>> Narrator: From around the globe it's theCUBE with digital coverage of AWS reinvent 2020. Sponsored by Intel, AWS and our community partners. >> All right, you're continuing coverage of AWS reinvent 2020 virtual event. We get the pleasure of covering this show like no other AWS reinvent. We are pulling in from the other side of the world Tomer Levy, CEO of Logz.io. First time Cuber so we're going to ease them into it but it's going to be a great conversation. I'm Keith Townsend at CTO advisor. Tomer, welcome to the show. >> Keith, thank you for having me. I'm super excited to be here. >> You know what? We love having founders here on theCUBE. We have a long history of having deep conversations with builders and we're probably the show for builders. AWS reinvent is virtual. However, I think the spirit of re-invent is highlighted in companies like this. We've seen a lot of observability companies sprout up around the industry. AWS is a big, big magnet for these types of solutions. What's the assets Logz.io and how are you guys differentiating yourselves in this crowded space? >> Yeah, absolutely Keith you see observability is so fundamental to building applications on AWS that as companies develop more applications, they have to have solid observability. And we have a mission and our mission is to enable develop engineers and any engineer out there to use open source to run their observability. So when we were developers we wanted to use open source but we had to compromise on a proprietary solution. We decided to build the company so engineers can use the observability tools they're already using for logging, for metrics, for tracing, Whatever they're already using we want to enable them to use that at scale on AWS. So it's easy to use, it's super smart and the data is coordinated. And I think fundamentally it's what we're doing very differently in the market. There is no other company in the market today that takes the best open sources and bring them together as one super strong platform and we're proud to be that company. >> Well, when you say there's no other company doing open source the way that you guys are doing it, that really intrigues me especially as we look at this from the angle of Cooper Netties, the CEO of the leading virtualization company called Kubernetes, the doubts home of the internet. How do you see the intersection of opensource observability in kubernetes especially in the public cloud? >> Yeah, for sure. People say that kubernetes is almost the operating system of the future and why do people use kubernetes? They use it to make sure they can run multiple microservices. They can take their application which used to be a monolith and put it in a distributed way. So it becomes so much harder to monitor or to troubleshoot even to secure applications. So the way we built Logz.io was really designed for companies that are moving into the cloud, companies moving into kubernetes, into microservices and by having logs and metrics and traces all work together through the best open sources. I think we can help customers really get the visibility and just accelerate the software delivery. Just provide better service to their customers. >> So Levy, walk me through that journey. What is it like for a developer to come from their traditional open source roots and enter the cloud where they're melding public cloud services in AWS alongside their tools that they're using in observability. How do you help ease that transition? >> Yeah, absolutely Keith because one of the main drivers for companies adopting tools like Logz.io is actually the migration to AWS. So imagine now migration to a new ground, what do you have to think about first? Do I have the glasses? Can I see what's going on? Like when I see what's going on, I feel more confident. So if I'm now using, let's call it elk or using the open-source Grafana or using tools like Jaeger, which are all open sources too that we offer as part of our platform. So when I use these tools I'm using them to get visibility into my own application, my own infrastructure. So Logz.io faster transition to Logz.io is super easy. This is the whole notion of having an open source compatible platform. So I want to move to Loz.io, everything that worked with my open source currently still works with Logz.io but now when you move to the cloud Logz.io on AWS, we have a very strong relationship so all the services are automatically monitored. You have pre-configured dashboard, everything is interconnected so just when I jump into the AWS platform I immediately get visibility of my existing apps and of the AWS infrastructure. And that eventually helped me become confident, grow and deliver faster on AWS. >> So again this is a conference full of builders but you used the term devOps. We're starting to see a bleeding of DevOps and builders or operations and builders come together. One of the big trans and DevOps and observability is AI and machine learning. What are some of the features of AI and Machine Learning you guys are bringing to bear to this market? >> Yeah, listen I'm a big believer in AI. You know, the amount of data that companies like Logz.io have to ingest and our customers have to process. It's just something a human being cannot possibly understand. It's like billions and millions of lines of data. So this is where we bring machines to help humans. I'll give you one example, right? If you're a DevOps engineer and you see an issue in your logs, what do you do? You usually copy that and putting it into Google and you'll end up on stack overflow, maybe on GitHub, maybe on another website. What we have done is we've scraped the web and we have learned from any user on our platform. So we actually know which log line is important and which one is not. So when companies send a log line, our AI automatically scans it and says, "Hey, here are the billion log lines. No one cares about but here is one that you should really look at right now because either you know half a million people that were searching for it. There are 7,000 alerts on this and it just happened to you. Keith look, maybe you should jump in and look at that". This is where AI makes us just better operate or better DevOp people and not kind of try to replace us. >> So I'm a technical founder, you're a technical founder, theCUBE loves supporting founders. One of the advantages of being the CEO of your company is that you get to decide the culture and the mission of your company. Talk to me about the people side of your organization and how you're making a change for the better. >> Yeah, absolutely. You know, it is a privilege and to the privilege to start and come with a mission that you want to change something in the world and we were just two developers, a staff, my co-founder and myself having to use a product we didn't want to use and you know still really wanted to use an open source product. So we said let's build the company around that and this is kind of set the mission for the company as the company evolved, so is our mission. It evolves from logging to monitoring, to tracing and we also added a cloud SIEM solution all based on open source. So we're going to DevOps engineers and any engineers and we tag any engineer we tell them, "Hey, you can use the best open source tools in the cloud is one platform without compromising". And that's something that really is very differentiated today and I'm very humbled and excited to be part of this journey and I think the team at Logz.io is as well. >> You know I'm always intrigued about this journey to the cloud. Security is one of these things that intrigues me especially as we look at something as mature in the way open source. We often associate open source with public cloud, cloud native but open source is as old as technology itself. So there is a lot of practices that we bring from legacy, traditional infrastructures into the public cloud. So talk to me about that transition of security and security models? How does observability help to either take our existing tools and migrate them to the public cloud or adopt all new cloud native tools in the public cloud? >> Yeah, for sure. I think security is probably together with observability. One of the top priority that when you think about CTOs and VP of Engineering and CSOs, they're concerned about. So we've taken the observability path and bringing better glasses to our users and then on the security side there's a whole market called the SIEM market where companies look at detecting threats, investigating them and most of these tools were that companies use our legacy, incumbents and for design on their own premises world. And are not really a fit for the dynamic world of kubernetes and the cloud. And this is when we decided a couple of years ago to launch a product in that space and today this product is extremely successful. We have customers protecting their AWS environments across the board. So basically with one product for observability, you can with a single checkbox enable security and then you can detect threats. You can look at kind of the common pitfalls of AWS environment and how you can avoid them. And eventually when you see a threat, you can use our tool to investigate and find the root cause in a tool which was designed on AWS for AWS. And it's really designed for the kind of the native cloud environment rather than the on-premise as well. >> Now, is there an integration between the AI ML law of management and the threat management solutions from our observability perspective? >> Yeah, for sure. This is the beauty, it's all one data platform. So customers ship their data, loads, metrics and traces into one place and then we start to look at how can we provide more value on the data, right? How can we look at the logs from an operational perspective and tell you, "Hey, your production might be going down because of a production risks or maybe we can provide you threat intelligence". We can enrich the data and tell you, "Hey, we think you're undergoing an attack right now". So this is all done by users and it is all enraged by AI that provides more visibility, more enrichment of the data and just advice on where to look. >> So Tomer levy, CEO, founder of Logz.io. You're now a few belong. Thank you for joining the show. I hope you have a very successful AWS reinvent. Speaking of AWS reinvent, theCUBE's nonstop coverage of AWS reinvent continues. Watch some of the world's greatest builders, innovators get challenged on their vision and for us to understand and appreciate the work that's been done in this dynamic community. Continue to watch this coverage and more. Talk to you next interview on the CUBE's coverage, of AWS reinvent 2020. (soft music)

>> Announcer: Live from Las Vegas, it's theCUBE covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with its ecosystem partners. >> Welcome back inside the Sands. We are in Las Vegas. We are live here on theCUBE along with Dave Vellante, I'm John Walls. We continue our coverage of AWS re:Invent 2019 by welcoming in Erik Kaulberg, VP of cloud solutions at Infinidat. Erik, good to see you today. Thanks for joining us. >> Thanks, it's nice to see you too. >> So share a little bit at home for the folks who might not be too familiar with Infindat. I know you guys, big in the, in data storage, in terms of what's happening in the enterprise, but shed a little bit of light on that for us. >> Yeah, so Infinidat's all about re-inventing the next generation of data storage at multi-petabyte scale, whether that's for on-prem appliances where we have over 5.4 exabytes deployed now around the world, large enterprises, or whether that's through our cloud services like Neutrix Cloud, which we're talking a lot about today and through the conference, we're solving large data challenges for customers with blocker file storage requirements. We're doing that through technology that gets the price point of hard drives with the performance capabilities of solid state media, DRAM and flash, and we're doing it at very large scale, even though we kind of fly under the radar a bit from a marketing standpoint. >> So there's a lot of interesting things going on. Good storage demand. There's no question that the cloud is eating away at some of the traditional on-prem, and there's very few companies that are gaining share rapidly. You happen to be one of them. You know, Pure Storage grew 15% this quarter. Much, much lower. You know, generally HBE's shrinking. I think Delium C grew a little bit. You know, IBM has been down. I don't think they've announced yet. So you're seeing a couple of things. Cloud eating away, and then all this injection of flash. You're really the only guys who can make spinning disk run faster, as fast as flash. Everybody else is just throwing flash at the problem. And that's created headroom. So what are you guys seeing, 'cause you're clearly growing. You're a market share gainer. You have the advantage of being new and smaller. Talk about your business and how you're growing and why you're growing. >> It's nothing but growth, and it comes from this increase in the overall data that, requirements that customers have, and it comes from the economic aspects of that data. Fundamentally, data storage is all about economics, and we're able to deliver through our technical advantage of blending disk, flash, and DRAM an order of magnitude cost basis advantage, and that translates into direct financial benefits that allow ultimately enterprises to do more with their data. That's what we're all about. >> So as workloads shift to the cloud, there's an on-prem component. We're going to talk about cloud, multicloud, hybrid cloud, et cetera. But you've got a product called Neutrix. Talk about that and where it fits into this big macro trend that we've just been talking about. >> Absolutely. So Neutrix fits into the broader landscape in a couple of ways. First of all, many of the clients that we deal with are large enterprises, and they're in their relatively early stages of cloud transformation. So Neutrix provides an easy on ramp for them to come from our best in class on-prem infrastructure and make that data accessible in one or multiple clouds. And that kind of, maybe it's for test dev. Maybe it's for a disaster recovery, a pilot light scenario, or a couple other use cases for general purpose primary data storage. That's their on ramp to then taking advantage of the more strategic value of Neutrix, which is allowing clouds to compete for the business on the compute side of things. >> You kind of hit a key word in there. I'm talking about transform. And we've talked about that a lot, transformation versus transition, in terms of storage capabilities, enterprise storage capabilities, whatever. Take us through that transformation, if you will, and not the transition, and what's the paradigm change? What's going on in that space that's requiring people tom ake this dive into the deep end, if you will, and not just tickling the water with their toes. >> Well, I think there's two elements to it. There's a business and kind of a philosophical reorientation around taking advantage of flexible resources and allowing infrastructure to change over time and pay opex-based business models, that sort of stuff, and getting comfortable with that honestly is a journey into and of itself, because many procurement organizations, especially large organizations, they don't know what to do with a monthly bill or an uncommitted reserve amount or things like that. So part of it is being able to walk with the customer as they transform on the business side of things, and then the other side is accepting and going down the path of variable workloads, being able to accommodate large varieties of mixed data environments, and be agile on the technology side so that you can put the data where it needs to be with the performance that it needs to be and with the capabilities that it needs to be. >> All right, so we're pressed for time, so I really want to get a few topics in. For now, I see three main opportunities, broadly. One is on-prem, stealing market share. We talked about that a little bit. Two is this multicloud thing, and we'll talk about that, as well. If you're an on-prem company, you got to have a multicloud