>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. >> Hi, I'm Stu Miniman, and welcome to theCUBE's Boston-area studio. Happy to welcome back to the program two CUBE alums. To my immediate right is Peter Smails, who's the CMO of Imanis Data, and joining him for the segment is Zeus Kerravala, who is founder and Principal at ZK Research. Gentlemen, thanks so much for joining us. >> Thank you. >> Thanks for having me. >> All right, so, we go out to so many shows, we're talking about massive change in the industry. Last two shows I've gone to, really looking at how hybrid and multi-cloud are shaping up, and change, and just the proliferation of options really seems to define what's happening in our industry. And Zeus, want to start with you because you've got some good research which looks at the data side of it. And of course, I'm an infrastructure guy, >> Yeah. >> but the reason we have infrastructure is to run my apps. And the only reason we have apps, really, is behind the data. And that transformation of data, and data at the core of everything, is something that we've loved to cover the last few years. So, what's new on your world? >> Yeah I, in fact, the word you said there, change, is apropos. Because I think I have never seen a time in IT, and I've been an analyst for 20 years and I was a CIO for a while, but I've never seen a period of change like this before. Where digital transformation is reshaping companies as fast as possible. Now, the key to being a successful digital organization is being able to take advantage the massive amounts of data that you have, and then be able to use some machine learning, or other analytic capabilities, to find those nuggets in there to be able to help you change your business process, make people more productive, improve customer service, whatever you're trying to do. I think it really stems from the analytics, that data. Now, what my research has found is that companies are really, and this shouldn't be a big surprise, but companies are really only using a very small slice of their data. Maybe five to 10% at the most in their data. Most data's kept in what's called secondary storage, and there what's happening is this concept called mass data fragmentation. Where we've always had data fragmentation, but it's becoming worse. Where data's now being stored, not only on local computers and servers, but also in the cloud, on IoT devices, out at the edge, within your organization. And so, this concept of mass data fragmentation has exploded. And it's hampering companies' ability to actually make critical decisions to be able to move fast and keep up with a lot of the cloud-native counterparts. And if they don't get a handle on this, they're going to wind falling further and further behind. I think it's absolutely critical today that this challenge of mass data fragmentation be solved. >> Yeah, Peter, want to pull you into this discussion. You talked to a lot of users, and we've talked to you at some of the Hadoop Shows. We look at what's happening in like the database world and there's so many options. >> Yeah. >> I know our team members that keep up to it, they keep spreadsheets. and they're trying to keep up with all of these, but seems like every week there's a new open-source this and that, >> Right, right. >> that's going to capture this segment of the market. But something that I found interesting from one of the previous interviews we'd done with you and your company is it's not that I took my main vendor of choice and I went to one other. It's that today, the database world is like everything else, I'm using a lot. >> Yeah, yeah. >> And it is, and, and therefore, we know that has ripple effects for what I do for security and what I do for things like data protection. Can you give us a little bit of, just kind of a view as to what customers, you know, why are they going to so many applications? What are some of the leading >> Sure. ones in the space? And we know that in IT nothing ever dies, >> and it's, >> Right. >> it tends to be additive. So, how are they dealing with this? >> Yeah, and it picks up directly on what Zeus was just saying before around this notion of fragmentation. So, Imanis Data, the genesis of Imanis Data was really around, if you look at it in the context of cloud, Could 1.0 was, it was essentially, let me take all my legacy applications, lift and shift. Right, let's just take everything on on-prem and let's put it in the cloud. People quickly realized that they were solving the wrong problem. The real answer to the problem was if I want to take advantage of all my data, if I want to take advantage of hybrid-cloud infrastructure. I've got to move from a traditional monolithic stack, application stack, to more of a microservices-based architecture. That led to a very rapid proliferation of new database platforms, both on the Hadoop side for big data, as well as the on the NoSQL side. So, the synergy here in why we like this research so much is because Hadoop, the key message is that Hadoop and NoSQL have both become significant contributors to the mass data fragmentation challenge. And that's really driven, ultimately, by digital transformation and organizations' desire to move to a true hybrid-cloud-based infrastructure. >> How does cloud and this data fragmentation, how does this all go together? >> Oh, our cloud and data fragmentation actually go hand in hand. People thought the cloud was actually solving a lot of their problems, but in a lot of ways it contributed to it, because, as you said, we never get rid of the old. We keep the old around and we add to it. In fact, what I've seen happens is with so many cloud repositories now, users are storing data in the place they were before and then making copies of it in these new cloud services. And in fact, almost all of the new app collaborative applications have their own cloud repositories. So, we've gone from an environment where we had a handful of storage repositories to manage to that absolutely exploding. And I think the cloud itself has matured. I think people are now starting to figure out how to really, to your point, use the cloud in a much different way than before. And so, they're reliant on it. The companies are dependent on it, but if we don't get a handle on where our data is we're going to wind up in a situation where it just becomes unmanageable. >> Yeah, and just to add to that, from additional researches, that according to recent research, 38% of interviewed companies had more than 25 databases. 20% of those same companies had over 100 databases. So, the point is there is a huge fragmentation issue. And if the problem you're trying to solve, ultimately, is insight to your data and intelligence on your business, you've got to create, you've got to solve this problem of fragmentation, because otherwise, you're never going to have any economies of scale. You're never going to be able to give visibility to all your data. That's ultimately the problem that needs to be solved. >> Yeah, it's funny, 'cause you talked about early cloud, and people thought oh, right, I'm going to move everything there and I'll have one cloud, it'll be the cloud. >> The cloud. >> Ah yeah, things like that. And of course, we understand, there's lots of reasons why I'm going to choose multiple solutions. But, too many companies I talk to, when you figure out how they got there. It wasn't like they said, well this is our strategy and we're going to do this, and this, and this. It was, well, different business units have different reasons. Just like I would build infrastructure for my various applications, I would have different groups with different needs. And then, hey IT, can you help us bring all these pieces together? So, how are we doing as an industry for helping customers get their arms around this? Is this just a mess today? Is there a wave, or a trend, as to how we put together, right? Who solves it from a vendor standpoint, and who, from the customer standpoint, kind of has the, is the champion of helping to solve this issue? >> Yeah, I think one of anything is unrealistic, right? And in fact, customers do want choice and they do want options. So, it's not the industry's job to force customers consolidate to one. In fact, it's better to let them use whatever they want. Now, where it becomes, where the work needs to be done now is creating that middleware layer, if you will, or that management layer, that sits above the infrastructure, that gives you the common view. So, I think this mythical single pane of glass we've been searching for for so long, actually, the cloud drives us in that direction, because we do need something to help us give that visibility. I know one of your partners, Cohesity, does that on the secondary storage side to actually make MDF, or mass data fragmentation, manageable. And there's other vendors that do that in other areas, but I think the concept here isn't to try and drive customers into selective choices, but it's to allow them to use whatever they want and then create a management layer over top that gives them that visibility to it looks like one environment. But in fact, it's whatever they want to use underneath. >> Yeah, and picking up on that, the notion of, if you look at the, you asked the question about, sort of, who owns the mantle of driving all this stuff together? And the answer isn't, you could say, oh, the chief data officer. Certain organizations have gone to the level of saying we have a chief data officer and they're trying to drive towards a consolidated strategy. That's a great idea, but, sort of the federation of how things have evolved is actually, is been a good model. Like, a lot of the folks that, from an Imanis Data standpoint, that we speak to, it's architects, it's developers, it's DevOps. And so, from an organizational standpoint, what's happening is you've got to have, over time, you've got to have the application folks, the DevOps folks, the architects, the DBAs, get more closely aligned with your traditional IT and infrastructure folks. That's evolving. And to Zeus' point there, that's not, you're not going to drive them all to one thing, because they have different viewpoints and such, but you need to provide that common layer. Sort of let them do their own thing, but then on the backend be able to sort of provide that common layer to be able to eliminate the backend silos. >> Okay, and drill us down a little bit. We brought up then that the notion of management being able to see across these environments as a piece of the solution, but what is Imanis doing? What are you seeing out there? And, I'll caution, we know a single pane of glass to solve everything is kind of the holy grail, but reality is we need to solve real problems for customers today, and yeah. >> Yeah, and our piece of the puzzle, our piece of the puzzle is Imanis Data is enterprise data management for Hadoop and NoSQL. That's where we focus. We're basically delivering industry-leading solutions for Hadoop and NoSQL. That has led to a very logical collaboration with Cohesity, who's one of the leaders in hyper-converged secondary storage. So, they're trying to provide that common layer of infrastructure to address mass data fragmentation. We see that as, we're the