>> Announcer: Live from San Jose, it's theCUBE, presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media and it's ecosystem partners. >> Welcome back to Big Data SV in San Jose. This theCUBE, the leader in live tech coverage. My name is Dave Vellante and this is day two of our wall-to-wall coverage. We've been here most of the week, had a great event last night, about 50 or 60 of our CUBE community members were here. We had a breakfast this morning where the Wikibon research team laid out it's big data forecast, the eighth big data forecast and report that we've put out, so check out that online. Jacques Nadeau is here. He is the CTO and co-founder of Dremio. Jacque, welcome to theCUBE, thanks for coming on. >> Thanks for having me here. >> So we were talking a little bit about what you guys do. Three year old company. Well, let me start. Why did you co-found Dremio? >> So, it was a very simple thing I saw, so, over the last ten years or so, we saw a regression in the ability for people to get at data, so you see all these really cool technologies that came out to store data. Data lakes, you know, SQL systems, all these different things that make developers very agile with data. But what we were also seeing was a regression in the ability for analysts and data consumers to get at that data because the systems weren't designed for analysts, they were designed for data producers and developers. And we said, you know what, there needs to be a way to solve this. We need to be able to empower people to be self-sufficient again at the data consumption layer. >> Okay, so you solved that problem how, you said, called it a self-service of a data platform. >> Yeah, yeah, so self-service data platform and the idea is pretty simple. It's that, no matter where the data is physically, people should be able to interact with a logical view of it. And so, we talk a little bit like it's Google Docs for your data. So people can go into the system, they can see the different data sets that are available to them, collaborate around those, create changes to those that they can then share with other people in the organization, always dealing with the logical layer and then, behind the scenes, we have physical capabilities to interact with all the different system we interact with. But that's something that business users shouldn't have to think as much about and so, if you think about how people interact with data today, it's very much about copies. So every time you want to do something, typically you're going to make a copy. I want to reshape the data, I make a copy. I want to make it go faster, I make a copy. And those copies are very, very difficult for people to manage and they could have mixed the business meaning of data with the physical, I'm making copies to make them faster or whatever. And so our perspective is that, if you can separate away the physical concerns from the logical, then business users have a much more, much more likelihood to be able to do something self-service. >> So you're essentially virtualizing my corpus of data, independent of location, is that right, I mean-- >> It's part of what we do, yeah. No, it's part of what we do. So, the way we look at it is, is kind of several different components to try to make something self-service. It starts with, yeah, virtualize or abstract away the details of the physical, right? But then, on top of that, expose a very, sort of a very user-friendly interface that allows people to sort of catalog and understand the different things, you know, search for things that they want to interact with, and then curate things, even if they're non-technical users, right? So the goal is that, if you talk to sort of even large internet companies in the Valley, it's very hard to even hire the amount of data engineering that you need to satisfy all the requests of your end-users of data. And so the, and so the goal of Dremio is basically to figure out different tools that can provide a non-technical experience for getting at the data. So that's sort of the start of it but then the second step is, once you've got access to this thing and people can collaborate and sort of deal with the data, then you've got these huge volumes of data, right? It's big data and so how do you make that go faster? And then we have some components that we deal with, sort of, speed and acceleration. >> So maybe talk about how people are leveraging this capability, this platform, what the business impact is, what have you seen there? >> So a lot of people have this problem, which is, they have data all over the place and they're trying to figure out "How do I expose this "to my end-users?" And those end-users might be analysts, they might be data scientists, they might be product managers that are trying to figure out how their product is working. And so, what they're doing today is they're typically trying to build systems internally that, to provide these capabilities. And so, for example, working with a large auto manufacturer. And they've got a big initiative where they're trying to make the data that they have, they have huge amounts of data across all sort of different parts of the organization and they're trying to make that available to different data consumers. Now, of course, there's a bunch of security concerns that you need to have around that, but they just want to make the data more accessible. And so, what they're doing is they're using Dremio to figure out ways to, basically, catalog all the data below, expose that to the different users, applying lots of different security rules around that, and then create a bunch of reflections, which make the things go faster as people are interacting with the things. >> Well, what about the governance factor? I mean, you heard this in the hadoop world years ago. "Ah, we're going to make, we're going to harden hadoop, "we're going to" and really, there was no governance and it became more and more important. How do you guys handle that? Do you partner with people? Is it up to the customer to figure that out? Do you provide that? >> It's several different things, right? It's a complex ecosystem, right? So it's a combination of things. You start with partnering with different systems to make sure that you integrate well with those things. So the different things that control some parts of credentials inside the systems all the way down to "What's the file system permissions?", right? "What are the permissions inside of something like Hive and the metastore there?" And then other systems on top of that, like Sentry or Ranger are also exposing different credentialing, right? And so we work hard to sort of integrate with those things. On top of that, Dremio also provides a full security model inside of the sort of virtual space that we work. And so people can control the permissions, the ability to access or edit any object inside of Dremio based on user roles and LDAP and those kinds of things. So it's, it's kind of multiple layers that have to be working together. >> And tell me more about the company. So founded three years ago, I think a couple of raises, >> Yep >> who's backing you? >> Yeah, yeah, yeah, so we founded just under three