>> Announcer: From around the globe, it's theCUBE with coverage of KubeCon + CloudNativeCon Europe 2020 Virtual brought to you by Red Hat, the Cloud Native Computing Foundation and Ecosystem partners. >> Welcome back I'm Stu Miniman, this is theCUBE's coverage of KubeCon + CloudNativeCon Europe 2020, the virtual event. Of course, when we talk about Cloud Native we talk about Kubernetes there's a lot that's happening to modernize the infrastructure but a very important thing that we're going to talk about today is also what's happening up the stack, what sits on top of it and some of the new use cases and applications that are enabled by all of this modern environment and for that we're going to talk about artificial intelligence and machine learning or AI and ML as we tend to talk in the industry, so happy to welcome to the program. We have two first time guests joining us from Red Hat. First of all, we have Abhinav Joshi and Tushar Katarki they are both senior managers, part of the OpenShift group. Abhinav is in the product marketing and Tushar is in product management. Abhinav and Tushar thank you so much for joining us. >> Thanks a lot, Stu, we're glad to be here. >> Thanks Stu and glad to be here at KubeCon. >> All right, so Abhinav I mentioned in the intro here, modernization of the infrastructure is awesome but really it's an enabler. We know... I'm an infrastructure person the whole reason we have infrastructure is to be able to drive those applications, interact with my data and the like and of course, AI and ML are exciting a lot going on there but can also be challenging. So, Abhinav if I could start with you bring us inside your customers that you're talking to, what are the challenges, the opportunities? What are they seeing in this space? Maybe what's been holding them back from really unlocking the value that is expected? >> Yup, that's a very good question to kick off the conversation. So what we are seeing as an organization they typically face a lot of challenges when they're trying to build an AI/ML environment, right? And the first one is like a talent shortage. There is a limited amount of the AI, ML expertise in the market and especially the data scientists that are responsible for building out the machine learning and the deep learning models. So yeah, it's hard to find them and to be able to retain them and also other talents like a data engineer or app DevOps folks as well and the lack of talent can actually stall the project. And the second key challenge that we see is the lack of the readily usable data. So the businesses collect a lot of data but they must find the right data and make it ready for the data scientists to be able to build out, to be able to test and train the machine learning models. If you don't have the right kind of data to the predictions that your model is going to do in the real world is only going to be so good. So that becomes a challenge as well, to be able to find and be able to wrangle the right kind of data. And the third key challenge that we see is the lack of the rapid availability of the compute infrastructure, the data and machine learning, and the app dev tools for the various personas like a data scientist or data engineer, the software developers and so on that can also slow down the project, right? Because if all your teams are waiting on the infrastructure and the tooling of their choice to be provisioned on a recurring basis and they don't get it in a timely manner, it can stall the projects. And then the next one is the lack of collaboration. So you have all these kinds of teams that are involved in the AI project, and they have to collaborate with each other because the work one of the team does has a dependency on a different team like say for example, the data scientists are responsible for building the machine learning models and then what they have to do is they have to work with the app dev teams to make sure the models get integrated as part of the app dev processes and ultimately rolled out into the production. So if all these teams are operating in say silos and there is lack of collaboration between the teams, so this can stall the projects as well. And finally, what we see is the data scientists they typically start the machine learning modeling on their individual PCs or laptops and they don't focus on the operational aspects of the solution. So what this means is when the IT teams have to roll all this out into a production kind of deployment, so they get challenged to take all the work that has been done by the individuals and then be able to make sense out of it, be able to make sure that it can be seamlessly brought up in a production environment in a consistent way, be it on-premises, be it in the cloud or be it say at the edge. So these are some of the key challenges that we see that the organizations are facing, as they say try to take the AI projects from pilot to production. >> Well, some of those things seem like repetition of what we've had in the past. Obviously silos have been the bane of IT moving forward and of course, for many years we've been talking about that gap between developers and what's happening in the operation side. So Tushar, help us connect the dots, containers, Kubernetes, the whole DevOps movement. How is this setting us up to actually be successful for solutions like AI and ML? >> Sure Stu I mean, in fact you said it right like in the world of software, in the world of microservices, in the world of app modernization, in the world of DevOps in the past 10, 15 years, but we have seen this evolution revolution happen with containers and Kubernetes driving more DevOps behavior, driving more agile behavior so this in fact is what we are trying to say here can ease up the cable to EIML also. So the various containers, Kubernetes, DevOps and OpenShift for software development is directly applicable for AI projects to make them move agile, to get them into production, to make them more valuable to organization so that they can realize the full potential of AI. We already touched upon a few personas so it's useful to think about who the users are, who the personas are. Abhinav I talked about data scientists these are the people who obviously do the machine learning itself, do the modeling. Then there are data engineers who do the plumbing who provide the essential data. Data is so essential to machine learning and deep learning and so there are data engineers that are app developers who in some ways will then use the output of what the data scientists have produced in terms of models and then incorporate them into services and of course, none of these things are purely cast in stone there's a lot of overlap you could find that data scientists are app developers as well, you'll see some of app developers being data scientist later data engineer. So it's a continuum rather than strict boundaries, but regardless what all of these personas groups of people need or experts need is self service to that preferred tools and compute and storage resources to be productive and then let's not forget the IT, engineering and operations teams that need to make all this happen in an easy, reliable, available manner and something that is really safe and secure. So containers help you, they help you quickly and easily deploy a broad set of machine learning tools, data tools across the cloud, the hybrid cloud from data center to public cloud to the edge in a very consistent way. Teams can therefore alternatively modify, change a shared container images, machine learning models with (indistinct) and track changes. And this could be applicable to both containers as well as to the data by the way and be transparent and transparency helps in collaboration but also it could help with the regulatory reasons later on in the process. And then with containers because of the inherent processes solution, resource control and protection from threat they can also be very secure. Now, Kubernetes takes it to the next level first of all, it forms a cluster of all your compute and data resources, and it helps you to run your containerized tools and whatever you develop on them in a consistent way with access to these shared compute and centralized compute and storage and networking resources from the data center, the edge or the public cloud. They provide things like resource management, workload scheduling, multi-tendency controls so that you can be a proper neighbors if you will, and quota enforcement right? Now that's Kubernetes now if you want to up level it further if you want to enhance what Kubernetes offers then you go into how do you write applications? How do you actually make those models into services? And that's where... and how do you lifecycle them? And that's sort of the power of Helm and for the more Kubernetes operators really comes into the picture and while Helm helps in installing some of this for a complete life cycle experience. A kubernetes operator is the way to go and they simplify the acceleration and deployment and life cycle management from end-to-end of your entire AI, ML tool chain. So all in all organizations therefore you'll see that they need to dial up and define models rapidly just like applications that's how they get ready out of it quickly. There is a lack of collaboration across teams as Abhinav pointed out earlier, as you noticed that has happened still in the world of software also. So we're talking about how do you bring those best practices here to AI, ML. DevOps approaches for machine learning operations or many analysts and others have started calling as MLOps. So how do you kind of bring DevOps to machine learning, and fosters better collaboration between teams, application developers and IT operations and create this feedback loop so that the time to production and the ability to take more machine learning into production and ML-powered applications into production increase is significant. So that's kind of the, where I wanted shine the light on what you were referring to earlier, Stu. >> All right, Abhinav of course one of the good things about OpenShift is you have quite a lot of customers that have deployed the solution over the years, bring us inside some of your customers what are they doing for AI, ML and help us understand really what differentiates OpenShift in the marketplace for this solution set. >> Yeah, absolutely that's a very good question as well and we're seeing a lot of traction in terms of all kinds of industries, right? Be it the financial services like healthcare, automotive, insurance, oil and gas, manufacturing and so on. For a wide variety of use cases and what we are seeing is at the end of the day like all these deployments are focused on helping improve the customer experience, be able to automate the business processes and then be able to help them increase the revenue, serve their customers better, and also be able to save costs. If you go to openshift.com/ai-ml it's got like a lot of customer stories in there but today I will not touch on three of the customers we have in terms of the different industries. The first one is like Royal Bank of Canada. So they are a top global financial institution based out of Canada and they have more than 17 million clients globally. So they recently announced that they build out an AI-powered private cloud platform that was based on OpenShift as well as the NVIDIA DGX AI compute system and this whole solution is actually helping them to transform the customer banking experience by being able to deliver an AI-powered intelligent apps and also at the same time being able to improve the operational efficiency of their organization. And now with this kind of a solution, what they're able to do is they're able to run thousands of simulations and be able to analyze millions of data points in a fraction of time as compared to the solution that they had before. Yeah, so like a lot of great work going on there but now the next one is the ETCA healthcare. So like ETCA is one of the leading healthcare providers in the country and they're based out of the Nashville, Tennessee. And they have more than 184 hospitals as well as more than 2,000 sites of care in the U.S. as well as in the UK. So what they did was they developed a very innovative machine learning power data platform on top of our OpenShift to help save lives. The first use case was to help with the early detection of sepsis like it's a life-threatening condition and then more recently they've been able to use OpenShift in the same kind of stack to be able to roll out the new applications that are powered by machine learning and deep learning let say to help them fight COVID-19. And recently they did a webinar as well that had all the details on the challenges they had like how did they go about it? Like the people, process and technology and then what the outcomes are. And we are proud to be a partner in the solution to help with such a noble cause. And the third example I want to share here is the BMW group and our partner DXC Technology what they've done is they've actually developed a very high performing data-driven data platform, a development platform based on OpenShift to be able to analyze the massive amount of data from the test fleet, the data and the speed of the say to help speed up the autonomous driving initiatives. And what they've also done is they've redesigned the connected drive capability that they have on top of OpenShift that's actually helping them provide various use cases to help improve the customer experience. With the customers and all of the customers are able to leverage a lot of different value-add services directly from within the car, their own cars. And then like last year at the Red Hat Summit they had a keynote as well and then this year at Summit, they were one of the Innovation Award winners. And we have a lot more stories but these are the three that I thought are actually compelling that I should talk about here on theCUBE. >> Yeah Abhinav just a quick follow up for you. One of the things of course we're looking at in 2020 is how has the COVID-19 pandemic, people working from home how has that impacted projects? I have to think that AI and ML are one of those projects that take a little bit longer to deploy, is it something that you see are they accelerating it? Are they putting on pause or are new project kicking off? Anything you can share from customers you're hearing right now as to the impact that they're seeing this year? >> Yeah what we are seeing is that the customers are now even more keen to be able to roll out the digital (indistinct) but we see a lot of customers are now on the accelerated timeline to be able to say complete the AI, ML project. So yeah, it's picking up a lot of momentum and we talk to a lot of analyst as well and they are reporting the same thing as well. But there is the interest that is actually like ramping up on the AI, ML projects like across their customer base. So yeah it's the right time to be looking at the innovation services that it can help improve the customer experience in the new virtual world that we live in now about COVID-19. >> All right, Tushar you mentioned that there's a few projects involved and of course we know at this conference there's a very large ecosystem. Red Hat is a strong contributor to many, many open source projects. Give us a little bit of a view as to in the AI, ML space who's involved, which pieces are important and how Red Hat looks at this entire ecosystem? >> Thank you, Stu so as you know technology partnerships and the power of open is really what is driving the technology world these days in any ways and particularly in the AI ecosystem. And that is mainly because one of the machine learning is in a bootstrap in the past 10 years or so and a lot of that emerging technology to take advantage of the emerging data as well as compute power has been built on the kind of the Linux ecosystem with openness and languages like popular languages like Python, et cetera. And so what you... and of course tons of technology based in Java but the point really here is that the ecosystem plays a big role and open plays a big role and that's kind of Red Hat's best cup of tea, if you will. And that really has plays a leadership role in the open ecosystem so if we take your question and kind of put it into two parts, what is the... what we are doing in the community and then what we are doing in terms of partnerships themselves, commercial partnerships, technology partnerships we'll take it one step at a time. In terms of the community itself, if you step back to the three years, we worked with other vendors and users, including Google and NVIDIA and H2O and other Seldon, et cetera, and both startups and big companies to develop this Kubeflow ecosystem. The Kubeflow is upstream community that is focused on developing MLOps as we talked about earlier end-to-end machine learning on top of Kubernetes. So Kubeflow right now is in 1.0 it happened a few months ago now it's actually at 1.1 you'll see that coupon here and then so that's the Kubeflow community in addition to that we are augmenting that with the Open Data Hub community which is something that extends the capabilities of the Kubeflow community to also add some of the data pipelining stuff and some of the data stuff that I talked about and forms a reference architecture on how to run some of this on top of OpenShift. So the Open Data Hub community also has a great way of including partners from a technology partnership perspective and then tie that with something that I mentioned earlier, which is the idea of Kubernetes operators. Now, if you take a step back as I mentioned earlier, Kubernetes operators help manage the life cycle of the entire application or containerized application including not only the configuration on day one but also day two activities like update and backups, restore et cetera whatever the application needs. Afford proper functioning that a "operator" needs for it to make sure so anyways, the Kubernetes operators ecosystem is also flourishing and we haven't faced that with the OperatorHub.io which is a community marketplace if you will, I don't call it marketplace a community hub because it's just comprised of community operators. So the Open Data Hub actually can take community operators and can show you how to run that on top of OpenShift and manage the life cycle. Now that's the reference architecture. Now, the other aspect of it really is as I mentioned earlier is the commercial aspect of it. It is from a customer point of view, how do I get certified, supported software? And to that extent, what we have is at the top of the... from a user experience point of view, we have certified operators and certified applications from the AI, ML, ISV community in the Red Hat marketplace. And from the Red Hat marketplace is where it becomes easy for end users to easily deploy these ISVs and manage the complete life cycle as I said. Some of the examples of these kinds of ISVs include startups like H2O although H2O is kind of well known in certain sectors PerceptiLabs, Cnvrg, Seldon, Starburst et cetera and then on the other side, we do have other big giants also in this which includes partnerships with NVIDIA, Cloudera et cetera that we have announced, including our also SaaS I got to mention. So anyways these provide... create that rich ecosystem for data scientists to take advantage of. A TEDx Summit back in April, we along with Cloudera, SaaS Anaconda showcased a live demo that shows all these things to working together on top of OpenShift with this operator kind of idea that I talked about. So I welcome people to go and take a look the openshift.com/ai-ml that Abhinav already referenced should have a link to that it take a simple Google search might download if you need some of that, but anyways and the other part of it is really our work with the hardware OEMs right? And so obviously NVIDIA GPUs is obviously hardware, and that accelerations is really important in this world but we are also working with other OEM partners like HP and Dell to produce this accelerated AI platform that turnkey solutions to run your data-- to create this open AI platform for "private cloud" or the data center. The other thing obviously is IBM, IBM Cloud Pak for Data is based on OpenShift that has been around for some time and is seeing very good traction, if you think about a very turnkey solution, IBM Cloud Pak is definitely kind of well ahead in that and then finally Red Hat is about driving innovation in the open-source community. So, as I said earlier, we are doing the Open Data Hub which that reference architecture that showcases a combination of upstream open source projects and all these ISV ecosystems coming together. So I welcome you to take a look at that at opendatahub.io So I think that would be kind of the some total of how we are not only doing open and community building but also doing certifications and providing to our customers that assurance that they can run these tools in production with the help of a rich certified ecosystem. >> And customer is always key to us so that's the other thing that the goal here is to provide our customers with a choice, right? They can go with open source or they can go with a commercial solution as well. So you want to make sure that they get the best in cloud experience on top of our OpenShift and our broader portfolio as well. >> All right great, great note to end on, Abhinav thank you so much and Tushar great to see the maturation in this space, such an important use case. Really appreciate you sharing this with theCUBE and Kubecon community. >> Thank you, Stu. >> Thank you, Stu. >> Okay thank you and thanks a lot and have a great rest of the show. Thanks everyone, stay safe. >> Thanks you and stay with us for a lot more coverage from KubeCon + CloudNativeCon Europe 2020, the virtual edition I'm Stu Miniman and thank you as always for watching theCUBE. (soft upbeat music plays)

