[Music] welcome to the great super cloud debate a power panel of three top technology industry analysts maribel lopez is here she's the founder and principal analyst at lopez research keith townsend is ceo and founder of the cto advisor and sanjeev mohan is principal at sanjmo super cloud is a term that we've used to describe the future of cloud architectures the idea is that super clouds are built on top of hyperscaler capex infrastructure and the idea is it goes beyond multi-cloud the premise being that multi-cloud is primarily a symptom of multi-vendor or m a or both and results in more stove we're going to talk about that super cloud's meant to connote a new architecture that leverages the underlying primitives of hyperscale clouds but hides and abstracts that complexity of each of their respective clouds and adds new value on top of that with services and a continuous experience a similar or identical experience across more than one cloud people may say hey that's multi-cloud we're going to talk about that as well so with that as brief background um i'd like to first welcome our painless guys thanks so much for coming on thecube it's great to see you all again great to be here thank you to be here so i'm going to start with maribel you know what i just described what's your reaction to that is it just like what like cloud is supposed to be is that really what multi-cloud is do you agree with the premise that multi-cloud has really been you know what like chuck whitten from dell calls it it's been multi-cloud by default i call it a symptom of multi-vendor what's your take on on what this is oh wow dave another term here we go right more more to define for people but okay the reality is i agree that it's time for something new something evolved right whether we call that super cloud or something else i you know i don't want to really debate the term but we need to move beyond where we are today in multi-cloud and into if we want to call it cloud 5 multi-cloud 2 whatever we want to call it i believe that we're at the next generation that we have to define what that next generation is but if you think about it we went from public to private to hybrid to multi and every time you have a discussion with somebody about cloud you spend 10 minutes defining what you're talking about so this doesn't seem any different to me so let's just go with super cloud for the moment and see where we go and you know if you're interested after everybody else makes their comments i got a few thoughts about what super cloud might mean as well yeah great so i and i agree with you when we like i said in a recent post you could call it cl cloud you know multi-cloud 2.0 but it's something different is happening and sanjeev i know you're not a you're not a big fan of buzz words either but i wonder if you could weigh in on this topic uh you mean by the way sanjeev is at the mit cdo iq conference a great conference uh in boston uh and so he's it's a public place so we're going to have i think you viewed his line when he's not speaking please go ahead yeah so you know i come from a pedigree of uh being an analyst of uh firms that love inventing new terms i am not a big fan of inventing new terms i feel that when we come up with a new term i spend all my time standing on a stage trying to define what it is it takes me away from trying to solve the problem so so i'm you know i find these terms to be uh words of convenience like for example big data you know big data to me may not mean anything but big data connotes some of this modern way of handling vast volumes of data that traditional systems could not handle so from that point of view i'm i'm completely okay with super cloud but just inventing a new term is what i have called in my previous sessions tyranny of jargons where we have just too many jargons and uh and they resonate with i.t people they do not resonate with the business people business people care about the problem they don't care about what we and i t called them yeah and i think this is a really important point that you make and by the way we're not trying to create a new industry category per se yeah we leave that to gartner that's why actually i like super cloud because nobody's going to use that no vendor's going to use the term super cloud it's just too buzzy so so but but but it brings up the point about practitioners and so keith i want to bring you in so the what we've talked about and i'll just sort of share some some thoughts on the problems that we see and and get keith get your practitioner view most clouds most companies use multiple clouds we all kind of agree on that i think and largely these clouds operate in silos and they have their own development environment their own operating environment different apis different primitives and the functionality of a particular cloud doesn't necessarily extend to other clouds so the problem is that increases friction for customers increases cost increases security risk and so there's this promise maribel multi-cloud 2.0 that's going to solve that problem so keith my question to you is is is that an accurate description of the problem that practitioners face today do what did i miss and i wonder if you could elaborate so i think we'll get into some of the detail later on why this is a problem specifically around technologies but if we think about it in the abstract most customers have their hands full dealing with one cloud like we'll you know through m a and such and you zoom in and you look at companies that have multiple clouds or multi-cloud from result of mma mna m a activity you'll see that most of that is in silos so organizationally the customer may have multiple clouds but sub orchid silos they're generally a single silo in a single cloud so as you think about being able to take advantage of of tooling across the multicloud of what dave you guys are calling the super cloud this becomes a serious problem it's just a skill problem it's too much capability uh across too many things that look completely different than another okay so dave can i pick up on that please i'd love i was gonna just go to you maribel please chime in here okay so if we think about what we're talking about with super cloud and what keith just mentioned remember when we went to see tcp ip and the whole idea was like how do we get computers to talk to each other in a more standardized way how do we get data to move in a more standardized way i think that the problem we have with multi-cloud right now is that we don't have that so i think that's sort of a ground level of getting us to your super cloud premise is that and and you know google's tried it with anthony's like everybody every hyperscaler has tried their like right one to run anywhere but that abstraction layer you talk about what whatever we want to call it is super necessary and it's sort of the foundation so if you really think about it we've spent like 15 years or so building out all the various components of cloud and now's the time to take it so that cloud is actually more of an operating model versus a place there's at least a base level of it that is vendor neutral and then to your point the value that's going to be built on top of that you know people been trying to commoditize the basic infrastructure for a while now and i think that's what you're seeing in your super cloud multi-cloud whatever you want to call it the infrastructure is the infrastructure and then what would have been traditionally that past layer and above is where we're going to start to see some real innovation but we still haven't gotten to that point where you can do visibility observability manageability across that really complex cloud stack that we have the reason i the reason i love that tcpip example hm is because it changed the industry and it had an ecosystem effect in sanjiv the the the example that i first example that i used was snowflake a company that you're very familiar with that is sort of hiding all that complexity and right and so we're not there yet but please chime in on this topic uh you gotta you gotta view it again uh after you building upon what maribel said you know to me uh this sounds like a multi-cloud operating system where uh you know you need that kind of a common uh set of primitives and layers because if you go in in the typical multi-cloud process you've got multiple identities and you can't have that you how can you govern if i'm if i have multiple identities i don't have observability i don't know what's going on across my different stacks so to me super cloud is that call it single pane of glass or or one way through which i'm unifying my experience my my technology interfaces my integration and uh and i as an end user don't even care which uh which cloud i'm in it makes no difference to me it makes a difference to the vendor the vendor may say this is coming from aws and this is coming from gcp or azure but to the end user it is a consistent experience with consistent id and and observability and governance so that to me makes it a big difference and so one of floyer's contribution conversation was in order to have a super cloud you got to have a super pass i'm like oh boy people are going to love that but the point being that that allows a consistent developer experience and to maribel's earlier point about tcp it explodes the ecosystem because the ecosystem can now write to that super pass if you will those apis so keith do you do do you buy that number one and number two do you see that industries financial services and healthcare are actually going to be on clouds or what we call super clouds so sanjeev hit on a really key aspect of this is identity let's make this real they you love talk about data collaboration i love senji's point on the business user kind of doesn't care if this is aws versus super cloud versus etc i was collaborating with the client and he wanted to send video file and the video file uh his organization's access control policy didn't allow him to upload or share the file from their preferred platform so he had to go out to another cloud provider and create yet another identity for that data on the cloud same data different identity a proper super cloud will enable me to simply say as a end user here's a set of data or data sets and i want to share a collaboration a collaborator and that requires cross identity across multiple clouds so even before we get to the past layer and the apis we have to solve the most basic problem which is data how do we stop data scientists from shipping snowballs to a location because we can't figure out the identity the we're duplicating the same data within the same cloud because we can't share identity across customer accounts or etc we we have to solve these basic thoughts before we get to supercloud otherwise we get to us a turtles all the way down thing so we'll get into snowflake and what snowflake can do but that's what happens when i want to share my snowflake data across multiple clouds to a different platform yeah you have to go inside the snowflake cloud which leads right so i would say to keith's question sanjeev snowflake i think is solving that problem but then he brings up the other problem which is what if i want to share share data outside the snowflake cloud so that gets to the point of visit open is it closed and so sanji chime in on the sort of snowflake example and in maribel i wonder if there are networking examples because that's that's keith's saying you got to fix the plumbing before you get these higher level abstractions but sanji first yeah so i so i actually want to go and talk a little bit about network but from a data and analytics point of view so i never built upon what what keith said so i i want to give an example let's say i am getting fantastic web logs i and i know who uh uh how much time they're spending on my web pages and which pages they're looking at so i have all of that now all of that is going into cloud a now it turns out that i use google analytics or maybe i use adobe's you know analytics uh suite now that is giving me the business view and i'm trying to do customer journey analytics and guess what i now have two separate identities two separate products two separate clouds if i and i as an id person no problem i can solve any problem by writing tons of code but why would i do that if i can have that super pass or a multi-cloud layout where i've got like a single way of looking at my network traffic my customer metrics and i can do my customer journey analytics it solves a huge problem and then i can share that data with my with my partners so they can see data about their products which is a combination of data from different uh clouds great thank you uh maribel please i think we're having a lord of the rings moment here with the run one room to rule them all concept and i'm not sure that anybody's actually incented to do that right so i think there's two levels of the stack i think in the basic we're talking a lot about we don't have the basic fundamentals of how do you move data authenticate data secure data do data lineage all that stuff across different clouds right we haven't even spoken right now i feel like we're really just talking about the public cloud venue and we haven't even pulled in the fact that people are doing hybrid cloud right so hybrid cloud you know then you're talking about you've got hardware vendors and you've got hyperscaler vendors and there's two or three different ways of doing things so i honestly think that something will emerge like if we think about where we are in technology today it's almost like we need back to that operating system that sanji was talking about like we need a next generation operating system like nobody wants to build the cloud mouse driver of the 21st century over and over again right we need something like that as a foundation layer but then on top of it you know there's obviously a lot of opportunity to build differentiation like when i think back on what happened with cloud amazon remained aws remained very powerful and popular because people invested in building things on amazon right they created a platform and it took a while for anybody else to catch up to that or to have that kind of presence and i still feel that way when i talk to companies but having said that i talked to retail the other day and they were like hey we spent a long time building an abstraction layer on top of the clouds so that our developers could basically write once and run anywhere but they were a massive global presence retailer that's not something that everybody can do so i think that we are still missing a gap i don't know if that exactly answers your question but i i do feel like we're kind of in this chicken and egg thing which comes first and nobody wants to necessarily invest in like oh well you know amazon has built a way to do this so we're all just going to do it the amazon way right it seems like that's not going to work either but i think you bring up a really important point which there is going to be no one ring to rule them all you're going to have you know vmware is going to solve its multi-cloud problem snowflake's going to do a very has a very specific you know purpose-built system for it itself databricks is going to do its thing and it's going to be you know more open source i would companies like aviatrix i would say cisco even is going to go out and solve this problem dell showed at uh at dell tech world a thing called uh project alpine which is basically storage across clouds they're going to be many super clouds we're going to get maybe super cloud stove pipes but but the point is however for a specific problem in a set of use cases they will be addressing those and solving incremental value so keith maybe we won't have that single cloud operating you know system but we'll have multiple ones what are your thoughts on that yeah we're definitely going to have multiple ones uh the there is no um there is no community large enough or influential enough to push a design take maribel's example of the mega retailer they've solved it but they're not going to that's that's competitive that's their competitive advantage they're not going to share that with the rest of us and open source that and force that upon the industry via just agreement from everyone else so we're not going to get uh the level of collaboration either originated by the cloud provider originated from user groups that solves this problem big for us we will get silos in which this problem is solved we'll get groups working together inside of maybe uh industry or subgroups within the industry to say that hey we're going to share or federate identity across our three or four or five or a dozen organizations we'll be able to share data we're going to solve that data problem but in the same individual organizations in another part of the super cloud problem are going to again just be silos i can't uh i can't run machine learning against my web assets for the community group that i run because that's not part of the working group that solved a different data science problem so yes we're going to have these uh bifurcations and forks within the super cloud the question is where is the focus for each individual organization where do i point my smart people and what problems they solve okay i want to throw out a premise and get you guys reaction to it because i think this again i go back to the maribel's tcpip example it changed the industry it opened up an ecosystem and to me this is what digital transformation is all about you've got now industry participants marc andreessen says every company is a software company you've now got industry participants and here's some examples it's not i wouldn't call them true super clouds yet but walmart's doing their hybrid thing with azure you got goldman sachs announced at the last reinvent and it's going to take its tools its software its data and which is on-prem and connect that to the aws cloud and actually deliver a service capital one we saw sanjiv at the snowflake summit is is taking their tooling and doing it now granted just within snowflake and aws but i fully expect them to expand that across other clouds these are industry examples capital one software is the name of the division that are now it's to the re reason why i don't get so worried that we're not solving the lord of the rings problem that maribel mentioned is because it opens up tremendous opportunities for companies we got like just under five minutes left i want to throw that out there and see what you guys think yeah i would just i want to build upon what maribel said i love what she said you're not going to build a mouse driver so if multi-cloud supercloud is a multi-cloud os the mouse driver would be identity or maybe it's data quality and to teach point that data quality is not going to come from a single vendor that is going to come from a different vendor whose job is to to harmonize data because there might be data might be for the same identity but it may be a different granularity level so you cannot just mix and match so you need to have some sort of like resolution and that is is an example of a driver for multi-cloud interesting okay so you know octa might be the identity cloud or z scaler might be the security cloud or calibre has its cloud etc any thoughts on that keith or maribel yeah so let's talk about where the practical challenges run into this we did some really great research that was sponsored by one of the large cloud providers in which we took all we looked at all the vmware cloud solutions when i say vmware cloud vmware has a lot of products across multi-cloud now in the rock broadcloud portfolio but we're talking about the og solution vmware vsphere it would seem like on paper if i put vmware vsphere in each cloud that is therefore a super cloud i think we would all agree to that in principle what we found in our research was that when we put hands on keyboard the differences of the clouds show themselves in the training gap and that skills gap between the clouds show themselves if i needed to expose less our favorite friend a friend a tc pip address to the public internet that is a different process on each one of the clouds that needs to be done on each one of the clouds and not abstracted in vmware vsphere so as we look at the nuance yes we can give the big controls but where the capital ones the uh jp morgan chase just spent two billion dollars on this type of capability where the spin effort is done is taking it from that 80 percent to that 90 95 experience and that's where the effort and money is spent on that last mile maribel we're out of time but please you know bring us home give us your closing thoughts hey i think we're still going to be working on what the multi-cloud thing is for a while and you know super cloud i think is a direction of the future of cloud computing but we got some real problems to solve around authentication uh identity data lineage data security so i think those are going to be sort of the tactical things that we're working on for the next couple years right guys always a pleasure having you on the cube i hope we see you around keith i understand you're you're bringing your airstream to vmworld or vmware explorer putting it on the on the floor i can't wait to see that and uh mrs cto advisor i'm sure we'll be uh by your side so looking forward to that hopefully sanjeev and maribel we'll see you uh on the circuit as well yes hope to see you there right looking forward to hopefully even doing some content with you guys at vmware explorer too awesome looking forward all right keep it right there for more content from super cloud 22 right back [Music] you

(upbeat music) >> Welcome, everyone, to theCUBE's presentation of the AWS Startup Showcase. And this episode two of season two is called "Data as Code," the ongoing series covering exciting new startups in the AWS ecosystem. I'm John Furrier, your host of theCUBE. Today, we're excited to be joined by Jon Dahl, who is the co-founder and CEO of MUX, a hot new startup building cloud video for developers, video with data. John, great to see you. We did an interview on theCube Conversation. Went into big detail of the awesomeness of your company and the trend that you're on. Welcome back. >> Thank you, glad to be here. >> So, video is everywhere, and video for pivot to video, you hear all these kind of terms in the industry, but now more than ever, video is everywhere and people are building with it, and it's becoming part of the developer experience in applications. So people have to stand up video into their code fast, and data is code, video is data. So you guys are specializing this. Take us through that dynamic. >> Yeah, so video clearly is a growing part of how people are building applications. We see a lot of trends of categories that did not involve video in the past making a major move towards video. I think what Peloton did five years ago to the world of fitness, that was not really a big category. Now video fitness is a huge thing. Video in education, video in business settings, video in a lot of places. I think Marc Andreessen famously said, "Software is eating the world" as a pretty, pretty good indicator of what the internet is actually doing to the economy. I think there's a lot of ways in which video right now is eating software. So categories that we're not video first are becoming video first. And that's what we help with. >> It's not obvious to like most software developers when they think about video, video industries, it's industry shows around video, NAB, others. People know, the video folks know what's going on in video, but when you start to bring it mainstream, it becomes an expectation in the apps. And it's not that easy, it's almost a provision video is hard for a developer 'cause you got to know the full, I guess, stack of video. That's like low level and then kind of just basic high level, just play something. So, in between, this is a media stack kind of dynamic. Can you talk about how hard it is to build video for developers? How is it going to become easier? >> Yeah, I mean, I've lived this story for too long, maybe 13 years now, when I first build my first video stack. And, you know, I'll sometimes say, I think it's kind of a miracle every time a video plays on the internet because the internet is not a medium designed for video. It's been hijacked by video, video is 70% of internet traffic today in an unreliable, sort of untrusted network space, which is totally different than how television used to work or cable or things like that. So yeah, so video is hard because there's so many problems from top to bottom that need to be solved to make video work. So you have to worry about video compression encoding, which is a complicated topic in itself. You have to worry about delivering video around the world at scale, delivering it at low cost, at low latency, with good performance, you have to worry about devices and how every device, Android, iOS, web, TVs, every device handles video differently and so there's a lot of work there. And at the end of the day, these are kind of unofficial standards that everyone's using. So one of the miracles is like, if you want to watch a video, somehow you have to get like Apple and Google to agree on things, which is not always easy. And so there's just so many layers of complexity that are behind it. I think one way to think about it is, if you want to put an image online, you just put an image online. And if you want to put video online, you build complex software, and that's the exact problem that MUX was started to help solve. >> It's interesting you guys have almost creating a whole new category around video infrastructure. And as you look at, you mentioned stack, video stack. I'm looking at a market where the notion of a media stack is developing, and you're seeing these verticals having similar dynamics with cloud. And if you go back to the early days of cloud computing, what was the developer experience or entrepreneurial experience, you had to actually do a lot of stuff before you even do anything, provision a server. And this has all kind of been covered in great detail in the glory of Agile and whatnot. It was expensive, and you had that actually engineer before you could even stand up any code. Now you got video that same thing's happening. So the developers have two choices, go do a bunch of stuff complex, building their own infrastructure, which is like building a data center, or lean in on MUX and say, "Hey, thank you for doing all that years of experience building out the stacks to take that hard part away," but using APIs that they have. This is a developer focused problem that you guys are solving. >> Yeah, that's right. my last company was a company called Zencoder, that was an API to video encoding. So it was kind of an API to a small part of what MUX does today, just one of those problems. And I think the thing that we got right at Zencoder, that we're doing again here at MUX, was building four developers first. So our number one persona is a software developer. Not necessarily a video expert, just we think any developer should be able to build with video. It shouldn't be like, yeah, got to go be a specialist to use this technology, because it should become just of the internet. Video should just be something that any developer can work with. So yeah, so we build for developers first, which means we spend a lot of time thinking about API design, we spend a lot of time thinking about documentation, transparent pricing, the right features, great support and all those kind of things that tend to be characteristics of good developer companies. >> Tell me about the pipe lining of the products. I'm a developer, I work for a company, my boss is putting pressure on me. We need video, we have all this library, it's all stacking up. We hired some people, they left. Where's the video, we've stored it somewhere. I mean, it's a nightmare, right? So I'm like, okay, I'm cloud native, I got an API. I need to get my product to market fast, 'cause that is what Agile developers want. So how do you describe that acceleration for time to market? You mentioned you guys are API first, video first. How do these customers get their product into the market as fast as possible? >> Yeah, well, I mean the first thing we do is we put what we think is probably on average, three to four months of hard engineering work behind a single API call. So if you want to build a video platform, we tell our customers like, "Hey, you can do that." You probably need a team, you probably need video experts on your team so hire them or train them. And then it takes several months just to kind of to get video flowing. One API call at MUX gives you on-demand video or live video that works at scale, works around the world with good performance, good reliability, a rich feature set. So maybe just a couple specific examples, we worked with Robin Hood a few years ago to bring video into their newsfeed, which was hugely successful for them. And they went from talking to us for the first time to a big launch in, I think it was three months, but the actual code time there was like really short. I want to say they had like a proof of concept up and running in a couple days, and then the full launch in three months. Another customer of ours, Bandcamp, I think switched from a legacy provider to MUX in two weeks in band. So one of the big advantages of going a little bit higher in the abstraction layer than just building it yourself is that time to market. >> Talk about this notion of video pipeline 'cause I know I've heard people I talk about, "Hey, I just want to get my product out there. I don't want to get stuck in the weeds on video pipeline." What does that mean for folks that aren't understanding the nuances of video? >> Yeah, I mean, it's all the steps that it takes to publish video. So from ingesting the video, if it's live video from making sure that you have secure, reliable ingest of that live feed potentially around the world to the transcoding, which is we talked a little bit about, but it is a, you know, on its own is a massively complicated problem. And doing that, well, doing that well is hard. Part of the reason it's hard is you really have to know where you're publishing too. And you might want to transcode video differently for different devices, for different types of content. You know, the pipeline typically would also include all of the workflow items you want to do with the video. You want to thumbnail a video, you want clip, create clips of the video, maybe you want to restream the video to Facebook or Twitter or a social platform. You want to archive the video, you want it to be available for downloads after an event. If it's just a, if it's a VOD upload, if it's not live in the first place. You have all those things and you might want to do simulated live with the video. You might want to actually record something and then play it back as a live stream. So, the pipeline Ty typically refers to everything from the ingest of the video to the time that the bits are delivered to a device. >> You know, I hear a lot of people talking about video these days, whether it's events, training, just want peer to peer experience, video is powerful, but customers want to own their own platform, right? They want to have the infrastructure as a service. They kind of want platform as a service, this is cloud talk now, but they want to have their own capability to build it out. This allows them to get what they want. And so you see this, like, is it SaaS? Is it platform? People want customization? So kind of the general purpose video solution does it really exist or doesn't? I mean, 'cause this is the question. Can I just buy software and work or is it going to be customized always? How do you see that? Because this becomes a huge discussion point. Is it a SaaS product or someone's going to make a SaaS product? >> Yeah, so I think one of the most important elements of designing any software, but especially when you get into infrastructure is choosing an abstraction level. So if you think of computing, you can go all the way down to building a data center, you can go all the way down to getting a colo and racking a server like maybe some of us used to do, who are older than others. And that's one way to run a server. On the other extreme, you have just think of the early days of cloud competing, you had app engine, which was a really fantastic, really incredible product. It was one push deploy of, I think Python code, if I remember correctly, and everything just worked. But right in the middle of those, you had EC2, which was, EC2 is basically an API to a server. And it turns out that that abstraction level, not Colo, not the full app engine kind of platform, but the API to virtual server was the right abstraction level for maybe the last 15 years. Maybe now some of the higher level application platforms are doing really well, maybe the needs will shift. But I think that's a little bit of how we think about video. What developers want is an API to video. They don't want an API to the building blocks of video, an API to transcoding, to video storage, to edge caching. They want an API to video. On the other extreme, they don't want a big application that's a drop in white label video in a box like a Shopify kind of thing. Shopify is great, but developers don't want to build on top of Shopify. In the payments world developers want Stripe. And that abstraction level of the API to the actual thing you're getting tends to be the abstraction level that developers want to build on. And the reason for that is, it's the most productive layer to build on. You get maximum flexibility and also maximum velocity when you have that API directly to a function like video. So, we like to tell our customers like you, you own your video when you build on top of MUX, you have full control over everything, how it's stored, when it's stored, where it goes, how it's published, we handle all of the hard technology and we give our customers all of the flexibility in terms of designing their products. >> I want to get back some use case, but you brought that up I might as well just jump to my next point. I'd like you to come back and circle back on some references 'cause I know you have some. You said building on infrastructure that you own, this is a fundamental cloud concept. You mentioned API to a server for the nerds out there that know that that's cool, but the people who aren't super nerdy, that means you're basically got an interface into a server behind the scenes. You're doing the same for video. So, that is a big thing around building services. So what wide range of services can we expect beyond MUX? If I'm going to have an API to video, what could I do possibly? >> What sort of experience could you build? >> Yes, I got a team of developers saying I'm all in API to video, I don't want to do all that transit got straight there, I want to build experiences, video experiences on my app. >> Yeah, I mean, I think, one way to think about it is that, what's the range of key use cases that people do with video? We tend to think about six at MUX, one is kind of the places where the content is, the prop. So one of the things that use video is you can create great video. Think of online courses or fitness or entertainment or news or things like that. That's kind of the first thing everyone thinks of, when you think video, you think Netflix, and that's great. But we see a lot of really interesting uses of video in the world of social media. So customers of ours like Visco, which is an incredible photo sharing application, really for photographers who really care about the craft. And they were able to bring video in and bring that same kind of Visco experience to video using MUX. We think about B2B tools, videos. When you think about it, all video is, is a high bandwidth way of communicating. And so customers are as like HubSpot use video for the marketing platform, for business collaboration, you'll see a lot of growth of video in terms of helping businesses engage their customers or engage with their employees. We see live events obviously have been a massive category over the last few years. You know, we were all forced into a world where we had to do live events two years ago, but I think now we're reemerging into a world where the online part of a conference will be just as important as the in-person component of a conference. So that's another big use case we see. >> Well, full disclosure, if you're watching this live right now, it's being powered by MUX. So shout out, we use MUX on theCUBE platform that you're experiencing in this. Actually in real time, 'cause this is one application, there's many more. So video as code, is data as code is the theme, that's going to bring up the data ops. Video also is code because (laughs) it's just like you said, it's just communicating, but it gets converted to data. So data ops, video ops could be its own new category. What's your reaction to that? >> Yeah, I mean, I think, I have a couple thoughts on that. The first thought is, video is a way that, because the way that companies interact with customers or users, it's really important to have good monitoring and analytics of your video. And so the first product we ever built was actually a product called MUX video, sorry, MUX data, which is the best way to monitor a video platform at scale. So we work with a lot of the big broadcasters, we work with like CBS and Fox Sports and Discovery. We work with big tech companies like Reddit and Vimeo to help them monitor their video. And you just get a huge amount of insight when you look at robust analytics about video delivery that you can use to optimize performance, to make sure that streaming works well globally, especially in hard to reach places or on every device. That's we actually build a MUX data platform first because when we started MUX, we spent time with some of our friends at companies like YouTube and Netflix, and got to know how they use data to power their video platforms. And they do really sophisticated things with data to ensure that their streams well, and we wanted to build the product that would help everyone else do that. So, that's one use. I think the other obvious use is just really understanding what people