>> Live from San Francisco, It's theCUBE. Covering Google Cloud Next 2018. Brought to you by Google Cloud and it's Ecosystem partners. >> Hey, welcome back everyone. We are live in San Francisco, CUBE coverage for Google Cloud Next 18. I'm John Furrier, Jeff Frick. Our next guest is Carol Carpenter, Vice President of product marketing here at Google Cloud, and Navid Erfani-Ghadimimi, welcome to theCUBE. >> Thank you very much. >> Thanks for coming on. So data for good has been a topic we were just talking about here, day three What do you guys do? And what's your relationship with Google? Because big data for good is really, with cloud computing more relevant than ever before. Take a minute to explain your project. >> Sure, so in South Africa we are a social nonprofit organization. We try and connect young people that are not employed, never employed to opportunities. And we are hosted in Google Cloud, and we use GCP as our sole provider. And what we try and do is we use data to be able to understand young people, understand the facets that make a young person employable and match them to opportunities that we find. So we describe opportunities using different data points. So all those data points that we have, we store them in Cloud Sequel, and we store them in BigQuery. And then we run analytics and matching to be able to find how these young people can contribute to the economy. >> How's it going so far? >> So far it's been great. It's allowed us to think about the 10X strategies. When we were an on PRAM business, we very limited by what could provide, bricks and mortar, and now we're looking and saying, well how do we provide as much capacity and capability to these young people using cell service channels? So it really has just opened up a world of possibilities. And we're really looking at it. And we're very excited because we've taken on some initiatives in Rwanda as well. And so we're taking on a global and Africa-wide kind of strategy, which I think without Cloud we really wouldn't be able to do. >> I wonder if you could just drill down, because what are some of the data points that you look at and you measure? And is it identifying the data points and then finding the match? Or is it finding the critical ones that you really need to address as a priority to get kids to that position where they can get a job? >> I mean it's really interesting because what we talk about, we talk about proxies for competence. So if you think about when you go apply for a job, you kind of say hello, here I am and I've done this job for so many years, and that's your proxy for competence. So if you're a young person that just has a high school education and you're stepping in, we need to be able to describe you as a human, right? So for those things we look and say, what are your biographic information? What's your socialization? What kind of grit and energy do you bring to the job? So we try and measure those things and we have as many contact points as we can get to be able to understand, who is this individual, really? And use those data points, and we have about 155 aspects that we use right now, and then match them to different entry-level jobs. >> So you're the Enterprise Architect of Harambee Youth and Employment Accelerator. I love that term, accelerator. >> Yes, right. >> And I also love the term Enterprise Architect, because both are indeed of some clout. One of the themes is digital transformation, which is kind of a generic term, the analysts all talk about it. But really we're talking about the cloud mobile digital world and the power that can bring. Accelerator on the youth side, they need an app. So you're essentially providing a digital capability, not the old brick and mortar. >> That's right. >> How do you architect all of this? Because you got to assume there's an app at the edge, either a downloadable app or website, phone-- >> So we have actually quite an interesting problem to solve, because for our young people, they don't have access to apps. The majority of our young people are on feature phones, basic phones, not smart phones. And data in South Africa is very expensive. So for that young person, we need to provide as low a touch at a connection point, to our services, without making that cost them something, right? So we built a very basic Mobi site, no JavaScript, as blank as you can get. It's very boring if you look at it. >> So lightweight. >> Very lightweight. But it's the tip of an iceberg. So from there we collect certain information, but then we have an award-winning contact center that makes 35 thousand calls every month. And we engage with a young person in an up down poll for about 15 minutes. And it's that 15 minutes that we use to talk to this young person, understand about them, figure out who they are and what they are, and use that to gather our data points. We then have assessments that we run. So we run psychometric assessments, we have competence assessments, and we gather all those data points and we start understanding this young person in a way that we can go to an employer, because on that side for the employer, we need to be able to say you trust us that when we give you this young person, that we say this person will do well in your job. Well you have to have trust in us to be able to do that. So we need to provide that data to say well, this is how we came up with it. So we take quite a lot of effort in that. >> You're verifying in a way, putting your reputation on the line with the candidates. >> Yes. >> At the same time, you don't know when the inbound touch is going to happen, so you got to have all that material ready to go. >> That's right. >> That's where the big data kicks in. >> That's right. So the big data, the collection of that information, and the understanding of it... And we're on a journey to start figuring out, how can we use artificial intelligence, how can we use ML in a way that improves our accuracy, but at the same time, leaves out anything that may be biased toward these young people. So we're taking a very cautious approach to it. But it's a lot of big data. We're trying to consume it as best we can. Plus, we're trying to think about, how do we provision our services for the employers? Because again, it's a demand at business, so we want to find as many jobs as we can so we can take young people to those jobs. So extend our reach to the employers and-- >> The heavy lifting, so that they don't have to. >> Yeah, so they don't have to. >> Carol, talk about the dynamic with Google Cloud, because this is the theme we're hearing all week. You guys do the heavy lifting, and at the edge of the user experience, you take the toil out of it. The word toil has been-- >> It keeps coming up. >> It keeps coming up. Thinking of that toil, the hard work, friction out of it. In this case, the connectivity costs, being productive at that point of transaction... >> Exactly. >> They're doing the back end heavy lifting. This is kind of like a core theme across. >> That is what the promise of the Cloud is supposed to be, right? Which is to remove all that back end toil, I love that word too, the toil, the mundaneness of it all, so that folks like Harambee can actually focus on delivering great service to both potential employers and employees. So we're trying to automate as much of that infrastructure, that's what we announced a lot around serverless, around containers, this idea of you don't need to worry about it. You don't have to provision the server now. You don't have to worry about patches. You don't have to worry about security. We'll take care of that for you. >> I just love your phrase proxy for competence, and I can't help but think, I've got kids in college that you know, that's the whole objective of the application, right? We've got SATs and PSATs and they take a couple data sets, but relative to the number of data sets that you describe. And I would the intimacy of those data sets, versus an ACT an SAT and a transcript. You probably have a really interesting insight, and if you can correlate to the proxies of competency, this is something that has a much greater kind of opportunity than just helping these kids that you need to help and it's really important. But that's a really interesting take, to use a much bigger data set, sophistication, great tools and infrastructure to do that mapping of competency to that job. >> Absolutely, and we're very focused on understanding, how do we use this data to provision a network for our young people to be able to describe themselves in entry? So one of the things we found in South Africa, and I'm sure it's a fairly universal problem, is that if you are unemployed, one of the things that prevents you from finding employment is you cannot access a network in which people that have jobs or describe jobs, you don't have access to that network. And so the ability to stand up and say, hey, this is who I am, these people have said, this is my profile as an individual, and say Harambee, or whoever it is, says that I am competent in these things. That gives them an in, that gives them some way of entering that network. And for instance, we've done a certain study that said that if a young lady takes just a basic CV that has a stamp on it from Harambee with a description of who they are and what their competencies are, that improves their chances of finding a job by 30%, up to 30%, and that's significant, right? And this is not us finding the job for them, this is them going out and looking for a job, so it's describing and helping this person enter that network by providing, again, a proxy for competence. >> Talk about the relationship with Google. What is Google working with you guys on? And what's next for you guys? >> Google has helped us immensely. We receive those credits, and those credits allowed us to take that first step into the cloud. They gave us a little bit of breathing room, alright, so we could take that step. We also have access to some Googlers, that have helped talk to us a little bit about ML and they have been helping us out on that. In terms of the next steps, it's 10X time. It's time to grow, it's time to use this scale, it's time to use the opportunity that we have to make the real impact that we've been searching for. >> Connect those jobs to those folks. >> Absolutely, because this is not a small problem. We've got a big problem to solve and we're really excited to be able to do it. >> I'm glad you're doing that. >> Awesome. >> It's a great, great mission. Carol, I want to get your thoughts finally, just to kind of end this segment and kind of end our time here at Google Cloud. Good opportunity for someone who's been looking at the landscape of the products. What's been the vide of the show, from your standpoint? Obviously you've been planning this for months, it's showtime, it's coming to a close, we're day three, you heard, it's going to close in 30 minutes. Are you happy? >> Yeah, I mean we're thrilled. We're thrilled. We were just talking earlier, it's been a tremendous three days of just great interaction with fantastic customers, partners, developers, it's just the level of engagement... Google Cloud is about making the Cloud available for everyone. We wanted this to be a place for people to engage, to make things, to try things, to be hands-on, to be in sessions with people like Harambee, to actually understand what the Cloud can do. And we're super excited. We've seen that in spades. The feedback has been tremendous. I hope you heard that as well. We're really excited. We believe that the capabilities we have around what we're doing in data analytics, machine learning, on top of this incredibly robust infrastructure, we really believe that there are amazing problems we can solve together. >> We had a couple of our reporters here earlier saying people who think Google is far behind is not here at the event. I got to say, give you guys some props, you guys are bringing... We know you've got great technology, everyone kind of knows that, who knows google, certainly knows the size and the scope of the great technology. But you're making it consumable. And you're thinking about the enterprise, versus we're Google. Use our great stuff because we use it. You're like Google. People aren't like Google because no one has that many servers. (laughs) Right. So it's self-awareness. This has really been a great stride you guys have shown. And the customers on stage. >> Oh, they're fantastic. >> That's the proof in the pudding. At the end of the day-- >> They're fantastic. Showing how you can actually apply it, how you can apply AI, machine learning to actually solve real world problems, that's what we were most excited about. Like you said, lots of great technology. What we want to do is connect the dots. >> And Diane Greene I thought of, my favorite soundbite was security is number one, worry, AI is the number one opportunity. >> Absolutely. >> I think if you look at it from that lens, everything falls into place. >> Absolutely. >> Well thanks for coming on, thanks for having theCUBE this week, Google. And congratulations on your great venture, and good luck with your initiative. >> Thank you very much. >> Thank you both. >> Alright that's theCUBE coverage here, live in San Francisco. I'm John Furrier, Jeff Frick, Dave Vellante went home last night. He's in our office taking care of some business. I want to thank everyone for watching. And that's a wrap here from San Francisco. Thanks for watching.

>> Live from San Francisco, It's the cube. Covering Google Cloud Next 2018. Brought to you by Google Cloud and it's Ecosystem partners. >> Hey, welcome back everyone. We are live in San Francisco, Cube coverage for Google Cloud Next 18. I'm John Furrier, Jeff Frick. Our next guest is Carol Carpenter, Vice President of product marketing here at Google Cloud, and Navid Erfani-Ghadimimi, welcome to the Cube. >> Thank you very much. >> Thanks for coming on. So data for good has been a topic we were just talking about here, day three What do you guys do? And what's your relationship with Google? Because big data for good is really, with cloud computing more relevant than ever before. Take a minute to explain your project. >> Sure, so in South Africa we are a social nonprofit organization. We try and connect young people that are not employed, never employed to opportunities. And we are hosted in Google Cloud, and we use GCP as our sole provider. And what we try and do is we use data to be able to understand young people, understand the facets that make a young person employable and match them to opportunities that we find. So we describe opportunities using different data points. So all those data points that we have, we store them in Cloud Sequel, and we store them in BigQuery. And then we run analytics and matching to be able to find how these young people can contribute to the economy. >> How's it going so far? >> So far it's been great. It's allowed us to think about the 10X strategies. When we were an on PRAM business, we very limited by what could provide, bricks and mortar, and now we're looking and saying, well how do we provide as much capacity and capability to these young people using cell service channels? So it really has just opened up a world of possibilities. And we're really looking at it. And we're very excited because we've taken on some initiatives in Rwanda as well. And so we're taking on a global and Africa-wide kind of strategy, which I think without Cloud we really wouldn't be able to do. >> I wonder if you could just drill down, because what are some of the data points that you look at and you measure? And is it identifying the data points and then finding the match? Or is it finding the critical ones that you really need to address as a priority to get kids to that position where they can get a job? >> I mean it's really interesting because what we talk about, we talk about proxies for competence. So if you think about when you go apply for a job, you kind of say hello, here I am and I've done this job for so many years, and that's your proxy for competence. So if you're a young person that just has a high school education and you're stepping in, we need to be able to describe you as a human, right? So for those things we look and say, what are your biographic information? What's your socialization? What kind of grit and energy do you bring to the job? So we try and measure those things and we have as many contact points as we can get to be able to understand, who is this individual, really? And use those data points, and we have about 155 aspects that we use right now, and then match them to different entry-level jobs. >> So you're the Enterprise Architect of Harambee Youth and Employment Accelerator. I love that term, accelerator. >> Yes, right. >> And I also love the term Enterprise Architect, because both are indeed of some clout. One of the themes is digital transformation, which is kind of a generic term, the analysts all talk about it. But really we're talking about the cloud mobile digital world and the power that can bring. Accelerator on the youth side, they need an app. So you're essentially providing a digital capability, not the old brick and mortar. >> That's right. >> How do you architect all of this? Because you got to assume there's an app at the edge, either a downloadable app or website, phone-- >> So we have actually quite an interesting problem to solve, because for our young people, they don't have access to apps. The majority of our young people are on feature phones, basic phones, not smart phones. And data in South Africa is very expensive. So for that young person, we need to provide as low a touch at a connection point, to our services, without making that cost them something, right? So we built a very basic Mobi site, no JavaScript, as blank as you can get. It's very boring if you look at it. >> So lightweight. >> Very lightweight. But it's the tip of an iceberg. So from there we collect certain information, but then we have an award-winning contact center that makes 35 thousand calls every month. And we engage with a young person in an up down poll for about 15 minutes. And it's that 15 minutes that we use to talk to this young person, understand about them, figure out who they are and what they are, and use that to gather our data points. We then have assessments that we run. So we run psychometric assessments, we have competence assessments, and we gather all those data points and we start understanding this young person in a way that we can go to an employer, because on that side for the employer, we need to be able to say you trust us that when we give you this young person, that we say this person will do well in your job. Well you have to have trust in us to be able to do that. So we need to provide that data to say well, this is how we came up with it. So we take quite a lot of effort in that. >> You're verifying in a way, putting your reputation on the line with the candidates. >> Yes. >> At the same time, you don't know when the inbound touch is going to happen, so you got to have all that material ready to go. >> That's right. >> That's where the big data kicks in. >> That's right. So the big data, the collection of that information, and the understanding of it... And we're on a journey to start figuring out, how can we use artificial intelligence, how can we use ML in a way that improves our accuracy, but at the same time, leaves out anything that may be biased toward these young people. So we're taking a very cautious approach to it. But it's a lot of big data. We're trying to consume it as best we can. Plus, we're trying to think about, how do we provision our services for the employers? Because again, it's a demand at business, so we want to find as many jobs as we can so we can take young people to those jobs. So extend our reach to the employers and-- >> The heavy lifting, so that they don't have to. >> Yeah, so they don't have to. >> Carol, talk about the dynamic with Google Cloud, because this is the theme we're hearing all week. You guys do the heavy lifting, and at the edge of the user experience, you take the toil out of it. The word toil has been-- >> It keeps coming up. >> It keeps coming up. Thinking of that toil, the hard work, friction out of it. In this case, the connectivity costs, being productive at that point of transaction... >> Exactly. >> They're doing the back end heavy lifting. This is kind of like a core theme across. >> That is what the promise of the Cloud is supposed to be, right? Which is to remove all that back end toil, I love that word too, the toil, the mundaneness of it all, so that folks like Harambee can actually focus on delivering great service to both potential employers and employees. So we're trying to automate as much of that infrastructure, that's what we announced a lot around serverless, around containers, this idea of you don't need to worry about it. You don't have to provision the server now. You don't have to worry about patches. You don't have to worry about security. We'll take care of that for you. >> I just love your phrase proxy for competence, and I can't help but think, I've got kids in college that you know, that's the whole objective of the application, right? We've got SATs and PSATs and they take a couple data sets, but relative to the number of data sets that you describe. And I would the intimacy of those data sets, versus an ACT an SAT and a transcript. You probably have a really interesting insight, and if you can correlate to the proxies of competency, this is something that has a much greater kind of opportunity than just helping these kids that you need to help and it's really important. But that's a really interesting take, to use a much bigger data set, sophistication, great tools and infrastructure to do that mapping of competency to that job. >> Absolutely, and we're very focused on understanding, how do we use this data to provision a network for our young people to be able to describe themselves in entry? So one of the things we found in South Africa, and I'm sure it's a fairly universal problem, is that if you are unemployed, one of the things that prevents you from finding employment is you cannot access a network in which people that have jobs or describe jobs, you don't have access to that network. And so the ability to stand up and say, hey, this is who I am, these people have said, this is my profile as an individual, and say Harambee, or whoever it is, says that I am competent in these things. That gives them an in, that gives them some way of entering that network. And for instance, we've done a certain study that said that if a young lady takes just a basic CV that has a stamp on it from Harambee with a description of who they are and what their competencies are, that improves their chances of finding a job by 30%, up to 30%, and that's significant, right? And this is not us finding the job for them, this is them going out and looking for a job, so it's describing and helping this person enter that network by providing, again, a proxy for competence. >> Talk about the relationship with Google. What is Google working with you guys on? And what's next for you guys? >> Google has helped us immensely. We receive those credits, and those credits allowed us to take that first step into the cloud. They gave us a little bit of breathing room, alright, so we could take that step. We also have access to some Googlers, that have helped talk to us a little bit about ML and they have been helping us out on that. In terms of the next steps, it's 10X time. It's time to grow, it's time to use this scale, it's time to use the opportunity that we have to make the real impact that we've been searching for. >> Connect those jobs to those folks. >> Absolutely, because this is not a small problem. We've got a big problem to solve and we're really excited to be able to do it. >> I'm glad you're doing that. >> Awesome. >> It's a great, great mission. Carol, I want to get your thoughts finally, just to kind of end this segment and kind of end our time here at Google Cloud. Good opportunity for someone who's been looking at the landscape of the products. What's been the vide of the show, from your standpoint? Obviously you've been planning this for months, it's showtime, it's coming to a close, we're day three, you heard, it's going to close in 30 minutes. Are you happy? >> Yeah, I mean we're thrilled. We're thrilled. We were just talking earlier, it's been a tremendous three days of just great interaction with fantastic customers, partners, developers, it's just the level of engagement... Google Cloud is about making the Cloud available for everyone. We wanted this to be a place for people to engage, to make things, to try things, to be hands-on, to be in sessions with people like Harambee, to actually understand what the Cloud can do. And we're super excited. We've seen that in spades. The feedback has been tremendous. I hope you heard that as well. We're really excited. We believe that the capabilities we have around what we're doing in data analytics, machine learning, on top of this incredibly robust infrastructure, we really believe that there are amazing problems we can solve together. >> We had a couple of our reporters here earlier saying people who think Google is far behind is not here at the event. I got to say, give you guys some props, you guys are bringing... We know you've got great technology, everyone kind of knows that, who knows google, certainly knows the size and the scope of the great technology. But you're making it consumable. And you're thinking about the enterprise, versus we're Google. Use our great stuff because we use it. You're like Google. People aren't like Google because no one has that many servers. (laughs) Right. So it's self-awareness. This has really been a great stride you guys have shown. And the customers on stage. >> Oh, they're fantastic. >> That's the proof in the pudding. At the end of the day-- >> They're fantastic. Showing how you can actually apply it, how you can apply AI, machine learning to actually solve real world problems, that's what we were most excited about. Like you said, lots of great technology. What we want to do is connect the dots. >> And Diane Greene I thought of, my favorite soundbite was security is number one, worry, AI is the number one opportunity. >> Absolutely. >> I think if you look at it from that lens, everything falls into place. >> Absolutely. >> Well thanks for coming on, thanks for having The Cube this week, Google. And congratulations on your great venture, and good luck with your initiative. >> Thank you very much. >> Thank you both. >> Alright that's The Cube coverage here, live in San Francisco. I'm John Furrier, Jeff Frick, Dave Vellante went home last night. He's in our office taking care of some business. I want to thank everyone for watching. And that's a wrap here from San Francisco. 