>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hi everyone, this is Dave Vellante and welcome to this CUBE conversation where we're going to dig in to this, the area of cloud databases. And Gartner just published a series of research in this space. And it's really a growing market, rapidly growing, a lot of new players, obviously the big three cloud players. And with me are three experts in the field, two long time industry analysts. Marc Staimer is the founder, president, and key principal at Dragon Slayer Consulting. And he's joined by David Floyer, the CTO of Wikibon. Gentlemen great to see you. Thanks for coming on theCUBE. >> Good to be here. >> Great to see you too Dave. >> Marc, coming from the great Northwest, I think first time on theCUBE, and so it's really great to have you. So let me set this up, as I said, you know, Gartner published these, you know, three giant tomes. These are, you know, publicly available documents on the web. I know you guys have been through them, you know, several hours of reading. And so, night... (Dave chuckles) Good night time reading. The three documents where they identify critical capabilities for cloud database management systems. And the first one we're going to talk about is, operational use cases. So we're talking about, you know, transaction oriented workloads, ERP financials. The second one was analytical use cases, sort of an emerging space to really try to, you know, the data warehouse space and the like. And, of course, the third is the famous Gartner Magic Quadrant, which we're going to talk about. So, Marc, let me start with you, you've dug into this research just at a high level, you know, what did you take away from it? >> Generally, if you look at all the players in the space they all have some basic good capabilities. What I mean by that is ultimately when you have, a transactional or an analytical database in the cloud, the goal is not to have to manage the database. Now they have different levels of where that goes to as how much you have to manage or what you have to manage. But ultimately, they all manage the basic administrative, or the pedantic tasks that DBAs have to do, the patching, the tuning, the upgrading, all of that is done by the service provider. So that's the number one thing they all aim at, from that point on every database has different capabilities and some will automate a whole bunch more than others, and will have different primary focuses. So it comes down to what you're looking for or what you need. And ultimately what I've learned from end users is what they think they need upfront, is not what they end up needing as they implement. >> David, anything you'd add to that, based on your reading of the Gartner work. >> Yes. It's a thorough piece of work. It's taking on a huge number of different types of uses and size of companies. And I think those are two parameters which really change how companies would look at it. If you're a Fortune 500 or Fortune 2000 type company, you're going to need a broader range of features, and you will need to deal with size and complexity in a much greater sense, and a lot of probably higher levels of availability, and reliability, and recoverability. Again, on the workload side, there are different types of workload and there're... There is as well as having the two transactional and analytic workloads, I think there's an emerging type of workload which is going to be very important for future applications where you want to combine transactional with analytic in real time, in order to automate business processes at a higher level, to make the business processes synchronous as opposed to asynchronous. And that degree of granularity, I think is missed, in a broader view of these companies and what they offer. It's in my view trying in some ways to not compare like with like from a customer point of view. So the very nuance, what you talked about, let's get into it, maybe that'll become clear to the audience. So like I said, these are very detailed research notes. There were several, I'll say analysts cooks in the kitchen, including Henry Cook, whom I don't know, but four other contributing analysts, two of whom are CUBE alum, Don Feinberg, and Merv Adrian, both really, you know, awesome researchers. And Rick Greenwald, along with Adam Ronthal. And these are public documents, you can go on the web and search for these. So I wonder if we could just look at some of the data and bring up... Guys, bring up the slide one here. And so we'll first look at the operational side and they broke it into four use cases. The traditional transaction use cases, the augmented transaction processing, stream/event processing and operational intelligence. And so we're going to show you there's a lot of data here. So what Gartner did is they essentially evaluated critical capabilities, or think of features and functions, and gave them a weighting, or a weighting, and then a rating. It was a weighting and rating methodology. On a s... The rating was on a scale of one to five, and then they weighted the importance of the features based on their assessment, and talking to the many customers they talk to. So you can see here on the first chart, we're showing both the traditional transactions and the augmented transactions and, you know, the thing... The first thing that jumps out at you guys is that, you know, Oracle with Autonomous is off the charts, far ahead of anybody else on this. And actually guys, if you just bring up slide number two, we'll take a look at the stream/event processing and operational intelligence use cases. And you can see, again, you know, Oracle has a big lead. And I don't want to necessarily go through every vendor here, but guys, if you don't mind going back to the first slide 'cause I think this is really, you know, the core of transaction processing. So let's look at this, you've got Oracle, you've got SAP HANA. You know, right there interestingly Amazon Web Services with the Aurora, you know, IBM Db2, which, you know, it goes back to the good old days, you know, down the list. But so, let me again start with Marc. So why is that? I mean, I guess this is no surprise, Oracle still owns the Mission-Critical for the database space. They earned that years ago. One that, you know, over the likes of Db2 and, you know, Informix and Sybase, and, you know, they emerged as number one there. But what do you make of this data Marc? >> If you look at this data in a vacuum, you're looking at specific functionality, I think you need to look at all the slides in total. And the reason I bring that up is because I agree with what David said earlier, in that the use case that's becoming more prevalent is the integration of transaction and analytics. And more importantly, it's not just your traditional data warehouse, but it's AI analytics. It's big data analytics. It's users are finding that they need more than just simple reporting. They need more in-depth analytics so that they can get more actionable insights into their data where they can react in real time. And so if you look at it just as a transaction, that's great. If you're going to just as a data warehouse, that's great, or analytics, that's fine. If you have a very narrow use case, yes. But I think today what we're looking at is... It's not so narrow. It's sort of like, if you bought a streaming device and it only streams Netflix and then you need to get another streaming device 'cause you want to watch Amazon Prime. You're not going to do that, you want one, that does all of it, and that's kind of what's missing from this data. So I agree that the data is good, but I don't think it's looking at it in a total encompassing manner. >> Well, so before we get off the horses on the track 'cause I love to do that. (Dave chuckles) I just kind of let's talk about that. So Marc, you're putting forth the... You guys seem to agree on that premise that the database that can do more than just one thing is of appeal to customers. I suppose that makes, certainly makes sense from a cost standpoint. But, you know, guys feel free to flip back and forth between slides one and two. But you can see SAP HANA, and I'm not sure what cloud that's running on, it's probably running on a combination of clouds, but, you know, scoring very strongly. I thought, you know, Aurora, you know, given AWS says it's one of the fastest growing services in history and they've got it ahead of Db2 just on functionality, which is pretty impressive. I love Google Spanner, you know, love the... What they're trying to accomplish there. You know, you go down to Microsoft is, they're kind of the... They're always good enough a database and that's how they succeed and et cetera, et cetera. But David, it sounds like you agree with Marc. I would say, I would think though, Amazon kind of doesn't agree 'cause they're like a horses for courses. >> I agree. >> Yeah, yeah. >> So I wonder if you could comment on that. >> Well, I want to comment on two vectors. The first vector is that the size of customer and, you know, a mid-sized customer versus a global $2,000 or global 500 customer. For the smaller customer that's the heart of AWS, and they are taking their applications and putting pretty well everything into their cloud, the one cloud, and Aurora is a good choice. But when you start to get to a requirements, as you do in larger companies have very high levels of availability, the functionality is not there. You're not comparing apples and... Apples with apples, it's two very different things. So from a tier one functionality point of view, IBM Db2 and Oracle have far greater capability for recovery and all the features that they've built in over there. >> Because of their... You mean 'cause of the maturity, right? maturity and... >> Because of their... Because of their focus on transaction and recovery, et cetera. >> So SAP though HANA, I mean, that's, you know... (David talks indistinctly) And then... >> Yeah, yeah. >> And then I wanted your comments on that, either of you or both of you. I mean, SAP, I think has a stated goal of basically getting its customers off Oracle that's, you know, there's always this urinary limping >> Yes, yes. >> between the two companies by 2024. Larry has said that ain't going to happen. You know, Amazon, we know still runs on Oracle. It's very hard to migrate Mission-Critical, David, you and I know this well, Marc you as well. So, you know, people often say, well, everybody wants to get off Oracle, it's too expensive, blah, blah, blah. But we talked to a lot of Oracle customers there, they're very happy with the reliability, availability, recoverability feature set. I mean, the core of Oracle seems pretty stable. >> Yes. >> But I wonder if you guys could comment on that, maybe Marc you go first. >> Sure. I've recently done some in-depth comparisons of Oracle and Aurora, and all their other RDS services and Snowflake and Google and a variety of them. And ultimately what surprised me is you made a statement it costs too much. It actually comes in half of Aurora for in most cases. And it comes in less than half of Snowflake in most cases, which surprised me. But no matter how you configure it, ultimately based on a couple of things, each vendor is focused on different aspects of what they do. Let's say Snowflake, for example, they're on the analytical side, they don't do any transaction processing. But... >> Yeah, so if I can... Sorry to interrupt. Guys if you could bring up the next slide that would be great. So that would be slide three, because now we get into the analytical piece Marc that you're talking about that's what Snowflake specialty is. So please carry on. >> Yeah, and what they're focused on is sharing data among customers. So if, for example, you're an automobile manufacturer and you've got a huge supply chain, you can supply... You can share the data without copying the data with any of your suppliers that are on Snowflake. Now, can you do that with the other data warehouses? Yes, you can. But the focal point is for Snowflake, that's where they're aiming it. And whereas let's say the focal point for Oracle is going to be performance. So their performance affects cost 'cause the higher the performance, the less you're paying for the performing part of the payment scale. Because you're paying per second for the CPUs that you're using. Same thing on Snowflake, but the performance is higher, therefore you use less. I mean, there's a whole bunch of things to come into this but at the end of the day what I've found is Oracle tends to be a lot less expensive than the prevailing wisdom. So let's talk value for a second because you said something, that yeah the other databases can do that, what Snowflake is doing there. But my understanding of what Snowflake is doing is they built this global data mesh across multiple clouds. So not only are they compatible with Google or AWS or Azure, but essentially you sign up for Snowflake and then you can share data with anybody else in the Snowflake cloud, that I think is unique. And I know, >> Marc: Yes. >> Redshift, for instance just announced, you know, Redshift data sharing, and I believe it's just within, you know, clusters within a customer, as opposed to across an ecosystem. And I think that's where the network effect is pretty compelling for Snowflake. So independent of costs, you and I can debate about costs and, you know, the tra... The lack of transparency of, because AWS you don't know what the bill is going to be at the end of the month. And that's the same thing with Snowflake, but I find that... And by the way guys, you can flip through slides three and four, because we've got... Let me just take a quick break and you have data warehouse, logical data warehouse. And then the next slide four you got data science, deep learning and operational intelligent use cases. And you can see, you know, Teradata, you know, law... Teradata came up in the mid 1980s and dominated in that space. Oracle does very well there. You can see Snowflake pop-up, SAP with the Data Warehouse, Amazon with Redshift. You know, Google with BigQuery gets a lot of high marks from people. You know, Cloud Data is in there, you know, so you see some of those names. But so Marc and David, to me, that's a different strategy. They're not trying to be just a better data warehouse, easier data warehouse. They're trying to create, Snowflake that is, an incremental opportunity as opposed to necessarily going after, for example, Oracle. David, your thoughts. >> Yeah, I absolutely agree. I mean, ease of use is a primary benefit for Snowflake. It enables you to do stuff very easily. It enables you to take data without ETL, without any of the complexity. It enables you to share a number of resources across many different users and know... And be able to bring in what that particular user wants or part of the company wants. So in terms of where they're focusing, they've got a tremendous ease of use, tremendous focus on what the customer wants. And you pointed out yourself the restrictions there are of doing that both within Oracle and AWS. So yes, they have really focused very, very hard on that. Again, for the future, they are bringing in a lot of additional functions. They're bringing in Python into it, not Python, JSON into the database. They can extend the database itself, whether they go the whole hog and put in transaction as well, that's probably something they may be thinking about but not at the moment. >> Well, but they, you know, they obviously have to have TAM expansion designs because Marc, I mean, you know, if they just get a 100% of the data warehouse market, they're probably at a third of their stock market valuation. So they had better have, you know, a roadmap and plans to extend there. But I want to come back Marc to this notion of, you know, the right tool for the right job, or, you know, best of breed for a specific, the right specific, you know horse for course, versus this kind of notion of all in one, I mean, they're two different ends of the spectrum. You're seeing, you know, Oracle obviously very successful based on these ratings and based on, you know their track record. And Amazon, I think I lost count of the number of data stores (Dave chuckles) with Redshift and Aurora and Dynamo, and, you know, on and on and on. (Marc talks indistinctly) So they clearly want to have that, you know, primitive, you know, different APIs for each access, completely different philosophies it's like Democrats or Republicans. Marc your thoughts as to who ultimately wins in the marketplace. >> Well, it's hard to say who is ultimately going to win, but if I look at Amazon, Amazon is an all-cart type of system. If you need time series, you go with their time series database. If you need a data warehouse, you go with Redshift. If you need transaction, you go with one of the RDS databases. If you need JSON, you go with a different database. Everything is a different, unique database. Moving data between these databases is far from simple. If you need to do a analytics on one database from another, you're going to use other services that cost money. So yeah, each one will do what they say it's going to do but it's going to end up costing you a lot of money when you do any kind of integration. And you're going to add complexity and you're going to have errors. There's all sorts of issues there. So if you need more than one, probably not your best route to go, but if you need just one, it's fine. And if, and on Snowflake, you raise the issue that they're going to have to add transactions, they're going to have to rewrite their database. They have no indexes whatsoever in Snowflake. I mean, part of the simplicity that David talked about is because they had to cut corners, which makes sense. If you're focused on the data warehouse you cut out the indexes, great. You don't need them. But if you're going to do transactions, you kind of need them. So you're going to have to do some more work there. So... >> Well... So, you know, I don't know. I have a different take on that guys. I think that, I'm not sure if Snowflake will add transactions. I think maybe, you know, their hope is that the market that they're creating is big enough. I mean, I have a different view of this in that, I think the data architecture is going to change over the next 10 years. As opposed to having a monolithic system where everything goes through that big data platform, the data warehouse and the data lake. I actually see what Snowflake is trying to do and, you know, I'm sure others will join them, is to put data in the hands of product builders, data product builders or data service builders. I think they're betting that that market is incremental and maybe they don't try to take on... I think it would maybe be a mistake to try to take on Oracle. Oracle is just too strong. I wonder David, if you could comment. So it's interesting to see how strong Gartner rated Oracle in cloud database, 'cause you don't... I mean, okay, Oracle has got OCI, but you know, you think a cloud, you think Google, or Amazon, Microsoft and Google. But if I have a transaction database running on Oracle, very risky to move that, right? And so we've seen that, it's interesting. Amazon's a big customer of Oracle, Salesforce is a big customer of Oracle. You know, Larry is very outspoken about those companies. SAP customers are many, most are using Oracle. I don't, you know, it's not likely that they're going anywhere. My question to you, David, is first of all, why do they want to go to the cloud? And if they do go to the cloud, is it logical that the least risky approach is to stay with Oracle, if you're an Oracle customer, or Db2, if you're an IBM customer, and then move those other workloads that can move whether it's more data warehouse oriented or incremental transaction work that could be done in a Aurora? >> I think the first point, why should Oracle go to the cloud? Why has it gone to the cloud? And if there is a... >> Moreso... Moreso why would customers of Oracle... >> Why would customers want to... >> That's really the question. >> Well, Oracle have got Oracle Cloud@Customer and that is a very powerful way of doing it. Where exactly the same Oracle system is running on premise or in the cloud. You can have it where you want, you can have them joined together. That's unique. That's unique in the marketplace. So that gives them a very special place in large customers that have data in many different places. The second point is that moving data is very expensive. Marc was making that point earlier on. Moving data from one place to another place between two different databases is a very expensive architecture. Having the data in one place where you don't have to move it where you can go directly to it, gives you enormous capabilities for a single database, single database type. And I'm sure that from a transact... From an analytic point of view, that's where Snowflake is going, to a large single database. But where Oracle is going to is where, you combine both the transactional and the other one. And as