>> 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.

>>Okay. We're here with the rest of the child who was the chief product officer at Altryx and Adam Wilson, the CEO of Trifacta. Now of course, part of Altryx just closed this quarter. Gentlemen. Welcome. >>Great to be here. >>Okay. So rest, let me start with you. In my opening remarks, I talked about Altrix is traditional position serving business analysts and how the hyper Anna acquisition brought you deeper into the business user space. What does Trifacta bring to your portfolio? Why'd you buy the company? >>Yeah. Thank you. Thank you for the question. Um, you know, we see, uh, we see a massive opportunity of helping, um, brands, um, democratize the use of analytics across their business. Um, every knowledge worker, every individual in the company should have access to analytics. It's no longer optional, um, as they navigate, uh, their businesses with that in mind, you know, we know designer and are the products that Ultrix has been selling the past decade or so do a really great job, um, addressing the business analysts, uh, with, um, hyperaware, um, now kind of renamed, um, Altrix auto insights. Uh, we even speak with the, uh, business owner of the line of business owner. Who's looking for insights that aren't real in traditional dashboards and so on. Um, but we see this opportunity of really helping the data engineering teams and it organizations, um, to also make better use of analytics. Um, and that's where the drive factor comes in for us. Um, drive factor has the best data engineering cloud in the planet. Um, they have an established track record of working across multiple cloud platforms and helping data engineers, um, do better data pipelining and work better with, uh, this massive kind of cloud transformation that's happening in every business. Um, and so Trifacta made so much sense for us. >>Yeah. Thank you for that. I mean, look, you could have built it yourself. Would've taken, you know, who knows how long, but, uh, so definitely a great time to market move, Adam. I wonder if we could dig into Trifacta some more, I mean, I remember interviewing Joe Hellerstein in the early days. You've talked about this as well, uh, on the cube coming at the problem of taking data from raw refined to an experience point of view. And Joe in the early days, talked about flipping the model and starting with data visualization, something Jeff, her was expert at. So maybe explain how we got here. We used to have this cumbersome process of ETL and you may be in some others changed that model with ELL and then T explain how Trifacta really changed the data engineering game. >>Yeah, that's exactly right. Uh, David, it's been a really interesting journey for us because I think the original hypothesis coming out of the campus research, uh, at Berkeley and Stanford that really birthed Trifacta was, you know, why is it that the people who know the data best can't do the work? You know, why is this become the exclusive purview of the highly technical and, you know, can we rethink this and make this a user experience, problem powered by machine learning that will take some of the more complicated things that people want to do with data and really helped to automate those. So, so a, a broader set of users can, um, can really see for themselves and help themselves. And, and I think that, um, there was a lot of pent up frustration out there because people have been told for, you know, for a decade now to be more data-driven and then the whole time they're saying, well, then give me the data, you know, in the shape that I can use it with the right level of quality and I'm happy to be, but don't tell me to be more data driven and then, and, and not empower me, um, to, to get in there and to actually start to work with the data in meaningful ways. >>And so, um, that was really, you know, what, you know, the origin story of the company. And I think as, as we, um, you know, saw over the course of the last 5, 6, 7 years that, um, you know, a real, uh, excitement to embrace this idea of, of trying to think about data engineering differently, trying to democratize the, the ETL process and to also leverage all of these exciting new, uh, engines and platforms that are out there that allow for processing, you know, ever more diverse data sets, ever larger data sets and new and interesting ways. And that's where a lot of the push down or the ELT approaches that, you know, I think it could really won the day. Um, and that, and that for us was a hallmark of the solution from the very beginning. >>Yeah, this is a huge point that you're making. This is first of all, there's a large business, it's probably about a hundred billion dollar Tam. Uh, and the, the point you're making is we've looked, we've contextualized most of our operational systems, but the big data pipelines hasn't gotten there. And maybe we could talk about that a little bit because democratizing data is Nirvana, but it's been historically very difficult. You've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome, but it's been hard. And so what's going to be different about Altryx as you bring these puzzle pieces together, how is this going to impact your customers who would like to take that one? >>Yeah, maybe, maybe I'll take a crack at it. And Adam will, um, add on, um, you know, there hasn't been a single platform, uh, for analytics automation in the enterprise, right? People have relied on, uh, different products, um, to solve kind of, uh, smaller problems, um, across this analytics, automation, data transformation domain. Um, and, um, I think uniquely altereds has that opportunity. Uh, we've got 7,000 plus customers who rely on analytics for, um, data management, for analytics or AI and ML, uh, for transformations, uh, for reporting and visualization for automated insights and so on. And so by bringing drive factor, we have the opportunity to scale this even further and solve for more use cases, expand the scenarios where it's gets applied and so multiple personas. Um, and now we just talked about the data engineers. They are really a growing stakeholder in this transformation of data and analytics. >>Yeah, good. Maybe we can stay on this for a minute cause you, you you're right. You bring it together. Now that at least 3% is the business analyst, the end user slash business user. And now the data engineer, which is really out of an it role in a lot of companies, and you've used this term, the data engineering cloud, what is that, how is it going to integrate in with, or support these other personas? And, and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores? >>Yeah, no, that's great. Uh, yeah, I think for us, we really looked at this and said, you know, we want to build an open and interactive cloud platform for data engineers, you know, to collaboratively profile pipeline, um, and prepare data for analysis. And that really meant collaborating with the analysts that were in the line of business. And so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are, uh, in the line of business that are driving a lot of the decision-making and allow for that, what I would describe as collaborative curation of the data together, so that you're starting to see, um, uh, you know, increasing returns to scale as this, uh, as this rolls out. I just think that is an incredibly powerful combination and, and frankly, something that the market has not cracked the code on yet. And so, um, I think when we, when I sat down with Suresh and with mark and the team at Ultrix, that was really part of the, the, the big idea, the big vision that that was painted and, and got us really energized about the acquisition and about the potential of the combination. >>Yeah. And you're really, you're obviously riding the cloud and the cloud native wave. Um, and, but specifically we're seeing, you know, I almost don't even want to call it a data warehouse anyway, because when you look at what's, for instance, snowflake is doing, of course their marketing is around the data cloud, but I actually think there's real justification for that because it's not like the traditional data warehouse, right. It's, it's simplified get there fast, don't necessarily have to go through the central organization to share data. Uh, and, and, and, but it's really all about simplification, right? Isn't that really what the democratization comes down to. >>Yeah. It's simplification and collaboration. Right. I don't want to, I want to kind of just, um, what Adam said resonates with me deeply, um, analytics is one of those, um, massive disciplines, an enterprise that's really had the weakest of tools. Um, and we just have interfaces to collaborate with, and I think truly this was Alteryx's and a superpower was helping the analysts get more out of their data, get more out of the analytics, like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources, um, understanding data models better, I think, um, uh, curating those insights. I boring Adam's phrase again. Um, I think that creates a real value inside the organization, uh, because frankly in scaling analytics and democratizing analytics and data, we're still in such early phases of this journey. >>So how should we think about designer cloud, which is from Altryx it's really been the on-prem and the server desktop offering. And of course Trifacta is with cloud cloud data warehouses. Right. Uh, how, how should we think about those two products? >>Yeah, I think, I think you should think about them and, uh, um, as, as very complimentary right design a cloud really shares a lot of DNA and heritage with, uh, designer desktop, um, the low code tooling and that interface, uh, that really appeals to the business analysts, um, and gets a lot of the things that they do well, we've also built it with interoperability in mind, right. So if you started building your workflows in designer desktop, you want to share that with design and cloud, we want to make it super easy for you to do that. Um, and I think over time now we're only a week into, um, this Alliance with, um, with Trifacta. Um, I think we have to get deeper inside to think about what does the data engineer really need what's business analysts really need and how to design a cloud, and Trifacta really support both of those requirements, uh, while kind of continue to build on the tri-factor on the amazing tri-factor cloud platform. >>You know, >>I was just going to say, I think that's one of the things that, um, you know, creates a lot of, uh, opportunity as we go forward, because ultimately, you know, Trifacta took a platform, uh, first mentality to everything that we built. So thinking about openness and extensibility and, um, and how over time people could build things on top of, by factor that are a variety of analytic tool chain, or analytic applications. And so, uh, when you think about, um, Ultrix now starting to, uh, to move some of its capabilities or to provide additional capabilities, uh, in the cloud, um, you know, Trifacta becomes a platform that can accelerate, you know, all of that work and create, uh, uh, a cohesive set of, of cloud-based services that, um, share a common platform. And that maintains independence because both companies, um, have been, uh, you know, fiercely independent, uh, and really giving people choice. >>Um, so making sure that whether you're, uh, you know, picking one cloud platform and other, whether you're running things on the desktop, uh, whether you're running in hybrid environments, that, um, no matter what your decision, um, you're always in a position to be able to get out your data. You're always in a position to be able to cleanse transform shape structure, that data, and ultimately to deliver, uh, the analytics that you need. And so I think in that sense, um, uh, you know, this, this again is another reason why the combination, you know, fits so well together, giving people, um, the choice. Um, and as they, as they think about their analytics strategy and their platform strategy going forward, >>Yeah. I make a chuckle, but I, one of the reasons I always liked Altryx is cause you kinda did the little end run on it. It can be a blocker sometimes, but that created problems, right? Because the current organization said, wow, there's big data stuff is taken off, but we need security. We need governance. And, and it was interesting because he got, you know, ETTL has been complex, whereas the visualization tools, they really, you know, really weren't great at governance and security. It took some time there. So that's not, not their heritage. You're bringing those worlds together. And I'm interested, you guys just had your sales kickoff, you know, what was their reaction like, uh, maybe Suresh, you could start off and maybe Adam, you could bring us home. >>Yeah. Um, thanks for asking about our sales kickoff. So we met for the first time and kind of two years, right. For, as, as it is for many of us, um, in person, uh, um, which I think was, uh, was a real breakthrough as Qualtrics has been on its transformation journey. Uh, we had a Trifacta to, um, the, the party such as the tour, um, and getting all of our sales teams and product organizations, um, to meet in person in one location. I thought that was very powerful for us, the company. Uh, but then I tell you, um, um, the reception for Trifacta was beyond anything I could have imagined. Uh, we were working Adam and I were working so hard on, on the deal and the core hypothesis and so on. And then you step back and you kind of share the vision, uh, with the field organization and it blows you away, the energy that it creates among our sellers, our partners, and I'm sure Adam will, and his team were mocked every single day with questions and opportunities to bring them in. >>But Adam, maybe he's chair. Yeah, I know it was, uh, it was through the roof. I mean, uh, uh, the, uh, the amount of energy, the, uh, certainly how welcoming everybody was, uh, uh, you know, just, I think the story makes so much sense together. I think culturally, the company is, are very aligned. Um, and, uh, it was a real, uh, real capstone moment, uh, to be able to complete the acquisition and to, and to close and announced, you know, at the kickoff event. And, um, I think, you know, for us, when we really thought about it, you know, when we ended the story, that we was just, you have this opportunity to really cater to what the end-users, you know, care about, which is a lot about interactivity and self-service, and at the same time. And that's, and that's a lot of the goodness that, um, that Ultrix has brought, you know, through, you know, you know, years and years of, of building a very vibrant community of, you know, thousands, hundreds of thousands of users. >>And on the other side, you know, Trifacta bringing in this data engineering focus, that's really about, uh, the governance things that you mentioned and the openness, um, that, that it cares deeply about. And all of a sudden, now you have a chance to put that together into a complete story where the data engineering cloud and analytics, automation, you know, coming together. And, um, and I just think, you know, the lights went on, um, you know, for people instantaneously and, you know, this is a story that, um, that I think the market is really hungry for. And certainly the reception we got from, uh, from the broader team at kickoff was, uh, was a great indication of that. >>Well, I think the story hangs together really well, you know, one of the better ones I've seen in, in this space, um, and, and you guys coming off a really, really strong quarter. So congratulations on that Jensen. We have to leave it there. I really appreciate your time today. Yeah. Take a look at this short video. And when we come back, we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses. You're watching the cube, your leader in enterprise tech coverage.