strategy, and even if you're a pure cloud company, you got to have a multicloud strategy. And the third is the cloud. You've got to embrace the cloud. If you deny the cloud, you're denying the biggest trend. So let's start with the cloud. What's your cloud strategy? What's your relationship with AWS and how are you taking advantage of that? >> So we're all about delivering our data services in whatever means, whatever physical infrastructure, whatever underlying business model the customer requires. With that in mind, we deliver Neutrix Cloud as a service for use with major public cloud environments, including AWS, and our relationship with AWS, you know, they recognize, I think, they would say that we bring access to large-scale, tier one environments all around the world coming from our base on the on-prem, and they're very interested in obviously working with the customers on cloud transformation at the scale that we operate, as well, so it's a mutually beneficial partnership. We're proud to be an APN member and all of that sort of thing. >> Yeah, I mean, if you can put your stack in the AWS cloud, which is what you're doing, it's going to drive other services, right? It's going to drive ML and SageMakers and backup and all kinds of great things. >> Absolutely. >> So the storage guys at AWS may not love you, but everybody else at AWS is going to be happy because you're driving other services. All right, let's talk about multicloud. It's obviously a controversial topic. We've got, John Furrier every year does a exclusive interview with Andy Jassy, and he's on the record, and I think he's right. He says, look it, multicloud is going to be more complex, less secure, and more expensive. He's right. And he goes, but he also recognizes that there are multiple clouds out there, and so organizations have to participate in multicloud strategies. I've predicted, as have Stu Miniman and John Furrier, Amazon's going to participate in that someday. They're going to do what they're doing in hybrid. So Amazon looks at multicloud as multiple public clouds and on-prem as hybrid. Coming back to Infinidat, what's your multicloud strategy? >> So the great thing about our strategy is that we're able to deliver the same data in whatever public cloud environments the customer wants to deploy. So we actually run our own independent infrastructure that sits just outside the walled gardens of all the major public clouds, and then we can provide network connectivity using their direct connect interfaces or similar private network interconnects, all API-driven, customer doesn't have to think about the underlying infrastructure, and fundamentally it allows them to subscribe to our storage as a service directly in whatever public clouds they choose. >> And now let's talk about the on-prem piece of that, which is the hybrid component, using Jassy's sort of definitional framework. You've got Flex. That extends your on-prem story. Talk about that a little bit. >> Absolutely. So our customers are saying, "Hey, I want the public cloud business model "on the on-prem environment," and Flex is our answer to that kind of question. So we deliver essentially hardware independence, price per gig per month. We maintain title to the asset, all that sort of stuff. And we're in charge of refreshing the infrastructure every three years, and we back it with a more than public cloud level availability guarantee, 100% availability guarantee for the Flex business model. >> We've seen companies, flash-based products as backup targets. Infinidat uses a combination of flash and spinning disks to keep costs down, and you've got math magic to make it as performant. One of the things I like what you're doing is you're partnering with I think most of, if not all the backup software vendors and opening up new market opportunities and expanding your TAM by partnering with those guys. Talk a little bit about, can you give us some specifics there? >> Absolutely. So, for example, we were presenting at the Veeam booth earlier this week about the intersection between InfiniBox and the Veeam backup software suite, and we have similar capabilities with some of the other backup platforms, as well. So two sides to that, one using the on-prem or cloud environments as a source, and there we have integrations with our snapshot technology specifically, and then two, using our InfiniGuard product on the on-prem side as a target, and there we have deep integration at an API level with the various backup platforms. So it's a cohesive universe where customers can take primary data, they can put it on Infinidat, they can use whatever enterprise backup platform. They can also put it as a target on Infinidat technology. >> And we're talking a lot about today. What about tomorrow? I mean, you know, what's the bigger picture down the road? What's your crystal ball telling you in terms of future complexities and challenges and what you see where this is headed? >> I think from a storage standpoint, at least, obviously lots of other complexities beyond that universe, but from a storage standpoint, people want to stop thinking about infrastructure. They want to think about cloud data services. They want to think about essentially going from storage arrays to storage clouds. We're doing that on on-prem, we're doing that in public cloud environments, and we're knitting it all together with our initiative called the Elastic Data Fabric. Our ultimate goal there and what we think customers really want is to be able to get the data services that they want at any given instant through the business model they care about independent of the underlying infrastructure, and that's what we're set up to deliver over the next couple of years at Infinidat. >> Well, Erik, thank you for the time. We appreciate that. By the way, Erik has become a very important Cuber, a VIC. His sixth appearance here on theCUBE. I wish we had a plaque or something to give you, but how about just an attaboy? >> Thanks very much. >> We appreciate that. >> Thanks, Erik. >> Back with more coverage here from AWS re:Invent 2019. You're watching us live. We're here on theCUBE. (techno music)

>> Announcer: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Welcome back to New York City. Along with Dave Vellante, I'm John Walls. We continue our coverage here on theCUBE of machine learning everywhere. Build your ladder to AI, IBM our host here today. We put together, occasionally at these events, a panel of esteemed experts with deep perspectives on a particular subject. Today our influencer panel is comprised of three well-known and respected authorities in this space. Glad to have Colin Sumpter here with us. He's the man with the mic, by the way. He's going to talk first. But, Colin is an IT architect with CrowdMole. Thank you for being with us, Colin. Jennifer Shin, those of you on theCUBE, you're very familiar with Jennifer, a long time Cuber. Founded 8 Path Solutions, on the faculty at NYU and Cal Berkeley, and also with us is Craig Brown, a big data consultant. And a home game for all of you guys, right, more or less here we are in the city. So, thanks for having us, we appreciate the time. First off, let's just talk about the title of the event, Build Your Path... Or Your Ladder, excuse me, to AI. What are those steps on that ladder, Colin? The fundamental steps that you've got to jump on, or step on, in order to get to that true AI environment? >> In order to get to that true AI environment, John, is a matter of mastering or organizing your information well enough to perform analytics. That'll give you two choices to do either linear regression or supervised classification, and then you actually have enough organized data to talk to your team and organize your team around that data to begin that ladder to successively benefit from your data science program. >> Want to take a stab at it, Jennifer? >> So, I would say, compute, right? You need to have the right processing, or at least the ability to scale out to be able to process the algorithm fast enough to be able to find value in your data. I think the other thing is, of course, the data source itself. Do you have right data to answer the questions you want to answer? So, I think, without those two things, you'll either have a lot of great data that you can't process in time, or you'll have a great process or a great algorithm that has no real information, so your output is useless. I think those are the fundamental things you really do need to have any sort of AI solution built. >> I'll take a stab at it from the business side. They have to adopt it first. They have to believe that this is going to benefit them and that the effort that's necessary in order to build into the various aspects of algorithms and data subjects is there, so I think adopting the concept of machine learning and the development aspects that it takes to do that is a key component to building the ladder. >> So this just isn't toe in the water, right? You got to dive in the deep end, right? >> Craig: Right. >> It gets to culture. If you look at most organizations, not the big five market capped companies, but most organizations, data is not at their core. Humans are at their core, human expertise and data is sort of bolted on, but that has to change, or they're going to get disrupted. Data has to be at the core, maybe the human expertise leverages that data. What do you guys seeing with end customers in terms of their readiness for this transformation? >> What I'm seeing customers spending time right now is getting out of the silos. So, when you speak culture, that's primarily what the culture surrounds. They develop applications with functionality as a silo, and data specific to that functionality is the component in which they look at data. They have to get out of that mindset and look at the data holistically, and ultimately, in these events, looking at it as an asset. >> The data is a shared resource. >> Craig: Right, correct. >> Okay, and again, with the exception of the... Whether it's Google, Facebook, obviously, but the Ubers, the AirBNB's, etc... With the exception of those guys, most customers aren't there. Still, the data is in silos, they've got myriad infrastructure. Your thoughts, Jennifer? >> I'm also seeing sort of a disconnect between the operationalizing team, the team that runs these codes, or has a real business need for it, and sometimes you'll see corporations with research teams, and there's sort of a disconnect between what the researchers do and what these operations, or marketing, whatever domain it is, what they're doing in terms of a day to day operation. So, for instance, a researcher will look really deep into these algorithms, and may know a lot about deep learning in theory, in theoretical world, and might publish a paper that's really interesting. But, that application part where they're actually being used every day, there's this difference there, where you really shouldn't have that difference. There should be more alignment. I think actually aligning those resources... I think companies are struggling with that. >> So, Colin, we were talking off camera about RPA, Robotic Process Automation. Where's the play for machine intelligence and RPA? Maybe, first of all, you could explain RPA. >> David, RPA stands for Robotic Process Automation. That's going to enable you to grow and scale a digital workforce. Typically, it's done in the cloud. The way RPA and Robotic Process Automation plays into machine learning and data science, is that it allows you to outsource business processes to compensate for the lack of human expertise that's available in the marketplace, because you need competency to enable the technology to take advantage of these new benefits coming in the market. And, when you start automating some of these processes, you can keep pace with the innovation in the marketplace and allow the human expertise to gradually grow into these new data science technologies. >> So, I was mentioning some of the big guys before. Top five market capped companies: Google, Amazon, Apple, Facebook, Microsoft, all digital. Microsoft you can argue, but still, pretty digital, pretty data oriented. My question is about closing that gap. In your view, can companies close that gap? How can they close that gap? Are you guys helping companies close that gap? It's a wide chasm, it seems. Thoughts? >> The thought on closing the chasm is... presenting the technology to the decision-makers. What we've learned is that... you don't know what you don't know, so it's impossible to find the new technologies if you don't have the vocabulary to just begin a simple research of these new technologies. And, to close that gap, it really comes down to the awareness, events like theCUBE, webinars, different educational opportunities that are available to line of business owners, directors, VP's of systems and services, to begin that awareness process, finding consultants... begin that pipeline enablement to begin allowing the business to take advantage and harness data science, machine learning and what's coming. >> One of the things I've noticed is that there's a lot of information out there, like everyone a webinar, everyone has tutorials, but there's a lot of overlap. There aren't that many very sophisticated documents you can find about how to implement it in real world conditions. They all tend to use the same core data set, a lot of these machine learning tutorials you'll find, which is hilarious because the data set's actually very small. And I know where it comes from, just from having the expertise, but it's not something I'd ever use in the real world. The level of skill you need to be able to do any of these methodologies. But that's what's out there. So, there's a lot of information, but they're kind of at a rudimentary level. They're not really at that sophisticated level where you're going to learn enough to deploy in real world conditions. One of the things I'm noticing is, with the technical teams, with the data science team, machine learning teams, they're kind of using the same methodologies I used maybe 10 years ago. Because the management who manage these teams are not technical enough. They're business people, so they don't understand how to guide them, how to explain hey maybe you shouldn't do that with your code, because that's actually going to cause a problem. You should use parallel code, you should make sure everything is running in parallel so compute's faster. But, if these younger teams are actually learning for the first time, they make the same mistakes you made 10 years ago. So, I think, what I'm noticing is that lack of leadership is partly one of the reasons, and also the assumption that a non-technical person can lead the technical team. >> So, it's just not skillset on the worker level, if you will. It's also knowledge base on the decision-maker level. That's a bad place to be, right? So, how do you get into the door to a business like that? Obviously, and we've talked about this a little bit today, that some companies say, "We're not data companies, we're not digital companies, we sell widgets." Well, yeah but you sell widgets and you need this to sell more widgets. And so, how do you get into the door and talk about this problem that Jennifer just cited? You're signing the checks, man. You're going to have to get up to speed on this otherwise you're not going to have checks to sign in three to five years, you're done! >> I think that speaks to use cases. I think that, and what I'm actually saying at customers, is that there's a disconnect and an understanding from the executive teams and the low-level technical teams on what the use case actually means to the business. Some of the use cases are operational in nature. Some of the use cases are data in nature. There's no real conformity on what does the use case mean across the organization, and that understanding isn't there. And so, the CIO's, the CEO's, the CTO's think that, "Okay, we're going to achieve a certain level of capability if we do a variety of technological things," and the business is looking to effectively improve some or bring some efficiency to business processes. At each level within the organization, the understanding is at the