you say, the cost of migration of databases is incredibly high, especially transaction databases, especially large complex transaction databases. >> So... >> And it takes a long time. So at least a two year... And it took five years for Amazon to actually succeed in getting a lot of their stuff over. And five years they could have been doing an awful lot more with the people that they used to bring it over. So it was a marketing decision as opposed to a rational business decision. >> It's the holy grail of the vendors, they all want your data in their database. That's why Amazon puts so much effort into it. Oracle is, you know, in obviously a very strong position. It's got growth and it's new stuff, it's old stuff. It's, you know... The problem with Oracle it has like many of the legacy vendors, it's the size of the install base is so large and it's shrinking. And the new stuff is.... The legacy stuff is shrinking. The new stuff is growing very, very fast but it's not large enough yet to offset that, you see that in all the learnings. So very positive news on, you know, the cloud database, and they just got to work through that transition. Let's bring up slide number five, because Marc, this is to me the most interesting. So we've just shown all these detailed analysis from Gartner. And then you look at the Magic Quadrant for cloud databases. And, you know, despite Amazon being behind, you know, Oracle, or Teradata, or whomever in every one of these ratings, they're up to the right. Now, of course, Gartner will caveat this and say, it doesn't necessarily mean you're the best, but of course, everybody wants to be in the upper, right. We all know that, but it doesn't necessarily mean that you should go by that database, I agree with what Gartner is saying. But look at Amazon, Microsoft and Google are like one, two and three. And then of course, you've got Oracle up there and then, you know, the others. So that I found that very curious, it is like there was a dissonance between the hardcore ratings and then the positions in the Magic Quadrant. Why do you think that is Marc? >> It, you know, it didn't surprise me in the least because of the way that Gartner does its Magic Quadrants. The higher up you go in the vertical is very much tied to the amount of revenue you get in that specific category which they're doing the Magic Quadrant. It doesn't have to do with any of the revenue from anywhere else. Just that specific quadrant is with that specific type of market. So when I look at it, Oracle's revenue still a big chunk of the revenue comes from on-prem, not in the cloud. So you're looking just at the cloud revenue. Now on the right side, moving to the right of the quadrant that's based on functionality, capabilities, the resilience, other things other than revenue. So visionary says, hey how far are you on the visionary side? Now, how they weight that again comes down to Gartner's experts and how they want to weight it and what makes more sense to them. But from my point of view, the right side is as important as the vertical side, 'cause the vertical side doesn't measure the growth rate either. And if we look at these, some of these are growing much faster than the others. For example, Snowflake is growing incredibly fast, and that doesn't reflect in these numbers from my perspective. >> Dave: I agree. >> Oracle is growing incredibly fast in the cloud. As David pointed out earlier, it's not just in their cloud where they're growing, but it's Cloud@Customer, which is basically an extension of their cloud. I don't know if that's included these numbers or not in the revenue side. So there's... There're a number of factors... >> Should it be in your opinion, Marc, would you include that in your definition of cloud? >> Yeah. >> The things that are hybrid and on-prem would that cloud... >> Yes. >> Well especially... Well, again, it depends on the hybrid. For example, if you have your own license, in your own hardware, but it connects to the cloud, no, I wouldn't include that. If you have a subscription license and subscription hardware that you don't own, but it's owned by the cloud provider, but it connects with the cloud as well, that I would. >> Interesting. Well, you know, to your point about growth, you're right. I mean, it's probably looking at, you know, revenues looking, you know, backwards from guys like Snowflake, it will be double, you know, the next one of these. It's also interesting to me on the horizontal axis to see Cloud Data and Databricks further to the right, than Snowflake, because that's kind of the data lake cloud. >> It is. >> And then of course, you've got, you know, the other... I mean, database used to be boring, so... (David laughs) It's such a hot market space here. (Marc talks indistinctly) David, your final thoughts on all this stuff. What does the customer take away here? What should I... What should my cloud database management strategy be? >> Well, I was positive about Oracle, let's take some of the negatives of Oracle. First of all, they don't make it very easy to rum on other platforms. So they have put in terms and conditions which make it very difficult to run on AWS, for example, you get double counts on the licenses, et cetera. So they haven't played well... >> Those are negotiable by the way. Those... You bring it up on the customer. You can negotiate that one. >> Can be, yes, They can be. Yes. If you're big enough they are negotiable. But Aurora certainly hasn't made it easy to work with other plat... Other clouds. What they did very... >> How about Microsoft? >> Well, no, that is exactly what I was going to say. Oracle with adjacent workloads have been working very well with Microsoft and you can then use Microsoft Azure and use a database adjacent in the same data center, working with integrated very nicely indeed. And I think Oracle has got to do that with AWS, it's got to do that with Google as well. It's got to provide a service for people to run where they want to run things not just on the Oracle cloud. If they did that, that would in my term, and my my opinion be a very strong move and would make make the capabilities available in many more places. >> Right. Awesome. Hey Marc, thanks so much for coming to theCUBE. Thank you, David, as well, and thanks to Gartner for doing all this great research and making it public on the web. You can... If you just search critical capabilities for cloud database management systems for operational use cases, that's a mouthful, and then do the same for analytical use cases, and the Magic Quadrant. There's the third doc for cloud database management systems. You'll get about two hours of reading and I learned a lot and I learned a lot here too. I appreciate the context guys. Thanks so much. >> My pleasure. All right, thank you for watching everybody. This is Dave Vellante for theCUBE. We'll see you next time. (upbeat music)

>> Male narrator: From around the globe, it's the CUBE with digital coverage of DockerCon Live 2020 brought to you by Docker and its ecosystem partners. >> Hey, welcome back everyone to the DockerCon Virtual Conference. DockerCon 20 being held digitally online is the CUBE's coverage. I'm John for your host of the CUBE. This is the CUBE virtual CUBE digital. We're getting all the remote interviews. We're here in our Palo Alto studio, quarantined crew, all getting the data for you. Got Peter Guangeti who's the Chief Marketing Officer Cockroach Labs, a company that we became familiar with last year. They had the first multicloud event in the history of the industry last year, notable milestone. Hey first, it's always good you're still around. So first you got the first position, Peter. Great to see you. Thanks for coming on the CUBE for DockerCon 20. >> Thank you, John. Thanks for having me. >> So it's kind of interesting, I mentioned that tidbit to give you a little bit of love on the fact that you guys ran or were a part of the first multicloud conference in the industry. Okay, now that's all everyone's talking about. You guys saw this early. Take a minute to explain Cockroach Labs. Why you saw this trend? Why you guys took the initiative and took the risk to have the first ever multicloud conference last year? >> So that's news to me that we were the first, actually. That's a bit of a surprise, cause for us we see multicloud and hybrid cloud as the obvious. I think the credit really for this belongs with folks like Gartner and others who took the time to listen to their customer, right? Took the time to understand what was the need in the market, which, you know, what I hear when I talk to CEOs is cloud is a capability, not a place, right? They're looking at us and saying, "yes, I have a go to cloud strategy, "but I also have made massive investments in my data center. "I believe I don't want to be locked in yet again "to another vendor with proprietary PIs, "proprietary systems, et cetera." So, what I hear when I talk to customers is, "I want to be multicloud show me how, "show me how to do that in a way "that isn't just buying from multiple vendors, right?" Where I've cost arbitrage, show me a way where I actually use the infrastructure in a creative way. And that really resonates with us. And it resonates with us for a few reasons. First is, we built a distributed SQL database for a reason, right? We believed that what you really need in the modern age for global applications is something that is truly diverse and distributed, right? You can have a database that behaves like a single database that lives in multiple locations around the world. But then you also have things like data locality. It's okay with German data stays in Germany because of German law. But when I write my application, I never write each of these things differently. Now, the other reason is, customers are coming to us and saying, "I want a single database that I can deploy "in any of the cloud providers." Azure SQL, and that is a phenomenal product. Google Spanner is a phenomenal product. But once I do that, I'm locked in. Then all I have is theirs. But if I'm a large global auto manufacturer, or if I'm a startup, that's trying to enter multiple markets at the same time. I don't want that. I want to be able to pick my infrastructure and deploy where I want, how I want. And increasingly, we talk to the large banks and they're saying, "I spent tens or even hundreds of millions of dollars "on data centers. "I don't want to throw them out. "I just want better utilization. "And the 15 to 20% that I get "from deploying software on bare metal, right? "I want to be able to containerize. "I want to be able to cloudify my data center "and then have ultimately what we see more and more "as what they call a tripod strategy "where your own data center and two cloud providers "behaving as a single unit "for your most important applications." >> That's awesome. I want to thank you for coming on to, for DockerCon 20, because this is an interesting time where developers are going to be called to the table in a very aggressive way because of COVID-19 crisis is going to accelerate until they pull the future forward ahead of most people thought. I mean, we, in the industry, we are inside the ropes, if you will. So we've been talking about stainless applications, stateful databases, and all the architectural things that's got that longer horizon. But this is an interesting time because now companies are realizing from whether it's the shelter in place at scale problems that emerge to the fact that I got to have high availability at a whole nother level. This kind of exposes a major challenge and a major opportunity. We're expecting projects to be funded, some not to be funded, things to move around. I think it's going to really change the conversation as developers get called in and saying, "I really got to look at my resources at scale. "The database is a critical one because you want data "to be part of that, this data plane, if you will, "across clouds." What's your reaction to this? Do you agree with that, the future has been pulled forward? And what's Cockroach doing to help developers do manage this? >> Yeah, John, I think you're exactly right. And I think that is a story that I'm glad that you're telling. Because, I think there's a lot of signal that's happening right now. But we're not really thinking about what the implications are. And we're seeing something that's I think quite remarkable. We're seeing within our existing customer base and the people we've been talking to, feast or famine. And in some cases, feast and famine in the same company. And what does that really mean? We've looked at these graphs for what's going to happen, for example, with online delivery services. And we've seen the growth rates and this is why they're all so valued. Why Uber invested so big in Uber eats and these other vendors. And we've seen these growth rates the same, and this is going to be amazing in the next 10 years, we're going to have this adoption. That five, 10 years happened overnight, right? We were so desperate to hold onto the things that are what mattered to us. And the things that make us happy on any given day. We're seeing that acceleration, like you said. It's all of that, the future got pulled forward, like you had said. >> Yeah. >> That's remarkable, but were you prepared for it? Many people were absolutely not prepared for it, right? They were on a steady state growth plan. And we have been very lucky because we built an architecture that is truly distributed and dynamic. So, scaling and adding more resilience to a database is something we all learned to do over the last 20 years, as data intensive applications matter. But with a distributed SQL and things like containerization on the stateless side, we know we can just truly elastically scale, right? You need more support for the application of something like Cockroach. You literally just add more nodes and we absorb it, right? Just like we did with containerization, where you need more concurrency, you just add more containers. And thank goodness, right, because I think those who were prepared for those things need to be worked with one of the large delivery services. Overnight, they saw a jump to what was their peak day at any point in time now happening every single day. And they were prepared for that because they already made these architectural decisions. >> Yeah. >> But if you weren't in that position, if you were still on legacy infrastructure, you were still trying to do this stuff manually, or you're manually sharding databases and having to increase the compute on your model, you are in trouble and you're feeling it. >> That's interesting Peter to bring that up and reminds me of the time, if you go back in history a little bit, just not too far back, I mean, I'm old enough to go back to the 80s, I remember all the different inflection points. And they all had their key characteristics as a computer revolution, TCP IP, and you pick your spots, there's always been that demarcation point or lions in where things change. But let's go back to around 2004 and then 2008. During that time, those legacy players out there kind of was sitting around, sleeping at the switch and incomes, open-source, incomes, Facebook, incomes, roll your own. Hey, I'm going to just run. I'm going to run open-source. I'm going to build my own database. And that was because there was nothing in the market. And most companies were buying from general purpose vendors because they didn't have to do all the due diligence. But the tech-savvy folks could build their own and scale. And that changed the game that became the hyperscale and the rest is history. Fast forward to today, because what you're getting at is, this new inflection point. There's going to be another tipping point of trajectory of knowledge, skill that's completely different than what we saw just a year ago. What's your reaction to that? >> I think you're exactly right. We saw and I've been lucky enough, same like you, I've been involved in the web since the very early days. I started my career at the beginning. And what we saw with web 1.0 and the shift to web 2.0, web 2.0 would not have happened without source. And I don't think we give them enough credit if it wasn't for the lamp stack, if it wasn't for Linux, if it wasn't for this wave of innovation and it wasn't even necessarily about rolling around. Yeah, the physics of the world to go hire their own engineers, to go and improve my SQL to make it scale. That was of course a possibility. But the democratization of that software is where all of the success really came from. And I lived on both sides of it in my career, as both an app developer and then as a software executive. In that window and got to see it from both sides and see the benefit. I think what we're entering now is yet another inflection point, like you said. We were already working at it. I think, the move from traditional applications with simple logic and simple rules to now highly data intensive applications, where data is driving the experience, models are driving the experience. I think we were already at a point where ML and AI and data intensive decision-making was going to make us rewrite every application we had and not needed a new infrastructure. But I think this is going to really force the issue. And it's going to force the issue at two levels. First is the people who are already innovating in each of these industries and categories, were already doing this. They were already cloud native. They were already built on top of very modern third generation databases, third generation programming languages, doing really interesting things with machine learning. So they were already out innovating, but now they have a bigger audience, right? And if you're a traditional and all of a sudden your business is under duress because substantial changes in what is happening in the market. Retailers still had strength with footprint as of last year, right? We don't be thinking about e-commerce versus traditional retail. Yeah, it was on a slow decline. There were lots of problems, but there was still a strength there, that happened changed overnight. Right now, that new sources have dried up, so what are you going to do? And how are you going to act? If you've built your entire business, for example, on legacy databases from folks like Oracle and old monolithic ways of building out patients, you're simply not adaptable enough to move with changing times. You're going to have to start, we used to talk about every company needed to become a software company. That mostly happened, but they weren't all very good software companies. I would argue that the next generation used to to be a great software company and great data scientists. We'll look at the software companies that have risen to prominence in the last five to 10 years. Folks like Facebook, folks like Google, folks like Uber, folks like Netflix, they use data better than anyone else in their category. So they have this amazing app experience and leverage data and innovate in such a way that allow them to just dominate their category. And I think that is going to be the change we see over the next 10 years. And we'll see who exits what is obviously going to be a jail term. We'll see who exits on top. >> Well, it's interesting to have you on. I love the perspective and the insights. I think that's great for the folks out there who haven't seen those ways before. Again, this wave is coming. Let's go back to the top when we were talking about what's in it for the developer. Because I believe there's going to be not a renaissance, cause it's always been great, but the developers even more are going to be called to the front lines for solutions. I mean, these are first-generation skill problems that are going to be in this whole next generation, modern era. That's upon us. What are some of the things that's going to be that lamp stack, like experience? What are some of the things that you see cause you guys are kind of at a tail sign, in my opinion, Cockroach, because you're thinking about things in a different construct. You're thinking about multicloud. You're thinking about state, which is a database challenge. Stateless has kind of been around restful API, stateless data service measures. Kubernetes is also showing a cloud native and the microservices or service orientation is the future. There's no debate on that. I think that's done. Okay, so now I'm a developer. What the hell am I going to be dealing with for the next five years? What's your thoughts? >> Well, I think the developer knows what they're already facing from an app perspective. I think you see the rapid evolution in languages, and then, in deployment and all of those things are super obvious. You need just need to go and say I'm sure that all the DockerCon sessions to see what the change to deployment looks like. I think there are a few other key trends that developers should start paying attention to, they are really critical. The first one, and only loosely related to us, is ML apps, right? I think just like