>> fly from San Francisco. It's the Cube covering Google Cloud next nineteen Tio by Google Cloud and its ecosystem partners. >> Welcome back, everyone live coverage here in San Francisco for the Cube, Google Cloud next twenty nineteen to show around Cloud, Google Cloud, I'm John Forest Do Minimum and Dave along. We've been here all week, three days of wall to wall coverage here on the floor with all the exhibitors. Write the mean all the action we've talked to all the thought leaders, Google executives, entrepreneurs, experts are in the cloud and around the ecosystem. Dave's stew wrapping up the wrap up segment. Kind of can I put the show to rest and look to next year and possibly Google summits. There's one in New York and some other shows we're looking to also cover. But if you look encapsulate the show, I want to get your guys reaction, too. What the main themes have been, we're seeing obviously anthems was the big news. That's the big deal. That's their platform. They want to bring all the connective tissue around data security and really on prim hybrid cloud multi cloud application modernization. Clearly, during my open source and enterprise developers, plus the ability to hybrid and multi cloud stew. Your thoughts on the show. >> Yeah. So, John, you know, when I first saw Antos, I was like, Well, this is CSP that they announced last year We were excited about that talk about things like Azure Stack and eight of us Outpost. But the more I learn about it, the more I understand it. It's more than just kind of g k e and a little bit of packaging here, Eric for David. I just interviewed a Google fellow and, you know, you expect the the Google Fellow to really be able to articulate, You know, the history of Google and the distributor architect doing is like we're going to enable cloud native. Of course, we always had that in the Google Cloud, but now we're going to make that easier for you to do that in your own environment. So when you're thinking about modernizing your applications, you know, I was a little bit tough on Google when I said, Oh, I hear a lot about lift and shift. Well, most customers can't lifted, shifted, not change, because then I'LL pull it back. It's too expensive, but if I could modernize wherever it makes the most sense. I talked to some customers here that said, Look, I need to kick the team and get it into the cloud And then I could modernize and start falling apart. But for someone customers, I can't move that. And they need to modernize it here and that Antos is the key enabler and therefore it's a good message, its extension of what they done with Cuba. Netease. That's a lot of other pieces here. But you know, I'm pretty impressed. >> They want to get your thoughts is one of things I'm seeing and, you know, in sports they wanna team, plays a game and wins. They call it a statement game. I think Google Cloud next twenty nineteen is a statement by Google saying, We're into the enterprise. We're not goingto waiver. We got hired Thomas Curry and mid savory. They're going to keep all the great talent. No one's believing. It's not like a new regime. Change came in. They're pivoting. They knows there's no pivot here. They put a stake in the ground saying we are going to invest in the clouds soon. DARPA Kai, the CEO of Google said that on stage of day one, they're clearly putting all the window dressing around enterprise with all the great phrases that we love. Digital transformation, data centric architecture, multi cloud hybrid monitors that applications They're invested, Dave. They are in it to play. They recognize that they're not gonna win right away because it's a long game. So Google clearly is playing the cards properly. They're saying, Look, if we're going to bring a lot of the table and this long time table, but we're in it to play and we're going to play well when invest. >> Yeah, I think it took a while for me to get there Stew, too. He is. I heard a lot about what Right we do get a global distributed infrastructure or we're doing the applications for digital transformation. We got industry specific solutions. Is what way d'Oh. Okay. Great. And I heard a lot of you know differentiators are unique value proposition. So, for civil, what I would have liked to hear it right up front was okay. We know that eighty percent of your workloads are on Prem. Well, guess what, and we're investing in scale and all that stuff, but We're the best at cloud native and and we're going to take and we have the tools and expertise. We're gonna bring those to you on your premises and show you how to get there. And then when you're ready, come to the cloud. If you're never ready, that's fine. But we're going to earn the right for your future business. Hey said that Stead that >> right way, the things we're wondering your business. But I don't think they can yet say were the best that cloud native and that I think that's that's still good self awareness studio for Google. >> I think they could say it now. Maybe it's debatable. >> I would debate that I do not think that Google is the best cloud native cloud at this point. I don't think they have the breath and depth Amazon has, but I don't think that that's the hard core stick in the ground. Because Cloud native is early cnc F, they're investing heavily in open source is a big bet that they're talking about. They got a lot more work to do but cloud needed. Still, it's still early because you said the workloads is still on premise for most of the enterprises, so we got plenty of time. The point is, if they had overplayed that card, I would have been more cautious. >> Well, I mean, Okay, fine, huh? Let's talk talk about that a little bit because it's new. It's Would you? Would you disagree that internally, Google's got the most sophisticated, the best cloud in the world internally, globally for Google. And they make that comment when they make that claim, right? That start there, we get the best cloud in the world. Yeah, >> well, I think it's got a great cloud, >> too. Okay, so there's stuff on there. I mean, they've got least got some credibility there, so I would have come from that position straight now. The other criticism I heard was where the numbers. Now, that doesn't bother me so much. How long did it take Amazon to show us the numbers? Nine years? I think so. Good. We'LL get there, it's clear it's growing. You look around here. There's what thirty thirty five thousand people don't know what was there last year. Twenty. Twenty five thousand. It's growing, it's growing nicely and the quality of the people is good. >> Here's what I'd say about Google Cloud Steward? Let's get your reaction. Sudhir has Bay said this. He's the director product. Mentioning about cloud fusion, he said This from a customer quote. Google's cloud is like an awesome highway, but I can't get my car on the road. So that's the on ramp. >> I can't get by giving car. Okay, so so this note about you Look at the >> technology from Spanner Cooper duties, which was founded inside Google. And they did that right. Big queries. Amazing. They have freaking amazing tech because they had to do it for Google. So I think that is a key strategy. And I, like other clouds that have come in and then died away, didn't have a lot of tech chops. So Cultural Shift is one of the big teams, but on ramping, getting people on board and the bed another source. I think there's a gestation period that's gives Google some time. I don't think they gotta have it overnight there some table stakes, but they're there checking the boxes just kind of grind it out. >> I mean, look, the critique has been for years is you know, Google's too smart for all of us. you know, way have love reading the papers and were really impressed with the technology. But the term you heard over and over again this week, we're going to meet customers where they are. And I I almost failed. They dialled it down a little too much here because I didn't have anything that I'm like. Wow, blown away. Like, you know, they had er's up on stage and it's like I'm used to seeing him flying out of a plane with a Google glass on his head. >> I was started by the way that was Google. I o like, you're >> gay. But, you know, you know, one of that's what you expect from a googol is you know, some of those pieces and there wasn't a G wow amazing moment for me, but the messaging solid, they absolutely you know, understanding or solving some real customer problems today and, you know, solid >> well and one hundred percent of the cloud providers now have a coherent and explainable hybrid on Prem strategy. You know, frankly, it's about time. I mean, they were denying that for a long time, and I think it's clear that's where the business is >> well to me. The big criteria on the cloud game is Do they have the global footprint? They do. Do they have the software at scale Check? Do they have the connective tissue to bring these disparity opportunity data services together Check working on it, continue to improve. And are they on the philosophy side of things? Meaning one of things that I am made Amazon really great. Wass they from day one. We're a P I center who will always has been part of web services. So they have that DNA. I think apogee is going to be the secret little dark horse. And all this is going to tell Signe because as a p, I become programmable. You saw Sisko of'em wear on stage. Can they build on ecosystem? Can they work with multiple vendors? Because the fact is, from our data and we've been reporting on this on silicon angle and Wiki bomb is that big enterprises and governments, whether it's a d, o. D. Or a big bank, are gonna have hundreds of cloud projects, hundreds of workloads that's going to require unique clouds selection criteria because you cannot separate real time data from software, and that's just the facts of the databases are moving all over the place. If I gotta work Lodi, any data? I gotta be agile with the data, but I then need a data plane to connect across other workload. So workload conversation, I don't think was front and center enough where workloads are for the key criteria. >> And still some of the message on where Google fits in that hybrid and multi cloud world is a little bit muddy to me. So how did they get, you know, on those in your data center? Well, it's a deep partnership with V m where, uh, you know, I heard some people here. It's like, Oh, well, the current Amazon VM wear deal, you know, is like up for renewal soon. It's like I don't see Veum Where an Amazon separating that Latino way. People engineering partnerships. We've heard directly from Andy Jazz sees talked about on the Cube how important that relationship is. S O Veum was going to play across all the cloud environment. But you know, where does Google, you know, really make their money? They're going to partner with all the open source companies. And you know, you're going to own your data. We're going to make sure the prophecies there. So is Dave Said the numbers and the business of how Google Khun start slow scaling and really growing the enterprise business beyond, you know, G sweets now, part of it. And we saw some of the android for enterprise, and they have lots of pieces, but the cloud revenue gets a little bit muddy like a Microsoft. So, you know, from the cloud piece itself, I'm not sure where you know they start gaining on a Microsoft or an Amazon today. >> Well, I think that they could gain ground, take territories. That said on on Day one, Jennifer Linds, demo of no code modification, migration of workloads. If that actually happens, that's going to be a critical piece of the pie that's going to move. Move the needle very quickly for at Google. But I >> want to get you >> guys take on surprises. What surprised you here at the show? What was something that you didn't expect happen? That was a surprise on a good way. To me, the big surprise is that the word customer was used a lot more here than ever before. Customer is the key to success in the enterprise, listening to customer and customer choice. That's the playbook from Amazon. You don't hear Andy Jassy or any other executive Amazon go three words without saying the word customer. If you had a tag cloud and be like customers, the biggest font here we've heard customer choice. That's been a big one for me. >> Surprises. I was going to say when you were asking that question to get to me. It was customer related as well. You know clearly when you in Amazon show it's just customer. Just get inundated with a cool injection of customers. It's very impressive, but you don't have that scale here. However, What did see is a lot of Fortune. One thousand company's senior people were here. Yeah, still kicking the tires but learning. And I think that usually leads to something. So I think Google's developing a lot of pipeline at this show that I think next year is going to translate. We had conversations John with companies that we can't mention on air, but they are seriously substantively looking at moving workloads into Google's Cloud Number one. Number two is if you look around here, Deloitte, Accenture at toes. You know, some of the biggest. I'd like to see more of those global s eyes, and I think you will. And that's where you're going to really start to see customers. >> Dave took the customer. I'll say partner. So we said in one of our analysis segments, that logo slides Good. But, you know, compare itto Microsoft or Amazon. It needs to quadruple where it is today. But in the conversations that I had from startups through some of those big logo's on here, partnering with Google is good for them and they're excited by it. And that's not necessarily the clay case for every one of the big cloud providers out there. >> All right, so a lot of multi cloud talk. I've said multi clouds all the rage, but it's really more a symptom of sort of multi vendor people going best of breed with different departments. Big news last night on Jet I John, I want to get your take. Google really wasn't I don't think ever in the running, but certainly, you know Amazon was the lead Oracle, IBM, Microsoft share the news in your analysis of that news. >> Well, yesterday there was news that the Department of Defense, this Jet I contract joint defense initiative that's going on joining the Price Defense Initiative system. The military cloud ten billion dollar contract was under a lot of It's the biggest story in Tech and DC in generations. It's the confluence of procurement being outdated. Clouds selection, one soul cloud for that workload, multi cloud across in the department and a lot of lost business, potentially for Oracle in IBM. So Amazon, Microsoft, Amazon, Webster's, Microsoft, Oracle and IBM. We're all fighting for this business. The incumbents IBM and Oracle. We're potentially at risk billions of dollars. So it's been a lot of dirty pool, so to speak, a lot of dirty politics, a lot of dirty smear campaigns going on, from Oracle to to Amazon to try to discredit them. So the D. O d. Oracle soothe d o d. Saying is unfair process conflict of interest? The D. O. D made a final selection. Amazon Web services and Microsoft are the final selections and basically kicking out Oracle and IBM at the process. So Oracle, IBM are out. Oracle's lawsuit's still pending that'LL probably be dismissed because Oracle tried three different times to claim conflict of interest. They tried to claim conflict of interest in. And where has three in my notes here July twenty eighteen, November twenty eighteen and April twenty nineteen. All three times competition has been not proven, and Oracle and IBM or out. The analysis here is, is that this proves what we've been saying on the Q and that is, is that you can have one cloud soul cloud for a workload. So the Department of Defense has hundreds of projects. But for the military project that ten billion dollar one Amazon or Microsoft, probably the Amazon to the front runner can serve that cloud. And that's the best architecture. That means that Microsoft will probably win the eight billion dollar contract of the D. O. E s contract for collaboration again. Soul Cloud Soul workload. This is the trendy. My analysis is that Oracle on IBM, mainly Oracle, knew that they were going to lose. They tried to do whatever it takes to kill the deal. And now the D. O. D. Has brought forward and their modernizing the application and all these lawsuits about procurement rules from nineteen eighty five all this trip wires, all these little nuances. This is a great win for the Department of Defense, and I think it is a tell sign for large enterprises because you could be multiple. You'd have multiple clouds, but you can have one cloud work on one workload. It could be a big monster workload like a ten billion dollar >> workload. >> There could be a small work. >> All the tech vendors want to eat it. The government trough, We know that. And so the why is this relevant? It's relevant to me because you're you're absolutely right for a particular set of workloads. Mission critical workloads, especially a single cloud, is going to be more cost effective, more secure, uh, higher availability, less complex. And that's really what the debate is here now is multi cloud gonna happen? Of course, for different workloads is going to be horses for courses. So multi cloud is a huge opportunity. Everybody's going after it stew uh, Google through its hat in the ring in a big way. We seem to have a couple of camps lining up and read. Had interesting, interesting leads in both camps. Kind of got the IBM redhead camp and of'em wear with now with Google Really interesting sort of chessboard matches going on? >> Yeah, absolutely. Every customer we talked to hear. There's no like, Oh, you know, I might be moving most of my stuff or even all of my stuff to the public cloud, but it is workload dependent, and that's how I'm choosing it. Google has some key strength. I took a little while to get the data and I and ML pieces that we know Google has some strength here. One of the questions I had coming into it Can they reclaim kind of that thought leadership space. I'd love to hear whether you guys think I think that was the case, but, you know, messaging point on good speed. You know T K has them talking to the Enterprise in a way that won't scare them away as to oh, geez, I'm not smart enough to work with Google so >> well, I think I think Google has to get enterprise compatible and they've been working really hard to do that, and they got it. Just grind it out. I said this on Tuesday. It's a grinding out game. They've got a got a fight to the trenches. We've got to get the check boxes, and this is what Amazon did that early on and helped them a lot. Google has been working hard, I think, their security angle with the from a device. I phoned the Android phone and onboard security at the edge is huge. I think data and Big Query and those kinds of on boarding tools is going to be a great accelerant. I think cloud code cloud Run Cloud build is a phenomenal construct. I think that's absolutely delivered Ella for friendly. If they can continue to serve the developer for the enterprise and make it easy to build and stand up applications that hit that sweet spot of the trend, which is the modernization of enterprise APS not develop, perhaps not like a startup started sort. Different styles are cloud born in the cloud enterprise that's gonna deal with legacy and all these compliance and all this risk. They could make that easy and make it Dev ops like That's a great check boxes. >> Just a quick note on that, because there was a lot of enterprise talk there. There's a nice group inside a Google, working with a lot of the startups, got to talk to a couple of the start up there, and Google's definitely company there looking to partner with. All >> right, guys, let's wrap this up. Google really leaning into the enterprise heavily. Obviously, they're not. They're not blinking. They're going to continue power forward thinking. I like the mojo they have here. They got a new CEO. We interviewed George Curry, and Thomas's brother Thomas couldn't make it on the Cube. He's super busy talking to customers were gonna get him on the cue soon, but you got a culture here. Google and the culture is innovation, and the cultures Dev ops. The culture's developed for the country's AP eyes D. That puts him in a good position, >> their thoughts. I mean, I've been saying for a decade I feel like a broken record. I said it so much. I stopped saying it that the marginal economics of the Cloud service providers who have scale are driving towards zero. In other words, the more volume they do, they're there. The cost of adding an extra customer goes down to zero, just like software. There's three companies in United States who have that scale Google, Amazon and Microsoft. Obviously some guys outside the U. S. And you look at the cap Ex numbers forty seven billion over the last three years by Google. Thirteen and a half billion year to date US data centers alone. It would take IBM three and a half years to spend that much on Affects Who take Oracle six years. Okay, they just do not have the marginal economics to compete. They'LL compete in other ways, but though these three are in it to win it this big market, they're trillion dollar market. There's enough room for each to carve out an opportunity and continue to grow for quite some time. Do >> and Google lining up their ecosystem of partners to help them get deep into the enterprise. Absolutely, There's good opportunity for Google to do a number of acquisitions. They have, you know, a big bank spend a lot of money not just on infrastructure, but all the partner engagements and definitely some acquisition to help them get there. Wouldn't be surprised if they, you know, made some nice acquisition to help them grow that enterprise. I am in a modern way way now that was mentioned to it was carrying twins could be back together, but sure, >> awesome stuff. Guys, I think my my final take is I've always said Google's the Dark Horse and the Cloud game. They don't have a lot of baggage like a lot of work to do, and they're they're working hard and they really bring in tech to the table that bringing that culture of innovation, they're there behind this. Opportunities for them to move the ball down the field in a big way. I think they can take territory and gain share quickly if global things follow the place. If those bets come home, this dark horse will be right up on number two really quickly. So great job. Wanna thank Google, Google's team Cool calms Team, Google's CMO and executive Thomas carrying for letting us come to the Cube. Bring the Cube here. Google's very co creation oriented. We appreciate the location. I want to thank Google one. Thanks to our sponsors about our sponsors, we wouldn't be here, so he city signal FX. We got net app. We got Saada. We got some great clients here supporting us. You, Fio. Thanks to our sponsors, they signal to the community they care and they support our programs. Our tenth year of Cube coverage at events one. Thank everyone for watching, listening, sharing hit us up on Twitter at Cube and also silken angle dot com. We now are adding on a new feature to our Cube, which is on silicon angle dot com special reports where we flow as many stories as it takes to get the truth out there. Get the story's right, of course. Used the cube and stream the data with you here on the Cube. We're here. Google Next in San Francisco. I'm John Faria student Min David Long. Thanks for watching.

