(upbeat music) >> Hello, and welcome back to theCUBE's coverage of Red Hat Summit 2021 Virtual. I'm John Furrier, host of theCUBE. We got a great guest here, Raymond Kok, Senior Vice President Cloud Application Solutions at Siemens Digital Industry Software. Raymond, thanks for remoting in with theCUBE Virtual all the way from the Netherlands. Great to see you. We're in Palo Alto, California. Great to see you. >> All right, thanks for having me. >> Love the international culture of the vibe with virtual, one of the benefits of having remote, which we were in person, but soon the pandemics coming around the corner, but great to see you. Let's get started, let's get into the Digital Industry Software Group that you're involved in, your relationship with Red Hat. But first let's start with, if you could take a minute to give us a brief overview of Siemens and your role there. >> Yeah, so first of all, let me talk a little bit about Siemens because Siemens is obviously a big company. So as you already announced, I'm part of Siemens Digital Industries Software. So Digital Industries is actually the vision at Siemens that is really focused on how to help companies to become a digital enterprise. And so as part of this IoT (faintly speaking) Industrial Internet of Things is obviously an important element of that. And so if you look at my role at Siemens, is really to be the business lead for the cloud part of IoT. And so what I mean with that is specifically a product line called MindSphere. And so Siemens, like I said, is looking at the overall digital transformation of customers, relay product landscape but also how we can support them with new technologies and IoT is very much part of that. >> One of the benefits of doing theCUBE interviews over the years and having the team that we have in the media side, we get to see things early. Industrial IoT, we've been blogging about and reporting for a couple of years now, now it's hard. Because with the pandemic, you still need things to run. And so Industrial IoT, not withstanding, there's still the other edges like consumer edge and other devices, but Industrial IoT is getting all the focus because of security and also because of just critical operations, critical infrastructure and for business and public sector, private sector, everything. This is a huge area. Could you talk about your strategy around Industrial IoT and specifically how you guys are using this analytics, MindSphere as you mentioned, what is that about? How does that help me if I'm a manufacturing organization? >> Yeah, so first of all, maybe it's good to clarify what we mean with Industrial IoT, because there's IoT and there's Industrial IoT. So, when people typically talk about Industrial IoT, it's really three main areas. It's smart grid. So it's really around IoT for energy management and energy usage. There is smart cities. So this is really IoT for smart buildings but also any kind of infrastructure that goes with smart cities. And then the last one is smart factories. And so, we typically, when we say Industrial IoT, we have clients that cover the three main areas that I just mentioned. And so really what it is about is to take advantage of data, right? So IoT is really about how you take advantage of data and how do you actually get insights from this data to run your business better? So maybe to give a specific example, if you look at one of our major customers, like for example, Coke Hellenic, and they just actually presented that (mumbles) last week. They are trying to use IoT to advance how they actually operate the bottling lines of the factories. And so it's really above operational excellence. So, meaning how to get more trooper, how to get more efficiency into how they do production. But in many cases, John, it's also about energy management because data is not just about, okay, operational excellence but also surrounding topics like, how can I better preserve energy as I produce something? And so, yeah. So in many cases, IoT is all about data, getting next levels of insight from the data and then put that to a particular use. So this can be answering the quality of production, getting better performance of your equipment, getting a better use of your equipment when it comes to energy consumption. So there are many use cases typically related to Industrial IoT. >> Yeah, and you got to love the industrial definition to the way you laid it out. That's critical infrastructure and emerging infrastructure and plant and equipment, all those things. But it's also a proxy for (faintly speaking) for business. So this kind of brings me to the kind of connecting the dots. If you don't mind, I'll jump to the convergence question I'd love to bring up, which is the convergence of IT, Information Technology and Operational Technology, OT, which has been discussed before, but you talked about culture clashes, different cultures. Also systems are different, purpose-built, potentially on one side, but they've got to come together, okay? These are both very important software pieces to the puzzle on the platform. How do you see that evolving? What's your take on resolving this dilemma of the priorities, of innovation and security and openness? What's your take on this? >> (Faintly speaking) Topic, John, because the reality is that OT has to ITinice and IT has to OTinice I guess, when we talk about IoT, right? So I think that's why at Siemens, we have kind of a unique viewpoint because Siemens looks at both the OT side of the world through, for example the context of discrete, the process industries look at the automation part of it, so meaning the actual operational automation and then obviously only equipment that comes with it, which is really typically an OT conversation. Then if you look at my business unit, so, Siemens Digital Industries Software, we look at it really from an IT point of view, and so how can we help these customers to become a digital enterprise? And so at Siemens, we're kind of bringing these two views together. And then to your point, we're trying to make the integration as seamless as possible. And to your point actually, it includes also making sure that we actually drive the standards that are going to make this enable, that are going to make this possible, can be open standards like OPC UA, for example, when you look at discrete manufacturing, but can also be standardizing on certain technologies, right? And so what we're seeing is that, for example, back to my word, talking is really Kubernetes and kind of the container technology that is out there, standard technology is helping this conversation as well. >> Yeah, the integration piece, that's the Kubernetes, containers and micro services. These are bringing cloud native integration points. And that's really going to be key, I'm going to get that in a second, but I want to come back to the MindSphere Analytics piece because data is critical as you mentioned. So integration data security and observability means security, monitoring all these things are evolving. You guys earlier this year, announced you expanding this MindSphere reach in partnership with IBM and Red Hat, so consumers could run on on-prem and cloud. That's the topic of this event. The main theme at Red Hat Summit this year is clearly hybrid cloud, in a distributed kind of computing paradigm which we all love. This is what we're talking about here. We're talking about distributed computing edge, Core Cloud. Why is this important for Siemens and your customers? Why did you decide to work with IBM and Red Hat on this initiative? >> Yeah, it kind of was already somewhat in your question, meaning that if we work with our customers, really the cloud conversation that we have with them is a hybrid cloud conversation. And what we mean with that is, yes, there're elements of public clouds, but especially when you talk about critical factory operations, many of these workloads that we're talking about are actually very close to the shop floor or are at least some what near, and therefore any kind of large enterprise OEM that we work with, so whether it's an automotive OEM, whether it's an aerospace and defense OEM, they all have a hybrid cloud strategy. And so what is interesting about IoT is that this is where hybrid cloud kind of comes together. It kind of goes back to your previous question about IT and OT coming together. As you can imagine OT has always been very on-premise because it's near real time critical factory operations. IT obviously much more comfortable with public cloud. So we're trying to bring this together and therefore, many of these conversations that we have with large enterprise OEMs is really a hybrid cloud conversation. So specifically, what we're doing here together with Red Hat is to enable exactly that. So meaning that we can take MindSphere or solution for IoT Analytics, we can bring that not just to a public cloud or make that available as a public cloud solution, but also on-premise private clouds. And I think it's very interesting because it opens a conversation that allows people to really now start talking about value as opposed to being worried about, okay, where is my data going? Is it secure? Is it actually going to be available when I need it for factory operations? So, yeah, I'm pretty excited about this work that we're doing together, because again, it's about value, making sure that our customers actually can fit what we do at Siemens into a landscape that they feel comfortable with. >> It feels to me, I may be a little bit old school but I feel like this is the innovation that we saw in the eighties and nineties as networks got more expansive and inter networking happened and you start to see that life blood of the action and the value get enabled. And I think your point about hybrid and operating around the environment is critical, because this brings up new challenges and new opportunities. For instance, you don't need to bolt on a caching layer to manage a slow database or you can get real time, and you can get better performance and compute. You don't need to move the data around. So, bringing compute and resource and scale to these edges when they need it, focuses more on the solution architect less on putting point technologies in place to solve. >> Yeah, exactly. Maybe to chime in on that, I think what is also interesting is that it allows the customers to optimize where to best place the workloads that they care about. And so maybe to make that a bit more specific, if you think about a use case like energy management. So let's say that I have a production line, 1500 assets that are consuming energy. If you then think about the data that is involved in analytics, you can imagine that if I start sending all this data to public cloud, maybe, maybe not the most efficient setup, because a first level of filtering and analytics, I can very much close do or do that close to a 2D equipment. And then when I get to aggregation of data, and some further filtering to figure out, okay, what is really happening at the line level? What is really happening at a particular production area level? Again, I think you can do that prior to actually sending some of this data to the cloud, meaning public cloud. Where the public cloud becomes interesting is when you want to aggregate, for example across multiple manufacturing facilities. Now you want to look at the KPIs of one factory versus another, you want to aggregate across multiple factories, you want to figure out, okay, why are certain trends happening just in this factory and it's better in this one? But I think that's why, what we're seeing with clients is that they're expecting from us a layered architecture and to your point, the most efficient way of actually dealing with their use cases across the infrastructure that is available to them. So yeah, if you look at Siemens, we're trying to kind of carefully think about all these layers from fields to edge, to on-premise private cloud, to public clouds, and then make sure that along the way each layer has value and that it's there for a purpose and for a real reason, right? And not just for the sake of having it. >> Yeah, or being limited by the architecture that you're stuck with, constrained by the architecture by what the solutions are. You're saying, the script is flipped upside down where you can optimize your business, which by the way will flow up more data to evaluate. So there's a new post analysis mode of post configuration, and you could align your resources best way you see fit to maximize your business model. This is the beautiful thing about this distributed edge concept is the software enablement of the business is there. So the data is critical. So, as more controlled data comes in, it's not just set it up and watch it run. Yeah, there's automation involved in a lot of software but you're getting new data coming in. If you have this new observation space, of new horizontally scalable data, this new data coming in. >> Yeah, exactly, exactly. And I think you said a key point there. We don't want our architecture to constrain. I guess, what kind of value the customer can actually get out of these use cases and therefore, I think it's kind of exciting that in this ecosystem, especially also the interplay between Red Hat and Siemens, that we kind of take it one step further and think about, okay, what is actually truly the most optimal way for customers to go do this? And that we've formed these kinds of partnerships to really help the customer even take another step forward. So I think it's pretty, pretty nice. >> Well, Raymond, I really appreciate it. That's a masterclass, a commentary, nice gems you're dropping here on theCUBE, I appreciate it. The way I look at it, I'd love to get your final reaction to kind of the world we're living in. Just my take on it is that, we have a new operating system of business, and we're kind of getting at, is that you guys now can have an operating model for your customers and software. It's not just another (faintly speaking) For a server and the server is the business, it's the world now. >> Yeah, exactly. And I think from my point of view, I think it's exciting to see us again in this world of complex technology always to find new ways to help the customer to kind of advance their use cases, right? Because the imperatives that, for example discreet manufacturing doesn't really change. They've been there for many, many years. And I think for us to be able to bring out technology closer together and then solve, and I do use use cases in an even more efficient way. I think that's pretty interesting. And yeah, so I see good things and I think ultimately IoT, I think those that can actually bring real value are going to be able to deal like we just talked about, the hybrid scenarios, but the people that are going to matter is the people that can bring the most insights out of this data, right? Because what I always say about IoT is, it's yet more a messy data. So it's only worth actually collecting all this data if you actually get next to levels and new levels of insight from it. And I think, yeah, it has to kind of fit that kind of a mantra, and I think together that we're really trying to figure that out, so- >> I know some people as well would agree with that statement, I do as well, but the other side of that question is, if you don't architect the edge properly or the IoT edge, the data costs could be compelling. You could get hit with some charges because most people have been burned by the idea of moving data around versus say, moving compute. So, back to this value, where's the edge? What're you optimizing for? That's kind of the big question. How do you react to that when someone says, Raymond, what should I be optimizing for as I lay out my architecture for the core to edge, data center cloud edge scenario, what am I optimizing for? >> Yeah, I think you kind of work backwards from what you're trying to achieve. I think it may sound kind of obvious, but quite often I get in discussions with customers where we first start talking technology, obviously it's exciting. I'll be kind of attacking myself. So it's exciting to talk about technology but they forget to start from, okay, what's the return of the invest and what's the use case, right? And so, what are we trying to solve? Who is trying to benefit from it? And what benefit are they looking for? And then if you carefully work backwards from there, you will actually see that as we just talk about data and insights into data are in many cases, leading some elements of the value that a particular person is looking for. And then working backwards from there, you will actually figure out that back to the layer of discussion that we just had, this data doesn't have to be available at every level, right? Every layer adds some value, and so therefore you have to have kind of an open discussion and that's meaning an open discussion about what layers to use. And that's why at Siemens, we kind of follow that approach. So meaning that we work backwards from the use case, then we think about, okay, what is most appropriate at the field and control level? Then what to your point, is the most appropriate at the edge level? Then what is the most appropriate at the cloud level? And then from there, you actually figure out, okay, where do I deploy? What kind of acquisition of data? What kind of insights am I interested in at that level? And then basically, what kind of machine learning am I going to deploy there? And then work all the way from there. And it seems to work. And that's why to your point, it's all about making sure that at every level data is there for a reason and you process it for a reason, because otherwise it's just acknowledging it, interesting still, but it doesn't have any value, right? >> Awesome. Raymond, great insight there. And this is all about engineering. You guys are doing a great job. Engineering, the solutions, this is DevOps, DevSecOps, it's some hybrid cloud, really bringing those that value to the edge, industrial edge. Congratulations for all the great work. Raymond Kok, Senior Vice President, Cloud Application Solutions at Siemens Digital Industries Software. Thanks for coming on theCUBE. >> Okay, yeah. Thanks for having me. >> Okay. >> Thank you. >> I'm John Furrier with theCUBE, Red Hat Summit 2021 Virtual. Thanks for watching. (upbeat music)

>> Live from Orlando, Florida It's the que covering Accelerate nineteen. Brought to you by important >> Welcome back to the Cubes Coverage of Fortinet. Accelerate twenty nineteen. Live from Orlando, Florida I'm Lisa Martin with Peter Burress, and we're welcoming to the keep for the first time. Jeff Fully senior business development manager from Siemens. Jeff. Thanks for joining Peter and me today. >> Thanks for having me appreciate >> it. So everybody knows Seaman's in some form or fashion or capacity. Ah, here we are in a cyber security event. Talk to us a little bit about what Seamen's and Fortinet are doing together as partners and a little bit about your role in business. >> Sure, so the organization the part of semen that I'm a part of, is more of a digital industries. So what we do is ah, lot of NT operational technology environment area. So it's it's more of the harsh environments oil and gas, waste water, rail transportation. So we do a lot of the communication and the cyber security around that. We're working with Fortinet in order to bring the best of the practices on cybersecurity into that OT environment. So we're doing a collaboration between the two because there's that communication that needs to happen. They still need that access point into at OT environment >> Now. Explain why? Because because, you know, guys have grown up presuming that everything was going to be connected and a lot of business leaders presume that everything's going to be connected. The okey guys have had to work in a very, very different world where they had to do real time work, sometimes for thirty years. So take us a little bit through that dynamic. And why is it that today we actually Khun, start having conversations about how these two things come together, work together and generate value together? >> Sure, so typically from operational technology environment when they put something together, is normally for a twenty or thirty year span. They want to put something in the network and the environment that's going to last. That's going to be out there. It's not. They don't change it. They don't upgrade it normally, as they do in the ninety environment, which typically has like a five year life cycle. So in OT environment, what's happening? Noah's know times are changing and all these cyber attacks are happening. They're being mandated to do this. A good example is, in two thousand five, President Obama signed into a legislative order that you, we must in the US secure critical infrastructure and part of that securing that, saying We're going to make sure that you know we're not going to be happy because in the utility market, if we take down four of the major interconnects between their power grid than that stated that us had become a third world country in eight days. So what we're doing is >> not do that. >> No, we're trying to help prevent that. So by doing so, we need to add security. And historically, from noti environment, it's always been about there's not been remote access. There's not been that connectivity. It's always been about electrical and mechanical devices. But now is these devices are getting smarter. They're getting Mohr intelligent. There's more information to get out of it. You get more efficiency and more information out of so you can know. Do your job better. You could do remote access. And like in Florida, here we have a bunch of hurricanes. There's the ability to say after a hurricane, I could get remote access or I could do that communication out to these devices where you wouldn't be able to do periodically in the past. So because of that, because connectivity we need to start securing our infrastructure to make sure that no, as we get access to that potential, that the bad guys get access to those devices, too. So we're working with our product portfolio and partners like Fortinet in order to make sure that we're applying the best of the security in the O t world. >> So when this convergence, we're talking probably with folks who are not used to change. Change is hard for everybody. However, as you said and back in two thousand five became a presidential mandate. But also it >> was two thousand fifteen >> two thousand five. Obama signed in tow the listen president till two thousand. And I'm sorry. >> Thank you for the mass >> housing one. >> Yeah, just years ago on the math expert. So just a few years ago, there was this mandate from President Obama which we clarified was only four years ago. So but historically, folks that are not used to having to change system so quickly. Yet here's this mandate. There's also this increasing abundance of separate tax. How do you have those, I presume, difficult conversations with Theo Teesside about the opportunity for OT convergence and the benefits and why they have to get on board with this. >> So historically, from the OT side, they've been very reluctant to do something like this. They want to own their own environment. They want to do that. It's always been the perception that if you bring that cyber security of the world into that OT environment, it's going to hinder their operations. But that's not really the case. The convergence of T and O. T has been happening for decades now. I started my job seem it's in two thousand in the telecom world, and we were doing that convergence of n ot back in two thousand when we're doing voiceover. I pay right because that was happening back then. So this convergence of Tino Tee is It's an ongoing thing. It's just in. Different markets are different industries, so now that we're doing that, we're bringing it in there. They're starting to have that conversation, but then it becomes a really who owns that The operation are who owns that security? No ot still wants to drive their own. They want to own their own. Where I saying, Look, I know we have the knowledge we have experience. Let's help you get there So there's generally a demarcation point that they've come to an agreement on where I Teo say we're going to help you to this point. And then you can own all the critical assets out on the far end. >> So let's talk about that demarcation point. What constitutes what characterizes that demarcation point? What are where are we today? Because we're moving from hardwired, uh, thirty year footprint to increasingly wireless, uh, faster. We're moving towards that, but we're how far our way when you talk to customers. What is what are the attributes of that demarcation point? It's >> interesting because we sew it. That goes everything we saw of customers that are on dialogue, communication, serial communication, Ethernet fiber, wireless. Lt s o. There's a broad range of that we call the pipe. So you know the pipe is the communication just between the side down to the OT side really helps to find that demarcation is when you get down to what are the critical assets, what's really the operation or what's making money for that company on those of the assets, which really the operational organization's own and then the side really provides that communication down to that to that ball. >> Got it. So it's really business specific. But are we starting to see Are we started to see? Well, it's got a little bit more processing power or it's got a little bit more. There's the's security attributes that are associated with it or ot guys picking up on T related security, starting elements of it faster than others >> they are on. But really, it's it's ah, it's region and as industry specific and it's really what's driving it So like in the U. S. Like I meant in the utility sector, three utility sector has requirements called no exit, and these narcs IP requirements said you must do these things and they get very specific to the point of. You must have something that will detect anti virus or malware. You must do this if you look into Canada. Canada just recently passed away. Be requirements for Ontario and those are based upon framework cyber security framework to do that. So it's really debate the industry that they're in and the region that they're in. That's what's really driving that our how deeper and far they're going to go. >> And it goes back to your original point that it's being driven by regulatory edict or a past exposure and trying to make sure it doesn't happen again. >> They don't want to make the news, and they don't want to be pushed by the government. But those were really the two things in the operational technology or environment that's really driving for that cyber. >> Thank you, Sierra, One of your favorite success stories that really highlights the opportunities that O. T. And I t Convergence have enabled for customers of forty nine Seaman's >> Oh yeah, there's, ah again because I'm global experience. No, I've got around the world, but actually one of the favorites is Actually, there's two of them that have happened just here in North America, Oneness in Texas and one is in Canada. And both of these requirements came to say that they had a specific date, that they needed to make requirements to meet the regulatory otherwise, that they were going to get fined and they came to us and it is both home were pretty last minute. So what we're able to do is to say, Look, we have this platform that's rated for harsh environment. That's no into your networking to provide that communication. So then what we could do is we can work with our partners, put that application on that OT environment and then install and get certified for your application. So there was two of you, Like I said, one in the U. S. And one in Canada. Which way made the deadline's where they came back and said, No, thank you very much, very appreciative. >> And how quickly were you able to get this up and running is that they didn't miss the deadline and we're able to certain gleanings value from this. >> I just did a write up on one. We got a phone call on a Friday that they needed to provide a solution. So we worked over the weekend, and on Monday we proposed a solution. So once you do that, no, obviously they need to go through their value chain to get to sign offs, and we have to go through our process. But it was within thirty days were able to install it make their deadline and make sure that they were compliant. >> That's a pretty good marketing message to deliver that you guys could enable Such It is such a big convergence and it's a month's period >> of thirty days. >> Pretty impressive. >> That was, That was That was one thing that I think we all worked out. There was a deadline. We all work toward that. It was a trusted partner thing, you know. The customer came to us, they were asking for some stuff. They trusted us to do that. So like I said, we worked over the weekend, help them do that. We felt we had the right solution to address their requirements and at the end of the day were ableto meet that thirty day deadline. >> But the trust is not just with you. It's not just a seaman's. It's with an expanding array of cos it Seaman's is working. That's correct. What is it like working with a company like Ford Net to try to ensure that these new domains that are characterized by enormous uncertainty, technological operational organisational are not undermined by challenges of crafting that sushi solution together in such ways, it can be implemented quickly and with a high degree facility. >> I think it's a great opportunity for saying it's important not to be working together only from the fact of Fortinet has got the history. They've got the technology. They've got the name in their market space on DH. They've got the capability to deliver that Siemens. And for if you look from our customer space in our environment, no, we're very well developed, well entrenched in our customer. So to be able to bring the technology and the experience and the know how and bring that those cyber security requirements which are now being pushed down into the OT environment in and no amount of time it's not. There's no development needed, there's no additional stuff fourteen and already has that knowledge from the space. So to bring that into the environment, it's very beneficial. I think both of us, in order to help drive their customer opportunities in our market. >> And they talked a lot this morning during the keynote about where they are from the competitive leadership perspective that was peppered, ah, lot throughout the first at least ninety minutes of the keynote. But presumably obviously everybody has choice. Everybody likes choice. Simons has choice there. I'm just curious to get your take on some of the announcements that came out today from Fortinet. Does that excite Seaman's? Were you involved in that? In terms of being able to take the next set of customers who have the same challenge that you describe with the Texas based on Canada based customers and show them we can help you together? Seaman's importing that transform in thirty days. >> I think it's very exciting with fourteen that's doing in the new capabilities and functional yet they're coming out with. I think that's really going to be able to enhance our offering because it's really a differentiator for us. If you look at us from the operational technology side, there's not a lot of people out there that can actually do with porting that's able to bring to the table. So all these additional features functionality that was coming out by Fortinet to be able to put it on to our platform and our environment and to be able to offer that in the operational technology side. So I think it's a big differentiator from our competitors for both forty and for seeming to be able to jointly provide this offering to our customers. >> Just one question about your competition. A lot of companies like Siemens, especially that especially strong in the OT space, not just your customers. But suppliers like yourselves have also struggled a bit as they try to find a Z trying navigate that way forward to convergence of tea. No tea on appropriate convergence of tea. No tea. What is it about Seamans that has allowed you to not stub your toe or cut off your leg? Like some of the competitors, >> I believe that's because we've had a long history in both the A I T o T space. If you look at the vertical, are the digital industry that we're in right now. It's been very much ot centric for the last twenty five thirty years, but we have seen minces No. Three hundred seventy nine thousand people worldwide strong. We're very embracing the newer technology and the newer capabilities myself coming. No, starting with Siemens twenty years ago with a nice background being able to bring that knowledge that ability and doing that convergence of the idea no t within Seaman's for so long. I think we understand our customers, and we've been listening to them. And then we're partnering up with companies like Fortinet. Health says, Bring that technology that capability to our customers >> said that expertise, that partnership, What's your recommendation has be wrapped things up here for customers who are at the precipice of being able to understand why I know Teenie to converge with your recommendation for them to tackle this challenge successfully. >> I think the best advice I could have is let's sit down and have that conversation. Let's see what the requirements are. Let's see what they're trying to accomplish because I believe with the solutions that Siemens has between communication, the network in the security and then they technology and the capability that forty nets bring to the table we can to help design a customized solution for their environment in order to make sure that they can address their cyber security needs >> and do so quickly. Well, Jeff, thank you so much for joining Peter and me on the cute this afternoon. We appreciate your time. >> Thank you for >> for Peter. Boris. I'm Lisa Martin. You're watching the Cube