>>Alteryx is a company with a long history that goes all the way back to the late 1990s. Now the one consistent theme over 20 plus years has been that Ultrix has always been a data company early in the big data and Hadoop cycle. It saw the need to combine and prep different data types so that organizations could analyze data and take action Altrix and similar companies played a critical role in helping companies become data-driven. The problem was the decade of big data, brought a lot of complexities and required immense skills just to get the technology to work as advertised this in turn limited, the pace of adoption and the number of companies that could really lean in and take advantage of the cloud began to change all that and set the foundation for today's theme to Zuora of digital transformation. We hear that phrase a ton digital transformation. >>People used to think it was a buzzword, but of course we learned from the pandemic that if you're not a digital business, you're out of business and a key tenant of digital transformation is democratizing data, meaning enabling, not just hypo hyper specialized experts, but anyone business users to put data to work. Now back to Ultrix, the company has embarked on a major transformation of its own. Over the past couple of years, brought in new management, they've changed the way in which it engaged with customers with the new subscription model and it's topgraded its talent pool. 2021 was even more significant because of two acquisitions that Altrix made hyper Ana and trifecta. Why are these acquisitions important? Well, traditionally Altryx sold to business analysts that were part of the data pipeline. These were fairly technical people who had certain skills and were trained in things like writing Python code with hyper Ana Altryx has added a new persona, the business user, anyone in the business who wanted to gain insights from data and, or let's say use AI without having to be a deep technical expert. >>And then Trifacta a company started in the early days of big data by cube alum, Joe Hellerstein and his colleagues at Berkeley. They knocked down the data engineering persona, and this gives Altryx a complimentary extension into it where things like governance and security are paramount. So as we enter 2022, the post isolation economy is here and we do so with a digital foundation built on the confluence of cloud native technologies, data democratization and machine intelligence or AI, if you prefer. And Altryx is entering that new era with an expanded portfolio, new go-to market vectors, a recurring revenue business model, and a brand new outlook on how to solve customer problems and scale a company. My name is Dave Vellante with the cube and I'll be your host today. And the next hour, we're going to explore the opportunities in this new data market. And we have three segments where we dig into these trends and themes. First we'll talk to Jay Henderson, vice president of product management at Ultrix about cloud acceleration and simplifying complex data operations. Then we'll bring in Suresh Vetol who's the chief product officer at Altrix and Adam Wilson, the CEO of Trifacta, which of course is now part of Altrix. And finally, we'll hear about how Altryx is partnering with snowflake and the ecosystem and how they're integrating with data platforms like snowflake and what this means for customers. And we may have a few surprises sprinkled in as well into the conversation let's get started. >>We're kicking off the program with our first segment. Jay Henderson is the vice president of product management Altryx and we're going to talk about the trends and data, where we came from, how we got here, where we're going. We get some launch news. Well, Jay, welcome to the cube. >>Great to be here, really excited to share some of the things we're working on. >>Yeah. Thank you. So look, you have a deep product background, product management, product marketing, you've done strategy work. You've been around software and data, your entire career, and we're seeing the collision of software data cloud machine intelligence. Let's start with the customer and maybe we can work back from there. So if you're an analytics or data executive in an organization, w J what's your north star, where are you trying to take your company from a data and analytics point of view? >>Yeah, I mean, you know, look, I think all organizations are really struggling to get insights out of their data. I think one of the things that we see is you've got digital exhaust, creating large volumes of data storage is really cheap, so it doesn't cost them much to keep it. And that results in a situation where the organization's, you know, drowning in data, but somehow still starving for insights. And so I think, uh, you know, when I talk to customers, they're really excited to figure out how they can put analytics in the hands of every single person in their organization, and really start to democratize the analytics, um, and, you know, let the, the business users and the whole organization get value out of all that data they have. >>And we're going to dig into that throughout this program data, I like to say is plentiful insights, not always so much. Tell us about your launch today, Jay, and thinking about the trends that you just highlighted, the direction that your customers want to go and the problems that you're solving, what role does the cloud play in? What is what you're launching? How does that fit in? >>Yeah, we're, we're really excited today. We're launching the Altryx analytics cloud. That's really a portfolio of cloud-based solutions that have all been built from the ground up to be cloud native, um, and to take advantage of things like based access. So that it's really easy to give anyone access, including folks on a Mac. Um, it, you know, it also lets you take advantage of elastic compute so that you can do, you know, in database processing and cloud native, um, solutions that are gonna scale to solve the most complex problems. So we've got a portfolio of solutions, things like designer cloud, which is our flagship designer product in a browser and on the cloud, but we've got ultra to machine learning, which helps up-skill regular old analysts with advanced machine learning capabilities. We've got auto insights, which brings a business users into the fold and automatically unearths insights using AI and machine learning. And we've got our latest edition, which is Trifacta that helps data engineers do data pipelining and really, um, you know, create a lot of the underlying data sets that are used in some of this, uh, downstream analytics. >>Let's dig into some of those roles if we could a little bit, I mean, you've traditionally Altryx has served the business analysts and that's what designer cloud is fit for, I believe. And you've explained, you know, kind of the scope, sorry, you've expanded that scope into the, to the business user with hyper Anna. And we're in a moment we're going to talk to Adam Wilson and Suresh, uh, about Trifacta and that recent acquisition takes you, as you said, into the data engineering space in it. But in thinking about the business analyst role, what's unique about designer cloud cloud, and how does it help these individuals? >>Yeah, I mean, you know, really, I go back to some of the feedback we've had from our customers, which is, um, you know, they oftentimes have dozens or hundreds of seats of our designer desktop product, you know, really, as they look to take the next step, they're trying to figure out how do I give access to that? Those types of analytics to thousands of people within the organization and designer cloud is, is really great for that. You've got the browser-based interface. So if folks are on a Mac, they can really easily just pop, open the browser and get access to all of those, uh, prep and blend capabilities to a lot of the analysis we're doing. Um, it's a great way to scale up access to the analytics and then start to put it in the hands of really anyone in the organization, not just those highly skilled power users. >>Okay, great. So now then you add in the hyper Anna acquisition. So now you're targeting the business user Trifacta comes into the mix that deeper it angle that we talked about, how does this all fit together? How should we be thinking about the new Altryx portfolio? >>Yeah, I mean, I think it's pretty exciting. Um, you know, when you think about democratizing analytics and providing access to all these different groups of people, um, you've not been able to do it through one platform before. Um, you know, it's not going to be one interface that meets the, of all these different groups within the organization. You really do need purpose built specialized capabilities for each group. And finally, today with the announcement of the alternates analytics cloud, we brought together all of those different capabilities, all of those different interfaces into a single in the end application. So really finally delivering on the promise of providing analytics to all, >>How much of this you've been able to share with your customers and maybe your partners. I mean, I know OD is fairly new, but if you've been able to get any feedback from them, what are they saying about it? >>Uh, I mean, it's, it's pretty amazing. Um, we ran a early access, limited availability program that led us put a lot of this technology in the hands of over 600 customers, um, over the last few months. So we have gotten a lot of feedback. I tell you, um, it's been overwhelmingly positive. I think organizations are really excited to unlock the insights that have been hidden in all this data. They've got, they're excited to be able to use analytics in every decision that they're making so that the decisions they have or more informed and produce better business outcomes. Um, and, and this idea that they're going to move from, you know, dozens to hundreds or thousands of people who have access to these kinds of capabilities, I think has been a really exciting thing that is going to accelerate the transformation that these customers are on. >>Yeah, those are good. Good, good numbers for, for preview mode. Let's, let's talk a little bit about vision. So it's democratizing data is the ultimate goal, which frankly has been elusive for most organizations over time. How's your cloud going to address the challenges of putting data to work across the entire enterprise? >>Yeah, I mean, I tend to think about the future and some of the investments we're making in our products and our roadmap across four big themes, you know, in the, and these are really kind of enduring themes that you're going to see us making investments in over the next few years, the first is having cloud centricity. You know, the data gravity has been moving to the cloud. We need to be able to provide access, to be able to ingest and manipulate that data, to be able to write back to it, to provide cloud solution. So the first one is really around cloud centricity. The second is around big data fluency. Once you have all of the data, you need to be able to manipulate it in a performant manner. So having the elastic cloud infrastructure and in database processing is so important, the third is around making AI a strategic advantage. >>So, uh, you know, getting everyone involved and accessing AI and machine learning to unlock those insights, getting it out of the hands of the small group of data scientists, putting it in the hands of analysts and business users. Um, and then the fourth thing is really providing access across the entire organization. You know, it and data engineers, uh, as well as business owners and analysts. So, um, cloud centricity, big data fluency, um, AI is a strategic advantage and, uh, personas across the organization are really the four big themes you're going to see us, uh, working on over the next few months and, uh, coming coming year. >>That's good. Thank you for that. So, so on a related question, how do you see the data organizations evolving? I mean, traditionally you've had, you know, monolithic organizations, uh, very specialized or I might even say hyper specialized roles and, and your, your mission of course is the customer. You, you, you, you and your customers, they want to democratize the data. And so it seems logical that domain leaders are going to take more responsibility for data, life cycles, data ownerships, low code becomes more important. And perhaps this kind of challenges, the historically highly centralized and really specialized roles that I just talked about. How do you see that evolving and, and, and what role will Altryx play? >>Yeah. Um, you know, I think we'll see sort of a more federated systems start to emerge. Those centralized groups are going to continue to exist. Um, but they're going to start to empower, you know, in a much more de-centralized way, the people who are closer to the business problems and have better business understanding. I think that's going to let the centralized highly skilled teams work on, uh, problems that are of higher value to the organization. The kinds of problems where one or 2% lift in the model results in millions of dollars a day for the business. And then by pushing some of the analytics out to, uh, closer to the edge and closer to the business, you'll be able to apply those analytics in every single decision. So I think you're going to see, you know, both the decentralized and centralized models start to work in harmony and a little bit more about almost a federated sort of a way. And I think, you know, the exciting thing for us at Altryx is, you know, we want to facilitate that. We want to give analytic capabilities and solutions to both groups and types of people. We want to help them collaborate better, um, and drive business outcomes with the analytics they're using. >>Yeah. I mean, I think my take on another one, if you could comment is to me, the technology should be an operational detail and it has been the, the, the dog that wags the tail, or maybe the other way around, you mentioned digital exhaust before. I mean, essentially it's digital exhaust coming out of operationals systems that then somehow, eventually end up in the hand of the domain users. And I wonder if increasingly we're going to see those domain users, users, those, those line of business experts get more access. That's your goal. And then even go beyond analytics, start to build data products that could be monetized, and that maybe it's going to take a decade to play out, but that is sort of a new era of data. Do you see it that way? >>Absolutely. We're actually making big investments in our products and capabilities to be able to create analytic applications and to enable somebody who's an analyst or business user to create an application on top of the data and analytics layers that they have, um, really to help democratize the analytics, to help prepackage some of the analytics that can drive more insights. So I think that's definitely a trend we're going to see more. >>Yeah. And to your point, if you can federate the governance and automate that, then that can happen. I mean, that's a key part of it, obviously. So, all right, Jay, we have to leave it there up next. We take a deep dive into the Altryx recent acquisition of Trifacta with Adam Wilson who led Trifacta for more than seven years. It's the recipe. Tyler is the chief product officer at Altryx to explain the rationale behind the acquisition and how it's going to impact customers. Keep it right there. You're watching the cube. You're a leader in enterprise tech coverage. >>It's go time, get ready to accelerate your data analytics journey with a unified cloud native platform. That's accessible for everyone on the go from home to office and everywhere in between effortless analytics to help you go from ideas to outcomes and no time. It's your time to shine. It's Altryx analytics cloud time. >>Okay. We're here with. Who's the chief product officer at Altryx and Adam Wilson, the CEO of Trifacta. Now of course, part of Altryx just closed this quarter. Gentlemen. Welcome. >>Great to be here. >>Okay. So let me start with you. In my opening remarks, I talked about Altrix is traditional position serving business analysts and how the hyper Anna acquisition brought you deeper into the business user space. What does Trifacta bring to your portfolio? Why'd you buy the company? >>Yeah. Thank you. Thank you for the question. Um, you know, we see, uh, we see a massive opportunity of helping, um, brands, um, democratize the use of analytics across their business. Um, every knowledge worker, every individual in the company should have access to analytics. It's no longer optional, um, as they navigate their businesses with that in mind, you know, we know designer and are the products that Altrix has been selling the past decade or so do a really great job, um, addressing the business analysts, uh, with, um, hyper Rana now kind of renamed, um, Altrix auto. We even speak with the business owner and the line of business owner. Who's looking for insights that aren't real in traditional dashboards and so on. Um, but we see this opportunity of really helping the data engineering teams and it organizations, um, to also make better use of analytics. Um, and that's where the drive factor comes in for us. Um, drive factor has the best data engineering cloud in the planet. Um, they have an established track record of working across multiple cloud platforms and helping data