>> Narrator: Live from Las Vegas, it's theCube. Covering Interconnect 2017. brought to you by IBM. >> Hey welcome back everyone. We're live here in Las Vegas for IBM Interconnect 2017. Siliconangle's theCube's Exclusive coverage of IBM Interconnect 2017, I'm John Furrier. My co-host Dave Vellante. Our next two guests, Eric Herzog, Vice President of Marketing for IBM Storage. Nice to see you again, you were on yesterday. And Mark Godard, Manager of Customer Success and Partnership at Sparkcognition, a customer. Guys, welcome to theCube, good to see you again. Welcome for the first time. >> Thank you. >> Thank you. >> Okay, so we're going to talk about some stories we did yesterday, but you've got the customer here. What's the relationship, why are you guys here? >> We provide the storage platform. They use our flash technology. Spark is a professional software company. It's not a custom house, they are a software company. >> And Spark, not related to Spark OpenSource. Just the name Spark, Sparkcognition. Make sure to get that out of the way. Go ahead, continue. >> So they're a hot startup. They have a number of different use case including cybersecurity, real-time IoT, predictive analytics and a whole bunch of other things that they do. When a customer goes on premise 'cause they deliver either through a service model or on premise, when it's in their service model they use our flash and our power servers. When it's on premise they recommend here's the hardware you should use to optimize the software if the customer buys a non-premised version. They offer it both ways, but part of the reason we thought it would be interesting is they're a professional software company. A lot of the people here as you know are regular developers, in-house developers. In this case these guys are a well-funded VC startup that delivers software to the end user base. >> Tell us more about Sparkcognition. Give us the highlights. >> Yeah, appreciate it. Sparkcognition, we're a cognitive algorithms company. We do data science, machine learning, natural language processing. Kind of the whole gambit there. Working, we have three products. SparkPredict is our predictive analytic, our predictive maintenance product. SparkSecure is our network log security product. And Deep Armor is a machine learning endpoint protection product. In that you kind of hear we're in the IoT, the industrial IoT, the IIoT of things. It also, in cybersecurity we've done use cases, other machine learning use cases as well. But the predictive maintenance and cybersecurity are two most, most advanced use cases, industrial areas. So we've been around about three years. We have around 100 people. Appreciate Eric talking about how well-financed we are and how our success really is budding this far. We're happy to be here. >> John: Where are you guys located? >> We're based out of Austin, Texas. >> John: Another Austin. >> Yeah Austin, Texas. >> Dominant with Austin. >> It's always good to have financing. You can't go out of business if you don't run out of money. Talk about the industrial aspect. One of the things that is hot, it's not a mainstream here, is blockchain is the big announcement. But IoT is the big one. But industrial IoT's interesting because now you have the digitization of business as a big factor. And that data is going to be throwing off massive analog digital data now. So analog to digital, what's going on there? What are you guys doing there to help and where does the storage fit in? >> Yeah, I appreciate that. So IIoT, industrial there's obviously there's big clients there. There's value in this information. For us it's predictive maintenance is the big play. A study I read the other day by a Boston consulting group talks about how its services and applications in the industrial internet of things. There's an $80 billion market in the next five years with predictive maintenance leading the way as the most mature application there. So we're happy to be kind of riding on the front of that wave, really pushing the state of the art there. Predictive maintenance is valuable to clients because the idea is to predict failures, do optimization of resources, so to get more energy out of your wind farm, get more gas out of the ground, you name it. Having the software that can provide those solutions efficiently to clients without a lot of start up, but each new iteration. So having a product that can deliver that intellectual property efficiently is important. The whole goal is to be able to reduce maintenance costs and extend the useful life of assets. So that's what SparkPredict is our product, SparkPredict our product, Sparkcognition has been laboring to do. We have a successful deployment of 1,100 turbines with Invenergy, which is the largest wind production company in the United States. We're doing work with Duke, Nexterra, several other large electrical production companies, oil and gas companies as well. In Austin we're near Houston, we have a lot of energy production opportunity there. So predictive maintenance for us is a big play. >> So you guys did a session this week. You hosted a panel, is that right? So I mean no offense, but what we're talking about now is really even more interesting than storage. But it's a storage panel you were hosting, right? So what was the conversation like? >> The conversation around that was we had three software companies, Sparkcognition and two other software companies. Then we had a federal integrator. All of them are doing cloud delivery. So for example, one of the other software companies Medicat, delivers medical record keeping as a service to hospitals. They're doing predictive analytics and predictive maintenance, and also some cybersecurity out. So there were three professional software companies, and integrator. And in each case the issues were A, we need to be up and going