>>Hello, and welcome to this special presentation. This is the cube on cloud startups, our special event of Amazon web services, startup showcase. I'm John furrier, host of the cube, and excited to be here to talk about the hottest startups around cloud cloud computing data and the future of the enterprise. We've got Rob Harris, vice president of solutions consulting for star dog. Great company, Rob. Great to see you. Thanks for coming on. So this is a showcase presentation with AWS showcase startup showcase. You guys are a fast growing startup knowledge graph. We did a video explaining kind of what we did in the cube conversation. Um, really interesting category this, uh, eight hubs cloud startups with you guys. Talk about what you got. Take a minute to explain star dog and what you got. >>Sure. Yeah, here at startup, we are really a knowledge graph platform company. So we help build a knowledge graph for our customers tying together the data inside the organization and with data on the cloud in order for them to be able to find search and understand the context and relationship of all that data within their own organization. So that's really what we try to facilitate and make successful for our customers. >>Awesome. What market are you guys targeting? What's the market opportunity. Can you explain the market space that you're building product value in and what's your focus? >>Sure. Yeah, it's, it's pretty exciting. We do a lot from an industry perspective, we target a lot, uh, life sciences or financial the services, and it just tends to be, those are the ones that are most excited and getting started with this, but we certainly have a much broader set of customers in government or in manufacturing. What we really look for is the horizontal type solution, where you have a lot of systems that you want to tie together, or you want to have that understanding of your data all within context throughout your organization. So anybody struggling with that kind of tying of your data together, whether it's on the cloud or on prem, that's what we really go after >>Disruption. Who are you disrupting as you come into the marketplace? I love Amazon so hot startups because they got an eye clean take on something, but someone usually is being impacted. Who is, who are you guys disrupting as you come into? >>Yeah, a lot of times we find we're disrupting traditional ETL, right? So centralizing of all your data into one big platform, a lot of people have gone down this path of trying to create these large repositories data lakes, data warehouses. Yeah. We try to provide the additional value on top of them by not forcing you to continue to invest in moving and centralizing all your data together, but connecting it and providing context, um, while leaving and leveraging the mid worries. >>Awesome. Cause there's a big market opportunity as data warehouses becomes modernized and horizontal control planes and cloud computing is data is the key competitive advantage. Uh, great disruption. Great opportunity. So let's talk about the business star dog. What do you guys, uh, talk about the company, uh, where the headquarters is? The, how many employees what's the business model? How do you guys make money? Yeah, >>Well, a headquarters is always a little bit tricky nowadays is we were also distributed, but officially it is in Arlington Virginia. Uh, although we are all over the globe, uh, mostly in the United States and Europe, certainly as we look at, uh, how, how do we go to market and what do we do related to that? We have a subscription-based model where we help our customers get started usually small, um, by leveraging a package that they can run either on prem or in the cloud or directly from the AWS marketplace and letting them connect to the data and then growing out as they grow within their organization, larger, more interplay enterprise wide type of installations. So that's how we kind of go after it, uh, from, from our company perspective. >>So your go to market then for the company, is it bottoms up organic growth, kind of a freemium get in there? Or is it kind of a mid, mid tier or how do you guys look at that, that entry? >>It's a great question. That's exactly right. A lot of times we do start with a freemium type of model. We do have free trials and use usability to get started very quickly without having to talk to a salesperson or without having to pay up front in order to see the value, because we want you to be able to understand the value you're going to get out of our platform right off the bat and get started. Then after you've really tried it out and you see where it could apply within your organization, we help make it enterprise. >>I have to ask you how the business model of SAS, obviously clouds. Great. Are you guys leveraging Amazon web services marketplace at all? >>We are we're on the marketplace today, um, with the, both the free trial, as well as the ability through, you know, private offers to do whole production instances. So we're really excited about being a part of the marketplace. What we found is that sometimes customers want to run on the cloud. Sometimes they want to run on prem, wherever they want to run. We want to be sure that we're there. >>Yeah. Alex, let's pull up that slide on the hybrid, uh, architecture for these guys. So I want to bring this up since you brought up the business model and you talk about hybrid. This is interesting. This gets into the business model and this is kind of transitions into kind of the technology architecture. Could you walk me through this slide, the knowledge graph and the hybrid cloud. Why is this important for you guys and why is it important for customers? >>This is great. Thank you for, uh, for pulling this up. What this is really showing is as we look toward the future, as we really look at how people are deploying knowledge, graphs, and managing their data, we see that one of the big problems they're trying to address is what about cloud, uh, data that's on the cloud would a bit dated it's on prem. Maybe it's in multiple VPCs that you have within the Amazon environment. How do you tie all this together? And we all know that moving data around between all of these zones can be expensive and time consuming and difficult. And so we've come up with an architecture that allows you to run the knowledge, graph an agent of the knowledge graph in each of these zones. And they can all talk to each other and coordinate with each other. So they can see data that exists within that zone and pass it on to the other pieces as required or as needed to minimize your kind of in and out fees. And to leverage that all that data in one, in one place >>I asked you because this comes up a lot in our coverage, um, data mobility, uh, moving data is expensive. Um, how does that impact you guys in customers? A lot of people have been looking at, Hey, you know, the economics of the cloud are phenomenal, but at some point, if you've got a lot of data, you move compute to the data or you kind of think differently, how do you guys look at that? That trend? >>Yeah, that's, that's really our key value prop is people struggle with this. As people try to figure out how do I handle this large amount of data without having to generate all this additional costs about moving it around. We really look about how do I push that compute down to the storage layers, where the data already exists. And so if you think about our product architecture and you know, we, I know we have a slide on how our product is really built and how it's pulled together. When you look at our core core architecture, we have the graph that represents that connected data, but the exciting part of our architecture, what we do differently than everyone else is by allowing you to keep the data in its existing data silos, whether it's applications or repositories documents that you already have out there, we allow you to connect to that data where it is cross zone, whether it's on prem or on the cloud. >>And by leveraging the power of start on the virtualization engine, you can connect that data and be able to represent it from one source without having to move it around. But because we also have a persistence layer that's built into our product, you can really determine where's the best home. Is it data that you're going to use a lot and thereby should be really close to where the query engine is? Or is it something where you want to federate it out and leverage that compute at that storage layer itself? That flexibility is really why our customers come to us and are excited to use, start off. >>That's awesome. Great, great stuff. Love, love. The slides. Love to look at some pictures that describe the architecture both as well as the product. I love how you got the enterprise high-grade applications and then you're integrating with other partners. I think that's a really key, uh, value. And I think if you're not integrating well in this modern era, you probably won't be surviving much longer. It's pretty much a game changer at this point when knows that a question on the technology and product. Now keeping it on this theme. What's your secret sauce. Every company's got a secret sauce. What is star dog's secret sauce? >>Our secret sauce is really how do we coordinate across all of those applications? So if you can imagine you have, you know, Oracle database or Redshift repository, and you're trying to be able to unify that data in real time across those applications. There's a lot of thought and needs to go about how to do that efficiently. You don't want to take all the database from both repositories, move them, all that data into one place and then figure it out. And so our query planner, how do we coordinate across the multiple applications is really what makes us different and special >>On the Symantec modeling that you're doing? Because I see there's a lot of data there. You got to kind of get an understanding context. Um, how do you guys look at reusability metadata on data? This has become a very key point on not just data warehouse, but it's becoming much more about addressability and discoverability in as fast as possible, low latency, uh, with intelligence, this has been a big discussion. How do you guys look at that aspect of the reusability of the data? >>Yeah, it's, it's one of the exciting parts about starting with a semantic graph and then extending into these capabilities around virtualization and reasoning and inference by starting with the semantic graph, we allow you to, you know, incrementally invest in building out your model and then being able to reuse that model as you, as you go through your implementations. Yeah. That's been a, a big failing as people have looked at the analytical movements recently is so many times people spin up a repository, they answer a particular question and they do an absolutely fine job, but then we have your next question. You have to spin up another repository, build more views, re ETL the data. And then the semantic technology is what allows you to create that common understanding and reuse it over and over and over again. And I think it's time for that to hit mainstream. You know, it's been around a while. It's something that has taken some time to get some adoption around, but now that we really have build up awareness around it and we've shelled, the technology can scale the large volumes. Uh, I think it's time to be able to leverage the value that reasonability brings. Yeah. >>One final question on the product and the technology and kind of the architecture is how do you guys connect the dots going forward as more and more edge nodes become available in the network as that architecture of hybrid that we talked earlier about becomes so complex and so connected. I mean, you could have more connectedness than ever before. Um, it's very complex networks graph theory, right? You're talking about a lot of edges and a lot of traversal it's billions and billions of edges. I mean, this is it's complicated. How do you guys create, how do you guys see that unfolding and how and why the star dog remained relevant in that configuration? >>Yeah. And the simple fact is that people need help, right? It can't be that you're going to define all those edges and connections by hand yourself through some systems or keys. It's a great way to get started, but it's not sufficient in order to really get the value out of that graph that you expect. And the ways we do that is twofold. The first bit is really an influencing or reasoning capability. Being able to look at this structure of the data, how it's composed and create connections between that data based on, you know, logical, logical rules. The second is machine learning, right? Machine learning is high. We use things like linear regression algorithms or other types of community detection algorithms in order to build more connections in the data so that you can get really unlock that value that you're looking for. When you're leveraging graph technology, >>A lot of secret sauce here, a lot of technology graph, super exciting. Let's get into the final segment around customer traction and what you guys have seen with customers. Um, what are some of the use cases that are popular and what happens if customers aren't going down this road? What are they missing out on? Um, I mean, it's the classic fear of missing out and fear of getting screwed over right. Are going out of business. I mean, that's, that's motivational at some level, but you know, there are