 Hi, I'm Stu Miniman, and this is the CUBE conversation and SiliconANGLE's Palo Alto office. Happy to welcome back to the program, Liron Zvibel, who is the co-founder and CEO of Weka IO. Thanks so much for joining me. Thank you for having me over. All right, so on our research side, we've really been saying that data is at the center of everything. It's in the cloud, it's in the network, and of course, in the storage industry. Data's always been there, but I think especially for customers, it's been more front and center. Why is data becoming more important? It's not data growth and some of the other things that we've talked about for decades, but how is it changing? What are you hearing from customers today? So I think the main difference is that organizations are starting to understand that the more data they have, the better service they're going to provide to their customers, and there will be an overall better company than their competitors. So about 10 years ago, we started hearing about big data and other ways that in a more simpler form, we just went over a sieve through a lot of data and tried to get some sort of high level meaning out of it. Last few years, people are actually employing deep learning, machine learning technique to their vast amounts of data, and they're getting much high level of intelligence out of their huge capacities of data, and actually with deep learning, the more data you have, the better outputs you get. Okay, before we go into kind of the ML and the deep learning piece, just kind of the focus on data itself, there's some that say, oh, digital transformation, it's this buzzword. When I talk to users, absolutely they're going through transformations. We're saying everybody's becoming a software company, but how does data specifically help them with that? What is your viewpoint there, and what are you hearing from your customers? So if you look at it from the consumer perspective, so people now keep track record of their lives at much higher resolution than they used to, and I'm not talking about the images, I'm talking about the vast amount of data that they store, so if I look at how many pictures I have of myself as a kid and how many pictures I have of my kids, like you could fit all of my pictures in two albums. I can probably fit my kids like a week's worth of time in two albums, so people keep a lot more data as consumers, and then organization keep a lot more data of their customers in order to provide better service and better overall product. The interest as an industry, we saw a real mixed bag when it came to big data. When it was saying, great, I have lots more volume of data, that doesn't necessarily mean that I got more value out of it. So what are the trends that you're seeing? Why are things like deep learning, machine learning, AI? Is it going to be different, or is this just kind of the next iteration of, well we're trying and maybe we didn't hit as well with big data, let's see if this does better. So I think that big data had its glory days, and now they're coming to the end of that crescendo because people realized that what they got was sort of aggregate of thing that they couldn't make too much sense of. And then people really understand that for you to make better use of your data, you need to employ ways, similarly to how the brain works. So look at a lot of data, and then you have to have some sense out of the data, and once you've made some sense out of that data, we can now get computers to go through way more data and make similar amount of sense out of that and actually get much, much better results. So just instead of going and finding anecdotes or these things that you were able to do with big data, you're actually now are able to generate intelligent systems. Yeah, one of the other things we saw is it used to be okay, I have this huge back catalog or I'm going to survey all the data I've collected. Today, it's much more real time's a word that's been thrown around for many years, whether it's you say live data or if you're at sensors where I need to have something where I can train models, react immediately, that kind of immediacy is much more important. I'm assuming that's something that you're seeing from customers too. Indeed, so what we see is that customers end up collecting vast amounts of data and then they train their models on these kind of data and then they're pushing these intelligent models to the edges and then you're going to have edges running inference and that could be a straight camera, it could be a camera in the store or it could be your car and then usually you run these inference at the end points using all the things you've trained the models back then and you will still keep the data, push it back and then you still run inference at the data center sort of doing QA and now the edges also know to mark where they couldn't make sense of what they saw. So the data center systems know what should we look at first, how we make our models smarter for the next iteration because these are closed loop systems. You train them, you push to the edges, the edges tell you how well you think, they think they understood, you train again and things improve. We're now at the infancy of a lot of these loops but I think the following probably two to five years will take us through a very, very fascinating revolution where systems all around us will become way, way more intelligent. Yeah, and there's interesting architectural discussions going on if you talk about this edge environments. If I'm an autonomous vehicle, if I'm an airplane, of course, I need to react there. I can't go back to the cloud but what happens in the cloud versus what happens at the edge? Where does WECA fit into that whole discussion? So where we currently are running, we're running at the data centers. So at WECA we created the fastest file system that's perfect for AI and machine learning and training and we make sure that your GPU field servers that are very expensive never sit idle. The second component of our system is tiering to very effective object storages