>> Announcer: From San Francisco, it's theCube. Covering Samsung Developer Conference 2017. Brought to you by Samsung. >> Hey, welcome back everyone, this is theCube's exclusive live coverage of Samsung Development Conference, SDC 2017. I'm John Furrier, the founder, co-founder of SiliconANGLE Media, co-host of theCube. My next guest is Kevin Hague, Vice President of Technology Strategy at Harman, now part of Samsung. His twitter handle is JSGuy. Welcome to theCube. >> Thank you, thank you having me. >> So HARMAN has a lot of cool things. Obviously, you're known for music, audio, in-car stuff, headphones, really the premier audio tech. >> Kevin: Yes, yeah. >> So give us the update. Part of Samsung. When did that happen? What have you guys done? Have you integrated in to the edge of the network? Is entertainment. >> It is these days. And it seems like more and more people are becoming interested in audio. Audio's becoming, you know, a big part of everybody's lives. Everybody will have headphones at work, connected devices at home, with AIs and voice assistance in their car. You know, we're huge in the car. A huge percentage of our automotive business is in audio, and infotainment, IVI systems, and we're really excited to be here at the Samsung Developer Conference, because this is our first conference, kind of together, and we're excited to show off a lot of cool developer tech. >> So we're huge on internet of things. I've been saying this for years, but now it's so clear to the developer community that internet of things includes people. Wearables, we had guests on doing dresses that are part of the internet, and technology with robotic arms and software. But headphones, you guys have a cool program called Hack Your Headphones. Which, tell a little about that, and then we'll talk about this new product that's here on the desk, I can't wait to get to, but >> Yeah, we have a couple of new products >> Hack your headphones, I mean, you got to get developers excited, because augmented reality and virtual reality, no one wants to put those damn goggles on. And it's got no audio. >> That's right. Yeah, so we're trying to fix that with this particular product, which is the JBL Everest Elite headphones. And it's probably one of the first consumer hackable headphones. We have an API, out for Android, that allows the developer to control many of the features and functions of this headphone. And we've added a lot of extra features, so this thing not only, when you put it on, and you're wearing virtual VR goggles, you're immersed, right? And you don't even know what's going on in the outside world. Well, we've come up with some tech that allows some of the outside world to come in programmatically. So within a game, or a VR game, or a VR application, you can do something where the outside noise can be added in to the gameplay. So let's say if you're playing Fruit Ninja, or something really crazy on your VR goggles, and you're about to hit somebody, it could warn you through audio signals. So we're really excited about these headphones, lots of other features that developers would like. >> So let's talk about the API, because this is a really cool feature, and I want to get to that again, the new thing, new device that's coming out of this new, breaking news here on theCube, which is, these headphones, is about, you guys have the normal coolness around, noise canceling, all that stuff, but you guys have tech that actually lets developers play with the settings. >> Kevin: That's right. >> So you actually reverse the settings. Right? Like, imagining, like, okay, what if I want to increase the noise out that comes in. Is that the concept? >> That's right. And so we can adjust, the developers can adjust, almost an infinite levels, the noise ratio from outside to inside. So if you want it perfectly quiet, you can set that. If you want it where a lot of outside noise is coming in, you can adjust that as well, without having to do this to talk to somebody. >> It's almost tap your phone, tap your app, or have some notifications sensing, so you're looking for creativity from the developer community. >> That's the objective. >> We are. And we don't actually know what developers are going to do. I always have a saying, that says, If I put ten of my smartest guys in a room for a week, they're going to come up with a 100 ideas. If I throw this out to the developer community, they're going to come up with a 1,000 ideas, and I think that's what we're looking for, is that kind of creative spark, and we're just going to give them platform to do that on. >> And that's super smart, because now you can let the creative development community tinker around, and kick the tires. You guys get the free access to the creative, but also you have APIs that make it kind of stable. >> That's right. And that's that something that we support. We love developers to play with. >> Alright, so now you have a new product. So this is the exclusive Cube coverage. So let's see this new product. >> Actually, we just sent boxes right before coming on set. >> Here, let me introduce this thing. So this is looks like a collar. Goes around your neck. So, first of all, what's the product name? >> So this is a JBL Sound Gear, and it's going to be available starting next month. So this is, as far as I know, the first one in the United States. I can't say that for sure, but that's the first one I've seen in the United States. >> So it looks like one of those old football collars, but you put it around like this, and it allows for music to come up only to my ears, right? Actually, let's turn on the music, and then they'll actually get to hear through my little headset here. >> Kevin: Yeah, we'll just throw something on. There's a little, it's kind of cranked up, actually. >> Okay, so this is cranked up. Can you hear this? >> Kevin: Just a little bit. >> So he can barely hear this. I'm, like, talking loud. >> Kevin: Yeah, yeah, that's right, because it's pretty loud to you. >> So I could be a gamer, I could be doing virtual reality with a headset. This is kind of like my ear experience, without and freeing my arms up. >> Yeah, that's right. And the nice thing is, we're looking in the future, and seeing augmented reality-type experiences are going to be important. But with augmented reality, you want that kind of pass through. So I want to be able to talk to you while you have your glasses on, or whatever the future brings us. >> So I can get a little notification, bing, you got this car coming, or about to get attacked by my app. >> Yeah, imagine walking down the street. Now you can listen to your music while walking down the street, and not worry about getting hit by a car or something. >> Or pissing people off. Hey, turn your headphones on! Or having some ambient noise coming in so I'm aware. >> Kevin: Yeah, that's right. >> Yeah, that's cool. >> And so that's a really exciting product. >> They're not that flexible. >> Yeah, it's a little bit. So I think a lot of people put it on from the side and twist it around, but it's actually a pretty solid product, and we're, you know, it's a transformative product. There's nobody else shipping anything like this that I know of. >> So it has a little bit of wiggle, but it's not, you could break it if you snap it, like a chicken bone. >> Kevin: Yeah, don't do that. As far as I know it's the only one in the United States. >> You can just throw it too, it's like horseshoes. Just toss it. >> We have other uses. Yes, we made it multiuse. >> Don't toss it. It's not horseshoes. That's awesome. And you've also got a little pow here, but also now, the problem with some of these devices is on, watching TV, or interfacing with a large screen, there's latency issues and if people are talking, and you're hearing it separately. A lot of internet streamings we see that. It's not like direct connected. >> Kevin: Yes. >> Talk about that. How does that address that? Does it have a feature where you could create a low latency connection to something that's either on the internet or TV? >> Sure, so there's a couple of different ways, so like audio latency's very important, especially if you're watching TV, and lip sync, it's always weird if you get that delay, and so, that's why we actually pair with this in the box comes a low latency transmitter. So it's plug and play, plug it into your TV, turn on this, it pairs up. Now you can watch TV seamlessly in the house without disturbing like everybody. >> I can watch my football games, make some dinner, lunch, whatever. >> Or even late night TV, somebody's asleep in the same room as you, and it won't disturb them, right? >> My wife, Linda, Linda, if you're watching, this is perfect, save our marriage. Turn the TV off! Maybe not that. But it'll be a first step, but this is exactly the use of these. Create a personal space, and the technology as it shoots up from the sides. >> Yeah, there's two speakers on each side. >> And it shoots up to the ear, so it comes up this way. >> That's right. And we do a lot of work to make sure that the beam of sound stays in the vertical space, so that a lot of people can't hear it from outside maybe three feet. Literally, when you first put it on, I couldn't even tell it was working, and I was going, can you hear it? And you're like, oh, it's loud! And so. >> And the folks listening heard it to, 'cause my microphone was right there. >> That's right, yes, and that's the side effect, is in this area here, you have full. >> Okay so this product >> Full awareness. >> will be shipped and it's called the >> AVL Sound Gear. >> Sound Gear, it's available next month. >> Next month. >> In Best Buy retail. >> Best Buy retail. MSRP I think is going to be 249. >> Which includes some accessories, right? >> It includes a couple of accessories, like the streaming unit and everything. >> Yeah, that's awesome. So it's not going to break the bank. >> I don't think so. >> Good. Well, so that's a good price point, I'm definitely going to buy one. >> It's definitely different. It's not like just a regular pair of headphones. This is also available in the stores today, the 750. >> And how about this being developer enabled? API's for this, too, or not yet? >> Not yet, but stay tuned. >> This is the total Star Trek device. >> Kevin: It is. >> If you're a Star Trek classic fan like me, you know the thrall collars. (laughter) >> Kevin: That's awesome. >> And certainly, I can use this. It's got voice in there just so I can talk to it, like on conference calls? >> That's right. You can do a conference call with it, or. >> Have intercommunications on gameplay, multiplayer? >> That's right. >> So yeah, I think gamers are going to love this. >> I think so, too. >> Yeah, my son plays Call of Duty and Destiny. >> It's very comfortable to wear. I think that's one of the key things, is once you get it on, it feels like, when I've tried some of our early prototypes of it, I forgot that I was even wearing it. >> I can listen to theCube music while talking to the guests. >> Kevin: I know, we need to get you one of these. I mean, we'll get you one soon so you can try it. >> Promotional considerations by Samsung. >> Kevin: That's right. >> Kevin, thanks for coming on, great tunes, old school classics. Yeah, crank it up a little bit more, we'll end on some music. Kevin Hague, Vice President of Technology at Harmon, (upbeat music) Samsung. Bringing all the developer action to you here, theCube. >> Kevin: Thanks for having me. >> Alright. More after this short break. (upbeat music)

