(gentle music playing) >> For centuries, we've been captivated by the concept of machines doing the job of humans. And over the past decade or so, we've really focused on AI and the possibility of intelligent machines that can perform cognitive tasks. Now in the past few years, with the popularity of machine learning models ranging from recent ChatGPT to Bert, we're starting to see how AI is changing the way we interact with the world. How is AI transforming the way we do business? And what does the future hold for us there. At theCube, we've covered Oracle's AI and ML strategy for years, which has really been used to drive automation into Oracle's autonomous database. We've talked a lot about MySQL HeatWave in database machine learning, and AI pushed into Oracle's business apps. Oracle, it tends to lead in AI, but not competing as a direct AI player per se, but rather embedding AI and machine learning into its portfolio to enhance its existing products, and bring new services and offerings to the market. Now, last October at Cloud World in Las Vegas, Oracle partnered with Nvidia, which is the go-to AI silicon provider for vendors. And they announced an investment, a pretty significant investment to deploy tens of thousands more Nvidia GPUs to OCI, the Oracle Cloud Infrastructure and build out Oracle's infrastructure for enterprise scale AI. Now, Oracle CEO, Safra Catz said something to the effect of this alliance is going to help customers across industries from healthcare, manufacturing, telecoms, and financial services to overcome the multitude of challenges they face. Presumably she was talking about just driving more automation and more productivity. Now, to learn more about Oracle's plans for AI, we'd like to welcome in Elad Ziklik, who's the vice president of AI services at Oracle. Elad, great to see you. Welcome to the show. >> Thank you. Thanks for having me. >> You're very welcome. So first let's talk about Oracle's path to AI. I mean, it's the hottest topic going for years you've been incorporating machine learning into your products and services, you know, could you tell us what you've been working on, how you got here? >> So great question. So as you mentioned, I think most of the original four-way into AI was on embedding AI and using AI to make our applications, and databases better. So inside mySQL HeatWave, inside our autonomous database in power, we've been driving AI, all of course are SaaS apps. So Fusion, our large enterprise business suite for HR applications and CRM and ELP, and whatnot has built in AI inside it. Most recently, NetSuite, our small medium business SaaS suite started using AI for things like automated invoice processing and whatnot. And most recently, over the last, I would say two years, we've started exposing and bringing these capabilities into the broader OCI Oracle Cloud infrastructure. So the developers, and ISVs and customers can start using our AI capabilities to make their apps better and their experiences and business workflow better, and not just consume these as embedded inside Oracle. And this recent partnership that you mentioned with Nvidia is another step in bringing the best AI infrastructure capabilities into this platform so you can actually build any type of machine learning workflow or AI model that you want on Oracle Cloud. >> So when I look at the market, I see companies out there like DataRobot or C3 AI, there's maybe a half dozen that sort of pop up on my radar anyway. And my premise has always been that most customers, they don't want to become AI experts, they want to buy applications and have AI embedded or they want AI to manage their infrastructure. So my question to you is, how does Oracle help its OCI customers support their business with AI? >> So it's a great question. So I think what most customers want is business AI. They want AI that works for the business. They want AI that works for the enterprise. I call it the last mile of AI. And they want this thing to work. The majority of them don't want to hire a large and expensive data science teams to go and build everything from scratch. They just want the business problem solved by applying AI to it. My best analogy is Lego. So if you think of Lego, Lego has these millions Lego blocks that you can use to build anything that you want. But the majority of people like me or like my kids, they want the Lego death style kit or the Lego Eiffel Tower thing. They want a thing that just works, and it's very easy to use. And still Lego blocks, you still need to build some things together, which just works for the scenario that you're looking for. So that's our focus. Our focus is making it easy for customers to apply AI where they need to, in the right business context. So whether it's embedding it inside the business applications, like adding forecasting capabilities to your supply chain management or financial planning software, whether it's adding chat bots into the line of business applications, integrating these things into your analytics dashboard, even all the way to, we have a new platform piece we call ML applications that allows you to take a machine learning model, and scale it for the thousands of tenants that you would be. 'Cause this is a big problem for most of the ML use cases. It's very easy to build something for a proof of concept or a pilot or a demo. But then if you need to take this and then deploy it across your thousands of customers or your thousands of regions or facilities, then it becomes messy. So this is where we spend our time making it easy to take these things into production in the context of your business application or your business use case that you're interested in right now. >> So you mentioned chat bots, and I want to talk about ChatGPT, but my question here is different, we'll talk about that in a minute. So when you think about these chat bots, the ones that are conversational, my experience anyway is they're just meh, they're not that great. But the ones that actually work pretty well, they have a conditioned response. Now they're limited, but they say, which of the following is your problem? And then if that's one of the following is your problem, you can maybe solve your problem. But this is clearly a trend and it helps the line of business. How does Oracle think about these use cases for your customers? >> Yeah, so I think the key here is exactly what you said. It's about task completion. The general purpose bots are interesting, but as you said, like are still limited. They're getting much better, I'm sure we'll talk about ChatGPT. But I think what most enterprises want is around task completion. I want to automate my expense report processing. So today inside Oracle we have a chat bot where I submit my expenses the bot ask a couple of question, I answer them, and then I'm done. Like I don't need to go to our fancy application, and manually submit an expense report. I do this via Slack. And the key is around managing the right expectations of what this thing is capable of doing. Like, I have a story from I think five, six years ago when technology was much inferior than it is today. Well, one of the telco providers I was working with wanted to roll a chat bot that does realtime translation. So it was for a support center for of the call centers. And what they wanted do is, Hey, we have English speaking employees, whatever, 24/7, if somebody's calling, and the native tongue is different like Hebrew in my case, or Chinese or whatnot, then we'll give them a chat bot that they will interact with and will translate this on the fly and everything would work. And when they rolled it out, the feedback from customers was horrendous. Customers said, the technology sucks. It's not good. I hate it, I hate your company, I hate your support. And what they've done is they've changed the narrative. Instead of, you go to a support center, and you assume you're going to talk to a human, and instead you get a crappy chat bot, they're like, Hey, if you want to talk to a Hebrew speaking person, there's a four hour wait, please leave your phone and we'll call you back. Or you can try a new amazing Hebrew speaking AI powered bot and it may help your use case. Do you want to try it out? And some people said, yeah, let's try it out. Plus one to try it out. And the feedback, even though it was the exact same technology was amazing. People were like, oh my God, this is so innovative, this is great. Even though it was the exact same experience that they hated a few weeks earlier on. So I think the key lesson that I picked from this experience is it's all about setting the right expectations, and working around the right use case. If you are replacing a human, the level is different than if you are just helping or augmenting something that otherwise would take a lot of time. And I think this is the focus that we are doing, picking up the tasks that people want to accomplish or that enterprise want to accomplish for the customers, for the employees. And using chat bots to make those specific ones better rather than, hey, this is going to replace all humans everywhere, and just be better than that. >> Yeah, I mean, to the point you mentioned expense reports. I'm in a Twitter thread and one guy says, my favorite part of business travel is filling out expense reports. It's an hour of excitement to figure out which receipts won't scan. We can all relate to that. It's just the worst. When you think about companies that are building custom AI driven apps, what can they do on OCI? What are the best options for them? Do they need to hire an army of machine intelligence experts and AI specialists? Help us understand your point of view there. >> So over the last, I would say the two or three years we've developed a full suite of machine learning and AI services for, I would say probably much every use case that you would expect right now from applying natural language processing to understanding customer support tickets or social media, or whatnot to computer vision platforms or computer vision services that can understand and detect objects, and count objects on shelves or detect cracks in the pipe or defecting parts, all the way to speech services. It can actually transcribe human speech. And most recently we've launched a new document AI service. That can actually look at unstructured documents like receipts or invoices or government IDs or even proprietary documents, loan application, student application forms, patient ingestion and whatnot and completely automate them using AI. So if you want to do one of the things that are, I would say common bread and butter for any industry, whether it's financial services or healthcare or manufacturing, we have a suite of services that any developer can go, and use easily customized with their own data. You don't need to be an expert in deep learning or large language models. You could just use our automobile capabilities, and build your own version of the models. Just go ahead and use them. And if you do have proprietary complex scenarios that you need customer from scratch, we actually have the most cost effective platform for that. So we have the OCI data science as well as built-in machine learning platform inside the databases inside the Oracle database, and mySQL HeatWave that allow data scientists, python welding people that actually like to build and tweak and control and improve, have everything that they need to go and build the machine learning models from scratch, deploy them, monitor and manage them at scale in production environment. And most of it is brand new. So we did not have these technologies four or five years ago and we've started building them and they're now at enterprise scale over the last couple of years. >> So what are some of the state-of-the-art tools, that AI specialists and data scientists need if they're going to go out and develop these new models? >> So I think it's on three layers. I think there's an infrastructure layer where the Nvidia's of the world come into play. For some of these things, you want massively efficient, massively scaled infrastructure place. So we are the most cost effective and performant large scale GPU training environment today. We're going to be first to onboard the new Nvidia H100s. These are the new super powerful GPU's for large language model training. So we have that covered for you in case you need this 'cause you want to build these ginormous things. You need a data science platform, a platform where you can open a Python notebook, and just use all these fancy open source frameworks and create the models that you want, and then click on a button and deploy it. And it infinitely scales wherever you need it. And in many cases you just need the, what I call the applied AI services. You need the Lego sets, the Lego death style, Lego Eiffel Tower. So we have a suite of these sets for typical scenarios, whether it's cognitive services of like, again, understanding images, or documents all the way to solving particular business problems. So an anomaly detection service, demand focusing service that will be the equivalent of these Lego sets. So if this is the business problem that you're looking to solve, we have services out there where we can bring your data, call an API, train a model, get the model and use it in your production environment. So wherever you want to play, all the way into embedding this thing, inside this applications, obviously, wherever you want to play, we have the tools for you to go and engage from infrastructure to SaaS at the top, and everything in the middle. >> So when you think about the data pipeline, and the data life cycle, and the specialized roles that came out of kind of the (indistinct) era if you will. I want to focus on two developers and data scientists. So the developers, they hate dealing with infrastructure and they got to deal with infrastructure. Now they're being asked to secure the infrastructure, they just want to write code. And a data scientist, they're spending all their time trying to figure out, okay, what's the data quality? And they're wrangling data and they don't spend enough time doing what they want to do. So there's been a lack of collaboration. Have you seen that change, are these approaches allowing collaboration between data scientists and developers on a single platform? Can you talk about that a little bit? >> Yeah, that is a great question. One of the biggest set of scars that I have on my back from for building these platforms in other companies is exactly that. Every persona had a set of tools, and these tools didn't talk to each other and the handoff was painful. And most of the machine learning things evaporate or die on the floor because of this problem. It's very rarely that they are unsuccessful because the algorithm wasn't good enough. In most cases it's somebody builds something, and then you can't take it to production, you can't integrate it into your business application. You can't take the data out, train, create an endpoint and integrate it back like it's too painful. So the way we are approaching this is focused on this problem exactly. We have a single set of tools that if you publish a model as a data scientist and developers, and even business analysts that are seeing a inside of business application could be able to consume it. We have a single model store, a single feature store, a single management experience across the various personas that need to play in this. And we spend a lot of time building, and borrowing a word that cellular folks used, and I really liked it, building inside highways to make it easier to bring these insights into where you need them inside applications, both inside our applications, inside our SaaS applications, but also inside custom third party and even first party applications. And this is where a lot of our focus goes to just because we have dealt with so much pain doing this inside our own SaaS that we now have built the tools, and we're making them available for others to make this process of building a machine learning outcome driven insight in your app easier. And it's not just the model development, and it's not just the deployment, it's the entire journey of taking the data, building the model, training it, deploying it, looking at the real data that comes from the app, and creating this feedback loop in a more efficient way. And that's our focus area. Exactly this problem. >> Well thank you for that. So, last week we had our super cloud two event, and I had Juan Loza on and he spent a lot of time talking about how open Oracle is in its philosophy, and I got a lot of feedback. They were like, Oracle open, I don't really think, but the truth is if you think about database Oracle database, it never met a hardware platform that it didn't like. So in that sense it's open. So, but my point is, a big part of of machine learning and AI is driven by open source tools, frameworks, what's your open source strategy? What do you support from an open source standpoint? >> So I'm a strong believer that you don't actually know, nobody knows where the next slip fog or the next industry shifting innovation in AI is going to come from. If you look six months ago, nobody foreseen Dali, the magical text to image generation and the exploding brought into just art and design type of experiences. If you look six weeks ago, I don't think anybody's seen ChatGPT, and what it can do for a whole bunch of industries. So to me, assuming that a customer or partner or developer would want to lock themselves into only the tools that a specific vendor can produce is ridiculous. 'Cause nobody knows, if anybody claims that they know where the innovation is going to come from in a year or two, let alone in five or 10, they're just wrong or lying. So our strategy for Oracle is to, I call this the Netflix of AI. So if you think about Netflix, they produced a bunch of high quality shows on their own. A few years ago it was House of Cards. Last month my wife and I binge watched Ginny and Georgie, but they also curated a lot of shows that they found around the world and bought them to their customers. So it started with things like Seinfeld or Friends and most recently it was Squid games and those are famous Israeli TV series called Founder that Netflix bought in, and they bought it as is and they gave it the Netflix value. So you have captioning and you have the ability to speed the movie and you have it inside your app, and you can download it and watch it offline and everything, but nobody Netflix was involved in the production of these first seasons. Now if these things hunt and they're great, then the third season or the fourth season will get the full Netflix production value, high value budget, high value location shooting or whatever. But you as a customer, you don't care whether the producer and director, and screenplay writing is a Netflix employee or is somebody else's employee. It is fulfilled by Netflix. I believe that we will become, or we are looking to become the Netflix of AI. We are building a bunch of AI in a bunch of places where we think it's important and we have some competitive advantage like healthcare with Acellular partnership or whatnot. But I want to bring the best AI software and hardware to OCI and do a fulfillment by Oracle on that. So you'll get the Oracle security and identity and single bill and everything you'd expect from a company like Oracle. But we don't have to be building the data science, and the models for everything. So this means both open source recently announced a partnership with Anaconda, the leading provider of Python distribution in the data science ecosystem where we are are doing a joint strategic partnership of bringing all the goodness into Oracle customers as well as in the process of doing the same with Nvidia, and all those software libraries, not just the Hubble, both for other stuff like Triton, but also for healthcare specific stuff as well as other ISVs, other AI leading ISVs that we are in the process of partnering with to get their stuff into OCI and into Oracle so that you can truly consume the best AI hardware, and the best AI software in the world on Oracle. 'Cause that is what I believe our customers would want the ability to choose from any open source engine, and honestly from any ISV type of solution that is AI powered and they want to use it in their experiences. >> So you mentioned ChatGPT, I want to talk about some of the innovations that are coming. As an AI expert, you see ChatGPT on the one hand, I'm sure you weren't surprised. On the other hand, maybe the reaction in the market, and the hype is somewhat surprising. You know, they say that we tend to under or over-hype things in the early stages and under hype them long term, you kind of use the internet as example. What's your take on that premise? >> So. I think that this type of technology is going to be an inflection point in how software is being developed. I truly believe this. I think this is an internet style moment, and the way software interfaces, software applications are being developed will dramatically change over the next year two or three because of this type of technologies. I think there will be industries that will be shifted. I think education is a good example. I saw this thing opened on my son's laptop. So I think education is going to be transformed. Design industry like images or whatever, it's already been transformed. But I think that for mass adoption, like beyond the hype, beyond the peak of inflected expectations, if I'm using Gartner terminology, I think certain things need to go and happen. One is this thing needs to become more reliable. So right now it is a complete black box that sometimes produce magic, and sometimes produce just nonsense. And it needs to have better explainability and better lineage to, how did you get to this answer? 'Cause I think enterprises are going to really care about the things that they surface with the customers or use internally. So I think that is one thing that's going to come out. And the other thing that's going to come out is I think it's going to come industry specific large language models or industry specific ChatGPTs. Something like how OpenAI did co-pilot for writing code. I think we will start seeing this type of apps solving for specific business problems, understanding contracts, understanding healthcare, writing doctor's notes on behalf of doctors so they don't have to spend time manually recording and analyzing conversations. And I think that would become the sweet spot of this thing. There will be companies, whether it's OpenAI or Microsoft or Google or hopefully Oracle that will use this type of technology to solve for specific very high value business needs. And I think this will change how interfaces happen. So going back to your expense report, the world of, I'm going to go into an app, and I'm going to click on seven buttons in order to get some job done like this world is gone. Like I'm going to say, hey, please do this and that. And I expect an answer to come out. I've seen a recent demo about, marketing in sales. So a customer sends an email that is interested in something and then a ChatGPT powered thing just produces the answer. I think this is how the world is going to evolve. Like yes, there's a ton of hype, yes, it looks like magic and right now it is magic, but it's not yet productive for most enterprise scenarios. But in the next 6, 12, 24 months, this will start getting more dependable, and it's going to change how these industries are being managed. Like I think it's an internet level revolution. That's my take. >> It's very interesting. And it's going to change the way in which we have. Instead of accessing the data center through APIs, we're going to access it through natural language processing and that opens up technology to a huge audience. Last question, is a two part question. And the first part is what you guys are working on from the futures, but the second part of the question is, we got data scientists and developers in our audience. They love the new shiny toy. So give us a little glimpse of what you're working on in the future, and what would you say to them to persuade them to check out Oracle's AI services? >> Yep. So I think there's two main things that we're doing, one is around healthcare. With a new recent acquisition, we are spending a significant effort around revolutionizing healthcare with AI. Of course many scenarios from patient care using computer vision and cameras through automating, and making better insurance claims to research and pharma. We are making the best models from leading organizations, and internal available for hospitals and researchers, and insurance providers everywhere. And we truly are looking to become the leader in AI for healthcare. So I think that's a huge focus area. And the second part is, again, going back to the enterprise AI angle. Like we want to, if you have a business problem that you want to apply here to solve, we want to be your platform. Like you could use others if you want to build everything complicated and whatnot. We have a platform for that as well. But like, if you want to apply AI to solve a business problem, we want to be your platform. We want to be the, again, the Netflix of AI kind of a thing where we are the place for the greatest AI innovations accessible to any developer, any business analyst, any user, any data scientist on Oracle Cloud. And we're making a significant effort on these two fronts as well as developing a lot of the missing pieces, and building blocks that we see are needed in this space to make truly like a great experience for developers and data scientists. And what would I recommend? Get started, try it out. We actually have a shameless sales plug here. We have a free deal for all of our AI services. So it typically cost you nothing. I would highly recommend to just go, and try these things out. Go play with it. If you are a python welding developer, and you want to try a little bit of auto mail, go down that path. If you're not even there and you're just like, hey, I have these customer feedback things and I want to try out, if I can understand them and apply AI and visualize, and do some cool stuff, we have services for that. My recommendation is, and I think ChatGPT got us 'cause I see people that have nothing to do with AI, and can't even spell AI going and trying it out. I think this is the time. Go play with these things, go play with these technologies and find what AI can do to you or for you. And I think Oracle is a great place to start playing with these things. >> Elad, thank you. Appreciate you sharing your vision of making Oracle the Netflix of AI. Love that and really appreciate your time. >> Awesome. Thank you. Thank you for having me. >> Okay. Thanks for watching this Cube conversation. This is Dave Vellante. We'll see you next time. (gentle music playing)

(electronic music) >> Hi everybody, welcome back to Supercloud 2. I'm Dave Vellante with my co-host John Furrier. We're here at our tricked out Palo Alto studio. We're going live wall to wall all day. We're inserting a number of pre-recorded interviews, folks like Walmart. We just heard from Nir Zuk of Palo Alto Networks, and I'm really pleased to welcome in David Flynn. David Flynn, you may know as one of the people behind Fusion-io, completely changed the way in which people think about storing data, accessing data. David Flynn now the founder and CEO of a company called Hammerspace. David, good to see you, thanks for coming on. >> David: Good to see you too. >> And Dr. Nelu Mihai is the CEO and founder of Cloud of Clouds. He's actually built a Supercloud. We're going to get into that. Nelu, thanks for coming on. >> Thank you, Happy New Year. >> Yeah, Happy New Year. So I'm going to start right off with a little debate that's going on in the community if you guys would bring out this slide. So Bob Muglia early today, he gave a definition of Supercloud. He felt like we had to tighten ours up a little bit. He said a Supercloud is a platform, underscoring platform, that provides programmatically consistent services hosted on heterogeneous cloud providers. Now, Nelu, we have this shared doc, and you've been in there. You responded, you said, well, hold on. Supercloud really needs to be an architecture, or else we're going to have this stove pipe of stove pipes, really. And then you went on with more detail, what's the information model? What's the execution model? How are users going to interact with Supercloud? So I start with you, why architecture? The inference is that a platform, the platform provider's responsible for the architecture? Why does that not work in your view? >> No, the, it's a very interesting question. So whenever I think about platform, what's the connotation, you think about monolithic system? Yeah, I mean, I don't know whether it's true or or not, but there is this connotation of of monolithic. On the other hand, if you look at what's a problem right now with HyperClouds, from the customer perspective, they're very complex. There is a heterogeneous world where actually every single one of this HyperClouds has their own architecture. You need rocket scientists to build a cloud applications. Always there is this contradiction between cost and performance. They fight each other. And I'm quoting here a former friend of mine from Bell Labs who work at AWS who used to say "Cloud is cheap as long as you don't use it too much." (group chuckles) So clearly we need something that kind of plays from the principle point of view the role of an operating system, that seats on top of this heterogeneous HyperCloud, and there's nothing wrong by having these proprietary HyperClouds, think about processors, think about operating system and so on, so forth. But in order to build a system that is simple enough, I think we need to go deeper and understand. >> So the argument, the counterargument to that, David, is you'll never get there. You need a proprietary system to get to market sooner, to solve today's problem. Now I don't know where you stand on this platform versus architecture. I haven't asked you, but. >> I think there are aspects of both for sure. I mean it needs to be an architecture in the sense that it's broad based and open and so forth. But you know, platform, you could say as long as people can instantiate it themselves, on their own infrastructure, as long as it's something that can be deployed as, you know, software defined, you don't want the concept of platform being the monolith, you know, combined hardware and software. So it really depends on what you're focused on when you're saying platform, you know, I'd say as long as they software defined thing, to where it can literally run anywhere. I mean, because I really think what we're talking about here is the original concept of cloud computing. The ability to run anything anywhere, without having to care about the physical infrastructure. And what we have today is not that, the cloud today is a big mainframe in the sky, that just happens to be large enough that once you select which region, generally you have enough resources. But, you know, nowadays you don't even necessarily have enough resources in one region. and then you're kind of stuck. So we haven't really gotten to that utility model of computing. And you're also asked to rewrite your application, you know, to abandon the conveniences of high performance file access. You got to rewrite it to use object storage stuff. We have to get away from that. >> Okay, I want to just drill on that, 'cause I think I like that point about, there's not enough availability, but on the developer cloud, the original AWS premise was targeting developers, 'cause at that time, you have to provision a Sun box get a Cisco DSU/CSU, now you get on the cloud. But I think you're giving up the scale question, 'cause I think right now, scale is huge, enterprise grade versus cloud for developers. >> That's Right. >> Because I mean look at, Amazon, Azure, they got compute, they got storage, they got queuing, and some stuff. If you're doing a startup, you throw your app up there, localhost to cloud, no big deal. It's the scale thing that gets me- >> And you can tell by the fact that, in regions that are under high demand, right, like in London or LA, at least with the clients we work with in the median entertainment space, it costs twice as much for the exact same cloud instances that do the exact same amount of work, as somewhere out in rural Canada. So why is it you have such a cost differential, it has to do with that supply and demand, and the fact that the clouds aren't really the ability to run anything anywhere. Even within the same cloud vendor, you're stuck in a specific region. >> And that was never the original promise, right? I mean it was, we turned it into that. But the original promise was get rid of the heavy lifting of IT. >> Not have to run your own, yeah, exactly. >> And then it became, wow, okay I can run anywhere. And then you know, it's like web 2.0. You know people say why Supercloud, you and I talked about this, why do you need a name for Supercloud? It's like web 2.0. >> It's what Cloud was supposed to be. >> It's what cloud was supposed to be, (group laughing and talking) exactly, right. >> Cloud was supposed to be run anything anywhere, or at least that's what we took it as. But you're right, originally it was just, oh don't have to run your own infrastructure, and you can choose somebody else's infrastructure. >> And you did that >> But you're still bound to that. >> Dave: And People said I want more, right? >> But how do we go from here? >> That's, that's actually, that's a very good point, because indeed when the first HyperClouds were designed, were designed really focus on customers. I think Supercloud is an opportunity to design in the right way. Also having in mind the computer science rigor. And we should take advantage of that, because in fact actually, if cloud would've been designed properly from the beginning, probably wouldn't have needed Supercloud. >> David: You wouldn't have to have been asked to rewrite your application. >> That's correct. (group laughs) >> To use REST interfaces to your storage. >> Revisist history is always a good one. But look, cloud is great. I mean your point is cloud is a good thing. Don't hold it back. >> It is a very good thing. >> Let it continue. >> Let it go as as it is. >> Yeah, let that thing continue to grow. Don't impose restrictions on the cloud. Just refactor what you need to for scale or enterprise grade or availability. >> And you would agree with that, is that true or is it problem you're solving? >> Well yeah, I mean it, what the cloud is doing is absolutely necessary. What the public cloud vendors are doing is absolutely necessary. But what's been missing is how to provide a consistent interface, especially to persistent data. And have it be available across different regions, and across different clouds. 'cause data is a highly localized thing in current architecture. It only exists as rendered by the storage system that you put it in. Whether that's a legacy thing like a NetApp or an Isilon or even a cloud data service. It's localized to a specific region of the cloud in which you put that. We have to delocalize data, and provide a consistent interface to it across all sites. That's high performance, local access, but to global data. >> And so Walmart earlier today described their, what we call Supercloud, they call it the Walmart cloud native platform. And they use this triplet model. They have AWS and Azure, no, oh sorry, no AWS. They have Azure and GCP and then on-prem, where all the VMs live. When you, you know, probe, it turns out that it's only stateless in the cloud. (John laughs) So, the state stuff- >> Well let's just admit it, there is no such thing as stateless, because even the application binaries and libraries are state. >> Well I'm happy that I'm hearing that. >> Yeah, okay. >> Because actually I have a lot of debate (indistinct). If you think about no software running on a (indistinct) machine is stateless. >> David: Exactly. >> This is something that was- >> David: And that's data that needs to be distributed and provided consistently >> (indistinct) >> Across all the clouds, >> And actually, it's a nonsense, but- >> Dave: So it's an illusion, okay. (group talks over each other) >> (indistinct) you guys talk about stateless. >> Well, see, people make the confusion between state and persistent state, okay. Persistent state it's a different thing. State is a different thing. So, but anyway, I want to go back to your point, because there's a lot of debate here. People are talking about data, some people are talking about logic, some people are talking about networking. In my opinion is this triplet, which is data logic and connectivity, that has equal importance. And actually depending on the application, can have the center of gravity moving towards data, moving towards what I call execution units or workloads. And connectivity is actually the most important part of it. >> David: (indistinct). >> Some people are saying move the logic towards the data, some other people, and you are saying actually, that no, you have to build a distributed data mesh. What I'm saying is actually, you have to consider all these three variables, all these vector in order to decide, based on application, what's the most important. Because sometimes- >> John: So the application chooses >> That's correct. >> Well it it's what operating systems were in the past, was principally the thing that runs and manages the jobs, the job scheduler, and the thing that provides your persistent data (indistinct). >> Okay. So we finally got operating system into the equation, thank you. (group laughs) >> Nelu: I actually have a PhD in operating system. >> Cause what we're talking about is an operating system. So forget platform or architecture, it's an operating environment. Let's use it as a general term. >> All right. I think that's about it for me. >> All right, let's take (indistinct). Nelu, I want ask you quick, 'cause I want to give a, 'cause I believe it's an operating system. I think it's going to be a reset, refactored. You wrote to me, "The model of Supercloud has to be open theoretical, has to satisfy the rigors of computer science, and customer requirements." So unique to today, if the OS is going to be refactored, it's not going to be, may or may not be Red Hat or somebody else. This new OS, obviously requirements are for customers too but is what's the computer science that is needed? Where are we, what's the missing? Where's the science in this shift? It's not your standard OS it's not like an- (group talks over each other) >> I would beg to differ. >> (indistinct) truly an operation environment. But the, if you think about, and make analogies, what you need when you design a distributed system, well you need an information model, yeah. You need to figure out how the data is located and distributed. You need a model for the execution units, and you need a way to describe the interactions between all these objects. And it is my opinion that we need to go deeper and formalize these operations in order to make a step forward. And when we design Supercloud, and design something that is better than the current HyperClouds. And actually that is when we design something better, you make a system more efficient and it's going to be better from the cost point of view, from the performance point of view. But we need to add some math into all this customer focus centering and I really admire AWS and their executive team focusing on the customer. But now it's time to go back and see, if we apply some computer science, if you try to formalize to build a theoretical model of cloud, can we build a system that is better than existing ones? >> So David, how do you- >> this is what I'm saying. >> That's a good question >> How do You see the operating system of a, or operating environment of a decentralized cloud? >> Well I think it's layered. I mean we have operating systems that can run systems quite efficiently. Linux has sort of one in the data center, but we're talking about a layer on top of that. And I think we're seeing the emergence of that. For example, on the job scheduling side of things, Kubernetes makes a really good example. You know, you break the workload into the most granular units of compute, the containerized microservice, and then you use a declarative model to state what is needed and give the system the degrees of freedom that it can choose how to instantiate it. Because the thing about these distributed systems, is that the complexity explodes, right? Running a piece of hardware, running a single server is not a problem, even with all the many cores and everything like that. It's when you start adding in the networking, and making it so that you have many of them. And then when it's going across whole different data centers, you know, so, at that level the way you solve this is not manually (group laughs) and not procedurally. You have to change the language so it's intent based, it's a declarative model, and what you're stating is what is intended, and you're leaving it to more advanced techniques, like machine learning to decide how to instantiate that service across the cluster, which is what Kubernetes does, or how to instantiate the data across the diverse storage infrastructure. And that's what we do. >> So that's a very good point because actually what has been neglected with HyperClouds is really optimization and automation. But in order to be able to do both of these things, you need, I'm going back and I'm stubborn, you need to have a mathematical model, a theoretical model because what does automation mean? It means that we have to put machines to do the work instead of us, and machines work with what? Formula, with algorithms, they don't work with services. So I think Supercloud is an opportunity to underscore the importance of optimization and automation- >> Totally agree. >> In HyperCloud, and actually by doing that, we can also have an interesting connotation. We are also contributing to save our planet, because if you think right now. we're consuming a lot of energy on this HyperClouds and also all this AI applications, and I think we can do better and build the same kind of application using less energy. >> So yeah, great point, love that call out, the- you know, Dave and I always joke about the old, 'cause we're old, we talk about, you know, (Nelu Laughs) old history, OS/2 versus DOS, okay, OS's, OS/2 is silly better, first threaded OS, DOS never went away. So how does legacy play into this conversation? Because I buy the theoretical, I love the conversation. Okay, I think it's an OS, totally see it that way myself. What's the blocker? Is there a legacy that drags it back? Is the anchor dragging from legacy? Is there a DOS OS/2 moment? Is there an opportunity to flip the script? This is- >> I think that's a perfect example of why we need to support the existing interfaces, Operating Systems, real operating systems like Linux, understands how to present data, it's called a file system, block devices, things that that plumb in there. And by, you know, going to a REST interface and S3 and telling people they have to rewrite their applications, you can't even consume your application binaries that way, the OS doesn't know how to pull that sort of thing. So we, to get to cloud, to get to the ability to host massive numbers of tenants within a centralized infrastructure, you know, we abandoned these lower level interfaces to the OS and we have to go back to that. It's the reason why DOS ultimately won, is it had the momentum of the install base. We're seeing the same thing here. Whatever it is, it has to be a real file system and not a come down file system >> Nelu, what's your reaction, 'cause you're in the theoretical bandwagon. Let's get your reaction. >> No, I think it's a good, I'll give, you made a good analogy between OS/2 and DOS, but I'll go even farther saying, if you think about the evolution operating system didn't stop the evolution of underlying microprocessors, hardware, and so on and so forth. On the contrary, it was a catalyst for that. So because everybody could develop their own hardware, without worrying that the applications on top of operating system are going to modify. The same thing is going to happen with Supercloud. You're going to have the AWSs, you're going to have the Azure and the the GCP continue to evolve in their own way proprietary. But if we create on top of it the right interface >> The open, this is why open is important. >> That's correct, because actually you're going to see sometime ago, everybody was saying, remember venture capitals were saying, "AWS killed the world, nobody's going to come." Now you see what Oracle is doing, and then you're going to see other players. >> It's funny, Amazon's trying to be more like Microsoft. Microsoft's trying to be more like Amazon and Google- Oracle's just trying to say they have cloud. >> That's, that's correct, (group laughs) so, my point is, you're going to see a multiplication of this HyperClouds and cloud technology. So, the system has to be open in order to accommodate what it is and what is going to come. Okay, so it's open. >> So the the legacy- so legacy is an opportunity, not a blocker in your mind. And you see- >> That's correct, I think we should allow them to continue to to to be their own actually. But maybe you're going to find a way to connect with it. >> Amazon's the processor, and they're on the 80 80 80 right? >> That's correct. >> You're saying you love people trying to get put to work. >> That's a good analogy. >> But, performance levels you say good luck, right? >> Well yeah, we have to be able to take traditional applications, high performance applications, those that consume file system and persistent data. Those things have to be able to run anywhere. You need to be able to put, put them onto, you know, more elastic infrastructure. So, we have to actually get cloud to where it lives up to its billing. >> And that's what you're solving for, with Hammerspace, >> That's what we're solving for, making it possible- >> Give me the bumper sticker. >> Solving for how do you have massive quantities of unstructured file data? At the end of the day, all data ultimately is unstructured data. Have that persistent data available, across any data center, within any cloud, within any region on-prem, at the edge. And have not just the same APIs, but have the exact same data sets, and not sucked over a straw remote, but at extreme high performance, local access. So how do you have local access to globally shared distributed data? And that's what we're doing. We are orchestrating data globally across all different forms of storage infrastructure, so you have a consistent access at the highest performance levels, at the lowest level innate built into the OS, how to consume it as (indistinct) >> So are you going into the- all the clouds and natively building in there, or are you off cloud? >> So This is software that can run on cloud instances and provide high performance file within the cloud. It can take file data that's on-prem. Again, it's software, it can run in virtual or on physical servers. And it abstracts the data from the existing storage infrastructure, and makes the data visible and consumable and orchestratable across any of it. >> And what's the elevator pitch for Cloud of Cloud, give that too. >> Well, Cloud of Clouds creates a theoretical model of cloud, and it describes every single object in the cloud. Where is data, execution units, and connectivity, with one single class of very simple object. And I can, I can give you (indistinct) >> And the problem that solves is what? >> The problem that solves is, it creates this mathematical model that is necessary in order to do other interesting things, such as optimization, using sata engines, using automation, applying ML for instance. Or deep learning to automate all this clouds, if you think about in the industrial field, we know how to manage and automate huge plants. Why wouldn't it do the same thing in cloud? It's the same thing you- >> That's what you mean by theoretical model. >> Nelu: That's correct. >> Lay out the architecture, almost the bones of skeleton or something, or, and then- >> That's correct, and then on top of it you can actually build a platform, You can create your services, >> when you say math, you mean you put numbers to it, you kind of index it. >> You quantify this thing and you apply mathematical- It's really about, I can disclose this thing. It's really about describing the cloud as a knowledge graph for every single object in the graph for node, an edge is a vector. And then once you have this model, then you can apply the field theory, and linear algebra to do operation with these vectors. And it's, this creates a very interesting opportunity to let the math do this thing for us. >> Okay, so what happens with hyperscale, or it's like AWS in your model. >> So in, in my model actually, >> Are they happy with this, or they >> I'm very happy with that. >> Will they be happy with you? >> We create an interface to every single HyperCloud. We actually, we don't need to interface with the thousands of APIs, but you know, if we have the 80 20 rule, and we map these APIs into this graph, and then every single operation that is done in this graph is done from the beginning, in an optimized manner and also automation ready. >> That's going to be great. David, I want us to go back to you before we close real quick. You've had a lot of experience, multiple ventures on the front end. You talked to a lot of customers who've been innovating. Where are the classic (indistinct)? Cause you, you used to sell and invent product around the old school enterprises with storage, you know that that trajectory storage is still critical to store the data. Where's the classic enterprise grade mindset right now? Those customers that were buying, that are buying storage, they're in the cloud, they're lifting and shifting. They not yet put the throttle on DevOps. When they look at this Supercloud thing, Are they like a deer in the headlights, or are they like getting it? What's the, what's the classic enterprise look like? >> You're seeing people at different stages of adoption. Some folks are trying to get to the cloud, some folks are trying to repatriate from the cloud, because they've realized it's better to own than to rent when you use a lot of it. And so people are at very different stages of the journey. But the one thing that's constant is that there's always change. And the change here has to do with being able to change the location where you're doing your computing. So being able to support traditional workloads in the cloud, being able to run things at the edge, and being able to rationalize where the data ought to exist, and with a declarative model, intent-based, business objective-based, be able to swipe a mouse and have the data get redistributed and positioned across different vendors, across different clouds, that, we're seeing that as really top of mind right now, because everybody's at some point on this journey, trying to go somewhere, and it involves taking their data with them. (John laughs) >> Guys, great conversation. Thanks so much for coming on, for John, Dave. Stay tuned, we got a great analyst power panel coming right up. More from Palo Alto, Supercloud 2. Be right back. (bouncy music)

