>> Narrator: Live from The Wigwam in Phoenix, Arizona, it's the CUBE. Covering Data Platforms 2017. Brought to you by Qubole. (techno music) >> Hey, welcome back everybody. Jeff Frick here with the CUBE. We are in The Wigwam Resort, historic Wigwam Resort, just outside of Phoenix, Arizona at Data Platforms 2017. It's a new Big Data event. You might say, god there's already a lot of Big Data events, but Qubole's taken a different approach to Big Data. Cloud-first, cloud-native, you're integrated with all the big public clouds and they all come from Big Data backgrounds, practitioner backgrounds. So it's a really cool thing and we're really excited to have our next guest, Colin Ridell, he's a Big Data architect from Epic Games, was up on a panel earlier today. Colin, Welcome. >> Thank you, thank you for having me. >> Absolutely, so, enjoyed your panel, a lot of topics that you guys covered. One of the ones we hear over and over again is get early wins. How do you drive adoption, change people's behaviors, it's not really a technology story. It's a human factors and behaviors story. So I wonder if you can share some of your experience, some best practices, some stories. >> So I don't know if there's really a rule book on best practices for that. Every environment is different, every company is different. But one thing that seems to be constant is resistance to change in a lot of the places, so... >> Jeff: That is consistent. >> We had some challenges when I came in. We were running a system that was on it's last legs basically, and we had to replace it. There was really no choice. There was no fixing it. And so, I did actually encounter a fair bit of resistance with regards to that when I started at Epic. >> Now it's interesting, you said a fair amount of resistance. Another one of your lessons was start slow, find some early wins, but you said, that you were thrown into a big project right off the bat. >> Colin: So, we were, yeah. >> I'm curious, how did the big project go, but when you do start slow, how small does it need to be where you can start to get these wins to break down the resistance. >> I think what we, the way we approached it was we looked at what was the most crucial process, or the most crucial set of processes. And that's where we started. So that was what we tried to convert first and then make that data available to people via an alternative method, which was Hive. And once people started using it and learned how to interact with it properly the barriers start to fall. >> What were some of the difficult change management issues? Where did you come from in terms of the technology platform and what resistance did you hit? >> So it was really a user interface was the main factor of resistance. So we were running a Hadoop cluster. It was fixed sized, it wasn't on PRaM, but it was in a private cloud. It was basically, simply being overloaded. We had to do constant maintenance on it. We had to prop it up. And it was, the performance was degrading and degrading and degrading. The idea behind the replacement was really to give us something that was scalable, that would grow in the future, that wouldn't run into these performance blockers that we were having. But again, like I said, the hardest factor was the user interface differences. People were used to the tool set that they were working with, they liked the way it worked. >> What was the tool set? >> I would rather not actually say that on camera, >> Jeff: That's fine. >> Does it source itself in Redmond or something? >> No, no it doesn't, they're not from Redmond. I just don't want to cast aspersions. >> No, you don't need to cast aspersions. The conflict was really just around familiarity with the tool, it wasn't really about a wholesale change in behavior and becoming more data-centric. >> No, because the tool that we replaced was an effort to become more data-centric to begin with. There definitely was a corporate culture of we want to be more data-informed. So that was not one of the factors that we had to overcome. It was really tool-based. >> But the games market is so competitive, right? You guys have to be on your game all the time and you got to keep an eye on what everybody else is doing in their games, and make course corrections as I understand, something becomes hot, or new, so you guys have to be super nimble on your feet. How does taking this approach help you be more nimble in the way that you guys get new code out, new functionality? >> It's really, really very easy for us now to inject new events into the game, we basically can break those events out and report on them or analyze what's going on in the game for free with the architecture that we have now. >> Does that mean it's the equivalent of, in IT operations, we instrument everything from the applications, to the middleware, down to the hardware. Are you essentially doing the same to the game so you can follow the pathway of a gamer, or the hotspots of all the gamers, that sort of thing? >> I'm