>>Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, or is modernized now. Yes, let's talk about that. And with me is Ravi, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >>Thank you so much. I'm so glad to be here with you. >>I asked you what the new requirements are around modern applications. I've seen some, you know, some of your comments, you gotta be flexible, distributed, multimodal, mobile edge. It, that those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >>Yeah, I think what has basically happened is that, uh, so far, uh, it's been a transition of sorts. And now we are come to a point where, uh, the tipping point and the tipping point has been, uh, uh, more because of COVID and there COVID has pushed us to a world where we are living, uh, in a sort of, uh, occasionally connected manner where our digital, uh, interactions, precede our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than, uh, in a digital manner, as opposed to sort of making a more specific human contact that has really been the, uh, sort of accelerant to this modernized. Now, as a team in this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. >>They're all sitting behind. Uh, they used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, uh, but they are all centralized still. Uh, but where our engagement happens with the data is, uh, at the edge, uh, at your point of convenience at your point of consumption, not where the data is actually sitting. So this has led to, uh, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? Uh, but it just basically comes down to the fact that the data needs to be where you are engaging with it. And that means if you are doing it on your mobile phone, or if you are sitting, uh, doing something in your body or traveling, or whether you are in a subway, whether you're in a plane or a ship, wherever the data needs to come to you, uh, and be available as opposed to every time you going to the data, which is centrally sitting in some place. >>And that is the fundamental shift in terms of how the modern architecture needs to think, uh, when they, when it comes to digital transformation and, uh, transitioning their old applications to, uh, the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Uh, otherwise people are basically waiting for that circle of death that we all know, uh, and blaming the networks and other pieces. The problem is actually, the data is not where you are engaging with. It has got to be fetched, you know, seven seas away. Um, and that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >>I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this, because date data by its very nature is distributed. It's always been distributed, but w w but distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, uh, of, uh, of a super rock solid database that can handle, you know, distributed data? Yes. >>So there are two issues that you're a little too over there with Forrest is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is, uh, like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data in one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, uh, when you have the data, you can first look at it to perform. >>Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five minutes. Again, this is a, there's a class of problem that we solve that same data. Now, eventually, without you ever having to, uh, sort of do a casting it to a different database, you can now do a solid, uh, acquire. These are classic sequel queries, which is our next magic. We are a no SQL database, but we have a full functional sequel. The sequel has been the language that has talked to data for 40 odd years successfully. Every other database has come and try to implement their own QL query language, but they've all failed only sequel as which stood the test of time of 40 odd years. >>Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is, uh, basically, uh, look at the data and any common tutorial, uh, any, uh, any which way you look at the data. All it will come, uh, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries, select star from where Canada stuff, because it's at an English level, it becomes easy to, so the same data, you didn't have to go move it to another database, do your, uh, sort of transformation of the data and all this stuff. Same day that you do this. >>Now, that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, but Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the ability to query the operational data in a different way. I'll talk budding. What was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. >>So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and find different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, uh, the database management system. And that's where the distributed, uh, platform that we have built enables us to get it to where you need the data to be, you know, in a classic way, we call it CDN in the data as in like content delivery networks. So far do static, uh, uh, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >>The first part of the, the answer to my question, are you saying you could do this without skiing with a no schema on, right? And then you can apply those techniques. >>Uh, fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read things. So because there is no schema, it is just a on document that is sitting inside. And Jason is the lingua franca of the web, as you very well know by now. So it just Jason that we manage, you can do key lookups of the Jason. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and the other sophisticated pieces of technology behind it. >>You can do searching on it, using the, um, the full textual analysis pipeline. You can do ad hoc wedding on the analytic side, and you can write your own custom logic on it using our eventing capabilities. So that's, that's what it allows because we keep the data in the native form of Jason. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing, uh, in the last 40 years because we developed various, uh, database systems and data processing systems of various points. In time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. >>We had queuing systems, all the systems, if you want to use any one of them, our answer has always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this is not one to fly instead, bring the logic to the data. So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this, >>As you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >>Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data casting because it required you to have it in seven schema in one sense at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably flooded, but it not really, uh, how do you say, um, keep to the promise that it actually meant to be? So that's why it was a swamp I need, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it, and you create different types of indexes to manage it. You distribute the index, you distribute the data you have, um, like we were discussing, you have acid semantics on top of, and when you, when you put all these things together, uh, it's, it's, it's a tough proposition, but they have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >>So you predicted the trend around multimodal and converged, uh, databases. Um, you kind of led Couchbase through that. I want to, I always ask this question because it's clearly a trend in the industry and it, it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, we have a little teeny scissors and a knife. That's not that sharp. How do you respond to that? Uh, >>A great one. Um, my answer is always, I use another analogy to tackle that, but is that, have you ever accused a smartphone of being a Swiss army knife? No. No. Nobody does that because it's actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Like as in a moment, it could be a Tom, Tom telling you all the directions, the next one, it's your PDA. >>Third one, it's a fantastic phone. Uh, four, it's a beautiful camera, which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment is a video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just taught that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, they missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app is the economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get the alert saying that today you got to leave home at eight 15 for your nine o'clock meeting. >>And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's gone there's notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place without that, you couldn't even do this simple function, uh, in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build, because half the time you're running sideline to sideline, just, you know, um, integrating data from one system to the other. >>So I love the analogy with the smartphone. I w I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? And so, so, but, but is there, is that a fair, where, in other words, those specialized databases, they say there still is a place for them, but they're getting >>Absolutely, absolutely great analogy and a great extension to the question. That's, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of the music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they haven't, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. >>Yes, it's 90% there or 80% there. It depends on your audio file mess of your, uh, I mean, you don't experience the super specialized ones do not go away. You know, there are, there are places where, uh, the specialized use cases will demand a separate system to exist, but even there that has got to be very closed. Um, how do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that, oh, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car and walk into my living room, that's same songs should continue and play in my living room speakers. Then it's a world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >>I love, I love that