all the time and the user doesn't know what storage we're using. But we can never fail because we're real time. In fact, one of the customers is the IRS. So the federal integrator, the IRS cloud runs on IBM storage. The entire IRS runs under IBM cloud. On our storage, but it's their cloud. It's their private cloud that they put together, that the integrator put together. The idea was we've got a cloud deployment. There's two key things your storage has to do. A, it needs to be resilient as heck because these guys and the other two companies on the software side if they cannot serve it as a service then no one's going to buy the software, right? Because software is the service. So for them it's critical in their own infrastructure that it be resilient. Then the second thing, it needs to be fast. You've got to meet the SLAs, right? So when you're thinking the system's integrator at the IRS, what do you think the SLAs are and they've got like 14 petabytes of all flash. >> You forgot dirt cheap. You got resilient as heck, lightning fast, and it's got to be dirt cheap, too. >> Well of course. >> They want all three, right? >> You have this panelist, so what Jenny, what were Jenny's three? Industrial ready, cloud based, and cognitive to the core. So you guys are, I'm on your website. It's cognitive this, cognitive that. You're cognitive to the core. You're presumably you're using industrial ready infrastructure and it's all cloud based, right? Talk about that a little bit, then I've got a follow up. >> To tie into what Eric is saying about the premium hardware, the cloud opportunity, for us to be able to to AI software, to be able to do machine learning models, these are very intensive applications that require massive amounts of CPU, IO, fast storage. To be able to get the value from that data quickly so that it's useful and actionable takes that premium hardware. So that's why we've done testing with flash system, with our cybersecurity product. One of the most innovative things that we did in the previous year was to move from a traditional architecture using X86, 64 where we had a cluster of eight servers there. Brought that down to one flash system array and we're able to get up to 20 times the performance doing things like analyzing, sorting, and ingesting data with our cybersecurity platform. So in that regard we were very much tied closely to the flash system product. That was a very successful use case. We offered a white paper on that. If anyone wants to read more that's available on the IBM website. >> Where do you find that, search it? >> Yeah, it's on IBM.com and it's basically how they used it to deliver software as a service. >> What do I search? >> If you search Sparkcognition IBM you'll find it on Google. >> My other question, my follow up is you talk about these IoT apps which are distributed by their very nature. Can we talk about the data flow? What are you seeing in terms of where the data flows? Everybody wants to instrument the windmill. You've got to connect it then you've got to instrument it. Where's the data going? You're doing analytics locally, you're sending data back. What are you seeing in the client base? >> Yeah, that's a great question. Those in the field use cases for the wind turbines for example, most of our clients they already have a data storage solution. We're not a data storage provider. The reason, and someone asked me this yesterday in a different conversation. They said why are wind turbines so ripe for the picking? It's because they're relatively modern assets. They were built with the sensors onboard. The data, they have been collecting the data since the invention of the modern wind turbine, they've been collecting this data. Generally it's sent in from the field at 10 minute intervals, usually stored in some sort of large data center. For our purposes though, we collect a feed off that data of the important information, run our models, store a small data set a few months, whatever we think we need to train that machine learning model and to retrain and balance that model. That's sort of an example where we're doing the analysis in a data center or in the cloud sort of out of the field. The other approach is sort of an edge analytics approach, you might have heard that term. That's usually for smaller devices where the value of the asset doesn't justify the infrastructure to relay the information and then deploy this large scale solution. So we actually are developing edge analytic solution, a version of our product as well working with a company called Flowserve, their the world's largest pump manufacturing company. To be able to say how can we add some intelligence the to these pumps that may operate near a pipeline or out in the oil field and be able to make those machines smarter even though they don't necessarily justify the robust IT infrastructure of a full wind turbine fleet. >> Is there a best practice that you guys see in terms of the storage? Because you bring out edge and the network. Great point, lot of diversity at the edge now, from industrial to people. But the data's got to be stored somewhere. I mean, is there a best practice? Is there a pattern to developing that you're seeing in terms of how people are approaching the data problem and applying algorithms to it? Just talk, do I move the data? Do I push to compute to the data? Thoughts on what you guys are seeing in terms of best practices. >> One of the other companies that was on the panel also is doing predictive modeling. They take 600 different feeds in real time then munge it for mostly for industrial markets, but mostly for the goods. So the raw goods that they need to make a machine or make a table or make the paper that is used behind us, or make the lights that are used here, they look at all that commodities and then they feed it out to all these consumers, not