 Okay, we're back live here in New York City for Strata. Hadoop World, this is Silicon Angles theCUBE. We go out to the events, they extract a signal from the noise. My co-host is Dave Vellante. I'm John Furrier, the founder of Silicon Angles. Jump right in with a venture capitalist who's been on theCUBE before. He's the emerging star in Big Data, VC. Mike Dauber, welcome back. You're also a very tall data kind of guy. You're tall. I mean, Michelle Bailey, I'm talking about. Everyone in Big Data is tall. Well, you know, we just have to stick with the theme, right? You know, we don't want someone who's a five, two company. Big, tall data. So, Mike, so you've been on last year. You're doing a lot of active investments. You're one of your investments continuity launch this week. We're going to have Todd Papiano, the CEO who's known for his business acumen as well as his technical chops. Yeah, that's right. If you want to really give him a hard time, make sure to just call him the business guy. If I want to talk to Jonathan, he's the tech guy. That's right. Well, that comes with being the CEO. So what are you seeing right now? Obviously, we talked to Mike Olson. The whole application space didn't explode last year. That was the big hope. But analytics did, right? Analytics did. But also, we saw adoption of Hadoop increase in context of overall big data. So simplicity and analytics see to be the themes. One, what did you see happen last year and what's happening now from startups that you're looking at from an investment standpoint? I think the biggest thing, just looking at the broader market for a second, is Hadoop's hit mainstream, right? I think if you talk to people a year ago, you could have argued, you know, people are still sort of just playing around with that it was a lot of test clusters. But now you're seeing, you know, Dow components, Fortune 500 companies, you know, really using Hadoop. And I think that's a really big deal, right? Because for any of these companies that we're investing in or any other VCs investing in, you know, someone has to be paying for this stuff. Right, so I think that's one. I think the other thing is, I was commenting to someone else about this earlier, you just look at the show to serve as a proxy. This place is packed. You know, when we went here last year, I mean, there were people here, but it was maybe 400 people, 500 people. I don't remember how many people were last year. 1,400. 1,400 last year? But they were at the Sheraton and they shut the Sheraton down. It was smaller. I don't remember last year being half this size. If they had more space, they'd probably easily get 5,000 people, 6,000 people. Yeah, it seems packed. And the quality of the people I'm seeing here, it's not all a bunch of VCs. I think on the company side, I think the broad theme that everyone is trying to figure out is some variation of how do you get big data to mere mortals, right? And every company you're seeing, be it, you know, Platform, you know, announced this morning after Olson, ClearStory, all these guys, it's all around how do you make big data accessible to people who don't have PhDs from Stanford or MIT? So tools above the Hadoop stack. Tools, but, and this is a theme that we believe really strongly in. You've mentioned continuity before, I think that's a good example of it, but, you know, continuity is still for developers. So that opens up the world for developers. But then, you know, business users, if I know how to use Excel, I don't know how to use Hadoop, you know, maybe I can't write SQL queries in Apollo. You know, what am I going to do? I need some kind of BI tool to make this a lot more accessible. I think you're going to see a lot more of those types of solutions. So, obviously, VC's invest in herds, and that's a good and bad thing. Well, bad thing if it's a Me Too market, but there's so much growth going on right now that it's not too bad. It's still early enough where there's a lot of Me Too. What are the Me Too investments right now in the big data space? And we hear that all the time. Oh yeah, big data business is coming in. I think the Me Too investments that worry me the most right now are these, you know, connectors between Tableau and Hadoop in some way, shape, or form, right? And I think being in that space, and Kurt Monash had a blog post about this yesterday, you know, he said he'd interviewed like five of these guys in the last couple of weeks. I mean, you can't sit there and just abstract all that value for Tableau. No one's going to pay for you, right? Because you just become interchangeable at that point. So I think that's an area where you're seeing there's a lot of interest, but also, you know, yes, it'd be great if you could run Tableau natively on Hadoop. I don't think you can do it. But a lot of people who are trying to run in and fill in that underlying layer, I think they're going to be in trouble. But I mean, to that point, I mean, Tableau really predated Hadoop. It