(upbeat music) >> Hello, everybody. This is Dave Vellante. Welcome back to Supercloud2, where we're exploring the intersection of data analytics and the future of cloud. In this segment, we're going to look at how the Supercloud will support a new class of applications, not just work that runs on multiple clouds, but rather a new breed of apps that can orchestrate things in the real world. Think Uber for many types of businesses. These applications, they're not about codifying forms or business processes. They're about orchestrating people, places, and things in a business ecosystem. And I'm pleased to welcome my colleague and friend, George Gilbert, former Gartner Analyst, Wiki Bond market analyst, former equities analyst as my co-host. And we're thrilled to have Tristan Handy, who's the founder and CEO of DBT Labs and Bob Muglia, who's the former President of Microsoft's Enterprise business and former CEO of Snowflake. Welcome all, gentlemen. Thank you for coming on the program. >> Good to be here. >> Thanks for having us. >> Hey, look, I'm going to start actually with the SuperCloud because both Tristan and Bob, you've read the definition. Thank you for doing that. And Bob, you have some really good input, some thoughts on maybe some of the drawbacks and how we can advance this. So what are your thoughts in reading that definition around SuperCloud? >> Well, I thought first of all that you did a very good job of laying out all of the characteristics of it and helping to define it overall. But I do think it can be tightened a bit, and I think it's helpful to do it in as short a way as possible. And so in the last day I've spent a little time thinking about how to take it and write a crisp definition. And here's my go at it. This is one day old, so gimme a break if it's going to change. And of course we have to follow the industry, and so that, and whatever the industry decides, but let's give this a try. So in the way I think you're defining it, what I would say is a SuperCloud is a platform that provides programmatically consistent services hosted on heterogeneous cloud providers. >> Boom. Nice. Okay, great. I'm going to go back and read the script on that one and tighten that up a bit. Thank you for spending the time thinking about that. Tristan, would you add anything to that or what are your thoughts on the whole SuperCloud concept? >> So as I read through this, I fully realize that we need a word for this thing because I have experienced the inability to talk about it as well. But for many of us who have been living in the Confluence, Snowflake, you know, this world of like new infrastructure, this seems fairly uncontroversial. Like I read through this, and I'm just like, yeah, this is like the world I've been living in for years now. And I noticed that you called out Snowflake for being an example of this, but I think that there are like many folks, myself included, for whom this world like fully exists today. >> Yeah, I think that's a fair, I dunno if it's criticism, but people observe, well, what's the big deal here? It's just kind of what we're living in today. It reminds me of, you know, Tim Burns Lee saying, well, this is what the internet was supposed to be. It was supposed to be Web 2.0, so maybe this is what multi-cloud was supposed to be. Let's turn our attention to apps. Bob first and then go to Tristan. Bob, what are data apps to you? When people talk about data products, is that what they mean? Are we talking about something more, different? What are data apps to you? >> Well, to understand data apps, it's useful to contrast them to something, and I just use the simple term people apps. I know that's a little bit awkward, but it's clear. And almost everything we work with, almost every application that we're familiar with, be it email or Salesforce or any consumer app, those are applications that are targeted at responding to people. You know, in contrast, a data application reacts to changes in data and uses some set of analytic services to autonomously take action. So where applications that we're familiar with respond to people, data apps respond to changes in data. And they both do something, but they do it for different reasons. >> Got it. You know, George, you and I were talking about, you know, it comes back to SuperCloud, broad definition, narrow definition. Tristan, how do you see it? Do you see it the same way? Do you have a different take on data apps? >> Oh, geez. This is like a conversation that I don't know has an end. It's like been, I write a substack, and there's like this little community of people who all write substack. We argue with each other about these kinds of things. Like, you know, as many different takes on this question as you can find, but the way that I think about it is that data products are atomic units of functionality that are fundamentally data driven in nature. So a data product can be as simple as an interactive dashboard that is like actually had design thinking put into it and serves a particular user group and has like actually gone through kind of a product development life cycle. And then a data app or data application is a kind of cohesive end-to-end experience that often encompasses like many different data products. So from my perspective there, this is very, very related to the way that these things are produced, the kinds of experiences that they're provided, that like data innovates every product that we've been building in, you know, software engineering for, you know, as long as there have been computers. >> You know, Jamak Dagani oftentimes uses the, you know, she doesn't name Spotify, but I think it's Spotify as that kind of example she uses. But I wonder if we can maybe try to take some examples. If you take, like George, if you take a CRM system today, you're inputting leads, you got opportunities, it's driven by humans, they're really inputting the data, and then you got this system that kind of orchestrates the business process, like runs a forecast. But in this data driven future, are we talking about the app itself pulling data in and automatically looking at data from the transaction systems, the call center, the supply chain and then actually building a plan? George, is that how you see it? >> I go back to the example of Uber, may not be the most sophisticated data app that we build now, but it was like one of the first where you do have users interacting with their devices as riders trying to call a car or driver. But the app then looks at the location of all the drivers in proximity, and it matches a driver to a rider. It calculates an ETA to the rider. It calculates an ETA then to the destination, and it calculates a price. Those are all activities that are done sort of autonomously that don't require a human to type something into a form. The application is using changes in data to calculate an analytic product and then to operationalize that, to assign the driver to, you know, calculate a price. Those are, that's an example of what I would think of as a data app. And my question then I guess for Tristan is if we don't have all the pieces in place for sort of mainstream companies to build those sorts of apps easily yet, like how would we get started? What's the role of a semantic layer in making that easier for mainstream companies to build? And how do we get started, you know, say with metrics? How does that, how does that take us down that path? >> So what we've seen in the past, I dunno, decade or so, is that one of the most successful business models in infrastructure is taking hard things and rolling 'em up behind APIs. You take messaging, you take payments, and you all of a sudden increase the capability of kind of your median application developer. And you say, you know, previously you were spending all your time being focused on how do you accept credit cards, how do you send SMS payments, and now you can focus on your business logic, and just create the thing. One of, interestingly, 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, you know, you would imagine that the business would be able to create applications around very easily, but in fact that's not the case. It's actually quite challenging to, and involves a lot of data engineering pipeline and all this work to make these available. And so if you really want to make it very easy to create some of 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. >> So how rich can that API layer grow if you start with metric definitions that you've defined? And DBT has, you know, the metric, the dimensions, the time grain, things like that, that's a well scoped sort of API that people can work within. How much can you extend that to say non-calculated business rules or governance information like data reliability rules, things like that, or even, you know, features for an AIML feature store. In other words, it starts, you started pragmatically, but how far can you grow? >> Bob is waiting with bated breath to answer this question. I'm, just really quickly, I think that we as a company and DBT as a product tend to be very pragmatic. We try to release the simplest possible version of a thing, get it out there, and see if people use it. But the idea that, the concept of a metric is really just a first landing pad. The really, there is a physical manifestation of the data and then there's a logical manifestation of the data. And what we're trying to do here is make it very easy to access the logical manifestation of the data, and metric is a way to look at that. Maybe an entity, a customer, a user is another way to look at that. And I'm sure that there will be more kind of logical structures as well. >> So, Bob, chime in on this. You know, what's your thoughts on the right architecture behind this, and how do we get there? >> Yeah, well first of all, I think one of the ways we get there is by what companies like DBT Labs and Tristan is doing, which is incrementally taking and building on the modern data stack and extending that to add a semantic layer that describes the data. Now the way I tend to think about this is a fairly major shift in the way we think about writing applications, which is today a code first approach to moving to a world that is model driven. And I think that's what the big change will be is that where today we think about data, we think about writing code, and we use that to produce APIs as Tristan said, which encapsulates those things together in some form of services that are useful for organizations. And that idea of that encapsulation is never going to go away. It's very, that concept of an API is incredibly useful and will exist well into the future. But what I think will happen is that in the next 10 years, we're going to move to a world where organizations are defining models first of their data, but then ultimately of their business process, their entire business process. Now the concept of a model driven world is a very old concept. I mean, I first started thinking about this and playing around with some early model driven tools, probably before Tristan was born in the early 1980s. And those tools didn't work because the semantics associated with executing the model were too complex to be written in anything other than a procedural language. We're now reaching a time where that is changing, and you see it everywhere. You see it first of all in the world of machine learning and machine learning models, which are taking over more and more of what applications are doing. And I think that's an incredibly important step. And learned models are an important part of what people will do. But if you look at the world today, I will claim that we've always been modeling. Modeling has existed in computers since there have been integrated circuits and any form of computers. But what we do is what I would call implicit modeling, which means that it's the model is written on a whiteboard. It's in a bunch of Slack messages. It's on a set of napkins in conversations that happen and during Zoom. That's where the model gets defined today. It's implicit. There is one in the system. It is hard coded inside application logic that exists across many applications with humans being the glue that connects those models together. And really there is no central place you can go to understand the full attributes of the business, all of the business rules, all of the business logic, the business data. That's going to change in the next 10 years. And we'll start to have a world where we can define models about what we're doing. Now in the short run, the most important models to build are data models and to describe all of the attributes of the data and their relationships. And that's work that DBT Labs is doing. A number of other companies are doing that. We're taking steps along that way with catalogs. People are trying to build more complete ontologies associated with that. The underlying infrastructure is still super, super nascent. But what I think we'll see is this infrastructure that exists today that's building learned models in the form of machine learning programs. You know, some of these incredible machine learning programs in foundation models like GPT and DALL-E and all of the things that are happening in these global scale models, but also all of that needs to get applied to the domains that are appropriate for a business. And I think we'll see the infrastructure developing for that, that can take this concept of learned models and put it together with more explicitly defined models. And this is where the concept of knowledge graphs come in and then the technology that underlies that to actually implement and execute that, which I believe are relational knowledge graphs. >> Oh, oh wow. There's a lot to unpack there. So let me ask the Colombo question, Tristan, we've been making fun of your youth. We're just, we're just jealous. Colombo, I'll explain it offline maybe. >> I watch Colombo. >> Okay. All right, good. So but today if you think about the application stack and the data stack, which is largely an analytics pipeline. They're separate. Do they, those worlds, do they have to come together in order to achieve Bob's vision? When I talk to practitioners about that, they're like, well, I don't want to complexify the application stack cause the data stack today is so, you know, hard to manage. But but do those worlds have to come together? And you know, through that model, I guess abstraction or translation that Bob was just describing, how do you guys think about that? Who wants to take that? >> I think it's inevitable that data and AI are going to become closer together? I think that the infrastructure there has been moving in that direction for a long time. Whether you want to use the Lakehouse portmanteau or not. There's also, there's a next generation of data tech that is still in the like early stage of being developed. There's a company that I love that is essentially Cross Cloud Lambda, and it's just a wonderful abstraction for computing. So I think that, you know, people have been predicting that these worlds are going to come together for awhile. A16Z wrote a great post on this back in I think 2020, predicting this, and I've been predicting this since since 2020. But what's not clear is the timeline, but I think that this is still just as inevitable as it's been. >> Who's that that does Cross Cloud? >> Let me follow up on. >> Who's that, Tristan, that does Cross Cloud Lambda? Can you name names? >> Oh, they're called Modal Labs. >> Modal Labs, yeah, of course. All right, go ahead, George. >> Let me ask about this vision of trying to put the semantics or the code that represents the business with the data. It gets us to a world that's sort of more data centric, where data's not locked inside or behind the APIs of different applications so that we don't have silos. But at the same time, Bob, I've heard you talk about building the semantics gradually on top of, into a knowledge graph that maybe grows out of a data catalog. And the vision of getting to that point, essentially the enterprise's metadata and then the semantics you're going to add onto it are really stored in something that's separate from the underlying operational and analytic data. So at the same time then why couldn't we gradually build semantics beyond the metric definitions that DBT has today? In other words, you build more and more of the semantics in some layer that DBT defines and that sits above the data management layer, but any requests for data have to go through the DBT layer. Is that a workable alternative? Or where, what type of limitations would you face? >> Well, I think that it is the way the world will evolve is to start with the modern data stack and, you know, which is operational applications going through a data pipeline into some form of data lake, data warehouse, the Lakehouse, whatever you want to call it. And then, you know, this wide variety of analytics services that are built together. To the point that Tristan made about machine learning and data coming together, you see that in every major data cloud provider. Snowflake certainly now supports Python and Java. Databricks is of course building their data warehouse. Certainly Google, Microsoft and Amazon are doing very, very similar things in terms of building complete solutions that bring together an analytics stack that typically supports languages like Python together with the data stack and the data warehouse. I mean, all of those things are going to evolve, and they're not going to go away because that infrastructure is relatively new. It's just being deployed by companies, and it solves the problem of working with petabytes of data if you need to work with petabytes of data, and nothing will do that for a long time. What's missing is a layer that understands and can model the semantics of all of this. And if you need to, if you want to model all, if you want to talk about all the semantics of even data, you need to think about all of the relationships. You need to think about how these things connect together. And unfortunately, there really is no platform today. None of our existing platforms are ultimately sufficient for this. It was interesting, I was just talking to a customer yesterday, you know, a large financial organization that is building out these semantic layers. They're further along than many companies are. And you know, I asked what they're building it on, and you know, it's not surprising they're using a, they're using combinations of some form of search together with, you know, textual based search together with a document oriented database. In this case it was Cosmos. And that really is kind of the state of the art right now. And yet those products were not built for this. They don't really, they can't manage the complicated relationships that are required. They can't issue the queries that are required. And so a new generation of database needs to be developed. And fortunately, you know, that is happening. The world is developing a new set of relational algorithms that will be able to work with hundreds of different relations. If you look at a SQL database like Snowflake or a big query, you know, you get tens of different joins coming together, and that query is going to take a really long time. Well, fortunately, technology is evolving, and it's possible with new join algorithms, worst case, optimal join algorithms they're called, where you can join hundreds of different relations together and run semantic queries that you simply couldn't run. Now that technology is nascent, but it's really important, and I think that will be a requirement to have this semantically reach its full potential. In the meantime, Tristan can do a lot of great things by building up on what he's got today and solve some problems that are very real. But in the long run I think we'll see a new set of databases to support these models. >> So Tristan, you got to respond to that, right? You got to, so take the example of Snowflake. We know it doesn't deal well with complex joins, but they're, they've got big aspirations. They're building an ecosystem to really solve some of these problems. Tristan, you guys are part of that ecosystem, and others, but please, your thoughts on what Bob just shared. >> Bob, I'm curious if, I would have no idea what you were talking about except that you introduced me to somebody who gave me a demo of a thing and do you not want to go there right now? >> No, I can talk about it. I mean, we can talk about it. Look, the company I've been working with is Relational AI, and they're doing this work to actually first of all work across the industry with academics and research, you know, across many, many different, over 20 different research institutions across the world to develop this new set of algorithms. They're all fully published, just like SQL, the underlying algorithms that are used by SQL databases are. If you look today, every single SQL database uses a similar set of relational algorithms underneath that. And those algorithms actually go back to system R and what IBM developed in the 1970s. We're just, there's an opportunity for us to build something new that allows you to take, for example, instead of taking data and grouping it together in tables, treat all data as individual relations, you know, a key and a set of values and then be able to perform purely relational operations on it. If you go back to what, to Codd, and what he wrote, he defined two things. He defined a relational calculus and relational algebra. And essentially SQL is a query language that is translated by the query processor into relational algebra. But however, the calculus of SQL is not even close to the full semantics of the relational mathematics. And it's possible to have systems that can do everything and that can store all of the attributes of the data model or ultimately the business model in a form that is much more natural to work with. >> So here's like my short answer to this. I think that we're dealing in different time scales. I think that there is actually a tremendous amount of work to do in the semantic layer using the kind of technology that we have on the ground today. And I think that there's, I don't know, let's say five years of like really solid work that there is to do for the entire industry, if not more. But the wonderful thing about DBT is that it's independent of what the compute substrate is beneath it. And so if we develop new platforms, new capabilities to describe semantic models in more fine grain detail, more procedural, then we're going to support that too. And so I'm excited about all of it. >> Yeah, so interpreting that short answer, you're basically saying, cause Bob was just kind of pointing to you as incremental, but you're saying, yeah, okay, we're applying it for incremental use cases today, but we can accommodate a much broader set of examples in the future. Is that correct, Tristan? >> I think you're using the word incremental as if it's not good, but I think that incremental is great. We have always been about applying incremental improvement on top of what exists today, but allowing practitioners to like use different workflows to actually make use of that technology. So yeah, yeah, we are a very incremental company. We're going to continue being that way. >> Well, I think Bob was using incremental as a pejorative. I mean, I, but to your point, a lot. >> No, I don't think so. I want to stop that. No, I don't think it's pejorative at all. I think incremental, incremental is usually the most successful path. >> Yes, of course. >> In my experience. >> We agree, we agree on that. >> Having tried many, many moonshot things in my Microsoft days, I can tell you that being incremental is a good thing. And I'm a very big believer that that's the way the world's going to go. I just think that there is a need for us to build something new and that ultimately that will be the solution. Now you can argue whether it's two years, three years, five years, or 10 years, but I'd be shocked if it didn't happen in 10 years. >> Yeah, so we all agree that incremental is less disruptive. Boom, but Tristan, you're, I think I'm inferring that you believe you have the architecture to accommodate Bob's vision, and then Bob, and I'm inferring from Bob's comments that maybe you don't think that's the case, but please. >> No, no, no. I think that, so Bob, let me put words into your mouth and you tell me if you disagree, DBT is completely useless in a world where a large scale cloud data warehouse doesn't exist. We were not able to bring the power of Python to our users until these platforms started supporting Python. Like DBT is a layer on top of large scale computing platforms. And to the extent that those platforms extend their functionality to bring more capabilities, we will also service those capabilities. >> Let me try and bridge the two. >> Yeah, yeah, so Bob, Bob, Bob, do you concur with what Tristan just said? >> Absolutely, I mean there's nothing to argue with in what Tristan just said. >> I wanted. >> And it's what he's doing. It'll continue to, I believe he'll continue to do it, and I think it's a very good thing for the industry. You know, I'm just simply saying that on top of that, I would like to provide Tristan and all of those who are following similar paths to him with a new type of database that can actually solve these problems in a much more architected way. And when I talk about Cosmos with something like Mongo or Cosmos together with Elastic, you're using Elastic as the join engine, okay. That's the purpose of it. It becomes a poor man's join engine. And I kind of go, I know there's a better answer than that. I know there is, but that's kind of where we are state of the art right now. >> George, we got to wrap it. So give us the last word here. Go ahead, George. >> Okay, I just, I think there's a way to tie together what Tristan and Bob are both talking about, and I want them to validate it, which is for five years we're going to be adding or some number of years more and more semantics to the operational and analytic data that we have, starting with metric definitions. My question is for Bob, as DBT accumulates more and more of those semantics for different enterprises, can that layer not run on top of a relational knowledge graph? And what would we lose by not having, by having the knowledge graph store sort of the joins, all the complex relationships among the data, but having the semantics in the DBT layer? >> Well, I think this, okay, I think first of all that DBT will be an environment where many of these semantics are defined. The question we're asking is how are they stored and how are they processed? And what I predict will happen is that over time, as companies like DBT begin to build more and more richness into their semantic layer, they will begin to experience challenges that customers want to run queries, they want to ask questions, they want to use this for things where the underlying infrastructure becomes an obstacle. I mean, this has happened in always in the history, right? I mean, you see major advances in computer science when the data model changes. And I think we're on the verge of a very significant change in the way data is stored and structured, or at least metadata is stored and structured. Again, I'm not saying that anytime in the next 10 years, SQL is going to go away. In fact, more SQL will be written in the future than has been written in the past. And those platforms will mature to become the engines, the slicer dicers of data. I mean that's what they are today. They're incredibly powerful at working with large amounts of data, and that infrastructure is maturing very rapidly. What is not maturing is the infrastructure to handle all of the metadata and the semantics that that requires. And that's where I say knowledge graphs are what I believe will be the solution to that. >> But Tristan, bring us home here. It sounds like, let me put pause at this, is that whatever happens in the future, we're going to leverage the vast system that has become cloud that we're talking about a supercloud, sort of where data lives irrespective of physical location. We're going to have to tap that data. It's not necessarily going to be in one place, but give us your final thoughts, please. >> 100% agree. I think that the data is going to live everywhere. It is the responsibility for both the metadata systems and the data processing engines themselves to make sure that we can join data across cloud providers, that we can join data across different physical regions and that we as practitioners are going to kind of start forgetting about details like that. And we're going to start thinking more about how we want to arrange our teams, how does the tooling that we use support our team structures? And that's when data mesh I think really starts to get very, very critical as a concept. >> Guys, great conversation. It was really awesome to have you. I can't thank you enough for spending time with us. Really appreciate it. >> Thanks a lot. >> All right. This is Dave Vellante for George Gilbert, John Furrier, and the entire Cube community. Keep it right there for more content. You're watching SuperCloud2. (upbeat music)

