hey everyone this is thecube's live coverage from los angeles of kubecon and cloud native con north america 21 lisa martin with dave nicholson we're going to be talking with the founder and ceo next of cloudnatics rohit seth rohit welcome to the program thank you very much lisa pleasure to meet you good to meet you too welcome so tell the the audience about cloudnatics what you guys do when you were founded and what was the gap in the market that you saw that said we need a solution so just to start uh cloud9x was started in 2019 by me and the reason for starting cloud netex was as i was starting to look at the cloud adoption and how enterprises are kind of almost blindly jumping on this cloud bandwagon i started reading what are the key challenges the market is facing and it started resonating with what i saw in google 15 years before when i joined google the first thing i noticed was of course the scale would just overwhelm anyone but at the same time how good they are utilized at that scale was the key that i was starting to look for and over the next couple of months i did all the scripting and such with my teams and found out that lower teens is the utilization of their computers servers and uh lower utilization means if you're spending a billion dollars you're basically wasting the major portion of that and a tech savvy company like google if that's a state of affair you can imagine what would be happening in other companies so in any case we actually now started work at that time started working on a technology so that more groups more business units could share the same machine in a efficient fashion and that's what led to the invention of containers over the next six years we rolled out containers across the whole google fleet the utilization went up at least three times right fast forward 15 years and you start reading 125 billion dollars are spent on a cloud and 60 billion dollars of waste someone would say 90 billion dollars a waste you know what i don't care whether 60 or 90 billion is a very large number and if tech savvy company google couldn't fix it on its own i bet you it it's not an easy problem for enterprises to fix it so we i started talking to several executives in the valley about is this problem for real or not the worst thing that i found was not only they didn't know how bad the problem was they actually didn't have any means to find out how bad the problem could be right one cfo just ran like headless chicken for about two months to figure out okay i know i'm spending this much but where is that spend going so i started kind of trading those waters and i started saying okay visibility is the first thing that we need to provide to the end customer saying that listen it doesn't need to be rocket science for you to figure out how much is your marketing spending how much your different business units so the first line of action is basically give them the visibility that they need to make the educated business decisions about how good or how bad they are doing their operations once they have the visibility the next thing is what to do if there is a waste there are a thousand different type of vms on aws alone people talk about complexity on multi-cloud hybrid cloud and that's all right but even on a single cloud you have thousand vms the heterogeneity of the vms with dynamic pricing that changes every so often is a killer and so and so rohit when you talk about driving levels of efficiency you're not just you're not just talking about abstraction versus bare metal utilization you're talking about even in environments that have used sort of traditional virtualization yes okay absolutely i think all clouds run in vms but within vms sometimes you have containers sometimes you don't have containers if you don't have containers there is no way for you to securely have a protagonist and antagonist job running on the same machines so containers basically came to the world just so that different applications could share the same resources in a meaningful fashion we are basically extending that landscape to to the enterprises so that that utilization benefit exists for everyone right so first of first order business for cloud natick is basically provide them the visibility on how well or bad they are doing the second is to give them the recommendation if you are not doing well what to do about it to do well and we can actually slice and dice the data based on what is important for you okay we don't tell you that these are the dimensions that you should be looking at of course we have our recommendations but we actually want you to figure out basically do you want to look at your marketing organization or your engineering organization or your product organization to see where they are spending money and you can slice and match that data according and we'll give you recommendations for those organizations but now you have the visibility now you have the recommendations but then what right if you ask a cubernities administrator to go and apply those recommendations i bet you the moment you have more than five cluster which is a kind of a very ordinary thing it'll take at least two hours just to figure out how to go from where you are to be able to log in and to be able to apply those recommendations and then changing back the ci cd pipelines and asking your developers to be cognizant about your resources next time is a month-long ordeal no one follows it that's why those recommendations falls on deaf ears most of the time what we do is we give you the