the, do I wait and people who waited on cloud computing by the way were left behind and some never survived. So we're almost in this same dynamic with customers. At some point you got to put the toe in the water, so to speak or get going to take us through some customer examples and use cases where, >>Or this is working. Yeah. I think both of those areas are, are, uh, great ones to hit on. So when you think about what are we missing out on one of our largest customer bases really in pharmaceuticals. Yeah. And they're using this technology in order to find more connections in the data so that they can really decrease the amount of time for getting a drug to market on the research and development. They can look more at leveraging the data they've already connected using related items to be able to accelerate their investments and waiting costs them hundreds of millions, if not billions of dollars. So there are certainly ones where being able to adopt this technology early and get value out of early, really pays off in. And they're not the only ones. That's the only, that's the only the life sciences space. But there's also the idea to use it, as you said, really about what else am I missing out on? >>And the data fabric movement, this movement around, how do I lower the cost in my organization about moving data around creating more ETL jobs, leveraging all these data assets already have that the data fabric movement is the idea of how do we really automate that? How do we accelerate that? How do we make that an easier process so that it just doesn't cost as much to manage all this data in an organization. And I've observed that more and more. We have customers coming to us, really interested in this type of use cases that relates to our technology and they are getting ahead of their competitors by really lowering their, it costs in line to focus on these higher value activities. >>Life of the customers is what for you with, with startup? Why, how do they win? What's the reason why they buy and take the freemium. And when do they convert over? Well, take me through the progression of value. When do they see something and why do they increase their sure. >>Assumption? Yeah. That, I mean, the bottom line is you want to try to get more value out of your data at a lower cost and make it easier and faster to do. And so getting started in a single use case, trying out our free version, representing your data and taking a look at what it could look like under a common model, connecting it up with our virtualization services is a great way to try out the technology and really, you know, put your toe in the water to see is this something that would be a value to organization as you see that value unlock is you really understand that you can leverage these days assets with this lower time to value, you know, days in order to unlock a whole repository and connected to another repository. That's where we love to engage with you and help show you how you can make that successful in a more production environment. >>I like about some of the things you're talking about star dog has kind of that aspirin aspect, but also a growth, um, uh, vitamin E as well, in terms of the value proposition, a lot of companies are overwhelmed with the data, but yet you have this path towards more creation of value through the knowledge graph and reasoning and other other value. When does a customer, and this is kind of comes back to the customers who are out there potentially watching prospects or future customers. When do they know they need to call you guys up? Is it because they have too many sources? Could you take me through what it, what it looks like in a prospect's environment where they would really win with start a what's it look like? What are some of the signs that they need to engage, start out? >>Yeah. The two big things that we've seen repeated in our customer base over and over again, is if you have a large number of systems out there that aren't connected, that you don't see how all the data it can be pulled together between those systems, because the different data formats or different languages or different ways that the data is created in those systems start off, can certainly help. The second is if you have a large data warehouse or a data Lake, and you don't see the value being generated out of that, because people don't understand where the data is or what context it has with other data within those repositories, both of those situations are one where we think you'd get a lot of value out of start off. And we'd love to talk to you. >>So would, so just secondly, understand this. So if you have a lot of systems that either are not connected or connected, whatever, that's great, a lot of sources sitting around, you know, whether it's spreadsheets or Oracle or >>Red shift, whatever it is, we've loved it that's right. >>Ingest as much as possible from sources >>That's right. Ingest or connect. I mean, that's really the value that we bring is you don't have to pull it all in. You can just map and leverage the data where it lives. We have customers that have petabyte repositories that just mapped that data in to start off, and we can really facilitate pulling out the value of those systems without you having to move it around again, to another request, >>Ingest, connect, and visually see value. That's right. It sounds, it sounds like a tagline, um, great stuff. So just give some examples of who's using it. What big names? Um, obviously you guys, aren't hot startup coming out of the Amazon cloud showcase. Uh, congratulations. What are some names that have worked with you guys that can give an indicator of the company that you're keeping right now in terms of, >>Yeah, I mean our largest customer by far right now, our longest customer has been NASA. Um, so they've been a really exciting user of the platform we've been really to see them leverage the platform. Schneider electric has been a long time user, uh, Bayer FINRA in the U S which is a financial services watchdog organization. These are customers that are getting a lot of value out of our platform today, and we're excited to work with them. >>Awesome, Rob, great to see you. Congratulations. Uh, take a minute to just give the plug for the commercial. How do we engage? What's the culture like, um, you guys hiring, what's the, what's the state of that? What's the state of the company. >>Yeah, no, it's a, it's a great thank you for, uh, for bringing that up where, you know, we're an exciting growing company. Um, as we really reach out more and more to connect more people's data, we find that we're always looking at more resources on building out more conductivity between the individual data sources. So more understanding on that front, as well as more, a professional services type folks to help people through the process. We've really been trying to minimize the amount of effort that you have to have in order to get started, but we know that people like a helping hands. So we're always looking for people we're always growing and we're excited to have the chance to, you know, bring this technology out beyond just the semantic group that is historically been here. >>You know, you've got a great job. Vice-president solutions consulting, essentially you're in a product role, but more like a solution architect meets products, uh, customer facing, and also product century. You're kind of the center of all the action. So what's the coolest thing you've seen, um, from a customer standpoint or an architecture or, um, a deployment or an engagement that you've been involved with. That's been kind of like, Oh, wow, that's cool. That's game. That's something new that we've been, we wouldn't have seen a few years ago. Take us through just an example, anecdotal, you don't have to share the company name or you. >>That's a great question. Um, there is a company that is working on self-driving cars and being able to leverage the knowledge graph to pull together all of the videos and material they get from the vehicles themselves, as well as static information about the sensors. Uh, that's been pretty exciting to see. I, I, I just recently purchased the festival myself. So I'm excited about the whole self-driving car world and to be able to help them participate with these companies is, is pretty exciting. Um, we, we just help one of the large drug manufacturers come to market with one of their drugs earlier than expected. You know, that's a, that's a pretty exciting feeling to know that you can really help people, um, by just connecting the data they already have and letting them leverage those resources, uh, that that really is something that we're going to be very calm >>And the bridge to the future that the customers have to cross with you is also pretty compelling. You got industrial IOT and more and more data to take a quick minute to describe what that future looks like. >>Yeah. You know, as we see more and more automation in this process, we see a couple of different really, you know, exploding areas. The first off, you know, you hit the nail on the head is data being able to bring in more edge devices, being able to really process that data on the fly and be able to help answer questions as these changes in data are occur within these sources. Um, that's certainly part of the future. And the other thing that we're really excited about is this more automatic data discovery with an organization. How can we have an agent that goes out and kind of can infer really even what your data is about in the structure of your data without a lot of input for you. And so we've been working a lot with building up these models automatically and letting you have the foundation for integrating your data, um, and just the push of a button. So we're excited about walking, Alexa, our customers in this journey as well. >>It's, it's a fun area. You talk about reasoning. That's one of the key value propositions that you guys have. You talk about AI, you talk about bots and soon it's going to be thinking machines for us. They're going to be doing all the work. >>I hope they're not too soon, but I am excited about that idea as well. I can go. I do think that, uh, you know, if you look at organizations today, it's fascinating how it's not, that the problems are different, but we're trying to automate as much of it as possible so that we can work on that, the real value clumps of our organizations. And it's not that kind of drudgery work. I started as a DBA back in my career, um, just trying to keep the database up and running, you know, nowadays, you know, all these autonomous databases and self indexing, and self-correcting, it's just not a passive lead as much anymore. You know, we hope we can bring that to the data infrastructure automation. >>It's a double-edged sword gun, right. It's amazing, done wrong. It could cause some damage and flipped some, some pain and hurt. And so you got to figure it out, got to have the right data sets, gotta have the right software, um, and a great future. Rob Harris, congratulations for being a cannabis startup showcase here on the cube on cloud startups, uh, with AWS, uh, led partnership. Thank you for coming on and being part of this event. Thank you again. Okay. Rob Harris, vice president solutions consulting at star dog here for the coupon cloud. I'm John furrier. Thanks for watching. >>Yeah.

>>hello. >>Welcome to the special key conversation. I'm John ferry, host of the queue here in Palo Alto, California, featuring star dog is a great hot start-up. We've got a great guest, Rob Harris, vice president of solutions consulting for star dog here talking about some of the cloud growth, um, knowledge graphs, the role of data. Obviously there's a huge sea change. You're seeing real value coming out of this COVID as companies coming out of the pandemic, new opportunities, new use cases, new expectations, highly accelerated shift happening, and we're here to break it down. Rob, thanks for joining us on the cube conversation. Great to be here. So got, I'm excited to talk to you guys about your company and specifically the value proposition I've been talking for almost since 2007 around graph databases with Neo four J came out and looking at how data would be part of a real part of the developer mindset. Um, early on, and this more of the development. Now it's mainstream, you're seeing value being created in graph structures. Okay. Not just relational. This has been, uh, very well verified. You guys are in this business. So this is a really hot area, a lot of value being created. It's cool. And it's relevant. So tell us first, what is star dog doing? What's uh, what is the company about? >>Yeah, so I mean, we are an enterprise knowledge graph platform company. We help people be successful at standing up knowledge graphs of the data that they have both inside their company and using public data and tying that all together in order to be able to leverage that connected data and really turn it into knowledge through context and understand it. >>So how did this all come about this from a tech standpoint? What is the, what is the, uh, what was the motivation around this? Because, um, obviously the unstructured wave hit, you're seeing successes like data bricks, for instance, just absolutely crushing it on, on their valuation and their relevance. You seeing the same kind of wave hit almost kind of born back on the Hadoop days with unstructured data. Is that a big part of it? Is it just evolution? What's the big