level at which the discussions are being made. And so, I'm in these meetings with senior executives and we have lots of ideas on how we can bring efficiencies and some operational productivity with technology. And then we get in a meeting with the data stewards and "What are these guys talking about? They don't understand what's going on at the data level and what data we have." And then that's where the data quality challenges come into the conversation, so I think that, to close that cataclysm, we have to figure out who needs to be in the room to effectively help us build the right understanding around the use cases and then bring the technology to those use cases then actually see within the organization how we're affecting that. >> So, to change the questioning here... I want you guys to think about how capable can we make machines in the near term, let's talk next decade near term. Let's say next decade. How capable can we make machines and are there limits to what we should do? >> That's a tough one. Although you want to go next decade, we're still faced with some of the challenges today in terms of, again, that adoption, the use case scenarios, and then what my colleagues are saying here about the various data challenges and dev ops and things. So, there's a number of things that we have to overcome, but if we can get past those areas in the next decade, I don't think there's going to be much of a limit, in my opinion, as to what the technology can do and what we can ask the machines to produce for us. As Colin mentioned, with RPA, I think that the capability is there, right? But, can we also ultimately, as humans, leverage that capability effectively? >> I get this question a lot. People are really worried about AI and robots taking over, and all of that. And I go... Well, let's think about the example. We've all been online, probably over the weekend, maybe it's 3 or 4 AM, checking your bank account, and you get an error message your password is wrong. And we swear... And I've been there where I'm like, "No, no my password's right." And it keeps saying that the password is wrong. Of course, then I change it, and it's still wrong. Then, the next day when I login, I can login, same password, because they didn't put a great error message there. They just defaulted to wrong password when it's probably a server that's down. So, there are these basics or processes that we could be improving which no one's improving. So you think in that example, how many customer service reps are going to be contacted to try to address that? How many IT teams? So, for every one of these bad technologies that are out there, or technologies that are not being run efficiently or run in a way that makes sense, you actually have maybe three people that are going to be contacted to try to resolve an issue that actually maybe could have been avoided to begin with. I feel like it's optimistic to say that robots are going to take over, because you're probably going to need more people to put band-aids on bad technology and bad engineering, frankly. And I think that's the reality of it. If we had hoverboards, that would be great, you know? For a while, we thought we did, right? But we found out, oh it's not quite hoverboards. I feel like that might be what happens with AI. We might think we have it, and then go oh wait, it's not really what we thought it was. >> So there are real limits, certainly in the near to mid to maybe even long term, that are imposed. But you're an optimist. >> Yeah. Well, not so much with AI but everything else, sure. (laughing) AI, I'm a little bit like, "Well, it would be great, but I'd like basic things to be taken care of every day." So, I think the usefulness of technology is not something anyone's talking about. They're talking about this advancement, that advancement, things people don't understand, don't know even how to use in their life. Great, great is an idea. But, what about useful things we can actually use in our real life? >> So block and tackle first, and then put some reverses in later, if you will, to switch over to football. We were talking about it earlier, just about basics. Fundamentals, get your fundamentals right and then you can complement on that with supplementary technologies. Craig, Colin? >> Jen made some really good points and brought up some very good points, and so has... >> John: Craig. >> Craig, I'm sorry. (laughing) >> Craig: It's alright. >> 10 years out, Jen and Craig spoke to false positives. And false positives create a lot of inefficiency in businesses. So, when you start using machine learning and AI 10 years from now, maybe there's reduced false positives that have been scored in real time, allowing teams not to have their time consumed and their business resources consumed trying to resolve false positives. These false positives have a business value that, today, some businesses might not be able to record. In financial services, banks count money not lended. But, in every day business, a lot of businesses aren't counting the monetary consequences of false positives and the drag it has on their operational ability and capacity. >> I want to ask you guys about disruption. If you look at where the disruption, the digital disruptions, have taken place, obviously retail, certainly advertising, certainly content businesses... There are some industries that haven't been highly disruptive: financial services, insurance, we were talking earlier about aerospace, defense rather. Is any business, any industry, safe from digital disruption? >> There are. Certain industries are just highly regulated: healthcare, financial services, real estate, transactional law... These are very extremely regulated technologies, or businesses, that are... I don't want to say susceptible to technology, but they can be disrupted at a basic level, operational efficiency, to make these things happen, these business processes happen more rapidly, more accurately. >> So you guys buy that? There's some... I'd like to get a little debate going here. >> So, I work with the government, and the government's trying to change things. I feel like that's kind of a sign because they tend to be a little bit slower than, say, other private industries, or private companies. They have data, they're trying to actually put it into a system, meaning like if they have files... I think that, at some point, I got contacted about putting files that they found, like birth records, right, marriage records, that they found from 100-plus years ago and trying to put that into the system. By the way, I did look into it, there was no way to use AI for that, because there was no standardization across these files, so they have half a million files, but someone's probably going to manually have to enter that in. The reality is, I think because there's a demand for having things be digital, we aren't likely to see a decrease in that. We're not going to have one industry that goes, "Oh, your files aren't digital." Probably because they also want to be digital. The companies themselves, the employees themselves, want to see that change. So, I think there's going to be this continuous move toward it, but there's the question of, "Are we doing it better?" It is better than, say, having it on paper sometimes? Because sometimes I just feel like it's easier on paper than to have to look through my phone, look through the app. There's so many apps now! >> (laughing) I got my index cards cards still, Jennifer! Dave's got his notebook! >> I'm not sure I want my ledger to be on paper... >> Right! So I think that's going to be an interesting thing when people take a step back and go like, "Is this really better? Is this actually an improvement?" Because I don't think all things are better digital. >> That's a great question. Will the world be a better, more prosperous place... Uncertain. Your thoughts? >> I think the competition is probably the driver as to who has to this now, who's not safe. The organizations that are heavily regulated or compliance-driven can actually use that as the reasoning for not jumping into the barrel right now, and letting it happen in other areas first, watching the technology mature-- >> Dave: Let's wait. >> Yeah, let's wait, because that's traditionally how they-- >> Dave: Good strategy in your opinion? >> It depends on the entity but I think there's nothing wrong with being safe. There's nothing wrong with waiting for a variety of innovations to mature. What level of maturity, I think, is the perspective that probably is another discussion for another day, but I think that it's okay. I don't think that everyone should jump in. Get some lessons learned, watch how the other guys do it. I think that safety is in the eyes of the beholder, right? But some organizations are just competition fierce and they need a competitive edge and this is where they get it. >> When you say safety, do you mean safety in making decisions, or do you mean safety in protecting data? How are you defining safety? >> Safety in terms of when they need to launch, and look into these new technologies as a basis for change within the organization. >> What about the other side of that point? There's so much more data about it, so much more behavior about it, so many more attitudes, so on and so forth. And there is privacy issues and security issues and all that... Those are real challenges for any company, and becoming exponentially more important as more is at stake. So, how do companies address that? That's got to be absolutely part of their equation, as they decide what these future deployments are, because they're going to have great, vast reams of data, but that's a lot of vulnerability too, isn't it? >> It's as vulnerable as they... So, from an organizational standpoint, they're accustomed to these... These challenges aren't new, right? We still see data breaches. >> They're bigger now, right? >> They're bigger, but we still see occasionally data breaches in organizations where we don't expect to see them. I think that, from that perspective, it's the experiences of the organizations that determine the risks they want to take on, to a certain degree. And then, based on those risks, and how they handle adversity within those risks, from an experience standpoint they know ultimately how to handle it, and get themselves to a place where they can figure out what happened and then fix the issues. And then the others watch while these risk-takers take on these types of scenarios. >> I want to underscore this whole disruption thing and ask... We don't have much time, I know we're going a little over. I want to ask you to pull out your Hubble telescopes. Let's make a 20 to 30 year view, so we're safe, because we know we're going to be wrong. I want a sort of scale of 1 to 10, high likelihood being 10, low being 1. Maybe sort of rapid fire. Do you think large retail stores are going to mostly disappear? What do you guys think? >> I think the way that they are structured, the way that they interact with their customers might change, but you're still going to need them because there are going to be times where you need to buy something. >> So, six, seven, something like that? Is that kind of consensus, or do you feel differently Colin? >> I feel retail's going to be around, especially fashion because certain people, and myself included, I need to try my clothes on. So, you need a location to go to, a physical location to actually feel the material, experience the material. >> Alright, so we kind of have a consensus there. It's probably no. How about driving-- >> I was going to say, Amazon opened a book store. Just saying, it's kind of funny because they got... And they opened the book store, so you know, I think what happens is people forget over time, they go, "It's a new idea." It's not so much a new idea. >> I heard a rumor the other day that their next big acquisition was going to be, not Neiman Marcus. What's the other high end retailer? >> Nordstrom? >> Nordstrom, yeah. And my wife said, "Bad idea, they'll ruin it." Will driving and owning your own car become an exception? >> Driving and owning your own car... >> Dave: 30 years now, we're talking. >> 30 years... Sure, I think the concept is there. I think that we're looking at that. IOT is moving us in that direction. 5G is around the corner. So, I think the makings of it is there. So, since I can dare to be wrong, yeah I think-- >> We'll be on 10G by then anyway, so-- >> Automobiles really haven't been disrupted, the car industry. But you're forecasting, I would tend to agree. Do you guys agree or no, or do you think that culturally I want to drive my own car? >> Yeah, I think people, I think a couple of things. How well engineered is it? Because if it's badly engineered, people are not going to want to use it. For instance, there are people who could take public transportation. It's the same idea, right? Everything's autonomous, you'd have to follow in line. There's going to be some system, some order to it. And you might go-- >> Dave: Good example, yeah. >> You might go, "Oh, I want it to be faster. I don't want to be in line with that autonomous vehicle. I want to get there faster, get there sooner." And there are people who want to have that control over their lives, but they're not subject to things like schedules all the time and that's their constraint. So, I think if the engineering is bad, you're going to have more problems and people are probably going to go away from wanting to be autonomous. >> Alright, Colin, one for you. Will robots and maybe 3D printing, for example RPA, will it reverse the trend toward offshore manufacturing? >> 30 years from now, yes. I think robotic process engineering, eventually you're going to be at your cubicle or your desk, or whatever it is, and you're going to be able to print office supplies. >> Do you guys think machines will make better diagnoses than doctors? Ohhhhh. >> I'll take that one. >> Alright, alright. >> I think yes, to a certain degree, because if you look at the... problems with diagnosis, right now they miss it and I don't know how people, even 30 years from now, will be different from that perspective, where machines can look at quite a bit of data about a patient in split seconds and say, "Hey, the likelihood of you recurring this disease is nil to none, because here's what I'm basing it on." I don't think doctors will be able to do that. Now, again, daring to be wrong! (laughing) >> Jennifer: Yeah so--6 >> Don't tell your own doctor either. (laughing) >> That's true. If anything happens, we know, we all know. I think it depends. So maybe 80%, some middle percentage might be the case. I think extreme outliers, maybe not so much. You think about anything that's programmed into an algorithm, someone probably identified that disease, a human being identified that as a disease, made that connection, and then it gets put into the algorithm. I think what w6ll happen is that, for the 20% that isn't being done well by machine, you'll have people who are more specialized being able to identify the outlier cases from, say, the standard. Normally, if you have certain symptoms, you have a cold, those are kind of standard ones. If you have this weird sort of thing where there's n6w variables, environmental variables for instance, your environment can actually lead to you having cancer. So, there's othe6 factors other than just your body and your health that's going to actually be important to think about wh6n diagnosing someone. >> John: Colin, go ahead. >> I think machines aren't going to out-decision doctors. I think doctors are going to work well the machine learning. For instance, there's a published document of Watson doing the research of a team of four in 10 minutes, when it normally takes a month. So, those doctors,6to bring up Jen and Craig's point, are going to have more time to focus in on what the actual symptoms are, to resolve the outcome of patient care and patient services in a way that benefits humanity. >> I just wish that, Dave, that you would have picked a shorter horizon that... 30 years, 20 I feel good about our chances of seeing that. 30 I'm just not so sure, I mean... For the two old guys on the panel here. >> The consensus is 20 years, not so much. But beyond 10 years, a lot's going to change. >> Well, thank you all for joining this. I always enjoy the discussions. Craig, Jennifer and Colin, thanks for being here with us here on theCUBE, we appreciate the time. Back with more here from New York right after this. You're watching theCUBE. (upbeat digital music)

