>> Good afternoon, fellow cloud nerds and welcome back to AWS Reinvent 2022. We are live here from fabulous Las Vegas, Nevada. My name is Savannah Peterson, joined by Lisa Martin. So excited to be here Lisa, it's my first reinvent. >> Is it really? >> Yeah. >> I think it's only like my fourth or fifth. >> Only your fourth or fifth. >> Only. >> You're such a pro here. >> There's some serious veterans here in attendance that have been to all 11. >> I love that. >> Yeah. Wow, go them. I know, maybe we'll be at that level sooner. >> One day we will. >> Are you enjoying the show so far? >> Absolutely, it is. I cannot believe how many people are here. We've had 70,000 and we're only seeing what's at the foundation Expo Hall, not at the other hotel. So, I can only imagine. >> I mean, there's a world outside of this. >> Yes, and there's sunlight. There's actual sunlight outside of this room. >> Nobel idea. Well, Lisa, I'm very excited to be sitting here next to you and to welcome our fabulous guests, from TEKsystems, we have Brandon and Srini. Thank you so much for being here. How is the show going for you gentlemen so far? >> It's great. Lot of new insights and the customers are going to love what AWS is releasing in this reinvent. >> There is such a community here, and I love that vibe. It's similar to what we had at Cloud Native con in Detroit. So much collaboration going on. I assume most folks know a lot about TEKsystems who are watching, but just in case they don't, Brandon, give us the pitch. >> You bet. So full stack IT solutions firm, been in business for over 40 years, 80,000 global employees, really specializing in digital transformation, enterprise modernization services. We have partners in One Strategy, which is an an acquisition we made, but a well known premier partner in the Amazon partner ecosystem, as well as One North Interactive, who is our boutique brand, creative and digital strategy firm. So together, we really feel like we can bring full end-to-end solutions for digital and modernization initiatives. >> So, I saw some notes where TEKsystems are saying organizations need experienced AWS partners that are not afraid doing the dirty work of digital transformation, who really can advise and execute. Brandon, talk to us about how TEKsystems and AWS are working together to help customers on that journey which is nebulous of digital transformation. >> So, our real hallmark is the ability to scale. We partner with AWS in a lot of different ways. In fact, we just signed our strategic collaboration agreement. So, we're in the one percenter group in the whole partner network. >> Savanna: That's a pretty casual flex there. >> Not bad. >> I love that, top 1%, that no wonder you're wearing that partner pin so proud today. (speaking indistinctly) >> But we're working all the way on the advisory and working with their pro serve organization and then transforming that into large scale mass migration services, a lot of data modernization that Srini is an absolute expert in. I'm sure he can add some context too, but it's been a great partnership for many years now. >> In the keynote, Adam spent almost 52 minutes on data, right? So, it emphasizes how organizations are ready to take data to cloud and actually make meaningful insights and help their own customers come out of it by making meaningful decisions. So, we are glad to be part of this entire ecosystem. >> I love that you quantified how many minutes. >> I know. >> Talked about it, that was impressive. There's a little bit of data driven thinking going on here. >> I think so. >> Yeah. >> Well, we can't be at an event like this without talking about data for copious amounts of time, 52 minutes, has just used this morning. >> Right, absolutely. >> But every company these days has to be a data company. There's no choice to be successful, to thrive, to survive. I mean, even to thrive and grow, if it's a grocery store or your local gas station or what? You name it, that company has to be a data company. But the challenge of the data volume, the explosion in data is huge for organizations to really try to figure out and sift through what they have, where is all of it? How do we make sense of it? How do we act on it and get insights? That's a big challenge. How is TEKsystems helping customers tackle that challenge? >> Yeah, that's a great question because that's the whole fun of handling data. You need to ensure its meaning is first understood. So, we are not just dumping data into a storage place, but rather assign a meaningful context. In today's announcement, again, the data zone was unveiled to give meaning to data. And I think those are key concrete steps that we take to our customers as well with some good blueprints, methodical ways of approaching data and ultimately gaining business insights. >> And maybe I'll add just something real quick to that. The theme we're seeing and hearing a lot about is data monetization. So, technology companies have figured it out and used techniques to personalize things and get you ads, probably that you don't want half the time. But now all industries are really looking to do that. Looking at ways to open new revenue channels, looking at ways to drive a better customer experience, a better employee experience. We've got a ton of examples of that, Big Oil and Gas leveraging like well and machine data, coming in to be more efficient when they're pumping and moving commodities around. We work a lot in the medium entertainment space and so obviously, getting targeted ads to consumers during the right periods of TV or movies or et cetera. Especially with the advert on Netflix and all your streaming videos. So, it's been really interesting but we really see the future in leveraging data as one of your biggest corporate assets. >> Brilliant. >> So, I'm just curious on the ad thing, just real quick and I'll let you go, Lisa. So, do you still fall victim to falling for the advertising even though you know it's been strategically put there for you to consume in that moment? >> Most of the time. >> I mean, I think we all do. We're all, (indistinct), you're behind the curtain so to speak. >> The Amazon Truck shows up every day at my house, which is great, right? >> Hello again >> Same. >> But I think the power of it is you are giving the customer what they're looking for. >> That's it. >> And you know... >> Exactly. We have that expectation, we want it. >> 100%. >> We know that. >> Agree. >> We don't need to buy it. But technology has made it so easy to transact. That's like when developers started going to the cloud years ago, it was just, it was a swipe. It was so simple. Brandon, talk about the changes in cloud and cloud migration that TEKsystems has seen, particularly in the last couple of years as every company was rushing to go digital because they had to. >> So several years ago, we kind of pushed away that cloud first mentality to the side and we use more of a cloud smart kind of fashion, right? Does everything need to go to the cloud? No. Do applications, data, need to go to the cloud in a way that's modern and takes advantages of what the cloud can provide and all the new services that are being released this week and ongoing. So, the other thing we're seeing is initiatives that have traditionally been in the CTO, CIO organization aren't necessarily all that successful because we're seeing a complete misalignment between business goals and IT achievements, outcomes, et cetera. You can automate things, you can move it to the cloud, but if you didn't solve a core business problem or challenge, what'd you really do? >> Yeah, just to add on that, it's all about putting data and people together. And then how we can actually ensure the workforce is equally brought up to speed on these new technologies. That has been something that we have seen tremendous improvement in the last 24 months where customers are ready to take up new challenges and the end users are ready to learn something new and not just stick onto that status quo mindset. >> Where do you guys factor in to bringing in AWS in the customer's cloud journeys? What is that partnership like? >> We always first look for where the customer is in their cloud journey path and make sure we advise them with the right next steps. And AWS having its services across the spectrum makes it even easier for us to look at what business problem they're solving and then align it according to the process and technology so that at the end of the day, we want end user adoption. We don't want to build a fancy new gadget that no one uses. >> Just because you built it doesn't mean they'll come. And I think that's the classic engineering marketing dilemma as well as balance to healthy tension. I would say between both. You mentioned Srini, you mentioned workforce just a second ago. What sort of trends are you seeing in workforce development? >> Generally speaking, there are a lot of services now that can quantify your code for errors and then make sure that the code that you're pushing into production is well tested. So what we are trying to make sure is a healthy mix of trying to solve a business problem and asking the right questions. Like today, even in the keynote, it was all about how QuickSight, for example, has additional features now that tells why something happened. And that's the kind of mindset we want our end users to adopt. Not just restricting themselves to a reactive analytics, but rather ask the question why, why did it happen? Why did my sales go down? And I think those technologies and mindset shift is happening across the workforce. >> From a workforce development standpoint, we're seeing there's not enough workforce and the core skills of data, DevOps, standard cloud type work. So, we're actually an ATP advanced training partner, one of the few within the AWS network. So, we've developed programs like our Rising Talent Program that are allowing us to bring the workforce up to the skills that are necessary in this new world. So, it's a more build versus buy strategy because we're on talents real, though it may start to wane a little bit as we change the macroeconomic outlook in 2023, but it's still there. And we still believe that building those workforce and investing in your people is the right thing to do. >> It is, and I think there's a strong alignment there with AWS and their focus on that as well. I wanted to ask you, Brandon. >> Brandon: Absolutely. >> One of the things, so our boss, John Furrier, the co CEO of theCUBE, talked with Adam Selipsky just a week or maybe 10 days ago. He always gets an exclusive interview with the CEO of AWS before reinvent, and one of the things that Adam shared with him is that customers, CEOs and CIOs are not coming to Adam, to this head of AWS to talk about technology, they want to talk about transformation. He's talking about... >> The topic this year. >> Moving away from amorphous topic of digital transformation to business transformation. Are you seeing the same thing in your customer? >> 100%, and if you're not starting at the business level, these initiatives are going to fail. We see it all the time. Again, it's about that misalignment and there's no good answer to that. But digital, I think is amorphous to some degree. We play a lot with the One North partnership that I mentioned earlier, really focusing on that strategy element because consumer dollars are shrinking via inflation, via what we're heading into, and we have to create the best experience possible. We have to create an omnichannel experience to get our products or services to market. And if we're not looking at those as our core goals and we're looking at them as IT or technology challenges, we're not looking in the right place. >> Well, and businesses aren't going to be successful if they're looking at it in those siloed organizations. Data has to be democratizing and we've spent same data democratization for so long, but really, we're seeing that it has to be moving out into the lines of business because another thing Adam shared with John Furrier is that he sees and I'm curious what your thoughts are on this, the title of data analysts going away because everybody in different functions and different lines of business within an organization are going to have to be data analysts to some degree, to use data whether it's marketing, ops, sales, finance, are you seeing the same? >> That is true. I mean, at this point, we are all in the connected world, right? Every data point is connected in some form or shape to another data point. >> Savanna: There are many data points, just sitting here, yeah. >> Absolutely, so I think if you are strategizing, data needs to be right in the center of it. And then your business problems need to be addressed with reliable data. >> No, I mean, advertising, supply chain, marketing, they're all interconnected now, and we're looking at ways to bring a lot of that siloed data into one place so we can make use to it. It goes back to that monetization element of our data. >> That's a lot about context and situational awareness. We want what we want, when we want it, even before we knew we needed it then. I think I said that right. But you know, it's always more faster, quicker and then scaling things up. You see a lot of different customers across verticals, you have an absolutely massive team. Give us a sneak peek into 2023. What does the future hold? >> 2023 is again, to today's keynote, I'm bringing it back because it was a keynote filled with vision and limitless possibilities. And that's what we see. Right now, our customers, they are no longer scared to go and take the plunge into the cloud. And as Brandon said, it's all about being smart about those decisions. So, we are very excited that together with the partnership that we recently acquired and the services and the depth, along with the horizontal domain expertise, we can actually help customers make meaningful message out of their data points. And that keeps us really excited for next year. >> Love that, Brandon, what about you? >> I think the obvious one is DevOps and a focus on optimization, financially, security, et cetera, just for the changing times. The other one is, I still think that digital is going to continue to be a big push in 2023, namely making sure that experience is at its best, whether that's employee and combating the war on talent, keeping your people or opening new revenue streams, enhancing existing revenue streams. You got to keep working on that. >> We got to keep the people happy with the machines and the systems that we are building as we all know. But it's very nice, it's been a lot of human-centric focus and a lot of customer obsession here at the show. We know it's a big thing for you all, for Amazon, for pretty much everyone who sat here. Hopefully it is in general. Hopefully there's nobody who doesn't care about their community, we're not talking to them, if that's the case, we have a new challenge on theCUBE for the show, this year as we kind of prepped you for and can call it a bumper sticker, you can call it a 30 second sizzle reel. But this is sort of your Instagram moment, your TikTok, your thought of leadership highlight. What's the most important story coming out of the show? Srini, you've been quoting the keynotes very well, so, I'm going to you first on this one. >> I think overall, it's all about owning the change. In our TEKsystems culture, it's all about striving for excellence through serving others and owning the change. And so it makes me very excited that when we get that kind of keynote resonating the same message that we invite culturally, that's a big win-win for all the companies. >> It's all about the shared vision. A lot of people with similar vision in this room right now, in this room, like it's a room, it's a massive expo center, just to be clear, I'm sure everyone can see in the background. Brandon >> I would say partnership, continuing to enhance our strategic partnership with AWS, continuing to be our customers' partners in transformation. And bringing those two things together here has been a predominance of my time this week. And we'll continue throughout the week, but we're in it together with our customers and with AWS and looking forward to the future. >> Yeah, that's a beautiful note to end on there. Brandon, Srini, thank you both so much for being here with us. Fantastic to learn from your insights and to continue to emphasize on this theme of collaboration. We look forward to the next conversation with you. Thank all of you for tuning in wherever you happen to be hanging out and watching this fabulous live stream or the replay. We are here at AWS Reinvent 2022 in wonderful sunny Las Vegas, Nevada with Lisa Martin. My name is Savannah Peterson, we are theCUBE, the leading source for high tech coverage.