engineers, um, do better data pipelining and work better with, uh, this massive kind of cloud transformation that's happening in every business. Um, and so fact made so much sense for us. >>Yeah. Thank you for that. I mean, you, look, you could have built it yourself would have taken, you know, who knows how long, you know, but, uh, so definitely a great time to market move, Adam. I wonder if we could dig into Trifacta some more, I mean, I remember interviewing Joe Hellerstein in the early days. You've talked about this as well, uh, on the cube coming at the problem of taking data from raw refined to an experience point of view. And Joe in the early days, talked about flipping the model and starting with data visualization, something Jeff, her was expert at. So maybe explain how we got here. We used to have this cumbersome process of ETL and you may be in some others changed that model with ELL and then T explain how Trifacta really changed the data engineering game. >>Yeah, that's exactly right. Uh, David, it's been a really interesting journey for us because I think the original hypothesis coming out of the campus research, uh, at Berkeley and Stanford that really birth Trifacta was, you know, why is it that the people who know the data best can't do the work? You know, why is this become the exclusive purview of the highly technical? And, you know, can we rethink this and make this a user experience, problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those. So, so a broader set of, of users can, um, can really see for themselves and help themselves. And, and I think that, um, there was a lot of pent up frustration out there because people have been told for, you know, for a decade now to be more data-driven and then the whole time they're saying, well, then give me the data, you know, in the shape that I could use it with the right level of quality and I'm happy to be, but don't tell me to be more data-driven and then, and, and not empower me, um, to, to get in there and to actually start to work with the data in meaningful ways. >>And so, um, that was really, you know, what, you know, the origin story of the company and I think is, as we, um, saw over the course of the last 5, 6, 7 years that, um, you know, uh, real, uh, excitement to embrace this idea of, of trying to think about data engineering differently, trying to democratize the, the ETL process and to also leverage all these exciting new, uh, engines and platforms that are out there that allow for processing, you know, ever more diverse data sets, ever larger data sets and new and interesting ways. And that's where a lot of the push-down or the ELT approaches that, you know, I think it could really won the day. Um, and that, and that for us was a hallmark of the solution from the very beginning. >>Yeah, this is a huge point that you're making is, is first of all, there's a large business, it's probably about a hundred billion dollar Tam. Uh, and the, the point you're making, because we've looked, we've contextualized most of our operational systems, but the big data pipeline is hasn't gotten there. But, and maybe we could talk about that a little bit because democratizing data is Nirvana, but it's been historically very difficult. You've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome, but it's been hard. And so what's going to be different about Altryx as you bring these puzzle pieces together, how is this going to impact your customers who would like to take that one? >>Yeah, maybe, maybe I'll take a crack at it. And Adam will, um, add on, um, you know, there hasn't been a single platform for analytics, automation in the enterprise, right? People have relied on, uh, different products, um, to solve kind of, uh, smaller problems, um, across this analytics, automation, data transformation domain. Um, and, um, I think uniquely Alcon's has that opportunity. Uh, we've got 7,000 plus customers who rely on analytics for, um, data management, for analytics, for AI and ML, uh, for transformations, uh, for reporting and visualization for automated insights and so on. Um, and so by bringing drive factor, we have the opportunity to scale this even further and solve for more use cases, expand the scenarios where it's applied and so multiple personas. Um, and we just talked about the data engineers. They are really a growing stakeholder in this transformation of data and analytics. >>Yeah, good. Maybe we can stay on this for a minute cause you, you you're right. You bring it together. Now at least three personas the business analyst, the end user slash business user. And now the data engineer, which is really out of an it role in a lot of companies, and you've used this term, the data engineering cloud, what is that? How is it going to integrate in with, or support these other personas? And, and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores? >>Yeah, no, that's great. Uh, yeah, I think for us, we really looked at this and said, you know, we want to build an open and interactive cloud platform for data engineers, you know, to collaboratively profile pipeline, um, and prepare data for analysis. And that really meant collaborating with the analysts that were in the line of business. And so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that, what I would describe as collaborative curation of the data together, so that you're starting to see, um, uh, you know, increasing returns to scale as this, uh, as this rolls out. I just think that is an incredibly powerful combination and, and frankly, something that the market is not crack the code on yet. And so, um, I think when we, when I sat down with Suresh and with mark and the team at Ultrix, that was really part of the, the, the big idea, the big vision that was painted and got us really energized about the acquisition and about the potential of the combination. >>And you're really, you're obviously writing the cloud and the cloud native wave. Um, and, but specifically we're seeing, you know, I almost don't even want to call it a data warehouse anyway, because when you look at what's, for instance, Snowflake's doing, of course their marketing is around the data cloud, but I actually think there's real justification for that because it's not like the traditional data warehouse, right. It's, it's simplified get there fast, don't necessarily have to go through the central organization to share data. Uh, and, and, and, but it's really all about simplification, right? Isn't that really what the democratization comes down to. >>Yeah. It's simplification and collaboration. Right. I don't want to, I want to kind of just what Adam said resonates with me deeply. Um, analytics is one of those, um, massive disciplines inside an enterprise that's really had the weakest of tools. Um, and we just have interfaces to collaborate with, and I think truly this was all drinks and a superpower was helping the analysts get more out of their data, get more out of the analytics, like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources, um, understanding data models better, I think, um, uh, curating those insights. I boring Adam's phrase again. Um, I think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data, we're still in such early phases of this journey. >>So how should we think about designer cloud, which is from Altrix it's really been the on-prem and the server desktop offering. And of course Trifacta is with cloud cloud data warehouses. Right. Uh, how, how should we think about those two products? Yeah, >>I think, I think you should think about them. And, uh, um, as, as very complimentary right designer cloud really shares a lot of DNA and heritage with, uh, designer desktop, um, the low code tooling and that interface, uh, the really appeals to the business analysts, um, and gets a lot of the things that they do well, we've also built it with interoperability in mind, right. So if you started building your workflows in designer desktop, you want to share that with design and cloud, we want to make it super easy for you to do that. Um, and I think over time now we're only a week into, um, this Alliance with, um, with, um, Trifacta, um, I think we have to get deeper inside to think about what does the data engineer really need? What's the business analysts really need and how to design a cloud, and Trifacta really support both of those requirements, uh, while kind of continue to build on the trifecta on the amazing Trifacta cloud platform. >>You know, >>I think we're just going to say, I think that's one of the things that, um, you know, creates a lot of, uh, opportunity as we go forward, because ultimately, you know, Trifacta took a platform, uh, first mentality to everything that we built. So thinking about openness and extensibility and, um, and how over time people could build things on top of factor that are a variety of analytic tool chain, or analytic applications. And so, uh, when you think about, um, Ultrix now starting to, uh, to move some of its capabilities or to provide additional capabilities, uh, in the cloud, um, you know, Trifacta becomes a platform that can accelerate, you know, all of that work and create, uh, uh, a cohesive set of, of cloud-based services that, um, share a common platform. And that maintains independence because both companies, um, have been, uh, you know, fiercely independent, uh, and, and really giving people choice. >>Um, so making sure that whether you're, uh, you know, picking one cloud platform and other, whether you're running things on the desktop, uh, whether you're running in hybrid environments, that, um, no matter what your decision, um, you're always in a position to be able to get out your data. You're always in a position to be able to cleanse transform shape structure, that data, and ultimately to deliver, uh, the analytics that you need. And so I think in that sense, um, uh, you know, this, this again is another reason why the combination, you know, fits so well together, giving people, um, the choice. Um, and as they, as they think about their analytics strategy and their platform strategy going forward, >>Yeah. I make a chuckle, but one of the reasons I always liked Altrix is cause you kinda did the little end run on it. It can be a blocker sometimes, but that created problems, right? Because the organization said, wow, this big data stuff has taken off, but we need security. We need governance. And it's interesting because you've got, you know, ETL has been complex, whereas the visualization tools, they really, you know, really weren't great at governance and security. It took some time there. So that's not, not their heritage. You're bringing those worlds together. And I'm interested, you guys just had your sales kickoff, you know, what was their reaction like? Uh, maybe Suresh, you could start off and maybe Adam, you could bring us home. >>Um, thanks for asking about our sales kickoff. So we met for the first time and you've got a two years, right. For, as, as it is for many of us, um, in person, uh, um, which I think was a, was a real breakthrough as Qualtrics has been on its transformation journey. Uh, we added a Trifacta to, um, the, the potty such as the tour, um, and getting all of our sales teams and product organizations, um, to meet in person in one location. I thought that was very powerful for other the company. Uh, but then I tell you, um, um, the reception for Trifacta was beyond anything I could have imagined. Uh, we were working out him and I will, when he's so hot on, on the deal and the core hypotheses and so on. And then you step back and you're going to share the vision with the field organization, and it blows you away, the energy that it creates among our sellers out of partners. >>And I'm sure Madam will and his team were mocked, um, every single day, uh, with questions and opportunities to bring them in. But Adam, maybe you should share. Yeah, no, it was, uh, it was through the roof. I mean, uh, uh, the, uh, the amount of energy, the, uh, certainly how welcoming everybody was, uh, uh, you know, just, I think the story makes so much sense together. I think culturally, the company is, are very aligned. Um, and, uh, it was a real, uh, real capstone moment, uh, to be able to complete the acquisition and to, and to close and announced, you know, at the kickoff event. And, um, I think, you know, for us, when we really thought about it, you know, when we ended, the story that we told was just, you have this opportunity to really cater to what the end users care about, which is a lot about interactivity and self-service, and at the same time. >>And that's, and that's a lot of the goodness that, um, that Altryx is, has brought, you know, through, you know, you know, years and years of, of building a very vibrant community of, you know, thousands, hundreds of thousands of users. And on the other side, you know, Trifacta bringing in this data engineering focus, that's really about, uh, the governance things that you mentioned and the openness, um, that, that it cares deeply about. And all of a sudden, now you have a chance to put that together into a complete story where the data engineering cloud and analytics, automation, you know, coming together. And, um, and I just think, you know, the lights went on, um, you know, for people instantaneously and, you know, this is a story that, um, that I think the market is really hungry for. And certainly the reception we got from, uh, from the broader team at kickoff was, uh, was a great indication. >>Well, I think the story hangs together really well, you know, one of the better ones I've seen in, in this space, um, and, and you guys coming off a really, really strong quarter. So congratulations on that jets. We have to leave it there. I really appreciate your time today. Yeah. Take a look at this short video. And when we come back, we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses. You're watching the cube you're leader in enterprise tech coverage. >>This is your data housed neatly insecurely in the snowflake data cloud. And all of it has potential the potential to solve complex business problems, deliver personalized financial offerings, protect supply chains from disruption, cut costs, forecast, grow and innovate. All you need to do is put your data in the hands of the right people and give it an opportunity. Luckily for you. That's the easy part because snowflake works with Alteryx and Alteryx turns data into breakthroughs with just a click. Your organization can automate analytics with drag and drop building blocks, easily access snowflake data with both sequel and no SQL options, share insights, powered by Alteryx data science and push processing to snowflake for lightning, fast performance, you get answers you can put to work in your teams, get repeatable processes they can share in that's exciting because not only is your data no longer sitting around in silos, it's also mobilized for the next opportunity. Turn your data into a breakthrough Alteryx and snowflake >>Okay. We're back here in the queue, focusing on the business promise of the cloud democratizing data, making it accessible and enabling everyone to get value from analytics, insights, and data. We're now moving into the eco systems segment the power of many versus the resources of one. And we're pleased to welcome. Barb Hills camp was the senior vice president partners and alliances at Ultrix and a special guest Terek do week head of technology alliances at snowflake folks. Welcome. Good to see you. >>Thank you. Thanks for having me. Good to see >>Dave. Great to see you guys. So cloud migration, it's one of the hottest topics. It's the top one of the top initiatives of senior technology leaders. We have survey data with our partner ETR it's number two behind security, and just ahead of analytics. So we're hovering around all the hot topics here. Barb, what are you seeing with respect to customer, you know, cloud migration momentum, and how does the Ultrix partner strategy fit? >>Yeah, sure. Partners are central company's strategy. They always have been. We recognize that our partners have deep customer relationships. And when you connect that with their domain expertise, they're really helping customers on their cloud and business transformation journey. We've been helping customers achieve their desired outcomes with our partner community for quite some time. And our partner base has been growing an average of 30% year over year, that partner community and strategy now addresses several kinds of partners, spanning solution providers to global SIS and technology partners, such as snowflake and together, we help our customers realize the business promise of their journey to the cloud. Snowflake provides a scalable storage system altereds provides the business user friendly front end. So for example, it departments depend on snowflake to consolidate data across systems into one data cloud with Altryx business users can easily unlock that data in snowflake solving real business outcomes. Our GSI and solution provider partners are instrumental in providing that end to end benefit of a modern analytic stack in the cloud providing platform, guidance, deployment, support, and other professional services. >>Great. Let's get a little bit more into the relationship between Altrix and S in snowflake, the partnership, maybe a little bit about the history, you know, what are the critical aspects that we should really focus on? Barb? Maybe you could start an Interra kindly way in as