(upbeat music) >> Welcome back to The Cube's coverage of Fal.Con 22. I'm Dave Vellante with Dave Nicholson. This is day one of our coverage. We had the big keynotes this morning. Derek Jeter was one of the keynotes. We have a big Yankee fan here: George Kurtz is the co-founder and CEO of CrowdStrike. George, thanks for coming on The Cube. >> It's great to be here. >> Boston fan, you know, I tweeted out Derek Jeter. He broke my heart many times, but I can't hate on Jeter. You got to have respect for the guy. >> Well, I still remember I was in Japan when Boston was down you know, by three games and came back to win. So I've got my own heartbreak as well. >> It did heal some wounds, but it almost changed the rivalry, you know? I mean, >> Yeah. >> Once, it's kind of neutralized it, you know? It's just not as interesting. I mean, I'm a season ticket holder. I go to all the games and Yankee games are great. A lot of it used to be, you would never walk into Fenway park with, you know pin stripes, when today there's as many Yankee fans as there are... >> I know. >> Boston fans. Anyway, at Fenway, I mean. >> Yeah. >> Why did you start CrowdStrike? >> Biggest thing for me was to really change the game in how people were looking at security. And at my previous company, I think a lot of people were buying security and not getting the outcome that they wanted. Not- I got acquired by a company, not my first company. So, to be clear, and before I started CrowdStrike, I was in the antivirus world, and they were spending a lot of money with antivirus vendors but not getting the outcome I thought they should achieve, which is to stop the breach, not just stop malware. And for me, security should be outcome based not sort of product based. And the biggest thing for us was how could we create the sales force of security that was focused on getting the right outcome: stopping the breach. >> And the premise, I've seen it, the unstoppable breach is a myth. No CSOs don't live by that mantra, but you do. How are you doing on that journey? >> Well I think, look, there's no 100% of anything in security, but what we've done is really created a platform that's focused on identifying and stopping breaches as well as now, extending that out into helping IT identify assets and their hygiene and basically providing more visibility into IT assets. So, we talked about the convergence of that. Maybe we'll get into it, but. >> Dave Vellante: Sure. >> We're doing pretty well. And from our standpoint, we've got a lot of customers, almost 20,000, that rely on us day to day to help stop the breach. >> Well, and when you dig into the CrowdStrike architecture, what's so fascinating is, you know, Dave, we've talked about this: agent bad. Well, not necessarily, if you can have a lightweight agent that can scale and support a number of modules, then you can consolidate all these point tools out there. You talked about in your keynote, your pillars, workloads, which really end points >> Right. >> ID, which we're going to talk about. Identity data and network security. You're not a network security specialist, >> Right. >> But the other three, >> Yes. >> You're knocking down. >> Yeah. >> You guys went deep into that today. Talk about that. >> We did, most folks are going to know us for endpoint and Cloud workload protection and visibility. We did an acquisition almost two years to the day on preempt. And that was our identity play, identity threat protection and detection. And that really turned out to be a smart move, because it's the hottest topic right now. If you look at all the breaches over the last couple years, it's all identity based. Big, big talking points in our keynotes today. >> Dave Vellante: Right. >> And then the third area is on data, and data is really the you know, the new currency that people trade in. So how do you identify and protect endpoints and workloads? How do you tie that together with identity, as well as understanding how you connect the dots and the data and where data flows? And that's really been our focus and we continue to deliver on that for customers. >> And you've had a real dogma, I'll call it, about Cloud Native. I've had this conversation with Frank Slootman, "No we're not going to do a halfway house." You, I think, said it really well today. I think it was you who said it. If you've got On-Prem and Cloud, you got two code bases, >> George Kurtz: Right. >> That you got to maintain. >> That's it, yeah. >> And that means you're taking away resources from one or the other. >> That's exactly right. And what a lot of our competitors have done is they started On-Prem as an AV vendor, and then they took what they had and they basically put it in a Cloud instance called a Cloud, which doesn't really scale. And then, you know, where they need to, they basically still keep their On-Prem, and that just diffuses your engineering team. And most of the On-Prem stuff doesn't even have the features of what they're trying to offer from the Cloud. So either you're Cloud Native or you're not. You can't be halfway. >> But it doesn't mean that you can't include and ingest On-Prem data- >> Well, absolutely. >> into your platform, and that's what I think most people just some reason don't seem to understand. >> Well our agents run wherever. They certainly run On-Prem. >> Dave Vellante: Right. Right. >> And they run in the Cloud, they run wherever. But the crowd in the CrowdStrike is the fact that we can crowdsource this threat information at scale into our threat graph, which gives us unique insight, 7 trillion events per week. And you can't do that if you're not Cloud Native. And that crowd gives the, we call, community immunity. We see all kinds of attacks across 176 different countries. That benefit accrues to all of our customers. >> But how do you envision and maintain and preserve a lightweight agent that can support so many modules? As you do more acquisitions and you knock down new areas and bring in new functionality, go after things like operations technology, how is it that you're able to keep that agent lightweight? >> Well, we started as a platform company, meaning that the whole idea was we're going to build a lightweight agent. First iteration had no security capabilities. It was collect data, get it into a common data architecture or threat graph, in one spot. And then once we had the data then we applied AI to it and we created different workflows. So, the first incarnation was get data into the Cloud at scale. And that still holds true today. So if you think about why we can actually have all these different modules without an impact on the performance, it's we collect data one time. It's a threat data, you know? We're not collecting user data, but threat data collection mechanism. Once we have all that data, then we can slice and dice and create other modules. So the new modules never have to even touch the agent 'cause we've already collected the data. >> I'm going to just keep going, Dave, unless you shove your way in. >> No, no, go ahead. No, no, no. I'm waiting to pounce. >> But okay, so, I think, George, but George, I need to ask you about a comment that you made about we're not just shoving it into a data lake. But you are collecting all the data. Can you explain that nuance? >> Yeah. So there's a difference between a collect and forward agent. It means they just collect a bunch of data. They'll probably store it in a lot of space on the endpoint. It's slow and cumbersome, and then they'll forward it up into another data lake. So you have no context going into no context. Our agent is a smart agent, which actually allows us to always track the context of all these processes in what's happening on the endpoint. And it's a mini graph, meaning we keep track of the relationships. And as we ship that contextual information to the Cloud, we never lose that context. And then it goes into the bigger graph database, always with the same level of context. So, we keep the context of each individual workload or endpoint, and then across the Cloud, we have the context of all of those put together. It's massive. And that allows us to create different insights rather than a data lake, which is, you know, you're looking for, you're creating a bigger needle stack looking for needles. >> And I'm envisioning almost an index that is super, super fast. I mean, you're talking about sub, well second kind of near real time responses, correct? >> Absolutely. So a lot of what we do in terms of protection is already pushed down to the endpoint , 'cause it has intelligence and the AI model. And then again, the Cloud is always looking for different anomalies, not only on each individual endpoint or workload, but across the entire spectrum of our customer base. And that's all real time. It continually self-learns from all the data we collect. >> So when, yeah, when you've made these architectural decisions over time, there was a time when saying that you needed to run an agent could be a deal killer somewhere for people who argued against that. >> George Kurtz: Right. >> You've made the right decision there, clearly. Having everything be crowdsourced into Cloud makes perfect sense. Has that, though, posed a challenge from a sovereignty perspective? If you were deploying stuff On-Prem all over the place, you don't need to worry about that. Everything is here >> George Kurtz: Yeah. >> in a given country. How do you address the challenges of sovereignty when these agents are sending data into some sort of centralized Cloud space that crosses boundaries? >> Well, yeah, I guess what we would, let me go back to the beginning. So I started company in 2011 and I had to convince people that delivering endpoint security from the Cloud was going to be a good thing. >> Dave Vellante: Right. (chuckles) >> You know, you go into a Swiss bank and a bunch of other places and they're like, you're crazy. Right? >> Dave Nicholson: Right. >> They all became customers afterwards, right? And you have to just look at what they're doing. And the question I would have in the early days is, well, let me ask you are you using Dropbox, Box? Are you using a Microsoft? You know, what are you using? Well, they're all sending data to the Cloud. So good news! You already have a model, you've already approved that, right? So let's talk about our benefit. And you know, you can either have an adversary steal your data or you can send threat data to our Cloud, which by the way is in a lot of sovereign Clouds that are out there. And when you actually break it down to what we're sending to the Cloud, it's threat data, right? It isn't user files and documents and stuff. It's threat data. So, we work through all of that. And the Cloud is bigger than CrowdStrike. So you look at Sales Force, Service Now, Workday, et cetera. That's being used all over the place, Box, Dropbox. We just tagged onto it. Like why shouldn't security be the platform of record, and why shouldn't CrowdStrike be the platform of record and be the pillar of Cloud security? >> Explain your observability strategy, 'cause you acquired Humio for, I mean, I think it was $400 million, which is a song. >> Yeah. >> And then Reposify is the latest acquisition. I see that as an extension, 'cause it gives you visibility. Is that part of your security, of your observability play? Explain where you do play and don't play. >> Sure. Well observability is a big, you know, fluffy word. Where we play is in probably the first two areas of observability, right? There's five, kind of, pillars. We're focused on event collection. Let's get events from the endpoints. Let's get events from really anywhere in the network. And we can do that with Humio is now log scale. And then the second piece is with our agents, let's get an understanding of their, the asset itself. What is the asset? What state is it in? Does it have vulnerabilities? Does it have, you know, is it running out of disc space? Is it have, does it have a performance issue? Those are really the first two, kind of, areas of observability. We're not in application performance, we're in let's collect data from the endpoint and other sources, and let's understand if the thing is working, right? And that's a huge value for customers. And we can do that because we already have a privileged spot on the endpoint with our agent. >> Got it. Question on the TAM. Like I look at your TAMs, your charts, I love it. You know, generally do. Were you taking known data from you know, firms like IDC >> George Kurtz: Yeah. >> and saying, okay we're going to play there, now we're made this acquisition. We're new modules, now we're playing there. Awesome. I think you got a big TAM. And I guess that's, that's the point. There's no lack of market for you. >> George Kurtz: Right. >> But I do feel like there's this unknown unquantifiable piece of your TAM. IDC can't see it, 'cause they're kind of looking back >> George Kurtz: Right. >> seein' what the market do last year and we'll forecast it out. It's almost, you got to be a futurist to see it. How do you think about your total available market and the opportunity that's out there? >> Well, it's well in excess of 120 billion and we've actually updated that recently. So it's even beyond that. But if you look at all the modules each module has a discreet TAM and again, for what, you know, what we're focused on is how do you give an outcome to a customer? So a lot of the modules map back into specific TAM and product categories. When you add 'em all up and when you look at, you know, some of the new things that we're coming out with, again, it's well in excess of 120 billion. So that's why we like to say like, you know, we're not an endpoint company. We're really, truly a security platform company that was born in the Cloud. And I think if you see the growth rates, and one of the things that we've talked about, and I think you might have pointed out in prior podcasts, is we're the second fastest company to 2 billion dollars in annual recurring revenue, only behind Zoom. And you know I would argue- great company, by the way, a customer- but that was a black Swan event in a pandemic, right? >> Dave Vellante: I'll say! >> Yeah. >> So we are rarefied air when you think about the capabilities that we have and the performance and the TAM that's available to us. >> The other thing I said in my breaking analysis was 'cause you guys aspire to be a generational company. And I think you got a really good shot at being one, but to be a generational company, you have to have an ecosystem. So I'd love you to talk about the ecosystem, but where you want to see it in five years. >> Well, it really is a good point and we are a partner first company. Ecosystem is really important. Cameras probably can't see all the vendors that are here that are our partners, right? It's a big part of this show that we're at. You see a lot of, well, you see some vendors behind us. >> Yep. >> We have to realize in 2022, and I think this is something that we did well and it's my philosophy, is we are not the only game in town. We like to be, and we are, for many companies the security platform on record, but we don't do everything. We talked about network in other areas. We can't do everything. You can't be good and try to do everything. So, for customers today, what they're looking at is best of platform. And in the early days of security, I've been in it over 30 years, it used to be best of breed products, then it was best of suite, now it's best of platform. So what do I mean by that? It means that customers don't want to engineer their own solution. They, like Lego blocks, they want to pull the platforms, and they want to stitch 'em together via API. And they want to say, okay, CrowdStrike works with Okta, works with Zscaler, works with Proofpoint, et cetera. And that's what customers want. So, ecosystem is incredibly important for us. >> Explain that. You mentioned Okta, I had another question for you. I was at Reinforce, and I saw this better together presentation, CrowdStrike and Okta talking about identity. You've got an identity module. Explain to people how you're not competing with Okta. You guys complement each other, there. >> Well, an identity kind of broker, if you will, is basically what Okta does in others, right? So you log in single sign on and you get access. They broker access to all these other applications. >> Dave Vellante: Right. >> That's not what we do. What we do is we look at those endpoints and workloads and domain controllers and directory services and we figure out, are there vulnerabilities and are there threats associated with them? And we call that out. The second piece, which is critical, is we prevent lateral movement. So if credentials are stolen we can prevent those credentials from being laundered or used and moved laterally, which is a key part of how breaches happen. We then create a trust score on those endpoints and workloads. And we basically say, okay, do we think the trust on the endpoint and workload is high or low? Do we think the identity, you know, is it George on the endpoint, or not? We give that a score. And we pass that along to Okta or Ping or whoever, and they then use that as part of their calculus in how they broker access to other resources. So it really is better together. >> So your execution has been stellar. This is my competition question. You obviously have competition out there. I think architecturally, you've got some advantages. You have a great relationship with AWS. I don't know what's going on with Google, but Kevin's up on stage. >> George Kurtz: Yeah. >> They're now part of Google. >> George Kurtz: We have a great relationship with them. >> Microsoft obviously, a competitor. You obviously do some things in, >> Right. >> in Azure. Are you building the security Cloud? >> We are. We think we are, because when you look at the amount of data that we actually ingest, when you look at companies using us for critical decisions and critical protection, not only on their On-Prem, but also in their Cloud environment, and the knowledge we have, we think it is a security Cloud. You know, you had, you had Salesforce and Workday and ServiceNow and each of them had their respective Clouds. When I started the company, there was no security Cloud. You know, it wasn't any of the companies that you know. It wasn't the firewall companies, wasn't the AV companies. And I think we really defined ourselves as the security Cloud. And the level of knowledge and insights we have in our Cloud, I think, are world class. >> But you know, it's a difference of being those- 'cause you mentioned those other, you know, seminal Clouds. They, like Salesforce, Workday, they're building their own Clouds. Maybe not so much Workday, but certainly Salesforce and ServiceNow built their own >> Yeah. >> Clouds, their own data centers. You're building on top of hyperscalers, correct? >> Well, >> Well you have your own data centers, too. >> We have our own data centers, yeah. So when we first started, we started in AWS as many do, and we have a great relationship there. We continue to build out. We are a huge customer and we also have, you know, with data sovereignty and those sort of things, we've got a lot of our sort of data that sits in our private Cloud. So it's a hybrid approach and we think it's the best of both worlds. >> Okay. And you mean you can manage those costs and it's, how do you make the decision? Is it just sovereignty or is it cost as well? >> Well, there's an operational element. There's cost. There's everything. There's a lot that goes into it. >> Right. >> And at the end of the day we want to make sure that we're using the right technology in the right Clouds to solve the right problem. >> Well, George, congratulations on being back in person. That's got to feel good. >> It feels really good. >> Got a really good audience here. I don't know what the numbers are but there's many thousands here, >> Thousands, yeah. >> at the ARIA. Really appreciate your time. And thanks for having The Cube here. You guys built a great set for us. >> Well, we appreciate all you do. I enjoy your programs. And I think hopefully we've given the audience a good idea of what CrowdStrike's all about, the impact we have and certainly the growth trajectory that we're on. So thank you. >> Fantastic. All right, George Kurtz, Dave Vellante for Dave Nicholson. We're going to wrap up day one. We'll be back tomorrow, first thing in the morning, live from the ARIA. We'll see you then. (calm music)

(light corporate music) >> Welcome back, everyone, to VMware Explore 22. I'm John Furrier, host of theCUBE with Dave Vellante. Our 12th year covering VMware's User Conference, formerly known as VMworld, now rebranded as VMware Explore. Two great cube alumnus coming down the cube. Ricky Cooper, SVP, Worldwide Partner Commercials VMware, great to see you. Thanks for coming on. >> Thank you. >> We just had a great chat- >> Good to see you again. >> With the Discovery and, of course, Joseph George, vice president of Compute Industry Alliances. Great to have you on. Great to see you. >> Great to see you, John. >> So guys this year is very curious in VMware. A lot goin' on, the name change, the event. Big, big move. Bold move. And then they changed the name of the event. Then Broadcom buys them. A lot of speculation, but at the end of the day, this conference kind of, people were wondering what would be the barometer of the event. We're reporting this morning on the keynote analysis. Very good mojo in the keynote. Very transparent about the Broadcom relationship. The expo floor last night was buzzing. >> Mhm. >> I mean, this is not a show that's lookin' like it's going to be, ya' know, going down. >> Yeah. >> This is clearly a wave. We're calling it Super Cloud. Multi-Cloud's their theme. Clearly the cloud's happenin'. We not to date ourselves, but 2013 we were discussing on theCUBE- >> We talked about that. Yeah. Yeah. >> Discover about DevOps infrastructure as code- >> Mhm. >> We're full realization now of that. >> Yep. >> This is where we're at. You guys had a great partnership with VMware and HPE. Talk about where you guys see this coming together because customers are refactoring. They are lookin' at Cloud Native. The whole Broadcom visibility to the VMware customer bases activated them. They're here and they're leaning in. >> Yeah. >> What's going on? >> Yeah. Absolutely. We're seeing a renewed interest now as customers are looking at their entire infrastructure, bottoms up, all the way up the stack, and the notion of a hybrid cloud, where you've got some visibility and control of your data and your infrastructure and your applications, customers want to live in that sort of a cloud environment and so we're seeing a renewed interest. A lot of conversations we're having with customers now, a lot of customers committing to that model where they have applications and workloads running at the Edge, in their data center, and in the public cloud in a lot of cases, but having that mobility, having that control, being able to have security in their own, you know, in their control. There's a lot that you can do there and, obviously, partnering with VMware. We've been partners for so long. >> 20 years about. Yeah. Yeah. >> Yeah. At least 20 years, back when they invented stuff, they were inventing way- >> Yeah. Yeah. Yeah. >> VMware's got a very technical culture, but Ricky, I got to say that, you know, we commented earlier when Raghu was on, the CEO, now CEO, I mean, legendary product. I sent the trajectory to VMware. Everyone knows that. VMware, I can't know whether to tell it was VMware or HP, HP before HPE, coined hybrid- >> Yeah. >> 'Cause you guys were both on. I can't recall, Dave, which company coined it first, but it was either one of you guys. Nobody else was there. >> It was the partnership. >> Yes. I- (cross talking) >> They had a big thing with Pat Gelsinger. Dave, remember when he said, you know, he got in my grill on theCUBE live? But now you see- >> But if you focus on that Multi-Cloud aspect, right? So you've got a situation where our customers are looking at Multi-Cloud and they're looking at it not just as a flash in the pan. This is here for five years, 10 years, 20 years. Okay. So what does that mean then to our partners and to our distributors? You're seeing a whole seed change. You're seeing partners now looking at this. So, look at the OEMs, you know, the ones that have historically been vSphere customers are now saying, they're coming in droves saying, okay, what is the next step? Well, how can I be a Multi-Cloud partner with you? >> Yep. Right. >> How can I look at other aspects that we're driving here together? So, you know, GreenLake is a great example. We keep going back to GreenLake and we are partaking in GreenLake at the moment. The real big thing for us is going to be, right, let's make sure that we've got the agreements in place that support this SaaS and subscription motion going forward and then the sky's the limit for us. >> You're pluggin' that right into GreenLake, right? >> Well, here's why. Here's why. So customers are loving the fact that they can go to a public cloud and they can get an SLA. They come to a, you know, an On-Premise. You've got the hardware, you've got the software, you've got the, you know, the guys on board to maintain this through its life cycle. >> Right. I mean, this is complicated stuff. >> Yeah. >> Now we've got a situation where you can say, hey, we can get an SLA On-Premise. >> Yeah. And I think what you're seeing is it's very analogous to having a financial advisor just manage your portfolio. You're taking care of just submitting money. That's really a lot of what the customers have done with the public cloud, but now, a lot of these customers are getting savvy and they have been working with VMware Technologies and HPE for so long. They've got expertise. They know how they want their workloads architected. Now, we've given them a model where they can leverage the Cloud platform to be able to do this, whether it's On-Premise, The Edge, or in the public cloud, leveraging HPE GreenLake and VMware. >> Is it predominantly or exclusively a managed service or do you find some customers saying, hey, we want to manage ourself? How, what are you seeing is the mix there? >> It is not predominantly managed services right now. We're actually, as we are growing, last time we talked to HPE Discover we talked about a whole bunch of new services that we've added to our catalog. It's growing by leaps and bounds. A lot of folks are definitely interested in the pay as you go, obviously, the financial model, but are now getting exposed to all the other management that can happen. There are managed services capabilities, but actually running it as a service with your systems On-Prem is a phenomenal idea for all these customers and they're opening their eyes to some new ways to service their customers better. >> And another phenomenon we're seeing there is where partners, such as HPA, using other partners for various areas of their services implementation as well. So that's another phenomenon, you know? You're seeing the resale motion now going into a lot more of the services motion. >> It's interesting too, you know, I mean, the digital modernization that's goin' on. The transformation, whatever you want to call it, is complicated. >> Yeah. >> That's clear. One of the things I liked about the keynote today was the concept of cloud chaos. >> Yeah. >> Because we've been saying, you know, quoting Andy Grove at Intel, "Let chaos rain and rain in the chaos." >> Mhm. >> And when you have inflection points, complexity, which is the chaos, needs to be solved and whoever solves it kicks the inflection point, that's up into the right. So- >> Prime idea right here. Yeah. >> So GreenLake is- >> Well, also look at the distribution model and how that's changed. A couple of points on a deal. Now they're saying, "I'll be your aggregator. I'll take the strain and I'll give you scale." You know? "I'll give you VMware Scale for all, you know, for all of the various different partners, et cetera." >> Yeah. So let's break this down because this is, I think, a key point. So complexity is good, but the old model in the Enterprise market was- >> Sure. >> You solve complexity with more complexity. >> Yeah. >> And everybody wins. Oh, yeah! We're locked in! That's not what the market wants. They want some self-service. They want, as a service, they want easy. Developer first security data ops, DevOps, is already in the cycle, so they're going to want simpler. >> Yeah. >> Easier. Faster. >> And this is kind of why I'll say, for the big announcement today here at VMware Explore, around the VMware vSphere Distributed Services Engine, Project Monterey- >> Yeah. >> That we've talked about for so long, HPE and VMware and AMD, with the Pensando DPU, actually work together to engineer a solution for exactly that. The capabilities are fairly straightforward in terms of the technologies, but actually doing the work to do integration, joint engineering, make sure that this is simple and easy and able to be running HPE GreenLake, that's- >> That's invested in Pensando, right? >> We are. >> We're all investors. Yeah. >> What's the benefit of that? What's, that's a great point you made. What's the value to the customer, bottom line? That deep co-engineering, co-partnering, what does it deliver that others don't do? >> Yeah. Well, I think one example would be, you know, a lot of vendors can say we support it. >> Yep. >> That's great. That's actually a really good move, supporting it. It can be resold. That's another great move. I'm not mechanically inclined to where I would go build my own car. I'll go to a dealership and actually buy one that I can press the button and I can start it and I can do what I need to do with my car and that's really what this does is the engineering work that's gone on between our two companies and AMD Pensando, as well as the business work to make that simple and easy, that transaction to work, and then to be able to make it available as a service, is really what made, it's, that's why it's such a winner winner with our- >> But it's also a lower cost out of the box. >> Yep. >> Right. >> So you get in whatever. Let's call it 20%. Okay? But there's, it's nuanced because you're also on a new technology curve- >> Right. >> And you're able to absorb modern apps, like, you know, we use that term as a bromide, but when I say modern apps, I mean data-rich apps, you know, things that are more AI-driven not the conventional, not that people aren't doing, you know, SAP and CRM, they are, but there's a whole slew of new apps that are coming in that, you know, traditional architectures aren't well-suited to handle from a price performance standpoint. This changes that doesn't it? >> Well, you think also of, you know, going to the next stage, which is to go to market between the two organizations that before. At the moment, you know, HPE's running off doing various different things. We were running off to it again, it's that chaos that you're talking about. In cloud chaos, you got to go to market chaos. >> Yeah. >> But by simplifying four or five things, what are we going to do really well together? How do we embed those in GreenLake- >> Mhm. >> And be known in the marketplace for these solutions? Then you get a, you know, an organization that's really behind the go to market. You can help with sales activation the enablement, you know, and then we benefit from the scale of HPE. >> Yeah. >> What are those solutions I mean? Is it just, is it I.S.? Is it, you know, compute storage? >> Yeah. >> Is it, you know, specific, you know, SAP? Is it VDI? What are you seeing out there? >> So right now, for this specific technology, we're educating our customers on what that could be and, at its core, this solution allows customers to take services that normally and traditionally run on the compute system and run on a DPU now with Project Monterey, and this is now allowing customers to think about, okay, where are their use cases. So I'm, rather than going and, say, use it for this, we're allowing our customers to explore and say, okay, here's where it makes sense. Where do I have workloads that are using a lot of compute cycles on services at the compute level that could be somewhere else like networking as a great example, right? And allowing more of those compute cycles to be available. So where there are performance requirements for an application, where there is timely response that's needed for, you know, for results to be able to take action on, to be able to get insight from data really quick, those are places where we're starting to see those services moving onto something like a DPU and that's where this makes a whole lot more sense. >> Okay. So, to get this right, you got the hybrid cloud, right? >> [Ricky And Joseph] Yes. >> You got GreenLake and you got the distributed engine. What's that called the- >> For, it's HPE ProLiant- >> ProLiant with- >> The VMware- >> With vSphere. >> That's the compute- >> Distributed. >> Okay. So does the customer, how do you guys implement that with the customer? All three at the same time or they mix and match? What's that? How does that work? >> All three of those components. Yeah. So the beauty of the HP ProLiant with VMware vSphere-distributed services engine- >> Mhm. >> Also known as Project Monterey for those that are keeping notes at home- >> Mhm. >> It's, again, already pre-engineered. So we've already worked through all the mechanics of how you would have to do this. So it's not something you have to go figure out how you build, get deployment, you know, work through those details. That's already done. It is available through HPE GreenLake. So you can go and actually get it as a service in partnership with our customer, our friends here at VMware, and because, if you're familiar and comfortable with all the things that HP ProLiant has done from a security perspective, from a reliability perspective, trusted supply chain, all those sorts of things, you're getting all of that with this particular (indistinct). >> Sumit Dhawan had a great quote on theCUBE just an hour or so ago. He said you have to be early to be first. >> Yeah. (laughing) >> I love that quote. Okay. So you were- >> I fought the urge. >> You were first. You were probably a little early, but do you have a lead? I know you're going to say yes, okay. Let's just- >> Okay. >> Let's just assume that. >> Okay. Yeah. >> Relative to the competition, how do you know? How do you determine that? >> If we have a lead or not? >> Yeah. If you lead. If you're the best. >> We go to the source of the truth which is our customers. >> And what do they tell you? What do you look at and say, okay, now, I mean, when you have that honest conversation and say, okay, we are, we're first, we're early. We're keeping our lead. What are the things that you- >> I'll say it this way. I'll say it this way. We've been in a lot of businesses where there, where we do compete head-to-head in a lot of places. >> Mhm. >> And we know how that sales process normally works. We're seeing a different motion from our customers. When we talk about HPE GreenLake, there's not a lot of back and forth on, okay, well, let me go shop around. It is HP Green. Let's talk about how we actually build this solution. >> And I can tell you, from a VMware perspective, our customers are asking us for this the other way around. So that's a great sign is that, hey, we need to see this partnership come together in GreenLake. >> Yeah. >> It's the old adage that Amazon used to coin and Andy Jassy, you know, they do the undifferentiated heavy lifting. >> [Ricky And Joseph] Yeah. >> A lot of that's now Cloud operations. >> Mhm. >> Underneath it is infrastructure's code to the developer. >> That's right. >> That's at scale. >> That's right. >> And so you got a lot of heavy lifting being done with GreenLake- >> Right. >> Which is why there's no objections probably. >> Right. >> What's the choice? What are you going to shop? >> Yeah. >> There's nothing to shop around. >> Yeah, exactly. And then we've got, you know, that is really icing on the cake that we've, you know, that we've been building for quite some time and there is an understanding in the market that what we do with our infrastructure is hardened from a reliability and quality perspective. Like, times are tough right now. Supply chain issues, all that stuff. We've talked, all talked about it, but at HPE, we don't skimp on quality. We're going to spend the dollars and time on making sure we got reliability and security built in. It's really important to us. >> We had a great use case. The storage team, they were provisioning with containers. >> Yes. >> Storage is a service instantly we're seeing with you guys with VMware. Your customers' bringing in a lot of that into the mix as well. I got to ask 'cause every event we talk about AI and machine learning- >> Mhm. >> Automation and DevOps are now infiltrating in with the CICD pipeline. Security and data become a big conversation. >> [Ricky And Joseph] Agreed. >> Okay. So how do you guys look at that? Okay. You sold me on Green. Like, I've been a big fan from day one. Now, it's got maturity on it. I know it's going to get a lot more headroom to do. There's still a lot of work to do, but directionally it's pretty accurate, you know? It's going to be a success. There's still concern about security, the data layer. That's agnostic of environment, private cloud, hybrid, public, and Edge. So that's important and security- >> Great. >> Has got a huge service area. >> Yeah. >> These are on working progress. >> Yeah. Yeah. >> How do you guys view those? >> I think you've just hit the net on the head. I mean, I was in the press and journalist meetings yesterday and our answer was exactly the same. There is still so much work that can be done here and, you know, I don't think anybody is really emerging as a true leader. It's just a continuation of, you know, tryin' to get that right because it is what is the most important thing to our customers. >> Right. >> And the industry is really sort of catching up to that. >> And, you know, when you start talking about privacy and when you, it's not just about company information. It's about individuals' information. It's about, you know, information that, if exposed, actually could have real impact on people. >> Mhm. >> So it's more than just an I.T. problem. It is actually, and from HPE's perspective, security starts from when we're picking our suppliers for our components. Like, there are processes that we put into our entire trusted supply chain from the factory on the way up. I liken it to my golf swing. My golf swing. I slice right like you wouldn't believe. (John laughing) But when I go to the golf pros, they start me back at the mechanics, the foundational pieces. Here's where the problems are and start workin' on that. So my view is, our