are doing with their video, who's watching what, what's engaging, those kind of things. >> Yeah, data is definitely there. You guys mentioned some great brands that are working with you guys, and they're doing it because of the developer experience. And I'd like you to explain, if you don't mind, in your words, why is the MUX developer experience so good? What are some of the results you're seeing from your customers? What are they saying to you? Obviously when you win, you get good feedback. What are some of the things that they're saying and what specific develop experiences do they like the best? >> Yeah, I mean, I think that the most gratifying thing about being a startup founder is when your customers like what you're doing. And so we get a lot of this, but it's always, we always pay attention to what customers say. But yeah, people, the number one thing developers say when they think about MUX is that the developer experience is great. I think when they say that, what they mean is two things, first is it's easy to work with, which helps them move faster, software velocity is so important. Every company in the world is investing and wants to move quickly and to build quickly. And so if you can help a team speed up, that's massively valuable. The second thing I think when people like our developer experience is, you know, in a lot of ways that think that we get out of the way and we let them do what they want to do. So well, designed APIs are a key part of that, coming back to abstraction, making sure that you're not forcing customers into decisions that they actually want to make themselves. Like, if our video player only had one design, that that would not be, that would not work for most developers, 'cause developers want to bring their own design and style and workflow and feel to their video. And so, yeah, so I think the way we do that is just think comprehensively about how APIs are designed, think about the workflows that users are trying to accomplish with video, and make sure that we have the right APIs, make sure they're the right information, we have the right webhooks, we have the right SDKs, all of those things in place so that they can build what they want. >> We were just having a conversation on theCUBE, Dave Vellante and I, and our team, and I'd love to get you a reaction to this. And it's more and more, a riff real quick. We're seeing a trend where video as code, data as code, media stack, where you're starting to see the emergence of the media developer, where the application of media looks a lot like kind of software developer, where the app, media as an app. It could be a chat, it could be a peer to peer video, it could be part of an event platform, but with all the recent advances, in UX designers, coders, the front end looks like an emergence of these creators that are essentially media developers for all intent and purpose, they're coding media. What's your reaction to that? How do you see that evolving? >> I think the. >> Or do you agree with it? >> It's okay. >> Yeah, yeah. >> Well, I think a couple things. I think one thing, I think this goes along through saying, but maybe it's disagreement, is that we don't think you should have to be an expert at video or at media to create and produce or create and publish good video, good audio, good images, those kind of things. And so, you know, I think if you look at software overall, I think of 10 years ago, the kind of DevOps movement, where there was kind of a movement away from specialization in software where the same software developer could build and deploy the same software developer maybe could do front end and back end. And we want to bring that to video as well. So you don't have to be a specialist to do it. On the other hand, I do think that investments and tooling, all the way from video creation, which is not our world, but there's a lot of amazing companies out there that are making it easier to produce video, to shoot video, to edit, a lot of interesting innovations there all the way to what we do, which is helping people stream and publish video and video experiences. You know, I think another way about it is, that tool set and companies doing that let anyone be a media developer, which I think is important. >> It's like DevOps turning into low-code, no-code, eventually it's just composability almost like just, you know, "Hey Siri, give me some video." That kind of thing. Final question for you why I got you here, at the end of the day, the decision between a lot of people's build versus buy, "I got to get a developer. Why not just roll my own?" You mentioned data center, "I want to build a data center." So why MUX versus do it yourself? >> Yeah, I mean, part of the reason we started this company is we have a pretty, pretty strong opinion on this. When you think about it, when we started MUX five years ago, six years ago, if you were a developer and you wanted to accept credit cards, if you wanted to bring payment processing into your application, you didn't go build a payment gateway. You just probably used Stripe. And if you wanted to send text messages, you didn't build your own SMS gateway, you probably used Twilio. But if you were a developer and you wanted to stream video, you built your own video gateway, you built your own video application, which was really complex. Like we talked about, you know, probably three, four months of work to get something basic up and running, probably not live video that's probably only on demand video at that point. And you get no benefit by doing it yourself. You're no better than anyone else because you rolled your own video stack. What you get is risk that you might not do a good job, maybe you do worse than your competitors, and you also get distraction where you've just taken, you take 10 engineers and 10 sprints and you apply it to a problem that doesn't actually really give you differentiated value to your users. So we started MUX so that people would not have to do that. It's fine if you want to build your own video platform, once you get to a certain scale, if you can afford a dozen engineers for a VOD platform and you have some really massively differentiated use case, you know, maybe, live is, I don't know, I don't have the rule of thumb, live videos maybe five times harder than on demand video to work with. But you know, in general, like there's such a shortage of software engineers today and software engineers have, frankly, are in such high demand. Like you see what happens in the marketplace and the hiring markets, how competitive it is. You need to use your software team where they're maximally effective, and where they're maximally effective is building differentiation into your products for your customers. And video is just not that, like very few companies actually differentiate on their video technology. So we want to be that team for everyone else. We're 200 people building the absolute best video infrastructure as APIs for developers and making that available to everyone else. >> John, great to have you on with the showcase, love the company, love what you guys do. Video as code, data as code, great stuff. Final plug for the company, for the developers out there and prospects watching for MUX, why should they go to MUX? What are you guys up to? What's the big benefit? >> I mean, first, just check us out. Try try our APIs, read our docs, talk to our support team. We put a lot of work into making our platform the best, you know, as you dig deeper, I think you'd be looking at the performance around, the global performance of what we do, looking at our analytics stack and the insight you get into video streaming. We have an emerging open source video player that's really exciting, and I think is going to be the direction that open source players go for the next decade. And then, you know, we're a quickly growing team. We're 60 people at the beginning of last year. You know, we're one 50 at the beginning of this year, and we're going to a add, we're going to grow really quickly again this year. And this whole team is dedicated to building the best video structure for developers. >> Great job, Jon. Thank you so much for spending the time sharing the story of MUX here on the show, Amazon Startup Showcase season two, episode two, thanks so much. >> Thank you, John. >> Okay, I'm John Furrier, your host of theCUBE. This is season two, episode two, the ongoing series cover the most exciting startups from the AWS Cloud Ecosystem. Talking data analytics here, video cloud, video as a service, video infrastructure, video APIs, hottest thing going on right now, and you're watching it live here on theCUBE. Thanks for watching. (upbeat music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> The pandemic has changed the way we think about and predict the future. As we enter the third year of a global pandemic, we see the significant impact that it's had on technology strategy, spending patterns, and company fortunes Much has changed. And while many of these changes were forced reactions to a new abnormal, the trends that we've seen over the past 24 months have become more entrenched, and point to the way that's coming ahead in the technology business. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we welcome our partner and colleague and business friend, Erik Porter Bradley, as we deliver what's becoming an annual tradition for Erik and me, our predictions for Enterprise Technology in 2022 and beyond Erik, welcome. Thanks for taking some time out. >> Thank you, Dave. Luckily we did pretty well last year, so we were able to do this again. So hopefully we can keep that momentum going. >> Yeah, you know, I want to mention that, you know, we get a lot of inbound predictions from companies and PR firms that help shape our thinking. But one of the main objectives that we have is we try to make predictions that can be measured. That's why we use a lot of data. Now not all will necessarily fit that parameter, but if you've seen the grading of our 2021 predictions that Erik and I did, you'll see we do a pretty good job of trying to put forth prognostications that can be declared correct or not, you know, as black and white as possible. Now let's get right into it. Our first prediction, we're going to go run into spending, something that ETR surveys for quarterly. And we've reported extensively on this. We're calling for tech spending to increase somewhere around 8% in 2022, we can see there on the slide, Erik, we predicted spending last year would increase by 4% IDC. Last check was came in at five and a half percent. Gardner was somewhat higher, but in general, you know, not too bad, but looking ahead, we're seeing an acceleration from the ETR September surveys, as you can see in the yellow versus the blue bar in this chart, many of the SMBs that were hard hit by the pandemic are picking up spending again. And the ETR data is showing acceleration above the mean for industries like energy, utilities, retail, and services, and also, notably, in the Forbes largest 225 private companies. These are companies like Mars or Koch industries. They're predicting well above average spending for 2022. So Erik, please weigh in here. >> Yeah, a lot to bring up on this one, I'm going to be quick. So 1200 respondents on this, over a third of which were at the C-suite level. So really good data that we brought in, the usual bucket of, you know, fortune 500, global 2000 make up the meat of that median, but it's 8.3% and rising with momentum as we see. What's really interesting right now is that energy and utilities. This is usually like, you know, an orphan stock dividend type of play. You don't see them at the highest point of tech spending. And the reason why right now is really because this state of tech infrastructure in our energy infrastructure needs help. And it's obvious, remember the Florida municipality break reach last year? When they took over the water systems or they had the ability to? And this is a real issue, you know, there's bad nation state actors out there, and I'm no alarmist, but the energy and utility has to spend this money to keep up. It's really important. And then you also hit on the retail consumer. Obviously what's happened, the work from home shift created a shop from home shift, and the trends that are happening right now in retail. If you don't spend and keep up, you're not going to be around much longer. So I think the really two interesting things here to call out are energy utilities, usually a laggard in IT spend and it's leading, and also retail consumer, a lot of changes happening. >> Yeah. Great stuff. I mean, I recall when we entered the pandemic, really ETR was the first to emphasize the impact that work from home was going to have, so I really put a lot of weight on this data. Okay. Our next prediction is we're going to get into security, it's one of our favorite topics. And that is that the number one priority that needs to be addressed by organizations in 2022 is security and you can see, in this slide, the degree to which security is top of mind, relative to some other pretty important areas like cloud, productivity, data, and automation, and some others. Now people may say, "Oh, this is obvious." But I'm going to add some context here, Erik, and then bring you in. First, organizations, they don't have unlimited budgets. And there are a lot of competing priorities for dollars, especially with the digital transformation mandate. And depending on the size of the company, this data will vary. For example, while security is still number one at the largest public companies, and those are of course of the biggest spenders, it's not nearly as pronounced as it is on average, or in, for example, mid-sized companies and government agencies. And this is because midsized companies or smaller companies, they don't have the resources that larger companies do. Larger companies have done a better job of securing their infrastructure. So these mid-size firms are playing catch up and the data suggests cyber is even a bigger priority there, gaps that they have to fill, you know, going forward. And that's why we think there's going to be more demand for MSSPs, managed security service providers. And we may even see some IPO action there. And then of course, Erik, you and I have talked about events like the SolarWinds Hack, there's more ransomware attacks, other vulnerabilities. Just recently, like Log4j in December. All of this has heightened concerns. Now I want to talk a little bit more about how we measure this, you know, relatively, okay, it's an obvious prediction, but let's stick our necks out a little bit. And so in addition to the rise of managed security services, we're calling for M&A and/or IPOs, we've specified some names here on this chart, and we're also pointing to the digital supply chain as an area of emphasis. Again, Log4j really shone that under a light. And this is going to help the likes of Auth0, which is now Okta, SailPoint, which is called out on this chart, and some others. We're calling some winners in end point security. Erik, you're going to talk about sort of that lifecycle, that transformation that we're seeing, that migration to new endpoint technologies that are going to benefit from this reset refresh cycle. So Erik, weigh in here, let's talk about some of the elements of this prediction and some of the names on that chart. >> Yeah, certainly. I'm going to start right with Log4j top of mind. And the reason why is because we're seeing a real paradigm shift here where things are no longer being attacked at the network layer, they're being attacked at the application layer, and in the application stack itself. And that is a huge shift left. And that's taking in DevSecOps now as a real priority in 2022. That's a real paradigm shift over the last 20 years. That's not where attacks used to come from. And this is going to have a lot of changes. You called out a bunch of names in there that are, they're either going to work. I would add to that list Wiz. I would add Orca Security. Two names in our emerging technology study, in addition to the ones you added that are involved in cloud security and container security. These names are either going to get gobbled up. So the traditional legacy names are going to have to start writing checks and, you know, legacy is not fair, but they're in the data center, right? They're, on-prem, they're not cloud native. So these are the names that money is going to be flowing to. So they're either going to get gobbled up, or we're going to see some IPO's. And on the other thing I want to talk about too, is what you mentioned. We have CrowdStrike on that list, We have SentinalOne on the list. Everyone knows them. Our data was so strong on Tanium that we actually went positive for the first time just today, just this morning, where that was released. The trifecta of these are so important because of what you mentioned, under resourcing. We can't have security just tell us when something happens, it has to automate, and it has to respond. So in this next generation of EDR and XDR, an automated response has to happen because people are under-resourced, salaries are really high, there's a skill shortage out there. Security has to become responsive. It can't just monitor anymore. >> Yeah. Great. And we should call out too. So we named some names, Snyk, Aqua, Arctic Wolf, Lacework, Netskope, Illumio. These are all sort of IPO, or possibly even M&A candidates. All right. Our next prediction goes right to the way we work. Again, something that ETR has been on for awhile. We're calling for a major rethink in remote work for 2022. We had predicted last year that by the end of 2021, there'd be a larger return to the office with the norm being around a third of workers permanently remote. And of course the variants changed that equation and, you know, gave more time for people to think about this idea of hybrid work and that's really come in to focus. So we're predicting that is going to overtake fully remote as the dominant work model with only about a third of the workers back in the office full-time. And Erik, we expect a somewhat lower percentage to be fully remote. It's now sort of dipped under 30%, at around 29%, but it's still significantly higher than the historical average of around 15 to 16%. So still a major change, but this idea of hybrid and getting hybrid right, has really come into focus. Hasn't it? >> Yeah. It's here to stay. There's no doubt about it. We started this in March of 2020, as soon as the virus hit. This is the 10th iteration of the survey. No one, no one ever thought we'd see a number where only 34% of people were going to be in office permanently. That's a permanent number. They're expecting only a third of the workers to ever come back fully in office. And against that, there's 63% that are saying their permanent workforce is going to be either fully remote or hybrid. And this, I can't really explain how big of a paradigm shift this is. Since the start of the industrial revolution, people leave their house and go to work. Now they're saying that's not going to happen. The economic impact here is so broad, on so many different areas And, you know, the reason is like, why not? Right? The productivity increase is real. We're seeing the productivity increase. Enterprises are spending on collaboration tools, productivity tools, We're seeing an increased perception in productivity of their workforce. And the CFOs can cut down an expense item. I just don't see a reason why this would end, you know, I think it's going to continue. And I also want to point out these results, as high as they are, were before the Omicron wave hit us. I can only imagine what these results would have been if we had sent the survey out just two or three weeks later. >> Yeah. That's a great point. Okay. Next prediction, we're going to look at the supply chain, specifically in how it's affecting some of the hardware spending and cloud strategies in the future. So in this chart, ETRS buyers, have you experienced problems procuring hardware as a result of supply chain issues? And, you know, despite the fact that some companies are, you know, I would call out Dell, for example, doing really well in terms of delivering, you can see that in the numbers, it's pretty clear, there's been an impact. And that's not not an across the board, you know, thing where vendors are able to deliver, especially acute in PCs, but also pronounced in networking, also in firewall servers and storage. And what's interesting is how companies are responding and reacting. So first, you know, I'm going to call the laptop and PC demand staying well above pre-COVID norms. It had peaked in 2012. Pre-pandemic it kept dropping and dropping and dropping, in terms of, you know, unit volume, where the market was contracting. And we think can continue to grow this year in double digits in 2022. But what's interesting, Erik, is when you survey customers, is despite the difficulty they're having in procuring network hardware, there's as much of a migration away from existing networks to the cloud. You could probably comment on that. Their networks are more fossilized, but when it comes to firewalls and servers and storage, there's a much higher propensity to move to the cloud. 30% of customers that ETR surveyed will replace security appliances with cloud services and 41% and 34% respectively will move to cloud compute and storage in 2022. So cloud's relentless march on traditional on-prem models continues. Erik, what do you make of this data? Please weigh in on this prediction. >> As if we needed another reason to go to the cloud. Right here, here it is yet again. So this was added to the survey by client demand. They were asking about the procurement difficulties, the supply chain issues, and how it was impacting our community. So this is the first time we ran it. And it really was interesting to see, you know, the move there. And storage particularly I found interesting because it correlated with a huge jump that we saw on one of our vendor names, which was Rubrik, had the highest net score that it's ever had. So clearly we're seeing some correlation with some of these names that are there, you know, really well positioned to take storage, to take data into the cloud. So again, you didn't need another reason to, you know, hasten this digital transformation, but here we are, we have it yet again, and I don't see it slowing down anytime soon. >> You know, that's a really good point. I mean, it's not necessarily bad news for the... I mean, obviously you wish that it had no change, would be great, but things, you know, always going to change. So we'll talk about this a little bit later when we get into the Supercloud conversation, but this is an opportunity for people who embrace the cloud. So we'll come back to that. And I want to hang on cloud a bit and share some recent projections that we've made. The next prediction is the big four cloud players are going to surpass 167 billion, an IaaS and PaaS revenue in 2022. We track this. Observers of this program know that we try to create an apples to apples comparison between AWS, Azure, GCP and Alibaba in IaaS and PaaS. So we're calling for 38% revenue growth in 2022, which is astounding for such a massive market. You know, AWS is probably not going to hit a hundred billion dollar run rate, but they're going to be close this year. And we're going to get there by 2023, you know they're going to surpass that. Azure continues to close the gap. Now they're about two thirds of the size of AWS and Google, we think is going to surpass Alibaba and take the number three spot. Erik, anything you'd like to add here? >> Yeah, first of all, just on a sector level, we saw our sector, new survey net score on cloud jumped another 10%. It was already really high at 48. Went up to 53. This train is not slowing down anytime soon. And we even added an edge compute type of player, like CloudFlare into our cloud bucket this year. And it debuted with a net score of almost 60. So this is really an area that's expanding, not just the big three, but everywhere. We even saw Oracle and IBM jump up. So even they're having success, taking some of their on-prem customers and then selling them to their cloud services. This is a massive opportunity and it's not changing anytime soon, it's going to continue. >> And I think the operative word there is opportunity. So, you know, the next prediction is something that we've been having fun with and that's this Supercloud becomes a thing. Now, the reason I say we've been having fun is we put this concept of Supercloud out and it's become a bit of a controversy. First, you know, what the heck's the Supercloud right? It's sort of a buzz-wordy term, but there really is, we believe, a thing here. We think there needs to be a rethinking or at least an evolution of the term multi-cloud. And what we mean is that in our view, you know, multicloud from a vendor perspective was really cloud compatibility. It wasn't marketed that way, but that's what it was. Either a vendor would containerize its legacy stack, shove it into the cloud, or a company, you know, they'd do the work, they'd build a cloud native service on one of the big clouds and they did do it for AWS, and then Azure, and then Google. But there really wasn't much, if any, leverage across clouds. Now from a buyer perspective, we've always said multicloud was a symptom of multi-vendor, meaning I got different workloads, running in different clouds, or I bought a company and they run on Azure, and I do a lot of work on AWS, but generally it wasn't necessarily a prescribed strategy to build value on top of hyperscale infrastructure. There certainly was somewhat of a, you know, reducing lock-in and hedging the risk. But we're talking about something more here. We're talking about building value on top of the hyperscale gift of hundreds of billions of dollars in CapEx. So in addition, we're not just talking about transforming IT, which is what the last 10 years of cloud have been like. And, you know, doing work in the cloud because it's cheaper or simpler or more agile, all of those things. So that's beginning to change. And this chart shows some of the technology vendors that are leaning toward this Supercloud vision, in our view, building on top of the hyperscalers that are highlighted in red. Now, Jerry Chan at Greylock, they wrote a piece called Castles in the Cloud. It got our thinking going, and he and the team at Greylock, they're building out a database of all the cloud services and all the sub-markets in cloud. And that got us thinking that there's a higher level of abstraction coalescing in the market, where there's tight integration of services across clouds, but the underlying complexity is hidden, and there's an identical experience across clouds, and even, in my dreams, on-prem for some platforms, so what's new or new-ish and evolving are things like location independence, you've got to include the edge on that, metadata services to optimize locality of reference and data source awareness, governance, privacy, you know, application independent and dependent, actually, recovery across clouds. So we're seeing this evolve. And in our view, the two biggest things that are new are the technology is evolving, where you're seeing services truly integrate cross-cloud. And the other big change is digital transformation, where there's this new innovation curve developing, and it's not just about making your IT better. It's about SaaS-ifying and automating your entire company workflows. So Supercloud, it's not just a vendor thing to us. It's the evolution of, you know, the, the Marc Andreessen quote, "Every company will be a SaaS company." Every company will deliver capabilities that can be consumed as cloud services. So Erik, the chart shows spending momentum on the y-axis and net score, or presence in the ETR data center, or market share on the x-axis. We've talked about snowflake as the poster child for this concept where the vision is you're in their cloud and sharing data in that safe place. Maybe you could make some comments, you know, what do you think of this Supercloud concept and this change that we're sensing in the market? >> Well, I think you did a great job describing the concept. So maybe I'll support it a little bit on the vendor level and then kind of give examples of the ones that are doing it. You stole the lead there with Snowflake, right? There is no better example than what we've seen with what Snowflake can do. Cross-portability in the cloud, the ability to be able to be, you know, completely agnostic, but then build those services on top. They're better than anything they could offer. And it's not just there. I mean, you mentioned edge compute, that's a whole nother layer where this is coming in. And CloudFlare, the momentum there is out of control. I mean, this is a company that started off just doing CDN and trying to compete with Okta Mite. And now they're giving you a full soup to nuts with security and actual edge compute layer, but it's a fantastic company. What they're doing, it's another great example of what you're seeing here. I'm going to call out HashiCorp as well. They're more of an infrastructure services, a little bit more of an open-source freemium model, but what they're doing as well is completely cloud agnostic. It's dynamic. It doesn't care if you're in a container, it doesn't matter where you are. They recently IPO'd and they're down 25%, but their data looks so good across both of our emerging technology and TISA survey. It's certainly another name that's playing on this. And another one that we mentioned as well is Rubrik. If you need storage, compute, and in the cloud layer and you need to be agnostic to it, they're another one that's really playing in this space. So I think it's a great concept you're bringing up. I think it's one that's here to stay and there's certainly a lot of vendors that fit into what you're describing. >> Excellent. Thank you. All right, let's shift to data. The next prediction, it might be a little tough to measure. Before I said we're trying to be a little black and white here, but it relates to Data Mesh, which is, the ideas behind that term were created by Zhamak Dehghani of ThoughtWorks. And we see Data Mesh is really gaining momentum in 2022, but it's largely going to be, we think, confined to a more narrow scope. Now, the impetus for change in data architecture in many companies really stems from the fact that their Hadoop infrastructure really didn't solve their data problems and they struggle to get more value out of their data investments. Data Mesh prescribes a shift to a decentralized architecture in domain ownership of data and a shift to data product thinking, beyond data for analytics, but data products and services that can be monetized. Now this a very powerful in our view, but they're difficult for organizations to get their heads around and further decentralization creates the need for a self-service platform and federated data governance that can be automated. And not a lot of standards around this. So it's going to take some time. At our power panel a couple of weeks ago on data management, Tony Baer predicted a backlash on Data Mesh. And I don't think it's going to be so much of a backlash, but rather the adoption will be more limited. Most implementations we think are going to use a starting point of AWS and they'll enable domains to access and control their own data lakes. And while that is a very small slice of the Data Mesh vision, I think it's going to be a starting point. And the last thing I'll say is, this is going to take a decade to evolve, but I think it's the right direction. And whether it's a data lake or a data warehouse or a data hub or an S3 bucket, these are really, the concept is, they'll eventually just become nodes on the data mesh that are discoverable and access is governed. And so the idea is that the stranglehold that the data pipeline and process and hyper-specialized roles that they have on data agility is going to evolve. And decentralized architectures and the democratization of data will eventually become a norm for a lot of different use cases. And Erik, I wonder if you'd add anything to this. >> Yeah. There's a lot to add there. The first thing that jumped out to me was that that mention of the word backlash you said, and you said it's not really a backlash, but what it could be is these are new words trying to solve an old problem. And I do think sometimes the industry will notice that right away and maybe that'll be a little pushback. And the problems are what you already mentioned, right? We're trying to get to an area where we can have more assets in our data site, more deliverable, and more usable and relevant to the business. And you mentioned that as self-service with governance laid on top. And that's really what we're trying to get to. Now, there's a lot of ways you can get there. Data fabric is really the technical aspect and data mesh is really more about the people, the process, and the governance, but the two of those need to meet, in order to make that happen. And as far as tools, you know, there's even cataloging names like Informatica that play in this, right? Istio plays in this, Snowflake plays in this. So there's a lot of different tools that will support it. But I think you're right in calling out AWS, right? They have AWS Lake, they have AWS Glue. They have so much that's trying to drive this. But I think the really important thing to keep here is what you said. It's going to be a decade long journey. And by the way, we're on the shoulders of giants a decade ago that have even gotten us to this point to talk about these new words because this has been an ongoing type of issue, but ultimately, no matter which vendors you use, this is going to come down to your data governance plan and the data literacy in your business. This is really about workflows and people as much as it is tools. So, you know, the new term of data mesh is wonderful, but you still have to have the people and the governance and the processes in place to get there. >> Great, thank you for that, Erik. Some great points. All right, for the next prediction, we're going to shine the spotlight on two of our favorite topics, Snowflake and Databricks, and the prediction here is that, of course, Databricks is going to IPO this year, as expected. Everybody sort of expects that. And while, but the prediction really is, well, while these two companies are facing off already in the market, they're also going to compete with each other for M&A, especially as Databricks, you know, after the IPO, you're going to have, you know, more prominence and a war chest. So first, these companies, they're both looking pretty good, the same XY graph with spending velocity and presence and market share on the horizontal axis. And both Snowflake and Databricks are well above that magic 40% red dotted line, the elevated line, to us. And for context, we've included a few other firms. So you can see kind of what a good position these two companies are really in, especially, I mean, Snowflake, wow, it just keeps moving to the right on this horizontal picture, but maintaining the next net score in the Y axis. Amazing. So, but here's the thing, Databricks is using the term Lakehouse implying that it has the best of data lakes and data warehouses. And Snowflake has the vision of the data cloud and data sharing. And Snowflake, they've nailed analytics, and now they're moving into data science in the domain of Databricks. Databricks, on the other hand, has nailed data science and is moving into the domain of Snowflake, in the data warehouse and analytics space. But to really make this seamless, there has to be a semantic layer between these two worlds and they're either going to build it or buy it or both. And there are other areas like data clean rooms and privacy and data prep and governance and machine learning tooling and AI, all that stuff. So the prediction is they'll not only compete in the market, but they'll step up and in their competition for M&A, especially after the Databricks IPO. We've listed some target names here, like Atscale, you know, Iguazio, Infosum, Habu, Immuta, and I'm sure there are many, many others. Erik, you care to comment? >> Yeah. I remember a year ago when we were talking Snowflake when they first came out and you, and I said, "I'm shocked if they don't use this war chest of money" "and start going after more" "because we know Slootman, we have so much respect for him." "We've seen his playbook." And I'm actually a little bit surprised that here we are, at 12 months later, and he hasn't spent that money yet. So I think this prediction's just spot on. To talk a little bit about the data side, Snowflake is in rarefied air. It's all by itself. It is the number one net score in our entire TISA universe. It is absolutely incredible. There's almost no negative intentions. Global 2000 organizations are increasing their spend on it. We maintain our positive outlook. It's really just, you know, stands alone. Databricks, however, also has one of the highest overall net sentiments in the entire universe, not just its area. And this is the first time we're coming up positive on this name as well. It looks like it's not slowing down. Really interesting comment you made though that we normally hear from our end-user commentary in our panels and our interviews. Databricks is really more used for the data science side. The MLAI is where it's best positioned in our survey. So it might still have some catching up to do to really have that caliber of usability that you know Snowflake is seeing right now. That's snowflake having its own marketplace. There's just a lot more to Snowflake right now than there is Databricks. But I do think you're right. These two massive vendors are sort of heading towards a collision course, and it'll be very interesting to see how they deploy their cash. I think Snowflake, with their incredible management and leadership, probably will make the first move. >> Well, I think you're right on that. And by the way, I'll just add, you know, Databricks has basically said, hey, it's going to be easier for us to come from data lakes into data warehouse. I'm not sure I buy that. I think, again, that semantic layer is a missing ingredient. So