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. >> Hello and welcome back to The Cube coverage here live in San Francisco for Google Cloud's conference Next 2018, #GoogleNext18. I'm John Furrier with Jeff Frick, my cohost all week. Third day of three days of wall to wall live coverage. Our next guest, Carol Carpenter, Vice President of Product Marketing for Google Cloud. And Ayin Vala, Chief Data Science Foundation for Precision Medicine. Welcome to The Cube, thanks for joining us. >> Thank you for having us. >> So congratulations, VP of Product Marketing. Great job getting all these announcements out, all these different products. Open source, big query machine learning, Istio, One dot, I mean, all this, tons of products, congratulations. >> Thank you, thank you. It was a tremendous amount of work. Great team. >> So you guys are starting to show real progress in customer traction, customer scale. Google's always had great technology. Consumption side of it, you guys have made progress. Diane Green mentioned on stage, on day one, she mentioned health care. She mentioned how you guys are organizing around these verticals. Health care is one of the big areas. Precision Medicine, AI usage, tell us about your story. >> Yes, so we are a very small non-profit. And we are at the intersection of data science and medical science and we work on projects that have non-profits impact and social impact. And we work on driving and developing projects that have social impact and in personalized medicine. >> So I think it's amazing. I always think with medicine, right, you look back five years wherever you are and you look back five years and think, oh my god, that was completely barbaric, right. They used to bleed people out and here, today, we still help cancer patients by basically poisoning them until they almost die and hopefully it kills the cancer first. You guys are looking at medicine in a very different way and the future medicine is so different than what it is today. And talk about, what is Presicion Medicine? Just the descriptor, it's a very different approach to kind of some of the treatments that we still use today in 2018. It's crazy. >> Yes, so Presicion Medicine has the meaning of personalized medicine. Meaning that we hone it into smaller population of people to trying to see what is the driving factors, individually customized to those populations and find out the different variables that are important for that population of people for detection of the disease, you know, cancer, Alzheimer's, those things. >> Okay, talk about the news. Okay, go ahead. >> Oh, oh, I was just going to say. And to be able to do what he's doing requires a lot of computational power to be able to actually get that precise. >> Right. Talk about the relationship and the news you guys have here. Some interesting stuff. Non-profits, they need compute power, they need, just like an eneterprise. You guys are bringing some change. What's the relationship between you guys? How are you working together? >> So one of our key messages here at this event is really around making computing available for everyone. Making data and analytics and machine learning available for everyone. This whole idea of human-centered AI. And what we've realized is, you know, data is the new natural resource. >> Yeah. >> In the world these days. And companies that know how to take advantage and actually mine insights from the data to solve problems like what they're solving at Precision Medicine. That is really where the new breakthroughs are going to come. So we announced a program here at the event, It's called Data Solutions for Change. It's from Google Cloud and it's a program in addition to our other non-profit programs. So we actually have other programs like Google Earth for non-profits. G Suite for non-profits. This one is very much focused on harnessing and helping non-profits extract insights from data. >> And is it a funding program, is it technology transfer Can you talk about, just a little detail on how it actually works. >> It's actually a combination of three things. One is funding, it's credits for up to $5,000 a month for up to six months. As well as customer support. One thing we've all talked about is the technology is amazing. You often also need to be able to apply some business logic around it and data scientists are somewhat of a challenge to hire these days. >> Yeah. >> So we're also proving free customer support, as well as online learning. >> Talk about an impact of the Cloud technology for the non-proit because6 I, you know, I'm seeing so much activity, certainly in Washington D.C. and around the world, where, you know, since the Jobs Act, fundings have changed. You got great things happening. You can have funding on mission-based funding. And also, the legacy of brand's are changing and open source changes So faster time to value. (laughs) >> Right. >> And without all the, you know, expertise it's an issue. How is Cloud helping you be better at what you do? Can you give some examples? >> Yes, so we had two different problems early on, as a small non-profit. First of all, we needed to scale up computationally. We had in-house servers. We needed a HIPAA complaint way to put our data up. So that's one of the reasons we were able to even use Google Cloud in the beginning. And now, we are able to run our models or entire data sets. Before that, we were only using a small population. And in Presicion Medicine, that's very important 'cause you want to get% entire population. That makes your models much more accurate. The second things was, we wanted to collaborate with people with clinical research backgrounds. And we need to provide a platform for them to be able to use, have the data on there, visualize, do computations, anything they want to do. And being on a Cloud really helped us to collaborate much more smoothly and you know, we only need their Gmail access, you know to Gmail to give them access and things. >> Yeah. >> And we could do it very, very quickly. Whereas before, it would take us months to transfer data. >> Yeah, it's a huge savings. Talk about the machine learning, AutoML's hot at the show, obviously, hot trend. You start to see AI ops coming in and disrupt more of the enterprise side but as data scientists, as you look at some of these machine learnings, I mean, you must get pretty excited. What are you thinking? What's your vision and how you going to use, like BigQuery's got ML built in now. This is like not new, it's Google's been using it for awhile. Are you tapping some of that? And what's your team doing with ML? >> Absolutely. We use BigQuery ML. We were able to use a few months in advance. It's great 'cause our data scientists like to work in BigQuery. They used to see, you know, you query the data right there. You can actually do the machine learning on there too. And you don't have to send it to different part of the platform for that. And it gives you sort of a proof of concept right away. For doing deep learning and those things, we use Cloud ML still, but for early on, you want to see if there is potential in a data. And you're able to do that very quickly with BigQuery ML right there. We also use AutoML Vision. We had access to about a thousand patients for MRI images and we wanted to see if we can detect Alzheimer's based on those. And we used AutoML for that. Actually works well. >> Some of the relationships with doctors, they're not always seen as the most tech savvy. So now they are getting more. As you do all this high-end, geeky stuff, you got to push it out to an interface. Google's really user-centric philosophy with user interfaces has always been kind of known for. Is that in Sheets, is that G Suite? How will you extend out the analysis and the interactions. How do you integrate into the edge work flow? You know? (laughs) >> So one thing I really appreciated for Google Cloud was that it was, seems to me it's built from the ground up for everyone to use. And it was the ease of access was very, was very important to us, like I said. We have data scientisits and statisticians and computer scientists onboard. But we needed a method and a platform that everybody can use. And through this program, they actually.. You guys provide what's called Qwiklab, which is, you know, screenshot of how to spin up a virtual machine and things like that. That, you know, a couple of years ago you have to run, you know, few command lines, too many command lines, to get that. Now it's just a push of a button. So that's just... Makes it much easier to work with people with background and domain knowledge and take away that 80% of the work, that's just a data engineering work that they don't want to do. >> That's awesome stuff. Well congratulations. Carol, a question to you is How does someone get involved in the Data Solutions for Change? An application? Online? Referral? I mean, how do these work? >> All of the above. (John laughs) We do have an online application and we welcome all non-profits to apply if they have a clear objective data problem that they want to solve. We would love to be able to help them. >> Does scope matter, big size, is it more mission? What's the mission criteria? Is there a certain bar to reach, so to speak, or-- >> Yeah, I mean we're most focused on... there really is not size, in terms of size of the non-profit or the breadth. It's much more around, do you have a problem that data and analytics can actually address. >> Yeah. >> So really working on problems that matter. And in addition, we actually announced this week that we are partnering with United Nations on a contest. It's called Sustainable.. It's for Visualize 2030 >> Yeah. >> So there are 17 sustainable development goals. >> Right, righr. >> And so, that's aimed at college students and storytelling to actually address one of these 17 areas. >> We'd love to follow up after the show, talk about some of the projects. since you have a lot of things going on. >> Yeah. >> Use of technology for good really is important right now, that people see that. People want to work for mission-driven organizations. >> Absolutely >> This becomes a clear citeria. Thanks for coming on. Appreciate it. Thanks for coming on today. Acute coverage here at Google Could Next 18 I'm John Furrier with Jeff Fricks. Stay with us. More coverage after this short break. (upbeat music)

>> Live from San Francisco, it's the Cube. Covering Google Cloud Next 2018. Brought to you by Google Cloud and it's ecosystem partners. >> Hello, welcome everyone back to the Cube's live coverage. This is day three of Google Cloud Cube coverage here. Google Next 2018 #GoogleNext18. I'm John Furrier, my co-host Jeff Frick. Our next two guests kicking off day three, is Ed Anuff, the director of product management at Google Cloud, part of the Apigee acquisition, really part of the APIs and really a big part of the story here at Google Next, and Chuck Knostman, vice-president of IT at T-mobile customer. Ed, thanks for coming on. Chuck, thanks for coming on. So Apigee, a big part of the story at Google Next is, you know, the role of APIs and services. Huge, and I won't say nuanced. I mean, certainly Istio is new to a lot of people. Kubernetes, superly a very important piece of this new cloud service platform, as well as just running work loads, multicloud, etc. What's the focus, what's going on for you guys at the event. Take a minute to explain the announcements and what you guys did here at the show. >> Sure, so, APIs are how software talks to software. And what we announced this week at the show with Kubernetes and Istio are new ways for people to build software and deploy it, in new distributive fashions. And so that's creating new ways for tying your software together. Microservices, a lot of people are talking about now, are a key part of this. And so, from an Apigee perspective, you know, we're looking at facilitating how to make that communications happen, how to make it secure, how to make it efficient, how to monitor it. So what we announced was that Apigee is making it now possible for you to have all the tools that we've given you for managing your APIs, for, you know, getting your mobile apps to talk to your cloud services and all that, now is also going to apply to these new microservices that you're building. And so we think it's a pretty exciting thing. Lot of our customers have been asking for this, and obviously, uh, Chuck being one of them, and so, you know, that's what it's been all about for us this week. >> Chuck, obviously, APIs, key part of dev ops. You know, it first started with slinging some APIs around, stitching them together. Developers voted with their code, clearly APIs is the way that software's working. Microservices takes us to a whole nother level. Now, operationalizing APIs seems easy, but it's, you've got to start managing things differently. How are you guys taking that API and this new service management piece of it and kind of operationalizing APIs into T-Mobile? >> Yeah, we've been using Apigee for about four years now, and so over the time I think we were have 200 plus internal APIs, so we've over that time we've kind of learned how to operationalize that piece of it. Over the last couple of years we've really been focused on the microservice layers. Writing cloud-native applications, essentially. And that layer, and now with the Apigee hook into Istio, we're going to have a much better way to manage it. And it's really nice to see the platform starting to grow and mature along with us, so that's really great. >> I can only imagine how complicated it is to run real-time, cloud-native and have also legacy, and I think one of the things I'd like to get your thoughts on is, containers have become a nice piece of, not ripping and replacing to bring in the new. You don't have to kill the old to bring in the new. And now with containers, Kubernetes, and microservices and Istio, you have an ability to kind of do both. Talk about how you guys do it, cause this is like a perfect storm, in a good way, for enterprises. >> Well yeah, and it's really good timing for us as well. We're just now starting our Kubernetes journey on premise, if you will. So we're a big cloud-foundry shop. We're starting to put our legacy applications into docker containers and moving them, we'll be moving them onto Kubernetes. And so you can see the whole, the containerization shift as we go, as we go through time. And it's really, for us, like you said, it's fortuitous that at this timing because now with Istio coming in and being able to control all that, that's a great thing for us. >> Ed, talk about, you give a lot of history. To use, as normal APIs, it's lingua franca, it's been around for a while, you've had a lot of experience in that. But a lot of the enterprises that we talk to are like, there's a lot of pressure in IT to do more now with cloud-native. And now with the new services that are out there, it kind of takes the pressure off IT because the pressure of, oh, I got to sunset that app or I don't know when to kill that workload. I know I want to maybe transform it, but I don't want to have to disrupt all this stuff. So talk about the importance of nondisruption, because this seems to be a conversation that's talked a lot in the hallways. >> That's exactly right. So, you know, what you see within enterprises is that there's a need to deliver a whole set of new applications, and a lot of these are connected to digital experiences. Basically everything that you experience on your mobile apps, every new form of engaging with your customer. That's where a lot of the business growth is that's bringing, you know, a lot of the funding for these new initiatives. But, a lot of the core data of the enterprise is locked up within systems that have been operating very efficiently, but siloed for many years. And so that's the part that we see the most, which is, you know, folks within IT come to us and say, "Look, you know, I've been building these legacy systems "for many years now, and I know that if I can just take "the data that's locked up in these and bring these "into these new ways of doing business, "that it's going to have a huge impact on my business." And that's, you know, that's where the question sits. And then the follow up on that is, "Hey, you know, we want to, "we want to make our businesses more like the way, you know, "you guys are doing it in Silicon Valley. "And we, we see what you're doing with containers, "and we see things like Kubernetes, and cloud-native, "and we know that's the right way to build things, "but there has to be a way for us to bring "all of these other assets that we've been building "for the last 30 years along for the ride." And in fact for most of these businesses, our response is, "Hey, it's not just a question "of building along for the ride. "That, that's your core, that's your, that is been "what you built your business on. "So don't even just think about it "as this thing that you somehow have to drag along. "Think about how you actually can amplify it "because it's been the source of your business for so long." >> Yeah, the other I would add to that is that it gives us scale and operation, a much better operational platform to work with. For us, we've grown tremendously, or our growth has been tremendous over the last five years. We've gone from I think 30 million customers to 73 million customers, and frankly, to scale those systems up, containerization is probably the only way we can go with it. And with, from an operational standpoint, having one platform like Kubernetes to have, to operate for all of this stuff just helps us out tremendously. >> We hear that all the time. I think that's the biggest story around containers outside of geeking out on the benefits of it is that it really allows a nice bridge to the future. You don't have to burn the boats, as they say, in Silicon Valley, you know. >> And you can pick your, you can pick on the applications you want to keep around, right. Then you refactor 'em to be cloud-native on the ones you don't. You don't have to go all the way, right, and so you can make it much better that way. >> Chuck, I'm curious to get your take on the changing competitive environment. Cause before, you know, you had these big complex systems and you wanted to keep them running. Now the pressure for more innovation, more applications, quicker applications, to leverage not only your inside stuff but outside stuff, and how some of these technologies are helping you deliver that to your customers or your internal development team. >> Yeah, like I said, scale is one aspect of it. Performance is another, and the ability to move those workloads close to the customer just like Google's trying to do with moving closer to the customer, we do the same thing. Right, and so the hybrid cloud is real for us. We run in almost all the clouds right now, and on premise we treat that as a cloud as well. But being able to do that can only happen when we containerize stuff and utilize similar platforms on all these places. >> Right, and then you'll have this huge transformational shift over the next several years with 5G right, that's coming-- >> Yeah, yeah, and we've been at it for a couple years now. >> For a couple years, so this is going to be another huge wave of change inside your infrastructure. >> Yeah, sounds fantastic. >> What attracted you to Google Cloud? Share, take a minute to explain. What was the interest in Google Cloud. Why Google Cloud for your guys? >> Well we're just getting started with it, but it's really, it's the partnership we've had with Apigee that's helped us kind of understand what's going on with Google Cloud, but then the open-source nature of it as well as the focus on AI and ML. That's why we're really taking a hard look at what's going on with Google Cloud, and the attitude towards enterprises is great as well. >> Culture's a good fit there. >> Yeah, yeah, absolutely. >> Yeah, it's interesting, a lot of people are attracted by some of the speed. I mean, we've been hearing here at the show, you know, Google obviously has built their business on being fast. >> Yeah, well and having your own network is massive as well, right. >> And now you got the API. And what's the future look like for APIs and Apigee inside Google? Give us a little taste of what you guys are working on, some of the projects you guys are passionate about, and some of the successes you've had or any anecdotal use case studies. >> So definitely, so, you know, APIs carry our customers' most important data. And data's the basis for machine learning and AI, and so you're going to see a lot of product innovation for us about bringing, you know, AI to the point of these data conduits that are what APIs are all about. It's the natural place to couple it with every business process. So that's a big deal for us. I think that, you know, the security aspect, you heard a lot about security in the key notes. Again, you know, APIs are the conduit in many cases for, again, the enterprises most important data. To get outside of the perimeter of the enterprise, it has to be done in a secure way. You know, and then finally, being able to go and leverage the sort of collaborative nature, the stuff you see within open-source, the community around all of this, again, you know, most APIs are about bringing a lot more developers to, you know, build more applications in less time around these APIs and that is, that collaboration component is something that we see a ton of opportunities in terms of leveraging, you know, Google's unique know-how in terms of advancing and pushing this data that are in an API management. So I think you're going to see a lot of that from us. >> Chuck, I'd love to get your thoughts on how you in IT, obviously and IT's transforming, we talk about it all the time, how you keep track of what's good, right. It used to be in the old days the stack was pretty not that complex. And you go to Gartner or magic quadrant, oh they're a leader, I'll kick the tires, they come in, a vendor will come in, but some of the best cloud providers don't even show up on a magic quadrant because it's horizontally scalable. APIs changes the stack a little bit. A new modern middleware is emerging with Istio and new sets of business models and services are emerging. So a lot of people are like trying to be, how do you determine who's good. You know, in IT, because ou want to move the needle, you want to transform, you got a lot a build up. How do you kind of evaluate, is there any new ways, or is it gut instinct or specific things that you look at? >> Really good question. We look, we try to adopt the open-source stuff first. But we, from the company standpoint we also look at the company themselves and who's really vested in what's going on with it. Like, Apigee four years ago was really the only ones that were really only doing APIs, right. And their knowledge and the depth and their road map, that's what we really kind of look for. But to your point, things are changing so rapidly that you kind of have to go with the, watch the open-source community. Where are all the pull requests coming from, or what platforms are they going after? And then track that, and that's where, that's what we try to do. And so when we see Kubernetes and the explosion that's happening on that, the tooling that's coming around that, we know that's going to be good for enterprises going forward. So, we're going to be heavily investing in that platform. >> It's interesting, we always talk about developers, but what's interesting that's coming out of the show that we're observing is, it's always about developers do building apps. But the role of an operator inside IT, used to be an operator would, you know, maybe provision some storage and some servers. Now the role of what an operator, I mean, network op guys, now it's kind of like a more of a holistic view. Your thoughts on this. I know it's super early, but the emergence of these two personas in IT is super critical. >> Yeah, we look at it like it's automation, right. That's where it all comes to play. So if you've got a platform like a Kubernetes where you can have all this automation built around it, and you let the developers just do their thing and focus on the business logic, it's huge. So there is kind of two personalities, and the caring and feeding of that platform is just as important as the guys writing the applications across the top. >> Yeah, it's really a great environment. Final question for you guys. Observations on the show, Google Next. What's your observation, obviously you've got an API perspective, just globally looking down. If you kind of look, zoom out and look at, look down at the show, thoughts and commentary on what's happening here. >> You know, I think the scale of it has been amazing, you know, we became part of Google two years ago. We were here at the show last year, looking at it this year. And, the level of growth, the activity, attendees, the number of announcements, it's just been amazing. It's been very exciting for us to be a part of. >> Cool, Chuck your thoughts? >> Super impressed. This is our first one, really, that we've come to. We were even participating on the stage on the Knative, we wrote some applications to work with Knative. But, it's a, it's a very diverse crowd which is awesome. I think you really need that. Some of the others, I don't see as much. So I think what Google is doing, and again their approaches to enterprise, looking more at solutions, vertical solutions, very impressed with what's going on here. >> It's a really great time. Congratulations on all your success with the APIs. You guys have done the work, and open-source, it's where the, your employees want to work. They want to meet other people, and this is where the co-creation, that's where the assessments of the vendors happen. >> Opensource.T-Mobile.com, that's where we want to be. >> Alright, great. Well, Chuck, Ed, thanks so much. Really appreciate the time. It's the Cube live coverage here in San Francisco covering Google Cloud's conference, Next '18. We'll be right back with more day three coverage. Stay with us, we'll be right back. (light jazzy music plays)

