>>Live from San Diego, California at the cube covering to clock in cloud native con brought to you by red hat, the cloud native computing foundation and its ecosystem Marsh. >>Welcome back. This is the cubes coverage of CubeCon cloud date of con 2019 in San Diego, 12,000 in attendance. I'm just two minute and my cohost is John trier. And welcome back to the program. A multi-time cube alumni. You're on Aviv, who is the CTO and cofounder of a Gwoza. We've had quite a lot of, you know, founders, CTOs, you know, their big brains at this show, your own. So you know, let, let, let's start, you know, there's, there's really a gathering, uh, there's a lot of effort building out, you know, a very complicated ecosystem. Give us first, kind of your overall impressions of the show in this ecosystem. Yeah, so we're very early on on Desecco system. We were one of the first in the first batch of CNCF members when there were a few dozens of those. Not like a thousand of those. Uh, so I've been, I've been to all those shows. >>Uh, we're part of the CNCF committees for different things. And any initiating, I think this has become much more mainstream. I told you before, it's sort of the new van world. You know, I lot a lot more, uh, all day infrastructure vendors along with middleware and application vendor are coming here. All right, so, so one of the things we like having you on the program you're on is you don't pull any punches. So we've seen certain waves of technology come with big promise and fall short, you know, big data was going to allow us to leverage everything and you know, large percentage of, uh, solutions, you know, had to stop or be pulled back. Um, give us, what's the cautionary tale that we should learn and make sure that we don't repeat, you know, so I've been a CTO for many years in different companies and, and what everyone used to say about it, I'm always right. >>I'm only one year off usually. I'm usually a little more optimistic. So, you know, we've been talking about Cloudera and Hadoop world sort of going down and Kubernetes and cloud services, essentially replacing them. We were talking about it four years ago and what do you see that's actually happening? You know, with the collapse of my par and whore, then we're going to Cloudera things are going down, customer now Denon guys, we need equivalent solution for Kubernetes. We're not going to maintain two clusters. So I think in general we've been, uh, picking on many of those friends. We've, we've invented serverless before it was even called serverless with, with nuclear and now we're expanding it further and now we see the new emerging trends really around machine learning and AI. That's sort of the big thing. I'm surprised, you know, that's our space where essentially you're doing a data science platform as a service fully automated around serverless constructs so people can, can develop things really, really quickly. >>And what I see that, you know, third of the people I talk to are, have some relations to machine learning and AI. Yeah. Maybe explain that for our audience a little bit. Because when, you know, Kubernetes first started very much an infrastructure discussion, but the last year or two, uh, very much application specific, we hear many people talking about those data use cases, AI and ML early days. But you know how, how does that fit into the overall? It's simple. You know there, if you're moving to the cloud are two workloads. There is lift and shift workloads and there are new workloads. Okay, lift and ship. Why? Why bother moving them to Kubernetes? Okay, so you end up with new workloads. Everyone is trying to be cloud native server, elastic services and all that. Everyone has to feed data and machine learning into those new applications. This is why you see those trends that talk about old data integration, various frameworks and all that in that space. >>So I don't think it's by coincidence. I think it's, that's because new applications incorporate the intelligence. That's why you hear a lot of the talk about those things. What I loved about the architecture, what you just said is like people don't want to run into another cluster. I don't want to run two versions of Kubernetes, you know, if I'm moving there you, because you, but you're still built on that, that kind of infrastructure framework and, and knowledge of, of how to do serverless and how to make more nodes and fewer nodes and persistent storage and all that sort of good stuff and uh, and, and run TensorFlow and run, you know, all these, all these big data apps. But you can, um, you can talk about that just as a, as a, the advantage to your customer cause you could, it seems like you could, you could run it on top of GKE. >>You could run it on prem. I could run my own Coobernetti's you could, you could just give me a, uh, so >> we, we say Kubernetes is not interesting. I didn't know. I don't want anyone to get offended. Okay. But Kubernetes is not the big deal. The big deal is organizations want to be competitive in this sort of digital world. They need to build new applications. Old ones are sort of in sort of a maintenance mode. And the big point is about delivering new application with elastic scaling because your, your customers may, may be a million people behind some sort of, uh, you know, uh, app. Okay. Um, so that's the key thing and Kubernetes is a way to deliver those microservices. But what we figured out, it's still very complicated for people. Okay. Especially in, in the data science work. Uh, he takes him a few weeks to deliver a model on a Jupiter notebook, whatever. >>And then productizing it is about the year. That's something we've seen between six months to a year to productize things that are relatively simple. Okay. And that's because people think about the container, the TensorFlow, the Kuda driver, whatever, how to scale it, how to make it perform, et cetera. So let's, we came up with is traditionally there's a notion of serverless, which is abstraction with very slow performance, very limited set of use cases. We sell services about elastic scaling paper, use, full automation around dev ops and all that. Okay. Why cannot apply to other use cases are really high concurrency, high-speed batch, no distributed training, distributed workload. Because we're coming, if you know my background, you know, been beeping in Mellanox and other high-performance companies. So where I have a, we have a high performance DNA so we don't know how to build things are extremely slow. >>It sort of irritates me. So the point is that how can we apply this notion of abstraction and scaling and all that to variety of workloads and this is essentially what it was. It is a combination of high speed data technology for like, you know, moving data around on between those function and extremely high speed set though functions that work on the different domains of data collection and ingestion, data analytics, you know, machine learning, training and CIN learning model serving. So a customer can come on on our platform and we have testimonials around that, that you know, things that they thought about building on Amazon or even on prem for months and months. They'd built in our platform in few weeks with fewer people because the focus is on building the application. The focus is not about joining your Kubernetes. Now we go to customers, some of them are large banks, et cetera. >>They say, Alrighty, likes Kubernetes, we have our own Kubernetes. So you know what, we don't butter. Initially we, we used to bring our own Kubernetes, but then you know, I don't mind, you know, we do struggle sometimes because our level of expertise in Coobernetti's is way more sophisticated than what they have to say. Okay, we've installed Kubernetes and we come with our software stack. No you didn't, you know, you didn't configure the security, they didn't configure ingress, et cetera. So sometimes it's easier for us to bring, but we don't want him to get into this sort of tension with it. Our focus is to accelerate development on the new application that are intelligent, you know, move applications from, if you think of the traditional data analytics and data science, it's about reporting and what people want to do. And some applications we've announced this week and application around real time cyber collection, it's being used in some different governments is that you can collect a lot of information, SMS, telephony, video, et cetera. >>And in real time you could detect terrorists. Okay. So those application requires high concurrency always on rolling upgrades, things that weren't there in the traditional BI, Oracle, you know, kind of reporting. So you have this wave of putting intelligence into more highly concurrent online application. It requires all the dev ops sort of aspects, but all the data analytics and machine learning aspects to to come to come along. Alright. So speaking of those workloads for, for machine learning, uh, cube flow is a project, uh, moving the, moving in that space along it. Give us the update there. Yeah. So, so there is sort of a rising star in the Kubernetes community around how to automate machine learning workflows. That's cube flow. Uh, I'm personally, I one of the committers and killed flow and what we've done, because it's very complicated cause Google developed the cube cube flow as one of the services on, on a GKE. >>Okay. And the tweaked everything. It works great in GK, even that it's relatively new technology and people want to move around it in a more generic. So one of the things in our platform is a managed cube flow that works natively with all the rest of the solutions. And other thing that we've done is we make it, we made it fully. So instead of queue flow approach is very con, you know, Kubernetes oriented containers, the ammos, all that. Uh, in our flavor of Coupa we can just create function and you just like chain functions and you click and it runs. Just, you've mentioned a couple of times, uh, how does serverless, as you defined it, fit in with, uh, Coobernetti's? Is that working together just functions on top or I'm just trying to make here, >> you'll, you'll hear different things. I think when most people say serverless, they mean sort of front end application things that are served low concurrency, a Terra, you know, uh, when we mean serverless, it's, we have eight different engines that each one is very good in, in different, uh, domain like distributed deep learning, you know, distributed machine learning, et cetera. >>And we know how to fit the thing into any workloads. So for me, uh, we deliver the elastic scaling, the paper use and the ease of use of sort of no dev ops across all the eight workloads that we're addressing. For most people it's like a single Dreek phony. And I think really that the future is, is moving to that. And if you think about serverless, there's another aspect here which is very important for machine learning and Israel's ability. I'm not going to develop any algorithm in the world. Okay. There are a bunch of companies or users or developers that can develop an algorithm and I can just consume it. So the future in data science but not just data science is essentially to have like marketplaces of algorithms premade or analytic tools or maybe even vendors licensing their technology through sort of prepackaged solution. >>So we're a great believer of forget about the infrastructure, focus on the business components and Daisy chain them in to a pipeline like UFO pipeline and run them. And that will allow you most reusability that, you know, lowest amount of cost, best performance, et cetera. That's great. I just want to double click on the serverless idea one more time, but, so you're, you're developing, it's an architectural pattern, uh, and you're developing these concepts yourself. You're not actually, sometimes the concept gets confused with the implementations of other people's serverless frameworks or things like that. Is that, is that correct? I think there are confusion. I'm getting asked a lot of times. How do you compare your technology compared to let's say a? You've heard the term gay native is just a technology or open FAS or, yeah. Hold on. Pfizer's a CGIs or Alito. An open community is very nice for hobbies, but if you're an enterprise and it's security, Eldep integration, authentication for anything, you need DUIs, you need CLI, you need all of those things. >>So Amazon provides that with Lambda. Can you compare Lambda to K native? No. Okay. Native is, I need to go from get and build and all that. Serverless is about taking a function and clicking and deploying. It's not about building. And the problem is that this conference is about people, it people in crowd for people who like to build. So they, they don't like to get something that work. They want to get the build the Lego building blocks so they can play. So in our view, serverless is not open FAS or K native. Okay. It's something that you click and it works and have all the enterprise set of features. We've extended it to different levels of magnitude of performance. I'll give you an anecdote. I did a comparison for our customer asking me the same question, not about Canadian, but this time Lambda. How do you guys compare with London? >>Know Nokia is extremely high performance. You know we are doing up to 400,000 events on a single process and the customer said, you know what, I have a use case. I need like 5,000 events per second. How do you guys compare a total across all my functions? How do you compare against Lambda? We went into, you know the price calculator, 5,000 events per second on Lambda. That's $50,000 okay. $50,000 we do about, let's say even in simple function, 60,000 per process, $500 VM on Amazon, $500 VM on Amazon with our technology stick, 2000 transactions per second, 5,000 events per second on Lambda. That's 50,000. Okay. 100 times more expensive. So it depends on the design point. We designed our solution to be extremely efficient, high concurrency. If you just need something to do a web hook, use Lambda, you know, if you are trying to build a high concurrency application efficient, you know, an enterprise application on it, on a serverless architecture construct come to us. >>Yeah. So, so just a, I'll pause at this for you because a, it reminds me what you were talking about about the builders here in the early days of VMware to get it to work the way I wanted to. People need to participate and build it and there's the Ikea effect. If I actually helped build it a little bit, I like it more to get to the vast majority, uh, to uh, adopt those things. It needs to become simplified and I can't have, you know, all the applications move over to this environment if I have to constantly tweak that. Everything. So that's the trend we've been really seeing this year is some of that simplification needs to get there. There's focus on, you know, the operators, the day two operations, the applications so that anybody can get there without having to build themselves. So we know there's still work to be done. >>Um, but if we've crossed the chasm and we want the majority to now adopt this, it can't be that I have to customize it. It needs to be more turnkey. Yeah. And I think it's a friendly and attitude between what you'll see in Amazon reinvent in couple of weeks. And then what you see here, because there is those, the focus of we're building application a what kind of tools and the Jess is gonna just launch today on the, on the floor. Okay. So we can just consume it and build our new application. They're not thinking, how did Andy just, he built his tools. Okay. And I think that's the opposite here is like how can you know Ali's is still working inside underneath dude who cares about his team. You know, you care about having connectivity between two points and and all that. How do you implement it that, you know, let someone else take care of it and then you can apply your few people that you have on solving your business problem, not on infrastructure. >>You know, I just met a guy, came to our booth, we've seen our demo. Pretty impressive how we rise people function and need scales and does everything automatically said we want to build something like you're doing, you know, not really like only 10% of what you just showed me. And we have about six people and for three months where it just like scratching our head. I said, okay, you can use our platform, pay us some software license and now you'll get, you know, 10 times more functionality and your six people can do something more useful. Says right, let's do a POC. So, so that's our intention and I think people are starting to get it because Kubernetes is not easy. Again, people tell me we installed Kubernete is now installed your stack and then they haven't installed like 20% of all the things that you need to stop so well your own have Eve always pleasure to catch up with you. Thanks for the all the updates and I know we'll catch up with you again soon. Sure. All right. For John Troyer, I'm Stu Miniman. We'll be back with more coverage here from CubeCon cloud date of con in San Diego. Thanks for watching the cube.

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Hello and welcome to Cube conversations. I'm James Kabila's lead analyst at Wicked Bond. Today we've got an excellent guest. Who's a Cube alumnus? Par excellence. It's your own Haviv who is the founder and CEO of a guajillo. Hello. You're wrong. Welcome in. I think you're you're coming in from Tel Aviv. If I'm not mistaken, >> right? Really? Close the deal of any thanks from my seeing you again. >> Yeah. Nice to see you again. So I'm here in our Palo Alto studios. And so I'm always excited when I can hear your own and meet with your room because he always has something interesting in new to share. But what they're doing in the areas of cloud and serve earless and really time streaming analytics And now, data science. I wasn't aware of how deeply they're involved in the whole data Science pipelines, so ah, your own. This is great to have you. So my first question really is. Can you sketch out? What are the emerging marketplace requirements that USA gua Si are seeing in the convergence of all these spaces? Especially riel time streaming analytics edge computing server lis and data science and A I can you give us a sort of ah broad perspective and outlook on the convergence and really the new opportunities or possibilities that the convergence of those technologies enable for enterprises that are making deep investments. >> Yeah, so I think we were serving dissipated. What's happening now? We just call them different names will probably get into into this discussion in a minute. I think what you see is the traditional analytics and even data scientist Science was starting at sort of a research labs, people exploring cancer, expressing, you know, impact. Whether on, you know, people's moved its era. And now people are trying to make real or a Y from a guy in their assigned, so they have to plug it within business applications. Okay, so it's not just a veil. A scientist Inning the silo, you know, with a bunch of large that he got from his friends, the data engineer in the scan them and Derrickson Namesake runs to the boss and says, You know what? You know, we could have made some money in a year ago. We've done something so that doesn't make a lot of impact on the business, where the impact on the business is happening is when you actually integrate a I in jackpot in recommendation engines in doing predictive analytics on analyzing failures and saving saving failures on, you know, saving people's life. Those kind of use cases. Doctors are the ones that record a tighter integration between the application and the data and algorithms that come from the day I. And that's where we started to think about our platform. Way worked on a real time data, which is where you know, when you're going into more production environment of not fatal accident. Very good, very fast integration with data. And we have this sort of fast computation layer, which was a one micro services, and now everyone talks about micro services. We sort of started with this area, and that is allowing people to build those intelligent application that are integrated into the business applications. And the biggest challenges they see today for organizations is moving from this process of books on research, on data in a historical data and translating that into a visit supplication or into impact on business application. This is where people can spend the year. You know, I've seen the tweet saying with build a machine learning model in, like, a few weeks. And now we've waited eleven months for the product ization. So that artifact, >> Yes, that's what we're seeing it wicked bomb. Which is that A. I is the heart of modern applications in business and the new generation of application developers, in many ways, our data scientists, or have you know, lovers the skills and tools for data science. Now, looking at a glass zeros portfolio, you evolve so rapidly and to address a broader range of use cases I've seen. And you've explained it over the years that in position to go, as well as being a continuous data platform and intelligent edge platform, a surveillance platform. And now I see that you're a bit of a data science workbench or pipeline tooling. Clever. Could you connect these dots here on explain what is a guajillo fully >> role, Earl? Nice mark things for this in technology that we've built, OK, just over the years, you know, people, four years when we started, So we have to call it something else. Well, that I thought that analytic sort of the corporate state of science. And when we said continued analytics, we meant essentially feeding data and running, some of them speaking some results. This is the service opposed to the trend of truth which was dating the lady Throw data in and then you run the batch that analytic and they're like, Do you have some insight? So continue statistics was served a term that we've came up with a B, not the basket. You know, describe that you're essentially thinking, needing from different forces crunching it, Prue algorithms and generating triggers and actions are responsible user requests. Okay on that will serve a pretty unique and serve the fireman here in this industry even before they called it streaming or in a real time, data science or whatever. Now, if you look at our architecture are architecture, as I explained before, is comprised of three components. The first event is a real time, full time model database. You know, you know about it really exceptional in his performance and its other capabilities. The second thing is a pursue miss engine that allows us to essentially inject applications. Various guys, initially we started with application. I sense you do analytics, you know, grouping joining, you know, correlating. And then we start just adding more functions and other things like inference, saying humans recognitions and analysis. It's Arab is we have dysfunction engine. It allows us a lot of flexibility and find the really fast for the engine on a really fast data there endure it, remarkable results and then this return calling this turn this micro assume it's finger serve Ellis who certainly even where have the game of this or service gang. And the third element of our platform is a sense she having a fully manage, passed a platform where a ll those micro services our data and it threw a self service into face surfing over there is a mini cloud. You know, we've recently the last two years we've shifted to working with coronaries versus using our own A proprietary micro spurs does or frustration originally. So we went into all those three major technologies. Now, those pit into different application when they're interesting application. If you think about edge in the engine in serving many clouds, you need variety of data, sources and databases. With you, no problem arose streaming files. Terra. We'LL support all of them when our integrated the platform and then you need to go micro services that developed in the cloud and then just sort of shift into the enforcement point in the edge. And you need for an orchestration there because you want to do suffer upgrades, you need to protect security. So having all the integrated separated an opportunity for us to work with providers of agin, you may have noticed our joint announcement with Google around solution for hedge around retailers and an i O. T. We've made some announcement with Microsoft in the fast. We're going to do some very interesting announcement very soon. We've made some joint that nonsense with Samsung and in video, all around those errands, we continue. It's not that we're limited to EJ just what happens because we have extremely high density data platform, very power of fish and very well integrated. It has a great feat in the India, but it's also the same platform that we sell in. The cloud is a service or we sell two on from customers s so they can run. The same things is in the clouds, which happens to be the fastest, most real time platform on the Advantage service. An essential feature cannot just ignore. >> So you're wrong. Europe. Yeah, Iguazu is a complete cloud, native development and run time platform. Now serve earless in many ways. Seems to be the core of your capability in your platform. New Cleo, which is your technology you've open