(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

(soothing music) >> Hey everyone, welcome to this event, "Build Your Cloud Center of Excellence." I'm your host, Lisa Martin. In the next 15 minutes or so my guest and I are going to be talking about redefining cloud operations, an application modernization for customers, and specifically how partners are helping to speed up that process. As you saw on our first two segments, we talked about problems enterprises are facing with cloud operations. We talked about redefining cloud operations as well to solve these problems. This segment is going to be focusing on how Hitachi Vantara's partners are really helping to speed up that process. We've got Johnson Controls here to talk about their partnership with Hitachi Vantara. Please welcome both of my guests, Prem Balasubramanian is with us, SVP and CTO Digital Solutions at Hitachi Vantara. And Suresh Mothikuru, SVP Customer Success Platform Engineering and Reliability Engineering from Johnson Controls. Gentlemen, welcome to the program, great to have you. >> Thank. >> Thank you, Lisa. >> First question is to both of you and Suresh, we'll start with you. We want to understand, you know, the cloud operations landscape is increasingly complex. We've talked a lot about that in this program. Talk to us, Suresh, about some of the biggest challenges and pin points that you faced with respect to that. >> Thank you. I think it's a great question. I mean, cloud has evolved a lot in the last 10 years. You know, when we were talking about a single cloud whether it's Azure or AWS and GCP, and that was complex enough. Now we are talking about multi-cloud and hybrid and you look at Johnson Controls, we have Azure we have AWS, we have GCP, we have Alibaba and we also support on-prem. So the architecture has become very, very complex and the complexity has grown so much that we are now thinking about whether we should be cloud native or cloud agnostic. So I think, I mean, sometimes it's hard to even explain the complexity because people think, oh, "When you go to cloud, everything is simplified." Cloud does give you a lot of simplicity, but it also really brings a lot more complexity along with it. So, and then next one is pretty important is, you know, generally when you look at cloud services, you have plenty of services that are offered within a cloud, 100, 150 services, 200 services. Even within those companies, you take AWS they might not know, an individual resource might not know about all the services we see. That's a big challenge for us as a customer to really understand each of the service that is provided in these, you know, clouds, well, doesn't matter which one that is. And the third one is pretty big, at least at the CTO the CIO, and the senior leadership level, is cost. Cost is a major factor because cloud, you know, will eat you up if you cannot manage it. If you don't have a good cloud governance process it because every minute you are in it, it's burning cash. So I think if you ask me, these are the three major things that I am facing day to day and that's where I use my partners, which I'll touch base down the line. >> Perfect, we'll talk about that. So Prem, I imagine that these problems are not unique to Johnson Controls or JCI, as you may hear us refer to it. Talk to me Prem about some of the other challenges that you're seeing within the customer landscape. >> So, yeah, I agree, Lisa, these are not very specific to JCI, but there are specific issues in JCI, right? So the way we think about these are, there is a common issue when people go to the cloud and there are very specific and unique issues for businesses, right? So JCI, and we will talk about this in the episode as we move forward. I think Suresh and his team have done some phenomenal step around how to manage this complexity. But there are customers who have a lesser complex cloud which is, they don't go to Alibaba, they don't have footprint in all three clouds. So their multi-cloud footprint could be a bit more manageable, but still struggle with a lot of the same problems around cost, around security, around talent. Talent is a big thing, right? And in Suresh's case I think it's slightly more exasperated because every cloud provider Be it AWS, JCP, or Azure brings in hundreds of services and there is nobody, including many of us, right? We learn every day, nowadays, right? It's not that there is one service integrator who knows all, while technically people can claim as a part of sales. But in reality all of us are continuing to learn in this landscape. And if you put all of this equation together with multiple clouds the complexity just starts to exponentially grow. And that's exactly what I think JCI is experiencing and Suresh's team has been experiencing, and we've been working together. But the common problems are around security talent and cost management of this, right? Those are my three things. And one last thing that I would love to say before we move away from this question is, if you think about cloud operations as a concept that's evolving over the last few years, and I have touched upon this in the previous episode as well, Lisa, right? If you take architectures, we've gone into microservices, we've gone into all these server-less architectures all the fancy things that we want. That helps us go to market faster, be more competent to as a business. But that's not simplified stuff, right? That's complicated stuff. It's a lot more distributed. Second, again, we've advanced and created more modern infrastructure because all of what we are talking is platform as a service, services on the cloud that we are consuming, right? In the same case with development we've moved into a DevOps model. We kind of click a button put some code in a repository, the code starts to run in production within a minute, everything else is automated. But then when we get to operations we are still stuck in a very old way of looking at cloud as an infrastructure, right? So you've got an infra team, you've got an app team, you've got an incident management team, you've got a soft knock, everything. But again, so Suresh can talk about this more because they are making significant strides in thinking about this as a single workload, and how do I apply engineering to go manage this? Because a lot of it is codified, right? So automation. Anyway, so that's kind of where the complexity is and how we are thinking, including JCI as a partner thinking about taming that complexity as we move forward. >> Suresh, let's talk about that taming the complexity. You guys have both done a great job of articulating the ostensible challenges that are there with cloud, especially multi-cloud environments that you're living in. But Suresh, talk about the partnership with Hitachi Vantara. How is it helping to dial down some of those inherent complexities? >> I mean, I always, you know, I think I've said this to Prem multiple times. I treat my partners as my internal, you know, employees. I look at Prem as my coworker or my peers. So the reason for that is I want Prem to have the same vested interest as a partner in my success or JCI success and vice versa, isn't it? I think that's how we operate and that's how we have been operating. And I think I would like to thank Prem and Hitachi Vantara for that really been an amazing partnership. And as he was saying, we have taken a completely holistic approach to how we want to really be in the market and play in the market to our customers. So if you look at my jacket it talks about OpenBlue platform. This is what JCI is building, that we are building this OpenBlue digital platform. And within that, my team, along with Prem's or Hitachi's, we have built what we call as Polaris. It's a technical platform where our apps can run. And this platform is automated end-to-end from a platform engineering standpoint. We stood up a platform engineering organization, a reliability engineering organization, as well as a support organization where Hitachi played a role. As I said previously, you know, for me to scale I'm not going to really have the talent and the knowledge of every function that I'm looking at. And Hitachi, not only they brought the talent but they also brought what he was talking about, Harc. You know, they have set up a lot and now we can leverage it. And they also came up with some really interesting concepts. I went and met them in India. They came up with this concept called IPL. Okay, what is that? They really challenged all their employees that's working for GCI to come up with innovative ideas to solve problems proactively, which is self-healing. You know, how you do that? So I think partners, you know, if they become really vested in your interests, they can do wonders for you. And I think in this case Hitachi is really working very well for us and in many aspects. And I'm leveraging them... You started with support, now I'm leveraging them in the automation, the platform engineering, as well as in the reliability engineering and then in even in the engineering spaces. And that like, they are my end-to-end partner right now? >> So you're really taking that holistic approach that you talked about and it sounds like it's a very collaborative two-way street partnership. Prem, I want to go back to, Suresh mentioned Harc. Talk a little bit about what Harc is and then how partners fit into Hitachi's Harc strategy. >> Great, so let me spend like a few seconds on what Harc is. Lisa, again, I know we've been using the term. Harc stands for Hitachi application reliability sectors. Now the reason we thought about Harc was, like I said in the beginning of this segment, there is an illusion from an architecture standpoint to be more modern, microservices, server-less, reactive architecture, so on and so forth. There is an illusion in your development methodology from Waterfall to agile, to DevOps to lean, agile to path program, whatever, right? Extreme program, so on and so forth. There is an evolution in the space of infrastructure from a point where you were buying these huge humongous servers and putting it in your data center to a point where people don't even see servers anymore, right? You buy it, by a click of a button you don't know the size of it. All you know is a, it's (indistinct) whatever that name means. Let's go provision it on the fly, get go, get your work done, right? When all of this is advanced when you think about operations people have been solving the problem the way they've been solving it 20 years back, right? That's the issue. And Harc was conceived exactly to fix that particular problem, to think about a modern way of operating a modern workload, right? That's exactly what Harc. So it brings together finest engineering talent. So the teams are trained in specific ways of working. We've invested and implemented some of the IP, we work with the best of the breed partner ecosystem, and I'll talk about that in a minute. And we've got these facilities in Dallas and I am talking from my office in Dallas, which is a Harc facility in the US from where we deliver for our customers. And then back in Hyderabad, we've got one more that we opened and these are facilities from where we deliver Harc services for our customers as well, right? And then we are expanding it in Japan and Portugal as we move into 23. That's kind of the plan that we are thinking through. However, that's what Harc is, Lisa, right? That's our solution to this cloud complexity problem. Right? >> Got it, and it sounds like it's going quite global, which is fantastic. So Suresh, I want to have you expand a bit on the partnership, the partner ecosystem and the role that it plays. You talked about it a little bit but what role does the partner ecosystem play in really helping JCI to dial down some of those challenges and the inherent complexities that we talked about? >> Yeah, sure. I think partners play a major role and JCI is very, very good at it. I mean, I've joined JCI 18 months ago, JCI leverages partners pretty extensively. As I said, I leverage Hitachi for my, you know, A group and the (indistinct) space and the cloud operations space, and they're my primary partner. But at the same time, we leverage many other partners. Well, you know, Accenture, SCL, and even on the tooling side we use Datadog and (indistinct). All these guys are major partners of our because the way we like to pick partners is based on our vision and where we want to go. And pick the right partner who's going to really, you know make you successful by investing their resources in you. And what I mean by that is when you have a partner, partner knows exactly what kind of skillset is needed for this customer, for them to really be successful. As I said earlier, we cannot really get all the skillset that we need, we rely on the partners and partners bring the the right skillset, they can scale. I can tell Prem tomorrow, "Hey, I need two parts by next week", and I guarantee it he's going to bring two parts to me. So they let you scale, they let you move fast. And I'm a big believer, in today's day and age, to get things done fast and be more agile. I'm not worried about failure, but for me moving fast is very, very important. And partners really do a very good job bringing that. But I think then they also really make you think, isn't it? Because one thing I like about partners they make you innovate whether they know it or not but they do because, you know, they will come and ask you questions about, "Hey, tell me why you are doing this. Can I review your architecture?" You know, and then they will try to really say I don't think this is going to work. Because they work with so many different clients, not JCI, they bring all that expertise and that's what I look from them, you know, just not, you know, do a T&M job for me. I ask you to do this go... They just bring more than that. That's how I pick my partners. And that's how, you know, Hitachi's Vantara is definitely one of a good partner from that sense because they bring a lot more innovation to the table and I appreciate about that. >> It sounds like, it sounds like a flywheel of innovation. >> Yeah. >> I love that. Last question for both of you, which we're almost out of time here, Prem, I want to go back to you. So I'm a partner, I'm planning on redefining CloudOps at my company. What are the two things you want me to remember from Hitachi Vantara's perspective? >> So before I get to that question, Lisa, the partners that we work with are slightly different from from the partners that, again, there are some similar partners. There are some different partners, right? For example, we pick and choose especially in the Harc space, we pick and choose partners that are more future focused, right? We don't care if they are huge companies or small companies. We go after companies that are future focused that are really, really nimble and can change for our customers need because it's not our need, right? When I pick partners for Harc my ultimate endeavor is to ensure, in this case because we've got (indistinct) GCI on, we are able to operate (indistinct) with the level of satisfaction above and beyond that they're expecting from us. And whatever I don't have I need to get from my partners so that I bring this solution to Suresh. As opposed to bringing a whole lot of people and making them stand in front of Suresh. So that's how I think about partners. What do I want them to do from, and we've always done this so we do workshops with our partners. We just don't go by tools. When we say we are partnering with X, Y, Z, we do workshops with them and we say, this is how we are thinking. Either you build it in your roadmap that helps us leverage you, continue to leverage you. And we do have minimal investments where we fix gaps. We're building some utilities for us to deliver the best service to our customers. And our intention is not to build a product to compete with our partner. Our intention is to just fill the wide space until they go build it into their product suite that we can then leverage it for our customers. So always think about end customers and how can we make it easy for them? Because for all the tool vendors out there seeing this and wanting to partner with Hitachi the biggest thing is tools sprawl, especially on the cloud is very real. For every problem on the cloud. I have a billion tools that are being thrown at me as Suresh if I'm putting my installation and it's not easy at all. It's so confusing. >> Yeah. >> So that's what we want. We want people to simplify that landscape for our end customers, and we are looking at partners that are thinking through the simplification not just making money. >> That makes perfect sense. There really is a very strong symbiosis it sounds like, in the partner ecosystem. And there's a lot of enablement that goes on back and forth it sounds like as well, which is really, to your point it's all about the end customers and what they're expecting. Suresh, last question for you is which is the same one, if I'm a partner what are the things that you want me to consider as I'm planning to redefine CloudOps at my company? >> I'll keep it simple. In my view, I mean, we've touched upon it in multiple facets in this interview about that, the three things. First and foremost, reliability. You know, in today's day and age my products has to be reliable, available and, you know, make sure that the customer's happy with what they're really dealing with, number one. Number two, my product has to be secure. Security is super, super important, okay? And number three, I need to really make sure my customers are getting the value so I keep my cost low. So these three is what I would focus and what I expect from my partners. >> Great advice, guys. Thank you so much for talking through this with me and really showing the audience how strong the partnership is between Hitachi Vantara and JCI. What you're doing together, we'll have to talk to you again to see where things go but we really appreciate your insights and your perspectives. Thank you. >> Thank you, Lisa. >> Thanks Lisa, thanks for having us. >> My pleasure. For my guests, I'm Lisa Martin. Thank you so much for watching. (soothing music)

