from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody welcome to this special cube in size powered by ET our enterprise Technology Research our partner who's got this database to solve the spending data and what we're gonna do is a braking analysis on the analytic database market we're seeing that cloud and cloud players are disrupting that marketplace and that marketplace really traditionally has been known as the enterprise data warehouse market so Alex if you wouldn't mind bringing up the first slide I want to talk about some of the trends in the traditional EDW market I almost don't like to use that term anymore because it's sort of a pejorative but let's look at it's a very large market it's about twenty billion dollars today growing it you know high single digits low double digits it's expected to be in the 30 to 35 billion dollar size by mid next decade now historically this is dominated by teradata who started this market really back in the 1980s with the first appliance the first converged appliance or coal with Exadata you know IBM I'll talk about IBM a little bit they bought a company called mateesah back in the day and they've basically this month just basically killed the t's and killed the brand Microsoft has entered the fray and so it's it's been a fairly large market but I say it's failed to really live up to the promises that we heard about in the late 90s early parts of the 2000 namely that you were going to be able to get a 360 degree view of your data and you're gonna have this flexible easy access to the data you know the reality is data warehouses were really expensive they were slow you had to go through a few experts to to get data it took a long time I'll tell you I've done a lot of research on this space and when you talked to the the data warehouse practitioners they would tell you we always had to chase the chips anytime Intel would come out with a new chip we forced it in there because we just didn't have the performance to really run the analytics as we need to it's took so long one practitioner described it as a snake swallowing a basketball so you've got all those data which is the sort of metaphor for the basketball just really practitioners had a hard time standing up infrastructure and what happened as a spate of new players came into the marketplace these these MPP players trying to disrupt the market you had Vertica who was eventually purchased by HP and then they sold them to Micro Focus greenplum was buy bought by EMC and really you know company is de-emphasized greenplum Netezza 1.7 billion dollar acquisition by IBM IBM just this month month killed the brand they're kind of you know refactoring everything par Excel was interesting was it was a company based on an open-source platform that Amazon AWS did a one-time license with and created a redshift it ever actually put a lot of innovation redshift this is really doing well well show you some data on that we've also at the time saw a major shift toward unstructured data and read much much greater emphasis on analytics it coincided with Hadoop which also disrupted the market economics I often joked it the ROI of a dupe was reduction on investment and so you saw all these data lakes being built and of course they turned into the data swamps and you had dozens of companies come into the database space which used to be rather boring but Mike Amazon with dynamodb s AP with HANA data stacks Redis Mongo you know snowflake is another one that I'm going to talk about in detail today so you're starting to see the blurring of lines between relational and non relational and what was was what once thought of is no sequel became not only sequel sequel became the killer app for Hadoop and so at any rate you saw this new class of data stores emerging and snowflake was one of the more interesting and and I want to share some of that data with you some of the spending intentions so over the last several weeks and months we've shared spending intentions from ETR enterprise technology research they're a company that that the manages of the spending data and has a panel of about 4,500 end-users they go out and do spending in tension surveys periodically so Alex if you bring up this survey data I want to show you this so this is spending intentions and and what it shows is that the public cloud vendors in snowflake who really is a database as a service offering so cloud like are really leading the pack here so the sector that I'm showing is the enterprise data warehouse and I've added in the the analytics business intelligence and Big Data section so what this chart shows is the vendor on the left-hand side and then this bar chart has colors the the red is we're leaving the platform the gray is our spending will be flat so this is from the July survey expect to expectations for the second half of 2019 so gray is flat the the dark green is increase and the lime green is we are a new customer coming on to the platform so if you take the the greens and subtract out the red and there's two Reds the dark red is leaving the lighter red is spending less so if you subtract the Reds from the greens you get what's