects which we added to the CD foundation this week is called Persia. So it's a, it's a decentralized package repository for getting open source libraries. And it solves a lot of the problems which you get when you have centralized infrastructure. You don't have the right security certificates, you don't have the right verification libraries. And these, these are all things which large companies provision and build out inside of their infrastructure. But the open source communities don't have the benefit of the same sort of really, really strong architecture. A lot of, a lot of the systems we depend upon. It's >>A good point, yeah. >>Yeah. I mean, if you think about the systems that developers depend upon, we depend upon, you know, npm, ruby Gems, Mayn Central, and these systems been around for a while. Like they serve the community well, right? They're, they're well supported by the companies and it's, it's, it's really a great contribution that they give us. But every time there's an outage or there's a security issue, guess, guess how many security issues that our, our research team found at npm? Just ballpark. >>74. >>So there're >>It's gotta be thousands. I mean, it's gotta be a lot of tons >>Of Yeah, >>They, they're currently up to 60,000 >>Whoa. >>Vulnerable, malicious packages in NPM and >>Oh my gosh. So that's a super, that's a jar number even. I know it was gonna be huge, but Holy mo. >>Yeah. So that's a software supply chain in actually right there. So that's, that's open source. Everything's out there. What's, how do, how does, how do you guys fix that? >>Yeah, so per peria kind of shifts the whole model. So when, when you think about a system that can be sustained, it has to be something which, which is not just one company. It has to be a, a, a set of companies, be vendor neutral and be decentralized. So that's why we donated it to the Continuous Delivery Foundation. So that can be that governance body, which, which makes sure it's not a single company, it is to use modern technologies. So you, you, you just need something which is immutable, so it can't be changed. So you can rely on it. It has to have a strong transaction ledger so you can see all of the history of it. You can build up your software, build materials off of it, and it, it has to have a strong peer-to-peer architecture, so it can be sustained long term. >>Steven, you mentioned something I want to just get back to. You mentioned outages and disruption. I, you didn't, you didn't say just the outages, but this whole disruption angle is interesting if something happens. Talk about the impact of the developer. They stalled, inefficiencies create basically disruption. >>No, I mean, if, if, so, so if you think about most DevOps teams in big companies, they support hundreds or thousands of teams and an hour of outage. All those developers, they, they can't program, they can't work. And that's, that's a huge loss of productivity for the company. Now, if you, if you take that up a level when MPM goes down for an hour, how many millions of man hours are wasted by not being able to get your builds working by not being able to get your codes to compile. Like it's, it's >>Like, yeah, I mean, it's almost hard to fathom. I mean, everyone's, It's stopped. Exactly. It's literally like having the plug pulled >>Exactly on whenever you're working on, That's, that's the fundamental problem we're trying to solve. Is it, it needs to be on a, like a well supported, well architected peer to peer network with some strong backing from big companies. So the company is working on Persia, include J Frog, which who I work for, Docker, Oracle. We have Deploy hub, Huawei, a whole bunch of other folks who are also helping out. And when you look at all of those folks, they all have different interests, but it's designed in a way where no single party has control over the network. So really it's, it's a system system. You, you're not relying upon one company or one logo. You're relying upon a well-architected open source implementation that everyone can rely >>On. That's shared software, but it's kind of a fault tolerant feature too. It's like, okay, if something happens here, you have a distributed piece of it, decentralized, you're not gonna go down. You can remediate. All right, so where's this go next? I mean, cuz we've been talking about the role of developer. This needs to be a modern, I won't say modern upgrade, but like a modern workflow or value chain. What's your vision? How do you see that? Cuz you're the center of the CD foundation coming together. People are gonna be coalescing multiple groups. Yeah. >>What's the, No, I think this is a good point. So there, there's a, a lot of different continuous delivery, continuous integration technologies. We're actually, from a Linux Foundation standpoint, we're coalescing all the continued delivery events into one big conference >>Next. You just made an announcement about this earlier this week. Tell us about CD events. What's going on, what's in, what's in the cooker? >>Yeah, and I think one of the big announcements we had was the 0.1 release of CD events. And CD events allows you to take all these systems and connect them in an event scalable, event oriented architecture. The first integration is between Tecton and Capin. So now you can get CD events flowing cleanly between your, your continuous delivery and your observability. And this extends through your entire DevOps pipeline. We all, we all need a standards based framework Yep. For how we get all the disparate continuous integration, continuous delivery, observability systems to, to work together. That's also high performance. It scales with our needs and it, it kind of gives you a future architecture to build on top of. So a lot of the companies I was talking with at the CD summit Yeah. They were very excited about not only using this with the projects we announced, but using this internally as an architecture to build their own DevOps pipelines on. >>I bet that feels good to hear. >>Yeah, absolutely. Yeah. >>Yeah. You mentioned Teton, they just graduated. I saw how many projects have graduated? >>So we have two graduated projects right now. We have Jenkins, which is the first graduated project. Now Tecton is also graduated. And I think this shows that for Tecton it was, it was time, the very mature project, great support, getting a lot of users and having them join the set of graduated projects. And the continuous delivery foundation is a really strong portfolio. And we have a bunch of other projects which also are on their way towards graduation. >>Feels like a moment of social proof I bet. >>For you all. Yeah, yeah. Yeah. No, it's really good. Yeah. >>How long has the CD Foundation been around? >>The CD foundation has been around for, i, I won't wanna say the exact number of years, a few years now. >>Okay. >>But I, I think that it, it was formed because what we wanted is we wanted a foundation which was purpose built. So CNCF is a great foundation. It has a very large umbrella of projects and it takes kind of that big umbrella approach where a lot of different efforts are joining it, a lot of things are happening and you can get good traction, but it produces its own bottlenecks in process. Having a foundation which is just about continuous delivery caters to more of a DevOps, professional DevOps audience. I think this, this gives a good platform for best practices. We're working on a new CDF best practices Yeah. Guide. We're working when use cases with all the member companies. And it, it gives that thought leadership platform for continuous delivery, which you need to be an expert in that area >>And the best practices too. And to identify the issues. Because at the end of the day, with the big thing that's coming out of this is velocity and more developers coming on board. I mean, this is the big thing. More people doing more. Yeah. Well yeah, I mean you take this open source continuous thunder away, you have more developers coming in, they be more productive and then people are gonna even either on the DevOps side or on the straight AP upside. And this is gonna be a huge issue. And the other thing that comes out that I wanna get your thoughts on is the supply chain issue you talked about is hot verifications and certifications of code is such big issue. Can you share your thoughts on that? Because Yeah, this is become, I won't say a business model for some companies, but it's also becoming critical for security that codes verified. >>Yeah. Okay. So I, I think one of, one of the things which we're specifically doing with the Peria project, which is unique, is rather than distributing, for example, libraries