(upbeat music) >> Announcer: From theCUBE studios in Palo Alto in Boston connecting with thought leaders all around the world, this is theCUBE Conversation. >> Well, welcome to theCUBE Conversation here in theCUBE studios in Palo Alto, California. I'm John Furrier, your host. Got a great conversation with two great guests, going to explore the edge, what it means in terms of commercial, but also national security. And as the world goes digital, we're going to have that deep dive conversation around how it's all transforming. We've got Ki Lee, Vice President of Booz Allen's Digital Business. Ki, great to have you. John Pisano, Principal at Booz Allen's Digital Cloud Solutions. Gentlemen, thanks for coming on. >> And thanks for having us, John. >> So one of the most hottest topics, obviously besides cloud computing having the most refactoring impact on business and government and public sector has been the next phase of cloud growth and cloud scale, and that's really modern applications and consumer, and then here for national security and for governments here in the U.S. is military impact. And as digital transformation starts to go to the next level, you're starting to see the architectures emerge where the edge, the IoT edge, the industrial IoT edge, or any kind of edge concept, 5G is exploding, making that much more of a dense, more throughput for connectivity with wireless. You got Amazon with Snowball, Snowmobile, all kinds of ways to deploy technology, that's IT like and operational technologies. It's causing quite a cloud operational opportunity and disruption, so I want to get into it. Ki, let's start with you. I mean, we're looking at an architecture that's changing both commercial and public sector with the edge. What are the key considerations that you guys see as people have to really move fast in this new architecture of digital? >> Yeah, John, I think it's a great question. And if I could just share our observation on why we even started investing in edge. You mentioned the cloud, but as we've reflected upon kind of the history of IT, then you take a look from mainframes to desktops to servers to cloud to mobile and now IoT, what we observed was that industry investing in infrastructure led to kind of an evolution of IT, right? So as you mentioned, with industry spending billions on IoT and edge, we just feel that that's going to be the next evolution. If you take a look at, you mentioned 5G, I think 5G will be certainly an accelerator to edge because of the resilience, the lower latency and so forth. But taking a look at what's happening in space, you mentioned space earlier as well, right, and what Starlink is doing by putting satellites to actually provide transport into the space, we're thinking that that actually is going to be the next ubiquitous thing. Once transport becomes ubiquitous, just like cloud allows storage to be ubiquitous. We think that the next generation internet will be space-based. So when you think about it, connected, it won't be connected servers per se, it will be connected devices. >> John: Yeah, yeah. >> That's kind of some of the observations and why we've been really focusing on investing in edge. >> I want to come back to that piece around space and edge and bring it from a commercial and then also tactical architecture in a minute 'cause there's a lot to unpack there, role of open source, modern application development, software and hardware supply chains, all are core issues that are going to emerge. But I want to get with John real quick on cloud impact, because you think about 5G and the future of work or future of play, you've got people, right? So whether you're at a large concert like Coachella or a 49ers or Patriots game or Redskins game if you're in the D.C. area, you got people there, of congestion, and now you got devices now serving those people. And that's their play, people at work, whether it's a military operation, and you've got work, play, tactical edge things. How is cloud connecting? 'Cause this is like the edge has never been kind of an IT thing. It's been more of a bandwidth or either telco or something else operationally. What's the cloud at scale, cloud operations impact? >> Yeah, so if you think about how these systems are architected and you think about those considerations that Ki kind of touched on, a lot of what you have to think about now is what aspects of the application reside in the cloud, where you tend to be less constrained. And then how do you architect that application to move out towards the edge, right? So how do I tier my application? Ultimately, how do I move data and applications around the ecosystem? How do I need to evolve where my application stages things and how that data and those apps are moved to each of those different tiers? So when we build a lot of applications, especially if they're in the cloud, they're built with some of those common considerations of elasticity, scalability, all those things; whereas when you talk about congestion and disconnected operations, you lose a lot of those characteristics, and you have to kind of rethink that. >> Ki, let's get into the aspect you brought up, which is space. And then I was mentioning the tactical edge from a military standpoint. These are use cases of deployments, and in fact, this is how people have to work now. So you've got the future of work or play, and now you've got the situational deployments, whether it's a new tower of next to a stadium. We've all been at a game or somewhere or a concert where we only got five bars and no connectivity. So we know what that means. So now you have people congregating in work or play, and now you have a tactical deployment. What's the key things that you're seeing that it's going to help make that better? Are there any breakthroughs that you see that are possible? What's going on in your view? >> Yeah, I mean, I think what's enabling all of this, again, one is transport, right? So whether it's 5G to increase the speed and decrease the latency, whether it's things like Starlink with making transport and comms ubiquitous, that tied with the fact that ships continue to get smaller and faster, right? And when you're thinking about tactical edge, those devices have limited size, weight, power conditions and constraints. And so the software that goes on them has to be just as lightweight. And that's why we've actually partnered with SUSE and what they've done with K3s to do that. So I think those are some of the enabling technologies out there. John, as you've kind of alluded to it, there are additional challenges as we think about it. We're not, it's not a simple transition and monetization here, but again, we think that this will be the next major disruption. >> What do you guys think, John, if you don't mind weighing in too on this as modern application development happens, we just were covering CloudNativeCon and KubeCon, DockerCon, containers are very popular. Kubernetes is becoming super great. As you look at the telco landscape where we're kind of converging this edge, it has to be commercially enterprise grade. It has to have that transit and transport that's intelligent and all these new things. How does open source fit into all this? Because we're seeing open source becoming very reliable, more people are contributing to open source. How does that impact the edge in your opinion? >> So from my perspective, I think it's helping accelerate things that traditionally maybe may have been stuck in the traditional proprietary software confines. So within our mindset at Booz Allen, we were very focused on open architecture, open based systems, which open source obviously is an aspect of that. So how do you create systems that can easily interface with each other to exchange data, and how do you leverage tools that are available in the open source community to do that? So containerization is a big drive that is really going throughout the open source community. And there's just a number of other tools, whether it's tools that are used to provide basic services like how do I move code through a pipeline all the way through? How do I do just basic hardening and security checking of my capabilities? Historically, those have tend to be closed source type apps, whereas today you've got a very broad community that's able to very quickly provide and develop capabilities and push it out to a community that then continues to adapt and add to it or grow that library of stuff. >> Yeah, and then we've got trends like Open RAN. I saw some Ground Station for the AWS. You're starting to see Starlink, you mentioned. You're bringing connectivity to the masses. What is that going to do for operators? Because remember, security is a huge issue. We talk about security all the time. Where does that kind of come in? Because now you're really OT, which has been very purpose-built kind devices in the old IoT world. As the new IoT and the edge develop, you're going to need to have intelligence. You're going to be data-driven. There is an open source impact key. So, how, if I'm a senior executive, how do I get my arms around this? I really need to think this through because the security risks alone could be more penetration areas, more surface area. >> Right. That's a great question. And let me just address kind of the value to the clients and the end users in the digital battlefield as our warriors to increase survivability and lethality. At the end of the day from a mission perspective, we know we believe that time's a weapon. So reducing any latency in that kind of observe, orient, decide, act OODA loop is value to the war fighter. In terms of your question on how to think about this, John, you're spot on. I mean, as I've mentioned before, there are various different challenges, one, being the cyber aspect of it. We are absolutely going to be increasing our attack surface when you think about putting processing on edge devices. There are other factors too, non-technical that we've been thinking about s we've tried to kind of engender and kind of move to this kind of edge open ecosystem where we can kind of plug and play, reuse, all kind of taking the same concepts of the open-source community and open architectures. But other things that we've considered, one, workforce. As you mentioned before, when you think about these embedded systems and so forth, there aren't that many embedded engineers out there. But there is a workforce that are digital and software engineers that are trained. So how do we actually create an abstraction layer that we can leverage that workforce and not be limited by some of the constraints of the embedded engineers out there? The other thing is what we've, in talking with several colleagues, clients, partners, what people aren't thinking about is actually when you start putting software on these edge devices in the billions, the total cost of ownership. How do you maintain an enterprise that potentially consists of billions of devices? So extending the standard kind of DevSecOps that we move to automate CI/CD to a cloud, how do we move it from cloud to jet? That's kind of what we say. How do we move DevSecOps to automate secure containers all the way to the edge devices to mitigate some of those total cost of ownership challenges. >> It's interesting, as you have software defined, this embedded system discussion is hugely relevant and important because when you have software defined, you've got to be faster in the deployment of these devices. You need security, 'cause remember, supply chain on the hardware side and software in that too. >> Absolutely. >> So if you're going to have a serviceability model where you have to shift left, as they say, you got to be at the point of CI/CD flows, you need to be having security at the time of coding. So all these paradigms are new in Day-2 operations. I call it Day-0 operations 'cause it should be in everyday too. >> Yep. Absolutely. >> But you've got to service these things. So software supply chain becomes a very interesting conversation. It's a new one that we're having on theCUBE and in the industry Software supply chain is a superly relevant important topic because now you've got to interface it, not just with other software, but hardware. How do you service devices in space? You can't send a break/fix person in space. (chuckles) Maybe you will soon, but again, this brings up a whole set of issues. >> No, so I think it's certainly, I don't think anyone has the answers. We sure don't have all the answers but we're very optimistic. If you take a look at what's going on within the U.S. Air Force and what the Chief Software Officer Nic Chaillan and his team, and we're a supporter of this and a plankowner of Platform One. They were ahead of the curve in kind of commoditizing some of these DevSecOps principles in partnership with the DoD CIO and that shift left concept. They've got a certified and accredited platform that provides that DevSecOps. They have an entire repository in the Iron Bank that allows for hardened containers and reciprocity. All those things are value to the mission and around the edge because those are all accelerators. I think there's an opportunity to leverage industry kind of best practices as well and patterns there. You kind of touched upon this, John, but these devices honestly just become firmware. The software is just, if the devices themselves just become firmware , you can just put over the wire updates onto them. So I'm optimistic. I think all the piece parts are taking place across industry and in the government. And I think we're primed to kind of move into this next evolution. >> Yeah. And it's also some collaboration. What I like about, why I'm bringing up the open source angle and I think this is where I think the major focus will shift to, and I want to get your reaction to it is because open source is seeing a lot more collaboration. You mentioned some of the embedded devices. Some people are saying, this is the weakest link in the supply chain, and it can be shored up pretty quickly. But there's other data, other collective intelligence that you can get from sharing data, for instance, which hasn't really been a best practice in the cybersecurity industry. So now open source, it's all been about sharing, right? So you got the confluence of these worlds colliding, all aspects of culture and Dev and Sec and Ops and engineering all coming together. John, what's your reaction to that? Because this is a big topic. >> Yeah, so it's providing a level of transparency that historically we've not seen, right? So in that community, having those pipelines, the results of what's coming out of it, it's allowing anyone in that life cycle or that supply chain to look at it, see the state of it, and make a decision on, is this a risk I'm willing to take or not? Or am I willing to invest and personally contribute back to the community to address that because it's important to me and it's likely going to be important to some of the others that are using it? So I think it's critical, and it's enabling that acceleration and shift that I talked about, that now that everybody can see it, look inside of it, understand the state of it, contribute to it, it's allowing us to break down some of the barriers that Ki talked about. And it reinforces that excitement that we're seeing now. That community is enabling us to move faster and do things that maybe historically we've not been able to do. >> Ki, I'd love to get your thoughts. You mentioned battlefield, and I've been covering a lot of the tactical edge around the DOD's work. You mentioned about the military on the Air Force side, Platform One, I believe, was from the Air Force work that they've done, all cloud native kind of directions. But when you talk about a war field, you talk about connectivity. I mean, who controls the DNS in Taiwan, or who controls the DNS in Korea? I mean, we have to deploy, you've got to stand up infrastructure. How about agility? I mean, tactical command and control operations, this has got to be really well done. So this is not a trivial thing. >> No. >> How are you seeing this translate into the edge innovation area? (laughs) >> It's certainly not a trivial thing, but I think, again, I'm encouraged by how government and industry are partnering up. There's a vision set around this joint all domain command control, JADC2. And then all the services are getting behind that, are looking into that, and this vision of this military, internet of military things. And I think the key thing there, John, as you mentioned, it's not just the connected of the sensors, which requires the transport again, but also they have to be interoperable. So you can have a bunch of sensors and platforms out there, they may be connected, but if they can't speak to one another in a common language, that kind of defeats the purpose and the mission value of that sensor or shooter kind of paradigm that we've been striving for for ages. So you're right on. I mean, this is not a trivial thing, but I think over history we've learned quite a bit. Technology and innovation is happening at just an amazing rate where things are coming out in months as opposed to decades as before. I agree, not trivial, but again, I think there are all the piece parts in place and being put into place. >> I think you mentioned earlier that the personnel, the people, the engineers that are out there, not enough, more of them coming in. I think now the appetite and the provocative nature of this shift in tech is going to attract a lot of people because the old adage is these are hard problems attracts great people. You got in new engineering, SRE like scale engineering. You have software development, that's changing, becoming much more robust and more science-driven. You don't have to be just a coder as a software engineer. You could be coming at it from any angle. So there's a lot more opportunities from a personnel standpoint now to attract great people, and there's real hard problems to solve, not just security. >> Absolutely. Definitely. I agree with that 100%. I would also contest that it's an opportunity for innovators. We've been thinking about this for some time, and we think there's absolute value from various different use cases that we've identified, digital battlefield, force protection, disaster recovery, and so forth. But there are use cases that we probably haven't even thought about, even from a commercial perspective. So I think there's going to be an opportunity just like the internet back in the mid '90s for us to kind of innovate based on this new kind of edge environment. >> It's a revolution. New leadership, new brands are going to emerge, new paradigms, new workflows, new operations, clearly great stuff. I want to thank you guys for coming on. I also want to thank Rancher Labs for sponsoring this conversation. Without their support, we wouldn't be here. And now they were acquired by SUSE. We've covered their event with theCUBE virtual last year. What's the connection with those guys? Can you guys take a minute to explain the relationship with SUSE and Rancher? >> Yeah. So it's actually it's fortuitous. And I think we just, we got lucky. There's two overall aspects of it. First of all, we are both, we partner on the Platform One basic ordering agreement. So just there we had a common mentality of DevSecOps. And so there was a good partnership there, but then when we thought about we're engaging it from an edge perspective, the K3s, right? I mean, they're a leader from a container perspective obviously, but the fact that they are innovators around K3s to reduce that software footprint, which is required on these edge devices, we kind of got a twofer there in that partnership. >> John, any comment on your end? >> Yeah, I would just amplify, the K3s aspects in leveraging the containers, a lot of what we've seen success in when you look at what's going on, especially on that tactical edge around enabling capabilities, containers, and the portability it provides makes it very easy for us to interface and integrate a lot of different sensors to close the OODA loop to whoever is wearing or operating that a piece of equipment that the software is running on. >> Awesome, I'd love to continue the conversation on space and the edge and super great conversation to have you guys on. Really appreciate it. I do want to ask you guys about the innovation and the opportunities of this new shift that's happening as the next big thing is coming quickly. And it's here on us and that's cloud, I call it cloud 2.0, the cloud scale, modern software development environment, edge with 5G changing the game. Ki, I completely agree with you. And I think this is where people are focusing their attention from startups to companies that are transforming and re-pivoting or refactoring their existing assets to be positioned. And you're starting to see clear winners and losers. There's a pattern emerging. You got to be in the cloud, you got to be leveraging data, you got to be horizontally scalable, but you got to have AI machine learning in there with modern software practices that are secure. That's the playbook. Some people are making it. Some people are not getting there. So I'd ask you guys, as telcos become super important and the ability to be a telco now, we just mentioned standing up a tactical edge, for instance. Launching a satellite, a couple of hundred K, you can launch a CubeSat. That could be good and bad. So the telco business is changing radically. Cloud, telco cloud is emerging as an edge phenomenon with 5G, certainly business commercial benefits more than consumer. How do you guys see the innovation and disruption happening with telco? >> As we think through cloud to edge, one thing that we realize, because our definition of edge, John, was actually at the point of data collection on the sensor themselves. Others' definition of edge is we're a little bit further back, what we call it the edge of the IT enterprise. But as we look at this, we realize that you needed this kind of multi echelon environment from your cloud to your tactical clouds where you can do some processing and then at the edge of themselves. Really at the end of the day, it's all about, I think, data, right? I mean, everything we're talking about, it's still all about the data, right? The AI needs the data, the telco is transporting the data. And so I think if you think about it from a data perspective in relationship to the telcos, one, edge will actually enable a very different paradigm and a distributed paradigm for data processing. So, hey, instead of bringing the data to some central cloud which takes bandwidth off your telcos, push the products to the data. So mitigate what's actually being sent over those telco lines to increase the efficiencies of them. So I think at the end of the day, the telcos are going to have a pretty big component to this, even from space down to ground station, how that works. So the network of these telcos, I think, are just going to expand. >> John, what's your perspective? I mean, startups are coming out. The scalability, speed of innovation is a big factor. The old telco days had, I mean, months and years, new towers go up and now you got a backbone. It's kind of a slow glacier pace. Now it's under siege with rapid innovation. >> Yeah, so I definitely echo the sentiments that Ki would have, but I would also, if we go back and think about the digital battle space and what we've talked about, faster speeds being available in places it's not been before is great. However, when you think about facing an adversary that's a near-peer threat, the first thing they're going to do is make it contested, congested, and you have to be able to survive. While yes, the pace of innovation is absolutely pushing comms to places we've not had it before, we have to be mindful to not get complacent and over-rely on it, assuming it'll always be there. 