>> Narrator: From theCUBE Studios in Palo Alto and Boston bringing you data-driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante >> Enterprise tech practitioners, like most of us they want to make their lives easier so they can focus on delivering more value to their businesses. And to do so, they want to tap best of breed services in the public cloud, but at the same time connect their on-prem intellectual property to emerging applications which drive top line revenue and bottom line profits. But creating a consistent experience across clouds and on-prem estates has been an elusive capability for most organizations, forcing trade-offs and injecting friction into the system. The need to create seamless experiences is clear and the technology industry is starting to respond with platforms, architectures, and visions of what we've called the Supercloud. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis we give you a preview of Supercloud 2, the second event of its kind that we've had on the topic. Yes, folks that's right Supercloud 2 is here. As of this recording, it's just about four days away 33 guests, 21 sessions, combining live discussions and fireside chats from theCUBE's Palo Alto Studio with prerecorded conversations on the future of cloud and data. You can register for free at supercloud.world. And we are super excited about the Supercloud 2 lineup of guests whereas Supercloud 22 in August, was all about refining the definition of Supercloud testing its technical feasibility and understanding various deployment models. Supercloud 2 features practitioners, technologists and analysts discussing what customers need with real-world examples of Supercloud and will expose thinking around a new breed of cross-cloud apps, data apps, if you will that change the way machines and humans interact with each other. Now the example we'd use if you think about applications today, say a CRM system, sales reps, what are they doing? They're entering data into opportunities they're choosing products they're importing contacts, et cetera. And sure the machine can then take all that data and spit out a forecast by rep, by region, by product, et cetera. But today's applications are largely about filling in forms and or codifying processes. In the future, the Supercloud community sees a new breed of applications emerging where data resides on different clouds, in different data storages, databases, Lakehouse, et cetera. And the machine uses AI to inspect the e-commerce system the inventory data, supply chain information and other systems, and puts together a plan without any human intervention whatsoever. Think about a system that orchestrates people, places and things like an Uber for business. So at Supercloud 2, you'll hear about this vision along with some of today's challenges facing practitioners. Zhamak Dehghani, the founder of Data Mesh is a headliner. Kit Colbert also is headlining. He laid out at the first Supercloud an initial architecture for what that's going to look like. That was last August. And he's going to present his most current thinking on the topic. Veronika Durgin of Sachs will be featured and talk about data sharing across clouds and you know what she needs in the future. One of the main highlights of Supercloud 2 is a dive into Walmart's Supercloud. Other featured practitioners include Western Union Ionis Pharmaceuticals, Warner Media. We've got deep, deep technology dives with folks like Bob Muglia, David Flynn Tristan Handy of DBT Labs, Nir Zuk, the founder of Palo Alto Networks focused on security. Thomas Hazel, who's going to talk about a new type of database for Supercloud. It's several analysts including Keith Townsend Maribel Lopez, George Gilbert, Sanjeev Mohan and so many more guests, we don't have time to list them all. They're all up on supercloud.world with a full agenda, so you can check that out. Now let's take a look at some of the things that we're exploring in more detail starting with the Walmart Cloud native platform, they call it WCNP. We definitely see this as a Supercloud and we dig into it with Jack Greenfield. He's the head of architecture at Walmart. Here's a quote from Jack. "WCNP is an implementation of Kubernetes for the Walmart ecosystem. We've taken Kubernetes off the shelf as open source." By the way, they do the same thing with OpenStack. "And we have integrated it with a number of foundational services that provide other aspects of our computational environment. Kubernetes off the shelf doesn't do everything." And so what Walmart chose to do, they took a do-it-yourself approach to build a Supercloud for a variety of reasons that Jack will explain, along with Walmart's so-called triplet architecture connecting on-prem, Azure and GCP. No surprise, there's no Amazon at Walmart for obvious reasons. And what they do is they create a common experience for devs across clouds. Jack is going to talk about how Walmart is evolving its Supercloud in the future. You don't want to miss that. Now, next, let's take a look at how Veronica Durgin of SAKS thinks about data sharing across clouds. Data sharing we think is a potential killer use case for Supercloud. In fact, let's hear it in Veronica's own words. Please play the clip. >> How do we talk to each other? And more importantly, how do we data share? You know, I work with data, you know this is what I do. So if you know I want to get data from a company that's using, say Google, how do we share it in a smooth way where it doesn't have to be this crazy I don't know, SFTP file moving? So that's where I think Supercloud comes to me in my mind, is like practical applications. How do we create that mesh, that network that we can easily share data with each other? >> Now data mesh is a possible architectural approach that will enable more facile data sharing and the monetization of data products. You'll hear Zhamak Dehghani live in studio talking about what standards are missing to make this vision a reality across the Supercloud. Now one of the other things that we're really excited about is digging deeper into the right approach for Supercloud adoption. And we're going to share a preview of a debate that's going on right now in the community. Bob Muglia, former CEO of Snowflake and Microsoft Exec was kind enough to spend some time looking at the community's supercloud definition and he felt that it needed to be simplified. So in near real time he came up with the following definition that we're showing here. I'll read it. "A Supercloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers." So not only did Bob simplify the initial definition he's stressed that the Supercloud is a platform versus an architecture implying that the platform provider eg Snowflake, VMware, Databricks, Cohesity, et cetera is responsible for determining the architecture. Now interestingly in the shared Google doc that the working group uses to collaborate on the supercloud de definition, Dr. Nelu Mihai who is actually building a Supercloud responded as follows to Bob's assertion "We need to avoid creating many Supercloud platforms with their own architectures. If we do that, then we create other proprietary clouds on top of existing ones. We need to define an architecture of how Supercloud interfaces with all other clouds. What is the information model? What is the execution model and how users will interact with Supercloud?" What does this seemingly nuanced point tell us and why does it matter? Well, history suggests that de facto standards will emerge more quickly to resolve real world practitioner problems and catch on more quickly than consensus-based architectures and standards-based architectures. But in the long run, the ladder may serve customers better. So we'll be exploring this topic in more detail in Supercloud 2, and of course we'd love to hear what you think platform, architecture, both? Now one of the real technical gurus that we'll have in studio at Supercloud two is David Flynn. He's one of the people behind the the movement that enabled enterprise flash adoption, that craze. And he did that with Fusion IO and he is now working on a system to enable read write data access to any user in any application in any data center or on any cloud anywhere. So think of this company as a Supercloud enabler. Allow me to share an excerpt from a conversation David Flore and I had with David Flynn last year. He as well gave a lot of thought to the Supercloud definition and was really helpful with an opinionated point of view. He said something to us that was, we thought relevant. "What is the operating system for a decentralized cloud? The main two functions of an operating system or an operating environment are one the process scheduler and two, the file system. The strongest argument for supercloud is made when you go down to the platform layer and talk about it as an operating environment on which you can run all forms of applications." So a couple of implications here that will be exploring with David Flynn in studio. First we're inferring from his comment that he's in the platform camp where the platform owner is responsible for the architecture and there are obviously trade-offs there and benefits but we'll have to clarify that with him. And second, he's basically saying, you kill the concept the further you move up the stack. So the weak, the further you move the stack the weaker the supercloud argument becomes because it's just becoming SaaS. Now this is something we're going to explore to better understand is thinking on this, but also whether the existing notion of SaaS is changing and whether or not a new breed of Supercloud apps will emerge. Which brings us to this really interesting fellow that George Gilbert and I RIFed with ahead of Supercloud two. Tristan Handy, he's the founder and CEO of DBT Labs and he has a highly opinionated and technical mind. Here's what he said, "One of the things that we still don't know how to API-ify is concepts that live inside of your data warehouse inside of your data lake. These are core concepts that the business should be able to create applications around very easily. In fact, that's not the case because it involves a lot of data engineering pipeline and other work to make these available. So if you really want to make it easy to create these data experiences for users you need to have an ability to describe these metrics and then to turn them into APIs to make them accessible to application developers who have literally no idea how they're calculated behind the scenes and they don't need to." A lot of implications to this statement that will explore at Supercloud two versus Jamma Dani's data mesh comes into play here with her critique of hyper specialized data pipeline experts with little or no domain knowledge. Also the need for simplified self-service infrastructure which Kit Colbert is likely going to touch upon. Veronica Durgin of SAKS and her ideal state for data shearing along with Harveer Singh of Western Union. They got to deal with 200 locations around the world in data privacy issues, data sovereignty how do you share data safely? Same with Nick Taylor of Ionis Pharmaceutical. And not to blow your mind but Thomas Hazel and Bob Muglia deposit that to make data apps a reality across the Supercloud you have to rethink everything. You can't just let in memory databases and caching architectures take care of everything in a brute force manner. Rather you have to get down to really detailed levels even things like how data is laid out on disk, ie flash and think about rewriting applications for the Supercloud and the MLAI era. All of this and more at Supercloud two which wouldn't be complete without some data. So we pinged our friends from ETR Eric Bradley and Darren Bramberm to see if they had any data on Supercloud that we could tap. And so we're going to be analyzing a number of the players as well at Supercloud two. Now, many of you are familiar with this graphic here we show some of the players involved in delivering or enabling Supercloud-like capabilities. On the Y axis is spending momentum and on the horizontal accesses market presence or pervasiveness in the data. So netscore versus what they call overlap or end in the data. And the table insert shows how the dots are plotted now not to steal ETR's thunder but the first point is you really can't have supercloud without the hyperscale cloud platforms which is shown on this graphic. But the exciting aspect of Supercloud is the opportunity to build value on top of that hyperscale infrastructure. Snowflake here continues to show strong spending velocity as those Databricks, Hashi, Rubrik. VMware Tanzu, which we all put under the magnifying glass after the Broadcom announcements, is also showing momentum. Unfortunately due to a scheduling conflict we weren't able to get Red Hat on the program but they're clearly a player here. And we've put Cohesity and Veeam on the chart as well because backup is a likely use case across clouds and on-premises. And now one other call out that we drill down on at Supercloud two is CloudFlare, which actually uses the term supercloud maybe in a different way. They look at Supercloud really as you know, serverless on steroids. And so the data brains at ETR will have more to say on this topic at Supercloud two along with many others. Okay, so why should you attend Supercloud two? What's in it for me kind of thing? So first of all, if you're a practitioner and you want to understand what the possibilities are for doing cross-cloud services for monetizing data how your peers are doing data sharing, how some of your peers are actually building out a Supercloud you're going to get real world input from practitioners. If you're a technologist, you're trying to figure out various ways to solve problems around data, data sharing, cross-cloud service deployment there's going to be a number of deep technology experts that are going to share how they're doing it. We're also going to drill down with Walmart into a practical example of Supercloud with some other examples of how practitioners are dealing with cross-cloud complexity. Some of them, by the way, are kind of thrown up their hands and saying, Hey, we're going mono cloud. And we'll talk about the potential implications and dangers and risks of doing that. And also some of the benefits. You know, there's a question, right? Is Supercloud the same wine new bottle or is it truly something different that can drive substantive business value? So look, go to Supercloud.world it's January 17th at 9:00 AM Pacific. You can register for free and participate directly in the program. Okay, that's a wrap. I want to give a shout out to the Supercloud supporters. VMware has been a great partner as our anchor sponsor Chaos Search Proximo, and Alura as well. For contributing to the effort I want to thank Alex Myerson who's on production and manages the podcast. Ken Schiffman is his supporting cast as well. Kristen Martin and Cheryl Knight to help get the word out on social media and at our newsletters. And Rob Ho is our editor-in-chief over at Silicon Angle. Thank you all. Remember, these episodes are all available as podcast. Wherever you listen we really appreciate the support that you've given. We just saw some stats from from Buzz Sprout, we hit the top 25% we're almost at 400,000 downloads last year. So really appreciate your participation. All you got to do is search Breaking Analysis podcast and you'll find those I publish each week on wikibon.com and siliconangle.com. Or if you want to get ahold of me you can email me directly at David.Vellante@siliconangle.com or dm me DVellante or comment on our LinkedIn post. I want you to check out etr.ai. They've got the best 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 week at Supercloud two or next time on breaking analysis. (light music)

(upbeat music) >> Welcome back to Vegas. Lisa Martin and Dave Vellante here with theCUBE live on the Venetian Expo Hall Floor, talking all things AWS re:Invent 2022. This is the first full day of coverage. It is jam-packed here. People are back. They are ready to hear all the new innovations from AWS. Dave, how does it feel to be back yet again in Vegas? >> Yeah, Vegas. I think it's my 10th time in Vegas this year. So, whatever. >> This year alone. You must have a favorite steak restaurant then. >> There are several. The restaurants in Vegas are actually really good. >> You know? >> They are good. >> They used to be terrible. But I'll tell you. My favorite? The place that closed. >> Oh! >> Yeah, closed. In between where we are in the Wynn and the Venetian. Anyway. >> Was it CUT? >> No, I forget what the name was. >> Something else, okay. >> It was like a Greek sort of steak place. Anyway. >> Now, I'm hungry. >> We were at Pure Accelerate a couple years ago. >> Yes, we were. >> When they announced Cloud Block Store. >> That's right. >> Pure was the first- >> In Austin. >> To do that. >> Yup. >> And then they made the acquisition of Portworx which was pretty prescient given that containers have been going through the roof. >> Yeah. >> So I'm sort of excited to have these guys on and talk about that. >> We're going to unpack all of this. We've got one of our alumni back with us, Venkat Ramakrishna, VP of Product, Portworx by Pure Storage. And Dan Kogan joins us for the first time, VP of Product Management and Product Marketing, FlashArray at Pure Storage. Guys, welcome to the program. >> Thank you. >> Hey, guys. >> Dan: Thanks for having us. >> Do you have a favorite steak restaurant in Vegas? Dave said there's a lot of good choices. >> There's a lot of good steak restaurants here. >> I like SDK. >> Yeah, that's a good one. >> That's the good one. >> That's a good one. >> Which one? >> SDK. >> SDK. >> Where's that? >> It's, I think, in Cosmopolitan. >> Ooh. >> Yeah. >> Oh, yeah, yeah, yeah. >> It's pretty good, yeah. >> There's one of the Western too that's pretty. >> I'm an Herbs and Rye guy. Have you ever been there? >> No. >> No. >> Herbs and Rye is off strip, but it's fantastic. It's kind of like a locals joint. >> I have to dig through all of this great stuff today and then check that out. Talk to me. This is our first day, obviously. First main day. I want to get both of your perspectives. Dan, we'll start with you since you're closest to me. How are you finding this year's event so far? Obviously, tons of people. >> Busy. >> Busy, yeah. >> Yeah, it is. It is old times. Bigger, right? Last re:Invent I was at was 2019 right before everything shut down and it's probably half the size of this which is a different trend than I feel like most other tech conferences have gone where they've come back, but a little bit smaller. re:Invent seems to be the IT show. >> It really does. Venkat, are you finding the same? In terms of what you're experiencing so far on day one of the events? >> Yeah, I mean... There's tremendous excitement. Overall, I think it's good to be back. Very good crowd, great turnout, lot of excitement around some of the new offerings we've announced. The booth traffic has been pretty good. And just the quality of the conversations, the customer meetings, have been really good. There's very interesting use cases shaping up and customers really looking to solve real large scale problems. Yeah, it's been a phenomenal first day. >> Venkat, talk a little bit about, and then we'll get to you Dan as well, the relationship that Portworx by Pure Storage has with AWS. Maybe some joint customers. >> Yeah, so we... Definitely, we have been a partner of AWS for quite some time, right? Earlier this year, we signed what is called a strategic investment letter with AWS where we kind of put some joint effort together like to better integrate our products. Plus, kind of get in front of our customers more together and educate them on how going to how they can deploy and build vision critical apps on EKS and EKS anywhere and Outpost. So that partnership has grown a lot over the last year. We have a lot of significant mutual customer wins together both on the public cloud on EKS as well as on EKS anywhere, right? And there are some exciting use cases around Edge and Edge deployments and different levels of Edge as well with EKS anywhere. And there are pretty good wins on the Outpost as well. So that partnership I think is kind of like growing across not just... We started off with the one product line. Now our Portworx backup as a service is also available on EKS and along with the Portworx Data Services. So, it is also expanded across the product lanes as well. >> And then Dan, you want to elaborate a bit on AWS Plus Pure? >> Yeah, it's for kind of what we'll call the core Pure business or the traditional Pure business. As Dave mentioned, Cloud Block Store is kind of where things started and we're seeing that move and evolve from predominantly being a DR site and kind of story into now more and more production applications being lifted and shifted and running now natively in AWS honor storage software. And then we have a new product called Pure Fusion which is our storage as code automation product essentially. It takes you from moving and managing of individual arrays, now obfuscates a fleet level allows you to build a very cloud-like backend and consume storage as code. Very, very similar to how you do with AWS, with an EBS. That product is built in AWS. So it's a SaaS product built in AWS, really allowing you to turn your traditional Pure storage into an AWS-like experience. >> Lisa: Got it. >> What changed with Cloud Block Store? 'Cause if I recall, am I right that you basically did it on S3 originally? >> S3 is a big... It's a number of components. >> And you had a high performance EC2 instances. >> Dan: Yup, that's right. >> On top of lower cost object store. Is that still the case? >> That's still the architecture. Yeah, at least for AWS. It's a different architecture in Azure where we leverage their disc storage more. But in AWS were just based on essentially that backend. >> And then what's the experience when you go from, say, on-prem to AWS to sort of a cross cloud? >> Yeah, very, very simple. It's our replication technology built in. So our sync rep, our async rep, our active cluster technology is essentially allowing you to move the data really, really seamlessly there and then again back to Fusion, now being that kind of master control plan. You can have availability zones, running Cloud Block Store instances in AWS. You can be running your own availability zones in your data centers wherever those may happen to be, and that's kind of a unification layer across it all. >> It looks the same to the customer. >> To the customer, at the end of the day, it's... What the customer sees is the purity operating system. We have FlashArray proprietary hardware on premises. We have AWS's hardware that we run it on here. But to the customer, it's just the FlashArray. >> That's a data super cloud actually. Yeah, it's a data super cloud. >> I'd agree. >> It spans multiple clouds- >> Multiple clouds on premises. >> It extracts all the complexity of the underlying muck and the primitives and presents a common experience. >> Yeah, and it's the same APIs, same management console. >> Dave: Yeah, awesome. >> Everything's the same. >> See? It's real. It's a thing, On containers, I have a question. So we're in this environment, everybody wants to be more efficient, what's happening with containers? Is there... The intersection of containers and serverless, right? You think about all the things you have to do to run containers in VMs, configure everything, configure the memory, et cetera, and then serverless simplifies all that. I guess Knative in between or I guess Fargate. What are you seeing with customers between stateless apps, stateful apps, and how it all relates to containers? >> That's a great question, right? I think that one of the things that what we are seeing is that as people run more and more workloads in the cloud, right? There's this huge movement towards being the ability to bring these applications to run anywhere, right? Not just in one public cloud, but in the data centers and sometimes the Edge clouds. So there's a lot of portability requirements for the applications, right? I mean, yesterday morning I was having breakfast with a customer who is a big AWS customer but has to go into an on-prem air gap deployment for one of their large customers and is kind of re-platforming some other apps into containers in Kubernetes because it makes it so much easier for them to deploy. So there is no longer the debate of, is it stateless versus it stateful, it's pretty much all applications are moving to containers, right? And in that, you see people are building on Kubernetes and containers is because they wanted multicloud portability for their applications. Now the other big aspect is cost, right? You can significantly run... You know, like lower cost by running with Kubernetes and Portworx and by on the public cloud or on a private cloud, right? Because it lets you get more out of your infrastructure. You're not all provisioning your infrastructure. You are like just deploying the just-enough infrastructure for your application to run with Kubernetes and scale it dynamically as your application load scales. So, customers are better able to manage costs. >> Does serverless play in here though? Right? Because if I'm running serverless, I'm not paying for the compute the whole time. >> Yeah. >> Right? But then stateless and stateful come into play. >> Serverless has a place, but it is more for like quick event-driven decision. >> Dave: The stateless apps. >> You know, stuff that needs to happen. The serverless has a place, but majority of the applications have need compute and more compute to run because there's like a ton of processing you have to do, you're serving a whole bunch of users, you're serving up media, right? Those are not typically good serverless apps, right? The several less apps do definitely have a place. There's a whole bunch of minor code snippets or events you need to process every now and then to make some decisions. In that, yeah, you see serverless. But majority of the apps are still requiring a lot of compute and scaling the compute and scaling storage requirements at a time. >> So what Venkat was talking about is cost. That is probably our biggest tailwind from a cloud adoption standpoint. I think initially for on-premises vendors like Pure Storage or historically on-premises vendors, the move to the cloud was a concern, right? In that we're getting out the data center business, we're going all in on the cloud, what are you going to do? That's kind of why we got ahead of that with Cloud Block Store. But as customers have matured in their adoption of cloud and actually moved more applications, they're becoming much more aware of the costs. And so anywhere you can help them save money seems to drive adoption. So they see that on the Kubernetes side, on our side, just by adding in things that we do really well: Data reduction, thin provisioning, low cost snaps. Those kind of things, massive cost savings. And so it's actually brought a lot of customers who thought they weren't going to be using our storage moving forward back into the fold. >> Dave: Got it. >> So cost saving is great, huge business outcomes potentially for customers. But what are some of the barriers that you're helping customers to overcome on the storage side and also in terms of moving applications to Kubernetes? What are some of those barriers that you could help us? >> Yeah, I mean, I can answer it simply from a core FlashArray side, it's enabling migration of applications without having to refactor them entirely, right? That's Kubernetes side is when they think about changing their applications and building them, we'll call quote unquote more cloud native, but there are a lot of customers that can't or won't or just aren't doing that, but they want to run those applications in the cloud. So the movement is easier back to your data super cloud kind of comment, and then also eliminating this high cost associated with it. >> I'm kind of not a huge fan of the whole repatriation narrative. You know, you look at the numbers and it's like, "Yeah, there's something going on." But the one use case that looks like it's actually valid is, "I'm going to test in the cloud and I'm going to deploy on-prem." Now, I dunno if that's even called repatriation, but I'm looking to help the repatriation narrative because- >> Venkat: I think it's- >> But that's a real thing, right? >> Yeah, it's more than repatriation, right? It's more about the ability to run your app, right? It's not just even test, right? I mean, you're going to have different kinds of governance and compliance and regulatory requirements have to run your apps in different kinds of cloud environments, right? There are certain... Certain regions may not have all of the compliance and regulatory requirements implemented in that cloud provider, right? So when you run with Kubernetes and containers, I mean, you kind of do the transformation. So now you can take that app and run an infrastructure that allows you to deliver under those requirements as well, right? So that portability is the major driver than repatriation. >> And you would do that for latency reasons? >> For latency, yeah. >> Or data sovereign? >> Data sovereignty. >> Data sovereignty. >> Control. >> I mean, yeah. Availability of your application and data just in that region, right? >> Okay, so if the capability is not there in the cloud region, you come in and say, "Hey, we can do that on-prem or in a colo and get you what you need to comply to your EDX." >> Yeah, or potentially moves to a different cloud provider. It's just a lot more control that you're providing on customer at the end of the day. >> What's that move like? I mean, now you're moving data and everybody's going to complain about egress fees. >> Well, you shouldn't be... I think it's more of a one-time move. You're probably not going to be moving data between cloud providers regularly. But if for whatever reasons you decide that I'm going to stop running in X Cloud and I'm going to move to this cloud, what's the most seamless way to do? >> So a customer might say, "Okay, that's certification's not going to be available in this region or gov cloud or whatever for a year, I need this now." >> Yeah, or various commercial. Whatever it might be. >> "And I'm going to make the call now, one-way door, and I'm going to keep it on-prem." And then worry about it down the road. Okay, makes sense. >> Dan, I got to talk to you about the sustainability element there because it's increasingly becoming a priority for organizations in every industry where they need to work with companies that really have established sustainability programs. What are some of the factors that you talk with customers about as they have choice in all FlashArray between Pure and competitors where sustainability- >> Yeah, I mean we've leaned very heavily into that from a marketing standpoint recently because it has become so top of mind for so many customers. But at the end of the day, sustainability was built into the core of the Purity operating system in FlashArray back before it was FlashArray, right? In our early generation of products. The things that drive that sustainability of high density, high data reduction, small footprint, we needed to build that for Pure to exist as a company. And we are maybe kind of the last all-flash vendor standing that came ground up all-flash, not just the disc vendor that's refactored, right? And so that's sort of engineering from the ground up that's deeply, deeply into our software as a huge sustainability payout now. And we see that and that message is really, really resonating with customers. >> I haven't thought about that in a while. You actually are. I don't think there's any other... Nobody else made it through the knothole. And you guys hit escape velocity and then some. >> So we hit escape velocity and it hasn't slowed down, right? Earnings will be tomorrow, but the last many quarters have been pretty good. >> Yeah, we follow you pretty closely. I mean, there was one little thing in the pandemic and then boom! It's just kept cranking since, so. >> So at the end of the day though, right? We needed that level to be economically viable as a flash bender going against disc. And now that's really paying off in a sustainability equation as well because we consume so much less footprint, power cooling, all those factors. >> And there's been some headwinds with none pricing up until recently too that you've kind of blown right through. You know, you dealt with the supply issues and- >> Yeah, 'cause the overall... One, we've been, again, one of the few vendors that's been able to navigate supply really well. We've had no major delays in disruptions, but the TCO argument's real. Like at the end of the day, when you look at the cost of running on Pure, it's very, very compelling. >> Adam Selipsky made the statement, "If you're looking to tighten your belt, the cloud is the place to do it." Yeah, okay. It might be that, but... Maybe. >> Maybe, but you can... So again, we are seeing cloud customers that are traditional Pure data center customers that a few years ago said, "We're moving these applications into the cloud. You know, it's been great working with you. We love Pure. We'll have some on-prem footprint, but most of everything we're going to do is in the cloud." Those customers are coming back to us to keep running in the cloud. Because again, when you start to factor in things like thin provisioning, data reduction, those don't exist in the cloud. >> So, it's not repatriation. >> It's not repatriation. >> It's we want Pure in the cloud. >> Correct. We want your software. So that's why we built CBS, and we're seeing that come all the way through. >> There's another cost savings is on the... You know, with what we are doing with Kubernetes and containers and Portworx Data Services, right? So when we run Portworx Data Services, typically customers spend a lot of money in running the cloud managed services, right? Where there is obviously a sprawl of those, right? And then they end up spending a lot of item costs. So when we move that, like when they run their data, like when they move their databases to Portworx Data Services on Kubernetes, because of all of the other cost savings we deliver plus the licensing costs are a lot lower, we deliver 5X to 10X savings to our customers. >> Lisa: Significant. >> You know, significant savings on cloud as well. >> The operational things he's talking about, too. My Fusion engineering team is one of his largest customers from Portworx Data Services. Because we don't have DBAs on that team, it's just developers. But they need databases. They need to run those databases. We turn to PDS. >> This is why he pays my bills. >> And that's why you guys have to come back 'cause we're out of time, but I do have one final question for each of you. Same question. We'll start with you Dan, the Venkat we'll go to you. Billboard. Billboard or a bumper sticker. We'll say they're going to put a billboard on Castor Street in Mountain View near the headquarters about Pure, what does it say? >> The best container for containers. (Dave and Lisa laugh) >> Venkat, Portworx, what's your bumper sticker? >> Well, I would just have one big billboard that goes and says, "Got PX?" With the question mark, right? And let people start thinking about, "What is PX?" >> I love that. >> Dave: Got Portworx, beautiful. >> You've got a side career in marketing, I can tell. >> I think they moved him out of the engineering. >> Ah, I see. We really appreciate you joining us on the program this afternoon talking about Pure, Portworx, AWS. Really compelling stories about how you're helping customers just really make big decisions and save considerable costs. We appreciate your insights. >> Awesome. Great. Thanks for having us. >> Thanks, guys. >> Thank you. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage. (upbeat music)

(soft upbeat music) >> Okay, we're kicking things off with Brian Gilmore. He's the director of IoT, an emerging technology at InfluxData. Brian, welcome to the program. Thanks for coming on. >> Thanks, Dave, great to be here. I appreciate the time. >> Hey, explain why InfluxDB, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >> No, no, not at all. I mean, I think, for us it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like SQL query support, things like that, we have to figure out a way to execute those for them in a way that will scale long term. And then we also want to make sure we're innovating, we're sort of staying ahead of the market as well, and sort of anticipating those future needs. So, you know, this is really a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine. But, you know, initially, the customers who are using us are going to see just great improvements in performance, you know, especially those that are working at the top end of the workload scale, you know, the massive data volumes and things like that. >> Yeah, and we're going to get into that today and the architecture and the like. But what was the catalyst for the enhancements? I mean, when and how did this all come about? >> Well, I mean, like three years ago, we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product. And sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was a long journey. (chuckles) I guess, you know, phase one was, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to optimize for like multi-tenant, multi-cloud, be able to host it in a truly like SAS manner where we could use, you know, some type of customer activity or consumption as the pricing vector. And that was sort of the birth of the real first InfluxDB cloud, you know, which has been really successful. We've seen, I think, like 60,000 people sign up. And we've got tons and tons of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a daily basis. And having that sort of big pool of very diverse and varied customers to chat with as they're using the product, as they're giving us feedback, et cetera, has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that, and then also making these big leaps as we're doing with this new engine. >> All right, so you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really want to understand how much of a pivot this is, and what does it take to make that shift from, you know, time series specialist to real time analytics and being able to support both? >> Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. Time series data is always going to be fundamental in sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. The time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics. If we're being honest though, I think our user base is well aware that the way we were architected was much more towards those sort of like backwards-looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a time to response on the queries, and can we get that to the point where the result sets are coming back so quickly from the time of query that we can like, limit that window down to minutes and then seconds? And now with this new engine, we're really starting to talk about a query window that could be like returning results in, you know, milliseconds of time since it hit the ingest queue. And that's really getting to the point where, as your data is available, you can use it and you can query it, you can visualize it, you can do all those sort of magical things with it. And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the real time queries, the multiple language query support. But, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a limited number of customers, strategic customers and strategic availabilities zones to start, but, you know, everybody over time. >> So you're basically going from what happened to, and you can still do that, obviously, but to what's happening now in the moment? >> Yeah. Yeah. I mean, if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the underlying data collection, the architecture, the infrastructure, the devices, and you know, the sort of highly distributed nature of all of this. So, yeah, I mean, getting a customer or a user to be able to use the data as soon as it is available, is what we're after here. I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >> Yeah, I mean, it is operationally, or operational real time is different. And that's one of the things that really triggered us to know that we were heading in the right direction is just how many sort of operational customers we have, you know, everything from like aerospace and defense. We've got companies monitoring satellites. We've got tons of industrial users using us as a process historian on the plant floor. And if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're going to do here is we're going to start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their historians and databases. >> Is this available, these innovations to InfluxDB cloud customers, only who can access this capability? >> Yeah, I mean, commercially and today, yes. I think we want to emphasize that for now our goal is to get our latest and greatest and our best to everybody over time of course. You know, one of the things we had to do here was like we doubled down on sort of our commitment to open source and availability. So, like, anybody today can take a look at the libraries on our GitHub and can inspect it and even can try to implement or execute some of it themselves in their own infrastructure. We are committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. And so just, you know, being careful, maybe a little cautious in terms of how big we go with this right away. Just sort of both limits, you know, the risk of any issues that can come with new software roll outs, we haven't seen anything so far. But also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products. But once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's going to be exciting time for the whole ecosystem. >> Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are going to help deliver on this vision. What should we know there? >> Well, I mean, I think, foundationally, we built the new core on Rust. This is a new very sort of popular systems language. It's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well, and if it does find error conditions. I mean, we've loved working with Go, and a lot of our libraries will continue to be sort of implemented in Go, but when it came to this particular new engine, that power performance and stability of Rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parquet for persistence. I think, for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our time series merge trees, this is a big break from that. You know, Arrow on the sort of in mem side and then Parquet in the on disk side. It allows us to present, you know, a unified set of APIs for those really fast real time queries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that Parquet format, which is also cool because there's an entire ecosystem sort of popping up around Parquet in terms of the machine learning community. And getting that all to work, we had to glue it together with Arrow Flight. That's sort of what we're using as our RPC component. It handles the orchestration and the transportation of the columnar data now, we're moving to like a true columnar database model for this version of the engine. You know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like, blurring that line between real time and historical data, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >> Yeah, again, I mean, it's funny. You mentioned Rust. It's been around for a long time but it's popularity is, you know, really starting to hit that steep part of the S-curve. And we're going to dig into more of that, but give us, is there anything else that we should know about, Brian? Give us the last word. >> Well, I mean, I think first, I'd like everybody sort of watching, just to like, take a look at what we're offering in terms of early access in beta programs. I mean, if you want to participate or if you want to work sort of in terms of early access with the new engine, please reach out to the team. I'm sure, you know, there's a lot of communications going out and it'll be highly featured on our website. But reach out to the team. Believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to, because we can flip a lot of stuff on, especially in cloud through feature flags. But if there's something new that you want to try out, we'd just love to hear from you. And then, you know, our goal would be, that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to sort of build the next versions of your business. Because, you know, the whole database, the ecosystem as it expands out into this vertically-oriented stack of cloud services, and enterprise databases, and edge databases, you know, it's going to be what we all make it together, not just those of us who are employed by InfluxDB. And then finally, I would just say, please, like, watch and Anais' and Tim's sessions. Like, these are two of our best and brightest. They're totally brilliant, completely pragmatic, and they are most of all customer-obsessed, which is amazing. And there's no better takes, like honestly, on the sort of technical details of this than theirs, especially when it comes to the value that these investments will bring to our customers and our communities. So, encourage you to, you know, pay more attention to them than you did to me, for sure. >> Brian Gilmore, great stuff. Really appreciate your time. Thank you. >> Yeah, thanks David, it was awesome. Looking forward to it. >> Yeah, me too. I'm looking forward to see how the community actually applies these new innovations and goes beyond just the historical into the real time. Really hot area. As Brian said, in a moment, I'll be right back with Anais Dotis-Georgiou to dig into the critical aspects of key open source components of the InfluxDB engine, including Rust, Arrow, Parquet, Data Fusion. Keep it right there. You don't want to miss this. (soft upbeat music)