sourced. It's bill for Prem bays to private clouds. But also it has is extensible to be usable in broader hybrid cloud scenarios. Now, give us a sense for how nuclear and civilised functions become valuable or useful for data science off or for executing services or functions of data of the data science pipeline kick you connect the dots of nuclear and data science and a I from the development standpoint >> church. So So I think you know, the two pillars that we have technology that the most important ones are the data. You know, we have things like twelve batons on our data engine is very high performance and nuclear functions, and also they're very well integrated because usually services stateless. So you know, you you end up. If you want to practice that they have some challenges with service with No, no, you can't. You stay for use cases. You can mount files. You have real time connections to data, so that makes it a lot more interesting than just along the functions. The other thing, with no clothes that is extremely high performance has about two hundred times faster than land. So that means that you can actually go and build things like the stream processing and joins in real time all over practice, their base activities. You can just go and do collectors. We call them those like things. Go fetch information from whether services from routers for the X cybersecurity analysis for all sorts of sensors. So those functions are becoming like, you know, those nanobots technology of off the movies is that you just send them over to go and do things for you, whether it's the daily collection and crunching, whether it's the influencing engines, those things that, for example, get a picture of very put the model, decide what's in the picture, and that this is where we're really comes into play. They nothing important you see now an emergence off a service patterns in data science. So there are many companies that do like mother influencing as a service city what they do, they launch an end point of your eleven point and serve runs the model inside you send the Vector America values and get back in the Americans and their conversion. It's not really different and service it just wait more limited because I don't just want to send a vector off numbers because usually I understand really like a geo location of my cellphone, which are user I D. And I need dysfunction to cross correlated with other information about myself with the location. Then came commendation of which a product they need to buy. So and then those functions also have all sorts of dependency exam on different packages. Different software environment, horribles, build structures, all those. This is really where service technologies are much more suitable now. It's interesting that if you'LL go to Amazon, they have a product called Sage Maker. I'm sure yes, which is dinner, then a science block. Okay, now sage mint for although you would say that's a deal use case for after Onda functions actually don't use Amazon London functions in sage maker, and you ask yourself, Why aren't they using Lambda Stage Maker just telling you, you know you could use Lambda is a blue logic around sage maker. And that's because because London doesn't feed the use case. Okay, because lambda doesn't it is not capable of storing large content and she learning miles could be hundreds of megabytes or Landa is extremely slow. So you cannot do hi concurrency influencing with will land the function so essentially had to create another surveillance and college with a different name. Although if they just would have approved Landa, maybe it was one or a Swiss are So we're looking, We've took it, were taken the other approach We don't have the resources that I have so we created a monster virus Engine one servant attention does batch Frost is saying scream processing, consort, lots of data, even rocketeer services to all the different computation pattern with a single engine. And that's when you started taking all this trend because that's about yeah, we need two version our code. We need to, you know, record all our back into dependencies. And although yes, service doesn't so if we just had to go and tied more into the existing frameworks and you've looked at our frantically product called Tokyo Jupiter, which is essentially a scientist, right, some code in his data's passport book and then in clicks. One command called nuclear Deploy, it automatically compiles, is their science artifact in notebooks, that server and converted into a real hand function that can listen in on your next city. People can listen on streams and keep the scheduled on various timing. It could do magic. So many other things. So, and the interesting point is that if you think about their scientists there, not the farmers, because they should be a scientist on this's means that they actually have a bigger barrier to write in code. So if you serve in this framework that also automates the law daughter scaling the security provisioning of data, the versions of everything in fact fantasies, they just need to focus on writing other them's. It's actually a bigger back for the book. Now, if you just take service into them, Epstein's and they will tell you, Yeah, you know, we know how to explain, Doctor. We know all those things, so they're very their eyes is smaller than the value in the eyes of their scientists. So that's why we're actually seeing this appeal that those those people that essentially focus in life trying math and algorithms and all sorts of those sophisticated things they don't want to deal with. Coding and maintenance are refreshed. And by also doing so by oppression analyzing their cool for service, you can come back to market. You can address calle ability to avoid rewriting of code. All those big challenges the organizations are facing. >> You're gonna have to ask you, that's great. You have the tools to build, uh, help customers build serve Ellis functions for and so forth inside of Jupiter notebooks. And you mentioned Sage Maker, which is in a WS solution, which is up in coming in terms of supporting a full data science tool chain for pipeline development. You know, among teams you have a high profile partnerships with Microsoft and Google and Silver. Do you incorporate or integrator support either of these cloud providers own data science workbench offerings or third party offerings from? There's dozens of others in this space. What are you doing in terms of partnerships in that area? >> Yeah, obviously we don't want to lock us out from any of those, and, you know, if someone already has his work bench that I don't know my customers say they were locking me into your world back in our work when things are really cool because like our Jupiter is connected for real time connections to the database. And yes, serve other cool features that sentir getting like a huge speed boost we have. But that's on A with an within vigna of round Heads and Integration, which reviews are creating a pool of abuse from each of one of the data scientist running on African essentially launch clubs on this full of civilians whose off owning the abuse, which are extremely expensive, is you? No. But what we've done is because of her. The technology beside the actual debate engine is open source. We can accept it essentially just going any sold packages. And we demonstrate that to Google in danger. The others we can essentially got just go and load a bunch of packages into their work match and make it very proposed to what we provide in our manage platform. You know, not with the same performance levels. Well, functionality wise, the same function. >> So how can you name some reference customers that air using a guajillo inside a high performance data science work flows is ah, are you Are there you just testing the waters in that market for your technology? Your technology's already fairly mature. >> That says, I told you before, although you know, sort of changed messaging along the lines. We always did the same thing. So when we were continuous analytics and we've spoken like a year or two ago both some news cases that we Iran like, you know, tell cooperators and running really time, you know, health, a predictive health, monitoring their networks and or killing birds and those kind of things they all use algorithms. You control those those positions. We worked with Brian nailing customers so we can feed a lot of there there in real time maps and do from detection. And another applications are on all those things that we've noticed that all of the use cases that we're working with involved in a science in some cases, by the way, because of sort of politics that with once we've said, we have analytics for continuous analytics, we were serving send into sent into the analytic schools with the organization, which more focused on survey data warehouse because I know the case is still serve. They were saying, and I do. And after the people that build up can serve those data science applications and serve real time. Aye, aye. OK, Ianto. Business applications or more, the development and business people. This is also why we sort of change are our name, because we wanted to make it very clear that we're aren't the carnage is about building a new applications. It's not about the warehousing or faster queries. On a day of Eros is about generating value to the business, if you ask it a specific amplification. And we just announced two weeks in the investment off Samsung in Iguazu, former that essentially has two pillars beyond getting a few million dollars, It says. One thing is that they're adopted. No cure. Is there a service for the internal clouds on the second one is, we're working with them on a bunch of us, Della sighs. Well, use case is one of them was even quoted in enough would make would be There are no I can not say, but says she knows our real business application is really a history of those that involves, you know, in in intercepting data from your sister's customers, doing real time on analytics and responding really quickly. One thing that we've announced it because of youse off nuclear sub picture. We're done with inferior we actually what were pulled their performance. >> You're onto you see if you see a fair number of customers embedding machine learning inside of Realtor time Streaming stream computing back ones. This is the week of Flink forward here in San San Francisco. I I was at the event earlier this week and I I saw the least. They're presenting a fair amount of uptake of ml in sight of stream computing. Do you see that as being a coming meet Mainstream best practice. >> Streaming is still the analytics bucket. OK, because what we're looking for is a weakness which are more interactive, you know, think about like, uh, like a chatterbox or like doing a predictive analytic. It's all about streaming. Streaming is still, you know, it's faster flow data, but it's still, sir has delay the social. It's not responses, you know. It's not the aspect of legacy. Is that pickle in streaming? Okay, the aspect of throughput is is higher on streaming, but not necessarily the response that I think about sparks streaming. You know, it's good at crossing a lot of data. It's definitely not good at three to one on would put spark as a way to respond to user request on the Internet S O. We're doing screaming, and we see that growth. But think where we see the real growth is panic to reel of inches. The ones with the customer logs in and sends a request or working with telcos on scenarios where conditions of LA car, if the on the tracks and they settled all sorts of information are a real time invent train. Then the customer closer says, I need a second box and they could say No, this guy needs to go away to that customer because how many times you've gotten technician coming to your house and said I don't have that more exactly. You know, they have to send a different guy. So they were. How do you impact the business on three pillars of business? Okay, the three pillars are one is essentially improving your china Reducing the risk is essentially reducing your calls. Ask him. The other one is essentially audio, rap or customer from a more successful. So this is around front and application and whether it's box or are doing, you know our thing or those kind of us kisses. And also under you grow your market, which is a together on a recommendation in at this time. So all those fit you if you want, have hey, I incorporated in your business applications. In few years you're probably gonna be dead. I don't see any bits of sustained competition without incorporating so ability to integrate really real data with some customer data and essentially go and react >> changes. Something slightly you mentioned in video as a partner recently, Of course, he announced that few weeks ago. At their event on, they have recently acquired Melon ox, and I believe you used to be with Melon Axe, so I'd like to get your commentary on that acquisition or merger. >> Right? Yes, yes, I was VP Data Center man Ox. Like my last job, I feel good friends off off the Guider, including the CEO and the rest of the team with medicines. And last week I was in Israel's with talk to the media. Kansas. Well, I think it's a great merger if you think about men in Ox Head as sort of the best that breaking and storage technology answer Silicon Side and the video has the best view technologies, man. It's also acquired some compute cheap technologies, and they also very, very nice. Photonics technologies and men are today's being by all the club providers. Remiss Troll was essentially only those technical engagement would like the seizures and you know the rest of the gas. So now VP running with the computation engine in and minerals coming, we serve the rest of the pieces were our storage and make them a very strong player. And I think it's our threatens intel because think about it until they haven't really managed to high speed networking recently. They haven't really managed to come with Jiffy use at your combat and big technology, and so I think that makes a video, sort of Ah, pretty. You know, vendor and suspect. >> And another question is not related to that. But you're in Tel Aviv, Israel. And of course, Israel is famous for the start ups in the areas of machine learning. And so, especially with a focus on cyber security of the Israel, is like near the top of the world in terms of just the amount of brainpower focused on cyber security there. What are the hot ML machine? Learning related developments or innovations you see, coming out of Israel recently related to cybersecurity and distributed cloud environments, anything in terms of just basic are indeed technology that we should all be aware of that will be finding its way into mainstream Cloud and Cooper Netease and civilised environments. Going forward, your thoughts. >> Yes, I think there are different areas, you know, The guys in Israel also look at what happens in sort of the U. S. And their place in all the different things. I think with what's unique about us is a small country is always trying to think outside of the box because we know we cannot compete in a very large market. It would not have innovation. So that's what triggers this ten of innovation part because of all this tippy expects in the country. And also there's a lot of cyber, you know, it's time. I think I've seen one cool startup. There's also backed by our VC selling. Serve, uh, think about like face un recognition, critical technology off sent you a picture and make it such that you machine learning will not be able to recognize Recognize that, you know, sort of out of the cyber attack for image recognition. So that's something pretty unique that I've heard. But there are other starts working on all the aspects on their ops and information in our animal and also cyber automated cyber security and hope. Curious aspect. >> Right, Right. Thank you very much. Your own. This has been an excellent conversation, and we've really enjoyed hearing your comments. And Iguazu. It was a great company. Quite quite an innovator is always a pleasure to have you on the Cube. With that, I'm going to sign off. This is James Kabila's with wicked bond with your own haviv on dh er we bid You all have a good day. >> Thank you.

>> Live, from Las Vegas, it's theCUBE, covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Welcome back to Las Vegas, as we continue our coverage here on theCUBE of AWS re:Invent, day two of our three days of coverage, happy Wednesday to you, wherever you might be watching. We're joined by Yaron Haviv, who is the founder and CPO of iguazio, and Yaron, thanks for joining us here on theCUBE, once again. >> Thank you, hi. >> For folks at home who might be watching or at their office and not familiar with iguazio, tell us a little bit about the history of the company, what you saw as the need, as the founder, and what your primary focus is. >> So our key focus is delivering advanced services, the same one that you see in the Cloud, high-performance for real time analytics, essentially what we've seen as a gap, you have all the Cloud services in the Cloud, but when you're fanning into an Edge or an on-prem environment, you're usually consuming, like IT, VAMs, et cetera. So what we are doing, we're matching the same level of services, we provide serverless functions, AI as a service, and manage databases that can run, either in the Cloud or on-prem, or in federated Edge environment. So one consistent application development environment brought where we are. >> So, on the AI side, you mentioned that, as you're looking at your client base, your customers, and you're introducing this concept now, right? For those who aren't there yet. What do you sell them on, if you will? Or what do they want to know, what don't they understand, you think, generally speaking? >> Yeah, so in AI and ML, there are a lot of companies solving that problem, okay? Where we master is the notion of real-time AI, okay? What people are looking, is into embedding AI into business applications. Okay? The traditional notion is, you have a data lake, you throw all the data, and then your data sign, just go learn stuff, create nice, you know, desk-origin tableau. Great. So what? You know? What people really want is to build recommendation engines. You know, someone is logging into a website, he gets recommendations, so that requires very short latency of response, okay? You are doing front-detection and financial applications, so you're freighting a lot of data. You need to make decisions now, okay? You're doing cyber security analysis, so you're feeding data from routers and firewalls and switches, and you need react immediately to whatever is happening. You think about retail stores, things like Amazon Go. Cameras examining your behavior et cetera, you need to respond very very quickly. And now this is a much harder problem to deliver AI in real time, than it is in a sort-of a data-science workbench or just a batching notion. And traditionally, the way people address that problem is by profiling, creating sort-of a, every time, I'm going to see something very similar to that, I'm going to go to a database, pull, compare, and contrast, but the problem is that you need more and more multi-environment analysis on objects that keep on updating. You know, my location keeps on changing. If I'm going to stand in front of this store, I need to get this advertisement, or if I've just done some purchase with my card, and the bank knows my GPS location, it can cross-correlate that, and know if it's a fraud or not, okay? So there are more inputs going into the decision. This is where we master is, the ability to ingest lots of data in real time, cross-correlate that, in real time, to generate what's called feature vector. It's all those things that make up a decision. Run the decision, based on the traditional AI and deep learning algorithms, and they act on it. Whether it's response to customer requests or, you know, block some firewall, or whatever. And our focus is time to action. And the way we are implementing it, is using two major components. One is, real time serverless functions, which is an open-source we're promoting, called nuclio. A second is a real-time database, extremely high performance, it attaches to those functions and allow and help stitching the data and calculating and getting the results. So that's the general thing we're doing. >> So that idea of the serverless functions with nuclio, that's really about bringing, what you're used to in the Cloud, and bringing that out into the Edge. Which, I think, we were talking before, and that's I think a focus for a lot of developers who, I want to use all of the things I'm used to in the Cloud, where it's, I can just consume them as services, and it's quite easy to deal with. But then I come back into the on-site or on onto the Edge in this kind-of hybrid Cloud model, I don't actually have access to all of those things anymore. And I want to. >> Right, and it's even beyond that, because, you the Lambda came from more of like, WebHooks, Seoscases, et cetera. Extremely not concurrent, extremely low performance. You're talking about hundreds of milliseconds of latencies, you know, you're talking about, like, thousand invocations per second, you know? That's sort-of the concurrency, single-threaded applications. We're talking about real-time applications, you know. Hundreds of thousands of events per second. We're talking about latency in the range of milliseconds response time, that you have to respond. So we had to build a different serverless. Something that's real-time, something that has real-time access to data, et cetera. So that's originally where nuclio came in. And then, we started seeing pull from customers, saying, yes, but you're also a multi-Cloud serverless. And I can run your serverless on a laptop for debugging. I can run it on a mini Edge appliance, because this is my enforcement point. I can run it on-prem, because, you know, I'm stuck with some old gear in my on-prem application, and this is what started making nuclio very popular in lots of getup starts et cetera. And the fact that we're provided as a fully managed platform you know, it's open-source, consume it, whatever, but when you're using our managed platform, you get security, integration with active directory, integration with data, logging, monitoring. So, it really provides an alternative to Lambda, where you need high concurrency and everywhere. You know, Edge, Cloud, on-prem, but also high performance, high concurrency for those new workloads of real-time analytics. >> Yeah, so what are some of things that customers are using the platform to develop on? Like, could you give us an example of someone who's using some of these serverless functions for real-time application? Yeah, so, one of the applications is a, we do a lot of work with the network operators. You know, Verizon is one of our investors, but also working with different, other tel-cos. So we're doing real-time network monitoring, across all their firewalls and network equipment et cetera, to predict the network behavior. So, if there's going to be a failure, is it a cyber-security attack right now, things like that. The next level that they went into doing is actually a remediation. It's essentially re-routing the networks to bypass faults automatically, based on the predicted behaviors. Or, you know, stopping some attacks as they occur. So that's one use case. Another use case, in financial services and many other places, is predictive network operations. It's monitoring, again, behavior of services et cetera, like in trading platforms. And knowing that there is going to be a latency spike that's going to impact the trading, and essentially going and fixing that, in order to not lose millions of dollars of trades. Or real time tick analytics, you know? Until now, all the financial applications were very sort-of event driven, and complex event driven, not incorporating deep learning, things like that. Now, I think that there are many variants. You know, the, your president, you know, is going to tweet something about some company, and then it's going to impact the buyover or with stock. So, the current high-frequency trading algorithms are not designed for that, okay? Now, if you build all those serverless functions that listens on Twitter and Muse and all those things, and they can start cross-correlating that information to a much smarter decision. They fit in the real-time decision of buying and selling stocks into a lot more intelligent decision, you can make more money, okay? Another application, retailers, okay? We're working with locations where they have a thousand cameras in a single supermarket, because they just inspect the shelves to look into inventory levels, and eventually they're going to like, an Amazon Go model, where they actually want to know, to track what you're buying et cetera. So a thousand cameras in a store, you cannot shape all that bandwidth to the Cloud. And this is where it comes to a federated application model. Where, as a developer, the guys that are Cloud-born, or Cloud-first, they know containers, they know APIs, they know that stuff. They don't know how to build a box that sits in a store, okay? This is the other world of VMs and Venix, they don't care about that, they want APIs. They want Lambda functions, Dynamo, et cetera. So what we're providing is a mechanism where they can develop in the Cloud, test, simulate, run CICD pipelines, push our defects to the store, to actually go and do the work. And there we have strong partnerships with at least a couple of the major Cloud providers. We have co-ceiling agreements with Azure, we're working with Google, and, I assume, Amazon will be next, but those two, we have a strong relations with already. >> Alright, before we cut you loose, just gimme your idea about the show in general here, from what you've seen, and kind of how you feel about the conversations that you're a part of. >> Yeah, I was very busy talking to customers all day, so I haven't had a lot of time. I think interesting announcements, you know, they've made announcements with VMware, I'm still trying to figure out, what have they announced. You know, again, we spoke about the fact that the whole idea of Cloud is about service obstructions. Not virtual machines, not Kubernetes containers. It's about using APIs, using serverless functions, using AI workbenches that you can develop this new logic. If I'm going to use this VMware on-prem with Amazon, am I going to get all the SageMaker, Lambda, all that on-prem, or just more of a tactical thing, like Azure Stack, like, we're bringing UVMs, we're calling it Cloud, you know, just for marketing's sake. Is that a real Cloud services platform, okay? I think it aligns with what we're seeing now with the Kubernetes, I think we had some discussion about it. You know, IBM buys Reddit, you know, Cisco collaborates with Amazon, VMware buys Apptio. Kubernetes is containers, it's infrastructure. We speak to customers, we show them what we do serverless, you know AI workbenches, databases, service. That's the interesting part. That eliminates IT. If you're putting Kubernetes, it perpetuates IT. Now they need to take Kubernetes, tie it to their security system, build Spark on top of a container et cetera. Now that is a lot of IT and dev ops work involved. But many customers need agility. The reason they're going to Cloud, is not to use VMs, you know? It's to be able to take some Lambda function, some pre-bagged services, glue them together, and really come fast to market with an application. >> So what we really want to do is just to Cloud all the things. I think? (group chuckles) Cloud all the things. >> Mission accomplished. Yaron, thanks for being with us. We appreciate the time you're on theCUBE. Good to see you, sir. >> Thank you. >> Alright, back with more, here at AWS re:Invent. You're watching it live, and we're on theCUBE. (techno music)