'Cause I know in my experience wearing the uniform, and even if I'm up against an adversary, that's the first thing I'm going to do is I'm going to do whatever I can to disrupt your ability to communicate. So how do you take it down to that lowest level and still make that squad, the platoon, whatever that structure is, continue survivable and lethal. So that's something I think, as we look at the innovations, we need to be mindful of that. So when I talk about how do you architect it? What services do you use? Those are all those things that you have to think about. What if I lose it at this echelon? How do I continue the mission? >> Yeah, it's interesting. And if you look at how companies have been procuring and consuming technology, Ki, it's been like siloed. "Okay, we've got a workplace workforce project, and we have the tactical edge, and we have the siloed IT solution," when really work and play, whether it's work here in John's example, is the war fighter. And so his concern is safety, his life and protection. >> Yeah. >> The other department has to manage the comms, (laughs) and so they have to have countermeasures and contingencies ready to go. So all this is, they all integrate it now. It's not like one department. It's like it's together. >> Yeah. John, I love what you just said. I mean, we have to get away from this siloed thinking not only within a single organization, but across the enterprise. From a digital battlefield perspective, it's a joint fight, so even across these enterprise of enterprises, So I think you're spot on. We have to look horizontally. We have to integrate, we have to inter-operate, and by doing that, that's where the innovation is also going to be accelerated too, not reinventing the wheel. >> Yeah, and I think the infrastructure edge is so key. It's going to be very interesting to see how the existing incumbents can handle themselves. Obviously the towers are important. 5G obviously, that's more deployments, not as centralized in terms of the spectrum. It's more dense. It's going to create more connectivity options. How do you guys see that impacting? Because certainly more gear, like obviously not the centralized tower, from a backhaul standpoint but now the edge, the radios themselves, the wireless transit is key. That's the real edge here. How do you guys see that evolving? >> We're seeing a lot of innovations actually through small companies who are really focused on very specific niche problems. I think it's a great starting point because what they're doing is showing the art of the possible. Because again, we're in a different environment now. There's different rules. There's different capabilities. But then we're also seeing, you mentioned earlier on, some of the larger companies, the Amazons, the Microsofts, also investing as well. So I think the merge of the, you know, or the unconstrained or the possible by these small companies that are just kind of driving innovations supported by the maturity and the heft of these large companies who are building out these hardened kind of capabilities, they're going to converge at some point. And that's where I think we're going to get further innovation. >> Well, I really appreciate you guys taking the time. Final question for you guys, as people are watching this, a lot of smart executives and teams are coming together to kind of put the battle plans together for their companies as they transition from old to this new way, which is clearly cloud-scale, role of data. We hit out all the key points I think here. As they start to think about architecture and how they deploy their resources, this becomes now the new boardroom conversation that trickles down and includes everyone, including the developers. The developers are now going to be on the front lines. Mid-level managers are going to be integrated in as well. It's a group conversation. What are some of the advice that you would give to folks who are in this mode of planning architecture, trying to be positioned to come out of this pandemic with a massive growth opportunity and to be on the right side of history? What's your advice? >> It's such a great question. So I think you touched upon it. One is take the holistic approach. You mentioned architectures a couple of times, and I think that's critical. Understanding how your edge architectures will let you connect with your cloud architecture so that they're not disjointed, they're not siloed. They're interoperable, they integrate. So you're taking that enterprise approach. I think the second thing is be patient. It took us some time to really kind of, and we've been looking at this for about three years now. And we were very intentional in assessing the landscape, how people were discussing around edge and kind of pulling that all together. But it took us some time to even figure it out, hey, what are the use cases? How can we actually apply this and get some ROI and value out for our clients? So being a little bit patient in thinking through kind of how we can leverage this and potentially be a disruptor. >> John, your thoughts on advice to people watching as they try to put the right plans together to be positioned and not foreclose any future value. >> Yeah, absolutely. So in addition to the points that Ki raised, I would, number one, amplify the fact of recognize that you're going to have a hybrid environment of legacy and modern capabilities. And in addition to thinking open architectures and whatnot, think about your culture, the people, your processes, your techniques and whatnot, and your governance. How do you make decisions when it needs to be closed versus open? Where do you invest in the workforce? What decisions are you going to make in your architecture that drive that hybrid world that you're going to live in? All those recipes, patience, open, all that, that I think we often overlook the cultural people aspect of upskilling. This is a very different way of thinking on modern software delivery. How do you go through this lifecycle? How's security embedded? So making sure that's part of that boardroom conversation I think is key. >> John Pisano, Principal at Booz Allen Digital Cloud Solutions, thanks for sharing that great insight. Ki Lee, Vice President at Booz Allen Digital Business. Gentlemen, great conversation. Thanks for that insight. And I think people watching are going to probably learn a lot on how to evaluate startups to how they put their architecture together. So I really appreciate the insight and commentary. >> Thank you. >> Thank you, John. >> Okay. I'm John Furrier. This is theCUBE Conversation. Thanks for watching. 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>> Announcer: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Partner Awards. Brought to you by Amazon Web Services. >> Hi friend, welcome to this CUBE coverage of AWS Public Sector Partner Awards Program. I'm John Furrier your host of theCUBE. We've two great guests here, Travis Hartman Director of Analytics and Weather at Maxar Technologies, and Vijay Tallapragada who's the Chief Modeling and Data Assimulation Branch at NOAH. Tell us about the success of this. What's the big deal? Take us through the award and why Maxar. What do you guys do? >> Yeah, so Maxar is an organization that does a lot of different activities in earth intelligence as well as space. We have about 4,000 employees around the world. One side of the economy works on space infrastructure actually building satellites, and all the infrastructure that's going to help get us back to the moon, and things like that, and then on the other side we have an earth intelligence group which is where I sit, and we leverage remote sensing information, earth science information to help people better understand how and what they do might impact the earth, or how the earth, in its activities, might impact their business mission or operations. So what we wanted to set out to do is help people better understand how weather could impact their mission, businesses, or operations. A big element of that was doing it with speed. So we knew NOAH had capabilities of running numerical weather prediction models and very traditional on-prem, big, beefy, high performance supercomputers, but we wanted to do it in the cloud. We wanted to use AWS as a key partner. So we collaborated with Vijay and NOAH and his teams there to help pull that off. They gave us access, public domain information but they showed us the right places to look. We've had some of our research scientists talkin' and yeah, after a pretty short effort, it didn't take a lot of time, we were able to pull something off a lot of people didn't think was possible. And we got pretty excited once we saw some of the outcomes. >> Travis, Vijay was just mentioning the relationship. Can you talk about the relationship together? Because this is not your classic Amazon Partner client relationship that you have. You guys have been partnering together, Vijay and your team, with AWS. Talk about the relationship and how Amazon played because it's a unique partnership. Explain in more detail, that specific relationship. >> Yeah, with Maxar and AWS, our partnership has gone back a number of years. Maxar being a fairly large organization, there's lots of different activities. I think Maxar was the first client of AWS Snowmobile where they had the big tractor trailer backed up to a data center, load all the data in, and then take it to an AWS data center. We were the first users of that 'cause we had over a hundred petabytes of satellite imagery in an archive that just movin' it across the internet it'd probably still be goin'. So the Snowmobile was a good success story for us but just with the amount of data that we have, the amount of data we collect every day, and all the analytics that we're running on it, whether it's in an HPC environment or the scalable AIML, we're able to scale out that architecture, scale out the compute, the much easier dynamic and really cost-effective way with AWS 'cause when we don't need to use the machines, we turn 'em off. We don't have a big data center sittin' somewhere where we have to have security, have all the overhead costs of just keeping the lights on, literally. AWS allows us to run our organization in a much more efficient way. And NOAH, they're seeing some of that same success story that we're seeing, as far as how they could use the cloud for accelerating research, accelerating how the advancement of numerical weather prediction from the United States can benefit from cloud, from cloud architecture, cloud compute, and things like that. And I think a lot of the stuff that we've done here at Maxar, with our HPC solution in the cloud is something that's pretty interesting to NOAH and it's a good opportunity for us to continue our collaboration. >> If I could drill down on that solution architecture for a minute, how did you guys set up the services and what lessons did you learn from that process? >> We're still learnin' is probably the short answer, but it all started with our people. We have some really strong engineers, really strong data scientists that fundamentally have a background in meteorology or atmospheric science, so they understand the physics of, you know, why the wind blows the way it does and why clouds do what clouds do. But we also, having a key strategic partnership with AWS, we were able to tap into some of their subject-matter experts, and we really put those people together and come up with new solutions and new, innovative ideas, stuff that people hadn't tried before. We were able to steer a little bit of AWS's product roadmap as far as what we were tryin' to do and how their current technology might not have been able to support it, but by interacting with us, gave them some ideas as far as what the tech had to move towards, and then that's what allowed us to move in a pretty quick fashion. It's neat stuff, technology, but it really comes down to the people. I feel very honored and privileged to work with both great people here, at Maxar, as well as AWS, as well as bein' able to collaborate with the great teams at NOAH. It's been a lot of fun. >> Well Travis, got a great example, I think it's a template that can be applied to many other areas, certainly even beyond. You've got a large scale, multi-scale situation, there. Congratulations. Final question, what does it mean to be an award winner for AWS Partner Awards? As part of the show, you're the best-in-show for HPC. What's it like? What's the feeling? Give is a quick stub from the field. >> Yeah, I mean, I don't know if there's really a lot of good words that can kind of sum it up. I shared the news with the team last night and you know, there were a lot of, lot of good responses that came from it. A lot of people think it's cool, and at the end of the day, a lot of people on our team took a hobby or a passion of weather and turned it into a career. And being acknowledged and recognized by groups like AWS for best solution in a particular thing, I think we take a lot of that to heart and we're very honored and proud of what we're able to do and proud that other people recognize the neat stuff that we're doin'. >> Well, certainly takin' advantage of the cloud which is large scale, but you're on a great wave, you've got a great area. I mean, weather, you talk about weather, it's exciting, dynamic, it's always changing, it's big data, it's large scale. So you got a lot of problems to solve and a lot of impact too, when you get it right. So congratulations on an excellent-- >> Thank you very much. >> Great mission. >> Thank you. >> Love what you do, love to followup again and maybe do another interview, and talk about the impact of weather and all the HPC kind of down the road. Travis, thank you very much. >> Thank you, appreciate it. >> Good to see you. >> Thank you, glad to be here. >> So NOAH, National Oceanic Atmospheric Administration, National Weather Center, National Center for Environmental Predictions, Environmental Modeling Center, that's your organization. You guys are competing to be the best in the world. Tell us what you guys do at a high level, then we'll jump into some of the successes. >> So the National Weather Service is responsible for providing weather forecasts to save lives and property, and improve the economy of the nation. And as part of that, the National Weather Service is responsible for providing data and also the forecast to the public and to the industry. We are responsible for providing the guidance on how they create the forecasts. So we are, at the Environmental Modeling Center, the nation's finest institute in advancing our numerical weather prediction modeling, government, and a nucleation of all the data that's available from the world to initialize our models and provide the future state of the atmosphere from hours all the way to seasons and years. And that's the kind of the range of products that we download and provide. Our key for managing the emergency of services and hazard management and mitigation, and also improve in the nation's economy by preparing well in advance, for the future events. And it's a science-based organization and we have world-class scientists working in this organization. I manage about 170 of them at the Environmental Modeling Center. They're all PhDs from various disciplines, mostly from meteorology, atmospheric sciences, oceanography, land surface modeling, space weather, all weather-related areas, and the mathematics and computer science. And we are at the stage where we are probably the most doubled up, advanced modeling center that we use almost all possible computational services available in the world, so this is heavily computational in terms of use of data, use of computers, use of all the power that we can get, and we have a 3.5 protoflop machine that we use to provide these weather forecasts. And they provide these services every hour for some census like we see the weather outbreaks and for every three hours for hurricanes, and for every six hours for the regular weather like precipitation, the temperature forecasts. So all the data that you see coming out from either the public media or the government agencies, they all are originated in our center and disseminated in various forms. And I think NOAH is the only center in the world that provides all this information free of cost. So it is a public service organization and we pride in our service to the society. >> Well, I love your title, Chief Modeling and Data Assimulation title, branch over all these organizations. This is, weather's critical. I want to get your thoughts 'cause we were talking before you came on about how the hurricane Katrina was something that really kind of forced everyone to kind of rethink things. Weather is an evolving system so it's always changing. Either there's a catastrophe or something happens, or you're trying to be proactive, predicting say, whether it's a fire season in California, all kinds of things goin' on. It's always hard to get a certain prediction. You have big jobs, there's a lot of data, you need horsepower, you need computing, you need to stand up some HPC. Take us through the thinking around the organization and what's the impact that you see, because weather does have that impact. >> So traditionally, you know, as you mentioned there are various weather phenomena that you described like the fiber of the hurricanes, the heavy precipitation, the flooding, so we download solutions for individual weather phenomena. And we have grown in that direction by downloading separate solutions for separate problems. And very soon, it became obvious that we cannot manage all these independent modeling systems to provide the best possible forecasts. So the thinking had to be changed. And then there is another bigger problem is that there's a lot of research going out in the community, like the academic institutes, the universities, other government labs. There are several people working in these areas and all their work is not necessarily a coordinated government act duty, that we cannot take advantage, and there are no incentives for people to come and contribute towards the mission that we are engaged in. So that actually prompted to change the direction of thinking, and as you mentioned, hurricane Katrina was an eye-opener. We have the best forecasts, but the dissemination of that information was not probably accurate enough, and also there is a lot of room for improvement in predicting these catastrophic events. >> How are you guys using AWS? Because HPC, high performance computing, I mean, you can't ask for more resources than the massive cloud that is Amazon. How has that helped you? Can you take a minute to explain, walk us through AWS partnership? >> There are a few examples I can cite, but before then, I would really like to appreciate Travis Hartman from Maxar who is probably the only private sector partner that we had in the beginning. And now, we are expanding on that. So we were able to share our immunity cords with Maxar and with our help, they were able to establish this entire modeling system as it is done in operations at NOAH. They were able to reproduce our operational forecasts using the cloud resources and then they went ahead and did even more by scaling the modeling systems as they can run even faster and quicker than what NOAH operations can do. So that gives you one example of how the cloud can be used. You know, the same forecast that we produce globally, which will take about eight minutes per day, and Maxar was able to do it much faster, like 50% improvement in the efficiency of the cords. And now, the one advantage of this is that the improvements that Maxar or other collaborators are using our cords, that they're putting into the system, are coming back to us. So we take advantage of that in improving the efficiency in operations. So this like a win-win situation for both of what part is fitting in the R&D and what using in operations. And on top of it, you can create multiple conflagrations of this model in various instances on the cloud where you can run it more efficiently and you can create an ensemble of solutions that can be catered to individual needs. And the one additional thing I wanted to mention about the user cloud is that this is like when you have a need, you can surge the compute, you can instantiate thousands of simulations to test a new innovation, for instance. You don't need to wait for the resources to be done in sequential manner. Instead, you can ramp up the production of these equipments in no time, and without worrying about, of course, the cost is a factor that we need to worry about, but otherwise the capacity is there, the facilities are there to take advantage of the cloud solutions. >> Well Vijay, I'm very impressed with your organization. I'd love to do a followup with you. I love the impact that you're doing. Certainly, the weather impacts society from forecasting disasters and giving people the ability to look at supply chain, whether it's planning for potentially a fire season or a water shortage, or anything goin' on, there. But also it's a template. You are succeeding a new kind of way to innovate with community, with large scale, multi-scale data points, so congratulations. >> Thank you. >> Thank you very much. I'm John Furrier here, part of AWS Partner Awards Program, best HPC solution. Great example, great use case, great conversation. Thanks for watching. Two great interviews here, as part of AWS Public Sector Partner Awards Program. I'm John Furrier. The best-in-show for HPC solutions, Travis Hartman, Maxar Technologies, and Vijay Tallapragada at NOAH, two great guests. Thanks for watching. (soft electronic music)