>> From every ISV to solve the problems. You want there to be tools in place that you can use, either open source tools or whatever it is that help you build it. And slowly over time, that building will become easier and easier. So my question to you was, where do you see you playing? Do you see yourself playing to ISVs as a set of tools, which will make their life a lot easier and provide that work? >> Absolutely. >> If they don't have, so they don't have to do it. Or you're providing this for the end users? Or both? >> So it's a progression. If you go to the ISVs first, you're doomed to starved before you have time for that other option. >> Yeah. >> Right? So it's a question of phase, the phasing of it. And also if you go directly to end users, you can demonstrate the power of it and get the attention of the ISVs. I believe that the ISVs, especially those with the biggest footprints and the most, you know, coveted estates, they have already made massive investments at trying to solve decentralization of their software stack. And I believe that they have used it as a hook to try to move to a software as a service model and rope people into leasing their infrastructure. So if you look at the clouds that have been propped up by Autodesk or by Adobe, or you name the company, they are building proprietary makeshift solutions for decentralizing or hybrid clouding. Or maybe they're not even doing that at all and all they're is saying hey, if you want to get location agnosticness, then what you should just, is just move into our cloud. >> Right. >> And then they try to solve on the background how to decentralize it between different regions so they can have decent offerings in each region. But those who are more advanced have already made larger investments and will be more averse to, you know, throwing that stuff away, all of their makeshift machinery away, and using a platform that gives them high performance parallel, low level file system access, while at the same time having metadata-driven, you know, policy-based, intent-based orchestration to manage the diffusion of data across a decentralized infrastructure. They are not going to be as open because they've made such an investment and they're going to look at how do they monetize it. So what we have found with like the movie studios who are using us already, many of the app they're using, many of those software offerings, the ISVs have their own cloud that offers that software for the cloud. But what we got when I asked about this, 'cause I was dealt specifically into this question because I'm very interested to know how we're going to make that leap from end user upstream into the ISVs where I believe we need to, and they said, look, we cannot use these software ISV-specific SAS clouds for two reasons. Number one is we lose control of the data. We're giving it to them. That's security and other issues. And here you're talking about we're doing work for Disney, we're doing work for Netflix, and they're not going to let us put our data on those software clouds, on those SAS clouds. Secondly, in any reasonable pipeline, the data is shared by many different applications. We need to be agnostic as to the application. 'Cause the inputs to one application, you know, the output for one application provides the input to the next, and it's not necessarily from the same vendor. So they need to have a data platform that lets them, you know, go from one software stack, and you know, to run it on another. Because they might do the rendering with this and yet, they do the editing with that, and you know, et cetera, et cetera. So I think the further you go up the stack in the structured data and dedicated applications for specific functions in specific verticals, the further up the stack you go, the harder it is to justify a SAS offering where you're basically telling the end users you need to park all your data with us and then you can run your application in our cloud and get this. That ultimately is a dead end path versus having the data be open and available to many applications across this supercloud layer. >> Okay, so-- >> Is that making any sense? >> Yes, so if I could just ask a clarifying question. So, if I had to take Snowflake as an example, I think they're doing exactly what you're saying is a dead end, put everything into our proprietary system and then we'll figure out how to distribute it. >> Yeah. >> And and I think if you're familiar with Zhamak Dehghaniis' data mesh concept. Are you? >> A little bit, yeah. >> But in her model, Snowflake, a Snowflake warehouse is just a node on the mesh and that mesh is-- >> That's right. >> Ultimately the supercloud and you're an enabler of that is what I'm hearing. >> That's right. What they're doing up at the structured level and what they're talking about at the structured level we're doing at the underlying, unstructured level, which by the way has implications for how you implement those distributed database things. In other words, implementing a Snowflake on top of Hammerspace would have made building stuff like in the first place easier. It would allow you to easily shift and run the database engine anywhere. You still have to solve how to shard and distribute at the transaction layer above, so I'm not saying we're a substitute for what you need to do at the app layer. By the way, there is another example of that and that's Microsoft Office, right? It's one thing to share that, to have a file share where you can share all the docs. It's something else to have Word and PowerPoint, Excel know how to allow people to be simultaneously editing the same doc. That's always going to happen in the app layer. But not all applications need that level of, you know, in-app decentralization. You know, many of them, many workflows are pipelined, especially the ones that are very data intensive where you're doing drug discovery or you're doing rendering, or you're doing machine learning training. These things are human in the loop with large stages of processing across tens of thousands of cores. And I think that kind of data processing pipeline is what we're focusing on first. Not so much the Microsoft Office or the Snowflake, you know, parking a relational database because that takes a lot of application layer stuff and that's what they're good at. >> Right. >> But I think... >> Go ahead, sorry. >> Later entrance in these markets will find Hammerspace as a way to accelerate their work so they can focus more narrowly on just the stuff that's app-specific, higher level sharing in the app. >> Yes, Snowflake founders-- >> I think it might be worth mentioning also, just keep this confidential guys, but one of our customers is Blue Origin. And one of the things that we have found is kind of the point of what you're talking about with our customers. They're needing to build this and since it's not commercially available or they don't know where to look for it to be commercially available, they're all building themselves. So this layer is needed. And Blue is just one of the examples of quite a few we're now talking to. And like manufacturing, HPC, research where they're out trying to solve this problem with their own scripting tools and things like that. And I just, I don't know if there's anything you want to add, David, but you know, but there's definitely a demand here and customers are trying to figure out how to solve it beyond what Hammerspace is doing. Like the need is so great that they're just putting developers on trying to do it themselves. >> Well, and you know, Snowflake founders, they didn't have a Hammerspace to lean on. But, one of the things that's interesting about supercloud is we feel as though industry clouds will emerge, that as part of company's digital transformations, they will, you know, every company's a software company, they'll begin to build their own clouds and they will be able to use a Hammerspace to do that. >> A super pass layer. >> Yes. It's really, I don't know if David's speaking, I don't want to speak over him, but we can't hear you. May be going through a bad... >> Well, a regional, regional talks that make that possible. And so they're doing these render farms and editing farms, and it's a cloud-specific to the types of workflows in the median entertainment world. Or clouds specifically to workflows in the chip design world or in the drug and bio and life sciences exploration world. There are large organizations that are kind of a blend of end users, like the Broad, which has their own kind of cloud where they're asking collaborators to come in and work with them. So it starts to even blur who's an end user versus an ISV. >> Yes. >> Right? When you start talking about the massive data is the main gravity is to having lots of people participate. >> Yep, and that's where the value is. And that's where the value is. And this is a megatrend that we see. And so it's really important for us to get to the point of what is and what is not a supercloud and, you know, that's where we're trying to evolve. >> Let's talk about this for a second 'cause I want to, I want to challenge you on something and it's something that I got challenged on and it has led me to thinking differently than I did at first, which Molly can attest to. Okay? So, we have been looking for a way to talk about the concept of cloud of utility computing, run anything anywhere that isn't addressed in today's realization of cloud. 'Cause today's cloud is not run anything anywhere, it's quite the opposite. You park your data in AWS and that's where you run stuff. And you pretty much have to. Same with with Azure. They're using data gravity to keep you captive there, just like the old infrastructure guys did. But now it's even worse because it's coupled back with the software to some degree, as well. And you have to use their storage, networking, and compute. It's not, I mean it fell back to the mainframe era. Anyhow, so I love the concept of supercloud. By the way, I was going to suggest that a better term might be hyper cloud since hyper speaks to the multidimensionality of it and the ability to be in a, you know, be in a different dimension, a different plane of existence kind of thing like hyperspace. But super and hyper are somewhat synonyms. I mean, you have hyper cars and you have super cars and blah, blah, blah. I happen to like hyper maybe also because it ties into the whole Hammerspace notion of a hyper-dimensional, you know, reality, having your data centers connected by a wormhole that is Hammerspace. But regardless, what I got challenged on is calling it something different at all versus simply saying, this is what cloud has always meant to be. This is the true cloud, this is real cloud, this is cloud. And I think back to what happened, you'll remember, at Fusion IO we talked about IO memory and we did that because people had a conceptualization of what an SSD was. And an SSD back then was low capacity, low endurance, made to go military, aerospace where things needed to be rugged but was completely useless in the data center. And we needed people to imagine this thing as being able to displace entire SAND, with the kind of capacity density, performance density, endurance. And so we talked IO memory, we could have said enterprise SSD, and that's what the industry now refers to for that concept. What will people be saying five and 10 years from now? Will they simply say, well this is cloud as it was always meant to be where you are truly able to run anything anywhere and have not only the same APIs, but you're same data available with high performance access, all forms of access, block file and object everywhere. So yeah. And I wonder, and this is just me throwing it out there, I wonder if, well, there's trade offs, right? Giving it a new moniker, supercloud, versus simply talking about how cloud is always intended to be and what it was meant to be, you know, the real cloud or true cloud, there are trade-offs. By putting a name on it and branding it, that lets people talk about it and understand they're talking about something different. But it also is that an affront to people who thought that that's what they already had. >> What's different, what's new? Yes, and so we've given a lot of thought to this. >> Right, it's like you. >> And it's because we've been asked that why does the industry need a new term, and we've tried to address some of that. But some of the inside baseball that we haven't shared is, you remember the Web 2.0, back then? >> Yep. >> Web 2.0 was the same thing. And I remember Tim Burners Lee saying, "Why do we need Web 2.0? "This is what the Web was always supposed to be." But the truth is-- >> I know, that was another perfect-- >> But the truth is it wasn't, number one. Number two, everybody hated the Web 2.0 term. John Furrier was actually in the middle of it all. And then it created this groundswell. So one of the things we wrote about is that supercloud is an evocative term that catalyzes debate and conversation, which is what we like, of course. And maybe that's self-serving. But yeah, HyperCloud, Metacloud, super, meaning, it's funny because super came from Latin supra, above, it was never the superlative. But the superlative was a convenient byproduct that caused a lot of friction and flack, which again, in the media business is like a perfect storm brewing. >> The bad thing to have to, and I think you do need to shake people out of their, the complacency of the limitations that they're used to. And I'll tell you what, the fact that you even have the terms hybrid cloud, multi-cloud, private cloud, edge computing, those are all just referring to the different boundaries that isolate the silo that is the current limited cloud. >> Right. >> So if I heard correctly, what just, in terms of us defining what is and what isn't in supercloud, you would say traditional applications which have to run in a certain place, in a certain cloud can't run anywhere else, would be the stuff that you would not put in as being addressed by supercloud. And over time, you would want to be able to run the data where you want to and in any of those concepts. >> Or even modern apps, right? Or even modern apps that are siloed in SAS within an individual cloud, right? >> So yeah, I guess it's twofold. Number one, if you're going at the high application layers, there's lots of ways that you can give the appearance of anything running anywhere. The ISV, the SAS vendor can engineer stuff to have the ability to serve with low enough latency to different geographies, right? So if you go too high up the stack, it kind of loses its meaning because there's lots of different ways to make due and give the appearance of omni-presence of the service. Okay? As you come down more towards the platform layer, it gets harder and harder to mask the fact that supercloud is something entirely different than just a good regionally-distributed SAS service. So I don't think you, I don't think you can distinguish supercloud if you go too high up the stack because it's just SAS, it's just a good SAS service where the SAS vendor has done the hard work to give you low latency access from different geographic regions. >> Yeah, so this is one of the hardest things, David. >> Common among them. >> Yeah, this is really an important point. This is one of the things I've had the most trouble with is why is this not just SAS? >> So you dilute your message when you go up to the SAS layer. If you were to focus most of this around the super pass layer, the how can you host applications and run them anywhere and not host this, not run a service, not have a service available everywhere. So how can you take any application, even applications that are written, you know, in a traditional legacy data center fashion and be able to run them anywhere and have them have their binaries and their datasets and the runtime environment and the infrastructure to start them and stop them? You know, the jobs, the, what the Kubernetes, the job scheduler? What we're really talking about here, what I think we're really talking about here is building the operating system for a decentralized cloud. What is the operating system, the operating environment for a decentralized cloud? Where you can, and that the main two functions of an operating system or an operating environment are the process scheduler, the thing that's scheduling what is running where and when and so forth, and the file system, right? The thing that's supplying a common view and access to data. So when we talk about this, I think that the strongest argument for supercloud is made when you go down to the platform layer and talk of it, talk about it as an operating environment on which you can run all forms of applications. >> Would you exclude--? >> Not a specific application that's been engineered as a SAS. (audio distortion) >> He'll come back. >> Are you there? >> Yeah, yeah, you just cut out for a minute. >> I lost your last statement when you broke up. >> We heard you, you said that not the specific application. So would you exclude Snowflake from supercloud? >> Frankly, I would. I would. Because, well, and this is kind of hard to do because Snowflake doesn't like to, Frank doesn't like to talk about Snowflake as a SAS service. It has a negative connotation. >> But it is. >> I know, we all know it is. We all know it is and because it is, yes, I would exclude them. >> I think I actually have him on camera. >> There's nothing in common. >> I think I have him on camera or maybe Benoit as saying, "Well, we are a SAS." I think it's Slootman. I think I said to Slootman, "I know you don't like to say you're a SAS." And I think he said, "Well, we are a SAS." >> Because again, if you go to the top of the application stack, there's any number of ways you can give it location agnostic function or you know, regional, local stuff. It's like let's solve the location problem by having me be your one location. How can it be decentralized if you're centralizing on (audio distortion)? >> Well, it's more decentralized than if it's all in one cloud. So let me actually, so the spectrum. So again, in the spirit of what is and what isn't, I think it's safe to say Hammerspace is supercloud. I think there's no debate there, right? Certainly among this crowd. And I think we can all agree that Dell, Dell Storage is not supercloud. Where it gets fuzzy is this Snowflake example or even, how about a, how about a Cohesity that instantiates its stack in different cloud regions in different clouds, and synchronizes, however magic sauce it does that. Is that a supercloud? I mean, so I'm cautious about having too strict of a definition 'cause then only-- >> Fair enough, fair enough. >> But I could use your help and thoughts on that. >> So I think we're talking about two different spectrums here. One is the spectrum of platform to application-specific. As you go up the application stack and it becomes this specific thing. Or you go up to the more and more structured where it's serving a specific application function where it's more of a SAS thing. I think it's harder to call a SAS service a supercloud. And I would argue that the reason there, and what you're lacking in the definition is to talk about it as general purpose. Okay? Now, that said, a data warehouse is general purpose at the structured data level. So you could make the argument for why Snowflake is a supercloud by saying that it is a general purpose platform for doing lots of different things. It's just one at a higher level up at the structured data level. So one spectrum is the high level going from platform to, you know, unstructured data to structured data to very application-specific, right? Like a specific, you know, CAD/CAM mechanical design cloud, like an Autodesk would want to give you their cloud for running, you know, and sharing CAD/CAM designs, doing your CAD/CAM anywhere stuff. Well, the other spectrum is how well does the purported supercloud technology actually live up to allowing you to run anything anywhere with not just the same APIs but with the local presence of data with the exact same runtime environment everywhere, and to be able to correctly manage how to get that runtime environment anywhere. So a Cohesity has some means of running things in different places and some means of coordinating what's where and of serving diff, you know, things in different places. I would argue that it is a very poor approximation of what Hammerspace does in providing the exact same file system with local high performance access everywhere with metadata ability to control where the data is actually instantiated so that you don't have to wait for it to get orchestrated. But even then when you do have to wait for it, it happens automatically and so it's still only a matter of, well, how quick is it? And on the other end of the spectrum is you could look at NetApp with Flexcache and say, "Is that supercloud?" And I would argue, well kind of because it allows you to run things in different places because it's a cache. But you know, it really isn't because it presumes some central silo from which you're cacheing stuff. So, you know, is it or isn't it? Well, it's on a spectrum of exactly how fully is it decoupling a runtime environment from specific locality? And I think a cache doesn't, it stretches a specific silo and makes it have some semblance of similar access in other places. But there's still a very big difference to the central silo, right? You can't turn off that central silo, for example. >> So it comes down to how specific you make the definition. And this is where it gets kind of really interesting. It's like cloud. Does IBM have a cloud? >> Exactly. >> I would say yes. Does it have the kind of quality that you would expect from a hyper-scale cloud? No. Or see if you could say the same thing about-- >> But that's a problem with choosing a name. That's the problem with choosing a name supercloud versus talking about the concept of cloud and how true up you are to that concept. >> For sure. >> Right? Because without getting a name, you don't have to draw, yeah. >> I'd like to explore one particular or bring them together. You made a very interesting observation that from a enterprise point of view, they want to safeguard their store, their data, and they want to make sure that they can have that data running in their own workflows, as well as, as other service providers providing services to them for that data. So, and in in particular, if you go back to, you go back to Snowflake. If Snowflake could provide the ability for you to have your data where you wanted, you were in charge of that, would that make Snowflake a supercloud? >> I'll tell you, in my mind, they would be closer to my conceptualization of supercloud if you can instantiate Snowflake as software on your own infrastructure, and pump your own data to Snowflake that's instantiated on your own infrastructure. The fact that it has to be on their infrastructure or that it's on their, that it's on their account in the cloud, that you're giving them the data and they're, that fundamentally goes against it to me. If they, you know, they would be a pure, a pure plate if they were a software defined thing where you could instantiate Snowflake machinery on the infrastructure of your choice and then put your data into that machinery and get all the benefits of Snowflake. >> So did you see--? >> In other words, if they were not a SAS service, but offered all of the similar benefits of being, you know, if it were a service that you could run on your own infrastructure. >> So did you see what they announced, that--? >> I hope that's making sense. >> It does, did you see what they announced at Dell? They basically announced the ability to take non-native Snowflake data, read it in from an object store on-prem, like a Dell object store. They do the same thing with Pure, read it in, running it in the cloud, and then push it back out. And I was saying to Dell, look, that's fine. Okay, that's interesting. You're taking a materialized view or an extended table, whatever you're doing, wouldn't it be more interesting if you could actually run the query locally with your compute? That would be an extension that would actually get my attention and extend that. >> That is what I'm talking about. That's what I'm talking about. And that's why I'm saying I think Hammerspace is more progressive on that front because with our technology, anybody who can instantiate a service, can make a service. And so I, so MSPs can use Hammerspace as a way to build a super pass layer and host their clients on their infrastructure in a cloud-like fashion. And their clients can have their own private data centers and the MSP or the public clouds, and Hammerspace can be instantiated, get this, by different parties in these different pieces of infrastructure and yet linked together to make a common file system across all of it. >> But this is data mesh. If I were HPE and Dell it's exactly what I'd be doing. I'd be working with Hammerspace to create my own data. I'd work with Databricks, Snowflake, and any other-- >> Data mesh is a good way to put it. Data mesh is a good way to put it. And this is at the lowest level of, you know, the underlying file system that's mountable by the operating system, consumed as a real file system. You can't get lower level than that. That's why this is the foundation for all of the other apps and structured data systems because you need to have a data mesh that can at least mesh the binary blob. >> Okay. >> That hold the binaries and that hold the datasets that those applications are running. >> So David, in the third week of January, we're doing supercloud 2 and I'm trying to convince John Furrier to make it a data slash data mesh edition. I'm slowly getting him to the knothole. I would very much, I mean you're in the Bay Area, I'd very much like you to be one of the headlines. As Zhamak Dehghaniis going to speak, she's the creator of Data Mesh, >> Sure. >> I'd love to have you come into our studio as well, for the live session. If you can't make it, we can pre-record. But you're right there, so I'll get you the dates. >> We'd love to, yeah. No, you can count on it. No, definitely. And you know, we don't typically talk about what we do as Data Mesh. We've been, you know, using global data environment. But, you know, under the covers, that's what the thing is. And so yeah, I think we can frame the discussion like that to line up with other, you know, with the other discussions. >> Yeah, and Data Mesh, of course, is one of those evocative names, but she has come up with some very well defined principles around decentralized data, data as products, self-serve infrastructure, automated governance, and and so forth, which I think your vision plugs right into. And she's brilliant. You'll love meeting her. >> Well, you know, and I think.. Oh, go ahead. Go ahead, Peter. >> Just like to work one other interface which I think is important. How do you see yourself and the open source? You talked about having an operating system. Obviously, Linux is the operating system at one level. How are you imagining that you would interface with cost community as part of this development? >> Well, it's funny you ask 'cause my CTO is the kernel maintainer of the storage networking stack. So how the Linux operating system perceives and consumes networked data at the file system level, the network file system stack is his purview. He owns that, he wrote most of it over the last decade that he's been the maintainer, but he's the gatekeeper of what goes in. And we have leveraged his abilities to enhance Linux to be able to use this decentralized data, in particular with decoupling the control plane driven by metadata from the data access path and the many storage systems on which the data gets accessed. So this factoring, this splitting of control plane from data path, metadata from data, was absolutely necessary to create a data mesh like we're talking about. And to be able to build this supercloud concept. And the highways on which the data runs and the client which knows how to talk to it is all open source. And we have, we've driven the NFS 4.2 spec. The newest NFS spec came from my team. And it was specifically the enhancements needed to be able to build a spanning file system, a data mesh at a file system level. Now that said, our file system itself and our server, our file server, our data orchestration, our data management stuff, that's all closed source, proprietary Hammerspace tech. But the highways on which the mesh connects are actually all open source and the client that knows how to consume it. So we would, honestly, I would welcome competitors using those same highways. They would be at a major disadvantage because we kind of built them, but it would still be very validating and I think only increase the potential adoption rate by more than whatever they might take of the market. So it'd actually be good to split the market with somebody else to come in and share those now super highways for how to mesh data at the file system level, you know, in here. So yeah, hopefully that answered your question. Does that answer the question about how we embrace the open source? >> Right, and there was one other, just that my last one is how do you enable something to run in every environment? And if we take the edge, for example, as being, as an environment which is much very, very compute heavy, but having a lot less capability, how do you do a hold? >> Perfect question. Perfect question. What we do today is a software appliance. We are using a Linux RHEL 8, RHEL 8 equivalent or a CentOS 8, or it's, you know, they're all roughly equivalent. But we have bundled and a software appliance which can be instantiated on bare metal hardware on any type of VM system from VMware to all of the different hypervisors in the Linux world, to even Nutanix and such. So it can run in any virtualized environment and it can run on any cloud instance, server instance in the cloud. And we have it packaged and deployable from the marketplaces within the different clouds. So you can literally spin it up at the click of an API in the cloud on instances in the cloud. So with all of these together, you can basically instantiate a Hammerspace set of machinery that can offer up this file system mesh. like we've been using the terminology we've been using now, anywhere. So it's like being able to take and spin up Snowflake and then just be able to install and run some VMs anywhere you want and boom, now you have a Snowflake service. And by the way, it is so complete that some of our customers, I would argue many aren't even using public clouds at all, they're using this just to run their own data centers in a cloud-like fashion, you know, where they have a data service that can span it all. >> Yeah and to Molly's first point, we would consider that, you know, cloud. Let me put you on the spot. If you had to describe conceptually without a chalkboard what an architectural diagram would look like for supercloud, what would you say? >> I would say it's to have the same runtime environment within every data center and defining that runtime environment as what it takes to schedule the execution of applications, so job scheduling, runtime stuff, and here we're talking Kubernetes, Slurm, other things that do job scheduling. We're talking about having a common way to, you know, instantiate compute resources. So a global compute environment, having a common compute environment where you can instantiate things that need computing. Okay? So that's the first part. And then the second is the data platform where you can have file block and object volumes, and have them available with the same APIs in each of these distributed data centers and have the exact same data omnipresent with the ability to control where the data is from one moment to the next, local, where all the data is instantiate. So my definition would be a common runtime environment that's bifurcate-- >> Oh. (attendees chuckling) We just lost them at the money slide. >> That's part of the magic makes people listen. We keep someone on pin and needles waiting. (attendees chuckling) >> That's good. >> Are you back, David? >> I'm on the edge of my seat. Common runtime environment. It was like... >> And just wait, there's more. >> But see, I'm maybe hyper-focused on the lower level of what it takes to host and run applications. And that's the stuff to schedule what resources they need to run and to get them going and to get them connected through to their persistence, you know, and their data. And to have that data available in all forms and have it be the same data everywhere. On top of that, you could then instantiate applications of different types, including relational databases, and data warehouses and such. And then you could say, now I've got, you know, now I've got these more application-level or structured data-level things. I tend to focus less on that structured data level and the application level and am more focused on what it takes to host any of them generically on that super pass layer. And I'll admit, I'm maybe hyper-focused on the pass layer and I think it's valid to include, you know, higher levels up the stack like the structured data level. But as soon as you go all the way up to like, you know, a very specific SAS service, I don't know that you would call that supercloud. >> Well, and that's the question, is there value? And Marianna Tessel from Intuit said, you know, we looked at it, we did it, and it just, it was actually negative value for us because connecting to all these separate clouds was a real pain in the neck. Didn't bring us any additional-- >> Well that's 'cause they don't have this pass layer underneath it so they can't even shop around, which actually makes it hard to stand up your own SAS service. And ultimately they end up having to build their own infrastructure. Like, you know, I think there's been examples like Netflix moving away from the cloud to their own infrastructure. Basically, if you're going to rent it for more than a few months, it makes sense to build it yourself, if it's at any kind of scale. >> Yeah, for certain components of that cloud. But if the Goldman Sachs came to you, David, and said, "Hey, we want to collaborate and we want to build "out a cloud and essentially build our SAS system "and we want to do that with Hammerspace, "and we want to tap the physical infrastructure "of not only our data centers but all the clouds," then that essentially would be a SAS, would it not? And wouldn't that be a Super SAS or a supercloud? >> Well, you know, what they may be using to build their service is a supercloud, but their service at the end of the day is just a SAS service with global reach. Right? >> Yeah. >> You know, look at, oh shoot. What's the name of the company that does? It has a cloud for doing bookkeeping and accounting. I forget their name, net something. NetSuite. >> NetSuite. NetSuite, yeah, Oracle. >> Yeah. >> Yep. >> Oracle acquired them, right? Is NetSuite a supercloud or is it just a SAS service? You know? I think under the covers you might ask are they using supercloud under the covers so that they can run their SAS service anywhere and be able to shop the venue, get elasticity, get all the benefits of cloud in the, to the benefit of their service that they're offering? But you know, folks who consume the service, they don't care because to them they're just connecting to some endpoint somewhere and they don't have to care. So the further up the stack you go, the more location-agnostic it is inherently anyway. >> And I think it's, paths is really the critical layer. We thought about IAS Plus and we thought about SAS Minus, you know, Heroku and hence, that's why we kind of got caught up and included it. But SAS, I admit, is the hardest one to crack. And so maybe we exclude that as a deployment model. >> That's right, and maybe coming down a level to saying but you can have a structured data supercloud, so you could still include, say, Snowflake. Because what Snowflake is doing is more general purpose. So it's about how general purpose it is. Is it hosting lots of other applications or is it the end application? Right? >> Yeah. >> So I would argue general purpose nature forces you to go further towards platform down-stack. And you really need that general purpose or else there is no real distinguishing. So if you want defensible turf to say supercloud is something different, I think it's important to not try to wrap your arms around SAS in the general sense. >> Yeah, and we've kind of not really gone, leaned hard into SAS, we've just included it as a deployment model, which, given the constraints that you just described for structured data would apply if it's general purpose. So David, super helpful. >> Had it sign. Define the SAS as including the hybrid model hold SAS. >> Yep. >> Okay, so with your permission, I'm going to add you to the list of contributors to the definition. I'm going to add-- >> Absolutely. >> I'm going to add this in. I'll share with Molly. >> Absolutely. >> We'll get on the calendar for the date. >> If Molly can share some specific language that we've been putting in that kind of goes to stuff we've been talking about, so. >> Oh, great. >> I think we can, we can share some written kind of concrete recommendations around this stuff, around the general purpose, nature, the common data thing and yeah. >> Okay. >> Really look forward to it and would be glad to be part of this thing. You said it's in February? >> It's in January, I'll let Molly know. >> Oh, January. >> What the date is. >> Excellent. >> Yeah, third week of January. Third week of January on a Tuesday, whatever that is. So yeah, we would welcome you in. But like I said, if it doesn't work for your schedule, we can prerecord something. But it would be awesome to have you in studio. >> I'm sure with this much notice we'll be able to get something. Let's make sure we have the dates communicated to Molly and she'll get my admin to set it up outside so that we have it. >> I'll get those today to you, Molly. Thank you. >> By the way, I am so, so pleased with being able to work with you guys on this. I think the industry needs it very bad. They need something to break them out of the box of their own mental constraints of what the cloud is versus what it's supposed to be. And obviously, the more we get people to question their reality and what is real, what are we really capable of today that then the more business that we're going to get. So we're excited to lend the hand behind this notion of supercloud and a super pass layer in whatever way we can. >> Awesome. >> Can I ask you whether your platforms include ARM as well as X86? >> So we have not done an ARM port yet. It has been entertained and won't be much of a stretch. >> Yeah, it's just a matter of time. >> Actually, entertained doing it on behalf of NVIDIA, but it will absolutely happen because ARM in the data center I think is a foregone conclusion. Well, it's already there in some cases, but not quite at volume. So definitely will be the case. And I'll tell you where this gets really interesting, discussion for another time, is back to my old friend, the SSD, and having SSDs that have enough brains on them to be part of that fabric. Directly. >> Interesting. Interesting. >> Very interesting. >> Directly attached to ethernet and able to create a data mesh global file system, that's going to be really fascinating. Got to run now. >> All right, hey, thanks you guys. Thanks David, thanks Molly. Great to catch up. Bye-bye. >> Bye >> Talk to you soon.

(gentle music) >> Hi everybody, this is Dave Vellante of TheCUBE. This is day two of Fal.Con 2022, CrowdStrike's big customer event. Over 2000 people here, a hundred sessions, a lot of deep security talk. Amol Kulkarni is here. He's the chief product and engineering officer at CrowdStrike, and we're going to get into it. Amol, thanks for coming to theCUBE. >> Great to be here. >> I enjoyed your keynote today. It was very informative. First of all, how's the show going for you? >> It's going fantastic. I mean, first and foremost, like to be having everyone here in person, after three years, that's just out the world, right? So great to meet and a lot of great conversations across the board with customers, partners. It's been fantastic. >> Yeah, so I want to start with Cloud Native, it's kind of your dogma. This whole, the new acronym is CNAP Cloud Native Application Protection Platform. >> Amol: That's right. >> There's a mouthful. What is that? How does it relate to what you guys are doing? >> Yeah, so CNAP is what Gartner has coined as the term for covering entire cloud security. And they have identified various components in it. The first and foremost is the runtime protection, cloud workload protection, as we call it. Second is posture management. That's CSBM cloud security posture management. Third is CIEM, which we announced today. And then the fourth is shift left, kind of Dev SecOps part of cloud security. And all together Gartner coins that as a solution or a suite, if you will, to cover various aspects of cloud security. >> Okay, so shift left and then shield right. You still got to shield right. Is that where network security comes in? Which is not your main focus, but okay. So now it explains... Gartner is an acronym. Now I get it. But the CIEM announcement cloud infrastructure entitlement management. So you're managing identities. Is that right? Explain that in more detail. >> So, yeah, so I mean, as in the on-premise world, but even more exacerbated in the crowd world you have lots and lots of identities, both human identities and service accounts that are accessing cloud services. And lot of the time the rigor is not there in terms of what permissions those identities are provisioned with. So are they over provisioned? Do they have lots of rights that they should not have? Are they able... Are services able to connect to resources that they should not be able to connect to all of that falls under the entitlement management, the identity entitlement management part. And that's where CIEM comes in. So what we said is, we have a great identity security story for on-premise, right? And now we are applying that to understand identities, the entitlements they have, secrets that are lying around, maybe leaked, or just, available for adversaries to exploit in the cloud security world. So taking all of that into account and giving you... Giving customers a snapshot view of one single view to say; these are the identities, these are their permissions, this is where you can trim them down because these are the dependencies that are present across services. And you see something that's not right from a dependency perspective, you can say, okay, this connection doesn't make sense. There's something malicious going on here. So there's a lot that you can do by having that scope of identities. Be very narrowed down. It's a first step in the zero trust journey for the cloud infrastructure. >> So I have to ask you when you now extend this conversation to the edge, and operations technology. Traditionally the infrastructure has been air gapped by, you know, brute force air gap. Don't worry about it. And maybe hasn't had to worry so much about the hygiene. So now as you... as the business drives and forces essentially digital connect... Digital transformation and connectivity >> Connectivity. Yeah. >> I mean, wow, that's a playground for the hackers. >> You absolutely nailed it. So most of these infrastructure was not designed with security in mind, unfortunately, right? As you said, most of it was air-gapped, disconnected. And now everything is getting to be connected because the updates are being pushed rapidly changes are happening. So, and that really, in some sense has changed the environment in which these devices are operating. The operational technology, industrial control. We had the colonial pipeline breach last year. And, that really opened people's eyes like, Hey, nation state adversaries are going to come after critical infrastructure. And that can... That is going to cause impact directly to the end end users, to the citizens. So we have to protect this infrastructure. And that's why we announced discover for IOT as a new module that looks at and understands all the IOT and industrial control systems assets. >> So that didn't require an architectural change though. Right? That was a capability that you introduced with partners. Right? Am I right about that? You don't have to re-architect anything. It's just... Your architecture fits perfectly into those scenarios. >> Absolutely, absolutely. Yeah, yeah, yeah. You actually... While the pace of change is there, architectural change is almost very difficult, because these are very large systems. They are built up over time. It take an industrial control system. The tracing speed is very different from a laptop. So yeah, you can't impose any architectural change. It has to be seamless from what the customers have. >> You were talking, I want to go back to CNAP. You were talking about the protecting the run time. You can do that with an agent. You had said agent... In your keynote. Agentless solutions don't give you runtime security protection. Can you double click on that and just elaborate? >> Yeah, absolutely. So what agentless solutions today are doing they're essentially tapping into APIs from AWS or Azure CloudTrail, for example and looking at misconfigurations. So that is indeed a challenge. So that is one part of the story, but that only gives you a partial view. Let's say that an attacker attacks and uses a existing credential. A legitimate credential to access one of the cloud services. And from there they escalate the privileges and then now start branching off the, the CSP, and the agentless-only solutions will not catch that. Right? So what you need is you, you need this agentless part but you have to couple that with; seeing the activity that's actually happening the living of the land attacks that cannot be caught by the CSP end-piece. So you need a combination of agentless and agent runtime to give that overall protection. >> What's the indicator of attack for a hacker that's living off the land, meaning using your own tools against you. >> That's right. So the indicators of attack are saying accessing services, for example, that are not normally accessed or escalating privileges. So you come in as a normal user, but then suddenly you have admin privileges because you have escalated those privileges, or you are moving laterally very rapidly from one place to another, or spraying across a lot of services in order to do reconnaissance and understand what is out there. So it's almost like looking for what is an abnormal attack path, abnormal behavior compared to what is normal and the good part is cloud. There's a lot that is normal, right? It's fairly constrained. It's not like a end user who is downloading stuff from the internet. And like doing all sorts of things. Cloud services are fairly constrained, so you can profile and you can figure out where there is a drift from the normal. And that's really the indicator of attack. In some sense, from cloud services >> In a previous life I want to change subjects. In a previous life. I spent a lot of time with CIOs. Helping them look at their application portfolio, understanding what to rationalize, what to get rid of, what to invest in, you know, bringing in new projects, cause you know, it's just you never throw a stuff away in IT. >> There is no obsolescence >> Right. So, but they wanted to... Anytime you go through these rationalization exercises change management is everything. And one of the hardest things to do was to map and understand the business impact of all the dependencies across the portfolio. Cause when application A needs this dataset. If you retire it, you're going to... It has ripple effects. And you talked about that in a security context today when you were talking about the asset graph and the threat graphs giving you the ability to understand those dependencies. Can you add some color to that? >> Absolutely. Absolutely. So what we've done with the asset graph; It's a fundamental piece of technology that we've been building now for some time that complements the thread graph. And the asset graph looks at: Assets, identities, applications, and configuration. All of those aspects. And the interconnections between them. So if a user is accessing an application on a server, all those, and in what role, all of that relationship is tied together in the asset graph. So what that does now is, it gives you an ability to say this application connects to this application. And that's the dependency on that port, for example. So you can now build up a dependency map and then the thread graph, what it does, it looks at the continuous activity that's happening. So if you now take the events that are coming into the thread graph and the graphical representation of those, combine it with the asset graph, you get that full dependency map. And now you can start doing that impact analysis that you talked about. Which is... It's an unsolved problem, right? And that's why security as I said in my keynote is most people do not have their security tools enabled to the highest level or they don't have full coverage just because the pace of change is so rapid. They cannot keep up with it. So we want to enable change management, at a rapid pace where businesses and customers can say; we are confident about the change management, about the change we are going to implement. Because we know what the potential impact would be. We can validate, test it in a smaller subset and then roll it out quickly. And that's the journey we are on. Sort of the theme of my talk was to make IT and security friends again. >> Right, you talked about that gap and bringing those two together. You also had a great quote in there; 'The pace of change and securities is insane.' And so this assets graph capability, dependencies and the threat graph, help you manage that accelerating pace of change. Before I forget, I want to ask you about your interview with Girls Who Code. What was that like? Who'd you interview? I unfortunately couldn't see it. I apologize. >> Yeah, fantastic. So, Reshma Saujani she heads Girls Who Code and she first off had a very very powerful talk just from her own own experiences. And essentially, like, what do we need to do to get more women into computer science first, but then within that, into cybersecurity. and what all have they done with Girls Who Code. So very, I mean, we were very touched at the audience was like super into her talk. And then I had a chance to chat with her for a few minutes, ask her a few questions. Just my view was more like, okay. What can we do together? What can CrowdStrike do in our position, in to attract more women? We've done a lot in terms of tailoring our job descriptions to make sure it's more... Remove the biases. Tuning the interview processes to be more welcoming and Reshma gave an example saying; 'Hey, many of these interviews, they start with a baseball discussion.' And I mean, some women may maybe interested in it but may not all maybe. And so is that the right? Is it a gender kind-of affirming or gender neutral kind-of discussion or do you want to have other topics? So a lot of that is about training the interviewers because most of the interviewers are men, unfortunately. That's the mix we have. And it was a great discussion. I mean, just like very practical. She's very much focused on increasing the number of people and increasing the pipeline which is honestly the biggest problem. Because if we have a lot of candidates we would definitely hire them and essentially improve the diversity. And we've done a great job with our intern program, for example, which has helped significantly improve the diversity on our workforce. >> And, but the gap keeps getting bigger in terms of unfulfilled jobs. That leads me to developers as a constituency. Because you guys are building the security cloud. You're on a mission to do that. And to me, if you have a security cloud, it's got to be programmable. You're going to have developers there. You don't... From what I can tell you have a specific developer platform, but it's organic. It's sort of happening out there. What's the strategy around, I mean, the developer today is so critical in terms of implementing a lot of security strategy and putting it into action. They've got to secure the run time. They got to worry about the APIs. They got to secure the PaaS. They got to secure the containers. Right, and so what's your developer strategy. >> Yeah, so within cloud security, enabling developers to implement DevSecOps as a as a philosophy, as a strategy, is critical. And so we, we have a lot of offerings there on the shift-left side, for example, you talked about securing containers. So we have container image assessment where we plug in into the container repositories to check for vulnerabilities and bad configuration in the container images. We then complement that with the runtime side where our agent can protect the container from runtime violations, from breakouts, for example. So it's a combination. It's a full spectrum, right? From the developer building an application, all the way to the end. Second I'd say is, we are a very much an API first company. So all of the things that you can do from a user interface perspective, you can do from APIs what is enable that is a bunch of partners a rich partner ecosystem that is building using those APIs. So the developers within our partners are leveraging those APIs to build very cool applications. And the manifestation of that is CrowdStrike store where essentially we have as Josh mentioned, in his ski-notes, we have a agent cloud architecture that is very rich. And we said, okay, why can't we open that up for partners to enable them to leverage that architecture for their scenarios? So we have a lot of applications that are built on the CrowdStrike store, leveraging our platform, right. Areas that we are not in, for example. >> And here, describe it. Is there a PaaS layer that's purpose-built for CrowdStrike so that developers can build applications? >> That's a great question. So I'll say that we have a beginnings of a PaaS layer. We definitely talked about CrowdStrike store as being passed for cybersecurity but there's a lot more to do. And we are in the process of building up an application platform so that customers can build the applications for their SOC workflow or IT workflow and and Falcon Fusion is a key part of that. So Falcon Fusion is our automation platform built right into the security cloud. And what that enables customers to do is to define... Encode their business process the way they want and leverage the platform the way they want. >> It seems like a logical next step. Because you're going to enable a consistent experience across the board. And fulfill your promise, your brand promise, and the capabilities that you bring. And this ecosystem will explode once you announce that. >> And that's the notion we talk about of being the sales force of security. >> Right, right. Yeah. That's the next step. Amol, thank you so much. I got to run and wrap. We really appreciate you coming on theCUBE. >> Thank you very much. >> Congratulations on your keynote and all the success and great event. >> Appreciate it. Thank you very much for the time and great chatting with you. >> You're very welcome. All right, keep it right there. We'll be back very shortly to wrap up from Fal.Con 2022. This is Dave Vellante for theCUBE. (soft electronic music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from the cube in ETR. This is Breaking Analysis with Dave Vellante. >> On Friday, December 10th, Oracle announced