>>Hey, welcome back everyone to cube Cod's coverage and cloud native con the I'm John for your husband, David Nicholson cube analyst, cloud analyst. Co-host you got two great guests, KIPP alumni, Jerry Chen needs no introduction partner at Greylock ventures have been on the case many times, almost like an analyst chair. It's great to see you. I got guest analyst and Martin mal who's the CEO co-founder of Chronosphere just closed a whopping $200 million series C round businesses. Booming. Great to see you. Thanks for coming on. Thank you. Hey, first of all, congratulations on the business translations, who would have known that observability and distributed tracing would be a big deal. Jerry, you predicted that in 2013, >>I think we predicted jointly cloud was going to be a big deal with 2013, right? And I think the rise of cloud creates all these markets behind it, right. This, you know, I always say you got to ride a wave bigger than you. And, uh, and so this ride on cloud and scale is the macro wave and, you know, Marty and Robin cryosphere, they're just drafted behind that wave, bigger scale, high cardinality, more data, more apps. I mean, that's, that's where the fuck. >>Yeah. Martin, all kidding aside. You know, we joke about this because we've had conversations where the philosophy of you pick the trend is your friend that you know, is going to be happening. So you can kind of see the big waves coming, but you got to stay true to it. And one of the things that we talk about is what's the next Amazon impact gonna look like? And we were watching the rise of Amazon. You go, if this continues a new way to do things is going to be upon us. Okay, you've got dev ops now, cloud native, but observability became really a key part of that. It's like almost the, I call it the network management in the cloud. It's like in a new way, you guys have been very successful. There's a lot of solutions out there. What's different. >>Yeah. I'd say for Kearney sphere, there's really three big differences. The first thing is that we're a platform. So we're still an observability platform. And by that, I mean, we solved the problem end to end. If thinking about observability and monitoring, you want to know when something's wrong, you want to be able to see how bad it is. And then you want to able to figure out what the root cause is. Often. There are solutions that do a part of that, that that problem might solve a part of the problem really well for a platform that does the whole thing. And 10 that's that's really the first thing. Second thing is we're really built for not just the cloud, but cloud native environments. So a microservices architecture on container-based infrastructure. And that is something that, uh, we, we have saw coming maybe 20 17, 20 18, but luckily for us, we were already solving this problem at Uber. That's where myself, my co-founder were back in 20 14, 20 15. So we already had the sort of perfect technology to solve this problem ahead of where the, the trend was going in the industry and therefore a purpose-built solution for this type of environment, a lot more effective than a lot of the existing. >>It's interesting, Jerry, you know, the view investing companies that have their problem, that they have to solve themselves as the new thing, versus someone says, Hey, there's a market. Let's build a solution for something. I don't really know. Well, that's kind of what's going on here. Right? It's >>That's why we love founders. Like Martin Marna, rod that come out with these hyperscale comes Uber's like we say, they've seen the future. You know, like there were Uber, they looked at the existing solutions out there trying to scale Promethease or you know, data dogs and the vendors. And it didn't work. It fell over, was too expensive. And so Martin Rob saw solid future. Like, this is where the world's going. We're going to solve it. They built MP3. It became cryosphere. And um, so I don't take any credit for that. You know, I just look fine folks that can see the future. >>Yeah. But they were solving their problem. No one else had anything. There's no general purpose software that managed servers you could buy, you guys were cutting your teeth into solving the pain. You had Uber. When did you guys figure out like, oh, well this is pretty big. >>Uh, probably about 20 17, 20 18 with a rise in popularity of Kubernetes. That's when we knew, oh wait, the whole world is shifting to this. It's not, no one could really it to just goober and the big tech giants of the world. And that's when we really knew, okay. The whole, the whole whole world is shifting here. And again, it's, it's sheer blind luck that we already had the ideal solution for this particular environment. It wasn't planned it. Wasn't what we were planning for back then. But, um, yeah. Get everything. >>It makes a lot of difference. When you walk into a customer and say, we had this problem, I can empathize with you. Not just say we've got solved. Exactly. Jerry, how do they compete in the cloud? We always talk about how Amazon and Azure want to eat up anything that they see that might, you know, something on AWS. Um, this castle in the cloud opportunity here. Okay. >>In the cloud. I mean, you know, we talked last time about how to fight the big three, uh, Amazon Azure and, uh, and Google. And I think for sure they have basic offerings, right. You know, Google Stackdriver years ago, they've done basically for Pete's offerings, basic modern offerings. I think you have like basic, simple needs. It's a great way to get started, but customers don't want kind of a piecemeal solution all the time. They want a full product. Like Datadog shows a better user experience, but full product is going to, you know, the better mousetrap the world will beat a path to your door. So first you can build a better product versus these point solutions. Number two is at some scale and some level complexity, those guys can handle like the demanding users that current affairs handling right now, right? The door dash, the world. >>And finally don't want the Fox guarding the hen house. You know, you don't want to say like Amazon monitoring, you can't depend on Amazon service monitoring your Amazon apps or Google service monitor your Google apps, having something that is independent and multi-cloud, that can dual things, Marta said, you know, see a triage, fixed your issues is