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks Summit here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Rob Bearden. He is the CEO of Hortonworks. So thanks so much for coming on theCUBE again, Rob. >> Thank you for having us. >> So you just got off of the keynote on the main stage. The big theme is really about modern data architecture. So we're going to have this modern data architecture. What is it all about? How do you think about it? What's your approach? And how do you walk customers through this process? >> Well, there's a lot of moving parts in enabling a modern data architecture. One of the first steps is what we're trying to do is unlock the siloed transactional applications, and to get that data into a central architecture so you can get real time insights around the inclusive dataset. But what we're really trying to accomplish then within that modern data architecture is to bring all types of data whether it be real time streaming data, whether it be sensor data, IoT data, whether it be data that's coming from a connected core across the network, and to be able to bring all that data together in real time, and give the enterprise the ability to be able to take best in class action so that you get a very prescriptive outcome of what you want. So if we bring that data under management from point of origination and out on the edge, and then have the platforms that move that through its entire lifecycle, and that's our HDF platform, it gives the customer the ability to, after they capture it at the edge, move it, and then have the ability to process it as an event happens, a condition changes, various conditions come together, have the ability to process and take the exact action that you want to see performed against that, and then bring it to rest, and that's where our HDP platform comes into play where then all that data can be aggregated so you can have a holistic insight, and have real time interactions on that data. But then it then becomes about deploying those datasets and workloads on the tier that's most economically and architecturally pragmatic. So if that's on-prem, we make sure that we are architected for that on-prem deployment or private cloud or even across multiple public clouds simultaneously, and give the enterprise the ability to support each of those native environments. And so we think hybrid cloud architecture is really where the vast majority of our customers today and in the future, are going to want to be able to run and deploy their applications and workloads. And that's where our DataPlane Service Offering gives them the ability to have that hybrid architecture and the architectural latitude to move workloads and datasets across each tier transparently to what storage file format that they did or where that application is, and we provide all the tooling to match the complexity from doing that, and then we ensured that it has one common security framework, one common governance through its entire lifecycle, and one management platform to handle that entire lifecycle data. And that's the modern data architecture is to be able to bring all data under management, all types of data under management, and manage that in real time through its lifecycle til it comes at rest and deploy that across whatever architecture tier is most appropriate financially and from a performance on-cloud or prem. >> Rob, this morning at the keynote here in day one at DataWorks San Jose, you presented this whole architecture that you described in the context of what you call hybrid clouds to enable connected communities and with HDP, Hortonworks Data Platform 3.0 is one of the prime announcements, you brought containerization into the story. Could you connect those dots, containerization, connected communities, and HDP 3.0? >> Well, HDP 3.0 is really the foundation for enabling that hybrid architecture natively, and what's it done is it separated the storage from the compute, and so now we have the ability to deploy those workloads via a container strategy across whichever tier makes the most sense, and to move those application and datasets around, and to be able to leverage each tier in the deployment architectures that are most pragmatic. And then what that lets us do then is be able to bring all of the different data types, whether it be customer data, supply chain data, product data. So imagine as an industrial piece of equipment is, an airplane is flying from Atlanta, Georgia to London, and you want to be able to make sure you really understand how well is that each component performing, so that that plane is going to need service when it gets there, it doesn't miss the turnaround and leave 300 passengers stranded or delayed, right? Now with our Connected platform, we have the ability to take every piece of data from every component that's generated and see that in real time, and let the airlines make that real time. >> Delineate essentially. >> And ensure that we know every person that touched it and looked at that data through its entire lifecycle from the ground crew to the pilots to the operations team to the service. Folks on the ground to the reservation agents, and we can prove that if somehow that data has been breached, that we know exactly at what point it was breached and who did or didn't get to see it, and can prevent that because of the security models that we put in place. >> And that relates to compliance and mandates such as the Global Data Protection Regulation GDPR in the EU. At DataWorks Berlin a few months ago, you laid out, Hortonworks laid out, announced a new product called the Data Steward Studio to enable GDPR compliance. Can you give our listeners now who may not have been following the Berlin event a bit of an update on Data Steward Studio, how it relates to the whole data lineage, or set of requirements that you're describing, and then going forward what does Hortonworks's roadmap for supporting the full governance lifecycle for the Connected community, from data lineage through like model governance and so forth. Can you just connect a few dots that will be helpful? >> Absolutely. What's important certainly, driven by GDPR, is the requirement to be able to prove that you understand who's touched that data and who has not had access to it, and that you ensure that you're in compliance with the GDPR regulations which are significant, but essentially what they say is you have to protect the personal data and attributes of that data of the individual. And so what's very important is that you've got to be able to have the systems that not just secure the data, but understand who has the accessibility at any point in time that you've ever maintained that individual's data. And so it's not just about when you've had a transaction with that individual, but it's the rest of the history that you've kept or the multiple datasets that you may try to correlate to try to expand relationship with that customer, and you need to make sure that you can ensure not only that you've secured their data, but then you're protecting and governing who has access to it and when. And as importantly that you can prove in the event of a breach that you had control of that, and who did or did not access it, because if you can't prove any breach, that it was secure, and that no one breached it, who has or access to this not supposed to, you can be opened up for hundreds of thousands of dollars or even multiple millions of dollars of fines just because you can't prove that it was not accessed, and that's what the variety of our platforms, you mentioned Data Studio, is part of. DataPlane is one of the capabilities that gives us the ability. The core engine that does that is Atlas, and that's the open source governance platform that we developed through the community that really drives all the capabilities for governance that moves through each of our products, HDP, HDF, then of course, and DataPlane and Data Studio takes advantage of that and how it moves and replicates data and manages that process for us. >> One of the things that we were talking about before the cameras were rolling was this idea of data driven business models, how they are disrupting current contenders, new rivals coming on the scene all the time. Can you talk a little bit about what you're seeing and what are some of the most exciting and maybe also some of the most threatening things that you're seeing? >> Sure, in the traditional legacy enterprise, it's very procedural driven. You think about classic Encore ERP. It's worked very hard to have a very rigid, very structural procedural order to cash cycle that has not a great deal of flexibility. And it takes through a design process, it builds product, that then you sell product to a customer, and then you service that customer, and then you learn from that transaction different ways to automate or improve efficiencies in their supply chain. But it's very procedural, very linear. And in the new world of connected data models, you want to bring transparency and real time understanding and connectivity between the enterprise, the customer, the product, and the supply chain, and that you can take real time best in practice action. So for example you understand how well your product is performing. Is your customer using it correctly? Are they frustrated with that? Are they using it in the patterns and the frequency that they should be if they are going to expand their use and buy more, and if they're not, how do we engage in that cycle? How do we understand if they're going through a re-review and another buying of something similar that may not be with you for a different reason. And when we have real time visibility to our customer's interaction, understand our product's performance through its entire lifecycle, then we can bring real time efficiency with linking those together with our supply chain into the various relationships we have with our customers. To do that, it requires the modern data architecture, bringing data under management from the point it originates, whether it's from the product or the customer interacting with the company, or the customer interacting potentially with our ecosystem partners, mutual partners, and then letting the best in practice supply chain techniques, make sure that we're bringing the highest level of service and support to that entire lifecycle. And when we bring data under management, manage it through its lifecycle and have the historical view at rest, and leverage that across every tier, that's when we get these high velocity, deep transparency, and connectivity between each of the constituents in the value chain, and that's what our platforms give them the ability to do. >> Not only your platform, you guys have been in business now for I think seven years or so, and you shifted from being in the minds of many and including your own strategy from being the premier data at rest company in terms of the a Hadoop platform to being one of the premier data in motion companies. Is that really where you're going? To be more of a completely streaming focus, solution provider in a multi-cloud environment? And I hear a lot of Kafka in your story now that it's like, oh yeah, that's right, Hortonworks is big on Kafka. Can you give us just a quick sense of how you're making that shift towards low latency real time streaming, big data, or small data for that matter, with embedded analytics and machine learning? >> So, we have evolved from certainly being the leader in global data platforms with all the work that we do collaboratively, and in through the community, to make Hadoop an enterprise viable data platform that has the ability to run mission critical workloads and apps at scale, ensuring that it has all the enterprise facilities from security and governance and management. But you're right, we have expanded our footprint aggressively. And we saw the opportunity to actually create more value for our customers by giving them the ability to not wait til they bring data under management to gain an insight, because in that case, they're happened to be reactive post event post transaction. We want to give them the ability to shift their business model to being interactive, pre-event, pre-conditioned. The way to do that we learned was to be able to bring the data under management from the point of origination, and that's what we used MiNiFi and NiFi for, and then HDF, to move it through its lifecycle, and your point, we have the intellect, we have the insight, and then we have the ability then to process the best in class outcome based on what we know the variables are we're trying to solve for as that's happening. >> And there's the word, the phrase asset which of course is a transactional data paradigm plan, I hear that all over your story now in streaming. So, what you're saying is it's a completely enterprise-grade streaming environment from n to n for the new era of edge computing. Would that be a fair way of-- >> It's very much so. And our model and strategy has always been bring the other best in class engines for what they do well for their particular dataset. A couple of examples of that, one, you brought up Kafka, another is Spark. And they do what they do really well. But what we do is make sure that they fit inside an overall data architecture that then embodies their access to a much broader central dataset that goes from point of origination to point of rest on a whole central architecture, and then benefit from our security, governance, and operations model, being able to manage those engines. So what we're trying to do is eliminate the silos for our customers, and having siloed datasets that just do particular functions. We give them the ability to have an enterprise modern data architecture, we manage the things that bring that forward for the enterprise to have the modern data driven business models by bringing the governance, the security, the operations management, ensure that those workflows go from beginning to end seamlessly. >> Do you, go ahead. >> So I was just going to ask about the customer concerns. So here you are, you've now given them this ability to make these real time changes, what's sort of next? What's on their mind now and what do you see as the future of what you want to deliver next? >> First and foremost we got to make sure we get this right, and we really bring this modern data architecture forward, and make sure that we truly have the governance correct, the security models correct. One pane of glass to manage this. And really enable that hybrid data architecture, and let them leverage the cloud tier where it's architecturally and financially pragmatic to do it, and give them the ability to leg into a cloud architecture without risk of either being locked in or misunderstanding where the lines of demarcation of workloads or datasets are, and not getting the economies or efficiencies they should. And we solved that with DataPlane. So we're working very hard with the community, with our ecosystem and strategic partners to make sure that we're enabling the ability to bring each type of data from any source and deploy it across any tier with a common security, governance, and management framework. So then what's next is now that we have this high velocity of data through its entire lifecycle on one common set of platforms, then we can start enabling the modern applications to function. And we can go look back into some of the legacy technologies that are very procedural based and are dependent on a transaction or an event happening before they can run their logic to get an outcome because that grinds the customer in post world activity. We want to make sure that we're bringing that kind of, for example, supply chain functionality, to the modern data architecture, so that we can put real time inventory allocation based on the patterns that our customers go in either how they're using the product, or frustrations they've had, or success they've had. And we know through artificial intelligence and machine learning that there's a high probability not only they will buy or use or expand their consumption of whatever that they have of our product or service, but it will probably to these other things as well if we do those things. >> Predict the logic as opposed to procedural, yes, AI. >> And very much so. And so it'll be bringing those what's next will be the modern applications on top of this that become very predictive and enabler versus very procedural post to that post transaction. We're little ways downstream. That's looking out. >> That's next year's conference. >> That's probably next year's conference. >> Well, Rob, thank you so much for coming on theCUBE, it's always a pleasure to have you. >> Thank you both for having us, and thank you for being here, and enjoy the summit. >> We're excited. >> Thank you. >> We'll do. >> I'm Rebecca Knight for Jim Kobielus. We will have more from DataWorks Summit just after this. (upbeat music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCube. Covering DataWorks Summit 2018. Brought to you by Hortonworks. >> Welcome back to day two of theCube's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight along with my co-host James Kobielus. James, it's great to be here with you in the hosting seat again. >> Day two, yes. >> Exactly. So here we are, this conference, 2,100 attendees from 32 countries, 23 industries. It's a relatively big show. They do three of them during the year. One of the things that I really-- >> It's a well-established show too. I think this is like the 11th year since Yahoo started up the first Hadoop summit in 2008. >> Right, right. >> So it's an established event, yeah go. >> Exactly, exactly. But I really want to talk about Hortonworks the company. This is something that you had brought up in an analyst report before the show started and that was talking about Hortonworks' cash flow positivity for the first time. >> Which is good. >> Which is good, which is a positive sign and yet what are the prospects for this company's financial health? We're still not seeing really clear signs of robust financial growth. >> I think the signs are good for the simple reason they're making significant investments now to prepare for the future that's almost inevitable. And the future that's almost inevitable, and when I say the future, the 2020s, the decade that's coming. Most of their customers will shift more of their workloads, maybe not entirely yet, to public cloud environments for everything they're doing, AI, machine learning, deep learning. And clearly the beneficiaries of that trend will be the public cloud providers, all of whom are Hortonworks' partners and established partners, AWS, Microsoft with Azure, Google with, you know, Google Cloud Platform, IBM with IBM Cloud. Hortonworks, and this is... You know, their partnerships with these cloud providers go back several years so it's not a new initiative for them. They've seen the writing on the wall practically from the start of Hortonworks' founding in 2011 and they now need to go deeper towards making their solution portfolio capable of being deployable on-prem, in cloud, public clouds, and in various and sundry funky combinations called hybrid multi-clouds. Okay, so, they've been making those investments in those partnerships and in public cloud enabling the Hortonworks Data Platform. Here at this show, DataWorks 2018 here in San Jose, they've released the latest major version, HDP 3.0 of their core platform with a lot of significant enhancements related to things that their customers are increasingly doing-- >> Well I want to ask you about those enhancements. >> But also they have partnership announcements, the deep ones of integration and, you know, lift and shift of the Hortonworks portfolio of HDP with Hortonworks DataFlow and DataPlane Services, so that those solutions can operate transparently on those public cloud environments as the customers, as and when the customers choose to shift their workloads. 'Cause Hortonworks really... You know, like Scott Gnau yesterday, I mean just laid it on the line, they know that the more of the public cloud workloads will predominate now in this space. They're just making these speculative investments that they absolutely have to now to prepare the way. So I think this cost that they're incurring now to prepare their entire portfolio for that inevitable future is the right thing to do and that's probably why they still have not attained massive rock and rollin' positive cash flow yet but I think that they're preparing the way for them to do so in the coming decade. >> So their financial future is looking brighter and they're doing the right things. >> Yeah, yes. >> So now let's talk tech. And this is really where you want to be, Jim, I know you. >> Oh I get sleep now and I don't think about tech constantly. >> So as you've said, they're really doing a lot of emphasis now on their public cloud partnerships. >> Yes. >> But they've also launched several new products and upgrades to existing products, what are you seeing that excites you and that you think really will be potential game changers? >> You know, this is geeky but this is important 'cause it's at the very heart of Hortonworks Data Platform 3.0, containerization of more... When you're a data scientist, and you're building a machine learning model using data that's maintained, and is persisted, and processed within Hortonworks Data Platform or any other big data platform, you want the ability increasingly for developing machine learning, deep learning, AI in general, to take that application you might build while you're using TensorFlow models, that you build on HDP, they will containerize it in Docker and, you know, orchestrate it all through Kubernetes and all that wonderful stuff, and deploy it out, those AI, out to increasingly edge computing, mobile computing, embedded computing environments where, you know, the real venture capital mania's happening, things like autonomous vehicles, and you know, drones, and you name it. So the fact is that Hortonworks has made that in many ways the premier new feature of HDP 3.0 announced here this week at the show. That very much harmonizes with what their partners, where their partners are going with containerization of AI. IBM, one of their premier partners, very recently, like last month, I think it was, announced the latest version of IBM, what do they call it, IBM Cloud Private, which has embedded as a core feature containerization within that environment which is a prem-based environment of AI and so forth. The fact that Hortonworks continues to maintain close alignment with the capabilities that its public cloud partners are building to their respective portfolios is important. But also Hortonworks with its, they call it, you know, a single pane of glass, the DataPlane Services for metadata and monitoring and governance and compliance across this sprawling hybrid multi-cloud, these scenarios. The fact that they're continuing to make, in fact, really focusing on deep investments in that portfolio, so that when an IBM introduces or, AWS, whoever, introduces some new feature in their respective platforms, Hortonworks has the ability to, as it were, abstract above and beyond all of that so that the customer, the developer, and the data administrator, all they need to do, if they're a Hortonworks customer, is stay within the DataPlane Services and environment to be able to deploy with harmonized metadata and harmonized policies, and harmonized schemas and so forth and so on, and query optimization across these sprawling environments. So Hortonworks, I think, knows where their bread is buttered and it needs to stay on the DPS, DataPlane Services, side which is why a couple months ago in Berlin, Hortonworks made a, I think, the most significant announcement of the year for them and really for the industry, was that they announced the Data Steward Studio in Berlin. Tech really clearly was who addressed the GDPR mandate that was coming up but really did a stewardship as an end-to-end workflow for lots of, you know, core enterprise applications, absolutely essential. Data Steward Studio is a DataPlane Service that can operate across multi-cloud environments. Hortonworks is going to keep on, you know... They didn't have a DPS, DataPlane Services, announcements here in San Jose this week but you can best believe that next year at this time at this show, and in the interim they'll probably have a number of significant announcements to deepen that portfolio. Once again it's to grease the wheels towards a more purely public cloud future in which there will be Hortonworks DNA inside most of their customers' environments going forward. >> I want to ask you about themes of this year's conference. The thing is is that you were in Berlin at the last big Hortonworks DataWorks Summit. >> (speaks in foreign language) >> And really GDPR dominated the conversations because the new rules and regulations hadn't yet taken effect and companies were sort of bracing for what life was going to be like under GDPR. Now the rules are here, they're here to stay, and companies are really grappling with it, trying to understand the changes and how they can exist in this new regime. What would you say are the biggest themes... We're still talking about GDPR, of course, but what would you say are the bigger themes that are this week's conference? Is it scalability, is it... I mean, what would you say we're going, what do you think has dominated the conversations here? >> Well scalability is not the big theme this week though there are significant scalability announcements this week in the context of HDP 3.0, the ability to persist in a scale-out fashion across multi-cloud, billions of files. Storage efficiency is an important piece of the overall announcement with support for erasure coding, blah blah blah. That's not, you know, that's... Already, Hortonworks, like all of their cloud providers and other big data providers, provide very scalable environments for storage, workload management. That was not the hugest, buzzy theme in terms of the announcements this week. The buzz of course was HDP 3.0. Containerization, that's important, but you know, we just came out of the day two keynote. AI is not a huge focus yet for a lot of the Hortonworks customers who are here, the developers. They're, you know, most of their customers are not yet that far along in their deep learning journeys and whatever but they're definitely going there. There's plenty of really cool keynote discussions including the guy with the autonomous vehicles or whatever that, the thing we just came out of. That was not the predominant theme this week here in terms of the HDP 3.0. I think what it comes down to is that with HDP 3.0... Hive, though you tend to take it for granted, it's been in Hadoop from the very start, practically, Hive is now a full enterprise database and that's the core, one of the cores, of HDP 3.0. Hive itself, Hive 3.0 now is its version, is ACID compliant and that may be totally geeky to the most of the world but that enables it to support transactional applications. So more big data in every environment is supporting more traditional enterprise application, transactional applications that require like two-phase commit and all that goodness. The fact is, you know, Hortonworks have, from what I can see, is the first of the big data vendors to incorporate those enhancements to Hive 3.0 because they're so completely tuned in to the Hive environment in terms of a committer. I think in many ways that is the predominant theme in terms of the new stuff that will actually resonate with the developers, their customers here at the show. And with the, you know, enterprises in general, they can put more of their traditional enterprise application workloads on big data environments and specifically, Hortonworks hopes, its HDP 3.0. >> Well I'm excited to learn more here at the on theCube with you today. We've got a lot of great interviews lined up and a lot of interesting content. We got a great crew too so this is a fun show to do. >> Sure is. >> We will have more from day two of the.

