(upbeat music) >> Hello, and welcome to the special Cube conversation that we're launching in conjunction with Pure Accelerate. Prakash Darji is here, is the general manager of Digital Experience. They actually have a business unit dedicated to this at Pure Storage. Prakash, welcome back, good to see you. >> Yeah Dave, happy to be here. >> So a few weeks back, you and I were talking about the Shift 2 and as a service economy and which is a good lead up to Accelerate, held today, we're releasing this video in LA. This is the fifth in person Accelerate. It's got a new tagline techfest so you're making it fun, but still hanging out to the tech, which we love. So this morning you guys made some announcements expanding the portfolio. I'm really interested in your reaffirmed commitment to Evergreen. That's something that got this whole trend started in the introduction of Evergreen Flex. What is that all about? What's your vision for Evergreen Flex? >> Well, so look, this is one of the biggest moments that I think we have as a company now, because we introduced Evergreen and that was and probably still is one of the largest disruptions to happen to the industry in a decade. Now, Evergreen Flex takes the power of modernizing performance and capacity to storage beyond the box, full stop. So we first started on a project many years ago to say, okay, how can we bring that modernization concept to our entire portfolio? That means if someone's got 10 boxes, how do you modernize performance and capacity across 10 boxes or across maybe FlashBlade and FlashArray. So with Evergreen Flex, we first are starting to hyper disaggregate performance and capacity and the capacity can be moved to where you need it. So previously, you could have thought of a box saying, okay, it has this performance or capacity range or boundary, but let's think about it beyond the box. Let's think about it as a portfolio. My application needs performance or capacity for storage, what if I could bring the resources to it? So with Evergreen Flex within the QLC family with our FlashBlade and our FlashArray QLC projects, you could actually move QLC capacity to where you need it. And with FlashArray X and XL or TLC family, you could move capacity to where you need it within that family. Now, if you're enabling that, you have to change the business model because the capacity needs to get build where you use it. If you use it in a high performance tier, you could build at a high performance rate. If you use it as a lower performance tier, you could build at a lower performance rate. So we changed the business model to enable this technology flexibility, where customers can buy the hardware and they get a pay per use consumption model for the software and services, but this enables the technology flexibility to use your capacity wherever you need. And we're just continuing that journey of hyper disaggregated. >> Okay, so you solve the problem of having to allocate specific capacity or performance to a particular workload. You can now spread that across whatever products in the portfolio, like you said, you're disaggregating performance and capacity. So that's very cool. Maybe you could double click on that. You obviously talk to customers about doing this. They were in pain a little bit, right? 'Cause they had this sort of stovepipe thing. So talk a little bit about the customer feedback that led you here. >> Well, look, let's just say today if you're an application developer or you haven't written your app yet, but you know you're going to. Well, you need that at least say I need something, right? So someone's going to ask you what kind of storage do you need? How many IOPS, what kind of performance capacity, before you've written your code. And you're going to buy something and you're going to spend that money. Now at that point, you're going to go write your application, run it on that box and then say, okay, was I right or was I wrong? And you know what? You were guessing before you wrote the software. After you wrote the software, you can test it and decide what you need, how it's going to scale, et cetera. But if you were wrong, you already bought something. In a hyper disaggregated world, that capacity is not a sunk cost, you can use it wherever you want. You can use capacity of somewhere else and bring it over there. So in the world of application development and in the world of storage, today people think about, I've got a workload, it's SAP, it's Oracle, I've built this custom app. I need to move it to a tier of storage, a performance class. Like you think about the application and you think about moving the application. And it takes time to move the application, takes performance, takes loan, it's a scheduled event. What if you said, you know what? You don't have to do any of that. You just move the capacity to where you need it, right? >> Yep. >> So the application's there and you actually have the ability to instantaneously move the capacity to where you need it for the application. And eventually, where we're going is we're looking to do the same thing across the performance hearing. So right now, the biggest benefit is the agility and flexibility a customer has across their fleet. So Evergreen was great for the customer with one array, but Evergreen Flex now brings that power to the entire fleet. And that's not tied to just FlashArray or FlashBlade. We've engineered a data plane in our direct flash fabric software to be able to take on the personality of the system it needs to go into. So when a data pack goes into a FlashBlade, that data pack is optimized for use in that scale out architecture with the metadata for FlashBlade. When it goes into a FlashArray C it's optimized for that metadata structure. So our Purity software has made this transformative to be able to do this. And we created a business model that allowed us to take advantage of this technology flexibility. >> Got it. Okay, so you got this mutually interchangeable performance and capacity across the portfolio beautiful. And I want to come back to sort of the Purity, but help me understand how this is different from just normal Evergreen, existing evergreen options. You mentioned the one array, but help us understand that more fully. >> Well, look, so in addition to this, like we had Evergreen Gold historically. We introduced Evergreen Flex and we had Pure as a service. So you had kind of two spectrums previously. You had Evergreen Gold on one hand, which modernized the performance and capacity of a box. You had Pure as a service that said don't worry about the box, tell me how many IOPS you have and will run and operate and manage that service for you. I think we've spoken about that previously on theCUBE. >> Yep. >> Now, we have this model where it's not just about the box, we have this model where we say, you know what, it's your fleet. You're going to run and operate and manage your fleet and you could move the capacity to where you need it. So as we started thinking about this, we decided to unify our entire portfolio of sub software and subscription services under the Evergreen brand. Evergreen Gold we're renaming to Evergreen Forever. We've actually had seven customers just crossed a decade of updates Forever Evergreen within a box. So Evergreen Forever is about modernizing a box. Evergreen Flex is about modernizing your fleet and Evergreen one, which is our rebrand of Pure as a service is about modernizing your labor. Instead of you worrying about it, let us do it for you. Because if you're an application developer and you're trying to figure out, where should I put my capacity? Where should I do it? You can just sign up for the IOPS you need and let us actually deliver and move the components to where you need it for performance, capacity, management, SLAs, et cetera. So as we think about this, for us this is a spectrum and a continuum of where you're at in the modernization journey to software subscription and services. >> Okay, got it. So why did you feel like now was the right time for the rebranding and the renaming convention, what's behind? What was the thing? Take us inside the internal conversations and the chalkboard discussion? >> Well, look, the chalkboard discussion's simple. It's everything was built on the Evergreen stateless architecture where within a box, right? We disaggregated the performance and capacity within the box already, 10 years ago within Evergreen. And that's what enabled us to build Pure as a service. That's why I say like when companies say they built a service, I'm like it's not a service if you have to do a data migration. You need a stateless architecture that's disaggregated. You can almost think of this as the anti hyper-converge, right? That's going the other way. It's hyper disaggregated. >> Right. >> And that foundation is true for our whole portfolio. That was fundamental, the Evergreen architecture. And then if Gold is modernizing a box and Flex is modernizing your fleet and your portfolio and Pure as a service is modernizing the labor, it is more of a continuation in the spectrum of how do you ensure you get better with age, right? And it's like one of those things when you think about a car. Miles driven on a car means your car's getting older and it doesn't necessarily get better with age, right? What's interesting when you think about the human body, yeah, you get older and some people deteriorate with age and some people it turns out for a period of time, you pick up some muscle mass, you get a little bit older, you get a little bit wiser and you get a little bit better with age for a while because you're putting in the work to modernize, right? But where in infrastructure and hardware and technology are you at the point where it always just gets better with age, right? We've introduced that concept 10 years ago. And we've now had proven industry success over a decade, right? As I mentioned, our first seven customers who've had a decade of Evergreen update started with an FA-300 way back when, and since then performance and capacity has been getting better over time with Evergreen Forever. So this is the next 10 years of it getting better and better for the company and not just tying it to the box because now we've grown up, we've got customers with like large fleets. I think one of our customers just hit 900 systems, right? >> Wow. >> So when you have 900 systems, right? And you're running a fleet you need to think about, okay, how am I using these resources? And in this day and age in that world, power becomes a big thing because if you're using resources inefficiently and the cost of power and energy is up, you're going to be in a world of hurt. So by using Flex where you can move the capacity to where it's needed, you're creating the most efficient operating environment, which is actually the lowest power consumption environment as well. >> Right. >> So we're really excited about this journey of modernizing, but that rebranding just became kind of a no brainer to us because it's all part of the spectrum on your journey of whether you're a single array customer, you're a fleet customer, or you don't want to even run, operate and manage. You can actually just say, you know what, give me the guarantee in the SLA. So that's the spectrum that informed the rebranding. >> Got it. Yeah, so to your point about the human body, all you got to do is look at Tom Brady's NFL combine videos and you'll see what a transformation. Fine wine is another one. I like the term hyper disaggregated because that to me is consistent with what's happening with the cloud and edge. We're building this hyper distributed or disaggregated system. So I want to just understand a little bit about you mentioned Purity so there's this software obviously is the enabler here, but what's under the covers? Is it like a virtualizer or megaload balancer, metadata manager, what's the tech behind this? >> Yeah, so we'll do a little bit of a double tech, right? So we have this concept of drives where in Purity, we build our own software for direct flash that takes the NAND and we do the NAND management as we're building our drives in Purity software. Now ,that advantage gives us the ability to say how should this drive behave? So in a FlashArray C system, it can behave as part of a FlashArray C and its usable capacity that you can write because the metadata and some of the system information is in NVRAM as part of the controller, right? So you have some metadata capability there. In a legend architecture for example, you have a distributed Blade architecture. So you need parts of that capacity to operate almost like a single layer chip where you can actually have metadata operations independent of your storage operations that operate like QLC. So we actually manage the NAND in a very very different way based on the persona of the system it's going into, right? So this capacity to make it usable, right? It's like saying a competitor could go ahead name it, Dell that has power max in Isilon, HPE that has single store and three power and nimble and like you name, like can you really from a technology standpoint say your capacity can be used anywhere or all these independent systems. Everyone's thinking about the world like a system, like here's this system, here's that system, here's that system. And your capacity is locked into a system. To be able to unlock that capacity to the system, you need to behave differently with the media type in the operating environment you're going into and that's what Purity does, right? So we are doing that as part of our direct Flex software around how we manage these drives to enable this. >> Well, it's the same thing in the cloud precaution, right? I mean, you got different APIs and primitive for object, for block, for file. Now, it's all programmable infrastructure so that makes it easier, but to the point, it's still somewhat stovepipe. So it's funny, it's good to see your commitment to Evergreen, I think you're right. You lay down the gauntlet a decade plus ago. First everybody ignored you and then they kind of laughed at you, then they criticized you, and then they said, oh, then you guys reached the escape velocity. So you had a winning hand. So I'm interested in that sort of progression over the past decade where you're going, why this is so important to your customers, where you're trying to get them ultimately. >> Well, look, the thing that's most disappointing is if I bought 100 terabytes still have to re-buy it every three or five years. That seems like a kind of ridiculous proposition, but welcome to storage. You know what I mean? That's what most people do with Evergreen. We want to end data migrations. We want to make sure that every software updates, hardware updates, non disruptive. We want to make it easy to deploy and run at scale for your fleet. And eventually we want everyone to move to our Evergreen one, formerly Pure as a service where we can run and operate and manage 'cause this is all about trust. We're trying to create trust with the customer to say, trust us, to run and operate and scale for you and worry about your business because we make tech easy. And like think about this hyper disaggregated if you go further. If you're going further with hyper disaggregated, you can think about it as like performance and capacity is your Lego building blocks. Now for anyone, I have a son, he wants to build a Lego Death Star. He didn't have that manual, he's toast. So when you move to at scale and you have this hyper disaggregated world and you have this unlimited freedom, you have unlimited choice. It's the problem of the cloud today, too much choice, right? There's like hundreds of instances of this, what do I even choose? >> Right. >> Well, so the only way to solve that problem and create simplicity when you have so much choice is put data to work. And that's where Pure one comes in because we've been collecting and we can scan your landscape and tell you, you should move these types of resources here and move those types of resources there, right? In the past, it was always about you should move this application there or you should move this application there. We're actually going to turn the entire industry on it's head. It's not like applications and data have gravity. So let's think about moving resources to where that are needed versus saying resources are a fixed asset, let's move the applications there. So that's a concept that's new to the industry. Like we're creating that concept, we're introducing that concept because now we have the technology to make that reality a new efficient way of running storage for the world. Like this is that big for the company. >> Well, I mean, a lot of the failures in data analytics and data strategies are a function of trying to jam everything into a single monolithic system and hyper centralize it. Data by its very nature is distributed. So hyper disaggregated fits that model and the pendulum's clearly swinging to that. Prakash, great to have you, purestorage.com I presume is where I can learn more? >> Oh, absolutely. We're super excited and our pent up by demand I think in this space is huge so we're looking forward to bringing this innovation to the world. >> All right, hey, thanks again. Great to see you, I appreciate you coming on and explaining this new model and good luck with it. >> All right, thank you. >> All right, and thanks for watching. This is David Vellante, and appreciate you watching this Cube conversation, we'll see you next time. (upbeat music)