Hadoop and NoSQL folks, so there's a very logical synergy, whereby the combination of Cohesity's solution and Imanis Data's solution essentially then provides, ultimately will provide that single pane of glass. But also, again, at the end of the day provides a common visibility and a common layer to all of your secondary storage whether traditional, relational, VM-based, cloud-based, whether it's your Hadoop and NoSQL-based data. >> Okay, so, bring us back to the customers. We know that simplification is something we want. You know, the cloud world doesn't feel like it's gotten things any simpler. So, where are we? What needs to happen down the road? What more can you share about customers? >> Yeah, I think that's fair to say it hasn't gotten more simple, and in fact, it's gotten more complicated. Everybody I talk to in IT is drowning today in whatever the task is. And I think the point you made of single pane of glass, of remain largely myth, I think the focus is wrong. I don't believe we actually need a single pane of glass that can manage, that can see everything. I think what we need are separate panes of glass that let us see what we need to see. And in fact, the way you guys do that for NoSQL and Hadoop makes some sense. Cohesity has their own that looks at things at more of a higher level, data plate. So, I think we're really in the early innings here, Stu. I think over the next few years, we will see a rise in better management tools and things to help us simplify. I know I just did some research on IT priority for 2019, and simplification actually is now ahead of even cybersecurity as the number one path for today's CIOs. So, I think we've gotten to the point where we've consumed so much stuff, now it's time to simplify it. And there's no one answer for that, but I think within the different departments within IT, they need to look at what those management tools are to let them do that. >> Yeah, I mean, going back, I think back to when I first became an analyst about nine years ago. A central premise is that enterprise IT doesn't necessarily have the skillset to go architect it. They're not a Google or a Yahoo. So, they will spend money from the vendors and the suppliers to help simplify that for them environment. But Peter, I want to ask you, brought up people who are drowning in information. >> Yeah, yes. >> Definitely, we know that today in 2019 there is more going on than they had a year from now, and when we look forward to 2020, we expect that there will be even more. So, the answer in the industry is AI and ML are going to come solve some of this for us. So, to tell us, how does that fits in to these sorts of solutions? >> Sure, and the answer is machine learning and AI will absolutely need to be. Our view is that they're critical pillars to the future of data management. They have to be, because the volume of data and the complexity of the infrastructure within which you're running. You can't, as human beings, we are drowning, and you need tools, you need help to solve this problem. And machine learning and AI are absolutely going to be key contributors. From an Imanis Data standpoint, our approach has been very much about completely avoiding the whole notion of machine learning whitewash. Let's talk about the practical application of machine learning. So, for example, what we do today is we apply machine learning to do what we call ThreatSense. So, it's very specifically applied to the automation of anomaly detection, okay. Build a model of what normal looks like from a backup and recovery standpoint. Anything that falls outside of normal gets flagged, so that administrators can then do something. Provide a human feedback loop to that machine running algorithm, so it can get smarter. We also recently introduced something that we call smart policies. That's about the automation of backup. So, again, it's not about the holy grail of machine learning. In the case of smart policies, it's instead of creating spreadsheets and having a human being trying to figure out how to address a particular RPO, it's tell us what's your RPO and what data do you want to protect. We'll go build a model and we'll address your RPOs, and if we can't, we'll tell you why we can't. So, very practical for today. To the point you made earlier about that fact that we're still in the early innings, today it's about the practical application of machine learning and AI to help people automate processes. >> I think the fear and doom and gloom around AI is, particularly in the IT circles, is completely misguided. I understand why people might think it's going to take their job, but AI and ML is the IT pro's best friend. There's so much data today, they're so much to do, that people just can't connect the dots between those data points fast enough. >> Right. >> Just like you look, today you wouldn't go to a radiologist that doesn't use machine learning to look at your brain scans, right? You know, it's getting harder and harder to work, to be a customer of a company that doesn't use AI or ML to analyze your data, and it becomes very apparent, because they're just not able to provide the same type of service. >> Yeah, totally agree. We've done some events with MIT and a couple of the professors there, Erik Brynjolfsson and Andy McAfee talk about racing with the machines. >> Yeah. >> So, the people that can actually harness and leverage that, the challenge is, if you're in IT and you're working on stuff that's five to 10 years old, and you can't take advantage of those new tools, well, you need to skill up, and you need to get ready. But most companies I talk to, it's not that they're looking to cut half the workforce, it's just that they can't add many more people, so most of them can be reskilled, or heck, if there's some automation they can have in there. There's lots of projects sittin' on the table that they've been trying to do for years. I don't find anybody that ever said, hey, if I could give ya an extra month in the year that you wouldn't have to figure out. >> The question is, do you want to be strategic to your organization, or tactical? And if you want to be tactical, your job's only as long as that tactic, right, so. >> Peter, when I was hearing you walk through some of that ML piece, things like security and ransomware kind of popped into my head. Is that a part of the solution in offer? >> Yeah, absolutely. So, ThreatSense is, specifically, we talk about as anomaly detection, because overall it really is about, ransomware is essentially about detecting anomalies. So, ransomeware is an application of anomaly detection. So, our ThreatSense capability is built into the product. What happens is, when we do backups, like I said, we build a model of what normal looks like, and then we flag anomalies. My dataset size, all of a sudden spike. My data type, all of a sudden I have a bunch of ZIP files, or something, all of a sudden. Something has changed that's outside of normal, and then we flag that, and you can take action against that. So, absolutely it is, but the initial application is specifically about ransomware. >> All right. Zeus, is there advice that you would want to give users, or when you're talking to customers, what's the profile of somebody that is handling their data, and leveraging it well? >> I don't always really hand it well. (all laughing) But I think the advice I'd give is you want to simplify and automate as much as you can, and ruthlessly automate. I think if you're trying to do things the old way, you're going to wind up falling behind. And so, I suppose to your question, what's the profile of a company that's doin' it well. It's one that's actually able to roll up new services quickly, and you see that in a lot of the big name cloud companies. They always new things comin' and new things goin', and they're able to transform the way they deal with customers and employees. That's the hallmark of a company that's using it's data well. Ones that aren't, frankly, we've seen a lot of 'em go out of business, right, over the last few years. And so, I think from an IT perspective, you want to embrace automation, embrace machine learning, right, embrace this concept of single pane of glass for your particular domain. Because what it lets you do is, it becomes a tool to help you do your job better. There's certain things people are good at and there's certain things people aren't, and connecting the dots, and terabits, petabytes of, bits of data isn't one of 'em. So, I think from an IT perspective, you want to automate, and you want to embrace machine learning, because it's going to be your best friend, and it's going to help you keep your skillset current. >> Yeah, and I would just pick up on that and say that the answer isn't constraining, to a large extent it's really embracing data diversity. Like the answer to mass data fragmentation isn't homogenization of your data, or limiting particular data types. The proliferation of different data types is a direct result of organizations trying to be more agile, and trying to be more nimble. So, the answer isn't sort of constraining data. The answer is making the strategic investments in the right tools, in sort of in some of the right policies and governance, if you will. So, that you keep everybody strategically going in the right direction in this sort of federated diverse type of environment. >> Yeah, if you look at any market in IT, well, really even in the consumer world, where there has been choice, it's create a rising tide for everybody. >> Right. >> The question is, you can't have it be chaotic. >> Right. >> Right, and so you're bringing a level of order to a world that was historically chaotic, and that untethers people to make whatever choice they want and use the best possible tools. >> Yeah. >> Right. >> Peter, I go back to the promise of big data, was that I was going to turn that proliferation of volume, velocity of data from a, oh my god, that's a problem, and flip it on its head, and become an opportunity for how we can leverage data. Give me the final word. How do we connect the dot from where that was a few years ago to this mass data fragmentation world today. >> Yeah, and the answer to that is don't treat, don't make big data sort of the three guys over in the corner who are the data scientist. Embrace big data. Embrace all your data types. So, our message, as the Hadoop and NoSQL data management folks, is simply, look Hadoop and NoSQL are a key part of your overall data strategy. Embrace those, include those in your overall strategy, and make sure you're basically taking the right contextual picture of what you're trying to do. Include all your different data types. Hadoop and NoSQL are contributors to mass data fragmentation, but as part of that salute, if they're part of the problem, then they need to be part of the solution, both from a data standpoint and from a solution standpoint. So, that's really the message that we're driving is that, embrace all your different data types, put the appropriate systems in place, take the right sort of approach to consolidating and solidifying your overall data strategy. >> All right, well, Peter and Zeus, thanks so much for sharing >> Thank you. the latest update. Absolutely, data at the center of it all, and need to embrace those new tools and opportunities out there. All right, I'm Stu Miniman. And be sure to check out thecube.net for all of our research and shows that we'll be at. And thank you, as always, for watching theCUBE. (electronic music)