view is, if your infrastructure is not secure, you're goin' to have troubles with security as you go further up. >> Stay in the sandbox. >> Yeah. >> Yeah. So to speak, you know, they're driving range on the golf analogy there. I love that. Talk about supply chain security real quick because you mentioned supply chain on the hardware side. You're seeing a lot of open source and supply chain in software, trusted software. >> Yep. >> How does GreenLake look at that? How do you guys view that piece of it? That's an important part. >> Yeah. Security is one of the key pillars that we're actually driving as a company right now. As I said, it's important to our customers as they're making purchasing decisions and we're looking at it from the infrastructure all the way up to the actual service itself and that's the beauty of having something like HPE GreenLake. We don't have to pick, is the infrastructure or the middle where, or the top of stack application- >> It's (indistinct), right? >> It's all of it. >> Yeah. >> It's all of it. That matters. >> Quick question on the ecosystem posture. So- >> Sure. >> I remember when HP was, you know, one company and then the GSIs were a little weird with HP because of EDS, you know? You had data protector so we weren't really chatting up Veeam at the time, right? And as soon as the split happened, ecosystem exploded. Now you have a situation where you, Broadcom, is acquiring VMware. You guys, big Broadcom customer. Has your attitude changed or has it not because, oh, we meet with the customers already. Well, you've always said that, but have you have leaned in more? I mean, culturally, is HPE now saying, hmm, now we have some real opportunities to partner in new ways that we don't have to sleep with one eye open, maybe. (John laughing) >> So first of all, VMware and HPE, we've got a variety of different partners. We always have. >> Mhm. >> Well before any Broadcom announcement came along. >> Yeah, sure. >> We've been working with a variety of partners. >> And that hasn't changed. >> And that hasn't changed. And, if your question is, has our posture toward VMware changed at all, the answer's absolutely not. We believe in what VMware is doing. We believe in what our customers are doing with VMware and we're going to continue to work with VMware and partner with the (indistinct). >> And of course, you know, we had to spin out ourselves in November of last year, which I worked on, you know, the whole Dell thing. >> Yeah. We still had the same chairman. >> Yeah. There- (Dave chuckling) >> Yeah, but since then, I think what's really become very apparent and not, it's not just with HPE, but with many of our partners, many of the OEM partners, the opportunity in front of us is vast and we need to rely on each other to help us as, you know, solve the customer problems that are out there. So there's a willingness to overlook some things that, in the past, may have been, you know, barriers. >> But it's important to note also that it's not that we have not had history- >> Yeah. >> Right? Over, we've got over 200,000 customers join- >> Hundreds of millions of dollars of business- >> 100,000, over 10,000, or 100,000 channel partners that we all have in common. >> Yeah. Yeah. >> Yep. >> There's numerous- >> And independent of the whole Broadcom overhang there. >> Yeah. >> There's the ecosystem floor. >> Yeah. >> The expo floor. >> Right. >> I mean, it's vibrant. I mean, there's clearly a wave coming, Ricky. We talked about this briefly at HPE Discover. I want to get an update from your perspectives, both of you, if you don't mind weighing in on this. Clearly, the wave, we're calling it the Super Cloud, 'cause it's not just Multi-Cloud. It's completely different looking successes- >> Smart Cloud. >> It's not just vendors. It's also the customers turning into clouds themselves. You look at Goldman Sachs and- >> Yep. >> You know, I think every vertical will have its own power law of Cloud players in the future. We believe that to be true. We're still testing that assumption, but it's trending in when you got OPEX- >> [Ricky And Joseph] Right. >> Has to go to in-fund statement- >> Yeah. >> CapEx goes too. Thanks for the Cloud. All that's good, but there's a wave coming- >> Yeah. >> And we're trying to identify it. What do you guys see as this wave 'cause beyond Multi-Cloud and the obvious nature of that will end up happening as a state and what happens beyond that interoperability piece, that's a whole other story, and that's what everyone's fighting for, but everyone out in that ecosystem, it's a big wave coming. They've got their surfboards. They're ready to go. So what do you guys see? What is the next wave that everyone's jacked up about here? >> Well, I think that the Multi-Cloud is obviously at the epicenter. You know, if you look at the results that are coming in, a lot of our customers, this is what's leading the discussion and now we're in a position where, you know, we've brought many companies over the last few years. They're starting to come to fruition. They're starting to play a role in, you know, how we're moving forward. >> Yeah. >> Some of those are a bit more applicable to the commercial space. We're finding commercial customers that never bought from us before. Never. Hundreds and hundreds are coming through our partner networks every single quarter, you know? So brand new to VMware. The trick then is how do you nurture them? How do you encourage them? >> So new logos are comin' in. >> New logos are coming in all the time, all the time, from, you know, from across the ecosystem. It's not just the OEMs. It's all the way back- >> So the ecosystem's back of VMware. >> Unbelievably. So what are we doing to help that? There's two big things that we've announced in the recent weeks is that Partner Connect 2.0. When I talked to you about Multi-Cloud and what the (indistinct), you know, the customers are doing, you see that trend. Four, five different separate clouds that we've got here. The next piece is that they're changing their business models with the partners. Their services is becoming more and more apparent, et cetera, you know? And the use of other partners to do other services, deployment, or this stuff is becoming prevalent. Then you've got the distributors that I talked about with their, you know, their, then you route to market, then you route to business. So how do you encapsulate all of that and ensure your rewarding partners on all aspects of that? Whether it's deployment, whether it's test and depth, it's a points-based system we've put in place now- >> It's a big pie that's developing. The market's getting bigger. >> It's getting so much bigger. And then you help- >> I know you agree, obviously, with that. >> Yeah. Absolutely. In fact, I think for a long time we were asking the question of, is it going to be there or is it going to be here? Which was the wrong question. (indistinct cross talking) Now it's everything. >> Yeah. >> And what I think that, what we're seeing in the ecosystem, is that people are finding the spots that, where they're going to play. Am I going to be on the Edge? >> Yeah. >> Am I going to be on Analytics Play? Am I going to be, you know, Cloud Transition Play? There's a lot of players are now emerging and saying, we're- >> Yeah. >> We're, we now have a place, a part to play. And having that industry view not just of, you know, a commercial customer at that level, but the two of us are lookin' at Teleco, are looking at financial services, at healthcare, at manufacturing. How do these new ecosystem players fit into the- >> (indistinct) lifting. Everyone can see their position there. >> Right. >> We're now being asked for simplicity and talk to me about partner profitability. >> Yes. >> How do I know where to focus my efforts? Am I spread too thin? And, you know, that's, and my advice that the partner ecosystem out there is, hey, let's pick out spots together. Let's really go to, and then strategic solutions that we were talking about is a good example of that. >> Yeah. >> Sounds like composability to me, but not to go back- (laughing) Guys, thanks for comin' on. I think there's a big market there. I think the fog is lifted. People seeing their spot. There's value there. Value creation equals reward. >> Yeah. >> Simplicity. Ease of use. This is the new normal. Great job. Thanks for coming on and sharing. (cross talking) Okay. Back to live coverage after this short break with more day one coverage here from the blue set here in Moscone. (light corporate music)

(bright intro music) >> Welcome back everyone to VMware Explore '22. I'm John Furrier, host of the key with David Lante, our 12th year covering VMware's user conference, formerly known as VM-World now rebranded as VMware Explore. You got two great Cube alumni coming on the Cube. Ricky Cooper, SVP worldwide partner commercial VMware. Great to see you, thanks for coming on. >> Thank you. >> We just had a great chat-- >> Good to see you again. >> At HPE discover. And of course, Joseph George, Vice President of Compute Industry Alliances. Great to have you on. Great to see you. >> Great to see you, John. >> So guys, this year is very curious, VMware, a lot going on. The name change of the event. Big move, Bold move. And then they changed the name of the event. Then Broadcom buys them. A lot of speculation, but at the end of the day, this conference... Kind of people were wondering what would be the barometer of the event. We were reporting this morning on the keynote analysis. Very good mojo in the keynote. Very transparent about the Broadcom relationship. The expo floor last night was buzzing. I mean, this is not a show that's looking like it's going to be, you know, going down. This is clearly a wave. We're calling it super cloud, multi-cloud's their theme. Clearly the cloud's happening. Not to date ourselves, but 2013 we were discussing on the-- >> We talked about that, yeah. >> HPE Discover about DevOps infrastructure as code. We're full realization now of that. This is where we're at. You guys had a great partnership with VMware and HPE. Talk about where you guys see this coming together because the customers are refactoring, they are looking at cloud native, the whole Broadcom visibility to the VMware customer bases activated them. They're here and they're leaning in. What's going on? >> Yeah absolutely, we're seeing a renewed interest now as customers are looking at their entire infrastructure, bottoms up all the way up the stack and the notion of a hybrid cloud, where you've got some visibility and control of your data and your infrastructure and applications. Customers want to live in that sort of a cloud environment. And so we're seeing a renewed interest, a lot of conversations we're having with customers now, a lot of customers committing to that model, where they have applications and workloads running at the edge in their data center and in the public cloud in a lot of cases. But having that mobility, having that control, being able to have security in their own control. There's a lot that you can do there. And obviously partnering with VMware. We've been partners for so long. >> 20 years, at least. >> At least 20 years. Back when they invented stuff. They were inventing way-- >> VMware's got a very technical culture, but Ricky, I got to say that we commented earlier when Ragu was on the CEO now CEO, I mean legendary product guy, set the trajectory to VMware, everyone knows that. I can't know whether it was VMware or HP, HP before HPE coined Hybrid. Cause you guys were both on, I can't recall Dave, which company coined it first, but it was either one of you guys. Nobody else was there. >> It was the partnership. (men chuckle) >> Hybrid Cloud I had a big thing with Pat Gelsinger, Dave. Remember when he said he got in my grill on theCube, live, but now you see. >> You focus on that multi-cloud aspect. So you've got a situation where our customers are looking at multi-cloud and they're looking at it, not just as a flash in the pan. This is here for five years, 10 years, 20 years. Okay. So what does that mean then to our partners and to our distributors, you're seeing a whole seed change. You're seeing partners now looking at this. So look at the OEMs, the ones that have historically been vSphere customers and now saying they're coming in, drove saying, okay, what is the next step? Well, how can I be a multi-cloud partner with you? How can I look at other aspects that we're driving here together? So GreenLake is a great example. We keep going back to GreenLake and we are partaking in GreenLake at the moment. The real big thing for us is going to be right. Let's make sure that we've got the agreements in place that support this Sasson subscription motion going forward. And then the sky's the limit for us. >> You're plugging that right into. >> Well, here's why, here's why, so customers are loving the fact that they can go to a public cloud and they can get an SLA. They come to an on-premise, you've got the hardware, you've got the software, you've got the guys on board to maintain this through its life cycle. I mean, this is complicated stuff. Now we've got a situation where you can say, Hey, we can get an SLA on premise. >> And I think what you're seeing is it's very analogous to having a financial advisor, just manage your portfolio. You're taking care of just submitting money. That's really a lot of what a lot of the customers have done with the public cloud. But now a lot of these customers are getting savvy. They have been working with VMware technologies and HPE for so long. they've got expertise. They know how they want their workloads architected. Now we've given them a model where they can leverage the cloud platform to be able to do this, whether it's on premise, the edge or in the public cloud, leveraging HPE GreenLake and VMware. >> Is it predominantly or exclusively a managed service or do you find some customers saying, hey, we want to manage ourself. What are you seeing is the mix there? >> It is not predominantly managed services right now. We're actually, as we are growing last time we talked at HPE discover. We talked about a whole bunch of new services that we've added to our catalog. It's growing by leaps and bounds. A lot of folks are definitely interested in the pay as you go, obviously the financial model, but are now getting exposed to all the other management that can happen. There are managed services capabilities, but actually running it as a service with your systems on-prem is a phenomenal idea for all these customers. And they're opening their eyes to some new ways to service their customers better. >> And another phenomenon we're seeing there is where partners such as HPA, using other partners for various areas of the services implementation as well. So that's another phenomenon. You're seeing the resale motion now going into a lot more of the services motion. >> It's interesting too. I mean the digital modernization that's going on, the transformation whatever you want to call it, is complicated, that's clear. One of the things I liked about the keynote today was the concept of cloud chaos, because we've been saying quoting Andy Grove, Next Intel, let chaos rain and rain in the chaos. And when you have inflection points, complexity, which is the chaos, needs to be solved and whoever solves it and kicks the inflection point, that's up and to the right. >> So prime idea right here. So. >> GreenLake is, well. >> Also look at the distribution model and how that's changed a couple of points on a deal. Now they're saying I'll be your aggregator. I'll take the strain and I'll give you scale. I'll give you VMware scale for all of the various different partners, et cetera. >> Yeah. So let's break this down because this is, I think a key point. So complexity is good, but the old model in the enterprise market was, you solve complexity with more complexity and everybody wins. Oh yeah, we're locked in. That's not what the market wants. They want self- service, they want as a service, they want easy, developer first security data ops. DevOps is already in the cycle. So they're going to want simpler, easier, faster. >> And this is kind of why I I'll say for the big announcement today here at VMware Explorer around the VMware vSphere distributed services engine, project Monterey that we've talked about for so long, HPE and VMware and AMD with the Pensando DPU actually work together to engineer a solution for exactly that. The capabilities are fairly straightforward in terms of the technologies, but actually doing the work to do integration, joint engineering, make sure that this is simple and easy and able to be running HPE GreenLake. >> We invested in Pensando right, we are investors. >> What's the benefit of that. That's a great point. You made what's the value to the customer bottom line, that deep, co-engineering, co-partnering, what is it deliver that others don't do? >> Yeah. Well, I think one example would be a lot of vendors can say we support it. >> Yep. That's great. That's actually a really good move, supporting it. It can be resold. That's another great move. I'm not mechanically inclined to where I would go build my own car. I'll go to a dealership and actually buy one that I can press the button and I can start it and I can do what I need to do with my car. And that's really what this does is the engineering work that's gone on between our two companies and AMD Pensando as well as the business work to make that simple and easy that transaction to work. And then to be able to make it available as a service is really what made, that's why it's such a winner here... >> But, it's also a lower cost out of the box. Yes. So you get in whatever it's called a 20%. Okay. But there's nuance because you're also on a new technology curve and you're able to absorb modern apps. We use that term as a promo, but when I say modern apps, I mean data, rich apps, things that are more AI driven. Not the conventional, not that people aren't doing, you know, SAP and CRM, they are. But, there's a whole slew of new apps that are coming in that traditional architectures aren't well suited to handle from a price performance standpoint. This changes that doesn't it? >> Well, you think also of going to the next stage, which is the go to market between the two organizations that before at the moment, HPE is running off doing various different things. We were running off to. Again, that chaos that you're talking about in cloud chaos, you got to go to market chaos, but by simplifying four or five things, what are we going to do really well together? How do we embed those in GreenLake and be known in the marketplace for these solutions? Then you get an organization that's really behind the go to market. You can help with sales, activation, the enablement. And then we benefit from the scale of HPE. >> Yeah. What are those solutions, I mean... Is it just, is it IS? Is it compute storage? Is it specific SAP? Is it VDI? What are you seeing out there? >> So right now for this specific technology, we're educating our customers on what that could be. And at its core, this solution allows customers to take services that normally and traditionally run on the compute system and run on a DPU now with project Monterey. And this is now allowing customers to think about where are their use cases. So I'm rather than going and say, use it for this. We're allowing our customers to explore and say, okay, here's where it makes sense. Where do I have workloads that are using a lot of compute cycles on services at the compute level? That could be somewhere else like networking as a great example, and allowing more of those compute cycles to be available. So where there are performance requirements for an application where there are timely response that's needed for results to be able to take action on, to be able to get insight from data really quick. Those are places where we're starting to see the services moving onto something like a DPU. And that's where this makes a whole lot more sense. >> Okay, so to get this right? You got the hybrid cloud, right? You got GreenLake and you got the distributed engine. What's that called? >> It's HPE Proliant Proliant with the VMware, VSphere. >> VSphere. That's the compute distributed. Okay. So does the customer, how do you guys implement that with the customer all three at the same time or they mix and match? How's that work? >> All three of those components. So the beauty of the HP Proliant with VMware vSphere distributed services engine also now is project Monterey for those that are keeping notes at home. Again already pre-engineered so we've already worked through all the mechanics of how you would have to do this. So it's not something you have to go figure out how you build, get deployment, work through those details. That's already done. It is available through HPE GreenLake. So you can go and actually get it as a service in partnership with our customer, our friends here at VMware. And because if you're familiar and comfortable with all the things that HP Proliant has done from a security perspective, from a reliability perspective, trusted supply chain, all those sorts of things, you're getting all of that with this particular solution. >> Sumit Dhawan had a great quote on theCube just a hour or so ago. He said you have to be early to be first. Love that quote. Okay. So you were first, you were probably a little early, but do you have a lead? I know you're going to say yes. Okay. Let's just assume that okay. Relative to the competition, how do you know? How do you determine that? >> If we have a lead or not? >> Yeah, if you lead, if you're the best. >> We go to the source of the truth, which is our customers. >> And what do they tell you? What do you look at and say, okay, now, I mean, when you have that honest conversation and say, okay, we are, we're first, we're early, we're keeping our lead. What are the things that you look at, as indicators? >> I'll say it this way. We've been in a lot of businesses where we do compete head-to-head in a lot of places and we know how that sales process normally works. We're seeing a different motion from our customers. When we talk about HPE GreenLake, there's not a lot of back and forth on, okay, well let me go shop around. It is HP GreenLake, let's talk about how we actually build this solution. >> And I can tell you from a VMware perspective, our customers are asking us for this the other way around. So that's a great sign. Is that, Hey, we need to see this partnership come together in GreenLake. >> Yeah. Okay. So you would concur with that? >> Absolutely. So third party validation. >> From Switzerland. Yeah. >> Bring it with you over here. >> We're talking about this earlier on, I mean, of course with I mentioned earlier on there's some contractual things that you've got to get in place as you are going through this migration into Sasson subscription, et cetera. And so we are working as hard as we can to make sure, Hey, let's really get this contract in place as quickly as possible, it's what the customers are asking us. >> We've been talking about this for years, you know, see containers being so popular. Now, Kubernetes becoming that layer of bringing people to bringing things together. It's the old adage that Amazon used to coin and Andy Jassy, they do the undifferentiated, heavy lifting. A lot of that's now that's now cloud operations. Underneath is infrastructure's code to the developer, right. That's at scale. >> That's right. >> And so you got a lot of heavy lifting being done with GreenLake. Which is why there's no objections probably. >> Right absolutely. >> What's the choice. What do you even shop? >> Yeah. There's nothing to shop around. >> Yeah, exactly. And then we've, that is really icing on the cake that we've, we've been building for quite some time. There is an understanding in the market that what we do with our infrastructure is hardened from a reliability and quality perspective. Times are tough right now, supply chain issues, all that stuff, we've talked about it. But at HPE, we don't skimp on quality. We're going to spend the dollars and time on making sure we got reliability and security built in. It's really important to us. >> We get a great use case, the storage team, they were provisioning with containers. Storage is a service, instantly. We're seeing with you guys with VMware, your customers bringing in a lot of that into the mix as well. I got to ask. Cause every event we talk about AI and machine learning, automation and DevOps are now infiltrating in with the Ci/CD pipeline security and data become a big conversation. >> Agreed. >> Okay. So how do you guys look at that? Okay. You sold me on green. I've been a big fan from day one. Now it's got maturity on it. I know it's going to get a lot more headroom to do there. It's still a lot of work to do, but directionally it's pretty accurate. It's going to be going to be success. There's still concerns about security, the data layer. That's agnostic of environment, private cloud hybrid, public and edge. So that's important and security has got a huge service area. These are a work in progress. How do you guys view those? >> I think you've just hit the nail on the head. I mean, I was in the press and journalist meetings yesterday and our answer was exactly the same. There is still so much work that can be done here. And I don't think anybody is really emerging as a true leader. It's just a continuation of trying to get that right. Because it is what is the most important thing to our customers. And the industry is really sort of catching up to that. >> And when you start talking about privacy and when you... It's not just about company information, it's about individuals information. It's about information that if exposed actually could have real impact on people. So it's more than just an IT problem. It is actually, and from HP's perspective, security starts from when we're picking our suppliers for our components. There are processes that we put into our entire trusted supply chain from the factory on the way up. I liken it to my golf swing, my golf swinging. I slice, right lik you wouldn't believe. But when I go to the golf pros, they start me back at the mechanics, the foundational pieces, here's where the problems are and start working on that. So my view is our view is if your infrastructure is not secure, you're going to have troubles with security as you go further up. >> Stay in the sandbox, so to speak, they're driving range on the golf analogy there. I love that. Talk about supply chain security real quick. Because you mentioned supply chain on the hardware side, you're seeing a lot of open source and supply chain in software trusted software. How does GreenLake look at that? How do you guys view that piece of it? That's an important part. >> Yeah, security is one of the key pillars that we're actually driving as a company right now. As I said, it's important to our customers as they're making purchasing decisions. And we're looking at it from the infrastructure all the way up to the actual service itself. And that's the beauty of having something like HP GreenLake, we don't have to pick is the infrastructure or the middle where, or the top of stack application, we can look at all of it. Yeah. It's all of it. That matters. >> Question on the ecosystem posture, so, I remember when HP was one company and then the GSIs were a little weird with HP because of EDS, you know, had data protector. So we weren't really chatting up Veeam at the time. And as soon as the split happened, ecosystem exploded. Now you have a situation where your Broadcom is acquiring VMware. You guys big Broadcom customer, has your attitude changed or has it not because, oh, we meet where the customers are. You've always said that, but have you have leaned in more? I mean, culturally is HPE, HPE now saying, hmm, now we have some real opportunities to partner in new ways that we don't have to sleep with one eye open, maybe. >> So I would some first of all, VMware and HPE, we've got a variety of different partners, we always have. If well, before any Broadcom announcement came along. We've been working with a variety of partners and that hasn't changed and that hasn't changed. And if your question is, has our posture toward VMware changed that all the answers absolutely not. We believe in what VMware is doing. We believe in what our customers are doing with VMware, and we're going to continue to work with VMware and partner with you. >> And of course we had to spin out ourselves in November of last year, which I worked on the whole Dell, whole Dell piece. >> But, you still had the same chairman. >> But since then, I think what's really become very apparent. And it's not just with HPE, but with many of our partners, many of the OEM partners, the opportunity in front of us is vast. And we need to rely on each other to help us solve the customer problems that are out there. So there's a willingness to overlook some things that in the past may have been barriers. >> But it's important to note also that it's not that we have not had history, right? Over... We've got over 200,000 customers join. >> Hundreds of millions of dollars of business. >> 100,000, over 10,000 or a 100,000 channel partners that we have in common. Numerous , numerous... >> And independent of the whole Broadcom overhang there, there's the ecosystem floor. Yeah, the expo floor. I mean, it's vibrant. I mean, there's clearly a wave coming. Ricky, we talked about this briefly at HPE Discover. I want to get an update from your perspective, both of you, if you don't mind weighing in on this, clearly the wave we calling it super cloud. Cause it's not just, multi-cloud completely different looking successes, >> Smart Cloud. >> It's not just vendors. It's also the customers turning into clouds themselves. You look at Goldman Sachs. I think every vertical will have its own power law of cloud players in the future. We believe that to be true. We're still testing that assumption, but it's trending in when you got OPEX has to go to in fund statement. CapEx goes to thanks for the cloud. All that's good, but there's a wave coming and we're trying to identify it. What do you guys see as this wave cause beyond multi-cloud and the obvious nature of that will end up happening as a state and what happens beyond that interoperability piece? That's a whole nother story and that's what everyone's fighting for. But everyone out in that ecosystem, it's a big wave coming. They got their surfboards. They're ready to go. So what do you guys see? What is the next wave that everyone's jacked up about here? >> Well, I think the multi-cloud is obviously at the epicenter. If you look at the results that are coming in, a lot of our customers, this is what's leading the discussion. And now we're in a position where we've brought many companies over the last few years, they're starting to come to fruition. They're starting to play a role in how we're moving forward. Some of those are a bit more applicable to the commercial space. We're finding commercial customers are never bought from us before never hundreds and hundreds are coming through our partner networks every single quarter. So brand new to VMware, the trick then is how do you nurture them? How do you encourage them? >> So new logos are coming in? >> New logos are coming in all the time, all the time from across the ecosystem. It's not just the OEMs, it's all the way back. >> So the ecosystem's back for VMware. >> Unbelievably. So what are we doing to help that? There's two big things that we've announced in the recent weeks is that partner connect 2.0. When I talk to you about multi-cloud and multicardt the customers are doing, you see that trend. Four, five different separate clouds that we've got here. The next piece is that they're changing their business models with the partners. Their services is becoming more and more apparent, etc. And the use of other partners to do other services deployment or this stuff is becoming prevalent. Then you've got the distributors that I talked about were there. Then you route to market, then you route to business. So how do you encapsulate all of that and ensure your rewarding partners on all aspects of that? Whether it's deployment, whether it's test and debt, it's a points based system we've put in place now. >> It's a big pie. That's developing the market's getting bigger. >> It's getting so much bigger and then help. >> You agree obviously with that. >> Yeah, absolutely, in fact, I think for a long time we were asking the question of, is it going to be there or is it going to be here? Which was the wrong question now it's everything. Yes. And what I think that what we're seeing in the ecosystem is people are finding the spots where they're going play. Am I going to be on the edge? Am I going to be an analytics play? Am I going to be a cloud transition play? A lot of players are now emerging and saying, we now have a place, a part to play. And having that industry view, not just of a commercial customer at that level, but the two of us are looking at Telco, are looking at financial services, at healthcare, at manufacturing. How do these new ecosystem players fit into it? >> ... is lifting, everyone can see their position there. >> We're now being asked for simplicity and talk to me about partner profitability. How do I know where to focus my efforts? Am I've spread too thin? And my advice that a partner ecosystem out there is, Hey, let's pick out spots together. Let's really go to, and then strategic solutions that we were talking about is good example of that. >> Sounds like composability to me, but not to go back guys. Thanks for coming on. I think there's a big market there. I think the fog is lifted, people seeing their spot there's value there. Value creation equals reward. Yeah. Simplicity, ease of use. This is the new normal great job. Thanks for coming on sharing. Okay. Back live coverage after this short break with more day one coverage here from the blue set here in Moscone.