that you developed on your laptop and compiled there, or maybe they were built on, you know, a runner somewhere like Travis CI or GitHub actions, all the libraries being distributed on Persia are built by the authorized nodes in the network. And then they're, they're verified across all of the authorized nodes. So you nice, you have a, a gar, the basic guarantee we're giving you is when you download something from the Peria network, you'll get exactly the same binary as if you built it yourself from source. >>So there's a lot of trust >>And, and transparency. Yeah, exactly. And if you remember back to like kind of the seminal project, which kicked off this whole supply chain security like, like whirlwind it was SolarWinds. Yeah. Yeah. And the exact problem they hit was the build ran, it produced a result, they modified the code of the bill of the resulting binary and then they signed it. So if you built with the same source and then you went through that same process a second time, you would've gotten a different result, which was a malicious pre right. Yeah. And it's very hard to risk take, to take a binary file Yep. And determine if there's malicious code in it. Cuz it's not like source code. You can't inspect it, you can't do a code audit. It's totally different. So I think we're solving a key part of this with Persia, where you're freeing open source projects from the possibility of having their binaries, their packages, their end reduces, tampered with. And also upstream from this, you do want to have verification of prs, people doing code reviews, making sure that they're looking at the source code. And I think there's a lot of good efforts going on in the open source security foundation. So I'm also on the governing board of Open ssf >>To Do you sleep? You have three jobs you've said on camera? No, I can't even imagine. Yeah. Didn't >>You just spin that out from this open source security? Is that the new one they >>Spun out? Yeah, So the Open Source Security foundation is one of the new Linux Foundation projects. They, they have been around for a couple years, but they did a big reboot last year around this time. And I think what they really did a good job of now is bringing all the industry players to the table, having dialogue with government agencies, figuring out like, what do we need to do to support open source projects? Is it more investment in memory, safe languages? Do we need to have more investment in, in code audits or like security reviews of opensource projects. Lot of things. And all of those things require money investments. And that's what all the companies, including Jay Frogger doing to advance open source supply chain security. I >>Mean, it's, it's really kind of interesting to watch some different demographics of the developers and the vendors and the customers. On one hand, if you're a hardware person company, you have, you talk zero trust your software, your top trust, so your trusted code, and you got zero trust. It's interesting, depending on where you're coming from, they're all trying to achieve the same thing. It means zero trust. Makes sense. But then also I got code, I I want trust. Trust and verified. So security is in everything now. So code. So how do you see that traversing over? Is it just semantics or what's your view on that? >>The, the right way of looking at security is from the standpoint of the hacker, because they're always looking for >>Well said, very well said, New >>Loop, hope, new loopholes, new exploits. And they're, they're very, very smart people. And I think when you, when you look some >>Of the smartest >>Yeah, yeah, yeah. I, I, I work with, well former hackers now, security researchers, >>They converted, they're >>Recruited. But when you look at them, there's like two main classes of like, like types of exploits. So some, some attacker groups. What they're looking for is they're looking for pulse zero days, CVEs, like existing vulnerabilities that they can exploit to break into systems. But there's an increasing number of attackers who are now on the opposite end of the spectrum. And what they're doing is they're creating their own exploits. So, oh, they're for example, putting malicious code into open source projects. Little >>Trojan horse status. Yeah. >>They're they're getting their little Trojan horses in. Yeah. Or they're finding supply chain attacks by maybe uploading a malicious library to NPM or to pii. And by creating these attacks, especially ones that start at the top of the supply chain, you have such a large reach. >>I was just gonna say, it could be a whole, almost gives me chills as we're talking about it, the systemic, So this is this >>Gnarly nation state attackers, like people who wanted serious >>Damages. Engineered hack just said they're high, highly funded. Highly skilled. Exactly. Highly agile, highly focused. >>Yes. >>Teams, team. Not in the teams. >>Yeah. And so, so one, one example of this, which actually netted quite a lot of money for the, for the hacker who exposed it was, you guys probably heard about this, but it was a, an attack where they uploaded a malicious library to npm with the same exact namespace as a corporate library and clever, >>Creepy. >>It's called a dependency injection attack. And what happens is if you, if you don't have the right sort of security package management guidelines inside your company, and it's just looking for the latest version of merging multiple repositories as like a, like a single view. A lot of companies were accidentally picking up the latest version, which was out in npm uploaded by Alex Spearson was the one who did the, the attack. And he simultaneously reported bug bounties on like a dozen different companies and netted 130 k. Wow. So like these sort of attacks that they're real Yep. They're exploitable. And the, the hackers >>Complex >>Are finding these sort of attacks now in our supply chain are the ones who really are the most dangerous. That's the biggest threat to us. >>Yeah. And we have stacker ones out there. You got a bunch of other services, the white hat hackers get the bounties. That's really important. All right. What's next? What's your vision of this show as we end Coan? What's the most important story coming outta Coan in your opinion? And what are you guys doing next? >>Well, I, I actually think this is, this is probably not what most hooks would say is the most exciting story to con, but I find this personally the best is >>I can't wait for this now. >>So, on, on Sunday, the CNCF ran the first kids' day. >>Oh. >>And so they had a, a free kids workshop for, you know, underprivileged kids for >>About, That's >>Detroit area. It was, it was taught by some of the folks from the CNCF community. So Arro, Eric hen my, my older daughter, Cassandra's also an instructor. So she also was teaching a raspberry pie workshop. >>Amazing. And she's >>Here and Yeah, Yeah. She's also here at the show. And when you think about it, you know, there's always, there's, there's, you know, hundreds of announcements this week, A lot of exciting technologies, some of which we've talked about. Yeah. But it's, it's really what matters is the community. >>It this is a community first event >>And the people, and like, if we're giving back to the community and helping Detroit's kids to get better at technology, to get educated, I think that it's a worthwhile for all of us to be here. >>What a beautiful way to close it. That is such, I'm so glad you brought that up and brought that to our attention. I wasn't aware of that. Did you know that was >>Happening, John? No, I know about that. Yeah. No, that was, And that's next generation too. And what we need, we need to get down into the elementary schools. We gotta get to the kids. They're all doing robotics club anyway in high school. Computer science is now, now a >>Sport, in my opinion. Well, I think that if you're in a privileged community, though, I don't think that every school's doing robotics. And >>That's why Well, Cal Poly, Cal Poly and the universities are stepping up and I think CNCF leadership is amazing here. And we need more of it. I mean, I'm, I'm bullish on this. I love it. And I think that's a really great story. No, >>I, I am. Absolutely. And, and it just goes to show how committed CNF is to community, Putting community first and Detroit. There has been such a celebration of Detroit this whole week. Stephen, thank you so much for joining us on the show. Best Wishes with the CD Foundation. John, thanks for the banter as always. And thank you for tuning in to us here live on the cube in Detroit, Michigan. I'm Savannah Peterson and we are having the best day. I hope you are too.