called a net score so the higher the net score the better so you can see here the net score of snowflake is 81% so that very very high you can also see AWS in Microsoft a very high and Google so the cloud vendors of which I would consider a snowflake at cloud vendor like at the cloud model all kicking butt now look at Oracle look at the the incumbents Oracle IBM and Tara data Oracle and IBM are in the single digits for a net score and the Terra data is in a negative 10% so that's obviously not a good sign for those guys so you're seeing share gains from the cloud company snowflake AWS Microsoft and Google at the expense of certainly of teradata but likely IBM and Oracle Oracle's little for animal they got Exadata and they're putting a lot of investments in there maybe talk about that a little bit more now you see on the right hand side this black says shared accounts so the N in this survey this July survey that ETR did is a thousand sixty eight so of a thousand sixty eight customers each er is asking them okay what's your spending going to be on enterprise data warehouse and analytics big data platforms and you can see the number of accounts out of that thousand sixty eight that are being cited so snowflake only had 52 and I'll show you some other data from from past surveys AWS 319 Microsoft the big you know whale here trillion dollar valuation 851 going down the line you see Oracle a number you know very large number and in Tara data and IBM pretty large as well certainly enough to get statistically valid results so takeaway here is snowflake you know very very strong and the other cloud vendors the hyper scale is AWS Microsoft and Google and their data stores doing very well in the marketplace and challenging the incumbents now the next slide that I want to show you is a time series for selected suppliers that can only show five on this chart but it's the spending intentions again in that EDW and analytics bi big data segment and it shows the spending intentions from January 17 survey all the way through July 19 so you can see the the period the periods that ETR takes this the snapshots and again the latest July survey is over a thousand n the other ones are very very large too so you can see here at the very top snowflake is that yellow line and they just showed up in the January 19 a survey and so you're seeing now actually you go back one yeah January 19 survey and then you see them in July you see the net score is the July next net score that I'm showing that's 35 that's the number of accounts out of the corpus of data that snowflake had in the survey back in January and now it's up to 52 you can see they lead the packet just in terms of the spending intention in terms of mentions AWS and Microsoft also up there very strong you see big gap down to Oracle and Terra data I didn't show I BM didn't show Google Google actually would be quite high to just around where Microsoft is but you can see the pressure that the cloud is placing on the incumbents so what are the incumbents going to do about it well certainly you're gonna see you know in the case of Oracle spending a lot of money trying to maybe rethink the the architecture refactor the architecture Oracle open worlds coming up shortly I'm sure you're gonna see a lot of new announcements around Exadata they're putting a lot of wood behind the the exadata arrow so you know we'll keep in touch with that and stay tuned but you can see again the big takeaways here is that cloud guys are really disrupting the traditional edw marketplace alright let's talk a little bit about snowflakes so I'm gonna highlight those guys and maybe give a little bit of inside baseball here but what you need to know about snowflakes so I've put some some points here just some quick points on the slide Alex if you want to bring that up very fast-growing cloud and SAS based data warehousing player growing that couple hundred percent annually their annual recurring revenue very high these guys are getting ready to do an IPO talk about that a little bit they were founded in 2012 and it kind of came out of stealth and hiding in 2014 after bringing Bob Moog Leon from Microsoft as the CEO it was really the background on these guys is they're three engineers from Oracle will probably bored out of their mind like you know what we got this great idea why should we give it to Oracle let's go pop out and start a company and that NIN's and as such they started a snowflake they really are disrupting the incumbents they've raised over 900 million dollars in venture and they've got almost a four billion dollar valuation last May they brought on Frank salute Minh and this is really a pivot point I think for the company and they're getting ready to do an IPO so and so let's talk a little bit about that in a moment but before we do that I want to bring up just this really simple picture of Alex if you if you'd bring this this slide up this block diagram it's like a kindergarten so that you know people like you know I can even understand it but basically the innovation around the snowflake architecture was that they they separated their claim is that they separated