choice you want to apply those recommendations manually or you can put the whole system on autopilot in which case once you have enough confidence in cloud native platform we will actually apply those recommendations for you dynamically on the fly as your workloads are increasing or decreasing in utilization and where are your customer conversations happening you mentioned the cfl you mentioned the billions in cloud waste where do you start having these conversations within an organization because clearly you mentioned marketing services you can give them that visibility across the organization who are you talking to within these customers so we start with mostly the cios ctos vp of engineering but it's very interesting we say it's a waste and i think the waste is most more of an effect than a cause the real cause is the complexity and who is having the complexity is the devops and the developers so in 99 of our customer interactions we basically start from cios and ctos but very soon we have these conversations over a week with developers and devops leads also sitting in the room saying that but this is a challenge on why i cannot do this so what we have done is to address the real cause and waste aspect of cloud computing we have we have what we call the management console through which we reduce the complexity of kubernetes operations themselves so think about how you can log into a crashing pod within two minutes rather than two hours right and this is where cloud native start differentiating from the rest of the competition out there because we provide you not only or do this recommendation do this right sizing of vm here or there but this is how you structurally fix the issue going forward right i'm not going to tell you that your containers are not going to crash loop their failures are regular part of distributed systems how you deal with them how you debug them and how you get it back up and running is a core integral part of how businesses get run that's what we provide in cloud natives platform a lot of this learning that we have is actually coming from our experience in hyperscalers we have a chief architect who is also from google he was a dl of a technology called borg and then we have sonic who was the head of products at mesosphere before so we understand what it takes for an enterprise who's primarily coming from on-prem or even the companies that are starting from cloud to scale in cloud often you hear this trillion dollar paradoxes that hey you're stupid if you don't start from cloud and you're stupid if you scale at cloud we are saying that if you're really careful about how you function on cloud it has a value prop that can actually take you to the web scalar heights without even blinking twice can you share an example of one of your favorite customer stories absolutely even by industry only where you've really shown them tremendous value in savings absolutely so a couple of discussions that happened that led like oh but we are we have already spent a team of four people trying to optimize our operations over the last year and we said that's fine uh you know what our onboarding exercise takes only 20 minutes right let's do the onboarding in about a week we will tell you if we could save you any money or not and put your best devops on this pov prove a value exercise to see if it actually help their daily life in terms of operations or not this particular customer only has 30 clusters so it's not very small but it's not very big in terms of what we are seeing in the market first thing the maximum benefit or the cost optimization that they could do over the past year using some of the tools and using their own top-class engineering shots were about seven to ten percent within a week we told them 38 without even having those engineers spend more than two hours in that week we gave them the recommendations right another two weeks because they did not want to put it on autopilot just because it's a new platform in production within next two hours they were able to apply i think at least close to 16 recommendations to their platform to get that 37 improvement in cost what are some examples of of recommendations um obviously you don't want to reveal too much of the secret sauce behind the scene but but but you know what are some what are some classic recommendations that are made so some of them could be as low-hanging fruit as or you have not right sized your vms right this is what i call a lot of companies you would find that oh you have not right side but for us that's the lowest hanging code you go in and you can tell them that whether you have right size that thing or not but in kubernetes in particular if you really look at how auto scaling up and how auto scaling down happens and particularly when you get a global federated view of the number of losses that's where our secret sources start coming and that's where we know how to load balance and how to scale vertically up or how to scale horizontally within the cluster right those kind of optimization we have not seen anywhere in the market so far and that's where the most of the value prop that our customers are seeing kind of comes out and it doesn't take uh too much time i think within a week we have enough data to to say that this service that has thousands of containers could benefit by about this much and just to kind of give you i wouldn't be able to go into the specific dollar numbers here but we are talking in at least a 5 million ish kind of a range of a spend for this cluster and think