>> 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)

>> 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.