 Live from Las Vegas, it's theCUBE, covering AWS re-invent 2017, presented by AWS, Intel, and our ecosystem of partners. Well, we are back live here at the Sands Expo Center, we're of course in Las Vegas live at re-invent, AWS putting on quite a show here, day one of three days of coverage that we're seeing right here on theCUBE. I'm John Walls along with Justin Warren, and we're now joined by a couple of folks from Datamere, Justin Rodatis is the CEO of that company in Pusher Palin, who's the senior product manager in Christian and Pusher, thanks for being with us. Good to have you here on theCUBE. Thank you for having us. You were just, so you were cubing it just recently up in New York, Christian? Yeah, absolutely. We were seeing you guys in New York, and we had actually, we've done some work with a couple of customers, probably two weeks ago in Palo Alto, I believe. I don't know how we can afford you. I mean, I'm going to have to look into our budget. I'm happy to be here again. It is great, thanks for taking the time here. I know this is a busy week for you all. First off, let's talk about Datamere in general, just to let the audience at home know, in case they're not familiar, with what you're doing from a core competency standpoint, let's talk about what you're doing here. Absolutely. Meet Datamere was founded eight years ago, and Datamere was on the onset of the big data wave that started in the 2009 and 2010 timeframe, and Datamere was actually the first commercial platform that provided a tool set to enable our customers to consume enterprise-scale Hadoop solutions for the enterprise analytics. So we do everything from ingesting the data into the data lake, over preparing the data for consumption by analytics tools throughout the enterprise, and we just recently also launched our own visualization capabilities for sophisticated analysis against very large data sets. We also are capable of integrating machine learning solutions and preparing data for machine learning throughout the organization, and probably the biggest push is into the cloud. And we've been in the cloud for a couple of years now, but we see increased momentum from our customers in the marketplace for about 15 months now, I would say. So before we dive a little deeper here, I'm just kind of curious about your work in general. It's kind of chicken in the egg, right? You're trying to come up with new products to meet customer demand. So are you producing to give them what you think they need, or are you producing on what they're telling you that they need? How does that work as far as trying to keep up with this? I can kick this off. So it's actually interesting that you asked this because the customers that did interviews with you guys two weeks ago were part of our customer advisory council, right? So we get direct feedback from leading customers that do really sophisticated things with data mirror. They are at the forefront of developing really mind-blowing analytical applications for high-value use cases throughout their organization. They help us understanding where these trends go. And I give you an example. So I was recently meeting with the chief data office of a large global bank in London and they have kicked off 32 Hadoop projects throughout the organization. And what he told me is just these projects will lead to an expansion of the physical footprints of the data centers in the UK by 30%. So and then he said, okay, we are not in the data center business. We don't want this. We need other people to take care of this. And they launched a massive initiative with Amazon to bring a big chunk of the enterprise analytics into AWS. It sounds like you're actually really ahead of the curve in many ways, because of the explosion in machine learning and AI, that data analytics side of things. Yeah, we had big data for a little while, but it's really hitting now where people are starting to really show some of the amazing things that you can do with data and analysis. So what are you seeing from these customers? Like, what are some of the things that they're saying? Actually, this thing here, this is what we really love about Datamir and this is something that we can do here that we wouldn't be able to do in any other way. Shall I take that? Yeah, you can do that. Oh yeah, so when it comes to heart of the matter, there's like three things that Datamir hits on really well. So in terms of our user personas, we look at all of our users, our analysts and data engineers. So what we provide them with that ease of use, being able to take data from anywhere and be able to use any multiple analytic capabilities within one tool without having to jump around in all different UIs, right? So it's like ease of use, single interface. The second one that they really like about us is being able to not have to, whatever, being able to not have to switch between interfaces to be able to get something done. So if they want to ingest data from different sources, it's one place to go to. If they want to access their data, all of it is within the single file browser, they want to munch their data, prepare data, analyze data. It's all within the same interface and they don't have to use 10 different tools to be able to do that. It's a very seamless workflow. And the same token, the third thing which comes up is that collaboration, it enables collaboration across different user groups within the same organization, which means that we're totally enabling the data democratization, which all of the self-service tools are trying to promote here, making the IT's job easier. And that's what Datamir enables. So it's kind of like a win-win situation between our users and the IT. And the third thing that I'm going to talk about, which is the IT making their lives easier, but at the same time not letting them go off, leaving the leash alone, enabling governance. And that's a key challenge, which is where Datamir comes into picture to be able to provide enterprise-ready governance to be able to deploy it across the board in the organization. Yeah, that's something that AWS has certainly led in, is that democratization of access to things so that you can, as individual developers, or individual users, go and make use of some of these cloud resources. And seeing here at the show, and we've been talking about that today, about this is becoming a much more enterprise-type issue. So being able to do that, have that self-service, but also have some of those enterprise-level controls, we're starting to see a lot of focus on that from enterprises who want to use cloud, but they really want to make sure that they do it properly and they do it securely. So what are some of the things that Datamir is doing that helps customers keep that kind of enterprise-level control, but without getting in the way of people being able to just use the cloud services to do what they want to do? So could you give us some examples of that maybe? You know, I'd like to check comment on the specifics on how we deploy in AWS for other cloud solutions for that matter. But what you see with on-premise