>>mhm. Welcome back to Boston. This is the cubes coverage of the Red Hat Summit 2022. The first Red Hat Summit we've done face to face in at least two years. 2019 was our last one. We're kind of rounding the far turn, you know, coming up for the home stretch. My name is Dave Valentin here with Paul Gillon. A J monger is here is a senior director of Iot. The Iot group for developer solutions and engineering at Intel. AJ, thanks for coming on the Cube. Thank you so much. We heard your colleague this morning and the keynote talking about the Dev Cloud. I feel like I need a Dev Cloud. What's it all about? >>So, um, we've been, uh, working with developers and the ecosystem for a long time, trying to build edge solutions. A lot of time people think about it. Solutions as, like, just computer the edge. But what really it is is you've got to have some component of the cloud. There is a network, and there is edge and edge is complicated because of the variety of devices that you need. And when you're building a solution, you got to figure out, like, where am I going to push the computer? How much of the computer I'm going to run in the cloud? How much of the computer? I'm gonna push it at the network and how much I need to run it at the edge. A lot of times what happens for developers is they don't have one environment where all of the three come together. And so what we said is, um, today the way it works is you have all these edge devices that customers by the instal, they set it up and they try to do all of that. And then they have a cloud environment they do to their development. And then they figure out how all of this comes together. And all of these things are only when they are integrating it at the customer at the solution space is when they try to do it. So what we did is we took all of these edge devices, put it in the cloud and gave one environment for cloud to the edge. Very good to your complete solution. >>Essentially simulates. >>No, it's not >>simulating span. So the cloud spans the cloud, the centralised cloud out to the edge. You >>know, what we did is we took all of these edge devices that will theoretically get deployed at the edge like we took all these variety of devices and putting it put it in a cloud environment. So these are non rack mountable devices that you can buy in the market today that you just have, like, we have about 500 devices in the cloud that you have from atom to call allusions to F. P. G s to head studio cards to graphics. All of these devices are available to you. So in one environment you have, like, you can connect to any of the cloud the hyper scholars, you could connect to any of these network devices. You can define your network topology. You could bring in any of your sources that is sitting in the gate repository or docker containers that may be sitting somewhere in a cloud environment, or it could be sitting on a docker hub. You can pull all of these things together, and we give you one place where you can build it where you can test it. You can performance benchmark it so you can know when you're actually going to the field to deploy it. What type of sizing you need. So >>let me show you, understand? If I want to test, uh, an actual edge device using 100 gig Ethernet versus an Mpls versus the five G, you can do all that without virtualizing. >>So all the H devices are there today, and the network part of it, we are building with red hat together where we are putting everything on this environment. So the network part of it is not quite yet solved, but that's what we want to solve. But the goal is here is you can let's say you have five cameras or you have 50 cameras with different type of resolutions. You want to do some ai inference type of workloads at the edge. What type of compute you need, what type of memory you need, How many devices do you need and where do you want to push the data? Because security is very important at the edge. So you gotta really figure out like I've got to secure the data on flight. I want to secure the data at Brest, and how do you do the governance of it. How do you kind of do service governance? So that all the services different containers that are running on the edge device, They're behaving well. You don't have one container hogging up all the memory or hogging up all the compute, or you don't have, like, certain points in the day. You might have priority for certain containers. So all of these mortals, where do you run it? So we have an environment that you could run all of that. >>Okay, so take that example of AI influencing at the edge. So I've got an edge device and I've developed an application, and I'm going to say Okay, I want you to do the AI influencing in real time. You got something? They become some kind of streaming data coming in, and I want you to persist, uh, every hour on the hour. I want to save that time stamp. Or if the if some event, if a deer runs across the headlights, I want you to persist that day to send that back to the cloud and you can develop that tested, benchmark >>it right, and then you can say that. Okay, look in this environment I have, like, five cameras, like at different angles, and you want to kind of try it out. And what we have is a product which is into, um, open vino, which is like an open source product, which does all of the optimizations you need for age in France. So you develop the like to recognise the deer in your example. I developed the training model somewhere in the cloud. Okay, so I have, like, I developed with all of the things have annotated the different video streams. And I know that I'm recognising a deer now. Okay, so now you need to figure out Like when the deer is coming and you want to immediately take an action. You don't want to send all of your video streams to the cloud. It's too expensive. Bandwidth costs a lot. So you want to compute that inference at the edge? Okay. In order to do that inference at the edge, you need some environment. You should be able to do it. And to build that solution What type of age device do you really need? What type of compute you need? How many cameras are you computing it? What different things you're not only recognising a deer, probably recognising some other objects could do all of that. In fact, one of the things happened was I took my nephew to San Diego Zoo and he was very disappointed that he couldn't see the chimpanzees. Uh, that was there, right, the gorillas and other things. So he was very sad. So I said, All right, there should be a better way. I saw, like there was a stream of the camera feed that was there. So what we did is we did an edge in friends and we did some logic to say, At this time of the day, the gorillas get fed, so there's likelihood of you actually seeing the gorilla is very high. So you just go at that point and so that you see >>it, you >>capture, That's what you do, and you want to develop that entire solution. It's based on whether, based on other factors, you need to bring all of these services together and build a solution, and we offer an environment that allows you to do it. Will >>you customise the the edge configuration for the for the developer If if they want 50 cameras. That's not You don't have 50 cameras available, right? >>It's all cameras. What we do is we have a streaming capability that we support so you can upload all your videos. And you can say I want to now simulate 50 streams. Want to simulate 30 streams? Or I want to do this right? Or just like two or three videos that you want to just pull in. And you want to be able to do the infant simultaneously, running different algorithms at the edge. All of that is supported, and the bigger challenge at the edge is developing. Solution is fine. And now when you go to actual deployment and post deployment monitoring, maintenance, make sure that you're like managing it. It's very complicated. What we have seen is over 50% 51% to be precise of developers are developed some kind of a cloud native applications recently, right? So that we believe that if you bring that type of a cloud native development model to the edge, then you're scaling problem. Your maintenance problem, you're like, how do you actually deploy it? All of these challenges can be better managed, Um, and if you run all of that is an orchestration later on kubernetes and we run everything on top of open shift, so you have a deployment ready solution already there it's everything is containerised everything. You have it as health charged Dr Composed. You have all their you have tested and in this environment, and now you go take that to the deployment. And if it is there on any standard kubernetes environment or in an open ship, you can just straight away deploy your application. >>What's that edge architecture looked like? What's Intel's and red hats philosophy around? You know what's programmable and it's different. I know you can run a S, a p a data centre. You guys got that covered? What's the edge look like? What's that architecture of silicon middleware? Describe that for us. >>So at the edge, you think about it, right? It can run traditional, Uh, in an industrial PC. You have a lot of Windows environment. You have a lot of the next. They're now in a in an edge environment. Quite a few of these devices. I'm not talking about Farage where there are tiny micro controllers and these devices I'm talking about those devices that connect to these forage devices. Collect the data. Do some analytics do some compute that type of thing. You have foraged devices. Could be a camera. Could be a temperature sensor. Could be like a weighing scale. Could be anything. It could be that forage and then all of that data instead of pushing all the data to the cloud. In order for you to do the analysis, you're going to have some type of an edge set of devices where it is collecting all this data, doing some decisions that's close to the data. You're making some analysis there, all of that stuff, right? So you need some analysis tools, you need certain other things. And let's say that you want to run like, UH, average costs or rail or any of these operating systems at the edge. Then you have an ability for you to manage all of that. Using a control note, the control node can also sit at the edge. In some cases, like in a smart factory, you have a little data centre in a smart factory or even in a retail >>store >>behind a closet. You have, like a bunch of devices that are sitting there, correct. And those devices all can be managed and clustered in an environment. So now the question is, how do you deploy applications to that edge? How do you collect all the data that is sitting through the camera? Other sensors and you're processing it close to where the data is being generated make immediate decisions. So the architecture would look like you have some club which does some management of this age devices management of this application, some type of control. You have some network because you need to connect to that. Then you have the whole plethora of edge, starting from an hybrid environment where you have an entire, like a mini data centre sitting at the edge. Or it could be one or two of these devices that are just collecting data from these sensors and processing it that is the heart of the other challenge. The architecture varies from different verticals, like from smart cities to retail to healthcare to industrial. They have all these different variations. They need to worry about these, uh, different environments they are going to operate under, uh, they have different regulations that they have to look into different security protocols that they need to follow. So your solution? Maybe it is just recognising people and identifying if they are wearing a helmet or a coal mine, right, whether they are wearing a safety gear equipment or not, that solution versus you are like driving in a traffic in a bike, and you, for safety reasons. We want to identify the person is wearing a helmet or not. Very different use cases, very different environments, different ways in which you are operating. But that is where the developer needs to have. Similar algorithms are used, by the way, but how you deploy it very, quite a bit. >>But the Dev Cloud make sure I understand it. You talked about like a retail store, a great example. But that's a general purpose infrastructure that's now customised through software for that retail environment. Same thing with Telco. Same thing with the smart factory, you said, not the far edge, right, but that's coming in the future. Or is that well, that >>extends far edge, putting everything in one cloud environment. We did it right. In fact, I put some cameras on some like ipads and laptops, and we could stream different videos did all of that in a data centre is a boring environment, right? What are you going to see? A bunch of racks and service, So putting far edge devices there didn't make sense. So what we did is you could just have an easy ability for you to stream or connect or a Plourde This far edge data that gets generated at the far edge. Like, say, time series data like you can take some of the time series data. Some of the sensor data are mostly camera data videos. So you upload those videos and that is as good as your streaming those videos. Right? And that means you are generating that data. And then you're developing your solution with the assumption that the camera is observing whatever is going on. And then you do your age inference and you optimise it. You make sure that you size it, and then you have a complete solution. >>Are you supporting all manner of microprocessors at the edge, including non intel? >>Um, today it is all intel, but the plan, because we are really promoting the whole open ecosystem and things like that in the future. Yes, that is really talking about it, so we want to be able to do that in the future. But today it's been like a lot of the we were trying to address the customers that we are serving today. We needed an environment where they could do all of this, for example, and what circumstances would use I five versus i nine versus putting an algorithm on using a graphics integrated graphics versus running it on a CPU or running it on a neural computer stick. It's hard, right? You need to buy all those devices you need to experiment your solutions on all of that. It's hard. So having everything available in one environment, you could compare and contrast to see what type of a vocal or makes best sense. But it's not >>just x 86 x 86 your portfolio >>portfolio of F. P. G s of graphics of like we have all what intel supports today and in future, we would want to open it up. So how >>do developers get access to this cloud? >>It is all free. You just have to go sign up and register and, uh, you get access to it. It is difficult dot intel dot com You go there, and the container playground is all available for free for developers to get access to it. And you can bring in container workloads there, or even bare metal workloads. Um, and, uh, yes, all of it is available for you >>need to reserve the endpoint devices. >>Comment. That is where it is. An interesting technology. >>Govern this. Correct. >>So what we did was we built a kind of a queuing system. Okay, So, schedule, er so you develop your application in a controlled north, and only you need the edge device when you're scheduling that workload. Okay, so we have this scheduling systems, like we use Kafka and other technologies to do the scheduling in the container workload environment, which are all the optimised operators that are available in an open shift, um, environment. So we regard those operators. Were we installed it. So what happens is you take your work, lord, and you run it. Let's say on an I seven device, when you're running that workload and I summon device, that device is dedicated to you. Okay, So and we've instrumented each of these devices with telemetry so we could see at the point your workload is running on that particular device. What is the memory looking like power looking like How hard is the device running? What is a compute looking like? So we capture all that metrics. Then what you do is you take it and run it on a 99 or run it on a graphic, so can't run it on an F p g a. Then you compare and contrast. And you say Huh? Okay for this particular work, Lord, this device makes best sense. In some cases, I'll tell you. Right, Uh, developers have come back and told me I don't need a bigger process that I need bigger memory. >>Yeah, sure, >>right. And some cases they've said, Look, I have I want to prioritise accuracy over performance because if you're in a healthcare setting, accuracy is more important. In some cases, they have optimised it for the size of the device because it needs to fit in the right environment in the right place. So every use case where you optimise is up to the solution up to the developer, and we give you an ability for you to do that kind >>of folks are you seeing? You got hardware developers, you get software developers are right, people coming in. And >>we have a lot of system integrators. We have enterprises that are coming in. We are seeing a lot of, uh, software solution developers, independent software developers. We also have a lot of students are coming in free environment for them to kind of play with in sort of them having to buy all of these devices. We're seeing those people. Um I mean, we are pulling through a lot of developers in this environment currently, and, uh, we're getting, of course, feedback from the developers. We are just getting started here. We are continuing to improve our capabilities. We are adding, like, virtualisation capabilities. We are working very closely with red hat to kind of showcase all the goodness that's coming out of red hat, open shift and other innovations. Right? We heard, uh, like, you know, in one of the open shift sessions, they're talking about micro shifts. They're talking about hyper shift, the talking about a lot of these innovations, operators, everything that is coming together. But where do developers play with all of this? If you spend half your time trying to configure it, instal it and buy the hardware, Trying to figure it out. You lose patience. What we have time, you lose time. What is time and it's complicated, right? How do you set up? Especially when you involve cloud. It has network. It has got the edge. You need all of that right? Set up. So what we have done is we've set up everything for you. You just come in. And by the way, not only just that what we realised is when you go talk to customers, they don't want to listen to all our optimizations processors and all that. They want to say that I am here to solve my retail problem. I want to count the people coming into my store, right. I want to see that if there is any spills that I recognise and I want to go clean it up before a customer complaints about it or I have a brain tumour segmentation where I want to identify if the tumour is malignant or not, right and I want to telehealth solutions. So they're really talking about these use cases that are talking about all these things. So What we did is we build many of these use cases by talking to customers. We open sourced it and made it available on Death Cloud for developers to use as a starting point so that they have this retail starting point or they have this healthcare starting point. All these use cases so that they have all the court we have showed them how to contain arise it. The biggest problem is developers still don't know at the edge how to bring a legacy application and make it cloud native. So they just wrap it all into one doctor and they say, OK, now I'm containerised got a lot more to do. So we tell them how to do it, right? So we train these developers, we give them an opportunity to experiment with all these use cases so that they get closer and closer to what the customer solutions need to be. >>Yeah, we saw that a lot with the early cloud where they wrapped their legacy apps in a container, shove it into the cloud. Say it's really hosting a legacy. Apps is all it was. It wasn't It didn't take advantage of the cloud. Never Now people come around. It sounds like a great developer. Free resource. Take advantage of that. Where do they go? They go. >>So it's def cloud dot intel dot com >>death cloud dot intel dot com. Check it out. It's a great freebie, AJ. Thanks very much. >>Thank you very much. I really appreciate your time. All right, >>keep it right there. This is Dave Volonte for Paul Dillon. We're right back. Covering the cube at Red Hat Summit 2022. >>Mhm. Yeah. Mhm. Mm.

