 From San Mateo, California. It's theCUBE, covering SnapLogic Innovation Day 2018. Brought to you by SnapLogic. Hey, welcome back to everybody. Jeff Frick here with theCUBE. We're in San Mateo at, they call it the Crossroads. It's 92 and 101. If you're coming by, you're probably sitting in traffic. Look up, you'll see SnapLogic. It's their new offices. We're really excited to be here for Innovation Day and we're excited to have the CTOs, James McCarrion. James, great to see you and we last talked a couple years ago in New York City. Yeah, that's right and why was I there? It was like a big data show and here we are again two years later talking about big data. Big data, but big data is fading a little bit because now big data is really an engine that's powering this new thing that's so exciting which is all about analytics and machine learning and we're going to eventually stop saying artificial intelligence and say augmented intelligence because there's really nothing artificial about it. And we might stop saying big data and just talk about data because it's becoming so ubiquitous. And I don't know that big data is necessarily going away but sort of how we're thinking about handling it is maybe kind of evolved over time, especially in the last couple of years. This is what we're kind of seeing from our customers. This is kind of an ingredient now, right? It's no longer this new shiny object now. It's just part of the infrastructure that helps you get everything else done. Yeah, and I think when you think about it from like an enterprise point of view, that shift is going from experimentation to operationalizing. And I think the things that you look for in experimentation, there's like one set of things that you're looking for, proving out the overall value regardless maybe of cost and uptime and other things. And then as you operationalize, you start thinking about other considerations that obviously like enterprise IT has to think about. So if you think back to like Hadoop Summit and Hadoop World when those are first cracking their teeth like in 2010 around that time frame, one of the big discussions that always comes up and that was before kind of the rise of public cloud which has really taken off over the last several years is just kind of ongoing debate between do you move the data to the compute or do you move the compute to the data? And there was always this like monster data gravity issue that was almost insurmountable and many would say you're never going to get all your data into the cloud. It's just way too hard and way too expensive. But you know now Amazon has snowballs and the snowball wasn't big enough. They actually had a diesel truck. That'll come help you move your data. Amazon, they rolled that thing across the stage a couple of years ago. The data gravity thing seems to be less and if you think of a world with infinite compute, infinite store, infinite networking, asymptotically approaching zero, not necessarily good news for some vendors out there but that's a world that we're eventually getting to. That changes the way that you organize all this stuff. Yeah, I think so much has changed and I was fortunate to be one of the like the early speakers like these Hadoop worlds and everything and I was like adamantly proclaiming the destiny of Hadoop is like bright and shiny and there's like this question about what really happened and I think that there's kind of a few different variables that kind of shifted at the same time. One is of course this like glut of computing and the cloud happened and there's like so many variables moving at once. It's like, how much time do you have, Jeff? Let's see, so like, you know, like when I think about- I was going to get a couple more drinks for you. So you're seeing our lovely like new headquarters here and like one of the things is, you know, there's like no big like data center. You know, there's no, we have like a little closet with some of the servers that we keep around but mostly everything that we do is like on Amazon and you're even looking at things now like commercial real estate is changing because, you know, I don't need all the cooling and the power and the space for my data center that I once had and so like I'm a lot more space efficient than I used to be and so like the cloud is really kind of changing everything and on the data side, you know, you mentioned this like interesting philosophical shift, you know, going from I couldn't possibly do it in the cloud to why in the world would we not do things in the cloud and with like maybe the one stalwart in there being like some fears about security, obviously there's been a lot of breaches. I think that there's still a lot of introspection everyone needs to do about, you know, is my on-premise, are my on-premise systems actually more secure than some of these cloud providers? And it's really not clear that we know the answer to that. In fact, we suspect that some of the cloud providers are actually more secure because they're kind of like professionals about it and they have best practice. And a whole lot of money. And the other thing that happened that you didn't mention like that's approaching infinity we're not quite there yet is interconnect speed. So it used to be the case, you know, I have a bunch of like, you know, mainframes and I have a Teradata system and I have a high speed interconnect that puts the two together. And now with fiber networks and you know, just in general you can run like super high speed like kind of when, you know, when especially if you don't care quite as much about latency. So if you, you know, if like 500 millisecond latency is still like okay with you you can do a heck of a lot and move a lot to the cloud. In fact, it's like so good that we went from worrying, you know, could I do this in the cloud at all to, well, why wouldn't I do some things in Amazon and some things in Microsoft and some things in Google even if it meant replicating my data across all of these environments. And the backdrop for some of that is, sorry, is we had a lot of customers and like I was thinking that people would approach it this way they would install on premise Hadoop, you know, whether it's like Apache or Cloudera or the other vendors. And I would like hire a bunch of folks that are like the administrators and I'm gonna like retire Teradata and I'm gonna put all my ETL jobs on there, et cetera. And it turned out to be like a great theory in the practice, you know, it was real for some folks but it turned out to be, you know, moving a lot of things to kind of shifting sands because Hadoop was evolving at the time that a lot of customers were putting a lot of pressure on it, operational pressure, again, moving from experimentation phase over to like