>>Hey, welcome back everyone. Day three of eight of us Reinvent 2022. I'm John Farmer with Dave Volante, co-host the q Dave. 10 years for us, the leader in high tech coverage is our slogan. Now 10 years of reinvent day. We've been to every single one except with the original, which we would've come to if Amazon actually marketed the event, but they didn't. It's more of a customer event. This is day three. Is the machine learning ai keynote sws up there. A lot of announcements. We're gonna break this down. We got, we got Andy Thra here, vice President, prince Constellation Research. Andy, great to see you've been on the cube before one of our analysts bringing the, bringing the, the analysis, commentary to the keynote. This is your wheelhouse. Ai. What do you think about Swami up there? I mean, he's awesome. We love him. Big fan Oh yeah. Of of the Cuban we're fans of him, but he got 13 announcements. >>A lot. A lot, >>A lot. >>So, well some of them are, first of all, thanks for having me here and I'm glad to have both of you on the same show attacking me. I'm just kidding. But some of the announcement really sort of like a game changer announcements and some of them are like, meh, you know, just to plug in the holes what they have and a lot of golf claps. Yeah. Meeting today. And you could have also noticed that by, when he was making the announcements, you know, the, the, the clapping volume difference, you could say, which is better, right? But some of the announcements are, are really, really good. You know, particularly we talked about, one of that was Microsoft took that out of, you know, having the open AI in there, doing the large language models. And then they were going after that, you know, having the transformer available to them. And Amazon was a little bit weak in the area, so they couldn't, they don't have a large language model. So, you know, they, they are taking a different route saying that, you know what, I'll help you train the large language model by yourself, customized models. So I can provide the necessary instance. I can provide the instant volume, memory, the whole thing. Yeah. So you can train the model by yourself without depending on them kind >>Of thing. So Dave and Andy, I wanna get your thoughts cuz first of all, we've been following Amazon's deep bench on the, on the infrastructure pass. They've been doing a lot of machine learning and ai, a lot of data. It just seems that the sentiment is that there's other competitors doing a good job too. Like Google, Dave. And I've heard folks in the hallway, even here, ex Amazonians saying, Hey, they're train their models on Google than they bring up the SageMaker cuz it's better interface. So you got, Google's making a play for being that data cloud. Microsoft's obviously putting in a, a great kind of package to kind of make it turnkey. How do they really stand versus the competition guys? >>Good question. So they, you know, each have their own uniqueness and the we variation that take it to the field, right? So for example, if you were to look at it, Microsoft is known for as industry or later things that they are been going after, you know, industry verticals and whatnot. So that's one of the things I looked here, you know, they, they had this omic announcement, particularly towards that healthcare genomics space. That's a huge space for hpz related AIML applications. And they have put a lot of things in together in here in the SageMaker and in the, in their models saying that, you know, how do you, how do you use this transmit to do things like that? Like for example, drug discovery, for genomics analysis, for cancer treatment, the whole, right? That's a few volumes of data do. So they're going in that healthcare area. Google has taken a different route. I mean they want to make everything simple. All I have to do is I gotta call an api, give what I need and then get it done. But Amazon wants to go at a much deeper level saying that, you know what? I wanna provide everything you need. You can customize the whole thing for what you need. >>So to me, the big picture here is, and and Swami references, Hey, we are a data company. We started, he talked about books and how that informed them as to, you know, what books to place front and center. Here's the, here's the big picture. In my view, companies need to put data at the core of their business and they haven't, they've generally put humans at the core of their business and data. And now machine learning are at the, at the outside and the periphery. Amazon, Google, Microsoft, Facebook have put data at their core. So the question is how do incumbent companies, and you mentioned some Toyota Capital One, Bristol Myers Squibb, I don't know, are those data companies, you know, we'll see, but the challenge is most companies don't have the resources as you well know, Andy, to actually implement what Google and Facebook and others have. >>So how are they gonna do that? Well, they're gonna buy it, right? So are they gonna build it with tools that's kind of like you said the Amazon approach or are they gonna buy it from Microsoft and Google, I pulled some ETR data to say, okay, who are the top companies that are showing up in terms of spending? Who's spending with whom? AWS number one, Microsoft number two, Google number three, data bricks. Number four, just in terms of, you know, presence. And then it falls down DataRobot, Anaconda data icu, Oracle popped up actually cuz they're embedding a lot of AI into their products and, and of course IBM and then a lot of smaller companies. But do companies generally customers have the resources to do what it takes to implement AI into applications and into workflows? >>So a couple of things on that. One is when it comes to, I mean it's, it's no surprise that the, the top three or the hyperscalers, because they all want to bring their business to them to run the specific workloads on the next biggest workload. As you was saying, his keynote are two things. One is the A AIML workloads and the other one is the, the heavy unstructured workloads that he was talking about. 80%, 90% of the data that's coming off is unstructured. So how do you analyze that? Such as the geospatial data. He was talking about the volumes of data you need to analyze the, the neural deep neural net drug you ought to use, only hyperscale can do it, right? So that's no wonder all of them on top for the data, one of the things they announced, which not many people paid attention, there was a zero eight L that that they talked about. >>What that does is a little bit of a game changing moment in a sense that you don't have to, for example, if you were to train the data, data, if the data is distributed everywhere, if you have to bring them all together to integrate it, to do that, it's a lot of work to doing the dl. So by taking Amazon, Aurora, and then Rich combine them as zero or no ETL and then have Apaches Apaches Spark applications run on top of analytical applications, ML workloads. That's huge. So you don't have to move around the data, use the data where it is, >>I, I think you said it, they're basically filling holes, right? Yeah. They created this, you know, suite of tools, let's call it. You might say it's a mess. It's not a mess because it's, they're really powerful but they're not well integrated and now they're starting to take the seams as I say. >>Well yeah, it's a great point. And I would double down and say, look it, I think that boring is good. You know, we had that phase in Kubernetes hype cycle where it got boring and that was kind of like, boring is good. Boring means we're getting better, we're invisible. That's infrastructure that's in the weeds, that's in between the toes details. It's the stuff that, you know, people we have to get done. So, you know, you look at their 40 new data sources with data Wrangler 50, new app flow connectors, Redshift Auto Cog, this is boring. Good important shit Dave. The governance, you gotta get it and the governance is gonna be key. So, so to me, this may not jump off the page. Adam's keynote also felt a little bit of, we gotta get these gaps done in a good way. So I think that's a very positive sign. >>Now going back to the bigger picture, I think the real question is can there be another independent cloud data cloud? And that's the, to me, what I try to get at my story and you're breaking analysis kind of hit a home run on this, is there's interesting opportunity for an independent data cloud. Meaning something that isn't aws, that isn't, Google isn't one of the big three that could sit in. And so let me give you an example. I had a conversation last night with a bunch of ex Amazonian engineering teams that left the conversation was interesting, Dave. They were like talking, well data bricks and Snowflake are basically batch, okay, not transactional. And you look at Aerospike, I can see their booth here. Transactional data bases are hot right now. Streaming data is different. Confluence different than data bricks. Is data bricks good at hosting? >>No, Amazon's better. So you start to see these kinds of questions come up where, you know, data bricks is great, but maybe not good for this, that and the other thing. So you start to see the formation of swim lanes or visibility into where people might sit in the ecosystem, but what came out was transactional. Yep. And batch the relationship there and streaming real time and versus you know, the transactional data. So you're starting to see these new things emerge. Andy, what do you, what's your take on this? You're following this closely. This seems to be the alpha nerd conversation and it all points to who's gonna have the best data cloud, say data, super clouds, I call it. What's your take? >>Yes, data cloud is important as well. But also the computational that goes on top of it too, right? Because when, when the data is like unstructured data, it's that much of a huge data, it's going to be hard to do that with a low model, you know, compute power. But going back to your data point, the training of the AIML models required the batch data, right? That's when you need all the, the historical data to train your models. And then after that, when you do inference of it, that's where you need the streaming real time data that's available to you too. You can make an inference. One of the things, what, what they also announced, which is somewhat interesting, is you saw that they have like 700 different instances geared towards every single workload. And there are some of them very specifically run on the Amazon's new chip. The, the inference in two and theran tr one chips that basically not only has a specific instances but also is run on a high powered chip. And then if you have that data to support that, both the training as well as towards the inference, the efficiency, again, those numbers have to be proven. They claim that it could be anywhere between 40 to 60% faster. >>Well, so a couple things. You're definitely right. I mean Snowflake started out as a data warehouse that was simpler and it's not architected, you know, in and it's first wave to do real time inference, which is not now how, how could they, the other second point is snowflake's two or three years ahead when it comes to governance, data sharing. I mean, Amazon's doing what always does. It's copying, you know, it's customer driven. Cuz they probably walk into an account and they say, Hey look, what's Snowflake's doing for us? This stuff's kicking ass. And they go, oh, that's a good idea, let's do that too. You saw that with separating compute from storage, which is their tiering. You saw it today with extending data, sharing Redshift, data sharing. So how does Snowflake and data bricks approach this? They deal with ecosystem. They bring in ecosystem partners, they bring in open source tooling and that's how they compete. I think there's unquestionably an opportunity for a data cloud. >>Yeah, I think, I think the super cloud conversation and then, you know, sky Cloud with Berkeley Paper and other folks talking about this kind of pre, multi-cloud era. I mean that's what I would call us right now. We are, we're kind of in the pre era of multi-cloud, which by the way is not even yet defined. I think people use that term, Dave, to say, you know, some sort of magical thing that's happening. Yeah. People have multiple clouds. They got, they, they end up by default, not by design as Dell likes to say. Right? And they gotta deal with it. So it's more of they're inheriting multiple cloud environments. It's not necessarily what they want in the situation. So to me that is a big, big issue. >>Yeah, I mean, again, going back to your snowflake and data breaks announcements, they're a data company. So they, that's how they made their mark in the market saying that, you know, I do all those things, therefore you have, I had to have your data because it's a seamless data. And, and Amazon is catching up with that with a lot of that announcements they made, how far it's gonna get traction, you know, to change when I to say, >>Yeah, I mean to me, to me there's no doubt about Dave. I think, I think what Swamee is doing, if Amazon can get corner the market on out of the box ML and AI capabilities so that people can make it easier, that's gonna be the end of the day tell sign can they fill in the gaps. Again, boring is good competition. I don't know mean, mean I'm not following the competition. Andy, this is a real question mark for me. I don't know where they stand. Are they more comprehensive? Are they more deeper? Are they have deeper services? I mean, obviously shows to all the, the different, you know, capabilities. Where, where, where does Amazon stand? What's the process? >>So what, particularly when it comes to the models. So they're going at, at a different angle that, you know, I will help you create the models we talked about the zero and the whole data. We'll get the data sources in, we'll create the model. We'll move the, the whole model. We are talking about the ML ops teams here, right? And they have the whole functionality that, that they built ind over the year. So essentially they want to become the platform that I, when you come in, I'm the only platform you would use from the model training to deployment to inference, to model versioning to management, the old s and that's angle they're trying to take. So it's, it's a one source platform. >>What about this idea of technical debt? Adrian Carro was on yesterday. John, I know you talked to him as well. He said, look, Amazon's Legos, you wanna buy a toy for Christmas, you can go out and buy a toy or do you wanna build a, to, if you buy a toy in a couple years, you could break and what are you gonna do? You're gonna throw it out. But if you, if you, if part of your Lego needs to be extended, you extend it. So, you know, George Gilbert was saying, well, there's a lot of technical debt. Adrian was countering that. Does Amazon have technical debt or is that Lego blocks analogy the right one? >>Well, I talked to him about the debt and one of the things we talked about was what do you optimize for E two APIs or Kubernetes APIs? It depends on what team you're on. If you're on the runtime gene, you're gonna optimize for Kubernetes, but E two is the resources you want to use. So I think the idea of the 15 years of technical debt, I, I don't believe that. I think the APIs are still hardened. The issue that he brings up that I think is relevant is it's an end situation, not an or. You can have the bag of Legos, which is the primitives and build a durable application platform, monitor it, customize it, work with it, build it. It's harder, but the outcome is durability and sustainability. Building a toy, having a toy with those Legos glued together for you, you can get the play with, but it'll break over time. Then you gotta replace it. So there's gonna be a toy business and there's gonna be a Legos business. Make your own. >>So who, who are the toys in ai? >>Well, out of >>The box and who's outta Legos? >>The, so you asking about what what toys Amazon building >>Or, yeah, I mean Amazon clearly is Lego blocks. >>If people gonna have out the box, >>What about Google? What about Microsoft? Are they basically more, more building toys, more solutions? >>So Google is more of, you know, building solutions angle like, you know, I give you an API kind of thing. But, but if it comes to vertical industry solutions, Microsoft is, is is ahead, right? Because they have, they have had years of indu industry experience. I mean there are other smaller cloud are trying to do that too. IBM being an example, but you know, the, now they are starting to go after the specific industry use cases. They think that through, for example, you know the medical one we talked about, right? So they want to build the, the health lake, security health lake that they're trying to build, which will HIPPA and it'll provide all the, the European regulations, the whole line yard, and it'll help you, you know, personalize things as you need as well. For example, you know, if you go for a certain treatment, it could analyze you based on your genome profile saying that, you know, the treatment for this particular person has to be individualized this way, but doing that requires a anomalous power, right? So if you do applications like that, you could bring in a lot of the, whether healthcare, finance or what have you, and then easy for them to use. >>What's the biggest mistake customers make when it comes to machine intelligence, ai, machine learning, >>So many things, right? I could start out with even the, the model. Basically when you build a model, you, you should be able to figure out how long that model is effective. Because as good as creating a model and, and going to the business and doing things the right way, there are people that they leave the model much longer than it's needed. It's hurting your business more than it is, you know, it could be things like that. Or you are, you are not building a responsibly or later things. You are, you are having a bias and you model and are so many issues. I, I don't know if I can pinpoint one, but there are many, many issues. Responsible ai, ethical ai. All >>Right, well, we'll leave it there. You're watching the cube, the leader in high tech coverage here at J three at reinvent. I'm Jeff, Dave Ante. Andy joining us here for the critical analysis and breaking down the commentary. We'll be right back with more coverage after this short break.