from around the globe it's thecube with digital coverage of exascale day made possible by hewlett packard enterprise welcome everyone to the cube celebration of exascale day ben bennett is here he's an hpc strategist and evangelist at hewlett-packard enterprise ben welcome good to see you good to see you too son hey well let's evangelize exascale a little bit you know what's exciting you uh in regards to the coming of exoskilled computing um well there's a couple of things really uh for me historically i've worked in super computing for many years and i have seen the coming of several milestones from you know actually i'm old enough to remember gigaflops uh coming through and teraflops and petaflops exascale is has been harder than many of us anticipated many years ago the sheer amount of technology that has been required to deliver machines of this performance has been has been us utterly staggering but the exascale era brings with it real solutions it gives us opportunities to do things that we've not been able to do before if you look at some of the the most powerful computers around today they've they've really helped with um the pandemic kovid but we're still you know orders of magnitude away from being able to design drugs in situ test them in memory and release them to the public you know we still have lots and lots of lab work to do and exascale machines are going to help with that we are going to be able to to do more um which ultimately will will aid humanity and they used to be called the grand challenges and i still think of them as that i still think of these challenges for scientists that exascale class machines will be able to help but also i'm a realist is that in 10 20 30 years time you know i should be able to look back at this hopefully touch wood look back at it and look at much faster machines and say do you remember the days when we thought exascale was faster yeah well you mentioned the pandemic and you know the present united states was tweeting this morning that he was upset that you know the the fda in the u.s is not allowing the the vaccine to proceed as fast as you'd like it in fact it the fda is loosening some of its uh restrictions and i wonder if you know high performance computing in part is helping with the simulations and maybe predicting because a lot of this is about probabilities um and concerns is is is that work that is going on today or are you saying that that exascale actually you know would be what we need to accelerate that what's the role of hpc that you see today in regards to sort of solving for that vaccine and any other sort of pandemic related drugs so so first a disclaimer i am not a geneticist i am not a biochemist um my son is he tries to explain it to me and it tends to go in one ear and out the other um um i just merely build the machines he uses so we're sort of even on that front um if you read if you had read the press there was a lot of people offering up systems and computational resources for scientists a lot of the work that has been done understanding the mechanisms of covid19 um have been you know uncovered by the use of very very powerful computers would exascale have helped well clearly the faster the computers the more simulations we can do i think if you look back historically no vaccine has come to fruition as fast ever under modern rules okay admittedly the first vaccine was you know edward jenner sat quietly um you know smearing a few people and hoping it worked um i think we're slightly beyond that the fda has rules and regulations for a reason and we you don't have to go back far in our history to understand the nature of uh drugs that work for 99 of the population you know and i think exascale widely available exoscale and much faster computers are going to assist with that imagine having a genetic map of very large numbers of people on the earth and being able to test your drug against that breadth of person and you know that 99 of the time it works fine under fda rules you could never sell it you could never do that but if you're confident in your testing if you can demonstrate that you can keep the one percent away for whom that drug doesn't work bingo you now have a drug for the majority of the people and so many drugs that have so many benefits are not released and drugs are expensive because they fail at the last few moments you know the more testing you can do the more testing in memory the better it's going to be for everybody uh personally are we at a point where we still need human trials yes do we still need due diligence yes um we're not there yet exascale is you know it's coming it's not there yet yeah well to your point the faster the computer the more simulations and the higher the the chance that we're actually going to going to going to get it right and maybe compress that time to market but talk about some of the problems that you're working on uh and and the challenges for you know for example with the uk government and maybe maybe others that you can you can share with us help us understand kind of what you're hoping to accomplish so um within the united kingdom there was a report published um for the um for the uk research institute i think it's the uk research institute it might be epsrc however it's the body of people responsible for funding um science and there was a case a science case done for exascale i'm not a scientist um a lot of the work that was in this documentation said that a number of things that can be done today aren't good enough that we need to look further out we need to look at machines that will do much more there's been a program funded called asimov and this is a sort of a commercial problem that the uk government is working with rolls royce and they're trying to research how you build a full engine model and by full engine model i mean one that takes into account both the flow of gases through it and how those flow of gases and temperatures change the physical dynamics of the engine and of course as you change the physical dynamics of the engine you change the flow so you need a closely coupled model as air travel becomes more and more under the microscope we need to make sure that the air travel we do is as efficient as possible and currently there aren't supercomputers that have the performance one of the things i'm going to be doing as part of this sequence of conversations is i'm going to be having an in detailed uh sorry an in-depth but it will be very detailed an in-depth conversation with professor mark parsons from the edinburgh parallel computing center he's the director there and the dean of research at edinburgh university and i'm going to be talking to him about the azimoth program and and mark's experience as the person responsible for looking at exascale within the uk to try and determine what are the sort of science problems that we can solve as we move into the exoscale era and what that means for humanity what are the benefits for humans yeah and that's what i wanted to ask you about the the rolls-royce example that you gave it wasn't i if i understood it wasn't so much safety as it was you said efficiency and so that's that's what fuel consumption um it's it's partly fuel consumption it is of course safety there is a um there is a very specific test called an extreme event or the fan blade off what happens is they build an engine and they put it in a cowling and then they run the engine at full speed and then they literally explode uh they fire off a little explosive and they fire a fan belt uh a fan blade off to make sure that it doesn't go through the cowling and the reason they do that is there has been in the past uh a uh a failure of a fan blade and it came through the cowling and came into the aircraft depressurized the aircraft i think somebody was killed as a result of that and the aircraft went down i don't think it was a total loss one death being one too many but as a result you now have to build a jet engine instrument it balance the blades put an explosive in it and then blow the fan blade off now you only really want to do that once it's like car crash testing you want to build a model of the car you want to demonstrate with the dummy that it is safe you don't want to have to build lots of cars and keep going back to the drawing board so you do it in computers memory right we're okay with cars we have computational power to resolve to the level to determine whether or not the accident would hurt a human being still a long way to go to make them more efficient uh new materials how you can get away with lighter structures but we haven't got there with aircraft yet i mean we can build a simulation and we can do that and we can be pretty sure we're right um we still need to build an engine which costs in excess of 10 million dollars and blow the fan blade off it so okay so you're talking about some pretty complex simulations obviously what are some of the the barriers and and the breakthroughs that are kind of required you know to to do some of these things that you're talking about that exascale is going to enable i mean presumably there are obviously technical barriers but maybe you can shed some light on that well some of them are very prosaic so for example power exoscale machines consume a lot of power um so you have to be able to design systems that consume less power and that goes into making sure they're cooled efficiently if you use water can you reuse the water i mean the if you take a laptop and sit it on your lap and you type away for four hours you'll notice it gets quite warm um an exascale computer is going to generate a lot more heat several megawatts actually um and it sounds prosaic but it's actually very important to people you've got to make sure that the systems can be cooled and that we can power them yeah so there's that another issue is the software the software models how do you take a software model and distribute the data over many tens of thousands of nodes how do you do that efficiently if you look at you know gigaflop machines they had hundreds of nodes and each node had effectively a processor a core a thread of application we're looking at many many tens of thousands of nodes cores parallel threads running how do you make that efficient so is the software ready i think the majority of people will tell you that it's the software that's the problem not the hardware of course my friends in hardware would tell you ah software is easy it's the hardware that's the problem i think for the universities and the users the challenge is going to be the software i think um it's going to have to evolve you you're just you want to look at your machine and you just want to be able to dump work onto it easily we're not there yet not by a long stretch of the imagination yeah consequently you know we one of the things that we're doing is that we have a lot of centers of excellence is we will provide well i hate say the word provide we we sell super computers and once the machine has gone in we work very closely with the establishments create centers of excellence to get the best out of the machines to improve the software um and if a machine's expensive you want to get the most out of it that you can you don't just want to run a synthetic benchmark and say look i'm the fastest supercomputer on the planet you know your users who want access to it are the people that really decide how useful it is and the work they get out of it yeah the economics is definitely a factor in fact the fastest supercomputer in the planet but you can't if you can't afford to use it what good is it uh you mentioned power uh and then the flip side of that coin is of course cooling you can reduce the power consumption but but how challenging is it to cool these systems um it's an engineering problem yeah we we have you know uh data centers in iceland where it gets um you know it doesn't get too warm we have a big air cooled data center in in the united kingdom where it never gets above 30 degrees centigrade so if you put in water at 40 degrees centigrade and it comes out at 50 degrees centigrade you can cool it by just pumping it round the air you know just putting it outside the building because the building will you know never gets above 30 so it'll easily drop it back to 40 to enable you to put it back into the machine um right other ways to do it um you know is to take the heat and use it commercially there's a there's a lovely story of they take the hot water out of the supercomputer in the nordics um and then they pump it into a brewery to keep the mash tuns warm you know that's that's the sort of engineering i can get behind yeah indeed that's a great application talk a little bit more about your conversation with professor parsons maybe we could double click into that what are some of the things that you're going to you're going to probe there what are you hoping to learn so i think some of the things that that are going to be interesting to uncover is just the breadth of science that can be uh that could take advantage of exascale you know there are there are many things going on that uh that people hear about you know we people are interested in um you know the nobel prize they might have no idea what it means but the nobel prize for physics was awarded um to do with research into black holes you know fascinating and truly insightful physics um could it benefit from exascale i have no idea uh i i really don't um you know one of the most profound pieces of knowledge in in the last few hundred years has been the theory of relativity you know an austrian patent clerk wrote e equals m c squared on the back of an envelope and and voila i i don't believe any form of exascale computing would have helped him get there any faster right that's maybe flippant but i think the point is is that there are areas in terms of weather prediction climate prediction drug discovery um material knowledge engineering uh problems that are going to be unlocked with the use of exascale class systems we are going to be able to provide more tools more insight [Music] and that's the purpose of computing you know it's not that it's not the data that that comes out and it's the insight we get from it yeah i often say data is plentiful insights are not um ben you're a bit of an industry historian so i've got to ask you you mentioned you mentioned mentioned gigaflop gigaflops before which i think goes back to the early 1970s uh but the history actually the 80s is it the 80s okay well the history of computing goes back even before that you know yes i thought i thought seymour cray was you know kind of father of super computing but perhaps you have another point of view as to the origination of high performance computing [Music] oh yes this is um this is this is one for all my colleagues globally um you know arguably he says getting ready to be attacked from all sides arguably you know um computing uh the parallel work and the research done during the war by alan turing is the father of high performance computing i think one of the problems we have is that so much of that work was classified so much of that work was kept away from commercial people that commercial computing evolved without that knowledge i uh i have done in in in a previous life i have done some work for the british science museum and i have had the great pleasure in walking through the the british science museum archives um to look at how computing has evolved from things like the the pascaline from blaise pascal you know napier's bones the babbage's machines uh to to look all the way through the analog machines you know what conrad zeus was doing on a desktop um i think i think what's important is it doesn't matter where you are is that it is the problem that drives the technology and it's having the problems that requires the you know the human race to look at solutions and be these kicks started by you know the terrible problem that the us has with its nuclear stockpile stewardship now you've invented them how do you keep them safe originally done through the ascii program that's driven a lot of computational advances ultimately it's our quest for knowledge that drives these machines and i think as long as we are interested as long as we want to find things out there will always be advances in computing to meet that need yeah and you know it was a great conversation uh you're a brilliant guest i i love this this this talk and uh and of course as the saying goes success has many fathers so there's probably a few polish mathematicians that would stake a claim in the uh the original enigma project as well i think i think they drove the algorithm i think the problem is is that the work of tommy flowers is the person who took the algorithms and the work that um that was being done and actually had to build the poor machine he's the guy that actually had to sit there and go how do i turn this into a machine that does that and and so you know people always remember touring very few people remember tommy flowers who actually had to turn the great work um into a working machine yeah super computer team sport well ben it's great to have you on thanks so much for your perspectives best of luck with your conversation with professor parsons we'll be looking forward to that and uh and thanks so much for coming on thecube a complete pleasure thank you and thank you everybody for watching this is dave vellante we're celebrating exascale day you're watching the cube [Music]

>> from our studios in the heart of Silicon Valley, Palo Alto, California. It is a cute conversation. >> Everyone. Welcome to this Special Cube conversation here at the Palo Alto Cube Studios. I'm John for a host of Cuba here. Moritz man is the head of the product management team at Open Systems A G. Great to see you again. Thanks for coming in. >> Hey, John. Thanks for having me. >> So last time we spoke, you had your event in Las Vegas. You guys are launching. You have a new headquarters here in Silicon Valley. Opened up this past spring. Congratulations. Thank you. >> Yeah, it's a great, great venue to start, and we set foot on the Silicon Valley ground. So to make our way to >> I know you've been super busy with the new building and rolling out, expanding heavily here in the Valley. But you guys were in the hottest area that we're covering Security Cloud security on premise, security. The combination of both has been the number one conversation pretty much in the cloud world right now. Honestly, besides a normal cloud, native cloud I t hybrid versus multi cloud out. See, that continues to be the discussion I think there's no more debate around multi cloud in hybrid public clouds. Great people gonna still keep their enterprises. But the security equation still is changing this new requirements. What's the latest that you guys are seeing with respect to security? >> Yeah. So, John, what we see is actually that cloud adoption had happens at different speeds. So you have usually the infrastructure of the service. Adoption would happens in a quite controlled way because there's a lift in shift. Do you have your old data center? You you take it and you transferred into azure I W S O G C P. But then there's also uncontrolled at option, which is in the SAS space. And I think this is where a lot off data risk occur, especially the wake off GDP are on where we see that this adoption happens. Maurin a sometimes control, but sometimes in a very uncontrolled way, >> explain that the uncontrolled and controlled expansion of of how security and multi cloud and cloud is going because this interesting control means this this plan's to do stuff uncontrolled means it's just by other forces explain uncontrolled versus controls >> eso controlled specifically means the IittIe team takes as a project plan and aches servers and workloads and moves them in a controlled fashion or in a dedicated project to the cloud. But what happened in the business world of business I t is actually did use those share content at any time with any device at any at any time and in all locations. So this is called the Mobile Enterprise on the Cloud First Enterprise. So it means that the classical security perimeter and the controls in that are my past, actually, by the path of least resistance or the shortest path >> available. And this is the classic case. People use Dropbox with some, you know, personal things. They're at home, they're at work, a p I based software. That's what you're getting at the >> and the issue of this is that that the data that has bean, like contained an pera meters where, you know, as it Caesar, where your data is. This has bean deployed too many edge devices, too many mobile devices, and it's get it gets shared, a nun controlled way. >> We'll get a couple talk tracks would like to drill down on that, because I think this is the trend. We're seeing a pea eye's dominant. The perimeter on the infrastructure has gone away. It's only getting bigger and larger. You got I, O. T and T Edge just and the networks are controlled and also owned by different people. So the packets of moving on it that's crazy so that that's the reality. First, talk track is the security challenge. What is the security challenge? How does a customer figure out what to do from an architectural standpoint when they're dealing with hybrid and multi cloud? So first of >> all, um, customers or BC enterprises try need to re think their infrastructure infrastructure centric view off the architecture's. So the architecture that had been built around data send us needs to become hybrid and multi cloud aware. So that means they need to define a new way off a perimeter, which is in cloud but also in the covering. Still the old, so to say, legacy hyper data center set up, which has the data still in the old data center and at the same time, they need to open up and become the cloud themselves, so to say, and but still draw a perimeter around their data and they users and not and their applications and not so much anymore around the physical infrastructure. >> So taking, changing their view of what a security product is, Is that really what you're getting at? >> Yeah, So the issues with the product