consumers but the companies that build these products. So for them, they need it real time so they need storage that's incredibly fast because what they're doing is they're putting out on super powerful CPUs loaded with D-ram, but you can only put so much D-ram in a server. They're building these giant clusters to analyze all this data and everything else is sitting on the flash. Then they push that out to their customers. Slightly different model from what Sparkcognition does, but a slightly similar except their taking it from 600 constant data sources in real time, 24 by seven, 365 and then feeding it back out to these manufacturing companies that are looking to buy all these commodities. >> You have "software defined" in your title. That was kind of the big buzzwords a few years ago. Everybody wanted to replicate the public cloud on prem. We think of it as programmable infrastructure, right? Set it and then you can start making API calls and set SLAs and thresholds, etc. Where are we at with software defined? Do you guys, does it resonate with you or is it just an industry buzzword? I'll start with Eric. >> For us we're the largest provider of software defined storage in the world. Hundreds and hundreds and hundreds of millions of dollars every year. We don't sell any infrastructure. We just sell the raw software and they use commodity infrastructure, whatever they want: hard drives, flash drives, CPUs, anything they buy from their local reseller and then create basically high-performance arrays using that software. So they create on their own. Everything is built around automation so we automatically can replicate data, snapshot data, migrate data around from box to box, move it from on-premise to a cloud through what we call transparent cloud tiering. All of that in the software defined storage is done based on automation play. So the software defined storage allows them to if you will, build their own version of our flash system by just buying the raw software and buying flash from someone else, which is okay with us because the real value's in the software, obviously as you know. That allows them to then create infrastructure of their own, but they've got the right kind of software. They're not home brewing the software it's all built around automation. That's what we're seeing in the software defined space across a number of different industries, whether it be cloud providers, banks. We have all kinds of banks that used our software defined storage and don't buy the actual underlying storage from us, just the storage software. >> Do you, you may not have visibility in this, but getting kind of geeky on it. Do you guys adopt that sort of software defined mentality in your approach? >> Yeah, so for us software defined storage is something that we've deployed for our proof of concept evaluations. The nature of the work that we do is the solution is innovative to the point where everyone needs to have some sort of proof point for themselves before the company or the client will invest in a large scale. So software defined storage and embracing that perspective has allowed us to deploy a small scale implementation without having our own dedicated hardware, for example, at different clients. That's enabled us to spin up an instance quickly, to provision that small scale deployment, to be able to prove out results at a low cost to our client. That's where we really leverage that approach. We also have used a similar approach in the cloud where we've used multi-tenant environments to be able to support our cybersecurity product, SparkSecure in a multi-tenant cloud hosted environment which brings down delivery costs as well. It allows us to slice up that data and deliver it at a low cost. As far as our large scale physical deployments for the asset monitoring and such, we really, we generally end up with a piece of a flash system or flash storage, bare metal deployment because that speed is critical whether that's the client wants to have instant monitoring of a critical asset or they have a financial services use case where we're looking for anomalies or looking for threats in the cybersecurity landscape. Having that real-time model building and model result is very critical. So having that bare metal flash system type installation is kind of our preferred route. The only other thing I would say on that is you asked earlier about our approach. For us, the security data is very important. Most of our assets are what are called critical assets. So clients are very sensitive to the security of the data. Some are still uncomfortable with a cloud deployment. Another reason why we have an affinity for the hardware deployment with IBM. >> Why IBM? >> Our company has really deep roots with IBM. My founder Amir Hussein, was actually on the board of directors of the original IBM Watson Project as well as Manoj Saxena was the original GM of the IBM Watson program. We have just a long relationship with IBM. We have a lot of mutual interest and respect for the entity. We've also found that the products are superior in many ways. We are hardware agnostic and we're an independent advisor to our clients when it comes to how to deliver our solutions. But our professional opinion based on the testing that we've done is that IBM is a top-tier option. So we continue to prescribe that to our clients. When they feel that's appropriate they make that purchase through IBM. >> Great testimonial. Eric, excited to hear that nice testimonial for you guys? Congratulations. >> He's done several panels with us and again, part of the reason for here was A, all about IoT which they're all into. All about commo which they're all into. And to show that you can do a software as a service model even in-house. They happen to be a professional software company but if you're a giant global enterprise you may