it's going to be really interesting to see how this plays out. And to your point, you know, Snowflake's got the war chest, they got the momentum, they've got the public presence now since November, 2020. And so, you know, they're probably going to start making some aggressive moves. Anyway, next prediction is something, Erik, that you and I have talked about many, many times, and that is observability. I know it's one of your favorite topics. And we see this world screaming for more consolidation it's going all in on cloud native. These legacy stacks, they're fighting to stay relevant, but the direction is pretty clear. And the same XY graph lays out the players in the field, with some of the new entrants that we've also highlighted, like Observe and Honeycomb and ChaosSearch that we've talked about. Erik, we put a big red target around Splunk because everyone wants their gold. So please give us your thoughts. >> Oh man, I feel like I've been saying negative things about Splunk for too long. I've got a bad rap on this name. The Splunk shareholders come after me all the time. Listen, it really comes down to this. They're a fantastic company that was designed to do logging and monitoring and had some great tool sets around what you could do with it. But they were designed for the data center. They were designed for prem. The world we're in now is so dynamic. Everything I hear from our end user community is that all net new workloads will be going to cloud native players. It's that simple. So Splunk has entrenched. It's going to continue doing what it's doing and it does it really, really well. But if you're doing something new, the new workloads are going to be in a dynamic environment and that's going to go to the cloud native players. And in our data, it is extremely clear that that means Datadog and Elastic. They are by far number one and two in net score, increase rates, adoption rates. It's not even close. Even New Relic actually is starting to, you know, entrench itself really well. We saw New Relic's adoption's going up, which is super important because they went to that freemium model, you know, to try to get their little bit of an entrenched customer base and that's working as well. And then you made a great list here, of all the new entrants, but it goes beyond this. There's so many more. In our emerging technology survey, we're seeing Century, Catchpoint, Securonix, Lucid Works. There are so many options in this space. And let's not forget, the biggest data that we're seeing is with Grafana. And Grafana labs as yet to turn on their enterprise. Elastic did it, why can't Grafana labs do it? They have an enterprise stack. So when you look at how crowded this space is, there has to be consolidation. I recently hosted a panel and every single guy on that panel said, "Please give me a consolidation." Because they're the end users trying to actually deploy these and it's getting a little bit confusing. >> Great. Thank you for that. Okay. Last prediction. Erik, might be a little out of your wheelhouse, but you know, you might have some thoughts on it. And that's a hybrid events become the new digital model and a new category in 2022. You got these pure play digital or virtual events. They're going to take a back seat to in-person hybrids. The virtual experience will eventually give way to metaverse experiences and that's going to take some time, but the physical hybrid is going to drive it. And metaverse is ultimately going to define the virtual experience because the virtual experience today is not great. Nobody likes virtual. And hybrid is going to become the business model. Today's pure virtual experience has to evolve, you know, theCUBE first delivered hybrid mid last decade, but nobody really wanted it. We did Mobile World Congress last summer in Barcelona in an amazing hybrid model, which we're showing in some of the pictures here. Alex, if you don't mind bringing that back up. And every physical event that we're we're doing now has a hybrid and virtual component, including the pre-records. You can see in our studios, you see that the green screen. I don't know. Erik, what do you think about, you know, the Zoom fatigue and all this. I know you host regular events with your round tables, but what are your thoughts? >> Well, first of all, I think you and your company here have just done an amazing job on this. So that's really your expertise. I spent 20 years of my career hosting intimate wall street idea dinners. So I'm better at navigating a wine list than I am navigating a conference floor. But I will say that, you know, the trend just goes along with what we saw. If 35% are going to be fully remote. If 70% are going to be hybrid, then our events are going to be as well. I used to host round table dinners on, you know, one or two nights a week. Now those have gone virtual. They're now panels. They're now one-on-one interviews. You know, we do chats. We do submitted questions. We do what we can, but there's no reason that this is going to change anytime soon. I think you're spot on here. >> Yeah. Great. All right. So there you have it, Erik and I, Listen, we always love the feedback. Love to know what you think. Thank you, Erik, for your partnership, your collaboration, and love doing these predictions with you. >> Yeah. I always enjoy them too. And I'm actually happy. Last year you made us do a baker's dozen, so thanks for keeping it to 10 this year. >> (laughs) We've got a lot to say. I know, you know, we cut out. We didn't do much on crypto. We didn't really talk about SaaS. I mean, I got some thoughts there. We didn't really do much on containers and AI. >> You want to keep going? I've got another 10 for you. >> RPA...All right, we'll have you back and then let's do that. All right. All right. Don't forget, these episodes are all available as podcasts, wherever you listen, all you can do is search Breaking Analysis podcast. Check out ETR's website at etr.plus, they've got a new website out. It's the best data in the industry, and we publish a full report every week on wikibon.com and siliconangle.com. You can always reach out on email, David.Vellante@siliconangle.com I'm @DVellante on Twitter. Comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (mellow music)

(upbeat pop music) >> Well, hi everyone, John Walls here on "theCUBE" as we continue our CUBE conversations as part of the "AWS Startup Showcase". And we welcome in today Krishna Gade who is the founder and the CEO of Fiddler AI. and Krishna, good to see you today. Thanks for joining us here on the "theCUBE". >> Hey John, thanks so much for inviting us and I'm glad to be here, and looking forward to our conversation. >> Yeah me two, and first off, I want to say congratulations as I look at your company's, this tremendous roster, this list of awards that just keep coming your way. Most recently recognized by "Forbes" as one of the Top 50 AI Companies To Watch here in 2021. I know Gartner called you one of their Cool Companies not too long ago. World Economic Forum also giving you a shout out. So whatever it is you're doing, you're doing it very well, but it's got to feel good I would think, some validation to get all this kind of recognition. >> Absolutely, I know we've been very fortunate to get all the recognition. You know, part of it is also because of the space we are playing in, right? A lot of companies are, you know, operationalizing AI and therefore, you know, this whole point of, you know, explainability monitoring and governance of AI is like forefront and it's in the news for various different reasons. So there's a lot of, you know, good sort of talk that is going on in the press around how one should bear responsible AI. And we are very fortunate to be, you know, in the space and pioneering, you know, some of the technologies here. >> Right. And talking about machine learning monitoring, obviously, in the AI space, and you mentioned explainability. So let's just talk about that concept broadly first off and explain to our viewers what you mean by explainability in this particular context. >> Yeah, that's a good question. So if you think about an AI system, one of the main differences between it and a traditional software system is that it's a black box in the sense that you cannot open it up and read it's code like a traditional software system. The reason is, you know, the AI systems that are built using data and training models which are represented in this non-human readable format. And you cannot really understand how a model is actually making a prediction at any given point of time. So therefore what happens is when you are deploying these AI systems at scale for a variety of use cases, let's say credit underwriting or, you know, screening resumes, or clinical diagnosis which are extremely, you know, important for general human beings. There is a need to understand how the AI system is working. You know, why did it approve a positive person's loan or reject someone's loan? Or why did it reject someone's, you know, resume from, you know, a job screening pipeline? How is it working overall? Right? And so this is where explainability becomes important because you need to understand the AI system, you need a way to probe it, to interrogate it, to understand how the system is making predictions, how is it being influenced by various inputs you're supplying to the system. And so this gamut of technologies or the algorithms that have come across in the last, you know, few years have really matured to a point where, you know, products like Fiddler are developing them and productizing them for the general enterprise to you know, put it in their machine learning and AI workflows. >> So you're talking about context basically, right? I mean, trying to give everybody an idea. This is, you know, kind of where this inputs coming, this is where the problem is, this is where the bottleneck might be, whatever it is, and and doing that in real time. Very efficient operation here. Well, let's talk about the ML world right now and in terms of how it relates to artificial intelligence and this interaction you know, that we're seeing and the, I guess, the problem that you are trying to fix, if you will, in terms of machine learning monitoring. So let's just deal with that first off. When you look at somebody's architecture and somebody set up, what do you see? What are you looking for? And what kind of problems are you trying to solve for your clients? >> Yeah. So just following up what I said. The two main problems with operationalizing AI is one is the black box nature of AI, which I already talked about. The other problem is that the AI system is fundamentally a stochastic system or a probabilistic system. By that, I mean that its performance, you know, its predictions can change over time based on the data it is receiving. So it's not a deterministic system like traditional software systems where you expect the same output all the time, right? So when you have a system that is stochastic in nature where its performance can vary based on the data it is receiving, then you are in a situation where you have uncertainty, right? You know, you let's say you have an AI system that is deployed for serving a credit underwriting model or a fraud, you know, detection use case. And you see that, okay, sometimes accuracy is up, sometimes accuracy is down. You know, when do you want, when do you trust your predictions, when you're not. How do you know if the model is actually performing in the same manner that you trained it? All of these issues open up the need for continuous monitoring of these AI systems, because without which you may have AI systems making bad predictions for your users, hurting your business metrics, potentially making biased decisions that can put your company into a compliance or a brand reputation risk scenario. To avoid all of these things you can actually monitor these AI systems continuously so that you know exactly if they're performing the way you expect them to be. Do you to retrain them right now, right? Or do you need to shut them down because they are actually not predicting the way that you expect them to be? So this is actually very important. And so that's what Fiddler tries to solve for our customers by helping them operationalize AI with full visibility and explainability, right? So you can essentially install Fiddler in your workflow to continuously monitor your AI systems and analyze and explain them when you have questions about how they're working. >> I mean, you talked about governance earlier a little bit, you know, compliance, obviously a great critical issue, big concern, fraud detection. Security, just in general here, as we know, I mean, we keep almost every day it seems like we're hearing about some kinds of security intrusion. So, in terms of identifying vulnerabilities or in terms of identifying anomalies, whatever it might be, what kind of work are you doing in that space to give your client base the kind of comfort and the peace of mind that everybody's searching for these days? >> Right, I mean, if you step back a little bit, John, we are truly living in the age of algorithms, right? So everything that we interact with on a day-to-day basis, the movies we watch, or when we request an Uber driver, or when we go to a financial institution and request for a loan application or a mortgage, there are algorithms behind the scenes that are processing our requests and delivering the experiences that we have. Now, increasingly these algorithms are becoming AI based algorithms. And when you have these AI based algorithms, they're trained on this data that's available, that an institution may collect from their users, or they may buy from other third parties. And when you develop these AI systems based on this data, if this data is not equally distributed amongst all different ethnicity backgrounds, people coming from different cultures, different religions, different races, different genders, you may actually build systems that can make very different decisions for different individuals based on like this bias that could creep into them. And so this actually needs, this means that at the end of the day, you can actually create a dystopian world where, you know, some people get like really great decisions from your systems, where some people are left out, right? So therefore, you know, this aspect of governing your AI systems so that you're validating what you're building upfront. You're validating the data that you're using to train the systems. You're continuously monitoring the systems there so that they're actually producing the right outcomes for your users. And then you can actually explain if some customer asks you or some regulator or a third party asks you how your system is working. It's very very important. This is an emerging area in industry, certain sectors already have this, for example, financial services. It's in companies like banks, where it is mandated to have model governance, so that every model that they are deploying needs to be validated and needs to be monitored. And we are seeing the emergence of generally AI governance creeping into other sectors as well. And so this is like a broader topic that covers explainability, covers monitoring, covers detecting bias in your AI systems and ensuring that you're building safe and responsible AI for your customers and your organization. >> Yeah, I find the bias point really interesting, actually, because I hadn't really thought about these prejudices or subjectivities, you know, it might bring to our work with us in terms of what we look at, what we ignore, what we process, how we don't. But it's a really interesting point you just raised. So thank you for that. And then there's also the kind of issue with data drift too a little bit, right? It's like, where did it go (laughing)? >> Right. >> What are we doing here? What happened to it? So maybe if you could talk about that a little bit in terms of all this data that's coming in and corralling it, right? Making sure that it stays organized and stays in a way that you can analyze and process it, and then glean insight from. >> Yeah, data drift is one of the main reasons why AI systems deteriorate in performance. So for example, let's say I'm trying to build a recommendation system that predicts the items that you want to buy when you go to an E-commerce website. Now, if I have used data pre-COVID, then the user behavior was very different, right? That kind of items people were probably buying before you know, February, 2020 was like probably much different than the kind of items that people were buying after it. So what happens is when you train your AI systems on datasets that are older but then that data has changed ever since because of an event like COVID-19 has happened, or some other seasonality has kicked in, then your AI systems are seeing different distribution data. For example, you may see that suddenly, you know, people who were shopping, let's say, in March or April last year, people were shopping for all kinds of, you know, toilet paper and all kinds of things to stock up, you know, to be ready for lockdown, right? And maybe they were not buying similar amounts in there previously. So therefore, if you have an inventory management system based on AI or an E-commerce recommendation system based on AI, you know, they would see data drift, because the buying patterns are different. The amount of stuff that people are buying in terms of toilet paper has completely shifted. And so their model is actually, may not be predicting as accurately as it would, right? So therefore identifying this data drift and alerting your AI engineer so that they can be prepared for this is very important. Otherwise, what you would see is if you're an E-commerce company, this has actually happened, you know? Instacart, a grocery delivery company and another company www.etsy.com, they blogged about it where they have seen their models go down in accuracy from 90% to 65% when this data shift happened, you know, especially during COVID-19. And so you need the ability to continuously monitor for drift so that when you can catch these things earlier, and then, you know, save your business from losing, you know, in terms of business metrics like such as number of sales that you may be making, number of bad recommendations that your systems are making to your users. >> So we've talked a lot about these various components of monitoring of which, you know, all of which you do extremely well. And I was reading earlier, just a little bit about the company, and we talked about accountability. We've already talked about that. We talked about fraud detection, we talked about reliability. There was also a point about ethical considerations, you know, and so I was interested in that, hearing from you about that in terms of why that's a pillar of your service or what exactly that was pointed toward in terms of monitoring, and what you can do. >> Right. So, I guess I'll just go back to like a famous quote from Marc Andreessen. He mentioned, you know, a few years ago that software is eating the world, right? Now, what's happening is AI is eating software. All the software that we are consuming is becoming AI based software, because basically at the end of the day some intelligence is being baked into the software to make it, you know, predict more interesting things for you to make those decisions. Instead of rule-based decisions, make it more AI based decisions. And so therefore it is very important that when we are building the software, we need to use ethical practices. You know, we need to know how, where you're collecting the data from. It can be very dangerous if you don't do it and you can land into trouble. And we have seen these incidents many times, right? For example, in 2019, when Apple and Goldman Sachs came up with a credit card, a lot of customers complained about gender bias with respect to the credit card limits that the algorithm was setting. You know, in the same household, the husband and wife were getting 10 times in terms of a difference between the credit limit between a male and a female, right? Even though they probably had similar salary ranges, similar FICO scores, right? So if you do not actually make sure that, you know, you're collecting data from the right sources that your datasets are not outbalanced. If your models, if your algorithms are tested for bias you know, before hand, before you deploy them and then you're continuously monitoring them, these are all ethical practices. These are all the responsible ways of building your AI. You can actually, you know, land into trouble. Your customers will complain about it. You know, you would lose your brand reputation. And at the end of the day you'll be essentially, and instead of actually adding value to the customers, you may be actually hurting them, right? And so this is actually why it's so important, and it's become more important when the more stakes, the higher the stakes are, right? You know, for example, when it's being used for criminal justice scenarios or when it's being used for clinical diagnosis scenarios. Being able to ensure that the system is making unbiased decisions is very, very important. >> Well, before I let you go, too, I like you to touch base on your AWS relationship about, you know, what was the Genesis of that. And currently what it is that you're working on together to provide this great value to your customers. >> Absolutely. So the follow-up to this ethical AI is like Amazon as a company is interested in pursuing, you know, the responsible AI but, you know, they have a lot of AI products. So they are looking for, you know, fostering a community and ecosystem of AI technologies. And in that hypothesis they actually invested in Fiddler last year in terms of enabling us to develop this explainable AI and ethical AI technology. And so we are working with Alexa Fund and also like AWS ecosystem in terms of partnering with how effectively Fiddler can be delivered to other AWS customers through, like, through their marketplace and other sort of areas that we can distribute the software. So it's a great partnership. We are very, very excited about the opportunity to work with Alexa Fund as well as the AWS ecosystem. It increases another opportunity for us to enable a lot more customers than we than we can otherwise. So this is a great win-win situation for both Amazon and Fiddler. >> Well, it sure is. And congratulations on that and developing that partnership. I know it's working well for your clients and it's working well for Fiddler AI obviously by the number of recognitions that have been coming your way. So Krishna, we wish you continued success and thanks for the time here today on "theCUBE". >> Yep. Thank you so much, John. It was a pleasure talking to you today. >> I enjoyed it. Thank you. John Walls here wrapping up our conversation with Fiddler AI's Krishna Gade, talking today about machine learning monitoring on the "AWS Startup Showcase". (upbeat pop music)

(uplifting music) >> Hello and welcome to this Cube Conversation. I'm John Furrier, host of theCube here in Palo Alto, California. We've got two great remote guests to talk about some big news hitting with Scality and Hewlett Packard Enterprise. Jerome Lecat CEO of Scality and Chris Tinker, Distinguished Technologist from HPE, Hewlett Packard Enterprise, Jerome, Chris, great to see you both Cube alumnis from an original gangster days as we'd say back then when we started almost 11 years ago. Great to see you both. >> It's great to be back. >> Good to see you John. >> So, really compelling news around kind of this next generation storage cloud native solution. Okay, it's really kind of an impact on the next gen, I call next gen, dev ops meets application, modern application world and something we've been covering heavily. There's some big news here around Scality and HPE offering a pretty amazing product. You guys introduced essentially the next gen piece of it, Artesca, we'll get into in a second, but this is a game-changing announcement you guys announced, this is an evolution continuing I think is more of a revolution, but I think, you know storage is kind of abstractionally of evolution to this app centric world. So talk about this environment we're in and we'll get to the announcement, which is object store for modern workloads, but this whole shift is happening Jerome. This is a game changer to storage and customers are going to be deploying workloads. >> Yeah, Scality really, I mean, I personally really started working on Scality more than 10 years ago, close to 15 now. And if we think about it I mean the cloud has really revolutionized IT. And within the cloud, we really see layers and layers of technology. I mean, it all start at around 2006 with Amazon and Google and Facebook finding ways to do initially what was consumer IT at very large scale, very low credible reliability and then slowly creeped into the enterprise. And at the very beginning, I would say that everyone was kind of wizards trying things and really coupling technologies together. And to some degree we were some of the first wizard doing this, but we, we're now close to 15 years later and there's a lot of knowledge and a lot of experience, a lot of tools. And this is really a new generation. I'll call it cloud native, or you can call it next gen whatever, but there is now enough experience in the world, both at the development level and at the infrastructure level to deliver truly distributed automated systems that run on industry standard servers. Obviously good quality server deliver a better service than others, but there is now enough knowledge for this to truly go at scale. And call this cloud or call this cloud native. Really the core concept here is to deliver scalable IT at very low cost, very high level of reliability, all based on software. And we've, we've been participated in this motion, but we feel that now the breadth of what's coming is at the new level, and it was time for us to think, develop and launch a new product that's specifically adapted to that. And Chris, I will let you comment on this because the customers or some of them, you can add a customer, you do that. >> Well, you know, you're right. You know, I've been in the, I've been like you I've been in this industry for a, well, along time. Give a long, 20 to 21 years in HPE in engineering. And look at the actual landscape has changed with how we're doing scale-out software-defined storage for particular workloads. And we're a catalyst has evolved here is an analytics normally what was only done in the three letter acronyms and massively scale-out parallel namespace file systems, parallel file systems. The application space has encroached into the enterprise world where the enterprise world needed a way to actually take a look at how to, how do I simplify the operations? How do I actually be able to bring about an application that can run in the public cloud or on premise or hybrid, be able to actually look at a workload optimized step that aligns the actual cost to the actual analytics that I'm going to be doing the workload that I'm going to be doing and be able to bridge those gaps and be able to spin this up and simplify operations. And you know, and if you, if you are familiar with these parallel processes which by the way we actually have on our truck, I, I do engineer those, but they are, they are, they are they have their own unique challenges, but in the world of enterprise where customers are looking to simplify operations, then take advantage of new application, analytic workloads whether it be smart, Mesa, whatever it might be, right. I mean, if I want to spin up a Mongo DB or maybe maybe a, you know, last a search capability how do I actually take those technologies, embrace a modern scale-out storage stack that without without breaking the bank, but also provide a simple operations. And that's, that's why we look for object storage capabilities because it brings us this massive parallelization. Back to you John. >> Well before we get into the product. I want to just touch on one thing Jerome you mentioned, and Chris, you, you brought up the DevOps piece next gen, next level, whatever term you use. It is cloud native, cloud native has proven that DevOps infrastructure is code is not only legit. It's being operationalized in all enterprises and add security in there, you have DevSecOps, this is the reality and hybrid cloud in particular has been pretty much the consensus is that standard. So our defacto center whatever you want to call it, that's happening. Multicloud are on the horizon. So these new workloads are have these new architectural changes, cloud on premises and edge. This is the number one story. And the number one challenge all enterprises are now working on. How do I build the architecture for the cloud on premises and edge? This is forcing the DevOps team to flex and build new apps. Can you guys talk about that particular trend? And is it, and is that relevant here? >> Yeah, I, I now talk about really storage anywhere and cloud anywhere and and really the key concept is edge to go to cloud. I mean, we all understand now that the edge will host a lot of that time and the edge is many different things. I mean, it's obviously a smartphone, whatever that is, but it's also factories, it's also production. It's also, you know, moving moving machinery, trains, planes, satellites that that's all the edge, cars obviously. And a lot of that I will be both produced and process there, but from the edge who will want to be able to send the data for analysis, for backup, for logging to a call, and that call could be regional, maybe not, you know, one call for the whole planet, but maybe one corporate region the state in the U.S. And then from there you will also want to push some of the data to public cloud. One of the thing that we see more and more is that the D.R that has centered the disaster recovery is not another physical data center. It's actually the cloud, and that's a very efficient infrastructure very cost efficient, especially. So really it, it, it's changing the paradigm on how you think about storage because you really need to integrate these three layers in a consistent approach especially around the topic of security because you want the data to be secure all along the way. And data is not just data, its data, and who can access the data, who can modify the data what are the conditions that allow modification all automatically erasure of the data? In some cases, it's super important that the data automatically erased after 10 years and all this needs to be transported from edge to core to cloud. So that that's one of the aspects. Another aspects that resonates for me with what you said is a word you didn't say, but it's actually crucial this whole revolution. It's Kubernetes I mean, Kubernetes is in now a mature technology, and it's, it's just, you know the next level of automatized operation for distributed system, which we didn't have 5 or 10 years ago. And that is so powerful that it's going to allow application developers to develop much faster system that can be distributed again edge to go to cloud, because it's going to be an underlying technology that spans the three layers. >> Chris, your thoughts hybrid cloud. I've been, I've been having questions with the HPE folks for God years and years on hybrid clouds, now here. >> Right (chuckles) >> Well, you know, and, and it's exciting in a layout right, so you look at like a, whether it be enterprise virtualization, that is a scale-out general purpose virtualization workloads whether it be analytic workloads, whether it be no data protection is a paramount to all of this, orchestration is paramount. If you look at that DevSecOps, absolutely. I mean, securing the actual data the digital last set is, is absolutely paramount. And if you look at how we do this look at the investments we're making, we're making enough and look at the collaborative platform development which goes to our partnership with Scality. It is, we're providing them an integral aspect of everything we do, whether we're bringing in Ezmeral which is our software we use for orchestration look at the veneer of its control plane, controlling Kubernetes. Being able to actually control the active clusters and the actual backing store for all the analytics that we just talked about. Whether it be a web-scale app that is traditionally using a politics namespace and now been modernized and take advantage of newer technologies running an NBME burst buffers or a hundred gig networks with Slingshot network of 200 and 400 gigabit looking at how do we actually get the actual analytics, the workload to the CPU and have it attached to the data at risk. Where's the data, how do we land the data? How do we actually align, essentially locality, locality of the actual asset to the computer. And this is where, you know, we can look leverage whether it be a Zair or Google or name your favorite hybrid, hyperscaler, leverage those technologies leveraging the actual persistent store. And this is where Scality is, with this object store capability has it been an industry trendsetter, setting the actual landscape of how provide an object store on premise and hybrid cloud run it in a public cloud, but being able to facilitate data mobility and tie it back to, and tie it back to an application. And this is where a lot of things have changed in the world of analytics, because the applications that you, the newer technologies that are coming on the market have taken advantage of this particular protocol as threes. So they can do web scale massively parallel concurrent workloads. >> You know what let's get into the announcement. I love cool and relevant products. And I think this hits the mark. Scality you guys have Artesca, which is just announced. And I think it, you know, we obviously we reported on it. You guys have a lightweight true enterprise grade object store software for Kubernetes. This is the announcement, Jerome, tell us about it. What's the big deal? Cool and relevant, come on, this is cool. Right, tell us. >> I'm super excited. I'm not sure, if you can see it as well on the screen, but I'm super, super excited. You know, we, we introduced the ring 11 years ago and they says our biggest announcements for the past 11 years. So yes, do pay attention. And, you know, after, after looking at, at all these trends and understanding where we see the future going. We decided that it was time to embark (indistinct) So there's not one line of code that's the same as our previous generation product. They will both exist, they both have a space in the market. And Artesca was specifically designed for this cloud native era. And what we see is that people want something that's lightweight especially because it had to go to the edge. They still want the enterprise grid that Scality is known for. And it has to be modern. What we really mean by modern is, we see object storage now being the primary storage for many application more and more applications. And so we have to be able to deliver the performance, that primary storage expects. This idea of a Scality of serving primary storage is actually not completely new. When we launched Scality 10 years ago, the first application that we were supporting was consumer email for which we were, and we are still today, the primary storage. So we have, we know what it is to be the primary store. We know what's the level of reliability you need to hit. We know what, what latency means and latency is different from throughput, you really need to optimize both. And I think that still today we're the only object storage company that protects data from both replication and original encoding Because we understand that replication is faster, but the original encoding is more better, and more, of file where fast internet latency doesn't matter so much. So we we've been being all that experience, but really rethinking of product for that new generation that really is here now. And so where we're truly excited, I guess people a bit more about the product. It's a software, Scality is a software company and that's why we love to partner with HPE who's producing amazing servers, you know for the record and the history. The very first deployment of Scality in 2010 was on the HP servers. So this is a long love story here. And so to come back to our desk is lightweight in the sense that it's easy to use. We can start small, we can start from just one server or one VM I mean, you would start really small, but he can grow infinitely. The fact that we start small, we didn't, you know limit the technology because of that. So you can start from one to many and it's cloud native in the sense that it's completely Kubernetes compatible it's Kubernetes office traded. It will deploy on many Kubernetes distributions. We're talking obviously with Ezmeral we're also talking with zoo and with the other all those of communities distribution it will also be able to be run in the cloud. Now, I'm not sure that there will be many true production deployment of Artesca going the cloud, because you already have really good object storage by the cloud providers but when you are developing something and you want to test that, you know just doing it in the cloud is very practical. So you'll be able to deploy our Kubernetes cloud distribution, and it's more than object storage in the sense that it's application centric. A lot of our work is actually validating that our storage is fit for this single purpose application. And making sure that we understand the requirement of these application, that we can guide our customers on how to deploy. And it's really designed to be the primary storage for these new workloads. >> The big part of the news is your relationship with Hewlett Packard Enterprise is some exclusivity here as part of this and as you mentioned the relationship goes back many, many years. We've covered the, your relationship in the past. Chris also, you know, we cover HP like a blanket. This is big news for HPE as well. >> This is very big news. >> What is the relationship, talk about this exclusivity Could you share about the partnership and the exclusivity piece? >> Well, there's the partnership expands into the pan HPE portfolio. we look, we made a massive