point solution was that they fixed a certain part off off a tactile issue. So if you take a firewall in itself, firewall back then it was like a entry door to a big building, and you could could decide who comes out goes in. Now. If the the kind of the walls of the building are vanishing or arm or more FIC, you need to come over the more integrated concept. So having these stacked appliance and stacked security solutions trying to work together and chain them doesn't work anymore. So we think and we see that, >> Why is that? Why doesn't it work? Because in >> the end, it's it's it's hardly two to operate them. Each of those points solutions have their own end off life. They have their own life cycle. They have their own AP eyes. They have their own TCO, as all that needs to be covered. And then there's the human aspect where you have the knowledge pools around >> those technologies. So as an enterprise you have to content to continuously keep the very scar security experts to maintain content continues the depreciating assets running right, >> and they're also in it. We weren't built for tying into a holistic kind of platform. >> Yeah, What we see is that that enterprises now realize we have data centers and it's not accepted reality that you can abstracted with the cloud. So you have You don't own your own servers and buildings anymore. So you have a PAX model to subscribe to Cloud Service is and we think that this has to happen to security to so shift from cap ex to our pecs and the same way also for operational matters >> securities. The service is a crepe is a small I want to ask you on that front you mentioned mobile users. How do you secure the mobile uses when they use cloud collaboration? Because this is really what uses expect, and they want How do you secure it? >> So be secured by by actually monitoring the data where it actually gravitates, and this is usually in the cloud. So we enforce the data that is in transit through, ah, proxies and gators towards the cloud from the endpoint devices, but also then looking by AP eyes in the cloud themselves to look for threats, data leakage and also sandbox. Certain activities that happened. There >> are the next talk talk I want to get into is the expansion to hybrid and multi cloud so that you guys do from a product standpoint, solution for your customers. But in general, this is in the industry conversation as well. How how do you look at this from a software standpoint? Because, you know, we've heard Pat Gelsinger of'em were talking about somewhere to find Data Center S d n. Everything's now software based. You talk about the premiere goes away. You guys were kind of bring up a different approaches. A software perimeter? Yeah, what is the challenge for expanding to multi cloud and hybrid cloud? >> So So the challenge for enterprise and customers we talked to is that they have to run their old business. Gardner once called it by motile business, and it's still adopting not one cloud, but we see in our surveys. And this is also what market research confirms is that customers end up with 2 to 3 loud vendors. So there were will be one or two platforms that will be the primary to their major majority of applications and data gravity. But they will end up and become much more flexible with have running AWS, the old Davis Center. But it was the G, C, P and Azure, or Ali Baba glowed even side by side, right tow cover the different speeds at what their own and the price runs. And >> so I gotta ask you about Cloud Needed was one of the things that you're bringing up that just jumps in my head. And when I got to ask, because this is what I see is a potential challenge. It might be a current challenges when you have kubernetes growing such a rapid rate. You see the level of service is coming online much higher rate. So okay, people, mobile users, they're using the drop boxes, the boxes and using all these FBI service's. But that's just those wraps. As a hundreds and thousands of micro service is being stood up and Tauron down in there, you guys are taking, I think, an approach of putting a perimeter software premieres around these kinds of things, but they get turned on enough. How do you know what's clean? It's all done automatically, so this is becoming a challenge. So is this what you guys mean when you say software perimeter that you guys could just put security around things at any time? Is that explain this? >> Yeah, So? So if you talk about the service match so really mashing cloudy but native functions, I think it's still in the face where it's, I would say, chaos chaotic when you have specific projects that are being ramped up them down. So we draw a perimeter in that specific contact. So let's say you have You're ramping up a lot off cloud a function AWS. We can build a pyramid around this kind off containment and look especially for threats in the activity locks off. The different component is containers, but from from a design perspective, this needs to be, uh, we need to think off the future because if you look at Mike soft on AWS strategy, those containers will eventually move Also back to the edge. Eso were in preparing that to support those models also cover. Bring these functions closer back again to the edge on We call that not any longer the when, ej but it will become a cloud at at actually. So it's not an extension of the land that comes to the data. It's actually the data and the applications coming back to the user and much closer. >> Yeah. I mean, in that case, you could define the on premises environment has an edge, big edge, because this is all about moving, were close and data around. This is what the new normal is. Yeah, So okay, I gotta ask the next question, which is okay, If that's true, that means that kubernetes becomes a critical part of all this. And containers. How do you guys play with that at all? >> So we play with us by by actually looking at data coming from that at the moment. We're looking at this from a from a data transit perspective. We But we will further Maur integrate into their eighties AP eyes and actually become part off the C I C D. Process that building then actually big become a security function in approval and rolling out a cannery to certain service mesh. And we can say, Well, this is safe for this is unsafe This is, I think, the eventual goal to get there. But But for now, it's It's really about tracking the locks of each of those containers and actually having a parent her and segmentation around this service mash cloud. So to say, >> I think you guys got a good thing going on when you talk about this new concept that's of softer to find perimeter. You can almost map that to anything you get. Really think everything has its own little perimeter workload. Could be moving around still in these three secure. So I gotta ask on the next talk Trek is this leads into hybrid cloud. This is the hottest topic. Hybrid cloud to me is the same as multi cloud. Just kind of get together a little bit different. But hybrid cloud means you're operating both on premises and in the cloud. This is becoming a channel most si si SOS Chief admission Security officers. I don't want to fork their teams and have multiple people coding different stacks. They don't want the vendor lock in, and so you're seeing a lot of people pulling back on premises building their own stacks, deploying in the cloud and having a seamless operation. What is your definition of hybrid? Where do you see hybrid going? And how important is it? Have a hybrid strategy. >> So I think the key successfactors of a hybrid strategy is that standards standardization is a big topic. So we think that a service platform that to secure that like the SD when secure service platform rebuilt, needs to be standardized on operational level, but also from a baseline security and detection level. And this means that if you run and create your own work, those on Prem you need to have the same security and standard security and deployment standard for the clout and have the seamless security primary perimeter and level off security no matter where these these deployments are. And the second factor of this is actually how do you ensure a secure data transfer between those different workloads? And this is where S T win comes into play, which acts as a fabric together with when backbone, where we connect all those pieces together in a secure fashion >> where it's great to have you on the Q and sharing your insight on the industry. Let's get into your company. Open systems. You guys provide an integrated solution for Dev Ops and Secure Service and Security Platform. Take a minute to talk about the innovations that you guys were doing because you guys talk a lot about Casby. Talk a lot about integrated esti when but first define what Casby is for. The audience doesn't know what Casby is. C. A S B. It's kicked around all of the security conscious of your new to security. It's an acronym that you should pay attention to so defined casby and talk about your solution. >> Eso casby isn't theory. Aviation means cloud access security we broker. So it's actually becoming this centralized orchestrator that that allows and defines access based on a trust level. So saying, um, first of all, it's between networks saying I have a mobile workforce accessing SAS or I s applications. Can't be it in the middle to provide security and visibility about Where's my data moving? Where's married? Where do I have exposure off off GDP, our compliance or P C. I or he power risks And where is it exposed to, Which is a big deal on it's kind of the lowest level to start with, But then it goes further by. You can use the Casby to actually pull in data that that is about I s were close to toe identified data that's being addressed and stored. So are there any incidentally, a shared data artifacts that are actually critical to the business? And are they shared with extra resource is and then going one step further, where we then have a complete zero trust access model where we say we know exactly who can talkto which application at any time on give access to. But as everything this needs to be is in embedded in an evolution >> and the benefit ultimately goes to the SAS applications toe, have security built in. >> That's the first thing that you need to tackle. Nowadays, it's get your sass, cloud security or policy enforced on, but without disrupting service on business on to actually empower business and not to block and keep out the business >> can make us the classic application developer challenge, which is? They love to co they love the build applications, and what cloud did with Dev Ops was abstracted away the infrastructure so that they didn't have to do all this configuration. Sister. Right? APs You guys air enabling that for security? >> Exactly. Yeah. So coming back to this multi protein product cloud would, which is not keeping up anymore with the current reality and needs of a business. So we took the approach and compared death ops with a great service platform. So we have engineers building the platform. That's Integrated Security Service Platform, which promotes Esti Wen managed Detection response and Caspi Service is in one on the one platform which is tightly integrated. But in the in the customer focus that we provide them on or Pecs model, which is pretty, very predictable, very transparent in their security posture. Make that a scalable platform to operate and expand their business on. >> And that's great. Congratulations. I wanna go back for the final point here to round up the interview for the I T. Folks watching or, um, folks who have to implement multi cloud and hybrid cloud they're sitting there could be a cloud architect that could be an I T. Operations or 90 pro. They think multi cloud this in hybrid club. This is the environment. They have to get their arms around. How? What >> should they >> be thinking about? Around multi cloud and hybrid cloud. What is it, really? What's the reality now? What >> should they be considering for evaluation? What are some of the key things that that should be on their mind when they're dealing with hybrid cloud and all the opportunity around it? >> So I think they're they're like, four key pieces. Oneness. Um, they think they still have to start to think strategic. So what? It's a platform and a partner That helps them to plan ahead for the next 3 to 5 years in a way that they can really focus on what their business needs are. This is the scalability aspect. Secondly, it's a do. We have a network on security, our architecture that allows me to grow confidently and go down different venues to to actually adopt multi clouds without worrying about the security implication behind it. Too much, uh, and to implement it. And third is have this baseline and have this standardized security posture around wherever the data is moving, being at Mobil's being it SAS or being on Prem and in clouds workloads, the fourth pieces again, reading, thinking off where did you spend most of my time? Where do I create? Create value by by defining this framework so it really can create a benefit and value for the enterprise? Because if you do it not right your not right. You will have a way. You will end up with a an architecture that will break the business and not accelerated. >> Or it's made head of product that open systems here inside the Cube studios. Um, great job. Must love your job. You got the keys. A lot of pressure. Security being a product. Head of product for security companies. A lot of pressure before we wrap up. Just give a quick plug for the company. You guys hiring you have a new office space here in Redwood City. Looks beautiful. Give a quick shared play for the company. >> Yeah. So open systems the great company to work with. We're expanding in the U. S. On also, Amy, uh, with all the work force. So we're hiring. So go on our website. We have a lot off open positions, exciting challenges in a growth or into workspace. Andi. Yeah. As you said, security at the moment, it's one of the hottest areas to be in, especially with all the fundamental changes happening in the enterprise and architecture. I d landscape. So yeah, >> and clouds securing specifically. Not just in point. The normal stuff that people used to classify as hot as hot as Hades could be right now. But thanks for coming on. Strong insights. I'm jumping with Cuba here in Palo Alto with more Morris Man is the head of product management for open systems. Thanks for watching.

>> from Boston, Massachusetts. It's the queue covering active eo 2019. Data driven you by activity. >> Welcome back to Boston, Everybody. Money >> belong here with my co host, a student of John >> Kerry's. Also here today You watching the Cuban leader and on the ground tech coverage. This is day one of active fio 19 data driven content Conference hashtag data driven 19 red cara minuses. Here is the senior vice president of fantasy Sports A draftkings Greg. Thanks for coming on. What a cool title. >> Yeah, it's It's, you know, I was joking with my wife. Anytime you could be working fantasy sports, it's a great place to be. Everybody's a little bit jealous. >> So the formula is easy, right? Offer big giant prizes and everybody comes And that's all there is suing. Anybody can come >> in. I just have the dream job right now. >> So hugely competitive market. You guys, you become the >> leader. We were in the radio. Check out your websites. I mean, take us through the draft kings and your ascendancy. How you got here? >> So, you know, company started in 2012 initially around sort of the major big American sports on DH. Then really a CZ. We started scale that we saw there was a huge consumer interest in the product players that would come on. We're very, very, very sticky. Um, and we've just been kind of, you know, pushing, pushing on growing that using these. So the initial founders are three former analyst. So come on. It's always been sort of a very analytically driven company. So they looked at what we were dealing with, and it was we had L TVs that were way higher than our cracks. So let's keep marketing and growing and growing and growing and finding out ways to offer a better product. So, over 2015 we did a major marketing blitz, blew up the company Absolutely huge. Um, and since then we've been just constantly innovating, adding new sports, adding new features on DH, adding ways toe on the product. And then even more recently, just about a year ago, we expanded also into online sports betting over New Jersey has that's become a legal product across the U. S. So it's been a great time to be at the company a lot of fun. >> What what was your first sport was like Amazon started in books and then, you know, scaled out what was your first sport. So it's actually the first sport was >> baseball because of the time that they actually launched. So is the middle of April. Sporting calendar is a little bit thin. Right then, so is it was baseball to start, and then once football season started, that's really when things take on >> 2015 is when you started the marketing blitz and I remember just here in the ads and it was just intense, like a while. This company's going for it. So you sort of took >> all the chips I went >> all in and it worked. Yeah, I mean, it's part of the, you know, the lifeblood of the company. It's We're a company that ends up being taking risks, but we take calculated risks. So at any given point, you sort of say, like, Hey, what is the what is the range of outcomes over here? We're not playing for second place. We want to be a market leader, so you have to take risks in order, be a market leader. So let's take calculated risks. Let's make sure we're not being insane, but you know we did the math. We figured out what? This is A This is a worthwhile shot. We pushed him for it. Andi really took off from their love to bet on >> sure things. Yeah, well, Greg, we know the people that play the fantasy for it feel that data is what differentiates whether they're going to live in, you know, winner lose. Talk to us a little bit about the data journey inside your business And how that helped differentiate draftkings in the market. Yes. So we think Death draftkings >> is one of the most analytically based companies in the, you know, definitely in the market, but also into sort of like General Cos right now we use our analytics platform to inform pretty much everything we dio on. Go to your point. You're joking. You know, it seems like fantasy sports is easy throughout some giant prizes there, and everything will take care of itself. You know, running a fantasy sports car company. If you throw out a contest that's too big, you lose a ton of money. There's a lot of asymmetric risk in the business where if we're right, we make a little bit more. But if we're wrong. We lose a ton very, very, very fast. So our ability to be very, very sound analytically is what allows us to sort of pushed the envelope and grow, grow, grow but not, you know, lose our heads along the way. You know, some of the fun of that is really, you know, when we first ran, I think one of the most game changing contest we ran was actually back in October of 2014. It was the very first millionaire maker contest I could still remember. It was Week five of the 2020 14 NFL season where we said, Hey, this it's crazy. We need crazy things that happen in order for it to work. But if we're on a $20 contest to enter with $1,000,000 top prize and 2,000,000 of total prizes, it could go viral, go absolutely crazy. And if it loses, here's how it'll losing. Here's how much will hurt us. It's a worthwhile risk. Let's go for it. So that sort of energy of, you know, doing discipline analysis and constantly sort of them. Taking the risk on the back of it is what allowed us to build >> up the brand value that you would have got out of that was sort of worth that risk in part anyway. And you wouldn't have to hurt presumably. >> Exactly. We knew our downside. As long as you know your downside, you're normally in a pretty good spot to take those risks. >> So where do you >> see this All going mean? So the company has grown. You're at this kind of critical mass now, Like we said, highly competitive, you know, knock down. You know, if you take your eye off the ball. So how do you guys keep this going? >> So we have a huge challenge ahead of us over the next couple of years, as sports betting becomes legal across the US, we need to make sure that we are one of the top competitors in that market. Sports betting in the US, we expect to be an absolutely enormous market. It will probably be significantly larger than the fantasy sports market in terms of absolute revenue and even, you know, on order of magnitude more competitive. So we need to be executing each step along the way a CZ markets open up. We need to be able to get into getting two market very, very fast. And that means our tech team needs to be working feverishly to make sure that we can hit the requirements that each legislator and each regulator puts on market entry in their state. We didn't mean making sure we're constantly figuring out what are the product elements that are absolutely critical for our for our users. Is it Maura around the live betting experiences that around the different markets that you offer? It's around pricing. And how do we find these things, these different lovers and told them to make sure that we're putting out a great product for users. And if we do that and throw a great product after users were pretty sure we can make you want >> to be one stop shopping presumably, right? I mean, all sports, right? But But then you've got these niche sports betting. I mean eggs, invest. Example. I could think of this horse racing. You know where it is alive. It's gonna video. It's got commentators on the ground that you know the business really well. Is >> that Is that the strategy to go sort of horizontal and so be a one stop shop or you >> gonna sort of pick your spots? What is the day to tell you? >> You know, I think we're constantly talking about it. One of the things that allowed our fantasy sports business to grow so fast was going a little bit more horizontal. So we