that can run into exabytes. So we have the system that makes sure you can have as many GPU servers churning all the time and getting the results, getting the new models while having the ability to read any form of data that was collected in the several years really through hundreds of petabytes of data sets and now we have customers talking about exabytes of data sets representing a single application, not throughout the organization just for that training application. So AI and ML, Keto, is that the killer use case for your customers today? So that's one killer application just because of the vast amount of data and the high performance nature of the clients. We actually show clients that Runwaka IO finished training sessions 10 times faster than how they would use traditional NFS based solutions just based on the different way we handle data. Another very strong application for us is around life sciences and genomics where we show that we're the only storage that let these processes remain CPU bound. So any other storage at some points becomes IO bound so you couldn't parallelize the processing anymore. We actually doesn't matter how many servers you run as clients, you double the amount of clients, you either get the twice the result the same amount of time or you get the same results at half the time and with genomics nowadays there are applications that are life saving. So hospitals run these things and they need results as fast as they can so faster storage means better healthcare. Yeah, without getting too deep in it because the storage industry has lots of wonkiness and there's so many pieces there but I hear life sciences, I think object storage, I hear NVMe, I think block storage, your file storage when it comes down to it, why is that the right architecture for today? And what advantages does that give you? So we are actually the only company that went through the hassles and the hurdles of utilizing NVMe and NVMe over fabrics for a parallel file system. All other solutions went the easier route and created the block and the reason we've created a file system is that this is what computers understand, this is what the operating system understand. When you go to university, you learn computer science, they teach you how to write programs, they need a file system. Now, if you want to run your program over two servers or 10 servers, what you need is a shared file system. Up until we came, Gold Standard was using NFS for sharing files across servers but NFS was actually created in the 80s when Ethernet ran at 10 megabit. So currently, most of our customers run already at 100 gigabits which is four orders of magnitude faster. So they're seeing that they cannot run a network protocol that was designed for four orders of magnitude less speed with the current demanding workloads. So this explains why we had to go and pick a totally different way of pushing data to the clients with regarding to object storages. Object storages are great because they allow customers to aggregate hard drives into inexpensive, large capacity solutions. The problem with object storages is that the programming model is different than the standard file system that computers can understand in two ways. A, when you write something you don't know when it's going to get actually stored. It's called eventual consistency and it's very difficult for mortal programmers to actually write a system that is sound, that is always correct when you're writing eventual consistency storage. The second thing is that objects cannot change. You cannot modify them. You either create them, you get them, or you can delete them, they can have versions. But this is also much different than how the average programmer is used to write its programs. So we're actually tying between the highest performance NVMe of the fabrics at the first tier and these object storages that are extremely efficient but very difficult to work with at the backend tier to a single solution that is high, highest performance and best economics. All right, Loran, I want to give you the last word. Give us a little bit of a long view. You talked about where we've gone, how parallel architecture helps now that we're at 100 gig. Look out five years in the future. What's going to happen? Blockchain takes over the world. Cloud dominates everything. But from an infrastructure application and storage world, where does Weka think that things look like? So one very strong trend that we are seeing is around encryption. So it doesn't matter what industry. I think storing things in clear text for many organizations just stops making sense and people will demand more and more of their data to be encrypted and tighter control around everything. That's one very strong trend that we're seeing. Another very strong trend that we're seeing is enterprises would like to leverage the public cloud but in an efficient way. So if you were to run economics, moving all your application to the public cloud may end up being more expensive than running everything on-prem. And I think a lot of organizations realize that the trick is going to be, each organization will have to find a balance to what kind of services are run on-prem. And these are going to be the services that are run around the clock. And what services have the more of a burst in nature. And then organization will learn how to leverage the public cloud for its elasticity. Because if you're just running on the cloud, you're not leveraging the elasticity, you're doing it wrong. And we're actually helping a lot of our customers do it with our hybrid cloud ability to have local workloads and the cloud workloads. And getting these whole workflows to actually run is a fascinating process. All right, Liron, thank you so much for joining us. Great to hear the update, not only on Weka but really where the industry's going. Dynamic times here in the industry, data at the center of it all, cubes looking to cover it at all the locations, including here in our lovely Palo Alto studio. I'm Stu Miniman, thanks so much for watching theCUBE. Thank you very much.