driver here? >>Yeah, no, I think it's a, it's a great question. The driver early is as these data sets have increased for so many companies trying to really bring some understanding to it as they roll it out in their organizations, you know, we've tried to just try to centralize it and that hasn't been sufficient in order to be able to unlock the value of most organization status. So being able to step beyond just, you know, pulling everything together into one place, but really putting that context and meaning around it that the graph can do. So that's where we've really got started at, uh, back in the day is we really looked at the inference and reasoning part of a knowledge graph. How do we bring more context and understanding that doesn't naturally exist within the data? And that really is how we launched off the product. >>I got to ask you around the use cases because one of the things that's really relevant right now is you're seeing a lot of front end development around agile application. Dev ops is brought infrastructure as code. You're seeing kind of this huge tsunami of new of applications, but one of the things that people are talking about in some of the developer circles and it's kind of hits the enterprise is this notion of state because you can have an application calling data, but if the data is not addressable and then keeping state and in real time and all these kinds of new, new technical problems, how do you guys look at that? When you look at trying to create knowledge graphs, because maintaining that level of connection, you need data, a ton of it it's gotta be exposed and addressable and then deal dealt with in real time. How do you guys look at it? >>Yeah, that's, that's a great question. What we've done to try to kind of move the ball forward on this is move past, trying to centralize that data into a knowledge graph that is separate from the rest of your data assets, but really build a data virtualization layer, which we have integrated into our product to look at the data where it is in the applications and the unstructured documents and the structure repositories, so that we can observe as state changes in that data and answer questions that are relevant at the time. And we don't have to worry about some sort of synchronous process, you know, loading information into the graph. So that ability to add that virtualization layer, uh, to the graph really enables you to get more of a real time, look at your data as it evolves. >>Yeah. I definitely want to double, double click on that and say, but I want to just drop step back and kind of set the table for the folks that aren't, um, getting in the weeds yet on this. There's kind of a specific definition of enterprise knowledge graph. Could you like just quickly define that? What is the enterprise knowledge graph? Sure. >>Yeah, we, we really see an enterprise knowledge graph as a connected set of data with context. So it's not just storing it like a graph, but connect again and putting meaning around that data through structure, through definitions, et cetera, across the entire enterprise. So looking at not just data within a single application or within a single silo, but broadly through your enterprise, what does your data mean? How is it connected and what does it look like within context each other? >>How should companies reuse their data? >>Boy, that's a broad question, right? Uh, you know, I mean, one of the things, uh, that I think is very important as so many companies have just collected data assets over the years, they collect more and more and more. We have customers that have eight petabytes of data within their data Lake. And they're trying to figure out how to leverage it by actually connecting and putting that context around the data. You can get a lot more meaning out of that old data or the stale data or the unknown data that the people are getting right today. So the ability to reuse the data assets with in context of meeting is where we see people really be able to make huge licks for in their organization like drug companies be able to get drugs to market faster. By looking at older studies, they've done where maybe the meeting was hidden because it was an old system. Nobody knew what the particular codes and meaning were in context of today. So being able to reuse and bring that forward brings real life application to people solving business problems today. >>Rob, I got to get your thoughts on something that we always riff on here on the cube, which is, um, you know, do you take down the data silos or do you leverage them? And you know, this came up a lot, many years ago when we first started discussing containers, for instance, and then that we saw that you didn't have to kill the old to bring in the new, um, there's one mindset of, you know, break the silos down, go horizontal scalability on the data, critical data, plane control, plane, other saying, Hey, you know what, just put it, you know, put a wrapper around those, those silos and you know, I'm oversimplifying, but you get the idea. So how should someone who's really struggling with, or, or not struggling, we're putting together an architecture around their future plans around dealing with data and data silos specifically, because certainly as new data comes in there's mechanism for that. But as you have existing data silos, what do companies do? What's the strategy in your opinion? >>Yeah, you know, it is a really interesting question. I was in data warehouse and for a long, long time and a big proponent of moving everything to one place. And, uh, then I really moved into looking into data virtualization and realized that neither of those solutions are complete, that there are some things that have to be centralized and moved the old systems aren't sufficient in order to be able to answer questions or process them. But there are many data silos that we've created within organizations that can be reused. You can leverage the compute, you can leverage the storage that already exist within us. And that's the approach we've taken at start off. We really want to be able to allow you to centralize the data that makes sense, right. To get it out of those old systems, that should be shut down from just a monetary perspective, but the systems that are have actual meeting or that it's too expensive in order to, to remove them, leverage those data silos. And by letting you have both approaches in the same platform, we hope to make this not an either or architectural decision, which is always the difficult question. >>Okay. So you got me on that one. So let me just say that. I want to leverage my data silos. What do I do? Take me through the playbook. What if I got the data silos? What is the star dog recommendation for me? >>Sure. So what, what we generally recommend is you start off with building kind of a model, uh, in the, in the lingo, we sometimes say ontology Euro, some sort of semantic understanding that puts context around what is my data and what does it mean? And then we allow you to map those data silos. We have a series of connectors in our product that whether it's an application and you're connecting through a rest connector, or whether it's a database and you're connecting through ODBC or JDBC map that data into the platform. And then when you issue queries to the startup platform, we federate those queries out to the downstream systems and answer as if that data existed on the graph. So that way we're leveraging the silos where they are without you having to move the data physically into the platform. So you guys are essentially building a >>Data fabric. >>We are, yeah. Data fabric is really the new term. That's been popping up more and more with our customers when they come to us to say, how can we kind of get past the traditional ways of doing data integration and unified data in a single place? Like you said, we don't think the answer is purely all about moving it all to one big Lake. We don't think the answer is all about just creating this virtualization plane, but really being able to leverage the festival. >>All right. So, so if you, if you believe that, then let's just go to the next level then. So if you believe that they can, don't have to move things around and to have one specific thing, how does a customer deal with their challenge of hybrid cloud and soon to be multi-cloud because that's certainly on the horizon. People want choice. There's going to be architectural. I mean, certainly a cloud operations will be in play, but this on-premise and this cloud, and then soon to be multiple cloud. How do you guys deal with that? That question? >>Yeah, that's a great question. And this is really a, an area that we're very excited about and we've been investing very heavily in is how to have multiple instances of StarTalk running in different clouds or on prem on the clown, coordinate to answer questions, to minimize data movement between the platforms. So we have the ability to run either an agent on prem. For example, if you're running the platform in the cloud or vice versa, you can run it in the cloud. You are two full instances that start off where they will actually cope plan queries to understand where does the data live? Where is it resident and how do I minimize moving data around in order to answer the question? So we really are trying to create that unified data fabric across on-prem or multiple cloud providers, so that any of the nodes in the platform can answer question from any of the datas >>S you know, complexity is always the issue. People cost go up. When you have complexity, you guys are trying to tame it. This is a huge conversation. You bring up multi-cloud and hybrid cloud. And multi-cloud when you think about the IOT edge, and you don't want to move data around, this is what everyone's saying, why move it? Why move data? It's expensive to move data processes where it is, and you kind of have this kind of flexibility. So this idea of unification is a huge concept. Is that enough? And how should customers think about the unification? Because if you can get there, it almost, it is the kind of the Holy grail you're talking about here. So, so this is kind of the prospect of, of having kind of an ideal architecture of unification. So take us, take me through that one step deeper. >>Well, it is, it is kind of interesting because as you really think about unifying your data and really bringing it together, of course it is the Holy grail. And that's what people have been talking about. Um, gosh, since I started in the industry over 20 years ago, how do I get this single plain view of my data, regardless of whether it's physically located or, uh, somehow stitched together, but what are the things that, you know, our founders really strongly believed on when they started the company? Was it isn't enough. It isn't sufficient. There is more value in your data that you don't even know. And unlocking that through either machine learning, which is, of course, we all know it's very hot right now to look at how do I derive new insights out of the data that I already have, or even through logical reasoning, right? And inference looking at, what do I understand about how that data is put together and how it's created in order to create more connections within the data and answer more questions. All those are ways to grow beyond just unifying your data, but actually getting more insights out of it. And I think that is the real Holy grail that people are looking for, not just bringing all the data together, but actually being able to get business value and insights out of that data. Yeah. >>Looking for it. You guys have obviously a pretty strong roster of clients that represent that. Um, but I got to ask you, since you brought up the founders, uh, the company, obviously having a founders' DNA, uh, mindset, um, tends to change the culture or drive the culture of the covenant change with age drives the culture of the company. What is the founder's culture inside star, dog? What is the vibe there, if you could, um, what do they talk about the most when you, when they get in that mode of being founders like, Hey, you know, this is the North star, what is, what's the rap like? What's the vibe share? It takes that, take us through some star star, dog culture. >>Sure. So our three founders came out of the rusty of Maryland, all in a PhD program around semantic reasoning and logical understanding and being able to understand data and be able to communicate that as easily as possible is really the core and the fiber of their being. And that's what we see continually under discussion every single day. How can we push the limits to take this technology and your gift easier to use more available, bring more insights to the customers beyond what we've seen in the past. And I find that really exciting to be able to constantly have conversations about how do we push the envelope? How do we look beyond even what Gartner says is five or eight years in the future, but looking even further ahead. So there >>They're into they're into this whole data scene. Then big time they are >>That they are very active in the conferences and posts and you know, all that great. >>They love this agility. They got to love dev ops. I mean, if you're into this knowledge graph scene, so I gotta, I gotta ask you, what's the machine learning angle here, obviously, AI, we know what AI is. AI is essentially combination of many things, machine learning and other computer science and data access. Um, what is