Live from New York It's theCUBE! Covering theCUBE New York City 2018 Brought to you by Silicon Angle Media and it's ecosystem partners >> Hey welcome back and we're live in theCUBE in New York city. It's our 2nd day of two days of coverage CUBE NYC. The hashtag CUBENYC Formerly Big data NYC renamed because it's about big data, it's about the server, it's about Cooper _________'s multi-cloud data. It's all about data, and that's the fundamental change in the industry. Our next guest is Yaron Haviv, who's the CTO of Iguazio, key alumni, always coming out with some good commentary smart analysis. Kind of a guest host as well as an industry participant supplier. Welcome back to theCUBE. Good to see you. >> Thank you John. >> Love having you on theCUBE because you always bring some good insight and we appreciate that. Thank you so much. First, before we get into some of the comments because I really want to delve into comments that David Richards said a few years ago, CEO of RenDisco. He said, "Cloud's going to kill Hadoop". And people were looking at him like, "Oh my God, who is this heretic? He's crazy. What is he talking about?" But you might not need Hadoop, if you can run server less Spark, Tensorflow.... You talk about this off camera. Is Hadoop going to be the open stack of the big data world? >> I don't think cloud necessary killed Hadoop, although it is working on that, you know because you go to Amazon and you know, you can consume a bunch of services and you don't really need to think about Hadoop. I think cloud native serve is starting to kill Hadoop, cause Hadoop is three layers, you know, it's a file system, it's DFS, and then you have server scheduling Yarn, then you have applications starting with map produce and then you evolve into things like Spark. Okay, so, file system I don't really need in the cloud. I use Asfree, I can use a database as a service, as you know, pretty efficient way of storing data. For scheduling, Kubernetes is a much more generic way of scheduling workloads and not confined to Spark and specific workloads. I can run with Dancerflow, I can run with data science tools, etc., just containerize. So essentially, why would I need Hadoop? If I can take the traditional tools people are now evolving in and using like Jupiter Notebooks, Spark, Dancerflow, you know, those packages with Kubernetes on top of a database as a service and some object store, I have a much easier stack to work with. And I could mobilize that whether it's in the cloud, you know on different vendors. >> Scale is important too. How do you scale it? >> Of course, you have independent scaling between data and computation, unlike Hadoop. So I can just go to Google, and use Vquery, or use, you know, DynamoDB on Amazon or Redchick, or whatever and automatically scale it down and then, you know >> That's a unique position, so essentially, Hadoop versus Kubernetes is a top-line story. And wouldn't that be ironic for Google, because Google essentially created Map Produce and Coudera ran with it and went public, but when we're talking about 2008 timeframe, 2009 timeframe, back when ventures with cloud were just emerging in the mainstream. So wouldn't it be ironic Kubernetes, which is being driven by Google, ends up taking over Hadoop? In terms of running things on Kubernetes and cloud eight on Visa Vis on premise with Hadoop. >> The poster is tend to give this comment about Google, but essentially Yahoo started Hadoop. Google started the technology  and couple of years after Hadoop started, with Google they essentially moved to a different architecture, with something called Percolator. So Google's not too associated with Hadoop. They're not really using this approach for a long time. >> Well they wrote the map-produced paper and the internal conversations we report on theCUBE about Google was, they just let that go. And Yahoo grabbed it. (cross-conversation) >> The companies that had the most experience were the first to leave. And I think it may respect what you're saying. As the marketplace realizes the outcomes of the dubious associate with, they will find other ways of achieving those outcomes. It might be more depth. >> There's also a fundamental shift in the consumption where Hadoop was about a ranking pages in a batch form. You know, just collecting logs and ranking pages, okay. The chances that people have today revolve around applying AI to business application. It needs to be a lot more concurring, transactional, real-time ish, you know? It's nothing to do with Hadoop, okay? So that's why you'll see more and more workers, mobilizing different black server functions, into service pre-canned services, etc. And Kubernetes playing a good role here is providing the trend. Transport for migrating workloads across cloud providers, because I can use GKE, the Google Kubenetes, or Amazon Kubernetes, or Azure Kubernetes, and I could write a similar application and deploy it on any cloud, or on Clam on my own private cluster. It makes the infrastructure agnostic really application focused. >> Question about Kubernetes we heard on theCUBE earlier, the VP of Project BlueData said that Kubernetes ecosystem and community needs to do a better job with Stapla, they nailed Stapflalis, Stafle application support is something that they need help on. Do you agree with that comment, and then if so, what alternatives do you have for customers who care about Stafe? >> They should use our product (laughing) >> (mumbling) Is Kubernetes struggling there? And if so, talk about your product >> So, I think that our challenge is rounded that there are many solutions in that. I think that they are attacking it from a different approach Many of them are essentially providing some block storage to different containers on really cloud 90. What you want to be able is to have multiple containers access the same data. That means either sharing through file systems, for objects or through databases because one container is generating, for example, ingestion or __________. Another container is manipulating that same data. A third container may look for something in the data, and generate a trigger or an action. So you need shared access to data from those containers. >> The rest of the data synchronizes all three of those things. >> Yes because the data is the form of state. The form of state cannot be associated with the same container, which is what most of where I am very active and sincere in those committees, and you have all the storage guys in the committees, and they think the block story just drag solution. Cause they still think like virtual machines, okay? But the general idea is that if you think about Kubernetes is like the new OS, where you have many processes, they're just scattered around. In OS, the way for us to share state between processes an OS, is whether through files, or through databases, in those form. And that's really what >> Threads and databases as a positive engagement. >> So essentially I gave maybe two years ago, a session at KubeCon in Europe about what we're doing on storing state. It's really high-performance access from those container processes to our database. Impersonate objects, files, streams or time series data, etc And then essentially, all those workloads just mount on top of and we can all share stape. We can even control the access for each >> Do you think you nailed the stape problem? >> Yes, by the way, we have a managed service. Anyone could go today to our cloud, to our website, that's in our cloud. It gets it's own Kubernetes cluster, a provision within less than 10 minutes, five to 10 minutes. With all of those services pre-integrated with Spark, Presto, ______________, real-time, these services functions. All that pre-configured on it's own time. I figured all of these- >> 100% compatible with Kubernetes, it's a good investment >> Well we're just expanding it to Kubernetes stripes, now it's working on them, Amazon Kubernetes, EKS I think, we're working on AKS and GK. We partner with Azure and Google. And we're also building an ad solution that is essentially exactly the same stock. Can run on an edge appliance in a factory. You can essentially mobilize data and functions back and forth. So you can go and develop your work loads, your application in the cloud, test it under simulation, push a single button and teleport the artifacts into the edge factory. >> So is it like a real-time Kubernetes? >> Yes, it's a real-time Kubernetes. >> If you _______like the things we're doing, it's all real-time. >> Talk about real-time in the database world because you mentioned time-series databases. You give objects store versus blog. Talk about time series. You're talking about data that is very relevant in the moment. And also understanding time series data. And then, it's important post-event, if you will, meaning How do you store it? Do you care? I mean, it's important to manage the time series. At the same time, it might not be as valuable as other data, or valuable at certain points and time, which changes it's relationship to how it's stored and how it's used. Talk about the dynamic of time series.. >> Figured it out in the last six or 12 months that since real-time is about time series. Everything you think about real-time censored data, even video is a time-series of frames, okay And what everyone wants to do is just huge amount of time series. They want to cross-correlate it, because for example, you think about stock tickers you know, the stock has an impact from news feeds or Twitter feeds, or of a company or a segment. So essentially, what they need to do is something called multi-volume analysis of multiple time series to be able to extract some meaning, and then decide if you want to sell or buy a stock, as in vacation example. And there is a huge gap in the solution in that market, because most of the time series databases were designed for operational databases, you know, things that monitor apps. Nothing that injects millions of data points per second, and cross-correlates and run real-time AI analytics. Ah, so we've essentially extended because we have a programmable database essentially under the hoop. We've extended it to support time series data with about 50 to 1 compression ratio, compared to some other solutions. You know we've break with the customer, we've done sizing, they told them us they need half a pitabyte. After a small sizing exercise, about 10 to 20 terabytes of storage for the same data they stored in Kassandra for 500 terabytes. No huge ingestion rates, and what's very important, we can do an in-flight with all those cross-correlations, so, that's something that's working very well for us. >> This could help on smart mobility. Kenex 5G comes on, certainly. Intelligent edge. >> So the customers we have, these cases that we applied right now is in financial services, two or three main applications. One is tick data and analytics, everyone wants to be smarter learning on how to buy and sell stocks or manage risk, the second one is infrastructure, monitoring, critical infrastructure, monitoring is SLA monitoring is be able to monitor network devices, latencies, applications, you now, transaction rate, or that, be able to predict potential failures or escalation We have similar applications; we have about three Telco customers using it for real-time time. Series analytics are metric data, cybersecurity attacks, congestion avoidance, SLA management, and also automotive. Fleet management, file linking, they are also essentially feeding huge data sets of time series analytics. They're running cross-correlation and AI logic, so now they can generate triggers. Now apply to Hadoop. What does Hadoop have anything to do with those kinds of applications? They cannot feed huge amounts of datasets, they cannot react in real-time, doesn't store time-series efficiently. >> Hapoop (laughing) >> You said that. >> Yeah. That's good. >> One, I know we don't have a lot of time left. We're running out of time, but I want to make sure we get this out here. How are you engaging with customers? You guys got great technical support. We can vouch for the tech chops that you guys have. We seen the solution. If it's compatible to Kubernetes, certainly this is an alternative to have really great analytical infrastructure. Cloud native, goodness of your building, You do PFC's, they go to your website, and how do you engage, how do you get deals? How do people work with you? >> So because now we have a cloud service, so also we engage through the cloud. Mainly, we're going after customers and leads, or from webinars and activities on the internet, and we sort of follow-up with those customers, we know >> Direct sales? >> Direct sales, but through lead generation mechanism. Marketplace activity, Amazon, Azure, >> Partnerships with Azure and Google now. And Azure joint selling activities. They can actually resale and get compensated. Our solution is an edge for Azure. Working on similar solution for Google. Very focused on retailers. That's the current market focus of since you think about stores that have a single supermarket will have more than a 1,000 cameras. Okay, just because they're monitoring shelves in real-time, think about Amazon go, kind of replication. Real-time inventory management. You cannot push a 1,000 camera feeds into the cloud. In order to analyze it then decide on inventory level. Proactive action, so, those are the kind of applications. >> So bigger deals, you've had some big deals. >> Yes, we're really not a raspberry pie-kind of solution. That's where the bigger customers >> Got it. Yaron, thank you so much. The CTO of Iguazio Check him out. It's actually been great commentary. The Hadoop versus Kubernetes narrative. Love to explore that further with you. Stay with us for more coverage after this short break. We're live in day 2 of CUBE NYC. Par Strata, Hadoop Strata, Hadoop World. CUBE Hadoop World, whatever you want to call it. It's all because of the data. We'll bring it to ya. Stay with us for more after this short break. (upbeat music)

>> Presenter: Live from Copenhagen, Denmark, it's the Cube. Covering Kubecon and CloudNativeCon Europe 2018. Brought to you by the Cloud Native Computing foundation, and it's ecosystem partners. >> Well, welcome back everyone, we're live here with the Cube in Copenhagen, Denmark, for KubeCon 2018 Europe, via the CFCF Cloud Native Computing foundation, part of the Linux foundation. I'm John Furrier, my co-host Lauren Cooney here this week. And up next to Yaron Haviv, the founder, and CTO of Iguazio, and Doug Davis, who is the co-chair of the serverless working group, And the CNCF, as well as a developer advocate for IBM, IBM cloud. Great to see you welcome to the Cube. >> Thank you. >> Thanks. >> Thanks for coming in. So love the serverless work, and want to dig into that with a bunch of questions. So, super important trend as we see in that success functions, and all the good stuff that's going on, programmable infrastructure. So I want to dig into that. But first, Yaron, I want to get into what's going on with the business, what's new with you? Iguazio, I saw you're on the sponsorship list here, you're doing a lot of work. You have some news as well. What's going on at KubeCon, Europe for you. >> Yeah, so we're expanding on the business side very nicely, taking more momentum, and this strength towards edge analytics, edge cloud, people starting to understand that central cloud is not the only way to build clouds. We're also progressing nicely on our serverless framework, called Nuclio. It just was published, maybe eight months ago, already made 2000 stars in GitHub, you know, users. We've got some quotes, NPR's around production version of that, including strong partnership with Acer, on being able to run the same functions in Acer, and the cloud in a joint development effort, as well as customers actually using it to build real-time analytics use case in development in the cloud, and deployment in different locations. >> Our audience knows you well, you've been on the cube many times. You also write for us, as well as other blogs with your opinion pieces and commentary. It's always edgy, and strong, and right on the money, I want to ask you your thoughts on serverless, because you were there from day one, I remember the conversation. It wasn't called serverless, we were talking about resource pools and looking at cloud computing, pontificating about, potentially, what Kubernetes and orchestration was going to look like. It's happening. So, are you happy with the progress of the industry, performance of the tech stack? What's your thoughts on serverless today, state of the union? What's your opinion? >> I think it's progressing nicely. I think many people call everything almost, serverless now. You have serverless data bases, you have serverless everything. I think serverless will become, more and more, a feature of a platform, not necessarily a thing. But, like Salesforce will have serverless functions, Wix will have serverless functions, for their own stuff. Obviously cloud platforms, analytic platforms, et cetera. So there'll be, maybe a family of generic ones, and a family of platform specific, that are more use case oriented. >> Does that connect with your business plan for Iguazio? Are you evolving with it? How are you navigating those waters on the adoption side. >> So, you know, I'm sort of trying to be inclusive, I think there's room for more than one serverless framework. There's also OpenWhisk, and Openfazzer, and a few of those. Our focus is mainly real-time analytics, and high performance in data processing. Yes, we can also do other things, but maybe we won't invest too much in some features that are more front-end oriented, or stuff like that. >> John: So you're staying focused on the core. >> Yes, on the other hand, other people to deal with front-end, we'll focus on HTTP, and Blue Logic, and things like that. Most of the frameworks don't have the same capabilities of Nuclio, like real-time stream distribution, real-time, low latencies, all that stuff. So, I think there's room for multiple frameworks, and that's also part of the relationship with Acer. Acer have their own product, which is very good with integration with the Acer stack, and the Acer components. On the other hand there is real-time analytics, in IOT Nuclio is stronger, So, there interest is, rather than saying, no we'll choose just one horse, why won't we enable the market, and allow the people the choice in solution. >> That's great. On IBM's side, Doug I want to get your thoughts on the working group, as well as IBM. You guys have done a lot of open source, IBM well known in the Linux history books, as we know. And now very active again, continuing that mission, congratulations, and thanks for doing that. But the serverless working group. This is a broader scope now, can you just give us some color on the commentary around how that's evolving, because you guys have a lot of blue chip customers. Cloud Foundry just did a survey, I was talking to Abby Kearns yesterday, about the results came back, mainstream tech, not middle of the country, but they heard about Kubenettis like, what's kubenettis? So you have people going, Okay, I've got a job to do, but now kubenettis has arrived, this is a key part of a micro-services focus. >> Right. Yeah, and so the way the serverless group got started was, about a year ago the CNCF TOC, technical oversight committee, decided serverless is kind of a new technology, we want to figure out what's going on in that space, and so they started up a working group. And our job wasn't to really decide what to do about it yet, it was to sort of give us the landscape of what's going on out there, what are people doing? What does serverless even mean, relative to function of the service, or even the other as's, and stuff like that What does a serverless framework generally look like? What do people use it for? Use cases, and stuff like that. And then at the end of that we produced a white paper with our results, as well as a landscape spreadsheet, to say all of the various technologies out there in that space, who's doing what. Without trying to pick winners, just saying what's there. And then we ended with a set of recommendations in terms of what possible next steps the CNCF could do in this space, with an eye towards interoperability building more than anything else, because that's what, really, we care about. We don't want vendor lock in and all the other good stuff. And so we had a set of recommendations, and one of the main ones was, two main things, one was function signatures was a very popular one, but we decided to focus on eventing first, because we thought that might be an easier fruit to pick off the tree first. And so we were going to focus on the formats, or meta data of an event, as it transfers between systems. And so from the service working group we create a cloud events, sort of little sub-group within our working group, to focus on creating a specification around what the meta-data around an event would look like, just so we can get some commonality. That way, at least the infrastructure between the two systems can transfer the events back and forth, much in the same way HTTP layer, doesn't have to understand the body of the message, but can look at common headers, and know how to route it properly. Same kind of thing with eventing. And again, this is all about trying to get interoperability, and portability for applications, and users more than anybody else. And so that's kind of where our focus has been on. How can we help the end user not get locked into one platform, not get locked into one solution, and make their life easier overall. >> Great. Where are you now with that? Is it running? Is it-- >> Overall done. No. >> Oh you're complete, yeah (laughs) >> Doug: But we did that last week. No, actually as of last week though, we just released our first version, 0.1. It's a very, very basic thing, and people might look at it and say, what's the big deal? But even with that simple little thing we've been able to get some level of interoperability between the various platforms. And if people actually join, when is it? Friday 11 o'clock? >> Yaron: Yeah. >> We have a session where someone's going to demonstrate interoperability between, oh gosh, IBM, you guys, Microsoft. >> Google. >> Dameware, Google. All the various companies involved in this thing. >> Love it, that's great. >> Huawei. >> Yeah. They're all going to be either sending or receiving events, using the cloud event format, to prove interoperability around the specification. So we're just at 0.1, we have some way to go, but that first step was huge just to get agreement, and everybody to the table to agree. So it's been really fun >> And it wasn't easy, it wasn't easy. And he's the peacemaker in the group. (laughs) I'm