>> Live from San Francisco it's theCUBE, covering Google Cloud Next 2018, brought to you by Google Cloud and its ecosystem partners. >> Hey welcome back everyone. This is theCUBE, live in San Francisco. Google Cloud Next '18. My co-host Jeff Frick and I are here with Suhail Dutta, VP of Cloud Services for Unity Technologies. Some of those popular game engines, developers of VR AR and mobile gaming, as well as game developers. A hot used case for Google Cloud. They love the speed. They love the features. We hearing that all, wait welcome to theCUBE, thanks for joining us. >> Thanks, thanks for having me. >> So I wish I was a kid again because the game experience now is so good and I'm kind of like a keyboard guy. So I'm not a good console player but now keyboard's back and now you got mobile games. I mean the games are amazing these days. >> They are, they are. They're amazing and they're amazing on every kind of platform. You could have mobile games, console games, PC games, input types, keyboards, controllers VR. It's stunning. >> I always say I've been observing the Internet. It used to be really the predictor of what's going to happen in computing and user experience really I think gaming leads a lot of it. Look at virtual currencies. Look at blockchain and crypto and virtual currencies in game for a long time. So it's a really leading indicator and certainly as you look at immersive experiences, gaming is not just gaming. It's potentially virtual reality, augmented reality around 3D is what you guys do. This is huge. >> Yeah. >> It's not just about gaming anymore. Talk about what you guys are doing. Take a minute to explain the company. Are you beyond gaming? What are some of the things you're working on. >> Yeah I think I said this at the keynote. One of the things we fundamentally believe is the world's better with more creators. So those creators for us traditionally have been in gaming but more and more we see that also happening in film. All kinds of media, animation, but also lots of industries like automotive and others. And so we more and more like to talk to ourselves as we're enabling, empowering creators to do what they love to do and we make their lives easier, and allow them to achieve what they wanted. >> It's the continuation of the democratization trend right because actually all the big hardware companies used to brag about how much time it took to render all the crazy scenes and all these beautiful big 70 millimeter movies. Everybody can't afford that horsepower, doesn't have the time, so with with engines like what you guys have you know you've been able to spread that developer ecosystem out, the creator ecosystem out dramatically to allow so much more points of view and people to contribute and to create all these cool new things. >> Yeah you know I think Diane actually said this on stage at the Unite event. Our founders may have coined democratized development and 15 years ago, we've always believed that to be true. We've been for the community every size of team for as long as we've been around, and it remains the first principle we use in our mission. We do solve our problems. We do enable our creators success but democratized development is core to everything we do, and we've been (mumbles). >> The younger generation is gravitating towards games. Obviously it's a gateway drug to software development if you think about it. Robotics is another one. You're seeing these maker culture kind of things really attracting developers at a whole another level. It's not computer science, software engineering degree, banging out raw machine language. This is like for fun. There's a whole new artistry going on. >> There is yeah. >> What is your view on this new trend around software artistry because there's engineering certainly involved. The engines are getting smarter. Distraction layers are becoming available. What's your take on that? >> Yeah I think the engineering side of it has always been about raising that level of abstraction so that people can focus on what they love to do. So if you're a game maker you probably got into making games because you love games and you love making them. You probably didn't get into it to make an engine. So that's always been very true for us, and we've gotten better at that. But some of the things we've learned along the way of course to your point are, the various kinds of artists that are actually just as or more critical through these kinds of creative endeavors, and we've actually been making great strides in not only helping artists of all kinds work themselves in Unity, or in other tools, but also then work seamlessly with engineers, which oftentimes ends up being a place where there is friction, but in an environment like Unity, you can't have a lot of separation right? We have a 3D environment. You put this on your computer, you work in it, you build your models, you write your scripts, you write all of that in one cohesive way, because otherwise games take way longer to build. They have all kinds of issues and communication. I think it's quite key for us. >> So I always love to watch the game threads on Reddit, EA and these guys, the corporate's taken over. You're seeing more younger artists coming in. You guys have to maintain your relevance to keep those developers happy. You got to continue to innovate. Gaming is a lot of pressure. >> Yes. >> How do you guys keep up? What are some of the things you're doing with tech? What do you bring into the market? How do you keep ratcheting up the capability so that they don't flock somewhere else or apparently so they can create better products. >> Right, I think probably the highest level principle there is leading on from democratized, but we focus a lot on our community of creators. Both in terms of the content, the samples, the learning, the tools, something Google does quite well actually. And that's been instrumental in empowering this community. That's very strong. I mean it is in many ways our greatest strength. We have a huge number of developers and artists and creators that work with Unity. So if you were to want to create something, and you were looking for answers, using our services or others, you can go out there. Now on the technology side, the way we look at it is in many ways we've looked at it as your engine team. So performance by default some of the things that we're doing to make really, really high performance, efficient computing, on all kinds of devices, letting you do more with them, but then also there's a responsible aspect which is if you think about improving the performance and power consumption on devices, is very important to us. And then an area where we're really putting in a lot of effort now is the cloud and with Google on connected games, which is why-- >> So let's talk about that because we're here and it's interesting the creator conversation because obviously Google owns YouTube, which has spawned a whole different kind of class of creators that are disrupting the media business. So you're here kind of what does Google give to you guys? Why are you partnering with them? What's kind of the story? >> Of course. So we talk about connected games right? So what we mean by that of course are games where players can connect to each other and or to the developers that create them. Oftentimes, we use the term multiplayer, which of course is a particular sub-genre of connected games. They run the gamut from a game that you might play on your phone and then you interact with other players through leaderboards and chat and things like that. So they're connected not necessarily real-time multiplayer and on the other side of the spectrum you might have a game where you run around and interact with each other in real time in a 3D environment or a massive multiplayer game where you stay in that world for many, many, many years and you act as a character. Because Unity has so many creators, the entire spectrum of those games, connected games are important to us, important to our users. For all those games, you need massive amounts of infrastructure. You need lots of infrastructure, you need performance items, like you need the best network and you need lots of services that help you again to the earlier point focus on making your game. This is an area that both Unity and Google care deeply about. If you take a small studio or even a large studio for that matter, that got in the business to create their game, they don't want to spend all of their time learning how to make an engine or set up a bunch of infrastructure. The area where we're focusing a lot now is that marriage between Google and Unity where you can because of our alliance, we can raise that level of abstract to your earlier point and let them build connected games in an easier way. >> Talk about the role of data because obviously you look at the data that's generated. I mean which could be user gesture data, I mean everything's tracked. >> Yeah. >> I mean that's a big data solution problem opportunity you guys have. >> Yeah and I think so one of the things we like to say of course is you know we're a platform. We enable our users to build and run successful games and our users being the developers and artists that data's theirs, and then they are able to then do really wonderful things with that data if they so choose. So you're right, for the games that have so many players online and all these actions, there is an amazing amount of data, but fundamentally in an anonymized way around what makes games more fun. And that's a hard problem to solve. It's why our creators have the hardest problem of it all is make something fun where data can play a huge role in that. >> How is the relation with Google Cloud and your engine with those developers? Do they get the magic of Google and you pass that through or is it built into your product that's abstracted away? >> Yeah it's a combination of things, so I think there's one side, which is us building services that run on top of Google Cloud so if for instance you need a matchmaker which is a very common piece of technology, but quite complicated piece of technology, for games is to match players into games quickly. We are working with Google, we're collaborating on an open source project, that we call Open Match that comes out later this summer, and then we're building a service on top of that that our users can just pick up and use. It runs on Google Cloud. At the same time, Google brings many other capabilities to bear, things like maps and other capabilities from GCP, that they can then bring to our users in a more direct way rather than building a product together, and then of course Unity actually now runs quite a few cloud services and we're going to migrate all of those to Google Cloud as well. So it's sort of three aspects of that. >> And what's your vision for Unity? If you look for and looking at what's coming on with Google, as to the future of your engine looking at the creator market, Hollywood. Just at Sundance I did a panel with Intel on the future of entertainment, and we talked about the new artists coming in. You have the social networks now reforming this game connector concept is pretty huge. >> Yeah. >> This is a new dynamic, so you got to build new services. What's your vision of how your going to build out these cloud services? Can you share your vision and thoughts on? >> Yeah we can yeah so I think the, within the space of connected games of course like I said there's many different categories of these games, but there are some fundamental building blocks that you can build that we can build together, Google and Unity can to empower all of these kinds of games. Matchmaking is a particular example, but at the end of the day, games that blur the lines between, they're running on a device, they're running on a PC, they're running on a console or they let players pick them up wherever they go, but also interact with each other right because as AR and VR and these virtual worlds come to fruition, more and more it's going to be about us interacting not just in the virtual world but also in the real world and able to do that and most of those things are predicated on this world that exists online, and it's all running on infrastructure. There's a lot of infrastructure that's required there, so we've got a really rich roadmap over the next many, many years to continue to invest in this area and help our users create these kinds of games because they are in the games world, the most influential kind, but more and more in other areas of our life they're also going to be the same technologies that are applied there. >> I just love to get your perspective. You've been in this space for a long time gaming but also 3D specifically. Now 3D is so still nascent. It's hard to do for most people. The experiences are still being developed, but it's come so long, so as you look at kind of where 3D is evolved, both to create it as well as to experience kind of what are your general thoughts of where we are on that path and what do you see kind of in the short term and near-term in terms of how that's really going to change the way we do things, whether it's work, gaming or experiencing other types of things? >> Sure I think that I'd like to go back to one of the things you said, where when you're playing games you have to stand up. We've come a long way. (all laughing) So we have come a long way. You look at some of the content of the games that are being produced, you even look at just the kinds of content and the interactive content that's being created in Unity, it's amazing if you look at how far we've come. I think to your point you're right. There is a long way to go, there's lots of it. I mean all our hardware capabilities just continue to get better, like the latest phones, the latest consoles. They're so powerful right we have these supercomputers in our pockets with amazing capabilities and consumers demand that kind of stuff, the latest level of graphics. I think all of that stuff continues. I think our CEO, John talked about in this sort of AR and VR, we're kind of going through this level of excitement and then we have the trough of disillusionment and all these kinds of things right. We've got some elements of that but there's a lot of great companies doing a lot of fantastic stuff, and I think that that's going to come to bear, and so I think Unity is there with them and we're really well positioned. >> The tell signs are there. You're seeing people using VR in areas that give them a unique thing that's so scarce in areas where that's pharmaceuticals, doctor, I see even heard Tom Brady uses a VR to look at defenses before he plays games, but this is an interesting question for you though, I want to get your thoughts. Do you have a unique position to see the data of what your game engine is doing? For the folks out there, the young kids who are in elementary school, high school that love games. That don't necessarily want to be computer science major. Maybe they don't even have a direction of any kind but want to start hacking away and start coding. What patterns do you see that would help someone get started and so they don't drop out or abandon it, get addicted if you will, what are some of the things you could share that you've seen successful getting someone involved in either coding games, getting involved in the community. What are some of those best practices or patterns that you've seen? >> Right I mean so I think there's probably a technical answer to that and then there's a non-technical one. I think your word community resonates with me a lot. So for anyone starting out I think there's a lot that an individual creator can accomplish but given the world we're in, we have these extremely rich communities that are helping each other, whether it's the open source community in a more general sense for web or servers, but even in machine learning if you hear the guy from Cal-vil talk, they were talking about machine learning community, and it was pretty amazing to hear him talk about that. For us it's the creator community and we have a really rich one and there's lots of people there that bring many skills to bear, which ends up being way more critical than things like very specific technology trends for this kind of thing, so I think-- >> Just mentoring and stuff going on in the creator community. People are helping each other big time. >> There's a huge amount. I mean this notion of developers and creators helping each other, sometimes not for any money, is a trend being seen everywhere, not just-- >> So advice is jump into a community, get a check in... >> I think it's probably cliched a little bit, if you can find a project or a set of projects or a type of thing that you really enjoy doing, you'd be surprised at what skills you can bring to bear and everyone needs help. >> So download the emulator, get some code in your hands, jump into a community-- >> Yeah Unity is free. Download it. It's easy to get started and then work with the community. I think almost always it's find the project that you really care about and start helping. >> Final question for you to wrap up the segment. For the people that are not inside the ropes in the industry that looking at Google, see Google Cloud, wow a lot of buzz on Google Cloud, knowing what we knew two years ago, oh gee the original app engine kind of concept was Google Cloud. Now so much more. What would you say to the people watching now how has Google Cloud changed? What's different? What are they doing right and where they need to improve? >> So even before Unity I've been a user of aspects of Google Cloud and App Engine. And I think they have come an amazing way in terms of the way they're approaching every other aspect that isn't just the technology aspect. I think the tech it's Google. They've always been impeccable. >> It's great tech. Yeah great tech, yeah. >> Their network is incredible. Their server is incredible. So they've always been extremely good at that, but the things that are so much better the level of support, they're working with us very closely all across their organization. We are enjoying working with them a lot and they're really trying to help us be successful much like we help our creators, so that's resonating with us a lot, and we found that to be great and I think that you know everything I see makes us quite happy that that we are partners with them. >> And they're bringing some goodies to the party. They've bred open source contributions, pretty phenomenal. I mean Kubernetes I mean that's just game-changing right there. You got BigQuery and they got some, they're contributing some jewels. >> They have some amazing tech that can be brought to bear on a lot of different things right? So we're are a heavy Kubernetes user and have been for a while. Even before we were Google partners, so I think this is great things that they announced with GKE, this conference really mattered to us, GKE on prem, and then they're also a very partner driven company, and I think they recognize our knowledge and expertise in games and I think that that's an area where their expertise in cloud and our expertise in games can be very very great. >> I think it's a great opportunity for Google to make the market on the partnership ecosystem side. They have a lot they could bring to the table. They can make people successful and people can make money and deliver great products. That's a winning formula. >> Yeah exactly. >> - So let's see. Congratulations on your success. >> Thank you. >> Thanks for coming on theCUBE. >> Thank you. >> Thanks for sharing the insight into Unity Technologies. It's theCUBE bringing you all the action here out in the open with Google Cloud. More coverage, stay with us. We are at day three of three days of live coverage. I'm John Furrier with Jeff Frick. Stay with us we'll be right back. (electronic music)

>> Live from San Francisco, it's theCUBE covering Google Cloud Next 2018. Brought to you by Google Cloud and it's ecosystem partners. >> Welcome back everyone. This is theCUBE live coverage of Google Cloud Next '18 in San Francisco. I'm John Furrier with Jeff Frick. We're at day three of three days of wall-to-wall coverage. Go to SiliconANGLE dot com on theCUBE dot net. Check out the on demand videos and the Cloud series special journalism report that we have out there, tons of articles, tons of coverage of Google Next with the news, analysis and opinion, of course, SiliconANGLE. Our next guest is Indranil Chakraborty, Project Manager for IoT Google Cloud. Certainly IoT part of the network part of the Cloud, one of the hottest areas in Cloud is IoT. We've been seeing that. Welcome to theCUBE. >> Thank you. >> Thanks for joining us. IoT is certainly the intersection of a lot of things: Cloud, data center, A.I., soon to be, you know, cryptocurrency and blockchain coming down, not for you guys, but in general those are the big hottest areas. >> IOT is not like, you can't say it's an IoT category, so IoT has to kind of sit in the intersection of a lot of different markets that are kind of pure playing. >> So I first want you to explain to the folks out there watching, what is the Google IoT philosophy? What is the products trying to do? And what are guys announcing here? >> Absolutely. Thanks for having me here, it's really great to be here. And if you think about IoT, and if you think about what we have on Google Cloud, we already have a great set of service for data storage, processing, and machine intelligence. Right, so we have Cloud Machine Learning Engine, we have an on start ML. So most of those data processing and intelligence services are already there. What we announced last year was Cloud IoT Core, which is our fully-managed service for our customers and partners who easily and securely connect their IoT devices to Google Cloud, so they can start transmitting data and then ingest and store in the user downstream services for analysis and machine intelligence. >> I mean, IoT is a great use case of Cloud because one, Cloud shows that you can be incented to collect data. >> Right. >> Cuz now you have the lower cost storage, You've got machine learning, all these things are going on. It's great. >> Exactly. >> But Iot is now the Edge of the network. You've got sensors. You've got cars, like Teslas, people can relate to. So everything's coming online has, not just an IP connection, anything that's a sensor. The IoT's been just evolving. What is the Edge to you guys? What does that mean when I say IoT Edge? What is Google view of the Edge? >> Yeah absolutely, it's a great question. You know, we identified early on the emergent trend of moving compute and intelligence to the edge and close to the device itself. So this week, as you already know, we've announced two products for Edge. One is Cloud IoT Edge, which is a software stack which can run on your gateway device, cameras, or any connected device that has some compute capabilities, which extends that powerful AI and machine learning capabilities of Google Cloud to your Edge device. And we also announced Edge TPU, which is a Google designed high performing chip for to run machine learning inference on the Edge device itself. And so with the combination of Cloud IoT Edge as a software stack and with our Edge TPU, we think we have an integrated machine learning solution for on Google Cloud platform. >> How does that get rolled out? So the chip, I'm assuming, you're doing OEM or deals with manufacturers. Same with the software stack. Is the software stack portable? Explain how you roll those out. >> Yeah, you know we are big into working with our ecosystem and we really want to build a robust part of ecosystem. So we are working with semiconductor companies, such as NXP and Arm, who will build a system-on-module using our Google Edge TPU, which can then be used by gateway device makers. So we have partnership with Harting, Nokia, NEXCOM. We're going to take those SOM, add it to their gateway devices, so to take it to the market. We're also working with a lot of computing companies, such as ADLINK, Acton, and a couple of others, Olya. So they can build an analytic solution using our Cloud IoT Edge software and Edge TPU to combine with the rest of Cloud IoT platform. So we're pretty excited about the partners. >> But every coin has two sides, right? So the kind of knock on the Edge is, now you're attack surface on the security side is growing exponentially. So clearly, security is an important part of what you guys do. And now this is kind of a different challenge when you're now, your point to presence is not like our point to presence, but are going to expand exponentially to all these connected autonomous devices. >> Yep, that's a great point. And you know, we take security very seriously. In fact, last year when we announced Cloud IoT Core, we reject any connection that doesn't use TLS, number one, right? And number