live from Las Vegas it's the cube covering service now knowledge 2018 brought to you by service now welcome back to the cubes live coverage of service now knowledge 18 here and Las Vegas Nevada I'm your host - Rebecca night along with my co-host Dave Volante we are joined by dr. Mateus Egelhoff he is the program director at Siemens AG thanks so much for coming on the problem yes great to see you again my friend veteran these two go way back they have a bromance brewing so Mateus at Siemens the now platform is really a key pillar of your digital transformation why is service integration so so it's such an important element of your vision of your strategy because service integration is really the place to be in the former days we concentrated to manage one service one provider but if you really want to integrate and be responsible end-to-end you really have to own the whole chain from the demand side to the supply side so you really have to span the whole value chain from the customer to the provider and back from the provider to the customer that's why it is so important to play the integrator role because if you own that whole value chain end-to-end you can optimize the value chain and also do some dramatic changes in that value change to kick out some of the providers that do not really add high value or you can optimize costs by combining some of the steps and that's why service integration is so key because then you have the whole end-to-end view and you gain the whole inside of that value chain and also the net the next topic I want to add is the typical service management topic is also changing over time because what to do with for example Microsoft Exchange Online you don't have to do much management on that one because that is used by millions of users so what to do actually and that's why it comes more important to have the overall view of the whole venue changer what if I could ask you as a seasoned ServiceNow practitioner you've seen a lot we were talking just kind of joking about sometimes tech company marketing is ahead of you know what they I can actually do service now obviously tremendous platform that makes it sound easy but it takes a lot of work to get there but once you get there you get a flywheel effect and you can add more and more because of the platform so talk a little bit about kind of where you started and how long it really took you to get to a point where you could really start driving major value for your organization so we we started our ServiceNow journey in January 2014 so roughly four years ago yeah and we started with the typical incident problem change service request portion but my goal was from the beginning to really have a high degree of automation and integration in that platform that's why we we set up the platform already in the integrated way of having not single processes single databases but rather having single source of record in the system and when we started of course we thought hey it's a great technology and it is a great technology it's a excellent tool but the challenge is not setting up the tool it is as Sean Donahoe said it's the change in the organization because by implementing such a huge tool with one process having it completely across all organizations in 149 countries with three hundred seventy seven thousand employees this is a scale where you need to have a focus on the change topic that they are really applying the process is because otherwise it's not of usage and this had a big impact on how we are providing the services because ServiceNow is more or less the window where it gets obvious how your services are looking like so it's not only about setting up ServiceNow you have to change the processes you have to change the organization you might simplify also the services they are quite a little bit too complicated to be handled in the portal and all that work has to be done in parallel and I always use the phrase there the dark side is coming up of an organization and I'm pretty sure each organization has a dark side of legacy system gaps in the process steps the data is not correct the data is not validated it is not one scene DP and all that stuff has to be pulled away connected otherwise you don't have the end-to-end chain you don't have the degree of automation that you want to leverage and this roughly took us two and a half years and and you knew that going in with ServiceNow kind of transparent or helpful in that or was it just gonna drop off the software and give us a call if you need help exactly we didn't you because otherwise we would have not started all those challenges and therefore ServiceNow was really helpful because there is out-of-the-box functionality that you can kick-start however if you want to leverage ServiceNow in that environment the out of box functionality is nice and a good starting point but you have to add some of the functionality like the integration layer is not there like data analytics not there yet so you have to add some of the topics but therefore it is good that ServiceNow was there that that's why we also procured licenses but on the other hand we engaged also professional services because we also wanted to make ServiceNow responsible for the implementation that this is really a lighthouse project also for ServiceNow and of course for us so it was a win-win so Evans now learned a lot and it was good to have them onboard and you're able to show quick enough value to get credibility in the organization to really fulfill your vision exactly so what we basically did we set up a road map based on savings because it's always easy to introduce a new tool a new portal a new process whatever always nice but when it comes to shutting down existing ones this is the difficult and nasty personnel but that's why I made a road map of clearly showing hey now we can shut down this portal now we can shut down this legacy tool and based on that the savings kicked in and the people really saw hey it works hey we really can shut down and get rid of some of the legacy dark side topic and then typically to a platform then the platform momentum starts where everybody wants to get on hey I have an additional provider I have initiative process I have additional services hey this country also wants to set em then the platform starts to grow and gain some momentum so that everybody gets up and this is also challenging then regarding the release how to handle all those demands I want to talk about data and because we just heard CJ Desai up there on the main stage preaching one thing but I know before the cameras are rolling yours you were telling us that you're actually doing a lot with the data that you're collecting so so talk about stop what it is you're doing it's because the collecting the data is the easy part in a lot of ways it's then figuring out okay what is the data telling us and then what do we do about it exactly so CJ in this main keynote mentioned that is not a good idea to pull out all the data outside of ServiceNow I'm agreeing but unfortunately only in two years or three years time when the intelligence is in service now that's why Siemens has decided to pull out really on a daily basis all the data from ServiceNow into a separate SQL database and then a first important step starts the qualification of the data is the data quality correct because the high degree of automation only works if the data is correct and of course if you wanted and display the data and do the analytics it's also key that the data is correct that's why we have established a data health - want to visualize is the data correct first step second one is then then we are displaying the data in tableau so with visualization layer doing the typical reports where you can slice down by division by country by service by cost cent or whatever the typical reporting but we are also doing that data and feeding it into for example Watson so we used Watson to see how intelligent he is so we gave Watson 1.3 million tickets and said hey Watson tell us what is exciting about 1.3 million tickets and that the first reaction was I don't understand because we have 5 languages a mix of languages Portuguese using Portuguese and English German and English and then Watson had some issues with understanding the tickets then we said ok then let's use just English portion 700,000 tickets and said hey Watson tell us now and he said issue ticket problems complained and whatnot and then I thought hey Watson you are telling me that those are tickets that is not the expectation I had based on what the Watson team is telling but to be fair to Watson that's not my point that I'm saying Watson is stupid I'm just saying 2 messages are important you really have to learn how to leverage that new technology and it really takes time so prepare your organization to apply those technology because also your organization needs a learning curve to apply that technology and the second example was with Asia so we gave or that the thesis was hey Asia can you tell us how to increase customer satisfaction and again we gave Asia with some nice mathematical formulas a lot of tickets and based on that model we learned what are the key success factors of satisfying a customer so it's of course how many times a ticket was routed how fast the ticket was picked up but we got really timestamps so we can also now adopt our SLA is to the providers to more satisfy the users and more excitingly based on four criterias we can now predict the satisfaction of the user so we can really say with 86% will that be rating between one and three what is not that good and if so this is now the next step we will feed that back into service now giving that ticket Aflac so the service desk agent can act on it and I think that is the exciting one not only collecting data learning out of it and then acting on it and now based on if a ticket is open we already can predict the customer satisfaction that is great providing guidance to the ServiceNow user so if I understand it correctly you're extracting data out of ServiceNow I think you've mentioned off-camera you bring some of that data into si P Hana yeah you mentioned your Watson tableau is the viz and you said Microsoft Azure exactly as well so like many big data problems you're solving it with a variety of tools that's challenging but you really have no choice is not one out-of-the-box solution is there nope well that's why we are now applying different technology to really learn what is in for us and quickly do is on POC check is it feasible is it a quick win or takes it longer or is the technology not that mature and then really follow up what is most promising is your expectation and desire that ServiceNow does sell all this in the platform for you and is that what you're pushing him to do I think the ratio which will get higher and higher what ServiceNow will be capable to do like the prediction of tickets and the route the automated routing that should be negative in ServiceNow but in regards to artificial intelligence I think there are other companies out there who are more at the front runner and really the lead us so I think it will be always a mixture out of ServiceNow but also pulling out some of the data to leverage other technology it's gonna be interesting to see what kind of merger and acquisition activity ServiceNow does certainly Mike Scarpelli and John Donahoe in the financial analysts meeting were hinting of acquisitions you would imagine they've done some in AI you would expect they do others I wonder if we could ask you about the climate in Germany with regard to machines replacing humans and cognitive functions obviously it's a very employee friendly environment what's the narrative like there what are you seeing yeah I think also big discussions in Germany about that digitalization is that disruptive to the job market and as I said with the example of Asia that is a core only artificial intelligent can do yeah no sense to use humans with a pocket calculator to do that doesn't make sense but on the other side I have also set up a team of 20 people who are doing let's say manual work they are monitoring the tickets for example three people and based on their experience and human factor to speak with the different resolve our groups applications they already reduced the ticket number they reduced the cycle time the number of the closing time was decreased by 20% so these are examples where you need humans because on the other side there are also humans and this optimization of looking at the data speaking with different people that have domain expertise this is really necessary where I see that humans are much more advanced than the machine learning so that's why I see balances of yes we are using Azure Watson and all those nice technologies but we are also ramping up people that really act on the data that they have at hand so there is less anxiety to this idea would you say exactly exactly so and that's why I am saying yes it will reduce some of the chops but hopefully the Nestea more administrative work and on the other hand it will create new opportunities especially in the integration layer where you need human intelligent and people who can act on and keep the ecosystem alive that is nothing a machine can do it is thanks so much for coming on the program it's always fun to have you on thank you we will have more from ServiceNow knowledge 18 of the cubes live coverage coming up just after this

>> Announcer: Live, from Las Vegas, it's theCUBE covering Dell Technologies World 2018. Brought to you by Dell EMC and its ecosystem partners. >> Welcome back. We are live here in Las Vegas at the Sands, along with Stu Miniman. I'm John Walls. You're watching theCUBE, of course, Dell Technologies World 2018. It's now a pleasure to welcome to the set, we have Bryan Bond, director of IT Infrastructure at Siemens eMeter. Bryan, thank you for being with us. >> Thank you for having me. >> John: And Andre Leibovici, who is the Vice President of Solutions and Alliances at Datrium. Andre, good afternoon to you. Good to see you. >> Great to see you. >> Alright, Bryan, tell us about Siemens eMeter, first, just for viewers who might not be familiar with the company and your mission. >> eMeter, basically, is a software development company. We do enterprise-level software for utilities, so gas, power, water, just about anything that has a meter. We do not make meters, but we deal with all the data that comes from those meters. So, data acquisition, meter data management, loss prevention, all those types of things that come from that data that's leaving your house or your business. We deal with that for the utilities. So, back-in billing systems, longterm data analytics, all of those types of things, that's what we do. >> Yeah, so, Bryan, most companies I talk to, it's like your industry's changing so fast, digital transformation, software, everything. Utilities are considered by most to be one of the slower moving pieces, so what's the reality in your world? >> It's like selling to a rock. (Stu laughs) A rock, right? It's tough, historically, it is very tough. Especially in the United States, with PUC regulations, with the way you can charge customers and can't, it makes it very hard. And I wish I was a real expert at that type of stuff, but... It's a slow-moving process. The good news is most countries in the planet have decided that they need to go full-on smart grid and they need to do it fast. So, in a lot of countries in Europe, there's an edict out, we're going to do this and that has helped move this along. So it's very helpful to us, as a business. I also think it's very helpful to us in general, you know, on the planet, being able to manage grids better and more efficiently. >> Okay, so we're not going to be talking about power grids and all the things on the utility. You're an IT guy. And that's what we love talking about on theCUBE here. So, give us a thumbnail sketch of your environment, your purview. What's going on? >> All right. So, like I said, so we're a software development house. It's all developers: dev test QA, sales, support, you know, all that type of stuff. I'm fortunate to be part of a very large company, so I don't have to worry about e-mail, SharePoint sites, or any of that stuff. I get to deal with the real fun stuff, which is our product, how it's deployed, how it's developed and tested. We're a pretty much a 100% virtualized. VMware shop. We use VMware-based cloud services for the appropriate things for that. And we do all of that work ourself with our own team. So we have a small team in the U.S., we have a small team in India, and we handle all of that ourselves, we don't really outsource any of that. >> Alright, so Andre, I want to pull you in here. You're software development in VMware environment. Brings me back; I remember early days of VMware was always only for test dev. Today, I hear developers, I hear this stuff, and it's like, "Oh, isn't that kind of public cloud "and some of those things?" So, give us your viewpoint on customers like Bryan and what kind of things Datrium brings to that environment, obviously virtualized and all that. >> Yeah, no, that's a good point. So... All types of customers know suddenly looking at how they can leverage private cloud, but also public cloud. Create the ideal, hybrid cloud. What does that mean, right? So we have Fortune 100 companies like Siemens who are leveraging our technology to deploy the private cloud, run the VMware infrastructure on us. At the same time, create, you know, DR strategies to their secondary sites. But there is also those customers who are looking to, "How can I actually push workloads to the cloud? "How can I create a strategy around disaster recovery "to the cloud?" And I believe that, as part of our journey as a company, embracing private data centers, we got to embrace, also, the cloud. And this is the next big thing for us at Datrium. Where are we going to help customers on the journey to take their workloads running on-premise to the cloud, but at the same time enabling them to use as as DR and also move back when needed. I may as well just spill the beans here. I'm not sure if I'm getting trouble with marketing or not. >> John: I'm sure you're not. >> So we actually releasing very soon a fully orchestrated DR from our platform to the VMware cloud, to VMC. Fully orchestrated and enables you to fire over environment to the cloud and back, once your DR site or your primary site is actually back. There's a lot of promise on this market. There's a lot of companies doing, saying that they would do, but, you know, I see that's something that customers are really excited... >> You know, how does it work when you're dealing with a customer who is dealing with a customer, who's dealing with customers who... You know, privacy's essential, right? And there's a lot of concern... They have to be the customer of a utility. So how do you treat them, you know, because they have very unique needs, I would assume and that's a major consideration, because of their position with their customer. I mean, that's got to create a new dynamic, or an interesting dynamic, for both of you to handle. >> Yeah, it does. You know, from a development standpoint, you know, you may not be actually dealing with that particular customer's data, but you're helping that customer deal with that data. So, we're having to go through and make sure that our software doesn't have any holes in it and it's patchable, and that it follows, you know, simple guidelines. But, at the same time, we make recommendations to customers all the time, you know. "Well, how are you guys doing X, Y, Z in-house, "because you seem to be doing okay." And we say, "Well, we're using this particular platform." And, their encryption is probably the best there is right now out there. De-duped encryption, it's just fantastic. And across different storage arrays. And being able to that to the cloud and be encrypted there, and not have to worry about that is a big bonus. And that's definitely something that we look at. Obviously, we don't encrypt all of our data, because a lot of it's just nonsense. But, we do have stuff that we do that with. And we do it both for testing purposes and to prove that this meets the requirements of the customer. Because those requirements are different, not just in different countries, but in every state you go to. So, being able to provide that level of assurance of yeah you can meet your requirements with our software regardless of what platform you're running on. >> Bryan, you mentioned a couple of features there. But I wonder if you could back us up a second. You've got a virtualized environment. There's, you know, so many options that you can choose on there. Walk us a little bit through the problems that you were having, the decision process, and ultimately what led to Datrium. >> So... The set of primary goals for us was the typical thing you see in IT is you're doing the same thing for a long period of time. You're buying the same stuff, you buy more of it, you renew, and then they tell you that the price is going to go way up on support. So you buy a new one and start over again, right? The hockey stick approach. And so that's the time I like to actually stop and say, "Hey, am I doing this right, still?" Because what I did five years ago may not be right, you know, going forward, knowing what the changes are in the business. We were looking for great cost to capacity. Right? And ease of management and overall cost of the deployment. And when we started looking at all the different players in the space... For us, the big thing was going to NFS. So, single file system for management. Prior to that, we were either fibre channel on or iSCSCI. So, mini management points. Hundreds of LUNs. Hundreds of LUNs. We're managing storage, right? A small group of people, three, four guys? You're spending 20 hours a week managing storage? That's nuts, right? So, day one, we put these guys in in a POC. And my guys are like, "This stuff's never leaving." Because now I'm down to one management point, right? Six months, seven months later, I'm down six hundred LUNs from where I was with three management points. I don't manage storage anymore. None of my guys manage storage anymore. That's a hidden cost, you know? And I'm not suggesting reduction in FTE or anything like that. I'm saying, "Oh, now those guys can go work "on operating system patching." You know, the other paying points that you've got in the business, rather than managing, you know, that platform. So, all of those things rolled in together. And when we tried to compare them to other vendors, we couldn't get an apples to apples comparison. We had to go with multiple vendors to get the same performance, to get the same capacity, and we could never get the pricing. The best-case scenario we got for capacity and performance was three times the cost. Best-case scenario. And I still had to manage LUNs. >> Yeah, Andre, I used to always joke simplicity in the enterprise was an oxymoron, because there's so much happening. You hear, "Okay, get rid of one thing, I got to patch the other thing." There's no such thing as eliminating bottlenecks, you just move them. But, you know, sounds like some common problems we've been hearing out there. What's typical about his environment? What are you hearing from customers in general that Datrium's helping? >> So, I think the first point is simplicity. And it's something that I know we've been evolving, it's a journey not only for Datrium, but the whole data center industry, right? Went through ACI and now it's open conversions. So the whole simplification of the data center and make sure that most of the task can be automated. So some of the things that we do, that we simplify from a management perspective: we have no knobs, you don't decide if it's compression, the de-duplication enable, the erasure codings. Everything is owned by default and that's the way it's going to be because it doesn't make sense for an organization with thousands of virtual machines and applications to start tweaking every single knob to make sure they're going to get the best possible performance. Across the board, once we've actually verified, you might get like one or 2% CPU back. So, simplicity's a big point. Also, the other point that we mitigate in the organization, especially compared to ACI's solutions, is the data resiliency. So we actually offer enterprise-grade data resiliency that for ACI... And when talking about evolution with data center, you know, taking like putting SSDs into the servers, ACI clusters, and moving forward. So we actually make all the management of this SSDs much simpler. I forgot the line, where I was going to, but I... (laughs) I think the message is simplicity, skill ability, back data resiliency. Making sure you get enterprise-greater data resiliency in the data center. And you don't compromise on that. You get capacity, data resiliency, simplicity at the same time. >> Keep it simple, make it work. >> Andre: Exactly. >> Right. Faster. Gentleman, thanks for joining us. We appreciate the time. Thanks for telling the Siemens eMeter story. We look forward to seeing you down the road. And good luck, continue success at Datrium, as well. Thanks, Andre. >> Yeah, thank you. >> Alright, thanks for having us. >> Back with more. You're watching Dell Technologies World 2018 right here on theCUBE. (techno music)

live from Las Vegas it's the cute covering knowledge 16 brought to you by service now here your host dave vellante and Jeff Frick we're back this is knowledge 16 this is the cube we go out to the events we extract the signal from the noise Matthias egg allah hafez here he's the program director at siemens AG worldwide conglomerate a US welcome to the cube thank you for inviting me star of the keynote this morning here we had that little snippet and but again you know welcome tell us what you do it siemens exactly so let me start i joined siemens in the crazy ebusiness world in two thousand but the journey i'm on currently is started in 2002 when we had the easy question of how many money is spending siemens on IT and this is an easy question but out in those days it was not to answer so we created a lot of transparency what is seaman spending in IT and we keep on driving that transparency based on the transparency we initiated different measure one was that we are focusing on service management in the infrastructure area for example in application we did a lot of consolidation and then we did several steps in the optimization based on the transparency we made we made some outsourcing deals we did a global IT organization and we managed our providers now since 12 years in service management but we thought what is the next step what is the big shot that is coming ahead of us and then we thought this is about service integration getting rid of the silos like network application data and the voice and having really an integration layer about the different topics as well as ensuring that we have end-to-end responsibility from the customer side and that was the challenge of answering that question was just so many stovepipes and so many systems and so many different systems of record and and is service now that integration layer is that exactly right so service now is our single service integration platform and it spans the word from the demand to the supply side demand of course our Siemens internal employees they were confronted in the old days with several portals my favorite example is always ordering an iPhone in Siemens was quite a difficult task you had to order in one part of the hardware in the other one the SIM card and in the third the messaging service now now we have the one-stop shop portal called my IT in Siemens and there we have a bundle where the customer has not to know which portal which provider we have our portal and the user can just concentrate what he needs and in that portal he can order and manage his IT and products as well as place incident so that's the demand side and then for the IT organization as you said we had a su of products and tools and processes and now with service management we have implemented a typical incident problem change service request config demand contract management's all the nice IT service management processes and rolled that out on a global scale so we have now one process one organization able to get the full transparency and the knowledge where is the status of IT and what is the head state is this so leading up to 2000 y2k we spend spend spend spend spend and then after y2k was a cut cut cut cut then how fast forward you've got visibility on the spend transparency what's the climate like Frank showed a chart today cost you know coming down but people are investing in IT because it's such an important part of the business are you able now to have much better line of sight on how those investments are producing for the business and has that affected your strategy totally and of course to a certain degree we have to go that path but we were able to cut the costs dramatically because we could shut down existing tool sets we could be faster in deploying new services so before we had the service now we had basically 15 tool sets that we had to an able to roll out the global service to Europe North America Latin and Asia 15 tools that now we have one that is of course dramatically faster in the deployment time in the time to customer in just enabling one tools that instead of 50 and you retired those other two sets or absolutely and people were screaming and kicking or was it absolutely they were all complaining about the tools but as soon as you tell them hey we are going shut down those tools they said hey that's the best we ever had and don't take my tool exactly but convincing them is quite easy with the ServiceNow product because it is from a usability standpoint modern integrated so you you catch that can catch them easily that they really can shut down the tools we even helped helped our providers also to shut down tools because they have basically the same environment they have not won cmdb one tool that they also have a fragmented environment and that's why we told all our providers in saying you can either work directly in our two or you have two connected to our tool but it's no longer that you have your own ecosystem so we have really generated an ecosystem from the employee that can order something in the mighty porter then it's going through our IT service management processes directly to our providers fully automated and that's where the providers also had to buy in in saying yeah we also see the benefits of consolidating our to landscape of getting better quality in our cmdb that that is the win-win situation what they don't like currently is that we have a much more transparency on how they perform now we don't have to wait until months end until we get a nice paper of green amber something we have now in our system real-time data anytime now we can really much more efficiently manage our provider can really dig into all the details that are available in our tools that and that's of course a big advantage but unfortunately provider has to get used to that that the customer is telling them how they provide it's a little bit a game changer it was that digit ID back to else la's or just new knowledge new information that you just didn't have before to help you manage that relationship now I mean of course SLA is an important topic but the SLA on a monthly basis speak one language but the experience on a daily basis tells another story so when we got the SAS on a month's end you always get an average ok level but if you then dig down into this as a in that country for this customer you saw some issues but that is now available in our ServiceNow instance and this is of course a great opportunity for us to manage the results of the providers and of course also as we have now our own tool set our own service integration architecture we can black in and out more easily the provider they don't like that as well but that is the advantage because I don't see and that is also a market trend that we get away from the 10 years 15 year outsourcing contracts this will be duration times of one year to year more cloud-based and to be really that or to support that velocity and speed that we can integrate much more easily the providers we have to have our own framework we have to have our own interfaces to the providers to really ensure we can plug in and out very easily one of the new acronyms that we are hearing at knowledge this year's asylum service integration and management it's been described as sort of son of ITIL i tilted Otto what is from your perspective matthias service i am so for me the big difference is i mean we we when we start with I to I think I too was a great achievement in defining the processes get a better maturity on incident problem and change and how this is done and also to generate the same verdict and so that you get a better understanding between the providers and the customers but what we missed in I tell is the integration so that we really if there is an incident and the we we allocate this incident to a provider and he doesn't feel responsible for it then we can transfer this incident to the next provider and hopefully he's then responsible and in charge so really the integration layer is for us very important because the market rent the competition in the different silos like end-user computing data and the application is pretty high so the value add for the customer is for me really in the integration layer getting the transparency across all your providers and of course the customers can you talk more about sort of how that is helped your your business and your providers business for example for the business I mean everybody talks about cloud services and all is fancy about cloud but to really manage cloud services is not that easy because what happened in in the in the old days the Siemens employs many they are cloud services with the credit card meaning if they wanted to have a server they look to Amazon or any other provider gave in the credit card and then this server was out of the IT well but now with the platform we have ensured that we can manage all the cloud services not only the ordering is done by our my IT porter we are getting also the reports not on a monthly basis but really on daily basis what is the usage of the cloud services and then we can either apps care downscale or even decommission those servers that are no longer needed because if they are managed by a credit card just we could to manage and that's why it was a key criteria for this platform to also managed cloud services and do you have a module for people to spin up just little dev instances of Amazon so you've actually brought shadow I tee underneath the ServiceNow platform absolutely and we have to expand that so we are at the beginning of that journey but as cloud is picking up and Siemens is also more addicted to cloud services we made sure that we have two examples of providers that are really working with that platform and that we can manage the whole life cycle of a cloud service so how would you describe sort of your strategy with respect to service now we're hearing a lot about obviously you know started in IT Service Management we're hearing a lot about other parts of the organization where are you guys in that whole journey I mean I could also start with talking about defenses staff outside of IT yeah but I think two topics are really important one is there are still a lot of IT service management processes within Siemens we have to shut down that means migration of existing tool sets is still a key activity for us because then we can fully leverage our investment we made into our ServiceNow platform in bringing in additional tools so this is one key component that we have to follow the second one is as we have a high degree of automation we really have to make sure that the system is mature for example we are exchanging data with our own application provider application for incident problem and change 13 million data sets per month that's quite a big number and if that data is not right if we have mixed up data the whole chain breaks the whole automation doesn't work so we spend also a lot of effort in hardening the system spending a lot of time in cleaning up the data that we are really sure we can also achieve the high degree of automation because it's always nice saying high degree of automation digitalization but getting there is an awful work because this is painful getting the right data cleaning the data and having the right data and as you do one then you just find your next point of failure right as you optimize the one then you move it it's a classic production line kind of band and that's why i say migration maturing the the system is one key then the third topic is of course we will bring in more providers more services more processes we we just downloaded from the ServiceNow App Store an application called Moby cod to manage our telecom expense management that we have also they are more transparency over the world and then of course we are in discussions to expand that platform also to facility management or HR but this will take some time because we still have in the HR facility area let's say Best of Breed applications and then it's hard to compete against the integration layer and saying okay but if it's integrated we have all the incidents in and all the data so that balance is not there but I heavenly believe that this will come as service now is more maturing in the HR facility area what not and then I think the the racial looks different and it's more promising also for Siemens to go beyond i TS so as you build out this integration architecture i have to ask you about security and my question is not one of a technical nature it's one of sort of a philosophical nature how is the conversation shifting right it's free decades we've spent money on the perimeter and protecting and and will keep the bad guys out now we all realize they can't keep the bad guys out it's how you respond to the bad guys and it seems as though ServiceNow could potentially is in some cases solving that problem how has the security conversation changed within your organization yeah i mean the security is high on the agenda with all the burn a little bit wilner abilities you here in the in the news and also did the day-to-day cases so security is a top agenda point and we had also some hard discussions within the siemens because the first reaction of demons was we have to have service now on premise we have to have our own nice server below the table and then we had some good discussions with herb is now we had discussions with other customers where they said no no we trust in this a solution then we did some penetration tests we did some assessment on side and that's where we said okay we will go with the SAS solution also for for semen and I'm pretty happy that we made the decision because now we don't have to focus on the operational topics we can really focus on the content bringing in new services bringing in you providers in rather discussing upgrading servers that you need to have the performance etc so that is also a next step we have on the agenda to bring in the vulnerability infosec topping also to our platform and but and is that the responsibility of sort of one group a group of gurus or is it a shared responsibility across the business or what should it be it is a shared responsibility because we are carefully watching how the ServiceNow cmdb is growing because we are collecting a lot of data we see exactly what is going on Adam and size of what are the customers ordering what in kind of incidents are they opening up we see exactly how our IT s doing and of course the provider data we have all the sea ice in our database and as we grow we grow the data that is in our system and that is of course a huge value but also security wise also a balance you have to make my last question I'll give you the last word we gotta wrap just knowledge you know your your experience here you know things you've learned anything that surprised you haha delighted you hear your you know I think the knowledge is really a great opportunity to meet at our customers to see what they have on the agenda were they working against and also it's a good opportunity to see what are the the hot topics for service now how does that match with our agenda that we have with our pipeline so we have a lot of discussions in the knowledge and that is a great value great Matthias egg will have thanks very much for coming in the cube preciate your time thank you good to have you all right keep right to everybody is the Cuba right back we're live at knowledge 16