the troublemaker, he's the peacemaker. >> We have a lot of vocal people in the group, yes. (laughs) >> We're not pointing at anyone. >> No, never. >> Important first step obviously, commonality, and having some sort of standardization kind of thinking. >> Doug: Yes. >> Yaron: Don't use the standard word. There are people allergic to that. >> Well yeah, the standard bodies and what not, but in terms of the community work going on, this is super important. What's the impact of that? Obviously it's a small step, but a big step, right? So, what's it going to impact? What's next, what's coming next now that you've got the meta-data, and you've got the interoperability, what's next? >> Well, obviously we need to finish it up, because 0.1 is obviously just the first step. As I said, I think beyond that people are really itching to do function signatures. Because I think if you can get the event format coming in to be somewhat similar, and then you can get portability of moving your function from one platform to another, with hopefully minimal changes from a function signature point of view, you're a long way there towards getting portability for people. And I think that's probably the next step we're going to be looking at. >> What's the technical case from a commercial entity like yourself, who's in business to make money, obviously you have a business to run. As you build out your architecture, where is this going to be applied for you? What's the impact of this project to your product? >> So beyond my strong religion around open APIs, and you've seen the blogs I've written about it, our interest is twofold. First, we're not the market leader, Amazon is the market leader, et cetera. So if we have a better technology, and things are standard, it's easier for customers to move. Second, is we believe in interoperability, closer to the data, closer to where the processing, especially when 5G is going to evolve, and we're going to see bottlenecks between metro locations. Our sales is, go develop in the cloud, and then push it, you know the diesel twin model. This is exactly what we're demonstrating with Acer. You could develop at Acer, our Nuclio functions and deploy in a factory. So it may not be the same platform, it may not be the same serverless framework. So having the ability to run the same code in different frameworks or different platforms is very important. >> And IBM, you're doing a lot of work. OpenWhisk has been something that's gotten a lot of press and notoriety. What's up with you guys and open source? Obviously we see you guys out there doing a lot of studies and a lot content, a lot of coding. What's new over on the IBM side of the house with serverless? >> From my point of view, I think probably the biggest thing is, we're leading the charge in putting OpenWhisk to run on top of Kubernetes. And I think what's interesting about that is we're going to see, probably, some changes to Kubernetes need to be made to get the better performance that we need. Because when OpenWhisk runs vanilla on top of, say run C, or the docker stuff, we have a lot more freedom there. Pausing containers, stuff like that. Stuff you can't do in Kubernetes. We're probably going to see some more pressure on Kubernetes to add some more features, to get the kind of performance numbers we need going forward. >> And scale too, is important to understand. I was just talking about the keynotes earlier with another guest, and Cern is up there. They have a thousand nodes, it's not massive numbers yet, at scale, I mean Amazon are the big clouds, you guys have clouds. You've got a lot of nodes, so it's a lot more scale going on in the cloud as Kubernetes starts to get it's footing. >> Doug: Yep. >> How do you explain Kubernetes, how do both of you guys explain Kubernetes to the IT transformation group out there, that's going cloud operations. >> So what we've seen, because we're also selling an appliance, a full integrated solution, people, in the enterprise, they don't necessarily want to understand low level of Kubernetes. And actually serverless is a nice way for doing that. If you look at the new Nuclio dashboard, you just go, you write some code, you click deploy, it auto scales, you don't need to think about the underlying cube cut whole, the underlying networking. It's all done there for you. And I think, what you see in the trend in the industry, some people call it serverless, some people call it other things, is more and more abstractions, where users will deploy code, will deploy containers, and some frameworks underneath will deal with the high availability, elasticity, all that. I think that's what enterprise customers are looking for. Not everyone is eBay, and Google, and Netflix. >> John: Your thoughts? >> What I think is interesting, I agree with what you said, but I think it's interesting is you actually have a wider range of people, right. You have some people who think Kubernetes, as you said, nice abstraction layer, you don't have to get into the nitty gritty if you don't need to. But Kubernetes does allow you to get under the covers and twiddle those lower level bits if you actually need to. I think that's one of the things that. People who start out with Docker, they like it, it's so simple to use, and it's wonderful, and they love it. But they found it a little bit limiting, because it was too opinionated, or it didn't give you access to things under the covers. Kubernetes, I think, is trying to find that right balance between the two, and I think for the most part they kind of hit it. There's a little bit more of a learning, because it's not quite as user friendly as Docker is. But once you get over that learning hump, all the flexibility it gives you, people seem to really, really, like that. >> What are some of the things that people do under the covers, you mentioned some tweaks here and there. Is it policy based stuff? What's happening under the covers that Kubernetes getting that their groove swing on now. >> There is something called custom resource definition. So for example, when we deploy a Nulio, maybe OpenWhisk or others have it as well. It's essentially, Nuclio becomes another resource that you can actually view when you're running the Kubernetes CLI, or all the other things that manage it's liveliness, et cetera. So those are services that you get for free as a platform. But if you want your function to keep being alive you need to code your functions into the liveliness API, the thing that monitors it staying alive. So you're getting a generic service, but you need to work with it. >> Yeah, actually I'd go one step further with that and abstract it a little. Because obviously Kubernetes has a lot of knobs you can turn, a lot more than other platforms, like Docker has. But I think, for me the biggest benefit of Kubernetes is the plugability. Custom resource definitions, one of them. Ripping out schedulers, or whatever controllers you want, and replace it with your own. That kind of flexibility to say, I don't have to leave the entire Kubernetes world just to run my own scheduler, or write the infrastructure around it, I can plug in my own. That's the kind of flexibility people seem to really, really like. That way they don't feel locked in, they can still play with part of the ecosystem, but get the flexibility and customization they need. >> Awesome, great commentary there. I want to get your thoughts on KubeCon 2018 Europe, for CNCF. Continuing to see growth in CNCF, fantastic to see. As the boat gets full of people, you've got to be the peacemaker if you're co-chair. As people want to start getting their claws into the projects, this imbalance on the community side, are you guys happy with the direction, obviously the success, and the visibility is increased. What's your take on the show here? What are you guys doing? What's going on around the event for you guys. >> So it only started today, but my impression, comparing it with the previous show in the U.S. There are a lot more decision makers here. I don't know if it's the European culture of not funding every student to every show, or just the maturity of the ecosystem. But that's something I've noticed, the discussions I had with decision makers. and they're also not everyone, like in the U.S.A. everyone wants to build it their own way. People here think about operationalizing solutions, so sometimes you need to take something that someone else already built and test. >> And what's the conversations like, that you're having? Is it architecture? Is it deploying production workloads? >> So for us it's a lot about use cases, because we're doing things in a very different way. We're doing some nice demos on how, we're running real-time analytics with the sample database as the core, and we're showing how it's equivalent to another solution that they may build. And that immediately clicks. The other aspect is really, there is so much technology, but we need someone to wrap it up for us as a package solution. >> Doug, your thoughts. First of all I love your shirt, it says code with all the words in the community. >> Doug: Yeah, it's one of my favorite shirts. I like it. >> Love that shirt. I'm just looking at it like, all these questions are popping in my head. What's your plan at the show here? What's your goal, what are you guys doing, what conversations are you hearing in the hallways? >> Well, obviously being from IBM, we just promote IBM as much as we can. But beyond that, really talk about interoperability around what we're doing here, and make sure people understand that we're not here to necessarily sell our products, which we obviously want to do. We want to make sure that we do it in a way that gives people choice. And that's why we have the serverless working group, the cloud events spec. It's all about giving everybody the choice to move from one platform to another, to get their job done. As much as we want people to buy our stuff, if the customer isn't happy in getting what they need, then we're all going to lose. >> And these projects are super important to get the solidarity around these, quote, standards. >> And just to follow on your previous question about the conference, and stuff that we'd like. Obviously it's great that it's growing so much, but what I really like about this conference, beyond some other ones that I've seen is, a lot of the other ones tend to have more marketing flair to them. And obviously there's a little bit of that here, people are promoting their stuff, but I love the fact that most of the stuff that I'm doing here aren't in the sessions. Because the sessions are great and interesting, but it's the hallway chatter, and interacting with people face to face, and not just to meet them, to actually have real technical, deep discussion with them, here at the conference, because everybody's here you can do that much better face to face than you can over a Zoom call, or something else. The productivity from that level is just astronomical, I love it. >> Yeah, I totally agree. And one thing I would add, just my observation, interviews in the hallways, is that we're living, and we talk about this on the Cube all the time, a modern software architectures here. And it's got some visibility around it, it's not filled in yet, but I think there's clear visibility. Cloud, micro-service, interoperability, portability, pretty clear. And I think people are engaged, people are excited. So you have the progressive new guard coming in, on board. Great job. Thanks for coming on the cube, we appreciate that. >> Thank you. >> Thank you. >> Iguazio and IBM, here on the Cube, breaking down KubeCon 2018 Europe. More live coverage, stay with us, we'll be right back after this short break. (electronic music)

Live from Las Vegas. It's the Cube covering AWS Reinvent 2017 presented by AWS, Intel, and our ecosystem of partners. >> Hello, welcome back. This is live coverage of the Cube's AWS re:Invent 2017. Two sets, a lot of action, day one of three days of wall to wall coverage. I'm John Furrier with my co-host Keith Townsend. Our next guest cube alumni is Yaron Haviv who's the founder and CTO of Iguazio, a hot new start up. And big news coming next. We got a big announcement. In following their work, Yaron, good to see you again. Thanks for coming back on. >> Hi, thanks! >> Hey you got a new shirt. Share that logo there. >> That's nuclio. That's our new serverless brainwork which is open source. Really kicks ass, it's about 100 times faster than Amazon. >> Word says it's 200 times faster. >> Yeah we don't want to shame. >> You set the bar. >> We doing 400,000 events per second on a single process. They do about 2000. Most of the open source project around the same ball park. >> Yaron, I got to get this off the bat. And then we can have a nice discussion afterwards. A pleasant discussion. Serverless. Let's first define what that means. Because there's a bunch of- I can take nuclio, install it in my data center, run it, am I serverless? >> You know so I mean I'm in the serverless working group. >> For CNCF >> for CNCF. And a we had a hot debate between the open source start ups. Doing what is called functional service and Amazon and others trying to push the notion of serverless. Which is serverless stands for server less. Meaning you don't manage server. And the way we position nucleo, it's actually both. Because on one end you can consume it as an open source project. Very easy to download. Single docker instruction and it's up and running unlike some other solutions. And on the other hand you can consume it as something within the Iguazio data platform. There is a slide from Amazon which I really like. Which is about serverless. They show serverless is attached to kinesis, DynaomoDB, S3 and Athena. Four services of data that attach to Lamda. Iguazio has API compatibility with kineses, DynamoDB with S3 and Presto, which is Athena as well. So exactly the same four data services that they position as far as the service ecosystem are supported on our platform. So we provide one platform, all the data services at Amazon has or at least interesting ones, serverless functions which are a hundred times faster, a few more tricks that they don't have-- >> So what is the definition then. In a pithy way, for someone out there who's learning about serverless. What is it? What's the definition? >> So the notion as a developer, you're sort of avoiding IT. You go, open a nice portal, you write the function, or you write your function in a get up repository somewhere. You click on a button and it gets deployed somewhere. Right now you know where it's going to get deployed. In the future, you may not know. >> Instead of an EC2 instance, get that prepared >> It's not really an EC2. >> The old way. The old way was. Right? >> The old way there were infrastructure guys building your EC2 instance, security layers, milware, etc. You go develop on your laptop and then you need to go and conform and all the continuous integration play was very complicated. Serverless comes inherently with scale out without the scale in, with continuous integration. You have versioning for the code. You can downgrade the version, you can upgrade the version. So essentially its a package version of a cloud native solution. That's the general idea. >> So I can do that if I'm doing it and managing it myself. It functions as a server. And if I'm doing it and it's a provided it as a cloud provider as a server, as a service, it's serverless. None of my operations team is dealing with servers. It's just writing code and just go. >> Yeah, you're writing a function. Push commit. You should play with nucleo, not just other things. But you'll see you're writing a function. Even see it has a built in editor. You write, you push deploy and it's already deployed somewhere. >> So give us some perspective before you move on. On the game what the impact is to a developer. Apples to oranges. Our old way you described it, new ways, it sounds easier! What's the impact? Is it time? Money? Can you quantify? >> The biggest challenge for businesses is to transform. I saw an interesting sentence. It's not about digital transformation, it's about businesses that need to work in a digital world. Okay? Because again, most of the communication of customers to businesses is becoming digital. Okay? Whether it's today from mobile apps tomorrow through Alexa. >> As Luke Cerney says, it's all software. Your business is the software. >> It's all about interactive really. Okay. As a business I always position there are two things you need to take care of as a business. One is increasing the revenue. And that's by engaging more customers. And increasing the revenue per customer. How do you engage more customer? Through digital services. Whether it's Twitters or proving a new service through your web portal. And the next thing is how do you generate more revenue from a customer is by showing recommendations. >> Finding more value. >> And the other aspect is operational efficiency. How do you automate your reparations to reduce the cost. You know Amazon uses robots to do the shipping and packing. So their margins can now be lower. So the generator is both those things. Reducing cost is becoming more and more dependent on automation which is digital. And increasing revenue become more about customer engagement which is digital. Okay so now you're a traditional enterprise. And you have your exchange to worry about. And all the legal stuff and the mainframes. But if you're not going to work on the transformation piece. You're going to die. Because some other start up is going to build insurance company which is sort of agile and all that. >> So you made an interesting comment earlier when you were talking about nucleo. And integrating the functions that really matter. The services that matter. Amazon releases 800 new services a year. >> Actually 1300. >> I'm sorry 1300. >> This time less, no? >> Right now they're at 1130 and they expect 1500, 1700 by the end of the year. Two years ago it was like 750 and then the year before that was 600. >> So is that an indicator as to Amazon's leading this race between the big, I don't know, three, four cloud providers. Rack and stack them for us. How do we assess the capability? >> It's a matter of mentality. Okay. Persos thinks like a supermarket. Just like an Amazon market. I could say I need a cover for my iPad. I'm gonna get 100 covers for my iPad. No one really, I need to now choose. So their strategy is we'll put dozens of services that do similar things. One is better at this, one is better at that. We control the market we'll sell more. We have a different approach. We do fewer services but each one sort of kicks ass. Each one is much better, much faster, much better engineered. Okay? This is also why we are on data plus provides 10 different data APIs and not 10 different individual data platforms. >> Alright so let's talk about the scoreboard. Even though they might be thinking about the supermarket. You've got Amazon, Azure Microsoft and Google. I've looked at some of the data. I mean, Microsoft's been international for a while from their MSN business. They now have Skype. They have data centers, they know a little bit about cloud. Amazon's got a lot more services. They support multiple versions of things. Google is kind of non-existent on the scale of comprehensiveness. >> Have you looked at their serverless functions? By the way? >> There's new stuff. Tensorflow, serverless. >> But serverless they only support an OJS. They have very few triggers and it's still defined as beta. >> That's the point, so people are touting my Forbes article. They're touting like a feature. There's a lot more that needs to get done. So the question I have for you is. There's a level of comprehensiveness that you need now. And I know you guys spend a lot of time building your solution. We've talked abut this at our last Cube interview. So the question is the whole MVP cousin, minimal viable product. Is great when you're building a consumer app for an iPhone. But when you start talking about a platform and now cloud. Question to you is there a level of completeness bar to be hurdled over for a legit cloud or cloud player? >> I don't think you need 1000 services to build a good cloud. But you do need a bunch of services. Okay? Now the way we see the world like Satya. Okay? Which is there is a core cloud. But there is sort of a belt around it which is what we call intelligence cloud. We would define ourselves as the intelligence cloud. So if someone is building a machine learning model and it needs a 5 year worth of data. And it just needs to do crawling on top of it. It's not really an interesting problem. It's commoditized, lots of CPO power, object storage. But the bigger challenge is doing game referencing close to the edge. This is what needs to happen in real time. You need fewer services but you need to be real time. >> Smarter integration to do that. Right? I mean. >> You have density problems. You don't have a lot of room to put a 100 servers. It needs to be a lot more integrated. You know look at Azure stack. Their slogan is consistency. Look at a slide that shows which Azure services are part of Azure stack. Less than 20%. Because it's a lot more complicated to take technology design whereas hyper scale and put them on few servers. >> How do customers figure it out? What does a customer do? It's all mind boggling. >> I love that concept of core services and then value around those core services. What are those core services that a cloud must have before I start to invest in that cloud providers strategy? >> So the point again, there's a lot of legacy that you need to grab with you. Especially someone like Amazon. So they have to have VMs and migration services from Oracle, etc. But let's assume I'm a start up and building a new client native applications. Do I need any of that? No. I can probably can do with containers. I don't really need to be VMs. I can use something like cybernetics, I can use sequel databases maybe some like sequel. So I can redesign my application differently with a lot fewer services. The problem for someone like Amazon in order to grow and be a supermarket, you have to have ten of everything. If I'm someone that focus on new applications I don't need so many services and so much legacy. >> Well I'll say one thing. You can call them a supermarket, use that retail analogy, I buy that analogy only to the extent that you used it. But if that's the case, then everyone's hungry for food. And they're the only supermarket in town. >> But Wholefoods maybe less stuff on the shelf. >> Everyone else is like a little hot dog stand compared to the supermarket. Amazon is crushing it. Your thoughts? I say that. Are they kicking ass? >> Obviously Amazon is kicking ass. But I think Azure is ramping up faster. Amazon is generating more alienation among people that they are starting to compete with. You know. >> Azure is copying Amazon. Right? >> Yeah. But they have a different angle. They know how to sell to enterprises. They already have the foot in the door for Office 365. I've talked to a customer. We're going Azure. I say why? >> Together: They've got 365. >> We already certify the security with 365 for us to use Azure it's a- >> Right up until that next breech. >> So the guys owning ITs, it's easier for them to go to Azure. The developers want Amazon. Because Amazon is sexier. >> We got to break. We debated this on the intro segment with he analyst. Question. IT buyers have been driven by a top down CIO driven, CXO driven waterfall, whatever you want to call it, old way. With developers now at the driver's seat, with all of this serverless function, serverless coming around the corner very fast. Are developers driving the buying decisions or not? Or is it IT? The budget's still there. They want to eliminate labor. They want more efficiencies. Are you seeing it again? Will it happen? >> Yeah because we are just in the middle. On one end we're an infrastructure. We're an infrastructure consumed by developers. So we keep on having those challenges within the accounts themselves. IT doesn't get what we're doing. Serverless, and database is serverless. Because they like to build stuff. They want to take the nutanix and take a hundred services on top of it. And it will take them two years to integrate it. By that time the business already moved somewhere else. >> So IT could be a dinosaur like the mainframe? >> Right. I think the smart ITs understand they need to adopt cloud instead of fight it. And more the line further up the step. And that sort of the thing we are trying to provide to them. When you are building stuff you are buying EMC storage. You are not just taking discs. So why do you focus on this low level block storage when you're buying infrastructure. Why no buy database as a service. And then you don't need all the hassle. Streaming is a service. Serverless is a service. And then you don't need all that stack. >> Yaron, you should be our guest analyst. But you're too busy building a company. We're going see you next week in Austin for Cubicon. Congratulations. I know you guys have worked hard. The founder and CTO of Iguazio. You're going to hear a lot about these guys. Smart team. They're either going to go big or go home. I think they're going to go big. Congratulations. More coverage here at AWS Re:Invent after this short break. I'm John Furrier with Keith Townsend.