>> 10, nine, eight, (clears throat) four, three. >> Good afternoon, fellow cloud nerds and welcome back to AWS Reinvent 2022. We are live here from fabulous Las Vegas, Nevada. My name is Savannah Peterson, joined by Lisa Martin. So excited to be here Lisa, it's my first reinvent. >> Is it really? >> Yeah. >> I think it's only like my fourth or fifth. >> Only your fourth or fifth. >> Only. >> You're such a pro here. >> There's some serious veterans here in attendance that have been to all 11. >> I love that. >> Yeah. Wow, go them. I know, maybe we'll be at that level sooner. >> One day we will. >> Are you enjoying the show so far? >> Absolutely, it is. I cannot believe how many people are here. We've had 70,000 and we're only seeing what's at the foundation Expo Hall, not at the other hotel. So, I can only imagine. >> I mean, there's a world outside of this. >> Yes, and there's sunlight. There's actual sunlight outside of this room. >> Nobel idea. Well, Lisa, I'm very excited to be sitting here next to you and to welcome our fabulous guests, from TEKsystems, we have Brandon and Srini. Thank you so much for being here. How is the show going for you gentlemen so far? >> It's great. Lot of new insights and the customers are going to love what AWS is releasing in this reinvent. >> There is such a community here, and I love that vibe. It's similar to what we had at Cloud Native con in Detroit. So much collaboration going on. I assume most folks know a lot about TEKsystems who are watching, but just in case they don't, Brandon, give us the pitch. >> You bet. So full stack IT solutions firm, been in business for over 40 years, 80,000 global employees, really specializing in digital transformation, enterprise modernization services. We have partners in One Strategy, which is an an acquisition we made, but a well known premier partner in the Amazon partner ecosystem, as well as One North Interactive, who is our boutique brand, creative and digital strategy firm. So together, we really feel like we can bring full end-to-end solutions for digital and modernization initiatives. >> So, I saw some notes where TEKsystems are saying organizations need experienced AWS partners that are not afraid doing the dirty work of digital transformation, who really can advise and execute. Brandon, talk to us about how TEKsystems and AWS are working together to help customers on that journey which is nebulous of digital transformation. >> So, our real hallmark is the ability to scale. We partner with AWS in a lot of different ways. In fact, we just signed our strategic collaboration agreement. So, we're in the one percenter group in the whole partner network. >> Savanna: That's a pretty casual flex there. >> Not bad. >> I love that, top 1%, that no wonder you're wearing that partner pin so proud today. (speaking indistinctly) >> But we're working all the way on the advisory and working with their pro serve organization and then transforming that into large scale mass migration services, a lot of data modernization that Srini is an absolute expert in. I'm sure he can add some context too, but it's been a great partnership for many years now. >> In the keynote, Adam spent almost 52 minutes on data, right? So, it emphasizes how organizations are ready to take data to cloud and actually make meaningful insights and help their own customers come out of it by making meaningful decisions. So, we are glad to be part of this entire ecosystem. >> I love that you quantified how many minutes. >> I know. >> Talked about it, that was impressive. There's a little bit of data driven thinking going on here. >> I think so. >> Yeah. >> Well, we can't be at an event like this without talking about data for copious amounts of time, 52 minutes, has just used this morning. >> Right, absolutely. >> But every company these days has to be a data company. There's no choice to be successful, to thrive, to survive. I mean, even to thrive and grow, if it's a grocery store or your local gas station or what? You name it, that company has to be a data company. But the challenge of the data volume, the explosion in data is huge for organizations to really try to figure out and sift through what they have, where is all of it? How do we make sense of it? How do we act on it and get insights? That's a big challenge. How is TEKsystems helping customers tackle that challenge? >> Yeah, that's a great question because that's the whole fun of handling data. You need to ensure its meaning is first understood. So, we are not just dumping data into a storage place, but rather assign a meaningful context. In today's announcement, again, the data zone was unveiled to give meaning to data. And I think those are key concrete steps that we take to our customers as well with some good blueprints, methodical ways of approaching data and ultimately gaining business insights. >> And maybe I'll add just something real quick to that. The theme we're seeing and hearing a lot about is data monetization. So, technology companies have figured it out and used techniques to personalize things and get you ads, probably that you don't want half the time. But now all industries are really looking to do that. Looking at ways to open new revenue channels, looking at ways to drive a better customer experience, a better employee experience. We've got a ton of examples of that, Big Oil and Gas leveraging like well and machine data, coming in to be more efficient when they're pumping and moving commodities around. We work a lot in the medium entertainment space and so obviously, getting targeted ads to consumers during the right periods of TV or movies or et cetera. Especially with the advert on Netflix and all your streaming videos. So, it's been really interesting but we really see the future in leveraging data as one of your biggest corporate assets. >> Brilliant. >> So, I'm just curious on the ad thing, just real quick and I'll let you go, Lisa. So, do you still fall victim to falling for the advertising even though you know it's been strategically put there for you to consume in that moment? >> Most of the time. >> I mean, I think we all do. We're all, (indistinct), you're behind the curtain so to speak. >> The Amazon Truck shows up every day at my house, which is great, right? >> Hello again >> Same. >> But I think the power of it is you are giving the customer what they're looking for. >> That's it. >> And you know... >> Exactly. We have that expectation, we want it. >> 100%. >> We know that. >> Agree. >> We don't need to buy it. But technology has made it so easy to transact. That's like when developers started going to the cloud years ago, it was just, it was a swipe. It was so simple. Brandon, talk about the changes in cloud and cloud migration that TEKsystems has seen, particularly in the last couple of years as every company was rushing to go digital because they had to. >> So several years ago, we kind of pushed away that cloud first mentality to the side and we use more of a cloud smart kind of fashion, right? Does everything need to go to the cloud? No. Do applications, data, need to go to the cloud in a way that's modern and takes advantages of what the cloud can provide and all the new services that are being released this week and ongoing. So, the other thing we're seeing is initiatives that have traditionally been in the CTO, CIO organization aren't necessarily all that successful because we're seeing a complete misalignment between business goals and IT achievements, outcomes, et cetera. You can automate things, you can move it to the cloud, but if you didn't solve a core business problem or challenge, what'd you really do? >> Yeah, just to add on that, it's all about putting data and people together. And then how we can actually ensure the workforce is equally brought up to speed on these new technologies. That has been something that we have seen tremendous improvement in the last 24 months where customers are ready to take up new challenges and the end users are ready to learn something new and not just stick onto that status quo mindset. >> Where do you guys factor in to bringing in AWS in the customer's cloud journeys? What is that partnership like? >> We always first look for where the customer is in their cloud journey path and make sure we advise them with the right next steps. And AWS having its services across the spectrum makes it even easier for us to look at what business problem they're solving and then align it according to the process and technology so that at the end of the day, we want end user adoption. We don't want to build a fancy new gadget that no one uses. >> Just because you built it doesn't mean they'll come. And I think that's the classic engineering marketing dilemma as well as balance to healthy tension. I would say between both. You mentioned Srini, you mentioned workforce just a second ago. What sort of trends are you seeing in workforce development? >> Generally speaking, there are a lot of services now that can quantify your code for errors and then make sure that the code that you're pushing into production is well tested. So what we are trying to make sure is a healthy mix of trying to solve a business problem and asking the right questions. Like today, even in the keynote, it was all about how QuickSight, for example, has additional features now that tells why something happened. And that's the kind of mindset we want our end users to adopt. Not just restricting themselves to a reactive analytics, but rather ask the question why, why did it happen? Why did my sales go down? And I think those technologies and mindset shift is happening across the workforce. >> From a workforce development standpoint, we're seeing there's not enough workforce and the core skills of data, DevOps, standard cloud type work. So, we're actually an ATP advanced training partner, one of the few within the AWS network. So, we've developed programs like our Rising Talent Program that are allowing us to bring the workforce up to the skills that are necessary in this new world. So, it's a more build versus buy strategy because we're on talents real, though it may start to wane a little bit as we change the macroeconomic outlook in 2023, but it's still there. And we still believe that building those workforce and investing in your people is the right thing to do. >> It is, and I think there's a strong alignment there with AWS and their focus on that as well. I wanted to ask you, Brandon. >> Brandon: Absolutely. >> One of the things, so our boss, John Furrier, the co CEO of theCUBE, talked with Adam Selipsky just a week or maybe 10 days ago. He always gets an exclusive interview with the CEO of AWS before reinvent, and one of the things that Adam shared with him is that customers, CEOs and CIOs are not coming to Adam, to this head of AWS to talk about technology, they want to talk about transformation. He's talking about... >> The topic this year. >> Moving away from amorphous topic of digital transformation to business transformation. Are you seeing the same thing in your customer? >> 100%, and if you're not starting at the business level, these initiatives are going to fail. We see it all the time. Again, it's about that misalignment and there's no good answer to that. But digital, I think is amorphous to some degree. We play a lot with the One North partnership that I mentioned earlier, really focusing on that strategy element because consumer dollars are shrinking via inflation, via what we're heading into, and we have to create the best experience possible. We have to create an omnichannel experience to get our products or services to market. And if we're not looking at those as our core goals and we're looking at them as IT or technology challenges, we're not looking in the right place. >> Well, and businesses aren't going to be successful if they're looking at it in those siloed organizations. Data has to be democratizing and we've spent same data democratization for so long, but really, we're seeing that it has to be moving out into the lines of business because another thing Adam shared with John Furrier is that he sees and I'm curious what your thoughts are on this, the title of data analysts going away because everybody in different functions and different lines of business within an organization are going to have to be data analysts to some degree, to use data whether it's marketing, ops, sales, finance, are you seeing the same? >> That is true. I mean, at this point, we are all in the connected world, right? Every data point is connected in some form or shape to another data point. >> Savanna: There are many data points, just sitting here, yeah. >> Absolutely, so I think if you are strategizing, data needs to be right in the center of it. And then your business problems need to be addressed with reliable data. >> No, I mean, advertising, supply chain, marketing, they're all interconnected now, and we're looking at ways to bring a lot of that siloed data into one place so we can make use to it. It goes back to that monetization element of our data. >> That's a lot about context and situational awareness. We want what we want, when we want it, even before we knew we needed it then. I think I said that right. But you know, it's always more faster, quicker and then scaling things up. You see a lot of different customers across verticals, you have an absolutely massive team. Give us a sneak peek into 2023. What does the future hold? >> 2023 is again, to today's keynote, I'm bringing it back because it was a keynote filled with vision and limitless possibilities. And that's what we see. Right now, our customers, they are no longer scared to go and take the plunge into the cloud. And as Brandon said, it's all about being smart about those decisions. So, we are very excited that together with the partnership that we recently acquired and the services and the depth, along with the horizontal domain expertise, we can actually help customers make meaningful message out of their data points. And that keeps us really excited for next year. >> Love that, Brandon, what about you? >> I think the obvious one is DevOps and a focus on optimization, financially, security, et cetera, just for the changing times. The other one is, I still think that digital is going to continue to be a big push in 2023, namely making sure that experience is at its best, whether that's employee and combating the war on talent, keeping your people or opening new revenue streams, enhancing existing revenue streams. You got to keep working on that. >> We got to keep the people happy with the machines and the systems that we are building as we all know. But it's very nice, it's been a lot of human-centric focus and a lot of customer obsession here at the show. We know it's a big thing for you all, for Amazon, for pretty much everyone who sat here. Hopefully it is in general. Hopefully there's nobody who doesn't care about their community, we're not talking to them, if that's the case, we have a new challenge on theCUBE for the show, this year as we kind of prepped you for and can call it a bumper sticker, you can call it a 30 second sizzle reel. But this is sort of your Instagram moment, your TikTok, your thought of leadership highlight. What's the most important story coming out of the show? Srini, you've been quoting the keynotes very well, so, I'm going to you first on this one. >> I think overall, it's all about owning the change. In our TEKsystems culture, it's all about striving for excellence through serving others and owning the change. And so it makes me very excited that when we get that kind of keynote resonating the same message that we invite culturally, that's a big win-win for all the companies. >> It's all about the shared vision. A lot of people with similar vision in this room right now, in this room, like it's a room, it's a massive expo center, just to be clear, I'm sure everyone can see in the background. Brandon >> I would say partnership, continuing to enhance our strategic partnership with AWS, continuing to be our customers' partners in transformation. And bringing those two things together here has been a predominance of my time this week. And we'll continue throughout the week, but we're in it together with our customers and with AWS and looking forward to the future. >> Yeah, that's a beautiful note to end on there. Brandon, Srini, thank you both so much for being here with us. Fantastic to learn from your insights and to continue to emphasize on this theme of collaboration. We look forward to the next conversation with you. Thank all of you for tuning in wherever you happen to be hanging out and watching this fabulous live stream or the replay. We are here at AWS Reinvent 2022 in wonderful sunny Las Vegas, Nevada with Lisa Martin. My name is Savannah Peterson, we are theCUBE, the leading source for high tech coverage.

(gentle melody) >> Welcome back to theCUBE's live coverage of AWS Summit in San Francisco, California. Events are back. AWS Summit in New York City this summer, theCUBE will be there as well. Check us out there. I'm glad to have events back. It's great to have of everyone here. I'm John Furrier, host of theCUBE. Dr. Matt Wood is with me, CUBE alumni, now VP of Business Analytics Division of AWS. Matt, great to see you. >> Thank you, John. It's great to be here. I appreciate it. >> I always call you Dr. Matt Wood because Andy Jackson always says, "Dr. Matt, we would introduce you on the arena." (Matt laughs) >> Matt: The one and only. >> The one and only, Dr. Matt Wood. >> In joke, I love it. (laughs) >> Andy style. (Matt laughs) I think you had walk up music too. >> Yes, we all have our own personalized walk up music. >> So talk about your new role, not a new role, but you're running the analytics business for AWS. What does that consist of right now? >> Sure. So I work. I've got what I consider to be one of the best jobs in the world. I get to work with our customers and the teams at AWS to build the analytics services that millions of our customers use to slice dice, pivot, better understand their data, look at how they can use that data for reporting, looking backwards. And also look at how they can use that data looking forward, so predictive analytics and machine learning. So whether it is slicing and dicing in the lower level of Hadoop and the big data engines, or whether you're doing ETL with Glue, or whether you're visualizing the data in QuickSight or building your models in SageMaker. I got my fingers in a lot of pies. >> One of the benefits of having CUBE coverage with AWS since 2013 is watching the progression. You were on theCUBE that first year we were at Reinvent in 2013, and look at how machine learning just exploded onto the scene. You were involved in that from day one. It's still day one, as you guys say. What's the big thing now? Look at just what happened. Machine learning comes in and then a slew of services come in. You've got SageMaker, became a hot seller right out of the gate. The database stuff was kicking butt. So all this is now booming. That was a real generational change over for database. What's the perspective? What's your perspective on that's evolved? >> I think it's a really good point. I totally agree. I think for machine learning, there's sort of a Renaissance in machine learning and the application of machine learning. Machine learning as a technology has been around for 50 years, let's say. But to do machine learning right, you need like a lot of data. The data needs to be high quality. You need a lot of compute to be able to train those models and you have to be able to evaluate what those models mean as you apply them to real world problems. And so the cloud really removed a lot of the constraints. Finally, customers had all of the data that they needed. We gave them services to be able to label that data in a high quality way. There's all the compute you need to be able to train the models. And so where you go? And so the cloud really enabled this Renaissance with machine learning. And we're seeing honestly a similar Renaissance with data and analytics. If you look back five to ten years, analytics was something you did in batch, your data warehouse ran an analysis to do reconciliation at the end of the month, and that was it. (John laughs) And so that's when you needed it. But today, if your Redshift cluster isn't available, Uber drivers don't turn up, DoorDash deliveries don't get made. Analytics is now central to virtually every business, and it is central to virtually every business's digital transformation. And being able to take that data from a variety of sources, be able to query it with high performance, to be able to actually then start to augment that data with real information, which usually comes from technical experts and domain experts to form wisdom and information from raw data. That's kind of what most organizations are trying to do when they kind of go through this analytics journey. >> It's interesting. Dave Velanta and I always talk on theCUBE about the future. And you look back, the things we're talking about six years ago are actually happening now. And it's not hyped up statement to say digital transformation is actually happening now. And there's also times when we bang our fists on the table saying, say, "I really think this is so important." And David says, "John, you're going to die on that hill." (Matt laughs) And so I'm excited that this year, for the first time, I didn't die on that hill. I've been saying- >> Do all right. >> Data as code is the next infrastructure as code. And Dave's like, "What do you mean by that?" We're talking about how data gets... And it's happening. So we just had an event on our AWS startups.com site, a showcase for startups, and the theme was data as code. And interesting new trends emerging really clearly, the role of a data engineer, right? Like an SRE, what an SRE did for cloud, you have a new data engineering role because of the developer onboarding is massively increasing, exponentially, new developers. Data science scientists are growing, but the pipelining and managing and engineering as a system, almost like an operating system. >> Kind of as a discipline. >> So what's your reaction to that about this data engineer, data as code? Because if you have horizontally scalable data, you've got to be open, that's hard (laughs), okay? And you got to silo the data that needs to be siloed for compliance and reason. So that's a big policy around that. So what's your reaction to data's code and the data engineering phenomenon? >> It's a really good point. I think with any technology project inside of an organization, success with analytics or machine learning, it's kind of 50% technology and then 50% cultural. And you have