years ago. We had great initial investors, in Red Point and Lightspeed, so two great initial investors and we raised about 15 million on that round. And then we actually just closed a B round in January of this year and we added Norwest to the portfolio there. >> Awesome, so you're now in the mode of, I mean, they always say, you know, software is such a capital-efficient business but you see software companies raising, you know, 900 million dollars and so, presumably, that's to compete, to go to market and, you know, differentiate with your messaging and branding. Is that sort of what the, the phase that you're in now? You kind of developed a product, it's technically sound, it's proven in the marketspace and now you're scaling the, the go-to-market, is that right? >> That's exactly right. So, so we've had a lot of early successes, a lot of Fortune 100 companies using Dremio today. For example, we're working with TransUnion. We're working with Intel. We actually have a great relationship with OVH, which is the third-largest hosting company in the world, so a lot of great, Daimler is another one. So working with a lot of great companies, seeing sort of great early success with the product with those companies, and really looking to say "Hey, we're out here." We've got a booth for the first time at Strata here and we're sort of letting people know about, sort of, a better way, or easier way, for people to deal with data >> Yeah. >> A happier way. >> I mean, it's a crowded space, right? There's a lot of tools out there, a lot of companies. I'm interested in how you sort of differentiate. Obviously simplification is a part of that, the breadth of your capabilities. But maybe, in your words, you could share with me how you differentiate from the competition and how you break out from the noise. >> Yeah, yeah, yeah, so it's, you're absolutely right, it's a very crowded space. Everybody's using the same words and that makes it very hard for people to understand what's going on. And so, what we've found is very simple is that typically we will actually, the first meeting we deal with a customer, within the first 10 minutes we'll demo the product. Because so many technologies are technologies, not, they're not products and so you have to figure out how to use the product. You've got to figure out how you would customize it for your certain use-case. And what we've found with our product is, by making it very, very simple, people start, the light goes on in a very short amount of time and so, we also do things on our website so that you can see, in a couple of minutes, or even less than that, little animations that sort of give you a sense of what it's about. But really, it's just "Hey, this is a product "which is about", there's this light bulb that goes on, it's great. And you figure this out over the course of working with different customers, right? But there's this light bulb that goes on for people that are so confused by all the things that are going on and if we can just sit down with them, show them the product for a few minutes, all of a sudden they're like "Wait a minute, "I can use this", right? So you're frequently talking to buyers that are not the most technical parts of the organization initially, and so most of the technologies they look at are technologies that are very difficult to understand and they have to look to others to try to even understand how it would fit into their architecture. With Dremio, we have customers that can, that have installed it and gotten up, and within an hour or two, started to see real value. And that sort of excitement happens even in the demo, with most people. >> So you kind of have this bifurcated market. Since the big data meme, everybody says they're data-driven and you've got a bifurcated market in that, you've got the companies that are data-driven and you've got companies who say they're data-driven but really aren't. Who are your customers? Are they in both? Are they predominantly in the data-driven side? Are they predominantly in the trying to be data-driven? >> Well, I would say that they all would say that they're data-driven. >> Yeah, everyone, who's going to say "Well, we're not data-driven." >> Yeah, yeah, yeah. So I would say >> We're dead. >> I would say that everybody has data and they've got some ways that they're using it well and other places where they feel like they're not using it as well as they should. And so, I mean, the reason that we exist is to make it so it's easier for people to get value out of data, and so, if they were getting all the value they think they could get out of data, then we probably wouldn't exist and they would be fully data-driven. So I think that everybody, it's a journey and people are responding well to us, in part, because we're helping them down that journey. >> Well, the reason I asked that question is that we go to a lot of shows and everybody likes to throw out the digital transformation buzzword and then use Uber and Airbnb as an example, but if you dig deeper, you see that data is at the core of those companies and they're now beginning to apply machine intelligence and they're leveraging all this data that they've built up, this data architecture that they built up over the last five or 10 years. And then you've got this set of companies where all the data lives in silos and I can see you guys being able to help them. At the same time, I can see you helping the disruptors, so how do you see that? I mean, in terms of your role, in terms of affecting either digital transformations or digital disruptions. >> Well, I'd say that in either case, so we believe in a very sort of simple thing, which is that, so going back to what I said at the beginning, which is just that I see this regression in terms of data access, right? And so what happens is that, if you have a tightly-coupled system between two layers, then it becomes very difficult for people to sort of accommodate two different sets of needs. And so, the change over the last 10 years was the rise of the developer as the primary person for controlling data and that brought a huge amount of great things to it but analysis was not one of them. And there's tools that try to make that better but that's really the problem. And so our belief is very simple, which is that a new tier needs to be introduced between the consumers and the, and the producers of data. And that, and so that tier may interact with different systems, it may be more complex or whatever, for certain organizations, but the tier is necessary in all organizations because the analysts shouldn't be shaken around every time the developers change how they're doing data. >> Great. John Furrier has a saying that "Data is the new development kit", you know. He said that, I don't know, eight years ago and it's really kind of turned out to be the case. Jacques Nadeau, thanks very much for coming on theCUBE. Really appreciate your time. >> Yeah. >> Great to meet you. Good luck and keep us informed, please. >> Yes, thanks so much for your time, I've enjoyed it. >> You're welcome. Alright, thanks for watching everybody. This is theCUBE. We're live from Big Data SV. We'll be right back. (bright music)