(upbeat music) >> Hello, welcome back to theCUBE's AWS RE:Invent 2022 Coverage. I'm John Furrier, host of theCUBE. Got a great lineup here, Itamar Ankorion SVP Technology Alliance at Qlik and Peter McDonald, vice President, cloud partnerships and business development Snowflake. We're going to talk about bringing SAP data to life, for joint Snowflake, Qlik and AWS Solution. Gentlemen, thanks for coming on theCUBE Really appreciate it. >> Thank you. >> Thank you, great meeting you John. >> Just to get started, introduce yourselves to the audience, then going to jump into what you guys are doing together, unique relationship here, really compelling solution in cloud. Big story about applications and scale this year. Let's introduce yourselves. Peter, we'll start with you. >> Great. I'm Peter MacDonald. I am vice president of Cloud Partners and business development here at Snowflake. On the Cloud Partner side, that means I manage AWS relationship along with Microsoft and Google Cloud. What we do together in terms of complimentary products, GTM, co-selling, things like that. Importantly, working with other third parties like Qlik for joint solutions. On business development, it's negotiating custom commercial partnerships, large companies like Salesforce and Dell, smaller companies at most for our venture portfolio. >> Thanks Peter and hi John. It's great to be back here. So I'm Itamar Ankorion and I'm the senior vice president responsible for technology alliances here at Qlik. With that, own strategic alliances, including our key partners in the cloud, including Snowflake and AWS. I've been in the data and analytics enterprise software market for 20 plus years, and my main focus is product management, marketing, alliances, and business development. I joined Qlik about three and a half years ago through the acquisition of Attunity, which is now the foundation for Qlik data integration. So again, we focus in my team on creating joint solution alignment with our key partners to provide more value to our customers. >> Great to have both you guys, senior executives in the industry on theCUBE here, talking about data, obviously bringing SAP data to life is the theme of this segment, but this reinvent, it's all about the data, big data end-to-end story, a lot about data being intrinsic as the CEO says on stage around in the organizations in all aspects. Take a minute to explain what you guys are doing as from a company standpoint. Snowflake and Qlik and the solutions, why here at AWS? Peter, we'll start with you at Snowflake, what you guys do as a company, your mission, your focus. >> That was great, John. Yeah, so here at Snowflake, we focus on the data platform and until recently, data platforms required expensive on-prem hardware appliances. And despite all that expense, customers had capacity constraints, inexpensive maintenance, and had limited functionality that all impeded these organizations from reaching their goals. Snowflake is a cloud native SaaS platform, and we've become so successful because we've addressed these pain points and have other new special features. For example, securely sharing data across both the organization and the value chain without copying the data, support for new data types such as JSON and structured data, and also advance in database data governance. Snowflake integrates with complimentary AWS services and other partner products. So we can enable holistic solutions that include, for example, here, both Qlik and AWS SageMaker, and comprehend and bring those to joint customers. Our customers want to convert data into insights along with advanced analytics platforms in AI. That is how they make holistic data-driven solutions that will give them competitive advantage. With Snowflake, our approach is to focus on customer solutions that leverage data from existing systems such as SAP, wherever they are in the cloud or on-premise. And to do this, we leverage partners like Qlik native US to help customers transform their businesses. We provide customers with a premier data analytics platform as a result. Itamar, why don't you talk about Qlik a little bit and then we can dive into the specific SAP solution here and some trends >> Sounds great, Peter. So Qlik provides modern data integration and analytics software used by over 38,000 customers worldwide. Our focus is to help our customers turn data into value and help them close the gap between data all the way through insight and action. We offer click data integration and click data analytics. Click data integration helps to automate the data pipelines to deliver data to where they want to use them in real-time and make the data ready for analytics and then Qlik data analytics is a robust platform for analytics and business intelligence has been a leader in the Gartner Magic Quadrant for over 11 years now in the market. And both of these come together into what we call Qlik Cloud, which is our SaaS based platform. So providing a more seamless way to consume all these services and accelerate time to value with customer solutions. In terms of partnerships, both Snowflake and AWS are very strategic to us here at Qlik, so we have very comprehensive investment to ensure strong joint value proposition to we can bring to our mutual customers, everything from aligning our roadmaps through optimizing and validating integrations, collaborating on best practices, packaging joint solutions like the one we'll talk about today. And with that investment, we are an elite level, top level partner with Snowflake. We fly that our technology is Snowflake-ready across the entire product set and we have hundreds of joint customers together and with AWS we've also partnered for a long time. We're here to reinvent. We've been here with the first reinvent since the inaugural one, so it kind of gives you an idea for how long we've been working with AWS. We provide very comprehensive integration with AWS data analytics services, and we have several competencies ranging from data analytics to migration and modernization. So that's our focus and again, we're excited about working with Snowflake and AWS to bring solutions together to market. >> Well, I'm looking forward to unpacking the solutions specifically, and congratulations on the continued success of both your companies. We've been following them obviously for a very long time and seeing the platform evolve beyond just SaaS and a lot more going on in cloud these days, kind of next generation emerging. You know, we're seeing a lot of macro trends that are going to be powering some of the things we're going to get into real quickly. But before we get into the solution, what are some of those power dynamics in the industry that you're seeing in trends specifically that are impacting your customers that are taking us down this road of getting more out of the data and specifically the SAP, but in general trends and dynamics. What are you hearing from your customers? Why do they care? Why are they going down this road? Peter, we'll start with you. >> Yeah, I'll go ahead and start. Thanks. Yeah, I'd say we continue to see customers being, being very eager to transform their businesses and they know they need to leverage technology and data to do so. They're also increasingly depending upon the cloud to bring that agility, that elasticity, new functionality necessary to react in real-time to every evolving customer needs. You look at what's happened over the last three years, and boy, the macro environment customers, it's all changing so fast. With our partnerships with AWS and Qlik, we've been able to bring to market innovative solutions like the one we're announcing today that spans all three companies. It provides a holistic solution and an integrated solution for our customer. >> Itamar let's get into it, you've been with theCUBE, you've seen the journey, you have your own journey, many, many years, you've seen the waves. What's going on now? I mean, what's the big wave? What's the dynamic powering this trend? >> Yeah, in a nutshell I'll call it, it's all about time. You know, it's time to value and it's about real-time data. I'll kind of talk about that a bit. So, I mean, you hear a lot about the data being the new oil, but it's definitely, we see more and more customers seeing data as their critical enabler for innovation and digital transformation. They look for ways to monetize data. They look as the data as the way in which they can innovate and bring different value to the customers. So we see customers want to use more data so to get more value from data. We definitely see them wanting to do it faster, right, than before. And we definitely see them looking for agility and automation as ways to accelerate time to value, and also reduce overall costs. I did mention real-time data, so we definitely see more and more customers, they want to be able to act and make decisions based on fresh data. So yesterday's data is just not good enough. >> John: Yeah. >> It's got to be down to the hour, down to the minutes and sometimes even lower than that. And then I think we're also seeing customers look to their core business systems where they have a lot of value, like the SAP, like mainframe and thinking, okay, our core data is there, how can we get more value from this data? So that's key things we see all the time with customers. >> Yeah, we did a big editorial segment this year on, we called data as code. Data as code is kind of a riff on infrastructure as code and you start to see data becoming proliferating into all aspects, fresh data. It's not just where you store it, it's how you share it, it's how you turn it into an application intrinsically involved in all aspects. This is the big theme this year and that's driving all the conversations here at RE:Invent. And I'm guaranteeing you, it's going to happen for another five and 10 years. It's not stopping. So I got to get into the solution, you guys mentioned SAP and you've announced the solution by Qlik, Snowflake and AWS for your customers using SAP. Can you share more about this solution? What's unique about it? Why is it important and why now? Peter, Itamar, we'll start with you first. >> Let me jump in, this is really, I'll jump because I'm excited. We're very excited about this solution and it's also a solution by the way and again, we've seen proven customer success with it. So to your point, it's ready to scale, it's starting, I think we're going to see a lot of companies doing this over the next few years. But before we jump to the solution, let me maybe take a few minutes just to clarify the need, why we're seeing, why we're seeing customers jump to do this. So customers that use SAP, they use it to manage the core of their business. So think order processing, management, finance, inventory, supply chain, and so much more. So if you're running SAP in your company, that data creates a great opportunity for you to drive innovation and modernization. So what we see customers want to do, they want to do more with their data and more means they want to take SAP with non-SAP data and use it together to drive new insights. They want to use real-time data to drive real-time analytics, which they couldn't do to date. They want to bring together descriptive with predictive analytics. So adding machine learning in AI to drive more value from the data. And naturally they want to do it faster. So find ways to iterate faster on their solutions, have freedom with the data and agility. And I think this is really where cloud data platforms like Snowflake and AWS, you know, bring that value to be able to drive that. Now to do that you need to unlock the SAP data, which is a lot of also where Qlik comes in because typical challenges these customers run into is the complexity, inherent in SAP data. Tens of thousands of tables, proprietary formats, complex data models, licensing restrictions, and more than, you have performance issues, they usually run into how do we handle the throughput, the volumes while maintaining lower latency and impact. Where do we find knowledge to really understand how to get all this done? So these are the things we've looked at when we came together to create a solution and make it unique. So when you think about its uniqueness, because we put together a lot, and I'll go through three, four key things that come together to make this unique. First is about data delivery. How do you have the SAP data delivery? So how do you get it from ECC, from HANA from S/4HANA, how do you deliver the data and the metadata and how that integration well into Snowflake. And what we've done is we've focused a lot on optimizing that process and the continuous ingestion, so the real-time ingestion of the data in a way that works really well with the Snowflake system, data cloud. Second thing is we looked at SAP data transformation, so once the data arrives at Snowflake, how do we turn it into being analytics ready? So that's where data transformation and data worth automation come in. And these are all elements of this solution. So creating derivative datasets, creating data marts, and all of that is done by again, creating an optimized integration that pushes down SQL based transformations, so they can be processed inside Snowflake, leveraging its powerful engine. And then the third element is bringing together data visualization analytics that can also take all the data now that in organizing inside Snowflake, bring other data in, bring machine learning from SageMaker, and then you go to create a seamless integration to bring analytic applications to life. So these are all things we put together in the solution. And maybe the last point is we actually took the next step with this and we created something we refer to as solution accelerators, which we're really, really keen about. Think about this as prepackaged templates for common business analytic needs like order to cash, finance, inventory. And we can either dig into that a little more later, but this gets the next level of value to the customers all built into this joint solution. >> Yeah, I want to get to the accelerators, but real quick, Peter, your reaction to the solution, what's unique about it? And obviously Snowflake, we've been seeing the progression data applications, more developers developing on top of Snowflake, data as code kind of implies developer ecosystem. This is kind of interesting. I mean, you got partnering with Qlik and AWS, it's kind of a developer-like thinking real solution. What's unique about this SAP solution that's, that's different than what customers can get anywhere else or not? >> Yeah, well listen, I think first of all, you have to start with the idea of the solution. This are three companies coming together to build a holistic solution that is all about, you know, creating a great opportunity to turn SAP data into value this is Itamar was talking about, that's really what we're talking about here and there's a lot of technology underneath it. I'll talk more about the Snowflake technology, what's involved here, and then cover some of the AWS pieces as well. But you know, we're focusing on getting that value out and accelerating time to value for our joint customers. As Itamar was saying, you know, there's a lot of complexity with the SAP data and a lot of value there. How can we manage that in a prepackaged way, bringing together best of breed solutions with proven capabilities and bringing this to market quickly for our joint customers. You know, Snowflake and AWS have been strong partners for a number of years now, and that's not only on how Snowflake runs on top of AWS, but also how we integrate with their complementary analytics and then all products. And so, you know, we want to be able to leverage those in addition to what Qlik is bringing in terms of the data transformations, bringing data out of SAP in the visualization as well. All very critical. And then we want to bring in the predictive analytics, AWS brings and what Sage brings. We'll talk about that a little bit later on. Some of the technologies that we're leveraging are some of our latest cutting edge technologies that really make things easier for both our partners and our customers. For example, Qlik leverages Snowflakes recently released Snowpark for Python functionality to push down those data transformations from clicking the Snowflake that Itamar's mentioning. And while we also leverage Snowpark for integrations with Amazon SageMaker, but there's a lot of great new technology that just makes this easy and compelling for customers. >> I think that's the big word, easy button here for what may look like a complex kind of integration, kind of turnkey, really, really compelling example of the modern era we're living in, as we always say in theCUBE. You mentioned accelerators, SAP accelerators. Can you give an example of how that works with the technology from the third party providers to deliver this business value Itamar, 'cause that was an interesting comment. What's the example? Give an example of this acceleration. >> Yes, certainly. I think this is something that really makes this truly, truly unique in the industry and again, a great opportunity for customers. So we kind talked earlier about there's a lot of things that need to be done with SP data to turn it to value. And these accelerator, as the name suggests, are designed to do just that, to kind of jumpstart the process and reduce the time and the risk involved in such project. So again, these are pre-packaged templates. We basically took a lot of knowledge, and a lot of configurations, best practices about to get things done and we put 'em together. So think about all the steps, it includes things like data extraction, so already knowing which tables, all the relevant tables that you need to get data from in the contexts of the solution you're looking for, say like order to cash, we'll get back to that one. How do you continuously deliver that data into Snowflake in an in efficient manner, handling things like data type mappings, metadata naming conventions and transformations. The data models you build all the way to data mart definitions and all the transformations that the data needs to go through moving through steps until it's fully analytics ready. And then on top of that, even adding a library of comprehensive analytic dashboards and integrations through machine learning and AI and put all of that in a way that's in pre-integrated and tested to work with Snowflake and AWS. So this is where again, you get this entire recipe that's ready. So take for example, I think I mentioned order to cash. So again, all these things I just talked about, I mean, for those who are not familiar, I mean order to cash is a critical business process for every organization. So especially if you're in retail, manufacturing, enterprise, it's a big... This is where, you know, starting with booking a sales order, following by fulfilling the order, billing the customer, then managing the accounts receivable when the customer actually pays, right? So this all process, you got sales order fulfillment and the billing impacts customer satisfaction, you got receivable payments, you know, the impact's working capital, cash liquidity. So again, as a result this order to cash process is a lifeblood for many businesses and it's critical to optimize and understand. So the solution accelerator we created specifically for order to cash takes care of understanding all these aspects and the data that needs to come with it. So everything we outline before to make the data available in Snowflake in a way that's really useful for downstream analytics, along with dashboards that are already common for that, for that use case. So again, this enables customers to gain real-time visibility into their sales orders, fulfillment, accounts receivable performance. That's what the Excel's are all about. And very similarly, we have another one for example, for finance analytics, right? So this will optimize financial data reporting, helps customers get insights into P&L, financial risk of stability or inventory analytics that helps with, you know, improve planning and inventory management, utilization, increased efficiencies, you know, so in supply chain. So again, these accelerators really help customers get a jumpstart and move faster with their solutions. >> Peter, this is the easy button we just talked about, getting things going, you know, get the ball rolling, get some acceleration. Big part of this are the three companies coming together doing this. >> Yeah, and to build on what Itamar just said that the SAP data obviously has tremendous value. Those sales orders, distribution data, financial data, bringing that into Snowflake makes it easily accessible, but also it enables it to be combined with other data too, is one of the things that Snowflake does so well. So you can get a full view of the end-to-end process and the business overall. You know, for example, I'll just take one, you know, one example that, that may not come to mind right away, but you know, looking at the impact of weather conditions on supply chain logistics is relevant and material and have interest to our customers. How do you bring those different data sets together in an easy way, bringing the data out of SAP, bringing maybe other data out of other systems through Qlik or through Snowflake, directly bringing data in from our data marketplace and bring that all together to make it work. You know, fundamentally organizational silos and the data fragmentation exist otherwise make it really difficult to drive modern analytics projects. And that in turn limits the value that our customers are getting from SAP data and these other data sets. We want to enable that and unleash. >> Yeah, time for value. This is great stuff. Itamar final question, you know, what are customers using this? What do you have? I'm sure you have customers examples already using the solution. Can you share kind of what these examples look like in the use cases and the value? >> Oh yeah, absolutely. Thank you. Happy to. We have customers across different, different sectors. You see manufacturing, retail, energy, oil and gas, CPG. So again, customers in those segments, typically sectors typically have SAP. So we have customers in all of them. A great example is like Siemens Energy. Siemens Energy is a global provider of gas par services. You know, over what, 28 billion, 30 billion in revenue. 90,000 employees. They operate globally in over 90 countries. So they've used SAP HANA as a core system, so it's running on premises, multiple locations around the world. And what they were looking for is a way to bring all these data together so they can innovate with it. And the thing is, Peter mentioned earlier, not just the SAP data, but also bring other data from other systems to bring it together for more value. That includes finance data, these logistics data, these customer CRM data. So they bring data from over 20 different SAP systems. Okay, with Qlik data integration, feeding that into Snowflake in under 20 minutes, 24/7, 365, you know, days a year. Okay, they get data from over 20,000 tables, you know, over million, hundreds of millions of records daily going in. So it is a great example of the type of scale, scalability, agility and speed that they can get to drive these kind of innovation. So that's a great example with Siemens. You know, another one comes to mind is a global manufacturer. Very similar scenario, but you know, they're using it for real-time executive reporting. So it's more like feasibility to the production data as well as for financial analytics. So think, think, think about everything from audit to texts to innovate financial intelligence because all the data's coming from SAP. >> It's a great time to be in the data business again. It keeps getting better and better. There's more data coming. It's not stopping, you know, it's growing so fast, it keeps coming. Every year, it's the same story, Peter. It's like, doesn't stop coming. As we wrap up here, let's just get customers some information on how to get started. I mean, obviously you're starting to see the accelerators, it's a great program there. What a great partnership between the two companies and AWS. How can customers get started to learn about the solution and take advantage of it, getting more out of their SAP data, Peter? >> Yeah, I think the first place to go to is talk to Snowflake, talk to AWS, talk to our account executives that are assigned to your account. Reach out to them and they will be able to educate you on the solution. We have packages up very nicely and can be deployed very, very quickly. >> Well gentlemen, thank you so much for coming on. Appreciate the conversation. Great overview of the partnership between, you know, Snowflake and Qlik and AWS on a joint solution. You know, getting more out of the SAP data. It's really kind of a key, key solution, bringing SAP data to life. Thanks for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you John. >> Okay, this is theCUBE coverage here at RE:Invent 2022. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

>> Good afternoon from Vegas, guys and gals. We're so happy that you're with us. This is theCUBE live at AWS re:Invent '22. This is our third day of coverage. We started Monday night, so we're counting that as day one. Loads of conversations we've had already. We know that you know that 'cause you've been watching. I'm here with Dave Vellante. Dave, great to be here with you with somewhere between 50,000 and 70,000 people. And we're excited for our next conversation. We've got two folks joining us who are new to theCUBE, soon will be alumni. Milan Bhatt joins us, the president and head of Cloud at Hexaware. And Nikhil Date, the Director of Engineering and Application Services at Domestic & General. Guys, welcome to the program. >> Thank you >> Thanks for having us. >> So Domestic & General, or D&G, is a customer of Hexaware, but Milan, we want to start with you. Give the audience an overview of Hexaware. What do you do? What's the business model? >> Yeah. So, Hexaware is a technology services company. We are a global partner of AWS, and essentially, we help customers like Domestic & General, you know, accelerate their digital transformation journeys. We like to think of ourselves as a billion dollar startup. And like Amazon, it is always day one at Hexaware. And, you know, I look forward to the conversation, but any company in the world that is looking at cloud-led digital transformation, they have to put Hexaware on the consideration list. Because, you know, not only do we work with a lot of customers, analysts like Gartner, they have rated us as a visionary in helping customers become, you know, digitally enabled, bring better customer experience to their end customers. >> Excellent. Well, we're glad to feature Hexaware on the program. >> Milan: Thank you. >> Nikhil let's bring you into the conversation. Talk to the audience about Domestic & General. What kind of business is it? What's the business model? >> Sure, thank you. So we are, you know, 110-year-old business, right? I mean, we started insuring sheep in Australia, if you believe it, you know, which is quite an origin story. But at the moment, you know, the primary business is keeping our customers world running. So what do I mean by that? We protect in warranty and out-of-warranty care for domestic appliances. You know, TVs, boilers, refrigerators, washing machines, that kind of thing. But we are also a B2B company in the sense that, you know, you might think you are getting a warranty from some of our biggest customers, like Whirlpool or, you know, Bosch, Siemens, or Samsung, but actually it's D&G at the back trying to administer that for you. So, you know, we are in 13 countries. Just launched in the US last year, but big plans. >> So it's really interesting because we all have appliances, and we can relate to, especially, you know pre or post-pandemic, how difficult it is to get service. So you're kind of like, in a way, you've got to build a digital platform like Uber, connecting drivers and passengers, right? And so you've got the supply of individuals who know how to fix stuff, right? And you want to make it as easy as possible for the customer. So was that the genesis of this digital transformation? Can you talk about those business drivers? >> It was, actually, and it's a fantastic point, because trying to become a platform business is what this journey has been all about for us, right? I think, you know, we are a pioneer in what we consider the subscription model. So customers pay a small amount per month as opposed to a big lump sum amount that they have to pay at the point you buy the appliance. And importantly, you can actually buy our product to pay in installments at the point something breaks down. So it's not just something that you buy at the point of sale or at the point you try to register. You can buy it at any time. And the goal really is to have warranty in a box that you can take anywhere, you know, anywhere in the world. So, you know, but it's a great point. Digital transformation is what it is all about. >> And there is a real lack right now of qualified technicians. >> That's right. >> Is there anything within the platform to incent those individuals to participate in your business? >> You know, this is what we consider a multi-tier approach. I think at the moment, the service that we offer is largely top tier, right? So we will get you an engineer that is certified by the manufacturer with the manufacturer warranty. And it's a no fix, no fee model, you know? So, you know, we guarantee either to repair or replace the appliance, you know? That's the model. But you are right, I think in the future stage would be, you know, why wouldn't we want to have anybody who's got the right skills to come in and work off the platform? Absolutely right. >> Nikhil, talk about, you said this is a legacy business, been around for quite some time. You've been there for not quite two years. What drew you to the organization? And where were they in their digital transformation journey? Because I always think legacy companies, this a big challenge, and it's cultural challenge to really transform, but companies these days have no choice. >> Again, a fantastic point, right? I think some of the, you know, 110-year-old business, right? And some of the tech, you would be forgiven for thinking it's that old. But the assets that we had are our people, right? Who are really passionate about the business. And I think what we had to do is to find a partner that can upskill the tech, but also upskill the people at the same time and upskill the delivery model, right? So we've a very traditional left-to-right waterfall, you know, planet first, big upfront planning, and then deliver kind of organization. And by working with a partner such as Hexaware and embracing cloud, because, you know, our first and our go-to will be a SaaS or a cloud provider. And, you know, doing that was the massive agenda that drew me to the company. But I think what is also fair is, you know, digitization or digitalization, is a misunderstood and often abused term, right? Because for the most part, when companies start, and I'm not saying it's right or wrong, but, you know, for the most part, when companies start on this journey, they take a journey that works in the brick and mortar world, and we were a contact center business, and just try to move it to the digital journey, right? It's not a great customer experience. I'll give you an example, right? Now, if you call our agent and say, "Yeah, I'm trying to register an appliance," they will tell you where to look for the serial number. But if you're on a digital channel, you don't know where to look. There's nobody, you know, who can help you. The model number, who remembers the model number of the washing machine they bought, right? I mean, you know, it's stuff like that, you know, which would feel, you know, for a digital native, my son, you know, for example, would think, "How can you even ask a customer for that?" But, you know, it's that change in the model, that's what this is all about. >> Yeah, it's like when you get to go, "What's your account number?" I have no idea what my account number is. So when did this whole project start? How was Hexaware involved? And where did Hexaware start? Like, how did you sort of gauge what the requirement was? Take us through that little- >> Sure. So, you know, when Nikhil and the rest of the management team came in, they came up with a competitive process where, you know, and it is refreshing to remember, I think they've stuck true to their vision. They were very clear that they were not looking for someone who can just digitize their paper processes, but who can help them completely re-imagine, you know, what the new process would look like what the new experience would look like. And, you know, remember, they were running this process at the height of the pandemic, so we couldn't meet anybody in person. We did everything virtual. And we were using cloud technology, but, you know, the way they run the process, they wanted to make sure that a provider brings in a mix of experience and engineering expertise. And that's really hard to find. But equally importantly, you remember those culture sessions that we did? They figured out some very creative ways of making sure that there is a cultural fit. So, for example, they did virtual breakout sessions where, you know, people were sort of asking each other, you know, if you want to have dinner with someone like a celebrity, who would it be? So, you know, these little things to make sure that there is a match and people can actually work. >> Relationship building too. >> The relationship building. It's hard to do in a virtual environment, but it was a competitive process. They looked at us in terms of engineering, you know, experience, our ability to transcend change and run, and, you know, really focus and align to keep their objectives first, right? Work as a true partnership. Do you agree? >> I would agree. And I think, you know, one of the biggest goals here was to make sure that, this is not an arms length vendor relationship, right? You know, this is an extension of our team. So these are our people, you know, for the people that work on D&G, you know, they work in the D&G way, you know, and that means that they can also challenge us, you know, which is quite refreshing, right? People stopping and saying, "Why are you asking me to do this?" You know, it's very refreshing, I think, you know, to work with a partner that is sold on the vision and committed to helping you achieve success. >> That synergy creates that flywheel. And like you said, at D&G, Hexaware, we're a team, we're working together. Nikhil, share with us some of the significant business outcomes that Hexaware services and AWS are helping the company to achieve? Because there's some big numbers there. >> Indeed. Yeah. So, you know, in the digital journey itself, like I said, we are also a B2B business. You know, one of the key challenges is every client wants their own brand, right? So, you know, a journey for customer X has to look like the customer X brand. And our journey for customer Y will have to do the same. You know, when you try to stretch this to a technology problem though, it means that, you know, we were trying to be too many things for too many people, and that slowed things down and increased complexity. So from our point of view, you know, when we started with the digital journey or in the middle of the digital journey, we thought, we need to have a library of reusable components. We need white labeling, right? So there was a root in branch re-engineering of the digital proposition to allow us to, you know, serve multiple clients with the same underlying technology. And that has meant that, you know, in some cases, we are going to market, you know, two, three times faster than what we were. Costs, obviously, you know, 50% cheaper. But, you know, I think the big thing here, and, you know, this is the unstated benefit, is because now there is a common underlying technology innovation that client X wants to do becomes available for client Y. You know, which means that, you know, there's a virtual circle of, you know, constant improvement. So, you know that, from my point of view, that's the big benefit. >> And would you agree that you are still only in the first quarter of a football game? >> Absolutely. >> I think a lot of ambitious plans. So, you know, this is just the beginning. And the way they have built the organization, the way they have driven the culture change, you know, I'm very hopeful for great things to come. >> Paint a picture of the tech. I'm interested in the architecture, and I'm really interested in the data component and how that's affected your business. >> So I mean, you know, multilayered tech architecture, as you can imagine. Then, you know, we still have a legacy, you know, legacy components running off our own PET mainframe, as we like to call it. But, you know, from a forward point of view, what we really want is to allow clients to self-serve, right? Not have to, you know, because at the moment, the only service we can offer is what I call the white glove, right? Which means, you know, somebody has to sit down with us, have a discussion on the requirements, but people should be able to self-serve, you know, look at the catalog of what it is we can do for them and go for it. Data is a very interesting point, right? Because not only are there, you know, geography restrictions around where customer data can go to, obviously, payments and PCI compliance is an issue. But last but not least, you know, some of this data is very, you know, unique to what the clients want to own and manage. And, you know, if you are a, you know, a typical homeowner, you will have appliance from all kinds of manufacturers, right? Many of whom would be our customers. But how much data we can share, because we recognize you as a person, but how much data we can share, there are restrictions. But, you know, building our data abstraction layer allows us to, you know, take care of that. But you're absolutely right, in terms of, But again, the potential for where the data can be mined, because, you know, the engineer also has to be local to where you live. You know, you can't come from 100 miles away. So, you know, the ability to use data to, you know, not just transform our business, but our client's business is phenomenal, you know? >> Do you actually have a mainframe? >> Yes >> We do do. (laughter) >> Adam Selinsky wants to move it into the cloud. (laughter) >> They have every possible technology that you can think of. I mean, 100-year-old business evolved over a period of time. And, you know, if I could add, you know, what has been really impressive about the decision making at D&G is that they have adopted cloud in the right way, right? So they are one of the few customers who have truly taken AWS well architected to heart. They have taken things like, you know, take the right workloads to the cloud and wait to do the right remediations before you take the rest of the workloads to the cloud. They've used native services available on AWS from apps perspective as well as a data perspective. So that's sort of a little bit more color on the technology and architecture. >> But you've essentially SaaSified your business and you basically have D&G cloud that you're delivering to your customers for self-serve. Is that fair? >> That's the vision, yes. The idea is to get there. And, you know, if we assemble what I call, you know, out-the-box solutions in a clever way, then that becomes the platform that we can replicate success on. And at the moment, our business needs what I call boots on the ground. When we are a true platform business, we should be able to operate without having, you know, any presence in country, with the partners leveraging the platform to do what what's next. >> I'm curious, Milan, you said that one of the great things that D&G has done is really adopted cloud in the right way. Do you, Nikhil, think of cloud first or cloud right approach? Because you've got a mainframe, so I'm just wondering if it's more what's right for cloud versus everything cloud first. >> Correct. I mean, I actually, you know, or we actually tend to start even two steps before that, right? I think it's really whether we need to buy or whether we need to build, right? And if we need to buy, then, you know, how easily would that thing that has been bought fit into what is a very complex architecture, as Milan said, right? I mean, any technology you can imagine we probably have it, but we want to simplify it, right? And this is a journey. So which means that, you know, we start with can SaaS product do it? And then we also want to go wherever we are building, then it has to be on the cloud. It has to be designed for scaling. It has to be designed to be in multiple geographies, multiple countries with the relevant data protection baked in. So, you know, that's the decision-thinking process. You know, that the goal is to not, I mean, you know, we had a project started 18 months ago that wanted to buy more tin, but we put a stop to that, right? And saying that, "You know, come on, you can't have that." Not in this day and age, you know, when the cloud can pretty much do everything that you need. >> Do you think of D&G, this is a question for you. We're almost out of time, but I'm just curious, I'm looking at your website, D&G, the experts who repair and replace the household products everyone relies on. Do you think about it as a repair company? Do you think about it as a tech company that delivers these repair services? >> I mean, this is the conversation we have in our teams all the time, right? That when our vision is successful, we will become a tech business. At the moment, I don't think we are, you know? At the moment, I think we are on a journey, you know, because, you know, we are multi-channel, you know, and our customers love us, you know, touch wood. But are we a true tech company? No, but we are getting there, right? I think, you know, that's the plan. >> You're on the journey? >> Yeah. >> Awesome stuff. Last question for each of you, a little bit different. Milan, question for you. You have a billboard or a bumper sticker, whichever, or maybe a sticker for your laptop and it's about Hexaware, and you want to really convey, in a compelling, but really short way, why are we so great? What would that sticker say? >> Awesome. Like I said at the beginning, if you are thinking about a digital transformation, if you are a company that has been around for a long time, you've got to think of us, you know, as a partner. So that's what I would say, because, you know, the purpose of our company is creating smiles through a combination of great people and technology. So that's what we live for. And, you know, brought a smile to me when Nikhil said that our customers love us, and somewhere, we have a small role to play in that. >> I love that. Nikhil, I'm going to ask the same question. I was going to ask you a different one, but I would love to, I mean, we talked a lot about D&G and the incredible business transformation that you've been on. What's that bumper sticker for D&G? What is that bumper sticker for D&G? >> Oh, yeah. Okay. We want keep your world running, right? I mean, you know, from our point of view, you know, you rely on the appliances to keep your home running, and we want you to rely on us to make sure your world keeps running. You know, that's what this is all about. It has to be slick. Touch wood, hopefully, you never have a problem, but if you do, we want to be there, you know, to make sure that your world keeps running. >> I love it. Awesome, guys. Thank you, Milan. Nikhil, thank you so much for joining Dave and me on the program. >> Thank you. I enjoyed the conversation. >> Great partnership. Hexaware, first time on theCUBE, now you're an alumni. You're an alumni too. We appreciate your insights, sharing the story. It's a really compelling story. Thank you. >> And thank you for all the support, Nikhil. >> Of course. >> All right. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage.