Hello and welcome back to Supercloud 2 where we examine the intersection of cloud and data in the 2020s. My name is Dave Vellante. Our Supercloud panel, our power panel is back. Maribel Lopez is the founder and principal analyst at Lopez Research. Sanjeev Mohan is former Gartner analyst and principal at Sanjeev Mohan. And Keith Townsend is the CTO advisor. Folks, welcome back and thanks for your participation today. Good to see you. >> Okay, great. >> Great to see you. >> Thanks. Let me start, Maribel, with you. Bob Muglia, we had a conversation as part of Supercloud the other day. And he said, "Dave, I like the work, you got to simplify this a little bit." So he said, quote, "A Supercloud is a platform." He said, "Think of it as a platform that provides programmatically consistent services hosted on heterogeneous cloud providers." And then Nelu Mihai said, "Well, wait a minute. This is just going to create more stove pipes. We need more standards in an architecture," which is kind of what Berkeley Sky Computing initiative is all about. So there's a sort of a debate going on. Is supercloud an architecture, a platform? Or maybe it's just another buzzword. Maribel, do you have a thought on this? >> Well, the easy answer would be to say it's just a buzzword. And then we could just kill the conversation and be done with it. But I think the term, it's more than that, right? The term actually isn't new. You can go back to at least 2016 and find references to supercloud in Cornell University or assist in other documents. So, having said this, I think we've been talking about Supercloud for a while, so I assume it's more than just a fancy buzzword. But I think it really speaks to that undeniable trend of moving towards an abstraction layer to deal with the chaos of what we consider managing multiple public and private clouds today, right? So one definition of the technology platform speaks to a set of services that allows companies to build and run that technology smoothly without worrying about the underlying infrastructure, which really gets back to something that Bob said. And some of the question is where that lives. And you could call that an abstraction layer. You could call it cross-cloud services, hybrid cloud management. So I see momentum there, like legitimate momentum with enterprise IT buyers that are trying to deal with the fact that they have multiple clouds now. So where I think we're moving is trying to define what are the specific attributes and frameworks of that that would make it so that it could be consistent across clouds. What is that layer? And maybe that's what the supercloud is. But one of the things I struggle with with supercloud is. What are we really trying to do here? Are we trying to create differentiated services in the supercloud layer? Is a supercloud just another variant of what AWS, GCP, or others do? You spoken to Walmart about its cloud native platform, and that's an example of somebody deciding to do it themselves because they need to deal with this today and not wait for some big standards thing to happen. So whatever it is, I do think it's something. I think we're trying to maybe create an architecture out of it would be a better way of saying it so that it does get to those set of principles, but it also needs to be edge aware. I think whenever we talk about supercloud, we're always talking about like the big centralized cloud. And I think we need to think about all the distributed clouds that we're looking at in edge as well. So that might be one of the ways that supercloud evolves. >> So thank you, Maribel. Keith, Brian Gracely, Gracely's law, things kind of repeat themselves. We've seen it all before. And so what Muglia brought to the forefront is this idea of a platform where the platform provider is really responsible for the architecture. Of course, the drawback is then you get a a bunch of stove pipes architectures. But practically speaking, that's kind of the way the industry has always evolved, right? >> So if we look at this from the practitioner's perspective and we talk about platforms, traditionally vendors have provided the platforms for us, whether it's distribution of lineage managed by or provided by Red Hat, Windows, servers, .NET, databases, Oracle. We think of those as platforms, things that are fundamental we can build on top. Supercloud isn't today that. It is a framework or idea, kind of a visionary goal to get to a point that we can have a platform or a framework. But what we're seeing repeated throughout the industry in customers, whether it's the Walmarts that's kind of supersized the idea of supercloud, or if it's regular end user organizations that are coming out with platform groups, groups who normalize cloud native infrastructure, AWS multi-cloud, VMware resources to look like one thing internally to their developers. We're seeing this trend that there's a desire for a platform that provides the capabilities of a supercloud. >> Thank you for that. Sanjeev, we often use Snowflake as a supercloud example, and now would presumably would be a platform with an architecture that's determined by the vendor. Maybe Databricks is pushing for a more open architecture, maybe more of that nirvana that we were talking about before to solve for supercloud. But regardless, the practitioner discussions show. At least currently, there's not a lot of cross-cloud data sharing. I think it could be a killer use case, egress charges or a barrier. But how do you see it? Will that change? Will we hide that underlying complexity and start sharing data across cloud? Is that something that you think Snowflake or others will be able to achieve? >> So I think we are already starting to see some of that happen. Snowflake is definitely one example that gets cited a lot. But even we don't talk about MongoDB in this like, but you could have a MongoDB cluster, for instance, with nodes sitting in different cloud providers. So there are companies that are starting to do it. The advantage that these companies have, let's take Snowflake as an example, it's a centralized proprietary platform. And they are building the capabilities that are needed for supercloud. So they're building things like you can push down your data transformations. They have the entire security and privacy suite. Data ops, they're adding those capabilities. And if I'm not mistaken, it'll be very soon, we will see them offer data observability. So it's all works great as long as you are in one platform. And if you want resilience, then Snowflake, Supercloud, great example. But if your primary goal is to choose the most cost-effective service irrespective of which cloud it sits in, then things start falling sideways. For example, I may be a very big Snowflake user. And I like Snowflake's resilience. I can move from one cloud to another cloud. Snowflake does it for me. But what if I want to train a very large model? Maybe Databricks is a better platform for that. So how do I do move my workload from one platform to another platform? That tooling does not exist. So we need server hybrid, cross-cloud, data ops platform. Walmart has done a great job, but they built it by themselves. Not every company is Walmart. Like Maribel and Keith said, we need standards, we need reference architectures, we need some sort of a cost control. I was just reading recently, Accenture has been public about their AWS bill. Every time they get the bill is tens of millions of lines, tens of millions 'cause there are over thousand teams using AWS. If we have not been able to corral a usage of a single cloud, now we're talking about supercloud, we've got multiple clouds, and hybrid, on-prem, and edge. So till we've got some cross-platform tooling in place, I think this will still take quite some time for it to take shape. >> It's interesting. Maribel, Walmart would tell you that their on-prem infrastructure is cheaper to run than the stuff in the cloud. but at the same time, they want the flexibility and the resiliency of their three-legged stool model. So the point as Sanjeev was making about hybrid. It's an interesting balance, isn't it, between getting your lowest cost and at the same time having best of breed and scale? >> It's basically what you're trying to optimize for, as you said, right? And by the way, to the earlier point, not everybody is at Walmart's scale, so it's not actually cheaper for everybody to have the purchasing power to make the cloud cheaper to have it on-prem. But I think what you see almost every company, large or small, moving towards is this concept of like, where do I find the agility? And is the agility in building the infrastructure for me? And typically, the thing that gives you outside advantage as an organization is not how you constructed your cloud computing infrastructure. It might be how you structured your data analytics as an example, which cloud is related to that. But how do you marry those two things? And getting back to sort of Sanjeev's point. We're in a real struggle now where one hand we want to have best of breed services and on the other hand we want it to be really easy to manage, secure, do data governance. And those two things are really at odds with each other right now. So if you want all the knobs and switches of a service like geospatial analytics and big query, you're going to have to use Google tools, right? Whereas if you want visibility across all the clouds for your application of state and understand the security and governance of that, you're kind of looking for something that's more cross-cloud tooling at that point. But whenever you talk to somebody about cross-cloud tooling, they look at you like that's not really possible. So it's a very interesting time in the market. Now, we're kind of layering this concept of supercloud on it. And some people think supercloud's about basically multi-cloud tooling, and some people think it's about a whole new architectural stack. So we're just not there yet. But it's not all about cost. I mean, cloud has not been about cost for a very, very long time. Cloud has been about how do you really make the most of your data. And this gets back to cross-cloud services like Snowflake. Why did they even exist? They existed because we had data everywhere, but we need to treat data as a unified object so that we can analyze it and get insight from it. And so that's where some of the benefit of these cross-cloud services are moving today. Still a long way to go, though, Dave. >> Keith, I reached out to my friends at ETR given the macro headwinds, And you're right, Maribel, cloud hasn't really been about just about cost savings. But I reached out to the ETR, guys, what's your data show in terms of how customers are dealing with the economic headwinds? And they said, by far, their number one strategy to cut cost is consolidating redundant vendors. And a distant second, but still notable was optimizing cloud costs. Maybe using reserve instances, or using more volume buying. Nowhere in there. And I asked them to, "Could you go look and see if you can find it?" Do we see repatriation? And you hear this a lot. You hear people whispering as analysts, "You better look into that repatriation trend." It's pretty big. You can't find it. But some of the Walmarts in the world, maybe even not repatriating, but they maybe have better cost structure on-prem. Keith, what are you seeing from the practitioners that you talk to in terms of how they're dealing with these headwinds? >> Yeah, I just got into a conversation about this just this morning with (indistinct) who is an analyst over at GigaHome. He's reading the same headlines. Repatriation is happening at large scale. I think this is kind of, we have these quiet terms now. We have quiet quitting, we have quiet hiring. I think we have quiet repatriation. Most people haven't done away with their data centers. They're still there. Whether they're completely on-premises data centers, and they own assets, or they're partnerships with QTX, Equinix, et cetera, they have these private cloud resources. What I'm seeing practically is a rebalancing of workloads. Do I really need to pay AWS for this instance of SAP that's on 24 hours a day versus just having it on-prem, moving it back to my data center? I've talked to quite a few customers who were early on to moving their static SAP workloads onto the public cloud, and they simply moved them back. Surprising, I was at VMware Explore. And we can talk about this a little bit later on. But our customers, net new, not a lot that were born in the cloud. And they get to this point where their workloads are static. And they look at something like a Kubernetes, or a OpenShift, or VMware Tanzu. And they ask the question, "Do I need the scalability of cloud?" I might consider being a net new VMware customer to deliver this base capability. So are we seeing repatriation as the number one reason? No, I think internal IT operations are just naturally come to this realization. Hey, I have these resources on premises. The private cloud technologies have moved far along enough that I can just simply move this workload back. I'm not calling it repatriation, I'm calling it rightsizing for the operating model that I have. >> Makes sense. Yeah. >> Go ahead. >> If I missed something, Dave, why we are on this topic of repatriation. I'm actually surprised that we are talking about repatriation as a very big thing. I think repatriation is happening, no doubt, but it's such a small percentage of cloud migration that to me it's a rounding error in my opinion. I think there's a bigger problem. The problem is that people don't know where the cost is. If they knew where the cost was being wasted in the cloud, they could do something about it. But if you don't know, then the easy answer is cloud costs a lot and moving it back to on-premises. I mean, take like Capital One as an example. They got rid of all the data centers. Where are they going to repatriate to? They're all in the cloud at this point. So I think my point is that data observability is one of the places that has seen a lot of traction is because of cost. Data observability, when it first came into existence, it was all about data quality. Then it was all about data pipeline reliability. And now, the number one killer use case is FinOps. >> Maribel, you had a comment? >> Yeah, I'm kind of in violent agreement with both Sanjeev and Keith. So what are we seeing here? So the first thing that we see is that many people wildly overspent in the big public cloud. They had stranded cloud credits, so to speak. The second thing is, some of them still had infrastructure that was useful. So why not use it if you find the right workloads to what Keith was talking about, if they were more static workloads, if it was already there? So there is a balancing that's going on. And then I think fundamentally, from a trend standpoint, these things aren't binary. Everybody, for a while, everything was going to go to the public cloud and then people are like, "Oh, it's kind of expensive." Then they're like, "Oh no, they're going to bring it all on-prem 'cause it's really expensive." And it's like, "Well, that doesn't necessarily get me some of the new features and functionalities I might want for some of my new workloads." So I'm going to put the workloads that have a certain set of characteristics that require cloud in the cloud. And if I have enough capability on-prem and enough IT resources to manage certain things on site, then I'm going to do that there 'cause that's a more cost-effective thing for me to do. It's not binary. That's why we went to hybrid. And then we went to multi just to describe the fact that people added multiple public clouds. And now we're talking about super, right? So I don't look at it as a one-size-fits-all for any of this. >> A a number of practitioners leading up to Supercloud2 have told us that they're solving their cloud complexity by going in monocloud. So they're putting on the blinders. Even though across the organization, there's other groups using other clouds. You're like, "In my group, we use AWS, or my group, we use Azure. And those guys over there, they use Google. We just kind of keep it separate." Are you guys hearing this in your view? Is that risky? Are they missing out on some potential to tap best of breed? What do you guys think about that? >> Everybody thinks they're monocloud. Is anybody really monocloud? It's like a group is monocloud, right? >> Right. >> This genie is out of the bottle. We're not putting the genie back in the bottle. You might think your monocloud and you go like three doors down and figure out the guy or gal is on a fundamentally different cloud, running some analytics workload that you didn't know about. So, to Sanjeev's earlier point, they don't even know where their cloud spend is. So I think the concept of monocloud, how that's actually really realized by practitioners is primary and then secondary sources. So they have a primary cloud that they run most of their stuff on, and that they try to optimize. And we still have forked workloads. Somebody decides, "Okay, this SAP runs really well on this, or these analytics workloads run really well on that cloud." And maybe that's how they parse it. But if you really looked at it, there's very few companies, if you really peaked under the hood and did an analysis that you could find an actual monocloud structure. They just want to pull it back in and make it more manageable. And I respect that. You want to do what you can to try to streamline the complexity of that. >> Yeah, we're- >> Sorry, go ahead, Keith. >> Yeah, we're doing this thing where we review AWS service every day. Just in your inbox, learn about a new AWS service cursory. There's 238 AWS products just on the AWS cloud itself. Some of them are redundant, but you get the idea. So the concept of monocloud, I'm in filing agreement with Maribel on this that, yes, a group might say I want a primary cloud. And that primary cloud may be the AWS. But have you tried the licensed Oracle database on AWS? It is really tempting to license Oracle on Oracle Cloud, Microsoft on Microsoft. And I can't get RDS anywhere but Amazon. So while I'm driven to desire the simplicity, the reality is whether be it M&A, licensing, data sovereignty. I am forced into a multi-cloud management style. But I do agree most people kind of do this one, this primary cloud, secondary cloud. And I guarantee you're going to have a third cloud or a fourth cloud whether you want to or not via shadow IT, latency, technical reasons, et cetera. >> Thank you. Sanjeev, you had a comment? >> Yeah, so I just wanted to mention, as an organization, I'm complete agreement, no organization is monocloud, at least if it's a large organization. Large organizations use all kinds of combinations of cloud providers. But when you talk about a single workload, that's where the program arises. As Keith said, the 238 services in AWS. How in the world am I going to be an expert in AWS, but then say let me bring GCP or Azure into a single workload? And that's where I think we probably will still see monocloud as being predominant because the team has developed its expertise on a particular cloud provider, and they just don't have the time of the day to go learn yet another stack. However, there are some interesting things that are happening. For example, if you look at a multi-cloud example where Oracle and Microsoft Azure have that interconnect, so that's a beautiful thing that they've done because now in the newest iteration, it's literally a few clicks. And then behind the scene, your .NET application and your Oracle database in OCI will be configured, the identities in active directory are federated. And you can just start using a database in one cloud, which is OCI, and an application, your .NET in Azure. So till we see this kind of a solution coming out of the providers, I think it's is unrealistic to expect the end users to be able to figure out multiple clouds. >> Well, I have to share with you. I can't remember if he said this on camera or if it was off camera so I'll hold off. I won't tell you who it is, but this individual was sort of complaining a little bit saying, "With AWS, I can take their best AI tools like SageMaker and I can run them on my Snowflake." He said, "I can't do that in Google. Google forces me to go to BigQuery if I want their excellent AI tools." So he was sort of pushing, kind of tweaking a little bit. Some of the vendor talked that, "Oh yeah, we're so customer-focused." Not to pick on Google, but I mean everybody will say that. And then you say, "If you're so customer-focused, why wouldn't you do a ABC?" So it's going to be interesting to see who leads that integration and how broadly it's applied. But I digress. Keith, at our first supercloud event, that was on August 9th. And it was only a few months after Broadcom announced the VMware acquisition. A lot of people, myself included said, "All right, cuts are coming." Generally, Tanzu is probably going to be under the radar, but it's Supercloud 22 and presumably VMware Explore, the company really... Well, certainly the US touted its Tanzu capabilities. I wasn't at VMware Explore Europe, but I bet you heard similar things. Hawk Tan has been blogging and very vocal about cross-cloud services and multi-cloud, which doesn't happen without Tanzu. So what did you hear, Keith, in Europe? What's your latest thinking on VMware's prospects in cross-cloud services/supercloud? >> So I think our friend and Cube, along host still be even more offended at this statement than he was when I sat in the Cube. This was maybe five years ago. There's no company better suited to help industries or companies, cross-cloud chasm than VMware. That's not a compliment. That's a reality of the industry. This is a very difficult, almost intractable problem. What I heard that VMware Europe were customers serious about this problem, even more so than the US data sovereignty is a real problem in the EU. Try being a company in Switzerland and having the Swiss data solvency issues. And there's no local cloud presence there large enough to accommodate your data needs. They had very serious questions about this. I talked to open source project leaders. Open source project leaders were asking me, why should I use the public cloud to host Kubernetes-based workloads, my projects that are building around Kubernetes, and the CNCF infrastructure? Why should I use AWS, Google, or even Azure to host these projects when that's undifferentiated? I know how to run Kubernetes, so why not run it on-premises? I don't want to deal with the hardware problems. So again, really great questions. And then there was always the specter of the problem, I think, we all had with the acquisition of VMware by Broadcom potentially. 4.5 billion in increased profitability in three years is a unbelievable amount of money when you look at the size of the problem. So a lot of the conversation in Europe was about industry at large. How do we do what regulators are asking us to do in a practical way from a true technology sense? Is VMware cross-cloud great? >> Yeah. So, VMware, obviously, to your point. OpenStack is another way of it. Actually, OpenStack, uptake is still alive and well, especially in those regions where there may not be a public cloud, or there's public policy dictating that. Walmart's using OpenStack. As you know in IT, some things never die. Question for Sanjeev. And it relates to this new breed of data apps. And Bob Muglia and Tristan Handy from DBT Labs who are participating in this program really got us thinking about this. You got data that resides in different clouds, it maybe even on-prem. And the machine polls data from different systems. No humans involved, e-commerce, ERP, et cetera. It creates a plan, outcomes. No human involvement. Today, you're on a CRM system, you're inputting, you're doing forms, you're, you're automating processes. We're talking about a new breed of apps. What are your thoughts on this? Is it real? Is it just way off in the distance? How does machine intelligence fit in? And how does supercloud fit? >> So great point. In fact, the data apps that you're talking about, I call them data products. Data products first came into limelight in the last couple of years when Jamal Duggan started talking about data mesh. I am taking data products out of the data mesh concept because data mesh, whether data mesh happens or not is analogous to data products. Data products, basically, are taking a product management view of bringing data from different sources based on what the consumer needs. We were talking earlier today about maybe it's my vacation rentals, or it may be a retail data product, it may be an investment data product. So it's a pre-packaged extraction of data from different sources. But now I have a product that has a whole lifecycle. I can version it. I have new features that get added. And it's a very business data consumer centric. It uses machine learning. For instance, I may be able to tell whether this data product has stale data. Who is using that data? Based on the usage of the data, I may have a new data products that get allocated. I may even have the ability to take existing data products, mash them up into something that I need. So if I'm going to have that kind of power to create a data product, then having a common substrate underneath, it can be very useful. And that could be supercloud where I am making API calls. I don't care where the ERP, the CRM, the survey data, the pricing engine where they sit. For me, there's a logical abstraction. And then I'm building my data product on top of that. So I see a new breed of data products coming out. To answer your question, how early we are or is this even possible? My prediction is that in 2023, we will start seeing more of data products. And then it'll take maybe two to three years for data products to become mainstream. But it's starting this year. >> A subprime mortgages were a data product, definitely were humans involved. All right, let's talk about some of the supercloud, multi-cloud players and what their future looks like. You can kind of pick your favorites. VMware, Snowflake, Databricks, Red Hat, Cisco, Dell, HP, Hashi, IBM, CloudFlare. There's many others. cohesive rubric. Keith, I wanted to start with CloudFlare because they actually use the term supercloud. and just simplifying what they said. They look at it as taking serverless to the max. You write your code and then you can deploy it in seconds worldwide, of course, across the CloudFlare infrastructure. You don't have to spin up containers, you don't go to provision instances. CloudFlare worries about all that infrastructure. What are your thoughts on CloudFlare this approach and their chances to disrupt the current cloud landscape? >> As Larry Ellison said famously once before, the network is the computer, right? I thought that was Scott McNeley. >> It wasn't Scott McNeley. I knew it was on Oracle Align. >> Oracle owns that now, owns that line. >> By purpose or acquisition. >> They should have just called it cloud. >> Yeah, they should have just called it cloud. >> Easier. >> Get ahead. >> But if you think about the CloudFlare capability, CloudFlare in its own right is becoming a decent sized cloud provider. If you have compute out at the edge, when we talk about edge in the sense of CloudFlare and points of presence, literally across the globe, you have all of this excess computer, what do you do with it? First offering, let's disrupt data in the cloud. We can't start the conversation talking about data. When they say we're going to give you object-oriented or object storage in the cloud without egress charges, that's disruptive. That we can start to think about supercloud capability of having compute EC2 run in AWS, pushing and pulling data from CloudFlare. And now, I've disrupted this roach motel data structure, and that I'm freely giving away bandwidth, basically. Well, the next layer is not that much more difficult. And I think part of CloudFlare's serverless approach or supercloud approaches so that they don't have to commit to a certain type of compute. It is advantageous. It is a feature for me to be able to go to EC2 and pick a memory heavy model, or a compute heavy model, or a network heavy model, CloudFlare is taken away those knobs. and I'm just giving code and allowing that to run. CloudFlare has a massive network. If I can put the code closest using the CloudFlare workers, if I can put that code closest to where the data is at or residing, super compelling observation. The question is, does it scale? I don't get the 238 services. While Server List is great, I have to know what I'm going to build. I don't have a Cognito, or RDS, or all these other services that make AWS, GCP, and Azure appealing from a builder's perspective. So it is a very interesting nascent start. It's great because now they can hide compute. If they don't have the capacity, they can outsource that maybe at a cost to one of the other cloud providers, but kind of hiding the compute behind the surplus architecture is a really unique approach. >> Yeah. And they're dipping their toe in the water. And they've announced an object store and a database platform and more to come. We got to wrap. So I wonder, Sanjeev and Maribel, if you could maybe pick some of your favorites from a competitive standpoint. Sanjeev, I felt like just watching Snowflake, I said, okay, in my opinion, they had the right strategy, which was to run on all the clouds, and then try to create that abstraction layer and data sharing across clouds. Even though, let's face it, most of it might be happening across regions if it's happening, but certainly outside of an individual account. But I felt like just observing them that anybody who's traditional on-prem player moving into the clouds or anybody who's a cloud native, it just makes total sense to write to the various clouds. And to the extent that you can simplify that for users, it seems to be a logical strategy. Maybe as I said before, what multi-cloud should have been. But are there companies that you're watching that you think are ahead in the game , or ones that you think are a good model for the future? >> Yes, Snowflake, definitely. In fact, one of the things we have not touched upon very much, and Keith mentioned a little bit, was data sovereignty. Data residency rules can require that certain data should be written into certain region of a certain cloud. And if my cloud provider can abstract that or my database provider, then that's perfect for me. So right now, I see Snowflake is way ahead of this pack. I would not put MongoDB too far behind. They don't really talk about this thing. They are in a different space, but now they have a lakehouse, and they've got all of these other SQL access and new capabilities that they're announcing. So I think they would be quite good with that. Oracle is always a dark forest. Oracle seems to have revived its Cloud Mojo to some extent. And it's doing some interesting stuff. Databricks is the other one. I have not seen Databricks. They've been very focused on lakehouse, unity, data catalog, and some of those pieces. But they would be the obvious challenger. And if they come into this space of supercloud, then they may bring some open source technologies that others can rely on like Delta Lake as a table format. >> Yeah. One of these infrastructure players, Dell, HPE, Cisco, even IBM. I mean, I would be making my infrastructure as programmable and cloud friendly as possible. That seems like table stakes. But Maribel, any companies that stand out to you that we should be paying attention to? >> Well, we already mentioned a bunch of them, so maybe I'll go a slightly different route. I'm watching two companies pretty closely to see what kind of traction they get in their established companies. One we already talked about, which is VMware. And the thing that's interesting about VMware is they're everywhere. And they also have the benefit of having a foot in both camps. If you want to do it the old way, the way you've always done it with VMware, they got all that going on. If you want to try to do a more cross-cloud, multi-cloud native style thing, they're really trying to build tools for that. So I think they have really good access to buyers. And that's one of the reasons why I'm interested in them to see how they progress. The other thing, I think, could be a sleeping horse oddly enough is Google Cloud. They've spent a lot of work and time on Anthos. They really need to create a certain set of differentiators. Well, it's not necessarily in their best interest to be the best multi-cloud player. If they decide that they want to differentiate on a different layer of the stack, let's say they want to be like the person that is really transformative, they talk about transformation cloud with analytics workloads, then maybe they do spend a good deal of time trying to help people abstract all of the other underlying infrastructure and make sure that they get the sexiest, most meaningful workloads into their cloud. So those are two people that you might not have expected me to go with, but I think it's interesting to see not just on the things that might be considered, either startups or more established independent companies, but how some of the traditional providers are trying to reinvent themselves as well. >> I'm glad you brought that up because if you think about what Google's done with Kubernetes. I mean, would Google even be relevant in the cloud without Kubernetes? I could argue both sides of that. But it was quite a gift to the industry. And there's a motivation there to do something unique and different from maybe the other cloud providers. And I'd throw in Red Hat as well. They're obviously a key player and Kubernetes. And Hashi Corp seems to be becoming the standard for application deployment, and terraform, or cross-clouds, and there are many, many others. I know we're leaving lots out, but we're out of time. Folks, I got to thank you so much for your insights and your participation in Supercloud2. Really appreciate it. >> Thank you. >> Thank you. >> Thank you. >> This is Dave Vellante for John Furrier and the entire Cube community. Keep it right there for more content from Supercloud2.