actually do software as a service to your remote branch offices which is very similar to what these guys to do other companies. This gives them an example, the other two software companies the same way, to show in-house developers if you're going to have a private cloud, not go public, you can deliver software as a service internally to your own company through the dev model and do it that way. Or you can use someone like Sparkcognition or Medicat or the other companies that we showed, Z-Power, all of which were using us to deliver their software as a service with IBM flash technology. >> Dave: And you're using Watson or Watson analytics? >> Yes, so we have done integrations with Watson for our cybersecurity product. We've also done integrations with Watson rank and retrieve using the NPL capabilities to advise the analysts both in the Predict space and in the Secure space. Sort of an advisor to say what a client user could see something happening on a turbine and say what does this mean? Using a Watson corpus. I was going to add one thing, we were talking about why IBM? IBM really has been a leader in the space of cognitive computing and they've invested in bringing and nurturing small companies and bringing up entrepreneurs in that space to build that out. So we appreciate that. I think it's important to mention that. >> All right Mark, thanks so much for joining in, the great testimonial, the great insight. Good luck with your business. Congratulations on the success startup taking names and kicking butt. Eric, great to see you again, thanks for the insight and congratulations on great, happy customers and see you again. Okay, we're watching theCube live here at Interconnect 2017. More great coverage, stay with us. There will be more after this short break. (upbeat instrumental music)

>> Voiceover: Live from Las Vegas, it's theCUBE, covering Discover 2016, Las Vegas. Brought to you by Hewlett Packard Enterprise. Now, here are your hosts, John Furrier and Dave Vellante. >> Okay, welcome back everyone. We are here live in Las Vegas for SiliconANGLE Media's theCUBE. It's our flagship program, we go out to the events to extract the signal from the noise, we're your exclusive coverage of HP Enterprise, Discover 2016, I'm John Furrier with my co-host, Dave Vellante, extracting the signals from the noise with two great guests, Dr. Tom Bradicich, VP and General Manager of the servers and IoT systems, and Eric Starkloff, the EVP of Global Sales and Marketing at National Instruments, welcome back to theCUBE. >> Thank you. >> John: Welcome for the first time Cube alumni, welcome to theCUBE. >> Thank you. >> So we are seeing a real interesting historic announcement from HP, because not only is there an IoT announcement this morning that you are the architect of, but the twist that you're taking with IoT, is very cutting edge, kind of like I just had Google IO, and at these big conferences they always have some sort of sexy demo, that's to kind of show the customers the future, like AI, or you know, Oculus Rift goggles as the future of their application, but you actually don't have something that's futuristic, it's reality, you have a new product, around IoT, at the Edge, Edgeline, the announcements are all online. Tom, but you guys did something different. And Eric's here for a reason, we'll get to that in a second, but the announcement represents a significant bet. That you're making, and HP's making, on the future of IoT. Please share the vision, and the importance of this event. >> Well thank you, and it's great to be back here with you guys. We've looked around and we could not find anything that existed today, if you will, to satisfy the needs of this industry and our customers. So we had to create not only a new product, but a new product category. A category of products that didn't exist before, and the new Edgeline1000, and the Edgeline4000 are the first entrance into this new product category. Now, what's a new product category? Well, whoever invented the first automobile, there was not a category of automobiles. When the first automobile was invented, it created a new product category called automobiles, and today everybody has a new entry into that as well. So we're creating a new product category, called converged IoT systems. Converged IoT systems are needed to deliver the real-time insights, real-time response, and advance the business outcomes, or the engineering outcomes, or the scientific outcomes, depending on the situation of our customers. They're needed to do that. Now when you have a name, converged, that means somewhat, a synonym is integration, what did we integrate? Now, I want to tell you the three major things we integrated, one of which comes from Eric, and the fine National Instruments company, that makes this technology that we actually put in, to the single box. And I can't wait to tell you more about it, but that's what we did, a new product category, not just two new products. >> So, you guys are bringing two industries together, again, that's not only just point technologies or platforms, in tooling, you're bringing disparate kind of players together. >> Yes. >> But it's not just a partnership, it's not like shaking hands and doing a strategic partnership, so there's real meat on the bone here. Eric, talk about one, the importance of this integration of two industries, basically, coming together, converged category if you will, or industry, and what specifically is in the box or in the technology. >> Yeah, I think you hit it exactly right. I mean, everyone talks about the convergence of OT, or operational technology, and IT. And we're actually doing it together. I represent the OT side, National Instruments is a global leader. >> John: OT, it means, just for the audience? >> Operational Technology, it's basically industrial equipment, measurement equipment, the