about it 37 of that if we could save that that kind of money is a real money that not only helps you save your bottom line but at that level you're actually impacting your top line of the business as well sure right that's our uh value crop that we are going to go in and completely automate you're not going to look for devops that don't exist anymore to hire one of the key challenges i'm pretty sure that you must have already heard 86 percent of businesses are not able to hire the devops and they want to hire 86 percent what happens when you don't have that devops that you want to have your existing devops want to move as fast cutting corners sometimes not because they don't know anywhere but just because there's so much pressure to do so much more they don't scale when things become brittle that's when um the fragility of the system comes up and when the demand goes up that's when the systems break but you're not prepared for that breakage just because you have not really done the all the things that you would have done if you had all the time that you needed to do the right thing it sounds like some of the microservices that are in containers that are that run the convention center here have just crashed i think it's gone hopefully the background noise didn't get picked up too much yeah but you're the so the the time to value the roi that you're able to deliver to customers is significant yes you talked about that great customer use case are there any kind of news or announcements anything that you want to kind of share here that folks can can be like looking forward to without the index absolutely so two things even though this is kubecon and everyone is focused on kubernetes kubernetes is still only about three to five percent of enterprise market okay we differentiate ourselves by saying that it doesn't matter whether you're running kubernetes or you're in running legacy vms we will come on board in your environment without you making a single line of change in less than 20 minutes and either we give you the value prop in one week or we don't all right that's number one number two we have a webinar coming on november 3rd uh please go to cloudnetix.com and subscribe or sign up for that webinar sonic and i will be presenting that webinar giving you the value proposition going through some use cases that oh we have seen with our customers so far so that we can actually educate the broader audience and let them know about this beautiful platform i think that my team has built up here all right cloudnatics.com rohit thank you for joining us sharing with us what you're doing at cloud natives why you founded the company and the tremendous impact and roi that you're able to give to your customers we appreciate learning more about the technology thank you so much and i really believe that cloud is here for stay for a long long time it's a trillion dollar market out there and if we do it right i do believe we will accelerate the adoption of cloud even further than what we have seen so far so thanks a lot lisa it's been a pleasure nice to meet you it's a pleasure we want to thank you for watching for dave nicholson lisa martin coming to you live from los angeles we are at kubecon cloudnativecon north america 21. dave and i will be right back with our next guest thank you you

>> Announcer: Live from Washington, DC, it's theCube, covering AWS Public Sector Summit 2017, brought to you by Amazon Web Services and its partner ecosystem. >> Welcome inside the convention center here in Washington, DC. You're looking at many of the attendees of the AWS Public Sector Summit 2017. We're coming to you live from our nation's capital. Several thousand people on hand here for this three-day event, we're here for two days. John Walls, along with John Furrier. John, good to see you again, sir. >> Sir, thank you. >> We're joined by Jay Littlepage, who is the VP of Infrastructure and Operations at Digital Globe, and Jay, thank you for being with us at theCube. >> My pleasure. >> John W: Good to have you. First off, your company, high-resolution, earth imagery satellite stuff. Out-of-this world business. >> Yep. >> Right, tell our viewers a little bit about what you do, I mean, the magnitude of, obviously, the environmental implications of that or defense, safety security, all those realms. >> Okay, well, stop me when I've said too much because I get pretty excited about this. We work for a very cool company. We've been taking earth imagery since 1999, when our first satellite went up in the sky. And, as we've increased our capabilities with our constellation, our latest satellite went up last November. We're flying, basically, a giant camera that we can fly like a drone. So, and when I say giant camera, it's about the size of a school bus, and the lens is about the size of the front of the school bus, and we can take imagery from 700 miles up in space and resolve a pixel about the size of a laptop. So, that gives us an incredible amount of capability, and the flying like a drone, besides just being really cool and geeky, we can sling the lens from basically Kansas City to here in Washington in 15 seconds and take a shot. And so, when world events happen, when an earthquake happens, you know, they're generally not scheduled events, we don't have to have the satellite right above the point where there's something going on on the ground, we can take a shot from an angle of 1,000 miles away, and with compute power and good algorithms, we can basically resolve the picture of the earth, and