>>Okay, welcome back. Everyone. Live cube coverage here in Las Vegas for Amazon re Mars hot event, machine learning, automation, robotics, and space. Two days of live coverage. We're talking to all the hot technologists. We got all the action startups and segment on sustainability and F pan hero for vet global lead, Amazon sustainability data initiative. Thanks for coming on the cube. Can I get that right? Can >>You, you, you did. >>Absolutely. Okay, great. <laugh> thank >>You. >>Great to see you. We met at the analyst, um, mixer and, um, blown away by the story going on at Amazon around sustainability data initiative, because we were joking. Everything's a data problem now, cuz that's cliche. But in this case you're using data in your program and it's really kind of got a bigger picture. Take a minute to explain what your project is, scope of it on the sustainability. >>Yeah, absolutely. And thank you for the opportunity to be here. Yeah. Um, okay. So, um, I, I lead this program that we launched several years back in 2018 more specifically, and it's a tech for good program. And when I say the tech for good, what that means is that we're trying to bring our technology and our infrastructure and lend that to the world specifically to solve the problems related to sustainability. And as you said, sustainability, uh, inherently needs data. You need, we need data to understand the baseline of where we are and also to understand the progress that we are making towards our goals. Right? But one of the big challenges that the data that we need is spread everywhere. Some of it is too large for most people to be able to, um, access and analyze. And so, uh, what we're trying to tackle is really the data problem in the sustainability space. >>Um, what we do more specifically is focus on Democrat democratizing access to data. So we work with a broader community and we try to understand what are those foundational data sets that most people need to use in the space to solve problems like climate change or food security or think about sustainable development goals, right? Yeah. Yeah. Like all the broad space. Um, and, and we basically then work with the data providers, bring the data to the cloud, make it free and open to everybody in the world. Um, I don't know how deep you want me to go into it. There's many other layers into that. So >>The perspective is zooming out. You're, you're, you're looking at creating a system where the democratizing data means making it freely available so that practitioners or citizens, data, Wrangler, people interested in helping the world could get access to it and then maybe collaborate with people around the world. Is that right? >>Absolutely. So one of the advantages of using the cloud for this kind of, uh, effort is that, you know, cloud is virtually accessible from anywhere where you have, you know, internet or bandwidth, right? So, uh, when, when you put data in the cloud in a centralized place next to compute, it really, uh, removes the, the need for everybody to have their own copy. Right. And to bring it into that, the traditional way is that you bring the data next to your compute. And so we have this multiple copies of data. Some of them are on the petabyte scale. There's obviously the, the carbon footprint associated with the storage, but there's also the complexity that not everybody's able to actually analyze and have that kind of storage. So by putting it in the cloud, now anyone in the world independent of where of their computer capabilities can have access to the same type of data to solve >>The problems. You don't remember doing a report on this in 2018 or 2017. I forget what year it was, but it was around public sector where it was a movement with universities and academia, where they were doing some really deep compute where Amazon had big customers. And there was a movement towards a open commons of data, almost like a national data set like a national park kind of vibe that seems to be getting momentum. In fact, this kind of sounds like what you're doing some similar where it's open to everybody. It's kinda like open source meets data. >>Uh, exactly. And, and the truth is that these data, the majority of it's and we primarily work with what we call authoritative data providers. So think of like NASA Noah, you came me office organizations whose mission is to create the data. So they, their mandate is actually to make the data public. Right. But in practice, that's not really the case. Right. A lot of the data is stored like in servers or tapes or not accessible. Um, so yes, you bring the data to the cloud. And in this model that we use, Amazon never actually touches the data and that's very intentional so that we preserve the integrity of the data. The data provider owns the data in the cloud. We cover all the costs, but they commit to making it public in free to anybody. Um, and obviously the computer is next to it. So that's, uh, evaluated. >>Okay. Anna. So give me some examples of, um, some successes. You've had some of the challenges and opportunities you've overcome, take me through some of the activities because, um, this is really needed, right? And we gotta, sustainability is top line conversation, even here at the conference, re Mars, they're talking about saving climate change with space mm-hmm <affirmative>, which is legitimate. And they're talking about all these new things. So it's only gonna get bigger. Yeah. This data, what are some of the things you're working on right now that you can share? >>Yeah. So what, for me, honestly, the most exciting part of all of this is, is when I see the impact that's creating on customers and the community in general, uh, and those are the stories that really bring it home, the value of opening access to data. And, and I would just say, um, the program actually offers in addition to the data, um, access to free compute, which is very important as well. Right? You put the data in the cloud. It's great. But then if you wanna analyze that, there's the cost and we want to offset that. So we have a, basically an open call for proposals. Anybody can apply and we subsidize that. But so what we see by putting the data in the cloud, making it free and putting the compute accessible is that like we see a lot, for instance, startups, startups jump on it very easily because they're very nimble. They, we basically remove all the cost of investing in the acquisition and storage of the data. The data is connected directly to the source and they don't have to do anything. So they easily build their applications on top of it and workloads and turn it on and off if you know, >>So they don't have to pay for it. >>They have to pay, they basically just pay for the computes whenever they need it. Right. So all the data is covered. So that makes it very visible for, for a lot of startups. And then we see anything like from academia and nonprofits and governments working extensively on the data, what >>Are some of the coolest things you've seen come out of the woodwork in terms of, you know, things that built on top of the, the data, the builders out there are creative, all that heavy, lifting's gone, they're being creative. I'm sure there's been some surprises, um, or obvious verticals that jump healthcare jumps out at me. I'm not sure if FinTech has a lot of data in there, but it's healthcare. I can see, uh, a big air vertical, obviously, you know, um, oil and gas, probably concern. Um, >>So we see it all over the space, honestly. But for instance, one of the things that is very, uh, common for people to use this, uh, Noah data like weather data, because no, basically weather impacts almost anything we do, right? So you have this forecast of data coming into the cloud directly streamed from Noah. And, um, a lot of applications are built on top of that. Like, um, forecasting radiation, for instance, for the solar industry or helping with navigation. But I would say some of the stories I love to mention because are very impactful are when we take data to remote places that traditionally did not have access to any data. Yeah. And for instance, we collaborate with a, with a program, a nonprofit called digital earth Africa where they, this is a basically philanthropically supported program to bring earth observations to the African continents in making it available to communities and governments and things like illegal mining fighting, illegal mining are the forestation, you know, for mangroves to deep forest. Um, it's really amazing what they are doing. And, uh, they are managing >>The low cost nature of it makes it a great use case there >>Yes. Cloud. So it makes it feasible for them to actually do this work. >>Yeah. You mentioned the Noah data making me think of the sale drone. Mm-hmm <affirmative> my favorite, um, use case. Yes. Those sales drones go around many them twice on the queue at reinvent over the years. Yeah. Um, really good innovation. That vibe is here too at the show at Remar this week at the robotics showcases you have startups and growing companies in the ML AI areas. And you have that convergence of not obvious to many, but here, this culture is like, Hey, we have, it's all coming together. Mm-hmm <affirmative>, you know, physical, industrial space is a function of the new O T landscape. Mm-hmm <affirmative>. I mean, there's no edge in space as they say, right. So the it's unlimited edge. So this kind of points to the major trend. It's not stopping this innovation, but sustainability has limits on earth. We have issues. >>We do have issues. And, uh, and I, I think that's one of my hopes is that when we come to the table with the resources and the skills we have and others do as well, we try to remove some of these big barriers, um, that make it things harder for us to move forward as fast as we need to. Right. We don't have time to spend that. Uh, you know, I've been accounted that 80% of the effort to generate new knowledge is spent on finding the data you need and cleaning it. Uh, we, we don't have time for that. Right. So can we remove that UN differentiated, heavy lifting and allow people to start at a different place and generate knowledge and insights faster. >>So that's key, that's the key point having them innovate on top of it, right. What are some things that you wanna see happen over the next year or two, as you look out, um, hopes, dreams, KPIs, performance metrics, what are you, what are you driving to? What's your north star? What are some of those milestones? >>Yeah, so some, we are investing heavily in some areas. Uh, we support, um, you know, we support broadly sustainability, which as, you know, it's like, it's all over, <laugh> the space, but, uh, there's an area that is, uh, becoming more and more critical, which is climate risk. Um, climate risk, you know, for obvious reasons we are experienced, but also there's more regulatory pressures on, uh, business and companies in general to disclose their risks, not only the physical, but also to transition risks. And that's a very, uh, data heavy and compute heavy space. Right. And so we are very focusing in trying to bring the right data and the right services to support that kind of, of activity. >>What kind of break was you looking for? >>Um, so I think, again, it goes back to this concept that there's all that effort that needs to be done equally by so many people that we are all repeating the effort. So I'll put a plug here actually for a project we are supporting, which is called OS climates. Um, I don't know if you're familiar with it, but it's the Linux foundation effort to create an open source platform for climate risk. And so they, they bought the SMP global Airbus, you know, Alliance all these big companies together. And we are one of the funding partners to basically do that basic line work. What are the data that is needed? What are the basic tools let's put it there and do the pre-competitive work. So then you can do the build the, the, the competitive part on top of it. So >>It's kinda like a data clean room. >>It kind of is right. But we need to do those things, right. So >>Are they worried about comp competitive data or is it more anonymized out? How do you, >>It has both actually. So we are primarily contributing, contributing with the open data part, but there's a lot of proprietary data that needs to be behind the whole, the walls. So, yeah, >>You're on the cutting edge of data engineering because, you know, web and ad tech technologies used to be where all that data sharing was done. Mm-hmm <affirmative> for the commercial reasons, you know, the best minds in our industry quoted by a cube alumni are working on how to place ads better. Yeah. Jeff Acker, founder of Cloudera said that on the cube. Okay. And he was like embarrassed, but the best minds are working on how to make ads get more efficient. Right. But that tech is coming to problem solving and you're dealing with data exchange data analysis from different sources, third parties. This is a hard problem. >>Well, it is a hard problem. And I'll, I'll my perspective is that the hardest problem with sustainability is that it goes across all kinds of domains. Right. We traditionally been very comfortable working in our little, you know, swimming lanes yeah. Where we don't need to deal with interoperability and, uh, extracting knowledge. But sustainability, you, you know, you touch the economic side, it touches this social or the environmental, it's all connected. Right. And you cannot just work in the little space and then go sets the impact in the other one. So it's going to force us to work in a different way. Right. It's, uh, big data complex data yeah. From different domains. And we need to somehow make sense of all of it. And there's the potential of AI and ML and things like that that can really help us right. To go beyond the, the modeling approaches we've been done so >>Far. And trust is a huge factor in all this trust. >>Absolutely. And, and just going back to what I said before, that's one of the main reasons why, when we bring data to the cloud, we don't touch it. We wanna make sure that anybody can trust that the data is nowhere data or NASA data, but not Amazon data. >>Yes. Like we always say in the cube, you should own your data plane. Don't give it up. <laugh> well, that's cool. Great. Great. To hear the update. Is there any other projects that you're working on you think might be cool for people that are watching that you wanna plug or point out because this is an area people are, are leaning into yeah. And learning more young, younger talents coming in. Um, I, whether it's university students to people on side hustles want to play with data, >>So we have plenty of data. So we have, uh, we have over a hundred data sets, uh, petabytes and petabytes of data all free. You don't even need an AWS account to access the data and take it out if you want to. Uh, but I, I would say a few things that are exciting that are happening at Mars. One is that we are actually got integrated into ADX. So the AWS that exchange and what that means is that now you can find the open data, free data from a STI in the same searching capability and service as the paid data, right. License data. So hopefully we'll make it easier if I, if you wanna play with data, we have actually something great. We just announced a hackathon this week, uh, in partnership with UNESCO, uh, focus on sustainable development goals, uh, a hundred K in prices and, uh, so much data <laugh> you >>Too years, they get the world is your oyster to go check that out at URL at website, I'll see it's on Amazon. It use our website or a project that can join, or how do people get in touch with you? >>Yeah. So, uh, Amazon SDI, like for Amazon sustainability, that initiative, so Amazon sdi.com and you'll find, um, all the data, a lot of examples of customer stories that are using the data for impactful solutions, um, and much more >>So, and these are, there's a, there's a, a new kind of hustle going out there, seeing entrepreneurs do this. And very successfully, they pick a narrow domain and they, they own it. Something really obscure that could be off the big player's reservation. Mm-hmm <affirmative> and they just become fluent in the data. And it's a big white space for them, right. This market opportunities. And at the minimum you're playing with data. So this is becoming kind of like a long tail domain expertise, data opportunity. Yeah, absolutely. This really hot. So yes. Yeah. Go play around with the data, check it outs for good cause too. And it's free. >>It's all free. >>Almost free. It's not always free. Is it >>Always free? Well, if you, a friend of mine said is only free if your time is worth nothing. <laugh>. Yeah, >>Exactly. Well, Anna, great to have you on the cube. Thanks for sharing the stories. Sustainability is super important. Thanks for coming on. Thank you for the opportunity. Okay. Cube coverage here in Las Vegas. I'm Sean. Furier, we've be back with more day one. After this short break.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Welcome back to the Cubes Live coverage of AWS reinvent 2020. It's virtual this year. We're not in person because of the pandemic. We're doing the remote Cube Cube Virtual were the Cube virtual. I'm your host, John for here with Andy Jassy, the CEO of Amazon Web services, in for his annual at the end of the show comes on the Cube. This year, it's virtual Andy. Good to see you remotely in Seattle or in Palo Alto. Uh, Dave couldn't make it in a personal conflict, but he says, Hello, great to see you. >>Great to see you as well, John. It's an annual tradition. On the last day of reinvent. I wish we were doing it in person, but I'm glad at least were able to do it. Virtually >>the good news is, I know you could arrested last night normally at reinvent you just like we're all both losing our voice at the end of the show. At least me more than you, your and we're just at the end of like okay, Relief. It happens here. It's different. It's been three weeks has been virtual. Um, you guys had a unique format this year went much better than I expected. It would go on because I was pretty skeptical about these long, um, multiple days or weeks events. You guys did a good job of timing it out and creating these activations and with key news, starting with your keynote on December 1st. Now, at the end of the three weeks, um, tell me, are you surprised by the results? Can you give us, Ah, a feeling for how you think everything went? What's what's your take So far as we close out reinvented >>Well, I think it's going really well. I mean, we always gnome or a Z get past, reinvent and you start, you know, collecting all the feedback. But we've been watching all the metrics and you know, there's trade offs. Of course, now I think all of us giving our druthers would be together in Las Vegas, and I think it's hard to replace that feeling of being with people and the excitement of learning about things together and and making decisions together after you see different sessions that you're gonna make big changes in your company and for your customer experience. And yeah, and there's a community peace. And there's, you know, this from being there. There's a concert. The answer. I think people like being with one another. But, you know, I think this was the best that any of us could imagine doing doing a virtual event. And we had to really reinvent, reinvent and all the pieces to it. And now I think that some of the positive trade offs are they. You get a lot mawr engagement than you would normally get in person So normally. Last year, with about 65,000 people in Las Vegas this year, we had 530,000 people registered to reinvent and over 300,000 participate in some fashion. All the sessions had a lot more people who are participating just because you remove the constraints of of travel in costs, and so there are trade offs. I think we prefer being together, but I think it's been a really good community event, um, in learning event for for our customers, and we've been really pleased with it so >>far. No doubt I would totally agree with you. I think a lot of people like, Hey, I love to walk the floor and discover Harry and Sarah Davis moments of finding an exhibit her and the exhibit hall or or attending a session or going to a party, bumping into friends and seeing making new friends. But I think one of the things I want to get your reaction to it. So I think this is comes up. And, you know, we've been doing a lot of Q virtual for the past year, and and everyone pretty much agrees that when we go back, it's gonna be a hybrid world in the sense of events as well as cloud. You know that. But you know, I think one of the things that I noticed this year with reinvent is it almost was a democratization of reinvent. So you really had to reinvent the format. You had 300,000 plus people attend 500 pending email addresses, but now you've got a different kind of beehive community. So you're a bar raiser thinker. It's with the culture of Amazon. So I gotta ask you do the economics does this new kind of extra epiphany impact you and how you raise the bar to keep the best of the face to face when it comes back. And then if you keep the virtual any thoughts on how to leverage this and kind of get more open, it was free. You guys made it free this year and people did show up. >>Yeah, it's a really good question, and it's probably a question will be better equipped to answer in a month or two after we kind of debrief we always do after reading that we spend. Actually, I really enjoy the meeting because the team, the Collective A. W s team, works so hard in this event. There's so many months across everything. All the product teams, um, you know, all the marketing folks, all the event folks, and I think they do a terrific job with it. And we we do about 2.5 3 hour debrief on everything we did, things that we thought was really well the things that we thought we could do better and all the feedback we get from our community and so I wouldn't be surprised if we didn't find things from what we tried this year that we incorporate into what we do when we're back to being a person again. You know, of course, none of us really know when we'll be back in person again. Re event happens to fall on the time of the year, which is early December. And so you with with a lot of people seemingly able to get vaccinated, probably by you know, they'd spring early summer. You could kind of imagine that we might be able to reinvent in person next year. We'll have to see e think we all hope we will. But I'm sure there are a number of pieces that we will take from this and incorporate into what we do in person. And you know, then it's just a matter of how far you go. >>Fingers crossed and you know it's a hybrid world for the Cube two and reinvent and clouds. Let's get into the announcement. I want to get your your take as you look back now. I mean, how many announcements is you guys have me and a lot of announcements this year. Which ones did you like? Which one did you think were jumping off the page, which ones resonated the most or had impact. Can you share kind of just some stats on e mean how many announcements launches you did this >>year? But we had about 100 50 different new services and features that we announced over the last three weeks and reinvent And there, you know the question you're asking. I could easily spend another three hours like my Kino. You know, answering you all the ones that I like thought were important. You know, I think that, you know, some of the ones I think that really stood out for people. I think first on the compute side, I just think the, um the excitement around what we're doing with chips, um, is very clear. I think what we've done with gravitas to our generalized compute to give people 40% better price performance and they could find in the latest generation X 86 processors is just It's a huge deal. If you could save 