>> Announcer: Live from midtown Manhattan, it's theCUBE, covering BigData New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Okay, welcome back everyone, we're live in New York City, this is theCUBE's coverage of BigData NYC, this is our own event for five years now we've been running it, been at Hadoop World since 2010, it's our eighth year covering the Hadoop World which has evolved into Strata Conference, Strata Hadoop, now called Strata Data, and of course it's bigger than just Strata, it's about big data in NYC, a lot of big players here inside theCUBE, thought leaders, entrepreneurs, and great guests. I'm John Furrier, the cohost this week with Jim Kobielus, who's the lead analyst on our BigData and our Wikibon team. Our next guest is Yaron Haviv, who's with iguazio, he's the founder and CTO, hot startup here at the show, making a lot of waves on their new platform. Welcome to theCUBE, good to see you again, congratulations. >> Yes, thanks, thanks very much. We're happy to be here again. >> You're known in the theCUBE community as the guy on Twitter who's always pinging me and Dave and team, saying, "Hey, you know, you guys got to "get that right." You really are one of the smartest guys on the network in our community, you're super-smart, your team has got great tech chops, and in the middle of all that is the hottest market which is cloud native, cloud native as it relates to the integration of how apps are being built, and essentially new ways of engineering around these solutions, not just repackaging old stuff, it's really about putting things in a true cloud environment, with an application development, with data at the center of it, you got a whole complex platform you've introduced. So really, really want to dig into this. So before we get into some of my pointed questions I know Jim's got a ton of questions, is give us an update on what's going on so you guys got some news here at the show, let's get to that first. >> So since the last time we spoke, we had tons of news. We're making revenues, we have customers, we've just recently GA'ed, we recently got significant investment from major investors, we raised about $33 million recently from companies like Verizon Ventures, Bosch, you know for IoT, Chicago Mercantile Exchange, which is Dow Jones and other properties, Dell EMC. So pretty broad. >> John: So customers, pretty much. >> Yeah, so that's the interesting thing. Usually you know investors are sort of strategic investors or partners or potential buyers, but here it's essentially our customers that it's so strategic to the business, we want to... >> Let's go with GA of the projects, just get into what's shipping, what's available, what's the general availability, what are you now offering? >> So iguazio is trying to, you know, you alluded to cloud native and all that. Usually when you go to events like Strata and BigData it's nothing to do with cloud native, a lot of hard labor, not really continuous development and integration, it's like continuous hard work, it's continuous hard work. And essentially what we did, we created a data platform which is extremely fast and integrated, you know has all the different forms of states, streaming and events and documents and tables and all that, into a very unique architecture, won't dive into that today. And on top of it we've integrated cloud services like Kubernetes and serverless functionality and others, so we can essentially create a hybrid cloud. So some of our customers they even deploy portions as an Opix-based settings in the cloud, and some portions in the edge or in the enterprise deployed the software, or even a prepackaged appliance. So we're the only ones that provide a full hybrid experience. >> John: Is this a SAS product? >> So it's a software stack, and it could be delivered in three different options. One, if you don't want to mess with the hardware, you can just rent it, and it's deployed in Equanix facility, we have very strong partnerships with them globally. If you want to have something on-prem, you can get a software reference architecture, you go and deploy it. If you're a telco or an IoT player that wants a manufacturing facility, we have a very small 2U box, four servers, four GPUs, all the analytics tech you could think of. You just put it in the factory instead of like two racks of Hadoop. >> So you're not general purpose, you're just whatever the customer wants to deploy the stack, their flexibility is on them. >> Yeah. Now it is an appliance >> You have a hosting solution? >> It is an appliance even when you deploy it on-prem, it's a bunch of Docker containers inside that you don't even touch them, you don't SSH to the machine. You have APIs and you have UIs, and just like the cloud experience when you go to Amazon, you don't open the Kimono, you know, you just use it. So our experience that's what we're telling customers. No root access problems, no security problems. It's a hardened system. Give us servers, we'll deploy it, and you go through consoles and UIs, >> You don't host anything for anyone? >> We host for some customers, including >> So you do whatever the customer was interested in doing? >> Yes. (laughs) >> So you're flexible, okay. >> We just want to make money. >> You're pretty good, sticking to the product. So on the GA, so here essentially the big data world you mentioned that there's data layers, like data piece. So I got to ask you the question, so pretend I'm an idiot for a second, right. >> Yaron: Okay. >> Okay, yeah. >> No, you're a smart guy. >> What problem are you solving. So we'll just go to the simple. I love what you're doing, I assume you guys are super-smart, which I can say you are, but what's the problem you're solving, what's in it for me? >> Okay, so there are two problems. One is the challenge everyone wants to transform. You know there is this digital transformation mantra. And it means essentially two things. One is, I want to automate my operation environment so I can cut costs and be more competitive. The other one is I want to improve my customer engagement. You know, I want to do mobile apps which are smarter, you know get more direct content to the user, get more targeted functionality, et cetera. These are the two key challenges for every business, any industry, okay? So they go and they deploy Hadoop and Hive and all that stuff, and it takes them two years to productize it. And then they get to the data science bit. And by the time they finished they understand that this Hadoop thing can only do one thing. It's queries, and reporting and BI, and data warehousing. How do you do actionable insights from that stuff, okay? 'Cause actionable insights means I get information from the mobile app, and then I translate it into some action. I have to enrich the vectors, the machine learning, all that details. And then I need to respond. Hadoop doesn't know how to do it. So the first generation is people that pulled a lot of stuff into data lake, and started querying it and generating reports. And the boss said >> Low cost data link basically, was what you say. >> Yes, and the boss said, "Okay, what are we going to do with this report? "Is it generating any revenue to the business?" No. The only revenue generation if you take this data >> You're fired, exactly. >> No, not all fired, but now >> John: Look at the budget >> Now they're starting to buy our stuff. So now the point is okay, how can I put all this data, and in the same time generate actions, and also deal with the production aspects of, I want to develop in a beta phase, I want to promote it into production. That's cloud native architectures, okay? Hadoop is not cloud, How do I take a Spark, Zeppelin, you know, a notebook and I turn it into production? There's no way to do that. >> By the way, depending on which cloud you go to, they have a different mechanism and elements for each cloud. >> Yeah, so the cloud providers do address that because they are selling the package, >> Expands all the clouds, yeah. >> Yeah, so cloud providers are starting to have their own offerings which are all proprietary around this is how you would, you know, forget about HDFS, we'll have S3, and we'll have Redshift for you, and we'll have Athena, and again you're starting to consume that into a service. Still doesn't address the continuous analytics challenge that people have. And if you're looking at what we've done with Grab, which is amazing, they started with using Amazon services, S3, Redshift, you know, Kinesis, all that stuff, and it took them about two hours to generate the insights. Now the problem is they want to do driver incentives in real time. So they want to incent the driver to go and make more rides or other things, so they have to analyze the event of the location of the driver, the event of the location of the customers, and just throwing messages back based on analytics. So that's real time analytics, and that's not something that you can do >> They got to build that from scratch right away. I mean they can't do that with the existing. >> No, and Uber invested tons of energy around that and they don't get the same functionality. Another unique feature that we talk about in our PR >> This is for the use case you're talking about, this is the Grab, which is the car >> Grab is the number one ride-sharing in Asia, which is bigger than Uber in Asia, and they're using our platform. By the way, even Uber doesn't really use Hadoop, they use MemSQL for that stuff, so it's not really using open source and all that. But the point is for example, with Uber, when you have a, when they monetize the rides, they do it just based on demand, okay. And with Grab, now what they do, because of the capability that we can intersect tons of data in real time, they can also look at the weather, was there a terror attack or something like that. They don't want to raise the price >> A lot of other data points, could be traffic >> They don't want to raise the price if there was a problem, you know, and all the customers get aggravated. This is actually intersecting data in real time, and no one today can do that in real time beyond what we can do. >> A lot of people have semantic problems with real time, they don't even know what they mean by real time. >> Yaron: Yes. >> The data could be a week old, but they can get it to them in real time. >> But every decision, if you think if you generalize round the problem, okay, and we have slides on that that I explain to customers. Every time I run analytics, I need to look at four types of data. The context, the event, okay, what happened, okay. The second type of data is the previous state. Like I have a car, was it up or down or what's the previous state of that element? The third element is the time aggregation, like, what happened in the last hour, the average temperature, the average, you know, ticker price for the stock, et cetera, okay? And the fourth thing is enriched data, like I have a car ID, but what's the make, what's the model, who's driving it right now. That's secondary data. So every time I run a machine learning task or any decision I have to collect all those four types of data into one vector, it's called feature vector, and take a decision on that. You take Kafka, it's only the event part, okay, you take MemSQL, it's only the state part, you take Hadoop it's only like historical stuff. How do you assemble and stitch a feature vector. >> Well you talked about complex machine learning pipeline, so clearly, you're talking about a hybrid >> It's a prediction. And actions based on just dumb things, like the car broke and I need to send a garage, I don't need machine learning for that. >> So within your environment then, do you enable the machine learning models to execute across the different data platforms, of which this hybrid environment is composed, and then do you aggregate the results of those models, runs into some larger model that drives the real time decision? >> In our solution, everything is a document, so even a picture is a document, a lot of things. So you can essentially throw in a picture, run tensor flow, embed more features into the document, and then query those features on another platform. So that's really what makes this continuous analytics extremely flexible, so that's what we give customers. The first thing is simplicity. They can now build applications, you know we have tier one now, automotive customer, CIO coming, meeting us. So you know when I have a project, one year, I need to have hired dozens of people, it's hugely complex, you know. Tell us what's the use case, and we'll build a prototype. >> John: All right, well I'm going to >> One week, we gave them a prototype, and he was amazed how in one week we created an application that analyzed all the streams from the data from the cars, did enrichment, did machine learning, and provided predictions. >> Well we're going to have to come in and test you on this, because I'm skeptical, but here's why. >> Everyone is. >> We'll get to that, I mean I'm probably not skeptical but I kind of am because the history is pretty clear. If you look at some of the big ideas out there, like OpenStack. I mean that thing just morphed into a beast. Hadoop was a cost of ownership nightmare as you mentioned early on. So people have been conceptually correct on what they were trying to do, but trying to get it done was always hard, and then it took a long time to kind of figure out the operational model. So how are you different, if I'm going to play the skeptic here? You know, I've heard this before. How are you different than say OpenStack or Hadoop Clusters, 'cause that was a nightmare, cost of ownership, I couldn't get the type of value I needed, lost my budget. Why aren't you the same? >> Okay, that's interesting. I don't know if you know but I ran a lot of development for OpenStack when I was in Matinox and Hadoop, so I patched a lot of those >> So do you agree with what I said? That that was a problem? >> They are extremely complex, yes. And I think one of the things that first OpenStack tried to bite on too much, and it's sort of a huge tent, everyone tries to push his agenda. OpenStack is still an infrastructure layer, okay. And also Hadoop is sort of a something in between an infrastructure and an application layer, but it was designed 10 years ago, where the problem that Hadoop tried to solve is how do you do web ranking, okay, on tons of batch data. And then the ecosystem evolved into real time, and streaming and machine learning. >> A data warehousing alternative or whatever. >> So it doesn't fit the original model of batch processing, 'cause if an event comes from the car or an IoT device, and you have to do something with it, you need a table with an index. You can't just go and build a huge Parquet file. >> You know, you're talking about complexity >> John: That's why he's different. >> Go ahead. >> So what we've done with our team, after knowing OpenStack and all those >> John: All the scar tissue. >> And all the scar tissues, and my role was also working with all the cloud service providers, so I know their internal architecture, and I worked on SAP HANA and Exodata and all those things, so we learned from the bad experiences, said let's forget about the lower layers, which is what OpenStack is trying to provide, provide you infrastructure as a service. Let's focus on the application, and build from the application all the way to the flash, and the CPU instruction set, and the adapters and the networking, okay. That's what's different. So what we provide is an application and service experience. We don't provide infrastructure. If you go buy VMware and Nutanix, all those offerings, you get infrastructure. Now you go and build with the dozen of dev ops guys all the stack above. You go to Amazon, you get services. Just they're not the most optimized in terms of the implementation because they also have dozens of independent projects that each one takes a VM and starts writing some >> But they're still a good service, but you got to put it together. >> Yeah right. But also the way they implement, because in order for them to scale is that they have a common layer, they found VMs, and then they're starting to build up applications so it's inefficient. And also a lot of it is built on 10-year-old baseline architecture. We've designed it for a very modern architecture, it's all parallel CPUs with 30 cores, you know, flash and NVMe. And so we've avoided a lot of the hardware challenges, and serialization, and just provide and abstraction layer pretty much like a cloud on top. >> Now in terms of abstraction layers in the cloud, they're efficient, and provide a simplification experience for developers. Serverless computing is up and coming, it's an important approach, of course we have the public clouds from AWS and Google and IBM and Microsoft. There are a growing range of serverless computing frameworks for prem-based deployment. I believe you are behind one. Can you talk about what you're doing at iguazio on serverless frameworks for on-prem or public? >> Yes, it's the first time I'm very active in CNC after Cloud Native Foundation. I'm one of the authors of the serverless white paper, which tries to normalize the definitions of all the vendors and come with a proposal for interoperable standard. So I spent a lot of energy on that, 'cause we don't want to lock customers to an API. What's unique, by the way, about our solution, we don't have a single proprietary API. We just emulate all the other guys' stuff. We have all the Amazon APIs for data services, like Kinesis, Dynamo, S3, et cetera. We have the open source APIs, like Kafka. So also on the serverless, my agenda is trying to promote that if I'm writing to Azure or AWS or iguazio, I don't need to change my app. I can use any developer tools. So that's my effort there. And we recently, a few weeks ago, we launched our open source project, which is a sort of second generation of something we had before called Nuclio. It's designed for real time >> John: How do you spell that? >> N-U-C-L-I-O. I even have the logo >> He's got a nice slick here. >> It's really fast because it's >> John: Nuclio, so that's open source that you guys just sponsor and it's all code out in the open? >> All the code is in the open, pretty cool, has a lot of innovative ideas on how to do stream processing and best, 'cause the original serverless functionality was designed around web hooks and HTTP, and even many of the open source projects are really designed around HTTP serving. >> I have a question. I'm doing research for Wikibon on the area of serverless, in fact we've recently published a report on serverless, and in terms of hybrid cloud environments, I'm not seeing yet any hybrid serverless clouds that involve public, you know, serverless like AWS Lambda, and private on-prem deployment of serverless. Do you have any customers who are doing that or interested in hybridizing serverless across public and private? >> Of course, and we have some patents I don't want to go into, but the general idea is, what we've done in Nuclio is also the decoupling of the data from the computation, which means that things can sort of be disjoined. You can run a function in Raspberry Pi, and the data will be in a different place, and those things can sort of move, okay. >> So the persistence has to happen outside the serverless environment, like in the application itself? >> Outside of the function, the function acts as the persistent layer through APIs, okay. And how this data persistence is materialized, that server separate thing. So you can actually write the same function that will run against Kafka or Kinesis or Private MQ, or HTTP without modifying the function, and ad hoc, through what we call function bindings, you define what's going to be the thing driving the data, or storing the data. So that can actually write the same function that does ETL drop from table one to table two. You don't need to put the table information in the function, which is not the thing that Lambda does. And it's about a hundred times faster than Lambda, we do 400,000 events per second in Nuclio. So if you write your serverless code in Nuclio, it's faster than writing it yourself, because of all those low-level optimizations. >> Yaron, thanks for coming on theCUBE. We want to do a deeper dive, love to have you out in Palo Alto next time you're in town. Let us know when you're in Silicon Valley for sure, we'll make sure we get you on camera for multiple sessions. >> And more information re:Invent. >> Go to re:Invent. We're looking forward to seeing you there. Love the continuous analytics message, I think continuous integration is going through a massive renaissance right now, you're starting to see new approaches, and I think things that you're doing is exactly along the lines of what the world wants, which is alternatives, innovation, and thanks for sharing on theCUBE. >> Great. >> That's very great. >> This is theCUBE coverage of the hot startups here at BigData NYC, live coverage from New York, after this short break. I'm John Furrier, Jim Kobielus, after this short break.

