 Okay, we're back live here at HP Discover to talk about big data, because that's what we'd love to talk about. With Jeff Kelly from Wikibon Jeff, welcome into theCUBE. Thanks for having me, good to see you, John. So what's going on with big data? What's your angle here at the show you've been scouting the landscape in the hallways? Yeah, we had a chance to talk with Colin Mahoney, who's the VP GM of Vertica. Yesterday on theCUBE had a good conversation, heard a little bit about kind of their, they recently released Vertica 6 and some of the updates there. And really, the Vertica team is focusing on building out Berga to be not just a tactical big data analytics engine, but really a big data hub. So they're making some really interesting additions to that platform. But yeah, big data, I mean, there's so many layers to it and it's the infrastructure, it's the storage layer, it's the analytics layer. So yeah, there's a lot happening, HP's in a pretty good spot. They've got all those layers kind of covered. I'm interested to see how they're going to bring them all together for kind of a unified solution. Okay, we're joined with Manoj Goyal. That get that right? Manoj Goyal. Manoj Goyal, okay. Senior director, data management solutions. What does that mean? Tell us what your job is. My job, or my team's job, I just get to talk about it, is to optimize our infrastructure, converge infrastructure, storage, server, networking, so that it absolutely can keep up with the volume that the big data is generating. We could constantly talk about the fact that, oh, just add another server. You know, this fallacy, I look at internet as a great source of information, but also sometimes as an opinion. So in the early days, some of the great web assets created some technologies that worked well on commodity servers, but they were coming with an army of software engineers who would highly tune the software to run on that stack. What we're finding out is as we analyze Hadoop and Vertica and autonomy, and we have run a variety of benchmarks that there's more than a commodity server to optimize these workloads. So what my team does is it works with all of these plus HANA and analyzes how all of these workloads can be optimized on our platform. And then we work very closely with the CTOs and the business unit heads on optimizing the infrastructure for big data. You know, obviously we've been covering, I mean, I've been covering HP for many years now and past, you know, three years in particular, we've been hyper all over HP in terms of coverage, especially with Donatelli over there, and converging infrastructure is a huge opportunity. It's half a trillion dollar market, just by itself, right? And you got all, you got the software teams over there. So HP's got all this stuff, but in comes the big data movement, cloud, mobile, social, and you guys are rolling out cloud, all this stuff, you guys are there, but you can't do cloud without doing big data. But big data isn't just about Hadoop. So try to share with the folks out there how HP is looking at the big data equation because it wasn't always a clean fit. Does it go into Donatelli's group? Does it go here? Who owns it? So it's kind of one of those groups that hits everything. So give us the holistic perspective of how HP's organized around big data right now. Absolutely. So first let me talk about the big data consumption problem. The industry has rapidly evolved and what used to be Hadoop MapReduce has now grown into a whole ecosystem. To take that to the market, first we had to simplify the big data equation. So an ESSN or Enterprise Group under Dave Donatelli, the focus is on pre-engineered workload optimized solutions that are factory integrated with, comes with bundle TS so that customers can just plug and play these clusters and get value almost immediately. So that was phase one. Had to make the sizing and complexity of provisioning big data easy. That's what Enterprise Group is doing. Then we are working now with our software partners, Autonomy and Vertica, to increase the value of Hadoop as you just talked about. Clearly Vertica is the best place to take the data once it's structured because you get denormalized unstructured data. Once you've structured it, Vertica is a great place to put that in. So we're working with our software partners to do that. Number three, HP Labs started well ahead of any of their other business units as you would expect and HP Labs started analyzing big data and their number of very key infrastructure, innovations going on. HP announced Memrister. Memrister is going to make petabytes of storage behave like DRAM. The phenomenal change there is that Hadoop is an IO bound environment and when you make so much of the storage very fast is going to do radical things to this environment. So we're working with HP Labs. HP IT is now working with Hadoop and working very closely with us to take our solutions and implement them in their data center as they're doing web commerce and analytics. So HP IT is working using it and we expect that collaboration to help us optimize. And then from a cloud perspective, what we're doing is first, making sure that every solution that we come out with is private cloud ready. That's something that Enterprise Group does but we're also working very closely with Public Cloud and Biri announced today that he's bringing autonomy and vertical to the cloud and he's selectively choosing the partners. We're working with them to make sure that whatever we do in the private cloud has elasticity so that as you lease capacity in Public Cloud, that leasing model works seamlessly. And so that's where the public cloud comes in. So for us in HP, as you pointed out, it's an end-to-end phenomena and every business has got an initiative and big data. But let me not stop there because none of this matters if you can't go the last mile and the Enterprise Services, oh my God. I mean, are both RTS, which is in our Enterprise Group and ES, are both working very closely with us to now take the whole ecosystem around Hadoop and make it easier from an accessibility to our customers because it's all about taking that last mile to consumption. And it's so new too. I mean, it's so new to even HP as they organize around big data. It's not new to HP. I mean, with the folks out there, I've covered HP's labs projects. They have big data in data center research they're doing around machine data and automation. I've interviewed Bernardo for sort of a social data. So killer stuff going on at HP for big data. It's just got to get its way into the operating environment of the divisions. And in some cases, HP will lead the way as in Memrister and in some cases will watch. And we've been watching big data for now five years. And we believe that this is a phenomenon that is going to disrupt the traditional database market with augmentation of capabilities to analyze stuff that was not analyzable. So HP is now all in. And one of the things that HP does is it takes one giant step and it's equal to 10 small steps of other companies. So the reason why I was telling you about big data being everywhere is because HP is ready to take giant step and we're going to just leave forward because all this innovation from HP labs, productization and release to market catalyze this once HP makes a decision, this is big and we're going to lead. And I'm on the record this morning and many times before, but today I actually said it on theCUBE, which was HP should buy Cloudera before Oracle does and be number one in Hadoop and number one in Enterprise Ready. So, okay, you don't have to say comment, you can just smirk. But with that Hadoop is really important. As you said, it's moving from batch, high availability has been released, MapReduce 2 is out with Cloudera. Real time is really important, it's still going there. What are you guys doing on the investment side for Hadoop? How much contribution? What is HP's role in the Apache Hadoop community? Great question, from our perspective, the investments are taking many directions. Number one, we got to make sure that we earn the trust of our customers and that comes from infrastructure investment. So the infrastructure investment, there isn't a day or a week where I'm not seeing from our hardware advanced labs benchmarks, results that are improving the big data performance. Number one. Number two, we are working with the ecosystem. So we're working with Cloudera, but we're working with 15 or 16 other software providers that are making no sequel relevant to the customers. Many of them have been mentioned, such as MongoDB, Couchbase. You've seen some of them on our foils in the presentation. So we're working with the ecosystem. A big area is development ecosystem. We're working with Cascading 2.0. It just came out. Cascading 2.0 is going to revolutionize the development environment and execution environments with big data. And then, how to make it all possible in the cloud. There's a lot of investment going on in that area. HP doesn't disclose its strategy on M&A, but we're always looking for ways to build partner and do gap fillers. And as time progresses, right things will happen, but we're all in on big data. Okay, we're getting the hook here. Thanks so much for talking to Hadoop. We love to talk about Hadoop. It's one of our favorite projects. It's the fastest growing open source project in the history of Apache, and it's great. So thanks for sharing. We'll be right back after this next break with our next guest.