often domain experts. Those could be physicians or drug design experts, or they could be financial experts or whoever they might be, got deep domain expertise, and then you've got technical implementation teams. And there's kind of a natural, often repulsive force. I don't mean that rudely, but they just don't talk the same language. And so the more complex a domain and the more complex the technology, the stronger their repulsive force. And it can become very difficult for domain experts to work closely with the technical experts to be able to actually get business decisions made. And so what data engineering does and data engineering is, in some cases a team, or it can be a role that you play. It's really allowing those two disciplines to speak the same language. You can think of it as plumbing, but I think of it as like a bridge. It's a bridge between the technical implementation and the domain experts, and that requires a very disparate range of skills. You've got to understand about statistics, you've got to understand about the implementation, you got to understand about the data, you got to understand about the domain. And if you can put all of that together, that data engineering discipline can be incredibly transformative for an organization because it builds the bridge between those two groups. >> I was advising some young computer science students at the sophomore, junior level just a couple of weeks ago, and I told them I would ask someone at Amazon this question. So I'll ask you, >> Matt: Okay. since you've been in the middle of it for years, they were asking me, and I was trying to mentor them on how do you become a data engineer, from a practical standpoint? Courseware, projects to work on, how to think, not just coding Python, because everyone's coding in Python, but what else can they do? So I was trying to help them. I didn't really know the answer myself. I was just trying to kind of help figure it out with them. So what is the answer, in your opinion, or the thoughts around advice to young students who want to be data engineers? Because data scientists is pretty clear on what that is. You use tools, you make visualizations, you manage data, you get answers and insights and then apply that to the business. That's an application. That's not the standing up a stack or managing the infrastructure. So what does that coding look like? What would your advice be to folks getting into a data engineering role? >> Yeah, I think if you believe this, what I said earlier about 50% technology, 50 % culture, the number one technology to learn as a data engineer is the tools in the cloud which allow you to aggregate data from virtually any source into something which is incrementally more valuable for the organization. That's really what data engineering is all about. It's about taking from multiple sources. Some people call them silos, but silos indicates that the storage is kind of fungible or undifferentiated. That's really not the case. Success requires you to have really purpose built, well crafted, high performance, low cost engines for all of your data. So understanding those tools and understanding how to use them, that's probably the most important technical piece. Python and programming and statistics go along with that, I think. And then the most important cultural part, I think is... It's just curiosity. You want to be able to, as a data engineer, you want to have a natural curiosity that drives you to seek the truth inside an organization, seek the truth of a particular problem, and to be able to engage because probably you're going to some choice as you go through your career about which domain you end up in. Maybe you're really passionate about healthcare, or you're really just passionate about transportation or media, whatever it might be. And you can allow that to drive a certain amount of curiosity. But within those roles, the domains are so broad you kind of got to allow your curiosity to develop and lead you to ask the right questions and engage in the right way with your teams, because you can have all the technical skills in the world. But if you're not able to help the team's truth seek through that curiosity, you simply won't be successful. >> We just had a guest, 20 year old founder, Johnny Dallas who was 16 when he worked at Amazon. Youngest engineer- >> Johnny Dallas is a great name, by the way. (both chuckle) >> It's his real name. It sounds like a football player. >> That's awesome. >> Rock star. Johnny CUBE, it's me. But he's young and he was saying... His advice was just do projects. >> Matt: And get hands on. Yeah. >> And I was saying, hey, I came from the old days where you get to stand stuff up and you hugged on for the assets because you didn't want to kill the project because you spent all this money. And he's like, yeah, with cloud you can shut it down. If you do a project that's not working and you get bad data no one's adopting it or you don't like it anymore, you shut it down, just something else. >> Yeah, totally. >> Instantly abandon it and move on to something new. That's a progression. >> Totally! The blast radius of decisions is just way reduced. We talk a lot about in the old world, trying to find the resources and get the funding is like, all right, I want to try out this kind of random idea that could be a big deal for the organization. I need $50 million and a new data center. You're not going to get anywhere. >> And you do a proposal, working backwards, documents all kinds of stuff. >> All that sort of stuff. >> Jump your hoops. >> So all of that is gone. But we sometimes forget that a big part of that is just the prototyping and the experimentation and the limited blast radius in terms of cost, and honestly, the most important thing is time, just being able to jump in there, fingers on keyboards, just try this stuff out. And that's why at AWS, we have... Part of the reason we have so many services, because we want, when you get into AWS, we want the whole toolbox to be available to every developer. And so as your ideas develop, you may want to jump from data that you have that's already in a database to doing realtime data. And then you have the tools there. And when you want to get into real time data, you don't just have kinesis, you have real time analytics, and you can run SQL against that data. The capabilities and the breadth really matter when it comes to prototyping. >> That's the culture piece, because what was once a dysfunctional behavior. I'm going to go off the reservation and try something behind my boss' back, now is a side hustle or fun project. So for fun, you can just code something. >> Yeah, totally. I remember my first Hadoop projects. I found almost literally a decommissioned set of servers in the data center that no one was using. They were super old. They're about to be literally turned off. And I managed to convince the team to leave them on for me for another month. And I installed Hadoop on them and got them going. That just seems crazy to me now that I had to go and convince anybody not to turn these servers off. But what it was like when you- >> That's when you came up with Elastic MapReduce because you said this is too hard, we got to make it easier. >> Basically yes. (John laughs) I was installing Hadoop version Beta 9.9 or whatever. It was like, this is really hard. >> We got to make it simpler. All right, good stuff. I love the walk down memory Lane. And also your advice. Great stuff. I think culture is huge. That's why I like Adam's keynote at Reinvent, Adam Selipsky talk about Pathfinders and trailblazers, because that's a blast radius impact when you can actually have innovation organically just come from anywhere. That's totally cool. >> Matt: Totally cool. >> All right, let's get into the product. Serverless has been hot. We hear a lot of EKS is hot. Containers are booming. Kubernetes is getting adopted, still a lot of work to do there. Cloud native developers are booming. Serverless, Lambda. How does that impact the analytics piece? Can you share the hot products around how that translates? >> Absolutely, yeah. >> Aurora, SageMaker. >> Yeah, I think it's... If you look at kind of the evolution and what customers are asking for, they don't just want low cost. They don't just want this broad set of services. They don't just want those services to have deep capabilities. They want those services to have as low an operating cost over time as possible. So we kind of really got it down. We got built a lot of muscle, a lot of services about getting up and running and experimenting and prototyping and turning things off and turning them on and turning them off. And that's all great. But actually, you really only in most projects start something once and then stop something once, and maybe there's an hour in between or maybe there's a year. But the real expense in terms of time and operations and complexity is sometimes in that running cost. And so we've heard very loudly and clearly from customers that running cost is just undifferentiated to them. And they want to spend more time on their work. And in analytics, that is slicing the data, pivoting the data, combining the data, labeling the data, training their models, running inference against their models, and less time doing the operational pieces. >> Is that why the service focuses there? >> Yeah, absolutely. It dramatically reduces the skill required to run these workloads of any scale. And it dramatically reduces the undifferentiated heavy lifting because you get to focus more of the time that you would have spent on the operations on the actual work that you want to get done. And so if you look at something just like Redshift Serverless, that we launched a Reinvent, we have a lot of customers that want to run the cluster, and they want to get into the weeds where there is benefit. We have a lot of customers that say there's no benefit for me, I just want to do the analytics. So you run the operational piece, you're the experts. We run 60 million instant startups every single day. We do this a lot. >> John: Exactly. We understand the operations- >> I just want the answers. Come on. >> So just give me the answers or just give me the notebook or just give me the inference prediction. Today, for example, we announced Serverless Inference. So now once you've trained your machine learning model, just run a few lines of code or you just click a few buttons and then you got an inference endpoint that you do not have to manage. And whether you're doing one query against that end point per hour or you're doing 10 million, we'll just scale it on the back end. I know we got not a lot of time left, but I want to get your reaction on this. One of the things about the data lakes not being data swamps has been, from what I've been reporting and hearing from customers, is that they want to retrain their machine learning algorithm. They need that data, they need the real time data, and they need the time series data. Even though the time has passed, they got to store in the data lake. So now the data lake's main function is being reusing the data to actually retrain. It works properly. So a lot of post mortems turn into actually business improvements to make the machine learnings smarter, faster. Do you see that same way? Do you see it the same way? >> Yeah, I think it's really interesting >> Or is that just... >> No, I think it's totally interesting because it's convenient to kind of think of analytics as a very clear progression from point A to point B. But really, you're navigating terrain for which you do not have a map, and you need a lot of help to navigate that terrain. And so having these services in place, not having to run the operations of those services, being able to have those services be secure and well governed. And we added PII detection today. It's something you can do automatically, to be able to use any unstructured data, run queries against that unstructured data. So today we added text queries. So you can just say, well, you can scan a badge, for example, and say, well, what's the name on this badge? And you don't have to identify where it is. We'll do all of that work for you. It's more like a branch than it is just a normal A to B path, a linear path. And that includes loops backwards. And sometimes you've got to get the results and use those to make improvements further upstream. And sometimes you've got to use those... And when you're downstream, it will be like, "Ah, I remember that." And you come back and bring it all together. >> Awesome. >> So it's a wonderful world for sure. >> Dr. Matt, we're here in theCUBE. Just take the last word and give the update while you're here what's the big news happening that you're announcing here at Summit in San Francisco, California, and update on the business analytics group. >> Yeah, we did a lot of announcements in the keynote. I encourage everyone to take a look at, that this morning with Swami. One of the ones I'm most excited about is the opportunity to be able to take dashboards, visualizations. We're all used to using these things. We see them in our business intelligence tools, all over the place. However, what we've heard from customers is like, yes, I want those analytics, I want that visualization, I want it to be up to date, but I don't actually want to have to go from my tools where I'm actually doing my work to another separate tool to be able to look at that information. And so today we announced 1-click public embedding for QuickSight dashboard. So today you can literally as easily as embedding a YouTube video, you can take a dashboard that you've built inside QuickSight, cut and paste the HTML, paste it into your application and that's it. That's what you have to do. It takes seconds. >> And it gets updated in real time. >> Updated in real time. It's interactive. You can do everything that you would normally do. You can brand it, there's no power by QuickSight button or anything like that. You can change the colors, fit in perfectly with your application. So that's an incredibly powerful way of being able to take an analytics capability that today sits inside its own little fiefdom and put it just everywhere. Very transformative. >> Awesome. And the business is going well. You got the Serverless detail win for you there. Good stuff. Dr. Matt Wood, thank you for coming on theCUBE. >> Anytime. Thank you. >> Okay, this is theCUBE's coverage of AWS Summit 2022 in San Francisco, California. I'm John Furrier, host of theCUBE. Stay with us for more coverage of day two after this short break. (gentle music)

>>Hello, everyone. Welcome to the cubes presentation of the 80 minutes startup showcase. Season two, episode two, the theme is data as code, the future of analytics. I'm John furry, your host. We had a great day lineup for you. Fast growing startups, great lineup of companies, founders, and stories around data as code. And we're going to kick it off here with our opening keynote with Rahul Pathak VP of analytics at AWS cube alumni. Right? We'll thank you for coming on and being the opening keynote for this awesome event. >>Yeah. And it's great to see you, and it's great to be part of this event, uh, excited to, um, to help showcase some of the great innovation that startups are doing on top of AWS. >>Yeah. We last spoke at AWS reinvent and, uh, a lot's happened there, service loss of serverless as the center of the, of the action, but all these start-ups rock set Dremio Cribble monks next Liccardo, a HANA imply all doing great stuff. Data as code has a lot of traction. So a lot of still momentum going on in the marketplace. Uh, pretty exciting. >>No, it's, uh, it's awesome. I mean, I think there's so much innovation happening and you know, the, the wonderful part of working with data is that the demand for services and products that help customers drive insight from data is just skyrocketing and has no sign of no sign of slowing down. And so it's a great time to be in the data business. >>It's interesting to see the theme of the show getting traction, because you start to see data being treated almost like how developers write software, taking things out of branches, working on them, putting them back in, uh, machine learnings, uh, getting iterated on you, seeing more models, being trained differently with better insights, action ones that all kind of like working like code. And this is a whole nother way. People are reinventing their businesses. This has been a big, huge wave. What's your reaction to that? >>Uh, I think it's spot on, I mean, I think the idea of data's code and bringing some of the repeatability of processes from software development into how people built it, applications is absolutely fundamental and especially so in machine learning where you need to think about the explainability of a model, what version of the world was it trained on? When you build a better model, you need to be able to explain and reproduce it. So I think your insights are spot on and these ideas are showing up in all stages of the data work flow from ingestion to analytics to I'm out >>This next way is about modernization and going to the next level with cloud-scale. Uh, thank you so much for coming on and being the keynote presenter here for this great event. Um, I'll let you take it away. Reinventing businesses, uh, with ads analytics, right? We'll take it away. >>Okay, perfect. Well, folks, we're going to talk about, uh, um, reinventing your business with, uh, data. And if you think about it, the first wave of reinvention was really driven by the cloud. As customers were able to really transform how they thought about technology and that's well on her way. Although if you stop and think about it, I think we're only about five to 10% of the way done in terms of it span being on the cloud. So lots of work to do there, but we're seeing another wave of reinvention, which is companies reinventing their businesses with data and really using data to transform what they're doing to look for new opportunities and look for ways to operate more efficiently. And I think the past couple of years of the pandemic, it really only accelerated that trend. And so what we're seeing is, uh, you know, it's really about the survival of the most informed folks for the best data are able to react more quickly to what's happening. >>Uh, we've seen customers being able to scale up if they're in, say the delivery business or scale down, if they were in the travel business at the beginning of all of this, and then using data to be able to find new opportunities and new ways to serve customers. And so it's really foundational and we're seeing this across the board. And so, um, you know, it's great to see the innovation that's happening to help customers make sense of all of this. And our customers are really looking at ways to put data to work. It's about making better decisions, finding new efficiencies and really finding new opportunities to succeed and scale. And, um, you know, when it comes to, uh, good examples of this FINRA is a great one. You may not have heard of them, but that the U S equities regulators, all trading that happens in equities, they keep track of they're look at about 250 billion records per day. >>Uh, the examiner, I was only EMR, which is our spark and Hadoop service, and they're processing 20 terabytes of data running across tens of thousands of nodes. And they're looking for fraud and bad actors in the market. So, um, you know, huge, uh, transformation journey for FINRA over the years of customer I've gotten to work with personally since really 2013 onward. So it's been amazing to see their journey, uh, Pinterest, not a great customer. I'm sure everyone's familiar with, but, um, you know, they're about visual search and discovery and commerce, and, um, they're able to scale their daily lot searches, um, really a factor of three X or more, uh, drive down their costs. And they're using the Amazon Opus search service. And really what we're trying to do at AWS is give our customers the most comprehensive set of services for the end-to-end journey around, uh, data from ingestion to analytics and machine learning. And we will want to provide a comprehensive set of capabilities for ingestion, cataloging analytics, and then machine learning. And all of these are things that our partners and the startups that are run on us have available to them to build on as they build and deliver value for their customers. >>And, you know, the way we think about this is we want customers to be able to modernize what they're doing and their infrastructure. And we provide services for that. It's about unifying data, wherever it lives, connecting it. So the customers can build a complete picture of their customers and business. And then it's about innovation and really using machine learning to bring all of this unified data, to bear on driving new innovation and new opportunities for customers. And what we're trying to do AWS is really provide a scalable and secure cloud platform that customers and partners can build on a unifying is about connecting data. And it's also about providing well-governed access to data. So one of the big trends that we see is customers looking for the ability to make self-service data available to that customer there and use. And the key to that is good foundational governance. >>Once you can define good access controls, you then are more comfortable setting data free. And, um, uh, the other part of it is, uh, data lakes play a huge role because you need to be able to think about structured and unstructured data. In fact, about 80% of the data being generated today, uh, is unstructured. And you want to be able to connect data that's in data lakes with data that's in purpose-built data stores, whether that's databases on AWS databases, outside SAS products, uh, as well as things like data warehouses and machine learning systems, but really connecting data as key. Uh, and then, uh, innovation, uh, how can we bring to bear? And we imagine all processes with new technologies like AI and machine learning, and AI is also key to unlocking a lot of the value that's in unstructured data. If you can figure out what's in an imagine the sentiment of audio and do that in real-time that lets you then personalize and dynamically tailor experiences, all of which are super important to getting an edge, um, in, uh, in the modern marketplace. And so at AWS, we, when we think about connecting the dots across sources of data, allowing customers to use data, lakes, databases, analytics, and machine learning, we want to provide a common catalog and governance and then use these to help drive new experiences for customers and their apps and their devices. And then this, you know, in an