(upbeat music) >> Hello, welcome back to theCUBE's AWS RE:Invent 2022 Coverage. I'm John Furrier, host of theCUBE. Got a great lineup here, Itamar Ankorion SVP Technology Alliance at Qlik and Peter McDonald, vice President, cloud partnerships and business development Snowflake. We're going to talk about bringing SAP data to life, for joint Snowflake, Qlik and AWS Solution. Gentlemen, thanks for coming on theCUBE Really appreciate it. >> Thank you. >> Thank you, great meeting you John. >> Just to get started, introduce yourselves to the audience, then going to jump into what you guys are doing together, unique relationship here, really compelling solution in cloud. Big story about applications and scale this year. Let's introduce yourselves. Peter, we'll start with you. >> Great. I'm Peter MacDonald. I am vice president of Cloud Partners and business development here at Snowflake. On the Cloud Partner side, that means I manage AWS relationship along with Microsoft and Google Cloud. What we do together in terms of complimentary products, GTM, co-selling, things like that. Importantly, working with other third parties like Qlik for joint solutions. On business development, it's negotiating custom commercial partnerships, large companies like Salesforce and Dell, smaller companies at most for our venture portfolio. >> Thanks Peter and hi John. It's great to be back here. So I'm Itamar Ankorion and I'm the senior vice president responsible for technology alliances here at Qlik. With that, own strategic alliances, including our key partners in the cloud, including Snowflake and AWS. I've been in the data and analytics enterprise software market for 20 plus years, and my main focus is product management, marketing, alliances, and business development. I joined Qlik about three and a half years ago through the acquisition of Attunity, which is now the foundation for Qlik data integration. So again, we focus in my team on creating joint solution alignment with our key partners to provide more value to our customers. >> Great to have both you guys, senior executives in the industry on theCUBE here, talking about data, obviously bringing SAP data to life is the theme of this segment, but this reinvent, it's all about the data, big data end-to-end story, a lot about data being intrinsic as the CEO says on stage around in the organizations in all aspects. Take a minute to explain what you guys are doing as from a company standpoint. Snowflake and Qlik and the solutions, why here at AWS? Peter, we'll start with you at Snowflake, what you guys do as a company, your mission, your focus. >> That was great, John. Yeah, so here at Snowflake, we focus on the data platform and until recently, data platforms required expensive on-prem hardware appliances. And despite all that expense, customers had capacity constraints, inexpensive maintenance, and had limited functionality that all impeded these organizations from reaching their goals. Snowflake is a cloud native SaaS platform, and we've become so successful because we've addressed these pain points and have other new special features. For example, securely sharing data across both the organization and the value chain without copying the data, support for new data types such as JSON and structured data, and also advance in database data governance. Snowflake integrates with complimentary AWS services and other partner products. So we can enable holistic solutions that include, for example, here, both Qlik and AWS SageMaker, and comprehend and bring those to joint customers. Our customers want to convert data into insights along with advanced analytics platforms in AI. That is how they make holistic data-driven solutions that will give them competitive advantage. With Snowflake, our approach is to focus on customer solutions that leverage data from existing systems such as SAP, wherever they are in the cloud or on-premise. And to do this, we leverage partners like Qlik native US to help customers transform their businesses. We provide customers with a premier data analytics platform as a result. Itamar, why don't you talk about Qlik a little bit and then we can dive into the specific SAP solution here and some trends >> Sounds great, Peter. So Qlik provides modern data integration and analytics software used by over 38,000 customers worldwide. Our focus is to help our customers turn data into value and help them close the gap between data all the way through insight and action. We offer click data integration and click data analytics. Click data integration helps to automate the data pipelines to deliver data to where they want to use them in real-time and make the data ready for analytics and then Qlik data analytics is a robust platform for analytics and business intelligence has been a leader in the Gartner Magic Quadrant for over 11 years now in the market. And both of these come together into what we call Qlik Cloud, which is our SaaS based platform. So providing a more seamless way to consume all these services and accelerate time to value with customer solutions. In terms of partnerships, both Snowflake and AWS are very strategic to us here at Qlik, so we have very comprehensive investment to ensure strong joint value proposition to we can bring to our mutual customers, everything from aligning our roadmaps through optimizing and validating integrations, collaborating on best practices, packaging joint solutions like the one we'll talk about today. And with that investment, we are an elite level, top level partner with Snowflake. We fly that our technology is Snowflake-ready across the entire product set and we have hundreds of joint customers together and with AWS we've also partnered for a long time. We're here to reinvent. We've been here with the first reinvent since the inaugural one, so it kind of gives you an idea for how long we've been working with AWS. We provide very comprehensive integration with AWS data analytics services, and we have several competencies ranging from data analytics to migration and modernization. So that's our focus and again, we're excited about working with Snowflake and AWS to bring solutions together to market. >> Well, I'm looking forward to unpacking the solutions specifically, and congratulations on the continued success of both your companies. We've been following them obviously for a very long time and seeing the platform evolve beyond just SaaS and a lot more going on in cloud these days, kind of next generation emerging. You know, we're seeing a lot of macro trends that are going to be powering some of the things we're going to get into real quickly. But before we get into the solution, what are some of those power dynamics in the industry that you're seeing in trends specifically that are impacting your customers that are taking us down this road of getting more out of the data and specifically the SAP, but in general trends and dynamics. What are you hearing from your customers? Why do they care? Why are they going down this road? Peter, we'll start with you. >> Yeah, I'll go ahead and start. Thanks. Yeah, I'd say we continue to see customers being, being very eager to transform their businesses and they know they need to leverage technology and data to do so. They're also increasingly depending upon the cloud to bring that agility, that elasticity, new functionality necessary to react in real-time to every evolving customer needs. You look at what's happened over the last three years, and boy, the macro environment customers, it's all changing so fast. With our partnerships with AWS and Qlik, we've been able to bring to market innovative solutions like the one we're announcing today that spans all three companies. It provides a holistic solution and an integrated solution for our customer. >> Itamar let's get into it, you've been with theCUBE, you've seen the journey, you have your own journey, many, many years, you've seen the waves. What's going on now? I mean, what's the big wave? What's the dynamic powering this trend? >> Yeah, in a nutshell I'll call it, it's all about time. You know, it's time to value and it's about real-time data. I'll kind of talk about that a bit. So, I mean, you hear a lot about the data being the new oil, but it's definitely, we see more and more customers seeing data as their critical enabler for innovation and digital transformation. They look for ways to monetize data. They look as the data as the way in which they can innovate and bring different value to the customers. So we see customers want to use more data so to get more value from data. We definitely see them wanting to do it faster, right, than before. And we definitely see them looking for agility and automation as ways to accelerate time to value, and also reduce overall costs. I did mention real-time data, so we definitely see more and more customers, they want to be able to act and make decisions based on fresh data. So yesterday's data is just not good enough. >> John: Yeah. >> It's got to be down to the hour, down to the minutes and sometimes even lower than that. And then I think we're also seeing customers look to their core business systems where they have a lot of value, like the SAP, like mainframe and thinking, okay, our core data is there, how can we get more value from this data? So that's key things we see all the time with customers. >> Yeah, we did a big editorial segment this year on, we called data as code. Data as code is kind of a riff on infrastructure as code and you start to see data becoming proliferating into all aspects, fresh data. It's not just where you store it, it's how you share it, it's how you turn it into an application intrinsically involved in all aspects. This is the big theme this year and that's driving all the conversations here at RE:Invent. And I'm guaranteeing you, it's going to happen for another five and 10 years. It's not stopping. So I got to get into the solution, you guys mentioned SAP and you've announced the solution by Qlik, Snowflake and AWS for your customers using SAP. Can you share more about this solution? What's unique about it? Why is it important and why now? Peter, Itamar, we'll start with you first. >> Let me jump in, this is really, I'll jump because I'm excited. We're very excited about this solution and it's also a solution by the way and again, we've seen proven customer success with it. So to your point, it's ready to scale, it's starting, I think we're going to see a lot of companies doing this over the next few years. But before we jump to the solution, let me maybe take a few minutes just to clarify the need, why we're seeing, why we're seeing customers jump to do this. So customers that use SAP, they use it to manage the core of their business. So think order processing, management, finance, inventory, supply chain, and so much more. So if you're running SAP in your company, that data creates a great opportunity for you to drive innovation and modernization. So what we see customers want to do, they want to do more with their data and more means they want to take SAP with non-SAP data and use it together to drive new insights. They want to use real-time data to drive real-time analytics, which they couldn't do to date. They want to bring together descriptive with predictive analytics. So adding machine learning in AI to drive more value from the data. And naturally they want to do it faster. So find ways to iterate faster on their solutions, have freedom with the data and agility. And I think this is really where cloud data platforms like Snowflake and AWS, you know, bring that value to be able to drive that. Now to do that you need to unlock the SAP data, which is a lot of also where Qlik comes in because typical challenges these customers run into is the complexity, inherent in SAP data. Tens of thousands of tables, proprietary formats, complex data models, licensing restrictions, and more than, you have performance issues, they usually run into how do we handle the throughput, the volumes while maintaining lower latency and impact. Where do we find knowledge to really understand how to get all this done? So these are the things we've looked at when we came together to create a solution and make it unique. So when you think about its uniqueness, because we put together a lot, and I'll go through three, four key things that come together to make this unique. First is about data delivery. How do you have the SAP data delivery? So how do you get it from ECC, from HANA from S/4HANA, how do you deliver the data and the metadata and how that integration well into Snowflake. And what we've done is we've focused a lot on optimizing that process and the continuous ingestion, so the real-time ingestion of the data in a way that works really well with the Snowflake system, data cloud. Second thing is we looked at SAP data transformation, so once the data arrives at Snowflake, how do we turn it into being analytics ready? So that's where data transformation and data worth automation come in. And these are all elements of this solution. So creating derivative datasets, creating data marts, and all of that is done by again, creating an optimized integration that pushes down SQL based transformations, so they can be processed inside Snowflake, leveraging its powerful engine. And then the third element is bringing together data visualization analytics that can also take all the data now that in organizing inside Snowflake, bring other data in, bring machine learning from SageMaker, and then you go to create a seamless integration to bring analytic applications to life. So these are all things we put together in the solution. And maybe the last point is we actually took the next step with this and we created something we refer to as solution accelerators, which we're really, really keen about. Think about this as prepackaged templates for common business analytic needs like order to cash, finance, inventory. And we can either dig into that a little more later, but this gets the next level of value to the customers all built into this joint solution. >> Yeah, I want to get to the accelerators, but real quick, Peter, your reaction to the solution, what's unique about it? And obviously Snowflake, we've been seeing the progression data applications, more developers developing on top of Snowflake, data as code kind of implies developer ecosystem. This is kind of interesting. I mean, you got partnering with Qlik and AWS, it's kind of a developer-like thinking real solution. What's unique about this SAP solution that's, that's different than what customers can get anywhere else or not? >> Yeah, well listen, I think first of all, you have to start with the idea of the solution. This are three companies coming together to build a holistic solution that is all about, you know, creating a great opportunity to turn SAP data into value this is Itamar was talking about, that's really what we're talking about here and there's a lot of technology underneath it. I'll talk more about the Snowflake technology, what's involved here, and then cover some of the AWS pieces as well. But you know, we're focusing on getting that value out and accelerating time to value for our joint customers. As Itamar was saying, you know, there's a lot of complexity with the SAP data and a lot of value there. How can we manage that in a prepackaged way, bringing together best of breed solutions with proven capabilities and bringing this to market quickly for our joint customers. You know, Snowflake and AWS have been strong partners for a number of years now, and that's not only on how Snowflake runs on top of AWS, but also how we integrate with their complementary analytics and then all products. And so, you know, we want to be able to leverage those in addition to what Qlik is bringing in terms of the data transformations, bringing data out of SAP in the visualization as well. All very critical. And then we want to bring in the predictive analytics, AWS brings and what Sage brings. We'll talk about that a little bit later on. Some of the technologies that we're leveraging are some of our latest cutting edge technologies that really make things easier for both our partners and our customers. For example, Qlik leverages Snowflakes recently released Snowpark for Python functionality to push down those data transformations from clicking the Snowflake that Itamar's mentioning. And while we also leverage Snowpark for integrations with Amazon SageMaker, but there's a lot of great new technology that just makes this easy and compelling for customers. >> I think that's the big word, easy button here for what may look like a complex kind of integration, kind of turnkey, really, really compelling example of the modern era we're living in, as we always say in theCUBE. You mentioned accelerators, SAP accelerators. Can you give an example of how that works with the technology from the third party providers to deliver this business value Itamar, 'cause that was an interesting comment. What's the example? Give an example of this acceleration. >> Yes, certainly. I think this is something that really makes this truly, truly unique in the industry and again, a great opportunity for customers. So we kind talked earlier about there's a lot of things that need to be done with SP data to turn it to value. And these accelerator, as the name suggests, are designed to do just that, to kind of jumpstart the process and reduce the time and the risk involved in such project. So again, these are pre-packaged templates. We basically took a lot of knowledge, and a lot of configurations, best practices about to get things done and we put 'em together. So think about all the steps, it includes things like data extraction, so already knowing which tables, all the relevant tables that you need to get data from in the contexts of the solution you're looking for, say like order to cash, we'll get back to that one. How do you continuously deliver that data into Snowflake in an in efficient manner, handling things like data type mappings, metadata naming conventions and transformations. The data models you build all the way to data mart definitions and all the transformations that the data needs to go through moving through steps until it's fully analytics ready. And then on top of that, even adding a library of comprehensive analytic dashboards and integrations through machine learning and AI and put all of that in a way that's in pre-integrated and tested to work with Snowflake and AWS. So this is where again, you get this entire recipe that's ready. So take for example, I think I mentioned order to cash. So again, all these things I just talked about, I mean, for those who are not familiar, I mean order to cash is a critical business process for every organization. So especially if you're in retail, manufacturing, enterprise, it's a big... This is where, you know, starting with booking a sales order, following by fulfilling the order, billing the customer, then managing the accounts receivable when the customer actually pays, right? So this all process, you got sales order fulfillment and the billing impacts customer satisfaction, you got receivable payments, you know, the impact's working capital, cash liquidity. So again, as a result this order to cash process is a lifeblood for many businesses and it's critical to optimize and understand. So the solution accelerator we created specifically for order to cash takes care of understanding all these aspects and the data that needs to come with it. So everything we outline before to make the data available in Snowflake in a way that's really useful for downstream analytics, along with dashboards that are already common for that, for that use case. So again, this enables customers to gain real-time visibility into their sales orders, fulfillment, accounts receivable performance. That's what the Excel's are all about. And very similarly, we have another one for example, for finance analytics, right? So this will optimize financial data reporting, helps customers get insights into P&L, financial risk of stability or inventory analytics that helps with, you know, improve planning and inventory management, utilization, increased efficiencies, you know, so in supply chain. So again, these accelerators really help customers get a jumpstart and move faster with their solutions. >> Peter, this is the easy button we just talked about, getting things going, you know, get the ball rolling, get some acceleration. Big part of this are the three companies coming together doing this. >> Yeah, and to build on what Itamar just said that the SAP data obviously has tremendous value. Those sales orders, distribution data, financial data, bringing that into Snowflake makes it easily accessible, but also it enables it to be combined with other data too, is one of the things that Snowflake does so well. So you can get a full view of the end-to-end process and the business overall. You know, for example, I'll just take one, you know, one example that, that may not come to mind right away, but you know, looking at the impact of weather conditions on supply chain logistics is relevant and material and have interest to our customers. How do you bring those different data sets together in an easy way, bringing the data out of SAP, bringing maybe other data out of other systems through Qlik or through Snowflake, directly bringing data in from our data marketplace and bring that all together to make it work. You know, fundamentally organizational silos and the data fragmentation exist otherwise make it really difficult to drive modern analytics projects. And that in turn limits the value that our customers are getting from SAP data and these other data sets. We want to enable that and unleash. >> Yeah, time for value. This is great stuff. Itamar final question, you know, what are customers using this? What do you have? I'm sure you have customers examples already using the solution. Can you share kind of what these examples look like in the use cases and the value? >> Oh yeah, absolutely. Thank you. Happy to. We have customers across different, different sectors. You see manufacturing, retail, energy, oil and gas, CPG. So again, customers in those segments, typically sectors typically have SAP. So we have customers in all of them. A great example is like Siemens Energy. Siemens Energy is a global provider of gas par services. You know, over what, 28 billion, 30 billion in revenue. 90,000 employees. They operate globally in over 90 countries. So they've used SAP HANA as a core system, so it's running on premises, multiple locations around the world. And what they were looking for is a way to bring all these data together so they can innovate with it. And the thing is, Peter mentioned earlier, not just the SAP data, but also bring other data from other systems to bring it together for more value. That includes finance data, these logistics data, these customer CRM data. So they bring data from over 20 different SAP systems. Okay, with Qlik data integration, feeding that into Snowflake in under 20 minutes, 24/7, 365, you know, days a year. Okay, they get data from over 20,000 tables, you know, over million, hundreds of millions of records daily going in. So it is a great example of the type of scale, scalability, agility and speed that they can get to drive these kind of innovation. So that's a great example with Siemens. You know, another one comes to mind is a global manufacturer. Very similar scenario, but you know, they're using it for real-time executive reporting. So it's more like feasibility to the production data as well as for financial analytics. So think, think, think about everything from audit to texts to innovate financial intelligence because all the data's coming from SAP. >> It's a great time to be in the data business again. It keeps getting better and better. There's more data coming. It's not stopping, you know, it's growing so fast, it keeps coming. Every year, it's the same story, Peter. It's like, doesn't stop coming. As we wrap up here, let's just get customers some information on how to get started. I mean, obviously you're starting to see the accelerators, it's a great program there. What a great partnership between the two companies and AWS. How can customers get started to learn about the solution and take advantage of it, getting more out of their SAP data, Peter? >> Yeah, I think the first place to go to is talk to Snowflake, talk to AWS, talk to our account executives that are assigned to your account. Reach out to them and they will be able to educate you on the solution. We have packages up very nicely and can be deployed very, very quickly. >> Well gentlemen, thank you so much for coming on. Appreciate the conversation. Great overview of the partnership between, you know, Snowflake and Qlik and AWS on a joint solution. You know, getting more out of the SAP data. It's really kind of a key, key solution, bringing SAP data to life. Thanks for coming on theCUBE. Appreciate it. >> Thank you. >> Thank you John. >> Okay, this is theCUBE coverage here at RE:Invent 2022. I'm John Furrier, your host of theCUBE. Thanks for watching. (upbeat music)