two, we individually authenticate each and every device using an asymmetry keypad. In addition to that, we've also announced partnership with Microchip. So Microchip has built this microcontroller crypto, which can have the private key inside the crypto, and we use JWT token that was signed by inside the chip itself. So your private key never leaves the chip at all. So that's one additional reinforcement for security. So we have end to end security. We make sure that the devices are connecting over TLS, but we also have hardware root of trust on the Edge device as well. >> The token model is interesting. Talk about blockchain because you know, David Floy on our analyst team, he and I are constantly riffing on that. IoT actually is interesting use case for blockchain and potentially token economics. How do you guys view that? I know that you just mentioned that this is kind of a thing there. Does it fit in your vision at all? What's your position on how that would work out? >> You know, we are closely looking at the blockchain technology. As of today, we don't have anything specific to announce in terms of a product perspective, but we do have, we do use JSON web token, which is standard on the web, use to sign those using our private keys. So that works beautifully, but we're closely monitoring and looking at it. We don't have anything to announce today. >> Not yet, but they're going to share that. Their research is working on it, interesting scenario. So in general, benefits to customers who're working with IoT, your team, cuz you have the core, you have the chip, you have the software stack. There's always an architectural discussion depending upon the environment. Do you move the compute to the data? Do you move the data to the Cloud? What's the role of data in all this cuz certainly you got the processing power. What's the architectural framework and benefits to the customers who are working with Google. >> Yeah, so let's make a specific example, LG CNS. They want to improve their productivity in the factory, and what they've done is they've built a machine learning model to detect defects on their assembly line using Cloud machine learning engine. And they've used this one engineer a couple of weeks and they would train the model on Cloud. Now with Cloud IoT Edge and the Edge TPU, they can run that train model locally on the camera itself, so they can do realtime defect analysis at a pretty fast moving assembly line. So that's the model which we are working on where you use Cloud for high compute for training, but you use the Edge TPU and the Cloud IoT Edge for local inference for real time detection as well. >> How do you guys look at the IoT market because depending on how you're looking at it, you can look at smart cities, you can look at self-driving cars? There's a huge aperture of different use cases. It could be humans with devices, also you guys have Android, so it's kind of a broad scope. You guys got to kind of have that core tech, which it sounds like you're putting in the center of all this. How do you guys look at that? How do you guys organize around that? I think Ann Green mentioned verticals, for instance, is there different verticals? I mean, how do you guys go at that mark with the product? >> IoT is a nation market. And what we offer as Google Cloud, is a horizontal platform, what we call it is Cloud IoT platform, which has got Cloud IoT core on the Cloud side, Cloud IoT Edge, the Edge TPU. And we really want to work with our partners our solution integrators and ISVs, to help build those vertical applications. And so we're working with partners on the healthcare side, manufacturing. We have Odin Technology as one of the partner to really build this vertical up. >> You guys are not going to be dogmatic, this is how our IoT sleeve. You're going to let a thousand flowers bloom kind of philosophy. Put it out there, connect, and let the innovation happen with the ecosystem. >> Yeah, we really believe in driving, moving the, having robust ecosystem. So we want to provide a horizontal platform, which really makes it easy for partners and customers to build vertical solutions. >> Another kind of unique IoT challenge, which you didn't have in the past, we've all seen great pictures of the inside of Google Data Centers. They're beautiful and tight and lots of pretty pictures, very different than out in a minefield or a lot of these challenging IT environments where power could be a challenge. The weather could be a challenge. Connectivity to the internet could be a challenge. Obviously, and then you need to power them. When you talk about how much store do you have locally, how much compute do you have locally. So as you look at that landscape, how has that shaped your guys' views? What are some of the unique challenges that you guys have faced? And how are you overcoming some of those? >> Yeah, that's a great question and this is one of the primary reasons why we announced Cloud IoT Edge, which is software stack, and Edge TPU. So that for use cases where you have limited connectivity, oil wells or farm field, windmills. Connectivity is limited, and you cannot rely on connectivity for reliable operations. But you can use Cloud IoT Edge with our partner device ecosystem to run some of the compute locally. You can store data locally. You can analyze locally, and then push some of the incremental data to the Cloud to further update your model in the Cloud. So that's how we were thinking about this. We have to have some compute locally for those reasons. >> Release the hard coupling, if you will. So it's really got to be a dynamic coupling based on the situation, based on the timing, maybe. >> Exactly. >> Schedule updates, and these type of things. So it's not just connected. >> Exactly. It doesn't need to be continuously connected, right? As long as there's enough connectivity to download some of the updated model, to download the latest firmware and the software. You can run local compute and local machine learning inference on the Edge itself. That's the model we're looking at. So you can train in Cloud, push down the updates to the Edge device, and you can run local compute and intelligence on the device itself. >> A lot of conscious we've been having lately has been about, how do you manage the Edge, has been an area of discussion. Why I want to have a multi-threaded computer, basically, on a device that could be attacked with malware, putting bounds around certain things. You need the IP there. You want to have as much compute, obviously, we'd agree. But there's going to be policies you're starting to think about. This is where I think it gets interesting when you look at what's going on at the abstractions up the stack that you guys are doing. How does that kind of thinking impact some rollouts of IoT because I'm looking to imagine that you won't have policies. Some might trickle data back. It might not be data intensive. Some might want more security. Containers, all this kind of tying in. Is that right? Am I getting that right? How do you see that happening? >> So when you think about Edge, there are different layers. There are different tiers. There are the gateway class devices, which has high compute, and all the way to sensors. Our focus really is on the Edge devices, which has some decent compute capabilities and you can scale up to high-end devices as well. And when you think about policies, on the Cloud side, we have IM policies, so you can define roles, and you can define policies, based on which you can decide which devices should get what software or which user should get access to particular data types as well. So we have the infrastructure already, and we're leveraging that for the IoT platform. >> Yeah, and automate a lot of those kind of activities as well. >> Exactly. >> Alright, so I got to ask you about the show. What's some of the cool things you're seeing, for the folks that couldn't make it that are watching this video live and on demand. What's happening here at Google? What's the phenomenon Google Cloud? What are some of the hot stories? What's the vibe? What are the cool things that you are seeing? >> Absolutely. So I'm biased, so I'm going to start with IoT. You know, we have an IoT showcase where we have a pedestal where we're showing the Edge TPU and the Edge TPU board as well. And there is a lot of work which is happening there. There's a maintenance team there as well, so I would highly encourage attendees to go check it out. >> What are people saying about that? The demos and the sessions, what are some of the feedback? Share some color commentary around reactions. >> Yeah, we've been getting a lot of positive reactions. In fact, we just had a couple of breakout sessions, and a lot of interest from partners across the board to engage with us. So we are pretty excited with our announcement on the Edge side. The whole orchestration of training model in the Cloud and then pushing it down and then sending updates, that's where it really makes it easy for a lot of the partners. So they're excited about it as well. >> They're going to make some good money with it too. You guys are making the mark, and not trying to go too far. Laying the foundational work, the horizontal scale. >> Yes, exactly. And we really focused, for the Edge TPU, we really focused on performance per dollar and performance per watt. And so that has been what we are striving to really have high performance for lower cost. So that's what we're targeting. And a couple of other things, the whole server-less capabilities, and the fact that Cloud functions have become GA, is pretty exciting. And Cloud IoT Core is also a fully managed server-less architecture in a machine. The AI and auto ML which we announced with NLP and text and speech is pretty exciting as well. And that works very well with some of our IoT use cases as well. So I think those are a couple of announcements, which I'm pretty excited about. >> Yeah, I think the automation theme too, really resonated well on all that. Cuz what comes out of that is, humans still got to be more proficient in doing the new stuff, but also they got to run this. And you've got developers enough to build apps that drives value, so you got the value development with the applications, and then also the operational side, which is, I don't want to say becoming generic, but it's not specialized as used to be. Network operator, this guys does this, this gal does that. I mean, it used to be very stove piped. Now it's much more of a how do you run the environment? >> Exactly, and to your point, even on the IoT space, it's also very relevant. I mean there are a lot of overlaps between what used to be just devops and OTE and IT. There are a lot of overlaps there. And so we're looking at it closely as well to make sure that we can really simplify the overall requirement and the tooling which is needed for building an IoT solution. >> For the people that are not following Google as closely as say we are, for instance, they're not inside the ropes, inside the baseball, if you will, in the industry. See Google Cloud, they know Google as Gmail, search, et cetera. They look a couple years ago, Google Cloud had app engine, the OG of Google Cloud, as it's called. What would you say to the folks now that are watching? What's different about Google Cloud now, and what should they know about Google Cloud that they may not know about. What would you say to that person? >> Absolutely, and the first thing is we are very serious about enterprise. You can see here the number of attendees who have come here and how we have multiple buildings where we organized the conference. We're very serious over enterprise. Second, back in the days, two years back, we were really focused on building products, which works for specific use cases. We didn't think about end to end solution, but now the focus has changed. And we're really thinking about, we always had the technology with packaging the products, and now we're thinking about providing end to end solutions, the framework where for a business user, enterprise user, they can just take the solution, and they know it will work. Alright, so there's been a lot of focus on that. And our key differentiator is about machine intelligence and AI, right? That's where Google thrives. We've been spending a lot of time on it, and now we're focused on democratizing AI. Not just on the Cloud, but also on the Edge with the announcement of HTPU. >> And I really think you guys have done a good job with the mindset of making it consumable. In an end to end framework with the option. We've got Kubernetes, and Container's been around for a while, but it's working with multiple environments. I think that is a real mindset shift. >> Exactly. >> So congratulations. >> Thank you. >> Thanks for coming on, appreciate it. >> Absolutely, was great having you guys. >> Google IoT, just plug into the Google Cloud. It'll suck all your data in. Give you some compute at the Edge. Open it up to partners, really focusing on the ecosystem and enabling new types of functionality. It's theCUBE, bringing you the data here on day three at Google Cloud Next '18. We'll be right back with more coverage. Stay with us after this short break. (modern music)

>> Live from San Francisco, its the CUBE. Covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. >> Welcome back everyone this is the CUBE's live coverage in San Francisco Google Cloud's big conference, Google Next 2018, #googlenext18, I'm John Furrier with Jeff Frick bringing you live coverage. Melody Meckfessel, Vice President of cloud engineering at Google is here in the CUBE. She leads a lot of the managing 40,000 plus engineers making them happy and creating great code, friendly environment, doing great work. Just featured her in a story we did about the power of women in Google Cloud. Melody great to see you, thanks for coming on. >> Thank you so much for having me, it's great to be here. >> Today is a lot of developer announcements, we saw a lot of community discussions, new code. You guys talked cloud build. What is some of the news, let's get that out of the way, what's going on here at Google Cloud Next? >> Great. Very excited to announce and demo today, and it was a live demo, I don't know if you saw that, so we had some dramatic excitement waiting for the actual build. Yeah we're very excited to announce Cloud Build, which is a fully managed continuous integration and delivery platform. It lets developers build and test their applications in the cloud at any scale, and it's based on a lot of the lessons learned that we had within Google, iterating over the last two decades with developer and operator tools. Google does some crazy scale internally, and we're really excited to bring that automation and scale out to our customers. >> We had the chance to meet a couple weeks ago, we went deep dive on developers. You have a job focus that's really to kind of keep the developers productive, happy, there's a lot of them at Google and they are tough customers, they want to be coding. They don't want any distractions. They don't want any toil, a word we've been using a lot and hearing a lot here. And so there's techniques that you guys have done within Google, this seems to be the theme of Google Next, taking the best of Google and trying to make it consumable for customers. In this case developers. What is the state of the art to keeping developers happy, making them productive, more cohesive in their work, what are some of the things that you guys are doing, I know there's a lot going on, Google Cloud Build is one. What are some of the things you guys do to keep developers productive and happy? >> Yeah, that's a really good question. What we've found is that there's a tremendous amount of value of automating away, you said toil, the things that developers want to do. So some of the research, industry research that we've done, developers want to write code, they want to do design, they want to work on requirements. They don't want to take care of the plumbing and the pipeline of how their build test and release happens. So we showed some pretty amazing features today around automated canary analysis. So it's almost in a way, we want these tools and the automation to have the developer and the operator's backs. Because we know, we've learned at Google, that when we do that, they take more risks. They move quickly. Because they know that the DevOps tools are going to catch the breakages for them, and we showed a couple of things today around running tasks, identify if they're vulnerability scans, trying to find vulnerability scans before they get pushed out to production. We're trying to move as much as we can into the front part of the pipeline. So what makes developers happy? Well one thing is, give them automation so that they can focus on code. The second thing is, the culture to support and empower them. We've found that 65% of developers believe that they have the ability to choose their own tools. So at GC we want to make that easier for them. >> Wow you mentioned something earlier I want to get into. What's a canary? Explain what that is. Because a lot people, they know what it is, but some people might not know this canary concept. >> So essentially what you're trying to do is take the release that you build and test, make sure it's secure, now you want to start routing traffic to it. So you take that you release it to a small set of production instances, you start routing traffic to it, you look at air rates, you look at traces you sort of see what's going on, if it's good, then you slowly deploy it out to all your production instances. So it's a really safe way, it reduces your risk. Right, you want to catch the errors before they get out. >> Canary in a coal mine. >> Yeah, there you go. >> So it's a great agile way to push code and test it. Well not push code, push users to code. >> That's right. >> And get a feel for does it break. >> Yes. >> And if it breaks you pull back. >> Yes, and we want to find things ahead of time. I know you're talking to Dave Resin, you know the alignment of having shared goals between developers and operators is really important culturally because when you're incented towards minimizing, we call it the MTTR right, the minimum time to respond. So when you do things like canary analysis, you're finding the issues before they roll out and impact your user community. >> It's super valuable. But it sounds so easy. Why don't we just roll it out to like, our top users or the ones who won't leave the platform, and then pull it back? And this is a DevOps principle. If done right, works great. But it's hard to do. How hard is it to do it if you didn't have all these tools? I mean think about, you got to push code, pull the servers back, re push the new code. >> Yeah, you don't want to do that, right? Human error. >> Without automation, without all these tools, how hard is it? >> It's very difficult and time consuming. And we know, as humans, we're prone to error. Right? So it was really fun to show a live demo today of a spinnaker pipeline, showing the canary, pushing it out to production, and then going back to the website and seeing the impact of the code fixes that we put in place. >> Right, so just on the culture side, you've been at Google for a while. And you know we still think of Google, I still think of it as a supersized startup. But you guys have been at it for a while. You've been there for 13 years on LinkedIn, they company's 20 years old. How do you maintain kind of that cool, the colored bikes, the great food, you know go play volleyball outside in the middle of the day, kind of culture as the company just grows and you have so many new people. How do you maintain that baseline culture that's been there and made Google what it is today? >> You know we have a very strong culture within Google. A very strong engineering community. And that engineering community really comes from, and I think this has been consistent for the almost 14 years I've been there, using data to guide our decisions. Right? We've also put things in place to help enable the trust between the humans, which when I talk to customers, this is a challenge. Throw it over the wall to the operators, you know they don't trust each other. We've had blameless post-mortems within the engineering culture for decades. We've abstracted away, it's about learning. It's about continuous improvement. We're a software a company, and everyone's a software company now. How do you accept and learn from failure? But when you create this shared goals, use the data not someone's opinion or someone's title, and then ground. And we're learning, we're always learning. We're always making it better. That inspires people, right? To have that impact together. Now, the culture, the benefits, you know I'm working on writing code, products, I don't know the last time I played volleyball. >> Beautiful court, though. >> It looks great when you come in the building. >> You're the second, Dave also mentioned this blameless post-morten, I'd love to dig a little bit more in, because obviously that must be an institutionalized thing that you guys do. How do you do it without hurting feelings? Because it's still people, and even when it's data-based, you still kind of risk hurting people. So how do you institutionalize it's the data, it's not you, and we're actually trying to use this to learn and grow, not necessarily get on that particular person or that team for something that didn't work. >> Well you know I love this quote, it comes from SRE, if your SLO target is perfection, it's the wrong target. So we know, in software development and systems, that things break. And as humans, we're writing the code. We are writing the services. So we're going to make mistakes. And I think that tolerance and that understanding, we have some structure, right, we track to-dos that came out of the outage, we make sure that they get closed so we don't have the outage again, but when you obstruct that away, and know that maybe I made the mistake this week and maybe someone else on my team's going to make the mistake the next week. But how we learn from it and how we come together as a team is what's important. >> Blameless post-mortem is a great concept. Most people think post-mortem, something bad happened. Someone needs to be charged with a crime. Oh my god, bad things. You're learning, blameless post-mortem is an iteration of learning. >> Mm hmm, continuous improvement. >> So this is a culture, now let's take that to open source, because one of the things that's happening here that's front and center, I mean it's just natural for you guys, the importance of open source. Software development is getting more power. And you mentioned the stats and some of the cycle graphics. They can choose any tool that they want. That's a challenge for companies. Retaining them, keeping them employed, because they can get a job anywhere, they get more power, open source seems to be this balance in the force if you will. It's kind of like open source is now operationalized for that's where recruiting happens, that's where social activity happens, conferences. How important is open source, and how are you guys organizing around it as you build the cloud out, what's the vision? >> I have been so inspired by Google's increased contributions and collaborations to open source. I think we had over, I hope I get these stats right, we were contributing over 30,000 repos last year, 1% of the total contributions in 2017 on GitHub came from Googlers. We're committed to it. And we really believe that Google Cloud platform is living the open cloud. And we do that through open APIs, we do that through collaboration around open source tooling, and by creating this abundance and community ecosystem around it. And if you think about, I'll throw out another stat, 70% of developers feel a connection with each other. That's how they get inspired, that's how they learn. Think about Stack Overflow, you think about GitHub. You think about contributing to a product that you're going to make better, it's incredibly inspiring. >> Co-creation creates a bond. >> Yeah it does, it's connection. So if you look in the DevOps base, we've made some commitments with Bazil, which is we've open-sourced our build system, if you look at the contributions in the Go community in terms of Go working really well on Cloud. And then I showed Spinnaker which is actually a project that Netflix started, with their workloads, and we stocked up an engineering team to contribute to that to make it work for multi-cloud. Right, it's the right thing to do for developers, to have these tools that they can use in different, irrespective of where they're deploying. Now Google Cloud platform is the best platform to deploy to, but choice is really important. >> But it's another piece to the puzzle that you contribute to keeping them happy, right? Their participation in open source is why they still have their day job, and the accolades and kind of the peer feedback that comes from that is an important piece. So to be able to do that while still having the day job has got to be a big piece of what keeps them at Google, keeps them happy. >> It is, and you look at the community aspect around Kubernetes and TensorFlow, and the ecosystem is having such a huge effect on the innovation that's happening. And we all get to be a part of that, that's what's inspiring around Cloud. >> Opens the new competitive advantage, certainly from a retention standpoint, recruiting, and productivity. >> Yeah and productivity, absolutely. >> We believe in open, we're open conduct, we're co-creating content here at Google Next with the best minds at Google. Melody thanks for coming on, we really appreciate your time. >> Thank you so much, great to see you again. >> It's the CUBE out in the open here on the floor at Google Next, we're got more coverage. Stay with us after this short break.

>> 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.