>> From San Jose, in the heart of Silicon Valley, it's theCube. Covering DataWorks Summit 2018. Brought to you by Hortonworks. (upbeat music) >> Welcome back to theCUBE's live coverage of Data Works here in sunny San Jose, California. I'm your host Rebecca Knight along with my co-host James Kobielus. We're joined by Pandit Prasad. He is the analytics, projects, strategy, and management at IBM Analytics. Thanks so much for coming on the show. >> Thanks Rebecca, glad to be here. >> So, why don't you just start out by telling our viewers a little bit about what you do in terms of in relationship with the Horton Works relationship and the other parts of your job. >> Sure, as you said I am in Offering Management, which is also known as Product Management for IBM, manage the big data portfolio from an IBM perspective. I was also working with Hortonworks on developing this relationship, nurturing that relationship, so it's been a year since the Northsys partnership. We announced this partnership exactly last year at the same conference. And now it's been a year, so this year has been a journey and aligning the two portfolios together. Right, so Hortonworks had HDP HDF. IBM also had similar products, so we have for example, Big Sequel, Hortonworks has Hive, so how Hive and Big Sequel align together. IBM has a Data Science Experience, where does that come into the picture on top of HDP, so it means before this partnership if you look into the market, it has been you sell Hadoop, you sell a sequel engine, you sell Data Science. So what this year has given us is more of a solution sell. Now with this partnership we go to the customers and say here is NTN experience for you. You start with Hadoop, you put more analytics on top of it, you then bring Big Sequel for complex queries and federation visualization stories and then finally you put Data Science on top of it, so it gives you a complete NTN solution, the NTN experience for getting the value out of the data. >> Now IBM a few years back released a Watson data platform for team data science with DSX, data science experience, as one of the tools for data scientists. Is Watson data platform still the core, I call it dev ops for data science and maybe that's the wrong term, that IBM provides to market or is there sort of a broader dev ops frame work within which IBM goes to market these tools? >> Sure, Watson data platform one year ago was more of a cloud platform and it had many components of it and now we are getting a lot of components on to the (mumbles) and data science experience is one part of it, so data science experience... >> So Watson analytics as well for subject matter experts and so forth. >> Yes. And again Watson has a whole suit of side business based offerings, data science experience is more of a a particular aspect of the focus, specifically on the data science and that's been now available on PRAM and now we are building this arm from stack, so we have HDP, HDF, Big Sequel, Data Science Experience and we are working towards adding more and more to that portfolio. >> Well you have a broader reference architecture and a stack of solutions AI and power and so for more of the deep learning development. In your relationship with Hortonworks, are they reselling more of those tools into their customer base to supplement, extend what they already resell DSX or is that outside of the scope of the relationship? >> No it is all part of the relationship, these three have been the core of what we announced last year and then there are other solutions. We have the whole governance solution right, so again it goes back to the partnership HDP brings with it Atlas. IBM has a whole suite of governance portfolio including the governance catalog. How do you expand the story from being a Hadoop-centric story to an enterprise data-like story, and then now we are taking that to the cloud that's what Truata is all about. Rob Thomas came out with a blog yesterday morning talking about Truata. If you look at it is nothing but a governed data-link hosted offering, if you want to simplify it. That's one way to look at it caters to the GDPR requirements as well. >> For GDPR for the IBM Hortonworks partnership is the lead solution for GDPR compliance, is it Hortonworks Data Steward Studio or is it any number of solutions that IBM already has for data governance and curation, or is it a combination of all of that in terms of what you, as partners, propose to customers for soup to nuts GDPR compliance? Give me a sense for... >> It is a combination of all of those so it has a HDP, its has HDF, it has Big Sequel, it has Data Science Experience, it had IBM governance catalog, it has IBM data quality and it has a bunch of security products, like Gaurdium and it has some new IBM proprietary components that are very specific towards data (cough drowns out speaker) and how do you deal with the personal data and sensitive personal data as classified by GDPR. I'm supposed to query some high level information but I'm not allowed to query deep into the personal information so how do you blog those queries, how do you understand those, these are not necessarily part of Data Steward Studio. These are some of the proprietary components that are thrown into the mix by IBM. >> One of the requirements that is not often talked about under GDPR, Ricky of Formworks got in to it a little bit in his presentation, was the notion that the requirement that if you are using an UE citizen's PII to drive algorithmic outcomes, that they have the right to full transparency. It's the algorithmic decision paths that were taken. I remember IBM had a tool under the Watson brand that wraps up a narrative of that sort. Is that something that IBM still, it was called Watson Curator a few years back, is that a solution that IBM still offers, because I'm getting a sense right now that Hortonworks has a specific solution, not to say that they may not be working on it, that addresses that side of GDPR, do you know what I'm referring to there? >> I'm not aware of something from the Hortonworks side beyond the Data Steward Studio, which offers basically identification of what some of the... >> Data lineage as opposed to model lineage. It's a subtle distinction. >> It can identify some of the personal information and maybe provide a way to tag it and hence, mask it, but the Truata offering is the one that is bringing some new research assets, after GDPR guidelines became clear and then they got into they are full of how do we cater to those requirements. These are relatively new proprietary components, they are not even being productized, that's why I am calling them proprietary components that are going in to this hosting service. >> IBM's got a big portfolio so I'll understand if you guys are still working out what position. Rebecca go ahead. >> I just wanted to ask you about this new era of GDPR. The last Hortonworks conference was sort of before it came into effect and now we're in this new era. How would you say companies are reacting? Are they in the right space for it, in the sense of they're really still understand the ripple effects and how it's all going to play out? How would you describe your interactions with companies in terms of how they're dealing with these new requirements? >> They are still trying to understand the requirements and interpret the requirements coming to terms with what that really means. For example I met with a customer and they are a multi-national company. They have data centers across different geos and they asked me, I have somebody from Asia trying to query the data so that the query should go to Europe, but the query processing should not happen in Asia, the query processing all should happen in Europe, and only the output of the query should be sent back to Asia. You won't be able to think in these terms before the GDPR guidance era. >> Right, exceedingly complicated. >> Decoupling storage from processing enables those kinds of fairly complex scenarios for compliance purposes. >> It's not just about the access to data, now you are getting into where the processing happens were the results are getting displayed, so we are getting... >> Severe penalties for not doing that so your customers need to keep up. There was announcement at this show at Dataworks 2018 of an IBM Hortonwokrs solution. IBM post-analytics with with Hortonworks. I wonder if you could speak a little bit about that, Pandit, in terms of what's provided, it's a subscription service? If you could tell us what subset of IBM's analytics portfolio is hosted for Hortonwork's customers? >> Sure, was you said, it is a a hosted offering. Initially we are starting of as base offering with three products, it will have HDP, Big Sequel, IBM DB2 Big Sequel and DSX, Data Science Experience. Those are the three solutions, again as I said, it is hosted on IBM Cloud, so customers have a choice of different configurations they can choose, whether it be VMs or bare metal. I should say this is probably the only offering, as of today, that offers bare metal configuration in the cloud. >> It's geared to data scientist developers and machine-learning models will build the models and train them in IBM Cloud, but in a hosted HDP in IBM Cloud. Is that correct? >> Yeah, I would rephrase that a little bit. There are several different offerings on the cloud today and we can think about them as you said for ad-hoc or ephemeral workloads, also geared towards low cost. You think about this offering as taking your on PRAM data center experience directly onto the cloud. It is geared towards very high performance. The hardware and the software they are all configured, optimized for providing high performance, not necessarily for ad-hoc workloads, or ephemeral workloads, they are capable of handling massive workloads, on sitcky workloads, not meant for I turned this massive performance computing power for a couple of hours and then switched them off, but rather, I'm going to run these massive workloads as if it is located in my data center, that's number one. It comes with the complete set of HDP. If you think about it there are currently in the cloud you have Hive and Hbase, the sequel engines and the stories separate, security is optional, governance is optional. This comes with the whole enchilada. It has security and governance all baked in. It provides the option to use Big Sequel, because once you get on Hadoop, the next experience is I want to run complex workloads. I want to run federated queries across Hadoop as well as other data storage. How do I handle those, and then it comes with Data Science Experience also configured for best performance and integrated together. As a part of this partnership, I mentioned earlier, that we have progress towards providing this story of an NTN solution. The next steps of that are, yeah I can say that it's an NTN solution but are the product's look and feel as if they are one solution. That's what we are getting into and I have featured some of those integrations. For example Big Sequel, IBM product, we have been working on baking it very closely with HDP. It can be deployed through Morey, it is integrated with Atlas and Granger for security. We are improving the integrations with Atlas for governance. >> Say you're building a Spark machine learning model inside a DSX on HDP within IH (mumbles) IBM hosting with Hortonworks on HDP 3.0, can you then containerize that machine learning Sparks and then deploy into an edge scenario? >> Sure, first was Big Sequel, the next one was DSX. DSX is integrated with HDP as well. We can run DSX workloads on HDP before, but what we have done now is, if you want to run the DSX workloads, I want to run a Python workload, I need to have Python libraries on all the nodes that I want to deploy. Suppose you are running a big cluster, 500 cluster. I need to have Python libraries on all 500 nodes and I need to maintain the versioning of it. If I upgrade the versions then I need to go and upgrade and make sure all of them are perfectly aligned. >> In this first version will you be able build a Spark model and a Tesorflow model and containerize them and deploy them. >> Yes. >> Across a multi-cloud and orchestrate them with Kubernetes to do all that meshing, is that a capability now or planned for the future within this portfolio? >> Yeah, we have that capability demonstrated in the pedestal today, so that is a new one integration. We can run virtual, we call it virtual Python environment. DSX can containerize it and run data that's foreclosed in the HDP cluster. Now we are making use of both the data in the cluster, as well as the infrastructure of the cluster itself for running the workloads. >> In terms of the layers stacked, is also incorporating the IBM distributed deep-learning technology that you've recently announced? Which I think is highly differentiated, because deep learning is increasingly become a set of capabilities that are across a distributed mesh playing together as is they're one unified application. Is that a capability now in this solution, or will it be in the near future? DPL distributed deep learning? >> No, we have not yet. >> I know that's on the AI power platform currently, gotcha. >> It's what we'll be talking about at next year's conference. >> That's definitely on the roadmap. We are starting with the base configuration of bare metals and VM configuration, next one is, depending on how the customers react to it, definitely we're thinking about bare metal with GPUs optimized for Tensorflow workloads. >> Exciting, we'll be tuned in the coming months and years I'm sure you guys will have that. >> Pandit, thank you so much for coming on theCUBE. We appreciate it. I'm Rebecca Knight for James Kobielus. We will have, more from theCUBE's live coverage of Dataworks, just after this.