>> Live from Orlando, Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome back to theCUBE'S coverage of Microsoft Ignite 2018 here in Orlando. I'm Stu Miniman, and happy to welcome back to the program John Mracek who's the CEO of a Imanis Data. It's our first time at the show, but not your first time on theCube. Thanks so much for joining us and tell us we caught up with you in New York City talking about kind of the AI, analytics, all those things there. what what what brings a Imanis to Microsoft Ignite? >> So this has been a great show for us. And what I really see happening here is there's a vibrancy that probably didn't exist in Microsoft events, maybe four or five years ago. Because Microsoft really getting their act together on the whole how you migrate and bring people to the Azure. Right, because that's their agenda. And so where we fit in there is in our data management platform. We help customers migrate to a Azure. So whether it's moving your Hadoop workloads to Azure, or one of the products that's been featured here that we've gotten a lot of Microsoft support on is our migration tool to move from MongoDB to Cosmo DB. So we play really well into the migration story and it really leverages our platform. >> Yeah, one of the questions we talk about all the time is customers trying to figure out where things live and, well, it's like your cloud strategy. Things are changing over time. Customers have really multi-cloud environments, which really means they're doing a lot of different things and a lot of times they need to move them and sort those out. So what are the challenges you're seeing? How do you help those businesses make decisions today and be able to move things as needed in the future? >> Yeah, what we see and what we're playing into is really this evolution. You know, solutions really drive technologies. So in a large enterprise, you might have a division or a particular group that says, I need this BI or analytics tool and I need a big data platform to do it. So they build this. They build on top of some either NoSQL or Hadoop and then they've got this great solution. Well, that happens four or five times across the enterprise, and at some point in the enterprise, the CIO or somebody says, "you know, "we kind of got all these distributed data systems, "and like, who's managing them? "How is that data being moved "to your point about cloud migration? "Well, these are on-prem, these are in the cloud. "We want to put them all in the cloud, how do we do that?" And so that's where we're seeing as kind of the call for our product, which is, okay, I need a central way to manage and manipulate this data, as a fundamental problem. >> Yeah, so we all know that data is fundamental to a business. It's one of the most important things. We can use all the tropes of, it's the new oil or anything like that. But when you dig down, it's a lot of complexity into how, how do I get data? How do I manage data? How do I share data? We're sitting here in Cohesity, is the where we are in the booth. Can you help us understand, what are the solutions that you complement in the data space? What are the solutions that you replace? or a modern version or compete against in this space? >> So the way to look at us, we're at our most general, we're a platform for moving data from one platform to another. Okay, and that has many different use cases. But where we're getting a lot of customer uptake is on the backup recovery. It's like, I've got it here, I want to make a backup. We also see a lot in terms of migration, whether it's the Mongo to DB or I want to move from on-prem to cloud or cloud to cloud. And where we fit is if you look there's a legacy providers who don't traditionally go after the NoSQL and Hadoop space. And so where were a perfect complement to either those companies or folks like Cohesity. We have partnerships with Cohesity, Veem and others where they get in RFP or they're talking to a customer and the customer has a specific request for data management solution for NoSQL or Hadoop platforms. And that's where we come in. Because that's what we focused on exclusively from day one. >> Yeah, well, being at a Microsoft show, I mean, applications are central to so much and Microsoft does. Everything from Office, but on the data side, we spend a lot of time this week talking about SQL. Talking about Cosmos DB and cool new things they're doing. And of course, Microsoft's playing in a lot of the modern areas. We see them, big developers base here, even more of it at the Microsoft Build show, what do you see in the Microsoft space on the application modernization? Sounds like that would tie in quite a bit to what you're helping customers with. >> Yeah, so we have customers across all the cloud providers. But what we see in the Microsoft case is really people looking for maybe global easy deployment, customer facing as typical examples. So people who are really pushing the envelope, frankly. And there's almost like a bi-modal distribution. There's kind of some folks who are still trying to retrofit the old world and then others who are really embracing some of the new platforms. >> I'm not sure if you were at the keynote on Monday, Satya Nadella unveiled the Open Data Initiative. We've got Adobe and SAP and Microsoft there. I was talking to one analyst and reading some reports, and I'm like, well, it's not a coincidence that this was launched the week of Salesforce. Salesforce has a lot of data. Maybe that's a little bit of an attack there. But data across these big providers is important. I want to be able to share and leverage my data. You're in the data business. But what viewpoint you have of some of these really big providers of the application as they're going through their digital transformation, and making how do customers get the best value out of their data? >> So, my background, most recent background, I was in an ad tech company, where we're all big data. And the whole play there, is how do you manage your audiences, right? How do you have a unifying way to look at audiences? And so this is what's playing out on a more higher level, a more general level of how do I normalize and create a unified view of the customer and consistent data so that I can then manage it. And so that's an essential requirement to get the maximum value at out of that. Once you have that and you're in your data repositories, it's incredibly critical to protect them, to be able to orchestrate and move around. Where we fit in and how we see it is, these things are data, to reuse the term is the new oil and the new gold. And companies are realizing that it's really time to protect this data. I put all this investment into getting unified view of data. Wow, what are we doing about how do we back it up, restore it and move it? >> It's interesting, I've watched the space long enough. You go back kind of BI and DW days, go through big data. Now, we talk about a lot more of the analytics in the intelligence there. Help us as to, what are we actually realizing today that we were been talking about for years, and what what are still some of the stumbling blocks as to what we need to mature as an industry to really help unlock data. >> So, I mean, there's clearly the, what's driven a lot of the machine learning AI is the availability of data. It wasn't so much algorithms change dramatically, it was, we have a, so all the machine learning applications are really benefiting from this. But what we see as you know, some of the immediate things with our customers, is they're using big data as they create their front ends, engage with their customers. So how do they have the most up to date, real-time information to whether it's present an offer to a customer, provide customer service. So a lot of the use case we see is in that really bread and butter customer-level interactions and having an appropriate database to front end that process. >> Alright, so one of the biggest challenges of our time is really talking about distributed architecture. When I talk to companies scale comes on a lot, but it means very different things to different people. Can maybe talk about what you're hearing from customers, and how your solution helps customers for a variety of implementations. >> Yeah, so, we typically are targeting and working with customers in the 10s to 100s of terabytes. Up to, and our system handles up into into the petabytes. Typically, what we see is an evolution is, as I said earlier, somebody will develop a solution in a particular division, and then realize we've got this asset to protect. And then so IT starts to get involved and basically look at it holistically. So, we had one of our prospects, we went in and pitched at an SVP level and said, "what are the problems you're facing?" and it was basically this, I have all these silos of data. To get the maximum value out of them, and have a uniform look, whether it's look at our customers, the market, I need a uniform view to do my BI and AI. And so they brought us in and said, "Okay, paint a picture of how I can continue to have "these groups run autonomously and run their solutions, "yet at the same time, give me a unified view "and make me feel comfortable "that I've been able to protect the data, "move the data, massage the data." >> Great. Talk to me, when I look at this show, I see a lot of customers are still doing things, I'm trying to think how to say it nicely. Kind of the old way, it's like, if you look at them five years ago, is like, okay, Windows 2019's there great. I'll get there in five years, you play with a lot of more modern applications. What do you hear from customers? What, what is the profile of a customer that is, taking advantage and being competitive in the world? And what do you advise companies that maybe are a little bit behind the eight ball. >> So, you're right, and there's a really big spectrum of where people are in the adoption curve. And the way we look at it, if people were waiting for it, you know, when somebody goes, "Yeah, we're looking at setting up a big data system", it's like, okay, we'll talk to you in a year once you get the basics set up. But I see kind of two types of things. There's, say, the smaller, more aggressive companies, who are willing to move forward and say, "I just got to create a product, I don't care how I do it, "I don't have legacy issues." And they've moved ahead, and they're starting to get to the point where they're like, "Okay, we're mature enough where we actually need to spend on data management." The more typical case though is, as I said earlier. It's like these these new apps, that larger companies might have bleeding edge groups. So it's not being driven centrally. And so my, you asked about advice, right? So if you're sitting in the top of large enterprise and say, "Well, how do we get there. "There's kind of the tops down, "I need somebody to help me figure out." But there's also, let 1,000 flowers and let there be some kind of anarchy, if you will. Breaking the