we saw with dev and ops, suddenly come together so we can actually develop and deploy in a super fast iterative manner. The same things now are going to start happening with data and all of the work that we do around deploying models. And I think that that's going to be a pretty massive change. You think about the rise of tools like TensorFlow, some of the developments that have happened inside of the cloud providers. I think you're seeing a lot there as a developer, you have to start thinking as much like a data scientist and a data engineer as simply somebody writing front end code, right? And I think that's a critical skill that the best developers already building will continue. I think then the data layer has become as important or more important than any other layer in the stack because of this. And you think about once again, how the leaders are using data and the interesting things that they're doing, the tools you use matter, right? If you are spending a lot of your time trying to figure out how to shard something how to make it scale, how to make it durable when instead you should be focused on just the pure capability, that's a ridiculous use of your time, right? That is not a good use of your time. We're still using 20 to 25 year old open-source databases for many of these applications when they gave up their value probably 10 years ago. Honestly, you know, we keep all paper over it, but it's not a great solution. And unfortunately, no SQL will fix some of the issues with scaling elasticity, it's like you and I starting a business and saying, "okay, everyone speaks English, "but because we're global, "everyone's going to learn Esperanto, right?" That doesn't work, right? So works for a developer. But if you're trying to do something where everyone can interact, this is why this entire new third generation of new SQL databases have risen. We took the distributed architecture SQL. >> Hold up for a second. Can you explain what that means? Cause I think a key topic. I want to just call that out. What is this third generation database mean? Sorry, I speak about it. Like everyone sees it. >> I think it's super important. It's just a highlight. Just take a minute to explain it and we can get into it. There is an entire new wave of database infrastructure that has risen in the last five years. And it started actually with Google. So it started with Google Spanner. So Google was the first to face most of these problems, right? They were the first to face web scale. At least at the scale, we now know it. They were the first to really understand the complexity of working with data. They have their own no SQL. They have their own way of doing things internally and they realized it wasn't working. And what they really needed was a relational database that spoke traditional ANSI SQL, but scaled, like there are no SQL counterparts. And there was a white paper that was released. That was the birth of Spanner. Spanner was an internal product for many, many years. They released the thinking into the wild and then they just started this way with innovation. That's where our company came from. And there were others like us who said, "you're right. "Let's go build something that behaves," like we expect a database to behave with structure and this relational model and like anyone can write simple to use it. It's the simplest API for most people with data, but it behaves like all the best distributed software that we've been using. And so that's how we were born. Our company was founded by ex Googlers who had lived in this space and decided to go and scratch the itch, right? And instead of doing a product that would be locked into a single cloud provider, a database that could be open-source, it could be deployed anywhere. It could cross actual power providers without hiccups and that's been the movement. And it's not just us, there were other vendors in this space and we're all focused on really trying to take the best of the both worlds that came before us. The traditional relational structure, the consistency and asset compliance that we all loved from tools like Oracle, right? And Microsoft who we really enjoyed. But then the developer friendly nature and the simple elastic scalability of distributed software and, that's what we're all seeing. Our company, for example, has only been selling a product for the last two years. We found it five years ago, it took us three years just to rank in the software that we would be happy selling to a customer. We're on what we believe is probably a 10 to 15 year product journey to really go and replace things like Oracle. But we started selling the product two years ago and there is 300% growth year over year. We're probably one of the fastest growing software companies in America, right? And it's all because of the latent demand for this kind of a tool. >> Yeah, that's a great point. I'm a big fan of this third wave. Can I see it? If you look at just the macro tailwinds in the industry, billions of edged devices, immersion of all kinds of software. So that means you can't have one database. I always said to someone, in (mumbles) and others. You can't have one database. It's physically impossible. You need data and whatever database fits the scene, wherever you want to have data being stored, but you got to have it real time. You got to have actionable, you have to have software intelligence into how to manage the data. So I think the data control plane or that layer, I think it's the next interoperability wave. Because without data, nothing really works. Machine learning doesn't really work well. You want the most data. I think cybersecurity is a great early use case because they have to leverage data fast. And so you start to see some interesting financial services, cyber, what's your thoughts on this? Can you share from the Cockroach Labs perspective, from your database, you've got a cloud. What are some of the adoption use cases? Who are those leaders? You can name names if you have them, if not, name the use case. What's the Cockroach approach? Who's winning with it? What's it look like? >> Yeah, that's a great question. And you nailed it, right? The data volumes are so large and they're so globally distributed. And then when you start layering again, the data streaming in from devices that then have to be weighed against all of these things. You want a single database. But you need one that will behave in a way that's going to support all of that and actually is going to live at the edge like you're saying. And that's where we have been shining. And so our use cases are, and unfortunate, I can't name any names, but, for example, in retail. We're seeing retailers who have that elasticity and that skill challenge with commerce. And what they're using us for is then, we're in all of the locations where they do business, right? And so we're able to have data locality associated with the businesses and the purchases in those countries. And however, only have single apps that actually bridge across all of those environments. And with the distributed nature, we were able to scale up and scale down truly elastically, right? Because we spread out the data across the nodes automatically. And, what we see there is, you know, retailers do you have up and down moments? Can you talk about people who can leverage the financial structure of the cloud in a really thoughtful way? Retail is a shining example of that. I remember having customers that had 64 times the amount of traffic on cyber Monday that they had on the average day. In the old data center world, that's what you bought for. That was horrendous. In a cloud environment, still horrendous, even public cloud providers. If you're having to go and change your app to ramp every time, that's a problem with something like a distributed database. and with containerization, you could scale much more quickly and scale down much more. That's a big one for streaming media, is another one. Same thing with data locality in each of these countries, you think about it, somebody like Netflix or Hulu, right? They have shows that are unique to specific countries, right? They haven't have that user behavior, all that user data. You know data sovereignty, you know, what you watch on Netflix, there's some very rich personal data. And we all know how that metadata has been used against people. Or so it's no surprise that you now have countries that I know there's going to be regulation around where that data can live and how it can. And so once again, something like Cockroach where you can have that global distribution, but take a locality, or we can lock data to certain nodes in certain locations. That's a big one. >> There's no doubt in my mind. I think there's such a big topic. We probably do more interviews just on the COVID-19 data problem that they have. The impact of getting this right, is a nerd problem today. But it is a technology solution for society globally in the future. Zero doubt in my mind on that. So, Peter, I want you to get the last word and to give a plugin to the developers that are watching out there about Cockroach. Why should they engage with you guys? What can you offer? Is there anything new you want to share about the company to the audience here at DockerCon 2020? Take us home in the next segment. >> Thank you, John. I'll keep the sales pitch to a minimum. I'm a former developer myself. I don't like being sold, so I appreciate it. But we believe we're building, what is the right database for the coming wave of cognitive applications. And specifically we've built what we believe is the ideal database for distributed applications and for containerized applications. So I would strongly encourage you to try it. It is open-source. It is truly cloud native. We have free education, so you can try it yourself. And once you get into it, it is traditional SQL that behaves like Postgres and other tools that you've already known of. And so it should be very familiar, you know, if you've come up through any of these other spaces will be very natural. Postgres compatible integrates with a number of ORM. So as a developer, just plugged right into the tools you use and we're on a rapid journey. We believe we can replace that first generation of technology built by the Oracles of the world. And we're committed to doing it. We're committed to spending the next five to 10 years in hard engineering to build that most powerful database to solve this problem. >> Well, thanks for coming on, sharing your awesome insight and historical perspective. get it out of experience. We believe and we want to share the audience in this time of crisis, more than ever to focus on critical nature of operations, because coming out of this, it is going to be a whole new reality. And I think the best tech will win the day and people will be building new things to grow, whether it's for profit or for societal benefit. The impact of what we do in the next year or two will determine a big trajectory and new technology, new approaches that are dealing with the realities of infrastructure, scale, working at home , sheltering in place to coming back to the hybrid world. We're coming virtualized, Peter. We've been virtualized, the media, the lifestyle, not just virtualization in the networking sense, but, fun times it was going to be challenging. So thanks for coming on. >> Thank you very much, John. >> Okay, we're here for DockerCon 20 virtual conferences, the CUBE Virtual Segment. I want to thank you for watching. Stay with me. We've got stream all day today and check out the sessions. Jump in, it's going to be on demand. There's a lot of videos it's going to live on and thanks for watching and stay with us for more coverage and analysis. Here at DockerCon 20, I'm John Furrier. Thanks for watching >> Narrator: From the CUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is the CUBE conversation.

>> Announcer: Live from San Francisco, it's theCUBE. Covering Google Cloud Next '19. Brought to you by Google Cloud and it's ecosystem partners. >> Welcome back to the Moscone Center in San Francisco everybody. This is theCUBE, the leader in live tech coverage. This is day two of our coverage of Google Cloud Next #GoogleNext19. I'm here with my co-host Stuart Miniman and I'm Dave Vellante, John Furrier is also here. Dominic Preuss is here, he's the Director of Product Management, Storage and Databases at Google. Dominic, good to see you. Thanks for coming on. >> Great, thanks to be here. >> Gosh, 15, 20 years ago there were like three databases and now there's like, I feel like there's 300. It's exploding, all this innovation. You guys made some announcements yesterday, we're gonna get into, but let's start with, I mean, data, we were just talking at the open, is the critical part of any IT transformation, business value, it's at the heart of it. Your job is at the heart of it and it's important to Google. >> Yes. Yeah, you know, Google has a long history of building businesses based on data. We understand the importance of it, we understand how critical it is. And so, really, that ethos is carried over into Google Cloud platform. We think about it very much as a data platform and we have a very strong responsibility to our customers to make sure that we provide the most secure, the most reliable, the most available data platform for their data. And it's a key part of any decision when a customer chooses a hyper cloud vendor. >> So summarize your strategy. You guys had some announcements yesterday really embracing open source. There's certainly been a lot of discussion in the software industry about other cloud service providers who were sort of bogarting open source and not giving back, et cetera, et cetera, et cetera. How would you characterize Google's strategy with regard to open source, data storage, data management and how do you differentiate from other cloud service providers? >> Yeah, Google has always been the open cloud. We have a long history in our commitment to open source. Whether be Kubernetes, TensorFlow, Angular, Golang. Pick any one of these that we've been contributing heavily back to open source. Google's entire history is built on the success of open source. So we believe very strongly that it's an important part of the success. We also believe that we can take a different approach to open source. We're in a very pivotal point in the open source industry, as these companies are understanding and deciding how to monetize in a hyper cloud world. So we think we can take a fundamentally different approach and be very collaborative and support the open source community without taking advantage or not giving back. >> So, somebody might say, okay, but Google's got its own operational databases, you got analytic databases, relational, non-relational. I guess Google Spanner kind of fits in between those. It was an amazing product. I remember that that first came out, it was making my eyes bleed reading the white paper on it but awesome tech. You certainly own a lot of your own database technology and do a lot of innovation there. So, square that circle with regard to partnerships with open source vendors. >> Yeah, I think you alluded to a little bit earlier there are hundreds of database technologies out there today. And there's really been a proliferation of new technology, specifically databases, for very specific use cases. Whether it be graph or time series, all these other things. As a hyper cloud vendor, we're gonna try to do the most common things that people need. We're gonna do manage MySQL, and PostgreS and SQL Server. But for other databases that people wanna run we want to make sure that those solutions are first class opportunities on the platform. So we've engaged with seven of the top and leading open source companies to make sure that they can provide a managed service on Google Cloud Platform that is first class. What that means is that as a GCP customer I can choose a Google offered service or a third-party offered service and I'm gonna have the same, seamless, frictionless, integrated experience. So I'm gonna get unified billing, I'm gonna get one bill at the end of the day. I'm gonna have unified support, I'm gonna reach out to Google support and they're going to figure out what the problem is, without blaming the third-party or saying that isn't our problem. We take ownership of the issue and we'll go and figure out what's happening to make sure you get an answer. Then thirdly, a unified experience so that the GCP customer can manage that experience, inside a cloud console, just like they would their Google offered serves. >> A fully-managed database as a service essentially. >> Yes, so of the seven vendors, a number of them are databases. But also for Kafka, to manage Kafka or any other solutions that are out there as well. >> All right, so we could spend the whole time talking about databases. I wanna spend a couple minutes talking about the other piece of your business, which is storage. >> Dominic: Absolutely. >> Dave and I have a long history in what we'd call traditional storage. And the dialog over the last few years has been we're actually talking about data more than the storing of information. A few years back, I called cloud the silent killer of the old storage market. Because, you know, I'm not looking at buying a storage array or building something in the cloud. I use storage is one of the many services that I leverage. Can you just give us some of the latest updates as to what's new and interesting in your world. As well as when customers come to Google where does storage fit in that overall discussion? >> I think that the amazing opportunity that we see for for large enterprises right now is today, a lot of that data that they have in their company are in silos. It's not properly documented, they don't necessarily know where it is or who owns it or the data lineage. When we pick all that date up across the enterprise and bring it in to Google Cloud Platform, what's so great about is regardless of what storage solution you choose to put your data in it's in a centralized place. It's all integrated, then you can really start to understand what data you have, how do I do connections across it? How do I try to drive value by correlating it? For us, we're trying to make sure that whatever data comes across, customers can choose whatever storage solution they want. Whichever is most appropriate for their workload. Then once the data's in the platform we help them take advantage of it. We are very proud of the fact that when you bring data into object storage, we have a single unified API. There's only one product to use. If you would have really cold data, or really fast data, you don't have to wait hours to get the data, it's all available within milliseconds. Now we're really excited that we announced today is a new storage class. So, in Google Cloud Storage, which is our object storage product, we're now gonna have a very cold, archival storage option, that's going to start at $0.12 per gigabyte, per month. We think that that's really going to change the game in terms of customers that are trying to retire their old tape backup systems or are really looking for the most cost efficient, long term storage option for their data. >> The other thing that we've heard a lot about this week is that hybrid and multi-cloud environment. Google laid out a lot of the partnerships. I think you had VMware up on stage. You had Cisco up on stage, I see Nutanix is here. How does that storage, the hybrid multi-cloud, fit together for your world. >> I think the way that we view hybrid is that every customer, at some point, is hybrid. Like, no one ever picks up all their data on day one and on day two, it's on the cloud. It's gonna be a journey of bringing that data across. So, it's always going to be hybrid for that period of time. So for us, it's making sure that all of our storage solutions, we support open standards. So if you're using an an S3 compliant storage solution on-premise, you can use Google Cloud Storage with our S3 compatible API. If you are doing block, we work with all the large vendors, whether be NetApp or EMC or any of the other vendors you're used to having on-premise, making sure we can support those. I'm personally very excited about the work that we've done with NetApp around NetApp cloud buying for Google Cloud Platform. If you're a NetApp shop and you've been leveraging that technology and you're really comfortable and really like it on-premise, we make it really easy to bring that data to the cloud and have the same exact experience. You get all the the wonderful features that NetApp offers you on-premise in a cloud native service where you're paying on a consumption based service. So, it really takes, kind of, the decision away for the customers. You like NetApp on-premise but you want cloud native features and pricing? Great, we'll give you NetApp in the cloud. It really makes it to be an easy transition. So, for us it's making sure that we're engaged and that we have a story with all the storage vendors that