>>from around the globe. It's >>the Cube with digital coverage of >>AWS Public Sector Partner Awards >>brought to you by >>Amazon Web services. Everyone, welcome to this cube coverage of AWS Public Sector Partner Awards program. I'm John Furrow, your host of the Cube with two great guests here. Travis Department director of analytics and Weather at Max. Our technologies and VJ teleplay Gotta Who's the chief? Modeling and data a simulation branch at Noah. Tell us about the success of this. What's the big deal? Take us through the award and why Max are what you guys do. >>Yeah, so Macs are is an organization. Does a lot of different activities unearth intelligence as well as space? We have about 4000 employees around the world. One side of the economy works on space infrastructure, actually building satellites on all the infrastructure that's going to help us get us back to the moon and things like that. And then on the other side we have a north of intelligence group, which is where, I said, and we leverage remote sensing information for science information to help people better understand how, how and what they do might impact the Earth or have the earth, and it's activities might impact their business mission. Our operation. So what we wanted to set out to do was help people better understand how weather could impact their mission, business or operations. And a big element of that was doing it with speed. Ah, so we we knew? No. I had capabilities running America weather prediction models and very traditional on Prem. Big, beefy ah, high performance compute supercomputers. But we wanted to do it in The cloud we want to do is AWS is a key part. So we collaborated with B. J and Noah and his team is there to help pull that off. They gave this access public domain information, but they showed us the right places to look. We've had some of the research scientists talking, and after pretty short effort, it didn't take a lot of time. We were able to pull something off that a lot of people didn't think was possible. I'm we got pretty excited. Once we saw some of the outcome >>Travis to be, Jay was just mentioning the relationship. Can you talk about the relationship together because this is not your classic Amazon partner client relationship that you have. You guys have been partnering together V. J and your team with AWS. Talk about the relationship and that and how Amazon plays because it's a unique partnership plane in more detail at specific relationship. >>Yeah, with Max or in AWS. You know, our partnership has gone back A number of years on Macs are being a fairly large organization. There's lots of different activities. I think Max Star was the first client of AWS Snowmobile, where they have the big tractor trailer back up to a data center, load all the data in and then take it to an AWS data center. We were the first users of that because we had over 100 petabytes of satellite imagery and archive that just moving across the Internet would probably still be going. Um, so the snowmobile is a good success story for us, but just with >>the >>amount of data that we have, the amount of data we collect every day and all the analytics that we're running on it, whether it's in an HPC environment or, you know, the scalable Ai ml were able to scale out that architecture scale out that compute the much easier, dynamic and really cost effective way with AWS, because when we don't need to use the machines, we turn them off. We don't have a big data center sitting somewhere. We have to have security, have all the overhead costs of just keeping the lights on. Literally. AWS allows us to run our organization and a much more efficient way. Um and Noah, you know, they're They're seeing some of that same success story that we're seeing as far as how they can use the cloud for accelerating research, accelerating how the advancement of numerical weather prediction from the United States can benefit from cloud from cloud architecture, cloud computer, things like that. And I think a lot of the stuff that we've done here, Max our with our HPC HPC solution in the cloud. It's something that's pretty interesting to know, and it's it's a good opportunity for us to continue our collaboration. >>If I could drill down on that solution architecture for a minute. How did you guys set up the services, and what lessons did you learn from that process? >>We're still learning. It was probably the the short answer, but it all started with our people. Uh, you know, we have some really strong engineers, really strong data scientists that fundamentally have a background in meteorology or atmospheric science, you know? So they understand the physics. So you know why the wind blows is the way it doesn't. Why Cloud's doing clouds to do, Um, but we also having a key strategic partnership with AWS. We really have to tap into some of their subject matter experts. And we really put those people together, you know, and come up with new solutions, new innovative ideas, stuff that people hadn't tried before. We're able to steer a little bit of AWS is product roadmap for is what we were trying to do and how their current technology might not have been able to support it. But by interacting with us gave them some ideas as far as what the tech had to move towards. And then that's that's what allowed us to move pretty quick fashion. Um, you know, it's it's neat stuff technology, but it really comes down to the people. Um, and I feel very honored and privileged to work with both great people here. Attacks are as well as aws, um, as well as being able to collaborate with your great teams. That power, it's been a lot of fun. Well, >>Travis gonna create example? I think it's a template that could be applied to many other areas, certainly even beyond. You've got large scale, multi scale situation there. Congratulations. Final question. What does it mean to be an award winner for AWS Partner Awards as part of the show? You're the best in show for HPC. What's it like? What's the feeling? Give us a quick side from the field? >>Yeah. I mean, I don't know if there's really a lot of good words that kind of sum it up. It's Ah, I shared the news with the team last night, and you know, there are a lot of a lot of good responses that came from a lot of people think it's cool. And at the end of the day, a lot of people on our team, you know, took a hobby or a passion of weather and turned it into a career. Ah, and being acknowledged and recognized by groups like AWS for best solution in a particular thing. Um, I think we take a lot of that to heart. And, ah, we're very honored and proud of what we were able to do and proud that other people recognize the need stuff that we're doing well, >>Certainly taking advantage. The cloud, which is large scale. But you you're on a great wave. You've got a great area. I mean, whether you talk about whether it's exciting, it's dynamic. It's always changing. It's big data. It's large scale. So you get a lot of problems to solve in a lot of impact to get it right. So congratulations on ECs. >>Thank you very much. Great mission. Thank you. >>Love what you do love to follow up again. Maybe do another interview and talk about the impact of weather and all the HPC kind of down the road. But, Travis, thank you very much. >>Thank you. Appreciate it. >>Good to see you. >>Thank you. Good to be here. >>So Noah, National Oceanic Atmospheric Administration, National Weather Center, National Center for Environmental Predictions, Environmental Modeling Center year. That's your organization? You guys are competing to be best in the world. Tell us what you guys do at a high level. Then we'll jump into some of the successes. >>So the national Weather Service is responsible for providing weather forecast to save lives and property and improve the economy of the nation. And that's part of that. That the national weather services responsible for providing data and also the forecasts to the public and the industry and be responsible for providing the guidance on how they create the forecasts. So we are at the Environmental Modeling Center, uh, the nation's finest institute in advancing our numerical weather prediction modelling development, and you play it off all the data that's available from the world to initialize our models and provide the future state of the atmosphere from hours all the way to seasons and years. That's that's the kind of a range of products that we don't lock and provide are our key for managing the emergency services and patch it management and mitigation and also improving the nation's economy by preparing well in advance for the future events. And it's it's a science based organization, and we have ah well class scientists working in this organization. I manage about 170 of them at the moment of modeling center. They're all PhDs from various disciplines, mostly from meteorology, atmospheric sciences, oceanography, land surface modelling space weather, all weather related areas and the mathematics and computer science. And we are at the stage where we are probably the most. Uh huh. Most developed, uh, advanced modelling center that we use almost all possible computational resources available in the world. So this is a really computational in terms of user data, user computer seems off. Uh, all the power that we can get and we have a 3.5 petaflop machine that we use to provide these weather forecasts, and they provide the services every hour. For some sense is like the CDO rather our rates for every three hours for hurricanes and for every six hours for the regular, Rather like the participation, uh, the temperature forecast. So all the data that you see coming out from either the public media, our department agencies, they are originated in our center and disseminated in various forms. I think no one is the only center in the world that provides all this information for your past. So it is, ah, public service organization and we riding on a visa with society. >>We'll I love your title, Chief modeling and data, a simulation title branch of a lot of these organizations. This >>is >>whether it's ever critical. I want to get your thoughts cause we were talking before we came on about how the Hurricane Katrina was something that really kind of forcing you to rethink things. Whether it is an evolving system, it's always changing. Either the catastrophe or something happens. Were you trying to proactive predicting, say, whether it's a fire season in California, all kinds of things going on that's not It's always hard to get a certain prediction. You have big job. It's a lot of data you need. Horsepower need computing. You need to stand up. Some HPC take us through like like the thinking around the organization. And what was The impact is that you see, because whether does have that impact. >>So traditionally, you know, as you mentioned, there are radius weather phenomenon that you describe like the five rather the Americans, every presentation, the flooding. So we developed solutions for individual weather phenomena, and, uh, we have grown in that direction by developing separate solutions for separate problems. And very soon it became obvious that we cannot manage all these independent modeling systems to provide the best possible forecasts. So the thinking has to be changed. And then there is Another big problem is that there's a lot of research going out in the community like the academic institutes, the universities, other government labs. There are several people working in these areas, and all their work is not necessarily a coordinated, uh, development activity that we cannot take advantage. And they have no incentive for people to come and contribute towards the mission that we are engaged in. So that actually prompted to change the direction of thinking. And as you mentioned, Hurricane Katrina was an eye opener. We had the best forecasts, but the dissemination of that information waas not probably accurate enough, and also there is a lot of room for improvement in predicting these catastrophic events. How are >>you guys using AWS? Because HPC high performance computing I mean you can't ask for more resources in the massive cloud that is Amazon. How is that help to you? Can you take a minute to explain, but walk us through? >>What? >>Aws? There >>are a few example. Second site. But before then, I would like to really appreciate a Travis Hartman from Max. Are you know who is probably the only private sector partner that we had in the beginning. And now we're expanding on. That s so we were able to share our community. Cores with Max are and without how they were able to establish this and drive modeling system as it is done in operations that Noah and they were able to reproduce operational forecast using the cloud resources. And then they went ahead and did even more by scaling the modeling systems is that it can run even faster and quicker them are what insert no operations can do. So that gives us one example of how the cloud can be used. You know, the same forecast that we produce, ah, globally, which will take about eight minutes per day. And, uh, Max I was able to do it much faster, like 50% improvement and in the efficiency of the colors. And now the one piece of this is that the improvements that matter are other collaborators are using, or cords that they're putting into the system are coming back to us. So we take advantage of that, improving the efficiency in operations. So this is that this is like a win win situation for both, uh, who are participating in the R and D on who are using it in operations, and on top of it, you can create multiple configurations of this model in various instances on the cloud when you can run it more efficiently and you can create an ensemble of solutions that can be captured toe individual needs. And the one additional thing I want to mention about User Cloud is, is that you know, this is like when you have a need, you can search the compute you can. Instead she 8000 sub simulations to test a new innovation. For instance, you don't need to wait for the resources to be done in a sequential manner. Instead, you can ramp up the production off these apartments in no kind and without Don't worry about. Of course, the cost is the fact that we need to worry about, but otherwise the capacity is there. The facilities are reacting to take advantage of the cloud solutions. If I'm a >>computer scientist person, I'm working on a project. Now I have all this goodness in the cloud, how's morale been and what's the reaction been like from from people doing the work. Because usually the bottleneck has been like I gotta provision resource. I gotta send a procurement request for some servers or I want to really push some load. And right now, I got a critical juncture. I mean, it's got a push morale up a bit, and you talk about the impact to the psychology of the people in your organization. >>Um, I haven't. I have two answers to this question. One from a scientist perspective like me. You know, I was not a computer scientist from the beginning, but I became a software engineer, kind of because I have to work with these software and hardware stuff more more on solving the computational problems than the critical problems. So people like us who have invested their careers in improving the science, they were not care whether it's ah, uh hbc on premise Cloud, what will be delighted to have, uh, resources available alleviate that they can drive. But on the other hand, the computer computational engineers are software engineers who are entering into this field. I think they are probably the most excited because of these emerging opportunities. And so there is a kind of a friction between the scientific and the computational aspects off personnel, I would say. But that difference is slowly raising on and we are working together as never before. So the collective moral is very high to take advantage of these resources and opportunities. I think way of making the we're going in the right direction. >>It's so much faster. I mean, in the old days, you write a paper, you got to get some traction. Gonna do a pilot now It's like you run an experiment, get it out there. VJ I'm very impressed with the organization. Love to do a follow up with you. I love the impact that you're doing certainly in the weather impact society from forecasting disasters and giving people the ability to look at supply chain, whether it's providing for potentially a fire season or water shortage or anything going on there. But also it's a template. You're exceeding a new kind of waiting to innovate with community with large scale, multi scale data points. So congratulations and >>thank you. >>Thank you very much. I'm John Furrier here part of AWS partner Awards program. Best HPC solution. Great. Great Example. Great use case. Great conversation. Thanks for watching two great interviews. Here is part of AWS Public Sector Partner Awards program. I'm John Furrier. The best in show for HPC Solutions. China's Hartman Max, our technologies and Vijay tell Apartado at Noah. Two great guests. Thanks for watching. Yeah, Yeah, yeah, yeah, yeah, yeah

