(bright upbeat music) >> Welcome back, everybody, to the Supercloud 2. My name is Dave Vellante. And I'm pleased to welcome Nir Zuk. He's the founder and CTO of Palo Alto Networks. Nir, good to see you again. Welcome. >> Same here. Good to see you. >> So let's start with the right security architecture in the context of today's fragmented market. You've got a lot of different tools, you've got different locations, on-prem, you've got hardware and software. Tell us about the right security architecture from your standpoint. What's that look like? >> You know, the funny thing is using the word security in architecture rarely works together. (Dave chuckles) If you ask a typical information security person to step up to a whiteboard and draw their security architecture, they will look at you as if you fell from the moon. I mean, haven't you been here in the last 25 years? There's no security architecture. The architecture today is just buying a bunch of products and dropping them into the infrastructure at some relatively random way without really any guiding architecture. And that's a huge challenge in cybersecurity. It's always been, we've always tried to find ways to put an architecture into writing blueprints, whatever you want to call it, and it's always been difficult. Luckily, two things. First, there's something called zero trust, which we can talk a little bit about more, if you want, and zero trust among other things is really a way to create a security architecture, and second, because in the cloud, in the supercloud, we're starting from scratch, we can do things differently. We don't have to follow the way we've always done cybersecurity, again, buying random products, okay, maybe not random, maybe there is some thinking going into it by buying products, one of the other, dropping them in, and doing it over 20 years and ending up with a mess in the cloud, we have an opportunity to do it differently and really have an architecture. >> You know, I love talking to founders and particularly technical founders from StartupNation. I think I saw an article, I think it was Erie Levine, one of the founders or co-founders of Waze, and he had a t-shirt on, it said, "Fall in love with the problem, not the solution." Is that how you approached architecture? You talk about zero trust, it's a relatively new term, but was that in your head when you thought about forming the company? >> Yeah, so when I started Palo Alto Networks, exactly, by the way, 17 years ago, we got funded January, 2006, January 18th, 2006. The idea behind Palo Alto Networks was to create a security platform and over time take more and more cybersecurity functions and deliver them on top of that platform, by the way, as a service, SaaS. Everybody thought we were crazy trying to combine many functions into one platform, best of breed and defense in death and putting all your eggs in the same basket and a bunch of other slogans were flying around, and also everybody thought we were crazy asking customers to send information to the cloud in order to secure themselves. Of course, step forward 17 years, everything is now different. We changed the market. Almost all of cybersecurity today is delivered as SaaS and platforms are ruling more and more the world. And so again, the idea behind the platform was to over time take more and more cybersecurity functions and deliver them together, one brain, one decision being made for each and every packet or system call or file or whatever it is that you're making the decision about and it works really, really well. As a side effect, when you combine that with zero trust and you end up with, let's not call it an architecture yet. You end up with with something where any user, any location, both geographically as well as any location in terms of branch office, headquarters, home, coffee shop, hotel, whatever, so any user, any geographical location, any location, any connectivity method, whether it is SD1 or IPsec or Client VPN or Client SVPN or proxy or browser isolation or whatever and any application deployed anywhere, public cloud, private cloud, traditional data center, SaaS, you secure the same way. That's really zero trust, right? You secure everything, no matter who the user is, no matter where they are, no matter where they go, you secure them exactly the same way. You don't make any assumptions about the user or the application or the location or whatever, just because you trust nothing. And as a side effect, when you do that, you end up with a security architecture, the security architecture I just described. The same thing is true for securing applications. If you try to really think and not just act instinctively the way we usually do in cybersecurity and you say, I'm going to secure my traditional data center applications or private cloud applications and public cloud applications and my SaaS applications the same way, I'm not going to trust something just because it's deployed in the private data center. I'm not going to trust two components of an application or two applications talking to each other just because they're deployed in the same place versus if one component is deployed in one public cloud and the other component is deployed in another public cloud or private cloud or whatever. I'm going to secure all of them the same way without making any trust assumptions. You end up with an architecture for securing your applications, which is applicable for the supercloud. >> It was very interesting. There's a debate I want to pick up on what you said because you said don't call it an architecture yet. So Bob Muglia, I dunno if you know Bob, but he sort of started the debate, said, "Supercloud, think of it as a platform, not an architecture." And there are others that are saying, "No, no, if we do that, then we're going to have a bunch of more stove pipes. So there needs to be standard, almost a purist view. There needs to be a supercloud architecture." So how do you think about it? And it's a bit academic, I know, but do you think of this idea of a supercloud, this layer of value on top of the hyperscalers, do you think of that as a platform approach that each of the individual vendors are responsible for the architecture? Or is there some kind of overriding architecture of standards that needs to emerge to enable the supercloud? >> So we can talk academically or we can talk practically. >> Yeah, let's talk practically. That's who you are. (Dave laughs) >> Practically, this world is ruled by financial interests and none of the public cloud providers, especially the bigger they are has any interest of making it easy for anyone to go multi-cloud, okay? Also, on top of that, if we want to be even more practical, each of those large cloud providers, cloud scale providers have engineers and all these engineers think they're the best in the world, which they are and they all like to do things differently. So you can't expect things in AWS and in Azure and GCP and in the other clouds like Oracle and Ali and so on to be the same. They're not going to be the same. And some things can be abstracted. Maybe cloud storage or bucket storage can be abstracted with the layer that makes them look the same no matter where you're running. And some things cannot be abstracted and unfortunately will not be abstracted because the economical interest and the way engineers work won't let it happen. We as a third party provider, cybersecurity provider, and I'm sure other providers in other areas as well are trying or we're doing our best. We're not trying, we are doing our best, and it's pretty close to being the way you describe the top of your supercloud. We're building something that abstracts the underlying cloud such that securing each of these clouds, and by the way, I would add private cloud to it as well, looks exactly the same. So we use, almost always, whenever possible, the same terminology, no matter which cloud we're securing and the same policy and the same alerts and the same information and so on. And that's also very important because when you look at the people that actually end up using the product, security engineers and more importantly, SOC, security operations center analysts, they're not going to study the details of each and every cloud. It's just going to be too much. So we need to abstract it for them. >> Yeah, we agree by the way that the supercloud definition is inclusive of on-prem, you know, what you call private cloud. And I want to pick up on something else you said. I think you're right that abstracting and making consistent across clouds something like object storage, get put, you know, whether it's an S3 bucket or an Azure Blob, relatively speaking trivial. When you now bring that supercloud concept to something more complex like security, first of all, as a technically feasible and inferring the answer there is yes, and if so, what do you see as the main technical challenges of doing so? >> So it is feasible to the extent that the different cloud provide the same functionality. Then you step into a territory where different cloud providers have different paths services and different cloud providers do things a little bit differently and they have different sets of permissions and different logging that sometimes provides all the information and sometimes it doesn't. So you end up with some differences. And then the question is, do you abstract the lowest common dominator and that's all you support? Or do you find a way to be smarter than that? And yeah, whatever can be abstracted is abstracted and whatever cannot be abstracted, you find an easy way to represent that to your users, security engineers, security analysts, and so on, which is what I believe we do. >> And you do that by what? Inventing or developing technology that presents that experience to users? Could you be more specific there? >> Yeah, so different cloud providers call their storage in different names and you use different ways to configure them and the logs come out the same. So we normalize it. I mean, the keyword is probably normalization. Normalize it. And we try to, you know, then you have to pick a winner here and to use someone's terminology or you need to invent new terminology. So we try to use the terminology of the largest cloud provider so that we have a better chance of doing that but we can't always do that because they don't support everything that other cloud providers provide, but the important thing is, with or thanks to that normalization, our customers both on the engineering side and on the user side, operations side end up having to learn one terminology in order to set policies and understand attacks and investigate incidents. >> I wonder if I could pick your brain on what you see as the ideal deployment model to achieve this supercloud experience. For example, do you think instantiating your stack in multiple regions and multiple clouds is the right way to do it? Or is building a single global instance on top of the clouds a more preferable way? Are maybe other models we should consider? What do you see as the trade off of these different deployment models and which one is ideal in your view? >> Yeah, so first, when you deploy cloud security, you have to decide whether you're going to use agents or not. By agents, I mean something working, something running inside the workload. Inside a virtual machine on the container host attached to function, serverless function and so on and I, of course, recommend using agents because that enables prevention, it enables functionality you cannot get without agents but you have to choose that. Now, of course, if you choose agent, you need to deploy AWS agents in AWS and GCP agents in GCP and Azure agents in Azure and so on. Of course, you don't do it manually. You do it through the CICD pipeline. And then the second thing that you need to do is you need to connect with the consoles. Of course, that can be done over the internet no matter where your security instances is running. You can run it on premise, you can run it in one of the other different clouds. Of course, we don't run it on premise. We prefer not to run it on premise because if you're secured in cloud, you might as well run in the cloud. And then the question is, for example, do you run a separate instance for AWS for GCP or for Azure, or you want to run one instance for all of them in one of these clouds? And there are advantages and disadvantages. I think that from a security perspective, it's always better to run in one place because then when you collect the information, you get information from all the clouds and you can start looking for cross-cloud issues, incidents, attacks, and so on. The downside of that is that you need to send all the information to one of the clouds and you probably know that sending data out of the cloud costs a lot of money versus keeping it in the cloud. So theoretically, you can build an architecture where you keep the data for AWS in AWS, Azure in Azure, GCP in GCP, and then you try to run distributed queries. When you do that, you find out you'd end up paying more for the compute to do that than you would've paid for sending all the data to a central location. So we prefer the approach of running in one place, bringing all the data there, and running all the security, the machine learning or whatever, the rules or whatever it is that you're running in one place versus trying to create a distributed deployment in order to try to save some money on the data, the network data transfers. >> Yeah, thank you for that. That makes a lot of sense. And so basically, should we think about the next layer building security data lake, if you will, and then running machine learning on top of that if I can use that term of a data lake or a lake house? Is that sort of where you're headed? >> Yeah, look, the world is headed in that direction, not just the cybersecurity world. The world is headed from being rule-based to being data-based. So cybersecurity is not different and what we used to do with rules in the past, we're now doing with machine learning. So in the past, you would define rules saying, if you see this, this, and this, it's an attack. Now you just throw the data at the machine, I mean, I'm simplifying it, but you throw data at a machine. You'll tell the machine, find the attack in the data. It's not that simple. You need to build the right machine learning models. It needs to be done by people that are both cybersecurity experts and machine learning experts. We do it mostly with ex-military offensive people that take their offensive knowledge and translate it into machine learning models. But look, the world is moving in that direction and cybersecurity is moving in that direction as well. You need to collect a lot of data. Like I said, I prefer to see all the data in one place so that the machine learning can be much more efficient, pay for transferring the data, save money on the compute. >> I think the drop the mic quote it ignite that you had was within five years, your security operation is going to be AI-powered. And so you could probably apply that to virtually any job over the next five years. >> I don't know if any job. Certainly writing essays for school is automated already as we've seen with ChatGPT and potentially other things. By the way, we need to talk at some point about ChatGPT security. I don't want to think what happens when someone spends a lot of money on creating a lot of fake content and teaches ChatGPT the wrong answer to a question. We start seeing ChatGPT as the oracle of everything. We need to figure out what to do with the security of that. But yeah, things have to be automated in cybersecurity. They have to be automated. They're just too much data to deal with and it's just not even close to being good enough to wait for an incident to happen and then going investigate the incident based on the data that we have. It's better to look at all the data all the time, millions of events per second, and find those incidents before they happen. There's no way to do that without machine learning. >> I'd love to have you back and talk about ChatGPT. I know they're trying to put in some guardrails but there are a lot of unintended consequences, aren't there? >> Look, if they're not going to have a person filtering the data, then with enough money, you can create thousands or tens of thousands of pieces of articles or whatever that look real and teach the machine something that is totally wrong. >> We were talking about the hyper skills before and I agree with you. It's very unlikely they're going to get together, band together, and create these standards. But it's not a static market. It's a moving train, if you will. So assuming you're building this cross cloud experience which you are, what do you want from the hyperscalers? What do you want them to bring to the table? What is a technology supplier like Palo Alto Networks bring? In other words, where do you see ongoing as your unique value add and that moat that you're building and how will that evolve over time vis-a-vis the hyperscaler evolution? >> Yeah, look, we need APIs. The more data we have, the more access we have to more data, the less restricted the access is and the cheaper the access is to the data because someone has to pay today for some reason for accessing that data, the more secure their customers are going to be. So we need help and are helping by the way a lot, all of them in finding easy ways for customers to deploy things in the cloud, access data, and again, a lot of data, very diversified data and do it in a cost-effective way. >> And when we talk about the edge, I presume you look at the edge as just another data center or maybe it's the reverse. Maybe the data center is just another edge location, but you're seeing specific edge security solutions come out. I'm guessing that you would say, that's not what we want. Edge should be part of that architecture that we talked about earlier. Do you agree? >> Correct, it should be part of the architecture. I would also say that the edge provides an opportunity specifically for network security, whereas traditional network security would be deployed on premise. I'm talking about internet security but half network security market, and not just network security but also the other network intelligent functions like routing and QS. We're seeing a trend of pushing those to the edge of the cloud. So what you deploy on premise is technology for bringing packets to the edge of the cloud and then you run your security at the edge, whatever that edge is, whether it's a private edge or public edge, you run it in the edge. It's called SASE, Secure Access Services Edge, pronounced SASE. >> Nir, I got to thank you so much. You're such a clear thinker. I really appreciate you participating in Supercloud 2. >> Thank you. >> All right, keep it right there for more content covering the future of cloud and data. This is Dave Vellante for John Furrier. I'll be right back. (bright upbeat music)

(upbeat music) >> Welcome back to Supercloud 2 this is Dave Vellante. We're here exploring the intersection of data and analytics and the future of cloud. And in this segment, we're going to look at the evolution of cloud, and try to test some of the Supercloud concepts and assumptions with Brian Gracely, is the founder and co-host along with Aaron Delp of the popular Cloudcast program. Amazing series, if you're not already familiar with it. The Cloudcast is one of the best ways to keep up with so many things going on in our industry. Enterprise tech, platform engineering, business models, obviously, cloud developer trends, crypto, Web 3.0. Sorry Brian, I know that's a sore spot, but Brian, thanks for coming >> That's okay. >> on the program, really appreciate it. >> Yeah, great to be with you, Dave. Happy New Year, and great to be back with everybody with SiliconANGLE again this year. >> Yeah, we love having you on. We miss working with you day-to-day, but I want to start with Gracely's theorem, which basically says, I'm going to paraphrase. For the most part, nothing new gets introduced in the enterprise tech business, patterns repeat themselves, maybe get applied in new ways. And you know this industry well, when something comes out that's new, if you take virtualization, for example, been around forever with mainframes, but then VMware applied it, solve a real problem in the client service system. And then it's like, "Okay, this is awesome." We get really excited and then after a while we pushed the architecture, we break things, introduce new things to fix the things that are broken and start adding new features. And oftentimes you do that through acquisitions. So, you know, has the cloud become that sort of thing? And is Supercloud sort of same wine, new bottle, following Gracely's theorem? >> Yeah, I think there's some of both of it. I hate to be the sort of, it depends sort of answer but, I think to a certain extent, you know, obviously Cloud in and of itself was, kind of revolutionary in that, you know, it wasn't that you couldn't rent things in the past, it was just being able to do it at scale, being able to do it with such amazing self-service. And then, you know, kind of proliferation of like, look at how many services I can get from, from one cloud, whether it was Amazon or Azure or Google. And then, you know, we, we slip back into the things that we know, we go, "Oh, well, okay, now I can get computing on demand, but, now it's just computing." Or I can get database on demand and it's, you know, it's got some of the same limitations of, of say, of database, right? It's still, you know, I have to think about IOPS and I have to think about caching, and other stuff. So, I think we do go through that and then we, you know, we have these sort of next paradigms that come along. So, you know, serverless was another one of those where it was like, okay, it seems sort of new. I don't have to, again, it was another level of like, I don't have to think about anything. And I was able to do that because, you know, there was either greater bandwidth available to me, or compute got cheaper. And what's been interesting is not the sort of, that specific thing, serverless in and of itself is just another way of doing compute, but the fact that it now gets applied as, sort of a no-ops model to, you know, again, like how do I provision a database? How do I think about, you know, do I have to think about the location of a service? Does that just get taken care of for me? So I think the Supercloud concept, and I did a thing and, and you and I have talked about it, you know, behind the scenes that maybe the, maybe a better name is Super app for something like Snowflake or other, but I think we're, seeing these these sort of evolutions over and over again of what were the big bottlenecks? How do we, how do we solve those bottlenecks? And I think the big thing here is, it's never, it's very rarely that you can take the old paradigm of what the thing was, the concept was, and apply it to the new model. So, I'll just give you an example. So, you know, something like VMware, which we all know, wildly popular, wildly used, but when we apply like a Supercloud concept of VMware, the concept of VMware has always been around a cluster, right? It's some finite number of servers, you sort of manage it as a cluster. And when you apply that to the cloud and you say, okay, there's, you know, for example, VMware in the cloud, it's still the same concept of a cluster of VMware. But yet when you look at some of these other services that would fit more into the, you know, Supercloud kind of paradigm, whether it's a Snowflake or a MongoDB Atlas or maybe what CloudFlare is doing at the edge, those things get rid of some of those old paradigms. And I think that's where stuff, you start to go, "Oh, okay, this is very different than before." Yes, it's still computing or storage, or data access, but there's a whole nother level of something that we didn't carry forward from the previous days. And that really kind of breaks the paradigm. And so that's the way I think I've started to think about, are these things really brand new? Yes and no, but I think it's when you can see that big, that thing that you didn't leave behind isn't there anymore, you start to get some really interesting new innovation come out of it. >> Yeah. And that's why, you know, lift and shift is okay, when you talk to practitioners, they'll say, "You know, I really didn't change my operating model. And so I just kind of moved it into the cloud. there were some benefits, but it was maybe one zero not three zeros that I was looking for." >> Right. >> You know, we always talk about what's great about cloud, the agility, and all the other wonderful stuff that we know, what's not working in cloud, you know, tie it into multi-cloud, you know, in terms of, you hear people talk about multi-cloud by accident, okay, that's true. >> Yep. >> What's not great about cloud. And then I want to get into, you know, is multi-cloud really a problem or is it just sort of vendor hype? But, but what's not working in cloud? I mean, you mentioned serverless and serverless is kind of narrow, right, for a lot of stateless apps, right? But, what's not great about cloud? >> Well, I think there's a few things that if you ask most people they don't love about cloud. I think, we can argue whether or not sort of this consolidation around a few cloud providers has been a good thing or a bad thing. I think, regardless of that, you know, we are seeing, we are hearing more and more people that say, look, you know, the experience I used to have with cloud when I went to, for example, an Amazon and there was, you know, a dozen services, it was easy to figure out what was going on. It was easy to figure out what my billing looked like. You know, now they've become so widespread, the number of services they have, you know, the number of stories you just hear of people who went, "Oh, I started a service over in US West and I can't find it anymore 'cause it's on a different screen. And I, you know, I just got billed for it." Like, so I think the sprawl of some of the clouds has gotten, has created a user experience that a lot of people are frustrated with. I think that's one thing. And we, you know, we see people like Digital Ocean and we see others who are saying, "Hey, we're going to be that simplified version." So, there's always that yin and yang. I think people are super frustrated at network costs, right? So, you know, and that's kind of at a lot of, at the center of maybe why we do or don't see more of these Supercloud services is just, you know, in the data center as an application owner, I didn't have to think about, well where, where does this go to? Where are my users? Yes, somebody took care of it, but when those things become front and center, that's super frustrating. That's the one area that we've seen absolutely no cost savings, cost reduction. So I think that frustrates people a lot. And then I think the third piece is just, you know, we're, we went from super centralized IT organizations, which, you know, for decades was how it worked. It was part of the reason why the cloud expanded and became a thing, right? Sort of shadow IT and I can't get things done. And then, now what we've seen is sort of this proliferation of little pockets of groups that are your IT, for lack of a better thing, whether they're called platform engineering or SRE or DevOps. But we have this, expansion, explosion if you will, of groups that, if I'm an app dev team, I go, "Hey, you helped me make this stuff run, but then the team next to you has another group and they have another group." And so you see this explosion of, you know, we don't have any standards in the company anymore. And, so sort of self-service has created its own nightmare to a certain extent for a lot of larger companies. >> Yeah. Thank you for that. So, you know, I want, I want to explore this multi-cloud, you know, by accident thing and is a real problem. You hear that a lot from vendors and we've been talking about Supercloud as this unifying layer across cloud. You know, but when you talk to customers, a lot of them are saying, "Yes, we have multiple clouds in our organization, but my group, we have mono cloud, we know the security, edicts, we know how to, you know, deal with the primitives, whether it's, you know, S3 or Azure Blob or whatever it is. And we're very comfortable with this." It's, that's how we're simplifying. So, do you think this is really a problem? Does it have merit that we need that unifying layer across clouds, or is it just too early for that? >> I think, yeah, I think what you, what you've laid out is basically how the world has played out. People have picked a cloud for a specific application or a series of applications. Yeah, and I think if you talk to most companies, they would tell you, you know, holistically, yes, we're multi-cloud, not, maybe not necessarily on, I don't necessarily love the phrase where people say like, well it happened by accident. I think it happened on purpose, but we got to multi-cloud, not in the way that maybe that vendors, you know, perceived, you know, kind of laid out a map for. So it was, it was, well you will lay out this sort of Supercloud framework. We didn't call it that back then, we just called it sort of multi-cloud. Maybe it was Kubernetes or maybe it was whatever. And different groups, because central IT kind of got disbanded or got fragmented. It turned into, go pick the best cloud for your application, for what you need to do for the business. And then, you know, multiple years later it was like, "Oh, hold on, I've got 20% in Google and 50% in AWS and I've got 30% in Azure. And, you know, it's, yeah, it's been evolution. I don't know that it's, I don't know if it's a mistake. I think it's now groups trying to figure out like, should I make sense of it? You know, should I try and standardize and I backwards standardize some stuff? I think that's going to be a hard thing for, for companies to do. 'cause I think they feel okay with where the applications are. They just happen to be in multiple clouds. >> I want to run something by you, and you guys, you and Aaron have talked about this. You know, still depending on who, which keynote you listen to, small percentage of the workloads are actually in cloud. And when you were with us at Wikibon, I think we called it true private cloud, and we looked at things like Nutanix and there were a lot of other examples of companies that were trying to replicate the hyperscale experience on Prem. >> Yeah. >> And, we would evaluate that, you know, beyond virtualization, and so we sort of defined that and, but I think what's, maybe what's more interesting than Supercloud across clouds is if you include that, that on Prem estate, because that's where most of the work is being done, that's where a lot of the proprietary tools have been built, a lot of data, a lot of software. So maybe there's this concept of sending that true private cloud to true hybrid cloud. So I actually think hybrid cloud in some cases is the more interesting use case for so-called Supercloud. What are your thoughts on that? >> Yeah, I think there's a couple aspects too. I think, you know, if we were to go back five or six years even, maybe even a little further and look at like what a data center looked like, even if it was just, "Hey we're a data center that runs primarily on VMware. We use some of their automation". Versus what you can, even what you can do in your data center today. The, you know, the games that people have seen through new types of automation through Kubernetes, through get ops, and a number of these things, like they've gotten significantly further along in terms of I can provision stuff really well, I can do multi-tenancy, I can do self-service. Is it, you know, is it still hard? Yeah. Because those things are hard to do, but there's been significant progress there. I don't, you know, I still look for kind of that, that killer application, that sort of, you know, lighthouse use case of, hybrid applications, you know, between data center and between cloud. I think, you know, we see some stuff where, you know, backup is a part of it. So you use the cloud for storage, maybe you use the cloud for certain kinds of resiliency, especially on maybe front end load balancing and stuff. But I think, you know, I think what we get into is, this being hung up on hybrid cloud or multi-cloud as a term and go like, "Look, what are you trying to measure? Are you trying to measure, you know, efficiency of of of IT usage? Are you trying to measure how quickly can I give these business, you know, these application teams that are part of a line of business resources that they need?" I think if we start measuring that way, we would look at, you know, you'd go, "Wow, it used to be weeks and months. Now we got rid of these boards that have to review everything every time I want to do a change management type of thing." We've seen a lot more self-service. I think those are the things we want to measure on. And then to your point of, you know, where does, where do these Supercloud applications fit in? I think there are a bunch of instances where you go, "Look, I have a, you know, global application, I have a thing that has to span multiple regions." That's where the Supercloud concept really comes into play. We used to do it in the data center, right? We'd had all sorts of technologies to help with that, I think you can now start to do it in the cloud. >> You know, one of the other things, trying to understand, your thoughts on this, do you think that you, you again have talked about this, like I'm with you. It's like, how is it that Google's losing, you know, 3 billion dollars a year, whatever. I mean, because when you go back and look at Amazon, when they were at that level of revenue where Google is today, they were making money, you know, and they were actually growing faster, by the way. So it's kind of interesting what's happened with Google. But, the reason I bring that up is, trying to understand if you think the hyperscalers will ever be motivated to create standards across clouds, and that may be a play for Google. I mean, obviously with Kubernetes it was like a Hail Mary and kind of made them relevant. Where would Google be without Kubernetes? But then did it achieve the objectives? We could have that conversation some other time, but do you think the hyperscalers will actually say, "Okay, we're going to lean in and create these standards across clouds." Because customers would love that, I would think, but it would sub-optimize their competitive advantage. What are your thoughts? >> I think, you know, on the surface, I would say they, they probably aren't. I think if you asked 'em the question, they would say, "Well, you know, first and foremost, you know, we do deliver standards, so we deliver a, you know, standard SQL interface or a SQL you know, or a standard Kubernetes API or whatever. So, in that, from that perspective, you know, we're not locking you into, you know, an Amazon specific database, or a Google specific database." You, you can argue about that, but I think to a certain extent, like they've been very good about, "Hey, we're going to adopt the standards that people want." A lot of times the open source standards. I think the problem is, let's say they did come up with a standard for it. I think you still have the problem of the costs of migration and you know, the longer you've, I think their bet is basically the longer you've been in some cloud. And again, the more data you sort of compile there, the data gravity concept, there's just going to be a natural thing that says, okay, the hurdle to get over to say, "Look, we want to move this to another cloud", becomes so cost prohibitive that they don't really have to worry about, you know, oh, I'm going to get into a war of standards. And so far I think they sort of realize like that's the flywheel that the cloud creates. And you know, unless they want to get into a world where they just cut bandwidth costs, like it just kind of won't happen. You know, I think we've even seen, and you know, the one example I'll use, and I forget the name of it off the top of my head, but there's a, there's a Google service. I think it's like BigQuery external or something along those lines, that allows you to say, "Look, you can use BigQuery against like S3 buckets and against other stuff." And so I think the cloud providers have kind of figured out, I'm never going to get the application out of that other guy's cloud or you know, the other cloud. But maybe I'm going to have to figure out some interesting ways to sort of work with it. And, you know, it's a little bit, it's a little janky, but that might be, you know, a moderate step that sort of gets customers where they want to be. >> Yeah. Or you know, it'd be interesting if you ever see AWS for example, running its database in other clouds, you started, even Oracle is doing that with, with with Azure, which is a form of Supercloud. My last question for you is, I want to get you thinking about sort of how the future plays out. You know, think about some of the companies that we've put forth this Supercloud, and by the way, this has been a criticism of the concept. Charles Fitzer, "Everything is Supercloud!" Which if true would defeat the purpose of course. >> Right. >> And so right with the community effort, we really tried to put some guardrails down on the essential characteristics, the deployment models, you know, so for example, running across multiple clouds with a purpose build pass, creating a common experience, metadata intelligence that solves a specific problem. I mean, the example I often use is Snowflake's governed data sharing. But yeah, Snowflake, Databricks, CloudFlare, Cohesity, you know, I just mentioned Oracle and Azure, these and others, they certainly claim to have that common experience across clouds. But my question is, again, I come back to, do customers need this capability? You know, is Mono Cloud the way to solve that problem? What's your, what are your thoughts on how this plays out in the future of, I guess, PAs, apps and cloud? >> Yeah, I think a couple of things. So, from a technology perspective, I think, you know, the companies you name, the services you've named, have sort of proven that the concept is viable and it's viable at a reasonable size, right? These aren't completely niche businesses, right? They're multi-billion dollar businesses. So, I think there's a subset of applications that, you know, maybe a a bigger than a niche set of applications that are going to use these types of things. A lot of what you talked about is very data centric, and that's, that's fine. That's that layer is, figuring that out. I think we'll see messaging types of services, so like Derek Hallison's, Caya Company runs a, sort of a Supercloud for messaging applications. So I think there'll be places where it makes a ton of sense. I think, the thing that I'm not sure about, and because again, we've been now 10 plus years of sort of super low, you know, interest rates in terms of being able to do things, is a lot of these things come out of research that have been done previously. Then they get turned into maybe somewhat of an open source project, and then they can become something. You know, will we see as much investment into the next Snowflake if, you know, the interest rates are three or four times that they used to be, do we, do we see VCs doing it? So that's the part that worries me a little bit, is I think we've seen what's possible. I think, you know, we've seen companies like what those services are. I think I read yesterday Snowflake was saying like, their biggest customers are growing at 30, like 50 or 60%. Like the, value they get out of it is becoming exponential. And it's just a matter of like, will the economics allow the next big thing to happen? Because some of these things are pretty, pretty costly, you know, expensive to get started. So I'm bullish on the idea. I don't know that it becomes, I think it's okay that it's still sort of, you know, niche plus, plus in terms of the size of it. Because, you know, if we think about all of IT it's still, you know, even microservices is a small part of bigger things. But I'm still really bullish on the idea. I like that it's been proven. I'm a little wary, like a lot of people have the economics of, you know, what might slow things down a little bit. But yeah, I, think the future is going to involve Supercloud somewhere, whatever people end up calling it. And you and I discussed that. (laughs) But I don't, I don't think it goes away. I don't think it's, I don't think it's a fad. I think it is something that people see tremendous value and it's just, it's got to be, you know, for what you're trying to do, your application specific thing. >> You're making a great point on the funding of innovation and we're entering a new era of public policy as well. R and D tax credit is now is shifting. >> Yeah. >> You know, you're going to have to capitalize that over five years now. And that's something that goes back to the 1950s and many people would argue that's at least in part what has helped the United States be so, you know, competitive in tech. But Brian, always great to talk to you. Thanks so much for participating in the program. Great to see you. >> Thanks Dave, appreciate it. Good luck with the rest of the show. >> Thank you. All right, this is Dave Vellante for John Furrier, the entire Cube community. Stay tuned for more content from Supercloud2.