>> Narrator: From around the globe, it's the CUBE with coverage of SUSECON Digital, brought to you by SUSE. >> Hi, and welcome back. I'm Stu Miniman, and this is the CUBE's coverage of SUSECON Digital '20. Happy to welcome to the program Glenn Fitzgerald, he is the Chief Data Officer for Fujitsu Products in Europe, coming to me from across the pond. Ah, Glenn, great to see you, so thanks so much for joining us. >> Hi Stu, thanks, very glad to be here. >> All right, so, first of all, you know, Fujitsu Products Europe, Chief Data Officer, give us a little bit, your role and responsibility inside Fujitsu. >> Of course, the Fujitsu Products Europe is as the name suggests, that part of the Fujitsu Corporation that is dedicated to delivering our products out through the European geography. Fujitsu's product sets runs the full range of ITC components from... tablets to PCs to servers to big storage devices to networks, which is to integrated systems and the software stacks that sit on top of them. It's a wide profile, yeah. And my role has been to be the Chief Technology Officer for that organization for several years. Recently, we have as an organization adopted a new approach to take to the marketplace. And that has necessitated a slight change in my role to one that's more focused on enabling customers to get value out of their data and their data repositories and the correlation of that data to generate business value. A long description, Stu, but I think necessary. >> Yeah, no, super important, Glenn. One thing we've actually been saying for more than a year on the CUBE now, is when you have that discussion of digital transformation, one of the things that differentiates companies before they've gone digital and if they are truly to call themself, you know, have gone through this transformation, is they need to be data-driven, you know. Data needs to be how they're making their decisions. It was definitely a key theme that we heard from SUSE in the keynote. So maybe talk a little bit about how digital transformation and the partnership with SUSE fits into your world. >> Absolutely. So, in terms of the transformation of our business and the changes that we're trying to make to it, as a product organization, traditionally our relationships with our customers is kind of transactional. You know, we sell stuff and they buy stuff. And that relationship with customers is increasingly less viable. It's increasingly challenged. And I think it's challenged by the many things that have happened in the marketplace. It's a sign of a maturing industry. So, you have the Cloud and you have the ISVs who are providing compute power and storage capacity and network capability to our customers in a different way. They're delivering it on the click of a button on an internet browser. Now, that's suitable for some customers in some situations, it isn't suitable for others, but it's definitely here to stay and it's definitely going to change the way the marketplace works, and it has. So we've recognized inside our organization that we need to leverage some of the capabilities that exists inside the Fujitsu services organizations. Fujitsu is a large company. It also has very significant manage services capabilities, we deliver to huge customers all across Europe in terms of German government, British government, a lot of the big manufacturing industrials in Europe and a lot of the travel and insurance financial sectors. So leveraging some of that to take a more consultant-led approach to our marketplace, to our customers. So what we want to do with them is take them through the story of data transformation. And as you said, and I quite agree, the marketplace is becoming increasingly data-driven. You've only got to look at some of the well-known examples, and I'm not going to rehearse them again because everybody's heard them and knows who they are. But, every organization, however large or small, has to derive business advantage and discrimination from its data. Otherwise, they'll go the way of... I hate to say it, the High Street. You can see, in this recent pandemic, the COVID-19 stuff. I don't know what it's like in the US, but absolutely in the UK and in Europe, those retailers that have been able to provide a online presence have survived, and some of them have thrived. And those retailers that haven't been able to provide that presence aren't here anymore. And that's just, it's a current and rather violent example of this change of how to manage data and get the best value out of it. Now, in order to take that to our customers, the Fujitsu Product team needs to change some of its capabilities, it needs to introduce some of those consulting capabilities into its portfolio, which we do. It needs to work with some of our partners to deliver the capabilities either as an installation or a service and SUSE are one of our prime partners in that sense. Both in terms of delivering the computing platform standards, the SUSE Data Hub, I believe it's changed its name now. The SUSE Data Hub as I know it, is core to our offerings in this space. We have just launched in Germany, for example, a manufacturing optimization application which runs off the SUSE infrastructure and uses the SAP database and database management systems above that to deliver things like predictive maintenance and just-in-time parts delivery, and in-factory automated routine of little robots carrying the bits to the right place. And that's an example of something that was led by a consulting activity between Fujitsu and our customer, in this case, a large manufacturer. We recognized during that consultancy that some of the stuff we needed to do to deliver the solution, that would deliver the data-derived business benefit the customer needed, was not in our immediate scope. We got some of our larger partners, SUSE and SAP in this case, involved in it, and they outcome has been happy for everybody. There are some lessons in all that. The Fujitsu is still learning, if I'm frank, like how to price it. When you have consultant-led activities that are generating very great benefit for your client, it's not too great for the supplier to still be charging that just on consultant day-rate. That can lead you to not getting the value out of what you're providing to your client. So there's lessons there. There's lessons in how to interact between ourselves and some of our services partners and clients. And making sure that the optimum route to market is delivered. But that essentially, Stu, is the story. It's a change from a transactional approach to a consultant-led approach, and the generation of a large ecosystem of partners, like SUSE, like SAP, with the capabilities to build stuff with us and deliver business outcomes to clients, not a stack of tin. >> Excellent. So, Glenn, what about kind of emerging requirements, what you're hearing from your customers, you know, AI is an area that we heard quite a bit in the keynote from SUSE. Where do you see that fitting into the entire discussion? Obviously, the key, leverage of data, when you talk about AI. >> Absolutely, and to talk about that in two ways. The first way, the first issue with that is exactly the point you make, Stu, around data. So, AI, which is not artificial and not intelligent, it's just maths. It's statistical mathematics acting upon a large set of data. And if you have a large set of the right data, it can produce fantastic results for the client. But without that data, it is a relatively meaningless exercise. Once that data are assembled, we're beginning to see very significant results produced by the application of new networking, the machine learning. To technology-based, data-derived solutions for our clients, and there are many examples. I'll give you just one or two. We are working with a large financial institution in the city of London that wants to produce, basically, an artificial knowledge base that will perform the task of insurance underwriting. Don't ask me how that works, I'm not a financial guy. But apparently, insurance underwriting is a relatively mechanical task. You have a set of actuarial tables, you have a set of risks, you compare one with the other and produce a premium. We're working with them on that. There is a lot in the manufacturing space, and a surprising amount in healthcare. One of the most personally rewarding examples I've been involved with was the delivery of intelligent heart monitoring to clients with pacemakers. So, the pacemaker is made intelligent and it dumps to a Bluetooth-connected device in the patient's home, and that uploads to an AI-based knowledge system in the Cloud, and the Cloud says, "Sit down, you're going to have a heart attack." And the important element of that is that it says, "Sit down, you're going to have a heart attack" before you've had the heart attack, so you don't have one. A really fantastic example of human-centric interest. So, I think, as a separate subject, AI is largely of academic interest. But as a component of a data-driven solution for a customer, it's rapidly emerging as an important element in our armory, as indeed some other technologies. Like data annealing, and like data analytics, and to a slightly lesser extent at the moment, but I think it will come, blockchain. >> Excellent. So, Glenn, one of the things we always like talking about when we talk to a CDO is how are companies getting along with their data strategy? And I think back four or five years ago when we were first hearing about CDO as a role, it was, you know, the CDO, where do they fit compared to the CIO, what is the changing role of the CIO? So, like you were saying about some of these things, data often can be an afterthought or not necessarily connected, but just as we were saying, data needs to be a critical piece of how companies plan. You gave a great example of medical, obviously. You know, the data can really help transform lives in that environment. So, bring us inside what you're hearing from customers, how are they structuring, and are they really being, I guess, data-driven is one of the terms that I... >> That's a very good question. And the answer is yes to everything. So, one of the most difficult things to estimate, if you're going in to a customer with a client, especially if it's a client that you don't know very well, is exactly what their point of reference is going to be, what their comfort with some of these things is. As a result, we at Fujitsu invested a good deal of effort in going out to our client base and asking them the necessary questions to generate a thing we call the Data Maturity Model. Now the Data Maturity Model is not a new concept, it's a very solid and sound concept, it's been around for a long time. I think what we're trying to do is bring more rigor to that with a very large sample base of our customers. And the model is what you'd expect. There are five levels within it from at Level 1, what is data? To Level 5, where data is continually monitored, continually exploited, and continually developed as part of the business that the organization delivers. So there's a spectrum. In my experience, slightly controversially perhaps, the state of organizations on that Maturity Index varies with geography. And I think it's something to do with acceptance of risk, I think it's something to do with security concerns and liabilities. It's my observation that in the Anglophone world, in England and in the US certainly, there is a higher average awareness of the importance of data and the need to derive business benefit from it than there is, for example, in the Germanophone world, where there are more concerns around security and more regulations around security. They're quite constraining. And as a result, where organizations are a bit more traditional and a bit less aware of the value to be derived from the data. So, people, organizations hit everywhere on this scope, this plane of awareness of data and its potential. But it's definitely the case that the average is always going up. >> Yeah-. >> You only have to look at some of the public stocks, under the stuff in the public domain, to observe that that's happening all the time. >> Yeah, Glenn, I'm curious with the global pandemic happening, are you seeing any impact on that? I've heard some anecdotal data that you talk about some of the companies that are, you know, might not be interested in doing Cloud adoption because they're concerned about security, and all of a sudden realizing they need to take advantage of certain solutions. Or if you look at something like the tracking and tracing, obviously, people are rightfully concerned about personal information and having rights infringed upon. So, what will, in your opinion, are you seeing any early indications as to what this impact will be on how we think about data? >> I think there, again, there are two different dimensions. There's a Darwinian element in the attitudes towards commercial data. As we said right at the start of the conversation, in the current environment, you can see large retailers disappearing at a rate of knots because they haven't been data-aware and data-adopting. That lesson is not lost on other retailers. So, retailers are beginning to do things that in the past they wouldn't have done because of that sort of security concern, but also because of concerns about things like function and performance... and the sheer security that you have in owning your own stuff and therefore being certain of its ownership by you and your retention of the IPR involved. So there is definitely a slackening of that concern and a faster adoption of data exploitation technologies in the commercial sector. In the domestic sector, I think it's very mixed. And again, extremely geography different. In the UK, we have, if I could just talk about my own country for a second, we have this trial of a smartphone COVID-19 tracking app going on on the Isle of Wight. The British media is full to brimming with discussions of the implications of that upon individual liberty, of whether or not it's the nanny state gone mad, of whether or not we should all be not cooperating with it and catching the damn disease anyway because it's a step too far. In Germany, they just implemented it. And everybody went, "Right." (makes click sound) So there are all these different cultural adoptions of these things. But always and forever the trend is upwards. Similar debates around video surveillance technology. So you've got the pressure of security and protecting the public, against intrusion and violation of individual rights. And that debate has got to the stage now where there have been some pilots for threat detection based on video surveillance in the UK that have been stopped. Not so much in Germany. In the US, I don't know, but I guess, you're even more Libertarian than we Brits are, so it's probably more the other way. But with all of these discussions of differences, of culture and nation and area and geography, the trend is definitely upward. So, however the British people resolve that stress, you have to have a tracking app if you want to beat this disease. And that will happen in due course. >> Excellent. Well, Glenn, I'll give you the final takeaway, SUSECON '20, talk about the importance of the Fujitsu and SUSE partnership. >> I think it's a growing part of the base of an ecosystem that's required for all organizations like Fujitsu, like SUSE, that want to reach out and deliver solutions to our customers' business problems, which is after all, what we're here for and what we're all about. Because let's face it. In any sizable organization, the data landscape is unbelievably complicated. You have different formats of data, in RBDs, in unstructured file store, in whatever floats around employees' devices, on social media, for God's sake. Getting all of that out, understanding its relationship to infrastructure, understanding its relation through infrastructure, through application stacks, and service delivery, and then being able to transform that into new applications and new service paradigms that deliver the business benefits that our customers are looking for, is an incredibly complex act. And no one organization is going to be able to do it on its own. So I see the future as one of these growing ecosystems of people that work together some of the time, compete some of the time. Are in what we might call a frenemy relationship. Because we all have to work together to deliver what the customers need. Fujitsu is working with SUSE and our other partners at the forefront of that trying to build economic and commercial and technical partnerships. And I'm sure that will continue through SUSECON '20 and into the future. >> All right, well, Glenn Fitzgerald, thank you so much for joining us. Really appreciate the updates. >> I've enjoyed it. Thank you for having me. >> All right, much more coverage from SUSECON '20 Digital. I'm Stu Miniman and thank you for watching the CUBE. (upbeat music)

>> Live from San Diego, California, it's theCUBE, covering Cisco Live US 2019. Brought to you by Cisco and its ecosystem partners. >> Welcome back to theCUBE. Our coverage of Cisco Live day three is in full effect. I am Lisa Martin with Dave Vellante and we have a couple of guests joining us. We've got Mike Bundy, head of Cisco Strategic Alliances from, guess where? The jacket should give it away, Pure Storage. And Tom Koppelman, the VP of Architecture Sales America for Cisco, hi guys! >> Hi. >> Hi. >> How ya doing? >> Thanks for bringing more brightness to our set. >> Yeah I forgot my sunglasses. >> I know, we're in the buzzy, bright DevNet Zone. We've been here all week. Great event, massive event, my goodness. 28,000 folks or so, Mike let's start with you. Give us a status of the Pure-Cisco relationship, the evolution of that, where you guys are now. What is exciting? >> Sure, so the relationship, it's unbelievable in terms of the amount of synergies and energy we have together. In fact, Tom at Cisco was really involved in the early genesis of this relationship, prior to me joining the company. And, in the last couple years, we've probably doubled in terms of our go-to-market and sell to customers together. So, tremendous growth. Partnership brings a value to us because of the strong heritage that we have from a DevNet tie-in, in terms of all the automation that we have on the platform, so. It's just a tremendous, tremendous, great partnership. >> And Tom, Cisco has a massive partner ecosystem, a lot of choice. What is it about Pure Storage that is providing advantages to Cisco? Where it's helping customers really kind of bridge this gap between hyper-converged, multi-cloud hybrid, all that jazz? >> Right, so Pure was a first mover in terms of flash storage, right. We saw demand from our customers wanting that technology to improve their data center environments. And when we partnered up early, we were able to kind of capture that momentum, right. And, when I think about our go-to-market with Pure, which is really where I kind of focus, there's very little friction in that relationship, right. There's not competitive overlap. There's not things like that. It's technology that the customers want, that they ask for, and a good field go-to-market in leadership on both sides that are willing to invest and get engaged and move the relationship forward. >> So what else are you guys doing besides just the go-to-market partnership because I got a hold of this timeline of Cisco Validated Designs that Pure and Cisco have put out over the last five years, four years. >> Right. >> And there's like 13 milestones on there. So that's roughly three a year. Of course, it started with Pure's IPO. So that's when Cisco said, all right, these guys are real. Start working with them. And in the early days, of course, you started with FlashStack. That was the flagship product. And then VDI, everybody does VDI, analysts are like, yeah, yeah, everybody does VDI. But then it started really accelerating the cadence. So it's more than just go-to-market. What's beneath that go-to-market? >> Yeah, good question. >> You want to? >> You hit the highlights of the CVD's and whatnot. >> I would say that Pure, this is our number one partnership that we have from an alliance perspective. The investment is far exceeding other partnerships we have. So, the amount of product integration that we're doing is tremendous, as you see there. We've focused on ACI and multi data centers the last couple years. We've focusing on AI and machine learning, most recently. And beyond that, we just signed an agreement and have released resell of Cisco SAN switches in the marketplace. It's the resell agreement we've ever done as a company and it just further shows the commitment in resources that we're willing to put into making sure the partnership is successful and continues to grow and evolve. >> And on top of that the investment in Cisco Intersight, in integrating with Cisco Intersight, the management platform, which is very important to us, it just shows commitment of the partnership. >> Let's talk more about that. So, how does that work? What problems is that solving for customers? >> Well, Cisco Intersight is our cloud based management offering for compute and Pure has integrated their storage platform as part of that solution. So allowing customers, whether it's a converged solution, just raw compute, a hyper-converged solution, but allowing them to manage those pools and deliver that via a cloud solution. >> So Pure plugs into the Cisco API. Now you're part of that stack, essentially. So it's transparent to the customer. And, Cisco's management plane takes care of all that. >> That's exactly right, correct, yes. >> Its' a big deal for us because it was the first integration with Intersight from any storage partner that Cisco has, right. So first to market. We want to embrace hyper-convergence, which is a big important priority for Cisco, and also bridging that gap. So as we compete against single vendor stacks, we have the right solution that customers are looking for. And ultimately, that's why it's so important for us. >> Yeah, Pure is big on firsts. First to flash, you just mentioned another first, you were the first with NVMe, before that you were with the evergreen. I mean, you like being first. >> First orange sport coat. >> That's definitely first there. (laughing) >> Let's talk about customer value though. Obviously, that's what it's all about. As we look at, not just the tremendous amount of choice that customer have when it comes to technology partners, but also the amount of data that's being generated, that's growing astronomically. Yet, organizations