(inspirational music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are in our Palo Alto studio. The conference season hasn't really kicked off yet into full swing so we can do a lot more kind of intimate stuff here in the studio, for a CUBE Conversation. And we're really excited to have a many time CUBE alum on, and a new guest, both from Western Digital. So Dave Tang, Senior Vice President at Western Digital. Great to see you again, Dave. >> Great to be here, Jeff. >> Absolutely and Martin Fink, he is the Chief Technology Officer at Western Digital, a longtime HP alum. I'm sure people recognized you from that and our great machine keynotes we were talking about it. So great to finally meet you, Martin. >> Thank you, nice to be here. >> Absolutely, so you guys are here talking about and we've got an ongoing program actually with Western Digital about Data Makes Possible, right. With all the things that are going on in tech at the end of the day, right, there's data, it's got to be stored somewhere and then of course there's processes and things going on. We've been exploring media and entertainment, sports, healthcare, autonomous vehicles, you know. All the places that this continues to reach out and it's such a fun project because you guys are a rising tide, lifts all boats, kind of company and really enjoy watching this whole ecosystem grow. So I really want to thank you for that. But now there's some new things that we want to talk about that you guys are doing to continue really in that same theme, and that's the support of this RISC-V. So first off, for people who have no idea, what is RISC-V? Let's jump into that and then kind of what is the announcement and why it's important. >> Sure, so RISC-V is an, you know, the tagline is, it's an open source instruction set architecture. So what does that mean, just so people can kind of understand. So today the world is dominated by two instruction set architectures. For the most part the, we'll call the desktop enterprise world is dominated by the Intel instruction set architecture and that's what's in most PCs, what people talk about as x86. And then the embedded and mobile space tends to be dominated by Arm, or by Arm Holdings. And so both of those are great architectures but they're also proprietary, they're owned by their respective companies. So RISC-V is essentially a third entrant, we'll say, into this world, but the distinction is that it's completely open source. So everything about the instruction set is available to all and anybody can implement it. We can all share the implementations. We can share the code that makes up that instruction set architecture, and very importantly for us and part of our motivation is the freedom to innovate. So we now have the ability to modify the instruction set or change the implementation of the instruction set, to optimize it for our devices and our storage and our drives, etc. >> So is this the first kind of open source play in microprocessor architecture? >> No, there's been other attempts at this. OpenSpark kind of comes to mind, and things like that, but the ability to get a community of individuals to kind of rally around this in a meaningful way has really been a challenge. And so I'd say that right now, RISC-V presents probably the best sort of clean slate, let's take some thing new to the market out there. >> So open source, obviously we've seen you know, take over the software world, first in the operating system which everybody is familiar with Linux but then we see it time and time again in different applications, Hadoop. I mean, there's just a proliferation of open source projects. The benefits are tremendous. Pretty easy to ascertain at a typical software case, how is that going to be applied do you think within the microprocessor world? >> So it's a little bit different. When we're talking about open source hardware or open source chips and microprocessors, you're dealing with a physical device. So even though you can open source all of the designs and the code associated with that device, you still have to fabricate it. You still have to create a physical design and you still have to call up a fab and say, will you make this for me at these particular volumes? And so that's the difference. So there are some differences between open source software where it's, you know, you create the bits and then you distribute those bits through the Internet and all is good. Whereas here, you still have a physical need to fabricate something. >> Now, how much more flexibility can you do then for the output when you can actually impact the architecture as opposed to just creating a custom chip design, on top of somebody else's architecture? >> Well, let me give you probably a really simple, concrete example that kind of people can internalize of some of our motivation behind this, because that might sort of help get people through this. If you think of a very typical surveillance application, you have a camera pointed into a room or a hallway. The reality is we're basically grabbing a ton of video frames but very few of them change, right? So the typical surveillance application is it never changes and you really want, only know when stuff changes. Well, today, in very simple terms, all of those frames get routed up to some big server somewhere and that server spends a lot of time trying to figure out, okay have I got a frame that changed? Have I got a frame that changed, and so on. And then eventually it'll find maybe two or three or five frames that have got something interesting. So in the world what we're trying to do is to say, okay well why don't we take that, find no changes, and