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in sunny San Jose, California. I'm your host Rebecca Knight along with my co-host James Kobielus. We're joined by Dan Potter. He is the VP Product Management at Attunity and also Ali Bajwah, who is the principal partner solutions engineer at Hortonworks. Thanks so much for coming on theCUBE. >> Pleasure to be here. >> It's good to be here. >> So I want to start with you, Dan, and have you tell our viewers a little bit about the company based in Boston, Massachusetts, what Attunity does. >> Attunity, we're a data integration vendor. We are best known as a provider of real-time data movement from transactional systems into data lakes, into clouds, into streaming architectures, so it's a modern approach to data integration. So as these core transactional systems are being updated, we're able to take those changes and move those changes where they're needed when they're needed for analytics for new operational applications, for a variety of different tasks. >> Change data capture. >> Change data capture is the heart of our-- >> They are well known in this business. They have changed data capture. Go ahead. >> We are. >> So tell us about the announcement today that Attunity has made at the Hortonworks-- >> Yeah, thank you, it's a great announcement because it showcases the collaboration between Attunity and Hortonworks and it's all about taking the metadata that we capture in that integration process. So we're a piece of a data lake architecture. As we are capturing changes from those source systems, we are also capturing the metadata, so we understand the source systems, we understand how the data gets modified along the way. We use that metadata internally and now we're built extensions to share that metadata into Atlas and to be able to extend that out through Atlas to higher data governance initiatives, so Data Steward Studio, into the DataPlane Services, so it's really important to be able to take the metadata that we have and to add to it the metadata that's from the other sources of information. >> Sure, for more of the transactional semantics of what Hortonworks has been describing they've baked in to HDP in your overall portfolios. Is that true? I mean, that supports those kind of requirements. >> With HTP, what we're seeing is you know the EDW optimization play has become more and more important for a lot of customers as they try to optimize the data that their EDWs are working on, so it really gels well with what we've done here with Attunity and then on the Atlas side with the integration on the governance side with GDPR and other sort of regulations coming into the play now, you know, those sort of things are becoming more and more important, you know, specifically around the governance initiative. We actually have a talk just on Thursday morning where we're actually showcasing the integration as well. >> So can you talk a little bit more about that for those who aren't going to be there for Thursday. GDPR was really a big theme at the DataWorks Berlin event and now we're in this new era and it's not talked about too, too much, I mean we-- >> And global business who have businesses at EU, but also all over the world, are trying to be systematic and are consistent about how they manage PII everywhere. So GDPR are those in EU regulation, really in many ways it's having ripple effects across the world in terms of practices. >> Absolutely and at the heart of understanding how you protect yourself and comply, I need to understand my data, and that's where metadata comes in. So having a holistic understanding of all of the data that resides in your data lake or in your cloud, metadata becomes a key part of that. And also in terms of enforcing that, if I understand my customer data, where the customer data comes from, the lineage from that, then I'm able to apply the protections of the masking on top of that data. So it's really, the GDPR effect has had, you know, it's created a broad-scale need for organizations to really get a handle on metadata so the timing of our announcement just works real well. >> And one nice thing about this integration is that you know it's not just about being able to capture the data in Atlas, but now with the integration of Atlas and Ranger, you can do enforcement of policies based on classifications as well, so if you can tag data as PCI, PII, personal data, that can get enforced through Ranger to say, hey, only certain admins can access certain types of data and now all that becomes possible once we've taken the initial steps of the Atlas integration. >> So with this collaboration, and it's really deepening an existing relationship, so how do you go to market? How do you collaborate with each other and then also service clients? >> You want to? >> Yeah, so from an engineering perspective, we've got deep roots in terms of being a first-class provider into the Hortonworks platform, both HDP and HDF. Last year about this time, we announced our support for acid merge capabilities, so the leading-edge work that Hortonworks has done in bringing acid compliance capabilities into Hive, was a really important one, so our change to data capture capabilities are able to feed directly into that and be able to support those extensions. >> Yeah, we have a lot of you know really key customers together with Attunity and you know maybe a a result of that they are actually our ISV of the Year as well, which they probably showcase on their booth there. >> We're very proud of that. Yeah, no, it's a nice honor for us to get that distinction from Hortonworks and it's also a proof point to the collaboration that we have commercially. You know our sales reps work hand in hand. When we go into a large organization, we both sell to very large organizations. These are big transformative initiatives for these organizations and they're looking for solutions not technologies, so the fact that we can come in, we can show the proof points from other customers that are successfully using our joint solution, that's really, it's critical. >> And I think it helps that they're integrating with some of our key technologies because, you know, that's where our sales force and our customers really see, you know, that as well as that's where we're putting in the investment and that's where these guys are also investing, so it really, you know, helps the story together. So with Hive, we're doing a lot of investment of making it closer and closer to a sort of real-time database, where you can combine historical insights as well as your, you know, real-time insights. with the new acid merge capabilities where you can do the inserts, updates and deletes, and so that's exactly what Attunity's integrating with with Atlas. We're doing a lot of investments there and that's exactly what these guys are integrating with. So I think our customers and prospects really see that and that's where all the wins are coming from. >> Yeah, and I think together there were two main barriers that we saw in terms of customers getting the most out of their data lake investment. One of them was, as I'm moving data into my data lake, I need to be able to put some structure around this, I need to be able to handle continuously updating data from multiple sources and that's what we introduce with Attunity composed for Hive, building out the structure in an automated fashion so I've got analytics-ready data and using the acid merge capabilities just made those updates much easier. The second piece was metadata. Business users need to have confidence that the data that they're using. Where did this come from? How is it modified? And overcoming both of those is really helping organizations make the most of those investments. >> How would you describe customer attitudes right now in terms of their approach to data because I mean, as we've talked about, data is the new oil, so there's a real excitement and there's a buzz around it and yet there's also so many high-profile cases of breeches and security concerns, so what would you say, is it that customers, are they more excited or are they more trepidatious? How would you describe the CIL mindset right now? >> So I think security and governance has become top of minds right, so more and more the serveways that we've taken with our customers, right, you know, more and more customers are more concerned about security, they're more concerned about governance. The joke is that we talk to some of our customers and they keep talking to us about Atlas, which is sort of one of the newer offerings on governance that we have, but then we ask, "Hey, what about Ranger for enforcement?" And they're like, "Oh, yeah, that's a standard now." So we have Ranger, now it's a question of you know how do we get our you know hooks into the Atlas and all that kind of stuff, so yeah, definitely, as you mentioned, because of GDPR, because of all these kind of issues that have happened, it's definitely become top of minds. >> And I would say the other side of that is there's real excitement as well about the possibilities. Now bringing together all of this data, AI, machine learning, real-time analytics and real-time visualization. There's analytic capabilities now that organizations have never had, so there's great excitement, but there's also trepidation. You know, how do we solve for both of those? And together, we're doing just that. >> But as you mentioned, if you look at Europe, some of the European companies that are more hit by GDPR, they're actually excited that now they can, you know, really get to understand their data more and do better things with it as a result of you know the GDPR initiative. >> Absolutely. >> Are you using machine learning inside of Attunity in a Hortonworks context to find patterns in that data in real time? >> So we enable data scientists to build those models. So we're not only bringing the data together but again, part of the announcement last year is the way we structure that data in Hive, we provide a complete historic data store so every single transaction that has happened and we send those transactions as they happen, it's at a big append, so if you're a data scientist, I want to understand the complete history of the transactions of a customer to be able to build those models, so building those out in Hive and making those analytics ready in Hive, that's what we do, so we're a key enabler to machine learning. >> Making analytics ready rather than do the analytics in the spring, yeah. >> Absolutely. >> Yeah, the other side to that is that because they're integrated with Atlas, you know, now we have a new capability called DataPlane and Data Steward Studio so the idea there is around multi-everything, so more and more customers have multiple clusters whether it's on-prem, in the cloud, so now more and more customers are looking at how do I get a single glass pane of view across all my data whether it's on-prem, in the cloud, whether it's IOT, whether it's data at rest, right, so that's where DataPlane comes in and with the Data Steward Studio, which is our second offering on top of DataPlane, they can kind of get that view across all their clusters, so as soon as you know the data lands from Attunity into Atlas, you can get a view into that across as a part of Data Steward Studio, and one of the nice things we do in Data Steward Studio is that we also have machine learning models to do some profiling, to figure out that hey, this looks like a credit card, so maybe I should suggest this as a tag of sensitive data and now the end user, the end administration has the option of you know saying that okay, yeah, this is a credit card, I'll accept that tag, or they can reject that and pick one of their own. >> Will any of this going forward of the Attunity CDC change in the capture capability be containerized for deployment to the edges in HDP 3.0? I mean, 'cause it seems, I mean for internetive things, edge analytics and so forth, change data capture, is it absolutely necessary to make the entire, some call it the fog computing, cloud or whatever, to make it a completely transactional environment for all applications from micro endpoint to micro endpoint? Are there any plans to do that going forward? >> Yeah, so I think what HDP 3.0 as you mentioned right, one of the key factors that was coming into play was around time to value, so with containerization now being able to bring third-party apps on top of Yarn through Docker, I think that's definitely an avenue that we're looking at. >> Yes, we're excited about that with 3.0 as well, so that's definitely in the cards for us. >> Great, well, Ali and Dan, thank you so much for coming on theCUBE. It's fun to have you here. >> Nice to be here, thank you guys. >> Great to have you. >> Thank you, it was a pleasure. >> I'm Rebecca Knight, for James Kobielus, we will have more from DataWorks in San Jose just after this. (techno music)

>> Announcer: From Berlin, Germany, it's the Cube covering Dataworks Summit Europe 2018. Brought to you by Hortonworks. (electronic music) >> Well hello welcome to the Cube I'm James Kobielus. I'm the lead analyst for big data analytics within the Wikibon team of SiliconANGLE Media. I'm hosting the Cube this week at Dataworks Summit 2018 in Berlin, Germany. It's been an excellent event. Hortonworks, the host, had... We've completed two days of keynotes. They made an announcement of the Data Steward Studio as the latest of their offerings and demonstrated it this morning, to address GDPR compliance, which of course is hot and heavy is coming down on enterprises both in the EU and around the world including in the U.S. and the May 25th deadline is fast approaching. One of Hortonworks' prime partners is IBM. And today on this Cube segment we have Mandy Chessell. Mandy is a distinguished engineer at IBM who did an excellent keynote yesterday all about metadata and metadata management. Mandy, great to have you. >> Hi and thank you. >> So I wonder if you can just reprise or summarize the main take aways from your keynote yesterday on metadata and it's role in GDPR compliance, so forth and the broader strategies that enterprise customers have regarding managing their data in this new multi-cloud world where Hadoop and open source platforms are critically important for storing and processing data. So Mandy go ahead. >> So, metadata's not new. I mean it's basically information about data. And a lot of companies are trying to build a data catalog which is not a catalog of, you know, actually containing their data, it's a catalog that describes their data. >> James: Is it different with index or a glossary. How's the catalog different from-- >> Yeah, so catalog actually includes both. So it is a list of all the data sets plus a links to glossary definitions of what those data items mean within the data sets, plus information about the lineage of the data. It includes information about who's using it, what they're using it for, how it should be governed. >> James: It's like a governance repository. >> So governance is part of it. So the governance part is really saying, "This is how you're allowed to use it, "this is how the data's classified," "these are the automated actions that are going to happen "on the data as it's used "within the operational environment." >> James: Yeah. >> So there's that aspect to it, but there is the collaboration side. Hey I've been using this data set it's great. Or, actually this data set is full of errors, we can't use it. So you've got feedback to data set owners as well as, exchange and collaboration between data scientists working with the data. So it's really, it is a central resource for an organization that has a strong data strategy, is interested in becoming a data-driven organization as such, so, you know, this becomes their major catalog over their data assets, and how they're using it. So when a regulator comes in and says, "can you show up, show me that you're "managing personal data?" The data catalog will have the information about where personal data's located, what type of infrastructure it's sitting on, how it's being used by different services. So they can really show that they know what they're doing and then from that they can show how to processes are used in the metadata in order to use the data appropriately day to day. >> So Apache Atlas, so it's basically a catalog, if I understand correctly at least for IBM and Hortonworks, it's Hadoop, it's Apache Atlas and Apache Atlas is essentially a metadata open source code base. >> Mandy: Yes, yes. >> So explain what Atlas is in this context. >> So yes, Atlas is a collection of code, but it supports a server, a graph-based metadata server. It also supports-- >> James: A graph-based >> Both: Metadata server >> Yes >> James: I'm sorry, so explain what you mean by graph-based in this context. >> Okay, so it runs using the JanusGraph, graph repository. And this is very good for metadata 'cause if you think about what it is it's connecting dots. It's basically saying this data set means this value and needs to be classified in this way and this-- >> James: Like a semantic knowledge graph >> It is, yes actually. And on top of it we impose a type system that describes the different types of things you need to control and manage in a data catalog, but the graph, the Atlas component gives you that graph-based, sorry, graph-based repository underneath, but on top we've built what we call the open metadata and governance libraries. They run inside Atlas so when you run Atlas you will have all the open metadata interfaces, but you can also take those libraries and connect them and load them actually into another vendor's product. And what they're doing is allowing metadata to be exchanged between repositories of different types. And this becomes incredibly important as an organization increases their maturity and their use of data because you can't just have knowledge about data in a single server, it just doesn't scale. You need to get that knowledge into every runtime environment, into the data tools that people are using across the organization. And so it needs to be distributed. >> Mandy I'm wondering, the whole notion of what you catalog in that repository, does it include, or does Apache Atlas support adding metadata relevant to data derivative assets like machine learning models-- >> Mandy: Absolutely. >> So forth. >> Mandy: Absolutely, so we have base types in the upper metadata layer, but also it's a very flexible and sensible type system. So, if you've got a specialist machine learning model that needs additional information stored about it, that can easily be added to the runtime environment. And then it will be managed through the open metadata protocols as if it was part of the native type system. >> Because of the courses in analysts, one of my core areas is artificial intelligence and one of the hot themes in artificial, well there's a broad umbrella called AI safety. >> Mandy: Yeah. >> And one of the core subsets of that is something called explicable AI, being able to identify the lineage of a given algorithmic decision back to what machine learning models fed from what data. >> Mandy: Yeah. >> Throw what action like when let's say a self-driving vehicle hits a human being for legal, you know, discovery whatever. So what I'm getting at, what I'm working through to is the extent to which the Hortonworks, IBM big data catalog running Atlas can be a foundation for explicable AI either now or in the future. We see a lot of enterprise, me as an analyst at least, sees lots of enterprises that are exploring this topic, but it's not to the point where it's in production, explicable AI, but where clearly companies like IBM are exploring building a stack or a architecture for doing this kind of thing in a standardized way. What are your thoughts there? Is IBM working on bringing, say Atlas and the overall big data catalog into that kind of a use case. >> Yes, yeah, so if you think about what's required, you need to understand the data that was used to train the AI how, what data's been fed to it since it was deployed because that's going to change its behavior, and then also a view of how that data's going to change in the future so you can start to anticipate issues that might arising from the model's changing behavior. And this is where the data catalog can actually associate and maintain information about the data that's being used with the algorithm. You can also associate the checking mechanism that's constantly monitoring the profile of the data so you can see where the data is changing over time, that will obviously affect the behavior of the machine learning model. So it's really about providing, not just information about the model itself, but also the data that's feeding it, how those characteristics are changing over time so that you know the model is continuing to work into the future. >> So tell us about the IBM, Hortonworks partnership on metadata and so forth. >> Mandy: Okay. >> How is that evolving? So, you know, your partnership is fairly tight. You clearly, you've got ODPI, you've got the work that you're doing related to the big data catalog. What can we expect to see in the near future in terms of, initiatives building on all of that for governance of big data in the multi-cloud environment? >> Yeah so Hortonworks started the Apache Atlas project a couple of years ago with a number of their customers. And they built a base repository and a set of APIs that allow it to work in the Hadoop environment. We came along last year, formed our partnership. That partnership includes this open metadata and governance layer. So since then we worked with ING as well and ING bring the, sort of, user perspective, this is the organization's use of the data. And, so between the three of us we are basically transforming Apache Atlas from an Hadoop focused metadata repository to an enterprise focused metadata repository. Plus enabling other vendors to connect into the open metadata ecosystem. So we're standardizing types, standardizing format, the format of metadata, there's a protocol for exchanging metadata between repositories. And this is all coming from that three-way partnership where you've got a consuming organization, you've got a company who's used to building enterprise middleware, and you've got Hortonworks with their knowledge of open source development in their Hadoop environment. >> Quick out of left field, as you develop this architecture, clearly you're leveraging Hadoop HTFS for storage. Are you looking to at least evaluating maybe using block chain for more distributed management of the metadata in these heterogeneous environments in the multi-cloud, or not? >> So Atlas itself does run on HTFS, but doesn't need to run on HTFS, it's got other storage environments so that we can run it outside of Hadoop. When it comes to block chain, so block chain is, for, sharing data between partners, small amounts of data that basically express agreements, so it's like a ledger. There are some aspects that we could use for metadata management. It's more that we actually need to put metadata management into block chain. So the agreements and contracts that are stored in block chain are only meaningful if we understand the data that's there, what it's quality, where it came from what it means. And so actually there's a very interesting distributor metadata question that comes with the block chain technology. And I think that's an important area of research. >> Well Mandy we're at the end of our time. Thank you very much. We could go on and on. You're a true expert and it's great to have you on the Cube. >> Thank you for inviting me. >> So this is James Kobielus with Mandy Chessell of IBM. We are here this week in Berlin at Dataworks Summit 2018. It's a great event and we have some more interviews coming up so thank you very much for tuning in. (electronic music)