are getting so little value out of that because they can't extract the insights. What are you guys doing together leveraging the superpowers of AI and machine learning to help customers in any industry search a really, not just monetize that data, but really accelerate their businesses. Tom you're smiling so let's start with you. >> Yeah, so we came out with an AI server, right, our ML 480, and we've integrated that. Pure has invested, we've both invested and done an integration between FlashBlade, and I'll let Mike talk a little about FlashBlade and the value proposition of FlashBlade, but integrated that with our AI server. And our AI server is an Nvidia powered server, so it essentially gives you scale of processing and capabilities to allow you capitalize on all that data so the customers can get the information they need out of that. If you want to take a second on FlashBlade. >> And you know, AI is the buzz. It's the hot two letter acronym in the industry these days. $13 billion infrastructure opportunity, et cetera, et cetera. So, what Pure is really focused on is, data is the new oil of commodities for customers and clients. What we've built is a platform called FlashBlade, an architecture called the Data Hub, that allows you to not have to copy data and move it around and create silos in data warehouses. So, you can much easier execute a data strategy with the Data Hub architecture, using FlashBlade. When you look at machine learning in terms of how you build a data pipeline so that you can then get to quicker results from a business application standpoint with AI. That's what we've built together with Cisco. We're uber, uber, super excited a number of customers already in the last couple months. >> So I'm going to push a little on that, AI server, AI storage, people don't associate storage and server guys with AI. But if I hear you correctly, there's a $13 billion opportunity for workloads. To manage workloads running on your servers and your storage. >> Correct. >> And so you're optimizing them for AI workloads. >> Absolutely, exactly right. >> So you're not necessarily inventing AI. You're providing infrastructure so that people can leverage AI, is that right? >> Yes. >> Yeah, and the same way that we've built APIs together to work with Intersight, we do that in a way that allows our customers to leverage Cafe, other applications that can help build that data pipeline. We build the platform from the infrastructure level, it makes the management easy and we partner with all of the applications at the top end, but also the middleware and that software prepackage layer that connects via APIs to us. So, it's easy, it's agile, it's manageable, it's a cloud-like experience for the customers, right. >> Easy, agile, all awesome but security. Absolutely critical today. What are you guys doing, Tom I'll start with you, how are you guys working together infuse and integrate security into the technology so that from a customer's perspective, those risks dial down. >> So, Cisco is integrating security across all of our product portfolio, right. And, that includes our data center portfolio, all the way through our campus, our WAN, all those portfolios. We continue to look for opportunities to integrate, whether it's dual-factor authentication or things like secure data center where the highly scalable, multi-instance firewall in front of a data center, things like that. So we're definitely looking for areas and angles and opportunities for us to, not only integrate it from a product standpoint, but also ensure that we are talking that story with our customers so that they know they can leverage Cisco for the full architecture from a security standpoint. >> And the same thing on the storage of the data from an encryption perspective, and as the data gets moved or is mobile, that level of security and policy follows it wherever the data is moved. >> So, what should we expect, what's next in the time? What's 14 going to look like? You don't have top give us specifics but are we going to see blockchain CVDs? What should observers think about the partnership going forward? What could we look forward to? >> Yeah, I mean, the adoption of Container capability is tremendous in our customers environment. Cisco has a cloud Container platform available today. We're integrating that into FlashStack in the very near future. Embracing the cloud. Disaster recovery and data protection it's very hot for customers. Improving that experience so that you have faster restoration times, you're able to look at multi-tier strategy that's very easy to manage from a storage perspective, leveraging S3 with Amazon, Azure, et cetera. So, that's a couple things that are on the short term building block together. >> Yeah, I was going to comment on certainly multi cloud and Containers, those would be two of the big ones that I'd hit on, right. And, in the event of multi cloud leveraging, converged and hyper-converged together to better solve a customer's problems. >> So I was going to ask you. So hyper-converged now becomes a bridge to the cloud if, in fact, that's where customers want to go. >> Yes, it can be. >> Absolutely. >> Yeah, it can be, yes. >> Absolutely. >> Well guys thank you so much for joining Dave and me on the program, sharing with us the momentum that the Pure-Cisco relationship has and what excites you for the future. We appreciate your time. >> Thank you. >> Thank guys. >> For Dave Vellante, I'm Lisa Martin, you're watching theCUBE live from Cisco Live San Diego. Thanks for watching. (electronic music)

>> live from Boston, Massachusetts. It's the you covering your red have some twenty nineteen rots. You buy bread hat. >> Good to have you back here on the Cube as we continue our coverage. Live at the Red Had Summit twenty nineteen, Day three of our coverage with you since Tuesday. And now it's just fresh off the keynote stage, joining stew, Minutemen and myself. Chris. Right? VP and chief technology officer at Red Hat. Good job there, Chris. Thanks for being with us this morning. Yeah. >> Thank you. Glad to be here. >> Great. Right? Among your central things, you talked about this, this new cycle of innovation and those components and how they're integrating to create all these great opportunities. So if you would just share for those with those at home who didn't have an opportunity to see the keynote this morning, it's what you were talking about. I don't think they play together. And where that lies with red hat. Yeah, you bet. >> So, I think an important first kind of concept is a lot of what we're doing. Is lane a foundation or a platform? Mean red hats focuses in the platform space. So we think of it as building this platform upon which you build an innovate. And so what we're seeing is a critical part of the future is data. So we're calling it a Kino data centric. It's the data centric economy. Along with that is machine learning. So all the intelligence that comes, what do you dividing? The insights you're grabbing from that data. It introduces some interesting challenges data and privacy and what we do with that data, I mean, we're all personally aware of this. You see the Cambridge Analytica stuff, and you know, we all have concerns about our own data when you combine all of us together techniques for how we can create insights from data without compromising privacy. We're really pushing the envelope into full distributed systems, EJ deployments, data coming from everywhere and the insights that go along with that. So it's really exciting time built on a consistent platform like lycopene shift. >> So, Chris, I always loved getting to dig in with you because that big trend of distributed systems is something that you know we've been working on for quite a long time. But, you know, we fully agree. You said data at the center of everything and that roll of even more distributed system. You know, the multi cloud world. You know, customers have their stuff everywhere and getting their arms around that, managing it, being about leverage and take advantage. That data is super challenging. So you know where where, you know, help us understand some of the areas that red hat in the communities are looking to solve those problems, you know, where are we and what's going well and what's still left to work on. >> Well, there's a couple of different aspect. So number one we're building these big, complex systems. Distributed systems are challenging distribute systems, engineers, air really solving harder problems. And we have to make that accessible to everybody operations teams. And it's one of the things that I think the cloud taught us when you sort of outsource your operations is somebody else. You get this encapsulated operational excellence. We need to bring that to wherever your work clothes are running. And so we talked a lot about a I ops, how you harness the value of data that's coming out of this complex infrastructure, feed it through models and gain insights, and then predict and really Ultimately, we're looking at autonomic computing how we can create autonomous clouds, things that really are operating themselves as much as possible with minimal human intervention. So we get massive scale. I think that's one of the key pieces. The other one really talking about a different audience. The developers. So developers air trying to incorporate similar types of intelligence into their applications were making recommendations. You're tryingto personalize applications for end users. They need easy access to that data. They need easy access to train models. So how do we do that? How do we make that challenging data scientist centric workflow accessible to developers? >> Yeah, just some of the challenges out there. I think about, you know, ten, fifteen years ago, you talk to people, it was like, Well, I had my central source of truth and it was a database. And you talk to most companies now and it's like, Well, I've got a least a dozen different database and you know, my all my different flavors of them and whether in the cloud or whether I have them in my environment, you know, things like a ops trying to help people get involved with them. You talked a little bit in your keynote about some of the partners that you're working on. So how do you, you know, bring these together and simplify them when they're getting, you know, even more and more fragmented? >> Well, it's part of the >> challenge of innovation. I mean, I think there's a there's a natural cycle. Creativity spawns new ideas. New ideas are encapsulated in projects, so there's a wave of expansion in any kind of new technology time frame. And then there's ultimately, you see some contraction as we get really clear winners and the best ideas and in the container orchestration space communities is a great example of that. We had a lot of proliferation of different ways of doing it. Today we're consolidating as an industry around Cooper Netease. So what we're doing is building a platform, building a rich ecosystem around that platform and bringing our partners in who have specific solutions. They look at whether it's the top side of the house, talking to the operations teams or whether it's giving developers easy access to data and training models through some partners that we had today, like perceptive labs and each to a A I this partnership. Bringing it to a common platform, I think, is a critical part of helping the industry move forward and ultimately will see where these best of breed tools come into play. >> Here, uh, you know, maybe help a little bit with with in terms of practical application, you got, you know, open source where you've got this community development going on and then people customized based on their individual needs all well, great, right? How does the inverse happen? Where somebody who does some custom ization and comes up with a revelation of some kind and that applies back to the general community. And we can think of a time where maybe something I'm thinking like Boston children, their imaging, that hospital we saw actually related to another industry somehow and gave them an ah ha moment that maybe they weren't expecting an open source. Roy was the driver that >> Yeah, I think what we showed today were some examples of what If you distill it down to the core, there's some common patterns. There's data, they're streaming data. There's the data processing, and there's a connection of that processed data or train model to an application. So we've been building an open source project called Open Data Hub, where we can bring people together to collaborate on what are the tools that we need to be in this stack of this kind of framework or stack And and then, as we do, that we're talking to banks. They're looking at any money laundering and fraud detection. We're talking to these hospitals that were looking at completely different use cases like HC Healthcare, which is taking data to reduce the amount of time nurses need to spend, gathering information from patients and clearly identify Septus sepsis concerns totally different applications, similar framework. And so getting that industry level collaboration, I think is the key, and that having common platforms and common tools and a place to rally around these bigger problems is exactly how we do that through open source. >> So Lynn exits and an interesting place in the stack is you talked about the one commonality and everything like that. But we're actually at a time where the proliferation of what's happen to get the hardware level is something that you know of an infrastructure and harbor guy by background, and it was like, Oh, I thought We're going to homogenize everything, standardize everything, and it's like, Oh, you're showing off Colin video stuff. And when we're doing all these pieces there, there's all these. You know, new things, Every been things you know you work from the mainframe through the latest armed processors. Give us a little insight as to how your team's geeking out, making sure that they provide that commonality yet can take advantage of some of the cool, awesome stuff that's out there that's enabling that next wave of innovation. >> Yeah, so I share that infrastructure geek nous with you. So I'm so stoked the word that we're in this cycle of harbor innovation, I'll say something that maybe you sounds controversial if we go back in time just five years or a little, a little more. The focus was around cloud computing and bringing massive number of APS to the cloud, and the cloud had kind of a T shirt size, small, medium, large view of the world of computer. It created this notion that Khun computers homogenous. It's a lie. If you go today to a cloud provider and count the number of different machine types they have or instance types it's It's not just three, it's a big number. And those air all specialized. It's for Io throughput. It's for storage acceleration. It's big memory, you know. It's all these different use cases that are required for the full set of applications. Maybe you get the eighty percent in a common core, but there's a whole bunch of specific use cases that require performance optimization that are unique. And what we're seeing, I think, is Moore's law. The laws of physics are kind of colliding a little bit, and the way to get increased acceleration is through specialized hardware. So we see things like TP use from Google. We see until doing deal boost. We've got GPS and even F p G A s and the operating system is there TIO give a consistent application run time while enabling all those hardware components and bringing it all together so the applications can leverage the performance acceleration without having to be tied directly to it. >> Yeah, you actually think you wrote about that right now, one of your a block post that came about how hardware plays this hugely important role. You also talked about innovation and change happening incrementally and And that's not how we kind of think about like big Banks, right? Yeah. Wow, this is But you pointed out in the open source, it really is step by step by step. Which way? Think about disruption is being very dramatic. And there's nothing sexy about step by step. Yeah, that's how we get to Yeah, disruption. I kind of >> hate this innovation, disruption and their buzz words. On the one hand, that's what captures attention. It's not necessarily clear what they mean. I like the idea that, you know, in open source, we do every day, incremental improvements. And it's the culmination of all these improvements over time that unlock new opportunities. And people ask me all the time, where is the future? What do we do and what's going on? You know, we're kind of doing the same thing we've been doing for a long time. You think about micro services as a way to encapsulate functionality, share and reuse with other developers. Well, object oriented programming decades ago was really tryingto tryingto established that same capability for developers. So, you know, the technologies change we're building on our history were always incrementally improving. You bring it all together. And yes, occasionally you can apply that in a business case that totally disrupts an industry and changes the game. But I really wanted encourage people to think about what are the incremental changes you can make to create something fundamentally new. >> All right, I need to poke it that a little bit, Chris, because there's one thing you know, I looked back in my career and look back a decade or two decades. We used to talk about things like intelligence and automation. Those have been around my entire career. Yeah, you look it today, though, you talk about intelligence and talk about automation, it's not what we were doing, you know, just the amount of degrees, what we're having there. It is like if we'd looked at it before, it was like, Oh, my gosh, science fiction's here so, you know, way sometimes lose when we're doing step by step, that something's there making step function, improvements. And now the massive compact, massive changes. So love your opinions there. >> Yeah, well, I think it's a combination, so I talk about the perpetual pursuit of excellence. So you pick up, pick a field, you know, we're talking about management. We got data and how you apply that data. We've been working towards autonomic computing for decades. Concepts and research are old, the details and the technologies and the tools that we have today are quite different. But I'm not. You know, I'm not sure that that's always a major step function. I think part of that is this incremental change. And you look at the number for the amount of kind of processing power and in the GPU today No, this is a problem that that industry has been working on for quite a long time. At some point, we realize, Hey, the vector processing capabilities in the GPU really, really suit the machine learning matrix multiplication world real world news case. So that was a fundamental shift which unlocked a whole bunch of opportunity in terms of how we harness data and turn it into knowledge. >> Yes. So are there any areas that you look at? Now that we've been working at that, you feel we're kind of getting to those tipping points or the thie waves of technology or coming together to really enable Cem Cem massive change? >> I do think our ability to move data around, like generate data. For one thing, move data around efficiently, have access to it from a processing capability. And turning that into ah, >> model >> has so fundamentally changed in the past couple of decades that we are tapping into the next generation of what's possible and things like having this. This holy grail of a self healing, self optimizing, self driving cluster is not as science fiction as it felt twenty years ago. It's >> kind of exciting. You talk about you've been there in the past, the president, but there is very much a place in the future, right? And how would that future looks like just from from again? That aye aye perspective. It's a little scary, sometimes through to some people. So how are you going about, I guess, working with your partners to bring them along and accept certain notions that maybe five six years ago I've been a little tough to swallow or Teo feel comfortable with? >> Yeah, well, there's a couple of different dimensions there. One is, uh, finding tasks that air computers are great at that augment tasks that humans were great at and the example we had today. I love the example, which was, Let's have computers, crunch numbers and nurses do what they do best, which is provide care and empathy for the patients. So it's not taking the nurse's job away. In fact, is taking the part that is drudgery ITT's computation >> and you forget what was the >> call it machine enhanced human intelligence right on a couple of different ways of looking at that, with the idea that we're not necessarily trying to eliminate humans out of the loop. We're trying to get humans to do what they do best and take away the drudgery that computers air awesome at repetitive tasks. Big number crunching. I think that's one piece. The other pieces really, from that developer point of view, how do you make it easily accessible? And then the one step that needs to come after that is understanding the black box. What happens inside the machine learning model? How is it creating the insights that it's creating and there's definitely work to be done there? There's work that's already underway. Tto help understand? Uh, the that's really what's behind the inside so that we don't just trust, which can create some problems when we're introducing data that itself might already be biased. Then we assumed because we gave data to a computer which is seemingly unbiased, it's going to give us an unbiased result, right? Garbage in garbage out. >> So we got really thoughtful >> about what the models are and what the data is that we're feeding >> It makes perfect sense it. Thanks for the time. Good job on the keynote stage again this morning. I know you've got a busy afternoon scheduled as well, so yeah, I will let you. We'Ll cut you loose. But thank you again. Always good to see you. >> Yeah. I always enjoy being here >> right at that's right. Joining us from red hat back with Wharton Red Hat Summit forty nineteen. You're watching live here on the Cube?