model, breaking the mold. Let people go build stuff and then over time start to figure out how to assimilate. So that'd be the biggest single biggest advice is, Yeah, you want to do the top down, but you really want to do the bottoms up. Because those people really know how to use the technology to provide a solution. >> Yeah, absolutely. Guy Kawasaki let 1,000 flowers bloom out there and everything. All right. Help bring this in. What kind of customer conversations are you having this week? We talked to the top about, there's real good energy to this show. Definitely, I felt that. What would you share with your peers that haven't been at the show? >> So the topics here are typically around the migration. Whether it's like to like, moving an existing workload into Azure, or the transformation. We also announced the show cooperation with Microsoft on moving any of your NoSQL workloads to Cosmos DB. So most of the conversations here have been related to migration. Either of, if you will, within the same Hadoop family, or, you know, like to unlike. Going from something to Cosmos DB. And that goes back to your earlier point about people trying to figure out what to do. They know there's this imperative to move to the cloud, and they're trying to figure out how they do it in bite-sized chunks. Right and protect their business at the same time. >> Yeah, so you mentioned Cosmos DB. We had an interview earlier this week about Cosmos DB. I definitely heard some good buzz at the show, What is it about that is drawing customers to it and what's that enable for them? >> Two things that I'm aware of, that I've seen is, again, the global nature and the ability to just kind of deploy anywhere. But also, I've seen a little bit around the dynamic schemas and the ability to map between them as a very quick way to ingest data. So you can get up and running quickly, instead of doing a lot of manual work to start using it. So those are things that are going to win developers 'cause it makes their life easier. >> Alright, John I want to give you the final word. What should we look to see from Imanis over the next six to 12 months. >> So we're going to continue to push forward with our platform around data management. You've seen in some recent announcements that, where leveraging machine learning in a very concrete way to do anomaly detection around ransomware. And also for administrators to be able to basically set rules or set goals and have the software do it. And that really steams from the fact that we're using a big data platform and machine learning to solve the problem of well, if you're running a big data platform, how do you manage the data? So the whole DNA of the company is built around that, and from a go-to-market standpoint, you know, partnering with folks like Cohesity and others where you've already got people in market selling a broad solution but they're missing a piece. So the other thing you'll see from us, is more partner announcements as we go forward. Alright, well, John Mracek really appreciate all the updates on a Imanis Data. Congrats on the progress so far. And look forward to catching with you up at future show. >> Great, thank you. >> Alright, we'll be back with more coverage here. Day three of three days live coverage. Microsoft Ignite here in Orlando. I'm Stu Miniman and thanks for watching theCUBE.

live from New York it's the cube covering the cube New York City 2018 brought to you by silicon angle media and its ecosystem partners i'm jeff workday Villante we're here nine years our nine years of coverage two days live in New York City and our next two guests shot Mrazek CEO amana stayed at fiendish males CMO mystic good to see you again welcome back thank you bad to be here guys so obviously this show we've been here nine years we were the first original Hadoop world we've seen a change Hadoop was gonna change the world it kind of didn't but we get the idea of it did not it did didn't but it would change our world it brought open source and the notion of low-cost Hardware into the big data game and then the big data became so much more powerful around these new tools but then the cloud comes in full throttles and while they can get horsepower that compute you can stand up infrastructure for analytics all this data goodness starts to change machine learning then becomes the the real utility that's showing this demand for using data right now not the set up using data this is a fundamental big trend so I don't get you guys reaction what do you see this evolving more cloud like how do you guys see the trend in this as data science certainly becoming more mainstream and productivity users to hardcore users and then you got cloud native developers doing things like kubernetes we've heard kubernetes here it's like a cloud is a data science what's going on what's your view of the market so I came from a company that was in an tech and we were built on big data and in looking at how big data is evolved and the movement towards analytics and machine learning it really being enabled by Big Data people have rushed to build these solutions and they've done a great job but it was always about what's the solution to my problem how do i leverage this data and they built out these platforms and in our context what we've seen is that enterprises get to a certain point where they say okay i've got all these different stacks that have been built these apps that have been built to solve my bi and analytics problems but what do I do about how do I manage all these and that's what I encounter my last company where we built everything ourselves and then so wait a minute but what we see at an enterprise level is fascinating because when I go to a large company I go you know we work with no sequel databases and Hadoop and you know how much Couchbase do you have how much Mongo etc the inevitable answer is yes and five of each right and they're cutting to this point where I've got all this distributed data distributed across my organization how am I going to actually manage it and make sure that that data is protected that I can migrate to the cloud or in a hybrid cloud environment and all these questions start to come up at an enterprise level we actually have had some very high-level discussions at a large financial institution here in New York where they literally brought 26 people to the meeting the initial meeting this was literally a second call where we were presenting our capability because they're they're now at the point where it's like this is mission-critical data this is not just some cool stuff somebody built off in one of our divisions it matters to the whole enterprise how do we make sure that data is protected backed up how do we move data around and that's really the the trend that we're tapping into and that the founders of our company saw many years ago and said I need to I need to we need to build a solution around this it's interesting you know you think about network data as a concept or data in general it's kind of got the same concepts we've seen in networking and/or cloud a control plane of some sorts out there and you know we're networking kind of went wrong as the management plane was different than the control plane so management and control or huge issues I mean you bring up this sprawl of data these companies are data full it's not like hey we might have data in the future right they got data now they're like bursting with data one what's the control plane look like what's the management plane look like these are all there's a technical concepts but with that with that in mind this is a big problem what our company is doing right now what are what are some of the steps that are taking now to get a handle on the management the data management it's not just your grandfather's data management so we anymore it's different it looks different your thoughts on on this chain of management so they're approaching the problem now and that's our sweet spot but I don't think they have in their minds yet come to exactly how to solve it it's there's this realization about we need to do this at this point and and and in fact doing it right is something that our founders when they built Lee said look if this problem of data management across big data needs to be solved by a data we're platform built on big data so let's use big data techniques to solve the problem all right so let's before getting some of the solution you guys are doing take a minute to explain what you guys are doing for the company the mission you know the value proposition status what do you guys do how are people gonna consume your product I mean take a particular type gen simple elevator pitch and we were enterprise data management focused specific than had you been no sequel so everyone's familiar with the traditional space of data management in the relational space relational world very large market very mature market well we're tapping into is what John was just saying which is you've got this proliferation but Dupin no sequel and people are hitting the wall they're hitting the ceiling because they don't have the same level of operational tools that they need to be able to mainstream these deployments whether it's data protection whether it's orchestration whether it's migration whatever the case may be so what we do that's essentially our value prophecy at a management for a Dupin no sequel we help organizations essentially drive that control plane really around three buckets data protection if it's business critical I got to protect it okay disaster recovery falls into protection bucket good old stuff everyone's familiar with but not in Hadoop in no single space orchestrations the second big bucket for us which is I'm moving to an agile development model how do i do things like automated test dev how do i do things like GD are the compliance management how do i do things like cloud migration you tut you know john touched on this one before a really interesting trend that we're seeing is you said what are customers doing they're trying to create a unified taxonomy they're trying to create a unified data strategy which is why 26 people end up in the but in lieu of that there's this huge opportunity because of what they need they know that it's got to be protected and they have 12 different platforms and they also want to be able to do things like one Cosmo I'm on go today but I'll be cosmos tomorrow I'm a dupe today but I might be HD inside tomorrow I want to just move from one to the other I want to be able to do intelligence so essentially the problem that we solve is we give them that agility and we give them that protection as they're sort of figuring this all out so we have this right you basically come in and say look it you can have whatever platform you want for your day there whether it's Hadoop and with most equals get unstructured and structured data together which makes sense but protections specifically does it have to morph and get swapped out based upon a decision correct make well now we're focused specifically Hadoop and no sequel so we would not be playing like if you we're not the 21st vendor to be helping s AP and Oracle you know customers backup their data it's basically if your Hadoop renewal sequel that's the platform