the storage from the compute and they've got this other layer called cloud services so let me talk about that for a minute snowflake fundamentally rethought the architecture of the data warehouse to really try to take advantage of the cloud so traditionally enterprise data warehouses are static you've got infrastructure that kind of dictates what you can do with the data warehouse and you got to predict you know your peak needs and you bring in a bunch of storage and compute and you say okay here's the infrastructure and this is what I got it's static if your workload grows or some new compliance regulation comes out or some new data set has to be analyzed well this is what you got you you got your infrastructure and yeah you can add to it in chunks of compute and storage together or you can forklift out and put in new infrastructure or you can chase more chips as I said it's that snake swallowing a basketball was not pretty so very static situation and you have to over provision whereas the cloud is all about you know pay buy the drink and it's about elasticity and on demand resources you got cheap storage and cheap compute and you can just pay for it as you use it so the innovation from snowflake was to separate the compute from storage so that you could independently scale those and decoupling those in a way that allowed you to sort of tune the knobs oh I need more compute dial it up I need more storage dial it up or dial it down and pay for only what you need now another nuance here is traditionally the computing and data warehousing happens on one cluster so you got contention for the resources of that cluster what snowflake does is you can spin up a warehouse on the fly you can size it up you can size it down based on the needs of the workload so that workload is what dictates the infrastructure also in snowflakes architecture you can access the same data from many many different houses so you got again that three layers that I'm showing you the storage the compute and the cloud services so let me go through some examples so you can really better understand this so you've got storage data you got customer data you got you know order data you got log files you might have parts data you know what's an inventory kind of thing and you want to build warehouses based on that data you might have marketing a warehouse you might have a sales warehouse you might have a finance warehouse maybe there's a supply chain warehouse so again by separating the compute from that sort of virtualized compute from the from the storage layer you can access any data leave the data where it is and I'll talk about this in more and bring the compute to the data so this is what in part the cloud layer does they've got security and governance they got data warehouse management in that cloud layer and and resource optimization but the key in in my opinion is this metadata management I think that's part of snowflakes secret sauce is the ability to leave data where it is and have the smarts and the algorithms to really efficiently bring the compute to the data so that you're not moving data around if you think about how traditional data warehouses work you put all the data into a central location so you can you know operate on it well that data movement takes a long long time it's very very complicated so that's part of the secret sauce is knowing what data lives where and efficiently bringing that compute to the data this dramatically improves performance it's a game changer and it's much much less expensive now when I come back to Frank's Luqman this is somebody that I've is a career that I've followed I've known had him on the cube of a number of times I first met Frank Sloot when he was at data domain he took that company took it public and then sold it originally NetApp made a bid for the company EMC Joe Tucci in the defensive play said no we're not gonna let Ned afgan it there was a little auction he ended up selling the company for I think two and a half billion dollars sloop and came in he helped clean up the the data protection business of EMC and then left did a stint as a VC and then took over service now when snoop and took over ServiceNow and a lot of people know this the ServiceNow is the the shiny toy on Wall Street today service that was a mess when saluteth took it over it's about 100 120 million dollar company he and his team took it to 1.2 billion dramatically increased the the valuation and one of the ways they did that was by thinking about the Tam and expanding that Tim that's part of a CEOs job as Tam expansion Steuben is also a great operational guy and he brought in an amazing team to do that I'll talk a little bit about that team effect uh well he just brought in Mike Scarpelli he was the CFO was the CFO of ServiceNow brought him in to run finance for snowflake so you've seen that playbook emerge you know be interesting Beth white was the CMO at data domain she was the CMO at ServiceNow helped take that company she's an amazing resource she kind of you know and in retirement she's young but she's kind of in retirement doing some advisory roles wonder if slooping will bring