>> From theCUBE Studios in Palo Alto and Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> The technology spending outlook is not pretty and very much unpredictable right now. The negative sentiment is of course being driven by the macroeconomic factors in earnings forecasts that have been coming down all year in an environment of rising interest rates. And what's worse, is many people think earnings estimates are still too high. But it's understandable why there's so much uncertainty. I mean, technology is still booming, digital transformations are happening in earnest, leading companies have momentum and they got cash runways. And moreover, the CEOs of these leading companies are still really optimistic. But strong guidance in an environment of uncertainty is somewhat risky. Hello and welcome to this week's Wikibon CUBE Insights Powered by ETR. In this breaking analysis, we share takeaways from ETR'S latest spending survey, which was released to their private clients on October 21st. Today, we're going to review the macro spending data. We're going to share where CIOs think their cloud spend is headed. We're going to look at the actions that organizations are taking to manage uncertainty and then review some of the technology companies that have the most positive and negative outlooks in the ETR data set. Let's first look at the sample makeup from the latest ETR survey. ETR captured more than 1300 respondents in this latest survey. Its highest figure for the year and the quality and seniority of respondents just keeps going up each time we dig into the data. We've got large contributions as you can see here from sea level executives in a broad industry focus. Now the survey is still North America centric with 20% of the respondents coming from overseas and there is a bias toward larger organizations. And nonetheless, we're still talking well over 400 respondents coming from SMBs. Now ETR for those of you who don't know, conducts a quarterly spending intention survey and they also do periodic drilldowns. So just by the way of review, let's take a look at the expectations in the latest drilldown survey for IT spending. Before we look at the broader technology spending intentions survey data, followers of this program know that we reported on this a couple of weeks ago, spending expectations that peaked last December at 8.3% are now down to 5.5% with a slight uptick expected for next year as shown here. Now one CIO in the ETR community said these figures could be understated because of inflation. Now that's an interesting comment. Real GDP in the US is forecast to be around 1.5% in 2022. So these figures are significantly ahead of that. Nominal GDP is forecast to be significantly higher than what is shown in that slide. It was over 9% in June for example. And one would interpret that survey respondents are talking about real dollars which reflects inflationary factors in IT spend. So you might say, well if nominal GDP is in the high single digits this means that IT spending is below GDP which is usually not the case. But the flip side of that is technology tends to be deflationary because prices come down over time on a per unit basis, so this would be a normal and even positive trend. But it's mixed right now with prices on hard to find hardware, they're holding more firms. Software, you know, software tends to be driven by lock in and competition and switching costs. So you have those countervailing factors. Services can be inflationary, especially now as wages rise but certain sectors like laptops and semis and NAND are seeing less demand and maybe even some oversupply. So the way to look at this data is on a relative basis. In other words, IT buyers are reporting 280 basis point drop in spending sentiment from the end of last year. Now, something that we haven't shared from the latest drilldown survey which we will now is how IT bar buyers are thinking about cloud adoption. This chart shows responses from 419 IT execs from that drilldown and depicts the percentage of workloads their organizations have in the cloud today and what the expectation is through years from now. And you can see it's 27% today and it's nearly 50% in three years. Now the nuance is if you look at the question, that ETRS, it's they asked about IaaS and PaaS, which to some could include on-prem. Now, let me come back to that. In particular, financial services, IT, telco and retail and services industry cited expectations for the future for three years out that we're well above the average of the mean adoption levels. Regardless of how you interpret this data there's most certainly plenty of public cloud in the numbers. And whether you believe cloud is an operating environment or a place out there in the cloud, there's plenty of room for workloads to move into a cloud model well beyond mid this decade. So you know, as ho hum as we've been toward recent as-a-service models announced from the likes of HPE with GreenLake and Dell with APEX, the timing of those offerings may be pretty good actually. Now let's expand on some of the data that we showed a couple weeks ago. This chart shows responses from 282 execs on actions their organizations are taking over the next three months. And the Deltas are quite traumatic from the early part of this charter than the left hand side. The brown line is hiring freezes, the black line is freezing IT projects, and the green line is hiring increases and that red line is layoffs. And we put a box around the sort of general area of the isolation economy timeframe. And you can see the wild swings on this chart. By mid last summer, people were kickstarting things and more hiring was going on and the black line shows IT project freezes, you know, came way down. And now, or on the way back up as our hiring freezes. So we're seeing these wild swings in organizational actions and strategies which underscores the lack of predictability. As with supply chains around the world, this is likely due to the fact that organizations, pre pandemic they were optimized for efficiency, not a lot of waste rather than business resilience. Meaning, you know, there's again not a lot of fluff in the system or if there was it got flushed out during the pandemic. And so the need for productivity and automation is becoming increasingly important, especially as actions that solely rely on headcount changes are very, very difficult to manage. Now, let's dig into some of the vendor commentary and take a look at some of the names that have momentum and some of the others possibly facing headwinds. Here's a list of companies that stand out in the ETR survey. Snowflake, once again leads the pack with a positive spending outlook. HashiCorp, CrowdStrike, Databricks, Freshworks and ServiceNow, they round out the top six. Microsoft, they seem to always be in the mix, as do a number of other security and related companies including CyberArk, Zscaler, CloudFlare, Elastic, Datadog, Fortinet, Tenable and to a certain extent Akamai, you can kind of put them sort of in that group. You know, CDN, they got to worry about security. Everybody worries about security, but especially the CDNs. Now the other software names that are highlighted here include Workday and Salesforce. On the negative side, you can see Dynatrace saw some negatives in the latest survey especially around its analytics business. Security is generally holding up better than other sectors but it's still seeing greater levels of pressure than it had previously. So lower spend. And defections relative to its observability peers, that's really for Dynatrace. Now the other one that was somewhat surprising is IBM. You see the IBM was sort of in that negative realm here but IBM reported an outstanding quarter this past week with double digit revenue growth, strong momentum in software, consulting, mainframes and other infrastructure like storage. It's benefiting from the Kyndryl restructuring and it's on track IBM to deliver 10 billion in free cash flow this year. Red Hat is performing exceedingly well and growing in the very high teens. And so look, IBM is in the midst of a major transformation and it seems like a company that is really focused now with hybrid cloud being powered by Red Hat and consulting and a decade plus of AI investments finally paying off. Now the other big thing we'll add is, IBM was once an outstanding acquire of companies and it seems to be really getting its act together on the M&A front. Yes, Red Hat was a big pill to swallow but IBM has done a number of smaller acquisitions, I think seven this year. Like for example, Turbonomic, which is starting to pay off. Arvind Krishna has the company focused once again. And he and Jim J. Kavanaugh, IBM CFO, seem to be very confident on the guidance that they're giving in their business. So that's a real positive in our view for the industry. Okay, the last thing we'd like to do is take 12 of the companies from the previous chart and plot them in context. Now these companies don't necessarily compete with each other, some do. But they are standouts in the ETR survey and in the market. What we're showing here is a view that we like to often show, it's net score or spending velocity on the vertical axis. And it's a measure, that's a measure of the net percentage of customers that are spending more on a particular platform. So ETR asks, are you spending more or less? They subtract less from the mores. I mean I'm simplifying, but that's what net score is. Now in the horizontal axis, that is a measure of overlap which is which measures presence or