example too. When I was a kid, we used to go to Twitter, et cetera. And we'd to play around with, we take off the big four foot speakers. Those stores are out of business too. Absolutely. Um, now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi. >>I believe so. Uh, because I think, uh, what had happened was the relational systems. Uh, I've been where the norm, they rule the roost, if you will, for the last 40 odd years, and then gain this no sequel movement, which was almost as though a rebellion from the relational world, we all inhibited, uh, uh, because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee, they required your DBA and your data architect. And you have to call them just to add one column and stuff like that. And the world had moved on. This was the world of blogs and tweets and, uh, you know, um, mashups and, um, uh, uh, a different generation of digital behavior, digital, native people now, um, who are operating in these and the, the applications, the, the consumer facing applications. >>We are living in this world. And yet the enterprise ones were still living in the, um, in the other, the other side of the divide. So all came this solution to say that we don't need SQL. Actually, the problem was never sequel. No sequel was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations, and the inability for these, the system to scale, the relational systems were built like, uh, airplanes, which is that if, uh, San Francisco Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set in from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the alarm to somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. >>These are called vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world, uh, is make the system how it is only scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guests. I'll add one more coach to it, one more car to it. And the better part of the way we have done this year is that, and we have super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have ID only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. >>You can attach the kind of coaches we call this multi-dimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it quite. So that's the beauty of this architecture. Now, why is that important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because you would say that I cannot run this analytical Barre because then my operational workload will suffer. Then my friend, then we'll slow down millions of customers that impacted that problem. We will solve the same data in which you can do analytical buddy, an operational query because they're separated by these cars, right? As in like we, we fence the, the, the resources, so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or equity. >>And then yet you can run this analytical body, which will take a couple of minutes to run one, not impeding the other. So that's in one sense, sort of the, part of the, um, uh, problems that we have solved here is that relational versus, uh, uh, the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same quality language on top. Y it's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, uh, the internal combustion engine, uh, I think gas, uh, you says, these are the issues we really wanted to solve. Um, so solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, or that are for your shifters. >>Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So, uh, even when you feed people the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blue harder to go fast and lean back for, for it to, you know, uh, to apply a break that's, that's how we seem to define, uh, design software. Instead, we should be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, uh, and the gas bottle and the, um, and the gear shifter is by putting cul back on underneath the surface, we have completely solved, uh, the relational, uh, uh, limitations of schema, as well as scalability. >>So in, in, in that way, and by bringing back the classic acid capabilities, which is what relational systems, uh, we accounted on and being able to do that with the sequel programming language, we call it like multi-state SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up the salt in the modern times, but rather than get, um, sort of pedantic about whether it's, we have no SQL or sequel or new sequel, or, uh, you know, any of that sort of, uh, jargon, oriented debate, uh, this, these are the debates of computer science that they are actually, uh, and they were the solve and they have solved them with, uh, the latest release of $7, which we released a few months ago. >>Right, right. Last July, Ravi, we got to leave it there. I, I love the examples and the analogies. I can't wait to be face to face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >>Fantastic. Thanks for the time. And the Aboriginal Dan was, I mean, very insightful questions really appreciate it. Thank you. >>Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

>> Welcome back to the cubes coverage of Couchbase connect online, where the theme of the event is, is modernize now. Yes, let's talk about that. And with me is Ravi mayor him, who's the senior vice president of engineering and the CTO at Couchbase Ravi. Welcome. Great to see you. >> Thank you so much. I'm so glad to be here with you. >> I want to ask you what the new requirements are around modern applications. I've seen some of your comments, you got to be flexible, distributed, multimodal, mobile, edge. Those are all the very cool sort of buzz words, smart applications. What does that all mean? And how do you put that into a product and make it real? >> Yeah, I think what has basically happened is that so far it's been a transition of sorts. And now we are come to a point where that tipping point and that tipping point has been more because of COVID and there are COVID has pushed us to a world where we are living in a in a sort of occasionally connected manner where our digital interactions precede, our physical interactions in one sense. So it's a world where we do a lot more stuff that's less than in a digital manner, as opposed to sort of making a more specific human contact. That does really been the sort of accelerant to this modernize Now, as a team. In this process, what has happened is that so far all the databases and all the data infrastructure that we have built historically, are all very centralized. They're all sitting behind. They used to be in mainframes from where they came to like your own data centers, where we used to run hundreds of servers to where they're going now, which is the computing marvelous change to consumption-based computing, which is all cloud oriented now. And so, but they are all centralized still, but where our engagement happens with the data is at the edge at your point of convenience, at your point of consumption, not where the data is actually sitting. So this has led to, you know, all those buzzwords, as you said, which is like, oh, well we need a distributed data infrastructure, where is the edge? But it just basically comes down to the fact that the data needs to be there, if you are engaging with it. And that means if you are doing it on your mobile phone, or if you're sitting, but doing something in your while you're traveling, or whether you're in a subway, whether you're in a plane or a ship, wherever the data needs to come to you and be available, as opposed to every time you going to the data, which is centrally sitting in some place. And that is the fundamental shift in terms of how the modern architecture needs to think when they, when it comes to digital transformation and, transitioning their old applications to the, the modern infrastructure, because that's, what's going to define your customer experiences and your personalized experiences. Otherwise, people are basically waiting for that circle of death that we all know, and blaming the networks and other pieces. The problem was actually, the data is not where you are engaging with it. It's got to be fetched, you know, seven sea's away. And that is the problem that we are basically solving in this modern modernization of that data, data infrastructure. >> I love this conversation and I love the fact that there's a technical person that can kind of educate us on, on this because date data by its very nature is distributed. It's always been distributed, but with the distributed database has always been incredibly challenging, whether it was a global SIS Plex or an eventual consistency of getting recovery for a distributed architecture has been extremely difficult. You know, I hate that this is a terrible term, lots of ways to skin a cat, but, but you've been the visionary behind this notion of optionality, how to solve technical problems in different ways. So how do you solve that, that problem of, of, of, of, of a super rock solid database that can handle, you know, distributed data? >> Yes. So there are two issues that you alluded little too over there. The first is the optionality piece of it, which is that same data that you have that requires different types of processing on it. It's almost like fractional distillation. It is like your crude flowing through the system. You start all over from petrol and you can end up with Vaseline and rayon on the other end, but the raw material, that's our data. In one sense. So far, we never treated the data that way. That's part of the problem. It has always been very purpose built and cast first problem. And so you just basically have to recast it every time we want to look at the data. The first thing that we have done is make data that fluid. So when you're actually, when you have the data, you can first look at it to perform. Let's say a simple operation that we call as a key value store operation. Given my ID, give him a password kind of scenarios, which is like, you know, there are customers of ours who have billions of user IDs in their management. So things get slower. How do you make it fast and easily available? Log-in should not take more than five milliseconds, this is, this is a class of problem that we solve that same data. Now, eventually, without you ever having to sort of do a casting it to a different database, you can now do solid queries. Our classic SQL queries, which is our next magic. We are a no SQL database, but we have a full functional SQL. The SQL has been the language that has talked to data for 40 odd years successfully. Every other database has come and tried to implement their own QL query language, but they've all failed only SQL has stood the test of time of 40 odd years. Why? Because there's a solid mathematics behind it. It's called a relational calculus. And what that helps you is, is basically a look at the data and any