offered Gulf in Mass at a time period when the primary competitors and the space vandal did not. On DH, we built that product into one of our largest sports. It's, you know, right up there with MLB in terms of the actual size that that comes in a Z have gone also horizontal, we pulled in other places, like NASCAR. Mm, a great sports that people are interested in. It gets more users into our platform. And honestly, if uses are interested in a product, we don't want them to have to go elsewhere. We want to be able to have the offerings that any sort of, you know, critical mass type environment is going toe is gonna have >> Well, it's that experience, right? Well, I like to shop in Amazon. You do, too, because I >> trusted. And it's the same user experience. So, Greg, one of things >> I'm hearing from you is something that everybody tries for, but it's really challenging that speed. How do you react that fast and move the company into new markets and new offerings and keep innovating? You know, culturally technology wise, you know, How does Draftkings do that? You know, I think a za company, you know, from really every single person that we recruit in higher We've been actually execution Aly disciplined throughout the company's history. It's It's something that our founders did a great job of instilling in the culture right at the gates. I mean, we've tried to foster all the way along the way, which is all the best strategies of the world. They're going to fail if you can't execute well and every single person down the company knows that. And we try to, you know, enable each person to be as autonomous as possible in their ability to execute their their portion of the business that allows us to move really, really, really fast. You know, we disseminate that responsibility quickly, and each leader and sort of each person knows what they have to do to execute. There's a high degree of accountability behind that, you know, I'd like to say there's some. There's some magic recipe that's, um, secret sauce, but it's a lot of just great people doing great work everyday. Well, Greg, you know it's any your competitors that they look at, You know, Boston's been been doing pretty well in Draftkings era, you know, for the last few years. ES o Boston's been a great market for us. We've expanded Conover here on DH. The sports teams have been fantastic, although the Bruins it was a little bit sad about Game seven over there, but it happens. >> So his m o be the flagship news that no, I wouldn't say >> that MLB was first, primarily just of the time of the year when we launched. NFL is always going to go, are not always going to be, but for the for the foreseeable future is the dominant US sport on will remain the dominant US for >> no reason. I mean, kids there watch MLB anymore. Maybe the maybe the playoffs and the games. It was a game. I think I'm some Father's day was like almost five hours long, you know, gets called. You can come in and out. But you know what some of the trends. You see soccer. Is that growing NFL? Obviously huge. Do you see so niche sports like lax coming on. >> So, uh, you know, starting point NFL has been huge. We actually launched a new product Ah, little over a year ago called Showdown, which allowed you start to do fantasy for a single game as opposed to the combination of games that's taken off fantastically because that's tapping into more of the I'm going to sit down and watch this game, and I would love to have a fantasy team on that on this game. That's really expanded the audience like that. That >> was genius because, look, if you're >> out of the running, it doesn't matter because I'm weak. On top of that N b A and NHL on fire. The embassy put out a great product is an actual sport league. You know, the Finals were great. You hate to see the injuries, but it was a great final. Siri's very competitive. The NHL Finals has been very, very competitive. Golf is growing phenomenally as a sport, way farm or interesting golf than I ever anticipated when I first started with the company and it's one of the most exciting things. When the Masters comes each year, every screen has turned to it and we see a huge player. Player number is kind of coming into that one. Beyond that, you know NASCAR. What's been interesting? NASCAR's been having a tough couple years, but the Truck series for us? We launched it this year and the trucks have been great. I don't know if you've watched NASCAR Trucks. They're wildly entertaining. Uh, you know, Emma, you got the big fighter. So every sport sort of has its moments. It's a matter of like picking those moments and figuring out how to make >> the most of them. Do you see boxing at all making a comeback? >> So we have thought about how to get boxing into a into a fantasy. We don't have it at the moment. We're putting a lot of thought into it, so we are actually seeing through. We've seen, you know, we've been in the M M A space and we've seen the growth out from there where that sports doing great and you look at places like Bela Tor. The Professional Fighters league is other leagues, and then boxing is the next step. There's a lot of interest there. I don't think they have the right products yet to be able to kind of engage with that extra way. So that's one of things we're working on. Also, you need a marquee fighter. You always need a marquee fighter. Kind of helped bring in the interest over on that side. So, um, be interesting to see with Taki on sort of the downside of his career. At this point on DH, Mayweather hasn't been fighting much. Will be interesting to see. Who's that next meeting with Adam. But >> I grew up in an era >> of Marquis fighters. What? They would fight, you know, they literally fight 6 70 times a year, you know, and you had used huge names on DSO, and then mm comes along and he's really hurt, >> but it feels like it's tryingto so to resuscitate. Yeah. I mean, I think these things could >> be a little bit cyclical. Like you get one Marquis fighter out there like so my wife, this Filipino. So I'm a huge backing out fan now way watch every fight. Even when we were living in remote locations that forces watching at weird hours. He's a type of athlete that could bring popularity of the sport. So if there was a major U. S. Fighter that gains that degree of sort of, you know that that degree of fame people will be into it, I think >> Do do do your analytics sort of have a probe into the activity at the at the fan level at the sports level, not just the fantasy level or the betting level? Is that a sort of ah ah predictor for you? Yet we >> see a lot of correlations between how many people play our sport are fantasy game, and how many people actually follow the underlying sport. Way can also see trends in terms of If I'm from Boston, I probably pick more patriots in my fantasy lineups than, uh, normal on DH. You can actually see that as people play different sports that you know, the number one Q. Be drafted in in Boston is almost always gonna be Tom Brady. And once you leave that you start seeing Aaron Rodgers pop up. Let's really, really fast. So you see these little micro trends where it's like you are still a sports fan of your local team in your local environment, but it manifest itself in the fantasy. >> So what you think that is? Do you think it's fan affinity >> or do you think it's just the sort of lack of knowledge out inside? You're sort of a circle of trust. >> I think it's probably a combination. I mean, I could say is, you know, following the Celtics in the mid to thousands, I knew the depth of the Celtics pension, how they would use their rotation better than anybody else, Probably better than anybody else in the coaches would probably disagree. But it's like I knew that James Posey was a huge value play on Saturday nights. I knew. I kind of with I feel the Eddie House nights. Uh, so, you know, on your local team, you probably know those players at the not the top top echelon All Stars, but the guy's right beneath. You know them a little bit better and probably more comfortable using >> what's your favorite sport. >> So my favorite sport, from a fantasy perspective, is I play all the basket. I play all football, played basketball just during play offs, and I played baseball. But baseball I'm strictly a fantasy player. I don't really follow the sport to play. I'm just playing fantasy. Okay, >> That's great. So, what do you think? The conference. Here. >> You have you Have you had any timeto interact? I know you were swamped after coming off the stage. >> You know, it looks like a great turnout over here. There's a lot of enthusiasm amongst them from people. I was a little bit late to the late to show up this morning, so I got a bit Swanson eager to go and be able to catch up a bit more. >> Okay, Well, Greg, thanks so much for coming on. The Cuba's great to have your every pleasure meeting you. >> All right, people. Right there. Still, when I >> was back with our next guest, John for it is also in the house. You wanted The Cube from active field data driven 19. Right back

>> from San Matteo. It's the Cube covering scaler. Innovation Day Brought to You by scaler. >> Welcome to this Special Cube Innovation Day. Here in San Mateo, California Scale is headquarters for a coast of the Cube. We're here with two great guests. Claudia Carpenter co founder Andy McAlister, Who's Dev evangelist? Uh, great to have you guys here a chat before we came on. Thanks for having us >> Great to be >> so scaler. It's all about the logs. The answer is in the logs. That's the title of the segment Them. I'll see the log files with a lot of exhaust in their data value extracting that, but it's got more operational impact. What's what's the Why is the answer in the locks? >> Because that's where the real information is. It's one thing to be able to tell that something is going around when your systems, but what is going wrong as engineers, what we tend to do is the old print. If it's like here's everything I can think of in this moment and leave it as breadcrumbs for myself to find later, then I need to go and look at those bread crumbs >> in a challenge. Of course, with this is that logs themselves are proliferating. There's lots of data. There's lots of services inside this logs, so you've gotta be able to find your answers as fast as possible. You can't afford Teo. Wait for something else. T lead you to them. You need to deep dive >> the way you guys have this saying it's the place to start. What does that mean? Why? Why is that the new approach? >> What We're trying to differentiate because there's this trend right now in the Dev Ops world towards metrics because they're much smaller to store it, pre digesting what's going on in your systems. And then you just play a lot of graphs and things like that. We agree with that. You do need to be able to see what's going on. You need to be able to set alerts. Metrics are good, but they only get you so far. A lot of people will go through. Look at metrics, dig through and then they stop, switch over and go to their logs. We like to start with the logs, build our metrics from them, and then we go direct to >> the source. I think a minute explain what you mean by metrics, because that has multiple meanings. Because the current way around metrics and you kind of talked about a new approach. Could you just take a minute? Explain what you meant by metrics and how logs are setting up the measures. The difference there. >> So to me, metrics is just counting things right? So at log files of these long textual representations of what's going on in my system and it's impossible to visually parce that I mean literally 10,000 lines. So you count. I've got five of this one in six of this one, and it's much smaller to store. I've got five of this one and six of this one, but that's also not very much information, so that's really the difference. >> But, you know, we have customers who use their metrics to help them indicate something might be wrong inside of here. The problem is, is that modern environments where we have instant gratification, needs and people you know, we'd be wait five seconds. Basically, it's a law sale online here. You need to know what's went wrong, not just where we went wrong or that something went wrong. So building for the logs to the metrics allows you to also have a perfect time back to that specific entrance ancient entrance that lets you be you out. What was wrong? >> He mention Claudia Death ops. And this is really kind of think of fun market because Dev Ops is now going mainstream and see the enterprise now started to adopt. It's still Jean Kim from Enterprise. Debs estimates only 3% of enterprise really there yet. So the action's on the cloud Native Public Cloud side where it's, you know, full blown, you know, cloud native more services. They're coming to see Cooper Netease things of that nature out there. And these services are being stood up and torn down while the rhythmically like. So with who the hell stores that data? That's the logs. The nature of log files and data is changing radically with Dev ops. I'm certainly this is going to be more complications but developers and figuring out what's what. How do you see that? What's your reaction to that trend? >> Yeah, so Dev Ops is a very exciting thing. At were Google. It was sort of like the new thing is the developers had to do their own operations, and that's where this comes from. Unfortunately, a lot of enterprise will just rename their ops people devil apps, and that's not the same thing. It's literally developers doing operations, Um, and right now that it's never been so exciting as as it is right now in the text axe, because you could get so much that's open source. Pre built glue this all these things together. But since you haven't written the code yourself, you've no way deal which going on. So it's kind of like Braille. You've got to go back and look and feel your way through it to figure out what's going on. And that's where logs come into play. >> The logs essentially, you know, lift up, get people eyesight into visibility of things that they care about. Absolute. So what's this red thing? Somebody read what is written? Rennes. >> One of the approaches. You'll hear things like golden signals. You'll hear youse, and you'll hear a red Corvette stands for rates, a rose and duration. And ready is a concept that says, How do you actually work with some of these complex technologies working with you're talking about and actually determined where your problems are. So if you think about it, rate is kind of how much traffic's going through a signal for this as a metric, it's accumulative number. So to back to Claudia's point, it's just number here. But if you're trapping goes up, you want to know what's going wrong here is self explanatory. Something broke, fix it, and then duration is how long things took. You talked about communities, Communities works hands in hands with this concept of micro services. Micro services are everywhere, and there were Khun B places that have thousands of little services, all serving the bigger need here. If one of them goes slow, you need to know what went slow as fast as possible. So rate duration and air is actually combined to give you the overall health of your system. While at the same point logs elect, you figure out what was causing >> the problem we'll take. I'm intrigued by what Claudia said. They're on this. You know, Braille concept is essentially a lot of people are flying blind date with what's going on, but you mentioned micro services. That's one area that's coming. Got state full data. Stateless data. They were given a P I economy. Certainly a state becomes important for these applications. You know, the developers don't may or may not know what's happening, so they need to have some intelligence. Also, security we've seen in the cloud. When you have a lot of people standing up instances whether it's on Amazon or other clouds, they don't actually have security on some of their things. So they got it. Figure out the trails of what the data looks like they need the log files to have understanding of. Did something happened? What happened? Why? What is the bottom line here? Claudia? What what people do to kind of get visibility So they're not flying blind as developers and organizations. >> Well, you gotta log everything you can within reason. They always have to take into account privacy and security. But logs much as you can and pull logs from every one of the components in your systems. The micro services that day was just talking about are so cool. And as engineers, we can't resist them way. Love, complexity >> and cool things. >> Things especially cool things and new things. >> New >> green things. Sorry, easily distracted. But there they are, harder to support. They can be a really difficult environment. So again it's back to bread crumbs, leaving that that trail and being able to go back and reconstruct what happened. >> Okay, what's the coolest thing about scaler since we thought about cool and relevant? You guys certainly in the relevant side thing. Check the box there. What's cool? What's cool about scaler telling us? >> That's great. Answer What isn't. But you know, honestly, when I came to work here, I no idea I was familiar with Log Management was really with long search and so forth. And the first time I actually saw the product, my jaw dropped. Okay, I now go to a trade show, for instance, and I'm showing people to use this. And I hit my return button to get my results. And you showed band with can be really bad and it stalls for 1/10 of a second, and I complain about it now. No, there is nothing quite as thrilling as getting your results as fast as you can think about them. Almost your thought processes the slow part of determining what's going on, and that is mind boggling. >> So the speed is the killer. >> The speed is like what killed me. But honestly, something that Chloe's been heavily involved in It takes you two minutes to get started. I mean, there's no long learning curve there. You get the product and you are there. You're ready to go >> close about ease of use and simplicity, because developers are fickle, but they're also loyal. Do you have a good product? They loved to get in that love the freebie. You know, the 30 day trial, They'll they'll kick the tires on anything. But the product isn't working. You hear about it when it does work. This mass traffic to people you know pound at the doorstep of the product. What's the compelling value proposition for the developer out there? Because they >> don't want to >> waste time. That's like the killer death to any product for development. Waste their time. They don't want to deal with it. >> So we live in the TL D our world right now. Frankly, if I have to read something, I usually move on on DH. That's the approach we take with scaler as well. Yes, we have some documentation, but I always feel like I have failed with the user interface design. If I require you to go read the documentation. So I try to take that into account with everything that we that we put out there making it really easy and fast it just jumping in, try stuff. >> How do you get to solve the complex complexity problem through attraction software? What's the secret sauce for the simplicity of this system? >> For me, it's a complete lack of patients. It's just like I wouldn't put up with that. I'm not gonna ask you to. Frankly, I view this sounds a little bit trite, but I've you Software's a relationship, and I view whoever is looking at it as a peer of mine, and I would be embarrassed if they couldn't figure it out if it wasn't obvious. But it is. We do have this sort of slope here of people who really know what's going on and people wanna optimize. This is your 80 20 split on people that don't know what just want to come in. I want both of them to be happy, so we need to blend those >> to talk about the value proposition of what you guys have because we've been covering you know log file mentioned Lock Management's Splunk events. We've gone, too. There's been no solution that I think may be going on 10 years old, that were once cutting edge. But the world changes so fast with Amazon Web services with Google Cloud with azure. Then get the international clouds out there as well. It's it's here. I mean, the scale is there, you got compute. You got the edge of the network right around the corner in the data problem's not going away. Log files going be needed. You have all this data exhausted, these value. >> If anything, there's always going to be more data that's out there. You're going to have more sources of that data coming in here. You're talking a little bit about you have the hybrid cloud. Where's part on prom? Part in the cloud. You could have multi clouds where across his boundaries. You're gonna have the wonderful coyote world where you have no idea when or where you're going to get an upload from too. This too and EJ environment. And you've got to worry about those and at the same time time, the logging, everything, the breadcrumbs. You have ephemeral events. They're not always there, and those are the ones that kill you. So the model is really simple and applaud Claudia for conning concept wise. But you're playing with concept of kiss, right? We'll hear its keep it simple and sophisticated at the same time. So I can teach you to do this demo in two minutes flat, and from there you can teach yourself everything else that this product's capable of doing it. That simple >> talk about who? The person out there that you want to use his product and why should they give scale or look what's in it for them. >> So for me, I think the perfect is to have Dev ops use it. It's developers. We really have designed a product less for ops and more for engineers. So one of the things that is different about scaler is you have somebody come in and set it up, parsed logs that ingestion of logs, which is different than splunk and sumo on DH. Then it's ready to use right out of the box. So for me, I think that our sweet spot, his engineers, because a lot of our formulations of things you do are more technical you're thinking about about you know what air the patterns here. I'm not going to say it's calculus, because then that wouldn't be simple. But it's along. Those >> engineers might be can also cloud Native is a really key party. People who were cloud native. We're actually looking at four in the cloud or cloud migration, >> right way C a lot. For instance, in the Croup. In any space from the Cloud Native Compute Foundation, we're seeing a tremendous instrument interest in Prometheus. We're seeing a lot of interest in usto with service mesh. The nice thing is that they are already all admitting logs themselves. And so, from our viewpoint, we bring them in. We put them together. So now you can look at each piece as it relates to the very other piece >> Claudia share with the folks who, watching this just some anecdotal use cases of what you guys have used internally, whether customers that give him a feel for how awesome scaler is and what's the what could they expect? >> Well, put me on the spot here. Um, >> I'll kick off. So we have a customer in Germany there need commerce shop, They have 1,000 engineers for here. When we started the product we replace because it was on a charge basis that was basically per user. They came back and they said, Oh my God, you don't understand our queries Air taking 15 minutes to get back By the time the quarry comes back, the engineer's forgotten why he asked the question for this. And so they loaded up. They rapidly discovered something unique. It's that they can discover things because anyone can use it. We now have 500 engineers that touch the log files every day, I will attest. Having written code myself, nobody reads log files for fun. But Scaler makes it easy to discover new things and new connections. And they actually look at what house >> discoveries of real value, proper >> discovery is a massive value proposition. Uh, where you figure out things that you don't know about back to that events thing that Claudia started about was, you can only measure the events that you can already considered. You can't measure things that didn't happen >> close. It quickly thought what the culture on David could chime in. What's the culture like here scaler? >> It is a unique culture and I know everyone probably says that about their startup, but we keep work life balance as a very important component. We're such nerds and unabashedly nerds. Wait, what we do. It's a joyful atmosphere to work in. Our founder, Steve Newman, is there in his flat, his flannel shirt, his socks cruising around. Um, and we are very much into our quality barcode. We have a lot of the principles of Google sort of combined into a start up. I mean to say it's a very honest environment, >> Sol. Heart problems make it a good environment. >> Yeah, and I value provide real values, are critical >> for me and have fun at the same point in time. The people here work hard, but they share what they're working on. They share information. They're not afraid to answer the what are you working on? Question. But we always managed to have fun. We are a pretty tight group that way. >> Well, thanks for sharing that insight. We have a lot of fun here in Innovation Day with the Q p. I'm John Furia. Thanks for watching