pervasiveness in the dataset. So bigger the better. We've inserted a table that informs how the dots in the companies are positioned. These companies are all in the green in terms of net score. And that right most column in the table insert is indicative of their presence in the dataset, the end. So higher, again, is better for both columns. Two other notes, the red dotted line there you see at 40%. Anything over that indicates an highly elevated spending momentum for a given platform. And we purposefully took Microsoft out of the mix in this chart because it skews the data due to its large size. Everybody else would cluster on the left and Microsoft would be all alone in the right. So we take them out. Now as we noted earlier, Snowflake once again leads with a net score of 64%, well above the 40% line. Having said that, while adoption rates for Snowflake remains strong the company's spending velocity in the survey has come down to Earth. And many more customers are shifting from where they were last year and the year before in growth mode i.e. spending more year to year with Snowflake to now shifting more toward flat spending. So a plus or minus 5%. So that puts pressure on Snowflake's net score, just based on the math as to how ETR calculates, its proprietary net score methodology. So Snowflake is by no means insulated completely to the macro factors. And this was seen especially in the data in the Fortune 500 cut of the survey for Snowflake. We didn't show that here, just giving you anecdotal commentary from the survey which is backed up by data. So, it showed steeper declines in the Fortune 500 momentum. But overall, Snowflake, very impressive. Now what's more, note the position of Streamlit relative to Databricks. Streamlit is an open source python framework for developing data driven, data science oriented apps. And it's ironic that it's net score and shared in is almost identical to those of data bricks, as the aspirations of Snowflake and Databricks are beginning to collide. Now, however, the Databricks net score has held up very well over the past year and is in the 92nd percentile of its machine learning and AI peers. And while it's seeing some softness, like Snowflake in the Fortune 500, Databricks has steadily moved to the right on the X axis over the last several surveys even though it was unable to get to the public markets and do an IPO during the lockdown tech bubble. Let's come back to the chart. ServiceNow is impressive because it's well above the 40% mark and it has 437 shared in on this cut, the largest of any company that we chose to plot here. The only real negative on ServiceNow is, more large customers are keeping spending levels flat. That's putting a little bit pressure on its net score, but that's just conservatives. It's kind of like Snowflakes, you know, same thing but in a larger scale. But it's defections, the ServiceNow as in Snowflake as well. It's defections remain very, very low, really low churn below 2% for ServiceNow, in fact, within the dataset. Now it's interesting to also see Freshworks hit the list. You can see them as one of the few ITSM vendors that has momentum and can potentially take on ServiceNow. Workday, on this chart, it's the other big app player that's above the 40% line and we're only showing Workday HCM, FYI, in this graphic. It's Workday Financials, that offering, is below the 40% line just for reference. Now let's talk about CrowdStrike. We attended Falcon last month, CrowdStrike's user conference and we're very impressed with the product visio, the company's execution, it's growing partnerships. And you can see in this graphic, the ETR survey data confirms the company's stellar performance with a net score at 50%, well above the 40% mark. And importantly, more than 300 mentions. That's second only to ServiceNow, amongst the 12 companies that we've chosen to highlight here. Only Microsoft, which is not shown here, has a higher net score in the security space than CrowdStrike. And when it comes to presence, CrowdStrike now has caught up to Splunk in terms of pervasion in the survey. Now CyberArk and Zscaler are the other two security firms that are right at that 40% red dotted line. CyberArk for names with over a hundred citations in the security sector, is only behind Microsoft and CrowdStrike. Zscaler for its part in the survey is seeing strong momentum in the Fortune 500, unlike what we said for Snowflake. And its pervasion on the X-axis has been steadily increasing. Again, not that Snowflake and CrowdStrike compete with each other but they're too prominent names and it's just interesting to compare peers and business models. Cloudflare, Elastic and Datadog are slightly below the 40% mark but they made the sort of top 12 that we showed to highlight here and they continue to have positive sentiment in the survey. So, what are the big takeaways from this latest survey, this really quick snapshot that we've taken. As you know, over the next several weeks we're going to dig into it more and more. As we've previously reported, the tide is going out and it's taking virtually all the tech ships with it. But in many ways the current market is a story of heightened expectations coming down to Earth, miscalculations about the economic patterns and the swings and imperfect visibility. Leading Barclays analyst, Ramo Limchao ask the question to guide or not to guide in a recent research note he wrote. His point being, should companies guide or should they be more cautious? Many companies, if not most companies, are actually giving guidance. Indeed, when companies like Oracle and IBM are emphatic about their near term outlook and their visibility, it gives one confidence. On the other hand, reasonable people are asking, will the red hot valuations that we saw over the last two years from the likes of Snowflake, CrowdStrike, MongoDB, Okta, Zscaler, and others. Will they return? Or are we in for a long, drawn out, sideways exercise before we see sustained momentum? And to that uncertainty, we add elections and public policy. It's very hard to predict right now. I'm sorry to be like a two-handed lawyer, you know. On the one hand, on the other hand. But that's just the way it is. Let's just say for our part, we think that once it's clear that interest rates are on their way back down and we'll stabilize it under 4% and we have clarity on the direction of inflation, wages, unemployment and geopolitics, the wild swings and sentiment will subside. But when that happens is anyone's guess. If I had to peg, I'd say 18 months, which puts us at least into the spring of 2024. What's your prediction? You know, it's almost that time of year. Let's hear it. Please keep in touch and let us know what you think. Okay, that's it for now. Many thanks to Alex Myerson. He is on production and he manages the podcast for us. Ken Schiffman as well is our newest addition to the Boston Studio. Kristin Martin and Cheryl Knight, they help get the word out on social media and in our newsletters. And Rob Hoff is our EIC, editor-in-chief over at SiliconANGLE. He does some wonderful editing for us. Thank you all. Remember all these episodes, they 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 at david.vellante@siliconangle.com or DM me @dvellante. Or feel free to comment on our LinkedIn posts. And please do check out etr.ai. They've got the best survey data in the enterprise tech business. If you haven't checked that out, you should. It'll give you an advantage. This is Dave Vellante for theCUBE Insights Powered by ETR. Thanks for watching. Be well and we'll see you next time on Breaking Analysis. (soft upbeat music)

>>In late 2009 when the industry was just beginning to offer so-called converged infrastructure, CI Nutanix was skating to the puck, so to speak, meaning unlike conversion infrastructure, which essentially bolted together compute and networking and storage into a single skew that was very hardware centric. Nutanix was focused on creating HCI hyperconverged infrastructure, which was a software led architecture that unified the key elements of data center infrastructure. Now, while both approaches saved time and money, HCI took the concept to new heights of cost savings and simplicity. Hyperconverged infrastructure became a staple of private clouds creating a cloudlike experience. OnPrem. As the public cloud evolved and grew, more and more customers are now taking a cloud first approach to it. So the challenge becomes how do you remodel your IT house so that you can connect your on-prem workloads to the cloud, to both simplify cloud migration, while at the same time creating an identical experience across your estate? >>Hello, and welcome to this special program, Accelerate Hybrid Cloud with Nutanix and Microsoft Made Possible by By Nutanix and produced by the Cube. I'm Dave Ante, one of your hosts today. Now, in this session, we'll hear how Nutanix is evolving its initial vision of simplifying infrastructure, deployment and management to support modern applications by partnering with Microsoft to enable that consistent experience that we talked about earlier, to extend hybrid cloud to Microsoft Azure and take advantage of cloud native tooling. Now, what's really important to stress here, and you'll hear this in our second segment, substantive engineering work has gone into this partnership. A lot of partnerships are sealed with a press release. We sometimes call it a Barney deal. You know, I love you, you love me. Like Barney, the once popular children's dinosaur character. We dig into the critical engineering