(bouncy music) >> Live, from Barcelona, Spain, it's theCUBE, covering Cisco Live Europe. Brought to you by Cisco and its ecosystem partners. >> Welcome back everyone. Live here in Barcelona it's theCUBE's exclusive coverage of Cisco Live 2019. I'm John Furrier. Dave Vellante, my co-host for the week, and Stu Miniman, who's also here doing interviews. Our next two guests is Mike Bundy, Senior Director of Global Cisco Alliance with Pure Storage, and Z, who's in charge of product strategy for Cisco. Welcome to theCUBE. Thanks for joining us. >> Thank you for having us here. >> You're welcome. >> Thank you. >> We're in the DevNet zone. It's packed with people learning real use cases, rolling up their sleeves. Talk about the Cisco Pure relationship. How do you guys fit into all this? What's the alliance? >> You want to start? >> Sure. So, we have a partnership with Cisco, primarily around a solution called Flashstack in the converged infrastructure space. And most recently, we've evolved a new use-case and application together for artificial intelligence that Z's business unit have just released a new platform that works with Cisco and NVIDEA to accomplish customer application needs mainly in machine learning but all aspects of artificial intelligence, so. >> So AI is obviously a hot trend in machine learning but today at Cisco, the big story was not about the data center as much anymore as it's the data at the center of the value proposition which spans the on-premises, IoT edge, and multiple clouds so data now is everywhere. You've got to store it. It's going to be stored in the cloud, it's on-premise. So data at the center means a lot of things. You can program with it. It's got to be addressable. It has to be smart and aware and take advantage of the networking. So with all of that as the background, backdrop, what is the AI approach? How should people think about AI in context to storing data, using data? Not just moving packets from point A to point B, but you're storing it, you're pulling it out, you're integrating it into applications. A lot of moving parts there. What's the-- >> Yeah, you got a really good point here. When people think about machine learning, traditionally they just think about training. But we look at it as more than just training. It's the whole data pack line that starts with collecting the data, store the data, analyze the data, train the data, and then deploy it. And then put the data back. So it's really a very, it's a cycle there. It's where you need to consider how you actually collect the data from edge, how you store them, in the speed that you can, and give the data to the training side. So I believe when we work with Pure, we try to create this as a whole data pack line and think about the entire data movement and the storage need that we look at here. >> So we're in the DevNet zone and I'm looking at the machine learning with Python, ML Library, (mumbles) Flow, Appache Spark, a lot of this data science type stuff. >> Yup. >> But increasingly, AI is a workload that's going mainstream. But what are the trends that you guys are seeing in terms of traditional IT's involvement? Is it still sort of AI off on an island? What are you seeing there? >> So I'll take a guess, a stab at it. So really, every major company industry that we work with have AI initiatives. It's the core of the future for their business. What we're trying to do is partner with IT to get ahead of the large infrastructure demands that will come from those smaller, innovative projects that are in pilot mode so that they are a partner to the business and the data scientists rather than a laggard in the business, the way that sometimes the reputation that IT gets. We want to be the infrastructure, solid, like a cloud-like experience for the data scientists so they can worry more about the applications, the data, what it means to the business, and less about the infrastructure. >> Okay. And so you guys are trying to simplify that infrastructure, whether it's converged infrastructure, and other unifying approaches. Are you seeing the shift of that heavy lifting, of people now shifting resources to new workloads like AI? Maybe you could discuss what the trends are there? >> Yeah, absolutely. So I think AI started with more like a data science experiment. You see a couple of data scientists experimenting. Now it's really getting into mainstream. More and more people are into that. And as, I apologize. >> Mike. >> Mike. >> Mike, can we restart that question? (all laughing) My deep apology. I need a GPU or something in my brain. I need to store that data better. >> You're on Fortnite. Go ahead. >> Yes, so as Mike has said earlier on, it's not just the data scientists. It's actually an IT challenge as well and I think with Cisco, what we're trying to do with Pure here is, you know that Cisco thing, we're saying, "We're a bridge." We want to bridge the gap between the data scientists and the IT and make it not just AI as an experiment but AI at scale, at production level, and be ready to actually create real impact with the technology infrastructure that we can enable. >> Mike, talk about Pure's position. You guys have announced Pure in the cloud? >> Yes. >> You're seeing that software focus. Software is the key here. >> Absolutely. >> You're getting into a software model. AI and machine learning, all this we're talking about is software. Data is now available to be addressed and managed in that software life cycle. How is the role of the software for you guys with converged infrastructure at the center of all the Cisco announcements. You were out on stage today with converged infrastructure to the edge. >> Yes, so, if you look at the platform that we built, it's referenced back, being called the Data Hub. The Data Hub has a very tight synergy with all the applications you're referring to: Spark, Tensor Flow, et cetera, et cetera, Cafe. So, we look at it as the next generation analytics and the platform has a super layer on top of all those applications because that's going to really make the integration possible for the data scientists so they can go quicker and faster. What we're trying to do underneath that is use the Data Hub that no matter what the size, whether it's small data, large data, transaction based or more bulk data warehouse type applications, the Data Hub and the FlashBlade solution underneath handle all of that very, very different and probably more optimized and easier than traditional legacy infrastructures. Even traditional, even Flash, from some of our competitors, because we built this purpose-built application for that. Not trying to go backwards in terms of technology. >> So I want to put both you guys on the spot for a question. We hear infrastructure as code going on many, many years since theCUBE started nine years ago. Infrastructure as code, now it's here. The network is programmable, the infrastructure is programmable, storage is programmable. When a customer or someone asks you, how is infrastructure, networks, and storage programmable and what do I do? I used to provision storage, I've got servers. I'm going to the cloud. What do I do? How do I become AI enabled so that I could program the infrastructure? How do you guys answer that question? >> So a lot of that comes to the infrastructure management layer. How do you actually, using policy and using the right infrastructure management to make the right configuration you want. And I think one thing from programmability is also flexibility. Instead of having just a fixed configuration, what we're doing with Pure here is really having that flexibility where you can put Pure storage, different kind of storage with different kind of compute that we have. No matter we're talking about two hour use, four hour use, that kind of compute power is different and can max with different storage, depending on what the customer use case is. So that flexibility driven to the programmability that is managed by the infrastructure management layer. And we're extending that. So Pure and Cisco's infrastructure management actually tying together. It's really single pane of glass within the side that we can actually manage both Pure and Cisco. That's the programmability that we're talking about. >> Your customers get Pure storage, end-to-end manageability? >> With the Cisco compute, it's a single pane of glass. >> Okay. >> So where do I buy? I want to get started. What do you got for me? (laughing) >> It's pretty simple. It's three basic components. Cisco Compute and a platform for machine learning that's powered by NVIDEA GPUs; Cisco FlashBlade, which is the Data Hub and storage component; and then network connectivity from the number one network provider in the world, from Cisco. It's very simple. >> And it's a SKU, it's a solution? >> Yup, it's very simple. It's data-driven. It's not tied to a specific SKU. It's more flexible than that so you have better optimization of the network. You don't buy a 1000 series X and then only use 50% of it. It's very customizable. >> Okay, do I can customize it for my, whatever, data science team or my IT workloads? >> Yes, and provision it for multi-purpose, same way a service provider would if you're a large IT organization. >> Trend around breaking silos has been discussed heavily. Can you talk about multiple clouds, on-premise in cloud and edge all coming together? How should companies think about their data architecture because silos are good for certain things, but to make multi-cloud work and all this end-to-end and intent-based networking and all the power of AI's around the corner, you got to have the data out there and it's got to be horizontally scalable, if you will. How do you break down those silos? What's your advice, is there a use case for an architecture? >> I think it's a classic example of how IT has evolved to not think just silos and be multi-cloud. So what we advocate is to have a data platform that transpires the entire community, whether it's development, test, engineering, production applications, and that runs holistically across the entire organization. That would include on-prem, it would include integration with the cloud because most companies now require that. So you can have different levels of high availability or lower cost if your data needs to be archived. So it's really building and thinking about the data as a platform across the company and not just silos for various applications. >> So replication never goes away. >> Never goes away. (laughing) >> It's going to be around for a long, long time. >> Dev Test never goes away either. >> Your thoughts on this? >> Yeah, so adding on top of that, we believe where your infrastructure should go is where the data goes. You want to follow where the data is and that's exactly why we want to partner with Pure here because we see a lot of the data are sitting today in the very important infrastructure which is built by Pure Storage and we want to make sure that we're not just building a silo box sitting there where you have to pour the data in there all the time, but actually connect to our server with Pure Storage in the most manageable way. And for IT, it's the same kind of manual layer. You're not thinking about, oh, I have to manage all this silo box, or the shadow IT that some data scientists would have under their desk. That's the least thing you want. >> And the other thing that came up in the key note today, which we've been saying on theCUBE, and all the experts reaffirm, is that moving data costs money. You've got latency costs and also just cost to move traffic around. So moving compute to the edge or moving compute to the data has been a big, hot trend. How has the compute equation changed? Because I've got storage. I'm not just moving packets around. I'm storing it, I'm moving it around. How does that change the compute? Does that put more emphasis on the compute? >> It's definitely putting a lot more emphasis on compute. I think it's where you want compute to happen. You can pull all the data and want it to happen in the center place. That's fine if that's the way you want to manage it. If you have already simplified the data, you want to put it in that's the way. If you want to do it at the edge, near where the data source is, you can also do the cleaning there. So we want to make sure that, no matter how you want to manage it, we have the portfolio that can actually help you to manage that. >> And it's alternative processors. You mentioned NVIDEA. >> Exactly. >> You guys are the first to do a deal with them. >> And other ways, too. You've got to take advantage of technology like Kubernetes, as an example. So you can move the containers where they need to be and have policy managers for the compute requirements and also storage, so that you don't have contention or data integrity issues. So embracing those technologies in a multi-cloud world is very, very essential. >> Mike, I want to ask you a question around customer trends. What are you seeing as a pattern from a customer standpoint, as they prepare for AI, and start re-factoring some of their IT and/or resources, is there a certain use-case that they set up with Pure in terms of how they set up their storage? Is it different by customer? Is there a common trend that you see? >> Yeah, there are some commonalities. Take financial services, quant-trading as an example. We have a number of customers that leverage our platform for that because it's very time-sensitive, high-availability data. So really, I think that the trend overall of that would be: step back, take a look at your data, and focus on, how can I correlate and organize that? And really get it ready so that whatever platform you use from a storage standpoint, you're thinking about all aspects of data and get it in a format, in a form, where you can manage and catalog, because that's kind of essential to the entire thing. >> It really highlights the key things that we've been saying in storage for a long time. High-availability, integrity of the data, and now you've got application developers programming with data. With APIs, you're slinging APIs around like it's-- >> The way it should be. >> That's the way it should be. This is like Nirvana finally got here. How far along are we in the progress? How far? Are we early? Are we moving the needle? Where are the customers? >> You mean in terms of a partnership? >> Partnership, customer AI, in general. You guys, you've got storage, you've got networking and compute all working together. It has to be flexible, elastic, like the cloud. >> My feeling, Mike can correct me, or you can disagree with me. (laughing) I think right now, if we look at what all the analysts are saying, and what we're saying, I think most of the companies, more than 50% of companies either have deployed AI MO or are considering a plan of deploying that. But having said that, we do see that we're still at a relatively early stage because the challenges of making AI deployment at scale, where data scientists and IT are really working together. You need that level of security and that level of skill of infrastructure and software and evolving DevNet. So my feeling is we're still at a relatively early stage. >> Yeah, I think we are in the early adopter phase. We've had customers for the last two years that have really been driving this. We work with about seven of the automated car-driving companies. But if you look at the data from Morgan Stanley and other analysts, there's about a $13 billion infrastructure that's required for AI over the next three years, from 2019-2021, so that is probably 6X, 7X what it is today, so we haven't quite hit that bell curve yet. >> So people are doing their homework right now, setting up their architecture? >> It's the leaders. It's leaders in the industry, not the mainstream. >> Got it. >> And everybody else is going to close that gap, and that's where you guys come in, is helping them do that. >> That's scale. (talking over one another) >> That's what we built this platform with Cisco on, is really, the Flashstack for AI is around scale, for tens and twenties of petabytes of data that will be required for these applications. >> And it's a targeted solution for AI with all the integration pieces with Cisco built in? >> Yes. >> Great, awesome. We'll keep track of it. It's exciting. >> Awesome. >> It's cliche to say future-proof but in this case, it literally is preparing for the future. The bridge to the future, as the new saying at Cisco goes. >> Yes, absolutely. >> This is theCube coverage live in Barcelona. We'll be back with more live coverage after this short break. Thanks for watching. I'm John Furrier with Dave Vallente. Stay with us. (upbeat electronic music)

>> Live, from Barcelona, Spain, it's theCUBE covering Cisco Live! Europe. Brought to you by Cisco and its Ecosystem partners. >> Hello everyone, welcome back to our live coverage here in Barcelona for Cisco Live! 2019's theCUBE. I'm John Furrier, your host with Stu Miniman. Our next guest is Dave Cope who's the senior director, market development, Cisco Cloud platform. Great to see you again. >> Great to see you. >> Thanks for coming on, I really appreciate it. One of your products is the big focus of the keynote, introducing the CloudCenter Suite. One of the core announcements, this was pretty critical for Cisco, obviously multicloud, we've seen the Kubernetes relationship with Amazon. You've got Azure, Google Cloud. Cisco's tied up with the clouds, which is good, >> Right. >> They have the on center core data center, but now dealing with cloud has been really the hot topic, so thanks for coming on. >> Absolutely. >> So I'm looking at your perspective first on cloud in general within Cisco and within your customer base and the industry. What is multicloud? Why is it important? Why is it a wave worth betting on? >> You know, it's a great question, I think. Actually, it's a really fun time right now because if you think about it, it's almost exactly 10 years ago where AWS's EC2 first came out of beta, and so, everybody's talking about the cloud, but it really hasn't been around that long. And even in that sort of ten-year period, it's gone through sort of skepticism to, I think, let me try some non-critical apps, to debate about public or private, or which is the best public, to, today, 94% of the businesses say they either are, or are planning to use, multicloud environments. And, so if you think about it, that's both provided a lot of advantages but also created a degree of complexity in how do I apply traditional disciplines like network management and security across environments that I control and don't control? So it's a whole new world. >> And the DevNet Zone, which for theCUBE is based out of again this year, has a hot growth vibe to it. People are joining the community in record numbers. The demos here aren't just like canned demos, they're actually real code. >> Exactly. >> So you're seeing a developer framework around the network, and the cloud, the cloud is not a one-vendor product. It's an architecture, it's a concept. And so cloud operations is in the cloud, it's also being done on premise and the edge, so everything's cloud now, if you think about it. >> Well I think what we saw is, obviously, huge initial growth of cloud and a lot of applications moving to the cloud, but it's always been my hypothesis and I think it's actually coming true that we're now, and some of the newer technologies support this, we're seeing this natural distribution of workloads across all these environments, whether it's the public cloud, or the edge, or the data center. And it's now technologies that allow you to put the workloads in the right place based on business priorities, not IT priorities. And now I believe you're starting to see this sort of natural stasis and the whole pie grow again. >> So I got to ask you the question from a customer perspective. So I'm a customer, I say, Dave, love it, you had me at cloud, I'm there. I got all this stuff to deal with. I've been working my business, running my business. Love it, what's in it for me though? What's the impact? What do I need to do differently? Is it, do I have to change anything? How does a customer engage with Cisco and the cloud and the multitude of technologies that are available to them? It can seem complex. >> Yeah, I think people had hoped that the cloud would make everything easy, but what they're finding is that the cloud is not the cloud. It's private clouds, public clouds, virtual private clouds. And if you think about it, good free market principles, all these cloud providers are competing with each other so they're all becoming very different. Cisco finds, I think, itself in a very unique position because of its heritage around network management and security, which is connecting everything together. We don't have our own cloud, so what we focus on is providing a very broad and deep solution to be able to manage workloads across all of these environments. So you truly can place the workload in the right place. >> I wonder if you could help us unpack a little bit what you just said, which is, the clouds are actually becoming more different, not more similar, you know. With the Kubernetes show >> That's right. >> we talked to Cisco, we talked to the whole ecosystem. The founders of Kubernetes said they weren't creating a magic layer, that's not what Kubernetes is. There's some base functionality, but everybody's building on top of it, and that's where a lot of the complexity comes in. So, how does CloudCenter Suite, you don't want to do what, in the past it was, you know, let's dumb down everything so that you get a least common denominator. I want to be able to leverage the individual features of my Azure and my AWS, and in my data center. But, I need to be able to get my arms around managing that whole environment. >> Yeah, and if you think about the old world, you know, if you had an application and a target, whether it's a cloud or any data center, you'd have to hard wire those together. And as you have more and more apps and they're changing faster and now more and more cloud environments with no standardization across those environments, this whole hard wiring together doesn't work anymore, so we have to rethink cloud management, and that's what CloudCenter's really all about. How do you describe an application, its components, sequence, and dependencies, independent of the nuances of those targets, and allow CloudCenter, once you define your application, to understand the resources on each of these environments and lay down that application