thing that is connected to the real world. Taking data and controlling the thing that is in the internet of things, or the industrial internet of things as we play. And we've been doing internet of... >> And IT is Information Technologies, we know what that is, OT is... >> I figured that one you knew, OT is Operational Technology. We've been doing IoT before it was a buzzword. Doing measurement and control systems on industrial equipment. So when we say we're making it real, this Edgeline system actually incorporates in National Instruments technology, on an industry standard called PXI. And it is a measurement and control standard that's ubiquitous in the industry, and it's used to connect to the real world, to connect to sensors, actuators, to take in image data, and temperature data and all of those things, to instrument the world, and take in huge amounts of analog data, and then apply the compute power of an Edgeline system onto that application. >> We don't talk a lot about analog data in the IT world. >> Yeah. >> Why is analog data so important, I mean it's prevalent obviously in your world. Talk a little bit more about that. >> It's the largest source of data in the world, as Tom says it's the oldest as well. Analog, of course if you think about it, the analog world is literally infinite. And it's only limited by how many things we want to measure, and how fast we measure them. And the trend in technology is more measurement points and faster. Let me give you a couple of examples of the world we live in. Our customers have acquired over the years, approximately 22 exabytes of data. We don't deal with exabytes that often, I'll give an analogy. It's streaming high definition video, continuously, for a million years, produces 22 exabytes of data. Customers like CERN, that do the Large Hadron Collider, they're a customer of ours, they take huge amounts of analog data. Every time they do an experiment, it's the equivalent of 14 million images, photographs, that they take per second. They create 25 petabytes of data each year. The importance of this and the importance of Edgeline, and we'll get into this some, is that when you have that quantity of data, you need to push processing, and compute technology, towards the edge. For two main reasons. One, is the quantity of data, doesn't lend itself, or takes up too much bandwidth, to be streaming all of it back to central, to cloud, or centralized storage locations. The other one that's very, very important is latency. In the applications that we serve, you often need to make a decision in microseconds. And that means that the processing needs to be done, literally the speed of light is a limiting factor, the processing must be done on the edge, at the thing itself. >> So basically you need a data center at the edge. >> A great way to say it. >> A great way to say it. And this data, or big analog data as we love to call it, is things like particulates, motion, acceleration, voltage, light, sound, location, such as GPS, as well as many other things like vibration and moisture. That is the data that is pent up in things. In the internet of things. And Eric's company National Instruments, can extract that data, digitize it, make it ones and zeroes, and put it into the IT world where we can compute it and gain these insights and actions. So we really have a seminal moment here. We really have the OT industry represented by Eric, connecting with the IT industry, in the same box, literally in the same product in the box, not just a partnership as you pointed out. In fact it's quite a moment, I think we should have a photo op here, shaking hands, two industries coming together. >> So you talk about this new product category. What are the parameters of a new product category? You gave an example of an automobile, okay, but nobody had ever seen one before, but now you're bringing together sort of two worlds. What defines the parameters of a product category, such that it warrants a new category? >> Well, in general, never been done before, and accomplishes something that's not been done before, so that would be more general. But very specifically, this new product, EL1000 and EL4000, creates a new product category because this is an industry first. Never before have we taken data acquisition and capture technology from National Instruments, and data control technology from National Instruments, put that in the same box as deep compute. Deep x86 compute. What do I mean by deep? 64 xeon cores. As you said, a piece of the data center. But that's not all we converged. We took Enterprise Class systems management, something that HP has done very well for many, many years. We've taken the Hewlett Packard Enterprise iLo lights-out technology, converged that as well. In addition we put storage in there. 10s of terabytes of storage can be at the edge. So by this combination of things, that did exist before, the elements of course, by that combination of things, we've created this new product category. >> And is there a data store out there as well? A database? >> Oh yes, now since we have, this is the profundity of what I said, lies in the fact that because we have so many cores, so close to the acquisition of the data, from National Instruments, we can run virtually any application that runs on an x86 server. So, and I'm not exaggerating, thousands. Thousands of databases. Machine learning. Manageability, insight, visualization of data. Data capture tools, that all run on servers and workstations, now run at the edge. Again, that's never been done before, in the sense that at the edge today, are very weak processing. Very weak, and you can't just run an unmodified app, at that level. >> And in terms of the value chain, National Instruments is a supplier to this new product category? Is that the right way to think about it? >> An ingredient, a