it looks like we're right overhead, and we're getting imagery out immediately to first responders, to governmental agencies so they can respond very quickly to a disaster happening to save lives. >> So, obviously, the ramifications are endless, almost, right? >> Yes. >> All that data, I mean, you can't even imagine the amount, talk about storage. So, that's certainly a complexity, and then, they are making it useful too all these different sectors. Without getting too simple, how do you manage that? >> Well, you know, it's a big trade-off because, ideally, if storage was free, all of our imagery in its highest consumable form would be available all the time to everybody. Each high-resolution image might be 35 gig by itself. So, you think of that long of flying a constellation, we've got 100 petabytes of imagery. That's too much, it's too expensive to have online all of the time. And so, we have to balance what's going to be relevant and useful to people versus cost. You know, a lot of the imagery goes through cycle where it's interesting until it's not, and it starts to age off. The thing about the planet, though, is we never know what's going to happen, and when something that aged off is going to be relevant again. And so, the balance for my team is really making sure we're hitting the sweet spot on there. The imagery that is relevant is readily accessible, and the imagery that's not is, in its cheapest form, fact or possible, which for us, is compressed, and it's in some sort of archival storage, which for us, now that we've used the Snowmobile, is Glacier. >> Jay, I want to ask to give your thoughts. I want you to talk about DigitalGlobe, before that, some context. This weekend, I was hanging out with my friends in Santa Cruz and kids were surfing. He's a big drone guy, he used to work for GoPro, and she used to buy the drones and, hey, how's it going with the drones. It got kind of boring, here's a great photo I created, but after a while, it just became like Google Earth, and it got boring. Kind of pointed out that he wanted more, and we got virtual reality, augmented reality, experience is coming to users. That puts imagery, place imagery, the globe, pictures, places and things is what you guys do. So, that's not going away any time soon. So, talk about your business, what you guys do, some of the things that you do, your business model, how that's changing, and how Amazon, here in the public sector, is changing that. >> Well, that's a fantastic questions, and our business is changing pretty rapidly. We have all that imagery, and it's beautiful imagery, but increasingly, there's so much of it, and so many of the use cases aren't about human eyeballs staring at pixels. They're about algorithms extracting information from the pixels. And, increasingly, from either the breadth of pixels, instead of just looking at a small area, you can look around it and see what's happening around it and use that as signal information, or you can go deep into an archive and see the same location on the planet over and over over years and see the changes that had happened in terms of time frame. So, increasingly, our market is about extracting information and extracting insights from the imagery more so than it is the imagery itself. And so that's driving an analytics business for us, and it's also driving a services business for us, which is particularly important in the public sector to actually use that for different purposes. >> You can imagine the creativity involved and developers out there watching or even thinking about using satellite imagery in conflux with other data. Remember, they're in the Web 2.0 craze earlier in the last decade. You saw mashups of API with Google Max. Oh yeah, pull a little pin, and then the mobile came. But now, you're seeing mashups go on with other data. And I've heard stats at Uber, for instance, remaps New York City every five days with all that GPS data of the cars, which are basically sensors. So, you can almost imagine the alchemy, the convergence of data. This is exciting for you, I can imagine. Won't you share with us, anecdotally or statistically what you're seeing, how this is playing out? >> Well, yes, some of our biggest commercial customers of our products now are location-based services. So, Uber's using our imagery because the size of the aperture of our lens means we have great resolution. And so, they've been consuming that and consuming our machine learning algorithms to basically understand where traffic is and where people are so that they can refine, on an ongoing basis, where the best pick-up and drop-off locations are. That really drives their business. Facebook's using the imagery to basically help build out the Internet. You know, they want to move into places on the planet where Internet doesn't exist. Well, in order to really understand that, they need to understand where to build, how to build, how many people are there, and you can actually extract all that from imagery by going in in detail and mapping roofs' shapes and roofs' sizes, and, from there, extracting pretty accurate estimates of how many people live in a particular area, and that's driving their project, which is ultimately going to drive access for... >> Intelligence in software, we look at imagery. I mean, we here at Amazon, recognition's their big product for facial recognition, among other pictures. But this is what's getting at, this notion of actually extracting that data. >> Well, you think about it. You know, once the data is available, once our