>> Announcer: Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2017. Presented by AWS, Intel and our ecosystem of partners. >> Hey, welcome back everyone. Live here this is theCUBE in Las Vegas for AWS Amazon Web Services re:Invent 2017. Our 5th year covering the event. Wall to wall coverage. Three days, this is our day two. 45,000 people here. Developers and business connecting together this year. Big show. Amazon continues its growth. I'm John Furrier, my co-host Justin Warren. Our next guest is from Washington County Sheriff's Office using Amazon, Amazon Recognition, Chris Adzima, who is the Senior Information Systems Analyst at the Washington County Sheriff. Welcome to theCUBE. >> Nice to have you. >> So Chris. >> be here. >> So, so tons of cool stuff we saw on stage today. You know they've had polylex out for awhile. But you're gonna start to see some of these multi-media services around. Human identification, transcription, Recognition's been out for awhile. With the power of the cloud, you can start rollin' out some pretty cool services. You have one of 'em, talk about your solution and what you guys are doing with it. >> Sure, about last year when Recognition was announced, I wanted to provide our deputies at the Sheriff's office with the way to identify people based on videos that we get from either surveillance or eyewitnesses. So, I looked into Recognition and decided that we should give it a try by giving all of our booking photos or mugshots up to the cloud for it to be indexed. So, that's what I did. I indexed all, about 300,000 booking photos, we have in the last 10 years, and put that into a Recognition Collection. And now I can use the simple tools that AWS gives me to search against that index for any new image that we get in, either from surveillance or an eyewitness, allowing us to get identification within seconds as opposed to having to go through all 700 employees at the Sheriff's Office for the chance that they might have known the person. >> So the old way was essentially grab the footage, and then do the old mugshot kinda scan manually, right? >> Yeah, manually. It wasn't in a book, it was on a website, but essentially, yeah, you had to-- >> I made my point, it sucks. It's hard as hell. >> It's very difficult, very difficult. >> You see on TV all the magic pictures goin' on and the facial recognition, you see on the movies and stuff. How close are we to that right now in terms of that capability? >> Well as far as facial recognition goes it all depends on the data that you have at your fingertips. Right now I have booking photos, so I can identify people with a very high level of certainty if they've been in our jail. If they haven't been in our jail, I obviously don't have much of a chance of identifying them. So, what you see on the TV where it's like, we looked through all the DMV records. We looked through all of the people on the street and all this stuff, We're pretty far off from that because nobody has a catalog of all those images. >> You need to incorporate of all the pictures, all the data. >> Yeah, but when you have the data, it's very simple. >> Right, and it's a lot like scanning for fingerprints. It's like, people would have seen that. You know, you have a fingerprint that you've collected from the crime scene-- >> Chris: Exactly. >> We see it on NCIS or something where you scan through all of that. So, it's pretty similar to that. >> Yeah, it's similar to that, or DNA, or anything like that. If you have the data set, it's very easy to search for those people. >> Yeah. >> So, faces are no different. >> So, how long did it take you to get up and running? Did you have to ingest the photos? How did you do that or? >> So... >> John: They're on a website so you had 'em on digital already. >> From never knowing anything about Amazon Web Services, to a fully-functional prototype of this product took me 30 days. >> John: Wow. >> I had the photos uploaded and the ability to actually run the searches via the API in three. So, extremely easy. Extremely easy. >> So, given the success that you've had with that particular producr, are there other services at AWS that you're looking into? That say, hey, that would actually be really useful for us? >> Yes, a couple that were announced today. First off, the recognition for video. Something that we have a problem with, and I'm hoping recognition for video's going to help with is when you have a surveillance camera, people are moving all the time. Therefore, trying to get a screenshot is going to get a blurry image. We're not getting good results with low-light or low frame rate. But recognition for video is gonna be able to take that movement and still look at the face. Hopefully we're gonna be able to get a better facial identification that way. >> Justin: Okay. >> Another thing that I want to look into is this DeepLens they just announced today. >> John: Awesome. >> That looks extremely promising in the way of me being able to teach it things that we need. A great example of what I would use this for is when a inmate comes in, we take pictures of scars, marks and tattoos. That way, we have a database of all the scars, marks and tattoos on somebody. In case, if they recommit a crime and our eye-witness says, "They had a skull tattoo on their chest" we can then look through all of the people that have a skull tattoo and say, "These are our list of possible suspects." The problem with that is, is that you may enter somebody in as a skull, and you may enter it in as crossbones. Somebody else might put an accidental I in there. So it's very hard to do a text search against that. But if recognition were to come through, or it wouldn't be recognition in this case. If whatever model I built with the DeepLens came through, and said this is a skull and this is the word we use, then I'd be able to index all of those images, quickly pull them up, so we wouldn't even need a picture. We would just need to know, from an eye-witness, that there was a skull on that person's chest. >> John: We had a guest on yesterday from Thorn, which Intel is doing AI for good, and they use essentially, and they didn't say Craigslist, but trying to look for women who were being sold for prostitution, and exploited children and whatnot. And it's all machine learning, and some natural language processing. When you look at the Sage announcement, that looks promising, 'cause they're gonna make, as