(upbeat music) >> Hey, gang. Welcome back to theCUBE's coverage of Snowflake Summit '22 live on the show floor at Caesar's Forum in Las Vegas. Lisa Martin here with Dave Vellante. Couple of guests joining us to unpack more of what we've been talking about today. George Fraser joins us, the CEO of Fivetran, and Veronika Durgin, the head of data at Saks Fifth Avenue. Guys, welcome to the program. >> Thank you for having us. >> Hello. >> George, talk to us about Fivetran for the audience that may not be super familiar. Talk to us about the company, your vision, your mission, your differentiation, and then maybe the partnership with Snowflake. >> Well, a lot of people in the audience here at Snowflake Summit probably are familiar with Fivetran. We have almost 2000 shared customers with them. So a considerable amount of the data that we're all talking about here, flows through Fivetran. But in brief, what Fivetran is, is we're data pipeline. And that means that we go get all the data of your company in all the places that it lives. So all your tools and systems that you use to run your company. We go get that data and we bring it all together in one place like Snowflake. And that is the first step in doing anything with data is getting it all in one place. >> So you've been considerable amount of shared customers. I think I saw this morning on the slide over 5,900, but you're saying you're already at around 2000 shared customers. Lots of innovation I'm sure, with between both companies, but talk to us about some of the latest developments at Fivetran, in terms of product, in terms of company growth, what's going on? >> Well, one of the biggest things that happened recently with Fivetran is we acquired another data integration company called HVR. And HVR specialty has always been replicating the biggest, baddest enterprise databases like Oracle and SQL Server databases that are enormous, that are run within an inch of their capabilities by their DBAs. And HVR was always known as the best in the business at that scenario. And by bringing that together with Fivetran, we now really have the full spectrum of capabilities. We can replicate all types of data for all sizes of company. And so that's a really exciting development for us and for the industry. >> So Veronika, head of data at Saks, what does that entail? How do you spend your time? What's your purview? >> So the cool thing abouts Saks is a very old company. Saks is the premier luxury e-commerce platform. And we help our Saks Fifth Avenue customers just express themselves through fashion. So we're trying to modernize very old company and we do have the biggest, baddest databases of any flavor you can imagine. So my job is to modernize, to bring us to near real-time data, to make sure data is available to all of our users so they can actually take advantage of it. >> So let's talk about some of those biggest, baddest hair balls that you've, and how you deal with that. So lot of over time, you've built up a lot of data. You've got different data stores. So, what are you doing with that? And what role does Fivetran and Snowflake play in helping you modernize? >> Yeah, Fivetran helps us ingest data from all of those data sources into Snowflake near real-time. It's very important to us. And like one of the examples that I give is within a matter of maybe a few weeks, we were able to get data from over a dozen of different data sources into Snowflake in near real-time. And some of those data sources were not available to our users in the past, and everybody was so excited. And the reason they weren't available is because they require a lot of engineering effort to actually build those data pipelines to manage them and maintain them. >> Lisa: Whoa, sorry. >> That was just a follow up. So, Fivetran is the consolidator of all that data and- >> That's right. >> Snowflake plays that role also. >> We bring it all together, and the place that it is consolidated is Snowflake. And from there you can really do anything with it. And there's really three things you were touching on it that make data integration hard. One is volume, and that's the one that people tend to talk about, just size of data. And that is important, but it's not the only thing. It's also latency. How fresh is the data in the locus of consolidation? Before Fivetran, the state of the art was nightly snapshots, once a day was considered pretty good. And we consider now once a minute pretty good and we're trying to make it even better. And then the last challenge, which people tend not to talk about, it's the dark secret of our industry is just incidental complexity. All of these data sources have a lot of strange behaviors and rules and corner cases. Every data source is a little bit different. And so a lot of what we bring that to the table, is that we've done the work over 10 years. And in the case of HVR, since the 90s', to map out all of these little complexities of all these data sources, that as a user, you don't have to see it. You just connect source, connect destination, and that's it. >> So you don't have to do the M word migrate off of all those databases. You can maybe allow them to dial them down over time, then create new value with using Fivetran and Snowflake. Is that the right way to think about it? >> Well, Fivetran, it's incredibly simple. You just connect it to whatever source, And then the matter of minutes you have a pipeline. And for us, it's in the matter of minutes, for Fivetran, there's hundreds of engineers, we're extending our data engineering team to now Fivetran. And we can pick and choose which tables we want to replicate which fields. And once data lands in Snowflake, now we have data across different sources in one place, in central place. And now we can do all kinds of different things. We can integrate it data together, we can do validations, we can do reconciliations. We now have ability to do point in time historical journey, in the past in transactional system, you don't see that, you only see data that's right now, but now that we replicate everything to Snowflake and Snowflake being so powerful as an analytical platform, we can do, what did it look like two months ago? What did it look like two years ago? >> You've got all that time series data, okay. >> And to address that word you mentioned a moment ago, migrate, this is something people often get confused about. What we're talking about here is not a migration, these source systems are not going away. These databases are the systems powering saks.com and they're staying right there. They're the systems you interact with when you place an order on this site. The purpose of our tool and the whole stack that Veronika has put together, is to serve other workloads in Snowflake that need to have access to all of the data together. >> But if you didn't have Snowflake, you would have to push those other data stores, try to have them do things that they have sometimes a tough time doing. >> Yeah, and you can't run analytical workloads. You cannot do reporting on the transactional database. It's not meant for that. It's supporting capability of an application and it's configured to be optimized for that. So we always had to offload those specific analytical reporting functionality, or machine learning somewhere else, and Snowflake is excellent for that. It's meant for that, yeah. >> I was going to ask you what you were doing before, you just answered that. What was the aha moment for realizing you needed to work with the power of Fivetran and Snowflake? If we look at, you talked about Saks being a legacy history company that's obviously been very successful at transforming to the digital age, but what was that one thing, as the head of the data you felt this is it? >> Great question. I've worked with Fivetran in the past. This is my third company, same with Snowflake. I actually brought Fivetran into two companies at this point. So my first experience with both Fivetran and Snowflake, was this like, this is where I want to be, this is the stack and the tooling, and just the engineering behind it. So as I moved on the next company, that that was, I'm bringing tools with me. So that was part. And the other thing I wanted to mention, when we evaluate tools for a new platform, we look at things in like three dimensions, right? One with cloud first, we want to have cloud native tools, and they have to be modular, but we also don't want to have too many tools. So Fivetran's certainly checks that off. They're first cloud native, and they also have a very long list of connectors. The other thing is for us, it's very important that data engineering effort is spent on actually analyzing data, not building pipelines and supporting infrastructure. In Fivetran, reliable, it's secure, it has various connectors, so it checks off that box as well. And another thing is that we're looking for companies we can partner with. So companies that help us grow and grow with us, we'll look in a company culture, their maturity, how they treat their customers and how they innovate. And again, Fivetran checks off that box as well. >> And I imagine Snowflake does as well, Frank Lutman on stage this morning talked about mission alignment. And it seemed to me like, wow, one of the missions of Snowflake is to align with its customer's missions. It sounds like from the conversations that Dave and I have had today, that it's the same with partners, but it sounds like you have that cultural alignment with Fivetran and Snowflake. >> Oh, absolutely. >> And Fivetran has that, obviously with 2000 shared customers. >> Yeah, I think that, well, not quite there yet, but we're close, (laughs) I think that the most important way that we've always been aligned with our customers is that we've been very clear on what we do and don't do. And that our job is to get the data from here to there, that the data be accurately replicated, which means in practice often joke that it is exactly as messed up as it was in the source. No better and no worse, but we really will accomplish that task. You do not need to worry about that. You can well and fully delegate it to us, but then what you do with the data, we don't claim that we're going to solve that problem for you. That's up to you. And anyone who claims that they're going to solve that problem for you, you should be very skeptical. >> So how do you solve that problem? >> Well, that's where modeling comes in, right? You get data from point A to point B, and it's like bad in, bad out. Like, that's it, and that's where we do those reconciliations, and that's where we model our data. We actually try to understand what our businesses, how our users, how they talk about data, how they talk about business. And that's where data warehouse is important. And in our case, it's data evolve. >> Talk to me a little bit before we wrap here about the benefits to the end user, the consumer. Say I'm on saks.com, I'm looking for a particular item. What is it about this foundation that Saks has built with Fivetran and with Snowflake, that's empowering me as a consumer, to be able to get, find what I want, get the transaction done like that? >> So getting access to, our end goal is to help our customers, right? Make their experience beautiful, luxurious. We want to make sure that what we put in front of you is what you're looking for. So you can actually make that purchase, and you're happy with it. So having that data, having that data coming from various different sources into one place enables us to do that near real-time analytics so we can help you as a customer to find what you're looking for. >> Magic on the back end, delighting customers. >> So the world is still messed up, right? Airlines are out of whack. There's supply imbalances. You've got the situation in Ukraine with oil prices. The Fed missed the mark. So can data solve these problems? If you think about the context of the macro environment, and you bring it down to what you're seeing at Saks, with your relationship with Fivetran and with Snowflake, do you see the light at the end of that confusion tunnel? >> That's such a great question. Very philosophical. I don't think data can solve it. Is the people looking at data and working together that can solve it. >> I think data can help, data can't stop a war. Data can help you forecast supply chain misses and mitigate those problems. So data can help. >> Can be a facilitator. >> Sorry, what? >> Can be a facilitator. >> Yeah, it can be a facilitator of whatever you end up doing with it. Data can be used for good or evil. It's ultimately up to the user. >> It's a tool, right? Do you bring a hammer to a gunfight? No, but t's a tool in the right hands, for the right purpose, it can definitely help. >> So you have this great foundation, you're able to delight customers as especially from a luxury brand perspective. I imagine that luxury customers have high expectations. What's next for Saks from a data perspective? >> Well, we want to first and foremost to modernize our data platform. We want to make sure we actually bring that near real-time data to our customers. We want to make sure data's reliable. That well understood that we do the data engineering and the modeling behind the scenes so that people that are using our data can rely on it. Because it's like, there is bad data is bad data but we want to make sure it's very clear. And what's next? The sky's the limit. >> Can you describe your data teams? Is it highly centralized? What's your philosophy in terms of the architecture of the organization? >> So right now we are starting with a centralized team. It just works for us as we're trying to rebuild our platform, and modernize it. But as we become more mature, we establish our practices, our data governance, our definitions, then I see a future where we like decentralize a little bit and actually each team has their own analytical function, or potentially data engineering function as well. >> That'll be an interesting discussion when you get there. >> That's a hot topic. >> It's one of the hardest problems in building a data team is whether decentralized or decentralized. We're still centralized at Fivetran, but companies now over 1000 people, and we're starting to feel the strain of that. And inevitably, you eventually have to find a way to find scenes and create specialization. >> You just have to be fluid, right? And then go with the company as the company grows and things change. >> Yeah, I've worked with some companies. JPMC is here, they've got a little, I'll call it a skunk works. They're probably under states what they're doing, but they're testing that out. A company like HelloFresh is doing some things 'cause their Hadoop cluster just couldn't scale. So they have to begin to decentralize. It is a hot topic these days. And I'm not sure there's a right or wrong. It's really a situational. But I think in a lot of situations, it's maybe the trend. >> Yeah. >> Yeah, I think centralized versus decentralized technology is a different question than centralized versus decentralized teams. >> Yes. >> They're both valid, but they're very different. And sometimes people conflate them, and that's very dangerous. Because you might want one to be centralized and the other to be decentralized. >> Well, it's true. And I think a lot of folks look at a centralized team and say, "Hey, it's more efficient to have these specialized roles, but at the same time, what's the outcome?" If the outcome can be optimized and it's maybe a little bit more people expensive, or I don't know. And they're in the lines of business where there's data context, that might be a better solution for a company. >> So to truly understand the value of data, you have to specialize in that specific area. So I see people like deep diving into specific vertical or whatever that is, and truly understanding what data they have and how to taken advantage of it. >> Well, all this talk about monetization and building data products, you're there, right? >> Yeah. >> You're on the cusp of that. And so who's going to build those data products? It's going to be somebody in the business. Today they don't "Own the life cycle" of the data. They don't feel responsible for it, but they complain when it's not what they want. And so, I feel as though what Snowflake is doing is actually attacking some of those problems. Not 100% there obviously, but a lot of work to do. >> Great analysts are great navigators of organizations amongst other things. And one of the best things that's happened as part of this evolution from technology like Hadoop to technology like Snowflake is the new stack is a lot simpler. There's a lot less technical knowledge that you need. You still need technical knowledge, but not nearly what you used to. And that has made it accessible to more people. People who bring different skills to the table. And in many cases, those are the skills you really need to deliver value from data is not, do you know the inner workings of HDFS? But do you know how to extract from your constituents in the organization, a precise version of the question that they're trying to ask? >> We really want them spending their time, the technical infrastructure is an operational detail, so you can put your teams on those types of questions, not how do we make it work? And that's what Hadoop was, "Hey, we got it to work." >> And that's something we're obsessed with. We're always trying to hide the technical complexities of the problem of data centralization behind the scenes. Even if it's harder for us, even if it's more expensive for us, we will pay any costs so that you don't have to see it. Because that allows our customers to focus on more high impact. >> Well, this is a case where a technology vendor's R&D is making your life easier. >> Veronika: Easier, right. >> I would presume you'd rather spend money to save time, than spend your time, to save engineering time, to save money. >> That's true. And at the end of the day, hiring three data engineers to do custom work that a tool does, it's actually not saving money. It costs more in the end. But to your point, pulling business people into those data teams gives them ownership, and they feel like they're part of the solution. And it's such a great feeling so that they're excited to contribute, they're excited to help us. So I love where the industry's going like in that direction. >> And of course, that's the theme of the show, the world around data collaborations. Absolutely critical, guys. Thank you so much for joining Dave and me, talking about Fivetran, Snowflake together, what you're doing to empower Saks, to be a data company. I'm going to absolutely have a different perspective next time I shop there. Thanks for joining us. Thank you. >> Dave: Thank you, guys. >> Thank you. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Snowflake Summit '22, from Vegas. Stick around, our next guest joins us momentarily. (upbeat music)

>>Welcome back to the cubes coverage of Veeam on 2022 at the RS. Nice to be at the aria. My co-host Dave Nicholson here. We spend a lot of time at the Venetian convention center, formerly the sand. So it's nice to have a more intimate venue. I really like it here. George Burg is joining us. He's the vice president of data protection at vast data, a company that some of you may not know about. George. >>Welcome a pleasure. Thank you so much for having me. >>So VAs is smoking hot, raised a ton of dough. You've got great founders, hard charging, interesting tech. We've covered a little bit on the Wikibon research side, but give us the overview of the company. Yeah, >>If I could please. So we're here at the, you know, the Veeam show and, you know, the theme is modern data protection, and I don't think there's any company that epitomizes modern data protection more than vast data. The fact that we're able to do an all flash system at exabyte scale, but the economics of cloud object based deep, cheap, and deep archive type solutions and an extremely resilient platform is really game changing for the marketplace. So, and quite frankly, a marketplace from a data protection target space that I think is, is ripe for change and in need of change based on the things that are going on in the marketplace today. >>Yeah. So a lot of what you said is gonna be surprising to people, wait a minute, you're talking about data protection and all flash sure. I thought you'd use cheap and deep disc or, you know, even tape for that or, you know, spin it up in the cloud in a, in a deep archive or a glacier. Explain your approach in, in architecture. Yeah. At a >>High level. Yeah. So great question. We get that question every day and got it in the booth yesterday, probably about 40 or 50 times. How could it be all flash that at an economic point that is the fitting that of, you know, data protection. Yeah. >>What is this Ferrari minivan of which you speak? >>Yeah, yeah, yeah. The minivan that goes 180 miles an hour, right. That, you know, it's, it's really all about the architecture, right? The component tree is, is somewhat similar to what you'll see in other devices. However, it's how we're leveraging them in the architecture and design, you know, from our founders years ago and building a solution that just not, was not available in the marketplace. So yeah, sure. We're using, you know, all flash QLC drives, but the technology, you know, the advanced next generation algorithms or erasure coding or rage striping allows us to be extremely efficient. We also have some technologies around what we call similarity, some advanced data reduction. So you need less, less capacity if you will, with a vast system. So that obviously help obviously helps us out tremendously with their economics. But the other thing is I could sell a customer exactly what they need. If you think about the legacy data protection market purpose built back of appliances, for example, you know, ALA, Adele, Aita, and HP, you know, they're selling systems that are somewhat rigid. There's always a controller in a capacity. It's tied to a model number right. Soon as you need more performance, you buy another, as soon as you need more capacity, you buy another, it's really not modular in any way. It's great >>Model. If you want to just keep, keep billing the >>Customer. Yeah. If, if that, if yeah. And, and I, I think, I think at this point, the purpose, you know, Dave, the purpose built backup appliance market is, is hungry for a change. Right. You know, there's, there's not anyone that has one. It doesn't exist. I'm not just talking about having two because of replication. I'm it's because of organic growth. Ransomware needs to have a second unit, a second copy. And just, and just scalability. Well, you >>Guys saw that fatigue with that model of, oh, you need more buy more, >>Right? Oh, without a doubt, you said we're gonna attack that. Yeah. Yeah. Sorry. No, no, no. That's great. Without a doubt. So, so we can configure a solution exactly. To the need. Cause let's face it. Every single data center, every single vertical market, it's a work of art. You know, everyone's retention policies are different. Everyone's compliance needs are different. There might be some things that are self mandated or government mandated and they're all gonna be somewhat different. Right? The fact of the matter is the way that our, our architecture works, disaggregated shared everything. Architecture is different because when we go back to those model numbers and there's more rigid purpose built back of appliances, or, or maybe a raise designed specifically for data protection, they don't offer that flexibility. And, you know, I, I, I think our, our, our, our entry point is sized to exactly what the need is. Our ease of scalability. You need more performance. We just add another compute, another compute box, what we call our C box. If you need more capacity, we just add another data box, a D box, you know, where the data resides. And, you know, I, you know, especially here at Veeam, I think customers are really clamoring for that next generation solution. They love the idea that there's a low point of entry, but they also love the idea that, that it's easy to scale on demand, you know, as, as needed and as needed basis. >>So just, I wanna be just, I want to go down another layer on that architecturally. Cause I think it's important for people to understand. Sure, exactly what you're saying. When you're talking about scaling, there's this concept of the, of the sort of devil's triangle, the tyranny of this combination of memory, CPU and storage. Sure. And if you're too rigid, like in an appliance, you end up paying for things you don't need. Correct. When all I need is a little more capacity. Correct. All I need is a little more horsepower. Well, you wanna horsepower? No, you gotta buy a bunch of capacity. Exactly. Oh, need capacity. No, no. You need to buy expensive CPUs and suck a bunch of power. All I need is capacity. So what, so go through that, just a little more detail in terms of sure. How you cobble these systems together. Sure. My, the way my brain works, it's always about Legos. So feel free to use Legos. >>Yeah. We, so, so with our disaggregated solution, right. We've separated basically hardware from software. Right. So, so, so that's a good thing, right? From an economic standpoint, but also a design and architecture standpoint, but also an underlining underpinning of that solution is we've also separated the capacity from the performance. And as you just mentioned, those are typically relatively speaking for every other solution on the planet. Those are tied together. Right? Right. So we've disaggregated that as well within our architecture. So we, we again have basically three tier, tier's not the right word, three components that build out a vast cluster. And again, we don't sell like a solution designed by a model number. And that's typically our C boxes connected via NVMe over fabric to a D box C is all the performance D is all the capacity because they're modular. You can end up like our, our baseline product would start out as a one by one, one C box one D box, right? >>Connected again, via different, different size and Vme fabrics. And that could scale to hundreds. When we do have customers with dozens of C boxes, meeting high performance requirements, keep in mind when, when vast data came to market, our founders brought it to the market for high performance computing machine learning, AI data protection was an afterthought, but those found, you know, foundational things that we're able to build in that modularity with performance at scale, it behooves itself, it's perfect fit for data protection. So we see in clients today, just yesterday, two clients standing next to each other in the same market in the same vertical. I have a 30 day retention. I have a 90 day retention. I have to keep one year worth of full backups. I have to keep seven years worth of full backups. We can accommodate both and size it to exactly what the need is. >>Now, the moment that they need one more terabyte, we license into 100 terabyte increments so they can actually buy it in a sense, almost in arrears, we don't turn it off. We don't, there's not a hard cat. They have access to that capacity within the solution that they provide and they can have access immediate access. And without going through, let's face it. A lot of the other companies that we're both thinking of that have those traditional again, purpose-built solutions or arrays. They want you to buy everything up front in advance, signing license agreements. We're the exact opposite. We want you to buy for the need as, and as needed basis. And also because the fact that we're, multi-protocol multi-use case, you see people doing many things within even a single vast cluster. >>I, I wanna come back to the architecture if I, I can and just understand it better. And I said, David, Flo's written a lot about this on our site, but I've had three key meetings in my life with Mosia and I, and I you've obviously know the first week you showed up in my offices at IDC in the late 1980s said, tell me everything, you know about the IBM mainframe IO subsystem. I'm like, oh, this is gonna be a short meeting. And then they came back a year later and showed us symmetric. I was like, wow, that's pretty impressive. The second one was, I gave a speech at 43 south of 42 south. He came up and gave me a big hug. I'm like, wow. He knows me. And the third one, he was in my offices at, in Mabo several years ago. And we were arguing about the flash versus spinning disc. And he's like, I can outperform an all flash array because we've tuned our algorithms for spinning disc. Everybody else is missing that. You're basically saying the opposite. Correct. We've turned tuned our algorithms to, for QC David Flos says Dave, there's a lot of ways to skin a cat in this technology industry. So I wanted to make sure I got that right. Basically you're skinning the cat with different >>Approach. Yeah. We've also changed really the approach of backup. I mean, the, the term backup is really legacy. I mean, that's 10, 12 years of our recovery. The, the story today is really about, about restore resiliency and recovery. So when you think about those legacy solutions, right, they were built to ingest fast, right? We wanna move the data off our primary systems, our, our primary applications and we needed to fit within a backup window. Restore was an afterthought. Restore was, I might occasionally need to restore something. Something got lost, something got re corrupted. I have to restore something today with the, you know, let's face it, the digital pandemic of, of, of cyber threats and, and ransomware it's about sometimes restoring everything. So if you look at a legacy system, they ingest, I'm sorry. They, they, they write very fast. They, they, they can bring the data in very quickly, but their restore time is typically about 20 to 25%. >>So their reading at only 20, 25% of their right speed, you know, is their rate speed. We flip the script on that. We actually read eight times faster than we write. So I could size again to the performance that you need. If you need 40 terabytes, an hour 50 terabytes an hour, we can do that. But those systems that write at 40 terabytes an hour are restoring at only eight. We're writing at a similarly size system, which actually comes out about 51 terabytes an hour 54 terabytes. We're restoring at 432 terabytes an hour. So we've broken the mold of data protection targets. We're no longer the bottleneck. We're no longer part of your recovery plan going to be the issue right now, you gotta start thinking about network connectivity. Do I have, you know, you know, with the, with our Veeam partners, do we have the right data movers, whether virtual or physical, where am I gonna put the data? >>We've really helped customer aided customers to rethinking their whole Dr. Plan, cuz let's face it. When, when ransomware occurs, you might not be able to get in the building, your phones don't work. Who do you call right? By the time you get that all figured out and you get to the point where you're start, you want to start recovering data. If I could recover 50 times faster than a purpose built backup appliance. Right? Think about it. Is it one day or is it 50 days? Am I gonna be back online? Is it one hour? Is it 50 hours? How many millions of dollars, tens of thousands of dollars were like, will that cost us? And that's why our architecture though our thought process and how the system was designed lends itself. So well for the requirements of today, data protection, not backup it's about data protection. >>Can you give us a sense as to how much of your business momentum is from data protection? >>Yeah, sure. So I joined VAs as we were talking chatting before I come on about six months ago. And it's funny, we had a lot of vast customers on their own because they wanted to leverage the platform and they saw the power of VAs. They started doing that. And then as our founders, you know, decided to lean in heavily into this marketplace with investments, not just in people, but also in technology and research and development, and also partnering with the likes of, of Veeam. We, we don't have a data mover, right. We, we require a data mover to bring us the data we've leaned in tremendously. Last quarter was really our, probably our first quarter where we had a lot of marketing and momentum around data protection. We sold five X last quarter than we did all of last year. So right now the momentum's great pipeline looks phenomenal and you know, we're gonna continue to lean in here. >>Describe the relationship with Veeam, like kind of, sort of started recently. It sounds like as customer demand. Yeah. But what's that like, what are you guys doing in terms of engineering integration go to market? >>Yeah. So, so we've gone through all the traditional, you know, verifications and certifications and, and, and I'm proud to say that we kind of blew the, the, the roof off the requirements of a Veeam environ. Remember Veeam was very innovative. 10, 12 years ago, they were putting flash in servers because they, they, they want a high performing environment, a feature such as instant recovery. We've now enabled. When I talked about all those things about re about restore. We had customers yesterday come to us that have tens of thousands of VMs. Imagine that I can spin them up instantaneously and run Veeam's instant recovery solution. While then in the background, restoring those items that is powerful and you need a very fast high performance system to enable that instant. Recovery's not new. It's been in the market for very long, but you can ask nine outta 10 customers walk in the floor. >>They're not able to leverage that today in the systems that they have, or it's over architected and very expensive and somewhat cost prohibitive. So our relationship with Veeam is really skyrocketing actually, as part of that, that success and our, our last quarter, we did seven figure deals here in the United States. We've done deals in Australia. We were chatting. I, I, I happened to be in Dubai and we did a deal there with the government there. So, you know, there's no, there's no specific vertical market. They're all different. You know, it's, it's really driven by, you know, they have a great, you know, cyber resilient message. I mean, you get seen by the last couple of days today and they just want that power that vast. Now there are other systems in the marketplace today that leverage all flash, but they don't have the economic solution that we have. >>No, your, your design anticipated the era that we're we're in right now from it, it anticipated the ability to scale in, to scale, you know, in >>A variety. Well, listen, anticipation of course, co coincidental architecture. It's a fantastic fit either way, either way. I mean, it's a fantastic fit for today. And that's the conversations that we're having with, with all the customers here, it's really all about resiliency. And they know, I mean, one of the sessions, I think it was mentioned 82 or 84% of, of all clients interviewed don't believe that they can do a restore after a cyber attack or it'll cost them millions of dollars. So that there's a tremendous amount of risk there. So time is, is, is ultimately equals dollars. So we see a, a big uptick there, but we're, we're actually continuing our validation work and testing with Veeam. They've been very receptive, very receptive globally. Veeam's channel has also been very receptive globally because you know, their customers are, you know, hungry for innovation as well. And I really strongly believe ASBO brings that >>George, we gotta go, but thank you. Congratulations. Pleasure on the momentum. Say hi to Jeff for us. >>We'll we'll do so, you know, and we'll, can I leave you with one last thought? Yeah, >>Please do give us your final thought. >>If I could, in closing, I think it's pretty important when, when customers are, are evaluating vast, if I could give them three data points, 100% of customers that Triva test vast POC, vast BVAs 100% Gartner peer insights recently did a survey. You know, they, they do it with our, you know, blind survey, dozens of vast customers and never happened before where 100% of the respondents said, yes, I would recommend VA and I will buy VAs again. It was more >>Than two respondents. >>It was more, it was dozens. They won't do it. If it's not dozens, it's dozens. It's not dozen this >>Check >>In and last but not. And, and last but not least our customers are, are speaking with their wallet. And the fact of the matter is for every customer that spends a dollar with vast within a year, they spend three more. So, I mean, if there's no better endorsement, if you have a customer base, a client base that are coming back and looking for more use cases, not just data protection, but again, high performance computing machine learning AI for a company like VA data. >>Awesome. And a lot of investment in engineering, more investment in engineering than marketing. How do I know? Because your capacity nodes, aren't the C nodes. They're the D nodes somehow. So the engineers obviously won that naming. >>They'll always win that one and we, and we, and we let them, we need them. Thank you. So that awesome product >>Sales, it's the golden rule. All right. Thank you, George. Keep it right there. VEON 20, 22, you're watching the cube, Uber, Uber right back.