her back I wonder if Dan Magee who was ServiceNow is operational you know guru wonder if he'll come out of retirement how about Dave Schneider who runs the sales team at at ServiceNow well he you know be be lord over we'll see the kinds of things that Sluman looks for just in my view of observing his playbook over the years he looks for great product he looks for a big market he looks for disruption and he looks for off-the-chart ROI so his sales teams can go in and really make a strong business case to disrupt the existing legacy players so I one of the things I said that snoopin looks for is a large market so let's look at this market and this is the thing that people missed around ServiceNow and to credit Pat myself and David for in the back you know we saw the Tam potential of ServiceNow is to be many many tens of billions you know Gartner when they when ServiceNow first came out said hey helpdesk it's a small market couple billion dollars we saw the potential to transform not only IT operations but go beyond helpdesk change management at cetera IT Service Management into lines of business and we wrote a piece on wiki Vaughn back then it's showing the potential Tam and we think something similar could happen here so the market today let's call 20 billion growing to 30 Billy big first of all but a lot of players in here what if so one of the things that we see snowflake potentially being able to do with its architecture and its vision is able to bring enterprise search you know to the marketplace 80% of the data that's out there today sits behind firewalls it's not searchable by Google what if you could unlock that data and access it in query at anytime anywhere put the power in the hands of the line of business users to do that maybe think Google search for enterprises but with provenance and security and governance and compliance and the ability to run analytics for a line of business users it's think of it as citizens data analytics we think that tam could be 70 plus billion dollars so just think about that in terms of how this company might this company snowflake might go to market you by the time they do their IPO you know it could be they could be you know three four five hundred billion dollar company so we'll see we'll keep an eye on that now because the markets so big this is not like the ITSM the the market that ServiceNow was going after they crushed BMC HP was there but really not paying attention to it IBM had a product it had all these products that were old legacy products they weren't designed for the cloud and so you know ServiceNow was able to really crush that market and caught everybody by surprise and just really blew it out there's a similar dynamic here in that these guys are disrupting the legacy players with a cloud like model but at the same time so the Amazon with redshift so is Microsoft with its analytics platform you know teradata is trying to figure it out they you know they've got an inertia of a large install base but it's a big on-prem install base I think they struggle a little bit but their their advantages they've got customers locked in or go with exudate is very interesting Oracle has burned the boats and in gone to cloud first in Oracle mark my words is is reacting everything for the cloud now you can say Oh Oracle they're old school they're old guard that's fine but one of the things about Oracle and Larry Ellison they spend money on R&D they're very very heavy investor in Rd and and I think that you know you can see the exadata as it's actually been a very successful product they will react attacked exadata believe you me to to bring compute to the data they understand you can't just move all this the InfiniBand is not gonna solve their problem in terms of moving data around their architecture so you know watch Oracle you've got other competitors like Google who shows up well in the ETR survey so they got bigquery and BigTable and you got a you know a lot of other players here you know guys like data stacks are in there and you've got you've got Amazon with dynamo DB you've got couch base you've got all kinds of database players that are sort of blurring the lines as I said between sequel no sequel but the real takeaway here from the ETR data is you've got cloud again is winning it's driving the discussion and the spending discussion with an IT watch this company snowflake they're gonna do an IPO I guarantee it hopefully they will see if they'll get in before the booth before the market turns down but we've seen this play by Frank Sluman before and his team and and and the spending data shows that this company is hot you see them all over Silicon Valley you're seeing them show up in the in the spending data so we'll keep an eye on this it's an exciting market database market used to be kind of boring now it's red-hot so there you have it folks thanks for listening is a Dave Volante cube insights we'll see you next time

