(upbeat music) >> Hello, everyone, and welcome to theCUBE's presentation of the "AWS Startup Showcase." This is season three, episode one. The focus of this episode is AI/ML: Top Startups Building Foundational Models, Infrastructure, and AI. It's great topics, super-relevant, and it's part of our ongoing coverage of startups in the AWS ecosystem. I'm your host, John Furrier, with theCUBE. Today, we're excited to be joined by Jay Marshall, VP of Business Development at Neural Magic. Jay, thanks for coming on theCUBE. >> Hey, John, thanks so much. Thanks for having us. >> We had a great CUBE conversation with you guys. This is very much about the company focuses. It's a feature presentation for the "Startup Showcase," and the machine learning at scale is the topic, but in general, it's more, (laughs) and we should call it "Machine Learning and AI: How to Get Started," because everybody is retooling their business. Companies that aren't retooling their business right now with AI first will be out of business, in my opinion. You're seeing massive shift. This is really truly the beginning of the next-gen machine learning AI trend. It's really seeing ChatGPT. Everyone sees that. That went mainstream. But this is just the beginning. This is scratching the surface of this next-generation AI with machine learning powering it, and with all the goodness of cloud, cloud scale, and how horizontally scalable it is. The resources are there. You got the Edge. Everything's perfect for AI 'cause data infrastructure's exploding in value. AI is just the applications. This is a super topic, so what do you guys see in this general area of opportunities right now in the headlines? And I'm sure you guys' phone must be ringing off the hook, metaphorically speaking, or emails and meetings and Zooms. What's going on over there at Neural Magic? >> No, absolutely, and you pretty much nailed most of it. I think that, you know, my background, we've seen for the last 20-plus years. Even just getting enterprise applications kind of built and delivered at scale, obviously, amazing things with AWS and the cloud to help accelerate that. And we just kind of figured out in the last five or so years how to do that productively and efficiently, kind of from an operations perspective. Got development and operations teams. We even came up with DevOps, right? But now, we kind of have this new kind of persona and new workload that developers have to talk to, and then it has to be deployed on those ITOps solutions. And so you pretty much nailed it. Folks are saying, "Well, how do I do this?" These big, generational models or foundational models, as we're calling them, they're great, but enterprises want to do that with their data, on their infrastructure, at scale, at the edge. So for us, yeah, we're helping enterprises accelerate that through optimizing models and then delivering them at scale in a more cost-effective fashion. >> Yeah, and I think one of the things, the benefits of OpenAI we saw, was not only is it open source, then you got also other models that are more proprietary, is that it shows the world that this is really happening, right? It's a whole nother level, and there's also new landscape kind of maps coming out. You got the generative AI, and you got the foundational models, large LLMs. Where do you guys fit into the landscape? Because you guys are in the middle of this. How do you talk to customers when they say, "I'm going down this road. I need help. I'm going to stand this up." This new AI infrastructure and applications, where do you guys fit in the landscape? >> Right, and really, the answer is both. I think today, when it comes to a lot of what for some folks would still be considered kind of cutting edge around computer vision and natural language processing, a lot of our optimization tools and our runtime are based around most of the common computer vision and natural language processing models. So your YOLOs, your BERTs, you know, your DistilBERTs and what have you, so we work to help optimize those, again, who've gotten great performance and great value for customers trying to get those into production. But when you get into the LLMs, and you mentioned some of the open source components there, our research teams have kind of been right in the trenches with those. So kind of the GPT open source equivalent being OPT, being able to actually take, you know, a multi-$100 billion parameter model and sparsify that or optimize that down, shaving away a ton of parameters, and being able to run it on smaller infrastructure. So I think the evolution here, you know, all this stuff came out in the last six months in terms of being turned loose into the wild, but we're staying in the trenches with folks so that we can help optimize those as well and not require, again, the heavy compute, the heavy cost, the heavy power consumption as those models evolve as well. So we're staying right in with everybody while they're being built, but trying to get folks into production today with things that help with business value today. >> Jay, I really appreciate you coming on theCUBE, and before we came on camera, you said you just were on a customer call. I know you got a lot of activity. What specific things are you helping enterprises solve? What kind of problems? Take us through the spectrum from the beginning, people jumping in the deep end of the pool, some people kind of coming in, starting out slow. What are the scale? Can you scope the kind of use cases and problems that are emerging that people are calling you for? >> Absolutely, so I think if I break it down to kind of, like, your startup, or I maybe call 'em AI native to kind of steal from cloud native years ago, that group, it's pretty much, you know, part and parcel for how that group already runs. So if you have a data science team and an ML engineering team, you're building models, you're training models, you're deploying models. You're seeing firsthand the expense of starting to try to do that at scale. So it's really just a pure operational efficiency play. They kind of speak natively to our tools, which we're doing in the open source. So it's really helping, again, with the optimization of the models they've built, and then, again, giving them an alternative to expensive proprietary hardware accelerators to have to run them. Now, on the enterprise side, it varies, right? You have some kind of AI native folks there that already have these teams, but you also have kind of, like, AI curious, right? Like, they want to do it, but they don't really know where to start, and so for there, we actually have an open source toolkit that can help you get into this optimization, and then again, that runtime, that inferencing runtime, purpose-built for CPUs. It allows you to not have to worry, again, about do I have a hardware accelerator available? How do I integrate that into my application stack? If I don't already know how to build this into my infrastructure, does my ITOps teams, do they know how to do this, and what does that runway look like? How do I cost for this? How do I plan for this? When it's just x86 compute, we've been doing that for a while, right? So it obviously still requires more, but at least it's a little bit more predictable. >> It's funny you mentioned AI native. You know, born in the cloud was a phrase that was out there. Now, you have startups that are born in AI companies. So I think you have this kind of cloud kind of vibe going on. You have lift and shift was a big discussion. Then you had cloud native, kind of in the cloud, kind of making it all work. Is there a existing set of things? People will throw on this hat, and then what's the difference between AI native and kind of providing it to existing stuff? 'Cause we're a lot of people take some of these tools and apply it to either existing stuff almost, and it's not really a lift and shift, but it's kind of like bolting on AI to something else, and then starting with AI first or native AI. >> Absolutely. It's a- >> How would you- >> It's a great question. I think that probably, where I'd probably pull back to kind of allow kind of retail-type scenarios where, you know, for five, seven, nine years or more even, a lot of these folks already have data science teams, you know? I mean, they've been doing this for quite some time. The difference is the introduction of these neural networks and deep learning, right? Those kinds of models are just a little bit of a paradigm shift. So, you know, I obviously was trying to be fun with the term AI native, but I think it's more folks that kind of came up in that neural network world, so it's a little bit more second nature, whereas I think for maybe some traditional data scientists starting to get into neural networks, you have the complexity there and the training overhead, and a lot of the aspects of getting a model finely tuned and hyperparameterization and all of these aspects of it. It just adds a layer of complexity that they're just not as used to dealing with. And so our goal is to help make that easy, and then of course, make it easier to run anywhere that you have just kind of standard infrastructure. >> Well, the other point I'd bring out, and I'd love to get your reaction to, is not only is that a neural network team, people who have been focused on that, but also, if you look at some of the DataOps lately, AIOps markets, a lot of data engineering, a lot of scale, folks who have been kind of, like, in that data tsunami cloud world are seeing, they kind of been in this, right? They're, like, been experiencing that. >> No doubt. I think it's funny the data lake concept, right? And you got data oceans now. Like, the metaphors just keep growing on us, but where it is valuable in terms of trying to shift the mindset, I've always kind of been a fan of some of the naming shift. I know with AWS, they always talk about purpose-built databases. And I always liked that because, you know, you don't have one database that can do everything. Even ones that say they can, like, you still have to do implementation detail differences. So sitting back and saying, "What is my use case, and then which database will I use it for?" I think it's kind of similar here. And when you're building those data teams, if you don't have folks that are doing data engineering, kind of that data harvesting, free processing, you got to do all that before a model's even going to care about it. So yeah, it's definitely a central piece of this as well, and again, whether or not you're going to be AI negative as you're making your way to kind of, you know, on that journey, you know, data's definitely a huge component of it. >> Yeah, you would have loved our Supercloud event we had. Talk about naming and, you know, around data meshes was talked about a lot. You're starting to see the control plane layers of data. I think that was the beginning of what I saw as that data infrastructure shift, to be horizontally scalable. So I have to ask you, with Neural Magic, when your customers and the people that are prospects for you guys, they're probably asking a lot of questions because I think the general thing that we see is, "How do I get started? Which GPU do I use?" I mean, there's a lot of things that are kind of, I won't say technical or targeted towards people who are living in that world, but, like, as the mainstream enterprises come in, they're going to need a playbook. What do you guys see, what do you guys offer your clients when they come in, and what do you recommend? >> Absolutely, and I think where we hook in specifically tends to be on the training side. So again, I've built a model. Now, I want to really optimize that model. And then on the runtime side when you want to deploy it, you know, we run that optimized model. And so that's where we're able to provide. We even have a labs offering in terms of being able to pair up our engineering teams with a customer's engineering teams, and we can actually help with most of that pipeline. So even if it is something where you have a dataset and you want some help in picking a model, you want some help training it, you want some help deploying that, we can actually help there as well. You know, there's also a great partner ecosystem out there, like a lot of folks even in the "Startup Showcase" here, that extend beyond into kind of your earlier comment around data engineering or downstream ITOps or the all-up MLOps umbrella. So we can absolutely engage with our labs, and then, of course, you know, again, partners, which are always kind of key to this. So you are spot on. I think what's happened with the kind of this, they talk about a hockey stick. This is almost like a flat wall now with the rate of innovation right now in this space. And so we do have a lot of folks wanting to go straight from curious to native. And so that's definitely where the partner ecosystem comes in so hard 'cause there just isn't anybody or any teams out there that, I literally do from, "Here's my blank database, and I want an API that does all the stuff," right? Like, that's a big chunk, but we can definitely help with the model to delivery piece. >> Well, you guys are obviously a featured company in this space. Talk about the expertise. A lot of companies are like, I won't say faking it till they make it. You can't really fake security. You can't really fake AI, right? So there's going to be a learning curve. They'll be a few startups who'll come out of the gate early. You guys are one of 'em. Talk about what you guys have as expertise as a company, why you're successful, and what problems do you solve for customers? >> No, appreciate that. Yeah, we actually, we love to tell the story of our founder, Nir Shavit. So he's a 20-year professor at MIT. Actually, he was doing a lot of work on kind of multicore processing before there were even physical multicores, and actually even did a stint in computational neurobiology in the 2010s, and the impetus for this whole technology, has a great talk on YouTube about it, where he talks about the fact that his work there, he kind of realized that the way neural networks encode and how they're executed by kind of ramming data layer by layer through these kind of HPC-style platforms, actually was not analogous to how the human brain actually works. So we're on one side, we're building neural networks, and we're trying to emulate neurons. We're not really executing them that way. So our team, which one of the co-founders, also an ex-MIT, that was kind of the birth of why can't we leverage this super-performance CPU platform, which has those really fat, fast caches attached to each core, and actually start to find a way to break that model down in a way that I can execute things in parallel, not having to do them sequentially? So it is a lot of amazing, like, talks and stuff that show kind of the magic, if you will, a part of the pun of Neural Magic, but that's kind of the foundational layer of all the engineering that we do here. And in terms of how we're able to bring it to reality for customers, I'll give one customer quote where it's a large retailer, and it's a people-counting application. So a very common application. And that customer's actually been able to show literally double the amount of cameras being run with the same amount of compute. So for a one-to-one perspective, two-to-one, business leaders usually like that math, right? So we're able to show pure cost savings, but even performance-wise, you know, we have some of the common models like your ResNets and your YOLOs, where we can actually even perform better than hardware-accelerated solutions. So we're trying to do, I need to just dumb it down to better, faster, cheaper, but from a commodity perspective, that's where we're accelerating. >> That's not a bad business model. Make things easier to use, faster, and reduce the steps it takes to do stuff. So, you know, that's always going to be a good market. Now, you guys have DeepSparse, which we've talked about on our CUBE conversation prior to this interview, delivers ML models through the software so the hardware allows for a decoupling, right? >> Yep. >> Which is going to drive probably a cost advantage. Also, it's also probably from a deployment standpoint it must be easier. Can you share the benefits? Is it a cost side? Is it more of a deployment? What are the benefits of the DeepSparse when you guys decouple the software from the hardware on the ML models? >> No you actually, you hit 'em both 'cause that really is primarily the value. Because ultimately, again, we're so early. And I came from this world in a prior life where I'm doing Java development, WebSphere, WebLogic, Tomcat open source, right? When we were trying to do innovation, we had innovation buckets, 'cause everybody wanted to be on the web and have their app and a browser, right? We got all the money we needed to build something and show, hey, look at the thing on the web, right? But when you had to get in production, that was the challenge. So to what you're speaking to here, in this situation, we're able to show we're just a Python package. So whether you just install it on the operating system itself, or we also have a containerized version you can drop on any container orchestration platform, so ECS or EKS on AWS. And so you get all the auto-scaling features. So when you think about that kind of a world where you have everything from real-time inferencing to kind of after hours batch processing inferencing, the fact that you can auto scale that hardware up and down and it's CPU based, so you're paying by the minute instead of maybe paying by the hour at a lower cost shelf, it does everything from pure cost to, again, I can have my standard IT team say, "Hey, here's the Kubernetes in the container," and it just runs on the infrastructure we're already managing. So yeah, operational, cost and again, and many times even performance. (audio warbles) CPUs if I want to. >> Yeah, so that's easier on the deployment too. And you don't have this kind of, you know, blank check kind of situation where you don't know what's on the backend on the cost side. >> Exactly. >> And you control the actual hardware and you can manage that supply chain. >> And keep in mind, exactly. Because the other thing that sometimes gets lost in the conversation, depending on where a customer is, some of these workloads, like, you know, you and I remember a world where even like the roundtrip to the cloud and back was a problem for folks, right? We're used to extremely low latency. And some of these workloads absolutely also adhere to that. But there's some workloads where the latency isn't as important. And we actually even provide the tuning. Now, if we're giving you five milliseconds of latency and you don't need that, you can tune that back. So less CPU, lower cost. Now, throughput and other things come into play. But that's the kind of configurability and flexibility we give for operations. >> All right, so why should I call you if I'm a customer or prospect Neural Magic, what problem do I have or when do I know I need you guys? When do I call you in and what does my environment look like? When do I know? What are some of the signals that would tell me that I need Neural Magic? >> No, absolutely. So I think in general, any neural network, you know, the process I mentioned before called sparcification, it's, you know, an optimization process that we specialize in. Any neural network, you know, can be sparcified. So I think if it's a deep-learning neural network type model. If you're trying to get AI into production, you have cost concerns even performance-wise. I certainly hate to be too generic and say, "Hey, we'll talk to everybody." But really in this world right now, if it's a neural network, it's something where you're trying to get into production, you know, we are definitely offering, you know, kind of an at-scale performant deployable solution for deep learning models. >> So neural network you would define as what? Just devices that are connected that need to know about each other? What's the state-of-the-art current definition of neural network for customers that may think they have a neural network or might not know they have a neural network architecture? What is that definition for neural network? >> That's a great question. So basically, machine learning models that fall under this kind of category, you hear about transformers a lot, or I mentioned about YOLO, the YOLO family of computer vision models, or natural language processing models like BERT. If you have a data science team or even developers, some even regular, I used to call myself a nine to five developer 'cause I worked in the enterprise, right? So like, hey, we found a new open source framework, you know, I used to use Spring back in the day and I had to go figure it out. There's developers that are pulling these models down and they're figuring out how to get 'em into production, okay? So I think all of those kinds of situations, you know, if it's a machine learning model of the deep learning variety that's, you know, really specifically where we shine. >> Okay, so let me pretend I'm a customer for a minute. I have all these videos, like all these transcripts, I have all these people that we've interviewed, CUBE alumnis, and I say to my team, "Let's AI-ify, sparcify theCUBE." >> Yep. >> What do I do? I mean, do I just like, my developers got to get involved and they're going to be like, "Well, how do I upload it to the cloud? Do I use a GPU?" So there's a thought process. And I think a lot of companies are going through that example of let's get on this AI, how can it help our business? >> Absolutely. >> What does that progression look like? Take me through that example. I mean, I made up theCUBE example up, but we do have a lot of data. We have large data models and we have people and connect to the internet and so we kind of seem like there's a neural network. I think every company might have a neural network in place. >> Well, and I was going to say, I think in general, you all probably do represent even the standard enterprise more than most. 'Cause even the enterprise is going to have a ton of video content, a ton of text content. So I think it's a great example. So I think that that kind of sea or I'll even go ahead and use that term data lake again, of data that you have, you're probably going to want to be setting up kind of machine learning pipelines that are going to be doing all of the pre-processing from kind of the raw data to kind of prepare it into the format that say a YOLO would actually use or let's say BERT for natural language processing. So you have all these transcripts, right? So we would do a pre-processing path where we would create that into the file format that BERT, the machine learning model would know how to train off of. So that's kind of all the pre-processing steps. And then for training itself, we actually enable what's called sparse transfer learning. So that's transfer learning is a very popular method of doing training with existing models. So we would be able to retrain that BERT model with your transcript data that we have now done the pre-processing with to get it into the proper format. And now we have a BERT natural language processing model that's been trained on your data. And now we can deploy that onto DeepSparse runtime so that now you can ask that model whatever questions, or I should say pass, you're not going to ask it those kinds of questions ChatGPT, although we can do that too. But you're going to pass text through the BERT model and it's going to give you answers back. It could be things like sentiment analysis or text classification. You just call the model, and now when you pass text through it, you get the answers better, faster or cheaper. I'll use that reference again. >> Okay, we can create a CUBE bot to give us questions on the fly from the the AI bot, you know, from our previous guests. >> Well, and I will tell you using that as an example. So I had mentioned OPT before, kind of the open source version of ChatGPT. So, you know, typically that requires multiple GPUs to run. So our research team, I may have mentioned earlier, we've been able to sparcify that over 50% already and run it on only a single GPU. And so in that situation, you could train OPT with that corpus of data and do exactly what you say. Actually we could use Alexa, we could use Alexa to actually respond back with voice. How about that? We'll do an API call and we'll actually have an interactive Alexa-enabled bot. >> Okay, we're going to be a customer, let's put it on the list. But this is a great example of what you guys call software delivered AI, a topic we chatted about on theCUBE conversation. This really means this is a developer opportunity. This really is the convergence of the data growth, the restructuring, how data is going to be horizontally scalable, meets developers. So this is an AI developer model going on right now, which is kind of unique. >> It is, John, I will tell you what's interesting. And again, folks don't always think of it this way, you know, the AI magical goodness is now getting pushed in the middle where the developers and IT are operating. And so it again, that paradigm, although for some folks seem obvious, again, if you've been around for 20 years, that whole all that plumbing is a thing, right? And so what we basically help with is when you deploy the DeepSparse runtime, we have a very rich API footprint. And so the developers can call the API, ITOps can run it, or to your point, it's developer friendly enough that you could actually deploy our off-the-shelf models. We have something called the SparseZoo where we actually publish pre-optimized or pre-sparcified models. And so developers could literally grab those right off the shelf with the training they've already had and just put 'em right into their applications and deploy them as containers. So yeah, we enable that for sure as well. >> It's interesting, DevOps was infrastructure as code and we had a last season, a series on data as code, which we kind of coined. This is data as code. This is a whole nother level of opportunity where developers just want to have programmable data and apps with AI. This is a whole new- >> Absolutely. >> Well, absolutely great, great stuff. Our news team at SiliconANGLE and theCUBE said you guys had a little bit of a launch announcement you wanted to make here on the "AWS Startup Showcase." So Jay, you have something that you want to launch here? >> Yes, and thank you John for teeing me up. So I'm going to try to put this in like, you know, the vein of like an AWS, like main stage keynote launch, okay? So we're going to try this out. So, you know, a lot of our product has obviously been built on top of x86. I've been sharing that the past 15 minutes or so. And with that, you know, we're seeing a lot of acceleration for folks wanting to run on commodity infrastructure. But we've had customers and prospects and partners tell us that, you know, ARM and all of its kind of variance are very compelling, both cost performance-wise and also obviously with Edge. And wanted to know if there was anything we could do from a runtime perspective with ARM. And so we got the work and, you know, it's a hard problem to solve 'cause the instructions set for ARM is very different than the instruction set for x86, and our deep tensor column technology has to be able to work with that lower level instruction spec. But working really hard, the engineering team's been at it and we are happy to announce here at the "AWS Startup Showcase," that DeepSparse inference now has, or inference runtime now has support for AWS Graviton instances. So it's no longer just x86, it is also ARM and that obviously also opens up the door to Edge and further out the stack so that optimize once run anywhere, we're not going to open up. So it is an early access. So if you go to neuralmagic.com/graviton, you can sign up for early access, but we're excited to now get into the ARM side of the fence as well on top of Graviton. >> That's awesome. Our news team is going to jump on that news. We'll get it right up. We get a little scoop here on the "Startup Showcase." Jay Marshall, great job. That really highlights the flexibility that you guys have when you decouple the software from the hardware. And again, we're seeing open source driving a lot more in AI ops now with with machine learning and AI. So to me, that makes a lot of sense. And congratulations on that announcement. Final minute or so we have left, give a summary of what you guys are all about. Put a plug in for the company, what you guys are looking to do. I'm sure you're probably hiring like crazy. Take the last few minutes to give a plug for the company and give a summary. >> No, I appreciate that so much. So yeah, joining us out neuralmagic.com, you know, part of what we didn't spend a lot of time here, our optimization tools, we are doing all of that in the open source. It's called SparseML and I mentioned SparseZoo briefly. So we really want the data scientists community and ML engineering community to join us out there. And again, the DeepSparse runtime, it's actually free to use for trial purposes and for personal use. So you can actually run all this on your own laptop or on an AWS instance of your choice. We are now live in the AWS marketplace. So push button, deploy, come try us out and reach out to us on neuralmagic.com. And again, sign up for the Graviton early access. >> All right, Jay Marshall, Vice President of Business Development Neural Magic here, talking about performant, cost effective machine learning at scale. This is season three, episode one, focusing on foundational models as far as building data infrastructure and AI, AI native. I'm John Furrier with theCUBE. Thanks for watching. (bright upbeat music)

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI/ML Top Startups Building Foundation Model Infrastructure. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem to talk about data and analytics. I'm your host, Lisa Martin and today we're excited to be joined by two guests from Astronomer. Steven Hillion joins us, it's Chief Data Officer and Jeff Fletcher, it's director of ML. They're here to talk about machine learning and data orchestration. Guys, thank you so much for joining us today. >> Thank you. >> It's great to be here. >> Before we get into machine learning let's give the audience an overview of Astronomer. Talk about what that is, Steven. Talk about what you mean by data orchestration. >> Yeah, let's start with Astronomer. We're the Airflow company basically. The commercial developer behind the open-source project, Apache Airflow. I don't know if you've heard of Airflow. It's sort of de-facto standard these days for orchestrating data pipelines, data engineering pipelines, and as we'll talk about later, machine learning pipelines. It's really is the de-facto standard. I think we're up to about 12 million downloads a month. That's actually as a open-source project. I think at this point it's more popular by some measures than Slack. Airflow was created by Airbnb some years ago to manage all of their data pipelines and manage all of their workflows and now it powers the data ecosystem for organizations as diverse as Electronic Arts, Conde Nast is one of our big customers, a big user of Airflow. And also not to mention the biggest banks on Wall Street use Airflow and Astronomer to power the flow of data throughout their organizations. >> Talk about that a little bit more, Steven, in terms of the business impact. You mentioned some great customer names there. What is the business impact or outcomes that a data orchestration strategy enables businesses to achieve? >> Yeah, I mean, at the heart of it is quite simply, scheduling and managing data pipelines. And so if you have some enormous retailer who's managing the flow of information throughout their organization they may literally have thousands or even tens of thousands of data pipelines that need to execute every day to do things as simple as delivering metrics for the executives to consume at the end of the day, to producing on a weekly basis new machine learning models that can be used to drive product recommendations. One of our customers, for example, is a British food delivery service. And you get those recommendations in your application that says, "Well, maybe you want to have samosas with your curry." That sort of thing is powered by machine learning models that they train on a regular basis to reflect changing conditions in the market. And those are produced through Airflow and through the Astronomer platform, which is essentially a managed platform for running airflow. So at its simplest it really is just scheduling and managing those workflows. But that's easier said than done of course. I mean if you have 10 thousands of those things then you need to make sure that they all run that they all have sufficient compute resources. If things fail, how do you track those down across those 10,000 workflows? How easy is it for an average data scientist or data engineer to contribute their code, their Python notebooks or their SQL code into a production environment? And then you've got reproducibility, governance, auditing, like managing data flows across an organization which we think of as orchestrating them is much more than just scheduling. It becomes really complicated pretty quickly. >> I imagine there's a fair amount of complexity there. Jeff, let's bring you into the conversation. Talk a little bit about Astronomer through your lens, data orchestration and how it applies to MLOps. >> So I come from a machine learning background and for me the interesting part is that machine learning requires the expansion into orchestration. A lot of the same things that you're using to go and develop and build pipelines in a standard data orchestration space applies equally well in a machine learning orchestration space. What you're doing is you're moving data between different locations, between different tools, and then tasking different types of tools to act on that data. So extending it made logical sense from a implementation perspective. And a lot of my focus at Astronomer is really to explain how Airflow can be used well in a machine learning context. It is being used well, it is being used a lot by the customers that we have and also by users of the open source version. But it's really being able to explain to people why it's a natural extension for it and how well it fits into that. And a lot of it is also extending some of the infrastructure capabilities that Astronomer provides to those customers for them to be able to run some of the more platform specific requirements that come with doing machine learning pipelines. >> Let's get into some of the things that make Astronomer unique. Jeff, sticking with you, when you're in customer conversations, what are some of the key differentiators that you articulate to customers? >> So a lot of it is that we are not specific to one cloud provider. So we have the ability to operate across all of the big cloud providers. I know, I'm certain we have the best developers that understand how best practices implementations for data orchestration works. So we spend a lot of time talking to not just the business outcomes and the business users of the product, but also also for the technical people, how to help them better implement things that they may have come across on a Stack Overflow article or not necessarily just grown with how the product has migrated. So it's the ability to run it wherever you need to run it and also our ability to help you, the customer, better implement and understand those workflows that I think are two of the primary differentiators that we have. >> Lisa: Got it. >> I'll add another one if you don't mind. >> You can go ahead, Steven. >> Is lineage and dependencies between workflows. One thing we've done is to augment core Airflow with Lineage services. So using the Open Lineage framework, another open source framework for tracking datasets as they move from one workflow to another one, team to another, one data source to another is a really key component of what we do and we bundle that within the service so that as a developer or as a production engineer, you really don't have to worry about lineage, it just happens. Jeff, may show us some of this later that you can actually see as data flows from source through to a data warehouse out through a Python notebook to produce a predictive model or a dashboard. Can you see how those data products relate to each other? And when something goes wrong, figure out what upstream maybe caused the problem, or if you're about to change something, figure out what the impact is going to be on the rest of the organization. So Lineage is a big deal for us. >> Got it. >> And just to add on to that, the other thing to think about is that traditional Airflow is actually a complicated implementation. It required quite a lot of time spent understanding or was almost a bespoke language that you needed to be able to develop in two write these DAGs, which is like fundamental pipelines. So part of what we are focusing on is tooling that makes it more accessible to say a data analyst or a data scientist who doesn't have or really needs to gain the necessary background in how the semantics of Airflow DAGs works to still be able to get the benefit of what Airflow can do. So there is new features and capabilities built into the astronomer cloud platform that effectively obfuscates and removes the need to understand some of the deep work that goes on. But you can still do it, you still have that capability, but we are expanding it to be able to have orchestrated and repeatable processes accessible to more teams within the business. >> In terms of accessibility to more teams in the business. You talked about data scientists, data analysts, developers. Steven, I want to talk to you, as the chief data officer, are you having more and more conversations with that role and how is it emerging and evolving within your customer base? >> Hmm. That's a good question, and it is evolving because I think if you look historically at the way that Airflow has been used it's often from the ground up. You have individual data engineers or maybe single data engineering teams who adopt Airflow 'cause it's very popular. Lots of people know how to use it and they bring it into an organization and say, "Hey, let's use this to run our data pipelines." But then increasingly as you turn from pure workflow management and job scheduling to the larger topic of orchestration you realize it gets pretty complicated, you want to have coordination across teams, and you want to have standardization for the way that you manage your data pipelines. And so having a managed service for Airflow that exists in the cloud is easy to spin up as you expand usage across the organization. And thinking long term about that in the context of orchestration that's where I think the chief data officer or the head of analytics tends to get involved because they really want to think of this as a strategic investment that they're making. Not just per team individual Airflow deployments, but a network of data orchestrators. >> That network is key. Every company these days has to be a data company. We talk about companies being data driven. It's a common word, but it's true. It's whether it is a grocer or a bank or a hospital, they've got to be data companies. So talk to me a little bit about Astronomer's business model. How is this available? How do customers get their hands on it? >> Jeff, go ahead. >> Yeah, yeah. So we have a managed cloud service and we have two modes of operation. One, you can bring your own cloud infrastructure. So you can say here is an account in say, AWS or Azure and we can go and deploy the necessary infrastructure into that, or alternatively we can host everything for you. So it becomes a full SaaS offering. But we then provide a platform that connects at the backend to your internal IDP process. So however you are authenticating users to make sure that the correct people are accessing the services that they need with role-based access control. From there we are deploying through Kubernetes, the different services and capabilities into either your cloud account or into an account that we host. And from there Airflow does what Airflow does, which is its ability to then reach to different data systems and data platforms and to then run the orchestration. We make sure we do it securely, we have all the necessary compliance certifications required for GDPR in Europe and HIPAA based out of the US, and a whole bunch host of others. So it is a secure platform that can run in a place that you need it to run, but it is a managed Airflow that includes a lot of the extra capabilities like the cloud developer environment and the open lineage services to enhance the overall airflow experience. >> Enhance the overall experience. So Steven, going back to you, if I'm a Conde Nast or another organization, what are some of the key business outcomes that I can expect? As one of the things I think we've learned during the pandemic is access to realtime data is no longer a nice to have for organizations. It's really an imperative. It's that demanding consumer that wants to have that personalized, customized, instant access to a product or a service. So if I'm a Conde Nast or I'm one of your customers, what can