40% price performance on computer, you get a lot more done for less on. Then you know some of the chip work we're doing in machine learning with inferential on the inference chips that we built And then what? We announced the trainee, um, on the machine learning training ship. People are very excited about the chip announcements. I think also, people on the container side is people are moving to smaller and smaller units of compute. I think people were very taken with the notion of E. K s and D. C s anywhere so they can run whatever container orchestration framework they're running in A. W s also on premises. To make it easier, Thio manage their deployments and containers. I think data stores was another space where I think people realize how much more data they're dealing with today. And we gave a couple statistics and the keynote that I think are kind of astonishing that, you know, every every hour today, people are creating mawr content that there was in an entire year, 20 years ago or the people expect more data to be created. The next three years in the prior 30 years combined these air astonishing numbers and it requires a brand new reinvention of data stores. And so I think people are very excited about Block Express, which is the first sand in the cloud and there really excited about Aurora in general, but then Aurora surveillance V two that allow you to scale up to hundreds of thousands of transactions per second and saved about 90% of supervision or people very excited about that. I think machine learning. You know, uh, Sage Maker has just been a game changer and the ease with which everyday developers and data scientists can build, train, tune into play machine learning models. And so we just keep knocking out things that are hard for people. Last year we launched the first i D for Machine Learning, the stage maker studio. This year, if you look at things that we announced, like Data Wrangler, which changes you know the process of Data Prep, which is one of the most time consuming pieces in machine learning or our feature store or the first see, I see deeper machine learning with pipelines or clarify, which allow you to have explain ability in your models. Those are big deals to people who are trying to build machine learning models, and you know that I'd say probably the last thing that we hear over and over again is really just the excitement around Connect, which is our call center service, which is just growing unbelievably fast and just, you know, the the fact that it's so easy to get started and so easy to scale so much more cost effective with, you know, built from the ground up on the cloud and with machine learning and ai embedded. And then adding some of the capabilities to give agents the right information, the right time about customers and products and real time capabilities for supervisors. Throw when calls were kind of going off the rails and to be ableto thio, stop the the contact before it becomes something, it hurts. The brand is there. Those are all big deals that people have been excited about. >>I think the connecting as I want to just jump on that for a second because I think when we first met many, many years ago, star eighth reinvent. You know the trends are always the same. You guys do a great job. Slew of announcements. You keep raising the bar. But one of the things that you mentioned to me when we talked about the origination of a W S was you were doing some stuff for Amazon proper, and you had a, you know, bootstrap team and you're solving your own problems, getting some scar tissue, the affiliate thing, all these examples. The trend is you guys tend to do stuff for yourself and then re factor it into potentially opportunities for your customers. And you're working backwards. All that good stuff. We'll get into that next section. But this year, more than ever, I think with the pandemic connect, you got chime, you got workspaces. This acceleration of you guys being pretty nimble on exposing these services. I mean, connect was a call center. It's an internal thing that you guys had been using. You re factored that for customer consumption. You see that kind of china? But you're not competing with Zoom. You're offering a service toe bundle in. Is this mawr relevant? Now, as you guys get bigger with more of these services because you're still big now you're still serving yourself. What? That seems to be a big trend now, coming out of the pandemic. Can you comment on um, >>yeah, It's a good question, John. And you know we do. We do a bunch of both. Frankly, you know, there there's some services where our customers. We're trying to solve certain problems and they tell us about those problems and then we build new services for him. So you know a good example that was red shift, which is our data warehouse and service, you know, two or three very large customers of ours. When we went to spend time with them and asked them what we could do to help them further, they just said, I wish I had a data warehousing service for the cloud that was built in the AWS style way. Um and they were really fed up with what they were using. Same thing was true with relation databases where people were just fed up with the old guard commercial, great commercial, great databases of Oracle and Sequel Server. And they hated the pricing and the proprietary nature of them and the punitive licensing. And they they wanted to move to these open engines like my sequel and post dress. But to get the same performance is the commercial great databases hard? So we solve that problem with them. With Aurora, which is our fastest growing service in our history, continues to be so there's sometimes when customers articulate a need, and we don't have a service that we've been running internally. But we way listen, and we have a very strong and innovative group of builders here where we build it for customers. And then there are other cases where customers say and connect with a great example of this. Connect with an example where some of our customers like into it. And Capital One said, You know, we need something for our contact center and customer service, and people weren't very happy with what they were using in that space. And they said, You, you've had to build something just to manage your retail business last 15, 20 years Can't you find a way to generalize that expose it? And when you have enough customers tell you that there's something that they want to use that you have experienced building. You start to think about it, and it's never a simple. It's just taking that technology and exposing it because it's often built, um, internally and you do a number of things to optimize it internally. But we have a way of building services and Amazon, where we do this working backwards process that you're referring to, where We build everything with the press release and frequently asked questions document, and we imagine that we're building it to be externalized even if it's an internal feature. But our feature for our retail business, it's only gonna be used as part of some other service that you never imagine Externalizing to third party developers. We always try and build it that way, and we always try to have well documented, hardened AP eyes so that other teams can use it without having to coordinate with those teams. And so it makes it easier for us to think about Externalizing it because we're a good part of the way there and we connect we. That's what we did way generalized it way built it from the ground up on top of the cloud. And then we embedded a bunch of AI and it so that people could do a number of things that would have taken him, you know, months to do with big development teams that they could really point, click and do so. We really try to do both. >>I think that's a great example of some of the scale benefits is worth calling out because that was a consistent theme this past year, The people we've reported on interviewed that Connect really was a lifeline for many during the pandemic and way >>have 5000 different customers who started using connect during the pandemic alone. Where they, you know, overnight they had to basically deal with having a a call center remotely. And so they picked up connect and they spun up call center remotely, and they didn't really quickly. And you know, it's that along with workspaces, which are virtual desktops in the cloud and things like Chime and some of our partners, Exume have really been lifelines for people. Thio have business continuity during a tandem. >>I think there's gonna be a whole set of new services that are gonna emerge You talked about in your keynote. We talked about it prior to the event where you know, if this pandemic hit with that five years ago, when there wasn't the advancements in, say, videoconferencing, it'd be a whole different world. And I think the whole world can see on full display that having integrated video communications and other cool things is gonna have a productivity benefit. And that's kind >>of could you imagine what the world would have been like the last nine months and we didn't have competent videoconferencing. I mean, just think about how different it would have been. And I think that all of these all of these capabilities today are kind of the occult 1.5 capabilities where, by the way, thank God for them. We've we've all been able to be productive because of them. But there's so early stage, they're all going to get evolved. I'm so significantly, I mean, even just today, you know, I was spending some time with with our team thinking about when we start to come back to the office and bigger numbers. And we do meetings with our remote partners, how we think about where the center of gravity should be and who should be on video conferencing and whether they should be allowed to kind of video conference in conference rooms, which are really hard to see them. We're only on their laptops, which are easier and what technology doesn't mean that you want in the conference rooms on both sides of the table, and how do you actually have it so that people who are remote could see which side of the table. I mean, all this stuff is yet to be invented. It will be very primitive for the next couple few years, even just interrupting one another in video conferencing people. When you do it, the sound counsel cancels each other out. So people don't really cut each other off and rip on one another. Same way, like all that, all that technology is going to get involved over time. It's a tremendous >>I could just see people fighting for the mute button. You know, that's power on these meetings. You know, Chuck on our team. All kidding aside, he was excited. We talked about Enron Kelly on your team, who runs product marketing on for your app side as well as computer networking storage. We're gonna do a green room app for the Q because you know, we're doing so many remote videos. We just did 112 here for reinvent one of things that people like is this idea of kind of being ready and kind of prepped. So again, this is a use case. We never would have thought off if there wasn't a pandemic. So and I think these are the kinds of innovation, thinking that seems small but works well when you start thinking about how easy it could be to say to integrate a chime through this sdk So this is the kind of things, that kind thing. So so with that, I want to get into your leadership principles because, you know, if you're a startup or a big company trying to reinvent, you're looking at the eight leadership principles you laid out, which were, um don't be afraid to reinvent. Acknowledge you can't fight gravity. Talent is hungry to reinvent solving real customer problems. Speed don't complex. If I use the platform with the broader set of tools, which is more a plug for you guys on cloud pull everything together with top down goals. Okay, great. How >>do you >>take those leadership principles and apply them broadly to companies and start ups? Because I think start ups in the garage are also gonna be there going. I'm going to jump on this wave. I'm inspired by the sea change. I'm gonna build something new or an enterprise. I'm gonna I'm gonna innovate. How do you How do you see these eight principles translating? >>Well, I think they're applicable to every company of every size and every industry and organization. Frankly, also, public sector organizations. I think in many ways startups have an advantage. And, you know, these were really keys to how to build a reinvention culture. And startups have an advantage because just by their very nature, they are inventive. You know, you can't you can't start a company that's a direct copy of somebody else that is an inventive where you have no chance. So startups already have, you know, a group of people that feel insurgent, and they wanted their passionate about certain customer experience. They want to invent it, and they know that they they only have so much time. Thio build something before money runs out and you know they have a number of those built in advantages. But I think larger companies are often where you see struggles and building a reinvention and invention culture and I've probably had in the last three weeks is part of reinvent probably about 40 different customer meetings with, you know, probably 75 different companies were accomplished in those or so and and I think that I met with a lot of leaders of companies where I think these reinvention principles really resonated, and I think they're they're battling with them and, you know, I think that it starts with the leaders if you, you know, when you have big companies that have been doing things a certain way for a long period of time, there's a fair bit of inertia that sets in and a lot of times not ill intended. It's just a big group of people in the middle who've been doing things a certain way for a long time and aren't that keen to change sometimes because it means ripping up something that they that they built and they remember how hard they worked on it. And sometimes it's because they don't know what it means for themselves. And you know, it takes the leadership team deciding that we are going to change. And usually that means they have to be able to have access to what's really happening in their business, what's really happening in their products in the market. But what customers really think of it and what they need to change and then having the courage and the energy, frankly, to pick the company up and push him to change because you're gonna have to fight a lot of inertia. So it always starts with the leaders. And in addition to having access that truth and deciding to make the change, you've gotta also set aggressive top down goal. The force of the organization moved faster than otherwise would and that also, sometimes leaders decide they're gonna want to change and they say they're going to change and they don't really set the goal. And they were kind of lessons and kind of doesn't listen. You know, we have a term the principal we have inside Amazon when we talk about the difference between good intentions and mechanisms and good intentions is saying we need to change and we need to invent, reinvent who we are and everyone has the right intentions. But nothing happens. Ah, mechanism, as opposed to good intention, is saying like Capital One did. We're going to reinvent our consumer digital banking platform in the next 18 months, and we're gonna meet every couple of weeks to see where we are into problem solved, like that's a mechanism. It's much harder to escape getting that done. Then somebody just saying we're going to reinvent, not checking on it, you know? And so, you know, I think that starts with the leaders. And then I think that you gotta have the right talent. You gotta have people who are excited about inventing, as opposed to really, Justin, what they built over a number of years, and yet at the same time, you're gonna make sure you don't hire people who were just building things that they're interested in. They went where they think the tech is cool as opposed to what customers want. And then I think you've got to Really You gotta build speed into your culture. And I think in some ways this is the very biggest challenge for a lot of enterprises. And I just I speak to so many leaders who kind of resigned themselves to moving slowly because they say you don't understand my like, companies big and the culture just move slow with regulator. There are a lot of reasons people will give you on why they have to move slow. But, you know, moving with speed is a choice. It's not something that your preordained with or not it is absolutely a leadership choice. And it can't happen overnight. You can't flip a switch and make it happen, but you can build a bunch of things into your culture first, starting with people. Understand that you are gonna move fast and then building an opportunity for people. Experiment quickly and reward people who experiment and to figure out the difference between one way doors and two way doors and things that are too way doors, letting people move quick and try things. You have to build that muscle or when it really comes, time to reinvent you won't have. >>That's a great point in the muscle on that's that's critical. You know, one of things I want to bring up. You brought on your keynote and you talk to me privately about it is you gave attribute in a way to Clay Christensen, who you called out on your keynote. Who was a professor at Harvard. Um, and he was you impressed by him and and you quoted him and he was He was your professor there, Um, your competitive person and you know, companies have strategy departments, and competitive strategy is not necessarily departments of mindset, and you were kind of brought this out in a zone undertone in your talk, we're saying you've got to be competitive in the sense of you got to survive and you've got to thrive. And you're kind of talking about rebuilding and building and, you know, Clay Christians. Innovative dilemma. Famous book is a mother, mother teachings around metrics and strategy and prescriptions. If he were alive today and he was with us, what would he be talking about? Because, you know, you have kind of stuck in the middle. Strategy was not Clay Christensen thing, but, you know, companies have to decide who they are. Their first principles face the truth. Some of the things you mentioned, what would we be talking with him about if we were talking about the innovator's dilemma with respect to, say, cloud and and some of the key decisions that have to be made right now? >>Well, then, Clay Christensen on it. Sounds like you read some of these books on. Guy had the fortunate, um, you know, being able to sit in classes that he taught. And also I got a chance. Thio, meet with him a couple of times after I graduated. Um, school, you know, kind of as more of a professional sorts. You can call me that. And, uh, he he was so thoughtful. He wasn't just thoughtful about innovation. He was thoughtful about how to get product market fit. And he was thoughtful about what your priorities in life were and how to build families. And, I mean, he really was one of the most thoughtful, innovative, um, you know, forward thinking, uh, strategist, I had the opportunity Thio encounter and that I've read, and so I'm very appreciative of having the opportunity Thio learn from him. And a lot of I mean, I think that he would probably be continuing to talk about a lot of the principles which I happen to think are evergreen that he he taught and there's it relates to the cloud. I think that one of the things that quite talked all the time about in all kinds of industries is that disruption always happens at the low end. It always happens with products that seem like they're not sophisticated enough. Don't do enough. And people always pooh pooh them because they say they won't do these things. And we learned this. I mean, I watched in the beginning of it of us. When we lost just three, we had so many people try and compare it Thio things like e m. C. And of course, it was very different than EMC. Um, but it was much simpler, but And it and it did a certain set of activities incredibly well at 1 1/100 of the price that's disrupted, you know, like 1 1/100 of the price. You find that builders, um, find a lot of utility for products like that. And so, you know, I think that it always starts with simple needs and products that aren't fully developed. That overtime continue to move their way up. Thio addressing Maura, Maura the market. And that's what we did with is what we've done with all our services. That's three and easy to and party ass and roar and things like that. And I think that there are lots of lessons is still apply. I think if you look at, um, containers and how that's changing what compute looks like, I think if you look at event driven, serverless compute in Lambda. Lambda is a great example of of really ah, derivative plays teaching, which is we knew when we were building Lambda that as people became excited about that programming model it would cannibalize easy to in our core compute service. And there are a lot of companies that won't do that. And for us we were trying to build a business that outlasts all of us. And that's you know, it's successful over a long period of time, and the the best way I know to do that is to listen to what customers We're trying to solve an event on their behalf, even if it means in the short term you may cannibalize yourself. And so that's what we always think about is, you know, wherever we see an opportunity to provide a better customer experience, even if it means in the short term, make cannibalism revenue leg lambda with complete with easy to our over our surveillance with provisions or are we're going to do it because we're gonna take the long view, and we believe that we serve customers well over a long period of time. We have a chance to do >>that. It's a cannibalize yourself and have someone else do it to you, right? That's that's the philosophy. Alright, fine. I know you've got tight for time. We got a you got a hard stop, But let's talk about the vaccine because you know, you brought up in the keynote carrier was a featured thing. And look at the news headlines. Now you got the shots being administered. You're starting to see, um, hashtag going around. I got my shot. So, you know, there's a There's a really Momenta. Mit's an uplifting vibe here. Amazon's involved in this and you talked about it. Can you share the innovation? There can just give us an update and what's come out of that and this supply chain factor. The cold chain. You guys were pretty instrumental in that share your your thoughts. >>We've been really excited and privileged partner with companies who are really trying to change what's possible for all of us. And I think you know it started with some of the companies producing vaccines. If you look at what we do with Moderna, where they built their digital manufacturing sweet on top of us in supply chain, where they used us for computing, storage and data warehousing and machine learning, and and on top of AWS they built, they're Cove in 19 vaccine candidate in 42 days when it normally takes 20 months. I mean, that is a total game changer. It's a game changer for all of us and getting the vaccine faster. But also, you just think about what that means for healthcare moving forward, it zits very exciting. And, yeah, I love what carriers doing. Kariya is building this product on top of AWS called links, which is giving them end and visibility over the transportation and in temperature of of the culture and everything they're delivering. And so it, uh, it changes what happens not only for food, ways and spoilage, but if you think about how much of the vaccine they're gonna actually transport to people and where several these vaccines need the right temperature control, it's it's a big deal. And what you know, I think there are a great example to what carrier is where. You know, if you think about the theme of this ring and then I talked about in my keynote, if you want to survive as an organization over a long period of time, you're gonna have to reinvent yourself. You're gonna have to probably do it. Multiple times over and the key to reinventing his first building, the right reinvention culture. And we talk about some of those principles earlier, but you also have to be aware of the technology that's available that allows you to do that. If you look at Carrier, they have built a very, very strong reinvention culture. And then, if you look at how they're leveraging, compute and storage and I o. T at the edge and machine learning, they know what's available, and they're using that technology to reinvent what's what's possible, and we're gonna all benefit because of >>it. All right. Well, Andy, you guys were reinventing the virtual space. Three weeks, it went off. Well, congratulations. Great to go along for the ride with the cube virtual. And again. Thank you for, um, keeping the show alive over there. Reinvent. Um, thanks for your team to for including the Cube. We really appreciate the Cube virtual being involved. Thank you. >>It's my pleasure. And thanks for having me, John and, uh, look forward to seeing you soon. >>All right? Take care. Have a hockey game in real life. When? When we get back, Andy Jesse, the CEO of a W s here to really wrap up. Reinvent here for Cuba, Virtual as well as the show. Today is the last day of the program. It will be online for the rest of the year and then into next month there's another wave coming, of course. Check out all the coverage. Come, come back, It's It's It's online. It's all free Cube Cube stuff is there on the Cube Channel. Silicon angle dot com For all the top stories, cube dot net tons of content on Twitter. Hashtag reinvent. You'll see all the commentary. Thanks for watching the Cube Virtual. I'm John Feehery.