>> Live from San Francisco, it's theCUBE covering Google Cloud Next 2018, brought to you by Google Cloud and its ecosystem partners. (techy music) >> Hey, welcome back, everyone, this is theCUBE Live in San Francisco coverage of Google Cloud Next '18, I'm John Furrier with Jeff Frick. Day three of three days of coverage, kind of getting day three going here. Our next guest, Sudhir, as the director of product management, Google Cloud, has the luxury and great job of managing BigTable, BigQuery, I'm sorry, BigQuery, I guess BigTable, BigQuery. (laughs) Welcome back to the table, good to see you. >> Thank you. >> So, you guys had a great demo yesterday, I want to get your thoughts on that, I want to explore some of the machine learning things that you guys announced, but first I want to get perspective of the show for you guys. What's going on with you guys at the show here, what are some of the big announcements, what's happening? >> A lot of different announcements across the board, so I'm responsible for data analytics on the Google Cloud. One of our key products is Google BigQuery. Large scale, cloud scale data warehouse, a lot of customers using it for bringing all their enterprise data into the data warehouse, analyzing it at scale, you can do petabyte scale queries in seconds, so that's the kind of scale we provide. So, a lot of momentum on that, we announced a lot of things, a lot of enhancements within that. For example, one of the things we announced was we have a new experience, new UI of BigQuery, now you can literally do the query, as I was saying, of petabyte scale or something, any queries that you want, and with one click you can go into Data Studio, which is our DI tool that's available, or you can go in Sheets and then from there quickly go ahead and fire up a connector, connect to BigQuery, get the data in Sheets and do analysis. >> So, ease of use is a focus. >> Ease of use is a major focus for us. As we are growing we want to make sure everybody in the organization can get access to their data, analyze it. That was one, one of the things, which is pretty unique to BigQuery, which is there is a real time collection of information, so you can... There are customers that are actually collecting real time data from click-stream, for example, on their websites or other places, and moving it directly into BigQuery and analyzing it. Example, in-game analytics, if in-game you're actually playing games and you're going to collect those events and do real time analysis, you're going to literally put it into BigQuery at scale and do that. So, a lot of customers using BigQuery at different levels. We also announced Clustering that allows you to reduce the cost, improve efficiency, and make queries almost two X faster for us. So, a lot of announcements other than the machine learning. >> Well, the one thing I saw in the demo I thought was, I mean, it was machine learning, so that's hot topic here, obviously. >> Yes. >> Is you don't have to move the data, and this is something that we've been covering, go back to the Hadoop, back when we first started doing theCUBE, you know, data pipeline, all the complexities involved in moving the data, and at the scale and size of the data all this wrangling was going on just to get some machine learning in. >> Yep. >> So, talk about that new feature where you guys are doing it inside BigQuery. I think that's important, take a minute to explain that. >> Yeah, so when we were talking to our customers one of the biggest challenges they were facing with machine learning in general, or a couple of them were, one, every time you want to do machine learning you are to take data from your core data warehouse, like in BigQuery you have petabytes of scaled data sets, terabytes of data sets. Now, if you want to do machine learning on any portion of it you take it out of BigQuery, move it into some machine learning engine, ML engine, auto-ML, anything, then you realize, "Oh, I missed some of the data that I needed." I go back then again take the data, move it, and you have to go back and forth too much time. There are analysis I think that different organizations have done. 80% of the time the data scientists say they're spending on the moving of data-- >> Right. >> Wrangling data and all of that, so that is one big problem. The second big challenge we were hearing was skillset gap, there are just not that many PhD data scientists in the industry, how do we solve that problem? So, what we said is first problem, how do we solve it, why do people have to move data to the machine learning engines? Why can't I take the machine learning capability, move it inside where the data is, so bring the machine learning closer to data rather than data closer to machine learning. So, that's what BigQuery ML is, it's an ability to run regression-like models inside the data warehouse itself in BigQuery so that you can do that. The second we said the interface can't be complex. Our audiences already know SQL, they're already analyzing data, these folks, business analysts that are using BigQuery are the experts on the data. So, what we said is use your standard SQL, write two lines of code, create model, type of the model you want to run, give us the data, we will just run the machine learning model on the backend and you can do predictions pretty easily. So, that's what we are doing with that. >> That's awesome. >> So, Sudhir, I love to hear that you were driven by that, by your customers, because one of the things we talk about all the time is democratization. >> Yeah. >> If you want innovation you've got to democratize access to the data, and then you got to democratize access to the tools to actually do stuff with the data-- >> Yes. >> That goes way beyond just the hardcore data scientist in the organization-- >> Yeah, exactly. >> And that's really what you're trying to enable the customers to be able to do. >> Absolutely, if you look at it, if you just go on LinkedIn and search for data analyst versus data scientist there is 100 X more analysts in the industry, and our thing was how do we empower these analysts that understand the data, that are familiar with SQL, to go ahead and do data science. Now, we realize they're not going to be expert machine learning folks who understand all the intricacies of how the gradient descent works, all that, that's not their skillset, so our thing was reduce the complexity, make it very simple for them to use. The framework, like just use SQL and we take care of the internal hyper-tuning, the complexity of it, model selection. We try to do that internally within the technology, and they just get a simple interface for that. So, it's really empowering the SQL analyst with an organization to do machine learning with very little to no knowledge of machine learning. >> Right. >> Talk about the history of BigQuery, where did it come from? I mean, Google has this DNA of they do it internally for themselves-- >> Yes. >> Which is a tough customer-- >> Yes. >> In Cloud Spatter we had the product manager on for Cloud Spatter. Dip Dee, she was, like amazing, like okay, baked internally, did that have the same-- >> Yes. >> BigQuery, take a minute to talk about that, because you're now making it consumable for enterprise customers. >> Yeah. >> It's not a just, "Here's BigQuery." >> No. >> Talk about the origination, how it started, why, and how you guys use it internally. >> So, BigQuery internally is called Dremel. There's a paper on Dremel available. I think in 2012 or something we published it. Dremel has been used internally for analytics across Google. So, if you think about Spanner being used for transaction management in the company across all areas, BigQuery, or Dremel internally, is what we use for all large scale data analytics within Google. So, the whole company runs on, analyzes data with it, so our things was how do we take this capability that we are driving, and imagine like, when you have seven products that are more than a billion active users, the amount of data that gets generated, the insights we are giving in Maps and all the different places, a lot of those things are first analyzed in Dremel internally and we're making it available. So, our thing was how do we take that capability that's there internally and make it available to all enterprises. >> Right. >> As Sundhir was saying yesterday, our goal is empower all our customers to go ahead and do more. >> Right. >> And so, this is a way of taking the piece of technology that's powered Google for a while and also make it available to enterprises. >> It's tested, hardened and tested. >> Yeah, absolutely. >> It's not like it's vaporware. >> Yeah, it's not. (laughs) >> No, I mean, this is what I think is important about the show this year. If you look at it, you guys have done a really good job of taking the big guns of Google, the big stuff, and not try to just say, "We're Google and you can be like Google." You've taken it and you've kind of made it consumable. >> Yes. >> This has been a big focus, explain the mindset behind the product management. >> Absolutely, there is actually one of the key things Google is good at doing is taking what's there internally used, but also the research part of it. Actually, Corinna Cortes, who is head of our AI side who does a lot of research in SQL-based machine learning, so again, the-- >> Yeah. >> BigQuery ML is nothing new, like we internally have a research team that has been developing it for a few years. We have been using it internally for running all these models and all, and so what we were able to do it bring product management from our side, like hey, this is really a problem we are facing, moving data, skillset gap, and then we were like, research team was already enabling it and then we had an engineering team which is pretty strong. We were like, okay, let's bring all three triads together and go ahead and make sure we provide a real value to our customers with all of that we're doing, so that's how it came to light. >> So, I just want to get your take, early days like when there was the early Google search appliance, I'll just pick that up, and that was ancient, ancient ago, but one of the digs was, right, it didn't work as well in the enterprise, per se, because you just didn't have the same amount of data when you applied that type of technique to a Google flow of data and a Google flow of queries. So, how's that evolved over time, because you guys, like you said, seven applications with a billion-- >> Yep. >> Users, most enterprises don't have that, so how do they get the same type of performance if they don't have the same kind of throughput to build the models and to get that data, how's that kind of evolved? >> So, this is why I think thinking about, when we think about scale we think about scaling up and scaling down, right? We have customers who are using BigQuery with a few terabytes of data. Not every customer has petabytes scale, but what we're also noticing is these same customers, when they see value in data they collect more. I will give you a real example, Zulily, one of our customers, I used to be there before, so when they started doing real time data collection for doing real time analytics they were collecting like 50 million events a day. Within 18 months they started collecting five billion a day, 100 x improvement, and the reason is they started seeing value. They could take this real time data, analyze it, make some real time experiences possible on their website and all, with all of that they were able to go out and get real valuer for their customers, drive growth, so when customers see that kind of value they collect more data. So, what I would say is yes, a lot of customers start small, but they all have an aspiration to have lots of data, leverage that to create operational efficiency as well as growth, and so as they start doing that I think they will need infrastructure that can scale down and up all the way, and I think that's what we're focusing on, providing that. >> You guys look at the possibility, and I've seen some examples where customers are just, like, they're shell-shocked, and you're almost too good, right? I mean, it's like, "We've been doing "Dremel on a large scale, I bought this "data warehouse like 10 years ago," like what are you talking about? (laughs) I mean, there's a reality of we've been buying IT, enterprises have been buying IT and in comes Google, the gunslinger saying, "Hey, man, you can do all this stuff." There's a little bit of shell-shock factor for some IT people. Some engineering organizations get it right away. How are you guys dealing with this as you make it consumable? >> Yeah. >> There's probably a lot of education. As a product manager do you see, is that something that you think about, is that something you guys talk about? >> Yes, we do, so I think I actually see a difference in how customers, what customers need, enterprise customers versus cloud native companies. As you said, cloud native companies starting new, starting fresh, so it's a very different set of requirement. Enterprise customers, thinking about scale, thinking about security and how do you do that. So, BigQuery is a highly secure data warehouse. The other thing BigQuery has is it's a completely serverless platform, so we take care of the security. We encrypt all the data at rest and when it's moving. The key thing is when we share what is possible and how easy it is to manage and how fast people can start analyzing, you can bring the data. Like you can actually get started with BigQuery in minutes, like you just bring your data in and start analyzing it. You don't have to worry about how many machines do I need, how do I provision it, how many servers do I need. >> Yeah. >> So, enterprises, when they look at-- >> Cloud native ready. >> Yeah. >> All right, so take a minute to explain BigTable versus, I mean, BigTable versus BigQuery. >> Yes. >> What's the difference between the two, one's a data warehouse and the other one is a system for managing data? What's the difference between Big-- >> So, it's a no-SQL system, so I will... The simple example, I will give you a real example how customers use it, right. BigQuery is great for large scale analytics, people who want to take, like, petabyte scale data or terabyte scale data and analyze historical patterns, all of that, and do complex analysis. You want to do machine learning model creation, you can do that. What BigTable is great at is once you have pre-aggregated data you want to go ahead and really fast serving. If you have a website, I don't expect you to run a website and back it with BigQuery, it's not built for that. Whereas BigTable is exactly for that scenario, so for example, you have millions of people coming on the website, they want to see some key metrics that have been pre-created ready to go, you go to BigTable and that can actually do high performance, high throughput. Last statement on that, like almost 10,000-- >> Yeah. >> Requests per second per node and you can just create as many as you want, so you can really create high scale-- >> Auto-scaling, all kinds of stuff there. >> Exactly. >> And that's good for unstructured data as well-- >> Exactly. >> And managing it. >> Absolutely. >> Okay, so structured data, SQL, basically large scale-- >> Yes. >> BigTable for real time-- >> Yes. >> New kinds of datas, different data types. >> Absolutely, yes. >> What else do you have in the bag of goodies in there that you're working on? >> The one big thing that we also announced with this week was a GIS capability within BigQuery. GIS is geographical information, like everything today is location-based, latitude, longitude. Our customers were telling us really difficult to analyze it, right, like I want to know... Example would be we are here, I want to know how many food restaurants are in a two-mile radius of here, which ones are those, how many, should we create the next one here or not. Those kind of analyses are really difficult, so we partnered with Earth Engine, Earth Engine team within Google with Maps, and then what we're launching is ability to do geospatial analysis within BigQuery. Additionally along with that we also have a visualization tool that we launched this week, so folks who haven't seen that should go check that out. One great example I will give you is Geotab, their CEO is here, Neil. He was showing a demo in one of the sessions and he was talking about how he was able to transform his business. I'll give you an example, Geotab is basically into vehicle tracking, so they have these sensors that track different things with vehicles, and then with, and they store everything in BigQuery, collect all of that and all, and his thing was with BigQuery ML and a GIS capability, what he's now able to do is create models that can predict what intersections in a city when it's snowing are going to be dangerous, and for smart cities he can now recommend to cities where and how to invest in these kind of scenarios. Completely transforming his business because his business is not smart cities, his business was vehicle tracking and all, he's like, but with these capabilities they're transforming what they were doing and solving-- >> New discoveries. >> New discoveries, solving new problems, it's amazing. I wonder if you could just dig at a little bit to, you know, the fact that you've got this, these seven billion activities or apps that you can leverage, you know, specific functionality or goals or objectives or priorities in those groups, and now apply those, pull that data, pull that knowledge, pull those use cases into a completely different application on the enterprise. I mean, is that an active process-- >> I don't think that's how people. >> Do people query? >> No, no. >> But how does that happen? >> No, we don't-- >> As a customer. >> As a customer completely different, right? Our focus in Google Cloud is primarily enabling enterprises to collect their data, process their data, innovate on their data. We don't bring in, like, the Google side of it at all, like that's their completely different area that way, so we basically, enterprises, all their data stays within their environment. They basically, we don't touch it, we don't get to access it at all, and they can know it. >> Yeah, yeah, no, I didn't mean that, I meant, you know, like say Maps for instance, it's interesting to see how Maps has evolved over all these years. Every time you open it, oh, and it's directions-- >> Yep. >> Oh, now it's better directions, oh, now it's got gas stations, oh, now it's where the... And it triggered because you said the restaurants that are close by, so it's kind of adding value to the core app on that side, and as you just said, now geolocation can be used on the enterprise side-- >> Yeah, yes. >> And lots of different things, so that-- >> Exactly. >> That's where I meant that kind of connection-- >> Exactly right, so-- >> In terms of the value of what can I do with geolocation. >> Absolutely, exactly, so like, that's exactly what we did. With Earth Engine we had a lot of learnings on geospatial analysis and our thing was how do you make it easy for our enterprise customers to do that. We've partnered with them closely and we said, "Okay, here are the core pieces of things "we can add in BigQuery that will allow you "to do better geospatial analysis, visualize it." One of the big challenges is lat longs, I don't think they're that friendly with analysts, like oh, numbers and all that. So, we actually will turn a UI visualization tool that allows you to just fire a query and see visually on a map where things are, all the points look like and all. >> Awesome. >> So, just simplifying what analysts can do with all these. >> Sudhir, thanks for coming on, really appreciate it and congratulations on your success. Got a lot of great, big products there, hardened internally, now-- >> Yes. >> Making consumable, it's clear here at Google Cloud you guys are recognized that making it consumable-- >> Yep. >> Pre-existing, proven technologies, so I want to give you guys props for that, congratulations. >> Thank you, thanks a lot. >> Thanks for coming on the show. >> Thanks for coming on. >> Thank you. >> It's theCUBE coverage here, Google Cloud coverage, Google Next 2018. I'm John Furrier with Jeff Frick, stay with us, we've got all day with more coverage for day three. Stay with us after this short break. (techy music)

>> Live from San Francisco, it's theCUBE, covering Google Cloud Next 2018. Brought to you by Google Cloud and it's ecosystem partners. >> Hey, welcome back everyone. We're live in San Francisco for Google Cloud's conference Next 18, #GoogleNext18. I'm John Furrier with Dave Vellante. Our next guest is Faizan Buzdar, Senior Director at Box, box.com, collaborative file sharing in the Cloud. No stranger to Cloud. Welcome to theCUBE. >> Thank you for having me. >> So you guys have a relationship with Google. First talk about the relationship with Google, and you have some breakouts you're doing on machine learning, which I want to dig into, but. Take a step back. Take a minute to explain the relationship between Box and Google Cloud. >> So Box has partnered Google for a few years now, and we have actually two areas of key, sort of, collaboration. One is around the Google Productivity Suite that was actually announced last year. But we actually demoed it for the first time in public today. Where, if you look at a bunch of customers, like about 60% of the Fortune 500, that chose Box as their secure content layer. These guys can now go into Box and say, "Create a new Google doc, Google spreadsheet, Google slide." And it will open up. It will fire up the Google editors. You can do, get all of the benefit of the rich editing, collaboration, but your content is long-term stored in Box. So it does not leave Box. So from a security and compliance layer, if you've chosen Box, you now get to use all of the power of the Google collaboration and >> It's Google Drive inside Google Box, but natively, you guys have the control for that backend, so the user experience feels native. >> Yeah, so in this case it doesn't touch Google Drive. It's basically, it never leaves Box. So that's the key benefit if you're a Box customer. >> That's awesome. That's great for the user. Great for you guys. That's awesome. Okay, so take a step back now. What's your role there? What do you do? >> So I'm Senior Director for Product Management, and I basically look after two areas. One is our sort of best of breed integration strategies, such as the one with Google Suite or Gmail. And then the second area is machine learning, especially as machine learning relates to specific business process problems in the Enterprise. So that's one of the areas that I look after. >> So how do you use data? You talked about the integration. How are you using data to solve some of those business process problems? Maybe give some examples, and tie it back into the Google Cloud. >> So, for example, so for us, we announced a product called Box Skills last year at BoxWorks. And we're going to talk about it next month at BoxWorks, too. So, the strategy there was we will bring the best of breed machine learning to apply to your content in Box, and we will take care of all of the piping. So, I keep hearing machine learning is the new electricity. But if you talk to CIOs, it's a weird kind of electricity for them because it actually feels like I have to uproot all of my appliances in factory, and take it to where the electricity is. It doesn't feel like electricity came to my factory, right? Or appliances or whatever. So, our job, we looked at it, and we said, "Hey, we have probably one of the biggest, most valuable repositories of content, Enterprise content. How do we enable it so that companies can use that without worrying about that?" So Box Skills actually has two components to it. One is what we would call, sort of, skills that are readily available out of the box. So as an example, today we are in beta with Google Vision. And the way that the admin turns that on is literally, he goes into his admin panel and he just turns on two check boxes, chooses which folders to apply it to, maybe apply it to all of the images in the Enterprise. So if you're a marketing company, now all of your images start to show these tags, which were basically returned by Google machine learning. But to the end user, it's still Box, they're still looking at their images, it still has all of those permissioning, it's just that now, we have the capability for metadata, for humans to add metadata manually, now that metadata is being added by machine learning. But in terms of adoption for the Enterprise, we made it super simple. And then, the framework also enables you to connect with any sort of best of breed machine learning. And we look at it, if you were to sort of make a, look at it as two axis, number of users that would use it, and the amount of business value that it brings. There are some things which are horizontal, like, say, the basic Google Vision, basic Google Video, basic Google Audio. Everybody would like an audio transcript, maybe. Everybody wants some data from their images. And that's something that a bunch of users will benefit from, but it might not be immense change in business process. And then there's another example, we'll say you're a ride sharing company, and you have to scan 50,000 driving licenses in every city that you go into. And currently you have that process where people submit their photos, and then people manually add that metadata. And if now you apply Google Vision to it, and you're extracting the metadata out of that, I actually love scenarios like this. Like, enterprises often ask me like where we should start. Where we should start in terms of applying machine learning, and my sort of candid advice is don't start with curing cancer. Start with something where there is some manual data being added. It's being added at scale. And take those scenarios, such as this driving license example, and now apply machine learning to that, so where previously it would take a month for you to get the data entered for 50,000 driving licenses, now you can do it in 50 minutes. And, um, yeah. >> And what's the quality impact? I mean, presumably the machines are going to get it right more often. >> Yeah. >> But do you have any data you can share with regard to that? >> So that's, actually, that's such an awesome question. And I'll connect it to my sort of previous advice to enterprises, which is that's why I love these processes because these processes have exception handling built into them already. So humans have at minimum a 5% error rate. Sometimes a 30% error rate. So, when we looked at, you know, captioned videos and TV from like 10 years ago, we could clearly see errors in that, which humans had transcribed, right? So, most of these manual processes at scale already have two processes built in, data entry, data validation and exception handling. So the reason that I love replacing the data entry portion is that machine learning is never 100%, but to the validation process, it still looks like kind of the same thing. You still saved all of your money. Not just money, but you saved sort of the time to market. And that's also what Box does, right? Because if you use Box in combination with Google Cloud, we actually, one of the things that I didn't talk about before, we looked at all of these machine learning providers, and we came up with standard JSON formats of how to represent machine learning output. So, as an example, you could imagine that getting machine learning applied in audio is a different problem than getting machine learning applied in video, is a different problem than getting machine learning applied from images. So we actually created these visual cards, which are developer components. And you can just get, put data in that JSON format, we will take care of the end user interactivity. So as an example, if it's a video, and you have topics. Now when you click on a topic, you see a timeline, which you didn't in images because there was no timeline. >> You matched the JSON configuration for the user expectation experience. >> Exactly. So now if you're in Enterprise and you're trying to turn that on, you're now, you could already see the content preview, and now you can also see the machine learning output, but it's also interactive. So if you, if you were recording this video, and you were like, "When did he say BoxWorks?" You click on that little timeline, and you will be able to jump to those portions in the timeline. >> That's awesome. I mean, you guys doing some great work. What's next? Final question, what are you guys going to do next? You got a lot to dig in. You got the AI, machine learning, store with Google. You got the Skills with Box to merge them together. What's next? >> So I think for us, the machine learning thing is just starting, so it's sort of, you'll learn more at BoxWorks. But for us I think the biggest thing there is how do we enable companies to experience machine learning faster? Which is why when we look at this two axis image audio video, we enable organizations to experience that quickly. And it actually is like an introduction to the drug because the guy who has to process insurance claims or the car damage photos, or the drone photos, he looks at that Google Vision output, and then he says, "Oh, if I can get these ties, maybe I can get these specialized business process ties." And then now he's looking at AutoML, announced today, and, you know, the adoption of that really, really >> Autonomous driving, machine learning. It's going to happen. Great stuff. Real quick question for you. When is BoxWorks? I don't think it's on our schedule. >> Next month, yeah. >> I think it's August 28th or 29th. It's coming up, yeah, yeah. >> So I'm going to go check. I don't think theCUBE is scheduled to be there, but I'm going to make a note. Follow up. >> We'd love to have you. Check with Jeff Frick on that. I think we were talking about covering the event. It's going to be local in San Francisco area? >> Uh, Moscone, yeah. >> Moscone, okay great. Well, thanks for coming on. Machine learning, certainly the future. You got auto drive, machine learning, all kinds of new stuff happening. Machine learning changing integrations, changing software, changing operations, and building better benefits, expectations for users. Box doing a great job. Congratulations on the work you're doing. Appreciate it. >> Thanks for coming on. >> Thanks for coming on. More CUBE coverage after the short break. We're going to wrap up day one. We got a special guest. Stay with us. One more interview, and then we got all day tomorrow. Be right back. (upbeat music)