I expect my business to be able to achieve as a result of data orchestration? >> Yeah, I think in a nutshell it's about providing a reliable, scalable, and easy to use service for developing and running data workflows. And talking of demanding customers, I mean, I'm actually a customer myself, as you mentioned, I'm the head of data for Astronomer. You won't be surprised to hear that we actually use Astronomer and Airflow to run all of our data pipelines. And so I can actually talk about my experience. When I started I was of course familiar with Airflow, but it always seemed a little bit unapproachable to me if I was introducing that to a new team of data scientists. They don't necessarily want to have to think about learning something new. But I think because of the layers that Astronomer has provided with our Astro service around Airflow it was pretty easy for me to get up and running. Of course I've got an incentive for doing that. I work for the Airflow company, but we went from about, at the beginning of last year, about 500 data tasks that we were running on a daily basis to about 15,000 every day. We run something like a million data operations every month within my team. And so as one outcome, just the ability to spin up new production workflows essentially in a single day you go from an idea in the morning to a new dashboard or a new model in the afternoon, that's really the business outcome is just removing that friction to operationalizing your machine learning and data workflows. >> And I imagine too, oh, go ahead, Jeff. >> Yeah, I think to add to that, one of the things that becomes part of the business cycle is a repeatable capabilities for things like reporting, for things like new machine learning models. And the impediment that has existed is that it's difficult to take that from a team that's an analyst team who then provide that or a data science team that then provide that to the data engineering team who have to work the workflow all the way through. What we're trying to unlock is the ability for those teams to directly get access to scheduling and orchestrating capabilities so that a business analyst can have a new report for C-suite execs that needs to be done once a week, but the time to repeatability for that report is much shorter. So it is then immediately in the hands of the person that needs to see it. It doesn't have to go into a long list of to-dos for a data engineering team that's already overworked that they eventually get it to it in a month's time. So that is also a part of it is that the realizing, orchestration I think is fairly well and a lot of people get the benefit of being able to orchestrate things within a business, but it's having more people be able to do it and shorten the time that that repeatability is there is one of the main benefits from good managed orchestration. >> So a lot of workforce productivity improvements in what you're doing to simplify things, giving more people access to data to be able to make those faster decisions, which ultimately helps the end user on the other end to get that product or the service that they're expecting like that. Jeff, I understand you have a demo that you can share so we can kind of dig into this. >> Yeah, let me take you through a quick look of how the whole thing works. So our starting point is our cloud infrastructure. This is the login. You go to the portal. You can see there's a a bunch of workspaces that are available. Workspaces are like individual places for people to operate in. I'm not going to delve into all the deep technical details here, but starting point for a lot of our data science customers is we have what we call our Cloud IDE, which is a web-based development environment for writing and building out DAGs without actually having to know how the underpinnings of Airflow work. This is an internal one, something that we use. You have a notebook-like interface that lets you write python code and SQL code and a bunch of specific bespoke type of blocks if you want. They all get pulled together and create a workflow. So this is a workflow, which gets compiled to something that looks like a complicated set of Python code, which is the DAG. I then have a CICD process pipeline where I commit this through to my GitHub repo. So this comes to a repo here, which is where these DAGs that I created in the previous step exist. I can then go and say, all right, I want to see how those particular DAGs have been running. We then get to the actual Airflow part. So this is the managed Airflow component. So we add the ability for teams to fairly easily bring up an Airflow instance and write code inside our notebook-like environment to get it into that instance. So you can see it's been running. That same process that we built here that graph ends up here inside this, but you don't need to know how the fundamentals of Airflow work in order to get this going. Then we can run one of these, it runs in the background and we can manage how it goes. And from there, every time this runs, it's emitting to a process underneath, which is the open lineage service, which is the lineage integration that allows me to come in here and have a look and see this was that actual, that same graph that we built, but now it's the historic version. So I know where things started, where things are going, and how it ran. And then I can also do a comparison. So if I want to see how this particular run worked compared to one historically, I can grab one from a previous date and it will show me the comparison between the two. So that combination of managed Airflow, getting Airflow up and running very quickly, but the Cloud IDE that lets you write code and know how to get something into a repeatable format get that into Airflow and have that attached to the lineage process adds what is a complete end-to-end orchestration process for any business looking to get the benefit from orchestration. >> Outstanding. Thank you so much Jeff for digging into that. So one of my last questions, Steven is for you. This is exciting. There's a lot that you guys are enabling organizations to achieve here to really become data-driven companies. So where can folks go to get their hands on this? >> Yeah, just go to astronomer.io and we have plenty of resources. If you're new to Airflow, you can read our documentation, our guides to getting started. We have a CLI that you can download that is really I think the easiest way to get started with Airflow. But you can actually sign up for a trial. You can sign up for a guided trial where our teams, we have a team of experts, really the world experts on getting Airflow up and running. And they'll take you through that trial and allow you to actually kick the tires and see how this works with your data. And I think you'll see pretty quickly that it's very easy to get started with Airflow, whether you're doing that from the command line or doing that in our cloud service. And all of that is available on our website >> astronomer.io. Jeff, last question for you. What are you excited about? There's so much going on here. What are some of the things, maybe you can give us a sneak peek coming down the road here that prospects and existing customers should be excited about? >> I think a lot of the development around the data awareness components, so one of the things that's traditionally been complicated with orchestration is you leave your data in the place that you're operating on and we're starting to have more data processing capability being built into Airflow. And from a Astronomer perspective, we are adding more capabilities around working with larger datasets, doing bigger data manipulation with inside the Airflow process itself. And that lends itself to better machine learning implementation. So as we start to grow and as we start to get better in the machine learning context, well, in the data awareness context, it unlocks a lot more capability to do and implement proper machine learning pipelines. >> Awesome guys. Exciting stuff. Thank you so much for talking to me about Astronomer, machine learning, data orchestration, and really the value in it for your customers. Steve and Jeff, we appreciate your time. >> Thank you. >> My pleasure, thanks. >> And we thank you for watching. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem. I'm your host, Lisa Martin. You're watching theCUBE, the leader in live tech coverage. (upbeat music)

>> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Hey everyone and welcome back to Barcelona, Spain, it's theCUBE. We are live at MWC 23. This is day two of our coverage, we're giving you four days of coverage, but you already know that because you were here yesterday. Lisa Martin with Dave Nicholson. Dave this show is massive. I was walking in this morning and almost getting claustrophobic with the 80,000 people that are joining us. There is, seems to be at MWC 23 more interest in enterprise-class technology than we've ever seen before. What are some of the things that you've observed with that regard? >> Well I've observed a lot of people racing to the highest level messaging about how wonderful it is to have the kiss of a breeze on your cheek, and to feel the flowing wheat. (laughing) I want to hear about the actual things that make this stuff possible. >> Right. >> So I think we have a couple of guests here who can help us start to go down that path of actually understanding the real cool stuff that's behind the scenes. >> And absolutely we got some cool stuff. We've got two guests from Dell. Dave Lincoln is here, the VP of Networking and Emerging the Server Solutions, and Deania Davidson, Director Edge Server Product Planning and Management at Dell. So great to have you. >> Thank you. >> Two Daves, and a Davidson. >> (indistinct) >> Just me who stands alone here. (laughing) So guys talk about, Dave, we'll start with you the newest generation of PowerEdge servers. What's new? Why is it so exciting? What challenges for telecom operators is it solving? >> Yeah, well so this is actually Dell's largest server launch ever. It's the most expansive, which is notable because of, we have a pretty significant portfolio. We're very proud of our core mainstream portfolio. But really since the Supercompute in Dallas in November, that we started a rolling thunder of launches. MWC being part of that leading up to DTW here in May, where we're actually going to be announcing big investments in those parts of the market that are the growth segments of server. Specifically AIML, where we in, to address that. We're investing heavy in our XE series which we, as I said, we announced at Supercompute in November. And then we have to address the CSP segment, a big investment around the HS series which we just announced, and then lastly, the edge telecom segment which we're, we had the biggest investment, biggest announce in portfolio launch with XR series. >> Deania, lets dig into that. >> Yeah. >> Where we see the growth coming from you mentioned telecom CSPs with the edge. What are some of the growth opportunities there that organizations need Dell's help with to manage, so that they can deliver what they're demanding and user is wanting? >> The biggest areas being obviously, in addition the telecom has been the biggest one, but the other areas too we're seeing is in retail and manufacturing as well. And, so internally, I mean we're going to be focused on hardware, but we also have a solutions team who are working with us to build the solutions focused on retail, and edge and telecom as well on top of the servers that we'll talk about shortly. >> What are some of the biggest challenges that retailers and manufacturers are facing? And during the pandemic retailers, those that were successful pivoted very quickly to curbside delivery. >> Deania: Yeah. >> Those that didn't survive weren't able to do that digitally. >> Deania: Yeah. >> But we're seeing such demand. >> Yeah. >> At the retail edge. On the consumer side we want to get whatever we want right now. >> Yes. >> It has to be delivered, it has to be personalized. Talk a little bit more about some of the challenges there, within those two verticals and how Dell is helping to address those with the new server technologies. >> For retail, I think there's couple of things, the one is like in the fast food area. So obviously through COVID a lot of people got familiar and comfortable with driving through. >> Lisa: Yeah. >> And so there's probably a certain fast food restaurant everyone's pretty familiar with, they're pretty efficient in that, and so there are other customers who are trying to replicate that, and so how do we help them do that all, from a technology perspective. From a retail, it's one of the pickup and the online experience, but when you go into a store, I don't know about you but I go to Target, and I'm looking for something and I have kids who are kind of distracting you. Its like where is this one thing, and so I pull up the Target App for example, and it tells me where its at, right. And then obviously, stores want to make more money, so like hey, since you picked this thing, there are these things around you. So things like that is what we're having conversations with customers about. >> It's so interesting because the demand is there. >> Yeah, it is. >> And its not going to go anywhere. >> No. >> And it's certainly not going to be dialed down. We're not going to want less stuff, less often. >> Yeah (giggles) >> And as typical consumers, we don't necessarily make the association between what we're seeing in the palm of our hand on a mobile device. >> Deania: Right. >> And the infrastructure that's actually supporting all of it. >> Deania: Right. >> People hear the term Cloud and they think cloud-phone mystery. >> Yeah, magic just happens. >> Yeah. >> Yeah. >> But in fact, in order to support the things that we want to be able to do. >> Yeah. >> On the move, you have to optimize the server hardware. >> Deania: Yes. >> In certain ways. What does that mean exactly? When you say that its optimized, what are the sorts of decisions that you make when you're building? I think of this in the terms of Lego bricks. >> Yes, yeah >> Put together. What are some of the decisions that you make? >> So there were few key things that we really had to think about in terms of what was different from the Data center, which obviously supports the cloud environment, but it was all about how do we get closer to the customer right? How do we get things really fast and how do we compute that information really quickly. So for us, it's things like size. All right, so our server is going to weigh one of them is the size of a shoe box and (giggles), we have a picture with Dave. >> Dave: It's true. >> Took off his shoe. >> Its actually, its actually as big as a shoe. (crowd chuckles) >> It is. >> It is. >> To be fair, its a pretty big shoe. >> True, true. >> It is, but its small in relative to the old big servers that you see. >> I see what you're doing, you find a guy with a size 12, (crowd giggles) >> Yeah. >> Its the size of your shoe. >> Yeah. >> Okay. >> Its literally the size of a shoe, and that's our smallest server and its the smallest one in the portfolio, its the XR 4000, and so we've actually crammed a lot of technology in there going with the Intel ZRT processors for example to get into that compute power. The XR 8000 which you'll be hearing a lot more about shortly with our next guest is one I think from a telco perspective is our flagship product, and its size was a big thing there too. Ruggedization so its like (indistinct) certification, so it can actually operate continuously in negative 5 to 55 C, which for customers, or they need that range of temperature operation, flexibility was a big thing too. In meaning that, there are some customers who wanted to have one system in different areas of deployment. So can I take this one system and configure it one way, take that same system, configure another way and have it here. So flexibility was really key for us as well, and so we'll actually be seeing that in the next segment coming. >> I think one of, some of the common things you're hearing from this is our focus on innovation, purpose build servers, so yes our times, you know economic situation like in itself is tough yeah. But far from receding we've doubled down on investment and you've seen that with the products that we are launching here, and we will be launching in the years to come. >> I imagine there's a pretty sizeable day impact to the total adjustable market for PowerEdge based on the launch what you're doing, its going to be a tam, a good size tam expansion. >> Yeah, absolutely. Depending on how you look at it, its roughly we add about $30 Billion of adjustable tam between the three purposeful series that we've launched, XE, HS and XR. >> Can you comment on, I know Dell and customers are like this. Talk about, I'd love to get both of your perspective, I'm sure you have a favorite customer stories. But talk about the involvement of the customer in the generation, and the evolution of PowerEdge. Where are they in that process? What kind of feedback do they deliver? >> Well, I mean, just to start, one thing that is essential Cortana of Dell period, is it all is about the customer. All of it, everything that we do is about the customer, and so there is a big focus at our level, from on high to get out there and talk with customers, and actually we have a pretty good story around XR8000 which is call it our flagship of the XR line that we've just announced, and because of this deep customer intimacy, there was a last minute kind of architectural design change. >> Hm-mm. >> Which actually would have been, come to find out it would have been sort of a fatal flaw for deployment. So we corrected that because of this tight intimacy with our customers. This was in two Thanksgiving ago about and, so anyways it's super cool and the fact that we were able to make a change so late in development cycle, that's a testament to a lot of the speed and, speed of innovation that we're driving, so anyway that was that's one, just case of one example. >> Hm-mm. >> Let talk about AI, we can't go to any trade show without talking about AI, the big thing right now is ChatGPT. >> Yeah. >> I was using it the other day, it's so interesting. But, the growing demand for AI, talk about how its driving the evolution of the server so that more AI use cases can become more (indistinct). >> In the edge space primarily, we actually have another product, so I guess what you'll notice in the XR line itself because there are so many different use cases and technologies that support the different use cases. We actually have a range form factor, so we have really small, I guess I would say 350 ml the size of a shoe box, you know, Dave's shoe box. (crowd chuckles) And then we also have, at the other end a 472, so still small, but a little bit bigger, but we did recognize obviously AI was coming up, and so that is our XR 7620 platform and that does support 2 GPUs right, so, like for Edge infrencing, making sure that we have the capability to support customers in that too, but also in the small one, we do also have a GPU capability there, that also helps in those other use cases as well. So we've built the platforms even though they're small to be able to handle the GPU power for customers. >> So nice tight package, a lot of power there. >> Yes. >> Beside as we've all clearly demonstrated the size of Dave's shoe. (crowd chuckles) Dave, talk about Dell's long standing commitment to really helping to rapidly evolve the server market. >> Dave: Yeah. >> Its a pivotal payer there. >> Well, like I was saying, we see innovation, I mean, this is, to us its a race to the top. You talked about racing and messaging that sort of thing, when you opened up the show here, but we see this as a race to the top, having worked at other server companies where maybe its a little bit different, maybe more of a race to the bottom source of approach. That's what I love about being at Dell. This is very much, we understand that it's innovation is that is what's going to deliver the most value for our customers. So whether its some of the first to market, first of its kind sort of innovation that you find in the XR4000, or XR8000, or any of our XE line, we know that at the end of day, that is what going to propel Dell, do the best for our customers and thereby do the best for us. To be honest, its a little bit surprising walking by some of our competitors booths, there's been like a dearth of zero, like no, like it's almost like you wouldn't even know that there was a big launch here right? >> Yeah. >> Or is it just me? >> No. >> It was a while, we've been walking around and yet we've had, and its sort of maybe I should take this as a flattery, but a lot of our competitors have been coming by to our booth everyday actually. >> Deania: Yeah, everyday. >> They came by multiple times yesterday, they came by multiple times today, they're taking pictures of our stuff I kind of want to just send 'em a sample. >> Lisa: Or your shoe. >> Right? Or just maybe my shoe right? But anyway, so I suppose I should take it as an honor. >> Deania: Yeah. >> And conversely when we've walked over there we actually get in back (indistinct), maybe I need a high Dell (indistinct). (crowd chuckles) >> We just had that experience, yeah. >> Its kind of funny but. >> Its a good position to be in. >> Yeah. >> Yes. >> You talked about the involvement of the customers, talk a bit more about Dell's ecosystem is also massive, its part of what makes Dell, Dell. >> Wait did you say ego-system? (laughing) After David just. >> You caught that? Darn it! The talk about the influence or the part of the ecosystem and also some of the feedback from the partners as you've been rapidly evolving the server market and clearly your competitors are taking notice. >> Yeah, sorry. >> Deania: That's okay. >> Dave: you want to take that? >> I mean I would say generally, one of the things that Dell prides itself on is being able to deliver the worlds best innovation into the hands of our customers, faster and better that any other, the optimal solution. So whether its you know, working with our great partners like Intel, AMD Broadcom, these sorts of folks. That is, at the end of the day that is our core mantra, again its retractor on service, doing the best, you know, what's best for the customers. And we want to bring the world's best innovation from our technology partners, get it into the hands of our partners you know, faster and better than any other option out there. >> Its a satisfying business for all of us to be in, because to your point, I made a joke about the high level messaging. But really, that's what it comes down to. >> Lisa: Yeah. >> We do these things, we feel like sometimes we're toiling in obscurity, working with the hardware. But what it delivers. >> Deania: Hm-mm. >> The experiences. >> Dave: Absolutely. >> Deania: Yes. >> Are truly meaningful. So its a fun. >> Absolutely. >> Its a really fun thing to be a part of. >> It is. >> Absolutely. >> Yeah. Is there a favorite customer story that you have that really articulates the value of what Dell is doing, full PowerEdge, at the Edge? >> Its probably one I can't particularly name obviously but, it was, they have different environments, so, in one case there's like on flights or on sea vessels, and just being able to use the same box in those different environments is really cool. And they really appreciate having the small compact, where they can just take the server with them and go somewhere. That was really cool to me in terms of how they were using the products that we built for them. >> I have one that's kind of funny. It around XR8000. Again a customer I won't name but they're so proud of it, they almost kinds feel like they co defined it with us, they want to be on the patent with us so, anyways that's. >> Deania: (indistinct). >> That's what they went in for, yeah. >> So it shows the strength of the partnership that. >> Yeah, exactly. >> Of course, the ecosystem of partners, customers, CSVs, telecom Edge. Guys thank you so much for joining us today. >> Thank you. >> Thank you. >> Sharing what's new with the PowerEdge. We can't wait to, we're just, we're cracking open the box, we saw the shoe. (laughing) And we're going to be dealing a little bit more later. So thank you. >> We're going to be able to touch something soon? >> Yes, yes. >> Yeah. >> In couple of minutes? >> Next segment I think. >> All right! >> Thanks for setting the table for that guys. We really appreciate your time. >> Thank you for having us. >> Thank you. >> Alright, our pleasure. >> For our guests and for Dave Nicholson, I'm Lisa Martin . You're watching theCUBE. The leader in live tech coverage, LIVE in Barcelona, Spain, MWC 23. Don't go anywhere, we will be right back with our next guests. (gentle music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

>> Hello everyone. Welcome to theCUBEs coverage of Compute Engineered for your Hybrid World sponsored by HPE and Intel. Today we're going to dive into advanced performance of VDI with the fourth gen Intel Zion scalable processors. Hello I'm John Furrier, the host of theCUBE. My guests today are Alan Chu, Director of Data Center Performance and Competition for Intel as well as Denis Kondakov who's the VDI product manager at HPE, and also joining us is Cynthia Sustiva, CAD/CAM product manager at HPE. Thanks for coming on, really appreciate you guys taking the time. >> Thank you. >> So accelerating VDI to the Edge. That's the topic of this topic here today. Let's get into it, Dennis, tell us about the new HPE ProLiant DL321 Gen 11 server. >> Okay, absolutely. Hello everybody. So HP ProLiant DL320 Gen 11 server is the new age center CCO and density optimized compact server, compact form factor server. It enables to modernize and power at the next generation of workloads in the diverse rec environment at the Edge in an industry standard designed with flexible scale for advanced graphics and compute. So it is one unit, one processor rec optimized server that can be deployed in the enterprise data center as well as at the remote office at end age. >> Cynthia HPE has announced another server, the ProLiant ML350. What can you tell us about that? >> Yeah, so the HPE ProLiant ML350 Gen 11 server is a powerful tower solution for a wide range of workloads. It is ideal for remote office compute with NextGen performance and expandability with two processors in tower form factor. This enables the server to be used not only in the data center environment, but also in the open office space as a powerful workstation use case. >> Dennis mentioned both servers are empowered by the fourth gen Intel Zion scale of process. Can you talk about the relationship between Intel HPE to get this done? How do you guys come together, what's behind the scenes? Share as much as you can. >> Yeah, thanks a lot John. So without a doubt it takes a lot to put all this together and I think the partnership that HPE and Intel bring together is a little bit of a critical point for us to be able to deliver to our customers. And I'm really thrilled to say that these leading Edge solutions that Dennis and Cynthia just talked about, they're built on the foundation of our fourth Gen Z on scalable platform that's trying to meet a wide variety of deployments for today and into the future. So I think the key point of it is we're together trying to drive leading performance with built-in acceleration and in order to deliver a lot of the business values to our customers, both HP and Intels, look to scale, drive down costs and deliver new services. >> You got the fourth Gen Z on, you got the Gen 11 and multiple ProLiants, a lot of action going on. Again, I love when these next gens come out. Can each of you guys comment and share what are the use cases for each of the systems? Because I think what we're looking at here is the next level innovation. What are some of the use cases on the systems? >> Yeah, so for the ML350, in the modern world where more and more data are generated at the Edge, we need to deploy computer infrastructure where the data is generated. So smaller form factor service will satisfy the requirements of S&B customers or remote and branch offices to deliver required performance redundancy where we're needed. This type of locations can be lacking dedicated facilities with strict humidity, temperature and noise isolation control. The server, the ML350 Gen 11 can be used as a powerful workstation sitting under a desk in the office or open space as well as the server for visualized workloads. It is a productivity workhorse with the ability to scale and adapt to any environment. One of the use cases can be for hosting digital workplace for manufacturing CAD/CAM engineering or oil and gas customers industry. So this server can be used as a high end bare metal workstation for local end users or it can be virtualized desktop solution environments for local and remote users. And talk about the DL320 Gen 11, I will pass it on to Dennis. >> Okay. >> Sure. So when we are talking about age of location we are talking about very specific requirements. So we need to provide solution building blocks that will empower and performance efficient, secure available for scaling up and down in a smaller increments than compared to the enterprise data center and of course redundant. So DL 320 Gen 11 server is the perfect server to satisfy all of those requirements. So for example, S&B customers can build a video solution, for example starting with just two HP ProLiant TL320 Gen 11 servers that will provide sufficient performance for high density video solution and at the same time be redundant and enable it for scaling up as required. So for VGI use cases it can be used for high density general VDI without GP acceleration or for a high performance VDI with virtual VGPU. So thanks to the modern modular architecture that is used on the server, it can be tailored for GPU or high density storage deployment with software defined compute and storage environment and to provide greater details on your Intel view I'm going to pass to Alan. >> Thanks a lot Dennis and I loved how you're both seeing the importance of how we scale and the applicability of the use cases of both the ML350 and DL320 solutions. So scalability is certainly a key tenant towards how we're delivering Intel's Zion scalable platform. It is called Zion scalable after all. And we know that deployments are happening in all different sorts of environments. And I think Cynthia you talked a little bit about kind of a environmental factors that go into how we're designing and I think a lot of people think of a traditional data center with all the bells and whistles and cooling technology where it sometimes might just be a dusty closet in the Edge. So we're defining fortunes you see on scalable to kind of tackle all those different environments and keep that in mind. Our SKUs range from low to high power, general purpose to segment optimize. We're supporting long life use cases so that all goes into account in delivering value to our customers. A lot of the latency sensitive nature of these Edge deployments also benefit greatly from monolithic architectures. And with our latest CPUs we do maintain quite a bit of that with many of our SKUs and delivering higher frequencies along with those SKUs optimized for those specific workloads in networking. So in the end we're looking to drive scalability. We're looking to drive value in a lot of our end users most important KPIs, whether it's latency throughput or efficiency and 4th Gen Z on scalable is looking to deliver that with 60 cores up to 60 cores, the most builtin accelerators of any CPUs in the market. And really the true technology transitions of the platform with DDR5, PCIE, Gen five and CXL. >> Love the scalability story, love the performance. We're going to take a break. Thanks Cynthia, Dennis. Now we're going to come back on our next segment after a quick break to discuss the performance and the benefits of the fourth Gen Intel Zion Scalable. You're watching theCUBE, the leader in high tech coverage, be right back. Welcome back around. We're continuing theCUBE's coverage of compute engineer for your hybrid world. I'm John Furrier, I'm joined by Alan Chu from Intel and Denis Konikoff and Cynthia Sistia from HPE. Welcome back. Cynthia, let's start with you. Can you tell us the benefits of the fourth Gen Intel Zion scale process for the HP Gen 11 server? >> Yeah, so HP ProLiant Gen 11 servers support DDR five memory which delivers increased bandwidth and lower power consumption. There are 32 DDR five dim slots with up to eight terabyte total on ML350 and 16 DDR five dim slots with up to two terabytes total on DL320. So we deliver more memory at a greater bandwidth. Also PCIE 5.0 delivers an increased bandwidth and greater number of lanes. So when we say increased number of lanes we need to remember that each lane delivers more bandwidth than lanes of the previous generation plus. Also a flexible storage configuration on HPDO 320 Gen 11 makes it an ideal server for establishing software defined compute and storage solution at the Edge. When we consider a server for VDI workloads, we need to keep the right balance between the number of cords and CPU frequency in order to deliver the desire environment density and noncompromised user experience. So the new server generation supports a greater number of single wide and global wide GPU use to deliver more graphic accelerated virtual desktops per server unit than ever before. HPE ProLiant ML 350 Gen 11 server supports up to four double wide GPUs or up to eight single wide GPUs. When the signing GPU accelerated solutions the number of GPUs available in the system and consistently the number of BGPUs that can be provisioned for VMs in the binding factor rather than CPU course or memory. So HPE ProLiant Gen 11 servers with Intel fourth generation science scalable processors enable us to deliver more virtual desktops per server than ever before. And with that I will pass it on to Alan to provide more details on the new Gen CPU performance. >> Thanks Cynthia. So you brought up I think a really great point earlier about the importance of achieving the right balance. So between the both of us, Intel and HPE, I'm sure we've heard countless feedback about how we should be optimizing efficiency for our customers and with four Gen Z and scalable in HP ProLiant Gen 11 servers I think we achieved just that with our built-in accelerator. So built-in acceleration delivers not only the revolutionary performance, but enables significant offload from valuable core execution. That offload unlocks a lot of previously unrealized execution efficiency. So for example, with quick assist technology built in, running engine X, TLS encryption to drive 65,000 connections per second we can offload up to 47% of the course that do other work. Accelerating AI inferences with AMX, that's 10X higher performance and we're now unlocking realtime inferencing. It's becoming an element in every workload from the data center to the Edge. And lastly, so with faster and more efficient database performance with RocksDB, we're executing with Intel in-memory analytics accelerator we're able to deliver 2X the performance per watt than prior gen. So I'll say it's that kind of offload that is really going to enable more and more virtualized desktops or users for any given deployment. >> Thanks everyone. We still got a lot more to discuss with Cynthia, Dennis and Allen, but we're going to take a break. Quick break before wrapping things up. You're watching theCUBE, the leader in tech coverage. We'll be right back. Okay, welcome back everyone to theCUBEs coverage of Compute Engineered for your Hybrid World. I'm John Furrier. We'll be wrapping up our discussion on advanced performance of VDI with the fourth gen Intel Zion scalable processers. Welcome back everyone. Dennis, we'll start with you. Let's continue our conversation and turn our attention to security. Obviously security is baked in from day zero as they say. What are some of the new security features or the key security features for the HP ProLiant Gen 11 server? >> Sure, I would like to start with the balance, right? We were talking about performance, we were talking about density, but Alan mentioned about the balance. So what about the security? The security is really important aspect especially if we're talking about solutions deployed at the H. When the security is not active but other aspects of the environment become non-important. And HP is uniquely positioned to deliver the best in class security solution on the market starting with the trusted supply chain and factories and silicon route of trust implemented from the factory. So the new ISO6 supports added protection leveraging SPDM for component authorization and not only enabled for the embedded server management, but also it is integrated with HP GreenLake compute ops manager that enables environment for secure and optimized configuration deployment and even lifecycle management starting from the single server deployed on the Edge and all the way up to the full scale distributed data center. So it brings uncompromised and trusted solution to customers fully protected at all tiers, hardware, firmware, hypervisor, operational system application and data. And the new intel CPUs play an important role in the securing of the platform. So Alan- >> Yeah, thanks. So Intel, I think our zero trust strategy toward security is a really great and a really strong parallel to all the focus that HPE is also bringing to that segment and market. We have even invested in a lot of hardware enabled security technologies like SGX designed to enhance data protection at rest in motion and in use. SGX'S application isolation is the most deployed, researched and battle tested confidential computing technology for the data center market and with the smallest trust boundary of any solution in market. So as we've talked about a little bit about virtualized use cases a lot of virtualized applications rely also on encryption whether bulk or specific ciphers. And this is again an area where we've seen the opportunity for offload to Intel's quick assist technology to encrypt within a single data flow. I think Intel and HP together, we are really providing security at all facets of execution today. >> I love that Software Guard Extension, SGX, also silicon root of trust. We've heard a lot about great stuff. Congratulations, security's very critical as we see more and more. Got to be embedded, got to be completely zero trust. Final question for you guys. Can you share any messages you'd like to share with the audience each of you, what should they walk away from this? What's in it for them? What does all this mean? >> Yeah, so I'll start. Yes, so to wrap it up, HPR Proliant Gen 11 servers are built on four generation science scalable processors to enable high density and extreme performance with high performance CDR five memory and PCI 5.0 plus HP engine engineered and validated workload solutions provide better ROI in any consumption model and prefer by a customer from Edge to Cloud. >> Dennis? >> And yeah, so you are talking about all of the great features that the new generation servers are bringing to our customers, but at the same time, customer IT organization should be ready to enable, configure, support, and fine tune all of these great features for the new server generation. And this is not an obvious task. It requires investments, skills, knowledge and experience. And HP is ready to step up and help customers at any desired skill with the HP Greenlake H2 cloud platform that enables customers for cloud like experience and convenience and the flexibility with the security of the infrastructure deployed in the private data center or in the Edge. So while consuming all of the HP solutions, customer have flexibility to choose the right level of the service delivered from HP GreenLake, starting from hardwares as a service and scale up or down is required to consume the full stack of the hardwares and software as a service with an option to paper use. >> Awesome. Alan, final word. >> Yeah. What should we walk away with? >> Yeah, thanks. So I'd say that we've talked a lot about the systems here in question with HP ProLiant Gen 11 and they're delivering on a lot of the business outcomes that our customers require in order to optimize for operational efficiency or to optimize for just to, well maybe just to enable what they want to do in, with their customers enabling new features, enabling new capabilities. Underpinning all of that is our fourth Gen Zion scalable platform. Whether it's the technology transitions that we're driving with DDR5 PCIA Gen 5 or the raw performance efficiency and scalability of the platform in CPU, I think we're here for our customers in delivering to it. >> That's great stuff. Alan, Dennis, Cynthia, thank you so much for taking the time to do a deep dive in the advanced performance of VDI with the fourth Gen Intel Zion scalable process. And congratulations on Gen 11 ProLiant. You get some great servers there and again next Gen's here. Thanks for taking the time. >> Thank you so much for having us here. >> Okay, this is theCUBEs keeps coverage of Compute Engineered for your Hybrid World sponsored by HP and Intel. I'm John Furrier for theCUBE. Accelerate VDI at the Edge. Thanks for watching.