common editorial, any, any which way you look at the data, all it will come, the data in a format that you can consume. That's the guarantee sort of gives you in one sense. And because of that, you can now do some really complex in the database signs, what we call us, predicate logic on top of that. And that gives you the ability to do the classic relational type queries select star from where, kind of stuff, because it's at an English level becomes easy to so the same day that you didn't have to go move it to another database, do your sort of transformation of the data and all the stuff, same day that you do this. Now that's where the optionality comes in. Now you can do another piece of logic on top of this, which we call search. This is built on this concept of inverted index and TF IDF, the classic Google in a very simple terms, what Google tokenized search, you can do that in the same data without you ever having to move the data to a different format. And then on top of it, they can do what is known as a eventing or your own custom logic, which we all which we do on a, on programming language called Java script. And finally analytics and analytics is the, your ability to query the operational data in a different way. And talk querying, what was my sales of this widget year over year on December 1st week, that's a very complex question to ask, and it takes a lot of different types of processing. So these are different types of that's optionality with different types of processing on the same data without you having to go to five different systems without you having to recast the data in five different ways and apply different application logic. So you put them in one place. Now is your second question. Now this has got to be distributed and made available in multiple cloud in your data center, all the way to the edge, which is the operational side of the, the database management system. And that's where the distributed platform that we have built enables us to get it to where you need the data to be, you know, in the classic way we call it CDN'ing the data as in like content delivery networks. So far do static, sort of moving of static content to the edges. Now we can actually dynamically move the data. Now imagine the richness of applications you can develop. >> And on the first part of, of the, the, the answer to my question, are you saying you could do this without scheme with a no schema on, right? And then you can apply those techniques. >> Fantastic question. Yes. That's the brilliance of this database is that so far classically databases have always demanded that you first define a schema before you can write a single byte of data. Couchbase is one of the rare databases. I, for one don't know any other one, but there could be, let's give the benefit of doubt. It's a database which writes data first and then late binds to schema as we call it. It's a schema on read thing. So, because there is no schema, it is just a Json document that is sitting inside. And Json is the lingua franca of the web, as you very well know by now. So it just Json that we manage, you can do key value look ups of the Json. You can do full credit capability, like a classic relational database. We even have cost-based optimizers and other sophisticated pieces of technology behind it. You can do searching on it, using the, the full textual analysis pipeline. You can do ad hoc webbing on the analytics side, and you can write your own custom logic on it using or inventing capabilities. So that's, that's what it allows because we keep the data in the native form of Json. It's not a data structure or a data schema imposed by a database. It is how the data is produced. And on top of it, bring, we bring different types of logic, five different types of it's like the philosophy is bringing logic to data as opposed to moving data to logic. This is what we have been doing in the last 40 years, because we developed various database systems and data processing systems at various points in time in our history, we had key value stores. We had relational systems, we had search systems, we had analytical systems. We had queuing systems, all these systems, if you want to use any one of them are answered. It always been, just move the data to that system. Versus we are saying that do not move the data as we get bigger and bigger and data just moving this data is going to be a humongous problem. If you're going to be moving petabytes of data for this, it's not going to fly instead, bring the logic to the data, right? So you can now apply different types of logic to the data. I think that's what, in one sense, the optionality piece of this. >> But as you know, there's plenty of schema-less data stores. They're just, they're called data swamps. I mean, that's what they, that's what they became, right? I mean, so this is some, some interesting magic that you're applying here. >> Yes. I mean, the one problem with the data swamps as you call them is that that was a little too open-ended because the data format itself could change. And then you do your, then everything became like a game data recasting because it required you to have it in seven schema in one sense at, at the end of the day, for certain types of processing. So in that where a lot of gaps it's probably related, but it not really, how do you say keep to the promise that it actually meant to be? So that's why it was a swamp I mean, because it was fundamentally not managing the data. The data was sitting in some file system, and then you are doing something, this is a classic database where the data is managed and you create indexes to manage it. And you create different types of indexes to manage it. You distribute the index, you distribute the data you have, like we were discussing, you have ACID semantics on top of, and when you, when you put all these things together, it's, it's, it's a tough proposition, but we have solved some really tough problems, which are good computer science stuff, computer science problems that we have to solve to bring this, to bring this, to bear, to bring this to the market. >> So you predicted the trend around multimodal and converged databases. You kind of led Couchbase through that. I, I want, I always ask this question because it's clearly a trend in the industry and it, and it definitely makes sense from a simplification standpoint. And, and, and so that I don't have to keep switching databases or the flip side of that though, Ravi. And I wonder if you could give me your opinion on this is kind of the right tool for the right job. So I often say isn't that the Swiss army knife approach, where you have have a little teeny scissors and a knife, that's not that sharp. How, how do you respond to that? >> A great one. My answer is always, I use another analogy to tackle that, and is that, have you ever accused a smartphone of being a Swiss army knife? - No. No. >> Nobody does. That because it actually 40 functions in one is what a smartphone becomes. You never call your iPhone or your Android phone, a Swiss army knife, because here's the reason is that you can use that same device in the full capacity. That's what optionality is. It's not, I'm not, it's not like your good old one where there's a keyboard hiding half the screen, and you can do everything only through the keyboard without touching and stuff like that. That's not the whole devices available to you to do one type of processing when you want it. When you're done with that, it can do another completely different types of processing. Right? As in a moment, it could be a TomTom, telling you all the directions, the next one, it's your PDA. Third one. It's a fantastic phone. Four. It's a beautiful camera which can do your f-stop management and give you a nice SLR quality picture. Right? So next moment, it's the video camera. People are shooting movies with this thing in Hollywood, these days for God's sake. So it gives you the full power of what you want to do when you want it. And now, if you just thought that iPhone is a great device or any smartphone is a great device, because you can do five things in one or 50 things in one, and at a certain level, he missed the point because what that device really enabled is not just these five things in one place. It becomes easy to consume and easy to operate. It actually started the app based economy. That's the brilliance of bringing so many things in one place, because in the morning, you know, I get an alert saying that today you got to leave home at >> 8: 15 for your nine o'clock meeting. And the next day it might actually say 8 45 is good enough because it knows where the phone is sitting. The geo position of it. It knows from my calendar where the meeting is actually happening. It can do a traffic calculation because it's got my map and all of the routes. And then it's got this notification system, which eventually pops up on my phone to say, Hey, you got to leave at this time. Now five different systems have to come together and they can because the data is in one place. Without that, you couldn't even do this simple function in a, in a sort of predictable manner in a, in a, in a manner that's useful to you. So I believe a database which gives you this optionality of doing multiple data processing on the same set of data allows you will allow you to build a class of products, which you are so far been able to struggling to build. Because half the time you're running sideline to sideline, just, you know, integrating data from one system to the other. >> So I love the analogy with the smartphone. I want to, I want to continue it and double click on it. So I use this camera. I used to, you know, my kid had a game. I would bring the, the, the big camera, the 35 millimeter. So I don't use that anymore no way, but my wife does, she still uses the DSLR. So is, is there a similar analogy here? That those, and by the way, the camera, the camera shop in my town went out of business, you know? So, so, but, but is there, is that a fair and where, in other words, those specialized databases, they say there still is a place for them, but they're getting. >> Absolutely, absolutely great analogy and a great extension to the question. That's like, that's the contrarian side of it in one sense is that, Hey, if everything can just be done in one, do you have a need for the other things? I mean, you gave a camera example where it is sort of, it's a, it's a slippery slope. Let me give you another one, which is actually less straight to the point better. I've been just because my, I, I listened to half of my music on the iPhone. Doesn't stop me from having my full digital receiver. And, you know, my Harman Kardon speakers at home because they, I mean, they produce a kind of sounded