a strong earnings beat and raise, on the strength of its licensed business, and slightly better than expected cloud performance. The stock was up sharply on the day and closed up nearly 16% surpassing 280 billion in market value. Oracle's success is due largely to its execution, of a highly differentiated strategy, that has really evolved over the past decade or more, deeply integrating its hardware and software, heavily investing in next generation cloud, creating a homogeneous experience across its application portfolio, and becoming the number one platform. Number one for the world's most mission critical applications. Now, while investors piled into the stock, skeptics will point to the beat being weighed toward licensed revenue and likely keep one finger on the sell button until they're convinced Oracle's cloud momentum, is more consistent and predictable. Hello and welcome to this week's Wikibond CUBE insights powered by ETR. In this breaking analysis, we'll review Oracle's most recent quarter, and pull in some ETR survey data, to frame the company's cloud business, the momentum of fusion ERP, where the company is winning and some gaps and opportunities that we see. The numbers this quarter was strong, particularly top line growth. Here are a few highlights. Oracle's revenues that grew 6% year on year that's in constant currency, surpassed $10 billion for the quarter. Oracle's non-gap operating margins, were an impressive 47%. Safra Catz has always said cloud is more profitable business and it's really starting to show in the income statement. Operating cash and free cash flow were 10.3 billion and 7.1 billion respectively, for the past four quarters, and would have been higher, if not for charges largely related to litigation expenses tied to the hiring of Mark Hurd, which the company said would not repeat in the future quarters. And you can see in this chart how Oracle breaks down its business, which is kind of a mishmash of items they lump into so-called the cloud. The largest piece of the revenue pie is cloud services, and licensed support, which in reading 10Ks, you'll find statements like the following; licensed support revenues are our largest revenue stream and include product upgrades, and maintenance releases and patches, as well as technical support assistance and statements like the following; cloud and licensed revenue, include the sale of cloud services, cloud licenses and on-premises licenses, which typically represent perpetual software licenses purchased by customers, for use in both cloud, and on-premises, IT environments. And cloud license and on-prem license revenues primarily represent amounts earned from granting customers perpetual licenses to use our database middleware application in industry specific products, which our customers use for cloud-based, on-premise and other IT environments. So you tell me, "is that cloud? I don't know." In the early days of Oracle cloud, the company used to break out, IaaS, PaaS and SaaS revenue separately, but it changed its mind, which really makes it difficult to determine what's happening in true cloud. Look I have no problem including same same hardware software control plane, et cetera. The hybrid if it's on-prem in a true hybrid environment like exadata cloud@customer or AWS outposts. But you have to question what's really cloud in these numbers. And Larry in the earnings call mentioned that Salesforce licenses the Oracle database, to run its cloud and Oracle doesn't count that in its cloud number, rather it counts it in license revenue, but as you can see it varies that into a line item that starts with the word cloud. So I guess I would say that Oracle's reporting is maybe somewhat better than IBM's cloud reporting, which is the worst, but I can't really say what is and isn't cloud, in these numbers. Nonetheless, Oracle is getting it done for investors. Here's a chart comparing the five-year performance of Oracle to some of its legacy peers. We excluded Microsoft because it skews the numbers. Microsoft would really crush all these names including Oracle. But look at Oracle. It's wedged in between the performance of the NASDAQ and the S&P 500, it's up over 160% in that five-year timeframe, well ahead of SAP which is up 59% in that time, and way ahead of the dismal -22% performance of IBM. Well, it's a shame. The tech tide is rising, it's lifting all boats but, IBM has unfortunately not been able to capitalize. That's a story for another day. As a market watcher, you can't help but love Larry Ellison. I only met him once at an IDC conference in Paris where I got to interview Scott McNealy, CEO at the time. Ellison is great for analysts because, he's not afraid to talk about the competition. He'll brag, he'll insult, he'll explain, and he'll pitch his stories. Now on the earnings call last night, he went off. Educating the analyst community, on the upside in the fusion ERP business, making the case that because only a thousand of the 7,500 legacy on-prem ERP customers from Oracle, JD Edwards and PeopleSoft have moved Oracle's fusion cloud ERP, and he predicted that Oracle's cloud ERP business will surpass 20 billion in five years. In fact, he said it's going to bigger than that. He slammed the hybrid cloud washing. You can see one of the quotes here in this chart, that's going on when companies have customers running in the cloud and they claim whatever they have on premise hybrid, he called that ridiculous. I would agree. And then he took an opportunity to slam the hyperscale cloud vendors, citing a telco customer that said Oracle's cloud never goes down, and of course, he chose the same week, that AWS had a major outage. And so to these points, I would say that Oracle really was the first tech company, to announce a true hybrid cloud strategy, where you have an entirely identical experience on prem and in the cloud. This was announced with cloud@customer, two years, before AWS announced outposts. Now it probably took Oracle two years to get it working as advertised, but they were first. And to the second point, this is where Oracle differentiates itself. Oracle is number one for mission critical applications. No other vendor really can come close to Oracle in this regard. And I would say that Oracle is recent quarterly performance to a large extent, is due to this differentiated approach. Over the past 10 years, we've talked to hundreds literally. Hundreds and hundreds of Oracle customers. And while they may not always like the tactics and licensing policies of Oracle in their contracting, they will tell you, that business case for investing and staying with Oracle are very strong. And yes, a big part of that is lock-in but R&D investments innovation and a keen sense of market direction, are just as important to these customers. When you're chairman and founder is a technologist and also the CTO, and has the cash on hand to invest, the results are a highly competitive story. Now that's not to say Oracle is not without its challenges. That's not to say Oracle is without its challenges. Those who follow this program know that when it comes to ETR survey data, the story is not always pretty for Oracle. So let's take a look. This chart shows the breakdown of ETR is net score methodology, Net score measures spending momentum and works ETR. Each quarter asks customers, are you adding in the platform, That's the lime green. Increasing spend by 6% or more, that's the fourth green. Is you're spending E+ or minus 5%, that's the gray. You're spending climbing by 6%, that's the pinkish. Or are you leaving the platform, that's the bright red retiring. You subtract the reds from the greens, and that yields a net score, which an Oracle's overall case, is an uninspiring -4%. This is one of the anomalies in the ETR dataset. The net score doesn't track absolute actual levels, of spending the dollars. Remember, as the leader in mission critical workloads, Oracle commands a premium price. And so what happens here is the gray, is still spending a large amount of money, enough to offset the declines, and the greens are spending more than they would on other platforms because Oracle could command higher prices. And so that's how Oracle is able to grow its overall revenue by 6% for example, whereas the ETR methodology, doesn't capture that trend. So you have to dig into the data a bit deeper. We're not going to go too deep today, but let's take a look at how some of Oracle's businesses are performing relative to its competitors. This is a popular view that we like to share. It shows net score or spending momentum on the vertical axis, and market share. Market share is a measure of pervasiveness in the survey. Think of it as mentioned share. That's on the x-axis. And we've broken down and circled Oracle overall, Oracle on prem, which is declining on the vertical axis, Oracle fusion and NetSuite, which are much higher than Oracle overall. And in the case of fusion, much closer to that 40% magic red horizontal line, remember anything above that line, we consider to be elevated. Now we've added SAP overall which has, momentum comparable to fusion in the survey, using this methodology and IBM, which is in between fusion and Oracle, overall on the y-axis. Oracle as you can see on the horizontal axis, has a larger presence than any of these firms that are below the 40% line. Now, above that 40% line, you see companies with a smaller presence in the survey like Workday, salesforce.com, pretty big presence still, Google cloud also, and Snowflake. Smaller presence but much much higher net score than anybody else on this chart. And AWS and Microsoft overall with both a strong presence, and impressive momentum, especially for their respective sizes. Now that view that we just showed you excluded on purpose Oracle specific cloud offering. So let's now take a look at that relative to other cloud providers. This chart shows the same XY view, but it cuts the data by cloud only. And you can see Oracle while still well below the 40% line, has a net score of +15 compared to a -4 overall that we showed you earlier. So here we see two key points. One, despite the convoluted reporting that we talked about earlier, the ETR data supports that Oracle's cloud business has significantly more momentum than Oracle's overall average momentum. And two, while Oracle is smaller and doesn't have the growth of the hyperscale giants, it's cloud is performing noticeably better than IBM's within the ETR survey data. Now a key point Ellison emphasized on the earnings call, was the importance of ERP, and the work that Oracle has done in this space. It lives by this notion of a cloud first mentality. It builds stuff for the cloud and then, would bring it on-prem. And it's been attracting new customers according to the company. He said Oracle has 8,500 fusion ERP customers, and 28,000 NetSuite customers in the cloud. And unlike Microsoft, it hasn't migrated its on-prem install base, to the cloud yet. Meaning these are largely new customers. Now this chart isolates fusion and NetSuite, within a sector ETR calls GPP. The very giant, public and private companies. And this is a bellwether of spending in the ETR dataset. They've gone back and it correlates to performance. So think large public companies, the biggest ones, and also privates big privates like Mars or Cargo or Fidelity. The chart shows the net score breakdown over time for fusion and NetSuite going back to 2019. And you can see, a big uptick as shown in the blue line from the October, 2020 survey. So Oracle has done a good job building and now marketing its cloud ERP to these important customers. Now, the last thing we want to show you is Oracle's performance within industry sectors. On the earnings call, Oracle said that it had a very strong momentum for fusion in financial services and healthcare. And this chart shows the net score for fusion, across each industry sector that ETR tracks, for three survey points. October, 2020, that's the gray bars, July 21, that's the blue bars and October, 2021, the yellow bars. So look it confirms Oracles assertions across the board that they're seeing fusion perform very well including the two verticals that are called out healthcare and banking slash financial services. Now the big question is where does Oracle go from here? Oracle has had a history of looking like it's going to break out, only to hit some bumps in the road. And so investors are likely going to remain a bit cautious and take profits off the table along the way. But since the Barron's article came out, we reported on that earlier this year in February, declaring Oracle a cloud giant, the stock is up more than 50% of course. 16 of those points were from Friday's move upward, but still, Oracle's highly differentiated strategy of integrating hardware and software together, investing in a modern cloud platform and selectively offering services that cater to the hardcore mission critical buyer, these have served the company, its customers and investors as well. From a cloud standpoint, we'd like to see Oracle be more inclusive, and aggressively expand its marketplace and its ecosystem. This would provide both greater optionality for customers, and further establish Oracle as a major cloud player. Indeed, one of the hallmarks of both AWS and Azure is the momentum being created, by their respective ecosystems. As well, we'd like to see more clear confirmation that Oracle's performance is being driven by its investments in technology IE cloud, same same hybrid, and industry features these modern investments, versus a legacy licensed cycles. We are generally encouraged and are reminded, of years ago when Sam Palmisano, he was retiring and leaving as the CEO of IBM. At the time, HP under the direction ironically of Mark Hurd, was the now company, Palmisano was asked, "do you worry about HP?" And he said in fact, "I don't worry about HP. I worry about Oracle because Oracle invests in R&D." And that statement has proven present. What do you think? Has Oracle hit the next inflection point? Let me know. Don't forget these episodes they're all available as podcasts wherever you listen, all you do is search it. Breaking Analysis podcast, check out ETR website at etr.plus. We also publish a full report every week on wikibon.com and siliconANGLE.com. You can get in touch with me on email David.vellante@siliconangle.com, you can DM me @dvellante on Twitter or, comment on our LinkedIn posts. This is Dave Vellante for theCUBE Insights. Powered by ETR. Have a great week everybody. Stay safe, be well, and we'll see you next time. (upbeat music)

(electronic music) >> The cloud is evolving. You know, it's no longer just a set of remote services accessed through a public cloud. Rather, it's expanding to on-premises, to multiple premises, across clouds, and eventually out to the edge. The challenge for customers is how to treat these locations as one. The opportunity for technology companies is to make that as simple as possible from an operational perspective. Welcome to this CUBE program where we're featuring Pure Storage in its latest innovations in bringing infrastructure and applications more closely together, fusing them, if you will. And today, we have a two-part program. First, we're going to hear from Rob Lee who's the CTO of Pure Storage and then my colleague John Walls is going to talk to Scott Sinclair of Enterprise Strategy Group. Scott will provide his expert analysis on infrastructure modernization and what to expect in today's changing world. So joining me right now is Rob Lee, CTO of Pure Storage. Welcome, Rob, good to see you. >> Good to see you again too, Dave. >> So take us through the announcements from today at a high level. What's most exciting about what you're delivering? >> Yeah, absolutely. So as you know, many announcement today, many things to discuss. But overall, I think what's most exciting is it's the expansion of our ability to help customers along the modern data journey. We've always thought of the journey to modern data as being formed by three pillars, if you will, certainly, modernizing infrastructure, modernizing operations and applications. And today's announcements are really in that kind of middle category of, like you said, bringing infrastructures and applications a lot more closely together. We've been modernizing infrastructure since day one, probably, people best know us for that and today's announcements are really about tackling that operations piece, bringing infrastructure and code and applications more closely together. So when we think about Pure Fusion, for example, that's really a huge step forward in how we're enabling our customers to manage large fleets of infrastructure, products, and components to deliver those services in a more automated, more tightly-integrated, seamlessly transparently delivered way to the applications that they serve, whether these services are being delivered by many different arrays in one location, many different arrays in different data center locations, or between the premise, on-premise environment and the cloud environment. Likewise, on the application front, when we think about today's announcements in Portworx Data Services, that's really all about how do we make the run and operate steps of a lot of the application building blocks that cloud-native developers are using and relying on, the database applications that are most poplar in open source, Cassandra, Mongo, so on and so forth, how dow we make the run and operate pieces of those applications a lot more intuitive, a lot more easily deployed, scaled, managed, monitored for those app developers? And so a ton of momentum. It's a big step forward on that front. And then right in the middle, when we think about today's announcements in Pure One, that's really all about how do we create more visibility, connecting the monitoring and management of the infrastructure running the apps and bring those closer together? So when we think about the visibility, we're now able to deliver for Portworx topologies allowing developers and DevOps teams to look at the entire tech stack, if you will, of a container environment from the application to the containers, to the Kubernetes cluster, to the compute nodes, all the way down to the storage, and be able to see everything that's going on, the root cause of any sort of problems that come up, that again, that's all in service of bringing infrastructure and applications a lot more closely together. So that's really how I view it and like I said, that's really the next step in our journey of helping customers modernize between infrastructure, operations, and their applications. >> Okay, so you got the control plane piece which is all about the operating model, you've got Pure One, you mentioned that which is for monitoring, you've got the Portworx piece which brings sort of development and deployment together in both infrastructure as code and better understanding of that full stack of, like you say, from applications through the clusters, the containers, all the way down to the storage. So I feel like it's not even the storage anymore. I mean, it's cloud. (chuckling) >> It is and you know, I chuckle a little bit because at the end of the day, we deliver storage but what customers are looking for is, and what they value and what they care about is their data. Now obviously, the storage is in service of the data and what we're doing with today's announcements is, again, just making it, extending our reach, helping customers work with their data a couple more steps down the road beyond just serving the bits and bytes of the storage but now getting into how do we connect the data that's sitting on our storage more quickly, get it, you know, in the hands of developers and the applications more seamlessly and more fluidly across these different environments. >> How does this news fit into Pure's evolution as a company? I mean, I don't see it as a pivot because a pivot's like, okay, we're going to go from here and now we're doin' this? >> Rob: Yeah, we were doing this, now we're doing that, right. >> And so it's more like a reinvention or a progression of the vision and the strategy. Can you talk to that? >> Absolutely. You know what, I think between those two words, I would say it's a progression, it's a next step in the journey as opposed to a reinvention. And again, I go back to, you know, I go back to the difference between storage and data and how customers are using data. We've been on a long-term path, long-term journey to continue to help customers modernize how they work with data, the results they're able to drive from the data. We got our start in infrastructure and just, you know, if you want to do bleeding edge things with data, you're not going to do it on decades-old infrastructure. So let's fix that component first, that's how we got our start. Today's announcement are really the next couple of steps along that journey. How do we make the core infrastructure more easily delivered, more flexible to operate, more automated in the hands of not just the DevOps teams, the IT teams, but the application developers? How do we deliver infrastructure more seamlessly as code? Well, why is that important? It's important because what customers are looking for out of their data is both speeds and feeds, the traditional kind of measures, bandwidth, iOps, latency, that sort of thing, but they're looking for speed of agility. You look at the modern application space around how data's being processed, it's a very, very fast-moving application space. The databases that are being used today may be different than the ones being used three months from now or six months from now. And so developers, application teams are looking for a ton more flexibility, a ton more agility than they were three, five, 10, 15 years ago. The other aspect is simplicity and reliability. As you know, that's a core component of everything we do. Our core products, you know, our arrays, our storage appliances, we're very well-known for the simplicity and reliability we drive at the individual product level. Well, as we scale and look at larger environments, as we look at customers' expectations for what they expect from a cloud-like service, there's the next level of scale and how we deliver that simplicity and reliability. And what do I mean by that? Well, a large enterprise customer who wants to operate like a cloud, wants to be able to manage large fleets of infrastructure resources, be able to package them up, deliver infrastructure services to their internal customers, they want to be able to do it in a self-service, policy-driven, easy to control, easy to manage way and that's the next level of fleet level simplicity and that's really what Pure Fusion is about is allowing operators that control plane to specify those attributes and how that service should be delivered. Same thing with Portworx, if we think about simplicity and reliability, containers, cloud-native applications, micro services, a lot of benefits there. A very fast-moving space, you can mix and match components, put them together very easily, but what goes hand in hand with that is now a need for a greater degree of simplicity 'cause you have more moving parts, and a greater need for reliability because, well now, you're not just serving one application but 30 or 40 working in unison. And that's really what we're after with Portworx and Portworx Data Services and the evolution of that family. So getting back to your original question, I really look at today's announcements as not a pivot, not a reinvention, but the next logical steps in our long-term journey to help customers modernize everything they do around data. >> Right, thanks for that, Rob. Hey, I want to switch topics. So virtually every infrastructure player now has an as-a-service offering and there're lots of claims out there about who was first, who's the best, et cetera. What's Pure's position on this topic? You claim you're ahead of the pack in delivering subscription and as-a-service offerings in the storage industry. You certainly were first with Evergreen. That was sort of a real change in how folks delivered. What about as-a-service and Pure as-a-service? What gives you confidence that you have the right approach and you're lead in the industry in this regard? >> Yeah, absolutely. I mean, I think of, first and foremost, we think of everything we do at Pure as a service and whether that's delivering products and helping customers to run and operate in an as-a-service model internally, or whether it's Pure taking on more of that run and operate as-a-service, ourselves, with Pure as a service. And so the second part of your question which is what is it that sets us apart, what are we doing differently, what gives us confidence that this is the right path, well, fundamentally, I think the difference is obviously this is a, you know, a hotter topic in the industry of late, but I think the difference is between us and the competitive set is we really look at this as a product and technology-led philosophy and strategy and we have since day one. And I think that's different than a lot of others in the industry who look at it as a little bit more of a packaging exercise between financial services, professional services, wrap it up in T(s) and C(s) and you call it a service. And what do I mean by that? So, you know, if you look internally at Pure, everything we do we think of as a service. We have a business unit organized around it, we have an engineering team, significant resources dedicated to it and building out service offerings. When we think about why this is technology-led, I think of a service. For something to be thought of as a service, it's got to be flexible, it's got to be adaptable. I've got to be able to grow as a customer and evolve as I need, whether that's changing needs in terms of performance and capacity, I've got to be able to do that without being locked into day-one, rigid kind of static some lands of having the capacity planned or plan out what my user's going to look like 18 months from now. I've got to be able to move and evolve and grow without disruption, right? You know, it's not a service if you're going to make me do a data migration or take a down time. And so when I net all that out, what are the things that you need the attributes that you need to be able to deliver a service? Well, you need a product set that is going to be able to be highly malleable, highly flexible, highly evolvable. You need something that's going to be able to cover the entire gamut of needs, whether it's price performance, tiers, you know, high performance capacity, lower cost, price points. You need something that's got a rich set of capabilities whether it's access protocols, file block object, whether it's data protection properties, you know, replications, snapshots, ransomware protection. So you need that full suite of capabilities but in order to deliver it as a service and enable me, as a customer, to seamlessly grow and change, that's got to be delivered on a very tight set of technology that can be repurposed and configured in different ways. You can't do this on 17 different products (chuckling) and expect me to change and move every single time that I have a service need change. And so when I net that out, that puts us in an absolutely differentiated position to be able to deliver this because again, everything we do is based on two core product families, Portworx adds a third. We're able to deliver all of the major storage protocols, all of the data protection capabilities across all of the price performance and service tiers, and we're able to do this on a very tight code base. And as you know, everything we do is completely non-disruptive so all of the elements really add up in our favor. And like I said, this is a huge area of a strategic focus for us. >> So these offerings, they're all part of the service-driven component of your portfolio, is that correct? >> Absolutely, yep. >> Great. You talk all the time about modern data experiences, modern application, the modern data changing the way customers think about infrastructure. What exactly does that mean and how are you driving that? >> Well, I think it means a couple of different things, but if I were to net it out, it's a greater demand for agility, a greater demand for flexibility and optionality. And if we look at why that is, you know, when I talk to customers, as they think about an infrastructure, largely, they think about their existing application demands and needs, what they're spending 90% of their time and budget dealing with today, and then the new stuff that they're getting more and more pressured to go off and build and support which is oftentimes the more strategic initiatives that they have to serve, so they're kind of balancing both worlds. And in the new world of modern applications, it's much more dynamic, meaning the application sets that are being deployed are changing all the time, the environments and what the infrastructure needs to deliver has to change more quickly in terms of scaling up, down, growing, it has to be a lot more elastic, and has much more variance. And what I mean by that is you look at a modern, cloud-native, micro services architecture-type application, it's really, you know, 20, 30, 40 different applications all working in concert with one another under the hood. This is a very different infrastructure demand than your more traditional application set. Back in the day, you have an Oracle application, you go design an environment for that. It's a big exercise, but once you put it in place, it has its own lifecycle. These days with modern applications, it's not just one application, it's 20 or 30, you've got to support all of them working in unison, you don't want to build separate infrastructures for each piece, and that set of 20 or 30 applications is changing very rapidly as open source ecosystem moves forward, as the application space moves forward. And so when customers think about the change in demands and infrastructure, this is kind of what they're thinking about and having to juggle. And so that, at the end of the day, drives them to demand much more flexibility in their infrastructure being able to use it for many different purposes, much more agility being able to adapt very, very quickly, and much more variance or dynamic range, the ability to support many different needs on the same set of infrastructure. And this is where we see very, very strong demand indicators and we're very invested in meeting these needs because they fit very well with our core product principles. >> Great, thank you for that. I really like that answer because it's not just a bunch of slideware mumbo-jumbo. You actually put some substance on it. Rob, we're going to have to leave it there. Thanks so much for joining us today. >> Thank you. >> And look forward to havin' you back soon. Now, in a moment, Scott Sinclair who's a senior analyst at Enterprise Strategy Group speaks with theCUBE's John Walls to give you the independent analyst's take. You're watching theCUBE, your global leader in high tech coverage. (techno music) >> Agility is what all digital organizations strive for, and for almost the entirety of the enterprise storage industry, agility and storage aren't words you'd often hear together. Since the founding of Pure Storage, we've been laser focused on taking what's painful about traditional enterprise storage and making it better. We imagined a world where consumers self-service the provisioning of their storage resources to match the performance and data protection capabilities that their applications require. No endless back and forth between application owners and storage teams, just true on-demand self-service. At the same time, imagine all of the complex storage management operations required to make this possible being automated through software. From the placement of the initial workload to storage adjusting with the unpredictable needs of an application and seamlessly migrating and rebalancing the fleet as needed, all with zero down time and no manual intervention. And finally, imagine almost limitless scale that adjusts to meet your business' data management needs over time. This is what we believe the future of enterprise storage looks like. >> Today, we are announcing Pure Fusion, a leap forward in enterprise storage, marrying the best parts of the public cloud with the storage experience and capabilities you've come to expect from Pure. By bringing the simplicity and scalability of the cloud operating model with on-demand consumption and automated provisioning, organizations can deliver an enterprise-grade managed, self-service storage model that unifies fleets of arrays and optimizes storage pulls on the fly. End users will be able to rapidly consume volumes, file systems, and advanced data services like replication without waiting for backend manual work making storage hardware truly invisible. And organizations will be able to scale seamlessly across block, file, and object workloads, leveraging the power of the entire Pure Storage family, including FlashArray, Pure Cloud Block Store, FlashBlade, and Portworx. (electronic music) >> It is time to take a look at what Pure's up to from a slightly different perspective. To help us do that is Scott Sinclair who's a senior analyst at the Enterprise Strategy Group. And Scott, thanks for joining us here, on theCUBE. Good to see ya today. >> Great to see you. >> All right, so let's jump into this. First, we'll get to the announcement in just a little bit. First off, in terms of Pure's strategy, as you've been watching this company evolve over years now, how has it evolved? And then we'll go to the announcements and how that fits into the strategy. But first off, let's just take them from your point of view where have they been and how are they doin'? >> You know, many people know of Pure or maybe they don't know of their history as an all-Flash array. I think Pure has always been, ever since they entered the IT industry as a pioneer, they're one of the early ones that said look, we're going all in on the all-Flash array business and a focus on Flash technology. Then they were early pioneers in things like Evergreen and things like storage-as-a-service capabilities for on-premises storage. And the entire time, they've had a really almost streamline focus on ease of use which, you know, from the outside, I think everyone talks about ease of use and making things simple for IT, but Pure has really made that almost like core as part of not only their product and their design but also part of their culture. And one of the things, and we'll get into this a little bit as we talk about the announcements, but, you know, if you look at these announcements and where Pure's going, they're trying to expand that culture, that DNA around ease of use or simplicity, and expanding it beyond just storage or IT operations, and really trying to see okay, how do we make the entire digital initiative process or the larger IT operations journey simpler. And I think that's part of where Pure is going is not just storage but focusing more on apps, operations, and data, and making it easier for the entire experience. >> So how do the announcements we're talking about, well, there're three phases here, and again, we'll unpack those separately, but in general, how do the announcements then, you think, fit into that strategy and fit into their view and your view, really, of the market trends? >> I think one of the big trends is, you know, IT in terms for most businesses is, it's not just an enabler anymore. IT's actually in the driver's seat. We see in our research at ESGU, we just did this study and I'm going to glance over my notes as I'm kind of talking, but we see one of the things is more than half of businesses are identifying some portion of their revenue is coming from digital products or digital services. So data is part of the revenue chain for a majority of organizations according to what we're seeing in our research. And so what that does is it puts IT right in that core, you know, that core delivery model of where the faster IT can operate, the faster organizations can realize these revenues opportunities. So what is that doing to IT organizations? Well first off, it makes their life a lot harder, it makes demands continue to increase. But also, this old adage or this old narrative that IT's about availability, it's about resiliency, it's about keeping the lights on and ensuring that the business doesn't go down, well none of that goes away. But now, IT organizations are being measured on their ability to accelerate operations. And in this world where everything's becoming more, you know, more complex, there're more demands, organizations are becoming more distributed, application demands are becoming more diverse and they're growing in breadth. All of this means that more pressure is falling not only on the IT operations but also on the infrastructure providers like Pure Storage to step up and make things even simpler with things like automation and simplification which, you know, we're going to talk about, but to help accelerate those operations. >> Yeah, I mean, if you're DevOps these days, I mean, and you're talkin' about kind of these quandaries that people are in, but what are these specific challenges do you think, on the enterprise level here, that Pure is addressing? >> Well so for example, you talked about developers and driving into that in particular, I want to say let's see, glance at my notes here, about two-thirds of organizations say they're under pressure to accelerate IT initiatives due to pressures specifically from DevOps teams as well as line of business teams. So what does that mean? It means that as organizations build up and try to accelerate either their revenue creation via the creation of software or products, or things of that, that drive, that support a DevOps team, maybe it's improving customer experience for example, as well as other line of business teams such as analytics and trying to provide better insights and better decision making off of data, what that means is this traditional process of IT operations of where you submit a trouble ticket and then it takes, after a few days, something happens and they start doing analysis in terms of basically what ends up being multiple days or multiple weeks, to end up to basically provision storage, it just takes too long. And so in these announcements what we're seeing is Pure delivering solutions that are all about automating the backend services and delivering storage in a way that is designed to be easily and quickly consumed by the new consumers of IT, the developers, the line of business teams via APIs where you can write to a standard API and it goes across basically lots of different technologies and happens very quickly where a lot of the backend processes are automated, and essentially, making the storage invisible to these new consumers. And all of that just delivers value because what these groups are doing is now they can access and get the resources that they need and they don't have to know about what's happening behind the scenes which, candidly, they don't really know much about, right now, and they don't really care. >> Right. (chuckling) That's right. Yeah, what I don't see, what I don't know won't hurt me. And it can, as we know, it can. So let's look at the announcements. Pure Fusion, I think we were hearing about that just a little bit before, earlier in the interview that Dave was conducting, but let's talk about Pure Fusion and your thoughts on that. >> Pure Fusion is what I was talking about a little bit where they're abstracting a lot of the storage capabilities and presenting it as an API, a consistent API that allows developers to provision things very quickly and where a lot of the backend services are automated and, you know, essentially invisible to the developer. And that is, I mean, it addresses where, you know, I kind of talk about this with some of the data that we just, you know, some of our research stats that we just discussed, but it's where a lot of organizations are going. The bottom line is, we used to, in a world where IT services weren't growing as fast and where everything had to be resilient and available, you could put a lot of personnel power or personal hours focused on okay, making sure every box and everything was checked prior to doing a new implementation.and all that was designed to reduce risk and possibly optimize the environment and reduce cost. Now in this world of acceleration what we've seen is organizations need faster responsiveness from the IT organization. Well that's all well and good, but the problem is it's difficult to do all those backend processes and make sure that data's fully being protected or making sure that everything is happening behind the scenes the way it should be. And so this is, again, just mounting more and more pressure. So with things like Pure Fusion what they're doing is they're essentially automating a lot of that on the backend and really simplifying it and making it so storage, or IT administrators can provide access to their line of business, to development teams to leverage infrastructure a lot faster while still ensuring that all those backend services, all those operations still happen. Portworx Data Services also announced and we're hearing it from Dave, for that perspective may be a game-changer in terms of storage. So your take on that and Portworx? >> You know, I really like Portworx. I've been following them ever since prior to the acquisition. One of the things that they were very early on is understanding the impact of micro services on the industry and really, the importance of designing infrastructure around for that environment. I think what they're doing around data services is really intriguing. I think it's really intriguing, first off, for Pure as a company because it elevates their visibility to a new audience and a new persona that may not have been familiar with them. As organizations are looking at, you know, one of the things that they're doing with this data services is essentially delivering a database-as-a-service platform where you can go provision and stand up databases very quickly and again, similar to we talked about fusion, a lot of those backend processes are automated. Really fascinating, again, aligns directly with this acceleration need that we talked about. So, you know, a huge value, but it's really fascinating for Pure because it opens them up to, you know, hey, there's this whole new world of possible consumers that where they're, that they can get experience to really, the ease of use that Pure is known for a lot of the capabilities that Portworx is known for, but also just increase really the value that Pure is able to deliver to some of these modern enterprises. >> And just to add, briefly, on the enhancements that Pure One also being announced today. Your take on those? >> I like that as well. I think one of the things if I kind of go through the list is a lot of insights and intelligence in terms of new app, sizing applications for the environment if I remember correctly, and more, you know, better capabilities to help ensure that your environment is optimized which candidly is a top challenge around IT organizations. We talk about, again, I keep hitting on this need to move faster, faster, faster. One of the big disconnects that we've seen and we saw it very early when organizations were moving to, for example, public cloud services, is this disconnect towards for this individual app, how many resources do I really need and I think that's something that, you know, vendors like Pure need to start integrating more and more intelligence. And that's, my understanding is they're doing with Pure One which is really impressive. >> I hope it's all it takes. Scott, we appreciate the time. Thank you for your insights into what has been a big day for Pure Storage. But thank you again for the time. Scott Sinclair at the Enterprise Strategy Group, senior analyst, there. Let's go back to Dave Vellante now with more on theCUBE. (electronic music) >> Thanks for watching this CUBE program made possible by Pure Storage. I want to say in summary, you know, sometimes it's hard to squint through all the vendor noise on cloud and as-a-service, and all the buzz words, and acronyms in the marketplace. But as I said at the top, the cloud is changing, it's evolving, it's expanding to new locations. The operating model is increasingly defining the cloud. There's so much opportunity to build value on top of the massive infrastructure build-out from the hyperscalers to $100 billion in CapEx last year, alone. This is not just true for technology vendors, but organizations are building their own layer to take advantage of the cloud. Now, of course, technology's critical so when you're evaluating technology solutions, look for the following. First, the ability of the solution to simplify your life. Can it abstract the underlying complexity of a cloud, multiple clouds, connect to on-prem workloads in an experience that is substantially identical, irrespective of location? Does the solution leverage cloud-native technologies and innovations and primitives and APIs or is it just a hosted stack that's really not on the latest technology curve, whether that's processor technology or virtualization, or machine learning, streaming, open source tech, et cetera? Third, how programmable is the infrastructure? Does it make developers more productive? Does it accelerate time to value? Does it minimize rework and increase the quality of your output? And four, what's the business impact? Will customers stand up and talk about the solution and how it contributed to their digital transformation by flexibly supporting emerging data-intensive workloads and evolving as their business rapidly changed? These are some of the important markers that we would suggest you monitor. Pure is obviously driving hard to optimize these and other areas, so watch closely and make your own assessment as to how, what they and others are building will fit into your business. Now as always, this content is available on demand on theCUBE.net, so definitely check that out. This I Dave Vellante for John Walls and the entire CUBE team, thanks for watching, everybody. We'll see ya next time. (soft electronic music)