>> Narrator: From Berlin, Germany, it's theCUBE. Covering Dataworks Summit Europe 2018. Brought to you by Hortonworks. >> Hello, welcome to theCUBE. We're here at Dataworks Summit 2018 in Berlin, Germany. I'm James Kobielus. I'm the lead analyst for Big Data Analytics, within the Wikibon team of SiliconAngle Media. Our guest is John Kreisa. He's the VP for Marketing at Hortonworks, of course, the host company of Dataworks Summit. John, it's great to have you. >> Thank you Jim, it's great to be here. >> We go long back, so you know it's always great to reconnect with you guys at Hortonworks. You guys are on a roll, it's been seven years I think since you guys were founded. I remember the founding of Hortonworks. I remember when it splashed in the Wall Street Journal. It was like oh wow, this big data thing, this Hadoop thing is actually, it's a market, it's a segment and you guys have built it. You know, you and your competitors, your partners, your ecosystem continues to grow. You guys went IPO a few years ago. Your latest numbers are pretty good. You're continuing to grow in revenues, in customer acquisitions, your deal sizes are growing. So Hortonworks remains on a roll. So, I'd like you to talk right now, John, and give us a sense of where Hortonworks is at in terms of engaging with the marketplace, in terms of trends that you're seeing, in terms of how you're addressing them. But talk about first of all the Dataworks Summit. How many attendees do you have from how many countries? Just give us sort of the layout of this show. >> I don't have all of the final counts yet. >> This is year six of the show? >> This is year six in Europe, absolutely, thank you. So it's great, we've moved it around different locations. Great venue, great host city here in Berlin. Super excited about it, I know we have representatives from more than 51 countries. If you think about that, drawing from a really broad set of countries, well beyond, as you know, because you've interviewed some of the folks beyond just Europe. We've had them from South America, U.S., Africa, and Asia as well, so really a broad swath of the open-source and big data community, which is great. The final attendance is going to be 1,250 to 1,300 range. The final numbers, but a great sized conference. The energy level's been really great, the sessions have been, you know, oversubscribed, standing room only in many of the popular sessions. So the community's strong, I think that's the thing that we really see here and that we're really continuing to invest in. It's something that Hortonworks was founded around. You referenced the founding, and driving the community forward and investing is something that has been part of our mantra since we started and it remains that way today. >> Right. So first of all what is Hortonworks? Now how does Hortonworks position itself? Clearly Hadoop is your foundation, but you, just like Cloudera, MapR, you guys have all continued to evolve to address a broader range of use-cases with a deeper stack of technology with fairly extensive partner ecosystems. So what kind of a beast is Hortonworks? It's an elephant, but what kind of an elephant is it? >> We're an elephant or riding on the elephant I'd say, so we're a global data management company. That's what we're helping organizations do. Really the end-to-end lifecycle of their data, helping them manage it regardless of where it is, whether it's on-premise or in the cloud, really through hybrid data architectures. That's really how we've seen the market evolve is, we started off in terms of our strategy with the platform based on Hadoop, as you said, to store, process, and analyze data at scale. The kind of fundamental use-case for Hadoop. Then as the company emerged, as the market kind of continued to evolve, we moved to and saw the opportunity really, capturing data from the edge. As IOT and kind of edge-use cases emerged it made sense for us to add to the platform and create the Hortonworks DataFlow. >> James: Apache NiFi >> Apache NiFi, exactly, HDF underneath, with associated additional open-source projects in there. Kafka and some streaming and things like that. So that was now move data, capture data in motion, move it back and put it into the platform for those large data applications that organizations are building on the core platform. It's also the next evolution, seeing great attach rates with that, the really strong interest in the Apache NiFi, you know, the meetup here for NiFi was oversubscribed, so really really strong interest in that. And then, the markets continued to evolve with cloud and cloud architectures, customers wanting to deploy in the cloud. You know, you saw we had that poll yesterday in the general session about cloud with really interesting results, but we saw that there was really companies wanting to deploy in a hybrid way. Some of them wanted to move specific workloads to the cloud. >> Multi-cloud, public, private. >> Exactly right, and multi-data center. >> The majority of your customer deployments are on prem. >> They are. >> Rob Bearden, your CEO, I think he said in a recent article on SiliconAngle that two-thirds of your deployments are on prem. Is that percentage going down over time? Are more of your customers shifting toward a public cloud orientation? Does Hortonworks worry about that? You've got partnerships, clearly, with the likes of IBM, AWS, and Microsoft Dasher and so forth, so do you guys see that as an opportunity, as a worrisome trend? >> No, we see it very much as an opportunity. And that's because we do have customers who are wanting to put more workloads and run things in the cloud, however, there's still almost always a component that's going to be on premise. And that creates a challenge for organizations. How do they manage the security and governance and really the overall operations of those deployments as they're in the cloud and on premise. And, to your point, multi-cloud. And so you get some complexity in there around that deployment and particularly with the regulations, we talked about GDPR earlier today. >> Oh, by the way, the Data Steward Studio demo today was really, really good. It showed that, first of all, you cover the entire range of core requirements for compliance. So that was actually the primary announcement at this show; Scott Gnau announced that. You demoed it today, I think you guys are off on a good start, yeah. We've gotten really, and thank you for that, we've gotten really good feedback on our DataPlane Services strategy, right, it provides that single pane of glass. >> I should say to our viewers that Data Steward Studio is the second of the services under the DataPlane, the Hortonworks DataPlane Services Portfolio. >> That's right, that's exactly right. >> Go ahead, keep going. >> So, you know, we see that as an opportunity. We think we're very strongly positioned in the market, being the first to bring that kind of solution to the customers and our large customers that we've been talking about and who have been starting to use DataPlane have been very, very positive. I mean they see it as something that is going to help them really kind of maintain control over these deployments as they start to spread around, as they grow their uses of the thing. >> And it's built to operate across the multi-cloud, I know this as well in terms of executing the consent or withdrawal of consent that the data subject makes through what is essentially a consent portal. >> That's right, that's right. >> That was actually a very compelling demonstration in that regard. >> It was good, and they worked very hard on it. And I was speaking to an analyst yesterday, and they were saying that they're seeing an increasing number of the customers, enterprises, wanting to have a multi-cloud strategy. They don't want to get locked into any one public cloud vendor, so, what they want is somebody who can help them maintain that common security and governance across their different deployments, and they see DataPlane Services is the way that's going to help them do that. >> So John, how is Hortonworks, what's your road map, how do you see the company in your go to market evolving over the coming years in terms of geographies, in terms of your focuses? Focus, in terms of the use-cases and workloads that the Hortonworks portfolio addresses. How is that shifting? You mentioned the Edge. AI, machine learning, deep learning. You are a reseller of IBM Data Science Experience. >> DSX, that's right. >> So, let's just focus on that. Do you see more customers turning to Hortonworks and IBM for a complete end-to-end pipeline for the ingest, for the preparation, modeling, training and so forth? And deployment of operationalized AI? Is that something you see going forward as an evolution path for your capabilities? >> I'd say yes, long-term, or even in the short-term. So, they have to get their data house in order, if you will, before they get to some of those other things, so we're still, Hortonworks strategy has always been focused on the platform aspect, right? The data-at-rest platform, data-in-motion platform, and now a platform for managing common security and governance across those different deployments. Building on that is the data science, machine learning, and AI opportunity, but our strategy there, as opposed to trying to trying to do it ourselves, is to partner, so we've got the strong partnership with IBM, resell their DSX product. And also other partnerships around to deliver those other capabilities, like machine learning and AI, from our partner ecosystem, which you referenced. We have over 2,300 partners, so a very, very strong ecosystem. And so, we're going to stick to our strategy of the platforms enabling that, which will subsequently enable data science, machine learning, and AI on top. And then, if you want me to talk about our strategy in terms of growth, so we already operate globally. We've got offices in I think 19 different countries. So we're really covering the globe in terms of the demand for Hortonworks products and beginning implements. >> Where's the fastest growing market in terms of regions for Hortonworks? >> Yeah, I mean, international generally is our fastest growing region, faster than the U.S. But we're seeing very strong growth in APAC, actually, so India, Asian countries, Singapore, and then up and through to Japan. There's a lot of growth out in the Asian region. And, you know, they're sort of moving directly to digital transformation projects at really large scale. Big banks, telcos, from a workload standpoint I'd say the patterns are very similar to what we've seen. I've been at Hortonworks for six and a half years, as it turns out, and the patterns we saw initially in terms of adoption in the U.S. became the patterns we saw in terms of adoption in Europe and now those patterns of adoption are the same in Asia. So, once a company realizes they need to either drive out operational costs or build new data applications, the patterns tend to be the same whether it's retail, financial services, telco, manufacturing. You can sort of replicate those as they move forward. >> So going forward, how is Hortonworks evolving as a company in terms of, for example with GDPR, Data Steward, data governance as a strong focus going forward, are you shifting your model in terms of your target customer away from the data engineers, the Hadoop cluster managers who are still very much the center of it, towards more data governance, towards more business analyst level of focus. Do you see Hortonworks shifting in that direction in terms of your focus, go to market, your message and everything? >> I would say it's not a shifting as much as an expansion, so we definitely are continuing to invest in the core platform, in Hadoop, and you would have heard of some of the changes that are coming in the core Hadoop 3.0 and 3.1 platform here. Alan and others can talk about those details, and in Apache NiFi. But, to your point, as we bring and have brought Data Steward Studio and DataPlane Services online, that allows us to address a different user within the organization, so it's really an expansion. We're not de-investing in any other things. It's really here's another way in a natural evolution of the way that we're helping organizations solve data problems. >> That's great, well thank you. This has been John Kreisa, he's the VP for marketing at Hortonworks. I'm James Kobielus of Wikibon SiliconAngle Media here at Dataworks Summit 2018 in Berlin. And it's been great, John, and thank you very much for coming on theCUBE. >> Great, thanks for your time. (techno music)

(techno music) >> (announcer) From Berlin, Germany it's theCUBE covering DataWorks Summit Europe 2018. Brought to you by Hortonworks. >> Well hello, welcome to theCUBE. We're here on day two of DataWorks Summit 2018 in Berlin, Germany. I'm James Kobielus. I'm lead analyst for Big Data Analytics in the Wikibon team of SiliconANGLE Media. And who we have here today, we have Alan Gates whose one of the founders of Hortonworks and Hortonworks of course is the host of DataWorks Summit and he's going to be, well, hello Alan. Welcome to theCUBE. >> Hello, thank you. >> Yeah, so Alan, so you and I go way back. Essentially, what we'd like you to do first of all is just explain a little bit of the genesis of Hortonworks. Where it came from, your role as a founder from the beginning, how that's evolved over time but really how the company has evolved specifically with the folks on the community, the Hadoop community, the Open Source community. You have a deepening open source stack with you build upon with Atlas and Ranger and so forth. Gives us a sense for all of that Alan. >> Sure. So as I think it's well-known, we started as the team at Yahoo that really was driving a lot of the development of Hadoop. We were one of the major players in the Hadoop community. Worked on that for, I was in that team for four years. I think the team itself was going for about five. And it became clear that there was an opportunity to build a business around this. Some others had already started to do so. We wanted to participate in that. We worked with Yahoo to spin out Hortonworks and actually they were a great partner in that. Helped us get than spun out. And the leadership team of the Hadoop team at Yahoo became the founders of Hortonworks and brought along a number of the other engineering, a bunch of the other engineers to help get started. And really at the beginning, we were. It was Hadoop, Pig, Hive, you know, a few of the very, Hbase, the kind of, the beginning projects. So pretty small toolkit. And we were, our early customers were very engineering heavy people, or companies who knew how to take those tools and build something directly on those tools right? >> Well, you started off with the Hadoop community as a whole started off with a focus on the data engineers of the world >> Yes. >> And I think it's shifted, and confirm for me, over time that you focus increasing with your solutions on the data scientists who are doing the development of the applications, and the data stewards from what I can see at this show. >> I think it's really just a part of the adoption curve right? When you're early on that curve, you have people who are very into the technology, understand how it works, and want to dive in there. So those tend to be, as you said, the data engineering types in this space. As that curve grows out, you get, it comes wider and wider. There's still plenty of data engineers that are our customers, that are working with us but as you said, the data analysts, the BI people, data scientists, data stewards, all those people are now starting to adopt it as well. And they need different tools than the data engineers do. They don't want to sit down and write Java code or you know, some of the data scientists might want to work in Python in a notebook like Zeppelin or Jupyter but some, may want to use SQL or even Tablo or something on top of SQL to do the presentation. Of course, data stewards want tools more like Atlas to help manage all their stuff. So that does drive us to one, put more things into the toolkit so you see the addition of projects like Apache Atlas and Ranger for security and all that. Another area of growth, I would say is also the kind of data that we're focused on. So early on, we were focused on data at rest. You know, we're going to store all this stuff in HDFS and as the kind of data scene has evolved, there's a lot more focus now on a couple things. One is data, what we call data-in-motion for our HDF product where you've got in a stream manager like Kafka or something like that >> (James) Right >> So there's processing that kind of data. But now we also see a lot of data in various places. It's not just oh, okay I have a Hadoop cluster on premise at my company. I might have some here, some on premise somewhere else and I might have it in several clouds as well. >> K, your focus has shifted like the industry in general towards streaming data in multi-clouds where your, it's more stateful interactions and so forth? I think you've made investments in Apache NiFi so >> (Alan) yes. >> Give us a sense for your NiFi versus Kafka and so forth inside of your product strategy or your >> Sure. So NiFi is really focused on that data at the edge, right? So you're bringing data in from sensors, connected cars, airplane engines, all those sorts of things that are out there generating data and you need, you need to figure out what parts of the data to move upstream, what parts not to. What processing can I do here so that I don't have to move upstream? When I have a error event or a warning event, can I turn up the amount of data I'm sending in, right? Say this airplane engine is suddenly heating up maybe a little more than it's supposed to. Maybe I should ship more of the logs upstream when the plane lands and connects that I would if, otherwise. That's the kind o' thing that Apache NiFi focuses on. I'm not saying it runs in all those places by my point is, it's that kind o' edge processing. Kafka is still going to be running in a data center somewhere. It's still a pretty heavy weight technology in terms of memory and disk space and all that so it's not going to be run on some sensor somewhere. But it is that data-in-motion right? I've got millions of events streaming through a set of Kafka topics watching all that sensor data that's coming in from NiFi and reacting to it, maybe putting some of it in the data warehouse for later analysis, all those sorts of things. So that's kind o' the differentiation there between Kafka and NiFi. >> Right, right, right. So, going forward, do you see more of your customers working internet of things projects, is that, we don't often, at least in the industry of popular mind, associate Hortonworks with edge computing and so forth. Is that? >> I think that we will have more and more customers in that space. I mean, our goal is to help our customers with their data wherever it is. >> (James) Yeah. >> When it's on the edge, when it's in the data center, when it's moving in between, when it's in the cloud. All those places, that's where we want to help our customers store and process their data. Right? So, I wouldn't want to say that we're going to focus on just the edge or the internet of things but that certainly has to be part of our strategy 'cause it's has to be part of what our customers are doing. >> When I think about the Hortonworks community, now we have to broaden our understanding because you have a tight partnership with IBM which obviously is well-established, huge and global. Give us a sense for as you guys have teamed more closely with IBM, how your community has changed or broadened or shifted in its focus or has it? >> I don't know that it's shifted the focus. I mean IBM was already part of the Hadoop community. They were already contributing. Obviously, they've contributed very heavily on projects like Spark and some of those. They continue some of that contribution. So I wouldn't say that it's shifted it, it's just we are working more closely together as we both contribute to those communities, working more closely together to present solutions to our mutual customer base. But I wouldn't say it's really shifted the focus for us. >> Right, right. Now at this show, we're in Europe right now, but it doesn't matter that we're in Europe. GDPR is coming down fast and furious now. Data Steward Studio, we had the demonstration today, it was announced yesterday. And it looks like a really good tool for the main, the requirements for compliance which is discover and inventory your data which is really set up a consent portal, what I like to refer to. So the data subject can then go and make a request to have my data forgotten and so forth. Give us a sense going forward, for how or if Hortonworks, IBM, and others in your community are going to work towards greater standardization in the functional capabilities of the tools and platforms for enabling GDPR compliance. 'Cause it seems to me that you're going to need, the industry's going to need to have some reference architecture for these kind o' capabilities so that going forward, either your ecosystem of partners can build add on tools in some common, like the framework that was laid out today looks like a good basis. Is there anything that you're doing in terms of pushing towards more Open Source standardization in that area? >> Yes, there is. So actually one of my responsibilities is the technical management of our relationship with ODPI which >> (James) yes. >> Mandy Chessell referenced yesterday in her keynote and that is where we're working with IBM, with ING, with other companies to build exactly those standards. Right? Because we do want to build it around Apache Atlas. We feel like that's a good tool for the basis of that but we know one, that some people are going to want to bring their own tools to it. They're not necessarily going to want to use that one platform so we want to do it in an open way that they can still plug in their metadata repositories and communicate with others and we want to build the standards on top of that of how do you properly implement these features that GDPR requires like right to be forgotten, like you know, what are the protocols around PIII data? How do you prevent a breach? How do you respond to a breach? >> Will that all be under the umbrella of ODPI, that initiative of the partnership or will it be a separate group or? >> Well, so certainly Apache Atlas is part of Apache and remains so. What ODPI is really focused up is that next layer up of how do we engage, not the programmers 'cause programmers can gage really well at the Apache level but the next level up. We want to engage the data professionals, the people whose job it is, the compliance officers. The people who don't sit and write code and frankly if you connect them to the engineers, there's just going to be an impedance mismatch in that conversation. >> You got policy wonks and you got tech wonks so. They understand each other at the wonk level. >> That's a good way to put it. And so that's where ODPI is really coming is that group of compliance people that speak a completely different language. But we still need to get them all talking to each other as you said, so that there's specifications around. How do we do this? And what is compliance? >> Well Alan, thank you very much. We're at the end of our time for this segment. This has been great. It's been great to catch up with you and Hortonworks has been evolving very rapidly and it seems to me that, going forward, I think you're well-positioned now for the new GDPR age to take your overall solution portfolio, your partnerships, and your capabilities to the next level and really in terms of in an Open Source framework. In many ways though, you're not entirely 100% like nobody is, purely Open Source. You're still very much focused on open frameworks for building fairly scalable, very scalable solutions for enterprise deployment. Well, this has been Jim Kobielus with Alan Gates of Hortonworks here at theCUBE on theCUBE at DataWorks Summit 2018 in Berlin. We'll be back fairly quickly with another guest and thank you very much for watching our segment. (techno music)