regardless of what Hadoop distribution you're doing or where it's no see you know change out your piece what they do as they evolve and are correct I feel exactly right you're filling white space right because when this whole movement started it was like you were saying commodity Hardware yeah and you had this this idea of pushing code to data and oh hey his life is so easy and all of a sudden there's no governance there's no data protection no business continuity is all his enterprise stuff I didn't you heard for a long time people were gonna bring enterprise grade to Hadoop but they really didn't focus on the data protection space correct or the orchestra either was in those buckets and you touch them just the last piece of that puzzle value wise is on the machine learning piece yeah we do protection we do orchestration and we're bringing machine learning to bear to automate protection what amazing we hear a lot and that's a huge concern because the HDFS clusters need to talk speech out there right so there's a lot of nuances and Hadoop that are great but also can create headache from a user human standpoint because you need exact errors can get folded I gotta write scripts it creates a huge problem on multiple fronts the whole notion of being eventually being clustered in the first base being eventually consistent in the second place it creates a huge opportunity for us because this notion of being a legs we get the question asked the question why well you know there are a lot of traditional vendors they're just getting into the space and then what do that that's actually good because it rises you know rises all boats if you will because we think we've got a pretty significant technology mode around our ability to provide protection orchestration for eventually consistent clustered environments which is radically different than the traditional I love the story about the 26 people showing them me take me through what happened because that's kind of like what your jonquil fishbowl what do they do it they sit in their auditing they take a node so they really raising their hand they peppering you with questions what what happened in that meeting tell us so so it's an interesting microcosm what's happening in these organizations because as the various divisions and kind of like the federated IT structure started building their own stuff and I think the cloud enabled that it's like you know basically giving a the middle finger to central IT and so I can do all this stuff myself and then the organization gets to this realization of like no we need a central way to approach data management so in this meeting basically so we had an initial meeting with a couple of senior people and said we are we are going about consolidating how we manage all this data across all these platforms we want you to come in and present so when we presented there was a lot of engagement a lot of questions you could also see people still though there's an element of I want to protect my world and so this organizational dynamic plays out but you know when you're at a fortune 50 company and data is everything there's the central control starts to assert itself again and that's what we saw in this because the consequences of not addressing it is what is potentially massive data you know data loss loss of millions hundreds of millions of dollars you know data is the gold now right is the new oil so the central organizations are starting to assert that so we say that see that playing out and that's why all these people were in this meeting which is good in a way because then we're not like okay we got to sell ten different groups or ten different organizations it's actually being so there's there's kind of this pull back to the center it's happened in the no sequel world of your perspectives on this I mean early on you had guys like Mongo took off because it was so simple to use and capture unstructured data and now you're hearing everybody's talking about you know acid compliance and enterprise you know great capabilities that's got to be a tailwind for you guys could you bring it in the data protection and orchestration component but yeah what do you see it in that world what do you guys support today and maybe give us a glimpse of the future sure so that what we see as well a couple different things we are we are agnostic to the databases in the sense that we are definitely in Switzerland we were we you know we support all commerce so it's you know it's follow the follow the follow of the market share if you will Cassandra Mongo couch data stacks right on down the line on the no sequel side and what's interesting so they have very there have all varying degrees of maturity in terms of what their enterprise capabilities are some of them offer sort of rudimentary backup type stuff some fancy they have more backup versus others but at the end of the day you know their core differentiation they each it's fascinating to each have sort of a unique value prop in terms of what they're good at so it's a very fragmented market so that's a challenge that's an opportunity for us but it's a challenge from a marketplace networkers they've got to carve out there they all want the biggest slice of the pie but it's very fragmented because each of them is good at doing something slightly different yeah okay and so that like the the situation described before is they've got yes so you got one of everything yeah so they've got 19 different backup and recovery right coordinate processes approach or the or nothing or scripting law so that they do have to they've got a zillion steps associated with that and they're all scripted and so their probability of a failure you know very you drop a mirror that's a human error to is another problem and you use the word tailwind and I think that's very appropriate because with most of these vendors they're there they've got their hands full just moving their database features forward right you know where the engagement so when we can come in and actually help them with a customer who's now like okay great thank you database platform what do you do for backup well we have a rudimentary thing we should belong with it but there is one of our partners a manas who can provide these like robust enterprise it really helps them so with some of those vendors were actually a lot of partner traction because they see it's like that's not what their their strength is and they got to focus on moving their database so I'll give you some stats I'm writing a piece right now a traditional enterprise back in recovery but I wonder if you could comment on how it applies to your world so these are these are research that David flora did and some survey work that we've done on average of global 2000 organizations will have 50 to 80 steps associated with its backup and recovery processes and they're generally automated with scripts which of course a fragile yeah right and their prefer own to era and it's basically because of all this complexity there's a 1 in 4 chance of encountering an error on recovery which is obviously going to lead to longer outages and you know if you look at I mean the average cost the downtime for a typical global global 2000 companies between 75 thousand and two hundred fifteen thousand dollars an hour right now I don't know is your world because it's data it's all digitally the worst built as a source is it probably higher end of the spectrum all those numbers go AHA all those numbers go up and here's why all those metrics tie back to a monolithic architecture the world is now micro services based apps and you're running these applications in clusters and distributor architectures drop a note which is common I mean think you know you're talking about you're talking about commodity hardware to come out of the infrastructure it's completely normal to drop notes drops off you just add one back in everything keeps going on if your script expects five nodes and now there's four everything goes sideways so the probability I would I don't have the same stats back but it's worse because the the likelihood of error based upon configuration changes something as simple as that and you said micro-services was interesting to is is that now is it just a data lake kind of idea of storing data and a new cluster with microservices now you're having data that's an input to another app check so now so that the level of outage 7so mole severity is multiple because there could be a revenue-generating app at good young some sort of recommendation engine for e-commerce or something yeah something that's important like sorry you can't get your bank balance right now can't you any transfers because the hadoo closes down okay this is pretty big yes so it's a little bit different than say oh well to have a guy go out there and add a new server maybe a little bit different yeah and this is the you know this is the type of those are the types of stats that organizations that we're talking to now are caring a lot more it speaks to the market maturity do you run into the problem of you know it's insurance yeah and so they don't want to pay for insurance but a big theme in that you know the traditional enterprises how do we get more out of this data whether it's helping manage you know this I guess where that that's where your orchestration comes in cloud management maybe cloud migration maybe talk about some of the non insurance value add to our components and how that's resonating with with cost yeah yeah I so I'll jump in but the yeah the non protection stuff the orchestration bucket we're actually seeing it comes back to the to the problem sting we just said before which is they don't have it's not a monolithic stack it's a micro services based stack they've got multiple data sources they've got multiple data types it's sort of a it's the it's the byproduct of essentially putting power into into divisions hands to drive these different data strategies so you know the whole cloud let me double click on cloud migrations is a is a huge value problem that we have we talked about this notion of being data where so the ability to I'm here today but I want to be somewhere else tomorrow is a very strong operational argument that we hear from customers that we also also hear from the SI community because they hear it from the other community the other piece of that puzzle is you also hear that from the cloud folks because you've got multiple data for platforms that you're dealing with that you need agility to move around and the second piece is you've got the cloud obviously there's a massive migration to the cloud particularly with the dubidouxs sequel workloads so how do I streamline that process how do I provide the agility to be able to go from point A to point B just from of migration standpoint so that's a very very important use case for us has a lot of strategic value like it's coming it's sort of the markets talking to us like no no no we have this is him but we have to be able to do this and then simple things like not simple but you know automated test step is a big deal for us everybody's moved agile development so they want to spin up you know I don't want it I don't want to basically I want 10% of my data set I want to mask out my PII data