>>We're back with the cube at Falcon 2022, Dave ante and Dave Nicholson. We're at the aria. We do of course, a lot of events in Las Vegas. It's the, it's the place to do events. Dave, I think is my sixth or seventh time here this year. At least. I don't know. I lose track. Jeff Swain is here. He's the vice president of global programs store and tech alliances at CrowdStrike. Jeff. Good to see you again. We saw each other at reinvent in July in Boston. >>Yes. Yeah, it was great to see you again, Dave, thank >>Very much. And we talked about making this happen so thrilled to be here at, at, at CrowdStrike Falcon. We're gonna talk today about the CrowdStrike XDR Alliance partners. First of all, what's XDR >>Well, I hope you were paying attention to George's George's keynote this morning. I guess. You know, the one thing we know is that if you ask 10, five people, what XDR is you'll get 10 answers. >>I like this answer a holistic approach to endpoint security. I, that was, >>It was good. Simple. >>That was a good one at black hat. So, but tell us about the XDR Alliance partners program. Give us the update there. >>Yeah, so I mean, we spoke about it reinforced, you know, the XDR program is really predicated on having a robust ecosystem of partners to help us share that telemetry across all of the different parts of our customers' environment. So we've done a lot of work over the last few weeks and trying to bolster that environment specifically, putting a lot of focus on firewall. You'll see that Cisco and fortunate have both joined the XD XDR Alliance. So we're working on that right now. A lot of customer demand for firewall data into the telemetry set. You know, obviously it's a very rich data environment. There's a lot of logs on firewalls. And so it drives a lot of, of, of information that we can, we can leverage. So we're continuing to grow that. And what we're doing is building out different content packs that support different use cases. So firewall is one CAS B is another emails another and we're building, building out the, the partner set right across the board. So it's, it's, it's been a, a great set of >>Activity. So it's it's partners that have data. Yep. There's probably some, you know, Joe Tuchi year old boss used to say that that overlap is better than gaps. So there's sometimes there's competition, but that's from a customer standpoint, overlap is, is better than gaps. So as gonna mention Cisco forte and there are a number of others, they've got data. Yes. And they're gonna pump it into your system, our platform, and you've got the, your platform. You've got the ability to ingest. You've got the cloud native architecture, you've got the analytics and you've got the near real time analysis capability. Right, right. >>Augmented by people as well, which is a really important part of our value proposition. You know, we, it's not just relying purely on AI, but we have a human, a human aspect to it as well to make sure we're getting extremely accurate responses. And then there's the final phase is the response phase. So being able to take action on a CASB, for example, when we have a known bad actor operating in the cloud is a really important, easy action for our customer to take. That's highly valuable. You're >>Talking about your threat hunting capability, right? >>So it's threat hunting and our Intel capability as well. We use all of that information as well as the telemetry to make sure we're making good, actionable >>Decisions, Intel being machine intelligence or, or human and machine >>Human and human and machine intelligence that we have. We have a whole business that's out there gathering Intel. I believe you think to Adam Myers who runs that business. And you know, that Intel is critical to making good decisions for our customers. >>So the X and XDR is extended, correct. Extending to things like firewalls. That's pretty obvious in the security space. Are there some less obvious data sources that you look to extend to at some point? >>Yeah, I think we're gonna continually go with where the customer demand is. And firewalls is one of the first and is very significant. Other one, you'll see that we're announcing support for Microsoft 365 as well as part of this, this announcement, but then we'll still grow out into the other areas. NDR is, you know, a specific area where we've already got a number of partners in that, in that space. And, and we'll grow that as we go. I think one of the really exciting additional elements is the, the OCS F announcement that we made at at, at, at, at reinforced, which also is a shared data scheme across a number of vendors as well. So talking to Mike's point, Microsoft ST's point this morning in his keynote, it's really about the industry getting together to do better job for our customers. And XDR is the platform to do that. And crowd strikes it way of doing it is the only really true, visible way for a customer to get their hands on all that information, make the decision, see the good from the bad and take the action. So I feel like we're really well placed to help our customers in >>That space. Well, Kevin mania referenced this too today, basically saying the industry's doing a better job of collaborations. I mean, sometimes I'm skeptical because we've certainly seen people try to, you know, commercialize private information, private reports. Yeah. But, but, but you're talking about, you know, some of your quasi competitors cooperatives, you know, actually partnering with you now. So that's a, that's a good indicator. Yeah. I want to step back a little bit, talk about the macro, the big conversation on wall street. Everybody wants to talk about the macro of course, for obvious reasons, we just published our breaking analysis, talking about you guys potentially being a generational company and sort of digging into that a little bit. We've seen, you know, cyber investments hold up a little bit better, both in terms of customer spending and of course the stock market better than tech broadly. Yeah. So in that case it would, it would suggest that cyber investments are somewhat non-discretionary. So, but that is my question are cyber investments non-discretionary if, if so, how, >>You know, I think George George calls that out directly in our analyst reports as well that, you know, we believe that cyber is a non-discretionary spend, but I, I actually think it's more than that. I think in this current macro or economic environment where CIOs and CSOs are being asked to sweat their assets for significantly longer period of time, that actually creates vulnerabilities because they have older kit, that's running for a longer period that they normally, you know, round out or churn out of their environment. They're not getting the investment to replace those laptops. They're not getting the, I placement to replace those servers. We have to sweat them for a little bit longer, longer, which means they need to be on top of the security posture of those devices. So that means that we need the best possible telemetry that we can get to protect those in the best possible way. So I actually think not only is it makes it non-discretionary, it actually increases the, the business case for, for, for taking on a, a cyber project. >>And I buy that. I buy that the business case is better potentially for cyber business case. And cyber is about, about risk reduction, right? It's about, it's about reducing expected loss. I, I, I, I, but the same time CISOs don't have an open wallet. They have to compete with other P and L managers. I also think the advantage for CrowdStrike I'm, I'm getting deeper into the architecture and beginning to understand the power of a lightweight agent that can do handle. I think you're up to 22 modules now, correct? Yes. I've got questions on how you keep that lightweight, but, but nonetheless, if you can consolidate the point tools, which is, you know, one of the biggest challenges that, that SecOps teams face that strengthens the ROI as well. >>Absolutely. And if you look at what George was saying this morning in the keynote, the combination of being able to provide tools, not only to the SecOps team, but the it ops team as well, being able to give the it ops team visibility on how many assets they have. I mean, these simple, these are simple questions that we should be able to answer. But often when we ask, you know, an operations leader, can you answer it? It sometimes it's hard for them. We actually have a lot of that information. So we are able to bring that into the platform. We're able to show them, we're able to show them where the assets are, where the vulnerabilities are against those assets and help it ops do a better job as well as SecOps. So the, the strength, the case strengthens, as you said, the CSO can also be talking to the it ops budget. >>The edge is getting more real. We're certainly hearing a lot about it now we're seeing a lot more and you kind of got the, the near edge, like the home Depot and the lows, you know, stores. Yeah. Okay. That I, I can get a better handle on, okay. How do I secure that? I've got some standards, but that's the far edge. It's, it's the, the OT yes. Piece of it. That's sort of the brave new world. What are you seeing there? How do you protect those far flowing estates? >>I think this gets back to the question of what's what's new or what's coming and where do we see the, the next set of workloads that we have to tackle? You know, when we came along first instance, we were really doing a lot of the on-prem on-prem and, and, and known cloud infrastructure suites. Then we started really tackling the broader crowd market with tools and technology to give visibility and control of the overall cloud environment. OT represents that next big addressable market for us, because there are so many questions around devices where they are, how old they are, what they're running. So visibility into the OT network is extremely, extremely important. And, you know, the, the wall that has existed again between the CISO and the OT environments coming down, we're seeing that's closer, closer alignment between the security on both those worlds. So the announcement that we've made around extending our Falcon discover product, to be able to receive and understand device information from the OT network and bring it into the same console as the, the it and the OT in the same console to give one cohesive picture of, of visibility of all of our devices is a major step forward for our customers and for, for the industry as well. >>And we see that being, being able to get the visibility will then lead us to a place of being able to build our AI models, build our response frameworks. So then we can go to a full EDR and then beyond that, there's, you know, all the other things that CrowdStrike do so well, but this is the first step to really the first step on control is visibility. And >>The OT guys are engineers. So they're obviously conscious of this stuff. It's, it's more it's again, you're extending that culture, isn't >>It? Yeah, yeah, yeah. Now when you're looking at threats, great, you want to do things to protect against those threats, but how much, how much of CrowdStrike's time is spent thinking about the friction that's involved in transactions? If I wanna go to the grocery store, think of me as an end point. If I wanna go to the grocery store, if I had to drive through three DUI checkpoints or car safety inspections. Yeah. Every time I went to the grocery store, I wouldn't be happy as an end point as an end user in this whole thing. Ideally, we'd be able just to be authenticated and then not have to worry about anything moving forward. Do you see that as your role, reducing friction 1%, >>That's again, one of the core tenants of, of, of why George founded the company. I mean, he tells the story of sitting on an airplane and seeing an executive who was also on the airplane, trying to boot their machine up and try and get an email out before the plane took off and watching the scanning happen, you know, old school virus scanning happening on the laptop and, and that executive not making it because, and he is like in this day and age, how can we be holding people back with that much friction in their day to day life? So that's one of the, again, founding principles of what we do at CrowdStrike was the security itself needs to support business growth, support, user growth, and actually get out of the way of how people do things. And we've seen progression along that lines. I think the zero trust work that we're doing right now really helps with that as well. >>Our integrations into other companies that play within the zero trust space makes that frictionless experience for the user, because yeah, we, we, we want to be there. We want to know everything that's happening, but we don't wanna see where we always want control points, but that's the value of the telemetry we take. We're taking all the data so we can see everything. And then we pick what we want to review rather than having to do the, the checkpoint approach of stop here. Now, let me see your credentials. Stop here. Let me see your credentials because we have a full field of, of knowledge and information on what the device is doing and what the user is doing. We're able to then do the trust with verify style approach. >>So coming back to the, to the edge in IOT, you know, bringing that zero trust concept to the, to the edge you've got, you've got it. And OT. Okay. So that's a new constituency, but you're consolidating that view. Your job gets harder. Doesn't it? So, so, so talk about how you resolve that. Do do the, do the concepts that you apply to traditional it endpoints apply at the edge. >>So first things we have to do is gain the visibility. And, and so the way in which we're doing that is effectively drawing information out from the OT environment at, by, by having a collector that's sitting there and bringing that into our console, which then will give us the ability to run our AI models and our other, you know, indications of attack or our indicators of misconfiguration into the model. So we can see whether something's good or bad whilst we're doing that. Obviously we're also working on building specific senses that will then sit in OT devices down, you know, one layer down from rather being collected and pulled and brought into the platform, being collected at the individual sensor level when we have that completed. And that requires a whole different ecosystem for us, it means that we have to engage with organizations like Rockwell and Siemens and Schneider, because they're the people who own the equipment, right? Yeah. And we have to certify with them to make sure that when we put technology onto their equipment, we're not going to cause any kind of critical failure that, you know, that could have genuine real world physical disastrous consequences. So we have to be super careful with how we build that, which we're we're in the process of >>Doing are the IOA signatures indicator as a tax. So I don't have to throw a dollar in the jar. Are the IOA signatures substantially similar at, at the edge, or >>I think we learn as we go, you know, first we have to gain the information and understand what good and bad looks like, what the kind of behaviors are there. But what we will see is that, you know, as someone's trying to, there's an actor, you know, making an attack, you know, will be able to see how they're affecting each of those endpoints individually, whether they're trying to take some form of control, whether they're switching them on and off in the edge and the far edge, it's a little bit more binary in terms of the kind of function of the device. It is the valve open or is the valve closed? It's is the production line running or is the production not line running, not running. So we need to be able to see that it's more about protecting the outcomes there as well. But again, you know, it's about first, we have to get the information. That's what this product will help us do, get it into the platform, get our teams over the top of it, learn more about what's going on there and then be able to take action. >>But the key point is the architecture will scale. And that's where the cloud native things comes >>Into. Yeah, it'll, it'll it'll scale. But to your, to your point about the lack of investment and infrastructure means older stuff means potentially wider gaps, bigger security holes, more opportunity for the security sector. Yep. I buy that. That makes sense. I think if it's a valid argument, when you, when you, when you know, we, we loosely talk about internet of things, edge, a lot of those things on the edge, there's probably a trillion dollars worth of a hundred year old garbage, and I'm only slightly exaggerating on the trillion and the a hundred years old, a lot of those critical devices that need to be sensed that are controlling our, our, our, our electrical grid. For example, a lot of those things need to be updated. So, so as you're pushing into that frontier, are you, you know, are, are you extending out developer kits and APIs to those people as they're developing those new things? Well, because some of the old stuff will never work. >>And that's what we're we're seeing is that there is a movement within the industrial control side of things to actually start, you know, doing this. Some, some simple things like removing the air gap from certain systems because you, now we can build a system around it. That's trustable and supportable. So now we can get access there over, over and over a network over the internet to, to, to kind of control a valve set that's down a pipeline or something like that. So there is, there is, there is willingness within the ecosystem, the, the IOT provider ecosystem to give us access to some of those, those controls, which, which wasn't there, which has led to some of some of these issues. Are we gonna be able to get to all of them? No, we're gonna have to make decisions based on customer demand, based on where the big, the big rock lie. And, and so we will continue to do that based on customer feedback on again, on what we see >>And the legacy air gaps in the OT worlds were by design for security reasons, or just sort of >>Mostly because there was no way to, to do before. Right. So it was, was like black >>Connectivity is >>So, so, so it was, people felt more comfortable sending an engineer route to the field truck roll. Yeah, yeah, yeah. To do it rather than expensive, rather. And, and exactly that, again, going back to our macro economic situation, you know, it's a very expensive way of managing and maintaining your fleet if you have to send someone to it every time. So there is a lot of there's, there's a lot of customer demand for change, and we're engaging in that change. And we want, we see a huge opportunity there >>Coming back to the X XDR Alliance, cuz that's kind of where we started. Where do you wanna see that go? What's your vision for that? >>So the Alliance itself has been fundamental in terms of now where we go with the overall platform. We are always constantly looking for customer feedback on where we go next on what additional elements to add that the Alliance members have been this fantastic time and effort in terms of engaging with us so that we can build in responses to their platforms, into, you know, into, into what we do. And they're seeing the value of it. I, I feel that over the next, you know, over the next two year period, we're gonna see those, our XDR Alliance and other XDR alliances growing out to get to each other and they will they'll touch each other. We will have to do it like the OSF project at AWS. And as that occurs, we're gonna be able to focus on customer outcomes, which is, you know, again, if you listen to George, you listen to Mike protecting the customers, the mission of CrowdStrike. So I think that's core to that, to, to that story. What we will see now is it's a great vehicle for us to give a structured approach to partnership. So we'll continue to invest in that. We've, we've got, we've got a pipeline of literally hundreds of, of partners who want to join. We've just gotta do that in a way that's consumable for us and consumable for the customer. >>Jeff Swain. Thanks so much for coming back in the cube. It's great to have you. Yeah. Thanks guys. Thank you. Okay. And thank you for watching Dave Nicholson and Dave ante. We'll be back right after this short break. You're watching the cube from Falcon 22 in Las Vegas, right back.

(intro music) >> Hi everybody. Dave Vellante, back with Day Two coverage, we're live at the ARIA Hotel in Las Vegas for fal.con '22. Several thousand people here today. The keynote was, it was a little light. I think people were out late last night, but the keynote was outstanding and it's still going on. We had to break early because we have to strike early today, but we're really excited to have Stephan Goldberg here, Vice President of Technology Alliances at Claroty. And we're going to talk about an extremely important topic, which is the internet of things, the edge, we talk about it a lot. We haven't covered securing the edge here at theCUBE this week. And so Stephan really excited to have you on. >> Thank you for having me. >> You're very welcome. Tell us more about Claroty, C-L-A-R-O-T-Y, a very interesting spelling, but what's it all about? >> Claroty is cybersecurity company that specializes in cyber physical systems, also known as operational technology systems and the extended internet of things. The difference between the traditional IoT and what what everyone calls an IoT in the cyber physical system is that an IoT device has anything connected on the network that traditionally cannot carry an agent, a security camera, a card reader. A cyber physical system is a system that has influence and operates in the physical world but is controlled from the cyberspace. An example would be a controller, a turbine, a robotic arm, or an MRI machine. >> Yeah, so those are really high-end systems, run, are looked after by engineers, not necessarily consumers. So what's what's happening in that world? I mean, we've talked a lot on theCUBE about the schism between OT and IT, they haven't really talked a lot, but in the last several years, they've started to talk more. You look at the ecosystem of IoT providers. I mean, it's companies like Hitachi and PTC and Siemens. I mean, it's the different names than we're used to in IT. What are the big trends that you're seeing the macro? >> So, first of all, traditionally, most manufacturers and environments that were heavy on operations, operational technology, they had the networks air-gapped, completely separated. You had your IT network for business administration, you had the OT network to actually build stuff. Today with emerging technologies and even modern switching architecture everything is being converged. You have the same physical infrastructure in terms of networking, that carries both networks. Sometimes a human error, sometimes a business logic that needs to interconnect these networks to transmit data from the OT side of the house, to the IT side of the house, exposes the OT environment to cyber threats. >> Was that air-gap by design or was it just that there wasn't connectivity? >> It was air-gap by design, due to security and operational reasons, and also ownership in these organizations. The IT-managed space was completely separate from the OT-managed space. So whoever built a network for the controllers to build a car, for example, was an automation engineer and the vendors, that have built these networks, were automation vendors, unlike the traditional Ciscos of the world, that we're specializing in IT. Today we're seeing the IT vendors on the OT side, and the OT vendors, they're worried about the IT side. >> But I mean, tradition, I mean, engineers are control freaks. No offense, but, I'm glad they are, I'm thankful for that. So there must have been some initial reticence to them connecting up these air-gap systems. They went wanted to make sure that they were secure, that they did it right, and presumably that's where you guys come in. What are the exposures and risks of these, of this critical infrastructure that we should be aware of? >> So you're completely right. And from an operational perspective let let's call it change control is very rigorous. So they did not want to go on the internet and just, we're seeing it with adoption of cloud technologies, for example. Cloud as in industry four ago, five ago, cloud as in cyber security. We all heard Amol's keynote from this morning talking about critical infrastructures and we'll touch upon our partnership in a second, but CrowdStrike, CrowdStrike being considered and deployed within these environments is a new thing. It's a new thing because the OT operation managers and the chief information security officers, they understand that air-gap is no longer a valid strategy. From a business perspective, these networks are already connected. We're seeing the trends of cyber attacks, IT cyber attacks, like not Patreon, I'm not talking about the Stoxnet, the targeted OT. I'm talking about WannaCry, EternalBlue, IT vulnerabilities that did not target OT, but due to the outdated and the specification of OT posture on the networks, they hit healthcare, they hit OT much harder than they did IT. >> Was Log4J, did that sleep into OT, or any IT that. >> So, absolutely. >> So Log4J right, which was so pervasive, like so many of these malwares. >> All these vulnerabilities that, it's a windows vulnerability, it has nothing to do with OT. But then when you stop and you say, hold on, my human machine interface workstation, although it has some proprietary software by Rockwell or Siemens running on it, what is the underlying operating system? Oh, hold on, it's Windows. We haven't updated that for like eight years. We were focused on updating the software but not the underlying operating system. The vulnerabilities exist to a greater extent on the OT side of the house because of the same characteristic of operational technology environments. >> So the brute force air-gap approach was no longer viable because the business imperative came in and said, no, we have to connect these systems to digitally transform, or advance our business, there's opportunities to monetize, whatever it was. The business laid that out as an imperative. So now OT engineers have to rethink how they secure it. So what are the steps that they're taking and how does Claroty help? Is there a sort of a playbook, a sequential playbook? >> Absolutely, so before we discussed the maturity curve of adopting an CPS security, or OT security technology, let's touch upon the characteristic of the space and what it led vendors like Claroty to build. So you have the rigorous chain control. You have the security in mind, operations, lowered the risk state of mind. That led vendors, likes of Claroty, to build a solution. And I'm talking about seven, eight years ago, to be passive, mostly passive or passive only to inspect network and to analyze network and focus on detection rather than taking action like response or preventative maintenance. >> Um-hmm. >> It made vendors to build on-prem solutions because of the cloud-averse state of mind of this industry. And because OT is very specific, it led vendors to focus only on OT devices, overlooking what we discussed as IoT, Unfortunately, besides HMI and PLC, the controller in the plant, you also have the security camera. So when you install an OT security solution I'm talking about the traditional ones, they traditionally overlook the security camera or anything that is not considered traditional OT. These three observations, although they were necessary in the beginning, you understand the shortcomings of it today. >> Um-hmm. >> So cloud-averse led to on-prem which leads to war security. It's like comparing CrowdStrike and one of its traditional competitors in the antivirus space. What CrowdStrike innovated is the SaaS first, cloud-native solution that is continuously being updated and provide the best in cloud security, right? And that is very much like what Claroty's building. We decided to go SaaS first and cloud-native solution. >> So, because of cloud-aversion, the industry shows somewhat outdated deployment models, on-prem, which limited scale and created greater diversity, more stovepipes, all the problems that we always talk about. Okay, and so is the answer to that, just becoming more cloud, having more of an affinity to cloud? That was a starting point, right. >> This is exactly it. Air-gap is perceived as secured, but you don't get updates and you don't really know what's going on in your network. If you have a Claroty or a crosswork installer, you have much higher probability detecting fast and responding fast. If you don't have it, you are just blind. You will be bridged, that's the. >> I was going to say, plus, air-gap, it's true, but people can get through air-gaps, too. I mean, it's harder, but Stoxnet. Yeah, look at Stoxnet right, oh, it's mopping the floor, boom, or however it happened, but so yeah. >> Correct. >> So, but the point being, you know, assume that breach, even though I know CrowdStrike thinks that the unstoppable breach is a myth, but you know, you talk to people like Kevin Mandia, it's like, we assume you're going to get breached, right? Let's make that assumption. Yeah, okay, and so that means you've got to have visibility into the network. So what are those steps that you would, what's that maturity model that you referenced before? >> So on top of these underlying principles, which is cloud-native, comprehensive, not OT only, but XIoT, and then bring that the verticalization and OT specificity. On top of that, you're exactly right. There is a maturity curve. You cannot boil the ocean, deploy protections, and change the environment within one day. It starts with discovering everything that is connected to your network. Everything from the traditional workstations to the cameras, and of course ending up with the cyber physical systems on the network. That discovery cannot be only a high level profile, it needs to be in depth to the level you need to know application versions of these devices. If you cannot tell the application version you cannot correlate it to a vulnerability, right? Just knowing that's an HMI or that's a PLC by Siemens is insufficient. You need to know the app version, then you can correlate to vulnerability, then you can correlate to risk. This is the next step, risk assessment. You need to put up a score basically, on each one of these devices. A vulnerability score, risk score, in order to prioritize action. >> Um-hmm. >> These two steps are discovery and thinking about the environment. The next two steps are taking action. After we have the prioritized devices discovered on your network, our approach is that you need to ladle in and deploy protections from a preventative perspective. Claroty delivers recommended policies in the form of access control lists or rules. >> Right. >> That can leverage existing infrastructure without touching a device without patching it, just to protect it. The next step would be detection and response. Once you have these policies deployed you also can leverage them to spot policy deviations. >> And that's where CrowdStrike comes in. So talk about how you guys partner with CrowdStrike, what that integration looks like and what the differentiation is. >> So actually the integration with CrowdStrike crosses the the entire customer journey. It starts with visibility. CrowdStrike and us exchange data on the asset level. With the announcement during FalCon, with Falcon Discover for IoT, we are really, really proud working on that with CrowdStrike. Traditionally CrowdStrike discovered and provided data about the IT assets. And we did the same thing with CPS and OT. Today with Falcon Discover for IoT, and us expanding to the XIoT space, both of us look at all devices but we can discover different things. When you merge these data sets you have an unparalleled visibility into any environment, and specifically OT. The integrations continue, and maybe the second spotlight I'll put, but without diminishing the other ones, is detection and response. It's the XDR Alliance. Claroty is very proud to be one of the first partners, XDR Alliance partners, for CrowdStrike, fitting in to the XDR, to CrowdStrike's XDR, the data that is needed to mitigate and respond and get more context about breaches in these OT environments, but also take action. Also trigger action, via Claroty and leverage Claroty's network-centric capabilities to respond. >> We hear a lot. We heard a lot in today's keynote note about the data, the importance of data, of the graph database. How unique is this Stephan, in the industry, in your view? >> The uniqueness of what exactly? >> Of this joint solution, if you will, this capability. >> I told my counterparts from CrowdStrike yesterday, the go-to market ones and the product management ones. If we are successful with Falcon Discover for IoT, and that product matures, as we plan for it to mature, it will change the industry, the OT security industry, for all of us. Not only for Claroty, for all players in this space. And this is why it's so important for us to stay coordinated and support this amazing company to enter this space and provide better security to organizations that really support our lives. >> We got to leave it there, but this is such an important topic. We're seeing in the war in Ukraine, there's a cyber component in the future of war. >> Yes. >> Today. And what do they do? They go after critical infrastructure. So protecting that critical infrastructure is so important, especially for a country like the United States, which has so much critical infrastructure and a lot to lose. So Stephan, thanks so much. >> Thank you. >> For the work that you're doing. It was great to have you on theCUBE. >> Thank you. >> All right, keep it right there. Dave Vellante for theCUBE. We'll be right back from fal.con '22. We're live from the ARIA in Las Vegas. (techno music)