you used to using on-premise today. >> Let me ask you a question, about go back, to the very cold, ice cold storage. You said $0.12 per gigabyte per month, which is kinda in between your other two major competitors. What was your thinking on the pricing strategy there? >> Yeah, basically everything we do is based on customer demand. So after talking to a bunch of customers, understanding the workloads, understanding the cost structure that they need, we think that that's the right price to meet all of those needs and allow us to basically compete for all the deals. We think that that's a really great price-point for our customers. And it really unlocks all those workloads for the cloud. >> It's dirt cheap, it's easy to store and then it takes a while to get it back, right, that's the concept? >> No, it is not at all. We are very different than other storage vendors or other public cloud offerings. When you drop your data into our system, basically, the trade up that you're making is saying, I will give you a cheaper price in exchange for agreeing to leave the data in the platform, for a longer time. So, basically you're making a time-based commitment to us, at which point we're giving you a cheaper price. But, what's fundamentally different about Google Cloud Storage, is that regardless of which storage class you use, everything is available within milliseconds. You don't have to wait hours or any amount of time to be able to get that data. It's all available to you. So, this is really important, if you have long-term archival data and then, let's say, that you got a compliance request or regulatory requests and you need to analyze all the data and get to all your data, you're not waiting hours to get access to that data. We're actually giving you, within milliseconds, giving you access to that data, so that you can get the answers you need. >> And the quid pro quo is I commit to storing it there for some period of time, is that you said? >> Correct. So, we have four storage classes. We have our Standard, our Nearline, our Coldline and this new Archival. Each of them has a lower price point, in exchange for a longer, committed time the you'll leave the product. >> That's cool. I think that adds real business value there. So, obviously, it's not sitting on tape somewhere. >> We have a number of solutions for how we store the data. For us, it's indifferent, how we store the data. It's all about how long you're willing to tell us it'll be there and that allows us to plan for those resources long term. >> That's a great story. Now, you also have this pay-as-you-go pricing tiers, can you talk about that a little bit? >> For which, for Google Cloud Storage? >> Dave: Yes. >> Yeah, everything is pay-as-you-go and so basically you write data to us and there's a charge for the operations you do and then you charge for however long you leave the data in the system. So, if you're using our Standard class, you're just paying our standard price. You can either use Regional or Multi-Regional, depending on the disaster recovery and the durability and availability requirements that you have. Then you're just paying us for that for however long you leave the data in the system. Once you delete it, you stop paying. >> So it must be, I'm not sure what kind of customer discussions are going on in terms of storage optionality. It used to be just, okay, I got block and I got file, but now you've got all different kind of. You just mentioned several different tiers of performance. What's the customer conversation like, specifically in terms of optionality and what are they asking you to deliver? >> I think within the storage space, there's really three things, there's object, block and file. So, on the object side, or on the block side we have our persistence product. Customers are asking for better price performance, more performance, more IOPS, more throughput. We're continuing to deliver a higher-performance, block device for them and that's going very, very well. For those that need file, we have our first-party service, which is Cloud Filestore, which is our manage NFS. So if you need managed NFS, we can provide that for you at a really low price point. We also partner with, you mentioned Elastifile earlier. We partner with NetApp, we're partnering with EMC. So all those options are also available for file. Then on the object side, if you can accept the object API, it's not POSIX-compliant it's a very different model. If your workloads can support that model then we give you a bunch of options with the Object Model API. >> So, data management is another hot topic and it means a lot of things to a lot of people. You hear the backup guys talking about data management. The database guys talk about data management. What is data management to Google and what your philosophy and strategy there? >> I think for us, again, I spend a lot of time making sure that the solutions are unified and consistent across. So, for us, the idea is that if you bring data into the platform, you're gonna get a consistent experience. So you're gonna have consistent backup options you're gonna have consistent pricing models. Everything should be very similar across the various products So, number one, we're just making sure that it's not confusing by making everything very simple and very consistent. Then over time, we're providing additional features that help you manage that. I'm really excited about all the work we're doing on the security side. So, you heard Orr's talk about access transparency and access approvals right. So basically, we can have a unified way to know whether or not anyone, either Google or if a third-party offer, a third-party request has come in about if we're having to access the data for any reason. So we're giving you full transparency as to what's going on with your data. And that's across the data platform. That's not on a per-product basis. We can basically layer in all these amazing security features on top of your data. The way that we view our business is that we are stewards of your data. You've given us your data and asked us to take care of it, right, don't lose it. Give it back to me when I want it and let me know when anything's happening to it. We take that very seriously and we see all the things we're able to bring to bear on the security side, to really help us be good stewards of that data. >> The other thing you said is I get those access logs in near real time, which is, again, nuanced but it's very important. Dominic, great story, really. I think clear thinking and you, obviously, delivered some value for the customers there. So thanks very much for coming on theCUBE and sharing that with us. >> Absolutely, happy to be here. >> All right, keep it right there everybody, we'll be back with our next guest right after this. You're watching theCUBE live from Google Cloud Next from Moscone. Dave Vellante, Stu Miniman, John Furrier. We'll be right back. (upbeat music)

(upbeat instrumental music) >> Welcome to the special CUBE Conversation here, in Palo Alto, I'm John Furrier, host of theCUBE. I'm here with David Richards the CEO of WANdisco, CUBE alumni, been on many times. WANdisco continues to make the right bets. The bet they recently made has been on cloud many years. We've covered it certainly on theCUBE. But live data is the new hot thing. Multiple clouds is turning out to be the trend. That's your friend. David, good to see you. >> Great to be back. >> Thanks for coming on. So we talk all the time about how you guys have always evolved the business and continued to stay out front in all the major waves. Now again, another good call. You've certainly bet on Cloud. We've talked about that, Open Source, Big Data, Cloud, you saw that coming, positioned for that. But now you got some great momentum and resonance with customers around live data, which is not a stretch, given what you guys have done with replication, things in the past, the core intellectual property. Give us the update. You guys have been in the news lately. >> So, thanks and I think you enumerated the past history over the past two or three years, which we like to say that we're living in dog years. Everything's happening seven times faster than it would do normally. So of course, we started out life by making a prediction that storage arrays would change. People are beginning to store, companies beginning to store structured and unstructured data, mammoth sizes that we've never seen previously. We're going to have to resort to Open Source software, running a commoditized hardware that we'd already seen the social media companies move to. Then we've seen, we began to see a problem emerge, even in that marketplace, where spike computes all the applications which were going to be heavily compute, would need to run in Cloud and Cloud environments where you have complete elastic compute at remarkably low cost. And that leads to a problem. So this iceberg kind of that we like to talk about underneath the oceans, so moving data for static archival data really simple problem. And that's not live data, that's archival data. You just FTP it from point A to point B. But if we're talking about transactional systems where 10, 20, 30, 40, 50 percent of the data set changes all of the time, that creates a humongous problem in moving data from one premises to cloud, either for hybrid cloud or between clouds for multi-cloud. And that's the precise problem that WANdisco solves. And we've seen customer attraction, recently we've just announced the deal, jointly with Microsoft Azure. Where a big healthcare company, who 12 months ago were not talking about cloud suddenly they got over that hump where security keys could be managed by themselves within the cloud, were able to move petabytes-scale data from their on-premise systems into the cloud, without any interruption to service, without any blocking. That's a trend that we're seeing our pipelines now full of companies, all trying to do that. >> It's like you hit the oil gusher with data, because the data tsunami has been there, and we've documented certainly on theCUBE, and our Research team at Wikibon, have been talking about it for years, and now you're starting to see it, and you guys are getting the benefits of it, is that people figured out that it's moving data around is expensive. And it's hard to do so you push compute to the edge, but you still got to move the data around because the key part of the latency piece of the cloud. So how do you do that at scale? So this is the thing that you guys have, and I want you to explain what it is. You guys have live data from multi-cloud. What does that mean? What is all the hubbub about? What's the buzz? Why is this such a hot topic, live data from multi-cloud. >> Okay so let's just take a step back and talk about what multi-cloud actually is in today's definition, which is the vendor's definition, which is very convenient. So what they mean is, moving, putting applications into a container, Kubernetes or whatever, picking it up and shifting it somewhere else. And hey presto, I've got applications running, the same applications running in two different clouds. That is not multi-cloud because you're forgetting about the data, and the iceberg underneath the ocean of this colossal amount of data. If I've got petabyte-scale, multi-terabyte-scale data sets, and I need to run the same applications, or different applications but against the same data set, I need guaranteed consistent data, and that is, by definition, a data consistency problem. It is not a data replication problem. So all of the stuff that we used to use in the past for gigabyte-scale data, for traditional, relational database problems, none of that stuff works in a live data world. And by live data, we're talking about multi-terabyte, petabyte-scale data. Data sets that are so large that we've never seen them before running in end cloud locations. It's different or same applications, but guaranteed consistent data in every location. >> So you guys have had this core composite around integrity around the data, whether it's in replication. Sounds like the same thing's true around moving data. >> Yep. >> You guys are managing the life cycle of end-to-end of data movement. >> Yep. >> Point A to point B. >> Yep. >> The other approach is to move compute to the data. >> Yep. >> We're just seeing Amazon do a deal with VMware on-premise. So there's two schools of thought. When should customers think about each approach? Can you just kind of debunk or just clarify those two positions? >> So it's not really a chicken and egg because we know which comes first. It's definitely the chicken. It's definitely the data. So if I'm going to rebuild my application infrastructure, in the cloud, I'm going to do it piece-by-piece. I can't do lift-and-shift for a thousand applications that are running against this data set and just hope that the data that block for six months because I've got petabyte-scale data, and wait for it to all arrive in the cloud, or put it to the back of you know, use a snowmobile or some physical device to move the data. I need to do this, I need to kind of build the aircraft while it's taking off and flying and that's probably a good analogy. So what we see, is companies the first step is to get consistent data on-premise to cloud, or between different clouds. Then what that enables me to do of course, is to piece-by-piece then rebuild my application infrastructure at the pace that I want to. I mean there's a great add that I keep on seeing on t.v. Where it's migration day. As though I can press a button and then suddenly you know, in this Alice in Wonderland magical world, everything just appears. Realistically, and I saw the CEO of VMware a couple of years ago talk about being in a hybrid cloud scenario for 20 years. I think that's probably accurate. We've got billions of applications. A mix of homegrown stuff, a mix of, you know, actuarial applications in the insurance industry that are impossible to build overnight. This is going to take an elongated period of time. >> I was talking on Twitter with a bunch of thought leaders. We were talking about hybrid cloud and multi-cloud, and the kindergarten class is hybrid, right? >> Yeah. >> So you got some public cloud, then you got some on-premise data center. So getting that operational thing nailed down is great. But as you get old, you know, you progress in the grades, and get smarter, as you increase your I.T. I.Q., you're dealing with multiple, potentially multiple data centers or bigger on site, or an IOT edge, and multiple clouds. >> Yep. >> So that sounds easy on paper, but when you have to move data around the different work loads, that's the core problem that people are talking about today. How do you guys address this problem? Because I buy multi-cloud, I can see that certain tools and certain clouds the right work load and the right cloud, I get that. >> Yeah. It makes a lot of sense to me. The data is the problem. >> Yep. >> So how do you guys address that? This is the number one concern. >> So the closest, people ask me all the time about competition. The closest is Google. Google have got a product called Google Spanner. And Google Spanner is a time-sensitive, active-active WAN-scope data replication solution. That looks on paper very close to what WANdisco does. It enables them to keep active data in all of their different geolocations that they've built for their add services years and years and years ago. The trouble with that is, it only works on their own proprietary network, against their own proprietary applications because they launched a satellite and stuck it in the sky, they put dark fiber under the ocean, and they put GPS atomic clocks on every single one of their servers because it uses time and time accuracy in order to synchronize all of their data. We can do all of that over the public internet. So we're not a hardware solution. This is a pure software solution that can work over the public internet. So we can do that for any cloud vendor, and any provider of applications. And that's what we do. We're licensing our I.P. all over the place at the moment. >> So which clouds are, I imagine there's a great uptake for the clouds. Which one are you working with now? Can you talk about the deals you've done? >> We're very close. We announced the Azure partnership with Microsoft, and their Azure product, and we've been very impressed with the traction that we're seeing with them, particularly an enterprise cloud. I mean the early stage of cloud obviously was dominated by Amazon, Amazon Web Services. And they did a fantastic job of really bringing cloud to the market by accident kind of inventing cloud and then bringing it to market very very quickly. The fastest ever company to, if it's and independent company to 15 billion dollars, but most of those applications and projects and companies were born in the cloud. I mean a lot of the modern companies today were actually of course, you have Airbnb et cetera, were born in the cloud. So that, the second inning of cloud is certainly enterprise. We've also been impressed with the traction that we've seen from Google GCP as being extremely impressive. And of course Amazon continued to thrive. In cloud we also have an OEM deal with Ali, with Alibaba with their cloud as well. So they're really the only full. >> If Google has Spanner, how do you differentiate between Google Spanner? >> So Google Spanner only works on their proprietary network. Which is great for Google and between their data centers, but what about 99.9 percent of the rest of the problem, which is the rest of us right, who operate on the public internet. So we can do what Google Spanner does active-active, geo, one scope replication of data but over the public internet. >> So you guys have been talking active-active for many times. We've had many conversations here on theCUBE. So I get that. How has your business changed with cloud? You had mentioned prior to coming on camera. You made a bet on cloud. It's paying off obviously. People who have made the right bets on cloud at the right time, it's certainly paying off. You're one of them. How does the live data in the multi-cloud change your business? Does it increase your trajectory? Is there a pivot? I mean what does it mean for WANdisco? >> So the very, so my thesis or the company's thesis, I won't take the credit for it, but the company's thesis was really simplistic, which is our bet was in the small data world of gigabyte-scale data, in order to do data replication, small data equals small outage. When you get data sets that are growing exponentially, and you get, you know, data sets through a thousand or a million times greater than what we've seen previously, what was a small outage or small blocking of client applications will become an elongated blocking of client applications that we're talking about, you know, six months to move 20 petabytes of data. You can't block applications, business critical applications for six months. That was the bet that we made. We expected initially to see that happen on-premise in the data like world, in the Hadoop world if you will. That didn't quite happen, or has not happen to date. We don't think that's probably going to happen. We're certainly seeing a huge desire of companies moving those data lakes into cloud, and we've actually innovated, we've got some new inventions coming out that enable you to move in a single pass, massive quantity of data that will be exponentially faster than anything else, and just doing a unidirectional data move into clouds. That was our bet that we said "Okay, companies in order to achieve the kind of scale "that they need to achieve, "they're going to have to do this in cloud." "In order to get to cloud, "they're going to have to move that data there, "and they're not going to be able to block even for a day "in order to move that data to cloud." And that was the bet we made, and it was the right bet. >> Talk about where you guys go from here. Give a company update. What's the status of the company? Get some new personnel? Any changes, notable updates? >> So we, really interestingly, my Co-Founder and Chief Scientist is a genius, Dr. Yeturu Aahlad, Ph.D. from UT, and undergrad from IIT, a new VP of Engineering Sakthi, IIT, Ph.D. at U.T. under Draxler. This fantastic Ph.D. program they did there. My new Head of Research came from, was Chairman of Computer Science at the University of Denver. He's was an IIT undergrad, Ph.D with Aahlad at UT. And I said jokingly to Aahlad: "There must be a fourth guy "that we can bring on board here "that went through the same program." He said, "We can but we can't hire him, "because he's the CTO of Microsoft, so." That was, he was the forth guy. Joel, who I know, is going to be coming on theCUBE shortly. He also has joined us from IBM to run Marketing for us. So we've made some fantastic new hires. The company's doing really well. You know cloud certainly has played a big part in the second half of last year. I think it's going to play a big part. It's definitely going to play a big part in 2019. We've seen a pivot in pipeline, that's moved away from possibly even disaster recovery, data lake in the first half of last year. We pivoted to more of a reliable subscription revenue in the second half of the year. We announced some pretty big deals, big healthcare companies. We've got really good public reference with AMD. We announced a motor vehicle company one of the new used cases there is four petabytes of data per day they're generating. That all has to be moved from on-premise to cloud. So we've got some ginormous deals in pipeline. We'll see how they play out in the coming weeks and months. >> It's great to see the change, and certainly on theCUBE. We've been talking, I think we've known each other for almost, this is our tenth year. >> Yeah. Ever since we first met. It's fun to see how you guys entered the market at Hadoop, staying on the data wave and thinking enterprise, integrity of the data, active-active, the key I.P. And how cloud is just assumed data, and it's not just data, it's large scale. So if you look at the new people you hired, you've got jobs in large scale systems. >> Yep. >> We're talking about a large systems, now data is just given. So you're really nailing the large scale, moving from an enterprise nice feature, certainly table stakes for fault tolerance, and active-active. Just add recovery to mission critical >> Yep. >> Ingredient in large scale cloud. >> Well it's ironic isn't it because our value actually increases with the volume of data. So we're an unusual company in that context where the larger the data site, the greater the problem, and the greater the problem that we solve. See we made a pretty good bet, the active-active replication, that live data would be a critical component of both hybrid cloud and multi-cloud. And that's playing out I think really well for us. >> And certainly a lot more changes to come. Great to have you on. >> Yeah. >> Cloud and multi-cloud. Certainly cloud has proven the economics proven large scale value of moving at cloud speed but now you have multiple clouds. That's going to change the game on applications, work loads. It's not going to change the data equation. There's still more tsunami of data that's not stopping. >> Exactly. >> I think you've got a good wave you're riding. >> Yeah. >> Data cloud wave. David Richards, CEO of WANdisco here in CUBE Conversations here in Palo Alto. I'm John Furrier, thanks for watching. (upbeat instrumental music)