I want to spin it up on Azure and I want to do that automatically every hour because I'm gonna run 16 I'm gonna run six builds today clouds certainly accelerates your opportunity big-time it forces everything to the table right yeah everybody's you can't hide anymore right what are you gonna do right you gotta answer the questions these are the questions so okay my final question I want to get on the table is for you in the segment is the product strategy how you guys looking at as an assassin gonna be software on premise cloud how's that look at how people consume the OP the offering and to opportunities because you guys are a young growing company you're kind of good good time you don't have the dog'll or the bagging it's Hadoop has changed a lot certainly there's a use case that neurons getting behind but clouds now a factor that product strategy and then when you're in deal why are you being called in why would someone want to call you rotor signs that would say you know call you guys up when with it when would a customer see signals and what signals would that be and to give you guys a ring or a digital connection product so the primary use cases are talking about recovery there's also data migration and the test step we have a big account right now that we're in final negotiations with where their primary use case is they're they're in health care and it's all about privacy and they need to securely mask and subset the data to your specific question around how are we getting called in basically you've got two things you've got the the administrators either the database architect or the IT or infrastructure people who are saying okay I need a backup solution I'm at a point now where I really need to protect my data as one and then there's this other track which is these higher-level strategic discussions where we're called in like the twenty six person meeting it's like okay we need an enterprise-wide data strategy so we're kind of attacking it both at the use case and at the higher level strategic and and and obviously the more we can drive that strategic discussion and get more of people wanting to talk to us about that that's gonna be better for our business and the stakeholders in that strategic discussion or whomever CIT is involved CIO maybe use their chief data officer and yeah database architect enterprise architecture head of enterprise architecture you know various flavors but you basically it kind of ways comes down to like two polls there's somebody who's kind of owns infrastructure and then there's somebody who kind of owns the data so it could be a chief data officer data architect or whatever depending on the scale of your and they're calling you because they're full they had to move the production workloads or they have production workloads that are from a bond from what uncared-for undershirt or is that the main reason they're in pain or you're the aspirin are you more others like we had a day loss and we didn't have any point in time recovery and that's what you guys provide so we don't want to go through this again so that's that's a huge impetus for us it is all about to your point it is mature its production workloads I mean the simple qualifying are you are you running a duper no sequel yes are you running in production yes you have a backup strategy sort of tip of the spear now to just briefly answer your question before we before we run out of time so it's an it's it's not a SAS basement we're software-defined solution will run in bare mantle running VMs will run in the cloud as your Google whatever you want to run on so we run anywhere you want we're sorry for be fine we use any storage that you want and basically it's an annual subscription base so it's not a SAS consumption model that may come down the road but it's basically in a license that you buy deploy it wherever you want customers choose what to do basically customers can do you know it's complete flexible flexible but back to you so let's go back to something you said you said they didn't have a point in time recovery what their point in time recovery was their last full backup or they just didn't have one or they just didn't have one all of the above you know see we've seen both yeah there's a market maturity issues so it's represented yeah you know that a lot its clustered I you know I just replicate my data and replication is not earth and truth be told my old company that was our approach we had a script but still it was like and the key thing is even if you write that script as you point out before the whole recovery thing so you know having a recovery sandbox is really in thing about this we designed everything exactly extract the value and show the use case prove it out yeah dupes real the history is repeating itself in that regard if you refuel a tional space there's a very in correlation to the Delton between the database platforms of the data mention logical hence they are involved coming in okay let's look at this in the big picture let's dad what's the recovery strategy how we gonna scale this exactly it's just a product Carson so your granularity for a point in time is you offer any point in time any point in time is varying and we'll have more news on that in the next couple weeks okay mantas data here inside the cube hot new startup growing companies really solving a real need need in the marketplace you're kind of an aspirant today but you know growth opportunity for as they scale up so congratulations good luck with the opportunity to secure bringing you live coverage here is part of Cuban YC our ninth year covering the big data ecosystem starting originally 2010 with a dupe world now it's a machine learning Hadoop clusters going at the production guys thanks for coming I really appreciate it this is the cube thanks for watching day one we'll be here all day tomorrow stay with us for more tomorrow be right back tomorrow I'll see you tomorrow

(bright, peppy music) >> Announcer: From Chicago, it's theCUBE. Covering Veritas Vision Solution Day 2018. Brought to you by Veritas. >> Hello everyone, welcome to Chicago. We're here covering the Veritas Solution Day. Veritas, last year, had the Veritas Vision Conference and they brought together all their customers. This year they decided to go around the world, I think they have six or seven of these across the globe. And we just were in New York a few weeks ago at Tavern on the Green. We're here at the Palmer House in Chicago. Iconic hotel. About 60 to 70 customers here. Of course Chicago's a big opportunity for companies like Veritas because there's such a good customer base here. But what I want to do now is set up what's going on in the data protection business. According to a number of sources, Gartner, IDC Data, other survey data, certainly anecdotally when we talk to customers, about half of the customers that we talk to are going to replace their data protection platform within the next five years. Why is that? Well, there are a number of factors that are affecting that and I want to talk about the reasons why, the implications to the market, and what that means for customers. So if you look back 10 years ago, there was a similar dynamic going on catalyzed by the ascendancy of virtualization. What was happening is that you had all these servers that were underutilized and so the brilliance of virtualization was we're going to consolidate those servers, virtualize the compute power, dramatically increase the utilization and reduce the physical capacity that's on the floor. So you can get rid of stuff. Get rid of servers, spend less, and get more value out of that asset. Because you had all these underutilized hardware assets. Data protection backup in particular was the one workload that actually could use all that compute power. Why, because at the end of the day, you're backing up this huge stream of data. And so as a result, when you had to do a full backup, you didn't have the physical resources. So people had to rethink how they architected backup because of virtualization. So you now have a similar dynamic, but for different reasons. Some of the big trends that are going on here. The first one is of course digital. So digital means data and it's all about how you get value out of your data because data is increasingly an important asset. People are realizing that protecting that data is more and more important. As a result, people are rethinking just the definition of recovery. Recovery has to be faster, you've got to be always on in this digital world. So digital transformation is critical. You can't just bolt on backup as you have for the last 20, 30, 40 years really. Backup has been a bolt on. You've also got cloud. Everybody wants cloud-like. So you're seeing a shift from improving or dealing with resource utilization and allocation, as I explained in the virtualization world, now to automation. Why automation? Because people want a cloud-like experience. They realize they can't just shove all their data into the public cloud. There's data all over the place, and I'll talk about that in a moment in terms of distributed data, but specifically people want a cloud-like experience. What does that mean? That means they want pay-as-you-go, they want simple deployment, they want fast seamless recovery, and they want a lot of automation. While the price of technology comes down year after year, the price of people doesn't. And you can't just keep throwing people at the infrastructure problem, because it's so complex, you have to automate. And you want to shift resources toward higher value activities. Digital transformation, dev opps, application development. So this distributed data world, this multi-cloud world, and I'll talk a little bit more about that in a moment when I discuss the Edge, it's becoming a forcing function. Multi-cloud is a forcing function to rethink your backup. Because you've got different infrastructures, a service providers, you've got SAS providers, you've got all kinds of clouds that are popping up all over the lines of business and within your own data centers. As a result, you need to think about how do I catalog all that data, how do I protect that data, how do I govern that data, how do I deal with things like GDPR and make sure that I'm in compliance. So it becomes a much more complicated equation, and the variables are distinct. For example, I don't really understand what point in time means anymore. If you have distributed data, what does it mean to have a point in time copy? Point in what time? Who's the master? So you need some kind of controls in that multi-cloud world. That's a forcing function to rethink your backup. The other thing is platform. Platform beats products. I'll talk about that in a moment. People for years have looked at backup as purely insurance. Everybody hates buying insurance, we all know that, so you're seeing people trying to get more out of their backup and recovery platforms. For instance, integrating disaster recovery. So that's becoming an integral part of people's strategies. You're also seeing analytics becoming more and more important. People are trying to, because all the data sits in the corpus of the backup, people are saying why don't we analyze that data and get more out of it. Why