>> Narrator: From Austin, Texas. It's the CUBE, covering DockerCon 2017. Brought to you by Docker, and support from it's ecosystem partners. >> Hi, I'm Stu Miniman, with my co-host, James Kobielus, who's been digging into all the application development angles. Happy to welcome back to the program, here at DockerCon, Yaron Haviv, who is the co-founder and CTO of iguazio. Yaron, great to see you. >> Thanks. >> How have you been? >> Great, great, been busy traveling a lot. >> We talked about how some of us celebrated Passover recently, I had brisket at home. We had Franklin's Barbecue brisket here. Anthony Bourdain said the only two people that know how to do brisket well, are Franklin's and the Jews. (all laugh) >> So we had Passover, a lot of good food, but also a lot of traveling. I was also in a Kubernetes conference in Europe and here. Prior to that, big data show, so it's a lot of traveling. >> Kubernetes, Docker, Ecosystem. You've been watching this, your company is involved in it. What's your take on the state of the ecosystem, and what do you think of the announcements this week? >> You know, I have also been to the Kubernetes conference, and you see those are still small, relatively small shows. And it's mostly developer focused. What we see is that Kubernetes is taking a lot of share from the others, because most of the guys that adopt are not enterprises yet. It's people that have a large enough infrastructure that they want to use it internally, and Kubernetes is a little more flexible. And on the other end, you see Docker trying to create a greenware-like, shrink wrapped, version of container infrastructure. So we see those two, and there's obviously the Public Cloud with their fully integrated stack. Now, what I notice here in the show, and also when, a couple of weeks ago, in the Kubernetes conference, think about the stack. It has, let's say, 20 components. So someone like Amazon brings the entire 20 components, and it's fully integrated and secure and networking and storage and data services and everything. And here, what you'll see, is a lot of vendors, this guy has those four components, the other guys have those five components, in some cases they actually overlap. So this guy will have three unique components, and two other components, et cetera. And it's very hard to assemble a full blown solution. So as a buyer, how do you decide which components am I going to choose? That's part of the challenge, and also helps serve the cloud guys. >> I remember when I first joined at Wikibon, we talked about, the hyperscale model was you take your team of PhDs, you just architect your application and software. You're the enterprise though, you don't have that talent. So you will spend money to buy that packaged solution. I want to buy it as a service, I want to buy it easy. Where do you see the maturity of this market, and how that fits for, and what can the enterprise consume, how do they do it? Or do they just go to platforms? >> So this is why our positioning was, it was a platform. We are not a component. We are a fully integrated system. We have multi-tendency, we have security, we have data lifecycle management. We integrate with applications, we have our own UI. But it's focused more on the data services. So if you take a dozen Amazon data services, you need to send Dynamo, and others, and object and file. We basically pack all of them, because data is the biggest challenge, as you know. High volubility, versioning, reliability, security. The biggest and toughest challenge is the data. And once you solve that one, the applications, they all become stateless, and that's much easier. There still needs to be a bigger ecosystem around it, which is why we are doing a lot more work with CNCF. And trying to create standards for the different interactions between those components. So when a buyer goes and buys a certain component from one vendor, it doesn't necessarily lock in to that. They can just go and modify it in the future. I think once you solve the data problem, of the persistency, which is sort of the toughest challenge in this environment, the rest of it becomes simpler. >> One of the questions James has been asking this week, is where analytics fits in? I look at your real-time continuous analytics piece, not an application that I heard talked about too much, maybe we can get your viewpoint on it? >> And the relevance is, of course, much of the application development that is going on, the hot stuff, is related to artificial intelligence, on streaming analytics, clearly continuous. >> Which is where we focus on. Some of the things that I try, to work with different communities, it's explained, that right now we have bifurcation, we have the Apache ecosystem, and we have the Docker ecosystem, totally separate ecosystems, and by the way, you know that cloud is where most analytics happen. >> James: Yes. >> So basically, analytics and cloud technology have to converge. This is what we have been trying to pitch, is why do you use YARN, as a scheduler, where I can use Kubernetes, and it's more generic. Because I can schedule any type of work. So this is something that we are trying to push, and all this notion of continuous integration, when we say continuous analytics, it's not just about the real-time aspect, it's also about the continuous development and integration. >> James: Yes. >> So you actually want this notion of server-less function, which is one of the things I like. Also, just immutable code and infrastructure, you want to adopt those notions, so analytics is going to go into real-time, more and more. So that means, unless I have my connected car pipeline that I get streams, and I process it, and I generate insights. What happens if I find a bug in my application, or I just want to enhance it, and create another feature? So I want to be able to just push a new version, of my analytics code into some platforms, hopefully ours. >> You also want to train that new algorithm as well, to make sure it's fit for whatever specific... >> Yeah, but you have to have this notion of continuity, which means all the integrations we did, have to be different, it has to be a lot more atomic. >> Yeah. >> It has to be check-pointed. All those things that I can basically knock down my analytic process, and relaunch it, and it goes seamlessly and continues. And that's not the Apache model, to play around at bootcamp enough, it's a lot more Legacy kind of approach, which I don't connect to too much. >> Yaron, maybe complete out the stack that you're building, how does serverless fit into this also? >> Okay, so basically, we are building all the data engines, we are doing streaming, we are doing objects, files, NoSQL, SQL, for us it's all integrated into the same very high performance engine. We also have built in analytics, so we can build things like joints and aggregations, and all of the computations on the data as it injects, and it could basically present itself as many different things. Now one of the things we get asked from customers, and we demonstrated that in Strata, let's assume I'm throwing an image into this thing, I want to be able to immediately analyze the image, and say if there is a face, if there is something suspicious about the picture, or maybe even simple things, like extract meta-data information, like geolocation of the picture, so I can do something with it. So we had to develop internally, an event driven process, we didn't call it serverless internally, where you throw data, and it immediately launches and triggers a process, which is a Docker container based process. It has high speed message bust integration into our data platform, that immediately invokes and processes that in a very elastic fashion. So if you throw thousands of objects, it elastically generates multiple workers to work on that, and that's also how we design things like DR, and backup internally in our platform to be very flexible, so we can build DR to S3. How do we do it? We basically have serverless functions that know how to convert the updates into a continuous stream of updates, and then they just go and there is a small code that says "Go right to S3". And that allows me a lot flexibility to develop new features. So this is all this notion of data lifecycle management, with every advance in our product, is actually based on serverless functions, we just didn't call it serverless. One of the things that we're working on with the community, is trying to detach that portion from our product, and contribute it as an open-source projects, because it's much faster and much more optimized than what you'll see, including IBM Whisk or Amazon Lambda implementation of that. >> Are you working with the Apache... Are you working in the context of the Apache framework to expose, for example, machine learning pipeline functions as serverless functions? >> So again, Apache is not the right necessarily place to do that. >> You can do them in Spark. >> I do them in Spark and all that, but we do want the Kubernetes environment to deal with all the constriction requirements for that thing. The way that we do, for example, tensorflow integration is we may expose file into tensor float, on one end, to be able to look at the image, and the same time the metadata updates, so what the image contains is exposed to tensorflow as sort of a key value store, or document store. It just updates attributes on the same image. So the way that we work now with healthcare, an MRI image lands and something looks at the MRI image, and senses cancer. Basically, you can mainly attack the same image, with records, which fields say contains cancer by this guy, take picture of this guy. And then, when you want to run a query, and say, you know what, give me all the MRIs pictures that contain query, it now flips and acts like a database, and you just pull all those images. It's a different approach to how to do those things. >> Yaron talked about Docker containers, Kubernetes, serverless, how do virtual machines fit into the environment? >> I had some interesting conversations at Kubernetes with some friends that are high ranked in this industry, without disclosing, do you really need openstack in between bare metal and containers? Because the traditional approach is, Okay, we have bare metal, we need to put virtualization layer for isolation, and then we need to put Kubernetes or Docker. And we figure out that very little amount of risk, actually, in putting, especially with the new security, things around containers and image signing, and what we do, which is authenticating the container, not the infrastructure on data access, network isolation, all those things that eventually can collapse and eliminate virtualization, but not for every application. Some applications which are more traditional Legacy, the application may still require VMs, because it's quite a different philosophy to develop microservices and develop VMs. Apart of what I see here in the show is not everyone internalizes that. People still think in the notion of Here's my lightweight VM, that happen to be called Docker container, and I'm going to give it the volume, and I'm going to create snapshots on that volume, and all that stuff. But if you think about it, what is really microservices? It's about allowing this elasticity, so the same workload can spawn multiple workers, it's the ability to go and create update versions, it's the ability to knock down this container anytime I want, and just kill it and launch it in a different place. You know how Google works, or Amazon or Ebay, or all those guys. You're basically killing containers on purpose, to basically test their system. All this notion that my configuration and my logs and all that stuff, sits inside the container, is not cloud native, and it doesn't allow this elasticity that you want if you're building a Netflix or an Ebay, or a modern enterprise infrastructure. So I think we need to put those two things aside. You have Legacy applications, keep them in the VMs. You have new workloads, you need to think of data, and data integration, and microservices differently on something which is entirely stateless. The image of the container builds from the get. OK? And create a Docker image. And if you want to go to a different image, you just go and recreate, from source, the same image. The data for that image needs to be stored in a data facility like a database or an object or something like that. >> Yaron, final question I have for you is, talk a little about the customers you're interacting with, talk about the people that are here, as you said, there's a spectrum of how far along they are in the thinking. You're pretty advanced in some of your architectural thoughts and opinionated as to where you're going. Where are the customers today, how many of them are ready for the future versus sticking to what they have got? >> So what you mentioned before, part of the key challenge for enterprises is they all want to move into the digital transformation, they all want to be competitive, because some have existential threats, think about even banks, today, where Apple comes with Apple Pay, it kills a lot of the margins they are making from all those small transactions. And now, no one really cares how many branches you have in the bank, because all the Y Generation just goes to their mobile app. Someone like a bank, have to immediately transition and be able to offer premium services, offer better experiences for the mobile application, be able to analyze user behavior, some things that are more strategic. The traditional things that IT deals with like exchange server management, SAP, all those Legacy things will move to the cloud, because there's no real value there. And what you see is more and more enterprises thinking about how do we generate the differentiation, which is more about analyzing data, and being able to provide better service to the customers, and the biggest challenge is they don't know how to do it. Because what the industry tells them, Go to Apache, and take a dozen of projects, and now integrate those and figure out the security problem, and you know what, you want to add Kubernetes, that's from a different story, but let's try and glue this together, and that's extremely complicated. So what we are trying to do is go to those customers, say you know what, we're building a full blown solution, fully integrated, security is baked in, all the different data services, it integrates with things like Kubernetes natively, we actually do the extra mile, we actually build Spark and tensorflow, and the images that contain everything, including support for us, that you can just launch Spark and it connects and works. We want to make life easier for those enterprises to solve those key challenges that they are working on. And this is working extremely well for us, actually the challenge we have, we only have, I think, two sales guys and we have a huge pipeline, and we can't really deliver for most of those projects. >> Good challenges to have sometimes, talk about scaling, which has been one of the themes of the week here. Yaron Haviv, great to catch up with you as always. We'll be back with two days of our coverage here, at DockerCon 2017. You're watching the CUBE. (electronic 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)

>> Announcer: Live from Washington DC it's The Cube. Covering .NEXT Conference. Brought to you by Nutanix. >> Good morning everybody. In 2009, infrastructure professionals, and the vendors who supplied products to them, started to realize that the cloud was real. They began to try to replicate that cloud on site. The first instantiation was what we call converged infrastructure. Converged infrastructure was essentially the combination of storage, server, and networking kind of bolted on together but prepackaged, pretested, and engineered to be installed and delivered in a more seamless fashion to minimize the amount of labor that had to go into the management of infrastructure. That was around 2009 and in that same year, Nutanix was born with a slightly different vision, to actually develop a solution that was truly cloud-like for on prem. Welcome everybody to Nutanix .NEXT 2017. This is The Cube, the leader, in live tech coverage. My name is Dave Vellante and I'm here with Stu Miniman from SiliconANGLE Wikibon, who is an expert on Nutanix, on hyper-converged infrastructure, which was the next instantiation of converged infrastructure. And now, Stu, we see this going into the cloud era. Nutanix is a company that's probably going to do close to a billion dollars this year, if not a billion. They got a two and a half billion dollar market cap. They got a gross margin model in the high fifties. They're growing at 65, 66% per year. The stock, when they did their IPO, was a meteoric rise, sort of flattened out since. It's up today, on some news that was broken by CNBC. They're doing a deal with Google. This is our, let's see, fourth? Third or fourth .NEXT >> Third year of the US show, The fourth >> Fourth overall. >> Nutanix conference overall >> we did some in Europe. >> Stu, great to be working with you. We've been together a lot this month at a number of shows. This is sort of the capstone of what we call true private cloud and a great instantiation and example of it. What are your thoughts, early thoughts, on what to hear. >> Steve, great to be with you as always, and excited to be here at this show. One of the things I love in our role as analysts is when you see something in some of the early stages. So, I first got introduced to Nutanix when they were about 20 people. Little bit smaller than say, when I first met VMware, when they were about 100 people. And to watch this company and this wave grow I remember John Furrier and I interviewed Dheeraj, CEO of Nutanix, back at VMworld 2012. And, right, infrastructure and converged infrastructure, I mean, I worked on some of the original Vblock architectures when I worked at EMC, and we're saying "Take my storage, take my network, "take my compute, put 'em together, "package 'em, integrate 'em, try to make it simpler." But when we talked to Dheeraj, it wasn't about the infrastructure, he really posited that the challenge of our day is building distributed systems, and it's really a software challenge, and that is at the core of what Nutanix's IP, what their engineering mentality is, and they've kept that going forward. We always talk about, oh, okay, in our infrastructure silos, Nutanix's vision from day one was to go beyond that, we talk about hybrid cloud or multicloud, today, that's what Nutanix is starting to deliver in their vision. When they first launched themselves as an enterprise cloud, as their marketing, everybody was like "What are you talking about? They're an HCI player, what's their cloud mission?" Making an announcement with Google shows that they are going to have some partnerships there to live in that hybrid or multicloud world. And Dave, I want to clarify, when I You know, we throw around these terms, hybrid and multi for me these days, I look at it as, hybrid is a lot of what I know you used to call federated, so how do I have a model that really looks the same whether that's just the application layer or even going down to the infrastructure layer, spreading between environments, typically my private cloud and my public cloud service writers can be in the mix there, multicloud means I've got data and I've got applications that are going to live in multiple public clouds in multiple data centers, of course we've got SaaS, so that differentiates a little differently. Things like Kubernetes are really helping that explosion and that discussion of multicloud, kind of replacing what we used to talk, with PAS, the platform as the service, where I could really be independent of the cloud, so there's a lot of nuance and detail which I'm looking forward to digging into. >> It's nuance >> Over the next two days >> But it's important nuance, because basically you're saying it's not just a bunch of hybrid, it's not just a bunch of clouds, >> Stu: Yeah. >> It's some kind of federated >> Stu: Yeah, we had >> It's a data plane and a control plane that spans multiple physical locations, essentially. >> Yeah, I worry about things like identity, I worry about things as to how to security SPAN these environments, governance of course plays in a lot, but I'm sure we'll hear things about consumption model because one of the things cloud consumption model used to be like, oh well some business guy just swipes a credit card and goes and does it, well shouldn't all infrastructure be as easy to buy as we did, I mean, you remember a decade ago we always talked about the consumerization of IT, now it's really that cloudification of IT with companies like Amazon really driving forth the model that everyone wants to copy. >> So since we've started researching Nutanix and talking to some of it's customers two years ago at the initial .NEXT conference, at the Fontainebleau in Miami. Really talked to a number of customers and you could see the enthusiasm for simplicity, you could see the desire to reduce their reliance of VMware and the vTax that was hanging over their heads, and at that time, a couple years ago Nutanix introduced Acropolis, which is essentially a hypervisor inside of what they call now the cloud operating system. But that is a strategic move by a company that's basically saying, "hey, we're not just hyper converged, "cause everybody's now pivoting "to hyper converged, we're cloud," so it's a message to the financial world, to the industry analysts, and to customers that, we have a vision and to the financial world, we have a large TAM. Financial guys are more worried about, they're always worried about TAM expansion, that's their TAM expansion plane. Now, bringing it back to some of their financials, just briefly, Nutanix essentially loses 37 cents from an operating basis, on every dollar of revenue it makes. Okay, that's I guess in vogue these days, but it's funny accounting, because they don't recognize the full value of their OEM deals, like the Dell deal, again some nuance, and there are some accounting changes that are occurring which should be a tailwind for this company from just from the optics of the income statement. So you're going to see, and I think the street's finally starting to understand this, again, stocks up today on the Google news, there's no press release yet, but CNBC broke that news, what do we know about that? >> Yeah, so first of all, right. The accounting changes coming this summer, if I remember right, I believe it's August, >> Dave: Yep. >> So that means that rather than taking the three year subscription and only taking a small piece of that today and pushing most of it out into the future, there will be a restatement of all of Nutanix's number, which, right, will be a tailwind as you said and push all that in. Google, number one, is you look at one of the most interesting dynamics of Nutanix is, been the relationship with VMware. I did an interview with Dheeraj right after the IPO, I was at the headquarters, sitting with him in this beautiful library that they have there, and he turns to me and he's like, you know, on camera, he says, "Stu, if VMware hadn't been so negative on us, and not allowing us to OEM or ship their product, I don't know if we would have launched our own hypervisor." It was not an initial plan. It was something that he's talked to the analyst community and said, this is something that was not simple, it was not something that was done lightly, it was something that, their hand was really forced because customers say, "I want the simplicity, "I want to be able to just stand it up, "I need that to come pre installed," and there was additional value that Nutanix felt they could bring. The DNA that they had, as they said, from companies like Facebook, from Google, and the like, where they understand the underlying code that they need to build to be able to build this distributor architecture. So, building off of KVM, they built the Acropolis hypervisor. One of the next big shifts coming is containerization, and this is where Google fits in. They should be able to accelerate that move, which is the thing I worried about with hyper converged, is we took our virtualized SAN environment and we just put them in slightly different form factor. It's simpler, yes, it's great, it's going to be, hopefully, a better economic environment, but how do we move the company forward faster? Two years ago, the customers were really excited cause they said, "I got my weekends back." What you want from an IT department is not to say, "hey, I'm no longer just overworked," but "I'm driving the business forward." But Dave, you've always said the role of the CIO is not just to maintain the business, but to transform and grow the business. So that's where we need things like containerization to help companies be more agile and move faster, and to have IT turn into a force that can help code, create new business lines, leverage our data more. The thing that we've heard at every storage show we've been at recently, Dave, is, NetApp actually say that "storing is boring" and that is something that we hear, is "I need to leverage our data, "we need to get it out there" and that's what I'm looking for this week, is to hear more of that cloud native piece. I was at the Cloud Foundry Summit a couple weeks ago, I can sit that on top of Nutanix. So how does partnerships like Google and some of these environments help with application modernization, analytics where I can get new, you know, take my data and create new business value, cause that's where we're going to really transform and grow companies into new revenue streams, and Nutanix is a platform to help deliver on that. >> So, Stu, since the spring when we released our latest true private cloud report, the Wikibon team, there's been a lot of talk about that report, it's implications, we had, there was a blog post today from Yaron Haviv at Iguazio saying, "you guys needs to rethink your definition of true private cloud, because the on prem guys have no chance against the cloud guys," we don't agree with that, by the way. But the notion of true private cloud, is this this on prem infrastructure that substantially mimics the public cloud, and as I said before, has a control plane that spans multiple physical locations. That's a concept that we've kind of put forth, and started to quantify, because there was so much cloud washing going on. And it really is, frankly, the savior of the existing infrastructure guys, or could be. Because their legacy business is not growing, in fact it's declining, and this is a very high growth market opportunity for these folks. So, my question to you is, can Nutanix participate in that high growth market? You know, there seems to be an aspiration, maybe I'm overstating this, but to be the next VMware, of sorts. >> Stu: Yeah, no, no. >> Can they do it? >> Exactly, and Dave you're not overstating it, I've heard people from Nutanix say that was their target. Their target wasn't to replace a flexpod, their overall vision was right, to be the next VMware. They want to be a platform to be able to grow on that. And the thing I've been looking at, at every show when we go to infrastructures, how do you live in that multicloud world? Because, you can't put blinders on and say, "Right, well, Amazon's in a corner." No, we know Amazon customers are using them. Right, as you said, Dave, I want that operational model in my own environment to build what we called a true private cloud, absolutely Nutanix is a player there. There's still work to be done for all of these infrastructure players, but what are they doing? How does their control plane span beyond that? And why should Nutanix be more than just a piece of the infrastructure? Because, there are lots and lots of players, not only the infrastructure players, lots of software companies, and the cloud companies themselves, that are going to say "I'm going to own that piece." I mean, Microsoft, identity is one of their greatest strengths. Google and Amazon, have been going after that, I mean, Amazon's eating everything, Dave. So, why should Nutanix not just eat some of the old guys, but be a player that lasts into the new world? Is looking forward to the announcements later tonight, we've got a lot of their partners and customers on here to be able to deliver proof points. And Nutanix is still growing, still doing real well, and you know, exciting stuff. >> Yeah, so we're going to be covering this, wall to wall coverage of two days. We've got some great outside guests coming in, we got Diane Greene's speaking at this event, Bill McDurmott's speaking at the event, and word is Peter McKay from Veeam is going to make another appearance, he's like The Cube, he's everywhere. (Stu chuckles) And then some really interesting outside speakers. Malhotra is here again, so really excellent line up that we have for you guys this week, two days, wall to wall coverage, this is The Cube, we'll be back from the district, live in Washington, D.C., this is Nutanix .NEXT. Be right back. (relaxed futuristic music) >> Announcer: Robert Herjavec