>> Announcer: From Berlin, Germany, it's the Cube covering Datawork Summit Europe 2018. Brought to you by Hortonworks. (electronic music) >> Hello and welcome to the Cube on day two of Dataworks Summit 2018 from Berlin. It's been a great show so far. We have just completed the day two keynote and in just a moment I'll bring ya up to speed on the major points and the presentations from that. It's been a great conference. Fairly well attended here. The hallway chatter, discussion's been great. The breakouts have been stimulating. For me the takeaway is the fact that Hortonworks, the show host, has announced yesterday at the keynote, Scott Gnau, the CTO of Hortonworks announced Data Steward Studio, DSS they call it, part of the data plane, Hotronworks data plane services portfolio and it could not be more timely Data Steward Studio because we are now five weeks away from GDPR, that's the General Data Protection Regulation becoming the law of the land. When I say the land, the EU, but really any company that operates in the EU, and that includes many U.S. based and Apac based and other companies will need to comply with the GDPR as of May 25th and ongoing. In terms of protecting the personal data of EU citizens. And that means a lot of different things. Data Steward Studio announced yesterday, was demo'd today, by Hortonworks and it was a really excellent demo, and showed that it's a powerful solution for a number of things that are at the core of GDPR compliance. The demo covered the capability of the solution to discover and inventory personal data within a distributed data lake or enterprise data environment, number one. Number two, the ability of the solution to centralize consent, provide a consent portal essentially that data subjects can use then to review the data that's kept on them to make fine grain consents or withdraw consents for use in profiling of their data that they own. And then number three, the show, they demonstrated the capability of the solution then to execute the data subject to people's requests in terms of the handling of their personal data. The three main points in terms of enabling, adding the teeth to enforce GDPR in an operational setting in any company that needs to comply with GDPR. So, what we're going to see, I believe going forward in the, really in the whole global economy and in the big data space is that Hortonworks and others in the data lake industry, and there's many others, are going to need to roll out similar capabilities in their portfolios 'cause their customers are absolutely going to demand it. In fact the deadline is fast approaching, it's only five weeks away. One of the interesting take aways from the, the keynote this morning was the fact that John Kreisa, the VP for marketing at Hortonworks today, a quick survey of those in the audience a poll, asking how ready they are to comply with GDPR as of May 25th and it was a bit eye opening. I wasn't surprised, but I think it was 19 or 20%, I don't have the numbers in front of me, said that they won't be ready to comply. I believe it was something where between 20 and 30% said they will be able to comply. About 40% I'm, don't quote me on that, but a fair plurality said that they're preparing. So that, indicates that they're not entirely 100% sure that they will be able to comply 100% to the letter of the law as of May 25th. I think that's probably accurate in terms of ballpark figures. I think there's a lot of, I know there's a lot of companies, users racing for compliance by that date. And so really GDPR is definitely the headline banner, umbrella story around this event and really around the big data community world-wide right now in terms of enterprise, investments in the needed compliance software and services and capabilities are needed to comply with GDPR. That was important. That wasn't the only thing that was covered in, not only the keynotes, but in the sessions here so far. AI, clearly AI and machine learning are hot themes in terms of the innovation side of big data. There's compliance, there's GDPR, but really innovation in terms of what enterprises are doing with their data, with their analytics, they're building more and more AI and embedding that in conversational UIs and chatbots and their embedding AI, you know manner of e-commerce applications, internal applications in terms of search, as well as things like face recognition, voice recognition, and so forth and so on. So, what we've seen here at the show is what I've been seeing for quite some time is that more of the actual developers who are working with big data are the data scientists of the world. And more of the traditional coders are getting up to speed very rapidly on the new state of the art for building machine learning and deep learning AI natural language processing into their applications. That said, so Hortonworks has become a fairly substantial player in the machine learning space. In fact, you know, really across their portfolio many of the discussions here I've seen shows that everybody's buzzing about getting up to speed on frameworks for building and deploying and iterating and refining machine learning models in operational environments. So that's definitely a hot theme. And so there was an AI presentation this morning from the first gentleman that came on that laid out the broad parameters of what, what developers are doing and looking to do with data that they maintain in their lakes, training data to both build the models and train them and deploy them. So, that was also something I expected and it's good to see at Dataworks Summit that there is a substantial focus on that in addition of course to GDPR and compliance. It's been about seven years now since Hortonworks was essentially spun off of Yahoo. It's been I think about three years or so since they went IPO. And what I can see is that they are making great progress in terms of their growth, in terms of not just the finances, but their customer acquisition and their deal size and also customer satisfaction. I get a sense from talking to many of the attendees at this event that Hortonworks has become a fairly blue chip vendor, that they're really in many ways, continuing to grow their footprint of Hortonworks products and services in most of their partners, such as IBM. And from what I can see everybody was wrapped with intention around Data Steward Studio and I sensed, sort of a sigh of relief that it looks like a fairly good solution and so I have no doubt that a fair number of those in this hall right now are probably, as we say in the U.S., probably kicking the tires of DSS and probably going to expedite their adoption of it. So, with that said, we have day two here, so what we're going to have is Alan Gates, one of the founders of Hortonworks coming on in just a few minutes and I'll be interviewing him, asking about the vibrancy in the health of the community, the Hortonworks ecosystem, developers, partners, and so forth as well as of course the open source communities for Hadoop and Ranger and Atlas and so forth, the growing stack of open source code upon which Hortonworks has built their substantial portfolio of solutions. Following him we'll have John Kreisa, the VP for marketing. I'm going to ask John to give us an update on, really the, sort of the health of Hortonworks as a business in terms of the reach out to the community in terms of their messaging obviously and have him really position Hortonworks in the community in terms of who's he see them competing with. What segments is Hortonworks in now? The whole Hadoop segment increasingly... Hadoop is there. It's the foundation. The word is not invoked in the context of discussions of Hortonworks as much now as it was in the past. And the same thing for say Cloudera one of their closest to traditional rivals, closest in the sense that people associate them. I was at the Cloudera analyst event the other week in Santa Monica, California. It was the same thing. I think both of these vendors are on a similar path to become fairly substantial data warehousing and data governance suppliers to the enterprises of the world that have traditionally gone with the likes of IBM and Oracle and SAP and so forth. So I think they're, Hortonworks, has definitely evolved into a far more diversified solution provider than people realize. And that's really one of the take aways from Dataworks Summit. With that said, this is Jim Kobielus. I'm the lead analyst, I should've said that at the outset. I'm the lead analyst at SiliconANGLE's Media's Wikibon team focused on big data analytics. I'm your host this week on the Cube at Dataworks Summit Berlin. I'll close out this segment and we'll get ready to talk to the Hortonworks and IBM personnel. I understand there's a gentleman from Accenture on as well today on the Cube here at Dataworks Summit Berlin. (electronic music)

>> Voiceover: From Berlin, Germany, it's theCUBE, covering DataWorks Summit Europe 2018, brought to you by Hortonworks. >> Hi, hello, welcome to theCUBE, I'm James Kobielus. I'm the lead analyst for Big Data Analytics at the Wikibon, which is the team inside of SiliconANGLE Media that focuses on emerging trends and technologies. We are here, on theCUBE at DataWorks Summit 2018 in Berlin, Germany. And I have a guest here. This is, Muggie, and if I get it wrong, Muggie Van Staden >> That's good enough, yep. >> Who is with Obsidian, which is a South Africa-based partner of Hortonworks. And I'm not familiar with Obsidian, so I'm going to ask Muggie to tell us a little bit about your company, what you do, your focus on open source, and really the opportunities you see for big data, for Hadoop, in South Africa, really the African continent as a whole. So, Muggie? >> Yeah, James great to be here. Yes, Obsidian, we started it 23 years ago, focusing mostly on open source technologies, and as you can imagine that has changed a lot over the last 23 years when we started the concept of selling Linux was basically a box with a hat and maybe a T-shirt in it. Today that's changed. >> James: Hopefully there's a stuffed penguin in there, too. (laughing) I could use that right now. >> Maybe a manual. So our business has evolved a lot over the last 23 years. And one of the technologies that has come around is Hadoop. And we actually started with some of the other Hadoop vendors out there as our first partnerships, and probably three or four years ago we decided to take on Hortonworks as one of our vendors. We found them an amazing company to work with. And together with them we've now worked in four of the big banks in South Africa. One of them is actually here at DataWorks Summit. They won an award last night. So it's fantastic to be part of all of that. And yes, South Africa being so far removed from the rest of the world. They have different challenges. Everybody's nervous of Cloud. We have the joys that we don't really have any Cloud players locally yet. The two big players are in Microsoft and Amazon are planning some data centers soon. So the guys have different challenges to Europe and to the States. But big data, the big banks are looking at it, starting to deploy nice Hadoop clusters, starting to ingest data, starting to get real business value out of it, and we're there to help, and hopefully the four is the start for us and we can help lots of customers on this journey. >> Are South African-based companies, because you are so distant in terms of miles on the planet from Europe, from the EU, is any company in South Africa, or many companies, concerned at all about the global, or say the general data protection regulation, GDPR? US-based companies certainly are 'cause they operate in Europe. So is that a growing focus for them? And we have five weeks until GDPR kicks in. So tell me about it. >> Yeah, so from a South African point of view, some of the banks and some of the companies would have subsidiaries in Europe. So for them it's a very real thing. But we have our own Act called PoPI, which is the protection of private information, so very similar. So everybody's keeping an eye on it. Everybody's worried. I think everybody's worried for the first company to be fined. And then they will all make sure that they get their things right. But, I think not just because of a legislation, I think it's something that everybody should worry about. How do we protect data? How do we make sure the right people have access to the correct data when they should and nobody violates that because I mean, in this day and age, you know, Google and Amazon and those guys probably know more about me than my family does. So it's a challenge for everybody. And I think it's just the right thing for companies to do is to make sure that the data that they do have that they really do take good care of it. We trust them with our money and now we're trusting them with our data. So it's a real challenge for everybody. >> So how long has Obsidian been a partner of Hortonworks and how has your role, or partnership I should say, evolved over that time, and how do you see it evolving going forward. >> We've been a partner about three or four years now. And started off as a value added reseller. We also a training partner in South Africa for them. And as they as company have evolved, we've had to evolve with them. You know, so they started with HTTP as the Hadoop platform. Now they're doing NiFi and HDF, so we have to learn all of those technologies as well. But very, very excited where they're going with DataPlane service just managing a customer's data across multiple clusters, multiple clouds, because that's realistically where we see all the customers going, is you know clusters, on-premise clusters in typically multiple Clouds and how do you manage that? And we are very excited to walk this road together with Hortonworks and all the South African customers that we have. >> So you say your customers are deploying multiple Clouds. Public Clouds or hybrid private-public Clouds? Give us a sense, for South Africa, whether public Cloud is a major, or is a major deployment option or choice for financial services firms that you work with. >> Not necessarily financial services, so most of them are kicking tires at this stage, nobody's really put major work loads in there. As I mentioned, both Amazon and Microsoft are planning to put data centers down in South Africa very soon, and I think that will spur a big movement towards Cloud, but we do have some customers, unfortunately not Hortonworks customers, that are actually mostly in the Cloud. And they are now starting to look at a multi-Cloud strategy. So to ideally be in the three or four major Cloud providers and spinning up the right workloads in the right Cloud, and we're there to help. >> One of the most predominant workloads that your customers are running in the Cloud, is it backend in terms of data ingest and transformation? Is it a bit of maybe data warehousing with unstructured data? Is it a bit of things like queriable archiving. I want to get a sense for, what is predominant right now in workloads? >> Yeah I think most of them start with (mumble) environments. (mumbles) one customer that's heavily into Cloud from a data point of view. Literally it's their data warehouse. They put everything in there. I think from the banking customers, most of them are considering DR of their existing Hadoop clusters, maybe a subset of their data and not necessarily everything. And I think some of them are also considering putting their unstructured data outside on the Cloud because that's where most of it's coming from. I mean, if you have Twitter, Facebook, LinkedIn data, it's a bit silly to pull all of that into your environment, why not just put it in the Cloud, that's where it's coming from, and analyze that and connect it back to your data where relevant. So I think a lot of the customers would love to get there, and now Hortonworks makes it so much easier to do that. I think a lot of them will start moving in that direction. Now, excuse me, so are any or many of your customers doing development and training of machine learning algorithms and models in their Clouds? And to the extent that they are, are they using tools like the IBM Data Science Experience that Hortonworks resells for that? >> I think it's definitely on the radar for a lot of them. I'm not aware of anybody using it yet, but lots of people are looking at it and excited about the partnership between IBM and Hortonworks. And IBM has been a longstanding player in the South African market, and it's exciting for us as well to bring them into the whole Hortonworks ecosystem, and together solve real world problems. >> Give us a sense for how built out the big data infrastructure is in neighboring countries like Botswana or Angola or Mozambique and so forth. Is that an area that your company, are those regions that your company operates in? Sells into? >> We don't have offices, but we don't have a problem going in and helping customers there, so we've had projects in the past, not data related, that we've flown in and helped people. Most of the banks from a South African point of view, have branches into Africa. So it's on the roadmap, some are a little bit ahead of others, but definitely on the roadmap to actually put down Hadoop clusters in some of the major countries all throughout Africa. There's a big debate, do you put it down there, do you leave the data in South Africa? So they're all going through their own legislation, but it's definitely on the roadmap for all of them to actually take their data, knowledge in data science, up into Africa. >> Now you say that in South Africa Proper, there are privacy regulations, you know, maybe not the same as GDPR, but equivalent. Throughout Africa, at least throughout Southern Africa, how is privacy regulation lacking or is it emerging? >> I think it's emerging. A lot of the countries do have the basic rule that their data shouldn't leave the country. So everybody wants that data sovereignty and that's why a lot of them will not go to Cloud, and that's part of the challenges for the banks, that if they have banks up in Botswana, etc. And Botswana rules are our data has to stay in country. They have to figure out a way how do they connect that data to get the value for all of their customers. So real world challenges for everybody. >> When you're going into and selling into an emerging, or developing nation, of you need to provide upfront consulting to help the customer bootstrap their own understanding of the technology and making the business case and so forth. And how consultative is the selling process... >> Absolutely, and what we see with the banks, most of them even have a consultative approach within their own environment, so you would have the South African team maybe flying into the team at (mumbles) Botswana, and share some of the learnings that they've had. And then help those guys get up to speed. The reality is the skills are not necessarily in country. So there's a lot of training, a lot of help to go and say, we've done this, let us upscale you. And be a part of that process. So we sometimes send in teams to come and do two, three day training, basics, etc., so that ultimately the guys can operationalize in each country by themselves. >> So, that's very interesting, so what do you want to take away from this event? What do you find most interesting in terms of the sessions you've been in around the community showcase that you can take back to Obsidian, back in your country and apply? Like the announcement this morning of the Data Steward Studio. Do you see a possible, that your customers might be eager to use that for curation of their data in their clusters? >> Definitely, and one of the key messages for me was Scott, the CTO's message about your data strategy, your Cloud strategy, and your business strategy. It is effectively the same thing. And I think that's the biggest message that I would like to take back to the South African customers is to go and say, you need to start thinking about this. You know, as Cloud becomes a bigger reality for us, we have to align, we have to go and say, how do we get your data where it belongs? So you know, we like to say to our customers, we help the teams get the right code to the right computer and the right data, and I think it's absolutely critical for all of the customers to go and say, well, where is that data going to sit? Where is the right compute for that piece of data? And can we get it then, can we manage it, etc.? And align to business strategy. Everybody's trying to do digital transformation, and those three things go very much hand-in-hand. >> Well, Muggie, thank you very much. We're at the end of our slot. This has been great. It's been excellent to learn more about Obsidian and the work you're doing in South Africa, providing big data solutions or working with customers to build the big data infrastructure in the financial industry down there. So this has been theCUBE. We've been speaking with Muggie Van Staden of Obsidian Systems, and here at DataWorks Summit 2018 in Berlin. Thank you very much.

(upbeat music) >> Announcer: From Berlin, Germany, it's The Cube, covering DataWorks Summit Europe 2018. Brought to you by Hortonworks. >> Hi, welcome to The Cube, we're separating the signal from the noise and tuning into the trends in data and analytics. Here at DataWorks Summit 2018 in Berlin, Germany. This is the sixth year, I believe, that DataWorks has been held in Europe. Last year I believe it was at Munich, now it's in Berlin. It's a great show. The host is Hortonworks and our first interviewee today is Scott Gnau, who is the chief technology officer of Hortonworks. Of course Hortonworks got established themselves about seven years ago as one of the up and coming start ups commercializing a then brand new technology called Hadoop and MapReduce. They've moved well beyond that in terms of their go to market strategy, their product portfolio, their partnerships. So Scott, this morning, it's great to have ya'. How are you doing? >> Glad to be back and good to see you. It's been awhile. >> You know, yes, I mean, you're an industry veteran. We've both been around the block a few times but I remember you years ago. You were at Teradata and I was at another analyst firm. And now you're with Hortonworks. And Hortonworks is really on a roll. I know you're not Rob Bearden, so I'm not going to go into the financials, but your financials look pretty good, your latest. You're growing, your deal sizes are growing. Your customer base is continuing to deepen. So you guys are on a roll. So we're here in Europe, we're here in Berlin in particular. It's five weeks--you did the keynote this morning, It's five weeks until GDPR. The sword of Damacles, the GDPR sword of Damacles. It's not just affecting European based companies, but it's affecting North American companies and others who do business in Europe. So your keynote this morning, your core theme was that, if you're in enterprise, your business strategy is equated with your cloud strategy now, is really equated with your data strategy. And you got to a lot of that. It was a really good discussion. And where GDPR comes into the picture is the fact that protecting data, personal data of your customers is absolutely important, in fact it's imperative and mandatory, and will be in five weeks or you'll face a significant penalty if you're not managing that data and providing customers with the right to have it erased, or the right to withdraw consent to have it profiled, and so forth. So enterprises all over the world, especially in Europe, are racing as fast as they can to get compliant with GDPR by the May 25th deadline time. So, one of the things you discussed this morning, you had an announcement overnight that Hortonworks has released a new solution in technical preview called The Data Steward Studio. And I'm wondering if you can tie that announcement to GDPR? It seems like data stewardship would have a strong value for your customers. >> Yeah, there's definitely a big tie-in. GDPR is certainly creating a milestone, kind of a trigger, for people to really think about their data assets. But it's certainly even larger than that, because when you even think about driving digitization of a business, driving new business models and connecting data and finding new use cases, it's all about finding the data you have, understanding what it is, where it came from, what's the lineage of it, who had access to it, what did they do to it? These are all governance kinds of things, which are also now mandated by laws like GDPR. And so it's all really coming together in the context of the new modern data architecture era that we live in, where a lot of data that we have access to, we didn't create. And so it was created outside the firewall by a device, by some application running with some customer, and so capturing and interpreting and governing that data is very different than taking derivative transactions from an ERP system, which are already adjudicated and understood, and governing that kind of a data structure. And so this is a need that's driven from many different perspectives, it's driven from the new architecture, the way IoT devices are connecting and just creating a data bomb, that's one thing. It's driven by business use cases, just saying what are the assets that I have access to, and how can I try to determine patterns between those assets where I didn't even create some of them, so how do I adjudicate that? >> Discovering and cataloging your data-- >> Discovering it, cataloging it, actually even... When I even think about data, just think the files on my laptop, that I created, and I don't remember what half of them are. So creating the metadata, creating that trail of bread crumbs that lets you piece together what's there, what's the relevance of it, and how, then, you might use it for some correlation. And then you get in, obviously, to the regulatory piece that says sure, if I'm a new customer and I ask to be forgotten, the only way that you can guarantee to forget me is to know where all of my data is. >> If you remember that they are your customer in the first place and you know where all that data is, if you're even aware that it exists, that's the first and foremost thing for an enterprise to be able to assess