>> on stew minimum, and this is a special on the ground here at the V tug Summer Slam. Of course, the V tug is the virtual ization in Technology User group, and the veto has always been great at getting us. Some of these users on the programs have any welcome back then. Valentine, who's in I t operating for Danon, the parent company of Dannon, spoke to you in 2017 >> at the >> winter warmer at Gillette Stadium. Since last we spoke, you no longer live in New England. But you have, ah ah, long history with this event. So let's start there what this event meant to you and what brought you back for the ultimate final V tug >> event here. Well, I have a long professional relationship with Chris Williams. He's one of the organizers of the events, and since he introduced me to it and I started coming, my career has really taken off the contacts that you congenital rate and the networking that you could do. An event like this is just unparalleled, and you can also learn a lot from the events, too. But it's almost a footnote because of everything else that you can gain from its ending something like this on a regular basis. Yeah, >> it's a great always look at this show. And when they do the breakout sessions, the Expo Hall gets pretty empty because people are wanting >> to learn. >> But it is the networking, you know, people sitting, you know, before the events, people sitting at lunch. And of course, you know, this evening at the lobster event, there's definitely some good networking, you know, going on, there s >> o, you know, explain. You >> know, from your standpoint, you know, this event started very heavily in virtualization, but it's gone through. You know what? What's changing into Industries Cloud and Dev ops in those environments Is that kind of followed, similar to what you've been seeing in your career? Oh, >> yes, absolutely. I mean, I started off his assists. Admin very heavy and B m wear like a lot of us in that field. Onda, Of course, you know everything's evolving now that the only constant is change. And what I like most about this event is that they have. They've changed the vendors that come in. They've changed the keynote. They've changed the different breakout sessions to keep the information that you're obtaining relevant. It's not redundant. And it allows you to just keep a good bead on what's out there and what to expect in the coming years. All >> right, Dan. What? What? What? What is what's interesting you These days I don't know if you've gotten a chance to go to any of the breakouts or you know what you were looking at coming at the event. But other than coming back and seeing some of the people you know, even though you're no longer in the area, you know what was catching your interest? >> Well, something that's very different since the last time I spoke to you is Cloud specifically for the company that I work for. At that time, it was just a research. It was a nice idea. It was something that, of course, tech was talking about. But the business wasn't interested. And now we're actually in the middle of a cloud implementation for all of our data centers were moving off KREM. We're taking things to the cloud, and we're in the infancy stage of actually, the implementation of the projects has been very beneficial to come here and gain that knowledge. >> Yeah, I heard that one of the themes that was over and over, you know, in the keynotes at this event as well as when I hear many Joe Joe's and just, you know it's not just changed, but, you know, how can I become more agile on? And how can I adopt new things? Theo? Enterprise is, you know, not known for change or speed. You know, what are you seeing in your world? And when you talk to your peers, you know, kind of the openness to be able to embrace technique, new technology that make changes in the way things are done. >> Well, from my personal experience, I would say that most companies intentionally stay a little bit behind when there's a lot of money involved. When you return on, your investment is high. Um, you're not going to jump right into the brand new thing, you know. So there's a There's an intentional, deliberate lag there behind what's brand new behind what your options are at that moment. Um, so I So I think that businesses do, and they do want to move along. They are interested in it, but the validity has to be proven first. All >> right, Dan. Want to give you, You know, your final thoughts that the final be tug any any memories from the events or, you know, last words that you have for the beach community. >> Well, there's definitely some memories that I wouldn't feel comfortable sharing, but ah, this will. This will be missed. I can say that this has been a huge part of my career up to this point. And I have every intention of keeping contact with many of the people that I've met here and continuing to build on those relationships throughout my career. And I'm pretty confident that it wouldn't be exactly where I am now. If it wasn't for my relationship with Chris and the other people, he's introduced me to this event. >> Waves of technology definitely come and go in the different tools, their environment. But those relationships are so important, you know, our careers in the communities that we're part of self. Thanks for coming back from Colorado, and thank you. We appreciate you sharing your story with our community on the cute. >> Yes, of course. Thank you. All >> right. Uh, I'm Sue Minutemen, as always. Thank you so much for watching the cue

(upbeat techno music) >> Narrator: Live, from San Jose, it's theCUBE! Presenting Big Data Silicon Valley, brought to you by SiliconANGLE Media and its ecosystem partners. >> Welcome back to Big Data SV, everybody. My name is Dave Vellante and this is theCUBE, the leader in live tech coverage. You know, this is our 10th big data event. When we first started covering big data, back in 2010, it was Hadoop, and everything was a batch job. About four or five years ago, everybody started talking about real time and the ability to affect outcomes before you lose the customer. Lewis Kaneshiro was here. He's the CEO of Streamlio and he's joined by Karthik Ramasamy who's the chief product officer. They're both co-founders. Gentlemen, welcome to theCUBE. My first question is, why did you start this company? >> Sure, we came together around a vision that enterprises need to access the value around fast data. And so as you mentioned, enterprises are moving out of the slow data era, and looking for a fast data value to their data, to really deliver that back to their users or their use cases. And so, coming together around that idea of real time action what we did was we realized that enterprises can't all access this data with projects right now that are not meant to work together, that are very difficult, perhaps, to stitch together. So what we did was create an intelligent platform for fast data that's really accessible to enterprises of all sizes. What we do is we unify the core components to access fast data, which is messaging, compute and stream storage, accessing the best of breed open-source technology that's really open-source out of Twitter and Yahoo! >> It's a good thing I was going to ask why does the world need to know there are, you know, streaming platforms, but Lewis kind of touched on it, 'cause it's too hard. It's too complicated, so you guys are trying to simplify all that. >> Yep, the reason mainly we wanted