>> Announcer: From theCUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is a CUBE Conversation. >> Hello. Welcome to today's session of the AWS Startup Showcase, The Next Big Thing in AI, Security & Life Sciences. Today featuring TetraScience for the life sciences track. I'm your host Natalie Erlich, and now we are joined by our special guests, Michelle Bradbury, VP of Product at TetraScience, as well as Mike Tarselli, the Chief Scientific Officer at TetraScience. We're going to talk about the R&D Data Cloud movement in life sciences, unlocking experimental data to accelerate discovery. Thank you both very much for joining us today. >> Thank you for having us. >> Yeah, thank you. Great to be here. >> Well, while traditionally slower to adopt cloud technology in R&D, global pharmas are now launching digital lab initiatives to improve time to market for therapeutics. Now, can you discuss some of the key challenges still facing big pharma in terms of digital transformation? >> Sure. I guess I'll start in. The big pharma sort of organization that we have today happens to work very well in its particular way, i.e., they have some architecture they've installed, usually on-premises. They are sort of tentatively sticking their foot into the cloud. They're learning how to move forward into that, and in order to process and automate their data streams. However, we would argue they haven't done enough fast enough and that they need to get there faster in order to deliver patient value and efficiencies to their businesses. >> Well, how specifically, now for Michelle, can R&D Data Cloud help big pharma in this digital transformation? >> So the big thing that large pharmas face is a couple different things. So the ecosystem within large pharma is a lot of diverse data types, a lot of diverse file types. So that's one thing that the data cloud handles very well to be able to parse through, harmonize, and bring together your data so that it can be leveraged for things like AI and machine learning at large-scale, which is sort of the other part where I think one of the large sort of challenges that pharma faces is sort of a proliferation of data. And what cloud offers, specifically, is a better way to store, more scalable storage, better ability to even tier your storage while still making it searchable, maintainable, and offer a lot of flexibility to the actual pharma companies. >> And what about security and compliance, or even governance? What are those implications? >> Sure. I'll jump into that one. So security and compliance, every large pharma is a regulated industry. Everyone watching this probably is aware of that. And so we therefore have to abide by the same tenets that they would. So 21 CFR Part 11 compliance, getting ready for GXP ready systems, And in fact, doing extra certifications around a SOC 2 Type 2, ISO 9001, really every single regulation that would allow our cloud solution to be quality, ready, inspectable, and really performant for what needs to be done for an eventual FDA submission. >> And can you also speak about some of the advances that we're seeing in machine learning and artificial intelligence, and how that will impact pharma, and what your role is in that at TetraScience? >> Sure. I'll pass this one to Michelle first. >> I was going to say I can take that one. So one of the things that we're seeing in terms of where AI and ML will go with large pharma is their ability to not only search and build models against the data that they have access to right now, which is very limited in the way they search, but the ability to go through the historical amount of data, the ability to leverage mass parallel compute on top of these giant data clusters, and what that means in terms of not only faster time to market for drugs, but also, I think, more accurate and precise testing coming in the future. So I think there's so much opportunity for this really data-rich vertical and industry to leverage in a lot of the modern tooling that it hasn't been able to leverage so far. >> And Mike, what would you say are the benefits that a fully automated lab could bring with increased fairness and data liquidity? >> Yeah, sure. Let's go five years into the future. I am a bench chemist, and I'm trying to get some results in, and it's amazing because I can look up everything the rest of my colleagues have ever done on this particular project with a single click of a button in a simple term set in natural language. I can then find and retrieve those results, easily visualize them in our platform or in any other platform I choose to use. And then I can inspect those, interrogate those, and say, "Actually, I'm going to be able to set up this automation cascade." I'll probably have it ready by the afternoon. All the data that's returned to me through this is going to be easily integratable, harmonized, and you're going to be able to find it, obviously. You're going to interoperate it with any system, so if I suddenly decide that I need to send a report over to another division in their preferred vis tool or data system of choice, great! I click three buttons, configure it. Boom. There goes that report to them. This should be a simple vision to achieve even faster than five years. And that data liquidity that enables you to sort of pass results around outside of your division, and outside of even your sort of company or division, to other who are able to see it should be fairly easy to achieve if all that data is ingested the right way. >> Well, I'd love to ask this next question to both of you. What is your defining contribution to the future of cloud scale? >> Mike, you want to go first? >> (chuckles) I would love to. So right now the pharmaceutical and life sciences companies, they aren't seeing data increase linearly. They're seeing it increase exponentially, right? We are living in the exabyte era, and really have on the internet since about 2016. It's only going to get bigger, and it's going to get bigger in a power law, right? So you're going to see, as sequencing comes on, as larger form microscopy comes on, and as more and more companies are taking on more and more data about each individual sample, retaining that data for longer, doing more analytics of that data, and also doing personalized medicine, right, more data about a specific patient, or animal, or cell line. You're just going to see this absolute data explosion. And because of that, the only thing you can really do to keep up with that is be in the cloud. On-prem, you will be buying disk drives and out of physical materials before you're going to outstrip the data. Michelle? >> Yeah. And, I think, to go along with not just the data storage scale, I think the compute scale. Mike is absolutely right. We're seeing personalized drugs. We're seeing customers that want to, within a matter of three, four hours, get to a personalized drug for patients. And that kind of scale on a compute basis not just requires a ton of data, but requires mass compute ability to be able to get it right, right? And so it really becomes this marriage of getting a huge amount of data, and getting the mass compute to be able to really leverage that per patient. And then the one thing that... Sort of enabling that ecosystem to come centrally together across such a diverse dataset is sort of that driving force. If you can get the data together but you can't compute it, if you can compute it but you can't get it together, it all needs to come together. Otherwise it just doesn't work. >> Yeah. Well, on your website you have all these great case studies, and I'd love it if you could outline some of your success stories for us, some specific, concrete examples. >> Sure. I'll take one first, and then they'll pass to Michelle. One really great concrete example is we were able to take data format processing for a biotech that had basically previously had instruments sitting off in a corner that they could not connect, were integratable for a high throughput screening cascade. We were able to bring them online. We were able to get the datasets interpretable, and get literally their processing time for these screens from the order of weeks to the order of minutes. So they could basically be doing probably a couple hundred more screens per year than they could have otherwise. Michelle? >> We have one customer that is in the process of automating their entire lab, even using robotics arms. So it's a huge mix of being able to ingest IoT data, send experiment data to them, understand sampling, getting the results back, and really automating that whole process, which when they even walked me through it, I was like, "Wow," and I'm like, "so cool." (chuckles) And there's a lot of... I think a lot of pharma companies want, and life science companies, want to move forward in innovation and do really creative and cool things for patients. But at the end of it, you sort of have to also realize it's like their core competency is focusing on drugs, and getting that to market, and making patients better. And we're just one part of that, really helping to enable that process and that ecosystem come to life, so it's really cool to watch. >> Right, right. And I mean, in this last year we've seen how critical the healthcare sector is to people all over the world. Now, looking forward, what do you anticipate some of the big innovations in the sector will be in the next five years, and where do you see TetraScience's role in that? >> So I think some of the larger innovations are... Mike mentioned one of them already. It's going to be sort of the personalized drugs the personalized health care. I think it is absolutely going to go to full lab automation to some degree, because who knows when the next pandemic will hit, right? And we're all going to have to go home, right? I think the days of trying to move around data manually and trying to work through that is just... If we don't plan for that to be a thing of the past, I think we're all going to do ourselves a disservice. So I think you'll see more automation. I think you'll see more personalization, and you'll see more things that leverage larger amounts of data. I think where we hope to sit is really at the ecosystem enablement part of that. We want to remain open. That's one of the cornerstones. We're not a single partner platform. We're not tied to any vendors. We really want to become that central aid and the ecosystem enabler for the labs. >> Yeah, to that point- >> And I'd also love to get your insight. >> Oh! Sorry. (chuckles) Thank you. To that point, we're really trying to unlock discovery, right? Many other horizontal cloud players will do something like you can upload files, or you can do some massive compute, but they won't have the vertical expertise that we do, right? They won't have the actual deep life sciences dedication. We have several PhDs, postdocs, et cetera, on staff who have done this for a living and can do this going forward. So you're going to see the realization of something that was really exciting in sort of 2005, 2006, that is fully automated experimentation. So get a robot to about an experiment, design it, have a human operator assist with putting together all the automation, and then run that over and over again cyclically until you get the result you want. I don't think that the compute was ready for that at the time. I don't think that the resources were up to snuff, but now you can do it, and you can do it with any tool, instrument, technique you want, because to Michelle's point, we're a vendor-agnostic partner networked platform. So you can actually assemble this learning automation cascade and have it run in the background while you go home and sleep. >> Yeah, and we often hear about automation, but tell us a little bit more specifically what is the harmonizing effect of TetraScience? I mean, that's not something that we usually hear, so what's unique about that? >> You want to take that, or you want me to go? >> You go, please. (chuckles) >> All right. So, really, it's about... It's about normalizing and harmonizing the data. And what does that... What that means is that whether you're a chromatography machine from, let's say Waters, or another vendor, ideally you'd like to be able to leverage all of your chromatography data and do research across all of it. Most of our customers have machinery that is of same sort from different customers, or sorry, from different vendors. And so it's really the ability to bring that data together, and sometimes it's even diverse instrumentation. So if I track a molecule, or a project, or a sample through one piece, one set of instrumentation, and I want to see how it got impacted in another set of instrumentation, or what the results were, I'm able to quickly and easily be able to sort of leverage that harmonized data and come to those results quickly. Mike, I'm sure you have a- >> May I offer a metaphor from something outside of science? Hopefully that's not off par for this, but let's say you had a parking lot, right, filled with different kinds of cars. And let's say you said at the beginning of that parking lot, "No, I'm sorry. We only have space right here for a Ford Fusion 2019 black with leather interior and this kind of tires." That would be crazy. You would never put that kind of limitation on who could park in a parking lot. So why do specific proprietary data systems put that kind of limitation on how data can be processed? We want to make it so that any car, any kind of data, can be processed and considered together in that same parking lot. >> Fascinating. Well, thank you both so much for your insights. Really appreciate it. Wonderful to hear about R&D Data Cloud movement in big pharma, and that of course is Michelle Bradbury, VP of Product at TetraScience, as well as Mike Tarselli, the Chief Scientific Officer at TetraScience. Thanks again very much for your insights. I'm your host for theCUBE, Natalie Erlich. Catch us again for the next session of the AWS Startup Session. Thank you. (smooth music)

(upbeat music) >> Hello everyone, and welcome to this "Cube Conversation." My name is Dave Vellante and you know, containers, they used to be stateless and ephemeral but they're maturing very rapidly. As cloud native workloads become more functional and they go mainstream persisting, and protecting the data that lives inside of containers, is becoming more important to organizations. Enterprise capabilities such as high availability or reliability, scalability and other features are now more fundamental and important and containers are linchpin of hybrid cloud, cross-cloud and edge strategies. Now fusing these capabilities together across these regions in an abstraction layer that hides that underlying complexity of the infrastructure, is where the entire enterprise technology industry is headed. But how do you do that without making endless copies of data and managing versions not to mention the complexities and costs of doing so. And with me to talk about how IBM thinks about and is solving these challenges are Eric Herzog, who's the Chief Marketing Officer and VP of Global Storage Channels. For the IBM Storage Division is Sam Werner is the vice president of offering management and the business line executive for IBM Storage. Guys, great to see you again, wish should, were face to face but thanks for coming on "theCUBE." >> Great to be here. >> Thanks Dave, as always. >> All right guys, you heard me my little spiel there about the problem statement. Eric, maybe you could start us off. I mean, is it on point? >> Yeah, absolutely. What we see is containers are going mainstream. I frame it very similarly to what happened with virtualization, right? It got brought in by the dev team, the test team, the applications team, and then eventually of course, it became the main state. Containers is going through exactly that right now. Brought in by the dev ops people, the software teams. And now it's becoming again, persistent, real use clients that want to deploy a million of them. Just the way they historically have deployed a million virtual machines, now they want a million containers or 2 million. So now it's going mainstream and the feature functions that you need once you take it out of the test sort of play with stage to the real production phase, really changes the ball game on the features you need, the quality of what you get, and the types of things you need the underlying storage and the data services that go with that storage,. to do in a fully container world. >> So Sam how'd we get here? I mean, container has been around forever. You look inside a Linux, right? But then they did, as Eric said, go mainstream. But it started out the, kind of little experimental, As I said, their femoral didn't really need to persist them, but it's changed very quickly. Maybe you could talk to that evolution and how we got here. >> I mean, well, it's been a look, this is all about agility right? It's about enterprises trying to accelerate their innovation. They started off by using virtual machines to try to accelerate access to IT for developers, and developers are constantly out, running ahead. They got to go faster and they have to deliver new applications. Business lines need to figure out new ways to engage with their customers. Especially now with the past year we had it even further accelerated this need to engage with customers in new ways. So it's about being agile. Containers promise or provide a lot of the capabilities you need to be agile. What enterprises are discovering, a lot of these initiatives are starting within the business lines and they're building these applications or making these architectural decisions, building dev ops environments on containers. And what they're finding is they're not bringing the infrastructure teams along with them. And they're running into challenges that are inhibiting their ability to achieve the agility they want because their storage needs aren't keeping up. So this is a big challenge that enterprises face. They want to use containers to build a more agile environment to do things like dev ops, but they need to bring the infrastructure teams along. And that's what we're focused on now. Is how do you make that agile infrastructure to support these new container worlds? >> Got it, so Eric, you guys made an announcement to directly address these issues. Like it's kind of a fire hose of innovation. Maybe you could take us through and then we can unpack that a little bit. >> Sure, so what we did is on April 27th, we announced IBM Spectrum Fusion. This is a fully container native software defined storage technology that integrates a number of proven battle-hardened technologies that IBM has been deploying in the enterprise for many years. That includes a global scalable file system that can span edge core and cloud seamlessly with a single copy of the data. So no more data silos and no more 12 copies of the data which of course drive up CapEx and OpEx. Spectrum Fusion reduces that and makes it easier to manage. Cuts the cost from a CapEx perspective and cuts a cost for an OpEx perspective. By being fully container native, it's ready to go for the container centric world and could span all types of areas. So what we've done is create a storage foundation which is what you need at the bottom. So things like the single global namespace, single accessibility, we have local caching. So with your edge core cloud, regardless of where the data is, you think the data's right with you, even if it physically is not. So that allows people to work on it. We have file locking and other technologies to ensure that the data is always good. And then of course we'd imbued it with the HA Disaster Recovery, the backup and restore technology, which we've had for years and have now made of fully container native. So spectrum fusion basically takes several elements of IBM's existing portfolio has made them container native and brought them together into a single piece of software. And we'll provide that both as a software defined storage technology early in 2022. And our first pass will be as a hyperconverged appliance which will be available next quarter in Q3 of 2021. That of course means it'll come with compute, it'll come with storage, come with a rack even, come with networking. And because we can preload everything for the end users or for our business partners, it would also include Kubernetes, Red Gat OpenShift and Red Hat's virtualization technology all in one simple package, all ease of use and a single management gooey to manage everything, both the software side and the physical infrastructure that's part of the hyperconverged system level technologies. >> So, maybe it can help us understand the architecture and maybe the prevailing ways in which people approach container storage, what's the stack look like? And how have you guys approached it? >> Yeah, that's a great question. Really, there's three layers that we look at when we talk about container native storage. It starts with the storage foundation which is the layer that actually lays the data out onto media and does it in an efficient way and makes that data available where it's needed. So that's the core of it. And the quality of your storage services above that depend on the quality of the foundation that you start with. Then you go up to the storage services layer. This is where you bring in capabilities like HA and DR. People take this for granted, I think as they move to containers. We're talking about moving mission critical applications now into a container and hybrid cloud world. How do you actually achieve the same levels of high availability you did in the past? If you look at what large enterprises do, they run three site, for site replication of their data with hyper swap and they can ensure high availability. How do you bring that into a Kubernetes environment? Are you ready to do that? We talk about how only 20% of applications have really moved into a hybrid cloud world. The thing that's inhibiting the other 80% these types of challenges, okay? So the storage services include HA DR, data protection, data governance, data discovery. You talked about making multiple copies of data creates complexity, it also creates risk and security exposures. If you have multiple copies of data, if you needed data to be available in the cloud you're making a copy there. How do you keep track of that? How do you destroy the copy when you're done with it? How do you keep track of governance and GDPR, right? So if I have to delete data about a person how do I delete it everywhere? So there's a lot of these different challenges. These are the storage services. So we talk about a storage services layer. So layer one data foundation, layer two storage services, and then there needs to be connection into the application runtime. There has to be application awareness to do things like high availability and application consistent backup and recovery. So then you have to create the connection. And so in our case, we're focused on open shift, right? When we talk about Kubernetes how do you create the knowledge between layer two, the storage services and layer three of the application services? >> And so this is your three layer cake. And then as far as like the policies that I want to inject, you got an API out and entries in, can use whatever policy engine I want. How does that work? >> So we're creating consistent sets of APIs to bring those storage services up into the application, run time. We in IBM have things like IBM cloud satellite which bring the IBM public cloud experience to your data center and give you a hybrid cloud or into other public cloud environments giving you one hybrid cloud management experience. We'll integrate there, giving you that consistent set of storage services within an IBM cloud satellite. We're also working with Red Hat on their Advanced Cluster Manager, also known as RACM to create a multi-cluster management of your Kubernetes environment and giving that consistent experience. Again, one common set of APIs. >> So the appliance comes first? Is that a no? Okay, so is that just time to market or is there a sort of enduring demand for appliances? Some customers, you know, they want that, maybe you could explain that strategy. >> Yeah, so first let me take it back a second. Look at our existing portfolio. Our award-winning products are both software defined and system-based. So for example Spectrum Virtualize comes on our flash system. Spectrum Scale comes on our elastic storage system. And we've had this model where we provide the exact same software, both on an array or as standalone piece of software. This is unique in the storage industry. When you look at our competitors, when they've got something that's embedded in their array, their array manager, if you will, that's not what they'll try to sell you. It's software defined storage. And of course, many of them don't offer software defined storage in any way, shape or form. So we've done both. So with spectrum fusion, we'll have a hyper-converged configuration which will be available in Q3. We'll have a software defined configuration which were available at the very beginning of 2022. So you wanted to get out of this market feedback from our clients, feedback from our business partners by doing a container native HCI technology, we're way ahead. We're going to where the park is. We're throwing the ball ahead of the wide receiver. If you're a soccer fan, we're making sure that the mid guy got it to the forward ahead of time so you could kick the goal right in. That's what we're doing. Other technologies lead with virtualization, which is great but virtualization is kind of old hat, right? VMware and other virtualization layers have been around for 20 now. Container is where the world is going. And by the way, we'll support everything. We still have customers in certain worlds that are using bare metal, guess what? We work fine with that. We worked fine with virtual as we have a tight integration with both hyper V and VMware. So some customers will still do that. And containers is a new wave. So with spectrum fusion, we are riding the wave not fighting the wave and that way we could meet all the needs, right? Bare metal, virtual environments, and container environments in a way that is all based on the end users applications, workloads, and use cases. What goes, where and IBM Storage can provide all of it. So we'll give them two methods of consumption, by early next year. And we started with a hyper-converged first because, A, we felt we had a lead, truly a lead. Other people are leading with virtualization. We're leading with OpenShift and containers where the first full container-native OpenShift ground up based hyper-converged of anyone in the industry versus somebody who's done VMware or some other virtualization layer and then sort of glommed on containers and as an afterthought. We're going to where the market is moving, not to where the market has been. >> So just follow up on that. You kind of, you got the sort of Switzerland DNA. And it's not just OpenShift and Red Hat and the open source ethos. I mean, it just goes all the way back to San Volume Controller back in the day where you could virtualize anybody's storage. How is that carrying through to this announcement? >> So Spectrum Fusion is doing the same thing. Spectrum Fusion, which has many key elements brought in from our history with Spectrum Scale supports not IBM storage, for example, EMC Isilon NFS. It will support, Fusion will support Spectrum Scale, Fusion will support our elastic storage system. Fusion will support NetApp filers as well. Fusion will support IBM cloud object storage both software defined storage, or as an array technology and Amazon S3 object stores and any other object storage vendor who's compliant with S3. All of those can be part of the global namespace, scalable file system. We can bring in, for example, object data without making a duplicate copy. The normal way to do that as you make a duplicate copy. So you had a copy in the object store. You make a copy and to bring that into the file. Well, guess what, we don't have to do that. So again, cutting CapEx and OpEx and ease of management. But just as we do with our flash systems product and our Spectrum Virtualize and the SAN Volume Controller, we support over 550 storage arrays that are not ours that are our competitors. With Spectrum Fusion, we've done the same thing, fusion, scale the IBM ESS, IBM cloud object storage, Amazon S3 object store, as well as other compliance, EMC Isilon NFS, and NFS from NetApp. And by the way, we can do the discovery model as well not just integration in the system. So we've made sure that we really do protect existing investments. And we try to eliminate, particularly with discovery capability, you've got AI or analytics software connecting with the API, into the discovery technology. You don't have to traverse and try to find things because the discovery will create real time, metadata cataloging, and indexing, not just of our storage but the other storage I'd mentioned, which is the competition. So talk about making it easier to use, particularly for people who are heterogeneous in their storage environment, which is pretty much the bulk of the global fortune 1500, for sure. And so we're allowing them to use multiple vendors but derive real value with Spectrum Fusion and get all the capabilities of Spectrum Fusion and all the advantages of the enterprise data services but not just for our own product but for the other products as well that aren't ours. >> So Sam, we understand the downside of copies, but then, so you're not doing multiple copies. How do you deal with latency? What's the secret sauce here? Is it the file system? Is there other magic in here? >> Yeah, that's a great question. And I'll build a little bit off of what Eric said, but look one of the really great and unique things about Spectrum Scale is its ability to consume any storage. And we can actually allow you to bring in data sets from where they are. It could have originated in object storage we'll cash it into the file system. It can be on any block storage. It can literally be on any storage you can imagine as long as you can integrate a file system with it. And as you know most applications run on top of the file system. So it naturally fits into your application stack. Spectrum Scale uniquely is a globally parallel file system. So there's not very many of them in the world and there's none that can achieve what Spectrum Scale can do. We have customers running in the exabytes of data and the performance improves with scales. So you can actually deploy Spectrum Scale on-prem, build out an environment of it, consuming whatever storage you have. Then you can go into AWS or IBM cloud or Azure, deploy an instance of it and it will now extend your file system into that cloud. Or you can deploy it at the edge and it'll extend your file system to that edge. This gives you the exact same set of files and visibility and we'll cash in only what's needed. Normally you would have to make a copy of data into the other environment. Then you'd have to deal with that copy later, let's say you were doing a cloud bursting use case. Let's look at that as an example, to make this real. You're running an application on-prem. You want to spin up more compute in the cloud for your AI. The data normally you'd have to make a copy of the data. You'd run your AI. They have to figure out what to do with that data. Do you copy some of the fact? Do we sync them? Do you delete it? What do you do? With Spectrum Scale just automatically cash in whatever you need. It'll run there and you get assigned to spin it down. Your copy is still on-prem. You know, no data is lost. We can actually deal with all of those scenarios for you. And then if you look at what's happening at the edge, a lot of say video surveillance, data pouring in. Looking at the manufacturing {for} looking for defects. You can run a AI right at the edge, make it available in the cloud, make that data available in your data center. Again, one file system going across all. And that's something unique in our data foundation built on Spectrum Scale. >> So there's some metadata magic in there as well, and that intelligence based on location. And okay, so you're smart enough to know where the data lives. What's the sweet spot for this Eric? Are there any particular use cases or industries that we should be focused on or is it through? >> Sure, so first let's talk about the industries. We see certain industries going more container quicker than other industries. So first is financial services. We see it happening there. Manufacturing, Sam already talked about AI based manufacturing platforms. We actually have a couple clients right now. We're doing autonomous driving software with us on containers right now, even before Spectrum Fusion with Spectrum Scale. We see public of course, healthcare and in healthcare don't just think delivery at IBM. That includes the research guys. So the genomic companies, the biotech companies, the drug companies are all included in that. And then of course, retail, both on-prem and off-prem. So those are sort of the industries. Then we see from an application workload, basically AI analytics and big data applications or workloads are the key things that Spectrum Fusion helps you because of its file system. It's high performance. And those applications are tending to spread across core ,edge and cloud. So those applications are spreading out. They're becoming broader than just running in the data center. And by the way they want to run it just into the data center, that's fine. Or perfect example, we had giant global auto manufacturer. They've got factories all over. And if you think there isn't compute resources in every factory, there is because those factories I just saw an article, actually, those factories cost about a billion dollars to build them, a billion. So they've got their own IT, now it's connected to their core data center as well. So that's a perfect example that enterprise edge where spectrum fusion would be an ideal solution whether they did it as software defined only, or of course when you got a billion dollar factory, just to make it let alone produce the autos or whatever you're producing. Silicon, for example, those fabs, all cost a billion. That's where the enterprise edge fits in very well with Spectrum Fusion. >> So are those industries, what's driving the adoption of containers? Is it just, they just want to modernize? Is it because they're doing some of those workloads that you mentioned or is there's edge? Like you mentioned manufacturing, I could see that potentially being an edge is the driver. >> Well, it's a little bit of all of those Dave. For example, virtualization came out and virtualization offered advantages over bare metal, okay? Now containerization has come out and containerization is offering advantage over virtualization. The good thing at IBM is we know we can support all three. And we know again, in the global fortune 2000, 1500 they're probably going to run all three based on the application workload or use case. And our storage is really good at bare metal. Very good at virtualization environments. And now with Spectrum Fusion are container native outstanding for container based environments. So we see these big companies will probably have all three and IBM storage is one of the few vendors if not the only vendor that could adroitly support all three of those various workload types. So that's why we see this as a huge advantage. And again, the market is going to containers. We are, I'm a native California. You don't fight the wave, you ride the wave. and the wave is containers and we're riding that wave. >> If you don't ride the wave you become driftwood as Pat Gelsinger would say. >> And that is true, another native California. I'm a whole boss. >> So okay, so, I wonder Sam I sort of hinted upfront in my little narrative there but the way we see this, as you've got on-prem hybrid, you got public clouds across cloud moving to the edge. Open shift is I said is the linchpin to enabling some of those. And what we see is this layer that abstracts the complexity, hides the underlying complexity of the infrastructure that becomes kind of an implementation detail. Eric talked about skating to the park or whatever sports analogy you want to use. Is that where the park is headed? >> Yeah, I mean, look, the bottom line is you have to remove the complexity for the developers. Again, the name of the game here is all about agility. You asked why these industries are implementing containers? It's about accelerating their innovation and their services for their customers. It's about leveraging AI to gain better insights about their customers and delivering what they want and proving their experience. So if it's all about agility developers don't want to wait around for infrastructure. You need to automate it as much as possible. So it's about building infrastructure that's automated, which requires consistent API APIs. And it requires abstracting out the complexity of things like HA and DR. You don't want every application owner to have to figure out how to implement that. You want to make those storage services available and easy for a developer to implement and integrate into what they're doing. You want to ensure security across everything you do as you bring more and more of your data of your information about your customers into these container worlds. You've got to have security rock solid. You can't leave any exposures there and you can't afford downtime. There's increasing threats from things like ransomware. You don't see it in the news every day but it happens every single day. So how do you make sure you can recover when an event happens to you? So yes, you need to build a abstracted layer of storage services and you need to make it simply available to the developers in these dev ops environments. And that's what we're doing with spectrum fusion. We're taking, I think, extremely unique and one of a kind storage foundation with Spectrum Scale that gives you single namespace globally. And we're building onto it an incredible set of storage services, making extremely simple to deploy enterprise class container applications. >> So what's the bottom line business impact. I mean, how does this change? I mean, Sam, you I think articulated very well through all about serving the developers versus you know, storage, admin provisioning, a LUN. So how does this change my organization, my business? What's the impact there? >> I've mentioned one other point that we talk about an IBM a lot, which is the AI ladder. And it's about how do you take all of this information you have and be able to take it to build new insights, to give your company and advantage. An incumbent in an industry shouldn't be able to be disrupted if they're able to leverage all the data they have about the industry and their customers. But in order to do that, you have to be able to get to a single source of data and be able to build it into the fabric of your business operations. So that all decisions you're making in your company, all services you deliver to your customers, are built on that data foundation and information and the only way to do that and infuse it into your culture is to make this stuff real time. And the only way to do that is to build out a containerized application environment that has access to real-time data. The ultimate outcome, sorry, I know you asked for business results is that you will, in real time understand your clients, understand your industry and deliver the best possible services. And the absolute, business outcome is you will continue to gain market share and your environment and grow revenue. I mean, that's the outcome every business wants. >> Yeah, it's all about speed. Everybody's kind of, everybody's last year was forced into digital transformation. It was sort of rushed into and compressed and now they get some time to do it right. And so modernizing apps, containers, dev ops developer led sort of initiatives are really key to modernization. All right, Eric, we've got, we're out of time but give us the bottom summary. We didn't talk, actually, we had to talk about the 3,200. Maybe you could give us a little insight on that before we close. >> Sure, so in addition to what we're doing with Fusion we also introduced a new elastic storage system, 3,200 and it's all flash. It gets 80 gigs, a second sustained at the node level and we can cluster them infinitely. So for example, I've got 10 of them. I'm delivering 800 gigabytes, a second sustained. And of course, AI, big data analytic workloads are extremely, extremely susceptible to bandwidth and or data transfer rate. That's what they need to deliver their application base properly. It comes with Spectrum Scale built in so that comes with it. So you get the advantage of Spectrum Scale. We talked a lot about Spectrum Scale because it is if you will, one of the three fathers of spectrum fusion. So it's ideal with it's highly parallel file system. It's used all over in high performance computing and super computing, in drug research, in health care in finance, probably about 80% of the world's largest banks in the world use Spectrum Scale already for AI, big data analytics. So the new 3,200 is an all flash version twice as fast as the older version and all the benefit of Spectrum Scale including the ability of seamlessly integrating into existing Spectrum Scale or ESS deployments. And when Fusion comes out, you'll be able to have Fusion. And you could also add 3,200 to it if you want to do that because of the capability of our global namespace and our single file system across edge, core and cloud. So that's the 3,200 in a nutshell, Dave. >> All right, give us a bottom line, Eric. And we got to go, what's the bumper sticker. >> Yeah, bumper sticker is, you got to ride the wave of containers and IBM storage is company that can take you there so that you win the big surfing context and get the big prize. >> Eric and Sam, thanks so much, guys. It's great to see you and miss you guys. Hopefully we'll get together soon. So get your jabs and we'll have a beer. >> All right. >> All right, thanks, Dave. >> Nice talking to you. >> All right, thank you for watching everybody. This is Dave Vellante for "theCUBE." We'll see you next time. (upbeat music)

>> From theCUBE studios in Palo Alto and Boston, Bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Recent survey data from ETR shows that enterprise tech spending is tracking with projected US GDP growth at six to 7% this year. Many markers continue to point the way to a strong recovery, including hiring trends and the loosening of frozen IT Project budgets. However skills shortages are blocking progress at some companies which bodes well for an increased reliance on external IT services. Moreover, while there's much talk about the rotation out of work from home plays and stocks such as video conferencing, VDI, and other remote worker tech, we see organizations still trying to figure out the ideal balance between funding headquarter investments that have been neglected and getting hybrid work right. In particular, the talent gap combined with a digital mandate, means companies face some tough decisions as to how to fund the future while serving existing customers and transforming culturally. Hello everyone, and welcome to this week's Wikibon CUBE's Insights powered by ETR. In this "Breaking Analysis", we welcome back Erik Porter Bradley of ETR who will share fresh data, perspectives and insights from the latest survey data. Erik, great to see you. Welcome. >> Thank you very much, Dave. Always good to see you and happy to be on the show again. >> Okay, we're going to share some macro data and then we're going to dig into some highlights from ETR's most recent March COVID survey and also the latest April data. So Erik, the first chart that we want to show, it shows CIO and IT buyer responses to expected IT spend for each quarter of 2021 versus 2020, and you can see here a steady quarterly improvement. Erik, what are the key takeaways, from your perspective? >> Sure, well, first of all, for everyone out there, this particular survey had a record-setting number of participation. We had a 1,500 IT decision makers participate and we had over half of the Fortune 500 and over a fifth of the Global 1000. So it was a really good survey. This is seventh iteration of the COVID Impact Survey specifically, and this is going to transition to an overlarge macro survey going forward so we can continue it. And you're 100% right, what we've been tracking here since March of last year was, how is spending being impacted because of COVID? Where is it shifting? And what we're seeing now finally is that there is a real re-acceleration in spend. I know we've been a little bit more cautious than some of the other peers out there that just early on slapped an eight or a 9% number, but what we're seeing is right now, it's at a midpoint of over six, about 6.7% and that is accelerating. So, we are still hopeful that that will continue, and really, that spending is going to be in the second half of the year. As you can see on the left part of this chart that we're looking at, it was about 1.7% versus 3% for Q1 spending year-over-year. So that is starting to accelerate through the back half. >> I think it's prudent to be cautious (indistinct) 'cause normally you'd say, okay, tech is going to grow a couple of points higher than GDP, but it's really so hard to predict this year. Okay, the next chart here that we want to show you is we asked respondents to indicate what strategies they're employing in the short term as a result of coronavirus and you can see a few things that I'll call out and then I'll ask Erik to chime in. First, there's been no meaningful change of course, no surprise in tactics like remote work and holding travel, however, we're seeing very positive trends in other areas trending downward, like hiring freezes and freezing IT deployments, a downward trend in layoffs, and we also see an increase in the acceleration of new IT deployments and in hiring. Erik, what are your key takeaways? >> Well, first of all, I think it's important to point out here that we're also capturing that people believe remote work productivity is still increasing. Now, the trajectory might be coming down a little bit, but that is really key, I think, to the backdrop of what's happening here. So people have a perception that productivity of remote work is better than hybrid work and that's from the IT decision makers themselves, but what we're seeing here is that, most importantly, these organizations are citing plans to increase hiring, and that's something that I think is really important to point out. It's showing a real following, and to your point right in the beginning of the intro, we are seeing deployments stabilize versus prior survey levels, which means early on, they had no plans to launch new tech deployments, then they said, "Nope, we're going to start." and now that stalling, and I think it's exactly right, what you said, is there's an IT skills shortage. So people want to continue to do IT deployments 'cause they have to support work from home and a hybrid back return to the office, but they just don't have the skills to do so, and I think that's really probably the most important takeaway from this chart, is that stalling and to really ask why it's stalling. >> Yeah, so we're going to get into that for sure, and I think that's a really key point, is that accelerating IT deployments, it looks like it's hit a wall in the survey, but before we get deep into the skills, let's take a look at this next chart, and we're asking people here how our return to the new normal, if you will, and back to offices is going to change spending with on-prem architectures and applications. And so the first two bars, they're Cloud-friendly, if you add them up, it's 63% of the respondents, say that either they'll stay in the Cloud for the most part, or they're going to lower their on-prem spend when they go back to the office. The next three bars are on-prem friendly. If you add those up it's 29% of the respondents say their on-prem spend is going to bounce back to pre-COVID levels or actually increase, and of course, 12% of that number, by the way, say they've never altered their on-prem spend. So Erik, no surprise, but this bodes well for Cloud, but isn't it also a positive for on-prem? We've had this dual funding premise, meaning Cloud continues to grow, but neglected data center spend also gets a boost. What's your thoughts? >> Really, it's interesting. It's people are spending on all fronts. You and I were talking in the prep, it's like we're in battle and I've got naval, I've got air, I've got land, I've got to spend on Cloud and digital transformation, but I also have to spend for on-prem. The hybrid work is here and it needs to be supported. So this is spending is going to increase. When you look at this chart, you're going to see though, that roughly 36% of all respondents say that their spending is going to remain mostly on Cloud. So that is still the clear direction, digital transformation is still happening, COVID accelerated it greatly, you and I, as journalists and researchers already know this is where the puck is going, but spend has always lagged a little bit behind 'cause it just takes some time to get there. Inversely, 27% said that their on-prem spending will decrease. So when you look at those two, I still think that the trend is the friend for Cloud spending, even though, yes, they do have to continue spending on hybrid, some of it's been neglected, there are refresh cycles coming up, so, overall it just points to more and more spending right now. It really does seem to be a very strong backdrop for IT growth. >> So I want to talk a little bit about the ETR taxonomy before we bring up the next chart. We get a lot of questions about this, and of course, when you do a massive survey like you're doing, you have to have consistency for time series, so you have to really think through what the buckets look like, if you will. So this next chart takes a look at the ETR taxonomy and it breaks it down into simple-to-understand terms. So the green is the portion of spending on a vendor's tech within a category that is accelerating, and the red is the portion that is decelerating. So Erik, what are the key messages in this data? >> Well, first of all, Dave, thank you so much for pointing that out. We used to do, just what we call a Net score. It's a proprietary formula that we use to determine the overall velocity of spending. Some people found it confusing. Our data scientists decided to break this sector, break down into what you said, which is really more of a mode analysis. In that sector, how many of the vendors are increasing versus decreasing? So again, I just appreciate you bringing that up and allowing us to explain the reasoning behind our analysis there. But what we're seeing here goes back to something you and I did last year when we did our predictions, and that was that IT services and consulting was going to have a true rebound in 2021, and that's what this is showing right here. So in this chart, you're going to see that consulting and services are really continuing their recovery, 2020 had a lot of the clients and they have the biggest sector year-over-year acceleration sector wise. The other thing to point out on this, which we'll get to again later, is that the inverse analysis is true for video conferencing. We will get to that, so I'm going to leave a little bit of ammunition behind for that one, but what we're seeing here is IT consulting services being the real favorable and video conferencing having a little bit more trouble. >> Great, okay, and then let's take a look at that services piece, and this next chart really is a drill down into that space and emphasizes, Erik, what you were just talking about. And we saw this in IBM's earnings, where still more than 60% of IBM's business comes from services and the company beat earnings, in part, due to services outperforming expectations, I think it had a somewhat easier compare and some of this pent-up demand that we've been talking about bodes well for IBM and other services companies, it's not just IBM, right, Erik? >> No, it's not, but again, I'm going to point out that you and I did point out IBM in our predictions when we did in late December, so, it is nice to see. One of the reasons we don't have a more favorable rating on IBM at the moment is because they are in the process of spinning out this large unit, and so there's a little bit of a corporate action there that keeps us off on the sideline. But I would also want to point out here, Tata, Infosys and Cognizant 'cause they're seeing year-over-year acceleration in both IT consulting and outsourced IT services. So we break those down separately and those are the three names that are seeing acceleration in both of those. So again, at the Tata, Infosys and Cognizant are all looking pretty well positioned as well. >> So we've been talking a little bit about this skills shortage, and this is what's, I think, so hard for forecasters, is that in the one hand, There's a lot of pent up demand, Scott Gottlieb said it's like Woodstock coming out of the COVID, but on the other hand, if you have a talent gap, you've got to rely on external services. So there's a learning curve, there's a ramp up, it's an external company, and so it takes time to put those together. So this data that we're going to show you next, is really important in my view and ties what we were saying at the top. It asks respondents to comment on their staffing plans. The light blue is "We're increasing staff", the gray is "No change" and the magenta or whatever, whatever color that is that sort of purplish color, anyway, that color is decreasing, and the picture is very positive across the board. Full-time staff, offshoring, contract employees, outsourced professional services, all up trending upwards, and this Erik is more evidence of the services bounce back. >> Yeah, it's certainly, yes, David, and what happened is when we caught this trend, we decided to go one level deeper and say, all right, we're seeing this, but we need to know why, and that's what we always try to do here. Data will tell you what's happening, it doesn't always tell you why, and that's one of the things that ETR really tries to dig in with through the insights, interviews panels, and also going direct with these more custom survey questions. So in this instance, I think the real takeaway is that 30% of the respondents said that their outsourced and managed services are going to increase over the next three months. That's really powerful, that's a large portion of organizations in a very short time period. So we're capturing that this acceleration is happening right now and it will be happening in real time, and I don't see it slowing down. You and I are speaking about we have to increase Cloud spend, we have to increase hybrid spend, there are refresh cycles coming up, and there's just a real skills shortage. So this is a long-term setup that bodes very well for IT services and consulting. >> You know, Erik, when I came out of college, somebody told me, "Read, read, read, read as much as you can." And then they said, "Read the Wall Street Journal every day." and so I did it, and I would read the tech magazines and back then it was all paper, and what happens is you begin to connect the dots. And so the reason I bring that up is because I've now taken a bath in the ETR data for the better part of two years and I'm beginning to be able to connect the dots. The data is not always predictive, but many, many times it is. And so this next data gets into the fun stuff where we name names. A lot of times people don't like it because they're either marketing people at organizations, say, "Well, data's