>>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.

(upbeat music) >> Okay, we're back to wrap up Fal.con 2022 CrowdStrike's customer event. You're watching theCUBE. My name is Dave Vellante. My co-host, Dave Nicholson, is on injured reserve today, so I'm solo. But I wanted to just give the audience a census to some of my quick takeaways. Really haven't given a ton of thought on this. We'll do review after we check out the videos and the transcripts, and do what we do at SiliconANGLE and theCUBE. I'd say the first thing is, look CrowdStrike continues to expand it's footprint. And, it's adding the identity module, through the preempt acquisition. Working very closely with managed service providers, MSPs, managed security service providers. Having an SMB play. So CrowdStrike has 20,000 customers. I think it could, it could 10X that, you know, over some period of time. As I've said earlier, it's on a path by mid-decade to be a 5 billion company, in terms of revenue. At the macro level, security is somewhat, I'd say it's less discretionary than some other investments. You know, you can, you can probably hold off buying a new storage device. You can maybe clean that up. You know, you might be able to hold off on some of your analytics, but at the end of the day, security is not completely non-discretionary. It's competing. The CISO is competing with other budgets. Okay? So it's, while it's less discretionary, it is still, you know, not an open checkbook for the CISO. Now, having said that, from CrowdStrike standpoint it has an excellent opportunity to consolidate tools. It's one of the biggest problems in the security business Go to Optiv and check out their security taxonomy. It'll make your eyes bleed. There's so many tools and companies that are really focused on one specialization. But really, what CrowdStrike can do with its 22 modules, to say, hey, we can give you ROI and consolidate those. And not only is it risk reduction, it's lowering the labor cost and labor intensity, so you can focus on other areas and free up the biggest problem that CISOs have. It's the lack of enough talent. So, really strong business value and value proposition. A lot of that is enabled by the architecture. We've talked about this. You can check out my breaking analysis that I dropped last weekend, on CrowdStrike. And, you know, can it become a generational company. But it's really built on a cloud-native architecture. George Kurtz and company, they shunned having an on-premise architecture. Much like Snowflake Frank Slootman has said, we're not doing a halfway house. We're going to put all our resources on a cloud-native architecture. The lightweight agent that allows them to add new modules and collect more data, and scale out. The purpose-built threat graph and and time series database, and asset graph that they've built. And very strong use of AI, to not only stop known malware, but stop unknown malware. Identify threats. Do that curation. And really, you know, support the SecOp teams. Product wise, I think the big three takeaways, and there were others, but the big three for me is EDR extending into XDR. You know, X is the extending for, in really, the core of endpoint detection and response, extending that further. Well, it seems to be a big buzzword these days. CrowdStrike, I think, is very focused on making a more complete, a holistic offering, beyond endpoint. And I think it's going to do very well in that space. They're not alone. There are others. It's a very competitive space. The second is identity. Through the acquisition of Preempt. CrowdStrike building that identity module. Partnering with leaders like Okta, to really provide that sort of, treating identity, if you will, as an endpoint. And then sort of Humio is now Falcon Log Scale. Bringing together, you know, the data and the observability piece, and the security piece, is kind of the three big product trends that I saw. I think the last point I'll make, before we wrap, is the ecosystem. The ecosystem here is good. It reminds me, I said, a number of times this week, of ServiceNow in 2013 I think the difference is, CrowdStrike has an SMB play it can go after many more customers, and actually have an even broader platform. And I think it can accelerate its ecosystem faster than ServiceNow was able to do that. I mean, it's got to be, sort of, an open and collaborative sort of ecosystem. You know, ServiceNow is kind of, more of, a one-way street. And I think the other piece of that ecosystem, that we see evolving, into IOT, into the operations technology and critical infrastructure. Which is so important, because critical infrastructure of nations is so vulnerable. We're seeing this in the Ukraine. Security is a key component now of any warfare. And going forward, it's always going to be a key component. Nation states are going to go after trust, or secure infrastructure, or critical infrastructure. Try to disable that and disrupt that. So securing those operation assets is going to be very critical. Not just the refrigerator and the coffee maker, but really going after those critical infrastructures. (chuckles) Getting asked to break. And the last thing I'll say, is the developer platform. We heard from ML that, the opportunity that's there, to build out a PaaS layer, super PaaS layer, if you will, so that developers can add value. I think if that happens, this ecosystem, which is breaking down, will explode. This is Dave Vellante, wrapping up at CrowdStrike, Fal.con 2022, Fal.con 2022. Go to SiliconAngle.com, for all the news. Check out theCUBE.net. You'll see these videos on demand and many others. Check out (indistinct).com for all the research. And look for where we'll be next. Of course, re:Invent is the big fall event, but there are many others in between. Thanks for watching. We're out. (music plays out)

(upbeat music) >> Hello everyone. This is Dave Vellante. We're diving into the deep end with AMD and Oracle on the topic of mySQL HeatWave performance. And we want to explore the important issues around machine learning. As applications become more data intensive and machine intelligence continues to evolve, workloads increasingly are seeing a major shift where data and AI are being infused into applications. And having a database that simplifies the convergence of transaction and analytics data without the need to context, switch and move data out of and into different data stores. And eliminating the need to perform extensive ETL operations is becoming an industry trend that customers are demanding. At the same time, workloads are becoming more automated and intelligent. And to explore these issues further, we're happy to have back in theCUBE Nipun Agarwal, who's the Senior Vice President of mySQL HeatWave and Kumaran Siva, who's the Corporate Vice President Strategic Business Development at AMD. Gents, hello again. Welcome back. >> Hello. Hi Dave. >> Thank you, Dave. >> Okay. Nipun, obviously machine learning has become a must have for analytics offerings. It's integrated into mySQL HeatWave. Why did you take this approach and not the specialized database approach as many competitors do right tool for the right job? >> Right? So, there are a lot of customers of mySQL who have the need to run machine learning on the data which is store in mySQL database. So in the past, customers would need to extract the data out of mySQL and they would take it to a specialized service for running machine learning. Now, the reason we decided to incorporate machine learning inside the database, there are multiple reasons. One, customers don't need to move the data. And if they don't need to move the data, it is more secure because it's protected by the same access controlled mechanisms as rest of the data There is no need for customers to manage multiple services. But in addition to that, when we run the machine learning inside the database customers are able to leverage the same service the same hardware, which has been provisioned for OTP analytics and use machine learning capabilities at no additional charge. So from a customer's perspective, they get the benefits that it is a single database. They don't need to manage multiple services. And it is offered at no additional charge. And then as another aspect, which is kind of hard to learn which is based on the IP, the work we have done it is also significantly faster than what customers would get by having a separate service. >> Just to follow up on that. How are you seeing customers use HeatWaves machine learning capabilities today? How is that evolving? >> Right. So one of the things which, you know customers very often want to do is to train their models based on the data. Now, one of the things is that data in a database or in a transaction database changes quite rapidly. So we have introduced support for auto machine learning as a part of HeatWave ML. And what it does is that it fully automates the process of training. And this is something which is very important to database users, very important to mySQL users that they don't really want to hire or data scientists or specialists for doing training. So that's the first part that training in HeatWave ML is fully automated. Doesn't require the user to provide any like specific parameters, just the source data and the task which they want to train. The second aspect is the training is really fast. So the training is really fast. The benefit is that customers can retrain quite often. They can make sure that the model is up to date with any changes which have been made to their transaction database. And as a result of the models being up to date, the accuracy of the prediction is high. Right? So that's the first aspect, which is training. The second aspect is inference, which customers run once they have the models trained. And the third thing, which is perhaps been the most sought after request from the mySQL customers is the ability to provide explanations. So, HeatWave ML provides explanations for any model which has been generated or trained by HeatWave ML. So these are the three capabilities- training, inference and explanations. And this whole process is completely automated, doesn't require a specialist or a data scientist. >> Yeah, that's nice. I mean, training obviously very popular today. I've said inference I think is going to explode in the coming decade. And then of course, AI explainable AI is a very important issue. Kumaran, what are the relevant capabilities of the AMD chips that are used in OCI to support HeatWave ML? Are they different from say the specs for HeatWave in general? >> So, actually they aren't. And this is one of the key features of this architecture or this implementation that is really exciting. Um, there with HeatWave ML, you're using the same CPU. And by the way, it's not a GPU, it's a CPU for both for all three of the functions that Nipun just talked about- inference, training and explanation all done on CPU. You know, bigger picture with the capabilities we bring here we're really providing a balance, you know between the CPU cores, memory and the networking. And what that allows you to do here is be able to feed the CPU cores appropriately. And within the cores, we have these AVX instruc... extensions in with the Zen 2 and Zen 3 cores. We had AVX 2, and then with the Zen 4 core coming out we're going to have AVX 512. But we were able to with that balance of being able to bring in the data and utilize the high memory bandwidth and then use the computation to its maximum we're able to provide, you know, build pride enough AI processing that we are able to get the job done. And then we're built to build a fit into that larger pipeline that that we build out here with the HeatWave. >> Got it. Nipun you know, you and I every time we have a conversation we've got to talk benchmarks. So you've done machine learning benchmarks with HeatWave. You might even be the first in the industry to publish you know, transparent, open ML benchmarks on GitHub. I mean, I, I wouldn't know for sure but I've not seen that as common. Can you describe the benchmarks and the data sets that you used here? >> Sure. So what we did was we took a bunch of open data sets for two categories of tasks- classification and regression. So we took about a dozen data sets for classification and about six for regression. So to give an example, the kind of data sets we used for classifications like the airlines data set, hex sensors bank, right? So these are open data sets. And what we did was for on these data sets we did a comparison of what would it take to train using HeatWave ML? And then the other service we compared with is that RedShift ML. So, there were two observations. One is that with HeatWave ML, the user does not need to provide any tuning parameters, right? The HeatWave ML using RML fully generates a train model, figures out what are the right algorithms? What are the right features? What are the right hyper parameters and sets, right? So no need for any manual intervention not so the case with Redshift ML. The second thing is the performance, right? So the performance of HeatWave ML aggregate on these 12 data sets for classification and the six data sets on regression. On an average, it is 25 times faster than Redshift ML. And note that Redshift ML in turn involves SageMaker, right? So on an average, HeatWave ML provides 25 times better performance for training. And the other point to note is that there is no need for any human intervention. That's fully automated. But in the case of Redshift ML, many of these data sets did not even complete in the set duration. If you look at price performance, one of the things again I want to highlight is because of the fact that AMD does pretty well in all kinds of workloads. We are able to use the same cluster users and use the same cluster for analytics, for OTP or for machine learning. So there is no additional cost for customers to run HeatWave ML if they have provision HeatWave. But assuming a user is provisioning a HeatWave cluster only to run HeatWave ML, right? That's the case, even in that case the price performance advantage of HeatWave ML over Redshift ML is 97 times, right? So 25 times faster at 1% of the cost compared to Redshift ML And all these scripts and all this information is available on GitHub for customers to try to modify and like, see, like what are the advantages they would get on their workloads? >> Every time I hear these numbers, I shake my head. I mean, they're just so overwhelming. Um, and so we'll see how the competition responds when, and if they respond. So, but thank you for sharing those results. Kumaran, can you elaborate on how the specs that you talked about earlier contribute to HeatWave ML's you know, benchmark results. I'm particularly interested in scalability, you know Typically things degrade as you push the system harder. What are you seeing? >> No, I think, I think it's good. Look, yeah. That's by those numbers, just blow me, blow my head too. That's crazy good performance. So look from, from an AMD perspective, we have really built an architecture. Like if you think about the chiplet architecture to begin with, it is fundamentally, you know, it's kind of scaling by design, right? And, and one of the things that we've done here is been able to work with, with the HeatWave team and heat well ML team, and then been able to, to within within the CPU package itself, be able to scale up to take very efficient use of all of the course. And then of course, work with them on how you go between nodes. So you can have these very large systems that can run ML very, very efficiently. So it's really, you know, building on the building blocks of the chiplet architecture and how scaling happens there. >> Yeah. So it's you're saying it's near linear scaling or essentially. >> So, let Nipun comment on that. >> Yeah. >> Is it... So, how about as cluster sizes grow, Nipun? >> Right. >> What happens there? >> So one of the design points for HeatWave is scale out architecture, right? So as you said, that as we add more data set or increase the size of the data, or we add the number of nodes to the cluster, we want the performance to scale. So we show that we have near linear scale factor, or nearly near scale scalability for SQL workloads in the case of HeatWave ML, as well. As users add more nodes to the cluster so the size of the cluster the performance of HeatWave ML improves. So I was giving you this example that HeatWave ML is 25 times faster compared to Redshift ML. Well, that was on a cluster size of two. If you increase the cluster size of HeatWave ML to a larger number. But I think the number is 16. The performance advantage over Redshift ML increases from 25 times faster to 45 times faster. So what that means is that on a cluster size of 16 nodes HeatWave ML is 45 times faster for training these again, dozen data sets. So this shows that HeatWave ML skills better than the computation. >> So you're saying adding nodes offsets any management complexity that you would think of as getting in the way. Is that right? >> Right. So one is the management complexity and which is why by features like last customers can scale up or scale down, you know, very easily. The second aspect is, okay What gives us this advantage, right, of scalability? Or how are we able to scale? Now, the techniques which we use for HeatWave ML scalability are a bit different from what we use for SQL processing. So in the case of HeatWave ML, they really like, you know, three, two trade offs which we have to be careful about. One is the accuracy. Because we want to provide better performance for machine learning without compromising on the accuracy. So accuracy would require like more synchronization if you have multiple threads. But if you have too much of synchronization that can slow down the degree of patterns that we get. Right? So we have to strike a fine balance. So what we do is that in HeatWave ML, there are different phases of training, like algorithm selection, feature selection, hyper probability training. Each of these phases is analyzed. And for instance, one of the ways techniques we use is that if you're trying to figure out what's the optimal hyper parameter to be used? We start up with the search space. And then each of the VMs gets a part of the search space. And then we synchronize only when needed, right? So these are some of the techniques which we have developed over the years. And there are actually paper's filed, research publications filed on this. And this is what we do to achieve good scalability. And what that results to the customer is that if they have some amount of training time and they want to make it better they can just provision a larger cluster and they will get better performance. >> Got it. Thank you. Kumaran, when I think of machine learning, machine intelligence, AI, I think GPU but you're not using GPU. So how are you able to get this type of performance or price performance without using GPU's? >> Yeah, definitely. So yeah, that's a good point. And you think about what is going on here and you consider the whole pipeline that Nipun has just described in terms of how you get you know, your training, your algorithms And using the mySQL pieces of it to get to the point where the AI can be effective. In that process what happens is you have to have a lot of memory to transactions. A lot of memory bandwidth comes into play. And then bringing all that data together, feeding the actual complex that does the AI calculations that in itself could be the bottleneck, right? And you can have multiple bottlenecks along the way. And I think what you see in the AMD architecture for epic for this use case is the balance. And the fact that you are able to do the pre-processing, the AI, and then the post-processing all kind of seamlessly together, that has a huge value. And that goes back to what Nipun was saying about using the same infrastructure, gets you the better TCO but it also gets you gets you better performance. And that's because of the fact that you're bringing the data to the computation. So the computation in this case is not strictly the bottleneck. It's really about how you pull together what you need and to do the AI computation. And that is, that's probably a more, you know, it's a common case. And so, you know, you're going to start I think the least start to see this especially for inference applications. But in this case we're doing both inference explanation and training. All using the the CPU in the same OCI infrastructure. >> Interesting. Now Nipun, is the secret sauce for HeatWave ML performance different than what we've discussed before you and I with with HeatWave generally? Is there some, you know, additive engine additive that you're putting in? >> Right? Yes. The secret sauce is indeed different, right? Just the way I was saying that for SQL processing. The reason we get very good performance and price performance is because we have come up with new algorithms which help the SQL process can scale out. Similarly for HeatWave ML, we have come up with new IP, new like algorithms. One example is that we use meta-learn proxy models, right? That's the technique we use for automating the training process, right? So think of this meta-learn proxy models to be like, you know using machine learning for machine learning training. And this is an IP which we developed. And again, we have published the results and the techniques. But having such kind of like techniques is what gives us a better performance. Similarly, another thing which we use is adaptive sampling that you can have a large data set. But we intelligently sample to figure out that how can we train on a small subset without compromising on the accuracy? So, yes, there are many techniques that you have developed specifically for machine learning which is what gives us the better performance, better price performance, and also better scalability. >> What about mySQL autopilot? Is there anything that differs from HeatWave ML that is relevant? >> Okay. Interesting you should ask. So mySQL Autopilot is think of it to be an application using machine learning. So mySQL Autopilot uses machine learning to automate various aspects of the database service. So for instance, if you want to figure out that what's the right partitioning scheme to partition the data in memory? We use machine learning techniques to figure out that what's the right, the best column based on the user's workload to partition the data in memory Or given a workload, if you want to figure out what is the right cluster size to provision? That's something we use mySQL autopilot for. And I want to highlight that we don't aware of any other database service which provides this level of machine learning based automation which customers get with mySQL Autopilot. >> Hmm. Interesting. Okay. Last question for both of you. What are you guys working on next? What can customers expect from this collaboration specifically in this space? Maybe Nipun, you can start and then Kamaran can bring us home. >> Sure. So there are two things we are working on. One is based on the feedback we have gotten from customers, we are going to keep making the machine learning capabilities richer in HeatWave ML. That's one dimension. And the second thing is which Kamaran was alluding to earlier, We are looking at the next generation of like processes coming from AMD. And we will be seeing as to how we can more benefit from these processes whether it's the size of the L3 cache, the memory bandwidth, the network bandwidth, and such or the newer effects. And make sure that we leverage the all the greatness which the new generation of processes will offer. >> It's like an engineering playground. Kumaran, let's give you the final word. >> No, that's great. Now look with the Zen 4 CPU cores, we're also bringing in AVX 512 instruction capability. Now our implementation is a little different. It was in, in Rome and Milan, too where we use a double pump implementation. What that means is, you know, we take two cycles to do these instructions. But the key thing there is we don't lower our speed of the CPU. So there's no noisy neighbor effects. And it's something that OCI and the HeatWave has taken full advantage of. And so like, as we go out in time and we see the Zen 4 core, we can... we see up to 96 CPUs that that's going to work really well. So we're collaborating closely with, with OCI and with the HeatWave team here to make sure that we can take advantage of that. And we're also going to upgrade the memory subsystem to get to 12 channels of DDR 5. So it should be, you know there should be a fairly significant boost in absolute performance. But more important or just as importantly in TCO value for the customers, the end customers who are going to adopt this great service. >> I love their relentless innovation guys. Thanks so much for your time. We're going to have to leave it there. Appreciate it. >> Thank you, David. >> Thank you, David. >> Okay. Thank you for watching this special presentation on theCUBE. Your leader in enterprise and emerging tech coverage.