>> From theCUBE Studios in Palo Alto, in Boston bringing you Data Driven Insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> In the pre-cloud era hardware companies would run benchmarks, showing how database and or application performance ran better on their systems relative to competitors or previous generation boxes. And they would make a big deal out of it. And the independent software vendors, you know they'd do a little golf clap if you will, in the form of a joint press release it became a game of leaprog amongst hardware competitors. That was pretty commonplace over the years. The Dell Snowflake Deal underscores that the value proposition between hardware companies and ISVs is changing and has much more to do with distribution channels, volumes and the amount of data that lives On-Prem in various storage platforms. For cloud native ISVs like Snowflake they're realizing that despite their Cloud only dogma they have to grit their teeth and deal with On-premises data or risk getting shut out of evolving architectures. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we unpack what little is known about the Snowflake announcement from Dell Technologies World and discuss the implications of a changing Cloud landscape. We'll also share some new data for Cloud and Database platforms from ETR that shows Snowflake has actually entered the Earth's orbit when it comes to spending momentum on its platform. Now, before we get into the news I want you to listen to Frank's Slootman's answer to my question as to whether or not Snowflake would ever architect the platform to run On-Prem because it's doable technically, here's what he said, play the clip >> Forget it, this will only work in the Public Cloud. Because it's, this is how the utility model works, right. I think everybody is coming through this realization, right? I mean, excuses are running out at this point. You know, we think that it'll, people will come to the Public Cloud a lot sooner than we will ever come to the Private Cloud. It's not that we can't run a private Cloud. It's just diminishes the potential and the value that we bring. >> So you may be asking yourselves how do you square that circle? Because basically the Dell Snowflake announcement is about bringing Snowflake to the private cloud, right? Or is it let's get into the news and we'll find out. Here's what we know at Dell Technologies World. One of the more buzzy announcements was the, by the way this was a very well attended vet event. I should say about I would say 8,000 people by my estimates. But anyway, one of the more buzzy announcements was Snowflake can now run analytics on Non-native Snowflake data that lives On-prem in a Dell object store Dell's ECS to start with. And eventually it's software defined object store. Here's Snowflake's clark, Snowflake's Clark Patterson describing how it works this past week on theCUBE. Play the clip. The way it works is I can now access Non-native Snowflake data using what materialized views, external tables How does that work? >> Some combination of the, all the above. So we've had in Snowflake, a capability called External Tables, which you refer to, it goes hand in hand with this notion of external stages. Basically there's a through the combination of those two capabilities, it's a metadata layer on data, wherever it resides. So customers have actually used this in Snowflake for data lake data outside of Snowflake in the Cloud, up until this point. So it's effectively an extension of that functionality into the Dell On-Premises world, so that we can tap into those things. So we use the external stages to expose all the metadata about what's in the Dell environment. And then we build external tables in Snowflake. So that data looks like it is in Snowflake. And then the experience for the analyst or whomever it is, is exactly as though that data lives in the Snowflake world. >> So as Clark explained, this capability of External tables has been around in the Cloud for a while, mainly to suck data out of Cloud data lakes. Snowflake External Tables use file level metadata, for instance, the name of the file and the versioning so that it can be queried in a stage. A stage is just an external location outside of Snowflake. It could be an S3 bucket or an Azure Blob and it's soon will be a Dell object store. And in using this feature, the Dell looks like it lives inside of Snowflake and Clark essentially, he's correct to say to an analyst that looks exactly like the data is in Snowflake, but uh, not exactly the data's read only which means you can't do what are called DML operations. DML stands for Data Manipulation Language and allows for things like inserting data into tables or deleting and modifying existing data. But the data can be queried. However, the performance of those queries to External Tables will almost certainly be slower. Now users can build things like materialized views which are going to speed things up a bit, but at the end of the day, it's going to run faster than the Cloud. And you can be almost certain that's where Snowflake wants it to run, but some organizations can't or won't move data into the Cloud for a variety of reasons, data sovereignty, compliance security policies, culture, you know, whatever. So data can remain in place On-prem, or it can be moved into the Public Cloud with this new announcement. Now, the compute today presumably is going to be done in the Public Cloud. I don't know where else it's going to be done. They really didn't talk about the compute side of things. Remember, one of Snowflake's early innovations was to separate compute from storage. And what that gave them is you could more efficiently scale with unlimited resources when you needed them. And you could shut off the compute when you don't need us. You didn't have to buy, and if you need more storage you didn't have to buy more compute and vice versa. So everybody in the industry has copied that including AWS with Redshift, although as we've reported not as elegantly as Snowflake did. RedShift's more of a storage tiering solution which minimizes the compute required but you can't really shut it off. And there are companies like Vertica with Eon Mode that have enabled this capability to be done On-prem, you know, but of course in that instance you don't have unlimited elastic compute scale on-Prem but with solutions like Dell Apex and HPE GreenLake, you can certainly, you can start to simulate that Cloud elasticity On-prem. I mean, it's not unlimited but it's sort of gets you there. According to a Dell Snowflake joint statement, the companies the quote, the companies will pursue product integrations and joint go to market efforts in the second half of 2022. So that's a little vague and kind of benign. It's not really clear when this is going to be available based on that statement from the two first, but, you know, we're left wondering will Dell develop an On-Prem compute capability and enable queries to run locally maybe as part of an extended apex offering? I mean, we don't know really not sure there's even a market for that but it's probably a good bet that again, Snowflake wants that data to land in the Snowflake data Cloud kind of makes you wonder how this deal came about. You heard Sloop on earlier Snowflake has always been pretty dogmatic about getting data into its native snowflake format to enable the best performance as we talked about but also data sharing and governance. But you could imagine that data architects they're building out their data mesh we've reported on this quite extensively and their data fabric and those visions around that. And they're probably telling Snowflake, Hey if you want to be a strategic partner of ours you're going to have to be more inclusive of our data. That for whatever reason we're not putting in your Cloud. So Snowflake had to kind of hold its nose and capitulate. Now the good news is it further opens up Snowflakes Tam the total available market. It's obviously good marketing posture. And ultimately it provides an on ramp to the Cloud. And we're going to come back to that shortly but let's look a little deeper into what's happening with data platforms and to do that we'll bring in some ETR data. Now, let me just say as companies like Dell, IBM, Cisco, HPE, Lenovo, Pure and others build out their hybrid Clouds. The cold hard fact is not only do they have to replicate the Cloud Operating Model. You will hear them talk about that a lot, but they got to do that. So it, and that's critical from a user experience but in order to gain that flywheel momentum they need to build a robust ecosystem that goes beyond their proprietary portfolios. And, you know, honestly they're really not even in the first inning most companies and for the likes of Snowflake to sort of flip this, they've had to recognize that not everything is moving into the Cloud. Now, let's bring up the next slide. One of the big areas of discussion at Dell Tech World was Apex. That's essentially Dell's nascent as a service offering. Apex is infrastructure as a Service Cloud On-prem and obviously has the vision of connecting to the Cloud and across Clouds and out to the Edge. And it's no secret that database is one of the most important ingredients of infrastructure as a service generally in Cloud Infrastructure specifically. So this chart here shows the ETR data for data platforms inside of Dell accounts. So the beauty of ETR platform is you can cut data a million different ways. So we cut it. We said, okay, give us the Cloud platforms inside Dell accounts, how are they performing? Now, this is a two dimensional graphic. You got net score or spending momentum on the vertical axis and what ETR now calls Overlap formally called Market Share which is a measure of pervasiveness in the survey. That's on the horizontal axis that red dotted line at 40% represents highly elevated spending on the Y. The table insert shows the raw data for how the dots are positioned. Now, the first call out here is Snowflake. According to ETR quote, after 13 straight surveys of astounding net scores, Snowflake has finally broken the trend with its net score dropping below the 70% mark among all respondents. Now, as you know, net score is measured by asking customers are you adding the platform new? That's the lime green in the bar that's pointing from Snowflake in the graph and or are you increasing spend by 6% or more? That's the forest green is spending flat that's the gray is you're spend decreasing by 6% or worse. That's the pinkish or are you decommissioning the platform bright red which is essentially zero for Snowflake subtract the reds from the greens and you get a net score. Now, what's somewhat interesting is that snowflakes net score overall in the survey is 68 which is still huge, just under 70%, but it's net score inside the Dell account base drops to the low sixties. Nonetheless, this chart tells you why Snowflake it's highly elevated spending momentum combined with an increasing presence in the market over the past two years makes it a perfect initial data platform partner for Dell. Now and in the Ford versus Ferrari dynamic. That's going on between the likes of Dell's apex and HPE GreenLake database deals are going to become increasingly important beyond what we're seeing with this recent Snowflake deal. Now noticed by the way HPE is positioned on this graph with its acquisition of map R which is now part of HPE Ezmeral. But if these companies want to be taken seriously as Cloud players, they need to further expand their database affinity to compete ideally spinning up databases as part of their super Clouds. We'll come back to that that span multiple Clouds and include Edge data platforms. We're a long ways off from that. But look, there's Mongo, there's Couchbase, MariaDB, Cloudera or Redis. All of those should be on the short list in my view and why not Microsoft? And what about Oracle? Look, that's to be continued on maybe as a future topic in a, in a Breaking Analysis but I'll leave you with this. There are a lot of people like John Furrier who believe that Dell is playing with fire in the Snowflake deal because he sees it as a one way ticket to the Cloud. He calls it a one way door sometimes listen to what he said this past week. >> I would say that that's a dangerous game because we've seen that movie before, VMware and AWS. >> Yeah, but that we've talked about this don't you think that was the right move for VMware? >> At the time, but if you don't nurture the relationship AWS will take all those customers ultimately from VMware. >> Okay, so what does the data say about what John just said? How is VMware actually doing in Cloud after its early missteps and then its subsequent embracing of AWS and other Clouds. Here's that same XY graphic spending momentum on the Y and pervasiveness on the X and the same table insert that plots the dots and the, in the breakdown of Dell's net score granularity. You see that at the bottom of the chart in those colors. So as usual, you see Azure and AWS up and to the right with Google well behind in a distant third, but still in the mix. So very impressive for Microsoft and AWS to have both that market presence in such elevated spending momentum. But the story here in context is that the VMware Cloud on AWS and VMware's On-Prem Cloud like VMware Cloud Foundation VCF they're doing pretty well in the market. Look, at HPE, gaining some traction in Cloud. And remember, you may not think HPE and Dell and VCF are true Cloud but these are customers answering the survey. So their perspective matters more than the purest view. And the bad news is the Dell Cloud is not setting the world on fire from a momentum standpoint on the vertical axis but it's above the line of zero and compared to Dell's overall net score of 20 you could see it's got some work to do. Okay, so overall Dell's got a pretty solid net score to you know, positive 20, as I say their Cloud perception needs to improve. Look, Apex has to be the Dell Cloud brand not Dell reselling VMware. And that requires more maturity of Apex it's feature sets, its selling partners, its compensation models and it's ecosystem. And I think Dell clearly understands that. I think they're pretty open about that. Now this includes partners that go beyond being just sellers has to include more tech offerings in the marketplace. And actually they got to build out a marketplace like Cloud Platform. So they got a lot of work to do there. And look, you've got Oracle coming up. I mean they're actually kind of just below the magic 40% in the line which is pro it's pretty impressive. And we've been telling you for years, you can hate Oracle all you want. You can hate its price, it's closed system all of that it's red stack shore. You can say it's legacy. You can say it's old and outdated, blah, blah, blah. You can say Oracle is irrelevant in trouble. You are dead wrong. When it comes to mission critical workloads. Oracle is the king of the hill. They're a founder led company that knows exactly what it's doing and they're showing Cloud momentum. Okay, the last point is that while Microsoft AWS and Google have major presence as shown on the X axis. VMware and Oracle now have more than a hundred citations in the survey. You can see that on the insert in the right hand, right most column. And IBM had better keep the momentum from last quarter going, or it won't be long before they get passed by Dell and HP in Cloud. So look, John might be right. And I would think Snowflake quietly agrees that this Dell deal is all about access to Dell's customers and their data. So they can Hoover it into the Snowflake Data Cloud but the data right now, anyway doesn't suggest that's happening with VMware. Oh, by the way, we're keeping an eye close eye on NetApp who last September ink, a similar deal to VMware Cloud on AWS to see how that fares. Okay, let's wrap with some closing thoughts on what this deal means. We learned a lot from the Cloud generally in AWS, specifically in two pizza teams, working backwards, customer obsession. We talk about flywheel all the time and we've been talking today about marketplaces. These have all become common parlance and often fundamental narratives within strategic plans investor decks and customer presentations. Cloud ecosystems are different. They take both competition and partnerships to new heights. You know, when I look at Azure service offerings like Apex, GreenLake and similar services and I see the vendor noise or hear the vendor noise that's being made around them. I kind of shake my head and ask, you know which movie were these companies watching last decade? I really wish we would've seen these initiatives start to roll out in 2015, three years before AWS announced Outposts not three years after but Hey, the good news is that not only was Outposts a wake up call for the On-Prem crowd but it's showing how difficult it is to build a platform like Outposts and bring it to On-Premises. I mean, Outpost isn't currently even a rounding era in the marketplace. It really doesn't do much in terms of database support and support of other services. And, you know, it's unclear where that that is going. And I don't think it has much momentum. And so the Hybrid Cloud Vendors they've had time to figure it out. But now it's game on, companies like Dell they're promising a consistent experience between On-Prem into the Cloud, across Clouds and out to the Edge. They call it MultCloud which by the way my view has really been multi-vendor Chuck, Chuck Whitten. Who's the new co-COO of Dell called it Multi-Cloud by default. (laughing) That's really, I think an accurate description of that. I call this new world Super Cloud. To me, it's different than MultiCloud. It's a layer that runs on top of hyperscale infrastructure kind of hides the underlying complexity of the Cloud. It's APIs, it's primitives. And it stretches not only across Clouds but out to the Edge. That's a big vision and that's going to require some seriously intense engineering to build out. It's also going to require partnerships that go beyond the portfolios of companies like Dell like their own proprietary stacks if you will. It's going to have to replicate the Cloud Operating Model and to do that, you're going to need more and more deals like Snowflake and even deeper than Snowflake, not just in database. Sure, you'll need to have a catalog of databases that run in your On-Prem and Hybrid and Super Cloud but also other services that customers can tap. I mean, can you imagine a day when Dell offers and embraces a directly competitive service inside of apex. I have trouble envisioning that, you know not with their historical posture, you think about companies like, you know, Nutanix, you know, or Cisco where they really, you know those relationships cooled quite quickly but you know, look, think about it. That's what AWS does. It offers for instance, Redshift and Snowflake side by side happily and the Redshift guys they probably hate Snowflake. I wouldn't blame them, but the EC Two Folks, they love them. And Adam SloopesKy understands that ISVs like Snowflake are a key part of the Cloud ecosystem. Again, I have a hard time envisioning that occurring with Dell or even HPE, you know maybe less so with HPE, but what does this imply that the Edge will allow companies like Dell to a reach around on the Cloud and somehow create a new type of model that begrudgingly accommodates the Public Cloud but drafts of the new momentum of the Edge, which right now to these companies is kind of mostly telco and retail. It's hard to see that happening. I think it's got to evolve in a more comprehensive and inclusive fashion. What's much more likely is companies like Dell are going to substantially replicate that Cloud Operating Model for the pieces that they own pieces that they control which admittedly are big pieces of the market. But unless they're able to really tap that ecosystem magic they're not going to be able to grow much beyond their existing install bases. You take that lime green we showed you earlier that new adoption metric from ETR as an example, by my estimates, AWS and Azure are capturing new accounts at a rate between three to five times faster than Dell and HPE. And in the more mature US and mere markets it's probably more like 10 X and a major reason is because of the Cloud's robust ecosystem and the optionality and simplicity of transaction that that is bringing to customers. Now, Dell for its part is a hundred billion dollar revenue company. And it has the capability to drive that kind of dynamic. If it can pivot its partner ecosystem mindset from kind of resellers to Cloud services and technology optionality. Okay, that's it for now? Thanks to my colleagues, Stephanie Chan who helped research topics for Breaking Analysis. Alex Myerson is on the production team. Kristen Martin and Cheryl Knight and Rob Hof, on editorial they helped get the word out and thanks to Jordan Anderson for the new Breaking Analysis branding and graphics package. Remember these episodes are all available as podcasts wherever you listen. All you do is search Breaking Analysis podcasts. You could check out ETR website @etr.ai. We publish a full report every week on wikibon.com and siliconangle.com. You want to get in touch. @dave.vellente @siliconangle.com. You can DM me @dvellante. You can make a comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (upbeat music)

(upbeat music) >> Hello, welcome to this CUBE conversation I'm John Furrier your host here in theCUBE in Palo Alto, California, featuring Cribl a hot startup taking over the enterprise when it comes to data pipelining, and we have a CUBE alumni who's the co-founder and CEO, Clint Sharp. Clint, great to see you again, you've been on theCUBE, you were on in 2013, great to see you, congratulations on the company that you co-founded, and leading as the chief executive officer over $200 million in funding, doing this really strong in the enterprise, congratulations thanks for joining us. >> Hey, thanks John it's really great to be back. >> You know, remember our first conversation the big data wave coming in, Hadoop World 2010, now the cloud comes in, and really the cloud native really takes data to a whole nother level. You've seeing the old data architectures being replaced with cloud scale. So the data landscape is interesting. You know, Data as Code you're hearing that term, data engineering teams are out there, data is everywhere, it's now part of how developers and companies are getting value whether it's real time, or coming out of data lakes, data is more pervasive than ever. Observability is a hot area, there's a zillion companies doing it, what are you guys doing? Where do you fit in the data landscape? >> Yeah, so what I say is that Cribl and our products and we solve the problem for our customers of the fundamental tension between data growth and budget. And so if you look at IDCs data data's growing at a 25%, CAGR, you're going to have two and a half times the amount of data in five years that you have today, and I talk to a lot of CIOs, I talk to a lot of CISOs, and the thing that I hear repeatedly is my budget is not growing at a 25% CAGR so fundamentally, how do I resolve this tension? We sell very specifically into the observability in security markets, we sell to technology professionals who are operating, you know, observability in security platforms like Splunk, or Elasticsearch, or Datadog, Exabeam, like these types of platforms they're moving, protocols like syslog, they're moving, they have lots of agents deployed on every endpoint and they're trying to figure out how to get the right data to the right place, and fundamentally you know, control cost. And we do that through our product called Stream which is what we call an observability pipeline. It allows you to take all this data, manipulate it in the stream and get it to the right place and fundamentally be able to connect all those things that maybe weren't originally intended to be connected. >> So I want to get into that new architecture if you don't mind, but let me first ask you on the problem space that you're in. So cloud native obviously instrumentating, instrumenting everything is a key thing. You mentioned data got all these tools, is the problem that there's been a sprawl of things being instrumented and they have to bring it together, or it's too costly to run all these point solutions and get it to work? What's the problem space that you're in? >> So I think customers have always been forced to make trade offs John. So the, hey I have volumes and volumes and volumes of data that's relevant to securing my enterprise, that's relevant to observing and understanding the behavior of my applications but there's never been an approach that allows me to really onboard all of that data. And so where we're coming at is giving them the tools to be able to, you know, filter out noise and waste, to be able to, you know, aggregate this high fidelity telemetry data. There's a lot of growing changes, you talk about cloud native, but digital transformation, you know, the pandemic itself and remote work all these are driving significantly greater data volumes, and vendors unsurprisingly haven't really been all that aligned to giving customers the tools in order to reshape that data, to filter out noise and waste because, you know, for many of them they're incentivized to get as much data into their platform as possible, whether that's aligned to the customer's interests or not. And so we saw an opportunity to come out and fundamentally as a customers-first company give them the tools that they need, in order to take back control of their data. >> I remember those conversations even going back six years ago the whole cloud scale, horizontally scalable applications, you're starting to see data now being stuck in the silos now to have high, good data you have to be observable, which means you got to be addressable. So you now have to have a horizontal data plane if you will. But then you get to the question of, okay, what data do I need at the right time? So is the Data as Code, data engineering discipline changing what new architectures are needed? What changes in the mind of the customer once they realize that they need this new way to pipe data and route data around, or make it available for certain applications? What are the key new changes? >> Yeah, so I think one of the things that we've been seeing in addition to the advent of the observability pipeline that allows you to connect all the things, is also the advent of an observability lake as well. Which is allowing people to store massively greater quantities of data, and also different types of data. So data that might not traditionally fit into a data warehouse, or might not traditionally fit into a data lake architecture, things like deployment artifacts, or things like packet captures. These are binary types of data that, you know, it's not designed to work in a database but yet they want to be able to ask questions like, hey, during the Log4Shell vulnerability, one of all my deployment artifacts actually had Log4j in it in an affected version. These are hard questions to answer in today's enterprise. Or they might need to go back to full fidelity packet capture data to try to understand that, you know, a malicious actor's movement throughout the enterprise. And we're not seeing, you know, we're seeing vendors who have great log indexing engines, and great time series databases, but really what people are looking for is the ability to store massive quantities of data, five times, 10 times more data than they're storing today, and they're doing that in places like AWSS3, or in Azure Blob Storage, and we're just now starting to see the advent of technologies we can help them query that data, and technologies that are generally more specifically focused at the type of persona that we sell to which is a security professional, or an IT professional who's trying to understand the behaviors of their applications, and we also find that, you know, general-purpose data processing technologies are great for the enterprise, but they're not working for the people who are running the enterprise, and that's why you're starting to see the concepts like observability pipelines and observability lakes emerge, because they're targeted at these people who have a very unique set of problems that are not being solved by the general-purpose data processing engines. >> It's interesting as you see the evolution of more data volume, more data gravity, then you have these specialty things that need to be engineered for the business. So sounds like observability lake and pipelining of the data, the data pipelining, or stream you call it, these are new things that they bolt into the architecture, right? Because they have business reasons to do it. What's driving that? Sounds like security is one of them. Are there others that are driving this behavior? >> Yeah, I mean it's the need to be able to observe applications and observe end-user behavior at a fine-grain detail. So, I mean I often use examples of like bank teller applications, or perhaps, you know, the app that you're using to, you know, I'm going to be flying in a couple of days. I'll be using their app to understand whether my flight's on time. Am I getting a good experience in that particular application? Answering the question of is Clint getting a good experience requires massive quantities of data, and your application and your service, you know, I'm going to sit there and look at, you know, American Airlines which I'm flying on Thursday, I'm going to be judging them based on off of my experience. I don't care what the average user's experience is I care what my experience is. And if I call them up and I say, hey, and especially for the enterprise usually this is much more for, you know, in-house applications and things like that. They call up their IT department and say, hey, this application is not working well, I don't know what's going on with it, and they can't answer the question of what was my individual experience, they're living with, you know, data that they can afford to store today. And so I think that's why you're starting to see the advent of these new architectures is because digital is so absolutely critical to every company's customer experience, that they're needing to be able to answer questions about an individual user's experience which requires significantly greater volumes of data, and because of significantly greater volumes of data, that requires entirely new approaches to aggregating that data, bringing the data in, and storing that data. >> Talk to me about enabling customer choice when it comes around controlling their data. You mentioned that before we came on camera that you guys are known for choice. How do you enable customer choice and control over their data? >> So I think one of the biggest problems I've seen in the industry over the last couple of decades is that vendors come to customers with hugely valuable products that make their lives better but it also requires them to maintain a relationship with that vendor in order to be able to continue to ask questions of that data. And so customers don't get a lot of optionality in these relationships. They sign multi-year agreements, they look to try to start another, they want to go try out another vendor, they want to add new technologies into their stack, and in order to do that they're often left with a choice of well, do I roll out like get another agent, do I go touch 10,000 computers, or a 100,000 computers in order to onboard this data? And what we have been able to offer them is the ability to reuse their existing deployed footprints of agents and their existing data collection technologies, to be able to use multiple tools and use the right tool for the right job, and really give them that choice, and not only give them the choice once, but with the concepts of things like the observability lake and replay, they can go back in time and say, you know what? I wanted to rehydrate all this data into a new tool, I'm no longer locked in to the way one vendor stores this, I can store this data in open formats and that's one of the coolest things about the observability late concept is that customers are no longer locked in to any particular vendor, the data is stored in open formats and so that gives them the choice to be able to go back later and choose any vendor, because they may want to do some AI or ML on that type of data and do some model training. They may want to be able to forward that data to a new cloud data warehouse, or try a different vendor for log search or a different vendor for time series data. And we're really giving them the choice and the tools to do that in a way in which was simply not possible before. >> You know you are bring up a point that's a big part of the upcoming AWS startup series Data as Code, the data engineering role has become so important and the word engineering is a key word in that, but there's not a lot of them, right? So like how many data engineers are there on the planet, and hopefully more will come in, come from these great programs in computer science but you got to engineer something but you're talking about developing on data, you're talking about doing replays and rehydrating, this is developing. So Data as Code is now a reality, how do you see Data as Code evolving from your perspective? Because it implies DevOps, Infrastructure as Code was DevOps, if Data as Code then you got DataOps, AIOps has been around for a while, what is Data as Code? And what does that mean to you Clint? >> I think for our customers, one, it means a number of I think sort of after-effects that maybe they have not yet been considering. One you mentioned which is it's hard to acquire that talent. I think it is also increasingly more critical that people who were working in jobs that used to be purely operational, are now being forced to learn, you know, developer centric tooling, things like GET, things like CI/CD pipelines. And that means that there's a lot of education that's going to have to happen because the vast majority of the people who have been doing things in the old way from the last 10 to 20 years, you know, they're going to have to get retrained and retooled. And I think that one is that's a huge opportunity for people who have that skillset, and I think that they will find that their compensation will be directly correlated to their ability to have those types of skills, but it also represents a massive opportunity for people who can catch this wave and find themselves in a place where they're going to have a significantly better career and more options available to them. >> Yeah and I've been thinking about what you just said about your customer environment having all these different things like Datadog and other agents. Those people that rolled those out can still work there, they don't have to rip and replace and then get new training on the new multiyear enterprise service agreement that some other vendor will sell them. You come in and it sounds like you're saying, hey, stay as you are, use Cribl, we'll have some data engineering capabilities for you, is that right? Is that? >> Yup, you got it. And I think one of the things that's a little bit different about our product and our market John, from kind of general-purpose data processing is for our users they often, they're often responsible for many tools and data engineering is not their full-time job, it's actually something they just need to do now, and so we've really built tool that's designed for your average security professional, your average IT professional, yes, we can utilize the same kind of DataOps techniques that you've been talking about, CI/CD pipelines, GITOps, that sort of stuff, but you don't have to, and if you're really just already familiar with administering a Datadog or a Splunk, you can get started with our product really easily, and it is designed to be able to be approachable to anybody with that type of skillset. >> It's interesting you, when you're talking you've remind me of the big wave that was coming, it's still here, shift left meant security from the beginning. What do you do with data shift up, right, down? Like what do you, what does that mean? Because what you're getting at here is that if you're a developer, you have to deal with data but you don't have to be a data engineer but you can be, right? So we're getting in this new world. Security had that same problem. Had to wait for that group to do things, creating tension on the CI/CD pipelining, so the developers who are building apps had to wait. Now you got shift left, what is data, what's the equivalent of the data version of shift left? >> Yeah so we're actually doing this right now. We just announced a new product a week ago called Cribl Edge. And this is enabling us to move processing of this data rather than doing it centrally in the stream to actually push this processing out to the edge, and to utilize a lot of unused capacity that you're already paying AWS, or paying Azure for, or maybe in your own data center, and utilize that capacity to do the processing rather than having to centralize and aggregate all of this data. So I think we're going to see a really interesting, and left from our side is towards the origination point rather than anything else, and that allows us to really unlock a lot of unused capacity and continue to drive the kind of cost down to make more data addressable back to the original thing we talked about the tension between data growth, if we want to offer more capacity to people, if we want to be able to answer more questions, we need to be able to cost-effectively query a lot more data. >> You guys had great success in the enterprise with what you got going on. Obviously the funding is just the scoreboard for that. You got good growth, what are the use cases, or what's the customer look like that's working for you where you're winning, or maybe said differently what pain points are out there the customer might be feeling right now that Cribl could fit in and solve? How would you describe that ideal persona, or environment, or problem, that the customer may have that they say, man, Cribl's a perfect fit? >> Yeah, this is a person who's working on tooling. So they administer a Splunk, or an Elastic, or a Datadog, they may be in a network operations center, a security operation center, they are struggling to get data into their tools, they're always at capacity, their tools always at the redline, they really wish they could do more for the business. They're kind of tired of being this department of no where everybody comes to them and says, "hey, can I get this data in?" And they're like, "I wish, but you know, we're all out of capacity, and you know, we have, we wish we could help you but we frankly can't right now." We help them by routing that data to multiple locations, we help them control costs by eliminating noise and waste, and we've been very successful at that in, you know, logos, like, you know, like a Shutterfly, or a, blanking on names, but we've been very successful in the enterprise, that's not great, and we continue to be successful with major logos inside of government, inside of banking, telco, et cetera. >> So basically it used to be the old hyperscalers, the ones with the data full problem, now everyone's got the, they're full of data and they got to really expand capacity and have more agility and more engineering around contributions of the business sounds like that's what you guys are solving. >> Yup and hopefully we help them do a little bit more with less. And I think that's a key problem for our enterprises, is that there's always a limit on the number of human resources that they have available at their disposal, which is why we try to make the software as easy to use as possible, and make it as widely applicable to those IT and security professionals who are, you know, kind of your run-of-the-mill tools administrator, our product is very approachable for them. >> Clint great to see you on theCUBE here, thanks for coming on. Quick plug for the company, you guys looking for hiring, what's going on? Give a quick update, take 30 seconds to give a plug. >> Yeah, absolutely. We are absolutely hiring cribl.io/jobs, we need people in every function from sales, to marketing, to engineering, to back office, GNA, HR, et cetera. So please check out our job site. If you are interested it in learning more you can go to cribl.io. We've got some great online sandboxes there which will help you educate yourself on the product, our documentation is freely available, you can sign up for up to a terabyte a day on our cloud, go to cribl.cloud and sign up free today. The product's easily accessible, and if you'd like to speak with us we'd love to have you in our community, and you can join the community from cribl.io as well. >> All right, Clint Sharp co-founder and CEO of Cribl, thanks for coming to theCUBE. Great to see you, I'm John Furrier your host thanks for watching. (upbeat music)