wrong." because that's the first thing they do, is attack the data. But you and I know, we've made some really great calls, work from home, for sure, you're talking about the services bounce back. We certainly saw the rise of CrowdStrike, Okta, Zscaler, well before people were talking about that, same thing with video conferencing. And so, anyway, this is the fun stuff and it looks at positive versus negative sentiment on companies. So first, how does ETR derive this data and how should we interpret it, and what are some of your takeaways? >> Sure, first of all, how we derive the data, are systematic survey responses that we do on a quarterly basis, and we standardize those responses to allow for time series analysis so we can do trend analysis as well. We do find that our data, because it's talking about forward-looking spending intentions, is really more predictive because we're talking about things that might be happening six months, three months in the future, not things that a lot of other competitors and research peers are looking at things that already happened, they're looking in the past, ETR really likes to look into the future and our surveys are set up to do so. So thank you for that question, It's a enjoyable lead in, but to get to the fun stuff, like you said, what we do here is we put ratings on the datasets. I do want to put the caveat out there that our spending intentions really only captures top-line revenue. It is not indicative of profit margin or any other line items, so this is only to be viewed as what we are rating the data set itself, not the company, that's not what we're in the game of doing. So I think that's very important for the marketing and the vendors out there themselves when they take a look at this. We're just talking about what we can control, which is our data. We're going to talk about a few of the names here on this highlighted vendors list. One, we're going to go back to that you and I spoke about, I guess, about six months ago, or maybe even earlier, which was the observability space. You and I were noticing that it was getting very crowded, a lot of new entrants, there was a lot of acquisition from more of the legacy or standard players in the space, and that is continuing. So I think in a minute, we're going to move into that observability space, but what we're seeing there is that it's becoming incredibly crowded and we're possibly seeing signs of them cannibalizing each other. We're also going to move on a little bit into video conferencing, where we're capturing some spend deceleration, and then ultimately, we're going to get into a little bit of a storage refresh cycle and talk about that. But yeah, these are the highlighted vendors for April, we usually do this once a quarter and they do change based on the data, but they're not usually whipsawed around, the data doesn't move that quickly. >> Yeah, so you can see some of the big names in the left-hand side, some of the SAS companies that have momentum. Obviously, ServiceNow has been doing very, very well. We've talked a lot about Snowflake, Okta, CrowdStrike, Zscaler, all very positive, as well as several others. I guess I'd add some things. I mean, I think if thinking about the next decade, it's Cloud, which is not going to be like the same Cloud as the last decade, a lot of machine learning and deep learning and AI and the Cloud is extending to the edge and the data center. Data, obviously, very important, data is decentralized and distributed, so data architectures are changing. A lot of opportunities to connect across Clouds and actually create abstraction layers, and then something that we've been covering a lot is processor performance is actually accelerating relative to Moore's law. It's probably instead of doubling every two years, it's quadrupling every two years, and so that is a huge factor, especially as it relates to powering AI and AI inferencing at the edge. This is a whole new territory, custom Silicon is really becoming in vogue and so something that we're watching very, very closely. >> Yeah, I completely, agree on that and I do think that the next version of Cloud will be very different. Another thing to point out on that too, is you can't do anything that you're talking about without collecting the data and organizations are extremely serious about that now. It seems it doesn't matter what industry they're in, every company is a data company, and that also bodes well for the storage goal. We do believe that there is going to just be a huge increase in the need for storage, and yes, hopefully that'll become portable across multi-Cloud and hybrid as well. >> Now, as Erik said, the ETR data, it's really focused on that top-line spend. So if you look on the right side of that chart, you saw NetApp was kind of negative, was very negative, right? But it is a company that's in transformation now, they've lowered expectations and they've recently beat expectations, that's why the stock has been doing better, but at the macro, from a spending standpoint, it's still stout challenged. So you have big footprint companies like NetApp and Oracle is another one. Oracle's stock is at an all time high, but the spending relative to sort of previous cycles are relative to, like for instance, Snowflake, much, much smaller, not as high growth, but they're managing expectations, they're managing their transition, they're managing profitability. Zoom is another one, Zoom looking negative, but Zoom's got to use its market cap now to transform and increase its TAM. And then Splunk is another one we're going to talk about. Splunk is in transition, it acquired SignalFX, It just brought on this week, Teresa Carlson, who was the head of AWS Public Sector. She's the president and head of sales, so they've got a go-to-market challenge and they brought in Teresa Carlson to really solve that, but Splunk has been trending downward, we called that several quarters ago, Erik, and so I want to bring up the data on Splunk, and this is Splunk, Erik, in analytics, and it's not trending in the right direction. The green is accelerating spend, the red is in the bars is decelerating spend, the top blue line is spending velocity or Net score, and the yellow line is market share or pervasiveness in the dataset. Your thoughts. >> Yeah, first I want to go back. There's a great point, Dave, about our data versus a disconnect from an equity analysis perspective. I used to be an equity analyst, that is not what we do here. And the main word you said is expectations, right? Stocks will trade on how they do compare to the expectations that are set, whether that's buy-side expectations, sell-side expectations or management's guidance themselves. We have no business in tracking any of that, what we are talking about is the top-line acceleration or deceleration. So, that was a great point to make, and I do think it's an important one for all of our listeners out there. Now, to move to Splunk, yes, I've been capturing a lot of negative commentary on Splunk even before the data turns. So this has been a about a year-long, our analysis and review on this name and I'm dating myself here, but I know you and I are both rock and roll fans, so I'm going to point out a Led Zeppelin song and movie, and say that the song remains the same for Splunk. We are just seeing recent spending attentions are taking yet another step down, both from prior survey levels, from year ago levels. This, we're looking at in the analytics sector and spending intentions are decelerating across every single group, and we went to one of our other slide analysis on the ETR+ platform, and you do by customer sub-sample, in analytics, it's dropping in every single vertical. It doesn't matter which one. it's really not looking good, unfortunately, and you had mentioned this is an analytics and I do believe the next slide is an information security. >> Yeah, let's bring that up. >> And unfortunately it's not doing much better. So this is specifically Fortune 500 accounts and information security. There's deep pockets in the Fortune 500, but from what we're hearing in all the insights and interviews and panels that I personally moderate for ETR, people are upset, that they didn't like the strong tactics that Splunk has used on them in the past, they didn't like the ingestion model pricing, the inflexibility, and when alternatives came along, people are willing to look at the alternatives, and that's what we're seeing in both analytics and big data and also for their SIM and security. >> Yeah, so I think again, I pointed Teresa Carlson. She's got a big job, but she's very capable. She's going to meet with a lot of customers, she's a go-to-market pro, she's going to to have to listen hard, and I think you're going to see some changes there. Okay, so sorry, there's more bad news on Splunk. So (indistinct) bring this up is Net score for Splunk and Elastic accounts. This is for analytics, so there's 106 Elastic accounts in the dataset that also have Splunk and it's trending downward for Splunk, that's why it's green for Elastic. And Erik, the important call out from ETR here is how Splunk's performance in Elastic accounts compares with its performance overall. The ELK stack, which obviously Elastic is a big part of that, is causing pain for Splunk, as is Datadog, and you mentioned the pricing issue, well, is it pricing in your assessment or is it more fundamental? >> It's multi-level based on the commentary we get from our ITDMs teams that take the survey. So yes, you did a great job with this analysis. What we're looking at is the spending within shared accounts. So if I have Splunk already, how am I spending? I'm sorry if I have Elastic already, how am I spending on Splunk? And what you're seeing here is it's down to about a 12% Net score, whereas Splunk overall, has a 32% Net score among all of its customers. So what you're seeing there is there is definitely a drain that's happening where Elastic is draining spend from Splunk and usage from them. The reason we used Elastic here is because all observabilities, the whole sector seems to be decelerating. Splunk is decelerating the most, but Elastic is the only one that's actually showing resiliency, so that's why we decided to choose these two, but you pointed out, yes, it's also Datadog. Datadog is Cloud native. They're more dev ops-oriented. They tend to be viewed as having technological lead as compared to Splunk. So a really good point. Dynatrace also is expanding their abilities and Splunk has been making a lot of acquisitions to push their Cloud services, they are also changing their pricing model, right? They're trying to make things a little bit more flexible, moving off ingestion and moving towards consumption. So they are trying, and the new hires, I'm not going to bet against them because the one thing that Splunk has going for them is their market share in our survey, they're still very well entrenched. So they do have a lot of accounts, they have their foothold. So if they can find a way to make these changes, then they will be able to change themselves, but the one thing I got to say across the whole sector is competition is increasing, and it does appear based on commentary and data that they're starting to cannibalize themselves. It really seems pretty hard to get away from that, and you know there are startups in the observability space too that are going to be even more disruptive. >> I think I want to key on the pricing for a moment, and I've been pretty vocal about this. I think the old SAS pricing model where you essentially lock in for a year or two years or three years, pay up front, or maybe pay quarterly if you're lucky, that's a one-way street and I think it's a flawed model. I like what Snowflake's doing, I like what Datadog's doing, look at what Stripe is doing, look at what Twilio is doing, you mentioned it, it's consumption-based pricing, and if you've got a great product, put it out there and damn, the torpedoes, and I think that is a game changer. I look at, for instance, HPE with GreenLake, I look at Dell with Apex, they're trying to mimic that model and apply it to infrastructure, it's much harder with infrastructure 'cause you've got to deploy physical infrastructure, but that is a model that I think is going to change, and I think all of the traditional SAS pricing is going to come under disruption over the next better part of the decades, but anyway, let's move on. We've been covering the APM space pretty extensively, application performance management, and this chart lines up some of the big players here. Comparing Net score or spending momentum from the April 20th survey, the gray is, sorry, the gray is the April 20th survey, the blue is Jan 21 and the yellow is April 21, and not only are Elastic and Datadog doing well relative to Splunk, Erik, but everything is down from last year. So this space, as you point out, is undergoing a transformation. >> Yeah, the pressures are real and it's sort of that perfect storm where it's not only the data that's telling us that, but also the direct feedback we get from the community. Pretty much all the interviews I do, I've done a few panels specifically on this topic, for anyone who wants to dive a little bit deeper. We've had some experts talk about this space and there really is no denying that there is a deceleration in spend and it's happening because that spend is getting spread out among different vendors. People are using a Datadog for certain aspects, they are using Elastic where they can 'cause it's cheaper. They're using Splunk because they have to, but because it's so expensive, they're cutting some of the things that they're putting into Splunk, which is dangerous, particularly on the security side. If I have to decide what to put in and whatnot, that's not really the right way to have security hygiene. So this space is just getting crowded, there's disruptive vendors coming from the emerging space as well, and what you're seeing here is the only bit of positivity is Elastic on a survey-over-survey basis with a slight, slight uptick. Everywhere else, year-over-year and survey-over-survey, it's showing declines, it's just hard to ignore. >> And then you've got Dynatrace who, based on the interviews you do in the (indistinct), one-on-one, or one-on-five, the private interviews that I've been invited to, Dynatrace gets very high scores for their roadmap. You've got New Relic, which has been struggling financially, but they've got a really good product and a purpose-built database just for this APM space, and then of course, you've got Cisco with AppD, which is a strong business for them, and then as you mentioned, you've got startups coming in, you got ChaosSearch, which Ed Walsh is now running, leave the data in place in AWS and really interesting model, Honeycomb is getting really disruptive, Jeremy Burton's company, Observed. So this space is it's becoming jumped ball. >> Yeah, there's a great line that came out of one of them, and that was that the lines are blurring. It used to be that you knew exactly that AppDynamics, what they were doing, it was APM only, or it was logging and monitoring only, and a lot of what I'm hearing from the ITDM experts is that the lines are blurring amongst all of these names. They all have functionality that kind of crosses over each other. And the other interesting thing is it used to be application versus infrastructure monitoring, but as you know, infrastructure is becoming code more and more and more, and as infrastructure becomes code, there's really no difference between application and infrastructure monitoring. So we're seeing a convergence and a blurring of the lines in this space, which really doesn't bode well, and a great point about New Relic, their tech gets good remarks. I just don't know if their enterprise level service and sales is up to snuff right now. As one of my experts said, a CTO of a very large public online hospitality company essentially said that he would be shocked that within 18 months if all of these players are still standalone, that there needs to be some M and A or convergence in this space. >> Okay, now we're going to call out some of the data that really has jumped out to ETR in the latest survey, and some of the names that are getting the most queries from ETR clients, many of which are investor clients. So let's start by having a look at one of the most important and prominent work from home names, Zoom. Let's look at this. Erik is the ride over for Zoom? >> Ah, I've been saying it for a little bit of a time now actually. I do believe it is, and we'll get into it, but again, pointing out, great, Dave, the reason we're presenting today Splunk, Elastic and Zoom, they are the most viewed on the ETR+ platform. Trailing behind that only slightly is F5, I decided not to bring F5 to the table today 'cause we don't have a rating on the data set. So then I went one deep, one below that and it's pure. So the reason we're presenting these to you today is that these are the ones that our clients and our community are most interested in, which is hopefully going to gain interest to your viewers as well. So to get to Zoom, yeah, I call Zoom the pandemic bull market baby. This was really just one that had a meteoric ride. You look back, January in 2020, the stock was at $60 and 10 months later, it was like 580, that's in 10 months. That's cooled down a little bit into the mid-300s, and I believe that cooling down should continue, and the reason why is because we are seeing huge deceleration in our spending intentions. They're hitting all-time lows, it's really just a very ugly dataset. More importantly than the spending intentions, for the first time, we're seeing customer growth in our survey flatten. In the past, we knew that the deceleration of spend was happening, but meanwhile, their new customer growth was accelerating, so it was kind of hard to really make any call based on that. This is the first time we're seeing flattening customer growth trajectory, and that in tandem with just dominance from Microsoft in every sector they're involved in, I don't care if it's IP telephony, productivity apps or the core video conferencing, Microsoft is just dominating. So there's really just no way to ignore this anymore. The data and the commentary state that Zoom is facing some headwinds. >> Well, plus you've pointed out to me that a lot of your private conversations with buyers says that, "Hey, we're, we're using the freebie version of Zoom, and we're not paying them." And that combined with Teams, I mean, it's... I think, look, Zoom, they've got to figure out how to use their elevated market cap to transform and expand their TAM, but let's move on. Here's the data on Pure Storage and we've highlighted a number of times this company is showing elevated spending intentions. Pure announced it's earnings in May, IBM just announced storage, it was way down actually. So still, Pure, more positive, but I'll on that comment in a moment, but what does this data tell you, Erik? >> Yeah, right now we started seeing this data last survey in January, and that was the first time we really went positive on the data set itself, and it's just really continuing. So we're seeing the strongest year-over-year acceleration in the entire survey, which is a really good spot to be. Pure is also a leading position among its sector peers, and the other thing that was pretty interesting from the data set is among all storage players, Pure has the highest positive public Cloud correlation. So what we can do is we can see which respondents are accelerating their public Cloud spend and then cross-reference that with their storage spend and Pure is best positioned. So as you and I both know, digital transformation Cloud spending is increasing, you need to be aligned with that. And among all storage sector peers, Pure is best positioned in all of those, in spending intentions and adoptions and also public Cloud correlation. So yet again, to start another really strong dataset, and I have an anecdote about why this might be happening, because when I saw the data, I started asking in my interviews, what's going on here? And there was one particular person, he was a director of Cloud operations for a very large public tech company. Now, they have hybrid, but their data center is in colo, So they don't own and build their own physical building. He pointed out that during COVID, his company wanted to increase storage, but he couldn't get into his colo center due to COVID restrictions. They weren't allowed. You had 250,000 square feet, right, but you're only allowed to have six people in there. So it's pretty hard to get to your rack and get work done. He said he would buy storage, but then the colo would say, "Hey, you got to get it out of here. It's not even allowed to sit here. We don't want it in our facility." So he has all this pent up demand. In tandem with pent up demand, we have a refresh cycle. The SSD depreciation cycle is ending. SSDs are moving on and we're starting to see a new technology in that space, NVMe sorry, technology increasing in that space. So we have pent up demand and we have new technology and that's really leading to a refresh cycle, and this particular ITDM that I spoke to and many of his peers think this has a long tailwind that storage could be a good sector for some time to come. >> That's really interesting, thank you for that extra metadata. And I want to do a little deeper dive on storage. So here's a look at storage in the industry in context and some of the competitive. I mean, it's been a tough market for the reasons that we've highlighted, Cloud has been eating away that flash headroom. It used to be you'd buy storage to get more spindles and more performance and we're sort of forced to buy more, flash, gave more headroom, but it's interesting what you're saying about the depreciation cycle. So that's good news. So ETR combines, just for people's benefit here, combines primary and secondary storage into a single category. So you have companies like Pure and NetApp, which are really pure play primary storage companies, largely in the sector, along with Veeam, Cohesity and Rubrik, which are kind of secondary data or data protection. So my quick thoughts here that Pure is elevated and remains what I call the one-eyed man in the land of the blind, but that's positive tailwinds there, so that's good news. Rubrik is very elevated but down, it's big competitor, Cohesity is way off its highs, and I have to say to me, Veeam is like the Steady Eddy consistent player here. They just really continue to do well in the data protection business, and the highs are steady, the lows are steady. Dell is also notable, they've been struggling in storage. Their ISG business, which comprises servers and storage, it's been softer in COVID, and during even this new product rollout, so it's notable with this new mid range they have in particular, the uptick in Dell, this survey, because Dell is so large, a small uptick can be very good for Dell. HPE has a big announcement next month in storage, so that might improve based on a product cycle. Of course, the Nimble brand continues to do well, IBM, as I said, just announced a very soft quarter, down double digits again, and they're in a product cycle shift. And NetApp, it looks bad in the ETR data from a spending momentum standpoint, but their management team is transforming the company into a Cloud play, which Erik is why it was interesting that Pure has the greatest momentum in Cloud accounts, so that is sort of striking to me. I would have thought it would be NetApp, so that's something that we want to pay attention to, but I do like a lot of what NetApp is doing, and other than Pure, they're the only big kind of pure play in primary storage. So long-winded, intro there, Erik, but anything you'd add? >> No, actually I appreciate it as long-winded. I'm going to be honest with you, storage is not my best sector as far as a researcher and analyst goes, but I actually think that a lot of what you said is spot on. We do capture a lot of large organizations spend, we don't capture much mid and small, so I think when you're talking about these large, large players like NetApp not looking so good, all I would state is that we are capturing really big organization spending attention, so these are names that should be doing better to be quite honest, in those accounts, and at least according to our data, we're not seeing it in. It's longterm depression, as you can see, NetApp now has a negative spending velocity in this analysis. So, I can go dig around a little bit more, but right now the names that I'm hearing are Pure, Cohesity. I'm hearing a little bit about Hitachi trying to reinvent themselves in the space, but I'll take a wait-and-see approach on that one, but pure Cohesity are the ones I'm hearing a lot from our community. >> So storage is transforming to Cloud as a service. You've seen things like Apex in GreenLake from Dell and HPE and container storage. A little, so not really a lot of people paying attention to it, but Pure bought a company called Portworx which really specializes in container storage, and there's many startups there, they're trying to really change the way. David Flynn, has a startup in that space, he's the guy who started Fusion-io. So a lot of transformations happening here. Okay, I know it's been a long segment, we have to summarize, and let me go through a summary and then I'll give you the last word, Erik. So tech spending appears to be tracking US GDP at 6 to 7%. This talent shortage could be a blocker to accelerating IT deployments, so that's kind of good news actually for services companies. Digital transformation, it remains a priority, and that bodes, well, not only for services, but automation. UiPath went public this week, we profiled that extensively, that went public last Wednesday. Organizations that sit at the top face some tough decisions on how to allocate resources. They're running the business, growing the business, transforming the business, and we're seeing a bifurcation of spending and some residual effects on vendors, and that remains a theme that we're watching. Erik, your final thoughts. >> Yeah, I'm going to go back quickly to just the overall macro spending, 'cause there's one thing I think is interesting to point out and we're seeing a real acceleration among mid and small. So it seems like early on in the COVID recovery or COVID spending, it was the deep pockets that moved first, right? Fortune 500 knew they had to support remote work, they started spending first. Around that in the Fortune 500, we're only seeing about 5% spend, but when you get into mid and small organizations, that's creeping up to eight, nine. So I just think it's important to point out that they're playing catch up right now. I also would point out that this is heavily skewed to North America spending. We're seeing laggards in EMEA, they just don't seem to be spending as much. They're in a very different place in their recovery, and I do think that it's important to point that out. Lastly, I also want to mention, I know you do such a great job on following a lot of the disruptive vendors that you just pointed out, with Pure doing container storage, we also have another bi-annual survey that we do called Emerging Technology, and that's for the private names. That's going to be launching in May, for everyone out there who's interested in not only the disruptive vendors, but also private equity players. Keep an eye out for that. We do that twice a year and that's growing in its respondents as well. And then lastly, one comment, because you mentioned the UiPath IPO, it was really hard for us to sit on the sidelines and not put some sort of rating on their dataset, but ultimately, the data was muted, unfortunately, and when you're seeing this kind of hype into an IPO like we saw with Snowflake, the data was resoundingly strong. We had no choice, but to listen to what the data said for Snowflake, despite the hype. We didn't see that for UiPath and we wanted to, and I'm not making a large call there, but I do think it's interesting to juxtapose the two, that when snowflake was heading to its IPO, the data was resoundingly positive, and for UiPath, we just didn't see that. >> Thank you for that, and Erik, thanks for coming on today. It's really a pleasure to have you, and so really appreciate the collaboration and look forward to doing more of these. >> Yeah, we enjoy the partnership greatly, Dave. We're very happy to have you on the ETR family and looking forward to doing a lot, lot more with you in the future. >> Ditto. Okay, that's it for today. Remember, these episodes are all available as podcasts wherever you listen. All you have to do is search "Breaking Analysis" podcast, and please subscribe to the series. Check out ETR website it's etr.plus. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me, david.vellante@siliconangle.com, you can DM me on Twitter @dvellante or comment on our LinkedIn posts. I could see you in Clubhouse. This is Dave Vellante for Erik Porter Bradley for the CUBE Insights powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (bright music)

(upbeat music) >> On February 19th of this year, Barron's dropped an article declaring Oracle, a cloud giant and the article explained why the stock was a buy. Investors took notice and the stock ran up 18% over the next nine trading days and it peaked on March 9th, the day before Oracle announced its latest earnings. The company beat consensus earnings on both top-line and EPS last quarter, but investors, they did not like Oracle's tepid guidance and the stock pulled back. But it's still, as you can see, well above its pre-Barron's article price. What does all this mean? Is Oracle a cloud giant? What are its growth prospects? Now many parts of Oracle's business are growing including Fusion ERP, Fusion HCM, NetSuite, we're talking deep into the double digits, 20 plus percent growth. It's OnPrem legacy licensed business however, continues to decline and that moderates, the overall company growth because that OnPrem business is so large. So the overall Oracle's growing in the low single digits. Now what stands out about Oracle is it's recurring revenue model. That figure, the company says now it represents 73% of its revenue and that's going to continue to grow. Now two other things stood out on the earnings call to us. First, Oracle plans on increasing its CapEX by 50% in the coming quarter, that's a lot. Now it's still far less than AWS Google or Microsoft Spend on capital but it's a meaningful data point. Second Oracle's consumption revenue for Autonomous Database and Cloud Infrastructure, OCI or Oracle Cloud Infrastructure grew at 64% and 139% respectively and these two factors combined with the CapEX Spend suggest that the company has real momentum. I mean look, it's possible that the CapEx announcements maybe just optics in they're front loading, some spend to show the street that it's a player in cloud but I don't think so. Oracle's Safra Catz's usually pretty disciplined when it comes to it's spending. Now today on March 17th, Oracle announced updates towards Autonomous Data Warehouse and with me is David Floyer who has extensively researched Oracle over the years and today we're going to unpack the Oracle Autonomous Data Warehouse, ADW announcement. What it means to customers but we also want to dig into Oracle's strategy. We want to compare it to some other prominent database vendors specifically, AWS and Snowflake. David Floyer, Welcome back to The Cube, thanks for making some time for me. >> Thank you Vellante, great pleasure to be here. >> All right, I want to get into the news but I want to start with this idea of the autonomous database which Oracle's announcement today is building on. Oracle uses the analogy of a self-driving car. It's obviously powerful metaphor as they call it the self-driving database and my takeaway is that, this means that the system automatically provisions, it upgrades, it does all the patching for you, it tunes itself. Oracle claims that all reduces labor costs or admin costs by 90%. So I ask you, is this the right interpretation of what Oracle means by autonomous database? And is it real? >> Is that the right interpretation? It's a nice analogy. It's a test to that analogy, isn't it? I would put it as the first stage of the Autonomous Data Warehouse was to do the things that you talked about, which was the tuning, the provisioning, all of that sort of thing. The second stage is actually, I think more interesting in that what they're focusing on is making it easy to use for the end user. Eliminating the requirement for IT, staff to be there to help in the actual using of it and that is a very big step for them but an absolutely vital step because all of the competition focusing on ease of use, ease of use, ease of use and cheapness of being able to manage and deploy. But, so I think that is the really important area that Oracle has focused on and it seemed to have done so very well. >> So in your view, is this, I mean you don't really hear a lot of other companies talking about this analogy of the self-driving database, is this unique? Is it differentiable for Oracle? If so, why, or maybe you could help us understand that a little bit better. >> Well, the whole strategy is unique in its breadth. It has really brought together a whole number of things together and made it of its type the best. So it has a single, whole number of data sources and database types. So it's got a very broad range of different ways that you can look at the data and the second thing that is also excellent is it's a platform. It is fully self provisioned and its functionality is very, very broad indeed. The quality of the original SQL and the query languages, etc, is very, very good indeed and it's a better agent to do joints for example, is excellent. So all of the building blocks are there and together with it's sharing of the same data with OLTP and inference and in memory data paces as well. All together the breadth of what they have is unique and very, very powerful. >> I want to come back to this but let's get into the news a little bit and the announcement. I mean, it seems like what's new in the autonomous data warehouse piece for Oracle's new tooling around four areas that so Andy Mendelsohn, the head of this group instead of the guy who releases his baby, he talked about four things. My takeaway, faster simpler loads, simplified transforms, autonomous machine learning models which are facilitating, What do you call it? Citizen data science and then faster time to insights. So tooling to make those four things happen. What's your take and takeaways on the news? >> I think those are all correct. I would add the ease of use in terms of being able to drag and drop, the user interface has been dramatically improved. Again, I think those, strategically are actually more important that the others are all useful and good components of it but strategically, I think is more important. There's ease of use, the use of apex for example, are more important. And, >> Why are they more important strategically? >> Because they focus on the end users capability. For example, one of other things that they've started to introduce is Python together with their spatial databases, for example. That is really important that you reach out to the developer as they are and what tools they want to use. So those type of ease of use things, those types of things are respecting what the end users use. For example, they haven't come out with anything like click or Tableau. They've left that there for that marketplace for the end user to use what they like best. >> Do you mean, they're not trying to compete with those two tools. They indeed had a laundry list of stuff that they supported, Talend, Tableau, Looker, click, Informatica, IBM, I had IBM there. So their claim was, hey, we're open. But so that's smart. That's just, hey, they realized that people use these tools. >> I'm trying to exclude other people, be a platform and be an ecosystem for the end users. >> Okay, so Mendelsohn who made the announcement said that Oracle's the smartphone of databases and I think, I actually think Alison kind of used that or maybe that was us planing to have, I thought he did like the iPhone of when he announced the exit data way back when the integrated hardware and software but is that how you see it, is Oracle, the smartphone of databases? >> It is, I mean, they are trying to own the complete stack, the hardware with the exit data all the way up to the databases at the data warehouses and the OLTP databases, the inference databases. They're trying to own the complete stack from top to bottom and that's what makes autonomy process possible. You can make it autonomous when you control all of that. Take away all of the requirements for IT in the business itself. So it's democratizing the use of data warehouses. It is pushing it out to the lines of business and it's simplifying it and making it possible to push out so that they can own their own data. They can manage their own data and they do not need an IT person from headquarters to help them. >> Let's stay in this a little bit more and then I want to go into some of the competitive stuff because Mendelsohn mentioned AWS several times. One of the things that struck me, he said, hey, we're basically one API 'cause we're doing analytics in the cloud, we're doing data in the cloud, we're doing integration in the cloud and that's sort of a big part of the value proposition. He made some comparisons to Redshift. Of course, I would say, if you can't find a workload where you beat your big competitor then you shouldn't be in this business. So I take those things with a grain of salt but one of the other things that caught me is that migrating from OnPrem to Oracle, Oracle Cloud was very simple and I think he might've made some comparisons to other platforms. And this to me is important because he also brought in that Gartner data. We looked at that Gardner data when they came out with it in the operational database class, Oracle smoked everybody. They were like way ahead and the reason why I think that's important is because let's face it, the Mission Critical Workloads, when you look at what's moving into AWS, the Mission Critical Workloads, the high performance, high criticality OLTP stuff. That's not moving in droves and you've made the point often that companies with their own cloud particularly, Oracle you've mentioned this about IBM for certain, DB2 for instance, customers are going to, there should be a lower risk environment moving from OnPrem to their cloud, because you could do, I don't think you could get Oracle RAC on AWS. For example, I don't think EXIF data is running in AWS data centers and so that like component is going to facilitate migration. What's your take on all that spiel? >> I think that's absolutely right. You all crown Jewels, the most expensive and the most valuable applications, the mission-critical applications. The ones that have got to take a beating, keep on taking. So those types of applications are where Oracle really shines. They own a very large high percentage of those Mission Critical Workloads and you have the choice if you're going to AWS, for example of either migrating to Oracle on AWS and that is frankly not a good fit at all. There're a lot of constraints to running large systems on AWS, large mission critical systems. So that's not an option and then the option, of course, that AWS will push is move to a Roller, change your way of writing applications, make them tiny little pieces and stitch them all together with microservices and that's okay if you're a small organization but that has got a lot of problems in its own, right? Because then you, the user have to stitch all those pieces together and you're responsible for testing it and you're responsible for looking after it. And that as you grow becomes a bigger and bigger overhead. So AWS, in my opinion needs to have a move towards a tier-one database of it's own and it's not in that position at the moment. >> Interesting, okay. So, let's talk about the competitive landscape and the choices that customers have. As I said, Mendelssohn mentioned AWS many times, Larry on the calls often take shy, it's a compliment to me. When Larry Ellison calls you out, that means you've made it, you're doing well. We've seen it over the years, whether it's IBM or Workday or Salesforce, even though Salesforce's big Oracle customer 'cause AWS, as we know are Oracle customer as well, even though AWS tells us they've off called when you peel the onion >> Five years should be great, some of the workers >> Well, as I said, I believe they're still using Oracle in certain workloads. Way, way, we digress. So AWS though, they take a different approach and I want to push on this a little bit with database. It's got more than a dozen, I think purpose-built databases. They take this kind of right tool for the right job approach was Oracle there converging all this function into a single database. SQL JSON graph databases, machine learning, blockchain. I'd love to talk about more about blockchain if we have time but seems to me that the right tool for the right job purpose-built, very granular down to the primitives and APIs. That seems to me to be a pretty viable approach versus kind of a Swiss Army approach. How do you compare the two? >> Yes, and it is to many initial programmers who are very interested for example, in graph databases or in time series databases. They are looking for a cheap database that will do the job for a particular project and that makes, for the program or for that individual piece of work is making a very sensible way of doing it and they pay for ads on it's clear cloud dynamics. The challenge as you have more and more data and as you're building up your data warehouse in your data lakes is that you do not want to have to move data from one place to another place. So for example, if you've got a Roller,, you have to move the database and it's a pretty complicated thing to do it, to move it to Redshift. It's a five or six steps to do that and each of those costs money and each of those take time. More importantly, they take time. The Oracle approach is a single database in terms of all the pieces that obviously you have multiple databases you have different OLTP databases and data warehouse databases but as a single architecture and a single design which means that all of the work in terms of moving stuff from one place to another place is within Oracle itself. It's Oracle that's doing that work for you and as you grow, that becomes very, very important. To me, very, very important, cost saving. The overhead of all those different ones and the databases themselves originate with all as open source and they've done very well with it and then there's a large revenue stream behind the, >> The AWS, you mean? >> Yes, the original database is in AWS and they've done a lot of work in terms of making it set with the panels, etc. But if a larger organization, especially very large ones and certainly if they want to combine, for example data warehouse with the OLTP and the inference which is in my opinion, a very good thing that they should be trying to do then that is incredibly difficult to do with AWS and in my opinion, AWS has to invest enormously in to make the whole ecosystem much better. >> Okay, so innovation required there maybe is part of the TAM expansion strategy but just to sort of digress for a second. So it seems like, and by the way, there are others that are doing, they're taking this converged approach. It seems like that is a trend. I mean, you certainly see it with single store. I mean, the name sort of implies that formerly MemSQL I think Monte Zweben of splice machine is probably headed in a similar direction, embedding AI in Microsoft's, kind of interesting. It seems like Microsoft is willing to build this abstraction layer that hides that complexity of the different tooling. AWS thus far has not taken that approach and then sort of looking at Snowflake, Snowflake's got a completely different, I think Snowflake's trying to do something completely different. I don't think they're necessarily trying to take Oracle head-on. I mean, they're certainly trying to just, I guess, let's talk about this. Snowflake simplified EDW, that's clear. Zero to snowflake in 90 minutes. It's got this data cloud vision. So you sign on to this Snowflake, speaking of layers they're abstracting the complexity of the underlying cloud. That's what the data cloud vision is all about. They, talk about this Global Mesh but they've not done a good job of explaining what the heck it is. We've been pushing them on that, but we got, >> Aspiration of moment >> Well, I guess, yeah, it seems that way. And so, but conceptually, it's I think very powerful but in reality, what snowflake is doing with data sharing, a lot of reading it's probably mostly read-only and I say, mostly read-only, oh, there you go. You'll get better but it's mostly read and so you're able to share the data, it's governed. I mean, it's exactly, quite genius how they've implemented this with its simplicity. It is a caching architecture. We've talked about that, we can geek out about that. There's good, there's bad, there's ugly but generally speaking, I guess my premise here I would love your thoughts. Is snowflakes trying to do something different? It's trying to be not just another data warehouse. It's not just trying to compete with data lakes. It's trying to create this data cloud to facilitate data sharing, put data in the hands of business owners in terms of a product build, data product builders. That's a different vision than anything I've seen thus far, your thoughts. >> I agree and even more going further, being a place where people can sell data. Put it up and make it available to whoever needs it and making it so simple that it can be shared across the country and across the world. I think it's a very powerful vision indeed. The challenge they have is that the pieces at the moment are very, very easy to use but the quality in terms of the, for example, joints, I mentioned, the joints were very powerful in Oracle. They don't try and do joints. They, they say >> They being Snowflake, snowflake. Yeah, they don't even write it. They would say use another Postgres >> Yeah. >> Database to do that. >> Yeah, so then they have a long way to go. >> Complex joints anyway, maybe simple joints, yeah. >> Complex joints, so they have a long way to go in terms of the functionality of their product and also in my opinion, they sure be going to have more types of databases inside it, including OLTP and they can do that. They have obviously got a great market gap and they can do that by acquisition as well as they can >> They've started. I think, I think they support JSON, right. >> Do they support JSON? And graph, I think there's a graph database that's either coming or it's there, I can't keep all that stuff in my head but there's no reason they can't go in that direction. I mean, in speaking to the founders in Snowflake they were like, look, we're kind of new. We would focus on simple. A lot of them came from Oracle so they know all database and they know how hard it is to do things like facilitate complex joints and do complex workload management and so they said, let's just simplify, we'll put it in the cloud and it will spin up a separate data warehouse. It's a virtual data warehouse every time you want one to. So that's how they handle those things. So different philosophy but again, coming back to some of the mission critical work and some of the larger Oracle customers, they said they have a thousand autonomous database customers. I think it was autonomous database, not ADW but anyway, a few stood out AON, lift, I think Deloitte stood out and as obviously, hundreds more. So we have people who misunderstand Oracle, I think. They got a big install base. They invest in R and D and they talk about lock-in sure but the CIO that I talked to and you talked to David, they're looking for business value. I would say that 75 to 80% of them will gravitate toward business value over the fear of lock-in and I think at the end of the day, they feel like, you know what? If our business is performing, it's a better business decision, it's a better business case. >> I fully agree, they've been very difficult to do business with in the past. Everybody's in dread of the >> The audit. >> The knock on the door from the auditor. >> Right. >> And that from a purchasing point of view has been really bad experience for many, many customers. The users of the database itself are very happy indeed. I mean, you talk to them and they understand why, what they're paying for. They understand the value and in terms of availability and all of the tools for complex multi-dimensional types of applications. It's pretty well, the only game in town. It's only DB2 and SQL that had any hope of doing >> Doing Microsoft, Microsoft SQL, right. >> Okay, SQL >> Which, okay, yeah, definitely competitive for sure. DB2, no IBM look, IBM lost its dominant position in database. They kind of seeded that. Oracle had to fight hard to win it. It wasn't obvious in the 80s who was going to be the database King and all had to fight. And to me, I always tell people the difference is that the chairman of Oracle is also the CTO. They spend money on R and D and they throw off a ton of cash. I want to say something about, >> I was just going to make one extra point. The simplicity and the capability of their cloud versions of all of this is incredibly good. They are better in terms of spending what you need or what you use much better than AWS, for example or anybody else. So they have really come full circle in terms of attractiveness in a cloud environment. >> You mean charging you for what you consume. Yeah, Mendelsohn talked about that. He made a big point about the granularity, you pay for only what you need. If you need 33 CPUs or the other databases you've got to shape, if you need 33, you've got to go to 64. I know that's true for everyone. I'm not sure if that's true too for snowflake. It may be, I got to dig into that a little bit, but maybe >> Yes, Snowflake has got a front end to hiding behind. >> Right, but I didn't want to push it that a little bit because I want to go look at their pricing strategies because I still think they make you buy, I may be wrong. I thought they make you still do a one-year or two-year or three-year term. I don't know if you can just turn it off at any time. They might allow, I should hold off. I'll do some more research on that but I wanted to make a point about the audits, you mentioned audits before. A big mistake that a lot of Oracle customers have made many times and we've written about this, negotiating with Oracle, you've got to bring your best and your brightest when you negotiate with Oracle. Some of the things that people didn't pay attention to and I think they've sort of caught onto this is that Oracle's SOW is adjudicate over the MSA, a lot of legal departments and procurement department. Oh, do