the secret sauce behind the machine learning and, and the vibe and the product of, of, uh, >>Yeah, a lot of times w we, the way that we leverage machine learning or the way that we look at it is how do we create those connections between data? So you have multiple different systems and you're trying to bring all that data together. Yeah. It's not always easy to tell, is this rod Harris the same as that rod Harris is this product the same as that product. So when possible we will leverage keys or we'll leverage very, uh, you know, systematic type of understanding of these things are the same, but sometimes you need to reach beyond that. And that's where we leverage a lot of machine learning within the platform, looking at things like linear regression or other approaches around the graph, you know, connectivity, analysis, page rank, things like that to say, where are things the same so that we can build that connections in that connectivity as automatically as possible. >>You don't get a lot of talks on the cube. Also. Now that's new news, new clubhouse app, where people are talking about misinformation, obviously we're in the media business. We love the digital network effect. Everything's networks, the network economy. You starting to see this power of information and value. You guys carved the knowledge graph. So I gotta, I gotta ask you, when you look at this kind of future where you have this, um, complexity and the network effect, um, how are you guys looking at that data access? Because if you don't have the data, you're not going to have that insight, right? So you need to have that, that network connection. Is that a limitation or for companies? Is that an, um, cause usually people aren't necessarily their blind spot is their data or their lack of their data. So having things network together is going to be more of the norm in the future. How do you guys see that playing out? Yeah, >>I think you're exactly right. And I think that as you look beyond where we are today, and a lot of times we focus today on the data that a company already has, what do I know? Right. What do I know about you? What, how do I interact with you? How have I interacted with you? I think that as we look at the future, we're going to talk more about data sharing, but leveraging publicly available information about being able to take these insights and leverage them, not just within the walls of my own organization, but being able to share them and, uh, work together with other organizations to bring up a better understanding of you as a person or as a consumer that we could all interact with. Yeah, you're absolutely right. You know, Metcons law still holds true that, you know, more network connections bring more value. I certainly see that growing in the future, probably more around, you know, more data sharing and more openness about leveraging publicly available. >>You know, it's interesting. You mentioned you came from a data warehouse background. I remember when I broken the businessmen 30 years ago, when I started getting computer science, you know, it was, it was, there was, there was pain having a product and an enabling platform. You guys seem to have this enabling platform where there's no one use case. I mean, you, you have an unlimited use case landscape. Um, you could do anything with what you guys have. It's not so much, I mean, there's, low-hanging fruit. So I got to ask you, if you have that, uh, enabling platform, you're creating value for customers. What are some of the areas you see developing, like now in terms of low-hanging fruit and where's the possibilities? How do you guys see that? I'm sure you've probably got a tsunami of activity around corner cases from media to every vertical we do. And that's, you know, >>The exciting part of this job. Uh, part of the exciting part of knowledge press in general is to see all the different ways that they are allowed to use. But we do see some use cases repeated over and over again. Uh, risk management is a very common one. How do I look at all the people and the assets with an organization, the interactions they have to look at hotspots for risk, uh, that I need to correct within my organization for the pre-commercial pharma, that has been a very, very hot area for us recently. How do we look at all the that's available with an organization that's publicly available in order to accelerate drug development in this post COVID world, that's become more and more relevant, uh, for organizations to be able to move forward faster and the kind of bio industry and my sciences. Um, that's a use case that we've seen repeated over and over again. And then this growing idea of the data fabric, the data fabric, looking at metadata within the organization to improve data integration processes, to really reduce the need for moving data without or around the organization as much. Those are the use cases we've seen repeated over and over again over the last >>Awesome Rob. My last question before we wrap up is for the solution architect that's out there that has, you know, got a real tall order. They have to put together a scalable organization, people process and technology around a data architecture. That's going to be part of, um, the next gen, the next gen next level activity. And they need headroom for IOT edge and industrial edge, uh, and all use cases. Um, what's your advice to them as they have to look out at and start thinking about architecture? >>Yeah, that's, it's a great question. Uh, I really think that it's important to keep your options open as the technology in the space continues to evolve, right? It's easy to get locked into a single vendor or a single mindset. Um, I've been an architect most of my career, and that's usually a lot of the pitfalls. Things like a knowledge graph are open and flexible. They adhere to standards, which then means you're not locked into a single vendor and you're allowed to leverage this type of technology to grow beyond originally envisioned. So thinking about how you can take advantage of these modern techniques to look at things and not just keep repeating what you've done in the past, the sins of the past have, uh, you know, a lot of times do reappear. So fighting against that as much as possible as gritty is my encouragement. >>Awesome, great insight. And I love this. I love this area. I know you guys got a great trend. You're riding on a very cool, very relevant final minute. Just take a quick minute to give a plug for the company. What's the business model. How do I deploy this? How do I get the software? How do you charge for it? If I'm going to buy this solution or engage with star DOE what do I do? Take me through that. Sure. >>Yeah. We, uh, we are like, uh, you've sat through this whole thing. We are enterprise knowledge graph platform company. So we really help you get started with your business, uh, uh, leveraging and using a knowledge graph fricking organization. We have the ability to deploy on prem. We have on the cloud, we're in the AWS marketplace today. So you can take a look at our software today, who generally are subscription-based based on the size of the install. And we are happy to talk to you any time, just drop by our website, reach out we'll we'll get doctors. >>Rob. Great. Thanks for coming. I really appreciate it. That gradients said, looking forward to seeing you in person, when we get back to real life, hopefully the vaccines are coming on. Thanks to, uh, companies like you guys providing awesome analytics and intelligence for these drug companies and pharma companies. Now you have a few of them in your, on your client roster. So congratulations, looking forward to following up great, great area. Cool and relevant data architecture is changing. Some of it's broken. Some it's being fixed started off as one of the hot startups scaling up beautifully in this new era of cloud computing meets applications and data. So I'm John. Forget the cube. This is a cube conversation from Palo Alto, California. Thanks for watching.

(upbeat music) >> Welcome to this special cloud virtual event, theCUBE on cloud. This is our continuing editorial series of the most important stories in cloud. We're going to explore the cutting edge most relevant technologies and companies that will impact business and society. We have special guests from Jeff Barr, Michael Liebow, Jerry Chen, Ben Haynes, Michael skulk, Mike Feinstein from AWS all today are presenting the top startups in the AWS ecosystem. This is the AWS showcase of startups. I'm showing with Dave Vellante. Dave great to see you. >> Hey John. Great to be here. Thanks for having me. >> So awesome day today. We're going to feature a 10 grade companies amplitude, auto grid, big ID, cordial Dremio Kong, multicloud, Reltio stardog wire wheel, companies that we've talked to. We've researched. And they're going to present today from 10 for the rest of the day. What's your thoughts? >> Well, John, a lot of these companies were just sort of last decade, they really, were keyer kicker mode, experimentation mode. Now they're well on their way to hitting escape velocity which is very exciting. And they're hitting tens of millions dollars of ARR, many are planning IPO's and it's just it's really great to see what the cloud has enabled and we're going to dig into that very deeply today. So I'm super excited. >> Before we jump into the keynote (mumbles) our non Huff from AWS up on stage Jeremy is the brains behind this program that we're doing. We're going to do this quarterly. Jeremy great to see you, you're in the global startups program at AWS. Your job is to keep the crops growing, keep the startups going and keep the flow of innovation. Thanks for joining us. >> Yeah. Made it to startup showcase day. I'm super excited. And as you mentioned my team the global startup program team, we kind of provide white glove service for VC backed startups and help them with go to market activities. Co-selling with AWS and we've been looking for ways to highlight all the great work they're doing and partnering with you guys has been tremendous. You guys really know how to bring their stories to life. So super excited about all the partner sessions today. >> Well, I really appreciate the vision and working with Amazon this is like truly a bar raiser from theCUBE virtual perspective, using the virtual we can get more content, more flow and great to have you on and bring that the top hot startups around data, data ops. Certainly the most important story in tech is cloud scale with data. You you can't look around and seeing more innovation happening. So I really appreciate the work. Thanks for coming on. >> Yeah, and don't forget, we're making this a quarterly series. So the next one we've already been working on it. The next one is Wednesday, June 16th. So mark your calendars, but super excited to continue doing these showcases with you guys in the future. >> Thanks for coming on Jeremy. I really appreciate it,. Dave so I want to just quick quickly before we get Jeff up here, Jeff Barr who's a luminary guests for us this week who has been in the industry has been there from the beginning of AWS the role of data, and what's happened in cloud. And we've been watching the evolution of Amazon web services from the beginning, from the startup market to dominate in the enterprise. If you look at the top 10 enterprise companies Amazon wasn't on that list in 2010 they weren't even bringing the top 10 Andy Jassy's keynote at reinvent this past year. Highlighted that fact, I think they were number five or four as vendor in just AWS. So interesting to see that you've been reporting and doing a lot of analysis on the role of data. What's your analysis for these startups and as businesses need to embrace the new technologies and be on the right side of history not part of that old guard, incumbent failed model. >> Well, I think again, if you look back on the early days of cloud, it was really about storage and networking and compute infrastructure. And then we collected all this data and now you're seeing the next generation of innovation and value. We're going to talk to Michael Liebow about this is really if you look at all the value points in the leavers, it's all around data and data is going through a massive change in the way that we think about it, that we talk about it. And you hear that a lot. Obviously you talk about the volumes, the giant volumes but there's something else going on as AWS brings the cloud to the edge. And of course it looks at the data centers, just another edge device, data is getting highly decentralized. And what we're seeing is data getting into the hands of business owners and data product builders. I think we're going to see a new parlance emerge and that's where you're seeing the competitive advantage. And if you look at all the real winners these days in the marketplace especially in the digital with COVID, it all comes back to the data. And we're going to talk about that a lot today. >> One of the things that's coming up in all of our cube interviews, certainly we've seen, I mean we've had a great observation space across all the ecosystems, but the clear thing that's coming out of COVID is speed, agility, scale, and data. If you don't have that data you are going to be a non-player. And I think I heard some industry people talking about the future of how the stock market's going to work and that if you're not truly in market with an AI or machine learning data value play you probably will be shorted