well. >>Yeah, so the relationship started in 2020 and all shirts made a big bag deep with snowflake co-innovating and optimizing cloud use cases together. We are supporting customers who are looking for that modern analytic stack to replace an old one or to implement their first analytic strategy. And our joint customers want to self-serve with data-driven analytics, leveraging all the benefits of the cloud, scalability, accessibility, governance, and optimizing their costs. Um, Altrix proudly achieved. Snowflake's highest elite tier in their partner program last year. And to do that, we completed a rigorous third party testing process, which also helped us make some recommended improvements to our joint stack. We wanted customers to have confidence. They would benefit from high quality and performance in their investment with us then to help customers get the most value out of the destroyed solution. We developed two great assets. One is the officer starter kit for snowflake, and we coauthored a joint best practices guide. >>The starter kit contains documentation, business workflows, and videos, helping customers to get going more easily with an altered since snowflake solution. And the best practices guide is more of a technical document, bringing together experiences and guidance on how Altryx and snowflake can be deployed together. Internally. We also built a full enablement catalog resources, right? We wanted to provide our account executives more about the value of the snowflake relationship. How do we engage and some best practices. And now we have hundreds of joint customers such as Juniper and Sainsbury who are actively using our joint solution, solving big business problems much faster. >>Cool. Kara, can you give us your perspective on the partnership? >>Yeah, definitely. Dave, so as Barb mentioned, we've got this standing very successful partnership going back years with hundreds of happy joint customers. And when I look at the beginning, Altrix has helped pioneer the concept of self-service analytics, especially with use cases that we worked on with for, for data prep for BI users like Tableau and as Altryx has evolved to now becoming from data prep to now becoming a full end to end data science platform. It's really opened up a lot more opportunities for our partnership. Altryx has invested heavily over the last two years in areas of deep integration for customers to fully be able to expand their investment, both technologies. And those investments include things like in database pushed down, right? So customers can, can leverage that elastic platform, that being the snowflake data cloud, uh, with Alteryx orchestrating the end to end machine learning workflows Alteryx also invested heavily in snow park, a feature we released last year around this concept of data programmability. So all users were regardless of their business analysts, regardless of their data, scientists can use their tools of choice in order to consume and get at data. And now with Altryx cloud, we think it's going to open up even more opportunities. It's going to be a big year for the partnership. >>Yeah. So, you know, Terike, we we've covered snowflake pretty extensively and you initially solve what I used to call the, I still call the snake swallowing the basketball problem and cloud data warehouse changed all that because you had virtually infinite resources, but so that's obviously one of the problems that you guys solved early on, but what are some of the common challenges or patterns or trends that you see with snowflake customers and where does Altryx come in? >>Sure. Dave there's there's handful, um, that I can come up with today, the big challenges or trends for us, and Altrix really helps us across all of them. Um, there are three particular ones I'm going to talk about the first one being self-service analytics. If we think about it, every organization is trying to democratize data. Every organization wants to empower all their users, business users, um, you know, the, the technology users, but the business users, right? I think every organization has realized that if everyone has access to data and everyone can do something with data, it's going to make them competitively, give them a competitive advantage with Altrix is something we share that vision of putting that power in the hands of everyday users, regardless of the skillsets. So, um, with self-service analytics, with Ultrix designer they've they started out with self-service analytics as the forefront, and we're just scratching the surface. >>I think there was an analyst, um, report that shows that less than 20% of organizations are truly getting self-service analytics to their end users. Now, with Altryx going to Ultrix cloud, we think that's going to be a huge opportunity for us. Um, and then that opens up the second challenge, which is machine learning and AI, every organization is trying to get predictive analytics into every application that they have in order to be competitive in order to be competitive. Um, and with Altryx creating this platform so they can cater to both the everyday business user, the quote unquote, citizen data scientists, and making a code friendly for data scientists to be able to get at their notebooks and all the different tools that they want to use. Um, they fully integrated in our snow park platform, which I talked about before, so that now we get an end to end solution caring to all, all lines of business. >>And then finally this concept of data marketplaces, right? We, we created snowflake from the ground up to be able to solve the data sharing problem, the big data problem, the data sharing problem. And Altryx um, if we look at mobilizing your data, getting access to third-party datasets, to enrich with your own data sets, to enrich with, um, with your suppliers and with your partners, data sets, that's what all customers are trying to do in order to get a more comprehensive 360 view, um, within their, their data applications. And so with Altryx alterations, we're working on third-party data sets and marketplaces for quite some time. Now we're working on how do we integrate what Altrix is providing with the snowflake data marketplace so that we can enrich these workflows, these great, great workflows that Altrix writing provides. Now we can add third party data into that workflow. So that opens up a ton of opportunities, Dave. So those are three I see, uh, easily that we're going to be able to solve a lot of customer challenges with. >>So thank you for that. Terrick so let's stay on cloud a little bit. I mean, Altrix is undergoing a major transformation, big focus on the cloud. How does this cloud launch impact the partnership Terike from snowflakes perspective and then Barb, maybe, please add some color. >>Yeah, sure. Dave snowflake started as a cloud data platform. We saw our founders really saw the challenges that customers are having with becoming data-driven. And the biggest challenge was the complexity of having imagine infrastructure to even be able to do it, to get applications off the ground. And so we created something to be cloud-native. We created to be a SAS managed service. So now that that Altrix is moving to the same model, right? A cloud platform, a SAS managed service, we're just, we're just removing more of the friction. So we're going to be able to start to package these end to end solutions that are SAS based that are fully managed. So customers can, can go faster and they don't have to worry about all of the underlying complexities of, of, of stitching things together. Right? So, um, so that's, what's exciting from my viewpoint >>And I'll follow up. So as you said, we're investing heavily in the cloud a year ago, we had two pre desktop products, and today we have four cloud products with cloud. We can provide our users with more flexibility. We want to make it easier for the users to leverage their snowflake data in the Alteryx platform, whether they're using our beloved on-premise solution or the new cloud products were committed to that continued investment in the cloud, enabling our joint partner solutions to meet customer requirements, wherever they store their data. And we're working with snowflake, we're doing just that. So as customers look for a modern analytic stack, they expect that data to be easily accessible, right within a fast, secure and scalable platform. And the launch of our cloud strategy is a huge leap forward in making Altrix more widely accessible to all users in all types of roles, our GSI and our solution provider partners have asked for these cloud capabilities at scale, and they're excited to better support our customers, cloud and analytic >>Are. How about you go to market strategy? How would you describe your joint go to market strategy with snowflake? >>Sure. It's simple. We've got to work backwards from our customer's challenges, right? Driving transformation to solve problems, gain efficiencies, or help them save money. So whether it's with snowflake or other GSI, other partner types, we've outlined a joint journey together from recruit solution development, activation enablement, and then strengthening our go to market strategies to optimize our results together. We launched an updated partner program and within that framework, we've created new benefits for our partners around opportunity registration, new role based enablement and training, basically extending everything we do internally for our own go-to-market teams to our partners. We're offering partner, marketing resources and funding to reach new customers together. And as a matter of fact, we recently launched a fantastic video with snowflake. I love this video that very simply describes the path to insights starting with your snowflake data. Right? We do joint customer webinars. We're working on joint hands-on labs and have a wonderful landing page with a lot of assets for our customers. Once we have an interested customer, we engage our respective account managers, collaborating through discovery questions, proof of concepts really showcasing the desired outcome. And when you combine that with our partners technology or domain expertise, it's quite powerful, >>Dark. How do you see it? You'll go to market strategy. >>Yeah. Dave we've. Um, so we initially started selling, we initially sold snowflake as technology, right? Uh, looking at positioning the diff the architectural differentiators and the scale and concurrency. And we noticed as we got up into the larger enterprise customers, we're starting to see how do they solve their business problems using the technology, as well as them coming to us and saying, look, we want to also know how do you, how do you continue to map back to the specific prescriptive business problems we're having? And so we shifted to an industry focus last year, and this is an area where Altrix has been mature for probably since their inception selling to the line of business, right? Having prescriptive use cases that are particular to an industry like financial services, like retail, like healthcare and life sciences. And so, um, Barb talked about these, these starter kits where it's prescriptive, you've got a demo and, um, a way that customers can get off the ground and running, right? >>Cause we want to be able to shrink that time to market, the time to value that customers can watch these applications. And we want to be able to, to tell them specifically how we can map back to their business initiatives. So I see a huge opportunity to align on these industry solutions. As BARR mentioned, we're already doing that where we've released a few around financial services working in healthcare and retail as well. So that is going to be a way for us to allow customers to go even faster and start to map two lines of business with Alteryx. >>Great. Thanks Derek. Bob, what can we expect if we're observing this relationship? What should we look for in the coming year? >>A lot specifically with snowflake, we'll continue to invest in the partnership. Uh, we're co innovators in this journey, including snow park extensibility efforts, which Derek will tell you more about shortly. We're also launching these great news strategic solution blueprints, and extending that at no charge to our partners with snowflake, we're already collaborating with their retail and CPG team for industry blueprints. We're working with their data marketplace team to highlight solutions, working with that data in their marketplace. More broadly, as I mentioned, we're relaunching the ultra partner program designed to really better support the unique partner types in our global ecosystem, introducing new benefits so that with every partner, achievement or investment with ultra score, providing our partners with earlier access to benefits, um, I could talk about our program for 30 minutes. I know we don't have time. The key message here Alteryx is investing in our partner community across the business, recognizing the incredible value that they bring to our customers every day. >>Tarik will give you the last word. What should we be looking for from, >>Yeah, thanks. Thanks, Dave. As BARR mentioned, Altrix has been the forefront of innovating with us. They've been integrating into, uh, making sure again, that customers get the full investment out of snowflake things like in database push down that I talked about before that extensibility is really what we're excited about. Um, the ability for Ultrix to plug into this extensibility framework that we call snow park and to be able to extend out, um, ways that the end users can consume snowflake through, through sequel, which has traditionally been the way that you consume snowflake as well as Java and Scala, not Python. So we're excited about those, those capabilities. And then we're also excited about the ability to plug into the data marketplace to provide third party data sets, right there probably day sets in, in financial services, third party, data sets and retail. So now customers can build their data applications from end to end using ultrasound snowflake when the comprehensive 360 view of their customers, of their partners, of even their employees. Right? I think it's exciting to see what we're going to be able to do together with these upcoming innovations. Great >>Barb Tara, thanks so much for coming on the program, got to leave it right there in a moment, I'll be back with some closing thoughts in a summary, don't go away. >>1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops make that 2.3. The sector times out the wazoo, whites are much of this velocity's pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into insights, they turn to Altryx Qualtrics analytics, automation, >>Okay, let's summarize and wrap up the session. We can pretty much agree the data is plentiful, but organizations continue to struggle to get maximum value out of their data investments. The ROI has been elusive. There are many reasons for that complexity data, trust silos, lack of talent and the like, but the opportunity to transform data operations and drive tangible value is immense collaboration across various roles. And disciplines is part of the answer as is democratizing data. This means putting data in the hands of those domain experts that are closest to the customer and really understand where the opportunity exists and how to best address them. We heard from Jay Henderson that we have all this data exhaust and cheap storage. It allows us to keep it for a long time. It's true, but as he pointed out that doesn't solve the fundamental problem. Data is spewing out from our operational systems, but much of it lacks business context for the data teams chartered with analyzing that data. >>So we heard about the trend toward low code development and federating data access. The reason this is important is because the business lines have the context and the more responsibility they take for data, the more quickly and effectively organizations are going to be able to put data to work. We also talked about the harmonization between centralized teams and enabling decentralized data flows. I mean, after all data by its very nature is distributed. And importantly, as we heard from Adam Wilson and Suresh Vittol to support this model, you have to have strong governance and service the needs of it and engineering teams. And that's where the trifecta acquisition fits into the equation. Finally, we heard about a key partnership between Altrix and snowflake and how the migration to cloud data warehouses is evolving into a global data cloud. This enables data sharing across teams and ecosystems and vertical markets at massive scale all while maintaining the governance required to protect the organizations and individuals alike. >>This is a new and emerging business model that is very exciting and points the way to the next generation of data innovation in the coming decade. We're decentralized domain teams get more facile access to data. Self-service take more responsibility for quality value and data innovation. While at the same time, the governance security and privacy edicts of an organization are centralized in programmatically enforced throughout an enterprise and an external ecosystem. This is Dave Volante. All these videos are available on demand@theqm.net altrix.com. Thanks for watching accelerating automated analytics in the cloud made possible by Altryx. And thanks for watching the queue, your leader in enterprise tech coverage. We'll see you next time.