>> Voiceover: Live from the Moscone Center, it's theCUBE. Covering AWS Summit San Francisco 2018. Brought to you by Amazon Web Services. >> Welcome back, I'm Stu Miniman and this is theCUBE's exclusive coverage of AWS Summit here in San Francisco. Happy to welcome back to the program Jagane Sundar, who is the CTO of WANdisco. Jagane, great to see you, how have you been? >> Well, been great Stu, thanks for having me. >> All right so, every show we go to now, data really is at the center of it, you know. I'm an infrastructure guy, you know, data is so much of the discussion here, here in the cloud in the keynotes, they were talking about it. IOT of course, data is so much involved in it. We've watched WANdisco from the days that we were talking about big data. Now it's you know, there's AI, there's ML. Data's involved, but tell us what is WANdisco's position in the marketplace today, and the updated role on data? >> So, we have this notion, this brand new industry segment called live data. Now this is more than just itty-bitty data or big data, in fact this is cloud-scale data located in multiple regions around the world and changing all the time. So you have East Coast data centers with data, West Coast data centers with data, European data centers with data, all of this is changing at the same time. Yet, your need for analytics and business intelligence based on that is across the board. You want your analytics to be consistent with the data from all these locations. That, in a sense, is the live data problem. >> Okay, I think I understand it but, you know, we're not talking about like, in the storage world there was like hot data, what's hot and cold data. And we talked about real-time data for streaming data and everything like that. But how do you compare and contrast, you know, you said global in scope, talked about multi-region, really talking distributed. From an architectural standpoint, what's enabling that to be kind of the discussion today? Is it the likes of Amazon and their global reach? And where does WANdisco fit into the picture? >> So Amazon's clearly a factor in this. The fact that you can start up a virtual machine in any part of the world in a matter of minutes and have data accessible to that VM in an instant changes the business of globally accessible data. You're not simply talking about a primary data center and a disaster recovery data center anymore. You have multiple data centers, the data's changing in all those places, and you want analytics on all of the data, not part of the data, not on the primary data center, how do you accomplish that, that's the challenge. >> Yeah, so drill into it a little bit for us. Is this a replication technology? Is this just a service that I can spin up? When you say live, can I turn it off? How do those kind of, when I think about all the cloud dynamics and levers? >> So it is indeed based on active-active replication, using a mathematically strong algorithm called Paxos. In a minute, I'll contrast that with other replication technologies, but the essence of this is that by using this replication technology as a service, so if you are going up to Amazon's web services and you're purchasing some analytics engine, be it Hive or Redshift or any analytics engine, and you want to have that be accessible from multiple data centers, be available in the face of data center or entire region failure, and the data should be accessible, then you go with our live data platform. >> Yeah so, we want you to compare and contrast. What I think about, you know, I hear active-active, speed of light's always a challenge. You know globally, you have inconsistency it's challenging, there's things like Google Spanner out there to look at those. You know, how does this fit compared to the way we've thought of things like replication and globally distributed systems in the past? >> Interesting question. So, ours great for analytics applications, but something like Google Spanner is more like a MySQL database replacement that runs into multiple data centers. We don't cater to that and database-transaction type of applications. We cater to analytics applications of batch, very fast streaming applications, enterprise data warehouse-type analytics applications, for all of those. Now if you take a look inside and see what kind of replication technology will be used, you'll find that we're better than the other two different types. There are two different types of existing replication technologies. One is log shipping. The traditional Oracle, GoldenGate-type, ship the log, once the change is made to the primary. The second is, take a snapshot and copy differences between snapshots. Both have their deficiencies. Snapshot of course is time-based, and it happens once in a while. You'll be lucky if you can get one day RTO with those sorts of things. Also, there's an interesting anecdote that comes to mind when I say that because the Hadoop folks in their HTFS, implemented a version of snapshot and snapdiff. The unfortunate truth is that it was engineered such that, if you have a lot of changes happening, the snapshot and snapdiff code might consume too much memory and bring down your NameNode. That's undesirable, now your backup facility just brought down your main data capability. So snapshot has its deficiencies. Log shipping is always active/passive. Contrast that with our technology of live data, whereat you can have multiple data centers filled with data. You can write your data to any of these data centers. It makes for a much more capable system. >> Okay, can you explain, how does this fit with AWS and can it live in multi-clouds, what about on-premises, the whole you know, multi and hybrid cloud discussion? >> Interesting, so the answer is yes. It can live in multiple regions within the same cloud, multiple reasons within different clouds. It'll also bridge data that exists on your on-prem, Hadoop or other big data systems, or object store systems within Cloud, S3 or Azure, or any of the BLOB stores available in the cloud. And when I say this, I mean in a live data fashion. That means you can write to your on-prem storage, you can also write to your cloud buckets at the same time. We'll keep it consistent and replicated. >> Yeah, what are you hearing from customers when it comes to where their data lives? I know last time I interviewed David Richards, your CEO, he said the data lakes really used to be on premises, now there's a massive shift moving to the public clouds. Is that continuing, what's kind of the breakdown, what are you hearing from customers? >> So I cannot name a single customer of ours who is not thinking about the cloud. Every one of them has a presence on premise. They're looking to grow in the cloud. On-prem does not appear to be on a growth path for them. They're looking at growing in the cloud, they're looking at bursting into the cloud, and they're almost all looking at multi-cloud as well. That's been our experience. >> At the beginning of the conversation we talked about data. How are customers doing you know, exploiting and leveraging or making sure that they aren't having data become a liability for them? >> So there are so many interesting use cases I'd love to talk about, but the one that jumps out at me is a major auto manufacturer. Telematics data coming in from a huge number, hundreds of thousands, of cars on the road. They chose to use our technology because they can feed their West Coast car telematics into their West Coast data center, while simultaneously writing East Coast car data into the East Coast data center. We do the replication, we build the live data platform for them, they run their standard analytics applications, be it Hadoop-sourced or some other analytics applications, they get consistent answers. Whether you run the analytics application on the East Coast or the West Coast, you will get the same exact answer. That is very valuable because if you are doing things like fault detection, you really don't want spurious detection because the data on the West Coast was not quite consistent and your analytics application was led astray. That's a great example. We also have another example with a top three bank that has a regulatory concern where they need to operate out of their backup data centers, so-called backup data center, once every three months or so. Now with live data, there is no notion of active data center and backup data center. All data centers are active, so this particular regulatory requirement is extremely simple for them to implement. They just run their queries on one of the other data centers and prove to the regulators that their data is indeed live. I could go on and on about a number of these. We also have a top two retailer who has got such a volume data that they cannot manage it in one Hadoop cluster. They use our technology to create the live data data link. >> One of the challenges always, customers love the idea of global but governance, compliance, things like GDPR pop up. Does that play into your world? Or is that a bit outside of what WANdisco sees? >> It actually turns out to be an important consideration for us because if you think about it, when we replicate the data flows through us. So we can be very careful about not replicating data that is not supposed to be replicated. We can also be very careful about making sure that the data is available in multiple regions within the same country if that is the requirement. So GDPR does play a big role in the reason why many of our customers, particularly in the financial industry, end up purchasing our software. >> Okay, so this new term live data, are there any other partners of yours that are involved in this? As always, you want like a bit of an ecosystem to help build out a wave. >> So our most important partners are the cloud vendors. And they're multi-region by nature. There is no idea of a single data center or a single region cloud, so Microsoft, Amazon with AWS, these are all important partners of ours, and they're promoting our live data platform as part of their strategy of building huge hybrid data lakes. >> All right, Jagane give us a little view looking forward. What should we expect to see with live data and WANdisco through the rest of 2018? >> Looking forward, we expect to see our footprint grow in terms with dealing with a variety of applications, all the way from batch, pig scripts that used to run once a day to hive that's maybe once every 15 minutes to data warehouses that are almost instant and queryable by human beings, to streaming data that pours things into Kafka. We see the whole footprint of analytics databases growing. We see cross-capability meaning perhaps an Amazon Redshift to an Azure or SQL EDW replication. Those things are very interesting to us, to our customers, because some of them have strengths in certain areas and other have strengths in other areas. Customers want to exploit both of those. So we see us as being the glue for all world-scale analytics applications. >> All right well, Jagane, I appreciate you sharing with us everything that's happening at WANdisco. This new idea of live data, we look forward to catching up with you and the team in the future and hearing more about the customers and everything on there. We'll be back with lots more coverage here from AWS Summit here in San Francisco. I'm Stu Miniman, you're watching theCUBE. (electronic music)

>> Narrator: Live from New York, it's the Cube. Covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. >> And welcome back here to New York City. We're at IBM's Machine Learning Everywhere: Build Your Ladder to AI, along with Dave Vellante, John Walls, and we're now joined by Sam Lightstone, who is an IBM fellow in analytics. And Sam, good morning. Thanks for joining us here once again on the Cube. >> Yeah, thanks a lot. Great to be back. >> Yeah, great. Yeah, good to have you here on kind of a moldy New York day here in late February. So we're talking, obviously data is the new norm, is what certainly, have heard a lot about here today and of late here from IBM. Talk to me about, in your terms, of just when you look at data and evolution and to where it's now become so central to what every enterprise is doing and must do. I mean, how do you do it? Give me a 30,000-foot level right now from your prism. >> Sure, I mean, from a super, if you just stand back, like way far back, and look at what data means to us today, it's really the thing that is separating companies one from the other. How much data do they have and can they make excellent use of it to achieve competitive advantage? And so many companies today are about data and only data. I mean, I'll give you some like really striking, disruptive examples of companies that are tremendously successful household names and it's all about the data. So the world's largest transportation company, or personal taxi, can't call it taxi, but (laughs) but, you know, Uber-- >> Yeah, right. >> Owns no cars, right? The world's largest accommodation company, Airbnb, owns no hotels, right? The world's largest distributor of motion pictures owns no movie theaters. So these companies are disrupting because they're focused on data, not on the material stuff. Material stuff is important, obviously. Somebody needs to own a car, somebody needs to own a way to view a motion picture, and so on. But data is what differentiates companies more than anything else today. And can they tap into the data, can they make sense of it for competitive advantage? And that's not only true for companies that are, you know, cloud companies. That's true for every company, whether you're a bricks and mortars organization or not. Now, one level of that data is to simply look at the data and ask questions of the data, the kinds of data that you already have in your mind. Generating reports, understanding who your customers are, and so on. That's sort of a fundamental level. But the deeper level, the exciting transformation that's going on right now, is the transformation from reporting and what we'll call business intelligence, the ability to take those reports and that insight on data and to visualize it in the way that human beings can understand it, and go much deeper into machine learning and AI, cognitive computing where we can start to learn from this data and learn at the pace of machines, and to drill into the data in a way that a human being cannot because we can't look at bajillions of bytes of data on our own, but machines can do that and they're very good at doing that. So it is a huge, that's one level. The other level is, there's so much more data now than there ever was because there's so many more devices that are now collecting data. And all of us, you know, every one of our phones is collecting data right now. Your cars are collecting data. I think there's something like 60 sensors on every car that rolls of the manufacturing line today. 60. So it's just a wild time and a very exciting time because there's so much untapped potential. And that's what we're here about today, you know. Machine learning, tapping into that unbelievable potential that's there in that data. >> So you're absolutely right on. I mean the data is foundational, or must be foundational in order to succeed in this sort of data-driven world. But it's not necessarily the center of the universe for a lot of companies. I mean, it is for the big data, you know, guys that we all know. You know, the top market cap companies. But so many organizations, they're sort of, human expertise is at the center of their universe, and data is sort of, oh yeah, bolt on, and like you say, reporting. >> Right. >> So how do they deal with that? Do they get one big giant DB2 instance and stuff all the data in there, and infuse it with MI? Is that even practical? How do they solve this problem? >> Yeah, that's a great question. And there's, again, there's a multi-layered answer to that. But let me start with the most, you know, one of the big changes, one of the massive shifts that's been going on over the last decade is the shift to cloud. And people think of the shift to cloud as, well, I don't have to own the server. Someone else will own the server. That's actually not the right way to look at it. I mean, that is one element of cloud computing, but it's not, for me, the most transformative. The big thing about the cloud is the introduction of fully-managed services. It's not just you don't own the server. You don't have to install, configure, or tune anything. Now that's directly related to the topic that you just raised, because people have expertise, domains of expertise in their business. Maybe you're a manufacturer and you have expertise in manufacturing. If you're a bank, you have expertise in banking. You may not be a high-tech expert. You may not have deep skills in tech. So one of the great elements of the cloud is that now you can use these fully managed services and you don't have to be a database expert anymore. You don't have to be an expert in tuning SQL or JSON, or yadda yadda. Someone else takes care of that for you, and that's the elegance of a fully managed service, not just that someone else has got the hardware, but they're taking care of all the complexity. And that's huge. The other thing that I would say is, you know, the companies that are really like the big data houses, they got lots of data, they've spent the last 20 years working so hard to converge their data into larger and larger data lakes. And some have been more successful than others. But everybody has found that that's quite hard to do. Data is coming in many places, in many different repositories, and trying to consolidate, you know, rip the data out, constantly ripping it out and replicating into some data lake where you, or data warehouse where you can do your analytics, is complicated. And it means in some ways you're multiplying your costs because you have the data in its original location and now you're copying it into yet another location. You've got to pay for that, too. So you're multiplying costs. So one of the things I'm very excited about at IBM is we've been working on this new technology that we've now branded it as IBM Queryplex. And that gives us the ability to query data across all of these myriad sources as if they are in one place. As if they are a single consolidated data lake, and make it all look like (snaps) one repository. And not only to the application appear as one repository, but actually tap into the processing power of every one of those data sources. So if you have 1,000 of them, we'll bring to bear the