>> Narrator: Live from Las Vegas, it's theCUBE! Covering IBM Think 2018. Brought to you by IBM. >> Hello and I'm John Furrier. We're here in theCUBE studios at Think 2018, IBM Think 2018 in Mandalay Bay, in Las Vegas. We're extracting the signal from the noise, talking to all the executives, customers, thought leaders, inside the community of IBM and theCUBE. Our next guest is Ritika Gunnar who is the VP of Product for Watson and AI, cloud data platforms, all the goodness of the product side. Welcome to theCUBE. >> Thank you, great to be here again. >> So, we love talking to the product people because we want to know what the product strategy is. What's available, what's the hottest features. Obviously, we've been talking about, these are our words, Jenny introduced the innovation sandwich. >> Ritika: She did. >> The data's in the middle, and you have blockchain and AI on both sides of it. This is really the future. This is where they're going to see automation. This is where you're going to see efficiencies being created, inefficiencies being abstracted away. Obviously blockchain's got more of an infrastructure, futuristic piece to it. AI in play now, machine learning. You got Cloud underneath it all. How has the product morphed? What is the product today? We've heard of World of Watson in the past. You got Watson for this, you got Watson for IOT, You got Watson for this. What is the current offering? What's the product? Can you take a minute, just to explain what, semantically, it is? >> Sure. I'll start off by saying what is Watson? Watson is AI for smarter business. I want to start there. Because Watson is equal to how do we really get AI infused in our enterprise organizations and that is the core foundation of what Watson is. You heard a couple of announcements that the conference this week about what we're doing with Watson Studio, which is about providing that framework for what it means to infuse AI in our clients' applications. And you talked about machine learning. It's not just about machine learning anymore. It really is about how do we pair what machine learning is, which is about tweaking and tuning single algorithms, to what we're doing with deep learning. And that's one of the core components of what we're doing with Watson Studio is how do we make AI truly accessible. Not just machine learning but deep learning to be able to infuse those in our client environments really seamlessly and so the deep learning as a service piece of what we're doing in the studio was a big part of the announcements this week because deep learning allows our clients to really have it in a very accessible way. And there were a few things we announced with deep learning as a service. We said, look just like with predictive analytics we have capabilities that easily allow you to democratize that to knowledge workers and to business analysts by adding drag-and-drop capabilities. We can do the same thing with deep learning and deep learning capabilities. So we have taken a lot of things that have come from our research area and started putting those into the product to really bring about enterprise capabilities for deep learning but in a really de-skilled way. >> Yeah, and also to remind the folks, there's a platform involved here. Maybe you can say it's been re-platformed, I don't know. Maybe you can answer that. Has it been re-platformed or is it just the platformization of existing stuff? Because there's certainly demand. TensorFlow at Google showed that there's a demand for machine learning libraries and then deep learning behind. You got Amazon Web Services with Sagemaker, Touting. As a service model for AI, it's definitely in demand. So talk about the platform piece underneath. What is it? How does it get rendered? And then we'll come back and talk about the user consumption side. >> So it definitely is not a re-platformization. You recall what we have done with a focus initially on what we did on data science and what we did on machine learning. And the number one thing that we did was we were about supporting open-source and open frameworks. So it's not just one framework, like a TensorFlow framework, but it's about what we can do with TensorFlow, Keras, PyTorch, Caffe, and be able to use all of our builders' favorite open-source frameworks and be able to use that in a way where then we can add additional value on top of that and help them accelerate what it means to actually have that in the enterprise and what it means to actually de-skill that for the organization. So we started there. But really, if you look at where Watson has focused on the APIs and the API services, it's bringing together those capabilities of what we're doing with unstructured, pre-trained services, and then allowing clients to be able to bring together the structured and unstructured together on one platform, and adding the deep learning as a service capabilities, which is truly differentiating. >> Well, I think the important point there, just to amplify, and for the people to know is, it's not just your version of the tools for the data, you're looking at bringing data in from anywhere the customer, your customer wants it. And that's super critical. You don't want to ignore data. You can't. You got to have access to the data that matters. >> Yeah, you know, I think one of the other critical pieces that we're talking about here is, data without AI is meaningless and AI without data is really not useful or very accurate. So, having both of them in a yin yang and then bringing them together as we're doing in the Watson Studio is extremely important. >> The other thing I want get now to the user side, the consumption side you mentioned making it easier, but one of the things we've been hearing, that's been a theme in the hallways and certainly in theCUBE here is; bad data equals bad AI. >> Bad data equals bad AI. >> It's not just about bolting a AI on, you really got to take a holistic approach and a hygiene approach to the data and understanding where the data is contextually is relevant to the application. Talk about, that means kind of nuance, but break that down. What's your reaction to that and how do you talk to customers saying, okay look you want to do AI here's the playbook. How do you explain that in a very simple way? >> Well you heard of the AI ladder, making your data ready for AI. This is a really important concept because you need to be able to have trust in the data that you have, relevancy in the data that you have, and so it is about not just the connectivity to that data, but can you start having curated and rich data that is really valuable, that's accurate that you can trust, that you can leverage. It becomes not just about the data, but about the governance and the self-service capabilities that you can have and around that data and then it is about the machine learning and the deep learning characteristics that you can put on there. But, all three of those components are absolutely essential. What we're seeing it's not even about the data that you have within the firewall of your organization, it's about what you're doing to really augment that with external data. That's another area that we're having pre-trained, enriched, data sets with what we're doing with the Wats and data kits is extremely important; industry specific data. >> Well you know my pet peeve is always I love data. I'm a data geek, I love innovation, I love data driven, but you can't have data without good human interaction. The human component is critical and certainly with seeing trends where startups like Elation that we've interviewed; are taking this social approach to data where they're looking at it like you don't need to be a data geek or data scientist. The average business person's creating the value in especially blockchain, we were just talking in theCUBE that it's the business model Innovations, it's universal property and the technology can be enabled and managed appropriately. This is where the value is. What's the human component? Is there like... You want to know who's using the data? >> Well-- >> Why are they using data? It's like do I share the data? Can you leverage other people's data? This is kind of a melting pot. >> It is. >> What's the human piece of it? >> It truly is about enabling more people access to what it means to infuse AI into their organization. When I said it's not about re-platforming, but it's about expanding. We started with the data scientists, and we're adding to that the application developer. The third piece of that is, how do you get the knowledge worker? The subject matter expert? The person who understand the actual machine, or equipment that needs to be inspected. How do you get them to start customizing models without having to know anything about the data science element? That's extremely important because I can auto-tag and auto-classify stuff and use AI to get them started, but there is that human element of not needing to be a data scientist, but still having input into that AI and that's a very beautiful thing. >> You know it's interesting is in the security industry you've seen groups; birds of a feather flock together, where they share hats and it's a super important community aspect of it. Data has now, and now with AI, you get the AI ladder, but this points to AI literacy within the organizations. >> Exactly. >> So you're seeing people saying, hey we need AI literacy. Not coding per se, but how do we manage data? But it's also understanding who within your peer group is evolving. So your seeing now a whole formation of user base out there, users who want to know who their; the birds of the other feather flocking together. This is now a social gamification opportunity because they're growing together. >> There're-- >> What's your thought on that? >> There're two things there I would say. First, is we often go to the technology and as a product person I just spoke to you a lot about the technology. But, what we find in talking to our clients, is that it really is about helping them with the skills, the culture, the process transformation that needs to happen within the organization to break down the boundaries and the silos exist to truly get AI into an organization. That's the first thing. The second, is when you think about AI and what it means to actually infuse AI into an enterprise organization there's an ethics component of this. There's ethics and bias, and bias components which you need to mitigate and detect, and those are real problems and by the way IBM, especially with the work that we're doing within Watson, with the work that we're doing in research, we're taking this on front and center and it's extremely important to what we do. >> You guys used to talk about that as cognitive, but I think you're so right on. I think this is such a progressive topic, love to do a deeper dive on it, but really you nailed it. Data has to have a consensus algorithm built into it. Meaning you need to have, that's why I brought up this social dynamic, because I'm seeing people within organizations address regulatory issues, legal issues, ethical, societal issues all together and it requires a group. >> That's right. >> Not just algorithm, people to synthesize. >> Exactly. >> And that's either diversity, diverse groups from different places and experiences whether it's an expert here, user there; all coming together. This is not really talked about much. How are you guys-- >> I think it will be more. >> John: It will, you think so? >> Absolutely it will be more. >> What do you see from customers? You've done a lot of client meetings. Are they talking about this? Or they still more in the how do I stand up AI, literacy. >> They are starting to talk about it because look, imagine if you train your model on bad data. You actually have bias then in your model and that means that the accuracy of that model is not where you need it to be if your going to run it in an enterprise organization. So, being able to do things like detect it and proactively mitigate it are at the forefront and by the way this where our teams are really focusing on what we can do to further the AI practice in the enterprise and it is where we really believe that the ethics part of this is so important for that enterprise or smarter business component. >> Iterating through the quality the data's really good. Okay, so now I was talking to Rob Thomas talking about data containers. We were kind of nerding out on Kubernetes and all that good stuff. You almost imagine Kubernetes and containers making data really easy to move around and manage effectively with software, but I mentioned consensus on the understanding the quality of the data and understanding the impact of the data. When you say consensus, the first thing that jumps in my mind is blockchain, cryptocurrency. Is there a tokenization economics model in data somewhere? Because all the best stuff going on in blockchain and cryptocurrency that's technically more impactful is the changing of the economics. Changing of the technical architectures. You almost can say, hmm. >> You can actually see over a time that there is a business model that puts more value not just on the data and the data assets themselves, but on the models and the insights that are actually created from the AI assets themselves. I do believe that is a transformation just like what we're seeing in blockchain and the type of cryptocurrency that exists within there, and the kind of where the value is. We will see the same shift within data and AI. >> Well, you know, we're really interested in exploring and if you guys have any input to that we'd love to get more access to thought leaders around the relationship people and things have to data. Obviously the internet of things is one piece, but the human relationship the data. You're seeing it play out in real time. Uber had a first death this week, that was tragic. First self-driving car fatality. You're seeing Facebook really get handed huge negative press on the fact that they mismanaged the data that was optimized for advertising not user experience. You're starting to see a shift in an evolution where people are starting to recognize the role of the human and their data and other people's data. This is a big topic. >> It's a huge topic and I think we'll see a lot more from it and the weeks, and months, and years ahead on this. I think it becomes a really important point as to how we start to really innovate in and around not just the data, but the AI we apply to it and then the implications of it and what it means in terms of if the data's not right, if the algorithm's aren't right, if the biases is there. It is big implications for society and for the environment as a whole. >> I really appreciate you taking the time to speak with us. I know you're super busy. My final question's much more share some color commentary on IBM Think this week, the event, your reaction to, obviously it's massive, and also the customer conversations you've had. You've told me that your in client briefings and meetings. What are they talking about? What are they asking for? What are some of the things that are, low-hanging fruit use cases? Where's the starting point? Where are people jumping in? Can you just share any data you have on-- >> Oh I can share. That's a fully loaded question; that's like 10 questions all in one. But the Think conference has been great in terms of when you think about the problems that we're trying to solve with AI, it's not AI alone, right? It actually is integrated in with things like data, with the systems, with how we actually integrate that in terms of a hybrid way of what we're doing on premises and what we're doing in private Cloud, what we're doing in public Cloud. So, actually having a forum where we're talking about all of that together in a unified manner has actually been great feedback that I've heard from many customers, many analysts, and in general from an IBM perspective, I believe has been extremely valuable. I think the types of questions that I'm hearing and the types of inputs and conversations we're having, are one of where clients want to be able to innovate and really do things that are in Horizon three type things. What are the things they should be doing in Horizon one, Horizon two, and Horizon three when it comes to AI and when it comes to AI and how they treat their data. This is really important because-- >> What's Horizon one, two and three? >> You think about Horizon one, those are things you should be doing immediately to get immediate value in your business. Horizon two, are kind of mid-term, 18 to 24. 24 plus months out is Horizon 3. So when you think about an AI journey, what is your AI journey really look like in terms of what you should be doing in the immediate terms. Small, quick wins. >> Foundational. >> What are things that you can do kind of projects that will pan out in a year and what are the two to three year projects that we should be doing. This are the most frequent conversations that I've been having with a lot of our clients in terms of what is that AI journey we should be thinking about, what are the projects right now, how do we work with you on the projects right now on H1 and H2. What are the things we can start incubating that are longer term. And these extremely transformational in nature. It's kind of like what do we do to really automate self-driving, not just cars, but what we do for trains and we do to do really revolutionize certain industries and professions. >> How does your product roadmap to your Horizons? Can you share a little bit about the priorities on the roadmap? I know you don't want to share a lot of data, competitive information. But, can you give an antidotal or at least a trajectory of what the priorities are and some guiding principals? >> I hinted at some of it, but I only talked about the Studio, right... During this discussion, but still Studio is just one of a three-pronged approach that we have in Watson. The Studio really is about laying the foundation that is equivalent for how do we get AI in our enterprises for the builders, and it's like a place where builders go to be able to create, build, deploy those models, machine learning, deep learning models and be able to do so in a de-skilled way. Well, on top of that, as you know, we've done thousands of engagements and we know the most comprehensive ways that clients are trying to use Watson and AI in their organizations. So taking our learnings from that, we're starting to harden those in applications so that clients can easily infuse that into their businesses. We have capabilities for things like Watson Assistance, which was announced this week at the conference that really helped clients with pre-existing skills like how do you have a customer care solution, but then how can you extend it to other industries like automotive, or hospitality, or retail. So, we're working not just within Watson but within broader IBM to bring solutions like that. We also have talked about compliance. Every organization has a regulatory, or compliance, or legal department that deals with either SOWs, legal documents, technical documents. How do you then start making sure that you're adhering to the types of regulations or legal requirements that you have on those documents. Compare and comply actually uses a lot of the Watson technologies to be able to do that. And scaling this out in terms of how clients are really using the AI in their business is the other point of where Watson will absolutely focus going forward. >> That's awesome, Ritika. Thank you for coming on theCUBE, sharing the awesome work and again gutting across IBM and also outside in the industry. The more data the better the potential. >> Absolutely. >> Well thanks for sharing the data. We're putting the data out there for you. theCUBE is one big data machine, we're data driven. We love doing these interviews, of course getting the experts and the product folks on theCUBE is super important to us. I'm John Furrier, more coverage for IBM Think after this short break. (upbeat music)