(upbeat techno music) >> Announcer: Live from San Jose, California it's theCUBE, covering Innovating to Fuel the Next Decade of Big Data, brought to you by Western Digital. >> Hey, welcome back everybody. Jeff Frick here at theCUBE. We're at the Western Digital Headquarters off Almaden down in San Jose, a really important place. Western Digital's been here for a while, their headquarters. A lot of innovation's been going on here forever. So we're excited to be here really for the next generation. The event's called Innovating to Fuel the Next Generation of big data, and we're joined by many time Cuber, Dave Tang. He is the SVP in corporate marketing from Western Digital. Dave, always great to see you. >> Yeah. Always great to be here, Jeff. Thanks. >> Absolutely. So you got to MC the announcement today. >> Yes. >> So for the people that weren't there, let's give them a quick overview on what the announcement was and then we can dive in a little deeper. >> Great, so what we were announcing was a major breakthrough in technology that's going to allow us to drive the increase in capacity in density to support big data for the next decade and beyond, right? So capacities and densities had been starting to level off in terms of hard drive technology capability. So what we announced was microwave-assisted magnetic recording technology that will allow us to keep growing that areal density up and reducing the cost per terabyte. >> You know, it's fascinating cause everyone loves to talk about Moore's Law and have these silly architectural debates, whether Moore's Law is alive or dead, but, as anyone who's lived here knows, Moore's Law is really an attitude much more it is than the specific physics of microprocessor density growth. And it's interesting to see. As we know the growth of data is growing in giant and the types of data, and not only regular big data, but now streaming data are bigger and bigger and bigger. I think you talked about stuff coming off of people and machines compared to business data is way bigger. >> Right. >> But you guys continue to push limits and break through, and even though we expect everything to be cheaper, faster, and better, you guys actually have to execute it-- >> Dave: Right. >> Back at the factory. >> Right, well it's interesting. There's this healthy tension, right, a push and pull in the environment. So you're right, it's not just Moore's Law that's enabling a technology push, but we have this virtuous cycle, right? We've realized what the value is of data and how to extract the possibilities and value of data, so that means that we want to store more of that data and access more of that data, which drives the need for innovation to be able to support all of that in a cost effective way. But then that triggers another wave of new applications, new ways to tap into the possibilities of data. So it just feeds on itself, and fortunately we have great technologists, great means of innovation, and a great attitude and spirit of innovation to help drive that. >> Yeah, so for people that want more, they can go to the press releases and get the data. We won't dive deep into the weeds here on the technology, but I thought you had Janet George speak, and she's chief data scientist. Phenomenal, phenomenal big brain. >> Dave: Yes. >> A smart lady. But she talked about, from her perspective we're still just barely even getting onto this data opportunity in terms of automation, and we see over and over at theCUBE events, innovation's really not that complicated. Give more people access to the data, give them more access to the tools, and let them try things easier and faster and feel quick, there's actually a ton of innovation that companies can unlock within their own four walls. But the data is such an important piece of it, and there's more and more and more of this. >> Dave: Right, right. >> What used to be digital exhaust now is, I think maybe you said, or maybe it was Dave, that there's a whole economy now built on data like we used to do with petroleum. I thought that was really insightful. >> Yeah, right. It's like a gold mine. So not only are the sources of data increasing, which is driving increased volume, but, as Janet was alluding to, we're starting to come up with the tools and the sophistication with machine learning and artificial intelligence to be able to put that data to new use as well as to find the pieces of data to interconnect, to drive these new capabilities and new insights. >> Yeah, but unlike petroleum it doesn't get used up. I mean that's the beauty of data. (laughing) >> Yeah, that's right. >> It's a digital process that can be used over and over and over again. >> And a self-renewing, renewing resource. And you're right, in that sense that it's being used over and over again that the longevity of that data, the use for life is growing exponentially along with the volume. >> Right, and Western Digital's in a unique position cause you have systems and you have big systems that could be used in data centers, but you also have the media that powers a whole bunch of other people's systems. So I thought one of the real important announcements today was, yes it's an interesting new breakthrough technology that uses energy assist to get more density on the drives, but it's done in such a way that the stuff's all backward compatible. It's plug and play. You've got production scheduled in a couple years I think with test out the customers-- >> Dave: That's right. >> Next year. So, you know, that is such an important piece beyond the technology. What's the commercial acceptance? What are the commercial barriers? And this sounds like a pretty interesting way to skin that cow. >> Right, often times the best answers aren't the most complex answers. They're the more elegant and simplistic answers. So it goes from the standpoint of a user being able to plug and play with older systems, older technologies. That's beautiful, and for us, to be able to, the ability to manufacture it in high volume reliably and cost effectively is equally as important. >> And you also talked, which I think was interesting, is kind of the relationship between hard drives and flash, because, obviously, flash is a, I want to say the sexy new toy, but it's not a sexy new toy anymore. >> Right. >> It's been around for a while, but, with that pressure on flash performance, you're still seeing the massive amounts of big data, which is growing faster than that. And there is a rule for the high density hard drives in that environment, and, based on the forecast you shared, which I'm presuming came from IDC or people that do numbers for a living, still a significant portion of a whole lot of data is not going to be on flash. >> Yeah, that's right. I think we have a tendency, especially in technology, to think either or, right? Something is going to take over from something else, but in this case it's definitely an and, right. And a lot of that is driven by this notion that there's fast data and big data, and, while our attention seems to shift over to maybe some fast data applications like autonomous vehicles and realtime applications, surveillance applications, there's still a need for big data because the algorithms that drive those realtime applications have to come from analysis of vast amounts of data. So big data is here to stay. It's not going away or shifting over. >> I think it's a really interesting kind of cross over, which Janet talked about too, where you need the algorithms to continue sharing the system that are feeding, continuing, and reacting to the real data, but then that just adds more vocabulary to their learning set so they can continue to evolve overtime. >> Yeah, what really helps us out in the market place is that because we have technologies and products across that full spectrum of flash and rotating magnetic recording, and we sell to customers who buy devices as well as platforms and systems, we see a lot of applications, a lot of uses of data, and we're able to then anticipate what those needs are going to be in the near future and in the distant future. >> Right, so we're getting towards the end of 2017, which I find hard to say, but as you look forward kind of to 2018 and this insatiable desire for more storage, cause this insatiable creation of more data, what are some of your priorities for 2018 and what are you kind of looking at as, like I said, I can't believe we're going to actually flip the calendar here-- >> Dave: Right, right. >> In just a few short months. (laughing) >> Well, I think for us, it's the realization that all these applications that are coming at us are more and more diverse, and their needs are very specialized. So it's not just the storage, although we're thought of as a storage company, it's not just about the storage of that data, but you have contrive complete environments to capture and preserve and access and transform that data, which means we have to go well beyond storage and think about how that data is accessed, technical interfaces to our memory products as well as storage products, and then where compute sits. Does it still sit in a centralized place or do you move compute to out closer to where the data sits. So, all this innovation and changing the way that we think about how we can mine that data is top of the mind for us for the next year and beyond. >> It's only job security for you, Dave. (laughing) >> Dave: Funny to think about. >> Alright. He's Dave Tang. Thanks for inviting us and again congratulations on the presentation. >> Always a pleasure. >> Alright, Dave Tang, I'm Jeff Frick. You're watching theCUBE from Western Digital headquarters in San Jose, California. Thanks for watching. (upbeat techno music)

>> Narrator: Live from Washington D.C. it's theCUBE, covering .conf2017. Brought to you by Splunk. (techno music) >> Well welcome back here on theCUBE as we continue our coverage at .conf2017. Splunks get together here in the nation's capital, Washington D.C. We are live here on theCUBE along with Dave Vellante. I'm John Walls. Glad to have you with us here for two days of coverage. We're joined now by Team Dell EMC I guess you could say. Colin Gallagher, who's the Senior Director of VxRail Product Marketing. Colin, good to see you, sir. >> Likewise. >> And Cory Minton, many time Cuber. Colin, you're a Cuber, as well. Principle Engineer, Data Analytical Leader at Dell EMC, and BigDataBeard.com, right? >> Yes, sir. >> Alright, and just in case, you have a special session going on. They're going to be handing these out a little bit later. So, I'm going to let you know that I'm prepared >> Cory: I love that, that's perfect. >> With you and your many legions of fans, allow me to join the club. >> That's awesome. Well welcome, we're so glad to have you. You've got a big data beard. You don't have to have a beard to talk big data at Dell EMC, but it certainly is not frowned upon if you do. >> John: Alright, well this would be the only way I'd ever grow one. >> There you go. >> I can promise you that. >> Looks good on you. >> I like the color, though, too. Anyway, they'll be handing these out at the special session. That'll be a lot of fun. Fellows, big announcement last week where you've got a marriage of sorts with Splunk technology and what Dell EMC is offering on VxRail. Tell us a little bit about that. Ready Systems is how you're branding this new offer. >> So we announced our Ready Systems for Splunk. These are turnkey offerings of Dell EMC technology pre-certified and pre-validated with Splunk and pre-sized. So we give you the option to buy from us both your Splunk solution and the underlying infrastructure that's been certified and validated in a wide variety of flavors based on top of VxRail, based on top of VxRack, based on top of some of our other storage products, as well, that gives you a full turnkey implementation for Splunk. So as Splunk is moving from the land of the hoodies and the experimenters to more mainstream running the business, these are the solutions that IT professionals can trust from both brands that IT professionals (mumbles). >> So you're both a Splunk reseller and a seller of infrastructure, is that right? >> Indeed. So we actually, we joined Splunk in a partnership as a strategic alliance partner a little over a year ago. And that gave us the opportunity to act as a reseller for Splunk. And we've recently gone through a rationalization of their catalog, so we actually have now an expanded offering. So, customers have more choice with us in terms of the offers that we provide from Splunk. And then part of our alliance relationship is that not only are we a reseller, but because of our relationship they now commit engineering and resources to us to help validate our solutions. So we actually work hand in hand with their partner engineering team to make sure that the solutions that we're designing from an infrastructure perspective at least meet or exceed the hardware requirements that Splunk wants to see their platform run on top of. >> Dave: Okay, cool. So you're a data guy. >> Indeed. >> You've been watching the evolution of things like Hadoop. When I look at the way in which customers deal with Hadoop, you know, ingest, you know, clean or transform, analyze, etc., etc., operationalize, there seem to be a lot of parallels between what goes on in that big data world and then the Splunk world, although Splunk is a package, it seems to be an integrated system. What are the similarities? What are the differences? And, what are the requirements for infrastructure? >> I think that the ecosystems, like you said, it's open source versus a commercial platform with a specific objective. And if you look at Splunk's deployment and their development over the years they've really started going from what was really a Google search for log, as Doug talked about today in the kickoff, to really being a robust analytics platform. So I think there's a lot of parallels in terms of technology. We're still ... It's designed to do many of the same things, which is I need to ingest data into somewhere, I need to make sense of it. So, we index it or do some sort of curation process to where then I can ask questions of it. And whether you choose to go the open source route, which is a very popular route, or you choose to go a commercial platform like Splunk, it really depends on your underlying call it ethos, right? It's that fundamental buy versus build, right? For somebody to achieve some of the business outcomes of like deploying a security event and information management tool like Splunk can do, to do that in open source may require some development, some integration of disparate open source platforms. I think Splunk is really good about focusing specifically on the business outcome that they're trying to drive and speeding their customers' time to value with that specific outcome in mind, whereas I think the open source community, like the Hadoop community, I think it offers maybe some ability to do some things that Splunk maybe wouldn't be interested in, things like rich media analytics, things that aren't good for Splunk indexing. >> Are there unique attributes of a data rich workload that you've accommodated that's maybe different from a traditional enterprise workload, and what are those? >> Yeah, so at the end of the day any application is going to have specific bottlenecks, right? One of the basis of performance engineering is move the bottleneck, right? In enterprise applications we had this evolution of originally they were kind of deployed in a server, and then we saw virtualization and shared storage really come in vogue for a number of years. And that's true in these applications, these data rich applications, as well. I think what we're starting to see is that regardless of what the workload is, whether it's a traditional business application like Oracle, SAP, or Microsoft or it's a data application like Splunk, anytime it becomes critical to