[Music] okay we're here with ceres vitale who's the chief product officer at alteryx and adam wilson the ceo of trifacta now of course part of alteryx just closed this quarter gentlemen welcome great to be here okay so rush let me start with you in my opening remarks i talked about alteryx's traditional position serving business analysts and how the hyperanna acquisition brought you deeper into the business user space what does trifacta bring to your portfolio why'd you buy the company yeah thank you thank you for the question um you know we see a we see a massive opportunity of helping brands democratize the use of analytics across their business every knowledge worker every individual in the company should have access to analytics it's no longer optional as they navigate their businesses with that in mind you know we know designer and our the products that alteryx has been selling the past decade or so do a really great job addressing the business analysts with hyper rana now kind of renamed alteryx auto insights we even speak with the business owner the line of business owner who's looking for insights that aren't revealed in traditional dashboards and so on um but we see this opportunity of really helping the data engineering teams and i.t organizations to also make better use of analytics and that's where trifacta comes in for us trifacta has the best data engineering cloud in the planet they have an established track record of working across multiple cloud platforms and helping data engineers um do better data pipelining and work better with this massive kind of cloud transformation that's happening in every business um and so trifecta made so much sense for us yeah thank you for that i mean look you could have built it yourself would have taken you know who knows how long you know but uh so definitely a great time to market move adam i wonder if we could dig into trifacta some more i mean i remember interviewing joe hellerstein in the early days you've talked about this as well on thecube coming at the problem of taking data from raw refined to an experience point of view and joe in the early days talked about flipping the model and starting with data visualization something jeff herr was expert at so maybe explain how we got here we used to have this cumbersome process of etl and you maybe and some others change that model with you know el and then t explain how trifacta really changed the data engineering game yeah that's exactly right uh dave and it's been a really interesting journey for us because i think the original hypothesis coming out of the campus research at berkeley and stanford that really birthed trifacta was you know why is it that the people who know the data best can't do the work you know why is this become the exclusive purview the highly technical and you know can we rethink this and make this a user experience problem powered by machine learning that will take some of the more complicated things that people want to do with data and really help to automate those so so a broader set of users can can really see for themselves and help themselves and and i think that um there was a lot of pent up frustration out there because people have been told for you know for a decade now to be more data driven and then the whole time they're saying well then give me the data you know in the shape that i can use it with the right level of quality and i'm happy to be but don't tell me to be more data driven and they'll don't then and and not empower me um to to get in there and to actually start to work with the data in meaningful ways and so um that was really you know what you know the origin story of the company and i think as as we saw over the course of the last five six seven years that um you know a real uh excitement to embrace this idea of of trying to think about data engineering differently trying to democratize the the etl process and to also leverage all these exciting new uh engines and platforms that are out there that allow for you know processing you know ever more diverse data sets ever larger data sets and new and interesting ways and that's where a lot of the push down or the elt approaches uh you know i think it really won the day um and that and that for us was a hallmark of the solution from the very beginning yeah this is a huge point that you're making this is first of all there's a large business probably about a hundred billion dollar tam uh and and the the point you're making is we look we've contextualized most of our operational systems but the big data pipelines hasn't gotten there but and maybe we could talk about that a little bit because democratizing data is nirvana but it's been historically very difficult you've got a number of companies it's very fragmented and they're all trying to attack their little piece of the problem to achieve an outcome but it's been hard and so what's going to be different about alteryx as you bring these puzzle pieces together how is this going to impact your customers who would like to take that one yeah maybe maybe i'll take a crack at it and adam will add on um you know there hasn't been a single platform [Music] for analytics automation in the enterprise right people have relied on different products to solve kind of smaller problems across this analytics automation data transformation domain and i think uniquely alteryx has that opportunity we've got 7000 plus customers who rely on analytics for data management for analytics for ai and ml for transformations for reporting and visualization for automated insights and so on and so by bringing trifecta we have the opportunity to scale this even further and solve for more use cases expand the scenarios where angles gets applied and serve multiple personas um and now we just talked about the data engineers they are really a growing stakeholder in this transformation of data analytics yeah good maybe we can stay on this for a minute because you're right you bring it together now at least three personas the business analyst the end user size business user and now the data engineer which is really out of an i.t role in a lot of companies and you've used this term the data engineering cloud what is that how is it going to integrate in with or support these other personas and and how's it going to integrate into the broader ecosystem of clouds and cloud data warehouses or any other data stores yeah you know that's great uh you know i think for us we really looked at this and said you know we want to build an open and interactive you know cloud platform for data engineers you know to collaboratively profile pipeline um and prepare data for analysis and and that really meant collaborating with the analysts that were in the line of business and so this is why a big reason why this combination is so magic because ultimately if we can get the data engineers that are creating the data products together with the analysts that are in the line of business that are driving a lot of the decision making and allow for that what i would describe as collaborative curation you know of the data together so that you're starting to see um uh you know increasing returns to scale as this uh as this rolls out i just think that is an incredibly uh powerful combination and frankly something that the market has not cracked the code on yet and so um i think when we when i sat down with surash and with mark and and the team at ultrix that was really part of the the big idea the big vision that that was painted and and got us really energized um about the acquisition and about the the potential of the combination yeah and you're really you're obviously riding the cloud and the cloud native wave um and but specifically we're seeing you know i almost don't even want to call it a data warehouse anyway because when you look at what princeton snowflake is doing of course their marketing is around the data cloud but i i actually think there's real justification for that because it's not like the traditional data warehouse right it's it's simplified get there fast don't necessarily have to go through this central organization to share data uh and and but it's really all about simplification right isn't that really what the democratization comes down to yeah it's simplification and collaboration right i don't want to i want to kind of just uh what what adam said resonates with me deeply um analytics is one of those massive disciplines inside an enterprise that's really had the weakest of tools um and weakest of interfaces to collaborate with and i think truly this was alteryx's end of superpower was helping the analysts get more out of their data get more out of the analytics like imagine a world where these people are collaborating and sharing insights in real time and sharing workflows and getting access to new data sources understanding data models better i think curating those insights i borrowing adam's phrase again i think that creates a real value inside the organization because frankly in scaling analytics and democratizing analytics and data we're still in such early phases of this journey so how should we think about designer cloud which is from alteryx it's really been the on-prem the server or desktop you know offering and of course trifecta is about cloud cloud data warehouses right um how should we think about those two products yeah i think i think you should think about them and as very complementary right designer cloud really shares a lot of dna and heritage with designer desktop the low code tooling and the interface that really appeals to the business analysts and gets a lot of the things that they do well we've also built it with interoperability in mind right so if you started building your workflows in designer desktop you want to share that with designer cloud we want to make it super easy for you to do that and i think over time now we're only a week into this alliance with uh with trifacta i think we have to get deeper and start to think about what does the data engineer really need what business analysts really need and how to design a cloud and try factor really support both of those requirements uh while kind of continue to build on the trifecta on the amazing trifecta cloud platform you know and i think let's go ahead i'm just to say i think that's one of the things that um you know creates a lot of opportunity as we go forward because ultimately you know trifacta took a platform uh first mentality to everything that we built so thinking about openness and extensibility and um and how over time people could build things on top of trifacta that are a variety of analytic tool chain or analytic applications and so when you think about um alteryx now starting to uh to move some of its capabilities or to provide additional capabilities uh in the cloud um you know trifacta becomes uh a a platform that can accelerate you know all of that work and create a cohesive set of of cloud-based services that share a common platform and that maintains independence because both companies um have been uh you know fiercely independent uh in really giving people choice um so making sure that whether you're uh you know picking one cloud platform another whether you're running things on the desktop uh whether you're running in hybrid environments that no matter what your decision you're always in a position to be able to get out your data you're always in a position to be able to cleanse transform shape structure that data and ultimately to deliver uh the analytics that you need and so i think in in that sense um uh you know this this again is another reason why the combination you know fits so well together giving people um the choice um and as they as they think about their analytics strategy and and their platform strategy going forward you know i make a chuckle but one of the reasons i always liked alteryx is because you kind of did did a little end run on i.t i.t can be a blocker sometimes but that created problems right because the organization said wow this big data stuff is taken off but we need security we need governance and and it's interesting because you got you know etl has been complex whereas the visualization tools they really you know really weren't great at governance and security it took some time there so that's not not their heritage you're bringing those worlds together and i'm interested you guys just had your sales kickoff you know what was the reaction like uh maybe suresh you could start off and maybe adam you could bring us home yeah um thanks for asking about our sales kickoff so we met uh for the first time in kind of two years right for as it is for many of us um in person uh um which i think was a was a real breakthrough as alteryx has been on its transformation journey uh we had a try factor to um the the party such as it were um and getting all of our sales teams and product organizations um to meet in person in one location i thought that was very powerful for us as a company but then i tell you um the reception for trifecta was beyond anything i could have imagined uh we were working adam and i were working so hard on on the the deal and the core hypotheses and so on and then you step back and kind of share the vision with the field organization and it blows you away the energy that it creates among our sellers our partners and i'm sure adam and his team were mobbed every single day with questions and opportunities to bring them in but adam maybe you should share yeah no it was uh it was through the roof i mean uh the uh the amount of energy the uh when so certainly how welcoming everybody was uh you know just i think the story makes so much sense together i think culturally the companies are very aligned um and uh it was a real uh real capstone moment uh to be able to complete the acquisition and to and to close and announce you know at the kickoff event and um i think you know for us when we really thought about it you know when we and the story that we told was just you have this opportunity to really cater to what the end users you know care about which is a lot about interactivity and self-service and at the same time and that's and that's a lot of the goodness that um that alteryx is has brought you know through you know you know years and years of of building a very vibrant community of you know thousands hundreds of thousands of users and on the other side you know trifecta bringing in this data engineering focus that's really about uh the governance things that you mentioned and the openness that that it cares deeply about and all of a sudden now you have a chance to put that together into a complete story where the data engineering cloud and analytics automation you know come together and um and i just think you know the lights went on um you know for people instantaneously and you know this is a story that um that i think the market is really hungry for and and certainly the reception we got from from the broader team at kickoff was uh was a great indication of that well i think the story hangs together really well you know one of the better ones i've seen in this space um and and you guys coming off a really really strong quarter so congratulations on that gents we have to leave it there really appreciate your time today yeah take a look at this short video and when we come back we're going to dig into the ecosystem and the integration into cloud data warehouses and how leading organizations are creating modern data teams and accelerating their digital businesses you're watching the cube your leader in enterprise tech coverage [Music]

>> Alteryx is a company with a long history that goes all the way back to the late 1990s. Now the one consistent theme over the past 20-plus years, however, is that Alteryx has always been a data company. Early in the big data and Hadoop cycle. It saw the need to combine and prep different data types, so that organizations could confidently analyze data and take action. Alteryx and similar companies played a critical role in helping, helping companies become, data driven. Alex, let me start over. Shit, sorry. Sorry, Leonard. Alteryx is a company with a long history that goes all the way back to the late 1990s. Now the one consistent theme over 20 plus years has been that Alteryx has always been a data company early in the big data and Hadoop cycle. It saw the need to combine and prep different data types so that organizations could analyze data and take action. Alteryx and similar companies played a critical role in helping companies become data driven. The problem was the decade of big data, brought a lot of complexities and required immense skills just to get the technology to work as advertised. This in turn limited, the pace of adoption and the number of companies that could really lean in and take advantage. Now, the cloud began to change all that, and set the foundation for today's themed, de jor of digital transformation. We hear that phrase a ton, digital transformation. People used to think it was a buzzword but of course we learn from the pandemic that if you're not a digital business, you're out of business. And a key tenant of digital transformation is democratizing data. Meaning enabling not just hyper specialized experts but anyone, business users to put data to work. Now back to Alteryx, the company has embarked on a major transformation of its own over the past couple of years. Brought in new management, they've changed the way in which it engaged it with customers with a new subscription model, and it's top graded. It's talent pool. 2021 was even more significant because of two acquisitions that Alteryx made, Hyper Anna and Trifecta. Why are these acquisitions important? While traditionally Altrix sold to business analysts that were part of the data pipeline. These were fairly technical people who had certain skills, and were trained in things like writing Python code. With Hyper Anna, Alteryx has added a new persona the business user, anyone in the business who wanted to gain insights from data and, or let's say use AI without having to be a deep technical expert. And then Trifecta, a company started in the early days of big data by Cubelum, Joe Hellerstein and his colleagues at Berkeley. They knock down the data engineering persona, and this gives Alteryx a complimentary extension into IT where things like governance and security are paramount. So as we enter 2022, the post isolation economy is here, and we do so with a digital foundation, built on the confluence of cloud native technologies, data democratization and machine intelligence or AI, if you prefer. And Alteryx is entering that new era with an expanded portfolio, new go to market vectors, a recurring revenue business model, and a brand new outlook on how to solve customer problems and scale a company. My name is Dave Volante with the Cube and I'll be your host today in the next hour we're going to explore the opportunities in this new data market. And we have three segments where we dig into these trends and themes. First we'll talk to Jay Henderson, vice president of product management at Alteryx about cloud accelerate and simplifying complex data operations. Then we'll bring in Crajesh vitall. Who's the chief product officer at Alteryx and Adam Wilson the CEO of trifecta, which of course is now part of Alteryx. And finally, we'll hear about how Alteryx is partnering with snowflake in the ecosystem and how they're integrating with data platforms like snow flick and what this means for customers. And we may have a few surprises sprinkled in as well into the conversation let's get started.

