>>Ed, welcome to the Cube. All right, Thanks guys. Good >>To see you. Thanks. Good to see you as well, >>John. Okay. Ed runs Biz dev for Cloudera, Industry veteran, worked at VMware. Ed, gotten to know you the past year. You guys have been doing great. What a difference one year makes, right? I mean, absolutely. Tell us, just let's start it off with what's happened in a year. I mean, you know, here at Hadoop World Cloudera, the ecosystem. Just give us your view of your perspective of what a difference one year makes. >>I think more than double is probably the, the fastest answer I could give you, which is, I mean, even looking around at the conference, it's, it itself is literally double from what it was last year. But in terms of the number of partners that have entered the market and really decided to work with, with Cloudera, but also in general, just the, the, the, the scope and size of the ecosystem itself, investors from every angle. You've got companies really well-branded marquee companies like Oracle coming into the mix and saying, Hey, Hadoop is the, is the real deal and we need to invest here. Marquee companies like IBM and EMC also doing the same. And of course, you know, as a result, you know, lots and lots of customer interest in the technology. And Cloudera's been fortunate to have been in the market early and really made the right investments with the right team. And so we're able to serve a lot of those customer needs. So it's been really, it's been a fantastic year for the company. >>So we had a great day yesterday with Cloudera. We had Kirk on, we had AER on twice, who by the way went viral with his modern warfare review, but we had Jeff Harmar Baer on, so we had pretty much the brain trust, Mike and Michaelson. Yep. The brain trust, the Cloudera. So we talked about the risk factors for Cloudera. Obviously you guys are number one, you've been kind of had untouchable lead and then all of a sudden boom competition. So Mike talked about that. So the strategy and the product side, they addressed, you're on the, the biz dev side, so you know, when you were number one, everyone wants to stand next to you and your phone rings off the hook from tier one partners all the way down to anyone's just getting in the business. Who wants a big data strategy on the execution. Now, what are you guys doing right now to, to continue your lead on the, on the sales marketing biz dev? I mean, I know you get the partner program, but what's your strategy for Phil, how to continue >>In that lead? The, the beautiful thing is honestly, our strategy hasn't changed at all. And I know that might sound counterintuitive, but we started off with a, a really crisp vision. And we want, what we wanna do is create a very attractive platform for partners. And, and, you know, one of the core, you know, sort of corporate strategy, Edix for Quadera is a recognition that the end of the day, the platform itself, Hado is an input into a solution. And Quadra is not likely to deliver the complete solution to market. Instead, it's going to be companies like Dell, for example, or it's going to be companies on the, on the ISV side like Informatica, which you're gonna deliver not only a base platform, but also the, the, the, the BI or analytics or data integration technologies on top. And as a result, what we've done is we've really focused in on creating a very attractive platform to vendors to build on. >>And one of the, I think one of the biggest misconceptions that I'm excited about that, you know, we are now having an opportunity to correct and that's a result, frankly, of the additional competitive dynamic. And I think the, the Wiki bond team pointed that out rather pointedly in their most recent articles. But is, is the sort of the lack of understanding around what CDH is and also the, some of the other investments that we're making to create a truly attractive platform for vendors to build on. And you know, I mean, I think you, you may have familiarity with exactly what CDH is, but for the sake of the audience here, what I'd like to do is say, say, first off, you know, first and foremost this is a hundred percent free in Apache license open source. But more importantly, it is everything that we build on the platform, meaning it's completely full featured. >>We put all of that out in the open. There's no turbo version of Hadoop that we've got hiding in the closet for our, our four pay customers. We're absolutely making investment. But I think, you know, when you think about it from the vendor perspective, and that's my bias. So I always think about, I treat all of the potential partners as really my customer. And when you think about it from that perspective, the things that matter most to vendors, number one, transparency. They need to understand exactly what our business model is, where we plan to make money and where we plan, don't make money. They need to know what we're really good at developing and what we're not so good at developing. And sort of where we draw the, the boundaries around that investment. I think, you know, a testament to that, for example, is tomorrow we're hosting a partner summit. >>So after this event, there are gonna be over 60 individuals, but they max two per per vendor. So we're gonna have over 35 vendors attending this event. And what they're gonna hear from is our entire management team is as deeply as we can and as open as we can. And you know, it, it's, it's, it's funny, you know, I think I saw this article in Forbes the other day about Cloudera. It was this, the title of the article was something like Spies Like Us. And it it, and it, what it highlighted was that some, some competitor of Cloudera had actually hired a, a, a competitive intelligence agency to go on and, and try to engage with, you know, and, and try to learn more about Cloudera. And so they went on to Cora, which we have a lot of active engineers on Cora. And they, you know, they went out and they asked a bunch of product related questions to our to, to someone on Cora. And our engineers immediately responded and they started being very transparent, completely open to what, what they're building and why they're building it. And the article basically summarized to say, Hey, you know what, you know, clearly some people aren't all that sophisticated in figuring out, you know, who they're talking to. And it's really important to do that. And they got the absolute wrong conclusion. Our engineers are actually encouraged and in fact rewarded for being extremely transparent in the market because we believe that it's transparency will ultimately allow us to be that platform vendor. >>And that's what attracts me. Jeff Hummer Bucker, who's active on core as well, he's recruiting there too. So you guys are out engaging the community. Yeah. So just let me just review, cuz this is cool that you're addressing this because Hortonworks and others, and I'll say the name Hortonworks has been pumping up the PR and creating a lot of noise around open and kind of Depositioning Cloudera. So you guys are completely open, a hundred percent Hadoop, open source, everything you build in, in every way, in every way. You have engineers building core, you've got tools and all the other stuff is being built in Cloudera then contributing into the community. >>Actually it's the other way around. We build it and the community@apache.org. So all of our technology is built@apache.org. It's, it's developed there. It's, it's, it's initially shared there. And then we have another team inside our company that pulls down bits from apache.org and then assembles them and integrates them. So it's really, it's a really key thing. And there's no, we do, we