aspects that enable that seamless connection between on-prem infrastructure and the public cloud. >>Now, in our first segment, Lisa Martin talks to Alro Salise, who is the vice president of Global ISD Commercial Solutions at Microsoft, and Michael Les Chica, who is the vice president of business development for the cloud and database partner ecosystem at Nutanix. Now, after that, Lisa will kick it back to me in our Boston studios to speak with Eric Lockard, who is the corporate vice president of Microsoft Azure specialized, along with Thomas Cornell, who is the senior vice president of products at Nutanix. And Indu Carey, who's the senior vice president of of engineering for NCI and NNC two at Nutanix. And we'll dig deeper into the announcement and it's salient features. Thanks for being with us. We hope you enjoy the program. Over to Lisa. >>Hi everyone. Welcome to our event Accelerate Hybrid Cloud with Nutanix and Microsoft. I'm your host Lisa Martin, and I've got two great guests here with me to give you some exciting news. Please welcome Alva Salise, the Vice President of Global ISD Commercial Solutions at Microsoft, and Michael Les Chika, VP of Business Development Cloud and database partner ecosystem at Nutanix. Guys, it's great to have you on the program. Thanks so much for joining me today. Great to be here. >>Thank you, Lisa. Looking forward, >>Yeah, so let's go ahead and start with you. Talk to me from your lens, what are you seeing in terms of the importance of the role of the the ISV ecosystem and really helping customers make their business outcomes successful? >>Oh, absolutely. Well, first of all, thank you for the invitation and thank you Michael and the Nutanix team for the partnership. The the ISV ecosystem plays a critical role as we support our customers and enable them in their data transformation journeys to create value, to move at their own pace, and more important to be sure that every one of them, as they transform themselves, have the right set of solutions for the long term with high differentiation, cost effectiveness and resiliency, especially given the times that we're living. >>Yeah, that resiliency is getting more and more critical as each day goes on. Ava was sticking with you. We got Microsoft Ignite going on today. What are some of the key themes that we should expect this year and how do they align to Microsoft's vision and strategy? >>Ah, great question. Thank you. When you think about it, we wanna talk about the topics that are very relevant and our customers have asked us to go deeper and, and share with them. One of them, as you may imagine, is how can we do more with less using Azure, especially given the current times that we're living in the, the business context has changed so much, they have different imperative, different different amount of pressure and priorities. How can we help? How can we combine the platform, the value that Microsoft can bring and our Microsoft ISV partner ecosystem to deliver more value and enable them to have their own journey? Actually, in that frame, if I may, we are making this announcement today with Nutanix. I, the Nutanix cloud clusters are often the fastest way on which customers will be able to do that journey into the cloud because it's very consistent with environments that they already know and use on premise. And once they go into the cloud, then they have all the benefit of scale, agility, resiliency, security, and cost benefits that they're looking for. So that topic and this type of announcements will be a big part of what we doing. Ignite, >>Exciting. Michael, let's bring you into the conversation now. Big milestone of our RDTs that the general availability of Nutanix Cloud clusters on Azure. Talk to us about that from Nutanix's perspective and also gimme a little bit of color, Michael, on the partnership, the relationship. >>Yeah, sure, absolutely. So we actually entered a partnership couple years ago, so we've been working on this solution quite a while, but really our ultimate goal from day one was really to make our customers journeys to hybrid cloud simpler and faster. So really for both companies, I think our goal is really being that trusted partner for our customers in their innovation journey. And as mentioned, you know, in the current macroeconomic conditions, really our customers really care about, but they have to be mindful of their bottom line as well. So they're really looking to leverage their existing investments in technology skill sets and leverage the most out of that. So the things like, for example, cost to operations and keeping those things consistent, cost on premises and the cloud are really important as customers are thinking about growth initiatives that they wanna implement. And of course, going to Azure public cloud is an important one as they think about flexibility, scale and modernizing their apps. >>And of course, as we look at the customer landscape, a lot of customers have an on on footprint, right? Whether that's for regulatory reasons for business or other technical reasons. So hybrid cloud has really become an ideal operating model for a lot of the customers that we see today. So really our partnership with Microsoft is critical because together, I really do see our US together simplifying that journey to the public cloud and making sure that it's not only easy but secure and really seamless. And really, I see our partnership as bringing the strengths of each company together, right? So Nutanix, of course, is known in the past versus hyperconverge infrastructure and really breaking down those silos between networking, compute, storage, and simplifying that infrastructure and operations. And our customers love that for the products and our, our NPS score of 90 over the last seven years. And if you look at Azure, at Microsoft, they're truly best in class cloud infrastructure with cutting edge services and innovation and really global scale. So when you think about those two combinations, right, that's really powerful for customers to be able to take their applications and whether they're on or even, and really combining all those various hybrid scenarios. And I think that's something that's pretty unique that we're to offer customers. >>Let's dig into that uniqueness of our, bringing you back into the conversation. You guys are meeting customers where they are helping them to accelerate their cloud transformations, delivering that consistency, you know, whether they're on-prem in Azure, in in the cloud. Talk to me about, from Microsoft's perspective about the significance of this announcement. I understand that the, the preview was oversubscribed, so the demand from your joint customers is clear. >>Thank you, Lisa. Michael, personally, I'm very proud and at the company we're very proud of the world that we did together with Nutanix. When you see two companies coming together with the mission of empowering customers and with the customer at the center and trying to solve real problems in this case, how to drive hybrid cloud and what is the best approach for them, opening more opportunities is, is, is extremely inspiring. And of course the welcome reception that we have from customer reiterates that we generating that value. Now, when you combine the power of Azure, that is very well known by resiliency, the scale, the performance, the elasticity, and the range of services with the reality of companies that might have hundreds or even thousands of different applications and data sources, those cloud journeys are very different for each and every one of them. So how do we combine our capabilities between Nutanix and Microsoft to be sure that that hybrid cloud journey that every one is gonna take can be simplified, you can take away the risk, the complexity on that transformation creates tones of value. >>And that's what a customers are asking us today. Either because they're trying to move and modernize their environment to Azure, or they're bringing their, you know, a enable ordinate services and cluster and data services on premise to a Nutanix platform, we together can combine and solve for that adding more value for any scenario that customers may have. And this is not once and done, this is not that we building, we forget it. It's a partnership that keeps evolving and also includes work that we do with our solution sales alliances that go to market seems to be sure that the customers have diverse service and support to make, to create the outcomes that they're asking us to deliver. >>Talk to me a little bit about the customers that were in the beta, as we mentioned, Alva, the, the preview was oversubscribed. So as I talked about earlier, the demand is clearly there. Talk to me about some of the customers in beta, you can even anonymize them or maybe talk about them by industry, but what, what were some of the, the key things they came to these two companies looking to, to solve, get to the cloud faster, be able to deliver the same sets of services with familiarity so that from a, they're able to do more with less? >>Maybe I could take that one out of our abital lines. It did. It means, but yeah, so like, like we, like you mentioned Lisa, you know, we've had a great preview oversubscribe, we had lots of, of cu not only customers, but also partners battle testing the solution. And you know, we're obviously very pleased now to have GN offered to everyone else, but one of our customers, Camper J was really looking forward to seeing how do they leverage Ncq and Azure to, like I mentioned, reduce that work workload, my, my