>> Live from San Francisco, it's theCUBE. Covering Google Cloud Next 2018. Brought to you by Google Cloud, and it's ecosystem partners. >> Hey, welcome back everyone. This is theCUBE live in San Francisco for coverage of Google Cloud Next 18, #GoogleCloudNext18 I'm John Furrier, Dave Vellante. Your next guest, Anthony Lye, Senior Vice President and General Manager of Cloud Data Services Business Unit at NetApp. Yes, Business Unit at NetApp, storage in the cloud. Anthony, welcome to theCUBE, Good to see you. >> Thank you very much. nice to see you guys again. >> Great to have you on, we have been, first of all, very complimentary of NetApp over the years. We've had some critical analysis, but one thing I will say that you guys were early on cloud. I remember talking to Tom Georgans years ago, >> Yup. >> You listened to the customers, and you saw cloud, and there was some work going on. Now, you're here at Google Cloud, you're in Amazon, kind of not conventional wisdom for a storage company selling boxes to be living in a cloud where there's serverless, and, some would argue, storageless soon. >> Well, you know-- >> How did this happen? How did this business unit happen? (mumbled speech) >> Well, I think George Kurian, our CEO, probably now about five years ago, I think saw that cloud computing had just too much, I think, going for it not for us to pay attention to it. And he took the top ten engineers at NetApp, and said, you know our flagship operating system ONTAP that runs on our engineered systems, he said, port it to Amazon. And so we spent time porting the operating system over directly to Amazon and today, now, it's a real business. Fully funded, staffed, growing, and you know to your point, you know, who'd have thought NetApp would be calling the cloud. Google chose us. >> Big announcement today, in the keynote-- >> Yup. >> Right >> Oh yeah. >> I mean it's-- >> Key partner >> Turns out that enterprises need enterprise level files, whether that's NFS or SMB, and we're the best in the business to do it. >> So talk about that a little more, because a lot of people get confused, and they say, well wait a minute, why do I need NetApp on Google Cloud or AWS? Why don't I just use whatever object store the cloud provider gives me? Explain that. >> So I think there's a number of use cases, certainly if you look at legacy, there's a lot of applications, databases, that need and demand file. And customers would rather not have to do all the work to translate them over to something like object. Now, you know, object is a very descriptive storage protocol, but it's not as fast as file. So, there are distinct advantages to file that I think the cloud companies have realized they need, to win the enterprise business, whether it's the lift-and-shift business, there's a lot of applications. If you look at oil and gas, all that seismic data is in a file in a volume. You look at CAD-CAM, all of those applications demand file. Oracle database runs incredibly fast on file, so file is certainly not to be discounted, and I think it's very much now a hot topic in public cloud. >> And there's more to this story than just running in the public cloud. THere's a whole business model around the economics, >> Yup. >> the pricings, can you explain that? >> The way we think about cloud is we think that we can build a business that's just in the cloud. We basically monetize a service, a set of services that we offer to our customers to help them manage their data, protect their data, secure their data, integrate and orchestrate their data. Whether it's on one cloud or many. Whether it's a combination of onprem and cloud. And we charge very, very simply based on capacity or API call. We provide a full service. And that's what I think the cloud has done is democratized and empowered many, many people to consume technology that, prior to these big public clouds, you'd have to go to IT and wait six months and get charged a lot of money. The clouds make everything instantly available. It's wonderful. >> You guys have a great history, and again we've been, not critical but complementary of NetApp. You listen to customers, got a very loyal customer base. No matter what the trend is against you, by the pundits, you guys persevere as a company. And it's been great to watch, classic Silicon Valley success story. But you got Solify, you got Flash, you've been doing some kicking the tires early in cloud, now you created a business unit out of it. As you listen to customers, you see DevOps, you see (mumble) Infrastructures go, massive amounts of new proliferation, there's going to be a renaissance in software development, it's coming very fast. You almost see it coming very, very fast. What are the use cases for NetApp in the cloud, what are some of the things that customers are talking to you about, what are the top use cases, and where do you think they're going to be? >> Yeah, yeah, yeah. Well, so people have been very ... in Google we've been in preview phase onboarding customers to test the system out, sort of flush water through the pipes. And we've been very lucky at Google, we've had really every use case that we wanted to test tested. At the low end, it can be as simple as just home directories shared across ... whether it's POSIX or Windows, people need access to those file systems and NetApp is the only company that offers that sort of dual protocol access. So we have home directories at the low end, all the way up to genome sequencing databases, big data, relational databases, data warehouses at the high end. And what's nice about our service is we have service level objectives. So we, for the first time, have actually put a performance guarantee on the volumes. And what's nice about that is the customer knows that that's something that we stand to. What's really nice is the customer can dial up or dial down, either the capacity that they want or the performance that they want. So they may say, Monday through Friday we want to run the volumes at this basic service level, and then over the weekend, through an API, we're going to crank them up and make them run at 128 MB/sec. So, we really are, I think, providing incredible value for all workload types. >> You just described what I consider chew software, defined strategy, programmable through an API, I mean that's something that is nuanced but dramatically simplified-- >> Oh, you know, I'm an application developer. >> I was going to say. >> And I can tell you the last thing application developers want to do is talk to IT. Second to last thing application developers want to do is mess around with UI's. So, you know, the cloud, where there are lots of pretty demos of Google Console, which is a very, very, I think, well written user interface. What we really want is the API. We want the code or application code to tell the cloud what to do and how to do it. And so, everything behind our cloud business is API first. >> The programmable aspect is critical. >> Yup. >> And this is where we're starting to see microservices >> Absolutely. >> Become interesting phenomenon. Because now you can have pure application developers, >> Yup. >> Never talking to anyone but other developers in collaboration space. They just collaborate, and they go play in open source communities, and they're-- >> Absolutely. >> Happy as a clam. >> We've now got NFS persisting in containers, so we've done ... we worked on a project called Trident. Which is an open source project and we contribute to that. On Google, you'll be able to mount file systems directly into containers. And persist storage now, with all the cool, new (mumble) things that Google brings. So, you know, the files are a very integral part, I think, of technology and strategy. And we seem to have, according to Google, the best one. What are the go-to-market aspects of your relationship with Google? Well that's the other thing I tell you I'm incredibly pleased with is Google sells our product. Google supports our product. Google bills the customers for our product. >> That's good. >> Google has kind of chosen us, and Google wants it to be part of Google. So, the experience is completely native to the console. We encapsulate all of the permissions, access control lists, it looks and feels exactly like any native Google service. >> And what's next now, obviously great relation with Google. You're almost embedded/operationalized with them. Congratulations. >> Thank you. >> What's next, what's going on, what's the agenda for you guys? >> For us it's really increased investment in two dimensions. I think the first dimension is now the roll-out. We've got a very aggressive schedule to roll this out to all the major Google data centers to support all their major regions. And that's probably a never ending task, cause Google ups its ante and increases its data centers, so that keeps us busy, making the service available. The second thing then is sort of integrating that service with more of our own services. And integrating our service into some of the other Google services like BigQuery, or Spanner, or obviously there's a huge opportunity for people to bring file based data into Google Cloud and take advantage of AI and ML. (overlapping voices) >> That's interesting, integration into Spanner, I mean you've pointed out, Anthony, that Oracle runs really well on file. You guys, decade ago or so, made that happen. We had a conversation yesterday with a customer that basically moved from Oracle to Spanner. So that level of integration is one to really watch, from a transaction/database in the cloud standpoint. >> Our mission is to make file a first class protocol. >> It was interesting, also, about this, and George Kurian was talking about this on the scene, I haven't yet interviewed him yet, I'll do that next time on theCUBE, but I've heard him speak publicly, I've seen comments, software is critical. You're a software company, >> Yeah, exactly. >> you happen to have hardware here and there. So this is actually ... >> We don't make the hardware, you know. >> You don't bend the metal. >> Right. >> Google loves software. >> Yeah. >> So, interesting, so you have a lot of range, potentially, looking out in the future. >> I tell you, you know, George asked me to come to NetApp, and he gave me a blank canvass, and told me to paint whatever picture I wanted. And so, as an application developer, I wanted to have a rich set of services to help me manage my data, and I wanted to be able to do it in the cloud. >> And you want to do it without storage. >> Yeah, I mean at the end of the day ... >> You're a developer, you just want it to be there working. >> Exactly right. You expect it to be like dial tone. When you pick up the phone, at home, you don't ask yourself, how does it work? >> Nor do you want to ask the operator to connect it for you. >> Exactly right. >> And that's what's been unique, I've been following NetApp since they took on Auspex. Early on, we realized that this is a company who, basically, has storage services, and makes calls to those storage services as required, like a software developer would. >> Exactly. >> Not things that are locked into some piece of hardware. >> No, I tell you, I think what the other thing that I'm particularly proud of is I think that all of those loyal customers who have built their careers on NetApp and ONTAP, we've now given them the next part of their journey. >> Yeah. >> We've now made all of their skills relevant for Google. >> That's another 20 year lease. >> Well, the other thing ... >> It's a beautiful thing. >> The other thing you've done is, by integrating with the cloud, you bring scale that has always been a challenge for clustered systems that the cloud resolves. It was a barrier to the adoption of the cluster concept. >> I tell you the other thing that customers say more than anything else is, you know, NetApp really provides probably the industry's best insurance. I mean, any customer that makes an onpremise decision, of which there are still many, are choosing NetApp onpremise because NetApp is in the cloud. >> That's interesting, because you see Oracle's marketing with same/same but Oracle's storage products are deficient. So (laughs) >> Well, when are we start to see storage functions and terms like storageless? We have serverless. I mean ... (laughs) >> We have some, let me tell you, we have some pretty cool tricks up our sleeve. We're not going to show our hand just yet, but the stuff we're doing with the Google guys, you know, I wouldn't underestimate the amount of work the teams have put into this. This is a amazing collaboration at the development level. It's something that I don't think Google has ever done before. And I think Google, like NetApp, we see each other as very, very strong partners at a very, very deep level. >> So you're talking about engineering resources that you're providing. Can you help us understand that? Or quantify that in any way? >> Oh yeah, so ... >> Couple of guys and a laptop, or we talking about ... >> It's a very large team, and a growing team. You know, my team at NetApp, just building software on the cloud, is six-seven hundred people strong now, all product managers and developers. I mean, we take this business very, very seriously. >> This is the future of NetApp. This is a competitive strategy for you guys. >> I think NetApp is cloud first. Just imagine, did you ever think you'd hear NetApp say we're a cloud first company? Because that's what we are. >> We don't hear your competitors saying that, I can tell you that right now. >> This is NetApp's fifth life. Like I said, I've been following this company a long time. It started with workstations, you brought file to dot-com. Then you went hard after that, dot-com blew up. You went hard into the enterprise. Bet the farm on virtualization. Now you're betting the farm on cloud. >> You know, I tell you the one thing that I've been at NetApp, as I said, for about 18 months. And the company has passion and conviction and belief. And what it does so amazingly well is it leans into the things that people think are going to kill it. >> Yeah. And there ... >> And you've met Dave, right? He's a wonderful guy. He founded the company, he's still involved in the company. He's here, he's learning cloud, and he loves it. >> We saw him last night, he's a great entrepreneur. And again, that's the kind of leadership, when the founders stay around, companies succeed. I've always said that, I wrote about it. And it statistically is proven. Lean in to anything you think will probably kill you, you'll probably come out stronger. And that's really an entrepreneurial lesson. >> I tell you, the other thing that I would say, more than anything else, and it was really the biggest part of my decision to join NetApp, is a technical CEO. >> Yeah. >> You have to have a technical CEO. No disrespect to sales guys that become CEO's, or finance guys that become CEO's, they're just not as good as the technical ones. And George is an engineer. >> Yup. And he gets it. He's very passionate and committed about the product. And that, that to me, I think-- >> More than ever now in a changing tide where technology decisions, the bets can be company killing or company making, about little things, how you deal with service meshes, >> Exactly right. >> How you deal with provisioning storage through software now, these are new things. >> You know, this stuff doesn't happen overnight, right. It takes a lot of time and a lot of effort. Software engineering, you know, is something that takes time. >> Well Anthony we really appreciate you taking the time to come on theCUBE. We love covering NetApp, we've been following your journey again, we see you at all the events, you guys are part of theCUBE community. We really appreciate that. And more than ever, we want to follow what you guys are doing in the cloud. We think it's competitive advantage vis-a-vis the competition. And want to see how it turns out. So... >> We're having so much fun. >> Let's keep in touch. >> So much fun. Thanks guys very much. >> Storageless is a big trend coming, trust me you heard it here first on theCUBE. I don't think they use that term yet, Dave. We'll be back with more live coverage, Day Two is coming to a close. Couple more segments, stay with us, for our three days of coverage of Google Cloud Google Next 2018. Be right back. (techno music)

>> Live from San Francisco. It's theCUBE covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. (electronic music) >> Hey, welcome back everyone. Live here in San Francisco it's theCUBE's coverage of Google Cloud and their big conference Google Next #GoogleNext18. I'm John Furrier, Dave Vellante. Our next guest is Adam Seligman, Vice President of Developer Relations at Google. Man, making it all happen, keeping the trains on time, keeping everyone motivated, welcome to theCUBE. Thanks for joining us. >> Thanks, glad to be here. >> So, first of all, take a step back, what is your job at Developer Relations? Are you herding cats, are you feeding them great code, are you overseeing a big team? Google's been very big on open-source, you've been part of the code program going back many many years. Google's always been a steward of open-source and developers are just devouring open-source in a big way right now. What's your job? >> I look after Developer Relations. There's around 20, 22 million developers in the world and we want to make every single one of them successful and build cool things, learn new technology, be part of community. That's something that's super important. I try to rally all of Google to sort of stand for developers. >> One of the big trends we're seeing now at open-source is that it's becoming such a good norm. I remember the days when I was getting into the business back in the late '80s, early '90s. Open-source, we'd kind of steal some code here and it kind of was radical. It's so normal now, and you start to see the clean, upstream etiquette, upstream projects, everyone's contributing, co-creating for a common good, monetizing downstream has been really well defined. There's some examples of probably where that could be better but for the most part, I think people are generally seeing a positive contribution. That's a community dynamic. How do you go to the next level for developers? Because this has turned out to be quite an opportunity to one: learn, meet new people, learn new skills and take advantage of some new technologies. How do you foster that community? What are you guys doing? Because no-one wants vendors to put their fingers in these upstream projects (laughs) but they're super important, they're all participating. What's the formula? How is that evolving? How do you see that? >> Google's been an open-source for maybe 20 years. Some big contributions early days, things like GCC, foundational compiler technology. And we have whole businesses that build around open-source, Chrome on the Web, Android for mobile, and now we see kubernetes in cloud and TensorFlow and AI and new things like Knative and Istio, so I think there's a course there where open-source can really shape whole ecosystems and create a lot of opportunity and a lot of innovation. And I think the challenge in all that is to do it in a really healthy, positive, community-centric way. And I think that's some real learning we've had in the last couple of years, is great leaders like Sarah Novotny have really helped guide us and her interface with open-source communities and foster the right kind of community interactions, and that's a big focus. We're trying to bring that here also. >> So, you had a keynote coming up, I know you got a hard stop and we want to try and get as many questions as we can. But I want to ask you, what are you going to be talking about at your keynote, what's the topic? 'Cos this is a, I won't say coming-out party for Google Cloud in particular, but clearly setting a couple stakes in the ground on what's going on. Enterprise focus, checking the boxes, table stakes are being met. And real tech: high performance, large-scale, really a good developer environment. What are you going to talk about at the keynote? >> Well, I think customers like HSBC and Target and others are coming to us, not for table stakes, they're coming to us for what's next. They're coming to us for massive-scale kubernetes, they're coming to us for AI. So, I think that the introductions we've had so far, things like the Cloud Services Platform, Istio 1.0, Knative, it really shows a bright future of service and AI-driven applications. What we're going to talk in the developer keynote, tomorrow, in day three, is really three themes: innovation, openness and open-source, and then that community theme that we were just talking about. And one area of innovation that we're going to talk about is Melody Meckfessel, who I think you talked to earlier, is going to talk about our approach to Cloud Build and integrated toolchains. We have a lot of technology we're going to open up in the DevOps space. But it's really a mentality, and this is the thing that I think is really needed coming to Google, is it's not just about pushing code down the waterfall to production, it's about building services for users and building services that the developers consume. And really flowing from code right out to running services, and then when you're done, the service is a turn on for everybody, you start routing traffic to it, you run canaries. So, it's a big step-change in how we think about continuous delivery and DevOps, we really want to land that in the keynote tomorrow. >> So I got to give some props to my partner, John Furrier, in 2010, John, you said, "Data is the new development kit." It was a while ago, and it's turned out, in my view anyway, to be true, but, Adam, it's also changed the profile of the developer. Data hackers, statisticians, mathematicians, artists. And so it's changed the way in which we think about a developer. I wonder, if you could talk about that, in terms of, how that's changed Developer Relations? >> Yufeng Guo is going to do a section AI in the keynote and he does these videos on YouTube that literally millions of people watch about how to get started on machine learning. And he's got a great line in there, which I think is attributed to him, that says, "AI is programming with data." And so I think we're in a world where all this data of user interactions and event streams and interactive things and mobile applications, we now have a lot of data to program the world on. And I think it's an incredible opportunity for developers. But the flip side, if we just restrict it to a couple thousand data scientists, it doesn't open up the world to everyone. So I think beyond that 20 million, what are the next 20 million we could pull in with AutoML? The next 20 million that can do SQL queries and can use BigQuery and do ML in BigQuery? So that's the vision of opening it up to more people, more developers. >> And the democratization of software, I mean, it's interesting, that's my background in software engineering, computer science, in the '80s you were called software engineering. Then it became software developer, then it became a software hacker. Now we're hearing words like software artisan. I interviewed Aparna, she said, "You don't need three PhDs, three degrees "in computer science, to do development anymore." The aperture's widening, big-time, because now craft is coming back to development. Because a lot of these abstractions, both on the business and tech side, are enabling different personas to come in. >> It's not legacy development anymore, it's heritage development, right?. (John laughs) I love that developers have the freedom to define their own titles and define their own tools they want to work with, and do a mix of the old and the new, and mix it up. So I think it's really important that we're not too narrow in how we define people and you don't have to be this tall to ride the ride, we really welcome everybody in to be a part of the community and if your entrance to ML is AutoML, but then eventually you graduate to TPUs, that's just fantastic. >> And how about crypto developers? They've exploded with innovation, what do you see in there? >> I could just go back to security, I think every company is really wrestling with security right now. How do they get two-factor everywhere? How do they stop phishing? How do they keep their employees safe? How do they have shielded VMs at every level of security? And it's a challenge to get developers to think about security sometimes. It's the operators that have to live with it, and so understanding your dependencies, way back up with developers are like, "Oh, I'll just use this library, "and I'll just use this library." How do you ensure you're using trusted dependencies back there, you don't have vulnerabilities you're introducing by taking dependencies in other codes. So I think there's a lot of education and best practice to share with developers to get them to care about security. >> My final question, I know you got to go. I just want to get it out there, years ago, when David and I used to hear on theCUBE, people come on, "We want to win the developers," no, they're not winnable. You don't win developers, you earn trust and you earn relationships and they might work with you and enjoy the services that they might provide to them. So I always kind of used to poo-poo that. But now with the Cloud you're seeing again, more range with developers. So, how do you keep developers happy? That might be a better question, because in order to earn and have a relationship with people who are going to be contributing IP and building IP, how do you keep harmonious relations? How do you keep people happy if you have things, like technical debt bothers people and people are like, "Oh, technical debt," you know, shipping codes, times. How do you think about that because keeping people happy is a broad answer, but in general, what's your view on keeping developers happy, harmonious, loving, working together, doing the things they love to do? >> It's a little different at Google, it's an interesting place, because there's never an "us and them" with developers, this is a company with tens of thousands of engineers on staff, most of the senior leadership team have an engineering background. So it's more like we live in the community of developers, my engineers are all over the world, living in developer communities. And so I think it really does matter how we show up and how we interact. But we sort of live it every day. So I don't think we have a hill to climb, so much as get to developers, I think we just have to have a really clear narrative, and then a really keen ear to listen to what they need and that's what I'm trying to orient them around. >> Listening, I think that's a great answer, listening. "What do you want?" you know, "What's important to you?" And then you have that perspective yourselves. Yeah, I mean, we're sort of a developer-centric company and I think the important thing is we put them at the center of everything we do, I use the word with my team, it's empathy. We have empathy for developers, you know, they have great jobs, great opportunities, but also great challenges, and as humans, can't we have empathy for them. >> I was hosting a panel one time, a night event, it was all out of fun, bunch of nerds on there were talking tech, getting on the hood, talking developers, all this stuff, range of questions, and one guy introduced himself as the, "I'm the CTO, I'm the Chief Toy Officer." (Adam laughs) Because we play with technology then we turn it into product. And you guys brought a lot of toys out here with Google, all this open-source. >> And then if we can amplify that for all the amazing talent that's in the world, at Google I/O, we host the developers' student clubs from Indonesia, and these young Indonesian women are teaching other college kids how to do android development. So, if we could bring that kind of magic to all of our assets, to the Cloud assets, I think there's this amazing, receptive community out there that could give us a bunch of whole new ideas that we don't just get in South of Market, San Francisco. >> It's inspiring to see people build things with open-source, pay it forward, contribute upstream, be part of a community, this is what it's all about, Developer Relations. Congratulations, thanks for coming on theCUBE. >> Thank you, so glad to be here, thanks guys! >> This is theCUBE paying it forward with content here from Google Next, all out in the open, co-creating with Google, Google's team, Google's customers, the best engineers, the best talent here at Google Cloud, I'm with theCUBE. I'm John Furrier, Dave Vellante, thanks for watching. Stay with us, more coverage after this short break. (electronic music)