(upbeat music) >> Announcer: Live from Munich, Germany, it's the Cube covering DataWorks Summit Europe, 2017. Brought to you by Hortonworks. >> Hey, welcome back everyone. We're here live in Munich, Germany, at the DataWorks 2017 Summit. I'm John Furrier, my co-host Dave Vellante with the Cube, and our next two guests are Gianthomas Volpe, head of customer development e-media for Alation. Welcome to the Cube. And we have Bertrand Cariou, who's the director of solution marketing at Trifecta with partners. Guys, welcome to the Cube. >> Thank you. >> Thank you for having us. >> Big fans of both your start-ups and growing. You guys are doing great. We had your CEO on our big data SV, Joe Hellerstein, he talked about the rang, all the cool stuff that's going on, and Alation, we know Stephanie has been on many times, but you guys are start ups that are doing very well and growing in this ecosystem, and, you know, everyone's going public. Cloud Air has filed their S1, great news for those guys, so the data world has changed beyond Hadoop. You're seeing it, obviously Hadoop is not dead, but it's still going to be a critical component of a larger ecosystem that's developing. You guys are part of that. So I want to get your thoughts of why you're here in Europe, okay? And how you guys are working together to take data to the next level, because, you know, we're hearing more and more data is a foundational conversation starter, because now there's other things happening, IOT, business analysts, you guys are in the heart of it. Your thoughts? >> You know, going to be you. >> All in, yeah, sure. So definitely at Alation what we're seeing is more and more people across the organization want to get access to the data, and we're kind of breaking out of the traditional roles around IP managing both metadata, data preparation, like Trifecta's focused on. So we're pretty squarely focused on how do we bring that access to a wider range of people? How do we enable that social and collaborative approach to working with that data, whether it's in a data lake so, or here at DataWorks. So clearly that's one of the main topics. But also other data sources within the organization. >> So you're freeing the data up and the whole collaboration thing is more of, okay, don't just look at IT as this black box of give me some data and now spit out some data at me. Maybe that's the old way. The new way is okay, all of the data's out there, they're doing their thing, but the collaboration is for the user to get into that data you know, ingestion. Playing with the data, using the data, shaping the data. Developing with the data. Whatever they're doing, right? >> It's just bringing transparency to not only what IT is doing and making that accessible to users, but also helping users collaborate across different silos within an organization, so. We look at things like logs to understand who is doing what with the data, so if I'm working in one group, I can find out that somebody in a completely different group in the organization is working with similar data, bringing new techniques to their analysis, and can start leveraging that and have a conversation that others can learn from, too. >> So basically it's like a discovery platform for saying hey, you know, Mary in department X has got these models. I can leverage that. Is that kind of what you guys are all about? >> Yeah, definitely. And breaking through that, enabling communication across the different levels of the organization, and teaching other people at all different levels of maturity within the company, how they can start interacting with data and giving them the tools to up skill throughout that process. >> Bertrand, how about the Trifecta? 'Cause one of the things that I find exciting about Europe value proposition and talking to Joe, the founder, besides the fact that they all have GitHub on their about page, which is the coolest thing ever, 'cause they're all developers. But the more reality is is that a business person or person dealing with data in some part of a geography, could be whether it's in Europe or in the US, might have a completely different view and interest in data than someone in another area. It could be sales data, could be retail data, it doesn't matter but it's never going to be the same schema. So the issue is, got to take that away from the user complexity. That is really fundamental change. >> Yeah. You're totally correct. So information is there, it is available. Alation helps identify what is the right information that can be used, so if I'm in marketing, I could reuse sales information, associating maybe with web logs information. Alation will give me the opportunity to know what information is available and if I can trust it. If someone in finance is using that information, I can trust that data. So now as a user, I want to take that data, maybe combine the data, and the data is always a different format, structure, level of quality, and the work of data wrangling is really for the end user, you can be an analyst. Someone in the line of business most of the time, these could be like some of the customers we are here in Germany like Munich Re would be actuaries. Building risk models and or claimed for casting, payment for casting. So they are not technologies at all, but they need to combine these data sets by themselves, and at scale, and the work they're doing, they are producing new information and this information is used directly to their own business, but as soon as they share this information, back to the data lake, Alation will index this information, see how it is used, and put it to this visibility to the other users for reuse as well. >> So you guys have a partnership, or is this more of a standard API kind of thing? >> So we do have a partnership, we have plan development on the road map. It's currently happening. So I think by the end of the quarter, we're going to be delivering a new integration where whether I'm in Alation and looking for data and finding something that I want to work with, I know needs to be prepared I can quickly jump into Trifecta to do that. Or the other way around in Trifecta, if I'm looking for data to prepare, I can open the catalog, quickly find out what exists and how to work with it better. >> So basically the relationship, if I get this right is, you guys pass on your expertise of the data wrangling all the back processes you guys have, and advertise that into Alation. They discover it, make it surfaceable for the social collaboration or the business collaboration. >> Exactly. And when the data is wrangled, it began indexed and so it's a virtual circle where all the data that is traded and combined is exposed to the user to be reused. >> So if I were Chief Data Officer, I'd say okay, there's three sequential things that I need to do, and you can maybe help me with a couple of them. So the first one is I need to understand how data contributes to the monetization of my company, if I'm a public company or a for profit company. That's, I guess my challenge. But then, there are other two things that I need to give people access to that data, and I need quality. So I presume Alation can help me understand what data's available. I can actually, it kind of helps with number one as well because like you said, okay, this is the type of data, this is how the business process works. Feed it. And then the access piece and quality. I guess the quality is really where Trifecta comes in. >> GianThomas: Yes. >> What about that sequential flow that I just described? Is that common? >> Yeah >> In your business, your customer base. >> It's definitely very common. So, kind of going back to the Munich Re examples, since we're here in Munich, they're very focused on providing better services around risk reduction for their customers. Data that can impact that risk can be of all kinds from all different places. You kind of have to think five, ten years ahead of where we are now to see where it might be coming from. So you're going to have a ton of data going in to the data lake. Just because you have a lot of data, that does not mean that people will know how to work with it they won't know that it exists. And especially since the volumes are so high. It doesn't mean that it's all coming in at a greatly usable format. So Alation comes in to play in helping you find not only what exists, by automating that process of extraction but also looking at what data people are actually using. So going back to your point of how do I know what data's driving value for the organization, we can tell you in this schema, this is what's actually being used the most. That's a pretty good starting point to focus in on what is driving value and when you do find something, then you can move over to Trifecta to prepare it and get it ready for analysis. >> So keying on that for a second, so in the example of Munich Re, the value there is my reduction in expected loss. I'm going to reduce my risk, that puts money in my bottom line. Okay, so you can help me with number one, and then take that Munich Re example into Trifecta. >> Yes, so the user will be the same user using Alation and Trifecta. So is an actuary. So as soon as the actuary items you find the data that is the most relevant for what you'll be planning, so the actuaries are working with terms like development triangles over 20 years. And usually it's column by column. So they have to pivot the data row by row. They have to associate that with the paid claims the new claims coming in, so all these information is different format. Then they have to look at maybe weather information, or additional third party information where the level of quality is not well known, so they are bringing data in the lake that is not yet known. And they're combining all this data. The outcome of that work, that helps in the Reese modeling so that could be used by, they could use Sass or our older technology for the risk modeling. But when they've done that modeling and building these new data sets. They're, again, available to the community because Alation would index that information and explain how it is used. The other things that we've seen with our users is there's also a very strong, if you think about insurances banks, farmer companies, there is a lot of regulation. So, as the user, as you are creating new data, said where the data coming from. Where the data is going, how is it used in the company? So we're capturing all that information. Trifecta would have the rules to transform the data, Alation will see the overall eye level picture from table to the source system where the data is come. So super important as well for the team. >> And just one follow up. In that example, the actuary, I know hard core data scientists hate this term, but the actuaries, the citizen data scientist. Is that right? >> The actuaries would know I would say statistics, usually. But you get multiple level of actuaries. You get many actuaries, they're Excel users. They have to prepare data. They have to pin up, structure the data to give it to next actuary that will be doing the pricing model or the next actuary that will risk modeling. >> You guys are hitting on a great formula which is cutting edge, which is why you guys are on the startups. But, Bertrand I want to talk to you about your experience at Informatica. You were the founder the Informatica France. And you're also involved in some product development in the old, I'd say old days, but like. Back in the days when structured data and enterprise data, which was once a hard problem, deal with metadata, deal with search, you had schemes, all kinds of stuff to deal with. It was very difficult. You have expertise. I want you to talk about what's different now in this environment. Because it's still challenging. But now the world has got so much fast data, we got so much new IOT data, especially here in Europe. >> Oh yes. >> Where you have an industrialized focus, certainly Germany, like case in point, but it's pretty smart mobility going on in Europe. You've always had that mobile environment. You've got smart cities. A lot of focus on data. What's the new world like now? How are people dealing with this? What's your perspective? >> Yes, so there's and we all know about the big data and with all this volume, additional volume and new structure of data. And I would say legacy technology can deal as you mentioned, with well structured information. Also you want to give that information to the masses. Because the people who know the data best, are the business people. They know what to do with the data, but the access of this data is pretty complicated. So where Trifecta is really differentiating and has been thinking through that is to say whatever the structure of the data, IOT, Web Logs, Value per J son, XML, that should be for an end user, just metrics. So that's the way you understand the data. The next thing when play with data, usually you don't know what the schema would be at the end. Because you don't know what the outcome is. So, you are, as an end user, you are exploring the data combining data set and the structure is trading as you discover the data. So that is also something new compared to the old model where an end user would go to the data engineer to say I need that information, can you give me that information? And engineers would look at that and say okay. We can access here, what is the schema? There was all this back and forth. >> There was so much friction in the old way, because the creativity of the user is independent now of all that scaffolding and all the wrangling, pre-processing. So I get that piece of the Citizen's Journal, Citizen Analyst. But the key thing here is you were shrecking with the complexity to get the job done. So the question then comes in, because it's interesting, all the theme here at DataWorks Summit in Europe and in the US is all the big transformative conversations are starting with business people. So this a business unit so the front lines if you will, not IT. Although IT now's got to support that. If that's the case, the world's shifting to the business owners. Hence your start up. Is that kind of getting that right? >> I think so. And I think that's also where we're positioning ourselves is you have a data lake, you can put tons of data in it, but if you don't find an easy way to make that accessible to a business user, you're not going to get a value out of it. It's just going to become a storage place. So really, what we've focused on is how do you make that layer easily accessible? How do you share around and bring some of the common business practices to that? And make sure that you're communicating with IT. So IT shouldn't be cast aside, but they should have an ongoing relationship with the business user. >> By the way, I'll point out that Dave knows I'm not really a big fan of the data lake concept mainly because they've turned it into data swamps because IT deploys it, we're done! You know, check the box. But, data's getting stale because it's not being leveraged. You're not impacting the data or making it addressable, or discoverable or even wrangleable. If that's a word. But my point is that's all complexities. >> Yes, so we call it sort of frozen data lake. You build a lake, and then it's frozen and nobody can go fishing. >> You play hockey on it. (laughs) >> You dig and you're fishing. >> And you need to have this collaboration ongoing with the IT people, because they own the infrastructure. They can feed the lake with data with the business. If there is no collaboration, and we've seen that multiple times. Data lake initiatives, and then we come back one year after there is no one using the lake, like one, two person of the processing power, or the data is used. Nobody is going to the lake. So you need to index the data, catalog the data to know what is available. >> And the psychology for IT is important here, and I was talking yesterday with IBM folks, Nevacarti here, but this is important because IT is not necessarily in a position of doing it because doing the frozen lake or data swamp because they want to screw over the business people, they just do their job, but here you're empowering them because you guys are got some tech that's enabling