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

>> Male: TheCUBE presents, UIPATH, Forward 5 brought to you by, UIPATH. >> Okay the party has started here at forward 5 UIPATH big customer event if you're watching the cube. We're wrapping up day one with the co-CE0 segment. Daniel Dines is here. He's the founder and Co-CEO of UIPATH and Rob Enslin, is co-CEO. Gents, great to see you. Thanks for spending some time with us. I know you're super busy. >> Thanks Dave. >> So I've been looking forward to this. Daniel you know I've followed the company for a long time. The really interesting path you took, to get to where you are today. How did you guys meet? And why did you decide to hire Rob? >> Male: (laughs) >> Rob: Well let me start. I uh, I was looking for a partner. Actually, in our work to your stand here, we are talking about how, how you feel in this job. You feel so alone. Because you are the center of all pressure points. And having a partner, having someone that has your back, it's kind of awesome. So I was looking for a partner. And our current friend, Carl Escenbach, he introduced us to each other, and we instantly clicked. And this is the type of job where it's uh either work well or it doesn't. It cannot be anything in the middle. >> Right, okay with Carl, we know Carl well. Awesome operator. Knows the business super well. So Rob, what attracted you to UIPATH? You had a great situation at google. You guys were growing like crazy. Why did you decide to come here? What did you see that attracted you? >> Yeah you know when I, when I went to google, I went to google because I really believed that data and AI was necessary for companies. And business is to be competitive in the future. And we did some great stuff at google cloud in the 3 years. But I knew UIPATH from a couple of years ago when they were mainly a RPA space. And I just felt that there was a place in time when automation was going expand. And as I sat down with Carl a couple of times, spoke to carl. And then I sat down with Daniel, I knew that there was something special with UIPATH, that could be a generational opportunity. Not any for myself but for the company in the future. And then I, you know I got to know Daniel. And at this stage of my career I was like, I'm pretty fussy about what I want to do and what I want and where I want to go. First of all, I want to go to a company that had great product, had a great culture, and I wanted to work with somebody that we could shake the future together and you know, Daniel and I just hit it off from the very first time we met. He got to meet my family, my dogs and we did the whole, we did the whole courting thing before we actually decided this was going to be a good thing for both of us. >> Dave: That's good. >> Rob: Yeah. >> Dave: You got to meet the family. That's very good. >> We just had, John Furrier and I just had, Mohit Aron and Sanjay Poonen into out studio. Cause Mohit, you know, formal google. Long time. And they decided to kind of split duties. Mohit's going into product, he didn't keep his CEO title. He walked. How are you guys splitting you time? What are each of you going to, responsible for? >> Daniel: Well its, its kind of similar. On a day by day operation I, I rely heavily on Rob. We do it together. Strategic decisions about the company's destiny. I'm doing mostly the product these days. Which is a big relief for me. And I think we also split a bit of customers visit. Which is great. I still enjoy meeting customers. I need, customers are food for my cause. >> Dave: (laughs) yeah and your awesome product visionary. You've been there since day one. Now Rob, you said in the key note today that you've seen around about a hundred customers. You've transverse the world. What did you learn from them that informed you? That gave you confidence that the the move to the internet platform, even though you had already started that. >> Male: Yeah. >> But you're really doubling down on that >> Rob: You know when I... >> from a stand point. >> Rob: You know Dave, when you think about it, like I was, I was so impressed that Daniel had the vision to create a platform 3 years ago. >> Dave: Yeah. >> All right. And as we went around the world. As I went around the world, and it was one of the very first things I've seen. I've got to understand how customers see UIPATH, from their advantage point. What are they looking for from us? Why is this company, why doe customers like this company so much? And as I went around the world. I went to Asia a couple, I went to Asia, Australia, Singapore, Japan. I was in Europe twice. We did the trip together. We went to visit customers. And it was very much the same thing. Helps us expand automation faster. And we are so surprise, at the break of your platform. We never knew that. And so it kind of just had, for me, it was conviction. It's like, this walls is the right decision you've made. There's so much opportunity there. And that's, you know that's kind of what I've learned through the last four five months. >> Dave: Now as you know Daniel, I've written a lot about your company. One of the things I've said is that, that start ups, if I can call you that back pre-IPO, typically don't have as much international exposure as UIPATH had. I mean you sort of, you sort of started as an international company and became more US centric. You said, in the, in the key note today, you're talking to Ray Wong about people may don't understand that challenges of FX. Point being, when you convert international dollars into US dollars there are less of them cause the dollars stronger. But still, I've always felt like that international footprint is an advantage. Rob you came from SAP, you know, again European based company. I don't, (stutters), do you regret that? Now? I mean I know it's technical, I'm sure you don't, but talk about that sort of international exposure? Why that's a long term benefit. >> Well, you, first of all, you expand faster. I think we expanded faster than our competition because our global footprint was larger. And we had the courage. Go in Japan, for instance. Everybody told me, it's impossible to make for such a small starter. It's impossible to make a business in Japan. But we didn't believe it. We're just crazy and we went there, and be built a very sizable business in Japan. Fifty-five percent of our revenue, even today, it's outside U.S. Now of course that has a down side. When uh, When the local currencies, you know, are losing the value compared to the dollars, we're impacted. As we go to... to investors, until now, so we are seeing like a (indistinct) in terms of ARI. It's huge. Only because (indistinct) and losing the business in Russia. But it still, it's the strength of our company. Things will come back. And then, you know, the growth engine will re-accelerate again. >> Dave: Yeah but when the dollars weakens that'll be in your favor. Rob I want to pick up on something you said today in your keynote. You went back and started, you know the cycles of ERP and you know, internet, et cetera. I kind of have a love hate with ERP. I have to be honest. >> Male: (laughing) >> But it, but but (chuckles) but if I go back to that. Late eighties nineties, you wouldn't have be able to pick SAP as the winner. And then SAP emerged. You know, very clearly. But the more interesting thing, is that the customers who are implementing ERP well. The practitioners did better than their peers, and dominated their industries. And their stocks went up. Their evaluations went up. Different worlds obviously but, do you see the same thing happening with RPA and automation? What gives you confidence that that's the case? >> I absolutely do see the same thing happening with automation and RPA being a part of, in being a part of that. The reason, the reason I believe that is speed is so critical. (stutters) And if you think about how hard it is for a CIO or a c level executive to consume the technology coming at them, plus all the changes in the world being thrown at them. It's compiling and compiling and compiling. We have an incredible solution, that can help companies. And there comes certain times, the love outcomes to the business. Like no one else gets. And when I see that, I view that as just like the beginning of what's going to happen in the future so, in many ways, and I've said this to many of my friends, it feels like 1992, 1993 to me. And it's interesting because no one really understood then why SAP would be great in 1992 and 93. And they got a couple of things right. They got the eco system right. Their new partners were important. And the knew they needed to drive business outcome for companies, in which they did. And so I feel like we are in a very similar place. Very different technology obviously. And the speed of change now is so dramatic, compared to what it was. And there's very few technology that can provide that level of speed and accomodation to their customers. >> All right, let's talk about priorities. You guys got a lot of work to do and you've, you've laid it out to the financial community. You've got to have profitable growth, because of FX, it part, you've lowered your forecast. But I think there's some conservative in their as well. Um, but you got to do that balance. You've given some guidance on gross margins. Cloud maybe brings that down a little bit. RnD I saw wide range. Thirteen to seventeen percent. I hope you keep spending on RnD. Big fan of that. You know stock buybacks and, RnD if in your position are going to be better. And the product priorities, continue to build that out. But question, let's start with the product. So you've got an on-prem stack and you've got a cloud stack that's emerging, how do you balance those out? How do you do the integration? You've done a great job with the integration. Does it, are you concerned about your ability to continue to work at that speed with two code bases? I wonder if you could address that? >> Daniel: We've become a cloud first company. We deliver all of our products first in the cloud. We've deliver on the two week (indistinct) in the cloud. So that helps us integrate quite fast. I think we made a very good business decision to build our cloud team in Seattle. In Bellevue to be specific. And we have access to great talent that knows how to build serious cloud service. Which is hard to find dollar. And uh, so, and also we, we have, we benef- one of our only benefits was, we have the really good architecture. We have an architecture that work easily on-prem and on the cloud. And even today, our work flow foundation, our local designers, were easy to modernize. So right now we are launching studio weapon. But behind the scene, it's the same workflow engine. Our customers don't have to rewrite anything. It just works. And it does the same to take our own brand product and brand it in the multicloud. So, it's, there is no friction at all. Actually cloud is just helping us accelerate. But we benefit then again of a really solid architectural foundation. >> Daniel: Architecture matters. We've seen that in this industry. We got the B52s rocking out in the background, I love it, but I've got so many questions for you guys. I want to talk about the go to market. Because Rob, it's obviously a strength of yours. You've come in. You've communicated to the street, that you're reshaping the sales floors. Are they lowering the ratios of sales? People, the customers at the high end, mid range as well, using digital. I mean the numbers are one to ten now. At the top. One to maybe fifty at the mid range. Where are you in terms of that journey? You've got to find people, you got to train them, how do you get the productivity out of those guys? Take us through your thinking there? >> Rob: Yeah firstly, I think we have enough resources. Having resources is not an issue. Um, we have an incredible vehicle to acquire customers inside the company. Our digital sales motion, it's probably the best I've seen. And so we have the ability to acquire customers really fast. And we get the first workload in really fast. The challenge is we need to, we need to be able to drive a (indistinct) model and we graduate customs when we acquire them into the direct sales floors. And then direct sales floors, we're not going to go one to thirty, we're talking one to ten for the direct sales floor. And even the high up in the pyramid, we want to have an even denser model than that. And the whole purpose is to drive the time to consumption much quicker, much faster. So we know exactly if we acquire a customer, will they spend? Do they have a (indistinct) spend? On what level do they have a (indistinct) spend? And therefore when we capture them, we can immediately surround them, and put the right resources so we can grow faster. We think this will have a significant impact on the organization. We'll start to implement certain pieces in the next quarter. Um, things like packaging solutions. Putting them in, enabling the sales organization. And buy the beginning of next year, we'll be ready to actually go full board, globally. We already put some pieces in place when I joined. Chris Weber, my chief business officer, did a great job doing some of those pieces. So we're on the journey already. >> Dave: Yeah and even before you guys were public and you weren't publishing your NRR numbers. Our ETR survey partner, we, we always thought you had very low churn. And I think you broke out just yesterday. The, the NRR for overseas vs U.S, U.S I think was 140 plus percent. >> Male: Yeah >> Very very strong. A little, a little less overseas but the churn is still very low. >> Male: Yep. >> Okay so that's super positive. Customer affinity, I was wanted to code these events. I listen to the key notes very carefully, and then interview customers on the cube, and I try to identify, is there alignment there? And I see very strong alignment, I have to say, and strong customer affinity. So that's in your favor. I have, Daniel, I got another question for you on product. What is Symantec automation? What the heck is that? Can you explain that? I don't understand >> Dave, have you seen the demo in my (indistinct)? >> Dave: You know, I had to leave and do interviews, so I, uh, I missed it. >> I think, I think that demo answer complete your question. So in the s-, you know there saying that great, you can not distinguish great technology by magic. I think technology should be simple. And we, we show today, one of the simplest demo that you can imagine. But it's so, such a complex technology behind the scene, that you also can not imagine. So what was demo? We show how one business user, without any technical skills, can build any type of document. Can be a passport, can be an invoice, can be a legal (indistinct), and just go, "I want to copy data from here, and I want to paste data there". Can be a spreadsheet, can be another obligation, and like a human user, without understanding, without having prior knowledge about data, document layout, about screens, screens layouts, nothing, we analyze real time. Document. We discover, we discover the meaning of the information. We analyze the screen. We understand the screen but we understand the meaning of the screen. And we understand how the information in one side relate to the other side. And we just connects the dots and we copy the information and we paste it. A job that you'll do as a human user, maybe three minutes, is done in ten seconds. This is powerful. >> Yeah that is powerful. Thank you for that. I mean, and you take the date, whether it's transaction data or unstructured data and and and bring meaning out of it. That's powerful. Last question and I'll let you guys go. Rob, you got traders, and you've got long term investors. All right traders going to be defensive, today. I get that. Make the case for UIPATH, for long term investors. >> Rob: I think we're going to be a multi-gern- multi-billion company and we're going to be a generational company of our time. And we will define enterprise automation. And it's going to be a long term game and we feel like really strong that we'll be the lead in that game. >> Dave: Guys, thanks so much for coming to the cube. Great show. Always fun at UiPath Forward. Really appreciate your time. Thank you. >> Thanks dave. >> Appreciate it as well. >> Okay wrap it up, day one, we're here tomorrow, first thing, Dave Vellante and Dave Nicholson. Thanks for watching, forward 5, Uipath big customer event, we'll see you tomorrow. (music)

>>The cube presents, Coon and cloud native con Europe, 2022, brought to you by red hat, the cloud native computing foundation and its ecosystem partners. >>Welcome to Valencia Spain in Coon and cloud native con Europe, 2022. I'm Keith Townsend, along with my host, Paul Gillon senior editor, enterprise architecture at Silicon angle. We are continuing the conversation here at KU con cloud native con around security app defense. Paul, were you aware it was this many security challenges and, and that were native to like cloud native >>Well there's security challenges with every new technology. And as we heard, uh, today from our, some of our earlier guests, uh, containers and Kubernetes naturally introduce new variables in the landscape and that creates the potential vulnerabilities. So there's a whole industry that's evolving around that. And what we've been looking at today, yesterday, we talked very much about managing Kubernetes today. We're talking about many of the nuances of building a, a Kubernetes based environment and security is clearly one of them. >>So welcome our guests on Garrett, head of products. >>Thank >>You and community at deep fence. You know what I'm going. I'm going to start out the question with a pretty interesting security at scale is one of your taglines. >>Absolutely. >>What does that mean? Exactly. >>So Kubernetes is all about scale securing applications and Kubernetes is a completely different game to securing your traditional monolithic legacy enterprise applications. Kubernetes grows it scales it's elastic, and the perimeter around a Kubernetes application is very, very porous. There are lots of entry points. So you can't think about securing a cloud native application. The way that you might have secured a monolith securing a monolith is like securing a castle. You build a wall around it. You put guards on the gate. You control, who comes in and out, and job is more or less done securing a cloud native application. It's like securing a city. People are roaming through the city without checks and balances. There are lots of services in the city that you've got to check and monitor. It's extremely porous. So sec, all of the security problems in Kubernetes with cloud native applications, they're amplified by scale, the size of the application, the number of nodes and the complexity of the application and the way that it's built and delivered. >>That's, uh, kind of a chilling phrase. The perimeter is porous. Uh, yeah, companies are adopting Kubernetes right now. Evidently bringing in all of these new, these new, uh, vulnerability points. Do they know what they're getting into >>Many don't, there's, there's a huge amount of work around trying to help organizations make the transition from thinking about applications as single components to thinking about them as microservices with multiple little, little components, it's a really essential step because that's what allows businesses to evolve, to digitize, to deliver services, using APIs, mobile, mobile apps. So it's a necessary technical change, but it brings with it. Lots of challenges and security is one of those biggest challenges. >>So as I'm thinking about that poorest nature, I can't help, but think, you know, if I have my, my traditional IPS does a really great job of blocking that centralized data center and access to that centralized data center. As I think about that city example that you gave me, I'm thinking, you know what? I have intruders or not even intruders. I have bad actors within my city. You >>Do you, how >>Do, how does deep defense help protect me from those bad actors that are inside or roaming the city? >>So this is the wonderful, unique technology we have within deep fence. So we install little sensors, little lightweight sensors on each host. That's running your application on Kubernetes nodes as a Damon set against Fargate instances on Docker hosts on bare metal. And those sensors install little taps into the network using E B P F and they monitor the workloads. So it's a little bit like having CCTV cameras throughout your city tracking what's happening. There are a lot of solutions which we'll look at what happens on a workload traditional XDR solutions that look for things like process changes or file system changes. And we gather those signals indicators of compromise, but those alone are too little too late. They tell you that a breach has probably already happened. What deep defense does is we also look at the network. We gather network signals. We can see someone using a, a reconnaissance tool roaming through your application, sending probe traffic to try and find weak points. >>We can see them then elevating the level of attack and trying to weaponize a particular exploit that they might have find, or vulnerability that they find. We can see everything that comes into each of the components, not just at the perimeter, but right inside your application. We see what happens in those components process file, integrity, changes. And we see what comes out, attempt exfiltrate, something that looks like a database file or et cetera password. And we put all of these little subtle signals, the indicators of attack, the network based signals and the indicators of compromise. We put those together and we build a picture of the threats against each of the workloads in your cloud, native application. There's lots and lots of background, recon traffic. We see that you generally don't need to worry about that. It's just noise. But as that elevates and you see evidence of exploits and later spread, we identify that we'll let you know, or we can step in and we can proactively block the behavior that's causing those problems. So we can stop someone from accessing a component, or if a component's compromised, we can, we can freeze it and restart it. And this is a key part of the technology within our threat striker security observability platform, >>Uh, false alerts are the bane of the security ministry's existence. What do you do to protect against those? >>So we use a range of heuristics and a degree, a small degree of machine learning to try and piece together. What's happening. It's a complicated picture. So some of your viewers will have heard of a might attack matrix. So a dictionary of techniques and tactics and, and protocols that attackers might use in order to attack an infrastructure. So we gather the signals, those TTPs, and we then build a model to try and understand how those little signals pieced together. So maybe there's, you know, there's a guy with a striped striped vest that is trying the doors in your city, you know, a low level criminal who isn't getting anywhere. We'll pick that up and that's low risk. But then if we see that person infiltrate a building, because they find an open door, then that raises the level of risk. So we monitor the growing level of risk against each workload. >>And once it hits a level of concern, then we let you know, but you can then forensically go back in time and look at all of the signals that surround that. So we don't just tell you, there was an alert and a file was compromised in your workload, do something about it. We tell you the file was compromised. And prior to that, there were these events, process failures. Those could have been caused by network events that are correlated to a vulnerability that we know. And those in, in turn could have been discovered by recon traffic. So we help you build that entire active picture up. Every application's different. You need to have the context to understand and interpret signals that a solution like threat striker gives you, and we give you that context. >>So I would push back. If I'm a platform team, say, you know what? I have a service mesh. I, I have trusted traffic going to trucked traffic going from trusted sources. I'm, I'm cutting off the problem even before it happens. Why should I use, uh, deep fix? >>So a service mesh won't cut off the problem. It'll just hide the problem because a service mesh will just encrypt the traffic between each of the components. It doesn't stop the bad traffic flowing. If a component is compromised, people can still talk to another component and the service mesh happily encrypts it and hides it. What we do. We love service meshes because we can decrypt the traffic or we can inspect the individual application components before they talk to the mesh side car. So we can pull out and see the plane, text traffic. We can identify things that other tools wouldn't have a hope of, of identifying. >>So, you know, you, you just, uh, triggered something. >>Yeah. >>A lot of companies do not like decrypting that traffic after it's been sent, they don't want anyone else, including security tools to see it. Yeah. How do you ensure, how do you serve those clients? >>So we serve those clients by having an architecture that sits entirely on premise in their infrastructure. Their sensitive data never leaves their network, their VPCs, their, their boundary. They install a threat striker console. So this is the tool that does all of the analysis and make the protection decisions. They run that themselves. They deploy the threat, striker sensors in their production environment. They talk over secure links, authenticated to the console. So everything sits within their power view, their level of their degree of control. >>So if, if they're building a, a, a cloud application though, or, or a hybrid cloud application, how do you connect? How do you deal with the cloud side? >>So whether their production environments are next to the threat striker console, whether they're running on remote clouds, our sensors will run in all of those environments and the console will manage a complex hybrid environment. It will show you traffic running in your Kubernetes cluster and AWS traffic Mon running on your VMs on Google traffic, running in your 4g instances on again, on AWS and on your on-prem instances, it gathers that data securely from each of those remote places, sends it to the console that you own and operate securely. So you have full control over what is captured. It's encrypted, it's authenticated, it's streamed back. So it never leaves your level of control. >>Talk to me about the overhead. How is this deployed and managed with MI environment? >>So there are two components, as we've learned, we have the console. All of the work is done on the console, the any necessary decryption, all the calculation that runs on a Kubernetes cluster, that, that you would deploy, that you would scale. So that's fully in your control. Then you need to install little sensors on each of your production environments to bring the data back to the console. >>Now those on pots, or are those in running inside of, uh, containers themselves. >>So they are container based. They're typically deployed as a demon set. So one instance per node in your Kubernetes cluster, they are, we have put a lot of engineering work into making those as lightweight as possible. They do very little analysis themselves. They do a little bit of pre-filtering of network traffic to reduce the bandwidth, and then they pass the packets back to the management console. So our goal is to have the minimal impact on customers, production environments, so that they can scale and operate without an impact on the performance or availability of their applications. And we have customers who are monitoring services running on literally thousands of Kubernetes nodes and streaming the data back to their management console and using that to analyze from a single point of control what's going on in their applications. >>So we hear time and again, CIOs complaining that they have too many point security products. Yes, I think average of 87 in, in, in the enterprise, according to, to one survey, aren't you just another, >>And that is the big challenge with security. There is no silver bullet product that will secure everything that you have. You have your, the what, you're the, what you're securing scales over space from your infrastructure to the containers and the workloads and the application code. It scales over time. Are you secure? Are you putting security measures in, at shift left development when you deploy or are you securing production? And it scales over the environments. There is no silver bullet that will provide best to breed security across that entire set of dimensions. There are large organizations that will present you with holistic solutions, which are a bunch of different solutions with the same logo on them, bundle together under the same umbrella. Those don't necessarily solve the problem. You need to understand the risks that your organization is faced. And then what are the best to breed solutions for each of those risks and for the life cycle of your application at deep fence, we are about securing your production environment. >>Your developers have built applications. They've secured those applications using tools like SNCC, and they've ticked and signed off saying with this list of documented vulnerabilities, my application is secure. It's now ready to go into production. But when I talk to, to application security people to ops people, and I say, are the applications in your Kubernetes environment? Are they secure? They say, look, honestly, I don't know, the developers have signed off something, but that's not what I'm running. I've had to inject things into the application. So it's different. There could have been issues that were, that were discovered after the developers signed it off. The developers made exceptions, but also 60, 80% of the code I'm running in production. Didn't come from my development team. It's infrastructure, it's third party modules. So when you look at security as a whole, you realize there are so many ax axis that you have to consider. There are so many points along these, a axis, and you need to figure out in a kind of a van diagram fashion, how are you going to address security issues at each of those points? So when it comes to production security, if you want a best breed solution for finding vulnerabilities in your production environment, threat map, open source, we'll do that. And then for monitoring attack behavior threat striker enterprise will do that. Then deep defense is a great set of solutions to look at. >>So on. Thanks for stopping by security at layers is a repetitive thing that we hear security experts talk about. Not one solution will solve every problem when it comes to security from Valencia Spain, I'm Keith Townson, along with Paul Gillon and you're watching the Q the leader in high tech coverage.