push that right down to the device? So we basically store all those frames, why don't we go figure out all the frames that mean nothing, and only ship up to that big bad server the frames that have something interesting and something you want to go analyze and do some work on? So that's a very typical application that's quite meaningful because we can do all of that work at the device. We can eliminate shipping a whole bunch of data to where it's just going to get discarded anyways, and we can allow the end customer to really focus on the data that matters, and get some intelligence. >> And that's critical as we get more and more immersed in a data-centric world, where we have realtime applications like Martin described as well as large data-centric applications like of course, big data analytics, but also training for AI systems or machine learning. These workloads are going to become more and more diverse and they're going to need more specialized architectures and more specialized processing. So big data is getting bigger and faster and these realtime fast data applications are getting faster and bigger. So we need ways to contend with that, that really go beyond what's available with general purpose architectures. >> So that's a great point because if we take this example of video frames, now if I can build a processor that is customized to only do that, that's the only thing it does. It can be very low power, very efficient, and do that one thing very very well, and the cost adder, if you want to call it that, to the device where we put it, is a tiny fraction, but the cost savings of the overall solution is significant. So this ability to customize the instruction set to only do what you need it to do for that very special purpose, that's gold. >> So I just wanted to, Dave, we've talked about a lot of interesting innovations that you guys have come up with over the years, with the helium launch. Which I don't know, a couple, two, three years ago, you were just at the MAMR event, really energy assisted recording. So this is really kind of foundational within the storage and the media itself and how you guys do better and take advantage of evolving land space. This is a kind of a different play for Western Digital, this isn't a direct kind of improvement in the way that storage media and architecture works but this is really more of, I'm going to ask you. What is the Western Digital play here? Why is this an important space for you guys in your core storage business? >> Well we're really broadening our focus to really develop and innovate around technologies that really help the world extract more value from data as a whole, right. So it's way beyond storage these days, right. We're looking for better ways to capture, preserve, access, and transform the data. And unless you transform it, you can't really extract the value out of it so as we see all these new applications for data and the vast possibilities for data, we really want to pave the path and help the industry innovate to bring all those applications to reality. >> It's interesting too because one of the great topics always in computing is you know, you got compute and store, which has to go to which, right. And nobody wants to move a lot of data, that's hard and may or may not be easy to get compute. Especially these IoT applications, remote devices, tough conditions and power, which we mentioned a little bit before we went on air. So the the landscape for the for the need for compute and store in networking is radically changing than either the desktop or what we're seeing a consolidation in clouds. So what's interesting here, where does the scale come, right? At the end of the day, scale always wins. And that's where we've seen historically where the general-purpose microprocessor architectures is dominated but used to be a slew of specialty purpose architectures but now there's an opportunity to bring scale to this. So how does that scale game continue to evolve? >> So it's a great point that scale does matter and we've seen that repeatedly and so it's a significant part of the reason why we decided to go early with a significant commitment was to tell the world that we were bringing scale to the equation. And so what we communicated to the marketplace is we ship on the order of a billion processor cores a year, most people don't realize that all of our devices from USB sticks to hard drives, all have processors on them. And so we said, hey we're going to basically go all-in and go big and that translates into a billion cores that we ship every year and we're going to go on a program to essentially migrate all of those cores to RISC-V. It'll take a few years to get there but we'll migrate all of those cores and so we basically were signaling to the market, hey scale is now here. Scale is here, you can make the investments, you can go forward, you can make that commitment to RISC-V because essentially we've got your back. >> So just to make sure we get that clear. So you guys have announced that you're going to slowly migrate over time your micro processors that power your devices to the tune of approximately a billion with a B, cores per year to this new architecture. >> That is correct. >> And has that started? >> So the design has started. So we have started to design and develop our first two cores but the actual manifestation into devices probably in the early stage of 2020. >> Okay, okay. But that's a pretty significant commitment and again, the ideas you explicitly said it's a signal to the ecosystem, this is worth your investment because there is some scale here. >> Martin: That's right. >> Yeah, pretty