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)

(lighthearted music) >> We're here now with Manoj Nair, who's the general manager of Metallic and Dave Totten CTO with Microsoft. And we're going to talk about some of the announcements that we heard earlier today and what Metallic and Microsoft are doing to meet customer needs around cyber threats and ensuring secure cloud data management. Gentlemen, welcome to theCUBE. Good to see you. >> Thanks Dave. >> Thank you. >> Hey Manoj, let me start with you. We heard early this morning, Dave Totten was here, David Noe, talk a lot about security. Has the conversation changed, how has it changed when you talk to customers, Manoj? What's top of mind. >> Yeah, thank you, Dave. And thank you, Dave Totten. You know, great conversation earlier. Dave, you and I have talked about this in the past, right? Security long a big passion of mine. You know, having lived through nation state attacks in the past and all that. We're seeing those kinds of techniques really just getting mainstream, right? Ransomware has become a mainstream problem in the scourge in our lives. Now, when you look at it from a lens of data and data management, data protection, backup, all of this was very much a passive you know, compliance centric use case. It was pretty static you know, put it in tapes, haul it all over. And what has really changed with this ransomware and cybercrime change rate is data, which is now your most precious asset, is under attack. So now you see security teams, just like you talked with Dave Martin, from ADP earlier, they are looking for that bridge between SecurityOps and ITOps. That data management solution needs to do more. It needs to be part of an active conversation, you know? Not just, you know, recovery readiness. Can you ensure that, are you testing that, is it recoverable? That is your last mile of defense. So then you get questions like that from security teams. You get you know, the need for doing more, signals. Can I get better signals from my data management stack to tell me I might be under attack? So what we're seeing in the conversation is the need to have more active conversations around data management and the bridge between ITOps and SecurityOps is really becoming paramount for our customers. >> Yeah, Dave Totten I mean, I often say that I think data protection used to be this bolt on. Now it's a fundamental component of the digital business stack. Anything you would add to what Manoj just said. >> Yeah, I would just say exactly that. Data is an asset, right? We talked about it a lot about the competitive advantage that customers are now realizing that no longer is IT considered sort of this cost center element. We need to be able to leverage our interactions with customers, with partners, with supply chains, with manufacturers, we need to be able to leverage that to sort of create differentiation and competitive advantage in the marketplace. And so if you think about it, as that way as the fuel for economic profitability and business growth, you would do everything in your power to secure it, to support it, to make sure you had access to it, to make sure that you didn't have you know, bad intent users accessing it. And I think we're seeing that shift with customers as they think more about how to be more efficient with their investments in information technology and then how just to make sure that they protect the lifeblood of their businesses. >> Yeah, and that just makes it harder because the adversary is very capable. They're coming in through the digital supply chain. So it's complicated. And so Dave and maybe Manoj, you can comment as well after, Microsoft and Commvault, you guys have been working together for decades and so you've seen a lot of the changes, a lot of the waves. So I'm curious as to how the partnership has evolved. You've got a recent strategic announcement around Azure with Metallic. Dave, take us through that. >> Yeah, I mean you know, Commvault and Microsoft aren't newlyweds, we've been together now for 25 plus years. We send each other anniversary gifts, all that good stuff. And you know, listen, there's a couple things that are key to our relationship. One, we started believing in each other's engineering organizations, right? We hire the best, we train and retain the best. And we both put a lot of investment behind our infrastructure and the ability to work together to really innovate at real time, rapid speeds. Two, we use Commvault products so you know, there's no greater I think, advantage that if a major supplier or platform partner like Microsoft uses your products. We've used it for years in our Xbox group to support and store the data for a hundred million XBox live users. And we're very avid with it with our data centers, our access to Azure data centers, our Microsoft office products. And so we use Commvault services as well. And through that mutual relationship you know, obviously Commvault has seen the ins and outs of what's great about our services and where we're continuing to build and invest. And so they've been able to really you know, dedicate a team of engineers and architects to support all that Azure as a platform, as a service can provide. And then how to take the best of those features and build it into their own first party products. I think when you get close enough to somebody for so many years right, 25 plus years, you figure out what they're great at and you learn to take those advantages like Commvault has with Microsoft and Azure and use it to your advantage, right? To build the best in class product that Metallic actually is. And you're right, the announcement this week it feels culminating, it feels like it's a major milestone in first off, industry innovation but also in our relationship. But it's really not that big of a step change from what we've been doing and building and innovating on for the past you know, 25 years. >> Yeah so Manoj, that's got to be music to your ears. Because you come at it with this rich data protection stack, Microsoft there's so many capabilities. One of the courses, which is Azure. It's like the secret weapon, it's become the secret weapon. How do you think about that relationship, Manoj? >> Absolutely Dave said it right. We are strong partners, 25 years, founding in Western Commvault, mutual customers, partnership. You know, really when you look at it from a customer lens, what our customers have appreciated, over the last year of that strengthening of that partnership basically the two pillars of Commvault the leader of data protection, or you know, for the last 25 years, 10 out of 10 in the Gartner MQ comes together with Azure, the enterprise secure cloud leader in creating Metallic. Metallic, now with 1,000 plus customers around the world, there's a reason they trust it. It's now become part of how they protect their Office 365. No workload left behind, which is very unique, you know? So what we have architected together and now we're taking it to the next phase, our joint partners, right? Our joint customers, that those are some of the things that are really changing in terms of how we're accelerating the partnership. >> Manoj, you and I have talked about ransomware a lot, we did a special segment a while back on that. The adversary is very capable. And you know, I put in the chat this morning, at Commvault Connections, you don't even need a high school diploma to be a ransomwarist. You can go on the dark web, you can buy ransomware as a service. All you need is access to a server and you can stick you know, some malware on it. So you know, it's very, very dangerous times. What is it about data management as a service that makes it a good fit right now from a customer perspective to solve this problem? >> Absolutely. Bad guys, real life, or in the cyber world, they have some techniques. First thing they do in a ransomware is you go after the exits. What are the exit doors? Now you back up data, they know that that backup data can be used to recover. So they go and try to defeat the backup products in that environment. That's number one game that changes with data management as a service. Your data management data protection environment is not inside your environment. Chances to do two simultaneous penetrations to try and anything is possible. But now you've got an additional layer of recovery readiness because that control plane secured on top of Microsoft, Azure, 3,500 security professionals, FedRAMP high standard only data management and service entity to get it. As one of our customers said, "A unicorn in the wild", that is what you have as your data management environment. So if something bad happens, worst case, this environment is ready. Our enterprise customers are starting to understand that this is becoming a big reason to shift to this model. You know, then it's okay if you're not ready to shift the entire model, you're given the easy button of just air gapping of your data. So if you're an existing Commvault customer, appliance, software, anything, secure air gap Metallic cloud storage on hardened Azure Blob protected jointly by us, start there. And finally things like active directory. Talk about shutting the exit path, right? Take that down, your entire environment is not accessible. We make it easy for you to recover that. And because of our partnership, we're able to get it for free to every one of our customers. Go protect your active directory environment using (speaks faintly) kind of three big reasons that we're seeing that entire conversation shift in the minds of our customers. >> Yeah, thank you for that. That's a no brainer. Dave, how do Metallic and Microsoft fit together? Where's the you know, kind of value chain if you will, when it comes to dealing with cyber protection or ransomware recovery, how are your customers thinking about that? >> Yeah well, first it's a shared responsibility model, right? When you've got the best in class platform like Azure with built in protections, scalable data centers all over the global footprint. But then also we spend 10 plus billion dollars a year in security and defense and our own data center environments, right? And so I always find it inspiring when companies believe that their investments in security and platform protection is going to do the job. That's true, that used to be true. Now with Azure, you can take advantage of this global scale and secure you know, footprint of investment that a company like Microsoft has done to really set your heart at ease. Now, what do you do with your actual applications and who has access to it, and how do you actually integrate like Manoj was talking about down to the individual or the individual account that's trying to get access to your environment? Well, that's where Commvault comes in at that point of attack or at that point of an actual data element. So if you've got that environment within Commvault system backed by the umbrella of the Azure security infrastructure, that's how the two sort of compliment each other. And again, it's about shared responsibility, right? We want every customer that leverages Azure to make sure that they know it's secure, it's protected. We've got a mechanism to protect your best interests. Commvault has that exact same mission statement, right? To make sure that every single element that comes into contact with their products is protected, is secure, is trustworthy. You know, I got a long lesson, long, long time ago, early in my career that says you can goof up a product feature, you can goof up the color scheme on a website but if you lose a customer's data or somebody trust, you never get it back. And so we don't take our relationships with customers very lightly. And I think our committed and joint responsibility to delight and support our customers is what has led to this partnership being so successful over the past couple of decades. >> Great, thank you, Dave. And so Manoj, I was saying earlier that data protection has become a fundamental component of your digital business stack. So that sounds good but what should customers be doing to make data protection and data management, a business value driver versus just a liability or exposure or cost factor that has to be managed? What do you think about that? >> No, and then David added earlier, right? It's no longer a liability. In fact it is you know, someone said data is the new oil, right? It is your crown jewels. You got to to start with thinking about an active data protection strategy, not you know, thinking about passive tools and looking at it in terms of a compliance or I need to keep the data around. So that's the number one part is like, how do I have something that protects all my workloads and everyone has a different pace of transformation. So unless you know, you're a company that just got created, you have environments that are on-prem, on the edge, in CoLOS, public cloud. You got you know, SaaS applications, all of those have a critical data that needs to come together. Look for breadth of data protection, something that doesn't leave your workloads behind. Siloed solutions, create a Swiss cheese that create light for the attackers to go after those gaps. You don't want to look for that, you know? And then finally trust. I mean you know, what are the pillars of trust that the solution is built on? You got to figure out how your teams can get to doing more productive things rather than patching systems. You know, making sure that the infrastructure is up. As Dave said you know, we invest a ton jointly in securing this infrastructure. Trust that and leverage that as a differentiator rather than trying to duplicate all of that. So those are some of the you know, key things. And you know, look for players who understand that hybrid is here, give you different entry points. Don't force you know, the single single mode of operation. Those are the things we have built to make it easier for our customers to have a more active data management strategy. >> Dave, Todd, I'll give you the last word we got to go but I want to hit on this notion of zero trust. It used to be a buzz word now it's mainstream. There's so much that this discussion, is it Prudentialist access? Every access is treated maybe as privileged but what does zero trust mean to you in less than a minute? >> Yeah you know, trust but verify, right? Every interaction you have with your infrastructure, with your data, with your applications and you do it at the identity level. We care about identity and we know that that's the core of how people are going to try and access infrastructure. Used to be protect the perimeter. The analogy I always use is we have locks on our houses. Now the bad guys are everywhere. They're getting inside our houses and they're not immediately taking things, they're hiding in the closet and they're popping out three weeks later before anybody knows it. And so being able to actually manage, measure, protect every interaction you have with your infrastructure and do it at the individual or application level, that's what zero trust is all about. So don't trust any interaction, make sure that you pass that authorization through with every ask. And then make sure you protect it from the inside out. >> Great stuff. Okay guys, we've got to leave it there. Thanks so much for the time today. All right next, right after a short break, we're headed into the CXL Power Panel to hear what's on the minds of the executives as it relates to data management in the digital era. Keep it right there, you're watching theCUBE. (lighthearted music)

(upbeat music) >> From around the globe, it's theCUBE. With digital coverage of VeeamON 2020. Brought to you by Veeam. >> Hi everybody, we're back. This is Dave Vellante, and you're watching theCUBE's continuous coverage of VeeamON 2020. Veeam Online 2020. And Danny Allen is here, he's the CTO and Senior Vice President of Product Strategy and he's joined by Anton Gostev, who's the Senior Vice President of Product Management. Gentlemen, good to see you again. Wish we were face-to-face, but thanks for coming on, virtually. >> Thanks Dave for having us. >> Always love being on with you. Thank you. >> So Danny, I want to start with you. In your keynote, you talked to, about great quote by Satya Nadella. He said "We basically compress two years of digital transformation in two months." And so, I'm interested in what that meant for Veeam but also specifically, for your customers and how you help. >> Yeah, I think about that in two different ways. So digital transformation is obviously the word that he used. But I think of this a lot about being remote. So in two months, every organization that we're ourselves included, has gone from, in person operations going into the office doing things to enabling remote operations. And so, I'm working from home today, Anton's working from home today. We're all working from home today. And so remote operations is a big part of that. And it's not just working from home, it's how do I actually conduct my operations, my backup, my archiving, my hearing, all of those things remotely. It's actually changed the way organizations think about their data management. Not just operations from the sense of internal processes, but also external processes as well. But I think about this as remote offering. So organizations say, "How can I take where we are today "in the world and turn this into competitive advantage? "How can I take the services that I offered today, "and help my customers be more successful remotely?" And so, it has those two aspects to it remote operations, remote offerings. And of course, all driven by data which we backed. >> So Anton, you know there's a saying "It's better to be lucky than good." And I say, "It's best to be lucky and good." So Danny was talking about some of the external processes, a lot of those processes were unknown. And people kind of making them up as they went along, with things that we've never seen before. So, I wonder if we could talk about your product suite, and how well you were able to adapt to some of these unknown. >> Well it's more customers using our product in creative ways. But, one feedback we got most recently in our annual user survey is that like, one of the customers was using tape as the off-site backups. And they had a process where obviously someone had to physically come to the office, pick up the exporter tapes and put them on the truck and move them some off-site location. And so this basically, the process was completely broken with COVID because of lockdown. And in that particular country, it was a stricter on the ground than in most and they were physically unable to basically leave the home. So they basically looked at, Luckily they upgraded already to version 10. And they looked at what version 10 has to offer. And then we're able to switch from using tape to fully automating this off-site backup and going directly to the public cloud to object storage. So, they still have the same off-site backups that, effectively air-gapped because of the first house you provide in virtual time for mutable backups. As soon as they created that they automatically ship to object storage, completely replacing this manual off-site process. So I don't know how long it will take them, if not COVID, to move to this process. Now they love it because it's so much better than what they did before. That's amazing. >> Yeah I bet, there's no doubt. That's interesting, that's an interesting use case. Do you see, others use cases that popped up. Again, I was saying that these processes were new. I mean, and I'm interested in from a product standpoint, how you guys were able to adapt to that. >> Well, another use case that seems to be on the rise is that the ability for customers to deploy the new machines to procure new hardware is severely limited now. Not only their supply chain issues, but also again, bring something into your data center. You have to physically be there and collaborate with other workers and doing installing the, whatever new hardware you purchase. So, we see a significant pick up of the functionality where that, we had in the product for a while, which we called direct resorts to cloud. So we support taking any backup, physical virtual machine. And restoring directory into cloud machine. So we see really the big uptick of migration, maybe a lot of migrations, maybe, not necessarily permanent migrations, but when people want to basically this, some of the applications start to struggle on their sources and they're unable to update the underlying hardware. So what they do is that they schedule the downtime, and then migrate, restore that latest backup into the cloud and continue using the machine in the cloud on much more powerful hardware. That's a lifesaver for them obviously in this situation. >> Yeah so the cloud, Danny is becoming a linchpin of these new models. In your keynote you talked about your vision. And it's interesting to note, I mean, VeeamON, last year, you actually talked about, what I call getting back to the basic of, backup, you kind of embrace backup, where a lot of the new entrants are like, "No no backup's, just one small part, it's data management." And, so I'd love to get your thoughts on that. But the vision you laid out was, backup and cloud data management. Maybe you could, unpack that a little bit. >> Yeah, the way I think about this is step one, in every infrastructure, it doesn't matter whether you're talking about on-prem or in the cloud. Step one is, to protect your data. So this is ingesting the data, whether be backup, whether it be replication, whether it be, long term retention. We have to do that, not only do we have to do that, but as you go to new cycles of infrastructure, it happens all over again. So, we backed up physical first and then virtual, and then we did, cloud and in some ways, containers we're going towards, we're not going backwards but people who are running containers on-prem so we always go back to the starting point of protect the data. And then of course, after you protect it then you, want to effectively begin to manage it. And that's exactly what Anton said. How do you automate the operational procedures to be able to make this part of the DNA of the organization and so, it doesn't matter whether it's on-prem or whether it's in the cloud, that protection of data and then the effective management and integration with existing processes, is fundamental for every infrastructure and will continue to be so into the future, including the cloud. And it's only then when you have this effective protection and management of it, can you begin to unleash the power of data, as you look out into the future, because you can reuse the data for additional purposes, you can move it to the optimal location, but we always start with protection and management of the data. >> So Anton, I want to come back to you on this notion of cloud being a portion of that, when you talk about security people say you layer, how should we think about the cloud? Is it a another layer of protection? And then Danny just said, "It doesn't really matter whether it's on-prem "or in the cloud, it well, it doesn't matter "if you can ensure the same experience." If it's a totally different experience well then it's problematic though. I wonder if you could address, both the layers. Is cloud just another layer and is the management of that, actually, how do you make it, quote, unquote, "Seamless"? I know it's an overused word, but from a product name? >> Well, for larger customers, it's not necessarily a new challenge, because it's rare when the customer had a single data center. And they had this challenge for always. How do I manage my multiple data centers with a single pane of glass? And, I will say public cloud does not necessarily mean that some new perspective in that sense. Yeah, maybe it even makes it easier because you no longer have to manage the physical aspect, the most important aspect of security, which is physical security. So someone else manages it for you and probably much better than most companies could ever afford. In terms of security answer, so then data center. But as far as networking security and how those multiple data centers interact with each other, that's essentially not a new challenge. It is a new challenge for smaller customers for SMBs that are just starting. So they have their own small data center, small world and now they are starting to move some workloads into the cloud. And I would say the biggest problem there is networking and VeeamON, sure provides some free tools to call Veeam PN to make it easier for them to make this step of, securing the networking aspect of public cloud and the private property also that they are in now as workloads move to the cloud, but also keeping some workloads on-prem. >> The other piece of cloud Danny, is SaaS. You weren't the first you were one of the first to offer SaaS back up particularly for Office 365. And a lot of people just, I think, rely on the SaaS vendor, "Hey, they've got me covered. "They've got me backed up", and maybe they do have them backed up, but they might not have them recovered. How is that market shaping up? What are the trends that you're seeing there? >> Well, you're absolutely right Dave. That the, focus here is not just on back up, but on recovery, and it's one of the things that Veeam is known for we don't just do the backup, but we have an Explorer for Exchange , an Explorer for SharePoint, an Explorer for OneDrive. You saw on stage today we demoed the Explorer for Microsoft Teams. So, it's not just about protecting the data, but getting back the specific element of data that you need for operations. And that is critically important. And our customers expect to need that. If you're depending on the SaaS vendor themselves to do that, and I won't, be derogatory or specific about any SaaS vendor, but what they'll often do is, take the entire data set from seven days ago, we'll say, and merge it back into the current data set. And that just results in, a complete chaos of your inbox, if that's what they're merging together. So having specific granularity to pull back that data, exactly the data you need when you need it, is critical. And that's why we're adding it, and the focus on Microsoft Teams now obviously, is because, as we have more intellectual property, in collaboration tools for remote operations, exactly what we're doing now, that only becomes more critical for the business. So, when you think about SaaS for backup, but we also think about it for recovery. And one thing that I'll credit Anton and the product management team for, we build all of this in-house, We don't give this to a third party to build it on our behalf because you need it to work and not only need it to work, but need it to work well, that completely integrated with the underlying cloud data management platform. >> So Anton, I wonder if I could ask you about that. So, from a recovery standpoint, there's one thing, is Dan was saying, you've got to have the granularity, you've got to be able to have a really relatively simple way to recover. But because it's the cloud, there's, latency involved and how are you from a product standpoint, dealing with, making that recovery as consistent and predictable and reliable as you have for a decade on-prem. >> So you mean recovery in the cloud or back to on-prem? >> Yeah, so, recovery from data that lives in the cloud. >> Okay. So basically, the most important feature of any cloud is the price of whatever you do. So, whenever we design anything, we always look at the costs even more than anything else. But, it in turn always translates into better performance as well. To give you example, without functionality where we can take the on-prem backup and make a copy in the public object storage for disaster recovery purposes, so that for example, when a hacker or ransomware wipes out your, entire data center, you have those backups in the cloud, and you can restore from them. So when you perform the restore from cloud backups, we are actually smart enough to understand that, we need to pull that and this in that block from the cloud backup, but many of those blocks actually shared with backups in another machines that are in your own prem backup repository. So we do this on the fly analysis, and we say, instead of pulling the 10 terabyte of the entire backup from the cloud, we can actually pull only 100 gigabytes off unique blocks. And the rest of the blocks we can take from on-prem repositories that have still survived the disaster. So, not only reduces the cost 20 times or whatever. The performance, obviously, of restoring from on-prem data versus pulling everything from the cloud through the internet links is dramatic. So again, we started from the cost, how do we reduce the cost of restore, because, that's where cloud vendors quote, unquote, "Get you." But in the end, it resulted in much better performance as well. >> Excellent, Anton as well in your keynote, you talked about the Veeam availability suite, gave a little sneak preview. You talked about continuous data protection. Cloud Tier, NAS recovery, which is oftentimes very challenging. What should we take away from that sneak peek? >> Three main directions basically, The first is Veeam CGP is we keep investing a lot in on-prem, data Protection, disaster recovery. VMware is a clear leader of on-prem virtualization. So, we keep building these, new ways to protect your web VMware with lower RPOs and RTOs that were never possible before with the classic snapshotting technologies. So that's one thing we keep investing on-prem. Second thing, we do major investments in the cloud in object storage specifically, from that regards, again, put a couple keynote in Google Cloud support. And we're adding the ability to work with coldest tier of object storage, which is Amazon Glacier Deep Archive or Microsoft Azure Blob Storage, archive tier. So that's the second big area of investment. And third, instant recovery Veaam has always been extremely well known for its instant recovery capabilities. And this race is going to be the biggest in terms of new instant recovery capabilities, that were introduced as many as three new major companies with capabilities there. (mumbles) >> So, Danny, I wonder if I could ask you. I'm interested in how you go from product strategy to actual product management and bring things to market. I mean, in the early days, Veeam. Very, very specific to virtualization. That of course, with the Bare-metal, you got a number of permutations and product capabilities. How do you guys work together in terms of assessing the market potential, the degree of difficulty, prioritizing, how does that all come to your customer value? >> Well, first of all, Anton and I, spend a lot of time together on the phone and collaborating just on a weekly basis about where we're going, what we're going to do. I always say there's four directions that we look at for the product strategy and what we're building. You look behind you, you have a, we have 375,000 customers and so those are the tail winds that are pushing you forward. We talked to them on all segments. What is it that you want? I say we look left and right, the left who are alliances. We have a rich ecosystem of partners and channel that we look to collect feedback from. Look right, we look out at the competitors in this space, what are they doing to make sure that we're not missing anything that we should be including and then look forward. Big focus of Veeam has always been not just creating check boxes and making sure that we have the required features but innovation. And you saw that on stage today when Anton was showing the NAS Instant Recovery in the database instant recovery and the capabilities that we have, we have a big focus on, not just checking a box but actually doing things better and differently than everyone else in the industry and that serve to see incredibly well. >> So I love that framework. But so now when you think about this pandemic, you look behind your customers have obviously been affected, your partners have been affected. Let's put your competitors to the side for a minute, we'll see how they respond. But then looking forward, future, as I've said many times, we're not just going back to 2019. We're new decade and really digital transformation is becoming real, for real this time around. So as you think about the pandemic and looking at those four dimensions, what initial conclusions are you drawing? >> Well, the first one would be that that Veeam is well positioned to win, continue to win and to win into the future. And the reason for that I would argue, is that we're software defined. Our whole model is based on being simple to use obviously, but software defined and because of the pandemic, as Anton said, can't go into the office anymore to switch your tapes from one system to another. And so being software defined set this apart positions as well for the future. And so make it simple, make it flexible. And ultimately, what our customers care about is the reliability of this end to the credit of research and development and Anton theme is, "We have product that as everyone says, it just worked". >> So Anton I wonder if I could ask you kind of a similar question. How has the pandemic affected your thinking along those dimensions and maybe some of your initial thinking on changes that you'll implement? >> Yes, sorry I wanted to add exactly on that. I will say that pandemic accelerated our vision becoming the reality. Basically, the vision we had and, I said a few years ago, one day that Veeam will become the biggest storage vendor without selling a single storage box. And this is just becoming the reality. We support a number of object storage providers today. Only a few of them actually track the consumption that is generated by different vendors. And just for those few who do track that and report numbers to us. We are already managing over hundreds of petabytes of data in the cloud. And we only just started a couple of years ago with object storage support. So that's the power of software defined. we don't need to sell you any storage to be eventually the biggest storage player on the market. And pandemic is clear accelerated that in the last three months we see the adoption, it was already like a hockey stick, but it's accelerating further. Because of the issues customers are facing today. Unable to actually physically go back to the office, do this backup handling the way they normally do it. >> Well guys, it's been really fun the last decade watching the ascendancy of Veeam, we've boarded on it and talked about it a lot. And as you guys have both said things have been accelerated. It's actually very exciting to see a company with, rich legacy, but also, very competitive with some of the new products and new companies that are hitting the market. So, congratulations, I know you've got a lot more to do here. You guys have been, for a private company, pretty transparent, more transparent than most and I have to say as an analyst, we appreciate that and, appreciate the partnership with theCUBE. So thanks very much for coming on. >> Thank you, Dave. Always a pleasure. >> Thanks Dave. >> All right, and thank you for watching everybody. This is Dave Vellante for theCUBE in our coverage of VeeamON 2020. Veeam Online. Keep it right there, I'll be right back. (upbeat music)