kind of what you want. And, um, that's where the market's skilling. So I do believe that cloud guys will have an offering the space, but in our castle and cloud research, we saw that, yeah, there's a plenty of startups being funded. There's plenty of opportunity. And that the scoreboard between Splunk Datadog and all these other companies, that there's a huge amount of market and value to be created in this piece. So, >>So with, at, at the time, when you, you know, uh, uh, necessity is the mother of invention, you're an Uber, you have a practical problem to solve and use you look around you and you see that you're not the only entity out there that has this problem. Where are we in that wave? So not everyone is at, cloud-scale not everyone has adopted completely Kubernetes and cloud native for everything. Are we just at the beginning of this wave? How far from the >>Beach are we, we think we're just at the beginning of this wave right now. Um, and if you think about most enterprises today, they're still using on, and they're not even in perhaps in the cloud at all right. Are you still using perhaps APM and solutions, uh, on premise? So, um, if you look at that wave, we're just at the beginning of it. But when, but when we talked to a lot of these companies and you ask them for their three year vision, Kubernetes is a huge piece of that because everyone wants to be multi-cloud everyone to be hybrid eventually. And that's going to be the enabler of that. So, uh, we're just in the beginning now, but it seems like an inevitable wave that is coming. >>So obviously people evaluated that exactly the way you're evaluating that. Right. Thus the funding, right. Because no one makes that kind of investment without thinking that there is a multiplier on that over time. So that's pretty, that's a pretty exciting place. >>Yeah. I think to your point, a lot of companies are running into that situation right now, and they're looking at existing solutions there for us. It was necessity because there wasn't anything out there now that there is a lot of companies are not using their sort of precious engineering resources to build their own there. They would prefer to buy a solution because this is something that we can offer to all the companies. And it's not necessarily a business differentiating technology for the businesses themselves >>Distributed tracing in that really platform. That's the news. Um, and you mentioned you've got this, a good bid. You do some good business. Is scale the big differentiator for you guys? Or is it the functionality? Because it sounds like with clients like door dash, and it looks a lot like Uber, they're doing a lot of stuff too, and I'm sure everyone needs the card. Other people doing the same kind of thing, that scale, massive amount of consumer data coming in on the edge. Yeah. Is that the differentiation or do you work for the old one, you know, main street enterprise, right. >>Um, that is a good part of the differentiation and for our product thus far before we had a distributor tracing for monitoring and metric data, that was the main differentiation is the sheer volume of data that gets produced so much higher, really excited about distributor tracing because that's actually not just a scale problem. It's, it's a space that everybody can see the potential distributor tracing yet. No one has really realized that potential. So our offering right now is fairly unique. It does things that no other vendors out there can do. And we're really excited about that because we think that that fundamentally solves the problem differently, not just at a larger scale, >>Because you're an expert, what is distributed tracing. >>Yeah. Uh, it's, it's, it's a great question. So really, if you look at this retracing, it captures the details of a particular request. So a particular customer interaction with your business and it captures how that request flows through your complex architecture, right? So you have every detail of that at every step of the way. And you can imagine this data is extremely rich and extremely useful to figure out what the underlying root causes of issues are. The problem with that is it's very bit boast. It's a lot of data gets produced. A ton of data gets produced, every interaction, every request. So one of the main issues are in this space is that people can't afford cost effectively to store all of this data. Right? So one of the main differentiators for our product is we made it cost efficient enough to store everything. And when you have all the data, you have far better analytics and you have >>Machine learning is better. Everything's better with data. That's right. Yeah. Great. What's the blind spot out. Different customers, as cybersecurity is always looking for corners and threats that some people say it's not what you want. It's what you don't see that kills you. That's, that's a tracing issue. That's a data problem. How do you see that evolving in your customer base clients, trying to get a handle of the visibility into the data? >>Yeah. Um, I think right now, again, it's, it's very early in this space of people are just getting started here and you're completely correct where, you know, you need that visibility. And again, this is why it's such a differentiator to have all the data. If you can imagine with only 10% of the data or 1% of data, how can you actually detect any of these particular issues? Right. So, uh, uh, data is key to solving that >>Feel great to have you guys on expert and congratulations on the funding, Jerry. Good to see you take a minute to give a plug for the company. What do you guys do? And actually close around the funding, told you a million dollars. Congratulations. What are you looking for for hiring? What are your milestones? What's on your plan plan. >>Yeah. Uh, so with the spanning, it's really to, to, uh, continue to grow the company, right? So we're sort of hiring, as I told you earlier, we are, uh, we grew our revenue this year by, by 10 X in the sense of the 10 months of this year, thus far. So our team hasn't really grown 10 X. So, so we, we need to keep up with that grid. So hiring across the board on engineering side, on the go to market side, and I just continue to >>Beat that. The headquarters, your virtual, if you don't mind, we've gone >>Completely distributed. Now we're mostly in the U S have a bunch of folks in Seattle and in