(bright music) >> Hey, welcome back everyone, theCUBE coverage here at AWS re:Mars 2022. I'm John, your host of the theCUBE. re:Mars, part of the three re big events, re:Invent is the big one, re:Inforce the security, re:MARS is the confluence of industrial space, of automation, robotics and machine learning. Got a great guest here, Muhammad Faisal senior consultant solutions architect at Capgemini. Welcome to theCUBE. Thanks for coming on. >> Thank you. >> So we, you just we're hearing the classes we had with the professor from Okta ML from Washington. So he's in the weeds on machine learning. He's down getting dirty with all the hardcore, uncoupling it from hardware. Machine learning has gone really super nova in the past couple years. And this show points to the tipping point where machine learning's driving space, it's driving robotics industrial edge at unprecedented rates. So it's kind of moving from the old I don't want to say old, couple years ago and the legacy AI, I mean, old school AI is kind of the same new school with a twist it's just modernized and has faster, cheaper, smaller chips. >> Yeah. I mean, but there is a change also in the way it's working. So you had the classical AI, where you are detecting something and then you're making an action. You are perceiving something, making an action, you're detecting something, and you're assuming something that has been perceived. But now we are moving towards more deeper learning, deep. So AI, where you have to train your model to do things or to detect things and hope that it will work. And there's like, of course, a lot of research going on into explainable AI to help facilitate that. But that's where the challenges come into play. >> Well, Muhammad , first let's take, what do you do over there? Talk about your role specifically. You're doing a lot of student architecting around AI machine learning. What's your role? What's your focus. >> Yeah. So we basically are working in automotive to help OEMs and tier-one suppliers validate ADAS functions that they're working on. So advanced driving assistance systems, there are many levels that are, are when we talk about it. So it can be something simple, like, you know, a blind spot detection, just a warning function. And it goes all the way. So SAE so- >> So there's like the easy stuff and then the hard stuff. >> Muhammad : Exactly. >> Yeah. >> That's what you're getting at. >> Yeah. Yeah. And, and the easy stuff you can test validate quite easily because if you get it wrong. >> Yeah. >> The impact is not that high. The complicated stuff, if you have it wrong, then that can be very dangerous. (John laughs) >> Well, I got to say the automotive one was one was that are so fascinating because it's been so archaic and just in the past recent years, and Tesla's the poster child for this. You see that you go, oh my God, I love that car. I want to have a software driven car. And it's amazing. And I don't get a Tesla on now because that's, it's more like I should have gotten it earlier. Now I'm going to just hold my ground. >> Everyone has- >> Everyone's got it in Palo Alto. I'm not going to get another car, no way. So, but you're starting to see a lot of the other manufacturers, just in the past five years, they're leveling up. It may not be as cool and sexy as the Tesla, but it's, they're there. And so what are they dealing with when they talk about data and AI? What's the, what's some of the challenges that you're seeing that they're grappling with in terms of getting things integrated, developing pipelines, R and D, they wrangling data. Take us through some of the things. >> Muhammad: I mean, like when I think about the challenges that autonomous or the automakers are facing, I can think of three big ones. So first, is the amount of data they need to do their training. And more importantly, the validation. So we are talking about petabytes or hundred of petabytes of data that has to be analyzed, validated, annotated. So labeling to create gen, ground truth processed, reprocessed many times with every creation of a new software. So that is a lot of data, a lot of computational power. And you need to ensure that all of the processing, all of handling of the data allows you complete transparency of what is happening to the data, as well as complete traceability. So your, for home allocations, so approval process for these functions so that they can be released in cars that can be used on public roads. You need to have traceability. Like you can, you are supposed to be able to reproduce the data to validate your work that was done. So you can, >> John: Yeah >> Like, prove that your function is successful or working as expected. So this, the big data is the first challenge. I see that all the automotive makers are tackling. The second big one I see is understanding how much testing is enough. So with AI or with classical approach, you have certain requirements, how a function is supposed to work. You can test that with some test cases based on your architecture, and you have a successful or failed result. With deep learning, it gets more complicated. >> John: What are they doing with deep learning? Give an example of some of things. >> I mean, so you are, you need to then start thinking about statistics that I will test enough data with like a failure rate of potentially like 0.0, 0.1%. How much data do I need to test to make sure that I am achieving that rate. So then we are talking about, in terms of statistics, which requires a lot of data, because the failure rate that we want to have is so low. And it's not only like, failure in terms of that something is always detected, and if it's there, but it's also having like, a low false positive rate. So you are only detecting objects which are there and not like, phantom objects. >> What's some of the trends you're seeing across the client base, in terms of the patterns that they're all kind of, what, where's the state of their mindset and position with AI and some of the work they're doing, are they feeling, you feel like they're all crossed over across the chasm so to speak, in terms of executing, are they still in experimental mode in driving with the full capabilities is conservative or is it progressive? >> Muhammad: I mean, it's a mixture of both. So I'm in German automotive where I'm from, there is for functions, which are more complicated ones. There's definitely hesitancy to release them too early in the car, unless we are sure that they are safe. But of course, for functions which are assisting the drivers everyday usage they are widely available. Like one of the things like, so when we talk about this complex function. >> John: Highly available or available? >> Muhammad: I would say highly available. >> Higher? Is that higher availability and highly available. >> Okay. Yeah. (both laughing) >> Yeah, so. >> I know there's a distinction. >> Yeah. I mean >> I bring up as a joke cuz of the Jedi contract. (Muhammad laughs) >> I mean, in like, our architecture. So when we are developing our solution, high availability is one of our requirements. It is highly available, but the ADAS functions are now available in more and more cars. >> John: Well, latency, man. I mean, it's kind of a joke of storage, but it's a storage joke, but you know, it's latency, you got it, okay. (Muhammad laughs) But these are decisions that have to be made. >> Muhammad: They... >> I mean. >> Muhammad: I mean, they are still being made. >> So I mean, we are... >> John: Good. >> We haven't reached like, level five, which is the highest level of autonomous driving yet on public roads. >> John: That's hard. That's hard to do. >> Yeah. And I mean, the biggest difference, like, as you go above these levels is in terms of availability. So are they these functions? >> John: Yeah. >> Can they handle all possible scenarios or are they only available in certain scenarios? And of course the responsibility. So, it's, in the end, so with Tesla, you would be like, if you had a one you would be the person who is in control or responsible to monitor it. >> John: Yeah. But as we go >> John: Actually the reason I don't have a Tesla all my family would want one. I don't want to get anyone a Tesla. >> But I mean, but that's the sort the liabilities is currently on you, if like, you're not monitoring. >> Allright, so, talk about AWS, the relationship that Capgemini has with AWS, obviously, the partnerships there, you're here and this show is really a commitment to, this is a future to me, this is the future. >> Muhammad: Yeah. >> This is it. All right here, industrial, innovation's going to come massive. Back-office cloud, done deal. Data centers, hybrid somewhat multi-cloud, I guess. But hybrid is a steady state in the back-office cloud, game over. >> Muhammad: Yeah. >> Amazon, Azure, Google, Alibaba done. So super clouds underneath. Great. This is a digital transformation in the industrial area. >> Muhammad: Yeah. >> This is the big thing. What's your relationship with AWS >> Muhammad: So, as I mentioned, the first challenge, data, like, we have so much data, so much computational power and it's not something that is always needed. You need it like on demand. And this is where like a hyperscale or cloud provider, like AWS, can be the key to achieve, like, the higher, the acceleration that we are providing to our customers using our technology built on top of AWS services. We did a breakout session, this during re:MARS, where we demonstrated a couple of small tools that we have developed out of our offering. One of them was ability to stream data from the vehicle that is collecting data worldwide. So during the day when we did it from Vegas, driving on the strip, as well as from Germany, and while we are while this data is uploaded, it's at the same time real time anonymized to make sure it you're privacy aligned with the, the data privacy >> Of course. Yeah. That's hard to do right there. >> Yeah. And so the faces are blurred. The licenses are blurred. We also, then at the same time can run object detection. So we have real time monitoring of what our feed is doing worldwide. And... >> John: Do you, just curious, do you do that blurring? Is that part of a managed service, you call an API or is that built into the go? >> Muhammad: So from like part of our DSV, we have many different service offerings, so data production, data test strategy orchestration. So part of data production is worldwide data collection. And we can then also offer data management services, which include then anonymization data, quality check. >> John: And that's service you provide. >> Yeah. >> To the customer. Okay. Got it. Okay. >> So of course, like, in collaboration with the customer, so our like, platform is very modular. Microservices based the idea being if the customer already has a good ML model for anonymization, we can plug it into our platform, running on AWS. If they want to use it, we can develop one or we can use one of our existing ones or something off the shelf or like any other supplier can provide one as well. And we all integrate. >> So you are, you're tight with Amazon web services in terms of your cloud, your service. It's a cloud. >> Yeah. >> It's so Capgemini Super Cloud, basically. >> Exactly. >> Okay. So this we call we call it Super Cloud, we made that a thing and re:Invent Charles Fitzgerald would disagree but we will debate him. It's a Super Cloud, but okay. You got your Super Cloud. What's the coolest thing that you think you're doing right now that people should pay attention to. >> I mean, the cool thing that we are currently working on, so from the keynote today, we talked about also synthetic data for validation. >> John: Now That was phenomenal. So that was phenomenal. >> We are working on digital twin creation. So we are capturing data in real world creating a virtual identity of it. And that allows you the freedom to create multiple scenarios out of it. So that's also something where we are using machine learning to determine what are the parameters you need to change between, or so, you have one scenario, such as like, the cut-in scenario and you can change. >> John: So what scenario? >> A cut-in scenario. So someone is cutting in front of you or overtake scenario. And so, I mean, in real world, someone will do it in probably a nicer way, but of course, in, it is possible, at some point. >> Cognition to the cars. >> Yeah. >> It comes up as a vehicle. >> I mean, at some point some might, someone would be very aggressive with it. We might not record it. >> You might be able to predict too. I mean, the predictions, you could say this guy's weaving, he's a potential candidate. >> It it is possible. Yes. But I mean, but to, >> That's a future scenario. >> Ensure that we are testing these scenarios, we can translate a real world scenario into a digital world, change the parameters. So the distance between those two is different and use ML. So machine learning to change these parameters. So this is exciting. And the other thing we are... >> That is pretty cool. I will admit that's very cool. >> Yeah. Yeah. The other thing we like are trying to do is reduce the cost for the customer in the end. So we are collecting petabytes of data. Every time they make updates to the software, they have to re-simulate it or replay this data, so that they can- >> Petabytes? >> Petabytes of data. And, and physically sometimes on a physical hardware in loop device. And then this >> That's called a really heavy edge. You got to move, you don't want to be moving that around the Amazon cloud. >> Yeah. That that's, that's the challenge. And once we have replayed this or re-simulated it. we still have to calculate the KPIs out of it. And what we are trying to do is optimize this test orchestration, so that we are minimizing the REAP simulation. So you don't want the data to be going to the edge, >> Yeah. >> Unnecessarily. And once we get this data back to optimize the way we are doing the calculation, so you're not calculating- >> There's a huge data, integrity management. >> Muhammad: Yeah. >> New kind of thing going on here, it's kind of is it new or is it? >> Muhammad: I mean, it's- >> Sounds new to me. >> The scale is new, so- >> Okay, got it. >> The management of the data, having the whole traceability, that has been in automotive. So also Capgemini involved in aerospace. So in aerospace. >> Yeah. >> Having this kind of high, this validation be very strictly monitored is norm, but now we have to think about how to do it on this large scale. And that's why, like, I think that's the biggest challenge and hopefully what we are trying to, yeah, solve with our DSV offering. >> All right, Muhammad, thanks for coming on theCUBE. I really appreciate it. Great way to close out re:MARS, our last interview our the show. Thanks for coming on. Appreciate your time. >> I mean like just one last comment, like, so I think in automotive, like, so part of the automation the future is quite exciting, and I think that's where like- >> John: Yeah. >> It's, we have to be hopeful that like- >> John: Well, the show is all about hope. I mean, you had, you had space, moon habitat, you had climate change, potential solutions. You have new functionality that we've been waiting for. And, you know, I've watch every episode of Star Trek and SkyNet and kind of SkyNet going on air. >> The robots. >> Robots running cubes, robot cubes host someday. >> Yeah. >> You never know. Yeah. Thanks for coming on. Appreciate it. >> Thank you. Okay. That's theCUBE here. Wrapping up re:MARS. I'm John Furrier You're watching theCUBE, stay with us for the next event. Next time. Thanks for watching. (upbeat music)

(upbeat music) >> Okay, welcome back everyone, Cube coverage live on the floor in the Moscone center in San Francisco, California. I'm John Furrier host of the Cube. AWS summit 2022 is here in San Francisco, we're back in live events. Of course, Amazon summit in New York city is coming, Amazon summit this summer we'll be there as well. We've got a great guest Mike Miller, GN of AI devices at AWS always one of my favorite interviews. We've got a little prop here, we got the car, DeepRacer, very popular at the events. Mike, welcome to the Cube. Good to see you. >> Hey John, thank you for having me. It's really exciting to be back and chat with you a little bit about DeepRacer. >> Well I want to get into the prop in a second, not the prop, the product. >> Yeah. >> So DeepRacer program, you got the race track here. Just explain what it is real quick, we'll get that out of the way. >> Absolutely so, well, you know that AI, AWS is passionate about making AI and ML more accessible to developers of all skill levels. So DeepRacer is one of our tools to do that. So DeepRacer is a 3D cloud-based racing simulator, a 1/18th scale autonomously driven car and a league to add a little spicy competition into it. So developers can start with the cloud-based simulator where they're introduced to reinforcement learning which basically teaches the, our car to drive around a track through trial and error and of course you're in a virtual simulator so it's easy for it to make mistakes and restart. Then once that model is trained, it's downloaded to the car which then can drive around a track autonomously, kind of making its own way and of course we track lap time and your successful lap completions and all of that data feeds into our league to try to top the leaderboard and win prizes. >> This is the ultimate gamification tool. (chuckles) >> Absolutely >> Making it fun to learn about machine learning. All right, let's get into the car, let's get into the showcase of the car. show everyone what's going on. >> Absolutely. So this is our 1/18th scale autonomously driven car. It's built off of a monster truck chassis so you can see it's got four wheel drive, it's got steering in the front, we've got a camera on the front. So the camera is the, does the sensing to the compute board that's driven by an Intel atom a processor on the, on the vehicle, that allows it to make sense of the in front of it and then decide where it wants to drive. So you take the car, you download your trained model to it and then it races around the track. >> So the front is the camera. >> The front is the camera, that's correct. >> Okay, So... >> So it's a little bit awkward but we needed to give it plenty of room here so that I can actually see the track in front of it. >> John: It needs eyes. >> Yep. That's exactly right. >> Awesome. >> Yes. >> And so I got to buy that if I'm a developer. >> So, developers can start in two ways, they can use our virtual racing experience and so there's no hardware cost for that, but once you want the experience, the hands on racing, then the car is needed but if you come to one of our AWS summits, like here in San Francisco or anywhere else around the world we have one or more tracks set up and you can get hands on, you can bring the model that you trained at home download it to a car and see it race around the track. >> So use a car here. You guys are not renting cars, but you're letting people use the cars. >> Absolutely. >> Can I build my own car or does it have to be assembled by AWS? >> Yeah, we, we sell it as a, as a kit that's already assembled because we've got the specific compute board in there, that Intel processor and all of the software that's already built on there that knows how to drive around the track. >> That's awesome, so talk about the results. What's going on? What's the feedback from developers? Obviously it's a nerd dream, people like race cars, people love formula one now, all the racing there. IOT is always an IOT opportunity as well. >> Absolutely, and as you said, gamification, right? And so what we found and what we thought we would find was that adding in those sort of ease of learning so we make it the on-ramp to machine learning very easy. So developers of all skill levels can take advantage of this, but we also make it fun by kind of gamifying it. We have different challenges every month, we have a leader board so you can see how you rank against your peers and actually we have split our league into two, there's an open division which is more designed for novices so you'll get rewarded for just participating and then we have a pro league. So if you're one of the top performers in the open league each month, you graduate and you get to race against the big boys in the pro leagues. >> What's the purse? >> Oh, the, (John laughing) we definitely have cash and prizes that happen, both every month. We have prizes cause we do races every month and those winners of those races all get qualified to race at the championship, which of course happens in Las Vegas at re:Invent. So we bring all the winners to re:Invent and they all race against each other for the grand prize the big trophy and the, and the, and the cash prize. >> Well, you know, I'm a big fan of what you guys are doing so I'm kind of obviously biased on this whole program but you got to look at trend of what's going on in eSports and the online engagement is off the charts, are there plans to kind of make this more official and bigger? Is there traction there or is this just all part of the Amazon goodness, love that you guys give back? I mean, obviously it's got traction. >> Yeah. I mean, the thing that's interesting about eSports is the number of young people who are getting into it and what we saw over the last couple years is that, there were a lot of students who were adopting DeepRacer but there were some hurdles, you know, it wasn't really designed for them. So what we did was we made some changes and at the beginning of this year we launched a student focused DeepRacer program. So they get both free training every month, they get free educational materials and their own private league so they know students can race against other students, as part of that league. >> John: Yeah. >> So that was really our first step in kind of thinking about those users and what do we need to do to cater to their kind of unique needs? >> Tell about some of the power dynamics or the, or not power dynamics, the group dynamics around teams and individuals, can I play as an individual? Do I, do I have to be on a team? Can I do teams? How does that look? How do you think about those things? >> Yeah, absolutely. Great, great question. The primary way to compete is individually. Now we do have an offering that allows companies to use DeepRacer to excite and engage their own employees and this is where operating as a team and collaborating with your coworkers comes into play so, if, if I may there's, you know, Accenture and JPMC are a couple big customers of ours, really strong partners. >> John: Yeah. >> Who've been able to take advantage of DeepRacer to educate their workforce. So Accenture ran a 24 hour round the, round the globe race a couple years ago, encouraging their employees to collaborate and form teams to race and then this past year JPMC, had over 3000 of their builders participate over a three month period where they ran a private league and they went on to win the top two spots, first place and second place. >> John: Yeah. >> At reinvent last year. >> It reminds me the NASCAR and all these like competitions, the owners have multiple cars on the race. Do you guys at re:Invent have to start cutting people like, only two submissions or is it free for all? >> Well, you have to qualify to get to the races at re:invent so it's very, it's very cutthroat leading up to that point. We've got winners of our monthly virtual contests, the winners like of the summit races will also get invited. So it's interesting, this dynamic, you'll have some people who won virtual races, some people who won physical races, all competing together. >> And do you guys have a name for the final cup or is it like what's the, what's the final, how do you guys talk about the prizes and the... >> It's, it's the DeepRacer Championship Cup of course. >> John: Of course. (laughter) >> Big silver cup, you get to hoist it and... >> Are the names inscribed in it, is it like the Stanley cup or is it just one. >> It's a unique one, so you get to hold onto it each year. The champion gets their own version of the cup. >> It's a lot of fun. I think it's really kind of cool. What's the benefits for a student? Talk about the student ones. >> Yeah. Yeah. >> So I'm a student I'm learning machine learning, what's in it for me is a career path and the fund's obvious, I see that. >> Yeah absolutely. You know, the, for students, it's a hands on way that's a very easy on-ramp to machine learning and you know, one of the things, as I mentioned we're passionate about making it accessible to all. Well, when we mean all we were really do mean all. So, we've got a couple partners who are passionate about the same thing, right? Which is how do we, if, if AI and ML is going to transform our world and solve our most challenging problems, how can we get the right minds from all walks of life and all backgrounds to learn machine learning and get engaged? So with two of our partners, so with Udacity and with Intel we launched a $10 million AWS, AI and ML scholarship program and we built it around DeepRacer. So not only can students who are college and high school students, age 16 and over can use DeepRacer, can learn about machine learning and then get qualified to win one of several thousand scholarships. >> Any other promotions going on that people should know about? >> Yeah, one, one final one is, so we talked about enterprises like JPMC and Accenture, so we've got a promotion that we just started yesterday. So if you are an enterprise and you want to host a DeepRacer event at your company to excite your employees and get 'em collaborating more, if you have over 50 employees participating, we're going to give you up to a hundred thousand dollars in AWS credits, to offset the costs of running your DeepRacer event at your, at your company so >> That's real money. >> Yeah. Real, real, real exciting I think for companies now to pick up DeepRacer. >> So, I mean, honestly, I know Andy Jassy, I have many sports car conversations with him. He's a sports guy, he's now the CEO of Amazon, gets to go all the sporting events, NFL. I wish I could bring the Cube there but, we'll stick with with cloud for now. You got to look at the purse kind of thing. I'm interested in like the whole economic point of cause I mean, forget the learning for side for a second which is by the way awesome. This is great competition. You got leader boards, you got regional activities, you got a funneling system laddering up to the final output. >> And we've really done a decent job and, and of adding capabilities into that user experience to make it more engaging. You can see the countries that the different competitors are from, you can see how the lap times change over time, you know, we give awards as I mentioned, the two divisions now. So if you're not super competitive, we'll reward you for just participating in that open league but if you want to get competitive, we'll even better rewards monthly in the Pro League. >> Do you guys have any conversations internally like, this is getting too big, we might have to outsource it or you keep it in inside the fold? (laughter) >> We, we love DeepRacer and it's so much fun running this, >> You see where I'm going with this. You see where I'm going with this right? The Cube might want to take this over. >> Hey. >> And you know >> We're always looking for partners and sponsors who can help us make it bigger so, absolutely. >> It's a good business opportunity. I just love it. Congratulations, great stuff. What's the big learning in this, you know, as a as an executive, you look back you got GM, AI super important and, and I think it is great community, communal activity as well. What's the learning, what have you learned from this over the years besides that it's working but like what's the big takeaway? >> Yeah, I mean. We've got such a wide range of developers and builders who are customers that we need to provide a variety of opportunities for people to get hands on and there's no better way to learn a complex technology like AI and ML than getting hands on and seeing, you know, physically the result of the AI and I think that's been the biggest learning, is that just having the hands on and the sort of element of watching what it does, just light bulbs go off. When, when developers look at this and they start piecing the, the puzzle pieces together, how they can benefit. >> So I have to ask the question that might be on other peoples minds, maybe it's not, maybe I'm just thinking really dark here but gamers love to hack and they love cheat codes, they love to get, you know, get into the system, any attempts to do a little hacking to win the, the the game, have you guys, is there, you know? >> Well, well, you know, last year we, we we released an open source version of the vehicle so that people could start using it as a platform to explore and do that kind of hacking and give them an opportunity build on top of it. >> So using mods, mods modules, we can mod out on this thing. >> Yeah, absolutely. If you go to deepracer.com, we have sort of extensions page there, and you can see, somebody mounted a Nerf cannon onto the top of this, somebody built a computer vision model that could recognize you know, rodents and this thing would kind of drive to scare 'em, all kinds of fun topics. >> So it's a feature, not a bug. >> Absolutely. >> Open it up. >> Yeah. >> And also on transparency, if you have the source code out there you guys can have some review. >> Yeah. The whole idea is like, let's see what developers, >> It's really not hackable. It's not hackable. >> Yeah, I mean, for the, if you think about it when we do the races, we bring the cars ourselves, the only way a developer interacts is by giving us their trained models so... >> And you, do you guys review the models? Nothing to review, right? >> Yeah. There's nothing really to review. It's all about, you know, there, there was a model that we saw one time where the car went backwards and then went forwards across the finish line but we, we, we gently told them, well that's really not a valid way to race. >> That was kind of a hack, not really a hack. That was a hack hack. (laughter) That was just a growth hack. >> Exactly, but everybody just has a lot of fun with it across the board. >> Mike, great, thanks for coming on. Love the prop. Thanks for bringing the car on, looks great. Success every year. I want to see the purse, you know, big up to $1,000,000 you know, the masters, you know, tournament. >> Someday. (John chuckles) >> You guys.. >> Thank you for having me John. >> DeepRacer again, Fun Start has a great way to train people on machine learning, IOT device, turns into a league of its own. Great stuff for people to learn, especially students and people in companies, but the competitive juices flowing. That's what it's all about, having fun, learning. It's the Cube here in San Francisco. Stay with us for more coverage after this short break. (gentle music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data driven insights from theCUBE in ETR, this is Breaking Analysis with Dave Vellante. >> The introduction in socialization of data mesh has caused practitioners, business technology executives, and technologists to pause, and ask some probing questions about the organization of their data teams, their data strategies, future investments, and their current architectural approaches. Some in the technology community have embraced the concept, others have twisted the definition, while still others remain oblivious to the momentum building around data mesh. Here we are in the early days of data mesh adoption. Organizations that have taken the plunge will tell you that aligning stakeholders is a non-trivial effort, but necessary to break through the limitations that monolithic data architectures and highly specialized teams have imposed over frustrated business and domain leaders. However, practical data mesh examples often lie in the eyes of the implementer, and may not strictly adhere to the principles of data mesh. Now, part of the problem is lack of open technologies and standards that can accelerate adoption and reduce friction, and that's what we're going to talk about today. Some of the key technology and architecture questions around data mesh. Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR, and in this Breaking Analysis, we welcome back the founder of data mesh and director of Emerging Technologies at Thoughtworks, Zhamak Dehghani. Hello, Zhamak. Thanks for being here today. >> Hi Dave, thank you for having me back. It's always a delight to connect and have a conversation. Thank you. >> Great, looking forward to it. Okay, so before we get into it in the technology details, I just want to quickly share some data from our friends at ETR. You know, despite the importance of data initiative since the pandemic, CIOs and IT organizations have had to juggle of course, a few other priorities, this is why in the survey data, cyber and cloud computing are rated as two most important priorities. Analytics and machine learning, and AI, which are kind of data topics, still make the top of the list, well ahead of many other categories. And look, a sound data architecture and strategy is fundamental to digital transformations, and much of the past two years, as we've often said, has been like a forced march into digital. So while organizations are moving forward, they really have to think hard about the data architecture decisions that they make, because it's going to impact them, Zhamak, for years to come, isn't it? >> Yes, absolutely. I mean, we are moving really from, slowly moving from reason based logical algorithmic to model based computation and decision making, where we exploit the patterns and signals within the data. So data becomes a very important ingredient, of not only decision making, and analytics and discovering trends, but also the features and applications that we build for the future. So we can't really ignore it, and as we see, some of the existing challenges around getting value from data is not necessarily that no longer is access to computation, is actually access to trustworthy, reliable data at scale. >> Yeah, and you see these domains coming together with the cloud and obviously it has to be secure and trusted, and that's why we're here today talking about data mesh. So let's get into it. Zhamak, first, your new book is out, 'Data Mesh: Delivering Data-Driven Value at Scale' just recently published, so congratulations on getting that done, awesome. Now in a recent presentation, you pulled excerpts from the book and we're going to talk through some of the technology and architectural considerations. Just quickly for the audience, four principles of data mesh. Domain driven ownership, data as product, self-served data platform and federated computational governance. So I want to start with self-serve platform and some of the data that you shared recently. You say that, "Data mesh serves autonomous domain oriented teams versus existing platforms, which serve a centralized team." Can you elaborate? >> Sure. I mean the role of the platform is to lower the cognitive load for domain teams, for people who are focusing on the business outcomes, the technologies that are building the applications, to really lower the cognitive load for them, to be able to work with data. Whether they are building analytics, automated decision making, intelligent modeling. They need to be able to get access to data and use it. So the role of the platform, I guess, just stepping back for a moment is to empower and enable these teams. Data mesh by definition is a scale out model. It's a decentralized model that wants to give autonomy to cross-functional teams. So it is core requires a set of tools that work really well in that decentralized model. When we look at the existing platforms, they try to achieve this similar outcome, right? Lower the cognitive load, give the tools to data practitioners, to manage data at scale because today centralized teams, really their job, the centralized data teams, their job isn't really directly aligned with a one or two or different, you know, business units and business outcomes in terms of getting value from data. Their job is manage the data and make the data available for then those cross-functional teams or business units to use the data. So the platforms they've been given are really centralized around or tuned to work with this structure as a team, structure of centralized team. Although on the surface, it seems that why not? Why can't I use my, you know, cloud storage or computation or data warehouse in a decentralized way? You should be able to, but some changes need to happen to those online platforms. As an example, some cloud providers simply have hard limits on the number of like account storage, storage accounts that you can have. Because they never envisaged you have hundreds of lakes. They envisage one or two, maybe 10 lakes, right. They envisage really centralizing data, not decentralizing data. So I think we see a shift in thinking about enabling autonomous independent teams versus a centralized team. >> So just a follow up if I may, we could be here for a while. But so this assumes that you've sorted out the organizational considerations? That you've defined all the, what a data product is and a sub product. And people will say, of course we use the term monolithic as a pejorative, let's face it. But the data warehouse crowd will say, "Well, that's what data march did. So we got that covered." But Europe... The primest of data mesh, if I understand it is whether it's a data march or a data mart or a data warehouse, or a data lake or whatever, a snowflake warehouse, it's a node on the mesh. Okay. So don't build your organization around the technology, let the technology serve the organization is that-- >> That's a perfect way of putting it, exactly. I mean, for a very long time, when we look at decomposition of complexity, we've looked at decomposition of complexity around technology, right? So we have technology and that's maybe a good segue to actually the next item on that list that we looked at. Oh, I need to decompose based on whether I want to have access to raw data and put it on the lake. Whether I want to have access to model data and put it on the warehouse. You know I need to have a team in the middle to move the data around. And then try to figure organization into that model. So data mesh really inverses that, and as you said, is look at the organizational structure first. Then scale boundaries around which your organization and operation can scale. And then the second layer look at the technology and how you decompose it. >> Okay. So let's go to that next point and talk about how you serve and manage autonomous interoperable data products. Where code, data policy you say is treated as one unit. Whereas your contention is existing platforms of course have independent management and dashboards for catalogs or storage, et cetera. Maybe we double click on that a bit. >> Yeah. So if you think about that functional, or technical decomposition, right? Of concerns, that's one way, that's a very valid way of decomposing, complexity and concerns. And then build solutions, independent solutions to address them. That's what we see in the technology landscape today. We will see technologies that are taking care of your management of data, bring your data under some sort of a control and modeling. You'll see technology that moves that data around, will perform various transformations and computations on it. And then you see technology that tries to overlay some level of meaning. Metadata, understandability, discovery was the end policy, right? So that's where your data processing kind of pipeline technologies versus data