>> Live from Las Vegas, it's theCUBE Covering Informatica World 2019 Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019 here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Ariel Kelman. He is the VP, Worldwide Marketing at AWS. Thank you so much for coming on theCUBE. >> Thanks so much for having me on today. >> So let's start out just at ten thousand feet and talk a little bit about what you're seeing as the major cloud and AI trends and what your customers are telling you. >> Yeah, so I mean, clearly, machine learning and AI is really the forefront of a lot of discussions in enterprise IT and there's massive interest but it's still really early. And one of the things that we're seeing companies really focused on now is just getting all their data ready to do the machine learning training. And as opposed to also, in addition I mean, training up all their people to be able to use these new skills. But we're seeing tons of interest, it's still very early, but you know one of the reasons here at Informatica World is that getting all the data imported and ready is, you know, it's almost doubled or tripled in importance as it was when people were just trying to do analytics. Now they're doing machine learning as well. You know, we're seeing huge interest in that. >> I want to get into some of the cloud trends with your business, but first, what's the relationship with Informatica, and you know we see them certainly at re:Invent. Why are you here? Was there an announcement? What's the big story? >> I mean, we've been working together for a long time and it's very complementary products and number varies. I think the relationship really started deepening when we released Redshift in 2013, and having so many customers that wanted to get data into the cloud to do data we're housing, we're already using Informatica in, to help get the data loaded and cleansed and so really they're one of the great partners that's fueling moving data into the cloud and helping our customers be more successful with Redshift. >> Yeah, one of the things I really admire about you guys is that you're very customer centric. We've been following Amazon as you know since their, actually second reinvent, Cube's been there every time, and just watching the growth, you know, Cloud certainly has been a power source for innovation, SAS companies that are born in the cloud have exponentially scaled faster than most enterprises because they use data. And so data's been a heart of all the successful SAS businesses, that's why start ups gravitated to the Cloud right away. But now that you guys got enterprise adoption, you guys have been customer centric and as you listen to customers, what are you guys hearing from that? Because the data on premises, you've got more compliance, you've got more regulation, you've got-- news today-- more privacy and now you've got regions, countries with different laws. So the complexity around even just regulatory, nevermind tech complexity, how are you guys helping customers when they say, you know what, I want to get to the cloud, love Amazon, love the cloud, but I've got my, I've got to clean up my on param house. >> Yeah, I would say like a lot, if you look at a lot of the professional services work that we do, a lot of it is around getting the company prepared and organized with all their data before they move to the cloud: segmenting it, understanding the different security regulatory requirements, coming up with a plan of what they need, what data they're going to maybe abstract up, before they load it, and there's a lot of work there. And, you know, we've been focused on trying to help customers.. >> And is there a part in you're helping migrate to the cloud, is that.. >> Yeah, there's technology pieces, companies like Informatica helping to extract and transform and load the data and on data governance policies. But then also, for a lot of our systems integrator partners, Cognizant, Accenture, Deloitte-- they're very involved in these projects. There's a lot of work that goes on; a lot of people don't talk about just before you can even start doing the machine learning, and a lot of that's getting your data ready. >> So how, what are some of the best practices that have emerged in working with companies that, as you said, there's a lot of pre-work that needs to be done and they need to be very thoughtful about about sort of getting their data sorted. >> Well I think the number one thing that I see and I recommend is to actually first take a step back from the data and to focus on what are the business requirements of, what questions are you trying to answer, let's say with machine learning, or with data science advanced analytics, and then back out the data from that. What we see a lot of, you know companies sometimes will have it be a data science driven project. Okay, here's all the data that we have, let's put it in one place, when you may not be spending time proportionate to the value of the data. And so that's one of the key things that we see, and to come up-- just come up with a strong plan around what answers you're, what business questions you're trying to answer. >> On the growth of Amazon, you guys certainly have had great record numbers, growth, even in the double digit kind of growth you're seeing on top of your baseline has been phenomenal. Clearly number one on the cloud. Enterprise has been a big focus. I noticed that on the NHL, your logo's on the ice during the playoffs; you've got the Statcast. You guys are creating a lot of aware-- I see a lot of billboards everywhere, a lot of TV ads. Is that part of the strategy is to get you guys more brand awareness? What's the.. >> We're trying, you know, it's part of our overall brand awareness strategy. What we're trying to do is to help, we're trying to communicate to the world how our customers are being successful using our technology, specifically machine learning and AI. It's one of these things where so many companies want to do it but they say, well, what am I supposed to use it for? And so, you know, one of, if you dumb down what marketing is at AWS, it's inspiring people about what they can run in the cloud with AWS, what use cases they should consider us for, and then we spend a lot of energy giving them the technical education and enablement so they can be successful using our products. At the end of the day, we make money when our customers are successful using our products. >> One of the hot products was SageMaker, we see in that group, AI's gone mainstream. That's a great tail wind for you guys because it kind of encapsulates or kind of doesn't have to get all nerdy about cloud, you know, infrastructure and SAS. AI kind of speaks to many people. It's one of the hottest curriculums and topics in the world. >> Yeah, and with SageMaker, we're trying to address a problem that we see in most of our customers where the everyday developer is not, does not have expertise in machine learning. They want to learn it, so we think that anything we can do to make it easier for every developer to ramp up on machine learning the better. So that's why we came up with SageMaker as a platform to really make all three stages of machine learning easier: getting your data prepared for training, training in optimized models, and then running inference to make the predictions and incorporate that into people's applications. >> One of the themes that's really emerging in this conversation is the need to make sure developers are ready and that your people are skilled up and know what they need to know. How are, how is AWS thinking about the skills gap, and what are you doing to remedy it? >> Yeah, a couple things. I mean, we're really, like a lot of things we do, we'll say what are all the ways we can attack the problem and let's try and help. So, we have free training that we've been creating online. We've been partnering with large online training firms like Udacity and Coursera. We have an ML solutions lab that help companies prototype, we have a pretty significant professional services team, and then we're working with all of out systems integrators partners to build up their machine learning practices. It's a new area for a lot of them and we've been pushing them to add more people so they can help their customers. >> Talk about the conferences, you have re:Invent, the CORE conference, we've been theCUBE there. We've just also covered London, Amazon's Web Services summit, and 22,000 registered, 14,000 showed up. Got huge global reach now. How do you keep up with this? I mean it's a... >> Well we're trying to help our customers keep up with all the technology. I mean, really, we have about, maybe 25 or so of these summits around the world-- usually around two days, several thousand people, free conferences. And what we're trying to do is >> They're free? >> The summits are free and it's like, we introduce so much new technology, new services, deeper functionality within our exiting services, and our customers are very hungry to learn the latest best practices and how they can use these, and so we're trying to be in all the major areas to come in and provide deep educational content to help our customers be more successful. >> And re:Invent's coming around the corner. Any themes there early on, numbers wise? Last year you had, again, record numbers. I mean at some point, is Vegas too small >> Yeah, we had over 50,000 people. We're going to have even more, and we've been expanding to more and more locations around Las Vegas and you know we're going to keep growing. There's a lot of demand. I mean, we want to be able to provide the re:Invent experience for as many people as want to attend. >> What's the biggest skill set, you know the folks graduating this month, my daughter's graduating from Cal Berkeley, and a lot of others are graduating >> Congratulations >> high school. Everyone wants to either jump into some sort of data related field, doesn't have to be computer science, those numbers are up. What's your view of skill sets that are needed right now that weren't in curriculum, or what pieces of curriculum should people be learning to be successful if machine learning continues to grow from helping videos surface to collecting customer data. Machine learning's going to be feeding the AI applications and SAS businesses. >> Yeah, I mean look, you just forget about machine learning, you go to a higher level. There's not enough good developers. I mean, we're in a world now where any enterprise that is going to be successful is going to have their own software developers. They're going to be writing their own software. That's not how the world was 15 years ago. But if you're a large corporation and you're outsourcing your technology, you're going to get disrupted by someone else who does believe in custom software and developers. So the demand for really good software engineers, I mean we deal with all the time, we're hiring. It is always going to outstrip supply. And so, for young people, I would encourage them to start coding and to not be over reliant on the university curriculums, which don't always keep pace with, you know, with the latest trends. >> And you guys got a ton of material online too, you can always go to your site. Okay, on the next question around, as someone figures out, okay, enterprise versus pure SAS, you guys have proven with the Cloud that start ups can grow very fast and then the list goes on: AirBnB, Pinterest, Zoom Communications, disrupting existing big, mature markets by having access to the data. So how do you talk about customers when you say, hey, you know, I want to be like a SAS company, like a consumer company, leverage data, but I've got a lot of stuff on premise. So how do I not make that data constrained? How do you guys feel about that conversation because that seems to be the top conversation here, is you know, it's not to say be consumer, it's consumer-like. Leveraging data, cause if data's not into AI, there's no, AI doesn't work, right? So >> Right >> It can't be constrained by anything. >> Well, you know, you talk to all these companies and at first they don't even know what they don't know in terms of what is that data? And where is it? And what are the pieces that are important? And so, you know, we encourage people to do a good amount of strategy work before they even start to move bits up to the cloud. And of course, then we have a lot of ways we can help them, from our Snowball machines that they can plug in, all the way to our Snowmobile, which is the semi truck that you can drive up to your data center and offload very large amounts of data and drive it over to our data centers. >> One of the things that is trending-- we had Ali from Data Bricks talk about, he absolutely believes a lot of the same philosophies you guys do-- data in the cloud. And one of his arguments was is that there's a lot of data sets in these marketplaces now where you can really leverage other people's data, and we see that on cybersecurity where people are starting to share data, and Cloud is a better model for that than trying to ship drives around, and there's a time for Snowball, I get that, and Snowmobile, the big trucks for large ingestion into the cloud, but the enterprise, this is a new phenomenon. No one really shared a lot in the old days. This is a new dynamic. Talk about that, is it-- >> I mean, sharing, selling, monetizing data. If there's something that is important, there will be a market for it. And I think we're seeing that just the hunger, everything from enterprises to startups, that want more data, whether it's for machine learning to train their models, or it's just to run analytics and compare against their data sets. So I think the commercial opportunity is pretty large. >> I think you're right on that. I think that's a great insight. I mean, no one ever thought about data as a service from our data set standpoint, 'cause data sets feed machine learning. All right, so let's do, give the plug on what's going on with AWS. What's new, what's on your plate, what's notable. I mean I love the NHL, I couldn't resist that plug for you being a hockey fan. But what's new in your world? >> Um, you know, we're, we're in early planning stages on our re:Invent conference, our engineers are hard at work on a lot of new technology that we're going to have ready between now and our re:Invent show. You know, also we're, my team's been doing a lot of work with the sports organizations. We've had some interesting machine learning work with major league baseball. They rolled out this year a new machine learning model to do stolen base predictions. So, you can see on some of the broadcasts, as a runner goes past first base, we'll have a ticker that will show what the probability is that they'll be successful stealing second base if they choose to run. Trying to make a little more entertaining all those scenes we've seen in the past of the pitcher throwing the ball back to first, trying to use AI machine leaning to give a little bit more insight into what's going on. >> And that's the Statcast. Part of that's the Statcast >> That's Statcast, yeah >> And you got anything new coming around that besides that new.. >> Yeah, I think that yeah, major league baseball is hard at work on some new models that I think will be announced fairly soon. >> All right, to wrap up Informatica real quick, an announcement here, news coming I hear. How are you guys working with Informatica in the field? Is there any, can you share more about relationship >> Yeah I mean I think we're going to have an announcement a little bit later today, I mean it's around the subject we've been talking about: making it easier for customers to, you know, be successful moving their data to the Cloud so that they can start to benefit from the agility, the speed and the cost savings of data analytics and machine learning in the Cloud. >> And so when you're working with customers, I mean, because this is the thing about Amazon. It is a famously innovative, cutting edge company, and when you talk about the hunger that you describe, that these customers, isn't it just that they want to be around Amazon and kind of rub shoulders with this really creative, thinking four steps ahead kind of company. I mean how do you let your innovation rub off on these customers? >> I mean there's a couple ways We do, one of the things we've done recently is these innovation workshops. We have this thing we talk about a lot this working backwards process where we force the engineers to write a press release before we'll green light the product because we feel like if you can't clearly articulate the customer benefit, then we probably shouldn't start investing, right? And so we, that's one of the processes that we use to help us innovate better, more effectively and so we've been walk-- we walk customers through this. We have them come, you know there's an international company that I was, part of one of the efforts we did in Palo Alto last year where we had a bunch of their leadership team out for two days of workshops where we worked a bunch of ideas through, through our process. And so we do some of that but the other area is we try and capture area where we think that we've innovated in some interesting way into a service that then customers can use. Like Amazon Connect I think is a good example of it. This is our contact center call routing technology and you know, one of the things Amazon's consumer business is known for is having great customer support, customer service, and they spent a lot of time and energy making sure that calls get routed intelligently to the right people, that you don't sit on hold forever, and so we figure we're probably not the only company that could benefit from that. Kind of like with AWS, when we figure out how to run infrastructure securely and high performance and availability, and so we turn that into a service and it's become a very successful service for us. A lot of companies have similar contact center problems. >> As a customer, I can attest to being on hold a lot. Ariel, thank you so much for coming on theCUBE. It's been great talking to you. >> I appreciate it. Thank you. >> Thanks for coming out, appreciate it. >> I'm Rebecca Knight, for John Furrier. You are watching theCUBE. Stay tuned. (upbeat music)

>> Narrator: Live from Manhattan, it's theCUBE, covering AWS Summit New York City 2017, brought to you by Amazon Web Services. >> And welcome back to New York, here. AWS Summit, theCUBE continue our coverage of what's happening here in the Big Apple. I'm John Walls along with Stu Miniman, and what this is is maybe not the most prolific CUBE guest of all time, but he's in the hall of fame. He really is a CUBE MVP for sure. It's good to have David Richards with us, the president, chairman, CEO of WANdisco. Good to see you, sir. >> It's a pleasure to be back again. It feels like home. >> It is like home. We need to get you your own microphone, I think, you know? >> David: I know it. I need my name on the back of the seat or something. >> This isn't quite a home game for you. All right, so you've got an office in Sheffield, England. >> David: Yeah. >> You've got an office out in the valley, Silicon Valley. We got ya right in the middle, I think. >> David: Yeah. >> Almost, don't we? So-- >> Exactly. >> We kind of split the difference for you this one. >> I always tell people I'm recolonizing the United States. I've been here for about 20 years. I can change the accent. >> Right. >> I'll get you all, eventually. >> All right, well, another year or two, we'll see how that works for ya. Big, big, I guess six, seven months for you, right? As far as some acquisitions you've done, some vice partnerships and arrangements you've done. >> Yes, as a business, we've really progressed well in the first half of the year. I've got to be a little bit careful. We've got results coming out September the sixth in London, but we did do a pre-announcement of a business update. We signed a record big data cloud contract with a very large bank for over four million dollars. That was our largest ever contract win. We signed a major retailer who we can't name, obviously, which is another sort of cloud ObjectStore on premises. A big data win, and interestingly, we stopped burning cash and investors really like this kind of perfect storm of, 175%, 173% growth in our cloud big data revenue, booking, sorry, combined with a flat cost-base, which meant, first half of last year, burning five point four million dollars down to virtually zero, just $600,000 in the first half. So, investors really like that. We really like that, and it demonstrates that perfect storm of flat cost-base and growing sales. >> David, I'm curious, does working with Amazon, and your customers being on Amazon, does the speed and agility and everything like that contribute to that profitability? >> Well, Amazon kind of changes the game for all vendors, right? Because nobody, it used to be this sort of big four, five, six, whatever it is these days, consulting companies that had to implement ERP systems and all those complex applications. I don't necessarily think they're the people, they're not the go-to people anymore for cloud. So, it's down to uniqueness of technology. Amazon have got such a wide array, we were talking earlier about some of their announcements out today as they continue to go up the stack with applications and so on. So, it does lend itself very well to small vendors with sticky, unique intellectual property and unique products and services that are going to really thrive in this kind of cloud environment. So, we've really enjoyed working with Amazon, but we're also working with the other cloud vendors, as well, and I have to say, when we first saw the Snowmobile and the Snowball, well, actually, the Snowmobile, drive out on stage in New York, was it 12, 18 months ago? It's dog years, so everything goes seven times faster. >> John: Right, right, right. >> I was laughing. I was like, "How on Earth can you possibly use a truck to move data?" But a customer came to us, a prospect came to us the other day, he wanted to move a hundred petabytes of data. Now, if you're going to use the public internet to do