migration and a risk for that and making sure, hey, some of the applications, maybe we are going to go and rewrite them, refactor them to take them natively to Azure. But there's others where we wanna lift and shift them to Azure. But like I mentioned, it's not just customers, right? We've been working with partners like PCs and Citrix where they share the same goal as Microsoft and Nutanix provides that superior customer experience where whatever the operating model might be for that customer. So they're going to be leveraging NC two on Azure to really provide those hybrid cloud experiences for their solutions on top of building on top of the, the work that we've done together. >>So this really kind of highlights the power of that Alva, the power of the ISV ecosystem and what you're all able to do together to really help customers achieve the outcomes that they individually need. >>A absolutely, look, I mean, we strongly believe that when you partner properly with an V you get to the, to the magical framework, one plus one equals three or more because you are combining superpowers and you are solving the problem on behalf of the customer so they can focus on their business. And this is a wonderful example, a very inspiring one where when you see the risk, the complexity that all these projects normally have, and Michael did a great job framing some of them, and the difference that they have now by having NC to on Azure, it's night and day. And we are fully committed to keep driving this innovation, this partnership on service of our customers and our partner ecosystem because at the same time, making our partners more successful, generating more value for customers and for all of us. >>Abar, can you comment a little bit on the go to market? Like how, how do your joint customers engage? What does that look like from their perspective? >>You know, when you think about the go to market, a lot of that is we have, you know, teams all over the world that will be aligned and working together in service of the customer. There is marketing and demand generation that will be done, that will be also work on enjoying opportunities that we will manage as well as a very tight connection on projects to be sure that the support experience for customers is well aligned. I don't wanna go into too much detail, but I will like to guarantee that our intent is not only to create an incredible technological experience, which the, the development teams are done, but also a great experience for the customers that are going through these projects, interacting with both teams that will work as one in service to empower the customer to achieve the outcomes that they need. >>Yeah, and just to comment maybe a little bit more on what Albar said, you know, it's not just about the product integration or it's really the full end to end experience for our customers. So when we embarked on this partnership with Microsoft, we really thought about what is the right product integration and with our engineering teams, but also how do we go and talk to customers with value prop together and all the way down through to support. So we actually been worked on how do we have a single joint support for our customers. So it doesn't really matter how the customer engages, they really see this as an end to end single solution across two companies. >>And that's so critical given just the, the natural challenges that that organizations face and the dynamics of the macro economic environment that we're living in. For them, for customers to be able to have that really seamless single point of interaction, they want that consistent experience on-prem to the cloud. But from an engagement perspective that you're, what sounds like what you're doing, Michael and Avaro is, is goes a long way to really giving customers a much more streamlined approach so that they can be laser focused on solving the business problems that they have, being competitive, getting products to market faster and all that good stuff. Michael, I wonder if you could comment on maybe the cultural alignment that Nutanix and Microsoft have. I know Microsoft's partner program has been around for decades and decades. Michael, what does that cultural alignment look like from, you know, the sales and marketing folks down to engineering, down to support? >>Yeah, I think honestly that was, that was something that kind of fit really well and we saw really a long alignment from day one. Of course, you know, Nutanix cares a lot about our customer experience, not just within the products, but again, through the entire life cycle to support and so forth. And Microsoft's no different, right? There's a huge emphasis on making sure that we provide the best customer experience and that we're also focusing on solving real world customer problems, right? And really focusing on the biggest problems that customers have. So really culturally it felt, it felt really natural. It felt like we were a single team, although it's, you know, two bar organizations working together, but I really felt like a single team working day in, day out on, on solving customer problems together. >>Yeah, >>Let, go ahead. >>No, I would say, well say Michael, the, the one element that we complement, the, I think the answer was super complete, is the, the fact that we work together from the outside in, look at it from the customer lenses is extremely powerful and inspire, as I mentioned, because that's what it's all about. And when you put the customer at the center, everything else falls in part on its its own place very, very quickly. And then it's hard work and innovation and, you know, doing what we do best, which is combining over superpowers in service of that customer. So that was the piece that, you know, I, I cannot emphasize enough how inspiring he's been. And again, the, the response for the previous is a great example of the opportunity that we have in there. >>And you've taken a lot of complexity out of the customer environment and I can imagine that the GA of Nutanix cloud clusters on Azure is gonna be a huge benefit for customers in every industry. Last question guys, I wanna get both your perspectives on Michael, we'll start with you and then Lvra will wrap with you. What's next? Obviously a lot of exciting stuff. What's next for the partnership of these, these two superheroes together, Michael? >>Yeah, so I think our goal doesn't change, right? I think our North star is to continue to make it easy for our customers to adopt, migrate and modernize their applications, leveraging Nutanix and Microsoft Azure, right? And I think NC two and Azure is just the start of that. So kind of maybe more immediate, like, you know, we mentioned obviously we have, we announced the ga that's J in Americas, but kind of the next more immediate step over the next few months look for us to continue expanding beyond Americas and making sure that we have support across all the global regions. And then beyond that, you know, again, as of our mentioned, it's working from kind of the s backwards. So we're, we're not, no, we're not waiting for ega. We're already working on the next set of solutions saying what are other problems that customer facing, especially across, they're running their workload cross on premises and public cloud, and what are the next set of solutions that we can deliver to the market to solve those real challenges for. >>It sounds really strongly that, that the partnership here, we're talking about Nutanix and Microsoft, it's really Nutanix and Microsoft with the customer at this center. I think you've both done a great job of articulating that there's laser focus there. Our last word to you, what excites you about the momentum that Microsoft and Nutanix have for the customers? >>Well, thank you Lisa. Michael, I will tell you, when you hear the customer feedback on the impact that you're having, that's the most inspiring part because you know you're generating value, you know, you're making a difference, especially in these complex times when the, the partnership gets tested where the, the right, you know, relationship gets built. We're being there for customers is extremely inspiring. Now, as Michael mentioned, this is all about what customer needs and how do we go even ahead of the game, being sure that we're ready not for what is the problem today, but the opportunities that we have tomorrow to keep working on this. We have a huge TA task ahead to be sure that we bring this value globally in the right way with the right quality. Every word, which is a, is never as small fist as you may imagine. You know, the, the world is a big place, but also the next wave of innovations that will be customer driven to keep and, and raise the bar on how, how much more value can we unlock and how much empowerment can we make for the customer to keep in innovating at their own pace, in their own terms. >>Absolutely that customer empowerment's key. Guys, it's been a pleasure talking to you about the announcement Nutanix cloud clusters on Azure of our Michael, thank you for your time, your inputs and helping us understand the impact that this powerhouse relationship is making. >>Thank you for having Lisa and thank you AAR for joining >>Me. Thank you Lisa, Michael, it's been fantastic. I looking forward and thank you to the audience for being here with us. Yeah, stay >>Tuned. Thanks to the audience. Exactly. And stay tuned. There's more to come. We have coming up next, a deeper conversation on the announcement with Dave and product execs from both Microsoft. You won't wanna.