have no bits that we don't develop@apache.org that are part of cdh. So there, I mean there can be no mistake that everything that that is in CDH is everything we got. >>So CDH is free. >>It is free >>And every it's open source. It's open you >>Charge enterprise edition. That's the only thing that's different you guys charge >>Yeah. Which is your management console, right. >>Management >>Suite and all kinds of >>The tools. And that's not free and that's not open source. That's correct. Just to be clear. Yep. But so AER took us yesterday through, I don't know, half a dozen probably open source projects and then the one is the, the management console. And that's what you charge for, that's where you're gonna make money? >>Yeah. We, we manufacture, essentially we manufacture two products, but we sell one. So we manufacture the Quadera distribution, including Apache Duke, that's free. It's free. And then we all in open source and built it Apache and, and really heavily tested and well documented and, and, and well integrated. And then we also manufacture quadera Enterprise, which includes support and indemnities and warranties for that full featured CDH product and also includes the Quadra management suite. And >>That's a subscription. >>And that's a subscription. And so customers can, can run cdh, they can then buy and license Cloudera Enterprise and then someday if they decide they don't need Cloud Air Enterprise for whatever reason, if they're, if their team are scripting wizards and they've decided that they, you know, they don't need the extra opportunity for being able to track all of the things that Cloudier Enterprise allows 'em to, they can step off of cloud enterprise and continue to use full feature to do as they see >>Fit. So take an example of one of your partners that you announced this week. NetApp NetApp's gonna package your cdh CDH and the subscription Correct. To their, their customers. And then they're gonna let their channel either, you know, they'll pre bule it or do a reference architecture, you'll get paid for that subscription that's bundled. That's correct. Will make money off of its filers. Yes. And the customer gets a package solution. >>Exactly. Right. And in fact, that's another important thing that you know, is probably worth discussing, which is our go to market model. I don't know if you guys had a chance to talk with anyone yesterday on that, but I'm responsible for our channel strategy and one of the key things that we've agreed to as a, as a company is that we really are gonna go to market through channel partners. Yeah. >>We covered sgi, that was a great announcement. >>Yep, a >>Hundred percent >>As, as close as we can get. Okay. I mean that is our, he's >>Still doing the direct deals. You still have that belly to belly sales force because it's still early, right? So there's a mix of direct and indirects, not a pure >>Indirect, but as, and that's only, that's only as we're able to, until we're able to ramp up our partners fully, in which case we really want our, the current team that is working belly to belly to really support our partners. >>So all so VMware like, but I I wanted to ask >>You VMware, like NetApp, like very similar. >>Yes. Very, very NetApp. Like NetApp probably 75%, you know. Exactly. What are the similarities and differences with VMware in, in the ecosystem? You know it well, >>I do know it well. Yeah. I spent several years working at VMware and you know, I think, I mean the first and most obvious difference is that when you think, when I think about platform software in general, you know, there are a few different flavors of platform. One of the things that makes Hadoop very unique, very unique relative to other platforms is that it, not only is it Apache license, but it really is, it's dependent upon other external innovators to, to create the entire full value of the ecosystem. So, or, or you know, of the solution, right? So unlike for example, so like, let's take a platform like everyone's familiar with like Apple iTunes, right? What happens is Apple creates the platform and they put it kind of in the middle on top of and behind the scenes is the innovator, the app builder, he builds it, he publishes it on Apple, and then Apple controls all access to the >>Customer. Yep. >>That's not adu, right? Right. Let's take VMware or Red Hat for example. So in that case, they publish a platform they own and control the, the absolute structure and boundaries of what that platform is. And then on top of that application vendors build and then they deliver to the, the customer. But you know, at the end of the day, the, you know, the relationship really is, you know, from that external innovator straight down, and there's no, there's, you know, there's no way for them to really modify the platform. And you take kadu, which is a hundred percent Apache licensed to open source, and you really, you really open up the opportunity for vendors to take ADU as an input into their system and then deliver it straight to their customers or for customers themselves to say, I want straight up vanilla Hadoop, I'm gonna go this way and I'm gonna add on my own be app of applications. So you're, we're seeing all sorts of variants right now in the market. We're seeing software as a service being delivered that's based on Hadoop. There was a great announcement a few weeks ago from a company named Tidemark, previously known as Per Ferry, and they're taking all of cdh. They're, but they're, the customer doesn't know that they're, and what they're doing is they're delivering software as a, as a service based on adu. >>Yeah. So I mean, you know, we are psyched that you're clearing this up because obviously we're seeing, we saw all that stuff, but I really think that indirect strategy as a home run, I'm said it when we talked about the SGI thing, and it's accelerates you guys, you enable, but you know, channels is an interesting business. I mean the, you have to have pure transparency as you mentioned, but they need comp, people need confidence and, and they don't, they worry about competition. So channel conflict is always the big issue, right? Right. Is Cloudera gonna compete with us? So talk that, talk us through that, that strategy. So obviously the market's growing, new solutions are coming around the corner, These guys wanna make money. I mean channel, it's all about, you know, what have you done for me today? >>Right. That, that is exactly right. And you know what, that's, that's why we decided on the channel strategy specifically around our product is because we recognize that each and every single potential channel partner of ours can actually innovate themselves on top of and create differentiation. And we're not an obstacle to that process. So we provide our platform as an input and we're capable of managing that platform, but ultimately creating differentiation is all in the hands of our partners and we're there to help, but it gives them wide latitudes. So take for example, the differences between Dell and NetApp solution, they are very different reference architectures leveraging the exact same platform. >>Yeah. And they have to make money. I mean, the money making side of it is, you know, people have kind of, don't really talk about that, but, you know, channel partners loyalty is all about who can help them make cash. Right. Right. Exactly. What are you hearing there in terms of the ecosystem? Has the channels Bess and the partnerships or the more as size, what's the profile of your, of your partners? I mean, can you give us the breakdown of Sure. We have what you look like from Dell. We know Dell and NetApp, but they're gear guys. But, >>So a big part