that, that's going to take a hell of a long time. So, this idea of a mix between physical and digital data movement I think is, when moving to cloud, is actually fascinating. I think it's a really fascinating subject area. One that customers are definitely going to use. >> Yeah, you've got a great vantage point looking at customers' migrations. >> David: Yeah. >> It was actually something big in the keynote talking about, there are so many migrations out there that Amazon released an AWS Migration Hubs. So, obviously, physics is always a challenge, my legacy mindset. Customers, we heard a customer up onstage and it's usually not lift and shift maybe for the private cloud, but for public cloud, I usually, I need to rewrite, I need to do micro-services. What is the friction for customers, and how are you and Amazon and the other clouds helping customers work through those challenges? >> OK, so, just to take a step back and think about the problems that happen at hyper-scale data movement. So, small-scale data, gigabyte-scale data, the stuff that you typically see in a relational database, they're not particularly big problems. It's kind of minimal outage, press pause, move data, make it consistent, and you're done. You can have a sort of, a small outage, maybe 15 minutes or even a day to move data, but when it gets to hyper-scale, when it gets to petabyte-scale, multi-terabyte-scale data moves, that's when you have a problem, and that's really the problem that we solve. So, the idea that you can move data that's moving and changing without an interruption to service from on-premise to cloud and support a hybrid cloud topology for an elongated period of time is fascinating. I was listening at an investor conference to the CEO of VMware who was talking about, we're going to be in a situation of hybrid cloud for the next 20, 25 years because, overnight, not everybody can just repurpose every single application that they're running on-premise, whether it's in the main frame application, or a relational data application, or wherever it is in the OP application, and repurpose that in cloud overnight. So, we're going to have to gradually move and migrate those applications over. So, it's highly likely we're going to be in a hybrid cloud environment for the foreseeable future, and that's actually fantastic news for us. We're moving, as I said, at scale companies into cloud with transactional data, and nobody else can touch us in terms of the uniqueness of the IP, which is fantastic news for us. >> In terms of just big data in general, Stu has one use for it, I have a different use for it. It's going to live in a lot of different places. How are you responding to different needs within your clients and trying to make them more effective, make them more efficient? And yet, when you're dealing with more and more data, that's a big storm to handle. >> That's a great question. I went to speak a couple of months ago to a new customer of ours who is a major healthcare provider on the east coast, and I kind of said to him, "OK, you've had this deep cluster for the past three years. Why are you calling us? Why now?" Which is the question that I always ask our customers. Why? What changed? Why are you doing this right now?" And maybe for the past three years they've been putting legal data into the system. That's data, but who cares if you can't get access to it? We can move to telephone. We can move to e-mails. We can go into an archive, into a paper archive even, to find it, but the why now is that they're now putting patient record data, patient information with regulated SLA's into this system, and that really is our sweet spot. As you get to, remember that investment thesis, small-scale gigabyte outage is small outage, when you get into petabyte, exabyte-scale, when you've got data sets that are a thousand, a million times greater, it's linear to the quantum of data. That outage becomes a thousand or a million times greater. So, that's kind of intolerable. So, we love it when strategic applications, regardless of what the use case is, we could all have different, it might be patient data, it might be retail information, it might be banking data, it might be customer retention information, when those strategic applications move onto this hyper-scale infrastructure, you have to support RTO and RTP, and that's what we do. >> And is a byte a byte a byte? You have these thousands of needles in haystacks, right? How do you assign value to one as opposed to another? >> So, this is another great question and one that investors kind of ask me a lot. So, we used to model our business from kind of the ground up. So, we take the classic enterprise sales team, you have a sales and marketing organization that's quite large, you would multiply that by their quota and then multiply it by 66% because that's how many of them are going to be successful in selling product. Well, we completely threw that away when we launched WANdisco Fusion, our new technology, early 2016. Then, we moved to a channel-based approach. So, we have IBM, we have an OAM, 5,000 quarter-carrying enterprise sales guys at IBM selling our products. That was a fantastic deal for us. We signed it in April 2016, and they've done the first half of this year, and made at least six million dollars in sales that we have also announced, and then, we've got strategic partnerships with Amazon, with Microsoft, with Google, and we model our business by those channels. So, we're not looking for needles in haystacks. We don't, we could never hire another, I mean, if we had to come into the market and say, "We need to go and hire 5,000 enterprise sales guys," we'd have to be raising, doing fund-raisers like Uber or something. We'd just be untenable. We couldn't do it. So, we have a product that lends itself very well to a channel-based approach, and that's working very nicely for us. So, we're not looking for, we're just looking for haystacks. Somebody else can go and find the needles. >> John: Find me and you, right? >> Right. >> David, how are your customers managing the pace of change these days? We've said Amazon is an example. It's like everyday there's three new services coming out. Are they excited? Are they completely overwhelmed? What do you see these days? >> So, I think it's classic sort of products and option lifecycle stuff. The sort of technical enthusiasts, they love all this change. The early-stage companies that are implementing this new cloud-based technology, ObjectStore technology and so on, they're managing very well. It's the later-stage companies you might go to and say, "ObjectStore," and they'll go, "What's ObjectStore? We're just getting our head around Hadoop, and Hive, and Pig, and all this other stuff that you were talking about three years ago," and sales guys go in there now and say, "Oh, no, no, no, don't worry about Hadoop. Nobody's going to run Hadoop in the cloud." It's like, "Well, that's what you told me three years ago." So, I think the market's certainly divided. I think you're going to see, as we move up products and option lifecycle, you're going to see lots and lots and lots of interesting moves happen. The companies that seem to be owning cloud, I think Alibaba is coming up really fast. We're seeing them doing some interesting things. Obviously, they've got dominoes in the Chinese market. Amazon First-Mover, Microsoft's futures dependent on cloud. So, they all have their different spin and different take on applications that they're going to run in cloud. I think there is, I think it's a bit like the cellphone industry. There's lot and lots of different plans, lots and lots of different confusing nomenclature, but that's going to settle out in the next couple of years, but there's unquestionably, if you look at the audience here today, unquestionably large-scale movement of applications and data to cloud. >> Well, we appreciate the time, as always. Great to see you. Another notch in your CUBE belt. (laughing) So, congratulations for that, and maybe you can settle in to New York for a day or two. You said your travels have had you flip-floppin' back and forth between England and here. So, maybe you can settle in for a day or two. >> Yeah, I need to replicate myself. I need to put myself in at least two different places at the same time. >> Live data replication right here. (laughing) All right, David, thanks for bein' with us. David Richards. >> Thank you. Thanks guys. >> Back with more here on theCUBE, we continue our coverage of AWS Summit from New York City right after this break. (upbeat music)

>> Announcer: Live from Washington, DC, it's theCube, covering AWS Public Sector Summit 2017, brought to you by Amazon Web Services and its partner ecosystem. >> Welcome inside the convention center here in Washington, DC. You're looking at many of the attendees of the AWS Public Sector Summit 2017. We're coming to you live from our nation's capital. Several thousand people on hand here for this three-day event, we're here for two days. John Walls, along with John Furrier. John, good to see you again, sir. >> Sir, thank you. >> We're joined by Jay Littlepage, who is the VP of Infrastructure and Operations at Digital Globe, and Jay, thank you for being with us at theCube. >> My pleasure. >> John W: Good to have you. First off, your company, high-resolution, earth imagery satellite stuff. Out-of-this world business. >> Yep. >> Right, tell our viewers a little bit about what you do, I mean, the magnitude of, obviously, the environmental implications of that or defense, safety security, all those realms. >> Okay, well, stop me when I've said too much because I get pretty excited about this. We work for a very cool company. We've been taking earth imagery since 1999, when our first satellite went up in the sky. And, as we've increased our capabilities with our constellation, our latest satellite went up last November. We're flying, basically, a giant camera that we can fly like a drone. So, and when I say giant camera, it's about the size of a school bus, and the lens is about the size of the front of the school bus, and we can take imagery from 700 miles up in space and resolve a pixel about the size of a laptop. So, that gives us an incredible amount of capability, and the flying like a drone, besides just being really cool and geeky, we can sling the lens from basically Kansas City to here in Washington in 15 seconds and take a shot. And so, when world events happen, when an earthquake happens, you know, they're generally not scheduled events, we don't have to have the satellite right above the point where there's something going on on the ground, we can take a shot from an angle of 1,000 miles away, and with compute power and good algorithms, we can basically resolve the picture of the earth, and it looks like we're right overhead, and we're getting imagery out immediately to first responders, to governmental agencies so they can respond very quickly to a disaster happening to save lives. >> So, obviously, the ramifications are endless, almost, right? >> Yes. >> All that data, I mean, you can't even imagine the amount, talk about storage. So, that's certainly a complexity, and then, they are making it useful too all these different sectors. Without getting too simple, how do you manage that? >> Well, you know, it's a big trade-off because, ideally, if storage was free, all of our imagery in its highest consumable form would be available all the time to everybody. Each high-resolution image might be 35 gig by itself. So, you think of that long of flying a constellation, we've got 100 petabytes of imagery. That's too much, it's too expensive to have online all of the time. And so, we have to balance what's going to be relevant and useful to people versus cost. You know, a lot of the imagery goes through cycle where it's interesting until it's not, and it starts to age off. The thing about the planet, though, is we never know what's going to happen, and when something that aged off is going to be relevant again. And so, the balance for my team is really making sure we're hitting the sweet spot on there. The imagery that is relevant is readily accessible, and the imagery that's not is, in its cheapest form, fact or possible, which for us, is compressed, and it's in some sort of archival storage, which for us, now that we've used the Snowmobile, is Glacier. >> Jay, I want to ask to give your thoughts. I want you to talk about DigitalGlobe, before that, some context. This weekend, I was hanging out with my friends in Santa Cruz and kids were surfing. He's a big drone guy, he used to work for GoPro, and she used to buy the drones and, hey, how's it going with the drones. It got kind of boring, here's a great photo I created, but after a while, it just became like Google Earth, and it got boring. Kind of pointed out that he wanted more, and we got virtual reality, augmented reality, experience is coming to users. That puts imagery, place imagery, the globe, pictures, places and things is what you guys do. So, that's not going away any time soon. So, talk about your business, what you guys do, some of the things that you do, your business model, how that's changing, and how Amazon, here in the public sector, is changing that. >> Well, that's a fantastic questions, and our business is changing pretty rapidly. We have all that imagery, and it's beautiful imagery, but increasingly, there's so much of it, and so many of the use cases aren't about human eyeballs staring at pixels. They're about algorithms extracting information from the pixels. And, increasingly, from either the breadth of pixels, instead of just looking at a small area, you can look around it and see what's happening around it and use that as signal information, or you can go deep into an archive and see the same location on the planet over and over over years and see the changes that had happened in terms of time frame. So, increasingly, our market is about extracting information and extracting insights from the imagery more so than it is the imagery itself. And so that's driving an analytics business for us, and it's also driving a services business for us, which is particularly important in the public sector to actually use that for different purposes. >> You can imagine the creativity involved and developers out there watching or even thinking about using satellite imagery in conflux with other data. Remember, they're in the Web 2.0 craze earlier in the last decade. You saw mashups of API with Google Max. Oh yeah, pull a little pin, and then the mobile came. But now, you're seeing mashups go on with other data. And I've heard stats at Uber, for instance, remaps New York City every five days with all that GPS data of the cars, which are basically sensors. So, you can almost imagine the alchemy, the convergence of data. This is exciting for you, I can imagine. Won't you share with us, anecdotally or statistically what you're seeing, how this is playing out? >> Well, yes, some of our biggest commercial customers of our products now are location-based services. So, Uber's using our imagery because the size of the aperture of our lens means we have great resolution. And so, they've been consuming that and consuming our machine learning algorithms to basically understand where traffic is and where people are so that they can refine, on an ongoing basis, where the best pick-up and drop-off locations are. That really drives their business. Facebook's using the imagery to basically help build out the Internet. You know, they want to move into places on the planet where Internet doesn't exist. Well, in order to really understand that, they need to understand where to build, how to build, how many people are there, and you can actually extract all that from imagery by going in in detail and mapping roofs' shapes and roofs' sizes, and, from there, extracting pretty accurate estimates of how many people live in a particular area, and that's driving their project, which is ultimately going to drive access for... >> Intelligence in software, we look at imagery. I mean, we here at Amazon, recognition's their big product for facial recognition, among other pictures. But this is what's getting at, this notion of actually extracting that data. >> Well, you think about it. You know, once the data is available, once our imagery is available, then the sky's the limit. You know, we have a certain set of algorithms that we apply to help different industries, you know, to look at rooftops, to look at water extractions. After a hurricane, we can actually see how the coverage has changed. But, you look at a Facebook, and they're applying their own algorithms. We don't force our algorithms to be used. We provide the information, we try to provide the data. Companies can bring their own algorithms, and then, it's all about what can you learn, and then, what can you do about it, and it's amazing. >> So, here's the question. With the whole polyglot conversation, multiple languages that people speak that's translated into the tech industry, and interdisciplinary forces are in play: Data science, coding, cognitive, machine learning. So, the question is, for you, is that, okay, as this stuff comes together, do you speak DevOps? It's kind of a word, and we hear people say, is that in Russian or is that like English? DevOps is a cloud language mindset. And so, that brings up the question of, are you guys friendly to developers, and because people want to have microservices, I'm from a developer, I'm like, hey, I want those maps. How do I get them, can I buy it as a service, are they loaded on Amazon, how to I gauge with DataGlobe, as a developer or a company? >> Well, you think about what you just said and the customers I just talked about. They're not geospatial customers. You know, they're not staffed with people that are PhDs in extracting information. They're developers that are working for high-tech companies that have a problem that want to solve. >> There are already mobile apps or doing some cool database working in here. >> So, we're providing the raw imagery and the algorithms to very tried and true systems where people can plug into work benches and build artificial intelligence without necessarily being experts in that. And, as a case in point, my team is an IT team. You know, we've got a part of the organization that is all staffed with PhDs. They're the ones that are driving our global... >> John W: PhD is a service. (laughter) >> Well, kind of. I mean, if you think about it, they're driving the leading edge, for these solutions to our customers. But, I've got an IT team, and I've got this problem with all this data that we talked about earlier. Well, how am I actually going to manage that? I'm going to be pulling in all sorts of different sources of data, and I'm going to be applying machine learning using IT guys that aren't PhDs to actually do that, and I'm not going to send them to graduate school. They're going to be using standard APIs, and they're going to be applying fairly generic algorithms, and... >> So, is that your model, is it just API, is there other... >> I think the real key is the API makes it accessible, but a machine learning algorithm is only as good as its training. So, the more it's used, the more it refines itself, the better our algorithm gets. And so, that is going to be the type of thing that the IT developer, the infrastructure engineer of the future becomes, and I've already, basically, in the last couple of years, as we started this journey at AWS, 20% of my staff now, same size staff, but they're software developers now. >> So, I'll take this to the government side. We talked a lot about commercial use. But at the government side, I'm thinking about FEMA, disaster response, maybe a core of engineers, you know, bridge construction, road construction, coastline management. Are all those kind of applications that we see on the dot gov side? >> There are all things that you see that can be done on the dot gov side, but we're doing them all in the commercial environment. The USC's region for AWS, and I think that's actually a really important distinction, and it's something that I think more and more of the government agencies are starting to see. We do a lot of work for one particular government agency and have for years. But 99 point something percent of our imagery is commercial unclassified, and it's available for the purposes that our customers use it for, but they're also available for all those other customers I've talked about. And more and more of what we do, we are doing on the completely open but secure commercial environment because it's ubiquitous for our customers. Not all of our customers do that type of work. They don't need to comply with those rigid standards. It's generally where all AWS products that are released are released to, with the other environments lagging, and they probably don't want me saying that on TV, but I just did. And it's cheaper, you know, we're a commercial company that does public sector work. We have to make a profit, and the best way to do that is to put your environment in a place where if you're going to repeat an operation, like pulling an image of Glacier and build it into something that is consumable