we have an MSA? With all, Yes, you do, okay, great and because they think the MSA, they then can run. If they have an MSA, they can rubber stamp it but the SOW really dictateS and Oracle's gotcha there and they're really smart about that. So you got to bring your best and the brightest and you've got to really negotiate hard with Oracle, you get trouble. >> Sure. >> So it is what it is but coming back to Oracle, let's sort of wrap on this. Dominant position in mission critical, we saw that from the Gartner research, especially for operational, giant customer base, there's cloud-first notion, there's investing in R and D, open, we'll put a question Mark around that but hey, they're doing some cool stuff with Michael stuff. >> Ecosystem, I put that, ecosystem they're promoting their ecosystem. >> Yeah, and look, I mean, for a lot of their customers, we've talked to many, they say, look, there's actually, a tail at the tail way, this saves us money and we don't have to migrate. >> Yeah. So interesting, so I'll give you the last word. We started sort of focusing on the announcement. So what do you want to leave us with? >> My last word is that there are platforms with a certain key application or key parts of the infrastructure, which I think can differentiate themselves from the Azures or the AWS. and Oracle owns one of those, SAP might be another one but there are certain platforms which are big enough and important enough that they will, in my opinion will succeed in that cloud strategy for this. >> Great, David, thanks so much, appreciate your insights. >> Good to be here. Thank you for watching everybody, this is Dave Vellante for The Cube. We'll see you next time. (upbeat 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. >> SaaS companies have been some of the strongest performers in this COVID era. They finally took a bit of a breather this month, but they remain generally well-positioned for the next several years with their predictable models and cloud platforms. Meanwhile, the demise of on-prem legacy players from COVID shock, seems to have been overstated, in part because of the return of the laptop and in the case of Oracle with some see as a cloud play, Hmm. Then there's Bitcoin which is seeing public companies use their balance sheet liquidity to invest in the cryptocurrency. (chuckles) Wow. What does that all mean? I'll leave that for another day. Hello everyone and welcome to this week on Cube insights powered by ETR. On this breaking analysis, we'll pick out some of the more recent themes from this month and share our thoughts in some major enterprise software players, the future of on-prem, a review of our take on cloud and what cloud will look like in the 2020s. Wow. It's true, trees really don't grow to the moon. As predicted, the stock market has been a little bit crazy this month. February saw some leading SaaS names like Workday, Salesforce, and ServiceNow take a bit of a breather in the second half of the month. Workday and Salesforce announced earnings on the 25th. Workday had its first billion dollar subscription revenue quarter at 16% revenue growth a revenue and earnings beat. And of course the stock closed down Friday, more than 2%. Salesforce had a nearly $6 billion revenue quarter 20% growth, a revenue and earnings beat. And the day after it announced earnings the stock was down more than 6%. The market is worried about rising interest rates, and maybe concerned that an inflation fears are going to kill the stimulus bill. And so any whiff of caution by company managements is met with dampened enthusiasm. Meanwhile, it's looking like some of the big on-prem legacy firms, notably Dell, HPQ and HPE are making it through COVID, and might even come out in the other side stronger maybe. Dell handily beat expectations on the 25th on the strength of 17% growth in its client business. That's PCs. It's the gift that keeps on giving. HPQ had a strong beat as well, and we're anticipating a solid quarter from HPE next week on March 2nd. And then there's Oracle. Barron's had a big article on February 19th, entitled, "Oracle is turning into a cloud giant and why it's stock is a buy". It moved the market. And many investors rotated out of growth stocks, tech growth tech stocks into Oracle, others who had owned Oracle for a while scooped up some profits. Is Oracle a cloud giant? Hmm. We'll discuss that in a moment. And then there's all this Bitcoin mania. You know, our interest there is much more beyond the price fluctuations rather we're interested in the innovations in crypto. Look, we're going to table this for another day, but it's an interesting side note of this February madness. Let's take a look closer look at the February chill for SaaS companies. Here's a chart showing the relative performance of some of the big SaaS names in the latter half of this month. Now despite the strong earnings for Workday and Salesforce you can see the market's negative response on the 26th. Snowflake and ServiceNow they had epic runs last year, and they've been softening although on Friday morning ServiceNow shut down quickly on the open on sympathy with Workday and ServiceNow and then investors, you know, came back in. Very weird action in the market these days, again, not surprising. And look at the reaction investors had to the Barron's article on the 19th. They anointed Oracle as a cloud giant. Kudos to the Oracle PR team for that one. Now, let's take a look at these companies and put them in context. Even though they're not direct competitors it's instructive to model some of the top enterprise software players in positions, and line them up against each other. This chart here shows two dimensions from the ETR data. On the vertical axis is net score or customer spending momentum. And the horizontal axis is market share or pervasiveness in the survey. The table inset shows the net score measurement in the shared end. That's the metric that plots the dots. In both cases bigger is better. Note, that red dotted-line there is the 40% line. 40% to us is the magic number. Anything above that line is considered elevated. So we have ServiceNow and Salesforce they're up to the right. They're both big companies. They have significant market presence amongst the CIO and both have elevated spending velocity in the 50% range. And I've said for years, these two companies are on a collision course and I stand by that. It started happening and McDermott Bill McDermott, new CEO he's going to accelerate that in our view. We put a cloud around Snowflake tongue in cheek, because they are literally in the clouds on this chart. They stand alone, with a solid market presence that continues to grow in an off the charts net score of 83.3% now. For context you can see Oracle Fusion, NetSuite and Taleo. In addition, we put Slack and Coupa on the graphic, two names that have been on the radar lately and SAP, which continues to show decent spending momentum despite its challenges. All right, let me make a few comments on some of these companies. Snowflake, we've talked about a lot. I said earlier that their IPO, that if you really wanted to own it and couldn't wait for a better price, which I thought you'd get. And by the way you did, but then if you really wanted to own it on day one hold your nose and buy it and then wait a few years. So, you know, good luck. And I think you'll, it'll turn out okay for you. Now, the data really continues to show strong demand for Snowflake. There's no signs of them slowing down. So they announced earnings on March 3rd. We didn't have more data there. So we would expect confirmation of our analysis but you never know. Now Workday, here's our take. In our view the market is catching up to Workday. They had about a three-year lead at least in human capital management and the cloud and that whole model. And they had the best product. It was really simple and it was quite disruptive. But now you got Oracle, ADP, Ceridian they're catching up. Workday's expansion into financial management has been much more challenging and as it gets bigger, things get tougher. It's still though an enduring name. Salesforce, we see a bit differently. Salesforce is so big now, it's really hard for it to move the needle. And so it's been on an acquisition binge, and to grow that's likely going to continue. It could work well for the company. I mean, similar to the ways in which Oracle consolidated software names and picked up a lot of customers. Salesforce is a great name, and we think is going to continue to grow. ServiceNow is interesting. It's entering a new chapter under CEO, Bill McDermott, new CEO. He wants to double the company's revenue. And I think he's got a reasonable chance at that through a combination of great go-to-market and expanding the platform and in McDermott style doing acquisitions. SAP's market value tripled under his watch, and he knows the customers. And he's a magnet for attracting talent. Now ServiceNow is not without its challenges. Its customers often complain that ServiceNow is pricing is really high and it's becoming the Oracle of service management. But as McDermott aims more at SAP and Oracle customers, they create a nice umbrella for ServiceNow to work with. And technically, we think ServiceNow has other challenges around its multi-instance. We call it, if you can't fix it feature it architecture. That may present some issues down the road at scale. We don't have time to go into that in detail but suffice it to say that ServiceNow runs on its own cloud. So it's not running on a hyper scale cloud. Yeah. Good news it doesn't have to pay it through that. The bad news is, has got to manage all that infrastructure. It's basically be a cloud supply supplier but it doesn't do multi-tenant which means fundamentally, it has a more expensive cost structure. Okay. Let's turn our attention to what's happening on-prem with some of the big legacy names. Here's the same X Y chart with some of the big names that have a presence on-prem. First you can see VMware and Cisco, Oracle, Dell, IBM and HP. Look at them on the horizontal scale. They've got a large market share of presence in the ETR dataset. Unlike the larger SaaS companies however, none is above that magic 40% net scoreline. Pure, Dell's laptop business, Red Hat, OpenShift. They're above the line with Nutanix just about there at the line. The other major laptop players, Lenovo and HPQ showing momentum from the whole remote work trend. And for context, we put in NetApp so you can get a sense of where they're at. Pure beat its earnings last week but only grew 2% last quarter. Now remember the ETR survey, this is a forward-looking survey. So this potentially bodes well for the companies that are above that 40% line. Okay. So most so sorry of the companies on this chart only IBM and Oracle, those two own a public cloud. And we'll dig further into that in a moment, but virtually every name shown here, even Oracle has a mandate to redefine cloud. Meaning it has to put forth in our view in North star vision and execute on it. That will unify the experience between on-prem, hybrid cloud, public clouds, cross clouds and the edge. Now I say even Oracle, because in my view, Oracle is in a stronger position than the others, because of it's more coherent software architecture. Now the other companies on this chart, they have to architect a platform that abstracts the underlying complexity of clouds, leverage cloud native tooling in the respective public clouds. Connect on-prem infrastructure and build a layer, that stretches out and accommodates edge workloads. I think Oracle will follow suit and is actually ahead of most in a narrower context, i.e hybrid. But it doesn't have to race toward this vision. It can sit back as it often does, watch everyone else fumble around and make mistakes. And then Oracle will keep investing in R&D, watch the market, you know make its own experimental mistakes, and then enter the market and act like we invented it. Now, Cisco will come at this from a strong networking and security perspective. And it has a nice story on programmable infrastructure with Cisco DevNet. But unfortunately it does not own VMware as does Dell, but Dell is in the middle of a fairly remarkable journey. And it will be interesting to see what happens with the VMware spin-out and the cozy commercial relationship that Dell is structuring with VMware as you know, and as we've reported, Dell has used VMware's cash for a lot of this restructuring. And so we'll see, as it exits the current phase and enters a new phase, how it will be able to pursue that vision. It's going to be, whatever it does it's going to be much different than that vertically integrated Oracle approach, which of course brings me to IBM. Potentially Red Hat with OpenShift is the most powerful card in the deck right now. OpenShift I mean, it's open it's everywhere. It has momentum as we showed. And I like their position. My concern is IBM, IBM is still unwinding and restructuring its business. And it's taking a long time as we've seen, with its outsourcing business. And now the Watson health assets, Irvine is continuing that downsizing trend that we saw under Ginny, shedding non-strategic businesses that don't fit, Irvine has a lot to deal with. And I want to point out that this idea of an abstraction layer across clouds is not trivial. First, all of these companies have to stop being so defensive about the public cloud. To a large extent, VMware and Red Hat have found a happy place. But in my view, they all should be thanking AWS, Azure, and Google for building out this great global distributed system, that they can leverage and build on top of. And second, this is going to be expensive. And Cisco, Dell VMware, IBM, they're all really stretched thin from an R&D perspective. They a lot of mouths to feed across the portfolio. So is HPE stretched thin, and it doesn't have the R&D budget at less than $2 billion annually. So my concern is that we're going to have lots of complexity across these obstructions layers by vendor. Now maybe the good news for companies. This may be good news for companies like Hashi or specialists with a vision to do this within a domain like a clumial, or a vast data, but this is big, and they are small. So it's going to take the better part of a decade to play out. Now, let's take a quick look at the cloud players. OMG when I saw that article in Barron's last weekend my mouth dropped. What a headline and it had this illustration of a stout Larry Ellison rising above the clouds. Here's a picture of the ETR data for the cloud players. It's the same X, Y plotting or net score and market share. If you follow this program, you know we believe there are four and only four hyper scale cloud players, with the resources to compete and differentiate as horizontal infrastructure players, which really is how we view the origination of modern cloud computing. AWS created it with S3 and EC2 with 2006. Those four are AWS and Azure, which have a large lead over the pack. Google cloud and Alibaba. And you can see we've circled the on-prem pack which comprises Oracle and IBM along with Dell VMware. And we snuck Google just stuck them at the edge of that circle because the differentiate they're cozying up to companies with strong enterprise sales teams and Google's, they're smart, they're patient. And so we, by no means, count them out. They're spending like mad and they have a lot of cash. They've done some really interesting open source things with containers. And so, you know, no doubt they're a player, but they are behind. Now in that on-prem pack, IBM and Oracle they actually own their own public clouds. IBM, they acquired soft layer which was a bare metal hosting company at the time to get IBM into the game. They retooled the platform over several years. Now here's the thing, try and unpack IBM's cloud business looking at its financial or in earnings reports. It's just a mess. I hope Irvine cuts the nonsense and actually develops and reports a set of metrics that are meaningful to cloud observers and IBM observers, because the way IBM reports its cloud business today is opaque and it's nonsense. It's frankly embarrassing to the company. It needs to end sooner rather than later is fundamentally meaningless to any observers. Now observers of cloud. If you care about the big chunk of whatever then maybe it has meaning. Now Oracle for its part, they announced the public cloud years ago, its version of one datto cloud was crap. And the company, they hired a bunch of really smart cloud engineers and they spent a lot of money to fix that. Now neither IBM nor Oracle have the CapEx resources of the big four, not even close, yet they'll build out data centers and yes they'll have a play, but they're different and that's okay. Now in the Barron's article on Oracle, the author was quite positive on Oracle, noting that quote, "On a recent earnings call CEO Safra Catz said that Oracle cloud infrastructure revenue was up 139% for the quarter". So, (laughs) we have really no sense or a stake in the ground is to up from what? Anyway, noting further the author said, quote, "Alas! Oracle doesn't break out OCI sales and comps can be messy". Hmm, indeed. Oracle is hiding the ball on OCI, that's because if they did break it out, which by the way they used to report, AIS revenue explicitly, but if they did break it out, they would only be highlighting that they are a minor player in AIS. Further, the article continues, quote, "Catz says that hers is the only tech company that has both a global cloud and a full set of enterprise applications". Unquote, bingo. There it is. That's why Oracle is in a better position than many of or most of the on-prem players listed in this chart. By the way, I would argue that Microsoft has a pretty impressive set of enterprise applications in a fairly global cloud. But what Safra is talking about is applications that support the world's most mission critical work. And when it comes to that, Oracle is number one. Don't fool yourself and get caught up in the Oracle lock-in and high pricing narrative, thinking that they're going to get crushed. They're not. Oracle is the best in the mission critical workload game. There is no one better, period. But guys, come on. The big four last year grew 41% and accounted for $86 billion in AIS revenue, AKA real cloud revenue. And they're going to surpass $115 billion this year combined. Real cloud companies don't grow in the single digits today. So talk to me when we reach equilibrium on that front. Okay. So let's wrap by looking at what does a cloud company look like in the 2020s? Now, I'm not saying that the rest of the pack shouldn't redefine cloud they should. But I hope we can all agree by now that modern day cloud computing was defined in business terms by AWS. They are number one in cloud computing, make no mistake. However, AWS is bringing the cloud into the wheelhouse of the on-prem players, cleverly saying that it's bringing AWS to the edge and it looks at the data centers. Just another edge node is great positioning but that is not trivial. Just look it out posts and how AWS has had to evolve its pricing strategy in terms, can't just turn it off like you can, the public cloud. I have an entire rant on all the, SaaS service transformations. It's really interesting to watch as AWS goes out, and the on-prem players come in and go hybrid. I got a lot of thought on what's happening there both in terms of SaaS, which I think is an outdated pricing model, and the infrastructure as a service players that are really getting into this game, we would love to do a session on that sometime. And it's a real disruption I think coming. Anyway, AWS competitors should absolutely try to redefine cloud. By AWS moving to the edge, it's opened up the door to that possibility. Microsoft is obviously in the best position I think by far here. They've earned the right and I'll never accuse them of cloud washing. Google, they got some work to do in this regard, but they probably have the largest physical cloud infrastructure in the world. As I've said, they just need to pull their heads out of their ads and quadruple down on cloud. But this idea of abstracting away the underlying complexity of the cloud, leveraging cloud native capabilities and building on top of the shoulders of the cloud giants such as David Floyer has expressed in this chart, moving from stateless to state full, integrating across clouds, advancing automation not only through the stack, but across domains and ultimately using metadata to govern where workloads should live or be moved, be disintegrated and recombined with low latency and be highly secured. I look at this, I think about this and I say one there is this technically feasible and smart techies tell me yes, so I keep trying to dig here for signs and I definitely see some movement in this direction. And two, I don't think any one vendor is going to do this themselves. They're not going to, it's not going to be owned by one company. I think what's going to happen is you'll get successes within layers of the stack. I mean, think about Snowflakes data cloud. We're going to see that for storage. See it for backup, data management, security maybe security within different domains. You see endpoint and identity access management. Maybe that cloud comes together as cloud security. You see it in applications, but without clear standards, it's going to be a challenge. And with respect to my friends at Snowflake, we might even see it in database sometime LOL, but look you all have a lot of work to do. And to my CIO friends, you know the drill much better than I, technology is going to keep relentlessly coming at you and you can deal with that. It's the people and the change management in the culture. Those are your bigger challenges, but don't screw up the tech. Okay. Thanks for watching. Remember, these episodes are all available as podcasts wherever you listen, just search breaking analysis podcasts, and please subscribe to the series, we appreciate that. Check out ETR's website at ETR.plus sorry, ETR.plus. We also publish a full report every week on wikibon.com and siliconangle.com. You can email me at David.Vellante@siliconangle.com or DM me on Twitter at DVellante that's @DVellante or comment, excuse me on my LinkedIn posts. This is Dave Vellante for theCUBE Insights powered by ETR. Stay safe, be well, get the jab if you have an opportunity. And we'll see you next time. (soft music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante as we watch an historic election unfold before our eyes we look back at the early days of the millennium with the memorable presidential race of 2000 that decade of course was defined by 911 which permanently reshaped our thinking and we exited that decade at the tail end of a massive financial crisis only to enter the 2010s with the hope and the momentum of fiscal stimulus a flat globe job growth and very importantly the ascendancy of the cloud cloud computing unquestionably powered the innovation engine over the last 10 years and the pandemic marks a new era where adoption of cloud data and ai have been accelerated by at least two to three years and that's what's going to shape the future of the technology industry and frankly all businesses and organizations hello everyone and welcome to this week's episode of thecube insights powered by etr in this breaking analysis we're going to update you on our latest cloud market share and dig in to some fresh october survey data from our partners over at etr let me start just with a brief summary of the latest action that's going on in cloud now quite interestingly each of the big three cloud players they showed nearly identical year-on-year growth rates in q3 as they did in q2 now we're going to dig into that in a moment but our data suggests that these three companies combined will account for more than 75 billion dollars in infrastructure as a service and platform as a service revenue in 2020 and they're potentially on track to hit 100 billion in 2021. customer survey data indicates that cio's top two infrastructure priorities remain security and cloud migration now that said as we previously reported the cloud it's not immune to the pandemic the remote worker pivot well it's a positive for cloud hasn't completely eradicated certain headwinds now what i mean here is that because the cloud vendors are now so large they're somewhat exposed to the softness in the overall i.t spending climate and also industries that have been hit hardest by the pandemic now would the cloud growth have been better if the pandemic didn't hit we'll never know for sure but our data suggests no covet has definitely been a benefactor to cloud in our view cloud will remain at the center of technological innovation for the foreseeable future the economics of cloud are becoming so compelling that we think the power of the big cloud companies will only increase this decade now importantly we're talking about the costs of running hyper-distributed systems we're not commenting here on what they charge customers that's a different story we believe the cost structure for the hyperscalers is superior to alternative approaches and we believe this advantage will only accelerate over the next several years we also believe that competition is going to continue to drive competitive pricing and innovation all right let's look at our latest market share numbers for the big three this chart shows our estimates of aws azure and the google cloud platform now viewers of this program know that these are is and pass figures and you also know that aws is the only company that provides clean numbers on that sector whereas azure and gcp are estimates that we make based on tidbits of guidance that the companies give us and survey data that we capture and other modeling that we do now as we've said we'll end this year it's about 75 billion in revenue or maybe even a little bit more note that for these three note that we've we've slightly restated some of our earlier estimates for azure to reconcile some differences that we had between constant currency and actual growth we try to keep things in constant currency where possible sorry for that but sometimes that happens azure according to our estimates as we reported last week is now 18 of microsoft's overall revenue number we had it at 19 that last week but when i dug in we made some adjustments so we toned it down a bit aws represents a much smaller percentage of course of amazon's revenues at about 12 percent but it represents 56 percent of amazon's profits gcp on the other hand accounts for less than five percent of google's overall revenue which as we've stated a few weeks ago needs more attention from google but look at the growth rates for these three platforms and the respective size of their is and pass businesses hear all this talk about repatriation i.e that what i mean by that is people go to the cloud but they're unhappy or the bill is too high it's too expensive so then they come back on prem well you just don't see that in the numbers so you gotta be careful when vendor a vendor tries to sell you on that trend i don't buy it except for selective situations now let's bring in some of the etr data and compare the spending momentum for each of the big three you've seen these wheel graphs before they show the breakdown of net score for aws microsoft and google now one note these figures represent these three companies overall within the etr technology taxonomy so for example they don't include amazon's retail business of course but they do include for example microsoft's entire tech portfolio not just the cloud the green portion of the wheel represents increases in spending via new adoptions and increased spending whereas the red sections show decreases via lower spending and defections net score which i've highlighted in the orange is calculated by subtracting the two reds from the two true greens in other words adoptions and increase minus decrease and replacements the takeaway here is these are all pretty strong with aws leading the pack microsoft is exceptionally strong as we pointed out last last week because they're so huge and they still have net scores comparable to aws which is a pure play gcp is a laggard and is showing softness in the data despite a sanguine outlook that we had back in 2019 based on survey data i don't know perhaps google's smaller presence muted their customers ability to take advantage of the platform the thinking there is the customers maybe needed to pivot to the cloud so quickly and aws and azure were the incumbents and that was maybe the most expedient path hence the higher increases in the spend more category but you do see gcp um they had 13 new adoptions which is pretty good so we'll keep looking at that regardless again these are not pure play cloud comparisons but they give a good indication of spending momentum i'd also note that all three show very low defections well each is showing solid increases in new adoptions especially google as i mentioned so that's kind of interesting to see but again google much much smaller you would expect that now i want to turn our attention to one of the hottest areas in cloud which is serverless and this is a pure play comparison so serverless let me start there it's a strange term because it's not really accurate but it's stuck serverless computing is a model where the cloud platform dynamically delivers services as the application requires so so you don't have to configure the compute and the containers for example rather when an application needs resources it goes and gets them and you only pay for when the services are actually invoked and in use so it's really good for workloads that spin up and spin down very frequently it kind of reminds me in concept anyway of the component tree that we saw in the days of soa if you remember that services oriented architecture but now this is cloud it's cloud native it's a whole new world and it's increasingly a popular model and as we'll show in a moment there's a lot of spending momentum in this area but before we do that i want to share some comments made by andy jassy a while back about serverless take a listen it's a good question and you know i really the comment i made was really about um directionally what amazon would do you know in this in the very earliest days of aws jeff used to say a lot if i were starting amazon today i'd have built it on top of aws we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point i think the same thing is true here with lambda which is i think if amazon were starting today it's a given they would build it on the cloud and i think with a lot of the applications that comprise amazon's consumer business we would build those on on our serverless capabilities now now lambda of course jesse referring to lambda that's amazon's serverless offering and if you think about amazon's retail business and take for example the frequent spin up and spin down of resources for something like black monday serverless would be a much more cost effective approach same for a managed data warehouse service for example where you know you don't want to pay for the compute if it's idle the app just calls for the compute when it's needed so it's a very popular model and it's got increased momentum today and you see that in this slide it shows the net score breakdown for serverless for azure aws is lambda which is again is their serverless offering and google cloud functions again you're shipping functions to the application that's why it's called functions look at the net scores azure functions nearly 70 aws at 65 google again lagging and that's a bit of a concern because this is a really really hot space all right let's move on and look at the competitive landscape as we like to do often and update you on that this xy graph is one of our favorites and it shows net score or spending momentum on the vertical axis and market share on the horizontal market share is a measure of pervasiveness in the data set in the upper right you also see a table that ranks each vendor my net score and it includes the shared n in other words the number of mentions in this sector for each vendor now you can you can see up top in the middle i've selected on the cloud computing category so this represents only the cloud businesses for each of these players there's a little bit of nuance here and that we've selected on microsoft azure there's a category in the etr taxonomy for that and we're comparing that with aws overall so there's there are things in the aws overall number that fit into the other parts of the taxonomy like maybe ai collaboration etc whereas azures and gcp are just the cloud segments so i i know it's a bit strange because aws is all cloud but don't get caught up in the taxonomical nuance the point is it's good to be azure in aws it's shown there when you look at the upper right of the chart here they stand out and they stand alone in cloud leadership google cloud is they have nice elevated levels but they're much much smaller they don't have the presence in the market now look at that hybrid cloud zone emerging we've talked about this sometimes in the past and and i want to call it vmware cloud on aws red hat open shift and vmware cloud itself like vmware cloud foundation and their other cloud services all of these appear to be gaining traction and you can see in the number of occurrences in the upper right that shared end that i talked about we're starting to see real numbers that are meaningful in this space vmware cloud on aws for example has a net score of 53 percent with 116 accounts within that total respondent sample that you see there in the middle left of 1438 that's how many cios and technology buyers responded to the etr survey in october you look at open shift at 45 net score and that's with 82 accounts now openshift is in beta with what looked to be some really strong offerings on aws and you can see for context i've added dell emc's cloud offerings hpe's cloud offerings and the oracle cloud and ibm cloud and also rackspace dell actually pretty strong with a net score of 20 and 185 shared accounts much much higher than dell overall which is kind of in the red zone oracle ibm you see those rackspace you know organizing not killing it rackspace is kind of in the big negative so that's a concern but anyway we'd like for these guys we'd like to see the data match the marketing rhetoric for the the guys that are in the red and look alibaba is starting to to show up in the server there's only 26 shared ends but we thought we'd we'd put it in there those three key points again aws and microsoft keep on trucking google needs to do better hybrid is becoming real and that bodes well for multi-cloud and the legacy on-prem guys they got a lot of work to do they're under a lot of pressure the pivot to cloud has not been easy for them uh and it's still a case where they're i've talked about this a lot they're they're declines in their on-premises offerings they're not being offset by the new stuff the cloud momentum all right i want to close out by sharing some of the conversations and thoughts that we've had in the community around sas and its impact on cloud we really have been focusing on ias and pass of the sas layer obviously up the stack so let me first share that there's a lot of talk around and has been for years about aws they're slowing growth rates and whether or not they'll have to enter the sas market to expand their total available market and i've said consistently while i never say never about aws i don't think so at least not yet this chart plots the big three cloud players note aws is a bigger piece of this pie now that i've turned off the cloud computing filter and i know more nuances but the data wonks will will find you know see this and they'll ask me about it this is all of aws portfolio and again it's only the microsoft azure portfolio so you see it aws now overtakes azure on the x-axis i.e market share now we've plotted some of the major sas vendors and you can see servicenow and salesforce both very large and they have really strong spending momentum and servicenow's you know pushing 100 billion dollars in market value they've surpassed workday quite some time ago workday's got less presence but they've got really really solid net score and i got to say i'm impressed with sap despite some of the earnings challenges that they've been having they're right up there with splunk and tableau splunk has softened in recent surveys and i've i've also plotted in there netsuite and oracle fusion which are just okay and that is i think for now anyway aws is going to position as the best place and the most friendly and highest quality cloud in which to run your sas for example workday runs on aws aws is salesforce's preferred infrastructure platform so my premise here is just like retail companies might want not want to run on aws a number of sas companies that compete with microsoft they might think twice about running on azure so aws would be better off for now trying to attract those sas players and drive their services and sticking to infrastructure and the pass layer snowflake is actually kind of interesting and i've added them for context because their netscore is always kind of a bellwether it's really off the charts and they're an isv running on the cloud they're different from some of the other sas players and the snowflake is a database okay and most of snowflake's business runs on aws and aws competes with snowflake with redshift but aws has the best cloud and drives a lot of business for snowflake and vice versa so it's kind of interesting snow snowflake to redshift and a much smaller example is kind of like netflix to amazon prime video to compete they both thrive so i think aws is going to continue to grow by attracting sas players as the preferred platform and they'll also attract developers and try to disrupt sas players like servicenow which runs on its own cloud i remember years ago david floyer and i said that servicenow was it was awesome but at some point its infrastructure cost structure its infrastructure cost structure is going to be less competitive than those companies that are running on hyperscale clouds certainly the hyperscale clouds themselves and servicenow they have this multi-instance architecture which just can't easily port over to the cloud but it can charge a lot which it does now at some point some sharp developers are going to look at all this and say whoa see that service now i can build this for less and they'll attack servicenow and their seat base license model maybe with the consumption pricing model and a platform that's perhaps or a set of services that are perhaps less expensive you're seeing this to a you know a certain degree with like elastic inside the application performance management space so there's some some things to watch there but there are those who firmly believe that aws will and must enter the sas space directly we talked last week about how beneficial microsoft's application business is for azure and what a flywheel that is but for me i think we're not there yet let's give it some time i think maybe four to five years before aws may even start to think about filling some of the space up the stack now maybe they'll find some unique opportunities to do that for instance at the edge but i think that's way off okay so bottom line it's good to be in tech these days it's even better to be in the cloud and it's best if you're aws and microsoft and i don't see that changing for a while now remember these episodes are all available as podcasts wherever you listen i publish each week on wikibon.com and siliconangle.com you can get in touch with me through email it's david at siliconangle.com feel free to dm me on twitter at d vallante i post on linkedin love your comments there thank you and don't forget to check out etr plus for all the survey action thanks for watching this episode of thecube insights powered by etr this is dave vellante stay safe stay sane and we'll see you next time you

(thoughtful electronic music) >> From theCUBE studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> Enterprise applications are an enormous market, and they're enormously important to organizations globally. Essentially, the world's businesses are running on enterprise applications. Companies' processes are wired into these systems, and the investments that they make in people, process, and technology are vital to these companies' success. But it's complicated because many of these systems are decades old. Markets have changed, but the ERP system for example fundamentally hasn't. Hello everyone, and welcome to this week's Wikibon CUBE Insights, powered by ETR. This week, we're going to do a data download on the enterprise software space, and put forth some themes in our thesis around this very important segment. I'd like to do a shout-out to my friend Sarbjeet Johal, who helped me frame this segment, and he's a strategic thinker and he shared some excellent insights for this episode. What I'd first like to do is let's lay out the scope of what we're going to talk about today. So we're going to focus on the core enterprise apps that companies rely on to run their businesses. Talkin' about the systems of record here, the ERP, the financial systems, HR, CRMs, service management we'll put in there. We may touch on some of the other areas, but this is core that we're going to drill into. This is a big, big market. Customers spend many hundreds of billions of dollars in this area, you could argue about a half a trillion. And it's a mature market, as you'll see from the data. Look, it's good to be in the technology business today. This business is doing better than most, and within the technology business, it's better to be in software because of the economics and scale. And if you have a SaaS cloud model, it's even better. But the market, it is fragmented, not nearly as much as it used to be, but there are many specialized areas where leaders have emerged. ServiceNow and ITSM or Workday and HCM are good examples of companies that've specialized and then exploded, first as we saw ServiceNow blow past Workday's valuation. It was nearly 2x at one point. Now, that was before Workday crushed its earnings this week. It's up 15% today. ServiceNow took a slight breather earlier this month, but it's up on Workday sympathy today. Salesforce also beat earnings, and of course replaced Exxon Mobile on the DOW Industrials, can you imagine that? But let's bring it back to this digital transformation that you hear about. This is the big cliche from all the tech companies and especially software players. Now a lot of this DX, I sometimes call it, is related to old systems. It's especially true for the mega-caps like Oracle, SAP, PeopleSoft, JD Edwards, and even Microsoft. Take ERP and some of the mature products for example, like Oracle R12, or SAP R3 or R4. Many of these systems were put in place 15 years ago, and yeah, they're going to need to transform. They are burnt in. They were installed in what, 2005? It was before the iPhone, before social media, before machine learning and AI made its big comeback, and before cloud. These systems were built on the 1.0 of cloud. The businesses have changed but the software really hasn't. It happens every 10 to 15 years, companies have to upgrade or re-implement their systems, and optimize for the way business now runs, because they had to be more competitive and more agile. They can't do it on their old software. And God help you if you made a bunch of custom modifications. Good lucking tryin' to rip those out. And this is why pure play companies in the cloud like ServiceNow and Workday have done so well. They're best-of-breed and they're cloud, and it sets up this age-old battle that we always talk about, best-of-breed versus integrated suites. So let's bring in some of the other themes and feedback that we get from the community. Now we've definitely seen this schism play out between on-prem and cloud plays. And that's created some challenges for the legacy players. People working remotely has meant less data center, less on-prem action for the legacy companies. Now, they have gone out and acquired to get to the cloud and/or they've had to rearchitect their software like Oracle has done with Fusion. But think about something like Oracle Financials. Oracle is tryna migrate them to Fusion, or think about SAP R3, with R4, SAP pushing HANA. All this is going to cloud-based SaaS. So the companies that've been pure play SaaS are doing better, and I say quasi-modern on this slide because Salesforce, ServiceNow, Workday, even Coupa, NetSuite which is now Oracle, SuccessFactors which SAP purchased, et cetera, these are actually pretty old companies, the earlier part of the 2000s or in the case of Salesforce, 1999. And you're seeing some really different pricing models in the market. Things are moving quickly to an OPEX model. You have the legacy perpetual pricing, and it's giving way to subscriptions, and now we even see companies like Datadog and Snowflake with so-called consumption-based pricing models, priced as a true cloud. And we think that that's going to eventually spill into the core SaaS applications. Now one of the concerns that we've heard from the community is that some of the traditional players that were able to hide from COVID earlier this year might not have enough deferred revenue dry powder to continue to power through the pandemic, but so far the picture continues to look pretty strong for the software companies. We'll get into some of that. Now, finally, this is a premise that I talked to Sarbjeet about, the disruption perhaps comes from cloud and developer ecosystems. Y'know I remember John Furrier and I had a conversation awhile back with Jerry Chen from Greylock. It was on theCUBE, and it was kind of like, went like this. People were talking about whether AWS was going to enter the applications market, and the thesis here is no, or not in the near future. Rather, the disruptive play, and this is really Sarbjeet's premise, is to provide infrastructure for innovation, and a PaaS layer for differentiation, and developers will build modern cloud-native apps to compete with the SaaS players on top of this. This is intriguing to me, and is likely going to play out over the next decade, but it's going to take a while, because these SaaS players are, they're very large, and they continue to pour money into their platforms. Now let's talk about the shift from CAPEX to OPEX and bring in some ETR data. Of course, this was well in play pre-COVID, but the trend has been accelerating. This chart shows data from the August ETR survey, and it was asking people to express their split between CAPEX and OPEX spend, and as you can see, the trend is clear. Goes from 48% last year, 55% today, and moving to over 62% OPEX a year from now. It's no surprise, but I think it could happen even faster depending on the technical debt that organizations have to shed. And hence, the attractiveness again of the SaaS cloud players. So now let's visualize some of the major players in this space, and do some comparisons. Here we show one of our favorite views, and what we're doing here is we juxtapose net score on the vertical axis with market share on the horizontal plane. Remember, net score is a measure of spending momentum. Each quarter, ETR asks buyers, are you planning to spend more or less, and they essentially subtract the lesses from the mores to derive net score. Market share on the other hand is a measure of pervasiveness in the dataset, and it's derived from the number of mentions in the sector divided by the total mentions in the survey, and you can see each metric in that embedded table that we put in there. So I said earlier, this was a pretty mature market and you can see that in the table. Eh, kind of middle-of-the-road net scores with pretty large shared ends, i.e. responses in the dataset, but a lot of red. There are some standouts, however, as you see in the upper right, namely, ServiceNow and Salesforce. These are two pretty remarkable companies. ServiceNow entered the market as a help desk or service management player, and has dramatically expanded its TAM, really to the point where they're aiming at $5 billion in revenue. Salesforce was the first in cloud CRM, and is pushing 20 billion in revenue. I've said many times, these companies are on a collision course, and I stand by that, as any of the next great software companies, and these are two, are going to compete with all the mega-caps, including Oracle, SAP, and Microsoft, and they'll bump into each other. Which brings us to those super-cap companies. You see Microsoft with Dynamics, they show up like they always do. I'm like a broken record on Microsoft. I mean they're everywhere in the survey data. Now Oracle and SAP, they've been extremely acquisitive over the years, and you can see some of their acquisitions on this chart. I've said many times in theCUBE that Larry Olsen used to denigrate his competitors for writing checks instead of code, but he saw the consolidation trend happening in the ERT, ERP space before anyone else did, and with the $10 billion PeopleSoft acquisition in 2005, set off a trend in enterprise software that did a few things. First, it solidified Oracle's position further up the stack. It also set Dave Duffield and Aneel Bhusri off to create a next-generation cloud software company, Workday, which you can see in the chart has a net score up there with ServiceNow, Salesforce, and Coupa, and it also led to Oracle Fusion Middleware, which is designed as an integration point for all these software components, and this is really important because Oracle is moving everything into its cloud. And you can see that its on-prem net score, which puts it deep into negative territory. Now SAP, take a look at them, they have much higher net scores than Oracle, and you can see it's acquired SaaS properties like Ariba, Concur, and SuccessFactors, which have decent momentum. But you know, SAP, and we've talked about this before, is not without its challenges. With SAP, HANA is the answer to all of its problems. The problem is that it's not necessarily the answer to all of SAP's customers' problems. Most of SAP's legacy customers run SAP on Oracle or other databases. HANA is used for the in-memory query workload, but most customers are going to continue to use other databases for their systems of record. So this adds complexity. But HANA is very good at the query piece. However, SAP never did what Oracle did with Fusion, which as you might recall, took more than a decade to get right. HANA is SAP's architectural attempt to unify the SAP portfolio and get, (laughs) really get off of Oracle, but it's many years away, and it's unclear when or if they'll ever get there. All right, let's move on. Here's a look at a similar set of companies, but I wanted to show you this view because it gives you a detailed look at ETR's net score approach, and it tells us a few things more. And remember, this is a survey of almost 1,200 technology buyers. That's the N, that's the respondent rate. So this chart shows the net score granularity for the enterprise players that we were just discussing. Let me explain this. Net score is actually more detailed than what I said before. It comprises responses in four categories. The lime green is new adoptions. The forest green is growth in spending of 6% or more, the gray is flat spend, the pink is a budget shrink of 6% or greater, and the red is retiring the platform. So what this tells us is that there's a big fat middle of stay the same. The lime green is pretty small, but you can see, NetSuite jumps out for new adoptions because they've been very aggressive going after smaller and mid-sized companies, and Coupa, the spend management specialist, shows reasonably strong new adoptions. Now ServiceNow is interesting to me. Not a ton of new adoptions. They've landed the ship and really penetrated larger organizations. And while new adoptions are not off the charts, look at the spending more categories, it's very very strong at 46%. And the other really positive thing for ServiceNow is there's very little red. This company is a beast. Now Salesforce similarly, not tons of new adoptions, but 40% spend more. For a company that size, that's pretty impressive. Workday similarly has a very strong spending profile. At the bottom of the chart, you see a fair amount of red, as we saw on the XY graph. But now, let's take another view of net score. Think of this as a zoom in, which takes those bar charts but shows it in a pie format for individual companies. So we're showing this here for ServiceNow, Workday, and Salesforce, and we've superimposed the net score for these three in green, so you can see ServiceNow at 48%, very good for a company headed toward five billion. Same with Workday, 40% for a company of similar size, and Salesforce has a comparable net score, and is significantly larger than those two revenue-wise. Now this is the same view, this next chart's the same view for SAP and Oracle, and you can see substantially lower than the momentum leaders in terms of net score. But these are much larger companies. SAP's about 33 billion, Oracle's closer to 40 billion. But Oracle especially has seen some headwinds from organizations spending less which drags its net score down. But you're not seeing a lot of replacement in Oracle's base because as I said at the top, these systems are fossilized and many are running on Oracle. And the vast majority of mission-critical workloads are especially running on Oracle. Now remember, this isn't a revenue-weighted view. Oracle charges a steep premium based on the number of cores, and it has a big maintenance stream. So while its net score is kind of sucky, its cashflow is not. All right, let's wrap it up here. We have a very large and mature market. But the semi-modern SaaS players like Salesforce and ServiceNow and Workday, they've gone well beyond escape velocity and solidified their positions as great software companies. Others are trying to follow that suit and compete with the biggest of the bigs, i.e. SAP and Oracle. Now I didn't talk much about Microsoft, but as always they show up prominently. They're huge and they're everywhere in this dataset. What I think is interesting is the competitive dynamics that we talked about earlier. These kind of newer SaaS leaders, they're disrupting Oracle and SAP, but they're also increasingly bumping into each other. You know, ServiceNow has HR for example, and they say that they don't compete with Workday, and that's true. But y'know, these two companies, they eye each other and they angle for account control. Same thing with Salesforce. It's that software mindset. The bigger a software company gets, the more they think they can own the world, because it's software, and if you're good at writing code and you see an opportunity that can add value for your customers, you tend to go after it. Now, we didn't talk much about M&A, but that's going to continue here, especially as these companies look for TAM expansion and opportunities to bring in new capabilities, particularly around data, analytics, machine learning, AI and the like, and don't forget industry specialization. You've seen Oracle pick up a number of industry plays and as digital transformation continues, you'll see more crossing of the industry streams because it's data. Now, the disruption isn't blatantly obvious in this market right now, other than SaaS clouds going after SAP and Oracle, and it's because these companies are deeply entrenched in their customer organizations and change is risky. But the cloud developer, the open source API trend, it could lead to disruptions, but I wouldn't expect that until the second half of this decade as cloud ecosystems really begin to evolve and take hold. Okay, well that's it for today. Remember, these Breaking Analysis episodes, they're all available as podcasts wherever you listen so please subscribe. I publish weekly on Wikibon.com and SiliconANGLE.com, so check that out, and please do comment on my LinkedIn posts. Don't forget, check out ETR.plus for all the survey action. Get in touch on Twitter, I'm @dvellante, or email me at David.Vellante@siliconangle.com. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching everybody. Be well, and we'll see you next time. (thoughtful electronic music)