of our strategy is to work with IHVs and then Ihv resellers. So you're talking about companies like Dell, like sgi, like NetApp, for example, independent hardware manufacturers. Another part of our strategy though, and a key, a key requirement from our customers is to work with a whole variety of ISVs, particularly in the data management space. So you've got really marquee companies in the database space like IBM's Netezza or Terradata. You've got in companies like Informatica and Talent, you've got companies on the BI side, like Micro Strategy and Tableau. These kinds of technologies are currently in play at our customers that have made substantial investments. And ultimately they want to be able to continue to leverage them with the data platform, whichever data platform that they end up choosing. So we invest considerably there. A big part of that has been our Qera Connect partner program. >>It's an opportunity for us to help the customer to understand which technologies work and work well with, with our platform. It's also an opportunity for us to engage directly and assist the vendor. So one of the things that we created as part of that program is first off, immediate and absolute discounted access to any part of our training. Second, lots of free information, access to our world class knowledge base, access to our support team, direct access to our support team. The, the vendors also get access to a developer portal that would created specifically for them. So if, if you think about it this way, Hadoop gets built@apache.org, but solutions don't get built@apache.org. Right? So what we're really trying to help our vendors do is be able to develop their solutions by having real clear visibility to the API level points of Hadoop. They're not necessarily interested in, in trying to figure out how, how MR two works or, or contributing code to that. >>But they absolutely are interested in figuring out how to run and execute their software on top of a do. So when I think about the things that matter to create an attractive platform, and at the end of the day, that's what we're really trying to do, first and foremost is transparency, right? Second really ultimately is really clear visibility to the APIs and the documentation of that platform so that there's no ambiguity that the, the vendor, this is the user in this case, it's building a solution, can absolutely absorb all of that content really cleanly. And then ultimately, you know, I think it's customers, right? Users of the technology. And I think our download numbers are, they're, they're, there's something we're proud of. >>We, we are, we're hearing good feedback. I mean, the feedback we hear from folks is, yeah, I love how they take away the complexity of handling versions and whatnot. So, you know, I think totally is a great way, The CDH is a great bundle. You know, the questions that we have for you is what are you hearing about the other products, the ones you're actually selling? Does that create the lock in? So that's something that we asked Elmer directly, you know, is that the, is that the lock in and what happens when the deployments get so big? You know, >>I mean, the way, I >>Don't really see an issue there, but that's what people are afraid of. I mean, that's kind of the, it's more of fear. I mean, some people can use that fear and, and >>Play against. I think, I think what we've seen in other markets is that management tools are ultimately interchangeable. And the only way that we're gonna retain a customer is by out innovating the competition on the management side, the lock in, the lock in component, as you will, is not really part of our business model. It's very difficult to achieve with an Apache licensed platform and a management suite that sits on outside of that, that licensed artifact. So ultimately, if we don't owe innovate, we're gonna lose. So we're working on the innovation and that's, >>How's the hiring go? Oh, go ahead. >>I, I had a, I wanted to come back to that. You mentioned download numbers. Can you share the numbers >>With the others? I can't, I can't share them publicly, but what I can say is that they've been on an incredible trajectory. Okay. That, and what we've seen is month to month growth rates, every single month we continue to see really significant growth rates. >>And then I, I had a follow up question on, you talked about the, the partner program. How do you manage all those partners? How do you prioritize them? I mean, the, the hardware vendors, it's pretty easy. There's a few big whales, but the, the ISVs, they're, I mean, your phone, like John said, must be ringing off the hook. How do you juggle that and, and can you do it better than VMware, for example? >>Well, we do it, we handle the, the influx of partner interest in two ways. One, we've been relatively structured with the Quadra Connect partner program, and we make real investments there. So we have dedicated folks that are there to help. We have our engineering team that is actually feeding inputs, and we're, we're leveraging some of the same resources that we provide to our customers and feeding those directly to our partners as well. So that's one way that we handle it. But the other way, frankly, is, I mean, customers help here having access to and, and a real customer population, they help you set priorities pretty quickly. And so we're able to understand what we track in inside of our systems, which, which technologies our customers use. So we know, for example, what percentage of our customer base has has SaaS installed, and we'd like to use that with a, do we know which percentage of our customer base is currently running on Red Hat and which is not. So having core visibility, that helps us to prioritize. >>How about incentives? I mean, obviously channel businesses as, like I said, very fickle people, you know, you know the channel business, I spent, you know, almost a decade in, in HP's channel organization and you know, you have to provide soft dollars. There's a lot of kind of blocking and tackling. You guys are clearly building out that tier one with the SGIs of the world and other vendors, and then get the partner connect program for kinda everyone else who's gonna grow up into a tier one. Yeah. Training, soft dollars incentives. You guys have that going yet, or is the >>Roadmap? We do. And in fact, you know, in addition to the sort of more wide publicized relationships you see with companies like Dell and Cloudera, we're actually building a very successful network of independent ours. And the VAs in general. What we do is we prioritize and select ours based on the top level relationships that we have, because that really helps them to hone in. They've got validation from, for, for example, someone that sells resells. SGI is an organization that now is heard really loud and clear from sgi the, the specific platform configurations that they're gonna represent to their customers, and they ultimately wanna represent them directly. And how we make investments is we're, I mean, the investments we're making ultimately in our sales org, I'm gonna lose the word direct from that conversation because our sales org is being built to help our partners succeed. And I think that's where you're, >>The end game is to go completely indirect and have all your support go into managing that channel. What, what's the mix of revenue generation from your partners? Obviously as a, you know, with sgi they have pre-built channels that you're funneling in, you got NetApp and they're wrapping their products and services around it. How much is services and how much is a solution specifically? Do you have any visibility or a feel for that at