>> Commentator: Live from San Jose, California. It's theCUBE covering Big Data Silicon Valley 2017. >> Okay, welcome back everyone. We are here live in Silicon Valley for Big Data SV (mumbles) event in conjunction with Strata + Hadoop. Our companion event, the Big Data NYC and we're here breaking down the Big Data world as it evolves and goes to the next level up on the step function, AI machine learning, IOT really forcing people to really focus on a clear line of the side of the data. I'm John Furrier with our announcer from Wikibon, George Gilbert and our next guest, our two executives from Trifacta. The founder and Chief Strategy Officer, Joe Hellerstein and Adam Wilson, the CEO. Guys, welcome to theCUBE. Welcome back. >> Great to be here. >> Good to be here. >> Founder, co-founder? >> Co-founder. >> Co-founder. He's a multiple co-founders. I remember it 'cause you guys were one of the first sites that have the (mumbles) in the about section on all the management team. Just to show you how technical you guys are. Welcome back. >> And if you're Trifacta, you have to have three founders, right? So that's part of the tri, right? >> The triple threat, so to speak. Okay, so a big year for you guys. Give us the update. I mean, also we had Alation announce this partnering going on and some product movement. >> Yup. >> But there's a turbulent time right now. You have a lot of things happening in multiple theaters to technical theater to business theater. And also within the customer base. It's a land grand, it seems to be on the metadata and who's going to control what. What's happening? What's going on in the market place and what's the update from you guys? >> Yeah, yeah. Last year was an absolutely spectacular year for Trifacta. It was four times growth in bookings, three times growth in customers. You know, it's been really exciting for us to see the technology get in the hands of some of the largest companies on the planet and to see what they're able to do with it. From the very beginning, we really believed in this idea of self service and democratization. We recognize that the wrangling of the data is often where a lot of the time and the effort goes. In fact, up to 80% of the time and effort goes in a lot of these analytic projects and to the extent that we can help take the data from (mumbles) in a more productive way and to allow more people in an organization to do that. That's going to create information agility that that we feel really good about and there are customers and they are telling us is having an impact on their use of Big Data and Hadoop. And I think you're seeing that transition where, you know, in the very beginning there was a lot of offloading, a lot of like, hey we're going to grab some cost savings but then in some point, people scratch their heads and said, well, wait a minute. What about the strategic asset that we were building? That was going to change the way people work with the data. Where is that piece of it? And I think as people started figuring out in order to get our (mumbles), we got to have users and use cases on these clusters and the data like itself is not a used case. Tools like Trifacta have been absolutely instrumental and really fueling that maturity in the market and we feel great about what's happening there. >> I want to get some more drilled out before we get to some of these questions for Joe too because I think you mentioned, you got some quotes. I just want to double up a click on that. It always comes up in the business model question for people. What's your business model? >> Sure. >> And doing democratization is really hard. Sometimes democratization doesn't appear until years later so it's one of those elusive things. You see it and you believe it but then making it happen are two different things. >> Yeah, sure. >> So. And appreciate that the vision they-- (mumbles) But ultimately, at the end of the day, that business model comes down to how you organized. Prove points. >> Yup. >> Customers, partnerships. >> Yeah. >> We had Alation on Stephanie (mumbles). Can you share just and connect the dots on the business model? >> Sure. >> With respect to the product, customers, partners. How was that specifically evolving? >> Adam: Sure. >> Give some examples. >> Sure, yeah. And I would say kind of-- we felt from the beginning that, you know, we wanted to turn what was traditionally a very complex messy problem dealing with data, you know, in the user experience problem that was powered by machine learning and so, a lot of it was down to, you know, how we were going to build and architect the technology needed (mumbles) for really getting the power in the hands of the people who know the data best. But it's important, and I think this is often lost in Silicon Valley where the focus on innovation is all around technology to recognize that the business model also has to support democritization so one of the first things we did coming in was to release a free version of the product. So Trifacta Wrangler that is now being used by over 4500 companies, ten of thousands of users and the power of that in terms of getting people something of value that they could start using right away on spreadsheets and files and small data and allowing them to get value but then also for us, the exchange is that we're actually