(upbeat music) >> Hi, everyone, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're wrapping up four days of coverage, two sets. Two remote sets, one in Boston, one in Palo Alto. And really, it's a pleasure to introduce Benoit Dageville. He's the Press Co-founder of Snowflake and President of Products. Benoit, thanks for taking some time out and coming to theCUBE. >> Yeah, thank you for having me, Dave. >> You know, it's really a pleasure. We've been watching Snowflake since, maybe not 2012, but mid last decade you hit our radar. We said, "Wow, this company is going to go places." And yeah, we made that call correctly. But it's been a pleasure to sort of follow you. We've talked a little bit remotely. I kind of want to go back to some of the fundamentals. First of all, I wanted mention your earnings last night. If you guys didn't see it, again, triple digit growth, $1.8 billion RPO, cashflow actually looking pretty good. So, pretty amazing. Oh, and 173% NRR, you know, wow. And Mike Scarpelli is kind of bummed that you did so well. And I know why, right? Because it's going to be at some point, and he dials it down for the expectations and Wall Street says, "Oh, he's sandbagging." And then at some point you're actually going to meet expectations and people are going to go, "Oh, they met expectations." But anyway, he's a smart guy, he know what he's doing. (Benoit laughing) I loved it, it was so funny listening to him last night. But anyway, I want to go back to, when I talked to practitioners about data warehousing pre-cloud, they would say sound bites like, it's like a snake swallowing a basketball, they would tell me. And the other thing they said, "We just chased the chips. Every time a new Intel chip comes out, we have to bring in new servers, and we're struggling." The cloud changed all that. Your vision and Terry's vision changed all that. Maybe go back to the fundamentals of what you saw. >> Yeah, we really wanted to address what we call the data challenges. And if you remember at that time, data challenge was first of the volume of data, machine-generated data. So it was way more than just structured data, right? Machine-generated data is weblogs, and it's at petabyte scale. And there was no good solution for that type of data. Big data was not a great solution, Hadoop was really bad. And there was no good solution for that. So we thought we should do something for big data. The other aspect was concurrency, right? Everyone wants to use these data analytic platform in an enterprise, right? And you have more and more workload running against the same data, and the systems that were built were not scaling for these workloads. So you had to silo data, right? That's the only way big enterprise could deal with that, is to create many different silos, Oracle, Teradata, data mass, you would hear data mass. All of it was to afloat, right, this data? And then there was the, what do we call, data sharing. How to get access to data which is not born inside the enterprise, right? So with Terry, we wanted to solve all these challenges and we thought the only way to solve it was the cloud. And the cloud has really two free aspects. One is the elasticity, for all of a sudden, you can run every workload that you want concurrently, in parallel, on different computer resources, and you can run them against the same data. So this is kind of the data lake model, if you want. At the same time, you can, in the cloud, create a service. So you can remove complexity from users and make it really easy for new workloads to be added to the system, because you can manage, you can create a managed service, where all the sudden our customers, they don't need to manage infrastructure, they don't need to patch, they don't need to tune. Everything is done by Snowflake, the service, and they can just load in and run their query. And the third aspect is really collaboration. Is how to connect data sets together. And that's almost a new product for Snowflake, this data sharing. So we really at Snowflake was all about combining big data and data warehouse in one system in the cloud, and have only one single system where you can put all your data and all your workload. >> So you weren't necessarily trying to solve the data warehouse problem, you were trying to solve a data problem. And then it just so happened data warehouse was a logical entry point for you. >> It's really not that. Yes, we wanted to solve the data problem. And for us big data was a really important problem to solve. So from day one, Snowflake was all about machine generated data, petabyte scale, but we wanted to do it right. And for us, right was not compromising on data warehouse principle, which is a CDT of transaction, which is really fast response time, and which is also simplicity. So as I said, we wanted to solve kind of all the problems at the time of volume of data, concurrency, and these sharing aspects. >> This was 2012. You knew at that time that Hadoop wasn't going to be the answer. >> No, I mean, we were really, I mean, everyone knew that. Everyone knew Hadoop was really bad. You know, complex to manage, really slow. It had good aspects, right? This was the only system that could manage petabyte scale data sets. That's the only thing- >> Cheaply. >> Yeah, and cheaply which was good. And we wanted really to do that, plus have all the good attributes of data warehouse system. And at the same time, we wanted to build a system where if you are data warehouse customer, if you are coming from Teradata, you can migrate to Snowflake and you will get to a system which is faster than what you had on-premise, right. That's why it's pretty cool. So we wanted to do big data without compromising on data warehouse. >> So several years ago we looked at the hyperscalers and said, "Wow, last year they spent $100 billion in CapEx." And so, we started to think about this abstraction layer. And then we saw what you guys announced with the data cloud. We call it super clouds. And we see that as exactly what you're building. So that's clearly not just a data warehouse or database, it's technology that really hides the underlying complexity of all those clouds, and it allows you to have federated governance and data sharing, all those things. Can you talk about sort of how you think about that architecture? >> So for me, what I say is that really Snowflake is the worldwide web of data. And we are indeed a super cloud, or we are super-posed to the infrastructure cloud, which is our friends at Amazon, and of course, Azure, I mean, Microsoft and Google. And as any cloud, we have regions, Snowflake regions all over the world, and located on different cloud providers. At the same time, our platform is global in the sense that every region interconnects with all the other regions, this is our snow grid and data mesh, if you want. So that as an organization you can have your presence on several Snowflake region. It doesn't matter which cloud provider, so you can mix AWS with Azure. You can use our cloud like that. And indeed you can, this is a cloud where you can store your data, that's the thing that really matters, and data is structured, but it's machine structure, as I say, machine generated, petabyte scale, but there's also unstructured, right? We have added support for images, text, videos, where you can process this data in our system, and that's the workload spout. And workload, what is very important is that you can run this workload, any number of workloads. So the number of workloads is effectively unlimited with Snowflake because each workload can have its dedicated set of compute resources all operating on the same data set. And the type of workloads is also very important. It's not only about dashboards and data warehouse, it's data engineering, it's data science, it's building application. We have many of our customers who are building full-scale cloud applications on top of Snowflake. >> Yeah so the other thing, if you're not familiar with Snowflake, I don't know, maybe your head has been in the sand for a while, but separating compute and storage, I don't know if you were the first, but you were certainly the first to popularize it. And that allowed you to solve that chasing the chips problem and the swallowing the basketball, right? Because you have virtually infinite resources now at your disposal. >> Yeah, this is really the concurrency challenge that I was mentioning. Everyone wants to access the data. And of course, if everyone runs on the same set of compute resources, you have a bottleneck. So Snowflake was really about this multi-workload. We call it Multi-Cluster Shared Data Architecture. But it's not difficult to run multiple cluster if you don't have consistency of data. So how to do that while maintaining transactional property of data as CDT, right? You cannot modify data from different clusters. And when you commit, every other cluster will immediately see the change, right, as if everyone was running on the same cluster. So that was the challenge that we solve when we started Snowflake. >> Used the term data mesh. What is data mesh to Snowflake? Is it a concept, is it fabric? >> No, it's a very interesting point. As much as we like to centralize data, this becomes a bottleneck, right? When you are a large organization with different independent units, everyone wants to manage their own data and they have domain-specific expertise about that data. So having it centralized in IT is not practical. At the same time, you really want to be able to connect these different data sets together and join different data together, right? So that's the data mesh architecture. Each data set is managed independently by business owners, and then there is a contract which is exposed to others, and you can combine. And Snowflake architectures with data sharing, right. Data sharing that can happen within an organization, or across organization, allows you to connect any data with any other data on our platform. >> Yeah, so when I first heard that term, you guys using the term data mesh, I got very excited because it was kind of the data mesh is, my view, anyway, is going to be the fundamental architecture of this decade and beyond. And the principles, if I understand it correctly, you're applying the principles of Jim Octagon's data mesh within Snowflake. So decentralized data doesn't have to be physically in one place. Logically it's in the data cloud. >> It's logically decentralized, right? It's independently managed, and the reason, right, is the data that you need to use is not produced by your, even if in your company you want to centralize the data and having only one organization, let's say IT managing that, let's say, pretend. Yet you need to connect with other datasets, which is managed by other organizations. So by nature, the data that you use cannot be centralized, right? So now that you have this principle, if you have a platform where you can store all the data, wherever it is, and you can connect these data very seamlessly, then we can use that platform for your enterprise, right? To have different business units independently manage their data sets, connects these together so that as a company you have a 360 view of your customers, for example. But you can expand that outside of your enterprise and connect with data sets, which are from your vertical, for example, financial data set that you don't have in your company, or any public data set. >> And the other key principles, I think, that you've touched on really is the line of business now. Increasingly they're building data products that are creating value, and then also there's a self-service component. Assuming there's the fourth principle, governance. You got to have federated governance. And it seems like you've kind of ticked the boxes, more than tick the boxes, but engineered a solution to solve for those. >> No, it's very true. So Snowflake was really built to be really simple to use. And you're right. Our vision was, it would be more than IT, right? Who is going to use Snowflake is going now to be business unit, because you do not have to manage infrastructure. You do not have to patch. You do not have to do these things that business cannot do. You just have to load your data and run your queries, and run your applications. So now business can directly use Snowflake and create value from that. And yes, you're right, then connect that data with other data sets and to get maximum insights. >> Can you please talk about some of the things you do with AWS here at the event. I'm interested in what you're doing with your machine learning initiatives that you've recently announced, the AI piece. >> Yes, so one key aspects is data is not only about SQL, right? We started with SQL, but we expanded our platform to what we call data programmability, which is really about running program at scale across a large volume of data. And this was made popular with a programming model which was introduced by Pendal, DataFrames. Later taken by Spark, and now we have DataFrames in Snowflake, Where we are different than other systems, is that these DataFrame programs, which are in Python, or Java, or Scala, you program with data. These DataFrames are compiled to our single execution platforms. So we have one single execution platform, which is a data flow execution platform, which can run both SQL very efficiently, as I said, data warehouse speed, and also these very complex programs running Python and Java against this data. And this is a single platform. You don't need to use two different systems. >> Now so, you kind of really attack the traditional analytics base. People said, "Wow, Snowflake's really easy." Now you're injecting AI and machine intelligence. I see Databricks coming at it from the other angle. They started with machine learning, now they're sort of going after the analytics. Does there need to be a semantic layer to connect, 'cause it's the same raw data. Does there need to be a semantic layer to connect those two worlds? >> Yes, and that's what we are doing in our platform. And that's very novel to Snowflake. As I said, you interact with data in different program. You pick your program. You are a SQL programmer, use SQL. You are a Python programmer, use DataFrames with Python. It doesn't really matter. And then the semantic layer is our compiler and our processing engine, is going to translate both your program and my program in Python, your program in SQL, to the same execution platform and to the same programming language that Snowflake internally, we don't expose our programming language, but it's a data flow programming language that our execution platform executes. So at the end, we might execute exactly the same program, potentially. And that's very important because we spent all our IP and all our time, engineering time to optimize this platform, to make it the fastest platform. And we want to use that platform for any type of workloads, whether it's data programs or SQL. >> Now, you and Terry were at Oracle, so you know a lot about bench marketing. As Larry would stand up and say, "We killed the competition." You guys are probably behind it, right. So you know all about that. >> We are very behind it. >> So you know a lot about that. I've had some experience, I'm not a technologist, but I'm an observer and analyst. You have to take benchmarking with a very big grain of salt. So you guys have generally stayed away from that. Databricks came out and they came up with all these benchmarks. So you had to respond, because otherwise it's out there. Now you reran the benchmarks, you took out the materialized views and all the expensive stuff that they included in your cost, your price performance, but then you wrote, I thought, a very cogent blog. Maybe you could talk about sort of why you did that and your general philosophy around bench marketing. >> Yeah, from day one, with Terry we say never again we will participate in this really stupid benchmark war, because it's really not in the interest of customers. And we have been really at the frontline of that war with Terry, both of us, really doing special tricks, right? And optimizing this query to death, this query that no one runs apart from the synthetic benchmark. We optimize them to death to have the best number when we were at Oracle. And we decided that this is really not helping customers in the end. So we said, with Snowflake, we'll not do that. And actually, we are not the only one not to do that. If you look at who has published TPC-DS, you will see no one, none of the big vendors. It's not because they cannot run TPC-DS, Oracle can run it, I know that. And all the other big data warehouse vendor can, but it's something of a little bit of past. And TPC was really important at some point, and is not really relevant now. So we are not going to compete. And that's what we said is basically now our blog. We are not interesting in participating in this war. We want to invest our engineering effort and our IP in solving real world issues and performance issues that we have. And we want to improve our engine for these real world customers. And the nice thing with Snowflake, because it's a service, we see exactly all the queries that our customers are executing. So we know where we are struggling as a system, and that's where we want to invest and we want to improve. And if you look at many announcements that we made, it's all about under-the-cover improving Snowflake and getting the benefit of this improvement to our customer. So that was the message of that blog. And yes, the message was okay. Mr. Databricks, it's nice, and it's perfect that, I mean, everyone makes a decision, right? We made the decision not to participate. Databricks made another decision, which is very fine, and that's fine that they publish their number on their system. Where it is not fine is that they published number using Snowflake and misrepresenting our performance. And that's what we wanted also to correct. >> Yeah, well, thank you for going into that. I know it's, look, leaders don't necessarily have to get involved in that mudslide. (crosstalk) Enough said about that, so that's cool. I want to ask you, I interviewed Frank last spring, right after the lockdown, he was kind enough to come on virtually, and I asked him about on-prem. And he was, you know Frank, he doesn't mix words, He said, "We're not getting into a halfway house. That's not going to happen." And of course, you really can't do what you do on-prem. You can't separate compute, some have tried, but it's not the same. But at the same time that you see like Andreessen comes out with this blog that says a huge portion of your cost of goods sold is going to be the cloud, so you're going to have to repatriate. Help me square that circle. Is it cloud forever? Is it will you never say never? What can you share of that? >> I will never say never, it's not my style. I always say you can always change your mind, and maybe different factors can change your mind. What was true at some point might not be true at a later point. But as of now, I don't see any reason for us to go on-premise. As you mentioned at the beginning, right, Snowflake is growing like crazy. The world is moving to the cloud. I think maybe it goes both ways, but I would say 90% or 99% of the world is moving to the cloud. Maybe 1% is coming back for some very specific reasons. I don't think that the world is going to move back on-premise. So in the end we might miss a small percentage of the workload that will stay on-premise and that's okay. >> And as well, if you dig into some of the financial statements you'll see, read the notes where you've renegotiated, right? We're talking big numbers. Hundreds and hundreds of millions of dollars of cost reduction, actually more, over a 10 year period. Billions of your cloud bills. So the cloud suppliers, they don't want to lose you as a customer, right? You're one of their biggest customer. So it's awesome. Last question is kind of, your work now is to really drive the data cloud, get adoption up, build that supercloud, we call it. Maybe you could talk a little bit about how you see the future. >> The future is really broadened, the scope of Snowflake, and really, I would say the marketplace, and data sharing, and services, which are directly built natively on Snowflake and are shared through our platform, and can operate, it can mix data on provider-side with data on consumer-side, and creating this collaboration within the Snowflake data cloud, I think is really the future. And we are really only scratching the surface of that. And you can see the enthusiasm of Snowflake data cloud and vertical industry We have nuanced the final show data cloud. Industry, complete vertical industry, latching on that concept and collaborating via Snowflake, which was not possible before. And I think you talked about machine learning, for example. Machine learning, collaboration through machine learning, the ones who are building this advanced model might not be the same as the one who are consuming this model, right? It might be this collaboration between expertise and consumer of that expertise. So we are really at the beginning of this interconnected world. And to me the world wide web of data that we are creating is really going to be amazing. And it's all about connecting. >> And I'm glad you mentioned the ecosystem. I didn't give enough attention to that. Because as a cloud provider, which essentially you are, you've got to have a strong ecosystem. That's a hallmark of cloud. And then the other, vertical, that we didn't touch on, is media and entertainment. A lot of direct-to-consumer. I think healthcare is going to be a huge vertical for you guys. All right we got to go, Terry. Thanks so much for coming on "theCUBE." I really appreciate you. >> Thanks, Dave. >> And thank you for watching. This a wrap from AWS re:Invent 2021. "theCUBE," the leader in global tech coverage. We'll see you next time. (upbeat music)

(upbeat music) >> Hey, welcome to theCUBE's coverage of AWS re:Invent 2021. I'm Lisa Martin, and I'm pleased to be joined by Jessica Alexander, who is the VP of Cloud Solutions Sales and Alliances at CrowdStrike. Jessica, welcome to the program. >> Thank you, Lisa. It's great to be here. >> So we're going to unpack a lot today, some news, what's going on with the threat landscape, what you're seeing across industries, but I want to get started talking a little bit about your team. As I mentioned, VP of Cloud Solutions Sales and Alliances. Talk to me about your team because you have a unique GTM here that I'd like to get into. >> Sure. Thank you, Lisa. Well, we recently launched our new cloud security products, Cloud Workload Protection and Horizon earlier this year. So we wanted to make sure that we accelerated our entry into this new product market, this new addressable market, and so we established not only a cloud sales specialist team that helps our core sellers as well as our partners sell our new cloud security products but we also wanted to make sure it was tightly integrated and aligned with our Cloud Alliances so specifically our co-sell relationship and partnership that we have with AWS. >> Got it. Let's talk about some of the things you mentioned, Aksino acceleration entering into the market. We saw a lot of acceleration in the last 20 months and counting, especially with respect to cloud adoption, digital transformation, but also the threat landscape things have accelerated. Wanted to get some information from you on what you've seen. We've seen and talked to a lot of folks on ransomware stats, you know, it's up nearly 11x in the first half of '21, but you guys have some unique stats and insights on that. Talk to me about what CrowdStrike is seeing with respect to that threat landscape and who it's impacting. >> Sure. You know, we have a unique perspective. CrowdStrike has millions of sensors out in our customer environments, they're feeding trillions of events into the cloud and we're able to correlate this data in real time, so this gives us a very unique perspective into what's happening in adversary activity out in the world. We also get feeds from our incident response teams that are actively responding to issues, as well as our Intel operatives out in the world. So, you know, we correlate these three sources of data into our threat graph in the cloud powered by AWS, which gives us very good insights into activity that we're seeing from an adversary perspective. So we also have a group called the OverWatch team, they are 24 by seven, you know, humans monitoring our cloud and monitoring our customer's networks to detect or, you know, get pre-breach activity information. And what they're seeing is that, you know, over this last year, an adversary is able to enter a network and move laterally into that network within one hour and 32 minutes. Now, you know, this is really fast, especially when you consider that in 2020, that average was four hours and 37 minutes for a threat actor to move laterally, you know, infiltrate a network and then move laterally. So, you know, the themes that we're seeing are adversaries are getting a lot faster and a lot more efficient, and, you know, as more companies are moving to remote work environments, you know, setting up virtual infrastructure for employees to use for work and productivity, you know, that threat landscape becomes more critical. >> Right? It becomes more critical. It becomes bigger. And of course we are in this work from anywhere environment that's going to last or some amount of it will persist permanently. So what you're saying is you're seeing a 4x increase in the speed with which adversaries can get in and laterally move within a network, so dramatically faster in a year over year period, where, so there's been so much flux in every market and of course in our lives, what are some of the things that you're helping customers do to combat this growing challenge? >> Well, it really goes back to being predictive and having that real time snapshot of what's going on and being able to proactively reach out to customers before anything bad happens and, you know, we're also seeing that ransomware continues to be an issue for customers, so, you know, having the ability to prevent these attacks and ransomware from happening in the first place and really taking the advantage that an adversary may have from a speed or intelligence perspective, taking that advantage away by having the Falcon Platform actively monitoring our customer environments is a big advantage. >> So let's talk about, speaking of advantages, what are you guys announcing at re:Invent this year? >> Sure. Well, we have two new service integrations with Amazon EKS, AWS Outpost and AWS Firelands to talk about this year. The cool thing is that, you know, customers are going to get our wonderful breach protection that we have, you know, the gold standard of breach protection, they'll have that available on various cloud services. And what it does is it provides consistent security and simplified operational management across AWS services, as customers extend those from public cloud to the data center, to the edge. And you know, the other great benefit is that it accelerates threat hunting, so we were talking about, you know, being able to predict and see what adversaries are doing. You know, one of the great customer benefits is that they can do that with their own teams and be able to do that on a cloud infrastructure as well. >> And how much of the events of the last 20 months was a catalyst or were catalysts for these integrations that you just mentioned? I imagine the threat landscape growing ransomware becoming a 'when we get hit not if' would have been some of those catalysts. >> Well, you know, we're seeing that the adoption of cloud services, especially for end user computing is growing much faster than traditional on-prem desktops, laptops, as people continue to work remotely and customers need to be, or corporations need to be efficient at how they manage end user computing environments. So, you know, we are seeing that adversary activity is picking up, they're getting smarter about, you know, leveraging cloud services and potential misconfigurations, there're really four key areas that we see customers struggle with, whether it be, you know, the complexity of cloud services, whether it be shadow IT, and a lot of the security folks don't necessarily know where all the cloud services are being deployed, then you've got, you know, kind of the advanced techniques that adversaries are using to get into networks. And then, you know, last but certainly not least is skills shortage. We're finding that a lot of customers want a turnkey solution, where they don't have to have a team of cloud security specialists to respond or handle any misconfigurations or issues that come up. They want to have a turnkey solution, a team that's already watching and reaching out to them to say, "Hey, you may want to look into XYZ and update a policy, or, you know, activate this new, you know, this feature in the platform." >> Yeah. That real time, the ability to have something that's turnkey is critical in this day and age where things are moving so quickly, there's so much being accelerated, good stuff and bad stuff. But also you mentioned that cybersecurity skills gap, which is in its, I think it's in its fifth year now, which is a big challenge for organizations as this scattered, work from anywhere persists as does the growth of the threat landscape. Let's get into now, for, you mentioned the adoption of cloud services has gone up considerably in this interesting time period, how is CrowdStrike helping customers do that securely, migrate from on-prem to the cloud with that security and that confidence that their landscape is protected? >> Yeah, well, we find obviously in the shared responsibility model, the great thing is that, you know, CrowdStrike and AWS team up to help, you know, customers have a better together experience as they migrate to the cloud. AWS is obviously responsible for the security of the cloud and customers are responsible for the security in the cloud. And in speaking with our customers who are moving or have moved to cloud services, and they really want a trusted and simple platform to use when securing their data and applications. So what, you know, they also have hybrid environments that can get complex to support, and, you know, we want to be able to provide them with a unified platform, a unified experience, regardless of where the workload is running or what services that it's using. You know, they have that unified visibility and protection across all of the cloud workloads. We're also, you know, seeing that, especially the reason we're doing this great integration with Outpost and EKS Anywhere is that customers are, you know, taking their cloud services out to their data centers as well as to the edge locations and branch offices, so they want to be able to run EKS on their own infrastructure. So it's important that customers have that portability that regardless of whether it's a laptop or an EC2 instance or an EKS container, you know, they have that portability throughout the continuum of their cloud journey. >> That continuum is absolutely critical as we, you know, talk about cloud and application or continuum from the customer's perspective, the cloud continuum is something that is front and center for customers, I imagine in every industry. >> Oh, for sure, 'cause every industry is adopting cloud maybe at a different speed, maybe for different applications, but, you know, everybody's moving to the cloud. >> So talk to me about what you're announcing with AWS, let's get into a little bit about the partnership that CloudStrike and AWS have, let's unpack that a bit. >> Sure. You know, we've been an AWS advanced technology partner for over five years. We've had our products, we now have six of our CrowdStrike products listed on AWS Marketplace. We're an active co-sell partner and, you know, have our security competency and our well-architected certification. And really it's about building trust with our customers. You know, AWS has a lot of wonderful partner products for customers to use and it's really about building trust that, you know, we're validated, we're vetted, we have a lot of customers who are using our products with AWS, and, you know, I think it's that tight collaboration, for example, if you look at what we're doing with Humio, we've implemented a quick start program, which AWS has to get customers quickly deployed with an integration or a new capability with a partner product. And what this does is it spins up a quick cloud formation template, customer can integrate it very quickly with the AWS Firelands and then, you know, all that log information coming from the AWS containers is easily ingested into the Humio platform. And so, you know, it really reduces the time to get the integration up and running as well as pulling all that data into the Humio platform so that customers can, like we said earlier, go back and threat hunt across, you know, different cloud service components in a quick and easy way. >> Quick and easy is good as is faster time to value. You mentioned the word trust, and, you know, we talk about trust, we've been talking about it for years as it relates to technology, but I'm curious, Jessica, in the last year and a half, if your customer conversations have changed, is trust now even more important than ever as there are so many things in flux, have you noticed any sort of change there in your customer conversations? >> Well, you know, I think trust is extensible. And over the last 10 years, CrowdStrike's done a really great job of building customer trust. And, you know, we started out as, you know, kind of primarily EDR and we've moved into prevention and now we're moving into identity protection and XDR so, you know, I see a pattern that, you know, we've built this amazing core of trust across our existing customers, and as we offer more capabilities, whether it be, you know, cloud security or XDR, identity protection, you know, customers trust us and so they're very willing to say, "ah well, I want to try out these new capabilities that CrowdStrike has because we trust you guys, you know, you've done a lot to protect our brand and, you know, really make our internal teams a lot more efficient and a lot smarter." So, you know, I think while trust is important, it's also something that we get to carry forward as we enter new markets and continue to innovate and provide new capabilities for our customers. >> And really extending that trusted, valued partner relationship that you've already established with customers in every industry. So where can customers go? So the joint GTM customers, and you said products available in the AWS marketplace, but where do you recommend customers go to learn more about how they can work with these joint solutions that CrowdStrike and AWS have together? >> Absolutely. We have a landing page on AWS, if you Google AWS and CrowdStrike, whether it be marketplace or EKS Anywhere, Amazon outposts, we're on all the joint product pages with Amazon, as well as always going to crowdstrike.com and looking up our cloud security products. >> Got it. And last question for you, Jessica, summarize the announcement in terms of business outcomes that it's going to enable your joint customers to achieve. >> Absolutely. You know, I think it goes back to probably the primary reason is complexity. And, you know, with complexity comes risk and blind spots so being able to have a unified platform that no matter where the workload is, or the employee may be, they are protected and have, you know, a unified platform and experience to manage their security risk. >> Excellent. Jessica, thank you so much for coming on the program today, sharing with me, what's new with CrowdStrike, some of the things that you're seeing, and what you're helping customers to accomplish in a very dynamic environment, we appreciate your time and your insights. >> Thank you for having me, Lisa. >> For Jessica Alexander, I'm Lisa Martin, and you're watching theCUBE's coverage of AWS re:Invent 2021. (gentle music)