this >>Point? I mean, services relative to, You mean for Cloudera particularly, or for our >>Partner? No, for the, for the part. I mean, if I'm a partner, I'm like, Hey, okay, I'm gonna use cdh. I'm on bundles. I don't mind paying you a wholesale if I'm gonna be able to throw off more cash on, you know, deployment and cloud and services, et cetera. And or if I'm a product manufacturer, a product, a solution I fund you in. I need to have that step >>Up a absolutely great question. So depending upon the partner we're dealing with, they like to either monetize or generate their revenue in different ways. So for example, NetApp, NetApp is a company that has very limited services, and their, their focus is a business is really on delivering hardware and software configured together. And they, they rely heavily on a services channel to fulfill, you take in, in contrast to a company like, for example, Dell, which has a very successful services business and really is excited about having service offerings around Hadoop. So it depends upon the company. But when we talk about our VAR channel in particular, one of the things that's a, in an internal acronym, but I'll share it publicly here. We, we call our, our supervisors and what makes them super and why, why we've selected the, the, the organizations that we are selecting right now to be our bar is that they not only can fulfill orders for hardware and software, particularly data management or infrastructure software, but they also have a services team on hand because we recognize that there is a services opportunity with every Hadoop deployment. And we want our partners to have that. So as an organization, we're structuring our, our services staff to facilitate and enable our partners not to be sold >>Directly. Okay. So that's the follow up that I had tomorrow when the partners ask, Okay, what do you want to be when you're really growing up? Is it services, is it software? >>Is it Carter is a software company, Crewing through, >>Oh, er we kind of got ett, well, he didn't say it, but we said it's a operating system. Yeah. >>So given that, so given that, I mean, you can make money on services, right? People need services. Okay, great. >>And partners will make that money for >>Us. And, and you know, early on you, you had to do some of that and you're, you've been very clear about where it's going. It's hard to make money in software when you're given all the software away for free. Well, >>We're not giving all >>The software. I know you've got that piece now, but, but here's my question. As ADU goes into the enterprise, which is clearly doing, is that that whole bundling, like what you're doing with NetApp is that really ultimately how you're gonna start to, to monetize and, and successfully monetize your software, >>Is by pushing it through >>Yeah. Packaging and that bundling that solution, in other words, our enterprise customer is gonna be more receptive to that solution package than say the, the fridge that has been using Hadoop for the last >>Two or three years. I think there's no question about it. If you, if you look at what Quadra Enterprise does, I don't know if, if you've had a chance to attend any of the sessions, maybe where Quadra Enterprise is, is currently being demonstrated. >>We just had Alex Williams as about on the air. Did a review, >>Okays >>Been going good and impressed with it? >>Yeah, there's no question about it. And I, I don't, and Alex probably hasn't seen the new version that, you know, our team is working on and it's, you know, quietly working on in the background. Incredible, incredible developments in, And that's really a function of when you have direct access to so many customers and you're getting so much input and feedback and they're the kinds of access to the kinds of customers we ultimately wanna serve. So real enterprises, what you get is really fast innovation from a really talented team that knows to do well. I mean, we are years ahead on the management side. Absolutely. Years ahead. And you know, I, so I was a guy who worked at VMware for several years, and I can tell you that while the hypervisor itself was, was a core component to VMware success, the monetization strategy was very squarely around vCenter. Yeah. Yes. Out. And we're not ignorant to that. Yeah. >>You can learn a lot from your VMware experience cause absolutely. The, the market changed significantly. And, you know, >>There were free hypervisors available all of a sudden. VMware itself had a free hypervisor. We had, we had VMware server and we had also our VMware player products, right? And those were all free. And they were very good technology. They were the best available in the market for free. And they were better, in my opinion, they were better than anything else. Open or not. No, our time >>Too, since still >>Are, they were, they, they were, they were superior products in every way. But yet how VMware was successful was recognizing that in the interest of running a production environment with an sola, you need management software. And they've also built the best management software. And there's no question that we understand that strategy and >>A phenomenal ecosystem. I mean, there's the >>Similarities, right? They did. And you, and the, and the ecosystem was in, in large part predicated on transparency act, very clear access to the APIs and a willingness to help partners be successful with those APIs. And ultimately drawing a very tight box about what the company wanted to do and didn't want to do. >>I mean, look, you're not, you're not gonna lose friends when you make people money. That's my philosophy, right? I agree. So when you're in that business where you can come in and enable a channel and have options on your growth strategy, which you do, I mean, you can say, Okay, bundling, I can go, you know, I can have this sold direct, or at least as long as you've got the options, you can grow with that market. So, you know, again, the, it's a money making opportunity for the partnerships, but there's >>More than that, right? Because you mentioned Apple, iTunes, Oracle's another example. And the way you make money with Apple and the way you make money with Oracle is different than the way you make money with VMware and presumably Cloudera. >>Yeah, I mean, our strategy is, if you make this base platform easier to install, more reliable, and you make it ultimately, you know, really rock solid from an integration standpoint, more people are gonna use it. So what happens when more people use it? First thing that happens is more solu, it's out there. So it's more solutions get built. When more solutions get built, then you see more clusters get developed. When more clusters are out there, they start to move into production. And then they, they need an sla when they need an sla, Cloudera and Enterprise gets purchased. But along that path, when those solutions got built, guess what else happened? More cloud units got sold, more servers got sold, more networking. Gear got sold, more services got created. You get, you get ultimately more operating systems got sold, more databases, got data into them, more BI clients got created. The ecosystem is deep and rich, and a lot of people stand to make money hop >>In people. The water's great. >>What about, what about support? Okay, so, you know, the other guys are saying, We're just gonna make money on support. I mean support, You guys still are doing support, right? I mean, you're selling >>Support. There's no question. Quad Enterprise contains two things, right? The management suite and support this is, this is not uncomplicated technology and having a world class support team is of value and