solution ingredient but just like we are, number one, but we are both reselling the product together. >> Dave: Okay. >> So we've jointly, collaboratively, developed this together. >> So it's engineers and engineers getting together, building the product. >> Exactly. His engineers, mine, we worked extremely close, and produced this beauty. >> We had a conversation yesterday, argument about the iPhone, I was saying hey, this was a game-changing category, if you will, because it was a computer that had software that could make phone calls. Versus the other guys, who had a phone, that could do text messages and do email. With a browser. >> Tom: With that converged product. >> So this would be similar, if I may, and you can correct me if I'm wrong, I want you to correct me and clarify, what you're saying is, you guys essentially looked at the edge differently, saying let's build the data center, at the edge, in theory or in concept here, in a little concept, but in theory, the power of a data center, that happens to do edge stuff. >> Tom: That's right. >> Is that accurate? >> I think it's very accurate. Let me make a point and let you respond. >> Okay. >> Neapolitan ice cream has three flavors. Chocolate, vanilla, strawberry, all in one box. That's what we did with this Edgeline. What's the value of that? Well, you can carry it, you can store it, you can serve it more conveniently, with everything together. You could have separate boxes, of chocolate, vanilla, and strawberry, that existed, right, but coming together, that convergence is key. We did that with deep compute, with data capture and control, and then systems management and Enterprise class device and systems management. And I'd like to explain why this is a product. Why would you use this product, you know, as well. Before I continue though, I want to get to the seven reasons why you would use this. And we'll go fast. But seven reasons why. But would you like to add anything about the definition of the conversion? >> Yeah, I was going to just give a little perspective, from an OT and an industrial OT kind of perspective. This world has generally lived in a silo away from IT. >> Mm-hmm. >> It's been proprietary networking standards, not been connected to the rest of the enterprise. That's the huge opportunity when we talk about the IoT, or the industrial IT, is connecting that to the rest of the enterprise. Let me give you an example. One of our customers is Duke Energy. They've implemented an online monitoring system for all of their power generation plants. They have 2,000 of our devices called CompactRIO, that connect to 30,000 sensors across all of their generation plants, getting real-time monitoring, predictive analytics, predictive failure, and it needs to have processing close to the edge, that latency issue I mentioned? They need to basically be able to do deep processing and potentially shut down a machine. Immediately if it's an a condition that warrants so. The importance here is that as those things are brought online, into IT infrastructure, the importance of deep compute, and the importance of the security and the capability that HPE has, becomes critical to our customers in the industrial internet of things. >> Well, I want to push back and just kind of play devil's advocate, and kind of poke holes in your thesis, if I can. >> Eric: Sure thing. >> So you got the probes and all the sensors and all the analog stuff that's been going on for you know, years and years, powering and instrumentation. You've got the box. So okay, I'm a customer. I have other stuff I might put in there, so I don't want to just rely on just your two stuff. Your technologies. So how do you deal with the corner case of I might have my own different devices, it's connected through IT, is that just a requirement on your end, or is that... How do you deal with the multi-vendor thing? >> It has to be an open standard. And there's two elements of open standard in this product, I'll let Tom come in on one, but one of them is, the actual IO standard, that connects to the physical world, we said it's something called PXI. National Instruments is a major vendor within this PXI market, but it is an open standard, there are 70 different vendors, thousands of products, so that part of it in connecting to the physical world, is built on an open standard, and the rest of the platform is as well. >> Indeed. Can I go back to your metaphor of the smartphone that you held up? There are times even today, but it's getting less and less, that people still carry around a camera. Or a second phone. Or a music player. Or the Beats headphones, et cetera, right? There's still time for that. So to answer your question, it's not a replacement for everything. But very frankly, the vision is over time, just like the smartphone, and the app store, more and more will get converged into this platform. So it's an introduction of a platform, we've done the inaugural convergence of the aforementioned data capture, high compute, management, storage, and we'll continue to add more and more, again, just like the smartphone analogy. And there will still be peripheral solutions around, to address your point. >> But your multi-vendor strategy if I get this right, doesn't prevent you, doesn't foreclose the customer's benefits in any way, so they connect through IT, they're connected into the box and benefits. You changed, they're just not converged inside the box. >> At this point. But I'm getting calls regularly, and you may too, Eric, of other vendors saying, I want in. I would like to relate that conceptually to the app store. Third party apps are being produced all the time that go onto this platform. And it's pretty exciting. >> And before you get to your seven killer attributes, what's the business model? So you guys have jointly engineered this product, you're jointly