New York, however, we going completely remote. So hiring anyone in the U S anywhere in Europe, uh, >>Oh, I got you here. What's your investment thesis. Now you got castles in the cloud, by the way, if you haven't seen the research from Greylock, Jerry and the team called castles in the cloud, you can Google it. What's your thesis now? What are you investing in? >>Yeah, it is. It is hard to always predict the next wave. I mean, my job is to find the right founders, but I'd say the three core areas are still the same one is this cloud disruption to Martin's point we're. So early days, the wave, I say, number two, uh, there's vertical apps, different SAS applications be finance, healthcare construction, all are changing. I think healthcare, especially the past couple of years through COVID, we've seen that's a market that needs to be digitized. And finally, FinTech, we talked about this before everything becomes a payments company, right? And that's why Stripe is such a huge juggernaut. You know, I don't think the world's all Stripe, but be it insurance payments, um, you know, stuff in crypto, whatever. I think fintechs still has a lot of, a lot of market to grow. >>It's making things easier. It's a good formula right now. If you can reduce complexity, it makes things easy in every market. You're going to seems to be the formula. >>And like the next great thing is making today's crappy thing better. Right? So the next, the next brace shows making this cube crappy thing. Yeah, >>We're getting better every day on our 11th season or year, I'm calling things seasons now, episodes and season for streaming, >>All the seasons drop a Netflix binge, watch them all the >>Cube plus and NFTs for our early videos. There'll be worth something because they're not that good, Jerry. How, of course you're great. Thank you. Thanks guys. Thanks for coming on it. Cubes coverage here in a physical event, 2021 cloud being the con CubeCon I'm John farrier and Dave Nicholson. Thanks for watching.

>> Live from San Francisco, it's The Cube. Covering Google Cloud Next 2018 brought to you by Google Cloud and its ecosystem partners. >> Welcome back everyone, it's The Cube live in San Francisco. At Google Cloud's big event, Next 18, GoogleNext18 is the hashtag. I'm John Furrier with Jeff Frick, our next guest, Dave Rensin, director of CRE and network capacity at Google. CRE stands for Customer Reliability Engineering, not to be confused with SRE which is Google's heralded program Site Reliability Engineering, categoric changer in the industry. Dave, great to have you on. Thanks for coming on. >> Thank you so much for having me. >> So we had a meeting a couple months ago and I was just so impressed by how much thought and engineering and business operations have been built around Google's infrastructure. It's a fascinating case study in history of computing, you guys obviously power yourselves and the Cloud is just massive. You've got the Site Reliability Engineer concept that now is, I won't say is a boiler plate, but it's certainly the guiding architecture for how enterprise is going to start to operate. Take a minute to explain the SRE and the CRE concept within Google. I think it's super important that you guys, again pioneered, something pretty amazing with the SRE program. >> Well, I mean, like everything it was just formed out of necessity for us. We did the calculation 12 or 13 years ago, I think. We sat down a piece of paper and we said, well, the number of people we need to run our systems scales linearly with the number of machines, which scales linearly with the number of users, and the complexity of the stuff you're doing. Alright, carry the two divide by six, plot line. In ten years, now this is 13 or 14 years ago, we're going to need one million humans to run google. And that was at the growth and complexity of 10 years ago or 12 years ago. >> Yeah, Search. (laughs) >> Search, right? We didn't have Android, we didn't have Cloud, we didn't have Assistant, we didn't have any of these things. We were like, well that's not going to work. We're going to have to do something different and so that's kind of where SRE came from. It's like, how do we automate, the basic philosophy is simple, give to the machines all the things machines can do. And keep for the humans all the things that require human judgment. And that's how we get to a place where like 2,500 SREs run all of Google. >> And that's massive and there's billions and billions of users. >> Yeah. >> Again, I think this is super important because at that time it was a tell sign for you guys to wake up and go, well I can't get a million humans. But it's now becoming, in my opinion, what this enterprise is going through in this digital transformation, whatever we call it these days, consumer's agent of IT now it's digital trasfor-- Whatever it is, the role of the human-machine interaction is now changing, people need to do more. They can collect more data than ever before. It doesn't cost them that much to collect data. >> Yeah. >> We just heard from the BigQuery guys, some amazing stuff happening. So now enterprises are almost going through the same changeover that you guys had to go through. And this I now super important because now you have the tooling and the scale that Google has. And so it's almost like it's a level up fast. So, how does an enterprise become SRE like, quickly, to take advantage of the Cloud? >> So, you know, I would like to say this is all sort of a deliberate march of a multi-year plan. But it wasn't, it was a little accidental. Starting two or three years ago, companies were asking us, they were saying, we're getting mired in toil. Like, we're not being able to innovate because we're spending all of our budget and effort just running the things and turning the crank. How do you have billions of users and not have this problem? We said, oh we use this thing called SRE. And they're like please use more words. And so we wrote a book. Right? And we expected maybe 20 people would read the book, and it was fine. And we didn't do it for any other reason other than that seemed like a very scalable way to tell people the words. And then it all just kind of exploded. We didn't expect that it was going to be true and so a couple of years ago we said, well, maybe we should formalize our interactions of, we should go out proactively and teach every enterprise we can