natively on those different environments. And it does provide both least common denominator support around core primitives like compute storage network security, but also provides access to these higher-level services, whether on case of AWS, it's RDS, ELB, et cetera, so you really get the best of both worlds. Move there easily, manage the workload and take advantage of all these rich services. >> You know, I love the keynote clever play on words, data center, center, data is the center of the value proposition. That kind of highlights just basic networking 101, move a packet from point A to point B. Now you have more intelligence in the data, so the data layer is now the enabling opportunity to build software. So look no further than microservices and containers, and you go, hey, this is pretty cool. Policy-based, sounds like the service meshes. So you got policy-based whatever, that's been a core competency in the network, moving to the application with applications programming. So we all kind of like to go, that's great, that's dev ops, thank you, check. Now, how do you deploy it? So, I got to ask you on the CloudCenter 5.0, the suite, so this is new, this is big news, how does that help me move to a microservices architecture? What is it offering? What's different than CloudCenter before it? >> So CloudCenter has always been this platform that allows you to manage the entire life cycle of applications across any private or public clouds. And it's always been a very comprehensive solution, perhaps too comprehensive for some people and so, with CloudCenter Suite 5.0 what we've announced is both new functionality and easier consumption. On the new functionality we've extended our price and performance benchmarking that allowed you to identify where to place workloads, to additional cost optimization capabilities that would actually make recommendations and allow you to remediate and take advantage of those cost optimization recommendations. We have a new Action Orchestrator workflow, which is a customizable workflow but with out-of-the-box connectors that allows you to integrate with both Cisco and third party products. Cisco security products, things like non-Cisco, ITSM ServiceNow applications. So you can provide users with a catalog. So new functionality-- >> That's the workload manager. >> That's the workload manager that provides those out-of-the-box connectors and a workflow to be able to reach out, run those routines. >> So can that do end-to-end management? >> Absolutely, absolutely. And we talk about CloudCenter, sort of full life cycle management, is the modeling of the app sort of the benchmarking or cost optimization, the deployment of the app, whether it be traditional VM based or microservice based, and those working together, and finally, the ongoing day two, day three management. >> So, I get that, you guys had a little bit of workflow management before, but the new things are orchestration, Action Orchestrator, and the cost optimizer. The cost optimizer I can get, that's like a TCO thing. >> Yes. >> The Action Orchestrator's interesting to me. What is that? What does it mean? Is that, like, just cloud-enabled? What is that, what does that mean? Action Orchestrator. >> It's really a dynamic workflow engine that allows you to either create customizable workflows or, if you've already invested in things like script libraries, in your application routine, it can reach out to say, go do a snapshot of the data and then reach back into the application technology. Or reach out to a third party tool, like an ITSM tool, or reach out to their CMDB and update their CMDB to do capacity management. So it gives you all of that flexibility. And, by the way, in all of this, while we were on-prem only, now we're going to provide both on-prem and CloudCenter Suite as a SaaS so now it really makes it nice. It also is available in three tiers, so it's never been easier to start simple and grow. Could be one app, one cloud, and then you could expand clouds, apps, and users, and functionality as you grow. >> But what if I have other systems under other management systems? Does it integrate into those? >> Yes. >> Do I have to toggle between them? What's the-- >> No, it will actually integrate into those management systems. But the whole idea is, if you think about the average Global 2000 company, today they have more than four public cloud providers, and many more regions than that, and this does not include SaaS apps, so what I think most companies realize is they don't want to have siloed management environments where they have to have expensive skills to manage everything. >> Yeah, we spent a lot of time talking about those technical pieces. How do we get something to work in multiple clouds or move them? But one of the biggest challenges I hear from users is the skillset. You know, I'm CCIE certified, I understand how to mange my environment. I've gone through my AWS certification and there's that. I need to learn a new language when I go, you know, go do Azure. So how are you, from a management standpoint, going to help, no matter which point I'm coming from, understand and use this tool simply? >> Yeah, it's sort of interesting. So a very large media company, I can't use their name, but you'll find this analogies, they found that, on average, they needed two fairly highly-paid skilled individuals for every target cloud environment. The other thing, by the way, is sort of interesting they measured, is that without sort of a cloud management platform, for every pairing of an app to a cloud, they had to custom-write about 1,200 lines of script. And every time the app or the cloud changed, and they did, they had to re-write 20% of those script libraries. So, between skilled resources and these manual script libraries, it just becomes unmanageable to have diverse apps across diverse cloud environments. >> And what's the status, just a quick update on the multicloud relationships? Google, AWS, Azure. The recent announcement we covered was the Amazon Kubernetes deal, congratulations, great deal. What's the status of the relationship with Cisco multicloud strategy for your customers that have Google, Azure, and AWS? >> Sure, well first of all, more broadly, CloudCenter today allows you to deploy and manage applications across all of the popular private and public clouds, and I think that adds up today to be about 15. So you can do that. From time to time, we'll see new technologies, in this case, Kubernetes, where we'll provide specific strategic partnership solutions to let our customers take advantage of that. So we announced the hybrid Kubernetes solution with Google and that with AWS. And these are very interesting because now we're taking Kubernetes, which is evolving from really a cool developer thing and now starting to move into production where IT ops gets involved and they say, how do I apply policies? How do I have governance, security? And these solutions with Google and AWS create really that transparency of the data center and those cloud environments. >> We were talking before we came on camera here about your history, and I want to get your perspective a little bit more on the entrepreneurial side in a bit, but I got to ask you, you go back, seen the early waves of IT. It started out single vendor, big mainframe, you know the history there, then it became the whole open systems, networking, the web and the internet. >> Client-server along the way. >> Client-server. But the one thing that was consistent over those decades was the word multi-vendor. Multi-vendor was important. Support multiple vendors, that became the interoperability and then growth happened. So good things came behind that. We're seeing the same trend with multicloud. Similar dynamic, >> I think you're right, yeah. >> But different environment, obviously cloud. If that's the case, multi-vendor created a lot of opportunities, how do you see multicloud creating opportunities for customers who are changing, as well as people building apps? >> I think we have actually seen that shift in the cloud, so I think for a lot of people the cloud may be reducing costs or shifting from CAPEX to OPEX, but today what I see is it's about accessing innovation and that these clouds are often becoming an extension of their engineering organizations and you never know where that innovation is going to be able to occur. And so I may want an Alexa API for a voice-driven application, or access AIML from, say, Google. And so now I think multiclouds, multi-vendor, is driven by access to innovation and it's also about optionality. CFOs talk a lot about optionality and maintaining purchasing power and they'll often put a value on that, 10 to 15% value. Just having that optionality as innovation occurs I can take advantage of it. >> And the speed too, on the agility. >> Oh yeah. >> I mean, this is like, real competitive advantage. People are building management practices around encouraging versus discouraging experiments or tests. >> Well think about where we started this talk, is that, it was just 10 years ago where there was really one person, there was AWS EC2, and today there are a lot of choices and a lot of technology and innovation. The whole idea is, how do I easily access that? >> Well I want to get your perspective, since you're here, on, people might not know that Dave has an entrepreneurial background, done eight startups. Last one was sold to Cisco, so you're now in the big company with a great product, congratulations. >> Thank you. >> But customers have to be entrepreneurial. We were just talking about being agile, that's an entrepreneurial vibe or spirit, >> Right. >> and you're starting to see agile really be very tactically like entrepreneurs. You know, taking new territory, trying things, failing, iterating. This is kind of the dog whistle for entrepreneurship. >> Right. >> How can customers, Cisco customers, be more entrepreneurial with this new set of technologies from Cisco and the cloud? Because that's really what's happening. I got to refactor my existing resources and be entrepreneurial. How can a customer be entrepreneurial? What's your advice? >> Well, I probably have a bit of a jaded position today, but I would say that technology enables that agility because now I can start to have an abstracted access to some of these capabilities. So we talked about hard wiring into different environments, once I did that, I made that investment, and I could not be very agile. Today, whether it's things like cloud management platforms, or things like Kubernetes, it gives me that agility to develop and deploy anywhere. Things like data hub technologies, like SAP's Data Hub that says, now I have apps anywhere accessing data anywhere, I no longer have to hard wire everything, multicloud doesn't have to mean lifting and shifting or refactoring everything, I can now start stretching these configurations across multiple environments which gives me that agility to set it up and to change as things change. >> So, more creative thinking probably going to come to the table. >> Well, more creative thinking, but more agile abilities to implement your creative thinking. I think technology-- >> Very valuable solutions. >> Exactly. >> You know, you got to make money. >> Yeah, exactly. >> And fun. >> Yeah. >> Dave, thanks so much for coming on. Great to see you, congratulations. Dave Cope is senior director, he's talking about CloudCenter here among other things, at Cisco Live! Barcelona. This is theCUBE, I'm Jeff Furrier, Stu Miniman. We'll be right back with more coverage after this short break. (electronic music)

>> Live, from Barcelona, Spain, it's theCUBE covering Cisco Live! Europe. Brought to you by Cisco and its Ecosystem partners. >> Hello everyone, welcome back to our live coverage here in Barcelona for Cisco Live! 2019's theCUBE. I'm John Furrier, your host with Stu Miniman. Our next guest is Dave Cope who's the senior director, market development, Cisco Cloud platform. Great to see you again. >> Great to see you. >> Thanks for coming on, I really appreciate it. One of your products is the big focus of the keynote, introducing the CloudCenter Suite. One of the core announcements, this was pretty critical for Cisco, obviously multicloud, we've seen the Kubernetes relationship with Amazon. You've got Azure, Google Cloud. Cisco's tied up with the clouds, which is good, >> Right. >> They have the on center core data center, but now dealing with cloud has been really the hot topic, so thanks for coming on. >> Absolutely. >> So I'm looking at your perspective first on cloud in general within Cisco and within your customer base and the industry. What is multicloud? Why is it important? Why is it a wave worth betting on? >> You know, it's a great question, I think. Actually, it's a really fun time right now because if you think about it, it's almost exactly 10 years ago where AWS's EC2 first came out of beta, and so, everybody's talking about the cloud, but it really hasn't been around that long. And even in that sort of ten-year period, it's gone through sort of skepticism to, I think, let me try some non-critical apps, to debate about public or private, or which is the best public, to, today, 94% of the businesses say they either are, or are planning to use, multicloud environments. And, so if you think about it, that's both provided a lot of advantages but also created a degree of complexity in how do I apply traditional disciplines like network management and security across environments that I control and don't control? So it's a whole new world. >> And the DevNet Zone, which for theCUBE is based out of again this year, has a hot growth vibe to it. People are joining the community in record numbers. The demos here aren't just like canned demos, they're actually real code. >> Exactly. >> So you're seeing a developer framework around the network, and the cloud, the cloud is not a one-vendor product. It's an architecture, it's a concept. And so cloud operations is in the cloud, it's also being done on premise and the edge, so everything's cloud now, if you think about it. >> Well I think what we saw is, obviously, huge initial growth of cloud and a lot of applications moving to the cloud, but it's always been my hypothesis and I think it's actually coming true that we're now, and some of the newer technologies support this, we're seeing this natural distribution of workloads across all these environments, whether it's the public cloud, or the edge, or the data center. And it's now technologies that allow you to put the workloads in the right place based on business priorities, not IT priorities. And now I believe you're starting to see this sort of natural stasis and the whole pie grow again. >> So I got to ask you the question from a customer perspective. So I'm a customer, I say, Dave, love it, you had me at cloud, I'm there. I got all this stuff to deal with. I've been working my business, running my business. Love it, what's in it for me though? What's the impact? What do I need to do differently? Is it, do I have to change anything? How does a customer engage with Cisco and the cloud and the multitude of technologies that are available to them? It can seem complex. >> Yeah, I think people had hoped that the cloud would make everything easy, but what they're finding is that the cloud is not the cloud. It's private clouds, public clouds, virtual private clouds. And if you think about it, good free market principles, all these cloud providers are competing with each other so they're all becoming very different. Cisco finds, I think, itself in a very unique position because of its heritage around network management and security, which is connecting everything together. We don't have our own cloud, so what we focus on is providing a very broad and deep solution to be able to manage workloads across all of these environments. So you truly can place the workload in the right place. >> I wonder if you could help us unpack a little bit what you just said, which is, the clouds are actually becoming more different, not more similar, you know. With the Kubernetes show >> That's right. >> we talked to Cisco, we talked to the whole ecosystem. The founders of Kubernetes said they weren't creating a magic layer, that's not what Kubernetes is. There's some base functionality, but everybody's building on top of it, and that's where a lot of the complexity comes in. So, how does CloudCenter Suite, you don't want to do what, in the past it was, you know, let's dumb down everything so that you get a least common denominator. I want to be able to leverage the individual features of my Azure and my AWS, and in my data center. But, I need to be able to get my arms around managing that whole environment. >> Yeah, and if you think about the old world, you know, if you had an application and a target, whether it's a cloud or any data center, you'd have to hard wire those together. And as you have more and more apps and they're changing faster and now more and more cloud environments with no standardization across those environments, this whole hard wiring together doesn't work anymore, so we have to rethink cloud management, and that's what CloudCenter's really all about. How do you describe an application, its components, sequence, and dependencies, independent of the nuances of those targets, and allow CloudCenter, once you define your application, to understand the resources on each of these environments and lay down that application natively on those different environments. And it does provide both least common denominator support around core primitives like compute storage network security, but also provides access to these higher-level services, whether on case of AWS, it's RDS, ELB, et cetera, so you really get the best of both worlds. Move there easily, manage the workload and take advantage of all these rich services. >> You know, I love the keynote clever play on words, data center, center, data is the center of the value proposition. That kind of highlights just basic networking 101, move a packet from point A to point B. Now you have more intelligence in the data, so the data layer is now the enabling opportunity to build software. So look no further than microservices and containers, and you go, hey, this is pretty cool. Policy-based, sounds like the service meshes. So you got policy-based whatever, that's been a core competency in the network, moving to the application with applications programming. So we all kind of like to go, that's great, that's dev ops, thank you, check. Now, how do you deploy it? So, I got to ask you on the CloudCenter 5.0, the suite, so this is new, this is big news, how does that help me move to a microservices architecture? What is it offering? What's different than CloudCenter before it? >> So CloudCenter has always been this platform that allows you to manage the entire life cycle of applications across any private or public clouds. And it's always been a very comprehensive solution, perhaps too comprehensive for some people and so, with CloudCenter Suite 5.0 what we've announced is both new functionality and easier consumption. On the new functionality we've extended our price and performance benchmarking that allowed you to identify where to place workloads, to additional cost optimization capabilities that would actually make recommendations and allow you to remediate and take advantage of those cost optimization recommendations. We have a new Action Orchestrator workflow, which is a customizable workflow but with out-of-the-box connectors that allows you to integrate with both Cisco and third party products. Cisco security products, things like non-Cisco, ITSM ServiceNow applications. So you can provide users with a catalog. So new functionality-- >> That's the workload manager. >> That's the workload manager that provides those out-of-the-box connectors and a workflow to be able to reach out, run those routines. >> So can that do end-to-end management? >> Absolutely, absolutely. And we talk about CloudCenter, sort of full life cycle management, is the modeling of the app sort of the benchmarking or cost optimization, the deployment of the app, whether it be traditional VM based or microservice based, and those working together, and finally, the ongoing day two, day three management. >> So, I get that, you guys had a little bit of workflow management before, but the new things are orchestration, Action Orchestrator, and the cost optimizer. The cost optimizer I can get, that's like a TCO thing. >> Yes. >> The Action Orchestrator's interesting to me. What is that? What does it mean? Is that, like, just cloud-enabled? What is that, what does that mean? Action Orchestrator. >> It's really a dynamic workflow engine that allows you to either create customizable workflows or, if you've already invested in things like script libraries, in your application routine, it can reach out to say, go do a snapshot of the data and then reach back into the application technology. Or reach out to a third party tool, like an ITSM tool, or reach out to their CMDB and update their CMDB to do capacity management. So it gives you all of that flexibility. And, by the way, in all of this, while we were on-prem only, now we're going to provide both on-prem and CloudCenter Suite as a SaaS so now it really makes it nice. It also is available in three tiers, so it's never been easier to start simple and grow. Could be one app, one cloud, and then you could expand clouds, apps, and users, and functionality as you grow. >> But what if I have other systems under other management systems? Does it integrate into those? >> Yes. >> Do I have to toggle between them? What's the-- >> No, it will actually integrate into those management systems. But the whole idea is, if you think about the average Global 2000 company, today they have more than four public cloud providers, and many more regions than that, and this does not include SaaS apps, so what I think most companies realize is they don't want to have siloed management environments where they have to have expensive skills to manage everything. >> Yeah, we spent a lot of time talking about those technical pieces. How do we get something to work in multiple clouds or move them? But one of the biggest challenges I hear from users is the skillset. You know, I'm CCIE certified, I understand how to mange my environment. I've gone through my AWS certification and there's that. I need to learn a new language when I go, you know, go do Azure. So how are you, from a management standpoint, going to help, no matter