selling it through your channels, >> Eric: Yes. >> If you have a large customer like GE for example, who just sort of made the public commitment to HPE infrastructure. How will you guys "split the booty," so to speak? (laughter) >> Well we are actually, as Tom said we are doing reselling, we'll be reselling this through our channel, but I think one of the key things is bringing together our mutual expertise. Because when we talk about convergence of OT and IT, it's also bringing together the engineering expertise of our two companies. We really understand acquiring data from the real world, controlling industrial systems. HPE is the world leader in IT technology. And so, we'll be working together and mutually with customers to bring those two perspectives together, and we see huge opportunity in that. >> Yeah, okay so it's engineering. You guys are primarily a channel company anyway, so. >> Actually, I can make it frankly real simple, knowing that if we go back to the Neapolitan ice cream, and we reference National Instruments as chocolate, they have all the contact with the chocolate vendor, the chocolate customers if you will. We have all the vanilla. So we can go in and then pull each other that way, and then go in and pull this way, right? So that's one way as this market develops. And that's going to very powerful because indeed, the more we talk about when it used to be separated, before today, the more we're expressing that also separate customers. That the other guy does not know. And that's the key here in this relationship. >> So talk about the trend we're hearing here at the show, I mean it's been around in IT for a long time. But more now with the agility, the DevOps and cloud and everything. End to end management. Because that seems to be the table stakes. Do you address any of that in the announcement, is it part, does it fit right in? >> Absolutely, because, when we take, and we shift left, this is one of our monikers, we shift left. The data center and the cloud is on the right, and we're shifting left the data center class capabilities, out to the edge. That's why we call it shift left. And we meet, our partner National Instruments is already there, and an expert and a leader. As we shift left, we're also shifting with it, the manageability capabilities and the software that runs the management. Whether it be infrastructure, I mean I can do virtualization at the edge now, with a very popular virtualization package, I can do remote desktops like the Citrix company, the VMware company, these technologies and databases that come from our own Vertica database, that come from PTC, a great partner, with again, operations technology. Things that were running already in the data center now, get to run there. >> So you bring the benefit to the IT guy, out to the edge, to management, and Eric, you get the benefit of connecting into IT, to bring that data benefits into the business processes. >> Exactly. And as the industrial internet of things scales to billions of machines that have monitoring, and online monitoring capability, that's critical. Right, it has to be manageable. You have to be able to have these IT capabilities in order to manage such a diverse set of assets. >> Well, the big data group can basically validate that, and the whole big data thesis is, moving data where it needs to be, and having data about physical analog stuff, assets, can come in and surface more insight. >> Exactly. The biggest data of all. >> And vice versa. >> Yup. >> All right, we've got to get to the significant seven, we only have a few minutes left. >> All right. Oh yeah. >> Hit us. >> Yeah, yeah. And we're cliffhanging here on that one. But let me go through them real quick. So the question is, why wouldn't I just, you know, rudimentary collect the data, do some rudimentary analytics, send it all up to the cloud. In fact you hear that today a lot, pop-up. Censored cloud, censored cloud. Who doesn't have a cloud today? Every time you turn around, somebody's got a cloud, please send me all your data. We do that, and we do that well. We have Helion, we have the Microsoft Azure IoT cloud, we do that well. But my point is, there's a world out there. And it can be as high as 40 to 50 percent of the market, IDC is quoted as suggesting 40 percent of the data collected at the edge, by for example National Instruments, will be processed at the edge. Not sent, necessarily back to the data center or cloud, okay. With that background, there are seven reasons to not send all the data, back to the cloud. That doesn't mean you can't or you shouldn't, it just means you don't have to. There are seven reasons to compute at the edge. With an Edgeline system. Ready? >> Dave: Ready. >> We're going to go fast. And there'll be a test on this, so. >> I'm writing it down. >> Number one is latency, Eric already talked about that. How fast do you want your turnaround time? How fast would you like to know your asset's going to catch on fire? How fast would you like to know when the future autonomous car, that there's a little girl playing in the road, as opposed to a plastic bag being blown against the road, and are you going to rely on the latency of going all the way to the cloud and back, which by the way may be dropped, it's not only slow, but you ever try to make a phone call recently, and it not work, right? So you get that point. So that's latency one. You need to time to incite, time to response. Number one of seven, I'll go real quick. Number two of seven is bandwidth. If you're going to send all this big analog data, the oldest, the fastest, and the biggest of all big data, all back, you need tremendous bandwidth. And sometimes it doesn't exist, or, as some of our mutual customers tell us, it exists but I don't want to use it all for edge data coming back. That's two of seven. Three of seven is cost. If