the operation of a business organizations have to start to do things that we've done to every enterprise IT app in the past, which is we align it to our strategy. Is it highly available? Is it redundant? Is it built on hardware that we can be confident in that's going to be up and running when we need it? So I think from a performance and an engineering perspective, we treat each workload special, right? So we look at what Splunk requirements are and we understand that their requirements may be slightly different than running SAP or Oracle, and that's why we build the bespoke systems like our Ready System for Splunk specifically, right? It's not a catch all that hey it works for everything. It is a specifically designed platform to run Splunk exceptionally well. >> So Colin, a lot of the data practitioners that I talk to at this show and other data oriented shows like, "Ah, infrastructure. "I don't care about infrastructure." Why should they care about infrastructure? Why does infrastructure matter, and what are the things that they should know? >> Infrastructure does matter. I mean infrastructure, if youre infrastructure isn't there, if your infrastructure isn't highly available, as Cory said, if it lets you down in the middle of something, your business is going to shut down, right? Any user can say, "Talk about what happened "the last time you had a data center event, "and how long were you offline, "and what did that really mean for your business? "What's the cost of downtime for you?" And everything we build at an application level and a software level really rests on an infrastructure foundation, right? Infrastructure is the foundation of your data center and the foundation of your IT, and so infrastructure does matter in the sense that, as Cory said, as you build mission critical platforms on it the infrastructure needs to be highly reliable, highly available, and trusted, and that's what we really focus on bringing. And as applications like Splunk evolve more into that mainstream world, they need to be built on that mission critical, reliable, managed infrastructure, right? It's one thing for infrastructure development, and this kind of happens in the history of IT, as well. It happened in client server back in the day. You know, new applications ... Even the web environment I remember a company was running, one of my clients was running a web server under their secretary's desk, and she was administering in half time. You would never have a large company doing that. >> They'd be back up (mumbles). Before you leave. >> As it becomes more important it becomes more central, but also it becomes more important to centrally manage those, right? I'm a 15 year storage veteran, for good or for worse, and what we really sell in storage is selling centralized management of that storage. That's the value that we bring from centralized infrastructure versus a bunch of servers that are sitting distributed around the environment under someone's desk is that centralized management, the ability to share the resources across them, the ability to take one down while the others keep running, shift that workload over and shift it back. And that's what we can do with our Ready Systems. We can bring that level of shared management, shared performance management, to the Splunk world. >> I'll tell you, one of the things that we talked about, we talked about in a number of sessions this week, is application owners, specifically the folks that are here at this conference, need to understand that when they decide to make changes at the application level, whether they like infrastructure or they think it's valuable or not, what they need to understand is that there are impacts, and that if you look at the exciting things that were announced today around Enterprise Security updates, right? Enterprise Security is an interesting app from Splunk, but if a customer goes from just having Splunk Enterprise to running Enterprise Security as a premium application, there's significant downstream impacts on infrastructure that if the application team doesn't account for they can basically put themselves in a corner from a performance and a capacity perspective that can cause serious problems and slow down the business outcome that they're trying to achieve because they didn't think about the infrastructure impacts. >> Well, and what they want really is they want infrastructure that they can code, right? And we talked about this at VMworld we were talking about off camera that cloud model, bringing that cloud model to your data as oppose to trying to force your business into the cloud. So what about Ready Systems mimics that cloud model? Is it a cloud like infrastructure? Wondering if you could talk-- >> Yeah, I think it's that cloud like experience. Because we know we're in a multi cloud world, right? Cloud is not a place, cloud is an operating model, right? And so I think that the Ready Systems specifically provides a couple of things that are that cloud like experience, which is simple ordering and configuration and consumption that is aligned to the application, right? So we actually align the sizing of the system to the license size and the expected experience that this one customer would have so they get that very curated bespoke system that's designed specifically for them, but in a very easy to consume fashion that's also validated by the software vendor, in this case Splunk, that they say, "These are known good configurations "that you will be successful with." So we give customers that comfort that, "Hey, this is a proven way "to deploy this application successfully, "and you don't have to go through "a significant architecture design concept "to get to that cloud like experience." Then you layer in the fact that what makes up the Ready System, which is it is a platform powered by, in the VxRail case powered by VMware, right, ESX and vSAN, which obviously if you look at any of the cloud providers everything is virtualized at the end of the day for the most part, or at least most of the environments are. And so we give, and VMware has been focused on that for years and years of giving that cloud like experience to their customers. >> You talk about, you mentioned selling, sort of reseller, you've got this partnership growing, you're a customer. So, you have all these hats, right, and connections with Splunk. What does that do for you you think just in general? What kind of value do you put on that having these multiple perspectives to how they operate whether it's in your environment or what you're doing for your customers using their insights? >> Yeah, I think at the end of the day we're here to make it simpler for customers. So if we do the work, and we invest the time and energy and resources in this partnership, and we go do the validation, we do the joint engineering, we do the joint certification, that's work that customers don't have to do, and that's value that we can deliver to them that whatever reason they buy Splunk for whatever workload or business outcome they're trying to achieve, we accelerate it. That's one of the biggest values, right? And then you look at who do they interact with in the field? Well, it's engineers from our awesome presales team from around the world that we've actually trained in Splunk. So we have now north of 25 folks that have Splunk SE certifications that are actually Dell EMC employees that are out working with Splunk customers to build platforms and achieve that value very, very quickly. And then them understanding that, "Oh, by the way, Dell EMC is also a user of Splunk, "a great customer of Splunk "and a number of interesting use cases "that we're actually replatforming now "and drinking our own Kool Aid so to say," that I think it just lends credibility to it. And that's a lot of the reason why we've made the investments in being part of this awesome show, but also in doing things like providing the applications. So we actually have four apps in Splunkbase that are available to monitor Dell EMC platforms using Splunk. So I think customers just get a wholistic experience that they've got a technology partner that wants to see them be successful deploying Splunk. >> I wonder if we could talk about stacks, because I've heard Chad Sack-edge talk about stack wars, tongue and cheek, but his point is that customers have to make bets. You've heard him talk about this. You've got the cloud stacks, whether it's Azure or AWS or Google. Obviously VMware has a prominent stack, maybe the most prominent stack. And there's still the open source, whether it's Hadoop or OpenStack. Should we be thinking about the Splunk stack? Is that emerging as a stack, or is it a combination of Splunk and these other? >> You know, we actually had that conversation today with some of the partner engineering team, and I don't know that I would today. I think Splunk continues to be, it's its own application in many cases. And I actually think that a lot of what Splunk is about is actually making sure that those stacks all work. So there was even announcements made today about a new app. So they have a new app for Pivotal Cloud Foundry, right? So if you think about stacks for application development, if you're going to hit push on a new application you're going to need to monitor it. Splunk is one of those things that persistent. The data is persistent. You want to keep large amounts of data for long periods of time so that you can build your models, understand what's really going on in the background, but then you need that real time reporting of, "Hey, if I hit push on a Cloud Foundry app "and all of a sudden I have an impact "to the service that's underlying it "because there's some microservice that gets broken, "if I don't have that monitoring platform "that can tell me that and correlate that event "and give me the guidance to not only alert against it "but actually go investigate it and act against it, "I'm in trouble." The stacks, I think many of them have their own monitoring capabilities, but I think Splunk has proven it that they are invested in being the monitoring and the data fabric that I think is wanting to help all the stacks be successful. So I don't necessarily put it in the stack. And I kind of don't put Hadoop in its own stack, either, because I think at the end of the day Hadoop needs a stack for deployment models. So you may see it go from a physical construct of being, a bit trying to be its own software that controls the underlying hardware, but I think you're seeing abstraction layers happen everywhere. They're containerizing Hadoop now. Virtualization of Hadoop is legit. Most of the big cloud providers talk about the decoupling of compute from storage in Hadoop for persistent and transient clusters. So I think the stacks will be interesting for application development, and applications like Splunk will be one of two things. They'll either consume one of those stacks for deployment or they'll be a standalone monitoring tool that makes us successful. >> So you don't see in the near term anyway Splunk becoming an application development platform the way that a lot of the-- >> Cory: They may have visions of it. That's not, yeah. >> They haven't laid that out there. It's something that we've been bounding around here. >> Yeah, I think it's interesting. Again, I think it goes back to .. Because the flexibility in what you can do with Splunk. I mean we've developed some of our own applications to help monitor Dell EMC storage platforms, and that's, it's interesting. But in terms of building what we'd I guess we'd consider like traditional seven factor app development, I don't know that it provides it. >> Yeah, well it's interesting because, I'm noodling here, Doug Merritt said, "Hey, we think we're going to be the next five billion, "10 billion, 20 billion dollar ecosystem slash company," and so you start to wonder, "Okay, how does that TAM grow to that point? That's one avenue that we considered. I want to talk about the anatomy of a transaction and how that's evolved. Colin, you mentioned Client Server, and you think about data rich applications going from sort of systems of record and the transactions associated with that. And while there were many going to Client Server and HTTP, and then now mobile apps really escalated that. And now with containers, with microservices, the amount of data and the complexity of transactions is greater and greater and greater. As a technologist, I wonder if you could sort of add some color to that. >> Yeah, I think as we kind of go down a path of application stacks are interesting, but at the end of the day we're still delivering a service, right? At the end of the day it's always about how do I deliver service, whether it's a business service, it's a mobile application, which is a service where I could get closer to my customer, I could transact business with them on a different model, I think all of it ... Because everything has gone digital, everything we do is digital, you're seeing more and more machines get created, there's more and more IP addressed devices out there on the planet that are creating data, and this machine generated data deluge that we're under right now it ain't slowing down, right? And so as we create these additional devices, somebody has got to make sense of this stuff. And if you listen to a lot of the analysts they talk about machine data is the most target rich in terms of business value, and it's their fastest growing. And it's now at a scale because we've now created so many devices that are creating their own logs, creating their own transactional data, right, there's just not that many tools that out of the box make it simple to collect the data, search the data, and derive value from it in the way that Splunk does. You can get to a lot of the things that Splunk can deliver from an outcome other ways with other platforms, but the simplicity and the ability to do it with a platform that out of the box does it and has a vibrant community of folks that will help you get there, it's a pretty big deal. So I think it's, you know, it's interesting. I don't know, like under the covers microservices are certainly interesting. They're still services. They're just smaller and packaged slightly differently and shared in a different way. >> And a lot more of them. >> Yeah, and scaled differently, right? And I totally get that, but at the end of the day we're still from a Splunk perspective and from a data perspective, we've still got to make sense of all of it. >> Right, well, I think the difference is just the amount of data. You talked about kind of new computing models, serverless sort of, stateless, IoT coming into play. It's just the data curve is reshaping. >> Well, it's not just the amount of data, it's the number of sources. The data is exploding, but also, as Cory mentioned, it's exploding because it's coming from so many places. Your refrigerator can generate data for you now, right? Every single ... Everything that generates Internet, anything doing anything now really has a microprocessor in it. I don't know if you guys saw my escape room at VMworld. There were 12 microprocessors running this escape room. So one of the things we played about doing was bring it here and trying to Splunk the escape room to actually see real time what the data was doing. And we weren't able to ship it back from Barcelona in time, but it would've been interesting to see, because you can see just the centers that are in that room real time and being able to correlate all that. And that's the value of Splunk is being able to pull that from those disparate sources altogether and give you those analytics. >> Yeah, it's funny you talk about an IoT use case. So we've got these... Our partner, who's a joint partner of both Dell EMC and Splunk, we actually have these Misfit devices that are activity trackers. And we're actually-- >> Misfit device? >> Misfit. Yeah, it's a brand. >> John: Love it. >> It's fitting, I think. But we have these devices that we gave away to a number of the attendees here, and we actually asked them if they're willing to participate. They can actually use the app on your phone to grab the data. And by simply going to a website they can allow us to pull the data from their device about their activity, about their sleep. And so we actually have in our booth and in Arrow's booth we're Splunking Conf and it's called How Happy is Conf? And so you can actually see Splunk running, and by the way, it's running in Arrow's lab. It's running on top of Dell EMC infrastructure designed for Splunk. You can actually see us Splunking how happy conf attendees are. And we're measuring happiness by their sleep. How much sleep-- >> John: Sleep quality and-- >> The exercise, the number of steps, right? So we have a little battle going between-- >> Is more sleep or less sleep happy? >> Are consumption behaviors also tracked on that? I just want to know. I'm curious. >> It's voluntary. You'd have to provide that. >> Alright, because that's another measure of happiness. >> It certainly is. But it's just a great use case where we talk about IoT and the number of sources of data that Splunk as a platform ... It's very, very simple to deploy that platform, have a web service that's able to pull that data from an API from a platform that's not ours, right, but bring that data into our environment, use Splunk to ingest and index that data, then actually create some interesting dashboards. It's a real world use case, right? Now, how much people really want to (mumbles) Splunk health devices we'll determine, but in the IoT context it's an absolute analog for what a lot of organizations are trying to do. >> Interesting, good stuff. Gentlemen, thanks for being with us. We appreciate that. Cory, it's probably not the real deal, but as close as I'm going to go. Good luck with your session. We appreciate the time to both of you, and you and your Misfit. Back with more here on theCUBE coming up in just a bit here in Washington D.C. (techno music)