customers do want to pay for that value. But we, we believe that support in and of itself is not enough. And that ultimately, when you wanna deliver an sla, being able to call when you have a problem is the wrong approach. You want to be proactive and understand the problem well in advance of it actually occurring. That's really important. When, for example, if you're a customer, a lot of our customers have a data pipeline that >>They, they're building out basically. I mean they're, it's, it's new and emerging. So they're building out, It's not just support. They need other tools. >>Yeah. And it building out I think is an understatement for some, where some of our customers are. I mean, when you have a thousand node cluster that you're operating Yeah, Yeah. To, that's mission critical to your business. I don't think that's building out anymore. I think that's an investment in a technology that's mission critical. And what you wanna see when you have a mission critical technology is you wanna know early and often when a problem may emerge. Not, Oh, oh my gosh, we have a problem now I need to go, you know, phone a friend, phone a friend is, is kind of a last resort. We offer that. But what we really do is, and that's the, that's the beau, That's why we don't decouple our support from our management suite. It's not about phone a friend. It's about understanding the operation of your cluster the entire way through 24. >>And the other op the other thing that people don't talk about in the support is that with open source, a lot of support gets handled in the community as well. So like That's right. So in a way, you're already pre cannibalized with the community >>By us and by others. Absolutely. But you, you'll never see to that Forbes article I referenced earlier. You will never, you will not see our, our engineers are not trained to withhold information and under any circumstances to anyone free or paying. Yeah. This is about getting, You >>Don't wanna hold back your business. I mean, you have nothing to hide. It's open rights. >>Open source. It's open. And we're here to help. We're here to help. Whether you're paying us or not, >>This is value to that anticipatory >>Remediation. Yeah. That's what you're packaging and clearing up the air. Great. Great cube guest, you're awesome on the cube. Gonna have you more on because great to get the info out there. Really impressed with the channel strategy. Love the love the growth strategy, the cloud air. You guys are really impressive. I'm really, really impressed to see that you guys got everything pumping on all cylinders, Kirk, and you are cranking out on the business execution. We're in the team playing this chest mask open. Perfect. So great. Congratulations. Great. Thanks. You guys just in the financing. >>Oh, thank you as >>Well. Hey, Ed from Cloudera, clearing it up here inside the cube. We're gonna take a quick break and we'll be right back with more video. >>Thanks guys. All right.

(upbeat music) >> Hello, everyone, welcome to theCUBE. I'm John Furrier, the host of theCUBE in Palo Alto, California. Also it's SiliconANGLE News. Got two great guests here to talk about AI, the impact of the future of the internet, the applications, the people. Amr Awadallah, the founder and CEO, Ed Alban is the CEO of Vectara, a new startup that emerged out of the original Cloudera, I would say, 'cause Amr's known, famous for the Cloudera founding, which was really the beginning of the big data movement. And now as AI goes mainstream, there's so much to talk about, so much to go on. And plus the new company is one of the, now what I call the wave, this next big wave, I call it the fifth wave in the industry. You know, you had PCs, you had the internet, you had mobile. This generative AI thing is real. And you're starting to see startups come out in droves. Amr obviously was founder of Cloudera, Big Data, and now Vectara. And Ed Albanese, you guys have a new company. Welcome to the show. >> Thank you. It's great to be here. >> So great to see you. Now the story is theCUBE started in the Cloudera office. Thanks to you, and your friendly entrepreneurship views that you have. We got to know each other over the years. But Cloudera had Hadoop, which was the beginning of what I call the big data wave, which then became what we now call data lakes, data oceans, and data infrastructure that's developed from that. It's almost interesting to look back 12 plus years, and see that what AI is doing now, right now, is opening up the eyes to the mainstream, and the application's almost mind blowing. You know, Sati Natel called it the Mosaic Moment, didn't say Netscape, he built Netscape (laughing) but called it the Mosaic Moment. You're seeing companies in startups, kind of the alpha geeks running here, because this is the new frontier, and there's real meat on the bone, in terms of like things to do. Why? Why is this happening now? What's is the confluence of the forces happening, that are making this happen? >> Yeah, I mean if you go back to the Cloudera days, with big data, and so on, that was more about data processing. Like how can we process data, so we can extract numbers from it, and do reporting, and maybe take some actions, like this is a fraud transaction, or this is not. And in the meanwhile, many of the researchers working in the neural network, and deep neural network space, were trying to focus on data understanding, like how can I understand the data, and learn from it, so I can take actual actions, based on the data directly, just like a human does. And we were only good at doing that at the level of somebody who was five years old, or seven years old, all the way until about 2013. And starting in 2013, which is only 10 years ago, a number of key innovations started taking place, and each one added on. It was no major innovation that just took place. It was a couple of really incremental ones, but they added on top of each other, in a very exponentially additive way, that led to, by the end of 2019, we now have models, deep neural network models, that can read and understand human text just like we do. Right? And they can reason about it, and argue with you, and explain it to you. And I think that's what is unlocking this whole new wave of innovation that we're seeing right now. So data understanding would be the essence of it. >> So it's not a Big Bang kind of theory, it's been evolving over time, and I think that the tipping point has been the advancements and other things. I mean look at cloud computing, and look how fast it just crept up on AWS. I mean AWS you back three, five years ago, I was talking to Swami yesterday, and their big news about AI, expanding the Hugging Face's relationship with AWS. And just three, five years ago, there wasn't a model training models out there. But as compute comes out, and you got more horsepower,, these large language models, these foundational models, they're flexible, they're not monolithic silos, they're interacting. There's a whole new, almost fusion of data happening. Do you see that? I mean is that part of this? >> Of course, of course. I mean this wave is building on all the previous waves. We wouldn't be at this point if we did not have hardware that can scale, in a very efficient way. We wouldn't be at this point, if we don't have data that we're collecting about everything we do, that we're able to process in this way. So this, this movement, this motion, this phase we're in, absolutely builds on the shoulders of all the previous phases. For some of the observers from the outside, when they see chatGPT for the first time, for them was like, "Oh my god, this just happened overnight." Like it didn't happen