how to do this and really work with them, and build up muscle memory. And that's where CRE comes from. That's my little corner of SRE. It's the part of SRE that, instead of being inward focused, we point out to companies. And our goal is that every firm from five to 50 thousand can follow these principles. And they can. wW know they can do it. And it's not as hard as they think. The funny thing about enterprises is they have this inferiority complex, like they've been told for years by Silicon Valley firms in sort of this derogatory way that, you're just an enterprise. We're the innovate-- That's-- >> Buy our stuff. Buy our software. Buy IT. >> We're smarter than you! And it's nonsense. There are hundreds and hundreds of thousands of really awesome engineers in these enterprises, right? And if you just give them a little latitude. And so anyway, we can walk these companies on this journey and it's been, I mean you've seen it, it's just been snowballing the last couple of years. >> Well the developers certainly have changed the game. We've seen with Cloud Native the role of developers doing toil and, or specific longer term projects at an app related IT would support them. So you had this traditional model that's been changed with agile et cetera. And dev ops, so that's great. So you know, golf clap for that. Now it's like scale >> No more than a golf clap it's been real. >> It's been a high five. Now it's like, they got to go to the next level. The next level is how do you scale it, how do I get more apps, how am I going to drive more revenue, not just reduce the cost? But now you got operators, now I have to operate things. So I think the persona of what operating something means, what you guys have hit with SRE, and CRE is part of that program, and that's really I think the aha moment. So that's where I see, and so how does someone read the book, put it in practice? Is it a cultural shift? Is it a reorganization? What are you guy seeing? What are some of the successes that you guys have been involved in? >> The biggest way to fail at doing SRE is try to do all of it at once. Don't do that. There are a few basic principles, that if you adhere to, the rest of it just comes organically at a pace that makes sense for your business. The easiest thing to think of, is simply-- If I had to distill it down to a few simple things, it's just this. Any system involving people is going to have errors. So any goal you have that assumes perfection, 100% uptime, 100% customer satisfaction, zero error, that kind of thing, is a lie. You're lying to yourself, you're lying to your customers. It's not just unrealistic its, in a way kind of immoral. So you got to embrace that. And then that difference between perfection and the amounts, the closeness to perfection that your customers really need, cuz they don't really need perfection, should be just a budget. We call it the error budget. Go spend the budget because above that line your customers are indifferent they don't care. And that unlocks innovation. >> So this is important, I want to just make sure I slow down on this, error budget is a concept that you're talking about. Explain that, because this is, I think, interesting. Because you're saying it's bs that there's no errors, because there's always errors, Right? >> Sure. >> So you just got to factor in and how you deal with them is-- But explain this error budget, because this operating philosophy of saying deal with errors, so explain this error budget concept. >> It comes from this observation, which is really fascinating. If you plot reliability and customer satisfaction on a graph what you will find is, for a while as your reliability goes up, your customer satisfaction goes up. Fantastic. And then there's a point, a magic line, after which you hit this really deep knee. And what you find is if you are much under that line your customers are angry, like pitchforks, torches, flipping cars, angry. And if you operate much above that line they are indifferent. Because, the network they connect with is less reliable than you. Or the phone they're using is less reliable than you. Or they're doing other things in their day than using your system, right? And so, there's a magic line, actually there's a term, it's called an SLO, Service Level Objective. And the difference between perfection, 100%, and the line you need, which is very business specific, we say treat as a budget. If you over spend your budget your customers aren't happy cuz you're less reliable than they need. But if you consistently under spend your budget, because they're indifferent to the change and because it is exponentially more expensive for incrementive improvement, that's literally resources you're wasting. You're wasting the one resource you can never get back, which is time. Spend it on innovation. And just that mental shift that we don't have to be perfect, less people do open and honest, blameless postmortems. It let's them embrace their risk in innovation. We go out of our way at Google to find people who accidentally broke something, took responsibility for it, redesigned the system so that the next unlucky person couldn't break it the same way, and then we promote them and celebrate them. >> So you push the error budget but then it's basically a way to do some experimentation, to do some innovation >> Safely. >> Safely. And what you're saying is, obviously the line of unhappy customers, it's like Gmail. When Gmail breaks people are like, the World freaks out, right? But, I'm happy with Gmail right now. It's working. >> But here's the thing, Gmail breaks very, very little. Very, very often. >> I never noticed it breaking. >> Will you notice the difference between 10 milliseconds of delivery time? No, of course not. Now, would you notice an hour or whatever? There's a line, you would for sure notice. >> That's the SLO line. >> That's exactly right. >> You're also saying that if you try to push above that, it costs more and there's not >> And you don't care >> An incremental benefit >> That's right. >> It doesn't effect my satisfaction. >> Yeah, you don't care. >> I'm at nirvana, now I'm happy. >> Yeah. >> Okay, and so what does that mean now for putting things in practice? What's the ideal error budget, that's an SLO? Is that part of the objective? >> Well that's part of the work to do as a business. And that's part of what my team does, is help