investment in edge IOT device. So we actually have how did we align the cost to the demand. Our customers come to us, wanting to looking at think about what we're doing with Greenlake, like in consumption based modeling. They want to be able to be able to consume the asset without having to do a capital outlay out of the gate. Number two, look at, you know how do you deploy technology, really demand. It depends on the scale, right? So in a lot of your web skill, you know, scale out technologies, it putting them on a diet is challenging. Meaning how skinny can you get it. Getting it down into the 50 terabyte range and then the complexities of those technologies at as you take a day one implementation and scale it out over you know, you know, multiple iterations over quarters, the growth becomes a challenge so working with Scality we, we believe we've actually cracked this nut. We figured out how to a number one, how to start small, but not limit a customer's ability to scale it out incrementally or grotesquely. You can eat depending on the quarters, the month, whatever whatever the workload is, how do you actually align and be able to consume it? So now whether it be on our Edgeline products our DL products go right there, now what that Jerome was talking about earlier you know, we, we, we ship a server every few seconds. That won't be a problem. But then of course, into our density optimized compute with the Apollo products. And this where our two companies have worked in an exclusivity where they scale the software bonds on the HP ecosystem. And then we can, of course provide you, our customers the ability to consume that through our GreenLake financial models or through a CapEx partners. >> Awesome, so Jerome and, and Chris, who's the customer here obviously, there's an exclusive period. Talk about the target customer and how the customers get the product and how they get the software. And how does this exclusivity with HP fit into it? >> Yeah, so there there's really a three types of customers and we've really, we've worked a lot with a company called UseDesign to optimize the user interface for each the types of customers. So we really thought about each customer role and providing with each of them the best product. So the, the first type of customer are application owners who are deploying an application that requires an object storage in the backend, you typically want a simple object store for one application, they want it to be simple and work. Honestly they want no thrill, just want an object store that works. And they want to be able to start as small as they start with their application. Often it's, you know, the first deployment maybe a small deployment, you know applications like a backup like VML, Rubrik, or analytics like (indistinct), file system that now, now available as a software, you know like CGI does a really great departmental NAS that works very well that needs an object store in the backend. Or for high performance computing a wake-up house system is an amazing file system. We will also have vertical application like road peak, for example, who provides origin and the view of the software broadcasters. So all these are application, they request an object store in the backend and you just need a simple high-performance working well object store and I'll discuss perfect for that. Now, the second type of people that we think will be interested by Artesca are essentially developer who are currently developing some capabilities or cloud native application, your next gen. And as part of their development stack, it's getting better and better when you're developing a cloud native application to really target an object storage rather than NFS, as you're persistent. It just, you know, think about generations of technologies and NFS and filesystem were great 25 years ago. I mean, it's an amazing technology. Now, when you want to develop a distributed scalable application object storage is a better fit because it's the same generation. And so same thing, I mean, you know, they're developing something they need an object store that they can develop on. So they want it very lightweight, but they also want the product that their enterprise or their customers will be able to rely on for years and years on. And this guy's really great fit to do that. The third type of customer are more architects, I would say are the architects that are designing a system where they are going to have 50 factories, a thousand planes, a million cars, they are going to have some local storage which will they want to replicate to the core and possibly also to the cloud. And as the design is really new generation workloads that are incredibly distributed but with local storage Artesca are really great for that. >> And tell about the HPE exclusive Chris. What's the, how does that fit in? Do they buy through Scality? Can they get it for the HP? Are you guys working together on how customers can procure it? >> Both ways, yeah both ways they can procure it through Scality. They can secure it through HPE and it's, it's it's the software stack running on our density optimized compute platforms which you would choose and align those and to provide an enterprise quality. Cause if it comes back to it in all of these use cases is how do we align up into a true enterprise stack, bringing about multitenancy bringing about the, the, the fact that you know, if you look at like a local coding one of the things that they're bringing to it, so that we can get down into the DL325. So with the exclusivity, you actually get choice. And that choice comes into our entire portfolio whether it be the Edgeline platform the DL325 AMD processing stack or the Intel 380, or whether it be the Apollos or like I said, there's, there's, there's so many ample choices there that facilitate this, and it's this allows us to align those two strategies. >> Awesome, and I think the Kubernetes piece is really relevant because, you know, I've been interviewing folks practitioners and Kubernetes is very much maturing fast. It's definitely the centerpiece of the cloud native both below the, the line, if you will below under the hood for the, for the infrastructure and then for apps, they want a program on top of it that's critical. I mean, Jerome, this is like, this is the future. >> Yeah, and if you don't mind like to come back to the myth on the exclusivity with HP. So we did a six month exclusive and the very reason we could do this is because HP has such breadth of server portfolio. And so we can go from, you know, really simple, very cheap you know, DL380, machine that we tell us for a few dollars. I mean, it's really like simple system, 50 terabyte. We can have the DL325 that Chris mentioned that is really a powerhouse all NVME, clash over storage is NVME, very fast processors you know, dense, large, large system, like the APOE 4,500. So it's a very large graph of portfolio. We support the whole portfolio and we work together on this. So I want to say that you know, one of the reason I want to send kudos to HP for the breadth of their server line really. As mentioned, Artesca can be ordered from either company. In hand-in-hand together, so anyway, you'll see both of us and our field working incredibly well together. >> Well, just on that point, I think just for clarification was this co-design by Scality and HPE, because Chris you mentioned, you know, the, the configuration of your systems. Can you guys, Chris quickly talk about the design. >> From, from, from the code base the software is entirely designed and developed by Scality, from testing and performance, so this really was a joint work with HP providing both a hardware and manpower so that we could accelerate the testing phase. >> You know, Chris HPE has just been doing such a great job of really focused on this. I know I've been covering it for years before it was fashionable. The idea of apps working no matter where it lives, public cloud, data center, edge. And you mentioned edge line's been around for awhile, you know, app centric, developer friendly, cloud first, has been an HPE kind of guiding first principle for many, many years. >> Well, it has. And, you know, as our CEO here intended, by 2022 everything will be able to be consumed as a service in our portfolio. And then this stack allows us the simplicity and the consumability of the technology and the granulation of it allows us to simplify the installation. Simplify the actual deployment bringing into a cloud ecosystem, but more importantly for the end customer. They simply get an enterprise quality product running on an optimized stack that they can consume through a orchestrated simplistic interface. That customers that's what they're wanting for today's but they come to me and ask, hey how do I need a, I've got this new app, new project. And, you know, it goes back to who's actually coming. It's no longer the IT people who are actually coming to us. It's the lines of business. It's that entire dimension of business owners coming to us, going this is my challenge. And how can you, HPE help us? And we rely on our breadth of technology, but also our breadth of partners to come together in our, of course Scality is hand in hand and our collaborative business unit our collaborative storage product engineering group that actually brought, brought this to market. So we're very excited about this solution. >> Chris, thanks for that input and great insight. Jerome, congratulations on a great partnership with HPE obviously great joint customer base. Congratulations on the product release here. Big moving the ball down the field, as they say. New functionality, clouds, cloud native object store. Phenomenal, so wrap, wrap, wrap up the interview. Tell us your vision for Scality and the future of storage. >> Yeah, I think I started in, Scality is going to be an amazing leader, it is already. But yeah, so, you know I have three things that I think will govern how storage is going. And obviously Marc Andreessen said it software is everywhere and software is eating the world. So definitely that's going to be true in the data center in storage in particular, but the three trends that are more specific are first of all, I think that security performance and agility is now basic expectation. It's, it's not, you know it's not like an additional feature. It's just the basic tables, security performance and our job. The second thing is, and we've talked about it during this conversation is edge to go. You need to think your platform with edge, core and cloud. You know, you, you don't want to have separate systems separate design interface point for edge and then think about the core and then think about cloud, and then think about the diverse power. All this needs to be integrated in a design. And the third thing that I see as a major trend for the next 10 years is data sovereignty. More and more, you need to think about where is the data residing? What are the legal challenges? What is the level of protection, against who are you protected? What is your independence strategy? How do you keep as a company being independent from the people you need to be in the band? And I mean, I say companies, but this is also true for public services. So these, these for me are the three big trends. And I do believe that software defined distributed architecture are necessary for these trends but you also need to think about being truly enterprise grade. and that has been one of our focus with design of Artesca. How do we combine a lightweight product with all of the security requirements and data sovereignty requirements that we expect to have in the next thing? >> That's awesome. Congratulations on the news Scality, Artesca. The big release with HPE exclusive for six months, Chris Tinker, Distinguished Engineer at HPE. Great to see you Jerome Lecat CEO of Scality, great to see you as well. Congratulations on the big news. I'm John Furrier from theCube. Thanks for watching. (uplifting music)

(informative tune) >> From San Francisco, it's The Cube. Covering D2 iQ. Brought to you by D2 iQ. (informative tune) >> Hey, welcome back everybody! Jeff Frick here with theCUBE. We're in downtown San Francisco at D2 iQ Headquarters, a beautiful office space here, right downtown. And we're talking about customers' journey to cloud data. We talk about it all the time, you hear about cloud native, everyone's rushing in, Kubernetes is the hottest thing since sliced bread, but the at the end of the day, you actually have to do it and we're really excited to talk to the founder who's been on his own company journey as he's watching his customers' company journeys and really kind of get into it a little bit. So, excited to have Tobi Knaup, he's a co-founder and CTO of D2 iQ. Tobi, great to see you! >> Thanks for having me. >> So, before we jump into the company and where you are now, I want to go back a little bit. I mean, looking through your resume, and your LinkedIn, etc. You're doing it kind of the classic dream-way for a founder. Did the Y Combinator thing, you've been at this for six years, you've changed the company a little bit. So, I wonder if you can just share form a founder's perspective, I think you've gone through four, five rounds of funding, raised a lot of money, 200 plus million dollars. As you sit back now, if you even get a chance, and kind of reflect, what goes through your head? As you've gone through this thing, pretty cool. A lot of people would like this, they think they'd like to be sitting in your seat. (chuckles) What can you share? >> Yeah, it's definitely been, you know, an exciting journey. And it's one that changes all the time. You know, we learned so many things over the years. And when you start out, you create a company, right? A tech company, you have you idea for the product, you have the technology. You know how to do that, right? You know how to iterate that and build it out. But there's many things you don't know as a technical founder with an engineering background, like myself. And so, I always joke with the team internally, this is that, you know, I basically try to fire myself every six months. And what I mean by that, is your role really changes, right? In the very beginning I wrote code and then is tarted managing engineers, when, you know, once you built up the team, then managed engineering managers and then did product and, you know. Nowadays, I spend a lot of time with customers to talk about our vision, you know, where I see the industry going, where things are going, how we fit into the greater picture. So, it's, you know, I think that's a big part of it, it's evolving with the company and, you know, learning the skills and evolving yourself. >> Right. It's just funny cause you think about tech founders and there's some big ones, right? Some big companies out there, to pick on Zuckerberg's, just to pick on him. But you know, when you start and kind of what your vision and your dream is and what you're coding in that early passion, isn't necessarily where you end up. And as you said, your role in more of a leadership position now, more of a guidance and setting strategy in communicating with the market, communicating with customers has changed. Has that been enjoyable for you, do you, you know, kind of enjoy more the, I don't want to say the elder states when you're a young guy, but more kind of that leadership role? Or just, you know, getting into the weeds and writing some code? >> Yeah. Yeah, what always excites me, is helping customers or helping people solve problems, right? And we do that with technology, in our case, but really it's about solving the problems. And the problems are not always technical problems, right? You know, the software that is at the core of our products, that's been running in production for many years and, you know, in some sense, what we did before we founded the company, when I worked at Airbnb and my co-founders worked at, you know, Airbnb and Twitter, we're still helping companies do those same things today. And so, where we need to help the most sometimes, it's actually on education, right? So, solving those problems. How do you train up, you know, a thousand or 10 thousand internal developers at a large organization, on what are containers, what is container management, cluster management, how does cloud native work? That's often the biggest challenge for folks and, you know, how did they transform their processes internally, how did they become really a cloud native organization. And so, you know, what motivates me is helping people solve problems in, whatever, you know, shape or form. >> Right >> It's funny because it's analogous to what you guys do, in that you got an open-source core, but people, I think, are often underestimate the degree of difficulty around all the activities beyond just the core software. >> Mm-hmm. >> Whether, as you said, it's training, it's implementation it's integration, it's best practices, it's support, it's connecting all these things together and staying on top of it. So, I think, you know, you're in a great position because it's not the software. That's not the hard part, that's arguably, the easy part. So, as you've watched people, you know, deal with this crazy acceleration of change in our industry and this rapid move to cloud native, you know, spawned by the success of the public clouds, you know, how do you kind of stay grounded and not jump too fast at the next shiny object, but still stay current, but still, you know, kind of keep to your kneading in terms of your foundation of the company and delivering real value for the customers? >> Yeah. Yeah, I know, it's exactly right. A lot of times, the challenges with adopting open-sourcing enterprise are, for example, around the skills, right? How do you hire a team that can manage that deployment and manage it for many years? Cause once software's introduced in an enterprise, it typically stays for a couple of years, right? And this gets especially challenging when you're using very popular open-source project, right? Because you're competing for those skills with, literally, everybody, right? A lot of folks want to deploy these things. And then, what people forget sometimes too is, so, a lot of the leading open-source projects, in the cloud native space, came out of, you know, big software companies, right? Kubernetes came from Google, Kafka came from LinkedIn, Cassandra from Facebook. And when those companies deploy these systems internally, they have a lot of other supporting infrastructure around it, right? And a lot of that is centered around day-two operations. Right? How do you monitor these things, how do you do lock management, how do you do do change management, how do you upgrade these things, keep current? So, all of that supporting infrastructure is what an enterprise also needs to develop in order to adopt open-source software and that's a big part of what we do. >> Right. So, I'd love to get your perspective. So, you said, you were at Airbnb, your founders were at Twitter. You know, often people, I think enterprises, fall into the trap of, you know, we want to be like the hyper-scale guys, you know. We want to be like Google or we want to be like Twitter. But they're not. But I'm sure there's a lot of lessons that you learned in watching the hyper-growth of Airbnb and Twitter. What are some of those ones that you can bring and hep enterprises with? What are some of the things that they should be aware of as, not necessarily maybe their sales don't ramp like those other companies, but their operations in some of these new cloud native things do? >> Right, right. Yeah, so, it's actually, you know, when we started the company, the key or one of the drivers was that, you know, we looked at the problems that we solved at Airbnb and Twitter and we realized that those problems are not specific to those two companies or, you know, Silicon Valley tech companies. We realized that most enterprises in the future will have, will be facing those problems. And a core one is really about agility and innovation. Right? Marc Andreessen, one of our early investors, said, "Software is eating the world." he wrote that up many years ago. And so, really what that means is that most enterprises, most companies on the planet, will transform into a software company. With all of that entails, right? With he agility that software brings. And, you know, if they don't do that, their competitors will transform into a software company and disrupt them. So, they need to become software companies. And so, a lot of the existing processes that these existing companies have around IT, don't work in that kind of environment, right? You just can't have a situation where, you know, a developer wants to deploy a new application that, you know, is very, you know, brings a lot of differentiation for the business, but the first thing they need to do in order to deploy that is file a ticket with IT and then someone will get to it in three months, right? That is a lot of waste of time and that's when people surpass you. So, that was one of the key-things we saw at Airbnb and Twitter, right? They were also in that old-school IT approach, where it took many months to deploy something. And deploying some of the software we work with, got that time down to even minutes, right? So it's empowering developers, right? And giving them the tools to make them agile so they can be innovative and bring the business forward. >> Right. The other big issue that enterprises have that you probably didn't have in some of those, you know, kind of native startups, is the complexity and the legacy. >> That's right. >> Right? So you've got all this old stuff that may or may not make any sense to redeploy, you've got stuff (laughing) stuff running in data centers, stuff running on public clouds, everybody wants to get the hyper-cloud to have a single point of view. So, it's a very different challenge when you're in the enterprises. What are you seeing, how are you helping them kind of navigate through that? >> Yeah, yeah. So, one of the first thongs we did actually, so, you know, most of our products are sort of open-core products. They have a lot of open-source at the center, but then, you know, we add enterprise components around that. Typically the first thing that shows up is around security, right? Putting the right access controls in place, making sure the traffic is encrypted. So, that's one of the first things. And then often, the companies we work with, are in a regulated environment, right? Banks, healthcare companies. So, we help them meet those requirements as well and often times that means, you know, adding features around the open-source products to get them to that. >> Right. So, like you said, the world has changed even in the six or seven years you've been at this. The, you know, containers, depending who you talk to, were around, not quite so hot. Docker's hot, Kubernetes is hot. But one of the big changes that's coming now, looking forward, is IOT and EDGE. So, you know, you just mentioned security, from the security point of view, you know, now you're tax services increased dramatically, we've done some work with Forescout and their secret sauce and they just put a sniffer on your network and find the hundreds and hundreds of devices (laughs)-- >> Yeah. >> That you don't even know are on your network. So do you look forward to kind of the opportunity and the challenges of IOT supported by 5G? What's that do for your business, where do you see opportunities, how are you going to address that? >> Yeah, so, I think IOT is really one of those big mega-trends that's going to transform a lot of things and create all kinds of new business models. And, really, what IOT is for me at the core, it's all around data, right? You have all these devices producing data, whether those are, you know, sensors in a factory in a production line, or those have, you know, cars on the road that send telemetry data in real time. IOT has been, you know, a big opportunity for us. We work with multiple customers that are in the space. And, you know, one fundamental problem with it is that, with IOT, a lot of the data that organizations need to process, are now, all of a sudden generated at the EDGE of the network, right? This wasn't the case many years for enterprises, right? Most of the data was generated, you know, at HQ or in some internal system, not at the EDGE of the network. And what always happens is when, with large-volume data is, compute generally moves where the data is and not the other way around. So, for many of these deployments, it's not efficient to move all that data from those IT devices to a central-cloud location or data-center location. So, those companies need to find ways to process data at the EDGE. That's a big part of what we're helping them with, it's automating real-time data services and machine-learning services, at the EDGE, where the EDGE can be, you know, factories all around the world, it could be cruise ships, it could be other types of locations where working with customers. And so, essentially what we're doing is we're bringing the automation that people are used to from the public cloud to the EDGE. So, you know, with the click of a button or a single command you can install a database or a machine-learning system or a message queue at all those EDGE locations. And then, it's not just that stuff is being deployed at the EDGE, I think the, you know, the standard type of infrastructure-mix, for most enterprises, is a hybrid one. I think most organizations will run a mix of EDGE, their data centers and typically multiple public cloud providers. And so, they really need a platform where they can manage applications across all of those environments and well, that's big value that our products bring. >> Yeah. I was at a talk the other day with a senior exec, formerly from Intel, and they thought that it's going to level out at probably 50-50, you know, kind of cloud-based versus on-prem. And that's just going to be the way it is cause it's just some workloads you just can't move. So, exciting stuff, so, what as you... I can't believe we're coming to the end of 2019, which is amazing to me. As you look forward to 2020 and beyond, what are some of your top priorities? >> Yeah, so, one of my top priorities is really, around machine-learning. I think machine-learning is one of these things that, you know, it's really a general-purpose tool. It's like a hammer, you can solve a lot of problems with it. And, you know, besides doing infrastructure and large-scale infrastructure, machine-learning has, you know, always been sort of my second baby. Did a lot of work during grad-school and at Airbnb. And so, we're seeing more and more customers adopt machine-learning to do all kinds of interesting, you know, problems like predictive maintenance in a factory where, you know, every minute of downtime costs a lot of money. But, machine-learning is such a new space, that a lot of the best practices that we know from software engineering and from running software into production, those same things don't always exist in machine-learning. And so, what I am looking at is, you know, what can we take from what we learned running production software, what can we take and move over to machine-learning to help people run these models in production and you know, where can we deploy machine-learning in our products too, internally, to make them smarter and automate them even more. >> That's interesting because the machine-learning and AI, you know, there's kind of the tools and stuff, and then there's the application of the tools. And we're seeing a lot of activity around, you know, people using ML in a specific application to drive better performances. As you just said,-- >> Mm-hmm. >> You could do it internally. >> Do you see an open-source play in machine-learning, in AI? Do you see, you know, kind of open-source algorithms? Do you see, you know, a lot of kind of open-source ecosystem develop around some of this stuff? So, just like I don't have time to learn data science, I won't necessarily have to have my own algorithms. How do you see that,-- >> Yeah. >> You know, kind of open-source meets AI and ML, of all things? >> Yeah. It's a space I think about a lot and what's really great, I think is that we're seeing a lot of the open-source, you know, best-practice that we know from software, actually, move over to machine-learning. I think it's interesting, right? Deep-learning is all the rage right now, everybody wants to do deep-learning, deep-learning networks. The theory behind deep-networks is actually, you know, pretty old. It's from the '70s and 80's. But for a long time, we dint have that much, enough compute-power to really use deep-learning in a meaningful way. We do have that now, but it's still expensive. So, you know, to get cutting edge results on image recognition or other types of ML problems, you need to spend a lot of money on infrastructure. It's tens of thousands or hundreds of thousands of dollars to train a model. So, it's not accessible to everyone. But, the great news is that, much like in software engineering, we can use these open-source libraries and combine them together and build upon them. There is, you know, we have that same kind of composability in machine-learning, using techniques like transfer-learning. And so, you can actually already see some, you know, open-community hubs spinning up, where people publish models that you can just take, they're pre-trained. You can take them and you know, just adjust them to your particular use case. >> Right. >> So, I think a lot of that is translating over. >> And even though it's expensive today, it's not going to be expensive tomorrow, right? >> Mm-hhm. >> I mean, if you look through the world in a lens, with, you know, the price of compute-store networking asymptotically approaching zero in the not-to-distant future and think about how you attack problems that way, that's a very different approach. And sure enough, I mean, some might argue that Moore's Law's done, but kind of the relentless march of Moore's Law types of performance increase it's not done, it's not necessarily just doubling up of transistors anymore >> Right >> So, I think there's huge opportunity to apply these things a lot of different places. >> Yeah, yeah. Absolutely. >> Can be an exciting future. >> Absolutely! (laughs) >> Tobi, congrats on all your successes! A really fun success story, we continue to like watching the ride and thanks for spending the few minutes with us. >> Thank you very much! >> All right. He's Tobi, I'm Jeff, you're watching The Cube, we're at D2 iQ Headquarters downtown in San Francisco. Thanks for watching, we'll catch you next time! (electric chime)

>> Announcer: From Sand Hill Road in the heart of Silicon Valley, it's theCUBE, presenting the People First Network, insights from entrepreneurs and tech leaders. (bright electronic music) >> Everyone, welcome to this special CUBE conversation. We're here at Sand Hill Road at Mayfield Fund. This is theCUBE, co-creation of the People First Network content series. I'm John Furrier, host of theCUBE. Our next guest, Yuvi Kochar, who's the Data-centric Digital Transformation Strategist at GameStop. Variety of stints in the industry, going in cutting-edge problems around data, Washington Post, comScore, among others. You've got your own practice. From Washington, DC, thanks for joining us. >> Thank you, thanks for hosting me. >> This is a awesome conversation. We were just talking before we came on camera about data and the roles you've had over your career have been very interesting, and this seems to be the theme for some of the innovators that I've been interviewing and were on the People First is they see an advantage with technology, and they help companies, they grow companies, and they assist. You did a lot of different things, most notably that I recognized was the Washington Post, which is on the mainstream conversations now as a rebooted media company with a storied, historic experience from the Graham family. Jeff Bezos purchased them for a song, with my opinion, and now growing still, with the monetization, with subscriber base growing. I think they're number one in subscribers, I don't believe, I believe so. Interesting time for media and data. You've been there for what, how many years were you at the Washington Post? >> I spent about 13 years in the corporate office. So the Washington Post company was a conglomerate. They'd owned a lot of businesses. Not very well known to have owned Kaplan, education company. We owned Slate, we owned Newsweek, we owned TV stations and now they're into buying all kinds of stuff. So I was involved with a lot of varied businesses, but obviously, we were in the same building with the Washington Post, and I had front row seat to see the digital transformation of the media industry. >> John: Yeah, we-- >> And how we responded. >> Yeah, I want to dig into that because I think that illustrates kind of a lot what's happening now, we're seeing with cloud computing. Obviously, Cloud 1.0 and the rise of Amazon public cloud. Clearly, check, done that, a lot of companies, startups go there. Why would you provision a data center? You're a startup, you're crazy, but at some point, you can have a data center. Now, hybrid cloud's important. Devops, the application development market, building your own stack, is shifting now. It seems like the old days, but upside down. It's flipped around, where applications are in charge, data's critical for the application, infrastructure's now elastic. Unlike the old days of here's your infrastructure. You're limited to what you can run on it based on the infrastructure. >> Right. >> What's your thoughts on that? >> My thoughts are that, I'm a very, as my title suggests, data-centric person. So I think about everything data first. We were in a time when cloud-first is becoming old, and we are now moving into data-first because what's happening in the marketplace is the ability, the capability, of data analytics has reached a point where prediction, in any aspect of a business, has become really inexpensive. So empowering employees with prediction machines, whether you call them bots, or you call them analytics, or you call them machine learning, or AI, has become really inexpensive, and so I'm thinking more of applications, which are built data-out instead of data-in, which is you build process and you capture data, and then you decide, oh, maybe I should build some reporting. That's what we used to do. Now, you need to start with what's the data I have got? What's the data I need? What's the data I can get? We were just talking about, everybody needs a data monetization strategy. People don't realize how much asset is sitting in their data and where to monetize it and how to use it. >> It's interesting. I mean, I got my computer science degree in the 80s and one of the tracks I got a degree in was database, and let's just say that my main one was operating system. Database was kind of the throwaway at that time. It wasn't considered a big field. Database wasn't sexy at all. It was like, database, like. Now, if you're a database, you're a data guru, you're a rock star. The world has changed, but also databases are changing. It used to be one centralized database rules the world. Oracle made a lot of money with that, bought all their competitors. Now you have open source came into the realm, so the world of data is also limited by where the data's stored, how the data is retrieved, how the data moves around the network. This is a new dynamic. How do you look at that because, again, lagging in business has a lot to do with the data, whether it's in an application, that's one thing, but also having data available, not necessarily in real time, but if I'm going to work on something, I want the data set handy, which means I can download it or maybe get real-time. What's your thoughts on data as an element in all that moving around? >> So I think what you're talking about is still data analytics. How do I get insights about my business? How do I make decisions using data in a better way? What flexibility do I need? So you talk about open source, you think about MongoDB and those kind of databases. They give you a lot of flexibility. You can develop interesting insights very quickly, but I think that is still very much thinking about data in an old-school kind of way. I think what's happening now is we're teaching algorithms with data. So data is actually the software, right? So you get an open source algorithm. I mean Google and everybody else is happy to open source their algorithms. They're all available for free. But what, the asset is now the data, which means how you train your algorithm with your data, and then now, moving towards deploying it on the edge, which is you take an algorithm, you train it, then you deploy it on the edge in an IoT kind of environment, and now you're doing decision-making, whether it's self-driving cars, I mean those are great examples, but I think it's going down into very interesting spaces in enterprise, which is, so we have to all think about software differently because, actually, data is a software. >> That's an interesting take on it, and I love that. I mean I wrote a blog post in 2007 when we first started playing with the, in looking at the network effects on social media and those platforms was, I wrote a post, it was called Data is the New Development Kit. Development kit was what people did back then. They had a development kit and they would download stuff and then code, but the idea was is that data has to be part of the runtime and the compilation of, as software acts, data needs to be resident, not just here's a database, access it, pull it out, use it, present it, where data is much more of a key ingredient into the development. Is that kind of what you're getting at? >> Yes. >> Notion of-- >> And I think we're moving from the age of arithmetic-based machines, which is we put arithmetic onto chips, and we then made general-purpose chips, which were used to solve a huge amount of problems in the world. We're talking about, now, prediction machines on a chip, so you think about algorithms that are trained using data, which are going to be available on chips. And now you can do very interesting algorithmic work right on the edge