imagery is available, then the sky's the limit. You know, we have a certain set of algorithms that we apply to help different industries, you know, to look at rooftops, to look at water extractions. After a hurricane, we can actually see how the coverage has changed. But, you look at a Facebook, and they're applying their own algorithms. We don't force our algorithms to be used. We provide the information, we try to provide the data. Companies can bring their own algorithms, and then, it's all about what can you learn, and then, what can you do about it, and it's amazing. >> So, here's the question. With the whole polyglot conversation, multiple languages that people speak that's translated into the tech industry, and interdisciplinary forces are in play: Data science, coding, cognitive, machine learning. So, the question is, for you, is that, okay, as this stuff comes together, do you speak DevOps? It's kind of a word, and we hear people say, is that in Russian or is that like English? DevOps is a cloud language mindset. And so, that brings up the question of, are you guys friendly to developers, and because people want to have microservices, I'm from a developer, I'm like, hey, I want those maps. How do I get them, can I buy it as a service, are they loaded on Amazon, how to I gauge with DataGlobe, as a developer or a company? >> Well, you think about what you just said and the customers I just talked about. They're not geospatial customers. You know, they're not staffed with people that are PhDs in extracting information. They're developers that are working for high-tech companies that have a problem that want to solve. >> There are already mobile apps or doing some cool database working in here. >> So, we're providing the raw imagery and the algorithms to very tried and true systems where people can plug into work benches and build artificial intelligence without necessarily being experts in that. And, as a case in point, my team is an IT team. You know, we've got a part of the organization that is all staffed with PhDs. They're the ones that are driving our global... >> John W: PhD is a service. (laughter) >> Well, kind of. I mean, if you think about it, they're driving the leading edge, for these solutions to our customers. But, I've got an IT team, and I've got this problem with all this data that we talked about earlier. Well, how am I actually going to manage that? I'm going to be pulling in all sorts of different sources of data, and I'm going to be applying machine learning using IT guys that aren't PhDs to actually do that, and I'm not going to send them to graduate school. They're going to be using standard APIs, and they're going to be applying fairly generic algorithms, and... >> So, is that your model, is it just API, is there other... >> I think the real key is the API makes it accessible, but a machine learning algorithm is only as good as its training. So, the more it's used, the more it refines itself, the better our algorithm gets. And so, that is going to be the type of thing that the IT developer, the infrastructure engineer of the future becomes, and I've already, basically, in the last couple of years, as we started this journey at AWS, 20% of my staff now, same size staff, but they're software developers now. >> So, I'll take this to the government side. We talked a lot about commercial use. But at the government side, I'm thinking about FEMA, disaster response, maybe a core of engineers, you know, bridge construction, road construction, coastline management. Are all those kind of applications that we see on the dot gov side? >> There are all things that you see that can be done on the dot gov side, but we're doing them all in the commercial environment. The USC's region for AWS, and I think that's actually a really important distinction, and it's something that I think more and more of the government agencies are starting to see. We do a lot of work for one particular government agency and have for years. But 99 point something percent of our imagery is commercial unclassified, and it's available for the purposes that our customers use it for, but they're also available for all those other customers I've talked about. And more and more of what we do, we are doing on the completely open but secure commercial environment because it's ubiquitous for our customers. Not all of our customers do that type of work. They don't need to comply with those rigid standards. It's generally where all AWS products that are released are released to, with the other environments lagging, and they probably don't want me saying that on TV, but I just did. And it's cheaper, you know, we're a commercial company that does public sector work. We have to make a profit, and the best way to do that is to put your environment in a place where if you're going to repeat an operation, like pulling an image of Glacier and build it into something that is consumable by either a human or an algorithm and put it back. If you're going to do something like that a million times, you want to do it really inexpensively. And so, that's where... (crosstalking) >> Lower prices, make things fast, that's Jeff Hayes' ethos, shipping products, that these books in the old days. Now, they're shipping code and making lower-latency systems. So, you guys are a big customer. What are the big implementation features that you have with AWS, and then, the second part of the question is, are you worried about locking. At some point, you're so big, the hours are going to be so massive, you're