you figure out is, what is the SLO, what is the error budget that makes sense for you for this application? And it's different. A medical device manufacturer is going to have a different SLO than a bank or a retailer, right? And the shapes are different. >> And it's interesting, we hear SLA, the Service Level Agreement, it's an old term >> Different things. >> Different things, here objective if I get this right, is not just about speed and feeds. There's also qualitative user experience objectives, right? So, am I getting that right? >> Very much so. SLOs and SLAs get confused a lot because they share two letters. But they don't mean anywhere near the same thing. An SLA is a legal agreement. It's a contract with your user that describes a penalty if you don't meet a certain performance. Lawyers, and sometimes sales or marketing people, drive SLAs. SLOs are different things driven by engineers. They are quantitative measures of your users happiness right now. And exactly to your point, it's always from the user's perspective. Like, your user does not care if the CPU and your fleet spiked. Or the memory usage went up x. They care, did my mail delivery slow down? Or is my load balancer not serving things? So, focus from your user backwards into your systems and then you get much saner things to track. >> Dave, great conversation. I love the innovation, I love the operating philosophy cuz you're really nailing it with terms of you want to make people happy but you're also pushing the envelope. You want to get these error budgets so we can experiment and learn, and not repeat the same mistake. That sounds like automation to me. But I want you to take a minute to explain, what SRE, that's an inward facing thing for Google, you are called a CRE, Customer Reliability Engineer. Explain what that is because I heard Diane Greene saying, we're taking a vertical focus. She mentioned healthcare. Seems like Google is starting to get in, and applying a lot of resources, to the field, customers. What is a CRE? What does that mean? How is that a part of SRE? Explain that. >> So a couple of years ago, when I was first hired at Google I was hired to build and run Cloud support. And one of the things I noticed, which you notice when you talk to customers a lot, is you know the industries done a really fabulous job of telling people how to get to Cloud. I used to work at Amazon. Amazon is a fantastic job! Telling people, how do you get to Cloud? How do you build a thing? But we're awful, as an industry, about telling them how to live there. How do you run it? Cuz it's different running a thing in a Cloud than it is running it in On-Prem. And you find that's the cause of a lot of friction for people. Not that they built it wrong, but they're just operating it in a way that's not quite compatible. It's a few degree off. And so we have this notion of, well we know how to operate these things to scale, that's what SRE is. What if, what if, we did a crazy thing? We took some of our SREs and instead of pointing them in at our production systems, we pointed them out at customers? Like what if we genetically screened our SREs for, can talk to human, instead of can talk to machine? Which is what you optimize for when you hire an engineer. And so we started Siri, it's this part of our SRE org that we point outwards to customer. And our job is to walk that path with you and really do it to get like-- sometimes we go so far as even to share a pager with you. And really get you to that place where your operations look a lot like we're talking that same language. >> It's custom too, you're looking at their environment. >> Oh yeah, it's bespoke. And then we also try to do scale things. We did the first SRE book. At the show just two days ago we launched the companion volume to the book, which is like-- cheap plug segment, where it's the implementation details. The first book's sort of a set of principles, these are the implementation details. Anything we can do to close that gap, I don't know if I ever told you the story, but when I was a little kid when I was like six. Like 1978, my dad who's always loved technology decided he was going to buy a personal computer. So he went to the largest retailer of personal computers in North America, Macy's in 1978, (laughs) and he came home with two things. He came home with a huge box and a human named Fred. And Fred the human unpacked the big box and set up the monitor, and the tape drive, and the keyboard, and told us about hardware and software and booting up, because who knew any of these things in 1978? And it's a funny story that you needed a human named Fred. My view is, I want to close the gap so that Siri are the Freds. Like, in a few years it'll be funny that you would ever need humans, from Google or anyone else, to help you learn how-- >> It's really helping people operate their new environment at a whole. It's a new first generation problem. >> Yeah. >> Essentially. Well, Dave great stuff. Final question, I want to get your thoughts. Great that we can have this conversation. You should come to the studio and go more and more deeper on this, I think it's a super important, and new role with SRES and CREs. But the show here, if you zoom out and look at Google Cloud, look down on the stage of what's going on this week, what's the most important story that should be told that's coming out of Google Cloud? Across all the announcements, what's the most important thing that people should be aware of? >> Wow, I have a definite set of biases, that won't lie. To me, the three most exciting announcements were GKE On-Prem, the idea that manage kubernetes you can actually run in your own environment. People have been saying for years that hybrid wasn't really a thing. Hybrid's a thing and it's going to be a thing for a long time, especially in enterprises. That's one. I think the introduction of machine learning to BigQuery, like anything we can do to bring those machine learning tools into these petabytes-- I mean, you mentioned it earlier. We are now collecting so much data not only can we not, as companies, we can't manage it. We can't even hire enough humans to figure out the right questions. So that's a big thing. And then, selfishly, in my own view of it because of reliability, the idea that Stackdriver will let you set up SLO dashboards and SLO alerting, to me that's a big win too. Those are my top three. >> Dave, great to have you on. Our SLO at The Cube is to bring the best content we possibly can, the most interviews at an event, and get the data and share that with you live. It's The Cube here at Google Cloud Next 18 I'm John Furrier with Jeff Frick. Stay with us, we've got more great content coming. We'll be right back after this short break.