overnight. (laughing) GPT itself, like GPT3, which is what chatGPT is based on, was released a year ahead of chatGPT, and many of us were seeing the power it can provide, and what it can do. I don't know if Ed agrees with that. >> Yeah, Ed? >> I do. Although I would acknowledge that the possibilities now, because of what we've hit from a maturity standpoint, have just opened up in an incredible way, that just wasn't tenable even three years ago. And that's what makes it, it's true that it developed incrementally, in the same way that, you know, the possibilities of a mobile handheld device, you know, in 2006 were there, but when the iPhone came out, the possibilities just exploded. And that's the moment we're in. >> Well, I've had many conversations over the past couple months around this area with chatGPT. John Markoff told me the other day, that he calls it, "The five dollar toy," because it's not that big of a deal, in context to what AI's doing behind the scenes, and all the work that's done on ethics, that's happened over the years, but it has woken up the mainstream, so everyone immediately jumps to ethics. "Does it work? "It's not factual," And everyone who's inside the industry is like, "This is amazing." 'Cause you have two schools of thought there. One's like, people that think this is now the beginning of next gen, this is now we're here, this ain't your grandfather's chatbot, okay?" With NLP, it's got reasoning, it's got other things. >> I'm in that camp for sure. >> Yeah. Well I mean, everyone who knows what's going on is in that camp. And as the naysayers start to get through this, and they go, "Wow, it's not just plagiarizing homework, "it's helping me be better. "Like it could rewrite my memo, "bring the lead to the top." It's so the format of the user interface is interesting, but it's still a data-driven app. >> Absolutely. >> So where does it go from here? 'Cause I'm not even calling this the first ending. This is like pregame, in my opinion. What do you guys see this going, in terms of scratching the surface to what happens next? >> I mean, I'll start with, I just don't see how an application is going to look the same in the next three years. Who's going to want to input data manually, in a form field? Who is going to want, or expect, to have to put in some text in a search box, and then read through 15 different possibilities, and try to figure out which one of them actually most closely resembles the question they asked? You know, I don't see that happening. Who's going to start with an absolute blank sheet of paper, and expect no help? That is not how an application will work in the next three years, and it's going to fundamentally change how people interact and spend time with opening any element on their mobile phone, or on their computer, to get something done. >> Yes. I agree with that. Like every single application, over the next five years, will be rewritten, to fit within this model. So imagine an HR application, I don't want to name companies, but imagine an HR application, and you go into application and you clicking on buttons, because you want to take two weeks of vacation, and menus, and clicking here and there, reasons and managers, versus just telling the system, "I'm taking two weeks of vacation, going to Las Vegas," book it, done. >> Yeah. >> And the system just does it for you. If you weren't completing in your input, in your description, for what you want, then the system asks you back, "Did you mean this? "Did you mean that? "Were you trying to also do this as well?" >> Yeah. >> "What was the reason?" And that will fit it for you, and just do it for you. So I think the user interface that we have with apps, is going to change to be very similar to the user interface that we have with each other. And that's why all these apps will need to evolve. >> I know we don't have a lot of time, 'cause you guys are very busy, but I want to definitely have multiple segments with you guys, on this topic, because there's so much to talk about. There's a lot of parallels going on here. I was talking again with Swami who runs all the AI database at AWS, and I asked him, I go, "This feels a lot like the original AWS. "You don't have to provision a data center." A lot of this heavy lifting on the back end, is these large language models, with these foundational models. So the bottleneck in the past, was the energy, and cost to actually do it. Now you're seeing it being stood up faster. So there's definitely going to be a tsunami of apps. I would see that clearly. What is it? We don't know yet. But also people who are going to leverage the fact that I can get started building value. So I see a startup boom coming, and I see an application tsunami of refactoring things. >> Yes. >> So the replatforming is already kind of happening. >> Yes, >> OpenAI, chatGPT, whatever. So that's going to be a developer environment. I mean if Amazon turns this into an API, or a Microsoft, what you guys are doing. >> We're turning it into API as well. That's part of what we're doing as well, yes. >> This is why this is exciting. Amr, you've lived the big data dream, and and we used to talk, if you didn't have a big data problem, if you weren't full of data, you weren't really getting it. Now people have all the data, and they got to stand this up. >> Yeah. >> So the analogy is again, the mobile, I like the mobile movement, and using mobile as an analogy, most companies were not building for a mobile environment, right? They were just building for the web, and legacy way of doing apps. And as soon as the user expectations shifted, that my expectation now, I need to be able to do my job on this small screen, on the mobile device with a touchscreen. Everybody had to invest in re-architecting, and re-implementing every single app, to fit within that model, and that model of interaction. And we are seeing the exact same thing happen now. And one of the core things we're focused on at Vectara, is how to simplify that for organizations, because a lot of them are overwhelmed by large language models, and ML. >> They don't have the staff. >> Yeah, yeah, yeah. They're understaffed, they don't have the skills. >> But they got developers, they've got DevOps, right? >> Yes. >> So they have the DevSecOps going on. >> Exactly, yes. >> So our goal is to simplify it enough for them that they can start leveraging this technology effectively, within their applications. >> Ed, you're the COO of the company, obviously a startup. You guys are growing. You got great backup, and good team. You've also done a lot of business development, and technical business development in this area. If you look at the landscape right now, and I agree the apps are coming, every company I talk to, that has that jet chatGPT of, you know, epiphany, "Oh my God, look how cool this is. "Like magic." Like okay, it's code, settle down. >> Mm hmm. >> But everyone I talk to is using it in a very horizontal way. I talk to a very senior person, very tech alpha geek, very senior person in the industry, technically. they're using it for log data, they're using it for configuration of routers. And in other areas, they're using it for, every vertical has a use case. So this is horizontally scalable from a use case standpoint. When you hear horizontally scalable, first thing I chose in my mind is cloud, right? >> Mm hmm. >> So cloud, and scalability that way. And the data is very specialized. So now you have this vertical specialization, horizontally scalable, everyone will be refactoring. What do you see, and what are you seeing from customers, that you talk to, and prospects? >> Yeah, I mean put yourself in the shoes of an application