(upbeat music) >> From around the globe, it's theCUBE. With digital coverage of VeeamON 2020. Brought to you by Veeam. Everybody, we're back. This is Dave Vellante, and you're watching theCUBE's continuous coverage of VeeamON 2020. Veeam Online 2020. And Danny Allen is here, he's the CTO and Senior Vice President of Product Strategy and he's joined by Anton Gostev, who's the Senior Vice President of Product Management. Gentlemen, good to see you again. Wish we were face-to-face, but thanks for coming on, virtually. >> Thanks Dave for having us. >> Always love being on with you. Thank you. >> So Danny, I want to start with you. In your keynote, you talked to, about great quote by Satya Nadella. He said "We basically compress two years of digital transformation in two months." And so, I'm interested in what that meant for Veeam but also specifically, for your customers and how you help. >> Yeah, I think about that in two different ways. So digital transformation is obviously the word that he used. But I think of this a lot about being remote. So in two months, every organization that we're ourselves included, has gone from, in person operations going into the office doing things to enabling remote operations. And so, I'm working from home today, Anton's working from home today. We're all working from home today. And so remote operations is a big part of that. And it's not just working from home, it's how do I actually conduct my operations, my backup, my archiving, my hearing, all of those things remotely. It's actually changed the way organizations think about their data management. Not just operations from the sense of internal processes, but also external processes as well. But I think about this as remote offering. So organizations say, "How can I take where we are today "in the world and turn this into competitive advantage? "How can I take the services that I offered today, "and help my customers be more successful remotely?" And so, it has those two aspects to it remote operations, remote offerings. And of course, all driven by data which we backed. >> So Anton, you know there's a saying "It's better to be lucky than good." And I say, "It's best to be lucky and good." So Danny was talking about some of the external processes, a lot of those processes were unknown. And people kind of making them up as they went along, with things that we've never seen before. So, I wonder if we could talk about your product suite, and how well you were able to adapt to some of these unknown. >> Well it's more customers using our product in creative ways. But, one feedback we got most recently in our annual user survey is that like, one of the customers was using tape as the off-site backups. And they had a process where obviously someone had to physically come to the office, pick up the exporter tapes and put them on the truck and move them some off-site location. And so this basically, the process was completely broken with COVID because of lockdown. And in that particular country, it was a stricter on the ground than in most and they were physically unable to basically leave the home. So they basically looked at, Luckily they upgraded already to version 10. And they looked at what version 10 has to offer. And then we're able to switch from using tape to fully automating this off-site backup and going directly to the public cloud to object storage. So, they still have the same off-site backups that, effectively air-gapped because of the first house you provide in virtual time for mutable backups. As soon as they created that they automatically ship to object storage, completely replacing this manual off-site process. So I don't know how long it will take them, if not COVID, to move to this process. Now they love it because it's so much better than what they did before. That's amazing. >> Yeah I bet, there's no doubt. That's interesting, that's an interesting use case. Do you see, others use cases that popped up. Again, I was saying that these processes were new. I mean, and I'm interested in from a product standpoint, how you guys were able to adapt to that. >> Well, another use case that seems to be on the rise is that the ability for customers to deploy the new machines to procure new hardware is severely limited now. Not only their supply chain issues, but also again, bring something into your data center. You have to physically be there and collaborate with other workers and doing installing the, whatever new hardware you purchase. So, we see a significant pick up of the functionality where that, we had in the product for a while, which we called direct resorts to cloud. So we support taking any backup, physical virtual machine. And restoring directory into cloud machine. So we see really the big uptick of migration, maybe a lot of migrations, maybe, not necessarily permanent migrations, but when people want to basically this, some of the applications start to struggle on their sources and they're unable to update the underlying hardware. So what they do is that they schedule the downtime, and then migrate, restore that latest backup into the cloud and continue using the machine in the cloud on much more powerful hardware. That's a lifesaver for them obviously in this situation. >> Yeah so the cloud, Danny is becoming a linchpin of these new models. In your keynote you talked about your vision. And it's interesting to note, I mean, VeeamON, last year, you actually talked about, what I call getting back to the basic of, backup, you kind of embrace backup, where a lot of the new entrants are like, "No no backup's, just one small part, it's data management." And, so I'd love to get your thoughts on that. But the vision you laid out was, backup and cloud data management. Maybe you could, unpack that a little bit. >> Yeah, the way I think about this is step one, in every infrastructure, it doesn't matter whether you're talking about on-prem or in the cloud. Step one is, to protect your data. So this is ingesting the data, whether be backup, whether it be replication, whether it be, long term retention. We have to do that, not only do we have to do that, but as you go to new cycles of infrastructure, it happens all over again. So, we backed up physical first and then virtual, and then we did, cloud and in some ways, containers we're going towards, we're not going backwards but people who are running containers on-prem so we always go back to the starting point of protect the data. And then of course, after you protect it then you, want to effectively begin to manage it. And that's exactly what Anton said. How do you automate the operational procedures to be able to make this part of the DNA of the organization and so, it doesn't matter whether it's on-prem or whether it's in the cloud, that protection of data and then the effective management and integration with existing processes, is fundamental for every infrastructure and will continue to be so into the future, including the cloud. And it's only then when you have this effective protection and management of it, can you begin to unleash the power of data, as you look out into the future, because you can reuse the data for additional purposes, you can move it to the optimal location, but we always start with protection and management of the data. >> So Anton, I want to come back to you on this notion of cloud being a portion of that, when you talk about security people say you layer, how should we think about the cloud? Is it a another layer of protection? And then Danny just said, "It doesn't really matter whether it's on-prem "or in the cloud, it well, it doesn't matter "if you can ensure the same experience." If it's a totally different experience well then it's problematic though. I wonder if you could address, both the layers. Is cloud just another layer and is the management of that, actually, how do you make it, quote, unquote, "Seamless"? I know it's an overused word, but from a product name? >> Well, for larger customers, it's not necessarily a new challenge, because it's rare when the customer had a single data center. And they had this challenge for always. How do I manage my multiple data centers with a single pane of glass? And, I will say public cloud does not necessarily mean that some new perspective in that sense. Yeah, maybe it even makes it easier because you no longer have to manage the physical aspect, the most important aspect of security, which is physical security. So someone else manages it for you and probably much better than most companies could ever afford. In terms of security answer, so then data center. But as far as networking security and how those multiple data centers interact with each other, that's essentially not a new challenge. It is a new challenge for smaller customers for SMBs that are just starting. So they have their own small data center, small world and now they are starting to move some workloads into the cloud. And I would say the biggest problem there is networking and VeeamON, sure provides some free tools to call Veeam PN to make it easier for them to make this step of, securing the networking aspect of public cloud and the private property also that they are in now as workloads move to the cloud, but also keeping some workloads on-prem. >> The other piece of cloud Danny, is SaaS. You weren't the first you were one of the first to offer SaaS back up particularly for Office 365. And a lot of people just, I think, rely on the SaaS vendor, "Hey, they've got me covered. "They've got me backed up", and maybe they do have them backed up, but they might not have them recovered. How is that market shaping up? What are the trends that you're seeing there? >> Well, you're absolutely right Dave. That the, focus here is not just on back up, but on recovery, and it's one of the things that Veeam is known for we don't just do the backup, but we have an Explorer for Exchange , an Explorer for SharePoint, an Explorer for OneDrive. You saw on stage today we demoed the Explorer for Microsoft Teams. So, it's not just about protecting the data, but getting back the specific element of data that you need for operations. And that is critically important. And our customers expect to need that. If you're depending on the SaaS vendor themselves to do that, and I won't, be derogatory or specific about any SaaS vendor, but what they'll often do is, take the entire data set from seven days ago, we'll say, and merge it back into the current data set. And that just results in, a complete chaos of your inbox, if that's what they're merging together. So having specific granularity to pull back that data, exactly the data you need when you need it, is critical. And that's why we're adding it, and the focus on Microsoft Teams now obviously, is because, as we have more intellectual property, in collaboration tools for remote operations, exactly what we're doing now, that only becomes more critical for the business. So, when you think about SaaS for backup, but we also think about it for recovery. And one thing that I'll credit Anton and the product management team for, we build all of this in-house, We don't give this to a third party to build it on our behalf because you need it to work and not only need it to work, but need it to work well, that completely integrated with the underlying cloud data management platform. >> So Anton, I wonder if I could ask you about that. So, from a recovery standpoint, there's one thing, is Dan was saying, you've got to have the granularity, you've got to be able to have a really relatively simple way to recover. But because it's the cloud, there's, latency involved and how are you from a product standpoint, dealing with, making that recovery as consistent and predictable and reliable as you have for a decade on-prem. >> So you mean recovery in the cloud or back to on-prem? >> Yeah, so, recovery from data that lives in the cloud. >> Okay. So basically, the most important feature of any cloud is the price of whatever you do. So, whenever we design anything, we always look at the costs even more than anything else. But, it in turn always translates into better performance as well. To give you example, without functionality where we can take the on-prem backup and make a copy in the public object storage for disaster recovery purposes, so that for example, when a hacker or ransomware wipes out your, entire data center, you have those backups in the cloud, and you can restore from them. So when you perform the restore from cloud backups, we are actually smart enough to understand that, we need to pull that and this in that block from the cloud backup, but many of those blocks actually shared with backups in another machines that are in your own prem backup repository. So we do this on the fly analysis, and we say, instead of pulling the 10 terabyte of the entire backup from the cloud, we can actually pull only 100 gigabytes off unique blocks. And the rest of the blocks we can take from on-prem repositories that have still survived the disaster. So, not only reduces the cost 20 times or whatever. The performance, obviously, of restoring from on-prem data versus pulling everything from the cloud through the internet links is dramatic. So again, we started from the cost, how do we reduce the cost of restore, because, that's where cloud vendors quote, unquote, "Get you." But in the end, it resulted in much better performance as well. >> Excellent, Anton as well in your keynote, you talked about the Veeam availability suite, gave a little sneak preview. You talked about continuous data protection. Cloud Tier, NAS recovery, which is oftentimes very challenging. What should we take away from that sneak peek? >> Three main directions basically, The first is Veeam CGP is we keep investing a lot in on-prem, data Protection, disaster recovery. VMware is a clear leader of on-prem virtualization. So, we keep building these, new ways to protect your web VMware with lower RPOs and RTOs that were never possible before with the classic snapshotting technologies. So that's one thing we keep investing on-prem. Second thing, we do major investments in the cloud in object storage specifically, from that regards, again, put a couple keynote in Google Cloud support. And we're adding the ability to work with coldest tier of object storage, which is Amazon Glacier Deep Archive or Microsoft Azure Blob Storage, archive tier. So that's the second big area of investment. And third, instant recovery Veaam has always been extremely well known for its instant recovery capabilities. And this race is going to be the biggest in terms of new instant recovery capabilities, that were introduced as many as three new major companies with capabilities there. (mumbles) >> So, Danny, I wonder if I could ask you. I'm interested in how you go from product strategy to actual product management and bring things to market. I mean, in the early days, Veeam. Very, very specific to virtualization. That of course, with the Bare-metal, you got a number of permutations and product capabilities. How do you guys work together in terms of assessing the market potential, the degree of difficulty, prioritizing, how does that all come to your customer value? >> Well, first of all, Anton and I, spend a lot of time together on the phone and collaborating just on a weekly basis about where we're going, what we're going to do. I always say there's four directions that we look at for the product strategy and what we're building. You look behind you, you have a, we have 375,000 customers and so those are the tail winds that are pushing you forward. We talked to them on all segments. What is it that you want? I say we look left and right, the left who are alliances. We have a rich ecosystem of partners and channel that we look to collect feedback from. Look right, we look out at the competitors in this space, what are they doing to make sure that we're not missing anything that we should be including and then look forward. Big focus of Veeam has always been not just creating check boxes and making sure that we have the required features but innovation. And you saw that on stage today when Anton was showing the NAS Instant Recovery in the database instant recovery and the capabilities that we have, we have a big focus on, not just checking a box but actually doing things better and differently than everyone else in the industry and that serve to see incredibly well. >> So I love that framework. But so now when you think about this pandemic, you look behind your customers have obviously been affected, your partners have been affected. Let's put your competitors to the side for a minute, we'll see how they respond. But then looking forward, future, as I've said many times, we're not just going back to 2019. We're new decade and really digital transformation is becoming real, for real this time around. So as you think about the pandemic and looking at those four dimensions, what initial conclusions are you drawing? >> Well, the first one would be that that Veeam is well positioned to win, continue to win and to win into the future. And the reason for that I would argue, is that we're software defined. Our whole model is based on being simple to use obviously, but software defined and because of the pandemic, as Anton said, can't go into the office anymore to switch your tapes from one system to another. And so being software defined set this apart positions as well for the future. And so make it simple, make it flexible. And ultimately, what our customers care about is the reliability of this end to the credit of research and development and Anton theme is, "We have product that as everyone says, it just worked". >> So Anton I wonder if I could ask you kind of a similar question. How has the pandemic affected your thinking along those dimensions and maybe some of your initial thinking on changes that you'll implement? >> Yes, sorry I wanted to add exactly on that. I will say that pandemic accelerated our vision becoming the reality. Basically, the vision we had and, I said a few years ago, one day that Veeam will become the biggest storage vendor without selling a single storage box. And this is just becoming the reality. We support a number of object storage providers today. Only a few of them actually track the consumption that is generated by different vendors. And just for those few who do track that and report numbers to us. We are already managing over hundreds of petabytes of data in the cloud. And we only just started a couple of years ago with object storage support. So that's the power of software defined. we don't need to sell you any storage to be eventually the biggest storage player on the market. And pandemic is clear accelerated that in the last three months we see the adoption, it was already like a hockey stick, but it's accelerating further. Because of the issues customers are facing today. Unable to actually physically go back to the office, do this backup handling the way they normally do it. >> Well guys, it's been really fun the last decade watching the ascendancy of Veeam, we've boarded on it and talked about it a lot. And as you guys have both said things have been accelerated. It's actually very exciting to see a company with, rich legacy, but also, very competitive with some of the new products and new companies that are hitting the market. So, congratulations, I know you've got a lot more to do here. You guys have been, for a private company, pretty transparent, more transparent than most and I have to say as an analyst, we appreciate that and, appreciate the partnership with theCUBE. So thanks very much for coming on. >> Thank you, Dave. Always a pleasure. >> Thanks Dave. >> All right, and thank you for watching everybody. This is Dave Vellante for theCUBE in our coverage of VeeamON 2020. Veeam Online. Keep it right there, I'll be right back. (upbeat music)

>> Joy: Welcome to the Virtual Big Data Conference. Vertica is so excited to host this event. I'm Joy King, and I'll be your host for today's Big Data Conference Keynote Session. It's my honor and my genuine pleasure to lead Vertica's product and go-to-market strategy. And I'm so lucky to have a passionate and committed team who turned our Vertica BDC event, into a virtual event in a very short amount of time. I want to thank the thousands of people, and yes, that's our true number who have registered to attend this virtual event. We were determined to balance your health, safety and your peace of mind with the excitement of the Vertica BDC. This is a very unique event. Because as I hope you all know, we focus on engineering and architecture, best practice sharing and customer stories that will educate and inspire everyone. I also want to thank our top sponsors for the virtual BDC, Arrow, and Pure Storage. Our partnerships are so important to us and to everyone in the audience. Because together, we get things done faster and better. Now for today's keynote, you'll hear from three very important and energizing speakers. First, Colin Mahony, our SVP and General Manager for Vertica, will talk about the market trends that Vertica is betting on to win for our customers. And he'll share the exciting news about our Vertica 10 announcement and how this will benefit our customers. Then you'll hear from Amy Fowler, VP of strategy and solutions for FlashBlade at Pure Storage. Our partnership with Pure Storage is truly unique in the industry, because together modern infrastructure from Pure powers modern analytics from Vertica. And then you'll hear from John Yovanovich, Director of IT at AT&T, who will tell you about the Pure Vertica Symphony that plays live every day at AT&T. Here we go, Colin, over to you. >> Colin: Well, thanks a lot joy. And, I want to echo Joy's thanks to our sponsors, and so many of you who have helped make this happen. This is not an easy time for anyone. We were certainly looking forward to getting together in person in Boston during the Vertica Big Data Conference and Winning with Data. But I think all of you and our team have done a great job, scrambling and putting together a terrific virtual event. So really appreciate your time. I also want to remind people that we will make both the slides and the full recording available after this. So for any of those who weren't able to join live, that is still going to be available. Well, things have been pretty exciting here. And in the analytic space in general, certainly for Vertica, there's a lot happening. There are a lot of problems to solve, a lot of opportunities to make things better, and a lot of data that can really make every business stronger, more efficient, and frankly, more differentiated. For Vertica, though, we know that focusing on the challenges that we can directly address with our platform, and our people, and where we can actually make the biggest difference is where we ought to be putting our energy and our resources. I think one of the things that has made Vertica so strong over the years is our ability to focus on those areas where we can make a great difference. So for us as we look at the market, and we look at where we play, there are really three recent and some not so recent, but certainly picking up a lot of the market trends that have become critical for every industry that wants to Win Big With Data. We've heard this loud and clear from our customers and from the analysts that cover the market. If I were to summarize these three areas, this really is the core focus for us right now. We know that there's massive data growth. And if we can unify the data silos so that people can really take advantage of that data, we can make a huge difference. We know that public clouds offer tremendous advantages, but we also know that balance and flexibility is critical. And we all need the benefit that machine learning for all the types up to the end data science. We all need the benefits that they can bring to every single use case, but only if it can really be operationalized at scale, accurate and in real time. And the power of Vertica is, of course, how we're able to bring so many of these things together. Let me talk a little bit more about some of these trends. So one of the first industry trends that we've all been following probably now for over the last decade, is Hadoop and specifically HDFS. So many companies have invested, time, money, more importantly, people in leveraging the opportunity that HDFS brought to the market. HDFS is really part of a much broader storage disruption that we'll talk a little bit more about, more broadly than HDFS. But HDFS itself was really designed for petabytes of data, leveraging low cost commodity hardware and the ability to capture a wide variety of data formats, from a wide variety of data sources and applications. And I think what people really wanted, was to store that data before having to define exactly what structures they should go into. So over the last decade or so, the focus for most organizations is figuring out how to capture, store and frankly manage that data. And as a platform to do that, I think, Hadoop was pretty good. It certainly changed the way that a lot of enterprises think about their data and where it's locked up. In parallel with Hadoop, particularly over the last five years, Cloud Object Storage has also given every organization another option for collecting, storing and managing even more data. That has led to a huge growth in data storage, obviously, up on public clouds like Amazon and their S3, Google Cloud Storage and Azure Blob Storage just to name a few. And then when you consider regional and local object storage offered by cloud vendors all over the world, the explosion of that data, in leveraging this type of object storage is very real. And I think, as I mentioned, it's just part of this broader storage disruption that's been going on. But with all this growth in the data, in all these new places to put this data, every organization we talk to is facing even more challenges now around the data silo. Sure the data silos certainly getting bigger. And hopefully they're getting cheaper per bit. But as I said, the focus has really been on collecting, storing and managing the data. But between the new data lakes and many different cloud object storage combined with all sorts of data types from the complexity of managing all this, getting that business value has been very limited. This actually takes me to big bet number one for Team Vertica, which is to unify the data. Our goal, and some of the announcements we have made today plus roadmap announcements I'll share with you throughout this presentation. Our goal is to ensure that all the time, money and effort that has gone into storing that data, all the data turns into business value. So how are we going to do that? With a unified analytics platform that analyzes the data wherever it is HDFS, Cloud Object Storage, External tables in an any format ORC, Parquet, JSON, and of course, our own Native Roth Vertica format. Analyze the data in the right place in the right format, using a single unified tool. This is something that Vertica has always been committed to, and you'll see in some of our announcements today, we're just doubling down on that commitment. Let's talk a little bit more about the public cloud. This is certainly the second trend. It's the second wave maybe of data disruption with object storage. And there's a lot of advantages when it comes to public cloud. There's no question that the public clouds give rapid access to compute storage with the added benefit of eliminating data center maintenance that so many companies, want to get out of themselves. But maybe the biggest advantage that I see is the architectural innovation. The public clouds have introduced so many methodologies around how to provision quickly, separating compute and storage and really dialing-in the exact needs on demand, as you change workloads. When public clouds began, it made a lot of sense for the cloud providers and their customers to charge and pay for compute and storage in the ratio that each use case demanded. And I think you're seeing that trend, proliferate all over the place, not just up in public cloud. That architecture itself is really becoming the next generation architecture for on-premise data centers, as well. But there are a lot of concerns. I think we're all aware of them. They're out there many times for different workloads, there are higher costs. Especially if some of the workloads that are being run through analytics, which tend to run all the time. Just like some of the silo challenges that companies are facing with HDFS, data lakes and cloud storage, the public clouds have similar types of siloed challenges as well. Initially, there was a belief that they were cheaper than data centers, and when you added in all the costs, it looked that way. And again, for certain elastic workloads, that is the case. I don't think that's true across the board overall. Even to the point where a lot of the cloud vendors aren't just charging lower costs anymore. We hear from a lot of customers that they don't really want to tether themselves to any one cloud because of some of those uncertainties. Of course, security and privacy are a concern. We hear a lot of concerns with regards to cloud and even some SaaS vendors around shared data catalogs, across all the customers and not enough separation. But security concerns are out there, you can read about them. I'm not going to jump into that bandwagon. But we hear about them. And then, of course, I think one of the things we hear the most from our customers, is that each cloud stack is starting to feel even a lot more locked in than the traditional data warehouse appliance. And as everybody knows, the industry has been running away from appliances as fast as it can. And so they're not eager to get locked into another, quote, unquote, virtual appliance, if you will, up in the cloud. They really want to make sure they have flexibility in which clouds, they're going to today, tomorrow and in the future. And frankly, we hear from a lot of our customers that they're very interested in eventually mixing and matching, compute from one cloud with, say storage from another cloud, which I think is something that we'll hear a lot more about. And so for us, that's why we've got our big bet number two. we love the cloud. We love the public cloud. We love the private clouds on-premise, and other hosting providers. But our passion and commitment is for Vertica to be able to run in any of the clouds that our customers choose, and make it portable across those clouds. We have supported on-premises and all public clouds for years. And today, we have announced even more support for Vertica in Eon Mode, the deployment option that leverages the separation of compute from storage, with even more deployment choices, which I'm going to also touch more on as we go. So super excited about our big bet number two. And finally as I mentioned, for all the hype that there is around machine learning, I actually think that most importantly, this third trend that team Vertica is determined to address is the need to bring business critical, analytics, machine learning, data science projects into production. For so many years, there just wasn't enough data available to justify the investment in machine learning. Also, processing power was expensive, and storage was prohibitively expensive. But to train and score and evaluate all the different models to unlock the full power of predictive analytics was tough. Today you have those massive data volumes. You have the relatively cheap processing power and storage to make that dream a reality. And if you think about this, I mean with all the data that's available to every company, the real need is to operationalize