going to be paying so much cash, should you build your own, that's the big debate. Do you go private cloud, do you stay in the public? Thoughts on those two options? >> Well, we have both. Right now, we're running a 15-year-old system, which is where we create the imagery that comes off the satellites, and it goes into a tape archive. Last year, Reinvent... >> John F: Tape's supposed to be dead! >> Tape will die someday! It's going to die really soon, but, at the Reinvent Conference last year, AWS rolled out a semi truck. Well, the real semi truck was in our parking lot getting loaded with all those tapes, and it's sad... >> John F: Did you actually use the semi? >> We were the first customer ever, I believe, of the Snowmobile. And so, it takes a lot of time and effort to move 12,000 LTO 5 tapes loaded onto a semi and send it off. You know, that represents every image ever taken by DG in the history of our company, and it's now in AWS. So, to your second part of your question, we're pretty committed now. >> John F: Are you okay with that? >> Well, we're okay with that for a couple of reasons. One is, I'm not constraining the business. AWS is cheaper. It will be even cheaper for us as we learn how to pull all the levers and turn all the dials in this environment. But, you know, you think about that, we ran a particular job last year for a customer that consumes 750,000 compute hours in 22 days. We couldn't have done that in our data center. We would have said no. And so, I would... >> I know, I can't do, you can't do it. >> We can't do it! Or, we can do it, come back, the answer will be here in six months. So, time is of the essence in situations like that, so we're comfortable with it for our business. We're also comfortable with it because, increasingly, that's where our customers already are. We are creating something in our current environment and shipping it to Amazon anyway. >> We're going to start a movie about you, with Jim Carrey, Yes Man. (laughter) You're going to say yes to everything now with Amazon. Okay, but this is a good point. Joking aside, this is interesting because we have this debate all the time, when is the cloud prohibitive. In this case, your business model, based on that fact that variables spend that you turn up your Compute is based upon cadence of the business. >> That's exactly right. You know, the thing that's really changed for the business with this model is historically, IT has been a call center, and moving into Amazon, I manage our storage, and I pay for our storage because it's a shared asset. It's something that is for the common good. The business units and different product managers in our business now have the dial for what they spend on the Compute and everything else. So, if they want to go to market really rapidly, they can. If they want to spin it up rapidly, they can. If they want to turn it down, they can. And it's not a fixed investment. So, it allows the business philosophy that we've never had before. >> Jay, I know we're getting tight on time, but I do want to ask you one question, and I did not know that you were the first Snowmobile customers, so, that's good trivia to have on theCube and great to have you. So, while we got you here, being the first customer of AWS Snowmobile when they rolled out at Amazon Reinvent, we covered on SiliconAngle. Why did you jump on that and how was your experience been, share some color onto that whole process. >> Okay, it's been an iterative learning process for both us and for Amazon. We were sitting on all this imagery. We knew we wanted to get in AWS. We started using the Snowballs almost a year and a half ago. But moving 100 petabytes, 80 terabytes at a time, it's like using a spoon to move a haystack. So, when Amazon approached us, knowing the challenge we had about moving it all at once, I initially thought they were kidding, and I realized it was Amazon, they don't kid about things like this, and so we jumped on pretty early and worked with them on this. >> John F: So, you've got blown away like, what? >> Just like. >> What's the catch? >> Really, a truck, really? Yeah, but really. So, it's as secure as it could possibly be. We're taking out the Internet and all the different variables in that, including a lot of cost in bandwidth and strengths, and basically parking and next to our data, and, you know, it's basically a big NFS file system, and we loaded data onto it, the constraint for us being, basically that tape library with 10,000 miles of movement on the tape pads. We had to balance between loading the Snowmobile and basically responding to our regular customers. You know, we pulled 4 million images a year off that tape library. And so, loading every single image we've ever created onto the Snowmobile at the same time was a technical challenge on our side more so than Amazon's side. So, we had to find that sweet spot and then just let it run. >> John F: Now, it's operational. >> So, the Snowmobile is gone. AWS has got it. They're adjusting it right now into the West Region, and we're looking forward to being able to just go wild with that data. >> We got Snowmobiles, we got semis, we have satellites, we have it all, right? >> We have it all, yeah. >> It's massive, obviously, but impressed with what you're doing with this. So, congratulations on that front, and thank you again for being with us. >> My pleasure, thanks for having me. >> You bet, we continue our coverage here from Washington, DC, live on theCube. SiliconAngle TV continues right after this. (theCube jingle)