by either a human or an algorithm and put it back. If you're going to do something like that a million times, you want to do it really inexpensively. And so, that's where... (crosstalking) >> Lower prices, make things fast, that's Jeff Hayes' ethos, shipping products, that these books in the old days. Now, they're shipping code and making lower-latency systems. So, you guys are a big customer. What are the big implementation features that you have with AWS, and then, the second part of the question is, are you worried about locking. At some point, you're so big, the hours are going to be so massive, you're going to be paying so much cash, should you build your own, that's the big debate. Do you go private cloud, do you stay in the public? Thoughts on those two options? >> Well, we have both. Right now, we're running a 15-year-old system, which is where we create the imagery that comes off the satellites, and it goes into a tape archive. Last year, Reinvent... >> John F: Tape's supposed to be dead! >> Tape will die someday! It's going to die really soon, but, at the Reinvent Conference last year, AWS rolled out a semi truck. Well, the real semi truck was in our parking lot getting loaded with all those tapes, and it's sad... >> John F: Did you actually use the semi? >> We were the first customer ever, I believe, of the Snowmobile. And so, it takes a lot of time and effort to move 12,000 LTO 5 tapes loaded onto a semi and send it off. You know, that represents every image ever taken by DG in the history of our company, and it's now in AWS. So, to your second part of your question, we're pretty committed now. >> John F: Are you okay with that? >> Well, we're okay with that for a couple of reasons. One is, I'm not constraining the business. AWS is cheaper. It will be even cheaper for us as we learn how to pull all the levers and turn all the dials in this environment. But, you know, you think about that, we ran a particular job last year for a customer that consumes 750,000 compute hours in 22 days. We couldn't have done that in our data center. We would have said no. And so, I would... >> I know, I can't do, you can't do it. >> We can't do it! Or, we can do it, come back, the answer will be here in six months. So, time is of the essence in situations like that, so we're comfortable with it for our business. We're also comfortable with it because, increasingly, that's where our customers already are. We are creating something in our current environment and shipping it to Amazon anyway. >> We're going to start a movie about you, with Jim Carrey, Yes Man. (laughter) You're going to say yes to everything now with Amazon. Okay, but this is a good point. Joking aside, this is interesting because we have this debate all the time, when is the cloud prohibitive. In this case, your business model, based on that fact that variables spend that you turn up your Compute is based upon cadence of the business. >> That's exactly right. You know, the thing that's really changed for the business with this model is historically, IT has been a call center, and moving into Amazon, I manage our storage, and I pay for our storage because it's a shared asset. It's something that is for the common good. The business units and different product managers in our business now have the dial for what they spend on the Compute and everything else. So, if they want to go to market really rapidly, they can. If they want to spin it up rapidly, they can. If they want to turn it down, they can. And it's not a fixed investment. So, it allows the business philosophy that we've never had before. >> Jay, I know we're getting tight on time, but I do want to ask you one question, and I did not know that you were the first Snowmobile customers, so, that's good trivia to have on theCube and great to have you. So, while we got you here, being the first customer of AWS Snowmobile when they rolled out at Amazon Reinvent, we covered on SiliconAngle. Why did you jump on that and how was your experience been, share some color onto that whole process. >> Okay, it's been an iterative learning process for both us and for Amazon. We were sitting on all this imagery. We knew we wanted to get in AWS. We started using the Snowballs almost a year and a half ago. But moving 100 petabytes, 80 terabytes at a time, it's like using a spoon to move a haystack. So, when Amazon approached us, knowing the challenge we had about moving it all at once, I initially thought they were kidding, and I realized it was Amazon, they don't kid about things like this, and so we jumped on pretty early and worked with them on this. >> John F: So, you've got blown away like, what? >> Just like. >> What's the catch? >> Really, a truck, really? Yeah, but really. So, it's as secure as it could possibly be. We're taking out the Internet and all the different variables in that, including a lot of cost in bandwidth and strengths, and basically parking and next to our data, and, you know, it's basically a big NFS file system, and we loaded data onto it, the constraint for us being, basically that tape library with 10,000 miles of movement on the tape pads. We had to balance between loading the Snowmobile and basically responding to our regular customers. You know, we pulled 4 million images a year off that tape library. And so, loading every single image we've ever created onto the Snowmobile at the same time was a technical challenge on our side more so than Amazon's side. So, we had to find that sweet spot and then just let it run. >> John F: Now, it's operational. >> So, the Snowmobile is gone. AWS has got it. They're adjusting it right now into the West Region, and we're looking forward to being able to just go wild with that data. >> We got Snowmobiles, we got semis, we have satellites, we have it all, right? >> We have it all, yeah. >> It's massive, obviously, but impressed with what you're doing with this. So, congratulations on that front, and thank you again for being with us. >> My pleasure, thanks for having me. >> You bet, we continue our coverage here from Washington, DC, live on theCube. SiliconAngle TV continues right after this. (theCube jingle)

>> Announcer: Please welcome Vice President of Worldwide Marketing, Amazon Web Services, Ariel Kelman. (applause) (techno music) >> Good afternoon, everyone. Thank you for coming. I hope you guys are having a great day here. It is my pleasure to introduce to come up on stage here, the CEO of Amazon Web Services, Andy Jassy. (applause) (techno music) >> Okay. Let's get started. I have a bunch of questions here for you, Andy. >> Just like one of our meetings, Ariel. >> Just like one of our meetings. So, I thought I'd start with a little bit of a state of the state on AWS. Can you give us your quick take? >> Yeah, well, first of all, thank you, everyone, for being here. We really appreciate it. We know how busy you guys are. So, hope you're having a good day. You know, the business is growing really quickly. In the last financials, we released, in Q four of '16, AWS is a 14 billion dollar revenue run rate business, growing 47% year over year. We have millions of active customers, and we consider an active customer as a non-Amazon entity that's used the platform in the last 30 days. And it's really a very broad, diverse customer set, in every imaginable size of customer and every imaginable vertical business segment. And I won't repeat all the customers that I know Werner went through earlier in the keynote, but here are just some of the more recent ones that you've seen, you know NELL is moving their their digital and their connected devices, meters, real estate to AWS. McDonalds is re-inventing their digital platform on top of AWS. FINRA is moving all in to AWS, yeah. You see at Reinvent, Workday announced AWS was its preferred cloud provider, and to start building on top of AWS further. Today, in press releases, you saw both Dunkin Donuts and Here, the geo-spatial map company announced they'd chosen AWS as their provider. You know and then I think if you look at our business, we have a really large non-US or global customer base and business that continues to expand very dramatically. And we're also aggressively increasing the number of geographic regions in which we have infrastructure. So last year in 2016, on top of the broad footprint we had, we added Korea, India, and Canada, and the UK. We've announced that we have regions coming, another one in China, in Ningxia, as well as in France, as well as in Sweden. So we're not close to being done expanding geographically. And then of course, we continue to iterate and innovate really quickly on behalf of all of you, of our customers. I mean, just last year alone, we launched what we considered over 1,000 significant services and features. So on average, our customers wake up every day and have three new capabilities they can choose to use or not use, but at their disposal. You've seen it already this year, if you look at Chime, which is our new unified communication service. It makes meetings much easier to conduct, be productive with. You saw Connect, which is our new global call center routing service. If you look even today, you look at Redshift Spectrum, which makes it easy to query all your data, not just locally on disk in your data warehouse but across all of S3, or DAX, which puts a cash in front of DynamoDB, we use the same interface, or all the new features in our machine learning services. We're not close to being done delivering and iterating on your behalf. And I think if you look at that collection of things, it's part of why, as Gartner looks out at the infrastructure space, they estimate the AWS is several times the size business of the next 14 providers combined. It's a pretty significant market segment leadership position. >> You talked a lot about adopts in there, a lot of customers moving to AWS, migrating large numbers of workloads, some going all in on AWS. And with that as kind of backdrop, do you still see a role for hybrid as being something that's important for customers? >> Yeah, it's funny. The quick answer is yes. I think the, you know, if you think about a few years ago, a lot of the rage was this debate about private cloud versus what people call public cloud. And we don't really see that debate very often anymore. I think relatively few companies have had success with private clouds, and most are pretty substantially moving in the direction of building on top of clouds like AWS. But, while you increasingly see more and more companies every month announcing that they're going all in to the cloud, we will see most enterprises operate in some form of hybrid mode for the next number of years. And I think in the early days of AWS and the cloud, I think people got confused about this, where they thought that they had to make this binary decision to either be all in on the public cloud and AWS or not at all. And of course that's not the case. It's not a binary decision. And what we know many of our enterprise customers want is they want to be able to run the data centers that they're not ready to retire yet as seamlessly as they can alongside of AWS. And it's why we've built a lot of the capabilities we've built the last several years. These are things like PPC, which is our virtual private cloud, which allows you to cordon off a portion of our network, deploy resources into it and connect to it through VPN or Direct Connect, which is a private connection between your data centers and our regions or our storage gateway, which is a virtual storage appliance, or Identity Federation, or a whole bunch of capabilities like that. But what we've seen, even though the vast majority of the big hybrid implementations today are built on top of AWS, as more and more of the mainstream enterprises are now at the point where they're really building substantial cloud adoption plans, they've come back to us and they've said, well, you know, actually you guys have made us make kind of a binary decision. And that's because the vast majority of the world is virtualized on top of VMWare. And because VMWare and AWS, prior to a few months ago, had really done nothing to try and make it easy to use the VMWare tools that people have been using for many years seamlessly with AWS, customers were having to make a binary choice. Either they stick with the VMWare tools they've used for a while but have a really tough time integrating with AWS, or they move to AWS and they have to leave behind the VMWare tools they've been using. And it really was the impetus for VMWare and AWS to have a number of deep conversations about it, which led to the announcement we made late last fall of VMWare and AWS, which is going to allow customers who have been using the VMWare tools to manage their infrastructure for a long time to seamlessly be able to run those on top of AWS. And they get to do so as they move workloads back and forth and they evolve their hybrid implementation without having to buy any new hardware, which is a big deal for companies. Very few companies are looking to find ways to buy more hardware these days. And customers have been very excited about this prospect. We've announced that it's going to be ready in the middle of this year. You see companies like Amadeus and Merck and Western Digital and the state of Louisiana, a number of others, we've a very large, private beta and preview happening right now. And people are pretty excited about that prospect. So we will allow customers to run in the mode that they want to run, and I think you'll see a huge transition over the next five to 10 years. >> So in addition to hybrid, another question we get a lot from enterprises around the concept of lock-in and how they should think about their relationship with the vendor and how they should think about whether to spread the workloads across multiple infrastructure providers. How do you think about that? >> Well, it's a question we get a lot. And Oracle has sure made people care about that issue. You know, I think people are very sensitive about being locked in, given the experience that they've had over the last 10 to 15 years. And I think the reality is when you look at the cloud, it really is nothing like being locked into something like Oracle. The APIs look pretty similar between the various providers. We build an open standard, it's like Linux and MySQL and Postgres. All the migration tools that we build allow you to migrate in or out of AWS. It's up to customers based on how they want to run their workload. So it is much easier to move away from something like the cloud than it is from some of the old software services that has created some of this phobia. But I think when you look at most CIOs, enterprise CIOs particularly, as they think about moving to the cloud, many of them started off thinking that they, you know, very well might split their workloads across multiple cloud providers. And I think when push comes to shove, very few decide to do so. Most predominately pick an infrastructure provider to run their workloads. And the reason that they don't split it across, you know, pretty evenly across clouds is a few reasons. Number one, if you do so, you have to standardize in the lowest common denominator. And these platforms are in radically different stages at this point. And if you look at something like AWS, it has a lot more functionality than anybody else by a large margin. And we're also iterating more quickly than you'll find from the other providers. And most folks don't want to tie the hands of their developers behind their backs in the name of having the ability of splitting it across multiple clouds, cause they actually are, in most of their spaces, competitive, and they have a lot of ideas that they want to actually build and invent on behalf of their customers. So, you know, they don't want to actually limit their functionality. It turns out the second reason is that they don't want to force their development teams to have to learn multiple platforms. And most development teams, if any of you have managed multiple stacks across different technologies, and many of us have had that experience, it's a pain in the butt. And trying to make a shift from what you've been doing for the last 30 years on premises to the cloud is hard enough. But then forcing teams to have to get good at running across two or three platforms is something most teams don't relish, and it's wasteful of people's time, it's wasteful of natural resources. That's the second thing. And then the third reason is that you effectively diminish your buying power because all of these cloud providers have volume discounts, and then you're splitting what you buy across multiple providers, which gives you a lower amount you buy from everybody at a worse price. So when most CIOs and enterprises look at this carefully, they don't actually end up splitting it relatively evenly. They predominately pick a cloud provider. Some will just pick one. Others will pick one and then do a little bit with a second, just so they know they can run with a second provider, in case that relationship with the one they choose to predominately run with goes sideways in some fashion. But when you really look at it, CIOs are not making that decision to split it up relatively evenly because it makes their development teams much less capable and much less agile. >> Okay, let's shift gears a little bit, talk about a subject that's on the minds of not just enterprises but startups and government organizations and pretty much every organization we talk to. And that's AI and machine learning. Reinvent, we introduced our Amazon AI services and just this morning Werner announced the general availability of Amazon Lex. So where are we overall on machine learning? >> Well it's a hugely exciting opportunity for customers, and I think, we believe it's exciting for us as well. And it's still in the relatively early stages, if you look at how people are using it, but it's something that we passionately believe is going to make a huge difference in the world and a huge difference with customers, and that we're investing a pretty gigantic amount of resource and capability for our customers. And I think the way that we think about, at a high level, the machine learning and deep learning spaces are, you know, there's kind of three macro layers of the stack. I think at that bottom layer, it's generally for the expert machine learning practitioners, of which there are relatively few in the world. It's a scarce resource relative to what I think will be the case in five, 10 years from now. And these are folks who are comfortable working with deep learning engines, know how to build models, know how to tune those models, know how to do inference, know how to get that data from the models into production apps. And for that group of people, if you look at the vast majority of machine learning and deep learning that's being done in the cloud today, it's being done on top of AWS, are P2 instances, which are optimized for deep learning and our deep learning AMIs, that package, effectively the deep learning engines and libraries inside those AMIs. And you see companies like Netflix, Nvidia, and Pinterest and Stanford and a whole bunch of others that are doing significant amounts of machine learning on top of those optimized instances for machine learning and the deep learning AMIs. And I think that you can expect, over time, that we'll continue to build additional capabilities and tools for those expert practitioners. I think we will support and do support every single one of the deep learning engines on top of AWS, and we have a significant amount of those workloads with all those engines running on top of AWS today. We also are making, I would say, a disproportionate investment of our own resources and the MXNet community just because if you look at running deep learning models once you get beyond a few GPUs, it's pretty difficult to have those scale as you get into the hundreds of GPUs. And most of the deep learning engines don't scale very well horizontally. And so what we've found through a lot of extensive testing, cause remember, Amazon has thousands of deep learning experts inside the company that have built very sophisticated deep learning capabilities, like the ones you see in Alexa, we have found that MXNet scales the best and almost linearly, as we continue to add nodes, as we continue to horizontally scale. So we have a lot of investment at that bottom layer of the stack. Now, if you think about most companies with developers, it's still largely inaccessible to them to do the type of machine learning and deep learning that they'd really like to do. And that's because the tools, I think, are still too primitive. And there's a number of services out there, we built one ourselves in Amazon Machine Learning that we have a lot of customers use, and yet I would argue that all of those services, including our own, are still more difficult than they should be for everyday developers to be able to build machine learning and access machine learning and deep learning. And if you look at the history of what AWS has done, in every part of our business, and a lot of what's driven us, is trying to democratize technologies that