don't we take snapshots of that data and make it available to dev opps. And what about ransomware, which again I'll talk about in a moment. Could I maybe look at anomalies in that data to determine if there are some problems. Many, many use cases emerging. Data classification, governance, I mentioned GDPR before, so you're seeing backup shift from pure insurance to a higher value business opportunity. And then of course, there's security, there's compliance, there's governance, ransomware is critical. Organizations are creating air gaps, meaning disconnecting from the internet, so that if they get hit with a ransomware attack they can isolate their data, but just even that is not enough. People can get through air gaps by physically putting in, whatever. Sticks or malware et cetera. So you still have to be able to use analytics to look at that corpus of backup data and identify anomalies. But again, because of those security risks and because of the importance of digital transformation and data people are rethinking how they do data protection. And finally, there's the Edge. We are living in a distributed world, it's a multi-cloud world, as I said before it's a forcing function, and the Edge is one of those clouds, if you will, which changes the way in which you think about backup. How does it change. Locality of the recovery data. If you've got Edge data, if you've got multi-cloud, you've, as I said before, got to have a global catalog and recover that data locally. Another thing to think about is SLAs. In a cloud world, you, the customer, are responsible for the recovery. Well, the cloud vendor can get the light back on on the disc system, or the computer, or the compute system, you are responsible for the people and the process to recover your business. That is not the cloud vendor's responsibility so you need to think about that. And think about recovery as recovery at the business level, not just recovery of the data, but recovery, getting your business back online. There's also the three laws of the cloud. We learned this from Pat Gelsinger this August at VMworld. The laws of physics, the laws of economics, and the law of the land. Those will dictate where you put data and how you back up that data. So all of this has created a new landscape in the data protection business. Let's run down that landscape. Who are the leaders. You've got Dell EMC, you've got Veritas, you've got Convault, and you've got IBM. Those guys comprise probably 2/3 or more of the marketplace. And you have startups like Cohesity and Rubrik who have raised hundreds of millions of dollars going after them and challenging them. You've got a whole new set of players that are taking new approaches. Actifio, for example, got the whole copy data management thing going. Datrium is creating end to end, both primary storage and data protection backup in the same platform with a software-based cloud-like, SAS-like offering. You've got companies like Zerto and Imanis Data that are specialists. You've got companies like WANdisco, again, taking new approaches. And then you have Oracle, with the Oracle recovery appliance, which is totally changing the way in which backup worked for Oracle databases exclusively. Taking a database-led approach to backup. And then of course you've got the storage players that are part of the ecosystem even though they're not directly competing with backup software vendors. Guys like Pure, NetApp, InfiniteApp. They're partnering with backup vendors. And then of course, there's the cloud guys. AWS, Azure, Google. The thing to think about as customers, really three things. Platform versus product. What's the platform look like? Is it an API-based platform? Because you want to program to that platform infrastructurer's code, you want to support your dev opps infrastructure. The second is cloud-like pricing, and cloud-like deployment. You want a cloud-based operating model to simplify your operations and lower your IT labor costs and shift those costs to more strategic efforts and initiatives such as digital transformation and application development. And the third is ecosystem alignment. Make sure that your backup software vendor and you backup solution vendors are all, their ecosystem is aligned with your ecosystem. Because you're going to get more facile integration and problem-solving and flexibility if those systems align. So take a look at that as well. Couple of things I want to mention and emphasize. New application development models. Cloud Native, Kubernetes. Function, you know people call it server-less, but function-based programming. Really to support dev opps and infrastructure as a code. That is going to have implications on how you protect data. And finally AI. How can you talk about anything today without talking about AI. Anticipatory staging of data for recovery, as in the example. Predicting where problems are going to occur. Machine intelligence will increasingly play a role in this whole landscape. So, as you can see, there's a lot going on. This is why data protection is such a hot space. That's why the VCs are getting in. It's why the incumbents like Veritas, Dell EMC, IBM, Convault, those that I mentioned are trying to re-platform and hang on to their large install bases and ultimately grow them. And it's why companies in the startup and the niche spaces, are tucking in and identifying new opportunities to participate. So that's a quick overview of what's going on here at the Veritas Vision Solution Day from Chicago. We'll be here all day talking to customers, talking to practitioners, technologists, and executives. So keep it right there, you're watching theCUBE. I'm Dave Vellante. Be right back. (bright music)

>> Live from San Jose in the heart of Silicon Valley, it's the Cube. Covering Dataworks Summit 2018 brought to you by Hortonworks. (upbeat music) >> Welcome back to The Cube's live coverage of Dataworks here in San Jose, California. I'm your host Rebecca Knight along with my co-host James Kobielus. We're joined by Peter Smails. He is the vice president of marketing at Imanis Data. Thanks so much for coming on The Cube. >> Thanks for having me, glad to be here. >> So you've been in the data storage solution industry for a long time, but you're new to Imanis, what made you jump? What was it about Imanis? >> Yep, so very easy to answer that. It's a hot market. So essentially what Imanis all about is we're an enterprise data management company. So the reason I jumped here is because if I put it in market context, if I take a small step back, I put it in market context, here's what happening. You've got your traditional application world, right? On prem typically already a mas based applications, that's the old world. New world is everybody's moving to the microservices based applications for IOT, for customer 360, for customer analysis, whatever you want. They're building these new modern applications. They're building those applications not in traditional RDMS, they're building them on microservices based architectures built on top of FEDOOP, or built on sequel databases. Those applications, as they go mainstream, and they go into production environments, they require data management. They require backup. They require backup and recovery. They require disaster recovery. They require archiving, etc. They require the whole plethora of data management capabilities. Nobody's touching that market. It's a blue ocean. So, that's why I'm here. >> Imanis as you were saying is one of the greatest little company no one's ever heard of. You've been around five years. (laughter) >> No, the company is not new. So, the thing that's exciting as a marketeer, what's exciting is that we're not sort of out there just pitching our wears untested technology. We have blue chip, we're getting into customers that people would die to get into. Big, blue chip companies because we're addressing a problem that's materialist. They roll out these new applications, they've got to have data management solutions for them. The company's been around five years. And I've only been on about a month, but what that's resulted is that over the last five years what they've had the opportunity, it's an enterprise product. And you don't build an enterprise product overnight. So they've had the last five years to really gestate the platform, gestate the technology, prove it in real world scenarios. And now, the opportunity for us as as a company is we're doubling down from a marketing standpoint. We're doubling down from the sales infrastructure standpoint. So the timing's right to essentially put this thing on the map, make sure everybody does know exactly what we do. Because we're solving a real world problem. >> Your backup and restore but much more. When you lay out the broad set of enterprise data and management capabilities, the mana state currently supports in your product portfolio on where you're going, on how you're going in terms of evolving in what you offer. >> Yeah, that's great. I love that question. So, think of us as the platform itself is this highly scalable distributed architecture. Okay, so we scale on multiple, and I'll come directly to your question. We scale on a number of different ways. One is we're infinitely scalable just in terms of computational power. So we're built for big data by definition. Number two is we're very, we scale very well from a storage efficiency standpoint. So we can store very large volumes of storage, which is a requirement. We also scale very much for the use case standpoint. So we support use cases throughout the life cycle. The one that gets all sort of the attention is obviously backup recovery. Because you have to protect your data. But if I look at it from a life cycle standpoint, our number use case is Test Def. So a lot of these organizations building these new apps now they want to spin up subsets of their data, cause they're supporting things like CICD. Okay, so they want to be able to do rapid testing and such. >> Develop Dev Opps and stuff like that. >> Yeah, Dev Opps and so worth. So, they need Test Def. So we help them automate the process and orchestrate the process of Test Def. Supporting things like sampling. I may have a one petabyte dataset, I'm not going to do Test Def against that. I want to do 10 percent of that and spin that up, and I want to do some masking of personal, PII data. So we can do masking and sampling against that Sport Test Def. We do backup and recovery. We do disaster recovery. So some customers, particularly in the big data space, they may for now say, well, I have replica so for some of this data it's not permanent data, it's transient data, but I do care about DR. So, DR is a key use case. We also do archiving. So if you just think of data through the life cycle, we support all of those. The piece in terms of where we're going is that what's truly unique, in addition to everything I just mentioned, is that we're the only data management platform that's machine learning based. Okay, so machine learning gets a lot of attention, and all that type of stuff, but we're actually delivering machine learning and abled capabilities today, so. >> And we discussed this before this interview. There's a bit of an anomaly detection. How exactly are you using