>>We're back with the cube at Falcon 2022, Dave ante and Dave Nicholson. We're at the aria. We do obvious of course, a lot of events in Las Vegas. It's the, it's the place to do events. Dave, I think is my sixth or seventh time here this year. At least. I don't know. I lose track. Jeff Swayne is here. He's the vice president of global programs store and tech alliances at CrowdStrike. Jeff. Good to see again. We saw each other at reinvent in July in Boston. >>Yes. Have it's great to see you again, Dave. Thank you very >>Much. And we talked about making this happen, so it's thrilled to be here at, at, at CrowdStrike Falcon. We're gonna talk today about the CrowdStrike XDR Alliance partners. First of all, what's XDR >>Well, I hope you were paying attention to George's George's keynote this morning. I guess. You know, the one thing we know is that if you ask 10, five people, what XDR is you'll get 10 answers. >>I like this answer a holistic approach to endpoint security. I, that was a, >>It was good. Simple. That >>Was a good one at black hat. So, but tell us about the XDR Alliance partners program. Give us the update there. >>Yeah, so I mean, we spoke about it reinforced, you know, the XDR program is really predicated on having a robust ecosystem of partners to help us share that telemetry across all of the different parts of our customers' environment. So we've done a lot of work over the last few weeks and trying to bolster that environment, specifically, putting a, a lot of focus on firewall. You'll see that Cisco and fortunate have both joined the XD XDR Alliance. So we're working on that right now. A lot of customer demand for firewall data into the telemetry set. You know, obviously it's a very rich data environment. There's a lot of logs on firewalls. And so it drives a lot of, of, of information that we can, we can leverage. So we're continuing to grow that. And what we're doing is building out different content packs that support different use cases. So firewall is one CAS B is another emails another and we're building, building out the, the partner set right across the board. So it's, it's, it's been a, a great set of >>Activity. So it's it's partners that have data. Yep. There's probably some, you know, Joe, Tuchi your old boss used to say that that overlap is better than gaps. So there's sometimes there's competition, but that's from a customer standpoint, overlap is, is better than gaps. So you gonna mention Cisco forte and there are a number of others. They've got data. Yes. And they're gonna pump it into your system, our platform, and you've got the, your platform. You've got the ability to ingest. You've got the cloud native architecture, you've got the analytics and you've got the near real time analysis capability, right. >>Augmented by people as well, which is a really important part of our value proposition. You know, we, it's not just relying purely on AI, but we have a human, a human aspect to it as well to make sure we're getting extremely accurate responses. And then there's the final phase is the response phase. So being able to take action on a CASB, for example, when we have a known bad actor operating in the cloud is a really important, easy action for our customer to take. That's highly valuable. You're >>Talking about your threat hunting capability, right? >>So threat hunting and our Intel capability as well. We use all of that information as well as the telemetry to make sure we're making good, actionable >>Decisions, Intel being machine intelligence or, or human in >>Machine human and human and machine intelligence that we have. We have a whole business that's out there gathering Intel. I believe you're thinking to Adam Myers who runs that business. And you know, that Intel is critical to making good decisions for our customers. >>So the X and XDR is extended, correct. Extending to things like firewalls. That's pretty obvious in the security space. Are there some less obvious data sources that you look to extend to at some point? >>Yeah, I think we're gonna continually go with where the customer demand is. Firewalls is one of the first and email is very significant. Other one, you'll see that we're announcing support for Microsoft 365 as well as part of this, this announcement, but then we'll still grow out into the other areas. NDR is, you know, a specific area where we've already got a number of partners in that, in that space. And, and we'll grow that as we go. I think one of the really exciting additional elements is the, the OCS F announcement that we made at at, at, at, at reinforced, which also is a shared data scheme across a number of vendors as well. So talking to Mike's point Microsoft's point this morning in his keynote, it's really about the industry getting together to do better job for our customers. And XDR is the platform to do that. And crowd strikes it way of doing it is the only really true, visible way for a customer to get their hands on all that information, make the decision, see the good from the bad and take the action. So I feel like we're really well placed to help our customers in >>That space. Well, Kevin, Mandy referenced this too today, basically saying the industry's doing a better job of collaboration. I mean, sometimes I'm skeptical because we've certainly seen people try to, you know, commercialize private information, private reports. Yeah. But, but, but you're talking about, you know, some of your quasi competitors cooperatives, you know, actually partnering with you now. So that's a, that's a good indicator. Yeah. I want to step back a little bit, talk about the macro, the big conversation on wall street. Everybody wants to talk about the macro of course, for obvious reasons, we just published our breaking analysis, talking about you guys potentially being a generational company and sort of digging into that a little bit. We've seen, you know, cyber investments hold up a little bit better, both in terms of customer spending and of course the stock market better than tech broadly. Yeah. So in that case it would, it would suggest that cyber investments are somewhat non-discretionary. So, but that's is my question are cyber investments non-discretionary if so, how, >>You know, I think George George calls that out directly in our analyst reports as well that, you know, we believe that cyber is a non-discretionary spend, but I, I actually think it's more than that. I think in this current macro of economic environment where CIOs and CSOs are being asked to sweat their assets for a significantly longer period of time, that actually creates vulnerabilities because they have older kit, that's running for a longer period that they normally, you know, round out or churn out of their environment. They're not getting the investment to replace those laptops. They're not getting the investment to replace those servers. We have to sweat them for a little bit longer, longer, which means they need to be on top of the security posture of those devices. So that means that we need the best possible telemetry that we can get to protect those in the best possible way. So I actually think not only is it makes it non-discretionary, it actually increases the, the business case for, for, for taking on a, a cyber project. >>And I buy that. I buy that the business case is better potentially for cyber business case. And cyber is about, about risk reduction, right? It's about, it's about reducing expected loss. I, I, I, I, but the same time CISOs don't have an open wallet. They have to compete with other P and L managers. I also think the advantage for CrowdStrike I'm, I'm getting deeper into the architecture and beginning to understand the power of a lightweight agent that can do handle. I think you're up to 22 modules now, correct? Yes. I've got questions on how you keep that lightweight, but, but nonetheless, if you can consolidate the point tools, which is, you know, one of the biggest challenges that, that SecOps teams face that strengthens the ROI as well. >>Absolutely. And if you look at what George was saying this morning in the keynote, the combination of being able to provide tools, not only to the SecOps team, but the it ops team as well, being able to give the it ops team visibility on how many assets they have. I mean, these simple, these are simple questions that we should be able to answer. But often when we ask, you know, an operations leader, can you answer it? It sometimes it's hard for them. We actually have a lot of that information. So we are able to bring that into the platform. We're able to show them, we're able to show them where the assets are, where the vulnerabilities are against those assets and help it ops do a better job as well as SecOps. So the, the strength, the case strengths, as you said, the CSO can also be talking to the it ops budget. >>The edge is getting more real. We're certainly hearing a lot about it. Now we're seeing a lot more and you kind of got the, the near edge. It's like the home Depot and the lows, you know, stores okay. That I, I can get a better handle on, okay. How do I secure that? I've got some standards, but that's the far edge. It's, it's the, the OT yes. Piece of it. That's sort of the brave new world. What are you seeing there? How do you protect those far flung estates? >>I think this gets back to the question of what's what's new what's coming and where do we see the, the next set of workloads that we have to tackle? You know, when we came along first instance, we were really doing a lot of the on-prem on-prem and, and, and known cloud infrastructure suites. Then we started really tackling the broader cloud market with tools and technology to give visibility and control of the overall cloud environment. OT represents that next big addressable market for us, because there are so many questions around devices where they are, how old they are, what they're running. So visibility into the OT network is extremely, extremely important. And, you know, the, the wall that has existed again between the CISO and the OT environments coming down, we're seeing that's closer, closer alignment between the security on both those worlds. So the announcement that we've made around extending our Falcon discover product, to be able to receive and understand device information from the OT network and bring it into the same console as the, the it and the OT in the same console to give one cohesive picture of, of visibility of all of our devices is a major step forward for our customers and for, for the industry as well. >>And we see that being, being able to get the visibility will then lead us to a place of being able to build our AI models, build our response frameworks. So then we can go to a full EDR and then beyond that, there's, you know, all the other things that CrowdStrike do so well, but this is the first step to really the first step on control is visibility. And >>The OT guys are engineers. So they're obviously conscious of this stuff. It's, it's more it's again, you're extending that culture, isn't it? >>Yeah, yeah, yeah. Now when you're looking at threats, great, you want to do things to protect against those threats, but how much, how much of CrowdStrike's time is spent thinking about the friction that's involved in transactions? If I wanna go to the grocery store, think of me as an end point. If I wanna go to the grocery store, if I had to drive through three DUI checkpoints or car safety inspections, every time I went to the grocery store, I wouldn't be happy as an end point as an end user in this whole thing. Ideally, we'd be able just to be authenticated and then not have to worry about anything moving forward. Do you see that as your role, reducing friction >>100%, that's again, one of the core tenants of, of, of why George founded the company. I mean, he tells the story of sitting on an airplane and seeing an executive who was also on the airplane, trying to boot their machine up and trying, and get an email out before the plane took off and watching the scanning happen, you know, old school virus scanning happening on the laptop and, and that executive not making it because, and he is like in this day and age, how can we be holding people back with that much friction in their day to day life? So that's one of the, again, founding principles of what we do at CrowdStrike was the security itself needs to support business growth, support, user growth, and actually get out of the way of how people do things. And we've seen progression along that lines. I think the zero trust work that we're doing right now really helps with that as well. >>Our integrations into other companies that play within the zero trust space makes that frictionless experience for the user, because yeah, we, we, we want to be there. We want to know everything that's happening, but we don't want to see where we always want control points, but that's the value of the telemetry we take. We're taking all the data so that we can see everything. And then we pick what we want to review rather than having to do the, the checkpoint approach of stop here. Now, let me see your credentials stop here. And let me see your credentials because we have a full field of, of knowledge and information on what the device is doing and what the user is doing. We're able to then do the trust with verify style approach. >>So coming back to the, to the edge and IOT, you know, bringing that zero trust concept to the, to the edge you've got, you've got it and OT. Okay. So that's a new constituency, but you're consolidating that view. Your job gets harder. Doesn't it? So, so, so talk about how you resolve that. Do do the, do the concepts that you apply to traditional it endpoints apply at the edge. >>So first things we have to do is gain the visibility. And, and so the way in which we're doing that is effectively drawing information out from the OT environment at, by, by having a collector that's sitting there and bringing that into our console, which then will give us the ability to run our AI models and our other, you know, indications of attack or our indications of misconfiguration into the model. So we can see whether something's good or bad whilst we're doing that. Obviously we're also working on building specific sensors that will then sit in OT devices down, you know, one layer down from rather being collected and pulled and brought into the platform, being collected at the individual sensor level when we have that completed. And that requires a whole different ecosystem for us, it means that we have to engage with organizations like Rockwell and Siemens and Schneider, because they're the people who own the equipment, right? Yeah. And we have to certify with them to make sure that when we put technology onto their equipment, we're not going to cause any kind of critical failure that, you know, that could have genuine real world physical disastrous consequences. So we have to be super careful with how we build that, which we're we're in the process of doing >>Are the IOA signatures indicator as a tax. So I don't have to throw a dollar in the jar, are the IOA signatures substantially similar at, at the edge? I think >>We learn as we go, you know, first we have to gain the information and understand what good and bad looks like, what the kind of behaviors are there. But what we will see is that, you know, as someone's trying to make, if there's an actor, you know, making an attack, you know, we'll be able to see how they're affecting each of those end points individually, whether they're trying to take some form of control, whether they're switching them on and off in the edge and the far edge, it's a little bit more binary in terms of the kind of function of the device. It is the valve open or is the valve closed? It's is the production line running or is the production not line running, not running. So we need to be able to see that it's more about protecting the outcomes there as well. But again, you know, it's about first, we have to get the information. That's what this product will help us do. Get it into the platform, get our teams over the top of it, learn more about what's going on there and then be able to take action. >>But the key point is the architecture will scale. That's where the cloud native things >>Comes into. Yeah, it'll, it'll it'll scale. But to your, to your point about the lack of investment and infrastructure means older stuff means potentially wider gaps, bigger security holes, more opportunity for the security sector. Yep. I buy that. That makes sense. I think if it's a valid argument, when you, when you, when you know, we, we loosely talk about internet of things, edge, a lot of those things on the edge, there's probably a trillion dollars worth of a hundred year old garbage, and I'm only slightly exaggerating on the trillion and the a hundred years old, a lot of those critical devices that need to be sensed that are controlling our, our, our, our electrical grid. For example, a lot of those things need to be updated. So, so as you're pushing into that frontier, are you, you know, are, are you extending out developer kits and APIs to those people as they're developing those new things, right? Because some of the old stuff will never work. >>And that's what we're we're seeing is that there is a movement within the industrial control side of things to actually start, you know, doing this. Some, some simple things like removing the air gap from certain systems, because now we can build a system around it, that's trustable and supportable. So now we can get access there over, over and over a network over the internet to, to, to kind of control a valve set that's down a pipeline or something like that. So there is a, there is, there is willingness within the ecosystem, the, the IOT provider ecosystem to give us access to some of those, those controls, which, which wasn't there, which has led to some of some of these issues. Are we gonna be able to get to all of them? No, we're gonna have to make decisions based on customer demand, based on where the big, the big rock lie. And, and so we will continue to do that based on customer feedback on again, on what we see >>And the legacy air gaps in the OT worlds were by design for security reasons, or just sort of, >>I see. Because there was no way to, to do before. Right. So it was, was like >>Lack connectivity is, >>Yeah. So, so, so it was, people felt more comfortable sending an engineer route to the field truck roll. Yeah, yeah, yeah. To do it rather than expensive, rather. And, and exactly that, again, going back to our macro economic situation, you know, it's a very expensive way of managing and maintaining your fleet if you have to send someone to it every time. So there is a lot of there's, there's a lot of customer demand for change, and we're engaging in that change. And we want to see a huge opportunity there >>Coming back to the XDR Alliance, cuz that's kind of where we started. Where do you wanna see that go? What's your vision for that? >>So the Alliance itself has been fundamental in terms of now where we go with the overall platform. We are always constantly looking for customer feedback on where we go next on what additional elements to add. The, the Alliance members have video this fantastic time and effort in terms of engaging with us so that we can build in responses to their platforms, into, you know, into, into what we do. And they're seeing the value of it. I, I feel that over the next, you know, over the next two year period, we're gonna see those, our XDR Alliance and other XDR alliances growing out to get to each other and they will they'll touch each other. We will have to do it like this O project at AWS. And as that occurs, we're gonna be able to focus on customer outcomes, which is, you know, again, if you listen to George, you listen to Mike protecting the customers, the mission of CrowdStrike. So I think that's core to that, to, to that story. What we will see now is it's a great vehicle for us to give a structured approach to partnership. So we'll continue to invest in that. We've, we've got, we've got a pipeline of literally hundreds of, of partners who want to join. We've just gotta do that in a way that's consumable for us and consumable for the customer. >>Jeff Swain. Thanks so much for coming back in the cube. It's great to have you. Yeah. Thanks guys. Thank you. Okay. And thank you for watching Dave Nicholson and Dave ante. We'll be back right to this short break. You're watching the cube from Falcon 22 in Las Vegas, right back.

(upbeat music) >> Okay, welcome back everyone. CUBE's coverage of re:MARS, here in Las Vegas, in person. I'm John Furrier, host of theCUBE. This is the analyst panel wrap up analysis of the keynote, the show, past one and a half days. We got two great guests here. We got Andy Thurai, Vice President, Principal Consultant, Constellation Research. Larry Carvalho, Principal Consultant at RobustCloud LLC. Congratulations going out on your own. >> Thank you. >> Andy, great to see you. >> Great to see you as well. >> Guys, thanks for coming out. So this is the session where we break down and analyze, you guys are analysts, industry analysts, you go to all the shows, we see each other. You guys are analyzing the landscape. What does this show mean to you guys? 'Cause this is not obvious to the normal tech follower. The insiders see the confluence of robotics, space, automation and machine learning. Obviously, it's IoTs, industrials, it's a bunch of things. But there's some dots to connect. Let's start with you, Larry. What do you see here happening at this show? >> So you got to see how Amazon started, right? When AWS started. When AWS started, it primarily took the compute storage, networking of Amazon.com and put it as a cloud service, as a service, and started selling the heck out of it. This is a stage later now that Amazon.com has done a lot of physical activity, and using AIML and the robotics, et cetera, it's now the second phase of innovation, which is beyond digital transformation of back office processes, to the transformation of physical processes where people are now actually delivering remotely and it's an amazing area. >> So back office's IT data center kind of vibe. >> Yeah. >> You're saying front end, industrial life. >> Yes. >> Life as we know it. >> Right, right. I mean, I just stopped at a booth here and they have something that helps anybody who's stuck in the house who cannot move around. But with Alexa, order some water to bring them wherever they are in the house where they're stuck in their bed. But look at the innovation that's going on there right at the edge. So I think those are... >> John: And you got the Lunar, got the sex appeal of the space, Lunar Outpost interview, >> Yes. >> those guys. They got Rover on Mars. They're going to have be colonizing the moon. >> Yes. >> I made a joke, I'm like, "Well, I left a part back on earth, I'll be right back." (Larry and Andy laugh) >> You can't drive back to the office. So a lot of challenges. Andy, what's your take of the show? Take us your analysis. What's the vibe, what's your analysis so far? >> It's a great show. So, as Larry was saying, one of the thing was that when Amazon started, right? So they were more about cloud computing. So, which means is they try to commoditize more of data center components or compute components. So that was working really well for what I call it as a compute economy, right? >> John: Mm hmm. >> And I call the newer economy as more of a AIML-based data economy. So when you move from a compute economy into a data economy, there are things that come into the forefront that never existed before, never popular before. Things like your AIML model creation, model training, model movement, model influencing, all of the above, right? And then of course the robotics has come long way since then. And then some of what they do at the store, or the charging, the whole nine yards. So, the whole concept of all of these components, when you put them on re:Invent, such a big show, it was getting lost. So that's why they don't have it for a couple of years. They had it one year. And now all of a sudden they woke up and say, "You know what? We got to do this!" >> John: Yeah. >> To bring out this critical components that we have, that's ripe, mature for the world to next component. So that's why- I think they're pretty good stuff. And some of the robotics things I saw in there, like one of them I posted on my Twitter, it's about the robot dog, sniffing out the robot rover, which I thought was pretty hilarious. (All laugh) >> Yeah, this is the thing. You're seeing like the pandemic put everything on hold on the last re:Mars, and then the whole world was upside down. But a lot of stuff pulled forward. You saw the call center stuff booming. You saw the Zoomification of our workplace. And I think a lot of people got to the realization that this hybrid, steady-state's here. And so, okay. That settles that. But the digital transformation of actually physical work? >> Andy: Yeah. >> Location, the walk in and out store right over here we've seen that's the ghost store in Seattle. We've all been there. In fact, I was kind of challenged, try to steal something. I'm like, okay- (Larry laughs) I'm pulling all my best New Jersey moves on everyone. You know? >> Andy: You'll get charged for it. >> I couldn't get away with it. Two double packs, drop it, it's smart as hell. Can't beat the system. But, you bring that to where the AI machine learning, and the robotics meet, robots. I mean, we had robots here on theCUBE. So, I think this robotics piece is a huge IoT, 'cause we've been covering industrial IoT for how many years, guys? And you could know what's going on there. Huge cyber threats. >> Mm hmm. >> Huge challenges, old antiquated OT technology. So I see a confluence in the collision between that OT getting decimated, to your point. And so, do you guys see that? I mean, am I just kind of seeing mirage? >> I don't see it'll get decimated, it'll get replaced with a newer- >> John: Dave would call me out on that. (Larry laughs) >> Decimated- >> Microsoft's going to get killed. >> I think it's going to have to be reworked. And just right now, you want do anything in a shop floor, you have to have a physical wire connected to it. Now you think about 5G coming in, and without a wire, you get minute details, you get low latency, high bandwidth. And the possibilities are endless at the edge. And I think with AWS, they got Outposts, they got Snowcone. >> John: There's a threat to them at the edge. Outpost is not doing well. You talk to anyone out there, it's like, you can't find success stories. >> Larry: Yeah. >> I'm going to get hammered by Amazon people, "Oh, what're you're saying that?" You know, EKS for example, with serverless is kicking ass too. So, I mean I'm not saying Outpost was wrong answer, it was a right at the time, what, four years ago that came out? >> Yeah. >> Okay, so, but that doesn't mean it's just theirs. You got Dell Technologies want some edge action. >> Yeah. >> So does HPE. >> Yes. >> So you got a competitive edge situation. >> I agree with that and I think that's definitely not Amazon's strong point, but like everything, they try to make it easy to use. >> John: Yeah. >> You know, you look at the AIML and they got Canvas. So Canvas says, hey, anybody can do AIML. If they can do that for the physical robotic processes, or even like with Outpost and Snowcone, that'll be good. I don't think they're there yet, and they don't have the presence in the market, >> John: Yeah. >> like HPE and, >> John: Well, let me ask you guys this question, because I think this brings up the next point. Will the best technology win or will the best solution win? Because if cloud's a platform and all software's open source, which you can make those assumptions, you then say, hey, they got this killer robotics thing going on with Artemis and Moonshot, they're trying to colonize the moon, but oh, they discovered a killer way to solve a big problem. Does something fall out of this kind of re:Mars environment, that cracks the code and radically changes and disrupts the IoT game? That's my open question. I don't know the answer. I'd love to get your take on what might be possible, what wild card's out there around, disrupting the edge. >> So one thing I see the way, so when IoT came into the world of play, it's when you're digitizing the physical world, it's IoT that does digitalization part of that actually, right? >> But then it has its own set of problems. >> John: Yeah. >> You're talking about you installing sensor everywhere, right? And not only installing your own sensor, but also you're installing competitor sensors. So in a given square feet how many sensors can you accommodate? So there are physical limitations on liabilities of bandwidth and networking all of that. >> John: And integration. >> As well. >> John: Your point. >> Right? So when that became an issue, this is where I was talking to the robotic guys here, a couple of companies, and one of the use cases they were talking about, which I thought was pretty cool, is, rather than going the sensor route, you go the robot route. So if you have either a factor that you want to map out, you put as many sensors on your robot, whatever that is, and then you make it go around, map the whole thing, and then you also do a surveillance in the whole nine yards. So, you can either have a fixed sensors or you can have moving sensors. So you can have three or four robots. So initially, when I was asking them about the price of it, when they were saying about a hundred thousand dollars, I was like, "Who would buy that?" (John and Larry laugh) >> When they then explained that, this is the use case, oh, that makes sense, because if you had to install, entire factory floor sensors, you're talking about millions of dollars. >> John: Yeah. >> But if you do the moveable sensors in this way, it's a lot cheaper. >> John: Yeah, yeah. >> So it's based on your use case, what are your use cases? What are you trying to achieve? >> The general purpose is over. >> Yeah. >> Which you're getting at, and that the enablement, this is again, this is the cloud scale open question- >> Yep. >> it's, okay, the differentiations isn't going to be open source software. That's open. >> It's going to be in the, how you configure it. >> Yes. >> What workflows you might have, the data streams. >> I think, John, you're bringing up a very good point about general purpose versus special purpose. Yesterday Zoox was on the stage and when they talked about their vehicle, it's made just for self-driving. You walk around in Vegas, over here, you see a bunch of old fashioned cars, whether they're Ford or GM- >> and they put all these devices around it, but you're still driving the same car. >> John: Yeah, exactly. >> You can retrofit those, but I don't think that kind of IoT is going to work. But if you redo the whole thing, we are going to see a significant change in how IoT delivers value all the way from the industrial to home, to healthcare, mining, agriculture, it's going to have to redo. I'll go back to the OT question. There are some OT guys, I know Rockwell and Siemens, some of them are innovating faster. The ones who innovate faster to keep up with the IT side, as well as the MLAI model are going to be the winners on that one. >> John: Yeah, I agree. Andy, your thoughts on manufacturing, you brought up the sensor thing. Robotics ultimately is, end of the day, an opportunity there. Obviously machine learning, we know what that does. As we move into these more autonomous builds, what does that look like? And is Amazon positioned well there? Obviously they have big manufacturers. Some are saying that they might want to get out of that business too, that Jassy's evaluating that some are saying. So, where does this all lead for that robotics manufacturing lifestyle, walk in, grab my food? 'Cause it's all robotics and AI at the end of the day, I got sensors, I got cameras, I got non-humans moving heavy lifting stuff, fixing the moon will be done by robots, not humans. So it's all coming. What's your analysis? >> Well, so, the point about robotics is on how far it has come, it is unbelievable, right? Couple of examples. One was that I was just talking to somebody, was explaining to them, to see that robot dog over there at the Boston Dynamics one- >> John: Yeah. >> climbing up and down the stairs. >> Larry: Yeah. >> That's more like the dinosaur movie opening the doors scene. (John and Larry laugh) It's like that for me, because the coordinated things, it is able to go walk up and down, that's unbelievable. But okay, it does that, and then there was also another video which is going on viral on the internet. This guy kicks the dog, robot dog, and then it falls down and it gets back up, and the sentiment that people were feeling for the dog, (Larry laughs) >> you can't, it's a robot, but people, it just comes at that level- >> John: Empathy, for a non-human. >> Yeah. >> But you see him, hey you, get off my lawn, you know? It's like, where are we? >> It has come to that level that people are able to kind of not look at that as a robot, but as more like a functioning, almost like a pet-level, human-level being. >> John: Yeah. >> And you saw that the human-like walking robot there as well. But to an extent, in my view, they are all still in an experimentation, innovation phase. It doesn't made it in the industrial terms yet. >> John: Yeah, not yet, it's coming. >> But, the problem- >> John: It's coming fast. That's what I'm trying to figure out is where you guys see Amazon and the industry relative to what from the fantasy coming reality- >> Right. >> of space in Mars, which is, it's intoxicating, let's face it. People love this. The nerds are all here. The geeks are all here. It's a celebration. James Hamilton's here- >> Yep. >> trying to get him on theCUBE. And he's here as a civilian. Jeff Barr, same thing. I'm here, not for Amazon, I bought a ticket. No, you didn't buy a ticket. (Larry laughs) >> I'm going to check on that. But, he's geeking out. >> Yeah. >> They're there because they want to be here. >> Yeah. >> Not because they have to work here. >> Well, I mean, the thing is, the innovation velocity has increased, because, in the past, remember, the smaller companies couldn't innovate because they don't have the platform. Now Compute is a platform available at the scale you want, AI is available at the scale. Every one of them is available at the scale you want. So if you have an idea, it's easy to innovate. The innovation velocity is high. But where I see most of the companies failing, whether startup or big company, is that you don't find the appropriate use case to solve, and then don't sell it to the right people to buy that. So if you don't find the right use case or don't sell the right value proposition to the actual buyer, >> John: Mm hmm. >> then why are you here? What are you doing? (John laughs) I mean, you're not just an invention, >> John: Eh, yeah. >> like a telephone kind of thing. >> Now, let's get into next talk track. I want to get your thoughts on the experience here at re:Mars. Obviously AWS and the Amazon people kind of combined effort between their teams. The event team does a great job. I thought the event, personally, was first class. The coffee didn't come in late today, I was complaining about that, (Larry laughs) >> people complaining out there, at CUBE reviews. But world class, high bar on the quality of the event. But you guys were involved in the analyst program. You've been through the walkthrough, some of the briefings. I couldn't do that 'cause I'm doing theCUBE interviews. What would you guys learn? What were some of the key walkaways, impressions? Amazon's putting all new teams together, seems on the analyst relations. >> Larry: Yeah. >> They got their mojo booming. They got three shows now, re:Mars, re:inforce, re:invent. >> Andy: Yeah. >> Which will be at theCUBE at all three. Now we got that coverage going, what's it like? What was the experience like? Did you feel it was good? Where do they need to improve? How would you grade the Amazon team? >> I think they did a great job over here in just bringing all the physical elements of the show. Even on the stage, where they had robots in there. It made it real and it's not just fake stuff. And every, or most of the booths out there are actually having- >> John: High quality demos. >> high quality demos. (John laughs) >> John: Not vaporware. >> Yeah, exactly. Not vaporware. >> John: I won't say the name of the company. (all laugh) >> And even the sessions were very good. They went through details. One thing that stood out, which is good, and I cover Low Code/No Code, and Low Code/No Code goes across everything. You know, you got DevOps No Low-Code Low-Code. You got AI Low Code/No Code. You got application development Low Code/No Code. What they have done with AI with Low Code/No Code is very powerful with Canvas. And I think that has really grown the adoption of AI. Because you don't have to go and train people what to do. And then, people are just saying, Hey, let me kick the tires, let me use it. Let me try it. >> John: It's going to be very interesting to see how Amazon, on that point, handles this, AWS handles this data tsunami. It's cause of Snowflake. Snowflake especially running the table >> Larry: Yeah. >> on the old Hadoop world. I think Dave had a great analysis with other colleagues last week at Snowflake Summit. But still, just scratching the surface. >> Larry: Yeah. >> The question is, how shared that ecosystem, how will that morph? 'Cause right now you've got Data Bricks, you've got Snowflake and a handful of others. Teradata's got some new chops going on there and a bunch of other folks. Some are going to win and lose in this downturn, but still, the scale that's needed is massive. >> So you got data growing so much, you were talking earlier about the growth of data and they were talking about the growth. That is a big pie and the pie can be shared by a lot of folks. I don't think- >> John: And snowflake pays AWS, remember that? >> Right, I get it. (John laughs) >> I get it. But they got very unique capabilities, just like Netflix has very unique capabilities. >> John: Yeah. >> They also pay AWS. >> John: Yeah. >> Right? But they're competing on prime. So I really think the cooperation is going to be there. >> John: Yeah. >> The pie is so big >> John: Yeah. >> that there's not going to be losers, but everybody could be winners. >> John: I'd be interested to follow up with you guys after next time we have an event together, we'll get you back on and figure out how do you measure this transitions? You went to IDC, so they had all kinds of ways to measure shipments. >> Larry: Yep. >> Even Gartner had fumbled for years, the Magic Quadrant on IaaS and PaaS when they had the market share. (Larry laughs) And then they finally bundled PaaS and IaaS together after years of my suggesting, thank you very much Gartner. (Larry laughs) But that just performs as the landscape changes so does the scoreboard. >> Yep. >> Right so, how do you measure who's winning and who's losing? How can we be critical of Amazon so they can get better? I mean, Andy Jassy always said to me, and Adam Salassi same way, we want to hear how bad we're doing so we can get better. >> Yeah. >> So they're open-minded to feedback. I mean, not (beep) posting on them, but they're open to critical feedback. What do you guys, what feedback would you give Amazon? Are they winning? I see them number one clearly over Azure, by miles. And even though Azure's kicking ass and taking names, getting back in the game, Microsoft's still behind, by a long ways, in some areas. >> Andy: Yes. In some ways. >> So, the scoreboard's changing. What's your thoughts on that? >> So, look, I mean, at the end of the day, when it comes to compute, right, Amazon is a clear winner. I mean, there are others who are catching up to it, but still, they are the established leader. And it comes with its own advantages because when you're trying to do innovation, when you're trying to do anything else, whether it's a data collection, we were talking about the data sensors, the amount of data they are collecting, whether it's the store, that self-serving store or other innovation projects, what they have going on. The storage compute and process of that requires a ton of compute. And they have that advantage with them. And, as I mentioned in my last article, one of my articles, when it comes to AIML and data programs, there is a rich and there is a poor. And the rich always gets richer because they, they have one leg up already. >> John: Yeah. >> I mean the amount of model training they have done, the billion or trillion dollar trillion parametrization, fine tuning of the model training and everything. They could do it faster. >> John: Yeah. >> Which means they have a leg up to begin with. So unless you are given an opportunity as a smaller, mid-size company to compete at them at the same level, you're going to start at the negative level to begin with. You have a lot of catch up to do. So, the other thing about Amazon is that they, when it comes to a lot of areas, they admit that they have to improve in certain areas and they're open and willing and listen to the people. >> Where are you, let's get critical. Let's do some critical analysis. Where does Amazon Websters need to get better? In your opinion, what criticism would you, in a constructive way, share? >> I think on the open source side, they need to be more proactive in, they are already, but they got to get even better than what they are. They got to engage with the community. They got to be able to talk on the open source side, hey, what are we doing? Maybe on the hardware side, can they do some open-sourcing of that? They got graviton. They got a lot of stuff. Will they be able to share the wealth with other folks, other than just being on an Amazon site, on the edge with their partners. >> John: Got it. >> If they can now take that, like you said, compute with what they have with a very end-to-end solution, the full stack. And if they can extend it, that's going to be really beneficial for them. >> Awesome. Andy, final word here. >> So one area where I think they could improve, which would be a game changer would be, right now, if you look at all of their solutions, if you look at the way they suggest implementation, the innovations, everything that comes out, comes out across very techy-oriented. The persona is very techy-oriented. Very rarely their solutions are built to the business audience or to the decision makers. So if I'm, say, an analyst, if I want to build, a business analyst rather, if I want to build a model, and then I want to deploy that or do some sort of application, mobile application, or what have you, it's a little bit hard. It's more techy-oriented. >> John: Yeah, yeah. >> So, if they could appeal or build a higher level abstraction of how to build and deploy applications for business users, or even build something industry specific, that's where a lot of the legacy companies succeeded. >> John: Yeah. >> Go after manufacturing specific or education. >> Well, we coined the term 'Supercloud' last re:Invent, and that's what we see. And Jerry Chen at Greylock calls it Castles in the Cloud, you can create these moats >> Yep. >> on top of the CapEx >> Yep. >> of Amazon. >> Exactly. >> And ride their back. >> Yep. >> And the difference in what you're paying and what you're charging, if you're good, like a Snowflake or a Mongo. I mean, Mongo's, they're just as big as Snow, if not bigger on Amazon than Snowflake is. 'Cause they use a lot of compute. No one turns off their database. (John laughs) >> Snowflake a little bit different, a little nuanced point, but, this is the new thing. You see Goldman Sachs, you got Capital One. They're building their own kind of, I call them sub clouds, but Dave Vellante says it's a Supercloud. And that essentially is the model. And then once you have a Supercloud, you say, great, I'm going to make sure it works on Azure and Google. >> Andy: Yep. >> And Alibaba if I have to. So, we're kind of seeing a playbook. >> Andy: Mm hmm. >> But you can't get it wrong 'cause it scales. >> Larry: Yeah, yeah. >> You can't scale the wrong answer. >> Andy: Yeah. >> So that seems to be what I'm watching is, who gets it right? Product market fit. Then if they roll it out to the cloud, then it becomes a Supercloud, and that's pure product market fit. So I think that's something that I've seen some people trying to figure out. And then, are you a supplier to the Superclouds? Like a Dell? Or you become an enabler? >> Andy: Yeah. >> You know, what's Dell Technologies do? >> Larry: Yeah. >> I mean, how do the box movers compete? >> Larry: I, the whole thing is now hybrid and you're going to have to see just, you said. (Larry laughs) >> John: Hybrid's a steady-state. I don't need to. >> Andy: I mean, >> By the way we're (indistinct), we can't get the chips, cause Broadcom and Apple bought 'em all. (Larry laughs) I mean there's a huge chip problem going on. >> Yes. I agree. >> Right now. >> I agree. >> I mean all these problems when you attract to a much higher level, a lot of those problems go away because you don't care about what they're using underlying as long as you deliver my solution. >> Larry: Yes. >> Yeah, it could be significantly, a little bit faster than what it used to be. But at the end of the day, are you solving my specific use case? >> John: Yeah. >> Then I'm willing to wait a little bit longer. >> John: Yeah. Time's on our side and now they're getting the right answers. Larry, Andy, thanks for coming on. This great analyst session turned into more of a podcast vibe, but you know what? (Larry laughs) To chill here at re:Mars, thanks for coming on, and we unpacked a lot. Thanks for sharing. >> Both: Thank you. >> Appreciate it. We'll get you back on. We'll get you in the rotation. We'll take it virtual. Do a panel. Do a panel, do some panels around this. >> Larry: Absolutely. >> Andy: Oh this not virtual, this physical. >> No we're live right now! (all laugh) We get back to Palo Alto. You guys are influencers. Thanks for coming on. You guys are moving the market, congratulations. Take a minute, quick minute each to plug any work you're doing for the people watching. Larry, what are you working on? Andy? You go after Larry, what you're working on. >> Yeah. So since I started my company, RobustCloud, since I left IDC about a year ago, I'm focused on edge computing, cloud-native technologies, and Low Code/No Code. And basically I help companies put their business value together. >> All right, Andy, what are you working on? >> I do a lot of work on the AIML areas. Particularly, last few of my reports are in the AI Ops incident management and ML Ops areas of how to generally improve your operations. >> John: Got it, yeah. >> In other words, how do you use the AIML to improve your IT operations? How do you use IT Ops to improve your AIML efficiency? So those are the- >> John: The real hardcore business transformation. >> Yep. >> All right. Guys, thanks so much for coming on the analyst session. We do keynote review, breaking down re:Mars after day two. We got a full day tomorrow. I'm John Furrier with theCUBE. See you next time. (pleasant music)