were really only available and accessible before to a select, small number of companies. And so we're doing a lot of work at what I would call that middle layer of the stack to get rid of a lot of the muck associated with having to do, you know, building the models, tuning the models, doing the inference, figuring how to get the data into production apps, a lot of those capabilities at that middle layer that we think are really essential to allow deep learning and machine learning to reach its full potential. And then at the top layer of the stack, we think of those as solutions. And those are things like, pass me an image and I'll tell you what that image is, or show me this face, does it match faces in this group of faces, or pass me a string of text and I'll give you an mpg file, or give me some words and what your intent is and then I'll be able to return answers that allow people to build conversational apps like the Lex technology. And we have a whole bunch of other services coming in that area, atop of Lex and Polly and Recognition, and you can imagine some of those that we've had to use in Amazon over the years that we'll continue to make available for you, our customers. So very significant level of investment at all three layers of that stack. We think it's relatively early days in the space but have a lot of passion and excitement for that. >> Okay, now for ML and AI, we're seeing customers wanting to load in tons of data, both to train the models and to actually process data once they've built their models. And then outside of ML and AI, we're seeing just as much demand to move in data for analytics and traditional workloads. So as people are looking to move more and more data to the cloud, how are we thinking about making it easier to get data in? >> It's a great question. And I think it's actually an often overlooked question because a lot of what gets attention with customers is all the really interesting services that allow you to do everything from compute and storage and database and messaging and analytics and machine learning and AI. But at the end of the day, if you have a significant amount of data already somewhere else, you have to get it into the cloud to be able to take advantage of all these capabilities that you don't have on premises. And so we have spent a disproportionate amount of focus over the last few years trying to build capabilities for our customers to make this easier. And we have a set of capabilities that really is not close to matched anywhere else, in part because we have so many customers who are asking for help in this area that it's, you know, that's really what drives what we build. So of course, you could use the good old-fashioned wire to send data over the internet. Increasingly, we find customers that are trying to move large amounts of data into S3, is using our S3 transfer acceleration service, which basically uses our points of presence, or POPs, all over the world to expedite delivery into S3. You know, a few years ago, we were talking to a number of companies that were looking to make big shifts to the cloud, and they said, well, I need to move lots of data that just isn't viable for me to move it over the wire, given the connection we can assign to it. It's why we built Snowball. And so we launched Snowball a couple years ago, which is really, it's a 50 terabyte appliance that is encrypted, the data's encrypted three different ways, and you ingest the data from your data center into Snowball, it has a Kindle connected to it, it allows you to, you know, that makes sure that you send it to the right place, and you can also track the progress of your high-speed ingestion into our data centers. And when we first launched Snowball, we launched it at Reinvent a couple years ago, I could not believe that we were going to order as many Snowballs to start with as the team wanted to order. And in fact, I reproached the team and I said, this is way too much, why don't we first see if people actually use any of these Snowballs. And so the team thankfully didn't listen very carefully to that, and they really only pared back a little bit. And then it turned out that we, almost from the get-go, had ordered 10X too few. And so this has been something that people have used in a very broad, pervasive way all over the world. And last year, at the beginning of the year, as we were asking people what else they would like us to build in Snowball, customers told us a few things that were pretty interesting to us. First, one that wasn't that surprising was they said, well, it would be great if they were bigger, you know, if instead of 50 terabytes it was more data I could store on each device. Then they said, you know, one of the problems is when I load the data onto a Snowball and send it to you, I have to still keep my local copy on premises until it's ingested, cause I can't risk losing that data. So they said it would be great if you could find a way to provide clustering, so that I don't have to keep that copy on premises. That was pretty interesting. And then they said, you know, there's some of that data that I'd actually like to be loading synchronously to S3, and then, or some things back from S3 to that data that I may want to compare against. That was interesting, having that endpoint. And then they said, well, we'd really love it if there was some compute on those Snowballs so I can do analytics on some relatively short-term signals that I want to take action on right away. Those were really the pieces of feedback that informed Snowball Edge, which is the next version of Snowball that we launched, announced at Reinvent this past November. So it has, it's a hundred-terabyte appliance, still the same level of encryption, and it has clustering so that you don't have to keep that copy of the data local. It allows you to have an endpoint to S3 to synchronously load data back and forth, and then it has a compute inside of it. And so it allows customers to use these on premises. I'll give you a good example. GE is using these for their wind turbines. And they collect all kinds of data from those turbines, but there's certain short-term signals they want to do analytics on in as close to real time as they can, and take action on those. And so they use that compute to do the analytics and then when they fill up that Snowball Edge, they detach it and send it back to AWS to do broad-scale analytics in the cloud and then just start using an additional Snowball Edge to capture that short-term data and be able to do those analytics. So Snowball Edge is, you know, we just launched it a couple months ago, again, amazed at the type of response, how many customers are starting to deploy those all over the place. I think if you have exabytes of data that you need to move, it's not so easy. An exabyte of data, if you wanted to move from on premises to AWS, would require 10,000 Snowball Edges. Those customers don't want to really manage a fleet of 10,000 Snowball Edges if they don't have to. And so, we tried to figure out how to solve that problem, and it's why we launched Snowmobile back at Reinvent in November, which effectively, it's a hundred-petabyte container on a 45-foot trailer that we will take a truck and bring out to your facility. It comes with its own power and its own network fiber that we plug in to your data center. And if you want to move an exabyte of data over a 10 gigabit per second connection, it would take you 26 years. But using 10 Snowmobiles, it would take you six months. So really different level of scale. And you'd be surprised how many companies have exabytes of data at this point that they want to move to the cloud to get all those analytics and machine learning capabilities running on top of them. Then for streaming data, as we have more and more companies that are doing real-time analytics of streaming data, we have Kinesis, where we built something called the Kinesis Firehose that makes it really simple to stream all your real-time data. We have a storage gateway for companies that want to keep certain data hot, locally, and then asynchronously be loading the rest of their data to AWS to be able to use in different formats, should they need it as backup or should they choose to make a transition. So it's a very broad set of storage capabilities. And then of course, if you've moved a lot of data into the cloud or into anything, you realize that one of the hardest parts that people often leave to the end is ETL. And so we have announced an ETL service called Glue, which we announced at Reinvent, which is going to make it much easier to move your data, be able to find your data and map your data to different locations and do ETL, which of course is hugely important as you're moving large amounts. >> So we've talked a lot about moving things to the cloud, moving applications, moving data. But let's shift gears a little bit and talk about something not on the cloud, connected devices. >> Yeah. >> Where do they fit in and how do you think about edge? >> Well, you know, I've been working on AWS since the start of AWS, and we've been in the market for a little over 11 years at this point. And we have encountered, as I'm sure all of you have, many buzzwords. And of all the buzzwords that everybody has talked about, I think I can make a pretty strong argument that the one that has delivered fastest on its promise has been IOT and connected devices. Just amazing to me how much is happening at the edge today and how fast that's changing with device manufacturers. And I think that if you look out 10 years from now, when you talk about hybrid, I think most companies, majority on premise piece of hybrid will not be servers, it will be connected devices. There are going to be billions of devices all over the place, in your home, in your office, in factories, in oil fields, in agricultural fields, on ships, in cars, in planes, everywhere. You're going to have these assets that sit at the edge that companies are going to want to be able to collect data on, do analytics on, and then take action. And if you think about it, most of these devices, by their very nature, have relatively little CPU and have relatively little disk, which makes the cloud disproportionately important for them to supplement them. It's why you see most of the big, successful IOT applications today are using AWS to supplement them. Illumina has hooked up their genome sequencing to AWS to do analytics, or you can look at Major League Baseball Statcast is an IOT application built on top of AWS, or John Deer has over 200,000 telematically enabled tractors that are collecting real-time planting conditions and information that they're doing analytics on and sending it back to farmers so they can figure out where and how to optimally plant. Tata Motors manages their truck fleet this way. Phillips has their smart lighting project. I mean, there're innumerable amounts of these IOT applications built on top of AWS where the cloud is supplementing the device's capability. But when you think about these becoming more mission-critical applications for companies, there are going to be certain functions and certain conditions by which they're not going to want to connect back to the cloud. They're not going to want to take the time for that round trip. They're not going to have connectivity in some cases to be able to make a round trip to the cloud. And what they really want is customers really want the same capabilities they have on AWS, with AWS IOT, but on the devices themselves. And if you've ever tried to develop on these embedded devices, it's not for mere mortals. It's pretty delicate and it's pretty scary and there's a lot of archaic protocols associated with it, pretty tough to do it all and to do it without taking down your application. And so what we did was we built something called Greengrass, and we announced it at Reinvent. And Greengrass is really like a software module that you can effectively have inside your device. And it allows developers to write lambda functions, it's got lambda inside of it, and it allows customers to write lambda functions, some of which they want to run in the cloud, some of which they want to run on the device itself through Greengrass. So they have a common programming model to build those functions, to take the signals they see and take the actions they want to take against that, which is really going to help, I think, across all these IOT devices to be able to be much more flexible and allow the devices and the analytics and the actions you take to be much smarter, more intelligent. It's also why we built Snowball Edge. Snowball Edge, if you think about it, is really a purpose-built Greengrass device. We have Greengrass, it's inside of the Snowball Edge, and you know, the GE wind turbine example is a good example of that. And so it's to us, I think it's the future of what the on-premises piece of hybrid's going to be. I think there're going to be billions of devices all over the place and people are going to want to interact with them with a common programming model like they use in AWS and the cloud, and we're continuing to invest very significantly to make that easier and easier for companies. >> We've talked about several feature directions. We talked about AI, machine learning, the edge. What are some of the other areas of investment that this group should care about? >> Well there's a lot. (laughs) That's not a suit question, Ariel. But there's a lot. I think, I'll name a few. I think first of all, as I alluded to earlier, we are not close to being done expanding geographically. I think virtually every tier-one country will have an AWS region over time. I think many of the emerging countries will as well. I think the database space is an area that is radically changing. It's happening at a faster pace than I think people sometimes realize. And I think it's good news for all of you. I think the database space over the last few decades has been a lonely place for customers. I think that they have felt particularly locked into companies that are expensive and proprietary and have high degrees of lock-in and aren't so customer-friendly. And I think customers are sick of it. And we have a relational database service that we launched many years ago and has many flavors that you can run. You can run MySQL, you can run Postgres, you can run MariaDB, you can run SQLServer, you can run Oracle. And what a lot of our customers kept saying to us was, could you please figure out a way to have a database capability that has the performance characteristics of the commercial-grade databases but the customer-friendly and pricing model of the more open engines like the MySQL and Postgres and MariaDB. What you do on your own, we do a lot of it at Amazon, but it's hard, I mean, it takes a lot of work and a lot of tuning. And our customers really wanted us to solve that problem for them. And it's why we spent several years building Aurora, which is our own database engine that we built, but that's fully compatible with MySQL and with Postgres. It's at least as fault tolerant and durable and performant as the commercial-grade databases, but it's a tenth of the cost of those. And it's also nice because if it turns out that you use Aurora and you decide for whatever reason you don't want to use Aurora anymore, because it's fully compatible with MySQL and Postgres, you just dump it to the community versions of those, and off you are. So there's really hardly any transition there. So that is the fastest-growing service in the history of AWS. I'm amazed at how quickly it's grown. I think you may have heard earlier, we've had 23,000 database migrations just in the last year or so. There's a lot of pent-up demand to have database freedom. And we're here to help you have it. You know, I think on the analytic side, it's just never been easier and less expensive to collect, store, analyze, and share data than it is today. Part of that has to do with the economics of the cloud. But a lot of it has to do with the really broad analytics capability that we provide you. And it's a much broader capability than you'll find elsewhere. And you know, you can manage Hadoop and Spark and Presto and Hive and Pig and Yarn on top of AWS, or we have a managed elastic search service, and you know, of course we have a very high scale, very high performing data warehouse in Redshift, that just got even more performant with Spectrum, which now can query across all of your S3 data, and of course you have Athena, where you can query S3 directly. We have a service that allows you to do real-time analytics of streaming data in Kinesis. We have a business intelligence service in QuickSight. We have a number of machine learning capabilities I talked about earlier. It's a very broad array. And what we find is that it's a new day in analytics for companies. A lot of the data that companies felt like they had to throw away before, either because it was too expensive to hold or they didn't really have the tools accessible to them to get the learning from that data, it's a totally different day today. And so we have a pretty big investment in that space, I mentioned Glue earlier to do ETL on all that data. We have a lot more coming in that space. I think compute, super interesting, you know, I think you will find, I think we will find that companies will use full instances for many, many years and we have, you know, more than double the number of instances than you'll find elsewhere in every imaginable shape and size. But I would also say that the trend we see is that more and more companies are using smaller units of compute, and it's why you see containers becoming so popular. We have a really big business in ECS. And we will continue to build out the capability there. We have companies really running virtually every type of container and orchestration and management service on top of AWS at this point. And then of course, a couple years ago, we pioneered the event-driven serverless capability in compute that we call Lambda, which I'm just again, blown away by how many customers are using that for everything, in every way. So I think the basic unit of compute is continuing to get smaller. I think that's really good for customers. I think the ability to be serverless is a very exciting proposition that we're continuing to to fulfill that vision that we laid out a couple years ago. And then, probably, the last thing I'd point out right now is, I think it's really interesting to see how the basic procurement of software is changing. In significant part driven by what we've been doing with our Marketplace. If you think about it, in the old world, if you were a company that was buying software, you'd have to go find bunch of the companies that you should consider, you'd have to have a lot of conversations, you'd have to talk to a lot of salespeople. Those companies, by the way, have to have a big sales team, an expensive marketing budget to go find those companies and then go sell those companies and then both companies engage in this long tap-dance around doing an agreement and the legal terms and the legal teams and it's just, the process is very arduous. Then after you buy it, you have to figure out how you're going to actually package it, how you're deploy to infrastructure and get it done, and it's just, I think in general, both consumers of software and sellers of software really don't like the process that's existed over the last few decades. And then you look at AWS Marketplace, and we have 35 hundred product listings in there from 12 hundred technology providers. If you look at the number of hours, that software that's been running EC2 just in the last month alone, it's several hundred million hours, EC2 hours, of that software being run on top of our Marketplace. And it's just completely changing how software is bought and procured. I think that if you talk to a lot of the big sellers of software, like Splunk or Trend Micro, there's a whole number of them, they'll tell you it totally changes their ability to be able to sell. You know, one of the things that really helped AWS in the early days and still continues to help us, is that we have a self-service model where we don't actually have to have a lot of people talk to every customer to get started. I think if you're a seller of software, that's very appealing, to allow people to find your software and be able to buy it. And if you're a consumer, to be able to buy it quickly, again, without the hassle of all those conversations and the overhead associated with that, very appealing. And I think it's why the marketplace has just exploded and taken off like it has. It's also really good, by the way, for systems integrators, who are often packaging things on top of that software to their clients. This makes it much easier to build kind of smaller catalogs of software products for their customers. I think when you layer on top of that the capabilities that we've announced to make it easier for SASS providers to meter and to do billing and to do identity is just, it's a very different world. And so I think that also is very exciting, both for companies and customers as well as software providers. >> We certainly touched on a lot