power 1,000 data sources and all that computing and all that memory on these analytics problems. >> Well, give me an example why that matters, of what would be a real-world application of that. >> Oh, sure, so there, you know, there's a couple of examples. I'll give you two extremes, two different extremes. One extreme would be what I'll call enterprise, enterprise data consolidation or virtualization, where you're a large institution and you have several of these repositories. Maybe you got some IBM repositories like DB2. Maybe you've got a little bit of Oracle and a little bit of SQL Server. Maybe you've got some open source stuff like Postgres or MySQL. You got a bunch of these and different departments use different things, and it develops over decades and to some extent you can't even control it, (laughs) right? And now you just want to get analytics on that. You just, what's this data telling me? And as long as all that data is sitting in these, you know, dozens or hundreds of different repositories, you can't tell, unless you copy it all out into a big data lake, which is expensive and complicated. So Queryplex will solve that problem. >> So it's sort of a virtual data store. >> Yeah, and one of the terms, many different terms that are used, but one of the terms that's used in the industry is data virtualization. So that would be a suitable terminology here as well. To make all that data in hundreds, thousands, even millions of possible data sources, appear as one thing, it has to tap into the processing power of all of them at once. Now, that's one extreme. Let's take another extreme, which is even more extreme, which is the IoT scenario, Internet of Things, right? Internet of Things. Imagine you've, have devices, you know, shipping containers and smart meters on buildings. You could literally have 100,000 of these or a million of these things. They're usually small; they don't usually have a lot of data on them. But they can store, usually, couple of months of data. And what's fascinating about that is that most analytics today are really on the most recent you know, 48 hours or four weeks, maybe. And that time is getting shorter and shorter, because people are doing analytics more regularly and they're interested in, just tell me what's going on recently. >> I got to geek out here, for a second. >> Please, well thanks for the warning. (laughs) >> And I know you know things, but I'm not a, I'm not a technical person, but I've been a molt. I've been around a long time. A lot of questions on data virtualization, but let me start with Queryplex. The name is really interesting to me. When I, and you're a database expert, so I'm going to tap your expertise. When I read the Google Spanner paper, I called up my colleague David Floyer, who's an ex-IBM, I said, "This is like global Sysplex. "It's a global distributed thing," And he goes, "Yeah, kind of." And I got very excited. And then my eyes started bleeding when I read the paper, but the name, Queryplex, is it a play on Sysplex? Is there-- >> It's actually, there's a long story. I don't think I can say the story on-air, but we, suffice it to say we wanted to get a name that was legally usable and also descriptive. >> Dave: Okay. >> And we went through literally hundreds and hundreds of permutations of words and we finally landed on Queryplex. But, you know, you mentioned Google Spanner. I probably should spend a moment to differentiate how what we're doing is-- >> Great, if you would. >> A different kind of thing. You know, on Google Spanner, you put data into Google Spanner. With Queryplex, you don't put data into it. >> Dave: Don't have to move it. >> You don't have to move it. You leave it where it is. You can have your data in DB2, you can have it in Oracle, you can have it in a flat file, you can have an Excel spreadsheet, and you know, think about that. An Excel spreadsheet, a collection of text files, comma delimited text files, SQL Server, Oracle, DB2, Netezza, all these things suddenly appear as one database. So that's the transformation. It's not about we'll take your data and copy it into our system, this is about leave your data where it is, and we're going to tap into your (snaps) existing systems for you and help you see them in a unified way. So it's a very different paradigm than what others have done. Part of the reason why we're so excited about it is we're, as far as we know, nobody else is really doing anything quite like this. >> And is that what gets people to the 21st century, basically, is that they have all these legacy systems and yet the conversion is much simpler, much more economical for them? >> Yeah, exactly. It's economical, it's fast. (snaps) You can deploy this in, you know, a very small amount of time. And we're here today talking about machine learning and it's a very good segue to point out in order to get to high-quality AI, you need to have a really strong foundation of an information architecture. And for the industry to show up, as some have done over the past decade, and keep telling people to re-architect their data infrastructure, keep modifying their databases and creating new databases and data lakes and warehouses, you know, it's just not realistic. And so we want to provide a different path. A path that says we're going to make it possible for you to have superb machine learning, cognitive computing, artificial intelligence, and you don't have to rebuild your information architecture. We're going to make it possible for you to leverage what you have and do something special. >> This is exciting. I wasn't aware of this capability. And we were talking earlier about the cloud and the managed service component of that as a major driver of lowering cost and complexity. There's another factor here, which is, we talked about moving data-- >> Right. >> And that's one of the most expensive components of any infrastructure. If I got to move data and the transmission costs and the latency, it's virtually impossible. Speed of light's still up. I know you guys are working on speed of light, but (Sam laughs) you'll eventually get there. >> Right. >> Maybe. But the other thing about cloud economics, and this relates to sort of Queryplex. There's this API economy. You've got virtually zero marginal costs. When you were talking, I was writing these down. You got global scale, it's never down, you've got this network effect working for you. Are you able to, are the standards there? Are you able to replicate those sort of cloud economics the APIs, the standards, that scale, even though you're not in control of this, there's not a single point of control? Can you explain sort of how that magic works? >> Yeah, well I think the API economy is for real and it's very important for us. And it's very important that, you know, we talk about API standards. There's a beautiful quote I once heard. The beautiful thing about standards is there's so many to choose from. (All laugh) And the reality is that, you know, you have standards that are official standards, and then you have the de facto standards because something just catches on and nobody blessed it. It just got popular. So that's a big part of what we're doing at IBM is being at the forefront of adopting the standards that matter. We made a big, a big investment in being Spark compatible, and, in fact, even with Queryplex. You can issue Spark SQL against Queryplex even though it's not a Spark engine, per se, but we make it look and feel like it can be Spark SQL. Another critical point here, when we talk about the API economy, and the speed of light, and movement to the cloud, and these topics you just raised, the friction of the Internet is an unbelievable friction. (John laughs) It's unbelievable. I mean, you know, when you go and watch a movie over the Internet, your home connection is just barely keeping up. I mean, you're pushing it, man. So a gigabyte, you know, a gigabyte an hour or something like that, right? Okay, and if you're a big company, maybe you have a fatter pipe. But not a lot fatter. I mean, not orders of, you're talking incredible friction. And what that means is that it is difficult for people, for companies, to en masse, move everything to the cloud. It's just not happening overnight. And, again, in the interest of doing the best possible service to our customers, that's why we've made it a fundamental element of our strategy in IBM to be a hybrid, what we call hybrid data management company, so that the APIs that we use on the cloud, they are compatible with the APIs that we use on premises. And whether that's software or private cloud. You've got software, you've got private cloud, you've got public cloud. And our APIs are going to be consistent across, and applications that you code for one will run on the other. And you can, that makes it a lot easier to migrate at your leisure when you're ready. >> Makes a lot of sense. That way you can bring cloud economics and the cloud operating model to your data, wherever the data exists. Listening to you speak, Sam, it reminds me, do you remember when Bob Metcalfe who I used to work with at IDG, predicted the collapse of the Internet? He predicted that year after year after year, in speech after speech, that it was so fragile, and you're bringing back that point of, guys, it's still, you know, a lot of friction. So that's very interesting, (laughs) as an architect. >> You think Bob's going to be happy that you brought up that he predicted the Internet was going to be its own demise? (Sam laughs) >> Well, he did it in-- >> I'm just saying. >> I'm staying out of it, man. >> He did it as a lightning rod. >> As a talking-- >> To get the industry to respond, and he had a big enough voice so he could do that. >> That it worked, right. But so I want to get back to Queryplex and the secret sauce. Somehow you're creating this data virtualization capability. What's the secret sauce behind it? >> Yeah, so I think, we're not the first to try, by the way. Actually this problem-- >> Hard problem. >> Of all these data sources all over the place, you try to make them look like one thing. People have been trying to figure out how to do that since like the '70s, okay, so, but-- >> Dave: Really hasn't worked. >> And it hasn't worked. And really, the reason why it hasn't worked is that there's been two fundamental strategies. One strategy is, you have a central coordinator that tries to speak to each of these data sources. So I've got, let's say, 10,000 data sources. I want to have one coordinator tap into each of them and have a dialogue. And what happens is that that coordinator, a server, an agent somewhere, becomes a network bottleneck. You were talking about the friction of the Internet. This is a great example of friction. One coordinator trying to speak to, you know, and collaborators becomes a point of friction. And it also becomes a point of friction not only in the Internet, but also in the computation, because he ends up doing too much of the work. There's too many things that cannot be done at the, at these edge repositories, aggregations, and joins, and so on. So all the aggregations and joins get done by this one sucker who can't keep up. >> Dave: The queue. >> Yeah, so there's a big queue, right. So that's one strategy that didn't work. The other strategy that people tried was sort of an end squared topology where every data source tries to speak to every other data source. And that doesn't scale as well. So what we've done in Queryplex is something that we think is unique and much more organic where we try to organize the universe or constellation of these data sources so that every data source speaks to a small number of peers but not a large number of peers. And that way no single source is a bottleneck, either in network or in computation. That's one trick. And the second trick is we've designed algorithms that can truly be distributed. So you can do joins in a distributed manner. You can do aggregation in a distributed manner. These are things, you know, when I say aggregation, I'm talking about simple things like a sum or an average or a median. These are super popular in, in analytic queries. Everybody wants to do a sum or an average or a median, right? But in the past, those things were hard to do in a distributed manner, getting all the participants in this universe to do some small incremental piece of the computation. So it's really these two things. Number one, this organic, dynamically forming constellation of devices. Dynamically forming a way that is latency aware. So if I'm a, if I represent a data source that's joining this universe or constellation, I'm going to try to find peers who I have a fast connection with. If all the universe of peers were out there, I'll try to find ones that are fast. And the second is having algorithms that we can all collaborate on. Those two things change the game. >> We're getting the two minute sign, and this is fascinating stuff. But so, how do you deal with the data consistency problem? You hear about eventual consistency and people using atomic clocks and-- Right, so Queryplex, you know, there's a reason we call it Queryplex not Dataplex. Queryplex is really a read-only operation. >> Dave: Oh, there you go. >> You've got all these-- >> Problem solved. (laughs) >> Problem solved. You've got all these data sources. They're already doing their, they already have data's coming in how it's coming in. >> Dave: Simple and brilliant. >> Right, and we're not changing any of that. All we're saying is, if you want to query them as one, you can query them as one. I should say a few words about the machine learning that we're doing here at the conference. We've talked about the importance of an information architecture and how that lays a foundation for machine learning. But one of the things that we're showing and demonstrating at the conference today, or at the showcase today, is how we're actually putting machine learning into the database. Create databases that learn and improve over time, learn from experience. In 1952, Arthur Samuel was a researcher at IBM who first, had one of the most fundamental breakthroughs in machine learning when he created a machine learning algorithm that will play checkers. And he programmed this checker playing game of his so it would learn over time. And then he had a great idea. He programmed it so it would play itself, thousands and thousands and thousands of times over, so it would actually learn from its own mistakes. And, you know, the evolution since then. Deep Blue playing chess and so on. The Watson Jeopardy game. We've seen tremendous potential in machine learning. We're putting into the database so databases can be smarter, faster, more consistent, and really just out of the box (snaps) performing. >> I'm glad you brought that up. I was going to ask you, because the legend Steve Mills once said to me, I had asked him a question about in-memory databases. He said ever databases have been around, in-memory databases have been around. But ML-infused databases are new. >> Sam: That's right, something totally new. >> Dave: Yeah, great. >> Well, you mentioned Deep Blue. Looking forward to having Garry Kasparov on a little bit later on here. And I know he's speaking as well. But fascinating stuff that you've covered here, Sam. We appreciate the time here. >> Thank you, thanks for having me. >> And wish you continued success, as well. >> Thank you very much. >> Sam Lightstone, IBM fellow joining us here live on the Cube. We're back with more here from New York City right after this. (electronic music)

(electronic music) >> Narrator: From the SiliconANGLE Media office in Boston Massachusetts, it's the CUBE. Now here are your hosts, Dave Vellante and Stu Miniman. >> Cloud is dead, it's all going to the edge. Or is it? Hi everybody, this is Dave Vellante and I'm here with Stu Miniman. Stu, where does this come from, this narrative that the cloud is over? >> Well Dave, you know, clouds had a good run, right? It's been over a decade. You know, Amazon's dominance in the marketplace but Peter Levine from Andreessen Horowitz did an article where he said, cloud is dead, the edge is killing the dead. The Edge is killing the cloud and really we're talking about IoT and IoT's huge opportunity. Wikibon, Dave we've been tracking for many years. We did you know the original forecast for the Industrial Internet and obviously there's going to be lots more devices at the edge so huge opportunity, huge growth, intelligence all over the place. But in our viewpoint Dave, it doesn't mean that cloud goes away. You know, we've been talking about distributed architectures now for a long time. The cloud is really at the core of this building services that surround the globe, live in just hundreds of places for all these companies so it's nuanced. And just as the cloud didn't overnight kill the data center and lots of discussion as to what lives in the data center, the edge does not kill the cloud and it's really, we're seeing some major transitions pull and push from some of these technologies. A lot of challenges and lots to dig into. >> So I've read Peter Levine's piece, I thought was very thought-provoking and quite well done. And of course, he's coming at that from the standpoint of a venture capitalist, all right. Do I want to start you know, do I want to pour money into the trend that is now the mainstream? Or do I want to get ahead of it? So I think that's what that was all about but here's my question Stu is, in your opinion will the activity that occurs at the edge, will it actually drive more demand from the cloud? So today we're seeing the infrastructure, the service business is growing at what? Thirty five percent? Forty percent? >> Sure, sure. Amazon's growing at the you know, 35 to 40 percent. Google, Microsoft are growing double that right now but overall you're right. >> Yeah, okay and so, and then of course the enterprise players are flat if they're lucky. So my question is will the edge actually be a tailwind for the cloud, in your opinion? >> Yeah, so first on your comment there from an investment standpoint, totally can understand why edge is greenfield opportunity. Lots of different places that I can place bets and probably can win as opposed to if I think that today I'm going to compete against the hyperscale cloud guys. You know, they're pouring 10 billion dollars a year into their infrastructure. They have huge massive employment so the bar to entry is a lot higher. I'm sorry, the second piece was? >> So will the edge drive more demand for the cloud? >> Yeah, absolutely. I think it does Dave because you know, let's take something like autonomous vehicles. Something that we talk about. I need intelligence of the edge. I can't wait for some instruction to go back to the cloud before my Tesla plows into an individual. I need to know that it's there but the models themselves, really I've got all the compute in the cloud. This is where I'm going to train all of my models but I need to be able to update and push those to the edge. If I think about a lot of the industrial applications. Flying a plane is, you know, things need to happen locally but all the anomalies and new things that we run into there's certain pieces that need to be updated to the cloud. So you know, it's kind of a multi-layer. If we look at how much data will there be at the edge, well there's probably going to be more data at the edge than there will be in the central cloud. But how much activity, how much compute do I need, how much things do I need to actually work on. The cloud is probably going to be that central computer still and it's not just a computer, as I said, a distributed architecture. That's where, you know. When we've looked at big data in the early days Dave, when we can put those data lines in the cloud. I've got thousands or millions of compute cycles that I can throw at this at such a lower price and use that there as opposed to at the edge especially. What kind of connectivity do I have? Am i isolated from those other pieces? If you go back to my premise of we're building distributed architectures, the edge is still very early. How do I make sure I secure that? Do I have the network? There's lots of things that I'm going to build in a tiny little component and have that be there. And there's lots of hardware innovation going on at that edge too. >> Okay, so let's talk about how this plays out a little bit and you're talking about a distributed model and it's really to me a distributed data model. The research analysts at Wikibon have envisioned this three-tier data model where you've got data at the edge, which you may or may not persist. You've got some kind of consolidation or aggregation layer where it's you know, it's kind of between the edge and the deep data center and then you've got the cloud. Now that cloud can be an on-prem cloud or it could be the public cloud. So that data model, how do you see that playing out with regard to the adoption of cloud, the morphing of cloud and the edge and the traditional data center? >> Yeah we've been talking