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. >>Welcome back to the cubes, ongoing coverage, AWS, AWS reinvent virtual. The cube has gone virtual too, and continues to bring our digital coverage of events across the globe. It's been a big week, big couple of weeks at reinvent and a big week for machine intelligence in learning and AI and new services for customers. And with me to discuss the trends in this space is broadened Sahab, who is the vice president and general manager of machine learning services at AWS Rodan. Great to see you. Thanks for coming on the cube. >>Thank you, Dave. Thank you for having me. >>You're very welcome. Let's get right into it. I mean, I remember when SageMaker was announced it was 2017. Uh, it was really a seminal moment in the whole machine learning space, but take us through the journey over the last few years. Uh, what can you tell us? >>So, you know, what, when we came out with SageMaker customers were telling us that machine learning is hard and it was within, you know, it's only a few large organizations that could truly deploy machine learning at scale. And so we released SageMaker in 2017 and we have seen really broad adoption of SageMaker across the entire spectrum of industries. And today, most of the machine learning in the cloud, the vast majority of it happens on AWS. In fact, AWS has more than two weeks of the machine learning than any other provider. And, you know, we saw this morning that more than 90% of the TensorFlow in the cloud and more than 92% of the pipe out in the cloud happens on AWS. So what has happened in that is customers saw that it was much easier to do machine learning once they were using tools like SageMaker. >>And so many customers started applying a handful of models and they started to see that they were getting real business value. You know, machine learning was no longer a niche machine learning was no longer a fictional thing. It was something that they were getting real business value. And then they started to proliferate across that use cases. And so these customers went from deploying like tens of models to deploying hundreds and thousands of models inside. We have one customer that is deploying more than a million models. And so that is what we have seen is really making machine learning broadly accessible to our customers through the use of SageMaker. >>Yeah. So you probably very quickly went through the experimentation phase and people said, wow, you got the aha moments. And, and, and so adoption went through the roof. What kind of patterns have you seen in terms of the way in which people are using data and maybe some of the problems and challenges that has created for organizations that they've asked you to erect help them rectify? Yes. >>And in fact, in a SageMaker is today one of the fastest growing services in AWS history. And what we have seen happen is as customer scaled out the machine learning deployments, they asked us to help them solve the issues that used to come when you deploy machine learning at scale. So one of the things that happens is when you're doing machine learning, you spend a lot of time preparing the data, cleaning the data, making sure the data is done correctly, so it can train your models. And customers wanted to be able to do the data prep in the same service in which they were doing machine learning. And hence we launched Sage, make a data and learn where with a few clicks, you can connect a variety of data stores, AWS data stores, or third party data stores, and do all of your data preparation. >>Now, once you've done your data preparation, customers wanted to be able to store that data. And that's why we came out with SageMaker feature store and then customers want to be able to take this entire end to end pipeline and be able to automate the whole thing. And that is why we came up with SageMaker pipelines. And then one of the things that customers have asked us to help them address is this issue of statistical bias and explainability. And so we released SageMaker clarify that actually helps customers look at statistical bias to the entire machine learning workflow before you do, when you're doing a data processing before you train your model. And even after you have deployed your model and it gives us insights into why your model is behaving in a particular way. And then we had machine learning in the cloud and many customers have started deploying machine learning at the edge, and they want to be able to deploy these models at the edge and wanted a solution that says, Hey, can I take all of these machine learning capabilities that I have in the cloud, specifically, the model management and the MLR SKP abilities and deploy them to the edge devices. >>And that is why we launched SageMaker edge manager. And then customers said, you know, we still need our basic functionality of training and so on to be faster. And so we released a number of enhancements to SageMaker distributed training in terms of new data, parallel models and new model parallelism models that give the fastest training time on SageMaker across both the frameworks. And, you know, that is one of the key things that we have at AWS is we give customers choice. We don't force them onto a single framework. >>Okay, great. And we, I think we hit them all except, uh, I don't know if you talked about SageMaker debugger, but we will. So I want to come back to and ask you a couple of questions about these features. So it's funny. Sometimes people make fun of your names, but I like them because they said, it says what it does because, because people tell me that I spend all my time wrangling data. So you have data Wrangler, it's, you know, it's all about transformation cleaning. And, and because you don't want to spend 80% of your time wrangling data, you want to spend 80 of your time, you know, driving insights and, and monetization. So, so how, how does one engage with, with data Wrangler and how do you see the possibilities there? >>So data angler is part of SageMaker studio. SageMaker studio was the world's first, fully integrated development run for machine learning. So you come to SageMaker studio, you have a tab there, which you SageMaker data angler, and then you have a visual UI. So that visual UI with just a single click, you can connect to AWS data stores like, you know, red shift or a Tina or third party data stores like snowflake and Databricks and Mongo DB, which will be coming. And then you have a set of built-in data processes for machine learning. So you get that data and you do some interactive processing. Once you're happy with the results of your data, you can just send it off as an automated data pipeline job. And, you know, it's really today the easiest and fastest way to do machine learning and really take out that 80% that you were talking about. >>Has it been so hard to automate the Sage, the pipelines to bring CIC D uh, to, uh, data pipelines? Why has that been such a challenge? And how did you resolve that? >>You know, what has happened is when you look at machine learning, machine learning deals with both code and data, okay. Unlike software, which really has to deal with only code. And so we had the CIC D tools for software, but someone needed to extend it to operating on both data and code. And at the same time, you know, you want to provide reproducibility and lineage and trackability, and really getting that whole end to end system to work across code and data across multiple capabilities was what made it hard. And, you know, that is where we brought in SageMaker pipelines to make this easy for our customers. >>Got it. Thank you. And then let me ask you about, uh, clarify. And this is a huge issue in, in machine intelligence, uh, you know, humans by the very nature of bias that they build models, the models of bias in them. Uh, and so you bringing transplant the other problem with, with AI, and I'm not sure that you're solving this problem, but please clarify if you are no pun intended, but it's that black box AI is a black box. I don't know how the answer, how we got to the answer. It seems like you're attacking that, bringing more transparency and really trying to deal with the biases. I wonder if you could talk about how you do that and how people can expect this to affect their operations. >>I'm glad you asked this question because you know, customers have also asked us about the SageMaker clarify is really intended to address the questions that you brought up. One is it gives you the tools to provide a lot of statistical analysis on the data set that you started with. So let's say you were creating a model for loan approvals, and you want to make sure that, you know, you have equal number of male applicants and equal number of female applicants and so on. So SageMaker clarify, lets you run these kinds of analysis to make sure that your data set is balanced to start with. Now, once that happens, you have trained the model. Once you've trained the model, you want to make sure that the training process did not introduce any unintended statistical bias. So then you can use, SageMaker clarify to again, say, well, is the model behaving in the way I expected it to behave based on the training data I had. >>So let's say your training data set, you know, 50% of all the male applicants got the loans approved after training, you can use, clarify to say, does this model actually predict that 50% of the male applicants will get approved? And if it's more than less, you know, you have a problem. And then after that, we get to the problem you mentioned, which is how do we unravel the black box nature of this? And you know, we took the first steps of it last year with autopilot where we actually gave notebooks. But SageMaker clarify really makes it much better because it tells you why our model is predicting the way it's predicting. It gives you the reasons and it tells you, you know, here is why the model predicts that, you know, you had approved a loan and here's why the model said that you may or may not get a loan. So it really makes it easier, gives visibility and transparency and helps to convert insights that you get from model predictions into actionable insights because you now know why the model is predicting what it's predicting. >>That brings out the confidence level. Okay. Thank you for that. Let me, let me ask you about distributed training on SageMaker help us understand what problem you're solving. You're injecting auto parallelism. Is that about, about scale? Help us understand that. >>Yeah. So one of the things that's happening is, you know, our customers are starting to train really large models like, you know, three years back, they will train models with like 20 million parameters. You know, last year they would train models with like couple of hundred million parameters. Now customers are actually training models with billions of parameters. And when you have such large models, that training can take days and sometimes weeks. And so what we have done E are two concepts. One is we introduced a way of taking a model and training it in parallel and multiple GPU's. And that's, you know what we call a data parallel implementation. We have our own custom libraries for this, which give you the fastest performance on AWS. And then the other thing that happens is customer stakes. Some of these models that are fairly large, you know, like billions of parameters and we showed one of them today called T five and these models are so big that they cannot fit in the memory of a single GPU. And so what happens is today customers have to train such a model. They spend weeks of effort trying to paralyze that Marlon, what we introduced in SageMaker today is a mechanism that automatically takes these large models and distributes it across multiple GPU's the auto parallelization that you were talking about, making it much easier and much faster for customers to really work with these big models. >>Well, the GPU is a very expensive resource. And prior to this, you would have the GPU waiting, waiting, waiting, load me up and you don't want to do that with it. Expensive resources. Yeah. >>And you know, one of the things I mentioned before is Sage make a debugger. So one of the things that we also came out with today is the SageMaker profiler, which is only part of the debugger that lets you look at your GPU utilization at your CPU utilization at, in network utilization and so on. And so now, you know, when your training job has started at which point has the GPU utilization gone down and you can go in and fix it. So this really lets you meet, utilize your resources much better and ultimately reducing your cost of training and making it more efficient. Awesome. >>Let's talk about edge manager because I, you know, Andy Jassy, his keynote was interesting. He his, where he's talking about hybrid and his vision is basically an Amazon's vision is we want to bring AWS to the edge. We see the data center as just another edge node. And so, so this is, to me, another example of, uh, of AWS is, you know, edge strategy, talk about how that works and, and, and, and in practice, uh, how does, how does it work? Am I doing inference at the edge and then bringing back data into the cloud? Uh, am I, am I doing things locally? >>Yes. So, you know what? See each man got edge manager does, is it helps you manage, deploy and manage and manage models at the edge. The inference is happening on the edge device. Now considers his case. So Lenovo has been working with us. And what Lenovo wants to do is to take these models and do predictive maintenance on laptops. So you want to get an it shop and you have a couple of hundred thousand laptops. You would want to know when something may go down. And so the deployed is predictive maintenance models on the laptop. They're doing inference locally on the laptop, but you want to see are the models getting degraded and you want to be able to see is the quality up. So what H manager does is number one, it takes your models, optimizes them so they can run on an edge device and we get up to 25 X benefit and then once you've deployed it, it helps you monitor the quality of the models by letting you upload data samples to SageMaker so that you can see if there is drift in your models, that if there's any other degradation, >>All right. And jumpstart is where I go to. It's kind of the portal that I go to, to access all these cool tools. Is that right? Yep. >>And you know, we have a lot of getting started material, lots of false party models, lots of open source models and solutions. >>I probably we're out of time, but I could go on forever and we did thanks so much for, for bringing this knowledge to the cube audience. Really appreciate your time. >>Thank you. Thank you, Dave, for having me. >>And you're very welcome and good luck with the, the announcements. And thank you for watching everybody. This is Dave Volante for the cube and our coverage of AWS reinvent 2020 continues right after this short break.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hi everyone. Welcome back to the cubes. Virtual coverage of AWS reinvent 2020 virtual. We are the cube virtual I'm John ferry, your host with my coach, Dave Alante for keynote analysis from Swami's machine learning, all things, data huge. Instead of announcements, the first ever machine learning keynote at a re-invent Dave. Great to see you. Thanks Johnny. And from Boston, I'm here in Palo Alto. We're doing the cube remote cube virtual. Great to see you. >>Yeah, good to be here, John, as always. Wall-to-wall love it. So, so, John, um, how about I give you my, my key highlights from the, uh, from the keynote today, I had, I had four kind of curated takeaways. So the first is that AWS is, is really trying to simplify machine learning and use machine intelligence into all applications. And if you think about it, it's good news for organizations because they're not the become machine learning experts have invent machine learning. They can buy it from Amazon. I think the second is they're trying to simplify the data pipeline. The data pipeline today is characterized by a series of hyper specialized individuals. It engineers, data scientists, quality engineers, analysts, developers. These are folks that are largely live in their own swim lane. Uh, and while they collaborate, uh, there's still a fairly linear and complicated data pipeline, uh, that, that a business person or a data product builder has to go through Amazon making some moves to the front of simplify that they're expanding data access to the line of business. I think that's a key point. Is there, there increasingly as people build data products and data services that can monetize, you know, for their business, either cut costs or generate revenue, they can expand that into line of business where there's there's domain context. And I think the last thing is this theme that we talked about the other day, John of extending Amazon, AWS to the edge that we saw that as well in a number of machine learning tools that, uh, Swami talked about. >>Yeah, it was great by the way, we're live here, uh, in Palo Alto in Boston covering the analysis, tons of content on the cube, check out the cube.net and also check out at reinvent. There's a cube section as there's some links to so on demand videos with all the content we've had. Dave, I got to say one of the things that's apparent to me, and this came out of my one-on-one with Andy Jassy and Andy Jassy talked about in his keynote is he kind of teased out this idea of training versus a more value add machine learning. And you saw that today in today's announcement. To me, the big revelation was that the training aspect of machine learning, um, is what can be automated away. And it's under a lot of controversy around it. Recently, a Google paper came out and the person was essentially kind of, kind of let go for this. >>But the idea of doing these training algorithms, some are saying is causes more harm to the environment than it does good because of all the compute power it takes. So you start to see the positioning of training, which can be automated away and served up with, you know, high powered ships and that's, they consider that undifferentiated heavy lifting. In my opinion, they didn't say that, but that's clearly what I see coming out of this announcement. The other thing that I saw Dave that's notable is you saw them clearly taking a three lane approach to this machine, learning the advanced builders, the advanced coders and the developers, and then database and data analysts, three swim lanes of personas of target audience. Clearly that is in line with SageMaker and the embedded stuff. So two big revelations, more horsepower required to process training and modeling. Okay. And to the expansion of the personas that are going to be using machine learning. So clearly this is a, to me, a big trend wave that we're seeing that validates some of the startups and I'll see their SageMaker and some of their products. >>Well, as I was saying at the top, I think Amazon's really trying, working hard on simplifying the whole process. And you mentioned training and, and a lot of times people are starting from scratch when they have to train models and retrain models. And so what they're doing is they're trying to create reusable components, uh, and allow people to, as you pointed out to automate and streamline some of that heavy lifting, uh, and as well, they talked a lot about, uh, doing, doing AI inferencing at the edge. And you're seeing, you know, they, they, uh, Swami talked about several foundational premises and the first being a foundation of frameworks. And you think about that at the, at the lowest level of their S their ML stack. They've got, you know, GPU's different processors, inferential, all these alternative processes, processors, not just the, the Xav six. And so these are very expensive resources and Swami talked a lot about, uh, and his colleagues talked a lot about, well, a lot of times the alternative processor is sitting there, you know, waiting, waiting, waiting. And so they're really trying to drive efficiency and speed. They talked a lot about compressing the time that it takes to, to run these, these models, uh, from, from sometimes weeks down to days, sometimes days down to hours and minutes. >>Yeah. Let's, let's unpack these four areas. Let's stay on the firm foundation because that's their core competency infrastructure as a service. Clearly they're laying that down. You put the processors, but what's interesting is the TensorFlow 92% of tensor flows on Amazon. The other thing is that pie torch surprisingly is back up there, um, with massive adoption and the numbers on pie torch literally is on fire. I was coming in and joke on Twitter. Um, we, a PI torch is telling because that means that TensorFlow is originally part of Google is getting, is getting a little bit diluted with other frameworks, and then you've got MX net, some other things out there. So the fact that you've got PI torch 91% and then TensorFlow 92% on 80 bucks is a huge validation. That means that the majority of most machine learning development and deep learning is happening on AWS. Um, >>Yeah, cloud-based, by the way, just to clarify, that's the 90% of cloud-based cloud, uh, TensorFlow runs on and 91% of cloud-based PI torch runs on ADM is amazingly massive numbers. >>Yeah. And I think that the, the processor has to show that it's not trivial to do the machine learning, but, you know, that's where the infrared internship came in. That's kind of where they want to go lay down that foundation. And they had Tanium, they had trainee, um, they had, um, infrared chow was the chip. And then, you know, just true, you know, distributed training training on SageMaker. So you got the chip and then you've got Sage makers, the middleware games, almost like a machine learning stack. That's what they're putting out there >>And how bad a Gowdy, which was, which