>> Announcer: Live, from Copenhagen, Denmark. It's theCUBE! Covering KubeCon and CloudNativeCon Europe 2018. Brought to you by the Cloud Native Computing Foundation, and its Ecosystem partners. >> Hi everyone, welcome back, this is theCUBE's exclusive coverage of the Linux Foundation's Cloud Native Compute Foundation KubeCon 2018 in Europe. I'm John Furrier, host of theCUBE and we're here with two Google folks. JD Velazquez who's the Product Manager for Stackdriver, got some news on that we're going to cover, and David Aronchick, who's the co-founder of Kubeflow, also with Google, news here on that. Guys, welcome to theCUBE, thanks for coming on. >> Thank you John. >> Thank you very much. >> So we're going to have Google Next coming out, theCUBE will be there this summer, looking forward to digging in to all the enterprise traction you guys have, and we had some good briefings at Google. Ton of movement on the Cloud for Google, so congratulations. >> JD: Thank you. >> Open source is not new to Google. This is a big show for you guys. What's the focus, you've got some news on Stackdriver, and Kubeflow. Kubeflow, not Cube flow, that's our flow. (laughing) David, share some of the news and then we'll get into Stackdriver. >> Absolutely, so Kubeflow is a brand new project. We launched it in December, and it is basically how to make machine learning stacks easy to use and deploy and maintain on Kubernetes. So we're not launching anything new. We support TensorFlow and PyTorch, Caffe, all the tools that you're familiar with today. But we use all the native APIs and constructs that Kubernetes rides to make it very easy and to let data scientists and researchers focus on what they do great, and let the I.T. Ops people deploy and manage these stacks. >> So simplifying the interactions and cross-functionality of the apps. Using Kubernetes. >> Exactly, when you go and talk to any researcher out there or data scientist, what you'll find is that while the model, TensorFlow, or Pytorch or whatever, that gets a little bit of the attention. 95% of the time is spent in all the other elements of the pipeline. Transforming your data, ingesting it, experimenting, visualizing. And then rolling it out toward production. What we want to do with Kubeflow is give everyone a standard way to interact with those, to interact with all those components. And give them a great workflow for doing so. >> That's great, and the Stackdriver news, what's the news we got going on? >> We're excited, we just announced the beta release of Stackdriver Kubernetes monitoring, which provides very rich and comprehensive observability for Kubernetes. So this is essentially simplifying operations for developers and operators. It's a very cool solution, it integrates many signals across the Kubernetes environment, including metrics, logs, events, as well as metadata. So what it allows is for you to really inspect your Kubernetes environment, regardless of the role, and regardless of where your deployment is running it. >> David is bringing up just the use cases. I just, my mind is exploding, 'cause you think about what Tensorflow is to a developer, and all the goodness that's going on with the app layer. The monitoring and the instrumentation is a critical piece, because Kubernetes is going to bring the people what is thousands and thousands of new services. So, how do you instrument that? I mean, you got to know, I want to provision this service dynamically, that didn't exist. How do you measure that, I mean this is, is this the challenge you guys are trying to figure out here? >> Yeah, for sure John. The great thing here is that we, and at Google primarily, many of our ancillary practices go beyond monitoring. It really is about observability, which I would describe more as a property of a system. How do you, are able to collect all these many signals to help you diagnose the production failure, and to get information about usage and so forth. So we do all of that for you in your Kubernetes environment, right. We take that toil away from the developer or the operator. Now, a cool thing is that you can also instrument your application in open source. You can use Prometheus, and we have an integration for that, so anything you've done in a Prometheus instrumentation, now you can bring into the cloud as needed. >> Tell about this notion, everyone gets that, oh my God, Google's huge. You guys are very open, you're integrating well. Talk about the guiding principles you guys have when you think about Prometheus as an example. Integrating in with these other projects. How are you guys treating these other projects? What's the standard practice? API Base? Is there integration plans? How do you guys address that question? >> Yeah, at a high level I would say, at Google, we really believe in contributing and helping grow open communities. I think that the best way to maintain a community open and portable is to help it grow. And Prometheus particularly, and Kubernetes of course, is a very vibrant community in that sense. So we are, from the start, designing our systems to be able to have integration, via APIs and so on, but also contributing directly to the projects. >> And I think that one thing that's just leveraging off that exact point, y'know, we realize what the world looks like. There's literally zero customers out there, like, "Well, I want be all in on one cloud. "Y'know, that 25 million dollar data center "I spent last year building. "Yeah, I'll toss that out so that I can get, "y'know, some special thing." The reality is, people are multi-cloud. And the only way to solve any problem is with these very open standards that work wherever people are. And that's very much core to our philosophy. >> Well, I mean, I've been critical of multi-cloud, by the definition. Statistically, if I'm on Azure, with 365, that's Azure. If I'm running something on Amazon, those are two clouds, they're not multi-cloud, by my definition. Which brings up where this is going, which is latency and portability, which you guys