ideal world, we'll create a closed loop. So you create a new experience. You observe our customers interact with it, that generates more data, which is a data source that feeds into the system. >>And, uh, you know, on AWS, uh, thinking about a modern data strategy, uh, really at the core is a data lakes built on us three. And I'll talk more about that in a second. Then you've got services like Athena included, lake formation for managing that data, cataloging it and querying it in place. And then you have the ability to use the right tool for the right job. And so we're big believers in purpose-built services for data because that's where you can avoid compromising on performance functionality or scale. Uh, and then as I mentioned, unification and inter interconnecting, all of that data. So if you need to move data between these systems, uh, there's well-trodden pathways that allow you to do that, and then features built into services that enable that. >>And, um, you know, some of the core ideas that guide the work that we do, um, scalable data lakes at key, um, and you know, this is really about providing arbitrarily scalable high throughput systems. It's about open format data for future-proofing. Uh, then we talk about purpose-built systems at the best possible functionality, performance, and cost. Uh, and then from a serverless perspective, this has been another big trend for us. We announced a bunch of serverless services and reinvented the goal here is to really take away the need to manage infrastructure from customers. They can really focus about driving differentiated business value, integrated governance, and then machine learning pervasively, um, not just as an end product for data scientists, but also machine learning built into data, warehouses, visualization and a database. >>And so it's scalable data lakes. Uh, data three is really the foundation for this. One of our, um, original services that AWS really the backbone of so much of what we do, uh, really unmatched your ability, availability, and scale, a huge portfolio of analytics services, uh, both that we offer, but also that our partners and customers offer and really arbitrary skin. We've got individual customers and estimator in the expert range, many in the hundreds of petabytes. And that's just growing. You know, as I mentioned, we see roughly a 10 X increase in data volume every five years. So that's a exponential increase in data volumes, Uh, from a purpose-built perspective, it's the right tool for the right job, the red shift and data warehousing Athena for querying all your data. Uh, EMR is our managed sparking to do, uh, open search for log analytics and search, and then Kinesis and Amex care for CAFCA and streaming. And that's been another big trend is, uh, real time. Data has been exploding and customers wanting to make sense of that data in real time, uh, is another big deal. >>Uh, some examples of how we're able to achieve differentiated performance and purpose-built systems. So with Redshift, um, using managed storage and it's led us and since types, uh, the three X better price performance, and what's out there available to all our customers and partners in EMR, uh, with things like spark, we're able to deliver two X performance of open source with a hundred percent compatibility, uh, almost three X and Presto, uh, with on two, which is our, um, uh, new Silicon chips on AWS, better price performance, about 10 to 12% better price performance, and 20% lower costs. And then, uh, all compatible source. So drop your jobs, then have them run faster and cheaper. And that translates to customer benefits for better margins for partners, uh, from a serverless perspective, this is about simplifying operations, reducing total cost of ownership and freeing customers from the need to think about capacity management. If we invent, we, uh, announced serverless redshifts EMR, uh, serverless, uh, Kinesis and Kafka, um, and these are all game changes for customers in terms of freeing our customers and partners from having to think about infrastructure and allowing them to focus on data. >>And, um, you know, when it comes to several assumptions in analytics, we've really got a very full and complete set. So, uh, whether that's around data warehousing, big data processing streaming, or cataloging or governance or visualization, we want all of our customers to have an option to run something struggles as well as if they have specialized needs, uh, uh, instances are available as well. And so, uh, really providing a comprehensive deployment model, uh, based on the customer's use cases, uh, from a governance perspective, uh, you know, like information is about easy build and management of data lakes. Uh, and this is what enables data sharing and self service. And, um, you know, with you get very granular access controls. So rule level security, uh, simple data sharing, and you can tag data. So you can tag a group of analysts in the year when you can say those only have access to the new data that's been tagged with the new tags, and it allows you to very, scaleably provide different secure views onto the same data without having to make multiple copies, another big win for customers and partners, uh, support transactions on data lakes. >>So updates and deletes. And time-travel, uh, you know, John talked about data as code and with time travel, you can look at, um, querying on different versions of data. So that's, uh, a big enabler for those types of strategies. And with blue, you're able to connect data in multiple places. So, uh, whether that's accessing data on premises in other SAS providers or, uh, clouds, uh, as well as data that's on AWS and all of this is, uh, serverless and interconnected. And, um, and really it's about plugging all of your data into the AWS ecosystem and into our partner ecosystem. So this API is all available for integration as well, but then from an AML perspective, what we're really trying to do is bring machine learning closer to data. And so with our databases and warehouses and lakes and BI tools, um, you know, we've infused machine learning throughout our, by, um, the state of the art machine running that we offer through SageMaker. >>And so you've got a ML in Aurora and Neptune for broths. Uh, you can train machine learning models from SQL, directly from Redshift and a female. You can use free inference, and then QuickSight has built in forecasting built in natural language, querying all powered by machine learning, same with anomaly detection. And here are the ideas, you know, how can we up our systems get smarter at the surface, the right insights for our customers so that they don't have to always rely on smart people asking the right questions, um, and you know, uh, really it's about bringing data back together and making it available for innovation. And, uh, thank you very much. I appreciate your attention. >>Okay. Well done reinventing the business with AWS analytics rural. That was great. Thanks for walking through that. That was awesome. I have to ask you some questions on the end-to-end view of the data. That seems to be a theme serverless, uh, in there, uh, Mel integration. Um, but then you also mentioned picking the right tool for the job. So then you've got like all these things moving on, simplify it for me right now. So from a business standpoint, how do they modernize? What's the steps that the clients are taking with analytics, what's the best practice? How do they, what's the what's the high order bit here? >>Uh, so the basic hierarchy is, you know, historically legacy systems are rigid and inflexible, and they weren't really designed for the scale of modern data or the variety of it. And so what customers are finding is they're moving to the cloud. They're moving from legacy systems with punitive licensing into more flexible, more systems. And that allows them to really think about building a decoupled, scalable future proof architecture. And so you've got the ability to combine data lakes and databases and data warehouses and connect them using common KPIs and common data protection. And that sets you up to deal with arbitrary scale and arbitrary types. And it allows you to evolve as the future changes since it makes it easy to add in a new type of engine, as we invent a better one a few years from now. Uh, and then, uh, once you've kind of got your data in a cloud and interconnected in this way, you can now build complete pictures of what's going on. You can understand all your touch points with customers. You can understand your complete supply chain, and once you can build that complete picture of your business, you can start to use analytics and machine learning to find new opportunities. So, uh, think about modernizing, moving to the cloud, setting up for the future, connecting data end to end, and then figuring out how to use that to your advantage. >>I know as you mentioned, modern data strategy gives you the best of both worlds. And you've mentioned, um, briefly, I want to get a little bit more, uh, insight from you on this. You mentioned open, open formats. One of the themes that's come out of some of the interviews, these companies we're going to be hearing from today is open source. The role opens playing. Um, how do you see that integrating in? Because again, this is just like software, right? Open, uh, open source software, open source data. It seems to be a trend. What does open look like to you? How do you see that progressing? >>Uh, it's a great question. Uh, open operates on multiple dimensions, John, as you point out, there's open data formats. These are things like JSI and our care for analytics. This allows multiple engines tend to operate on data and it'll, it, it creates option value for customers. If you're going to data in an open format, you can use it with multiple technologies and that'll be future-proofed. You don't have to migrate your data. Now, if you're thinking about using a different technology. So that's one piece now that sort of software, um, also, um, really a big enabler for innovation and for customers. And you've got things like squat arc and Presto, which are popular. And I know some of the startups, um, you know, that we're talking about as part of the showcase and use these technologies, and this allows for really the world to contribute, to innovating and these engines and moving them forward together. And we're big believers in that we've got open source services. We contribute to open-source, we support open source projects, and that's another big part of what we do. And then there's open API is things like SQL or Python. Uh, again, uh, common ways of interacting with data that are broadly adopted. And this one, again, create standardization. It makes it easier for customers to inter-operate and be flexible. And so open is really present all the way through. And it's a big part, I think, of, uh, the present and the future. >>Yeah. It's going to be fun to watch and see how that grows. It seems to be a lot of traction there. I want to ask you about, um, the other comment I thought was cool. You had the architectural slides out there. One was data lakes built on S3, and you had a theme, the glue in lake formation kind of around S3. And then you had the constellation of, you know, Kinesis SageMaker and other things around it. And you said, you know, pick the tool for the right job. And then you had the other slide on the analytics at the center and you had Redshift and all the other, other, other services around it around serverless. So one was more about the data lake with Athena glue and lake formation. The other one's about serverless. Explain that a little bit more for me, because I'm trying to understand where that fits. I get the data lake piece. Okay. Athena glue and lake formation enables it, and then you can pick and choose what you need on the serverless side. What does analytics in the center mean? >>So the idea there is that really, we wanted to talk about the fact that if you zoom into the analytics use case within analytics, everything that we offer, uh, has a serverless option for our customers. So, um, you could look at the bucket of analytics across things like Redshift or EMR or Athena, or, um, glue and league permission. You have the option to use instances or containers, but also to just not worry about infrastructure and just think declaratively about the data that you want to. >>Oh, so basically you're saying the analytics is going serverless everywhere. Talking about volumes, you mentioned 10 X volumes. Um, what are other stats? Can you share in terms of volumes? What are people seeing velocity I've seen data warehouses can't move as fast as what we're seeing in the cloud with some of your customers and how they're using data. How does the volume and velocity community have any kind of other kind of insights into those numbers? >>Yeah, I mean, I think from a stats perspective, um, you know, take Redshift, for example, customers are processing. So reading and writing, um, multiple exabytes of data there across from each shift. And, uh, you know, one of the things that we've seen in, uh, as time has progressed as, as data volumes have gone up and did a tapes have exploded, uh, you've seen data warehouses get more flexible. So we've added things like the ability to put semi-structured data and arbitrary, nested data into Redshift. Uh, we've also seen the seamless integration of data warehouses and data lakes. So, um, actually Redshift was one of the first to enable a straightforward acquiring of data. That's sitting in locally and drives as well as feed and that's managed on a stream and, uh, you know, those trends will continue. I think you'll kind of continue to see this, um, need to query data wherever it lives and, um, and, uh, allow, uh, leaks and warehouses and purpose-built stores to interconnect. >>You know, one of the things I liked about your presentation was, you know, kind of had the theme of, you know, modernize, unify, innovate, um, and we've been covering a lot of companies that have been, I won't say stumbling, but like getting to the future, some go faster than others, but they all kind of get stuck in an area that seems to be the same spot. It's the silos, breaking down the silos and get in the data lakes and kind of blending that purpose built data store. And they get stuck there because they're so used to silos and their teams, and that's kind of holding back the machine learning side of it because the machine learning can't do its job if they don't have access to all the data. And that's where we're seeing machine learning kind of being this new iterative model where the models are coming in faster. And so the silo brake busting is an issue. So what's your take on this part of the equation? >>Uh, so there's a few things I plan it. So you're absolutely right. I think that transition from some old data to interconnected data is always straightforward and it operates on a number of levels. You want to have the right technology. So, um, you know, we enable things like queries that can span multiple stores. You want to have good governance, you can connect across multiple ones. Uh, then you need to be able to get data in and out of these things and blue plays that role. So there's that interconnection on the technical side, but the other piece is also, um, you know, you want to think through, um, organizationally, how do you organize, how do you define it once data when they share it? And one of the asylees for enabling that sharing and, um, think about, um, some of the processes that need to get put in place and create the right incentives in your company to enable that data sharing. And then the foundational piece is good guardrails. You know, it's, uh, it can be scary to open data up. And, uh, the key to that is to put good governance in place where you can ensure that data can be shared and distributed while remaining protected and adhering to the privacy and compliance and security regulations that you have for that. And once you can assert that level of protection, then you can set that data free. And that's when, uh, customers really start to see the benefits of connecting all of it together, >>Right? And then we have a batch of startups here on this episode that are doing a lot of different things. Uh, some have, you know, new lake new lakes are forming observability lakes. You have CQL innovation on the front end data, tiering innovation at the data tier side, just a ton of innovation around this new data as code. How do you see as executive at AWS? You're enabling all this, um, where's the action going? Where are the white spaces? Where are the opportunities as this architecture continues to grow, um, and get traction because of the relevance of machine learning and AI and the apps are embedding data in there now as code where's the opportunities for these startups and how can they continue to grow? >>Yeah, the, I mean, the opportunity is it's amazing, John, you know, we talked a little bit about this at the beginning, but the, there is no slow down insight for the volume of data that we're generating pretty much everything that we have, whether it's a watch or a phone or the systems that we interact with are generating data and, uh, you know, customers, uh, you know, we talk a lot about the things that'll stay the same over time. And so, you know, the data volumes will continue to go up. Customers are gonna want to keep analyzing that data to make sense of it. They're going to want to be able to do it faster and more cheaply than they were yesterday. And then we're going to want to be able to make decisions and innovate, uh, in a shorter cycle and run more experiments than they were able to do. >>And so I think as long as, and they're always going to want this data to be secure and well-protected, and so I think as long as we, and the startups that we work with can continue to push on making these things better. Can I deal with more data? Can I deal with it more cheaply? Can I make it easier to get insight? And can I maintain a super high bar in security investments in these areas will just be off. Um, because, uh, the demand side of this equation is just in a great place, given what we're seeing in terms of theater and the architect for forum. >>I also love your comment about, uh, ML integration being the last leg of the equation here or less likely the journey, but you've got that enablement of the AIP solves a lot of problems. People can see benefits from good machine learning and AI is creating opportunities. Um, and also you also have mentioned the end to end with security piece. So data and security are kind of going hand in hand these days, not just the governments and the compliance stuff we're talking about security. So machine learning integration kind of connects all of this. Um, what's it all mean for the customers, >>For customers. That means that with machine learning and really enabling themselves to use machine learning, to make sense of data, they're able to find patterns that can represent new opportunities, um, quicker than ever before. And they're able to do it, uh, dynamically. So, you know, in a prior version of the world, we'd have little bit of systems and they would be relatively rigid and then we'd have to improve them. Um, with machine learning, this can be dynamic and near real time and you can customize them. So, uh, that just represents an opportunity to deepen relationships with customers and create more value and to find more efficiency in how businesses are run. So that piece is there. Um, and you know, your ideas around, uh, data's code really come into play because machine learning needs to be repeatable and explainable. And that means versioning, uh, keeping track of everything that you've done from a code and data and learning and training perspective >>And data sets are updating the machine learning. You got data sets growing, they become code modules that can be reused and, uh, interrogated, um, security okay. Is a big as a big theme data, really important security is seen as one of our top use cases. Certainly now in this day and age, we're getting a lot of, a lot of breaches and hacks coming in, being defended. It brings up the open, brings up the data as code security is a good proxy for kind of where this is going. What's your what's take on that and your reaction to that. >>So I'm, I'm security. You can, we can never invest enough. And I think one of the things that we, um, you know, guide us in AWS is security, availability, durability sort of jobs, you know, 1, 2, 3, and, um, and it operates at multiple levels. You need to protect data and rest with encryption, good key management and good practices though. You need to protect data on the wire. You need to have a good sense of what data is allowed to be seen by whom. And then you need to keep track of who did what and be able to verify and come back and prove that, uh, you know, uh, only the things that were allowed to happen actually happened. And you can actually then use machine learning on top of all of this apparatus to say, uh, you know, can I detect things that are happening that shouldn't be happening in near real time so they could put a stop to them. So I don't think any of us can ever invest enough in securing and protecting my data and our systems, and it is really fundamental or adding customer trust and it's just good business. So I think it is absolutely crucial. And we think about it all the time and are always looking for ways to raise >>Well, I really appreciate you taking the time to give the keynote final word here for the folks watching a lot of these startups that are presenting, they're doing well. Business wise, they're being used by large enterprises and people buying their products and using their services for customers are implementing more and more of the hot startups products they're relevant. What's your advice to the customer out there as they go on this journey, this new data as code this new future of analytics, what's your recommendation. >>So for customers who are out there, uh, recommend you take a look at, um, what, uh, the startups on AWS are building. I think there's tremendous innovation and energy, uh, and, um, there's really great technology being built on top of a rock solid platform. And so I encourage customers thinking about it to lean forward, to think about new technology and to embrace, uh, move to the cloud suite, modernized, you know, build a single picture of our data and, and figure out how to innovate and when >>Well, thanks for coming on. Appreciate your keynote. Thanks for the insight. And thanks for the conversation. Let's hand it off to the show. Let the show begin. >>Thank you, John pleasure, as always.