immersive experience. This teeny little speaker has never in its lifetime intended to produce, right? It's the convenience. Yes. It's the convenience of convergence that I can put my earphones on and listen to all the great music. Yes, it's 90% there or 80% there. It depends on your audio file-ness of your, I mean, your experience super specialized ones do not go away. You know, there are, there are places where the specialized use cases will demand a separate system to exist. But even there that has got to be very closed. How do you say close, binding or late binding? I should be able to stream that song from my phone to that receiver so I can get it from those speakers. You can say that all, there's a digital divide between these two things done, and I can only play CDs on that one. That's not how it's going to work going forward. It's going to be, this is the connected world, right? As in, if I'm listening to the song in my car and then step off the car, walk into my living room, that same songs should continue and play in my living room speakers. Then it's a connected world because it knows my preference and what I'm doing that all happened only because of this data flowing between all these systems. >> I love, I love that example too. When I was a kid, we used to go to Tweeter, et cetera. And we used to play around with three, take home, big four foot speakers. Those stores are out of business too. Absolutely. And now we just plug into Sonos. So that is the debate between relational and non-relational databases over Ravi? >> I believe so, because I think what had happened was relational systems. I've mean where the norm, they rule the roost, if you will, for the last 40 odd years and then gain this no SQL movement, which was almost as though a rebellion from the relational world, we all inhabited because we, it was very restrictive. It, it had the schema definition and the schema evolution as we call it, all those things, they were like, they required a committee. They required your DBA and your data architect. And you had to call them just to add one column and stuff like that. And the world had moved on. This was a world of blogs and tweets and, you know, mashups and a different generation of digital behavior, There are digital, native people now who are operating in these and the, the applications, the, the consumer facing applications. We are living in this world. And yet the enterprise ones were still living in the, in the other, the other side of the divide. So out came this solution to say that we don't need SQL. Actually the problem was never SQL. No SQL was, you know, best approximation, good marketing name, but from a technologist perspective, the problem was never the query language, no SQL was not the problem, the schema limitations and the inability for these, the system to scale, the relational systems were built like airplanes, which is that if a San Francisco, Boston, there is a flight route, it's so popular that if you want to add 50 more seats to it, the only way you can do that is to go back to Boeing and ask them to get you a set from 7 3 7 2 7 7 7, or whatever it is. And they'll stick you with a billion dollar bill on the allowance that you'll somehow pay that by, you know, either flying more people or raising the rates or whatever you have to do. These are all vertically scaling systems. So relational systems are vertically scaling. They are expensive. Versus what we have done in this modern world is make the system horizontally scaling, which is more like the same thing. If it's a train that is going from San Francisco to Boston, you need 50 more people be my guest. I'll add one more coach to it, one more car to it. And the better part of the way we have done this here is that, and we are super specialized on that. This route actually requires three, three dining cars and only 10 sort of sleeper cars or whatever. Then just pick those and attach the next route. You can choose to have, I need only one dining car. That's good enough. So the way you scale the plane is also can be customized based on the route along the route, more, more dining capabilities, shorter route, not an abandoned capability. You can attach the kind of coaches we call this multidimensional scaling. Not only do we scale horizontally, we can scale to different types of workloads by adding different types of coaches to it, right? So that's the beauty of this architecture. Now, why is that architecture important? Is that where we land eventually is the ability to do operational and analytical in the same place. This is another thing which doesn't happen in the past, because, you would say that I cannot run this analytical query because then my operational workload will suffer. Then my front end, then we'll slow down millions of customers that impacted that problem. They'll solve the same data once again, do analytical query, an operational query because they're separated by these cars, right? As in like we, we, we fence the, the, the resources so that one doesn't impede the other. So you can, at the same time, have a microsecond 10 million ops per second, happening of a key value or a query. And then yet you can run this analytical query, which will take a couple of minutes to them. One, not impeding the other. So that's in one sense, sort of the part of the problems that we have solved it here is that relational versus the no SQL portion of it. These are the kinds of problems we have to solve. We solve those. And then we yet put back the same query language on top. Why? It's like Tesla in one sense, right underneath the surface is where all the stuff that had to be changed had to change, which is like the gasoline, the internal combustion engine the gas, you says, these were the issues we really wanted to solve. So solve that, change the engine out, you don't need to change the steering wheel or the gas pedal or the, you know, the battle shifters or whatever else you need, over there your gear shifters. Those need to remain in the same place. Otherwise people won't buy it. Otherwise it does not even look like a car to people. So even when you feed people, the most advanced technology, it's got to be accessible to them in the manner that people can consume. Only in software, we forget this first design principle, and we go and say that, well, I got a car here, you got the blow harder to go fast. And they lean back for, for it to, you know, to apply a break that's, that's how we seem to define design software. Instead, we shouldn't be designing them in a manner that it is easiest for our audience, which is developers to consume. And they've been using SQL for 40 years or 30 years. And so we give them the steering wheel on the, and the gas pedal and the, and the gear shifters by putting SQL back on underneath the surface, we have completely solved the relational limitations of schema, as well as scalability. So in, in, in that way, and by bringing back the classic ACID capabilities, which is what relational systems we accounted on, and being able to do that with the SQL programming language, we call it like multi-statement SQL transaction. So to say, which is what a classic way all the enterprise software was built by putting that back. Now, I can say that that debate between relational and non-relational is over because this has truly extended the database to solve the problems that the relational systems had to grow up to solve in the modern times, rather than get sort of pedantic about whether it's we have no SQL or SQL or new SQL, or, you know, any of that sort of jargon oriented debate. This is, these are the debates of computer science that they are actually, and they were the solve, and they have solved them with the latest release of 7.0, which we released a few months ago. >> Right, right. Last July, Ravi, we got got to leave it there. I love the examples and the analogies. I can't wait to be face-to-face with you. I want to hang with you at the cocktail party because I've learned so much and really appreciate your time. Thanks for coming to the cube. >> Fantastic. Thanks for the time. And the opportunity I was, I mean, very insightful questions really appreciate it. - Thank you. >> Okay. This is Dave Volante. We're covering Couchbase connect online, keep it right there for more great content on the cube.

>> Announcer: Live, from Las Vegas, it's the Cube. Covering I.B.M. Think 2018. Brought to you by I.B.M. >> Welcome back to the Cube. We are live at I.B.M. Think 2018, our inaugural event. I'm Lisa Martin with Dave Vellante. We're joined by another Vegas veteran, as we all are. First time guest to the Cube, Bret Greenstein, the V.P. of Watson I.o.T. Offerings. Bret, welcome to the Cube. >> Thank you very much, exciting to be here. >> This is the inaugural Think 2018 event. >> Yes. >> 40,000 plus attendees, expected over 10 keynotes, lots of cool stuff. Speaking of cool stuff, I.o.T. What is happening in I.o.T. this year? >> Yeah, so we've been here in Vegas several times over the last several years talking about the Internet of Things, but what's really pivoted, what's really changed, is people talking about applied I.o.T. How are they using it to get business outcomes. Something different happening. And I think when we all started with the Internet of Things we talked a lot about, connecting stuff and devices. But really, it was always about the data and the effect that data had on changing business, changing user engagement, changing outcomes. And so here, on stage, you're going to see people talking about how their businesses have been changed, how their customers are changing as a result of I.o.T. >> Yeah so, I've always felt like I.o.T. is the intersection of devices, data, and machine intelligence. >> Bret: Yeah. >> How are those sort of three things coming together and what's the data model look like? >> Data model is every type of data. I think what people really didn't expect was it wasn't just machine data coming off sensors, temperatures, vibrations. It's all this unstructured data coming in from connected things that are everywhere in our lives. So sensors with cameras for example, being able to see. It's not just recorded images, but it's information. Tons of information that you need A.I. systems and other systems to interpret. So we're able to take all that data, structured data, numeric stuff coming off of devices and sensors, but images and sound and vibration. Even emotional content in people's dialogue. All of that is relevant to the Internet of Things. >> What's the conversation like with customers? For example, when we say, what physical assets do we have that we can instrument. >> Bret: Right. >> Parking meters or whatever, okay. >> Bret: Right. >> What physical assets don't we have that we should