the speed and the scale of machine learning so that these organizations can actually take advantage of it where they need to. I mean, we've seen this for years with Vertica, going back to some of the most advanced gaming companies in the early days, they were incorporating this with live data directly into their gaming experiences. Well, every organization wants to do that now. And the accuracy for clickability and real time actions are all key to separating the leaders from the rest of the pack in every industry when it comes to machine learning. But if you look at a lot of these projects, the reality is that there's a ton of buzz, there's a ton of hype spanning every acronym that you can imagine. But most companies are struggling, do the separate teams, different tools, silos and the limitation that many platforms are facing, driving, down sampling to get a small subset of the data, to try to create a model that then doesn't apply, or compromising accuracy and making it virtually impossible to replicate models, and understand decisions. And if there's one thing that we've learned when it comes to data, prescriptive data at the atomic level, being able to show end of one as we refer to it, meaning individually tailored data. No matter what it is healthcare, entertainment experiences, like gaming or other, being able to get at the granular data and make these decisions, make that scoring applies to machine learning just as much as it applies to giving somebody a next-best-offer. But the opportunity has never been greater. The need to integrate this end-to-end workflow and support the right tools without compromising on that accuracy. Think about it as no downsampling, using all the data, it really is key to machine learning success. Which should be no surprise then why the third big bet from Vertica is one that we've actually been working on for years. And we're so proud to be where we are today, helping the data disruptors across the world operationalize machine learning. This big bet has the potential to truly unlock, really the potential of machine learning. And today, we're announcing some very important new capabilities specifically focused on unifying the work being done by the data science community, with their preferred tools and platforms, and the volume of data and performance at scale, available in Vertica. Our strategy has been very consistent over the last several years. As I said in the beginning, we haven't deviated from our strategy. Of course, there's always things that we add. Most of the time, it's customer driven, it's based on what our customers are asking us to do. But I think we've also done a great job, not trying to be all things to all people. Especially as these hype cycles flare up around us, we absolutely love participating in these different areas without getting completely distracted. I mean, there's a variety of query tools and data warehouses and analytics platforms in the market. We all know that. There are tools and platforms that are offered by the public cloud vendors, by other vendors that support one or two specific clouds. There are appliance vendors, who I was referring to earlier who can deliver package data warehouse offerings for private data centers. And there's a ton of popular machine learning tools, languages and other kits. But Vertica is the only advanced analytic platform that can do all this, that can bring it together. We can analyze the data wherever it is, in HDFS, S3 Object Storage, or Vertica itself. Natively we support multiple clouds on-premise deployments, And maybe most importantly, we offer that choice of deployment modes to allow our customers to choose the architecture that works for them right now. It still also gives them the option to change move, evolve over time. And Vertica is the only analytics database with end-to-end machine learning that can truly operationalize ML at scale. And I know it's a mouthful. But it is not easy to do all these things. It is one of the things that highly differentiates Vertica from the rest of the pack. It is also why our customers, all of you continue to bet on us and see the value that we are delivering and we will continue to deliver. Here's a couple of examples of some of our customers who are powered by Vertica. It's the scale of data. It's the millisecond response times. Performance and scale have always been a huge part of what we have been about, not the only thing. I think the functionality all the capabilities that we add to the platform, the ease of use, the flexibility, obviously with the deployment. But if you look at some of the numbers they are under these customers on this slide. And I've shared a lot of different stories about these customers. Which, by the way, it still amaze me every time I talk to one and I get the updates, you can see the power and the difference that Vertica is making. Equally important, if you look at a lot of these customers, they are the epitome of being able to deploy Vertica in a lot of different environments. Many of the customers on this slide are not using Vertica just on-premise or just in the cloud. They're using it in a hybrid way. They're using it in multiple different clouds. And again, we've been with them on that journey throughout, which is what has made this product and frankly, our roadmap and our vision exactly what it is. It's been quite a journey. And that journey continues now with the Vertica 10 release. The Vertica 10 release is obviously a massive release for us. But if you look back, you can see that building on that native columnar architecture that started a long time ago, obviously, with the C-Store paper. We built it to leverage that commodity hardware, because it was an architecture that was never tightly integrated with any specific underlying infrastructure. I still remember hearing the initial pitch from Mike Stonebreaker, about the vision of Vertica as a software only solution and the importance of separating the company from hardware innovation. And at the time, Mike basically said to me, "there's so much R&D in innovation that's going to happen in hardware, we shouldn't bake hardware into our solution. We should do it in software, and we'll be able to take advantage of that hardware." And that is exactly what has happened. But one of the most recent innovations that we embraced with hardware is certainly that separation of compute and storage. As I said previously, the public cloud providers offered this next generation architecture, really to ensure that they can provide the customers exactly what they needed, more compute or more storage and charge for each, respectively. The separation of compute and storage, compute from storage is a major milestone in data center architectures. If you think about it, it's really not only a public cloud innovation, though. It fundamentally redefines the next generation data architecture for on-premise and for pretty much every way people are thinking about computing today. And that goes for software too. Object storage is an example of the cost effective means for storing data. And even more importantly, separating compute from storage for analytic workloads has a lot of advantages. Including the opportunity to manage much more dynamic, flexible workloads. And more importantly, truly isolate those workloads from others. And by the way, once you start having something that can truly isolate workloads, then you can have the conversations around autonomic computing, around setting up some nodes, some compute resources on the data that won't affect any of the other data to do some things on their own, maybe some self analytics, by the system, etc. A lot of things that many of you know we've already been exploring in terms of our own system data in the product. But it was May 2018, believe it or not, it seems like a long time ago where we first announced Eon Mode and I want to make something very clear, actually about Eon mode. It's a mode, it's a deployment option for Vertica customers. And I think this is another huge benefit that we don't talk about enough. But unlike a lot of vendors in the market who will dig you and charge you for every single add-on like hit-buy, you name it. You get this with the Vertica product. If you continue to pay support and maintenance, this comes with the upgrade. This comes as part of the new release. So any customer who owns or buys Vertica has the ability to set up either an Enterprise Mode or Eon Mode, which is a question I know that comes up sometimes. Our first announcement of Eon was obviously AWS customers, including the trade desk, AT&T. Most of whom will be speaking here later at the Virtual Big Data Conference. They saw a huge opportunity. Eon Mode, not only allowed Vertica to scale elastically with that specific compute and storage that was needed, but it really dramatically simplified database operations including things like workload balancing, node recovery, compute provisioning, etc. So one of the most popular functions is that ability to isolate the workloads and really allocate those resources without negatively affecting others. And even though traditional data warehouses, including Vertica Enterprise Mode have been able to do lots of different workload isolation, it's never been as strong as Eon Mode. Well, it certainly didn't take long for our customers to see that value across the board with Eon Mode. Not just up in the cloud, in partnership with one of our most valued partners and a platinum sponsor here. Joy mentioned at the beginning. We announced Vertica Eon Mode for Pure Storage FlashBlade in September 2019. And again, just to be clear, this is not a new product, it's one Vertica with yet more deployment options. With Pure Storage, Vertica in Eon mode is not limited in any way by variable cloud, network latency. The performance is actually amazing when you take the benefits of separate and compute from storage and you run it with a Pure environment on-premise. Vertica in Eon Mode has a super smart cache layer that we call the depot. It's a big part of our secret sauce around Eon mode. And combined with the power and performance of Pure's FlashBlade, Vertica became the industry's first advanced analytics platform that actually separates compute and storage for on-premises data centers. Something that a lot of our customers are already benefiting from, and we're super excited about it. But as I said, this is a journey. We don't stop, we're not going to stop. Our customers need the flexibility of multiple public clouds. So today with Vertica 10, we're super proud and excited to announce support for Vertica in Eon Mode on Google Cloud. This gives our customers the ability to use their Vertica licenses on Amazon AWS, on-premise with Pure Storage and on Google Cloud. Now, we were talking about HDFS and a lot of our customers who have invested quite a bit in HDFS as a place, especially to store data have been pushing us to support Eon Mode with HDFS. So as part of Vertica 10, we are also announcing support for Vertica in Eon Mode using HDFS as the communal storage. Vertica's own Roth format data can be stored in HDFS, and actually the full functionality of Vertica is complete analytics, geospatial pattern matching, time series, machine learning, everything that we have in there can be applied to this data. And on the same HDFS nodes, Vertica can actually also analyze data in ORC or Parquet format, using External tables. We can also execute joins between the Roth data the External table holds, which powers a much more comprehensive view. So again, it's that flexibility to be able to support our customers, wherever they need us to support them on whatever platform, they have. Vertica 10 gives us a lot more ways that we can deploy Eon Mode in various environments for our customers. It allows them to take advantage of Vertica in Eon Mode and the power that it brings with that separation, with that workload isolation, to whichever platform they are most comfortable with. Now, there's a lot that has come in Vertica 10. I'm definitely not going to be able to cover everything. But we also introduced complex types as an example. And complex data types fit very well into Eon as well in this separation. They significantly reduce the data pipeline, the cost of moving data between those, a much better support for unstructured data, which a lot of our customers have mixed with structured data, of course, and they leverage a lot of columnar execution that Vertica provides. So you get complex data types in Vertica now, a lot more data, stronger performance. It goes great with the announcement that we made with the broader Eon Mode. Let's talk a little bit more about machine learning. We've been actually doing work in and around machine learning with various extra regressions and a whole bunch of other algorithms for several years. We saw the huge advantage that MPP offered, not just as a sequel engine as a database, but for ML as well. Didn't take as long to realize that there's a lot more to operationalizing machine learning than just those algorithms. It's data preparation, it's that model trade training. It's the scoring, the shaping, the evaluation. That is so much of what machine learning and frankly, data science is about. You do know, everybody always wants to jump to the sexy algorithm and we handle those tasks very, very well. It makes Vertica a terrific platform to do that. A lot of work in data science and machine learning is done in other tools. I had mentioned that there's just so many tools out there. We want people to be able to take advantage of all that. We never believed we were going to be the best algorithm company or come up with the best models for people to use. So with Vertica 10, we support PMML. We can import now and export PMML models. It's a huge step for us around that operationalizing machine learning projects for our customers. Allowing the models to get built outside of Vertica yet be imported in and then applying to that full scale of data with all the performance that you would expect from Vertica. We also are more tightly integrating with Python. As many of you know, we've been doing a lot of open source projects with the community driven by many of our customers, like Uber. And so now with Python we've integrated with TensorFlow, allowing data scientists to build models in their preferred language, to take advantage of TensorFlow. But again, to store and deploy those models at scale with Vertica. I think both these announcements are proof of our big bet number three, and really our commitment to supporting innovation throughout the community by operationalizing ML with that accuracy, performance and scale of Vertica for our customers. Again, there's a lot of steps when it comes to the workflow of machine learning. These are some of them that you can see on the slide, and it's definitely not linear either. We see this as a circle. And companies that do it, well just continue to learn, they continue to rescore, they continue to redeploy and they want to operationalize all that within a single platform that can take advantage of all those capabilities. And that is the platform, with a very robust ecosystem that Vertica has always been committed to as an organization and will continue to be. This graphic, many of you have seen it evolve over the years. Frankly, if we put everything and everyone on here wouldn't fit on a slide. But it will absolutely continue to evolve and grow as we support our customers, where they need the support most. So, again, being able to deploy everywhere, being able to take advantage of Vertica, not just as a business analyst or a business user, but as a data scientists or as an operational or BI person. We want Vertica to be leveraged and used by the broader organization. So I think it's fair to say and I encourage everybody to learn more about Vertica 10, because I'm just highlighting some of the bigger aspects of it. But we talked about those three market trends. The need to unify the silos, the need for hybrid multiple cloud deployment options, the need to operationalize business critical machine learning projects. Vertica 10 has absolutely delivered on those. But again, we are not going to stop. It is our job not to, and this is how Team Vertica thrives. I always joke that the next release is the best release. And, of course, even after Vertica 10, that is also true, although Vertica 10 is pretty awesome. But, you know, from the first line of code, we've always been focused on performance and scale, right. And like any really strong data platform, the execution engine, the optimizer and the execution engine are the two core pieces of that. Beyond Vertica 10, some of the big things that we're already working on, next generation execution engine. We're already actually seeing incredible early performance from this. And this is just one example, of how important it is for an organization like Vertica to constantly go back and re-innovate. Every single release, we do the sit ups and crunches, our performance and scale. How do we improve? And there's so many parts of the core server, there's so many parts of our broader ecosystem. We are constantly looking at coverages of how we can go back to all the code lines that we have, and make them better in the current environment. And it's not an easy thing to do when you're doing that, and you're also expanding in the environment that we are expanding into to take advantage of the different deployments, which is a great segue to this slide. Because if you think about today, we're obviously already available with Eon Mode and Amazon, AWS and Pure and actually MinIO as well. As I talked about in Vertica 10 we're adding Google and HDFS. And coming next, obviously, Microsoft Azure, Alibaba cloud. So being able to expand into more of these environments is really important for the Vertica team and how we go forward. And it's not just running in these clouds, for us, we want it to be a SaaS like experience in all these clouds. We want you to be able to deploy Vertica in 15 minutes or less on these clouds. You can also consume Vertica, in a lot of different ways, on these clouds. As an example, in Amazon Vertica by the Hour. So for us, it's not just about running, it's about taking advantage of the ecosystems that all these cloud providers offer, and really optimizing the Vertica experience as part of them. Optimization, around automation, around self service capabilities, extending our management console, we now have products that like the Vertica Advisor Tool that our Customer Success Team has created to actually use our own smarts in Vertica. To take data from customers that give it to us and help them tune automatically their environment. You can imagine that we're taking that to the next level, in a lot of different endeavors that we're doing around how Vertica as a product can actually be smarter because we all know that simplicity is key. There just aren't enough people in the world who are good at managing data and taking it to the next level. And of course, other things that we all hear about, whether it's Kubernetes and containerization. You can imagine that that probably works very well with the Eon Mode and separating compute and storage. But innovation happens everywhere. We innovate around our community documentation. Many of you have taken advantage of the Vertica Academy. The numbers there are through the roof in terms of the number of people coming in and certifying on it. So there's a lot of things that are within the core products. There's a lot of activity and action beyond the core products that we're taking advantage of. And let's not forget why we're here, right? It's easy to talk about a platform, a data platform, it's easy to jump into all the functionality, the analytics, the flexibility, how we can offer it. But at the end of the day, somebody, a person, she's got to take advantage of this data, she's got to be able to take this data and use this information to make a critical business decision. And that doesn't happen unless we explore lots of different and frankly, new ways to get that predictive analytics UI and interface beyond just the standard BI tools in front of her at the right time. And so there's a lot of activity, I'll tease you with that going on in this organization right now about how we can do that and deliver that for our customers. We're in a great position to be able to see exactly how this data is consumed and used and start with this core platform that we have to go out. Look, I know, the plan wasn't to do this as a virtual BDC. But I really appreciate you tuning in. Really appreciate your support. I think if there's any silver lining to us, maybe not being able to do this in person, it's the fact that the reach has actually gone significantly higher than what we would have been able to do in person in Boston. We're certainly looking forward to doing a Big Data Conference in the future. But if I could leave you with anything, know this, since that first release for Vertica, and our very first customers, we have been very consistent. We respect all the innovation around us, whether it's open source or not. We understand the market trends. We embrace those new ideas and technologies and for us true north, and the most important thing is what does our customer need to do? What problem are they trying to solve? And how do we use the advantages that we have without disrupting our customers? But knowing that you depend on us to deliver that unified analytics strategy, it will deliver that performance of scale, not only today, but tomorrow and for years to come. We've added a lot of great features to Vertica. I think we've said no to a lot of things, frankly, that we just knew we wouldn't be the best company to deliver. When we say we're going to do things we do them. Vertica 10 is a perfect example of so many of those things that we from you, our customers have heard loud and clear, and we have delivered. I am incredibly proud of this team across the board. I think the culture of Vertica, a customer first culture, jumping in to help our customers win no matter what is also something that sets us massively apart. I hear horror stories about support experiences with other organizations. And people always seem to be amazed at Team Vertica's willingness to jump in or their aptitude for certain technical capabilities or understanding the business. And I think sometimes we take that for granted. But that is the team that we have as Team Vertica. We are incredibly excited about Vertica 10. I think you're going to love the Virtual Big Data Conference this year. I encourage you to tune in. Maybe one other benefit is I know some people were worried about not being able to see different sessions because they were going to overlap with each other well now, even if you can't do it live, you'll be able to do those sessions on demand. Please enjoy the Vertica Big Data Conference here in 2020. Please you and your families and your co-workers be safe during these times. I know we will get through it. And analytics is probably going to help with a lot of that and we already know it is helping in many different ways. So believe in the data, believe in data's ability to change the world for the better. And thank you for your time. And with that, I am delighted to now introduce Micro Focus CEO Stephen Murdoch to the Vertica Big Data Virtual Conference. Thank you Stephen. >> Stephen: Hi, everyone, my name is Stephen Murdoch. I have the pleasure and privilege of being the Chief Executive Officer here at Micro Focus. Please let me add my welcome to the Big Data Conference. And also my thanks for your support, as we've had to pivot to this being virtual rather than a physical conference. Its amazing how quickly we all reset to a new normal. I certainly didn't expect to be addressing you from my study. Vertica is an incredibly important part of Micro Focus family. Is key to our goal of trying to enable and help customers become much more data driven across all of their IT operations. Vertica 10 is a huge step forward, we believe. It allows for multi-cloud innovation, genuinely hybrid deployments, begin to leverage machine learning properly in the enterprise, and also allows the opportunity to unify currently siloed lakes of information. We operate in a very noisy, very competitive market, and there are people, who are in that market who can do some of those things. The reason we are so excited about Vertica is we genuinely believe that we are the best at doing all of those things. And that's why we've announced publicly, you're under executing internally, incremental investment into Vertica. That investments targeted at accelerating the roadmaps that already exist. And getting that innovation into your hands faster. This idea is speed is key. It's not a question of if companies have to become data driven organizations, it's a question of when. So that speed now is really important. And that's why we believe that the Big Data Conference gives a great opportunity for you to accelerate your own plans. You will have the opportunity to talk to some of our best architects, some of the best development brains that we have. But more importantly, you'll also get to hear from some of our phenomenal Roth Data customers. You'll hear from Uber, from the Trade Desk, from Philips, and from AT&T, as well as many many others. And just hearing how those customers are using the power of Vertica to accelerate their own, I think is the highlight. And I encourage you to use this opportunity to its full. Let me close by, again saying thank you, we genuinely hope that you get as much from this virtual conference as you could have from a physical conference. And we look forward to your engagement, and we look forward to hearing your feedback. With that, thank you very much. >> Joy: Thank you so much, Stephen, for joining us for the Vertica Big Data Conference. Your support and enthusiasm for Vertica is so clear, and it makes a big difference. Now, I'm delighted to introduce Amy Fowler, the VP of Strategy and Solutions for FlashBlade at Pure Storage, who was one of our BDC Platinum Sponsors, and one of our most valued partners. It was a proud moment for me, when we announced Vertica in Eon mode for Pure Storage FlashBlade and we became the first analytics data warehouse that separates compute from storage for on-premise data centers. Thank you so much, Amy, for joining us. Let's get started. >> Amy: Well, thank you, Joy so much for having us. And thank you all for joining us today, virtually, as we may all be. So, as we just heard from Colin Mahony, there are some really interesting trends that are happening right now in the big data analytics market. From the end of the Hadoop hype cycle, to the new cloud reality, and even the opportunity to help the many data science and machine learning projects move from labs to production. So let's talk about these trends in the context of infrastructure. And in particular, look at why a modern storage platform is relevant as organizations take on the challenges and opportunities associated with these trends. The answer is the Hadoop hype cycles left a lot of data in HDFS data lakes, or reservoirs or swamps depending upon the level of the data hygiene. But without the ability to get the value that was promised from Hadoop as a platform rather than a distributed file store. And when we combine that data with the massive volume of data in Cloud Object Storage, we find ourselves with a lot of data and a lot of silos, but without a way to unify that data and find value in it. Now when you look at the infrastructure data lakes are traditionally built on, it is often direct attached storage or data. The approach that Hadoop took when it entered the market was primarily bound by the limits of networking and storage technologies. One gig ethernet and slower spinning disk. But today, those barriers do not exist. And all FlashStorage has fundamentally transformed how data is accessed, managed and leveraged. The need for local data storage for significant volumes of data has been largely mitigated by the performance increases afforded by all Flash. At the same time, organizations can achieve superior economies of scale with that segregation of compute and storage. With compute and storage, you don't always scale in lockstep. Would you want to add an engine to the train every time you add another boxcar? Probably not. But from a Pure Storage perspective, FlashBlade is uniquely architected to allow customers to achieve better resource utilization for compute and storage, while at the same time, reducing complexity that has arisen from the siloed nature of the original big data solutions. The second and equally important recent trend we see is something I'll call cloud reality. The public clouds made a lot of promises and some of those promises were delivered. But cloud economics, especially usage based and elastic scaling, without the control that many companies need to manage the financial impact is causing a lot of issues. In addition, the risk of vendor lock-in from data egress, charges, to integrated software stacks that can't be moved or deployed on-premise is causing a lot of organizations to back off the all the way non-cloud strategy, and move toward hybrid deployments. Which is kind of funny in a way because it wasn't that long ago that there was a lot of talk about no more data centers. And for example, one large retailer, I won't name them, but I'll admit they are my favorites. They several years ago told us they were completely done with on-prem storage infrastructure, because they were going 100% to the cloud. But they just deployed FlashBlade for their data pipelines, because they need predictable performance at scale. And the all cloud TCO just didn't add up. Now, that being said, well, there are certainly challenges with the public cloud. It has also brought some things to the table that we see most organizations wanting. First of all, in a lot of cases applications have been built to leverage object storage platforms like S3. So they need that object protocol, but they may also need it to be fast. And the said object may be oxymoron only a few years ago, and this is an area of the market where Pure and FlashBlade have really taken a leadership position. Second, regardless of where the data is physically stored, organizations want the best elements of a cloud experience. And for us, that means two main things. Number one is simplicity and ease of use. If you need a bunch of storage experts to run the system, that should be considered a bug. The other big one is the consumption model. The ability to pay for what you need when you need it, and seamlessly grow your environment over time totally nondestructively. This is actually pretty huge and something that a lot of vendors try to solve for with finance programs. But no finance program can address the pain of a forklift upgrade, when you need to move to next gen hardware. To scale nondestructively over long periods of time, five to 10 years plus is a crucial architectural decisions need to be made at the outset. Plus, you need the ability to pay as you use it. And we offer something for FlashBlade called Pure as a Service, which delivers exactly that. The third cloud characteristic that many organizations want is the option for hybrid. Even if that is just a DR site in the cloud. In our case, that means supporting appplication of S3, at the AWS. And the final trend, which to me represents the biggest opportunity for all of us, is the need to help the many data science and machine learning projects move from labs to production. This means bringing all the machine learning functions and model training to the data, rather than moving samples or segments of data to separate platforms. As we all know, machine learning needs a ton of data for accuracy. And there is just too much data to retrieve from the cloud for every training job. At the same time, predictive analytics without accuracy is not going to deliver the business advantage that everyone is seeking. You can kind of visualize data analytics as it is traditionally deployed as being on a continuum. With that thing, we've been doing the longest, data warehousing on one end, and AI on the other end. But the way this manifests in most environments is a series of silos that get built up. So data is duplicated across all kinds of bespoke analytics and AI, environments and infrastructure. This creates an expensive and complex environment. So historically, there was no other way to do it because some level of performance is always table stakes. And each of these parts of the data pipeline has a different workload profile. A single platform to deliver on the multi dimensional performances, diverse set of applications required, that didn't exist three years ago. And that's why the application vendors pointed you towards bespoke things like DAS environments that we talked about earlier. And the fact that better options exists today is why we're seeing them move towards supporting this disaggregation of compute and storage. And when it comes to a platform that is a better option, one with a modern architecture that can address the diverse performance requirements of this continuum, and allow organizations to bring a model to the data instead of creating separate silos. That's exactly what FlashBlade is built for. Small files, large files, high throughput, low latency and scale to petabytes in a single namespace. And this is importantly a single rapid space is what we're focused on delivering for our customers. At Pure, we talk about it in the context of modern data experience because at the end of the day, that's what it's really all about. The experience for your teams in your organization. And together Pure Storage and Vertica have delivered that experience to a wide range of customers. From a SaaS analytics company, which uses Vertica on FlashBlade to authenticate the quality of digital media in real time, to a multinational car company, which uses Vertica on FlashBlade to make thousands of decisions per second for autonomous cars, or a healthcare organization, which uses Vertica on FlashBlade to enable healthcare providers to make real time decisions that impact lives. And I'm sure you're all looking forward to hearing from John Yavanovich from AT&T. To hear how he's been doing this with Vertica and FlashBlade as well. He's coming up soon. We have been really excited to build this partnership with Vertica. And we're proud to provide the only on-premise storage platform validated with Vertica Eon Mode. And deliver this modern data experience to our customers together. Thank you all so much for joining us today. >> Joy: Amy, thank you so much for your time and your insights. Modern infrastructure is key to modern analytics, especially as organizations leverage next generation data center architectures, and object storage for their on-premise data centers. Now, I'm delighted to introduce our last speaker in our Vertica Big Data Conference Keynote, John Yovanovich, Director of IT for AT&T. Vertica is so proud to serve AT&T, and especially proud of the harmonious impact we are having in partnership with Pure Storage. John, welcome to the Virtual Vertica BDC. >> John: Thank you joy. It's a pleasure to be here. And I'm excited to go through this presentation today. And in a unique fashion today 'cause as I was thinking through how I wanted to present the partnership that we have formed together between Pure Storage, Vertica and AT&T, I want to emphasize how well we all work together and how these three components have really driven home, my desire for a harmonious to use your word relationship. So, I'm going to move forward here and with. So here, what I'm going to do the theme of today's presentation is the Pure Vertica Symphony live at AT&T. And if anybody is a Westworld fan, you can appreciate the sheet music on the right hand side. What we're going to what I'm going to highlight here is in a musical fashion, is how we at AT&T leverage these technologies to save money to