which point I'm coming from, understand and use this tool simply? >> Yeah, it's sort of interesting. So a very large media company, I can't use their name, but you'll find this analogies, they found that, on average, they needed two fairly highly-paid skilled individuals for every target cloud environment. The other thing, by the way, is sort of interesting they measured, is that without sort of a cloud management platform, for every pairing of an app to a cloud, they had to custom-write about 1,200 lines of script. And every time the app or the cloud changed, and they did, they had to re-write 20% of those script libraries. So, between skilled resources and these manual script libraries, it just becomes unmanageable to have diverse apps across diverse cloud environments. >> And what's the status, just a quick update on the multicloud relationships? Google, AWS, Azure. The recent announcement we covered was the Amazon Kubernetes deal, congratulations, great deal. What's the status of the relationship with Cisco multicloud strategy for your customers that have Google, Azure, and AWS? >> Sure, well first of all, more broadly, CloudCenter today allows you to deploy and manage applications across all of the popular private and public clouds, and I think that adds up today to be about 15. So you can do that. From time to time, we'll see new technologies, in this case, Kubernetes, where we'll provide specific strategic partnership solutions to let our customers take advantage of that. So we announced the hybrid Kubernetes solution with Google and that with AWS. And these are very interesting because now we're taking Kubernetes, which is evolving from really a cool developer thing and now starting to move into production where IT ops gets involved and they say, how do I apply policies? How do I have governance, security? And these solutions with Google and AWS create really that transparency of the data center and those cloud environments. >> We were talking before we came on camera here about your history, and I want to get your perspective a little bit more on the entrepreneurial side in a bit, but I got to ask you, you go back, seen the early waves of IT. It started out single vendor, big mainframe, you know the history there, then it became the whole open systems, networking, the web and the internet. >> Client-server along the way. >> Client-server. But the one thing that was consistent over those decades was the word multi-vendor. Multi-vendor was important. Support multiple vendors, that became the interoperability and then growth happened. So good things came behind that. We're seeing the same trend with multicloud. Similar dynamic, >> I think you're right, yeah. >> But different environment, obviously cloud. If that's the case, multi-vendor created a lot of opportunities, how do you see multicloud creating opportunities for customers who are changing, as well as people building apps? >> I think we have actually seen that shift in the cloud, so I think for a lot of people the cloud may be reducing costs or shifting from CAPEX to OPEX, but today what I see is it's about accessing innovation and that these clouds are often becoming an extension of their engineering organizations and you never know where that innovation is going to be able to occur. And so I may want an Alexa API for a voice-driven application, or access AIML from, say, Google. And so now I think multiclouds, multi-vendor, is driven by access to innovation and it's also about optionality. CFOs talk a lot about optionality and maintaining purchasing power and they'll often put a value on that, 10 to 15% value. Just having that optionality as innovation occurs I can take advantage of it. >> And the speed too, on the agility. >> Oh yeah. >> I mean, this is like, real competitive advantage. People are building management practices around encouraging versus discouraging experiments or tests. >> Well think about where we started this talk, is that, it was just 10 years ago where there was really one person, there was AWS EC2, and today there are a lot of choices and a lot of technology and innovation. The whole idea is, how do I easily access that? >> Well I want to get your perspective, since you're here, on, people might not know that Dave has an entrepreneurial background, done eight startups. Last one was sold to Cisco, so you're now in the big company with a great product, congratulations. >> Thank you. >> But customers have to be entrepreneurial. We were just talking about being agile, that's an entrepreneurial vibe or spirit, >> Right. >> and you're starting to see agile really be very tactically like entrepreneurs. You know, taking new territory, trying things, failing, iterating. This is kind of the dog whistle for entrepreneurship. >> Right. >> How can customers, Cisco customers, be more entrepreneurial with this new set of technologies from Cisco and the cloud? Because that's really what's happening. I got to refactor my existing resources and be entrepreneurial. How can a customer be entrepreneurial? What's your advice? >> Well, I probably have a bit of a jaded position today, but I would say that technology enables that agility because now I can start to have an abstracted access to some of these capabilities. So we talked about hard wiring into different environments, once I did that, I made that investment, and I could not be very agile. Today, whether it's things like cloud management platforms, or things like Kubernetes, it gives me that agility to develop and deploy anywhere. Things like data hub technologies, like SAP's Data Hub that says, now I have apps anywhere accessing data anywhere, I no longer have to hard wire everything, multicloud doesn't have to mean lifting and shifting or refactoring everything, I can now start stretching these configurations across multiple environments which gives me that agility to set it up and to change as things change. >> So, more creative thinking probably going to come to the table. >> Well, more creative thinking, but more agile abilities to implement your creative thinking. I think technology-- >> Very valuable solutions. >> Exactly. >> You know, you got to make money. >> Yeah, exactly. >> And fun. >> Yeah. >> Dave, thanks so much for coming on. Great to see you, congratulations. Dave Cope is senior director, he's talking about CloudCenter here among other things, at Cisco Live! Barcelona. This is theCUBE, I'm Jeff Furrier, Stu Miniman. We'll be right back with more coverage after this short break. (electronic music)

(futuristic electronic music) >> Live from London, England, it's theCUBE covering .Next Conference Europe 2018. Brought to you buy Nutanix. >> Welcome back to Nutanix .Next 2018 in beautiful London, England. I'm Stu Miniman, my co-host Joep Piscaer, and happy to welcome back to the program, third time guest, I believe, Chris Hallenbeck, who's the senior vice president of database and data management with SAP. Fresh off the keynote stage this morning. Were you were with CEO Dheeraj Panday? >> I was, a great time. >> So, SAP, things are going well. I see SAP at lots of shows. You've been on our program at a few different ones. You are based here in Europe now, you're from the US. Chris, introduce us a little bit. Give us some of the summary of what brings you specifically to the event. >> Well, I mean, several things. So, my responsibility is looking after data platform. And what we're doing from a strategy perspective, what we're doing, what applications we're building on that in the cloud, what we're doing, everyone asks what are you doing with HANA? What are you doing with Data Hub? And so that's the core of what I spend time on. But equally I think you need to step back and look at SAP's business 'cause we're also, we're our own OEM, right? HANA's what makes S4 possible. HANA's what powers all of our cloud applications. We're going to announce now that everyone one of those, everyone of the acquired companies now runs on HANA and not on any other database. And so you really see these three pillars of SAP. You talk about I've been with SAP seven years ago, and everyone said, why would you go there? Because there's this old applications company that seems to be getting, oh, and even Hasso Plattner, our founder, was saying that was true. Came out with HANA, that we quickly streamed up. Passed Teradata, become the number four database company in the world. Still growing phenomenally. They used HANA as a method of rejuvenation for originally S4 and now that's gone to the cloud. And during that time, we were able to acquire all these cloud applications and build those, SuccessFactors, Ariba, and other stuff, and that's become a wildly successful business. >> Yeah, Chris, I wanted to step back for a second because you talk about data products. >> Yeah. >> You know, I've watched databases for my entire career. I've watched the huge growth of the importance of data. Especially the last few years. You know, we went through that big data wave, which was kind of middle end success, but everything today, data is the center of it all. You know database is where a lot of data live, but how am I getting, and how are customer getting more advantage out of their data when they are using your products? >> It's a great question. So, one is it continues to be the fact that now, people now have realtime access to that information. And it continues to actually be the biggest driver, to be honest. The other one where we see HANA getting picked, especially, is when you have tens or even hundreds of data feeds coming in simultaneously. Frequently, some are streaming, some are traditionally relational, coming from all different systems, and people then want to do analytics on that. But when we talk about analytics, I don't just mean a BI tool, although you could, but now we're doing predictive on that. And, in fact, and then figuring out how does a data scientist then go through, do machine learning, build a model, deploy for scoring, from a full lifecycle perspective. And that's where HANA's getting used tremendously, is in these analytic systems, and data warehousing, and in particularly people going, I want a realtime data warehouse. The other one where we see it being a lot more is in applications where HANA originally was only for SAP applications. We got a huge amount of work on that to make it work for OEM, ISVs, to port their applications over. And you've been seeing that continuously. I think there's some phenomenal work we've done with Esri. HANA's now the fastest geospatial database in the world. And Esri has about 80% of the geospatial market. Now prefers and runs on HANA. So that's been huge. So customers are beginning to use it in more areas. Not just SAP customers, or the CIO who ran the SAP systems, we're getting used a lot by the chief data officer's division. We're getting used out by other groups. We're getting used by specialty firms doing things like geospatial, doing text analytics. And so it's been kind of exciting. I don't know if I answered your question, by the way, but-- >> No, I think that was really good. >> So that sounds like you positioned yourself to enable customers to make the most out of the cloud, make the most out of data, make the most out of IoT. But I'm curious, how are helping customers succeed in that digital transformation? >> Yeah, well, with the digital transformation, and the way I always look at digital transformation, well, it's like big data, what does it mean, right? But what you see the patterns are is people are trying to remove layers between them and the actual consumer or the product. And if I can take those layers out, now you have people like Netflix who went all the way from just saying, let's make it easier to get a DVD, but now they are the movie studio directly to the consumer. They got rid of the 18-year-old kid at the video store, they got rid of everything through streaming. They went out on the, business. They took out all these layers and got closer. Whether it's Airbnb and all these pure plays, that's exactly, they've reduced the number of layers. Our existing customers are trying to do the same thing. They're saying, how do I get closer? How do I understand them? That requires, like if I'm running machinery, IoT data will tell me exactly how they use my machinery. If I can then start to take a look at that, now they want to work with me in different ways. Customers dictate how they're going to work with me. That means if they want to come over the web one time, other time they want to phone, they should always be treated equally based on how important they are to me. Reducing layers. Equally, though, you always have to be worried about someone coming out of nowhere, the pure play that comes in with a brilliant idea in your division, and you can't let 'em just take you out. So what we're seeing is these traditional companies, not necessarily know what the digital transformation is, but saying, I've basically got to get fit. And I can't do that with a really complicated landscape. If my department says, oh, that's great, new business model? We got to have the accounting up and ready in three years to compete with this new entrant. It's not going to work. Yet you upgrade your systems, and let's say SAP is financials, somebody comes up with a new business model, that's a day change in the system. You want to reorganize, that's a few clicks in the system, and I have a new hierarchy. That used to be a two year process. And so we working in all different aspects. We can do the IoT, we can do the agile work, we can have the data science machine learning understand the customer, all the way back to the applications that are agile now as people upgrade to the S4 system. >> Alright, I want to bring us back to the Nutanix show here, Chris. >> We like Nutanix, let's help them here. >> That's great, let's talk about platforms out there. You have applications that they all want to get certified on. Your application certified on their platform, so it's always, okay, am I SAP certified? And, okay, Nutanix even went through some redesign in there file system to make sure that they run really well for HANA and we're real excited for the certification there. Talk a little bit about what goes into that. Is there joint efforts between the companies? Or is it just their going through and following the process that you've got to describe? >> While I was on stage with Dheeraj and this wasn't, although it's nice to say supported database, this was a year and half effort. In memory computing, people get in and go, okay, it's not just a big data cache, this is a fundamentally different way software runs. How data stored in memory uses caches. So Nutanix worked with us, back and forth, on we would have this happen. Now it was worth it to us. Our customers have been demanding simpler infrastructure. And these hyper-converged infrastructures are exactly that. And Nutanix being the leader, we wanted to be supportive. This is good for both of us. If our customers can have agility on both sides of the business, running traditional SAP applications, they've got to ramp up, they need to add 100,000 users at quarter end, they can do that with a Nutanix platform. Equally, they want to quickly bring up an agile data mark for project basis, click a button, have a new data mark in seven minutes like they did on stage. And maybe they don't even want to do that when they're on on-prem/cloud. They want to do that on AWS or somewhere, GCP, they can do that. Yet that's all controlled from a single interface running through Nutanix. So really, really good for both of us. >> SAP is partial with a lot of companies out there, so you have kind of a neutral view when it comes down to everything. I'm sure you have certain partners you work more with and less. But what are you hearing from your customers? How do they think of cloud today? And any more about the Nutanix connection along the way. >> Yeah, it's interesting 'cause talk about data density, the most valuable data a company has is sitting, you typically, if they're an SAP customer, it's in their SAP system. It's exactly who is my customer, what did they buy, what is their service, what is their bill of material? All that, it's very value dense. It's the huge amount of security governance. What we've actually been seeing is a lot of them, yes, we're moving those workloads to the cloud to save money, I've actually seen a fair number come back on-premise. 'Cause they're saying, look, I'm not getting rid of SAP for easily the next seven, but we have no plans. So then they're realizing, I can run this on a private cloud infrastructure and actually save a ton of money. So they've been pulling back on prem, and we've been hearing that from all, the Forrester, and Gartner, and IDC are saying the same things. We have a lot of folks who don't want to go to the cloud with that core system yet, or they're saying, look, I got to save money and I think I'm going to the cloud, but I'm not ready. And so that's exactly where we see private cloud being really, really crucial, and then the ability to then push out and be ready to go to the cloud. Nutanix really is a good solution for that. And in particular, on-prem database right now, depends who you get your estimates on, is roughly growing at 5% to 8%, five year kay-ger. On-prem private cloud is forecasted to go up 26%. I mean, that is massive. Cloud's only 40 overall for databases. So you see it's a close second. So, huge, huge growth. What's declining is bare metal on-prem, it's gone. Everyone wants to run an either virtualized or fully hyper-converged infrastructure now, even on-prem. So we see people, like I said, staying on, getting ready to go to the cloud. A lot of people pushing workloads to the cloud, but even some repatriation. >> Alright, well, Chris Hallenbeck, really appreciate the updates. Thanks for everything and-- >> Well, thanks for having me. I always love speaking with you guys, thank you. >> Awesome, thanks so much. Joep Piscaer, I'm Stu Miniman, we'll be back with more programming from Nutanix .Next 2018, thanks for watching theCUBE. (futuristic buzzing) (futuristic electronic music)

(techno music) >> From Orlando, Florida, it's The Cube. Covering SAP Sapphire Now 2018. Brought to you by NetApp. >> Welcome to The Cube. I'm Lisa Martin with Keith Townsend and we are at SAP Sapphire Now 2018 in Orlando. This is a massive event. Not only are there 20,000 people here but there's about a million engaging with SAP this week online. Amazing! We're joined by a Cube alumni. Welcome back to The Cube >> Thank you Lisa. Chris Hallenbeck. You are the SVP of Database and Data Management at SAP. >> What they tell me. (laughter) >> That's what they tell you. That's what your cards say? >> It is. >> Alright. Well, thanks for coming onto The Cube. So this event is enormous. Sixteen American football fields is this space. You really can close your rings. >> Well, and it is, is the energy is just crazy. It's actually different than other years. I don't know why but it really it is. >> You know yesterday, that's what Keith and I were saying yesterday. Bill McDermott really kicked things off with such enthusiasm and genuine energy. It was really amazing to see that. You don't see that with a lot of, see levels on day one. That energy was really palpable as was. >> Enterprise applications aren't that sexy huh? (crosstalk) >> Apparently they are. >> Well, apparently they are now. >> Who knew? >> Well, and that's the thing too. You guys wanting to be one of the top ten most valuable brands in the world. Up there with Apple, Google. And one of the cool things I saw yesterday on a bus out here was ERP that you can talk to and hear from. So taking this, what was an invisible product and making it now something that people can engage with like a digital assistant at home. Remarkable. >> Well, yeah. No. The user interface which has been a huge, huge thing. We have these massive UX labs throughout the world. We have ones in Palo Alto. We have ones throughout Germany and other locations. And we've been really looking at how people engage with the software. And it's not only through a screen although that's it and we win all these Red Dot awards, the Preeminent Design Award. We get those consistently now, many a year, for the work we're doing within UI which is fabulous work. But we're also again, a lot of people aren't in front of computers anymore. So how can I actually just speak into my phone and get all the information I need? How can I have the device speak to me? How can somebody wearing gloves on an assembly line, automatically they vibrate if they're reaching for the wrong bin and would have grabbed the wrong part which create a faulty defective product. So it's all built in, our actually shoes vibrating if something else happens. And so actually this interaction of sensors in two way, taking IOT data in, and then also feeding it back into signals but that's part of the interface of the software. It's not always sitting in a screen and if you are in front of a screen, they're actually pretty great to use. >> So speaking of these consumer technologies, we've had this expectation and these technologies have changed the expectations of what our business tech is. We expect to be able to do things such as, hey, say what's the latest score from last night's game. And now there's these intelligent streams of having conversations with computers. All that is powered by the data on the backend. SAP traditionally hadn't been. We talked about it on stage this morning. SAP hadn't been known for the type of company to sub at to the real-time data entry, real-time data analytics. >> Yeah. You're all about data management. We heard something on the stage this morning. What was it? Data management suite? (crosstalk) The mature database now. (crosstalk) What is that? What's that about? >> Well, now what we're finding, you know, HANA enabled these incredible use cases and originally we were all, we actually didn't run underneath SAP applications an entire database but really a data platform that people were doing these incredible innovations on. And then of course it really started to get swept underneath and it went under BW and then it became part of Sweden HANA and everyone just focused said, oh yeah, HANA is just gonna be like Netweaver. It's just a system that runs underneath SAP and we kept saying no, it's not, no, it's not. And it was sort of but that was its main, that was where it was mostly getting deployed. And then what you're actually seeing here at Sapphire is this massive breakout of