>> Narrator: Live from Las Vegas, it's The Cube. Covering IBM Think 2018. Brought to you by IBM. >> Hello everyone and welcome to The Cube here at IBM Think in Las Vegas, the Mandalay Bay. I'm John Furrier, the host of The Cube. We're here in this Cube studio as a set for IBM Think. My next guest is Jennifer Shiin who's the founder of 8 Path Solutions. Twitter handle Jenn, J-S-H-I-N. Great to see you. Thanks for joining me. >> Yeah, happy to be here. >> I'm glad you stopped by. I wanted to get your thoughts. You're thought leader in the industry. You've been on multiple Cube panels. Thank you very much. And also Cube alumni. You know, IBM with the data center of the value proposition. The CEO's up on the stage today saying you got data, you got blockchain and you got AI, which is such the infrastructure of the future. And AI is the software of the future, data's at the middle. Dave and I were talking about that as the innovation sandwich. The data is being sandwiched between blockchain and AI, two super important things. And she also mentioned Moore's law. Faster, smaller, cheaper. Every 6 months doubling in speed and performance. And then Metcalfe's law, which is more of a network effect. Kind of teasing out token economics. You see kind of where the world's going. This is an interesting position from IBM. I like it. Is it real? >> Well it sounds very data sciency, right? You have the economics part, you have the networking. You have all these things in your plane. So I think it's very much in line with what you would expect if data science actually sustains (mumbles), which thankfully it has. >> Yeah. >> And I think the reality is you know, we like to boil things down into nice, simple concepts but in the real world when you're actually figuring it all out its going to be multiple effects. It's going to be, you know a lot of different things that interact. >> And they kind of really tease out their cloud strategy in a very elegant way. I mean they essentially said, 'Look we're into the cloud and we're not going to try to.' They didn't say it directly, but they basically said it. We're not going to compete with Amazon head-to-head. We're going to let our offerings to do the talking. We're going to use data and give customers choice with multi cloud. How does that jive for you? How does that work because at the end of the day I got to have business logics. I need applications. >> Yes. >> You know whether its blockchains, cryptocurrency or apps. The killer app's now money. >> Yep. >> If no one's making any money. >> Sure. >> No commerce is being done. >> Right. I mean I think it makes sense. You know, Amazon has such a strong hold in the infrastructure part, right? Being able to store your data elsewhere and have it be cloud. I don't think that was really IBM's core business. You know, a lot of I think their business model was built around business and business relationships and these days, one of the great things about all these data technologies is that one company doesn't have to do all of it, right? You have partnerships and actually partners so that you know, one company does AI. You partner with another company that has data. And that way you can actually both make money, right? There's more than enough work to go around and that much you can say having worked in data science teams right? If I can offload some of my work to different divisions, fantastic. That'd be great. Saves us time. You get to market faster. You can build things quicker. So I think that's one of the great things about what's happening with data these days, right? There's enough work to get around. >> And it's beautiful too because if you think about the concept that made cloud great is DevOps. Blockchain is an opportunity to use desensualization to take away a lot of inefficiencies. AI is also an automation opportunity to create value. So you got inefficiencies on block chains side and AI to create value, your thoughts and reaction to where that's going to go. You know, in light of the first death on a Uber self-driving car. Again, historic yesterday right? And so you know, the reality is right there. We're not perfect. >> Yeah. >> But there's a path. >> Well so most of its inefficiency out there. It's not the technology. It's all the people using technology, right? You broke the logic by putting in something you shouldn't have put in that data set, you know? The data's now dirty because you put in things that you know, the developer didn't think you'd put in there. So the reality is we're going to keep making mistakes and there will be more and more opportunities for new technologies to help you know, cheer that up. >> So I was talking to Rob Thomas, GM of the analytics team. You know Rob, great guy. He's smart. He's also an executive but he knows the tech. He and I were talking about this notion of data containers. So with Kubernetes now front and center as an orchestration layer for cloud and application workloads, IBM has an interesting announcement with this cloud private approach. Where data is the central thing in this. Because you've got things like GDPR out there and the regulatory environment not going to get any easier. You got blockchain crypto. That's a regulatory nightmare. We know a GDBR. That's a total nightmare. So this is happening, right? So what should customers be doing, in your experience? Customers are scratching their head. They don't want to make a wrong bet, but they need good data, good strategy. They need to do things differently. How do they get the best out of their data architecture knowing that there's hurdles and potential blockers in front of them? >> Well so I think you want to be careful of what you select. and how much are you going to be indebted to that one service that you selected, right? So if you're not sure yet maybe you don't want to invest all of your budget into this one thing you're not sure is going to be what you really want to be paying for a year or two, right? So I think being really open to how you're going to plan for things long term and thinking about where you can have some flexibility, whereas certain things you can't. For instance, if you're going to be in an industry that is going to be you know, strict on regulatory requirements right? Then you have less wiggle room than let's say an industry where that's not going to be an absolute necessary part of your technology. >> Let me ask you a question and being kind of a historian you know, what say one year is seven dog years or whatever the expression is in the data space. It just seems like yesterday that Hadoop was going to save the world. So that as kind of context, what is some technologies that just didn't pan out? Is the data link working? You know, what didn't work and what replaced it if you can make an observation? >> Well, so I think that's hard because I think the way I understood technology is probably not the way everyone else did right? I mean, you know at the end of the day it just is being a way to store data right? And just being able to use you know, more information store faster, but I'll tell you what I think is hilarious. I've seen people using Hadoop and then writing sequel queries the same way we did like ten plus years ago, same inefficiencies and they're not leveling the fact that it's Hadoop. Right? They're treating it like I want to create eight million tables and then use joins. So they're not really using the technology. I think that's probably the biggest disappointment is that without that knowledge sharing, without education you have people making the same mistakes you made when technology wasn't as efficient. >> I mean if you're a hammer, everything else is like a nail I guess if that's the expression. >> Right. >> On the exciting side, what are you excited about in technology right now? What are you looking at that's a you know, next 20 mile stare of potential goodness that could be coming out of the industry? >> So I think anytime you have better science, better measurements. So measurement's huge, right? If you think about media industry, right? Everyone's trying to measure. I think there was an article that came out about some of YouTube's failure about measurement, right? And I think in general like Facebook is you know, very well known for measurement. That's going to be really interesting to see, right? What methodologies come out in terms of how well can we measure? I think another one will be say, target advertising right? That's another huge market that you know, a lot of companies are going after. I think what's really going to be cool in the next few years is to see what people come up with, right? It's really the human ingenuity of it, right? We have the technology now. We have data engineers. What can we actually build? And how are we going to be able to partner to be able to do that? >> And there's new stacks that are developing. You think about the ecommerce stack. It's a 30 year old stack. AdTech and DNS and cookiing, now you've got social and network effects going on. You mentioned you know, the Metcalfe's law. So with all that, I want to get just your personal thoughts on blockchain. Beyond blockchain, token economics because there are a lot people who are doing stuff with crypto. But what's really kind of pointing as a mega trands standpoint is a new class of desensualized application developers are coming in. >> Right. >> Okay. They're dealing with data now on a desensualized basis. At the heart of that is the token economics, which is changing some of the business model dynamics. Have you seen anything? Your thoughts on token economics? >> So I haven't seen it from the economics standpoint. I've seen it from more of the algorithms and that standpoint. I actually have a good friend of mine, she's at Yale. And she actually runs the, she's executive director of their corporate law center. So I hear some from her on the legal side. I think what's really interesting is there's all these different arenas. Legal being a very important component in blockchain. As well as, from the mathematical standpoint. You know when I was in school way back when, we studied things like hash keys and you know, RSA keys and so from a math standpoint that's also a really cool aspect of it. So I think it's probably too early to say for sure what the economics part is going to actually look like. I think that's going to be a little more longterm. But what is exciting about this, is you actually see different parts of businesses, right? Not just the financial sector but also the legal sector and then you know say, the math and algorithms and you know. Having that integration of being able to build cooler things for that reason. >> Yeah the math's certainly exciting. Machine learning, obviously that's well documented. The growth and success of what, and certainly the interests are there. You seeing Amazon celebrating all the time. I just saw Werner Vogels, the CTO. Talking about another SageMaker, a success. They're looking at machine learning that way. You got Google with TensorFlow. You've got this goodness in these libraries now that are in the community. It's kind of a perfect storm of innovation. What's new in the ML world that developers are getting excited about that companies are harnessing for value? You seeing anything there? Can you share some commentary on the current machine learning trends? >> So I think a lot of companies have gotten a little more adjusted to the idea of ML. At the beginning everyone was like, 'Oh this is all new.' They loved the idea of it but they didn't really know what they were doing, right? Right now they know a little bit more. I think in general everyone thinks deep learning is really cool, neural networks. I think what's interesting though is everyone's trying to figure out where's the line. What's the different between AI versus machine learning versus deep learning versus neural networks. I think it's a little bit fun for me just to see everyone kind of struggle a little bit and actually even know the terminology so we can have a conversation. So I think all of that, right? Just anything related to that you know, when do you TensorFlow? What do you use it for? And then also say, from Google right? Which parts do you actually send through an API? I mean that's some of the conversations I've been having with people in the business industry, like which parts do you send through an API. Which parts do you actually have in house versus you know, having to outsource out? >> And that's really kind of your thinking there is what, around core competencies where people need to kind of own it and really build a core competency and then outsource where its more a femoral invalue. Is there a formula, I guess to know when to bring it in house and build around? >> Right. >> What's your thoughts there? >> Well part of it, I think is scalability. If you don't have the resources or the time, right? Sometimes time. If you don't have the time to build it in house, it does make sense actually to outsource it out. Also if you don't think that's part of your core business, developing that within house do you're spending all that money and resources to hire the best data scientists, may not be worth it because in fact the majority of your actual sales is with the sale department. I mean they're the ones that actually bring in that revenue. So I think it's finding a balance of what investment's actually worth it. >> And sometimes personnel could leave and you could be a big problem, you know. Someone walks about the door, gets another job because its a hot commodity to be. >> That's actually one of the big complaints I've heard is that we spend all this time investing in certain young people and then they leave. I think part of this is actually that human factor. How do you encourage them to stay? >> Let's talk about you. How did you get here? School? Interests? Did you go off the path? Did you come in from another vector? How did you get into what you're doing now and share a little bit about who you are? >> Yeah so I studied economics, mathematics, creative writing as an undergrad and statistics as a grad student. So you know, kind of perfect storm. >> Natural math, bring it all together. >> Yeah but you know its funny because I actually wrote about and talked about how data is going to be this big thing. This is like 2009, 2010 and people didn't think it was that important, you know? I was like next three to five years mathematicians are going to be a hot hire. No one believed me. So I ended up going, 'Okay well, the economy crashed.' I was in management consulting in finance, private equity hedge funds. Everyone swore like, if you do this you're going to be set for life, right? You're on the path. You'll make money and then the economy crashed. All the jobs went away. And I went, 'Maybe not the best career choice for me.' So I did what I did at companies. I looked at the market and I went, 'Where's their growth?' I saw tech had growth and decided I'm going to pick up some skills I've never had before, learn to develop more. I mean in the beginning I had no idea what an application development process was, right? I'm like, 'What does that mean to actually develop an application?' So the last few years I've really just been spending, just learning these things. What's really cool though is last year when my patents went through and I was able to actually able to launch something with Box at their keynote. That was really awesome. >> Awesome. >> So I became a long way from I think, have the academic knowledge to being able to apply it and then learn the technologies and then developing the technologies, which is a cool thing. >> Yeah and that's a good path because you came in with a clean sheet of paper. You didn't have any dogma of waterfall and all the technologies. So you kind of jumped in. Did you use like a cloud to build on? Was it Amazon? Was it? >> Oh that's funny too. Actually I do know Legacy's technology quite well because I was in corporate America before. Yeah, so like Sequel. For instance like when I started working data science, funny enough we didn't call it data science. We just called it like whatever you call it, you know. There was no data science term at that point. You know we didn't have that idea of whether to use R or Python. I mean I've used R over ten years, but it was for statistics. It was never for like actual data science work. And then we used Sequel in corporate America. When I was taking data it was like in 2012. Around then, everyone swore that no, no. They're going to programmers. Got to know programming. To which, I'm like really? In corporate America, we're going to have programmers? I mean think about how long it's going to take to get someone to learn any language and of course, now everyone's learning. It's on Sequel again right? So. >> Isn't it fun to like, when you see someone on Facebook or Linkdin, 'Oh man data's a new oil.' And then you say, 'Yeah here's a blog post I wrote in 2009.' >> Right. Yeah, exactly. Well so funny enough Ginni Rometty today was saying about exponential versus linear and that's one of the things I've been saying over the last year about because you know, you want exponential growth. Because linear anyone can do. That's a tweet. That's not really growth. >> Well we value your opinion. You've been great on The Cube. Great to help us out on those panels, got a great view. What's going on with your company? What are you working on now? What's exciting you these days? >> Yeah so one of the cool things we worked on, it's very much in line with what the IBM announcement was, so being smarter, right? So I developed some technology in the photo industry, digital assent management as well as being able to automate the renaming of files, right? So you think you probably a picture on your digital camera you never moved over because you, I remember the process. You open it, you rename it, you saved it. You open the next one. Takes forever. >> Sometimes its the same number. I got same version files. It's a nightmare. >> Exactly. So I basically automated that process of having all of that automatically renamed. So the demo that I did I had 120 photos renamed in less than two minutes, right? Just making it faster and smarter. So really developing technologies that you can actually use every day and leverage for things like photography and some cooler stuff with OCR, which is the long term goal. To be able to allow photographers to never touch the computer and have all of their clients photos automatically uploaded, renamed and sent to the right locations instantly. >> How did you get to start that app? Are you into photography or? >> No >> More of, I got a picture problem and I got to fix it? >> Well actually its funny. I had a photographer taking my picture and she showed me what she does, the process. And I went, 'This is not okay. You can do better than this.' So I can code so I basically went to Python and went, 'Alright I think this could work,' built a proof of concept and then decided to patent it. >> Awesome. Well congratulations on the patent. Final thoughts here about IBM Think? Overall sentiment of the show? Ginni's keynote. Did you get a chance to check anything out? What's the hallway conversations like? What are some of the things that you're hearing? >> So I think there's a general excitement about what might be coming, right? So a lot of the people who are here are actually here to, I think share notes. They want to know what everyone else is doing, so that's actually great. You get to see more people here who are actually interested in this technology. I think there's probably some questions about alignment, about where does everything fit. That seems to be a lot of the conversation here. It's much bigger this year as I'm sure you've noticed, right? It's a lot bigger so that's probably the biggest thing I've heard like there's so many more people than we expected there to be so. >> I like the big tent events. I'm a big fan of it. I think if I was going to be critical I would say, they should do a business event and do a technical one under the same kind of theme and bring more alpha geeks to the technical one and make this much more of a business conversation because the business transformation seems to be the hottest thing here but I want to get down in the weeds, you know? Get down and dirty so I would like to see two. That's my take. >> I think its really hard to cater to both. Like whenever I give a talk, I don't give a really nerdy talk to say a business crowd. I don't give a really business talk to a nerdy crowd, you know? >> It's hard. >> You just have to know, right? I think they both have a very different sensibility, so really if you want to have a successful talk. Generally you want both. >> Jennifer thanks so much for coming by and spending some time with The Cube. Great to see you. Thanks for sharing your insights. Jennifer Shin here inside The Cube at IBM Think 2018. I'm John Furrier, host of The Cube. We'll be back with more coverage after this short break.