(upbeat music) >> Hello, everyone. Welcome back to theCUBE's coverage of AWS re:Invent! 2021. I'm John Furrier, your host for the theCUBE, a lot of great action here. A lot of great solutions. Great keynote. The future of cloud's going to be all about purpose-built software platforms, enabling more and more SaaS, faster performance with custom chips, all enabling great stuff. I have two great guests here. Who are going to talk about it from Accenture. We've got Karthik Narain, global lead of Accenture's Cloud First. Welcome to the program. Good to see you and Chris Wegmann, AABG Accenture Amazon Business Group. Technology leads senior manager. Thanks for coming on. >> Great to be here. >> I was commenting before we came on about Accenture's work you guys been doing with the clouds in my article, I posted before re:Invent!. Dave Vellante coined the term superclouds, which we kind of just put out there, but the idea that people can build really strong platforms that enable a new kind of Saas has been the big wave. Connect has been a great example. We heard on stage from Adam, the CEO. Chris, this has been something that's been a real change where it's not just lift and shift and refactor, it's build value in a platform and new SaaS capabilities. What's your reaction to that? >> Yeah, I would absolutely agree. We've seen this change over time. We've seen the lift and shift and modernize and it's definitely moved into the Superclouds. I like the term, but you know, we call them cloud continuums, which we'll talk a little bit about, it's about building these purpose-built solutions. I think if you look at the keynote today, you look at, everybody that was on stage. United and everyone talking about what they're building, their technology companies now, they're not just the business. >> You guys did some new research, coining new terms and Cloud First. What is this all about? What is this new wave you guys are talking about? >> Yeah, so John, you know, few years ago, when people talked about cloud, they generally meant public cloud. I think the definition of cloud is changing and expanding. And from now on, whenever people talk about cloud, it's actually a cloud continuum. It's a continuum of capability from public to Edge and everything in between all seamlessly connected by Cloud First networks, which means all the capabilities that customers used to get from one public cloud destination. They can actually access that across the continuum, whether that be in their own private data center, using the capability of cloud with AWS's Outpost and other capabilities. Or they could use the capability in their Edge location, whether it's their retail centers, their warehouse locations, manufacturing and so on and so forth. So organizations are using the power of cloud beyond one purpose and one destination, but more as an operating system going forward. >> Chris, what's your take on this redefinition of cloud what's your take on it? >> I think it's much needed. I think Andy kicked it off last year when he recognized the term hybrid. We all, who've has been around a while kind of chuckled because they finally said the word. But if you look at the keynote today, they just continued it. Adam picked it up and ran with it. If you look at all the services, Wavelength and all the different services, there's not a single customer that I have, that's just using EC2 or S3 right. They're using all these different services you saw today. You saw all the different services that United put up on the screen. That DISH put up on the screen. So yeah, it's how people and companies, if they're truly going to transform and truly use cloud to transform, you have to use the whole continuum. >> Yeah. And I think the continuum message is a good one because if you look at what the evolution is, that was interesting to. Adam went on and did kind of a history lesson in the beginning, it felt like I was in the Star Wars movie, like back in the old days. And then you kind of progressed. You had to be really elite to roll your own cloud. And the hyperscalers did that, you saw that. Now you still have elite technical people, but now it's general purpose, or purpose built. It's like having prefabricated platforms and open source. We've learned that why do you want to reinvent the wheel if you don't have to? So if I want a call center I get Connect, if I want to have a big plugin platform, I can still build on top of and have that SaaS unique application. This seems logical. This is new. (laughter) This is the continuum. I mean, it seems obvious now looking at it, but how far along in are people getting this. Karthik, what's your take on this? >> I think customers are getting it. They are looking at cloud more as an operating system for their future innovation. They liked the concept that they got from the public cloud, which is easy configurability, consumability and automatability of their infrastructure assets. And when you can get that capability as an operating system for your entire enterprise, and you could innovate across the spectrum, that's extremely powerful. We see companies accelerating their adoption to cloud, but we are also seeing over the last three years, a lot of that adoption was using cloud as a migration destination. But now with the power of the cloud continuum, where innovation is available, that so many new services that Adam launched today, you could use truly cloud as an innovation engine. And we're actually seeing that the clients who are using the cloud continuum for innovation are doing much better than the ones that are using cloud as a migration destination. In fact, they're doing two X to three X use of cloud for innovation and uplifting knowlEdge where they are actually using three X more cloud for sustainability purposes. So huge, huge value. >> Yeah, I mean, this is a great point. Great insight, because what you're saying is essentially you can't hide anymore. The projects are either going to be successful or not. You can see whether it's useful or not, and now you're tying cloud adoption and outcomes together. Where you can look it and saying, we need to make this outcome work. Not for building, for building sake. Those projects were discovered during the pandemic. Why are we doing that? So you can't hide that ball anymore. >> Right and everybody's got to do it now, right? I mean, you don't have a choice. The pandemic is now forcing companies to change. They've changed. And that the research shows that the companies that have truly adopted the whole continuum are doing much better than the companies that didn't. >> What's pattern in this continuum research you guys, what's the big takeaway that you guys have found in that study, in that customer experience that you're having. What's the big, Aha moment. >> I think there are a few things. Number one surprising aspect is that the companies that use cloud for a broader innovation objective, actually, were saving more than the ones that use cloud just as a cost saving initiative. That was a big, Aha moment. Number two, when you talk about all of this innovation that AWS provides, sometimes it's easy for organizations to shrug it off saying, this looks like this is only for the elite companies, or this is only for the digitally native companies to follow. But our research showed that the companies that were successful adopting cloud continuum, the ones that we call less continuum competitors, 60% of them are pre-digitally born organizations. And they were reaping the benefits and they were growing faster, saving more, being more innovative than all others. So this is truly usable across the spectrum of the G 2000 enterprise. >> Yeah, and I think it's a no brainer, but now that you have, customers are transforming, they have multiple clouds. You have AWS, Azure, Google cloud, people were trying to find their swim lane. We heard about skill gap shortage. We did some reporting on that, that this idea of multi-cloud maybe not, I can't hire enough people. I'm going to bet on this cloud, maybe use that cloud. How are people looking at that? How do you guys see that the cloud competitive continuum, or how is the cloud competition affecting the cloud continuum from a customer standpoint? >> Yeah. I mean, you got to look at it, do you use the whole continuum? You've got a lot of cases, you got to be on the same cloud, right. You can use the whole, you got to use all the different components, all the different services. So I think we are seeing customers that are picking one and starting with one and then adding others. I see a lot of my customers who are using multiple clouds, but they're using them in different business units, right? So they may pick one business unit to go deep with AWS on, they may go use another business unit to go deep on another cloud, right? So yeah, I mean, everyone is getting multiple, but a lot of they're starting with one and then adding a second one or a third along the way. >> Karthik, this is what I was trying to get out of my story. It's a hard, very nuanced point. But if you look at the success of say Snowflake and Databricks, all bet on Amazon and their superclouds, they are on Amazon, but they're now working with Azure as well, because why wouldn't you want to open up your market? >> Exactly. And even the industry companies that want to monetize their capabilities using the digital ecosystems are doing that. For example, Siemens wanted to bring all their capabilities in manufacturing and machine operating system into a platform called MindSphere. And they knew that their end goal was going to be multi-cloud, but they want to practice, leveraging the power of cloud with one platform. And when they created MindSphere, they started with AWS and they created that solution in the public cloud and private cloud also at the Edge by leveraging the power of cloud from public to Edge and proved it out. And once it started working and they were able to roll it out for customers. Now they are giving customers the choice to be able to use it in other clouds as well. >> Yeah Karthik, you mentioned earlier at the top of our interview about the platform of the cloud and Dave and I were talking on our keynote review. We did a little history lesson of when Microsoft owned the monopoly of windows, the system software, and they had the application suite with office, but they still wanted developers to build on top of windows. Okay. But now with cloud that's one big windows platform like thing. So the developers ecosystem is evolving. And so one of the things we're watching, I want to get your reaction to this. Is in every major inflection point in the computer industry, when new ways to build and write code rolled out, the application owners always wanted their software to run on the fastest platform. Speeds and feeds matter in these shifts, because why would I want to have my software run slower? >> Yeah. >> What is your reaction to that? >> Yeah, absolutely. And again, there's a lot of things that the industry is going through and we are pushing the envelope on digitization. And today's keynote. When you saw the CEO of NASDAQ talking about the technology bottlenecks that were preventing the matching algorithm to be finally taken to cloud. Now that capability that's available at with AWS is what is enabling that matching algorithm to be taken to cloud through the power of Edge. So there's so much technology innovation, that's happening. That's constantly expanding the boundaries of posibilities. >> I mean, that's exactly the point. And I wrote this in my story and it came out on the keynote today, which was Adam saying, the clouds expanding that's the continuum. If it's running cloud operations, does it matter what it is? I mean, it's, if you're at the Edge and you're running cloud, maybe cause you want latency, of course you want to have low latency. Why wouldn't you want outposts. Again, this is all cloud operations. DevSecOps data is now kind of cloud operationalized. That seems to be what's happening. >> Yeah, I think the developers love the fact that they can write for one and put it anywhere, right? And whether it's a EKS on Inside, I don't even know what you call anymore, the public cloud, right? Or all the way out at the Edge, right? You write it once, you can deploy it there and it makes their lives a lot easier. And you know, as you said, it's all about performance. So they get the best option. >> Well, We love having you guys on the theCUBE, Accenture. You guys have really smart, talented people, always great commentary. Dave and I were looking at reviewing the tape so to speak. It's not really tape anymore. It's it's digitally stored on a S3, but we were looking back at 2016 when we first started talking about horizontally scalable cloud and vertically specialized applications. If you look at the keynote today and squint through the announcements, Amazon's going to offer full horizontal scalability and vertical specialization at the app level with machine learning capabilities. This means that you need data to be horizontally addressable, which is kind of counterintuitive, but you're seeing all the success on data lakes and lakes. This is the new architecture. It's kind of proven now, what do you guys think? >> Yeah, again, the aspect of cloud is about democratised innovation. The first element is, even though there's so much infrastructure build-out and infrastructural elements where there's continuous innovation going on, the enterprises and developers are moving from Bivives built decisions to assembling and consuming options. And when they assemble and consume, they want newer and newer services to be available. That is very specific to their industry and specific to functions, whether it is supply chain function or manufacturing function or so on and so forth. For this, there are going to be specific data that is going to be required, or operational for that particular use-case. But the whole idea of predictive analytics and AI and machine learning and data science is about how do you find correlations between operational data for a particular capability, with things that in the previous world was unrelated. For that you need to bring all of this data together. Time will tell whether all the data is going to move to one location or is there going to be distributed computing of that data with more technology, but that's the role that data is going to play in these verticalized solutions. >> Yeah, I mean, that's awesome. I want to get you guys while I got one, a couple of minutes left. Advice to people that look into go this next level. They know the continuum is coming, you guys been providing great solutions and advice to your customers. For the folks watching, what advice can you give where they're just putting their toe in the water or want to go full in? >> Yeah, so, we found in that research that there were some common patterns that were followed by these continuum competitors, the ones that were succeeding or winning in the cloud. And there was namely four of them, the first one, and these four can be adopted by others for them to also win in the continuum. The first one was looking at the power of the continuum, how the technology is evolving and creating a strategy to take advantage of the evolution of the continuum. That's number one. Number two, this is about organizational change. So don't go about this change in a soft manner. There are elements that you need to change within your organization to imbibe this wholeheartedly. That's the second thing. Third thing is one common aspect that all the continuum competitors followed was they put experience at the forefront for everything. For their end customers. Last but not the least. This is a holistic journey and an enterprise wide journey. And this would require CSO level, CEO level commitment on a longer term to achieve this. So with these four things, most companies can achieve the successes that the continuum competitors are seeing. >> Awesome insight, Chris, real quick, 30 seconds. What's your advice. >> Chris: Don't be afraid. (laughter) It's pretty simple. >> The water's warm, come on in >> Yeah, come on in. A lot of gone before you, right? It can be scary. It can be daunting, right? A lot of services. Don't be scared to get in and go at it. >> Yeah, one of the jobs I love about being theCUBE host is, you talk to people many years earlier, you guys got it right at Accenture. Congratulations. You were deploying, you saw this wave of purpose-built before anyone else and congratulations. Great success. >> Thanks, thanks for having us on theCUBE. >> Okay, I'm John Furrier. You're watching us here live in Las Vegas, for AWS re:Invent 2021 coverage. TheCUBE, the leader in tech coverage. (upbeat music)

>>Well, welcome back to the cubes coverage of AWS reinvent 2021. We're here joined by Ian buck, general manager and vice president of accelerated computing at Nvidia I'm. John Ford, your host of the QB. And thanks for coming on. So in video, obviously, great brand congratulates on all your continued success. Everyone who has does anything in graphics knows the GPU's are hot and you guys get great brand great success in the company, but AI and machine learning was seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing, uh, in ML and AI, that's accelerating computing to the cloud. Yeah, >>I mean, AI is kind of drape bragging breakthroughs innovations across so many segments, so many different use cases. We see it showing up with things like credit card, fraud prevention and product and content recommendations. Really it's the new engine behind search engines is AI. Uh, people are applying AI to things like, um, meeting transcriptions, uh, virtual calls like this using AI to actually capture what was said. Um, and that gets applied in person to person interactions. We also see it in intelligence systems assistance for a contact center, automation or chat bots, uh, medical imaging, um, and intelligence stores and warehouses and everywhere. It's really, it's really amazing what AI has been demonstrated, what it can do. And, uh, it's new use cases are showing up all the time. >>Yeah. I'd love to get your thoughts on, on how the world's evolved just in the past few years, along with cloud, and certainly the pandemics proven it. You had this whole kind of full stack mindset initially, and now you're seeing more of a horizontal scale, but yet enabling this vertical specialization in applications. I mean, you mentioned some of those apps, the new enablers, this kind of the horizontal play with enablement for specialization, with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >>Yeah, it's the innovations on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIS as well as machine learning techniques that are, um, just being invented by researchers for, uh, and the community at large, including Amazon. Um, you know, it started with these convolutional neural networks, which are great for image processing, but as it expanded more recently into, uh, recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic graph neural networks, where the actual graph now is trained as a, as a neural network, you have this underpinning of great AI technologies that are being adventure around the world in videos role is try to productize that and provide a platform for people to do that innovation and then take the next step and innovate vertically. Um, take it, take it and apply it to two particular field, um, like medical, like healthcare and medical imaging applying AI, so that radiologists can have an AI assistant with them and highlight different parts of the scan. >>Then maybe troublesome worrying, or requires more investigation, um, using it for robotics, building virtual worlds, where robots can be trained in a virtual environment, their AI being constantly trained, reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box, um, to do, to activate that we co we are creating different vertical solutions, vertical stacks for products that talk the languages of those businesses, of those users, uh, in medical imaging, it's processing medical data, which is obviously a very complicated large format data, often three-dimensional boxes in robotics. It's building combining both our graphics and simulation technologies, along with the, you know, the AI training capabilities and different capabilities in order to run in real time. Those are, >>Yeah. I mean, it's just so cutting edge. It's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just to go back to the late two thousands, you know, how unstructured data or object store created, a lot of people realize that the value out of that now you've got graph graph value, you got graph network effect, you've got all kinds of new patterns. You guys have this notion of graph neural networks. Um, that's, that's, that's out there. What is, what is a graph neural network and what does it actually mean for deep learning and an AI perspective? >>Yeah, we have a graph is exactly what it sounds like. You have points that are connected to each other, that established relationships and the example of amazon.com. You might have buyers, distributors, sellers, um, and all of them are buying or recommending or selling different products. And they're represented in a graph if I buy something from you and from you, I'm connected to those end points and likewise more deeply across a supply chain or warehouse or other buyers and sellers across the network. What's new right now is that those connections now can be treated and trained like a neural network, understanding the relationship. How strong is that connection between that buyer and seller or that distributor and supplier, and then build up a network that figure out and understand patterns across them. For example, what products I may like. Cause I have this connection in my graph, what other products may meet those requirements, or also identifying things like fraud when, when patterns and buying patterns don't match, what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two captured by the frequency half I buy things or how I rate them or give them stars as she used cases, uh, this application graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, it's very exciting to a new application, but applying AI to optimizing business, to reducing fraud and letting us, you know, get access to the products that we want, the products that they have, our recommendations be things that, that excited us and want us to buy things >>Great setup for the real conversation that's going on here at re-invent, which is new kinds of workloads are changing. The game. People are refactoring their business with not just replatform, but actually using this to identify value and see cloud scale allows you to have the compute power to, you know, look at a note on an arc and actually code that. It's all, it's all science, all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS before? >>Yeah. 80 of us has been a great partner and one of the first cloud providers to ever provide GPS the cloud, uh, we most more recently we've announced two new instances, uh, the instance, which is based on the RA 10 G GPU, which has it was supports the Nvidia RTX technology or rendering technology, uh, for real-time Ray tracing and graphics and game streaming is their highest performance graphics, enhanced replicate without allows for those high performance graphics applications to be directly hosted in the cloud. And of course runs everything else as well, including our AI has access to our AI technology runs all of our AI stacks. We also announced with AWS, the G 5g instance, this is exciting because it's the first, uh, graviton or ARM-based processor connected to a GPU and successful in the cloud. Um, this makes, uh, the focus here is Android gaming and machine learning and France. And we're excited to see the advancements that Amazon is making and AWS is making with arm and the cloud. And we're glad to be part of that journey. >>Well, congratulations. I remember I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was getting, he was teasing this out, that they're going to build their own, get in there and build their own connections, take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new new interfaces and the new servers, new technology that you guys are doing, you're enabling applications. What does, what do you see this enabling as this, as this new capability comes out, new speed, more, more performance, but also now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >>Well, so first off I think arm is here to stay and you can see the growth and explosion of my arm, uh, led of course, by grab a tiny to be. I spend many others, uh, and by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to arm, we can help bring that innovation. That arm allows that open innovation because there's an open architecture to the entire ecosystem. Uh, we can help bring it forward, uh, to the state of the art in AI machine learning, the graphics. Um, we all have our software that we released is both supportive, both on x86 and an army equally, um, and including all of our AI stacks. So most notably for inference the deployment of AI models. We have our, the Nvidia Triton inference server. Uh, this is the, our inference serving software where after he was trained to model, he wanted to play it at scale on any CPU or GPU instance, um, for that matter. So we support both CPS and GPS with Triton. Um, it's natively integrated with SageMaker and provides the benefit of all those performance optimizations all the time. Uh, things like, uh, features like dynamic batching. It supports all the different AI frameworks from PI torch to TensorFlow, even a generalized Python code. Um, we're activating how activating the arm ecosystem as well as bringing all those AI new AI use cases and all those different performance levels, uh, with our partnership with AWS and all the different clouds. >>And you got to making it really easy for people to use, use the technology that brings up the next kind of question I want to ask you. I mean, a lot of people are really going in jumping in the big time into this. They're adopting AI. Either they're moving in from prototype to production. There's always some gaps, whether it's knowledge, skills, gaps, or whatever, but people are accelerating into the AI and leaning into it hard. What advancements have is Nvidia made to make it more accessible, um, for people to move faster through the, through the system, through the process? >>Yeah, it's one of the biggest challenges. The other promise of AI, all the publications that are coming all the way research now, how can you make it more accessible or easier to use by more people rather than just being an AI researcher, which is, uh, uh, obviously a very challenging and interesting field, but not one that's directly in the business. Nvidia is trying to write a full stack approach to AI. So as we make, uh, discover or see these AI technologies come available, we produce SDKs to help activate them or connect them with developers around the world. Uh, we have over 150 different STKs at this point, certain industries from gaming to design, to life sciences, to earth scientist. We even have stuff to help simulate quantum computing. Um, and of course all the, all the work we're doing with AI, 5g and robotics. So, uh, we actually just introduced about 65 new updates just this past month on all those SDKs. Uh, some of the newer stuff that's really exciting is the large language models. Uh, people are building some amazing AI. That's capable of understanding the Corpus of like human understanding, these language models that are trained on literally the continent of the internet to provide general purpose or open domain chatbots. So the customer is going to have a new kind of experience with a computer or the cloud. Uh, we're offering large language, uh, those large language models, as well as AI frameworks to help companies take advantage of this new kind of technology. >>You know, each and every time I do an interview with Nvidia or talk about Nvidia my kids and their friends, they first thing they said, you get me a good graphics card. Hey, I want the best thing in their rig. Obviously the gaming market's hot and known for that, but I mean, but there's a huge software team behind Nvidia. This is a well-known your CEO is always talking about on his keynotes, you're in the software business. And then you had, do have hardware. You were integrating with graviton and other things. So, but it's a software practices, software. This is all about software. Could you share kind of more about how Nvidia culture and their cloud culture and specifically around the scale? I mean, you, you hit every, every use case. So what's the software culture there at Nvidia, >>And it is actually a bigger, we have more software people than hardware people, people don't often realize this. Uh, and in fact that it's because of we create, uh, the, the, it just starts with the chip, obviously building great Silicon is necessary to provide that level of innovation, but as it expanded dramatically from then, from there, uh, not just the Silicon and the GPU, but the server designs themselves, we actually do entire server designs ourselves to help build out this infrastructure. We consume it and use it ourselves and build our own supercomputers to use AI, to improve our products. And then all that software that we build on top, we make it available. As I mentioned before, uh, as containers on our, uh, NGC container store container registry, which is accessible for me to bus, um, to connect to those vertical markets, instead of just opening up the hardware and none of the ecosystem in develop on it, they can with a low-level and programmatic stacks that we provide with Kuda. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make as well, >>Ram a little software is so much easier. I want to get that plug for, I think it's worth noting that you guys are, are heavy hardcore, especially on the AI side. And it's worth calling out, uh, getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about and looking at how they're doing? >>Yeah. Um, for training, it's all about time to solution. Um, it's not the hardware that that's the cost, it's the opportunity that AI can provide your business and many, and the productivity of those data scientists, which are developing, which are not easy to come by. So, uh, what we hear from customers is they need a fast time to solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it often. So in training is time to solution for inference. It's about our, your ability to deploy at scale. Often people need to have real time requirements. They want to run in a certain amount of latency, a certain amount of time. And typically most companies don't have a single AI model. They have a collection of them. They want, they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure leveraging the trading infant server. I mentioned before can actually run multiple models on a single GPU saving costs, optimizing for efficiency yet still meeting the requirements for latency and the real time experience so that your customers have a good, a good interaction with the AI. >>Awesome. Great. Let's get into, uh, the customer examples. You guys have obviously great customers. Can you share some of the use cases, examples with customers, notable customers? >>Yeah. I want one great part about working in videos as a technology company. You see, you get to engage with such amazing customers across many verticals. Uh, some of the ones that are pretty exciting right now, Netflix is using the G4 instances to CLA um, to do a video effects and animation content. And, you know, from anywhere in the world, in the cloud, uh, as a cloud creation content platform, uh, we work in the energy field that Siemens energy is actually using AI combined with, um, uh, simulation to do predictive maintenance on their energy plants, um, and, and, uh, doing preventing or optimizing onsite inspection activities and eliminating downtime, which is saving a lot of money for the engine industry. Uh, we have worked with Oxford university, uh, which is Oxford university actually has over two, over 20 million artifacts and specimens and collections across its gardens and museums and libraries. They're actually using convenient GPS and Amazon to do enhance image recognition, to classify all these things, which would take literally years with, um, uh, going through manually each of these artifacts using AI, we can click and quickly catalog all of them and connect them with their users. Um, great stories across graphics, about cross industries across research that, uh, it's just so exciting to see what people are doing with our technology together with, >>And thank you so much for coming on the cube. I really appreciate Greg, a lot of great content there. We probably going to go another hour, all the great stuff going on in the video, any closing remarks you want to share as we wrap this last minute up >>Now, the, um, really what Nvidia is about as accelerating cloud computing, whether it be AI, machine learning, graphics, or headphones, community simulation, and AWS was one of the first with this in the beginning, and they continue to bring out great instances to help connect, uh, the cloud and accelerated computing with all the different opportunities integrations with with SageMaker really Ks and ECS. Uh, the new instances with G five and G 5g, very excited to see all the work that we're doing together. >>Ian buck, general manager, and vice president of accelerated computing. I mean, how can you not love that title? We want more, more power, more faster, come on. More computing. No, one's going to complain with more computing know, thanks for coming on. Thank you. Appreciate it. I'm John Farrell hosted the cube. You're watching Amazon coverage reinvent 2021. Thanks for watching.