deliver a more efficient platform, and to actually just to make our customers happier overall. So as we look back, and back as early as just maybe a few years ago here at AT&T, I realized that we had many musicians to help the company. Or maybe you might want to call them data scientists, or data analysts. For the theme we'll stay with musicians. None of them were singing or playing from the same hymn book or sheet music. And so what we had was many organizations chasing a similar dream, but not exactly the same dream. And, best way to describe that is and I think with a lot of people this might resonate in your organizations. How many organizations are chasing a customer 360 view in your company? Well, I can tell you that I have at least four in my company. And I'm sure there are many that I don't know of. That is our problem because what we see is a repetitive sourcing of data. We see a repetitive copying of data. And there's just so much money to be spent. This is where I asked Pure Storage and Vertica to help me solve that problem with their technologies. What I also noticed was that there was no coordination between these departments. In fact, if you look here, nobody really wants to play with finance. Sales, marketing and care, sure that you all copied each other's data. But they actually didn't communicate with each other as they were copying the data. So the data became replicated and out of sync. This is a challenge throughout, not just my company, but all companies across the world. And that is, the more we replicate the data, the more problems we have at chasing or conquering the goal of single version of truth. In fact, I kid that I think that AT&T, we actually have adopted the multiple versions of truth, techno theory, which is not where we want to be, but this is where we are. But we are conquering that with the synergies between Pure Storage and Vertica. This is what it leaves us with. And this is where we are challenged and that if each one of our siloed business units had their own stories, their own dedicated stories, and some of them had more money than others so they bought more storage. Some of them anticipating storing more data, and then they really did. Others are running out of space, but can't put anymore because their bodies aren't been replenished. So if you look at it from this side view here, we have a limited amount of compute or fixed compute dedicated to each one of these silos. And that's because of the, wanting to own your own. And the other part is that you are limited or wasting space, depending on where you are in the organization. So there were the synergies aren't just about the data, but actually the compute and the storage. And I wanted to tackle that challenge as well. So I was tackling the data. I was tackling the storage, and I was tackling the compute all at the same time. So my ask across the company was can we just please play together okay. And to do that, I knew that I wasn't going to tackle this by getting everybody in the same room and getting them to agree that we needed one account table, because they will argue about whose account table is the best account table. But I knew that if I brought the account tables together, they would soon see that they had so much redundancy that I can now start retiring data sources. I also knew that if I brought all the compute together, that they would all be happy. But I didn't want them to tackle across tackle each other. And in fact that was one of the things that all business units really enjoy. Is they enjoy the silo of having their own compute, and more or less being able to control their own destiny. Well, Vertica's subclustering allows just that. And this is exactly what I was hoping for, and I'm glad they've brought through. And finally, how did I solve the problem of the single account table? Well when you don't have dedicated storage, and you can separate compute and storage as Vertica in Eon Mode does. And we store the data on FlashBlades, which you see on the left and right hand side, of our container, which I can describe in a moment. Okay, so what we have here, is we have a container full of compute with all the Vertica nodes sitting in the middle. Two loader, we'll call them loader subclusters, sitting on the sides, which are dedicated to just putting data onto the FlashBlades, which is sitting on both ends of the container. Now today, I have two dedicated storage or common dedicated might not be the right word, but two storage racks one on the left one on the right. And I treat them as separate storage racks. They could be one, but i created them separately for disaster recovery purposes, lashing work in case that rack were to go down. But that being said, there's no reason why I'm probably going to add a couple of them here in the future. So I can just have a, say five to 10, petabyte storage, setup, and I'll have my DR in another 'cause the DR shouldn't be in the same container. Okay, but I'll DR outside of this container. So I got them all together, I leveraged subclustering, I leveraged separate and compute. I was able to convince many of my clients that they didn't need their own account table, that they were better off having one. I eliminated, I reduced latency, I reduced our ticketing I reduce our data quality issues AKA ticketing okay. I was able to expand. What is this? As work. I was able to leverage elasticity within this cluster. As you can see, there are racks and racks of compute. We set up what we'll call the fixed capacity that each of the business units needed. And then I'm able to ramp up and release the compute that's necessary for each one of my clients based on their workloads throughout the day. And so while they compute to the right before you see that the instruments have already like, more or less, dedicated themselves towards all those are free for anybody to use. So in essence, what I have, is I have a concert hall with a lot of seats available. So if I want to run a 10 chair Symphony or 80, chairs, Symphony, I'm able to do that. And all the while, I can also do the same with my loader nodes. I can expand my loader nodes, to actually have their own Symphony or write all to themselves and not compete with any other workloads of the other clusters. What does that change for our organization? Well, it really changes the way our database administrators actually do their jobs. This has been a big transformation for them. They have actually become data conductors. Maybe you might even call them composers, which is interesting, because what I've asked them to do is morph into less technology and more workload analysis. And in doing so we're able to write auto-detect scripts, that watch the queues, watch the workloads so that we can help ramp up and trim down the cluster and subclusters as necessary. There has been an exciting transformation for our DBAs, who I need to now classify as something maybe like DCAs. I don't know, I have to work with HR on that. But I think it's an exciting future for their careers. And if we bring it all together, If we bring it all together, and then our clusters, start looking like this. Where everything is moving in harmonious, we have lots of seats open for extra musicians. And we are able to emulate a cloud experience on-prem. And so, I want you to sit back and enjoy the Pure Vertica Symphony live at AT&T. (soft music) >> Joy: Thank you so much, John, for an informative and very creative look at the benefits that AT&T is getting from its Pure Vertica symphony. I do really like the idea of engaging HR to change the title to Data Conductor. That's fantastic. I've always believed that music brings people together. And now it's clear that analytics at AT&T is part of that musical advantage. So, now it's time for a short break. And we'll be back for our breakout sessions, beginning at 12 pm Eastern Daylight Time. We have some really exciting sessions planned later today. And then again, as you can see on Wednesday. Now because all of you are already logged in and listening to this keynote, you already know the steps to continue to participate in the sessions that are listed here and on the previous slide. In addition, everyone received an email yesterday, today, and you'll get another one tomorrow, outlining the simple steps to register, login and choose your session. If you have any questions, check out the emails or go to www.vertica.com/bdc2020 for the logistics information. There are a lot of choices and that's always a good thing. Don't worry if you want to attend one or more or can't listen to these live sessions due to your timezone. All the sessions, including the Q&A sections will be available on demand and everyone will have access to the recordings as well as even more pre-recorded sessions that we'll post to the BDC website. Now I do want to leave you with two other important sites. First, our Vertica Academy. Vertica Academy is available to everyone. And there's a variety of very technical, self-paced, on-demand training, virtual instructor-led workshops, and Vertica Essentials Certification. And it's all free. Because we believe that Vertica expertise, helps everyone accelerate their Vertica projects and the advantage that those projects deliver. Now, if you have questions or want to engage with our Vertica engineering team now, we're waiting for you on the Vertica forum. We'll answer any questions or discuss any ideas that you might have. Thank you again for joining the Vertica Big Data Conference Keynote Session. Enjoy the rest of the BDC because there's a lot more to come

>> Announcer: Live from San Francisco. Celebrating 10 years of high tech coverage, it's theCUBE. Covering VMWorld 2019, brought to you by VMware and it's ecosystem partners. >> Stu: Welcome back. I'm Stu Miniman, my co-host Justin Warren. And you are watching theCUBE. We have two sets, three days, here at VMWorld 2019. Our 10th year of the show. And happy to welcome back to our program, two of our theCUBE Alumni. We were at VeeamON earlier this year down in Miami, but sitting to my right is Ratmir Timashev, who is the co-founder and executive vice president of global sales and marketing with Veeam, and joining us also is Danny Allan, who's the vice president of product strategy also at Veeam. Thank you so much both for joining us. >> Thanks for having us Stu. >> Thank you. >> All right so, Ratmir, let's start. Veeam has been very transparent as to how the company is doing. You know, there's all this talks about unicorns and crazy evaluations or anything like that? But give us the update on, you know, actual dollars and actually what's happening in your business. >> Ratmir: Absolutely, we're always transparent. So actually, there's this term, unicorn, right? So does it mean one billion in valuation, or one billion in revenue (chuckles)? >> Stu: It is valuation. >> Yeah, I know that. So, Veeam is not unicorn anymore, right? Veeam is one billion in bookings. So, yeah, the major trend in the industry, is that we're moving from perpetual to subscription, because we're moving on-prem to hybrid cloud. And Veeam is actually leading that wave. So where we've been always known to be very customer friendly to do business with, easy to do business with, from the channel, from the customer perspective, and that's the major trend. If the customers are moving to hybrid cloud, we have to move to there, from our business model to a hybrid cloud. So we're changing our business model, to make it very easy for customers. >> Ratmir, that's not an easy adjustment. We've watched some public companies go through a little bit of challenges as you work through, you know there's the financial pieces, there's the sales pieces of that, since... Give us a little bit of the, how that works? You know, you just retrain the sales force and go or-- >> That is awesome, awesome question. That that is awesome point, that it's extremely painful. Extremely painful, and for some company, like everybody says Adobe is the best example of moving from perpetual or traditional business model to a subscription, right. So annual, even monthly subscription. For us it's even ten times more difficult than Adobe, because, we're not only moving from perpetual to subscription. We're moving, we're changing our licensing unit, per socket which is VMware traditional to pure VM or pure workload or pure instance, right. What we call instance, basically means, so it's extremely painful, we have to change how we do business, how we incentivize our sales people, how we incentivize our channel, how we incentivize our customers. But that's inevitable, we're moving to a hybrid cloud where sockets don't exist. Sockets, there are no sockets in the hybrid cloud. There are workloads and data. Data and applications. So we have to change our business model, but we also have to keep our current business model. And it's very difficult in terms of the bookings and revenue, when we give a customer an option to buy this way or that way. Of course they will choose the way that is the less expensive for them, and we're ready to do that. We can absorb that, because we're a private company, and we're approachable and we're fast growing. So we can afford that, unlike some of the public companies or companies that, venture capital finance. >> So how do you make that kind of substantial change to the... I mean changing half your company, really. To change that many structures. How do do you do that without losing the soul of the company? And like Veeam, Veeam is famous for being extremely Veeamy. How do you make all those sorts of changes and still not lose the soul of the company like that? How do you keep that there? >> That's an awesome question, because that's 50% of executive management discussions, are about that questions, right. What made Veeam successful? Core value, what we call, core values, there are family values, there are company core values every company has. So that's the most important. And one of them is, be extremely customer friendly, right. So easy to do business with. That's the number one priority. Revenue, projects, number two, number three, being doing the right things for the customer is number one. That's how we're discussing, and we're introducing a major change on October 1st. >> Ah yes. >> Another major change. We've done this major changes in the last two years, moving to subscription. So we started that move, two, two-and-a-half years ago, by introducing our product for Office 365, backup, when that was available only for, on subscription basis, not perpetual. So we're moving in subscription, to the subscription business model in the the last three years. On October 1st, 2019, in one month, we introducing another major change. We are extremely simplifying our subscription licensing and introducing, what we will call Veeam Universal License. Where you can buy once and move or close everywhere. From physical to VMware to Hyper-V to a double SS, ash or back to VMware and back to physical. I'm joking. (lauging) >> All right, Danny, bring us inside the product. We've watched the maturity, ten years of theCUBE here, Veeam was one of the early big ecosystems success stories, of course it went into Multi-Hypervisor, went into Multicloud. You know Ratmir, just went through all of the changes there. Exciting the VUL I guess we'll call it. >> Ratmir: VUL >> VUL, absolutely. So on the product piece, how's the product keeping in line with all these things. >> So our vision is to be the most trusted provider, backup solutions that enable high data management. So backup is still a core of it and it's the start of everything that we do. But if you look what we've done over the course of this year, it's very much about the cloud. So we added the ability, for example, to tier things into object storage in the hyperscale public cloud and that has been taking off, gang busters into S3 and into Azure Blob storage. And so that's a big part of it. Second part of it, in cloud data management is the ability to recover, if you're sending your data into the cloud, why not recover there? So we've added the ability to recover workloads in Azure, recover workloads in EC2. And lastly of course, once your workloads are in the cloud, then you want to protect it, using cloud-native technology. So we've addressed all of these solutions, and we've been announcing all these exciting things over the course of 2019. >> The product started off as being VM-centrical, VM Only back in the day. And then you've gradually added different capabilities to it as customers demanded, and it was on a pretty regular cadence as well. And you've recently added, added cloud functionality and backups there. What's the next thing, customers are asking for? 'Cause we've got lots of workloads being deployed in edge, we've got lots of people doing things with NoSQL backups, we've got Kubernetes, is mentioned every second breath at this show. So where are you seeing demand for customers that you need to take the product next? So we've heard a lot about Kubernetes obviously, the shows, the containers it's obviously a focus point. But one of the things we demoed yesterday. We actually had a breakout session, is leveraging an API from VMR called the VCR API for IO filtering. So it basically enables you to fork the rights when you're writing down to the storage level, so that you have continuous replication in two environments. And that just highlights the relationship we have with VMware. 80% of our customers are running on VMware. But that's the exciting things that we're innovating on. Things like making availability better. Making the agility and movement between clouds better. Making sure that people can take copies of their data to accelerate their business. These all areas that we are focusing on. >> Yeah, a lot of companies have tried to, multiple times have tried to go away from backup and go into data management. I like that you don't shy away from, ah, yeah we do backup and it's an important workload, and you're not afraid to mention that. Where's some other companies seem to be quite scared of saying, we do backup, 'cause it's not very cool or sexy. Although well, it doesn't have to be cool and sexy to be important. So I like that you actually say that yes we do backup. But we are also able to do some of these other bits and pieces. And it's enabled by that backup. So you know, copy, data management, so we can take copies of things and do this. Where is some of the demand coming around what to do with that data management side of things. I know there's, people are interested in things like, for example, data masking, where you want to take a copy of some data and use it for testing. There's a whole bunch of issue and risks around in doing that. So companies look for assistance from companies like Veeam to do that sort of thing. Is that where you're heading with some of that product? >> It is, there's four big use cases, DevOps is certainly one of them, and we've been talking about Kubernetes, right, which is all about developers and DevOps type development, so that's a big one. And one of the interesting things about that use case is, when you make copies of data, compliance comes into play. If you need to give a copy of the data to the developer, you don't want to give them credit card numbers or health information, so you probably want to mask that out. We have the capability today in Veeam, we call it, Staged Restore, that you could actually open the data in the sandbox to manipulate it, before you give it to the developer. But that's certainly one big use case, and it's highlighted at conferences like this. Another one is security, I spent a decade in security. I get passionate about it, but pentesting or forensics. If you do an invasive test on a production system, you'll bring the system down. And so another use case of the data is, take a copy, give it to the security team to do that test without impacting the production workload. A third one would be, IT operations, patching and updating all the systems. One of the interesting things about Veeam customers. They're far more likely to be on the most recent versions of software, because you can test it easily, by taking a copy. Test the patch, test the update and then roll it forward. And then a forth huge use case that we can not ignore is the GDPR in analytics and compliance. There's just this huge demand right now. And I think there's going to be market places opened in the public cloud, around delegating access to the data, so that they can analyze it and give you more intelligence about it. So GDPR is just a start, right. Were is my personally identifiable information? But I can imagine workload where a market place or an offering, where someone comes in and says, hey, I'll pay you some money and I'll classify your data for you, or I'll archive it smartly for you. And the business doesn't have to that. All they have to do is delegate access to the data, so that they can run some kind of machine learning algorithm on that data. So these are all interesting use cases. I go back, DevOps, security IT operations and analytics, all of those. >> So Ratmir, when I go to the keynote, it did feel like it was Kubernetes world? When I went down the show floor it definitely felt like data protection world. So it's definitely been one of the buzzier conversations the last couple of years at this show. But you look, walk through the floor, whether it be some of the big traditional vendors, lots of brand new start ups, some of the cloud-native players in this space. How do you make sure that Veeam gets the customers, keeps the customers that they have and can keep growing on the momentum that you've been building on? >> That's a great question, Stu. Like Pat Gelsinger mention that, number of applications has grown in the last five years, from 50 million to something like 330 million, and will grow to another almost 800 million in the next five years, by 2024. Veeam is in the right business, Veeam is the leader, Veeam is driving the vision and the strategy, right. Yeah, we have good competition in the form of legacy vendors and emerging vendors, but we have very good position because we own the major part of your hybrid cloud, which is the private cloud. And we're providing a good vision for how the hybrid cloud data management, not just data protection, which just Danny explained, should be done, right. I think we're in a good position and I feel very comfortable for the next five, ten years for Veeam. >> It's a good place to be. I mean feeling confident about the future is... I don't know five to ten years, that's a long way out. I don't know. >> Yeah I agree, I agree, it used to be like that, now you cannot predict more than six moths ahead, right. >> Justin I'm not going to ask him about Simon now, it's-- >> Six months is good yeah, six months maximum, what we can predict-- >> We were asking Michael Dell about the impact of China these days, so there's a lot of uncertainty in the world these day. >> Ratmir: Totally. >> Anything macro economic, you know that, you look at your global footprint. >> No we're traditional global technology company that generates most of the revenue between Europe and North America and we have emerging markets like Asia-Pac and Latin. We're no different than any other global technology company, in terms of the revenue and our investment. The fastest growing region of course is Asia-Pac, but our traditional markets is North America and Europe. >> Hailing from Asia-Pac, I do know the region reasonably well and Veeam is, yeah Veeam is definitely, has a very strong presence there and growing. Australia used to be there, one of our claims to fame, was one of the highest virtualized workload-- >> And Mohai is the cloud adapter. >> Cloud adoption. >> Yes, we like new shiny toys, so adopt it very, very quickly. Do you see any innovation coming out of Asia-Pac, because we use these things so much, and we tend to be on that leading edge. Do you see things coming out of the Asia-Pac teams that notice how customers are using these systems and is that placing demand on Veeam. >> Absolutely, but Danny knows better because he just came back from the Asia-Pacific trip. >> Justin: That's right, you did. >> Yeah, I did, I always say you live in the future, because you're so many hours ahead. But the reality is actually, the adoption of things like Hyper-convergence infrastructure, was far faster in areas like NZ, the adoption of the cloud. And it's because of New Zealand is part of the DAid, Australia is very much associated with taking that. One of the things that we're seeing there is consumption based model. I was just there a few weeks ago and the move to a consumption and subscription based model is far further advanced in other parts of the world. So I go there regularly, mostly because it gives me a good perspective on what the US is going to do two years later, And maybe AMEA three years later. It gives us a good perspective of where the industry is going-- >> It's not to the US it comes to California first then it spreads from there. (lauging) >> Are you saying he's literally using the technology of tomorrow in his today, is what we're saying. >> Maybe me I can make predictions a little bit further ahead there. >> Well you live in the future. >> All right I want to give you the both, just a final word here, VMWorld 2019. >> It's always the best show for us. VMWorld is the, I mean like Danny said, 80% of our customers is VMware, so it's always the best. We've been here for the last 12 years, since 2007. I have so many friends, buddies, love to come here, like to spend three, four days with my best friends, in the industry and just in life. >> I love the perspective here of the Multicloud worlds, so we saw some really interesting things, the moving things across clouds and leveraging Kubernetes and containers. And I think the focus on where the industry is going is very much aligned with Veeam. We also believe that, while it starts with backup up, the exciting thing is what's coming in two, three years. And so we have a close alignment, close relationship. It's been a great conference. >> Danny, Ratmir, thank you so much for the updates as always and yeah, have some fun with some of your friends, in the remaining time that we have. >> We have a party tonight Stu, so Justin too. >> Yeah, I think most people that have been to VMWorld are familiar with the Veeam party, it is famous, definitely. >> For Justin Warren, I'm Stu Miniman, we'll be back with more coverage here, from VMWorld 2019. Thanks for watching theCUBE. (electronic music)

>> Hello everyone. Welcome to this CUBE conversation. I'm John Furrier host like you here in our Palo Alto studio here in Palo Alto. Here with Dominic Wilde, known as Dom, CEO of SnapRoute, a hot new startup. A great venture. Backers don. Welcome to skip conversation. So love having to start ups. And so talk about Snape route the company because you're doing something interesting that we've been covering your pretty aggressively the convergence between Dev Ops and Networking. We've known you for many, many years. You were a former Hewlett Packard than you woodpecker enterprise running the networking group over there. You know, networking. And you're an operator. Snap rows. Interesting, because, um, great names back behind it. Big venture backers. Lightspeed Norwest, among others. Yes. Take a minute. Explain what? A SnapRoute. >> So SnapRoute was founded to really address one of the big, big problems we see in infrastructure, which is that, you know, essentially the network gets in the way of the deployment the rapid and angel deployment of applications. And so in the modern environment that we're in, you know, the business environment, highly competitive environment of disruption, continuous disruption going on in our industry, every company out there is constantly looking over their shoulder is, you know, making sure that they're moving fast enough there innovating fast enough that they don't want to be disrupted. They don't want to be overrun by, you know, a new upstart. And in order to do that, you know the application is is actually the work product that you really want to deploy, that you you want to roll out, and you want to be able to do that on a continuous basis. You want to be really agile about how you do it. And, quite frankly, when it comes to infrastructure, networking has been fifteen years behind the rest of the infrastructure and enabling that it's, ah, it's a big roadblock. It's obviously, you know, some of the innovations and developments and networking of lag behind other areas on what we snap Brown set out to do was to say, You know, look, if we're if we're going to bring networking forward and we're going to try and solve some of these problems, how do we do that? In a way, architecturally, that will enable networking to become not just a part of Ah, you know a cloud native infrastructure but actually enable those those organizations to drive forward. And so what we did was we took all of our sort of devops principles and Dev ups tools, and we built a network operating system from the ground up using devops principles, devops architectures and devops tools. And so what we're delivering is a cloud native network operating system that is built entirely on containers and is delivered is a micro services architecture on the big...one of the big value propositions that we deliver is what we call see a CD for networking, which is your continuous integration. Continuous deployment is obviously, you know, Big devops principal there. But doing that for networking, allowing a network to be constantly up enabling network Teo adapt to immutable infrastructure principles. You know we're just replacing pieces that need to be replaced. Different pieces of the operating system can be replaced If there's a security vulnerability, for instance, or if there's ah, bugger and you feature needed so you know we can innovate quicker. We can enable the network to be more reliable, allow it to be more agile, more responsive to the needs of the organization on all of this, fundamentally means that your Operation shins model now becomes ah, lot more unified. A lot more simple. You. Now, we now enable the net ox teams to become a sort of more native part of the conversation with devils. Reduce the tension there, eliminate any conflicts and everything. And we do that through this. You know, this innovative offices. >> Classically, the infrastructure is code ethos. >> Yeah, exactly right. I mean, it's you know, a lot of people have been talking about infrastructure is code for a long, long time. But what we really do, I mean, if if you deploy our network operating system you employ onto the bare metal switching, then you really enable Dev ops to hang have, you know, I take control and to drive the network in the way they want using their native tool chains. So, you know, Cuba Netease, for instance, ears. You know that the big growing dev ops orchestration to all of the moment. In fact, we think it's more than of the moment. You know, I've never seen in the industry that sort of, you know, this kind of momentum behind on open source initiative like there is behind Cuba. Netease. And we've taken communities and baked it natively into the operating system. Such that now our network operating system that runs on a physical switch can be a native part off that communities and develops tool >> Dom, I want to get to the marketplace, dynamics. Kind of what's different. Why now? But I think what's interesting about SnapRoute you're the chief of is that it's a venture back with big names? Yeah. Lightspeed, Norwest, among others. It's a signal of a wave that we've been covering people are interested in. How do you make developers deploy faster, more agility at scale, on premises and in clouds. But I want you to before we get there, want to talk about the origin story of company? Yeah. Why does it exist? How did it come to bear you mentioned? Operation is a big part of cloud to cloud is about operating model so much a company. Yes. This is the big trend. That's the big way. But how did it all get started? What's the SnapRoute story? >> Yeah, it's an interesting story. Our founders were actually operators at at Apple back in the day, and they were responsible for building out some of Apple's biggest. You know, data centers for their sort of customer facing services, like, you know, like loud iTunes, all those good things and you know they would. They were tasked with, sort of, you know, sort of modernizing the operational model with with those data centers and, you know, and then they, like many other operators, do you know, had a sense of community and worked with their peers. You know, another big organizations, even you know, other hyper scale organizations and wanted to learn from what they did on DH. What they recognised was that, you know, cos like, you know, Google and Facebook and Microsoft is urine things. They had done some incredible things and some incredible innovations around infrastructure and particularly in networking, that enabled them to Dr Thie infrastructure from A from a Devil ops perspective and make it more native. But those words that if you know, fairly tailored for there, if you know, for their organizations and so what they saw was the opportunity to say, Well, you know, there's there's many other organizations who are delivering, you know, infrastructure is a service or SAS, or you know, who are just very large enterprises who are acting as these new cloud service providers. And they would have a need to, you know, to also have, you know, tools and capabilities, particularly in the network, to enable the network to be more responsive, more to the devil apps like. And so, you know, they they they founded SnapRoute on that principle that, you know, here's the problem that we know we can solve. It's been solved, you know, some degree, but it's an architectural problem, and it's not about taking, You know, all of the, you know, the last twenty five years of networking knowledge and just incrementally doing a sort of, you know, dot upgrade and, you know, trying to sort of say, Hey, we're just add on some AP eyes and things. You really needed to start from the ground up and rethink this entirely from an architectural perspective and design the network operating system as on with Dev ups, tools and principles. So they started the company, you know, been around just very late two thousand fifteen early two thousand sixteen. >> And how much money have you read >> The last around. We are Siri's, eh? We took in twenty five million. >> And who were the venture? >> It was Lightspeed Ventures on DH Norwest. And we also had some strategic investment from Microsoft Ventures and from teams >> from great name blue chips. What was their interest? What was their thesis? Well, and you mentioned the problem. What was the core problem that you're solving that they were attracted to? Why would that why was the thirst with such big name VCs? >> Yeah, I mean, I think it was, you know, a zip said, I think it's the the opportunity to change the operational more. And I think one of the big things that was very different about our company is and, you know, we like to say, you know, we're building for effort. Operators, by operators, you know, I've found is, as I said, well, more operators from Apple, they have lived and breathed what it is to be woken up at three. A. M. On Christmas Eve toe. You know, some outage and have to, you know, try and figure that out and fight your way through a legacy kind of network and figure out what's going on. So you know, so they empathize with what that means and having that DNA and our company is incredibly meaningful in terms of how we build that you know the product on how we engage with customers. We're not just a bunch of vendors who you know we're coming from, you know, previous spender backgrounds. Although I do, you know, I bring to the table the ability to, you know, to deliver a package and you know, So there's just a cloud scale its clouds, Gail. It's it's but it's It's enabling a bridge