>> Live from San Francisco, it's theCube, covering Google Cloud Next 2018. Brought to you by Google Cloud and its ecosystem partners. >> Hello everyone, welcome back, this is theCUBE's live coverage, we're here in San Francisco, Moscone West for Google Cloud's big conference called Next 2018. The hashtag is GoogleNext18. I'm John Furrier, Dave Vellante, our next guest is Traci Gusher, Principal, Data and Analytics at KPMG. Great to have you on, thanks for joining us today. >> Yeah, thanks for having me. >> We love bringing on the big system, global, some integrators, you guys have great domain expertise. You also work with customers, you have all the best stories. You work with the best tech. Google Cloud is like a kid in the candy store >> It sure is. when it comes to tech, so my first question is obviously AI in super important to Google. Huge scale, they bring out all the goodies to the party. Spanner, Bigtable, BigQuery, I mean they got a lot of good stuff. TensorFlow, all this open source goodness, pretty impressive, right, >> Yeah, absolutely. the past couple years what they've done. How are you guys partnering with Google, because now that's out there, they need help, they've been acknowledging it for a couple years, they're building an ecosystem, and they want to help end user customers. >> Yeah, we've been working with Google for quite some time, but we actually just formalized our partnership with Google in May of this year. From our perspective, all of the good work that we have done, we're ready to hit the accelerator on and really move forward fast. Some of the things that were announced this week, I think, are prime examples of areas where we see opportunity for us to hit the accelerator on. Something like what was announced this week with their new contact center, API suite, launched by the Advanced Solutions Lab. We had early access to test some of that and really were able to witness just how accelerated some of these things can help us be when we're building end-to-end solutions for clients. >> There's a shortcut to the solutions because with Cloud, the time to value is so much faster, so it's almost an innovator's dilemma. The longer deployments probably meant more billings, ( laughs) right, for a lot of integrators. We've heard people saying hey we've gone, the old days were eight months to eight weeks to eight minutes on some of these techs, so the engagements have changed. At the end of the day, there's still a huge demand for architectural shift. How has the delivery piece of tech helped you guys serve your customers, because I think that's now a conversation that we're hearing is that look, I can move faster, but I don't want to break anything. The old Facebook move fast, break stuff, that doesn't fly in enterprise. >> No, it doesn't (laughs). >> I want to move fast, but I need to have some support there. What are some of the things that you're seeing that are impacting the delivery from integrators? >> Well, some of the technology that's come, that's reduced the length of time to deliver, we see and a lot of our customers see as opportunity to do the next thing, right? If you can implement a solution to a problem quicker, better, faster, than you can move on to the next problem and implement that one quicker, better, faster. I think the first impact is just being able to solve more problems, just being able to really apply some benefits in a lot more areas. The second thing is that we're looking at problems differently, the way that problems used to be solved is changing, and that's most powerfully noted, as we see, at this conference by what's happening with artificial intelligence and with all the accelerators that are being released in machine learning and the like. There's a big difference in just how we're solving the problems that impacts it. >> What are some of the problems that you guys are attacking now, obviously AI's got a lot of goodness to it. What are some of the challenges that you're attacking for customers, what are some examples? >> Our customers have varying problems as they're looking to capitalize on artificial intelligence. One of the big problems is where do I start, right? Often you'll have a big hype cycle where people are really interested, executives are really interested, and I want to use AI, I want to be an AI-enabled company. But they're not really sure where to start. One of the areas that we're really hoping a lot of our customers do is identify where the low hanging fruit is to get immediate value. And at the same time, plan for longer strategic types of opportunities. The second area is that one of the faults that we're seeing, or failure points that we're seeing in using artificial intelligence is failure to launch. What I mean by that is there's a lot of great modeling, a lot of great prototyping and experimentation happening in the lab as it relates to applying AI to different problems and opportunities, but they're staying in the lab, they're not making it in to production, they're not making it in to BAU, business as usual processes inside organizations. So a big area that we're helping our clients in is actually bridging that gap, and that's actually how I refer to it, I refer to it as mind the gap. >> That is a great example, I hear this all the time, classic. Is it, what's the reasons, just group think, I'm nervous, there's no process, what's holding that back from the failure to launch? >> There's a few things. The first is that a lot of traditional IT organizations embedded in enterprises don't necessarily have all of the skills and capabilities or the depth of skills and capabilities that they need to deploy these models in to production. There's even just basic programming types of gaps, where a lot of models are being constructed using things like Python, and a lot of traditional IT organizations are Java shops and they're saying what do I do now? Do I convert, do I learn, do I use different talent? There's technology areas that prove to be challenging. The other area is in the people, and I actually spoke with an analyst this morning about this very topic. There's a lot of organizations that have started productionalizing some of these systems and some of these applications, and they're a little bit discouraged that they're not seeing the kind of lift and the kind of benefits that they thought they would. In most cases-- >> Who, the customers or the analysts? >> The customers. >> OK, alright. >> Yeah, I was having a conversation with an analyst about it. But in most cases, it's not that the technology is falling short, it's not that the model isn't as accurate as you need it to be, it's that the workforce hasn't been transitioned to utilize it, the processes haven't been changed. >> Operationalizing it, yeah. >> The user interfaces aren't transitioning the workforce to a new type of model, they're not being retrained on how to utilize the new technology or the new insights coming from these models. >> That's a huge issue, I agree. >> Isn't there also, Traci, some complacency in certain industries? I mean you think about businesses that haven't yet totally transformed, I think of healthcare, I think of financial services, as examples that are ripe for transformation but really haven't yet. You hear a lot of people say well, it's not really urgent for us, we're doing pretty well, I'll be retired by then, there seems to be a sense of complacency in certain segments of enterprises. Do you see that? >> I do. And I'll say that we've seen a lot more movement in some of those complacent industries in the last six to 18 months than we have previously. I'll also say going back to that where do I start element, there's a lot of organizations that have pressing business challenges, those burning platforms, and that's where they're starting and I'm not advocating against it, I'm actually advocating very much for that, because that's how you can prove some real immediate value. Some organizations, particularly in life sciences or financial services, they're starting to use these technologies to solve their regulatory challenges. How do I comply faster, how do I comply better, how do I avoid any type of compliance issues in the future, how do I avoid other challenges that could come in those areas? The answer to a lot of those questions is if I use AI, I can do it quicker, more accurately, etc. >> Are you able to help them get ancillary value out of that or is it just sort of, compliance a lot of times is like insurance, if I don't do it I get in trouble or I get fined. But are you able to, this is like the holy grail of compliance and governance, are you able to get additional value out of that when you sort of apply machine intelligence to solve those problems? >> That's always the goal. Solving the regulatory problem is certainly what I would say are the table stakes, right? The must-have. But the ability to gain insight that can actually drive value in the organization, that's where your aim really is. In fact, we've worked with a lot of organizations, take life sciences, we've worked with some life sciences organizations that are trying to solve some compliance issues and what we've found is that many times in helping them solve these compliance issues, we're actually gathering insights that significantly increase the capability of their sales organization, because the insights are giving them real information about their customers, their customers' buying patterns, how they're buying, where they might be buying improperly. And it's not the table stake of what we're trying to do, the table stake was maybe contract compliance, but the value that they're actually getting out of it is not only the compliance over their distributors or their pharmacies, but it's also over the impact that they're going to have on their sales organization. For something like an internal audit department to have value to sales, that' like holy grail stuff. >> Yeah, right, yeah. >> What about the data challenges? Even in a bank, who's essentially a data company, the data tends to be very siloed, maybe tucked away in different business units. How are you seeing organizations, how are you helping organizations deal with that data silo problem, specifically as it relates to AI? >> It used to be that the devil was in the details, but now the devil's in the data, right? >> I love that. >> There was a great Harvard Business Review article that came out, and I think Diane Green actually quoted this in one of her presentations, that companies that can't do analytics well can't do AI yet. A lot of companies that can't do analytics well yet, it isn't because they don't have the analytical talent, it's not because they don't know the insights they want to drive, it's because the data isn't in the right format, isn't usable to be able to gain value from it. There's a few different ways that we're helping our clients deal with those things. Just at the very basic level is good data governance. Do you have data stewards that are owning data, that are making sure that data is being created and governed the right way? >> That's a huge deal, I imagine-- >> Inequality and. >> It's huge. >> Inequality-- >> inequality, meta data. >> Garbage in, garbage out. >> Lineage of data, how it's transformed. Being able to govern those things is just imperative. >> It could be just a database thing, could be a database thing, too, it's one of those things where there's so many areas that could be mistakes on the data side. Want to get your thoughts on the point you said earlier which I thought was about technology not coming out and getting commercialized or operationalized. For a variety of reasons, one of them being processes in place, and we hear this a lot. This is a big opportunity, because the human side of these new jobs, whether you're operating the network, really they need help, customers need help. I think you guys should do a great job there given the history. The other trend that came out of the keynote today I want to get your reaction to is there's a tweet here, I'll read it, it says "GCB Cloud will start serving "managing services, enterprise workloads, including Oracle, RAC and Oracle exit data, and SAP HANA through partners." Interesting mind shift again, talk about a mind shift, OK. Partners aren't used to dealing with multi-vendors, but now as a managed service will change the mechanism a bit on delivery because now it's like OK, hey, you want to sling some APIs around, no problem. You want to manage it, we got Kubernetes and Istio. You want a little Oracle with a little bit of HANA? It brings up a much more diverse landscape of solutions. >> It does. Which makes the partners like sous chefs. You can cut the solutions up any way you want. To your point about going faster, to the next challenge. Normal, is that going to be the new normal, this kind of managed service dashboarding? You see that as the... >> I think it is, and I'll take it a step, sir, I'll take it a step further beyond managed service and actually get a little more discreet. One of the things that we're doing increasingly more of is insights as a service, right? If you think about managed service in the traditional sense of I've got a process and you're going to manage that process end to end for me, that technology end to end for me, I do think that that's going to slowly become more and more prevalent. That has to happen with our movement to putting our applications in the cloud, and our ERPs in the cloud. I think it is going to become more of the norm than the less but I also think that it's opening the door for a lot of other things as a service, including insights as a service. Organizations can't find the data science talent that they need to do the really complex types of analysis. >> Your insights as a service comment just gave me an insightful, original idea, thank you very much. >> You're welcome. >> I'll put this in the wrap-up, Dave, when we talk about it. Think about insight as a service, to make that happen with all the underpinning tech, whether it's Oracle or whatever, the insights are an abstraction layer on top of that so if the job is to create great experiences or insights, it should be independent of that. Google Cloud is bringing out a lot more of the concept of abstractions. Kubernetes, Istio, so this notion of an abstraction layer is not just technical, there's also business logic involved. >> Yeah, absolutely. >> This is going to be a dream scenario for KPMG, >> We think so. for your customers, for other partners. Cause now you can add value in those abstraction layers. >> Absolutely. >> By reducing the complexity. Well Oracle, that's not my department, that's HANA's, that's SAP, who does that? He or she's the product lead over it, gone. Insights as a service completely horizontally flattens that. >> Yeah, and to that point, there's magic that happens when you bring different data together. Having data silos because their data's in different systems just, that's the analytics of 1990. Organizations can't operate on that anymore, and real analytics comes when you are working at a layer above the system's and working with the data that's coming from those systems and in fact even creating signals from the data. Not even using the data anymore, creating a signal from the data as an input to a model. I couldn't agree with you more. >> Whole new way of doing business. This is digital transmitting, this is the magic of Cloud. Traci, great to have you on. >> Yeah, thanks for having me. >> It's going to be a whole new landscape changeover, new way to do business. You guys are doing a great job, KPMG, Traci Gusher. Here inside theCUBE talking about analytics AI. If you can't do analytics good, why even go to AI? Love that line. theCUBE bringing you all the data here, stick with us for more after this short break. (bubbly electronic tones)

live from San Francisco it's the cube covering Google cloud next 2018 brought to you by Google cloud and its ecosystem partners ok welcome back everyone we're live here in San Francisco this is the cubes exclusive coverage of Google clouds event next 18 Google next 18 s the hashtag we got two great guests talking about services kubernetes sto and the future of cloud aparna scene how's the group product manager of kubernetes and we have hen goldberg director of engineering of google cloud - amazing cube alumni x' really awesome guests here to break down why kubernetes why is Google cloud really doubling down on that is do a variety of other great multi cloud and on-premise activities guys welcome to the queue great to see you guys again thank you always a pleasure and again you know we love kubernetes the CN CF and we've talked many times about you know we were riffing and you know Luke who Chuck it was on Francisco who loves sto we thought service meshes are amazing you guys had a great open source presence with cube flow and a variety of other great things the open source contribution is recognized by Diane green and the whole industry as number one congratulations why is this deal so important we're seeing the big news at least for me this kind of nuances one datos available you get general availability we're supposed to be kind of after kubernetes made it but now sto is now happening faster why so what we've seen in the industry is that it only becomes too easy to create micro services or services overall but we still want to move fast so with the industry today how can you make sure that you have the right security policies how do you manage those services at scale and what if tio does really in one sense is to expand it it's decoupled the service development from the service operations so developers are free they don't need to take care of monitoring audit logging network traffic for example but instead the operation team has really sophisticated tool to manage all of that on behalf of the developers in a consistent way you know Penn and I did a session yesterday a spotlight session and it covered cloud services platform including ISTE oh we had a guest from eBay and eBay has been with Google kubernetes engine for a long time and they're also a contributor to the kubernetes open source project they talked about how they have hundreds of micro services and they're written in different languages so they're using gold Python Ruby everything under the Sun and as an operator how do you figure out how the services are communicating with each other how do you know which ones are healthy so they I asked him you know so how did you solve that complexity problem and he said boom you assist EO and I deployed this deal it deploys as just kind of like a sidecar proxy and it's auto injected so none of your developers have to do anything and then it's available in every service and it gives you so much out of the box it gives you traffic management it gives you security it gives you observability it gives you the ability to set quotas and to have SL o--'s and and that's really you know something that operators haven't had before describe SL lows for a second what is why is that important objectives so you can see an example so you can have an availability objective that this service should always always be available you know 99.9 percent of the time that's an SLO or you know the response rate needs to be have a certain type of latency so you can have a latency SLO but the key here with this deal is that as an operator previously Jeff was working Jeff from eBay he was working at the at the VM or container or network port level now he's working at the service level so he understands intelligence about the parts of the application that weren't there before and that has two things it makes him powerful right and more intelligent and secondly the developer doesn't need to worry about those things and I think one of the things for network guys out there is that it's like policy breeze policy to the equation now I want to ask course on the auto injections what's the role of the how much coding is involved in doing this zero coding how much how much developer times involved in injecting the sidecar proxies zero from a developer perspective that's not something that you need to worry about you you can focus on you know the chatbot your writing or the webpage your writing or whatever logic you're developing that's critical for your business that's gonna make you more competitive that's why you were hired as a developer right so you don't have to worry about the auto injection of sto and what we announced was really managed it's d1 gke so that's something that Google will manage for you in the future oh go ahead I want less thing about sto I think it also represented changing the transformation because before we were all about kubernetes and containers but definitely when we see the adoption the complexity is much broader so in DCP were actually introducing new solutions that are appropriate for that so easier for example works on both container eyes applications and VM based applications cloud build that we announced right it also works across applications of all types doesn't have to be only containers we introduced some tools for multi cluster management because we know all customers have multi cluster the large ones so really thinking about it how is in a holistic way we are solving those problems we've seen Google evolve its position in the enterprise clearly when we John and I first started talking to Google about cloud is like everything's going to cloud now we're seeing a lot of recognition of some of the challenges that enterprises face we heard a lot of announcements today that are resonating or going to resonate with the enterprise can you talk about the cloud services platform is that essentially your hybrid strategy is it encompass that maybe you could talk about that little bit closer services platform is a big part of our hybrid cloud strategy I mean for as a Google platform we also have networking and compute and we bridge private and public and that's a foundation but cloud services platform it comes from our heritage with open source it comes from our engagement with many large enterprises banks healthcare institutions retailers do so many of them here you know we had HSBC speaking we had target speaking we know that there are large portions of enterprise IT that are going to remain on premise that have to remain on premise because you know they're in a branch office or they have some sort of regulatory compliance or you know that's just where their developers are and they want to have a local environment so so we're very very sensitive and and knowledgeable about that and that's why we introduced cloud services platform as Google's technology in your environment on Prem so you can modernize where you are at your own pace so some of the things we heard today in the keynote we heard support for Oracle RAC and Exadata and sa P that's obviously traditional enterprises partnership with NetApp cloud armor shielded VMs these are all you know traditional enterprise things what enterprise grade features should we be looking for from cloud services platform so the first one which I actually love the most is the G key policy management one of the things we've heard from our customers they say okay portability is great consistency great but we want security portability right they now have those all of those environment how can they ensure that they're combined with the gtp are in all of their environments how they manage tenants in all of their environments in the same way and G key policy measurement is exactly that okay we're allowing customers to apply the same policy while not locking them in okay we're fully compatible with the kubernetes approach and the primitives of our bug enrolls but it is also aligned with G CPI M so you can actually manage it once and apply it to all your environment including clusters kubernetes cluster everywhere you have so I expect we'll have more and more effort in this area I'm making sure that everything is secured and consistent auto-scaling is that enterprise greed auto-scaling yes yes I mean auto-scaling is a inherent part of kubernetes so kubernetes scales your pods automatically that's a very mature I mean it's been stable for more than a year or probably two years and it's used everywhere so auto skip on auto scaling is something that's used and everywhere the thing about gke is that we also do cluster auto scaling cluster auto scaling is actually harder and we not only do it for CPU as we do it for GPUs which is innovative you know so we can scale an auto scale and auto implements Auto provision your GPUs if you machine learning we're gonna bring that on-prem - it's not in the first version but that's something that with the approach that we've taken to GK on Prem we're gonna be adding those kinds of capabilities that gonna be the go on parameters it's just an extension just got to get the job done or what time frame we look API that we've built it's a downward API that works with some sort of hardware clustering technology right now it's working with vSphere right and so it basically if you're under an underlying technology has that capability we will auto scale the cluster in the future you know I got to say you guys are like the dynamic duo of kubernetes seen you in the shows you had Linux Foundation events talk about the relationship between you guys you have an engineering your product management how were you guys organizer you're moving fast I mean just the progress since we've been interviewing you to CN CF segoe all just been significant since we started talking on the cube you see in kubernetes obviously you guys have some inside knowledge of that but it's really moving fast how is the team organized what's the magic internal formula that you guys are engineering and you guys are working as a team I've seen you guys opens is it just open stores is the internal talk about some of the dynamics we're working as one team one thing I love mostly about the Google culture is about doing the right thing for the user like the announcements you've seen yesterday on the on the keynote there are many many teams and I've been working together you know to get that done but you cannot see that right you don't see that there are so many different teams and different product managers and different engineering managers all working together but well I I think where we are right now I know is that really Google is backing up kubernetes and you can see it everywhere right you can see with ours our announcement about key native yeah for example so the idea of portability the