getting a chance to curate at scale usage data across all of these-- >> Is this a (mumbles) product? >> It's a hybrid product. >> Okay. >> So the data stays local. It never leaves their local laptop. The metadata is hashed and put into the cloud and now we're-- >> (mumbles) to that. >> Absolutely. And so now we can use that as training data that actually has more people wrangle, the product itself gets smarter based on that. >> That's good. >> So that's creating real tangible value for customers and for us is a source of very strategic advantage and so we think that combination of the technology innovation but also making sure that we can get this in the hands of users and they can get going and as their problem grows up to be bigger and more complicated, not just spreadsheets and files on the desktop but something more complicated, then we're right there along with them for products that would have been modified. >> How about partnerships with Alation? How they (mumbles)? What are all the deals you got going on there? >> So Alation has been a great partner for us for a while and we've really deepened the integration with the announcements today. We think that cataloging and data wrangling are very complimentary and they're a natural fit. We've got customers like Munich Re, like eBay as well as MarketShare that are using both solutions in concert with one another and so, we really felt that it was natural to tighten that coupling and to help people go from inventorying what's going on in their data legs and their clusters to then cleansing, standardizing. Essentially making it fit for purpose and then ensuring that metadata can roundtrip back into the catalog. And so that's really been an extension of what we're doing also at the technical level with technologies like Cloudera Navigator with Atlas and with the project that Joe's involved with at Berkeley called Ground. So I don't know if you want to talk-- >> Yeah, tell him about Ground. >> Sure. So part of our outlook on this and this speaks to the kind of way that the landscape in the industry's shaping out is that we're not going to see customers buying until it's sort of lock in on the key components of the area for (mumbles). So for example, storage, HD (mumbles). This is open and that's key, I think, for all the players in this base at HTFS. It's not a product from a storage vendor. It's an open platform and you can change vendors along the way and you could role your own and so on. So metadata, to my mind, is going to move in the same direction. That the storage of metadata, the basic component tree that keeps the metadata, that's got to be open to give people the confidence that they're going to pour the basic descriptions of what's in their business and what their people are doing into a place that they know they can count on and it will be vendor neutral. So the catalog vendors are, in my mind, providing a functionality above that basic storage that relates to how do you search the catalog, what does the catalog do for you to suggest things, to suggest data sets that you should be looking at. So that's a value we have on top but below that what we're seeing is, we're seeing Horton and Cloudera coming out with either products re opensource and it's sort of the metadata space and what would be a shame is if the two vendors ended up kind of pointing guns inward and kind of killing the metadata storage. So one of the things that I got interested in as my dual role as a professor at Berkeley and also as a founder of a company in this space was we want to ensure that there's a free open vendor neutral metadata solution. So we began building out a project called Ground which is both a platform for metadata storage that can be sitting underneath catalog vendors and other metadata value adds. And it's also a platform for research much as we did with Spark previously at Berkeley. So Ground is a project in our new lab at Berkeley. The RISELab which is the successor to the AMPLab that gave us Spark. And Ground has now got, you know, collaboratives from Cloudera, from LinkedIn. Capital One has significantly invested in Ground and is putting engineers behind it and contributors are coming also from some startups to build out an open-sourced platform for metadata. >> How old has Ground been around? >> Joe: Ground's been around for about 12 months. It's very-- >> So it's brand new. How do people get involved? >> Brand new. >> Just standard similar to the way the AMPLab was? Just jump in and-- >> Yeah, you know-- >> Go away and-- >> It comes up on GitHub. There's (mumbles) to go download and play with. It's in alpha. And you know, we hope we (mumbles) and the usual opensource still. >> This is interesting. I like this idea because one thing you've been riffing on the cue ball of time is how do you make data addressable? Because ultimately, you know, real time you need to have access to data really really low (mumbles) to see the inside to make it work. Hence the data swamp problem right? So, how do you guys see that? 