exciting. And how do you think it's going to open up the ability for you to do new things with your devices that you before either couldn't do or we're too expensive with dollars or power. >> Martin: So we're going to step and iterate through this and one key point here is a lot of people tend to want to start in this processor world at the very high end, right. I'm going to go take on a Xeon processor or something like that. It's not what we're doing. We're basically saying, we're going to go at the small end, the tiny end where power matters. Power matters a lot in our devices and where can we achieve the optimum combination of power and performance. So even in our small devices like a USB stick or a client SSD or something like that, if we can reduce power consumption and even just maintain performance that's a huge win for our customers, you know. If you think about your laptop and if I reduce the power consumption of that SSD in there so that you have longer battery life and you can get you know through the day better, that's a huge win, right. And I don't impact performance in the process, that's a huge win. So what we do, what we're doing right now is we're developing the cores based on the RISC-V architecture and then what we're going to do is once we've got that sort of design, sort of complete is we want to take all of the typical client workloads and profile them on that. Then we want to find out, okay where are the hot spots? What are the two or three things that are really consuming all the power and how do we go optimize, by either creating two or three instructions or by optimizing the micro architecture for an existing instruction. And then iterate through that a few times so that we really get a big win, even at the very low end of the spectrum and then we just iterate through that with time. >> We're in a unique position I think in that the technologies that we develop span everything from the actual media where the bits are stored, whether it's solid-state flash or rotating magnetic disk and the recording heads. We take those technologies and build them all the way up into devices and platforms and full-fledged data center systems. And if we can optimize and tune all the way from that core media level all the way up through into the system level, we can deliver significantly higher value, we believe, to the marketplace. So this is the start of that, that enables us to customize command sets and optimize the flow of data so that we can we can allow users to access it when and where they need it. >> So I think there's another actually really cool point, which goes back to the open source nature of this and we try to be very clear about this. We're not going to develop our cores for all applications. We want the world to develop all sorts of different cores. And so for many applications somebody else might come in and say, hey we've got a really cool core. So one of the companies we've partnered with and invested in for example, is Esperanto. They've actually decided to go at the high end and do a machine learning accelerator. Hey, maybe we'll use that for some machine learning applications in our system level performance. So we don't have to do it all but we've got a common architecture across the portfolio and that speaks to that sort of open source nature of the RISC-V architecture is we want the world to get going. We want our competitors to get on board, we want partners, we want software providers, we want everybody on board. >> It's such a different ecosystem with open-source and the way the contributions are made and the way contributions are valued and the way that people can go find niches that are underserved. It's this really interesting kind of bifurcation of the market really, you don't really want to be in the big general-purpose middle anymore. That's not a great place to be, there's all kinds of specialty places where you can build the competence and with software and you know with, thank goodness for Moore's law decreasing prices of the power of the compute and now the cloud, which is basically always available. Really a exciting time to develop a myriad of different applications. >> Right and you talked before about scale in terms of points of implementation that will drive adoption and drive this to critical mass but there's another aspect of scale relative to the architecture within a single system that's also important that I think RISC-V helps to break down some barriers. Because with general purpose computer architectures, they assume a certain ratio of memory and storage and processing and bandwidth for interconnect and if you exceed those ratios, you have to add a whole new processor. Even though you don't need to need the processing capability, you need it for scale. So that's another great benefit of these new architectures is that the diversity of data needs where some are going to be large data sets, some are going to be small data sets that need need high bandwidth. You can customize and blend that recipe as you need to, you're not at the mercy of these fixed ratios. >> Yeah and I think you know it's so much of kind of what is cloud computing. And the atomic nature of it, that you can apply the ratios, the amount that you need as you need, you can change it on the fly, you can tone it up, tone it down. And I think the other interesting thing that you touched on is some of these new, which are now relatively special-purpose but are going to be general-purpose very soon in terms of machine learning and AI and applying those to different places and applying them closer to the problem. It's a very very interesting evolution of the