>>Hey, welcome back everyone. We're live here in the cube in Las Vegas Raiders reinvent 2021. I'm Jeffrey hosted the key we're in person this year. It's a hybrid event online. Great action. Going on. I'm rolling. Vice-president of ADF analytics. David is great to see you. Thanks for coming on. >>It's great to be here, John. Thanks for having me again. >>Um, so you've got a really awesome job. You've got serverless, you've got analytics. You're in the middle of all the action for AWS. What's the big news. What are you guys announcing? What's going on? >>Yeah, well, it's been an awesome reinvent for us. Uh, we've had a number of several us analytics launches. So red shift, our petabyte scale data warehouse, EMR for open source analytics. Uh, and then we've also had, uh, managed streaming for Kafka go serverless and then on demand for Kinesis. And then a couple of other big ones. We've got RO and cell based security for AWS lake formation. So you can get really fine grain controls over your data lakes and then asset transactions. You can actually have a inserts, updates and deletes on data lakes, which is a big step forward. >>Uh, so Swami on stage and the keynote he's actually finishing up now. But even last night I saw him in the hallway. We were talking about as much as about AI. Of course, he's got the AI title, but AI is the outcome. It's the application of all the data and this and a new architecture. He said on stage just now like, Hey, it's not about the old databases from the nineties, right? There's multiple data stores now available. And there's the unification is the big trend. And he said something interesting. Governance can be an advantage, not an inhibitor. This is kind of this new horizontally scalable, um, kind of idea that enables the vertical specialization around machine learning to be effective. It's not a new architecture, but it's now becoming more popular. People are realizing it. It's sort of share your thoughts on this whole not shift, but the acceleration of horizontally scalable and vertically integrated. Yeah, >>No, I think the way Swami put it is exactly right. What you want is the right tool for the right job. And you want to be able to deliver that to customers. So you're not compromising on performance or functionality of scale, but then you wanted all of these to be interconnected. So they're, well-integrated, you can stay in your favorite interface and take advantage of other technologies. So you can have things like Redshift integrated with Sage makers, you get analytics and machine learning. And then in Swami's absolutely right. Governance is actually an enabler of velocity. Once you've got the right guardrails in place, you can actually set people free because they can innovate. You don't have to be in the way, but you know that your data is protected. It's being used in the way that you expect by the people that you are allowing to use that data. And so it becomes a very powerful way for customers to set data free. And then, because things are elastic and serverless, uh, you can really just match capacity with demand. And so as you see spikes in usage, the system can scale out as those dwindle, they can scale back down, and it just becomes a very efficient way for customers to operate with data at scale >>Every year it reinvented. So it was kind of like a pinch me moment. It's like, well, more that's really good technology. Oh my God, it's getting easier and easier. As the infrastructure as code becomes more programmable, it's becoming easier, more Lambda, more serverless action. Uh, you got new offerings. How are customers benefiting for instance, from the three new offerings that you guys announced here? What specifically is the value proposition that you guys are putting out there? Yeah, so the, >>Um, you know, as we've tried to do with AWS over the years, customers get to focus on the things that really differentiate them and differentiate their businesses. So we take away in Redshift serverless, for example, all of the work that's needed to manage clusters, provision them, scale them, optimize them. Uh, and that's all been automated and made invisible to customers, the customers to think about data, what they want to do with it, what insights they can derive from it. And they know they're getting the most efficient infrastructure possible to make that a reality for them with high performance and low costs. So, uh, better results, more ability to focus on what differentiates their business and lower cost structure over time. >>Yeah. I had the essential guys on it's interesting. They had part of the soul cloud. Continuous is their word for what Adam was saying is clouds everywhere. And they're saying it's faster to match what you want to do with the outcomes, but the capabilities and outcomes kind of merging together where it's easy to say, this is what we want to do. And here's the outcome it supports that's right with that. What are some of the key trends on those outcomes that you see with the data analytics that's most popular right now? And kind of where's that, where's that going? >>Yeah. I mean, I think what we've seen is that data's just becoming more and more critical and top of mind for customers and, uh, you know, the pandemic has also accelerated that we found that customers are really looking to data and analytics and machine learning to find new opportunities. How can they, uh, really expand their business, take advantage of what's happening? And then the other part is how can they find efficiencies? And so, um, really everything that we're trying to do is we're trying to connect it to business outcomes for customers. How can you deepen your relationship with your customers? How can you create new customer experiences and how can you do that more efficiently, uh, with more agility and take advantage of, uh, the ability to be flexible. And you know, what is a very unpredictable world, as we've seen, >>I noticed a lot of purpose-built discussion going on in the keynote with Swami as well. How are you creating this next layer of what I call purpose-built platform like features? I mean, tools are great. You see a lot of tools in the data market tools are tools of your hammer. You want to look for a nail. We see people over by too many tools and you have ultimately a platform, but this seems to be a new trend where there's this connect phenomenon was showing me that you've got these platform capabilities that people can build on top of it, because there's a huge ecosystem of data tools out there that you guys have as partners that want to snap together. So the trend is things are starting to snap together, less primitive, roll your own, which you can do, but there's now more easier ways. Take me through that. Explain that, unpack that that phenomenon role rolling your own firm is, which has been the way now to here. Here's, here's some prefabricated software go. >>Yeah. Um, so it's a great observation and you're absolutely right. I mean, I think there's some customers that want to roll their own and they'll start with instances, they'll install software, they'll write their own code, build their own bespoke systems. And, uh, and we provide what the customers need to do that. But I think increasingly you're starting to see these higher level abstractions that take away all of that detail. And mark has Adam put it and allow customers to compose these. And we think it's important when you do that, uh, to be modular. So customers don't have to have these big bang all or nothing approaches you can pick what's appropriate, uh, but you're never on a dead end. You can always evolve and scale as you need to. And then you want to bring these ideas of unified governance and cohesive interfaces across so that customers find it easy to adopt the next thing. And so you can start off say with batch analytics, you can expand into real time. You can bring in machine learning and predictive capabilities. You can add natural language, and it's a big ecosystem of managed services as well as third parties and partners. >>And what's interesting. I want to get your thoughts while I got you here, because I think this is such an important trend and historic moment in time, Jerry chin, who one of the smartest VCs that we know from Greylock and coin castles in the cloud, which kind of came out of a cube conversation here in the queue years ago, where we saw the movement of that someone's going to build real value on AWS, not just an app. And you see the rise of the snowflakes and Databricks and other companies. And he was pointing out that you can get a very narrow wedge and get a position with these platforms, build on top of them and then build value. And I think that's, uh, the number one question people ask me, it's like, okay, how do I build value on top of these analytic packages? So if I'm a startup or I'm a big company, I also want to leverage these high level abstractions and build on top of it. How do you talk about that? How do you explain that? Because that's what people kind of want to know is like, okay, is it enabling me or do I have to fend for myself later? This is kind of, it comes up a lot. >>That's a great question. And, um, you know, if you saw, uh, Goldman's announcement this week, which is about bringing, building their cloud on top of AWS, it's a great example of using our capabilities in terms of infrastructure and analytics and machine learning to really allow them to take what's value added about Goldman and their position to financial markets, to build something value, add, and create a ton of value for Goldman, uh, by leveraging the things that we offer. And to us, that's an ideal outcome because it's a win-win for us in Goldman, but it's also a win for Goldman and their customers. >>That's what we call the Supercloud that's the opportunity. So is there a lot of Goldmans opportunities out there? Is that just a, these unicorns, are these sites? I mean, how do you, I mean, that's Goldman Sachs, they're huge. Is there, is this open to everybody? >>Absolutely. I mean, that's been one of the, uh, you know, one of the core ideas behind AWS was we wanted to give anybody any developer access to the same technology that the world's largest corporations had. And, uh, that's what you have today. The things that Goldman uses to build that cloud are available to anybody. And you can start for a few pennies scale up, uh, you know, into the petabytes and beyond >>When I was talking to Adams, Lipski when I met with him prior to re-invent, I noticed that he was definitely had an affinity towards the data, obviously he's Amazonia, but he spent time at Tableau. So, so as he's running that company, so you see that kind of mindset of the data advantage. So I have to ask you, because it's something that I've been talking about for a while and I'm waiting for it to emerge, but I'm not sure it's going to happen yet. But what infrastructure is code was for dev ops and then dev sec ops, there's almost like a data ops developing where data as code or programmable data. If I can connect the dots of what Swami's saying, what you're doing is this is like a new horizontal layer of data of freely available data with some government governance built in that's right. So it's, data's being baked into everything. So data is any ingredient, not a query to some database, it's gotta be baked into the apps, that's data as code that's. Right. So it's almost a data DevOps kind of vibe. >>Yeah, no, you're absolutely right. And you know, you've seen it with things like ML ops and so on. It's all the special case of dev ops. But what you're really trying to do is to get programmatic and systematic about how you deal with data. And it's not just data that you have. It's also publicly available data sets and it's customers sharing with each other. So building the ecosystem, our data, and we've got things like our open data program where we've got publicly hosted data sets or things like the AWS data exchange where customers can actually monetize data. So it's not just data as code, but now data as a monetizeable asset. So it's a really exciting time to be in the data business. >>Yeah. And I think it's so many too. So I've got to ask you while I got you here since you're an expert. Um, okay. Here's my problem. I have a lot of data. I'm nervous about it. I want to secure it. So if I try to secure it, I'm not making it available. So I want to feed the machine learning. How do I create an architecture where I can make it freely available, but yet maintain the control and the comfort that this is going to be secure. So what products do I buy? >>Yeah. So, uh, you know, a great place to start at as three. Um, you know, it's one of the best places for data lakes, uh, for all the reasons. That's why we talked about your ability scale costs. You can then use lake formation to really protect and govern that data so you can decide who's allowed to see it and what they're allowed to see, and you don't have to create multiple copies. So you can define that, you know, this group of partners can see a, B and C. This group can see D E and F and the system enforces that. And you have a central point of control where you can monitor what's happening. And if you want to change your mind, you can do that instantly. And all access can be locked down that you've got a variety of encryption capabilities with things like KMS. And so you can really lock down your data, but yet keep it open to the parties that you want and give them specifically the access that you want to give them. And then once you've done that, they're free to use that data, according to the rules that you defined with the analytics tools that we offer to go drive value, create insight, and do something >>That's lake formation. And then you got a Thena querying. Yes, we got all kinds of tooling on top of it. >>It's all right. You can have, uh, Athena query and your data in S3 lake formation, protecting it. And then SageMaker is integrated with Athena. So you can pull that data into SageMaker for machine learning, interrogate that data, using natural language with things like QuickSight Q a like we demoed. So just a ton of power without having to really think too deeply about, uh, developing expert skill sets in this. >>So the next question I want to ask you is because that first part of the great, great, great description, thank you very much. Now, 5g in the edges here, outpost, how was the analytics going on that as edge becomes more pervasive in the architecture? >>Yeah, it's going to be a key part of this ecosystem and it's really a continuum. So, uh, you know, we find customers are collecting data at the edge. They might be making local ML or inference type decisions on edge devices, or, you know, automobiles, for example. Uh, but typically that data with some point will come back into the cloud, into S3 will be used to do heavy duty training, and then those models get pushed back out to the edge. And then some of the things that we've done in Athena, for example, with federated query, as long as you have a network path, and you can understand what the data format or the database is, you can actually run a query on that data. So you can run real-time queries on data, wherever it lives, whether it's on an edge device, on an outpost, in a local zone or in your cloud region and combine all of that together in one place. >>Yeah. And I think having that data copies everywhere is a big thing deal. I've got to ask you now that we're here at reinvent, what's your take we're back in person last year was all virtual. Finally, not 60,000 people, like a couple of years ago, it's still 27,000 people here, all lining up for the sessions, all having a great time. Um, all good. What's the most important story from your, your area that people should pay attention to? What's the headline, what's the top news? What should people pay attention to? >>Yeah, so I think first off it is awesome to be back in person. It's just so fun to see customers and to see, I mean, you, like, we've been meeting here over the years and it's, it's great to so much energy in person. It's been really nice. Uh, you know, I think from an analytics perspective, there's just been a ton of innovation. I think the core idea for us is we want to make it easy for customers to use the right tool for the right job to get insight from all of their data as cost effectively as possible. And I think, uh, you know, I think if customers walk away and think about it as being, it's now easier than ever for me to take advantage of everything that AWS has to offer, uh, to make sense of all the data that I'm generating and use it to drive business value, but I think we'll have done our jobs. Right. >>What's the coolest thing that you're seeing here is that the serverless innovation, is it, um, the new abstraction layer with data high level services in your mind? What's the coolest thing. Got it. >>It's hard to pick the coolest that sticks like kicking the candies. I mean, I think the, uh, you know, the continued innovation in terms of, uh, performance and functionality in each of our services is a big deal. I think serverless is a game changer for customers. Uh, and then I think really the infusion of machine learning throughout all of these systems. So things like Redshift ML, Athena ML, Pixar, Q a just really enabling new experiences for customers, uh, in a way that's easier than it ever has been. And I think that's a, that's a big deal and I'm really excited to see what customers do with it. >>Yeah. And I think the performance thing to me, the coolest thing that I'm seeing is the graviton three and the gravitron progression with the custom stacks with all this ease of use, it's just going to be just a real performance advantage and the costs are getting lowered. So I think the ECE two instances around the compute is phenomenal. No, >>Absolutely. I mean, I think the hardware and Silicon innovation is huge and it's not just performance. It's also the energy efficiency. It's a big deal for the future reality. >>We're at an inflection point where this modern applications are being built. And in my history, I'm old, my birthday is today. I'm in my fifties. So I remember back in the eighties, every major inflection point when there was a shift in how things were developed from mainframe client server, PC inter network, you name it every time the apps change, the app owners, app developers all went to the best platform processing. And so I think, you know, that idea of system software applications being bundled together, um, is a losing formula. I think you got to have that decoupling large-scale was seeing that with cloud. And I think now if I'm an app developer, whether whether I'm in a large ISV in your ecosystem or in the APN partner or a startup, I'm going to go with my software runs the best period and where I can create value. That's right. I get distribution, I create value and it runs fast. I mean, that's, I mean, it's pretty simple. So I think the ecosystem is going to be a big action for the next couple of years. >>Absolutely. Right. And I mean, the ecosystem's huge and I think, um, and we're also grateful to have all these partners here. It's a huge deal for us. And I think it really matters for customers >>What's on your roadmap this year, what you got going on. What can you share a little bit of a trajectory without kind of, uh, breaking the rules of the Amazonian, uh, confidentiality. Um, what's, what's the focus for the year? What do you what's next? >>Well, you know, as you know, we're always talking to customers and, uh, I think we're going to make things better, faster, cheaper, easier to use. And, um, I think you've seen some of the things that we're doing with integration now, you'll see more of that. And, uh, really the goal is how can customers get value as quickly as possible for as low cost as possible? That's how we went to >>Yeah. They're in the longterm. Yeah. We've always say every time we see each other data is at the center of the value proposition. I've been saying that for 10 years now, it's actually the value proposition, powering AI. And you're seeing because of it, the rise of superclouds and then the superclouds are emerging. I think you guys are the under innings of these emerging superclouds. And so it's a huge treading, the Goldman Sachs things of validation. So again, more data, the better, sorry, cool things happening. >>It is just it's everywhere. And the, uh, the diversity of use cases is amazing. I mean, I think from, you know, the Australia swimming team to, uh, to formula one to NASDAQ, it's just incredible to see what our >>Customers do. We see the great route. Good to see you. Thanks for coming on the cube. >>Pleasure to be here as always John. Great to see you. Thank you. Yeah. >>Thanks for, thanks for sharing. All of the data is the key to the success. Data is the value proposition. You've seen the rise of superclouds because of the data advantage. If you can expose it, protect it and govern it, unleashes creativity and opportunities for entrepreneurs and businesses. Of course, you got to have the scale and the price performance. That's what doing this is the cube coverage. You're watching the leader in worldwide tech coverage here in person for any of us reinvent 2021 I'm John ferry. Thanks for watching.