>> Narrator: Live, from Las Vegas, it's The Cube, covering HPE Discover 2017. Brought to you by Hewlett Packard Enterprise. >> Hi everybody, welcome back to Las Vegas, my name is Dave Vellante and this is day three of The Cube's live wall to wall coverage of Hewlett Packard Enterprise, HPE Discover. This is The Cube, the leader and live tech coverage. We have a little reveal here, JR Fuller is here, he's the Global Business Development Manager IoT Edgeline at Hewlett Packard Enterprise and he's joined by Doug Smith, who is the CEO of Texmark. Gentleman, welcome. >> Thank you. >> Thank you for having us. >> Alright lay it on us Doug, what is Texmark all about? We're going to have, like I say, a little virtual reveal here-- >> Sure. And first of all, thanks for having me here-- >> Dave: You're very welcome. >> And, Texmark Chemical is a 50 year-old company, located in Galena Park, Texas, which is right on the Houston Ship Channel outside of the city of Houston. We are a manufacturer of specialty chemicals, one being DCPD, which stands for dicyclopentadiene. We have been making significant capital investments in the physical plant, over the last 20 years. And about two years ago, we realized we needed to move forward in a control system, a new control system, initiative at the plant, as well as a baseline mechanical integrity. Initiative. And so we're a small organization of 53 people and we looked to our contacts and got in touch with HPE and started a conversation. We don't have a normal client-customer relationship. We have a partnership of people, HPE people, Texmark people. >> Absolutely. >> So JR, pick it up from HPE's side. So, you guys have made a big push into this whole IoT business and you need partners like Doug's firm. >> Yeah, absolutely. So it's kind of interesting the way we got started. You probably remember last year, we had the big pump. The pump demo, the Filzer pump demo, so that was a project of mine, and Dough had heard about that from a mutual friend and ... Gracious. Very gracious of him, he invited us to come out at Texmark and actually install that at his facility. And he said "I got this bug pond over there, you can put that in there." And then you have a production version of that, 'cause we had the proof of concept version in our lap, and I said "That is really nice and very sweet, but no. "Let's figure out what we can do that will really benefit you, 'cause that won't really benefit you." And that started a dialogue that's, been about a year that we've been talking about this and I think it was in August, I proposed to him and said, "What do you think about "doing a refinery of the future?" And his words to me were, "JR, I don't know what "it is, but I love it." And I said, "Well, let's figure out what it is "for Texmark and let's go from there." And that's kind of how we started the genesis of this entire journey, of what we're doing. >> So you kind of laid out the vision, which is fantastic-- >> JR: Right. >> Sort of your North Star. And then just for the audiences benefit, you know, everyone here discovered there was this amazing floor exhibit, and it was pumps and tubes and pipes. >> JR: We've seen learning and, yeah. >> And it was all kinds of data, that was flowing through there, and sort of I guess, a digital twin if you will. >> Exactly. >> Of the factory floor ... >> Doug: Well of a plant, yes. And that's a great segway into Texmark and how we have synergy between our two organizations is that Texmark, although a small chemical process facility, we have all the equipment that the huge companies have. We have boilers, we have pipes, we have distillation columns, and we need to move forward, with our people to instrument, to gather data, to data analytics on the edge to have a connected facility with wifi capabilities, so that's where the conversation started. >> So much of the data ... Maybe even most of data today, historically anyway, analog data, is that correct? >> It is a combination. >> Dave: Okay. >> What we are doing, once again, we are a small organization. We have one IT person. And that person is contract, so how we're approaching it is, Texmark stays in the chemical, we use the analogy of, swim lanes. We are swimming towards profitability in the chemical business. HPE is swimming in the lane with-- >> All the technology. >> Technology. And then we're working together on this voyage of discovery, out here, that we're figuring out along the way. >> And for sure, you're not IT, you're operations. >> Doug: Yes, sir. >> Right? And you guys are IT. >> Exactly. >> So talk more about the partnership. What is that all about? >> Doug: People. >> JR: It's totally about people and it's interacting with each other, it's showing up ever day, it's working towards things. It's, when you do run into a problem ... And Doug's got a great story of when we had a problem. When you do run into a problem, you have the mutual of how to solve this problem together. In a typical customer-vendor relationship, there's some kind of built-in tension that's there and you know, you're worried about, "Oh, the vendor's trying to do this to me" , or "Oh, the customer if trying to get something from me." And we don't have any of that. We actually have a very solid partnership and occasionally, if one of my team or one of his team gets off track on that, we bring them back to the fold and say, "No, no, no. We're plowing road here." We need them to cut trees, we need us to cut trees, we all need to be heading in the same direction. You can't stop and go, "How come this isn't paved?" Because it hasn't been done before. >> And it's that shared objective of the refinery of the future that you're working towards. So, can we describe in a little bit more detail, the refinery of the future. >> Doug: Sure. Let me just jump in on that, because in this voyage of discovery, with these conversations, we talked about, what do we need to achieve the goals that we want? And so, first there is the hardware component. What do we here to achieve these goals? We'll just take the example of the pump. The pump is the heart of any process facility. If you have a critical pump go down, it can put you out of operation. There's a cost associated with that, and so what we need to do ... There's a cost associated with putting wiring from our control center to an actual pump. If we can have a wireless network and a censor on a pump, we eliminate the cost of physical wiring. The wireless network was provided by one of our content partners, Aruba, and so that is installed. We are working-- >> Dave: You know those guys? >> JR: I do, I do. >> He's heard of them. So then, what do we do with that data when it comes in? So, we have two Edgeline servers in there, and we have one in our control room, and then we have one, and it's super. They have one here, on the floor here, at the Discovery, the Micro-data center, which is for our place, everybody's like ... (sings) (laughter) >> It's fantastic-- >> Dave: It's data in a box. >> Yes, sir.And what that does, we have the ... I'll just give you an example. So we have our old system, the old server over here, size of a refrigerator, and I have used this numerous times when explaining the project to people here at Discover is that, I have to explain what we're doing to my 81 year-old mother. And when I say we have a refrigerator over there that used to run the plant, and now we have this one little thing the size of a little tablet-- (JR laughs) >> She goes ... And it saves money. It increases efficiency, she gets that. So those are some of the phases of the project, and now I'll pass it over to JR 'cause we then identify how are we going to use this cool hardware to achieve objectives? >> Yeah. So when we look at the refinery future, we usually have a three phrase project, alright? You don't boil the ocean, you bring it down into ... So phase one for us was putting in the Aruba wifi network out in the entire facility. We've done that. And because it's a petro chemical plant, it needs to go into a special enclosure. So we have a partner with Extronics, out in the U.K., that creates this protective enclosure. >> Dave: Like militarize. >> Yeah. Well, it's actually even beyond that, because in type one, dib one environments, there is a potential for hazardous gas to be out in there, and so electronic equipment would be sparking and stuff like that, and gas that can explode. Not a good combination. So, these div one boxes, make it so that, if there is an interaction with a spark, and some flammable gas, and there's an explosion, it's contained in that box, and not contaminates to the whole factory, which would be-- >> Plant. (laughs) >> Plant, the whole plant. Where it would actually create problems for everybody else, so that first phase was putting those div one compliant wifi AP's out there from Aruba. We also put in our beacons, with our location-based services, the meridian system out there, so they can do wave-finders and get to the right pump to fix it. And also, they're clear pass, so putting clear pass out there so it's a secured network, right? We don't want anybody to be able to go in there and mess with anything. >> So basic productivity, the security to allow that, all that basic infrastructure. >> So that was to-- >> To connect the ... >> Exactly. That was phase one. Phase two was, they had rack of other people's compute in there and we replaced all of that, like Doug said, with two of our Edgeline EL 4000 Converge systems. >> Dave: Okay. >> One of those, we actually mounted on the control room floor, so right out on the Edge, not in a data center environment, not in a temperature-controlled place, per say, and what we consider our data center. And then that other one, we actually did get an HPE Micro-data center, and we put the other one in there. It's secured, it's badge-access only. Only a couple people in Texmark have badge access to actually be able to get that. And when we look at the compute needs growing, that's where they're going to probably grow into, is that data. >> So phase two was bring the the compute. >> So I call those two, phase one and phase two, my infrastructure phase, 'cause now I've got what I need to do. Now phase three is really interesting because that's where we're going to start doing IoT stuff, right? So there are five projects that we're doing on IoT. So the first one is predictive analytics. This is both at the discreet and the process level. So, when we talk about that pump that we saw last year, that's a discreet machine. We're doing predicted analytics on that machine. But that machine feeds a process, so how can we predict what's happening on this machine, what's the impact of that to this process? So that's the first one. >> Doug: Can I hop in? >> Yeah, go for it. >> So, JR is using the example of the pump, and I mentioned the pump earlier, being the heart of the organization. So, it's been interesting being at Discover for the first time for me and the way that I have been talking with people, you have people that are extremely interested in the human component, and how is it affecting people? Also there is, the critical bottom line. How is it going to make me money and save me money? >> Dave: Right. >> So this pump is an excellent example that addresses both of those. So, if have a pump fail, there is a significant cost if it shuts us down for the day. We're a seven acre facility, and let's just throw a number out for easy math. Let's just say it costs us $100,000 a day, if that pump goes down. If you have a facility that's 100,000 times larger, just let me pull out my calculator and your math can tell this solves a problem. From a human perspective, it's just like your heart stopping, there's a risk associated with that pump going down within the facility. >> Okay, so we're very tight on time. >> Sorry. >> That's okay. So, you got the five phases for five IoT projects, within phase three, predictive analytics. Let's run through them and ... >> The second one is video is a sensor, so this is-- >> Cool. >> Using video to detect things that are going on and using the Edge analytics to be able to power that. The third one is safety and security. So these are things like, man down. Directive response, those types of things. The fourth one is, connected worker. And I define this as, location-based context-aware content. So, just very quickly, if you have three different people at the pump. One is a operations person, one's a maintenance person, one's a finance person, and they're all using that augmented reality that we saw, they're going to see three different dashboards. Locations base, context-aware content. And then the fifth one is, we're going to tie into the two sister projects that are going on out there with the DCS upgrade and the aneo-spalatio mechanical integrity program, and do a full life cycle as that management. So these are big projects. >> Dave: So now you've got the fully instrumented refinery is where you're at. Now you got all this data flowing. What happens to the data? Where does it get analyzed, where does it end up? Where do you go from there? >> Sure, so of course, having the Edgeline servers there, we're doing data analytics on the Edge so we can have real time, right there information to help our workers work safely and efficiently. And then we have this wealth of historical data that we can start analyzing, either on-premise or off-premise, to help us-- >> JR: Help probe the models. >> Better. And then also, this is one really cool aspect from a Texmark perspective is, we do a significant amount of total processing. That means, somebody comes to us and says, "Here, Dave. Make this for us." And we will run it through our equipment and give them an end product. If we can improve the way we cook, whatever our process, whatever it is that they want, there is a significant value added to that. >> Dave: And that historical data, in the lake if you will, lives on Prim, it lives in the Cloud, or you don't know yet. >> Everything is on Prim. The Cloud applications that we'll probably use are around safety and security when talking about weather, humidity, and those types of things. >> Dave: So bring in some outside data or models that you apply. >> Right. Yes. Texmark is a single facility, so leveraging the Cloud to communicate to other locations and things like that isn't really a necessary driver. Although it would be, completely would be, for some of the target customers that we want to sell this to initially. >> But the vast majority of the data is staying at-- >> JR: On Prim, yeah. >> Correct? So, it confirms the assumptions that we've been making, that 90% of the data is this world is going to be analyzed at the Edge and maybe trickle some stuff back, some nuggets back to the Cloud. >> Absolutely. >> Guys, we got to go. That's a fascinating story. Thank you so much. >> Thanks, you could tell I like the camera a lot in this. Thank you, Dave, I really appreciate it. >> Dave: My pleasure, thank you. Alright, keep it right there, everybody. We'll be back with our next guest as The Cuber live from HPE Discover in Las Vegas, 2017. We'll be right back. (electronic music)