have? How can we leverage our existing data? What's the conversation like in terms of transformations that are going on? >> I think the conversations have shifted a lot. Over the couple years people were talking about we want to connect our thing, whatever the thing is, whether it's an elevator or car or whatever. We want to connect it, what does that mean? And that's shifted very quickly to customers who are coming in talking about information data and insights and they want to know, what should I do to get more of those insights? So I'm seeing customers now with Chief Data Officers or heads of digital transformation. Totally new roles that didn't exist before. And they're coming in with a data centric view. They're saying, we're going to be a digital business. We need to understand all of these live data about our customers and our things and our business process. Help us do that. And so that's much more than just instrumenting the individual devices now. And I find that conversation is really, really focused on the value of the data. >> What about the industry impact in this context? Do you see, does I.B.M.'s perspective, is I.o.T., it's certainly transformative. >> Bret: Right. >> But is it disruptive or it is sort of the guys with infrastructure are going to evolve to it? Is it more evolutionary, is it more disruptive? How do you see it? >> I think there's room for both. Obviously traditional players are going to instrument their business process. They're bringing in connected cars and all that. But you could also look at those same industries and say there's new players emerging who are coming in with software defined products that are digital by design. And they can come in and suddenly become leaders in their field. I don't think people would've expected companies like Tesla to be so disruptive in automotive, but coming in as electric changes the game without having to build on a hundred years of mechanical design. You're building on some new principles. And now we see some new players coming in to automotive who've never built cars at all before. Like Dyson for example, that recently announced they were working on electric cars. So I think a digital platform, a digital way of thinking, also creates opportunities for new entrance in every market. >> I think automobiles is a great example because it's an industry that hasn't been largely disrupted. But then you use an example of Tesla which is extremely innovative, you could actually pretend disruptions coming out. And you see whole ecosystems form around that. >> Right, right. And I think what was so powerful about the effect they had was it's a software defined product. The software in it is upgraded constantly. Sometimes you buy the car, the next day you get a new feature you didn't even expect. And this is the way we've come to appreciate, experience through mobile and everything else. Software that continues to improve products that get more valuable over time. Not less valuable over time. >> So let's talk about Watson and I.o.T. I'd also love to maybe take a slice on how I.B.M. is helping customers that maybe have been around maybe the flip side of a Tesla. They've been around a long time. How are they leveraging Watson and I.o.T. to transform their businesses? So kind of start with, what's new with Watson and I.o.T. >> Sure, so I mentioned before that there's a whole part of many data types now that previously were very hard to interpret through traditional analytics. But A.I. and machine learning give you the ability to absorb and consume some of that data. Unstructured sound, images, video, vibration, all of that stuff is now able to become part of a business process. So even traditional companies that have been around a long time can start to look at the data coming off of cameras, visual inspection in manufacturing, sound and voice for example. We work with Jefferson Hospital where they brought Watson into patients rooms so you could ask questions like visiting hours, or set the temperature. Put the patients in control of their experience in a hospital. That takes a traditional experience, like a hospital recovery room, and turns it into something A.I. driven, I.o.T. powered and puts the patient at the center. So very big changes can occur when you do that. >> How far do you see us being able to take A.I. in this whole world of I.o.T.? How far should we take it? >> I think we have to start become more appreciative of the power of machine learning to drive outcomes that are not as easily prescribed with code. So all of us, all of our business processes, all of our businesses will be enhanced with A.I. And we shouldn't look at that in any other way as a better tool to understand data in a way that's different than the way you interpret data. And so it wasn't long ago when big data just meant writing an algorithm across large volumes of data. And now we literally have algorithms whose job is to find patterns. Whose job is to understand data from training. And deliver an outcome that you couldn't have prescribed before. And so those type of problems, it just opens up a class of problems we can all solve now that we couldn't before. >> You're seeing a whole set of digital services emerge. The lingua franca is changing. It's sense, hear, see, respond. >> Bret: Right. >> Optimize. >> Right. >> Fix. (chuckles) >> And all that comes from comprehending. So having a system that can look. For example, I have a camera outside the window of my house and every once in a while I feed the images into Watson to see what it sees. When I first did it, it would say truck. But later, as we make Watson better, now it says FedEx truck or U.P.S. truck. It can read the writing, it can see the patterns. Every camera should know what it sees. Whether it's in a car or a home or somewhere else. Because it's much more valuable than just taking a picture and letting a human being interpret it later. So cameras should know what they see. Machines should know what they hear. Machines should tell us when they're about to break based on vibration or sound. And so this is possible with machine learning. >> So you're saying machines actually take on a whole new set of human-like activities. Digital twins is an example. >> Bret: Okay. >> What's your perspective on, let's start there, digital twins? >> Digital twins, for me, represents sort of the evolution of I.o.T. and that it's digitalizing things. And so, a thing that has no connectivity and very few sensors, is just a thing, it's just a box, it's a block. But as you start to put sensors on it and start to understand it's behavior, it's motion, it's vibration, it's location. Any of the mechanisms, the angels, all this stuff. Then you add a virtual representation of that thing. And if you can do that with all the things in your business, you can start to look for patterns. You can start to assess what's working and what's not working. So I think it just represents a true digitization of a business, of a class of objects in your business. >> Does I.o.T. make security a do-over in your opinion? >> No, but it certainly raises the bar. And so, when we all started connecting our computers to the internet, I remember everyone being panicked. It you put a disc in your machine, you might get a virus. Then we connected them to the internet, we all panicked, but the tools evolved and we start to get things that can help detect zero day problems. In the case of I.o.T. we've got these software defined products that are connected. That are inherently vulnerable cause they're in the real world. They can be touched by other things. So it raises the bar in the expectation of monitoring normal behavior for things. Monitoring all kinds of different threats and stuff, So companies like I.B.M. they focus so much on security and security services, we build that right into our platform so we can keep an eye on that. And also, when things occur, be able to push out new software that is protected. So for more updates, keeping the products live and current is a huge security protection. >> Bret, how would you describe the ecosystem. I.B.M.'s point of view on the ecosystem that you've got to form and catalog in order to succeed in I.o.T.? What does that look like? >> Yeah so, there are so many things for people to do in the world of I.o.T. That I.B.M. doesn't prescribe to do all of them, at all. There's certain things that we're really, really good at. We're certainly good at our cloud infrastructure and analytics and the platforms that enable this and deep industry knowledge. But the ability to apply that in businesses, to take on machine learning algorithms and make it work on the thousands of classes of machines in manufacturing, requires a huge partner ecosystem. So we work very openly on contributions to standards and open source. We certainly work with partners to build a lot of value around our stuff. So for example, on stage this week, we have several partners who are going to be up there. One of them is Harmen, who builds all kinds of things that's including info-tainment units in cars and the professional equipment that goes into hotels and buildings. So we work with them to build great innovative value together and they do things that they're experts in and we do what we're experts in. >> So, from an I.o.T. perspective, what are some of the cool things that are here at I.B.M. Think 2018, that those that are attending are going to get to see and feel and touch and smell? >> Well there are some things I can talk about, things that I can't. Tomorrow we have some very exciting announcements coming up. Going to talk a lot more about Watson and I.o.T. coming together, that's all I can say about that. You'll also see physical representations of things. There's a Jaguar Land Rover out here on the floor. To look at where we have contributed significantly to the engineering and the software development inside these kinds of products like J.L.R. So they're going to be up on stage talking about some of the things we're doing together. You'll hear A.B.B. here talking about some of the work we're doing around manufacturing techniques and helping manage wind turbines. So all kinds of really cool, industrial use cases. It's really exciting and I think working in I.o.T. is great because not only do you get to talk about the technology and the analytics and the data, but you actually get to see things. So it makes all of this feel very real when you walk up to and see a thing that's infused with I.o.T. and made better because of I.B.M. >> What inning are we in? >> What's that? >> What inning are we in? >> Oh it's still early, still early. Third inning still, mostly because so much of the market is still working to figure out how to take advantage of the data and the insights