devices, and so I think a lot of businesses, and I've seen that recently at GameStop, I think business leaders have a hard time understanding the change because we have moved from process-centric, process automation, how can I do it better? How can I be more productive? How can I make better decisions? We have trained our business partners on that kind of thinking, and now we are starting to say, no, no, no, we've got something that's going to help you make those decisions. >> It's interesting, you mentioned GameStop. Obviously, well-known, my sons are all gamers. I used to be a gamer back before I had kids, but then, can't keep up anymore. Got to be on that for so long, but GameStop was a retail giant in gaming. Okay, when they had physical displays, but now, with online, they're under pressure, and I had interviewed, again, at an Amazon event, this Best Buy CIO, and he says, "We don't compete with price anymore. "If they want to buy from Amazon, no problem, "but our store traffic is off the charts. "We personalize 50,000 emails a day." So personalization became their strategy, it was a data strategy. This is a user experience, not a purchase decision. Is this how you guys are thinking about it at GameStop? >> I think retail, if you look at the segment per se, personalization, Amazon obviously led the way, but it's obvious that personalization is key to attract the customer. If I don't know what games you play, or if I don't know what video you watched a little while ago, about which game, then I'm not offering you the product that you are most prone or are looking for or what you want to buy, and I think that's why personalization is key. I think that's-- >> John: And data drives that, and data drives that. >> Data drives that, and for personalization, if you look at retail, there's customer information. You need to know the customer. You need to know, understand the customer preferences, but then there's the product, and you need to marry the two. And that's where personalization comes into play. >> So I'll get your thoughts. You have, obviously, a great perspective on how tech has been built and now working on some real cutting-edge, clear view on what the future looks like. Totally agree with you, by the way, on the data. There's kind of an old guard/new guard, kind of two sides of the street, the winners and the losers, but hey, look, I think the old guard, if they don't innovate and become fresh and new and adopt the modern things that need to attract the new expectations and new experiences from their customers, are going to die. That being said, what is the success formula, because some people might say, hey, I'm data-driven. I'm doing it, look at me, I'm data. Well, not really. Well, how do you tell if someone's really data-driven or data-centric? What's the difference? Is there a tell sign? >> I think when you say the old guard, you're talking about companies that have large assets, that have been very successful in a business model that maybe they even innovated, like GameStop came up with pre-owned games, and for the longest of times, we've made huge amount of revenue and profit from that segment of our business. So yes, that's becoming old now, but I think the most important thing for large enterprises at least, to battle the incumbent, the new upstarts, is to develop strategies which are leveraging the new technologies, but are building on their existing capability, and that's what I drive at GameStop. >> And also the startups too, that they were here in a venture capital firm, we're at Mayfield Fund, doing this program, startups want to come and take a big market down, or come in on a narrow entry and get a position and then eat away at an incumbent. They could do it fast if they're data-centric. >> And I think it's speed is what you're talking about. I think the biggest challenge large companies have is an ability to to play the field at the speed of the new upstarts and the firms that Mayfield and others are investing in. That's the big challenge because you see this, you see an opportunity, but you're, and I saw that at the Washington Post. Everybody went to meetings and said, yes, we need to be digital, but they went-- >> They were talking. >> They went back to their desk and they had to print a paper, and so yes, so we'll be digital tomorrow, and that's very hard because, finally, the paper had to come out. >> Let's take us through the journey. You were the CTO, VP of Technology, Graham Holdings, Washington Post, they sold it to Jeff Bezos, well-documented, historic moment, but what a storied company, Washington Post, local paper, was the movie about it, all the historic things they've done from a reporting and journalism standpoint. We admire that. Then they hit, the media business starts changing, gets bloated, not making any money, online classifieds are dying, search engine marketing is growing, they have to adjust. You were there. What was the big, take us through that journey. >> I think the transformation was occurring really fast. The new opportunities were coming up fast. We were one of the first companies to set up a website, but we were not allowed to use the brand on the website because there was a lot of concern in the newsroom that we are going to use or put the brand on this misunderstood, nearly misunderstood opportunity. So I think it started there, and then-- >> John: This is classic old guard mentality. >> Yes, and it continued down because people had seen downturns. It's not like media companies hadn't been through downturns. They had, because the market crashes and we have a recession and there's a downturn, but it always came back because-- >> But this was a wave. I mean the thing is, downturns are economic and there's business that happens there, advertisers, consumption changes. This was a shift in their user base based upon a technology wave, and they didn't see it coming. >> And they hadn't ever experienced it. So they were experiencing it as it was happening, and I think it's very hard to respond to a transformation of that kind in a very old-- >> As a leader, how did you handle that? Give us an example of what you did, how you make your mark, how do you get them to move? What were some of the things that were notable moments? >> I think the main thing that happened there was that we spun out washingtonpost.com. So it became an independent business. It was actually running across the river. It moved out of the corporate offices. It went to a separate place. >> The renegades. >> And they were given-- >> John: Like Steve Jobs and the Macintosh team, they go into separate building. >> And we were given, I was the CTO of the dotcom for some time while we were turning over our CTO there, and we were given a lot of flexibility. We were not held accountable to the same level. We used the, obviously, we used-- >> John: You were running fast and loose. >> And we were, yes, we had a lot of flexibility and we were doing things differently. We were giving away the content in some way. On the online side, there was no pay wall. We started with a pay wall, but advertising kind of was so much more lucrative in the beginning, that the pay wall was shut down, and so I think we experimented a lot, and I think where we missed, and a lot of large companies miss, is that you need to leave your existing business behind and scale your new business, and I think that's very hard to do, which is, okay, we're going to, it's happening at GameStop. We're no longer completely have a control of the market where we are the primary source of where, you talk about your kids, where they go to get their games. They can get the games online and I think-- >> It's interesting, people are afraid to let go because they're so used to operating their business, and now it has to pivot to a new operating model and grow. Two different dynamics, growth, operation, operating and growing. Not all managers have that growth mindset. >> And I think there's also an experience thing. So most people who are in these businesses, who've been running these businesses very successfully, have not been watching what's happening in technology. And so the technology team comes out and says, look, let me show you what we can do. I think there has to be this open and very, very candid discussion around how we are going to transform-- >> How would you talk about your peer, developed peers out there, your peers and other CIOs, and even CISOs on the security side, have been dealing with the same suppliers over, and in fact, on the security side, the supplier base is getting larger. There's more tools coming out. I mean who wants another tool? So platform, tool, these are big decisions being made around companies, that if you want to be data-centric, you want to be a data-centric model, you got to understand platforms, not just buying tools. If you buy a hammer, they will look like a nail, and you have so many hammers, what version, so platform discussions come in. What's your thoughts on this? Because this is a cutting-edge topic we've been talking about with a lot of senior engineering leaders around Platform 2.0 coming, not like a classic platform to... >> Right, I think that each organization has to leverage or build their, our stack on top of commodity platforms. You talked about AWS or Azure or whatever cloud you use, and you take all their platform capability and services that they offer, but then on top of that, you structure your own platform with your vertical capabilities, which become your differentiators, which is what you take to market. You enable those for all your product lines, so that now you are building capability, which is a layer on top of, and the commodity platforms will continue to bite into your platform because they will start offering capabilities that earlier, I remember, I started at this company called BrassRing, recruitment automation. One of the first software-as-a-service companies, and I, we bought a little company, and the CTO there had built a web server. It was called, it was his name, it was called Barrett's Engine. (chuckles) And so-- >> Probably Apache with something built around it. >> So, in those days, we used to build our own web servers. But now today, you can't even find an engineer who will build a web server. >> I mean the web stack and these notions of just simple Web 1.0 building blocks of change. We've been calling it Cloud 2.0, and I want to get your thoughts on this because one of the things I've been riffing on lately is this, I remember Marc Andreessen wrote the famous article in Wall Street Journal, Software is Eating the World, which I agree with in general, no debate there, but also the 10x Engineer, you go into any forum online, talking about 10x Engineers, you get five different opinions, meaning, a 10x Engineer's an engineer who can do 10 times more work than an old school, old classical engineer. I bring this up because the notion of full stack developer used to be a real premium, but what you're talking about here with cloud is a horizontally scalable commodity layer with differentiation at the application level. That's not full stack, that's half stack. So you think the world's kind of changing. If you're going to be data-centric, the control plane is data. The software that's domain-specific is on top. That's what you're essentially letting out. >> That's what I'm talking about, but I think that also, what I'm beginning to find, and we've been working on a couple of projects, is you put the data scientists in the same room with engineers who write code, write software, and it's fascinating to see them communicate and collaborate. They do not talk the same language at all. >> John: What's it like? Give us a mental picture. >> So a data scientist-- >> Are they throwing rocks at each other? >> Well, nearly, because the data scientists come from the math side of the house. They're very math-oriented, they're very algorithm-oriented. Mathematical algorithms, whereas software engineers are much more logic-oriented, and they're thinking about scalability and a whole lot of other things, and if you think about, a data scientist develops an algorithm, it rarely scales. You have to actually then hand it to an engineer to rewrite it in a scalable form. >> I want to ask you a question on that. This is why I got you and you're an awesome guest. Thanks for your insights here, and we'll take a detour into machine learning. Machine learning really is what AI is about. AI is really nothing more than just, I love AI, it gets people excited about computer science, which is great. I mean my kids talk about AI, they don't talk about IoT, which is good that AI does that, but it's really machine learning. So there's two schools of thought on machine. I call it the Berkeley school on one end, not Berkeley per se but Berkeley talks about math, machine learning, math, math, math, and then you have other schools of thought that are on cognition, that machine learning should be more cognitive, less math-driven, spectrum of full math, full cognition, and everything in between. What's your thoughts on the relationship between math and cognition? >> Yeah, so it's interesting. You get gray hair and you kind of move up the stack, and I'm much more business-focused. These are tools. You can get passionate about either school of thought, but I think that what that does is you lose sight of what the business needs, and I think it's most important to start with what are we here trying to do, and what is the best tool? What is the approach that we should utilize to meet that need? Like the other day, we were looking at product data from GameStop, and we know that the quality of data should be better, but we found a simple algorithm that we could utilize to create product affinity. Now whether it's cognition or math, it doesn't matter. >> John: The outcome's the outcome. >> The outcome is the outcome, and so-- >> They're not mutually exclusive, and that's a good conversation debate but it really gets to your point of does it really matter as long as it's accurate and the data drives that, and this is where I think data is interesting. If you look at folks who are thinking about data, back to the cloud as an example, it's only good as what you can get access to, and cybersecurity, the transparency issue around sharing data becomes a big thing. Having access to the data's super important. How do you view that for, as CIOs, and start to think about they're re-architecting their organizations for these digital transformations. Is there a school of thought there? >> Yes, so I think data is now getting consolidated. For the longest time, we were building data warehouses, departmental data warehouses. You can go do your own analytics and just take your data and add whatever else you want to do, and so the part of data that's interesting to you becomes much more clean, much more reliable, but the rest, you don't care much about. I think given the new technologies that are available and the opportunity of the data, data is coming back together, and it's being put into a single place. >> (mumbles) Well, that's certainly a honeypot for a hacker, but we'll get that in a second. If you and I were doing a startup, we say, hey, let's, we've got a great idea, we're going to build something. How would we want to think about the data in terms of having data be a competitive advantage, being native into the architecture of the system. I'll say we use cloud unless we need some scale on premise for privacy reasons or whatever, but we would, how would we go to market, and we have an app, as apps defined, great use case, but I want to have extensibility around the data, I don't want to foreclose any future options, How should I think about my, how should we think about our data strategy? >> Yes, so there was a very interesting conversation I had just a month ago with a friend of mine who's working at a startup in New York, and they're going to build a solution, take it to market, and he said, "I want to try it only in a small market "and learn from it," and he's going very old school, focus groups, analytics, analysis, and I sat down, we sat at Grand Central Station, and we talked about how, today, he should be thinking about capturing the data and letting the data tell him what's working and what's not working, instead of trying to find focus groups and find very small data points to make big decisions. He should actually utilize the target, the POC market, to capture data and get ready for scale because if you want to go national after having run a test in... >> Des Moines, Iowa. >> Part of New York or wherever, then you need to already have built the data capability to scale that business in today's-- >> John: Is it a SaaS business? >> No, it's a service and-- >> So he can instrument it, just watch the data. >> And yes, but he's not thinking like that because most business people are still thinking the old way, and if you look at Uber and others, they have gone global at such a rapid pace because they're very data-centric, and they scale with data, and they don't scale with just let's go to that market and then let's try-- >> Yeah, ship often, get the data, then think of it as part of the life cycle of development. Don't think it as the old school, craft, launch it, and then see how it goes and watch it fail or succeed, and know six months later what happened, know immediately. >> And if you go data-centric, then you can turn the R&D crank really fast. Learn, test and learn, test and learn, test and learn at a very rapid pace. That changes the game, and I think people are beginning to realize that data needs to be thought about as the application and the service is being developed, because the data will help scale the service really fast. >> Data comes into applications. I love your line of data is the new software. That's better than the new oil, which has been said before, but data comes into the app. You also mentioned that app throws off data. >> Yuvi: Yes. >> We know that humans have personal, data exhaust all the time. Facebook made billions of dollars on our exhaust and our data. The role of data in and out of the application, the I/O of the application, is a new concept, you brought that up. I like that and I see that happening. How should we capture that data? This used to be log files. Now you got observability, all kinds of new words kind of coming into this cloud equation. How should people think about this? >> I think that has to be part of the design of your applications, because data is application, and you need to design the application with data in mind, and that needs to be thought of upfront, and not later. >> Yuvi, what's next for you? We're here in Sand Hill Road, VC firm, they're doing a lot of investments, you've got a great project with GameStop, you're advising startups, what's going on in your world? >> Yes, so I'm totally focused, as you probably are beginning to sense, on the opportunity that data is enabling, especially in the enterprise. I'm very interested in helping business understand how to leverage data, because this is another major shift that's occurring in the marketplace. Opportunities have opened up, prediction is becoming cheap and at scale, and I think any business runs on their capability to predict, what is the shirt I should buy? How many I should buy? What color should I buy? I think data is going to drive that prediction at scale. >> This is a legit way that everyone should pay attention to. All businesses, not just one-- >> All businesses, everything, because prediction is becoming cheap and automated and granular. That means you need to be able to not just, you need to empower your people with low-level prediction that comes out of the machines. >> Data is the new software. Yuvi, thanks so much for great insight. This is theCUBE conversation. I'm John Furrier here at Sand Hill Road at the Mayfield Fund, for the People First Network series. Thanks for watching. >> Yuvi: Thank you. (bright electronic music)

(upbeat techno music) >> From Mountain View California, it's the Cube covering the 15th Annual Grow Awards. Brought to you by ACG SV. >> Hi, Lisa Martin with the Cube on the ground at the Computer History Museum for the 15th annual ACG SV Awards. And in Mountain View California excited to welcome to the Cube for the first time, John Hennessy, the chairman of Alphabet and the co-founder of the Knight-Hennessy Scholars Program at Stanford. JOHN, it's truly a pleasure to have you on the Cube today. >> Well delighted to be here, Lisa. >> So I was doing some research on you. And I see Marc Andreessen has called you the godfather of Silicon Valley. >> Marc very generous (loughs) >> so I thought I was pretty cool I'm going to sit down with the godfather tonight. (loughs) >> I have not done that yet. So you are keynoting the 15th Annual ACG SV Awards tonight. Talk to us a little bit about the takeaways that the audience is going to hear from you tonight. >> Well, they're going to hear some things about leadership the importance of leadership, obviously the importance of innovation. We're in the middle of Silicon Valley innovation is a big thing. And the role that technology plays in our lives and how we should be thinking about that, and how do we ensure the technology is something that serves the public good. >> Definitely. So there's about I think over 230 attendees expected tonight over 100 sea levels, the ACG SV Is has been it's it's much more than a networking organization. there's a lot of opportunities for collaboration for community. Tell me a little bit about your experience with that from a collaboration standpoint? >> Well, I think collaboration is a critical ingredient. I mean, for so many years, you look at the collaboration is gone. Just take between between the universities, my own Stanford and Silicon Valley and how that collaboration has developed over time and lead the founding of great companies, but also collaboration within the valley. This is the place to be a technology person in the whole world it's the best place partly because of this collaboration, and this innovative spirit that really is a core part of what we are as a place. >> I agree. The innovative spirit is one of the things that I enjoy, about not only being in technology, but also living in Silicon Valley. You can't go to a Starbucks without hearing a conversation or many conversations about new startups or cloud technology. So the innovative spirit is pervasive here. And it's also one that I find in an in an environment like ASG SV. You just hear a lot of inspiring stories and I was doing some research on them in the last 18 months. Five CEO positions have been seated and materialized through ACG SV. Number of venture deals initiated several board positions. So a lot of opportunity in this group here tonight. >> Right, well I think that's important because so much of the leadership has got to come by recruiting new young people. And with the increase in concerned about diversity and our leadership core and our boards, I think building that network out and trying to stretch it a little bit from the from perhaps the old boys network of an earlier time in the Valley is absolutely crucial. >> Couldn't agree more. So let's now talk a little bit about the Knight-Hennessy Scholars Program at Stanford. Tell us a little bit about it. When was it founded? >> So we are we are in our very first year, actually, this year, our first year of scholars, we founded it in 2016. The motivation was, I think, an increasing gap we perceived in terms of the need for great leadership and what was available. And it was in government. It was in the nonprofit world, it was in the for profit world. So I being a lifelong educator said, What can we do about this? Let's try to recruit and develop a core of younger people who show that they're committed to the greater good and who are excellent, who are innovative, who are creative, and prepare them for leadership roles in the future. >> So you're looking for are these undergraduate students? >> They are graduate students, so they've completed their undergraduate, it's a little hard to tell when somebody's coming out of high school, what their civic commitment is, what their ability to lead is. But coming out of coming out of undergraduate experience, and often a few years of work experience, we can tell a lot more about whether somebody has the potential to be a future leader. >> So you said, found it just in 2016. And one of the things I saw that was very interesting is projecting in the next 50 years, there's going to be 5000 Knight-Hennessy scholars at various stages of their careers and government organizations, NGOs, as you mentioned, so looking out 50 years you have a strong vision there, but really expect this organization to be able to make a lasting impact. >> That's what our goal is lasting impact over decades, because people who go into leadership positions often take a decade or two to rise to that position. But that's what our investment is our investment is in the in the future. And when I went to Phil Knight who's my co-founder and donor, might lead donor to the program, he was enthusiastic. His view was that we had a we had a major gap in leadership. And we needed to begin training, we need to do multiple things. We need to do things like we're doing tonight. But we also need to think about that next younger generation is up and coming. >> Some terms of inspiring the next generation of innovative diversity thinkers. Talk to me about some of the things that this program is aimed at, in addition to just, you know, some of the knowledge about leadership, but really helping them understand this diverse nature in which we now all find ourselves living. >> So one of the things we do is we try to bring in leaders from all different walks of life to meet and have a conversation with our scholars. This morning, we had the UN High Commissioner for Human Rights in town, Michelle Bachelet, and she sat down and talked about how she thought about her role as addressing human rights, how to move things forward in very complex situations we face around the world with collapse of many governments and many human rights violations. And how do you how do you make that forward progress with a difficult problem? So that kind of exposure to leaders who are grappling with really difficult problems is a critical part of our program. >> And they're really seeing and experiencing real world situations? >> Absolutely. They're seeing them up close as they're really occurring. They see the challenges we had, we had Governor Brown and just before he went out of office here in California, to talk about criminal justice reform a major issue in California and around the country. And how do we make progress on that on that particular challenge? >> So you mentioned a couple of other leaders who the students I've had the opportunity to learn from and engage with, but you yourself are quite the established leader. You went to Stanford as a professor in 1977. You are a President Emeritus you were president of Stanford from 2000 to 2016. So these students also get the opportunity to learn from all that you have experienced as it as a professor of Computer Science, as well as in one of your current roles as chairman of Alphabet. Talk to us a little bit about just the massive changes that you have seen, not just in Silicon Valley, but in technology and innovation over the last 40 plus years. >> Well, it is simply amazing. When I arrived at Stanford, there was no internet. The ARPANET was in its young days, email was something that a bunch of engineers and scientists use to communicate, nobody else did. I still remember going and seeing the first demonstration of what would become Yahoo. Well, while David Filo and Jerry Yang had it set up in their office. And the thing that immediately convinced me Lisa was they showed me that their favorite Pizza Parlor would now allow orders to go online. And when I saw that I said, the World Wide Web is not just about a bunch of scientists and engineers exchanging information. It's going to change our lives and it did. And we've seen wave after wave that with Google and Facebook, social media rise. And now the rise of AI I mean this this is a transformative technology as big as anything I think we've ever seen. In terms of its potential impact. >> It is AI is so transformative. I was I was in Hawaii recently on vacation and Barracuda Networks was actually advertising about AI in Hawaii and I thought that's interesting that the people that are coming to to Hawaii on vacation, presumably, people have you know, many generations who now have AI as a common household word may not understand the massive implications and opportunities that it provides. But it is becoming pervasive at every event we're at at the Cube and there's a lot of opportunity there. It's it's a very exciting subject. Last question for you. You mentioned that this that the Knight-Hennessy Scholars Program is really aimed towards graduate students. What is your advice to those BB stem kids in high school right now who are watching this saying, oh, John, what, what? How do you advise me to be able to eventually get into a program like this? >> Well, I think it begins by really finding your passion, finding something you're really dedicated to pushing yourself challenging yourself, showing that you can do great things with it. And then thinking about the bigger role you want to have with technology. In the after all, technology is not an end in itself. It's a tool to make human lives better and that's the sort of person we're looking for in the knight-Hennessy Scholars Program, >> Best advice you've ever gotten. >> Best advice ever gotten is remember that leadership is about service to the people in the institution you lead. >> It's fantastic not about about yourself but really about service to those. >> About service to others >> JOHN, it's been a pleasure having you on the Cube tonight we wish you the best of luck in your keynote at the 15th annual ACG SV Awards and we thank you for your time. >> Thank you, Lisa. I've enjoyed it. Lisa Martin, you're watching the Cube on the ground. Thanks for watching. (upbeat tech music)

(upbeat techno music) >> Live from Las Vegas, it's theCUBE. Covering Boomi World 2018. Brought to you by Dell Boomi. >> Good afternoon, welcome back to theCUBE's continuing coverage of Boomi World 2018 from Las Vegas. I'm Lisa Martin with John Furrier and we're welcoming back to theCUBE one of our alumni Nima Badiey, Head of Technology Ecosystems from Pivotal. Nima, welcome back. >> Thank you for having me back. >> So Pivotal, part of the Dell technologies part of the companies, >> Yeah. >> You guys IPOd recently. And I did read that of the first half 2018, eight of the 10 tech IPOs were powered by Boomi. >> Well, I don't know about that specific. I know that tech IPOs are making a big comeback. We did IPO on the 20th of April, so we've passed out six-month anniversary if you can say. But it's been a distinct privilege to be part of the overall Dell family of businesses. I think what you have in Michael as a leader, who, he has a specific vision, but he's left the independent operating units to work on their own, to find their path through that journey, and to help each other as brethren, as like sisters and brothers. And the fact that Pivotal is here supporting Boomi. That Boomi is within our conference of supporting our customers that we're working together really speaks volumes. I think if you take a look at it, a lot of things happened this week, right? So a couple weeks ago, IBM's acquiring RedHat, this morning VMWare's acquiring Heptio. That's a solid signal that the enterprise transformation and adoption of cloud native model is really taking off. So the new middleware is really all about the cloud native polyglock, multiglock environment. >> And what's interesting, I want to get your thoughts on this because first of all congratulations on the IP, some are saying Pivotal's never going to go public, and they did, you guys were spectacular, great success. But what's going on now is interesting. We're hearing here at this show, as other shows is, cloud scale and data are really at the center of this horizontally scalable cloud poly proposition. Okay great, you mention Kubernetes and Heptio and VM where, that's all great. The question that is how do you compete when ecosystems become the most important thing. You worked at VMware you're at Pivotal. Dell knows ecosystems. Boomi's got an ecosystem. Partners, which is also suppliers and integrators. >> Yeah. >> They integrate and also developers. This is a key competitive advantage. What's your take on that here? >> So I think you touched on the right point. You compete because of your ecosystem, not despite your ecosystem. We can't be completely hedgemonic like Microsoft or Cisco or Amazon can afford to be. And I don't think customers really want that. Customers actually want choice. They want the best options but from a variety of sources. And that's why one of the reasons that we not only invest Dell ecosystem but also in Pivotal's own ecosystem is to cultivate the right technologies that will help our customers on that journey. And our philosophy's always find the leaders in the quadrant. The Cadillac vendors, the Lexus vendors onboard them and the most important thing you can do is, to ensure a pristine customer experience. We're not measuring whether feature A from one partner is better than feature B from another partner. We really don't care. What we care about is we can hand wire and automate what would have been a very manual process for customers, so that, let's say Boomi with Cloud Foundry works perfectly out of the box. So the customers doesn't have to go through and hire consultants and additional external resources just to figure out how two pieces of software should work together, they just should. So when they make that buying decision they know that the day after that buying decision, everything's going to be installed and their developers and their app dev teams and their ops teams can be productive. So that's the power of the ecosystem. >> Can you talk about the relationship between Pivotal and Boomi, because Boomi's been born in the Cloud as start up. Acquired eight years ago. You're part of the Dell Technologies family. VMware's VMware, we know about VMware doing great. You guys doing great. Now Boomi's out there. So how do they factor into and what's the relationship you have with them and how does that work, how do you guys work together? >> Perfect question. So, in my primary role at Pivotal is to manage all of our partner ecosystems, specifically the technology partners. And what I look for are any force multipliers. Any essentially ISVs who can help us accomplish more together than we could on our own. Boomi's a classic example of that. What do they enable? So take your classic customer. Classic customer has, let's say, 100 applications in inventory that they have built, managed, and purchased procured off from shelf-to-shelf components. And roughly 20 or 30% are newish, green field applications, perfect for the cloud native transformation. Most 80% of them or 70% are going to be older, ground field applications that will have to be refactored. But there's always going to be that 15% towards the end that's legacy mainframe. It can't be changed, you cannot afford to modernize it, to restructure it, to refactor it. You're going to have to leave it alone, but you need it. Your inventory systems are there. >> These are critical systems, those people who think legacy as outdated, but they're actually just valued. >> No, they're critically valuable. >> Yes. >> We just cannot be modernized. >> Bingo. >> So a partner like Boomi will allow you to access the full breadth of those resources without having to change them. So I could potentially put Boomi in front of any number of older business applications and effectively modernize them by bridging those older legacy systems with the new systems that I want to build. So let's do an example. I am the Gap and I want to build a new version of our in-store procurement system that runs on my iPhone, that I can just point to a garment and it will automatically put it in my, ya know, check out box. How do I do that? Well I can build all the intelligence. And I can use AI and functions and I can build everything it's out of containers, that's great. But I still have to connect to the inventory system. Inventory system... >> Which is a database. All these systems are out there. >> Somewhere, something. And my developers don't know enough about the old legacy database to be able to use it. But if I put a restful interface using Boomi in front of it and a business connector that's not older XML or kind of inflexible, whatever, solo gateways. Then I have enabled my developer to actually build something that is real. That is customer focused. It is appropriate for that market without being hamstrung by my existing legacy infrastructure. And now my legacy infrastructure is not an anchor that's holding me back. >> You had mentioned force, me and Lisa talk about this all the time on theCUBE, where that scenario's totally legit and relevant because in the old version of IT you have to essentially build inventory management into the new app. You'd have to essentially kill the old to bring in the new. I think with containers and cloud native has shown is you can keep the old and sunset it if you want on your own time table or keep it there and make it productive. Make the data exposeble, but you can bring the cool relevant new stuff in. >> Yeah. >> I think that is what I see and we see from customers, like OK cool, I don't have to kill the old. I'll take care of it on my own timetable versus a complete switching cost analysis. Take down a production system. >> Exactly. >> Build something new, will it work. Ya know cross your fingers. Okay, again and this is a key IT different dynamic. >> It is and it's a realization that there are things you can move and those are immutable. They're simply just monolithic that will never move. And you're going to work within those confines. You can have the best of both worlds. You can maintain your legacy applications. They're still fine, they run most of your business. And still invent the new and explore new markets and new industries and new verticals. And just new capabilities all through and through without having to touch in your back end systems. Without having to bring the older vendors in and say can you please modernize your stuff because my business is dependent and I am going to lose that. I'm going to become the new Sears, I going to become the new Woolworth or whoever. Blockbuster that has missed an opportunity to vector into a new way of delivering their services. >> When you're having customer conversations, Nima, I'm curious, talking with enterprise organizations who have tons of data, all the systems including the legacy, which I'm glad that you brought up that that's not just old systems. There's a lot of business critical, mission critical application running on 'em. Where do you start that conversation with the large enterprise, who doesn't want to become a Blockbuster to your point, and going this is the suite of applications we have, where do we start? Talk to us about that customer journey that you help enable. >> That's great 'cause in most cases the customers already know exactly what they want. It's not the what that you have to have the conversation around, it's the how do I get there. I know what I want, I know what I want to be, I know what I want to design. And it's how do I transform my business fundamentally do an app transformation, enterprise transformation, digital transformation? Where do I begin? And so, ya know, our perspective at Pivotal is, ya know, we're diehard adopters of agile methodology. We truly, truly believe that you can be an agile development organization. We truly believe in Marc Andreessen's vision of software eating the world. Which let's unpack what that means. It just means that if you're going to survive the next 10 years you have to fundamentally become a software company, right? So look at all the companies we work with. Are you an insurance company or are you delivering an insurance product through software? Are you a bank or are you delivering banking product through software? Well, when was the last time you talked to a bank teller? Or the atm, most of your banking's done online. Your computer or your mobile device. Even my check cashing, I don't have to talk to anyone. It's wonderful. Ford Motor Company, do they bend sheet metal and put wheels on it or are they a software company? Well consider that your modern pickup truck has... >> They're an IOT company now. (laughing) (crosstalking) Manufacturing lines. >> That's what's crazy. You have a 150 million lines of code in your pickup truck. Your car, your pickup truck, your whatever is more software than it is anything else. >> But also data's key. I want to get your thoughts since this is super important Michael Dell brought up on the keynote today here at Boomi World was, okay the data's got to stay in the car. I don't need to have a latency issue of hey, I need to know nanosecond results. With data, cloud has become a great use case. With multicloud on the horizon, some people are going to throw data in multiple clouds and that's clear use case, and everyone can see the benefits of that. How do you guys look at this? 