I was try to democratize the heavy-lifting around all of this, you know, voodoo machine learning. Which, I mean, if you're totally a computer science geek and that's all you do, yeah, you could probably master machine learning. But if you're a practitioner, you're just whipping up. >> Well, yeah, and that's a good example. Because I am not a data scientist. I have no idea how this stuff works in the back end. But being able to utilize, stand on the shoulders of these giants, so to speak, is allowing people like me who A, I only have seven people on my team to devote to this kind of thing. We don't have a lot of resources. We wouldn't be able to get a data scientist. But opening this stuff up to us allows us to build these things, like this facial recognition and other things based on machine learning. And ultimately keep our citizens safe through the work that AWS does in getting this to us. >> Justin: Yeah, and we've been saying at a couple of different interviews so far, that humans don't scale. So these tools that provide the humans that you do have a lot more leverage to get things done. So, we were talking just before, before we started recording that these are tools that assist the humans. You're not replacing the humans with machines that just go oh we're gonna cede all decision-making to you. This is just another tool like being able to fingerprint people and search that. It's one more way of doing the standard policing that you are already doing. >> Exactly, and the tool that I've already created, and any tool I create after that, doesn't ever look to replace our deputies or our detectives. We give them things so that they don't have to do the things like flipping through that book for hours upon hours. They can be out in the field, following the leads, keeping the community safe and apprehending these criminals. >> Do they have on body cameras too? >> Not yet. We are currently looking into body cameras. >> John: That's a trend. They're gonna be instrumented basically like warriors: fully loaded, you know, cameras. >> I tend not to think of it like that. Only because, again, that's a tool that we use. Not to, you know, be that land-warrior so to speak. But more of a-- >> Documentation, I mean, you see 'em on cars when people get pulled over. >> Exactly. >> You've got the evidence. >> It's documentation, just like anything else. It's just that one more tool that helps that deputy, that detective, that police officer get a better idea of the entire situation. >> Maybe I shouldn't have said war. Maybe I'm just into the Twitch culture where they're all geared up with all the gear. Okay, so next question for you is what's your vibe on the show? Obviously you have great experience working at Amazon. You're a success study because you're trying to get a job done, you got some tools and, >> Right. >> making it happen. What's your take this year? What's your vibe of the show? >> I'm really excited about a lot of stuff I'm seeing at the show. A lot of the announcements seemed like they were almost geared towards me. And I know they weren't obviously, but it really felt like announcement after announcement were these things that I'm wanting to go home and immediately start to play with. Anywhere from the stuff that was in the machine learning to the new elastic containers that they are announcing, to the new LAM defunctions that they're talking about. I mean, just all over the board. I'm very excited for all these new things that I get to go home and play with. >> What do you think, Justin? What's your take on the vibe show? >> I find that it's an interesting show. I'm finding it a little different than what I was expecting. This is my first time here at AWS re:Invent. I go to a lot of other trade shows and I was expecting more of like a developer show. Like I'm going to CubeCon next week and that's full of people with spiky hair, and pink shoes, and craziness. >> John: That's the area, by the way. >> Oh that's the area, right. It's a bit more casual than some of the other more businessy sort of conferences. I mean, here I am, wearing a jacket. So I don't feel completely out of place here, but it does feel like it's that blending of business and use cases and the things that you actually get done with it as well as there being people who have the tools that they want to go and build amazing new things with. >> Chris: Right, right, yeah. >> So it's a nice blend, I think. >> Yeah, I've found that it definitely doesn't feel like any other developer conference I've been to. But being in the public sector, I tend to go to the more business-suit conferences. >> John: This is like total developer for you, from a public sector perspective. >> From where I'm coming from, this is very laid back. And extremely... >> Oh yeah. >> But at the same time, it's very like a mixture. Like you said, you see executives mingling with the developers talking about things-- >> John: You're a good example I think of Amazon. First of all, there's the builder thing in the area is supposed to be pretty cool. I was told to go there last night. People came back, it was very much builder, kind of maker culture. They're doing prototypes, it was very developer-oriented. But the public sector, I'm astonished by Amazon's success there because the stuff is easy and low-cost to get in. And public sector is not known for its agility. >> Chris: No. >> I mean, it's music to your ears, right? I mean, if you're in the public sector, you're like, "What? Now I can get it done?" >> Very much so. And one thing I love to share about our solution is the price, right? Because I spent $6 a month for my AWS bill. Right? >> John: Wow. >> That's extremely easy to sell to tax payers, right? It's extremely easy to sell to the higher-ups in government to say, I'm gonna tinker around with this, but even if we solve one crime, we've already seen a return on our investment above and beyond what we expected. >> Yeah. >> No brainer, no brainer. Chris, thanks so much for sharing your story. We really appreciate it. Congratulations on your success and keep in touch with theCube. Welcome to theCube Alumni Club. >> Alright. >> John: For coming out, it's theCube here. Amazon re:Invent, bringing all the action down, all of the success stories, all of the analysis. I'm John Furrier with theCube. More live coverage after this short break. (upbeat music)

>> 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)