here. And we have a lot going on, and you know, while we have customers asking us a lot about how they can use all these new services and new features, we also tend to get a lot of questions from customers on how we innovate so quickly, and they can think about applying some of those lessons learned to their own businesses. >> So you're asking how we're able to innovate quickly? >> Mmm hmm. >> I think there's a few things that have helped us, and it's different for every company. But some of these might be helpful. I'll point to a few. I think the first thing is, I think we disproportionately index on hiring builders. And we think of builders as people who are inventors, people who look at different customer experiences really critically, are honest about what's flawed about them, and then seek to reinvent them. And then people who understand that launch is the starting line and not the finish line. There's very little that any of us ever built that's a home run right out of the gate. And so most things that succeed take a lot of listening to customers and a lot of experimentation and a lot of iterating before you get to an equation that really works. So the first thing is who we hire. I think the second thing is how we organize. And we have, at Amazon, long tried to organize into as small and separable and autonomous teams as we can, that have all the resources in those teams to own their own destiny. And so for instance, the technologists and the product managers are part of the same team. And a lot of that is because we don't want the finger pointing that goes back and forth between the teams, and if they're on the same team, they focus all their energy on owning it together and understanding what customers need from them, spending a disproportionate amount of time with customers, and then they get to own their own roadmaps. One of the reasons we don't publish a 12 to 18 month roadmap is we want those teams to have the freedom, in talking to customers and listening to what you tell us matters, to re-prioritize if there are certain things that we assumed mattered more than it turns out it does. So, you know I think that the way that we organize is the second piece. I think a third piece is all of our teams get to use the same AWS building blocks that all of you get to use, which allow you to move much more quickly. And I think one of the least told stories about Amazon over the last five years, in part because people have gotten interested in AWS, is people have missed how fast our consumer business at Amazon has iterated. Look at the amount of invention in Amazon's consumer business. And they'll tell you that a big piece of that is their ability to use the AWS building blocks like they do. I think a fourth thing is many big companies, as they get larger, what starts to happen is what people call the institutional no, which is that leaders walk into meetings on new ideas looking to find ways to say no, and not because they're ill intended but just because they get more conservative or they have a lot on their plate or things are really managed very centrally, so it's hard to imagine adding more to what you're already doing. At Amazon, it's really the opposite, and in part because of the way we're organized in such a decoupled, decentralized fashion, and in part because it's just part of our DNA. When the leaders walk into a meeting, they are looking for ways to say yes. And we don't say yes to everything, we have a lot of proposals. But we say yes to a lot more than I think virtually any other company on the planet. And when we're having conversations with builders who are proposing new ideas, we're in a mode where we're trying to problem-solve with them to get to yes, which I think is really different. And then I think the last thing is that we have mechanisms inside the company that allow us to make fast decisions. And if you want a little bit more detail, you should read our founder and CEO Jeff Bezos's shareholder letter, which just was released. He talks about the fast decision-making that happens inside the company. It's really true. We make fast decisions and we're willing to fail. And you know, we sometimes talk about how we're working on several of our next biggest failures, and we hope that most of the things we're doing aren't going to fail, but we know, if you're going to push the envelope and if you're going to experiment at the rate that we're trying to experiment, to find more pillars that allow us to do more for customers and allow us to be more relevant, you are going to fail sometimes. And you have to accept that, and you have to have a way of evaluating people that recognizes the inputs, meaning the things that they actually delivered as opposed to the outputs, cause on new ventures, you don't know what the outputs are going to be, you don't know consumers or customers are going to respond to the new thing you're trying to build. So you have to be able to reward employees on the inputs, you have to have a way for them to continue to progress and grow in their career even if they work on something didn't work. And you have to have a way of thinking about, when things don't work, how do I take the technology that I built as part of that, that really actually does work, but I didn't get it right in the form factor, and use it for other things. And I think that when you think about a culture like Amazon, that disproportionately hires builders, organizes into these separable, autonomous teams, and allows them to use building blocks to move fast, and has a leadership team that's looking to say yes to ideas and is willing to fail, you end up finding not only do you do more inventing but you get the people at every level of the organization spending their free cycles thinking about new ideas because it actually pays to think of new ideas cause you get a shot to try it. And so that has really helped us and I think most of our customers who have made significant shifts to AWS and the cloud would argue that that's one of the big transformational things they've seen in their companies as well. >> Okay. I want to go a little bit deeper on the subject of culture. What are some of the things that are most unique about the AWS culture that companies should know about when they're looking to partner with us? >> Well, I think if you're making a decision on a predominant infrastructure provider, it's really important that you decide that the culture of the company you're going to partner with is a fit for yours. And you know, it's a super important decision that you don't want to have to redo multiple times cause it's wasted effort. And I think that, look, I've been at Amazon for almost 20 years at this point, so I have obviously drank the Kool Aid. But there are a few things that I think are truly unique about Amazon's culture. I'll talk about three of them. The first is I think that we are unusually customer-oriented. And I think a lot of companies talk about being customer-oriented, but few actually are. I think most of the big technology companies truthfully are competitor-focused. They kind of look at what competitors are doing and then they try to one-up one another. You have one or two of them that I would say are product-focused, where they say, hey, it's great, you Mr. and Mrs. Customer have ideas on a product, but leave that to the experts, and you know, you'll like the products we're going to build. And those strategies can be good ones and successful ones, they're just not ours. We are driven by what customers tell us matters to them. We don't build technology for technology's sake, we don't become, you know, smitten by any one technology. We're trying to solve real problems for our customers. 90% of what we build is driven by what you tell us matters. And the other 10% is listening to you, and even if you can't articulate exactly what you want, trying to read between the lines and invent on your behalf. So that's the first thing. Second thing is that we are pioneers. We really like to invent, as I was talking about earlier. And I think most big technology companies at this point have either lost their will or their DNA to invent. Most of them acquire it or fast follow. And again, that can be a successful strategy. It's just not ours. I think in this day and age, where we're going through as big a shift as we are in the cloud, which is the biggest technology shift in our lifetime, as dynamic as it is, being able to partner with a company that has the most functionality, it's iterating the fastest, has the most customers, has the largest ecosystem of partners, has SIs and ISPs, that has had a vision for how all these pieces fit together from the start, instead of trying to patch them together in a following act, you have a big advantage. I think that the third thing is that we're unusually long-term oriented. And I think that you won't ever see us show up at your door the last day of a quarter, the last day of a year, trying to harass you into doing some kind of deal with us, not to be heard from again for a couple years when we either audit you or try to re-up you for a deal. That's just not the way that we will ever operate. We are trying to build a business, a set of relationships, that will outlast all of us here. And I think something that always ties it together well is this trusted advisor capability that we have inside our support function, which is, you know, we look at dozens of programmatic ways that our customers are using the platform and reach out to you if you're doing something we think's suboptimal. And one of the things we do is if you're not fully utilizing resources, or hardly, or not using them at all, we'll reach out and say, hey, you should stop paying for this. And over the last couple of years, we've sent out a couple million of these notifications that have led to actual annualized savings for customers of 350 million dollars. So I ask you, how many of your technology partners reach out to you and say stop spending money with us? To the tune of 350 million dollars lost revenue per year. Not too many. And I think when we first started doing it, people though it was gimmicky, but if you understand what I just talked about with regard to our culture, it makes perfect sense. We don't want to make money from customers unless you're getting value. We want to reinvent an experience that we think has been broken for the prior few decades. And then we're trying to build a relationship with you that outlasts all of us, and we think the best way to do that is to provide value and do right by customers over a long period of time. >> Okay, keeping going on the culture subject, what about some of the quirky things about Amazon's culture that people might find interesting or useful? >> Well there are a lot of quirky parts to our culture. And I think any, you know lots of companies who have strong culture will argue they have quirky pieces but I think there's a few I might point to. You know, I think the first would be the first several years I was with the company, I guess the first six years or so I was at the company, like most companies, all the information that was presented was via PowerPoint. And we would find that it was a very inefficient way to consume information. You know, you were often shaded by the charisma of the presenter, sometimes you would overweight what the presenters said based on whether they were a good presenter. And vice versa. You would very rarely have a deep conversation, cause you have no room on PowerPoint slides to have any depth. You would interrupt the presenter constantly with questions that they hadn't really thought through cause they didn't think they were going to have to present that level of depth. You constantly have the, you know, you'd ask the question, oh, I'm going to get to that in five slides, you want to do that now or you want to do that in five slides, you know, it was just maddening. And we would often find that most of the meetings required multiple meetings. And so we made a decision as a company to effectively ban PowerPoints as a communication vehicle inside the company. Really the only time I do PowerPoints is at Reinvent. And maybe that shows. And what we found is that it's a much more substantive and effective and time-efficient way to have conversations because there is no way to fake depth in a six-page narrative. So what we went to from PowerPoint was six-page narrative. You can write, have as much as you want in the appendix, but you have to assume nobody will read the appendices. Everything you have to communicate has to be done in six pages. You can't fake depth in a six-page narrative. And so what we do is we all get to the room, we spend 20 minutes or so reading the document so it's fresh in everybody's head. And then where we start the conversation is a radically different spot than when you're hearing a presentation one kind of shallow slide at a time. We all start the conversation with a fair bit of depth on the topic, and we can really hone in on the three or four issues that typically matter in each of these conversations. So we get to the heart of the matter and we can have one meeting on the topic instead of three or four. So that has been really, I mean it's unusual and it takes some time getting used to but it is a much more effective way to pay attention to the detail and have a substantive conversation. You know, I think a second thing, if you look at our working backwards process, we don't write a lot of code for any of our services until we write and refine and decide we have crisp press release and frequently asked question, or FAQ, for that product. And in the press release, what we're trying to do is make sure that we're building a product that has benefits that will really matter. How many times have we all gotten to the end of products and by the time we get there, we kind of think about what we're launching and think, this is not that interesting. Like, people are not going to find this that compelling. And it's because you just haven't thought through and argued and debated and made sure that you drew the line in the right spot on a set of benefits that will really matter to customers. So that's why we use the press release. The FAQ is to really have the arguments up front about how you're building the product. So what technology are you using? What's the architecture? What's the customer experience? What's the UI look like? What's the pricing dimensions? Are you going to charge for it or not? All of those decisions, what are people going to be most excited about, what are people going to be most disappointed by. All those conversations, if you have them up front, even if it takes you a few times to go through it, you can just let the teams build, and you don't have to check in with them except on the dates. And so we find that if we take the time up front we not only get the products right more often but the teams also deliver much more quickly and with much less churn. And then the third thing I'd say that's kind of quirky is it is an unusually truth-seeking culture at Amazon. I think we have a leadership principle that we say have backbone, disagree, and commit. And what it means is that we really expect people to speak up if they believe that we're headed down a path that's wrong for customers, no matter who is advancing it, what level in the company, everybody is empowered and expected to speak up. And then once we have the debate, then we all have to pull the same way, even if it's a different way than you were advocating. And I think, you always hear the old adage of where, two people look at a ceiling and one person says it's 14 feet and the other person says, it's 10 feet, and they say, okay let's compromise, it's 12 feet. And of course, it's not 12 feet, there is an answer. And not all things that we all consider has that black and white answer, but most things have an answer that really is more right if you actually assess it and debate it. And so we have an environment that really empowers people to challenge one another and I think it's part of why we end up getting to better answers, cause we have that level of openness and rigor. >> Okay, well Andy, we have time for one more question. >> Okay. >> So other than some of the things you've talked about, like customer focus, innovation, and long-term orientation, what is the single most important lesson that you've learned that is really relevant to this audience and this time we're living in? >> There's a lot. But I'll pick one. I would say I'll tell a short story that I think captures it. In the early days at Amazon, our sole business was what we called an owned inventory retail business, which meant we bought the inventory from distributors or publishers or manufacturers, stored it in our own fulfillment centers and shipped it to customers. And around the year 1999 or 2000, this third party seller model started becoming very popular. You know, these were companies like Half.com and eBay and folks like that. And we had a really animated debate inside the company about whether we should allow third party sellers to sell on the Amazon site. And the concerns internally were, first of all, we just had this fundamental belief that other sellers weren't going to care as much about the customer experience as we did cause it was such a central part of everything we did DNA-wise. And then also we had this entire business and all this machinery that was built around owned inventory business, with all these relationships with publishers and distributors and manufacturers, who we didn't think would necessarily like third party sellers selling right alongside us having bought their products. And so we really debated this, and we ultimately decided that we were going to allow third party sellers to sell in our marketplace. And we made that decision in part because it was better for customers, it allowed them to have lower prices, so more price variety and better selection. But also in significant part because we realized you can't fight gravity. If something is going to happen, whether you want it to happen or not, it is going to happen. And you are much better off cannibalizing yourself or being ahead of whatever direction the world is headed than you are at howling at the wind or wishing it away or trying to put up blockers and find a way to delay moving to the model that is really most successful and has the most amount of benefits for the customers in question. And that turned out to be a really important lesson for Amazon as a company and for me, personally, as well. You know, in the early days of doing Marketplace, we had all kinds of folks, even after we made the decision, that despite the have backbone, disagree and commit weren't really sure that they believed that it was going to be a successful decision. And it took several months, but thankfully we really were vigilant about it, and today in roughly half of the units we sell in our retail business are third party seller units. Been really good for our customers. And really good for our business as well. And I think the same thing is really applicable to the space we're talking about today, to the cloud, as you think about this gigantic shift that's going on right now, moving to the cloud, which is, you know, I think in the early days of the cloud, the first, I'll call it six, seven, eight years, I think collectively we consumed so much energy with all these arguments about are people going to move to the cloud, what are they going to move to the cloud, will they move mission-critical applications to the cloud, will the enterprise adopt it, will public sector adopt it, what about private cloud, you know, we just consumed a huge amount of energy and it was, you can see both in the results in what's happening in businesses like ours, it was a form of fighting gravity. And today we don't really have if conversations anymore with our customers. They're all when and how and what order conversations. And I would say that this going to be a much better world for all of us, because we will be able to build in a much more cost effective fashion, we will be able to build much more quickly, we'll be able to take our scarce resource of engineers and not spend their resource on the undifferentiated heavy lifting of infrastructure and instead on what truly differentiates your business. And you'll have a global presence, so that you have lower latency and a better end user customer experience being deployed with your applications and infrastructure all over the world. And you'll be able to meet the data sovereignty requirements of various locales. So I think it's a great world that we're entering right now, I think we're at a time where there's a lot less confusion about where the world is headed, and I think it's an unprecedented opportunity for you to reinvent your businesses, reinvent your applications, and build capabilities for your customers and for your business that weren't easily possible before. And I hope you take advantage of it, and we'll be right here every step of the way to help you. Thank you very much. I appreciate it. (applause) >> Thank you, Andy. And thank you, everyone. I appreciate your time today. >> Thank you. (applause) (upbeat music)