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 >>sponsored by >>Intel and AWS. Yeah, Welcome back to the cubes. Ongoing coverage of aws reinvent virtual the Cuba has gone virtual to. We're gonna talk about machine intelligence, cloud and transformation in healthcare. An industry that is rapidly evolving and reinventing itself to provide better quality care faster and more accurate diagnoses. And this has to be done at lower cost. And with me to talk about This is Dr Taha. Awesome. Who? Who is the director of machine learning at Amazon Web services? Doctor, good to see you again. Thanks for coming on. >>Thank you so much. Good to see Dave. >>Yeah, last time we talked, I think it was a couple of years ago. We remember we were talking about Amazon. Comprehend medical. And, of course, you've been so called so called raising the bar, so to speak, Over the past 24 months, you made some announcements today, including Amazon Health Lake, which we're gonna talk about. Tell us about it. >>Well, we're really excited about eso our customers. Amazon Half Lake, a new hip eligible service for health care providers health insurance companies and pharmaceutical companies to securely store, transform Aquarian, analyze health data in the cloud at petabytes scale, a Amazon health lake uses machine learning models trained to automatically understand context and extract meaningful data from medical data from raw, disparate information such as medications, procedures, Um, and diagnosis. Um Therefore, revolutionizing a process that was traditionally manual Arab prone and highly costly requires a lot of expertise on teams within these organizations. What healthcare Catholic does is it tags and indexes every piece of information on then structure in an open standard. The fire standard, or that's the fast healthcare interoperability resource, is in order to provide a complete view 360 degree view of each patient in a consistent way so you'll be able to curry and share that data securely. It also integrates with other machine learning services and a lot of services that AWS offers, such as Amazon Quicksight or Amazon sage maker. In order to visualize and understand the relationships in the data identify trends, Andi also make predictions. The other great benefit is since the Amazon health lake automatically structures all the health care organizations data into open standard. The fire industry format. The information now can be easily and securely shared between systems. Health systems onda with third party applications. So eso providers, health care providers will will enjoy the ability to collaborate more effectively with each other but also allowing patients and federal access to their medical information. >>I think now, so one of things that people are gonna ask is Okay, wait a minute. Hip eligible Is that like cable ready or HD ready? And but people need to understand that it's a shared responsibility. But you can't come out of the box and be HIPPA compliant there a number of things and processes, uh, that that your customer has to do. Maybe you could explain that a little >>bit. Absolutely. I mean, in practice a little bit. This is a very, very important thing, and and it's something that we really fully baked into the service and how we built Also the service, especially dealing with health care information. First off, AWS, as you know, is vigilant about customers, privacy and security. It is job zero for us. Your data and Health Lake is secure, compliant, and auditable data version is enabled to protect um, the data against any accident collision, for example, and per fire sophistication. If you are to delete one piece of data, it will be version it will be on Lee. Hidden from analysis is a result not believed from the service. So your dad is always encrypted on by using your own customer. Manage key in a keys in a single tenant. Architectures is another added benefit to provide the additional level of protection when the data is access and search for example, every time inquiry a value, for example, someone's glucose level if the data is encrypted and decrypted and and and and so on and so forth. So, additionally, this system in a single tenant architectures so that that way the data, uh, the key. The same key is not shared across multiple customers. So you're saying full ownership and control of your data along with the ability to encrypt, protect move, deleted in alignment with organization, security and policies. Now a little bit about the hip eligibility. It's a term that AWS uses eso for customers storing protected health information or P h. I A. DBS by its business associate agreement on also Business Associate amendment require customers to encrypt data addressed in transit when they're using area services. There are over 100 services today. They're hip eligible, including the Amazon. Health like this is very important, especially for, uh enabling discovered entities and their business associates subject to HIPAA regulations, and is be able to kind of and this shared model between what a the best protection and services and how it can process and store and managed ph I. But there's additional level of compliance is required on the on the customer side, um, about you know, anywhere from physical security thio how each application can be built, which is no different than how you manage it. For example, today in your own that data center, what not? But this is why many cats, growing number of health care providers, um, players as well as I, because professionals are using AWS utility based cloud services today to process, store and transmit pH. I. >>So tell us more about who was gonna benefit from this new capability, what types of organizations and would be some of the outcomes for for for patients, >>absolutely every healthcare provider today, or a payer like a health insurance company or a life. Science companies such as Pharma Company is just trying to solve the problem of organizing instruction their data. Because if you do, you make better sense of this information from better patient support decisions. Design better clinical trials, operate more efficiently, understand population health trends on be able them to share that that security. It's really all starts with making sense of that of that data. And those are the ultimate customers that we're trying to empower with the Amazon Amazon Health Lake. Um, >>well, And of course, there's downstream benefits for the patient. Absolutely. That's ultimately what we're trying to get to. I mean, absolutely. I mean, I set up front. I mean, it's it's everybody you know, feels the pain of high health care costs. A lot of times you're trying to get to see a doctor, and it it takes a long time now, especially with with covitz so and much of this, oftentimes it's even hard to get access to your own data s. So I think you're really trying to attack that problem. Aren't >>you absolutely give you a couple of examples like I mean, today, the most widely used clinical models, uh, in practice to predict. Let's say someone's disease risk lack personalization. Um, it's you and I can be lumped in the same in the same bucket, for example, based on a few attributes that common, UM, data elements or data points, which is problematic also because the resulting models produce are imprecise. However, if you look at an individual's medical records, for example, you know a diabetic type two diabetic patients there, if you look at the entire history and from all this information coming to them, whether it's doctor knows medication dosages, which line of treatment the second line treatment, uh, continuous monitoring of glucose and that sort of thing is over hundreds. You know, there are hundreds of thousands of data points in their entire medical history, but none of this is used today. At the point of care on. You want all this information to be organized, aggregated, structured in a way that you will be able to build even better models easily queried this information, aan den observed the health of the individual in comparison with the rest of the population because at that point you'll be able to make those personalized decisions and then also for patient engagement with the health lake ability to now emit data back on dshea air securely the a p i s that conform to the fire standard. So third party applications can be built also, um, Thio provide the access whether that's for analytics or digital health application, for example, a patient accident, that information all that is very, very, very important. Because ultimately you wanna, um, get at better care of these these populations better. In Roma, clinical trials reduce duplicative tests and waste and health care systems. All that comes when you have your entire information available in a way that structured and normalize on be able to Korean and analyze andan the seamless integration between the health lake and the arrest of the services like Amazon sage maker. You can really start to understand relationships and meaning of the information, build better, better decision support models and predictive models at the individual on a population level. >>Yeah, right. You talked about all this data that's not not really used on. It's because it's not accessible. I presume it's not in in one place that somebody can analyze its not standardized. It's not normalized. Uh, is that >>right, that is the biggest. That is the biggest challenge for every healthcare provider, pair or life science organization today. If you look at this data, it's difficult to work with. Medical health. Data is really different that I siloed spread out across multiple systems, and it's sort of not incompatible formats. If you look at the last decade, I mean, one of the greatest things is we witnessed a great transformation healthcare towards digitization of the record. But your data is scattered across many of these systems anywhere from found your family history, the clinical observation, diagnosis and treatment. When you see the vast majority of that data is contained in unstructured medical records like Dr Notes P. D efs of insurance, um, of laboratory reports or insurance claims and forms with the With With Covad, we've seen in quite a bit of uptake of digital sort of, um uh, delivery of care such as telemedicine and recorded audios and videos, X rays and images, uh, the large propagation of digital health, APS and and digital assistances and on and wearables and as well as these sort of monitors like glucose, monitor or not, data come in all shapes and form and form and start across all these things. It's a huge heavy lift for any health care organization to be able to aggregate normalized stored securely on. Then also be able to kind of analyze this information and structure in a way that zizi to scale. Um uh, with regards, Thio, the kind of problems that you're going after. >>Well, Dr Cox, who We have to leave it there. Thank you so much. I have been saying for years in the Cube. When is it? That machine's gonna be able to make it make better diagnoses than doctors. Maybe that's the wrong question. Maybe it's machines helping doctors make faster and more accurate diagnoses and lowering our costs. Thanks so much for coming. >>Thank you very much. Appreciate it. Thank you. >>Thank you for watching everybody keep it right there. This is Dave Volonte. We'll be back with more coverage of aws reinvent 2020. You virtual right after this short break

>>From around the globe. It's the queue with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hello, and welcome back to the cube live coverage of reinvent 2020 virtual. We're not there this year. It's the cube virtual. We are the cube virtual. I'm your host, John fro with Dave Alante and analyzing our take on the partner day. Um, keynotes and leadership sessions today was AWS APN, which is Amazon partner network global partner network day, where all the content being featured today is all about the partners and what Amazon is doing to create an ecosystem, build the ecosystem, nurture the ecosystem and reinvent what it means to be a partner. Dave, thanks for joining me today on the analysis of Amazon's ecosystem and partner network and a great stuff today. Thanks for coming on. >>Yeah, you're welcome. I mean, watch the keynote this morning. I mean, partners are critical to AWS. Look, the fact is that when, when AWS was launched, it was the developers ate it up. You know, if you're a developer, you dive right in infrastructure is code beautiful. You know, if you're mainstream it, this thing's just got more complex with the cloud. And so there's, there's a big gap right between how I, where I am today and where I want to be. And partners are critical to help helping people get there. And we'll talk about the details of specifically what Amazon did, but I mean, especially when John, when you look at things like smaller outposts, you know, going hybrid, Andy Jassy redefining hybrid, you need partners to really help you plan design, implement, manage at scale. >>Yeah. You know, one of the things I'm always, um, you know, saying nice things about Amazon, but one of the things that they're vulnerable on in my opinion is how they balanced their own SAS offerings and with what they develop in the ecosystem. This has been a constant, um, challenge and, and they've balanced it very well. Um, so other vendors, they are very clear. They make their own software, right. And they have a channel and it's kind of the old playbook. Amazon's got to reinvent the playbook here. And I think that's, what's key today on stage Doug Yom. He's the, uh, the leader you had, um, also Dave McCann who heads up marketplace and Sandy Carter who heads up worldwide public sector partners. So Dave interesting combination of three different teams, you had the classic ISV partners in the ecosystem, the cohesiveness of the world, the EMCs and so on, you had the marketplace with Dave McCann. That's where the future of procurement is. That's where people are buying product and you had public sector, huge tsunami of innovation happening because of the pandemic and Sandy is highlighting their partners. So it's partner day it's partner ecosystem, but multiple elements. They're moving marketplace where you buy programs and competencies with public sector and then ISV, all of those three areas are changing. Um, I want to get your take because you've been following ecosystems years and you've been close to the enterprise and how they buy your, >>And I think, I think John, Oh, a couple of things. One is, you know, Dave McCann was talking a lot about how CIO is one of modernize applications and they have to rationalize, and it will save some of that talk for later on, you know, Tim prophet on. But there's no question that Amazon's out to reinvent, as you said, uh, the whole experience from procurement all the way through, and, you know, normally you had to, to acquire services outside of the marketplace. And now what they're doing is bundling the services and software together. You know, it's straightforward services, implementation services, but those are well understood. The processes are known. You can pretty much size them and price them. So I think that's a huge opportunity for partners and customers to reduce friction. I think the other thing I would say is ecosystems are, are critical. >>Uh, one of the themes that we've been talking about in the cube as we've gone from a product centric world in the old days of it to a platform centric world, which has really been the last decade has been about SAS platforms and cloud platforms. And I think ecosystems are going to be a really power, the new innovation in the coming decade. And what I mean by that is look, if you're just building a service and Amazon is going to do that same service, you know, you got to keep innovating. And one of the ways you can innovate is you can build on ecosystems. There's all this data within industries, across industries, and you can through the partner network and through customer networks within industry start building new innovation around ecosystems and partners or that glue, Amazon's not going to go in. And like Jandy Jesse even said in the, uh, in his fireside chat, you know, customers will ask us for our advice and we're happy to give it to them, but frankly partners are better at that nitty gritty hardcore stuff. They have closer relationships with the customers. And so that's a really important gap that Amazon has been closing for the last, you know, frankly 10 years. And I think that to your point, they've still got a long way to go, but that's a huge opportunity in that. >>A good call out on any Jess, I've got to mention that one of the highlights of today's keynote was on a scheduled, um, Andy Jassy fireside chat. Uh, normally Andy does his keynote and then he kind of talks to customers and does his thing normally at a normal re-invent this time he came out on stage. And I think what I found interesting was he was talking about this builder. You always use the word builder customer, um, solutions. And I think one of the things that's interesting about this partner network is, is that I think there's