to simplify it because, based on all our experiences at Twitter and Yahoo! one of the key aspects was to to simplify it so that it's conceivable by regular enterprise because Twitter and Yahoo! kind of our position can afford the talent and the expertise in order to do this real time platforms. But when it goes to normal enterprises, they don't have access to the expertise and the cost benefits that they might have to reincur. So, because of that we wanted to use these open-source projects, the Twitter and the Yahoo!'s provider, combine them, and make sure that you have a simple, easy, drag and drop kind of interface, so that it's easily conceivable for any enterprise. Essentially, what we are trying to do is reduce the (mumbles) for enterprises for real time, for all enterprises. >> Dave: Yeah, enterprises will pay up... >> Yes. >> For a solution. The companies that you used to work for, they all gladly throw engineering at the problem. >> Yeah. >> Sure. >> To save time, but most organizations, they don't have the resources and so. Okay, so how does it, would it work prior to Streamlio? Maybe take us through sort of how a company would attack this problem, the complexities of what they have to deal with, and what life is like with you guys. >> So, current state of the world is it's fragmented solution, today. So the state of the world is where you take multiple pieces of different projects and you'd assemble them together in formats so that you can do (mumbles) right? So the reason why people end up doing is each of these big data projects that people use was the same for completely different purpose. Like messaging is one, and compute is another one, and third one is storage one. So, essentially what we have done as company is to simplify this aspect by integrating this well-known, best-of-the-breed projects called, for messaging we use something called Apache Poser, for compute we use something called Apache Krem, from Twitter, and similarly for storage, for real time storage, we use something called Apache Bookkeeper, so and to unify them, so that, under the hoods, it may be three systems, but, as a user, when you are using it, it serves or functions as a single system. So you install the system, and ingest your data, express your computation, and get the results out, in one single system. >> So you've unified or converged these functions. If I understand it correctly, we talking off camera a little bit, the team, Lewis, that you've assembled actually developed a lot of these, or hugely committed to these open-source projects, right? >> Absolutely, co-creators of each of the projects and what that allows us to do is to really integrate, at a deep level, each project. For example, Pulsar is actually a pub/sub system that is built on Bookkeeper, and Bookkeeper, in our minds, is a pure list best-of-breed stream storage solution. So, fast and durable storage. That storage is also used in Apache Heron to store State. So, as you can see, enterprises, rather than stitching together multiple different solutions for queuing, streaming, compute, and storage, now have one option that they can install in a very small cluster, and operationally it's very simple to scale up. We simply add nodes if you get data spikes. And what this allows is enterprises to access new and exciting use cases that really weren't possible before. For example, machine learning model deployment to real time. So I'm a data scientist and what I found is in data science, you spend a lot of time training models in batch mode. It's a legacy type of approach, but once the model is trained, you want to put that model into production in real time so that you can deliver that value back to a user in real time. Let's call it under two second SLA. So, that has been a great use case for Streamlio because we are a ready made intelligent platform for fast data, for MLai deployment. >> And the use cases are typically stateful and your persisting data, is that right? >> Yes, use cases, it can be used for stateless use cases also, but the key advantage that we bring to a table is stateful storage. And since we ship along with the storage (mumbles) stateful storage becomes much easier because of the fact that it can be used to store a real intermediate state of the computation or it can be used for the staging (mumbles) data when it spills over from what the memory is it's automatically stored to disk or you can even in the data for as long as you want so that you can unlock the value later after the data has been processed for the fast data. You can access the lazy data later, in time. >> So give us the run-down on the company, funding, you know, VCs, head count. Give us the basics. >> Sure, we raise Series A from Lightspeed Venture Partners, lead by John Vrionis and Sudip Chakrabarti. We've raised seven and a half million and emerged from stealth back in August. That allowed us to ramp up our team to 17, now, mainly engineers, in order to really have a very solid product, but we launched post rev, prelaunch and some of our customers are really looking at geo replication across multiple data centers and so active, active geo replication is an open-source feature in Apache Pulsar, and that's been a huge draw, compared to some other solutions that are out there. As you can see, this theme of simplifying architecture is where Streamlio sits, so unifying, queuing and streaming allows us to replace a number of different legacy systems. So that's been one avenue to help growth. The other, obviously is on the compute piece. As enterprises are finding new and exciting use cases to deliver back to their users, the compute piece needs to scale up and down. We also announce Pulsar Functions, which is stream-native compute that allows very simple function computation in native Python and Java, so you spin out the Apache Python cluster or Streamlio platform, and you simply have compute functionality. That allows us to access edge use cases, so IOT is a huge, kind of exciting POC's for us right now where we have connected car examples that don't need heavyweight schedule or deployment at the edge. It's Pulsar Pulsar functions. What that allows us to do are things like fraud detection, anomaly detection at the edge, model deployment at the edge, interpolation, observability, and alerts. >> And, so how do you charge for this? Is it usage based. >> Sure. What we found is enterprise are more comfortable on a per node basis, simply because we have the ambition to really scale up and help enterprises really use Streamlio as their fast data platform across the entire enterprise. We found that having a per data charge rate actually would limit that growth, and so per node and shared architecture. So, we took an early investment in optimizing around Kubernetes. And so, as enterprises are adopting Kubernetes, we are the most simple installation on Kubernetes, so on-prem, multicloud, at the edge. >> I love it, so I mean for years we've just been talking about the complexity headwinds in this big data space. We certainly saw that with Hadoop. You know, Spark was designed to certainly solve some of those problems, but. Sounds like you're doing some really good work to take that further. Lewis and Karthik, thank you so much for coming on theCUBE. I really appreciate it. >> Thanks for having us, Dave. >> All right, thank you for watching. We're here at Big Data SV, live from San Jose. We'll be right back. (techno music)