is, which is a patrol also for training, which is an Intel based chip. Uh, so that was kind of interesting. So a lot of new chips and, and specialized just, we've been talking about this for awhile, particularly as you get to the edge and do AI inferencing, you need, uh, you know, a different approach than we're used to with the general purpose microbes. >>So what gets your take on tenant? Number two? So tenant number one, clearly infrastructure, a lot of announcements we'll go through those, review them at the end, but tenant number two, that Swami put out there was creating the shortest path to success for builders or machine learning builders. And I think here you lays out the complexity, Dave butts, mostly around methodology, and, you know, the value activities required to execute. And again, this points to the complexity problem that they have. What's your take on this? >>Yeah. Well you think about, again, I'm talking about the pipeline, you collect data, you just data, you prepare that data, you analyze that data. You, you, you make sure that it's it's high quality and then you start the training and then you're iterating. And so they really trying to automate as much as possible and simplify as much as possible. What I really liked about that segment of foundation, number two, if you will, is the example, the customer example of the speaker from the NFL, you know, talked about, uh, you know, the AWS stats that we see in the commercials, uh, next gen stats. Uh, and, and she talked about the ways in which they've, well, we all know they've, they've rearchitected helmets. Uh, they've been, it's really a very much database. It was interesting to see they had the spectrum of the helmets that were, you know, the safest, most safe to the least safe and how they've migrated everybody in the NFL to those that they, she started a 24%. >>It was interesting how she wanted a 24% reduction in reported concussions. You know, you got to give the benefit of the doubt and assume some of that's through, through the data. But you know, some of that could be like, you know, Julian Edelman popping up off the ground. When, you know, we had a concussion, he doesn't want to come out of the game with the new protocol, but no doubt, they're collecting more data on this stuff, and it's not just head injuries. And she talked about ankle injuries, knee injuries. So all this comes from training models and reducing the time it takes to actually go from raw data to insights. >>Yeah. I mean, I think the NFL is a great example. You and I both know how hard it is to get the NFL to come on and do an interview. They're very coy. They don't really put their name on anything much because of the value of the NFL, this a meaningful partnership. You had the, the person onstage virtually really going into some real detail around the depth of the partnership. So to me, it's real, first of all, I love stat cast 11, anything to do with what they do with the stats is phenomenal at this point. So the real world example, Dave, that you starting to see sports as one metaphor, healthcare, and others are going to see those coming in to me, totally a tale sign that Amazon's continued to lead. The thing that got my attention was is that it is an IOT problem, and there's no reason why they shouldn't get to it. I mean, some say that, Oh, concussion, NFL is just covering their butt. They don't have to, this is actually really working. So you got the tech, why not use it? And they are. So that, to me, that's impressive. And I think that's, again, a digital transformation sign that, that, you know, in the NFL is doing it. It's real. Um, because it's just easier. >>I think, look, I think, I think it's easy to criticize the NFL, but the re the reality is, is there anything old days? It was like, Hey, you get your bell rung and get back out there. That's just the way it was a football players, you know, but Ted Johnson was one of the first and, you know, bill Bellacheck was, was, you know, the guy who sent him back out there with a concussion, but, but he was very much outspoken. You've got to give the NFL credit. Uh, it didn't just ignore the problem. Yeah. Maybe it, it took a little while, but you know, these things take some time because, you know, it's generally was generally accepted, you know, back in the day that, okay, Hey, you'd get right back out there, but, but the NFL has made big investments there. And you can say, you got to give him, give him props for that. And especially given that they're collecting all this data. That to me is the most interesting angle here is letting the data inform the actions. >>And next step, after the NFL, they had this data prep data Wrangler news, that they're now integrating snowflakes, Databricks, Mongo DB, into SageMaker, which is a theme there of Redshift S3 and Lake formation into not the other way around. So again, you've been following this pretty closely, uh, specifically the snowflake recent IPO and their success. Um, this is an ecosystem play for Amazon. What does it mean? >>Well, a couple of things, as we, as you well know, John, when you first called me up, I was in Dallas and I flew into New York and an ice storm to get to the one of the early Duke worlds. You know, and back then it was all batch. The big data was this big batch job. And today you want to combine that batch. There's still a lot of need for batch, but when people want real time inferencing and AWS is bringing that together and they're bringing in multiple data sources, you mentioned Databricks and snowflake Mongo. These are three platforms that are doing very well in the market and holding a lot of data in AWS and saying, okay, Hey, we want to be the brain in the middle. You can import data from any of those sources. And I'm sure they're going to add more over time. Uh, and so they talked about 300 pre-configured data transformations, uh, that now come with stage maker of SageMaker studio with essentially, I've talked about this a lot. It's essentially abstracting away the, it complexity, the whole it operations piece. I mean, it's the same old theme that AWS is just pointing. It's its platform and its cloud at non undifferentiated, heavy lifting. And it's moving it up the stack now into the data life cycle and data pipeline, which is one of the biggest blockers to monetizing data. >>Expand on that more. What does that actually mean? I'm an it person translate that into it. Speak. Yeah. >>So today, if you're, if you're a business person and you want, you want the answers, right, and you want say to adjust a new data source, so let's say you want to build a new, new product. Um, let me give an example. Let's say you're like a Spotify, make it up. And, and you do music today, but let's say you want to add, you know, movies, or you want to add podcasts and you want to start monetizing that you want to, you want to identify, who's watching what you want to create new metadata. Well, you need new data sources. So what you do as a business person that wants to create that new data product, let's say for podcasts, you have to knock on the door, get to the front of the data pipeline line and say, okay, Hey, can you please add this data source? >>And then everybody else down the line has to get in line and Hey, this becomes a new data source. And it's this linear process where very specialized individuals have to do their part. And then at the other end, you know, it comes to self-serve capability that somebody can use to either build dashboards or build a data product. In a lot of that middle part is our operational details around deploying infrastructure, deploying, you know, training machine learning models that a lot of Python coding. Yeah. There's SQL queries that have to be done. So a lot of very highly specialized activities, what Amazon is doing, my takeaway is they're really streamlining a lot of those activities, removing what they always call the non undifferentiated, heavy lifting abstracting away that it complexity to me, this is a real positive sign, because it's all about the technology serving the business, as opposed to historically, it's the business begging the technology department to please help me. The technology department obviously evolving from, you know, the, the glass house, if you will, to this new data, data pipeline data, life cycle. >>Yeah. I mean, it's classic agility to take down those. I mean, it's undifferentiated, I guess, but if it actually works, just create a differentiated product. So, but it's just log it's that it's, you can debate that kind of aspect of it, but I hear what you're saying, just get rid of it and make it simpler. Um, the impact of machine learning is Dave is one came out clear on this, uh, SageMaker clarify announcement, which is a bias decision algorithm. They had an expert, uh, nationally CFUs presented essentially how they're dealing with the, the, the bias piece of it. I thought that was very interesting. What'd you think? >>Well, so humans are biased and so humans build models or models are inherently biased. And so I thought it was, you know, this is a huge problem to big problems in artificial intelligence. One is the inherent bias in the models. And the second is the lack of transparency that, you know, they call it the black box problem, like, okay, I know there was an answer there, but how did it get to that answer and how do I trace it back? Uh, and so Amazon is really trying to attack those, uh, with, with, with clarify. I wasn't sure if it was clarity or clarified, I think it's clarity clarify, um, a lot of entirely certain how it works. So we really have to dig more into that, but it's essentially identifying situations where there is bias flagging those, and then, you know, I believe making recommendations as to how it can be stamped. >>Nope. Yeah. And also some other news deep profiling for debugger. So you could make a debugger, which is a deep profile on neural network training, um, which is very cool again on that same theme of profiling. The other thing that I found >>That remind me, John, if I may interrupt there reminded me of like grammar corrections and, you know, when you're typing, it's like, you know, bug code corrections and automated debugging, try this. >>It wasn't like a better debugger come on. We, first of all, it should be bug free code, but, um, you know, there's always biases of the data is critical. Um, the other news I thought was interesting and then Amazon's claiming this is the first SageMaker pipelines for purpose-built CIC D uh, for machine learning, bringing machine learning into a developer construct. And I think this started bringing in this idea of the edge manager where you have, you know, and they call it the about machine, uh, uh, SageMaker store storing your functions of this idea of managing and monitoring machine learning modules effectively is on the edge. And, and through the development process is interesting and really targeting that developer, Dave, >>Yeah, applying CIC D to the machine learning and machine intelligence has always been very challenging because again, there's so many piece parts. And so, you know, I said it the other day, it's like a lot of the innovations that Amazon comes out with are things that have problems that have come up given the pace of innovation that they're putting forth. And, and it's like the customers drinking from a fire hose. We've talked about this at previous reinvents and the, and the customers keep up with the pace of Amazon. So I see this as Amazon trying to reduce friction, you know, across its entire stack. Most, for example, >>Let me lay it out. A slide ahead, build machine learning, gurus developers, and then database and data analysts, clearly database developers and data analysts are on their radar. This is not the first time we've heard that. But we, as the kind of it is the first time we're starting to see products materialized where you have machine learning for databases, data warehouse, and data lakes, and then BI tools. So again, three different segments, the databases, the data warehouse and data lakes, and then the BI tools, three areas of machine learning, innovation, where you're seeing some product news, your, your take on this natural evolution. >>Well, well, it's what I'm saying up front is that the good news for, for, for our customers is you don't have to be a Google or Amazon or Facebook to be a super expert at AI. Uh, companies like Amazon are going to be providing products that you can then apply to your business. And, and it's allowed you to infuse AI across your entire application portfolio. Amazon Redshift ML was another, um, example of them, abstracting complexity. They're taking, they're taking S3 Redshift and SageMaker complexity and abstracting that and presenting it to the data analysts. So that, that, that individual can worry about, you know, again, getting to the insights, it's injecting ML into the database much in the same way, frankly, the big query has done that. And so that's a huge, huge positive. When you talk to customers, they, they love the fact that when, when ML can be embedded into the, into the database and it simplifies, uh, that, that all that, uh, uh, uh, complexity, they absolutely love it because they can focus on more important things. >>Clearly I'm this tenant, and this is part of the keynote. They were laying out all their announcements, quick excitement and ML insights out of the box, quick, quick site cue available in preview all the announcements. And then they moved on to the next, the fourth tenant day solving real problems end to end, kind of reminds me of the theme we heard at Dell technology worlds last year end to end it. So we are starting to see the, the, the land grab my opinion, Amazon really going after, beyond I, as in pass, they talked about contact content, contact centers, Kendra, uh, lookout for metrics, and that'll maintain men. Then Matt would came on, talk about all the massive disruption on the, in the industries. And he said, literally machine learning will disrupt every industry. They spent a lot of time on that and they went into the computer vision at the edge, which I'm a big fan of. I just loved that product. Clearly, every innovation, I mean, every vertical Dave is up for grabs. That's the key. Dr. Matt would message. >>Yeah. I mean, I totally agree. I mean, I see that machine intelligence as a top layer of, you know, the S the stack. And as I said, it's going to be infused into all areas. It's not some kind of separate thing, you know, like, Coobernetti's, we think it's some separate thing. It's not, it's going to be embedded everywhere. And I really like Amazon's edge strategy. It's this, you, you are the first to sort of write about it and your keynote preview, Andy Jassy said, we see, we see, we want to bring AWS to the edge. And we see data center as just another edge node. And so what they're doing is they're bringing SDKs. They've got a package of sensors. They're bringing appliances. I've said many, many times the developers are going to be, you know, the linchpin to the edge. And so Amazon is bringing its entire, you know, data plane is control plane, it's API APIs to the edge and giving builders or slash developers, the ability to innovate. And I really liked the strategy versus, Hey, here's a box it's, it's got an x86 processor inside on a, throw it over the edge, give it a cool name that has edge in it. And here you go, >>That sounds call it hyper edge. You know, I mean, the thing that's true is the data aspect at the edge. I mean, everything's got a database data warehouse and data lakes are involved in everything. And then, and some sort of BI or tools to get the data and work with the data or the data analyst, data feeds, machine learning, critical piece to all this, Dave, I mean, this is like databases used to be boring, like boring field. Like, you know, if you were a database, I have a degree in a database design, one of my degrees who do science degrees back then no one really cared. If you were a database person. Now it's like, man data, everything. This is a whole new field. This is an opportunity. But also, I mean, are there enough people out there to do all this? >>Well, it's a great point. And I think this is why Amazon is trying to extract some of the abstract. Some of the complexity I sat in on a private session around databases today and listened to a number of customers. And I will say this, you know, some of it I think was NDA. So I can't, I can't say too much, but I will say this Amazon's philosophy of the database. And you address this in your conversation with Andy Jassy across its entire portfolio is to have really, really fine grain access to the deep level API APIs across all their services. And he said, he said this to you. We don't necessarily want to be the abstraction layer per se, because when the market changes, that's harder for us to change. We want to have that fine-grained access. And so you're seeing that with database, whether it's, you know, no sequel, sequel, you know, the, the Aurora the different flavors of Aurora dynamo, DV, uh, red shift, uh, you know, already S on and on and on. There's just a number of data stores. And you're seeing, for instance, Oracle take a completely different approach. Yes, they have my SQL cause they know got that with the sun acquisition. But, but this is they're really about put, is putting as much capability into a single database as possible. Oh, you only need one database only different philosophy. >>Yeah. And then obviously a health Lake. And then that was pretty much the end of the, the announcements big impact to health care. Again, the theme of horizontal data, vertical specialization with data science and software playing out in real time. >>Yeah. Well, so I have asked this question many times in the cube, when is it that machines will be able to make better diagnoses than doctors and you know, that day is coming. If it's not here, uh, you know, I think helped like is really interesting. I've got an interview later on with one of the practitioners in that space. And so, you know, healthcare is something that is an industry that's ripe for disruption. It really hasn't been disruption disrupted. It's a very high, high risk obviously industry. Uh, but look at healthcare as we all know, it's too expensive. It's too slow. It's too cumbersome. It's too long sometimes to get to a diagnosis or be seen, Amazon's trying to attack with its partners, all of those problems. >>Well, Dave, let's, let's summarize our take on Amazon keynote with machine learning, I'll say pretty historic in the sense that there was so much content in first keynote last year with Andy Jassy, he spent like 75 minutes. He told me on machine learning, they had to kind of create their own category Swami, who we interviewed many times on the cube was awesome. But a lot of still a lot more stuff, more, 215 announcements this year, machine learning more capabilities than ever before. Um, moving faster, solving real problems, targeting the builders, um, fraud platform set of things is the Amazon cadence. What's your analysis of the keynote? >>Well, so I think a couple of things, one is, you know, we've said for a while now that the new innovation cocktail is cloud plus data, plus AI, it's really data machine intelligence or AI applied to that data. And the scale at cloud Amazon Naylor obviously has nailed the cloud infrastructure. It's got the data. That's why database is so important and it's gotta be a leader in machine intelligence. And you're seeing this in the, in the spending data, you know, with our partner ETR, you see that, uh, that AI and ML in terms of spending momentum is, is at the highest or, or at the highest, along with automation, uh, and containers. And so in. Why is that? It's because everybody is trying to infuse AI into their application portfolios. They're trying to automate as much as possible. They're trying to get insights that, that the systems can take action on. >>And, and, and actually it's really augmented intelligence in a big way, but, but really driving insights, speeding that time to insight and Amazon, they have to be a leader there that it's Amazon it's, it's, it's Google, it's the Facebook's, it's obviously Microsoft, you know, IBM's Tron trying to get in there. They were kind of first with, with Watson, but with they're far behind, I think, uh, the, the hyper hyper scale guys. Uh, but, but I guess like the key point is you're going to be buying this. Most companies are going to be buying this, not building it. And that's good news for organizations. >>Yeah. I mean, you get 80% there with the product. Why not go that way? The alternative is try to find some machine learning people to build it. They're hard to find. Um, so the seeing the scale of kind of replicating machine learning expertise with SageMaker, then ultimately into databases and tools, and then ultimately built into applications. I think, you know, this is the thing that I think they, my opinion is that Amazon continues to move up the stack, uh, with their capabilities. And I think machine learning is interesting because it's a whole new set of it's kind of its own little monster building block. That's just not one thing it's going to be super important. I think it's going to have an impact on the startup scene and innovation is going, gonna have an impact on incumbent companies that are currently leaders that are under threat from new entrance entering the business. >>So I think it's going to be a very entrepreneurial opportunity. And I think it's going to be interesting to see is how machine learning plays that role. Is it a defining feature that's core to the intellectual property, or is it enabling new intellectual property? So to me, I just don't see how that's going to fall yet. I would bet that today intellectual property will be built on top of Amazon's machine learning, where the new algorithms and the new things will be built separately. If you compete head to head with that scale, you could be on the wrong side of history. Again, this is a bet that the startups and the venture capitals will have to make is who's going to end up being on the right wave here. Because if you make the wrong design choice, you can have a very complex environment with IOT or whatever your app serving. If you can narrow it down and get a wedge in the marketplace, if you're a company, um, I think that's going to be an advantage. This could be great just to see how the impact of the ecosystem this will be. >>Well, I think something you said just now it gives a clue. You talked about, you know, the, the difficulty of finding the skills. And I think that's a big part of what Amazon and others who were innovating in machine learning are trying to do is the gap between those that are qualified to actually do this stuff. The data scientists, the quality engineers, the data engineers, et cetera. And so companies, you know, the last 10 years went out and tried to hire these people. They couldn't find them, they tried to train them. So it's taking too long. And now that I think they're looking toward machine intelligence to really solve that problem, because that scales, as we, as we know, outsourcing to services companies and just, you know, hardcore heavy lifting, does it doesn't scale that well, >>Well, you know what, give me some machine learning, give it to me faster. I want to take the 80% there and allow us to build certainly on the media cloud and the cube virtual that we're doing. Again, every vertical is going to impact a Dave. Great to see you, uh, great stuff. So far week two. So, you know, we're cube live, we're live covering the keynotes tomorrow. We'll be covering the keynotes for the public sector day. That should be chock-full action. That environment is going to impact the most by COVID a lot of innovation, a lot of coverage. I'm John Ferrari. And with Dave Alante, thanks for watching.