not sure I fully understand your question. >> When you're running analytics on a massively multi-player game, what questions are you seeking to answer? >> Really what we are seeking to answer at the moment is what brings people back? What behaviors can we foster in-- >> Engagement. >> in our players. Yeah, engagement, exactly. >> And that's how you measure engagement, it's just as simple as, do they come back or time on game? >> That's the most simple measure that we use for it, yeah. >> So Colin, we're short on time, want to give you the last word. When you come to a conference like this, there's a lot of peer interaction, there's some great questions coming out of the panel, around specifically, how do you measure success? It wasn't technical at all. It's, what are the things that you're using to measure whether stuff is working. I wonder if you can talk to the power of being in an ecosystem of peers here. Any surprises or great insights that you've got. I know we've only been here for a couple days. >> I would say that one of the biggest values, obviously the sessions and the breakouts are great, but I think one of the greatest values of here is simply the networking aspect of it. The being able to speak to people who are facing similar challenges, or doing similar things. Even although they're in a completely different domain, the problems are constant. Or common at least. How do you do machine learning to categorize player behaviors in our case and in other cases it's categorization of feedback that people get from websites, stuff like that. I really think the networking aspect is the most valuable thing to conferences like this. >> Alright, awesome. Well, Colin Ridell, Epic Games, thanks for taking a few minutes to stop by the CUBE. >> You're welcome, more than welcome, thank you very much. >> Absolutely, alright, George Gilbert, I'm Jeff Frick, you're watching the CUBE from Data Platforms 2017 at the historic Wigwam Resort. Thanks for watching. (upbeat techno music)

>> Narrator: Live from the Wigwam in Phoenix Arizona, it's theCUBE, covering data platforms 2017, brought to you by Qubole. >> Hey welcome back everybody, Jeff Frick here with theCUBE. I'm joined by George Gilbert from Wikibond and we're at DataPlatforms 2017. Small conference down at the historic Wigwam Resort, just outside of Phoenix, talking about, kind of a new approach to big data really. A Cloud native approach to big data and really kind of flipping the old model on it's head. We're really excited to be joined by Tripp Smith, he's the CTO of Clarity Insights, up on a panel earlier today. So first off, welcome Tripp. >> Thank you. >> For the folks that aren't familiar with Clarity Insights Give us a little background. >> So Clarity is a pure play data analytics professional services company. That's all we do. We say we advise, build and enable for our client. So what that means, is data strategy, data engineering and data science and making sure that we can action the insights that our customers get out of their data analytics platforms. >> Jeff: So not a real busy area these days. >> It's growing pretty well. >> Good for you. So a lot of interesting stuff came up on the panel. But one of the things that you reacted to, I reacted to as well from the keynote. Was this concept of, you know before you had kind of the data scientist with the data platform behind them, being service providers to the basic business units. Really turning that model on it's head. Giving access to the data to all the business units, and people that want to consume that. Making the data team really enablers of kind of a platform play. Seemed to really resonate with you as well. >> Yeah absolutely, so if you think about it, a lot of the focus on legacy platforms was driven by, scarcity around the resources to deal with data. So you created this almost pyramid structure with IT and architecture at the top. They were the gatekeepers and kind of the single door where Insights got out to the business. >> Jeff: Right. >> So in the big data world and with Cloud, with elastic scale, we've been able to turn that around and actually create much more collaborative friction in parallel with the business. Putting the data engineers, data scientists and business focus analystist together and making them more of partners, than just customers of IT. >> Jeff: Right, very interesting way, to think of it as a partner. It's a very different mindset. The other piece that came up over and over in the Q&A at the end. Was how do people get started? How are they successful? So you deal with a lot of customers, right? That's your business. What are some stories, or one that you can share of best practices, when people come and they say, we obviously hired you, we wrote a check. But how do we get started, where do we go first? How do you help people out? >> We focus on self funding analytic programs. Getting those early wins, tend to pay for more investment in analytics. So if you look at the ability