a huge opportunity for companies to be customer centric and build on top of Amazon. And what I mean by that is, is that Amazon is pretty cool with you doing things on top of their platform that does two things serves the customer's needs better than they do, and they can make more money on and other services look at snowflake as an example, um, that's a company built on AWS. I know they've got other clouds going on, but mainly Amazon Zoom's the same way. They're doing a great solution. They've got Redshift, Amazon, Amazon's got Redshift, Dave, but also they're a customer and a partner. So this is the dynamic. If you can be successful on Amazon serving customers better than Amazon does, that's the growth hack. That's the hack on Amazon's partner network. If you could. >>I think, I think Snowflake's a really good example. You snowflake you use new Relic as an example, I've heard Andy Jess in the past use cloud air as an example, I like snowflake better because they're, they're sort of thriving. And so, but, but I will say this there's a, they're a great example of that ecosystem that we just talked about because yes, not only are they building on AWS, they're connecting to other clouds and that is an ecosystem that they're building out. And Amazon's got a lot of snowflake, I guess, unless you're the Redshift team, but, but generally speaking, Snowflake's driving a lot of business for Amazon and Andy Jesse addressed that in that, uh, in that fireside chat, he's asked that question a lot. And he said, look, we, we, we have our primary services. And at the same time we want to enable our partners to be successful. And snowflake is a really good example of that. >>Yeah. I want to call out also, uh, yesterday. Um, I had our Monday, I should say Tuesday, December 1st, uh, Jesse's keynote. I did an interview with Jerry chin with gray lock. He's investing in startups and one of the things he observed and he pointed out Dave, is that with Amazon, if you're, if you're a full all-in in the cloud, you're going to take advantage of things that are just not available on say on premises that is data patterns, other integrations. And I think one of the things that Doug pointed out was with interoperability and integration with say things like the SAS factor that they put out there there's advantages for being in the cloud specifically with Amazon, that you can get on integrations. And I think Dave McCann teases that out with the marketplace when they talk about integrations. But the idea of being in the cloud with all these other partners makes integration and interoperability different and unique and better potentially a differentiator. This is going to become a huge deal. >>I didn't pick up on that because yesterday I thought I wasn't in the keynote. I think it was in the analyst one-on-one with, with Jesse, he talked about, you know, this notion that people, I think he was addressing multi-cloud he didn't use that term, but this notion of an abstraction layer and how it does simplify things in, in his basic, he basically said, look, our philosophy is we want to have, you know, the, the ability to go deep with the primitives and have that fine grain access, because that will give us control. A lot of times when you put in this abstraction layer, which people are trying to do across clouds, you know, it limits your ability to really move fast. And then of course it's big theme is, is this year, at the same time, if you look at a company who was called out today, like, like Octa, you know, when you do an identity management and single sign-on, you're, you're touching a lot of pieces, there's a lot of integration to your point. >>So you need partners to come in and be that glue that does a lot of that heavy lifting that needs to needs to be done. Amazon. What Jessie was essentially saying, I think to the partner network is, look, we're not going to put in that abstraction layer. You're going to you, you got to do that. We're going to do stuff maybe between our own own services like they did with the, you know, the glue between databases, but generally speaking, that's a giant white space for partner organizations. He mentioned Okta. He been talked about in for apt Aptio. This was Dave McCann, actually Cohesity came up a confluent doing fully managed Kafka. So that to me was a signal to the partners. Look, here's where you guys should be playing. This is what customers need. And this is where we're not going to, you know, eat your lunch. >>Yeah. And the other thing McCann pointed out was 200 new Dave McCann pointed out who leads these leader of the, of the marketplace. He pointed out 200 new ISP. ISV is out there, huge news, and they're going to turn already. He went, he talked with his manage entitlements, which got my attention. And this is kind of an, um, kind of one of those advantage points that it's kind of not sexy and mainstream to talk about, but it's really one of those details. That's the heavy lifting. That's a pain in the butt to deal with licensing and tracking all this compliance stuff that goes on under the covers and distribution of software. I think that's where the cloud could be really advantaged. And also the app service catalog registry that he talked about and the professional services. So these are areas that Amazon is going to kind of create automation around. >>And as Jassy always talks about that undifferentiated heavy lifting, they're going to take care of some of these plumbing issues. And I think you're right about this differentiation because if I'm a partner and I could build on top of Amazon and have my own cloud, I mean, let's face it. Snowflake is a born in the cloud, in the cloud only solution on Amazon. So they're essentially Amazon's cloud. So I think the thing that's not being talked about this year, that is probably my come up in future reinvents is that whoever can build their own cloud on top of Amazon's cloud will be a winner. And I, I talked about this years ago, data around this tier two, I call it tier two clouds. This new layer of cloud service provider is going to be kind of the, on the power law, the, the second wave of cloud. >>In other words, you're on top of Amazon differentiating with a modern application at scale inside the cloud with all the other people in there, a whole new ecosystem is going to emerge. And to me, I think this is something that is not yet baked out, but if I was a partner, I would be out there planning like hell right now to say, I'm going to build a cloud business on Amazon. I'm going to take advantage of the relationships and the heavy lifting and compete and win that way. I think that's a re redefining moment. And I think whoever does that will win >>And a big theme around reinventing everything, reinvent the industry. And one of the areas that's being reinvented as is the, you know, the VAR channel really well, consultancies, you know, smaller size for years, these companies made a ton of dough selling boxes, right? All the, all the Dell and the IBM and the EMC resellers, you know, they get big boats and big houses, but that business changed dramatically. They had to shift toward value, value, value add. So what did they do? They became VMware specialists. They came became SAP specialists. There's a couple of examples, maybe, you know, adding into security. The cloud was freaking them out, but the cloud is really an opportunity for them. And I'll give you an example. We've talked a lot about snowflake. The other is AWS glue elastic views. That's what the AWS announced to connect all their databases together. Think about a consultancy that is able to come in and totally rearchitect your big data life cycle and pipeline with the people, the processes, the skillsets, you know, Amazon's not going to do that work, but the upside value for the organizations is tremendous. So you're seeing consultancies becoming managed service providers and adding all kinds of value throughout the stack. That's really reinvention of the partnership. >>Yeah. I think it's a complete, um, channel strategy. That's different. It doesn't, it looks like other channels, but it's not, it's, it's, it's driven by value. And I think this idea of competing on value versus just being kind of a commodity play is shifting. I think the ISV and the VARs, those traditional markets, David, as you pointed out, are going to definitely go value oriented. And you can just own a specialty area because as data comes in and when, and this is interesting. And one of the key things that Andy Jassy said in his fireside chat want to ask directly, how do partners benefit when asked about his keynote, how that would translate to partners. He really kind of went in and he was kind of rambling, but he, he, he hit the chips. He said, well, we've got our own chips, which means compute. Then he went into purpose-built data store and data Lake data, elastic views SageMaker Q and QuickSight. He kind of went down the road of, we have the horsepower, we have the data Lake data, data, data. So he was kind of hinting at innovate on the data and you'll do okay. >>Well, and this is again, we kind of, I'm like a snowflake fan boy, you know, in the way you, you like AWS. But look, if you look at AWS glue elastic views, that to me is like snowflakes data cloud is different, a lot of pushing and moving a date, a lot of copying data. But, but this is a great example of where like, remember last year at reinvent, they said, Hey, we're separating compute from storage. Well, you know, of course, snowflake popularized that. So this is great example of two companies thriving that are both competitors and partners. >>Well, I've got to ask you, you know, you, you and I always say we kind of his stories, we've been around the block on the enterprise for years. Um, where do you Mark the, um, evolution of their partner? Because again, Amazon has been so explosive in their growth. The numbers have been off the charts and they've done it well with and pass. And now you have the pandemic which kind of puts on full display, digital transformation. And then Jassy telegraphing that the digital global it spend is their next kind of conquering ground, um, to take, and they got the edge exploding with 5g. So you have this kind of range and they doing all kinds of stuff with IOT, and they're doing stuff in you on earth and in space. So you have this huge growth and they still don't have their own fully oriented business model. They rely on people to build on top of Amazon. So how do you see that evolving in your opinion? Because they're trying to add their own Amazon only, we've got Redshift that competes with others. How do you see that playing out? >>So I think it's going to be specialized and, and something that, uh, that I've talked about is Amazon, you know, AWS in the old day, old days being last decade, they really weren't that solution focused. It was really, you know, serving the builders with tooling, with you, look at something like what they're doing in the call center and what they're doing at the edge and IOT there. I think they're, so I think their move up the stack is going to be very solution oriented, but not necessarily, you know, horizontal going after CRM or going after, you know, uh, supply chain management or ERP. I don't think that's going to be their play. I think their play is going to be to really focus on hard problems that they can automate through their tooling and bring special advantage. And that's what they'll SAS. And at the same time, they'll obviously allow SAS players. >>It's just reminds me of the early days when you and I first met, uh, VMware. Everybody had to work with VMware because they had a such big ecosystem. Well, the SAS players will run on top. Like Workday does like Salesforce does Infour et cetera. And then I think you and I, and Jerry Chen talked about this years ago, I think they're going to give tools to builders, to disrupt the service now is in the sales forces who are out buying companies like crazy to try to get a, you know, half, half a billion dollar, half a trillion dollar market caps. And that is a really interesting dynamic. And I think right now, they're, they're not even having to walk a fine line. I think the lines are reasonably clear. We're going up to database, we're going to do specialized solutions. We're going to enable SAS. We're going to compete where we compete, come on, partner ecosystem. And >>Yeah, I, I, I think that, you know, the Slack being bought by Salesforce is just going to be one of those. I think it's a web van moment, you know, um, you know, where it's like, okay, Slack is going to go die on Salesforce. Okay. I get that. Um, but it's, it's just, it's just, it's just, it's just old school thinking. And I think if you're an entrepreneur and if you're a developer or a partner, you could really reinvent the business model because if you're, dis-aggregating all these other services like you can compete with Salesforce, Slack has now taken out of the game with Salesforce, but what Amazon is doing with say connect, which they're promoting heavily at this conference. I mean, you hear it, you heard it on Andy Jessie's keynote, Sandy Carter. They've had huge success with AWS connect. It's a call center mindset, but it's not calling just on phones. >>It's contact that is descent, intermediating, the Salesforce model. And I think when you start getting into specialists and specialism in channels, you have customer opportunity to be valuable. And I think call center, these kinds of stories that you can stand up pretty quickly and then integrate into a business model is going to be game changing. And I think that's going to going to a lot of threat on these big incumbents, like Salesforce, like Slack, because let's face it. Bots is just the chat bot is just a call center front end. You can innovate on the audio, the transcriptions there's so much Amazon goodness there, that connect. Isn't just a call center that could level the playing field and every vertical >>Well, and SAS is getting disrupted, you know, to your, to your point. I mean, you think about what happened with, with Oracle and SAP. You had, you know, these new emerging players come up like, like Salesforce, like Workday, like service now, but their pricing model, it was all the same. We lock you in for a one-year two-year three-year term. A lot of times you have to pay up front. Now you look at guys like Datadog. Uh, you, you look at a snowflake, you look at elastic, they're disrupting the Splunks of the world. And that model, I think that SAS model is right for disruption with a consumption pricing, a true cloud pricing model. You combine that with new innovation that developers are going to attack. I mean, you know, people right now, they complain about service now pricing, they complain about Splunk pricing. They, you know, they talk about, Oh, elastic. We can get that for half the price Datadog. And so I'm not predicting that those companies service now Workday, the great companies, but they are going to have to respond much in the same way that Oracle and SAP had to respond to the disruption that they saw. >>Yeah. It's interesting. During the keynote, they'll talk about going out to the mainframes today, too. So you have Amazon going into Oracle and Microsoft, and now the mainframes. So you have Oracle database and SQL server and windows server all going to being old school technologies. And now mainframe very interesting. And I think the, this whole idea of this SAS factory, um, got my attention to Cohesity, which we've been covering Dave on the storage front, uh, Mo with the founder was on stage. I'm a data management as a service they're part of this new SAS factory thing that Amazon has. And what they talk about here is they're trying to turn ISV and VARs into full-on SAS providers. And I think if they get that right with the SAS factory, um, then that's going to be potentially game changing. And I'm gonna look at to see if what the successes are there, because if Amazon can create more SAS applications, then their Tam and the global it market is there is going to, it can be mopped up pretty quickly, but they got to enable it. They got to enable that quickly. Yeah. >>Enabling to me means not just, and I think, you know, when Jesse answered your question, I saw it in the article that you wrote about, you know, you asked them about multi-cloud and it, to me, it's not about running on AWS and being compatible with Azure and being compatible with Google. No, it's about that frankly abstraction layer that he talked about, and that's what Cohesity is trying to do. You see others trying to do it as well? Snowflake for sure. It's about abstracting that complexity away and adding value on top of the cloud. In other words, you're using the cloud for scale being really expert at taking advantage of the native cloud services, which requires is that Jessie was saying different API APIs, different control, plane, different data plane, but taking that complexity away and then adding new value on top that's white space for a lot of players there. And, and, and I'll tell you, it's not trivial. It takes a lot of R and D and it takes really smart people. And that's, what's going to be really interesting to see, shake out is, you know, can the Dell and HPE, can they go fast enough to compete with the, the Cohesity's you've got guys like CLU Mayo coming in that are, that are brand new. Obviously we talked about snowflake a lot and many others. >>I think there's going to be a huge change in expectations, experience, huge opportunity for people to come in with unique solutions. We're going to have specialty programming on the cube all day today. So if you're watching us here on the Amazon channel, you know that we're going to have an all of a sudden demand. There's a little link on our page. On the, on the, um, the Amazon reinvent virtual event platform, click here, the bottom, it's going to be a landing page, check out all the interviews as we roll them out all day. We got a great lineup, Dave, we got Nutanix pure storage, big ID, BMC, Amazon leaders, all coming in to talk today. Uh, chaos search ed Walsh, Rachel Rose, uh, Medicar Kumar, um, Mike Gill, flux, tons of great, great, uh, partners coming in and they're going to share their story and what's working for them and their new strategies. And all throughout the day, you're going to hear specific examples of how people are changing and reinventing their business development, their partnership strategies on the product, and go to market with Amazon. So really interesting learnings. We're going to have great conversations all throughout the day. So check it out. And again, everything's going to be on demand. And when in doubt, go to the cube.net, we have everything there and Silicon angle.com, uh, for all the great coverage. So >>I don't think John is, we're going to have a conversation with him. David McCann touched on this. You talked about the need for modernization and rationalization, Tim Crawford on, on later. And th this is, this is sort of the, the, uh, the call-out that Andy Jassy made in his keynote. He gave the story of that one. CIO is a good friend of his who said, Hey, I love what you're doing, but it's not going to happen on