>> Live from San Diego, California It's the queue covering Sisqo Live US 2019 Tio by Cisco and its ecosystem. Barker's >> back to San Diego. Everybody watching the Cube, the leader and live tech coverage. My name is Dave Volante, and I'm with my co host, Steuben. Amanda, this is Day two for Sisqo. Live 2019. We're in the definite. So still. I was walking around earlier in the last interview, and I think I saw Ron Burgundy out there. Stay classy Sleeping Gow is here. He's the founder and CEO of Met Net Brain Technology's just outside of Boston. Thanks very much for coming on the Q. Thank you there. So you're very welcome. So I want to ask you, I always ask Founders passion for starting companies. Why did you start? >> Well, maybe tired of doing things, Emmanuel. Well, that's alongside the other side of Yes, I used Teo took exam called a C C. I a lot of folks doing here. I failed on my first try. There was a big blow to my eagle, so I decided that we're gonna create a softer help them the past. This is actually the genesis of nettle. I met a friend help people three better doing their network management. >> That's a great story. So tell us more about that brain. What do you guys all about? >> Sure, we're the industry. First chasing time. Little confirmations after our mission is to Democrat ties. Merrick Automation. Every engineer, every task. They should've started with automation before human being touched. This task, >> you know, way go back. Let's say, 10 years ago people were afraid of automation. You know, they thought I was going to take away their jobs. They steal and they still are. We'll talk about that. You get this and I want to ask you about the blockers. They were fearful they wanted the touch thing. But the reality is people talk about digital transformation. And it's really all about how you use data, how your leverage data. And you can't be spending your time doing all this stuff that doesn't add value to your business. You have to automate that and move up to more valuable test. But so people are still afraid of automation. Why, what's the blocker there? >> They have the right reason to be afraid. Because so many automation was created a once used exactly wass right. And then you have the cost ofthe tradition automation. You have the complexity to create in their dark automation. You guys realize that middle confirmation You cannot have little gotta measure only work on a portion of your little way. You have to walk on maturity if not all of your narrow right. So that's became very complex. Just like a You wanna a self driving car? 10 You can't go buy a Tesla a new car. You can drive on a song. But if you want to your Yoder Puta striving always song Richard feared it. That's a very complex Well, let's today, Netto. Condemnation had to deal with you. Had a deal with Marty Venna Technology Marty, years of technology. So people spent a lot of money return are very small. There's so they have a right to a fair afraid of them. But the challenges there is what's alternative >> way before you're there. So there, if I understand it, just playing back there, solving a very narrow problem, they do it once, maybe twice. Maybe a rudimentary example would be a script. Yeah, right, right. And then it breaks or it doesn't afford something else in the network changes, and it really doesn't affect that, right? >> Yeah. I mean, you know, I think back to money network engineers. It's like, Well, I'm sitting there, I've got all my keep knobs and I get everything done and they say, No, don't breathe on it because it's just the way I want it less. It can't be that doesn't scale. It doesn't respond to the business. I need to be able to, you know, respond fast what is needed. And things are changing in every environment. So it's something that I couldn't, as you know, a person or a team keep up with myself, and therefore I need to have more standardized components, and I need to have intelligence that can help me. >> Let's sit and let's >> s so we've laid out the generalized way that we've laid out the problem. What's what's the better approach? >> Well, give you looking out of the challenge today is you have to have Dave ups, which a lot of here they have not engineer know howto script and the mid off the engineer who know how little cooperates walk together. So there's a date, a part of it. There's a knowledge. A part of this too has to meet to create a narrow coordination and that Ned Ogata may have to be a scale. So the challenge traditional thoracotomy here, why is for short lie on if you're going down? Technical level is wise A terra, too many data and structure and the otherwise Our knowledge knowledge cannot be codified. So you have the knowledge sitting people's head, right, Eh Programa had to walk in with a narrow canyon near together. You make it a cost hire. You make it a very unskilled apple. So those are the challenge. So how fast Motor way have to do so neither brand for last 15 years You decide to look differently that we created some saying called operating system off total network and actually use this to manage over 1,000 of mental models technology. And he threw problem. You can't continually adding new savings into this problem. So the benefit of it is narrow. Canyon near anybody can create automation. They don't have to know how to writing a code. Right? And Deborah, who knows