(upbeat music) >> Hey, welcome to theCUBE's coverage of AWS re:Invent 2021. I'm Lisa Martin, and I'm pleased to be joined by Jessica Alexander, who is the VP of Cloud Solutions Sales and Alliances at CrowdStrike. Jessica, welcome to the program. >> Thank you, Lisa. It's great to be here. >> So we're going to unpack a lot today, some news, what's going on with the threat landscape, what you're seeing across industries, but I want to get started talking a little bit about your team. As I mentioned, VP of Cloud Solutions Sales and Alliances. Talk to me about your team because you have a unique GTM here that I'd like to get into. >> Sure. Thank you, Lisa. Well, we recently launched our new cloud security products, Cloud Workload Protection and Horizon earlier this year. So we wanted to make sure that we accelerated our entry into this new product market, this new addressable market, and so we established not only a cloud sales specialist team that helps our core sellers as well as our partners sell our new cloud security products but we also wanted to make sure it was tightly integrated and aligned with our Cloud Alliances so specifically our co-sell relationship and partnership that we have with AWS. >> Got it. Let's talk about some of the things you mentioned, Aksino acceleration entering into the market. We saw a lot of acceleration in the last 20 months and counting, especially with respect to cloud adoption, digital transformation, but also the threat landscape things have accelerated. Wanted to get some information from you on what you've seen. We've seen and talked to a lot of folks on ransomware stats, you know, it's up nearly 11x in the first half of '21, but you guys have some unique stats and insights on that. Talk to me about what CrowdStrike is seeing with respect to that threat landscape and who it's impacting. >> Sure. You know, we have a unique perspective. CrowdStrike has millions of sensors out in our customer environments, they're feeding trillions of events into the cloud and we're able to correlate this data in real time, so this gives us a very unique perspective into what's happening in adversary activity out in the world. We also get feeds from our incident response teams that are actively responding to issues, as well as our Intel operatives out in the world. So, you know, we correlate these three sources of data into our threat graph in the cloud powered by AWS, which gives us very good insights into activity that we're seeing from an adversary perspective. So we also have a group called the OverWatch team, they are 24 by seven, you know, humans monitoring our cloud and monitoring our customer's networks to detect or, you know, get pre-breach activity information. And what they're seeing is that, you know, over this last year, an adversary is able to enter a network and move laterally into that network within one hour and 32 minutes. Now, you know, this is really fast, especially when you consider that in 2020, that average was four hours and 37 minutes for a threat actor to move laterally, you know, infiltrate a network and then move laterally. So, you know, the themes that we're seeing are adversaries are getting a lot faster and a lot more efficient, and, you know, as more companies are moving to remote work environments, you know, setting up virtual infrastructure for employees to use for work and productivity, you know, that threat landscape becomes more critical. >> Right? It becomes more critical. It becomes bigger. And of course we are in this work from anywhere environment that's going to last or some amount of it will persist permanently. So what you're saying is you're seeing a 4x increase in the speed with which adversaries can get in and laterally move within a network, so dramatically faster in a year over year period, where, so there's been so much flux in every market and of course in our lives, what are some of the things that you're helping customers do to combat this growing challenge? >> Well, it really goes back to being predictive and having that real time snapshot of what's going on and being able to proactively reach out to customers before anything bad happens and, you know, we're also seeing that ransomware continues to be an issue for customers, so, you know, having the ability to prevent these attacks and ransomware from happening in the first place and really taking the advantage that an adversary may have from a speed or intelligence perspective, taking that advantage away by having the Falcon Platform actively monitoring our customer environments is a big advantage. >> So let's talk about, speaking of advantages, what are you guys announcing at re:Invent this year? >> Sure. Well, we have two new service integrations with Amazon EKS, AWS Outpost and AWS Firelands to talk about this year. The cool thing is that, you know, customers are going to get our wonderful breach protection that we have, you know, the gold standard of breach protection, they'll have that available on various cloud services. And what it does is it provides consistent security and simplified operational management across AWS services, as customers extend those from public cloud to the data center, to the edge. And you know, the other great benefit is that it accelerates threat hunting, so we were talking about, you know, being able to predict and see what adversaries are doing. You know, one of the great customer benefits is that they can do that with their own teams and be able to do that on a cloud infrastructure as well. >> And how much of the events of the last 20 months was a catalyst or were catalysts for these integrations that you just mentioned? I imagine the threat landscape growing ransomware becoming a 'when we get hit not if' would have been some of those catalysts. >> Well, you know, we're seeing that the adoption of cloud services, especially for end user computing is growing much faster than traditional on-prem desktops, laptops, as people continue to work remotely and customers need to be, or corporations need to be efficient at how they manage end user computing environments. So, you know, we are seeing that adversary activity is picking up, they're getting smarter about, you know, leveraging cloud services and potential misconfigurations, there're really four key areas that we see customers struggle with, whether it be, you know, the complexity of cloud services, whether it be shadow IT, and a lot of the security folks don't necessarily know where all the cloud services are being deployed, then you've got, you know, kind of the advanced techniques that adversaries are using to get into networks. And then, you know, last but certainly not least is skills shortage. We're finding that a lot of customers want a turnkey solution, where they don't have to have a team of cloud security specialists to respond or handle any misconfigurations or issues that come up. They want to have a turnkey solution, a team that's already watching and reaching out to them to say, "Hey, you may want to look into XYZ and update a policy, or, you know, activate this new, you know, this feature in the platform." >> Yeah. That real time, the ability to have something that's turnkey is critical in this day and age where things are moving so quickly, there's so much being accelerated, good stuff and bad stuff. But also you mentioned that cybersecurity skills gap, which is in its, I think it's in its fifth year now, which is a big challenge for organizations as this scattered, work from anywhere persists as does the growth of the threat landscape. Let's get into now, for, you mentioned the adoption of cloud services has gone up considerably in this interesting time period, how is CrowdStrike helping customers do that securely, migrate from on-prem to the cloud with that security and that confidence that their landscape is protected? >> Yeah, well, we find obviously in the shared responsibility model, the great thing is that, you know, CrowdStrike and AWS team up to help, you know, customers have a better together experience as they migrate to the cloud. AWS is obviously responsible for the security of the cloud and customers are responsible for the security in the cloud. And in speaking with our customers who are moving or have moved to cloud services, and they really want a trusted and simple platform to use when securing their data and applications. So what, you know, they also have hybrid environments that can get complex to support, and, you know, we want to be able to provide them with a unified platform, a unified experience, regardless of where the workload is running or what services that it's using. You know, they have that unified visibility and protection across all of the cloud workloads. We're also, you know, seeing that, especially the reason we're doing this great integration with Outpost and EKS Anywhere is that customers are, you know, taking their cloud services out to their data centers as well as to the edge locations and branch offices, so they want to be able to run EKS on their own infrastructure. So it's important that customers have that portability that regardless of whether it's a laptop or an EC2 instance or an EKS container, you know, they have that portability throughout the continuum of their cloud journey. >> That continuum is absolutely critical as we, you know, talk about cloud and application or continuum from the customer's perspective, the cloud continuum is something that is front and center for customers, I imagine in every industry. >> Oh, for sure, 'cause every industry is adopting cloud maybe at a different speed, maybe for different applications, but, you know, everybody's moving to the cloud. >> So talk to me about what you're announcing with AWS, let's get into a little bit about the partnership that CloudStrike and AWS have, let's unpack that a bit. >> Sure. You know, we've been an AWS advanced technology partner for over five years. We've had our products, we now have six of our CrowdStrike products listed on AWS Marketplace. We're an active co-sell partner and, you know, have our security competency and our well-architected certification. And really it's about building trust with our customers. You know, AWS has a lot of wonderful partner products for customers to use and it's really about building trust that, you know, we're validated, we're vetted, we have a lot of customers who are using our products with AWS, and, you know, I think it's that tight collaboration, for example, if you look at what we're doing with Humio, we've implemented a quick start program, which AWS has to get customers quickly deployed with an integration or a new capability with a partner product. And what this does is it spins up a quick cloud formation template, customer can integrate it very quickly with the AWS Firelands and then, you know, all that log information coming from the AWS containers is easily ingested into the Humio platform. And so, you know, it really reduces the time to get the integration up and running as well as pulling all that data into the Humio platform so that customers can, like we said earlier, go back and threat hunt across, you know, different cloud service components in a quick and easy way. >> Quick and easy is good as is faster time to value. You mentioned the word trust, and, you know, we talk about trust, we've been talking about it for years as it relates to technology, but I'm curious, Jessica, in the last year and a half, if your customer conversations have changed, is trust now even more important than ever as there are so many things in flux, have you noticed any sort of change there in your customer conversations? >> Well, you know, I think trust is extensible. And over the last 10 years, CrowdStrike's done a really great job of building customer trust. And, you know, we started out as, you know, kind of primarily EDR and we've moved into prevention and now we're moving into identity protection and XDR so, you know, I see a pattern that, you know, we've built this amazing core of trust across our existing customers, and as we offer more capabilities, whether it be, you know, cloud security or XDR, identity protection, you know, customers trust us and so they're very willing to say, "ah well, I want to try out these new capabilities that CrowdStrike has because we trust you guys, you know, you've done a lot to protect our brand and, you know, really make our internal teams a lot more efficient and a lot smarter." So, you know, I think while trust is important, it's also something that we get to carry forward as we enter new markets and continue to innovate and provide new capabilities for our customers. >> And really extending that trusted, valued partner relationship that you've already established with customers in every industry. So where can customers go? So the joint GTM customers, and you said products available in the AWS marketplace, but where do you recommend customers go to learn more about how they can work with these joint solutions that CrowdStrike and AWS have together? >> Absolutely. We have a landing page on AWS, if you Google AWS and CrowdStrike, whether it be marketplace or EKS Anywhere, Amazon outposts, we're on all the joint product pages with Amazon, as well as always going to crowdstrike.com and looking up our cloud security products. >> Got it. And last question for you, Jessica, summarize the announcement in terms of business outcomes that it's going to enable your joint customers to achieve. >> Absolutely. You know, I think it goes back to probably the primary reason is complexity. And, you know, with complexity comes risk and blind spots so being able to have a unified platform that no matter where the workload is, or the employee may be, they are protected and have, you know, a unified platform and experience to manage their security risk. >> Excellent. Jessica, thank you so much for coming on the program today, sharing with me, what's new with CrowdStrike, some of the things that you're seeing, and what you're helping customers to accomplish in a very dynamic environment, we appreciate your time and your insights. >> Thank you for having me, Lisa. >> For Jessica Alexander, I'm Lisa Martin, and you're watching theCUBE's coverage of AWS re:Invent 2021. (gentle music)

>>mhm >>Hello and welcome to this cube conversation here in Palo alto California, I'm john for a year host of the cube, we're here with a great guest Jt gear, Ceo and founder and ops Hot Startup. Jt Welcome to the cube conversation. >>Hey, that sound, thanks for having me. It sounds like we know each other, we used to run into each other at meat out. So yeah, >>it's fun to talk to you because I know you're, you know, scratching the devops it from the beginning before devops was devops before infrastructure of code was infrastructure as code. All that's played out. So it's really a great ride. I know you had a good time doing it a lot of action though. If you look at devops it's kind of like this new, I won't say devops two point because it kind of cliche but you're starting to see the mature ization of companies besides the early adopters and the people who are hardcore adopting and they realize this is amazing and then they? Re platform in the cloud and they go great, let's do more and next thing, you know, they have an operations issue and they got a really kind of stabilize and then also not break anything. So this is kind of the wheelhouse of what you guys are doing in ops reminds me of no ops, no operations, you know, we don't want to have a lot of extra stuff. This is a big thing. Take a but take them in to explain the company, you're what you guys stand for and what you're all about. >>Yeah, so you know, our main focus is more on the operation side, so, you know the reason why you move to cloud or the reason why you have devops practices, you want to go fast. Um but you know when you're building cloud infrastructure, you have to make trade offs right? You have to maybe some environment, maybe you have to optimize for S L A. And maybe another workload, you have to optimize for um you know, maybe costs, right? So what we're on a mission to do is to make sure that companies are able to make the right trade offs, right? We help companies to make sure all their workload, every single resource in the cloud is aligned with the business needs, you know, so we do a lot of cool things by like, you know, bringing accountability mapping and we're close to different genes. But yeah, the end goal is, can we make sure that every single resource on data Bs is aligned with the business needs >>and they're also adding stuff. Every reinvent zillion more services get announced. So a lot, a lot of stuff going on, I gotta ask you while I got you here, what is the definition of cloud apps these days, from your standpoint and why is it important? A lot of folks are looking at this and they want to have stable operations. They love the cloud really can't deny the cloud value at all. But cloud ops has become a big topic. What is cloud apps and why is it important. >>Right? I mean, first of all, Like you just mentioned, right? Like Amazon keeps on launching more services. It's over 200. So the environment is very complex, Right? And then mm complexity within the services is uh pretty uh you really need to be the main expert for example, know everything about do So, you know, our question to us is, let's say if you find a critical issue, uh let's say you want to uh you know, enable multi AZ on your RDS for example. Uh and it's critical because you know, you're running a uh high availability workloads on AWS. How do you follow up on that right to us. Operation is how do you build a cloud backlog? How do you prioritize, how do you come together as a team to actually remediate those issues? No one is tackling that job, everyone's surfaces like, hey, here's 1000 things that are wrong with your environment. No one is focused on like how do you go from these issues to prioritization to backlog to actually coming together as a team. You know, I've been fixing some of those issues. That's that's what operation means is >>I know it's totally hard because sometimes I don't even know what's going on. I gotta ask you why, why is it harder now? Why are people, I mean I get the impression that people like looking the other way? I hope it goes the problem kind of goes away. What are the challenges? What's the big blocker from getting at the root cause or trying to solve these problems? What's the big thing that's holding people back? >>Yeah, I mean, when I first got into, you know, I t you know, I was working in data center and every time we needed a server, you know, we have to ask for approvals, right? And you finally got a server, but nowadays anyone could provision resources. And normally you have different people within the team's provisioning resources and you can have hundreds of different teams who are provisioning resources. So the complexity uh and the speed that we are, you know, provisioning resources across multiple people, it just continues to go higher and higher. So that's why uh you know, on the surface it might look that hey, this, you know, maybe the biggest instance uh is, you know, aligned with the business needs, you know, looking at the changes, it's hard to know, are those aligned with the business? They're not? So that's that's that's where the complexity and player. >>So the question I get a lot from people we talk about devops and cloud, cloud apps or cloud management or whatever kind of buzz words out there, it kind of comes down to cloud apps and cloud management seems to be the category, people focus on. How is cloud ops different then? Say the traditional cloud management and what impact does it have for customers and why should they care and what do they need an option. >>Right. So one of the things we do uh and and we do think that cloud operation is sort of an evolution from cloud management. We make sure that Every single resource 1st, first of all blondes and workload. So and you know, workload could be a group of microservices uh and then uh you know every single workload has owners like define owners who are responsible for making sure they managed budget that they're responsible for security that normally doesn't exist. Right? Cloud is this black box, you know where multiple people are provisioning resources, you know, everyone tries to sort of build sort of a structure to kind of see like what are these resources for? What are these resources for as part of onboarding to end up? So what we do, we actually, you know, analyze all your metadata. We create like 56 workloads and then we say here is a bucket where there's there, this is totally unassigned, right? And then we actually walked them through assigning different roles and also we walk them through to kinda looking under this unallocated resources and assign resources for those as well. So once you're done, every single resource has clear definition, right? Is this a compliant? Uh you know hip hop workload, what are the run books, what is this for? John I don't know if he heard that before. Sometimes there are workloads running and how people don't know, I don't even know who is the owner, right? So after you're done with an office and after you're managing and uh, you know, uh, managing your workload on and off, you have full visibility and clear understanding of what are the. It's funny, it's >>funny you mentioned the workloads being kind of either not knowing the owners, but also we see people um, with the workloads sometimes it's like throwing a switch and leaving the hose on the water on. And next thing you know, they get the bill. They're like, oh my God, what happened? Why did I leave? What, what is this? So there's a lot of things that you could miss. This brings up the point you just said and what you said earlier aligning resources across the cloud uh and and having accountability. And then you, you mentioned at the top of this interview that aligning with the business needs. I find that fastest. I would like to take him in to explain because it sounds really hard. I get how you can align the resources and do some things, identify what's going on, accountability kind of map that that's, that's good tech. How does that, how do you get that to the alignment on the business side. >>Yeah. I mean we start by, first of all, like I said, you know, we use machine learning to play these workloads? And then we asked basic questions about the workload. You know, what is this workload for? Uh Do you need to meet with any kind of compliance is for this workload? Uh What is your S. O. A. For this workload? You know, depending on that. We we make recommendations. Uh So we kind of ask those questions and we also walk them through where they create roles. Like we asked who was responsible for creating budgets or managing security for this workload and guess what also the you know the bucket where resources are allocated for. We ask for you know, owners for that as well like in this bucket who's the owner for who's going to monitor the budget and things like that. So you know we asked, you know, we start by just asking the question, having teams complete that sort of information and also you know, why do you a little bit more information on how this aligns with the business needs? You know, >>talk about the complexity side of it. I love that conversation around the number of services. You said 200 services depending how you count what you call services in the thousands of so many different things uh knobs to turn on amazon uh web services. So why are people um focused on the complexity and the partnering side? Because you know, it's the clouds at E. P. I. Based system. So you're dealing with a lot of different diverse resources. So you have complexity and diversity. Can you talk me through how that works? Because that's that seems to be a tough beast to tame the difference between the complexity of services and also working with other people. >>Yeah for sure like this this normal to have um you know maybe thousands of lambda functions in their application. We're working with a customer where within last month there were nine million containers that launched and got terminated right there, pretty much leveraging, auto scaling and things like that. So these environments are like very complex. You know, there's a lot of moving pieces even, you know, depending on the type of services they're using. So again what we do, you know we when we look at tags and we look at other variables like environments and we look at who's provisioning resources, those resources and we try to group them together and that way there's accountability uh you know if the cost goes up for one workload were able to show that team like your cost is going up uh And also we can show uh unallocated bucket that hey within last week Your cost is you know, $4,000 higher in the unallocated bucket. Where would you like to move this these resources to just like an ongoing game. You >>know, you know jt I was talking with my friend jerry Chen is that Greylock partners is a V. C. Has been on the cube many times a couple of years ago. We're talking about how you can build a business within the cloud, in the shadows of the clouds, what he called it, but I called it more the enabling side and and that's happened now, you're seeing the massive growth. I'm also talking to some C X O C IOS or CSOs and they're like trying to figure out which companies that are evolving and growing to be to buy from, get to get the technology. Uh and they always say to me john I'm looking for game changing kind of impact. I'm looking for the efficiency and you know, enablement, the classic kind of criteria. So how would you guys position yourself to those buyers out there that might want to look at you guys as a solution and ups what game changing aspect of what you do is out there, how would you talk to that that C I O or C. So or buyer um out in the end the enterprise and the thieves ran his piece. What would you say to them? >>Yeah, I think the biggest uh advantage and I think right now it's a necessity, you hear these stories where, you know, people provision resources, they don't even know which project is it for. It's just very hard to govern the cloud environment, but I believe we're the only tool. Mhm where you want to compromise on the speed, right? The whole reason um cloud but they want to innovate faster. No one wants to follow that. Right? But I think what's important. We need to make sure everything is aligned with the business value. Uh, we allow people to do that. You know, we, we, we can both fast at the same time. You can have some sort of guard rails. So there are proper ownership. There's accountability. People are collaborating and people are also rightsizing terminating resources, they're not using. It's like, you know, I think if companies are looking for a tool that's gonna drive better accountability on how people build and collaborate on cloud, I think reply the best solution. >>So people are evolving with the cloud and you mentioned terminating services. That's a huge deal in cloud. Native things are being spun up and turned off all the time. So you need to have good law, You have a good visibility, observe ability is one of the hottest buzzwords out there. We see a zillion companies saying, hey, we're observe ability, which is to me is just monitoring stuff. They can sure you're tracking everything. So when you have all this and you start to operationalize this next gen, next level cloud scale, cost optimization and visibility is huge. Um, what is the, what is the secret sauce uh, for that you guys offer? Because the change management is a big 12 teams are changing too cost team accountability. All this is kind of, it's not just speeds and feeds, there's, it's kind of intersection of both. What's your take on that reaction to that? >>Yeah, I think it's the Delta. Right? So change management, What you're really looking for is not a, like a fire hose, you're looking for. What changed what the root cause who did it, what happened? Right. Because it's totally normal for someone to provision maybe thousands or even millions containers. But how many of those got shut down? What is the delta and uh, you know, if there is a, there is an anomaly, what is the root cause? Right? Uh, how we fix it. So you know the way we've changed managers, change management is a lot different. We really get to the root cause analysis and we really help companies to make, really show what changed and how they can take action to a media. But if there were issues, >>I want to put a little plug in for you guys. I noticed you guys have a really strong net promoter score. You have happy customers also get partners. A lot of enablement there. You kind of got a lot of things going on. Um, explain what you guys are all about. How did you get here? What's the day in the life of a customer that you're serving? Why then why are the scores so high? Um, take us through a use case of someone getting that value. >>Yeah. So I, I come from like a consulting background, john so you know, I was migrating companies to read the Bs when the institute was in beta and then I, you know, founded a consulting company over 100 employees. Really successful interview. S premier partner called in clouds. And so Enos was born there because because you know it was, it was born out a consulting company, there are a lot of other partners who are leveraging the tools to help their customers and it goes back to our point earlier, john like amazon has to wonder services, right? We are noticing customers are open to work with partners and uh you know with different partners that really helped them to make sure they're making the right decisions when they are building on cloud. So a lot of the partners, a lot of the consulting companies are leveraging uh and hopes to deliver value to their customers as far as uh you know how we actually operate. You know, we pay attention to uh you know what, what customers are looking for, what, where are the next sort of challenges uh you know, customers are facing in a cloud environment world like super obsessed, you know, like we're trying to figure out how do we make sure every single resource is aligned with the business value without slowing companies down so that really drives us, we're constantly welcome customers to stay true to the admission >>and that's the ethos of devops moving fast. The old quote Mark Zuckerberg used to have move fast, break stuff and then he revised it to move move fast and make it stable, which is essentially operational thing. Right, so you're starting to see that maturity, I noticed that you guys also have a really cool pricing model, very easy to get in and you have a high end too. So talk us through about how to engage with you guys, how do people get involved? Just click and just jump in there, buying software buying services, take a minute to explain how people can, can work with you. >>Yeah, it's just, it's just signing up on our site, you know, our pricing is tier model, uh you know, once you sign up, if you do need help with, you know, remediating high risk issues we can bring in partners, we have a strong partner ecosystem. Uh we could definitely help you do interviews to the right partners but it's as simple as just signing up and just taking me out. First thing I guess. >>Jt great chatting with you have been there from early days of devops, born in the field, getting, getting close to the customers and you mentioned ec two and beta, they just celebrate their 15th birthday and I remember one of my starts that didn't actually get off the off the blocks, they didn't even have custom domains at that time was still the long remember the long you are else >>everything was ephemeral like when you restart server, everything will go away a cool >>time. And I just remember saying to myself man, every entrepreneur is going to use this service who would ever go out and buy and host the server. So you were there from the beginning and it's been great to see the success. Thanks for coming on the cube >>all That's >>okay. Jt thanks so much as a cube conversation here in Palo alto. I'm john for your host. Thanks for watching. Mhm.