'Cause now I can just pop in. I can hear the objections. Oh, security! You know. How do you guys see the protections? I'd love to help get my data in there and get something back in return in a community model. Security? Is it the hashing? What's the-- How do you get any security (mumbles)? Or what are the issues? >> Yeah, so I mean the straightforward issues are the traditional issues of authorization and encryption and those are issues that are reasonably well-plumed out in the industry and you can go out and you can take the solutions from people like Clutter or from Horton and those solutions have plugin quite nicely actually to a variety of platforms. And I feel like that level of enterprise security is understood. It's work for vendors to work with that technology so when we went out, we make sure we were carburized in all the right ways at Trifacta to work with these vendors and that we integrated well with Navigator, we integrated with Atlas. That was, you know, there was some labor there but it's understood. There's also-- >> It's solvable basically. >> It's solvable basically and pluggable. There are research questions there which, you know, on another day we could talk about but for instance if you don't trust your cloud hosting service what do you do? And that's like an open area that we're working on at Berkeley. Intel SGX is a really interesting technology and that's based probably a topic for another day. >> But you know, I think it's important-- >> The sooner we get you out of the studio, Paolo Alto would love to drill on that. >> I think it's important though that, you know, when we talk about self service, the first question that comes up is I'm only going to let you self service as far as I can govern what's going on, right? And so I think those things-- >> Restrictions, guard rails-- >> Really going hand in here. >> About handcuffs. >> Yeah so, right. Because that's always a first thing that kind of comes out where people say, okay wait minute now is this-- if I've now got, you know-- you've got an increasing number of knowledge workers who think that is their-- and believe that it is their unalienable right to have access to data. >> Well that's the (mumbles) democratization. That's the top down, you know, governance control point. >> So how do you balance that? And I think you can't solve for one side of that equation without the other, right? And that's really really critical. >> Democratization is anarchization, right? >> Right, exactly. >> Yes, exactly. But it's hard though. I mean, and you look at all the big trends where there was, you know, web one data, web (mumbles), all had those democratization trends but they took six years to play out and I think there might be a more auxiliary with cloud when you point about this new stop. Okay George, go ahead. You might get in there. >> I wanted to ask you about, you know, what we were talking about earlier and what customers are faced with which is, you know, a lot of choice and specialization because building something end to end and having it fully functional is really difficult. So... What are the functional points where you start driving the guard rails in that Ikee cares about and then what are the user experience points where you have critical mass so that the end users then draw other compliant tools in. You with me? On sort of the IT side and the user side and then which tools start pulling those standards? >> Well, I would say at the highest level, to me what's been very interesting especially would be with that's happened in opensource is that people have now gotten accustomed to the idea that like I don't have to go buy a big monolithic stacks where the innovation moves only as fast as the slowest product in the stack or the portfolio. I can grab onto things and I can download them today and be using them tomorrow. And that has, I think, changed the entire approach that companies like Trifacta are taking to how we how we build and release product to market, how we inter operate with partners like Alation and Waterline and how we integrate with the platform vendors like Cloudera, MapR, and Horton because we recognize that we are going to have to be meniacal focused on one piece of this puzzle and to go very very deep but then play incredibly well both, you know, with all the rest of the ecosystem and so I think that is really colored our entire product strategy and how we go to market and I think customers, you know, they want the flexibility to change their minds and the subscription model is all about that, right? You got to earn it every single year. >> So what's the future of (mumbles)? 'Cause that brings up a good point we were kind of critical of Google and you mentioned you guys had-- I saw in some news that you guys were involved with Google. >> Yup. >> Being enterprise ready is not just, hey we have the great tech and you buy from us, damn it we're Google. >> Right. >> I mean, you have to have sales people. You have to have automation mechanism to create great product. Will the future of wrangling and data prep go into-- where does it end up? Because enterprises want, they want certain things. They're finicky of things. >> Right, right. >> As you guys know. So how does the future of data prep deal with the, I won't say the slowness of the enterprise, but they're more conservative, more SLA driven than they are price performance. >> But they're also more fragmented than ever before and you know, while that may not be a great thing for the customers for a company that's all about harmonizing data that's actually a phenomenal opportunity, right? Because we want