>> Announcer: Live from the Wigwam in Phoenix, Arizona. It's theCUBE. Covering Data Platforms 2017. Brought to you by Kubo. >> Hey welcome back everybody. Jeff Frick here with theCUBE along with George Gilbert from Wikibon. We've had a tremendous day here at DataPlatforms 2017 at the historic Wigwam Resort, just outside of Phoenix, Arizona. George, you've been to a lot of big data shows. What's your impression? >> I thought we're at the, we're sort of at the edge of what could be a real bridge to something new, which is, we've built big data systems for like out of traditional, as traditional software for deployment on traditional infrastructure. Even if you were going to put it in a virtual machine, it's still not a cloud. You're still dealing with server abstractions. But what's happening with Kubo is, they're saying, once you go to the cloud, whether it's Amazon, Azure, Google or Oracle, you're going to be dealing with services. Services are very different. It greatly simplifies the administrative experience, the developer experience, and more than that, they're focused on, they're focused on turning Kubo, the product on Kubo the service, so that they can automate the management of it. And we know that big data has been choking itself on complexity. Both admin and developer complexity. And they're doing something unique, both on sort of the big data platform management, but also data science operations. And their point, their contention, which we still have to do a little more homework on, is that the vendors who started with software on-prem, can't really make that change very easily without breaking what they've done on-prem. Cuz they have traditional perpetual license physical software as opposed to services, which is what is in the cloud. >> The question is, are people going to wait for them to figure it out. I talked to somebody in the hallway earlier this morning and we were talking about their move to put all their data into, it was S3, on their data lake. And he said, it's part of a much bigger transformational process that we're doing inside the company. And so, this move, from his cloud, public cloud viable, to tell me, give me a reason why it shouldn't go to the cloud, has really kicked in big time. And hear over and over and over that speed and agility, not just in deploying applications, but in operating as a company, is the key to success. And we hear over and over how many, how short the tenure is on the Fortune 500 now, compared to what it used to be. So if you're not speed and agile, which you pretty much have to use cloud, and software driven automated decision-making >> Yeah. >> that's powered by machine learning to eat. >> Those two things. >> A huge percentage of your transaction and decision-making, you're going to get smoked by the person that is. >> Let's let's sort of peel that back. I was talking to Monte Zweben who is the co-founder of Splice Machine, one of the most advance databases that sort of come out of nowhere over the last couple of years. And it's now, I think, in close beta on Amazon. He showed me, like a couple of screens for spinning it up and configuring it on Amazon. And he said, if I were doing that on-prem, he goes I needed Hadoop cluster with HBase. It would take me like four plus months. And that's an example of software versus services. >> Jeff: Right. >> And when you said, when you pointed out that, automated decision-making, powered by machine learning, that's the other part, which is these big data systems ultimately are in the service of creating machine learning models that will inform ever better decisions with ever greater speed and the key then is to plug those models into existing systems of record. >> Jeff: Right. Right. >> Because we're not going to, >> We're not going to to rip those out and rebuild them from scratch. >> Right. But as you just heard, you can pull the data out that you need, run it through a new age application. >> George: Yeah. >> And then feed it back into the old system. >> George: Yes. >> The other thing that came up, it was Oskar, I have to look him up, Oskar Austegard from Gannett was on one of the panels. We always talk about the flexibility to add capacity very easily in a cloud-based solution. But he talked about in the separation of storage and cloud, that they actually have times where they turn off all their compute. It's off. Off. >> And that was If you had to boil down the fundamental compatibility break between on-prem and in the cloud, the Kubo folks, both the CEO and CMO said, look, you cannot reconcile what's essentially server send, where the storage is attached to the compute node, the server. With cloud where you have storage separate from compute and allowing you to spin it down completely. He said those are just the fundamentally incompatible. >> Yeah, yeah. And also, Andretti, one of the founders in his talk, he talked about the big three trends, which we just kind of talked about, he summarized them right in serverless. This continual push towards smaller and smaller units >> George: Yeah. >> of store compute. And the increasing speed of networks is one, from virtual servers to just no servers, to just compute. The second one is automation, you've got to move to automation. >> George: Right. If you're not, you're going to get passed by your competitor that is. Or the competitor you that