(electronic music) >> Announcer: Live from Austin, Texas, it's theCUBE, covering DockercCon 2017. Brought to you by Docker and support from Asseco System Partners. >> Welcome back to theCUBE, I'm Stu Mittleman, with my co-host, Jim Kobielus. Happy to have on the program, I'm shocked to say a first time guest. Someone that I've known in the community here for many years, but Michael Ducy, who is Director of Product Marketing at Chef Software. Not a chef. Maybe you might-- >> Not a chef, although I do cook at home (laughing). >> Maybe in Chef. Not a puppeteer. >> Not a puppeteer. >> But you work for Chef Software. So thank you so much for joining us. >> Yes, thanks for having me. >> Alright, so Michael, for the audience that doesn't know you... I think a lot of people here in the community would know you. I've known you through Twitter for many years. What's your role at Chef? What do you work on? What's your passion? >> Sure, so right now I do product marketing for our open source projects. So Chef Software actually has a commercial product, and then we also have three open source projects that we maintain. The first was the original one that we're named after, which is Chef, which is open source automation or configuration management. The second one being Inspect, which is all about how do you basically write compliance rules as code. And then third one, as you can see from my shirt, is called Habitat. So Habitat is a new way of thinking about how do you package up automation for your application. And then how can you easily export that application and the automation into something like a container. I've had various roles at Chef though over the four years that I've worked for them. My passion's always kind of been open source communities, an involvement in open source communities and helping grow those communities. >> Yeah, and people send you lots of stuff about goats. >> People send me lots of stuff about goats (laughing). There was a joke that was made at a conference about waking up next to a goat. This was a conference in Amsterdam, which is I'm sure I wouldn't be the first one that woke up next to a goat in Amsterdam (laughing). But since then, the whole goat thing kind of took off after that. >> Yeah, so, Chef, you understand many things about Docker. So one of the things, we come in and we talk about there's Docker, the company, there's Docker, the community. A lot of what was talked about in the keynote today was about open source. >> Umm-hmm. >> So how's Docker doing? What interested you in the keynote? How do you as an individual in Chef see what's going on in the Docker ecosystem? And what do you think? >> Yeah. >> Yeah. >> So we've been put in a little bit of an interesting position as Chef, the company. And not only has Chef, the company, been put in this position, but all of our competitors have as well. So there's been a movement as Docker and containers got more popular that the idea that configuration management is no longer needed. And from a inside the container perspective, configuration management really isn't needed. But what you do end up realizing is that there's this whole idea of what you need to actually run a container in production effectively, that still needs to go into that container. And we kind of call it The Learning Cliff of Containers. And I tweeted out an image about... that why co-worker draw on a whiteboard. That shows in development you just have Docker and it's really easy, but then when you move it to production there's this whole other stack of concerns. And Docker or your container runtime is just one of them. And so, we've been focusing more on kind of shifting into those ideas of how do you actually run containers effectively in production. What we saw in the keynote today is more of an emphasis on things like security, right. That's definitely been an area that we're interested in, especially from a compliance perspective, and doing work around having our open source projects, being able to scan containers for compliance. >> Yeah, it's funny before the keynote they have this fun little thing. They have this 8-bit video game playing. >> Right. >> And it was like they were collecting coins and they were leveling up, but they kept hitting lots of bombs (laughing) and things were exploding all the time. And everybody was joking online. It was like, Oh, it's like putting Docker in production. I will level up (laughing) and I will get past everything, but, Boy, I'm going to have lots of bombs going off and things-- >> Sure. >> And things that I'll have to deal with, and there were lots of fun little comments that they threw out there. It's like, Checking documentation. Oh, documentation says you don't have documentation. (laughing) So just fun stuff like that. But it's challenging. Solomon says, We want this put in deployment, but as we know it's not quite there yet. There's lots of things, that's where you guys fit in. >> Umm-hmm. >> A lot of the ecosystem helps to solidify that about you here. >> Michael, what are those concerns that you allude to? There's security, and what other concerns are there for containers in production that need to be represented in the configuration management portfolio or profile you're describing? >> Sure, so there's the security aspects of it is focused on what vulnerabilities are in your container. >> Yeah. >> And there's been some interesting studies recently that showed 24% of the official images are shipping with some sort of a vulnerability. Some of that you have to accept, and then also realize can you do risk mitigation around that vulnerability. There's concerns about how the application is actually configured when you ship it as well. So am I doing things like storing secrets in config files. Am I disabling versions of ISOCELL that's no longer a best practice anymore because it's actually broken. And then there's other aspects around how do you things like service discovery, how do you do credentials or secrets. And how do you get them into the container securely. There's networking aspects. There's last malconfiguration of the application, so-- >> Right. >> If you take a container from one environment to another environment and kind of work it through a lifecycle. There are things at runtime that you have to change in its configuration to make it run in that particular environment. >> Right. >> So it's all of those little knobs that you still have to turn. And that's why-- >> The entire DevOps lifecycle essentially there's all those little knobs and... >> There's all these little knobs and this has always been a little bit of a frustration for me, in that PaaS sounds great, platform as a service sounds great. And this idea that you can just take this blob and go run it. But What people don't realize is there still are tons of knobs that you have to turn, and there are tons of concerns that you have to worry about as an operations person or as a DevOps person or as a developer when you actually are taking that code into production. >> Right. >> Michael, we've seen the cloud providers and some of the other open source providers kind of chipping away. Red Hat bought Ansible, every time I go to Amazon re:Invent or Google, it seems like they're trying to build more things up the stack and into their platforms. >> Umm-hmm. >> So what is Chef's position here? How do you guys play across all these environments and kind of maintain and grow what you're doing? >> Yeah, so we've started to take a little bit more of a different focus and... Well, not a different focus... A different focus for us. Traditionally, we focus on infrastructure and operations people and then as we moved up the stack and DevOps became more popular. We definitely focused on that because that's kind of our bread and butter. But what we started to do with Habitat is focus more on building a developer experience. So how can a developer take their code-- >> Yeah. >> Easily wrap automation around it, and then ship it out into production. And this is the new world for us, as coming from the operations side of things. And really starting to think about what does the developer tooling look like and the developer experience look like. We're taking source code, building that source code, and then deploying that source code to production. >> Yeah, and it's interesting, it sounds... We talk about Docker. They very much started out in the developer world, and then they're kind of moving to kind of the Op side more. >> Umm-hmm. >> And to the enterprise side more. You're almost going-- >> Michael: And we're kind of-- >> A little bit in reverse, huh. >> Yeah, going a little bit in reverse, yeah. >> Yeah, it's interesting because usually it's like, Okay, I start with developers, get them excited and then figure out to monetize. So, yeah, what are you seeing in your customer base? >> Sure. >> Who do you sell to in that aspect? Yeah, I'm just curiosity at some of the buyers. >> Well, so, traditionally, a tool like Chef or, even some of our competitors would be bought by what's called the Shared Services Team, right. And that Shared Services Team is going to take that and try and work economies of scale, right. And try and deploy that across all of the different BMs or machines that they have to manage, right. And we've seen this shift as we moved more up the stack and as the industry's shifted more up the stack. Of what the Shared Services Team actually needs to transform themselves into is more of a developer services team. So how can I offer the services that a developer can get via an API, to quickly deploy the application services that they need. And when I say application services, I'm thinking about all of the things that you need to actually go and persist the data. The business logic side of things are very easy to do in containers or PaaS. But when you're actually having to go and persist data in something like Red-S are Mongo or MySQL, that's a whole other area of concern that you have to worry about. So what we've actually had started to do is the core team that actually works on Habitat has a very, very big background in distributive systems. So what we've started to do is bake a lot of that foundational ideas about how you effectively run large-scale distributive systems into Habitat, which makes it very easy to then go and take that developer, take their source code, and deploy it using Habitat, using this knowledge that we have from distributive systems. So we actually see it as a benefit that we come from this infrastructure background because we have experience of actually running things in production, right. >> Umm-hmm, what do you see as some of the challenges that we still need to face in this kind of container ecosystem? I know one of the questions I have coming in is you talked about stateful applications. We know storage still needs some time to mature. Networking seems to be a little bit further along in what they're doing. >> Umm-hmm. >> What's your take as to what's doing well? What still needs some more work? >> Yeah, storage is one of those areas that... And persisting data is one of those areas that we're not able to get around, right. And if you look at some people's recommendations, so Pivotal, for example, recommends running persistent services on BMs, right. If you look at the Google approach or the Cuber-netee's approach, they actually recommend that you use a cloud provider services to go and run those data services for you, until you think you're good enough to actually go and run it like Google. (laughing) And they're also hedging on the fact that you'll probably never be good enough to run it like Google. >> Yeah, yeah. >> So, kind of building that expertise of running those distributive systems in an effective way is kind of the area in running those persistent data services in a highly scalable way is kind of the big challenge that operations still hasn't figured out. And developers also need work to... Need help to help figure that out as well. >> Yeah, the big theme this morning was really about scalability. When you talked to customers, what does scale mean to them? What are the limitations they're having? I loved when you talked about what you're doing with Habitat. Helping customers, so that they don't have to have the expertise to build distributive systems because that's the software challenge of our time-- >> Yeah. >> Is moving to that. What we talk at Wicky-bon, it's moving from the old enterprise where it was like kind of baked in the hardware to a distributive, where the software model, anything had failed, there's no single point of failure, I can scale. >> Yeah. >> What do you think? >> Well, to kind of paraphrase our CTO, Adam Jacob, he always likes to say ignore scaling problems because you don't have a scaling problem. (laughing) And you don't have a scaling problem until you have a scaling problem, right. So if you kind of look at where your time's most effectively spent, your time is more effectively spent at actually building an application that people want to use, and worry about the scaling problem when the scaling problem comes up, right. And the other thing is that you might never hit that scaling problem, so everyone wants to be the next Uber, everyone wants to be the next Netflix, and so forth. And so, if you go in as a startup or, even a startup inside of a large enterprise trying to do a new application. If you start by trying to solve the scaling problem out the door, then what you end up losing is a lot of development cycles that you could actually be spending on building something that people actually want to use. And then worrying about the scaling problem when you hit the scaling problem. >> So, Mike, last question I have for you. A month from now, you're going to be back in Austin. >> A month from now, I'm going to be back in Austin. >> So tell us about ChefConf. >> Yes. >> What can people expect? Give us a compare and contrast to kind of the communities, the type of people that attend. I expect we'll see more shorts because it's going to be a little bit warmer and more humid here in Austin (laughing). >> Yes, so we're back at Austin for the second ChefConf in Austin. We were here also last year. We were in Austin in July last year. >> Ooooh. >> Which was not a fun experience (laughing). The air conditioning was very nice. The pool was also very nice. (laughing) But what you can expect is more practical advice to how to actually run these things in production. We have a lot of talks about Habitat. I think we're going to have a lot... Nine talks on Habitat. We have a lot of talks from the Chef community about running actual systems in production in a lot of real world experience, which is something that we always try and hover into our conferences. We also have a day that's going to be focused on our open source community as well, so where our open source and contributors can get together to talk about problems that they're trying to solve in our open source communities as well. And then on the last day, of course, as every conference does we're going to have a hack day, where you can contribute to open source, our open source, or we can help you get started solving a problem that you have, but there'll be a lot of people there that can answer questions for you about the problems that you're trying to solve in running distributive systems. >> Alright, well, Michael Ducy, happy to welcoming you into the ranks of theCUBE alumni, finally. >> Yes, finally, thank you very much. >> And thank you for sharing all the updates with us. And thank you for watching theCUBE. (electronic music) >> I remember...