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante nvidia wants to completely transform enterprise computing by making data centers run 10x faster at one tenth the cost and video's ceo jensen wang is crafting a strategy to re-architect today's on-prem data centers public clouds and edge computing installations with a vision that leverages the company's strong position in ai architectures the keys to this end-to-end strategy include a clarity of vision massive chip design skills a new arm-based architecture approach that integrates memory processors i o and networking and a compelling software consumption model even if nvidia is unsuccessful at acquiring arm we believe it will still be able to execute on this strategy by actively participating in the arm ecosystem however if its attempts to acquire arm are successful we believe it will transform nvidia from the world's most valuable chip company into the world's most valuable supplier of integrated computing architectures hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll explain why we believe nvidia is in the right position to power the world's computing centers and how it plans to disrupt the grip that x86 architectures have had on the data center for decades the data center market is in transition like the universe the cloud is expanding at an accelerated pace no longer is the cloud an opaque set of remote services i always say somewhere out there sitting in a mega data center no rather the cloud is extending to on-premises data centers data centers are moving into the cloud and they're connecting through adjacent locations that create hybrid interactions clouds are being meshed together across regions and eventually will stretch to the far edge this new definition or view of cloud will be hyper distributed and run by software kubernetes is changing the world of software development and enabling workloads to run anywhere open apis external applications expanding the digital supply chains and this expanding cloud they all increase the threat surface and vulnerability to the most sensitive information that resides within the data center and around the world zero trust has become a mandate we're also seeing ai being injected into every application and it's the technology area that we see with the most momentum coming out of the pandemic this new world will not be powered by general purpose x86 processors rather it will be supported by an ecosystem of arm-based providers in our opinion that are affecting an unprecedented increase in processor performance as we have been reporting and nvidia in our view is sitting in the poll position and is currently the favorite to dominate the next era of computing architecture for global data centers public clouds as well as the near and far edge let's talk about jensen wang's clarity of vision for this new world here's a chart that underscores some of the fundamental assumptions that he's leveraging to expand his market the first is that there's a lot of waste in the data center he claims that only half of the cpu cores deployed in the data center today actually support applications the other half are processing the infrastructure all around the applications that run the software defined data center and they're terribly under utilized nvidia's blue field three dpu the data processing unit was described in a blog post on siliconangle by analyst zias caravala as a complete mini server on a card i like that with software defined networking storage and security acceleration built in this product has the bandwidth and according to nvidia can replace 300 general purpose x86 cores jensen believes that every network chip will be intelligent programmable and capable of this type of acceleration to offload conventional cpus he believes that every server node will have this capability and enable every packed of every packet and every application to be monitored in real time all the time for intrusion and as servers move to the edge bluefield will be included as a core component in his view and this last statement by jensen is critical in our opinion he says ai is the most powerful force of our time whether you agree with that or not it's relevant because ai is everywhere an invidious position in ai and the architectures the company is building are the fundamental linchpin of its data center enterprise strategy so let's take a look at some etr spending data to see where ai fits on the priority list here's a set of data in a view that we often like to share the horizontal axis is market share or pervasiveness in the etr data but we want to call your attention to the vertical axis that's really really what really we want to pay attention today that's net score or spending momentum exiting the pandemic we've seen ai capture the number one position in the last two surveys and we think this dynamic will continue for quite some time as ai becomes the staple of digital transformations and automations an ai will be infused in every single dot you see on this chart nvidia's architectures it just so happens are tailor made for ai workloads and that is how it will enter these markets let's quantify what that means and lay out our view of how nvidia with the help of arm will go after the enterprise market here's some data from wikibon research that depicts the percent of worldwide spending on server infrastructure by workload type here are the key points first the market last year was around 78 billion dollars worldwide and is expected to approach 115 billion by the end of the decade this might even be a conservative figure and we've split the market into three broad workload categories the blue is ai and other related applications what david floyer calls matrix workloads the orange is general purpose think things like erp supply chain hcm collaboration basically oracle saps and microsoft work that's being supported today and of course many other software providers and the gray that's the area that jensen was referring to is about being wasted the offload work for networking and storage and all the software defined management in the data centers around the world okay you can see the squeeze that we think compute infrastructure is gonna gonna occur around that orange area that general-purpose workloads that we think is going to really get squeezed in the next several years on a percentage basis and on an absolute basis it's really not growing nearly as fast as the other two and video with arm in our view is well positioned to attack that blue area and the gray area those those workload offsets and the new emerging ai applications but even the orange as we've reported is under pressure as for example companies like aws and oracle they use arm-based designs to service general purpose workloads why are they doing that cost is the reason because x86 generally and intel specifically are not delivering the price performance and efficiency required to keep up with the demands to reduce data center costs and if intel doesn't respond which we believe it will but if it doesn't act arm we think will get 50 percent of the general purpose workloads by the end of the decade and with nvidia it will dominate the blue the ai and the gray the offload work when we say dominate we're talking like capture 90 percent of the available market if intel doesn't respond now intel they're not just going to sit back and let that happen pat gelsinger is well aware of this in moving intel to a new strategy but nvidia and arm are way ahead in the game in our view and as we've reported this is going to be a real challenge for intel to catch up now let's take a quick look at what nvidia is doing with relevant parts of its pretty massive portfolio here's a slide that shows nvidia's three chip strategy the company is shifting to arm-based architectures which we'll describe in more detail in a moment the slide shows at the top line nvidia's ampere architecture not to be confused with the company ampere computing nvidia is taking a gpu centric approach no surprise obvious reasons there that's their sort of stronghold but we think over time it may rethink this a little bit and lean more into npus the neural processing unit we look at what apple's doing what tesla are doing we see opportunities for companies like nvidia to really sort of go after that but we'll save that for another day nvidia has announced its grace cpu a nod to the famous computer scientist grace hopper grace is a new architecture that doesn't rely on x86 and much more efficiently uses memory resources we'll again describe this in more detail later and the bottom line there that roadmap line shows the bluefield dpu which we described is essentially a complete server on a card in this approach using arm will reduce the elapsed time to go from chip design to production by 50 we're talking about shaving years down to 18 months or less we don't have time to do a deep dive into nvidia's portfolio it's large but we want to share some things that we think are important and this next graphic is one of them this shows some of the details of nvidia's jetson architecture which is designed to accelerate those ai plus workloads that we showed earlier and the reason is that this is important in our view is because the same software supports from small to very large including edge systems and we think this type of architecture is very well suited for ai inference at the edge as well as core data center applications that use ai and as we've said before a lot of the action in ai is going to happen at the edge so this is a good example of leveraging an architecture across a wide spectrum of performance and cost now we want to take a moment to explain why the moved arm-based architectures is so critical to nvidia one of the biggest cost challenges for nvidia today is keeping the gpu utilized typical utilization of gpu is well below 20 percent here's why the left hand side of this chart shows essentially racks if you will of traditional compute and the bottlenecks that nvidia faces the processor and dram they're tied together in separate blocks imagine there are thousands thousands of cores in a rack and every time you need data that lives in another processor you have to send a request and go retrieve it it's very overhead intensive now technologies like rocky are designed to help but it doesn't solve the fundamental architectural bottleneck every gpu shown here also has its own dram and it has to communicate with the processors to get the data i.e they can't communicate with each other efficiently now the right hand side side shows where nvidia is headed start in the middle with system on chip socs cpus are packaged in with npus ipu's that's the image processing unit you know x dot dot dot x pu's the the alternative processors they're all connected with sram which is think of that as a high speed layer like an layer one cache the os for the system on a chip lives inside of this and that's where nvidia has this killer software model what they're doing is they're licensing the consumption of the operating system that's running this system on chip in this entire system and they're affecting a new and really compelling subscription model you know maybe they should just give away the chips and charge for the software like a razer blade model talk about disruptive now the outer layer is the the dpu and the shared dram and other resources like the ampere computing the company this time cpus ssds and other resources these are the processors that will manage the socs together this design is based on nvidia's three chip approach using bluefield dpu leveraging melanox that's the networking component the network enables shared dram across the cpus which will eventually be all arm based grace lives inside the system on a chip and also on the outside layers and of course the gpu lives inside the soc in a scaled-down version like for instance a rendering gpu and we show some gpus on the outer layer as well for ai workloads at least in the near term you know eventually we think they may reside solely in the system on chip but only time will tell okay so you as you can see nvidia is making some serious moves and by teaming up with arm and leaning into the arm ecosystem it plans to take the company to its next level so let's talk about how we think competition for the next era of compute stacks up here's that same xy graph that we love to show market share or pervasiveness on the horizontal tracking against next net score on the vertical net score again is spending velocity and we've cut the etr data to capture players that are that are big in compute and storage and networking we've plugged in a couple of the cloud players these are the guys that we feel are vying for data center leadership around compute aws is a very strong position we believe that more than half of its revenues comes from compute you know ec2 we're talking about more than 25 billion on a run rate basis that's huge the company designs its own silicon graviton 2 etc and is working with isvs to run general purpose workloads on arm-based graviton chips microsoft and google they're going to follow suit they're big consumers of compute they sell a lot but microsoft in particular you know they're likely to continue to work with oem partners to attack that on-prem data center opportunity but it's really intel that's the provider of compute to the likes of hpe and dell and cisco and the odms which are the odms are not shown here now hpe let's talk about them for a second they have architectures and i hate to bring it up but remember the machine i know it's the butt of many jokes especially from competitors it had been you know frankly hpe and hp they deserve some of that heat for all the fanfare and then that they they put out there and then quietly you know pulled the machine or put it out the pasture but hpe has a strong position in high performance computing and the work that it did on new computing architectures with the machine and shared memories that might be still kicking around somewhere inside of hp and could come in handy for some day in the future so hpe has some chops there plus hpe has been known hp historically has been known to design its own custom silicon so i would not count them out as an innovator in this race cisco is interesting because it not only has custom silicon designs but its entry into the compute business with ucs a decade ago was notable and they created a new way to think about integrating resources particularly compute and networking with partnerships to add in the storage piece initially it was within within emc prior to the dell acquisition but you know it continues with netapp and pure and others cisco invests they spend money investing in architectures and we expect the next generation of ucs oh ucs2 ucs 2.0 will mark another notable milestone in the company's data center business dell just had an amazing quarterly earnings report the company grew top line revenue by around 12 percent and it wasn't because of an easy compare to last year dells is simply executing despite continued softness in the legacy emc storage business laptop the laptop demand continued to soar in dell server business it's growing again but we don't see dell as an architectural innovator per se in compute rather we think the company will be content to partner with suppliers whether it's intel nvidia arm-based partners or all of the above dell we think will rely on its massive portfolio its excellent supply chain and execution ethos to compete now ibm is notable for historical reasons with its mainframe ibm created the first great compute monopoly before it unwind and wittingly handed it to intel along with microsoft we don't see ibm necessarily aspiring to retake that compute platform mantle that once once held with mainframes rather red hat in the march to hybrid cloud is the path that we think in our view is ibm's approach now let's get down to the elephants in the room intel nvidia and china inc china is of course relevant because of companies like alibaba and huawei and the chinese chinese government's desire to be self-sufficient in semiconductor technology and technology generally but our premise here is that the trends are favoring nvidia over intel in this picture because nvidia is making moves to further position itself for new workloads in the data center and compete for intel's stronghold intel is going to attempt to remake itself but it should have been doing this seven years ago what pat gelsinger is doing today intel is simply far behind and it's going to take at least a couple years for them to really start to to make inroads in this new model let's stay on the nvidia v intel comparison for a moment and take a snapshot of the two companies here's a quick chart that we put together with some basic kpis some of these figures are approximations or they're rounded so don't stress over it too much but you can see intel is an 80 billion dollar company 4x the size of nvidia but nvidia's market cap far exceeds that of intel why is that of course growth in our view it's justified due to that growth and nvidia's strategic positioning intel used to be the gross margin king but nvidia has much higher gross margins interesting now when it comes down to free cash flow intel is still dominant as it pertains to the balance sheet intel is way more capital intensive than nvidia and as it starts to build out its foundries that's going to eat into intel's cash position now what we did is we put together a little pro forma on the third column of nvidia plus arm circa let's say the end of 2022. we think they could get to a run rate that is about half the size of intel and that can propel the company's market cap to well over half a trillion dollars if they get any credit for arm they're paying 40 billion dollars for arm a company that's you know sub 2 billion the risk is that because of the arm because the arm deal is based on cash plus tons of stock it could put pressure on the market capitalization for some time arm has 90 percent gross margins because it pretty much has a pure license model so it helps the gross margin line a little bit for this in this pro forma and the balance sheet is a swag arm has said that it's not going to take on debt to do the transaction but we haven't had time to really dig into that and figure out how they're going to structure it so we took a took a swag in in what we would do with this low interest rate environment but but take that with a grain of salt we'll do more research in there the point is given the momentum and growth of nvidia its strategic position in ai is in its deep engineering they're aimed at all the right places and its potential to unlock huge value with arm on paper it looks like the horse to beat if it can execute all right let's wrap up here's a summary look the architectures on which nvidia is building its dominant ai business are evolving and nvidia is well positioned to drive a truck right to the enterprise in our view the power has shifted from intel to the arm ecosystem and nvidia is leaning in big time whereas intel it has to preserve its current business while recreating itself at the same time this is going to take a couple of years but intel potentially has the powerful backing of the us government too strategic to fail the wild card is will nvidia be successful in acquiring arm certain factions in the uk and eu are fighting the deal because they don't want the u.s dictating to whom arm can sell its technology for example the restrictions placed on huawei for many suppliers of arm-based chips based on u.s sanctions nvidia's competitors like broadcom qualcomm at all are nervous that if nvidia gets armed they will be at a competitive disadvantage they being invidious competitors and for sure china doesn't want nvidia controlling arm for obvious reasons and it will do what it can to block the deal and or put handcuffs on how business can be done in china we can see a scenario where the u.s government pressures the uk and eu regulators to let this deal go through look ai and semiconductors you can't get much more strategic than that for the u.s military and the u.s long-term competitiveness in exchange for maybe facilitating the deal the government pressures nvidia to guarantee some feed to the intel foundry business while at the same time imposing conditions that secure access to arm-based technology for nvidia's competitors and maybe as we've talked about before having them funnel business to intel's foundry actually we've talked about the us government enticing apple to do so but it could also entice nvidia's competitors to do so propping up intel's foundry business which is clearly starting from ground zero and is going to need help outside of intel's own semiconductor manufacturing internally look we don't have any inside information as to what's happening behind the scenes with the us government and so forth but on its earning call on its earnings call nvidia said they're working with regulators that are on track to complete the deal in early 2022. we'll see okay that's it for today thank you to david floyer who co-created this episode with me and remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you're going to do is search breaking analysis podcast and you can always connect with me on twitter at dvalante or email me at david.valante siliconangle.com i always appreciate the comments on linkedin and in the clubhouse please follow me so you can be notified when we start a room and riff on these topics and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