you don't even know that exists that's going to come out from over your shoulder. And the third one was the intelligence, right. There is a lot of intelligence that can be applied. And I think the other cusp that we're on, is this continuing crazy increase in compute horsepower. Which just keeps going. That the speed and the intelligence of these machines is growing at an exponential curve, not a linear curve. It's going to be bananas in the not too distance future. >> We're soaking up more and more that intelligence with machine learning. The training part of machine learning where the datasets to train a model are immense. Not only the dataset are large, but the amount of time to sort of chug through them to come up with the, just the right mix of variables and values for those variables. Or maybe even multiple models. So that we're going to see in the cloud. And that's going to chew up more and more cycles. Even as we have >> Jeff: Right. Right. >> specialized processors. >> Jeff: Right. But in the data ops world, in theory yes, but I don't have to wait to get it right. Right? I can get it 70% right. >> George: Yeah. >> Which is better than not right. >> George: Yeah. >> And I can continue to iterate over time. In that, I think was the the genius of dev-ops. To stop writing PRDs and MRDs. >> George: Yeah. >> And deliver something. And then listen and adjust. >> George: Yeah. >> And within the data ops world, it's the same thing. Don't try to figure it all out. Take the data you know, have some hypothesis. Build some models and iterate. That's really tough to compete with. >> George: Yeah. >> Fast, fast, fast iteration. >> We're doing actually a fair amount of research on that. On the Wikibon side. Which is, if you build, if you build an enterprise application that has, that is reinforced or informed by models in many different parts, in other words, you're modeling more and more digital entities within the business. >> Jeff: Right. >> Each of those has feedback loops. >> Jeff: Right. Right. >> And when you get the whole thing orchestrated and moving or learning in concert then you have essentially what Michael Porter many years ago called competitive advantage. Which is when each business process reinforces all the other business processes in service of a delivering a value proposition. And those models represent business processes and when they're learning and orchestrated all together, you have a, what Trump called a fined-tuned machine. >> I won't go there. >> Leaving out that it was Bigley and it was finely-tuned machine. >> Yeah, yeah. But the end of the day, if you're using resources and effort to improve an different resource and effort, you're getting a multiplier effect. >> Yes. >> And that's really the key part. Final thought as we go out of here. Are you excited about this? Do you see, they showed the picture the NASA headquarters with the big giant snowball truck loading up? Do you see more and more of this big enterprise data going into S3, going into Google Cloud, going into Microsoft Azure? >> You're asking-- >> Is this the solution for the data lake swamp issue that we've been talking about? >> You're asking the 64 dollar question. Which is, companies, we sensed a year ago at the at the Hortonworks DataWorks Summit in, was in June, down in San Jose last year. That was where we first got the sense that, people were sort of throwing in the towel on trying to build, large scale big data platforms on-prem. And what changes now is, are they now evaluating Hortonworks versus Cloudera versus MapR in the cloud or are they widening their consideration as Kubo suggests. Because now they want to look, not only at Cloud Native Hadoop, but they actually might want to look at Cloud Native Services that aren't necessarily related to Hadoop. >> Right. Right. And we know as a service wins. It's continue. PAS is a service. Software is a service. Time and time again, as a service either eats a lot of share from the incumbent or knocks the incumbent out. So, Hadoop as a service, regardless of your distro, via one of these types of companies on Amazon, it seems like it's got to win, right. It's going to win. >> Yeah but the difference is, so far, so far, the Clouderas and the MapRs and the Hortonworks of the world are more software than service when they're in the cloud. They don't hide all the knobs. You still need You still a highly trained admin to get them up-- >> But not if you buy it as a service, in theory, right. It's going to be packaged up by somebody else and they'll have your knobs all set. >> They're not designed yet that way. >> HD Insight >> Then, then, then, then, They better be careful cuz it might be a new, as a service distro, of the Hadoop system. >> My point, which is what this is. >> Okay, very good, we'll leave it at that. So George, thanks for spending the day with me. Good show as always. >> And I'll be in a better mood next time when you don't steal my candy bars. >> All right. He's George Goodwin. I'm Jeff Frick. You're watching theCUBE. We're at the historic 99 years young, Wigwam Resort, just outside of Phoenix, Arizona. DataPlatforms 2017. Thanks for watching. It's been a busy season. It'll continue to be a busy season. So keep it tuned. SiliconAngle.TV or YouTube.com/SiliconAngle. Thanks for watching.

>> 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)