>> 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