are really behind. How are you guys looking at that, because you mentioned observation. Let's talk about the observation space of clouds. How are you guys looking at, 'cause that's what people are talking about. When are we going to get to the future state, which is, I need to have workload portability, in real time, if I want to move something from Azure to AWS or Google Cloud, that would be cool. Can't do that today. >> That is actually the core of what we did around Kubeflow. What we are able to do is describe in code all the layers of your pipeline, all the steps of your pipeline. That works based on any conformant Kubernetes cluster. So, you have a Kubernetes conformant cluster on Azure, or on AWS, or on Google Cloud, or on your laptop, or in your private data center, that's great. And to be clear, I totally agree. I don't think that having single workloads spread across cloud, that's not just unrealistic, because of all the things you identified. Latency, variability, unknown failures, y'know. Cap theorem is a thing because, y'know, it's well-known. But what people want to do is, they want to take advantage of different clouds for the efforts that they provide. Maybe my data is here, maybe I have a legal reason, maybe this particular cloud has a unique chip, or unique service-- >> Use cases can drive it. >> Exactly, and then I can take my workload, which has been described in code and deploy it to that place where it makes sense. Keeping it within a single cloud, but as an organization I'll use multiple clouds together. >> Yeah, I agree, and the data's key, because if you can have data moving between clouds, I think that's something I would like to see, because that's going to be, because the metadata you mentioned is a real critical piece of all these apps. Whether it's instrumentation logging, and/or, y'know, provisioning new services. >> Yeah, and as soon as you have, as David is mentioning, if you have deployments on, y'know, with public or private clouds, then the difficult part is that of severability, that we were talking before. Because now you're trying to stitch together data, and tools to help you get that diagnosed, or get signals when you need them. This is what we're doing with Stackdriver Kubernetes monitoring, precisely. >> Y'know, we're early days in the cloud. It stills feels like we're 10 years in, but, y'know, a lot of people are now coming to realize cloud native, so. Y'know, I'm not a big fan of the whole, y'know, Amazon, although they do say Amazon's winning, they are doing quite well with the cloud, 'cause they're a cloud. It's early days, and you guys are doing some really specific good things with the cloud, but you don't have the breadth of services, say, Amazon has. And you guys are above board about that. You're like, "Hey, we're not trying to meet them "speed for speed on services." But you do certain things really, really well. You mentioned SRE. Site Reliability Engineers. This is a scale best practice that you guys have bringing to the table. But yet the customers are learning about Kubernetes. Some people who have never heard of it before say, "Hey, what's this Kubernetes thing?" >> Right. >> What is your perspectives on the relevance of Kubernetes at this point in history? Because it really feels like a critical mass, de facto, standard movement where everyone's getting behind Kubernetes, for all the right reasons. It feels a lot like interoperability is here. Thoughts on Kubernetes' relevance. >> Well I think that Alexis Richardson summed it up great today, the chairperson of the technical oversight committee. The reality is that what we're looking for, what operators and software engineers have been looking for forever, is clean lines between the various concerns. So as you think about the underlying infrastructure, and then you think about the applications that run on top of that, potentially services that run on top of that, then you think about applications, then you think about how that shows up to end users. Before, if you're old like me, you remember that you buy a $50,000 machine and stick it in the corner, and you'd stack everything on there, right? That never works, right? The power supply goes out, the memory goes out, this particular database goes out. Failure will happen. The only way to actually build a system that is reliable, that can meet your business needs, is by adopting something more cloud native, where if any particular component fails, your system can recover. If you have business requirements that change, you can move very quickly and adapt. Kubernetes provides a rich, portable, common set of APIs, that do work everywhere. And as a result, you're starting to see a lot of adoption, because it gives people that opportunity. But I think, y'know and let me hand off to JD here, y'know, the next layer up is about observability. Because without observing what's going on in each of those stacks, you're not going to have any kind of-- >> Well, programmability comes behind it, to your point. Talk about that, that's a huge point. >> Yeah, and just to build on what David is saying, one thing that is unique about Google is that we've been doing for more than a decade now, we've been very good at being able to provide innovative services without compromising reliability. Right, and so what we're doing is in that commitment, and you see that with Kubernetes and Istio, we're externalizing many of our, y'know, opinionated infrastructure, and platforms in that sense, but it's not just the platforms. You need those methodologies and best practices. And now the toolset. So that's what we're doing now, precisely. >> And you guys have made great strides, just to kind of point out to the folks watching, in the enterprise, I know you've got a lot more work to do but you're pedaling as fast as you can. I want to ask you specifically around this, because again, we're still early days with the cloud, if you think about it, there are now table stakes that are on the table that you got to get done. Check boxes if you will. Certainly on the government side there's like, compliance issues, and you guys are now checking those boxes. What is the key thing, 'cause you guys are operating at a scale that enterprises can't even fathom. I mean, millions of services, on and on up a huge scale. That's going to be helpful for them down the road, no doubt about it. But today, what is