>> From theCUBE studios in Palo Alto and Boston, connecting with thought-leaders all around the world, this is a Cube Conversation. >> Hi everybody, this is Dave Vellante. Welcome to this Cube Conversation. We're really excited to have Wim Coekaerts in, he is the senior vice-president of software development at Oracle. Wim, it's great to have you on, and, you know I often say I wish we were face-to-face but if we were you'd have to cut off my tie, cause developers and ties just don't go together. >> No, I know, and this is my normal outfit, so this is me wherever I go. Hi again, good to see you. >> Yeah, great to see you. So, of course, you know a lot of people are confused about Oracle, and open-source, they say "Oracle? Open-source? What is that all about?" But I think you're misunderstood. People don't, first of all, realize you as the leader of the software-development community inside of Oracle, I mean, you've been involved in Linux since the early 90s. But you guys have a lot of committers, you do a lot. I want to talk about that. What is up with Oracle, and open-source? >> Ah, well, it's a broad question. So, you know, a couple of things. One is, we have many different areas within the company that are dealing with open-source. So we have the cloud team doing a lot of stuff around cloud SDKs and support for different languages like Python and Go, and of course Java and so forth, so they do a lot around ensuring that the Oracle ecosystem is integrated in the open-source tools that customers use, or developers use, Terraform companies and so forth. And then you have the Java team, and so forth. Java is open-source and then the Graal project, GraalVM which is a polyglot compiler that can run Java, and Python, and Javascript and so forth together in one. VM do really cool optimizations, that's an open-source project, also on GitHub. There's of course MySQL, which is along with Java, they're probably the two most popular and widely used open-source projects out there. There's VirtualBox which is of course also a very popular project that's open-source. There's all the work we do around Linux. And I think one of the things is that, when you have so many different areas, doing things that are for that area, then as a developer or as a customer, you typically just deal with that group. And what you see is, oh you're talking to the Java developers, so you know what's going on around Java. The Java developers might not necessarily say, "Oh well we also do MySQL, and we do Linux and VirtualBox and so forth," and so you get a rather myopic, narrow view of the larger company. When you add all these things up, and there will be one big slide that says "This is Oracle, these are all these open source projects," and there's multiple ways. One is, we have projects that we've open-sourced and all the code came from us and we made it publicly available, we're the main contributor and we get contributions back. There are other projects where we contribute to third-party in terms of enhancing things, like I said with the Cloud Team, and then in general something like Linux where we're part of an external project and we participate in development of that project at large. And so there's these three different ways, when you count up all the developers that we have that deal with open-source on a daily basis. And in terms of contributions, in terms of bug fixes, testing, and so forth, it's thousands, literally, full-time paid developers. And of course, all the projects are all either on GitHub or similar sites that are very popular. So yeah, I think the misunderstood is probably a lack of knowledge of the breadth of what we do. And, you know, our primary goal is to provide services and products to customers, and so the open-source part is sort of embedded in a development methodology. But that's not something we sell or market separately, we just work with customers and products and services, and so in some cases it's not well-understood. >> Yeah. Well, we're talking of course, we're talking about the state of the penguin, I think it's important for people to understand, Oracle got into the Linux game in the 90s, maybe the latter part of the 90s and Oracle, of course, wants to make Linux-- wants to make Oracle, it's applications and database run better on Linux, but as you're pointing out, your Linux distro, full support, end-to-end, thousands of people in your open-source community, and the contributions that you make to Linux, many if not most, they go upstream, everybody can benefit from those, but of course you want an Oracle distro that is going to make Oracle stuff run better, that's always kind of been the Oracle way. >> Well, so, yes, two things though. One is, so everything we do is upstream. So we have no Linux patches that are not contributed upstream; There's no proprietary code in Oracle Linux at all, it's all completely open, publicly available: the source code, the change log, all the commits, it's fully open and public, which sometimes is not well-understood, but it's completely open. And, everything we do in terms of feature development or functionality or bug fixes goes upstream to the Linux kernel mail-list. It's actually, it's the only way to be able to manage a Linux distribution and be a Linux vendor is to live in that eco-system. Otherwise, the cost of maintaining your own fork, so to speak, is very high and it doesn't really solve the problem. Now, the functionality we work on obviously is focused on making Oracle products run better, making Oracle Cloud run better, and so forth. However, again, what's important to understand, though, is an Oracle database is a program running on an operating system. It does IO, it does networking, it deals with memory management, lots of processing. So, for the most part, the things that we work on to improve that helps everyone out, right? It helps every other database run better, or helps every other language run better. So none of these changes are specific to Oracle, they're just things that we found doing performance benchmarks and testing and so forth, where we say "Hey, if Linux did the following, it would make boot-up faster. Now boot-up has nothing to do with the database. But our customers run on 1-terabyte, 4-terabyte, 8-terabyte systems, and so booting up, and Linux starting up, and cleaning up memory takes a long time. So we want to reduce that from an availability point of view. So here, we're now talking about just enterprise for you. So there's this broad set of things we work on that definitely help us, but they're actually really completely generic and help everyone out. >> Yeah, that's great. So I wanted to kind of get that out of the way and help our audience understand that. So let's get into it a little bit; What are you seeing, what's going on in IT, pick your observation space and your vision of what you see happening out there. >> Well, you know, it's very interesting, it's sort of, there's two... there's sort of two worlds, right, there's the cloud world and the move to cloud, and there's the on-premises world, where people run their systems on their own. And, one of the things that we've learned is, when you talk about machine-learning, obviously, is something that's very popular these days, and automation. And so in order to rely on machine-learning well, and have algorithms that are very effective, you need lots of data. And so being a cloud vendor, and having Linux in our cloud on tens of thousands, or hundreds of thousands of servers, or more, allows us to have a view of how an operating system works across an incredibly large scale. So we get lots of data. And so for us to figure out which algorithms work well in terms of how can we do network optimizations, how can we discover anomalies on the storage site, and deal with it and so forth, we can do that at scale. And what's interesting is, how do we then bring that on-prem? Well, if we can get the data and the learning done, the training done, in our cloud directly, then when we provide that service also for people running Oracle Linux on premises then that will work. The alternative is to have point solutions where you provide something to a customer, and he needs to learn something from small amounts of data. That doesn't work so well. So I think having both worlds, on-prem and cloud directly, allows us to kind of benefit from that. And I think that's important, because lots of customers are interested in going to cloud. Many of the enterprises have not yet. You know, they're starting, but there's still a huge on-premises space that's important. And so by being able to get them familiar with how these things work at scale, autonomy is again important, right, Autonomous Database is incredibly popular and so forth, that allows us to then say, "Here, try these things out here, it's a service. We can show you the benefits right away," and then as that improves we bring that, to a certain extent, on-premises as well. And then they can have it in both places. And that, I think, is something, again, that's relatively unique but also very important, is that we want to provide services and products that act similarly on-premises as well as in cloud, because at some point when people move we want to make that transition seamless. And what you have today for the most part is one world that's on-prem, and then the cloud world is completely different. And that is a big barrier of moving, and so we want to reduce that, we can run the same operating system local as well as cloud, you can the same functionality, and then that helps transition people over much easier. >> Yeah, well Oracle actually was one of the -- I think Oracle was the first company to actually market same-same, you actually used that term. Others put forth that concept, but Oracle was the first to announce products like Cloud at Customer, that were same-same, now it took some time to actually get it perfected, and get it to market, but the point is, and we've written about this, is Oracle, because of the ascendancy of cloud, flipped and has a cloud-first mentality, and you just kind of referenced that, you just said, "And you can bring that to on-prem." So I wonder if you could talk about that cloud-first mentality, and the impact on hybrid. >> So yeah, I think the cloud-first part is of course in cloud we work on services moreso than products that we deliver. And there's a number of things that are happening. So one is that we obviously continue to provide products to customers, you can download Oracle Linux, you can download the database and what not, you can install it on your own, you can do the traditional way of working. Then in the cloud-world, what typically happens is "Oh, I use a database service. I'm not installing anything, I push a button and I get an IP address and a SQL that connects extremely quickly to the database." And we take care of everything underneath that is on this database. Now, in order to do that, you need a whole infrastructure in place, you need log-in agents, you need a back-end that captures all that stuff, you need monitoring tools, you need all the automation scripts for bringing the service up and monitor it. And so, that takes a lot of time to do right, and we learn a lot by doing this. And so the cloud-first part of these services means that we get to experience this ourselves with direct access to everything. Now taking that service with all of the additional features like autonomy, and bringing that to an on-premises world, we have to make sure we can package that so that all these pieces around it go along with it. And that takes a little bit more time, so we can do everything at the same time. And so what we've done with Autonomous Database is we created everything in Oracle Cloud, we have the whole system running really well, and then we've been able to sort of package that and shrink it into something that can be installed on-premises, but then connected into Oracle Cloud again. And so that way we can get all the telemetry over the metric, and that allows us to scale. Because part of providing a cloud service that runs on-prem in the customer environment is that we need to be able to remotely manage that similar to how that runs in our own cloud. Right, otherwise it doesn't scale. And so that takes a little bit of time, but we've done all that work, and now with Cloud at Customer Database that's really in place. >> Yeah, you really want to have that same cloud experience, whether with on-prem, in the public cloud, hybrid, et cetera. So, I want to explore a little bit more who is using Oracle Linux, and what's the driver for using it. Can you describe maybe some of the types of customers and why they buy? >> Sure, so we started this fourteen years ago, in 2006, October 25th, 2006. I remember that day very well; Penguins on stage and a big launch for Oracle Linux in San Francisco Moscone Center. So, look, the initial driver for Oracle Linux was to ensure that Oracle database customers or Oracle product customers had a good operating system experience, and the ability to be able to handle critical issues when that occurs, because typically a database runs the company's critical data: the most essential stuff that a company has is typically in a database, an Oracle database. And so when that thing has issues with the operating system, you don't want just to talk to multiple vendors and have finger-pointing, and having to explain to an operating system vendor how the database works. In the Unix world, we had a good relationship with the OS vendors, and the hardware vendors, they were the same. And they knew our products really well, and in the Linux world, that was very different. The OS vendor basically did not want to understand or learn anything about the products living on top. And so while to a certain extent that makes sense, it's an enterprise world where time is of the essence, and downtime needs to be limited absolutely. We can't have these arguments and such. And that was the driver, initially, for doing Oracle Linux. It was to ensure there was a Linux distribution really backed by us, that we could fix, that we could fully support. That was completely the original intent. And so the early customer base was database customers. Database and middleware. Mostly database. But that has then evolved quickly, and so what happened was, people say "Look, I have a thousand servers, a hundred run Oracle, so we'll run Oracle Linux on those hundred, and we'll run something else on those other nine-hundred." Now after a year or so, they realize that our support is really good; We fix all these issues, and so then they're like "Why are we having two Linux distributions? This thing works really well, it runs any application, it's fully compatible, so we'll do a thousand with Oracle Linux." And so the early days, the first few years, was definitely Oracle Database as the core driver, and then it sort of expanded to the rest of the estate. And over the years, we've added lots of features and functionality, like Ksplice, and so forth. We have an attractive pricing model for running on servers, and so now lots of our customers have a very small Oracle percentage running and many other things running. So it's really become a all-or-nothing play in the Linux space, and we're well-known now, so it's actually very good. >> You just mentioned Ksplice. We've been talking about cloud, and on-prem, and hybrid. Let's talk about security, because security really is a differentiator, particularly if you're going to start to put stuff in the cloud. Talk about Ksplice specifically, but generally security and your policy there. >> So, "Security first" is sort of what you hear us say and do, in everything we do. The database obviously security, on the Linux site security matters. Ksplice as a technology is there to do critical bug-fixing and make sure that we can apply security vulnerability fixes without affecting the customer, and not have downtime. And if you look at most of the cases or many of the cases where you have security vulnerabilities and exploits, it tends to be because systems were not patched. Why were they not patched? Well not that our customer doesn't understand that it's important, but it's a whole train of events that needs to happen. You have to, you get notified that there's a security issue in your operating system or application. Then, well, an application typically means it's a multi-layered setup. So if you have to bring your database server down, then you first have to coordinate with the application users to bring the app server down, cause that talks to the database. So to patch one system, you basically have to bring down the whole application stack. You have to negotiate with the DBAs, you have to negotiate with the app admins, you have to negotiate with the user. It takes weeks to do that and find time. Well during that time, you're vulnerable. So the only way, really, to address security in a scalable and reducing that window of time is to do it without affecting the customer. And so Casewise is something that, it's a company we acquired in 2009, and have since evolved in terms of capabilities, and so it allows us to patch the Linux terminal without downtime. We lock the kernel for 8 microseconds. It's literally no downtime. You don't have to bring down applications, the user doesn't see it, there's no hang, there's no delay. And so by doing that, you can run a Linux operating system, or gLinux, and you can be fully patched on a system that hasn't rebooted for 3 years. You don't even know it. And so by doing that type of stuff, it makes customers more secure, and it avoids them-- It saves them a lot of money in terms of dealing with project management and so forth, but it really keeps them secure. And so we do that for the Linux kernel, we do that for some of the libraries on top that are critical like OpenSSL and 2 LVC, and, you know one example-- I can give you two examples. So one example is, Heartbleed was this bug in OpenSSL a number of years ago. And so everyone had to patch their SSH server. And that meant, basically, systems around the world had to reboot. Like a whole IT reboot across the world. With Ksplice today, if Heartbleed were to happen tomorrow, we would be able to patch this online for all the Oracle Linux customers without any downtime. No reboots, no restarting of applications, everything keeps running. The amount of money saved would be massive, and also, of course, the headache. Another example is, and this was in Oracle Cloud, when some of these CPU bugs that happened a few years ago that were rather damaging on the cloud side, where you could basically see memory potentially of other CPUs running, the cloud is incredibly critical. We were basically able to basically patch our entire cloud in four hours. And the customer didn't know, right, a hundred and twenty million patches, or something, that we applied within four hours, all online, without any downtime. And so that technology has been really helpful, both for us to run our cloud, but the exact same patches and same fixes go to customers on-premises as well. But this comes back to the whole, what we do in cloud we also do for customer. And I think that's a unique thing that we have at Oracle which is quite fascinating. The operating system we run for our customers, the operating system that's the host part of VMs, is the exact same binary and source code that we make available, just to be clear, the exact same binaries are the ones that you run as a customer on-premises. So if you run Oracle Linux with KVM, you run VMs, you're actually running the exact same stuff as we run underneath our customer's stuff. Nobody else does that, everyone else has a black box. So I think that helps a little bit with transparency as well. >> Yeah, and that homogeneity just creates an environment, you're talking about that sort of security mindset, it's critical, you're not just bolting it on, it's part of the culture. But you started your career, and then of course you were a Linux person when you came to Oracle, but then I think you spent some time in database, back in the day when there were serious database wars going on, before Oracle became the king of database. So now you've got, obviously, this great portfolio, and a lot of really sharp software developers; What should we expect going forward, from Oracle? What should we look for? >> You know, I was talking to some, I was welcoming some interns to the company, for their summer internship yesterday, and one of the things I mentioned to them was that -- so cloud obviously gives us a lot of opportunities, but there's a number of things. One is, we have such a breadth of applications and software and hardware together. We have the servers, we have the storage, we have the operating systems, we have the database layer and so forth, and we have the cloud side, and one of the great opportunities, and I think we've shown a lot of this happening with the ability to create something like Autonomous Database, is to combine all these things. Right, we have such a broad portfolio of really cool technology that by itself is okay, but if you combine the things it really becomes awesome. You cannot create autonomous database without having autonomous learning. You cannot create those two and make them really safe without also controlling the firmware on the hardware and so forth. So by being able to combine all these layers, and by having a really great relationship across the teams within the company, that opens up a lot of opportunities to do stuff really quickly. And having the scale for that. I think that has been, for the last few years, a really great thing, but I can see that being one of the advantages that we have going forward. We have Oracle Fusion Applications, which is incredibly popular, and has great growth, and then we have that running on Oracle Cloud, that talks to Oracle Autonomous Database, so we bring all these pieces together. And no other SaaS vendor can do that, because they don't have these other pieces. They have one area, we have all of them. And so that's the exciting part for me, it's not so much about making my own world better, and having Linux be better, and Casewise and so forth, which is important, but that becoming part of the bigger picture. And that's the exciting part. >> Well, Oracle's always invested in RND, we've made that point many, many times. Whether it's database, you know Fusion was a painful but worthy effort, the whole public cloud piece, obviously many acquisitions, but the investments that you've made in open-source as well, Wim, you're a great spokesperson, and a great representative of the open-source community generally, and then Oracle specifically, so thanks very much for coming on theCUBE and sharing with us the state of the penguin, and best of luck. >> You're welcome. Thank you, thanks for having me. >> Alright, and thank you for watching, everybody. This is Dave Vellante for theCUBE. We'll see you next time. (cheerful music).

>> From theCUBE studios in Palo Alto and Boston, connecting with thought-leaders all around the world, this is a Cube Conversation. >> Hi everybody, this is Dave Vellante. Welcome to this Cube Conversation. We're really excited to have Wim Coekaerts in, he is the senior vice-president of software development at Oracle. Wim, it's great to have you on, and, you know I often say I wish we were face-to-face but if we were you'd have to cut off my tie, cause developers and ties just don't go together. >> No, I know, and this is my normal outfit, so this is me wherever I go. Hi again, good to see you. >> Yeah, great to see you. So, of course, you know a lot of people are confused about Oracle, and open-source, they say "Oracle? Open-source? What is that all about?" But I think you're misunderstood. People don't, first of all, realize you as the leader of the software-development community inside of Oracle, I mean, you've been involved in Linux since the early 90s. But you guys have a lot of committers, you do a lot. I want to talk about that. What is up with Oracle, and open-source? >> Ah, well, it's a broad question. So, you know, a couple of things. One is, we have many different areas within the company that are dealing with open-source. So we have the cloud team doing a lot of stuff around cloud SDKs and support for different languages like Python and Go, and of course Java and so forth, so they do a lot around ensuring that the Oracle ecosystem is integrated in the open-source tools that customers use, or developers use, Terraform companies and so forth. And then you have the Java team, and so forth. Java is open-source and then the Graal project, GraalVM which is a polyglot compiler that can run Java, and Python, and Javascript and so forth together in one. VM do really cool optimizations, that's an open-source project, also on GitHub. There's of course MySQL, which is along with Java, they're probably the two most popular and widely used open-source projects out there. There's VirtualBox which is of course also a very popular project that's open-source. There's all the work we do around Linux. And I think one of the things is that, when you have so many different areas, doing things that are for that area, then as a developer or as a customer, you typically just deal with that group. And what you see is, oh you're talking to the Java developers, so you know what's going on around Java. The Java developers might not necessarily say, "Oh well we also do MySQL, and we do Linux and VirtualBox and so forth," and so you get a rather myopic, narrow view of the larger company. When you add all these things up, and there will be one big slide that says "This is Oracle, these are all these open source projects," and there's multiple ways. One is, we have projects that we've open-sourced and all the code came from us and we made it publicly available, we're the main contributor and we get contributions back. There are other projects where we contribute to third-party in terms of enhancing things, like I said with the Cloud Team, and then in general something like Linux where we're part of an external project and we participate in development of that project at large. And so there's these three different ways, when you count up all the developers that we have that deal with open-source on a daily basis. And in terms of contributions, in terms of bug fixes, testing, and so forth, it's thousands, literally, full-time paid developers. And of course, all the projects are all either on GitHub or similar sites that are very popular. So yeah, I think the misunderstood is probably a lack of knowledge of the breadth of what we do. And, you know, our primary goal is to provide services and products to customers, and so the open-source part is sort of embedded in a development methodology. But that's not something we sell or market separately, we just work with customers and products and services, and so in some cases it's not well-understood. >> Yeah. Well, we're talking of course, we're talking about the state of the penguin, I think it's important for people to understand, Oracle got into the Linux game in the 90s, maybe the latter part of the 90s and Oracle, of course, wants to make Linux-- wants to make Oracle, it's applications and database run better on Linux, but as you're pointing out, your Linux distro, full support, end-to-end, thousands of people in your open-source community, and the contributions that you make to Linux, many if not most, they go upstream, everybody can benefit from those, but of course you want an Oracle distro that is going to make Oracle stuff run better, that's always kind of been the Oracle way. >> Well, so, yes, two things though. One is, so everything we do is upstream. So we have no Linux patches that are not contributed upstream; There's no proprietary code in Oracle Linux at all, it's all completely open, publicly available: the source code, the change log, all the commits, it's fully open and public, which sometimes is not well-understood, but it's completely open. And, everything we do in terms of feature development or functionality or bug fixes goes upstream to the Linux kernel mail-list. It's actually, it's the only way to be able to manage a Linux distribution and be a Linux vendor is to live in that eco-system. Otherwise, the cost of maintaining your own fork, so to speak, is very high and it doesn't really solve the problem. Now, the functionality we work on obviously is focused on making Oracle products run better, making Oracle Cloud run better, and so forth. However, again, what's important to understand, though, is an Oracle database is a program running on an operating system. It does IO, it does networking, it deals with memory management, lots of processing. So, for the most part, the things that we work on to improve that helps everyone out, right? It helps every other database run better, or helps every other language run better. So none of these changes are specific to Oracle, they're just things that we found doing performance benchmarks and testing and so forth, where we say "Hey, if Linux did the following, it would make boot-up faster. Now boot-up has nothing to do with the database. But our customers run on 1-terabyte, 4-terabyte, 8-terabyte systems, and so booting up, and Linux starting up, and cleaning up memory takes a long time. So we want to reduce that from an availability point of view. So here, we're now talking about just enterprise for you. So there's this broad set of things we work on that definitely help us, but they're actually really completely generic and help everyone out. >> Yeah, that's great. So I wanted to kind of get that out of the way and help our audience understand that. So let's get into it a little bit; What are you seeing, what's going on in IT, pick your observation space and your vision of what you see happening out there. >> Well, you know, it's very interesting, it's sort of, there's two... there's sort of two worlds, right, there's the cloud world and the move to cloud, and there's the on-premises world, where people run their systems on their own. And, one of the things that we've learned is, when you talk about machine-learning, obviously, is something that's very popular these days, and automation. And so in order to rely on machine-learning well, and have algorithms that are very effective, you need lots of data. And so being a cloud vendor, and having Linux in our cloud on tens of thousands, or hundreds of thousands of servers, or more, allows us to have a view of how an operating system works across an incredibly large scale. So we get lots of data. And so for us to figure out which algorithms work well in terms of how can we do network optimizations, how can we discover anomalies on the storage site, and deal with it and so forth, we can do that at scale. And what's interesting is, how do we then bring that on-prem? Well, if we can get the data and the learning done, the training done, in our cloud directly, then when we provide that service also for people running Oracle Linux on premises then that will work. The alternative is to have point solutions where you provide something to a customer, and he needs to learn something from small amounts of data. That doesn't work so well. So I think having both worlds, on-prem and cloud directly, allows us to kind of benefit from that. And I think that's important, because lots of customers are interested in going to cloud. Many of the enterprises have not yet. You know, they're starting, but there's still a huge on-premises space that's important. And so by being able to get them familiar with how these things work at scale, autonomy is again important, right, Autonomous Database is incredibly popular and so forth, that allows us to then say, "Here, try these things out here, it's a service. We can show you the benefits right away," and then as that improves we bring that, to a certain extent, on-premises as well. And then they can have it in both places. And that, I think, is something, again, that's relatively unique but also very important, is that we want to provide services and products that act similarly on-premises as well as in cloud, because at some point when people move we want to make that transition seamless. And what you have today for the most part is one world that's on-prem, and then the cloud world is completely different. And that is a big barrier of moving, and so we want to reduce that, we can run the same operating system local as well as cloud, you can the same functionality, and then that helps transition people over much easier. >> Yeah, well Oracle actually was one of the -- I think Oracle was the first company to actually market same-same, you actually used that term. Others put forth that concept, but Oracle was the first to announce products like Cloud at Customer, that were same-same, now it took some time to actually get it perfected, and get it to market, but the point is, and we've written about this, is Oracle, because of the ascendancy of cloud, flipped and has a cloud-first mentality, and you just kind of referenced that, you just said, "And you can bring that to on-prem." So I wonder if you could talk about that cloud-first mentality, and the impact on hybrid. >> So yeah, I think the cloud-first part is of course in cloud we work on services moreso than products that we deliver. And there's a number of things that are happening. So one is that we obviously continue to provide products to customers, you can download Oracle Linux, you can download the database and what not, you can install it on your own, you can do the traditional way of working. Then in the cloud-world, what typically happens is "Oh, I use a database service. I'm not installing anything, I push a button and I get an IP address and a SQL that connects extremely quickly to the database." And we take care of everything underneath that is on this database. Now, in order to do that, you need a whole infrastructure in place, you need log-in agents, you need a back-end that captures all that stuff, you need monitoring tools, you need all the automation scripts for bringing the service up and monitor it. And so, that takes a lot of time to do right, and we learn a lot by doing this. And so the cloud-first part of these services means that we get to experience this ourselves with direct access to everything. Now taking that service with all of the additional features like autonomy, and bringing that to an on-premises world, we have to make sure we can package that so that all these pieces around it go along with it. And that takes a little bit more time, so we can do everything at the same time. And so what we've done with Autonomous Database is we created everything in Oracle Cloud, we have the whole system running really well, and then we've been able to sort of package that and shrink it into something that can be installed on-premises, but then connected into Oracle Cloud again. And so that way we can get all the telemetry over the metric, and that allows us to scale. Because part of providing a cloud service that runs on-prem in the customer environment is that we need to be able to remotely manage that similar to how that runs in our own cloud. Right, otherwise it doesn't scale. And so that takes a little bit of time, but we've done all that work, and now with Cloud at Customer Database that's really in place. >> Yeah, you really want to have that same cloud experience, whether with on-prem, in the public cloud, hybrid, et cetera. So, I want to explore a little bit more who is using Oracle Linux, and what's the driver for using it. Can you describe maybe some of the types of customers and why they buy? >> Sure, so we started this fourteen years ago, in 2006, October 25th, 2006. I remember that day very well; Penguins on stage and a big launch for Oracle Linux in San Francisco Moscone Center. So, look, the initial driver for Oracle Linux was to ensure that Oracle database customers or Oracle product customers had a good operating system experience, and the ability to be able to handle critical issues when that occurs, because typically a database runs the company's critical data: the most essential stuff that a company has is typically in a database, an Oracle database. And so when that thing has issues with the operating system, you don't want just to talk to multiple vendors and have finger-pointing, and having to explain to an operating system vendor how the database works. In the Unix world, we had a good relationship with the OS vendors, and the hardware vendors, they were the same. And they knew our products really well, and in the Linux world, that was very different. The OS vendor basically did not want to understand or learn anything about the products living on top. And so while to a certain extent that makes sense, it's an enterprise world where time is of the essence, and downtime needs to be limited absolutely. We can't have these arguments and such. And that was the driver, initially, for doing Oracle Linux. It was to ensure there was a Linux distribution really backed by us, that we could fix, that we could fully support. That was completely the original intent. And so the early customer base was database customers. Database and middleware. Mostly database. But that has then evolved quickly, and so what happened was, people say "Look, I have a thousand servers, a hundred run Oracle, so we'll run Oracle Linux on those hundred, and we'll run something else on those other nine-hundred." Now after a year or so, they realize that our support is really good; We fix all these issues, and so then they're like "Why are we having two Linux distributions? This thing works really well, it runs any application, it's fully compatible, so we'll do a thousand with Oracle Linux." And so the early days, the first few years, was definitely Oracle Database as the core driver, and then it sort of expanded to the rest of the estate. And over the years, we've added lots of features and functionality, like Ksplice, and so forth. We have an attractive pricing model for running on servers, and so now lots of our customers have a very small Oracle percentage running and many other things running. So it's really become a all-or-nothing play in the Linux space, and we're well-known now, so it's actually very good. >> You just mentioned Ksplice. We've been talking about cloud, and on-prem, and hybrid. Let's talk about security, because security really is a differentiator, particularly if you're going to start to put stuff in the cloud. Talk about Ksplice specifically, but generally security and your policy there. >> So, "Security first" is sort of what you hear us say and do, in everything we do. The database obviously security, on the Linux site security matters. Ksplice as a technology is there to do critical bug-fixing and make sure that we can apply security vulnerability fixes without affecting the customer, and not have downtime. And if you look at most of the cases or many of the cases where you have security vulnerabilities and exploits, it tends to be because systems were not patched. Why were they not patched? Well not that our customer doesn't understand that it's important, but it's a whole train of events that needs to happen. You have to, you get notified that there's a security issue in your operating system or application. Then, well, an application typically means it's a multi-layered setup. So if you have to bring your database server down, then you first have to coordinate with the application users to bring the app server down, cause that talks to the database. So to patch one system, you basically have to bring down the whole application stack. You have to negotiate with the DBAs, you have to negotiate with the app admins, you have to negotiate with the user. It takes weeks to do that and find time. Well during that time, you're vulnerable. So the only way, really, to address security in a scalable and reducing that window of time is to do it without affecting the customer. And so Casewise is something that, it's a company we acquired in 2009, and have since evolved in terms of capabilities, and so it allows us to patch the Linux terminal without downtime. We lock the kernel for 8 microseconds. It's literally no downtime. You don't have to bring down applications, the user doesn't see it, there's no hang, there's no delay. And so by doing that, you can run a Linux operating system, or gLinux, and you can be fully patched on a system that hasn't rebooted for 3 years. You don't even know it. And so by doing that type of stuff, it makes customers more secure, and it avoids them-- It saves them a lot of money in terms of dealing with project management and so forth, but it really keeps them secure. And so we do that for the Linux kernel, we do that for some of the libraries on top that are critical like OpenSSL and 2 LVC, and, you know one example-- I can give you two examples. So one example is, Heartbleed was this bug in OpenSSL a number of years ago. And so everyone had to patch their SSH server. And that meant, basically, systems around the world had to reboot. Like a whole IT reboot across the world. With Ksplice today, if Heartbleed were to happen tomorrow, we would be able to patch this online for all the Oracle Linux customers without any downtime. No reboots, no restarting of applications, everything keeps running. The amount of money saved would be massive, and also, of course, the headache. Another example is, and this was in Oracle Cloud, when some of these CPU bugs that happened a few years ago that were rather damaging on the cloud side, where you could basically see memory potentially of other CPUs running, the cloud is incredibly critical. We were basically able to basically patch our entire cloud in four hours. And the customer didn't know, right, a hundred and twenty million patches, or something, that we applied within four hours, all online, without any downtime. And so that technology has been really helpful, both for us to run our cloud, but the exact same patches and same fixes go to customers on-premises as well. But this comes back to the whole, what we do in cloud we also do for customer. And I think that's a unique thing that we have at Oracle which is quite fascinating. The operating system we run for our customers, the operating system that's the host part of VMs, is the exact same binary and source code that we make available, just to be clear, the exact same binaries are the ones that you run as a customer on-premises. So if you run Oracle Linux with KVM, you run VMs, you're actually running the exact same stuff as we run underneath our customer's stuff. Nobody else does that, everyone else has a black box. So I think that helps a little bit with transparency as well. >> Yeah, and that homogeneity just creates an environment, you're talking about that sort of security mindset, it's critical, you're not just bolting it on, it's part of the culture. But you started your career, and then of course you were a Linux person when you came to Oracle, but then I think you spent some time in database, back in the day when there were serious database wars going on, before Oracle became the king of database. So now you've got, obviously, this great portfolio, and a lot of really sharp software developers; What should we expect going forward, from Oracle? What should we look for? >> You know, I was talking to some, I was welcoming some interns to the company, for their summer internship yesterday, and one of the things I mentioned to them was that -- so cloud obviously gives us a lot of opportunities, but there's a number of things. One is, we have such a breadth of applications and software and hardware together. We have the servers, we have the storage, we have the operating systems, we have the database layer and so forth, and we have the cloud side, and one of the great opportunities, and I think we've shown a lot of this happening with the ability to create something like Autonomous Database, is to combine all these things. Right, we have such a broad portfolio of really cool technology that by itself is okay, but if you combine the things it really becomes awesome. You cannot create autonomous database without having autonomous learning. You cannot create those two and make them really safe without also controlling the firmware on the hardware and so forth. So by being able to combine all these layers, and by having a really great relationship across the teams within the company, that opens up a lot of opportunities to do stuff really quickly. And having the scale for that. I think that has been, for the last few years, a really great thing, but I can see that being one of the advantages that we have going forward. We have Oracle Fusion Applications, which is incredibly popular, and has great girth, and then we have that running on Oracle Cloud, that talks to Oracle Autonomous Database, so we bring all these pieces together. And no other SaaS vendor can do that, because they don't have these other pieces. They have one area, we have all of them. And so that's the exciting part for me, it's not so much about making my own world better, and having Linux be better, and Casewise and so forth, which is important, but that becoming part of the bigger picture. And that's the exciting part. >> Well, Oracle's always invested in RND, we've made that point many, many times. Whether it's database, you know Fusion was a painful but worthy effort, the whole public cloud piece, obviously many acquisitions, but the investments that you've made in open-source as well, Wim, you're a great spokesperson, and a great representative of the open-source community generally, and then Oracle specifically, so thanks very much for coming on theCUBE and sharing with us the state of the penguin, and best of luck. >> You're welcome. Thank you, thanks for having me. >> Alright, and thank you for watching, everybody. This is Dave Vellante for theCUBE. We'll see you next time. (cheerful music).