idea of no lock-in is really important for us the idea of open cloud freedom of choice so because we're all aligned to that direction and we all agree about the principles is actually super easy to the she's very modest you know this type of thing doesn't just happen by itself right I mean of course google has a wonderful culture and we have a great team but I you know I really enjoy working with hen and she is an amazing leader she is the leader of the engineering team she also brings together these other teams you know every large company has many teams and the announcement at the scale that we made it and the vision that you see the cohesiveness of it right it comes from collaboration it comes from thinking as a team and you know the management and leadership depend has brought to the kubernetes project and to kubernetes and gke and cloud services platform is phenomenal it's an inspiration I really enjoy the progress congratulate and it's been great progress so I hear a lot of customers talk about things like hey you know they evaluate vendors you know those guys have done the work and it's kind of a categorical way of saying it's complete they're working hard they're doing the right things as you guys continue this mission what's some of the work that you're continuing to what's the work that you guys are doing the work we see some of that evidence if it does ascribe to someone says hey have you done the work to earn the cred in the crowd cloud what would it be how would you describe the work that you've done and the work that you're doing and continue to do what does that work what would you say that I mean I hope that we have done the work to you know to earn the credit I think we're very very conscientious you know in the kubernetes open source project I can say we have 300 plus contributors we are working not just on the future functionality but we work on the testing and the we work on the QA we work on all the documentation stuff we work on all the nitty-gritty details so I think that's where we earn the credit on the open source side I think in cloud and in Enterprise do well you're seeing a lot of it here today you know the announcements that you mentioned we're very very cognizant and I think the thing I like about one of the things that Diane said I liked very much as I think the industry underestimates us well when you talk about well we look at the kubernetes if I can call it a playbook it took the world by storm obviously solving some of your own problems you open source it develop the community should we think about it Co the same it's still the same way are you going to use that sort of similar approach it seems to be working yes doing open source is not easy okay managing and investing and building something like kubernetes requires a lot of effort by the way not just from Google we have a lot of people that working full time just on kubernetes the way we look at that we we look about the thing that we have valued the most like portability for example if there is anything that you would like to make a standard like with K native those are kind of thing that we really want to bring to the industry as open source technologies because we want to make sure that they will work for customers everywhere right we need we need to be genuine and really stand behind what we were saying to our customers so this is the way we look at things again another example you can see about Q flow right so we actually have a lot of examples or we want to make sure that we give those options so that's one it's one is for the customer the second thing I want actually the emphasize is the ecosystem and partners yeah we know that innovation not a lot of innovation will come from Google and we want to make sure that we empower our powders and the ecosystem to build new solutions and is again another way to do it yes I mean because we're talking before we came on camera about the importance of ecosystems Dave and I have covered many industries within you know enterprise and now cloud and big data and I see blockchain on the horizon another part of our coverage area ecosystems are super important when you have openness and you have inclusion inclusion Airy culture around building together and co-creation this is the ethos of open source but people need to make money right so at the end of the day we're you guys are not you're not a non-profit you know it's gonna make profit so instead of the partners so as the world turns to cloud there's going to be new value opportunities how do you guys view that ecosystem because is it yeah is it more educational is it more just keep up a lot of people want to be on the right side of history with cloud and begin a lot of things are changing how do you guys view that ecosystem in terms of nurturing it identifying it working with it building it sharing what's your thoughts sure you know I I believe that new technology comes with lots of opportunity we've seen this with kubernetes and I think going forward we see it it's not a zero-sum game you know there's a huge ecosystem that's grown up around kubernetes and now we see actually around sto a huge ecosystem as well the types of opportunities in the value chain I think that it changes it's not what it used to be right it's not so much I think taking care of hardware racking and stacking hardware it's higher level when we talked about SEO and how that raises the level of management I think there's a huge role for operators it's a transformative role you know and we've seen it at Google we have this thing called site reliability engineering sre it's a big thing like those people are God you know when it comes to your services I think that's gonna happen in the enterprise that's gonna be a real role that's an Operations role and then of course developers their life changes and I think even like for regular people you know for kids for you and I and normal people they can become developers and start writing applications so I think there's a huge shift that's a huge thing you're touching on a lot of areas of IT transformation you know talking about the operations piece we've touched upon some of the application development how do you guys look at IT transformation and what are some of your customers doing IT transformation is enabled by you know this raising of the level of abstraction by having a multi cluster multi cloud environment what I see in in the customer base is that they don't want to be limited to one type of cloud they don't want to be limited to just what's on Prem or just what's in one you know in any one cloud they want to be able to consume best-of-breed they want to be able to take what they have and modernize it even if it's even if they can't completely rewrite or even if they can't completely transform it they want to be able they wanted to be able to participate so they even they want their mainframes to be able to participate but yeah I had one customers say you know I I don't want to have two platforms a slow platform and a fast platform I want just a fast platform know about the future now as we end the segment here I want to get your thoughts we're gonna see CN CF s coming up to Seattle in a couple months and also his ST O's got great traction with I'll see with the support and and general availability but what's the impact of the customers because gke Google Cabernets engine is evolving to be the single in her face it's almost as ease of use because that's a real part of what you guys are trying to do is make it easy the abstraction layer is gonna create new business models obviously we see that with the transformation fee she were just mentioning the end of the day I got to operate something I'm a network guy I'm now gonna might be a operating the entire environment I'm gonna enable my developers to be modern fast or whatever they want to be in the day you got to run things got to manage it so what does gke turn into what's the vision can you share your thoughts on on how this transforms and what's the trajectory look like so our goal is actually to help automate that for our customers so they can focus elsewhere as we said from the operations perspective making things more reliable defining the SLO understanding what kind of service they want to provide their customers and our hope you know you can again you can see in other things that we are building like Auto ml okay actually giving more tools to provide those capabilities to the application I think that's really see more and more so the operators will manage services and they will do it across clusters and across environments this is this is a new skill set you know it's the sre skill set but but even bigger because it's not just in one cloud it's across clouds yeah it's not easy they're gonna do it with centralized policy centralized control security compliance all of that so you see us re which is site reliability engineers at Google term but you see that being a role in enterprises and it's also knowing what services to use when what's going to be the most cost effective the right service for the right job that's really an important point I agree I think yeah I think security I think cost perspective was something definitely that will see enterprises investing more in and understanding and how they can leverage that right for their own benefit the admin the operator is gonna say okay I've got this on Prem I've got these three different regions I have to be that traffic coordinator to figure out who can talk to who where should this traffic go there's who should have how much quota all of that right that's the operator role that's the new roles so it's a it's an opportunity for operations people who might have spent their lives managing lawns to really transform their careers yes there's no better time to be an operator I mean you can I want to be an operator and I can't tell you how my dear sorry impacts our team like the engineering team how much they bring the focus on customer the service we are giving to our customers thinking about our services in different ways I think that actually is super important for any engineering team to have that balance okay final questions just put you on the spot real quick answer great stuff congratulations on the work you guys are doing great to follow the progress but I'm a customer I'll put my customer hat on par in ahead I can get that on Amazon Microsoft's got kubernetes why Google cloud what makes Google cloud different if kubernetes is open why should I use Google Cloud so you're right and the wonderful thing is that Google is actually all in kubernetes and we are the first public cloud that actually providing a managed kubernetes on-prem well the first cloud provider to have a GCP marketplace with a kubernetes application production-ready with our partners so if you're all in kubernetes I would say that it's obvious yeah III see most of the customers wanting to be multi cloud and to have choice and that is something that you know is very aligned with what we're look at this crowd win open source is winning great to have you on a part of hend thanks for coming on dynamic duo and kubernetes is - a lot of new services are happening we're bringing all those services here in the cube it's our content here from Google cloud Google next I'm Jennifer and David Lonnie we'll be right back stay with us for more day two coverage after this short break thank you

>> Live from San Francisco, it's The Cube, covering Google Cloud Next 2018. Brought to you by, Google Cloud and its ecosystem partners. >> Hello everyone, welcome back this is The Cube, live in San Francisco for Google Cloud's big event. It's called Google Next for 2018, it's their big cloud show. They're showcasing all their hot technology. A lot of breaking news, a lot of new tech, a lot of new announcements, of course we're bringing it here for three days of wall-to-wall coverage live. It's day two, our next guest is Tim Kelton, co-founder of Descartes Labs, doing some amazing work with imagery and data science, AI, TensorFlow, using the Google Cloud platform to analyze nearly 15 petabytes of data. Tim, welcome to The Cube. >> Thanks, great to be here >> Thanks for coming on. So we were just geeking out before we came on camera of the app that you have, really interesting stuff you guys got going on. Again, really cool, before we get into some of the tech, talk to me about Descartes Labs, you're co-founder, where did it come from? How did it start? And what are some of the projects that you guys are working on? >> I think, therefore I am. >> Exactly, exactly. Yeah, so we're a little different story than maybe a normal start-up. I was actually at a national research laboratory, Los Alamos National Laboratory, and there was a team of us that were focused on machine learning and using datasets, like remotely sensing the Earth with satellite and aerial imagery. And we were working on that from around 2008 to 2014 and then we saw just this explosion in things like, use cases for machine learning and applying that to real world use cases. But then, at the same time, there was this explosion in cloud computing and how much data you could store and train and things like that. So we started the company in late 2014 and now here we are today, we have around 80 employees. >> And what's the main thing you guys do from a data standpoint, where does the data come from? Take a minute to explain that. >> Yeah, so we focus on kind of a lot of often geospatial-centric data, but a lot of satellite and aerial imagery. A lot of what we call remote sensing, sensors orbiting the Earth or at low aerial over the Earth. All different modalities, such as different bands of light, different radio frequencies, all of those types of things. And then we fuse them together and have them in our models. And what we've seen is there's not just the magic data set that gives you the pure answer, right? It's fusing of a lot of these data sets together to tell you what's happening and then building models to predict how those changes affect our customers, their businesses, their supply chain, all those types of things. >> Let's talk about, I want to riff on something real quick, I know I want to get to some of the tech in a second. But my kids and I talk about this all the time, I got four kids and they're now, two in high school, two in college and they see Uber. And they see Uber remapping New York City every five minutes with the data that they get from the GPS. And we started riffing on drones and self-driving cars or aerial cars, if we want to fly in the air with automated helicopters or devices, you got to have some sort of coordinate system. We need this geospatial, and so, I know it's fantasy now, but what you guys are kind of getting at could be an indicator of the kind of geospatial work that's coming down later. Right now there's some cool things happening but you'd need kind of a name space or coordinates so you don't bump into something or are these automated drones don't fly near airports, or cell towers, or windmills, wind farms. >> Yeah, and those are the types of problems we solve or we look to solve, change is happening over time. Often it's the temporal cadence that's almost the key indicator in seeing how things are actually changing over time. And people are coming to us and saying, "Can you quantify that?" We've done things like agriculture and looking at crops grown, look at every single farm all over the whole U.S. and then build that into our models and say how much corn is grown at this field? And then test it back over the last 15 years and then say, as we get new imagery coming in, just daily flooding in through our Cloud Native platform, then just rerunning those models and saying, are we producing more today or less today? >> And then how is that data used, for example, take the agriculture example and that's used to say, okay, this region is maybe more productive than this region? Is it because of weather? Is it because of other things that they're doing? >> You can go back through all different types of use cases, everything from maybe if you're insuring that crop, you would might want to know if that's flooded more on the left side of the road or the right side of the road, as a predictive indicator. You might say, this is looking like a drought year. How have we done in drought years of 2007 and-- >> You look at irrigation trends. >> And you were talking off-camera about the ground truth, can you use IOT to actually calibrate the ground truth? >> Yeah and that's the sensor infusion we're seeing, everywhere around us we're seeing just floods and floods of sensors, so we have the sensors above the Earth looking down, but then as you have more and more sensors on the ground, that's the set of ground truth that you can train and calibrate. You could go back and train and train over again. It's a lot harder problem than, is this a cat or a dog? >> Yeah that's why I was riffing on the concept of a name space, the developer concept around, this is actually space. If you want to have flying drones deliver packages to transportation, you're going to need to know, some sort of triangulation, know what to do. But I got to ask you a question, so what are some of the problems that you're asked to look at, now that you have, you have the top-down view geospace, you got some ground truth sensor exploding in with more and more devices at the network, as a instrument anywhere it can have the IP or whatnot. What are some of the problems that you guys get asked to look at, you mentioned the agriculture, what else are you guys solving? >> Any sort of land use or land classification, or facilities and facility monitoring. It could be any sort of physical infrastructure that you're wanting to quantify and predict how those changes over time might impact that business vertical. And they're really varied, they're everything from energy and agriculture, and real estate, and things like that. Just last Friday, I was talking with, we have a two parts to our company. We have from the tech side, we have the engineering side which is normal engineering, but then we also have this applied science, where we have a team of scientists that are trying to build models often for our customers. 'Cause they're not, this is geospatial and machine learning, that's a rare breed of person. >> You don't want to cross pollinate. >> Yeah, and that's just not everywhere. Not all of our customers have that type of individual. But they were telling me, they were looking at the hurricane season coming up this Fall, and they had a building detector and they can detect all the buildings. So in just a couple hours, they ran that over all of the state of Florida and identified every building in the whole state of Florida. So now, as the seasons come in, they have a way to track that. >> They can be proactive and notify someone, hey you're building might need some boards on it or some sort of risk. >> Yeah and the last couple years look at all the weather events. In California we've had droughts and fires, but then you have flooding and things like that. And you're even able to start taking new types of sensors that are coming out, like the European Space Agency has a sensor that we ingest and it does synthetic aperture radar, where it's sending a radar signal down to the Earth and capturing it. So you can do things like water levels in reservoirs and things like that. >> And look at irrigation for farming, where is the droughts going to be? Where is the flooding going to be? So, for the folks watching, go to descarteslabs.com/search they got a search engine there, I wish we could show it on screen here but we don't have the terminal for it on this show. But it's a cool demo, you can search and find, you can pick an area, football field, and irrigation ditch, anything, cell tower, wind farm, and find duplicates and it gives you a map around the country. So the question is, is that, what is going on in the tech? 'Cause you got to use Cloud for this, so how do you make it all happen? >> Yeah, so we have two real big components to our tech space the first is, obviously we have lots and lots of satellite and aerial imagery, that's one of the biggest and messiest data sets and there's all types of calibration workloads that we have to do. So we have this ingest pipeline that processes it, cleans it, calibrates it, removes the clouds, not as in cloud computing infrastructure, but as in clouds over the head and then the shadows they emit down on the Earth. And we have this big ingestion process that cleans it all. And then finally compresses it and then we use things like GCS as an infinitely scalable object store. And what we really like on the GCS side is the performance we get 'cause we're reading and pulling in and out that compressed imagery all day long. So every time you zoom in or zoom out, like we're expanding it and removing that, but then our models, sometimes what we've done is, we'll want to maybe we're making a model in vegetation and we just want to look at the infrared bands. So we'll want to fuse together satellites from many different sources, fuse together ground sources, sensor sources, and just maybe pull in just one of those bands of light, not pull the whole files in. So that's what we've been building on our API. >> So how do you find GCP? What do you like? We've been all the users this week, what are the strengths? What are some of the weaknesses? What's on their to-do list? Documentation comes up a lot, we'd like to see better documentation, okay that's normal but what's your perspective? >> If you write code or develop, you always want something, you know it's always out of feature parody and stuff. From our perspective, the biggest strengths of GCP, one of the most core strengths is the network. The performance we've been able to see from the network is basically on par with what used to have, when we were at national laboratories we'd have access to high performance, super computing, some of the biggest clusters in the world. And in the network, in GCS and how we've been able scale linearly, like our ingest pipelines, we processed a petabyte of data on GCP in 16 hours through our processing pipeline on 30,000 cores. And we'll just scale that network bandwidth right up. >> Do you tap the premium network service or is it just the standard network? >> This is just stock. That was actually three years ago that we got to our bandwidth. >> How many cores? >> That was 30,000. >> Cause Google talked this morning about their standard network and the premium network, I don't know if you saw the keynote, with you get the low latency, if you pay a little bit more, proximate to your users, but you're saying on the standard network, you're getting just incredible... >> That was early 2015, it's just a few people in our company scaling up our ingest pipeline. We look at that, from then that was 40 years of imagery from NASA's Landsat program that we pulled in. And not that far off in the future, that petabyte's going to be a daily occurrence. So we wanted our ingest to scale and one of our big questions early on is actually, could the cloud actually even handle that type of scale? So that was one of the earliest workloads on things like-- >> And you feel good now about right? >> Oh yeah, and that was one of the first workloads on preemptible instances as well. >> What's on the to-do list? What would make your life better? >> So we've been working a lot with Istio that was shown here. So we actually gave a demo, we were in a couple talks yesterday on how we leverage and use Istio on our microservices. Our APIs are all built on that and so is our multi tenant SAS platform. So our ML team, when they're building models, they're all building models off different use cases, different bands of light, different geographic regions, different temporal windows. So we do all of that in Kubernetes and so those are all-- >> And what does Istio give you guys? What's the benefit of Istio? >> For us, we're using it on a few of our APIs and it's things like, really being able to see when you've start splitting out these microservices that network and that node-to-node or container-to-container latency and where things break down. Being about to do circuit retries or being able to try a response three different times before I return back a 500 or rate limit some of your APIs so they don't get crushed or you can scale them appropriately. And then actually being able to make custom metrics and to be able to fuse that back into how GKE scales on the node pools and stuff like that. >> So okay, that's how you're using it. So you were talking about Istio before, there's things that you'd like to see that aren't there today? More maturity or? >> Yeah I think Istio's like a very early starting point on all of this types of tools. >> So you want more? >> Oh yeah, definitely, definitely but I love the direction they're going and I love that it's open and if I ever wanted to I could build it on prem. But we were built basically native in the cloud so all of our infrastructure's in the cloud. We don't even have a physical server. >> What does open do for you, for your business? Is it just a good feeling? Do you feel like you're less locked in? Does it feel like you're giving back to the community? >> We read the Kubernetes source code. We've committed changes. Just recently, there's Google's open source, the OpenCensus library for tracing and things like that. We committed PRs back into that last week. We're looking for change. Something that doesn't quite work how we want, we can actually go.. >> Cause you're upstream >> Add value... >> For your business. >> We get in really hard problems, you kind of need to understand that code sometimes at that level. Build Tools, where Google took their internal tool, Blaze and opened source that bezel and so we're been using that. We're using that on our monorepos to do all of our builds. >> So you guys take it downstream, you work on it, and then all upstream contributions, is that how it works? >> Sometimes. >> Whenever you need to. >> Even Kubernetes, we've looked, if nothing else we've looked at the code multiple times and say, "Oh, this is why that autoscaler is behaving this way." Actually now I can understand how to change my workload a little bit and alter that so that the scaler works a little bit more performantly or we extract that last 10% of performance out to try and save that last 10%. >> This is a fascinating, I would love to come visit you guys and check out the facilities. It's the coolest thing ever. I think it's the future, there's so much tech going on. So many problems that are new and cool. You got the compute to boot behind it. Final question for you, how are you using analytics and machine learning? What's the key things you're using from Google? What are you guys building on your own? If anything, can you share a quick note on the ML and the analytics, how you guys are scaling that up? >> We've been using TensorFlow since very early days that geovisual search that you were saying, where we user TensorFlow models in some of those types of products. So we're big fans of that as well. And we'll keep building out models where it's appropriate. Sometimes we use very simple packages. You're just doing linear regression or things like that. >> So you're just applying that in. >> Yeah, it's the right tool for the right problem and always picking that and applying that. >> And just quick are you guys are for-profit, non-profit? What's the commercial? >> Yeah, we're for-profit, we're a Silicon Valley VC-backed company, even though we're in the mountains. >> Who's in the VCs? Which VCs are in? >> CrosslinK Capital is one our leading VCs, Eric Chin and that team down there and they've been great to work with. So they took a chance in a crazy bunch of scientists from up in the mountains of New Mexico. >> That sounds like a good VC back opportunity. >> Yeah and we had a CEO that was kind of from the Bay Area, Mark Johnson, and so we needed kind of both of those to really be successful. >> I mean I'm a big believer you throw money at great smart people and then merging markets like this. And you got a mission that's super cool, it's obvious that it's a lot to do and there's opportunities as well. >> Tremendous opportunities. Congratulations, Tim. Thanks for coming on The Cube. Tim Kelton, he's the co-founder at Descartes Labs. Here in The Cube, breaking down, bringing the technology, they got applied physicists, all these brains working on the geospatial future for The Cube. We are geospatial here in The Cube, in Google Next in San Francisco, I'm John Furrier, Dave Vellante, stay with us, for more coverage after this short break.