their degree of exposure to GDPR. >> So, right. It's like a whole new use case. It's a microcosm of all of these really big things that are going on. And so what we've been trying to do is really leverage our expertise in metadata management using the Apache Atlas project. >> Interviewer: You and IBM have done some major work-- >> We work with IBM and the community on Apache Atlas. You know, metadata tagging is not the most interesting topic for some people, but in the context that I just described, it's kind of important. And so I think one of the areas where we can really add value for the industry is leveraging our lowest common denominator, open source, open community kind of development to really create a standard infrastructure, a standard open infrastructure for metadata tagging, into which all of these use cases can now plug. Whether it's I want to discover data and create metadata about the data based on patterns that I see in the data, or I've inherited data and I want to ensure that the metadata stay with that data through its life cycle, so that I can guarantee the lineage of the data, and be compliant with GDPR-- >> And in fact, tomorrow we will have Mandy Chessell from IBM, a key Hortonworks partner, discussing the open metadata framework you're describing and what you're doing. >> And that was part of this morning's keynote close also. It all really flowed nicely together. Anyway, it is really a perfect storm. So what we've done is we've said, let's leverage this lowest common denominator, standard metadata tagging, Apache Atlas, and uplevel it, and not have it be part of a cluster, but actually have it be a cloud service that can be in force across multiple data stores, whether they're in the cloud or whether they're on prem. >> Interviewer: That's the Data Steward Studio? >> Well, Data Plane and Data Steward Studio really enable those things to come together. >> So the Data Steward Studio is the second service >> Like an app. >> under the Hortonworks DataPlane service. >> Yeah, so the whole idea is to be able to tie those things together, and when you think about it in today's hybrid world, and this is where I really started, where your data strategy is your cloud strategy, they can't be separate, because if they're separate, just think about what would happen. So I've copied a bunch of data out to the cloud. All memory of any lineage is gone. Or I've got to go set up manually another set of lineage that may not be the same as the lineage it came with. And so being able to provide that common service across footprint, whether it's multiple data centers, whether it's multiple clouds, or both, is a really huge value, because now you can sit back and through that single pane, see all of your data assets and understand how they interact. That obviously has the ability then to provide value like with Data Steward Studio, to discover assets, maybe to discover assets and discover duplicate assets, where, hey, I can save some money if I get rid of this cloud instance, 'cause it's over here already. Or to be compliant and say yeah, I've got these assets here, here, and here, I am now compelled to do whatever: delete, protect, encrypt. I can now go do that and keep a record through the metadata that I did it. >> Yes, in fact that is very much at the heart of compliance, you got to know what assets there are out there. And so it seems to me that Hortonworks is increasingly... the H-word rarely comes up these days. >> Scott: Not Hortonworks, you're talking about Hadoop. >> Hadoop rarely comes up these days. When the industry talks about you guys, it's known that's your core, that's your base, that's where HDP and so forth, great product, great distro. In fact, in your partnership with IBM, a year or more ago, I think it was IBM standardized on HDP in lieu of their distro, 'cause it's so well-established, so mature. But going forward, you guys in many ways, Hortonworks, you have positioned yourselves now. Wikibon sees you as being the premier solution provider of big data governance solutions specifically focused on multi-cloud, on structured data, and so forth. So the announcement today of the Data Steward Studio very much builds on that capability you already have there. So going forward, can you give us a sense to your roadmap in terms of building out DataPlane's service? 'Cause this is the second of these services under the DataPlane umbrella. Give us a sense for how you'll continue to deepen your governance portfolio in DataPlane. >> Really the way to think about it, there are a couple of things that you touched on that I think are really critical, certainly for me, and for us at Hortonworks to continue to repeat, just to make sure the message got there. Number one, Hadoop is definitely at the core of what we've done, and was kind of the secret sauce. Some very different stuff in the technology, also the fact that it's open source and community, all those kinds of things. But that really created a foundation that allowed us to build the whole beginning of big data data management. And we added and expanded to the traditional Hadoop stack by adding Data in Motion. And so what we've done is-- >> Interviewer: NiFi, I believe, you made a major investment. >> Yeah, so we made a large investment in Apache NiFi, as well as Storm and Kafka as kind of a group of technologies. And the whole idea behind doing that was to expand our footprint so that we would enable our customers to manage their data through its entire lifecycle, from being created at the edge, all the way through streaming technologies, to landing, to analytics, and then even analytics being pushed back out to the edge. So it's really about having that common management infrastructure for the lifecycle of all the data, including Hadoop and many other things. And then in that, obviously as we discuss whether it be regulation, whether it be, frankly, future functionality, there's an opportunity to uplevel those services from an overall security and governance perspective. And just like Hadoop kind of upended traditional thinking... and what I mean by that was not the economics of it, specifically, but just the fact that you could land data without describing it. That seemed so unimportant at one time, and now it's like the key thing that drives the difference. Think about sensors that are sending in data that reconfigure firmware, and those streams change. Being able to acquire data and then assess the data is a big deal. So the same thing applies, then, to how we apply governance. I said this morning, traditional governance was hey, I started this employee, I have access to this file, this file, this file, and nothing else. I don't know what else is out there. I only have access to what my job title describes. And that's traditional data governance. In the new world, that doesn't work. Data scientists need access to all of the data. Now, that doesn't mean we need to give away PII. We can encrypt it, we can tokenize it, but we keep referential integrity. We keep the integrity of the original structures, and those who have a need to actually see the PII can get the token and see the PII. But it's governance thought inversely as it's been thought about for 30 years. >> It's so great you've worked governance into an increasingly streaming, real-time in motion data environment. Scott, this has been great. It's been great to have you on The Cube. You're an alum of The Cube. I think we've had you at least two or three times over the last few years. >> It feels like 35. Nah, it's pretty fun.. >> Yeah, you've been great. So we are here at Dataworks Summit in Berlin. (upbeat music)

>> Narrator: From Berlin, Germany, it's theCUBE! Covering DataWorks Summit, Europe 2018. (upbeat music) Brought to you by Hortonworks. (upbeat music) >> Hello, and welcome to theCUBE. I'm James Kobielus. I'm the lead analyst for Big Data analytics in the Wikibon team of SiliconANGLE Media, and we're here at DataWorks Summit 2018 in Berlin, Germany. And it's an excellent event, and we are here for two days of hard-hitting interviews with industry experts focused on the hot issues facing customers, enterprises, in Europe and the world over, related to the management of data and analytics. And what's super hot this year, and it will remain hot as an issue, is data privacy and privacy protection. Five weeks from now, a new regulation of the European Union called the General Data Protection Regulation takes effect, and it's a mandate that is effecting any business that is not only based in the EU but that does business in the EU. It's coming fairly quickly, and enterprises on both sides of the Atlantic and really throughout the world are focused on GDPR compliance. So that's a hot issue that was discussed this morning in the keynote, and so what we're going to be doing over the next two days, we're going to be having experts from Hortonworks, the show's host, as well as IBM, Hortonworks is one of their lead partners, as well as a customer, Munich Re, will appear on theCUBE and I'll be interviewing them about not just GDPR but really the trends facing the Big Data industry. Hadoop, of course, Hortonworks got started about seven years ago as one of the solution providers that was focused on commercializing the open source Hadoop code base, and they've come quite a ways. They've had their recent financials were very good. They continue to rock 'n' roll on the growth side and customer acquisitions and deal sizes. So we'll be talking a little bit later to Scott Gnau, their chief technology officer, who did the core keynote this morning. He'll be talking not only about how the business is doing but about a new product announcement, the Data Steward Studio that Hortonworks announced overnight. It is directly related to or useful, this new solution, for GDPR compliance, and we'll ask Scott to bring us more insight there. But what we'll be doing over the next two days is extracting signal from noise. The Big Data space continues to grow and develop. Hadoop has been around for a number of years now, but in many ways it's been superseded in the agenda as the priorities of enterprises that are building applications from data by some newer primarily open source technology such as Apache Spark, TensorFlow for building deep learning and so forth. We'll be discussing the trends towards the deepening of the open source data analytics stack with our guest. We'll be talking with a European based reinsurance company, Munich Re, about the data lake that they have built for their internal operations, and we'll be asking their, Andres Kohlmaier, their lead of data engineering, to discuss how they're using it, how they're managing their data lake, and possibly to give us some insight about it will serve them in achieving GDPR compliance and sustaining it going forward. So what we will be doing is that we'll be looking at trends, not just in compliance, not just in the underlying technologies, but the applications that Hadoop and Spark and so forth, these technologies are being used for, and the applications are really, the same initiatives in Europe are world-wide in terms of what enterprises are doing. They're moving away from Big Data environments built primarily on data at rest, that's where Hadoop has been, the sweet spot, towards more streaming architectures. And so Hortonworks, as I said the show's host, has been going more deeply towards streaming architectures with its investments in NiFi and so forth. We'll be asking them to give us some insight about where they're going with that. We'll also be looking at the growth of multi-cloud Big Data environments. What we're seeing is that there's a trend in the marketplace away from predominately premises-based Big Data platforms towards public cloud-based Big Data platforms. And so Hortonworks, they are partners with a number of the public cloud providers, the IBM that I mentioned. They've also got partnerships with Microsoft Azure, with Amazon Web Services, with Google and so forth. We'll be looking, we'll be asking our guest to give us some insight about where they're going in terms of their support for multi-clouds, support for edge computing, analytics, and the internet of things. Big Data increasingly is evolving towards more of a focus on serving applications at the edge like mobile devices that have autonomous smarts like for self-driving vehicles. Big Data is critically important for feeding, for modeling and building the AI needed to power the intelligence and endpoints. Not just self-driving cars but intelligent appliances, conversational user interfaces for mobile devices for our consumer appliances like, you know, Amazon's got their Alexa, Apple's got their Siri and so forth. So we'll be looking at those trends as well towards pushing more of that intelligence towards the edge and the power and the role of Big Data and data driven algorithms, like machine learning, and driving those kinds of applications. So what we see in the Wikibon, the team that I'm embedded within, we have published just recently our updated forecast for the Big Data analytics market, and we've identified key trends that are... revolutionizing and disrupting and changing the market for Big Data analytics. So among the core trends, I mentioned the move towards multi-clouds. The move towards a more public cloud-based big data environments in the enterprise, I'll be asking Hortonworks, who of course built their business and their revenue stream primarily on on-premises deployments, to give us a sense for how they plan to evolve as a business as their customers move towards more public cloud facing deployments. And IBM, of course, will be here in force. We have tomorrow, which is a Thursday. We have several representatives from IBM to talk about their initiatives and partnerships with Hortonworks and others in the area of metadata management, in the area of machine learning and AI development tools and collaboration platforms. We'll be also discussing the push by IBM and Hortonworks to enable greater depths of governance applied to enterprise deployments of Big Data, both data governance, which is an area where Hortonworks and IBM as partners have achieved a lot of traction in terms of recognition among the pace setters in data governance in the multi-cloud, unstructured, Big Data environments, but also model governments. The governing, the version controls and so forth of machine learning and AI models. Model governance is a huge push by enterprises who increasingly are doing data science, which is what machine learning is all about. Taking that competency, that practice, and turning into more of an industrialized pipeline of building and training and deploying into an operational environment, a steady stream of machine-learning models into multiple applications, you know, edge applications, conversational UIs, search engines, eCommerce environments that are driven increasingly by machine learning that's able to process Big Data in real time and deliver next best actions and so forth more intelligence into all applications. So we'll be asking Hortonworks and IBM to net out where they're going with their partnership in terms of enabling a multi-layered governance environment to enable this pipeline, this machine-learning pipeline, this data science pipeline, to be deployed it as an operational capability into more organizations. Also, one of the areas where I'll be probing our guest is to talk about automation in the machine learning pipeline. That's been a hot theme that Wikibon has seen in our research. A lot of vendors in the data science arena are adding automation capabilities to their machine-learning tools. Automation is critically important for productivity. Data scientists as a discipline are in limited supply. I mean experienced, trained, seasoned data scientists fetch a high price. There aren't that many of them, so more of the work they do needs to be automated. It can be automated by a mature tool, increasingly mature tools on the market, a growing range of vendors. I'll be asking IBM and Hortonworks to net out where they're going with automation in sight of their Big Data, their machine learning tools and partnerships going forward. So really what we're going to be doing over the next few days is looking at these trends, but it's going to come back down to GDPR as a core envelope that many companies attending this event, DataWorks Summit, Berlin, are facing. So I'm James Kobielus with theCUBE. Thank you very much for joining us, and we look forward to starting our interviews in just a little while. Our first up will be Scott Gnau from Hortonworks. Thank you very much. (upbeat music)

>> Announcer: Live from San Juan, Puerto Rico, it's the Cube, covering blockchain unbound. Brought to you by Blockchain Industries. (upbeat Cuban music) >> Hello there, and welcome back to our exclusive coverage. This is the Cube's coverage in Puerto Rico for Blockchain Unbound. We start week of variety of activities here on the island around blockchain, cryptocurrency, the decentralized internet, the future of work, the future of play, the future of society, all here, happening. My next guest is an entrepreneur. Steve Steward is the CEO and co-founder of that's V-E-Z-T. Really changing the game around music, relationship to fans, and using blockchain and tokens to enable that. Welcome to The Cube. >> Thank you so much John, it's great to be here. >> Thanks for coming on, so first talk a little bit about what your value proposition, what you guys are doing. Obviously, people who ever downloaded iTunes, and then said, "This sucks, let's go to Spotify." Now are going, "Hey, I'm on Instagram. "I have access to my artist directly." The internet is a response vehicle; one on one. Tell them about your opportunity. >> There's two value props. One for the consumer, right? So, if you're an artist fan, and you love a song. You love an artist. You want to be involved with that artist on a one to one basis, there's no way to do that right now. You can follow somebody on Twitter, you can like their YouTube, that doesn't connect you with them. Our platform let's you buy in, and by buy in I mean ownership. You own a piece of the IP with that artist in their song, so it's on a song by song basis. But if Ariana Grande's my favorite artist, I want to buy a little slice of her song for $10 or $100, I now have the opportunity to put that out there, and I can share in that royalty stream with her. And she and I will connect on a level. If she wants to take my information and send other things to me like concert tickets or backstage passes, that's possible now. So the value prop for the fan, is connection with the artist and ability to say, "I own a piece of that royalty stream. "I own a piece of that song." And on the artist side the value prop is, "I now get to actually share directly with my fans, "build that community directly. "There's no gate keeper like a label "or publishing company in the middle, "and I have the ability to reach out "and monetize directly based on demand and merit. "Then take that and do whatever I want "and build up my brand." >> So this is a great example where artists that have direct relationships, might be undervalued. Also, in a way there doing their own mini ICO, so to speak, with their fans by sharing in the future value of the success with the people that got 'em there. >> They are, we call it an ISO, Initial Song Offering. So just like a ticket on sale, it allows an artist to pick a time and date and say, "At noon on Thursday, I'm putting out 5% of my song "to raise $10,000." They pick the pricing, they pick the amount they want to put up, we admin the actual royalty stream for those people that put money into it, and the artist keeps the rest of it. >> I've seen a lot of pitches, I've seen a lot of stuff online, "Oh yeah, we're going to revolutionize "the new music industry, were going to use tokens." I've seen I feel pitches, but again, if you look at the smart money investors, they're looking at deals and saying, "Is there a network effect? "Is there a protocol of some sort in there?" Obviously you've identified a relationship that has tokenization or token economics built into the business model. Take a minute to explain that key tokenization. Why you're business is set for token economics? Why you, over someone else? >> So my backgrounds in the music business, I used to manage a band called Stone Temple Pilots for 20 years. Actually for 10 years, from 1990 to 2000. I had 20 other artist in that meantime. I understand the pain points from an artists perspective. I also know where the value is in the industry. It's in the publishing. Most of these entertainment businesses, the IP is where the real value is. Film, books, T.V., music, it's all in the underlying content. Not the distribution, not how many times I've downloaded it, but the actual ownership of the content. What we want to do, is put that in a basis so the artist can now take that on a fractional basis. We can use a tokenized product to let the fans buy in. The blockchain helps us track those rights, keep them secure, make them transparent, and allow the ownership to be shared between thousands or hundreds of thousands of people. >> And this also helps build community. I want to get your thoughts on something. I held a panel on Sundance this year, Sundance Film Festival, called The New Creative. What you're seeing emerging is a new artist. The new artists are digital native, their fan base is direct. Things we just talked about. But they're undervalued, because the gatekeepers, either the studios and or labels in your instance, are controlling distribution and they're also controlling the activities. So we all know what Apple's done with some of their artists, and artists have to go on the road and do all this work. Well digital changes all that, so from your perspective as a industry guru in music, how has digital changed that dynamic? And talk about this new artist breed, this new young upcoming digital generation of artists. >> There's two things. First, internet really hasn't delivered what it said it was going to the music community, right? When you had Napster come out, it's great for the fan base. The artist and the creators actually lost out. Music got valued from here to here. It went almost to zero. People were sharing files for free, so at some point we thought-- >> Regulatory tried to solve that legal-- >> Tried and tried, but once you build a generation on free, it's hard to change that. On the fan side it was great. There was a lot more distribution. On the artist and creator side, it wasn't so great. What we're trying to do is bring value back to that. We're going to use digital in a way that lets people share what they believe in, without these gatekeepers like you said; fully demand based. If I'm the small artist who plays banjo in Kentucky, but I've got a 100,000 fans who really love me, and they can show that by buying in, forget the labels. Forget the publishers. Forget the brands. I now have a direct connection. I'm earning a living directly from my fan base, which is how it should be. >> Kind of like we do open source content. We were talking about our business, you are enabling people to self-identify with the artist, letting the artist be open to that, make that handshake or if you will, digital handshake, and have a relationship beyond just being a fan. >> Most of the labels, in fact all the labels: Spotify, YouTube, Pandora. None of those platforms let the artist share directly with the consumer, right? If I say, "Look, I've got 20,000 streams today, "can you tell me who they were, no. "Can you show me where the downloads are, no." Why aren't they letting those people connect. The artist has a natural connection with their fans. >> That's because the tech platforms are optimized for a different business model. Look at Facebook, they're living in their own problem. Their success is almost killing them. They have this centralized data optimization for the wrong incentive. They're optimizing data for advertising, not user experience. In this case, you're saying, "Hey, lets use the infrastructure and crypto "to optimize the fan relationship and expand it." >> The reason artists get on stage, the reason they write a song, is to connect with people, right? We've disembodied that connection to the point where they're out there in the ether and the fans are over here. They're like, "How do we get together?" If we can bring that back, there's a very powerful connection there that we can take advantage of and let people actually make money from their craft. >> Well Steve, great to have you on The Cube because one, you have domain expertise, you're business model solid, and we've been saying yesterday and on The Cube that it's a reverse of the old stack model. The top of the