>> Thank you for joining us today for this session entitled, Deploy any Workload as a Service, When General Purpose Technology isn't Enough. This session today will be on our HPE GreenLake platform. And my name is Mark Seamans, and I'm a member of our GreenLake cloud services team. And I'll be kind of leading you through the material today which will include both a slide presentation as well as an interactive demo to get some experience in terms of how the process goes for interacting with your initial experience with our GreenLake system. So, let's go ahead and get started. One of the things that we've noticed over the last decade and I'm sure that you have as well has been the tremendous focus on accelerating business while concurrently trying to increase agility and to reduce costs. And one of the ways a lot of businesses have gone about doing that has been leveraging a cloud based technology set. And in many cases, that's involved moving some of the workloads to the public cloud. And so with that much said, though, while organizations have been able to enjoy that cost control and the agility associated with the public cloud. What we've seen is that the easy to move workloads have been moved but there's a significant amount as much as 70% in many cases of workloads that organizations run which still remain on prem. And there's reasons for that. Some cases it's due to data privacy and security concerns. Other times it's due to latency of really needing high-performance access to data. And the other times, it's really just related to the interconnected nature of systems and that you need to have a whole bunch of systems which form an overall experience and they need to be located close together. So, one of the challenges that we've worked with customers and have actually developed our GreenLake solution to address is this idea of trying to achieve this cloud-like experience for all of your apps and data in a way that leverages the best of the public cloud with also that same type of experience delivered on premise. So as you think about some of the challenges, again, we touched on this that customers are trying to address. One of the ones is this idea of agility, being able to move quickly and to be able to take a set of IT resources that you have and deploy them for different use cases and different models. So, it's one of the things as we built GreenLake, we really had a strong focus on is how do we provide a common foundation, a common framework to deliver that kind of agility. The next one is this term on the top right called scale. And one of the words you may hear is you hear cloud talked about regularly is this notion of what's called elasticity and the ability to have something stretch and get larger kind of on an on demand basis. That's another challenge and premise that we've really tried to work through. And you'll see how we've addressed that. Now, obviously, as you do this, you can achieve scale if you just put a ton of equipment in place much more maybe than you need at any given time but with that comes a lot of costs. And so as you think about wanting to have an agile and flexible system, what you'd also like is something where the costs flexes as your needs grow and it's elastic and that it can get larger and then it can get smaller as needed as well. So, we'll talk about how we do that with our GreenLake solution. And then finally it's complexity, it's trying to abstract away the vision for people of having to be aware of all the complexity it takes to build these systems and provide a single interface, a single experience for people to manage all of their IT assets. So we do that through this solution called HPE GreenLake and really we call it the cloud that comes to you. And as you think about what we're really trying to do here is take the notion of a cloud from being a place where people have thought about the public cloud and turning that to an idea of the cloud being an experience. And so it's regardless of whether it's in the public cloud or running on premise or as is the case with GreenLake, whether it's a mixture of those and maybe even a mixture of multiple public clouds with on-prem experience, the cloud now becomes something you experience and that you leverage as opposed to a place where you have an account and that can include edge computing combined with co-location or data center based computing. It could include equipment stored in your own data center and certainly it can include resources in the public cloud. So, let's take a look at how we go about delivering the experience and what some of those benefits are as we put these solutions in place. So, as you think about why you'd want to do this and the benefits you get from GreenLake, what we've seen in terms of both working with customers and actually having studies done with analysts is the benefits are numerous, but they come in areas that are shown here, one time to deployment. And that once you get this flexible and easily to manage environment in place with what we'll show you are these prebuilt, pre-configured and managed as a service solutions, your time to deployment for putting new workloads in place can shrink dramatically. The next in terms of having these pre-configured solutions and combining both the hardware and software technology with a set of managed services through our GreenLake managed services team, what you can do is dramatically reduce the risk of putting a new workload in place. So for example, if you wanted to deploy virtual desktop infrastructure and maybe you haven't done that in the past, you can leverage a GreenLake VDI solution along with GreenLake management services to very predictably and very reliably put that solution in place. So you're up and running focusing on the needs of your users with incredibly lowered risk, because this was built on a pre-validated and a pre-certified foundation. Obviously, I talked earlier about the idea with GreenLake is that you have flexibility in terms of scaling up your use of the resources, even though they're computers that may be in your data center or a colo, and also scaling them back down. So if you have workloads over time, that may be even an end of month cycle or an end to quarter cycle where certain workloads get larger and then would get smaller again, the ability with GreenLake on a consumption billing basis is there where your costs can flow as your use of the systems flow. And again, I'll show you a screen in just a few minutes, that kind of illustrates what that looks like. And then the last piece is the single pane of glass for control and insight into what's going on. And what we mean by that is not just what's going on from a cost perspective, but also what's going on from a system utilization perspective. You'll see in one of the screens I'll show that there's a system utilization report of all of your GreenLake resources that you can view at any time. And so what you can get visibility to, for example, with storage capacity as your storage capacity is being consumed over time as you generate more data, the system will tell you, hey, you're getting up to about 60, 70% utilized. And then at that point, we would be able to work with you to automatically deploy even though you won't be paying for it yet, additional storage capacity so it's ready as your needs grow to encompass that. So in terms of what are some of these services that we deliver as part of GreenLake? Well, they range and you see here a portfolio of services that we offer. If you start at the bottom, it's simple things, right? Things like compute as a service, and I'll show you examples of that today, networking as a service, hyper-converged infrastructure as a service. And then if we work our way up the stack, we move from kind of basic services to platform services, things like VMware and containers as a service. And then if we go to the top layer of this, we actually can offer complete solutions for targeted workloads. So if your need was for example, to run machine learning and AI, and you wanted to have a complete environment put in place that you could leverage for machine learning and AI and use it and consume it on a consumption as a service basis, we've got our MLOps solution that delivers that. And similarly, I mentioned earlier, VDI for virtual desktops or a solution for SAP HANA. So, the solutions range from very basic compute at the foundation all the way up to complete workload solutions that you can achieve. And the portfolio of what these are is expanding all the time. And as you'll see, you can go out to our hpe.com site and see a complete catalog of all the GreenLake services that are available. So let's take a minute and let's drill in like on that MLOps solution. And we can take a look at how that fits together and what makes that up. So, if you think about GreenLake for MLOps, it's a fast path for data scientists, and it's really oriented around the needs of data scientists within your organization who have a desire to be able to get in and start to analyze data for advantage in your business. So, what comes with an MLOps solution from GreenLake starts at the left side of the slide here with a fully curated hardware platform, including GPU based nodes, data science, optimized hardware, all the storage that you're going to need to run at scale and that performance to make these workloads work. And so that's one piece of it is a curated hardware stack for machine learning. Next in the software component, we pre-validated a whole bunch of the common stack elements that you would need. So beyond operating systems, but things for doing continuous integration, for things like TensorFlow and Jupyter notebooks are already pre-validated and delivered with this solution. So, the tools that your data scientists will need come with this, ready to go, out of the box. And then finally, as this solution gets delivered, there's a services component to it beyond just us installing this full thing and delivering a complete solution to you. But the GreenLake management services options where our services teams can work side by side with data scientists to assist them in getting up to speed on the solution, to leveraging the tools, to understanding best practices if you want those, if you want that assistance for deploying MLOps and the whole thing's delivered as a service. As similar, we similar solutions for other workloads like SAP HANA that would leverage again, different compute building blocks, but always in a way that's done for workload optimized solutions, best practice and that build up that stack. And so your experience in consuming this is always consistent, but what's running under the hood isn't just a generic solution that you might see in for example, a public cloud environment, it's a best practice, hardware optimized, software optimized environment built for each one of the workloads that we can deploy. So I like to do at this point is actually show you what's the process like for actually specifying a GreenLake solution. And maybe we'll take a look at compute as our example today. So, what I've got here is a browser experience, I'm just in my web browser, I'm on the hpe.com website and what I'd like to do. I mean the GreenLake section and I've actually clicked on this services menu and I'm going to go ahead and scroll down. And one of the things you can see here is that catalog of GreenLake services that I referenced. So, just like we showed you on the slide, this is that catalog of services that you can consume. I'm going to go to compute and we'll go about quoting a GreenLake compute solution. So we see when I clicked on that, one of the options I have is to get a price in my inbox. And I'll click on that to go in here to our GreenLake quick quote environment where if in my case here for our demonstration, I'll specify that I'd like to purchase to add to my GreenLake environment some additional general compute capability for some workloads that I might like to run. If I click on this, I go in and you notice here that I'm not going to specify server types. I'm really going to tell the system about the types of workloads that I'd like to run and the characteristics of those workloads. So for example, my workload choices would be adaptable performance or maybe densely optimized compute for highly scalable and high performance computing requirements. So, I'll select adaptable performance. I have a choice of processor types, my case, I'll pick Intel. And I then say, how many servers for the workloads that I want to run would be part of the solution. Again, in my case, maybe we'll quote a 20 server configuration. Now, as we think about the plans here, what you can see is we're really looking at the different options in terms of a balanced performance and price option which is the recommended option. But if I knew that the workloads I were going to run were more performance optimized, I could simply click on that option. And in the system under the hood does all the work to reconfigure the system. I'm not having to pick individual server options as you see. So once I picked between cost optimized balance or performance, I can go in here and select the rest of the options. Now, we'll start at the top right and you see here from a services perspective, this is where it specifies how much services content and in services assistance I'd like all the way from just doing proactive metering of my solution all the way through being able to do actual workload deployment and assistance with me physically managing the equipment myself. The other piece I'll focus on is this variable usage. And this comes back to how much of the variable time, variable capacity of additional capacity, what I like to have available in my data center for this solution. So if I know that my flex could be larger in the future of the capacity, I want to flex up and down. I might pick a slightly larger amount of flex capacity at my location as part of this solution. With that, I'd select that workload. And the less steps would be, I could click on get price and this whole thing will be packaged up and shipped to you in terms of the price of the solution. And any other details that you might like to see. And I encourage you to go out to hpe.com and to go through this process yourself for one of the workloads that might be of interest for you to get a flavor of that experience. So if we move forward, once you've deployed your GreenLake solution, one of the things you see here is that single pane of glass experience in terms of managing the system, right? We've got a single panel that all in one place provides you access to your cost information for billing, and what's driving that billing, your middle and the middle of the top center, you can see we've got information on the capacity planning but then we can actually drill in and actually look at additional things like services we offer around continuous compliance, capacity planning data for you to build and see how things like storage or filling, cost control information with recommendations around how you could reduce or minimize your costs based on the usage profile that you have. So, all of this is a fully integrated experience that can span components running both on-premise and also incorporating services that could be in the public cloud. Now, when we think about who's using this and why is this becoming attractive? You can imagine just looking at this capability that this ability to blend public cloud capabilities with on-premise or in a co-location, private data center capabilities provides tremendous power and provides tremendous flexibility for users. And so we're seeing this adopted broadly as kind of a new way, people are looking to take the advantages of cloud, but bring them into a much more self-managed or on-premise experience. And so some example, customers here include deployments in the automotive field, both at Porsche or over on the right at Zenseact, which is the autonomous driving division of Volvo where they're doing research with tremendous amounts of data to produce the best possible autonomous driving experience. And then in the center, Danfoss who is one of the world's leading manufacturers of both electric and hydraulic control components. And so as they produce components themselves, that drive an optimized management of physical infrastructure, power, liquids and cooling, they're leveraging GreenLake for the same type of control and best practice deployment of their data centers and of their IT infrastructure. So again, somebody who's innovating in their own world taking advantage of compute innovations to get the benefits of the cloud and the flexibility of a cloud-like environment but running within their own premise. And it's not just those three customers clearly. I mean, what we're seeing is, as you see on the slide, it's a unique solution in the market today. It provides the true benefits of the cloud, but with your own on-premise experience, it provides expertise in terms of services to help you take best advantage of it. And if you look at the adoption by customers, over a thousand customers in 50 countries have now deployed GreenLake based solutions as the foundation on which they're building their next generation IT architecture. So, there's a lot of unique capabilities that as we built GreenLake, that we have that really make this a single pane of glass and a very, very unified and elegant experience. So as we kind of wrap up, there's three things I want to call your attention to, one, GreenLake, which we focused a lot on today. I'd also like to call your attention to the point next services, which are an extension of those GreenLake services that I talked about earlier but there's a much broader portfolio of what Pointnext can do in delivering value for your organization. And then again, HPE financial services who much like what we do with GreenLake in this as a service consumption environment can provide a lot of financial flexibility in other models and other use cases. So, I'd encourage you to take time to learn about each of those three areas. And then there's obviously many many resources available online. And again, there's some that are listed here but it kind of as a single point takeaway from this slide, I encourage you to go to hpe.com. If you're interested in GreenLake, click on our GreenLake icon and you can take yourself through that quoting experience for what would be interesting and certainly as well for our compute solutions, there's a tremendous amount of information about the leading solutions that HPE brings to market. So with that, I hope that's been an informative set of experience. I'm thanking you for spending a little bit of time with us today and hopefully you'll take some time to learn more about GreenLake and how it might be a benefit for you within your organization. Thanks again.

>>Okay, We're now going into the technical deep dive. We're gonna geek out here a little bit. Ben Wa Dodgeville is here. He's co founder of Snowflake and president of products. And also joining us is Christian Kleinerman. Who's the senior vice president of products. Gentlemen, welcome. Good to see you. >>Yeah, you that >>get this year, they Thanks for having us. >>Very welcome. So it been well, we've heard a lot this morning about the data cloud, and it's becoming my view anyway, the linchpin of your strategy. I'm interested in what technical decisions you made early on. That that led you to this point and even enabled the data cloud. >>Yes. So? So I would say that that a crowd was built in tow in three phases. Really? The initial phase, as you call it, was it was really about 20 minutes. One regions Teoh, Data Cloud and and that region. What was important is to make that region infinity, infinity scalable, right. And that's our architectural, which we call the beauty cross to share the architectural er so that you can plug in as many were clues in that region as a Z without any limits. The limit is really the underlying prop Provide the, you know, resource is which you know, Cal provide the region as a really no limits. So So that z you know, region architecture, I think, was really the building block of the snowflake. That a cloud. But it really didn't stop there. The second aspect Waas Well, it was really data sharing. How you know munity internets within the region, how to share data between 10 and off that region between different customers on that was also enabled by architectures Because we discover, you know, compute and storage so compute You know clusters can access any storage within the region. Eso that's based off the data cloud and then really faced three Which is critical is the expansion the global expansion how we made you know, our cloud domestic layers so that we could talk You know the snowflake vision on different clouds on DNA Now we are running in three cloud on top of three cloud providers. We started with the ws and US West. We moved to assure and then uh, Google g c p On how this this crowd region way started with one crowd region as I said in the W S U S West, and then we create we created, you know, many you know, different regions. We have 22 regions today, all over the world and all over the different in the cloud providers. And what's more important is that these regions are not isolated. You know, Snowflake is one single, you know, system for the world where we created this global data mesh which connects every region such that not only there's no flex system as a whole can can be aware of for these regions, But customers can replicate data across regions on and, you know, share. There are, you know, across the planet if need be. So So this is one single, you know, really? I call it the World Wide Web. Off data that, that's, you know, is this vision of the data cloud. And it really started with this building block, which is a cloud region. >>Thank you for that. Ben White Christian. You and I have talked about this. I mean, that notion of a stripping away the complexity and that's kind of what the data cloud does. But if you think about data architectures, historically they really had no domain knowledge. They've really been focused on the technology toe ingest and analyze and prepare And then, you know, push data out to the business and you're really flipping that model, allowing the sort of domain leaders to be first class citizens if you will, uh, because they're the ones that creating data value, and they're worrying less about infrastructure. But I wonder, do you feel like customers air ready for that change? >>I I love the observation. They've that, uh, so much energy goes in in in enterprises, in organizations today, just dealing with infrastructure and dealing with pipes and plumbing and things like that and something that was insightful from from Ben Juan and and our founders from from Day one WAAS. This is a managed service. We want our customers to focus on the data, getting the insights, getting the decisions in time, not just managing pipes and plumbing and patches and upgrades, and and the the other piece that it's it's it's an interesting reality is that there is this belief that the cloud is simplifying this, and all of a sudden there's no problem but actually understanding each of the public cloud providers is a large undertaking, right? Each of them have 100 plus services, uh, sending upgrades and updates on a constant basis. And that just distracts from the time that it takes to go and say, Here's my data. Here's my data model. Here's how it make better decisions. So at the heart of everything we do is we wanna abstract the infrastructure. We don't wanna abstract the nuance of each of the cloud providers. And as you said, have companies focus on This is the domain expertise or the knowledge for my industry. Are all companies ready for it? I think it's a It's a mixed bag. We we talk to customers on a regular basis every way, every week, every day, and some of them are full on. They've sort of burned the bridges and, like I'm going to the cloud, I'm going to embrace a new model. Some others. You can see the complete like, uh, shock and all expressions like What do you mean? I don't have all these knobs. 2 to 3 can turn. Uh, but I think the future is very clear on how do we get companies to be more competitive through data? >>Well, Ben Ben. Well, it's interesting that Christian mentioned to manage service and that used to be in a hosting. Guys run around the lab lab coats and plugging things in. And of course, you're looking at this differently. It's high degrees of automation. But, you know, one of those areas is workload management. And I wonder how you think about workload management and how that changes with the data cloud. >>Yeah, this is a great question. Actually, Workload management used to be a nightmare. You know, traditional systems on it was a nightmare for the B s and they had to spend most a lot of their time, you know, just managing workloads. And why is that is because all these workloads are running on the single, you know, system and a single cluster The compete for resources. So managing workload that always explain it as explain Tetris, right? You had the first to know when to run. This work will make sure that too big workers are not overlapping. You know, maybe it really is pushed at night, you know, And And you have this 90 window which is not, you know, efficient. Of course, for you a TL because you have delays because of that. But but you have no choice, right? You have a speaks and more for resource is and you have to get the best out of this speaks resource is. And and for sure you don't want to eat here with her to impact your dash boarding workload or your reports, you know, impact and with data science and and And this became a true nine man because because everyone wants to be that a driven meaning that all the entire company wants to run new workers on on this system. And these systems are completely overwhelmed. So so, well below management was, and I may have before Snowflake and Snowflake made it really >>easy. The >>reason is it's no flag. We leverage the crowds who dedicates, you know, compute resources to each work. It's in the snowflake terminology. It's called a warehouse virtual warehouse, and each workload can run in its own virtual warehouse, and each virtual warehouse has its own dedicated competition resources. It's on, you know, I opened with and you can really control how much resources which workload gas by sizing this warehouses. You know, I just think the compute resources that they can use When the workload, you know, starts to execute automatically. The warehouse, the compute resources are turned off, but turned on by snowflake is for resuming a warehouse and you can dynamically resized this warehouse. It can be done by the system automatically. You know if if the conference see of the workload increases or it can be done manually by the administrator or, you know, just suggesting, you know, uh, compute power. You know, for each workload and and the best off that model is not only it gives you a very fine grain. Control on resource is that this work can get Not only workloads are not competing and not impacting it in any other workload. But because of that model, you can hand as many workload as you want. And that's really critical because, as I said, you know, everyone in the organization wants to use data to make decisions, So you have more and more work roads running. And then the Patriots game, you know, would have been impossible in in a in a centralized one single computer, cross the system On the flip side. Oh, is that you have to have a zone administrator off the system. You have to to justify that. The workload is worth running for your organization, right? It's so easy in literally in seconds, you can stand up a new warehouse and and start to run your your crazy on that new compute cluster. And of course, you have to justify if the cost of that because there is a cost, right, snowflake charges by seconds off compute So that cost, you know, is it's justified and you have toe. You know, it's so easy now to hire new workflow than you do new things with snowflake that that that you have to to see, you know, and and look at the trade off the cost off course and managing costs. >>So, Christian been while I use the term nightmare, I'm thinking about previous days of workload management. I mean, I talked to a lot of customers that are trying to reduce the elapsed time of going from data insights, and their nightmare is they've got this complicated data lifecycle. Andi, I'm wondering how you guys think about that. That notion of compressing elapsed time toe data value from raw data to insights. >>Yeah, so? So we we obsess or we we think a lot about this time to insight from the moment that an event happens toe the point that it shows up in a dashboard or a report or some decision or action happens based on it. There are three parts that we think on. How do we reduce that life cycle? The first one which ties to our previous conversation is related toe. Where is their muscle memory on processes or ways of doing things that don't actually make us much sense? My favorite example is you say you ask any any organization. Do you run pipelines and ingestion and transformation at two and three in the morning? And the answer is, Oh yeah, we do that. And if you go in and say, Why do you do that? The answer is typically, well, that's when the resource is are available Back to Ben Wallace. Tetris, right? That's that's when it was possible. But then you ask, Would you really want to run it two and three in the morning? If if you could do it sooner, we could do it. Mawr in time, riel time with when the event happened. So first part of it is back to removing the constraints of the infrastructures. How about running transformations and their ingestion when the business best needs it? When it's the lowest time to inside the lowest latency, not one of technology lets you do it. So that's the the the easy one out the door. The second one is instead of just fully optimizing a process, where can you remove steps of the process? This is where all of our data sharing and the snowflake data marketplace come into place. How about if you need to go in and just data from a SAS application vendor or maybe from a commercial data provider and imagine the dream off? You wouldn't have to be running constant iterations and FTP s and cracking C S V files and things like that. What if it's always available in your environment, always up to date, And that, in our mind, is a lot more revolutionary, which is not? Let's take away a process of ingesting and copying data and optimize it. How about not copying in the first place? So that's back to number two on, then back to number three is is what we do day in and day out on making sure our platform delivers the best performance. Make it faster. The combination of those three things has led many of our customers, and and And you'll see it through many of the customer testimonials today that they get insights and decisions and actions way faster, in part by removing steps, in part by doing away with all habits and in part because we deliver exceptional performance. >>Thank you, Christian. Now, Ben Wa is you know, we're big proponents of this idea of the main driven design and data architecture. Er, you know, for example, customers building entire applications and what I like all data products or data services on their data platform. I wonder if you could talk about the types of applications and services that you're seeing >>built >>on top of snowflake. >>Yeah, and And I have to say that this is a critical aspect of snowflake is to create this platform and and really help application to be built on top of this platform. And the more application we have, the better the platform will be. It is like, you know, the the analogies with your iPhone. If your iPhone that no applications, you know it would be useless. It's it's an empty platforms. So So we are really encouraging. You know, applications to be belong to the top of snowflake and from there one actually many applications and many off our customers are building applications on snowflake. We estimated that's about 30% are running already applications on top off our platform. And the reason is is off course because it's it's so easy to get compute resources. There is no limit in scale in our viability, their ability. So all these characteristics are critical for for an application on DWI deliver that you know from day One Now we have improved, you know, our increased the scope off the platform by adding, you know, Java in competition and Snow Park, which which was announced today. That's also you know, it is an enabler. Eso in terms off type of application. It's really, you know, all over and and what I like actually needs to be surprised, right? I don't know what well being on top of snowflake and how it will be the world, but with that are sharing. Also, we are opening the door to a new type of applications which are deliver of the other marketplace. Uh, where, You know, one can get this application died inside the platform, right? The platform is distributing this application, and today there was a presentation on a Christian T notes about, >>you >>know, 20 finds, which, you know, is this machine learning, you know, which is providing toe. You know, any users off snowflake off the application and and machine learning, you know, to find, you know, and apply model on on your data and enrich your data. So data enrichment, I think, will be a huge aspect of snowflake and data enrichment with machine learning would be a big, you know, use case for these applications. Also, how to get there are, you know, inside the platform. You know, a lot of applications led him to do that. Eso machine learning. Uh, that engineering enrichments away. These are application that we run on the platform. >>Great. Hey, we just got a minute or so left in. Earlier today, we ran a video. We saw that you guys announced the startup competition, >>which >>is awesome. Ben, while you're a judge in this competition, what can you tell us about this >>Yeah, >>e you know, for me, we are still a startup. I didn't you know yet, you know, realize that we're not anymore. Startup. I really, you know, you really feel about you know, l things, you know, a new startups, you know, on that. That's very important for Snowflake. We have. We were started yesterday, and we want to have new startups. So So the ends, the idea of this program, the other aspect off that program is also toe help, you know, started to build on top of snowflake and to enrich. You know, this this pain, you know, rich ecosystem that snowflake is or the data cloud off that a cloud is And we want to, you know, add and boost. You know that that excitement for the platform, so So the ants, you know, it's a win win. It's a win, you know, for for new startups. And it's a win, ofcourse for us. Because it will make the platform even better. >>Yeah, And startups, or where innovation happens. So registrations open. I've heard, uh, several, uh, startups have have signed up. You goto snowflake dot com slash startup challenge, and you can learn mawr. That's exciting program. An initiative. So thank you for doing that on behalf of of startups out there and thanks. Ben Wa and Christian. Yeah, I really appreciate you guys coming on Great conversation. >>Thanks for David. >>You're welcome. And when we talk, Thio go to market >>pros. They >>always tell us that one of the key tenets is to stay close to the customer. Well, we want to find out how data helps us. To do that in our next segment. Brings in to chief revenue officers to give us their perspective on how data is helping their customers transform. Business is digitally. Let's watch.