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

>> Announcer: Live from New York, it's theCUBE covering Big Data New York City 2016. Brought to you by headline sponsors Cisco, IBM, Nvidia, and our ecosystem sponsors. Now, here are your hosts, Dave Vellante and Peter Burris. >> We're back. Goutham Belliappa is here. He's with Capgemini. He's the Big Data Integration and Analytics Leader at Capgemini. Welcome to theCUBE. >> Thank you. Happy to be here with you. >> So a lot going on this week at Big Data. You guys have one of the top SI's consultants in the world. What are you seeing as far as the transformation of organizations to become data driven? What are some of the drivers that you're seeing out there? >> It's a good question. So a couple of years ago, we started on this journey with Cloudera about four years ago. When we started this journey on LinkedIn, you saw the poster that said, "Big Data is like teenage sex - everybody talks about it, nobody does it." Right? The reality shifted considerably. So while the technology's evolved considerably over the last four years, the most important thing is most of our clients are feeling pressure from the disruptors in Silicon Valley. You see the AirBnb's and the Amazon's and the Google apply pressure's on traditional industries that didn't exist before. For example, a lot of our auto clients don't believe auto clients are the biggest threat. They believe Apple, and Google, and Amazon are the biggest threat. Right? Because what our clients are afraid of, the incumbents, the traditional companies are afraid of, is they don't want to become a commodity manufacturer of components for a software company. They don't want, for example, GM manufacturing a part that Apple is putting the wrapper on, selling and making the margin on. So, more and more tech is driving the industry to where GE made the announcement they no longer want to be known as an engine manufacturer, they want to be an IT company. >> Peter: Or a financial services firm. >> Or a financial services firm. And you see the same thing in pharma as well. We see the pharma companies don't want to be known as manufacturers of med devices, they want to own the service industry. Move up the value chain and secure the revenue stream. So that's what's changing the industry as a whole and then Big Data Central to the strategy of data-enabled transformation. >> So it's like the death, what was the article we saw yesterday? Who wrote that? "The Death of Tech". It was Rob Thomas, right? The death of tech companies is now the rebirth of... all companies are tech companies. >> All companies are tech companies and that's the future of all companies: to be a tech company and move from selling commodities to selling services and having a vested interest in the outcome that the clients receive at the end of the day. >> Yeah, I once wrote a piece many year ago that suggested that we would see more non-tech companies generate SAS and Cloud applications than tech companies themselves. And while it's still hasn't come true there's evidence on the horizon that it very well likely will be a major feature of how companies engage their customers through their own version of SAS or deploying their own Clouds for their own ecosystem. And you can go back, thirty years, thirty-five years and look at MAP/TOP for example and the promise of what it meant to define and deploy standards that could integrate whole industries around data. Hasn't happened, but we can see it actually happening on the horizon. What industry? I mean, you're still looking at things through an industry lenses, right? Where do you see it happening before it's happening elsewhere? >> So, the first place it happens naturally is tech because they're closest to it, right? To give you the classic example, I can go anywhere and buy an Office license today. I have to subscribe to Office, right? So, what it's done to Microsoft, it's changed the fundamentals of the balance sheet from selling perpetual licenses, getting revenue once and then having the prospect of not having a customer later, to selling it over a sustained period of time. So moving from one-time revenue hits to perpetual revenue. So tech is where it's starting off. And even in tech, we're actually pushing the boundaries by working some of our providers like Cloudera and some of the other providers out there to move from a perpetual license model to as-a-service model. So what this enables people like us to do is to offer as-a-service to our customers because our customers need to offer as-a-service to their end users as well, right? I gave you the example of GE because it's public knowledge. They want to move up the spectrum of not selling an engine but leasing an engine to an airplane manufacturer and then owning the services revenue on it, right? So when Delta, let's say, that's leasing the engine is no longer owning a commodity, they're becoming asset light, right? The companies like GE and other companies when they become tech, they need to become asset light as well, which means not being burdened by land, labor, and capital but, as they get paid for outcome, they want to pay for outcome as well. >> Somebody's got to own the asset eventually. This is not a game of musical chairs where the asset-owning music keeps playing and then it stops and somebody's got all the assets. >> Ghoutham: Exactly. >> So how do you see... the global sense of how organization, how is this going to get institutionalized? Are we just going to have a few companies with enormous assets and everybody else running software? How do you think it's going to play out? >> Good question. So Jeff Bezos was at a manufacturing company outside of Arland recently and he pointed at and antique generator sitting next to the plane and said, 'Back in the day, everybody had 'a generator sitting next to the 'company producing electricity.' But today we have a big distribution plan and we get it off the grid, right? So to your point, yes, we see the scale and the price reduction coming from a few companies owning those pieces of assets. For example, it's almost impossible to compete with the Amazon's and Google's of the world today because at the scale that they receive. And the customers get the benefit of that. Similarly, you'll see the software, right? So software, you see the software companies owning the assets and title and leasing it back to the customer. So to your point, yes, we're moving to a model where it's more scalable and the price efficiencies of them, they're passed on to the end consumer. >> Peter: So historically, in a more asset-oriented company, historically, if you take a look, for example, at Porter. Porter's competitive strategy. So Porter would say, 'Pick your industry' where an industry is a way of categorizing companies with similarly procured and deployed assets. Automobile had a collection of assets and hotelery had a collection of assets. So pick your industry based on your knowledge and what kind of returns you're likely to get. Pick your position in that industry and then decide what games you're going to play using the five-factor analysis you did. But it was all tied back to assets. So if the world's getting less asset-oriented, hard asset-oriented >> Ghoutham: Hard assets >> What does that do to competitive strategy? >> Good point. So the hard assets are getting commoditized. The value comes in what you can build on top of the hard assets, which is your IP, right? So the soft assets of IP and software is where the value's going to be. So there's a lot of pressure on hard-asset companies. You see many companies getting at the server market because they can't compete with the Amazon's and the Google's. They can wide-label and manufacture all their stuff. The differentiation is going to come in the software. That's the reason companies like GE and the other pharma companies and automobile companies want to become tech companies, because that's where the margin is, that's where the differentiation is. It's no longer in the tangible, hard-assets but it's in what you can do with them. >> Dave: Well, and it says data's going to be one of those differentiators. >> Yeah, yeah. >> And a big asset so what... Everybody in theory has to become data-driven, maybe in fact has to be- >> Data is their asset, is their differentiator. >> You've pointed out many times all this digitization is data. >> Peter: Well, yeah. >> Digital equals data. >> So our basic proposition is that increasingly the whole notion of being a digital business is about how you differentially use data to create and sustain customers. So let me build on that for a second and say that there's this term in economics known as "asset specificity" which essentially is the degree to which an asset is applied to a single or limited numbers of uses. Programmability reduces asset specificity so if we go back to the airline engine example, GE added programmability to an airplane engine and was able to turn it into a service. Uber was able to add programmability to a bunch of consumer cars and was able to turn it into a ride sharing capability. What does that say about the future of an industry-oriented approach to conducting business if I am now able to reconfigure my asset base very quickly and the industry's based on how my assets are reconfigured. What does that say about the future of industry? >> Ghoutham: So, in my opinion, I don't think the future of industry is going to change because you still going to have a specialization based on the domain you're selling to and the expertise that you have. >> Peter: So it's customer-focused industry definitions not asset-based industry definition. >> Ghoutham: The hard assets or going to get commoditized and get moved out to a few specialty players. But the differentiation is going to be on how you serve the customers and the type of customer that you serve. >> Dave: So what are the head winds you're seeing in terms of customers getting to this data nirvana? What are the challenges that they're facing? >> So, Peter Drucker. There's an attribute of Peter Drucker, regardless of who said it, 'Culture eats strategy for breakfast.' We work with retailers all the time who understand that they face an existential threat from Amazon, however their culture prevents them from being like Amazon. It prevents them from experimenting. It prevents them from failing fast. It prevents them from acting together. For example, a lot of customers want to have an OmniChannel strategy. It's a seamless commerce strategy but then they have a silo for the stores they have a silo for the call centers, they have a silo for the web, but they don't act together. So culture is one of the biggest barriers we see in enabling that journey. Tech, we know that tech works. Two years ago we're doing technical POC's. Today, we're not anymore. We know that tech works, right? So get over it. So it's a culture and the attitude and the ability to change how you go to market that's to me the biggest challenge. >> Peter: But isn't there also finance? Because hard assets still are associated with a rate of amortization, depreciation, and utilization. There's expertise and what not built up around that, and this becomes especially critical when you start thinking about the impedance mismatch between agile development and budgeting, for example. So how do you anticipate that not only culture has to change, but also the way we think about finance? Or is financing disciplines end up being a part of the culture? >> Ghoutham: So you're absolutely right. So, financing discipline has to be part of the culture. To give you an abstract example, back in the day when we did a data warehouse or a data project, we'd do a huge, let's say for lack of an argument, 10 million dollar project. Today we're doing 40, 50, 50k, 100k projects. So Agile has gone from fixed scope where you laid out a two-year project with an end in mind and by the time you achieve that end the requirements have changed and the business has moved on, to achieving small objectives. So we're consuming it in chunks. You're going from fixed scope to fixed budget. So I've got a certain allocation that I need to use and I prioritize it on a regular basis on how I want to consume that basis that I have. >> So it's almost a subscription? Are you going in basically almost subscription-basis? Going to a customer and saying, here's the outcome. We will achieve that outcome over a period of time. You'll sign up to achieve that outcome over a 12-month period and will consume that budget in 12-month increments? >> First and second, in any given period, you can re-prioritize the outcome that you want to achieve. During the journey for 12 months, if you realize something new, you have the flexibility to change. Let me take out this chunk of work and do something else so I have the flexibility. >> Peter: So you can redefine the outcomes? >> Yes. >> It's almost like, I don't know if you'd call it this, I'd be interested to know what you guys call it, but it's almost like a subscription-to-outcome business model. >> Ghoutham: Exactly. >> Dave: Service is a service. >> Ghoutham: We call it sprint as a service. >> Service is a service. >> We call it sprint as a service is our defined model of how to go to market around that is we know two sprints ahead what we're going to deliver. Everything else is indicative, right? Because not everything we do has to succeed. That's a mindset change that our customers need to realize. We believe the biggest reason clients fail is because failure is not an option. They put so much behind it, when they fail, it's catastrophic. >> Peter: Because careers fail- >> Yes >> Peter: And not the project fails. >> Exactly. >> Dave: You're not saying "failure equals fire" mentality. If that's the culture, then people refuse to fail and they end up failing. >> Until it's catastrophic. >> (Dave laughing) >> So I was having a conversation last week at Oracle OpenWorld when theCUBE was here, great show, and had a really good conversation with a competitor of yours who talked about how they were going to use machine-learning in the contracting process by sweeping up all kinds of data and that would help them actually define the characteristics of what they were going to deliver. How much work was going to take, how much labor, what other resources? And they were able to get rid of the 500 thousand to five million dollar part of the assessment or the assessment part of a deal, drive it down to 50 thousand dollars or less and in the process come up with contracts who are much more customer-friendly. What other types of changes are happening in the services business as we do a better job of packaging intellectual property whether it's this "service as a service" or "service subscription" or whatever you mentioned or even thinking about machine learning being applied to the contracting process. >> Dave: "Sprint as a service" >> That's correct. Sorry. Thank you. >> You've asked a number of questions so first thing >> I did. >> Let me talk about machine learning and human task automation. So one of the biggest things we're doing today is learning to understand and automate human tasks. One of the biggest things we've seen, supply chain companies for example, is they don't have enough planners, right? So you hire a bunch of planners. You have different variations and skills. So we're taking the top 5% of planners, automating what everybody else does and letting them handle exceptions. And workforce automation, in many of those areas, we're beginning to automate human tasks and letting the human handle exceptions that a machine cannot handle. So machine learning has becoming fundamental in everything, and not just contract negotiation, but actually enabling companies to scale in areas where they could never scale because they never had enough people to do it. We're not just doing it externally to our clients. One of the things we're doing internally is we don't have an Big Data developers so we're beginning to use machine learning to automate a lot of tasks that developers will do. Industrialize a lot of it so we can scale in our delivery approach as well. >> Peter: Excellent. >> Come back to this event. You guys are here, you're on the floor. We've been talking all week about, you know, Hadoop is kind of yesterday's news. >> Ghoutham: Yes, yes. >> What are you guys seeing? You got a big chunk of customers that said alright, we're going to invest in Hadoop. We have the skill sets. And then a big chunk of... I'm not going there. And now they're sort of looking at new ways. Whether it's Cloud, whether it's Spark. >> Peter: And a big chunk of customers will say I do want to go there, but I'm having problems getting there. >> Yeah, right. And I got some serious challenges. So what are you seeing there, and how is CapGemini helping them? >> So we did an analysis with Forrester and one thing we'll say that 100% of our clients are going to Hadoop. It's not 95%. So everybody's going to Hadoop in one way, shape, or form. Whether you go with the traditional distribution, go with an Amazon as your whatever, everybody's going to Hadoop in some way, shape, or form. To address the reluctance, we spoke about the Uberization of the industry, which is you have a contract, which is an outcome-based contract. So we go to our clients who have fears about moving to Hadoop and say, 'We'll take the risk'. Let's write an outcome-based contract to move you guys into the noob because you know you need to go there. You're afraid to go there so we'll take the risk, we'll shift the risk over to us and we'll move you onto Hadoop. The last piece is industrialization. So back two years ago, we designed code for every little thing that we needed to do. Today, we've automated a lot of our code generation from existing systems, from knowledge we've gained, including machine learning to we're able to mechanize a lot of the code. Frankly, we did it because we had a developer shortage. So we started industrializing a lot of our IPN, our assets, and our learnings, but this is also helping our customers move on to the new world. It's improved the quality of a delivery. It's improved the velocity of a delivery. It's reduced the price where we're much more competitive. To give you an example in the BPO space back in the day we did labor arbitrage. But more and more, like with our clients who use manual auditing, we're using machine learning to automate a lot of that. And that more than pays for the cost of Hadoop. So to answer your specific question, gone are the days of 'Hey, I want to get into Hadoop.' The question is what business value can I achieve? How fast can I achieve it, and if you're afraid, can I take the risk for you? >> And that business value, historically, if I can use that term on such a nascent industry, Has been... the ROI's been a Reduction on Investment. >> Ghoutham: Correct. I'm going to lower the cost of my enterprise data warehouse. >> Ghoutham: That was two years ago. >> Okay so what is it today? >> Today, it is 'How can I reduce your marketing span? 'How can I optimize your marketing span? 'How can I improve the accuracy 'of your supply chain planning?' So it's more in terms of directly delivering business value versus the cost reduction. Many of our clients say the cost reduction is irrelevant. Frankly, because the business case is so huge. To give you an example of one of our supply chain clients, their fill-rate for orders is 60% which means they're a big manufacturer, they're only to fill 60% of the orders that come through. That's because they're not able to plan where to deploy product and so on and so forth. So if you increase it by 5%, it's a 300 million dollar annual business case. My two million dollar data warehouse optimization, it's irrelevant. It's peanuts in a 300 million dollar annual business case. It's things like that that's helping machine learning and Hadoop evolve in the ecosystem. The cost-reduction play was just a way to slide the infrastructure in. You can do a lot more with it. >> And when you're selling to the CIO's and business leaders, that resonates. >> Ghoutham: Yeah. Absolutely. >> Great. We'll have to leave it there. Thanks very much for coming to theCUBE, Ghou. >> Ghoutham: My pleasure. My pleasure. >> Alright keep it right there everybody. We'll be back with our next guest. This is theCUBE. We're live at Big Data NYC. Be right back. (techno music)