about this to transform their business. I think if you thought of the dot com era and how long it took for companies to emerge to be truly digital e-businesses, on demand businesses. The I.o.T. businesses, the A.I. driven businesses of the future, still very early. Some of them, you probably don't even know their names yet. But they're going to be the leaders that's coming. >> Do you think it'll happen faster because there is an internet? Or not so much because of the physical infrastructure that has to get built out? >> The infrastructure is actually not the gate at all. >> Dave: Okay. >> The real gate is the cultural difference of having people who are data driven, data thinkers. Having a leadership role in our clients. If you can think about it, mechanical things have dominated for a hundred years. Software engineers are still not even the most senior people in most of the companies that build physical things. But to have the data scientists, have the data leaders have a strong enough role to define business process. It's really the readiness and maturity of those data leaders. >> Yeah so the culture of a mechanical engineering culture that says "don't touch my things," >> Right. >> I'm not going to let a software engineer come in and mess with it because it works, it's secure, I trust it. >> Right. >> So that's the cultural one of the cultural dimensions. >> It's to look at what the data might mean. Just understand how your users use your things or if you want to understand what they're doing with those things somewhere else. Or even with the value of your insights of your users are and building entirely new ecosystems of the data of I.o.T. >> Alright, so we're in the third inning. We'll say the top of the third. >> Okay. >> But one of the things that you shared with us is that you're excited about is this is about applied I.o.T. To get business outcomes. >> Yes. >> Shared some examples that attendees of the event are going to hear from A.B.B., you mentioned, you mentioned the >> Bret: J.L.R. >> Land Rover that's here. Harman as well. And maybe some best practices for how to advise companies to get through some of those cultural hurdles, we'll say, to start embracing the opportunities that are within the I.o.T. space. >> I think the best thing people could do is to start to really, I'm going to say it again, put value on data science. It doesn't mean everyone has to be a data geek. But it does mean you have to have a certain value on the skills and the insights that come from a data driven business. What does it mean to make decisions in real time based on your customers? For a hundred years when companies shipped a washing machine it went into someone's house and sat for 10 years and they never heard from the person ever again until they bought another one 10 years later. But now when you ship a washing machine, you want people to connect it to the wi-fi. You want to know the features that are used. Suddenly as a manufacturer of things, you have to respect the data coming off those things because they inform you on how to design better. How to deliver better service and value. Which means those engineers who were the experts in washing machines, now have to be the experts in the data of washing machines and the data of their users. So, I would say, focus on the education, the recruitment, the enablement, the empowerment of people who are data centric by nature and who are looking for the transformation of a digital business from a physical business. >> Awesome, Bret thank you so much for stopping by the Cube and sharing your insights. >> You're very welcome. >> Good luck tomorrow with your presentations and we are going to be waiting on the edge of our seats for those lots of I.o.T. announcements. >> Very exciting. >> Very exciting. >> Okay. >> Alright you heard it here. >> Thank you so much. >> You can watch all of our good stuff on thecube.net live, of course, as we are now as well as the interviews that we've already done and those that we'll be doing for the next two days as our coverage continues of I.B.M. Think 2018. Also check out siliconangle.com our media site for all of your real time coverage of this event and others. For Dave Vellante and Bret, two Vegas Veterans, I'm Lisa Martin. Stick around, Dave and I are going to be right back after a short break. (upbeat music)

>> Narrator: From San Francisco, it's theCUBE covering Samsung Developer Conference 2017. Brought to you by Samsung. >> Welcome back everyone. Live here in San Francisco at Moscone West is the exclusive coverage from theCUBE SiliconANGLE Media of the SDC 2017. I'm John Furrier the co-founder of SiliconANGLE Media and the co-host of theCUBE. My next guest is Amy Jo Kim who is the CEO of Shufflebrain. It's the parent company of gamethinking.io, a variety of other projects, and expert in the convergence of design, gaming, computer science, and et cetera. Welcome to theCUBE. >> It's a pleasure to be here. >> Thanks for coming on. Obviously we've been seeing the trend, the convergence trend for a while certainly in the tech industry. Computer science and social science coming together, that was our motto when we started our company eight years ago. But really to me the flashpoint was Steve Jobs had the technology-liberal arts crossroads. That really kind of spawned the beginning of a creative generation start thinking about the devices, how it all intersects, and not the pure play handheld. So gamers here at Samsung Development Conference and developers bring game mechanics in. That's communities, gamification, games themselves, user interface. What's your reaction to all this? You've designed a great bunch of interfaces. >> I'm, I think it's fantastic. I think what we're seeing is really a flashpoint that has several trends converging. One of the trends we have is developers, the folks here, you know are right here at this wonderful conference, they've grown up with games. They're familiar with the lexicon of games, with how games work. And so it's very natural for them when they start to build their own apps and say what will make this engaging to turn to games and look for inspiration in games? So that's been going on for a while and it's accelerating. We're also seeing that mobile technology, mobile phones, have become so ubiquitous that most of the traffic coming in on many people's experiences 70%, I recently ran a promotion for Shufflebrain, 70% of our traffic was mobile total traffic. So the ubiquity of mobile phones means that everybody's got a potential gaming machine or a machine where they can have a light, fun, engaging experience right in their pocket. So as you noted, we've moved away from single purpose game consoles, handheld or otherwise they still exist, but more and more what we see is the best games and the best game like experiences that might not be games but they the feel and the pull of games. Those are showing up on mobile phones like Samsung. >> And the screens are awesome. I'll say my Note 8 here is awesome and bigger and better and the graphics. But it's a generational shift too. Like my son was, we're designing a new app and we're kind of sitting at the drawing board and he's like, "Dad, you're a search generation. "No one searches anymore. "You actually type on the keyboard, that's like so old." So he brings up a point which is illuminated here. Which is you see voice touch, voice activation. Harman's got now the kind of interface with this audio. You're seeing cars all over the air with software. This is really the computer science, computer engineering culture interfacing with art. Where new user experiences are coming that quite frankly don't look the same. >> Exactly that's such a good point. So what's happening is that a lot of the user experiences, the back end neural networks, the AI, the sophisticated bots that we've been seeing in gaming for the last five or six years are trickling into the mainstream. And that's what you always see. Gaming is the canary in a coal mine. What we see now happening in games and what we saw a few years ago is becoming more mainstream. So if we look now at what's happening in gaming, that gives us a clue to 18 to 24 months out for app developers. >> Yeah we brought this up on day one. You nailed it. It's an early indicator. >> That's right. >> What are you seeing in that area? Because you're in the vanguard of the user interface so you have a computer science background. You understand how communities work. Which by the way, you look at anything from blockchain ICOs to game communities, community is the most important aspect right now in the world. The community role of the people are so important. You don't have a network effect. You don't have input output into the quote neural aspect of the interface because now people are involved. Not just software and data bits. I need a notification from my friend if they're right around the corner from me. So it's the role of people. >> Exactly, so I'm a multiplayer game designer. The teams I work with, because it's always a team effort, are multiplayer games. Rock Band, Covet Fashion is a more recent one. And so we've known for a long time in the gaming industry that if you want to drive deep lasting engagement, you need to create a multiplayer experience and some sort of community around that. What you'll hear gamers say is "You know, I'm kind of tired of that game "but my friends need me. "It's where my friends are, my team needs me." So that's part of what drives long term engagement. >> John: The socialization piece. >> Exactly. What we're seeing now and the opportunity I think for developers even outside of gaming is we're seeing the intersection of gaming, a style of gaming that's sort of I would call them gaming systems versus game mechanics. We're seeing gaming systems find their way into social media. Musical.ly is a great example. And Discord is another example. Discord is a platform started by gamers but now it's merging into just other people. That's for communication. Sort of like a next generation Slack but mobile and for gamers. Covet Fashion, a game I worked on with a brilliant team who actually came up with the idea at CrowdStar, really merged a cooperative game mechanic like you might see in say Portal 2 or Left for Dead with social media and very lightweight voting systems of the users themselves playing a crucial role in what's good or not. Just like in Facebook or in Instagram, your feed is going to show you what gets liked a lot, what gets popular. And games are starting to incorporate this too so that the players themselves become almost like the game pieces and become a big part of what's entertaining. We see networks like Twitch with a huge rush of popularity. That is people delivering entertainment to each other. It's