on the stock market or delisted. I think people are looking at that as a table stakes competitive advantage item, where if you don't have some sort of data competitive strategy you're going to be either delisted or sold short. And that's, I don't think delisted but the point is this table-stakes Dave. >> Well, I think too, I think the whole language the lingua franca of data is changing. We talk about data as an asset all the time, but you think about it now, what do we do with assets? We protect it, we hide it. And we kind of we don't share it. But then on the other hand, everybody talks about sharing the data and that is a huge trend in the marketplace. And so I think that everybody is really starting to rethink the whole concept of data, what it is, its value and how we think about it, talk about it, share it make it accessible, and at the same time, protect it and make it governed. And I think you're seeing, computational governance and automation really hidden. Couldn't do this without the cloud. I mean, that's the bottom line. >> Well, I'm super excited to have Jeff Barr here from AWS as our special keynote guests. I've been following Jeff's career for a long, long time. He's a luminaries, he's a technical, he's in the industry. He's part of the community, he's been there from the beginning AWS just celebrate its 15th birthday as he was blogging hard. He's been a hardcore blogger. I think Jeff, you had one of the original ping service. If I remember correctly, you were part of the web services foundational kind of present at creation. No better guests to have you Jeff thanks for coming up on our stage. >> John and Dave really happy to be here. >> So I got to ask you, you've been blogging hard for the past decade or so, going hard and your job has evolved from blogging about what's new with Amazon. A couple of building blocks a few services to last reinvent them. You must have put out I don't know how many blog posts did you put out last year at every event? I mean, it must have been a zillion. >> Not quite a zillion. I think I personally wrote somewhere between 20 and 25 including quite a few that I did in the month or so run up to reinvent and it's always intense, but it's always really, really fun. >> So I've got to ask you in the past couple of years, I mean I quoted Andy Jassy's keynote where we highlight in 2010 Amazon wasn't even on the top 10 enterprise players. Now in the top five, you've seen the evolution. What is the big takeaway from your standpoint as you look at the enterprise going from Amazon really dominating the start of a year startups today, you're in the cloud, you're born in the cloud. There's advantage to that. Now enterprises are kind of being reborn in the cloud at the same time, they're building these new use cases rejuvenating themselves and having innovation strategy. What's your takeaway? >> So I love to work with our customers and one of the things that I hear over and over again and especially the last year or two is really the value that they're placing on building a workforce that has really strong cloud skills. They're investing in education. They're focusing on this neat phrase that I learned in Australia called upskilling and saying let's take our set of employees and improve their skill base. I hear companies really saying we're going to go cloud first. We're going to be cloud native. We're going to really embrace it, adopt the full set of cloud services and APIs. And I also see that they're really looking at cloud as part of often a bigger picture. They often use the phrase digital transformation, in Amazon terms we'd say they're thinking big. They're really looking beyond where they are and who they are to what they could be and what they could grow into. Really putting a lot of energy and creativity into thinking forward in that way. >> I wonder Jeff, if you could talk about sort of how people are thinking about the future of cloud if you look at where the spending action is obviously you see it in cloud computing. We've seen that as the move to digital, serverless Lambda is huge. If you look at the data it's off the charts, machine learning and AI also up there containers and of course, automation, AWS leads in all of those. And they portend a different sort of programming model a different way of thinking about how to deploy workloads and applications maybe different than the early days of cloud. What's driving that generally and I'm interested in serverless specifically. And how do you see the next several years folding out? >> Well, they always say that the future is the hardest thing to predict but when I talked to our enterprise customers the two really big things that I see is there's this focus that says we need to really, we're not simply like hosting the website or running the MRP. I'm working with one customer in particular where they say, well, we're going to start on the factory floor all the way up to the boardroom effectively from IOT and sensors on the factory floor to feed all the data into machine learning. So they understand that the factory is running really well to actually doing planning and inventory maintenance to putting it on the website to drive the analytics, to then saying, okay, well how do we know that we're building the right product mix? How do we know that we're getting it out through the right channels? How are our customers doing? So they're really saying there's so many different services available to us in the cloud and they're relatively easy and straightforward to deploy. They really don't think in the old days as we talked about earlier that the old days where these multi-year planning and deployment cycles, now it's much more straightforward. It's like let's see what we can do today. And this week and this month, and from idea to some initial results is a much, much shorter turnaround. So they can iterate a lot more quickly which is just always known to produce better results. >> Well, Jeff and the spirit of the 15th birthday of AWS a lot of services have been built from the original three. I believe it was the core building blocks and there's been a lot of history and it's kind of like there was a key decoupling of compute from storage, those innovations what's the most important architectural change if any has happened or built upon those building blocks with AWS that you could share with companies out there as many people are coming into the cloud not just lifting and shifting and having that innovation but really building cloud native and now hybrid full cloud operations, day two operations. However you want to look at it. That's a big thing. What architecturally has changed that's been innovative from those original building blocks? >> Well, I think that the basic architecture has proven to be very, very resilient. When I wrote about the 15 year birthday of Amazon S3 a couple of weeks ago one thing that I thought was really incredible was the fact that the same APIs that you could have used 15 years ago they all still work. The put, the get, the list, the delete, the permissions management, every last one of those were chosen with extreme care. And so they all still work. So one of the things you think about when you put APIs out there is in Amazon terms we always talk about going through a one-way door and a one way door says, once you do it you're committed for the indefinite future. And so you we're very happy to do that but we take those steps with extreme care. And so those basic building blocks so the original S3 APIs, the original EC2 APIs and the model, all those things really worked. But now they're running at this just insane scale. One thing that blows me away I routinely hear my colleagues talking about petabytes and exabytes, and we throw around trillions and quadrillions like they're pennies. It's kind of amazing. Sometimes when you hear the scale of requests per day or request per month, and the orders of magnitude are you can't map them back to reality anymore. They're simply like literally astronomical. >> If I can just jump in real quick Dave before you ask Jeff, I was watching the Jeff Bezos interview in 1999 that's been going around on LinkedIn in a 60 minutes interview. The interviewer says you are reporting that you can store a gigabyte of customer data from all their purchases. What are you going to do with that? He basically nailed the answer. This is in 99. We're going to use that data to create, that was only a gig. >> Well one of the things that is interesting to me guys, is if you look at again, the early days of cloud, of course I always talked about that in small companies like ours John could have now access to information technology that only big companies could get access to. And now you've seen we just going to talk about it today. All these startups rise up and reach viability. But at the same time, Jeff you've seen big companies get the aha moment on cloud and competition drives urgency and that drives innovation. And so now you see everybody is doing cloud, it's a mandate. And so the expectation is a lot more innovation, experimentation and speed from all ends. It's really exciting to see. >> I know this sounds hackneyed and overused but it really, really still feels just like day one. We're 15 plus years into this. I still wake up every morning, like, wow what is the coolest thing that I'm going to get to learn about and write about today? We have the most amazing customers, one of the things that is great when you're so well connected to your customers, they keep telling you about their dreams, their aspirations, their use cases. And we can just take that and say we can actually build awesome things to help you address those use cases from the ground on up, from building custom hardware things like the nitro system, the graviton to the machine learning inferencing and training chips where we have such insight into customer use cases because we have these awesome customers that we can make these incredible pieces of hardware and software to really address those use cases. >> I'm glad you brought that up. This is another big change, right? You're getting the early days of cloud like, oh, Amazon they're just using off the shelf components. They're not buying these big refrigerator sized disc drives. And now you're developing all this custom Silicon and vertical integration in certain aspects of your business. And that's because workload is demanding. You've got to get more specialized in a lot of cases. >> Indeed they do. And if you watch Peter DeSantis' keynote at re-invent he talked about the fact that we're researching ways to make better cement that actually produces less carbon dioxide. So we're now literally at the from the ground on up level of construction. >> Jeff, I want to get a question from the crowd here. We got, (mumbles) who's a good friend of theCUBE cloud Arate from the beginning. He asked you, he wants to know if you'd like to share Amazon's edge aspirations. He says, he goes, I mean, roadmaps. I go, first of all, he's not going to talk about the roadmaps, but what can you share? I mean, obviously the edge is key. Outpost has been all in the news. You obviously at CloudOps is not a boundary. It's a distributed network. What's your response to-- >> Well, the funny thing is we don't generally have technology roadmaps inside the company. The roadmap is always listen really well to customers not just where they are, but the customers are just so great at saying, this is where we'd like to go. And when we hear edge, the customers don't generally come to us and say edge, they say we need as low latency as possible between where the action happens within our factory floors and our own offices and where we might be able to compute, analyze, store make decisions. And so that's resulted in things like outposts where we can put outposts in their own data center or their own field office, wavelength, where we're working with 5G telecom providers to put computing storage in the carrier hubs of the various 5G providers. Again, with reducing latency, we've been doing things like local zones, where we put zones in an increasing number of cities across the country with the goal of just reducing the average latency between the vast majority of customers and AWS resources. So instead of thinking edge, we really think in terms of how do we make sure that our customers can realize their dreams. >> Staying on the flywheel that AWS has built on ship stuff faster, make things faster, smaller, cheaper, great mission. I want to ask you about the working backwards document. I know it's been getting a lot of public awareness. I've been, that's all I've learned in interviewing Amazon folks. They always work backwards. I always mentioned the customer and all the interviews. So you've got a couple of customer references in there check the box there for you. But working backwards has become kind of a guiding principles, almost like a Harvard Business School case study approach to management. As you guys look at this working backwards and ex Amazonians have written books about it now so people can go look at, it's a really good methodology. Take us back to how you guys work back from the customers because here we're featuring 10 startups. So companies that are out there and Andy has been preaching this to customers. You should think about working backwards because it's so fast. These companies are going into this enterprise market your ecosystem of startups to provide value. What things are you seeing that customers need to think about to work backwards from their customer? How do you see that? 