>>My name is Dave Volonte, and with me are two world class technologists, visionaries and entrepreneurs. And Wa Dodgeville is the he co founded Snowflake, and he's now the president of the product division. And Florian Duetto is the co founder and CEO of Data Aiko. Gentlemen, welcome to the Cube to first timers. Love it. >>Great to be here >>now, Florian you and Ben Wa You have a number of customers in common. And I have said many times on the Cube that you know, the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud is really the scale platform. So is that premise your relevant to you? Do you buy that? And and why do you think snowflake and data ICU make a good match for customers? >>I think that because it's our values that are aligned when it's all about actually today allowing complexity for customers. So you close the gap or the democratizing access to data access to technology. It's not only about data data is important, but it's also about the impact of data. Who can you make the best out of data as fast as possible as easily as possible within an organization. And another value is about just the openness of the platform building the future together? Uh, I think a platform that is not just about the platform but also full ecosystem of partners around it, bringing the level off accessibility and flexibility you need for the 10 years away. >>Yeah, so that's key. But it's not just data. It's turning data into insights. Have been why you came out of the world of very powerful but highly complex databases. And we know we all know that you and the snowflake team you get very high marks for really radically simplifying customers lives. But can you talk specifically about the types of challenges that your customers air using snowflake to solve? >>Yeah, so So the really the challenge, you know, be four. Snowflake. I would say waas really? To put all the data, you know, in one place and run all the computers, all the workloads that you wanted to run, You know, against that data and off course, you know, existing legacy platforms. We're not able to support. You know that level of concurrency, Many workload. You know, we we talk about machine learning that a science that are engendering, you know, that our house big data were closed or running in one place didn't make sense at all. And therefore, you know what customers did is to create silos, silos of data everywhere, you know, with different system having a subset of the data. And of course, now you cannot analyze this data in one place. So, snowflake, we really solve that problem by creating a single, you know, architectural where you can put all the data in the cloud. So it's a really cloud native we really thought about You know how to solve that problem, how to create, you know, leverage, Cloud and the lessee cc off cloud to really put all the die in one place, but at the same time not run all workload at the same place. So each workload that runs in Snowflake that is dedicated, You know, computer resource is to run, and that makes it very Ajai, right? You know, Floyd and talk about, you know, data scientists having to run analysis, so they need you know a lot of compute resources, but only for, you know, a few hours on. Do you know, with snowflake they can run these new work lord at this workload to the system, get the compute resources that they need to run this workload. And when it's over, they can shut down. You know that their system, it will be automatically shut down. Therefore, they would not pay for the resources that they don't use. So it's a very Ajai system where you can do this, analyzes when you need, and you have all the power to run all this workload at the same time. >>Well, it's profound what you guys built to me. I mean, of course, everybody's trying to copy it now. It was like, remember that bringing the notion of bringing compute to the data and the Hadoop days, and I think that that Asai say everybody is sort of following your suit now are trying to Florian I gotta say the first data scientist I ever interviewed on the Cube was amazing. Hilary Mason, right after she started a bit Lee. And, you know, she made data science that sounds so compelling. But data science is hard. So same same question for you. What do you see is the biggest challenges for customers that they're facing with data science. >>The biggest challenge, from my perspective, is that owns you solve the issue of the data. Seidel with snowflake, you don't want to bring another Seidel, which would be a side off skills. Essentially, there is to the talent gap between the talented label of the market, or are it is to actually find recruits trained data scientist on what needs to be done. And so you need actually to simplify the access to technologies such as every organization can make it, whatever the talent, by bridging that gap and to get there, there is a need of actually breaking up the silos. And in a collaborative approach where technologists and business work together and actually put some their hands into those data projects together, >>it makes sense for flooring. Let's stay with you for a minute. If I can your observation spaces, you know it's pretty, pretty global, and and so you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends may be differences between regions or maybe within different industries. What are you seeing? >>Yes. Yeah, definitely. I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to start data in the last few years and no age, a level of maturity where they can invest more and get to the next steps. And it's really rely on the ability of certain medial certain organization actually to have built this long term strategy a few years ago and no start raping up the benefits. >>You know, a decade ago, Florian Hal Varian, we, you know, famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of change that to data scientists and then everybody. All the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What what skills >>do >>you see as critical for the next generation of data science? >>Yeah, it's a good question because I think the first generation of the patient is became the licenses because they could done some pipe and quickly on be flexible. And I think that the skills or the next generation of data sentences will definitely be different. It will be first about being able to speak the language of the business, meaning, oh, you translate data inside predictive modeling all of this into actionable insight or business impact. And it would be about you collaborate with the rest of the business. It's not just a farce. You can build something off fast. You can do a notebook in python or your credit models off themselves. It's about, oh, you actually build this bridge with the business. And obviously those things are important. But we also has become the center of the fact that technology will evolve in the future. There will be new tools and technologies, and they will still need to keep this level of flexibility and get to understand quickly, quickly. What are the next tools they need to use the new languages or whatever to get there. >>As you look back on 2020 what are you thinking? What are you telling people as we head into next year? >>Yeah, I I think it's Zaveri interesting, right? We did this crisis, as has told us that the world really can change from one day to the next. And this has, you know, dramatic, you know, and perform the, you know, aspect. For example, companies all the sudden, you know, So their revenue line, you know, dropping. And they had to do less meat data. Some of the companies was the reverse, right? All the sudden, you know, they were online, like in stock out, for example, and their business, you know, completely, you know, change, you know, from one day to the other. So this GT off, You know, I, you know, adjusting the resource is that you have tow the task a need that can change, you know, using solution like snowflakes, you know, really has that. And we saw, you know, both in in our customers some customers from one day to the to do the next where, you know, growing like big time because they benefited, you know, from from from from co vid and their business benefited, but also, as you know, had to drop. And what is nice with with with cloud, it allows to, you know, I just compute resources toe, you know, to your business needs, you know, and really adjusted, you know, in our, uh, the the other aspect is is understanding what is happening, right? You need to analyze the we saw all these all our customers basically wanted to understand. What is that going to be the impact on my business? How can I adapt? How can I adjust? And and for that, they needed to analyze data. And, of course, a lot of data which are not necessarily data about, you know, their business, but also data from the outside. You know, for example, coffee data, You know, where is the States? You know, what is the impact? You know, geographic impact from covitz, You know, all the time and access to this data is critical. So this is, you know, the promise off the data crowd, right? You know, having one single place where you can put all the data off the world. So our customers, all the Children you know, started to consume the cov data from our that our marketplace and and we had the literally thousands of customers looking at this data analyzing this data, uh, to make good decisions So this agility and and and this, you know, adapt adapting, you know, from from one hour to the next is really critical. And that goes, you know, with data with crowding adjusting, resource is on and that's, you know, doesn't exist on premise. So So So indeed, I think the lesson learned is is we are living in a world which machines changing all the time and we have for understanding We have to adjust and and And that's why cloud, you know, somewhere it's great. >>Excellent. Thank you. You know the kid we like to talk about disruption, of course. Who doesn't on And also, I mean, you look at a I and and the impact that is beginning to have and kind of pre co vid. You look at some of the industries that were getting disrupted by, you know, we talked about digital transformation and you had on the one end of the spectrum industries like publishing which are highly disrupted or taxis. And you could say Okay, well, that's, you know, bits versus Adam, the old Negroponte thing. But then the flip side of that look at financial services that hadn't been dramatically disrupted. Certainly healthcare, which is ripe for disruption Defense. So the number number of industries that really hadn't leaned into digital transformation If it ain't broke, don't fix it. Not on my watch. There was this complacency and then, >>of >>course, co vid broke everything. So, florian, I wonder if you could comment? You know what industry or industries do you think you're gonna be most impacted by data science and what I call machine intelligence or a I in the coming years and decades? >>Honestly, I think it's all of them artist, most of them because for some industries, the impact is very visible because we're talking about brand new products, drones like cars or whatever that are very visible for us. But for others, we are talking about sport from changes in the way you operate as an organization, even if financial industry itself doesn't seems to be so impacted when you look it from the consumer side or the outside. In fact, internally, it's probably impacted just because the way you use data on developer for flexibility, you need the kind off cost gay you can get by leveraging the latest technologies is just enormous, and so it will actually transform the industry that also and overall, I think that 2020 is only a where, from the perspective of a I and analytics, we understood this idea of maturity and resilience, maturity, meaning that when you've got a crisis, you actually need data and ai more than before. You need to actually call the people from data in the room to take better decisions and look for a while and not background. And I think that's a very important learning from 2020 that will tell things about 2021 and the resilience it's like, Yeah, Data Analytics today is a function consuming every industries and is so important that it's something that needs to work. So the infrastructure is to work in frustration in super resilient. So probably not on prime on a fully and prime at some point and the kind of residence where you need to be able to plan for literally anything like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we're just getting to the next step for the analytics. >>I wonder, Benoit, if you have anything to add to that. I mean, I often wonder, you know, winter machine's gonna be able to make better diagnoses than doctors. Some people say already, you know? Well, the financial services traditional banks lose control of payment systems. Uh, you know what's gonna happen to big retail stores? I mean, maybe bring us home with maybe some of your final thoughts. >>Yeah, I would say, you know, I I don't see that as a negative, right? The human being will always be involved very closely, but the machine and the data can really have, you know, see, Coalition, you know, in the data that that would be impossible for for for human being alone, you know, you know, to to discover so So I think it's going to be a compliment, not a replacement on. Do you know everything that has made us you know faster, you know, doesn't mean that that we have less work to do. It means that we can doom or and and we have so much, you know, to do, uh, that that I would not be worried about, You know, the effect off being more efficient and and and better at at our you know, work. And indeed, you know, I fundamentally think that that data, you know, processing off images and doing, you know, I ai on on on these images and discovering, you know, patterns and and potentially flagging, you know, disease, where all year that then it was possible is going toe have a huge impact in in health care, Onda and And as as as Ryan was saying, every you know, every industry is going to be impacted by by that technology. So So, yeah, I'm very optimistic. >>Great guys. I wish we had more time. I gotta leave it there. But so thanks so much for coming on. The Cube was really a pleasure having you.

>> My name is Dave Vellante. And with me are two world-class technologists, visionaries and entrepreneurs. Benoit Dageville, he co-founded Snowflake and he's now the President of the Product Division, and Florian Douetteau is the Co-founder and CEO of Dataiku. Gentlemen, welcome to the cube to first timers, love it. >> Yup, great to be here. >> Now Florian you and Benoit, you have a number of customers in common, and I've said many times on theCUBE, that the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation, is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake, and Dataiku make a good match for customers? >> I think that because it's our values that aligned, when it gets all about actually today, and knowing complexity of our customers, so you close the gap. Where we need to commoditize the access to data, the access to technology, it's not only about data. Data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible, within an organization. And another value is about just the openness of the platform, building a future together. Having a platform that is not just about the platform, but also for the ecosystem of partners around it, bringing the level of accessibility, and flexibility you need for the 10 years of that. >> Yeah, so that's key, that it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we know we all know that you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so the challenge before snowflake, I would say, was really to put all the data in one place, and run all the computes, all the workloads that you wanted to run against that data. And of course existing legacy platforms were not able to support that level of concurrency, many workload, we talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place didn't make sense at all. And therefore be what customers did this to create silos, silos of data everywhere, with different system, having a subset of the data. And of course now, you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data into cloud. So it's a really cloud native. We really thought about how solve that problem, how to create, leverage cloud, and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake, at its dedicated compute resources to run. And that makes it agile, right? Florian talked about data scientist having to run analysis, so they need a lot of compute resources, but only for a few hours. And with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system, it will automatically shut down. Therefore they would not pay for the resources that they don't use. So it's a very agile system, where you can do this analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. I mean to me, I mean of course everybody's trying to copy it now, it was like, I remember that bringing the notion of bringing compute to the data, in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say the first data scientist I ever interviewed on theCUBE, it was the amazing Hillary Mason, right after she started at Bitly, and she made data sciences sounds so compelling, but data science is a hard. So same question for you, what do you see as the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective, is that once you solve the issue of the data silo, with Snowflake, you don't want to bring another silo, which will be a silo of skills. And essentially, thanks to the talent gap, between the talent available to the markets, or are released to actually find recruits, train data scientists, and what needs to be done. And so you need actually to simplify the access to technologies such as, every organization can make it, whatever the talent, by bridging that gap. And to get there, there's a need of actually backing up the silos. Having a collaborative approach, where technologies and business work together, and actually all puts up their ends into those data projects together. >> It makes sense, Florain let's stay with you for a minute, if I can. Your observation space, it's pretty, pretty global. And so you have a unique perspective on how can companies around the world might be using data, and data science. Are you seeing any trends, maybe differences between regions, or maybe within different industries? What are you seeing? >> Yeah, definitely I do see trends that are not geographic, that much, but much more in terms of maturity of certain industries and certain sectors. Which are, that certain industries invested a lot, in terms of data, data access, ability to store data. As well as experience, and know region level of maturity, where they can invest more, and get to the next steps. And it's really relying on the ability of certain leaders, certain organizations, actually, to have built these long-term data strategy, a few years ago when no stats reaping of the benefits. >> A decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientist. And then everybody, all the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What skills do you see as critical for the next generation of data science? >> Yeah, it's a great question because I think the first generation of data scientists, became data scientists because they could have done some Python quickly, and be flexible. And I think that the skills of the next generation of data scientists will definitely be different. It will be, first of all, being able to speak the language of the business, meaning how you translates data insight, predictive modeling, all of this into actionable insights of business impact. And it would be about how you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python, or do predictive models of some sorts. It's about how you actually build this bridge with the business, and obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools, new technologies, and they will still need to keep this level of flexibility to understand quickly what are the next tools they need to use a new languages, or whatever to get there. >> As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right? This crises has told us that the world really can change from one day to the next. And this has dramatic and perform the aspects. For example companies all of a sudden, show their revenue line dropping, and they had to do less with data. And some other companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely changed from one day to the other. So this agility of adjusting the resources that you have to do the task, and need that can change, using solution like Snowflake really helps that. Then we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID, and their business benefited. But others had to drop. And what is nice with cloud, it allows you to adjust compute resources to your business needs, and really address it in house. The other aspect is understanding what happening, right? You need to analyze. We saw all our customers basically, wanted to understand what is the going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data which are not necessarily data about their business, but also they are from the outside. For example, COVID data, where is the States, what is the impact, geographic impact on COVID, the time. And access to this data is critical. So this is the premise of the data cloud, right? Having one single place, where you can put all the data of the world. So our customer obviously then, started to consume the COVID data from that our data marketplace. And we had delete already thousand customers looking at this data, analyzing these data, and to make good decisions. So this agility and this, adapting from one hour to the next is really critical. And that goes with data, with cloud, with interesting resources, and that doesn't exist on premise. So indeed I think the lesson learned is we are living in a world, which is changing all the time, and we have to understand it. We have to adjust, and that's why cloud some ways is great. >> Excellent thank you. In theCUBE we like to talk about disruption, of course, who doesn't? And also, I mean, you look at AI, and the impact that it's beginning to have, and kind of pre-COVID. You look at some of the industries that were getting disrupted by, everyone talks about digital transformation. And you had on the one end of the spectrum, industries like publishing, which are highly disrupted, or taxis. And you can say, okay, well that's Bits versus Adam, the old Negroponte thing. But then the flip side of, you say look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is ripe for disruption, defense. So there a number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science, and what I call machine intelligence, or AI, in the coming years and decade? >> Honestly, I think it's all of them, or at least most of them, because for some industries, the impact is very visible, because we have talking about brand new products, drones, flying cars, or whatever that are very visible for us. But for others, we are talking about a part from changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted, when you look at it from the consumer side, or the outside insights in Germany, it's probably impacted just because the way you use data (mumbles) for flexibility you need. Is there kind of the cost gain you can get by leveraging the latest technologies, is just the numbers. And so it's will actually comes from the industry that also. And overall, I think that 2020, is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience, maturity meaning that when you've got to crisis you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions, and look for one and a backlog. And I think that's a very important learning from 2020, that will tell things about 2021. And the resilience, it's like, data analytics today is a function transforming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient, so probably not on prem or not fully on prem, at some point. And the kind of resilience where you need to be able to blend for literally anything, like no hypothesis in terms of BLOs, can be taken for granted. And that's something that is new, and which is just signaling that we are just getting to a next step for data analytics. >> I wonder Benoir if you have anything to add to that. I mean, I often wonder, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? What's going to happen to big retail stores? I mean, maybe bring us home with maybe some of your finals thoughts. >> Yeah, I would say I don't see that as a negative, right? The human being will always be involved very closely, but then the machine, and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So I think it's going to be a compliment not a replacement. And everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do, that I will not be worried about the effect of being more efficient, and bare at our work. And indeed, I fundamentally think that data, processing of images, and doing AI on these images, and discovering patterns, and potentially flagging disease way earlier than it was possible. It is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, guys, I wish we had more time. I've got to leave it there, but so thanks so much for coming on theCUBE. It was really a pleasure having you.

>> Hello everyone, welcome back to theCUBE'S wall to wall coverage of the Snowflake Data Cloud Summit. My name is Dave Vellante and with me are two world-class technologists, visionaries, and entrepreneurs. Benoit Dageville is the, he co-founded Snowflake. And he's now the president of the Product division and Florian Douetteau is the co-founder and CEO of Dataiku. Gentlemen, welcome to theCUBE, two first timers, love it. >> Great time to be here. >> Now Florian, you and Benoit, you have a number of customers in common. And I've said many times on theCUBE that, the first era of cloud was really about infrastructure, making it more agile taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake and Dataiku make a good match for customers? >> I think that because it's our values that align. When it gets all about actually today, and knowing complexity per customer, so you close the gap or we need to commoditize the access to data, the access to technology, it's not only about data, data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible within an organization? And another value is about just the openness of the platform, building a future together. I think a platform that is not just about the platform but also for the ecosystem of partners around it, bringing the little bit of accessibility and flexibility, you need for the 10 years of that. >> Yes, so that's key, but it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we all know that, you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so really the challenge before Snowflake, I would say, was really to put all the data, in one place and run all the computes, all the workloads that you wanted to run, against that data. And of course, existing legacy platforms were not able to support that level of concurrency, many workload. We talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place, didn't make sense at all. And therefore, what customers did, is to create silos, silos of data everywhere, with different systems having a subset of the data. And of course now you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data in the cloud. So it's a really cloud native. We really thought about how to solve that problem, how to create leverage cloud and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake at least dedicate compute resources to run. And that makes it very agile, right. Florian talked about data scientist having to run analysis. So they need a lot of compute resources, but only for few hours and with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system. It will automatically shut down. Therefore they would not pay for the resources that they don't choose. So it's a very agile system, where you can do these analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. To me, I mean, because everybody's trying to copy it now. It's like, I remember the notion of bringing compute to the data in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say, the first data scientist I ever interviewed on theCUBE was the amazing Hilary Mason, right after she started at Bitly. And she made data science sounds so compelling, but data science is hard. So same question for you. What do you see is the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective is that once you solve the issue of the data silo with Snowflake, you don't want to bring another silo, which would be a silo of skills. And essentially, thanks to that talent gap between the talent and labor of the markets, or how it is to actually find, recruit and train data scientists and what needs to be done. And so you need actually to simplify the access to technology such as every organization can make it, whatever the talents by bridging that gap. And to get there, there is a need of actually breaking up the silos. I think a collaborative approach, where technologies and business work together and actually all put some of their ends into those data projects together. >> Yeah, it makes sense. So Florian, Let's stay with you for a minute, if I can. Your observation spaces, is pretty, pretty global. And so, you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends, maybe differences between regions or maybe within different industries? What are you seeing? >> Yep. Yeah, definitely, I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to store data as well as few years and know each level of maturity where they can invest more and get to the next steps. And it's really reliant to reach out to certain details, certain organization, actually to have built this longterm data strategy a few years ago, and no stocks ripping off the benefits. >> You know, a decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientists. And then everybody, all the statisticians became data scientists and they got a raise. But data science requires more than just statistics acumen. What skills do you see is critical for the next generation of data science? >> Yeah, it's a good question because I think the first generation of data scientists became better scientists because they could learn some Python quickly and be flexible. And I think that skills of the next generation of data scientists will definitely be different. It will be first about being able to speak the language of the business, meaning all you translate data insight, predictive modeling, all of this into actionable insights or business impact. And it will be about who you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python or do quantity models of some sorts. It's about how you actually build this bridge with the business. And obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools in technologies, and they will still need to get this level of flexibility and get to understand quickly what are the next tools, they need to use or new languages or whatever to get there. >> Thank you for that. Benoit, let's come back to you. This year has been tumultuous to say the least for everyone, but it's a good time to be in tech, ironically. And if you're in cloud, it's even better. But you look at Snowflake and Dataiku, you guys had done well, despite the economic uncertainty and the challenges of the pandemic. As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right. We, this crisis has told us that the world really can change from one day to the next. And this has dramatic and profound aspects. For example, companies all of a sudden, saw their revenue line dropping and they had to do less with data. And some of the companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely change from one day to the other. So this agility of adjusting the resources that you have to do the task, a need that can change, using solution like Snowflake, really helps that. And we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID and their business benefited, but also, as you know, had to drop and what is nice with cloud, it allows to adjust compute resources to your business needs and really address it in-house. The other aspect is understanding what is happening, right? You need to analyze. So we saw all our customers basically wanted to understand, what is it going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data, which are not necessarily data about their business, but also data from the outside. For example, COVID data. Where is the state, what is the impact, geographic impact on COVID all the time. And access to this data is critical. So this is the promise of the data cloud, right? Having one single place where you can put all the data of the world. So, our customers all of a sudden, started to consume the COVID data from our data marketplace. And we have the unit already thousands of customers looking at this data, analyzing this data to make good decisions. So this agility and this adapting from one hour to the next is really critical and that goes with data, with cloud, more interesting resources and that's doesn't exist on premise. So, indeed I think the lesson learned is, we are living in a world which is changing all the time, and we have to understand it. We have to adjust and that's why cloud, some way is great. >> Excellent, thank you. You know, in theCUBE, we like to talk about disruption, of course, who doesn't. And also, I mean, you look at AI and the impact that it's beginning to have and kind of pre-COVID, you look at some of the industries that were getting disrupted by, everybody talks about digital transformation and you had on the one end of the spectrum, industries like publishing, which are highly disrupted or taxis, and you can say, "Okay well, that's Bits versus Adam, the old Negroponte thing." But then the flip side of this, it says, "Look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is right for disruption, defense." So the more the number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian, I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science and what I call machine intelligence or AI in the coming years and decades? >> Honestly, I think it's all of them, or at least most of them. Because for some industries, the impact is very visible because we are talking about brand new products, drones, flying cars, or whatever is that are very visible for us. But for others, we are talking about spectrum changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted when you look at it from the consumer side or the outside. In fact internally, it's probably impacted just because of the way you use data to develop for flexibility you need, is there kind of a cost gain you can get by leveraging the latest technologies, is just enormous. And so it will, actually comes from the industry, that also. And overall, I think that 2020 is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience. Maturity, meaning that when you've got a crisis, you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions and look forward and not backward. And I think that's a very important learning from 2020 that will tell things about 2021. And resilience, it's like, yeah, data analytics today is a function consuming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient. So probably not on trend and not fully on trend, at some point and the kind of residence where you need to be able to plan for literally anything. like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we are just getting into a next step for all data analytics. >> I wonder Benoit, if you have anything to add to that, I mean, I often wonder, you know, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? You know, what's going to happen to big retail stores? I mean, may be bring us home with maybe some of your final thoughts. >> Yeah, I would say, I don't see that as a negative, right? The human being will always be involved very closely, but then the machine and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So, I think it's going to be a compliment, not a replacement and everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do. That I would not be worried about the effect of being more efficient and better at our work. And indeed, I fundamentally think that, data, processing of images and doing AI on these images and discovering patterns and potentially flagging disease, way earlier than it was possible, it is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, Guys, I wish we had more time. We got to leave it there but so thanks so much for coming on theCUBE. It was really a pleasure having you. >> [Benoit & Florian] Thank you. >> You're welcome but keep it right there, everybody. We'll back with our next guest, right after this short break. You're watching theCUBE.