(bright music) >> Well, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're here joined by Ian Buck, general manager and vice president of Accelerated Computing at NVIDIA. I'm John Furrrier, host of theCUBE. Ian, thanks for coming on. >> Oh, thanks for having me. >> So NVIDIA, obviously, great brand. Congratulations on all your continued success. Everyone who does anything in graphics knows that GPU's are hot, and you guys have a great brand, great success in the company. But AI and machine learning, we're seeing the trend significantly being powered by the GPU's and other systems. So it's a key part of everything. So what's the trends that you're seeing in ML and AI that's accelerating computing to the cloud? >> Yeah. I mean, AI is kind of driving breakthroughs and innovations across so many segments, so many different use cases. We see it showing up with things like credit card fraud prevention, and product and content recommendations. Really, it's the new engine behind search engines, is AI. People are applying AI to things like meeting transcriptions, virtual calls like this, using AI to actually capture what was said. And that gets applied in person-to-person interactions. We also see it in intelligence assistance for contact center automation, or chat bots, medical imaging, and intelligence stores, and warehouses, and everywhere. It's really amazing what AI has been demonstrating, what it can do, and its new use cases are showing up all the time. >> You know, Ian, I'd love to get your thoughts on how the world's evolved, just in the past few years alone, with cloud. And certainly, the pandemic's proven it. You had this whole kind of fullstack mindset, initially, and now you're seeing more of a horizontal scale, but yet, enabling this vertical specialization in applications. I mean, you mentioned some of those apps. The new enablers, this kind of, the horizontal play with enablement for, you know, specialization with data, this is a huge shift that's going on. It's been happening. What's your reaction to that? >> Yeah. The innovation's on two fronts. There's a horizontal front, which is basically the different kinds of neural networks or AIs, as well as machine learning techniques, that are just being invented by researchers and the community at large, including Amazon. You know, it started with these convolutional neural networks, which are great for image processing, but has expanded more recently into recurrent neural networks, transformer models, which are great for language and language and understanding, and then the new hot topic, graph neural networks, where the actual graph now is trained as a neural network. You have this underpinning of great AI technologies that are being invented around the world. NVIDIA's role is to try to productize that and provide a platform for people to do that innovation. And then, take the next step and innovate vertically. Take it and apply it to a particular field, like medical, like healthcare and medical imaging, applying AI so that radiologists can have an AI assistant with them and highlight different parts of the scan that may be troublesome or worrying, or require some more investigation. Using it for robotics, building virtual worlds where robots can be trained in a virtual environment, their AI being constantly trained and reinforced, and learn how to do certain activities and techniques. So that the first time it's ever downloaded into a real robot, it works right out of the box. To activate that, we are creating different vertical solutions, vertical stacks, vertical products, that talk the languages of those businesses, of those users. In medical imaging, it's processing medical data, which is obviously a very complicated, large format data, often three-dimensional voxels. In robotics, it's building, combining both our graphics and simulation technologies, along with the AI training capabilities and difference capabilities, in order to run in real time. Those are just two simple- >> Yeah, no. I mean, it's just so cutting-edge, it's so relevant. I mean, I think one of the things you mentioned about the neural networks, specifically, the graph neural networks, I mean, we saw, I mean, just go back to the late 2000s, how unstructured data, or object storage created, a lot of people realized a lot of value out of that. Now you got graph value, you got network effect, you got all kinds of new patterns. You guys have this notion of graph neural networks that's out there. What is a graph neural network, and what does it actually mean from a deep learning and an AI perspective? >> Yeah. I mean, a graph is exactly what it sounds like. You have points that are connected to each other, that establish relationships. In the example of Amazon.com, you might have buyers, distributors, sellers, and all of them are buying, or recommending, or selling different products. And they're represented in a graph. If I buy something from you and from you, I'm connected to those endpoints, and likewise, more deeply across a supply chain, or warehouse, or other buyers and sellers across the network. What's new right now is, that those connections now can be treated and trained like a neural network, understanding the relationship, how strong is that connection between that buyer and seller, or the distributor and supplier, and then build up a network to figure out and understand patterns across them. For example, what products I may like, 'cause I have this connection in my graph, what other products may meet those requirements? Or, also, identifying things like fraud, When patterns and buying patterns don't match what a graph neural networks should say would be the typical kind of graph connectivity, the different kind of weights and connections between the two, captured by the frequency of how often I buy things, or how I rate them or give them stars, or other such use cases. This application, graph neural networks, which is basically capturing the connections of all things with all people, especially in the world of e-commerce, is very exciting to a new application of applying AI to optimizing business, to reducing fraud, and letting us, you know, get access to the products that we want. They have our recommendations be things that excite us and want us to buy things, and buy more. >> That's a great setup for the real conversation that's going on here at re:Invent, which is new kinds of workloads are changing the game, people are refactoring their business with, not just re-platforming, but actually using this to identify value. And also, your cloud scale allows you to have the compute power to, you know, look at a note in an arc and actually code that. It's all science, it's all computer science, all at scale. So with that, that brings up the whole AWS relationship. Can you tell us how you're working with AWS, specifically? >> Yeah, AWS have been a great partner, and one of the first cloud providers to ever provide GPUs to the cloud. More recently, we've announced two new instances, the G5 instance, which is based on our A10G GPU, which supports the NVIDIA RTX technology, our rendering technology, for real-time ray tracing in graphics and game streaming. This is our highest performance graphics enhanced application, allows for those high-performance graphics applications to be directly hosted in the cloud. And, of course, runs everything else as well. It has access to our AI technology and runs all of our AI stacks. We also announced, with AWS, the G5 G instance. This is exciting because it's the first Graviton or Arm-based processor connected to a GPU and successful in the cloud. The focus here is Android gaming and machine learning inference. And we're excited to see the advancements that Amazon is making and AWS is making, with Arm in the cloud. And we're glad to be part of that journey. >> Well, congratulations. I remember, I was just watching my interview with James Hamilton from AWS 2013 and 2014. He was teasing this out, that they're going to build their own, get in there, and build their own connections to take that latency down and do other things. This is kind of the harvest of all that. As you start looking at these new interfaces, and the new servers, new technology that you guys are doing, you're enabling applications. What do you see this enabling? As this new capability comes out, new speed, more performance, but also, now it's enabling more capabilities so that new workloads can be realized. What would you say to folks who want to ask that question? >> Well, so first off, I think Arm is here to stay. We can see the growth and explosion of Arm, led of course, by Graviton and AWS, but many others. And by bringing all of NVIDIA's rendering graphics, machine learning and AI technologies to Arm, we can help bring that innovation that Arm allows, that open innovation, because there's an open architecture, to the entire ecosystem. We can help bring it forward to the state of the art in AI machine learning and graphics. All of our software that we release is both supportive, both on x86 and on Arm equally, and including all of our AI stacks. So most notably, for inference, the deployment of AI models, we have the NVIDIA Triton inference server. This is our inference serving software, where after you've trained a model, you want to deploy it at scale on any CPU, or GPU instance, for that matter. So we support both CPUs and GPUs with Triton. It's natively integrated with SageMaker and provides the benefit of all those performance optimizations. Features like dynamic batching, it supports all the different AI frameworks, from PyTorch to TensorFlow, even a generalized Python code. We're activating, and help activating, the Arm ecosystem, as well as bringing all those new AI use cases, and all those different performance levels with our partnership with AWS and all the different cloud instances. >> And you guys are making it really easy for people to use use the technology. That brings up the next, kind of, question I wanted to ask you. I mean, a lot of people are really going in, jumping in big-time into this. They're adopting AI, either they're moving it from prototype to production. There's always some gaps, whether it's, you know, knowledge, skills gaps, or whatever. But people are accelerating into the AI and leaning into it hard. What advancements has NVIDIA made to make it more accessible for people to move faster through the system, through the process? >> Yeah. It's one of the biggest challenges. You know, the promise of AI, all the publications that are coming out, all the great research, you know, how can you make it more accessible or easier to use by more people? Rather than just being an AI researcher, which is obviously a very challenging and interesting field, but not one that's directly connected to the business. NVIDIA is trying to provide a fullstack approach to AI. So as we discover or see these AI technologies become available, we produce SDKs to help activate them or connect them with developers around the world. We have over 150 different SDKs at this point, serving industries from gaming, to design, to life sciences, to earth sciences. We even have stuff to help simulate quantum computing. And of course, all the work we're doing with AI, 5G, and robotics. So we actually just introduced about 65 new updates, just this past month, on all those SDKs. Some of the newer stuff that's really exciting is the large language models. People are building some amazing AI that's capable of understanding the corpus of, like, human understanding. These language models that are trained on literally the content of the internet to provide general purpose or open-domain chatbots, so the customer is going to have a new kind of experience with the computer or the cloud. We're offering those large language models, as well as AI frameworks, to help companies take advantage of this new kind of technology. >> You know, Ian, every time I do an interview with NVIDIA or talk about NVIDIA, my kids and friends, first thing they say is, "Can you get me a good graphics card?" They all want the best thing in their rig. Obviously the gaming market's hot and known for that. But there's a huge software team behind NVIDIA. This is well-known. Your CEO is always talking about it on his keynotes. You're in the software business. And you do have hardware, you are integrating with Graviton and other things. But it's a software practice. This is software. This is all about software. >> Right. >> Can you share, kind of, more about how NVIDIA culture and their cloud culture, and specifically around the scale, I mean, you hit every use case. So what's the software culture there at NVIDIA? >> Yeah, NVIDIA's actually a bigger, we have more software people than hardware people. But people don't often realize this. And in fact, that it's because of, it just starts with the chip, and obviously, building great silicon is necessary to provide that level of innovation. But it's expanded dramatically from there. Not just the silicon and the GPU, but the server designs themselves. We actually do entire server designs ourselves, to help build out this infrastructure. We consume it and use it ourselves, and build our own supercomputers to use AI to improve our products. And then, all that software that we build on top, we make it available, as I mentioned before, as containers on our NGC container store, container registry, which is accessible from AWS, to connect to those vertical markets. Instead of just opening up the hardware and letting the ecosystem develop on it, they can, with the low-level and programmatic stacks that we provide with CUDA. We believe that those vertical stacks are the ways we can help accelerate and advance AI. And that's why we make them so available. >> And programmable software is so much easier. I want to get that plug in for, I think it's worth noting that you guys are heavy hardcore, especially on the AI side, and it's worth calling out. Getting back to the customers who are bridging that gap and getting out there, what are the metrics they should consider as they're deploying AI? What are success metrics? What does success look like? Can you share any insight into what they should be thinking about, and looking at how they're doing? >> Yeah. For training, it's all about time-to-solution. It's not the hardware that's the cost, it's the opportunity that AI can provide to your business, and the productivity of those data scientists which are developing them, which are not easy to come by. So what we hear from customers is they need a fast time-to-solution to allow people to prototype very quickly, to train a model to convergence, to get into production quickly, and of course, move on to the next or continue to refine it. >> John Furrier: Often. >> So in training, it's time-to-solution. For inference, it's about your ability to deploy at scale. Often people need to have real-time requirements. They want to run in a certain amount of latency, in a certain amount of time. And typically, most companies don't have a single AI model. They have a collection of them they want to run for a single service or across multiple services. That's where you can aggregate some of your infrastructure. Leveraging the Triton inference server, I mentioned before, can actually run multiple models on a single GPU saving costs, optimizing for efficiency, yet still meeting the requirements for latency and the real-time experience, so that our customers have a good interaction with the AI. >> Awesome. Great. Let's get into the customer examples. You guys have, obviously, great customers. Can you share some of the use cases examples with customers, notable customers? >> Yeah. One great part about working at NVIDIA is, as technology company, you get to engage with such amazing customers across many verticals. Some of the ones that are pretty exciting right now, Netflix is using the G4 instances to do a video effects and animation content from anywhere in the world, in the cloud, as a cloud creation content platform. We work in the energy field. Siemens energy is actually using AI combined with simulation to do predictive maintenance on their energy plants, preventing, or optimizing, onsite inspection activities and eliminating downtime, which is saving a lot of money for the energy industry. We have worked with Oxford University. Oxford University actually has over 20 million artifacts and specimens and collections, across its gardens and museums and libraries. They're actually using NVIDIA GPU's and Amazon to do enhanced image recognition to classify all these things, which would take literally years going through manually, each of these artifacts. Using AI, we can quickly catalog all of them and connect them with their users. Great stories across graphics, across industries, across research, that it's just so exciting to see what people are doing with our technology, together with Amazon. >> Ian, thank you so much for coming on theCUBE. I really appreciate it. A lot of great content there. We probably could go another hour. All the great stuff going on at NVIDIA. Any closing remarks you want to share, as we wrap this last minute up? >> You know, really what NVIDIA's about, is accelerating cloud computing. Whether it be AI, machine learning, graphics, or high-performance computing and simulation. And AWS was one of the first with this, in the beginning, and they continue to bring out great instances to help connect the cloud and accelerated computing with all the different opportunities. The integrations with EC2, with SageMaker, with EKS, and ECS. The new instances with G5 and G5 G. Very excited to see all the work that we're doing together. >> Ian Buck, general manager and vice president of Accelerated Computing. I mean, how can you not love that title? We want more power, more faster, come on. More computing. No one's going to complain with more computing. Ian, thanks for coming on. >> Thank you. >> Appreciate it. I'm John Furrier, host of theCUBE. You're watching Amazon coverage re:Invent 2021. Thanks for watching. (bright music)

(intro music playing) >> Hello everyone, and welcome to the power of n where HPE Aruba and Pensando are changing the game, the way customers scale with the cloud, and what's next in the evolution in switching. Hey everyone, I'm John furrier with the cube, and I'm here with Shane Corbin, director of technical product management at Pensando, and William show vice president of product management, Aruba HPE. Gentlemen, thank you for coming on and doing a deep dive and, and going into the, the big news. So the first question I want to ask you guys is um, what do you guys see from a market customer perspective that kicked this project off? um, amazing um, results um, over the past year or so? Where did it all come from? >> No, it's a great question, John. So when we were doing our homework, there were actually three very clear customer challenges. First, security threats were largely spawn with on, within the perimeter. In fact, Forrester highlighted 80% of threats originate within the internal network. Secondly, workloads are largely distributed creating a ton of east-west traffic. And then lastly, network services such as firewalls, load balancers, VPN aggregators are expensive, they're centralized, and they ultimately result in service chaining complexity. >> John: So, so, >> John: Go ahead, Shane. >> Yeah. Additionally, when we spoke to our customers after launching initially the distributed services platform, these compliance challenges clearly became apparent to us and while they saw the architecture value of adopting what the largest public cloud providers have done by putting a smart NIC in each compute node to provide these stateful services. Enterprise customers were still, were struggling with the need to upgrade fleets and brown field servers and the associated per node cost of adding a smart NIC to every compute node. Typically the traffic volumes for on a per node basis within an enterprise data center are significantly lower than cloud. Thus, we saw an opportunity here to, in conjunction with Aruba, develop a new category of switching product um, to share the processing capabilities of our unique intellectual property around our DPU across a rack of servers that net net delivers the same set of services through a new category of platform, enabling a distributed services architecture, and ultimately addressing the compliance and TCO generating huge TCO and ROI for customers. >> You know, one of the things that we've been reporting on with you guys, as well as the cloud scale, this is the volume of data and just the performance and scale. I think the timing of the, of this partnership and the product development is right on point. And you've got the edge right around the corner, more, more distributed nature of cloud operations, huge, huge change in the marketplace. So great timing on the origination story there. Great stuff. Tell me more about the platform itself, the details, what's under the hood, the hardware OS, what are the specs? >> Yeah, so we started with a very familiar premise. Rubik customers are already leveraging CX with an edge to cloud common operating model, in deploying leaf and spine networks. Plus we're excited to introduce the industry's first distributed services switch, where the first configuration has 48-25 gig ports with a hundred gig couplings running Aruba CX cloud native operating system, Pensando Asic's software inside, enabling layer four through six, seven stateful services. Shane, do you want to elaborate on. >> Yeah, let me elaborate on that a little bit further, um, you know, as we spoke existing platforms and how customers were seeking to address these challenges were, are inherently limited by the ASIC dye size, and that does limit their scale and performance and ability in traditional switching platforms to deliver truly stateful functions in, in, in a switching platform, this was, you know, architecturally from the ground up, when we developed our DPU, first and second generation, we delivered it, or we, we built it with stateful services in mind from the get-go, we leveraged the clean state design with our P four program with DPU. We evolved to our seven nanometers based pro DPU right now, which is essentially enabling software and Silicon. And this has generated a new level of performance scale, flexibility and capability in terms of services. This serves as the foundation for our 200 gig card, were we taking the largest cloud providers into production for. And the DPU itself is, is designed inherently to process stage, track stateful connections, and stateful flow is at very, very large scale without impacting performance. And in fact, the two of these DPU components server disk, services foundation of the CX 10 K, and this is how we enable stateful functions in a switching platform functions like stateful network fire-walling, stateful segmentation, enhanced programmable telemetry, which we believe will bring a whole lot of value to our customers. And this is a platform that's inherently programmable from the ground up. We can, we can build and leverage this platform to build new use cases around encryption, enabling stateful load balancing, stateful NAT to name a few, but, but the key message here is, this is, this is a platform with the next generation of architecture's in mind, is programmed, but at all, there's the stack, and that's what makes it fundamentally different than anything else. >> I want to just double click on that if you don't mind, before we get to the competitive question, because I think you brought up the state thing. I think this is worth calling out, if you guys don't mind commenting more on this states issue, because this is big. Cloud native developers right now, want speed, they're shifting left at the CICD pipeline with programmability. So going down and having the programmability, and having state is a really big deal. Can you guys just expand on that a little bit more and why it's important and, and how hard it really is to pull off? >> I, I can start, I guess, um, it's very hard to pull off because of the sheer amount of connections you need to track when you're developing something like a stateful firewall or a stateful load balancer, a key component of that is managing the connections at very, very large scale and understanding what's happening with those connections at scale, without impacting application performance. And this is fundamentally different at traditional switching platform, regardless of how it's deployed today in Asics, don't typically process and manage state like this. Um, memory resources within the chip aren't sufficient, um, the policy scale that you can um, implement on a platform aren't sufficient to address and fundamentally enable deployable firewalling, or load balancing, or other stateful services. >> That's exactly right. And so the other kind of key point here is that, if you think about the sophistication of different security threats, it does really require you to be able to look at the entire packet, and, and more so be able to look at the entire flow and be able to log that history, so that you can get much better heuristics around different anomalies, security threats that are emerging today. >> That's a great, great point. Thanks for, for, um, bringing that extra, extra point out. I would just add to this, we're reporting this all the time on Silicon angle in the cube is that, you know, the, you know, the, the automation wave that's coming with around data, you know, it's a center of data, not data centers we heard earlier on with the, in, in, in the presentation. Data drives automation, having that enabled with the state is a real big deal. So, I think that's really worth calling out. Now, I've got to ask the competition question, how is this different? I mean, this is an evolution. I would say, it's a revolution. You guys are being being humble, um, but how is this different from what customers can deploy today? >> Architecturally, if you take a look at it. We've, we've spoken about the technology and fundamentally in the platform what's unique, in the architecture, but, foundationally when customers deploy stateful services they're typically deployed leveraging traditional big box appliances for east-west our workload based agents, which seek to implement stateful security for each east-west. Architecturally what we're enabling is stateful services like firewalling, segmentation, can scale with the fabric and are delivered at the optimal point for east west which is through leaf for access layer of the network. And we do this for any type of workload. Be it deployed on a virtualized compute node, be a deployed on a containerized worker node, be deployed on bare metal, agnostic up typology, it can be in the access layer of a three tier design and a data center. It can be in the leaf layer of a VX VPN based fabric, but the goal is an all centrally managed to a single point of orchestration and control of which William will talk about shortly. The goal of this is to drive down the TCO of your data center as a whole, by allowing you to retire legacy appliances that are deployed in an east-west roll, and not utilize host based agents, and thus save a whole lot of money and we've modeled on the order of 60 to 70% in terms of savings in terms of the traditional data center pod design of a thousand compute nodes which we'll be publishing. And as, as we go forward additional services, as we mentioned, like encryption, this platform has the capability to terminate up to 800 gigs of our line rates encryption, IP sec, VPN per platform, stateful Nat load balancing, and this is all functionality we'll be adding to this existing platform because it's programmable as we've mentioned from the ground up. >> What are some of the use cases lead? And what's the top use cases, what's the low hanging fruit and where does this go? You've got service providers, enterprises. What are the types of customers you guys see implementing? >> Yeah, that's, what's really exciting about the CX 10,000. We actually see customer interest from all types of different markets, whether it be higher education, service providers to financial services, basically all enterprises verticals with private cloud or edge data centers. For example, it could be a hospital, a big box retailer, or a colon such as Iniquinate So it's really the CX 10,000 that creates a new switching category, enabling stateful services in that leaf node right at the workload, unifying network and security automation policy management. Second, the CX 10,000 greatly improves security posture and eliminates the need for hair-pinning east-west traffic all the way back to the centralized deployments. Lastly, As Shane highlighted, there's a 70% TCO savings by eliminating that appliance sprawl and ultimately collapsing the network security operations. >> I love the category creation um, vibe here. Love it. And also the technical and the cloud alignment's great. But how do the customers manage all this? Okay, I got a new category. I just put the box in, throw away some other ones? I mean, how does this all get done? And how does the customers manage all this? >> Yeah, so we're, we're looking to build on top of the river fabric composer. It's another familiar site for our customers, and what's already provides for compute storage and network automation, with a broad ecosystem integrations, such as VMware vSphere Vcenter as with Nutanix prism and so aligned with the CX 10,000 FGA, now you have a fabric composer, unified security and policy orchestration, and management with the ability to find firewall policies efficiently and provide that telemetry to collect your such a Splunk. >> John: So the customer environments right now involve a lot of multi-vendor and new frameworks, obviously, cloud native. How does this fit into the customer's existing environment with the ecosystem? How do they get, get going here? >> Yeah, great question. Um, Our customers can get going as we, we've built a flexible platform that can be deployed in either Greenfield or brownfield. Obviously it's a best of breed architecture for distributed services we're building in conjunction with Aruba. But if customers want to gradually integrate this into their existing environments and they're using other vendors, spines or cores, this can be inserted seamlessly as, as a lead for an access, access tier switch to deliver the exact same set of services within that architecture. So it plugs seamlessly in because it supports all the standard control plan protocols, a VX 90 VPN, and a traditional attitude, three tier designs easily. Now, for any enterprise solution deployment, it's critical that you build a holistic ecosystem around it. It's clear that, this will get customer deployments and the ecosystem being diverse and rich is very, very important. And as part of our integrations with the controller, we're building a broad suite of integrations across threat detection, application dependency mapping, Siemens sooam, dev ops infrastructure as code tools. (inaudible) And it's clear if you look at these categories of integrations, you know, XDR or threat detection requires full telemetric from within the data center, it's been hard to accomplish to date because you typically need agents on, on your compute nodes to give you the visibility into what's going on or firewalls for east west fuels. Now, our platform can natively provide full visibility into all flows east- west in the data center. And this can become the source of telemetry truth that these MLX CR engines require to work. The other aspects of ecosystem around application dependency mapping, this single core challenge with deploying segmentation east west is understanding the rules to put in & Right, first is how do you insert the service, um, service device in such a way that it won't add more complexity? We don't add any complexity because we're in line natively. How you would understand it, would allow you to build the rules that are necessary to do segmentation. We integrate with tools like Guardi core, we provide our flogs as source of data, and they can provide room recommendations and policy recommendations for customers. Around, we're building integrations around Siemen soam with, with tools like Splunk and elastic, elastic search that will allow NetOps and SecOps teams to visualize trend and manage the services delivered by the CX 10 K. And the other aspect of ecosystem, from a security standpoint is clearly how do I get policy for these traditional appliances and enforce them on this next generation architecture that you've built, that can enable stateful services. So we're building integrations with tools like turf and an algo sec third-party sources of policy that we can ingest and enforce on the infrastructure, allowing you to gradually, um, migrate to this new architecture over time. >> John: It's really a cloud native switch. I mean, you solve people's problems, pin- points, but yet positioned for growth. I mean, it sounds that's my takeaway, but I got to ask you guys both, what's the takeaway for the customers because it's not that simple for them, I mean it's, we a have complicated environment. (all giggling) >> Yeah, I think it's, I think it's really simple, um, you know, every 10 years or so, we see major evolutions in the data center and the switching environment, but we do believe we've created a new category with the distributed services, distributed services switch, delivering cloud scale distributed services, where the local, where the workloads reside greatly, simplifying network, security provisioning, and operations with the urban fabric composer while improving security posture and the TCO. But that's not all the folks, it's a journey, right Shane? >> Yeah, it's absolutely a journey. And this is the first step in a long journey with a great partner like Aruba. There's other platforms, hundred or 400 gig hardware platforms where we're looking at and then this additional services that we can enable over time, allowing customers to drive even more TCO value out of the platform of the architecture services like encryption for securing the cloud on-ramp, services like stateful load balancing to deploy east-west in the data center and, you know, holistically that's, that's the goal, deliver value for customers. And we believe we have an architecture and a platform, and this is a first step in a long journey. >> It's a great way of, I just ask one final, final question for both of you as product leaders, you got to be excited having a category creation product here in this market, this big wave, but what's your thoughts? >> Yeah, exactly right, it doesn't happen that often, and so we're, we're all in it's, it's exciting to be able to work with a great team like Pensando and Shane here. Um, so we're really, really excited about this launch. >> Yeah, it's awesome. The team is great. It's a great partnership between Pensando and Aruba. You know, we, we look forward to delivering value for our joint customers. >> John: Thank you both for sharing under the hood and more details on the product. Thanks for coming on. >> [William And Shane] Thank you. >> Okay. The next evolution in switching, I'm John Furrier here with the power of nHPE Aruba and Pensando changing the game, the way customers scale up in the cloud and networking. Thanks for watching. (music playing)