warehouse, storage, lake technologies, and then the governance come to play. And over time, we decomposed and we compose, right? Deconstruct and reconstruct back this together. But, right now that's where we stand. I think for data mesh really to become a reality, as in independent sources of data and teams can responsibly share data in a way that can be understood right then and there can impose policies, right then when the data gets accessed in that source and in a resilient manner, like in a way that data changes structure of the data or changes to the scheme of the data, doesn't have those downstream down times. We've got to think about this new nucleus or new units of data sharing. And we need to really bring back transformation and governing data and the data itself together around these decentralized nodes on the mesh. So that's another, I guess, deconstruction and reconstruction that needs to happen around the technology to formulate ourselves around the domains. And again the data and the logic of the data itself, the meaning of the data itself. >> Great. Got it. And we're going to talk more about the importance of data sharing and the implications. But the third point deals with how operational, analytical technologies are constructed. You've got an app DevStack, you've got a data stack. You've made the point many times actually that we've contextualized our operational systems, but not our data systems, they remain separate. Maybe you could elaborate on this point. >> Yes. I think this is, again, has a historical background and beginning. For a really long time, applications have dealt with features and the logic of running the business and encapsulating the data and the state that they need to run that feature or run that business function. And then we had for anything analytical driven, which required access data across these applications and across the longer dimension of time around different subjects within the organization. This analytical data, we had made a decision that, "Okay, let's leave those applications aside. Let's leave those databases aside. We'll extract the data out and we'll load it, or we'll transform it and put it under the analytical kind of a data stack and then downstream from it, we will have analytical data users, the data analysts, the data sciences and the, you know, the portfolio of users that are growing use that data stack. And that led to this really separation of dual stack with point to point integration. So applications went down the path of transactional databases or urban document store, but using APIs for communicating and then we've gone to, you know, lake storage or data warehouse on the other side. If we are moving and that again, enforces the silo of data versus app, right? So if we are moving to the world that our missions that are ambitions around making applications, more intelligent. Making them data driven. These two worlds need to come closer. As in ML Analytics gets embedded into those app applications themselves. And the data sharing, as a very essential ingredient of that, gets embedded and gets closer, becomes closer to those applications. So, if you are looking at this now cross-functional, app data, based team, right? Business team, then the technology stacks can't be so segregated, right? There has to be a continuum of experience from app delivery, to sharing of the data, to using that data, to embed models back into those applications. And that continuum of experience requires well integrated technologies. I'll give you an example, which actually in some sense, we are somewhat moving to that direction. But if we are talking about data sharing or data modeling and applications use one set of APIs, you know, HTTP compliant, GraQL or RAC APIs. And on the other hand, you have proprietary SQL, like connect to my database and run SQL. Like those are very two different models of representing and accessing data. So we kind of have to harmonize or integrate those two worlds a bit more closely to achieve that domain oriented cross-functional teams. >> Yeah. We are going to talk about some of the gaps later and actually you look at them as opportunities, more than barriers. But they are barriers, but they're opportunities for more innovation. Let's go on to the fourth one. The next point, it deals with the roles that the platform serves. Data mesh proposes that domain experts own the data and take responsibility for it end to end and are served by the technology. Kind of, we referenced that before. Whereas your contention is that today, data systems are really designed for specialists. I think you use the term hyper specialists a lot. I love that term. And the generalist are kind of passive bystanders waiting in line for the technical teams to serve them. >> Yes. I mean, if you think about the, again, the intention behind data mesh was creating a responsible data sharing model that scales out. And I challenge any organization that has a scaled ambitions around data or usage of data that relies on small pockets of very expensive specialists resources, right? So we have no choice, but upscaling cross-scaling. The majority population of our technologists, we often call them generalists, right? That's a short hand for people that can really move from one technology to another technology. Sometimes we call them pandric people sometimes we call them T-shaped people. But regardless, like we need to have ability to really mobilize our generalists. And we had to do that at Thoughtworks. We serve a lot of our clients and like many other organizations, we are also challenged with hiring specialists. So we have tested the model of having a few specialists, really conveying and translating the knowledge to generalists and bring them forward. And of course, platform is a big enabler of that. Like what is the language of using the technology? What are the APIs that delight that generalist experience? This doesn't mean no code, low code. We have to throw away in to good engineering practices. And I think good software engineering practices remain to exist. Of course, they get adopted to the world of data to build resilient you know, sustainable solutions, but specialty, especially around kind of proprietary technology is going to be a hard one to scale. >> Okay. I'm definitely going to come back and pick your brain on that one. And, you know, your point about scale out in the examples, the practical examples of companies that have implemented data mesh that I've talked to. I think in all cases, you know, there's only a handful that I've really gone deep with, but it was their hadoop instances, their clusters wouldn't scale, they couldn't scale the business and around it. So that's really a key point of a common pattern that we've seen now. I think in all cases, they went to like the data lake model and AWS. And so that maybe has some violation of the principles, but we'll come back to that. But so let me go on to the next one. Of course, data mesh leans heavily, toward this concept of decentralization, to support domain ownership over the centralized approaches. And we certainly see this, the public cloud players, database companies as key actors here with very large install bases, pushing a centralized approach. So I guess my question is, how realistic is this next point where you have decentralized technologies ruling the roost? >> I think if you look at the history of places, in our industry where decentralization has succeeded, they heavily relied on standardization of connectivity with, you know, across different components of technology. And I think right now you are right. The way we get value from data relies on collection. At the end of the day, collection of data. Whether you have a deep learning machinery model that you're training, or you have, you know, reports to generate. Regardless, the model is bring your data to a place that you can collect it, so that we can use it. And that leads to a naturally set of technologies that try to operate as a full stack integrated proprietary with no intention of, you know, opening, data for sharing. Now, conversely, if you think about internet itself, web itself, microservices, even at the enterprise level, not at the planetary level, they succeeded as decentralized technologies to a large degree because of their emphasis on open net and openness and sharing, right. API sharing. We don't talk about, in the API worlds, like we don't say, you know, "I will build a platform to manage your logical applications." Maybe to a degree but we actually moved away from that. We say, "I'll build a platform that opens around applications to manage your APIs, manage your interfaces." Right? Give you access to API. So I think the shift needs to... That definition of decentralized there means really composable, open pieces of the technology that can play nicely with each other, rather than a full stack, all have control of your data yet being somewhat decentralized within the boundary of my platform. That's just simply not going to scale if data needs to come from different platforms, different locations, different geographical locations, it needs to rethink. >> Okay, thank you. And then the final point is, is data mesh favors technologies that are domain agnostic versus those that are domain aware. And I wonder if you could help me square the circle cause it's nuanced and I'm kind of a 100 level student of your work. But you have said for example, that the data teams lack context of the domain and so help us understand what you mean here in this case. >> Sure. Absolutely. So as you said, we want to take... Data mesh tries to give autonomy and decision making power and responsibility to people that have the context of those domains, right? The people that are really familiar with different business domains and naturally the data that that domain needs, or that naturally the data that domains shares. So if the intention of the platform is really to give the power to people with most relevant and timely context, the platform itself naturally becomes as a shared component, becomes domain agnostic to a large degree. Of course those domains can still... The platform is a (chuckles) fairly overloaded world. As in, if you think about it as a set of technology that abstracts complexity and allows building the next level solutions on top, those domains may have their own set of platforms that are very much doing agnostic. But as a generalized shareable set of technologies or tools that allows us share data. So that piece of technology needs to relinquish the knowledge of the context to the domain teams and actually becomes domain agnostic. >> Got it. Okay. Makes sense. All right. Let's shift gears here. Talk about some of the gaps and some of the standards that are needed. You and I have talked about this a little bit before, but this digs deeper. What types of standards are needed? Maybe you could walk us through this graphic, please. >> Sure. So what I'm trying to depict here is that if we imagine a world that data can be shared from many different locations, for a variety of analytical use cases, naturally the boundary of what we call a node on the mesh will encapsulates internally a fair few pieces. It's not just the boundary of that, not on the mesh, is the data itself that it's controlling and updating and maintaining. It's of course a computation and the code that's responsible for that data. And then the policies that continue to govern that data as long as that data exists. So if that's the boundary, then if we shift that focus from implementation details, that we can leave that for later, what becomes really important is the scene or the APIs and interfaces that this node exposes. And I think that's where the work that needs to be done and the standards that are missing. And we want the scene and those interfaces be open because that allows, you know, different organizations with different boundaries of trust to share data. Not only to share data to kind of move that data to yes, another location, to share the data in a way that distributed workloads, distributed analytics, distributed machine learning model can happen on the data where it is. So if you follow that line of thinking around the centralization and connection of data versus collection of data, I think the very, very important piece of it that needs really deep thinking, and I don't claim that I have done that, is how do we share data responsibly and sustainably, right? That is not brittle. If you think about it today, the ways we share data, one of the very common ways is around, I'll give you a JDC endpoint, or I give you an endpoint to your, you know, database of choice. And now as technology, whereas a user actually, you can now have access to the schema of the underlying data and then run various queries or SQL queries on it. That's very simple and easy to get started with. That's why SQL is an evergreen, you know, standard or semi standard, pseudo standard that we all use. But it's also very brittle, because we are dependent on a underlying schema and formatting of the data that's been designed to tell the computer how to store and manage the data. So I think that the data sharing APIs of the future really need to think about removing this brittle dependencies, think about sharing, not only the data, but what we call metadata, I suppose. Additional set of characteristics that is always shared along with data to make the data usage, I suppose ethical and also friendly for the users and also, I think we have to... That data sharing API, the other element of it, is to allow kind of computation to run where the data exists. So if you think about SQL again, as a simple primitive example of computation, when we select and when we filter and when we join, the computation is happening on that data. So maybe there is a next level of articulating, distributed computational data that simply trains models, right? Your language primitives change in a way to allow sophisticated analytical workloads run on the data more responsibly with policies and access control and force. So I think that output port that I mentioned simply is about next generation data sharing, responsible data sharing APIs. Suitable for decentralized analytical workloads. >> So I'm not trying to bait you here, but I have a follow up as well. So you schema, for all its good creates constraints. No schema on right, that didn't work, cause it was just a free for all and it created the data swamps. But now you have technology companies trying to solve that problem. Take Snowflake for example, you know, enabling, data sharing. But it is within its proprietary environment. Certainly Databricks doing something, you know, trying to come at it from its angle, bringing some of the best to data warehouse, with the data science. Is your contention that those remain sort of proprietary and defacto standards? And then what we need is more open standards? Maybe you could comment. >> Sure. I think the two points one is, as you mentioned. Open standards that allow... Actually make the underlying platform invisible. I mean my litmus test for a technology provider to say, "I'm a data mesh," (laughs) kind of compliant is, "Is your platform invisible?" As in, can I replace it with another and yet get the similar data sharing experience that I need? So part of it is that. Part of it is open standards, they're not really proprietary. The other angle for kind of sharing data across different platforms so that you know, we don't get stuck with one technology or another is around APIs. It is around code that is protecting that internal schema. So where we are on the curve of evolution of technology, right now we are exposing the internal structure of the data. That is designed to optimize certain modes of access. We're exposing that to the end client and application APIs, right? So the APIs that use the data today are very much aware that this database was optimized for machine learning workloads. Hence you will deal with a columnar storage of the file versus this other API is optimized for a very different, report type access, relational access and is optimized around roles. I think that should become irrelevant in the API sharing of the future. Because as a user, I shouldn't care how this data is internally optimized, right? The language primitive that I'm using should be really agnostic to the machine optimization underneath that. And if we did that, perhaps this war between warehouse or lake or the other will become actually irrelevant. So we're optimizing for that human best human experience, as opposed to the best machine experience. We still have to do that but we have to make that invisible. Make that an implementation concern. So that's another angle of what should... If we daydream together, the best experience and resilient experience in terms of data usage than these APIs with diagnostics to the internal storage structure. >> Great, thank you for that. We've wrapped our ankles now on the controversy, so we might as well wade all the way in, I can't let you go without addressing some of this. Which you've catalyzed, which I, by the way, I see as a sign of progress. So this gentleman, Paul Andrew is an architect and he gave a presentation I think last night. And he teased it as quote, "The theory from Zhamak Dehghani versus the practical experience of a technical architect, AKA me," meaning him. And Zhamak, you were quick to shoot back that data mesh is not theory, it's based on practice. And some practices are experimental. Some are more baked and data mesh really avoids by design, the specificity of vendor or technology. Perhaps you intend to frame your post as a technology or vendor specific, specific implementation. So touche, that was excellent. (Zhamak laughs) Now you don't need me to defend you, but I will anyway. You spent 14 plus years as a software engineer and the better part of a decade consulting with some of the most technically advanced companies in the world. But I'm going to push you a little bit here and say, some of this tension is of your own making because you purposefully don't talk about technologies and vendors. Sometimes doing so it's instructive for us neophytes. So, why don't you ever like use specific examples of technology for frames of reference? >> Yes. My role is pushes to the next level. So, you know everybody picks their fights, pick their battles. My role in this battle is to push us to think beyond what's available today. Of course, that's my public persona. On a day to day basis, actually I work with clients and existing technology and I think at Thoughtworks we have given the talk we gave a case study talk with a colleague of mine and I intentionally got him to talk about (indistinct) I want to talk about the technology that we use to implement data mesh. And the reason I haven't really embraced, in my conversations, the specific technology. One is, I feel the technology solutions we're using today are still not ready for the vision. I mean, we have to be in this transitional step, no matter what we have to be pragmatic, of course, and practical, I suppose. And use the existing vendors that exist and I wholeheartedly embrace that, but that's just not my role, to show that. I've gone through this transformation once before in my life. When microservices happened, we were building microservices like architectures with technology that wasn't ready for it. Big application, web application servers that were designed to run these giant monolithic applications. And now we're trying to run little microservices onto them. And the tail was riding the dock, the environmental complexity of running these services was consuming so much of our effort that we couldn't really pay attention to that business logic, the business value. And that's where we are today. The complexity of integrating existing technologies is really overwhelmingly, capturing a lot of our attention and cost and effort, money and effort as opposed to really focusing on the data product themselves. So it's just that's the role I have, but it doesn't mean that, you know, we have to rebuild the world. We've got to do with what we have in this transitional phase until the new generation, I guess, technologies come around and reshape our landscape of tools. >> Well, impressive public discipline. Your point about microservice is interesting because a lot of those early microservices, weren't so micro and for the naysayers look past this, not prologue, but Thoughtworks was really early on in the whole concept of microservices. So be very excited to see how this plays out. But now there was some other good comments. There was one from a gentleman who said the most interesting aspects of data mesh are organizational. And that's how my colleague Sanji Mohan frames data mesh versus data fabric. You know, I'm not sure, I think we've sort of scratched the surface today that data today, data mesh is more. And I still think data fabric is what NetApp defined as software defined storage infrastructure that can serve on-prem and public cloud workloads back whatever, 2016. But the point you make in the thread that we're showing you here is that you're warning, and you referenced this earlier, that the segregating different modes of access will lead to fragmentation. And we don't want to repeat the mistakes of the past. >> Yes, there are comments around. Again going back to that original conversation that we have got this at a macro level. We've got this tendency to decompose complexity based on technical solutions. And, you know, the conversation could be, "Oh, I do batch or you do a stream and we are different."' They create these bifurcations in our decisions based on the technology where I do events and you do tables, right? So that sort of segregation of modes of access causes accidental complexity that we keep dealing with. Because every time in this tree, you create a new branch, you create new kind of new set of tools and then somehow need to be point to point integrated. You create new specialization around that. So the least number of branches that we have, and think about really about the continuum of experiences that we need to create and technologies that simplify, that continuum experience. So one of the things, for example, give you a past experience. I was really excited around the papers and the work that came around on Apache Beam, and generally flow based programming and stream processing. Because basically they were saying whether you are doing batch or whether you're doing streaming, it's all one stream. And sometimes the window of time, narrows and sometimes the window of time over which you're computing, widens and at the end of today, is you are just getting... Doing the stream processing. So it is those sort of notions that simplify and create continuum of experience. I think resonate with me personally, more than creating these tribal fights of this type versus that mode of access. So that's why data mesh naturally selects kind of this multimodal access to support end users, right? The persona of end users. >> Okay. So the last topic I want to hit, this whole discussion, the topic of data mesh it's highly nuanced, it's new, and people are going to shoehorn data mesh into their respective views of the world. And we talked about lake houses and there's three buckets. And of course, the gentleman from LinkedIn with Azure, Microsoft has a data mesh community. See you're going to have to enlist some serious army of enforcers to adjudicate. And I wrote some of the stuff down. I mean, it's interesting. Monte Carlo has a data mesh calculator. Starburst is leaning in, chaos. Search sees themselves as an enabler. Oracle and Snowflake both use the term data mesh. And then of course you've got big practitioners J-P-M-C, we've talked to Intuit, Orlando, HelloFresh has been on, Netflix has this event based sort of streaming implementation. So my question is, how realistic is it that the clarity of your vision can be implemented and not polluted by really rich technology companies and others? (Zhamak laughs) >> Is it even possible, right? Is it even possible? That's a yes. That's why I practice then. This is why I should practice things. Cause I think, it's going to be hard. What I'm hopeful, is that the socio-technical, Leveling Data mentioned that this is a socio-technical concern or solution, not just a technology solution. Hopefully always brings us back to, you know, the reality that vendors try to sell you safe oil that solves all of your problems. (chuckles) All of your data mesh problems. It's just going to cause more problem down the track. So we'll see, time will tell Dave and I count on you as one of those members of, (laughs) you know, folks that will continue to share their platform. To go back to the roots, as why in the first place? I mean, I dedicated a whole part of the book to 'Why?' Because we get, as you said, we get carried away with vendors and technology solution try to ride a wave. And in that story, we forget the reason for which we even making this change and we are going to spend all of this resources. So hopefully we can always come back to that. >> Yeah. And I think we can. I think you have really given this some deep thought and as we pointed out, this was based on practical knowledge and experience. And look, we've been trying to solve this data problem for a long, long time. You've not only articulated it well, but you've come up with solutions. So Zhamak, thank you so much. We're going to leave it there and I'd love to have you back. >> Thank you for the conversation. I really enjoyed it. And thank you for sharing your platform to talk about data mesh. >> Yeah, you bet. All right. And I want to thank my colleague, Stephanie Chan, who helps research topics for us. Alex Myerson is on production and Kristen Martin, Cheryl Knight and Rob Hoff on editorial. Remember all these episodes are available as podcasts, wherever you listen. And all you got to do is search Breaking Analysis Podcast. Check out ETR's website at etr.ai for all the data. And we publish a full report every week on wikibon.com, siliconangle.com. You can reach me by email david.vellante@siliconangle.com or DM me @dvellante. Hit us up on our LinkedIn post. This is Dave Vellante for theCUBE Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (bright music)

(upbeat music) >> Once a staid and boring business dominated by IBM, Oracle, and at the time newcomer Microsoft, along with a handful of wannabes, the database business has exploded in the past decade and has become a staple of financial excellence, customer experience, analytic advantage, competitive strategy, growth initiatives, visualizations, not to mention compliance, security, privacy and dozens of other important use cases and initiatives. And on the vendor's side of the house, we've seen the rapid ascendancy of cloud databases. Most notably from Snowflake, whose massive raises leading up to its IPO in late 2020 sparked a spate of interest and VC investment in the separation of compute and storage and all that elastic resource stuff in the cloud. The company joined AWS, Azure and Google to popularize cloud databases, which have become a linchpin of competitive strategies for technology suppliers. And if I get you to put your data in my database and in my cloud, and I keep innovating, I'm going to build a moat and achieve a hugely attractive lifetime customer value in a really amazing marginal economics dynamic that is going to fund my future. And I'll be able to sell other adjacent services, not just compute and storage, but machine learning and inference and training and all kinds of stuff, dozens of lucrative cloud offerings. Meanwhile, the database leader, Oracle has invested massive amounts of money to maintain its lead. It's building on its position as the king of mission critical workloads and making typical Oracle like claims against the competition. Most were recently just yesterday with another announcement around MySQL HeatWave. An extension of MySQL that is compatible with on-premises MySQLs and is setting new standards in price performance. We're seeing a dramatic divergence in strategies across the database spectrum. On the far left, we see Amazon with more than a dozen database offerings each with its own API and primitives. AWS is taking a right tool for the right job approach, often building on open source platforms and creating services that it offers to customers to solve very specific problems for developers. And on the other side of the line, we see Oracle, which is taking the Swiss Army Knife approach, converging database functionality, enabling analytic and transactional workloads to run in the same data store, eliminating the need to ETL, at the same time adding capabilities into its platform like automation and machine learning. Welcome to this database Power Panel. My name is Dave Vellante, and I'm so excited to bring together some of the most respected industry analyst in the community. Today we're going to assess what's happening in the market. We're going to dig into the competitive landscape and explore the future of database and database platforms and decode what it means to customers. Let me take a moment to welcome our guest analyst today. Matt Kimball is a vice president and principal analysts at Moor Insights and Strategy, Matt. He knows products, he knows industry, he's got real world IT expertise, and he's got all the angles 25 plus years of experience in all kinds of great background. Matt, welcome. Thanks very much for coming on theCUBE. Holgar Mueller, friend of theCUBE, vice president and principal analyst at Constellation Research in depth knowledge on applications, application development, knows developers. He's worked at SAP and Oracle. And then Bob Evans is Chief Content Officer and co-founder of the Acceleration Economy, founder and principle of Cloud Wars. Covers all kinds of industry topics and great insights. He's got awesome videos, these three minute hits. If you haven't seen 'em, checking them out, knows cloud companies, his Cloud Wars minutes are fantastic. And then of course, Marc Staimer is the founder of Dragon Slayer Research. A frequent contributor and guest analyst at Wikibon. He's got a wide ranging knowledge across IT products, knows technology really well, can go deep. And then of course, Ron Westfall, Senior Analyst and Director Research Director at Futurum Research, great all around product trends knowledge. Can take, you know, technical dives and really understands competitive angles, knows Redshift, Snowflake, and many others. Gents, thanks so much for taking the time to join us in theCube today. It's great to have you on, good to see you. >> Good to be here, thanks for having us. >> Thanks, Dave. >> All right, let's start with an around the horn and briefly, if each of you would describe, you know, anything I missed in your areas of expertise and then you answer the following question, how would you describe the state of the database, state of platform market today? Matt Kimball, please start. >> Oh, I hate going first, but that it's okay. How would I describe the world today? I would just in one sentence, I would say, I'm glad I'm not in IT anymore, right? So, you know, it is a complex and dangerous world out there. And I don't envy IT folks I'd have to support, you know, these modernization and transformation efforts that are going on within the enterprise. It used to be, you mentioned it, Dave, you would argue about IBM versus Oracle versus this newcomer in the database space called Microsoft. And don't forget Sybase back in the day, but you know, now it's not just, which SQL vendor am I going to go with? It's all of these different, divergent data types that have to be taken, they have to be merged together, synthesized. And somehow I have to do that cleanly and use this to drive strategic decisions for my business. That is not easy. So, you know, you have to look at it from the perspective of the business user. It's great for them because as a DevOps person, or as an analyst, I have so much flexibility and I have this thing called the cloud now where I can go get services immediately. As an IT person or a DBA, I am calling up prevention hotlines 24 hours a day, because I don't know how I'm going to be able to support the business. And as an Oracle or as an Oracle or a Microsoft or some of the cloud providers and cloud databases out there, I'm licking my chops because, you know, my market is expanding and expanding every day. >> Great, thank you for that, Matt. Holgar, how do you see the world these days? You always have a good perspective on things, share with us. >> Well, I think it's the best time to be in IT, I'm not sure what Matt is talking about. (laughing) It's easier than ever, right? The direction is going to cloud. Kubernetes has won, Google has the best AI for now, right? So things are easier than ever before. You made commitments for five plus years on hardware, networking and so on premise, and I got gray hair about worrying it was the wrong decision. No, just kidding. But you kind of both sides, just to be controversial, make it interesting, right. So yeah, no, I think the interesting thing specifically with databases, right? We have this big suite versus best of breed, right? Obviously innovation, like you mentioned with Snowflake and others happening in the cloud, the cloud vendors server, where to save of their databases. And then we have one of the few survivors of the old guard as Evans likes to call them is Oracle who's doing well, both their traditional database. And now, which is really interesting, remarkable from that because Oracle it was always the power of one, have one database, add more to it, make it what I call the universal database. And now this new HeatWave offering is coming and MySQL open source side. So they're getting the second (indistinct) right? So it's interesting that older players, traditional players who still are in the market are diversifying their offerings. Something we don't see so much from the traditional tools from Oracle on the Microsoft side or the IBM side these days. >> Great, thank you Holgar. Bob Evans, you've covered this business for a while. You've worked at, you know, a number of different outlets and companies and you cover the competition, how do you see things? >> Dave, you know, the other angle to look at this from is from the customer side, right? You got now CEOs who are any sort of business across all sorts of industries, and they understand that their future success is going to be dependent on their ability to become a digital company, to understand data, to use it the right way. So as you outline Dave, I think in your intro there, it is a fantastic time to be in the database business. And I think we've got a lot of new buyers and influencers coming in. They don't know all this history about IBM and Microsoft and Oracle and you know, whoever else. So I think they're going to take a long, hard look, Dave, at some of these results and who is able to help these companies not serve up the best technology, but who's going to be able to help their business move into the digital future. So it's a fascinating time now from every perspective. >> Great points, Bob. I mean, digital transformation has gone from buzzword to imperative. Mr. Staimer, how do you see things? >> I see things a little bit differently than my peers here in that I see the database market being segmented. There's all the different kinds of databases that people are looking at for different kinds of data, and then there is databases in the cloud. And so database as cloud service, I view very differently than databases because the traditional way of implementing a database is changing and it's changing rapidly. So one of the premises that you stated earlier on was that you viewed Oracle as a database company. I don't view Oracle as a database company anymore. I view Oracle as a cloud company that happens to have a significant expertise and specialty in databases, and they still sell database software in the traditional way, but ultimately they're a cloud company. So database cloud services from my point of view is a very distinct market from databases. >> Okay, well, you gave us some good meat on the bone to talk about that. Last but not least-- >> Dave did Marc, just say Oracle's a cloud company? >> Yeah. (laughing) Take away the database, it would be interesting to have that discussion, but let's let Ron jump in here. Ron, give us your take. >> That's a great segue. I think it's truly the era of the cloud database, that's something that's rising. And the key trends that come with it include for example, elastic scaling. That is the ability to scale on demand, to right size workloads according to customer requirements. And also I think it's going to increase the prioritization for high availability. That is the player who can provide the highest availability is going to have, I think, a great deal of success in this emerging market. And also I anticipate that there will be more consolidation across platforms in order to enable cost savings for customers, and that's something that's always going to be important. And I think we'll see more of that over the horizon. And then finally security, security will be more important than ever. We've seen a spike (indistinct), we certainly have seen geopolitical originated cybersecurity concerns. And as a result, I see database security becoming all the more important. >> Great, thank you. Okay, let me share some data with you guys. I'm going to throw this at you and see what you think. We have this awesome data partner called Enterprise Technology Research, ETR. They do these quarterly surveys and each period with dozens of industry segments, they track clients spending, customer spending. And this is the database, data warehouse sector okay so it's taxonomy, so it's not perfect, but it's a big kind of chunk. They essentially ask customers within a category and buy a specific vendor, you're spending more or less on the platform? And then they subtract the lesses from the mores and they derive a metric called net score. It's like NPS, it's a measure of spending velocity. It's more complicated and granular than that, but that's the basis and that's the vertical axis. The horizontal axis is what they call market share, it's not like IDC market share, it's just pervasiveness in the data set. And so there are a couple of things that stand out here and that we can use as reference point. The first is the momentum of Snowflake. They've been off the charts for many, many, for over two years now, anything above that dotted red line, that 40%, is considered by ETR to be highly elevated and Snowflake's even way above that. And I think it's probably not sustainable. We're going to see in the next April survey, next month from those guys, when it comes out. And then you see AWS and Microsoft, they're really pervasive on the horizontal axis and highly elevated, Google falls behind them. And then you got a number of well funded players. You got Cockroach Labs, Mongo, Redis, MariaDB, which of course is a fork on MySQL started almost as protest at Oracle when they acquired Sun and they got MySQL and you can see the number of others. Now Oracle who's the leading database player, despite what Marc Staimer says, we know, (laughs) and they're a cloud player (laughing) who happens to be a leading database player. They dominate in the mission critical space, we know that they're the king of that sector, but you can see here that they're kind of legacy, right? They've been around a long time, they get a big install base. So they don't have the spending momentum on the vertical axis. Now remember this is, just really this doesn't capture spending levels, so that understates Oracle but nonetheless. So it's not a complete picture like SAP for instance is