>>on >>the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Okay. Welcome back, everyone. That's the cubes. Live coverage here in Palo Alto, California. I'm John for your host with David Lantana in Boston. Massachusetts. Uh, we got a great panel here. Analysts just gonna break it down. Keynote analysis. Day one, we got Ah, longtime Web services expert analyst Diane Hinchcliffe, principal researcher at N V. P. It constantly research, but he goes way back. Dan, I remember, uh, 2000 and one time frame you and I'm >>reading Last time you and I hang out with Michael Arrington's house back in the TechCrunch days >>back when, you know you were on this was Web services. I mean, that's always, uh, serves on the architectures. They called it back then. This was the beginning. This really was the catalyst of cloud. If you think about virtualization and Web services in that era, that really spawned where we are today so great to >>have you on as an Amazon got their start saying that everyone could get whatever they want to on a P. I now right, >>all right? Well, we've been riding this wave. Certainly it's cotton now more clear for the mainstream America. And I quoted you in my story, uh, on Andy Jassy when I had my one on one with them because I saw your talk with star Bit of the weekend and in the way you kicked it off was the Pandemic four was forced upon everybody, which is true, and that caught my attention was very notable because you talked to a lot of C E. O s. Does jazz sees pitch resonate with them? In your opinion, what's your take on on that on that posture? Because we heard, hey, you know, get busy building or you're dying, right? So get busy building. That's what >>I thought that was a good message. But I mean on and certainly I saw tweets and said, Hey, he's just he's just directly talking to the CEO. But if you ask me, he's still talking to the CTO, right? The technology officer who's got a feels all this technology and bend it into the shape that it will serve the business. You talk to a CEO who wants is trying to get on the cloud their biggest challenges. I know I need armies of people who know all these brand new services. You saw the development velocity of all the things that they announced and things they re emphasized there was There was a lot of things that were bringing back again because they have so many things that they're offering to the public. But the developer skills or not, they're the partner skills are not there. So you talked to CEO, says All right, I buy in and and I have had to transform overnight because of the pandemic, my customers have moved, my workers have moved on, and I have to like, you know, redirect all my I t Overnight and Cloud is the best way to do that. Where's my where's all the skills for the training programs, the department programs that allow me to get access to large amounts of talent? Those are the types of things that the CEO is concerned about is from an operational perspective. We didn't hear anything about, like a sales force type trailhead where we're going to democratize cloud skills to the very far end of your organization. >>Yeah, they're just kind of scratching the service. They didn't mention that, you know, far Gates away to get into server list. I mean, this is ultimately the challenge Dave and Deena like, don't get your thoughts on this because I was talking Teoh a big time CTO and a big time see so and that perspectives were interesting. And here's the Here's the Here's what I want you to react Thio the sea level Say everything is gonna be a service. Otherwise we're gonna be extinct. Okay, that's true. I buy that narrative, Okay, Make it as a service. That's why not use it. And then they go to the C t. And they say, implement, They go Well, it's not that easy. So automation becomes a big thing. And then so there's this debate. Automate, automate, automate. And then everything becomes a service. Is it the cart before the horse? So is automation. It's the cart before the horse, for everything is a service. What do you guys think about that? >>We'll see. I mean, CEO is to Diane's point, are highly risk averse and they like services. And those services generally are highly customized. And I think the tell in the bevy of announcements the buffet have announces that we heard today was in the marketplace what you guys thought of this or if you caught this. But there was a discussion about curated professional services that were tied to software, and there were classic PDM services. But they were very, you know, tight eso sort of off the shelf professional services, and that's kind of how Amazon plays it. And they were designed to be either self serve. It's a Diane's point. Skill sets aren't necessarily there or third parties, not directly from Amazon. So that's a gap that Amazon's got too close. I mean, you talk about all the time without post installations, you know, going on Prem. You know who's gonna support and service those things. You know, that's a that's a white space right now. I think >>e think we're still reading the tea leaves on the announcements. But there was one announcement that was, I thought really important. And that was this VM Ware cloud for a W s. It says, Let's take your VM ware skills, which you've honed and and cultivated and built a talent base inside your organization to run VMS and let's make that work for a W s. So I thought the VM Ware cloud for a W s announcement was key. It was a sleeper. It didn't spend a lot of time on it. But the CEO ears are gonna perk up and say, Wait, I can use native born skills. I already have to go out to the cloud So I didn't think that they did have 11 announcement I thought was compelling in that >>in the spending data shows of VM Ware Cloud on AWS is really gaining momentum by the way, As you see in that open shift So you see in that hybrid zone really picking up. And we heard that from AWS today. John, you and I talked about it at the open I produces in >>Yeah, I want to double down on that point you made because I want to get your thoughts on this a Z analyst because you know, the VM ware is also tell. Sign to what I'm seeing as operating and developing Dev ops as they be called back in the day. But you gotta operate, i t. And if Jassy wants to go after this next tier of spend on premise and edge. He's gotta win the global i t posture game. He's gotta win hybrid. He's got to get there faster to your point. You gotta operate. It's not just develop on it. So you have a development environment. You have operational environment. I think the VM Ware thing that's interesting, cause it's a nice clean hand in glove. VM Ware's got operators who operate I t. And they're using Amazon to develop, but they work together. There's no real conflict like everyone predicted. So is that the tell sign is the operational side. The challenge? The Dev, How does Amazon get that global I t formula down? Is it the VM Ware partnership? >>I think part of it is there, finally learning to say that the leverage that the vast pool of operational data they have on their literally watching millions of organizations run all the different services they should know a lot and I say made that point today, he said, Well, people ask us all the time. You must have all these insights about when things were going right or wrong. Can you just tell us? And so I think the announcement around the Dev ops guru was very significant, also saying you don't necessarily have to again teach all your staff every in and out about how to monitor every aspect of all these new services that are much more powerful for your business. But you don't yet know how to manage, especially at scale. So the Dev Ops guru is gonna basically give a dashboard that says, based on everything that we've known in the past, we could give you insights, operational insights you can act on right away. And so I think that is again a tool that could be put in place on the operational side. Right. So b m where for cloud gives you migration ability, uh, of existing skills and workloads. And then the Dev Ops crew, if it turns out to be everything they say it is, could be a really panacea for unlocking the maturity curve that these operators have to climb >>on. AWS is in the business now of solving a lot of the problems that it sort of helped create. So you look at, for instance, you look at the sage maker Data Wrangler trying to simplify data workloads. The data pipeline in the cloud is very very complex and so they could get paid for helping simplify that. So that's a wonderful, virtuous circle. We've seen it before. >>Yeah. I mean, you have a lot of real time contact lens you've got, um, quick site. I mean, they have to kind of match the features. And And I want to get your guys thoughts on on hybrid because I think, you know, I'm still stuck on this, Okay? They won the as path and their innovations Great. The custom chips I buy that machine learning all awesome. So from the classic cloud I as infrastructure and platform as a service business looking good. Now, if you're thinking global, I t I just don't just not connecting the dots there. See Outpost? What's riel today for Amazon? Can you guys share E? I mean, if you were watching this keynote your head explode because you've got so many announcements. What's actually going on if you're looking at this is the CEO. >>So the challenge you have is the CEO. Is that your you have 10, 20 or 30 or more years of legacy hardware, including mainframes, right. Like so big insurance companies don't use mainframe because their claims systems have been developed in their very risk averse about changing them. Do you have to make all of this work together? Like, you know, we see IBM and Redhead are actually, you know, chasing that mainframe. Which angle, which is gonna die out where Amazon, I think is smart is saying, Look, we understand that container is gonna be the model container orchestration is gonna be how I t goes forward. The CEO is now buy into that. Last year, I was still saying, Are we gonna be able to understand? Understand? Kubernetes is the regular average i t person, which are not, you know, Google or Facebook level engineers Are there gonna be able to do do containers? And so we see the open sourcing of of the AWS is, uh, kubernetes, uh, server on. We see plenty of container options. That's how organizations could build cloud native internally. And when they're ready to go outside because we're gonna move, they're gonna move many times slower than a cloud native company to go outside. Everything is ready there. Um, I like what I'm seeing without posts. I like what I'm seeing with the hybrid options. The VM ware for cloud. They're building a pathway that says you can do real cloud. And I think the big announcement that was that. That s a really, uh, spend time on which is that PCs for everywhere. Um, a saying you're gonna be able to put Amazon services are compute services anywhere. You need it, e think that's a smart message. And that allows people to say I could eventually get toe one model to get my arms around this over time >>day. What does that mean for the numbers? I know you do a lot of research on spend customer data. Um, CEO is clearly no. This is gonna be the world's never go back to the same way it was. They certainly will accelerate cloud toe. What level depends upon where they are in their truth, as Jassy says. But >>what does >>the numbers look at? Because you're looking at the data you got Microsoft, You got Amazon. What's the customer spend look like where they're gonna be spending? >>Well, so a couple things one is that when you strip out the the SAS portion of both Google and Azure, you know, as we know, I asked him pass A W S is the leader, but there's no question that Microsoft is catching up. Says that we were talking about earlier. Uh, it's the law of large numbers Just to give you a sense Amazon this year we'll add. Q four is not done yet, but they'll add 10 billion over last year. And Jesse sort of alluded to that. They do that in 12 months. You know, uh, azure will add close to nine billion this year of incremental revenue. Google much, much smaller. And so So that's, you know, just seeing, uh, as you really catch up there for sure, you know, closing that gap. But still Amazon's got the lead. The other thing I would say is die on you and I were talking about this Is that you know Google is starting. Thio do a little bit better. People love their analytics. They love the built in machine learning things like like big query. And you know, even though they're much, much smaller there, another hedge people don't necessarily want to goto Microsoft unless they're Microsoft Shop. Google gives them that alternative, and that's been a bit of a tailwind for Google. Although I would say again, looking at the numbers. If I look back at where Azure and AWS were at this point where Google is with a few billion dollars in cloud the growth rates, I'd like to see Google growing a little faster. Maybe there's a covert factor there. >>Diane. I want to get your thoughts on this transition. Microsoft Oracle competition Um, Jesse knows he's got a deal with the elite Salesforce's out there. Oracle, Microsoft. Microsoft used to be the innovator. They had the they had the phrase embracing extend back in the day. Now Amazon's embracing and extending, but they gotta go through Oracle and Microsoft if they wanna win the enterprise on premise business and everybody else. Um, eso welcome to the party like Amazon. You What's your take on them versus Microsoft? Calling them out on sequel server licensing practices almost thrown him under the bus big time. >>Well, I think that's you know, we saw the evidence today that they're actually taking aim at Microsoft now. So Babel Fish, which allows you to run Microsoft sequel server workloads directly on Aurora. Uh, that that is what I call the escape pod that gives organizations an easy way That isn't Will parliament to redesign and re architect their applications to say, Just come over to AWS, right? We'll give you a better deal. But I think you've got to see Amazon have, um, or comprehensive sales plan to go into the C. E. O s. Go after the big deals and say, You know, we want to say the whole cloud suite, we have a stack that's unbeatable. You see our velocities, you know, best in class. Arguably against Microsoft is the big challenger, but we'll beat you on on a total cost of ownership. You know, your final bill. At the end of the day, we could we commit to being less than our competitors. Things like that will get the attention. But, you know, uh, Amazon is not known for cutting customized deals. Actually, even frankly, I'm hearing from very CEO is a very large, like Fortune 20 companies. They have very little wiggle room with Microsoft's anybody who's willing to go to the big enterprise and create custom deals. So if you build a sales team that could do that, you have a real shot and saying getting into the CEO's office and saying, You know, we want to move all the I t over and I'm seeing Microsoft getting winds like that. I'm not yet seeing Amazon and they're just gonna have to build a specialized sales team that go up against those guys and migration tools like we saw with Babel fish that says, If you want to come, we can get you over here pretty quick. >>I want to chime in on Oracle to John. I do. I think this is a blind spot somewhat for AWS, Oracle and mainframes. Jesse talks that talks like, Oh yeah, these people, they wanna get off there. And there's no question there are a number of folks that are unhappy, certainly with Oracle's licensing practices. But I talked to a lot of Oracle customers that are running the shops on Oracle database, and it's really good technology. It is world class for mission critical transaction workloads. Transaction workloads tend to be much, much smaller data set sizes, and so and Oracle's got, you know, decades built up, and so their their customers air locked in and and they're actually reasonably happy with the service levels they're getting out of Oracle. So yes, licensing is one thing, but there's more to the story and again, CEO or risk averse. To Diane's point, you're not just gonna chuck away your claim system. It's just a lot of custom code. And it's just the business case isn't there to move? >>Well, I mean, I would argue that Well, first of all, I see where you're coming from. But I would also argue that one of the things that Jesse laid out today that I thought was kind of a nuanced point was during the vertical section. I think it was under the manufacturing. He really laid out the case that I saw for startups and or innovation formula, that horizontal integration around the data. But then being vertically focused with the modern app with same machine learning. So what he was saying, and I don't think he did a good job doing it was you could disrupt horizontally in any industry. That's a that's a disruption formula, but you still could have that scale. That's cloud horizontal scalability, cloud. But the data gives you the ability to do both. I think bringing data together across multiple silos is critical, but having that machine learning in the vertical is the way you could different so horizontally. Scalable vertical specialization for the modern app, I think is a killer formula. And I think >>I think that's a I think it's a really strong point, John, and you're seeing that you're seeing in industries like, for instance, Amazon getting into grocery. And that's a data play. But I do like Thio following your point. The Contact Center solutions. I like the solutions play there and some of the stuff they're doing at the edge with i o T. The equipment optimization, the predictive maintenance, those air specialized solutions. I really like the solutions Focus, which several years ago, Amazon really didn't talk solution. So that's a positive sign, >>Diane, what do you think? The context And I think that was just such low hanging fruit for Amazon. Why not do it? You got the cloud scale. You got the Alexa knowledge, you know, got machine learning >>zone, that natural language processing maturity to allow them to actually monitor that. You know that that contact lens real time allows them a lot of supervisors to intervene them conversations before they go completely south, right? So allowing people to get inside decision windows they couldn't before. I think that's a really important capability. And that's a challenge with analytics in general. Is that generates form or insights than people know how to deal with? And it solutions like contact lens Real time? This is Let's make these insights actionable before it's broken. Let's give you the data to go and fix it before it even finishes breaking. And this is the whole predictive model is very powerful. >>Alright, guys, we got four minutes left. I wanted Segway and finish up with what was said in the keynote. That was a tell sign that gives us some direction of where the dots will connect in the future. There's a lot of stuff that was talked about that was, you know, follow on. That was meat on the bone from previous announcements. Where did Jassy layout? What? I would call the directional shift. Did you see anything particular that you said? Okay, that is solid. I mean, the zones was one I could see. What clearly is an edge piece. Where did you guys see? Um, some really good directional signaling from Jassy in terms of where they really go. Deal with start >>e I felt like Jassy basically said, Hey, we invented cloud. Even