'Cause you've been on the community side, you see the tech side customers have to move fast and work backwards. What are the things that they need to focus on? What's your observation? >> So there's actually a brand new book called "Working Backwards," which I actually learned a lot about our own company from simply reading the book. And I think to me, a principal part of learning backward it's really about humility and being able to be a great listener. So you don't walk into a customer meeting ready to just broadcast the latest and greatest that we've been working on. You walk in and say, I'm here from AWS and I simply want to learn more about who you are, what you're doing. And most importantly, what do you want to do that we're not able to help you with right now? And then once we hear those kinds of things we don't simply write down kind of a bullet item of AWS needs to improve. It's this very active listening process. Tell me a little bit more about this challenge and if we solve it in this way or this way which one's a better fit for your needs. And then a typical AWS launch, we might talk to between 50 and 100 customers in depth to make sure that we have that detailed understanding of what they would like to do. We can't always meet all the needs of these customers but the idea is let's see what is the common base that we can address first. And then once we get that first iteration out there, let's keep listening, let's keep making it better and better and better as quickly. >> A lot of people might poopoo that John but I got to tell you, John, you will remember this the first time we ever met Andy Jassy face-to-face. I was in the room, you were on the speaker phone. We were building an app on AWS at the time. And he was asking you John, for feedback. And he was probing and he pulled out his notebook. He was writing down and he wasn't just superficial questions. He was like, well, why'd you do it that way? And he really wanted to dig. So this is cultural. >> Yeah. I mean, that's the classic Amazon. And that's the best thing about it is that you can go from zero startups zero stage startup to traction. And that was the premise of the cloud. Jeff, I want to get your thoughts and commentary on this love to get your opinion. You've seen this grow from the beginning. And I remember 'cause I've been playing with AWS since the beginning as well. And it says as an entrepreneur I remember my first EC2 instance that didn't even have custom domain support. It was the long URL. You seen the startups and now that we've been 15 years in, you see Dropbox was it just a startup back in the day. I remember these startups that when they were coming they were all born on Amazon, right? These big now unicorns, you were there when these guys were just developers and these gals. So what's it like, I mean, you see just the growth like here's a couple of people with them ideas rubbing nickels together, making magic happen who knows what's going to turn into, you've been there. What's it been like? >> It's been a really unique journey. And to me like the privilege of a lifetime, honestly I've like, you always want to be part of something amazing and you aspire to it and you study hard and you work hard and you always think, okay, somewhere in this universe something really cool is about to happen. And if you're really, really lucky and just a million great pieces of luck like lineup in series, sometimes it actually all works out and you get to be part of something like this when it does you don't always fully appreciate just how awesome it is from the inside, because you're just there just like feeding the machine and you are just doing your job just as fast as you possibly can. And in my case, it was listening to teams and writing blog posts about their launches and sharing them on social media, going out and speaking, you do it, you do it as quickly as possible. You're kind of running your whole life as you're doing that as well. And suddenly you just take a little step back and say, wow we did this kind of amazing thing, but we don't tend to like relax and say, okay, we've done it at Amazon. We get to a certain point. We recognize it. And five minutes later, we're like, okay, let's do the next amazingly good thing. But it's been this just unique privilege and something that I never thought I'd be fortunate enough to be a part of. >> Well, then the last few minutes we have Jeff I really appreciate you taking the time to spend with us for this inaugural launch of theCUBE on cloud startup showcase. We are showcasing 10 startups here from your ecosystem. And a lot of people who know AWS for the folks that don't you guys pride yourself on community and ecosystem the global startups program that Jeremy and his team are running. You guys nurture these startups. You want them to be successful. They're vectoring out into the marketplace with growth strategy, helping customers. What's your take on this ecosystem? As customers are out there listening to this what's your advice to them? How should they engage? Why is these sets of start-ups so important? >> Well, I totally love startups and I've spent time in several startups. I've spent other time consulting with them. And I think we're in this incredible time now wheres, it's so easy and straightforward to get those basic resources, to get your compute, to get your storage, to get your databases, to get your machine learning and to take that and to really focus on your customers and to build what you want. And we see this actual exponential growth. And we see these startups that find something to do. They listen to one of their customers, they build that solution. And they're just that feedback cycle gets started. It's really incredible. And I love to see the energy of these startups. I love to hear from them. And at any point if we've got an AWS powered startup and they build something awesome and want to share it with me, I'm all ears. I love to hear about them. Emails, Twitter mentions, whatever I'll just love to hear about all this energy all those great success with our startups. >> Jeff Barr, thank you for coming on. And congratulations, please pass on to Andy Jassy who's going to take over for Jeff Bezos and I saw the big news that he's picking a successor an Amazonian coming back into the fold, Adam. So congratulations on that. >> I will definitely pass on your congratulations to Andy and I worked with Adam in the past when AWS was just getting started and really looking forward to seeing him again, welcoming back and working with him. >> All right, Jeff Barr with AWS guys check out his Twitter and all the social coordinates. He is pumping out all the resources you need to know about if you're a developer or you're an enterprise looking to go to the next level, next generation, modern infrastructure. Thanks Jeff for coming on. Really appreciate it. Our next guests want to bring up stage Michael Liebow from McKinsey cube alumni, who is a great guest who is very timely in his McKinsey role with a paper he and his colleagues put out called cloud's trillion dollar prize up for grabs. Michael, thank you for coming up on stage with Dave and I. >> Hey, great to be here, John. Thank you. >> One of the things I loved about this and why I wanted you to come on was not only is the report awesome. And Dave has got a zillion questions, he want us to drill into. But in 2015, we wrote a story called Andy Jassy trillion dollar baby on Forbes, and then on medium and silken angle where we were the first ones to profile Andy Jassy and talk about this trillion dollar term. And Dave came up with the calculation and people thought we were crazy. What are you talking about trillion dollar opportunity. That was in 2015. You guys have put this together with a serious research report with methodology and you left a lot on the table. I noticed in the report you didn't even have a whole section quantified. So I think just scratching the surface trillion. I'd be a little light, Dave, so let's dig into it, Michael thanks for coming on. >> Well, and I got to say, Michael that John's a trillion dollar baby was revenue. Yours is EBITDA. So we're talking about seven to X, seven to eight X. What we were talking back then, but great job on the report. Fantastic work. >> Thank you. >> So tell us about the report gives a quick lowdown. I got some questions. You guys are unlocking the value drivers but give us a quick overview of this report that people can get for free. So everyone who's registered will get a copy but give us a quick rundown. >> Great. Well the question I think that has bothered all of us for a long time is what's the business value of cloud and how do you quantify it? How do you specify it? Because a lot of people talk around the infrastructure or technical value of cloud but that actually is a big problem because it just scratches the surface of the potential of what cloud can mean. And we focus around the fortune 500. So we had to box us in somewhat. And so focusing on the fortune 500 and fast forwarding to 2030, we put out this number that there's over a trillion dollars worth of value. And we did a lot of analysis using research from a variety of partners, using third-party research, primary research in order to come up with this view. So the business value is two X the technical value of cloud. And as you just pointed out, there is a whole unlock of additional value where organizations can pioneer on some of the newest technologies. And so AWS and others are creating platforms in order to do not just machine learning and analytics and IOT, but also for quantum or mixed reality for blockchain. And so organizations specific around the fortune 500 that aren't leveraging these capabilities today are going to get left behind. And that's the message we were trying to deliver that if you're not doing this and doing this with purpose and with great execution, that others, whether it's others in your industry or upstarts who were motioning into your industry, because as you say cloud democratizes compute, it provides these capabilities and small companies with talent. And that's what the skills can leverage these capabilities ahead of slow moving incumbents. And I think that was the critical component. So that gives you the framework. We can deep dive based on your questions. >> Well before we get into the deep dive, I want to ask you we have startups being showcased here as part of the, it will showcase, they're coming out of the ecosystem. They have a lot of certification from Amazon and they're secure, which is a big issue. Enterprises that you guys talk to McKinsey speaks directly to I call the boardroom CXOs, the top executives. Are they realizing that the scale and timing of this agility window? I mean, you want to go through these key areas that you would break out but as startups become more relevant the boardrooms that are making these big decisions realize that their businesses are up for grabs. Do they realize that all this wealth is shifting? And do they see the role of startups helping them? How did you guys come out of them and report on that piece? >> Well in terms of the whole notion, we came up with this framework which looked at the opportunity. We talked about it in terms of three dimensions, rejuvenate, innovate and pioneer. And so from the standpoint of a board they're more than focused on not just efficiency and cost reduction basically tied to nation, but innovation tied to analytics tied to machine learning, tied to IOT, tied to two key attributes of cloud speed and scale. And one of the things that we did in the paper was leverage case examples from across industry, across-region there's 17 different case examples. My three favorite is one is Moderna. So software for life couldn't have delivered the vaccine as fast as they did without cloud. My second example was Goldman Sachs got into consumer banking is the platform behind the Apple card couldn't have done it without leveraging cloud. And the third example, particularly in early days of the pandemic was Zoom that added five to 6,000 servers a night in order to scale to meet the demand. And so all three of those examples, plus the other 14 just indicate in business terms what the potential is and to convince boards and the C-suite that if you're not doing this, and we have some recommendations in terms of what CEOs should do in order to leverage this but to really take advantage of those capabilities. >> Michael, I think it's important to point out the approach at sometimes it gets a little wonky on the methodology but having done a lot of these types of studies and observed there's a lot of superficial studies out there, a lot of times people will do, they'll go I'll talk to a customer. What kind of ROI did you get? And boom, that's the value study. You took a different approach. You have benchmark data, you talked to a lot of companies. You obviously have a lot of financial data. You use some third-party data, you built models, you bounded it. And ultimately when you do these things you have to ascribe a value contribution to the cloud component because fortunate 500 companies are going to grow even if there were no cloud. And the way you did that is again, you talk to people you model things, and it's a very detailed study. And I think it's worth pointing out that this was not just hey what'd you get from going to cloud before and after. This was a very