not in here, no Hana. I think people are actually buying it, but it doesn't show up here, (laughs) but it does give an indication of momentum and presence. So Bob Evans, I'm going to start with you. You've commented on many of these companies, you know, what does this data tell you? >> Yeah, you know, Dave, I think all these compilations of things like that are interesting, and that folks at ETR do some good work, but I think as you said, it's a snapshot sort of a two-dimensional thing of a rapidly changing, three dimensional world. You know, the incidents at which some of these companies are mentioned versus the volume that happens. I think it's, you know, with Oracle and I'm not going to declare my religious affiliation, either as cloud company or database company, you know, they're all of those things and more, and I think some of our old language of how we classify companies is just not relevant anymore. But I want to ask too something in here, the autonomous database from Oracle, nobody else has done that. So either Oracle is crazy, they've tried out a technology that nobody other than them is interested in, or they're onto something that nobody else can match. So to me, Dave, within Oracle, trying to identify how they're doing there, I would watch autonomous database growth too, because right, it's either going to be a big plan and it breaks through, or it's going to be caught behind. And the Snowflake phenomenon as you mentioned, that is a rare, rare bird who comes up and can grow 100% at a billion dollar revenue level like that. So now they've had a chance to come in, scare the crap out of everybody, rock the market with something totally new, the data cloud. Will the bigger companies be able to catch up and offer a compelling alternative, or is Snowflake going to continue to be this outlier. It's a fascinating time. >> Really, interesting points there. Holgar, I want to ask you, I mean, I've talked to certainly I'm sure you guys have too, the founders of Snowflake that came out of Oracle and they actually, they don't apologize. They say, "Hey, we not going to do all that complicated stuff that Oracle does, we were trying to keep it real simple." But at the same time, you know, they don't do sophisticated workload management. They don't do complex joints. They're kind of relying on the ecosystems. So when you look at the data like this and the various momentums, and we talked about the diverging strategies, what does this say to you? >> Well, it is a great point. And I think Snowflake is an example how the cloud can turbo charge a well understood concept in this case, the data warehouse, right? You move that and you find steroids and you see like for some players who've been big in data warehouse, like Sentara Data, as an example, here in San Diego, what could have been for them right in that part. The interesting thing, the problem though is the cloud hides a lot of complexity too, which you can scale really well as you attract lots of customers to go there. And you don't have to build things like what Bob said, right? One of the fascinating things, right, nobody's answering Oracle on the autonomous database. I don't think is that they cannot, they just have different priorities or the database is not such a priority. I would dare to say that it's for IBM and Microsoft right now at the moment. And the cloud vendors, you just hide that right through scripts and through scale because you support thousands of customers and you can deal with a little more complexity, right? It's not against them. Whereas if you have to run it yourself, very different story, right? You want to have the autonomous parts, you want to have the powerful tools to do things. >> Thank you. And so Matt, I want to go to you, you've set up front, you know, it's just complicated if you're in IT, it's a complicated situation and you've been on the customer side. And if you're a buyer, it's obviously, it's like Holgar said, "Cloud's supposed to make this stuff easier, but the simpler it gets the more complicated gets." So where do you place your bets? Or I guess more importantly, how do you decide where to place your bets? >> Yeah, it's a good question. And to what Bob and Holgar said, you know, the around autonomous database, I think, you know, part of, as I, you know, play kind of armchair psychologist, if you will, corporate psychologists, I look at what Oracle is doing and, you know, databases where they've made their mark and it's kind of, that's their strong position, right? So it makes sense if you're making an entry into this cloud and you really want to kind of build momentum, you go with what you're good at, right? So that's kind of the strength of Oracle. Let's put a lot of focus on that. They do a lot more than database, don't get me wrong, but you know, I'm going to short my strength and then kind of pivot from there. With regards to, you know, what IT looks at and what I would look at you know as an IT director or somebody who is, you know, trying to consume services from these different cloud providers. First and foremost, I go with what I know, right? Let's not forget IT is a conservative group. And when we look at, you know, all the different permutations of database types out there, SQL, NoSQL, all the different types of NoSQL, those are largely being deployed by business users that are looking for agility or businesses that are looking for agility. You know, the reason why MongoDB is so popular is because of DevOps, right? It's a great platform to develop on and that's where it kind of gained its traction. But as an IT person, I want to go with what I know, where my muscle memory is, and that's my first position. And so as I evaluate different cloud service providers and cloud databases, I look for, you know, what I know and what I've invested in and where my muscle memory is. Is there enough there and do I have enough belief that that company or that service is going to be able to take me to, you know, where I see my organization in five years from a data management perspective, from a business perspective, are they going to be there? And if they are, then I'm a little bit more willing to make that investment, but it is, you know, if I'm kind of going in this blind or if I'm cloud native, you know, that's where the Snowflakes of the world become very attractive to me. >> Thank you. So Marc, I asked Andy Jackson in theCube one time, you have all these, you know, data stores and different APIs and primitives and you know, very granular, what's the strategy there? And he said, "Hey, that allows us as the market changes, it allows us to be more flexible. If we start building abstractions layers, it's harder for us." I think also it was not a good time to market advantage, but let me ask you, I described earlier on that spectrum from AWS to Oracle. We just saw yesterday, Oracle announced, I think the third major enhancement in like 15 months to MySQL HeatWave, what do you make of that announcement? How do you think it impacts the competitive landscape, particularly as it relates to, you know, converging transaction and analytics, eliminating ELT, I know you have some thoughts on this. >> So let me back up for a second and defend my cloud statement about Oracle for a moment. (laughing) AWS did a great job in developing the cloud market in general and everything in the cloud market. I mean, I give them lots of kudos on that. And a lot of what they did is they took open source software and they rent it to people who use their cloud. So I give 'em lots of credit, they dominate the market. Oracle was late to the cloud market. In fact, they actually poo-pooed it initially, if you look at some of Larry Ellison's statements, they said, "Oh, it's never going to take off." And then they did 180 turn, and they said, "Oh, we're going to embrace the cloud." And they really have, but when you're late to a market, you've got to be compelling. And this ties into the announcement yesterday, but let's deal with this compelling. To be compelling from a user point of view, you got to be twice as fast, offer twice as much functionality, at half the cost. That's generally what compelling is that you're going to capture market share from the leaders who established the market. It's very difficult to capture market share in a new market for yourself. And you're right. I mean, Bob was correct on this and Holgar and Matt in which you look at Oracle, and they did a great job of leveraging their database to move into this market, give 'em lots of kudos for that too. But yesterday they announced, as you said, the third innovation release and the pace is just amazing of what they're doing on these releases on HeatWave that ties together initially MySQL with an integrated builtin analytics engine, so a data warehouse built in. And then they added automation with autopilot, and now they've added machine learning to it, and it's all in the same service. It's not something you can buy and put on your premise unless you buy their cloud customers stuff. But generally it's a cloud offering, so it's compellingly better as far as the integration. You don't buy multiple services, you buy one and it's lower cost than any of the other services, but more importantly, it's faster, which again, give 'em credit for, they have more integration of a product. They can tie things together in a way that nobody else does. There's no additional services, ETL services like Glue and AWS. So from that perspective, they're getting better performance, fewer services, lower cost. Hmm, they're aiming at the compelling side again. So from a customer point of view it's compelling. Matt, you wanted to say something there. >> Yeah, I want to kind of, on what you just said there Marc, and this is something I've found really interesting, you know. The traditional way that you look at software and, you know, purchasing software and IT is, you look at either best of breed solutions and you have to work on the backend to integrate them all and make them all work well. And generally, you know, the big hit against the, you know, we have one integrated offering is that, you lose capability or you lose depth of features, right. And to what you were saying, you know, that's the thing I found interesting about what Oracle is doing is they're building in depth as they kind of, you know, build that service. It's not like you're losing a lot of capabilities, because you're going to one integrated service versus having to use A versus B versus C, and I love that idea. >> You're right. Yeah, not only you're not losing, but you're gaining functionality that you can't get by integrating a lot of these. I mean, I can take Snowflake and integrate it in with machine learning, but I also have to integrate in with a transactional database. So I've got to have connectors between all of this, which means I'm adding time. And what it comes down to at the end of the day is expertise, effort, time, and cost. And so what I see the difference from the Oracle announcements is they're aiming at reducing all of that by increasing performance as well. Correct me if I'm wrong on that but that's what I saw at the announcement yesterday. >> You know, Marc, one thing though Marc, it's funny you say that because I started out saying, you know, I'm glad I'm not 19 anymore. And the reason is because of exactly what you said, it's almost like there's a pseudo level of witchcraft that's required to support the modern data environment right in the enterprise. And I need simpler faster, better. That's what I need, you know, I am no longer wearing pocket protectors. I have turned from, you know, break, fix kind of person, to you know, business consultant. And I need that point and click simplicity, but I can't sacrifice, you know, a depth of features of functionality on the backend as I play that consultancy role. >> So, Ron, I want to bring in Ron, you know, it's funny. So Matt, you mentioned Mongo, I often and say, if Oracle mentions you, you're on the map. We saw them yesterday Ron, (laughing) they hammered RedShifts auto ML, they took swipes at Snowflake, a little bit of BigQuery. What were your thoughts on that? Do you agree with what these guys are saying in terms of HeatWaves capabilities? >> Yes, Dave, I think that's an excellent question. And fundamentally I do agree. And the question is why, and I think it's important to know that all of the Oracle data is backed by the fact that they're using benchmarks. For example, all of the ML and all of the TPC benchmarks, including all the scripts, all the configs and all the detail are posted on GitHub. So anybody can look at these results and they're fully transparent and replicate themselves. If you don't agree with this data, then by all means challenge it. And we have not really seen that in all of the new updates in HeatWave over the last 15 months. And as a result, when it comes to these, you know, fundamentals in looking at the competitive landscape, which I think gives validity to outcomes such as Oracle being able to deliver 4.8 times better price performance than Redshift. As well as for example, 14.4 better price performance than Snowflake, and also 12.9 better price performance than BigQuery. And so that is, you know, looking at the quantitative side of things. But again, I think, you know, to Marc's point and to Matt's point, there are also qualitative aspects that clearly differentiate the Oracle proposition, from my perspective. For example now the MySQL HeatWave ML capabilities are native, they're built in, and they also support things such as completion criteria. And as a result, that enables them to show that hey, when you're using Redshift ML for example, you're having to also use their SageMaker tool and it's running on a meter. And so, you know, nobody really wants to be running on a meter when, you know, executing these incredibly complex tasks. And likewise, when it comes to Snowflake, they have to use a third party capability. They don't have the built in, it's not native. So the user, to the point that he's having to spend more time and it increases complexity to use auto ML capabilities across the Snowflake platform. And also, I think it also applies to other important features such as data sampling, for example, with the HeatWave ML, it's intelligent sampling that's being implemented. Whereas in contrast, we're seeing Redshift using random sampling. And again, Snowflake, you're having to use a third party library in order to achieve the same capabilities. So I think the differentiation is crystal clear. I think it definitely is refreshing. It's showing that this is where true value can be assigned. And if you don't agree with it, by all means challenge the data. >> Yeah, I want to come to the benchmarks in a minute. By the way, you know, the gentleman who's the Oracle's architect, he did a great job on the call yesterday explaining what you have to do. I thought that was quite impressive. But Bob, I know you follow the financials pretty closely and on the earnings call earlier this month, Ellison said that, "We're going to see HeatWave on AWS." And the skeptic in me said, oh, they must not be getting people to come to OCI. And then they, you remember this chart they showed yesterday that showed the growth of HeatWave on OCI. But of course there was no data on there, it was just sort of, you know, lines up and to the right. So what do you guys think of that? (Marc laughs) Does it signal Bob, desperation by Oracle that they can't get traction on OCI, or is it just really a smart tame expansion move? What do you think? >> Yeah, Dave, that's a great question. You know, along the way there, and you know, just inside of that was something that said Ellison said on earnings call that spoke to a different sort of philosophy or mindset, almost Marc, where he said, "We're going to make this multicloud," right? With a lot of their other cloud stuff, if you wanted to use any of Oracle's cloud software, you had to use Oracle's infrastructure, OCI, there was no other way out of it. But this one, but I thought it was a classic Ellison line. He said, "Well, we're making this available on AWS. We're making this available, you know, on Snowflake because we're going after those users. And once they see what can be done here." So he's looking at it, I guess you could say, it's a concession to customers because they want multi-cloud. The other way to look at it, it's a hunting expedition and it's one of those uniquely I think Oracle ways. He said up front, right, he doesn't say, "Well, there's a big market, there's a lot for everybody, we just want on our slice." Said, "No, we are going after Amazon, we're going after Redshift, we're going after Aurora. We're going after these users of Snowflake and so on." And I think it's really fairly refreshing these days to hear somebody say that, because now if I'm a buyer, I can look at that and say, you know, to Marc's point, "Do they measure up, do they crack that threshold ceiling? Or is this just going to be more pain than a few dollars savings is worth?" But you look at those numbers that Ron pointed out and that we all saw in that chart. I've never seen Dave, anything like that. In a substantive market, a new player coming in here, and being able to establish differences that are four, seven, eight, 10, 12 times better than competition. And as new buyers look at that, they're going to say, "What the hell are we doing paying, you know, five times more to get a poor result? What's going on here?" So I think this is going to rattle people and force a harder, closer look at what these alternatives are. >> I wonder if the guy, thank you. Let's just skip ahead of the benchmarks guys, bring up the next slide, let's skip ahead a little bit here, which talks to the benchmarks and the benchmarking if we can. You know, David Floyer, the sort of semiretired, you know, Wikibon analyst said, "Dave, this is going to force Amazon and others, Snowflake," he said, "To rethink actually how they architect databases." And this is kind of a compilation of some of the data that they shared. They went after Redshift mostly, (laughs) but also, you know, as I say, Snowflake, BigQuery. And, like I said, you can always tell which companies are doing well, 'cause Oracle will come after you, but they're on the radar here. (laughing) Holgar should we take this stuff seriously? I mean, or is it, you know, a grain salt? What are your thoughts here? >> I think you have to take it seriously. I mean, that's a great question, great point on that. Because like Ron said, "If there's a flaw in a benchmark, we know this database traditionally, right?" If anybody came up that, everybody will be, "Oh, you put the wrong benchmark, it wasn't audited right, let us do it again," and so on. We don't see this happening, right? So kudos to Oracle to be aggressive, differentiated, and seem to having impeccable benchmarks. But what we really see, I think in my view is that the classic and we can talk about this in 100 years, right? Is the suite versus best of breed, right? And the key question of the suite, because the suite's always slower, right? No matter at which level of the stack, you have the suite, then the best of breed that will come up with something new, use a cloud, put the data warehouse on steroids and so on. The important thing is that you have to assess as a buyer what is the speed of my suite vendor. And that's what you guys mentioned before as well, right? Marc said that and so on, "Like, this is a third release in one year of the HeatWave team, right?" So everybody in the database open source Marc, and there's so many MySQL spinoffs to certain point is put on shine on the speed of (indistinct) team, putting out fundamental changes. And the beauty of that is right, is so inherent to the Oracle value proposition. Larry's vision of building the IBM of the 21st century, right from the Silicon, from the chip all the way across the seven stacks to the click of the user. And that what makes the database what Rob was saying, "Tied to the OCI infrastructure," because designed for that, it runs uniquely better for that, that's why we see the cross connect to Microsoft. HeatWave so it's different, right? Because HeatWave runs on cheap hardware, right? Which is the breadth and butter 886 scale of any cloud provider, right? So Oracle probably needs it to scale OCI in a different category, not the expensive side, but also allow us to do what we said before, the multicloud capability, which ultimately CIOs really want, because data gravity is real, you want to operate where that is. If you have a fast, innovative offering, which gives you more functionality and the R and D speed is really impressive for the space, puts away bad results, then it's a good bet to look at. >> Yeah, so you're saying, that we versus best of breed. I just want to sort of play back then Marc a comment. That suite versus best of breed, there's always been that trade off. If I understand you Holgar you're saying that somehow Oracle has magically cut through that trade off and they're giving you the best of both. >> It's the developing velocity, right? The provision of important features, which matter to buyers of the suite vendor, eclipses the best of breed vendor, then the best of breed vendor is in the hell of a potential job. >> Yeah, go ahead Marc. >> Yeah and I want to add on what Holgar just said there. I mean the worst job in the data center is data movement, moving the data sucks. I don't care who you are, nobody likes it. You never get any kudos for doing it well, and you always get the ah craps, when things go wrong. So it's in- >> In the data center Marc all the time across data centers, across cloud. That's where the bleeding comes. >> It's right, you get beat up all the time. So nobody likes to move data, ever. So what you're looking at with what they announce with HeatWave and what I love about HeatWave is it doesn't matter when you started with it, you get all the additional features they announce it's part of the service, all the time. But they don't have to move any of the data. You want to analyze the data that's in your transactional, MySQL database, it's there. You want to do machine learning models, it's there, there's no data movement. The data movement is the key thing, and they just eliminate that, in so many ways. And the other thing I wanted to talk about is on the benchmarks. As great as those benchmarks are, they're really conservative 'cause they're underestimating the cost of that data movement. The ETLs, the other services, everything's left out. It's just comparing HeatWave, MySQL cloud service with HeatWave versus Redshift, not Redshift and Aurora and Glue, Redshift and Redshift ML and SageMaker, it's just Redshift. >> Yeah, so what you're saying is what Oracle's doing is saying, "Okay, we're going to run MySQL HeatWave benchmarks on analytics against Redshift, and then we're going to run 'em in transaction against Aurora." >> Right. >> But if you really had to look at what you would have to do with the ETL, you'd have to buy two different data stores and all the infrastructure around that, and that goes away so. >> Due to the nature of the competition, they're running narrow best of breed benchmarks. There is no suite level benchmark (Dave laughs) because they created something new. >> Well that's you're the earlier point they're beating best of breed with a suite. So that's, I guess to Floyer's earlier point, "That's going to shake things up." But I want to come back to Bob Evans, 'cause I want to tap your Cloud Wars mojo before we wrap. And line up the horses, you got AWS, you got Microsoft, Google and Oracle. Now they all own their own cloud. Snowflake, Mongo, Couchbase, Redis, Cockroach by the way they're all doing very well. They run in the cloud as do many others. I think you guys all saw the Andreessen, you know, commentary from Sarah Wang and company, to talk about the cost of goods sold impact of cloud. So owning your own cloud has to be an advantage because other guys like Snowflake have to pay cloud vendors and negotiate down versus having the whole enchilada, Safra Catz's dream. Bob, how do you think this is going to impact the market long term? >> Well, Dave, that's a great question about, you know, how this is all going to play out. If I could mention three things, one, Frank Slootman has done a fantastic job with Snowflake. Really good company before he got there, but since he's been there, the growth mindset, the discipline, the rigor and the phenomenon of what Snowflake has done has forced all these bigger companies to really accelerate what they're doing. And again, it's an example of how this intense competition makes all the different cloud vendors better and it provides enormous value to customers. Second thing I wanted to mention here was look at the Adam Selipsky effect at AWS, took over in the middle of May, and in Q2, Q3, Q4, AWS's growth rate accelerated. And in each of those three quotas, they grew faster than Microsoft's cloud, which has not happened in two or three years, so they're closing the gap on Microsoft. The third thing, Dave, in this, you know, incredibly intense competitive nature here, look at Larry Ellison, right? He's got his, you know, the product that for the last two or three years, he said, "It's going to help determine the future of the company, autonomous database." You would think he's the last person in the world who's going to bring in, you know, in some ways another database to think about there, but he has put, you know, his whole effort and energy behind this. The investments Oracle's made, he's riding this horse really hard. So it's not just a technology achievement, but it's also an investment priority for Oracle going forward. And I think it's going to form a lot of how they position themselves to this new breed of buyer with a new type of need and expectations from IT. So I just think the next two or three years are going to be fantastic for people who are lucky enough to get to do the sorts of things that we do. >> You know, it's a great point you made about AWS. Back in 2018 Q3, they were doing about 7.4 billion a quarter and they were growing in the mid forties. They dropped down to like 29% Q4, 2020, I'm looking at the data now. They popped back up last quarter, last reported quarter to 40%, that is 17.8 billion, so they more doubled and they accelerated their growth rate. (laughs) So maybe that pretends, people are concerned about Snowflake right now decelerating growth. You know, maybe that's going to be different. By the way, I think Snowflake has a different strategy, the whole data cloud thing, data sharing. They're not trying to necessarily take Oracle head on, which is going to make this next 10 years, really interesting. All right, we got to go, last question. 30 seconds or less, what can we expect from the future of data platforms? Matt, please start. >> I have to go first again? You're killing me, Dave. (laughing) In the next few years, I think you're going to see the major players continue to meet customers where they are, right. Every organization, every environment is, you know, kind of, we use these words bespoke in Snowflake, pardon the pun, but Snowflakes, right. But you know, they're all opinionated and unique and what's great as an IT person is, you know, there is a service for me regardless of where I am on my journey, in my data management journey. I think you're going to continue to see with regards specifically to Oracle, I think you're going to see the company continue along this path of being all things to all people, if you will, or all organizations without sacrificing, you know, kind of richness of features and sacrificing who they are, right. Look, they are the data kings, right? I mean, they've been a database leader for an awful long time. I don't see that going away any time soon and I love the innovative spirit they've brought in with HeatWave. >> All right, great thank you. Okay, 30 seconds, Holgar go. >> Yeah, I mean, the interesting thing that we see is really that trend to autonomous as Oracle calls or self-driving software, right? So the database will have to do more things than just store the data and support the DVA. It will have to show it can wide insights, the whole upside, it will be able to show to one machine learning. We haven't really talked about that. How in just exciting what kind of use case we can get of machine learning running real time on data as it changes, right? So, which is part of the E5 announcement, right? So we'll see more of that self-driving nature in the database space. And because you said we can promote it, right. Check out my report about HeatWave latest release where I post in oracle.com. >> Great, thank you for that. And Bob Evans, please. You're great at quick hits, hit us. >> Dave, thanks. I really enjoyed getting to hear everybody's opinion here today and I think what's going to happen too. I think there's a new generation of buyers, a new set of CXO influencers in here. And I think what Oracle's done with this, MySQL HeatWave, those benchmarks that Ron talked about so eloquently here that is going to become something that forces other companies, not just try to get incrementally better. I think we're going to see a massive new wave of innovation to try to play catch up. So I really take my hat off to Oracle's achievement from going to, push everybody to be better. >> Excellent. Marc Staimer, what do you say? >> Sure, I'm going to leverage off of something Matt said earlier, "Those companies that are going to develop faster, cheaper, simpler products that are going to solve customer problems, IT problems are the ones that are going to succeed, or the ones who are going to grow. The one who are just focused on the technology are going to fall by the wayside." So those who can solve more problems, do it more elegantly and do it for less money are going to do great. So Oracle's going down that path today, Snowflake's going down that path. They're trying to do more integration with third party, but as a result, aiming at that simpler, faster, cheaper mentality is where you're going to continue to see this market go. >> Amen brother Marc. >> Thank you, Ron Westfall, we'll give you the last word, bring us home. >> Well, thank you. And I'm loving it. I see a wave of innovation across the entire cloud database ecosystem and Oracle is fueling it. We are seeing it, with the native integration of auto ML capabilities, elastic scaling, lower entry price points, et cetera. And this is just going to be great news for buyers, but also developers and increased use of open APIs. And so I think that is really the key takeaways. Just we're going to see a lot of great innovation on the horizon here. >> Guys, fantastic insights, one of the best power panel as I've ever done. Love to have you back. Thanks so much for coming on today. >> Great job, Dave, thank you. >> All right, and thank you for watching. This is Dave Vellante for theCube and we'll see you next time. (soft music)

(upbeat music) >> When Oracle acquired Sun in 2009, it paid $5.6 billion net of Sun's cash and debt. Now I argued at the time that Oracle got one of the best deals in the history of enterprise tech, and I got a lot of grief for saying that because Sun had a declining business, it was losing money, and its revenue was under serious pressure as it tried to hang on for dear life. But Safra Catz understood that Oracle could pay Sun's lower profit and lagging businesses, like its low index 86 product lines, and even if Sun's revenue was cut in half, because Oracle has such a high revenue multiple as a software company, it could almost instantly generate $25 to $30 billion in shareholder value on paper. In addition, it was a catalyst for Oracle to initiate its highly differentiated engineering systems business, and was actually the precursor to Oracle's Cloud. Oracle saw that it could capture high margin dollars that used to go to partners like HP, it's original exit data partner, and get paid for the full stack across infrastructure, middleware, database, and application software, when eventually got really serious about cloud. Now there was also a major technology angle to this story. Remember Sun's tagline, "the network is the computer"? Well, they should have just called it cloud. Through the Sun acquisition. Oracle also got a couple of key technologies, Java, the number one programming language in the world, and MySQL, a key ingredient of the LAMP stack, that's Linux, Apache, MySQL and PHP, Perl or Python, on which the internet is basically built, and is used by many cloud services like Facebook, Twitter, WordPress, Flicker, Amazon, Aurora, and many other examples, including, by the way, Maria DB, which is a fork of MySQL created by MySQL's creator, basically in protest to Oracle's acquisition; the drama is Oscar worthy. It gets even better. In 2020, Oracle began introducing a new version of MySQL called MySQL HeatWave, and since late 2020 it's been in sort of a super cycle rolling, out three new releases in less than a year and a half in an attempt to expand its Tam and compete in new markets. Now we covered the release of MySQL Autopilot, which uses machine learning to automate management functions. And we also covered the bench marketing that Oracle produced against Snowflake, AWS, Azure, and Google. And Oracle's at it again with HeatWave, adding machine learning into its database capabilities, along with previously available integrations of OLAP and OLTP. This, of course, is in line with Oracle's converged database philosophy, which, as we've reported, is different from other cloud database providers, most notably Amazon, which takes the right tool for the right job approach and chooses database specialization over a one size fits all strategy. Now we've asked Oracle to come on theCUBE and explain these moves, and I'm pleased to welcome back Nipun Agarwal, who's the senior vice president for MySQL Database and HeatWave at Oracle. And today, in this video exclusive, we'll discuss machine learning, other new capabilities around elasticity and compression, and then any benchmark data that Nipun wants to share. Nipun's been a leading advocate of the HeatWave program. He's led engineering in that team for over 10 years, and he has over 185 patents in database technologies. Welcome back to the show Nipun. Great to see you again. Thanks for coming on. >> Thank you, Dave. Very happy to be back. >> Yeah, now for those who may not have kept up with the news, maybe to kick things off you could give us an overview of what MySQL HeatWave actually is so that we're all on the same page. >> Sure, Dave, MySQL HeatWave is a fully managed MySQL database service from Oracle, and it has a builtin query accelerator called HeatWave, and that's the part which is unique. So with MySQL HeatWave, customers of MySQL get a single database which they can use for transactional processing, for analytics, and for mixed workloads because traditionally MySQL has been designed and optimized for transaction processing. So in the past, when customers had to run analytics with the MySQL based service, they would need to move the data out of MySQL into some other database for running analytics. So they would end up with two different databases and it would take some time to move the data out of MySQL into this other system. With MySQL HeatWave, we have solved this problem and customers now have a single MySQL database for all their applications, and they can get the good performance of analytics without any changes to their MySQL application. >> Now it's no secret that a lot of times, you know, queries are not, you know, most efficiently written, and critics of MySQL HeatWave will claim that this product is very memory and cluster intensive, it has a heavy footprint that adds to cost. How do you answer that, Nipun? >> Right, so for offering any database service in the cloud there are two dimensions, performance and cost, and we have been very cognizant of both of them. So it is indeed the case that HeatWave is a, in-memory query accelerator, which is why we get very good performance, but it is also the case that we have optimized HeatWave for commodity cloud services. So for instance, we use the least expensive compute. We use the least expensive storage. So what I would suggest is for the customers who kind of would like to know what is the price performance advantage of HeatWave compared to any database we have benchmark against, Redshift, Snowflake, Google BigQuery, Azure Synapse, HeatWave is significantly faster and significantly lower price on a multitude of workloads. So not only is it in-memory database and optimized for that, but we have also optimized it for commodity cloud services, which makes it much lower price than the competition. >> Well, at the end of the day, it's customers that sort of decide what the truth is. So to date, what's been the customer reaction? Are they moving from other clouds from on-prem environments? Both why, you know, what are you seeing? >> Right, so we are definitely a whole bunch of migrations of customers who are running MySQL on-premise to the cloud, to MySQL HeatWave. That's definitely happening. What is also very interesting is we are seeing that a very large percentage of customers, more than half the customers who are coming to MySQL HeatWave, are migrating from other clouds. We have a lot of migrations coming from AWS Aurora, migrations from RedShift, migrations from RDS MySQL, TerriData, SAP HANA, right. So we are seeing migrations from a whole bunch of other databases and other cloud services to MySQL HeatWave. And the main reason we are told why customers are migrating from other databases to MySQL HeatWave are lower cost, better performance, and no change to their application because many of these services, like AWS Aurora are ETL compatible with MySQL. So when customers try MySQL HeatWave, not only do they get better performance at a lower cost, but they find that they can migrate their application without any changes, and that's a big incentive for them. >> Great, thank you, Nipun. So can you give us some names? Are there some real world examples of these customers that have migrated to MySQL HeatWave that you can share? >> Oh, absolutely, I'll give you a few names. Stutor.com, this is an educational SaaS provider raised out of Brazil. They were using Google BigQuery, and when they migrated to MySQL HeatWave, they found a 300X, right, 300 times improvement in performance, and it lowered their cost by 85 (audio cut out). Another example is Neovera. They offer cybersecurity solutions and they were running their application on an on-premise version of MySQL when they migrated to MySQL HeatWave, their application improved in performance by 300 times and their cost reduced by 80%, right. So by going from on-premise to MySQL HeatWave, they reduced the cost by 80%, improved performance by 300 times. We are Glass, another customer based out of Brazil. They were running on AWS EC2, and when they migrated, within hours they found that there was a significant improvement, like, you know, over 5X improvement in database performance, and they were able to accommodate a very large virtual event, which had more than a million visitors. Another example, Genius Senority. They are a game designer in Japan, and when they moved to MySQL HeatWave, they found a 90 times percent improvement in performance. And there many, many more like a lot of migrations, again, from like, you know, Aurora, RedShift and many other databases as well. And consistently what we hear is (audio cut out) getting much better performance at a much lower cost without any change to their application. >> Great, thank you. You know, when I ask that question, a lot of times I get, "Well, I can't name the customer name," but I got to give Oracle credit, a lot of times you guys have at your