operational phase. And like when you don't have the uptime guarantee and when I can't just hire somebody off the street to administer this, it has to be a very sharp, you know, knowledgeable person, like that's very expensive. You know, people started saying, well, what am I really getting from this? And can I just dump it all in S3 and apply like a bunch of technology there and let Amazon, you know, worry about keeping this thing up and running and people started saying, you know, I used to, you know, I used to reject that idea. Now it's sounding like a very smart idea. It's so funny, we talk about people processing tech all the time, right? But they call them tech shows, they don't call them people in process shows. Right. It's not the ones we go to, time and time again. And I remember talking to some people about the Hadoop situations, like there's just no Hadoop people. Talk about the technology all day long. There just aren't enough people with the skills to actually implement it. It's probably changed now, but I remember that was such a big problem. And it's funny, talk about the security and cloud security, you know, at AWS, I think on Tuesday night of re-invent, they have a special kind of a technical keynote. It used to be James Hamilton would go. And in the amount of resources, I mean, I remember one talk he gave just on their cabling across the ocean. You know, the amount of resources that he can bring to bear relative to any individual company is so different, much less a mid-tier company or a small company. I mean, you can bring so much more resources, expertise, knowledge. Yeah, the economy is a scale, or just there, you know, that's right. And that's why, you know, like you sort of assume that the cloud sort of, you know, eventually eats everything, right? And so there's no reason to believe this won't be one of those cases. So you guys are getting extreme. So what is SnapLogic Extreme? Well, SnapLogic Extreme is kind of like a response to this trend of data moving from on-premise to the cloud. And there are some like interesting dynamics of that movement. First of all, you need to get data into the cloud, you know, first of all, and we've been doing that for years, connect to everything, dump it in S3, ADLS, et cetera, you know, like no problem. The thing that we're seeing with cloud computing is like there's another interesting shift. Not only is it like kind of like mess for less, like let Amazon sort of like manage all this. And I, you know, I've probably referred to Amazon more than like, you know, other vendors would appreciate. But yeah, like, you know, let's, let's. They're the leaders. Yeah, yeah, yeah. So like, call us, pay to spade. Yeah. Clearly Google and Microsoft are out there as well. So those are the top three we've acknowledged that. Yeah, so one of the, one of the interesting things about it is that you couldn't really adequately achieve on-premises this burstiness of your compute. So, you know, I run at a steady state where I need, you know, 10 servers, 100 servers, but every once in a while, I need like a thousand or like 10,000 servers to apply to something. So what's the on-premise model? Rack and stack, 10,000 machines, and it's like, you know, waiting for the great pumpkin, like waiting, waiting for that workload to come that I've been waiting like months and months for, and maybe it never comes. But I'm paying for it. I paid for a software license for the thing that I need to run there. I'm paying for like, you know, the cabling and the racking and everything and the person administering. Make sure the disks are all operating in the case, you know, where it gets used. Now all of a sudden, like, you know, we're taking Amazon and they're saying, hey, you know, pay us for what you're using. You can use, you know, reserve pricing and pay like a lower rate for the things that you might actually care about on a consistent basis. But then I'm going to allow you to like spike and I'll just run the meter. And so this has caused software vendors like us to look at the way that we, you know, charge in the way that we deploy our resources and say, hey, that's a very good model. So we want to follow that. And so we introduced SnapLogic Extreme, which has a few different components, but basically it enables us to operate in these elastic environments, shift our thinking and pricing so that we don't think about like node-based or God forbid, like core-based pricing and say like, hey, you know, basically pay us for what you do with your data and don't worry about how many servers it's running on and let SnapLogic worry about spinning up and spinning down these machines because a lot of these workloads are, you know, data integration or application workloads that we know lots about. So first of all, we manage these ephemera, what we call ephemeral or elastic clusters. Second of all, the way that we distribute our workload is by generating Spark code currently. So we use the same graphic environment that used for everything, but instead of running on our engines, we kind of spit out Spark code in the end that takes advantage of the massive scale-out potential for these ephemeral environments. Then we've also kind of built this in such a way that it's Spark today. It could be like native or it could be some other engine like Flink or other things that come up and we really don't care like what that backend engine actually is as long as it can run certain types of data-oriented jobs. So it's like actually like lots of things in one. So we combine our kind of like data acquisition and distribution capability with this like massive elastic scale-out capability. Yeah, it's unbelievable how you can spin that up and then of course most people forget you need to spin it down after the event. Yeah, that's right. We talked to a great vendor who talked about, you know, my customer spends no money with me on the weekend, zero, and I'm thrilled because they're not using me, but when they do use me, then they're buying stuff. And I think what's really interesting is how that changes also your relationship with your customer. If you have a recurring revenue model, you have to continue to deliver value. You have to stay close to your customer. You have to stay engaged because it's not a one-time pop and then you send them the 15% or 20% maintenance bill. It's really kind of this ongoing relationship and they're actually getting value from your products each and every time they use it, very different way. Yeah, that's right. I think it creates better relationships because you feel like, you know, what we do is in proportion to what they do and vice versa. So it has this kind of like fundamental fairness about it if you will. It's a good relationship, but I want to go down another