the Google table stakes that are done, and what are enterprises need to have for table stakes to do cloud native right, from your perspective? >> Well, I think more than anything, y'know, I agree with you. The reality is all the hyperscale cloud providers have the same table stakes, all the check boxes are checked, we're ready to go. I think what will really differentiate and move the ball forward for so many people is this adoption of cloud native. And really, how cloud native is your cloud, right? How much do you need to spin up an entire SRE team like Netflix in order to operate in the Netflix model of, y'know, complete automation and building your own services and things like that. Does your cloud help you get cloud native? And I think that's where we really want to lean in. It's not about IAS anymore, it's about does your cloud support the reliability, support the distribution, all the various services, in order to help you move even faster and achieve higher velocity. >> And standing up that is critical, because now these applications are the business model of companies, when you talk about digital. So I tweeted, I want to get your reaction to this, yesterday I got a quote I overheard from a person here in the hallways. "I need to get away from VPNs and firewalls. "I need user application layer security "with unphishable access, otherwise I'm never safe." Again this talks about the perimeterless cloud, spearphishing is really hot right now, people are getting killed with security concerns. So, I'm going to stop if I'm enterprise, I'm going to say, "Hold on, I'm not going," Y'know, I'm going to proceed with caution. What are you guys doing to take away the fear, and also the reality that as you provision all these, stand up all this infrastructure, services for customers, what are you guys doing to prevent phishing attacks from happening, security concerns, what's the Google story? >> So I think that more than anything, what we're trying to do is exactly what JD just said, which is externalize all the practices that we have. So, for example, at Google we have all sorts of internal tools that we've used, and internal practices. For example, we just published a whitepaper about our security practices where you need to have two vulnerabilities in order to break out of any system. We have all that written up there. We just published a whitepaper about encryption and how to do encryption by default, encryption between machines and so on. But I think what we're really doing is, we're helping people to operate like Google without having to spin up an entire SRE team as big as Google's to do it. An example is, we just released something internally, we have something called BeyondCorp. It's a non-firewall, non-VPN based way for you to authenticate against any Google system, using two-factor authentication, for our internal employees. Externally, we just released it, it's called, Internet, excuse me, IdentityAware proxy. You can use with literally any service that you have. You can provision a domain name, you can integrate with OAuth, you can, including Google OAuth or your own private OAuth. All those various things. That's simply a service that we offer, and so, really, y'know, I think-- >> And there's also multi, more than two-factor coming down the road, right? >> Exactly, actually IdentityAware proxy already supports two-factor. But I will say, one of the things that I always tell people, is a lot of enterprises say exactly what you said. "Jeez, this new world looks very scary to me. "I'm going to slow down." The problem is they're mistaken, under the mistaken impression that they're secure today. More than likely, they're not. They already have firewall, they already have VPN, and it's not great. In many ways, the enterprises that are going to win are the ones that lean in and move faster to the new world. >> Well, they have to, otherwise they're going to die, with IOT and all these benefits, they're exposed even as they are, just operationally. >> Yep. >> Just to support it. Okay, I want to get your thoughts, guys, on Google's role here at the Linux Foundation's CNCF KubeCon event. You guys do a lot of work in open source. You've got a lot of great fan base. I'm a fan of what you guys do, love the tech Google brings to the table. How do people get involved, what are you guys connecting with here, what's going on at the show, and how does someone get on board with the Google train? Certainly TensorFlow has been, it's like, great open source goodness, developers are loving it, what's going on? >> Well we have over almost 200 people from Google here at the show, helping and connecting with people, we have a Google booth which I invite people to stop by and tell about the different project we have. >> Yeah, and exactly like you said, we have an entire repo on Github. Anyone can jump in, all our things are open source and available for everyone to use no matter where they are. Obviously I've been on Kubernetes for a while. The Kubernetes project is on fire, Tensorflow is on fire, KubeFlow that we mentioned earlier is completely open source, we're integrating with Prometheus, which is a CNCF project. We are huge fans of these open source foundations and we think that's the direction that most software projects are going to go. >> Well congratulations, I know you guys invested a lot. I just want to highlight that. Again, to show my age, y'know these younger generation have no idea how hard open source was in the early days. I call it open bar and open source, you guys are bringing so much, y'know, everyone's drunk on all this goodness. Y'know, just these libraries you guys bringing to the table. >> David: Right. >> I mean Tensorflow is just the classic poster-child example. I mean, you're bringing a lot of stuff to the table. I mean, you invented Kubernetes. So much good stuff coming in. >> Yeah, I couldn't agree more. I hesitate to say we invented it. It really was a community effort, but yeah, absolutely-- >> But you opened it up, and you did it right, and did a good job. Congratulations. Thanks for coming on theCUBE, I'm going to see you at Google Next. theCUBE will be broadcasting live at Google Next in July. Of course we'll do a big drill-down on Google Cloud platform at that show. It's theCUBE here at KubeCon 2018 in Copenhagen, Denmark. More live coverage after this short break, stay with us. (upbeat music)