fingertips. So you're not the only one, but it's somewhat rare in this industry. So, okay, so you got some good feedback from those customers that did migrate to MySQL HeatWave. What else did they tell you that they wanted? Did they, you know, kind of share a wishlist and some of the white space that you guys should be working on? What'd they tell you? >> Right, so as customers are moving more data into MySQL HeatWave, as they're consolidating more data into MySQL HeatWave, customers want to run other kinds of processing with this data. A very popular one is (audio cut out) So we have had multiple customers who told us that they wanted to run machine learning with data which is stored in MySQL HeatWave, and for that they have to extract the data out of MySQL (audio cut out). So that was the first feedback we got. Second thing is MySQL HeatWave is a highly scalable system. What that means is that as you add more nodes to a HeatWave cluster, the performance of the system improves almost linearly. But currently customers need to perform some manual steps to add most to a cluster or to reduce the cluster size. So that was other feedback we got that people wanted this thing to be automated. Third thing is that we have shown in the previous results, that HeatWave is significantly faster and significantly lower price compared to competitive services. So we got feedback from customers that can we trade off some performance to get even lower cost, and that's what we have looked at. And then finally, like we have some results on various data sizes with TPC-H. Customers wanted to see if we can offer some more data points as to how does HeatWave perform on other kinds of workloads. And that's what we've been working on for the several months. >> Okay, Nipun, we're going to get into some of that, but, so how did you go about addressing these requirements? >> Right, so the first thing is we are announcing support for in-database machine learning, meaning that customers who have their data inside MySQL HeatWave can now run training, inference, and prediction all inside the database without the data or the model ever having to leave the database. So that's how we address the first one. Second thing is we are offering support for real time elasticity, meaning that customers can scale up or scale down to any number of nodes. This requires no manual intervention on part of the user, and for the entire duration of the resize operation, the system is fully available. The third, in terms of the costs, we have double the amount of data that can be processed per node. So if you look at a HeatWave cluster, the size of the cluster determines the cost. So by doubling the amount of data that can be processed per node, we have effectively reduced the cluster size which is required for planning a given workload to have, which means it reduces the cost to the customer by half. And finally, we have also run the TPC-DS workload on HeatWave and compared it with other vendors. So now customers can have another data point in terms of the performance and the cost comparison of HeatWave with other services. >> All right, and I promise, I'm going to ask you about the benchmarks, but I want to come back and drill into these a bit. How is HeatWave ML different from competitive offerings? Take for instance, Redshift ML, for example. >> Sure, okay, so this is a good comparison. Let's start with, let's say RedShift ML, like there are some systems like, you know, Snowflake, which don't even offer any, like, processing of machine learning inside the database, and they expect customers to write a whole bunch of code, in say Python or Java, to do machine learning. RedShift ML does have integration with SQL. That's a good start. However, when customers of Redshift need to run machine learning, and they invoke Redshift ML, it makes a call to another service, SageMaker, right, where so the data needs to be exported to a different service. The model is generated, and the model is also outside RedShift. With HeatWave ML, the data resides always inside the MySQL database service. We are able to generate models. We are able to train the models, run inference, run explanations, all inside the MySQL HeatWave service. So the data, or the model, never have to leave the database, which means that both the data and the models can now be secured by the same access control mechanisms as the rest of the data. So that's the first part, that there is no need for any ETL. The second aspect is the automation. Training is a very important part of machine learning, right, and it impacts the quality of the predictions and such. So traditionally, customers would employ data scientists to influence the training process so that it's done right. And even in the case of Redshift ML, the users are expected to provide a lot of parameters to the training process. So the second thing which we have worked on with HeatWave ML is that it is fully automated. There is absolutely no user intervention required for training. Third is in terms of performance. So one of the things we are very, very sensitive to is performance because performance determines the eventual cost to the customer. So again, in some benchmarks, which we have published, and these are all available on GitHub, we are showing how HeatWave ML is 25 times faster than Redshift ML, and here's the kicker, at 1% of the cost. So four benefits, the data all remain secure inside the database service, it's fully automated, much faster, much lower cost than the competition. >> All right, thank you Nipun. Now, so there's a lot of talk these days about explainability and AI. You know, the system can very accurately tell you that it's a cat, you know, or for you Silicon Valley fans, it's a hot dog or not a hot dog, but they can't tell you how the system got there. So what is explainability, and why should people care about it? >> Right, so when we were talking to customers about what they would like from a machine learning based solution, one of the feedbacks we got is that enterprise is a little slow or averse to uptaking machine learning, because it seems to be, you know, like magic, right? And enterprises have the obligation to be able to explain, or to provide a answer to their customers as to why did the database make a certain choice. With a rule based solution it's simple, it's a rule based thing, and you know what the logic was. So the reason explanations are important is because customers want to know why did the system make a certain prediction? One of the important characteristics of HeatWave ML is that any model which is generated by HeatWave ML can be explained, and we can do both global explanations or model explanations as well as we can also do local explanations. So when the system makes a specific prediction using HeatWave ML, the user can find out why did the system make such a prediction? So for instance, if someone is being denied a loan, the user can figure out what were the attribute, what were the features which led to that decision? So this ensures, like, you know, fairness, and many of the times there is also like a need for regulatory compliance where users have a right to know. So we feel that explanations are very important for enterprise workload, and that's why every model which is generated by HeatWave ML can be explained. >> Now I got to give Snowflakes some props, you know, this whole idea of separating compute from storage, but also bringing the database to the cloud and driving elasticity. So that's been a key enabler and has solved a lot of problems, in particular the snake swallowing the basketball problem, as I often say. But what about elasticity and elasticity in real time? How is your version, and there's a lot of companies chasing this, how is your approach to an elastic cloud database service different from what others are promoting these days? >> Right, so a couple of characteristics. One is that we have now fully automated the process of elasticity, meaning that if a user wants to scale up or scale down, the only thing they need to specify is the eventual size of the cluster and the system completely takes care of it transparently. But then there are a few characteristics which are very unique. So for instance, we can scale up or scale down to any number of nodes. Whereas in the case of Snowflake, the number of nodes someone can scale up or scale down to are the powers of two. So if a user needs 70 CPUs, well, their choice is either 64 or 128. So by providing this flexibly with MySQL HeatWave, customers get a custom fit. So they can get a cluster which is optimized for their specific portal. So that's the first thing, flexibility of scaling up or down to any number of nodes. The second thing is that after the operation is completed, the system is fully balanced, meaning the data across the various nodes is fully balanced. That is not the case with many solutions. So for instance, in the case of Redshift, after the resize operation is done, the user is expected to manually balance the data, which can be very cumbersome. And the third aspect is that while the resize operation is going on, the HeatWave cluster is completely available for queries, for DMLS, for loading more data. That is, again, not the case with Redshift. Redshift, suppose the operation takes 10 to 15 minutes, during that window of time, the system is not available for writes, and for a big part of that chunk of time, the system is not even available for queries, which is very limiting. So the advantages we have are fully flexible, the system is in a balanced state, and the system is completely available for the entire duration operation. >> Yeah, I guess you got that hypergranularity, which, you know, sometimes they say, "Well, t-shirt sizes are good enough," but then I think of myself, some t-shirts fit me better than others, so. Okay, I saw on the announcement that you have this lower price point for customers. How did you actually achieve this? Could you give us some details around that please? >> Sure, so there are two things for announcing this service, which lower the cost for the customers. The first thing is that we have doubled the amount of data that can be processed by a HeatWave node. So if we have doubled the amount of data, which can be a process by a node, the cluster size which is required by customers reduces to half, and that's why the cost drops to half. The way we have managed to do this is by two things. One is support for Bloom filters, which reduces the amount of intermediate memory. And second is we compress the base data. So these are the two techniques we have used to process more data per node. The second way by which we are lowering the cost for the customers is by supporting pause and resume of HeatWave. And many times you find customers of like HeatWave and other services that they want to run some other queries or some other workloads for some duration of time, but then they don't need the cluster for a few hours. Now with the support for pause and resume, customers can pause the cluster and the HeatWave cluster instantaneously stops. And when they resume, not only do we fetch the data, in a very, like, you know, a quick pace from the object store, but we also preserve all the statistics, which are used by Autopilot. So both the data and the metadata are fetched, extremely fast from the object store. So with these two capabilities we feel that it'll drive down the cost to our customers even more. >> Got it, thank you. Okay, I promised I was going to get to the benchmarks. Let's have it. How do you compare with others but specifically cloud databases? I mean, and how do we know these benchmarks are real? My friends at EMC, they were back in the day, they were brilliant at doing benchmarks. They would produce these beautiful PowerPoints charts, but it was kind of opaque, but what do you say to that? >> Right, so there are multiple things I would say. The first thing is that this time we have published two benchmarks, one is for machine learning and other is for SQL analytics. All the benchmarks, including the scripts which we have used are available on GitHub. So we have full transparency, and we invite and encourage customers or other service providers to download the scripts, to download the benchmarks and see if they get any different results, right. So what we are seeing, we have published it for other people to try and validate. That's the first part. Now for machine learning, there hasn't been a precedence for enterprise benchmarks so we talk about aiding open data sets and we have published benchmarks for those, right? So both for classification, as well as for aggression, we have run the training times, and that's where we find that HeatWave MLS is 25 times faster than RedShift ML at one percent of the cost. So fully transparent, available. For SQL analytics, in the past we have shown comparisons with TPC-H. So we would show TPC-H across various databases, across various data sizes. This time we decided to use TPC-DS. the advantage of TPC-DS over TPC-H is that it has more number of queries, the queries are more complex, the schema is more complex, and there is a lot more data skew. So it represents a different class of workloads, and which is very interesting. So these are queries derived from the TPC-DS benchmark. So the numbers we have are published this time are for 10 terabyte TPC-DS, and we are comparing with all the four majors services, Redshift, Snowflake, Google BigQuery, Azure Synapse. And in all the cases, HeatWave is significantly faster and significantly lower priced. Now one of the things I want to point out is that when we are doing the cost comparison with other vendors, we are being overly fair. For instance, the cost of HeatWave includes the cost of both the MySQL node as well as the HeatWave node, and with this setup, customers can run transaction processing analytics as well as machine learning. So the price captures all of it. Whereas with the other vendors, the comparison is only for the analytic queries, right? So if customers wanted to run RDP, you would need to add the cost of that database. Or if customers wanted to run machine learning, you would need to add the cost of that service. Furthermore, with the case of HeatWave, we are quoting pay as you go price, whereas for other vendors like, you know, RedShift, and like, you know, where applicable, we are quoting one year, fully paid upfront cost rate. So it's like, you know, very fair comparison. So in terms of the numbers though, price performance for TPC-DS, we are about 4.8 times better price performance compared to RedShift We are 14.4 times better price performance compared to Snowflake, 13 times better than Google BigQuery, and 15 times better than Synapse. So across the board, we are significantly faster and significantly lower price. And as I said, all of these scripts are available in GitHub for people to drive for themselves. >> Okay, all right, I get it. So I think what you're saying is, you could have said this is what it's going to cost for you to do both analytics and transaction processing on a competitive platform versus what it takes to do that on Oracle MySQL HeatWave, but you're not doing that. You're saying, let's take them head on in their sweet spot of analytics, or OLTP separately and you're saying you still beat them. Okay, so you got this one database service in your cloud that supports transactions and analytics and machine learning. How much do you estimate your saving companies with this integrated approach versus the alternative of kind of what I called upfront, the right tool for the right job, and admittedly having to ETL tools. How can you quantify that? >> Right, so, okay. The numbers I call it, right, at the end of the day in a cloud service price performance is the metric which gives a sense as to how much the customers are going to save. So for instance, for like a TPC-DS workload, if we are 14 times better price performance than Snowflake, it means that our cost is going to be 1/14th for what customers would pay for Snowflake. Now, in addition, in other costs, in terms of migrating the data, having to manage two different databases, having to pay for other service for like, you know, machine learning, that's all extra and that depends upon what tools customers are using or what other services they're using for transaction processing or for machine learning. But these numbers themselves, right, like they're very, very compelling. If we are 1/5th the cost of Redshift, right, or 1/14th of Snowflake, these numbers, like, themselves are very, very compelling. And that's the reason we are seeing so many of these migrations from these databases to MySQL HeatWave. >> Okay, great, thank you. Our last question, in the Q3 earnings call for fiscal 22, Larry Ellison said that "MySQL HeatWave is coming soon on AWS," and that caught a lot of people's attention. That's not like Oracle. I mean, people might say maybe that's an indication that you're not having success moving customers to OCI. So you got to go to other clouds, which by the way I applaud, but any comments on that? >> Yep, this is very much like Oracle. So if you look at one of the big reasons for success of the Oracle database and why Oracle database is the most popular database is because Oracle database runs on all the platforms, and that has been the case from day one. So very akin to that, the idea is that there's a lot of value in MySQL HeatWave, and we want to make sure that we can offer same value to the customers of MySQL running on any cloud, whether it's OCI, whether it's the AWS, or any other cloud. So this shows how confident we are in our offering, and we believe that in other clouds as well, customers will find significant advantage by having a single database, which is much faster and much lower price then what alternatives they currently have. So this shows how confident we are about our products and services. >> Well, that's great, I mean, obviously for you, you're in MySQL group. You love that, right? The more places you can run, the better it is for you, of course, and your customers. Okay, Nipun, we got to leave it there. As always it's great to have you on theCUBE, really appreciate your time. Thanks for coming on and sharing the new innovations. Congratulations on all the progress you're making here. You're doing a great job. >> Thank you, Dave, and thank you for the opportunity. >> All right, and thank you for watching this CUBE conversation with Dave Vellante for theCUBE, your leader in enterprise tech coverage. We'll see you next time. (upbeat music)

>>Hey, welcome back everyone. We're live here in the cube in Las Vegas Raiders reinvent 2021. I'm Jeffrey hosted the key we're in person this year. It's a hybrid event online. Great action. Going on. I'm rolling. Vice-president of ADF analytics. David is great to see you. Thanks for coming on. >>It's great to be here, John. Thanks for having me again. >>Um, so you've got a really awesome job. You've got serverless, you've got analytics. You're in the middle of all the action for AWS. What's the big news. What are you guys announcing? What's going on? >>Yeah, well, it's been an awesome reinvent for us. Uh, we've had a number of several us analytics launches. So red shift, our petabyte scale data warehouse, EMR for open source analytics. Uh, and then we've also had, uh, managed streaming for Kafka go serverless and then on demand for Kinesis. And then a couple of other big ones. We've got RO and cell based security for AWS lake formation. So you can get really fine grain controls over your data lakes and then asset transactions. You can actually have a inserts, updates and deletes on data lakes, which is a big step forward. >>Uh, so Swami on stage and the keynote he's actually finishing up now. But even last night I saw him in the hallway. We were talking about as much as about AI. Of course, he's got the AI title, but AI is the outcome. It's the application of all the data and this and a new architecture. He said on stage just now like, Hey, it's not about the old databases from the nineties, right? There's multiple data stores now available. And there's the unification is the big trend. And he said something interesting. Governance can be an advantage, not an inhibitor. This is kind of this new horizontally scalable, um, kind of idea that enables the vertical specialization around machine learning to be effective. It's not a new architecture, but it's now becoming more popular. People are realizing it. It's sort of share your thoughts on this whole not shift, but the acceleration of horizontally scalable and vertically integrated. Yeah, >>No, I think the way Swami put it is exactly right. What you want is the right tool for the right job. And you want to be able to deliver that to customers. So you're not compromising on performance or functionality of scale, but then you wanted all of these to be interconnected. So they're, well-integrated, you can stay in your favorite interface and take advantage of other technologies. So you can have things like Redshift integrated with Sage makers, you get analytics and machine learning. And then in Swami's absolutely right. Governance is actually an enabler of velocity. Once you've got the right guardrails in place, you can actually set people free because they can innovate. You don't have to be in the way, but you know that your data is protected. It's being used in the way that you expect by the people that you are allowing to use that data. And so it becomes a very powerful way for customers to set data free. And then, because things are elastic and serverless, uh, you can really just match capacity with demand. And so as you see spikes in usage, the system can scale out as those dwindle, they can scale back down, and it just becomes a very efficient way for customers to operate with data at scale >>Every year it reinvented. So it was kind of like a pinch me moment. It's like, well, more that's really good technology. Oh my God, it's getting easier and easier. As the infrastructure as code becomes more programmable, it's becoming easier, more Lambda, more serverless action. Uh, you got new offerings. How are customers benefiting for instance, from the three new offerings that you guys announced here? What specifically is the value proposition that you guys are putting out there? Yeah, so the, >>Um, you know, as we've tried to do with AWS over the years, customers get to focus on the things that really differentiate them and differentiate their businesses. So we take away in Redshift serverless, for example, all of the work that's needed to manage clusters, provision them, scale them, optimize them. Uh, and that's all been automated and made invisible to customers, the customers to think about data, what they want to do with it, what insights they can derive from it. And they know they're getting the most efficient infrastructure possible to make that a reality for them with high performance and low costs. So, uh, better results, more ability to focus on what differentiates their business and lower cost structure over time. >>Yeah. I had the essential guys on it's interesting. They had part of the soul cloud. Continuous is their word for what Adam was saying is clouds everywhere. And they're saying it's faster to match what you want to do with the outcomes, but the capabilities and outcomes kind of merging together where it's easy to say, this is what we want to do. And here's the outcome it supports that's right with that. What are some of the key trends on those outcomes that you see with the data analytics that's most popular right now? And kind of where's that, where's that going? >>Yeah. I mean, I think what we've seen is that data's just becoming more and more critical and top of mind for customers and, uh, you know, the pandemic has also accelerated that we found that customers are really looking to data and analytics and machine learning to find new opportunities. How can they, uh, really expand their business, take advantage of what's happening? And then the other part is how can they find efficiencies? And so, um, really everything that we're trying to do is we're trying to connect it to business outcomes for customers. How can you deepen your relationship with your customers? How can you create new customer experiences and how can you do that more efficiently, uh, with more agility and take advantage of, uh, the ability to be flexible. And you know, what is a very unpredictable world, as we've seen, >>I noticed a lot of purpose-built discussion going on in the keynote with Swami as well. How are you creating this next layer of what I call purpose-built platform like features? I mean, tools are great. You see a lot of tools in the data market tools are tools of your hammer. You want to look for a nail. We see people over by too many tools and you have ultimately a platform, but this seems to be a new trend where there's this connect phenomenon was showing me that you've got these platform capabilities that people can build on top of it, because there's a huge ecosystem of data tools out there that you guys have as partners that want to snap together. So the trend is things are starting to snap together, less primitive, roll your own, which you can do, but there's now more easier ways. Take me through that. Explain that, unpack that that phenomenon role rolling your own firm is, which has been the way now to here. Here's, here's some prefabricated software go. >>Yeah. Um, so it's a great observation and you're absolutely right. I mean, I think there's some customers that want to roll their own and they'll start with instances, they'll install software, they'll write their own code, build their own bespoke systems. And, uh, and we provide what the customers need to do that. But I think increasingly you're starting to see these higher level abstractions that take away all of that detail. And mark has Adam put it and allow customers to compose these. And we think it's important when you do that, uh, to be modular. So customers don't have to have these big bang all or nothing approaches you can pick what's appropriate, uh, but you're never on a dead end. You can always evolve and scale as you need to. And then you want to bring these ideas of unified governance and cohesive interfaces across so that customers find it easy to adopt the next thing. And so you can start off say with batch analytics, you can expand into real time. You can bring in machine learning and predictive capabilities. You can add natural language, and it's a big ecosystem of managed services as well as third parties and partners. >>And what's interesting. I want to get your thoughts while I got you here, because I think this is such an important trend and historic moment in time, Jerry chin, who one of the smartest VCs that we know from Greylock and coin castles in the cloud, which kind of came out of a cube conversation here in the queue years ago, where we saw the movement of that someone's going to build real value on AWS, not just an app. And you see the rise of the snowflakes and Databricks and other companies. And he was pointing out that you can get a very narrow wedge and get a position with these platforms, build on top of them and then build value. And I think that's, uh, the number one question people ask me, it's like, okay, how do I build value on top of these analytic packages? So if I'm a startup or I'm a big company, I also want to leverage these high level abstractions and build on top of it. How do you talk about that? How do you explain that? Because that's what people kind of want to know is like, okay, is it enabling me or do I have to fend for myself later? This is kind of, it comes up a lot. >>That's a great question. And, um, you know, if you saw, uh, Goldman's announcement this week, which is about bringing, building their cloud on top of AWS, it's a great example of using our capabilities in terms of infrastructure and analytics and machine learning to really allow them to take what's value added about Goldman and their position to financial markets, to build something value, add, and create a ton of value for Goldman, uh, by leveraging the things that we offer. And to us, that's an ideal outcome because it's a win-win for us in Goldman, but it's also a win for Goldman and their customers. >>That's what we call the Supercloud that's the opportunity. So is there a lot of Goldmans opportunities out there? Is that just a, these unicorns, are these sites? I mean, how do you, I mean, that's Goldman Sachs, they're huge. Is there, is this open to everybody? >>Absolutely. I mean, that's been one of the, uh, you know, one of the core ideas behind AWS was we wanted to give anybody any developer access to the same technology that the world's largest corporations had. And, uh, that's what you have today. The things that Goldman uses to build that cloud are available to anybody. And you can start for a few pennies scale up, uh, you know, into the petabytes and beyond >>When I was talking to Adams, Lipski when I met with him prior to re-invent, I noticed that he was definitely had an affinity towards the data, obviously he's Amazonia, but he spent time at Tableau. So, so as he's running that company, so you see that kind of mindset of the data advantage. So I have to ask you, because it's something that I've been talking about for a while and I'm waiting for it to emerge, but I'm not sure it's going to happen yet. But what infrastructure is code was for dev ops and then dev sec ops, there's almost like a data ops developing where data as code or programmable data. If I can connect the dots of what Swami's saying, what you're doing is this is like a new horizontal layer of data of freely available data with some government governance built in that's right. So it's, data's being baked into everything. So data is any ingredient, not a query to some database, it's gotta be baked into the apps, that's data as code that's. Right. So it's almost a data DevOps kind of vibe. >>Yeah, no, you're absolutely right. And you know, you've seen it with things like ML ops and so on. It's all the special case of dev ops. But what you're really trying to do is to get programmatic and systematic about how you deal with data. And it's not just data that you have. It's also publicly available data sets and it's customers sharing with each other. So building the ecosystem, our data, and we've got things like our open data program where we've got publicly hosted data sets or things like the AWS data exchange where customers can actually monetize data. So it's not just data as code, but now data as a monetizeable asset. So it's a really exciting time to be in the data business. >>Yeah. And I think it's so many too. So I've got to ask you while I got you here since you're an expert. Um, okay. Here's my problem. I have a lot of data. I'm nervous about it. I want to secure it. So if I try to secure it, I'm not making it available. So I want to feed the machine learning. How do I create an architecture where I can make it freely available, but yet maintain the control and the comfort that this is going to be secure. So what products do I buy? >>Yeah. So, uh, you know, a great place to start at as three. Um, you know, it's one of the best places for data lakes, uh, for all the reasons. That's why we talked about your ability scale costs. You can then use lake formation to really protect and govern that data so you can decide who's allowed to see it and what they're allowed to see, and you don't have to create multiple copies. So you can define that, you know, this group of partners can see a, B and C. This group can see D E and F and the system enforces that. And you have a central point of control where you can monitor what's happening. And if you want to change your mind, you can do that instantly. And all access can be locked down that you've got a variety of encryption capabilities with things like KMS. And so you can really lock down your data, but yet keep it open to the parties that you want and give them specifically the access that you want to give them. And then once you've done that, they're free to use that data, according to the rules that you defined with the analytics tools that we offer to go drive value, create insight, and do something >>That's lake formation. And then you got a Thena querying. Yes, we got all kinds of tooling on top of it. >>It's all right. You can have, uh, Athena query and your data in S3 lake formation, protecting it. And then SageMaker is integrated with Athena. So you can pull that data into SageMaker for machine learning, interrogate that data, using natural language with things like QuickSight Q a like we demoed. So just a ton of power without having to really think too deeply about, uh, developing expert skill sets in this. >>So the next question I want to ask you is because that first part of the great, great, great description, thank you very much. Now, 5g in the edges here, outpost, how was the analytics going on that as edge becomes more pervasive in the architecture? >>Yeah, it's going to be a key part of this ecosystem and it's really a continuum. So, uh, you know, we find customers are collecting data at the edge. They might be making local ML or inference type decisions on edge devices, or, you know, automobiles, for example. Uh, but typically that data with some point will come back into the cloud, into S3 will be used to do heavy duty training, and then those models get pushed back out to the edge. And then some of the things that we've done in Athena, for example, with federated query, as long as you have a network path, and you can understand what the data format or the database is, you can actually run a query on that data. So you can run real-time queries on data, wherever it lives, whether it's on an edge device, on an outpost, in a local zone or in your cloud region and combine all of that together in one place. >>Yeah. And I think having that data copies everywhere is a big thing deal. I've got to ask you now that we're here at reinvent, what's your take we're back in person last year was all virtual. Finally, not 60,000 people, like a couple of years ago, it's still 27,000 people here, all lining up for the sessions, all having a great time. Um, all good. What's the most important story from your, your area that people should pay attention to? What's the headline, what's the top news? What should people pay attention to? >>Yeah, so I think first off it is awesome to be back in person. It's just so fun to see customers and to see, I mean, you, like, we've been meeting here over the years and it's, it's great to so much energy in person. It's been really nice. Uh, you know, I think from an analytics perspective, there's just been a ton of innovation. I think the core idea for us is we want to make it easy for customers to use the right tool for the right job to get insight from all of their data as cost effectively as possible. And I think, uh, you know, I think if customers walk away and think about it as being, it's now easier than ever for me to take advantage of everything that AWS has to offer, uh, to make sense of all the data that I'm generating and use it to drive business value, but I think we'll have done our jobs. Right. >>What's the coolest thing that you're seeing here is that the serverless innovation, is it, um, the new abstraction layer with data high level services in your mind? What's the coolest thing. Got it. >>It's hard to pick the coolest that sticks like kicking the candies. I mean, I think the, uh, you know, the continued innovation in terms of, uh, performance and functionality in each of our services is a big deal. I think serverless is a game changer for customers. Uh, and then I think really the infusion of machine learning throughout all of these systems. So things like Redshift ML, Athena ML, Pixar, Q a just really enabling new experiences for customers, uh, in a way that's easier than it ever has been. And I think that's a, that's a big deal and I'm really excited to see what customers do with it. >>Yeah. And I think the performance thing to me, the coolest thing that I'm seeing is the graviton three and the gravitron progression with the custom stacks with all this ease of use, it's just going to be just a real performance advantage and the costs are getting lowered. So I think the ECE two instances around the compute is phenomenal. No, >>Absolutely. I mean, I think the hardware and Silicon innovation is huge and it's not just performance. It's also the energy efficiency. It's a big deal for the future reality. >>We're at an inflection point where this modern applications are being built. And in my history, I'm old, my birthday is today. I'm in my fifties. So I remember back in the eighties, every major inflection point when there was a shift in how things were developed from mainframe client server, PC inter network, you name it every time the apps change, the app owners, app developers all went to the best platform processing. And so I think, you know, that idea of system software applications being bundled together, um, is a losing formula. I think you got to have that decoupling large-scale was seeing that with cloud. And I think now if I'm an app developer, whether whether I'm in a large ISV in your ecosystem or in the APN partner or a startup, I'm going to go with my software runs the best period and where I can create value. That's right. I get distribution, I create value and it runs fast. I mean, that's, I mean, it's pretty simple. So I think the ecosystem is going to be a big action for the next couple of years. >>Absolutely. Right. And I mean, the ecosystem's huge and I think, um, and we're also grateful to have all these partners here. It's a huge deal for us. And I think it really matters for customers >>What's on your roadmap this year, what you got going on. What can you share a little bit of a trajectory without kind of, uh, breaking the rules of the Amazonian, uh, confidentiality. Um, what's, what's the focus for the year? What do you what's next? >>Well, you know, as you know, we're always talking to customers and, uh, I think we're going to make things better, faster, cheaper, easier to use. And, um, I think you've seen some of the things that we're doing with integration now, you'll see more of that. And, uh, really the goal is how can customers get value as quickly as possible for as low cost as possible? That's how we went to >>Yeah. They're in the longterm. Yeah. We've always say every time we see each other data is at the center of the value proposition. I've been saying that for 10 years now, it's actually the value proposition, powering AI. And you're seeing because of it, the rise of superclouds and then the superclouds are emerging. I think you guys are the under innings of these emerging superclouds. And so it's a huge treading, the Goldman Sachs things of validation. So again, more data, the better, sorry, cool things happening. >>It is just it's everywhere. And the, uh, the diversity of use cases is amazing. I mean, I think from, you know, the Australia swimming team to, uh, to formula one to NASDAQ, it's just incredible to see what our >>Customers do. We see the great route. Good to see you. Thanks for coming on the cube. >>Pleasure to be here as always John. Great to see you. Thank you. Yeah. >>Thanks for, thanks for sharing. All of the data is the key to the success. Data is the value proposition. You've seen the rise of superclouds because of the data advantage. If you can expose it, protect it and govern it, unleashes creativity and opportunities for entrepreneurs and businesses. Of course, you got to have the scale and the price performance. That's what doing this is the cube coverage. You're watching the leader in worldwide tech coverage here in person for any of us reinvent 2021 I'm John ferry. Thanks for watching.