use these words we invented cloud and we're gonna define what hybrid looks like We're gonna bring our cloud model to the edge. And the data center just happens to be another edge point. And hey, I thought he laid down the gauntlet. E think it's a very powerful message. >>What do you think Jesse has been saying? That he laid out here, That's >>you laid out a very clear path to the edge that the Amazons marching to the edge. That's the next big frontier in the cloud. It isn't well defined. And that just like they defined cloud in the early days that they don't get out there and be the definitive leader in that space. Then they're gonna be the follower. I think so. We saw announcement after announcement around that you know, from the zones Thio the different options for outpost um, the five g announcement wavelength. All of those things says we're gonna go out to the very tippy edge is what I heard right out to your mobile devices. Right after the most obscure field applications imaginable. We're gonna have an appliance So we're gonna have a service that lets you put Amazon everywhere. And so I think the overarching message was This is a W s everywhere it z gonna go after 100% of I t. Eventually on DSO you can move to that. You know, this one stop shop? Um and you know, we saw him or more discussions about multi cloud, but it was interesting how they stand away from that. And this is what I think One area that they're going to continue to avoid. So it was interesting, >>John, I think I think the edges one by developers. And that's good news for Amazon. And good news for Microsoft. >>We'll see the facilities is gonna be good for me. I think guys, the big take away You guys nailed two of them there, but I think the other one was I think he's trying to speak to this new generation in a very professorial way. Talk about Clay Christensen was a professor at his business school at Harvard. We all know the book. Um, but there was this There was this a posture of speaking to the younger generation like hey, the old guy, the old that was running the mainframe. Wherever the old guys there, you could take over and run this. So it's kind of like more of a leadership preach of preaching like, Hey, it's okay to be cool and innovative, right now is the time to get in cloud. And the people who are blocking you are either holding on to what they built or too afraid to shift. Eso I think a Z we've seen through waves of innovation. You always have those people you know who are gonna stop that innovation. So I was very interesting. You mentioned that would service to the next generation. Um, compute. So he had that kind of posture. Interesting point. Yeah, just very, very preachy. >>E think he's talking to a group of people who also went through the through 2020 and they might be very risk averse and not bold anymore. And so, you know, I think that may have helped address that as well. >>All right, gentlemen, great stuff. Final word in the nutshell. Kena, What do you think about it in general? Will take away. >>Yeah, I I think we saw the continued product development intensity that Amazon is going to use to try and thrash the competition? Uh, the big vision. Um, you know, the real focus on developers first? Um and I think I t and C e O's second, I think before you could say they didn't really think about them too much at all. But now it's a close second. You know, I really liked what I saw, and I think it's It's the right move. I'd like to Seymour on on hybrid cloud migration than that, even when we saw them. >>All right, leave it there. Don. Thanks for coming on from this guest analyst segment. Appreciate you jumping in Cuba. Live. Thank you. >>Thanks. Alright. >>With acute virtual. I'm your host John per day Volonte here covering A W s live covering the keynote in real time State more for more coverage after the break

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Everyone welcome to the cubes Live coverage of AWS reinvent 2020 virtual were virtual this year We are the Cube Virtual I'm your host John for a joint day Volonte for keynote analysis Andy Jassy just delivered his live keynote. This is our live keynote analysis. Dave. Great to see you, Andy Jassy again. You know their eight year covering reinvent their ninth year. We're virtual. We're not in person. We're doing it. >>Great to see you, John. Even though we're 3000 miles apart, we both have the covert here. Do going Happy birthday, my friend. >>Thank you. Congratulations. Five years ago I was 50 and they had the cake on stage and on the floor. There's no floor, this year's virtual and I think one of the things that came out of Andy Jessie's keynote, obviously, you know, I met with him earlier. Telegraph some of these these moves was one thing that surprised me. He came right out of the gate. He acknowledged that social change, the cultural shift. Um, that was interesting but he went in and did his normal end to end. Slew of announcements, big themes around pivoting. And he brought kind of this business school kind of leadership vibe to the table early talking about what people are experiencing companies like ourselves and others around the change and cultural change around companies and leadership. It takes for the cloud. And this was a big theme of reinvent, literally like, Hey, don't hold on to the old And I kept thinking to myself, David, you and I both are Historians of the tech industry remind me of when I was young, breaking into the business, the mainframe guys and gals, they were hugging onto those mainframes as long as they could, and I looked at it like That's not gonna be around much longer. And they kept No, it's gonna be around. This is this is the state of the art, and then the extinction. Instantly this feels like cloud moment, where it's like it's the wake up call. Hey, everyone doing it the old way. You're done. This is it. But you know, this is a big theme. >>Yes. So, I mean, how do you curate 2.5 3 hours of Andy Jassy. So I tried to break it down at the three things in addition to what you just mentioned about him acknowledging the social unrest and and the inequalities, particularly with black people. Uh, but so I had market leadership. And there's some nuance there that if we have time, I'd love to talk about, uh, the feature innovation. I mean, that was the bulk of his presentation, and I was very pleased. I wrote a piece this weekend. As you know, talk about Cloud 2030 and my main focus was the last 10 years about I t transformation the next 10 years. They're gonna be about organizational and business and industry transformation. I saw a lot of that in jazz ces keynote. So you know, where do you wanna go? We've only got a few minutes here, John, >>but let's break. Let's break down the high level theme before we get into the announcement. The thematic part was, it's about reinventing 2020. The digital transformation is being forced upon us. Either you're in the cloud or you're not in the cloud. Either way, you got to get to the cloud for to survive in this post covert error. Um, you heard a lot about redefining compute new chips, custom chips. They announced the deal with Intel, but then he's like we're better and faster on our custom side. That was kind of a key thing, this high idea of computing, I think that comes into play with edge and hybrid. The other thing that was notable was Jessie's almost announcement of redefining hybrid. There's no product announcement, but he was essentially announcing. Hybrid is changed, and he was leaning forward with his definition of redefining what hybrid cloud is. And I think that to me was the biggest, um, signal. And then finally, what got my attention was the absolute overt call out of Microsoft and Oracle, and, you know, suddenly, behind the scenes on the database shift we've been saying for multiple times. Multiple databases in the cloud he laid that out, said there will be no one thing to rule anything. No databases. And he called out Microsoft would look at Microsoft. Some people like cloud wars. Bob Evans, our good friend, claims that Microsoft been number one in the cloud for like like year, and it's just not true right. That's just not number one. He used his revenue a za benchmark. And if you look at Microsoft's revenue, bulk of it is from propped up from Windows Server and Sequel Server. They have Get up in there that's new. And then a bunch of professional services and some eyes and passed. If you look at true cloud revenue, there's not much there, Dave. They're definitely not number one. I think Jassy kind of throws a dagger in there with saying, Hey, if you're paying for licenses mawr on Amazon versus Azure that's old school shenanigans or sales tactics. And he called that out. That, to me, was pretty aggressive. And then So I finally just cove in management stuff. Democratizing machine learning. >>Let me pick up on a couple things. There actually were a number of hybrid announcements. Um, E C s anywhere E k s anywhere. So kubernetes anywhere containers anywhere smaller outposts, new local zones, announced 12 new cities, including Boston, and then Jesse rattle them off and made a sort of a joke to himself that you made that I remembered all 12 because the guy uses no notes. He's just amazing. He's up there for three hours, no notes and then new wavelength zones for for the five g edge. So actually a lot of hybrid announcements, basically, to your point redefining hybrid. Basically, bringing the cloud to the edge of which he kind of redefined the data center is just sort of another edge location. >>Well, I mean, my point was Is that my point is that he Actually, Reid said it needs to be redefined. Any kind of paused there and then went into the announcements. And, you know, I think you know, it's funny how you called out Microsoft. I was just saying which I think was really pivotal. We're gonna dig into that Babel Babel Fish Open source thing, which could be complete competitive strategy, move against Microsoft. But in a way, Dave Jassy is pulling and Amazon's pulling the same move Microsoft did decades ago. Remember, embrace and extend right Bill Gates's philosophy. This is kind of what they're doing. They have embraced hybrid. They have embraced the data center. They're extending it out. You're seeing outpost, You see, five g, You're seeing these I o t edge points. They're putting Amazon everywhere. That was my take away. They call it Amazon anywhere. I think it's everywhere. They want cloud operations everywhere. That's the theme that I see kind of bubbling out there saying, Hey, we're just gonna keep keep doing this. >>Well, what I like about it is and I've said this for a long time now that the edge is gonna be one by developers. And so they essentially taking AWS and the data center is an AP, and they're bringing that data center is an A P I virtually everywhere. As you're saying, I wanna go back to something you said about leadership and Microsoft and the numbers because I've done a lot of homework on this Aziz, you know, And so Jassy made the point. He makes this point a lot that it's not about the the actual growth rate. Yeah, the other guys, they're growing faster. But there were growing from a much larger base and I want to share with you a nuance because he said he talked about how AWS grew incrementally 10 billion and only took him 12 months. I have quarterly forecast and I've published these on Wiki Bond, a silicon angle. And if you look at the quarterly numbers and now this is an estimate, John. But for Q four, I've got Amazon growing at 25%. That's a year on year as you're growing to 46% and Google growing at 50% 58%. So Google and and Azure much, much higher growth rates that than than Amazon. But what happens when you look at the absolute numbers? From Q three to Q four, Amazon goes from 11.6 billion to 12.4 billion. Microsoft actually stays flat at around 6.76 point eight billion. Google actually drops sequentially. Now I'm talking about sequentially, even though they have 58% growth. So the point of the Jazz is making is right on. He is the only company growing at half the growth rate year on year, but it's sequential. Revenues are the only of the Big Three that are growing, so that's the law of large numbers. You grow more slowly, but you throw off more revenue. Who would you rather be? >>I think I mean, it's clearly that Microsoft's not number one. Amazon's number one cloud certainly infrastructure as a service and pass major themes in the now so we won't go through. We're digging into the analyst Sessions would come at two o'clock in three o'clock later, but they're innovating on those two. They want they one that I would call this member. Jasio says, Oh, we're in the early innings Inning one is I as and pass. Amazon wins it all. They ran the table, No doubt. Now inning to in the game is global. I t. That was a really big part of the announcement. People might have missed that. If you if you're blown away by all the technical and complexity of GP three volumes for EBS and Aurora Surveillance V two or sage maker Feature store and Data Wrangler Elastic. All that all that complex stuff the one take away is they're going to continue to innovate. And I, as in past and the new mountain that they're gonna Klima's global I t spin. That's on premises. Cloud is eating the world and a W s is hungry for on premises and the edge. You're going to see massive surge for those territories. That's where the big spend is gonna be. And that's why you're seeing a big focus on containers and kubernetes and this kind of connective tissue between the data machine layer, modern app layer and full custom. I as on the on the bottom stack. So they're kind of just marching along to the cadence of, uh, Andy Jassy view here, Dave, that, you know, they're gonna listen to customers and keep sucking it in Obama's well and pushing it out to the edge. And and we've set it on the Cube many years. The data center is just a big edge. And that's what Jassy is basically saying here in the keynote. >>Well, and when when Andy Jassy gets pushed on Well, yes, you listen to customers. What about your partners? You know, he'll give examples of partners that are doing very well. And of course we have many. But as we've often said in the Cube, John, if you're a partner in the ecosystem, you gotta move fast. There were three interesting feature announcements that I thought were very closely related to other things that we've seen before. The high performance elastic block storage. I forget the exact name of it, but SAN in a cloud the first ever SAN in the cloud it reminds me of something that pure storage did last year and accelerate so very, very kind of similar. And then the aws glue elastic views. It was sort of like snowflake's data cloud. Now, of course, AWS has many, many more databases that they're connecting, You know, it, uh, stuff like as one. But the way AWS does it is they're copying and moving data and doing change data management. So what snowflake has is what I would consider a true global mesh. And then the third one was quicksight que That reminded me of what thought spots doing with search and analytics and AI. So again, if you're an ecosystem partner, you gotta move fast and you've got to keep innovating. Amazon's gonna do what it has to for customers. >>I think Amazon's gonna have their playbooks when it's all said and done, you know, Do they eat the competition up? I think what they do is they have to have the match on the Amazon side. They're gonna have ah, game and play and let the partners innovate. They clearly need that ecosystem message. That's a key thing. Um, love the message from them. I think it's a positive story, but as you know it's Amazons. This is their Kool Aid injection moment, David. Educational or a k A. Their view of the world. My question for you is what's your take on what wasn't said If you were, you know, as were in the virtual audience, what should have been talk about? What's the reality? What's different? What didn't they hit home? What could they have done? What, your critical analysis? >>Well, I mean, I'm not sure it should have been said, but certainly what wasn't said is the recognition that multi cloud is an opportunity. And I think Amazon's philosophy or belief at the current time is that people aren't spreading workloads, same workload across multiple clouds and splitting them up. What they're doing is they're hedging bets. Maybe they're going 70 30 90 10, 60 40. But so multi cloud, from Amazon standpoint is clearly not the opportunity that everybody who doesn't have a cloud or also Google, whose no distant third in cloud says is a huge opportunity. So it doesn't appear that it's there yet, so that was I wouldn't call it a miss, but it's something that, to me, was a take away that Amazon does not currently see that there's something that customers are clamoring for. >>There's so many threads in here Were unpacked mean Andy does leave a lot of, you know, signature stories that lines in there. Tons of storylines. You know, I thought one thing that that mass Amazon's gonna talk about this is not something that promotes product, but trend allies. I think one thing that I would have loved to Seymour conversation around is what I call the snowflake factor. It snowflake built their business on Amazon. I think you're gonna see a tsunami of kind of new cloud service providers. Come on the scene building on top of AWS in a major way of like, that kind of value means snowflake went public, uh, to the level of no one's ever seen ever in the history of N Y s e. They're on Amazon. So I call that the the next tier cloud scale value. That was one thing I'd like to see. I didn't hear much about the global i t number penetration love to hear more about that and the thing that I would like to have heard more. But Jassy kind of touched a little bit on it was that, he said at one point, and when he talked about the verticals that this horizontal disruption now you and I both know we've been seeing on the queue for years. It's horizontally scalable, vertically specialized with the data, and that's kind of what Amazon's been doing for the past couple of years. And it's on full display here, horizontal integration value with the data and then use machine learning with the modern applications, you get the best of both worlds. He actually called that out on this keynote. So to me, that is a message to all entrepreneurs, all innovators out there that if you wanna change the position in the industry of your company, do those things. There's an opportunity right now to integrate with the cloud to disrupt horizontally, but then on the vertical. So that will be very interesting to see how that plays out. >>And eventually you mentioned Snowflake and I was talking about multi cloud snowflake talks about multi cloud a lot, but I don't even think what they're doing is multi cloud. I think what they're doing is building a data cloud across clouds and their abstracting that infrastructure and so to me, That's not multi Cloud is in. Hey, I run on Google or I run on the AWS or I run on Azure ITT's. I'm abstracting that making that complexity disappeared, I'm creating an entirely new cloud at scale. Quite different. >>Okay, we gotta break it there. Come back into our program. It's our live portion of Cube Live and e. K s Everywhere day. That's multi cloud. If they won't say, that's what I'll say it for them, but the way we go, more live coverage from here at reinvent virtual. We are virtual Cuban John for Dave a lot. They'll be right back.