if you like. If you look at what the hyper scales have done, what they're achieving and the operational models they have, where a if you like a bridge to enable that capability for a much broader set of operators and C. S. P s and as a service companies and dry forward a an aggressive Angela innovation agenda for companies, >> businesses. You know, we always discussing the Cuban. Everyone who watches the Kiev knows I'm always ranting about how cloud providers make their market share numbers, and lot of people include sass, right? I think everyone will be in the SAS business, so I kind of look at the SAS numbers on, say, it's really infrastructures service platform to service Amazon, Google, Microsoft and then, you know, Ali Baba in China. Others. Then you got IBM or one of it's kind of in the big kind of cluster there top. That is a whole nother set of business requirements that sass driven this cloud based. Yeah, this seems to be a really growing market. Is that what you're targeting? And the question is, how do you relate Visa? Visa Cooper? Netease trend? Because communities and these abstraction layers, you're starting to hear things like service mesh, policy based state Full application states up. Is that you trying to that trend explain. >> We're very complimentary, Teo. Those trends, we're, you know, we're not looking to replace any of that, really. And and my big philosophy is, if you're not simplifying something, then you're not really adding back here, you know, what you're doing is complicating matters or adding another layer on top. So so yeah, I mean, we are of value to those companies who are looking at hybrid approaches or have some on prime asset. Our operating system will land on a physical, bare metal switch So you know what? What we do is when you look at it, you know, service most is your message measures and all the other, You know, technologies you talked about with very, very complimentary to those approaches because we're delivering the on underlying network infrastructure on network fabric. Whatever you'd like to call it, that can be managed natively with class native tools, squeezing the alliteration there. But but, you know, it means that you don't need toe add overlays. We don't need to sort of say, Hey, look, the network is this static, archaic thing that's really fragile. And And I mean, if we touch it, it's going to break. So let's just leave it alone and let's let's put some kind of overlay over the top of it on do you know, run over the top? What we're saying is you can collapse that down. Now what you can say, what you can do is you can say, Well, let's make the network dynamic responsive. Let's build a network operating system out of micro services so you can replace parts of it. You can, you know, fix bugs. You can fix security vulnerabilities and you can do all that on the fly without having to schedule outage windows, which is, you know, for a cloud native company or a sass or infrastructure service company. I mean, that's your business. You can't take outage windows. Your business depends on being available all the time. And so we were really changing that fundamentals of a principle of networking and saying, You know, networking is now dynamic, you know, in a very, very native way, but it also integrates very closely with Dev ops. Operational model >> is a lot of innovation that network. We're seeing that clearly around the industry. No doubt everyone sees late and see that comes into multi Cloud was saying that the trend moving the data to the compute coyote again that's a network issue network is now an innovation opportunity. So I gotta ask you, where do you guys see that happening? And I want to ask you specifically talking about the cloud architects out in the marketplace in these enterprises who were trying to figure out about the architecture of clowns. So they know on premises there, moving that into a cloud operations. We see Amazon, they see Google and Microsoft has clouds that might want to engage with have cloud native presence in a hybrid and multi cloud fashion for those cloud architects. What are the things that you like to see them doing? More of that relates to your value problems. In other words, if they're using containers or they're using micro services, is this good or bad? What? What you should enterprise to be working on that ties into your value proposition. >> So I think about this the other way around, actually, if I can kind of turn that turn that question. But on his head, I think what you know, enterprises, you know, organization C, S. P s. I think what they should be doing is focusing on their business and what their business needs. They shouldn't be looking at their infrastructure architecture and saying, you know, okay, how can we, you know, build all these pieces? And then you know what can the business and do on top of that infrastructure? You wanna look at it the other way around? I need to deploy applications rapidly. I need to innovate those applications. I need to, you know, upgrade, change whatever you need to do with those applications. And I need an infrastructure that can be responsive. I need an infrastructure that can be hybrid. I need infrastructure that can be, you know, orchestrated in the hybrid manner on DH. Therefore, I want to go and look for the building blocks out there of those those architectural and infrastructure building blocks out there that can service that application in the most appropriate way to enable the velocity of my business and the innovation from my business. Because at the end of the day, I mean, you know, when we talk to customers, the most important thing T customers, you know, is the velocity of their business. It is keeping ahead in the highly competitive environment and staying so far ahead that you're not going to be disrupted. And, you know, if any element of your infrastructure is holding you back and even you know, you know the most mild way it's a problem. It's something you should address. And we now have the capability to do that for, you know, for many, many years. In fact, you know, I would claim up to today without snap route that you know, you you do not have the ability to remove the network problem. The network is always going to be a boat anchor on your business. It introduces extra cycles. It introduces big security, of underplaying >> the problems of the network and the consequences that prior to snap her out that you guys saw. >> So I take the security issue right? I mean, everybody is very concerned about security today. One of the biggest attack vectors in the security world world today is the infrastructure. It's it's it's so vulnerable. A lot of infrastructure is is built on sort of proprietary software and operating systems. You know, it's very complex. There's a lot of, you know, operations, operational, moves out and change it. So there's there's a lot of opportunity for mistakes to be made. There's a lot of opportunity for, you know, for vulnerabilities to be exposed. And so what you want to do is you want to reduce the threat surface of, you know, your your infrastructure. So one of the things that we can do it SnapRoute that was never possible before is when you look at a traditional network operating system. Andreas, A traditional. I mean, any operating system is out there, other you know, Other >> than our own. >> It's basically a monolithic Lennox blob. It is one blob of code that contains all of the features. And it could be, you know, architect in in a way that it Sze chopped up nicely. But if you're not using certain features, they're still there. And that increases the threat surface with our sat proud plant native network operating system. Because it is a micro services are key picture. If you are not using certain services or features, you can destroy and remove the containers that contain those features and reduce the threat surface of the operating system. And then beyond that, if you do become aware ofthe vulnerability or a threat that you know is somewhere in there, you can replace it in seconds on the fly without taking the infrastructure. Damn, without having to completely replace that whole blob of software causing, you know, an outage window. So that's just one example of, you know, the things we can do. But even when it comes to simple things, like, you know, adding in new services or things because we're containerized service is a ll boot together. It's no, eh? You know it doesn't. It doesn't have a one after the other. It it's all in parallel. So you know this this operating system comes up faster. It's more reliable. It eliminates the risk factors, the security, you know, the issues that you have. It provides native automation capabilities. It natively integrates with, You know, your Dev Ops tool chain. It brings networking into the cloud. Native >> really, really isn't in frustrations. Code is an operating system, so it sounds like your solution is a cloud native operating system. That's correct. That's pretty much the solution. That's it. How do customers engage with you guys? And what do you say? That cloud architect this is Don't tell me what to do. What's the playbook, right? How you guys advice? Because I see this is a new solution. Talk about the solution and your recommendation to architects as they start thinking about building that elastic in that flexible environment. >> Yeah. I mean, I think you know, Ah, big recommendation is, you know, is to embrace, you know, that all the all of the cloud native principles and most of the companies that were talking to, you know, definitely doing that and moving very quickly. But, you know, my recommendation. You know, engaging with us is you should be looking for the network to in naval, your your goals and your you know your applications rather than limiting. I mean, that's that's the big difference that, you know, the people who really see the value in what we do recognize that, you know, the network should be Andi is an asset. It should be enabling new innovation, new capabilities in the business rather than looking at the network as necessary evil where we you know, where we have to get over its limitations or it's holding us back. And so, you know, for any organization that is, you know, is looking at deploying, you know, new switching infrastructure in any way, shape or form. I think, you know, you should be looking at Well, how am I going to integrate this into a dev ops? You know, world, how may going to integrate this into a cloud native world. So as my business moves forward, I'm actually servicing the application in enabling a faster time to service for the application for the business. At the end of the day, that's that's everybody's going, >> you know, we've been seeing in reporting this consistently, and it's even more mainstream now that cloud computing has opened up the aperture of the value and the economics and also the technical innovation around application developers coding faster having the kind of resource is. But it also created a CZ creating a renaissance and networking. So the value of networking and application development that collision is coming together very quickly. So the intersection you guys play. So I'm sure this will resonate well with customers Will as they try to figure out the role the network because against security number one analytics all the things that go into what Sadiq they care about share data, shared coat all this is kind of coming together. So if someone hears this story, they'll go, OK, love this snap around store. I gotta I gotta dig in. How do they engage you? What do you guys sell to them? What's the pitch? Give the quick plug for the company real >> quick. Engaging with us is, you know, is a simple issue. No, come to www snapper out dot com. And you know, you know contacts are up there. You know, we were currently obviously we're a small company. We sell direct, more engaged with, you know, our first customers and deploying our product, you know, right now, and it's going very, very well, and, you know, it's a PSE faras. You know how you know what and when to engage us. I would say you can engage us at any stage and and value whether or not your architect ing a whole new network deploying a new data center. Obviously. Which is, you know, it is an ideal is built from the ground up, but we add value to the >> data center preexisting data saying that wants >> the modernizing data centers. I mean, very want >> to modernize my data center, my candidate. >> So one of the biggest challenges in an existing data center in when one of the biggest areas of tension is at the top of rack switch the top of racks, which is where you connect in your you know, your your application assets, your servers are connected. You're connecting into the into the, you know, first leap into the network. One of the challenges there is. You know, Dev ops engineers, They want Teo, you know, deploy containers. They want to deploy virtual machines they wantto and stuff move stuff, change stuff and they need network engineers to help them to do that. For a network engineer, the least interesting part of the infrastructure is the top Arax. Which it is a constant barrage day in, day, out of request. Hey, can I have a villain? Can have an i p address. Can we move this? And it's not interesting. It just chews up time we alleviate that tension. What we enable you to do is network engineer can you know, deploy the network, get it up and running, and then control what needs to be controlled natively from their box from debits tool chains and allow the devil ups engineers to take control as infrastructure. So the >> Taelon is taking the stress out of the top of racks. Wedge, take the drama out of this. >> Take that arm around the network. Right. >> So okay, you have the soul from a customer. What am I buying? What do you guys offering? Is that a professional services package? Is it software? Is it a sad solution? Itself is the product. >> It is software, you know. We are. We're selling a network operating system. It lands on, you know, bare metal. He liked white box switching. Ah, nde. We offer that as both perpetual licenses or as a subscription. We also office, um, you know, the value and services around that as well. You know, Andre, right now that is, you know, that is our approach to market. You know, we may expand that, you know, two other services in the future, but that is what we're selling right now. It is a network operating >> system down. Thanks for coming and sharing this story of SnapRoute. Final question for you is you've been in this century. While we've had many conversations we'd love to talk about gear, speeds and feeds. I'll see softwares eating. The world was seeing that we're seeing cloud create massive amounts. Opportunity. You're in a big wave, right? What is this wave look like for the next couple of years? How do you see this? Playing out as Cloud continues to go global and you start to Seymour networking becoming much more innovative. Part of the equation with Mohr developers coming onboard. Faster, more scale. How do you see? It's all playing out in the industry. >> Yeah. So I think the next sort of, you know, big wave of things is really around the operational. But I mean, we've we've we've concentrated for many years in the networking industry on speeds and feeds. And then it was, you know, it's all about protocols and you know how protocol stacks of building stuff. That's all noise. It's really about How do you engage with the network? How do you how do you operate your network to service your business? Quite frankly, you know, you should not even know the network is there. If we're doing a really good job of network, you shouldn't even know about it. And that's where we need to get to is an industry. And you know that's that's my belief is where, where we can take >> it. Low latent. See programmable networks. Great stuff. SnapRoute Dominic. While no one is dominant industry friend of the Cube also keep alumni CEO of Snapper Out. Hot new start up with some big backers. Interesting signal. Programmable networks software Cloud Global all kind of big Party innovation equation. Here in Silicon Valley, I'm showing for with cube conversations. Thanks for watching

>> Live from Orlando, Florida it's theCUBE. Covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome back, everyone, to theCUBE's live coverage of Microsoft Ignite here in Orlando. I'm your host Rebecca Knight along with my cohost Stu Miniman. We are joined by Sai Mukundan. He is the Director of Product Management, Cloud Solutions at Cohesity. Thanks so much for coming on the show. >> Thanks, Rebecca, thanks. So nice to have you guys here at the Cohesity booth. >> And thank you for hosting us, I should say, yes. >> Absolutely, it's been wonderful. >> So we already had you colleague Lynn Lucas on this morning, she was terrific. And she gave us a high level vision of the news. Why don't you break it down for us. Explain to our viewers exactly what Cohesity was announcing here at Ignite. >> Sure. So, broadly speaking, we announced three things this morning. The first one, we've seen a lot of customers, Optic Office 365, in fact, that's one of the first or initial use cases of how they adopt Microsoft's solutions more off as a service. So the ability to now backup and recover old 365 has come up quite a bit in our customer conversations. So we announced a solution that will be available shortly, so customers can leverage the same Cohesity platform that we had up until now to also backup and recover old 365. So that was number one. Number two was around Azure Databox. So, this is a relatively new offering from Azure. It was up until now, it was in preview, and now it's going GA. So the fact that we can now integrate with Azure Databox as a means for customers to move data from on-premise to Azure, a great initial seeding for long term retention. And the fact that we integrate seamlessly with that, that was the second piece of the news. And then the third one is really around a hybrid Cloud message in the margin. Really, hybrid, I know-- Stu, you like to refer to it more as it's an operational model. It's not about what the Cloud is but it's more of an operation model. And in that model, customers are always looking to leverage it for disaster recovery purposes. And their ability to fade over to Azure and then bring it back on-premise, fade back, that capability is the third underpinning of the announcement this morning. >> And Sai, one of the challenges that we have is, if we look at Cloud and say it's an operating model. Well, the challenge we have is it really is a multi-cloud world. If you look especially here in the Microsoft ecosystem, absolutely, start with Office 365. Microsoft pushed a lot of customers to the SAS model. I have my data center, I'm probably modernizing things there, and then I have the public cloud. Well, when I look at my data, I want to be able to manage and interact and leverage my data no matter where it lives. So, that's where-- I said Microsoft lives in all those places, and it sounds like your integrations are going to help customers span and get their arms around their data and leverage their data no matter where it lives. >> Yeah, I particularly like the use of the word span, because as you may know, we call our underlying distributor file system the spanifest. (laughing) Right? So the idea is that it spans on-premise Cloud, and your point, multi-cloud as well. So the ability to use the same platform, and that's really what drives customers today. When you look at what are the three aspects of our solution that they like, I would say one is the scale ability. The fact that they can start small and then scale as their environment grows, that's important. The second is around, everything plays around automation, API driven, API first architecture, right. And the fact that we are policy based, API driven really really resonates with them. And the third one is the simplicity and ease of management. I mean, you can build all these solutions, but at the end of the day, it has to be simple for customers to consume. And that's something that really resonates with prospects, partners, and customers we talk to. >> Sai, wondering on the Azure Databox, if you could help unpack that a little. We have some Microsoft guests on, Jeffery Snover walked us through. There's a couple of different versions of them. Some are for data movement, some of them there will be really kind of edge, compute, and AI capabilities there. Which ones do Cohesity use, what do you see is the use cases that you'll be playing in? >> Sure, so before I go into the solution and the use case. I think one of the key aspects of why that announcement is important for us, is it also shows the kind of engagement and close technology partnership that we have established with Microsoft, Azure, right. The fact that we are one of their launch partners, both during the preview and now in the GA timeframe. It's important for both customers and partners, because that gives them a good, sort of, understanding that we are there in establishing thought leadership. We are there in working closely with Microsoft in this case, along with other technology partners out there. Just coming back to the solution itself, there are a couple of flavors of Databox. So the one that we have done extensive integration with is Databox. There's another version offered, which is called the Databox Edge, which also has Compute in it. But the idea here, the use case is really around when customers are looking at Cohesity, there is backup and recovery that they can do from on-premise. But Azure and Azure Blob Storage in particular becomes a seamless extension for long term retention. Now, there are a few customers, and I can relate to several who asked, "Hey, I have a large enough "data set that needs to be seeded initially." And obviously the network becomes a bottle neck in that case. So with Databox, the ability to now transfer the data into your on-prem, like you get the Databox shipped to your on-premise, get it loaded, true Cohesity. Seamlessly get it hydrated in our Azure account, and from that point on we only send the changes or the incremental data. So that is really appealing to both customers, as well as partners who are really engaged in these migration projects in some cases. >> I'm really interesting what you're talking about with the thought leadership and your approach to partnerships, because Microsoft selecting Cohesity as a partner, it's a real stamp of approval for Cohesity, a real validation that this company's for real. How do you then think about who you will partner with? Particularly if the company is, say, only five years old or pretty new to the space or maybe not as well known. >> I think one of the things that Mohit Aron, and he's a pioneer in the spirit systems and is the founder of Cohesity. One of the things that he established, right from the get go is the ability for the product to scale, scale on-premise, but also that the Cloud has to be very seamless. It's a natural extension of what the architecture is intended to do or achieve. And so that kind of made it easier for us on the product team to figure out who is it that we need to partner with. Azure is obviously a leader in that space, particularly over the last few years. I want to go back to something that was mentioned in the keynote yesterday. It's not a know it all, but it's a learn it all, right. The learning that we have had as we have grown Cohesity and the product has grown and as we acquired customers and talked to prospects is they want to work with the likes of Microsoft Azure, leverage the infrastructure that they have to offer. So we started there. We said if customers are asking for it, we do it and we learn along with them on why and what the use cases are. And it started with, going back to my earlier comment, long term retention. And now, as an extension to that, with the hybrid cloud where not only storage, but leveraging disks, leveraging Azure Compute, that's now become an extension of what we started off with. And so we have Azure DataPlatform Cloud Edition, which is Cohesity running on Azure. So I would say how we made the decision in this case, A. the product and the foundation really set that for us, but B., more importantly, the customers really asking for it and asking for that integration made it easier for us to determine that, hey we absolutely need to partner with the cloud renders. >> Sai, I'd like to build off of that, the customers and what they're asking for. This is a very large ecosystem here. To be honest, we know that Azure, Microsoft is a big player in Cloud, when I look at this show, Azure's a piece of the overall discussion. So, I was a little surprised. Not that we're hearing more about Azure here, but, it's because if you look at just order magnitude, how many customers Microsoft has on Windows and Office, obviously that's going to dwarf customer adopts in general. Where are your customers when the talk about Cloud adoption, your customers? Do you find them more in a Windows customers in their own data center versus Azure? What are your customers doing and adoption of Cohesity Cloud products in general? >> So if you look at the typical on ramp of customers, more often than not, at least I would say over the last couple of years, our customers have typically started with the on-premise. Because their immediate pain point was the platform can do a lot of things. Customers are always looking to also solve that immediate pain point while looking into the future. So the immediate pain point was really around how do I make my backup and data protection systems, first of all, simple, efficient, and less fragmentation. And while I'm doing that, how can I then potentially invest in the platform that is capable of doing more. And that's something that Cohesity offered in the on-premise world. And as a natural extension to that, as both from the bottoms up, as storage admins and backup admins started looking at leveraging Cloud or Azure in particular for as an extension of their storage infrastructure, as well as from the top down. You know, more of like the business decision makers and the CIOs driving that mandate of, hey, I want you to think about Cloud first and have that mindset. I think it really appealed to them. Because now they could start leveraging Azure Blob, again, back to that long term retention, legal hold, compliance standpoint. And then building off of that, building off of that to do test dev. We have a great feature, it's called Cloud Spend. The ability to take some of the on-premise infrastructure. And your earlier questions too, we have seen customers both VMware, Windows Hyper-v environments. Believe it or not, some customers still have physical systems. And the fact that Cohesity can take care of all that in the on-prem world, while seamlessly helping them adopt Cloud is really the kind of customers that we have seen in this journey that we have taken along with our customers and partners. >> Well this is theCUBE's first time at Ignite. I know you're relatively new to Ignite. >> I'm even surprised about that. I would think you guys would have made a number of appearances, but I'm glad it's the first time and it's at the Cohesity booth, so wonderful. >> We're so excited, but what are some of the things you're going to take back with you from this conference? >> I think for me, this conference, as has any other such conference in particular, it's really the excitement. You go back and you reflect on the last three, four days you spend here, and it's about all the great conversations that we have had with customers, prospects, and partners. Secondly, we heard a session earlier this morning, a Cohesity session, we had Brown University join us. And then there's going to be another one tomorrow. We're going to have UPenn and HKS. We are working on your alma mater Cornell, by the way, Stu. So we'll get them soon. >> Excellent, excellent. Go Big Red. >> So the fact that we have all these sessions and some really great attendance. And attendance from folks who are yet to embrace the Cohesity solutions. So it's great for us to get our message out. >> Getting the word out. >> Get our word out there. And I would say the last thing for us is also showcasing to Microsoft here in particular, the fact that we have this big presence here and the excitement it's having is a great message to the Microsoft executives and the leadership team that we work with as well to show more love, we already have enough that we get attention from them. But this is more of a validation for them to say there's more that we should be doing and could be doing with Cohesity. So I think those are probably the three things I'll walk away with and build on what we learned from Ignite here. >> Excellent, well thank you so much, Sai, for coming on the show. It was great having you here. >> Thanks, likewise. >> I'm Rebecca Knight for Stu Miniman, we will have more at theCUBE's live coverage of Microsoft's Ignite in just a little bit. (techno music)

(electronic music) >> Live from Orlando, Florida, it's theCUBE! Covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back everyone to theCUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We're joined by Jeffrey Snover. He is the technical fellow and chief architect for Azure Storage and Cloud Edge at Microsoft. Thanks so much for coming, for returning to theCUBE, I should say, Jeffrey, you're a CUBE alum. >> Yes, I enjoyed the last time. So can't wait to do it again this time. >> Well we're excited to have you. So before the camera's were rolling, we were talking about PowerShell. You invented PowerShell. >> Yeah, I did. >> It was invented in the early 2000's, it took a few years to ship, as you said. But can you give our viewers an update of where we are? >> Yeah, you know, it's 2018, and it's never been a better time for PowerShell. You know, basically the initial mission is sort of complete. And the mission was provide sort of general purpose scripting for Windows. But now we have a new mission. And that new mission is to manage anything, anywhere. So we've taken PowerShell, we've open sourced it. It's now running, we've ported it to macOS and Linux. There's a very large list of Linux distributions that we support it on, and it runs everywhere. And so, now, you can manage from anywhere. Your Windows box, your Linux box, your Mac box, even in the browser, you can manage, and then anything. You can manage Windows, you can manage Linux, you can manage macOS. So manage anything, anywhere. Any cloud, Azure, or AWS, or Google. Any hypervisor, Hyper-V or VMware, or any physical server. It's amazing. In fact, our launch partners, when we launched this, our launch partners, VMware, Google, AWS. Not Microsoft's traditional partners. >> That's great to hear. It was actually, one of the critiques we had, at the key note this morning, was partnerships are critically important. But felt that Satya gave a little bit of a jab towards, the kind of, the Amazon's out there. When we talk to customers, we know it's a heterogeneous, multi-cloud world. You know, you work all over the place, with your solutions that you had. There's not, like, Azure, Azure Stack, out to The Edge. The Edge, it is early, it's going to be very heterogeneous. So connect the dots for us a little. You know, we love having the technical fellows on, as to, you go from PowerShell, to now this diverse set of solutions that you work on today. >> Yeah, exactly. So basically, from PowerShell, they asked me to be the chief architect for Windows Server. Right, because if you think about it, an operating system is largely management, right? And, so, that's what I did, resource management. And, so, I was the chief architect for that, for many years, and we decided that, as part of that, we were developing cloud-inspired infrastructure. So, basically, you know, Windows Server had grown up. You know, sort of focused in on a machine. Azure had gone and needed to build a new set of infrastructure for the cloud. And we looked at what they were doing. And they say, hey, that's some great ideas. Let's take the ideas there, and put them into the general purpose operating system. And that's what we call our software-defined data center. And the reason why we couldn't use Azure's directly is, Azure's, really, design center is very, very, very large systems. So, for instance, the storage stamp, that starts at about 10 racks. No customer wants to start with 10 racks. So we took the inspiration from them and re-implemented it. And now our systems can start with two servers. Our Azure Stack systems, well, so, then, what we decided was, hey, this is great technology. Let's take the great cloud-inspired infrastructure of Windows Server, and match it with the Azure services themselves. So we take Azure, put it on top of Windows Server, package it as an appliance experience, and we call that Azure Stack. And that's where I have been mostly focused for the last couple of years. >> Right, can you help us unpack a little bit. There's a lot of news today. >> Yes. >> You know, Windows 2019 was announced. I was real interested in the Data Box Edge solution, which I'm sure. >> Isn't that crazy? >> Yeah, really interesting. You're like, let's do some AI applications out at the Edge, and with the same kind of box that we can transport data. Because, I always say, you got to follow customers applications and data, and it's tough to move these things. You know, we've got physics that we still have to, you know, work on until some of these smart guys figure out how to break that. But, yeah, maybe give us a little context, as to news of the show, things your teams have been working on. >> Yeah, so the Data Box Edge, big, exciting stuff. Now, there's a couple scenarios for Data Box Edge. First is, first it's all kind of largely centered on storage and the Edge. So Storage, you've got a bunch of data in your enterprise, and you'd like it to be in Azure. One flavor of Data Box Edge is a disk. You call us up, we send you a disk, you fill up that disk, you send it back to us, it shows up in Azure. Next. >> A pretty big disk, though? >> Well, it can be a small disk. >> Oh, okay. >> Yeah, no, it can be a single SSD, okay. But then you can say, well, no, I need a bunch more. And so we send you a box, the box is over there. It's like 47 pounds, we send you this thing, it's about 100 terabytes of data. You fill that thing up, send it to us, and we upload it. Or a Data Box Heavy. Now this thing has a handle and wheels. I mean, literally, wheels, it's specially designed so that a forklift can pick this thing up, right? It's like, I don't know, like 400 pounds, it's crazy. And that's got about a petabyte worth of storage. Again, we ship it to you, you fill it up, ship it back to us. So that's one flavor, Data Box transport. Then there's Data Box Edge. Data Box Edge, you go to the website, say, I'd like a Data Box Edge, we send you a 1u server. You plug that in, you keep it plugged in, then you use it. How do you use it? You connect it to your Azure storage, and then all your Azure storage is available through here. And it's exposed through SMB. Later, we'll expose it through NFS and a Blob API. But, then, anything you write here is available immediately, it gets back to Azure, and, effectively, it looks like near-infinite storage. Just use it and it gets backed up, so it's amazing. Now, on that box, we're also adding the ability to say, hey, we got a bunch of compute there. You can run IoT Edge platforms. So you run the IoT Edge platform, you can run gateways, you can run Kubernetes clusters on this thing, you can run all sorts of IoT software. Including, we're integrating in brainwave technology. So, brainwave technology is, and, by the way, we'll want to talk about this a little bit, in a second. It is evidence of the largest transformation we'll see in our industry. And that is the re-integration of the industry. So, basically, what does that mean? In the past, the industry used to be, back when the big key players were