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase Open Cloud Innovations. This is season two of the ongoing series. We're covering exciting start startups in the AWS ecosystem to talk about open source community stuff. I'm your host, Dave Nicholson. And I'm delighted today to have two guests from Weaveworks. Steve George, COO of Weaveworks, and Steve Waterworth, technical marketing engineer from Weaveworks. Welcome, gentlemen, how are you? >> Very well, thanks. >> Very well, thanks very much. >> So, Steve G., what's the relationship with AWS? This is the AWS Startup Showcase. How do Weaveworks and AWS interact? >> Yeah sure. So, AWS is a investor in Weaveworks. And we, actually, collaborate really closely around EKS and some specific EKS tooling. So, in the early days of Kubernetes when AWS was working on EKS, the Elastic Kubernetes Service, we started working on the command line interface for EKS itself. And due to that partnership, we've been working closely with the EKS team for a long period of time, helping them to build the CLI and make sure that users in the community find EKS really easy to use. And so that brought us together with the AWS team, working on GitOps and thinking about how to deploy applications and clusters using this GitOps approach. And we've built that into the EKS CLI, which is an open source tool, is a project on GitHub. So, everybody can get involved with that, use it, contribute to it. We love hearing user feedback about how to help teams take advantage of the elastic nature of Kubernetes as simply and easily as possible. >> Well, it's great to have you. Before we get into the specifics around what Weaveworks is doing in this area that we're about to discuss, let's talk about this concept of GitOps. Some of us may have gotten too deep into a Netflix series, and we didn't realize that we've moved on from the world of DevOps or DevSecOps and the like. Explain where GitOps fits into this evolution. >> Yeah, sure. So, really GitOps is an instantiation, a version of DevOps. And it fits within the idea that, particularly in the Kubernetes world, we have a model in Kubernetes, which tells us exactly what we want to deploy. And so what we're talking about is using Git as a way of recording what we want to be in the runtime environment, and then telling Kubernetes from the configuration that is stored in Git exactly what we want to deploy. So, in a sense, it's very much aligned with DevOps, because we know we want to bring teams together, help them to deploy their applications, their clusters, their environments. And really with GitOps, we have a specific set of tools that we can use. And obviously what's nice about Git is it's a very developer tool, or lots and lots of developers use it, the vast majority. And so what we're trying to do is bring those operational processes into the way that developers work. So, really bringing DevOps to that generation through that specific tooling. >> So Steve G., let's continue down this thread a little bit. Why is it necessary then this sort of added wrinkle? If right now in my organization we have developers, who consider themselves to be DevOps folks, and we give them Amazon gift cards each month. And we say, "Hey, it's a world of serverless, "no code, low code lights out data centers. "Go out and deploy your code. "Everything should be fine." What's the problem with that model, and how does GitOps come in and address that? >> Right. I think there's a couple of things. So, for individual developers, one of the big challenges is that, when you watch development teams, like deploying applications and running them, you watch them switching between all those different tabs, and services, and systems that they're using. So, GitOps has a real advantage to developers, because they're already sat in Git, they're already using their familiar tooling. And so by bringing operations within that developer tooling, you're giving them that familiarity. So, it's one advantage for developers. And then for operations staff, one of the things that it does is it centralizes where all of this configuration is kept. And then you can use things like templating and some other things that we're going to be talking about today to make sure that you automate and go quickly, but you also do that in a way which is reliable, and secure, and stable. So, it's really helping to bring that run fast, but don't break things kind of ethos to how we can deploy and run applications in the cloud. >> So, Steve W., let's start talking about where Weaveworks comes into the picture, and what's your perspective. >> So, yeah, Weaveworks has an engine, a set of software, that enables this to happen. So, think of it as a constant reconciliation engine. So, you've got your declared state, your desired state is declared in Git. So, this is where all your YAML for all your Kubernetes hangs out. And then you have an agent that's running inside Kubernetes, that's the Weaveworks GitOps agent. And it's constantly comparing the desired state in Git with the actual state, which is what's running in Kubernetes. So, then as a developer, you want to make a change, or an operator, you want to make a change. You push a change into Git. The reconciliation loop runs and says, "All right, what we've got in Git does not match "what we've got in Kubernetes. "Therefore, I will create story resource, whatever." But it also works the other way. So, if someone does directly access Kubernetes and make a change, then the next time that reconciliation loop runs, it's automatically reverted back to that single source of truth in Git. So, your Kubernetes cluster, you don't get any configuration drift. It's always configured as you desire it to be configured. And as Steve George has already said, from a developer or engineer point of view, it's easy to use. They're just using Git just as they always have done and continue to do. There's nothing new to learn. No change to working practices. I just push code into Git, magic happens. >> So, Steve W., little deeper dive on that. When we hear Ops, a lot of us start thinking about, specifically in terms of infrastructure, and especially since infrastructure when deployed and left out there, even though it's really idle, you're paying for it. So, anytime there's an Ops component to the discussion, cost and resource management come into play. You mentioned this idea of not letting things drift from a template. What are those templates based on? Are they based on... Is this primarily an infrastructure discussion, or are we talking about the code itself that is outside of the infrastructure discussion? >> It's predominantly around the infrastructure. So, what you're managing in Git, as far as Kubernetes is concerned, is always deployment files, and services, and horizontal pod autoscalers, all those Kubernetes entities. Typically, the source code for your application, be it in Java, Node.js, whatever it is you happen to be writing it in, that's, typically, in a separate repository. You, typically, don't combine the two. So, you've got one set of repository, basically, for building your containers, and your CLI will run off that, and ultimately push a container into a registry somewhere. Then you have a separate repo, which is your config. repo, which declares what version of the containers you're going to run, how many you're going to run, how the services are bound to those containers, et cetera. >> Yeah, that makes sense. Steve G., talk to us about this concept of trusted application delivery with GitOps, and frankly, it's what led to the sort of prior question. When you think about trusted application delivery, where is that intertwinement between what we think of as the application code versus the code that is creating the infrastructure? So, what is trusted application delivery? >> Sure, so, with GitOps, we have the ability to deploy the infrastructure components. And then we also define what the application containers are, that would go to be deployed into that environment. And so, this is a really interesting question, because some teams will associate all of the services that an application needs within an application team. And sometimes teams will deploy sort of horizontal infrastructure, which then all application teams services take advantage of. Either way, you can define that within your configuration, within your GitOps configuration. Now, when you start deploying speed, particularly when you have multiple different teams doing these sorts of deployments, one of the questions that starts to come up will be from the security team, or someone who's thinking about, well, what happens if we make a deployment, which is accidentally incorrect, or if there is a security issue in one of those dependencies, and we need to get a new version deployed as quickly as possible? And so, in the GitOps pipeline, one of the things that we can do is to put in various checkpoints to check that the policy is being followed correctly. So, are we deploying the right number of applications, the right configuration of an application? Does that application follow certain standards that the enterprise has set down? And that's what we talk about when we talk about trusted policy and trusted delivery. Because really what we're thinking about here is enabling the development teams to go as quickly as possible with their new deployments, but protecting them with automated guard rails. So, making sure that they can go fast, but they are not going to do anything which destroys the reliability of the application platform. >> Yeah, you've mentioned reliability and kind of alluded to scalability in the application environment. What about looking at this from the security perspective? There've been some recently, pretty well publicized breaches. Not a lot of senior executives in enterprises understand that a very high percentage of code that their businesses are running on is coming out of the open source community, where developers and maintainers are, to a certain degree, what they would consider to be volunteers. That can be a scary thing. So, talk about why an enterprise struggles today with security, policy, and governance. And I toss this out to Steve W. Or Steve George. Answer appropriately. >> I'll try that in a high level, and Steve W. can give more of the technical detail. I mean, I'll say that when I talk to enterprise customers, there's two areas of concern. One area of concern is that, we're in an environment with DevOps where we started this conversation of trying to help teams to go as quickly as possible. But there's many instances where teams accidentally do things, but, nonetheless, that is a security issue. They deploy something manually into an environment, they forget about it, and that's something which is wrong. So, helping with this kind of policy as code pipeline, ensuring that everything goes through a set of standards could really help teams. And that's why we call it developer guard rails, because this is about helping the development team by providing automation around the outside, that helps them to go faster and relieves them from that mental concern of have they made any mistakes or errors. So, that's one form. And then the other form is the form, where you are going, David, which is really around security dependencies within software, a whole supply chain of concern. And what we can do there, by, again, having a set of standard scanners and policy checking, which ensures that everything is checked before it goes into the environment. That really helps to make sure that there are no security issues in the runtime deployment. Steve W., anything that I missed there? >> Yeah, well, I'll just say, I'll just go a little deeper on the technology bit. So, essentially, we have a library of policies, which get you started. Of course, you can modify those policies, write your own. The library is there just to get you going. So, as a change is made, typically, via, say, a GitHub action, the policy engine then kicks in and checks all those deployment files, all those YAML for Kubernetes, and looks for things that then are outside of policy. And if that's the case, then the action will fail, and that'll show up on the pull request. So, things like, are your containers coming from trusted sources? You're not just pulling in some random container from a public registry. You're actually using a trusted registry. Things like, are containers running as route, or are they running in privileged mode, which, again, it could be a security? But it's not just about security, it can also be about coding standards. Are the containers correctly annotated? Is the deployment correctly annotated? Does it have the annotation fields that we require for our coding standards? And it can also be about reliability. Does the deployment script have the health checks defined? Does it have a suitable replica account? So, a rolling update. We'll actually do a rolling update. You can't do a rolling update with only one replica. So, you can have all these sorts of checks and guards in there. And then finally, there's an admission controller that runs inside Kubernetes. So, if someone does try and squeeze through, and do something a little naughty, and go directly to the cluster, it's not going to happen, 'cause that admission controller is going to say, "Hey, no, that's a policy violation. "I'm not letting that in." So, it really just stops. It stops developers making mistakes. I know, I know, I've done development, and I've deployed things into Kubernetes, and haven't got the conflict quite right, and then it falls flat on its face. And you're sitting there scratching your head. And with the policy checks, then that wouldn't happen. 'Cause you would try and put something in that has a slightly iffy configuration, and it would spit it straight back out at you. >> So, obviously you have some sort of policy engine that you're you're relying on. But what is the user experience like? I mean, is this a screen that is reminiscent of the matrix with non-readable characters streaming down that only another machine can understand? What does this look like to the operator? >> Yeah, sure, so, we have a console, a web console, where developers and operators can use a set of predefined policies. And so that's the starting point. And we have a set of recommendations there and policies that you can just attach to your deployments. So, set of recommendations about different AWS resources, deployment types, EKS deployment types, different sets of standards that your enterprise might be following along with. So, that's one way of doing it. And then you can take those policies and start customizing them to your needs. And by using GitOps, what we're aiming for here is to bring both the application configuration, the environment configuration. We talked about this earlier, all of this being within Git. We're adding these policies within Git as well. So, for advanced users, they'll have everything that they need together in a single unit of change, your application, your definitions of how you want to run this application service, and the policies that you want it to follow, all together in Git. And then when there is some sort of policy violation on the other end of the pipeline, people can see where this policy is being violated, how it was violated. And then for a set of those, we try and automate by showing a pull request for the user about how they can fix this policy violation. So, try and make it as simple as possible. Because in many of these sorts of violations, if you're a busy developer, there'll be minor configuration details going against the configuration, and you just want to fix those really quickly. >> So Steve W., is that what the Mega Leaks policy engine is? >> Yes, that's the Mega Leaks policy engine. So, yes, it's a SaaS-based service that holds the actual policy engine and your library of policies. So, when your GitHub action runs, it goes and essentially makes a call across with the configuration and does the check and spits out any violation errors, if there are any. >> So, folks in this community really like to try things before they deploy them. Is there an opportunity for people to get a demo of this, get their hands on it? what's the best way to do that? >> The best way to do it is have a play with it. As an engineer, I just love getting my hands dirty with these sorts of things. So, yeah, you can go to the Mega Leaks website and get a 30-day free trial. You can spin yourself up a little, test cluster, and have a play. >> So, what's coming next? We had DevOps, and then DevSecOps, and now GitOps. What's next? Are we going to go back to all infrastructure on premises all the time, back to waterfall? Back to waterfall, "Hot Tub Time Machine?" What's the prediction? >> Well, I think the thing that you set out right at the start, actually, is the prediction. The difference between infrastructure and applications is steadily going away, as we try and be more dynamic in the way that we deploy. And for us with GitOps, I think we're... When we talk about operations, there's a lots of depth to what we mean about operations. So, I think there's lots of areas to explore how to bring operations into developer tooling with GitOps. So, that's, I think, certainly where Weaveworks will be focusing. >> Well, as an old infrastructure guy myself, I see this as vindication. Because infrastructure still matters, kids. And we need sophisticated ways to make sure that the proper infrastructure is applied. People are shocked to learn that even serverless application environments involve servers. So, I tell my 14-year-old son this regularly, he doesn't believe it, but it is what it is. Steve W., any final thoughts on this whole move towards GitOps and, specifically, the Weaveworks secret sauce and superpower. >> Yeah. It's all about (indistinct)... It's all about going as quickly as possible, but without tripping up. Being able to run fast, but without tripping over your shoe laces, which you forgot to tie up. And that's what the automation brings. It allows you to go quickly, does lots of things for you, and yeah, we try and stop you shooting yourself in the foot as you're going. >> Well, it's been fantastic talking to both of you today. For the audience's sake, I'm in California, and we have a gentleman in France, and a gentlemen in the UK. It's just the wonders of modern technology never cease. Thanks, again, Steve Waterworth, Steve George from Weaveworks. Thanks for coming on theCUBE for the AWS Startup Showcase. And to the rest of us, keep it right here for more action on theCUBE, your leader in tech coverage. (upbeat music)

(bright upbeat music) >> Welcome back to theCube's coverage of AWS re:Invent 2021. This is "theCube". We go out to the events. We extract the signal from the noise. We're here at a live event, hybrid event, two sets. We had two remote studios prior to the event, over 100 interviews. Really excited to have George Elissaios here. He's the director of product management for EC2 Edge, really interesting topic at AWS. George, great to see you. Thanks for coming on. >> Yeah, great to be here. Thanks for having me. >> So, everybody's talking about Edge, IoT, EC2. What's the scope of your portfolio, your responsibility? >> Yeah, well, our vision here at AWS is to really bring the power of the AWS platform wherever customers need it. AWS wherever our customers want it is our long-term vision. And we have a bunch of products in this space that help us do that and help us enable our customers whatever their use case is. So we have things like Wavelength. I know we talked about Wavelength before here in "theCube", where we bring full AWS service at the edge of the 5G network, so with 5G edge computing in partnership with telcos worldwide, our partnership with Verizon in the US has been flourishing. We're up to, I think, 15 or more Wavelength zones right now in many of the major cities in the US, but also in Japan and Korea, and in Europe with Vodafone. So that's one of the portfolio kind of offerings. And that helps you as a customer of AWS if you want to have the best latency to mobile devices, whether they are sensors, or mobile phones, or what have you. But we're also feeling out that Edge portfolio with local zones. Earlier today in Werner's keynote, we announced that we're going to launch another 30 local zones in 20 new countries, everywhere from South America, Africa, Asia, Australia, and Europe, obviously. So a lot of expansion there. Very excited about that. And that is kind of a similar offering, but it basically brings you closer to customers in metropolitan areas over the internet. >> So, Wavelength's a big feature. George, I want to get just to touch on it because I think latency comes up a lot in Edge conversations, low latency issues, whether it's cars, factories. You guys gave a demo yesterday to the press corps in the press room, I was there, where you had someone in San Francisco from the Opera and someone in person here in Vegas, and you had 13 milliseconds going back and forth demoing, real time- >> Collaboration. >> The benefit of low latency in remote. It wasn't next door. It was San Francisco. This is kind of the purpose of what Edge is about. Can you explain what that means, that demo, why it was important, and what you were trying to show, and how does it mean for the Edge? >> So there is multiple use cases. One of them is human collaboration, right? Like, we spent the last two years of our lives over conferences and kind of like the teleconferences, and trying to talk over each other and unmute ourselves desperately. But existing solutions kind of work, generally, for most of the things that we do, but when it comes to music collaboration where milliseconds matter, it's a lot harder with existing solutions to get artists to collaborate when they're hundreds of miles away. Last night, we saw a really inspiring demo, I think, of how two top tier musicians, one located in San Francisco and one located in Vegas, can collaborate in opera, which is one of the most precise art forms in the music world. There are no beats in opera to kind of synchronize, so you really need to play off each other, right? So we provided a latency between them of less than 30 milliseconds, which translates, if you're thinking about audio or if you're thinking about the speed of sound, that's like being in the same stage. And that was very inspiring. But there's also a lot of use cases that are machine to machine communications, where even lower latencies matter, and we can think of latencies down to one millisecond, like single digit milliseconds when it comes to, for example, vehicles or robots, and things like that. So we're, with our products, we're enabling customers to drive down that latency, but also the jitter, which is the variation of latency. Especially in human communications, that is almost more important than latency itself. Your mind can adapt to latency, and you can start predicting what's going to happen, but if I'm keep changing that for you, that becomes even harder. >> Well, this is what I want to get to because you got outcomes and applications like this opera example. That's an application, I guess. So working backwards from the application, that's one thing, but now people are really starting to trying to figure out, "What is the Edge?" So I have to ask you, what is AWS's Edge? Is it Outpost, Wavelength? What do people buy to make the Edge work? >> Well, for us, is providing a breadth of services that our customers can either use holistically or combine multiple of those. So a really good example, for example, is DISH Wireless. I'm sure you know we're building with DISH the first in the world mobile network, 5G mobile network fully on cloud, right? So these combines Outposts and combines local zones in order to distribute the 5G network across nationwide. And different parts of their applications live in different edges, right? The local zone, the Outputs, and the region itself. So we have our customers... You know, I talked about how local zones is going to be, you know, in total, 45 cities in the world, right? We're already in 15 in the U.S. We're going to do another 30. But customers might still come, and say, "Oh, why are you not," you know, "in "in Costa Rica?" Well, we'll have Outposts in Costa Rica. So you could build your own offering there, or you could build on top of Outputs while you distribute the rest of your workload in existing AWS offering. So to answer your question, John, there is no single answer. I think that it is per use case and per workload that customers are going to combine or choose which one of- >> Okay, so let's go through local zones. Explain what a local zone is real quick. I know we covered it a bit last year with the virtual event, but local zones are now part of the nomenclature of the AWS language. >> Yes. >> And we know what a region is, right? So regions are regions. What's a local zone? >> When your region's saying new availability zones, and then we're just (chuckles)- >> You got availability zones. Now you got local zones. Take us through the topology, if you will, of how to think about this. >> Right, so a local zone is a fully-managed AWS infrastructure deployment. So it's owned and managed and operated by AWS. And because of that, it offers you the same elasticity, and security, and all of the goodies of the cloud, but it's positioned closer to your end customers or to your own deployment. So it's positioned in the local urban, metropolitan or industrial center closer to you. So if you think about the U.S., for example, we have a few regions, like, in the East Coast and in the West Coast, but now, we're basically extending these regions, and we're bringing more and more services to 15 cities. So if you are in Miami, there is a local zone there. If you are in LA, there is two locals zones actually in LA. That enables customers to run two different types of workloads. One is these distributed clouds or distributed Edge kind of workload that we've been hearing more and more about. Think of gaming, for example, right? Like, we have customers that are, like Supercell, that need to be closer to the gamers, wherever they are. So they're going to be using a bunch of local zones to deploy. And also, we have these hyper-local use cases, where we're talking, for example, about Netflix that are enabling in LA their creative artists to connect locally and get like as low as single millisecond latencies. So local zone is like an availability zone, but it's closer to you. It offers the same scalability, the same elasticity, the same security and the same services as the AWS cloud. And it connects back to the regions to offer you the full breadth of the platform. >> So just to clarify, so the Edge strategy essentially is to bring the cloud, AWS, the primitives, the APIs, to where the customers are in instances where they either can't move or won't move their resources into the cloud, or there's no connectivity? >> Right, we have a bunch of use cases where customers either need to be there because of regulation or because of some data gravity, so data is being generated in a specific place and you need to locally process it, or we'll have customers in this distributed use case. But I think that you're pointing out a very important thing, which is a common factor across all of these offerings. It's it is the cloud. It's not like a copycat of the cloud. It's the same API. It's the same services that you already know and use, et cetera. So extending the cloud rather than copying it around is our vision, and getting those customers who, well, connectivity obviously needs to be there. We were offering AWS Private 5G. We talked about it yesterday. >> Now, a premise that we've had is that a lot of Edge use cases will be driven by AI inferencing. And so... First of all, is that a reasonable premise, that's growing, we think, very quickly, and it has huge potential. What does the compute, if that's the correct premise, what does the compute look like for that type of workload? >> That is a great premise, and that's why we think that the model that we're offering is so powerful, because you have the Edge and the cloud fully cooperating and being connected together. You know, the Edge is a resource that's more limited than the full cloud in the AWS region. So when you're doing inferencing, what you really want to do is you want to train your models back up in the region where you get more scalability and the best prices. You know, you have the full scale of AWS. But for the latency-sensitive parts of your applications, you want to push those to the Edge. So when you're doing the actual inferencing, not the training of the models- >> Real time. Yeah. >> Real time, you push that to the Edge, whether that's if your connectivity is 5G, you can push that into a Wavelength zone. If your connectivity is wired, you can push it into a local zone. If you really need it to be in your data center, you can push it in your Outposts. So you see how our kind of like building out for all of those use cases. >> But in those instances, I'm interested in what the compute looks like, 'cause I presume it's got to be low power, low cost, super high performance. I mean, all of those things that are good for data-driven workloads. >> Right, the power, if we think here, is the same compute that you know and love in the cloud. So the same EC2 instance types, the EBS volumes, the S3 for storage, or RDS for your databases and EMR clusters. You can use the same service. And the compute is the same powerful all the way down from the hardware up to the service. >> And is the promise to customers that eventually those... It's not all of those services, right? I mean, you go to Outposts today, it continues to grow. >> Continuing to grow, yeah. Right, so but conceptually, as many services you could possibly push to the Edge, you intend to do so? >> We are pushing services according to customer requests, but also there is a nuance here. The nuance is that you push down the services that are truly latency-sensitive. You don't need to push everything down to the Edge when you're talking about latency- >> Like, what's an example of what you wouldn't push down? >> So management tools, right? So when you're doing monitoring and management, yeah, you don't need these to be at the Edge. You can do that, and you can scale that. Or, you know, batch processing, it doesn't have to be at the Edge because it's, by definition, not online, not like a latency service. So we're keeping those, like AWS Batch, for example, that's in the region because, you know, that's where customers really use it. But things like EC2, EBS, EMR, we're pushing those to the Edge because those are more- >> We got two minutes left. I want to get the Outposts kind of update. I remember when Outposts launched. It was really a seminal moment for re:Invent. Hybrid. "Oh, Andy Jassy said hybrid." Yeah. "I'll never say hybrid." But now hybrid's kind of translated into all cloud operations. Now you got local zones. A lot's changed from Amazon Web Services standpoint since Outposts launched. Local zones, things are happening. 5G, DISH. Now what's the status of Outposts? Are you guys happy with it? What has it morphed into? Is it still the same game? What is Outposts today, vis-a-vis what people may think it is or isn't? >> Yeah, we've been focusing in what we're talking about, building out a number of services that customers request, but also being in more and more places. So I think we're in more than 60, now, countries with Outposts. We've seen very good adoption. We've seen very good feedback. You know, half of my EBCs have been on Outposts, but this year, I think that one of the most exciting announcements were the Outposts servers. So the smaller form factors that enable an additional use cases, like for example, retail or even building your 5G networks. You know, one of our partners, Mavenir, is moving their 5G core, so the smarts of the network that does all the routing, on Outposts servers, and we can distribute those all over the place. So, we're keeping on the innovation. We're keeping on the expansion. And we've been getting very good customer feedback- >> So all steam ahead, full steam ahead? >> Full steam ahead plus 10%. (John laughs) >> All right, guys. Thank you so much, George. Really appreciate it. We're seeing the cloud expand. The definition is growing, kind of like the universe, John. Dave Vellante for John John Furrier. You're watching "theCube" at AWS re:Invent, the leader in high tech coverage globally. We'll be right back.

>>Yeah. Hey, everyone, Welcome to the cubes. Continuous coverage of AWS Re invent 2021. I'm Lisa Martin with John Furrier were running one of the industry's largest and most important hybrid tech events with AWS and massive ecosystem of partners. Right now there are two live cube sets to remote sets over 100 guests on the programme and we're pleased to welcome back one of our alum I to talk about the next generation and cloud innovation. Georgia Lisa is joins John to me, the director of product management for EC two edge at A. W S George. Welcome to the programme. >>Glad to be here in person. Thanks Great to be here in person. Awesome to be here in person. Finally, >>one of the things that is very clear is the US flywheel of innovation and there was no slowdown with what's happened in the last 22 months. Amazing announcements, new leadership. We talked a little bit about five g yesterday, but let's talk more about that. Everyone is excited about five g consumers businesses. What's going on? >>So, yeah, I wanted to talk to you today about the new service that we launched called AWS Private. Five g. Essentially, it's a service that allows any AWS customer to build their own private five g network and what we try to do with the services make it that simple and cost effective for anyone without any telco experience or expertise, really, to build their own private five g network. So you just have to go to your AWS console. Um, describe the parameters for network simple stuff like, Where do you want it to be located? The throughput, the number of devices and AWS will build a plan for your network and seep you everything that you need. Just plug it together. Uh, turn it on and the network automatically configures itself. All you got to do is popular sim cards that we send you into your mobile devices and you have a private five g network working in your your premise is >>one of the things that we know and love about AWS is its customer obsession. It's focused on the customer's that whole flywheel of all the innovation that comes out as Adam was saying yesterday to the customers, we deliver this, but but you wanted more. We said we deliver this, but you wanted more. Talk to me a little bit about some of the customer catalysts for private five G. >>Actually, one of the good examples is where we are right now. More and more AWS customers need to connect an increased number of devices, and these devices become more data hungry. You know they need to push data around. They also become more and more wireless, right? Uh, so when you are trying to connect devices in the manufacturing floor, bit sensors, you know, connect the tracks, forklifts or in a convention centre. You look at how many devices there are around us. When you're trying to connect these devices with a wired network, you quickly run into physical problems like it's. It's hard to lay cable anywhere, and customers try to use for many of these use cases. But as a number of devices grows into the thousands and you know you need to put more and more data around, you quickly reach the limitations of what the WiFi technology and also WiFi is not really great at covering really open, large space. So that's where these customers, you know, think of college campuses, convention centres, manufacturing floors, all of these customers. Really? What they need to be able to do is to level the power of the mobile networks. However, doing that by yourself is pretty hard. So that's what we aim to to enable here we are waiting to enable these customers to build very easily and cost effectively their own. Uh, >>Okay, George. So I have to ask. I'm truly curious. I love this announcement. Um, because it brings together kind of the edge story. But also, I'm a band with love. I love more broad. Give me more broadband. Faster, cheaper and more broadband. How does it work? So take me through the use case of what do I need to deploy? Do I need to have a back haul connection? What does that look like? Is there a certain band with requirements? How big is the footprint? What's the radius? Just walk me through. How do I roll this out? >>Yeah, sure. Some of that stuff actually depends on your requirements, right. How How big? How much of a space do you want to cover? Basically, what we see, if you were in preview right now, so we're sipping you. The simplest configuration, which is basically these things called small cells there, you know, radio units and antennas. And all you have to do is connect them to your local. The network has Internet access. These things connect and automatically had, you know, connect home to the cloud and basically integrate and build up your whole network. All all you need is that Internet connection, and I don't know what to do. Now, how big is the network? You can You can make it pretty big. You can cover hundreds of thousands of square feet with with cellular networks with mobile networks. Um, you know, the bigger you they especially want to cover the more of these radio units. We're gonna stop you, uh, >>classic wireless radios. >>Yes. You >>light up the area with five g connected to the network. That's your choke point. The big of the pipe >>took the bigger pipe. That toxic. I mean, well, there, there's two. There's two things to consider here. There is local connectivity. So devices talking to each other, and there was connectivity back to somewhere else, like the Internet or the cloud. There are use cases, for example. Let's say data video feeds that you want to push up to do some inference in the cloud. In these use cases, you're basically pushing all of the data up. There is no left. There's no East West connectivity locally, and that's where our simplest configuration works best. There are other, uh, use cases where there is a lot of connectivity and devices talk to each other locally, like in this place, for example, right in this. In these cases, we can sip you that second configuration where we actually see Pew, a managed hardware WS managed hardware on premises, and that runs the smart of the network and allows all of your data traffic to remain local. That's >>wavelength Outpost, or both. >>A different configuration of A. W s private five G. It's a managed service. We take. We take care of it. You basically it's very It has a pricing model, which is very customer friendly because you like multi W services. You can start with no upfront fees. You can scale and pay as you scale because >>it's designed to deploy easily. >>Yep, deploys the >>footprint. Just I'm just curious if the poll is it like, it's like an antenna. Is it like so and >>yeah, well, the antenna is, you know, the small cell. They call them small cells in, you know, in in cellular land there, this big. And you can you can hide this. There is actually a demo in the Venetian of the private service. So you can you can actually see it in action, but yeah, that thing can cover 10,000 square feet, just one of them. So you can >>go out and put a five g network downtown and be like the king. >>You could Yes. You could have your own private network. You can monetise that next >>on the Q. >>Great stuff. >>So in terms of industries adopting this, you gave us some examples. Obviously. Convention centres, campuses, universities. I'm just curious, given the amount of acceleration that we've seen in every industry the last 22 months where organisations must become digital. They depend on that for their livelihood. And we saw this all these pivots, right? 22 months ago. How do we survive this? How do we thrive? Are consumers now are whether it's an injury or consumer or enterprise. Have this expectation that we're gonna be able to communicate no matter where we are 24 by seven. Whether it's health care, financial services. I'm just curious if you're seeing any industries in particular that you think are really prime for this private five >>G. Yeah. So manufacturing is a is a really great example because you have to cover large spaces. You have thousands of devices, sensors, etcetera and using other solutions like WiFi does not provide you the depth of capabilities like, for example, you know, advanced security capabilities or even capabilities to prioritise traffic from some devices over others, which is what a five G network can do for you. But also, you know, it involves large spaces both indoors and outdoors. We, you know, actually, Amazon is a really great example of you know of using this. We're working with Amazon fulfilment centres. These are the warehouses that fulfil your orders when you order online. Um, and they are a mix of indoor space and outer space, and you can think of, you know, I don't know if you've seen pictures or videos. There's robots running around their sensors everywhere. There is packing lines, etcetera, all of these things in order to operate performantly, but also securely and safely for the people that are around. You need to be well connected at a very high reliability rate. Right? So, uh, Amazon for two networks is actually using private A W s private five G to connect all of these devices. The really key thing here is you don't have to go drop 1000 of these access points we're talking about you. Can you can. You can probably cover your space with 5 10 of these. So your operational expenses, your maintenance goes down and there is less interruption of your normal operations like you can't. You don't have to stop your manufacturing line for someone to come in and fix your WiFi access. >>It's great for campuses like college campuses, college >>campuses, a great one. We you know, we've worked with college campuses, including the CME University in the past two, you know, with some of our partners to, uh, to to deploy. So >>that's how close you have these distribution, gas systems, distribution, whatever they call it accelerate whatever amplifies into get extra coverage, this seems to be a good fit. Um, for that how you mentioned in the preview? How do people get involved? Is there like a criteria. How was it going to >>be available to get priority? Don't get you >>tell them ready to jump in. Take us through the programme. What's the plants? >>So currently we're you know, we're in that preview mode. So we're keeping you this small configuration, the simpler configuration. You can sign up on the AWS website and you know, we, as we scale our operations are supply chain. Because this involves also, you know, hardware, etcetera. We're gonna go to general availability g A over the next few months and we have both configurations open. So I I encourage everyone who is interested go to the W s website and sign up. We're asking to get that in customers' hands because we're getting overwhelmingly positive feedback on what we built. >>This is transformative. I mean, clearly what you're talking about here is going to transform industry and help organisations transform themselves and outpaced the competitors that are in the rear view mirror Aren't going to be able to take advantage of this were on the show floor. We've got lots of people here. Where can people actually go and see this preview tested up? >>There is an actual demo in the Venetian. I can't remember. Sorry, I can't remember the room. I think it's on the Yes, actually, it's on the floor on the third floor where the meeting rooms are on outside 35 or one. If anyone wants to go, we're >>going to start buying lunch time. >>Yes. Yeah, you can see it in action. And, you know, you could You could see a future where everything, You know, you look around. There's thousands of devices here. You could power all of these devices with a single cell and, you know, really scaled throughput >>in the five G. Just curious, um on the range is better than wifi >>ranges. Better outdoors, >>obviously, or factories. What's the throughput on the >>depending on the spectrum that you choose? And that's actually a really good save way. The device, the service that we built, its spectrum agnostic so it can be used on right now. We're using it on what we call C BRS spectrum, which is the free for all you can. You know, you can you can use it yourself. But also, customers can bring their own spectrum. And we're working with a batch of, uh, CSP operators to build advanced bundles where you can work this on licence spectrum. So if you're going up the spectrum in what they called millimetre wave >>spectrum owner to bring your own licence, >>you could So telco right? You could be a telco, bring your, you know, and work with us as a partner or some actually, actually, manufacturing customers have purchased rights to small spectrum bands so they can use those in combination with this service to deploy. So to your original question, as you're going back up the spectrum, you can drive more and more throughput. You it's not. It's not unheard of to drive one gig. You know what's so >>The low hanging fruit is the the use cases that have critical need for edge connectivity manufacturing? Um, certainly the retail or whatever that they help do the deployment >>we can. We can. We can see this being applicable because because you can start super small. You can see this being applicable even to branch offices, right? Like, uh, let's say I was talking to a customer yesterday. They were thinking or have all these branch offices. I don't even I don't even want to have I thought either he just wants something that's very quickly and easily. You know, I can manage centrally and it just connects. >>Can I should have fixed wireless shot to the wavelength order to have back all with wire >>too. Oh, they actually we are planning to. You know, I talked about where the smarts of the network live in the they can live in a region, they can live in the locals, and they can live in a wave election. So we're combining more and more of these products as well. And it's computing, obviously, is a is an obvious thing that, you know, we should be working on >>incredible work, George, that you and the team have done transforming industries. And I don't know if a feeling there might be a cube to Is it? Would it be too dot >>Oh, John, >>he's ready. Big George, Thank you so much for joining joining me today. It's great >>to be here. Thanks for having that >>for John Ferrier. I'm Lisa Martin. You're watching the Cube, the global leader in live coverage. Mhm