technology in full use use cases. That people are using it outside even non-SAP customers are using it to solve their individual problems. Really going after that huge, that 80% of data which is non-SAP but the challenge there with is how do you handle that? Data is now sitting out in all these different clouds. HANA was known for orchestrating data but it was really designed to do it on premise because we knew not everyone's gonna put data into our system. We came in late, right. And yeah we're the fastest growing but data was sitting in Oracle, and the TIZA and that's coming up and going into data lakes, running on ADO and we could orchestrate and move that data into HANA or do it in place. Go to the cloud, it's totally different. Average customer and CIOs are telling you they have six to eight clouds and you're like, wait, how did you get to six to eight? And you're like, yeah, they've got data in storage just in Azure, in AWS, and in Google but they've also got in all these different cloud applications and a lot are from SAP but a lot aren't and yet and so companies are telling us we've lost the view of who our customer is. We've lost view of our business. Which is the opposite of what you would have expect from this data explosion and, you know, digital transformation which was like showed up and disappeared in like two years but so how do you handle that? If I have data. So much data sitting out there. IOT data in the edge, love file data sitting in object stores, I've got data in different applications, data still on Fram. How can I actually possibly move that? You can't. There's no way to put it all together in one cloud. Everyone says, oh, bring it to my cloud. It's not viable. >> Right. So how do I actually push compute, get the data I need, refine it in place, and orchestrate and move that together with the ultimate security in governance? Which is what our customers are wanting. They're saying, how Chris for our non-SAP data and SAP, can I move data for application integration? How do I do analytics? How can I pre-press data and load it into a data lake, into a data warehouse and then I'll come back and do some other cool stuff on it with data science? And that's all about by combining HANA and data hub together in a suite with deep integrations, technically from a data center readiness it's all as a service runs in the cloud but because we're SAP it's also on Prem enabled if you still want to run it that way. And it allows you to solve these huge data problems and we also help you. We bring SAPs intellectual property of data models to this so you can use things like Enterprise Architecture designer and say look we don't have a model of customer. I'm like, well yeah, what kind of industry are you in? Okay, I've got a high tech customer model pre-built for you so then you don't have to build that from scratch. We bring the things to you. So now you can get very, very quick value right from the implementation within weeks. >> And that speed is obviously essential. >> Well, how does it. (crosstalk) >> HANA's a terror, which it's known for. >> But you're right, sorry Keith, you're right that in the consumer world because we have access to everything everywhere from so many devices, we as business people expect the same thing. >> Yeah. And so that speed is critical. You talk about, you know, multiple clouds, data in so many different sources. It's not valuable unless you can actually harness it and extract insights that may only be viable for a quarter or something like that. >> But nobody even knows where the data is and so you look at like we're about to, we were talking about HANA. I just came back and we're coming out a little bit later the year with HANA data hub 2.3 which is part of HANA data management suite and that actually has a whole metadata repository. So someone who knows what they're doing goes in and maps out where all this data is located and actually they don't have to do it all themselves, it's got heuristic-al and semantic search to automatically map and categorize data. I can then map that back to like my definition of customer or supplier and other things. Now everyone doing all the analytics and doing exactly what you're talking about Keith where can I just say into my phone, hey, someone in board meeting goes hey what were our results within two peak last year over this year and show and break that down by city and have it just pop up. Just like you say to somebody, hey high school football game, didn't those two play together? Anyone can do that on a mobile device but we don't know the data in our own company. How do you do that? And then let HANA data management suite will automatically know where the data is, orchestrate, go get it, pull it together, and deliver that back to a mobile device that you might have spoken into. >> Do you have a favorite customer that articulates just what you said? >> I do. I just actually walked out of a session. It was just and it sounds a little boring but it's incredible what people are doing. So I just walked out of a thing with the Swiss Federal Railways. Sounds boring but you know where. I live in Europe and everything is by rail, right? And so they're doing about 60 percent of the rail traffic there is passengers, 1.25 million passengers a day plus the balance of 40 percent of the trains are freight. They're having a huge problem because you use huge, it's all electrical and they're trying and so when you get up and it's growing rapidly. So they're, and they do their own power with power plants and when they go up with power plants, when they go over peak they have to spot by at just massive times a premium on that data on that. And we're actually doing this a lot of place out of rail but they also use electricity on heaters and other stuff in the cold winters and air conditioners. They're now streaming information off the trains, off of the points all the way along the signals and from all the power plants. They know peak usage. It automatically detects when they're going to go over and rather than going into the plants, it actually cuts the heaters off for a second here or there. There's heaters in all the switching equipment. They know how long they can do it. HANA managed this, this is automatically so it's IOT in but it's automatically making automated business decisions, shutting down systems programmatically, intelligently actually using machine learning and keeping it. So now what they do, so now they don't need to go out to the spot market in buy energy anymore. It has cut their electrical usage by a third. >> How much money have they saved? >> No, what's a third is how much money they've saved. The electricity is still high but they're not buying that really, really >> The premium. expensive premium and so you're streaming data, it's all over, it's all happening in real time, and it's automatically kicking out business processes without human intervention. And then it's a platform for them where they're adding all this new capability to save in other ways and so it's just, you know, simple but clean really good use. Good for the planet. It's great for the customers. And now they have, and by the way, when you hit those peaks, that's when they short-out systems and that's when trains stall out. So actually you're getting better servicing of the trains. So, yeah, it's good storage. >> So edge core cloud, great breakdown of kind of the use case. The data is being collected at the edge. Data may not even be collected in a SAP system? (crosstalk) We're doing great! >> It's reality. >> It is reality and one of the things that I think architecturally that enterprises have a hard time wrapping their head around, HANA in-memory database defeats latency when you're inside the database, when you're inside of the data center, however you were thinking about HANA data management. How does the in-memory database impact and data management impact data retrieved from the edge? Help explain the importance of metadata and willing down that data so that we can get it back to the cloud and process their important data. >> Keith, it's a great question. Sometimes, HANA is not, you know. Although we like to go it's a hammer and we think everything's a nail but sometimes you don't which is why we have data hub. And it has unique capabilities for doing something called data pipelines and movement. So we can actually do all the data transformation movement calling tensor flow in flight. We do this as the data is in movement so we're actually doing all of that processing as it's moving through. If you need extra horsepower and want to combine different data types and there's certain capabilities pipeline engines don't solve well. HANA is a service which HANA is now completely cloud native. They can actually bring up HANA in a few seconds. It will take the data flow in, compute it, it's not being used as database, it's a compute layer out at the edge, the data flows out to move on to the next step usually via a data pipeline from data hub and that service gets shut off. So you just pay from small compute when you need to bring out the big guns and then it moves on. And maybe that data never comes back into a HANA system, maybe it does, but you're using the technological underpinnings of in-memory computing in this way as just literally a flow through compute engine. >> And I think that's the disconnect a lot of organizations have because you associate s4 bases, BW, all these applications on top of the database. They don't think of HANA as something that you can spin up, spin down. >> But that's brand-new and that is what we just announced and went live last week. So HANA was, there's traditional on-prem system, bare-metal, it run virtualized but I mean talking about big arm running HANA systems. Now to actually have it, so HANA as a service came up. We rewrote the entire thing to make it completely cloud native and orchestrated. It's all containerized in elastic. It runs, it came up last week running an AWS and available also in GCP. Our target is a little bit later this year. I always have to use a safe harbor language. It'll be coming up on, it'll be coming up in Azure and after all the rest of SAPs data centers and then also coming out and in Asia through Huawei and coming up in those data centers as well as some others we have planned. And that's where you actually get this fully elastic HANA that's able to come up and come down automatically. >> So this massive transformation that you guys have achieved in 46 years, say 46 years young, 390,000 customers. >> Yeah. SAP didn't get to where it is without having a really robust symbiotic partner relationship ecosystem. We're here in the NetApp booth. There's a 150 partner sessions alone at Sapphire this week. Talk to us a little bit about how the partner ecosystem is helping you guys give customers the flexibility and the choice that they need. >> Yeah, no, and it is. SAP can't do everything. And so a lot of the aspects are that we look at in very different ways. Of course, some companies and the big corporations we deal with need strategic SIs, these strategic integrators to do consulting and other pieces and we work really closely with them on and they have specialized practices and other things on both HANA. They're extending out into the HANA data management suite. We do the same thing since we realize you need boutiques. We're the fastest geospatial engine in the world but that's a very niche piece although geospatials may be the hottest data type out there happening right now. Those are very specialized boutique firms. So we work with all of those and to help our customers when they need that. So we work with a lot of specialists. We work boutiques but we couldn't do this without hardware partners, with storages which is why we allow. There's still a lot of folks running on Prem. So we still have to have all these things so we have HANA tailor data center integration so you can certify your systems like NetApp. You can certify everything else on prem so you don't have to rebuy new hardware. Use what you have. I'm not trying to get you to buy a bunch of new appliances. And then the other one is a lot of is via and OEMs have started building out on HANA but now what they really want to do is go directly on HDMS as the cloud offering because it runs both in any cloud, which is a very unique differentiator that we run in every major cloud out there, as well as coming back and running on-premise. They can play their applications very risk-free with the extreme security and governance we're providing within that stack to build applications that they want to sell and use for enterprises. >> So you've been with SAP about six years you said and even Bill McDermott said in his keynote on day one, biggest Sapphire ever. You've seen a tremendous amount of growth. The momentum here is so palpable. The types of validation that SAP is getting through the voice of the customer, through partners like Netta, the different partner ecosystem. That validation is electric. >> Yeah. >> What excites you about everything that was just announced in the last couple of days about the rest of 2018? Where do you go from here? >> Oh my god! Okay, it's like asking me to pick my favorite child. (crosstalk) But, you know, honestly I get to. You get to see the innovations that I still enjoy. I love the full use use cases because I'm like a compute guy at heart but I see all the applications that we've done in these demonstrations. The fact that people have applications that are giving all of the analytics in line with the transactions on these gorgeous UIs. I mean you run these things on a mobile device that means the data layer has 20 milliseconds to actually not only grab the data but to do all the predictive analytics and everything you see to give you that nice two second screen to screen time on your mobile device and that's what we've worked for six years to enable. And now we're seeing that potential coming out at places like Swiss Rail. Just was talking with Gustav Rossi through the biggest cancer research labs and hospitals throughout all of Europe. They're doing all this genomic research, personalized medicine for cancer patients throughout Europe using HANA. I didn't even know about it, you know, or other ones we talked about beef farmers. Talking about smart farming throughout all the Netherlands. Reducing pesticide use, water usage dramatically down, and they increased yields by 10 percent. I mean and they're doing this on native HANA. So this area for me, the excitement of people and busting out of the SAP core traditional CIO market and moving into this 80% of data is to me exciting that people are seeing that HANA is not just an SAP appliance but it's really a general-purpose data platform for these innovation use cases. >> Helping customers change their business, change industries, save lives, pretty cool stuff. >> Yeah, I think so. >> Chris, thank you so much for stopping by The Cube and sharing with us your enthusiasm and your excitement for what you're doing at SAP. We appreciate it. >> Well, thank you very much. This was awesome. Thank you guys. >> We want to thank you for watching The Cube. Lisa Martin with Keith Townsend at SAP Sapphire 2018. Thanks for watching! (techno music)

>> From Orlando, Florida, it's The Cube. Covering SAP Sapphire Now 2018. Brought to you by NetApp. >> Welcome to The Cube. I'm Lisa Martin with Keith Townsend in Orlando, at SAP Sapphire Now 2018. We're in the NetApp booth and we are having some great conversations, really understanding how SAP and their ecosystem of partners have really helped to transform 390,000 plus customers. We're joined next by Sue Waite, who is one of the directors of the Global Center of Excellence for Database and Data Management at SAP. Sue, thanks for coming to The Cube. >> Thank you very much for letting me join you. >> So SAP, 46 years young company, like I said, 390,000 customers and 25 plus industries. You guys have, probably, many Centers of Excellence. Give us a little bit of understanding of the COE for database and data management. >> I will, happy to do so. So, the team that I'm very, it's my pleasure to be a part of, focuses on helping our customers understand what are the new opportunities that are out there. Many customers are so driven by the day-to-day operations. How do we take that opportunity to step back and look at what perhaps other competitors have done in their space, or in completely different industries. And what are new ways that they could be looking at approaching their business, approaching their engagements with their customers, and helping them grow, as well. And our database and data management solutions are the platform that helps enable that in a truly comprehensive data management way. >> It sounds pretty symbiotic. >> Very much so. >> Where, they're actually, you're helping them, but customers are also helping you. Tell us maybe some examples, like Data Hub, for example, of one of the things that maybe that symbiotic relationship-- >> Love to. >> Helped to evolve. >> Yes, yes. So, a little history in our database and data management solutions. Of course, SAP HANA is a cornerstone to our core platforms. Very much a groundbreaking technology eight years ago in introducing a completely comprehensive platform. But one of the things we've learned as we've worked with our customers over time, so many clients, and in fact SAP itself, has our different pockets of enterprise systems. We have our CRM applications, our ERP, our finance, our, you know, supply chain. But in today's environments, we have so much more information coming at us. There's the whole big data space. Everybody's trying to pull and collect information from, of course, social media feeds. That's the one everybody thinks of. But another new space is internet of things. Collecting information from sensors off their machines, from, you know, telemetry from where their trucks are, to facial recognition as people are coming into our stores, or image recognition as we're manufacturing sheet metal on the plant floor. It's amazing the amount of information that is now available to be collected and mined to bring further insight into business operations. So, great, we can collect all of that fabulous new data and store it in Hadoop or Amazon S3 or object stores, but how do we get at that information? >> Right, and extract valuable insights-- >> Exactly. >> That they can then use to generate new products, new revenue streams, new businesses. >> Completely so. >> Yeah, and a simple example is, well, not example, but S/4 and HANA. The journey to S/4 and HANA starts mostly with BW. So if the original data warehouse and the capability that that brings to organizations, one of the first things that happens when you deploy BW on HANA is other businesses look up, other business units look up and say, "Hey! I want that capability. I want that instant analytics, that instant search." >> Yes, yes. >> Talk to the evolution of that. After we go BW and the focus is still on analytics and data intelligence. >> And it should be, you know. It is about making important decisions, in an instant now. >> Right. >> I mean, everybody looks at their phone when we make deposit. We expect to see that deposit instantaneously. >> Yes. >> Right. >> The business needs to operate just as instantaneously and with BW it has a tremendously powerful system that works hand-in-hand, as you said, with S/4, ERP, and the whole business suite itself. But then the goal was, as well, to bring in this larger context, from these other large-data environments that are being captured in Hadoop or S3. So the genesis of the idea to help address that marrying up of data, stored in our classic enterprise data warehouse, like BW, is the solution that we call Data Hub. And what Data Hub does, what's different about it, is it truly is an umbrella solution that transcends the big data environment as well as the classic enterprise systems. And in doing so, one of the first problems was we have all this fabulous information collected in our data lake. How do we get to the information that's truly useful, to combine with information in BW? Or even feed into S/4 itself? So Data Hub helps pre-process, refine, and enrich that information, and the key is doing so where the data lives. Let's not move petabytes of data around, just trying to derive intelligence from it. So Data Hub allows customers to pre process, refine, and enrich that data in their data lake itself. Get from petabytes of information to, say, gigabytes of data that is useful to combine with information in BW, or within HANA, or S/4, or whatever other systems may be useful to bring that together. And the trick with all of that is having visibility into the information that truly lives within each of those systems, which is also something that Data Hub brings to the table, because it has the ability to collect metadata. So, information about the data that lives within each of those environments, so the data analysts, who are bringing those data sets together, can intelligently know, this is the data set I want, this is how I need to refine it, and I want to combine it here, and they can set that up through pipelines and orchestration within Data Hub. It is tremendously powerful in simplifying that end-to-end scenario, and the whole goal is to make it easier for the business to get to those useful insights. Really help me have a competitive differentiator because of the great set of information I can now bring together, and bubble that up through our analytics tools. >> Yeah, access at speed, that was one of the things that Hasso Plattner-- >> Completely so. >> Plattner talked about this morning, is, everything has to be realtime. We expect it, as consumers, right? >> Yes, yes. >> And then as consumers who are also business people, which many are, you also expect that. One of the things, too, that you reminded me of, that Bill MCDermott talked about yesterday, was customers in every industry need a 360 of their customers, right? But, SAP is moving it's away from the 360 of just sales automation to really having a true, enabling a true 360 of the entire customer experience. And one of the things I liked yesterday was the notion, in a not-creepy way, but we expect that, and customers have to connect. If you can connect finance and procurement and supply chain and marketing and sales and extract those really valuable insights, faster than your competition, that's what today's digital businesses need. >> One of the simplest statements I've heard that I think is so