>> 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)