about intelligent devices at the edge for a couple decades now. I mean, I remember I built a house in like 1999 and the smart home was already something that people were talking about then. Today, great, I've got you know. I've got my Nest if I have, I probably have smart assistants. There's a lot of things I love-- >> Alexa. >> Saw on Twitter today, somebody's talking like I'm waiting for my light bulbs to update their firmware from the latest push so, some of its coming but it's just this slow gradual adoption. So there's the consumer piece and then there's the business aspect. So, you know, we are still really really early in some of these exciting edge uses. Talk about the enterprise. They're all working on their strategy for how devices and how they're going to work through IoT but you know this is not something that's going to happen overnight. It's they're figuring out their partnerships, they're figuring out where they work, and that three-tiered model that you talked about. My cloud provider, absolutely hugely important for how I do that and I really see it Dave, not as an or but it's an and. So I need to understand where I collect my data, where it's at certain aspects are going to live, and the public cloud players are spending a lot of time working on on that intelligence, the intelligence layer. >> And Stu, I should mention, so far we're talking about really, the infrastructure as a service layer comprises database and middleware. We haven't really addressed the the SAS space and we're not going to go deep into that but just to say. I mean look, packaged software as we knew it is dead, right? SAS is where all the action is. It's the highest growth area, it's the highest value area, so we'll cover that in another segment. So we're really talking about that, the stack up to the middleware, the database, and obviously the infrastructures as a service. So when you think about the players here, let's start with AWS. You've been to I think, every AWS re:Invent maybe, with the exception of one. You've seen the evolution. I was just down in D.C. the other day and they have this chart on the wall, which is their releases, their functional releases by year. It's just, it's overwhelming what they've done. So they're obviously the leader. I saw a recent Gartner Magic Quadrant. It looked like, I tweeted it, it looked like Ronnie Turcotte looking back on Secretariat from the Belmont and whatever it was. 1978, I think it was. (laughs) 31 lengths. I mean, massive domination in the infrastructure as a service space. What do you see going on? >> Yeah so, Dave, absolutely. Today the cloud is, it's Amazon's market out there. Interestingly if you say, okay what's some of the biggest threats in the infrastructure as a service? Well, maybe China, Dave. You know, Alibaba was one that you look at there. But huge opportunity for what's happened at the edge. If you talk about intelligence, you talk about AI, talk about machine learning. Google is actually the company that most people will talk about it, can kind of have a leadership. Heck, I've even seen discussion that maybe we need antitrust to look at Google because they're going to lock things up. You know, they have Android, they have Google Home, they have all these various pieces. But we know Dave, they are far behind Amazon in the public cloud market and Amazon has done a lot, especially over the last two years. You're right, I've been to every Amazon re:Invent except for the first one and the last two years, really seen a maturation of that growth. Not just you know, devices and partnerships there but how do they bring their intelligence and push that out to the edge so things like their serverless technology, which is Lambda. They have Lambda Greengrass that can put to the edge. The serverless is pervading all of their solutions. They've got like the Aurora database-- >> And serverless is profound, not just that from the standpoint of application development but just an entire new business model is emerging on top of serverless and Lambda really started all that but but carry on. >> Yeah and when you look in and you say okay, what better use case than IoT for, well I need infrastructure but I only need it when I need it and I want to call it for when it's there. So that kind of model where I should be able to build by the microsecond and only use what I need. That's something that Amazon is at the forefront, clear leadership position there and they should be able to plug in and if they can extend that out to the edge, starting new partnerships. Like the VMware partnerships, interesting. Red Hat's another partnership they have with OpenShift to be able to get that out to more environments and Amazon has a tremendous ecosystem out there and absolutely is on their radar as to how their-- >> They're crushing it So we were at Google Next last year. Big push, verbally anyway, to the enterprise. They've been making some progress, they're hiring a lot of people out of formerly Cisco, EMC, folks that understand the enterprise but beyond sort of the AI and sort of data analytics, what kind of progress has Google made relative to the leader? >> So in general, enterprise infrastructure service, they haven't made as much progress as most of us watching would expect them to make. But Dave, you mentioned something, data. I mean, at the center of everything we're talking about is the data. So in some ways is Google you know, come on Google, they're smarter than the rest of us. They're skating to where the puck is Dave and infrastructure services, last decades argument if it's the data and the intelligence, Google's got just brilliant people. They're working at the some of these amazing environments. You look at things like Google's Spanner. This is distributed architecture. Say how do I plug in all of these devices and help the work in a distributed gradual work well. You know, heck, I'd be reading the whitepapers that Google's doing in understanding that they might be really well positioned in this 3D chess match that were playing. >> Your eyes might bleed. (laughs) I've read the Google Spanner, I was very excited about it. Understood, you know, a little bit of it. Okay, let's talk about Microsoft. They're really of the big cloud guys. They're really the one that has a partnership strategy to do both on-prem and public cloud. What are your thoughts on that now that sort of Azure stack is starting to roll out with some key partners? >> Yeah absolutely, it's the one that you know. Dave, if you use your analogy looking back, it's like well the next one, it's gaining a little bit, gaining a little bit but still far back. There is Microsoft. Where Microsoft has done best of course is their portfolio of business applications that they have. That they've really turned the green light on for enterprises to adopt SAS with Office 365. Azure stack, it's early days still but companies that use Microsoft, they trust Microsoft. Microsoft's done phenomenal working with developers over the last couple of years. Very prominent like the Kubernetes shows that I've been attending recently. They've absolutely got a play for serverless that we were talking about. I'm not as up to speed as to where Microsoft sits for kind of the IoT edge discussions. >> But you know they're playing there. >> Yeah, absolutely. I mean, Microsoft does identity better than anyone. Active Directory is still the standard in enterprises today. So you know, I worry that Microsoft could be caught in the middle. If Google's making the play for what's next, Microsoft is still chasing a little bit what Amazon's already winning. >> Okay and then we don't have enough time to really talk about China, you mentioned it before. Alibaba's you know, legit. Tencent, Baidu obviously with their captive market in China, they're going to do a lot of business and they're going to move a lot of compute and storage and networking but maybe address that in another segment. I want to talk about the traditional enterprise players. Dell EMC, IBM, HPE, Cisco, where do they stand? We talk a lot at Wikibond about true private cloud. The notion that you can't just stick all your data into the public cloud. Andy Jassy may disagree with that but there are practical realities and certainly when you talk to CIOs they they underscore that. But that notion of true private cloud hasn't allowed these companies to really grow. Now of course IBM and Oracle, I didn't mention Oracle, have a different strategy and Oracle's strategy is even more different. So let's sort of run through them. Let's take the arms dealers. Dell EMC, HPE, Cisco, maybe you put Lenovo in there. What's their cloud strategy? >> Well first of all Dave I think most of them, they went through a number of bumps along the road trying to figure out what their cloud strategy is. Most of them, especially let's take, if you take the compute or server side of the business, they are suppliers to all the service providers trying to get into the hyperscalers. Most of them have, they all have some partnership with Microsoft. There's a Assure stack and they're saying, okay hey, if I want an HPE server in my own data center and in Azure, Microsoft's going to be happy to provide that for you. But David, it's not really competing against infrastructure as a service and the bigger question is as that market has kind of flattened out and we kind of understand it, where is the opportunity for them in IoT. We saw, you know Dave. Last five years or so, can I have a consumer business and an enterprise business in the same? HPE tore those two apart. Michael Dell has kept them together. IBM spun off to Lenovo everything that was on the more consumer side of the business. Where will they play or will companies like Google, like Apple, the ones that you know, Dave. They are spending huge amounts of money in chips. Look at Google and what they're doing with TP use. Look at Apple, I believe it was, there was an Israeli company that they bought and they're making chips there. There's a different need at the edge and sure, company like Dell can create that but will they have the margin, will they have the software, will they have the ecosystem to be able to compete there? Cisco, I haven't seen on the compute side, them going down that path but I was at Cisco Live and a big talk there. I really like the opening keynote and we had a sit down on the CUBE with the executive, it said really if I look out to like 2030. If Cisco still successful and we're thinking about them, we don't think of them as a network company anymore. They are a software company and therefore, things like collaboration, things like how it's kind of a new version of networking that's not on ports and boxes. But really as I think about my data, think about my privacy and security, Cisco absolutely has a play there. They've done some very large acquisitions in that space and they've got some deep expertise there. >> But again, Dell, HPE, Cisco, predominantly arms dealers. Obviously don't have, HPE at one point had a public cloud, they've pulled back. HP's cloud play really is cloud technology partners that they acquire. That at least gives them a revenue stream into the cloud. Now maybe-- >> But it's a consultancy. >> It's a consultancy, maybe it's a one-way trip to the cloud but I will say this about CTP. What it does is it gives HPE a footprint in that business and to the extent that they're a trusted service provider for companies trying to move into the cloud. They can maybe be in the catbird seat for the on-prem business but again, largely an arms dealer. it's going to be a lower margin business certainly than IBM and Oracle, which have applications. They own their own public cloud with the Oracle public cloud and IBM cloud, formerly SoftLayer, which was a two billion dollar acquisition several years ago. So those companies from a participation standpoint, even a tiny market share is compared to Amazon, Google, and Microsoft. They're at least in that cloud game and they're somewhat insulated from that disruption because of their software business and their large install base. Okay, I want to sort of end with, sort of where we started. You know, the Peter Levine comment, cloud is dead, it's all going to the edge. I actually think the cloud era, it's kind of, it's here, we're kind of. It's kind of playing out as many of us had expected over the last five years. You know what blew me away? Is Alexa, who would have thought that Amazon would be a leader in this sort of natural language processing marketplace, right? You would have thought it would come from, certainly Google with all the the search capability. You would have thought Apple with Siri, you know compared to Alexa. So my point is Amazon is able to do that because it's got a data model. It's a data company, all these companies, including Apple, Google, Microsoft, Amazon, Facebook. The largest market cap companies in the world, they have data at the core. Data is foundational for those companies and that's why they are in such a good position to disrupt. So cloud, SAS, mobile, social, big data, to me still these are kind of the last 10 years. The next 10 years are going to be about AI, machine intelligence, deep learning, machine learning, cognitive. We're trying to even get the names right but it starts with the data. So let me put forth the premise and get your commentary. and tie it back in the cloud. So the innovation, in the next 10 years is going to come from data and to the extent that your data is not in silos, you're going to be in a much better position than if it is. Number two is your application of artificial intelligence, you know whatever term you want to use, machine intelligence, etc. Data plus AI, plus I'll bring it back to cloud, cloud economics. If you don't have those cloud economics then you're going to be at a disadvantage of innovation. So let's talk about what we mean by cloud economics. You're talking about the API economy, talking about global scale, always on. Very importantly something we've talked about for years, virtually zero marginal costs at volume, which you're never going to get on-prem because this creates a network effect. And the other thing it does from an innovation context, it attracts startups. Or startups saying, hey I want to build on-prem. No, they don't want to build in the cloud. So it's data plus artificial intelligence plus cloud economics that's going to drive innovation in the next ten years. What are your thoughts? >> Yeah Dave, absolutely. Something I've been saying for the last couple of years, we watched kind of the the customer flywheel that the public clouds have. Data is that next flywheel so companies that can capture that. You mentioned Amazon and Alexa, one of the reasons that Amazon can basically sell that as a loss is lots of those people, they're all Amazon Prime customers and they're ordering more things from Amazon and they're getting so much data that drive all of those other services. Where is Amazon going to threaten in the future? Everywhere. It is basically what they see. The thing we didn't discuss there Dave, you know I love your premise there, is it's technology plus people. What's going to happen with jobs? You and I did the sessions with Andy McAfee and Eril Brynjolfsson, it's racing with the machine. Where is, we know that people plus machines always beat so we spent the last five years talking about data scientist, the growth of developers and developers and the new king makers. So you know what are those new jobs, what are those new roles that are going to help build the solutions where people plus machine will win and what does that kind of next generation of workforce going to look like? >> Well I want to add to that Stu, I'm glad you brought that up. So a friend of mine David Michelle is just about to publish a new book called Seeing Digital. And in that book, I got an advance copy, in there he talks about companies that have data at their core and with human expertise around the data but if you think about the vast majority of companies, it's human expertise and the data is kind of bolted on. And the data lives in silos. Those companies are in a much more vulnerable position in terms of being disrupted, than the ones that have a data model that everybody has access to with human expertise around it. And so when you think about digital disruption, no industry is safe in my opinion, and every industry has kind of its unique attributes. You know, obviously publishing and books and music have disrupted very quickly. Insurance hasn't been disrupted, banking hasn't been disrupted, although blockchain it's probably going to affect that. So again, coming back to this tail-end premise is the next 10 years is going to be about that digital disruption. And it's real, it's not just a bunch of buzzwords, a cloud is obviously a key component, if not the key component of the underlying infrastructure with a lot of activity in terms of business models being built on top. All right Stu, thank you for your perspectives. Thanks for covering this. We will be looking for this video, the outputs, the clips from that. Thanks for watching everybody. This is Dave Vellante with Stu Miniman, we'll see you next time. (electronic music)

(mellow electronic music) >> Welcome back, we're here live in Las Vegas. This is SiliconANGLE and Wikibon's theCUBE, our flagship program. We go out to the events, extract the signal from the noise. We are live in Las Vegas at Amazon Web Services re:Invent conference, about developers, large-scale cloud, big data, the future. I'm John Furrier, the founder of SiliconANGLE. I'm joined by co-host, Dave Vellante, co-founder of Wikibon.org, and our guest is James Hamilton, VP and Distinguished Engineer at Amazon Web Services. Welcome to theCUBE. >> Well thank you very much. >> You're a tech athlete, certainly in our book, is a term we coined, because we love to use sports analogies You're kind of the cutting edge. You've been the business and technology innovating for many years going back to the database days at IBM, Microsoft, and now Amazon. You gave a great presentation at the analyst briefing. Very impressive. So I got to ask you the first question, when did you first get addicted to the notion of what Amazon could be? When did you first taste the Cool-Aide? >> Super good question. Couple different instances. One is I was general manager of exchange hosts and services and we were doing a decent job, but what I noticed was customers were loving it, we're expanding like mad, and I saw opportunity to improve by at least a factor of two I'm sorry, 10, it's just amazing. So that was a first hint that this is really important for customers. The second one was S3 was announced, and the storage price pretty much froze the whole industry. I've worked in storage all my life, I think I know what's possible in storage, and S3 was not possible. It was just like, what is this? And so, I started writing apps against it, I was just blown away. Super reliable. Unbelievably priced. I wrote a fairly substantial app, I got a bill for $7. Wow. So that's really the beginnings of where I knew this was going to change the world, and I've been, as you said, addicted to it since. >> So you also mentioned some stats there. We'll break it down, 'cause we love to talk about the software defined data center, which is basically not even at the hype stage yet. It's just like, it's still undefined, but software virtualization, network virtualization really is pushing that movement of the software focus, and that's essentially you guys are doing. You're talking about notifications and basically it's a large-scale systems problem. That you guys are building a global operating system as Andy Jassy would say. Well, he didn't say that directly, he said internet operating system, but if you believe that APIs are critical services. So I got to ask you that question around this notion of a data center, I mean come on, nobody's really going to give up their data center. It might change significantly, but you pointed out the data center costs are in the top three order, servers, power circulation systems, or cooling circulation, and then actual power itself. Is that right, did I get that right? >> Pretty close, pretty close. Servers dominate, and then after servers if you look at data centers together, that's power, cooling, and the building and facility itself. That is the number two cost, and the actual power itself is number three. >> So that's a huge issue. When we talk like CIOs, it's like can you please take the facility's budget off my back? For many reasons, one, it's going to be written off soon maybe. All kinds of financial issues around-- >> A lot of them don't see it, though, which is a problem. >> That is a problem, that is a problem. Real estate season, and then, yes. >> And then they go, "Ah, it's not my problem" so money just flies out the window. >> So it's obviously a cost improvement for you. So what are you guys doing in that area and what's your big ah-ha for the customers that you walk in the door and say, look, we