>>The Cube presents UI Path Forward five. Brought to you by UI Path. >>We're back the cube's coverage of UI path forward. Five. And we're live. Dave Velante with Dave Nicholson. AJ Gupta is here. He's the Chief Digital Transformation Officer at the Motor Vehicles of California dmv. Welcome Jay. Good to see you. >>Thank you. >>Good to see you. Wow, you, you have an interesting job. I would just say, you know, I've been to going to conferences for a long time. I remember early last decade, Frank Sluman put up a slide. People ho hanging out, waiting outside the California dmv. You were the butt of many jokes, but we have a happy customer here, so we're gonna get it to your taste >>Of it. Yeah, very happy >>Customer, obviously transform the organization. I think it's pretty clear from our conversations that that automation has played a role in that. But first of all, tell us about yourself, your role and what's going on at the dmv. >>Sure. Myself, a j Gupta, I am the Chief Digital Transformation Officer at the dmv. Somewhat of i, one would say a made up title, but Governor's office asked me, Okay, we need help. And that's what >>Your title though? >>Yeah, yeah. So I'm like, well we are doing business and technology transformation. So that's, that's what I've been doing for the last three years at the dmv. Before that I was in private sector for 25 years, decided first time to give back cuz I was mostly doing public sector consulting. So here I am. >>Okay. So you knew the industry and that's cool that you wanted to give back because I mean obviously you just, in talking off camera, you're smart, you're very cogent and you know, a lot of times people in the private sector, they don't want to go work in the, in the public sector unless they're, unless they're power crazy, you know? Anyway, so speaking with David Nicholson, the experience has gone from really crappy to really great. I mean, take >>It from here. Yeah. Well, am I gonna be, I'm, because I'm from California, I was just, I was just, you know, we >>Got a dual case study >>Eloquently about, about the, the, the change that's happened just in, just in terms of simple things like a registration renewal. It used to be go online and pray and weed through things and now it's very simple, very, very fast. Tell us more about, about some of the things that you've done in the area of automation that have increased the percentage of things that could be done online without visiting a field office. Just as an >>Example. Yeah, what's the story? >>Yeah, so first of all, thank you for saying nice things about dmv, you as a customer. It means a lot because we have been very deliberately working towards solving all customer po pain points, whether it's in person experiences, online call centers, kiosks, so all across the channels. So we started our journey, myself and director Steve Gordon about three years ago, almost at the same time with the goal of making Department of Mo no motor vehicles in California as the best retail experience in the nation across industries. So that's our goal, right? Not there yet, but we are working towards it. So for, for our in person channels, which is what you may be familiar with, first of all, we wanna make sure brick and click and call all the customer journeys can be done across the channels. You can decide to start journey at one place, finish at another place. >>All that is very deliberate. We are also trying to make sure you don't have to come to field office at all. We would welcome you to come, we love you, but we don't want you to be there. You have better things to do for the economy. We want you to do that instead of showing up in the field office, being in the weight line. So that's number one. Creating more digital channels has been the key. We have created virtual field office. That's something that you would become familiar with if you are not as a DMV customer. During Covid, the goal was we provide almost all the services. We connect our technicians to the customer who are in need of a live conversation or a email or a text or a, or a SMS conversation or chat conversation in multiple languages or a video call, right? >>So we were able to accomplish that while Covid was going on, while the riots were going on. Those of your, you know about that, we, our offices were shut down. We created this channel, which we are continuing because it's a great disaster recovery business continuity channel, but also it can help keep people away from field office during peak hours. So that's been very deliberate. We have also added additional online services using bots. So we have created these web and process bots that actually let you do the intake, right? You, we could set up a new service in less than four weeks, a brand new service online. We have set up a brand new IVR service on call centers in less than a month for our seniors who didn't want to come to the field office and they were required certain pieces of information and we were able to provide that for our customers by creating this channel in less than less than four. >>And the pandemic was an accelerant to this was, was it the catalyst really? And then you guys compressed it? Or were, had you already started on the >>Well, we were >>Ready. I mean you, but you came on right? Just about just before the pandemic. >>Yeah. Yeah. So I came on in 2019, pandemic started in 2020 early. So we got lucky a little bit because we had a head start at, I was already working with u UI paths and we had come up with design patterns that we gonna take this journey for all DMV channels with using UiPath. So it was about timing that when it happened, it accelerated the need and it accelerated the actual work. I was thinking, I'll have a one year plan. I executed all of the one year plan items in less than two months out of necessity. So it accelerated definitely the execution of my plan. >>So when you talk about the chat channel, is that bots, is that humans or a combination? Yeah, >>It's a, it's a combination of it. I would say more AI than bots. Bots to the service fulfillment. So there is the user interaction where you have, you're saying something, the, the chat answers those questions, but then if you want something, hey, I want my, my registration renewed, right? It would take you to the right channel. And this is something we do today on our IVR channel. If you call in the DMV number in California, you'll see that your registration renewal is all automatic. You also have a AI listening to it. But also when you are saying, Yep, I wanna do it, then bot triggers certain aspects of the service fulfillment because our legacy is still sitting about 60 years old and we are able to still provide this modern facade for our customers with no gap and as quickly as possible within a month's time. How >>Many DMVs are in the state? >>Okay, so we have 230 different field locations out of which 180 are available for general public services. >>Okay. So and then you're, you're creating a digital overlay that's right >>To all of >>That, right? >>Yeah, it's digital and virtual overlay, right? Digital is fully self-service. Bots can do all your processing automation, can do all the processing. AI can do all the processing, but then you have virtual channels where you have customer interacting with the technicians or technicians virtually. But once a technician is done solving the problem, they click a button and bot does rest of the work for the technician. So that's where we are able to get some back office efficiency and transaction reduction. >>When was the last time you walked into a bank? >>Oh man. >>I mean, is that where we're going here where you just don't have to >>Go into the branch and that is the goal. In fact, we already have a starting point. I mean, just like you have ATM machines, we have kiosks already that do some of this automation work for us today. The goal is to not have to have to, unless you really want to, We actually set up these personas. One of them was high touch Henry. He likes to go to the field office and talk to people. We are there for them. But for the millennials, for the people who are like, I don't have time. I wanna like quickly finish this work off hours 24 by seven, which is where bots come in. They do not have weekends, HR complaint, they don't have overtime. They're able to solve these problems for me, 24 >>By seven. And what's the scope of your, like how many automations, how many bots? Can you give us a sense? >>Sure. So right now we are sitting at 36 different use cases. We have collected six point of eight point, well, we