>> Voiceover: Live from Orlando, Florida, It's The Cube covering Sapphire Now, headlining sponsored by SAP HANA Cloud, the leader in platform-as-a-service with support from Console Inc., the cloud internet company. Now, here are your hosts, John Furrier and Peter Burris. >> Hey, welcome back, everyone, We are here, live, in Orlando, Florida SAP Sapphire Now. This is SiliconANGLE Media's The Cube. It's our flagship program, we go out to the events and extract the signal from the noise. I'm John Furrier with my co-host, Peter Burris, want to give a shout out to our sponsors, SAP Hana Cloud Platform, Console Inc, Capgemini, EMC, thanks so much for sponsoring us. Our next guest is Pat Bakey who's the president of SAP's Industry Cloud group. It's the core of the cloud, all SAP. Welcome to The Cube. >> Hi, it's great to be in The Cube, first time in The Cube. >> First time on The Cube, congratulations first time Cuber. Great to have you. You have, as holistically viewing across all the different lines of business, Cloud will be a very big part of the future and across all of SAP, that's the core business. Yet, now you have Hana cloud platform, you got all this other stuff going on. Now, you have cloudification of SAP in kind of a real time happening in this show, it's going to have an impact to the deployment model, the consumption model, and the economics. What's the take, what's the internal discussions? How you guys talk about it externally with costumers and how is it received? >> Right, so, you know what, I'll tell you what, this is the industry cloud organization, so, maybe I can start there. What's industry and cloud doing in the same sentence, in the same title? So, when you talk about digitization, what customers are looking for today, it's value and speed, right, speed and agility. So, the industry part of the equation is all about value. How do we communicated the value of our innovations in a message and understanding that gives the customers confidence to invest in a innovation agenda and that's kind of, historically, has always been the strength of SAP, is the language that we speak with our customers, it's well understood, we just make sure that we express that well across all industries and line of business with the digitization agenda. The cloud portion is where speed and agility comes into play. How do you move quickly, how do you move fast? If in yesterday's business the strength was your ownership of assets, the strength today, the attributes in which these companies compete on is speed, innovation, agility, and that's where cloud comes into play. >> And knowledge of the customer. How are you then bringing those two things together for your customerS? >> So, we're helping, actually, customers across all industries get closer to the customer. If there's one strategy that every customer in every industry is pursuing is get close to the customer. This is important, it may seem sort of simplistic, but it's easy to say, it's hard to do. So, we are helping customers understand what their customers and what their customer's customers are doing. It's driving a blurring of industries. You may say that I'm responsible for 26 industries, maybe oversimplifying 'cause we see this massive blurring of industries because as customers in industries are trying to get closer to their customers, they cross boundaries. >> And conversation let's them do that. >> Yeah, it's like we were talking about before, in this world of atoms, very restrictive, very kind of two-dimensional. Digital, it defies gravity, it defies boundaries, and that's why you see this blurring of boundaries in cross industry plays. >> Yeah, we're seeing that, too, you guys talk about it here, I heard it many times, breaking down the silos and the keynotes, but at the same time, you want to have that getting close to your customer mindset which means that the apps, the workloads are domain specific and there's some blurring, so the question is, how can you be vertically integrated at some level for that domain expertise and then be horizontally scalable because the data really becomes the blurring component, too, you have data moving around, so how do you guys look at that and are customers asking for this kind of architecture? >> Yeah, it's exactly, so... It's interesting, in the old world, you either had deep industry expertise in your applications, your technology, or you had sort of a broad, horizontal, and that got you a seat at the table. You had to be best in class in either of those. So, those still get you to the table, if you have those, but it may be a small table like the table that we deal with, with our customers, is an innovation table, it's a growth table, and it involves the whole board, the whole enterprise. If you get to that table, you need to have deep industry expertise and what do I mean by that? First, you speak the language, you understand their industry from a process and the capability area and then you have to express that across their businesses, so whether that business are traditional COM, the customer business around people, HR, or around procurement or even in the industries where you're taking look at supply chain or you're looking at planning, you need to be able to integrate the industry with the horizontal. When you have that conversation and that message, which we have, you're at the big table. >> The big boy table, so what are some of the conversations at the table, is it really more revenue-driving for the customer's customers? Is it cost-saving, both, is it implementation? What are some of the trending conversations that are happening at the big table? So, at the big table, at the top of the house, strategically, around this topic of digitization, the world of digitization, competition is at the business model level, that's what they're talking about which is, I know I'm in this business today, will I be in this business tomorrow and how do I compete tomorrow? It's less about the assets as we said before, what do you have, but it's the insight that you have and that's opening up a lot of new business opportunities, so at the big table, it's around business model innovation, that's what they're talking about. >> Let me see if I can connect a couple of things you said here, so it used to be that when you thought about industry, you thought about the organization of assets, your organization of assets looks like your organization of assets, how do you handle your balance sheets, but now we're talking about customers and in many respects, the new industry is defined by the things that your customers want to do that are common to your competitor's customers. >> Exactly. >> And sometimes they're the same customers. So, as SAP's ecosystem grows, as it expands, as you're able to attract, through new sources of value, to things like this wonderful Apple partnership that we want to give you guys a chance to talk about, do you see SAP's role moving from a provider of software to actually increasing the provider of a way of thinking about doing business, where SAP, in many respects, becomes an element, almost a core element, of the business model that your customers are using to make things happen. >> That is a great statement and I actually can point you in two directions and I want to get to the Apple relation because it actually expresses our strategy on taking advantage of that. So, I would say, historically, when we were just an application company, the source of innovation came from SAP, we understood business process, we understood industry, we built these remarkable applications, and our ecosystem took 'em, implemented, and customers enjoyed the success. We're in the world now of digitization and massive innovation and there's no way that we can be the single source of innovation, this is why you heard Burn, this is why you heard Robyn Bell talking about the Hana cloud platform. So, we still need to be the catalyst when it comes to defining what is remarkable about our technology and capability to solve business problems, but then we have to enable a massive ecosystem to innovate on top of that, to extend it, to innovate, and that's where the Hana cloud platform comes into play. We are setting the agenda, we are setting the expectation of what great looks like and then tapping into the ecosystems that we have. >> What's interesting about what you just said and Peter brought this up yesterday with the global CTO of Capgemini and your premise was, the old days, you knew the processes, but didn't know the technologies, and you automated those processes, now we know the technology and don't know the processes as their developing. So, you look at IOT, it's an unknown future, but you can kind of guess it's going to be a lot of data, it's going to be an edge of the network, so that reinforces this whole ecosystem point that the innovation will come in an unknown innovation way meaning that you can't say, "I'm going to automate that" 'cause it's not known yet, it's evolving. That to me seems to tie what you just said. Can you expand your thoughts on that because this is what everyone's chasing that's the startup mentality, that's the agile, that's the jump on a grenade, win the beachhead, grow a business, that's going to be the startups and the white space for you guys. >> Look, I'm a lousy dart player, all right, but I could win if I'm throwing a thousand darts at a target and the other guy's throwing three, that's the environment we're in with Hana cloud platform, we got massive darts to throw at the target because it changes so fast you need to have a couple things, you need to have that great ecosystem, you need to be able to innovate, and you need to be able to address volatility. Let me give you a practical example of that. If you take a look at digitization and one of the key dimensions which is how work will be done in this new digital world, we have some pretty good ideas how it's going to be done such as it's not going to be done inside of the enterprise, whether that work is a manufacturing environment or that work is knowledge management in a typical office, it's going to be increasingly mobile and these mobile workers will be connected. And the challenge there is one, how do you understand what the processes will be? We have an idea, but they're going to evolve and second, how do you enable them with real time information 'cause the mobile experience isn't just taking the desktop and putting a different form factor on it, so we take a look at the Apple and SAP announcement, what does this mean? When you hear Tim and you hear Bill discuss it, it's a step change in how these two great companies believe work will be done in the digital world. The way that we execute on that is, again, back to what I said before, we will bring the best of a consumer, user experience, with the best of a business insight experience and bring those together and if you take a look then at what is the standard of a mobile platform, it's iOS which, by the way, is severely underutilized. It's chat, it's phone, it's email. If you take a look at your iPhone and how we're using it as consumers, that's massively underutilized in an enterprise setting, same thing with business information, when you leave the office, you're leaving all that behind, SAP will bring all that, the business process, the business insight, you bring it together and you have these new native applications. >> Interesting, too, on the Apple, by the way, congratulations it's a real phenomenal announcement, super happy to see that. The other nuance there, too, is that swift programming languages is very popular among developers right now and there's also another trend in the developer community what they're calling the non-coding developer, the tools are getting so damn good now that you don't have to go to be a computer science major to write code and there's other, Python, other languages that are good on-ramps, so you have an ecosystem that has the glam of Apple and the sexiness of swift. There's all this monetization opportunities. There's a developer saying, "Hey, I have an ecosystem "I can work with, that I can ride on the back of, "to the marketplace," so it's a great avenue for someone or now business to pick a white space and dominate it, whether it's a tool or a feature, they can come in and be a feature and still be a business, you'll be saying, so could I, was, "Oh, that's a feature not a company." That was the old way, now that's the innovation coming from these entrepreneurs, that, to me, is interesting. Are you guys seeing that kind of excitement from developers and do you see the developers as the core of the ecosystem? Well, what's your thoughts on that, overall? >> We're seeing the developer community becoming a more critical part because it's not just about implementing, remember when I said we're the source of innovation and other people implement it, that the skill set of the ecosystem, now when it's innovation, the source of the innovation needs to come from the ecosystem, and that's the developing community. So, if you take a look again at this Apple announcement, the reference applications and what we're building right now because that's what we and Apple think would look great in specific industries, but then it's this SDK and the Hana cloud platform. If you take 2.5 million SAP developers and you take 12 million iOS developers, you bring 'em together, not only just to work together, but to redefine what this new developing environment is, swift, right, the best of how you design enterprise applications or commercial applications and then the third leg of this is the iOS university because these are new classes of developers and my final point is as much as we think we know how work will be done in this mobile work environment, it's going to change, it's going to change. >> IOT's important, but people are going to work together with people over distance over agendas over boundaries, that's going to change the world. Let me ask you a question. We'd asked a couple of times to some of your folks on The Cube, Is it going to possible at some point in time, I'm going to get an Apple developer who decides to enter into an enterprise space by creating a solution, have an Apple phone customer go up, pull something off the app store because it is SAP complaint, is that going to happen? >> I can envision that happening, I can envision it. It's we are the standard for a trusted enterprise partner. >> Well, think about it, so now you got a situation where you your CIO and your IT organization who wants stable, comply in SAP, and then all the folks out in the field that are doing the work, that are identifying new problems and finding software that they can apply to solve the problem and having SAP and Apple bring both of those sides together, so that the CIO can be certain that what was just grabbed works and is compliant, but also, at the same time, that person knows that this innovative thing is not going to create problems in the backend. Very, very powerful vision, loved to see that notion. >> Yeah, and I think that's what you get when you combine those two brands and those two experiences. As quickly we're innovating and moving forward, you still need to have predictability in the business and a strong core, right? It's the business continuity, so you need to be able to innovate very quickly, rapid innovation, quick failure, fast learning, that's at the edge. So, if we can enable that, but give the predictability and the stability in the business relationships, security, you bring that together, this is the new world that we're creating, calls for new developers, calls for new ecosystems, and new leadership, and that's what we and Apple bring to the equation. >> So, Pat, share the roadmap on the Apple thing, just to kind of just to take the final close, square this out in little bits. Ecosystem, I get the ecosystem, I would evision that's a great outcome. >> Yeah, absolutely. >> Certified SAP apps in the Apple, I'm sure that's the plan. On the SAP side, you're going for the low hanging fruit, you mentioned that you're doing a couple of things, what's the roadmap for the sequence and the progression of SAP-Apple relationship? What are you guys bringing to the table from the core software? >> Yeah, so we've identified specific industries where the dynamics play to the favor of the dynamic at work, so they're mobile, they're standardizing already on iOS and they're connected and they need the rich enterprise information and we've identified high-values cases and those where we'll build the applications, but what we want to do-- >> John: That's a low hanging fruit for you guys. >> That's a low hanging fruit. And create that kind of references of what a great mobile experience looks like and then we're going to enable through the SDK, the ecosystem, so that's where the massive innovation is going to come from and then we'll try to figure out where this takes us. This is a series of six month sprints that we're on. >> Business sprints Love that concept. >> You know, this phrase, a couple of years ago, the speed of business, I forget which SAP soft, I remember in 2013, McDermott's phrase was "Running at the speed of business" with the mobile. Final question for you is, on the Industry Cloud, what's your plans, what's your goals, how do you see it evolving, can you share some anecdotal, you don't have to reveal any sensitive information, but the visions for how you see the Industry Cloud group that you're running, evolving over the next 12, 48 months? >> So, I see us, right now, that there's some things your core values and your core competencies shouldn't change, they should sort of leverage the environment that you're in and so, we're caring for our industry in sight, our focus on an end-to-end capability, high-values cases, and integration where it needs to be and that's what we express. So, we're going to take that and we're going to apply it to helping customers digitize on that journey. Here at Sapphire, the focus has been not on what we're announcing because ask any customer here, we have the requisite capabilities, what they want to get is busy on their journey and they want us to help them reduce uncertainty, reduce risk, and realize value. So, all the conversations here on what are we doing, industry, clear road maps, where we going? What capabilities? Second, road map on value, what value? S4, fastest launch in our history, customers, right now, are saying, "How do we double that, how do we triple that? Is by showing the business value associated with it. So that's what we're doing with industry, is showing a clear path of what great looks like, a road map on how to get there, the business values associated with it, and how working our digital business services customers, how we can help them realize that. >> And the road map is key because that clarifies the ecosystem. They understand kind of the rules of engagement. They can see the line. >> Yeah, what their overall is used. You know, it's interesting, Pat, you look around, there's 60,000 people, the amount of activity, the amount of deal making, that's going on here, it's probably the 25th largest economy in the world right here. >> Oh, it is, in Orlando, that's amazing. Yeah, I need to take a knee guys, I was just hearing about that. >> Final question and I'll let you go 'cause we got to go, we know you're tight on time, what's the coolest thing you've seen at Sapphire this week? >> Coolest thing, boy, I've been in so many meetings, I haven't seen cool. >> Peter: Other than this one. >> Oh, yeah, this is definitely a cool meeting. Oh, geez, coolest thing? >> Coolest phrase, sound bite, feedback, hallway conversation. >> What are you going to tell, in your next management meeting, what's the one thing you're going to tell 'em about Sapphire? >> I'd say that there is so much demand for us to help customers. We're not pushing, we're getting pulled. So, it's about prioritization like how do we focus on what's most important for our customers? That's such a lame answer. >> Peter: Well, but the prioritization of-- >> When you're looking for cool, but it's true. >> There's drones somewhere, I saw a beer tap that got IOT on it for-- >> I did see the guy in kind of the transformer outfit, that was pretty cool, but I'll tell you what, as we become more and more of consumer business oriented, my kids start developing a better understanding of what I actually do when I leave home. It's cool, I mean, SAP is cool. Actually, I'll tell you the one thing. The one thing I heard here from customers that either went to original Sapphires and are back after a while or coming for the first time, they can't believe how fast we're moving. They really can believe how fast we're moving. It's that speed, it's not just the pace of this conversation or the pace of the traffic around here, it's the pace of how quickly business is moving and that we're leading it. >> Pat Bakey, president of Industry Cloud, SAP, this is The Cube, I'm John Furrier with Peter Burris. Be right back, this is The Cube, SiliconANGLE's flagship program. This is The Cube, you're watching The Cube, we'll be right back. (fun, upbeat melody) >> Voiceover: There'll be millions of people in the near future that aren't allowed to be involved in their own personal well-being and wellness. Nobody wants to.