data lakes, customers are struggling with it, right? So the stack has become outrageously complicated. So they try to stitch all these various solutions together. The open-source community, I believe now, supports 27 different technology platforms, right? And then there's dozens over dozens of commercial tools that they play into that, right? And what they want, they actually just want this thing to work, right? They want to deploy what they used from the enterprise IT. Scalability, security, seamlessness across the platforms, appropriate service-level agreements with the end-user communities and so on and so forth, right? So they really struggle to make this happen on-premise. The cloud addresses a lot of these issues and takes a lot of that burden away. And it becomes way more flexible, scalable, and adjustable to whatever they need, right? And when it comes to the specific deployments and how we do this and how we give them enterprise-grade solutions that make sense for them, Pooja, maybe you can comment on that. Sure, absolutely. And more specific to cloud, I would love to talk about this. So in the recent times, one of our very first financial services customers went on cloud and that pretty much brings us over here, being even more excited about it. And trust me, even before elasticity, their number one requirement is security, right? And as part of security, it's not just like one, two, three, Amazon takes care of it, it's sorted. We have security as part of data mirror. It's been deployed before it's sorted. It's not enough. So when it comes to security, it's security at multiple levels. It's security about data in motion. It's security about data at rest, right? So encryption across the board. And then specifically, right now while we're at the Amazon conference, we were talking about enabling key management services, being able to have server-side encryption that Amazon enables, being able to support that. And then besides that, there's a lot of other custom requirements, specifically around how do you, because it's more of a hybrid architecture, they do have applications on prem, right? They do have like a deployed cloud infrastructure to do compute in the cloud as and when needed for any kind of burst workloads. So as part of that, when data moves between, you know, within their land to the cloud, within that VPC, you know, that itself, those connectivity has to be secured. And they want to make sure that all of those, you know, user passwords, all of that authentication is also, you know, kind of secure. So we've enabled a bunch of capabilities around that, specifically for customers who are like, you know, super keen on having security, taking care of, you know, rule number one, right? Even before they go. Financial services, I mean, you mentioned that, both of you are talking about it. That's a pretty big target market for you, right? I mean, you've really made it a point of emphasis. Are there concerns, I get it, we'll obviously understand how treasured that data can be. But do you provide anything different for them? I mean, as a data point, a point of point is a point as opposed to another business. You just protect it the same way, or do you have unique processes and procedures and treatments in place that give them maybe whatever that additional oomph of comfort is that needs? So that's a good question, right? So in principle, we service a couple of industries that are very demanding. So it's financial services, it's telecommunications and media, it's government agencies, insurance companies. And when you look at the complexities of the stack that I've described, it's very challenging to make security, scalability, and these things really happen, right? You cannot inherit security protocols throughout the stack, right? So you stack a data prep piece together with a BI accelerator, with an InChest tool, right? These things don't make sense. So the big advantage of Datamere is it's an end-to-end tool, right? We do everything from InChest data preparation to enterprise scale analytics and provide this out of the box in a seamless fashion to our customers. Right. Yeah, it is fascinating how the whole ecosystem has sort of changed in what feels like only a couple of years and how much customers are taking some of these things and putting them together to create some of the amazing new products and new ways of doing things. So can you give us a bit of an idea of, you were saying earlier that Cloud was sort of, it was about two years ago, three years ago, what was it that finally tipped you over and said, you know what, we've got to do this? We're hearing a lot of talk about people wanting hybrid solutions, wanting to be able to do bursting. What was it really that drove you from the customer perspective to say, you know what, we have to do this and we have to go into AWS? Did you catch the entire question? Just repeat the last one. What drove it to the cloud? What drove you to the cloud? What pushed you over the top? I mean, so this is a very interesting question because DataMirror was always innovating ahead of the curve. Right, so this is probably a big piece to the story. And if you look back, I think the first cloud solutions with Microsoft Azure, so first I think we did our own cloud solution and we moved to Microsoft Azure and this was already maybe two and a half years ago, right? Or even longer. So we were ahead of the curve. Then I would say it was even too early, right? You saw some adoptions in Microsoft Azure, so there's some good adoption but now you see this accelerating, right? And it's related to the complexity of the stack, to the multiple points of failure of on-premise solutions, to the fact that people want, really they want elasticity. They want flexibility in rolling this out. The primary, interestingly enough, the primary motivator is actually not cost, right? It's really a breathable solution that allows them to spin up clusters, to manage certain workloads that come for a compliance report every quarter, right? They need another 50 nodes, spin them up, run them for a week or two and spin them down again, right? So it's really the customers are buying elasticity, they're buying elasticity from a technology perspective, they're buying elasticity from a commercial perspective. Yeah, we certainly, customers really like that flexibility, yeah. And I think we are now at a tipping point where customers see that they can actually do this in a highly secure and governed way, so especially our demanding customers and that it really makes sense from a commercial and elasticity perspective, right? Well, as you were saying, that's what they're buying, but they're buying what you're selling. So congratulations on that, obviously it's working. So good luck, continued success down the road and thanks for the time here today, we appreciate it. Thanks for having us. Always good to have you on the queue. It's cocktail time, thanks for having us. It's just five o'clock somewhere here right now. Back with more live coverage and re-event. We'll be back here for Las Vegas Live in just a bit.