the IT to do a data lake or data environment that allows them to free up the hassles, but more importantly, satisfy the business customer. >> GeanThomas: Exactly. >> There's a lot of tech involved. And certainly we've talked to you guys about that. Talk about that dynamic of the psychology because that's what IT wants. So what's that dev ops mindset for data, data ops if you will or you know, data as code if you will, constantly what we've been calling it but that's now the cloud ethos hits the date ethos. Kind of coming together. >> Yes, I think data catalogs are subtly different in that traditionally they are more of an IT function, but to some extent on the metadata side, where as on the business side, they tended to be a siloed organization of information that business itself kept to maintain very manually. So we've tried to bring that together. All the different parties within this process from the IT side to the govern stewardship all the way down to the analysts and data scientists can get value out of a data catalog that can help each other out throughout that process. So if it's communicating to end users what kind of impact any change IT will make, that makes their life easier, and have one way to communicate that out and see what's going to happen. But also understand what the business is doing for governance or stewardship. You can't really govern or curate if you don't know what exists and what matters to the business itself. So bring those different stages together, helping them help each other is really what Alation does. >> Tell about the prospects that you guys are engaging in from a customer standpoint. What are some of the conversations of those customers you haven't gotten yet together. And and also give an example of a customer that you guys have, and use cases where they've been successful. >> Absolutely. So typically what we see, is that an organization is starting up a data lake or they already have legacy data warehouses. Often it's both, together. And they just need a unified way of making information about those environments available to end users. And they want to have that better relationship. So we're often seeing IT engaged in trying to develop that relationship along with the business. So that's typically how we start and we in the process of deploying, work in to that conversation of now that you know what exists, what you might want to work with, you're often going to have to do some level of preparation or transformation. And that's what makes Trifecta a great fit for us, as a partner, is coming to that next step. >> Yeah, on Mobile Market Share, one of our common customers, we have DNSS, also a common customer, eBay, a common customer. So we've got already multiple customers and so some information about the issue Market Share, they have to deal with their customer information. So the first thing they receive is data, digital information about ads, and so it's really marketing type of data. They have to assess the quality of the data. They have to understand what values and combine the value with their existing data to provide back analytics to their customers. And that use case, we were talking to the business users, my people selling Market Share to their customers because the fastest they can unboard their data, they can qualify the quality of the data the easiest it is to deliver right level of quality analytics. And also to engage more customers. So it was really was to be fast onboarding customer data and deliver analytics. And where Alatia explain is that they can then analyze all the sequel statement that the customers, maybe I'll let you talk about use case, but there's also, it was the same users looking at the same information, so we engage with the business users. >> I wonder if we can talk about the different roles. You hear about the data scientists obviously, the data engineer, there might be a data quality professional involved, there's certainly the application developer. These guys may or may not even be in IT. And then you got a DVA. Then you may have somebody who's statistician. They might sit in the line of business. Am I overcomplicating it? Do larger organizations have these different roles? And how do you help bring them together? >> I'd say that those roles are still influx in the industry. Sometimes they sit on IT's legs, sometimes they sit in the business. I think there's a lot of movement happening it's not a consistent definition of those different roles. So I think it comes down to different functions. Sometimes you find those functions happening within different places in the company. So stewardship and governance may happen on the IT side, it might happen on the business side, and it's almost a maturity scale of how involved the two sides are within that. So we play with all of those different groups so it's sometimes hard to narrow down exactly who it is. But generally it's on the consumptions side whether it's the analyst or data scientists, and there's definitely a crossover between the two groups, moving up towards the governance and stewardship that wants to enable those users or document curing the data for them all the way to the IT data engineers that operationalize a lot of the work that the data scientists and analysts might be hypothesizing and working with in their research. >> And you sell to all of those roles? Who's your primary user constituency, or advocate? >> We sell both to the analytics groups as well as governance and they often merge together. But we tend to talk to all of those constituencies throughout a sales cycle. >> And how prominent in your customer base do you see that the role of the Chief Data Officer? Is it only reconfined within regulated industries? Does he seep into non-regulated industries? >> I'd say for us, it seeps with non-regulated industries. >> What percent of the customers, for instance have, just anecdotally, not even customers, just people that you talk to, have a Chief Data Officer? Formal Chief Data Officer? >> I'd say probably about 60 to 70 percent. >> That high? >> Yeah, same for us. In regulated industries (mumbles). I think they play a role. The real advantage a Chief Data and Analytical Officer, it's data and analytics, and they have to look at governance. Governance could be for regulation, because you have to, you've got governance policy, which data can be combined with which data, there is a lot. And you need to add that. But then, even if you are less regulated, you need to know what data is available, and what data is (mumbles). So you have this requirement as well. We see them a lot. We are more and more powerful, I would say in the enterprise where they are able to collaborate with the business to enable the business. >> Thanks so much for coming on the Cube, I really appreciate it. Congratulations on your partnership. Final word I'll give you guys before we end the segment. Share a story, obviously you guys have a unique partnership, you've been in the business for awhile, breaking into the business with Alation. Hot startups. What observations out there that people should know about that might not be known in this data world. Obviously there's a lot of false premises out there on what the industry may or may not be, but there's a lot of certainly a sea change happening. You see AI, it gives a mental model for people, Eugene Learning, Autonomous Vehicles, Smart Cities, some amazing, kind of magical things going on. But for the basic business out there, they're struggling. And there's a lot of opportunities if they get it right, what thing, observation, data, pattern you're seeing that people should know about that may not be known? It could be something anecdotal or something specific. >> You go first. (laughs) >> So maybe there will be surprising, but like Kaiser is a big customer of us. And you know Kaiser in California in the US. They have hundreds or thousands of hospitals. And surprisingly, some of the supply chain people where I've been working for years, trying to analyze, optimizing the relationship with their suppliers. Typically they would buy a staple gun without staples. Stupid. But they see that happening over and over with many products. They were never able to sell these, because why? There will be one product that have to go to IT, they have to work, it would take two months and there's another supplier, new products. So how to know- >> John: They're chasing their tail! >> Yeah. It's not super excited, they are now to do that in a couple of hours. So for them, they are able, by going to the data lakes, see what data, see how this hospital is buying, they were not able to do it. So there is nothing magical here, it's just giving access to the data who know the data best, the analyst. >> So your point is don't underestimate the innovation, as small as it may seem, or inconsequential, could have huge impacts. >> The innovation goes with the process to be more efficient with the data, not so much building new products, just basically being good at what you do, so then you can focus on the value you bring to the company. >> GianThomas what's your thoughts? >> So it's sort of related. I would actually say something we've seen pretty often is companies, all sizes, are all struggling with very similar, similar problems in the data space specifically so it's not a big companies have it all figured out, small companies are behind trying to catch up, and small companies aren't necessarily super agile and aren't able to change at the drop of a hat. So it's a journey. It's a journey and it's understanding what your problems are with the data in the company and it's about figuring out what works best for your solution, or for your problems. And understanding how that impacts everyone in the business. So it's really a learning process to understand what's going- >> What are your friends who aren't in the tech business say to you? Hey, what's this data thing? How do you explain it? The fundamental shift, how do you explain it? What do you say to them? >> I'm more and more getting people that already have an idea of what this data thing is. Which five years ago was not the case. Five years ago, it was oh, what's data? Tell me more about that? Why do you need to know about what's in these databases? Now, they actually get why that's important. So it's becoming a concept that everyone understands. Now it's just a matter of moving its practice and how that actually works. >> Operationalizing it, all the things you're talking about. Guys, thanks so much for bringing the insights. We wrangled it here on the Cube. Live. Congratulations to Trifecta and Alation. Great startups, you guys are doing great. Good to see you guys successful again and rising tide floats all boats in this open source world we're living in and we're bringing you more coverage here at DataWowrks 2017, I'm John Furrier with Dave Vellante. Stay with us, more great content coming after this short break. (upbeat music)

>>Okay. We're back here, live in New York city for big data week. This is siliconangle.tvs, exclusive coverage of Hadoop world strata plus Hadoop world big event, a big data week. And we just wrote a blog post on siliconangle.com calling this the south by Southwest for data geeks and, and, um, it's my prediction that this is going to turn into a, quite the geek Fest. Uh, obviously the crowd here is enormous packed and an amazing event. And, uh, we're excited. This is siliconangle.com. I'm the founder John ferry. I'm joined by cohost update >>Volante of Wiki bond.org, where people go for free research and peers collaborate to solve problems. And we're here with Jack Norris. Who's the vice president of market marketing at map are a company that we've been tracking for quite some time. Jack, welcome back to the cube. Thank you, Dave. I'm going to hand it to you. You know, we met quite a while ago now. It was well over a year ago and we were pushing at you guys and saying, well, you know, open source and nice look, we're solving problems for customers. We got the right model. We think, you know, this is, this is our strategy. We're sticking to it. Watch what happens. And like I said, I have to hand it to you. You guys are really have some great traction in the market and you're doing what you said. And so congratulations on that. I know you've got a lot more work to do, but >>Yeah, and actually the, the topic of openness is when it's, it's pretty interesting. Um, and, uh, you know, if you look at the different options out there, all of them are combining open source with some proprietary. Uh, now in the case of some distributions, it's very small, like an ODBC driver with a proprietary, um, driver. Um, but I think it represents that that any solution combining to make it more open is, is important. So what we've done is make innovations, but what we've made those innovations we've opened up and provided API. It's like NFS for standard access, like rest, like, uh, ODBC drivers, et cetera. >>So, so it's a spectrum. I mean, actually we were at Oracle open world a few weeks ago and you listen to Larry Ellison, talk about the Oracle public cloud mix of actually a very strong case that it's open. You can move data, it's all Java. So it's all about standards. Yeah. And, uh, yeah, it from an opposite, but it was really all about the business value. That's, that's what the bottom line is. So, uh, we had your CEO, John Schroeder on yesterday. Uh, John and I both were very impressed with, um, essentially what he described as your philosophy of we, we not as a product when we have, we have customers when we announce that product and, um, you know, that's impressive, >>Is that what he was also given some good feedback that startup entrepreneurs out there who are obviously a lot of action going on with the startup community. And he's basically said the same thing, get customers. Yeah. And that's it, that's all and use your tech, but don't be so locked into the tech, get the cutters, understand the needs and then deliver that. So you guys have done great. And, uh, I want to talk about the, the show here. Okay. Because, uh, you guys are, um, have a big booth and big presence here at the show. What, what did you guys are learning? I'll say how's the positioning, how's the new news hitting. Give us a quick update. So, >>Uh, a lot of news, uh, first started, uh, on Tuesday where we announced the M seven edition. And, uh, yeah, I brought a demo here for me, uh, for you all. Uh, because the, the big thing about M seven is what we don't have. So, uh, w we're not demoing Regents servers, we're not demoing compactions, uh, we're not demoing a lot of, uh, manual administration, uh, administrative tasks. So what that really means is that we took this stack. And if you look at HBase HBase today has about half of dupe users, uh, adopting HBase. So it's a lot of momentum in the market, uh, and, you know, use for everything from real-time analytics to kind of lightweight LTP processing. But it's an infrastructure that sits on top of a JVM that stores it's data in the Hadoop distributed file system that sits on a JVM that stores its data in a Linux file system that writes to disk. >>And so a lot of the complexity is that stack. And so as an administrator, you have to worry about how data gets permit, uh, uh, you know, kind of basically written across that. And you've got region servers to keep up, uh, when you're doing kind of rights, you have things called compactions, which increased response time. So it's, uh, it's a complex environment and we've spent quite a bit of time in, in collapsing that infrastructure and with the M seven edition, you've got files and tables together in the same layer writing directly to disc. So there's no region servers, uh, there's no compactions to deal with. There's no pre splitting of tables and trying to do manual merges. It just makes it much, much simpler. >>Let's talk about some of your customers in terms of, um, the profile of these guys are, uh, I'm assuming and correct me if I'm wrong, that you're not selling to the tire kickers. You're selling to the guys who actually have some experience with, with a dupe and have run into some of the limitations and you come in and say, Hey, we can solve some of those problems. Is