to scale out as a starting point. Then aligning that business value and the roadmap in a way that going to both demonstrate the value along the way, and contribute to that capability is important. I think we also recommend to our clients that they solve the hard problems around security and data governance and compliance first. Because that allows them to deal with more valuable data and put that to work for their business. >> So is there any kind of low hanging fruit that you see time and time and time again? That just is like, ah we can do this. We know it's got huge ROI. It's either neglected cause they don't think it's valuable or it's neglected because it's in the backroom. Or is there any easy steps that you find some patterns? >> Yeah, absolutely. So we go to market by industry vertical. So within each vertical, we've defined the value maps and ROI levers within that business. Then align a lot of our analytic solutions to those ROI levers. In doing that, we focus this on being able to build a small, multifunctional team that can work directly with the business. Then deliver that in real time in an interactive way. >> Right, another thing you just talked about security and government, are we past the security concerns about public Cloud? Does that even come up as an issue anymore? >> You know, I think there was a great comment today that if you had money, you wouldn't put it in your safe at home. You'd put it in a bank. >> Jeff: I missed that one, that's a good one. >> The Cloud providers are really focused on security in a way that they can invest in it. That an individual enterprise really can't. So in a lot of cases, moving to the Cloud means, letting the experts take on the area that they're really good at and letting you focus on your business. >> Jeff: Right, interesting they had, Amazon is here, Google's here, Oracle's here and Azure is here. AWS reinvent one of my favorite things, is Tuesday night with James Hamilton. Which I don't know if you've ever been, it's a can't miss presentation. But he talks about the infrastructure investments that Amazon, AWS can make. Which again, compared to any individual enterprise are tremendous in not only security, but networking and all these other things that they do. So it really seems that the scale that these huge Cloud providers have now reach, gives them such an advantage over any individual enterprise, whether it's for security, or networking or anything else. So it's very different kind of a model. >> Yeah, absolutely, or even the application platform, like Google now having Spanner. Which has the scale advantage of Cassandra or H Based. The transactional capabilities of a traditional RDB mess. I guess my question is. Once a customer is considering Qubole, as a Cloud first data platform. How do you help the customer evaluate it? Relative to the dist rose that started out on Prim, and then the other Cloud native ones that are from Azure and Google and Amazon. >> You know I think that's a great question. It kind of focuses back on, letting the experts do what they're really good at. My business may not be differentiated by my ability to operate and support Hadoop. But it's really putting Hadoop to work in order to solve this business problems that makes me money. So when I look at something like Qubole, it's actually going to that expert and saying, "Hey own this for me and deliver this in a reliable way." Rather than me having to solve those problems over and over again myself. >> Do you think that those problems are not solved to the same degree by the Cloud native services? >> So I think there's definitely an ability to leverage Cloud data services. But there's also this aspect of administration and management, and understanding how those integrate within an ecosystem. That I don't think necessarily every company is going to be able to approach in the same way, that a company like Qubole can. So again, being able to shift that off and having that kind of support gives you the ability to focus back on what really makes a difference for you. >> So Tripp we're running out of time. We got a really tight schedule here. I'm just curious, it's a busy conference season. Big data's all over the place. How did you end up here? What is it about this conference and this technology that got you to come down to the, I think it's only a 106 today, weather to take it in. What do you see that's a special opportunity here? >> Yeah you know, this is Data Platforms 2017. It's been a really great conference, just in the focus on being able to look at Cloud and look at this differentiation. Outside of the realm of inventing new shiny objects and really putting it to work for new business cases and that sort of thing. >> Jeff: Well Tripp Smith, thanks for stopping by theCUBE. >> Excellent, Thank you guys for having me. >> All right, he's George Gilbert, I'm Jeff Frick. You're watching Data Platforms 2017 from the historic Wigwam Resort in Phoenix Arizona. Thanks for watching. (techno music)