>> Announcer: LIVE from San Francisco, it's theCUBE, covering Pure Accelerate 2017. Brought to you by Pure Storage. >> Welcome back to San Francisco everybody, this is Dave Vellante with David Floyer, and this is theCUBE, the leader in live tech coverage, we go out to the events, we Extract the Signal from the Noise, this is Pure Accelerate 2017. This is the second year of Pure Accelerate. Last year was a little north of here at, right outside AT&T Park. Pure, it's pretty funny, Pure chose this venue, it's like this old, rusted out, steel warehouse, where they used to make battleships, and they're going to tear this down after the show, so of course the metaphor is spinning rust, old legacy systems that Pure is essentially replacing, this is like a swan song, goodbye to the old days, welcome in the new. So very clever marketing by Pure. I mean they did a great job setting up this rusty old building-- >> It's bad. Nice, it's a nice building. >> Hopefully it doesn't fall down on our heads and, so, but let's get to the event. The messaging was very strong here. I mean, they pull no punches. >> You know, legacy, slow, expensive, not agile, we're fast and simple, come with us. Of course the narrative from the big guys is, "Oh Pure, they're small, they're losing money, "you know, they're in a little niche." But you see this company as I said earlier when Matt Kixmoeller was on. They've hit escape velocity. >> Absolutely. >> They're not going out of business-- >> Nope. Okay, there's a lot of companies you see them-- >> And they're making a profit. >> Yeah, you read their financials and you say ah oh, this company's in deep you know what. No, they're not making a profit yet, Pure. >> They are projecting to make a profit in the next six months. >> But they basically got you know, 500 and what, twenty-five million dollars in the balance sheet, their negative-free cash flow gets them through by my calculation, in the next nine or 10 years, because they have zero debt. They could easily take out debt if they wanted to, growing at 30% a year. They'll do a billion dollars this year, 2.4 billion dollar market cap. They didn't have a big brain drain six months after the IPO, which was really important, it was like, you know business as usual. They've maintained the core management team. I know Jonathan Martin's you know, moving on, but they're bringing in Todd Forsythe to run marketing. A very seasoned marketing executive so, you know, things are really pretty interesting. The fact is, we haven't seen a billion dollar storage company that's independent since NetApp, there's only one left, NetApp. EMC is now Dell EMC. 3PAR never made it even close to a billion outside of HPE. Isilon couldn't make it, Compellent couldn't make it, Data Domain you know, couldn't make it as a billion dollar company. None of those guys could ever reach that level of escape velocity, that it appears that Pure and Nutanix are both on. Your thoughts David Floyer. >> I couldn't agree more. They have made their whole mantra, simplicity. They've really brought in the same sort of simplicity as Nutanix is doing. Those are the companies that seem to have been really making it, because the fundamental value proposition to their customers is, "You don't need to put in lots of people "to manage this, it'll manage itself." And I think that's, they've stuck to that, and they are been very successful with that simple message. Obviously taking a flash product, and replacing old rusts with it is, makes it much simpler, they're starting off from a very good starting point. But they've extended that right the way up to a whole lot of Cloud services with Pure. They've extended it in the whole philosophy of how they put data services together. I'm very impressed with that. It reminds me of Ashley, the early days of-- >> Of NetApp. >> No, of NetApp and also of the 3PAR. >> Oh, yeah, yeah, absolutely, simplicity, great storage services, Tier 1. When I say NetApp, I'm thinking, you know, simplicity in storage services as well. But you know, this is the joke that I been making all week is that you talk to a practitioner you say, "What's your storage strategy?" Oh, I buy EMC for block, I buy NetApp for file. At Pure it's sort of, not only challenging that convention, but they're trying to move the market to the big data, and analytics, and they also have a unique perspective on converge and hyper-converge. They count a deep position hyper-converge that's you know, okay for certain use cases, not really scalable, not really applicable to a lot of the things we're doing. You know, Nutanix could, might even reach a billion dollars before Pure, so it's going to be interesting. >> Well, I think they have a second strategy there, which is to be an OEM supplier. Their work with Cisco for example. They're an OEM supplier there. They are bending to the requirements of being an OEM supplier, and I think that's their way into the hyper-converge market is working with certain vendors, certain areas, providing the storage in the way that that integrator wants, and acting in that way, and I think that's a smart strategy. I think that's the way that they're going to survive in the traditional market. But what's, to me, interesting anyway, is that they are really starting to break out into different markets, into the AI market, into flash for big data, into that type of market, and with a very interesting approach, which is, you can't afford to take all the data from the edge to the center, so you need us, and you need to process that data using us, because it's in real time these days. You need that speed, and then you want to minimize the amount of data that you move up the stack to the center. I think it's a very interesting strategy. >> So their competing against, you know, a lot of massive companies I mean, and they're competing with this notion of simplicity, some speed and innovation in these new areas. I mean look at, compare this with you know, EMC's portfolio, now Dell, EMC's portfolio. It's never been more complicated right? But, they got one of everything. They've got a massive distribution channel. They can solve a lot of problems. HPE, a little bit more focused, then Dell EMC. Really going hard after the edge. So they bring some interesting competition there. >> And they bring their service side, which is-- >> As does Dell. So they got servers right? Which is something that Pure has to partner on. And then IBM it's like you know, they kind of still got their toe in infrastructure, but you know they're, Ginni Rometty's heart is not in it you know? But they, they have it, they can make money at it, and you know, they're making the software to find but... And then you get a lot of little guys kind of bubbling. Well, Nimble got taken out, SimpliVity, which of course was converged, hyper-converged. A lot of sort of new emerging guys, you got, you know guys like Datrium out there, Iguazio. Infinidat is another one, much, much smaller, growing pretty rapidly. You know, what are your thoughts, can any of these guys become a billion-dollar company, I mean we've talked for years David about... Remember we wrote a piece? Can EMC remain independent? Well, the answer was no, right? Can Pure remain independent in your view? >> I don't believe it could do it, it was, as just purely storage, except by taking the OEM route. But I think if they go after it as a data company, as a information company, information processing company, and focus on the software that's required to do that, along with the processes, I think they can, yes. I think there's room for somebody-- >> Well, you heard what Kix said. Matt Kixmoeller said, "We might have to take storage "out of the name." >> Out of the name, that's right. >> Maybe, right? >> Yes, I think they will, yeah. >> So they're playing in a big (mumbles), and the (mumbles) enormous, so let's talk about some of the stuff we've been working on. The True Private Cloud report is hot. I think it's very relevant here. On-Prem customers want to substantially mimic the Public Cloud. Not just virtualization, management, orchestration, simplified provisioning, a business model that provides elasticity, including pricing elasticity. HPE actually had some interesting commentary there, on their On-Demand Pricing. Not just the rental model, so they're doing some interesting things, I think you'll see others follow suit there. I find Pure to be very Cloud-like in that regard, in terms of Evergreen, I mean they essentially have a Sass subscription model for their appliance. >> And they're going after the stacked vendors as well, in this OEM mode. >> Yeah, they call it four to one thousand Cloud vendors, so you're True Private Cloud Report, what was significant about that was, to me anyway, was a hundred and fifty billion dollars approximately, is going to exit the market in terms of IT labor that's doing today, non-differentiated lifting of patching, provisioning, server provisioning, (mumbles) provisioning, storage management, performance management, tuning, all the stuff that adds no value to the business, it just keeps the lights on. That's going to go away, and it's going to shift into Public Cloud, and what we call True Private Cloud. Now True Private Cloud is going, in our view, to be larger than infrastructures that serve us in the Public Cloud, not as large as Sass, and it's the fastest growing part of the market today, from a smaller base. >> And also will deal with the edge. It will go down to the edge. >> So punctuate down, so also down to the edge so, what's driving that True Private Cloud market? >> What's driving it is (mumbles), to a large extent, because you need stuff to be low latency, and you need therefore, Private Clouds on the edge, in the center. Data has a high degree of gravity, it's difficult to move out. So you want to move the application to where that data is. So if data starts in the Cloud, it should keep stay in the Cloud, if it starts in the edge, you want to keep it there and let it die, most of it die there, and if it starts in headquarters again, no point in moving it just for the sake of moving it. So where possible, Private Cloud is going to be the better way of dealing with data at the edge, and data in headquarters, which is a lot of data. >> Okay, so a lot of announcements here today, NVMe, and NVMe Fabric you know, pushing hard, into file and object, which really they're the only ones with all-flash doing that. I think again, I think others will follow suit, once they have, start having some success there. What are some of the things that you are working on with the Wikibon Team these days? >> Well, the next thing we're doing is the update of the, well two things. We're doing a piece on what we call Unigrid, which is this new NVMe of a fabric architecture, which we think is going to be very, very important to all enterprise computing. The ability to merge the traditional state applications, applications of record with the large AI, and other big data applications. >> Relevations, what we've talking about here. >> Very relevant indeed, and that's the architecture that we believe will bring that together. And then after that we're doing our service end, and converged infrastructure report and the how, showing how the two of those are merging. >> Great, that's a report that's always been, been very highly anticipated. I think this is our third or fourth doing that right? >> Fourth year. >> Right, fourth year so great looking forward to that. Well David, thanks very much for co-hosting with me-- >> Your very welcome. >> And it's been a pleasure working with you. Okay that's it, we're one day here at Pure Accelerate. Tomorrow we're at Hortonworks, DataWorks Summit, we were there today actually as well, and Cloud Foundry Summit. Of course we're also at the AWS Public Sector, John Furrier is down there. So yeah, theCUBE is crazy busy. Next week we're in Munich at, IBM has an event, the Data Summit, and then the week after that we're at Nutanix dot next. There's a lot going on theCUBE, check out SiliconANGLE.tv, to find out where we're going to be next. Go to Wiki.com for all the research, and SiliconANGLE.com for all the news, thanks you guys, great job, thanks to Pure, we're out, this is theCUBE. See you next time. (retro music)

>> Voiceover: Live from Austin, Texas, it's the CUBE. Covering DockerCon 2017. Brought to you by Docker in support from its ecosystem partners. >> Hi I'm Stu Miniman here with the final wrap with Jim Kobielus at DockerCon 2017. The CUBE's really excited that we were here for the third year. Have to have a big shout out to our partners and our sponsors that allow us to be here. Of course, Docker's been a great partnership. They talk a lot about ecosystem, really bringing some media people like ourselves giving us some of the great speakers from their company, the partner ecosystem and their customers, and the sponsors for the show, for ourselves, App Lariat, CISCO, Iguazio, Skelety, Cononical, and Red Hat. Without them we couldn't bring you this programming. Really excited to be able to be here. They're starting to tear down the show here, so not a lot of time, so many things to dock to. >> The show itself is containerized. >> We're not even going to be able to talk about the Franklin's barbeque. >> You just did. >> But Jim ... Absolutely. Jim, you've gotten to be on the CUBE here, see some of the show. Give us your quick hits. >> Sure. >> on your takeaways from the show. >> First of all, my first takeaway is this is a vibrant developer ecosystem, clearly. This show is much larger than the year before, and the year before that. It'll probably be twice as large next year. That's my prediction based on the sheer amount of developers migrating into the Docker ecosystem because so many organizations are Dockerizing their applications, containerizing their applications. That's a huge focus for me and Wikibon, as an analyst, is the containerization of application development with microservices and all that, for cloud deployment and multi-clouds, hot, hot, hot across all niches. So, vibrant ecosystem. Docker as the core solution-provider and the centerpiece of this community. Amazing show. The Enterprise Edition, of course, that preceded the announcement of that and the release preceded this show. That's critically important in getting Docker into new accounts that, with a full stack. Clearly it's enterprise ready. Developers, more developers will be exposed to Docker through the EE. Docker, at this show, had a couple of really important announcements for developers. Moby. Project Moby, for customization of container images and so forth, clearly that's going to be a multiplier effect on the ecosystem of developers, ISVs and so forth, Building applications, and customizing containerized Docker applications and images for a wide range of opportunities. >> Yeah, Jim, just want to comment on the Moby piece here 'cause it was really interesting. I think the last couple of years, it's been that pull and tug as to what was the open-source piece, what is the company itself doing, and I think it's clarifying. Kubernetes is a big rising tide in the environment, and all they cared about is they've got the open-source pieces that they need to be able to do Kubernetes. So, with Moby Project it's like okay, now I understand what's out and open. I understand what Docker's doing. I saw some humility from Solomon Hykes, talking, it's like we're listening. We're working, you know, ecosystem, ecosystem, ecosystem. So it was good to see that maturity. I mean, there were some people that I talk to, and they're like, "Oh, will this be the last DockerCon?" I'm like, "I don't think anybody watching this show would say that coming out." As you said, I expect the show to grow; it's doing really well. >> Solomon's totally partner-focused. Look at him. >> Kudos to what they're doing. The partners are excited. It's not just lip service. "Oh yeah. We did some little announcement on the side." No. We're excited. This is there. I know you've got a bunch of pieces, but I want to ask you, are developers excited about taking this legacy ... >> There's lots of news I'm going to analyze. >> Legacy applications, and like helping to move those in, or they only want to work on the cool new stuff? >> Oh, that's a huge theme. MTA. I forget what exactly the acronym stands for, but it's wrapping legacy applications, containerizing them in the Docker ecosystem. That is so important so all of these legacy applications will be Dockerized before long, and refactored, in addition to all the Greenfield development of containerized applications. So the MTA announcement, just as critical as the Moby announcement and so forth in terms, and EE as part of the show, of getting Docker, getting their ecosystem, getting developers working in this environment, more and more developers. This entire Docker, this entire ecosystem has a magnetic force on the developer community, or will. Those are very important. Also I thought the announcements with Microsoft, in terms of containers are going into Windows in a larger way, Linux containers and so forth, that also, 'cause Microsoft has a huge presence obviously in not only enterprise but small to midsize businesses. We're going to see Docker in ever-smaller deployments, hosts and so forth, across the board. More buyers, in other words, more companies will be Dockerizing more applications thanks to, in part, Microsoft as clearly a forerunner. >> Jim, absolutely. I say it at almost every cloud show. I want to follow the data and I want to follow the applications, and you had Microsoft and you had Oracle. You had two of the big players from an application standpoint, Oracle's now in the Docker store. >> Oracle's in the Docker store. That is huge. >> Yeah. >> That has validated containers and Docker for ... >> How about you? From the data standpoint, I heard, we talked to Iguazio about some of the analytics and things ... >> Jim: I'm a data guy, yeah. >> Yeah, you're a data guy. What's a data guy think at a show like this? Is it too infrastructure-focused, or did you see some of the data future here? >> No. It's infrastructure-focused in the sense that it needs to be to harden this technology for enterprise deployment, but it's really dev-ops focused, you know, Kubernetes and everything, and Swarm and whatnot. Look at all these vendors. Here are these tools for the dev-ops life cycle, Kubernetes and everything. That's really, really important. It's all about developers and speeding of development, and putting containerized Docker applications and images into production, and managing them and securing them and so forth. Just the sheer range of dev-ops tools on this show floor that's packing up now was amazing. I'm just uncracking my research here. Very important. So I'm going to wrap up. So, the adoption is amazing. I mean, all these industries, including like Visa. We had a swap-meet, who have adopted Docker into core applications that they're running major businesses on. That's some serious validation in its own right. >> Jim, one of the feedback I got from, it was actually John White from Expedient. >> Okay. >> talked about, and he said he deals with kind of small to mid, to little bit large enterprises, and he said, all that this keynote reminds us of AWS Re:Invent a couple of years ago. >> Oh yeah. >> Big global names. I mean, it's, you know, Visa. You know. Around the globe. Northern Trust. These are not, you know, your regional companies that did a little initiative. It's virtualization started in a lot of small environments. Containerization really started in the likes of Google. I remember the first DockerCon. It was Google and Facebook, and they're the ones that have been doing these projects pre-Docker, and it's slowly moving down. Part of the things I look at is where's the watermark >> Jim: Yeah. >> Where below this you're probably not going to do containers because you're going to go live on a platform that leverages container. The service writers I talked to ... >> Jim: They're going to live in a public cloud like Microsoft, or you know. >> Stu: The cloud guys. I'm going to go to, right, I'm going to go to Microsoft. I'm going to go to Oracle. >> Jim: AWS or IBM. >> Stu: I'm going to go AWS. >> Jim: Whoever it might be. >> Right. Any of them because they're going to just take care of that, and I won't care that it was containerized, so at the end of the day, it's not that tool, it's the wave of that modernization. >> Oh. Yeah, I want to end on a data note because we were talking about data. Okay. I thought Iguazio, I thought Yaron was very, that was very good to have him. There's a lot of storage foundations like Veritas and so forth, so storage in a Docker environment and persistent storage and data protection, pretty important, but also containerizing the new wave of applications that are machine-learning and deep learning and artificial intelligence. We got a fair look at some of that from Solomon yesterday because Solomon mentioned that the open AI consortium is based in their internal test bed training network on Docker, on Swarm and so forth. I, in my prior life, I just joined Wikibon a few weeks ago, I've focused on data science, which is a key development theme, by the way, I'll focus on for Wikibon. I saw a lot of containerization. I saw a fair amount of Docker and a lot of the data science oriented app dev that was going on in the business world. That's going to be a huge theme for me under Wikibon, but also, I mean Solomon sort of alluded to a lot, and so did Yaron, a lot of the work that's going on in the AI community Dockerized their application. Tenser flowing, all that. Huge theme we'll probably see much more of at next year's DockerCon I predict containerizing AI. >> All right. Well. >> For deployment into autonomous vehicles. Whatever. >> Jim, you've long been a CUBE alumn, but now you are a veteran of doing the CUBE. I really appreciate you coming on. >> I'm on this side of the table now. It's amazing. >> Stu: I want to give a shout out to the whole team here. John Furrier, I know, was really disappointed. He loves this show. Usually my co-host. A lot of these open-sourced shows. John, you better be down here in Austin for CUBECon at the end of the year with me. So many shows now through July 4th. The CUBE has so many activities going on. If you go to theCUBE.net, you can see all of our upcoming shows. Always watch us live. If we're not at the show that you think we should be at, go ahead and Ping us. Reach out to us through Twitter or through the website. Jim's research, a lot of it's going to be on Wikibon.com. Siliconangle.com is also where we have some research corner, some of the other pieces there, so check out the whole SiliconANGLE Media for Jim, myself, Ava, Leonard, Brandon, Jay, Sam, who's already heading to the airport. Thank you so much for watching The CUBE. Hope to see you at lots of shows coming around and thank you for sharing.