to be the decision that customers make that guarantee that all their other decisions are changeable, right? And I go and-- >> Well they have legacy systems of record. This is the challenge, right? So I got the old oracle monolithic-- >> That's fine. And that's good-- >> So how do you-- >> The more the merrier, right? >> Does that impact you guys at all? How did you guys handle that situation? >> To me, to us that is more fragmentation which creates more need for wrangling because that introduces more complexity, right? >> You guys do well in that environment. >> Absolutely. And that, you know, is only getting bigger, worse, and more complicated. And especially as people go from (mumbles) to cloud as people start thinking about moving from just looking at transactions to interactions to now looking at behavior data and the IOT-- >> You're welcome in that environment. >> So we welcome that. In fact, that's where-- we went to solve this problem for Hadoop and Big Data first because we wanted to solve the problems at scale that were the most complicated and over time we can always move downstream to sort of more structured and smaller data and that's kind of what's happened with our business. >> I guess I want to circle back to this issue of which part of this value chain of refining data is-- if I'm understanding you right, the data wrangling is the anchor and once a company has made that choice then all the other tool choices have to revolve around it? Is that a-- >> Well think about this way, I mean, the bulk of the time when you talk to the analysts and also the bulk of the labor cost and these things isn't getting the data from its raw form into usage. That whole process of wrangling which is not really just data prep. It's all the things you do all day long to kind of massage these data sets and get 'em from here to there and make 'em work. That space is where the labor cost is. That also means that's spaces were the value add is because that's where your people power or your business context is really getting poured in to understand what do I have, what am I doing with it and what do I want to get out of it. As we move from bottom line IT to top line value generation with data, it becomes all the more so, right? Because now it's not just the matter of getting the reports out every month. It's also what did that brilliant in sales do to that dataset to get that much left? I need to learn from her and do a similar thing. Alright? So, that whole space is where the value is. What that means is that, you know, you don't want that space to be tied to a particular BI tool or a particular execution edge. So when we say that we want to make a decision in the middle of that enables all the other decisions, what you really want to make sure is that that work process in there is not tightly bound to the rest of the stack. Okay? And so you want to particularly pick technologies in that space that will play nicely with different storage, that play nicely with different execution environments. Today it's a dupe, tomorrow it's Amazon, the next day it's Google and they have different engines back there potentially. And you want it certainly makes your place with all the analytic and visualizations-- >> So decouple from all that? >> You want to decouple that and you want to not lock yourself in 'cause that's where the creativity's happening on the consumption side and that's where the mess that you talked about is just growing on the production side so data production is just getting more complicated. Data consumption's getting more interesting. >> That's actually a really really cool good point. >> Elaborating on that, does that mean that you have to open up interfaces with either the UI layer or at the sort of data definition layer? Or does that just mean other companies have to do the work to tie in to the styles? The styles and structures that you have already written? >> In fact it's sort of the opposite. We do the work to tie in to a lot of this, these other decisions in this infrastructure, you know. We don't pretend for a minute that people are going to sort of pick a solution like Trifacta and then build their organization around it. As your point, there's tons of legacy, technology out there. There is all kinds of things moving. Absolutely. So we, a big part of being the decoder ring for data for Trifacta and saying it's like listen, we are going to inter operate with your existing investments and we're going to make sure that you can always get at your data, you can always take it from whatever state its in to whatever state you need to be in, you can change your mind along the way. And that puts a lot of owners on us and that's the reason why we have to be so focused on this space and not jump into visualization and analytics and not jump in to its storage and processing and not try to do the other things to the right or left. Right? >> So final question. I'd like you guys both to take a stab at it. You know, just going to pivot off at what Joe was saying. Some of the most interesting things are happening in the data exploration kind of discovery area from creativity to insights to game changing stuff. >> Yup. >> Ventures potentially. >> Joe: Yup. >> The problem of the complexity, that's conflict. >> Yeah. >> So how does we resolve this? I mean, besides