have this cloud, we have this system and all those headaches can be, not shifted, or relieved if you will, some big asprin for them. What's the communication like? What do you talk to them about? >> Really it depends an awful lot on who it is. I mean, different people care about different things. What gets me excited is I know that this is the dominate cost of offering a service is all of this muck. It's all of this complexity, it's all of this high, high capital cost up front. Facility will run 200 million before there's servers in it. This is big money, and so from my perspective, taking that way from most companies is one contribution. Second contribution is, if you build a lot of data centers you get good at it, and so as a consequence of that I think we're building very good facilities. They're very reliable, and the costs are plummeting fast. That's a second contribution. Third contribution is because... because we're making capacity available to customers it means they don't have to predict two years in advance what they're going to need, and that means there's less wastage, and that's just good for the industry as a whole. >> So we're getting some questions on our crowd chat application. If you want to ask a question, ask him anything. It's kind of like Reddit. Go to crowdchat.net/reinvent. The first question came in was, "James, when do you think ARM will be in the data center?" >> Ah ha, that's a great question. Well, many people know that I'm super excited about ARM. It's early days, the reason why I'm excited is partly because I love seeing lots of players. I love seeing lots of innovation. I think that's what's making our industry so exciting right now. So that's one contribution that ARM brings. Another is if you look at the history of server-side computing, most of the innovation comes from the volume-driven, usually on clients first. The reason why X86 ended up in such a strong position is so many desktops we running X86 processors and as a consequence it became a great server processor. High R&D flow into it. ARM is in just about every device that everyone's carrying around. It's almost every disk drive, it's just super broadly deployed. And whenever you see a broadly deployed processor it means there's an opportunity to do something special for customers. I think it's good for the industry. But in a precise answer to your question, I really don't have one right now. It's something that we're deeply interested in and investigating deeply, but at this point it hasn't happened yet, but I'm excited by it. >> Do you think that... Two lines of questioning here. One is things that are applicable to AWS, other's just your knowledge of the industry and what you think. We talked about that yesterday with OCP, right? >> Yep. >> Not a right fit for us, but you applaud the effort. We should talk about that, too, but does splitting workloads up into little itty, bitty processors change the utilization factor and change the need for things like virtualization, you know? What do you think? >> Yeah, it's a good question. I first got excited about the price performance of micro-servers back in 2007. And at that time it was pretty easy to produce a win by going to a lower-powered processor. At that point memory bandwidth wasn't as good as it could be. It was actually hard on some workloads to fully use a processor. Intel's a very smart company, they've done great work on improving the memory bandwidth, and so today it's actually harder to produce a win, and so you kind of have workloads in classes. At the very, very high end we've got database workloads. They really love single-threaded performance, and performance really is king, but there are lots of highly parallel workloads where there's an opportunity for a big gain. I still think virtualization is probably something where the industry's going to want to be there, just because it brings so many operational advantages. >> So I got to ask the question. Yesterday we had Jason Stowe on, CEO of Cycle Computing, and he had an amazing thing that he did, sorry, trumping it out kids say, but it's not new to you, but it's new to us. He basically created a supercomputer and spun up hundreds of thousands of cores in 30 minutes, which is like insane, but he did it for like 30 grand. Which would've cost, if you try to provision it to the TUCO calculator or whatever your model, it'd be months and years, maybe, and years. But the thing that he said I want to get your point on and I'm going to ask you questions specifically on is, Spot instances were critical for him to do that, and the creativity of his solutions, so I got to ask you, did you see Spot pricing instances being a big deal, and what impact has that done to AWS' vision of large scale? >> I'm super excited by Spot. In fact, it's one of the reasons I joined Amazon. I went through a day of interviews, I met a bunch of really smart people doing interesting work. Someone probably shouldn't have talked to me about Spot because it hadn't been announced yet, and I just went, "This is brilliant! "This is absolutely brilliant!" It's taking the ideas from financial markets, where you've got high-value assets, and saying why don't we actually sell it off, make a market on the basis of that and sell it off? So two things happen that make Spot interesting. The first is an observation up front that poor utilization is basically the elephant in the room. Most folks can't use more than 12% to 15% of their overall server capacity, and so all the rest ends up being wasted. >> You said yesterday 30% is outstanding. It's like have a party. >> 30% probably means you're not measuring it well. >> Yeah, you're lying. >> It's real good, yeah, basically. So that means 70% or more is wasted, it's a crime. And so the first thing that says is, that one of the most powerful advertisements for cloud computing is if you bring a large number of non-correlated workloads together, what happens is when you're supporting a workload you've got to have enough capacity to support the peak, but you only get to monetize the average. And so as the peak to average gets further apart, you're wasting more. So when you bring a large number of non-correlated workloads together what happens is it flattens out just by itself. Without doing anything it flattens out, but there's still some ups and downs. And the Spot market is a way of filling in those ups and downs so we get as close to 100%. >> Is there certain workloads that fit the spot, obviously certain workloads might fit it, but what workloads don't fit the Spot price, because, I mean, it makes total sense and it's an arbitrage opportunity for excess capacity laying around, and it's price based on usage. So is there a workload, 'cause it'll be torrent up, torrent down, I mean, what's the use cases there? >> Workloads that don't operate well in an interrupted environment, that are very time-critical, those workloads shouldn't be run in Spot. It's just not what the resource is designed for. But workloads like the one that we were talking to with Cycle Computing are awesome, where you need large numbers of resources. If the workload needs to restart, that's absolutely fine, and price is really the focus. >> Okay, and question from crowd chat. "Ask James what are his thoughts "on commodity networking and merchant silicon." >> I think an awful lot about that. >> This guy knows you. (both laughing) >> Who's that from? >> It's your family. >> Yeah, exactly! >> They're watching. >> No, network commoditization is a phenomenal thing that the whole industry's needed that for 15 years. We've got a vertical ecosystem that's kind of frozen in time. Vertically-integrated ecosystem kind of frozen in time. Costs everywhere are falling except in networking. We just got to do something, and so it's happening. I'm real excited by that. It's really changing the Amazon business and what we can do for customers. >> Let's talk a little bit about server design, because I was fascinated yesterday listening to you talk how you've come full circle. Over the last decade, right, you started with what's got to be stripped down, basic commodity and now you're of a different mindset. So describe that, and then I have some follow-up questions for you. >> Yeah, I know what you're alluding to. Is years ago I used to argue you don't want hardware specialization, it's crazy. It's the magic's in software. You want to specialize software running on general-purpose processors, and that's because there was a very small number of servers out there, and I felt like it was the most nimble way to run. However today, in AWS when we're running ten of thousands of copies of a single type of server, hardware optimizations are absolutely vital. You end up getting a power-performance advantage at 10X. You can get a price-performance advantage that's substantial and so I've kind of gone full circle where now we're pulling more and more down into the hardware, and starting to do hardware optimizations for our customers. >> So heat density is a huge problem in data centers and server design. You showed a picture of a Quanta package yesterday. You didn't show us your server, said "I can't you ours," but you said, "but we blow this away, "and this is really good." But you describe that you're able to get around a lot of those problems because of the way you design data centers. >> Yep. >> Could you talk about that a little bit? >> Sure, sure, sure. One of the problems when you're building a server it could end up anywhere. It could end up in a beautiful data center that's super well engineered. It could end up on the end of a row on a very badly run data center. >> Or in a closet. >> Or in a closet. The air is recirculating, and so the servers have to be designed with huge headroom on cooling requirements, and they have to be able to operate in any of those environments without driving warranty costs for the vendors. We take a different approach. We say we're not going to build terrible data centers. We're going to build really good data centers and we're going to build servers that exploit the fact those data centers are good, and what happens is more value. We don't have to waste as much because we know that we don't have to operate in the closet. >> We got some more questions coming here by the way. This is awesome. This ask me anything crowd chat thing is going great. We got someone, he's from Nutanix, so he's a geek. He's been following your career for many years. I got to ask you about kind of the future of large-scale. So Spot, in his comment, David's comment, Spot instances prove that solutions like WMare's distributed power management are not valuable. Don't power off the most expensive asset. So, okay, that brings up an interesting point. I don't want to slam on BMWare right now, but I just wanted to bring to the next logical question which is this is a paradigm shift. That's a buzz word, but really a lot's happening that's new and innovative. And you guys are doing it and leading. What's next in the large-scale paradigm of computing and computer science? On the science-side you mentioned merchant silicon. Obviously that's, the genie's out of the bottle there, but what's around the corner? Is it the notifications at the scheduling? Was it virtualization, is it compiler design? What are some of the things that you see out on the horizon that you got your eyes on? >> That's interesting, I mean. I've got, if you name your area, and I'll you some interesting things happening in the area, and it's one of the cool things of being in the industry right now. Is that 10 years ago we had a relatively static, kind of slow-pace. You really didn't have to look that far ahead, because of anything was coming you'd see it coming for five years. Now if you ask me about power distribution, we've got tons of work going on in power distribution. We're researching different power distribution topologies. We're researching higher voltage distribution, direct current distribution. Haven't taken any of those steps yet, but we're were working in that. We've got a ton going on in networking. You'll see an announcement tomorrow of a new instance type that is got some interesting characteristics from a networking perspective. There's a lot going on. >> Let's pre-announce, no. >> Gary's over there like-- >> How 'about database, how 'about database? I mean, 10 years ago, John always says database was kind of boring. You go to a party say, oh welcome to database business, oh yeah, see ya. 25 years ago it was really interesting. >> Now you go to a party is like, hey ah! Have a drink! >> It a whole new ballgame, you guys are participating. Google Spanner is this crazy thing, right? So what are your thoughts on the state of the database business today, in memory, I mean. >> No, it's beautiful. I did a keynote at SIGMOD a few years ago and what I said is that 10 years ago Bruce Linsey, I used to work with him in the database world, Bruce Linsey called it polishing the round ball. It's just we're making everything a little, tiny bit better, and now it's fundamentally different. I mean what's happening right now is the database world, every year, if you stepped out for a year, you wouldn't recognize it. It's just, yeah, it's amazing. >> And DynamoDB has had rapid success. You know, we're big users of that. We actually built this app, crowd chat app that people are using on Hadoop and Hbase, and we immediately moved that to DynamoDB and your stack was just so much faster and scalable. So I got to ask you the-- >> And less labor. >> Yeah, yeah. So it's just been very reliable and all the other goodness of the elastic B socket and SQS, all that other good stuff we're working with node, et cetera So I got to ask you, the area that I want your opinion around the corner is versioning control. So at large-scale one of the challenges that we have is as we're pushin' new code, making sure that the integrated stack is completely updated and synchronized with open-source projects. So where does that fit into the scaling up? 'Cause at large scale, versioning control used to be easy to manage, but downloading software and putting in patches, but now you guys handle all that at scale. So that, I'm assuming there's some automation involved, some real tech involved, but how are you guys handling the future of making sure the code is all updated in the stack? >> It's a great question. It's super important from a security perspective that the code be up to date and current. It's super important from a customer perspective and you need to make sure that these upgrades are just non-disruptive. One customer, best answer I heard was yesterday from a customer was on a panel, they were asked how did they deal with Amazon's upgrades, and what she said is, "I didn't even know when they were happening. "I can't tell when they're happening." Exactly the right answer. That's exactly our goal. We monitor the heck out of all of our systems, and our goal, and boy we take it seriously, is we need to know any issue before a customer knows it. And if you fail on that promise, you'll meet Andy really quick. >> So some other paradigm questions coming in. Floyd asks, "Ask James what his opinion of cloud brokerage "companies such as Jamcracker or Graviton. "Do they have a place, or is it wrong thinking?" (James laughs) >> From my perspective, the bigger and richer the ecosystem, the happier our customers all are. It's all goodness. >> It's Darwinism, that's the answer. You know, the fit shall survive. No, but I think that brings up this new marketplace that Spot pricing came out of the woodwork. It's a paradigm that exists in other industries, apply it to cloud. So brokering of cloud might be something, especially with regional and geographical focuses. You can imagine a world of brokering. I mean, I don't know, I'm not qualified to answer that. >> Our goal, honestly, is to provide enough diversity of services that we completely satisfy customer's requirements, and that's what we intend to do. >> How do you guys think about the make versus buy? Are you at a point now where you say, you know what, we can make this stuff for our specific requirements better than we can get it off the shelf, or is that not the case? >> It changes every few minutes. It really does. >> So what are the parameters? >> Years ago when I joined the company we were buying servers from OEM suppliers, and they were doing some tailoring for our uses. It's gotten to the point now where that's not the right model and we have our own custom designs that are being built. We've now gotten to the point where some of the components in servers are being customized for us, partly because we're driving sufficient volume that it's justified, and partly because the partners that the component suppliers are happy to work with us directly and they want input from us. And so it's every year it's a little bit more specialized and that line's moving, so it's shifting towards specialization pretty quickly. >> So now I'm going to be replaced by the crowd, gettin' great questions, I'm going to be obsolete! No earbud, I got it right here. So the question's more of a fun one probably for you to answer, or just kind of lean back and kind of pull your hair out, but how the heck does AWS add so much infrastructure per day? How do you do it? >> It's a really interesting question. I kind of know how much infrastructure, I know abstractly how much infrastructure we put out every day, but when you actually think about this number in context, it's mind boggling. So here's the number. Here's the number. Every day, we deploy enough servers to support Amazon when it was a seven billion dollar company. You think of how many servers a seven billion dollar e-commerce company would actually require? Every day we deploy that many servers, and it's just shocking to me to think that the servers are in the logistics chain, they're being built, they're delivered to the appropriate data centers, there's back positions there, there's networking there, there's power there. I'm actually, every day I'm amazed to be quite honest with you. >> It's mind-boggling. And then for a while I was there, okay, wait a minute. Would that be Moors' Law? Uh no, not even in particular. 'Cause you said every day. Not every year, every day. >> Yeah, it really is. It's a shocking number and one, my definition of scale changes almost every day, where if you look at the number of customers that are trusting with their workloads today, that's what's driving that growth, it's phenomenal! >> We got to get wrapped up, but I got to ask the Hadoob World SQL over Hadoob question solutions. Obviously Hadoob is great, great for storing stuff, but now you're seeing hybrids come out. Again this comes back down to the, you can recognize the database world anymore if you were asleep for a year. So what's your take on that ecosystem? You guys have a lasting map or a decent a bunch of other things. There's some big data stuff going on. How do you, from a database perspective, how do you look at Hadoob and SQL over Hadoob? >> I personally love 'em both, and I love the diversity that's happening in the database world. There's some people that kind of have a religion and think it's crazy to do anything else. I think it's a good thing. Map reduce is particularly, I think, is a good thing, because it takes... First time I saw map reduce being used was actually a Google advertising engineer. And what I loved about his, I was actually talking to him about it, and what I loved is he had no idea how many servers he was using. If you ask me or anyone in the technology how many servers they're using, they know. And the beautiful thing is he's running multi-thousand node applications and he doesn't know. He doesn't care, he's solving advertising problems. And so I think it's good. I think there's a place for everything. >> Well my final question is asking guests this show. Put the bumper sticker on the car leaving re:Invent this year. What's it say? What does the bumper sticker say on the car? Summarize for the folks, what is the tagline this year? The vibe, and the focus? >> Yeah, for me this was the year. I mean, the business has been growing but this is the year where suddenly I'm seeing huge companies 100% dependent upon AWS or on track to be 100% dependent upon AWS. This is no longer an experiment, something people want to learn about. This is real, and this is happening. This is running real businesses. So it's real, baby! >> It's real baby, I like, that's the best bumper... James, distinguished guest now CUBE alum for us, thanks for coming on, you're a tech athlete. Great to have you, great success. Sounds like you got a lot of exciting things you're working on and that's always fun. And obviously Amazon is killing it, as we say in Silicon Valley. You guys are doing great, we love the product. We've been using it for crowd chats. Great stuff, thanks for coming on theCUBE. >> Thank you. >> We'll be right back with our next guest after this short break. This is live, exclusive coverage with siliconANGLE theCUBE. We'll be right back.