my watch. And, and so, you know, Jessie's sort of poking at that, that, uh, complacency saying, guys, you have to reinvent, you have to go fast, you have to keep moving. And so we're gonna talk a little bit about what, what does that mean to modernize applications, why the CIO is want to rationalize what is the role of AWS and its ecosystem and providing that, that, that level of innovation, and really try to understand what the next five to seven years are gonna look like in that regard. >>Funny, you mentioned, uh, Andy Jesuit that story. When I had my one-on-one conversation with them, uh, he was kind of talking about that anonymous CIO and I, if people don't know Andy, he's a big movie buff, too, right? He loves it goes to Sundance every year. Um, so I said to him, I said, this error of digital transformation, uh, is kind of like that scene in the godfather, Dave, where, um, Michael Corleone goes to Tom Hagen, Tom, you're not a wartime conciliary. And what he meant by that was is that, you know, they were going to war with the other five families. I think now I think this is what chassis pointed out is that, that this is such an interesting, important time in history. And he pointed this out. If you don't have the leadership chops to lean into this, you're going to get swept away. >>And that story about the CIO being complacent. Yeah. He didn't want to shift. And the new guy came in or gal and they, and they, and they lost three years, three years of innovation. And the time loss, you can't get that back. And during this time, I think you have to have the stomach for the digital transformation. You have to have the fortitude to go forward and face the truth. And the truth is you got to learn new stuff. So the old way of doing things, and he pointed that out very aggressively. And I think for the partners, that same thing is true. You got to look in the mirror and say, where are we? What's the opportunity. And you gotta gotta go there. If not, you can wait, be swept away, be driftwood as Pat Gelsinger would say, or lean in and pick up a, pick up a shovel and start digging the new solution. >>You know what the other interesting thing, I mean, every year when you listen to Jassy and his keynotes and you sort of experienced re-invent culture comes through and John you're live in Silicon Valley, you talked to leaders of Silicon Valley, you know, well, what's the secret of success though? Nine times out of 10, they'll talk about culture, maybe 10 times out of 10. And, and, and so that's, that comes through in Jesse's keynotes. But one of the things that was interesting this year, and it's been thematic, you know, Andy, you know, repetition is important, uh, to, to him because he wants to educate people and make sure it sticks. One of the things that's really been he's been focused on is you actually can change your culture. And there's a lot of inertia. People say, well, not on my watch. Well, it doesn't work that way around here. >>And then he'll share stories about how AWS encourages people to write papers. Anybody in the organization say we should do it differently. And, and you know, they have to follow their protocol and work backwards and all of those stuff. But I believe him when he says that they're open to what you have a great example today. He said, look, if somebody says, well, it's 10 feet and somebody else says, well, it's, it's five feet. He said, okay, let's compromise and say it's seven and a half feet. Well, we know it's not seven and a half feet. We don't want to compromise. We either want to be a 10, Oh, we want to be at five, which is the right answer. And they push that. And that that's, he gives examples like that for the AWS culture, the working backwards, the frequently asked questions, documents, and he's always pushing. And that to me is very, very important and fundamental to understanding AWS. >>It's no doubt that Andy Jassy is the best CEO in the business. These days. If you look at him compared to everyone else, he's hands down, more humble as keynote who does three hour keynotes, the way he does with no notes with no, he memorize it all. So he's competitive and he's open. And he's a good leader. I think he's a great CEO. And I think it will be written and then looked back at his story this time in history. The next, I think post COVID Dave is going to be an error. We're going to look back and say the digital transformation was accelerated. Yes, all that good stuff, people process technology. But I think we're gonna look at this time, this year and saying, this was the year that there was before COVID and after COVID and the people who change and modernize will build the winners and not, and the losers will, will be sitting still. So I think it's important. I think that was a great message by him. So great stuff. All right. We gotta leave it there. Dave, the analysis we're going to be back within the power panel. Two sessions from now, stay with us. We've got another great guest coming on next. And then we have a pair of lb talk about the marketplace pricing and how enterprises have CIO is going to be consuming the cloud in their ecosystem. This is the cube. Thanks for watching..

>> 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)

>>From the sands convention center in Las Vegas, Nevada extracting the signal from the noise. It's the cube covering AWS reinvent 2015. Now your host John furrier. >>Okay. Welcome back. And we are here, live in Las Vegas, Amazon web services, AWS reinvent 2015. This is Silicon angles, the cube, our flagship program. We go out to the events, extract the signal from the noise. I'm John furry, the founders to look in an angle I'm joined here today. Special guests on the cube. Andy Jassy senior vice president of Amazon web services. Basically the CEO of AWS. Uh, great to have you on the queue. >>Great to see you. Thanks for having me. Uh, >>Great. We always tell our tech athletes, uh, on the cube and you're, I know you're a sports fan and we love the MLB highlights, great company. Uh, you're a sportsman. We want to have kind of a, uh, sports chat here about tech. Um, my first question is the keynote, your smile, this year up there, you really had some color, some Andy Jassy, you know, some, some good vibes going, you showed a picture of your daughter. You had dynamic, you were, it was good. You feel different this year. I mean, you just introduced a lot of stuff. So you had good, good support. >>Yeah. Well, you know, first of all, being in re-invent is the best time of the year for all of us data Ws. So we're always very happy to be here and be here with our customers and our partners. And then we had so much to deliver and announced to our customers that we've been holding as a secret for so long that we couldn't wait to get it out. So it was fun to be, uh, asked to be the one to actually share all that information with our customers. >>You even showed a picture of your daughter up on stage. I was talking with too many men, uh, after that, I was like, did he get permission for that to ask? So did you get permission from your daughter? Cause my kids will never let me take a picture and put it on any social media. Nevermind. A keynote. >>Uh, you know, I, I saw a bunch of tweets where people said when I got home after the conference, that I was going to be in trouble at home. But the reality is I actually told Emma that I was thinking about doing it the next morning. And she was the biggest proponent of my thinking about doing it. In fact, she had, she had suggestions of what else I could say about her in the keynote. I said, no, no, no, really this is just about a story and a bridge to the security point, in which case she lost interest, but she was absolutely fine with having her picture >>When you're on the Snapchat, you know, you made it to the top grade of the, in the family community. Sure. That'll ever happen for them. Um, I want to get your take on just your mindset right now. I mean, you've been very successful. Obviously the numbers are all in the press, you know, 7 billion David, David, Jonathan, I always speculate probably 10 billion. You built the largest storage business since NetApp was founded. You built the biggest server business you have now business Intel, all this good stuff happening. You've built a disruption machine. That's really, really changing the industry. The big whales are kind of scratching their heads. They're in turmoil. Um, how do you feel about this? I mean like I know we've talked in the past privately one-on-one you kind of didn't plan it. You're going to go with the customer's going, but you've got an engine of that's also disrupting >>Well, you know, our, our goal is to try to build a technology infrastructure platform that companies and developers to build their applications on top of. And we started off with just this core set of building blocks that were compute and storage and database. And then we've iterated really quickly over the last nine and a half years such that we now have over 50 services and lots of features within those services. And we don't think of it so much as trying to be disruptive as much as just what customers tell us they want, that allow them to move more of their workloads to the cloud and for them to be disruptive in their businesses. They're pursuing what we're about is really enabling other businesses to be successful, whether it's a startup getting going, or whether it's an enterprise is trying to reinvent themselves or whether it's a government is trying to do more for the constituency for less money, >>You know, culture and a is defined, not so much with what the company says, but what the employees do. And, and AWS has a cadence. I call it Jazziz law and you guys are always shipping products. It's kind of a dev ops ethos, but it's also one of discipline. And I know you're a humble guy, but I want to get your take on that. How has that culture fostered internally? I mean, you're constantly putting out with people on coming on the cube. They're like, man, I'm so happy. They filled in the white spaces. Is that part of the cadence now within AWS just to keep shipping more and more, >>More features? Yeah, well, you know, first of all, fairly obvious point, which is anytime you've got a, a significant size business, it's never one person and it's never one person's culture. And we have a leadership team at AWS. That's very strong, has been together for a long time. And, and that group is very committed to iterating quickly on behalf of our customers. And you know, some of that, you set a culture around what are the dates that you're going to ship? What do you ask about meetings on where we are and whether we're on track and then what's your philosophy and on when you ship the products and we have a very strong principle that we don't try to ship all singing, all dancing, monolithic products. We try to pick the minimal amount of functionality that allow our customers to use the service in some meaningful way. And then we organize ourselves and hold ourselves to the standard, to execute on iterating quickly based on what they give us feedback and what they want next. >>You know, the, the business is changing the industry all over the place. The computer industry is now integrated. You guys have led that way, that, that disruption and the innovation, what's the biggest learnings that you've personally have walked away with over the past three years, maybe 10, but in the last three years, because you guys really have moved the needle in the past three years before that certainly the foundation has said been successful, but what's the biggest learnings that's been magnified for you personally? >>Well, I mean, there've been so many. We, we could spend 20 minutes just on the learnings, but I know the one I would probably pick is that I think when we were starting AWS, we started insignificant part because we saw a very strong technology company and Amazon the retailer that was thirsty to move more quickly and needed reliable, scalable cost, effective centralized infrastructure services and what you know, so we thought it had a chance to take off because Amazon needed it. And lots of other companies that may be less technical might need it as well. But I don't think any of us really internalized just how constrained developers and companies have been over the last 30 years. They, you know, builders really want the freedom and the control over their own destiny to pursue the ideas they have that could make their businesses better. And for so long at enterprises, they were so unable to move quickly that all the people inside the company just gave up hope and thinking about new innovations because they knew it was so unlikely to get done. And when you actually give them access to infrastructure in minutes and all the supporting services, so they can get from an idea to actually testing it quickly, all of a sudden it opens up all of the ideas that a company and you get lots of people thinking constantly about your customers and how you can solve problems for them instead of a tiny thing. >>You know, I, I know you're a competitive person. I know you're humble. They don't wanna admit it, but you always say to me privately, we don't think about the competition. We think about our customers and I get that, but you are actually executing a really strong competitive strategy just by playing offense. You guys are shipping more product, but the ecosystem is also now a competitive opportunity. But for you guys and your customers talk about your mindset on that. Because on the business side, you're creating a lot of value for people to make money. Yeah. Certainly in the ecosystem side. So describe your philosophy there. And is it still early days for you guys? It's still a lot more to do. Um, and some of the opportunities that the partners are >>So many opportunities for companies of all sizes to build on top of our platform and build successful businesses and it's astounding. And then we are totally blown away with what our ecosystem partners have built on top of the platform and the success they're having in their businesses. And there's no end in sight to that. I mean, all of these areas, every single area of technology. And I think every application area too, is being reinvented and has an opportunity to have new experimentation quicker than ever because the cloud allows them >>Move much faster. And you did take some shot at the competition with Oracle, obviously they're higher priced and you and you guys are w some of the calls were like a 10th of the cost. You offering products for free migration products. So you guys have that advantage with the cost. >>You know, we've built these database products from the ground up with the cloud in mind. So the power by the cloud, they're highly scalable. They're really flexible. And they have a cost structure that's much more affordable than what the old guard products were. It's why we've been able to add a Redshift, which is our data warehouse service, which is as performant as the old guard data warehouses, but a 10th of the cost same goes for Aurora, which is our new database engine, same goes for QuickSight, which is our new business intelligence service. And so we're building them from the ground up with the cloud in mind so that our customers can move more quickly, have whatever scalability they need, and also have a better cost for the internet >>Of things. Things we're pretty pumped about that we were talking about this morning. Um, but that's kind of one of those things it's kind of out there and edge of the network, connected device, connected cars, you know, pretty obvious it's not anything new per se, but now the way the market's evolving, it's a huge opportunity, right? So I want, is that a pinch me moment for you? We, we kind of saw it out there, but now that you're on top of it, you look at and say, wow, we're really poised for this. And then how do you see that evolving for Amazon? Cause it's almost like you were where the puck came to you guys. >>Yeah, well, you know, most of the big IOT applications today are built on top of AWS. If you look at nest or drop cam or Amazon's echo in the consumer space or alumina or Tata their, their truck fleet application, they build, uh, or Phillips lighting. Those are all built on top of AWS. And yet we always believed that it was more challenging than it should have been for device manufacturers to be able to leverage the cloud. Remember the smaller the device, the less CPU it has and the less disk it has. And the more important the cloud becomes and supplementing its capabilities. So we always felt like it was more difficult than it should have been to connect to AWS. And also for application developers were building the applications that really control these devices. They didn't have tools to deal with things like identity or to deal with things like the state of these devices and be able to build applications that have much more sophisticated capabilities. So that's what our AWS IOT platform capability that Verner announced today is about. And, you know, they're going to be millions of these devices in people's homes and in people's workplaces and oil fields. And we hope that it will be much easier for a customer for companies to build these devices. Now >>I know you're super busy. Thank you so much for that time. We got to ask you one final question. Is it a, is it a thesis, a thesis internally of your business that making things easier is part of the part of the core design cause you guys keep seeping, making easier and easier is that part of the cultural directive to the theme, make things simpler and easier and elegant. >>Everything we do is about the customer and the customer experience. And we're very blessed that we have all kinds of customer feedback loops. And one of the things customers say is we'd actually love using these services. There are some folks in the organization that don't want to have to dig into the details as much, if you can provide abstractions and make it even easier, even better. So, >>So I got to ask you, the baseball question says MLP was on the keynote. What inning are we in in the cloud? >>I still think we're in the first inning. I mean, it's amazing. You know, AWS is a $7.3 billion revenue run rate business. And yet I would argue that that, that we're in really the beginning stages of the meat of enterprise and public sector adoption. And if you look at the segments that AWS has addresses infrastructure, software, hardware, and data center services, that's trillions of dollars globally. So we're, we're in the really beginning stage >>You're Ignacio to who works on your platform. You can have MLB to TV, to, you know, IOT. Yeah. >>We want to enable all of our customers build on top of our infrastructure. Thanks so much for >>Spending the time real quick, Andy Jassy here inside the cube, the CEO of ADFS, I'm sorry. SVP of AWS, senior vice president. Um, built a great team. Congratulations. Great to have you we're live here at AWS reinvent, go to siliconangle.tv to check out all the footage. Next week will be a Grace Hopper celebration of women in technology computing. Uh, watch us there. We're going to continue our coverage after this >>Short break..