detailed deep dive with really a lot of good background work going into it. >> Yeah, we're very fortunate to have the McKinsey Global Institute which has done extensive studies in these areas. So there was a base of knowledge that we could leverage. In fact, we looked at over 700 use cases across 19 industries in order to unpack the value that cloud contributed to those use cases. And so getting down to that level of specificity really, I think helps build it from the bottom up and then using cloud measures or KPIs that indicate the value like how much faster you can deploy, how much faster you can develop. So these are things that help to kind of inform the overall model. >> Yeah. Again, having done hundreds, if not thousands of these types of things, when you start talking to people the patterns emerge, I want to ask you there's an exhibit tool in here, which is right on those use cases, retail, healthcare, high-tech oil and gas banking, and a lot of examples. And I went through them all and virtually every single one of them from a value contribution standpoint the unlocking value came down to data large data sets, document analysis, converting sentiment analysis, analytics. I mean, it really does come down to the data. And I wonder if you could comment on that and why is it that cloud is enabled that? >> Well, it goes back to scale. And I think the word that I would use would be data gravity because we're talking about massive amounts of data. So as you go through those kind of three dimensions in terms of rejuvenation one of the things you can do as you optimize and clarify and build better resiliency the thing that comes into play I think is to have clean data and data that's available in multiple places that you can create an underlying platform in order to leverage the services, the capabilities around, building out that structure. >> And then if I may, so you had this again I want to stress as EBITDA. It's not a revenue and it's the EBITDA potential as a result of leveraging cloud. And you listed a number of industries. And I wonder if you could comment on the patterns that you saw. I mean, it doesn't seem to be as simple as Negroponte bits versus Adam's in terms of your ability to unlock value. What are the patterns that you saw there and why are the ones that have so much potential why are they at the top of the list? >> Well, I mean, they're ranked based on impact. So the five greatest industries and again, aligned by the fortune 500. So it's interesting when you start to unpack it that way high-tech oil, gas, retail, healthcare, insurance and banking, right? Top. And so we did look at the different solutions that were in that, tried to decipher what was fully unlocked by cloud, what was accelerated by cloud and what was perhaps in this timeframe remaining on premise. And so we kind of step by step, expert by expert, use case by use case deciphered of the 700, how that applied. >> So how should practitioners within organizations business but how should they use this data? What would you recommend, in terms of how they think about it, how they apply it to their business, how they communicate? >> Well, I think clearly what came out was a set of best practices for what organizations that were leveraging cloud and getting the kind of business return, three things stood out, execution, experience and excellence. And so for under execution it's not just the transaction, you're not just buying cloud you're changing their operating model. And so if the organization isn't kind of retooling the model, the processes, the workflows in order to support creating the roles then they aren't going to be able, they aren't going to be successful. In terms of experience, that's all about hands-on. And so you have to dive in, you have to start you have to apply yourself, you have to gain that applied knowledge. And so if you're not gaining that experience, you're not going to move forward. And then in terms of excellence, and it was mentioned earlier by Jeff re-skilling, up-skilling, if you're not committed to your workforce and pushing certification, pushing training in order to really evolve your workforce or your ways of working you're not going to leverage cloud. So those three best practices really came up on top in terms of what a mature cloud adopter looks like. >> That's awesome. Michael, thank you for coming on. Really appreciate it. Last question I have for you as we wrap up this trillion dollar segment upon intended is the cloud mindset. You mentioned partnering and scaling up. The role of the enterprise and business is to partner with the technologists, not just the technologies but the companies talk about this cloud native mindset because it's not just lift and shift and run apps. And I have an IT optimization issue. It's about innovating next gen solutions and you're seeing it in public sector. You're seeing it in the commercial sector, all areas where the relationship with partners and companies and startups in particular, this is the startup showcase. These are startups are more relevant than ever as the tide is shifting to a new generation of companies. >> Yeah, so a lot of think about an engine. A lot of things have to work in order to produce the kind of results that we're talking about. Brad, you're more than fair share or unfair share of trillion dollars. And so CEOs need to lead this in bold fashion. Number one, they need to craft the moonshot or the Marshot. They have to set that goal, that aspiration. And it has to be a stretch goal for the organization because cloud is the only way to enable that achievement of that aspiration that's number one, number two, they really need a hardheaded economic case. It has to be defined in terms of what the expectation is going to be. So it's not loose. It's very, very well and defined. And in some respects time box what can we do here? I would say the cloud data, your organization has to move in an agile fashion training DevOps, and the fourth thing, and this is where the startups come in is the cloud platform. There has to be an underlying platform that supports those aspirations. It's an art, it's not just an architecture. It's a living, breathing live service with integrations, with standardization, with self service that enables this whole program. >> Awesome, Michael, thank you for coming on and sharing the McKinsey perspective. The report, the clouds trillion dollar prize is up for grabs. Everyone who's registered for this event will get a copy. We will appreciate it's also on the website. We'll make sure everyone gets a copy. Thanks for coming, I appreciate it. Thank you. >> Thanks, Michael. >> Okay, Dave, big discussion there. Trillion dollar baby. That's the cloud. That's Jassy. Now he's going to be the CEO of AWS. They have a new CEO they announced. So that's going to be good for Amazon's kind of got clarity on the succession to Jassy, trusted soldier. The ecosystem is big for Amazon. Unlike Microsoft, they have the different view, right? They have some apps, but they're cultivating as many startups and enterprises as possible in the cloud. And no better reason to change gears here and get a venture capitalist in here. And a friend of theCUBE, Jerry Chen let's bring them up on stage. Jerry Chen, great to see you partner at Greylock making all the big investments. Good to see you >> John hey, Dave it's great to be here with you guys. Happy marks.Can you see that? >> Hey Jerry, good to see you man >> So Jerry, our first inaugural AWS startup showcase we'll be doing these quarterly and we're going to be featuring the best of the best, you're investing in all the hot startups. We've been tracking your careers from the beginning. You're a good friend of theCUBE. Always got great commentary. Why are startups more important than ever before? Because in the old days we've talked about theCUBE before startups had to go through certain certifications and you've got tire kicking, you got to go through IT. It's like going through security at the airport, take your shoes off, put your belt on thing. I mean, all kinds of things now different. The world has changed. What's your take? >> I think startups have always been a great way for experimentation, right? It's either new technologies, new business models, new markets they can move faster, the experiment, and a lot of startups don't work, unfortunately, but a lot of them turned to be multi-billion dollar companies. I thing startup is more important because as we come out COVID and economy is recovery is a great way for individuals, engineers, for companies for different markets to try different things out. And I think startups are running multiple experiments at the same time across the globe trying to figure how to do things better, faster, cheaper. >> And McKinsey points out this use case of rejuvenate, which is essentially retool pivot essentially get your costs down or and the next innovation here where there's Tam there's trillion dollars on unlock value and where the bulk of it is is the innovation, the new use cases and existing new use cases. This is where the enterprises really have an opportunity. Could you share your thoughts as you invest in the startups to attack these new waves these new areas where it may not look the same as before, what's your assessment of this kind of innovation, these new use cases? >> I think we talked last time about kind of changing the COVID the past year and there's been acceleration of things like how we work, education, medicine all these things are going online. So I think that's very clear. The first wave of innovation is like, hey things we didn't think we could be possible, like working remotely, e-commerce everywhere, telemedicine, tele-education, that's happening. I think the second order of fact now is okay as enterprises realize that this is the new reality everything is digital, everything is in the cloud and everything's going to be more kind of electronic relation with the customers. I think that we're rethinking what does it mean to be a business? What does it mean to be a bank? What does it mean to be a car company or an energy company? What does it mean to be a retailer? Right? So I think the rethinking that brands are now global, brands are all online. And they now have relationships with the customers directly. So I think if you are a business now, you have to re experiment or rethink about your business model. If you thought you were a Nike selling shoes to the retailers, like half of Nike's revenue is now digital right all online. So instead of selling sneakers through stores they're now a direct to consumer brand. And so I think every business is going to rethink about what the AR. Airbnb is like are they in the travel business or the experience business, right? Airlines, what business are they in? >> Yeah, theCUBE we're direct to consumer virtual totally opened up our business model. Dave, the cloud premise is interesting now. I mean, let's reset this where we are, right? Andy Jassy always talks about the old guard, new guard. Okay we've been there done that, even though they still have a lot of Oracle inside AWS which we were joking the other day, but this new modern era coming out of COVID Jerry brings this up. These startups are going to be relevant take territory down in the enterprises as new things develop. What's your premise of the cloud and AWS prospect? >> Well, so Jerry, I want to to ask you. >> Jerry: Yeah. >> The other night, last Thursday, I think we were in Clubhouse. Ben Horowitz was on and Martine Casado was laying out this sort of premise about cloud startups saying basically at some point they're going to have to repatriate because of the Amazon VIG. I mean, I'm paraphrasing and I guess the premise was that there's this variable cost that grows as you scale but I kind of shook my head and I went back. You saw, I put it out on Twitter a clip that we had the a couple of years ago and I don't think, I certainly didn't see it that way. Maybe I'm getting it wrong but what's your take on that? I just don't see a snowflake ever saying, okay we're going to go build our own data center or we're going to repatriate 'cause they're going to end up like service now and have this high cost infrastructure. What do you think? >> Yeah, look, I think Martin is an old friend from VMware and he's brilliant. He has placed a lot of insights. There is some insights around, at some point a scale, use of startup can probably run things more cost-effectively in your own data center, right? But I think that's fewer companies more the vast majority, right? At some point, but number two, to your point, Dave going on premise versus your own data center are two different things. So on premise in a customer's environment versus your own data center are two different worlds. So at some point some scale, a lot of the large SaaS companies run their own data centers that makes sense, Facebook and Google they're at scale, they run their own data centers, going on premise or customer's environment like a fortune 100 bank or something like that. That's a different story. There are reasons to do that around compliance or data gravity, Dave, but Amazon's costs, I don't think is a legitimate reason. Like if price is an issue that could be solved much faster than architectural decisions or tech stacks, right? Once you're on the cloud I think the thesis, the conversation we had like a year ago was the way you build apps are very different in the cloud and the way built apps on premise, right? You