>> Announcer: Live from San Jose, California, it's the CUBE, covering Big Data Silicon Valley 2017. (upbeat synthesizer music) >> Live with the CUBE coverage of Big Data Silicon Valley or Big Data SV, #BigDataSV in conjunction with Strata + Hadoop. I'm John Furrier with the CUBE and my co-host George Gilbert, analyst at Wikibon. I'm excited to have our next guest, Yaron Haviv, who's the founder and CTO of iguazio, just wrote a post up on SiliconANGLE, check it out. Welcome to the CUBE. >> Thanks, John. >> Great to see you. You're in a guest blog this week on SiliconANGLE, and always great on Twitter, cause Dave Alante always liked to bring you into the contentious conversations. >> Yaron: I like the controversial ones, yes. (laughter) >> And you add a lot of good color on that. So let's just get right into it. So your company's doing some really innovative things. We were just talking before we came on camera here, about some of the amazing performance improvements you guys have on many different levels. But first take a step back, and let's talk about what this continuous analytics platform is, because it's unique, it's different, and it's got impact. Take a minute to explain. >> Sure, so first a few words on iguazio. We're developing a data platform which is unified, so basically it can ingest data through many different APIs, and it's more like a cloud service. It is for on-prem and edge locations and co-location, but it's managed more like a cloud platform so very similar experience to Amazon. >> John: It's software? >> It's software. We do integrate a lot with hardware in order to achieve our performance, which is really about 10 to 100 times faster than what exists today. We've talked to a lot of customers and what we really want to focus with customers in solving business problems, Because I think a lot of the Hadoop camp started with more solving IT problems. So IT is going kicking tires, and eventually failing based on your statistics and Gardner statistics. So what we really wanted to solve is big business problems. We figured out that this notion of pipeline architecture, where you ingest data, and then curate it, and fix it, et cetera, which was very good for the early days of Hadoop, if you think about how Hadoop started, was page ranking from Google. There was no time sensitivity. You could take days to calculate it and recalibrate your search engine. Based on new research, everyone is now looking for real time insights. So there is sensory data from (mumbles), there's stock data from exchanges, there is fraud data from banks, and you need to act very quickly. So this notion of and I can give you examples from customers, this notion of taking data, creating Parquet file and log files, and storing them in S3 and then taking Redshift and analyzing them, and then maybe a few hours later having an insight, this is not going to work. And what you need to fix is, you have to put some structure into the data. Because if you need to update a single record, you cannot just create a huge file of 10 gigabyte and then analyze it. So what we did is, basically, a mechanism where you ingest data. As you ingest the data, you can run multiple different processes on the same thing. And you can also serve the data immediately, okay? And two examples that we demonstrate here in the show, one is video surveillance, very nice movie-style example, that you, basically, ingest pictures for S3 API, for object API, you analyze the picture to detect faces, to detect scenery, to extract geolocation from pictures and all that, all those through different processes. TensorFlow doing one, serverless functions that we have, do other simpler tasks. And in the same time, you can have dashboards that just show everything. And you can have Spark, that basically does queries of where was this guys last seen? Or who was he with, you know, or think about the Boston Bomber example. You could just do it in real time. Because you don't need this notion of pipeline. And this solves very hard business problems for some of the customers we work with. >> So that's the key innovation, there's no pipe lining. And what's the secret sauce? >> So first, our system does about a couple of million of transactions per second. And we are a multi-modal database. So, basically, you can ingest data as a stream, exactly the same data could be read by Spark as a table. So you could, basically, issue a query on the same data. Give me everything that has a certain pattern or something, and could also be served immediately through RESTful APIs to a dashboard running AngularJS or something like that. So that's the secret sauce, is by having this integration, and this unique data model, it allows you all those things to work together. There are other aspects, like we have transactional semantics. One of the challenges is how do you make sure that a bunch of processes don't collide when they update the same data. So first you need a very low ground alert. 'cause each one may update to different field. Like this example that I gave with GeoData, the serverless function that does the GeoData extraction only updates the GeoData fields within the records. And maybe TensorFlow updates information about the image in a different location in the record or, potentially, a different record. So you have to have that, along with transaction safety, along with security. We have very tight security at the field level, identity level. So that's re-thinking the entire architecture. And I think what many of the companies you'll see at the show, they'll say, okay, Hadoop is given, let's build some sort of convenience tools around it, let's do some scripting, let's do automation. But serve the underlying thing, I won't use dirty words, but is not well-equipped to the new challenges of real time. We basically restructured everything, we took the notions of cloud-native architectures, we took the notions of Flash and latest Flash technologies, a lot of parallelism on CPUs. We didn't take anything for granted on the underlying architecture. >> So when you found the company, take a personal story here. What was the itch you were scratching, why did you get into this? Obviously, you have a huge tech advantage, which is, will double-down with the research piece and George will have some questions. What got you going with the company? You got a unique approach, people would love to do away with the pipeline, that sounds great. And the performance, you said about 100x. So how did you get here? (laughs) Tell the story. >> So if you know my background, I ran all the data center activities in Mellanox, and you know Mellanox, I know Kevin was here. And my role was to take Mellanox technology, which is 100 gig networking and silicon, and fit it into the different applications. So I worked with SAP HANA, I worked with Teradata, I worked on Oracle Exadata, I work with all the cloud service providers on building their own object storage and NoSQL and other solutions. I also owned all the open source activities around Hadoop and Saf and all those projects, and my role was to fix many of those. If a customer says I don't need 100 gig, it's too fast for me, how do I? And my role was to convince him that yes, I can open up all the bottleneck all the way up to your stack so you can leverage those new technologies. And for that we basically sowed inefficiencies in those stacks. >> So you had a good purview of the marketplace. >> Yaron: Yes. >> You had open source on one hand, and then all the-- >> All the storage players, >> vendors, network. >> all the database players and all the cloud service providers were my customers. So you're a very unique point where you see the trajectory of cloud. Doing things totally different, and sometimes I see the trajectory of enterprise storage, SAN, NAS, you know, all Flash, all that, legacy technologies where cloud providers are all about object, key value, NoSQL. And you're trying to convince those guys that maybe they were going the wrong way. But it's pretty hard. >> Are they going the wrong way? >> I think they are going the wrong way. Everyone, for example, is running to do NVMe over Fabric now that's the new fashion. Okay, I did the first implementation of NVMe over Fabric, in my team at Mellanox. And I really loved it, at that time, but databases cannot run on top of storage area networks. Because there are serialization problems. Okay, if you use a storage area network, that mean that every node in the cluster have to go and serialize an operation against the shared media. And that's not how Google and Amazon works. >> There's a lot more databases out there too, and a lot more data sources. You've got the Edge. >> Yeah, but all the new databases, all the modern databases, they basically shared the data across the different nodes so there are no serialization problems. So that's why Oracle doesn't scale, or scale to 10 nodes at best, with a lot of RDMA as a back plane, to allow that. And that's why Amazon can scale to a thousand nodes, or Google-- >> That's the horizontally-scalable piece that's happening. >> Yeah, because, basically, the distribution has to move into the higher layers of the data, and not the lower layers of the data. And that's really the trajectory where the traditional legacy storage and system vendors are going, and we sort of followed the way the cloud guys went, just with our knowledge of the infrastructure, we sort of did it better than what the cloud guys did. 'Cause the cloud guys focused more on the higher levels of the implementation, the algorithms, the Paxos, and all that. Their implementation is not that efficient. And we did both sides extremely efficient. >> How about the Edge? 'Cause Edge is now part of cloud, and you got cloud has got the compute, all the benefits, you were saying, and still they have their own consumption opportunities and challenges that everyone else does. But Edge is now exploding. The combination of those things coming together, at the intersection of that is deep learning, machine learning, which is powering the AI hype. So how is the Edge factoring into your plan and overall architectures for the cloud? >> Yeah, so I wrote a bunch of posts that are not published yet about the Edge, But my analysis along with your analysis and Pierre Levin's analysis, is that cloud have to start distribute more. Because if you're looking at the trends. Five gig, 5G Wi-Fi in wireless networking is going to be gigabit traffic. Gigabit to the homes, they're going to buy Google, 70 bucks a month. It's going to push a lot more bend with the Edge. On the same time, a cloud provider, is in order to lower costs and deal with energy problems they're going to rural areas. The traditional way we solve cloud problems was to put CDNs, so every time you download a picture or video, you got to a CDN. When you go to Netflix, you don't really go to Amazon, you got to a Netflix pop, one of 250 locations. The new work loads are different because they're no longer pictures that need to be cashed. First, there are a lot of data going up. Sensory data, upload files, et cetera. Data is becoming a lot more structured. Censored data is structured. All this car information will be structured. And you want to (mumbles) digest or summarize the data. So you need technologies like machine learning, NNI and all those things. You need something which is like CDNs. Just mini version of cloud that sits somewhere in between the Edge and the cloud. And this is our approach. And now because we can string grab the mini cloud, the mini Amazon in a way more dense approach, then this is a play that we're going to take. We have a very good partnership with Equinox. Which has 170 something locations with very good relations. >> So you're, essentially, going to disrupt the CDN. It's something that I've been writing about and tweeting about. CDNs were based on the old Yahoo days. Cashing images, you mentioned, give me 1999 back, please. That's old school, today's standards. So it's a whole new architecture because of how things are stored. >> You have to be a lot more distributive. >> What is the architecture? >> In our innovation, we have two layers of innovation. One is on the lower layers of, we, actually, have three main innovations. One is on the lower layers of what we discussed. The other one is the security layer, where we classify everything. Layer seven at 100 gig graphic rates. And the third one is all this notion of distributed system. We can, actually, run multiple systems in multiple locations and manage them as one logical entity through high level semantics, high level policies. >> Okay, so when we take the CUBE global, we're going to have you guys on every pop. This is a legit question. >> No it's going to take time for us. We're not going to do everything in one day and we're starting with the local problems. >> Yeah but this is digital transmissions. Stay with me for a second. Stay with this scenario. So video like Netflix is, pretty much, one dimension, it's video. They use CDNs now but when you start thinking in different content types. So, I'm going to have a video with, maybe, just CGI overlayed or social graph data coming in from tweets at the same time with Instagram pictures. I might be accessing multiple data everywhere to watch a movie or something. That would require beyond a CDN thinking. >> And you have to run continuous analytics because it can not afford batch. It can not afford a pipeline. Because you ingest picture data, you may need to add some subtext with the data and feed it, directly, to the consumer. So you have to move to those two elements of moving more stuff into the Edge and running into continuous analytics versus a batch on pipeline. >> So you think, based on that scenario I just said, that there's going to be an opportunity for somebody to take over the media landscape for sure? >> Yeah, I think if you're also looking at the statistics. I seen a nice article. I told George about it. That analyzing the Intel cheap distribution. What you see is that there is a 30% growth on Intel's cheap Intel Cloud which is faster than what most analysts anticipate in terms of cloud growth. That means, actually, that cloud is going to cannibalize Enterprise faster than what most think. Enterprise is shrinking about 7%. There is another place which is growing. It's Telcos. It's not growing like cloud but part of it is because of this move towards the Edge and the move of Telcos buying white boxes. >> And 5G and access over the top too. >> Yeah but that's server chips. >> Okay. >> There's going to be more and more computation in the different Telco locations. >> John: Oh you're talking about computer, okay. >> This is an opportunity that we can capitalize on if we run fast enough. >> It sounds as though because you've implemented these industry standard APIs that come from the, largely, the open source ecosystem, that you can propagate those to areas on the network that the vendors, who are behind those APIs can't, necessarily, do. Into the Telcos, towards the Edge. And, I assume, part of that is cause of the density and the simplicity. So, essentially, your footprint's smaller in terms of hardware and the operational simplicity is greater. Is that a fair assessment? >> Yes and also, we support a lot of Amazon compatible APIs which are RESTful, typically, HTTP based. Very convenient to work with in a cloud environment. Another thing is, because we're taking all the state on ourself, the different forms of states whether it's a message queue or a table or an object, et cetera, that makes the computation layer very simple. So one of the things that we are, also, demonstrating is the integration we have with Kubernetes that, basically, now simplifies Kubernetes. Cause you don't have to build all those different data services for cloud native infrastructure. You just run Kubernetes. We're the volume driver, we're the database, we're the message queues, we're everything underneath Kubernetes and then, you just run Spark or TensorFlow or a serverless function as a Kubernetes micro service. That allows you now, elastically, to increase the number of Spark jobs that you need or, maybe, you have another tenant. You just spun a Spark job. YARN has some of those attributes but YARN is very limited, very confined to the Hadoop Ecosystem. TensorFlow is not a Hadoop player and a bunch of those new tools are not in Hadoop players and everyone is now adopting a new way of doing streaming and they just call it serverless. serverless and streaming are very similar technologies. The advantage of serverless is all this pre-packaging and all this automation of the CICD. The continuous integration, the continuous development. So we're thinking, in order to simplify the developer in an operation aspects, we're trying to integrate more and more with cloud native approach around CICD and integration with Kubernetes and cloud native technologies. >> Would it be fair to say that from a developer or admin point of view, you're pushing out from the cloud towards the Edge faster than if the existing implementations say, the Apache Ecosystem or the AWS Ecosystem where AWS has something on the edge. I forgot whether it's Snowball or Green Grass or whatever. Where they at least get the lambda function. >> They're field by the way and it's interesting to see. One of the things they allowed lambda functions in their CDS which is going the direction I mentioned just for a minimal functionality. Another thing is they have those boxes where they have a single VM and they can run lambda function as well. But I think their ability to run computation is very limited and also, their focus is on shipping the boxes through mail and we want it to be always connected. >> Our final question for you, just to get your thoughts. Great save up, by the way. This is very informative. Maybe be should do a follow up on Skype in our studio for Silocon Friday show. Google Next was interesting. They're serious about the Enterprise but you can see that they're not yet there. What is the Enterprise readiness from your perspective? Cause Google has the tech and they try to flaunt the tech. We're great, we're Google, look at us, therefore, you should buy us. It's not that easy in the Enterprise. How would you size up the different players? Because they're all not like Amazon although Amazon is winning. You got Amazon, Azure and Google. Your thoughts on the cloud players. >> The way we attack Enterprise, we don't attack it from an Enterprise perspective or IT perspective, we take it from a business use case perspective. Especially, because we're small and we have to run fast. You need to identify a real critical business problem. We're working with stock exchanges and they have a lot of issues around monitoring the daily trade activities in real time. If you compare what we do with them on this continuous analytics notion to how they work with Excel's and Hadoops, it's totally different and now, they could do things which are way different. I think that one of the things that Hadook's customer, if Google wants to succeed against Amazon, they have to find the way of how to approach those business owners and say here's a problem Mr. Customer, here's a business challenge, here's what I'm going to solve. If they're just going to say, you know what? My VM's are cheaper than Amazon, it's not going to be a-- >> Also, they're doing the whole, they're calling lift and shift which is code word for rip and replace in the Enterprise. So that's, essentially, I guess, a good opportunity if you can get people to do that but not everyone's ripping and replacing and lifting and shifting. >> But a lot of Google advantages around areas of AI and things like that. So they should try and leverage, if you think about Amazon approach to AI, this fund the university to build a project and then set it's hours where Google created TensorFlow and created a lot of other IPs and Dataflow and all those solutions and consumered it to the community. I really love Google's approach of contributing Kubernetes, to contributing TensorFlow. And this way, they're planting the seeds so the new generation this is going to work with Kubernetes and TensorFlow who are going to say, "You know what?" "Why would I mess with this thing on (mumbles) just go and. >> Regular cloud, do multi-cloud. >> Right to the cloud. But I think a lot of criticism about Google is that they're too research oriented. They don't know how to monetize and approach the-- >> Enterprise is just a whole different drum beat and I think that's the only thing on my complaint with them, they got to get that knowledge and/or buy companies. Have a quick final point on Spanner or any analysis of Spanner that went from paper, pretty quickly, from paper to product. >> So before we started iguazio, I started Spanner quite a bit. All the publication was there and all the other things like Spanner. Spanner has the underlying layer called Colossus. And our data layer is very similar to how Colossus works. So we're very familiar. We took a lot of concepts from Spanner on our platform. >> And you like Spanner, it's legit? >> Yes, again. >> Cause you copied it. (laughs) >> Yaron: We haven't copied-- >> You borrowed some best practices. >> I think I cited about 300 research papers before we did the architecture. But we, basically, took the best of each one of them. Cause there's still a lot of issues. Most of those technologies, by the way, are designed for mechanical disks and we can talk about it in a different-- >> And you have Flash. Alright, Yaron, we have gone over here. Great segment. We're here, live in Silicon Valley, breakin it down, getting under the hood. Looking a 10X, 100X performance advantages. Keep an eye on iguazio, they're looking like they got some great products. Check them out. This is the CUBE. I'm John Furrier with George Gilbert. We'll be back with more after this short break. (upbeat synthesizer music)