'Cause now data needs to be addressable across horizontal systems. You mentioned the Gap and the Gap example. >> That's great, so, one of the biggest trends we see in data is really event streaming. Is the idea that the ability to generate data far out exceeds the ability to consume it. So, what if we treated data as just a river? And I'm going to cast my line and only pick up what I want out of that stream. And this is where CAFCA and companies like Solice and any venturing networks and spring cloud functions and spring cloud data are really coming into play, is acknowledgement that yes we are not in a world where we can store all of the data all the time and figure out what to do with it after the fact. We need timely, and timely is within milliseconds, if not seconds. Action taken on an event or data even coming through. So why don't we modernize around, ya know, that type of data structure and data event and data horizon. So that's one of the trends we see. The second is that there is no one database to rule them all anymore. I can't get away with having oracle and that's my be all, end all. I now have my ESQL and SQL and Mongo and Cassandra and Redis and any other number of databases that are form, fit and function specific for a utility and they're perfect for that. I see graph databases, I see key value stores, I see distributed data warehouse. And so my options as a developer, as a user is really expanding, which means the total types of data components that I can use are also expanding exponentially. And that gives me a lot more flexibility on the types of products that I can build and the services that I can ultimately deliver. >> And that highlights micro services trend, because you have now a multitude of databases, it's not the one database rules them all. They'll be literally thousands of database on censors, so micro service has become the key element to connect all these systems. >> All of it together. And micro services really a higher level of abstraction. So we started with virtual machines and then we went to containers and then we went to functions and micro services. It's on an upward trend necessarily as it is an expansion. Into different ways of being able to do work. So some of my work products are going to be very, very small. They can afford to be ephemeral, but there may be many of them. How do I manage a cluster of millions of these potential work loads? Backing off I can have an ephemeral applications that run inside of containers or I can have ridged fixed applications that have to run inside a virtual machines. I'm going to have all of them. What I need is a platform that delivers all of this for me without me having to figure out how to hand wire these bits and pieces from various different either proprietary or open source kits just to make it work. I'm going to need a 60 to 100 or 200 person team just to maintain this very bespoke thing that I have developed. I'll just pull it off the shelf 'cause this is a solved problem. Right, Pivotal has already solved this problem. Other companies have already solved this problem. Let me start there and so now I'm here. I don't have to worry about all this left over plumbing. Now I can actually build on top of my business. The analogy I'd use is you don't bring furniture with you every time you check into a hotel. And we're telling customers every time you want to move to a different city just for business meeting or for work trip we're going to build you a house and you need to furnish it. Well, that's ridiculous. I'm going to check into a hotel and my expectation is I can check out of any other room and they'll all be the same, it doesn't really matter what floor I'm on, what room I'm in. But they'll have the same facilities, the same bed, the same, ya know, restroom facilities. That's what I want. That's what containers are. Eventually all the services surrounding that hotel room experience will be micro services. >> And we're the work load, the people. >> And we are the work load and we're the most important thing, we are the application, you're right. >> I love that. That's probably best analogy I've heard of containers. Nima, thanks so much for stopping by theCUBE, joining John and me today. And talking to us about what's going on with Pivotal and how you guys are really helping as part of Dell business dramatically transform. >> Been my pleasure. Thank you both. >> Thank you. >> Thank you. Thank you for watching theCUBE. I'm Lisa Martin with John Furrier. We are in Las Vegas at Boomi World '18. Stick around, John and I will be right back with our next guest. (light techno music)

>> Live from Bahrain, it's theCUBE! Covering AWS Summit Bahrain. Brought to you by Amazon Web Services. >> Okay, welcome back everyone. We are here, live in Bahrain, for the exclusive CUBE coverage of AWS Summit here in the region. Obviously, huge news, Amazon's having a region here, a full region, that's going to create a lot of connections, new opportunities, and hopefully make the life easier for all the developers and whatnot. Great guest here, so we're just talking with Kim on camera, about all the exciting developments on Amazon. We've got Abdul Raman, who's the group EVP of tech, at the ATG, which is the Al Tayyar Travel Group, in Saudi Arabia. >> Yep. >> Thanks for joining me today. >> Thanks a lot for having me. >> So, I'll quickly fast forward, you guys started in 2015, programming in the cloud, your like, we were late. I think that's actually a good time, 'cause Amazon had a lot of mature services ready. Went from zero to billions in revenue. >> Correct. >> Really big success story, that's large scale, all cloud based right? >> Yep, correct. >> Tell your story, what do you guys do, real quick, take a minute to explain your group, what you guys do, and then, what were the architectural things you decided, how did you get the growth? >> So, we are a 40 years old company, we started in 1979, we are the largest travel and tourism company in the Middle East. We went public, through our IPO in 2012. And 2015, our new board, and new management, including myself, we started building our ten-year strategy plan. And we said, we need to diversify our investment, so it mandated that we need to have an online presence. In 2015, we had a choice to build our online presence, which is very late, either on-premise using, building a data center, or we go to the cloud. We had multiple metrics including the cost efficiency, including scalability, security and so on, and all these metrics, when we compared on-premise versus cloud, cloud always win. And we selected Amazon to build our online presence. And beginning of 2015, we had zero presence, zero revenue. Our total revenue from the classic legacy systems, for the retail was almost two billion dollars. But we had zero revenue from the online. We were able, within six weeks, to build the proof of concept, and launch it immediately, and we started heavily investing in various components, from back-end, front-end, DevOps, and so on. And this year, we anticipate, we're going to be generating more than two billion riyal of revenue, that's about 450 >> Online >> Online only. >> Via cloud. >> Exactly, only on Amazon. And for us, that has been the best success story we had for years. >> It's an amazing success story actually. >> We look backward to our decision back then. >> I'll break for you, that's like actually really amazing. This is something that I think people don't really understand, what about the cloud, and certainly Amazon, and the kind of scale that you can get, if you get something right, both on the business model side and architecturally, you can be a unicorn. You're really a unicorn in revenue, that's the word that they hear in the startup world, unicorn, but mostly that's stock value, that's not actually real cash, in how many years? This is pretty phenomenal. This is the entrepreneurial dream, that is now a reality. >> Yep, that's correct. >> This is the story here. >> Exactly, and I'm happy that you mentioned that. We actually, when we started this venture, we said, to the founders, you guys are a startup. We rented out, in 2015, a garage, literally. >> Yeah, get out of the way. >> A house, A very old warehouse, we brought like, five guys, you are the core team, we told them, you are a startup, give us whatever you want to do. And it has been very successful since then. >> It's kind of like the Steve Jobs story, you got Apple, with the Mac II, and then the little group over here, you know, doing the Macintosh. >> Yep, yep, yep. >> That's your group, because you got to get out of their way, it's a mindset, I want to ask you that, that was one of my questions, but we got there a little early, but, this is a cultural shift. Cloud is a different mindset. >> Yep. >> It's not the old way of planning, team-building. >> Yep. >> It really is a different dynamic both execution wise, but team makeup. >> Correct. >> Can you share that piece of it? >> We gave our founders complete freedom, in how they're going to make up their management style. So we have a complete agile team, we have diverse geographical locations, we have people from India, developers in Egypt, in Dubai, in Saudi, and be all work and collaborate, using DevOp tools from Amazon, so we divide the work load, our product teams, weekly launch feature list. They tell us when they would like to launch every two weeks, or three weeks, a new version of the website, or the mobile apps. So, we have a completely agile development methodology, and we give our new venture a truly startup culture. >> And the key for you, if I get this right, is to have executive leadership say, we're doing this? >> Yep. >> Was that in place, did you drive that? >> Absolutely, so when our board said, told us, the new board in 2015, guys we don't have an online, go and get it, me and the CEO said, the best way to do it, is just spin off a completely different unit, completely independent, startup mentality, intro manuals, and told them, guys, sky is limit. We need to be the number one player in the Middle East. >> So, I got to dig deeper, 'cause I love, you know, it's all sexy, and great story when you say, this is how we started, and we finished strong, but as Andy Jassy would say, the CEO of AWS, the learning's in the middle, the ups and downs, as you figure things out, 'cause a lot of things about cloud, is iteration. >> Yep. >> 'Cause you have the ability to move very fast, and you get smart people together, so there's a glorious start and a glorious outcome, but in the middle is the experimentation, that's where the real work gets done. Can you share some of the learnings? Was it a technology selection? Did you really, do you have more queuing, more database, as you start to play with Amazon, this becomes, actually, a business process. >> Our biggest, yeah. >> Playing with the different pieces and which services are right for which process. Can you share something? >> Correct. So our biggest challenge was finding the right skillset, who are people who understand how Amazon, AWS, works. In the Middle East, we don't have that many skillset, or skillful people, so we had to wait, train the people, send them to Amazon workshops, be very patient with the mistakes, we don't mind people refactoring all the old code. Every month we start from scratch. We were very aware that this is, what we are doing, is never been done before in the Middle East. And what we have developed, in terms of, for example, the big data, the big data platform we build today, is one of the largest, we are processing terrabytes of data every week. It's one of the largest in the Middle East. The number of developers we have today, more than 500, working on AWS. I don't think any company in the Middle East, have that number of developers, working on this platform. So we're very proud that we gave our developers the trust and we are aware that you need to fail fast, learn, and quickly adapt. >> And it's a contagious mindset too, when you start seeing success. >> Yep. >> So talk about some of the architectural, talk about the stack that you're using. Obviously, you must be using a variety of the Amazon goodness, EC2, that's pretty obvious, are you guys using the queuing, are you using Kinesis? How you, can you talk about some of the architectural things, if you can? >> Yep, so we have, the front-end that we have today, is completely built on Node.js and AngularJS, so it's very fast, very agile. Our back end is built on Java, most of the code built on Java. We have multiple messaging buses, that asynchronous mode, so whenever there is something that needs to be given to a certain component, we don't have to wait for serial queuing. It's all parallel. At the same time, we have a lot of Auto Scaling components. One of the examples I gave earlier today, is that, we had, the beginning of this summer, we had so many marketing campaigns, and we were surprised by how successful these marketing campaigns. We have noticed, in one marketing campaign, that our demand, from our customer, have reached 300 percent, within 24 hours, and the Auto Scaling that we have in place, have been very successful. We were able to immediately meet that demand. >> Talk about how good the Auto Scaling is. Isn't that a relief? >> Absolutely. >> I mean, explain how it works because, essentially, when the demand comes in, explain how it works. >> Yep, so, just to give an example, if we had this infrastructure on-premise, we would have needed six weeks to procure a new infrastructure, install it, configure it, and we would have lost all this six weeks of revenue. >> And then, by the way, you would have lost the first 24 hour surge, then you'd go over-billed, and then wait around, and then not know if you over-provisioned. >> Absolutely. >> This is, the old way. The new way is, you configure Auto Scaling, based on policy, and then it just spins up. >> Absolutely. >> Resources. >> Absolutely. >> While you're sleeping. >> Exactly, so in a few seconds, the Auto Scaling fires up a lot of instances, and we immediately cope with the demand. >> You know, it's funny you mentioned that. One of the comments we have inside our company is, you know you're successful online, when you're making money while you're sleeping. And, you know, if you have Auto Scaling, and things of that nature, these things are programmatic, this is what elastic is all about, this is what coders, >> Yep. >> Not system administrators do >> True. >> And once they do it, they're highly motivated not to manage it again. >> Correct, absolutely. >> Again, this is back to the culture of DevOps. >> Yep, yep. >> How have you guys innovated on that piece, can you give some other examples? >> Yes, so today we have, our big data has feeds from all the buys from the big social networks, Twitter and Facebook, and also from Google, and we have all this analytical data, into our big data, and we analyze all our customer behavior, what they're looking for, what kind of destinations, holidays, business travel, and we try to adapt every two, three weeks, our product and services to meet our customer demand. Next year, we're going to be launching our machine learning, and AI infrastructure. This way, we'll be able to do real time, predictive analysis, and we will be able to serve each customer, unique, fully personalized, customized, web page and experience. We will be able to exceed our customer expectations, and we'll be able to give our customer exactly what they're looking for. >> Abdul, I got to ask you a personal question. >> Sure. >> What are you most proud of, of this success story? What are some of the things, that you look back and say, wow, we really knocked it out of the park, we did great on this, and then an example where you had a good learning experience. Maybe a trip and a fall, that was a learning opportunity. What are you most proud of? And areas that you learned the most about from, tripping and falling, and failure. >> Yep, so I think the most thing I'm proud of, is we have gathered great minds, and we have created great culture. I think great companies have great people behind them, and this, I've learned from reading the stories of Apple or Microsoft, or Google and so on. So, I think we've been very successful in this area, in the Middle East, where the resources are very scarce, and the ability to attract very smart people is very difficult, to bring them in the Middle East. And I think, we've been very successful in that regard, we've been able to gather a lot of smart people, and create great culture. >> You know, Marc Andreessen wrote that article, book about, or maybe it was a tweet, I can't even remember, the 10x engineer. >> Yep. >> And that concept is one engineer, that does cloud and DevOps right is worth ten engineers in the old world. And so, if you can collect, a selection of these 10x multipliers, that can do architecture. >> Correct. >> Now I personally believe that the full-stack developer, might be obsoleted with the cloud, or reduce the requirement for full-stack developer, but you'll still need full-stack developers for cloud, in general, but you don't need to stockpile full-stack developers. >> True, true, I agree. >> If you have good full-stack developers, you then can hire application developers >> True. >> Because the full-stack takes care of all the scale. >> Exactly, you can always repurpose those guys, and up-skill them to do something different. Instead of being a full-stack, you really want to focus on solution developer. >> Google's proven this with their SRE, if you've seen, they have operators, and developers. And this, as you scale, you're operating infrastructure, or you're writing code for applications. >> Correct. >> Alright, so what's the learnings that have been magnified for you? In the middle of the journey here, there's always the, you know, situation were, you know, you have to take care of personnel issue, or technology selection tweak or change, iteration, I won't say pivot, 'cause people don't pivot, when they're succeeding, it's just navigating through the journey. What was something that you've experienced that was magnified in the learnings, that have helped you get better? >> Yep, I believe that the multi-culture and the multi-nationalities and multi-discipline and people coming from different backgrounds. We have people from Asia, from Europe, from the U.S., in our company, and this helped having different backgrounds, different experiences, and this has helped us to build a nice, multi-dimensional solutions. And people have been able to share this experience, in a very nice way. >> That's great, Abdul, thanks so much for sharing, taking the time. >> Thank you. >> Here on theCUBE, and sharing your insight, and amazing success story, congratulations to you and your team, really love to hear these amazing success stories, essentially building from zero start, online, to billions in revenue, that's an amazing success story. >> Thank you very much for having me. >> And it certainly is great. Exclusive coverage here, we are in Bahrain, this exclusive CUBE coverage, I'm John Furrier. You can reach me on Twitter @furrier, or just search my name, reach out to me, let me know what you think. Stay with us for more coverage, after this break. (techno music fades out)

>> Narrator: Live from Copenhagen, Denmark. It's theCUBE covering KubeCon and CloudNativeCon Europe 2018. Brought to you by the Cloud Native Computing Foundation, and its ecosystem partners. >> Welcome back everyone. This is the theCUBE's exclusive coverage here in Copenhagen, Denmark for KubeCon 2018, part of the Cloud Native Compute Foundation, also known as CNCF. I'm John Furrier with Lauren Cooney, the founder of Spark Labs. We have two of the main players here at the Linux Foundation, CNCF, Dan Kohn, Cube alumni, Executive Director, and Dee Kumar, Vice President of product marketing. Great to see you guys. Welcome back. >> Oh, thrilled to be here. >> So you guys, not to build your head up a little bit, but you're doing really well. Successful, we're excited to be a part of the seeing, witnessing the growth. I know you work hard, we've talked in the past and off camera. Just, it's working. CNCF's formula is working. The Linux Foundation has brought a lot to the table, you've taken the ball with this cloud-native community, with Kubernetes' growth, good actors in the community, a lot of things clicking on all cylinders. >> Thanks, we're thrilled to be here. And, yeah, 43 hundred people is the biggest ever for KubeCon CloudNativeCon. It's actually the biggest conference the Linux Foundation has ever thrown, which is incredibly exciting, and also here in Europe to show it's not just a North American focus. >> And you've got the big North American event in Seattle. What's the over-under on that? Six thousand, eight thousand? >> (laughing) I think we could probably go a little higher. 75 hundred we're going to max out, so we'll see if we hit that or not. But we had 42 hundred six months ago when you were with us in Austin, and so we think a ton of people, you know people joke about Seattle being the cloudy city, because it's not just Amazon there, but Microsoft, Google, Oracle, and IBM all have huge Cloud offices. >> Yeah, and University of Washington has an amazing program in computer science, a lot of tech there. Seattle's certainly an awesome city. I got to ask you, you know, you do a lot of work with the members in the organization. Obviously the success is well-documented. We're seeing that Kubernetes is now going to main stream tech. And still learning, a lot of people learning about Kubernetes, but there's a lot going on. You talk to a lot of people. What's the vibe? What's the conversation like? What is actually happening in the membership organization that's notable, that you'd like to share and get the word out on? >> Actually Dee's been working directly with all the members since we've been putting together our marketing plan. >> So one thing I can do share, in terms of the vibe, and some of the feedback that we have received from the members, is they really, I think it's about what we've heard from all the keynotes and the sessions, it's about really us coming together as a community and defining, what is Cloud-native? And what's that journey? And so as a step towards that, what we have done as in CNCF is we have launched the interactive landscape which kind of showcases a lot of the member work that we are jointly working on. And secondly, the trail map is our attempt to define what is the cloud-native journey. So we've kind of highlighted about 10 steps and the processes to get to a cloud-native journey. And I think the next steps, in terms of the vision and the goal, is to really engage the member community and to start building on that. What is containerization? What is orchestration? Microservices? CICD? And Dan, I think in his keynote, touched upon continuous integration. We really need to figure out integration, testing, development, deployment, and what does that, all that narrative mean, and how as a community we have a common understanding and a framework. And then the next step would again be in terms of building use cases, and also really showcasing some heroes in the community which is our developers. So our developers and contributors end of the day are the heart and soul of the cloud-native ecosystem. So we really want to bring their stories, match that up with our end users. We're seeing incredible growth with just leveraging the cloud-native different types of architectures. >> One of the things I'm looking at, the cloud-native Interactive Landscape map, which is, by the way, pretty impressive. The market cap numbers in the trillions, of course includes Amazon, (Dee laughing) so let's take that out, but good healthy distribution. I want to talk about the startups, because they are going to be the lifeblood of the future. The total funding to date is 4.7 billion of cloud-native compute foundation members, startups. Significant investment. They got to build, they're building products. What do they care about? What is the most important thing for them? You guys, can you share what they're asking for, is there a profile that you're seeing emerge? Because there's a new era coming, right? It's the new guard. The new guard of startups. >> There's incredible diversity of startups there, and what I love about the startup ecosystem, kind of like the open source ecosystem, is they're all looking for their niche. And so there's kind of an evolutionary strategy for it. But it's really amazing to see different approaches towards attacking different markets, consulting specific products and such. One of the neat things about CNCF is that we like to think of ourselves as a commercially friendly startup. All 20 of our projects, commercially friendly open source foundation. All 20 of our projects use the Apache 2.0 license which allows you to create a commercial product on top of it. We are very cognizant of the fact that most large enterprises are going to want support from a business startup or an established industry player and in many cases, both, in order to roll this out. And so we love the fact that that's available if they need it, but they also could download the projects directly and work with it themselves if they want. >> Well I think that's an important point. I always want to highlight, because what you said I think is really, I think, is a big part of the success. You guys do a great job of balancing community, and the role of the people within the community, and the traditional Linux Foundation mission of having great open source. But at the same time, you're like, hey, it's okay to have a business model with Open. And I think this new era is being highly accelerated on commercialization. And I think this is, I think, a unique part of the digital fabric, the digital businesses of the future. And Cloud hits that right on. So that's, to me, a great step. The question I have for you is, how do you keep it going? What's next? Because the bar is high. Now you got to do more. What's the strategy? What's the plan? >> So one thing we can do is, like a highlighter to get back to the cloud-native journey, as a story. Today we kind of have a lot of emphasis on Kubernetes. And it's just not limited to containers and orchestration, and we really want to expand the narrative and the story to address all the 20, 19 different projects that is all housed under the cloud-native computing foundation umbrella. And we really want to bring out use cases, value props, and I think there's a lot to be told here. Like how do we address security? There's a lot of sessions and keynotes today that bring about security applications, testing, CICD, how does it develop a community, can enable all these different amazing technologies. So we've had a lot of talk about it, but I think it's something that startups that I've been talking to have asked me to help or the CNCF in terms of just simplifying these conversations. Like how do we make it simple? And to your earlier point, like they want to start with simplicity and that eventually leads to monetization, and they want to take the fabric from CNCF so they can then start building a narrative in terms of a solution, and what does that mean in terms of value creation? >> Exactly and I actually work with a couple startups inside of the CNCF, and work with them on their business model, and what they're doing, and what is that narrative that they're going to start telling? You know, I think it's interesting because you have all these communities actually coming together in that ecosystem. And when you take a look at that, you probably, you talk about use cases. And I think those are really what the developers are going to be driven towards is their, you know, onboarding to this platform, basically. And what are the top use cases that you guys see kind of across the board? >> So I think there are three main use cases and I think our partner did a great job of summarizing that today. So I think it's primarily security, because that's the enterprise audience, and most Fortune 100 companies are dealing with that. Second, I would say it's about agility. It's about who gets to market first, and back to the startup point. It's about addressing that. Thirdly I would just say it's scalability. I think it's about going beyond, you know, a science project where you just have Kubernetes, or a couple containers deployed in your own QA or staging environments. And people are really thinking about, how do you adopt Kubernetes on a large scale? How do you take it to a production type of environment? And what does that mean? And I think, today, "Financial Times" Sarah Wells, she did an amazing job of just taking us through what it took them in terms of getting from where they were and how they had to deal with, you know, all the challenges and I think she made a great point about technologies can be boring. So I think that was some of the key takeaways in terms of the three use cases that we could build on collectively would be agility, scalability, and security. >> Well, you're also changing the conversation, really. You know, we had the great customer of, you know, Kubernetes on here earlier. And they were talking about, really, how their whole infrastructure, they don't have to worry about it, it's, you know, based on AWBS now and they were phenomenal and, really, what the point was is that, you know, they are not just an energy company, they're actually a technology company and a software company. And that's really what, you know, folks want to be working with today. And are you seeing more of that as, you know, with the startups, is that they have the opportunity to start shifting their companies more in the direction of technology for the end users? >> Absolutely. Yeah. But it is amazing the just range of different approaches that they're taking. But we think there's every level of the stack. We have this, you referred to the Interactive Landscape before, and I will give the quick pitch, it's a l.cncf.io, but it is amazing to see all of the different layers of which these startups are operating. >> And you guys do a good job of breaking down which ones are open source, which ones are not, funding, public, private, category. So, good job. So what's the numbers look like? Dan, I'd like you to just take a minute, just, I know you do this a lot, but just do it on the record, what's the numbers? Members, growth? How many cities are you going to be doing KubeCon in? You mentioned Shanghai before we came on. Just run us through the numbers, inside the numbers. >> So, the first number that I think's the most exciting is we've over 20 thousand developers actively engaged across our 20 projects. And so those aren't users, I mean the users is hundreds of thousands. But those are people who've actually found issues with it, made a documentation fix, or, you know, added some significant new feature in order to scratch the itch that they were having. We have 43 hundred people here in KubeCon CloudNativeCon. These events are always a great check-in. We were together in Seattle just a year and a half ago and had a thousand people, 15 hundred here a year ago, 42 hundred in Austin in six months. What we're very excited to do is head to Shanghai in November for our first ever KubeCon CloudNativeCon China, where we now have three platinum members there, three gold members, just a huge level of engagement and interest. >> John: And a big developer community there in China. >> Definitely. >> Lauren: Huge developer community there. >> And obviously the language issue is a barrier, and we're going to be investing real resources to have simultaneous interpretation for all of our talks and all of our tracks. >> John: In real time or post-- >> Definitely in real time. >> Primarily in English and then-- >> No, we can do it both ways, and so we're telling every speaker that they can present in Chinese or English, and then the question can be in Chinese or English. >> I love that. And it's a cost, but we think that that can really help bridge those two different parts. And then we'll be in Seattle in December 11th through 13th for our biggest ever event, KubeCon CloudNativeCon. Along that journey, we've been increasing members and so we had, I believe, 68 in Berlin a year ago, and we're at 216 today, and of those we have 52 members are end user community, who we're particularly proud of. >> Well, congratulations. I want to get those numbers out in the end, because last time we talked about they had more projects coming, coming so good job. Dee, I want to get your thoughts on the branding. Obviously, CNCF, Linux Foundation, separate group, part of the Linux Foundation. I noticed you got CloudNativeCon built into it, still. Branding, guys, thoughts in here, because there's more than Kubernetes here, right, these Cloud-natives, so what's the, are you going to keep one, both, dual branding, what's the thoughts? >> So, I would say the branding will be defined by the community and the fact that we have 20 different projects. I wouldn't put a very strong emphasis on just having one type of a branding associated with cloud-natives. One of the things that I'm thinking about is I've been talking to the community, and I think it's the developers and contributors, again, who's going to define the branding of cloud-native in general. And I think it's still something that we, as a community, have to figure it out. But, essentially, it's going to be beyond containers, orchestration. There's a lot of talks around Prometheus, we talked about Code OS, Redhead. So I think it's just, you know, a combination of how all these projects work together, in a way, it's going to define the branding strategy. So I think it's a little bit too early for me to make some comments on that. >> The best move is not to move at this point. (Dan laughs) I'm a big fan of cloud-native, but KubeCon... Little bit of a conflict with theCUBE, because people-- >> Oh yeah (laughs). >> But we're not going to put a trademark and bring it on you guys, yet. >> We appreciate that. >> We love the confusion. You're in good company, vice versa. Okay, serious question, Dan. I want to ask you, and Dee you can weigh in, too, on this. You're a student of the industry. You've also been around a while, you've seen many waves. For folks that-- >> I'm not that old. (Dan laughs) >> This is a new wave. You're younger than me. For the folks that are looking at this going, "Okay, the numbers are there. I'm seeing growth, "you've got my attention." And they're still trying to grok what this wave is about, this new modern era, cloud-native, KubeCon, Kubernetes. Certainly insiders kind of see it, and there's a lot of people who are kind of high-fiving each other, but, yet, it's not yet fully here. >> Dan: No. >> How important, how do you describe it to someone at a cocktail party or in the elevator. How do I explain to them the historic nature of what's happening. In your own words, what's happening? >> And it is tricky because, you know, at my kids' little leagues games, if we're just chatting about what we do, I sometimes describe it as the plumbing software for the internet. And it's not a bad metaphor; Linux has also been described that way, because plumbing is really important. Now, most of us never think about it, we don't have to worry about it, but if it breaks, we all get extremely upset. And, so, I do think of our sort of overarching method is to say that the whole way this software is being developed, being deployed, especially being pushed into production, is changing. And it's almost all for the positive, where, in the last decade, you had virtualization, but that was often through a proprietary solution that you were paying a tax for every new application you deployed. And the idea today, that you can pick this software platform and then deploy to any public, private, or hybrid cloud and avoid that lock-in, but get all these advantages in terms of higher velocity, lower cost, better efficiency, the slack of lock-in. Those are really amazing stories that lots of enterprises are just now hearing. There's this cliche of crossing the chasm. And I do think we can make the argument that 2018 is really the year that Kubernetes crosses the chasm outside of just innovators and into the early majority. >> You know, I think that's definitely the case. I've been walking around and talking to people and one of the things that I'm hearing is that folks are here to learn, and there are actually kind of beginners on Kubernetes and they actually want to learn more and their companies have sent them here in order to actually figure out if the technology is going to work back at their home company, which is, you know, ranges from tech companies to banks to different types of, you know, manufacturing and things along those lines. It's really a tremendous, you know, growth. What do you see in terms of end users? What types of end users are you seeing mostly? Or what kind of categories do those fall into? >> So we've 52 companies in our end user community now, and a number of them are up on the stage, including folks like Spotify I thought gave a really inspiring talk today about not just being a user of software, but how to engage with the community and contribute back and such. But the thing that I love is that there really is not sort of one industry that we're focused on or avoiding. So, finance who have tons of issues around regulation and such, they're much more likely to be deploying Kubernetes in their own infrastructure on bare-metal. But we have just fantastic stories. Bloomberg won our first ever end user award. We're very big on publishing, so to have not just "The New York Times", but Reddit and Wikipedia. And then a number of just very interesting consumer-oriented companies like a Pinterest or a Twitter, Spotify, and then the list sort of keeps going and going. >> Yeah, it's impressive, and I got to say, you know, you're agnostic as everyone needs plumbing, right, so plumbing is vertical agnostics. So, it's-- >> Well, in the cliche from Marc Andreessen, that software's eating the world is, again, somewhat true. That there really is not a company today that can avoid writing its own software. I mean, as I was saying in my keynote yesterday, that software tends to just be the tip of the pyramid that they're building on tons of open source. But, every company today needs to-- >> And your point of commercialization-friendly or membership organization, which you've built, is important. And I got to say, for the first time, we heard on theCUBE multiple times, not from the visionary to believe and drink the Kool-Aid, so to speak, like us and you guys and users and other commercial entities have used the word "de facto standard" to describe Kubernetes. Now, there's only a few times in history when you've heard that word. There's been inflection points. >> Dan: Linux, certainly one of them. (laughs) >> Yes so, again, when you have a de facto standard that's determined by the community, just really good things happen. So we're hopeful and we'll keep monitoring it. >> Yeah, and I do want to say that we take that responsibility very seriously. And so we have thing like our certified Kubernetes program about making sure the Kubernetes remains compatible between the carefulness that we do apply to new projects coming in, so we hope to live up to that. >> Great and, Dee, we talked yesterday, going to get that share that information with our team, happy to amplify it. There's a lot of people who want to learn, they want to discover and find out who to connect with, so a robust community. >> We really appreciate you going with us on this journey. >> It's been fun, we're going to hang along for the ride. We're going to be a sidecar, pun intended. (laughing) Well, theCUBE, Dan, thanks so much. Congratulations, executive director. >> Oh, thank you very much. >> Dee, good work. CNCF, here inside the cube at their event, here at KubeCon 2018, I'm John Furrier and Lauren Cooney. We'll be back with more live coverage. Stay with us after this short break. (techno music)