machine learning? What value does it provide to a enterprise data administrator? They have ML inside your tool. >> Inside our platform, Great question. Very specifically, the product we're delivering today essentially there's a capability in the product called threat sets. Okay, so the number one use cases I mentioned is backup and recovery. So within backup and recovery, threat sense, what it will do with no user intervention whatsoever, what it will do is it will analyze your backups, as they go forward. And what it will do is it will learn what a normal pattern looks like across like 50 different metrics. The details of which I couldn't give you right now. But essentially, a whole bunch of different metrics that we look at to establish this is what a normal baseline looks like for you or for you, kind of thing. Great, that's number one. Number two is then we look and constantly analyze is anything occurring that is knocking things outside of that? Creating an anomaly, does something fall outside of that, and when it does, we're notifying the administrators. You might want to look at this, something could've happened. So the value very specifically is around ransomware typically one of the ways you're going to detect ransomware is you will see an anomaly in your backup set, because your data set will change materially. So we will be able to tell you, >> Cause somebody's holding for ransom is what you're saying. >> Correct, so something's going to happen in your data pattern. >> You've lost data that should be there, or whatever it might be. >> Correct, it could be that you lost data. Your change rate went way up, or something. >> Yeah, gotcha. >> There's any number of things that could trigger it. And then we let the administrator know, it happened here. So today we don't then turn around and just automatically solve that. But your point about where we're going. We've already broken the ice on delivering machine learning and abled data management. >> That might indicate you want to check point your backups to like a few days before this was detected. So the least you have, you know what data is most likely missing, so yeah, I understand. >> Bingo, that's exactly right now where we're going with that. As you could imagine, having a machine learning power data management platform at our core, how many different ways we can go with that. When do I backup? What data do I backup? How do I create the optimal RTO and IRPO? From a storage management standpoint, when do I put what data wear? There's all kinds of the whole library science of data management. The future of data management is machine learning based. There's too much data. There's too much complexity for humans to just be able to, you need to bring machine learning into the equation to help you harness the power of your data. We've broken the ice, we've got a long way to go. But we've got the platform to start with. And we've already introduced the first use case around this. And you can imagine all the places we can take this going forward. >> Very exciting. >> So you were the company that's using machine learning right now. What in your opinion will separate the winners from the losers? >> In terms of vendors, or in terms of the customers? >> Well, in terms of both. >> Yeah, let me answer that two ways. So, let me answer it sort of the inward/outward versus how we are unique. We are very unique, and since we're infinitely scalable, We are a single pane of glass for all of your distributed systems. We are very unique in terms of our multi-staged data reduction. And we're the only vendor that's doing, from a technology differentiation standpoint, we're the only vendor that's doing machine learning based stuff. >> Multi-stage data reduction, I want to break that down. What does that actually mean in practice? >> Sure, so we get the question frequently. Is that compression or duplication or is there something else in there? >> There's a couple different things actually. So why does that matter? So a lot of customers will ask a question, well by definition, no sequel or redo based environments, it's all based on replica, so how to back things up. First of all, replication isn't backup. So that's lesson number one. Point in time backup is very different than replication. Replication replicates bad data just as quickly as it replicates good. When you back up these very large data sets, you have to be incredibly efficient in how you do that. What we do with multi-stage data reduction is one, we will do de duplication, we'll do variable length, de duplication, we will do compression, we will do erasure coding, but the other thing that we'll also do in there, is what we call a global de plication pool. So when we're de duping your data, we're actually de duping it against a very large data set. So there's value in, this is where size matters. So the larger the data set, your data's all secured. But the larger the size of the data that I'm actually storing, the higher percentage I could get of de duplication. Because I've got a higher pool to reduce against. So the net result is we're incredibly efficient when you're talking about petabyte scale data management. We're incredibly efficient to the tune of 10 X easily 10 X over traditional de duplication, and multi X over technologies that are more current, if you will. So back to your question about, we are confident that we have a very strong head start. Our opportunity now is we got to drive why we're here. Cause we got to drive awareness. We got to make sure everybody knows who we are and how we're unique and how we're different. And you guys are great. Love being on The Cube. From a customer standpoint, the customers are going to win, and this is sort of a cliche, but it's true, the customer's the best harness of their data. They're the ones that are going to win. They're going to be more competitive, they're going to be able to find ways to be differentiated. And the only way they're going to do that is they're make the appropriate investments in their data infrastructure, in their data lakes, in their data management tool, so that they can harness all that data. >> Where do you see the future of your Hortonworks partnership going? So Hortonworks is, so we support a broad ecosystem. So, Hortonworks is just as important as any of our other data source partners. So, we are where we see that enfolding, is they're going to, we play an important part in, we feel our value, let me put it that way. We feel our value in helping Hortonworks, is as more and more organizations go mainstream with these applications. These are not corner cases anymore. This is not sort of in the lab. This is like the real deal. This is mainstream enterprises running business critical applications. The value we bring is you're not going to rely on those platforms without an enterprise data management solution that delivers what we deliver. So our value there is we can go to market, too. There's all kinds of ways we can go to market together. But net and that our value there is that we provide a very important enterprise data management capability that's important for customers that are deploying in these business critical environments. >> Great. >> Very good, as more of the data gets persisted out at the edge devices and the Internet of things, and so forth, what are the challenges in terms of protecting that data, backup and restore, de duplication, and so forth, and to what extent is your company's Imanis data maybe addressing those kinds of more distributed data management requirements going forward? Do you see that on the rise? Are you hearing that from customers? They want to do more of that? More of an edge cloud environment? Or is that way too far in the future? >> I don't think it's way too far in the future, but I do think there's an inside out. So my position on that is that it's not that there isn't edge work going on. What I would contend is that the big problem right now from an enterprise mainstreaming standpoint, is more getting the house is order, just your core house in order, from you move from sort of a traditional four wall data center to a hybrid cloud environment. Maybe not quite as edge. Combination of how do I leverage on prem and the cloud, so to speak. And how do I get the core data lake and the case of Hortonworks, how do I get that big data lake sorted out? You're touching on, I think, a longer discussion, which is where is the analysis going on? Where is the data going to persist? You know, where do you do some of that computational work? So you get all this information out at the edge. Does all that information end up going into the data lake? So, do you move the storage to where the lake is? Do you start pushing some of the lake functionale out to the edge where you have to then start doing some of the, so it's a much more complicated discussion. >> I know we had this discussion over lunch. This may be outside your wheelhouse, but let me just ask it anyway. We've seen more at Wikibon, I cover AI and distributed training and distributed inference and things so the edges are capturing the data and for more and more, there's a trend to where they're performing local training of their models, their embedded models, from the data they capture. But quite often, edge devices don't have a ton of storage and they're not going to retain that long. But some of that data will need to be archived. Will need to be persisted in a way and managed as a core resource, so we see that kind of requirement maybe not now, but in a few years time distributed training in persistence of that data, protection of that data, becoming a mainstream enterprise requirement. Where AI and machine learning, the whole pipeline is a concern. That's like I said, that's probably outside you guys wheelhouse. That's probably outside the realm for your customers But that kind of thing is coming out, as the likes of Hortonworks and IBM and everybody else, is starting to look at it and implement it, containerization of analytics and data management out to all these micro devices. >> Yes, and I think you're right there. And to your point about the, we're kind of going where the data is, if you will in volumes, kind of thing. And it's going that direction. And frankly, where we see that happening is, that's where the cloud plays a big role as well, because there's edge, but how do you get to the edge? You can get to the edge through the cloud. So, again, we run on AWS. We run on GCP, we run on Asher. So, to be clear, in terms of the data we can rotect, we got a broad portfolio, broad ecosystem of adute based big data, data sources that we support as well as no sequel. If they're running on AWS or GCP or Asher, we support ADLS, we support Asher's data lake stuff, HD Inside, we support a whole bunch of different things both from a cloud standpoint as on prem. Which is where we're seeing some of that edge work happening. >> Great, well Peter thank you so much for coming on The Cube. It's always a pleasure to have you on. >> Yes, thanks for having me and I look forward to being back sometime soon. >> We'll have you. >> Thank you both. >> When the time is right. >> Indeed, we will have more from The Cube's live coverage of Dataworks just after this. (upbeat music)