>> Commentator: Live from San Jose, California. It's theCUBE covering Big Data Silicon Valley 2017. >> Okay, welcome back everyone. We are here live in Silicon Valley for Big Data SV (mumbles) event in conjunction with Strata + Hadoop. Our companion event, the Big Data NYC and we're here breaking down the Big Data world as it evolves and goes to the next level up on the step function, AI machine learning, IOT really forcing people to really focus on a clear line of the side of the data. I'm John Furrier with our announcer from Wikibon, George Gilbert and our next guest, our two executives from Trifacta. The founder and Chief Strategy Officer, Joe Hellerstein and Adam Wilson, the CEO. Guys, welcome to theCUBE. Welcome back. >> Great to be here. >> Good to be here. >> Founder, co-founder? >> Co-founder. >> Co-founder. He's a multiple co-founders. I remember it 'cause you guys were one of the first sites that have the (mumbles) in the about section on all the management team. Just to show you how technical you guys are. Welcome back. >> And if you're Trifacta, you have to have three founders, right? So that's part of the tri, right? >> The triple threat, so to speak. Okay, so a big year for you guys. Give us the update. I mean, also we had Alation announce this partnering going on and some product movement. >> Yup. >> But there's a turbulent time right now. You have a lot of things happening in multiple theaters to technical theater to business theater. And also within the customer base. It's a land grand, it seems to be on the metadata and who's going to control what. What's happening? What's going on in the market place and what's the update from you guys? >> Yeah, yeah. Last year was an absolutely spectacular year for Trifacta. It was four times growth in bookings, three times growth in customers. You know, it's been really exciting for us to see the technology get in the hands of some of the largest companies on the planet and to see what they're able to do with it. From the very beginning, we really believed in this idea of self service and democratization. We recognize that the wrangling of the data is often where a lot of the time and the effort goes. In fact, up to 80% of the time and effort goes in a lot of these analytic projects and to the extent that we can help take the data from (mumbles) in a more productive way and to allow more people in an organization to do that. That's going to create information agility that that we feel really good about and there are customers and they are telling us is having an impact on their use of Big Data and Hadoop. And I think you're seeing that transition where, you know, in the very beginning there was a lot of offloading, a lot of like, hey we're going to grab some cost savings but then in some point, people scratch their heads and said, well, wait a minute. What about the strategic asset that we were building? That was going to change the way people work with the data. Where is that piece of it? And I think as people started figuring out in order to get our (mumbles), we got to have users and use cases on these clusters and the data like itself is not a used case. Tools like Trifacta have been absolutely instrumental and really fueling that maturity in the market and we feel great about what's happening there. >> I want to get some more drilled out before we get to some of these questions for Joe too because I think you mentioned, you got some quotes. I just want to double up a click on that. It always comes up in the business model question for people. What's your business model? >> Sure. >> And doing democratization is really hard. Sometimes democratization doesn't appear until years later so it's one of those elusive things. You see it and you believe it but then making it happen are two different things. >> Yeah, sure. >> So. And appreciate that the vision they-- (mumbles) But ultimately, at the end of the day, that business model comes down to how you organized. Prove points. >> Yup. >> Customers, partnerships. >> Yeah. >> We had Alation on Stephanie (mumbles). Can you share just and connect the dots on the business model? >> Sure. >> With respect to the product, customers, partners. How was that specifically evolving? >> Adam: Sure. >> Give some examples. >> Sure, yeah. And I would say kind of-- we felt from the beginning that, you know, we wanted to turn what was traditionally a very complex messy problem dealing with data, you know, in the user experience problem that was powered by machine learning and so, a lot of it was down to, you know, how we were going to build and architect the technology needed (mumbles) for really getting the power in the hands of the people who know the data best. But it's important, and I think this is often lost in Silicon Valley where the focus on innovation is all around technology to recognize that the business model also has to support democritization so one of the first things we did coming in was to release a free version of the product. So Trifacta Wrangler that is now being used by over 4500 companies, ten of thousands of users and the power of that in terms of getting people something of value that they could start using right away on spreadsheets and files and small data and allowing them to get value but then also for us, the exchange is that we're actually getting a chance to curate at scale usage data across all of these-- >> Is this a (mumbles) product? >> It's a hybrid product. >> Okay. >> So the data stays local. It never leaves their local laptop. The metadata is hashed and put into the cloud and now we're-- >> (mumbles) to that. >> Absolutely. And so now we can use that as training data that actually has more people wrangle, the product itself gets smarter based on that. >> That's good. >> So that's creating real tangible value for customers and for us is a source of very strategic advantage and so we think that combination of the technology innovation but also making sure that we can get this in the hands of users and they can get going and as their problem grows up to be bigger and more complicated, not just spreadsheets and files on the desktop but something more complicated, then we're right there along with them for products that would have been modified. >> How about partnerships with Alation? How they (mumbles)? What are all the deals you got going on there? >> So Alation has been a great partner for us for a while and we've really deepened the integration with the announcements today. We think that cataloging and data wrangling are very complimentary and they're a natural fit. We've got customers like Munich Re, like eBay as well as MarketShare that are using both solutions in concert with one another and so, we really felt that it was natural to tighten that coupling and to help people go from inventorying what's going on in their data legs and their clusters to then cleansing, standardizing. Essentially making it fit for purpose and then ensuring that metadata can roundtrip back into the catalog. And so that's really been an extension of what we're doing also at the technical level with technologies like Cloudera Navigator with Atlas and with the project that Joe's involved with at Berkeley called Ground. So I don't know if you want to talk-- >> Yeah, tell him about Ground. >> Sure. So part of our outlook on this and this speaks to the kind of way that the landscape in the industry's shaping out is that we're not going to see customers buying until it's sort of lock in on the key components of the area for (mumbles). So for example, storage, HD (mumbles). This is open and that's key, I think, for all the players in this base at HTFS. It's not a product from a storage vendor. It's an open platform and you can change vendors along the way and you could role your own and so on. So metadata, to my mind, is going to move in the same direction. That the storage of metadata, the basic component tree that keeps the metadata, that's got to be open to give people the confidence that they're going to pour the basic descriptions of what's in their business and what their people are doing into a place that they know they can count on and it will be vendor neutral. So the catalog vendors are, in my mind, providing a functionality above that basic storage that relates to how do you search the catalog, what does the catalog do for you to suggest things, to suggest data sets that you should be looking at. So that's a value we have on top but below that what we're seeing is, we're seeing Horton and Cloudera coming out with either products re opensource and it's sort of the metadata space and what would be a shame is if the two vendors ended up kind of pointing guns inward and kind of killing the metadata storage. So one of the things that I got interested in as my dual role as a professor at Berkeley and also as a founder of a company in this space was we want to ensure that there's a free open vendor neutral metadata solution. So we began building out a project called Ground which is both a platform for metadata storage that can be sitting underneath catalog vendors and other metadata value adds. And it's also a platform for research much as we did with Spark previously at Berkeley. So Ground is a project in our new lab at Berkeley. The RISELab which is the successor to the AMPLab that gave us Spark. And Ground has now got, you know, collaboratives from Cloudera, from LinkedIn. Capital One has significantly invested in Ground and is putting engineers behind it and contributors are coming also from some startups to build out an open-sourced platform for metadata. >> How old has Ground been around? >> Joe: Ground's been around for about 12 months. It's very-- >> So it's brand new. How do people get involved? >> Brand new. >> Just standard similar to the way the AMPLab was? Just jump in and-- >> Yeah, you know-- >> Go away and-- >> It comes up on GitHub. There's (mumbles) to go download and play with. It's in alpha. And you know, we hope we (mumbles) and the usual opensource still. >> This is interesting. I like this idea because one thing you've been riffing on the cue ball of time is how do you make data addressable? Because ultimately, you know, real time you need to have access to data really really low (mumbles) to see the inside to make it work. Hence the data swamp problem right? So, how do you guys see that? 'Cause now I can just pop in. I can hear the objections. Oh, security! You know. How do you guys see the protections? I'd love to help get my data in there and get something back in return in a community model. Security? Is it the hashing? What's the-- How do you get any security (mumbles)? Or what are the issues? >> Yeah, so I mean the straightforward issues are the traditional issues of authorization and encryption and those are issues that are reasonably well-plumed out in the industry and you can go out and you can take the solutions from people like Clutter or from Horton and those solutions have plugin quite nicely actually to a variety of platforms. And I feel like that level of enterprise security is understood. It's work for vendors to work with that technology so when we went out, we make sure we were carburized in all the right ways at Trifacta to work with these vendors and that we integrated well with Navigator, we integrated with Atlas. That was, you know, there was some labor there but it's understood. There's also-- >> It's solvable basically. >> It's solvable basically and pluggable. There are research questions there which, you know, on another day we could talk about but for instance if you don't trust your cloud hosting service what do you do? And that's like an open area that we're working on at Berkeley. Intel SGX is a really interesting technology and that's based probably a topic for another day. >> But you know, I think it's important-- >> The sooner we get you out of the studio, Paolo Alto would love to drill on that. >> I think it's important though that, you know, when we talk about self service, the first question that comes up is I'm only going to let you self service as far as I can govern what's going on, right? And so I think those things-- >> Restrictions, guard rails-- >> Really going hand in here. >> About handcuffs. >> Yeah so, right. Because that's always a first thing that kind of comes out where people say, okay wait minute now is this-- if I've now got, you know-- you've got an increasing number of knowledge workers who think that is their-- and believe that it is their unalienable right to have access to data. >> Well that's the (mumbles) democratization. That's the top down, you know, governance control point. >> So how do you balance that? And I think you can't solve for one side of that equation without the other, right? And that's really really critical. >> Democratization is anarchization, right? >> Right, exactly. >> Yes, exactly. But it's hard though. I mean, and you look at all the big trends where there was, you know, web one data, web (mumbles), all had those democratization trends but they took six years to play out and I think there might be a more auxiliary with cloud when you point about this new stop. Okay George, go ahead. You might get in there. >> I wanted to ask you about, you know, what we were talking about earlier and what customers are faced with which is, you know, a lot of choice and specialization because building something end to end and having it fully functional is really difficult. So... What are the functional points where you start driving the guard rails in that Ikee cares about and then what are the user experience points where you have critical mass so that the end users then draw other compliant tools in. You with me? On sort of the IT side and the user side and then which tools start pulling those standards? >> Well, I would say at the highest level, to me what's been very interesting especially would be with that's happened in opensource is that people have now gotten accustomed to the idea that like I don't have to go buy a big monolithic stacks where the innovation moves only as fast as the slowest product in the stack or the portfolio. I can grab onto things and I can download them today and be using them tomorrow. And that has, I think, changed the entire approach that companies like Trifacta are taking to how we how we build and release product to market, how we inter operate with partners like Alation and Waterline and how we integrate with the platform vendors like Cloudera, MapR, and Horton because we recognize that we are going to have to be meniacal focused on one piece of this puzzle and to go very very deep but then play incredibly well both, you know, with all the rest of the ecosystem and so I think that is really colored our entire product strategy and how we go to market and I think customers, you know, they want the flexibility to change their minds and the subscription model is all about that, right? You got to earn it every single year. >> So what's the future of (mumbles)? 'Cause that brings up a good point we were kind of critical of Google and you mentioned you guys had-- I saw in some news that you guys were involved with Google. >> Yup. >> Being enterprise ready is not just, hey we have the great tech and you buy from us, damn it we're Google. >> Right. >> I mean, you have to have sales people. You have to have automation mechanism to create great product. Will the future of wrangling and data prep go into-- where does it end up? Because enterprises want, they want certain things. They're finicky of things. >> Right, right. >> As you guys know. So how does the future of data prep deal with the, I won't say the slowness of the enterprise, but they're more conservative, more SLA driven than they are price performance. >> But they're also more fragmented than ever before and you know, while that may not be a great thing for the customers for a company that's all about harmonizing data that's actually a phenomenal opportunity, right? Because we want to be the decision that customers make that guarantee that all their other decisions are changeable, right? And I go and-- >> Well they have legacy systems of record. This is the challenge, right? So I got the old oracle monolithic-- >> That's fine. And that's good-- >> So how do you-- >> The more the merrier, right? >> Does that impact you guys at all? How did you guys handle that situation? >> To me, to us that is more fragmentation which creates more need for wrangling because that introduces more complexity, right? >> You guys do well in that environment. >> Absolutely. And that, you know, is only getting bigger, worse, and more complicated. And especially as people go from (mumbles) to cloud as people start thinking about moving from just looking at transactions to interactions to now looking at behavior data and the IOT-- >> You're welcome in that environment. >> So we welcome that. In fact, that's where-- we went to solve this problem for Hadoop and Big Data first because we wanted to solve the problems at scale that were the most complicated and over time we can always move downstream to sort of more structured and smaller data and that's kind of what's happened with our business. >> I guess I want to circle back to this issue of which part of this value chain of refining data is-- if I'm understanding you right, the data wrangling is the anchor and once a company has made that choice then all the other tool choices have to revolve around it? Is that a-- >> Well think about this way, I mean, the bulk of the time when you talk to the analysts and also the bulk of the labor cost and these things isn't getting the data from its raw form into usage. That whole process of wrangling which is not really just data prep. It's all the things you do all day long to kind of massage these data sets and get 'em from here to there and make 'em work. That space is where the labor cost is. That also means that's spaces were the value add is because that's where your people power or your business context is really getting poured in to understand what do I have, what am I doing with it and what do I want to get out of it. As we move from bottom line IT to top line value generation with data, it becomes all the more so, right? Because now it's not just the matter of getting the reports out every month. It's also what did that brilliant in sales do to that dataset to get that much left? I need to learn from her and do a similar thing. Alright? So, that whole space is where the value is. What that means is that, you know, you don't want that space to be tied to a particular BI tool or a particular execution edge. So when we say that we want to make a decision in the middle of that enables all the other decisions, what you really want to make sure is that that work process in there is not tightly bound to the rest of the stack. Okay? And so you want to particularly pick technologies in that space that will play nicely with different storage, that play nicely with different execution environments. Today it's a dupe, tomorrow it's Amazon, the next day it's Google and they have different engines back there potentially. And you want it certainly makes your place with all the analytic and visualizations-- >> So decouple from all that? >> You want to decouple that and you want to not lock yourself in 'cause that's where the creativity's happening on the consumption side and that's where the mess that you talked about is just growing on the production side so data production is just getting more complicated. Data consumption's getting more interesting. >> That's actually a really really cool good point. >> Elaborating on that, does that mean that you have to open up interfaces with either the UI layer or at the sort of data definition layer? Or does that just mean other companies have to do the work to tie in to the styles? The styles and structures that you have already written? >> In fact it's sort of the opposite. We do the work to tie in to a lot of this, these other decisions in this infrastructure, you know. We don't pretend for a minute that people are going to sort of pick a solution like Trifacta and then build their organization around it. As your point, there's tons of legacy, technology out there. There is all kinds of things moving. Absolutely. So we, a big part of being the decoder ring for data for Trifacta and saying it's like listen, we are going to inter operate with your existing investments and we're going to make sure that you can always get at your data, you can always take it from whatever state its in to whatever state you need to be in, you can change your mind along the way. And that puts a lot of owners on us and that's the reason why we have to be so focused on this space and not jump into visualization and analytics and not jump in to its storage and processing and not try to do the other things to the right or left. Right? >> So final question. I'd like you guys both to take a stab at it. You know, just going to pivot off at what Joe was saying. Some of the most interesting things are happening in the data exploration kind of discovery area from creativity to insights to game changing stuff. >> Yup. >> Ventures potentially. >> Joe: Yup. >> The problem of the complexity, that's conflict. >> Yeah. >> So how does we resolve this? I mean, besides the Trifacta solution which you guys are taming, creating a platform for that, how do people in industry work together to solve that problem? What's the approach? >> So I think actually there's a couple sort of heartening trends on this front that make me pretty optimistic. One of these is that the inside of structures are in the enterprises we work with becoming quite aligned between IT and the line of business. It's no longer the case that the line of business that are these annoying people that they're distracting IT from their bottom line function. IT's bottom line function is being translated into a what's your value for the business question? And the answer for a savvy IT management person is, I will try to empower the people around me to be rabid fans and I will also try to make sure that they do their own works so I don't have to learn how to do it for them. Right? And so, that I think is happening-- >> Guys to this (mumbles) business guys, a bunch of annoying guys who don't get what I need, right? So it works both ways, right? >> It does, it does. And I see that that's improving sort of in the industry as the corporate missions around data change, right? So it's no longer that the IT guys really only need to take care of executives and everyone else doesn't matter. Their function really is to serve the business and I see that alignment. The other thing that I think is a huge opportunity and the part of who I-- we're excited to be so tightly coupled with Google and also have our stuff running in Amazon and at Microsoft. It's as people read platform to the cloud, a lot of legacy becomes a shed or at least become deprecated. And so there is a real-- >> Or containerized or some sort of microservice. >> Yeah. >> Right, right. >> And so, people are peeling off business function and as part of that cost savings to migrate it to the cloud, they're also simplified. And you know, things will get complicated again. >> What's (mumbles) solution architects out there that kind of re-boot their careers because the old way was, hey I got networks, I got apps and stacks and so that gives the guys who could be the new heroes coming in. >> Right. >> And thinking differently about enabling that creativity. >> In the midst of all that, everything you said is true. IT is a massive place and it always will be. And tools that can come in and help are absolutely going to be (mumbles). >> This is obvious now. The tension's obviously eased a bit in the sense that there's clear line of sight that top line and bottom line are working together now on. You mentioned that earlier. Okay. Adam, take a stab at it. (mumbling) >> I was just going to-- hey, I know it's great. I was just going to give an example, I think, that illustrates that point so you know, one of our customers is Pepsi. And Pepsi came to us and they said, listen we work with retailers all over the world and their reality is that, when they place orders with us, they often get it wrong. And sometimes they order too much and then they return it, it spoils and that's bad for us. Or they order too little and they stock out and we miss revenue opportunities. So they said, we actually have to be better at demand planning and forecasting than the orders that are literally coming in the door. So how do we do that? Well, we're getting all of the customers to give us their point of sale data. We're combining that with geospatial data, with weather data. We're like looking at historical data and industry averages but as you can see, they were like-- we're stitching together data across a whole variety of sources and they said the best people to do this are actually the category managers and the people responsible for the brands 'cause they literally live inside those businesses and they understand it. And so what happened was they-- the IT organization was saying, look listen, we don't want to be the people doing the janitorial work on the data. We're going to give that work over to people who understand it and they're going to be more productive and get to better outcomes with that information and that brings us up to go find new and interesting sources and I think that collaborative model that you're starting to see emerge where they can now be the data heroes in a different way by not being the ones beating the bottleneck on provisioning but rather can go out and figure out how do we share the best stuff across the organization? How do we find new sources of information to bring in that people can leverage to make better decisions? That's in incredibly powerful place to be and you know, I think that that model is really what's going to be driving a lot of the thinking at Trifacta and in the industry over the next couple of years. >> Great. Adam Wilson, CEO of Trifacta. Joe Hellestein, CTO-- Chief Strategy Officer of Trifacta and also a professor at Berkeley. Great story. Getting the (mumbles) right is hard but under the hood stuff's complicated and again, congratulations about sharing the Ground project. Ground open source. Open source lab kind of thing at-- in Berkeley. Exciting new stuff. Thanks so much for coming on theCUBE. I appreciate great conversation. I'm John Furrier, George Gilbert. You're watching theCUBE here at Big Data SV in conjunction with Strata and Hadoop. Thanks for watching. >> Great. >> Thanks guys.