stack is the business model. You nail the business model, the underlying plumbing will sort itself out. With that in mind, how are you guys looking at the plumbing? What are you doing here in Puerto Rico? Are you raising money? Are you doing an ICO? Take a little bit to explain your relationship to the plumbing under the hood, in the blockchain, crypto world. And then what you guys are doing here in Puerto Rico. >> We started building our platform the traditional way. We took traditional VC funding about a year ago. As we were building the platform, we understood the importance of a blockchain, some type of decentralized ledger that allows people to look transparently under the ownership stack. As we were building that, one of our engineers said, "hey, have you guys heard of an ICO?" we had no idea what this was. It was about a year ago. Got educated very quickly, dove deep on it, and realized there's an opportunity, not really for the fact that it's crypto, but to actually capitalize the company in a meaningful way. We want to scale this very quickly. We've got strategic partners in Asia, other parts of the world, that we need to grow very quickly into. We realized it was an opportunity to have. We did a raise close of December 1st; oron exchanges. >> An equity raise or a token raise? >> The token raise. We did a U.S. based PPM SAFT. >> So a security token. >> It's a utility token, but we followed a process that our legal advisors advised us. In the U.S., keep it as a PPM SAFT. If it's offshore, it's offshore. >> So accredited investors? >> Accredited investors only, small cap, try to keep it reasonable, because we don't need 100 billion dollars to build this platform right now. We're looking to get this in a traditional business sense, so we're building a real platform with a real team. We took advantage of that. We got listed on an exchange January 12th. At this point, we're head down in product. We're looking to launch this in 45 days at Coachella. We had an event two nights ago at South by Southwest. We came up here from Austin, so we're going back to California tomorrow. >> John: You're on a plane. >> Yeah, we're on a roadshow. We've got artist brand partners now. We're signing people, two or three artists a week that come in. We've got publishing catalogs that are coming on board realizing that there's a B to be played, because publishers only monetize the top two or 3% of their catalogs. The other 98% get no love. If they can put that on a retail platform like us, and allow consumers to buy directly into it, it's a whole windfall for them. >> Everyone's a media company these days. We've been saying it, and that's the new media model. You got a great formula, good luck. We'd love to keep in touch. >> Absolutely. >> What are you guys looking to do next six months as you get the product out the door? Ecosystem, you got to recruit more artists? What's the plan? >> My goal is 100,000 songs in the platform by the end of summer. Like I said, we're doing a lot of brand activations at music festivals. We see people, you know, exponential growth. Each song comes with an artist fan base. This builds into it. We're also supporting producers, co-writers, performers, the other guys that aren't on the stage. We realize this platforms for them, because the own live ownership in these songs, but have never had a way to monetize it. We're growing this very quickly. >> Steve Steward, CEO/co-founder of that's V-E-Z-T. Check 'em out. If you like music, this is a great way to actually take part in being a fan and owner of the actual property; great business model. We'll keep in touch. Thanks for sharing on The Cube. More live coverage here on The Cube, bringing you all the action, and extracting the signal from the noise. I'm John Furrier, thanks for watching. We'll be right back with more coverage after this break. >> Thanks guys, thanks John. (electronic instrumental music)

>> Narrator: Live from Las Vegas, it's theCUBE. Covering Oracle Modern Customer Experience, 2017. Brought to you by, Oracle. (upbeat music) >> Welcome back to theCUBE. I'm Peter Burris, and once again theCUBE is here at Oracle Modern Marketing... Modern Customer Experience, having a great series of conversations about the evolution of marketing, the role technology is playing, and especially important, the centerpiece that data now has within a overall orientation towards customer experience. Now one of the key features of that notion of customer experience is what's going on with service. And this is a great session, because we've got a representative from Oracle, but also a customer, as well. Welcome to Steve Fioretti, who's the VP of Product Management, Oracle Service Cloud and Heather Miksch, who's the Vice President of Field and Product Operations at Carbon. >> Thank you. >> Peter: Welcome to the (mumbles) >> Thanks. >> Glad to be here. >> So, Steve why don't we start with you. >> Steve: Sure. >> Oracle is here talking about how the cloud can help transform field and service operations. >> Steve: Right. >> How is it transforming it, what're the trends? >> Well, there's a lot of interesting trends that are affecting customer service, and I would, you talked about marketing and a lot of people say customer service is the new marketing. A lot of, a lot of interactions that people have with a company is in the customer service group and that really affects their impact on the brand. And there's a lot of things going on in the industry that are affecting customer service. There's new dynamic channels emerging, for example, people want to use Facebook Messenger, or WeChat, or WhatsApp as customer service channels to interact with their brand. It's much beyond just email, phone, chat, things like that. So, new channels are emerging and companies have to think about how do I integrate that into my customer service organization. Automation has really come into the fore. So, you know, in our personal lives we use Siri, and other V, you know, interactions we have with Alexa. So, those are coming into businesses to automate those, perhaps more simple, customer service processes. The internet of things is really taking off, where connected devices are allowing organizations to deliver predictive and proactive service. And on the automation front, they're even extending to where organizations are taking robotics and making robots agents in a retail store, for example. >> Are you talking about me? >> Wow it's Pepper. Hi, Pepper, what are...(Peter laughs) I didn't know you were here, that's awesome. So, Pepper, I'll ask you a question. What makes you a great Customer Service Agent? >> I'm smart, I'm connected, and I'm cool and, most importantly, I'm effective. (Steve laughs) >> And we replaced John Furrier with Pepper. >> Steve: Excellent.(Heather laughs) >> So, going to the next question about the, as we use robotics, as we use many of these things: we have to remember that these are not magic, they're really is no intelligence, in the classical sense, in them, they are still being driven to perform functions, take action, based on the availability of data that is coming off of customers. So talk a bit about the role the data, data integration, and some of these new tools: AI, or Adaptive Intelligence as you're calling it, are playing in ensuring that we can, enhance Customer Experience with new devices, and these new channels. >> You're absolutely right. I mean, if, you know, it's all about making the experience with a device like, like Pepper personalized and effective, and data, knowing what a consumer wants, what their preferences, and perhaps anticipating their preferences before, you know, they even know that; their past buying history, and taking all that, first-party data and third-party data, combining that with artificial intelligence, to deliver those personalized smart experiences is what's really happening. You heard a lot at this conference about Oracle's Adaptive Intelligence Initiative, and in the context of service, we're going to be building applications for things like account health, predictive field service, so, you know, you can predict ahead of time that a machine may, you know, may need service or break. And, you know, our customer here, Heather from Carbon is going to talk a lot about what they're doing with-- >> Well, so-- >> You know, smarts and the experience-- >> Got it, so how does this resonate with Carbon? >> Well, so, Carbon, is a, we manufacture an industrial 3D printer, and we have a process we call Digital Light Synthesis, which allows us to make photo-polymer materials that are robust enough to use in final production. So, our goal is to take customers from their design, of their part, straight into production, using the 3D printer as a means of production. And the reason why this is so exciting to Carbon, is our printer is actually an IOT device. It operates over the internet, and it operates through a browser. As a result, all types of data, from machine data from the printer, are flowing into our databases; as well as operational data, how long is the print taking, what type of resin is the customer using, how often are they printing, are they running into problems with their print? We've also built in a feedback system for the user, directly in the user interface, that flows directly through our channels into our databases, and it actually opens tickets in our Oracle Service Cloud for agents to contact the customers. The way we use this in a very practical standpoint, to give you one example, is for machine failures. The idea that we can monitor our printers in the field, and we can see if a part is having problems, and might fail, and we can actually proactively reach out to the customer and say, "We'd like to be there "in a couple weeks, change out this part. "It's not affecting your machine yet. "It's not affecting your prints." And, the customer is now able, instead of having unplanned downtime, which can be very difficult for a production environment, they now have planned downtime. This technology is nothing new. The example I like to use is, in the nuclear power industry, you don't wait until you have a core meltdown and then call your service engineer.(Steve laughs) Like, it's been around for for decades. >> Form has been around for a while. >> But what's new, is actually taking this technology and putting it in capital equipment, or putting it in devices like Peppper. I mean, she's also an IOT device; or even putting it into some of our wearables, or just other consumer products as well. And once you actually have this data coming through to the manufacturer of the device, it's really almost limitless what you can do with it. And, just in our short time of Carbon actually working on this problem, we have about 70% of our hardware failures are actually predictive. So that we're able to go out and repair the printer before the customer even realizes they have a problem. And some of the problems, we can actually fix before the customer knows anything, and we can fix them remotely from our offices in Redwood City. >> And it's interesting, theCUBE this week was also at the National Association of Broadcasters, in the NEB show, and we actually had an astronaut present over theCUBE. >> Yes, yes. >> One of the things that's interesting is there are 3D printers now on-- >> There are. >> Up on the Space Station. >> Yes, yes. >> So that you can print things a long ways away. That's one of the advantages, one of the great use cases of 3D printers >> Yes. >> Is that you can actually assemble, or you can create and assemble things, in very very, you know, unfriendly environments. >> Yes, yes. So, being able to schedule, and being able to plan that, is absolutely essential. >> Yes, yes and you can see, so for us, for 3D printers, some of the use cases that our customers are coming to us with, is they are companies, their own capital equipment manufacturers that have hundreds of thousands of spare parts, and they don't want to have to keep these inventories of massive spare parts. They want to have a design sent directly to a printer, maybe it's located in another country, closer to the point of use for that part, print out the part, and get it to the user faster. The idea is to actually move, one of the ideas, is to move manufacturing closer to the point of use. So that we're not spending all this time shipping products, you know, across the entire world, when we can actually be producing them much closer to the user. >> So that suggests, when we think about, again, the role of integration, the role of data, the idea of the Service Cloud; that there will be circumstances in which the part is printed and the capital equipment, Lessor, or the person who sold it, is on site to then put it in place, and assemble it. So now we're talking about multiple people operating very very quickly with a lot of new technology. >> Right. >> And, we now see why these types of devices and the need for that data sharing is so crucial. So, how is Oracle, in Oracle's vision of how service is going to be performed in the future, facilitating these types of interactions. >> So, I mean what we have to do is think about the technologies that are powering devices like robots, that are, providing technologies that are powering virtual assistants to automate customer interactions, to deliver technologies that help customers serve themselves. Another example is, more and more people, particularly younger generation, they don't want to phone. You've got a phone in home, they don't want to call you. They don't want to have anything to do with the phone. So, that's why things like messaging, self-service, going to a website and finding their own answer are critical. So, enabling and anticipating the data, the technologies, the way, the channels that people want to use, are all going to allow brands like Carbon and others to deliver great customer service for-- >> How are you using the Oracle Service Cloud, then, to facilitate many of these changes in your organization. >> So right now, what we have is for... We actually have a database we use for our big machine data. So, all the big machine data comes through, all the data coming off of our printers. And then we've integrated that database into Oracle Service Cloud; so then, instead of a customer having to phone up if they have a problem, we actually have, on our user interface, a little button, it just says "Request Help", that's all they need to do, and it's within the print job that they've been working on. All of that data about their print job: who the user is, what the company is, which printer they were using, how long was the print. Any specific information they want to say about the print, like why they're having trouble with it, it flows through into Oracle Service Cloud, and within the Oracle Service Cloud environment we can open up our big machine database, within that same environment, we can look at the actual print job. And then, we have an escalation tool we use for our engineering team. If we need to escalate, we can do that out of Service Cloud as well. And the idea is that there's very little manual entry of any other information. All of that is just flowing through, and everybody within the organization, whether it's the people that are first in front of the customer, or whether it's our engineers, have access to the exact same data. >> But is the system also then, through the escalation process, saying, well, we really got to get someone at the hardware level, or someone here, or someone at the design level. So you're flowing it to the right person. >> Yes, yes, absolutely. And the other fabulous thing about having these internet connected devices, is even when we do need to send somebody out on site to make a hardware fix, because of the diagnostic data we have from the device, we have, until now, 100% success rate in having the right part on-hand. Which is, if you've ever had much experience with capital equipment repairs, or even a repair of your dishwasher, sometimes the people don't have the right parts. We always have the right parts. >> That's too bad you couldn't >> So far, nothing-- >> print the part with the printer when it's down.(laughs) >> That's an interesting thing. We actually do have some parts within our printer that are printed on our printers, so its (laughs) it's pretty fun >> Can I talk about one other short example-- >> Of course. >> Of another customer that actually Heather's met here at the show, Denon & Marantz, so, they make all sorts of audio equipment, high-end audio equipment, and they've got a new brand of speakers, wireless speakers, called HEOS. And, when they first started, selling those to consumers they noticed, these are connected as well, they noticed that a number of them were having, a chip problem, remotely. People were calling in. So they went out, and they, they pinged, if you will, because they're connected, all of their consumer deployments, and they could tell that, you know, a small percentage of them are going to fail. They actually shipped speakers to those consumers before they even knew they had a problem, and they arranged to pick up the old ones, and you can imagine the value the customer, loyalty, and customer sat that that had. So that proactive predictive customer service example in the consumer world, and in a business world, really makes service that much-- >> Yeah. >> So, customer service, increasingly, is taking some degree of responsibility for ensuring that things operate within the threshold, as opposed to fixing things after they've broken. >> Yes, absolutely. >> Exactly. >> Heather: Yes, yeah. >> So how does that tie back into marketing and sales. So, at Carbon what is the, what is the way these feedback loops are being used to also inform marketing and selling. >> So, the interesting thing is that because we're also gathering operational data, we actually use the data coming off our printers for much more than just a service organization. In fact, our entire company is becoming more and more dependent on this printer data. So, for instance, our product group, when they're looking at bringing out a new feature they're actually looking at the data of the actual prints and the features that the customers are currently using, and deciding, do we need to augment this feature? Do we need to bring out another tool for our customers to use? And then looking at the printer data to make those decisions, and to prioritize what projects to work on because as you can imagine we've just got a ton of projects that we'd like to work on, and we need to make some priorities. The other thing that we're looking at is changing customer dynamics. Like we have, all of our customers are broken down into different industries, and we monitor the different printing behaviors, across industries, and we've been surprised. Like, there's certain industries that have grown faster than we would have expected, and because we've got this data that we look at every single day, we're looking at our customers' print data, we can actually make much faster corrections to either marketing campaigns, or sales strategies, or things like that, rather than waiting for a monthly roll-up or a quarterly roll-up or something like that. >> So who's the steward of data within Carbon? >> Who is the steward of data? We actually have a Director of Business Operations, his name is Chris Hutton. He actually works a lot with Oracle. He recently spoke at the Modern Finance Experience with Safra Catz, and I would say that if anyone's the steward of the data, he's probably the Grand Poobah of this data? But many of us have access to it. I mean, I can go into some of these databases and pull all the data I need. We don't really restrict it. >> But he's making sure that every, he's making sure that the data works for everybody in the organization. >> Yeah. Yeah, I'd say to some degree, yes. We also have our software engineers, making sure the printer data is actually-- >> Well, they're always... >> Heather, I think I would... >> Always behind the scenes. >> I think I would like the title Steward of Data. >> Yeah. (laughs) >> I think that's, I think I just found my new title. >> It's a little geeky.(laughs) >> Well it won't be long. Somebody's going to be called, and-- >> Exactly. One other quick example of how that feedback's happening between a customer service experience and let's say marketing, is, back to my Denon & Marantz example. They had another set of speakers, and they can tell, they often, the consumer will label the speaker, based upon, you know, this is the living room, this is the bedroom... And they had some failures on another brand of speakers, and they noticed a commonality, they were all labeled Bathroom. And, basically, they realized that their speakers... Some of these speakers couldn't handle the humidity that was happening in the bathroom; drove that back into product development, built a new series of speakers quickly for bathroom that were more waterproof, >> Yeah. >> Or, more moisture resistant, and created a new product extension that actually sells quite well. So, there's just a simple example of how that data flowed back into product development and marketing. >> So, Heather, you're not feeling like a fish out water here at a customer experience show with all of the-- >> Oh, no, of course not. No, I love this kind of stuff. >> What's exciting you about listening to, mainly marketers, but a lot of customer experience, too? >> I, you know people-- >> Talk about customer service >> That are in service, they get excited. I mean, fundamentally, there's all kinds of reasons for growing the business, and increasing revenue, and cutting costs, and all those things, but fundamentally, people are in service to help other people. Like, that's what gets us up in the morning. That's what makes us jump out of bed. So, the idea that there's all these companies doing these super-cool things, where you can, really, proactively be helping people instead of waiting till they're already in trouble. That's like, you've just burst through a barrier that's existed for millennia; the fact that we can actually start predicting problems. >> But that's also, we also talked a lot here on theCUBE this week about the role that talent's going to play. And, while I've never been in a hardcore customer service job, I know that people who have gone in, often got demoralized because they were always being yelled at because there was problem. >> Yes, yes, yes. >> And I had to believe it's attracting a new class of person because they can actually be participating, and anticipating, and solving problems >> Yes, yes, yes. Well, and I, it does take a certain type to be a customer, to be in front of customers all the time. We always say that the number one rule is you have to hire happy people to be put in that position, because (laughs) >> Peter: So, how about (Heather laughs) >> Actually, that was a very insightful question, because we were on a panel yesterday with an analyst, Denis Pombriant from the Beagle Research and he talked about, well, a couple of dynamics. One is, agents, the profile of the agents that you hire is changing. Because all the simple things are being solved online through self-service, and now that agent has to be a more gifted, even arguably, he called it a controller, a more aggressive agent that's going to be a problem-solver, able to collaborate with others. So, more empowered, and that's one thing, so I thought your question was really insightful. The nature of that agent is changing. And another thing that smart companies do, is they empower those agents. You know, not just with technology, but they give them the ability to, you know, the a brand of hotels, high-end hotels, I won't use the brand, but their agents are given a couple thousand dollars a day, and are empowered to use that to fix any issues. You know, somebody shows up and the room's booked, they don't drag them out of the hotel. (all laugh) They actually find them... Maybe they upgrade the room or they get them a meal if they have a problem so, empowering them also makes the agent feel much better about delivering customer service-- >> Alright, so Steve Fioretti, VP Product Management Oracle Service Cloud. Heather Miksch the Vice President of Field and Product Operations at Carbon, and Pepper from SoftBank. >> Yay! >> Thank you all for being a part of theCUBE here at the Oracle-- >> Thank you. >> Modern Customer Experience >> Thank you Peter. >> And talking about the role that service is now playing in driving customer experience and the role that the Cloud is playing in improving customer service. >> Steve: Great, awesome. >> We'll be back with a wrap-up in a few minutes, and in fact, John will magically reappear. Give us a few minutes and we'll be back with more from theCUBE. (upbeat music)