powerful is, "Understand more about your customers, so that you can do more for your customers." That's what it's all about. Truly providing that end service to help them achieve their goals and move to that. >> So let's talk about some of the, from a high level, some of the technology to makes this capable. When you're talking about petabytes and petabytes of data, you can't move all the data, different systems have different capabilities when it comes to data transformation. I love the insight that you provided that data analysts need to be aware of the metadata, so that they can set up the transformations needed to get the reporting that they need. How does Data Hub enable the power of metadata to all these different systems, whether it's Hadoop, unstructured data, systems that we don't even control, such as social media data. How does Data Hub bring all that metadata together? >> So one of the capabilities that enables that visibility into data content is through what we call a data discovery mechanism. And Data Hub includes metadata crawlers. So it literally, anytime Data Hub has a system that it's been authorized to connect to, we can then go out and collect the metadata about the information, the data itself, that lives within those environments. And so it comes back and there's a repository within Data Hub that holds information about the tables, the column names, and then things like data types, as well as, even basic profiling information, such as, you know, minimum, maximum, how often values showing up, cardinality, even the frequency of different values that are there, down to the ability to even preview, literally look at the content within the tables. And that's so powerful for the data analysts, because they no longer have to alright, go, you know literally crack open a file, to look at the content. It's at their fingertips. And that's just an amazing tool, that, once they have that, then they can move on to the truly value-added activity of how they want to refine, enrich, mashup, that information to get to those insights that are at their fingertips. >> With so many, the C-suite, like we've talked about before, is changing so dramatically, the CDO, the CIO, the CMO, the CXO, they all have need, different needs, a need for this data. Your customer conversations, where do you start at the C-suite, in terms of, you know, they've got all of this data that they know, there's golden nuggets in there. How do we find it? And also, exploit insights for marketing, for sales, for finance, for procurement. Where do you start in terms of that conversation within a customer? Do you help unite the C-suite to understand how they can team together? >> That is always the goal, of course. And it's important to understand each customer's individual, you know, what their business is, what their market is, as well as, that company themselves, what their goals are, what they're trying to achieve. So that we can truly be, I know you've heard the term trusted advisor, but we really take that seriously, because understanding what their challenges are and where they're trying to grow their business, along with, you know, the very technical aspects of which technologies they're using today, and what roadblocks are they experiencing that are preventing them from achieving those goals. Of course, our objective is to help them cross those roadblocks, cross those bridges, and if we can help with SAP solutions to achieve those goals, it's not about rip and replace, it's helping them bridge those challenges to reach those goals. And that's the role we play. I love what I do. >> So, the Data Hub is a great example of a platform that can be expanded upon. Can you share about some of the successes that you've had with the ecosystem around Data Hub? To extend, not just the analysts who can interact with Data Hub directly, but what we like to call bolt-on applications, that extend the overall capability of whether it's analytics, AI, machine learning, the examples, or automation, business process automation. What are some of the successes coming out of making Data Hub? I know it's only a year old, but what are making the Data Hub available to your ecosystem of partners? >> Yep, so, some of the successes have been, truly, you know, efficiency, obviously, but in that ability to bring those data sets together. For example, we've been working with one customer who's, we'll just say they're a manufacturer. And they have their own team of data scientists, and they have petabytes of information they've been collecting in their data lake, and we talked with them about Data Hub and what we were seeing, and they're like, "Yeah, love the story. But, you know, our data science team is really good. I think we've got this." They literally came back to us six months later and said, "It's a whole lot more work, than we ever expected it would be." Because in a classic environment, it's a lot of hand coding, it's a lot of scripting, it's creating those predictive models which is the lifeblood, that's why we hire data scientists. But they were spending so much time and data manipulation and trying to find the right information. They're like, "Please, you know, white flag." >> Yeah, the can bring back a lot of data. >> Yeah, it literally seems like a great opportunity for the overall market to start adding value on top the, on top of Data Hub to basically shorten that timeframe for internal data scientists. They should be figuring out what questions to ask, versus figuring out how to organize the data. >> Exactly so, that's why they're being paid the big bucks. Let them do the job that we hired them to do, you know. >> Well, Sue, you said you love your job, and it's evident. Thank you so much for stopping by The Cube and sharing what you're doing within the COE for database and database management, specifically, >> Thank you very much. A pleasure to speak with you this morning. >> With Data Hub, we can't wait to hear what's next for next year. >> Alright, excellent. >> We wanna thank you for watching The Cube. I'm Lisa Martin with Keith Townsend, from SAP Sapphire Now 2018. Thanks for watching. (upbeat music)

>> From Orlando, Florida, it's theCUBE, covering SAP SAPPHIRE NOW 2018, brought to you by NetApp. >> Hey, welcome to theCUBE, I'm Lisa Martin, with Keith Townsend on the ground at SAP SAPPHIRE NOW 2018, we're in the NetApp booth, and we are joined by a CUBE alumni, Brian Ferrar, Marketing Manager for SAP at Cisco, welcome. >> Thank you, thank you, it's great to be here. >> So you are a veteran, you've been at Cisco, you said, about four years. But you have been in the SAP community for a long time. This is, I think I was reading, the 25th time that they've done an event like this. Now, obviously, an event with north of 20,000 people, a million people, Bill McDermott said, engaging online. Wow, we're in the NetApp booth. Tell us, Brian, about this trifecta: NetApp, Cisco, SAP. >> Well sure, thank you very much, first of all. We appreciate the invitation to be here. We've been working with Cisco, Cisco's been working with NetApp and SAP on solutions for our customers together for about 10 years. And in that time, our joint solution for SAP, which we called a FlexPod, which combines Cisco UCS servers with NetApp storage, and of course then there's Cisco networking. That's become one of the most preferred platforms to run SAP HANA on. There was a recent IDC survey, in fact, end of last year, in which they went out, without any consultation with us vendors, and did an independent, true market research survey, with over 300 end users of HANA, and they asked, what was the best platform, what was the most preferred platform to run on. And by far, it was FlexPod, with Cisco and NetApp. And the favorite storage platform, by far NetApp. So we think we're doing a really good job for our customers, but there's always room for improvement, so we're ever innovating, and that, I think, is the secret to our success. Constant, repetitive innovation, making it better and better and better. >> So if we look at these digital transformation platforms of the future: SAP HANA, Leonardo, and then we think about the Cisco, NetApp value prop. How does those individual components play in that equation? >> Well a couple of ways I think, it's a great question. First of all, you gotta start with the very core of what you're concerned about. This is a risky situation. You're running your companies most valuable asset, your supply chain, on this stuff. And so you wanna make sure that the platform you're using is rigorously tested and even more rigorously secured. So one of the things we're known for, and we do this with NetApp, on the FlexPod platform, is our CVDs, Cisco Validated Designs, in which we pretest and precertify everything that you would have to do to implement your SAP solution on a FlexPod. And that's all documented. So if you follow the instructions, you're gonna get a foolproof installation. Then the second way, is we need to make operation and management of these environments simpler and easier. Everybody's looking to reduce cost, reduce resources, improve performance. So one of the ways we've really distinguished ourselves in this market, especially with NetApp, is something we call policy-based infrastructure. We have a product called ACI, Application. (laughs) ACI, what's ACI? It's Application Centric Infrastructure. And it allows us to automate the deployment and management of HANA on these solutions. In fact, one SAP executive saw this and coined the term, One Click Deployment. And I won't say it's just one click deployment, there is some tweaking, but that speaks to the simplicity of deploying it on a FlexPod. But more than that, then we apply that automation to the management and the ongoing orchestration of that environment. And so, for example, if you wanna keep security threats out of your environment, you can automate our Tetration solution on top of that platform that looks at incoming code, looks for patterns, and detects inappropriate activity before it has time to harm your system. Another way we do that was with a product we're unveiling here at SAPPHIRE, which is AppDynamics for SAP. AppDynamics is a fairly famous company around monitoring applications, and Cisco acquired them about a year ago, and we're unveiling their solution for SAP in our booth with number 550, in fact. And that allows you to look all the way down to the code level and see what's happening. >> So, let's pick that apart a little bit, that's pretty amazing. I'm familiar with AppDynamics, it was a born-in-the-cloud solution. So when you think about SAP, and you think about traditional applications built on SAP, you don't think about AppDynamics, you know. AppDynamics was this thing that could allow you to monitor and troubleshoot code across clouds. What's their play with SAP. >> It's hard to say, anymore that anybody's running SAP just on premise, or just in cloud. We live in a hybrid world, a lot of people call it the multi cloud world. And you have to have these management and monitoring tools work both on prem and in the cloud. Basically, they gotta follow your data. And that's the beauty of AppDynamics, is it works across all those multi cloud environments. And I think that's the big play for us with them. We're very concerned about security, coming from a network background, we're very aware of intrusion capabilities, the size of your attack surface, how cloud actually increases the size of your attack surface. So you need a tool like AppDynamics, and other tools that Cisco has, as I mentioned our Tetration tool, to really watch that code and that data going across your infrastructure. And also to keep an eye out for bad actors. It's unfortunately a dangerous world now. Just read the news and see all the companies that have had their brand essentially held hostage with ransomware, for example. >> So let's talk about support. I love the idea of being able to take the infrastructure, outsource the engineering of that to Cisco, FlexPod, Tetration, these validated designs that makes deployment simple. But support, when there's a problem with a query, that's supporting a digital transformation initiative, who do I call? Do I call NetApp, do I call SAP, do I call Cisco? >> It's a great question, it's a great question, 'cause nobody here, not just Cisco, but no vendor here at the show today, implements a solution just on their own, and every environment has multiple pieces in the solution. Cisco takes ownership of the support of all the components, even our partner components, of any solution we deploy. So it's one stop shopping for your support calls. Now if we find it escalates to a higher and higher level, we have direct connections to our partners, third level support escalation teams, and we bring them in, and we solve the problem, but we never let go of it. We don't hand it off, we maintain that incident. No finger pointing, and if you've ever had any personal issues at home on your laptop, and tried to get somebody to help, and you call one person and they point you to another, Yeah, it just doesn't happen. >> My better half just always blames it on the network. And I'm not a network guy anymore, so it's never my fault. (laughing) >> But speaking of needing to delight customers, one of the things that, thematically, was talked about this morning in Bill Mcdermott's keynote is enabling the intelligent enterprise and really being able to embed AI into the technologies to unite the humans with the machines. I loved how he talked about augmenting humanity, and what he talked about there was really. >> The Brave New World, huh? >> Right, and kind of, not calling out their competitors by name, but we all know who they are, and really saying that what SAP is now doing is connecting, synchronizing, the demand chain with the supply chain. So enabling the customers who don't care what's under the hood, right? To focus on their customers, to get this comprehensive customer view. >> I actually really liked that part of the keynote because that description characterizes Cisco ourselves as SAP's customer. So we eat our own dog food, to use the cliche, but you talk about artificial intelligence and machine learning. Last year at SAPPHIRE, we won the HANA innovation award, for the innovation that we did on HANA with AI and machine learning. And we implement that internally, not just for our customers, but internally for ourselves, we do all our sales forecasting, and supply chain management with HANA using AI and machine learning for better insights. And it has made a world of difference to our internal supply chain and IT teams. I mean it's funny because, 20 years ago, we would have called it magic, and it's not, it's innovation. In fact that's the theme of our booth here at SAPPHIRE this week, is it's not magic, it's innovation. We actually have a magician in the booth sawing people in half. You're welcome to come by. If you fit in the booth, you can be sawed in half. >> I might be in trouble here. >> You have to be rather small, but we'll show you how the trick is even done. And that's the thing with innovation, differentiating it from magical claims that other vendors might make. We show you under the covers, how it's done, and we share everything and document everything. And that's actually going back to those CVDs that are so valuable to our customers. >> So let's talk about one of the pillars of Cisco, which is security. As we look at where data's at, we're talking about Edge, the data center, and somewhere in between, >> Yeah, everywhere in between. >> Everywhere in between, security has to follow the data. How does Cisco with NetApp help administrators follow the data? >> Oh, that's another good question. I was in a presentation from an analyst recently, it said the world's data is now increasing, it's doubling every two years. The entire world's data is doubling every two years. So how do you keep track of that and how do you manage it? One of the ways we do it, and we do this with NetApp as well and the FlexPod, is we have security embedded in every aspect, so we talk about computing at the Edge, with IOT devices, you know? Smart cars is an example everyone understands. But there's supply chain IOT out there on the Edge as well. Tracking shipments and ballots, and widgets, and units. And we talk about computing at the fog, and trying to get computing as close to the transaction as possible, for low latency, high performance. But then for deep analytics, you're bringing that data back to the core. So you've got a lot of places where you could be attacked, as I mentioned earlier, that attack surface has now grown dramatically, it's no longer isolated within the four walls of your data center. So we embed security at every place along that chain. Coming from a network heritage, we have intelligent routers, often ruggedized, that we can put out there in the Edge, with security, to catch inappropriate activity happening, coming in from an IOT source for example, from a sensor. That is not what we were expecting and could potentially be an attack. And then we can analyze it before it ever gets into your valuable data center. And so we're putting that security at the Edge, in the fog, on the servers, in the data center, on the routers, on the network, you name it. We think there's no one solution. You have to have an all encompassing end to end solution, that literally surrounds you with that security bubble, and that's what we're doing. In fact, we, by the way, to put a plugin for Cisco, we just came out with our annual Cybersecurity Report, which is one of the most popular supports on cyber security trends every year in the industry. So Cisco puts that together, and obviously takes it very seriously. >> So you mentioned AppDynamics before, monitoring SAP apps, you just mentioned security. Put that in the context of this next generation data center. What does a customer, what can they expect working with Cisco, NetApp, and SAP, to evolve to a next gen data center. >> It's an interesting question, because the very nature of the data center is changing now. I mean, you know if I'm on the road and I'm processing end of year financial closes or end of quarter financial closes, am I a data center? If I'm processing IOT data on the Edge, and because it's so critical, for example, take oil and gas. They can measure that remote oil well in dollars per second, or tens of thousands of dollars per second of down time. And so you want the data coming in from that well. Pressure, temperature, potential downtime, coming in in time to fix it before it breaks, is that now a data center? So we're talking about, what does it mean? The definitions of compute, of data capture have all changed. The idea is you've gotta follow that data. And that's what we're looking at for the future, I think, is the data center is no longer an internal monolithic, controlled environment, that you can be very certain of. Now you've gotta follow that data and adapt your security to the type of processing you're doing, whether it's in the data center core or out there on the Edge. And I think that's what we're evolving to. Someday we'll all be data centers. >> So let's talk about that, all on the data center. Developers are now developing applications, containers, they practically have data centers on their laptop. Connect the dots for us, where Cisco plays in. >> This is actually one of the latest developments, I think, in the industry, is the emergence of something called containers. And we're the first vendor to work with SAP to implement our Cisco container platform, to provide their SAP data hub with containerized access. So now, that SAP data hub can become the nucleus of all incoming data and processing all big data for new insights, regardless of the source of that data or the application that data's running on. And that's what the beauty of containers is, is it encapsulates that application, so those rules come with that data, and so now you can, literally, connect everything to that central SAP data hub, and have complete, what did Bill Mcdermott call it, 360 degree visibility. And that's made possible by the ability to tap into not just new big data solutions that you have out there, but legacy big data solutions. I mean, I'm old enough to remember when there was such a thing called the data warehouse. And they were all proprietary and there were a whole bunch of them. And there are still our customers out there running not only the new stuff, but the legacy stuff, 'cause it works, they figured it out, and they don't wanna change it, it gives them good insights. So how do you take that legacy stuff now, and link it and combine it with all the new stuff coming out of, for example, your SAP supply chain, and the answer is the containers on top of that SAP data hub will do that for you. And that's really where we're taking it. There was a language, Esperanto, years and years ago, that was created in the '60s, '50s even. And I think the idea was it was gonna be a universal language that anybody could speak. So I don't speak Spanish, if they don't speak English, but we both speak Esperanto. And of course it never took off, because it was yet another language to learn. But the idea, the concept of having this in between piece that makes anything connect to anything is still a very intriguing idea for the human mind. And you can apply that to this data sphere, this global data sphere, and now, with something like a data hub tool and containers, that serve that encapsulation purpose, you can actually have a nucleus of big data and analytics in your company, that doesn't care where the data was originated from or what application it's running. It's still available to plug into your analysis, your planning. >> Well who knew, SAP, we heard Kubernetes, and AppDynamics in one interview at SAP SAPPHIRE, that's amazing. >> Mind blown? >> I get paid by the buzzword, you know. >> Wow! >> Yeah, I'm in marketing. >> So am I, I gotta tap into your expertise now. Brian, thank you so much for stopping by theCUBE, and talking with Keith and me about what's new with Cisco, your partnership with SAP, and NetApp, and happy birthday. >> Thank you very much, I appreciate it. Come to the party. I'm actually having Justin Timberlake perform this year. >> That's very nice of you. >> You're all invited. >> Well thank you, wow, I'm glad I could make it to your birthday party. >> You guys have a great day. >> We wanna thank you for watching theCUBE, we are at SAP SAPPHIRE 2018. I'm Lisa Martin with Keith Townsend, thanks for watching.