(upbeat music) Welcome back to theCUBE's coverage of AWS re:Invent 2021. I'm John Furrier, host of theCUBE. We have George Watkins, the product marketing manager, cloud gaming and visual cloud at AMD. George, thanks for coming on theCUBE. >> Thank you for having me. >> Love this segment, accelerating game development. AWS cloud, big topic on how the gaming developer environment's changing and how AMD is powering it. Let's get into it. So streaming remote, working remote, flexible collaboration, all powered by the G4ad virtual workstations, it's been a big part of success. Take us through what's going on there. >> Yeah, certainly. So obviously from a remote working perspective, there was a huge impact on collaboration and productivity for many industries out there. But, a collaborative environment like game design, it was even more so. First off, happy to have these big bulky workstation ship to local artists, so they can actually carry on working was a massive nightmare for IT management. Making sure that they have the right hardware, the right resources, the right applications and security. So it was a real mean task. And on top of that, working remotely also brings in other efficiencies when it comes to collaboration. So for example, working on a data sets, as I mentioned before, it's a huge team collaboration effort when it comes to game development, and using the same dataset happens very very often. So if you're actually working remotely and an artist, for example pulled a dataset, from a server, worked on it, then took it back up into the cloud. I'll tell you now, it takes some time to do. And at the same time you might have one or two other artists trying to use that data set. The problem or the big issue that comes here is version control. And essentially because these artists are using the older version, there's creating errors, and keeping that production timed longer. So it's very very inefficient. And then this is where the cloud really comes to end zone. First off the cloud, and then obviously in this case, the AWS cloud, with G4ad instances, really does bring the whole pipeline together. It brings the data sets and the virtual workstations, obviously, as I mentioned, G4ad, as well as all the applications into one place. It's all centralized. And from an IT perspective, this is fantastic. And actually sending out a workstation now is really really simple. It's log in details into an email to your new staff, and there's some really great benefits as well from a staff perspective. Not only are they not tethered to a local workstation, they have the flexibility of work where they need to, and also how they like to. But it's also really interesting about how they work on a day-to-day basis. So a good example of this is, if a artist is using or working on a very very heavy dataset and the configuration from their VM or virtual workstation, isn't up to snuff because of the such a large dataset, all they need to do is call up IT and say, I need more resource. And literally in a couple of minutes time, they can actually have that resource, again, improving that productivity, reducing that time. So it's really really important. And just a final note here as well, with having all that data and all that resource in the cloud, version control tools, really do help bring that efficiency as it's all built into the applications and that data sets really, really exciting staff and ultimately, bring in that productivity and reducing that time and errors down. >> I could see your point too because, when you don't bring it to the cloud, people are going to be bored, waiting for things to happen. And they say I want to take a shortcut. Shortcuts equal mistakes. So, I can see that the G4ad with focus for artists is cool because it's purpose-built for what you're talking about. So take me through how you see the improved efficiencies in the development pipeline with cloud computing around this area because, obviously it makes a lot of sense. Everything's in the cloud, you've got the instances there. Now what happens next? How does the coding all work? What's going on around the game development pipeline? >> 3D applications today, particularly at use in the game industry, I'll be honest, they are still based on legacy hardware. And what I mean by this is that the applications typically require higher CPU Hertz the typically single threaded, maybe some kind of multi threaded functionality there. But generally they are limited by what the traditional workstation has been. And obviously why not? They've been built over the last 10-15 years to access that type of data. Now that is great, but it's not accessing what could be, all the resources that are available in the cloud. And this is what's really really exciting in my part. So ultimately what we're saying is that is that you have this great virtual workstation experience. You have all your applications running on there, you can be efficient, but then there's these really specific and really interesting use cases that aren't accessing the cloud. And I've got a couple of examples, so first off there's a feature inside Unreal 4 engine, called Unreal Swarm. And this feature helps actually reduce the time it takes, in this case and to bake light maps into auto scale, to bake light maps into a game. And this is done by auto scaling, the compiling in AWS cloud. So for example, after making the amends to a light map, we're ready to essentially recompile, but instead of doing this on the local workstation, using the traditional CPU and memory resource, which you would expect to see in a workstation, and actually in this case, it takes around about 50 minutes to do. When you actually use Unreal Swarm, you can, the coordinator as part of this functionality, bursts the requirement or the actual compiling into the cloud. And actually in this case, it's using, like, 10 C5a instances. So these are all CPU high-performance computing instances. And because you have this ability to auto-scale, you actually essentially bring that time, that original 50 minutes, down to 4 minutes. And this type of kind of functionality or this type of task that you would typically see with a 3d artist or with a programmer, basically happens multiple times a day. So when you start factoring in a saving of 45 minutes multiple times a day, it starts really bringing down, the amount of time saved, and obviously the amount of cost saved as well for that artist's time. So it's really really exciting and, certainly something to talk about. >> That's totally cool. I got to ask you since you're here, because it brings up the question that pops into my head, which is okay. What's the state of the art development trends that you're seeing because, on the cloud side, on non gaming world, so shift left to security. You start to see more agile kind of methods around what used to be different modules, right? So you mentioned compiling, acceleration, what's going on in the actual workflows for the developers? What are some of the cool things that you could share that people might not know about that are important? >> Well certainly it's really about finding, those bursty computational expensive and time consuming processes, and actually moving them to the cloud. So really, from a compiling standpoint, they are usually CPU bound. So essentially the GPU does all the work when it comes to the view pole, all that high rendering frames per second, that's what it's really designed for. And it does a very good job with that. But actually the compiling aspect, the compute aspect is all done on the CPU side. And, the work that we've been doing with AWS and the game tech team is actually finding certain ways of actually helping to reduce the compiling nature because ultimately that is always restricted by the amount of calls that you actually have on a local device. So again, another example is there's a company out there called Incredibuild, and they specialize in accelerating the development of that programming code. And obviously in this case, it's the game code. And if an artist, entered a clean source code built on unreal engine full, it would take approximately around about 60 minutes to do on a local machine. However, using the Incredibuild solution to accelerate that type of workload, you can complete it in just 6 minutes. Because again, it's auto scaled out that compiling to several in this case 16 C5a large instances, which essentially reduces all that time for the artist freeing them up to do more stuff. >> And the more creativity is just the classic use case of the clouds, beautiful thing. It's just reminds me of how good this is, because, when you think about what you guys are doing, pushing the envelope for cloud with the creators. gaming is such a state-of-the-art pressure point to make high performance come better. It really is putting a lot of pressure on AMD and everyone else's to get faster and stronger because, it truly is pushing state-of-the-art in general. It's always been that way. If you look at the gaming world. This is a whole 'nother level. I mean, you starting to see that. What's your view on that? If you look at the gaming as a tail sign for the trends and the tech side, better, faster, cheaper processors and speeds and feeds, and how codes work in between processes GPU's and CPU's, all this is cool. All kind of new, if you will. New patterns, new usage, what's your view on that? >> Well certainly, cloud gaming is a really exciting topic and, we believe that cloud gaming with the introduction of various key elements are reading revolutionalize the way that some people are actually using their complaint gamings and interacting with games. And what I mean by this is like, today we can do cloud gaming, it's a fantastic experience. You're usually hardwired, using a broadband connection to actually play those games. And you tend to try and be close to an actual data sensors, to try to reduce that latency. However this is only going to get better with the introduction of 5G coverage and also just, as important edge computing. And because of these two elements, what we're going to be seeing is very high speeds wirelessly, and more importantly, low latency. And this is very important for, that very dynamic cinematic gaming experiences. But not only this, what it can actually do is bring, 4k, 8K gaming to people wirelessly. It can also bring VR and AR experiences wirelessly, and also it can access, these new emerging technologies that are making higher fidelity gaming experiences like hardware retraces. All this can be done with these new technologies. And it's incredibly incredibly exciting. But more importantly, what's really great about this is, from a game publisher perspective, because it's actually helping them simplify their business processes, particularly from a game development standpoint. And actually what I mean by this is, if we take a typical example of what a game developer has to do for a mobile game, there's certain considerations that they need to think about when they actually comes to developing and validate. First off they'll have to understand what type of OS to account for. And actually what type of version of that OS to account for. What type of IPA they're going to be building on. And also finally, what type of resources, are actually on that end point device. So there's a lot of considerations here, and a lot of testing. So ultimately a lot of work to get that game out, to those gamers who might be on a couple of these different mobile platforms. However, when it comes to game streaming, it really does kind of change all this because ultimately what the game developer is actually doing is that they're developing and they're validating on one source. And that is going to be the server that is essentially pairing that game streaming service. Because how game streaming works is that we essentially trans code the actual game via H.264 to a software client on any end point device. So this could be those mobile devices I just mentioned. It can also be TVs, it could be consoles, it can be even low powered laptops. And what's very exciting is that, from an end user perspective, they're getting the ultimate in gaming experiences and usually these types of solutions are traditionally subscription-based. So you're actually reducing the requirement of this kind of high-end thousands of dollars gaming solution or simply a high-end next gen console. All of this is actually been given to you and delivered as part of a game streaming service. So it's very very exciting and, certainly we can see the adoption on both the game development side, as well as the gamer's side. That's a great way to put an end to this awesome segment. I think that business model innovation around making it easier, and making it better to develop environment, that's just how they work. So that's good, check. But really the business model here, the gaming as a service, you're making it possible for the developer and the artist to see an outcome faster. That's the cloud way. >> Thank you >> And they doubled down on success and they could do that. So again, this is all new and exciting and certainly the edge and having data being processed at the edge as well. Again, all this is coming in to create more good choice. Thank you so much for coming on and sharing that insight with us from the AMD perspective. And again, more power, more speed, we always say, no one's going to complain, they get more compute, that's what I always. >> Absolutely absolutely. >> Thanks for coming I appreciate it. >> Thank you. >> theCUBE coverage here at AWS re:Invent 2021. I'm John Furrier host of theCUBE. Thanks for watching. (upbeat music)

>>from the cube studios in Palo alto in boston connecting with thought leaders all around the world. >>This is a cute conversation. Welcome to the cubes coverage of HP discover 2021 I'm lisa martin. I've got three guests with me here. They're going to be talking about the partnership between HP and AMG. Please welcome George hope worldwide Head of partner sales at HP terry, Richardson north american channel chief for AMG and Peter chan, the director of media channel sales at AMG Gentlemen, it's great to have you on the cube. >>Well, thanks for having us lisa. >>All right, >>we're excited to talk to you. We want to start by talking about this partnership terry. Let's go ahead and start with you. H P E and M D have been partners for a very long time, very long history of collaboration. Talk to us about the partnership >>HB named, He do have a rich history of collaboration spinning back to the days of chapter on and then when A M. D brought the first generation AMG equity process department back in 2017, HP was a foundational partner providing valuable engineering and customer insights from day one AmY has a long history of innovation that created a high performance CP roadmap for value partners like HP to leverage in their workload optimized product portfolios, maximizing the synergies between the two companies. We've kicked off initiatives to grow the chain of business together with workload focused solutions and together we define the future. >>Thanks terry George, let's get your perspective as worldwide had a partner sales at HP. Talked to me about H P S perspective of that AMG partnership. >>Yeah, they say it's uh the introduction of the third generation AMG Epic processors, we've we've doubled our A. M. D. Based Pro Lion portfolio. We've even extended it to our follow systems. And with this we have achieved a number of world records across a variety of workloads and are seeing real world results. The third generation am the epic processor delivers strong performance, expand ability and the security our customers need as they continue their digital transformation, We can deliver better outcomes and lay a strong foundation for profitable apartment growth. And we're incorporating unmatched workload optimization and intelligent automation with 360° security. And of course, uh with that as a service experience. >>But as a service experience becoming even more critical as is the security as we've seen some of the groundbreaking numbers and data breaches in 2020 alone. Peter I want to jump over to you now. One of the things that we see H P E and M. D. Talking about our solutions and workloads that are key areas of focus for both companies. Can you explain some of those key solutions and the value that they deliver for your customers? >>Absolutely. It's from computing to HPC to the cloud and everything in between and the young HB have been focused on delivering not just servers but meaningful solutions that can solve customer challenges. For example, we've seen here in India, the DL- 325 has been really powerful for customers that want to deploy video. Hp nmD have worked together with icy partners in the industry to tune the performance and ensure that the user experience is exceptional. Um This just one example of many of course, for instance, the 3 45 with database 3 65 for dense deployments, it's key the 35 That has led the way in big data analytics. Um the Apollo 60 500 breaking new path in terms of AI and Machine learning, quite a trending topic and m D H p are always in the news when it comes to groundbreaking HPC solutions and oh by the way, we're able to do this due to an unyielding commitment to the data center and long term laser focused execution on the M the road map. >>Excellent. Thanks. Peter. Let's talk about the channel expansion a little bit more terry with you. You know, you and the team here. Channel Chief focused on the channel. What is A. M. D. Doing specifically to expand your channel capabilities and support all of the Channel partners that work with Andy >>great question lisa Campbell is investing in so many areas around the channel. Let's start with digital transformation. Our Channel partners consistently provided feedback that customers need to do more with less between A and B and H P. E. We have solutions that increase capabilities and deliver faster time to value for the customer looking to do more with less. We have a tool on our website called the and metrics server virtualization, Tco estimation tool and those who have visually see the savings. We also have lots of other resources such as technical documentation, A and E arena for training and general CPU's departments can take advantage of aside from solution examples, AMG is investing in headcount internally and at our channel part race. I'm actually an example of the investment MD is making to build out the channel. One more thing that I'll mention is the investment that are, you know, lisa su and Andy are making to build out the ecosystem from head Count to code development and is investing to have a more powerful user experience with our software partners in the ecosystem. From my discussions with our channel partners, they're glad to see A and d expanding our our channel through the many initiatives and really bringing that ecosystem. >>Here's another question for you as channel chief. I'm just curious in the last year, speaking and you talked about digital transformation. We've seen so much acceleration of the adoption of that since the last 15 months has presented such challenges. Talk to me a little bit about some of the feedback from your channel partners about what you am, D N H B are doing together to help those customers needed to deliver that fast time to value, >>you know, so really it's all about close collaboration. Um we we work very closely with our counterparts at H P. E just to make sure we understand partner and customer requirements and then we work to craft solutions together from engaging, technically to collaborating on on, you know, when products will be shipped and delivered and also just what are we doing to uh to identify the next key workloads and projects that are going to be engaged in together? So it's it's really brought the companies I think even closer together, >>that's excellent as a covid catalyst. As I say, there's a lot of silver linings that we've seen and it sounds like the collaboration terry that you mentioned has become even stronger George. I want to go to you. Let's HP has been around for a long time. My first job in tech was Hewlett Packard by the way, many years ago. I won't mention how long but talk to me about the partnership with AMG from H P s perspective, is this part of H P S D N A? >>Absolutely. Partnering is our D N A. We've had 80 years of collaboration with an ever expanding ecosystem of partners that that all play a key role in our go to market strategy. We actually design and test our strategic initiatives in close collaboration with our partners so that we can meet their most pressing needs. We do that through like farmer advisory boards and things of that nature. Um but we have we have one of the most profitable partner programs in the industry, 2-3 times higher rebates than most of our competitors. And we continue to invest in the partner experience in creating that expertise so partners can stand out in a highly competitive market. Uh And Andy is in direct alignment with that strategy. We have strong synergies and a common focus between the two companies. >>And I also imagine George one question and one question to that there's tremendous value in it for your end user customers, especially those that have had to everyone pivot so many times in the last year and have talked to me a little bit about George What you're saying from the customer's perspective. >>Well as Antonio Neri said a couple of years back, the world is going to be hybrid and uh, he was right. We continue uh we continue to see that evolution and we continue to deliver solutions around a hybrid digital world with, with Green Lake and the new wave of digital transformation that we refer to now as the age of insight customers want a cloud experience everywhere. And 70% of today's workloads can easily be re factored for the public cloud or they need to stay physically close to the data and other apps at the emerging edge or in polos are in the data centers. So as a result, most organizations are forced to deal with the complexity of having two divergent operating models and they're paying higher cost to maintain them both with Green Lake, we provide one consistent operating model with visibility and control across public clouds and on prem environments. And that applies to all workloads, you know, whether it's cloud native or non cloud native applications. Um we also have other benefits like no cloud block in or no data. Egress charges, so you have to pay a steep price just to move workloads out of the public cloud. And then we're expanding collaboration opportunities within for our partner ecosystem so that we can bring that cloud experience to a faster growing number of customers worldwide. So we've launched new initiatives uh in support of the core strategy as we accelerate our as a service vision and then work with partners to unlock better customer outcomes with Green Lake and of course, hb compute of which I am d is part of is, is the underlying value added technology. >>Can you expand on some of those customer outcomes as we look at, as I mentioned before, this very dynamic market in which we live. It's all about customer outcomes. What are some of those that from a hybrid cloud environment perspective with Green like that you're helping customers achieve? >>Well, at least Greenland has come out with with about 30 different different offerings that package up some solutions. So you're not just buying infrastructure as a service. We have offerings like HPC as a service. We have offerings like uh, V D I as a service, ml, ops as a service. So we're packaging in technology, some are are some are not ours, but into completing some solutions. So that creates the outcome that the customers are looking for. >>Excellent. Thanks, George and Peter, last question to you again with the hybrid cloud environment being something that we're seeing more and more of the benefits that Green Lake is delivering through the channel. What's your perspective from a. M decide? >>Absolutely lisa. So, so I mean I think it's clear with a MD based systems, customers get the benefit of performance, security and fast time to value whether deployed on prem and cloud on a hybrid model. So please come try out our HP system based on name the processors and see how we can accelerate and protect your applications. Thank you lisa. >>Excellent, Peter George terry, thank you for joining me today. I'm sure there's a lot more that folks are going to be able to learn about what AM D and H. P. Are doing together on the virtual show floor. We appreciate your time. Thank you. Yeah, for my guests, I'm lisa martin. You're watching the cubes coverage of HP discover 2021 Yeah.

(bright upbeat music) >> Hi well, this is Dave Vellante. We're digging deeper into the world of database. You know, there are a lot of ways to skin a cat and different vendors take different approaches and we're reaching out to the technologists to get their perspective on the major trends that they're seeing in the market, 'cause we want to understand the different ways in which you can solve problems. So look, if you have thoughts and the technical chops on this topic, I'd love to interview you. Just ping me at at DVellante, on Twitter, a lot of ways to get ahold of me. Anyway, we recently spoke with Andrew Mendelsohn, who is Oracle's EVP and he's responsible for database server technologies. And we talked a lot about Oracle's ADW, Autonomous Data Warehouse. And we looked at the cloud database strategy that Oracle is taking and the company's plans and how they're different maybe from other solutions in the marketplace, but I wanted to dig deeper. And so today we have two members of Mendelsohn's team on The Cube, and we're going to probe a little bit. George Lumpkin, is the Vice President of Autonomous Data Warehouse. And Neil Mendelson is the VP of Modern Data Warehouse, that business for Oracle. They're both 20-year veterans of Oracle. When I reached out to Steve Savannah, who's a colleague of mine for many years, he's always telling me how great Oracle is relative to the competition. So I said, okay, come on The Cube and talk about this, give me your best people. And he said, whatever these two don't know about cloud data warehouse, it isn't worth knowing anyway. So with that said gentlemen, welcome to The Cube. Thanks so much for coming on. >> Thank you. >> Hey, glad to be here. >> So George, let's start with you. And maybe we could recap for some of the viewers who might not be familiar with the interview that I did with Andy. In your words, what exactly is an Autonomous Data Warehouse? Is this cloud native? Is it an Oracle buzzword? What is it? >> Well, I mean, Autonomous Data Warehouse is Oracle's cloud data warehouse. It's a service that built to allow business users to get more value from their data. That's what the cloud data warehouse market is. Autonomous Data Warehouse is absolutely cloud native. This is a huge misconception that people might have when they first sort of hear about something, this service because they think this is a Oracle database, right? Oracle makes databases. This is the same old database I knew from 10 years ago. And that's absolutely not true. We built a cloud native service or data warehousing built it with cloud features. You know, if your understanding of the cloud data warehouse market is based upon how you thought things look 10 years ago, like Snowflake wouldn't have even existed, right? You can't base your understanding of Oracle based upon that. We have a modern service that's highly elastic, provides cloud capabilities like online patching and it's fully autonomous. It's really built the business users so they don't need to worry about administering their database. >> So I want to come back and actually ask you some questions about that, but let me follow up and talk about some of the evolution of the ADW. And where did you start? I think it was 2018, maybe where you came from, where you are today, maybe you can take us through the technological progression and maybe the path you took to get here. >> And so 2018, was when we released the service and made generally available, but of course, you know we started much earlier than that. And this was started within my product management team, and other organization. So we really sat down with a blank sheet of paper and we said, what should the data warehouse in the cloud look like? You know, let's put aside everything that Oracle does for its on-prem customers and think about how the cloud should be different. And the first thing that we said was, well, you know, if Oracle writes the database software, and Oracle builds its own hardware, and Oracle has created its own cloud, why do we need customers to manage a database? And that's where the idea of autonomous database came from. That Oracle is managing the entire ecosystem. And therefore we built a database that we believe it's far and away the simplest to use simplest data warehouse in the market. And that's been our focus since we started with 2018. And that continues to be our focus, looking at more ways that we can make an Autonomous Data Warehouse as simpler and easier for business users to get more value out of their data. >> Awesome, one more question. And actually Neil, you might want to chime in on this as well. So just from a technical perspective, you know forget the marketing claims and all the BS. How do you compare ADW to the so-called born in the cloud data warehouses? You mentioned Snowflake, you know Redshift, is Redshift born in the cloud. Well, it was par XL but Amazon's done some good work around Redshift. I think big query is maybe probably a better example 'cause it was, you know, like Snowflake started in the cloud but how do you compare ADW to some of these other so-called born in the cloud data warehouses? >> I think part of this, you mentioned Redshift wasn't important in the cloud. It was, you know, a code base taken from a prior company that was on-premise company. So they adapted it to the cloud, right? And you know, we have done, as George said, much of the same, which is, you know, our starting point was not you know another company's code base, but our starting point was our own code base. But as George said, it's less about the starting point and it's more about where you envision the end point, right? Which is that, you know, whatever your starting point is, I think we have a fundamental different view of the endpoint. Amazon talks about how they're literally built for you know, a cloud built for developers, right? You know, builders, right? And you know Oracle wasn't first in the infrastructure business, we entered through applications business. And all of a sudden, you know we began taking on 100s of 1000s and 100s of even more customers that were SAS customers. Underneath was the database and all the infrastructure. One of the things that we took away from that was that we couldn't possibly hire enough people DBA, to manage all the infrastructure below our applications customers. So one of the things that influenced this is that, you know customers expect SAS applications to just take care of themselves, right? So we had to essentially modify the infrastructure to allow it to do so as well, right? And we're bringing that capability to those people who, you know, may or may not have an application, but their interest is, you know more of this self-service agility type of aspect. >> So it seems to me and Georgia was sort of alluding to this before. I mean, when you mentioned Snowflake a couple of times, and then Neil, something you just said, I'm going to pick up on is you've been around for a long time. And you know, when I talked to the Snowflake people, they know Oracle, a lot of them came from Oracle. They understand I think how you can't just build Oracle overnight and build in the capabilities that Oracle has and the recovery. And you talk to customers and you know you are the gold standard of, you know especially mission critical databases, so I get that. But now you just sort of hit on it, is it takes a lot of people and skill to run the database. So that's the problem that you're saying you were attacking, is that, am I getting that right? >> Right, right, so the people that you talked about who originally built Snowflake came from Oracle, but they came from Oracle more than a decade ago. So their context is over a decade old, right? In the meantime, we've been busy, you know building a economies and many other capabilities, right? Their view of Oracle is that view that was back more than 10 years ago, right? They're still adding capability. So a really good example of this illustration is Oracle as you said, it's the most capable system that's out there and has been for many years. We've been focusing on how do we simplify that and how do we use machine learning embedded within the system itself? Because core to the concept of autonomous is that inside, is this machine learning system that's continually improving, right? That's the whole notion. Where in Snowflakes case, they're still adding functionality. Last year, they added masking which you know functionality they didn't have, but when they added the capability, they added it without, you know, the ability for a business user to actually take advantage of it. There's no capability for a business user to actually find the information that needs to be masked. And then after the information is found, you require a technical person to actually implement the mask. In Oracle's case, we've had masking and those capabilities for a long time, our focus was to be able to provide a simple tool that a business user can use that doesn't need technical or security experience. Find the data that needs to be masked PII data, and then hit a button and have it masked for you. So, you know, they're still, you know, without this notion of a strategy to move toward the system to heal itself and to manage itself, they're just going to continue. As they continue to add more capability, they will in turn add more complexity. What we're trying to do is take complexity out while others are adding it in, its an ironic twist. >> It is an ironic twist. It is interesting to look at it. And I don't want to make this about Snowflake. But I mean, Hey, I like what they're doing. I like them. I know the management, they're growing like crazy and you know and the customers tell me, hey, this is really simple. And it's simple by design. I mean, to your point over time it's going to get, you know, more and more complex. I was talking to Andy, I think it was Andy. He was saying, you know, they've got the different sizes you've got to shape some, you know, they call it t-shirt sizes. And I was like, okay, I got a small, I got a medium and a large, maybe that's okay. But you guys would say, we give more granular you know, a scaling, I guess is the point there, right? I mean George, I don't know if you can comment on that. It just a different strategy. You've got a company that was founded well, I guess, 2015 versus one that was founded in 1977. So you would think the latter has, you know way more function than the former, but George, anything you'd add to this conversation? >> Yeah, I mean, I'm always amazed that there are these database systems that are perceived as cloud native and they do things like sell you database sizes by t-shirt sizes, as you described. I mean, if you look at Snowflake, it's small, medium, large extra large too extra large, but they're all factors of two. You're getting a size of your database of two, four, eight, six, 32, et cetera. Or if you look at AWS Redshift, you're buying your database by the nodes. You say, how many nodes do you want? And in both those cases, this is a cloud native. This is saying we have some hardware underneath our database and we need you, Mr. Customer, to tell us how many servers you want. That's not the way the clouds should work, right? And I think this is one of the things that we did with Autonomous Data Warehouse. We said, no, that's not how the rules should work. We still run our database on hardware, we still have nodes and servers. We should tell the customer, how many CPU's you would like for your data warehouse? You want 16? Sounds good. You want 18? Yeah, we can give you 18. We're not, you know, we're not selling