landscape but what I want to do is kind of close on you Martin, especially because again kind of back to the machine. Not the machine specifically but you have been in the business of looking way down the road for a long time. So you came out, I'd looked at your LinkedIn, you retired for three months, congratulations. (laughs) Hope you got some my golf in but you came back to Western Digital so why did you come back? And as you look down the road a ways, what do you see that it excites you, that got you off that three-month little tour around the golf course and I'm sorry I had to tease about that. But what do you see? What are you excited about that you came back and got involved in an open source microprocessor project? >> So the the short answer was that, I saw the opportunity at Western Digital to be where data lives. So I had spent my entire career, will call it at the compute or the server side of things and the interesting thing is I had a very close relationship with SanDisk, which was acquired by Western Digital. And so I had, we'll call it an insider view, of what was possible there and so what triggered was essentially what we're talking about here was given that about half the world's data lands on Western Digital devices, taking that from a real position of strength in the marketplace and say, what could we go do to make data more intelligent and rather than start kind of at that server end and so that I saw that potential there and it was just incredible, so that's that's what made me want to join. >> Exciting times. Dave good get. (laughs) >> We're delighted to have Martin with us. >> All right, well we look forward to watch it evolve. We've got another another whole set of events we're going to do again together with Western Digital that we're excited about. Again, covering Data Makes Possible but you know kind of uplifting into the application space as a lot of the cool things that people are doing in innovation. So Martin, great to finally meet you and thanks for stopping by. >> Thanks for the time. >> David as always and I think we'll see in a month or so. >> Right, always a pleasure Jeff, thanks. >> All right Martin Fink, Dave Tang. I'm Jeff Frick, you're watching theCUBE. Thanks for watching, we'll catch you next time. (inspirational 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)

>> Presenter: From the Fairmont Hotel, in the heart of Silicon Valley, it's theCUBE. Covering When IoT Met AI The Intelligence of Things. Brought to you by Western Digital. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel, at an event called When IoT Met AI The Intelligence of Things. You've heard about the internet of things, and on the intelligence of things, it's IoT, it's AI, it's AR, all this stuff is really coming to play, it's very interesting space, still a lot of start-up activity, still a lot of big companies making plays in this space. So we're excited to be here, and really joined by our host, big thanks to Western Digital for hosting this event with WDLabs' Dave Tang. Got newly promoted since last we spoke. The SVP of corporate marketing and communications, for Western Digital, Dave great to see you as usual. >> Well, great to be here, thanks. >> So I don't think the need for more storage is going down anytime soon, that's kind of my takeaway. >> No, no, yeah. If this wall of data just keeps growing. >> Yeah, I think the term we had yesterday at the Ag event that we were at, also sponsored by you, is really the flood of data using an agricultural term. But it's pretty fascinating, as more, and more, and more data is not only coming off the sensors, but coming off the people, and used in so many more ways. >> That's right, yeah we see it as a virtual cycle, you create more data, you find more uses for that data to harness the power and unleash the promise of that data, and then you create even more data. So, when that virtual cycle of creating more, and finding more uses of it, and yeah one of the things that we find interesting, that's related to this event with IoT and AI, is this notion that data is falling into two general categories. There's big data, and there's fast data. So, big data I think everyone is quite familiar with by this time, these large aggregated likes of data that you can extract information out of. Look for insights and connections between data, predict the future, and create more prescriptive recommendations, right? >> Right. >> And through all of that you can gain algorithms that help to make predictions, or can help machines run based on that data. So we've gone through this phase where we focused a lot on how we harness big data, but now we're taking these algorithms that we've gleaned from that, and we're able to put them in real time applications, and that's sort of been the birth of fast data, it's been really-- >> Right, the streaming data. We cover Spark Summit, we cover Flink, and New, a new kind of open source project that came out of Berlin. That some people would say the next generation of Spark, and the other thing, you know, good for you guys, is that it used to be, not only was it old data, but it was a sampling of old data. Now on this new data, and the data stream that's all of the data. And I would actually challenge, I wonder if that separation as you describe, will stay, because I got to tell you, the last little drive I bought, just last week, was an SSD drive, you know, one terabyte. I needed some storage, and I had a choice between spinning disc and not, and I went with the flat. I mean, 'cause what's fascinating to me, is the second order benefits that we keep hearing time, and time, and time again, once people