developer, who is actually trying to make their application a bit more like magic. And to have that soon-to-be, honestly, expected experience. They've got to think about things like performance, and how efficiently that they can actually execute a query, or a question. They've got to think about cost. Generative isn't cheap, like the inference of it. And so you've got to be thoughtful about how and when you take advantage of it, you can't use it as a, you know, everything looks like a nail, and I've got a hammer, and I'm going to hit everything with it, because that will be wasteful. Developers also need to think about how they're going to take advantage of, but not lose their own data. So there has to be some controls around what they feed into the large language model, if anything. Like, should they fine tune a large language model with their own data? Can they keep it logically separated, but still take advantage of the powers of a large language model? And they've also got to take advantage, and be aware of the fact that when data is generated, that it is a different class of data. It might not fully be their own. >> Yeah. >> And it may not even be fully verified. And so when the logical cycle starts, of someone making a request, the relationship between that request, and the output, those things have to be stored safely, logically, and identified as such. >> Yeah. >> And taken advantage of in an ongoing fashion. So these are mega problems, each one of them independently, that, you know, you can think of it as middleware companies need to take advantage of, and think about, to help the next wave of application development be logical, sensible, and effective. It's not just calling some raw API on the cloud, like openAI, and then just, you know, you get your answer and you're done, because that is a very brute force approach. >> Well also I will point, first of all, I agree with your statement about the apps experience, that's going to be expected, form filling. Great point. The interesting about chatGPT. >> Sorry, it's not just form filling, it's any action you would like to take. >> Yeah. >> Instead of clicking, and dragging, and dropping, and doing it on a menu, or on a touch screen, you just say it, and it's and it happens perfectly. >> Yeah. It's a different interface. And that's why I love that UIUX experiences, that's the people falling out of their chair moment with chatGPT, right? But a lot of the things with chatGPT, if you feed it right, it works great. If you feed it wrong and it goes off the rails, it goes off the rails big. >> Yes, yes. >> So the the Bing catastrophes. >> Yeah. >> And that's an example of garbage in, garbage out, classic old school kind of comp-side phrase that we all use. >> Yep. >> Yes. >> This is about data in injection, right? It reminds me the old SQL days, if you had to, if you can sling some SQL, you were a magician, you know, to get the right answer, it's pretty much there. So you got to feed the AI. >> You do, Some people call this, the early word to describe this as prompt engineering. You know, old school, you know, search, or, you know, engagement with data would be, I'm going to, I have a question or I have a query. New school is, I have, I have to issue it a prompt, because I'm trying to get, you know, an action or a reaction, from the system. And the active engineering, there are a lot of different ways you could do it, all the way from, you know, raw, just I'm going to send you whatever I'm thinking. >> Yeah. >> And you get the unintended outcomes, to more constrained, where I'm going to just use my own data, and I'm going to constrain the initial inputs, the data I already know that's first party, and I trust, to, you know, hyper constrain, where the application is actually, it's looking for certain elements to respond to. >> It's interesting Amr, this is why I love this, because one we are in the media, we're recording this video now, we'll stream it. But we got all your linguistics, we're talking. >> Yes. >> This is data. >> Yep. >> So the data quality becomes now the new intellectual property, because, if you have that prompt source data, it makes data or content, in our case, the original content, intellectual property. >> Absolutely. >> Because that's the value. And that's where you see chatGPT fall down, is because they're trying to scroll the web, and people think it's search. It's not necessarily search, it's giving you something that you wanted. It is a lot of that, I remember in Cloudera, you said, "Ask the right questions." Remember that phrase you guys had, that slogan? >> Mm hmm. And that's prompt engineering. So that's exactly, that's the reinvention of "Ask the right question," is prompt engineering is, if you don't give these models the question in the right way, and very few people know how to frame it in the right way with the right context, then you will get garbage out. Right? That is the garbage in, garbage out. But if you specify the question correctly, and you provide with it the metadata that constrain what that question is going to be acted upon or answered upon, then you'll get much better answers. And that's exactly what we solved Vectara. >> Okay. So before we get into the last couple minutes we have left, I want to make sure we get a plug in for the opportunity, and the profile of Vectara, your new company. Can you guys both share with me what you think the current situation is? So for the folks who are now having those moments of, "Ah, AI's bullshit," or, "It's not real, it's a lot of stuff," from, "Oh my god, this is magic," to, "Okay, this is the future." >> Yes. >> What would you say to that person, if you're at a cocktail party, or in the elevator say, "Calm down, this is the first inning." How do you explain the dynamics going on right now, to someone who's either in the industry, but not in the ropes? How would you explain like, what this wave's about? How would you describe it, and how would you prepare them for how to change their life around this? >> Yeah, so I'll go first and then I'll let Ed go. Efficiency, efficiency is the description. So we figured that a way to be a lot more efficient, a way where you can write a lot more emails, create way more content, create way more presentations. Developers can develop 10 times faster than they normally would. And that is very similar to what happened during the Industrial Revolution. I always like to look at examples from the past, to read what will happen now, and what will happen in the future. So during the Industrial Revolution, it was about efficiency with our hands, right? So I had to make a piece of cloth, like this piece of cloth for this shirt I'm wearing. Our ancestors, they had to spend month taking the cotton, making it into threads, taking the threads, making them into pieces of cloth, and then cutting it. And now a machine makes it just like that, right? And the ancestors now turned from the people that do the thing, to manage the machines that do the thing. And I think the same thing is going to happen now, is our efficiency will be multiplied extremely, as human beings, and we'll be able to do a lot more. And many of us will be able to do things they couldn't do before. So another great example I always like to use is the example of Google Maps, and GPS. Very few of us knew how to drive a car from one location to another, and read a map, and get there correctly. But once that efficiency of an AI, by the way, behind these things is very, very complex AI, that figures out how to do that for us. All of us now became amazing navigators that can go from any point to any point. So that's kind of how I look at the future. >> And that's a great