the code can also use this problem. All the people who are familiar with technology like and people they can integrate that never >> pray. Okay, so you have all this data I wish I could say is unstructured So he doesn't have any meaning. Data's plentiful insights aren't, uh And then you have this what I call tribal knowledge. Joe knows how to do it, but nobody else knows how to do it. So you're marrying those two. How are you doing that? Using machine intelligence and and iterating building models, can you get that's amore colors? Tow How you go about that? What's the secret sauce >> way? Took a hybrid approach. First call on you have to more than the entire network. With this we'll kind of operating system called on their own way have about 20 12,000 valuables modeling a device and that 12,000 valuable adults across your let's say 1,000 known there or there will be 12,000,000 valuables describing your medal. That's that's first. Zang on top of 12,000,000 valuables will be continually monitored. A slow aye aye, and the machine learning give something called a baseline data. But on top of it, the user, the human being will have the knowledge young what is considered normal what is considered abnormal. They can add their intelligence through something called excludable rumble on couple of this system, and their system now can be wrong at any time. Which talking about where somebody attacking you when that OK is un afford all you through a human being, all our task Now the automation can be wrong guessing time. So >> this the expert, the subject matter expert, the main expert that the person with the knowledge he or she can inject that neck knowledge into your system, and then it generates and improves overtime. That's right, >> and it always improve, and other people can open the hood. I can't continue improving. Tell it so the whole automation in the past, it was. Why is the writer wants only used once? Because it's a colossal? It's a script. You I you input and output just text. So it wasn't a designer with a company, has a motive behind it. So you do it, You beauty your model. You're writing a logical whizzing a same periods off, we decided. We think that's you. Cannot a scale that way. >> OK, so obviously you can stop Dave from inputting his lack of knowledge into the system with, you know, security control and access control. Yeah, but there must be a bell curve in terms of the quality of the knowledge that goes into the system. You know, Joe might be a you know, a superstar. And, you know, stew maybe doesn't know as much about it. No offense, too. Student. So good. So how do you sort of, you know, balance that out? Do you tryto reach an equilibrium or can you wait? Jos Knowledge more than Stu's knowledge. How does that work? >> So the idea that this automation platform has something called excludable Rambo like pseudo Rambo can sure and implacably improved by Sri source One is any near themselves, right? The otherwise by underlying engine. So way talk about a I and the machine learning we have is that we also have a loo engine way. Basically, adjusting that ourselves certainly is through Claverie Partner, for example, Sisko, who run many years of Qatar where they have a lot of no house. Let's attack that knowledge can be pushed to the user. We actually have a in our system that a partnership with Cisco attack South and those script can be wrong. slow. Never prayer without a using woman getting the benefit of without talking with attack. Getting the answer? >> Yes, I think you actually partially answered. The question I have is how do you make sure we don't automata bad process? Yeah. So And maybe talk a little bit about kind of the training process to your original. Why of the company is to make things easier. You know, What's the ramp up period for someone that gets in giving me a bit of a how many engineers you guys have >> worked with? The automatic Allied mission. Our mission statement of neda prayer is to Democrat ties. Network automation, you know, used to be network automation on ly the guru's guru to it. Right, Dave off. Send a satchel. And a young generation. My generation who used come, Ally, this is not us, right? This is the same, you know. But we believe nowadays, with the complicity of middle with a cloud, computing with a cybersecurity demand the alternative Genetic automation is just no longer viable. So way really put a lot of starting to it and say how we can put a network automation into everyone's hand. So the things we tell as three angle of it, while his other missions can be created by anyone, the second meaning they've ofthe net off. Anyone who know have knowledge on metal can create automation. Second piece of automation can lunched at any time. Somebody attacking you middle of the night. They don't tell you Automation can lunch to protect Theo, and they're always out. You don't have people the time of the charter. Automation can lunch the tax losses, so it's called a lunch. Any time certain want is can adapt to any work follow. You have trouble shooting. You have nettle changes. You have compliance, right? You have documentation workflow. The