was developed, you know, before this whole distributed system craze took off. So you're seeing some innovation around visualization, aren't you? You are. And actually, you know, if you look at one of our core theses, it's always, look where innovation is taking place and where the fundamental infrastructure is changing and try to find who's going to be the next winner in that new area. So look at a totally different space for a second. Look at what happened in the cloud, right? You had CRMs that were native apps like Siebel and you had Salesforce. You know, it was very, very, very hard for someone who is a native CRM application to be successful in the cloud. And we saw that all the way down the cloud stack. And so I think the question here is, we talked about this a little bit back in February, the question here is what are the big disruptions that Hadoop creates that necessitate a new innovation? And I would argue BI is actually probably first and foremost on that list. I think Tableau is a great company. I have tremendous respect for them. If they were to sell me shares today, I would buy them, but not for their big data piece. I think Tableau is a great tool, but it was built in a different era. It still runs on a Windows laptop, right? And I think the sort of capability that Platformer was talking about today, this notion of scale out running natively on Hadoop, that notion will win out over time. So is the EDW, the legacy BI space, is that the new mainframe in your view? I don't know, it's just a new mainframe. I think there's still a lot of reasons that people will use it, but if you think about what makes Hadoop great, Hadoop is schemalice on the fly. And so giving me a BI tool that doesn't take advantage of that capability to me just doesn't make sense because the whole point of me dumping data in Hadoop is I don't know the questions I want to ask until I want to ask them and think about how we all use Google. If you had to use Google, but you could only write queries once a day and the query, you know, you would write questions much differently, but when you're looking for a webpage, if you don't get the first hit with Google, you'd be like, oh, those are the search results. Let me refine that, let me come back with it. And I think a BI tool on Hadoop has to have that same, again, these are business users. This isn't, the data scientists aren't going to use these BI tools that much. You need to get the volume and the dollars. You need to have someone who's really going to go out and service the Google users. So final question for you is, what are the investment areas that you're looking at relative to the new emerging areas right now that what people aren't seeing. That's always been a VC success trade is to find the deals that aren't hot. The ones that aren't hot might be the hot ones. Yeah, that's right. I think, well certainly, we didn't think there was enough focus on developers. So we've already sort of made our bet there. I think BI obviously talking here is something that we need to spend more time looking at. You know, I think the big thing on big data is moving towards more verticalized apps. I talked to you guys a little bit about this back in February, but I just continue to believe that everyone's trying to solve sort of the one ring to rule them all. And that's just not. Matt, I would joke, believes the same thing, by the way. Yeah, that's not traditionally how problems get solved, right? It's very difficult, say, for maybe BI or a couple of things to really solve problems around broad markets. You need to have very specific solutions for very specific markets. And some of these markets might not be very sexy, right? But if there's big enough markets, I think you're going to see quote unquote big data solutions as big data is brought to those end applications. We were just out at the IBM conference, you know, the IBM's basically super gluing its analytics business to the big data meme. But one of the areas I was most impressed with is exactly what you're saying, Mike, is they are deep into all these verticals and they're actually crushing it within that space. Because they got the resources, they have the knowledge, they have the domain expertise. So that's definitely a trend to watch. Yeah, I think it's being able to productize that is where you'll see a lot more successes, right? And everything doesn't have to be, everything doesn't have to be a soup to nut solution. Right? You can have a vertical slice of a large pie and still make a really good company. Mike Dabber with Battery Ventures Guys, if you're an entrepreneur out there to go see Mike, we endorse him. He's a great guy, good venture firm. He's got his hands in a lot of pies here in the big data space, so go give Mike a call. Battery Ventures on the Cube. This is SiliconANGLE, we'll be right back with our next guest, Jeff Hummerbacher, Chief Data Scientist from CloudAir right after this short break. Thanks, guys. All the best, Mike.