digital. Remember digital, from DEC? We're all Massachusetts people. (Rebecca laughs) So, DEC was the number one employer in Massachusetts, gone. IBM dominant, much diminished, a whole bunch of people. They were dominant when the industry was vertically integrated. Vertically integrated meant all those companies designed their own silicone, they built their own boards, they built their own systems, they built their OS, they built the applications, the serviced them. Then there was the disintegration of the computer industry. Where, basically, we went vertically integrated. You got your chips from Intel or Motorola. The operating system, you got from Sun or Microsoft. The applications you got from a number of different vendors. Okay, so we got vertically integrated. What you're seeing, and what's so exciting, is a shift back to vertical integration. So Microsoft is designing its own hardware, right? We're designing our own chips. So we've designed a chip specially for AI, we call it a brainwave chip, and that's available in the Data Box Edge. So, now, when you do this AI stuff, guess what? The processing is very different. And it can be very, very fast. So that's just one example of Microsoft's innovation in hardware. >> Wow, so, I mean. >> What do you do with that? >> One of the things that we keep hearing so much, at this conference, is that Microsoft products and services are helping individual employees tap into their own creativity, their ingenuity, and then, also, collaborate with colleagues. I'm curious about where you get your ideas, and how you actually put that into practice, as a technical fellow. >> Yeah. >> How do you think about the future, and envision these next generation technologies? >> Yeah, well, you know, it's one of those things, honestly, where your strength is your weakness, your weakness is your strength. So my weakness is, I can't deal with complexity, right. And, so, what I'm always doing is I'm taking a look at a very complex situation, and I'm saying, what's the heart of it, like, give me the heart of it. So my background's physics, right? And so, in physics, you're not doing, you're looking for the F equals M A. And if you have that, when you find that, then you can apply it over, and over, and over again. So I'm always looking at what are the essential things here. And so that's this, well, you see a whole bunch of confusing things, like, what's up with this? What's with this? That idea of there is this narrative about the reintegration of the computer industry. How very large vendors, be it Microsoft, or AWS, are, because we operate at such large scales, we are going to be vertically integrated. We're developing our own hardware, we do our own systems, et cetera. So, I'm always looking for the simple story, and then applying it. And, it turns out, I do it pretty accurately. And it turns out, it's pretty valuable. >> Alright, so that's a good set up to talk about Azure Stacks. So, the value proposition we heard, of course, is, you know, start everything in the cloud first, you know, Microsoft does Azure, and then lets, you know, have some of those services in the same operating model in your data center, or in your hosting service provider environment. So, first of all, did I get that right? And, you know, give us the update on Azure Stack. I've been trying to talk to customers that are using it, talking to your partners. There is a lot of excitement around it. But, you know, proof points, early use cases, you know, where is this going to be pointing towards, where the future of the data center is? >> So, it's a great example. So what I figured out, when I thought about this, and kind of drilled in, like what's really, what really matters here? What I realized was that what the gestalt of Azure Stack is different than everything we've done in the past. And it really is an appliance, okay? So, in the past, I just had a session the other day, and people were asking, well, when are you going to, when is Azure Stack going to have the latest version of the operating system? I said, no, no, no, no, no. Internals are internal, it's an appliance. Azure Stack is for people who want to use a cloud, not for people who want to build it. So you shouldn't be concerned about all the internals. You just plug it in, fill out some forms, and then you use it, just start using it. You don't care about the details of how it's all configured, you don't do the provisioning, we do all that for you. And so that's what we've done. And it turns out that that message resonates really well. Because, as you probably know, most private clouds fail. Most private clouds fail miserably. Why? And there's really two reasons. There's two flavors of failure. But one is they just never work. Now that's because, guess what, it's incredibly hard. There are so many moving pieces and, guess what, we learned that ourselves. The numbers of times we stepped on the rakes, and, like, how do you make all this work? There's a gazillion moving parts. So if any of your, you have a team, that's failed at private cloud, they're not idiots. It's super, super, super hard. So that's one level of failure. But even those teams that got it working, they ultimately failed, as well, because of lack of usage. And the reason for that is, having done all that, they then built a snowflake cloud. And then when someone said, well, how do I use this? How do I add another NIC to a VM? The team that put it together were the only ones that could answer that. Nope, there was no ecosystem around it. So, with Azure Stack, the gestalt is, like, this is for people who want to use it, not for people who want to build it. So you just plug it in, you pick a vendor, and you pick a capacity. This vendor, four notes, this vendor 12 or 16 notes. And that's it. You come in, we ask you what IP range is, how do I integrate with your identity? Within a day, it's up and running, and your users are using it, really using it. Like, that's craziness. And then, well what does it mean to use it? Like, oh, hey, how do I ad a NIC to a VM? It's Azure, so how does Azure do it? I have an entire Azure ecosystem. There's documentation, there's training, there's videos, there's conferences. You can go and put on a resume, I'd like to hire someone with Azure skills, and get someone, and then they're productive that day. Or, and here's the best part, you can put on your resume, I have Azure skills, and you knock on 10 doors, and nine of them are going to say, come talk to me. So, that was the heart of it. And, again, it goes back to your question of, like, the value, or what does a technical fellow do. It's to figure out what really matters. And then say, we're all in on that. There was a lot of skepticism, a lot of customers like, I must have my security agent on there. It's like, well, no, then you're not a good candidate. What do you mean? I say, well, look, we're not going to do this. And they say, well you'll never be able to sell to anyone in my industry. I said, no, you're wrong. They say, what do you mean, I'm wrong? I say, well, let me prove it to ya, do you own a SAN? They say, well, of course we own a SAN. I said, I know you own a SAN. Let me ask you this, a SAN is a general purpose server with a general purpose operating system. So do you put your security and managing agents on there? And they said, no, we're not allowed to. I said, right, and that's the way Azure Stack is. It's a sealed appliance. We take care of that responsibility for you. And it's worked out very, very well. >> Alright, you got me thinking. One of the things we want to do is, we want to simplify the environment. That's been the problem we've had in IT, for a long time, is it's this heterogeneous mess. Every group did their own thing. I worry a multi-cloud world has gotten us into more silos. Because, I've got lots of SAS providers, I've got multiple cloud providers, and, boy, maybe when I get to the Edge, every customer is going to have multiple Edge applications, and they're going to be different, so, you know. How do you simplify this, over time, for customers? Or do we? >> Here's the hard story, back to getting at the heart of it. Look, one of the benefits of having done this a while, is I've stepped on a lot of these rakes. You're looking at one of the biggest, earliest adopters of the Boolean cross-platform, Gooey Framework. And, every time, there is this, oh, there's multiple platforms? People say, oh, that's a problem, I want a technology that allows me to bridge all of those things. And it sound so attractive, and generates a lot of early things, and then it turned out, I was rocking with this Boolean cross-breed platform. I wrote it, and it worked on Mac's and Windows. Except, I couldn't cut and paste. I couldn't print, I couldn't do anything. And so what happens is it's so attractive, blah, blah, blah. And then you find out, and when the platforms aren't very sophisticated, the gap between what these cross-platform things do, and the platform is not so much, so it's like, eh, it's better to do this. But, over time, the platform just grows and grows and grows. So the hard message is, people should pick. People should pick. Now, one of the benefits of Azure, as a great choice, is that, with the other guys, you are locked to vendor. Right, there is exactly one provider of those API's. With Azure, you can get an implementation of Azure from Microsoft, the Azure Public Cloud. Or you can get an implementation from one of our hardware vendors, running Azure Stack. They provide that to you. Or you can get it from a service provider. So, you don't have to get, you buy into these API's. You optimize around that, but then you can still use vendor. You know, hey, what's your price for this? What's your price for that, what can you give me? With the other guys, they're going to give you whatcha give ya, and that's your deal. (Rebecca laughs) >> That's a good note to end on. Thank you so much, Jeffrey, for coming on theCUBE again. It was great talking to you. >> Oh, that was fast. (Rebecca laughs) Enjoyed it, this was great. >> Great. I'm Rebecca Knight, for Stu Miniman, stay tuned to theCUBE. We will have more from Microsoft Ignite in just a little bit. (electronic music)

(regal music) >> Hi, I'm Peter Burris And welcome to another Cube conversation from our beautiful studios here in Palo Alto, California. Got another great guest today, Jagane Sundar is the CTO of WANdisco Jagane, welcome back to the Cube. >> Good morning, Peter. >> So, Jagane, I want to talk about something that I want to talk about. And I want you to help explicate for our clients what this actually means. So there's two topics that I want to discuss. We've done extensive research in both of them, and one is this notion that we call plastic infrastructure. And the other one, related, is something we call networks of data. Let's start with networks of data because I think that that's perhaps foundational for plastic infrastructure. If we look back at the history of computing, we've seen increasing decentralization of data. Yet today many people talk about data gravity and how the Cloud is going to bring all data into the Cloud. Our belief, however, is that there's a relationship between where data is located and the actions that have to be taken. And data locality has a technical reality to it. We think we're going to see more distribution of data, but in a way that nonetheless allows us to federate. To bring that data into structures that nonetheless can ensure that the data is valuable wherever it needs to be. When you think of the notion of networks of data, what does that make you think about? >> That's a very interesting concept, Peter. When you consider the Cloud, and you talk about S3 for example and buckets of objects, people automatically assume that it's a global storage system for objects. But if you scratch a little deeper under the surface you'll find that each bucket is located in one region. If you want it available in other regions you've got to set up something called cross-region replication, which replicates in an eventual consistent fashion. It may or may not get there in time. So even in the Cloud storage systems, there is a notion of locality of data. It's something that you have to pay attention to. Now you hit the nail on the head when you said networks of data. What does that mean? Where does the data go? How it is used. Our own platform, the Fusion platform for replication of data is a strongly consistent platform which helps you conform to legal requirements and locality of data and many such things. It was built with such a thing in mind. Of course, we didn't quite call it that way, but I like your way of describing it. >> So as we think then about where this is, the idea is, 40 years ago, ARPANET allowed us to create networks of devices in a relatively open application-oriented way. And the web allowed us to create networks of pages of content. But again, that content was highly stylized. More recently social media's allowed us great networks of identities. All very important stuff. Now as we start talking about digital business and the fact that we want to be able to rearrange our data assets very quickly in response to new business opportunities whether it's customer experience or operational-oriented, this notion of networks of data allows us to think about the approach to doing that, so that we can have the data being in service to existing business opportunities, new business opportunities, and even available for future activities. So we're talking about creating networks out of these data sources, but as you said, to do that properly, we need to worry about consistency, we need to worry about cost. The platform for doing this, Fusion is a good one, it's going to require over time, however we think, some additional types of capabilities. The ability to understand patterns of data usage, the ability to stage data in advance and predictably, et cetera. Where do you think this goes as we start conceiving of networks of data as a fundamental value proposition for technology and business? >> Sure, one of the first things that occurs to me when you talk about a network of data, if you consider that as parallel to a network of computers, you don't have a notion of things like read-only computers whereas read-write computers. That's just silly. You want all computers to be roughly equal in the world. If you have a network of servers, and a network of computers, any of them can read. Any of them can write, and any of them can store. Now our Fusion platform brings about that capability to your definition of a network of data. What we call live data is the ability for you to store replicas of the data in different data centers around the world with the ability to write to any of those locations. If one of the locations happens to go down, it's a non-event. You can continue writing and reading from the other locations. That truly makes the first step towards building this network of data that you're talking about feasible. >> But I want to build on that notion a little bit because we are seeing increased specialization for example, AI, or GPUs. >> Sure. >> AI-specific processors, so even though we are still looking forward to general purpose nonetheless we see some degree of specialization. But let me also take that notion of live data and say I expect that we're going to see something similar. So for example, the same data set can be applied to multiple different classes of applications where each application may take advantage of underlying hardware advantages. But you don't have a restriction on how you deploy it built into the data. Have I got that right? >> Absolutely. Our Fusion platform includes the capability to replicate across Cloud vendors. You can replicate your storage between Amazon S3 and Azure Blob store. Now this is interesting because suddenly, you may discover that Redshift is great for certain applications while Azure SQLDW is better for others. We give you the freedom to invent new applications based on what location is best suited for that purpose. You've taken this concept of network of data, you've applied a consistent replication platform, now you have the ability to build applications in different worlds, in completely different worlds. And that's very interesting to us because if we look at data as the primary asset of any company, consider a company like Netflix, their data and the way they manage their data is the most important thing to that company. We bring the capability to distribute that data across different Cloud vendors, different storage systems, and run different applications. Perhaps you have a GPU heavy Cloud that maybe a GPU vendor offers. Replicate your data into that Cloud, and run your AI applications against that particular replica. We give you truly the freedom to invent new applications for your purpose. >> But very importantly, you are also providing, and I think this is essential, a certainty that there's consistency no matter how you do it. And I think that's the basis of the whole, the Paxos algorithms you guys are using. >> Exactly. The fundamental fact is this. Data scientists hate to deal with outdated data. Because all the work they're doing may be for no use if the data that they're applying it to is outdated, invalid, or partially consistent. We give you guarantees that the data is constantly updated, live data, it's completely consistent. If you ask the same question of two replicas of your data, you will get exactly the same answer. There is no other product in the industry today that can offer that guarantee. And that's important for our customers. >> Now building on the foundation, we're going to have to add some additional things to it. So pattern recognition, ML inside the tool. Is that on the drawing board? And I don't want you to go too far in the future, but is that kind of the future that you see too? >> We are a platform company with an excellent plug-in API. And one of the uses of our plug-in API, I'll give you a simple example, we have banking customers and they need to prevent credit card numbers from flying over the wire under certain circumstances. Our plug-in API enables them to do that. Applying an ML intelligence program into the plug-in API, again, a very simple development effort to do that. We are facilitating such capabilities. We expect third-party developers. We already have a host of third-party developers and companies building to our plug-in API. We expect that to be the vehicle for this. We won't claim expertise in ML, but there are plenty of companies that will do that on our platform. >> All right, so that leads to the second set of questions that I wanted to ask you about. We've defined what we call plastic infrastructure as a future for the industry. And to make sense of that, what we've done is we've said let's take a look at three phases of infrastructure, not based on the nature of the hardware, but based on the fundamental capabilities of the infrastructure. Static infrastructure is when we took an application, we wired it to a particular class of infrastructure. New load hits it, often you broke the infrastructure. Elastic infrastructure is the ability to be able to take a set of workloads and have it vary up and down, so that you can consume more and release the infrastructure so it has a kind of a rubber orientation. You hit it with a new load, it will deform for as long as you need it to, then it snaps back into shape. So you've predictability about what your costs are. We think that increasingly digital business is going to have to think about plastic infrastructure. The ability to very rapidly have the infrastructure deform in response to new loads, but persist that new shape, that new structure in response to how the load has impacted the business if in fact that is a source of value for the business. >> Sure. >> What do you think about that notion of plastic infrastructure? >> I love the way you describe it. In our own internal terminology we have this notion of live data and freedom to invent. What you've described is exactly that. The plastic infrastructure matches exactly with our notion of freedom to invent. Once you've solved the problem of making your data consistently available in different Clouds, different regions, different data centers, the next step of course is the freedom to invent new applications. You're going to throw experimental things at it. You're going to find that there are specific business intelligence that you can draw from this by virtue of a new application. Use it to make some critical decisions, improve profitability perhaps. That results in what you describe as plastic infrastructure. I really love that description by the way. Because we've gone from, the Cloud brought us plastic infrastructure, we've replicated, we've built a system that enables innovation and invention of new ideas. That's plastic infrastructure. I really like the idea that you're proposing. >> So as you think about this concept of plastic infrastructure, obviously there's a lot of changes that're going to take place in the industry. But Fusion in particular, by providing consistency, by increasing the availability, more importantly even the delivery of data where it's required facilitates at that data level, that notion of plasticity. >> Absolutely. The notion that you can throw brand new applications at it in a Cloud vendor of your choice, the fact that we can replicate across different Clouds is important for plastic infrastructure. Perhaps there are certain applications that work better in one Cloud versus the other. You definitely want to try it out that. And if that results in some real valuable applications, continue running it. So your definition that elastic becomes plastic infrastructure matches perfectly with that. We love this notion that we take the CIO's problems of mundane data management away and introduce the capability to invent and innovate in their space. >> So let me give you a very, or let me ask you a very practical, simple question. Historically, the back-up and restore people, and the application development people didn't spend a lot of time with each other, and that has created some tension. Are we now because of our ability to do this live data, are we able to bring those two worlds more closely together so that developers can now think about building increasingly complex, increasingly rich applications? And at the same time ensure that the data that they're building and testing with is in fact very close to the live data that they're actually going to use. >> Absolutely. We do bridge that gap. We enabled application developers to think of more complex, more sophisticated applications without actually worrying about the availability or the consistency of data. And the IT administrators and the CIO run operations that need to deliver that, have the confidence that they can in fact deliver it with the levels of consistency and availability that they need. >> So I'm going to give you the last word in this. I talked about a fair amount now, about this notion of networks of data, and infrastructure plasticity, where do you think this kind of matures over the course of the next four or five years? And what's your peer CTOs of large businesses that are thinking about these challenges of data management be focusing on? >> So the first thing that you have to acknowledge is that people need to stop thinking about machines and servers, and consider this as infrastructure that they acquire from different Cloud vendors. Different Cloud vendors because in fact there is going to be a few, a handful of good Cloud vendors that'll give you different capabilities. Once you get to that conclusion, you need your data available in all of these different Cloud vendors perhaps on your on-prem location as well, with strong consistency. Our platform enables you to do that. Once you get to that point, you have the freedom to build new applications, build business-critical systems that can depend on the consistency and availability of data. That is your definition of plasticity and networks of data. I truly like that. >> Yeah, and so we, great, great summary. We would say that we would agree with you, that increasing with the CIO, or the CDO, whoever it's going to be, has to focus on how do I increase returns on my business's data, and to do that they need to start thinking differently about their data, about their data assets, both now and in the future. Very, very important stuff. Jagane, thank you very much for being on the Cube. >> Thank you, Peter. >> And once again, I'm Peter Burris, and this has been a Cube conversation with Jagane Sundar, CTO of WANdisco. Thanks again. (regal music)

>> Voiceover: Live from the Moscone Center, it's theCUBE. Covering AWS Summit San Francisco 2018. Brought to you by Amazon Web Services. >> Welcome back, I'm Stu Miniman and this is theCUBE's exclusive coverage of AWS Summit here in San Francisco. Happy to welcome back to the program Jagane Sundar, who is the CTO of WANdisco. Jagane, great to see you, how have you been? >> Well, been great Stu, thanks for having me. >> All right so, every show we go to now, data really is at the center of it, you know. I'm an infrastructure guy, you know, data is so much of the discussion here, here in the cloud in the keynotes, they were talking about it. IOT of course, data is so much involved in it. We've watched WANdisco from the days that we were talking about big data. Now it's you know, there's AI, there's ML. Data's involved, but tell us what is WANdisco's position in the marketplace today, and the updated role on data? >> So, we have this notion, this brand new industry segment called live data. Now this is more than just itty-bitty data or big data, in fact this is cloud-scale data located in multiple regions around the world and changing all the time. So you have East Coast data centers with data, West Coast data centers with data, European data centers with data, all of this is changing at the same time. Yet, your need for analytics and business intelligence based on that is across the board. You want your analytics to be consistent with the data from all these locations. That, in a sense, is the live data problem. >> Okay, I think I understand it but, you know, we're not talking about like, in the storage world there was like hot data, what's hot and cold data. And we talked about real-time data for streaming data and everything like that. But how do you compare and contrast, you know, you said global in scope, talked about multi-region, really talking distributed. From an architectural standpoint, what's enabling that to be kind of the discussion today? Is it the likes of Amazon and their global reach? And where does WANdisco fit into the picture? >> So Amazon's clearly a factor in this. The fact that you can start up a virtual machine in any part of the world in a matter of minutes and have data accessible to that VM in an instant changes the business of globally accessible data. You're not simply talking about a primary data center and a disaster recovery data center anymore. You have multiple data centers, the data's changing in all those places, and you want analytics on all of the data, not part of the data, not on the primary data center, how do you accomplish that, that's the challenge. >> Yeah, so drill into it a little bit for us. Is this a replication technology? Is this just a service that I can spin up? When you say live, can I turn it off? How do those kind of, when I think about all the cloud dynamics and levers? >> So it is indeed based on active-active replication, using a mathematically strong algorithm called Paxos. In a minute, I'll contrast that with other replication technologies, but the essence of this is that by using this replication technology as a service, so if you are going up to Amazon's web services and you're purchasing some analytics engine, be it Hive or Redshift or any analytics engine, and you want to have that be accessible from multiple data centers, be available in the face of data center or entire region failure, and the data should be accessible, then you go with our live data platform. >> Yeah so, we want you to compare and contrast. What I think about, you know, I hear active-active, speed of light's always a challenge. You know globally, you have inconsistency it's challenging, there's things like Google Spanner out there to look at those. You know, how does this fit compared to the way we've thought of things like replication and globally distributed systems in the past? >> Interesting question. So, ours great for analytics applications, but something like Google Spanner is more like a MySQL database replacement that runs into multiple data centers. We don't cater to that and database-transaction type of applications. We cater to analytics applications of batch, very fast streaming applications, enterprise data warehouse-type analytics applications, for all of those. Now if you take a look inside and see what kind of replication technology will be used, you'll find that we're better than the other two different types. There are two different types of existing replication technologies. One is log shipping. The traditional Oracle, GoldenGate-type, ship the log, once the change is made to the primary. The second is, take a snapshot and copy differences between snapshots. Both have their deficiencies. Snapshot of course is time-based, and it happens once in a while. You'll be lucky if you can get one day RTO with those sorts of things. Also, there's an interesting anecdote that comes to mind when I say that because the Hadoop folks in their HTFS, implemented a version of snapshot and snapdiff. The unfortunate truth is that it was engineered such that, if you have a lot of changes happening, the snapshot and snapdiff code might consume too much memory and bring down your NameNode. That's undesirable, now your backup facility just brought down your main data capability. So snapshot has its deficiencies. Log shipping is always active/passive. Contrast that with our technology of live data, whereat you can have multiple data centers filled with data. You can write your data to any of these data centers. It makes for a much more capable system. >> Okay, can you explain, how does this fit with AWS and can it live in multi-clouds, what about on-premises, the whole you know, multi and hybrid cloud discussion? >> Interesting, so the answer is yes. It can live in multiple regions within the same cloud, multiple reasons within different clouds. It'll also bridge data that exists on your on-prem, Hadoop or other big data systems, or object store systems within Cloud, S3 or Azure, or any of the BLOB stores available in the cloud. And when I say this, I mean in a live data fashion. That means you can write to your on-prem storage, you can also write to your cloud buckets at the same time. We'll keep it consistent and replicated. >> Yeah, what are you hearing from customers when it comes to where their data lives? I know last time I interviewed David Richards, your CEO, he said the data lakes really used to be on premises, now there's a massive shift moving to the public clouds. Is that continuing, what's kind of the breakdown, what are you hearing from customers? >> So I cannot name a single customer of ours who is not thinking about the cloud. Every one of them has a presence on premise. They're looking to grow in the cloud. On-prem does not appear to be on a growth path for them. They're looking at growing in the cloud, they're looking at bursting into the cloud, and they're almost all looking at multi-cloud as well. That's been our experience. >> At the beginning of the conversation we talked about data. How are customers doing you know, exploiting and leveraging or making sure that they aren't having data become a liability for them? >> So there are so many interesting use cases I'd love to talk about, but the one that jumps out at me is a major auto manufacturer. Telematics data coming in from a huge number, hundreds of thousands, of cars on the road. They chose to use our technology because they can feed their West Coast car telematics into their West Coast data center, while simultaneously writing East Coast car data into the East Coast data center. We do the replication, we build the live data platform for them, they run their standard analytics applications, be it Hadoop-sourced or some other analytics applications, they get consistent answers. Whether you run the analytics application on the East Coast or the West Coast, you will get the same exact answer. That is very valuable because if you are doing things like fault detection, you really don't want spurious detection because the data on the West Coast was not quite consistent and your analytics application was led astray. That's a great example. We also have another example with a top three bank that has a regulatory concern where they need to operate out of their backup data centers, so-called backup data center, once every three months or so. Now with live data, there is no notion of active data center and backup data center. All data centers are active, so this particular regulatory requirement is extremely simple for them to implement. They just run their queries on one of the other data centers and prove to the regulators that their data is indeed live. I could go on and on about a number of these. We also have a top two retailer who has got such a volume data that they cannot manage it in one Hadoop cluster. They use our technology to create the live data data link. >> One of the challenges always, customers love the idea of global but governance, compliance, things like GDPR pop up. Does that play into your world? Or is that a bit outside of what WANdisco sees? >> It actually turns out to be an important consideration for us because if you think about it, when we replicate the data flows through us. So we can be very careful about not replicating data that is not supposed to be replicated. We can also be very careful about making sure that the data is available in multiple regions within the same country if that is the requirement. So GDPR does play a big role in the reason why many of our customers, particularly in the financial industry, end up purchasing our software. >> Okay, so this new term live data, are there any other partners of yours that are involved in this? As always, you want like a bit of an ecosystem to help build out a wave. >> So our most important partners are the cloud vendors. And they're multi-region by nature. There is no idea of a single data center or a single region cloud, so Microsoft, Amazon with AWS, these are all important partners of ours, and they're promoting our live data platform as part of their strategy of building huge hybrid data lakes. >> All right, Jagane give us a little view looking forward. What should we expect to see with live data and WANdisco through the rest of 2018? >> Looking forward, we expect to see our footprint grow in terms with dealing with a variety of applications, all the way from batch, pig scripts that used to run once a day to hive that's maybe once every 15 minutes to data warehouses that are almost instant and queryable by human beings, to streaming data that pours things into Kafka. We see the whole footprint of analytics databases growing. We see cross-capability meaning perhaps an Amazon Redshift to an Azure or SQL EDW replication. Those things are very interesting to us, to our customers, because some of them have strengths in certain areas and other have strengths in other areas. Customers want to exploit both of those. So we see us as being the glue for all world-scale analytics applications. >> All right well, Jagane, I appreciate you sharing with us everything that's happening at WANdisco. This new idea of live data, we look forward to catching up with you and the team in the future and hearing more about the customers and everything on there. We'll be back with lots more coverage here from AWS Summit here in San Francisco. I'm Stu Miniman, you're watching theCUBE. (electronic music)