automation should be able to attack to any of this will clothe topping digression tomorrow. We have when service now. So there's a ticket. Human being shouldn't touches a ticket before automation has dies, she'll write. Is a human should come in and then use continually use automation. So >> So you talk about democratizing automation network automation. So it's so anybody who sees a manual process that's wasting time. I can sort of solve that problem is essentially what you're >> doing. That's what I did exactly what we >> know So is there, uh, is there a pattern emerging in terms of best practice in terms of how customers are adopting your technology? >> Yes. Now we see more animal customer creating This thing's almost like a club, the power user, and we haven't caught it. Normal user. They have knowledge in their heads. Pattern immunity is emergent. We saw. Is there now work proactively say, How can I put that knowledge into a set of excludable format so that I don't get escalate all the time, right? So that I can do the same and more meaningful to me that I be repeating the same scene 10 times a month? Right? And I should want it my way. Caught a shift to the left a little while doing level to the machine doing the Level one task level two. Level three are doing more meaningful sex. >> How different is what you're doing it net brain from what others are doing in the marketplace. What's the differentiation? How do you compete? >> Yeah, Little got 1,000,000 so far has being a piecemeal, I think, a fragment. It's things that has done typical in a sweeping cracker. Why is wholesale Hardaway approach you replace the hardware was esti N S P. Where's d? Let there's automation Capitol Building Fifth, I caught a Tesla approached by a Tesla, and you can drive and a self driving. The second approaches softer approach is as well. We are leading build a model of your partner or apply machine learning and statistics and was behind but also more importantly, open architecture. Allow a human being to put their intelligence into this. Let's second approach and insert approaches. Actually service little outsourcer take you, help you We're moving way or walk alone in the cloud because there's a paid automation there, right so way are focusing on the middle portion of it. And the landscaper is really where we have over 2,000 identifies customer and they're automating. This is not a just wall twice a week, but 1,000 times a day. We really excited that the automation in that escape scale is transforming how metal and is being managed and enable things like collaboration. But I used to be people from here. People from offshore couldn't walk together because knowledge, data and knowledge is hard to communicate with automation. We see collaboration is happening more collaboration happening. So we've >> been talking about automation in the network for my entire career. Feels like the promise has been there for decades. That site feels like over the last couple of years, we've really seen automation. Not just a networking, but we've been covering a lot like the robotic process automation. All the different pieces of it are seeing automation. Bring in, gives a little bit look forward. What? What do you predict is gonna happen with automation in I t over the next couple of years? A >> future that's great Way have a cloud computing. We have cyber security. We have the share of scale middle driving the network automation to the front and center as a solution. And my prediction in the next five years probably surrounded one izing automation gonna be ubiquitous. Gonna be everywhere. No human being should touch a ticket without automation through the first task. First right second way. Believe things called a collaborative nature of automation will be happy. The other was a local. Automation is following the packet from one narrow kennedy to the other entity. Example would be your manager service provider and the price they collaborated. Manager Nettle common little But when there's something wrong we don't know each part Which part? I have issues so automation define it by one entity Could it be wrong Across multiple So is provider like cloud provider also come Automation can be initiated by the Enterprise Client way also see the hado A vendor like Cisco and their customer has collaborated Automation happening So next five years will be very interesting The Manu away to manage and operate near Oca will be finally go away >> Last question Give us the business update You mentioned 2,000 customers You're hundreds of employees Any other business metrics you Khun, you can share with us Where do you want to take this company >> way really wanted behind every enterprise. Well, Misha is a Democrat. Eyes network automation way Looking at it in the next five years our business in a girl 10 times. >> Well, good luck. Thank you. Thanks very much for coming on the queue of a great story. Thank you. Thank you for the congratulations For all your success. Think Keep right! Everybody stew and I will be back. Lisa Martin as well as here with an X guest Live from Cisco Live 2019 in San Diego. You watching the cube right back