have saved 8.8 million just using the bots overall savings. If you were to look at virtual field office, which bots are part of, we have collected 388 million so far in that particular channel bots. I've also saved paper. I've saved a million sheets of paper through the bot, which I'm trying to remember how many trees it equates to, but it's a whole lot of trees that I've saved. And >>How many bots are we talking about? >>So it's 36 different use cases. So 36 >>Bots? >>Well, no, there's more bots I wanna say. So we are running at 85% efficiency, 50 bots. Oh wow. Yeah. >>Wow. Okay. So you, you asked the question about, you know, when was the last time someone was in a bank? The last time I was in a bank it was to deposit, you know, more than $10,000 in cash because of a cash transaction. Someone bought a car from me. It was more of a nuisance. I felt like I was being treated like a criminal. I was very clear what I was doing. I had just paid off a loan with that bank and I was giving them the cash for that transaction as opposed to the DMV transaction transferring title. That was easy. The DMV part was easier than the bank. And you're trying to make it even easier and it shouldn't, it shouldn't be that way. Yes. Right. But, but I, I have a, I have a question for you on, on that bot implementation. Can you give us, you've sort of give it us examples of how they interact. Yeah. But as your kind of prototypical California driver's license holder, how has that improved a specific transaction that I would be involved with? Can >>You, so well you as a Californian and you as a taxpayer, you as a Californian getting services and you as a taxpayer getting the most out of the money Okay. That the DMV spending on providing services, Right. Both are benefits to you. Sure. So bots have benefited in both of those areas. If you were used to the DMV three years ago, there was a whole lot of paper involved. You gotta fill this form out, you gotta fill this other form out and you gotta go to dmv. Oh by the way, your form, you didn't bring this thing with you. Your form has issues. We are calculated that about 30% of paper workloads are wasted because they just have bad data, right? There is no control. There's nobody telling you, hey, do this. Right. Even dates could be wrong, names could be wrong fields, maybe incomplete and such. >>So we were able to automate a whole lot of that by creating self-service channels, which are accelerated by bot. So we have these web acceleration platforms that collect the data, bots do the validation, they also verify the information, give you real time feedback or near real time feedback that hey, this is what you need to change. This is when you need to verify. So all the business rules are in the bot. And then once you're done, it'll commit the information to our legacy systems, which wouldn't have been possible unless a technician was punching it in manually. So there is a third cohort of Californians, which is our employees. We have 10,000 of those. They, I don't want them to get carpal tunnel. I want them to make sure they're spending more time thinking and helping our customers, looking at the customers rather than typing things. And that's what we are able to accomplish with the bots where you press that one button, which will have required maybe 50 more keystrokes and that's gone. And now you're saving time, you're also saving the effort and the attention loss of serving the best. >>Jay, what does it take to get a new process on board? So I'm thinking about real id, I just went through that in Massachusetts. I took, it was gonna be months to get to the dmv. So I ended up going through a aaa, had to get all these documents, I uploaded all the documents. Of course when I showed up, none were there. Thankfully I had backup copies. But it was really a pleasant experience. Are you, describe what you're doing with real ID and what role bots play? >>Yeah, sure. So with real id, what we are doing today and what I, what we'll be doing in the future, so I can talk about both. What we are doing today is that we are aligning most of the work to be done upfront by the customer. Because real ID is a complex transaction. You've gotta have four different pieces of documentation. You need to provide your information, it needs to match our records. And then you show up to the field office. And by the way, oh man, I did not upload this information. We are getting about 15 to 17% returns customers. And that's a whole lot of time. Every single mile our customer travels to the DMV office, which averages to about 13 miles. In my calculation for average customer, it's a dollar spent in carbon footprint in the time lost in the technician time trying to triage out some other things. So you're talking $26 per visit to the economy. >>Yeah. An amazing frustration, Yes. >>That has to come back and, and our customer satisfaction scores, which we really like to track, goes down right away. So in general, for real, id, what we have been, what we have done is created bunch of self-service channels, which are accelerated by workflow engines, by AI and by bots to collect the documentation, verify the documentation against external systems because we actually connect with Department of Homeland Security verify, you know, what's your passport about? We look at your picture and we verify that yep, it is truly a passport and yours and not your wives. Right? Or not a picture of a dog. And it's actually truly you, right? I mean, people do all kind of fun stuff by mistake or intentionally. So we wanna make sure we save time for our customer, we save time for our, for our employees, and we have zero returns required when employees, where customer shows up, which by the way is requirement right now. But the Department of Homeland Security is in a rule making process. And we are hopeful, very hopeful at this point in time that we'll be able to take the entire experience and get it done from home. And that'll give us a whole lot more efficiency, as you can imagine. And bots are at the tail end of it, committing all the data and transactions into our systems faster and with more accuracy. >>That's a great story. I mean, really congratulations and, and I guess I'll leave it. Last question is, where do you want to take this? What's the, what's your roadmap look like? What's your runway look like? Is it, is there endless opportunities to automate at the state or do you see a sort of light at the end of the tunnel? >>Sure. So there is a thing I shared in the previous session that I was in, which is be modern while we modernize. So that's been the goal with the bot. They are integral part of my transition architecture as I modernize the entire dmv, bring them from 90 60, bringing us from 1960 to 2022 or even 2025 and do it now, right? So bots are able to get me to a place where customers expectations are managed. They are getting their online, they're getting their mobile experience, they are avoiding making field off his trips and avoiding any kind of paper based processing right? For our employees and customers as well. So bots are serving that need today as part of the transition strategy going from 1960 to 2022 in the future. They're continue gonna continue to service. I think it's one thing that was talked about by the previous sessions today that we, they, they're looking at empowering the employees to do their own work back office work also in a full automation way and self-power them to automate their own processes. So that's one of the strategies we're gonna look for. But also we'll continue to have a strategy where we need to remain nimble with upcoming needs and have a faster go to market market plan using the bot. >>Outstanding. Well thanks so much for sharing your, your story and, and thanks for helping Dave. >>Real life testimony. I never, never thought I'd be coming on to praise the California dmv. Here I am and it's legit. Yeah, >>Well done. Can I, can I make an introduction to our Massachusetts colleagues? >>Good to, well actually we have, we have been working with state of New York, Massachusetts, Nevara, Arizona. So goal is to share but also learn from >>That. Help us out, help us out. >>But nice to be here, >>Great >>To have you and looking for feedback next time you was at dmv. >>All right. Oh, absolutely. Yeah. Get that, fill out that NPS score. All right. Thank you for watching. This is Dave Valante for Dave Nicholson. Forward five UI customer conference from the Venetian in Las Vegas. We'll be right back.

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