real example of impact. Ed, your take on how you would talk to a friend, or colleague, or anyone who asks like, "How do I make sense of the current situation? "Is it real? "What's in it for me, and what do I do?" I mean every company's rethinking their business right now, around this. What would you say to them? >> You know, I usually like to show, rather than describe. And so, you know, the other day I just got access, I've been using an application for a long time, called Notion, and it's super popular. There's like 30 or 40 million users. And the new version of Notion came out, which has AI embedded within it. And it's AI that allows you primarily to create. So if you could break down the world of AI into find and create, for a minute, just kind of logically separate those two things, find is certainly going to be massively impacted in our experiences as consumers on, you know, Google and Bing, and I can't believe I just said the word Bing in the same sentence as Google, but that's what's happening now (all laughing), because it's a good example of change. >> Yes. >> But also inside the business. But on the crate side, you know, Notion is a wiki product, where you try to, you know, note down things that you are thinking about, or you want to share and memorialize. But sometimes you do need help to get it down fast. And just in the first day of using this new product, like my experience has really fundamentally changed. And I think that anybody who would, you know, anybody say for example, that is using an existing app, I would show them, open up the app. Now imagine the possibility of getting a starting point right off the bat, in five seconds of, instead of having to whole cloth draft this thing, imagine getting a starting point then you can modify and edit, or just dispose of and retry again. And that's the potential for me. I can't imagine a scenario where, in a few years from now, I'm going to be satisfied if I don't have a little bit of help, in the same way that I don't manually spell check every email that I send. I automatically spell check it. I love when I'm getting type ahead support inside of Google, or anything. Doesn't mean I always take it, or when texting. >> That's efficiency too. I mean the cloud was about developers getting stuff up quick. >> Exactly. >> All that heavy lifting is there for you, so you don't have to do it. >> Right? >> And you get to the value faster. >> Exactly. I mean, if history taught us one thing, it's, you have to always embrace efficiency, and if you don't fast enough, you will fall behind. Again, looking at the industrial revolution, the companies that embraced the industrial revolution, they became the leaders in the world, and the ones who did not, they all like. >> Well the AI thing that we got to watch out for, is watching how it goes off the rails. If it doesn't have the right prompt engineering, or data architecture, infrastructure. >> Yes. >> It's a big part. So this comes back down to your startup, real quick, I know we got a couple minutes left. Talk about the company, the motivation, and we'll do a deeper dive on on the company. But what's the motivation? What are you targeting for the market, business model? The tech, let's go. >> Actually, I would like Ed to go first. Go ahead. >> Sure, I mean, we're a developer-first, API-first platform. So the product is oriented around allowing developers who may not be superstars, in being able to either leverage, or choose, or select their own large language models for appropriate use cases. But they that want to be able to instantly add the power of large language models into their application set. We started with search, because we think it's going to be one of the first places that people try to take advantage of large language models, to help find information within an application context. And we've built our own large language models, focused on making it very efficient, and elegant, to find information more quickly. So what a developer can do is, within minutes, go up, register for an account, and get access to a set of APIs, that allow them to send data, to be converted into a format that's easy to understand for large language models, vectors. And then secondarily, they can issue queries, ask questions. And they can ask them very, the questions that can be asked, are very natural language questions. So we're talking about long form sentences, you know, drill down types of questions, and they can get answers that either come back in depending upon the form factor of the user interface, in list form, or summarized form, where summarized equals the opportunity to kind of see a condensed, singular answer. >> All right. I have a. >> Oh okay, go ahead, you go. >> I was just going to say, I'm going to be a customer for you, because I want, my dream was to have a hologram of theCUBE host, me and Dave, and have questions be generated in the metaverse. So you know. (all laughing) >> There'll be no longer any guests here. They'll all be talking to you guys. >> Give a couple bullets, I'll spit out 10 good questions. Publish a story. This brings the automation, I'm sorry to interrupt you. >> No, no. No, no, I was just going to follow on on the same. So another way to look at exactly what Ed described is, we want to offer you chatGPT for your own data, right? So imagine taking all of the recordings of all of the interviews you have done, and having all of the content of that being ingested by a system, where you can now have a conversation with your own data and say, "Oh, last time when I met Amr, "which video games did we talk about? "Which movie or book did we use as an analogy "for how we should be embracing data science, "and big data, which is moneyball," I know you use moneyball all the time. And you start having that conversation. So, now the data doesn't become a passive asset that you just have in your organization. No. It's an active participant that's sitting with you, on the table, helping you make decisions. >> One of my favorite things to do with customers, is to go to their site or application, and show them me using it. So for example, one of the customers I talked to was one of the biggest property management companies in the world, that lets people go and rent homes, and houses, and things like that. And you know, I went and I showed them me searching through reviews, looking for information, and trying different words, and trying to find out like, you know, is this place quiet? Is it comfortable? And then I put all the same data into our platform, and I showed them the world of difference you can have when you start asking that question wholeheartedly, and getting real information that doesn't have anything to do with the words you asked, but is really focused on the meaning. You know, when I asked like, "Is it quiet?" You know, answers would come back like, "The wind whispered through the trees peacefully," and you know, it's like nothing to do with quiet in the literal word sense, but in the meaning sense, everything to do with it. And that that was magical even for them, to see that. >> Well you guys are the front end of this big wave. Congratulations on the startup, Amr. I know you guys got great pedigree in big data, and you've got a great team, and congratulations. Vectara is the name of the company, check 'em out. Again, the startup boom is coming. This will be one of the major waves, generative AI is here. I think we'll look back, and it will be pointed out as a major inflection point in the industry. >> Absolutely. >> There's not a lot of hype behind that. People are are seeing it, experts are. So it's going to be fun, thanks for watching. >> Thanks John. (soft music)