not scripted. So this user generated content, this systems which let people be entertaining to each other, is the huge push that's going on in gaming. And we have, part of what makes a game so exciting, is when the game makes interacting with other people lower friction or more magical but it's still the people that makes it exciting. >> Amy Jo this is amazing. I think that you're right on it. Because remember when I was a gamer, single player game on the computer, you got bored. I mastered it. Then comes multiplayer. But you're bringing up a new dynamic which is the dynamic nature of the people themselves. And I think Twitch had an interesting experiment where the comments, which we know on Twitch are pretty bad, drove the game experience. So now you have the people being part of the input to the game itself. I mean isn't Life a game in a way? >> Sure, you could look at Life the game. I think that that's a semantic issue. There are people that really enjoy looking at life as a game And if you define a game as a structured activity with roles and goals, sure you could look at it that way. What I think is most exciting is not so much what is and isn't a game but the bleeding over of gaming systems into places like digital health and education and enterprise and fashion, and those are, and genealogy. Right now I have a client who's merging a game like experience with a genealogy crowd source experience. So I think what I'd like to leave you with and to understand is the first wave of this we called gamification where people got very excited about the visible markers of progress that are in games like points and badges and leaderboards. And that's a great opening door, but that's not where the magic is. Where the magic is is in the underlying systems that drive you toward mastery of something you care about. And that's the explosion we're seeing now. So you say what am I seeing? I'm seeing clients come to me, a game designer, in all kinds, banking, call centers, SaaS products, change transformation in companies as well as all kinds of consumer products, saying we tried gamification. It just worked in the short term. We want what makes games interesting in the long term. First of all you said the most important thing which is other people. But it's not just other people. It's other people in a playful and mastery based environment that helps you get better at something you care about getting better at. >> So this great so take me through what game system. What I hear you saying is, okay, people think of gamification as a one trick pony, a shortcut to something. You're taking a much more wholistic approach saying the game system. What does that mean? What is a game system? Because you're, what I hear you saying, is that this is like a fabric. It's not like, or an operating system maybe. How should people think of a game... >> It's a methodology or a system. A good way to think about this, are you familiar with design thinking? >> Mm-hmm. >> Are you familiar with an agile approach or Agile Lean UX? Those are systems. Those are methodologies. Those are approaches to creating great products. And they help you. Game thinking is similar. It's got elements of design thinking, elements of Agile, but it adds game design. The difference between strong game design and gamification is game design is about bringing systems to life from the inside out. And so game thinking is as much about how you bring your product to life as it is about anything that you put into the product once it's brought to life. Which is where gamification usually comes in. So it's really about building a learning architecture into the core of your game using feedback loops and using simple systems. And one more thing. Every complex system starts as a simple system that works. So it's really about building core systems and then bringing them to life with the right approach and the right people. >> It's like having a kernel or a small building block. If you overthink it you could get in trouble. >> Right. But you also have to have the right building block so you build a strong foundation. >> Yeah I remember the old days when game engines came out. There was no market for game engines when the first games came out. Then someone said hey why don't we just take the game engine and become a game engine. That was an interesting dynamic that spawned a lot of innovation. Is there an analogy to that happening now where there's new innovations that people can build on top of? Is it open source? Is there an equivalent? I'm trying to figure out where that next level up is going to be because right now we've gone like this and then we see a new level with AR and these new kinds of games and you're bringing this kind of integrated system approach is coming. >> Right so I think there's two thing that have to happen for those to take off. One of which is technology based. You have to have engines. So Unity's rise has been tremendous for the gaming industry. Many many simple game-like experiences are being built in Unity, not from scratch. And other tools like that. And then ARKit from Apple is causing an explosion of really interesting work happening, making it easier to create and experiment with an experience like Pokemon Go. So those are the bottom-up tools based changes that are really accelerating innovation in our industry. Now at the time, none of that will work if you don't have the customer demand and the customer hunger. So the other thing that's happening is that customers are being trained by Pokemon Go and things like that that oh, this is how AR could work. We've seen that VR has kind of stalled out but again, that's a special purpose hardware that's not something easy that you can get on your mobile phone in between all the other things you do. So I think it can't be overstated how powerful it is to have these platforms combined with a huge consumer base on mobile, with phones in their pocket, ready to have a compelling game-like experience that doesn't necessarily have to be a game. The world is waiting for those. >> Yeah and your point about VR, you don't want a build it they will come mentality. You got to focus on the magic formula which is-- >> Customer demand. >> Call it sticky. But some could say look it's got to be a utility and that mastery component is critical whether it's learning, friendship, or some human dopamine effect right. >> Well that's exactly what we do at gamethinking.io. We help teams and companies create a product that customers love and come back to from the ground up using gaming techniques. So anyone who's interested, that's what we do. And the reason we help people do that is it's hard, and it's incredibly high leverage. >> Yeah and you got to have the expertise to do it. And it really is. It sounds like gamethinking.io, you're going to bring architecture. It's not just going to be jump on the grenade that someone throws a project at you. Sure, if it's a big project maybe. But you're kind of train the trainer it sounds like, you're teaching people to fish if you will. >> It's product development. Gamification is often a marketing campaign. We're talking about product development. If you want to build lasting engagement and you're a product leader, then you can use these techniques to build it from the ground up but it's not a silver bullet. >> Give a plug for what you do at Shufflebrain about your company and share some advice for folks watching that might be interested. Like I want to transform my Web 2.0, my 1.0 web responsive app, or my offshore built mobile app that I hired someone to just iOS it and Android it. I want to actually build from the ground up a new architecture that's going to be, have a lot of headroom, I really want to build it from the ground up with good design thinking, game system, game thinking, with the game systems, all the magic potentially in there. What do they do? I don't know do you call the, you know there's no Yellow Pages anymore. Do you Google search it? >> Thank you that was a great setup because that's, I mean I wish that I had had this years ago when I doing a venture funded startup. I needed help. So that's why I do what I do. So what we do is take 20 years of what works and what doesn't in game and product design and turn it into a step by step toolkit with templates, instruction, training, and coaching. And let me give you a specific tip. So there's, it's a whole system we use, but one of the things that you do and if anybody wants to try this it will amaze you if you're able to do it right, one of the things that the greatest game designers, the Will Wrights and folks at CrowdStar and Harmonics, what they do is when they're bringing a new game idea to life, first of all they find out aggressively as much about what's wrong with their ideas, what's right with it, through iterative, low fidelity testing early. Secondly they test it on their superfans that shortcut for high need, high value, early adopters. Not your target market but people that can get you to your target market. Knowing how to find and identify and then leverage your superfans for very early product testing and iteration, that's how you bring your core systems to life. Not with your ultimate target market. Most people don't know this. Knowing this, and then finding those people and leveraging them will turn what's often a failure into success. >> John: That's gold. >> It's complete gold. Let me just tell you why. Because if you're able to ask very product-focused questions, again with my guidance, of these people, you can build your product around what you know they want rather than guessing. >> And you can also help the person, might have blind spot, your customer, understand what superfans are saying. Sometimes it's like they're just giving you the answer right there early on. >> That's such a good point. And when you're inside of it- >> And I have bias. I'm an entrepreneur. Oh no I want to hear what I want to hear. I'm going to change the world. (laughs) Not really. >> That's why when I was an entrepreneur I knew all this stuff but I needed a coach when I was doing this. Because you can't see outside of your bubble and that's part of the value of doing this. >> Amy, the URL is? >> Gamethinking.io. >> Gamthinking.io. Amy Jo is a coach, she is an entrepreneur, venture backed, probably has some scar tissue from that but now she's kicking ass and taking names on gamethinking.io. Great mind. Thank you for sharing an amazing tutorial. You know that's free consulting here on theCUBE right here from and expert. >> It's what I love to do. Thank you for having me. >> Amy Jo here on theCUBE. Live in San Francisco at the Samsung Developer Conference, I'm John Furrier back with more here in theCUBE after this short break. (techno music)