 Live from the wigwam in Phoenix, Arizona, it's theCUBE, covering Data Platforms 2017, brought to you by CUBE. Hey, welcome back everybody. Jeff Frick here with theCUBE, along with George Gilbert from Wikibon. We've had a tremendous day here at Data Platforms 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 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 CUBE all 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. And services are very different. It greatly simplifies the administrative experience, the developer experience. And more than that, they're focused on turning CUBE all the product on CUBE all 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 homocon, is that the vendors who started with software on Prim can't really make that change very easily without breaking what they've done on Prim. Because they have traditional perpetual license, physical software, as opposed to services, which is what's in the cloud. The question is, are people going to wait for them to figure it out? I mean, 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 us three, on their data lake. And he said, it's part of a much bigger transformational process that we are doing inside the company. And so, this move from is cloud, public cloud viable to tell me, give me a reason why I shouldn't go to the cloud, has really kicked in big time. And we 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, that's powered by machine learning, to eat a huge percentage of your transaction in decision making, you're going to get smoked by the person that is. Let's sort of peel that back. I was talking to Montice Weven, who's the co-founder of Splice Machine, and one of the most advanced databases that sort of come out of nowhere over the last couple of years. And it's now, I think in closed beta on Amazon, he showed me a couple screens for spinning it up and configuring it on Amazon. And he said, if I were doing that on-prem, because I needed a Hadoop cluster with HBase, it would take me like four plus months. And that's an example of software versus services. 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. Because we're not going to rip those out and rebuild them from scratch. Right, but what we just heard, you can pull the data out that you need, run it through a new age application, and then feed it back into the old system. The other thing that came up, it was Oscar, I'm looking up Ostegard from Gannett, was on one of the panels. And 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. And that was off. If you had to boil down the fundamental compatibility break between on-prem and in the cloud, the cue ball folks, both the CEO and the CMO said, look, you cannot reconcile what's essentially a server stand 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 fundamentally incompatible. It's, and also, and Joyty, 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. So this just continual push towards smaller and smaller units 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. We've got to move to automation. If you're not, you're going to get passed by your competitor that is, or the competitor 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 distant future. And what that, we're soaking up more and more of that intelligence with machine learning, but the training part of machine learning, where the data sets to train a model are immense. And not only the data sets are large, but the amount of time to sort of chug through them to come up with just the right mix of variables and values for those variables, it takes, 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 specialized processors. 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, which is better than not right. And I can continue to iterate over time. And that I think was the genius of DevOps to start, stop writing PRDs and MRDs and deliver something and then listen and adjust. 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 hypotheses, build some models and iterate. And that's really tough to compete with. Fast, fast, fast iteration. And 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. Each of those has feedback loops. 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 a competitive advantage, which is when each business process reinforces all the other business processes in service of delivering a value proposition. And those models represent business processes. And when they're learning and orchestrated altogether, you have what Trump called a find-tuned machine. Well, I don't know. I won't go there. Leaving out that it was bigly and it was finely-tuned machine. Yeah, but at the end of the day, right, if you're using resources and effort to improve a different resource and effort, you're getting a multiplier effect, right? And that's really a key part. So final thoughts as we go out of here. You excited about this? Do you see, they showed the picture of 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? Is this the solution for the data lake swamp issue that we've been talking about? You're asking the $64 question, which is companies, we sensed a year ago at the Hortonworks data works summit in, it 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 Kubel 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. And we know that as-a-service wins, right? It passes the service, softwares the service. Time and time again, the 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. But the difference is, so far, the Cloudera's and the MapR's 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 highly-trained admins to get them out. 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 inside. Then they better be careful, because it might be a new as-a-service distro. Of the Duke data 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. It's George Gilbert. I'm Jeff Frick. You're watching theCUBE. We're at the historic 99-years-young wigwam resort just outside of Phoenix, Arizona. Data Platform's 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 slash SiliconANGLE. Thanks for watching.