you're going to use the bandwidth, you've got to pay for it. Even if you have money to pay for it, you might not want to, so again that's three, let's go to four. (coughs) Excuse me. Number four of seven is threats. If you're going to send all the data across sites, you have threats. It doesn't mean we can't handle the threats, in fact we have the best security in the industry, with our Aruba security, ClearPass, we have ArcSight, we have Volt. We have several things. But the point is, again, it just exposes it to more threats. I've had customers say, we don't want it exposed. Anyway, that's four. Let's move on to five, is duplication. If you're going to collect all the data, and then send it all back, you're going to duplicate at the edge, you're going to duplicate not all things, but some things, both. All right, so duplication. And here we're coming up to number six. Number six is corruption. Not hostile corruption, but just package dropped. Data gets corrupt. The longer you have it in motion, e.g. back to the cloud, right, the longer it is as well. So you have corruption, you can avoid. And number three, I'm sorry, number seven, here we go with number seven. Not to send all the data back, is what we call policies and compliance, geo-fencing, I've had a customer say, I am not allowed to send all the data to these data centers or to my data scientists, because I can't leave country borders. I can't go over the ocean, as well. Now again, all these seven, create a market for us, so we can solve these seven, or at least significantly ameliorate the issues by computing at the edge with the Edgeline systems. >> Great. Eric, I want to get your final thoughts here, and as we wind down the segment. You're from the ops side, ops technologies, this is your world, it's not new to you, this edge stuff, it's been there, been there, done that, it is IoT for you, right? So you've seen the evolution of your industry. For the folks that are in IT, that HP is going to be approaching with this new category, and this new shift left, what does it mean? Share your color behind, and reasoning and reality check, on the viability. >> Sure. >> And relevance. >> Yeah, I think that there are some significant things that are driving this change. The rise of software capability, connecting these previously siloed, unconnected assets to the rest of the world, is a fundamental shift. And the cost point of acquisition technology has come down the point where we literally have a better, more compelling economic case to be made, for the online monitoring of more and more machine-type data. That example I gave of Duke Energy? Ten years ago they evaluated online monitoring, and it wasn't economical, to implement that type of a system. Today it is, and it's actually very, very compelling to their business, in terms of scheduled downtime, maintenance cost, it's a compelling value proposition. And the final one is as we deliver more analytics capability to the edge, I believe that's going to create opportunity that we don't even really, completely envision yet. And this deep computing, that the Edgeline systems have, is going to enable us to do an analysis at the edge, that we've previously never done. And I think that's going to create whole new opportunities. >> So based on your expert opinion, talk to the IT guys watching, viability, and ability to do this, what's the... Because some people are a little nervous, will the parachute open? I mean, it's a huge endeavor for an IT company to instrument the edge of their business, it's the cutting, bleeding edge, literally. What's the viability, the outcome, is it possible? >> It's here now. It is here now, I mean this announcement kind of codifies it in a new product category, but it's here now, and it's inevitable. >> Final word, your thoughts. >> Tom: I agree. >> Proud papa, you're like a proud papa now, you got your baby out there. >> It's great. But the more I tell you how wonderful the EL1000, EL4000 is, it's like my mother calling me handsome. Therefore I want to point the audience to Flowserve. F-L-O-W, S-E-R-V-E. They're one of our customers using Edgeline, and National Instruments equipment, so you can find that video online as well. They'll tell us about really the value here, and it's really powerful to hear from a customer. >> John: And availability is... >> Right now we have EL1000s and EL4000s in the hands of our customers, doing evaluations, at the end of the summer... >> John: Pre-announcement, not general availability. >> Right, general availability is not yet, but we'll have that at the end of the summer, and we can do limited availability as we call it, depending on the demand, and how we roll it out, so. >> How big the customer base is, in relevance to the... Now, is this the old boon shot box, just a quick final question. >> Tom: It is not, no. >> Really? >> We are leveraging some high-performance, low-power technology, that Intel has just announced, I'd like to shout out to that partner. They just announced and launched... Diane Bryant did her keynote to launch the new xeon, E3, low-power high-performance xeon, and it was streamed, her keynote, on the Edgeline compute engine. That's actually going into the Edgeline, that compute blade is going into the Edgeline. She streamed with it, we're pretty excited about that as well. >> Tom and Eric, thanks so much for sharing the big news, and of course congratulations, new category. >> Thank you. >> Let's see how this plays out, we'll be watching, got to get the draft picks in for this new sports league, we're calling it, like IoT, the edge, of course we're theCUBE, we're living at the edge, all the time, we're at the edge of HPE Discovery. Have one more day tomorrow, but again, three days of coverage. You're watching theCUBE, I'm John Furrier with Dave Vellante, we'll be right back. (electronic music)