path before you turn the cameras on. Talked a little bit about kind of the race always between, you know, the need for compute and the compute and they used to be personified best with Microsoft and Intel, Intel will come out with a new chip and then Microsoft OS would eat up all the extra capacity and they come up with a new chip and it was an ongoing thing. You made an interesting comment that, especially in a cloud world where the scale of these things is much, much bigger, that we're in a world now where the compute and the storage have kind of outpaced the applications, if you will. And there's an opportunity for the applications to catch up. Oh, by the way, we have this new cool thing called machine learning and augmented intelligence. So I wonder if you could, you know, is that what's gonna fill the kind of rebalance the consumption pattern? It seems that way. And I always think about like kind of like, you know, compute and software spiraling around each other like a Helix. And like at one point, one is leading the other and they sort of just, one eventually surpasses the other and then you need innovation on the other side. And I think for a while, like if you turn the clock like way back to like, you know, when the Pentium was like, you know, introduced everyone's like, how are we ever gonna use all of the compute power, you know, power of the like Pentium? Like, you know, do I really need to run my spreadsheets like a hundred percent faster? Like there's no business value whatsoever and transacting faster or in like general user interface, like, you know, graphical user interfaces or rendering web pages. But then, so then you start seeing like this new, you know, glut often led by like researchers first of like software applications coming up that use all of this power because you can, in academia, you can start saying, well, what if I did have infinite compute? Like, what would I do differently? And so you see things like, you know, VR and like advanced gaming come up on the consumer side. And then I think the real, you know, answer on the business side is AI and ML. And it sort of, you know, the general trend I start, you know, thinking of is something that I was used to talk about back in the old days, which is this conversion of like having machines work for us instead of us working for machines. And like the only way that we're ever gonna like get there is by having higher and higher intelligence on the application side so that it kind of intuits more based on what it's seen before, what it knows about you, et cetera, and then there's like this whole new breed of person that you need in order to like, you know, wield all of that power because, you know, like Hadoop, it's not just like natural. Like you don't just have people floating around like, hey, you know, I'm gonna be like an Uzi expert or a Yarn expert. It's like, you know, I don't, you don't run into people every day. It's like, oh yeah, I know, you know, neural nets well, like I'm a gradient descent expert, you know, or whatever, you know, your model is. So it's really gonna drive like a lots of, lots of changes, I think. Right. Well, hopefully it does. And especially like we were talking about earlier, you know, within core curriculums at schools and stuff, we had Grace Hopper and Brenda Wilkerson, the new head of the Anita Borg organization was at the Chicago Public School District and they actually starting to make CS a requirement along with biology and physics and chemistry and some of these other things. So, you know, we do have a huge, a huge dearth of that, but I want to just close out on one last concept before I let you go. And you guys are way on top of this, Greg talked about what you just talked about, which is making the computers work for us versus the other way around. And that's really the democratization of the power. And we heard a lot about democratization of big data and the tools. Now you guys are talking about the democratization of the integration, especially when you got a bunch of cloud-based applications that everybody has access to and maybe needs to stitch together a different way. But when you look at the, just this whole concept of democratization of that power, how do you see that kind of playing out over the next several years? Yeah, that's a very, you know, I'm sorry, I didn't bring a couple of beer squirts. It's a very big, no, I got you covered. Oh, I'm sorry. So, it's a very big, interesting question because I think, you know, first of all, it's like one of these, like I know, is we can't predict with a lot of like accuracy how exactly that's gonna look because we're sort of juxtaposing two things. One is, you know, part of the initial move to the cloud was the failure to properly democratize data inside the enterprise for whatever reason. And we didn't do it. Now we have the compute resources and the central, you know, kind of web-based access to everything, great. Now we have, you know, Cambridge Analytica and like Facebook and people really thinking about like data privacy and the fact that we want, you know, ubiquitous safe access. And I think we know how to make things ubiquitous. The question is, do we know how to make it sort of safe and fair so that the right people are using the right data in the right way? And it's a little bit like, you know, nobody, you know, there's all these like cautionary tales out there, like beware of, you know, AI and robotics and everything. And nobody really thinks about like the danger of this like data that's there, but it's a much more immediate problem. And yet it's sort of like the silent like killer until some scandal, scandal comes up. And then we started thinking about these different ways we can tackle it. Yeah, obviously like there's like, you know, great, you know, solutions for tokenization and encryption and everything at the data level. But even if you have access to it, the question is how you control that wildfire that could happen as soon as like the horse leaves a barn. So, you know, it's maybe not in its current form, but when you look at things like blockchain, you know, there's been a lot of predictions about how blockchain can be used around like data. And I think that this privacy and this curation and tracking of who, you know, has the data, who has access to it and can we control it. I think you are looking at even more like kind of centralized and guarded access to, you know, this private data. Right, interesting times. Yeah, for sure. All right, James. Thanks for taking a couple of minutes with us. I really enjoyed the conversation. Yeah, it's always great. Thanks for having me, Jeff. All right. Hey James, I'm Jeff. You're watching theCUBE. SnapLogic headquarters in the San Mateo, California. Thanks for watching.