(upbeat music) >> Announcer: Live from San Jose, it's theCUBE presenting Big Data Silicon Valley brought to you by SiliconANGLE Media and its ecosystem partner. >> What am I going to spend time, next 15, 20 minutes or so, talking about. I'm going to answer three things. Our research has gone deep into where are we now in the big data community. I'm sorry, where is the big data community going, number one. Number two is how are we going to get there and number three, what do the numbers say about where we are? So those are the three things. Now, since when we want to get out of here, I'm going to fly through some of these slides but again there's a lot of opportunity for additional conversation because we're all about having conversations with the community. So let's start here. The first thing to know, when we think about where this is all going is it has to be bound. It's inextricably bound up with digital transformation. Well, what is digital transformation? We've done a lot of research on this. This is Peter Drucker who famously said many years ago, that the purpose of a business is to create and keep a customer. That's what a business is. Now what's the difference between a business and a digital business? What's the business between Sears Roebuck, or what's the difference between Sears Roebuck and Amazon? It's data. A digital business uses data as an asset to create and keep customers. It infuses data and operations differently to create more automation. It infuses data and engagement differently to catalyze superior customer experiences. It reformats and restructures its concept of value proposition and product to move from a product to a services orientation. The role of data is the centerpiece of digital business transformation and in many respects that is where we're going, is an understanding and appreciation of that. Now, we think there's going to be a number of strategic capabilities that will have to be built out to make that possible. First off, we have to start thinking about what it means to put data to work. The whole notion of an asset is an asset is something that can be applied to a productive activity. Data can be applied to a productive activity. Now, there's a lot of very interesting implications that we won't get into now, but essentially if we're going to treat data as an asset and think about how we could put more data to work, we're going to focus on three core strategic capabilities about how to make that possible. One, we need to build a capability for collecting and capturing data. That's a lot of what IoT is about. It's a lot of what mobile computing is about. There's going to be a lot of implications around how to ethically and properly do some of those things but a lot of that investment is about finding better and superior ways to capture data. Two, once we are able to capture that data, we have to turn it into value. That in many respects is the essence of big data. How we turn data into data assets, in the form of models, in the form of insights, in the form of any number of other approaches to thinking about how we're going to appropriate value out of data. But it's not just enough to create value out of it and have it sit there as potential value. We have to turn it into kinetic value, to actually do the work with it and that is the last piece. We have to build new capabilities for how we're going to apply data to perform work better, to enact based on data. Now, we've got a concept we're researching now that we call systems of agency, which is the idea that there's going to be a lot of new approaches, new systems with a lot of intelligence and a lot of data that act on behalf of the brand. I'm not going to spend a lot of time going into this but remember that word because I will come back to it. Systems of agency is about how you're going to apply data to perform work with automation, augmentation, and actuation on behalf of your brand. Now, all this is going to happen against the backdrop of cloud optimization. I'll explain what we mean by that right now. Very importantly, increasingly how you create value out of data, how you create future options on the value of your data is going to drive your technology choices. For the first 10 years of the cloud, the presumption is all data was going to go to the cloud. We think that a better way of thinking about it is how is the cloud experience going to come to the data. We've done a lot of research on the cost of data movement and both in terms of the actual out-of-pocket costs but also the potential uncertainty, the transaction costs, etc, associated with data movement. And that's going to be one of the fundamental pieces or elements of how we think about the future of big data and how digital business works, is what we think about data movement. I'll come to that in a bit. But our proposition is increasingly, we're going to see architectural approaches that focus on how we're going to move the cloud experience to the data. We've got this notion of true private cloud which is effectively the idea of the cloud experience on or near premise. That doesn't diminish the role that the cloud's going to play on industry or doesn't say that Amazon and AWS and Microsoft Azure and all the other options are not important. They're crucially important but it means we have to start thinking architecturally about how we're going to create value of data out of data and recognize that means that it, we have to start envisioning how our organization and infrastructure is going to be set up so that we can use data where it needs to be or where it's most valuable and often that's close to the action. So if we think then about that very quickly because it's a backdrop for everything, increasingly we're going to start talking about the idea of where's the workload going to go? Where's workload the dog going to be against this kind of backdrop of the divorce of infrastructure? We believe that and our research pretty strongly shows that a lot of workloads are going to go to true private cloud but a lot of big data is moving into the cloud. This is a prediction we made a few years ago and it's clearly happening and it's underway and we'll get into what some of the implications are. So again, when we say that a lot of the big data elements, a lot of the process of creating value out of data is going to move into the cloud. That doesn't mean that all the systems of agency that build or rely on that data, the inference engines, etc, are also in a public cloud. A lot of them are going to be distributed out to the edge, out to where the action needs to be because of latency and other types of issues. This is a fundamental proposition and I know I'm going fast but hopefully I'm being clear. All right, so let's now get to the second part. This is kind of where the industry's going. Data is an asset. Invest in strategic business capabilities to appreciate, to create those data assets and appreciate the value of those assets and utilize the cloud intelligently to generate and ensure increasing returns. So the next question is well, how will we get there? Now. Right now, not too far from here, Neil Raden for example, was on the show floor yesterday. Neil made the observation that, as he wandered around, he only heard the word big data two or three times. The concept of big data is not dead. Whether the term is or is not is somebody else's decision. Our perspective, very simply, is that the notion is bifurcating. And it's bifurcating because we see different strategic imperatives happening at two different levels. On the one hand, we see infrastructure convergence. The idea that increasingly we have to think about how we're going to bring and federated data together, both from a systems and a data management standpoint. And on the other hand, we're going to see infrastructure or application specialization. That's going to have an enormous implication over next few years, if only because there just aren't enough people in the world that understand how to create value out of data. And there's going to be a lot of effort made over the next few years to find new ways to go from that one expertise group to billions of people, billions of devices, and those are the two dominant considerations in the industry right now. How can we converge data physically, logically, and on the other hand, how can we liberate more of the smarts associated with this very, very powerful approach so that more people get access to the capacities and the capabilities and the assets that are being generated by that process. Now, we've done at Wikibon, probably I don't know, 18, 20, 23 predictions overall on the role that or on the changes being wrought by digital business. Here I'm going to focus on four of them that are central to our big data research. We have many more but I'm just going to focus on four. The first one, when we think about infrastructure convergence we worry about hardware. Here's a prediction about what we think is going to happen with hardware and our observation is we believe pretty strongly that future systems are going to be built on the concept of how do you increase the value of data assets. The technologies are all in place. Simpler parts that it more successfully bind specifically through all its storage and network are going to play together. Why, because increasingly that's the fundamental constraint. How do I make data available to other machines, actors, sources of change, sources of process within the business. Now, we envision or we are watching before our very eyes, new technologies that allow us to take these simple piece parts and weave them together in very powerful fabrics or grids, what we call UniGrid. So that there is almost no latency between data that exists within one of these, call it a molecule, and anywhere else in that grid or lattice. Now again, these are not systems that are going to be here in five years. All the piece parts are here today and there are companies that are actually delivering them. So if you take a look at what Micron has done with Mellanox and other players, that's an example of one of these true private cloud oriented machines in place. The bottom line though is that there is a lot of room left in hardware. A lot of room. This is what cloud suppliers are building and are going to build but increasingly as we think about true private cloud, enterprises are going to look at this as well. So future systems for improving data assets. The capacity of this type of a system with low latency amongst any source of data means that we can now think about data not as... Not as a set of sources that have to be each individually, each having some control over its own data and sinks woven together by middleware and applications but literally as networks of data. As we start to think about distributing data and distributing control and authority associated with that data more broadly across systems, we now have to think about what does it mean to create networks of data? Because that, in many respects, is how these assets are going to be forged. I haven't even mentioned the role that security is going to play in all of this by the way but fundamentally that's how it's likely to play out. We'll have a lot of different sources but from a business standpoint, we're going to think about how those sources come together into a persistent network that can be acted upon by the business. One of the primary drivers of this is what's going on at the edge. Marc Andreessen famously said that software is eating the world, well our observation is great but if software's eating the world, it's eating it at the edge. That's where it's happening. Secondly, that this notion of agency zones. I said I'm going to bring that word up again, how systems act on behalf of a brand or act on behalf of an institution or business is very, very crucial because the time necessary to do the analysis, perform the intelligence, and then take action is a real constraint on how we do things. And our expectation is that we're going to see what we call an agency zone or a hub zone or cloud zone defined by latency and how we architect data to get the data that's necessary to perform that piece of work into the zone where it's required. Now, the implications of this is none of this is going to happen if we don't use AI and related technologies to increasingly automate how we handle infrastructure. And technologies like blockchain have the potential to provide a interesting way of imagining how these networks of data actually get structured. It's not going to solve everything. There's some people that think the blockchain is kind of everything that's necessary but it will be a way of describing a network of data. So we see those technologies on the ascension. But what does it mean for DBMS? In the old way, in the old world, the old way of thinking, the database manager was the control point for data. In the new world these networks of data are going to exist beyond a single DBMS and in fact, over time, that concept of federated data actually has a potential to become real. When we have these networks of data, we're going to need people to act upon them and that's essentially a lot of what the data scientist is going to be doing. Identifying the outcome, identifying the data that's required, and weaving that data through the construction and management, manipulation of pipelines, to ensure that the data as an asset can persist for the purposes of solving a near-term problem or over whatever duration is required to solve a longer term problem. Data scientists remain very important but we're going to see, as a consequence of improvements in tooling capable of doing these things, an increasing recognition that there's a difference between a data scientist and a data scientist. There's going to be a lot of folks that participate in the process of manipulating, maintaining, managing these networks of data to create these business outcomes but we're going to see specialization in those ranks as the tooling is more targeted to specific types of activities. So the data scientist is going to become or will remain an important job, going to lose a little bit of its luster because it's going to become clear what it means. So some data scientists will probably become more, let's call them data network administrators or networks of data administrators. And very importantly as I said earlier, there's just not enough of these people on the planet and so increasingly when we think about again, digital business and the idea of creating data assets. A central challenge is going to be how to create the data or how to turn all the data that can be captured into assets that can be applied to a lot of different uses. There's going to be two fundamental changes to the way we are currently conceiving of the big data world on the horizon. One is well, it's pretty clear that Hadoop can only go so far. Hadoop is a great tool for certain types of activities and certain numbers of individuals. So Hadoop solves problems for an important but relatively limited subset of the world. Some of the new data science platforms that we just talked about, that I just talked about, they're going to help with a degree of specialization that hasn't been available before in the data world, will certainly also help but it also will only take it so far. The real way that we see the work that we're doing, the work that the big data community is performing, turned into sources of value that extend into virtually every single corner of humankind is going to be through these cloud services that are being built and increasingly through packaged applications. A lot of computer science, it still exists between what I just said and when this actually happens. But in many respects, that's the challenge of the vendor ecosystem. How to reconstruct the idea of packaged software, which has historically been built around operations and transaction processing, with a known data model and an unknown or the known process and some technology challenges. How do we reapply that to a world where we now are thinking about, well we don't know exactly what the process is because the data tells us at the moment that the actions going to be taking place. It's a very different way of thinking about application development. A very different way of thinking about what's important in IT and very different way of thinking about how business is going to be constructed and how strategy's going to be established. Packaged applications are going to be crucially important. So in the last few minutes here, what are the numbers? So this is kind of the basis for our analysis. Digital business, role of data is an asset, having an enormous impact in how we think about hardware, how do we think about database management or data management, how we think about the people involved in this, and ultimately how we think about how we're going to deliver all this value out to the world. And the numbers are starting to reflect that. So why don't you think about four numbers as I go through the two or three slides. Hundred and three billion, 68%, 11%, and 2017. So of all the numbers that you will see, those are four of the most important numbers. So let's start by looking at the total market place. This is the growth of the hardware, software, and services pieces of the big data universe. Now we have a fair amount of additional research that breaks all these down into tighter segments, especially in software side. But the key number here is we're talking about big numbers. 103 billion over the course of next 10 years and let's be clear that 103 billion dollars actually has a dramatic amplification on the rest of the computing industry because a lot of the pricing models associated with, especially the software, are tied back to open source which has its own issues. And very importantly, the fact that the services business is going to go through an enormous amount of change over the next five years as service companies better understand how to deliver some of these big data rich applications. The second point to note here is that it was in 2017 that the software market surpassed the hardware market in big data. Again, for first number of years we focused on buying the hardware and the system software associated with that and the software became something that we hope to discover. So I was having a conversation here in theCUBE with the CEO of Transwarp which is a very interesting Chinese big data company and I asked what's the difference between how you do things in China and how we do things in the US? He said well, in the US you guys focus on proof of concept. You spend an enormous amount of time asking, does the hardware work? Does the database software work? Does the data management software work? In China we focus on the outcome. That's what we focus on. Here you have to placate the IT organization to make sure that everybody in IT is comfortable with what's about to happen. In China, were focused on the business people. This is the first year that software is bigger than hardware and it's only going to get bigger and bigger over time. It doesn't mean again, that hardware is dead or hardware is not important. It's going to remain very important but it does mean that the centerpiece of the locus of the industry is moving. Now, when we think about what the market shares look like, it's a very fragmented market. 60%, 68% of the market is still other. This is a highly immature market that's going to go through a number of changes over the next few years. Partly catalyzed by that notion of infrastructure convergence. So in four years our expectation is that, that 68% is going to start going down pretty fast as we see greater consolidation in how some of these numbers come together. Now IBM is the biggest one on the basis of the fact that they operate in all these different segments. They operating the hardware, software, and services segment but especially because they're very strong within the services business. The last one I want to point your attention to is this one. I mentioned earlier on, that our expectation is that the market increasingly is going to move to a packaged application orientation or packaged services orientation as a way of delivering expertise about big data to customers. Splunk is the leading software player right now. Why, because that's the perspective that they've taken. Now, perhaps we're a limited subset. It's perhaps for a limited subset of individuals or markets or of sectors but it takes a packaged application, weaves these technologies together, and applies them to an outcome. And we think this presages more of that kind of activity over the course of the next few years. Oracle, kind of different approach and we'll see how that plays out over the course of the next five years as well. Okay, so that's where the numbers are. Again, a lot more numbers, a lot of people you can talk to. Let me give you some action items. First one, if data was a core asset, how would IT, how would your business be different? Stop and think about that. If it wasn't your buildings that were the asset, it wasn't the machines that were the asset, it wasn't your people by themselves who were the asset, but data was the asset. How would you reinstitutionalize work? That's what every business is starting to ask, even if they don't ask it in the same way. And our advice is, then do it because that's the future of business. Not that data is the only asset but data is a recognized central asset and that's going to have enormous impacts on a lot of things. The second point I want to leave you with, tens of billions of users and I'm including people and devices, are dependent on thousands of data scientists that's an impedance mismatch that cannot be sustained. Packaged apps and these cloud services are going to be the way to bridge that gap. I'd love to tell you that it's all going to be about tools, that we're going to have hundreds of thousands or millions or tens of millions or hundreds of millions of data scientists suddenly emerge out of the woodwork. It's not going to happen. The third thing is we think that big businesses, enterprises, have to master what we call the big inflection. The big tech inflection. The first 50 years were about known process and unknown technology. How do I take an accounting package and do I put on a mainframe or a mini computer a client/server or do I do it on the web? Unknown technology. Well increasingly today, all of us have a pretty good idea what the base technology is going to be. Does anybody doubt it's going to be the cloud? We got a pretty good idea what the base technology is going to be. What we don't know is what are the new problems that we can attack, that we can address with data rich approaches to thinking about how we turn those systems into actors on behalf of our business and customers. So I'm a couple minutes over, I apologize. I want to make sure everybody can get over to the keynotes if you want to. Feel free to stay, theCUBE's going to be live at 9:30. If I got that right. So it's actually pretty exciting if anybody wants to see how it works, feel free to stay. Georgia's here, Neil's here, I'm here. I mentioned Greg Terrio, Dave Volante, John Greco, I think I saw Sam Kahane back in the corner. Any questions, come and ask us, we'll be more than happy. Thank you very much for, oh David Volante. >> David: I have a question. >> Yes. >> David: Do you have time? >> Yep. >> David: So you talk about data as a core asset, that if you look at the top five companies by market cap in the US, Google, Amazon, Facebook, etc. They're data companies, they got data at the core which is kind of what your first bullet here describes. How do you see traditional companies closing that gap where humans, buildings, etc at the core as we enter this machine intelligence era, what's your advice to the traditional companies on how they close that gap? >> All right. So the question was, the most valuable companies in the world are companies that are well down the path of treating data as an asset. How does everybody else get going? Our observation is you go back to what's the value proposition? What actions are most important? what's data is necessary to perform those actions? Can changing the way the data is orchestrated and organized and put together inform or change the cost of performing that work by changing the cost transactions? Can you increase a new service along the same lines and then architect your infrastructure and your business to make sure that the data is near the action in time for the action to be absolute genius to your customer. So it's a relatively simple thought process. That's how Amazon thought, Apple increasingly thinks like that, where they design the experience and they think what data is necessary to deliver that experience. That's a simple approach but it works. Yes, sir. >> Audience Member: With the slide that you had a few slides ago, the market share, the big spenders, and you mentioned that, you asked the question do any of us doubt that cloud is the future? I'm with Snowflake, I don't see many of those large vendors in the cloud and I was wondering if you could speak to what are you seeing in terms of emerging vendors in that space. >> What a great question. So the question was, when you look at the companies that are catalyzing a lot of the change, you don't see a lot of the big companies being at the leadership. And someone from Snowflake just said, well who's going to lead it? That's a big question that has a lot of implications but at this point time it's very clear that the big companies are suffering a bit from the old, from the old, trying to remember what the... RCA syndrome. I think Clay Christensen talked about this. You know, the innovators dilemma. So RCA actually is one of the first creators. They created the transistor and they held a lot of original patents on it. They put that incredible new technology, back in the forties and fifties, under the control of the people who ran the vacuum tube business. When was the last time anybody bought RCA stock? The same problem is existing today. Now, how is that going to play out? Are we going to see a lot of, as we've always seen, a lot of new vendors emerge out of this industry, grow into big vendors with IPO related exits to try to scale their business? Or are we going to see a whole bunch of gobbling up? That's what I'm not clear on but it's pretty clear at this point in time that a lot of the technology, a lot of the science, is being done in smaller places. The moderating feature of that is the services side. Because there's limited groupings of expertise that the companies that today are able to attract that expertise. The Googles, the Facebooks, the AWSs, etc, the Amazons. Are doing so in support of a particular service. IBM and others are trying to attract that talent so they can apply it to customer problems. We'll see over the next few years whether the IBMs and the Accentures and the big service providers are able to attract the kind of talent necessary to diffuse that knowledge into the industry faster. So it's the rate at which that the idea of internet scale computing, the idea of big data being applied to business problems, can diffuse into the marketplace through services. If it can diffuse faster that will have both an accelerating impact for smaller vendors, as it has in the past. But it may also again, have a moderating impact because a lot of that expertise that comes out of IBM, IBM is going to find ways to drive in the product faster than it ever has before. So it's a complicated answer but that's our thinking at this point time. >> Dave: Can I add to that? >> Yeah. (audience member speaking faintly) >> I think that's true now but I think the real question, not to not to argue with Dave but this is part of what we do. The real question is how is that knowledge going to diffuse into the enterprise broadly? Because Airbnb, I doubt is going to get into the business of providing services. (audience member speaking faintly) So I think that the whole concept of community, partnership, ecosystem is going to remain very important as it always has and we'll see how fast those service companies that are dedicated to diffusing knowledge, diffusing knowledge into customer problems actually occurs. Our expectation is that as the tooling gets better, we will see more people be able to present themselves truly as capable of doing this and that will accelerate the process. But the next few years are going to be really turbulent and we'll see which way it actually ends up going. (audience member speaking faintly) >> Audience Member: So I'm with IBM. So I can tell you 100% for sure that we are, I hired literally 50 data scientists in the last three months to go out and do exactly what you're saying. Sit down with clients and help them figure out how to do data science in the enterprise. And so we are in fact scaling it, we're getting people that have done this at Google, Facebook. Not a whole lot of those 'cause we want to do it with people that have actually done it in legacy fortune 500 Companies, right? Because there's a little bit difference there. >> So. >> Audience Member: So we are doing exactly what you said and Microsoft is doing the same thing, Amazon is actually doing the same thing too, Domino Data Lab. >> They don't like they're like talking about it too much but they're doing it. >> Audience Member: But all the big players from the data science platform game are doing this at a different scale. >> Exactly. >> Audience Member: IBM is doing it on a much bigger scale than anyone else. >> And that will have an impact on ultimately how the market gets structured and who the winners end up being. >> Audience Member: To add too, a lot of people thought that, you mentioned the Red Hat of big data, a lot of people thought Cloudera was going to be the Red Hat of big data and if you look at what's happened to their business. (background noise drowns out other sounds) They're getting surrounded by the cloud. We look at like how can we get closer to companies like AWS? That was like a wild card that wasn't expected. >> Yeah but look, at the end of the day Red Hat isn't even the Red Hat of open source. So the bottom line is the thing to focus on is how is this knowledge going to diffuse. That's the thing to focus on. And there's a lot of different ways, some of its going to diffuse through tools. If it diffuses through tools, it increases the likelihood that we'll have more people capable of doing this in IBM and others can hire more. That Citibank can hire more. That's an important participant, that's an important play. So you have something to say about that but it also says we're going to see more of the packaged applications emerge because that facilitates the diffusion. This is not, we haven't figured out, I don't know exactly, nobody knows exactly the exact shape it's going to take. But that's the centerpiece of our big data researches. How is that diffusion process going to happen, accelerate, and what's the resulting structure going to look like? And ultimately how are enterprises going to create value with whatever results. Yes, sir. (audience member asks question faintly) So the recap question is you see more people coming in and promising the moon but being incapable of delivering because they are, partly because the technology is uncertain and for other reasons. So here's our approach. Or here's our observation. We actually did a fair amount of research on this. When you take a look at what we call a approach to doing big data that's optimized for the costs of procurement i.e. let's get the simplest combination of infrastructure, the simplest combination of open-source software, the simplest contracting, to create that proof of concept that you can stand things up very quickly if you have enough expertise but you can create that proof of concept but the process of turning that into actually a production system extends dramatically. And that's one of the reasons why the Clouderas did not take over the universe. There are other reasons. As George Gilbert's research has pointed out, that Cloudera is spending 53, 55 % of their money right now just integrating all the stuff that they bought into the distribution five years ago. Which is a real great recipe for creating customer value. The bottom line though is that if we focus on the time to value in production, we end up taking a different path. We don't focus as much on whether the hardware is going to work and the network is going to work and the storage can be integrated and how it's going to impact the database and what that's going to mean to our Oracle license pool and all the other things that people tend to think about if they're focused on the technology. And so as a consequence, you get better time to value if you focus on bringing the domain expertise, working with the right partner, working with the appropriate approach, to go from what's the value proposition, what actions are associated with a value proposition, what's stated in that area to perform those actions, how can I take transaction costs out of performing those actions, where's the data need to be, what infrastructure do I require? So we have to focus on a time to value not the time to procure. And that's not what a lot of professional IT oriented people are doing because many of them, I hate say it, but many of them still acquire new technology with the promise to helping the business but having a stronger focus on what it's going to mean to their careers. All right, I want to be really respectful to everybody's time. The keynotes start in about five minutes which means you just got time. If you want to stay, feel free to stay. We'll be here, we'll be happy to talk but I think that's pretty much going to close our presentation broadcast. Thank you very much for being an attentive audience and I hope you found this useful. (upbeat music)