become a data-driven enterprise, are way more than just that kind of top-level thing that they thought. >> Exactly, and that's sort of that virtual cycle, you got to taste, and you learn how to use it, and then you want more. >> Jeff: Right, right. >> And that's the great thing about the breadth of technologies and products that Western Digital has, is from the solid state products, the higher performance flash products that we have, to the higher capacity helium-filled drive technologies, as well as devices going on up into systems, we cover this whole spectrum of fast data and big data. >> Right, right. >> I'll give an example. So credit card fraud detection is an interesting area. Billions of dollars potentially being lost there. Well to learn how to predict when transactions are fraudulent, you have to study massive amounts of data. Billions of transactions, so that's the big data side of it, and then as soon as you do that, you can take those algorithms and run them in real time. So as transactions come in for authorization, those algorithms can determine, before they're approved, that one's fraudulent, and that one's not. Save a lot of time and processing for fraud claims. So that's a great example of once you learn something from big data, you apply it to the real-time realm, and it's quite dire right? And then that spawned you to collect even more data, because you want to find new applications and new uses. >> Right, and too kind of this wave of computing back and forth from the shared services computer, then the desktop computer, now it's back to the cloud, and then now it's-- >> Dave: Out with the edge. >> IoT, it's all about the edge. >> Yeah, right. >> And at the end of the day, it's going to be application-specific. What needs to be processed locally, what needs to be processed back at the computer, and then all the different platforms. We were again at a navigation for autonomous vehicles show, who knew there was such a thing that small? And even the attributes of the storage required in the ecosystem of a car, right? And the environmental conditions-- >> That's right. >> Is the word I'm looking for. Completely different, new opportunity, kind of new class of hardware required to operate in that environment, and again that still combines cloud and Edge, sensors and maps. So just the, I don't think that the man's going down David. >> Yeah, absolutely >> I think you're in a good spot. (Jeff laughing) >> You're absolutely right, and even though we try to simplify into fast data, and big data, and Core and Edge, what we're finding is that applications are increasingly specialized, and have specialized needs in terms of the type of data. Is it large amounts of data, is it streaming? You know, what are the performance characteristics, and how is it being transformed, what's the compute aspect of it? And what we're finding, is that the days of general-purpose compute and storage, and memory platforms, are fading, and we're getting into environments with increasingly specialized architectures, across all those elements. Compute, memory and storage. So that's what's really exciting to be in our spot in the industry, is that we're looking at creating the future by developing new technologies that continue to fuel that growth even further, and fuel the uses of data even further. >> And fascinating just the ongoing case of Moore's law, which I know is not, you know you're not making microprocessors, but I think it's so powerful. Moore's law really is a philosophy, as opposed to an architectural spec. Just this relentless pace of innovation, and you guys just continue to push the envelope. So what are your kind of priorities? I can't believe we're halfway through 2017 already, but for kind of the balance of the year kind of, what are some of your top-of-mind things? I know it's exciting times, you're going through the merger, you know, the company is in a great space. What are your kind of top priorities for the next several months? >> Well, so, I think as a company that has gone through serial acquisitions and integrations, of course we're continuing to drive the transformation of the overall business. >> But the fun stuff right? It's not to increase your staff (Jeff laughing). >> Right, yeah, that is the hardware. >> Stitching together the European systems. >> But yeah, the fun stuff includes pushing the limits even further with solid state technologies, with our 3D NAND technologies. You know, we're leading the industry in 64 layer 3D NAND, and just yesterday we announced a 96 layer 3D NAND. So pushing those limits even further, so that we can provide higher capacities in smaller footprints, lower power, in mobile devices and out on the Edge, to drive all these exciting opportunities in IoT an AI. >> It's crazy, it's crazy. >> Yeah it is, yeah. >> You know, terabyte SD cards, terabyte Micro SD cards, I mean the amount of power that you guys pack into these smaller and smaller packages, it's magical. I mean it's absolutely magic. >> Yeah, and the same goes on the other end of the spectrum, with high-capacity devices. Our helium-filled drives are getting higher and higher capacity, 10, 12, 14 terabyte high-capacity devices for that big data core, that all the data has to end up with at some point. So we're trying to keep a balance of pushing the limits on both ends. >> Alright, well Dave, thanks for taking a few minutes out of your busy day, and congratulations on all your success. >> Great, good to be here. >> Alright, he's Dave Tang from Western Digital, he's changing your world, my world, and everyone else's. We're here in San Jose, you're watching theCUBE, thanks for watching.