that, is that, is that right? Can you talk about that a little bit >>Characterization? I think part of it is when you're in the evaluation process and when you first hear about Hadoop, it's kind of like the Gartner hype curve, right. And, uh, you know, this stuff, it does everything. And of course you got data protection, cause you've got things replicated across the cluster. And, uh, of course you've got scalability because you can just add nodes and so forth. Well, once you start using it, you realize that yes, I've got data replicated across the cluster, but if I accidentally delete something or if I've got some corruption that's replicated across the cluster too. So things like snapshots are really important. So you can return to, you know, what was it, five minutes before, uh, you know, performance where you can get the most out of your hardware, um, you know, ease of administration where I can cut this up into, into logical volumes and, and have policies at that whole level instead of at an individual file. >>So there's a, there's a bunch of features that really resonate with users after they've had some experience. And those tend to be our, um, you know, our, our kind of key customers. There's a, there's another phase two, which is when you're testing Hadoop, you're looking at, what's possible with this platform. What, what type of analytics can I do when you go into production? Now, all of a sudden you're looking at how does this fit in with my SLS? How does this fit in with my data protection, uh, policies, you know, how do I integrate with my different data sources? And can I leverage existing code? You know, we had one customer, um, you know, a large kind of a systems integrator for the federal government. They have a million lines of code that they were told to rewrite, to run with other distributions that they could use just out of the box with Matt BARR. >>So, um, let's talk about some of those customers. Can you name some names and get >>Sure. So, um, actually I'll, I'll, I'll talk with, uh, we had a keynote today and, uh, we had this beautiful customer video. They've had to cut because of times it's running in our booth and it's screaming on our website. And I think we've got to, uh, actually some of the bumper here, we kind of inserted. So, um, but I want to shout out to those because they ended up in the cutting room floor running it here. Yeah. So one was Rubicon project and, um, they're, they're an interesting company. They're a real-time advertising platform at auction network. They recently passed a Google in terms of number one ad reach as mentioned by comScore, uh, and a lot of press on that. Um, I particularly liked the headline that mentioned those three companies because it was measured by comScore and comScore's customer to map our customer. And Google's a key partner. >>And, uh, yesterday we announced a world record for the Hadoop pterosaur running on, running on Google. So, um, M seven for Rubicon, it allows them to address and replace different point solutions that were running alongside of Hadoop. And, uh, you know, it simplifies their, their potentially simplifies their architecture because now they have more things done with a single platform, increases performance, simplifies administration. Um, another customer is ancestry.com who, uh, you know, maybe you've seen their ads or heard, uh, some of their radio shots. Um, they're they do a tremendous amount of, of data processing to help family services and genealogy and figure out, you know, family backgrounds. One of the things they do is, is DNA testing. Uh, so for an internet service to do that, advanced technology is pretty impressive. And, uh, you know, you send them it's $99, I believe, and they'll send you a DNA kit spit in the tube, you send it back and then they process that and match and give you insights into your family background. So for them simplifying HBase meant additional performance, so they could do matches faster and really simplified administration. Uh, so, you know, and, and Melinda Graham's words, uh, you know, it's simpler because they're just not there. Those, those components >>Jack, I want to ask you about enterprise grade had duped because, um, um, and then, uh, Ted Dunning, because he was, he was mentioned by Tim SDS on his keynote speech. So, so you have some rockstars stars in the company. I was in his management team. We had your CEO when we've interviewed MC Sri vis and Google IO, and we were on a panel together. So as to know your team solid team, uh, so let's talk about, uh, Ted in a minute, but I want to ask you about the enterprise grade Hadoop conversation. What does that mean now? I mean, obviously you guys were very successful at first. Again, we were skeptics at first, but now your traction and your performance has proven this is a market for that kind of platform. What does that mean now in this, uh, at this event today, as this is evolving as Hadoop ecosystem is not just Hadoop anymore. It's other things. Yeah, >>There's, there's, there's three dimensions to enterprise grade. Um, the first is, is ease of use and ease of use from an administrator standpoint, how easy does it integrate into an existing environment? How easy does it, does it fit into my, my it policies? You know, do you run in a lights out data center? Does the Hadoop distribution fit into that? So that's, that's one whole dimension. Um, a key to that is, is, you know, complete NFS support. So it functions like, uh, you know, like standard storage. Uh, a second dimension is undependability reliability. So it's not just, you know, do you have a checkbox ha feature it's do you have automated stateful fail over? Do you have self healing? Can you handle multiple, uh, failures and, and, you know, automated recovery. So, you know, in a lights out data center, can you actually go there once a week? Uh, and then just, you know, replace drives. And a great example of that is one of our customers had a test cluster with, with Matt BARR. It was a POC went on and did other things. They had a power field, they came back a week later and the cluster was up and running and they hadn't done any manual tasks there. And they were, they were just blown away to the recovery process for the other distributions, a long laundry list of, >>So I've got to ask you, I got to ask you this, the third >>One, what's the third one, third one is performance and performance is, is, you know, kind of Ross' speed. It's also, how do you leverage the infrastructure? Can you take advantage of, of the network infrastructure, multiple Knicks? Can you take advantage of heterogeneous hardware? Can you mix and match for different workloads? And it's really about sharing a cluster for different use cases and, and different users. And there's a lot of features there. It's not just raw >>The existing it infrastructure policies that whole, the whole, what happens when something goes wrong. Can you automate that? And then, >>And it's easy to be dependable, fast, and speed the same thing, making HBase, uh, easy, dependable, fast with themselves. >>So the talk of the show right now, he had the keynote this morning is that map. Our marketing has dropped the big data term and going with data Kozum. Is that true? Is that true? So, Joe, Hellerstein just had a tweet, Joe, um, famous, uh, Cal Berkeley professor, computer science professor now is CEO of a startup. Um, what's the industry trifecta they're doing, and he had a good couple of epic tweets this week. So shout out to Joe Hellerstein, but Joel Hellison's tweet that says map our marketing has decided to drop the term big data and go with data Kozum with a shout out to George Gilder. So I'm kind of like middle intellectual kind of humor. So w w w what's what's your response to that? Is it true? What's happening? What is your, the embargo, the VP of marketing? >>Well, if you look at the big data term, I think, you know, there's a lot of big data washing going on where, um, you know, architectures that have been out there for 30 years or, you know, all about big data. Uh, so I think there's a, uh, there's the need for a more descriptive term. Um, the, the purpose of data Kozum was not to try to coin something or try to, you know, change a big data label. It was just to get people to take a step back and think, and to realize that we are in a massive paradigm shift. And, you know, with a shout out to George Gilder, acknowledging, you know, he recognized what the impact of, of making available compute, uh, meant he recognized with Telekom what bandwidth would mean. And if you look at the combination of we've got all this, this, uh, compute efficiency and bandwidth, now data them is, is basically taking those resources and unleashing it and changing the way we do things. >>And, um, I think, I think one of the ways to look at that is the new things that will be possible. And there's been a lot of focus on, you know, SQL interfaces on top of, of Hadoop, which are important. But I think some of the more interesting use cases are taking this machine J generated data that's being produced very, very rapidly and having automated operational analytics that can respond in a very fast time to change how you do business, either, how you're communicating with customers, um, how you're responding to two different, uh, uh, risk factors in the environment for fraud, et cetera, or, uh, just increasing and improving, um, uh, your response time to kind of cost events. We met earlier called >>Actionable insight. Then he said, assigning intent, you be able to respond. It's interesting that you talk about that George Gilder, cause we like to kind of riff and get into the concept abstract concepts, but he also was very big in supply side economics. And so if you look at the business value conversation, one of things we pointed out, uh, yesterday and this morning, so opening, um, review was, you know, the, the top conversations, insight and analytics, you know, as a killer app right now, the app market has not developed. And that's why we like companies like continuity and what you guys are doing under the hood is being worked on right at many levels, performance units of those three things, but analytics is a no brainer insight, but the other one's business value. So when you look at that kind of data, Kozum, I can see where you're going with that. >>Um, and that's kind of what people want, because it's not so much like I'm Republican because he's Republican George Gilder and he bought American spectator. Everyone knows that. So, so obviously he's a Republican, but politics aside, the business side of what big data is implementing is massive. Now that I guess that's a Republican concept. Um, but not really. I mean, businesses is, is, uh, all parties. So relative to data caused them. I mean, no one talks about e-business anymore. We talking to IBM at the IBM conference and they were saying, Hey, that was a great marketing campaign, but no one says, Hey, uh, you and eat business today. So we think that big data is going to have the same effect, which is, Hey, are you, do you have big data? No, it's just assumed. Yeah. So that's what you're basically trying to establish that it's not just about big. >>Yeah. Let me give you one small example, um, from a business value standpoint and, uh, Ted Dunning, you mentioned Ted earlier, chief application architect, um, and one of the coauthors of, of, uh, the book hoot, which deals with machine learning, uh, he dealt with one of our large financial services, uh, companies, and, uh, you know, one of the techniques on Hadoop is, is clustering, uh, you know, K nearest neighbors, uh, you know, different algorithms. And they looked at a particular process and they sped up that process by 30,000 times. So there's a blog post, uh, that's on our website. You can find out additional information on that. And I, >>There's one >>Point on this one point, but I think, you know, to your point about business value and you know, what does data Kozum really mean? That's an incredible speed up, uh, in terms of, of performance and it changes how companies can react in real time. It changes how they can do pattern recognition. And Google did a really interesting paper called the unreasonable effectiveness of data. And in there they say simple algorithms on big data, on massive amounts of data, beat a complex model every time. And so I think what we'll see is a movement away from data sampling and trying to do an 80 20 to looking at all your data and identifying where are the exceptions that we want to increase because there, you know, revenue exceptions or that we want to address because it's a cost or a fraud. >>Well, that's what I, I would give a shout out to, uh, to the guys that digital reasoning Tim asked he's plugged, uh, Ted. It was idolized him in terms of his work. Obviously his work is awesome, but two, he brought up this concept of understanding gap and he showed an interesting chart in his keynote, which was the date explosion, you know, it's up and, you know, straight up, right. It's massive amount of data, 64% unstructured by his calculation. Then he showed out a flat line called attention. So as data's been exploding over time, going up attention mean user attention is flat with some uptick maybe, but so users and humans, they can't expand their mind fast enough. So machine learning technologies have to bridge that gap. That's analytics, that's insight. >>Yeah. There's a big conversation now going on about more data, better models, people trying to squint through some of the comments that Google made and say, all right, does that mean we just throw out >>The models and data trumps algorithms, data >>Trumps algorithms, but the question I have is do you think, and your customer is talking about, okay, well now they have more data. Can I actually develop better algorithms that are simpler? And is it a virtuous cycle? >>Yeah, it's I, I think, I mean, uh, there are there's, there are a lot of debate here, a lot of information, but I think one of the, one of the interesting things is given that compute cycles, given the, you know, kind of that compute efficiency that we have and given the bandwidth, you can take a model and then iterate very quickly on it and kind of arrive at, at insight. And in the past, it was just that amount of data in that amount of time to process. Okay. That could take you 40 days to get to the point where you can do now in hours. Right. >>Right. So, I mean, the great example is fraud detection, right? So we used the sample six months later, Hey, your credit card might've been hacked. And now it's, you know, you got a phone call, you know, or you can't use your credit card or whatever it is. And so, uh, but there's still a lot of use cases where, you know, whether is an example where modeling and better modeling would be very helpful. Uh, excellent. So, um, so Dana custom, are you planning other marketing initiatives around that? Or is this sort of tongue in cheek fun? Throw it out there. A little red meat into the chum in the waters is, >>You know, what really motivated us was, um, you know, the cubes here talking, you know, for the whole day, what could we possibly do to help give them a topic of conversation? >>Okay. Data cosmos. Now of course, we found that on our proprietary HBase tools, Jack Norris, thanks for coming in. We appreciate your support. You guys have been great. We've been following you and continue to follow. You've been a great support of the cube. Want to thank you personally, while we're here. Uh, Matt BARR has been generous underwriter supportive of our great independent editorial. We want to recognize you guys, thanks for your support. And we continue to look forward to watching you guys grow and kick ass. So thanks for all your support. And we'll be right back with our next guest after this short break. >>Thank you. >>10 years ago, the video news business believed the internet was a fat. The science is settled. We all know the internet is here to stay bubbles and busts come and go. But the industry deserves a news team that goes the distance coming up on social angle are some interesting new metrics for measuring the worth of a customer on the web. 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