these to you in bundles of eight or bundles of six or powers of two. We'll sell you what you need. That's what cloud elasticity should be. Not this idea that oh, we are a database that should be managed by IT. IT already knows about servers and nodes. Therefore it's okay if we tell people your cloud data warehouse runs on nodes. Within Oracle as Neil said, we wouldn't. The data warehouse should be used by the people who want to actually analyze their data, should be used by the business users. >> Well, and so the other piece of cloud native that has become popular, is this idea of separating compute from storage and being able to scale those two independent of each other which is pretty important, right? Because you don't want to have to pay for a chunk of compute if you don't need the storage and vice versa. Maybe you could talk about that, how you solve that problem, to the extent that you solve that problem. >> Absolutely, we do separate compute print storage with Autonomous Data Warehouse. When you come in and you say, I need 10 CPU's for my data warehouse and I need two terabytes of storage. Those are two dependent decisions that you make. So they're not tied together in any way. And, you are exactly right, Dave, this is how things should work in the cloud. You should pay for what you need, pay for what you use, not be constrained by having big sets of storage you have to use for a given amount CPU or vice versa. >> Okay, go ahead Neil, please. >> Oh, just to add on to that, you know, the other aspect that comes into play is that, you know, so your starting point is X, whatever that happens to be. Over time that changes. And we all know that workloads vary right throughout the day throughout the month, throughout the year by various events that occur maybe the close of the year, close of business at the end of the quarter, it maybe you know, holiday season for retailers and so forth. So, you know, it's not only the starting point, but how do you actually manage the growth, right? scaling up and scaling down, right? In our case, we tried, as George said, we abstracted that completely for the customer basically said check a box, which has auto scale. So, if the system is required more resources, will apply more resources. And we do so instantaneously without any downtime whatsoever, right? Because you know, again, you know, people think in terms of these systems have now become business critical. So if the business critical, you can't just shut down to expand. Imagine during the holiday season is your business is ramping up. And then all of a sudden you have to scale, right? And your system either shuts down, reboots itself, right? Or it slows down to the point that it's a crawl and all your customers get frustrated. We don't do that. You click a button, auto scale and we take care of it for you smoothing out those lumps, right? Without any technical assistance. And again, if you look at Redshift, you look at all these various systems, they require technical assistance to be able to figure out not only your initial data, but how you scale out over time. >> Interesting, okay. So all is said, you know, a lot of companies are using Azure, AWS Google for infrastructure, why would these customers not just use their database? Why would they switch to Oracle or ADW? >> Well, I think Neil will probably add something. I want to start by saying a huge number of our existing Autonomous Data Warehouse customers today are customers of AWS and Azure. They are pulling data from AWS and Azure and bringing it into an Oracle Autonomous Data Warehouse. And we built feature Joe, I focused on product managers. We feel featured for that. And so it's perfectly viable and it it's almost commonplace, that the very largest enterprises to be doing that. But then coming to the question of why would they want to do it? I don't know, Neil, you want to take that? >> Yeah, yeah, so one of the things that we've really see emerge here is you know, a data warehouse doesn't generate the transactions on itself, right? So the data has to come from somewhere, right? And you ask yourself, well, where does the data come from? Well, in a lot of cases, that data is coming from applications and increasingly SAS applications that the company has deployed. And those are, you know, HR applications, you know, CRM applications, you know ERP applications and many vertical applications. In Oracle's case, what we've done is we say, okay, well, we have the application, this transactional thing, we have the infrastructure from the economist data warehouse, why don't we just make it really, really easy? And if you're an Oracle applications customer, that's already running on the Oracle cloud, we will essentially provide you the ability to create a data warehouse from that information, right? With a clicker, with largely either with a product and service or quick start kit. You don't start from scratch, you start from where you are. And there are many cases that where you are has data, very much as George mentioned before telcos, banks, insurance companies, governments, all of the data that they want to analyze, a lot of that data guess where it's coming from, it's coming from Oracle applications. So it makes sense to be able to have both the data that's generated and the data that's being analyzed close to the same place. Because at the end of the day, the payoff pitch for any form of analysis is not coming up with an insight, oh, I realized X, Y, Z, but it's rather putting the insight directly into production. And that's where, when you have this stuff spread all over God's greener trying to go from insight into action can take months, if not years. The reason that a lot of customers are now turning to us is that they need to be much more agile and they need to be able to turn that insight into action immediately without it being a science project. >> Okay, thank you for that. So let's tick them off. Like what are the top things that customers can get from Oracle Autonomous Data Warehouse, that they couldn't get from say a Snowflake or Redshift or Big query or SQL server or something yet. I appreciate you guys' willingness to talk about the competition. Let's tick them off. What are the most important things that we should know about that they can't get elsewhere? >> So first, I mean, we already talked about a couple of what we think are really the major themes of Autonomous Data Warehouse. The services is autonomous. You don't need to worry about managing it, anyone can manage the data warehouse. The service is elastic. You can buy and pay for what you use. You know, those are just what we think of as being the general characteristics of Autonomous Data Warehouse. But you know, when you come to your question of, hey, what do we give that other vendors don't provide? And I think the one angle that Autonomous Data Warehouse does a really good job is and Neil was just discussing this, it focuses on the business problems, right? We have years and years of experience with not just database security, but data security, right? You know, every cloud vendor can say, oh we encrypt all your data, we have these compliance certifications, all of these things. And what they're saying is, we are securing your database, we are securing your database infrastructure. At Oracle of course has to do those as well. But where we go further, is we say, hey, no, no, no, no, no, we know what business users want. They want to secure their data. What kind of data am I storing? Do I have PII data? Could you detect whether there's PII data and tell me about it in case some user loaded something that I wasn't aware of? What kind of privileges did I give my users? Can you make sure that those privileges are right? And can you tell me if users were given privileges that they're not using maybe I need to take them away. These are the problems that Oracle's tackled in security over the last 20 years. It's really more about the business problem. Yeah, some other, oh, go ahead. >> Oh, I'm sorry, I got so many questions for you guys. We'll get back to that 'cause it sounds like there's a long list. (laughs) >> We have nowhere to go.(laughs) I want to pick up with George on something you said about elasticity. Is it true pay by the drink? Do you have a consumption pricing? I mean, can I dial it up and dial it down whenever I want? How does that work? >> Yes, I mean not to be too many technical details, but you say, I want 14 CPU's that's what your database runs at. You can change that default number anytime you want online, right? You can say, okay, I'm coming up on my quarter end, I'm going to raise my database 20 CPU. We just do it on the ply. We just adjust the size--- >> What about the other way? What about coming down? Can I go down to one? >> You go down, you can go down to one--- >> And you're not going to charge me for 14 if I go down to one? >> No, if you set it down to one, you get charged for one, right? >> Okay, that's good, that's good. >> In the background, you know we are also allowing levels of auto scaling. You say, if you say hey, I want to charged for 14 and Oracle, can you take care of all those scaling for me? So if a bunch of people jump on at 5:00 PM, to run some queries, 'cause the executive said, hey, I need a report by tomorrow morning. We'll take care of that for you. We'll let you go beyond 14 and only charge you for exactly what you use for those extra CPU's beyond 14. >> Okay, thank you. Go ahead, Neil. >> And maybe, if we add, you know, Andy talked about this when he was on that show with you last week, right? And you know, he talked about this concept of a converged database, but let me talk about it in the way that we see it from a business point of view, right? You know, business users are looking to, you know ask a variety of questions, right? And those questions need to be able to relate to both you know, the customer themselves, the relationship that the customer might have with others. You know, today we talk about like the social network and who are influencers within that, and then where they actually conduct business. Which is really, you know, in every case, it's on some form of increasingly on a mobile device. So in that case, you want to be able to ask questions, which is not only, you know, who should I focus on, but who are the key influencers within this community, right? That could influence others? And does that happen in a particular place in time? Meaning, you know, let's say pre COVID, it might happen at a coffee shop or somewhere else. We can answer all of those questions and more inside of the autonomous system without having to replicate the data out to one system that does graph and another system that does spatial, a third system that does this. It's like a business user. It's like, wait a minute, come on, you're trying to tell me that I need a separate system and replicate the data just be able to understand location? The answer in many cases is yes, you have to have separate, which a business person says, well, that's absurd. Can't I just do this all in one system? You can with Oracle. >> So look, I'm not trying to be the snarky journalist or analyst here but I want to keep pushing on this issue. So here we are, it's 2021. It's April. We're like a third of the way through the year. And so far, nobody has come out and said, okay, we're going to deliver Autonomous Data Warehouse just like Oracle. So I asked myself, well, why is Oracle doing this? You guys answered, you know, to reduce the labor cost. But I asked myself, is this how they're solving the problem of keeping relevant a database that spans five decades? And you guys said, no, no, this is cloud native born in the cloud, you know started essentially with a new mindset. But is this a trend that others are going to follow? You know, and if so, why haven't we seen it this idea of a self-driving databases? Why is it right now unique to Oracle? What's really going on here? >> So I think there's a really interesting thing that's happening, it's not visible outside of Oracle. It's very visible for those of us who work inside of the development organization. You know, if you look at Oracle, I can tell you bad. I mean, I think it's safe to presume Oracle has the largest database development organization on the planet, right? I mean, it was kind of the largest database or large most used database for the past two decades. And what's happened is we pivoted to building a cloud platform. We're not just building a database, we're taking all of these resources that we have with all these expertise of building database software. We were saying, we now have to build the platform to run and manage the database software in the cloud, right? And it's a little bit like, you know I think to make people relate to it a little better, there was a really good quote from Elon Musk couple of years ago, talking about Tesla. Like everyone looks at the car, right? Tesla, the car is really great. The hard part of this, is building the factory, and that's analogy holds for Oracle. What we're building is the cloud battery. And what we have transitioned is our database development organization is now building as robust a cloud as possible. So that you know, when we increase the number of databases by 10 X, we don't add 10 X, more cloud ops people to manage it. We are ramping up developer building features to automate the management of our cloud infrastructure. And with that automation, we get better ability, less errors, more security. We give benefits to our cloud data warehouse customers with it. And I think this something really important to realize, right? We build database software. We build, you know, an engineered system built for databases called exit data, and we build a cloud platform. And these are really equal tiers in what we are building and developing today in 2021 from Oracle database development organization. >> Well, you mentioned exit data, I want to shift gears here a little bit and talk about we're seeing this hybrid cloud on-premises clouds, they're finally gaining some traction. I got to give props Oracle's cloud of customers really the early to that game. I think it was the first in my view anyway, true same same vision, took you guys a little while to get there but it was the right vision. And the thing I always say about Oracle people don't understand is Oracle invest in R and D, your chairman is also the CTO. You guys are serious about technical investment so you know, that's where innovation comes from. But, and we heard during your recent earnings call, we heard some positive comments on this. So what's your take on delivering autonomous data warehouse on-prem and how do you compare with say Snowflake and AWS in that area? Snowflake, Frank Slootman, I've had him on record saying we're not going to do that halfway house. Forget it, we are always going to be in the cloud. We're never going to do an on-prem installation. AWS, we'll see to date. Yeah, I don't think you can get a Redshift for instance in outposts, but maybe that'll come. But, how do you see that emerging? What's your difference there? Maybe Neil, you could talk about that. >> Yeah, so, you know, I think, you know, customers had a lot of regulated industries, right? Still have concerns about the public cloud. And I think that when you hear statements like, you know, we're never going to do, you know, on-prem. Well, economist cloud at customer, it's not a classic on-prem solution. What it is, it's a piece of our cloud delivered in your data center. It's still the cloud software. Oracle manages it, Oracle, you know, the system itself manages itself and we take care of that responsibility so you don't have to. The differences is that we can make that available in a public cloud as well as in a private cloud, right? And there are so many use cases, you know, that you can imagine from a regulatory point of view, or just from a comfort point of view, where customers are choosing, they want the ability to decide for themselves where to place this stuff as compared to only having one option, right? And you know, you look at a lot of what's happening in the emerging world where, you know, there are a lot of places in the world that may not have, you know, really really high-speed internet connections to make, you know a public cloud feasible. Well, in that case, whether you're talking about, you know an oil rig or you're talking about something else, right? We can put that capability where it needs to be close to the operation that you're talking about, irrespective of the deployment option. >> Well, let me just follow up on that because I think it's interesting that, you know Frank Slootman said that to me, I oftentimes around AWS I say, never say never 'cause they'll surprise you, right? And I've learned that with Andy Jassy, but one of the things that seems difficult for on-prem, would be to separate that compute from storage because you have to actually physically move in resources. I think about Vertica Xeon mode. It's not quite the same, same. So, I mean, in that regard, maybe you're not the same same. And maybe that dogma makes sense for some companies. For Oracle, obviously you've got a huge on-prem state, thoughts on that. >> So, you know, clearly, you know, so typically what we'll do is that we'll provide additional hardware beyond what the customer might expect and that allows them to use the capabilities of expansion, right? We also have the ability to allow the customer to expand from their cloud of customer into the public cloud as well, of which we have a lot of those situations. So we can provide a level of elasticity, even on-premises by over provisioning the systems, well not charging the customer until they use only based on what they consume, right? Combined together with the ability for us to augment their usage in the public cloud as well, right? Where others, again are constraint, right? Because they only have a single option. >> Right, well, you've got the capital resources to do that as well which is not to be overlooked. Okay, I mean, I've blown our time here but you guys are so awesome. (laughs) I appreciate the candor. So last question and George, if you want to throw in a couple of those other tick boxes, you know the differentiators, please feel free, but for both of you, if you can leave customers with the one key point or the top key points on how Oracle Autonomous Data Warehouse can really help them improve their business in the near term, what would they be? Maybe George, you could start and then Neil you bring us home. >> Yeah, I mean, I think that, as I said before, our starting point with Autonomous Data Warehouse, is how can we build a better customer experience in the cloud? And I think, and this continues throughout 2021, and I think that the big theme here is the business users should be able to get value directly from their data warehouses. We talked a few times about how a line of business user should be able to manage their own data, should be able to load their own data warehouse, should be able to start to work with their own data, should be able to run machine learning, model of build machine learning, models against that data and all of that built in, and delivered in Autonomous Data Warehouse. And we think that this is, you know we see our customer organizations large and small, the light bulbs starting to go on how easy the services to use to and how completed it is for helping business users get value from their data. And just adding onto what George said, you know, the development organization has done a tremendous job of really simplifying this cooperation. What we also tried to do that on the business side. You know, when a customer has an on-prem situation, they're looking at moving to the cloud, whether lift and shift or modernized, they're looking at costs, they're looking at risk and they're looking at time. So one of the things we look at is how do we mitigate that? How do we mitigate the cost, the risk and the time? Well, this week, I think we announced our new cloud lift program and the cloud lift program is what Oracle will provide to its cloud engineering resources around the world is that we will do, we will take the cost, the risk and the time out of the equation and Oracle will work directly with the customer or the customer's partner of choice, maybe an Accenture or Deloitte, and we will move them, right? You know, at little or no cost, most cases there's no cost whatsoever, right? We mitigate the risk because we're taking the risk on. And we've built a lot of automated tools to make that go very quickly, right? And securely, and then finally, we do it in a very very short amount of time as compared to what you would need to do with, you know 'cause there is no Redshift on-premises. There is no Snowflake on-premises. You have to convert from what you already have to that, right? And, but the company beyond the technological barriers that George talked about were also trying to smooth the operation so that a business itself can make a decision that not only did they not need the technical people to operate it, they won't need an entire consulting contract with millions of dollars in order to actually do the movement to the cloud. >> Well, guys, I really appreciate you coming on the program and again, your candor to speak openly about you know, your approach, the competitors. And so it's great having you, really really thank you for, for your time. >> Appreciate it. >> And thank you for watching everybody. Look, if you guys want to come back, go toe to toe with these guys, say the word you're always welcome to come on The Cube. One thing for sure, Oracle are serious, when it comes to database. Thank you for watching. This is Dave Vellante. We'll see you next time. (bright music)

(lighthearted music) >> Okay. We're here with Keith White, Senior Vice President and General Manager for GreenLake at HPE, and George Hope, who's the Worldwide Head of Partner Sales at Hewlett Packard Enterprise. Welcome gentlemen. Good to see you. >> Awesome to be here. >> Yeah. Thanks so much. >> You're welcome. Keith, last we spoke, we talked about how you guys were enabling high performance computing workloads to get GreenLake right, for enterprise markets. And you got some news today which we're going to get to, but you guys, you put out a pretty bold position with GreenLake, basically staking a claim, if you will. The Edge, Cloud, as a service, all in. How are you thinking about its impacts for your customers so far? >> You know, the impact has been amazing. And, you know, in essence, I think the pandemic has really brought forward this real need to accelerate our customers' digital transformation, their modernization efforts, and, you know, frankly help them solve what was amounting to a bunch of new business problems. And so, you know, this manifests itself in a set of workloads, a set of solutions, and across all industries, across all customer types. And as you mentioned, you know, GreenLake is really bringing that value to them. It brings the Cloud to the customer in their data center, in their Colo or at the Edge. And so frankly, being able to do that with that full Cloud experience all as a pay per use, you know, fully consumption-based scenario, all managed for them, so they get that, as I mentioned, true Cloud experience, it's really sort of landing really well with customers. And we continue to see accelerated growth. We're adding new customers, we're adding new technology and we're adding a whole new set of partner ecosystem folks as well that we'll talk about. >> You know, it's interesting, you mentioned that, just as a quick aside. The definition of cloud is evolving, and it's because customers ... It's the way customers look at it. It's not just vendor marketing. It's what customers want, that experience across Cloud, Edge, you know, multi clouds on prem. So George, what's your take? Anything you'd add to Keith's response? >> I would, you've heard Antonio Neri say it several times and you probably see it again for yourself. The cloud is an experience. It's not a destination and digital transformation is pushing new business models and that demands more flexible IT. The first round of digital transformation focused on a Cloud first strategy, where our customers were looking to get more agility. As Keith mentioned, the next phase of transformation will be characterized by bringing the Cloud speed, the agility to all apps and data, regardless of where they live. According to IDC, by the end of 2021 80% of the businesses will have some mechanism in place to shift the cloud centric, infrastructure and apps, and twice as fast as before the pandemic. So the pandemic has actually accelerated the impact of the digital divide. Specifically in the small and medium companies, which are adapting to technology change even faster and emerging stronger as a result. You know, they, the analyst degree cloud computing and digitalization will be key differentiators for small and medium business in years to come and speed and automation will be pivotal as well. And by 2022, at least 30% of the lagging SMBs will accelerate digitalization. But the focus will be on internal processes and operations the digital leaders, however, will differentiate by delivering their customers dynamic experience. And with our partner ecosystem we're helping our customers embrace our as a service vision and stand out wherever they are on their transformation journey. >> Well thanks for those stats. I always liked the data. I mean, look, if you're not a digital business today I feel like you're out of business and-- I'm sure there's some exceptions but you got to get on the, on the digital bandwagon. I think pre pandemic, a lot of times people really didn't know what it meant. We know now what it means. Okay, Keith. Let's get into the news when we do these things. I love that you guys always have som-- something new to share. What do you have? >> No, you got it. And you know, as we said, you know the world is hybrid and the world is multi-cloud and so customers are expecting these solutions. And so we're continuing to really drive up the innovation and we're adding additional cloud services to GreenLake. We just recently went to a general availability of our ML ops, mach-- machine learning operations and our containers for cloud services along with our virtual desktop which has become very big in a pandemic world where a lot more people are working from home. And then we have shipped our SAP HEC customer edition which allows SAP customers to run on their premise whether it's the data center or the Colo. And then today we're introducing our new bare metal capabilities as well as containers on bare metal as a service, for those folks that are running cloud native applications that don't require any sort of hypervisor. So we're really excited about that. And then second, I'd say similar to that HPC as a service experience, we talked about before where we were bringing HPC down to a broader set of customers. We're expanding the entry point for our private cloud which is virtual machines, containers, storage compute type capabilities in workload optimized systems. So again, this is one of the key benefits that HPE brings is it combines all of the best of our hardware, software, third-party software and our services and financial services into a package. And we've workload optimized this for small, medium, large and extra large. So we have a real sort of broader base for our customers to take advantage of and to really get that cloud experience through HPE GreenLake. And, you know, from a partner standpoint we also want to make sure that we continue to make this super easy. So we're adding self service capabilities or integrating into our distributors through a core set of APIs to to make sure that it plugs in for a very smooth customer experience. And this expands our reach to over a hundred thousand additional value add resellers. And, you know, we saw just fantastic growth in the channel in Q1 over 118% year over year growth for GreenLake cloud services through the channel. And we're continuing to expand our ex-- extend and expand our partner ecosystem with additional key partnerships. Like our Colos, that co-location centers are really key. So Equinix, Cirrus 1 and others that we're working with. And I'll let George talk more about that. >> Yeah. I wonder if you could pick up on that George I mean, look, if I'm a partner and and I mean, I see that I see opportunity here. Maybe, you know, I made a lot of money in the in the old days moving iron, but I got to move. I got to pivot my business. You know, COVID is actually, you know accelerating a lot of those changes, but, but there's a lot of complexity out there and partners can be critical in in helping customers make that journey. What do you see this meaning to partners, Georgia? >> So I completely agree with Keith the-- through and through in with our partners, we we give our customers choice, right? They don't have to worry about security or cost as they would with public cloud or the hyperscalers we're driving special initiatives via Cloud 28, which we run which is the World's largest Cloud aggregator. And also in collaboration with our distributors and their marketplaces. As, as Keith mentioned, in addition customers can leverage our expertise and support of our service provider ecosystem, our SIs, our ISBs to find the right mix of hybrid IT and decide where each application or workload should be hosted. Because customers are now demanding robust ecosystems, cloud adjacency, and efficient, low latency networks and the modern workload demands, secure compliant highly available, and cost optimized environments. And Keith touched on co-location, we're partnering with co-location facilities to provide our customers the ability to expand bandwidth, reduced latency and get access to a robust ecosystem of adjacent providers. We touched on Equinix a bit as one of them but we're partnering with them to enable customers to connect to multiple clouds with private on demand interconnections from hundreds of data center locations around the globe. We continue to invest in the partner and customer experience, you know making ourselves easier to do business with we've now fully integrated partners in GreenLake central. And can provide their customers end to end support in managing the entire hybrid IT estate. And lastly, we're providing partners with dedicated and exclusive enablement opportunities. So customers can rely on both HPE and partner experts and we have a competent team of specialists that can help them transform and differentiate themselves. >> Yeah. So I'm hearing a theme of simplicity. You know, I talked earlier about this being customer driven to me what the customer wants is they want to come in. They want simple, like you mentioned, self-serve. I don't care if it's on prem in the cloud, across clouds at the edge, abstract, all that complexity away from me make it simple to do not only the technology to work you know, you figure out where the workload should run and let the metadata decide. And that's a, that's a bold vision and then make it easy to do business. Let me buy as a service if that's the way I want to consume. And, and partners are all about, you know, making, you know reducing friction and driving that. So anyway guys, final thoughts. Maybe Keith, you can close it out here and maybe George-- >> Yeah. You summed it up really nice. You know, we're excited to continue to provide what we view as the largest and most flexible hybrid cloud for our customers apps, data, workloads, and solutions and really being that leading on-prem solution to meet our customer's needs. At the same time, we're going to continue to innovate. You know, our ears are wide open and we're listening to our customers on what their needs are, what their requirements are. So we're going to expand the use cases, expand the solution sets that we provide in these workload optimized offerings to a very very broad set of customers as they drive forward with that digital transformation and modernization efforts. >> Great. George, any final thoughts? >> Yeah, I would say, you know, with our partners we work as one team and continue to hone our skills in and embrace our confidence. We're looking to help them evolve their businesses and thrive, and we're here to help now more than ever. So, you know, please reach out to our team and our partners so we can show you where we've already been successful together. >> So that's great. We're seeing the expanding GreenLake portfolio partners are key part of it. We're seeing new tools for them and then this ecosystem evolution and build out an expansion. Guys, thanks so much. >> You bet, thank you. >> Thank you. Appreciate it. >> You're welcome.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, welcome back to the cubes. Live coverage here for eight of us. Reinvent 2020. Virtual normally were on the show floor getting all of the interviews and talking about the top newsmakers and we have one of them here on the Cube were remote. I'm John for your host of the Cube. George Ellis Eros, GM and director of product manager for AWS. Talking about Wavelength George. Welcome to the remote Cube Cube. Virtual. Thanks for coming on. >>Good to be here. Thanks for having a John >>Eso Andy's Kino. One of the highlights last year, I pointed out that the five g thing is gonna be huge with the L A Wavelength Metro thing going on this year. Same thing. Mawr Proofpoint S'more expansion. Take us through what was announced this year. What's the big update on wavelength? >>Yes, so John Wavelength essentially brings a W services at the edge of the five G network, allowing our AWS customers and developers to reach their own end users and devices. Five devices with very low latency enabling a number off emerging applications ranging from industrial automation and I O. T. All the way to weigh AR VR smart cities, connected vehicles and much more this year we announced earlier in the year the general availability of wavelength in two locations one in the Bay Area and one in the Boston area. And since then we've seen we've been growing with Verizon or five D partner in the U. S. And and increasing that coverage in multiple off the larger U. S cities, including Miami and D. C in New York. And we launched Las Vegas yesterday at Andy's keynote with Verizon. We also announced that we are going toe to have a global footprint with K d D I in Japan launching a wavelength in Tokyo with SK detail SK Telecom in in South Korea or launching indigestion and with Vodafone in London >>so significant its expansion. Um, we used to call these points of presence back in the old days. I don't know what you call them now. I guess they're just zones like you calling them zones, but this really is gonna be a critical edge network, part of the edge, whether it's stadiums, metro area things and the density and the group is awesome. And everyone loves at about five gs. More of a business at less consumer. When you think about it, what has been some of the response as you guys had deployed mawr, What's the feedback? Um, can you take us through what the response has been? What's it been like? What have been some of the observations? >>Yeah, customers air really excited with the promise of five G and really excited to get their hands on these new capabilities that we're offering. Um, And they're telling us, you know, some consistent feedback that we're getting is that they're telling us that they love that they can use the same A W s, a P I S and tools and services that they used today in the region to get their hands on this new capabilities. So that's being pretty pretty consistent. Feedback these off use and the you know, Sometimes customers tell us that within a day they are able to deploy their applications in web. So that's a that's pretty consistent there. We've seen customers across a number of areas arranging, you know, from from manufacturing to healthcare to a ar and VR and broadcasting and live streaming all the way to smart cities and and connected vehicles. So a number of customers in these areas are using wavelength. Some of my favorite you know, examples are in in actually connected vehicles where you really can see that future materialized. You get, you know, customers like LG that are building the completely secularized vehicle, tow everything platform, and customers like safari that allow multiple devices to do, you know, talkto the Waveland, the closest Waveland Zone process. All of those device data streams at the edge. And then, um, it back. You know messages to the drivers, like for emergency situations, or even construct full dynamic maps for consumption off the off the vehicle themselves. >>I mean, it's absolutely awesome. And, you know, one of things that someone Dave Brown yesterday around the C two and the trend with smaller compute. You have the compute relationship at the edge to moving back and forth so I can see those dots connecting and looking forward to see how that plays out. Sure, and it will enable more capabilities. I do want to get your your thoughts, or you could just for the audience and our perspective just define the difference between wavelength and local zones because we know what regions are. Amazon regions are well understood all around the world. But now you have this new concept called locals owns part of wavelength, not part of wavelengths. Are they different technology? Can you just explain? Take him in to exclaim wavelength versus local zones how they work together? >>Yeah, So let me take a step back at AWS. Basically, what we're trying to do is we're trying to enable our customers to reach their end users with low latency and great performance, wherever those end users are and whatever network they're they're using to get connected, whether that's the five g mobile network with the Internet or in I o t Network. So we have a number of products that help our customers do that. And we expect, like, in months off other areas of the AWS platform, that customers are gonna pick and twos and mix and match and combine some of these products toe master use case. So when you're talking about wavelength and local zones, wavelength is about five g. There is obviously a lot off excitement as you said yourself about five g about the promise off those higher throughput. They're Lowell agencies. You know, the large number of devices supported and with wavelengths were enabling our customers toe to make the most of that. You know, of the five G technology and toe work on these emerging new use cases and applications that we talked about When it comes to local zones, we're talking more about extending AWS out two more locations. So if you think about you mentioned AWS regions, we have 24 regions in another five coming. Those are worldwide and enabled most of our customers to run their workloads. You know all of their workloads with low latency and adequate performance across the world. But we are hearing from customers that they want AWS in more locations. So local zones basically bring a W S extend those regions to more locations by bringing a W s closer to population I t and industrial centers. You know, l A is a great example of that. We launched the lay last year toe to local zones in L. A and toe toe a mainly at the media and entertainment customers that are, you know, in the L. A Metro, and we've seen customers like Netflix, for example, moving their artist workstations to the local zones. If they were to move that somewhere, you know, to the cloud somewhere further out the Laden's, he might have been too much for their ass artists work clothes and having some local AWS in the L. A. Metro allows them to finally move those workstation to the cloud while preserving that user experience. You know, interacting with the workstations that's happened. The cloud. >>So just like in conceptualizing is local zone, like a base station is in the metro point of physical location. Is it outpost on steroids? Been trying to get the feel for what it is >>you can think off regions consisting off availability zones. So these are, you know, data center clusters that deliver AWS services. So a local zone is much like an availability zone. But instead of being co located with the rest of the region, is in another locations that, for example, in L. A. Rather than being, you know, in in Virginia, let's say, um, they are internally. We use the same technology that we use for outpost, I suppose, is another great example of how AWS is getting closer to customers for on premises. Deployments were using much of the same technology that you you probably know as Nitro System and a number of other kind of technology that we've been working on for years, actually, toe make all this possible. >>You know, anyone who's been to a football game or any kind of stadium knows you got a great WiFi signal, but you get terrible bandwidth that is essentially kind of the back hall component for the telecom geeks out there. This is kind of what we're talking about here, right? We're talking about more of an expansionary at that edge on throughput, not just signal. So there's, you know, there's there's a wireless signal, and it's like really conductivity riel functionality for applications. >>Yeah, and many. Many of those use case that we're talking about are about, you know, immersive experiences for for end users. So with five t, you get that increasing throughput, you can get up to 10 GPS. You know, it is much higher with what you get 40. You also get lower latents is, but in order to really get make the most out of five G. You need to have the cloud services closer to the end user. So that's what Wavelength is doing is bringing all of those cloud services closer to the end user and combined with five G delivers on these on these applications. You know, um, a couple of customers are actually doing very, very, very exciting things on immersive application, our own immersive experiences. Um, why be VR is a customer that's working on wavelength today to deliver a full 3 60 video off sports events, and it's like you're there. They basically take all of those video streams. They process them in the waving zone and then put them back down to your to your VR headset. But don't you have seen those? We are headsets there, these bulky, awkward, big things because we can do a lot of the processing now at the edge rather than on the heads of itself. We are envisioning that these headsets will Will will string down to something that's indistinguishable potential from, you know, your glasses, making that user experience much better. >>Yeah, from anything from first responders toe large gatherings of people having immersive experiences, it's only gonna get better. Jorge. Thanks for coming on. The Cuban explaining wavelength graduates on the news and expansion. A lot more cities. Um, what's your take for reinvent while I got you? What's the big take away for you this year? Obviously. Virtual, but what's the big moment for you? >>Well, I think that the big moment for me is that we're continuing to, you know, to deliver for our customers. Obviously, a very difficult year for everyone and being able to, you know, with our help off our customers and our partners deliver on the reinvent promised this year as well. It is really impressed for >>me. All right. Great to have you on. Congratulations on local news. Great to see Andy pumping up wavelength. Ah, lot more work. We'll check in with you throughout the year. A lot to talk about. A lot of societal issues and certainly a lot of a lot of controversy as well as tech for good, great stuff. Thanks for coming. I appreciate it. >>Thanks for having me. Thanks. >>Okay, That's the cube. Virtual. I'm John for your host. Thanks for watching. We'll be back with more coverage from reinvent 2023 weeks of coverage. Walter Wall here in the Cube. Thanks for watching. Yeah,