(upbeat soft intro music) >> Hey everyone. Welcome back to theCube's live coverage from Los Angeles of KubeCon and CloudNativeCon 2021. Lisa Martin with Dave Nicholson, Dave and I are pleased to welcome our next guest remotely. Steven Huels joins us, the senior director of Cloud Services at Red Hat. Steven, welcome to the program. >> Steven: Thanks, Lisa. Good to be here with you and Dave. >> Talk to me about where you're seeing traction from an AI/ML perspective? Like where are you seeing that traction? What are you seeing? Like it. >> It's a great starter question here, right? Like AI/ML is really being employed everywhere, right? Regardless of industry. So financial services, telco, governments, manufacturing, retail. Everyone at this point is finding a use for AI/ML. They're looking for ways to better take advantage of the data that they've been collecting for these years. It really, it wasn't all that long ago when we were talking to customers about Kubernetes and containers, you know, AI/ML really wasn't a core topic where they were looking to use a Kubernetes platform to address those types of workloads. But in the last couple of years, that's really skyrocketed. We're seeing a lot of interest from existing customers that are using Red Hat open shift, which is a Kubernetes based platform to take those AI/ML workloads and take them from what they've been doing for additionally, for experimentation, and really get them into production and start getting value out of them at the end of it. >> Is there a common theme, you mentioned a number of different verticals, telco, healthcare, financial services. Is there a common theme, that you're seeing among these organizations across verticals? >> ^There is. I mean, everyone has their own approach, like the type of technique that they're going to get the most value out of. But the common theme is really that everyone seems to have a really good handle on experimentation. They have a lot of very brig data scientists, model developers that are able to take their data and out of it, but where they're all looking to get, get our help or looking for help, is to put those models into production. So ML ops, right. So how do I take what's been built on, on somebody's machine and put that into production in a repeatable way. And then once it's in production, how do I monitor it? What am I looking for as triggers to indicate that I need to retrain and how do I iterate on this sequentially and rapidly applying what would really be traditional dev ops software development, life cycle methodologies to ML and AI models. >> So Steve, we're joining you from KubeCon live at the moment. What's, what's the connection with Kubernetes and how does Kubernetes enable machine learning artificial intelligence? How does it enable it and what are some of the special considerations to in mind? >> So the immediate connection for Red Hat, is Red Hat's open shift is basically an enterprise grade Kubernetics. And so the connection there is, is really how we're working with customers and how customers in general are looking to take advantage of all the benefits that you can get from the Kubernetes platform that they've been applying to their traditional software development over the years, right? The, the agility, the ability to scale up on demand, the ability to have shared resources, to make specialized hardware available to the individual communities. And they want to start applying those foundational elements to their AI/Ml practices. A lot of data science work traditionally was done with high powered monolithic machines and systems. They weren't necessarily shared across development communities. So connecting something that was built by a data scientist, to something that then a software developer was going to put into production was challenging. There wasn't a lot of repeatability in there. There wasn't a lot of scalability, there wasn't a lot of auditability and these are all things that we know we need when talking about analytics and AI/ML. There's a lot of scrutiny put on the auditability of what you put into production, something that's making decisions that impact on whether or not somebody gets a loan or whether or not somebody is granted access to systems or decisions that are made. And so that the connection there is really around taking advantage of what has proven itself in kubernetes to be a very effective development model and applying that to AI/ML and getting the benefits in being able to put these things into production. >> Dave: So, so Red Hat has been involved in enterprises for a long time. Are you seeing most of this from a Kubernetes perspective, being net new application environments or are these extensions of what we would call legacy or traditional environments. >> They tend to be net new, I guess, you know, it's, it's sort of, it's transitioned a little bit over time. When we first started talking to customers, there was desire to try to do all of this in a single Kubernetes cluster, right? How can I take the same environment that had been doing our, our software development, beef it up a little bit and have it applied to our data science environment. And over time, like Kubernetes advanced rights. So now you can actually add labels to different nodes and target workloads based on specialized machinery and hardware accelerators. And so that has shifted now toward coming up with specialized data science environments, but still connecting the clusters in that's something that's being built on that data science environment is essentially being deployed then through, through a model pipeline, into a software artifact that then makes its way into an application that that goes live. And, and really, I think that that's sensible, right? Because we're constantly seeing a lot of evolution in, in the types of accelerators, the types of frameworks, the types of libraries that are being made available to data scientists. And so you want the ability to extend your data science cluster to take advantage of those things and to give data scientists access to that those specialized environments. So they can try things out, determine if there's a better way to, to do what they're doing. And then when they find out there is, be able to rapidly roll that into your production environment. >> You mentioned the word acceleration, and that's one of the words that we talk about when we talk about 2020, and even 2021, the acceleration in digital transformation that was necessary really a year and a half ago, for companies to survive. And now to be able to pivot and thrive. What are you seeing in terms of customers appetites for, for adopting AI/ML based solutions? Has it accelerated as the pandemic has accelerated digital transformation. >> It's definitely accelerated. And I think, you know, the pandemic probably put more of a focus for businesses on where can they start to drive more value? How can they start to do more with less? And when you look at systems that are used for customer interactions, whether they're deflecting customer cases or providing next best action type recommendations, AI/ML fits the bill there perfectly. So when they were looking to optimize, Hey, where do we put our spend? What can help us accelerate and grow? Even in this virtual world we're living in, AI/ML really floated to the top there, that's definitely a theme that we've seen. >> Lisa: Is there a customer example that you think that you could mention that really articulates the value over that? >> You know, I think a lot of it, you know, we've published one specifically around HCA health care, and this had started actually before the pandemic, but I think especially, it's applicable because of the nature of what a pandemic is, where HCA was using AI/ML to essentially accelerate diagnosis of sepsis, right. They were using it for, for disease diagnoses. That same type of, of diagnosis was being applied to looking at COVID cases as well. And so there was one that we did in Canada with, it's called 'how's your flattening', which was basically being able to track and do some predictions around COVID cases in the Canadian provinces. And so that one's particularly, I guess, kind of close to home, given the nature of the pandemic, but even within Red Hat, we started applying a lot more attention to how we could help with customer support cases, right. Knowing that if folks were going to be out with any type of illness. We needed to be able to be able to handle that case, you know, workload without negatively impacting work-life balance for, for other associates. So we looked at how can we apply AI/ML to help, you know, maintain and increase the quality of customer service we were providing. >> it's a great use case. Did you have a keynote or a session, here at KubeCon CloudNative? >> I did. I did. And it really focused specifically on that whole ML ops and model ops pipeline. It was called involving Kubernetes and bracing model ops. It was for a Kubernetes AI day. I believe it aired on Wednesday of this week. Tuesday, maybe. It all kind of condenses in the virtual world. >> Doesn't it? It does. >> So one of the questions that Lisa and I have for folks where we sit here, I don't know, was it year seven or so of the Dawn of Kubernetes, if I have that, right. Where do you think we are, in this, in this wave of adoption, coming from a Red Hat perspective, you have insight into, what's been going on in enterprises for the last 20 plus years. Where are we in this wave? >> That's a great question. Every time, like you, it's sort of that cresting wave sort of, of analogy, right? That when you get to top one wave, you notice the next wave it's even bigger. I think we've certainly gotten to the point where, where organizations have accepted that Kubernetes can, is applicable across all the workloads that they're looking to put in production. Now, the focus has shifted on optimizing those workloads, right? So what are the things that we need to run in our in-house data centers? What are things that we need, or can benefit from using commodity hardware from one of the hyperscalers, how do we connect those environments and more effectively target workloads? So if I look at where things are going to the future, right now, we see a lot of things being targeted based on cluster, right? We say, Hey, we have a data science cluster. It has characteristics because of X, Y, and Z. And we put all of our data science workloads into that cluster. In the future, I think we want to see more workload specific, type of categorization of workloads so that we're able to match available hardware with workloads rather than targeting a workload at a specific cluster. So a developer or data scientist can say, Hey, my particular algorithm here needs access to GPU acceleration and the following frameworks. And then it, the Kubernetes scheduler is able to determine of the available environments. What's the capacity, what are the available resources and match it up accordingly. So we get into a more dynamic environment where the developers and those that are actually building on top of these platforms actually have to know less and less about the clusters they're running on. It just have to know what types of resources they need access to. >> Lisa: So sort of democratizing that. Steve, thank you for joining Dave and me on the program tonight, talking about the traction that you're seeing with AI/ML, Kubernetes as an enabler, we appreciate your time. >> Thank you. >> Thanks Steve. >> For Dave Nicholson. I'm Lisa Martin. You're watching theCube live from Los Angeles KubeCon and CloudNativeCon 21. We'll be right back with our next guest. (subtle music playing) >> Lisa: I have been in the software and technology industry for over 12 years now. So I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCube. Being a host on the cube has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, Hey, I'm really interested in this. I love talking with customers...

>>Welcome back to our coverage of HBs. Green Lake announcement's gonna talk about transformation acceleration, who doesn't wanna go faster as they're transforming, right? Everybody is transforming and they want to go as fast as possible to get time to value keith White is here, he's the senior vice president and general manager of Green Lakes commercial business at HP. Michelle LaU is Green Lake cloud services solutions at HP gents. Welcome. Good to see you >>awesome to be here. Thanks so much. Great to be here. >>Dave keith, we've we've been talking virtually for >>quite some time now. >>Q three earnings beaten raise uh focusing on, you know, some real momentum uh want to understand where it's coming from. A r I've said it's headed toward a billion, I think you said 700 million was where you were at last quarter, 1100 customers, orders were up 46%,, Last quarter revenue up over 30%. Where's the momentum >>coming from? No, it's fantastic. And I think what you're seeing is, you know, the world is hybrid. So in essence customers are looking for that solution that says, hey, mere my public cloud with my on premise scenario and give me that hybrid solution and we're just seeing just tremendous momentum and interest across a variety of workloads across a variety of vertical solutions and frankly we're seeing customers basically uh lean in on really running their business on HP. Green Lake, so you know, we had a pretty exciting announcement with the s a a couple weeks back, $2 billion deal, um but again, this shows the value of what Green Lake and the on prem requirements are high level of security, high level of capability? They're doing analytics on all the data that's out there. I mean this is the number one intelligence agency in the world. Right? So super excited about that and it just validates our strategy and validates where we're going. Um the other thing that's really exciting is we're seeing a lot of customers with this whole S. A. P migration, right? Um so ongc, one of the largest oil and gas companies in India, I want to say it's one of the top five S. A. P. Implementations in the world has chosen. Green Lake is their opportunity as well, huge retailers like wal wars. Uh so worldwide we're seeing tremendous momentum. >>That's great. Congratulations on the momentum. I know you're not done uh Michelle new role for you. Awesome. Um when we covered uh discover this year in the cube, we talked about sort of new workload solutions that you guys had. Uh S. A. P. As keith was just mentioning Ml Ops V. D. I. A number of of those workloads that you were really focused on the solution side. How's that going? Give us the update there? >>No, it's coming along really well. I mean you highlighted some of the big ones there. I mean the way we are thinking about Green Lake. Right? I mean, you know, we talked about the great momentum that we've had. The question is why are we having that right? Why are missing that momentum in the market? And I think I'll kind of call out a few features of the green platform that's really making it attractive to customers. Right? What is the experience? What we're trying to do is make it a very, very seamless experience for them? Right. Quick provisioning, easy to manage, easy to monitor, kind of an automated solution. Right? So that's kind of a key element of what we're trying to offer performances. Another one. Right? I mean, the end of the day, what we're doing is we are building out our infrastructure stack and the software stack in such a way that is optimized for the performance. Right? I mean, if you take data for example, it's called the right elements to make sure that the analytics can be done in a machine learning algorithms can be run. So those are like, you know, some of the performance, I think it's a great experience is a big factor. Tco right? I mean customers are very, very focused on their cost base. Right? Especially as they are starting to run up the bills in public cloud. They're like, man, this is expensive, I need to start thinking about costs here because costs catch up pretty fast. So that's kind of another element that people are really focused on and I would say the last one being choice. Right? I mean we provide this platform which is open. Alright. So customers can use it if they want to migrate off it, they can migrate off it. We're not locking them in. So those are some of the value propositions that are really resonating in the marketplace and you're seeing that in the numbers that we just talked about. >>So keep speaking of transformation you guys are undergoing obviously a transformation your your cloud company now. Okay, so part of that is the ecosystem. The partners talk about your strategy in that regard, why you're so excited about welcoming the partners into this old Green Lake world, >>you bet and you know I'm a big fan of one plus one equals three. My seven year old daughter tells me that doesn't actually add up correctly but at the same time it's so true with what we're doing and as official just said an open platform that allows partners to really plug in so that we can leverage the power of S. A. P. Or the power of Nutanix. So the power of Citrix at the same time, all of these are solutions that require, you know deep system integration and capabilities to really be customized for that customers environment. So whether that's infosys or accenture or we pro you know that we need we need those partners as well along with our own advisory and professional services to help customers. But at the same time, you know we talked about the fact that this is really about bringing that cloud experience to the on prem world might be a data center but we're seeing a lot of customers get out of the data center management business and move into a Coehlo. And so the fact that we can partner with the ECU annexes and the Cyrus ones of the world really enable a whole new environment so that customers again can run their business and not get caught up with keeping the lights on and managing power and those types of things. And then finally I'll say, look, the channel itself is actually migrating to offer more services to their customers managed service providers, telcos, distance and resellers and now what we're providing them is that platform with which to offer their own manage services to customers in a much more cost effective cloud experience way with all the benefits of being on prem secure latency app integration and that sort of thing. So it's exciting to see the ecosystem really gate Gardner the momentum and really partner with us closely >>follow up on the partner question if I could. So partner services are part of Green Lake. It's a journey, not everything all at once. Uh but so it's essentially as simple as saying, okay, I want that service, that's my choice. Uh you've given them optionality and it's ideally as seamless as it is in HP services, that the direction that you're >>going. That's right, yeah. So the set that api set that Stalin team are building are basically saying, hey, leverage our cost analytics capabilities, leverage our capacity management, leverage the interface so that you can plug into that single control plane. And so they're making it super simple for our partner ecosystem to do that. And what I think is really important is that if you are a partner, you want to basically offer choice to the customer and if the customer decides, hey, I want to use um red hats open shift for the container platform versus rs morale offering, then they can get just as good of a first class offering with respect to that. Someone wants to use Citrix or Nutanix or VM ware for their video solution. They have that choice. And so we want to make sure we're offering customer choice for what's best for their situation, but also making sure that it's fully integrated with what we do. God thank >>You. So we see more software content of the show. I wonder if you could. I mean certainly as morale is a big piece of that. I talked earlier about margins hit record for HPE. Almost 35% gross margins. This course of software is gonna obviously push that further along um, Lighthouses, another one. How should we think about the direction that you're going >>software. Absolutely. So if you think about what we are building out here is a solution, right? This is solution that's very tightly integrated between the infrastructure stack and the soft and this software that enables it. So really there three or four components to the solution day. Right. So think about Lighthouse, which is an infrastructure stack that is optimized for what's going to run on that. Right? If it's a general purpose compute it will the infrastructure will look different. If it's a storage intensive workload, it will look different. If it's a machine learning workloads will look different. Right? So that's kind of the first component and just optimizing it for what's going to run on it. Second is, um, what we call the Green light platform, which is all about managing and orchestrating it. And what we want to do is we want to have a completely automated experience right from from the way you provisioned it to the way you run the workloads to the way you manage it, to the way you monitor it to the way partners link into it. Right to the way in the software vendors kind of sit on top of that. Right. And then we talked about escrow as well as the engine that runs it right from a container platform perspective or we spend some time talking about unified analytics today. Those are the types of data integration that power Green Lake and the last piece of software I would say is as we kind of think about the ecosystem that runs on top of Green Lake, whether it's our software or third party software. Right? They all have a place equal place on top of the green light platform. And we are very focused on building on the ecosystem. Right? So as a customer or an enterprise who wants to use you should have the choice to run you know 40 50 102 105 100 different software packages on top of Green Lake. And it should be all an automated fashion. But we have tested that in advance. There's there's commercials behind that. It becomes a very very self service provision, seamless experience from the customer's perspective. >>Great. Thank you. So keep 2020 was sort of like sometimes called the force marched to digital right? And some some customers they were already there. Uh so there's a majority now that we've been through this awful year and change, customers are kind of rethinking their digital strategies and their transformations that there can be a little bit more planned fel now you know the world didn't end and and you know I. T. Budgets kind of stabilized a bit actually, you know did better than perhaps we thought. So where are we in terms of transformations? What's the business angle? What are you seeing out there? >>Yeah. I mean customers found a lot of holes that they had in their environment because of the pandemic. I think customers are also seeing opportunities to grow pretty aggressively. You know we just announced Patrick terminals, one of the largest shipping companies in south pack and you know that whole shipping craziness that's going on right now they needed a new digital transformation in order to really make sure they could orchestrate their container ships effectively. Even we talked about Woolworth's there now, changing how they deal with their suppliers because of the Green Lake platform that they have. And so what you're seeing is, hey, you know, first phase of digital transformation public cloud was an interesting scenario. Now they're being able to be planned for like you said and say, where's the best place for me to run this for the latency required with that data, for the choice that we have from an I. S. V. Standpoint, you know, for the on prem capabilities of what we're trying to do from a security standpoint etcetera. So the nice thing is we've seen it move from, you know, hey, we're just trying to get the basic things modernized into truly modernizing data centers, monetizing the data that I have and continuing to transform that environment for their customers, partners, employees and products >>kind of a left field question a bit off topic, but certainly related edge. You guys talk about edge a lot. Hybrid is clear. I think in people's minds you've got an on prem you're connecting to a cloud maybe across clouds? Is edge an extension of hybrid or is it today sort of a bespoke opportunity that maybe we'll come back to this new version of cloud, What's happening at the edge >>that you see? Yeah. So let me just uh I mean think of the edge as it's a continuum. Right? The way at least we think about it, it's not data center or the edge. Right. Think of it as, you know, there's a data center, uh there's a hyper scale data center, there's a data center, there's a closet somewhere, right? There's a cola opportunity, Right? And then you're running something in the store. Right? So let's take the example of a retailer. They're running something in the store and what are they running? They're running? Point of service applications or they're running IOT devices. Right. And at some point they have to connect back into the cloud. Right. So we actually have, you know, something to find van capabilities that connect, you know, uh you know, the Edge devices or edge analytics back into the cloud, we actually have a small form factor kubernetes um operating system that runs on the edge. Right. So we think of all of that as kind of a distributed environment in which Edge is one place where the application runs and where the data sites but it needs to be connected back and so we provide the connectivity back, we provide the mechanism by which we run it and then there's a security model, especially around sassy that is emerging on securing that. So that's kind of how we think about it as part of the overall distributed architecture that we are building and that's where the world will be >>another node in the cloud. >>Another note in the distributed world. Exactly >>yeah. I think the other thing to think about with the edges that this is where the majority of your data is actually getting created. Right? You talked about IOT devices, you know, you'll hear from Zen's Act and what they're doing with respect to autonomous driving with vehicles. You know, we talk about folks like ab that are building the factory of the future and robotics as a service in order to be able to really make sure that that precision happens at that at that point. So a ton of data is coming from that. And so again, how do you analyze that? How do you monetize that? How do you make decisions off of it? And it's it's an exciting place for us. So it's great to have all the connectivity we talked >>about last question, maybe both could address it. Uh we've we we used to see this cadence of of products often times in the form of boxes come out from HP and HP. Now we're seeing a cadence of services, we're seeing more capabilities across this, this this this green lake uh state that you guys are building out. What should we expect in the future? What are the kinds of things that we should evaluate you on? >>Well, I'll start and then maybe you can jump in but you know, the reality is we are becoming much deeper partners with our customers right there looking to us to say help me run my data center, help me improve my data and analytics. Help me at the edge so that I can have the most effective scenario. So what you're seeing from us is this flip from hardware provider into deep partnerships with that with the open platform. I'd say the second thing that we're doing is we're helping them fuel that digital transformation because again, they're looking for that hybrid solution. And so now they're saying, hey HP come and showcase all the experience you have from point next from your advisor and professional services and help me understand what other customers are doing so that I can implement that faster, better, cheaper, easier, etcetera. And then from a product standpoint, kind of a ton of great things. >>That's exactly right. I mean uh we are taking a very, very focused customer back view as we are looking at the future of Green Lake. Right. And exactly the way kids said, right, I mean it's all about solving customer problems for us. Some customer problems are still in the data center, some of them are in close, some customer problems are in the edge. So they're all uh fair game for us as we think about, you know, what we are going to be building out and do your point earlier. Dave it's not about, you know, a server or storage is the institutions right. And the solutions have to have integrated hardware, integrated software, staff, integrated services. Right. There are partners who sell that, who service that and all that entire experience from a customer perspective has to be a seamless. Right? And it's just in our cloud platform, we kind of help the customer run it and manage it and we give them kind of the best performance at the lowest cost, which is what they're looking for. So that's kind of what you'll see us. You'll see more of a cadence of these services can come out, but it's all going in that direction in helping customers with new solutions. >>A lot of customer problems out there, which your opportunities and you know, generally the hyper scale as they are good at solutions. They don't, you know, there's not a lot of solution folks like that. That's a that's a wonderful opportunity for you to build on on top of that huge gift, that Capex gift >>at the hyper scholars have given us all. That's right. And we're seeing the momentum happen. So it's exciting. That's cool guys. Hey, thanks a lot for coming to the cube. Yeah, Yeah. All right, >>okay. And thank you for watching keep it right there more action from HP. Es Green Lake announcements, you're watching the cube. Mm. Mm

>>text, you know, consumer opens up their iphone and says, oh my gosh, I love the technology behind my eyes. What's it been like being on the shark tank? You know, filming is fun, hang out, just fun and it's fun to be a celebrity at first your head gets really big and you get a good tables at restaurants who says texas has got a little possess more skin in the game today in charge of his destiny robert Hirschbeck, No stars. Here is CUBA alumni. Yeah, okay. >>Hi. I'm john Ferry, the co founder of silicon angle Media and co host of the cube. I've been in the tech business since I was 19 1st programming on many computers in a large enterprise and then worked at IBM and Hewlett Packard total of nine years in the enterprise brian's jobs from programming, Training, consulting and ultimately as an executive salesperson and then started my first company with 1997 and moved to Silicon Valley in 1999. I've been here ever since. I've always loved technology and I love covering you know, emerging technology as trained as a software developer and love business and I love the impact of software and technology to business to me creating technology that starts the company and creates value and jobs is probably the most rewarding things I've ever been involved in. And I bring that energy to the queue because the Cubans were all the ideas are and what the experts are, where the people are and I think what's most exciting about the cube is that we get to talk to people who are making things happen, entrepreneur ceo of companies, venture capitalists, people who are really on a day in and day out basis, building great companies and the technology business is just not a lot of real time live tv coverage and, and the cube is a non linear tv operation. We do everything that the T. V guys on cable don't do. We do longer interviews. We asked tougher questions, we ask sometimes some light questions. We talked about the person and what they feel about. It's not prompted and scripted. It's a conversation authentic And for shows that have the Cube coverage and makes the show buzz. That creates excitement. More importantly, it creates great content, great digital assets that can be shared instantaneously to the world. Over 31 million people have viewed the cube and that is the result. Great content, great conversations and I'm so proud to be part of you with great team. Hi, I'm john ferrier. Thanks for watching the cube. >>Hello and welcome to the cube. We are here live on the ground in the expo floor of a live event. The AWS public sector summit. I'm john for your host of the cube. We're here for the next two days. Wall to wall coverage. I'm here with Sandy carter to kick off the event. Vice president partner as partners on AWS public sector. Great to see you Sandy, >>so great to see you john live and in person, right? >>I'm excited. I'm jumping out of my chair because I did a, I did a twitter periscope yesterday and said a live event and all the comments are, oh my God, an expo floor a real events. Congratulations. >>True. Yeah. We're so excited yesterday. We had our partner day and we sold out the event. It was rock them and pack them and we had to turn people away. So what a great experience. Right, >>Well, I'm excited. People are actually happy. We tried, we tried covering mobile world congress in Barcelona. Still, people were there, people felt good here at same vibe. People are excited to be in person. You get all your partners here. You guys have had had an amazing year. Congratulations. We did a couple awards show with you guys. But I think the big story is the amazon services for the partners. Public sector has been a real game changer. I mean we talked about it before, but again, it continues to happen. What's the update? >>Yeah, well we had, so there's lots of announcements. So let me start out with some really cool growth things because I know you're a big growth guy. So we announced here at the conference yesterday that our government competency program for partners is now the number one industry in AWS for are the competency. That's a huge deal. Government is growing so fast. We saw that during the pandemic, everybody was moving to the cloud and it's just affirmation with the government competency now taking that number one position across AWS. So not across public sector across AWS and then one of our fastest growing areas as well as health care. So we now have an A. T. O. Authority to operate for HIPPA and Hi trust and that's now our fastest growing area with 85% growth. So I love that new news about the growth that we're seeing in public sector and all the energy that's going into the cloud and beyond. >>You know, one of the things that we talked about before and another Cuban of you. But I want to get your reaction now current state of the art now in the moment the pandemic has highlighted the antiquated outdated systems and highlighted help inadequate. They are cloud. You guys have done an amazing job to stand up value quickly now we're in a hybrid world. So you've got hybrid automation ai driving a complete change and it's happening pretty quick. What's the new things that you guys are seeing that's emerging? Obviously a steady state of more growth. But what's the big success programs that you're seeing right now? >>Well, there's a few new programs that we're seeing that have really taken off. So one is called proserve ready. We announced yesterday that it's now G. A. And the U. S. And a media and why that's so important is that our proserve team a lot of times when they're doing contracts, they run out of resources and so they need to tap on the shoulder some partners to come and help them. And the customers told us that they wanted them to be pro served ready so to have that badge of honor if you would that they're using the same template, the same best practices that we use as well. And so we're seeing that as a big value creator for our partners, but also for our customers because now those partners are being trained by us and really helping to be mentored on the job training as they go. Very powerful program. >>Well, one of the things that really impressed by and I've talked to some of your MSP partners on the floor here as they walk by, they see the cube, they're all doing well. They're all happy. They got a spring in their step. And the thing is that this public private partnerships is a real trend we've been talking about for a while. More people in the public sector saying, hey, I want I need a commercial relationship, not the old school, you know, we're public. We have all these rules. There's more collaboration. Can you share your thoughts on how you see that evolving? Because now the partners in the public sector are partnering closer than ever before. >>Yeah, it's really um, I think it's really fascinating because a lot of our new partners are actually commercial partners that are now choosing to add a public sector practice with them. And I think a lot of that is because of these public and private partnerships. So let me give you an example space. So we were at the space symposium our first time ever for a W. S at the space symposium and what we found was there were partners, they're like orbital insight who's bringing data from satellites, There are public sector partner, but that data is being used for insurance companies being used for agriculture being used to impact environment. So I think a lot of those public private partnerships are strengthening as we go through Covid or have like getting alec of it. And we do see a lot of push in that area. >>Talk about health care because health care is again changing radically. We talked to customers all the time. They're like, they have a lot of legacy systems but they can't just throw them away. So cloud native aligns well with health care. >>It does. And in fact, you know, if you think about health care, most health care, they don't build solutions themselves, they depend on partners to build them. So they do the customer doesn't buy and the partner does the build. So it's a great and exciting area for our partners. We just launched a new program called the mission accelerator program. It's in beta and that program is really fascinating because our healthcare partners, our government partners and more now can use these accelerators that maybe isolate a common area like um digital analytics for health care and they can reuse those. So it's pretty, I think it's really exciting today as we think about the potential health care and beyond. >>You know, one of the challenge that I always thought you had that you guys do a good job on, I'd love to get your reaction to now is there's more and more people who want to partner with you than ever before. And sometimes it hasn't always been easy in the old days like to get fed ramp certified or even deal with public sector. If you were a commercial vendor, you guys have done a lot with accelerating certifications. Where are you on that spectrum now, what's next? What's the next wave of partner onboarding or what's the partner trends around the opportunities in public sector? >>Well, one of the new things that we announced, we have tested out in the U. S. You know, that's the amazon way, right, Andy's way, you tested your experiment. If it works, you roll it out, we have a concierge program now to help a lot of those new partners get inundated into public sector. And so it's basically, I'm gonna hold your hand just like at a hotel. I would go up and say, hey, can you direct me to the right restaurant or to the right museum, we do the same thing, we hand hold people through that process. Um, if you don't want to do that, we also have a new program called navigate which is built for brand new partners. And what that enables our partners to do is to kind of be guided through that process. So you are right. We have so many partners now who want to come and grow with us that it's really essential that we provide a great partner, experienced a how to on board. >>Yeah. And the A. P. M. Was the amazon partner network also has a lot of crossover. You see a lot a lot of that going on because the cloud, it's you can do both. >>Absolutely. And I think it's really, you know, we leverage all of the ap in programs that exist today. So for example, there was just a new program that was put out for a growth rebate and that was driven by the A. P. N. And we're leveraging and using that in public sector too. So there's a lot of prosecutes going on to make it easier for our partners to do business with us. >>So I have to ask you on a personal note, I know we've talked about before, your very comfortable the virtual now hybrid space. How's your team doing? How's the structure looks like, what are your goals, what are you excited about? >>Well, I think I have the greatest team ever. So of course I'm excited about our team and we are working in this new hybrid world. So it is a change for everybody uh the other day we had some people in the office and some people calling in virtually so how to manage that, right was really quite interesting. Our goals that we align our whole team around and we talked a little bit about this yesterday are around mission which are the solution areas migration, so getting everything to the cloud and then in the cloud, we talk about modernization, are you gonna use Ai Ml or I O T? And we actually just announced a new program around that to to help out IOT partners to really build and understand that data that's coming in from I O T I D C says that that idea that IOT data has increased by four times uh in the, during the covid period. So there's so many more partners who need help. >>There's a huge shift going on and you know, we always try to explain on the cube. Dave and I talked about a lot and it's re platform with the cloud, which is not just lift and shift you kind of move and then re platform then re factoring your business and there's a nuance there between re platform in which is great. Take advantage of cloud scale. But the re factoring allows for this unique advantage of these high level services. >>That's right >>and this is where people are winning. What's your reaction to that? >>Oh, I completely agree. I think this whole area of modernizing your application, like we have a lot of folks who are doing mainframe migrations and to your point if they just lift what they had in COBOL and they move it to a W S, there's really not a lot of value there, but when they rewrite the code, when they re factor the code, that's where we're seeing tremendous breakthrough momentum with our partner community, you know, Deloitte is one of our top partners with our mainframe migration. They have both our technology and our consulting um, mainframe migration competency there to one of the other things I think you would be interested in is in our session yesterday we just completed some research with r C T O s and we talked about the next mega trends that are coming around Web three dato. And I'm sure you've been hearing a lot about web www dot right? Yeah, >>0.04.0, it's all moving too fast. I mean it's moving >>fast. And so some of the things we talked to our partners about yesterday are like the metaverse that's coming. So you talked about health care yesterday electronic caregiver announced an entire application for virtual caregivers in the metaverse. We talked about Blockchain, you know, and the rise of Blockchain yesterday, we had a whole set of meetings, everybody was talking about Blockchain because now you've got El Salvador Panama Ukraine who have all adopted Bitcoin which is built on the Blockchain. So there are some really exciting things going on in technology and public sector. >>It's a societal shift and I think the confluence of tech user experience data, new, decentralized ways of changing society. You're in the middle of it. >>We are and our partners are in the middle of it and data data, data data, that's what I would say. Everybody is using data. You and I even talked about how you guys are using data. Data is really a hot topic and we we're really trying to help our partners figure out just how to migrate the data to the cloud but also to use that analytics and machine learning on it too. Well, >>thanks for sharing the data here on our opening segment. The insights we will be getting out of the Great Sandy. Great to see you got a couple more interviews with you. Thanks for coming on. I appreciate you And thanks for all your support. You guys are doing great. Your partners are happy you're on a great wave. Congratulations. Thank you, john appreciate more coverage from the queue here. Neither is public sector summit. We'll be right back. Mhm Yeah. >>Mhm. Mhm robert Herjavec. People obviously know you from shark tank