>> Narrator: Live from Nice, France It's theCUBE covering .NEXT Conference 2017 Europe Brought to you by Nutanix. (electronic music) >> Hi, I'm Stu Miniman and here with SiliconANGLE Media's exclusive coverage of theCUBE live from Nutanix's .NEXT conference here in Nice, France. It's the fifth Nutanix conference. theCUBE has had the pleasure of broadcasting from all five of them. It's the second annual European show. Over 2,200 in attendance here. We're in the Acropolis, which is a little ironic because, of course, Acropolis is one of the product names of Nutanix. To help me with the introduction today, happy to have Martin Veitch... Is the Contributing Editor of IDG Connect. Martin, thank you so much for joining us. >> My pleasure. >> Alright, so, Nutanix. It's a year after they IPO'd. I've been tracking them since they were a very small company. I think a friend of mine was somewhere between the number 20 and 30 employee in there. They now have 2,800 employees worldwide. Talked about, they have, you know, thousands of Nutanix certified, you know, people just in Europe alone between the employees, the partners, and the customers. You know, what's the vibe been for you so much? Tell us, you know, bring us in for the Nutanix show. >> Yeah, like you, I followed them from pretty much the early days. I always thought they were a hot-to-trot, you know. They were an exciting company back in the day. The narrative made a lot of sense. It looked like they were a company very capable of executing. They seemed to have great management. What really surprises me is, if anything, you know, in this business we have a habit of, you know, overdoing it and praising these people to the skies and saying "this is the next big thing." I think these guys really undersell themselves sometimes. To me, you know, the Goldman Sachs line that Dheeraj Pandey, the CEO, used earlier on when he was talking about the Goldman Sachs comment that it was a once-in-a-decade opportunity, to me, the company they remind me of a lot these days is VMware. I think, you know, that's a company they're going to work with, go up against, and they remind me a lot of that infrastructure revolution kind of play, you know. >> Yeah, absolutely, and I think Nutanix would like that analogy, because number one, >> I hope so. >> I love the line, they did a little song at the intro with the clapping and everything. >> Yeah that was pretty wacky, wasn't it? >> They have a little fun, they've got a fun culture. Dheeraj always says they try to be humble. From a marketing, from a sales, sometimes a little aggressive, but you need that to kind of break in to the enterprise space. But they said, in the song, they said "We used to sell boxes, now it's all about the software you know" You know, so what they've been pounding on is, it's one OS, one click, any cloud. So the question I've been asking at all of these events I go to this year is, you talk to customers, it's a choose your pick, hybrid or multi-cloud world, but how do you live in that environment? You're absolutely, you know, customers, they doing lots of SASS, they're doing Amazon, they're doing things with Microsoft or Google, and if you just live in the data center you're limiting where you're going to play. If you're just, you know, the public cloud is obviously lots of growth. Nutanix is trying to fit in all these other environments, as they said many people when they first saw them, was like, "Oh, well they sell you an appliance that goes into your data center? That's not all that interesting." They positioned themselves as enterprise cloud. What do you take, the message in, you know, they said, you know, hyper-converge was kind of the baseline, but I don't think I even heard that word in the keynote this morning. >> I was going to say the same thing >> It's now clouds, so... >> Yeah, enterprise cloud, which isn't a tag I'm particularly fond of, I must admit, but you can see what the appeal is, right? I mean, people are going to build these, they're going to have these data canters on premise. They're going to have private clouds, going to have public clouds. They're going to go for data center co-location, and what you really need is a layer of management, a layer that sits over there. So I think what they're building is something analogous to the systems management frameworks that we saw back in the day for the multi-cloud era, and really, that adds such another arrow to the quiver, and that's why I say, you know, you look at the stock price on this one and you kind of wonder whether they're under-priced in a way, you know, or whether people realize quite what the power they potentially yield is, you know. Obviously they're going to go up against some of the world's largest organizations, but I think it's going to be an extraordinarily ambitious and bullish play. Yeah, absolutely, I think it's a really fascinating story. >> Yeah, well, top line revenue Nutanix now sitting right around a billion dollars on an annual basis and from a market cap, talk about the stocks undervalued, they're still over four billion dollars in revenue. Kind of, you know, if you look at the similar compare company that, you know, Pure Storage, Nutanix now has about the same revenue but, you know, higher marker cap, so, you know, they're doing okay. But as they are trying to emphasize, and I think your point, I would agree with you, it is early still. This is not the final Nutanix. CloudPlay at the DC show made a big announcement with Google, and starting to see some of that come to fruition here at the show, and a big push of theirs is their Calm. Calm really is that layer that's going to live in the multi-cloud. It's still, most customers haven't touched it or really seen more than kind of some slides and demo. I did talk to a couple of customers already that have used it, and at least the early customers, of course heavily involved, it's a little bit self-selecting when you come to an event like this, but excited about how that is, you know, can be that layer that spans between my various environments, whether that be my core, the public cloud, or potentially even the edge. They did an example in the keynote of an oil and gas going out to the rigs. So, you know, you think the Nutanix, you know, if we look to a year from now, when I think multi-cloud is Nutanix a company that comes to mind? >> Absolutely, I've just thought of this, so tell me if you like it or not, but they've kind of gone from stack to PAC, okay. So, hyper-convergence was the play where you would conflate compute networking storage et cetera, and really this combination of Prism, Acropolis and Calm is a whole other level. And you know, again, they didn't really hammer it with the audience today, but they're moving to also a very much a software-centric view of the world. You know, and that was always the question that people like me would ask of them, "Hey, why do you bother having the appliances? Why do you have the hardware cell when, you know, software is the high-margin kind of business in technology?" And "software is eating the world" as Marc Andreessen said. And now I think they're really pivoting towards being very much a software-centric company and flying the flag for that, you know, and I think that whole combination of management layers, of virtualization, of orchestration that they have is exactly what the sweet spot is in the future of enterprise software management. >> Yeah, I've heard some companies talk about the "new stack" and you took their products and P, A, C >> See what I did? >> I do, I think maybe the marketing organization, you know, give you a call, see if they can leverage that. >> 500 bucks. >> So, you know, we've got two days of the show coming up here. Absolutely the kind of cloud story is one that I'm looking to tease apart and talk to the customers. Since I've already had a chance to talk to some customers and it's very much a spectrum. You talk to some customers, especially here in Europe, you go to Germany and it's like well, you know governage, regulation, yeah a public cloud might not be something that they can do because we have to dig into it. >> Yeah >> As opposed to, there's a customer giving a presentation today that, very much, they said everything was going to be public cloud, but they found even when they tried to put everything either in SASS or, like, infrastructures of service with Amazon, there were certain things that, well, in certain countries I just don't have the networking or it was going to be too expensive. >> Yeah. >> So I need to put something in my own data center, and that's where Nutanix has been a fit for them, so it's that good story, as they said, "Where is the center?" and Nutanix being a softer play, it's not about, "Oh I have to sell, you know, thousands and millions of boxes", and even, I've read financial reports that there have been hints from Nutanix that you've said, "Why do they offer the appliances?" Well maybe in the future they won't. It will be through a partner and they'll do that. You need to qualify it, but, you know, absolutely position themselves. They are the, you know, enterprise, you know, software company is what they want to play. Infrastructure is a piece of it. >> Yeah, you're absolutely right. I mean, you've, we've both been around the block a few times. When I started writing about this business, people used to say, "Well, mainframes, they're the dinosaurs who are about to fall off the edge of a cliff." People are still buying a lot of mainframes now. Look at IBM's revenue sheet, a lot of that's mainframe-centric. So I think you're absolutely right. People are going to persist putting stuff close their vest in internal data centers, and they're going to selectively source in various different types of cloud. And you're right, governance is a big one over here in Europe, you know GDPR is a thing that scares all the CIO's and CEO's, for that matter, witless, you know. So they're all terrified of that one PSD2 and payments. So when you have these regulatory landscapes, you know, there's a tendency to be very cautious, very calm, and keep it behind the firewall, and you know, I think probably as long as I live, God willing, you know, we're going to see this combination of deployment models. >> Yeah, GDPR absolutely something we're going to be talking about. Nutanix actually has a couple of experts here talking to customers >> Good. >> As to how they play into it, because that's a question I've had for Nutanix, is, okay, they have kind of their core focus but as they start to go in adjacencies, you know we see companies all the time, alright, I've reached a certain level and then how do I get a little bit further, and how do I have a reason to play into those environments. You know, Nutanix says push into IOT. Nutanix is not the first company that I think of, you know, they don't make sensors, they're not a GE, even Hitachi Vantara has arms that play there so, you know, Satcham Vigani, they've got a small team working on that. So, you want a company of Nutanix' size to start, right, poking out, but where will they be successful and where will they gain traction? Anything catching your eye or interest from Nutanix as they go kind of beyond, you know, kind of the core kind of infrastructure status? >> I think it's a management layer. You know, very similar, I guess, VMware initially was known for their hypervisor and then later on they were really tooling around that to become the control pane, you know, the command center of the data center. That's where I see them. You know, frankly Stu, I'd be pretty worried if they'd made a lot of noise on, I don't know, virtual reality, augmented reality in the net of things, you know. I think they, to a certain extent, can be still have to stick to the netting, and this is a company that's very much geared around being the 21st century data center nexus, and for me, that's where the real value is, and that is a multi multi multi billion dollar segment in its own right. >> Yeah, a big question I have this week, as always, is, you know, what are the relationships that are going to help Nutanix, you know, move further. One that we always look at is the Dell relationship. >> Sure. >> Dell is their largest partner, but also their largest competitor between the VXrail that they're doing, all the Vsan pieces. I'm interested to see IBM up on stage. The power announcement is one that I don't think a lot of people really understand, how that fits. You know, Bumpage Yano was talking about, you know, AI and all of those pieces. Of course, you know, Lenovo, another hardware partner, so, you know. What are the partners that are going to drive them? Which are they, you know, what's the headwinds, what are the tailwinds as they go. Anything from the partner standpoint that you're looking into? >> Well one of the ways, you know, I guess we all try to judge companies is by the company they keep. >> Yes. >> And they've got some nice partners, as you said. The complicated one is a lot of co-optition and frenemy-type stuff going on. It's a bit like Game of Thrones-type complexity of scenario there, you know? Behind the scenes is Dell telling it's sales guys to sell this rather than this and what do they do to objection handling and are they going to eventually try and stitch up Nutanix? I don't know, I think, my feeling is now companies are mature enough that if they can get significant revenues and please the customer, then that's probably the way to go. And you know, those are big, big names and those are companies that you might think would have a history of wanting to do their own thing and go their own way, but they're not. They're going with Nutanix because, you know, it's a USP. That's a unique selling point, and it's a high-quality product, and the customers are very happy. Very high net promoter score, which was an interesting little aspect, you know, a 90+ year after year, clocking at that. You speak to the customers here, they're a happy crowd. You know, you can't say that at every enterprise IT conference, I promise you. >> Yeah, absolutely, it's the channel partners and the customers. Every single one of these events I've come to, this one's a little bit self-selecting, but the people are super excited, digging into it. Alright, Martin, why don't I give you the final word. Things you're looking into, any kind of undercurrent, you know, that we should be aware of. What should Nutanix be concerned about, or people that are looking at it? >> The one thing I would say that would be kind of a risk factor, if you are saying you're reporting into the financial markets and so on is, you know, as I said, they're really up against some of the world's largest organizations here. You know, there's a lot of very, very big companies with skin in the game. And, you know, it depends. They could flip and get much more aggressive. They could decide to go their own way. They could make strategic acquisitions. We saw HPE buying Simplivity, and maybe that would be an interesting turn in the market, but I think they're sat fair for quite a while. Now, I think they've become part of the data center landscape rather than the disruptor. I think they're now part of the status quo in a good way, anyway. >> Yeah, last year they made, you know, it was one or two small software acquisitions >> Yeah. >> That's where we would expect, you know, Nutanix to make those. Alright, well, Martin Veitche, really appreciate you helping me kick off. >> Pleasure, Stu. >> We've got two days of coverage here at the Acropolis in Nice, France. Be sure to stay with us. I have the executives on, customers, and the partners. I'm Stu Miniman here with Martin. Thank you so much for watching theCUBE.

(light adventurous music) >> Hello everyone, welcome to theCUBE Studios here in Palo Alto. I'm John Furrier, the co-host of theCUBE, co-founder of SiliconANGLE Media. This is our CUBE Conversation Thought Leader Thursday and I'm here with Val Bercovici, who's the founder and CEO of a new startup called PencilDATA. Val, CUBE alumni, been on many times with NetApp and then a variety of other great startups, but now you're doing your own thing around cryptocurrency, blockchain, enterprise-like technical infrastructure. You've been a CTO, now entrepreneur, founder and CEO of PencilDATA. Congratulations, you're on the crypto wave, this wave is coming. >> I believe it's here. >> It's here. >> Timing couldn't be better. >> So, I interviewed Dr. Jian Wang who's the chairman of Alibaba's technology steering committee, also the founder of Alibaba Cloud, just recently in China. Presented by Intel, plug for Intel there, thanks Intel for supporting theCUBE. He said to me, and I put the clip out on Twitter, natively on the video clip, which was, I asked him about blockchain, you know China, they blocked the ICOs, he said, "Blockchain is fundamental, part of the Internet. "It's as fundamental as TCP/IP was." This is the nuance that is attracting a lot of tier one entrepreneurs. Obviously the money side is hyped up beyond all recognition right now. As Don Klein on our team was saying, "It's melting up in terms of hype." But this really speaks to the transformation of the web, and the Internet now, the web is the Internet, from distributed and decentralized. This is a big sea change. Kind of building on the fundamentals of the internet, formerly called the information superhighway, before the web came along, but the web was designed to withstand nuclear disaster, be resilient, be decentralized. >> It reminds me of Back to the Future in many, many ways, because if you're as old as we are, you remember those DARPA origins of the Internet and exactly that decentralized nature, and we've gone away from that, right? As Tim Berners-Lee brought on the HTTP protocol, we've had web protocols, and as major, the FANG vendors have really dominated their usage of that existing layer of technology we've gone away, we've gone to a very, very centralized approach, which as we're seeing with the tech hearings this week, carries all sorts of risks, it's not just business and legal and political. >> And you're referring to the senate hearings, where Facebook, Google, or Alphabet, and Twitter were in front of the senate committee, you're going to tell them about the Russians, the Russian political thing, but they're bringing up the issue of the role of these mega platforms that have all this data and the problem is that this is not what the users bargained for. I mean, I use Facebook as a free app, I love Facebook, Facebook, we love you, WhatsApp here and there, and Instagram, but you know, my bargain was simple. I'll use your free app and I'll let you use some of my data but now you're making billions, $10 billion quarter, fake news has infiltrated the country, I have a poor user experience every day, it's getting worse and worse, a lot of hate and division. This is not what I bargained for. >> Val: Exactly. >> So the world's kind of revolting against these mega-siloed platforms. >> That's the risk of having such centralized control of the technology. If you remember in the old days when Microsoft's dominance was rising, all you had to do was target Windows as a virus platform and you're able to impact thousands of businesses, even in the early Internet days, within hours. And it's the same thing happening right now, there's a weaponization of these social media platforms and Google's search engine technology and so forth. It's the same side effect now, the centralization of that control is the problem. One of the reasons I love the blockstack technology, and blockchain in general is the ability to decentralize these things right now, and the most passionate thing I care about nowadays is being driven out of Europe, where they have a lot more maturity in terms of handling these new scenarios. >> You mean the tech being driven out of Europe. >> The laws. >> The laws, okay. >> Being driven out of Europe. >> Be specific, we'd like an example. >> The major deadline that's coming up in May 25th of 2018 is GDPR, General Data Protection Regulation, where European citizens now in any company, American or otherwise, catering to European citizens, has to respond to things like the right to be forgotten request. You've got 24 hours, as a global corporation with European operations, to respond to European citizens', EU citizens', right to be forgotten request, where all the personally identifiable information, the PII, has to be removed and an audit trail, proving it's been removed, has to be gone from two, three hundred internal systems within 24 hours. And this has teeth by the way, it's not like the $2.7 billion fine that Google just flipped away casually, this has up to 4% of your global profits per incident where you don't meet that requirement. >> Well you bring up a good point, the GDPR is a good one, it has teeth and it's kind of in the weeds with the folks who might not know that regulation, but really it's about the privacy and the rights of the individual. But coming back to Facebook, to connect another dot is, what we're seeing with Facebook, Twitter, and Alphabet with the senate hearings is, and this is why the industry and the media is crumbling, publications are dying, the newspapers, the media's changing, is because knowing your customer is a really important thing. The people who want to be served need to have a closed loop with the publication, and these platforms are bogarting all the data, and so the right of the customer, the users are suffering, and that's what people are generally talking about. You know, personally, a guy can rent a truck and go mow people down in Manhattan, we should know who these people are, like the neighbors, so I think there's going to be a trend towards knowing who your neighbor is, knowing who the customers are, at a level that's not scary privacy violation, but we're going to know who the crazies are, we're going to know what's going on and then that's kind of out there, that's kind of my general feeling. But now, getting back to the impact. GDPR, these big mega platforms where the users are at the center of the value proposition, really comes down to the shift in user expectations around a decentralized Internet. That means agile goes to a whole other level. If I'm a user and I say, "Hey Facebook, "delete my digital exhaust or digital footprints "from Facebook over the past 10 years." I mean, that's hard to do. >> That's hard for them. >> That's not, technically is a really serious problem. >> And it's actually not just a technology challenge, I always love to go back to Conway's Law in these discussions, the org chart, you know, how information, infrastructure is budgeted for, and managed through various different departments within any large enterprise, data-savvy or not, is a challenge, as is coordinating these efforts, actually going beyond the talking phase, towards implementing a master data model. Those are the main challenges right now, and it's a movement that I believe now has political strength to actually migrate across the pond. Over here as well there's a groundswell movement called Digital Sovereignty as a response to GDPR in Europe, where people are realizing that they have the right to be sovereign over their data, their digital exhaust, their digital footprints online and that's a two-way street. You want and demand control over your data, but on the other hand your identity, which you control, has to be authentic as opposed to a fake identity, and your reputation has to be out there as well. >> These signals and these trends you were just referring to, to me are just like little tremors of the tectonic plates that are going to be changing, because if you look at the major shift in technology, let's take blockchain for instance, and look at the impact of a decentralized internet, now global, immutability with the ability now for more agile capability and not just permanent, "I want to erase things" that you're talking about, but three, the younger generation, if we look at what the young kids are doing, I have four kids, my oldest is 22, it's a gaming culture, right? It's a gaming culture, they're online all the time. They're not old like us, my son's like, "Dad, Google Search is for old people." I mean, that's a general sentiment, over-categorizing, but a combination of the new user experience, this younger generation, entrepreneurs and users, and these tremors we're seeing in the marketplace, signaling that, "hey Facebook, you might be too big for your britches," or, "hey Twitter, you got a bot problem, "hey all you gamers using Twitch," this is now a signal, where is it leading to? And where does blockchain in particular impact it? Because this is kind of where everything's converging to. >> So what I'd like to say right now is, you've got Marc Andreessen's premise that software is eating the world. If you extend that, data is feeding it, blockchain is valuing it, and it's AI that's automating it. So in my mind, particularly in my experience earlier this year in the AI industry, you realize that AI today really boils down to machine learning, which in itself boils down to deep learning, which boils down to data, your access to data. Professor Andrew Wang did this at the recent O'Reilly conference up in the city, he got up and lectured as the keynote instead of sharing slides and his number one, two, and three advice to everyone in the audience was, get the right datasets to train your model. If you don't have that you don't have a differentiated business, and that's what inspired PencilDATA, is my encountering of the cold start AI problem where the IP's in a public domain, public datasets are ubiquitous which is fantastic for academics, but as a business you can't differentiate unless you have access to the right datasets to train your models more specifically. >> Okay, as the founder and CEO of PencilDATA, that's your new startup, let's get into some of the reasons why you're starting it. What problem are you attacking? Obviously a pencil, I can see pencil and you erase things, it's got data... >> The internet is no longer written in ink, that's the premise. Now with Pencil you can erase some data. >> Well blockchain is immutable, so this is conflicting in my mind. Help me kind of rationalize this. The benefit of blockchain is everything's permanent, if you're on-chain as they say. >> Exactly. >> If you're off-chain, you could do some things. Is that kind of what we're getting at? >> We're mixing the best of both. So our premise is that again, whether you're an organization or an individual, you need to have, to survive in a new digital economy, control over your data. The blockchain part of it is the visibility side. If you don't know who's doing what to your data, you're far less likely to share it. And once you know who's doing what to your data, in an immutable blockchain, with a detailed audit trail, with strong authentication, of literally who's doing what to your data, gives you that visibility. Then you do what modern asset managers do. You can't really value an asset until you fully control it. And our premise is, you can't control something until you can take it back. So the notion of PencilDATA is the ability to go on-chain for the visibility and off-chain for managing data in encrypted containers, and if a data owner or publisher doesn't like how the subscriber's consuming their data, they have the power to revoke all downloaded copies. >> So is this kind of like a shadow blockchain model? I'm trying to find a mental model because I remember the old days back, I was breaking into the industry in the late '80s, early '90s, WORM drives, write once, read many. And you write it once, it's a laser, it was optical drives at the time. Also, demilitarized zones in networking was an area where there was a safe harbor kind of thing, where people could play around. What metaphor, what mental model can people take away from some of the things that you're trying to solve? Is it like a DMZ, is it like a-- >> The implementation's a lot like a DMZ and the business challenge and opportunity is that there's a lot of tension between protecting data, because we have an epidemic of data breaches right now, I think you're foolish if you're assuming that you haven't been breached yet but you might be, because everyone has been breached, personally and organizationally, so we have to deal with the rising need to protect data more and more. But at the same time, you can't stay in business if you don't optimize the monetization of the data you have. And so PencilDATA walks that fine line between letting you do both, letting you not just protect infrastructure, that's a whole other industry that we're not involved in, but literally protect data at the data level. If you look up terms like crypto anchor you'll see some of the technologies we're taking advantage of there. But being able to monetize data by unlocking all that latent value of data hidden behind firewalls. If you use a physics analogy of potential and kinetic energy, applied to data behind firewalls, there's hundreds of billions of dollars of value in latent data basically, potential data hiding behind firewalls, and when you can safely share it, give the owners control they've never had before, then you expose the value of that data for the first time. >> Alright, so let's take us through where you're at. Obviously super exciting, you're leveraging the blockchain and you've got an ICO, initial coin offering coming up but you're not just doing that for the sake of doing, there's a lot of scams out there, you're taking a little bit more of a pragmatic approach. Give us the status because you're the founder and CEO, what's the makeup of the team, how big are you guys, what are you guys looking for, obviously you're looking for team members most likely. >> We're looking for developers obviously. >> Where in the process are you? >> We are a two-month-old company. We're at the seed stage. And we've actually assembled a world-class team. You hear that a lot, but I'm really, really proud of the team members we have right now. >> World-class, are they from around the world and then they have class? Define world-class. >> They're worldly, like myself, I travel a lot. (laughter) An example, my chief privacy officer is Sheila Fitzpatrick, she's a worldwide recognized leader in data privacy, she's on many, many privacy boards in the US and EU and so forth, and she now is traveling nonstop lecturing on GDPR, itself specifically. She's one of those recognized-- >> Should you see yourself as a solution for GDPR, because that's, again, it does have teeth, I'll just say that we've been reporting on this through Wikibon, our research team as well as theCUBE, it comes up all the time and there's heavy fines associated with it, so it's not like- >> GDPR is the perfect use case because on the one hand, we have that audit trail that proves what you're doing with data. On the other hand we have a kill switch, that revocable use clause for data where you can literally comply with GDPR in minutes or seconds, as opposed to take a full 24 hours to scour database and delete selected records. >> Alright, so what about the product? Give us an example of the product. Will you be, first of all that's right around the corner, it's next year. >> Val: Yeah. >> I think it was a March or April's timeframe, I don't have the exact date but it's pretty soon. >> Public beta before the end of this year, version 1.0 first of second quarter next year. >> For you guys, PencilDATA. >> Yes. >> Clients, are you working with anyone right now, you have a handful? >> So we've actually got really interesting distribution partnerships that we're not in a position to announce right now but the top-tier brand name enterprise cloud vendors, both on the SaaS and infrastructure and database side, they're lining up to work with us. Because we're enabling amazing use cases in healthcare and life sciences, the ability to selectively share patient data with insurers, with healthcare providers, clinical trials now to share more information through differential privacy and collectively have more data to be processed and analyzed. Use cases are just off the charts. >> Well you know we go to all the big data shows, we're horizontally scaled on the event site circuit, but this is the number one thing that comes up, I want to move from batch marketing, batch process, batch business to real-time business, speed is essential, but it's always been a conflict between, how do I enable data to move really fast and be available for applications but protecting the privacy. >> Yeah. >> Do you solve that problem, is that something that you see yourselves solving? >> We aren't necessarily innovating on speed, of data movement, it's going to be a SaaS service. >> So it's availability model. >> It's availability of data that's really never been shared before and I think that's the key here, is we know there's a lot of value locked up behind corporate firewalls. The irony is, we don't even have to sell this outside firewalls initially, when you go to any medium-to-large size enterprise that has more than one site or more than one department, Sales doesn't trust Marketing and vice versa, Engineering doesn't trust Customer Support, neither of the four of them trust each other, so we're actually going to enable more data shared within an enterprise at first. >> So that's a starting point for you guys. >> That's a starting point, that's the easiest low-hanging fruit sale we have. >> Well PencilDATA, it's great stuff, Val, congratulations on that startup. I mean, you've got a world-class management team, and this kind of brings up a point that I've been banging on theCUBE pretty much every time I go out I'll talk about blockchain and ICO because you know, theCUBE is a very decentralized audience and that's a value that we're looking at as well with blockchain. I've got to ask you the personal question, from your own personal perspective, experience, executive and CTO, why is blockchain attracting so many A players? Because you're seeing a lot of what I call A players, entrepreneurs, technical geeks, really jumping into this because they can see it, they can smell the opportunity, and also, it also attracts the scammers as well, but specifically, why are these A players coming in? Is it, what are you hearing, what's the general vibe, what's the anecdotal reason? >> So as you said earlier on, it's a fundamental evolution of the core internet as a technology, as fundamental as HTTP and web was on top of TCP/IP back 20 years ago, but it's got that rare combination of not only being a technical innovation that empowers new use cases on the web, on the internet, it's also got immediate, amazing business applications as a store of value initially, as an actual valuation of various business processes, or datasets in my case, as an ability to exchange that value so transparently, so, in such a friction-less liquid manner, those are some of the amazing innovations it brings to the table and I think the most important thing is not to think of this as being able to do digital transformation or faster analog, it's about completely reimagining the exchange of value, measurement of value, and new kinds of businesses that just weren't possible before. >> And at all points of the stack, not the low levels and at the application level, the business logic, and to the geek side, right? >> Absolutely. >> You agree. I mean, that's great and as you know, theCUBE is looking at a blockchain ICO on down the horizon so keep an eye out for that, CUBEcoins could be in everyone's future, so we're super excited like you. >> I'm looking forward to your presale, just like I'm looking forward to mine. (laughing) >> Well, we'll see. But the bottom line is that this is what the reality is, you know, reimagining the applications is what people are thinking and I think people should beware of the scams out there, and then final question I want to ask you is, obviously we're both in the community together, with our teams. Share your perspective on the ecosystem, because obviously decentralization will change the nature of traditional ecosystems. >> Very much so. >> What's your vision on how the ecosystem will evolve, and how big is it now relative to these early markets? >> We're actually starting to enter the middle innings of the cloud game, if you will, we're seeing a very good maturity, a good diversification of profitable earnings and outcomes for the major cloud players, so I think we've gone well down the cloud path so far. But the decentralized world is in its infancy. It's embryonic right now. And I've always been a proponent of the multi-cloud environment and a multi-cloud world, and decentralization fundamentally is based on and depends on a multi-cloud, not just multi-region, but multi-data-center-in-a-closet scenario as well, to be able to actually have a democratic model for determining where the value is, where the value isn't, blockchain node style. And that is incredibly exciting to me, because that really cements this rebalancing of the pendulum between core and edge in terms of where processing and value happens. >> Yeah, and value exchange obviously now, markup links are becoming the du jour way to exchange value, users are in control, infrastructure equilibrium is interesting. Great stuff. And I'll say, perfect storm for innovation. The waves are coming. (laughing) >> You know, one thing I've learned over the years is, the innovation, change never stops. There's always an opportunity to innovate, and that's what I love about this movement. >> Blockchain, ICO, PencilDATA, check 'em out, Val Bercovici, founder and CEO, great friend of theCUBE, also really strong CTO, check these guys out. This wave of innovation around blockchain ICOs and infrastructure, reimagining, the future is here upon us at theCUBE, be right back with more, thanks for watching. (electronic music)