from the cube studios in Palo Alto in Boston it's the cube covering empowering the autonomous enterprise brought to you by Oracle consulting welcome back everybody to this special digital event coverage that the cube is looking into the rebirth of Oracle consulting Janet George is here she's group vp autonomous for advanced analytics with machine learning and artificial intelligence at oracle and she's joined by grant gibson is a group vp of growth and strategy at oracle folks welcome to the cube thanks so much for coming on thank you thank you great I want to start with you because you get strategy in your title like just start big picture what is the strategy with Oracle specifically as it relates to autonomous and also consulting sure so I think you know Oracle has a deep legacy of strengthened data and over the company's successful history it's evolved what that is from steps along the way if you look at the modern enterprise of Oracle client I think there's no denying that we've entered the age of AI that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward and while generally it's acknowledge that it's a transformative technology and people know that they need to take advantage of it it's the how that's really tricky and that most enterprises in order to really get an enterprise level ROI on an AI investment need to engage in projects of significant scope and going from realizing there's an opportunity to realize and there's a threat to mobilizing yourself to capitalize on it is a is a daunting task for an enemy certainly one that's you know anybody that's got any sort of legacy of success has built-in processes that's built in systems has built in skillsets and making that leap to be an autonomous enterprise is is challenging for companies to wrap their heads around so as part of the rebirth of Oracle consulting we've developed a practice around how to both manage the the technology needs for that transformation as well as the human needs as well as the data science needs to it so rather there's about five or six things that I want to followup with you there so there's gonna be good conversations Janet so ever since I've been in the industry we're talking about AI in sort of start stop start stop we had the AI winter and now it seems to be here it's almost feel like that the the technology never lived up to its promise you didn't have the horsepower a compute power you know enough data maybe so we're here today feels like we are entering a new era why is that and and how will the technology perform this time so for AI to perform it's very reliant on the data we entered the age of AI without having the right data for AI so you can imagine that we we just launched into AI without our data being ready to be training sex for AI so we started with bi data or we started the data that was already historically transformed formatted had logical structures physical structures this data was sort of trapped in many different tools and then suddenly AI comes along and we say take this data our historical data we haven't tested to see if this has labels in it this has learning capability in it we just thrust the data to AI and that's why we saw the initial wave of AI sort of failing because it was not ready to fall AI ready for the generation of AI and part of I think the leap that clients are finding success with now is getting the Apple data types and you're moving from the zeros and ones of structured data to image language written language spoken language you're capturing different data sets in ways that prior tools never could and so the classifications that come out of it the insights that come out of it the business process transformation comes out of it is different than what we would have understood under the structured data format so I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale that is what I think is the combination that takes it to the next plateau for sure the language that we use today I feel like is going to change and you just started to touch on some of them you know sensing you know they're our senses and you know the visualization and the the the the auditory so it's it's sort of this new experience that customers are saying a lot of this machine intelligence behind them I call it the autonomous enterprise right the journey to be the autonomous enterprise and when you're on this journey to be the autonomous enterprise you need really the platform that can help you be cloud is that platform which can help you get to the autonomous journey but the autonomous journey does not end with the cloud right or doesn't end with the dead lake these are just infrastructures that are basic necessary necessities for being on that on that autonomous journey but at the end it's about how do you train and scale at a very large scale training that needs to happen on this platform for AI to be successful and if you are an autonomous enterprise then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value if you will so you've got the platform you've got the data and now you're actually tapping into the autonomous components AI and machine learning to derive business intelligence and business value so I want to get into a little bit of Oracle's role but to do that I want to talk a little bit more about the industry so if you think about the way this the industry seems to be restructuring around data there historically Industries had their own stack or value chain and if you were in the finance industry you were there for life you know so when you think about banking for example highly regulated industry think about our geek culture these are highly regulated industries they're come it was very difficult to disrupt these industries but now you look at an Amazon right and what does an Amazon or any other tech giant like Apple have they have incredible amounts of data they understand how people use or how they want to do banking and so they've cut off the tap of cash or Amazon pay and these things are starting to eat into the market right so you would have never thought an Amazon could be a competition to your banking industry just because of regulations but they are not hindered by the regulations because they're starting at a different level and so they become an instant threat and an instant destructor to these highly regulated industries that's what data does right then you use data as you DNA for your business and you are sort of born in data or you figured out how to be autonomous if you will capture value from that data in a very significant manner then you can get into industries that are not traditionally your own industry it can be like the food industry it can be the cloud industry the book industry you know different industries so you know that that's what I see happening with the tech giants so great this is a really interesting point that Gina is making that you mentioned you started off with like a couple of industries that are highly regulated harder to disrupt you know music got disrupted publishing got disrupted but you've got these regulated businesses you know defense automotive actually hasn't been truly disrupted yet so I'm Tesla maybes a harbinger and so you've got this spectrum of disruption but is anybody safe from disruption okay I don't think anyone's ever safe from it it's it's changed in evolution right that you whether it's you know swapping horseshoes for cars or TV for movies or Netflix or any sort of evolution of a business you I wouldn't coast on any of them and I think to earlier question around the value that we can help bring to Oracle customers is that you know we have a rich stack of applications and I find that the space between the applications the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company but it's trapped from both a technology and a business perspective and that's where I think really any company can take advantage of knowing its data better and changing itself to take advantage of what's already there yet powerful bit people always throw the bromide out the data is the new oil and we've said no data is far more valuable because you can use it in a lot of different places or you can use once and it's has to follow laws of scarcity data if you can unlock it and so a lot of the incumbents they have built a business around whatever a factory or you know process and people a lot of the the trillion-dollar start in us that they're become trillionaires you know I'm talking about data is at the core their data company so so it seems like a big challenge for you you're incumbent customers clients is to put data hit the core be able to break down those silos how do they do that grading down silos is really super critical for any business it was okay to operate in a silo for example you would think that oh you know I could just be payroll in expense reports and it wouldn't man matter if I get into vendor performance management or purchasing that can operate as a silo but anymore we are finding that there are tremendous insights between vendor performance management I expensive all these things are all connected so you can't afford to have your data set in silos so grading down that silo actually gives the business very good performance right insights that they didn't have before so that's one way to go but but another phenomena happens when you start to great down the silos you start to recognize what data you don't have to take your business to the next level right that awareness will not happen when you're working with existing data so that awareness comes into form when you great the silos and you start to figure out you need to go after different set of data to get you to new product creation what would that look like new test insights or new capex avoidance then that data is just you have to go through the eye tration to be able to figure that out which takes is what you're saying happy so this notion of the autonomous under president help me here because I get kind of autonomous and automation coming into IT IT ops I'm interested in how you see customers taking that beyond the technology organization into the enterprise I think when AI is a technology problem the company is it at a loss ai has to be a business problem ai has to inform the business strategy ai has two main companies the successful companies that have done so 90 percent of our investments are going towards data we know that and and most of it going towards AI data out there about this right and so we looked at what are these ninety cup ninety percent of the company's investments where are these going and who is doing this right and who's not doing this right one of the things we are seeing as results is that the companies that are doing it right have brought data into their business strategy they've changed their business model right so it's not like making a better taxi but coming up with uber right so it's not like saying okay I'm going to have all these I'm going to be the drug manufacturing company I'm going to put drugs out there in the market versus I'm going to do connected health right and so how does data serve the business model of being connected health rather than being a drug company selling drugs to my customers right it's a completely different way of looking at it and so now I is informing drug discovery AI is not helping you just put more drugs to the market rather it's helping you come up with new drugs that will help the process of connected game there's a lot of discussion in the press about you know the ethics of AI and how far should we take AI and how far can we take it from a technology standpoint long roadmap there but how far should we take it do you feel as though public policy will take care of that a lot of that narrative is just kind of journalists looking for you know the negative story well that's sort itself out how much time do you spend with your customers talking about that we in Oracle we're building our data science platform with an explicit feature called explain ability off the model on how the model came up with the features what features it picked we can rearrange the features that the model picked so I think explain ability is very important for ordinary people to trust AI because we can't trust AI even even data scientists contrast AI right to a large extent so for us to get to that level where we can really trust what AI is picking in terms of a model we need to have explained ability and I think a lot of the companies right now are starting to make that as part of their platform well we're definitely entering a new era the the age of AI of the autonomous enterprise folks thanks very much for a great segment really appreciate it yeah our pleasure thank you for having us thank you alright and thank you and keep it right there we're right back with our next guest for this short break you're watching the cubes coverage of the rebirth of Oracle consulting right back you [Music]

>>Yeah, yeah, >>yeah! >>Welcome back, everybody. To this special digital event coverage, the Cube is looking into the rebirth of Oracle Consulting. Janet George is here. She's group VP Autonomous for Advanced Analytics with machine learning and artificial intelligence at Oracle. And she's joined by Grant Gibson Group VP of growth and strategy at Oracle. Folks, welcome to the Cube. Thanks so much for coming on. Great. I want to start with you because you get strategy in your title like this. Start big picture. What is the strategy with Oracle specifically as it relates to autonomous and also consulting? >>Sure. So I think you know, Oracle has a deep legacy of strength and data and, uh uh, over the company's successful history. It's evolved what that is from steps along the way. And if you look at the modern enterprise Oracle client, I think there's no denying that we've entered the age of AI, that everyone knows that artificial intelligence and machine learning are a key to their success in the business marketplace going forward. And while generally it's acknowledged that it's a transformative technology and people know that they need to take advantage of it, it's the how that's really tricky and that most enterprises, in order to really get an enterprise level, are rely on AI investment. Need to engage in projects of significant scope, and going from realizing there's an opportunity of realizing there's a threat to mobilize yourself to capitalize on it is a daunting task or certainly one that's, you know, Anybody that's got any sort of legacy of success has built in processes as building systems has built in skill sets, and making that leap to be an autonomous enterprise is challenging for companies to wrap their heads around. So as part of the rebirth of Oracle Consulting, we've developed a practice around how to both manage the technology needs for that transformation as well as the human needs as well as the data science needs. >>So there's about five or six things that I want to follow up with you there. So this is a good conversation. Ever since I've been in the industry, we were talking about a sort of start stop start stop at the Ai Winter, and now it seems to be here is almost feel like the technology never lived up to its promise. If you didn't have the horsepower compute power data may be so we're here today. It feels like we are entering a new era. Why is that? And how will the technology perform this time? >>So for AI to perform it's very remind on the data we entered the age of Ai without having the right data for AI. So you can imagine that we just launched into Ai without our data being ready to be training sex for AI. So we started with B I data or we started the data that was already historically transformed. Formatted had logical structures, physical structures. This data was sort of trapped in many different tools. And then suddenly Ai comes along and we see Take this data, our historical data we haven't tested to see if this has labels in it. This has learning capability in it. Just trust the data to AI. And that's why we saw the initial wave of ai sort of failing because it was not ready to full ai ready for the generation of Ai, if you will. >>So, to me, this is I always say, this was the contribution that Hadoop left us, right? I mean, the dupe everybody was crazy. It turned into big data. Oracle was never that nuts about it is gonna watch, Setback and wash obviously participated, but it gathered all this data created Chief Data Lakes, which people always joke turns into data swamps. But the data is often times now within organizations least present. Now it's a matter of what? What what's The next step is >>basically about Hadoop did to the world of data. Was her dupe freed data from being stuck in tools it basically brought forth. This concept of a platform and platform is very essential because as we enter the age of AI and be entered, the better wide range of data. We can't have tools handling all of the state of the data needs to scale. The data needs to move, the data needs to grow. And so we need the concept of platforms so we can be elastic for the growth of the data, right, it can be distributed. It can grow based on the growth of the data, and it can learn from that data. So that is that's the reason why Hadoop sort of brought us into the platform board, >>right? A lot of that data ended up in the cloud. I always say, You know, for years we marched to the cadence of Moore's law. That was the innovation engine in this industry and fastest, you could get a chip in, you know, you get a little advantage, and then somebody would leapfrog. Today it's got all this data you apply machine intelligence and cloud gives you scale. It gives you agility of your customers. Are they taking advantage of the new innovation cocktail? First of all, do you buy that? How do you see them taking >>advantage of? Yeah, I think part of what James mentioned makes a lot of sense is that at the beginning, when you know you're taking the existing data in an enterprise and trying to do AI to it, you often get things that look a lot like what you already knew because you're dealing with your existing data set in your existing expertise. And part of I think the leap that clients are finding success with now is getting novel data types, and you're moving from, uh, zeros and ones of structured data, too. Image language, written language, spoken language. You're capturing different data sets in ways that prior tools never could. And so the classifications that come out of it, the insights that come out of it, the business process transformation comes out of it is different than what we would have understood under the structure data format. So I think it's that combination of really being able to push massive amounts of data through a cloud product to be able to process it at scale. That is what I think is the combination that takes it to the next plateau for sure. >>So you talked about sort of. We're entering a new era Age of a AI. You know, a lot of people, you know, kind of focus on the cloud is the current era, but it really does feel like we're moving beyond that. The language that we use today, I feel like it's going to change, and you just started to touch on some of it. Sensing, you know, there are senses and you know the visualization in the the auditory. So it's It's sort of this new experience that customers are seeing a lot of this machine intelligence behind. >>I call it the autonomous and a price right. The journey to be the autonomous enterprise. And then you're on this journey to be the autonomous enterprise you need. Really? The platform that can help you be cloud is that platform which can help you get to the autonomous journey. But the autonomous journey does not end with the cloud or doesn't end with the data lake. These are just infrastructures that are basic necessary necessities for being on that on that autonomous journey. But at the end, it's about how do you train and scale at, um, very large scale training that needs to happen on this platform for AI to be successful. And if you are an autonomous and price, then you have really figured out how to tap into AI and machine learning in a way that nobody else has to derive business value, if you will. So you've got the platform, you've got the data, and now you're actually tapping into the autonomous components ai and machine learning to derive business, intelligence and business value. >>So I want to get into a little bit of Oracle's role. But to do that I want to talk a little bit more about the industry. So if you think about the way that the industry seems to be restructuring around data. Historically, industries had their own stack value chain, and if you were in in in the finance industry, you were there for life. We had your own sales channel distribution, etcetera. But today you see companies traversing industries, which has never happened before. You know, you see apple getting into content and music, and there's so many examples are buying whole foods data is sort of the enabler. There you have a lot of organizations, your customers, that are incumbents that they don't wanna get disrupted your part big party roles to help them become that autonomous and press so they don't get disrupted. I wonder if you could maybe maybe comment on How are you doing? >>Yeah, I'll comment and then grant you China, you know. So when you think about banking, for example, highly regulated industry think about RG culture. These are highly regulated industries there. It was very difficult to destruct these industries. But now you look at an Amazon, right? And what is an Amazon or any other tech giants like Apple have? They have incredible amounts of data. They understand how people use for how they want to do banking. And so they've come up with Apple cash or Amazon pay, and these things are starting to eat into the market, right? So you would have never thought and Amazon could be a competition to a banking industry just because of regulations. But they're not hindered by the regulations because they're starting at a different level. And so they become an instant threat in an instant destructive to these highly regulated industries. That's what data does, right when you use data as your DNA for your business and you are sort of born in data or you figured out how to be autonomous. If you will capture value from that data in a very significant manner, then you can get into industries that are not traditionally your own industry. It can be like the food industry can be the cloud industry, the book industry, you know, different industries. So you know that that's what I see happening with the tech giants. >>So great, there's a really interesting point that the Gina is making that you mentioned. You started off with a couple of industries that are highly regulated, the harder to disrupt use, it got disrupted, publishing got disrupted. But you've got these regulated businesses. Defense or automotive actually hasn't been truly disrupted yet. Some Tesla, maybe a harbinger. And so you've got this spectrum of disruption. But is anybody safe from disruption? >>Kind of. I don't think anyone's ever say from it. It's It's changing evolution, right? That you whether it's, you know, swapping horseshoes for cars are TV for movies or Netflix are any sort of evolution of a business You're I wouldn't coast on any of them. And I think to the earlier question around the value that we can help bring the Oracle customers is that you know, we have a rich stack of applications, and I find that the space between the applications, the data that that spans more than one of them is a ripe playground for innovations that where the data already exists inside a company. But it's trapped from both a technology and a business perspective. Uh, and that's where I think really any company can take advantage of knowing it's data better and changing itself to take advantage of what's already there. >>Yet powerful people always throw the bromide out. The data is the new oil, and we've said. No data is far more valuable because you can use it in a lot of different places. Oil you can use once and it's follow the laws of scarcity data if you can unlock it. And so a lot of the incumbents they have built a business around, whatever a factory or a process and people, a lot of the trillion are starting us that have become billionaires. You know, I'm talking about Data's at the core. They're data companies. So So it seems like a big challenge for your incumbent customers. Clients is to put data at the core, be able to break down those silos. How do they do that? >>Grading down silos is really super critical for any business. It was okay to operate in a silo, for example. You would think that, Oh, you know, I could just be payroll and expense reports and it wouldn't matter matter if I get into vendor performance management or purchasing that can operate as a silo. But any movie of finding that there are tremendous insights between vendor performance management I expensive for these things are all connected, so you can't afford to have your data sits in silos. So grading down that silo actually gives the business very good performance, right? Insights that they didn't have before. So that's one way to go. But but another phenomena happens when you start to great down the silos, you start to recognize what data you don't have to take your business to the next level, right. That awareness will not happen when you're working with existing data so that a Venice comes into form when you great the silos and you start to figure out you need to go after a different set of data to get you to a new product creation. What would that look like? New test insights or new cap ex avoidance that that data is just you have to go through the iteration to be able to figure that out. >>It becomes it becomes a business problem, right? If you got a process now where you can identify 75% of the failures and you know the value of the other 25% of failures, that becomes a simple investment. How much money am I willing to invest to knock down some portion that 25% and it changes it from simply an I t problem or expense management problem to you know, the cash problem. >>But you still need a platform that has AP eyes that allows you to bring in those data sets that you don't have access to this enable an enabler. It's not the answer. It's not the outcome in and of itself, but it enables. And >>I always say, you can't have the best toilet if you're coming, doesn't work. You know what I mean? So you have to have your plumbing. Your plumbing has to be more modern. So you have to bring in modern infrastructure distributed computing that that you cannot. There's no compromise there, right? You have to have the right equal system for you to be able to be technologically advanced on a leader in that >>table. Stakes is what you're saying. And so this notion of the autonomous enterprise I would help me here cause I get kind of autonomous and automation coming into I t I t ops. I'm interested in how you see customers taking that beyond the technology organization into the enterprise. >>Yeah, this is this is such a great question, right? This is what I've been talking about all morning. Um, I think when AI is a technology problem, the company is that at a loss AI has to be a business problem. AI has to inform the business strategy. AI has to been companies. The successful companies that have done so. 90% of my investments are going towards state. We know that and most of it going towards AI. There's data out there about this, right? And so we look at what are these? 90 90% of the company's investments. Where are these going and whose doing this right? Who's not doing this right? One of the things we're seeing as results is that the companies that are doing it right have brought data into their business strategy. They've changed their business model, right? So it's not like making a better taxi, but coming up with a bow, right? So it's not like saying Okay, I'm going to have all these. I'm going to be the drug manufacturing company. I'm gonna put drugs out there in the market forces. I'm going to do connected help, right? And so how does data serve the business model of being connected? Help rather than being a drug company selling drugs to my customers, right? It's a completely different way of looking at it. And so now you guys informing drug discovery is not helping you just put more drugs to the market. Rather, it's helping you come up with new drugs that would help the process of connected games. There's a >>lot of discussion in the press about, you know, the ethics of AI, and how far should we take? A far. Can we take it from a technology standpoint, Long road map there? But how far should we take it? Do you feel as though of public policy will take care of that? A lot of that narrative is just kind of journalists looking for, You know, the negative story. Well, that's sort itself out. How much time do you spend with your customers talking about that and is what's Oracle's role there? I mean, Facebook says, Hey, the government should figure this out. What's your point? >>I think everybody has a role. It's a joint role, and none of us could give up our responsibilities as data scientists. We have heavy responsibility in this area on. We have heavy responsibility to advise the clients on the state area. Also, the data we come from the past has to change. That is inherently biased, right? And we tend to put data signs on biased data with the one dimensional view of the data. So we have to start looking at multiple dimensions of the data. It's got to start examining. I call it a responsible AI when you just simply take one variable or start to do machine learning with that because that's not that's not right. You have to examine the data. You got to understand how much biases in the data are you training a machine learning model with the bias? Is there diversity in the models? Is their diversity in the data? These are conversations we need to have. And we absolutely need policy around this because unless our lawmakers start to understand that we need the source of the data to change. And if we look at this, if we look at the source of the data and the source of the data is inherently biased or the source of the data has only a single representation, we're never going to change that downstream. AI is not going to help us. There so that has to change upstream. That's where the policy makers come into into play. The lawmakers come into play, but at the same time as we're building models, I think we have a responsibility to say can be triangle can be built with multiple models. Can we look at the results of these models? How are these feature's ranked? Are they ranked based on biases, sex, HP II, information? Are we taking the P I information out? Are we really looking at one variable? Somebody fell to pay their bill, but they just felt they they build because they were late, right? Voices that they don't have a bank account and be classified. Them is poor and having no bank account, you know what I mean? So all of this becomes part of response >>that humans are inherently biased, and so humans or building algorithms right there. So you say that through iteration, we can stamp out, the buyers >>can stamp out, or we can confront the bias. >>Let's make it transparent, >>make transparent. So I think that even if we can have the trust to be able to have the discussion on, is this data the right data that we're doing the analysis on On start the conversation day, we start to see the change. >>We'll wait so we could make it transparent. And I'm thinking a lot of AI is black box. Is that a problem? Is the black box you know, syndrome an issue or we actually >>is not a black box. We in Oracle, we're building our data science platform with an explicit feature called Explained Ability. Off the model on how the model came up with the features what features they picked. We can rearrange the features that the model picked, citing Explain ability is very important for ordinary people. Trust ai because we can't trust even even they designed This contrast ai right to a large extent. So for us to get to that level, where we can really trust what ai speaking in terms of a modern, we need to have explain ability. And I think a lot of the companies right now are starting to make that as part of their platform. >>So that's your promise. Toe clients is that your AI will be a that's not everybody's promised. I mean, there's a lot of black box and, you know, >>there is, if you go to open source and you start downloading, you'll get a lot of black boss. The other advantage to open source is sometimes you can just modify the black box. You know they can give you access, and you could modify the black box. But if you get companies that have released to open, source it somewhat of a black box, so you have to figure out the balance between you. Don't really worry too much about the black box. If you can see that the model has done a pretty good job as compared to other models, right if I take if I triangulate the results off the algorithm and the triangulation turns out to be reasonable, the accuracy on our values and the Matrix is show reasonable results. Then I don't really have to brief one model is to bias compared to another moderate. But I worry if if there's only one dimension to it. >>Well, ultimately much too much of the data scientists to make dismay, somebody in the business side is going to ask about cause I think this is what the model says. Why is it saying that? And you know, ethical reasons aside, you're gonna want to understand why the predictions are what they are, and certainly as you're going to examine those things as you look at the factors that are causing the predictions on the outcomes, I think there's any sort of business should be asking those responsibility questions of everything they do, ai included, for sure. >>So we're entering a new era. We kind of all agree on that. So I want to just throw a few questions out, have a little fun here, so feel free to answer in any order. So when do you think machines will be able to make better diagnoses than doctors? >>I think they already are making better diagnosis. And there's so much that I found out recently that most of the very complicated cancel surgeries are done by machines doctors to standing by and making sure that the machines are doing it well, right? And so I think the machines are taking over in some aspects. I wouldn't say all aspects. And then there's the bedside manners. You really need the human doctor and you need the comfort of talking to >>a CIO inside man. Okay, when >>do you >>think that driving and owning your own vehicle is going to be the exception rather than the rule >>that I think it's so far ahead. It's going to be very, very near future, you know, because if you've ever driven in an autonomous car, you'll find that after your initial reservations, you're going to feel a lot more safer in an autonomous car because it's it's got a vision that humans don't. It's got a communication mechanism that humans don't right. It's talking to all the fleets of cars. Richardson Sense of data. It's got a richer sense of vision. It's got a richer sense of ability to react when a kid jumps in front of the car where a human will be terrified, not able to make quick decisions, the car can right. But at the same time we're going to have we're gonna have some startup problems, right? We're going to see a I miss file in certain areas, and junk insurance companies are getting gearing themselves up for that because that's just but the data is showing us that we will have tremendously decreased death rates, right? That's a pretty good start to have AI driving up costs right >>believer. Well, as you're right, there's going to be some startup issues because this car, the vehicle has to decide. Teoh kill the person who jumped in front of me. Or do I kill the driver killing? It's overstating, but those are some of the stories >>and humans you don't. You don't question the judgment system for that. >>There's no you person >>that developed right. It's treated as a one off. But I think if you look back, you look back five years where we're way. You figure the pace of innovation and the speed and the gaps that we're closing now, where we're gonna be in five years, you have to figure it's I mean, I don't I have an eight year old son. My question. If he's ever gonna drive a car, yeah, >>How about retail? Do you think retail stores largely will disappear? >>I think retail. Will there be a customer service element to retail? But it will evolve from where it's at in a very, very high stakes, right, because now, with our if I did, you know we used to be invisible as we want. We still aren't invisible as you walk into a retail store, right, Even if you spend a lot of money in in retail. And you know now with buying patterns and knowing who the customer is and your profile is out there on the Web, you know, just getting a sense of who this person is, what their intent is walking into the store and doing doing responsible ai like bringing value to that intent right, not responsible. That will gain the trust. And as people gain the trust and then verify these, you're in the location. You're nearby. You normally by the sword suits on sale, you know, bring it all together. So I think there's a lot of connective tissue work that needs to happen. But that's all coming. It's coming together, >>not the value and what the what? The proposition of the customers. If it's simply there as a place where you go and buy, pick up something, you already know what you're going to get. That story doesn't add value. But if there's something in the human expertise and the shared felt, that experience of being in the store, that's that's where you'll see retailers differentiate themselves. I >>like, yeah, yeah, yeah, >>you mentioned Apple pay before you think traditional banks will lose control of payment systems, >>They're already losing control of payment systems, right? I mean, if you look at there was no reason for the banks to create Siri like assistance. They're all over right now, right? And we started with Alexa first. So you can see the banks are trying to be a lot more customized customer service, trying to be personalized, trying to really make it connect to them in a way that you have not connected to the bank before. The way we connected to the bank is you know, you knew the person at the bank for 20 years or since when you had your first bank account, right? That's how you connect with the banks. And then you go to a different branch, and then all of a sudden you're invisible, right? Nobody knows you. Nobody knows that you were 20 years with the bank. That's changing, right? They're keeping track of which location you're going to and trying to be a more personalized. So I think ai is is a forcing function in some ways to provide more value. If anything, >>we're definitely entering a new era. The age of of AI of the autonomous enterprise folks, thanks very much for great segment. Really appreciate it. >>Yeah. Pleasure. Thank you for having us. >>All right. And thank you and keep it right there. We'll be back with our next guest right after this short break. You're watching the Cube's coverage of the rebirth of Oracle consulting right back. Yeah, yeah, yeah, yeah.