the Trifacta solution which you guys are taming, creating a platform for that, how do people in industry work together to solve that problem? What's the approach? >> So I think actually there's a couple sort of heartening trends on this front that make me pretty optimistic. One of these is that the inside of structures are in the enterprises we work with becoming quite aligned between IT and the line of business. It's no longer the case that the line of business that are these annoying people that they're distracting IT from their bottom line function. IT's bottom line function is being translated into a what's your value for the business question? And the answer for a savvy IT management person is, I will try to empower the people around me to be rabid fans and I will also try to make sure that they do their own works so I don't have to learn how to do it for them. Right? And so, that I think is happening-- >> Guys to this (mumbles) business guys, a bunch of annoying guys who don't get what I need, right? So it works both ways, right? >> It does, it does. And I see that that's improving sort of in the industry as the corporate missions around data change, right? So it's no longer that the IT guys really only need to take care of executives and everyone else doesn't matter. Their function really is to serve the business and I see that alignment. The other thing that I think is a huge opportunity and the part of who I-- we're excited to be so tightly coupled with Google and also have our stuff running in Amazon and at Microsoft. It's as people read platform to the cloud, a lot of legacy becomes a shed or at least become deprecated. And so there is a real-- >> Or containerized or some sort of microservice. >> Yeah. >> Right, right. >> And so, people are peeling off business function and as part of that cost savings to migrate it to the cloud, they're also simplified. And you know, things will get complicated again. >> What's (mumbles) solution architects out there that kind of re-boot their careers because the old way was, hey I got networks, I got apps and stacks and so that gives the guys who could be the new heroes coming in. >> Right. >> And thinking differently about enabling that creativity. >> In the midst of all that, everything you said is true. IT is a massive place and it always will be. And tools that can come in and help are absolutely going to be (mumbles). >> This is obvious now. The tension's obviously eased a bit in the sense that there's clear line of sight that top line and bottom line are working together now on. You mentioned that earlier. Okay. Adam, take a stab at it. (mumbling) >> I was just going to-- hey, I know it's great. I was just going to give an example, I think, that illustrates that point so you know, one of our customers is Pepsi. And Pepsi came to us and they said, listen we work with retailers all over the world and their reality is that, when they place orders with us, they often get it wrong. And sometimes they order too much and then they return it, it spoils and that's bad for us. Or they order too little and they stock out and we miss revenue opportunities. So they said, we actually have to be better at demand planning and forecasting than the orders that are literally coming in the door. So how do we do that? Well, we're getting all of the customers to give us their point of sale data. We're combining that with geospatial data, with weather data. We're like looking at historical data and industry averages but as you can see, they were like-- we're stitching together data across a whole variety of sources and they said the best people to do this are actually the category managers and the people responsible for the brands 'cause they literally live inside those businesses and they understand it. And so what happened was they-- the IT organization was saying, look listen, we don't want to be the people doing the janitorial work on the data. We're going to give that work over to people who understand it and they're going to be more productive and get to better outcomes with that information and that brings us up to go find new and interesting sources and I think that collaborative model that you're starting to see emerge where they can now be the data heroes in a different way by not being the ones beating the bottleneck on provisioning but rather can go out and figure out how do we share the best stuff across the organization? How do we find new sources of information to bring in that people can leverage to make better decisions? That's in incredibly powerful place to be and you know, I think that that model is really what's going to be driving a lot of the thinking at Trifacta and in the industry over the next couple of years. >> Great. Adam Wilson, CEO of Trifacta. Joe Hellestein, CTO-- Chief Strategy Officer of Trifacta and also a professor at Berkeley. Great story. Getting the (mumbles) right is hard but under the hood stuff's complicated and again, congratulations about sharing the Ground project. Ground open source. Open source lab kind of thing at-- in Berkeley. Exciting new stuff. Thanks so much for coming on theCUBE. I appreciate great conversation. I'm John Furrier, George Gilbert. You're watching theCUBE here at Big Data SV in conjunction with Strata and Hadoop. Thanks for watching. >> Great. >> Thanks guys.