have assume storage, networking and compute elasticity that's independent each other. You don't really get that in a customer's data center or their own environment even with all the new technologies. So you can't really go from cloud back to on-premise because the way you build your apps look very, very different. So I would say for sure at some scale run your own data center that's why the hyperscale guys do that. On-premise for customers, data gravity, compliance governance, great reasons to go on premise but for vast majority of startups and vast majority of customers, the network effects you get for being in the cloud, the network effects you get from having everything in this alas cloud service I think outweighs any of the costs. >> I couldn't agree more and that's where the data is, at the way I look at it is your technology spend is going to be some percentage of revenue and it's going to be generally flat over time and you're going to have to manage it whether it's in the cloud or it's on prem John. >> Yeah, we had a quote on theCUBE on the conscious that had Jerry I want to get your reaction to this. The executive said, if you don't have an AI strategy built into your value proposition you will be shorted as a stock on wall street. And I even went further. So you'll probably be delisted cause you won't be performing with a tongue in cheek comment. But the reality is that that's indicating that everyone has to have AI in their thing. Mainly as a reality, what's your take on that? I know you've got a lot of investments in this area as AI becomes beyond fashion and becomes table stakes. Where are we on that spectrum? And how does that impact business and society as that becomes a key part of the stack and application stack? >> Yeah, I think John you've seen AI machine learning turn out to be some kind of novelty thing that a bunch of CS professors working on years ago to a funnel piece of every application. So I would say the statement of the sentiment's directionally correct that 20 years ago if you didn't have a web strategy or a website as a company, your company be sure it, right? If you didn't have kind of a internet website, you weren't real company. Likewise, if you don't use AI now to power your applications or machine learning in some form or fashion for sure you'd be at a competitive disadvantage to everyone else. And just like if you're not using software intelligently or the cloud intelligently your stock as a company is going to underperform the rest of the market. And the cloud guys on the startups that we're backing are making AI so accessible and so easy for developers today that it's really easy to use some level of machine learning, any applications, if you're not doing that it's like not having a website in 1999. >> Yeah. So let's get into that whole operation side. So what would you be your advice to the enterprises that are watching and people who are making decisions on architecture and how they roll out their business model or value proposition? How should they look at AI and operations? I mean big theme is day two operations. You've got IT service management, all these things are being disrupted. What's the operational impact to this? What's your view on that? >> So I think two things, one thing that you and Dave both talked about operation is the key, I mean, operations is not just the guts of the business but the actual people running the business, right? And so we forget that one of the values are going to cloud, one of the values of giving these services is you not only have a different technology stack, all the bits, you have a different human stack meaning the people running your cloud, running your data center are now effectively outsource to Amazon, Google or Azure, right? Which I think a big part of the Amazon VIG as Dave said, is so eloquently on Twitter per se, right? You're really paying for those folks like carry pagers. Now take that to the next level. Operations is human beings, people intelligently trying to figure out how my business can run better, right? And that's either accelerate revenue or decrease costs, improve my margin. So if you want to use machine learning, I would say there's two areas to think about. One is how I think about customers, right? So we both talked about the amount of data being generated around enterprise individuals. So intelligently use machine learning how to serve my customers better, then number two AI and machine learning internally how to run my business better, right? Can I take cost out? Can I optimize supply chain? Can I use my warehouses more efficiently my logistics more efficiently? So one is how do I use AI learning to be a more familiar more customer oriented and number two, how can I take cost out be more efficient as a company, by writing AI internally from finance ops, et cetera. >> So, Jerry, I wonder if I could ask you a little different subject but a question on tactical valuations how coupled or decoupled are private company valuations from the public markets. You're seeing the public markets everybody's freaking out 'cause interest rates are going to go up. So the future value of cash flows are lower. Does that trickle in quickly into the private markets? Or is it a whole different dynamic? >> If I could weigh in poly for some private markets Dave I would have a different job than I do today. I think the reality is in the long run it doesn't matter as much as long as you're investing early. Now that's an easy answer say, boats have to fall away. Yes, interest rates will probably go up because they're hard to go lower, right? They're effectively almost zero to negative right now in most of the developed world, but at the end of the day, I'm not going to trade my Twilio shares or Salesforce shares for like a 1% yield bond, right? I'm going to hold the high growth tech stocks because regardless of what interest rates you're giving me 1%, 2%, 3%, I'm still going to beat that with a top tech performers, Snowflake, Twilio Hashi Corp, bunch of the private companies out there I think are elastic. They're going to have a great 10, 15 year run. And in the Greylock portfolio like the things we're investing in, I'm super bullish on from Roxanne to Kronos fear, to true era in the AI space. I think in the long run, next 10 years these things will outperform the market that said, right valuation prices have gone up and down and they will in our careers, they have. In the careers we've been covering tech. So I do believe that they're high now they'll come down for sure. Will they go back up again? Definitely, right? But as long as you're betting these macro waves I think we're all be good. >> Great answer as usual. Would you trade them for NFTs Jerry? >> That $69 million people piece of artwork look, I mean, I'm a longterm believer in kind of IP and property rights in the blockchain, right? And I'm waiting for theCUBE to mint this video as the NFT, when we do this guys, we'll mint this video's NFT and see how much people pay for the original Dave, John, Jerry (mumbles). >> Hey, you know what? We can probably get some good bang for that. Hey it's all about this next Jerry. Jerry, great to have you on, final question as we got this one minute left what's your advice to the people out there that either engaging with these innovative startups, we're going to feature startups every quarter from the in the Amazon ecosystem, they are going to be adding value. What's the advice to the enterprises that are engaging startups, the approach, posture, what's your advice. >> Yeah, when I talk to CIOs and large enterprises, they often are wary like, hey, when do I engage a startup? How, what businesses, and is it risky or low risk? Now I say, just like any career managing, just like any investment you're making in a big, small company you should have a budget or set of projects. And then I want to say to a CIO, Hey, every priority on your wish list, go use the startup, right? I mean, that would be 10 for 10 projects, 10 startups. Probably too much risk for a lot of tech companies. But we would say to most CIOs and executives, look, there are strategic initiatives in your business that you want to accelerate. And I would take the time to invest in one or two startups each quarter selectively, right? Use the time, focus on fewer startups, go deep with them because we can actually be game changers in terms of inflecting your business. And what I mean by that is don't pick too many startups because you can't devote the time, but don't pick zero startups because you're going to be left behind, right? It'd be shorted as a stock by the John, Dave and Jerry hedge fund apparently but pick a handful of startups in your strategic areas, in your top tier three things. These really, these could be accelerators for your career. >> I have to ask you real quick while you're here. We've got a couple minutes left on startups that are building apps. I've seen DevOps and the infrastructure as code movement has gone full mainstream. That's really what we're living right now. That kind of first-generation commercialization of DevOps. Now DevSecOps, what are the trends that you've seen that's different from say a couple of years ago now that we're in COVID around how apps are being built? Is it security? Is it the data integration? What can you share as a key app stack impact (mumbles)? >> Yeah, I think there're two things one is security is always been a top priority. I think that was the only going forward period, right? Security for sure. That's why you said that DevOps, DevSecOps like security is often overlooked but I think increasingly could be more important. The second thing is I think we talked about Dave mentioned earlier just the data around customers, the data on premise or the cloud, and there's a ton of data out there. We keep saying this over and over again like data's new oil, et cetera. It's evolving and not changing because the way we're using data finding data is changing in terms of sources of data we're using and discovering and also speed of data, right? In terms of going from Basser real-time is changing. The speed of business has changed to go faster. So I think these are all things that we're thinking about. So both security and how you use your data faster and better. >> Yeah you were in theCUBE a number of years ago and I remember either John or I asked you about you think Amazon is going to go up the stack and start developing applications and your answer was you know what I think no, I think they're going to enable a new set of disruptors to come in and disrupt the SaaS world. And I think that's largely playing out. And one of the interesting things about Adam Selipsky appointment to the CEO, he comes from Tableau. He really helped Tableau go from that sort of old guard model to an ARR model obviously executed a great exit to Salesforce. And now I see companies like Salesforce and service now and Workday is potential for your scenario to really play out. They've got in my view anyway, outdated pricing models. You look at what's how Snowflake's pricing and the consumption basis, same with Datadog same with Stripe and new startups seem to really be a leading into the consumption-based pricing model. So how do you, what are your thoughts on that? And maybe thoughts on Adam and thoughts on SaaS disruption? >> I think my thesis still holds that. I don't think Selipsky Adam is going to go into the app space aggressively. I think Amazon wants to enable next generation apps and seeing some of the new service that they're doing is they're kind of deconstructing apps, right? They're deconstructing the parts of CRM or e-commerce and they're offering them as services. So I think you're going to see Amazon continue to say, hey we're the core parts of an app like payments or custom prediction or some machine learning things around applications you want to buy bacon, they're going to turn those things to the API and sell those services, right? So you look at things like Stripe, Twilio which are two of the biggest companies out there. They're not apps themselves, they're the components of the app, right? Either e-commerce or messaging communications. So I can see Amazon going down that path. I think Adam is a great choice, right? He was a longterm early AWS exact from the early days latent to your point Dave really helped take Tableau into kind of a cloud business acquired by Salesforce work there for a few years under Benioff the guy who created quote unquote cloud and now him coming home again and back to Amazon. So I think it'll be exciting to see how Adam runs the business. >> And John I think he's the perfect choice because he's got operations chops and he knows how to... He can help the startups disrupt. >> Yeah, and he's been a trusted soldier of Jassy from the beginning, he knows the DNA. He's got some CEO outside experience. I think that was the key he knows. And he's not going to give up Amazon speed, but this is baby, right? So he's got him in charge and he's a trusted lieutenant. >> You think. Yeah, you think he's going to hold the mic? >> Yeah. We got to go. Jerry Chen thank you very much for coming on. Really appreciate it. Great to see you. Thanks for coming on our inaugural cube on cloud AWS startup event. Now for the 10 startups, enjoy the sessions at 12:30 Pacific, we're going to have the closing keynote. I'm John Ferry for Dave Vellante and our special guests, thanks for watching and enjoy the rest of the day and the 10 startups. (upbeat music)